From e0fdb33e12c5049392012a5a6caaf3c995b8d967 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 9 Sep 2019 11:12:10 +0200 Subject: [PATCH 001/624] Local Outlier Factor --- docs/modules/exploratory/outliers.rst | 24 +- skfda/_neighbors/__init__.py | 1 + skfda/_neighbors/base.py | 3 +- skfda/_neighbors/classification.py | 3 + skfda/_neighbors/outlier.py | 324 +++++++++++++++++++++++++ skfda/_neighbors/regression.py | 2 + skfda/_neighbors/unsupervised.py | 1 + skfda/exploratory/outliers/__init__.py | 1 + 8 files changed, 354 insertions(+), 5 deletions(-) create mode 100644 skfda/_neighbors/outlier.py diff --git a/docs/modules/exploratory/outliers.rst b/docs/modules/exploratory/outliers.rst index 290a1e377..4ee0c70c2 100644 --- a/docs/modules/exploratory/outliers.rst +++ b/docs/modules/exploratory/outliers.rst @@ -4,12 +4,15 @@ Outlier detection Functional outlier detection is the identification of functions that do not seem to behave like the others in the dataset. There are several ways in which a function may be different from the others. For example, a function may have a different shape than the others, or its values could be more extreme. Thus, outlyingness is difficult to -categorize exactly as each outlier detection method looks at different features of the functions in order to +categorize exactly as each outlier detection method looks at different features of the functions in order to identify the outliers. Each of the outlier detection methods in scikit-fda has the same API as the outlier detection methods of `scikit-learn `_. +Interquartilic Range Outlier Detector +------------------------------------ + One of the most common ways of outlier detection is given by the functional data boxplot. An observation is marked as an outlier if it has points :math:`1.5 \cdot IQR` times outside the region containing the deepest 50% of the curves (the central region), where :math:`IQR` is the interquartilic range. @@ -18,7 +21,11 @@ as an outlier if it has points :math:`1.5 \cdot IQR` times outside the region co :toctree: autosummary skfda.exploratory.outliers.IQROutlierDetector - + + +DirectionalOutlierDetector +-------------------------- + Other more novel way of outlier detection takes into account the magnitude and shape of the curves. Curves which have a very different shape or magnitude are considered outliers. @@ -26,11 +33,20 @@ a very different shape or magnitude are considered outliers. :toctree: autosummary skfda.exploratory.outliers.DirectionalOutlierDetector - + For this method, it is necessary to compute the mean and variation of the directional outlyingness, which can be done with the following function. .. autosummary:: :toctree: autosummary - skfda.exploratory.outliers.directional_outlyingness_stats \ No newline at end of file + skfda.exploratory.outliers.directional_outlyingness_stats + + +Local Outlier Factor +-------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.outliers.LocalOutlierFactor diff --git a/skfda/_neighbors/__init__.py b/skfda/_neighbors/__init__.py index 58316566d..0a9866b6a 100644 --- a/skfda/_neighbors/__init__.py +++ b/skfda/_neighbors/__init__.py @@ -10,5 +10,6 @@ """ from .unsupervised import NearestNeighbors from .regression import KNeighborsRegressor, RadiusNeighborsRegressor +from .outlier import LocalOutlierFactor from .classification import (KNeighborsClassifier, RadiusNeighborsClassifier, NearestCentroids) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 5e73364cd..3a1b1a2fc 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -97,11 +97,12 @@ def multivariate_metric(x, y, _check=False, **kwargs): class NeighborsBase(ABC, BaseEstimator): """Base class for nearest neighbors estimators.""" - @abstractmethod + def __init__(self, n_neighbors=None, radius=None, weights='uniform', algorithm='auto', leaf_size=30, metric='l2', metric_params=None, n_jobs=None, multivariate_metric=False): + """Initializes the nearest neighbors estimator""" self.n_neighbors = n_neighbors self.radius = radius diff --git a/skfda/_neighbors/classification.py b/skfda/_neighbors/classification.py index c8f63482d..4a0547372 100644 --- a/skfda/_neighbors/classification.py +++ b/skfda/_neighbors/classification.py @@ -96,6 +96,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` Notes ----- @@ -253,6 +254,7 @@ class RadiusNeighborsClassifier(NeighborsBase, NeighborsMixin, :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` Notes ----- @@ -357,6 +359,7 @@ class and return a :class:`FData` object with only one sample :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` """ diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py new file mode 100644 index 000000000..a7fd2c8b3 --- /dev/null +++ b/skfda/_neighbors/outlier.py @@ -0,0 +1,324 @@ + + +from sklearn.base import OutlierMixin +from .base import (NeighborsBase, NeighborsMixin, KNeighborsMixin, + _to_multivariate_metric) + +from ..misc.metrics import lp_distance + +class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, + OutlierMixin): + """Unsupervised Outlier Detection. + + Unsupervised Outlier Detection using Local Outlier Factor (LOF). + + The anomaly score of each sample is called Local Outlier Factor. + It measures the local deviation of density of a given sample with + respect to its neighbors. + It is local in that the anomaly score depends on how isolated the object + is with respect to the surrounding neighborhood. + More precisely, locality is given by k-nearest neighbors, whose distance + is used to estimate the local density. + By comparing the local density of a sample to the local densities of + its neighbors, one can identify samples that have a substantially lower + density than their neighbors. These are considered outliers. + + Parameters + ---------- + n_neighbors : int, optional (default=20) + Number of neighbors to use by default for :meth:`kneighbors` queries. + If n_neighbors is larger than the number of samples provided, + all samples will be used. + algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional + Algorithm used to compute the nearest neighbors: + - 'ball_tree' will use :class:`BallTree` + - 'kd_tree' will use :class:`KDTree` + - 'brute' will use a brute-force search. + - 'auto' will attempt to decide the most appropriate algorithm + based on the values passed to :meth:`fit` method. + Note: fitting on sparse input will override the setting of + this parameter, using brute force. + leaf_size : int, optional (default=30) + Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can + affect the speed of the construction and query, as well as the memory + required to store the tree. The optimal value depends on the + nature of the problem. + metric : string or callable, default 'minkowski' + metric used for the distance computation. Any metric from scikit-learn + or scipy.spatial.distance can be used. + If 'precomputed', the training input X is expected to be a distance + matrix. + If metric is a callable function, it is called on each + pair of instances (rows) and the resulting value recorded. The callable + should take two arrays as input and return one value indicating the + distance between them. This works for Scipy's metrics, but is less + efficient than passing the metric name as a string. + Valid values for metric are: + - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', + 'manhattan'] + - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', + 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', + 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', + 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', + 'yule'] + See the documentation for scipy.spatial.distance for details on these + metrics: + https://docs.scipy.org/doc/scipy/reference/spatial.distance.html + p : integer, optional (default=2) + Parameter for the Minkowski metric from + :func:`sklearn.metrics.pairwise.pairwise_distances`. When p = 1, this + is equivalent to using manhattan_distance (l1), and euclidean_distance + (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. + metric_params : dict, optional (default=None) + Additional keyword arguments for the metric function. + contamination : float in (0., 0.5), optional (default='auto') + The amount of contamination of the data set, i.e. the proportion + of outliers in the data set. When fitting this is used to define the + threshold on the decision function. If "auto", the decision function + threshold is determined as in the original paper. + novelty : boolean, default False + By default, LocalOutlierFactor is only meant to be used for outlier + detection (novelty=False). Set novelty to True if you want to use + LocalOutlierFactor for novelty detection. In this case be aware that + that you should only use predict, decision_function and score_samples + on new unseen data and not on the training set. + n_jobs : int or None, optional (default=None) + The number of parallel jobs to run for neighbors search. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + Affects only :meth:`kneighbors` and :meth:`kneighbors_graph` methods. + + Attributes + ---------- + negative_outlier_factor_ : numpy array, shape (n_samples,) + The opposite LOF of the training samples. The higher, the more normal. + Inliers tend to have a LOF score close to 1 (``negative_outlier_factor_`` + close to -1), while outliers tend to have a larger LOF score. + The local outlier factor (LOF) of a sample captures its + supposed 'degree of abnormality'. + It is the average of the ratio of the local reachability density of + a sample and those of its k-nearest neighbors. + n_neighbors_ : integer + The actual number of neighbors used for :meth:`kneighbors` queries. + offset_ : float + Offset used to obtain binary labels from the raw scores. + Observations having a negative_outlier_factor smaller than `offset_` + are detected as abnormal. + The offset is set to -1.5 (inliers score around -1), except when a + contamination parameter different than "auto" is provided. In that + case, the offset is defined in such a way we obtain the expected + number of outliers in training. + + References + ---------- + .. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). + LOF: identifying density-based local outliers. In ACM sigmod record. + + Notes + ----- + This estimator wraps the scikit-learn analogous class + :class:`~sklearn.neighbors.LocalOutlierFactor` employing functional + metrics instead of the multivariate ones. + + See also + -------- + :class:`~skfda.ml.classification.KNeighborsClassifier` + :class:`~skfda.ml.classification.RadiusNeighborsClassifier` + :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.regression.KNeighborsRegressor` + :class:`~skfda.ml.regression.RadiusNeighborsRegressor` + :class:`~skfda.ml.clustering.NearestNeighbors` + """ + def __init__(self, n_neighbors=20, algorithm='auto', + leaf_size=30, metric='l2', metric_params=None, + contamination='auto', novelty=False, + n_jobs=1, multivariate_metric=False): + """Initialize the Local Outlier Factor estimator.""" + + super().__init__(n_neighbors=n_neighbors, algorithm=algorithm, + leaf_size=leaf_size, metric=metric, + metric_params=metric_params, n_jobs=n_jobs, + multivariate_metric=multivariate_metric) + self.contamination = contamination + self.novelty = novelty + + def _init_estimator(self, sklearn_metric): + """Initialize the sklearn nearest neighbors estimator. + + Args: + sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with + sklearn API or matrix (n_samples, n_samples) with precomputed + distances. + + Returns: + Sklearn LocalOutlierFactor estimator initialized. + + """ + from sklearn.neighbors import LocalOutlierFactor as _LocalOutlierFactor + + return _LocalOutlierFactor( + n_neighbors=self.n_neighbors, algorithm=self.algorithm, + leaf_size=self.leaf_size, metric=self.metric, + metric_params=self.metric_params, contamination=self.contamination, + novelty=self.novelty, n_jobs=self.n_jobs) + + def _store_fit_data(self): + """Store the parameters created during the fit.""" + self.negative_outlier_factor_ = self.estimator_.negative_outlier_factor_ + self.n_neighbors_ = self.estimator_.n_neighbors_ + self.offset_ = self.estimator_.offset_ + + + def fit(self, X, y=None): + """Fit the model using X as training data. + + Parameters + ---------- + X : :class:`~skfda.FDataGrid` or array_like + Training data. FDataGrid containing the samples, + or array with shape [n_samples, n_samples] if metric='precomputed'. + y : Ignored + not used, present for API consistency by convention. + Returns + ------- + self : object + """ + + super().fit(X, y) + self._store_fit_data() + + return self + + def predict(self, X, y=None): + """Predict the labels (1 inlier, -1 outlier) of X according to LOF. + + This method allows to generalize prediction to *new observations* (not + in the training set). Only available for novelty detection (when + novelty is set to True). + + Parameters + ---------- + X : :class:`~skfda.FDataGrid` or array_like + FDataGrid containing the query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples. + + Returns + ------- + is_inlier : array, shape (n_samples,) + Returns -1 for anomalies/outliers and +1 for inliers. + """ + + self._check_is_fitted() + X_multivariate = self._transform_to_multivariate(X) + + return self.estimator_.predict(X_multivariate) + + + def fit_predict(self, X, y=None): + """"Fits the model to the training set X and returns the labels. + + Label is 1 for an inlier and -1 for an outlier according to the LOF + score and the contamination parameter. + + Parameters + ---------- + X : :class:`~skfda.FDataGrid` or array_like + Training data. FDataGrid containing the samples, + or array with shape [n_samples, n_samples] if metric='precomputed'. + y : Ignored + not used, present for API consistency by convention. + Returns + ------- + is_inlier : array, shape (n_samples,) + Returns -1 for anomalies/outliers and 1 for inliers. + """ + + # In this estimator fit_predict cannot be wrapped as fit().predict() + + if self.metric == 'precomputed': + self.estimator_ = self._init_estimator(self.metric) + res = self.estimator_.fit_predict(X, y) + else: + self._sample_points = X.sample_points + self._shape = X.data_matrix.shape[1:] + + if not self.multivariate_metric: + # Constructs sklearn metric to manage vector + if self.metric == 'l2': + metric = lp_distance + else: + metric = self.metric + sklearn_metric = _to_multivariate_metric(metric, + self._sample_points) + else: + sklearn_metric = self.metric + + self.estimator_ = self._init_estimator(sklearn_metric) + X_multivariate = self._transform_to_multivariate(X) + res = self.estimator_.fit_predict(X_multivariate, y) + + self._store_fit_data() + + return res + + def decision_function(self, X, y=None): + """Shifted opposite of the Local Outlier Factor of X. + + Bigger is better, i.e. large values correspond to inliers. + The shift offset allows a zero threshold for being an outlier. + Only available for novelty detection (when novelty is set to True). + The argument X is supposed to contain *new data*: if X contains a + point from training, it considers the later in its own neighborhood. + Also, the samples in X are not considered in the neighborhood of any + point. + + Parameters + ---------- + X : :class:`~skfda.FDataGrid` or array_like + FDataGrid containing the query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples. + + Returns + ------- + shifted_opposite_lof_scores : array, shape (n_samples,) + The shifted opposite of the Local Outlier Factor of each input + samples. The lower, the more abnormal. Negative scores represent + outliers, positive scores represent inliers. + """ + self._check_is_fitted() + X_multivariate = self._transform_to_multivariate(X) + + return self.estimator_.decision_function(X_multivariate) + + def score_samples(self, X): + """Opposite of the Local Outlier Factor of X. + + It is the opposite as bigger is better, i.e. large values correspond + to inliers. + + Only available for novelty detection (when novelty is set to True). + The argument X is supposed to contain *new data*: if X contains a + point from training, it considers the later in its own neighborhood. + Also, the samples in X are not considered in the neighborhood of any + point. + + The score_samples on training data is available by considering the + the ``negative_outlier_factor_`` attribute. + + Parameters + ---------- + X : :class:`~skfda.FDataGrid` or array_like + FDataGrid containing the query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples. + + Returns + ------- + opposite_lof_scores : array, shape (n_samples,) + The opposite of the Local Outlier Factor of each input samples. + The lower, the more abnormal. + """ + self._check_is_fitted() + X_multivariate = self._transform_to_multivariate(X) + + return self.estimator_.decision_function(X_multivariate) diff --git a/skfda/_neighbors/regression.py b/skfda/_neighbors/regression.py index 8300215ee..7f04680b1 100644 --- a/skfda/_neighbors/regression.py +++ b/skfda/_neighbors/regression.py @@ -110,6 +110,7 @@ class KNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` Notes ----- @@ -279,6 +280,7 @@ class RadiusNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` Notes ----- diff --git a/skfda/_neighbors/unsupervised.py b/skfda/_neighbors/unsupervised.py index 9e2fbee1a..c6189e80e 100644 --- a/skfda/_neighbors/unsupervised.py +++ b/skfda/_neighbors/unsupervised.py @@ -87,6 +87,7 @@ class NearestNeighbors(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` + :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` Notes ----- diff --git a/skfda/exploratory/outliers/__init__.py b/skfda/exploratory/outliers/__init__.py index 666ee83f6..10595a551 100644 --- a/skfda/exploratory/outliers/__init__.py +++ b/skfda/exploratory/outliers/__init__.py @@ -1,3 +1,4 @@ from ._directional_outlyingness import (directional_outlyingness_stats, DirectionalOutlierDetector) from ._iqr import IQROutlierDetector +from ..._neighbors import LocalOutlierFactor From 19977765474c63296c7c40735160f2f38cd7e499 Mon Sep 17 00:00:00 2001 From: pablomm Date: Tue, 10 Sep 2019 00:38:33 +0200 Subject: [PATCH 002/624] Documentation of Local outlier factor --- skfda/_neighbors/base.py | 6 +- skfda/_neighbors/classification.py | 5 +- skfda/_neighbors/outlier.py | 107 +++++++++++++++++++---------- 3 files changed, 77 insertions(+), 41 deletions(-) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 3a1b1a2fc..4412ad599 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -204,7 +204,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): Indices of the nearest points in the population matrix. Examples: - Firstly, we will create a toy dataset with 2 classes + Firstly, we will create a toy dataset. >>> from skfda.datasets import make_sinusoidal_process >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0) @@ -261,7 +261,7 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'): A[i, j] is assigned the weight of edge that connects i to j. Examples: - Firstly, we will create a toy dataset with 2 classes. + Firstly, we will create a toy dataset. >>> from skfda.datasets import make_sinusoidal_process >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0) @@ -330,7 +330,7 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True): within a ball of size ``radius`` around the query points. Examples: - Firstly, we will create a toy dataset with 2 classes. + Firstly, we will create a toy dataset. >>> from skfda.datasets import make_sinusoidal_process >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0) diff --git a/skfda/_neighbors/classification.py b/skfda/_neighbors/classification.py index 4a0547372..33822ffe0 100644 --- a/skfda/_neighbors/classification.py +++ b/skfda/_neighbors/classification.py @@ -59,8 +59,9 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, Doesn't affect :meth:`fit` method. multivariate_metric : boolean, optional (default = False) Indicates if the metric used is a sklearn distance between vectors (see - :class:`sklearn.neighbors.DistanceMetric`) or a functional metric of - the module :mod:`skfda.misc.metrics`. + :class:`~sklearn.neighbors.DistanceMetric`) or a functional metric of + the module `skfda.misc.metrics` if ``False``. + Examples -------- Firstly, we will create a toy dataset with 2 classes diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index a7fd2c8b3..f91f5bce3 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -15,10 +15,13 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, The anomaly score of each sample is called Local Outlier Factor. It measures the local deviation of density of a given sample with respect to its neighbors. + It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood. + More precisely, locality is given by k-nearest neighbors, whose distance is used to estimate the local density. + By comparing the local density of a sample to the local densities of its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers. @@ -31,51 +34,30 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, all samples will be used. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors: + - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. - Note: fitting on sparse input will override the setting of - this parameter, using brute force. + leaf_size : int, optional (default=30) Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. - metric : string or callable, default 'minkowski' - metric used for the distance computation. Any metric from scikit-learn - or scipy.spatial.distance can be used. - If 'precomputed', the training input X is expected to be a distance - matrix. - If metric is a callable function, it is called on each - pair of instances (rows) and the resulting value recorded. The callable - should take two arrays as input and return one value indicating the - distance between them. This works for Scipy's metrics, but is less - efficient than passing the metric name as a string. - Valid values for metric are: - - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', - 'manhattan'] - - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', - 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', - 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', - 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', - 'yule'] - See the documentation for scipy.spatial.distance for details on these - metrics: - https://docs.scipy.org/doc/scipy/reference/spatial.distance.html - p : integer, optional (default=2) - Parameter for the Minkowski metric from - :func:`sklearn.metrics.pairwise.pairwise_distances`. When p = 1, this - is equivalent to using manhattan_distance (l1), and euclidean_distance - (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. + metric : string or callable, (default + :func:`lp_distance `) + the distance metric to use for the tree. The default metric is + the L2 distance. See the documentation of the metrics module + for a list of available metrics. metric_params : dict, optional (default=None) Additional keyword arguments for the metric function. contamination : float in (0., 0.5), optional (default='auto') The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. If "auto", the decision function - threshold is determined as in the original paper. + threshold is determined as in the original paper [Rca479bb49841-1]_. novelty : boolean, default False By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=False). Set novelty to True if you want to use @@ -88,13 +70,18 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, ``-1`` means using all processors. See :term:`Glossary ` for more details. Affects only :meth:`kneighbors` and :meth:`kneighbors_graph` methods. + multivariate_metric : boolean, optional (default = False) + Indicates if the metric used is a sklearn distance between vectors (see + :class:`~sklearn.neighbors.DistanceMetric`) or a functional metric of + the module `skfda.misc.metrics` if ``False``. Attributes ---------- negative_outlier_factor_ : numpy array, shape (n_samples,) The opposite LOF of the training samples. The higher, the more normal. - Inliers tend to have a LOF score close to 1 (``negative_outlier_factor_`` - close to -1), while outliers tend to have a larger LOF score. + Inliers tend to have a LOF score close to 1 + (``negative_outlier_factor_`` close to -1), while outliers tend to have + a larger LOF score. The local outlier factor (LOF) of a sample captures its supposed 'degree of abnormality'. It is the average of the ratio of the local reachability density of @@ -110,10 +97,57 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, case, the offset is defined in such a way we obtain the expected number of outliers in training. + Examples: + + **Local Outlier Factor (LOF) for outlier detection**. + + >>> from skfda.exploratory.outliers import LocalOutlierFactor + + Creation of simulated dataset with 2 outliers to be used with LOF. + + >>> from skfda.datasets import make_sinusoidal_process + >>> fd_clean = make_sinusoidal_process(n_samples=25, error_std=0, + ... phase_std=0.1, random_state=0) + >>> fd_outliers = make_sinusoidal_process( + ... n_samples=2, error_std=0, phase_mean=0.5, random_state=5) + >>> fd = fd_outliers.concatenate(fd_clean) # Dataset with 2 outliers + + Detection of outliers with LOF. + + >>> lof = LocalOutlierFactor() + >>> is_outlier = lof.fit_predict(fd) + >>> is_outlier # -1 for anomalies/outliers and +1 for inliers + array([ -1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) + + The negative outlier factor stored. + + >>> lof.negative_outlier_factor_.round(2) + array([ -7.07, -1.54, -1. , -0.99, ..., -0.97, -1, -0.99]) + + **Novelty detection with LOF**. + + Creation of a dataset without outliers. + + >>> fd_train = make_sinusoidal_process(n_samples=25, error_std=0, + ... phase_std=0.1, random_state=9) + + Fit of LOF using the dataset without outliers. + + >>> lof = LocalOutlierFactor(novelty=True) + >>> lof.fit(fd_train) + LocalOutlierFactor(algorithm='auto', ..., novelty=True) + + Detection of annomalies for new samples. + + >>> lof.predict(fd) # Predict with samples not used in fit + array([ -1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) + + References ---------- - .. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). - LOF: identifying density-based local outliers. In ACM sigmod record. + .. [Rca479bb49841-1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, + J. (2000, May). LOF: identifying density-based local outliers. In ACM + sigmod record. Notes ----- @@ -190,7 +224,7 @@ def fit(self, X, y=None): return self - def predict(self, X, y=None): + def predict(self, X): """Predict the labels (1 inlier, -1 outlier) of X according to LOF. This method allows to generalize prediction to *new observations* (not @@ -201,7 +235,8 @@ def predict(self, X, y=None): ---------- X : :class:`~skfda.FDataGrid` or array_like FDataGrid containing the query sample or samples to compute the - Local Outlier Factor w.r.t. to the training samples. + Local Outlier Factor w.r.t. to the training samples or array with + the distances to the training samples if metric='precomputed'. Returns ------- @@ -262,7 +297,7 @@ def fit_predict(self, X, y=None): return res - def decision_function(self, X, y=None): + def decision_function(self, X): """Shifted opposite of the Local Outlier Factor of X. Bigger is better, i.e. large values correspond to inliers. From 399013a3d4c208b144340beffe0dd5ee5d2a8751 Mon Sep 17 00:00:00 2001 From: pablomm Date: Tue, 10 Sep 2019 19:02:34 +0200 Subject: [PATCH 003/624] Example of LOF and new dataset --- docs/modules/datasets.rst | 1 + examples/plot_local_outlier_factor.py | 73 +++++++++++++++++++++++++++ skfda/_neighbors/outlier.py | 8 +-- skfda/datasets/__init__.py | 3 +- skfda/datasets/_real_datasets.py | 68 +++++++++++++++++++++++++ 5 files changed, 148 insertions(+), 5 deletions(-) create mode 100644 examples/plot_local_outlier_factor.py diff --git a/docs/modules/datasets.rst b/docs/modules/datasets.rst index 6946376c9..4121e988d 100644 --- a/docs/modules/datasets.rst +++ b/docs/modules/datasets.rst @@ -17,6 +17,7 @@ The following functions are used to retrieve specific functional datasets: skfda.datasets.fetch_medflies skfda.datasets.fetch_weather skfda.datasets.fetch_aemet + skfda.datasets.fetch_octane Those functions return a dictionary with at least a "data" field containing the instance data, and a "target" field containing the class labels or regression values, diff --git a/examples/plot_local_outlier_factor.py b/examples/plot_local_outlier_factor.py new file mode 100644 index 000000000..aff9381c3 --- /dev/null +++ b/examples/plot_local_outlier_factor.py @@ -0,0 +1,73 @@ +""" +Outlier detection with Local Outlier Factor +=========================================== + +Shows the use of the Local Outlier Factor to detect outliers in the octane +dataset. +""" + +# Author: Pablo Marcos Manchón +# License: MIT + +# sphinx_gallery_thumbnail_number = 2 + +import matplotlib.pyplot as plt + +from skfda.datasets import fetch_octane +from skfda.exploratory.outliers import LocalOutlierFactor + + +############################################################################## +# First, we load the *octane dataset* consisting of 39 near infrared +# (NIR) spectra of gasoline samples, with wavelengths ranging from 1102nm to +# 1552nm with measurements every two nm. +# +# This dataset contains six outliers, studied in [RDEH2006]_ and [HuRS2015]_, +# to which ethanol was added. This different +# composition has an effect on the shape of the spectra of gasoline samples. +# + +fd, labels = fetch_octane(return_X_y=True) +fd.plot() + + +############################################################################## +# :class:`~skfda.exploratory.outliers.LocalOutlierFactor` +# (`LOF `_), based on +# the local density of the curves as described in [BKNS2000]_, may be used to +# detect these outliers. In order to get the results the +# :meth:`~skfda.exploratory.outliers.LocalOutlierFactor.fit_predict` +# method is used. +# + +lof = LocalOutlierFactor() +is_outlier = lof.fit_predict(fd) + +print(is_outlier) # 1 for inliners / -1 for outliers + +############################################################################## +# The curves detected as outliers correspond to the samples to which +# ethanol was added. +# + +# TODO: Use one hot encoding internally to allow arbitrary sample_labels +is_outlier[is_outlier == -1] = 0 + +fd.plot(sample_labels=is_outlier, label_colors=['C1', 'C0'], + label_names=["outlier", "inliner"]) + + +############################################################################## +# .. rubric:: References +# .. [RDEH2006] Rousseeuw, Peter & Debruyne, Michiel & Engelen, Sanne & +# Hubert, Mia. (2006). Robustness and Outlier Detection in +# Chemometrics. Critical Reviews in Analytical Chemistry. 36. +# 221-242. 10.1080/10408340600969403. +# .. [HuRS2015] Hubert, Mia & Rousseeuw, Peter & Segaert, Pieter. (2015). +# Multivariate functional outlier detection. Statistical Methods and +# Applications. 24. 177-202. 10.1007/s10260-015-0297-8. +# .. [BKNS2000] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, +# J. (2000, May). LOF: identifying density-based local outliers. In ACM +# sigmod record. + +plt.show() diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index f91f5bce3..181b810b4 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -34,7 +34,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, all samples will be used. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors: - + - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. @@ -57,7 +57,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. If "auto", the decision function - threshold is determined as in the original paper [Rca479bb49841-1]_. + threshold is determined as in the original paper [BKNS2000]_. novelty : boolean, default False By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=False). Set novelty to True if you want to use @@ -145,7 +145,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, References ---------- - .. [Rca479bb49841-1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, + .. [BKNS2000] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying density-based local outliers. In ACM sigmod record. @@ -251,7 +251,7 @@ def predict(self, X): def fit_predict(self, X, y=None): - """"Fits the model to the training set X and returns the labels. + """Fits the model to the training set X and returns the labels. Label is 1 for an inlier and -1 for an outlier according to the LOF score and the contamination parameter. diff --git a/skfda/datasets/__init__.py b/skfda/datasets/__init__.py index ec3dcc9ab..c2e84fc5a 100644 --- a/skfda/datasets/__init__.py +++ b/skfda/datasets/__init__.py @@ -2,7 +2,8 @@ fetch_ucr, fetch_phoneme, fetch_growth, fetch_tecator, fetch_medflies, - fetch_weather, fetch_aemet) + fetch_weather, fetch_aemet, + fetch_octane) from ._samples_generators import (make_gaussian_process, make_sinusoidal_process, make_multimodal_samples, diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index ca5767837..d51ded976 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -531,3 +531,71 @@ def fetch_aemet(return_X_y: bool = False): if hasattr(fetch_aemet, "__doc__"): # docstrings can be stripped off fetch_aemet.__doc__ += _aemet_descr + _param_descr + + +_octane_descr = """ + Near infrared (NIR) spectra of gasoline samples, with wavelengths ranging + from 1102nm to 1552nm with measurements every two nm. + This dataset contains six outliers to which ethanol was added, which is + required in some states. See [RDEH2006]_ and [HuRS2015]_ for further + details. + + The data is labeled according to this different composition. + + Source: + Esbensen K. (2001). Multivariate data analysis in practice. 5th edn. + Camo Software, Trondheim, Norway. + + References: + .. [RDEH2006] Rousseeuw, Peter & Debruyne, Michiel & Engelen, Sanne & + Hubert, Mia. (2006). Robustness and Outlier Detection in + Chemometrics. Critical Reviews in Analytical Chemistry. 36. + 221-242. 10.1080/10408340600969403. + .. [HuRS2015] Hubert, Mia & Rousseeuw, Peter & Segaert, Pieter. (2015). + Multivariate functional outlier detection. Statistical Methods and + Applications. 24. 177-202. 10.1007/s10260-015-0297-8. + +""" + +def fetch_octane(return_X_y: bool = False): + """Load near infrared spectra of gasoline samples. + + This function fetchs the octane dataset from the R package 'mrfDepth' + from CRAN. + + """ + DESCR = _octane_descr + + # octane file from mrfDepth R package + raw_dataset = fetch_cran("octane", "mrfDepth", version="1.0.11") + data = raw_dataset['octane'][..., 0].T + + # The R package only stores the values of the curves, but the paper + # describes the rest of the data. According to [RDEH2006], Section 5.4: + + # "wavelengths ranging from 1102nm to 1552nm with measurements every two + # nm."" + sample_points = np.linspace(1102, 1552, 226) + + # "The octane data set contains six outliers (25, 26, 36–39) to which + # alcohol was added". + target = np.zeros(len(data), dtype=int) + target[24] = target[25] = target [35:39] = 1 # Outliers 1 + + axes_labels = ["wavelength (nm)", "absorbances"] + + curves = FDataGrid(data, + sample_points=sample_points, + dataset_label="Octane", + axes_labels=axes_labels) + + if return_X_y: + return curves, target + else: + return {"data": curves, + "target": target, + "target_names": ['inliner', 'outlier'], + "DESCR" : DESCR} + +if hasattr(fetch_octane, "__doc__"): # docstrings can be stripped off + fetch_octane.__doc__ += _octane_descr + _param_descr From d80a112cc7f09913a38630a6187e7b6da0d49339 Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 11 Sep 2019 21:26:42 +0200 Subject: [PATCH 004/624] Space in doctest --- skfda/_neighbors/outlier.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index 181b810b4..40659de13 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -117,7 +117,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, >>> lof = LocalOutlierFactor() >>> is_outlier = lof.fit_predict(fd) >>> is_outlier # -1 for anomalies/outliers and +1 for inliers - array([ -1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) + array([-1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) The negative outlier factor stored. @@ -140,7 +140,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, Detection of annomalies for new samples. >>> lof.predict(fd) # Predict with samples not used in fit - array([ -1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) + array([-1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) References From c0533cb5096b2ef8c52c7d71018cb37b4364540f Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 11 Sep 2019 22:12:52 +0200 Subject: [PATCH 005/624] Space in doctest --- skfda/_neighbors/outlier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index 40659de13..f0a744d1b 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -122,7 +122,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, The negative outlier factor stored. >>> lof.negative_outlier_factor_.round(2) - array([ -7.07, -1.54, -1. , -0.99, ..., -0.97, -1, -0.99]) + array([-7.07, -1.54, -1. , -0.99, ..., -0.97, -1, -0.99]) **Novelty detection with LOF**. From 8c03cab15b148ae3061e19b535c5ed80ba8e3c1a Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 11 Sep 2019 22:17:11 +0200 Subject: [PATCH 006/624] Format in doctest --- skfda/_neighbors/outlier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index f0a744d1b..d75a7fbe4 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -122,7 +122,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, The negative outlier factor stored. >>> lof.negative_outlier_factor_.round(2) - array([-7.07, -1.54, -1. , -0.99, ..., -0.97, -1, -0.99]) + array([-7.07, -1.54, -1. , -0.99, ..., -0.97, -1. , -0.99]) **Novelty detection with LOF**. From 2a6fed589d225202eb5f111f0ab59fd0d88dba31 Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 12 Sep 2019 14:21:37 +0200 Subject: [PATCH 007/624] Coverage of LocalOutlierFactor --- examples/plot_local_outlier_factor.py | 2 +- skfda/_neighbors/base.py | 1 + skfda/_neighbors/outlier.py | 12 +-- tests/test_neighbors.py | 111 +++++++++++++++++++++++++- 4 files changed, 116 insertions(+), 10 deletions(-) diff --git a/examples/plot_local_outlier_factor.py b/examples/plot_local_outlier_factor.py index aff9381c3..4c70dfee4 100644 --- a/examples/plot_local_outlier_factor.py +++ b/examples/plot_local_outlier_factor.py @@ -53,7 +53,7 @@ # TODO: Use one hot encoding internally to allow arbitrary sample_labels is_outlier[is_outlier == -1] = 0 -fd.plot(sample_labels=is_outlier, label_colors=['C1', 'C0'], +fd.plot(sample_labels=is_outlier, label_colors=['darkorange', 'lightgrey'], label_names=["outlier", "inliner"]) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 4412ad599..919e086c4 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -167,6 +167,7 @@ def fit(self, X, y=None): metric = lp_distance else: metric = self.metric + sklearn_metric = _to_multivariate_metric(metric, self._sample_points) else: diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index d75a7fbe4..99c0404de 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -151,9 +151,9 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, Notes ----- - This estimator wraps the scikit-learn analogous class + This estimator wraps the scikit-learn class :class:`~sklearn.neighbors.LocalOutlierFactor` employing functional - metrics instead of the multivariate ones. + metrics and data instead of the multivariate ones. See also -------- @@ -193,7 +193,7 @@ def _init_estimator(self, sklearn_metric): return _LocalOutlierFactor( n_neighbors=self.n_neighbors, algorithm=self.algorithm, - leaf_size=self.leaf_size, metric=self.metric, + leaf_size=self.leaf_size, metric=sklearn_metric, metric_params=self.metric_params, contamination=self.contamination, novelty=self.novelty, n_jobs=self.n_jobs) @@ -224,13 +224,15 @@ def fit(self, X, y=None): return self - def predict(self, X): + def predict(self, X=None): """Predict the labels (1 inlier, -1 outlier) of X according to LOF. This method allows to generalize prediction to *new observations* (not in the training set). Only available for novelty detection (when novelty is set to True). + If X is None, returns the same as fit_predict(X_train). + Parameters ---------- X : :class:`~skfda.FDataGrid` or array_like @@ -356,4 +358,4 @@ def score_samples(self, X): self._check_is_fitted() X_multivariate = self._transform_to_multivariate(X) - return self.estimator_.decision_function(X_multivariate) + return self.estimator_.score_samples(X_multivariate) diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index d4df75fdc..77c63f77d 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -3,7 +3,7 @@ import unittest import numpy as np -from skfda.datasets import make_multimodal_samples +from skfda.datasets import make_multimodal_samples, make_sinusoidal_process from skfda.exploratory.stats import mean as l2_mean from skfda.misc.metrics import lp_distance, pairwise_distance from skfda.ml.classification import (KNeighborsClassifier, @@ -11,6 +11,7 @@ NearestCentroids) from skfda.ml.clustering import NearestNeighbors from skfda.ml.regression import KNeighborsRegressor, RadiusNeighborsRegressor +from skfda.exploratory.outliers import LocalOutlierFactor from skfda.representation.basis import Fourier @@ -41,6 +42,13 @@ def setUp(self): self.probs = np.array(15 * [[1., 0.]] + 15 * [[0., 1.]])[idx] + # Dataset with outliers + fd_clean = make_sinusoidal_process(n_samples=25, error_std=0, + phase_std=0.1, random_state=0) + fd_outliers = make_sinusoidal_process(n_samples=2, error_std=0, + phase_mean=0.5, random_state=5) + self.fd_lof = fd_outliers.concatenate(fd_clean) + def test_predict_classifier(self): """Tests predict for neighbors classifier""" @@ -86,27 +94,31 @@ def test_kneighbors(self): nn = NearestNeighbors() nn.fit(self.X) + lof = LocalOutlierFactor(n_neighbors=5) + lof.fit(self.X) + knn = KNeighborsClassifier() knn.fit(self.X, self.y) knnr = KNeighborsRegressor() knnr.fit(self.X, self.modes_location) - for neigh in [nn, knn, knnr]: + for neigh in [nn, knn, knnr, lof]: dist, links = neigh.kneighbors(self.X[:4]) + np.testing.assert_array_equal(links, [[0, 7, 21, 23, 15], [1, 12, 19, 18, 17], [2, 17, 22, 27, 26], [3, 4, 9, 5, 25]]) + graph = neigh.kneighbors_graph(self.X[:4]) + dist_kneigh = lp_distance(self.X[0], self.X[7]) np.testing.assert_array_almost_equal(dist[0, 1], dist_kneigh) - graph = neigh.kneighbors_graph(self.X[:4]) - for i in range(30): self.assertEqual(graph[0, i] == 1.0, i in links[0]) self.assertEqual(graph[0, i] == 0.0, i not in links[0]) @@ -325,6 +337,97 @@ def test_multivariate_response_score(self): with np.testing.assert_raises(ValueError): neigh.score(self.X[:5], y) + def test_lof_fit_predict(self): + """ Test same results with different forms to call fit_predict""" + + # Outliers + expected = np.ones(len(self.fd_lof)) + expected[0:2] = -1 + + # With default l2 distance + lof = LocalOutlierFactor() + res = lof.fit_predict(self.fd_lof) + np.testing.assert_array_equal(expected, res) + + # With explicit l2 distance + lof2 = LocalOutlierFactor(metric=lp_distance) + res2 = lof2.fit_predict(self.fd_lof) + np.testing.assert_array_equal(expected, res2) + + d = pairwise_distance(lp_distance) + distances = d(self.fd_lof, self.fd_lof) + + # With precompute distances + lof3 = LocalOutlierFactor(metric="precomputed") + res3 = lof3.fit_predict(distances) + np.testing.assert_array_equal(expected, res3) + + # With multivariate sklearn + lof4 = LocalOutlierFactor(metric="euclidean", multivariate_metric=True) + res4 = lof4.fit_predict(self.fd_lof) + np.testing.assert_array_equal(expected, res4) + + # Other way of call fit_predict, undocumented in sklearn + lof5 = LocalOutlierFactor(novelty=True) + res5 = lof5.fit(self.fd_lof).predict() + np.testing.assert_array_equal(expected, res5) + + + # Check values of negative outlier factor + negative_lof = [-7.1068, -1.5412, -0.9961, -0.9854, -0.9896, -1.0993, + -1.065 , -0.9871, -0.9821, -0.9955, -1.0385, -1.0072, + -0.9832, -1.0134, -0.9939, -1.0074, -0.992, -0.992, + -0.9883, -1.0012, -1.1149, -1.002, -0.9994, -0.9869, + -0.9726, -0.9989, -0.9904] + + np.testing.assert_array_almost_equal( + lof.negative_outlier_factor_.round(4), negative_lof) + + # Check same negative outlier factor + np.testing.assert_array_almost_equal(lof.negative_outlier_factor_, + lof2.negative_outlier_factor_) + + np.testing.assert_array_almost_equal(lof.negative_outlier_factor_, + lof3.negative_outlier_factor_) + + + def test_lof_decision_function(self): + """ Test decision function and score samples of LOF""" + + lof = LocalOutlierFactor(novelty=True) + lof.fit(self.fd_lof[5:]) + + score = lof.score_samples(self.fd_lof[:5]) + + np.testing.assert_array_almost_equal( + score.round(4),[-5.9726, -1.3445, -0.9853, -0.9817, -0.985 ], + err_msg='Error in LocalOutlierFactor.score_samples') + + # Test decision_function = score_function - offset + np.testing.assert_array_almost_equal( + lof.decision_function(self.fd_lof[:5]), score - lof.offset_, + err_msg='Error in LocalOutlierFactor.decision_function') + + + def test_lof_exceptions(self): + """ Test error due to novelty attribute""" + + lof = LocalOutlierFactor(novelty=True) + + # Error in fit_predict function + with np.testing.assert_raises(AttributeError): + lof.fit_predict(self.fd_lof[5:]) + + lof.set_params(novelty=False) + lof.fit(self.fd_lof[5:]) + + # Error in predict function + with np.testing.assert_raises(AttributeError): + lof.predict(self.fd_lof[5:]) + + + + if __name__ == '__main__': print() From 33bce9a01fce0afbfb40c68f923aba84d6960562 Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 12 Sep 2019 14:23:25 +0200 Subject: [PATCH 008/624] Format code with autopep8 --- skfda/_neighbors/base.py | 1 - skfda/_neighbors/outlier.py | 6 +++--- tests/test_neighbors.py | 26 +++++++++----------------- 3 files changed, 12 insertions(+), 21 deletions(-) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 919e086c4..499d18cb8 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -97,7 +97,6 @@ def multivariate_metric(x, y, _check=False, **kwargs): class NeighborsBase(ABC, BaseEstimator): """Base class for nearest neighbors estimators.""" - def __init__(self, n_neighbors=None, radius=None, weights='uniform', algorithm='auto', leaf_size=30, metric='l2', metric_params=None, diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index 99c0404de..cbc0c2eb8 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -2,10 +2,11 @@ from sklearn.base import OutlierMixin from .base import (NeighborsBase, NeighborsMixin, KNeighborsMixin, - _to_multivariate_metric) + _to_multivariate_metric) from ..misc.metrics import lp_distance + class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, OutlierMixin): """Unsupervised Outlier Detection. @@ -164,6 +165,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` """ + def __init__(self, n_neighbors=20, algorithm='auto', leaf_size=30, metric='l2', metric_params=None, contamination='auto', novelty=False, @@ -203,7 +205,6 @@ def _store_fit_data(self): self.n_neighbors_ = self.estimator_.n_neighbors_ self.offset_ = self.estimator_.offset_ - def fit(self, X, y=None): """Fit the model using X as training data. @@ -251,7 +252,6 @@ def predict(self, X=None): return self.estimator_.predict(X_multivariate) - def fit_predict(self, X, y=None): """Fits the model to the training set X and returns the labels. diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index 77c63f77d..dde2a9094 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -107,7 +107,6 @@ def test_kneighbors(self): dist, links = neigh.kneighbors(self.X[:4]) - np.testing.assert_array_equal(links, [[0, 7, 21, 23, 15], [1, 12, 19, 18, 17], [2, 17, 22, 27, 26], @@ -165,7 +164,7 @@ def test_knn_functional_response(self): def test_knn_functional_response_sklearn(self): # Check sklearn metric knnr = KNeighborsRegressor(n_neighbors=1, metric='euclidean', - multivariate_metric=True) + multivariate_metric=True) knnr.fit(self.X, self.X) res = knnr.predict(self.X) @@ -174,7 +173,7 @@ def test_knn_functional_response_sklearn(self): def test_knn_functional_response_precomputed(self): knnr = KNeighborsRegressor(n_neighbors=4, weights='distance', - metric='precomputed') + metric='precomputed') d = pairwise_distance(lp_distance) distances = d(self.X[:4], self.X[:4]) @@ -186,8 +185,8 @@ def test_knn_functional_response_precomputed(self): def test_radius_functional_response(self): knnr = RadiusNeighborsRegressor(metric=lp_distance, - weights='distance', - regressor=l2_mean) + weights='distance', + regressor=l2_mean) knnr.fit(self.X, self.X) @@ -245,7 +244,7 @@ def test_radius_outlier_functional_response(self): # Test response knnr = RadiusNeighborsRegressor(radius=0.001, - outlier_response=self.X[0]) + outlier_response=self.X[0]) knnr.fit(self.X[:6], self.X[:6]) res = knnr.predict(self.X[:7]) @@ -302,7 +301,6 @@ def test_score_scalar_response(self): r = neigh.score(self.X, self.modes_location) np.testing.assert_almost_equal(r, 0.9975889963743335) - def test_score_functional_response(self): neigh = KNeighborsRegressor() @@ -372,12 +370,11 @@ def test_lof_fit_predict(self): res5 = lof5.fit(self.fd_lof).predict() np.testing.assert_array_equal(expected, res5) - # Check values of negative outlier factor negative_lof = [-7.1068, -1.5412, -0.9961, -0.9854, -0.9896, -1.0993, - -1.065 , -0.9871, -0.9821, -0.9955, -1.0385, -1.0072, - -0.9832, -1.0134, -0.9939, -1.0074, -0.992, -0.992, - -0.9883, -1.0012, -1.1149, -1.002, -0.9994, -0.9869, + -1.065, -0.9871, -0.9821, -0.9955, -1.0385, -1.0072, + -0.9832, -1.0134, -0.9939, -1.0074, -0.992, -0.992, + -0.9883, -1.0012, -1.1149, -1.002, -0.9994, -0.9869, -0.9726, -0.9989, -0.9904] np.testing.assert_array_almost_equal( @@ -390,7 +387,6 @@ def test_lof_fit_predict(self): np.testing.assert_array_almost_equal(lof.negative_outlier_factor_, lof3.negative_outlier_factor_) - def test_lof_decision_function(self): """ Test decision function and score samples of LOF""" @@ -400,7 +396,7 @@ def test_lof_decision_function(self): score = lof.score_samples(self.fd_lof[:5]) np.testing.assert_array_almost_equal( - score.round(4),[-5.9726, -1.3445, -0.9853, -0.9817, -0.985 ], + score.round(4), [-5.9726, -1.3445, -0.9853, -0.9817, -0.985], err_msg='Error in LocalOutlierFactor.score_samples') # Test decision_function = score_function - offset @@ -408,7 +404,6 @@ def test_lof_decision_function(self): lof.decision_function(self.fd_lof[:5]), score - lof.offset_, err_msg='Error in LocalOutlierFactor.decision_function') - def test_lof_exceptions(self): """ Test error due to novelty attribute""" @@ -426,9 +421,6 @@ def test_lof_exceptions(self): lof.predict(self.fd_lof[5:]) - - - if __name__ == '__main__': print() unittest.main() From d4def93e96f0be1c98ee2aae5363ef16116eb329 Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 12 Sep 2019 14:26:57 +0200 Subject: [PATCH 009/624] Change in doctest due to bug fixed --- skfda/_neighbors/outlier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index cbc0c2eb8..e353da4a0 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -123,7 +123,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, The negative outlier factor stored. >>> lof.negative_outlier_factor_.round(2) - array([-7.07, -1.54, -1. , -0.99, ..., -0.97, -1. , -0.99]) + array([-7.11, -1.54, -1. , -0.99, ..., -0.97, -1. , -0.99]) **Novelty detection with LOF**. From a0de069317e19c456f55b3746747312be7a167cc Mon Sep 17 00:00:00 2001 From: pablomm Date: Sat, 21 Sep 2019 15:17:47 +0200 Subject: [PATCH 010/624] Improvements in documentation index. * Add information in the index, and best presentation * Remove old index broken links * Update copyright in conf.py according to the current license --- docs/conf.py | 6 ++-- docs/index.rst | 77 +++++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 72 insertions(+), 11 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 18a8d6170..177af1566 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,9 +17,6 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # -# import os -# import sys -# sys.path.insert(0, '/home/miguel/Desktop/fda/fda') import os import sys @@ -79,7 +76,8 @@ # General information about the project. project = 'scikit-fda' -copyright = '2017, Author' +copyright = ('2019, Grupo de Aprendizaje Automático - ' + + 'Universidad Autónoma de Madrid') author = 'Author' # The language for content autogenerated by Sphinx. Refer to documentation diff --git a/docs/index.rst b/docs/index.rst index f5f999aff..a8aedd176 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -5,18 +5,81 @@ Welcome to scikit-fda's documentation! ====================================== +This package offers classes, methods and functions to give support to +Functional Data Analysis in Python. Includes a wide range of utils to work with +functional data, and its representation, exploratory analysis, or +preprocessing, among other tasks such as inference, classification, regression +or clustering of functional data. + +In the `project page `_ hosted by +Github you can find more information related to the development of the package. + + .. toctree:: - :includehidden: - :maxdepth: 4 + :maxdepth: 2 :caption: Contents: :titlesonly: apilist + + +.. toctree:: + :maxdepth: 1 + :titlesonly: + auto_examples/index -Indices and tables -================== +An exhaustive list of all the contents of the package can be found in the +:ref:`genindex`. + +Installation +------------ + +Currently, scikit-fda is available in Python 3.6 and 3.7, regardless of the +platform. The stable version can be installed via +`PyPI `_: + +.. code-block:: bash + + pip install scikit-fda + + +It is possible to install the latest version of the package, available in +the develop branch, by cloning this repository and doing a manual installation. + +.. code-block:: bash + + git clone https://github.com/GAA-UAM/scikit-fda.git + cd scikit-fda/ + pip install -r requirements.txt + python setup.py install + + +In this type of installation make sure that your default Python version is +currently supported, or change the python and pip commands by specifying a +version, such as python3.6. + + +Contributions +------------- + +All contributions are welcome. You can help this project grow in multiple ways, +from creating an issue, reporting an improvement or a bug, to doing a +repository fork and creating a pull request to the development branch. +The people involved at some point in the development of the package can be +found in the `contributors file +`_. + +Citation +-------- + +If you find this project useful, please cite: + +.. todo:: Include citation to scikit-fda paper. + +License +------- -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` +The package is licensed under the BSD 3-Clause License. A copy of the +`license `_ +can be found along with the code or in the project page. From 164073518458dbf13dd4f1f2f8239bc33f4bb6fa Mon Sep 17 00:00:00 2001 From: pablomm Date: Sat, 21 Sep 2019 15:38:43 +0200 Subject: [PATCH 011/624] Remove public references to LocalOutlierFactor --- docs/modules/exploratory/outliers.rst | 9 ---- examples/plot_local_outlier_factor.py | 73 -------------------------- skfda/_neighbors/__init__.py | 1 - skfda/_neighbors/classification.py | 8 +-- skfda/_neighbors/outlier.py | 2 +- skfda/_neighbors/regression.py | 4 +- skfda/_neighbors/unsupervised.py | 2 +- skfda/exploratory/outliers/__init__.py | 1 - tests/test_neighbors.py | 3 +- 9 files changed, 10 insertions(+), 93 deletions(-) delete mode 100644 examples/plot_local_outlier_factor.py diff --git a/docs/modules/exploratory/outliers.rst b/docs/modules/exploratory/outliers.rst index 4ee0c70c2..ef79c6367 100644 --- a/docs/modules/exploratory/outliers.rst +++ b/docs/modules/exploratory/outliers.rst @@ -41,12 +41,3 @@ with the following function. :toctree: autosummary skfda.exploratory.outliers.directional_outlyingness_stats - - -Local Outlier Factor --------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.outliers.LocalOutlierFactor diff --git a/examples/plot_local_outlier_factor.py b/examples/plot_local_outlier_factor.py deleted file mode 100644 index 4c70dfee4..000000000 --- a/examples/plot_local_outlier_factor.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -Outlier detection with Local Outlier Factor -=========================================== - -Shows the use of the Local Outlier Factor to detect outliers in the octane -dataset. -""" - -# Author: Pablo Marcos Manchón -# License: MIT - -# sphinx_gallery_thumbnail_number = 2 - -import matplotlib.pyplot as plt - -from skfda.datasets import fetch_octane -from skfda.exploratory.outliers import LocalOutlierFactor - - -############################################################################## -# First, we load the *octane dataset* consisting of 39 near infrared -# (NIR) spectra of gasoline samples, with wavelengths ranging from 1102nm to -# 1552nm with measurements every two nm. -# -# This dataset contains six outliers, studied in [RDEH2006]_ and [HuRS2015]_, -# to which ethanol was added. This different -# composition has an effect on the shape of the spectra of gasoline samples. -# - -fd, labels = fetch_octane(return_X_y=True) -fd.plot() - - -############################################################################## -# :class:`~skfda.exploratory.outliers.LocalOutlierFactor` -# (`LOF `_), based on -# the local density of the curves as described in [BKNS2000]_, may be used to -# detect these outliers. In order to get the results the -# :meth:`~skfda.exploratory.outliers.LocalOutlierFactor.fit_predict` -# method is used. -# - -lof = LocalOutlierFactor() -is_outlier = lof.fit_predict(fd) - -print(is_outlier) # 1 for inliners / -1 for outliers - -############################################################################## -# The curves detected as outliers correspond to the samples to which -# ethanol was added. -# - -# TODO: Use one hot encoding internally to allow arbitrary sample_labels -is_outlier[is_outlier == -1] = 0 - -fd.plot(sample_labels=is_outlier, label_colors=['darkorange', 'lightgrey'], - label_names=["outlier", "inliner"]) - - -############################################################################## -# .. rubric:: References -# .. [RDEH2006] Rousseeuw, Peter & Debruyne, Michiel & Engelen, Sanne & -# Hubert, Mia. (2006). Robustness and Outlier Detection in -# Chemometrics. Critical Reviews in Analytical Chemistry. 36. -# 221-242. 10.1080/10408340600969403. -# .. [HuRS2015] Hubert, Mia & Rousseeuw, Peter & Segaert, Pieter. (2015). -# Multivariate functional outlier detection. Statistical Methods and -# Applications. 24. 177-202. 10.1007/s10260-015-0297-8. -# .. [BKNS2000] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, -# J. (2000, May). LOF: identifying density-based local outliers. In ACM -# sigmod record. - -plt.show() diff --git a/skfda/_neighbors/__init__.py b/skfda/_neighbors/__init__.py index 0a9866b6a..58316566d 100644 --- a/skfda/_neighbors/__init__.py +++ b/skfda/_neighbors/__init__.py @@ -10,6 +10,5 @@ """ from .unsupervised import NearestNeighbors from .regression import KNeighborsRegressor, RadiusNeighborsRegressor -from .outlier import LocalOutlierFactor from .classification import (KNeighborsClassifier, RadiusNeighborsClassifier, NearestCentroids) diff --git a/skfda/_neighbors/classification.py b/skfda/_neighbors/classification.py index 33822ffe0..228ea4e2a 100644 --- a/skfda/_neighbors/classification.py +++ b/skfda/_neighbors/classification.py @@ -61,7 +61,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, Indicates if the metric used is a sklearn distance between vectors (see :class:`~sklearn.neighbors.DistanceMetric`) or a functional metric of the module `skfda.misc.metrics` if ``False``. - + Examples -------- Firstly, we will create a toy dataset with 2 classes @@ -97,7 +97,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + Notes ----- @@ -255,7 +255,7 @@ class RadiusNeighborsClassifier(NeighborsBase, NeighborsMixin, :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + Notes ----- @@ -360,7 +360,7 @@ class and return a :class:`FData` object with only one sample :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + """ diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index e353da4a0..8ce41cb49 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -102,7 +102,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, **Local Outlier Factor (LOF) for outlier detection**. - >>> from skfda.exploratory.outliers import LocalOutlierFactor + >>> from skfda._neighbors.outlier import LocalOutlierFactor Creation of simulated dataset with 2 outliers to be used with LOF. diff --git a/skfda/_neighbors/regression.py b/skfda/_neighbors/regression.py index 7f04680b1..715d87935 100644 --- a/skfda/_neighbors/regression.py +++ b/skfda/_neighbors/regression.py @@ -110,7 +110,7 @@ class KNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + Notes ----- @@ -280,7 +280,7 @@ class RadiusNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + Notes ----- diff --git a/skfda/_neighbors/unsupervised.py b/skfda/_neighbors/unsupervised.py index c6189e80e..b786cd425 100644 --- a/skfda/_neighbors/unsupervised.py +++ b/skfda/_neighbors/unsupervised.py @@ -87,7 +87,7 @@ class NearestNeighbors(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.classification.NearestCentroids` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` - :class:`~skfda.ml.exploratory.outliers.LocalOutlierFactor` + Notes ----- diff --git a/skfda/exploratory/outliers/__init__.py b/skfda/exploratory/outliers/__init__.py index 10595a551..666ee83f6 100644 --- a/skfda/exploratory/outliers/__init__.py +++ b/skfda/exploratory/outliers/__init__.py @@ -1,4 +1,3 @@ from ._directional_outlyingness import (directional_outlyingness_stats, DirectionalOutlierDetector) from ._iqr import IQROutlierDetector -from ..._neighbors import LocalOutlierFactor diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index dfe8337ad..60dffc190 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -11,7 +11,8 @@ NearestCentroids) from skfda.ml.clustering import NearestNeighbors from skfda.ml.regression import KNeighborsRegressor, RadiusNeighborsRegressor -from skfda.exploratory.outliers import LocalOutlierFactor +#from skfda.exploratory.outliers import LocalOutlierFactor +from skfda._neighbors.outlier import LocalOutlierFactor # Pending theory from skfda.representation.basis import Fourier From ffcb066517b4b9076454f8dd6795ab848513fba9 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 23 Sep 2019 17:29:39 +0200 Subject: [PATCH 012/624] Remove paper section --- README.rst | 8 ++++---- docs/index.rst | 9 ++++----- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/README.rst b/README.rst index 07a5a7017..c83e56535 100644 --- a/README.rst +++ b/README.rst @@ -88,11 +88,11 @@ The people involved at some point in the development of the package can be found in the `contributors file `_. -Citation -======== -If you find this project useful, please cite: +.. Citation + ======== + If you find this project useful, please cite: -.. todo:: Include citation to scikit-fda paper. + .. todo:: Include citation to scikit-fda paper. License ======= diff --git a/docs/index.rst b/docs/index.rst index a8aedd176..a3ecf34e1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -70,12 +70,11 @@ The people involved at some point in the development of the package can be found in the `contributors file `_. -Citation --------- +.. Citation + -------- + If you find this project useful, please cite: -If you find this project useful, please cite: - -.. todo:: Include citation to scikit-fda paper. + .. todo:: Include citation to scikit-fda paper. License ------- From 11f908160c8c79569a54d6abab2151d740dca810 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 23 Sep 2019 17:58:14 +0200 Subject: [PATCH 013/624] Change installation command --- README.rst | 12 ++++-------- docs/index.rst | 4 +--- 2 files changed, 5 insertions(+), 11 deletions(-) diff --git a/README.rst b/README.rst index c83e56535..757866897 100644 --- a/README.rst +++ b/README.rst @@ -44,22 +44,18 @@ Installation from source It is possible to install the latest version of the package, available in the develop branch, by cloning this repository and doing a manual installation. -.. code:: +.. code:: bash git clone https://github.com/GAA-UAM/scikit-fda.git - cd scikit-fda/ - pip install -r requirements.txt # Install dependencies - python setup.py install + pip install ./scikit-fda Make sure that your default Python version is currently supported, or change the python and pip commands by specifying a version, such as ``python3.6``: -.. code:: +.. code:: bash git clone https://github.com/GAA-UAM/scikit-fda.git - cd scikit-fda/ - python3.6 -m pip install -r requirements.txt # Install dependencies - python3.6 setup.py install + python3.6 -m pip install ./scikit-fda Requirements ------------ diff --git a/docs/index.rst b/docs/index.rst index a3ecf34e1..f451e872b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,9 +50,7 @@ the develop branch, by cloning this repository and doing a manual installation. .. code-block:: bash git clone https://github.com/GAA-UAM/scikit-fda.git - cd scikit-fda/ - pip install -r requirements.txt - python setup.py install + pip install ./scikit-fda In this type of installation make sure that your default Python version is From 9a0d5b693c9416a132801a9b79734dc99b0f4cdf Mon Sep 17 00:00:00 2001 From: pablomm Date: Tue, 1 Oct 2019 18:26:43 +0200 Subject: [PATCH 014/624] Shift Registration refactorized --- skfda/preprocessing/registration/__init__.py | 2 +- .../registration/_shift_registration.py | 534 +++++++++--------- skfda/preprocessing/registration/base.py | 43 ++ .../preprocessing/registration/validation.py | 479 ++++++++++++++++ 4 files changed, 781 insertions(+), 277 deletions(-) create mode 100644 skfda/preprocessing/registration/base.py create mode 100644 skfda/preprocessing/registration/validation.py diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 3ac379682..704c23371 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -9,7 +9,7 @@ landmark_registration_warping, landmark_registration) -from ._shift_registration import shift_registration, shift_registration_deltas +from ._shift_registration import ShiftRegistration from ._registration_utils import (mse_decomposition, invert_warping, diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 491188654..c75fb1205 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -9,285 +9,267 @@ import numpy as np from ..._utils import constants +from .base import RegistrationTransformer +from ... import FData, FDataGrid __author__ = "Pablo Marcos Manchón" __email__ = "pablo.marcosm@estudiante.uam.es" +class ShiftRegistration(RegistrationTransformer): + + def __init__(self, maxiter=5, tol=1e-2, restrict_domain=False, + template="mean", extrapolation=None, step_size=1, + initial=None, output_points=None, **kwargs): + self.max_iter = maxiter + self.tol = tol + self.template = template + self.restrict_domain = restrict_domain + self.extrapolation = extrapolation + self.step_size = step_size + self.initial = initial + self.output_points = output_points + + + def _shift_registration_deltas(self, fd, template): + r"""Return the lists of shifts used in the shift registration procedure. + + Realizes a registration of the curves, using shift aligment, as is + defined in [RS05-7-2-1]_. Calculates :math:`\delta_{i}` for each sample + such that :math:`x_i(t + \delta_{i})` minimizes the least squares + criterion: + + .. math:: + \text{REGSSE} = \sum_{i=1}^{N} \int_{\mathcal{T}} + [x_i(t + \delta_i) - \hat\mu(t)]^2 ds + + Estimates the shift parameter :math:`\delta_i` iteratively by + using a modified Newton-Raphson algorithm, updating the mean + in each iteration, as is described in detail in [RS05-7-9-1-1]_. + + Method only implemented for Funtional objects with domain and image + dimension equal to 1. + + Args: + fd (:class:`FData`): Functional data object to be registered. + maxiter (int, optional): Maximun number of iterations. + Defaults to 5. + tol (float, optional): Tolerance allowable. The process will stop if + :math:`\max_{i}|\delta_{i}^{(\nu)}-\delta_{i}^{(\nu-1)}|>> from skfda.datasets import make_sinusoidal_process + >>> from skfda.representation.basis import Fourier + >>> from skfda.preprocessing.registration import ( + ... shift_registration_deltas) + >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, + ... random_state=1) + + Registration of data in discretized form: + + >>> shift_registration_deltas(fd).round(3) + array([-0.022, 0.03 ]) + + Registration of data in basis form: + + >>> fd = fd.to_basis(Fourier()) + >>> shift_registration_deltas(fd).round(3) + array([-0.022, 0.03 ]) + + + References: + .. [RS05-7-2-1] Ramsay, J., Silverman, B. W. (2005). Shift + registration. In *Functional Data Analysis* (pp. 129-132). + Springer. + .. [RS05-7-9-1-1] Ramsay, J., Silverman, B. W. (2005). Shift + registration by the Newton-Raphson algorithm. In *Functional + Data Analysis* (pp. 142-144). Springer. + """ + + # Initial estimation of the shifts + + if fd.dim_codomain > 1 or fd.dim_domain > 1: + raise NotImplementedError("Method for unidimensional data.") + + domain_range = fd.domain_range[0] + + if self.initial is None: + delta = np.zeros(fd.n_samples) + + elif len(self.initial) != fd.n_samples: + raise ValueError(f"the initial shift ({len(self.initial)}) must have the " + f"same length than the number of samples " + f"({fd.n_samples})") + else: + delta = np.asarray(self.initial) + + # Fine equispaced mesh to evaluate the samples + if self.output_points is None: + + try: + output_points = fd.sample_points[0] + nfine = len(output_points) + except AttributeError: + nfine = max(fd.n_basis * constants.BASIS_MIN_FACTOR + 1, + constants.N_POINTS_COARSE_MESH) + output_points = np.linspace(*domain_range, nfine) + + else: + nfine = len(self.output_points) + output_points = np.asarray(self.output_points) + + # Auxiliar array to avoid multiple memory allocations + delta_aux = np.empty(fd.n_samples) + + # Computes the derivate of originals curves in the mesh points + D1x = fd.evaluate(output_points, derivative=1, keepdims=False) + + # Second term of the second derivate estimation of REGSSE. The + # first term has been dropped to improve convergence (see references) + d2_regsse = scipy.integrate.trapz(np.square(D1x), output_points, + axis=1) + + max_diff = self.tol + 1 + self.n_iter_ = 0 + + # Case template fixed + if isinstance(template, FData): + original_template = template + tfine_aux = template.evaluate(output_points, keepdims=False) -def shift_registration_deltas(fd, *, maxiter=5, tol=1e-2, - restrict_domain=False, extrapolation=None, - step_size=1, initial=None, eval_points=None): - r"""Return the lists of shifts used in the shift registration procedure. - - Realizes a registration of the curves, using shift aligment, as is - defined in [RS05-7-2-1]_. Calculates :math:`\delta_{i}` for each sample - such that :math:`x_i(t + \delta_{i})` minimizes the least squares - criterion: - - .. math:: - \text{REGSSE} = \sum_{i=1}^{N} \int_{\mathcal{T}} - [x_i(t + \delta_i) - \hat\mu(t)]^2 ds - - Estimates the shift parameter :math:`\delta_i` iteratively by - using a modified Newton-Raphson algorithm, updating the mean - in each iteration, as is described in detail in [RS05-7-9-1-1]_. - - Method only implemented for Funtional objects with domain and image - dimension equal to 1. - - Args: - fd (:class:`FData`): Functional data object to be registered. - maxiter (int, optional): Maximun number of iterations. - Defaults to 5. - tol (float, optional): Tolerance allowable. The process will stop if - :math:`\max_{i}|\delta_{i}^{(\nu)}-\delta_{i}^{(\nu-1)}|>> from skfda.datasets import make_sinusoidal_process - >>> from skfda.representation.basis import Fourier - >>> from skfda.preprocessing.registration import ( - ... shift_registration_deltas) - >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, - ... random_state=1) - - Registration of data in discretized form: - - >>> shift_registration_deltas(fd).round(3) - array([-0.022, 0.03 ]) - - Registration of data in basis form: - - >>> fd = fd.to_basis(Fourier()) - >>> shift_registration_deltas(fd).round(3) - array([-0.022, 0.03 ]) - - - References: - .. [RS05-7-2-1] Ramsay, J., Silverman, B. W. (2005). Shift - registration. In *Functional Data Analysis* (pp. 129-132). - Springer. - .. [RS05-7-9-1-1] Ramsay, J., Silverman, B. W. (2005). Shift - registration by the Newton-Raphson algorithm. In *Functional - Data Analysis* (pp. 142-144). Springer. - """ - - # Initial estimation of the shifts - - if fd.dim_codomain > 1 or fd.dim_domain > 1: - raise NotImplementedError("Method for unidimensional data.") - - domain_range = fd.domain_range[0] - - if initial is None: - delta = np.zeros(fd.n_samples) - - elif len(initial) != fd.n_samples: - raise ValueError(f"the initial shift ({len(initial)}) must have the " - f"same length than the number of samples " - f"({fd.n_samples})") - else: - delta = np.asarray(initial) - - # Fine equispaced mesh to evaluate the samples - if eval_points is None: - - try: - eval_points = fd.sample_points[0] - nfine = len(eval_points) - except AttributeError: - nfine = max(fd.n_basis * constants.BASIS_MIN_FACTOR + 1, - constants.N_POINTS_COARSE_MESH) - eval_points = np.linspace(*domain_range, nfine) - - else: - nfine = len(eval_points) - eval_points = np.asarray(eval_points) - - # Auxiliar arrays to avoid multiple memory allocations - delta_aux = np.empty(fd.n_samples) - tfine_aux = np.empty(nfine) - - # Computes the derivate of originals curves in the mesh points - D1x = fd.evaluate(eval_points, derivative=1, keepdims=False) - - # Second term of the second derivate estimation of REGSSE. The - # first term has been dropped to improve convergence (see references) - d2_regsse = scipy.integrate.trapz(np.square(D1x), eval_points, - axis=1) - - max_diff = tol + 1 - iter = 0 - - # Auxiliar array if the domain will be restricted - if restrict_domain: - D1x_tmp = D1x - tfine_tmp = eval_points - tfine_aux_tmp = tfine_aux - domain = np.empty(nfine, dtype=np.dtype(bool)) - - ones = np.ones(fd.n_samples) - eval_points_rep = np.outer(ones, eval_points) - - # Newton-Rhapson iteration - while max_diff > tol and iter < maxiter: - - # Updates the limits for non periodic functions ignoring the ends - if restrict_domain: - # Calculates the new limits - a = domain_range[0] - min(np.min(delta), 0) - b = domain_range[1] - max(np.max(delta), 0) - - # New interval is (a,b) - np.logical_and(tfine_tmp >= a, tfine_tmp <= b, out=domain) - eval_points = tfine_tmp[domain] - tfine_aux = tfine_aux_tmp[domain] - D1x = D1x_tmp[:, domain] - # Reescale the second derivate could be other approach - # d2_regsse = - # d2_regsse_original * ( 1 + (a - b) / (domain[1] - domain[0])) - d2_regsse = scipy.integrate.trapz(np.square(D1x), - eval_points, axis=1) - eval_points_rep = np.outer(ones, eval_points) - - # Computes the new values shifted - x = fd.evaluate(eval_points_rep + np.atleast_2d(delta).T, - aligned_evaluation=False, - extrapolation=extrapolation, - keepdims=False) - - x.mean(axis=0, out=tfine_aux) - - # Calculates x - mean - np.subtract(x, tfine_aux, out=x) - - d1_regsse = scipy.integrate.trapz(np.multiply(x, D1x, out=x), - eval_points, axis=1) - # Updates the shifts by the Newton-Rhapson iteration - # delta = delta - step_size * d1_regsse / d2_regsse - np.divide(d1_regsse, d2_regsse, out=delta_aux) - np.multiply(delta_aux, step_size, out=delta_aux) - np.subtract(delta, delta_aux, out=delta) - - # Updates convergence criterions - max_diff = np.abs(delta_aux, out=delta_aux).max() - iter += 1 - - return delta - - -def shift_registration(fd, *, maxiter=5, tol=1e-2, restrict_domain=False, - extrapolation=None, step_size=1, initial=None, - eval_points=None, **kwargs): - r"""Perform shift registration of the curves. - - Realizes a registration of the curves, using shift aligment, as is - defined in [RS05-7-2]_. Calculates :math:`\delta_{i}` for each sample - such that :math:`x_i(t + \delta_{i})` minimizes the least squares - criterion: - - .. math:: - \text{REGSSE} = \sum_{i=1}^{N} \int_{\mathcal{T}} - [x_i(t + \delta_i) - \hat\mu(t)]^2 ds - - Estimates the shift parameter :math:`\delta_i` iteratively by - using a modified Newton-Raphson algorithm, updating the mean - in each iteration, as is described in detail in [RS05-7-9-1]_. - - Args: - fd (:class:`FData`): Functional data object to be registered. - maxiter (int, optional): Maximun number of iterations. - Defaults to 5. - tol (float, optional): Tolerance allowable. The process will stop if - :math:`\max_{i}|\delta_{i}^{(\nu)}-\delta_{i}^{(\nu-1)}|>> from skfda.datasets import make_sinusoidal_process - >>> from skfda.representation.basis import Fourier - >>> from skfda.preprocessing.registration import shift_registration - >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, - ... random_state=1) - - Registration of data in discretized form: - - >>> shift_registration(fd) - FDataGrid(...) - - Registration of data in basis form: - - >>> fd = fd.to_basis(Fourier()) - >>> shift_registration(fd) - FDataBasis(...) - - References: - .. [RS05-7-2] Ramsay, J., Silverman, B. W. (2005). Shift - registration. In *Functional Data Analysis* (pp. 129-132). - Springer. - .. [RS05-7-9-1] Ramsay, J., Silverman, B. W. (2005). Shift - registration by the Newton-Raphson algorithm. In *Functional - Data Analysis* (pp. 142-144). Springer. - """ - - delta = shift_registration_deltas(fd, maxiter=maxiter, tol=tol, - restrict_domain=restrict_domain, - extrapolation=extrapolation, - step_size=step_size, initial=initial, - eval_points=eval_points) - - # Computes the values with the final shift to construct the FDataBasis - return fd.shift(delta, restrict_domain=restrict_domain, - extrapolation=extrapolation, - eval_points=eval_points, **kwargs) + if self.restrict_domain: + template_points_aux = tfine_aux + + template="fixed" + else: + tfine_aux = np.empty(nfine) + + # Auxiliar array if the domain will be restricted + if self.restrict_domain: + D1x_tmp = D1x + tfine_tmp = output_points + tfine_aux_tmp = tfine_aux + domain = np.empty(nfine, dtype=np.dtype(bool)) + + ones = np.ones(fd.n_samples) + output_points_rep = np.outer(ones, output_points) + + # Newton-Rhapson iteration + while max_diff > self.tol and self.n_iter_ < self.max_iter: + + # Updates the limits for non periodic functions ignoring the ends + if self.restrict_domain: + # Calculates the new limits + a = domain_range[0] - min(np.min(delta), 0) + b = domain_range[1] - max(np.max(delta), 0) + + # New interval is (a,b) + np.logical_and(tfine_tmp >= a, tfine_tmp <= b, out=domain) + output_points = tfine_tmp[domain] + tfine_aux = tfine_aux_tmp[domain] + D1x = D1x_tmp[:, domain] + # Reescale the second derivate could be other approach + # d2_regsse = + # d2_regsse_original * ( 1 + (a - b) / (domain[1] - domain[0])) + d2_regsse = scipy.integrate.trapz(np.square(D1x), + output_points, axis=1) + + # Recompute base points for evaluation + output_points_rep = np.outer(ones, output_points) + + # Computes the new values shifted + x = fd.evaluate(output_points_rep + np.atleast_2d(delta).T, + aligned_evaluation=False, + extrapolation=self.extrapolation, + keepdims=False) + + if template == "mean": + print("Updating mean") + x.mean(axis=0, out=tfine_aux) + elif template == "fixed" and self.restrict_domain: + print("Restricting mean") + tfine_aux = template_points_aux[domain] + + # Calculates x - mean + np.subtract(x, tfine_aux, out=x) + + d1_regsse = scipy.integrate.trapz(np.multiply(x, D1x, out=x), + output_points, axis=1) + # Updates the shifts by the Newton-Rhapson iteration + # delta = delta - step_size * d1_regsse / d2_regsse + np.divide(d1_regsse, d2_regsse, out=delta_aux) + np.multiply(delta_aux, self.step_size, out=delta_aux) + np.subtract(delta, delta_aux, out=delta) + + # Updates convergence criterions + max_diff = np.abs(delta_aux, out=delta_aux).max() + self.n_iter_ += 1 + + + if template == "fixed": + + # Stores the original template instead of build it again + template = original_template + else: + + # Stores the template in an FDataGrid + template = FDataGrid(tfine_aux, sample_points=output_points) + + return delta, template + + + def fit_transform(self, X: FData, y=None): + + deltas, template = self._shift_registration_deltas(X, self.template) + self.template_ = template + self.deltas_ = deltas + + # Computes the values with the final shift to construct the FDataBasis + return X.shift(deltas, restrict_domain=self.restrict_domain, + extrapolation=self.extrapolation, + eval_points=self.output_points) + + def fit(self, X: FData, y=None): + + deltas, template = self._shift_registration_deltas(X, self.template) + + self.template_ = template + + return self + + def transform(self, X: FData, y=None): + + deltas, template = self._shift_registration_deltas(X, self.template_) + self.template_ = template + self.deltas_ = deltas + + # Computes the values with the final shift to construct the FDataBasis + return X.shift(deltas, restrict_domain=self.restrict_domain, + extrapolation=self.extrapolation, + eval_points=self.output_points) diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py new file mode 100644 index 000000000..0ae75c3b4 --- /dev/null +++ b/skfda/preprocessing/registration/base.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- +"""Registration method. +This module contains the abstract base class for all registration methods. +""" + +from abc import ABC +from sklearn.base import BaseEstimator, TransformerMixin +from ... import FData + +class RegistrationTransformer(ABC, BaseEstimator, TransformerMixin): + + def score(self, X: FData, y=None): + r"""Returns the percentage of total variation removed. + + Computes the squared multiple correlation index of the proportion of + the total variation due to phase, defined as: + + .. math:: + R^2 = \frac{\text{MSE}_{phase}}{\text{MSE}_{total}}, + + where :math:`\text{MSE}_{total}` is the mean squared error and + :math:`\text{MSE}_{phase}` is the mean squared error due to the phase + explained by the registration procedure. See :func:`mse_decomposition` + for a detailed explanation. + + Args: + X (FData): Functional data to be registered + y : Ignored + + Returns: + float. + + See also: + :class:`RegistrationScorer ` + :func:`mse_r_squared ` + :func:`least_squares ` + :func:`sobolev_least_squares ` + :func:`pairwise_correlation ` + + """ + from .validation import RegistrationScorer + + return RegistrationScorer()(self, X, y) diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py new file mode 100644 index 000000000..0fb01d456 --- /dev/null +++ b/skfda/preprocessing/registration/validation.py @@ -0,0 +1,479 @@ +"""Methods and classes for validation of the registration procedures""" + +import numpy as np +from typing import NamedTuple + + +def _to_grid(X, y, eval_points=None): + """Transforms the functional data in grids to perform calculations.""" + + from ... import FDataGrid + x_is_grid = isinstance(X, FDataGrid) + y_is_grid = isinstance(y, FDataGrid) + + if eval_points is not None: + X = X.to_grid(eval_points) + y = y.to_grid(eval_points) + elif x_is_grid and not y_is_grid: + y = y.to_grid(X.sample_points[0]) + elif not x_is_grid and y_is_grid: + X = X.to_grid(y.sample_points[0]) + elif not x_is_grid and not y_is_grid: + X = X.to_grid() + y = y.to_grid() + + return X, y + + +class RegistrationScorer(): + r"""Cross validation scoring for registration procedures. + + It calculates the score of a registration procedure, used to perform + model validation or parameter selection. + + Attributes: + score_function (callable): Function to compute the score. By default + it is used :func:`mse_r_squared`. See other + available metrics in the module :mod:`registration.validation + `. + + Args: + estimator (Estimator): Registration method estimator. The estimator + should be fitted. + X (:class:`FData `): Functional data to be registered. + y (:class:`FData `, optional): Functional data target. + If provided should be the same as `X` in general. + + Returns: + float: Cross validation score. + + Note: + The scorer passes the warpings generated in the registration procedure + to the `score_function` when necessary. + + See also: + :func:`mse_r_squared ` + :func:`least_squares ` + :func:`sobolev_least_squares ` + :func:`pairwise_correlation ` + + """ + + def __init__(self, score_function=None): + self.score_function = score_function + + def __call__(self, estimator, X, y=None): + + # By default it is used the R^2 coefficient of Ramsay + if self.score_function is None: + score_function = mse_r_squared + else: + score_function = self.score_function + + if y is None: + y = X + + # Register the data + X_reg = estimator.transform(X) + + # Pass the warpings if needed in the score function + # and the estimator generates warpings + # By the moment only used in the mse_r_squared + if (hasattr(estimator, 'warping_') and + 'warping' in score_function.__kwdefaults__): + return score_function(y, X_reg, warping=estimator.warping_) + else: + return score_function(y, X_reg) + + +class AmplitudePhaseDecomposition(NamedTuple): + r"""Named tuple to store the values of the amplitude-phase decomposition. + + Values of the amplitude phase decomposition computed in + :func:`mse_r_squared`, returned when `return_stats` is `True`. + + Args: + r_square (float): Squared correlation index :math:`R^2`. + mse_amp (float): Mean square error of amplitude + :math:`\text{MSE}_{amp}`. + mse_pha (float): Mean square error of phase :math:`\text{MSE}_{pha}`. + c_r (float): Constant :math:`C_R`. + + """ + r_squared: float + mse_amp: float + mse_pha: float + c_r: float + + +def mse_r_squared(X, y, *, warping=None, return_stats=False, eval_points=None): + r"""Compute mean square error measures for amplitude and phase variation. + + Once the registration has taken place, this function computes two mean + squared error measures, one for amplitude variation, and the other for + phase variation and returns a squared multiple correlation index + of the amount of variation in the unregistered functions is due to phase. + + Let :math:`x_i(t),y_i(t)` be the unregistered and registered functions + respectively. The total mean square error measure (see [RGS09-8-5]_) is + defined as + + + .. math:: + \text{MSE}_{total}= + \frac{1}{N}\sum_{i=1}^{N}\int[x_i(t)-\overline x(t)]^2dt + + The measures of amplitude and phase mean square error are + + .. math:: + \text{MSE}_{amp} = C_R \frac{1}{N} + \sum_{i=1}^{N} \int \left [ y_i(t) - \overline{y}(t) \right ]^2 dt + + .. math:: + \text{MSE}_{phase}= + \int \left [C_R \overline{y}^2(t) - \overline{x}^2(t) \right]dt + + where the constant :math:`C_R` is defined as + + .. math:: + + C_R = 1 + \frac{\frac{1}{N}\sum_{i}^{N}\int [Dh_i(t)-\overline{Dh}(t)] + [ y_i^2(t)- \overline{y^2}(t) ]dt} + {\frac{1}{N} \sum_{i}^{N} \int y_i^2(t)dt} + + whose structure is related to the covariation between the deformation + functions :math:`Dh_i(t)` and the squared registered functions + :math:`y_i^2(t)`. When these two sets of functions are independents + :math:`C_R=1`, as in the case of shift registration. + + The total mean square error is decomposed in the two sources of + variability. + + .. math:: + \text{MSE}_{total} = \text{MSE}_{amp} + \text{MSE}_{phase} + + The squared multiple correlation index of the proportion of the total + variation due to phase is defined as: + + .. math:: + R^2 = \frac{\text{MSE}_{phase}}{\text{MSE}_{total}} + + See [KR08-3]_ for a detailed explanation. + + + Args: + X (:class:`FData`): Unregistered functions. + y (:class:`FData`, optional): Target data, generally the same as X. By + default 'None', which uses `X` as target. + return_stats (boolean, optional): If `true` returns a named tuple + with four values: :math:`R^2`, :math:`MSE_{amp}`, :math:`MSE_{pha}` + and :math:`C_R`. Otherwise the squared correlation index + :math:`R^2` is returned. Default `False`. + eval_points: (array_like, optional): Set of points where the + functions are evaluated to obtain a discrete representation and + perform the calculation. + + Returns: + (float or :class:`NamedTuple `): squared correlation + index :math:`R^2` if `return_stats` is `False`. Otherwise a named + tuple containing: + + * `r_squared`: Squared correlation index :math:`R^2`. + * `mse_amp`: Mean square error of amplitude + :math:`\text{MSE}_{amp}`. + * `mse_pha`: Mean square error of phase :math:`\text{MSE}_{pha}`. + * `c_r`: Constant :math:`C_R`. + + + Raises: + ValueError: If the functional data is not univariate. + + References: + .. [KR08-3] Kneip, Alois & Ramsay, James. (2008). Quantifying + amplitude and phase variation. In *Combining Registration and + Fitting for Functional Models* (pp. 14-15). Journal of the American + Statistical Association. + .. [RGS09-8-5] Ramsay J.O., Giles Hooker & Spencer Graves (2009). In + *Functional Data Analysis with R and Matlab* (pp. 125-126). + Springer. + + See also: + :class:`RegistrationScorer ` + :func:`least_squares ` + :func:`sobolev_least_squares ` + :func:`pairwise_correlation ` + + """ + from scipy.integrate import simps + + # Parameter checks + if not X._univariate or not y._univariate: + raise ValueError("Scorer only valid for univariate data.") + + if len(y) != len(X): + raise ValueError(f"the registered and unregistered curves must have " + f"the same number of samples ({len(y)})!=({len(X)})") + + if warping is not None and len(warping) != len(X): + raise ValueError(f"The registered curves and the warping functions " + f"must have the same number of samples " + f"({len(X)})!=({len(warping)})") + + # Creates the mesh to discretize the functions + if eval_points is None: + try: + eval_points = y.sample_points[0] + + except AttributeError: + nfine = max(y.basis.nbasis * 10 + 1, 201) + eval_points = np.linspace(*y.domain_range[0], nfine) + else: + eval_points = np.asarray(eval_points) + + x_fine = X.evaluate(eval_points, keepdims=False) + y_fine = y.evaluate(eval_points, keepdims=False) + mu_fine = x_fine.mean(axis=0) # Mean unregistered function + eta_fine = y_fine.mean(axis=0) # Mean registered function + mu_fine_sq = np.square(mu_fine) + eta_fine_sq = np.square(eta_fine) + + # Total mean square error of the original funtions + # mse_total = scipy.integrate.simps( + # np.mean(np.square(x_fine - mu_fine), axis=0), + # eval_points) + + cr = 1. # Constant related to the covariation between the deformation + # functions and y^2 + + # If the warping functions are not provided, are suppose to be independent + if warping is not None: + # Derivates warping functions + dh_fine = warping.evaluate(eval_points, derivative=1, keepdims=False) + dh_fine_mean = dh_fine.mean(axis=0) + dh_fine_center = dh_fine - dh_fine_mean + + y_fine_sq = np.square(y_fine) # y^2 + y_fine_sq_center = np.subtract(y_fine_sq, eta_fine_sq) # y^2-E[y2] + + covariate = np.inner(dh_fine_center.T, y_fine_sq_center.T) + covariate = covariate.mean(axis=0) + cr += np.divide(simps(covariate, eval_points), + simps(eta_fine_sq, eval_points)) + + # mse due to phase variation + mse_pha = simps(cr * eta_fine_sq - mu_fine_sq, eval_points) + + # mse due to amplitude variation + # mse_amp = mse_total - mse_pha + y_fine_center = np.subtract(y_fine, eta_fine) + y_fine_center_sq = np.square(y_fine_center, out=y_fine_center) + y_fine_center_sq_mean = y_fine_center_sq.mean(axis=0) + + mse_amp = simps(y_fine_center_sq_mean, eval_points) + + # Total mean square error of the original funtions + mse_total = mse_pha + mse_amp + + # squared correlation measure of proportion of phase variation + rsq = mse_pha / (mse_total) + + if return_stats is True: + stats = AmplitudePhaseDecomposition(rsq, mse_amp, mse_pha, cr) + return stats + + return rsq + + +def least_squares(X, y, *, eval_points=None): + r"""Cross-validated measure of the registration procedure. + + Computes a cross-validated measure of the level of synchronization + [James07]_: + + .. math:: + ls=1 - \frac{1}{N} \sum_{i=1}^{N} \frac{\int\left(\tilde{f}_{i}(t)- + \frac{1}{N-1} \sum_{j \neq i} \tilde{f}_{j}(t)\right)^{2} dt}{\int + \left(f_{i}(t)-\frac{1}{N-1} \sum_{j \neq i} f_{j}(t)\right)^{2} dt} + + where :math:`f_i` and :math:`\tilde f_i` are the original and the + registered data respectively. + + The :math:`ls` measures the total cross-sectional variance of the aligned + functions, relative to the original value. + A value of :math:`1` would indicate an identical shape for all registered + curves, while zero corresponds to no improvement in the synchronization. It + can be negative because the model can be arbitrarily worse. + + Args: + X (:class:`FData `): Original functional data. + y (:class:`FData `): Registered functional data. + + + Note: + The original least square measure used in [S11-5-2-1]_ is defined as + :math:`1 - ls`, but has been modified according to the scikit-learn + scorers, where higher values correspond to better cross-validated + measures. + + + References: + .. [James07] G. James. Curve alignments by moments. Annals of Applied + Statistics, 1(2):480–501, 2007. + .. [S11-5-2-1] Srivastava, Anuj et. al. Registration of Functional Data + Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* + (p. 18). arXiv:1103.3817v2. + + See also: + :class:`RegistrationScorer ` + :func:`mse_r_squared ` + :func:`sobolev_least_squares ` + :func:`pairwise_correlation ` + + """ + from ...misc.metrics import pairwise_distance, lp_distance + + X, y = _to_grid(X, y, eval_points=eval_points) + + # Instead of compute f_i - 1/(N-1) sum(j!=i)f_j for each i = 1 ... N + # It is used (1 + 1/(N-1))f_i - 1/(N-1) sum(j=1 ... N) f_j = + # (1 + 1/(N-1))f_i - N/(N-1) mean(f) = + # C1 * f_1 - C2 mean(f) for each i= 1 ... N + N = len(X) + C1 = 1 + 1 / (N - 1) + C2 = N / (N - 1) + + X = C1 * X + y = C1 * y + mean_X = C2 * X.mean() + mean_y = C2 * y.mean() + + # Compute distance to mean + distance = pairwise_distance(lp_distance) + ls_x = distance(X, mean_X).flatten() + ls_y = distance(y, mean_y).flatten() + + # Quotient of distance + quotient = ls_y / ls_x + + return 1 - 1. / N * quotient.sum() + + +def sobolev_least_squares(X, y, *, eval_points=None): + r"""Cross-validated measure of the registration procedure. + + Computes a cross-validated measure of the level of synchronization + [S11-5-2-3]_: + + .. math:: + sls=1 - \frac{\sum_{i=1}^{N} \int\left(\dot{\tilde{f}}_{i}(t)- + \frac{1}{N} \sum_{j=1}^{N} \dot{\tilde{f}}_{j}\right)^{2} dt} + {\sum_{i=1}^{N} \int\left(\dot{f}_{i}(t)-\frac{1}{N} \sum_{j=1}^{N} + \dot{f}_{j}\right)^{2} dt} + + where :math:`f_i` and :math:`\tilde f_i` are the derivatives of the + original and the registered data respectively. + + This criterion measures the total cross-sectional variance of the + derivatives of the aligned functions, relative to the original value. + A value of :math:`1` would indicate an identical shape for all registered + curves, while zero corresponds to no improvement in the registration. It + can be negative because the model can be arbitrarily worse. + + Args: + X (:class:`FData `): Original functional data. + y (:class:`FData `): Registered functional data. + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain a discrete representation and + perform the calculation. + + Note: + The original sobolev least square measure used in [S11-5-2-3]_ is + defined as :math:`1 - sls`, but has been modified according to the + scikit-learn scorers, where higher values correspond to better + cross-validated measures. + + + References: + .. [S11-5-2-3] Srivastava, Anuj et. al. Registration of Functional Data + Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* + (p. 18). arXiv:1103.3817v2. + + See also: + :class:`RegistrationScorer ` + :func:`mse_r_squared ` + :func:`least_squares ` + :func:`pairwise_correlation ` + + """ + from ...misc.metrics import pairwise_distance, lp_distance + + # Compute derivative + X = X.derivative() + y = y.derivative() + + # Discretize if needed + X, y = _to_grid(X, y, eval_points=eval_points) + + # L2 distance to mean + distance = pairwise_distance(lp_distance) + + sls_x = distance(X, X.mean()) + sls_y = distance(y, y.mean()) + + return 1 - sls_y.sum() / sls_x.sum() + + +def pairwise_correlation(X, y, *, eval_points=None): + r"""Cross-validated measure of pairwise correlation between functions. + + Computes a cross-validated pairwise correlation between functions + to compare registration methods [S11-5-2-2]_ : + + .. math:: + pc=\frac{\sum_{i \neq j} \operatorname{cc}\left(\tilde{f}_{i}(t), + \tilde{f}_{j}(t)\right)}{\sum_{i \neq j} + \operatorname{cc}\left(f_{i}(t), f_{j}(t)\right)} + + where :math:`f_i` and :math:`\tilde f_i` are the original and registered + data respectively and :math:`cc(f, g)` is the pairwise Pearson’s + correlation between functions. + + The larger the value of :math:`pc`, the better the alignment between + functions in general. + + Args: + X (:class:`FData `): Original functional data. + y (:class:`FData `): Registered functional data. + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain a discrete representation and + perform the calculation. + + Note: + Pearson’s correlation between functions is calculated assuming + the samples are equiespaciated. + + References: + .. [S11-5-2-2] Srivastava, Anuj et. al. Registration of Functional Data + Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* + (p. 18). arXiv:1103.3817v2. + + See also: + :class:`RegistrationScorer ` + :func:`mse_r_squared ` + :func:`least_squares ` + :func:`sobolev_least_squares ` + + """ + # Discretize functional data if needed + X, y = _to_grid(X, y, eval_points=eval_points) + + # Compute correlation matrices with zeros in diagonal + # corrcoefs computes the correlation between vector, without weights + # due to the sample points + X_corr = np.corrcoef(X.data_matrix[..., 0]) + np.fill_diagonal(X_corr, 0.) + + y_corr = np.corrcoef(y.data_matrix[..., 0]) + np.fill_diagonal(y_corr, 0.) + + return y_corr.sum() / X_corr.sum() From 0d8d55bcbc4d0268f75d368a5c0eca3e9769feee Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 1 Oct 2019 19:11:31 +0200 Subject: [PATCH 015/624] Add Zenodo DOI. --- README.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 757866897..ad382cc08 100644 --- a/README.rst +++ b/README.rst @@ -4,7 +4,7 @@ scikit-fda: Functional Data Analysis in Python =================================================== -|python|_ |build-status| |docs| |Codecov|_ |PyPIBadge|_ |license|_ +|python|_ |build-status| |docs| |Codecov|_ |PyPIBadge|_ |license|_ |doi|_ Functional Data Analysis, or FDA, is the field of Statistics that analyses data that depend on a continuous parameter. @@ -120,3 +120,6 @@ license_ can be found along with the code. .. |license| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg .. _license: https://github.com/GAA-UAM/scikit-fda/blob/master/LICENSE.txt + +.. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468127.svg + :target: https://doi.org/10.5281/zenodo.3468127 From 84d7c53eb8dad80a7c19a7ac8ea78b5da39281b6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 1 Oct 2019 19:12:46 +0200 Subject: [PATCH 016/624] Fix Zenodo DOI --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index ad382cc08..d568bcaf8 100644 --- a/README.rst +++ b/README.rst @@ -122,4 +122,4 @@ license_ can be found along with the code. .. _license: https://github.com/GAA-UAM/scikit-fda/blob/master/LICENSE.txt .. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468127.svg - :target: https://doi.org/10.5281/zenodo.3468127 + :target: https://doi.org/10.5281/zenodo.3468127 From c8a253d344ff2d8805aa588bf2cdd52064b6e06f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Tue, 1 Oct 2019 19:15:22 +0200 Subject: [PATCH 017/624] Fix Zenodo DOI --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index d568bcaf8..6b58b841e 100644 --- a/README.rst +++ b/README.rst @@ -4,7 +4,7 @@ scikit-fda: Functional Data Analysis in Python =================================================== -|python|_ |build-status| |docs| |Codecov|_ |PyPIBadge|_ |license|_ |doi|_ +|python|_ |build-status| |docs| |Codecov|_ |PyPIBadge|_ |license|_ |doi| Functional Data Analysis, or FDA, is the field of Statistics that analyses data that depend on a continuous parameter. @@ -122,4 +122,4 @@ license_ can be found along with the code. .. _license: https://github.com/GAA-UAM/scikit-fda/blob/master/LICENSE.txt .. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3468127.svg - :target: https://doi.org/10.5281/zenodo.3468127 + :target: https://doi.org/10.5281/zenodo.3468127 From 985345d0b3b394f8bc6149ff707d8980ef78e207 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 1 Oct 2019 19:26:24 +0200 Subject: [PATCH 018/624] Fix covariance function. --- skfda/representation/grid.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 7adfdbb69..e216f8f64 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -545,7 +545,11 @@ def cov(self): else: dataset_label = None - return self.copy(data_matrix=np.cov(self.data_matrix, + if self.dim_domain != 1 or self.dim_codomain != 1: + raise NotImplementedError("Covariance only implemented " + "for univariate functions") + + return self.copy(data_matrix=np.cov(self.data_matrix[..., 0], rowvar=False)[np.newaxis, ...], sample_points=[self.sample_points[0], self.sample_points[0]], From df491086210481eebf6f7d4eeda3554fe7a6842c Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 16:06:37 +0200 Subject: [PATCH 019/624] Documentation of shift registration --- .gitignore | 2 + docs/modules/preprocessing/registration.rst | 7 +- .../registration/_shift_registration.py | 346 ++++++++++++------ 3 files changed, 235 insertions(+), 120 deletions(-) diff --git a/.gitignore b/.gitignore index 765aa793c..741aff5c6 100644 --- a/.gitignore +++ b/.gitignore @@ -105,3 +105,5 @@ ENV/ #IDE metadata .idea/ + +pip-wheel-metadata/ diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 941b063f6..14bfbcace 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -14,14 +14,13 @@ Many of the issues involved in registration can be solved by considering the simplest case, a simple shift in the time scale. This often happens because the time at which the recording process begins is arbitrary, and is unrelated to the beginning of the interesting segment of the data. In the -`Shift Registration Example <../auto_examples/plot_shift_registration_basis.html>`_ -it is shown the basic usage of this methods applied to periodic data. +:ref:`sphx_glr_auto_examples_plot_shift_registration_basis.py` example +is shown the basic usage of this method. .. autosummary:: :toctree: autosummary - skfda.preprocessing.registration.shift_registration - skfda.preprocessing.registration.shift_registration_deltas + skfda.preprocessing.registration.ShiftRegistration Landmark Registration diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index c75fb1205..8af5a2a18 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -1,10 +1,9 @@ -"""Shift Registration of functional data module. +"""Class to apply Shift Registration to functional data""" -This module contains methods to perform the registration of -functional data using shifts, in basis as well in discretized form. -""" +# Pablo Marcos Manchón +# pablo.marcosm@protonmail.com -import scipy.integrate +from scipy.integrate import simps import numpy as np @@ -13,15 +12,117 @@ from ... import FData, FDataGrid -__author__ = "Pablo Marcos Manchón" -__email__ = "pablo.marcosm@estudiante.uam.es" - class ShiftRegistration(RegistrationTransformer): - - def __init__(self, maxiter=5, tol=1e-2, restrict_domain=False, - template="mean", extrapolation=None, step_size=1, - initial=None, output_points=None, **kwargs): - self.max_iter = maxiter + r"""Register a functional dataset using shift alignment. + + Realizes the registration of a set of curves using a shift aligment + [RS05-7-2-1]_. Let :math:`\{x_i(t)\}_{i=1}^{N}` be a functional dataset, + calculates :math:`\delta_{i}` for each sample such that + :math:`x_i(t + \delta_{i})` minimizes the least squares criterion: + + .. math:: + \text{REGSSE} = \sum_{i=1}^{N} \int_{\mathcal{T}} + [x_i(t + \delta_i) - \hat\mu(t)]^2 ds + + Estimates each shift parameter :math:`\delta_i` iteratively by + using a modified Newton-Raphson algorithm, updating the template + :math:`\mu` in each iteration as is described in detail in [RS05-7-9-1-1]_. + + Method only implemented for univariate functional data. + + Args: + max_iter (int, optional): Maximun number of iterations. + Defaults sets to 5. Generally 2 or 3 iterations are sufficient to + obtain a good alignment. + tol (float, optional): Tolerance allowable. The process will stop if + :math:`\max_{i}|\delta_{i}^{(\nu)}-\delta_{i}^{(\nu-1)}|>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> from skfda.representation.basis import Fourier + + + Registration and creation of dataset in discretized form: + + >>> fd = make_sinusoidal_process(n_samples=10, error_std=0, + ... random_state=1) + >>> reg = ShiftRegistration(extrapolation="periodic") + >>> fd_registered = reg.fit_transform(fd) + >>> fd_registered + FDataGrid(...) + + Shifts applied during the transformation + + >>> reg.deltas_.round(3) + array([ -0.126, 0.19 , 0.029, 0.036, -0.104, 0.116, ..., -0.058]) + + + Registration and creation of a dataset in basis form using the + transformation previosly fitted: + + >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, + ... random_state=2) + >>> fd_basis = fd.to_basis(Fourier()) + >>> reg.transform(fd_basis) + FDataBasis(...) + + + References: + .. [RS05-7-2-1] Ramsay, J., Silverman, B. W. (2005). Shift + registration. In *Functional Data Analysis* (pp. 129-132). + Springer. + .. [RS05-7-9-1-1] Ramsay, J., Silverman, B. W. (2005). Shift + registration by the Newton-Raphson algorithm. In *Functional + Data Analysis* (pp. 142-144). Springer. + """ + + def __init__(self, max_iter=5, tol=1e-2, template="mean", + extrapolation=None, step_size=1, restrict_domain=False, + initial="zeros", output_points=None, **kwargs): + self.max_iter = max_iter self.tol = tol self.template = template self.restrict_domain = restrict_domain @@ -30,102 +131,34 @@ def __init__(self, maxiter=5, tol=1e-2, restrict_domain=False, self.initial = initial self.output_points = output_points - - def _shift_registration_deltas(self, fd, template): - r"""Return the lists of shifts used in the shift registration procedure. - - Realizes a registration of the curves, using shift aligment, as is - defined in [RS05-7-2-1]_. Calculates :math:`\delta_{i}` for each sample - such that :math:`x_i(t + \delta_{i})` minimizes the least squares - criterion: - - .. math:: - \text{REGSSE} = \sum_{i=1}^{N} \int_{\mathcal{T}} - [x_i(t + \delta_i) - \hat\mu(t)]^2 ds - - Estimates the shift parameter :math:`\delta_i` iteratively by - using a modified Newton-Raphson algorithm, updating the mean - in each iteration, as is described in detail in [RS05-7-9-1-1]_. - - Method only implemented for Funtional objects with domain and image - dimension equal to 1. + def _compute_deltas(self, fd, template): + r"""Compute the shifts to perform the registration. Args: - fd (:class:`FData`): Functional data object to be registered. - maxiter (int, optional): Maximun number of iterations. - Defaults to 5. - tol (float, optional): Tolerance allowable. The process will stop if - :math:`\max_{i}|\delta_{i}^{(\nu)}-\delta_{i}^{(\nu-1)}|>> from skfda.datasets import make_sinusoidal_process - >>> from skfda.representation.basis import Fourier - >>> from skfda.preprocessing.registration import ( - ... shift_registration_deltas) - >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, - ... random_state=1) - - Registration of data in discretized form: - - >>> shift_registration_deltas(fd).round(3) - array([-0.022, 0.03 ]) - - Registration of data in basis form: - - >>> fd = fd.to_basis(Fourier()) - >>> shift_registration_deltas(fd).round(3) - array([-0.022, 0.03 ]) - + tuple: A tuple with an array of deltas and an FDataGrid with the + template. - References: - .. [RS05-7-2-1] Ramsay, J., Silverman, B. W. (2005). Shift - registration. In *Functional Data Analysis* (pp. 129-132). - Springer. - .. [RS05-7-9-1-1] Ramsay, J., Silverman, B. W. (2005). Shift - registration by the Newton-Raphson algorithm. In *Functional - Data Analysis* (pp. 142-144). Springer. """ - - # Initial estimation of the shifts - if fd.dim_codomain > 1 or fd.dim_domain > 1: raise NotImplementedError("Method for unidimensional data.") domain_range = fd.domain_range[0] - if self.initial is None: + # Initial estimation of the shifts + if self.initial is "zeros": delta = np.zeros(fd.n_samples) elif len(self.initial) != fd.n_samples: - raise ValueError(f"the initial shift ({len(self.initial)}) must have the " - f"same length than the number of samples " - f"({fd.n_samples})") + raise ValueError(f"the initial shift ({len(self.initial)}) must " + f"have the same length than the number of samples" + f" ({fd.n_samples})") else: delta = np.asarray(self.initial) @@ -152,8 +185,7 @@ def _shift_registration_deltas(self, fd, template): # Second term of the second derivate estimation of REGSSE. The # first term has been dropped to improve convergence (see references) - d2_regsse = scipy.integrate.trapz(np.square(D1x), output_points, - axis=1) + d2_regsse = simps(np.square(D1x), output_points, axis=1) max_diff = self.tol + 1 self.n_iter_ = 0 @@ -166,7 +198,7 @@ def _shift_registration_deltas(self, fd, template): if self.restrict_domain: template_points_aux = tfine_aux - template="fixed" + template = "fixed" else: tfine_aux = np.empty(nfine) @@ -196,9 +228,8 @@ def _shift_registration_deltas(self, fd, template): D1x = D1x_tmp[:, domain] # Reescale the second derivate could be other approach # d2_regsse = - # d2_regsse_original * ( 1 + (a - b) / (domain[1] - domain[0])) - d2_regsse = scipy.integrate.trapz(np.square(D1x), - output_points, axis=1) + # d2_regsse_original * ( 1 + (a - b) / (domain[1] - domain[0])) + d2_regsse = simps(np.square(D1x), output_points, axis=1) # Recompute base points for evaluation output_points_rep = np.outer(ones, output_points) @@ -210,17 +241,15 @@ def _shift_registration_deltas(self, fd, template): keepdims=False) if template == "mean": - print("Updating mean") x.mean(axis=0, out=tfine_aux) elif template == "fixed" and self.restrict_domain: - print("Restricting mean") tfine_aux = template_points_aux[domain] # Calculates x - mean np.subtract(x, tfine_aux, out=x) - d1_regsse = scipy.integrate.trapz(np.multiply(x, D1x, out=x), - output_points, axis=1) + d1_regsse = simps(np.multiply(x, D1x, out=x), + output_points, axis=1) # Updates the shifts by the Newton-Rhapson iteration # delta = delta - step_size * d1_regsse / d2_regsse np.divide(d1_regsse, d2_regsse, out=delta_aux) @@ -231,7 +260,6 @@ def _shift_registration_deltas(self, fd, template): max_diff = np.abs(delta_aux, out=delta_aux).max() self.n_iter_ += 1 - if template == "fixed": # Stores the original template instead of build it again @@ -243,33 +271,119 @@ def _shift_registration_deltas(self, fd, template): return delta, template - def fit_transform(self, X: FData, y=None): + """Fit the estimator and transform the data. - deltas, template = self._shift_registration_deltas(X, self.template) - self.template_ = template - self.deltas_ = deltas + Args: + X (FData): Functional dataset to be transformed. + y (ignored): not used, present for API consistency by convention. - # Computes the values with the final shift to construct the FDataBasis - return X.shift(deltas, restrict_domain=self.restrict_domain, + Returns: + FData: Functional data registered. + + """ + self.deltas_, self.template_ = self._compute_deltas(X, self.template) + + return X.shift(self.deltas_, restrict_domain=self.restrict_domain, extrapolation=self.extrapolation, eval_points=self.output_points) def fit(self, X: FData, y=None): + """Fit the estimator. - deltas, template = self._shift_registration_deltas(X, self.template) + Args: + X (FData): Functional dataset used to construct the template for + the alignment. + y (ignored): not used, present for API consistency by convention. - self.template_ = template + Returns: + RegistrationTransformer: self + + """ + if self.restrict_domain: + raise AttributeError("fit and predict are not available when " + "restrict_domain=True, fitting and " + "transformation should be done together. Use " + "an extrapolation method with " + "restrict_domain=False or fit_predict") + + _, self.template_ = self._compute_deltas(X, self.template) return self def transform(self, X: FData, y=None): + """Register the data. + + Transforms the data using the template previously learned during + fitting. + + Args: + X (FData): Functional dataset to be transformed. + y (ignored): not used, present for API consistency by convention. + + Returns: + FData: Functional data registered. - deltas, template = self._shift_registration_deltas(X, self.template_) + Raises: + AttributeError: If it is call when restrict_domain=True. + + """ + + if self.restrict_domain: + raise AttributeError("fit and predict are not available when " + "restrict_domain=True, fitting and " + "transformation should be done together. Use " + "an extrapolation method with " + "restrict_domain=False or fit_predict") + + deltas, template = self._compute_deltas(X, self.template_) self.template_ = template self.deltas_ = deltas - # Computes the values with the final shift to construct the FDataBasis return X.shift(deltas, restrict_domain=self.restrict_domain, extrapolation=self.extrapolation, eval_points=self.output_points) + + def inverse_transform(self, X: FData, y=None): + """Applies the inverse transformation. + + Applies the opossite shift used in the last call to `transform`. + + Args: + X (FData): Functional dataset to be transformed. + y (ignored): not used, present for API consistency by convention. + + Returns: + FData: Functional data registered. + + Examples: + + Creation of a synthetic functional dataset. + + >>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> fd = make_sinusoidal_process(error_std=0, random_state=1) + >>> fd.extrapolation = 'periodic' + + Dataset registration and centering + + >>> reg = ShiftRegistration() + >>> fd_registered = reg.fit_transform(fd) + >>> fd_centered = fd_registered - fd_registered.mean() + + Reverse the translation applied during the registration + + >>> reg.inverse_transform(fd_centered) + FDataGrid(...) + + """ + if not hasattr(self, "deltas_"): + raise AttributeError("Data must be previously transformed to learn" + " the inverse transformation") + elif len(X) != len(self.deltas_): + raise ValueError("Data must contain the same number of samples " + "than the dataset previously transformed") + + return X.shift(-self.deltas_, restrict_domain=self.restrict_domain, + extrapolation=self.extrapolation, + eval_points=self.output_points) From b9f4eb766003cb458b63aa14ef87cbff8ccf0bde Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 22:24:29 +0200 Subject: [PATCH 020/624] Refactor shift registration example --- docs/modules/preprocessing/registration.rst | 2 +- ...on_basis.py => plot_shift_registration.py} | 52 ++++++++----------- skfda/preprocessing/registration/__init__.py | 2 +- ..._registration.py => shift_registration.py} | 11 ++-- 4 files changed, 32 insertions(+), 35 deletions(-) rename examples/{plot_shift_registration_basis.py => plot_shift_registration.py} (64%) rename skfda/preprocessing/registration/{_shift_registration.py => shift_registration.py} (97%) diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 14bfbcace..103ee7e1b 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -14,7 +14,7 @@ Many of the issues involved in registration can be solved by considering the simplest case, a simple shift in the time scale. This often happens because the time at which the recording process begins is arbitrary, and is unrelated to the beginning of the interesting segment of the data. In the -:ref:`sphx_glr_auto_examples_plot_shift_registration_basis.py` example +:ref:`sphx_glr_auto_examples_plot_shift_registration.py` example is shown the basic usage of this method. .. autosummary:: diff --git a/examples/plot_shift_registration_basis.py b/examples/plot_shift_registration.py similarity index 64% rename from examples/plot_shift_registration_basis.py rename to examples/plot_shift_registration.py index 79dd8bdff..e4838186f 100644 --- a/examples/plot_shift_registration_basis.py +++ b/examples/plot_shift_registration.py @@ -1,6 +1,6 @@ """ -Shift Registration of basis -=========================== +Shift Registration +================== Shows the use of shift registration applied to a sinusoidal process represented in a Fourier basis. @@ -12,8 +12,10 @@ # sphinx_gallery_thumbnail_number = 3 import matplotlib.pyplot as plt -import skfda +from skfda.datasets import make_sinusoidal_process +from skfda.preprocessing.registration import ShiftRegistration +from skfda.representation.basis import Fourier ############################################################################## # In this example we will use a @@ -24,7 +26,7 @@ # # In this example we want to register the curves using a translation # and remove the phase variation to perform further analysis. -fd = skfda.datasets.make_sinusoidal_process(random_state=1) +fd = make_sinusoidal_process(random_state=1) fd.plot() @@ -32,26 +34,23 @@ # We will smooth the curves using a basis representation, which will help us # to remove the gaussian noise. Smoothing before registration # is essential due to the use of derivatives in the optimization process. -# # Because of their sinusoidal nature we will use a Fourier basis. -basis = skfda.representation.basis.Fourier(n_basis=11) -fd_basis = fd.to_basis(basis) - +fd_basis = fd.to_basis(Fourier(n_basis=11)) fd_basis.plot() ############################################################################## -# We will apply the -# :func:`~skfda.preprocessing.registration.shift_registration`, +# We will use the +# :func:`~skfda.preprocessing.registration.ShiftRegistration` transformer, # which is suitable due to the periodicity of the dataset and the small # amount of amplitude variation. - -fd_registered = skfda.preprocessing.registration.shift_registration(fd_basis) - -############################################################################## +# # We can observe how the sinusoidal pattern is easily distinguishable # once the alignment has been made. +shift_registration = ShiftRegistration() +fd_registered = shift_registration.fit_transform(fd_basis) + fd_registered.plot() ############################################################################## @@ -63,28 +62,23 @@ # curves varying their amplitude with respect to the original process, # however, this effect is mitigated after the registration. -fig = fd_basis.mean().plot() -fd_registered.mean().plot(fig=fig) - # sinusoidal process without variation and noise -sine = skfda.datasets.make_sinusoidal_process(n_samples=1, phase_std=0, - amplitude_std=0, error_std=0) +sine = make_sinusoidal_process(n_samples=1, phase_std=0, + amplitude_std=0, error_std=0) -sine.plot(fig=fig, linestyle='dashed') +fig = fd_basis.mean().plot() +fd_registered.mean().plot(fig) +sine.plot(fig, linestyle='dashed') fig.axes[0].legend(['original mean', 'registered mean', 'sine']) ############################################################################## -# The values of the shifts :math:`\delta_i` may be relevant for further -# analysis, as they may be considered as nuisance or random effects. +# The values of the shifts :math:`\delta_i`, stored in the attribute `deltas_` +# may be relevant for further analysis, as they may be considered as nuisance +# or random effects. # -deltas = skfda.preprocessing.registration.shift_registration_deltas(fd_basis) -print(deltas) +print(shift_registration.deltas_) -############################################################################## -# The aligned functions can be obtained from the :math:`\delta_i` list -# using the `shift` method. -# -fd_basis.shift(deltas).plot() +plt.show() diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 704c23371..34f50d5e4 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -9,7 +9,7 @@ landmark_registration_warping, landmark_registration) -from ._shift_registration import ShiftRegistration +from .shift_registration import ShiftRegistration from ._registration_utils import (mse_decomposition, invert_warping, diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/shift_registration.py similarity index 97% rename from skfda/preprocessing/registration/_shift_registration.py rename to skfda/preprocessing/registration/shift_registration.py index 8af5a2a18..49f17bfb2 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -299,6 +299,9 @@ def fit(self, X: FData, y=None): Returns: RegistrationTransformer: self + Raises: + AttributeError: If this method is call when restrict_domain=True. + """ if self.restrict_domain: raise AttributeError("fit and predict are not available when " @@ -325,7 +328,7 @@ def transform(self, X: FData, y=None): FData: Functional data registered. Raises: - AttributeError: If it is call when restrict_domain=True. + AttributeError: If this method is call when restrict_domain=True. """ @@ -358,20 +361,20 @@ def inverse_transform(self, X: FData, y=None): Examples: - Creation of a synthetic functional dataset. + Creates a synthetic functional dataset. >>> from skfda.preprocessing.registration import ShiftRegistration >>> from skfda.datasets import make_sinusoidal_process >>> fd = make_sinusoidal_process(error_std=0, random_state=1) >>> fd.extrapolation = 'periodic' - Dataset registration and centering + Dataset registration and centering. >>> reg = ShiftRegistration() >>> fd_registered = reg.fit_transform(fd) >>> fd_centered = fd_registered - fd_registered.mean() - Reverse the translation applied during the registration + Reverse the translation applied during the registration. >>> reg.inverse_transform(fd_centered) FDataGrid(...) From db557bb6fecabb2e203588f7a8c61c36157b35b8 Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 22:48:55 +0200 Subject: [PATCH 021/624] Refactor shift registration tests --- tests/test_registration.py | 67 ++++++++++++++++++++++++++++++-------- 1 file changed, 53 insertions(+), 14 deletions(-) diff --git a/tests/test_registration.py b/tests/test_registration.py index f23e86690..b2b9cadcc 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -9,8 +9,7 @@ make_sinusoidal_process) from skfda.preprocessing.registration import ( normalize_warping, invert_warping, landmark_shift_deltas, landmark_shift, - landmark_registration_warping, landmark_registration, mse_decomposition, - shift_registration_deltas, shift_registration) + landmark_registration_warping, landmark_registration, ShiftRegistration) class TestWarping(unittest.TestCase): @@ -147,7 +146,8 @@ def test_landmark_registration(self): np.testing.assert_array_almost_equal(fd_reg(center), original_values, decimal=2) - def test_mse_decomposition(self): + def _test_mse_decomposition(self): + # Test disabled fd = make_multimodal_samples(n_samples=3, random_state=1) landmarks = make_multimodal_landmarks(n_samples=3, random_state=1) landmarks = landmarks.squeeze() @@ -160,26 +160,65 @@ def test_mse_decomposition(self): np.testing.assert_almost_equal(ret.rsq, 0.9915489952877273) np.testing.assert_almost_equal(ret.cr, 0.9999963424653829) - def test_shift_registration_deltas(self): +class TestShiftRegistration(unittest.TestCase): + """Test shift registration""" - fd = make_sinusoidal_process(n_samples=2, error_std=0, random_state=1) + def setUp(self): + """Initialization of samples""" + self.fd = fd = make_sinusoidal_process(n_samples=2, error_std=0, + random_state=1) + self.fd.extrapolation = "periodic" + + + def test_fit_transform(self): - deltas = shift_registration_deltas(fd).round(3) + reg = ShiftRegistration() + + # Test fit transform with FDataGrid + fd_reg = reg.fit_transform(self.fd) + + # Check attributes fitted + self.assertTrue(hasattr(reg, 'deltas_')) + self.assertTrue(hasattr(reg, 'template_')) + self.assertTrue(hasattr(reg, 'n_iter_')) + self.assertTrue(isinstance(fd_reg, FDataGrid)) + + deltas = reg.deltas_.round(3) np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) - fd = fd.to_basis(Fourier()) - deltas = shift_registration_deltas(fd).round(3) + # Test with Basis + fd = self.fd.to_basis(Fourier()) + reg.fit_transform(fd) + deltas = reg.deltas_.round(3) np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) - def test_shift_registration(self): + + def test_fit_and_transform(self): """Test wrapper of shift_registration_deltas""" - fd = make_sinusoidal_process(n_samples=2, error_std=0, random_state=1) + fd = make_sinusoidal_process(n_samples=2, error_std=0, random_state=10) + + reg = ShiftRegistration() + response = reg.fit(self.fd) + + # Check attributes and returned value + self.assertTrue(hasattr(reg, 'template_')) + self.assertTrue(response is reg) + + fd_registered = reg.transform(fd) + deltas = reg.deltas_.round(3) + np.testing.assert_array_almost_equal(deltas, [ 0.071, -0.071]) + + def test_inverse_transform(self): + + reg = ShiftRegistration() + fd = reg.fit_transform(self.fd) + fd = reg.inverse_transform(fd) + + np.testing.assert_array_almost_equal(fd.data_matrix, + self.fd.data_matrix, decimal=3) + - fd_reg = shift_registration(fd) - deltas = shift_registration_deltas(fd) - np.testing.assert_array_almost_equal(fd_reg.data_matrix, - fd.shift(deltas).data_matrix) if __name__ == '__main__': From 523338974788fd1fd82b9cfd927dd47fcae5d36e Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 22:56:29 +0200 Subject: [PATCH 022/624] Add validation to the docs and fix doctest --- docs/modules/preprocessing/registration.rst | 15 +- skfda/preprocessing/registration/__init__.py | 7 +- .../registration/_registration_utils.py | 202 ------------------ .../registration/shift_registration.py | 2 +- 4 files changed, 15 insertions(+), 211 deletions(-) diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 103ee7e1b..8b1fa283a 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -83,17 +83,20 @@ on the elastic framework. -Amplitude and Phase Decomposition ---------------------------------- +Validation +---------- -The amplitude and phase variation may be quantified by comparing a sample before -and after registration. The package contains an implementation of the -decomposition procedure developed by *Kneip and Ramsay (2008)*. +This module contains several classes methods for the quantification and +validation of the registration procedure. .. autosummary:: :toctree: autosummary - skfda.preprocessing.registration.mse_decomposition + skfda.preprocessing.registration.validation.RegistrationScorer + skfda.preprocessing.registration.validation.mse_r_squared + skfda.preprocessing.registration.validation.least_squares + skfda.preprocessing.registration.validation.sobolev_least_squares + skfda.preprocessing.registration.validation.pairwise_correlation Utility functions diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 34f50d5e4..f3311a4ab 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -11,11 +11,14 @@ from .shift_registration import ShiftRegistration -from ._registration_utils import (mse_decomposition, - invert_warping, +from ._registration_utils import (invert_warping, normalize_warping, _normalize_scale) +from .validation import (RegistrationScorer, AmplitudePhaseDecomposition, + mse_r_squared, least_squares, sobolev_least_squares, + pairwise_correlation) + from ._elastic import (to_srsf, from_srsf, elastic_registration, elastic_registration_warping, diff --git a/skfda/preprocessing/registration/_registration_utils.py b/skfda/preprocessing/registration/_registration_utils.py index e8735c584..4f13e42a8 100644 --- a/skfda/preprocessing/registration/_registration_utils.py +++ b/skfda/preprocessing/registration/_registration_utils.py @@ -14,208 +14,6 @@ __email__ = "pablo.marcosm@estudiante.uam.es" -def mse_decomposition(original_fdata, registered_fdata, warping_function=None, - *, eval_points=None): - r"""Compute mean square error measures for amplitude and phase variation. - - Once the registration has taken place, this function computes two mean - squared error measures, one for amplitude variation, and the other for - phase variation. It also computes a squared multiple correlation index - of the amount of variation in the unregistered functions is due to phase. - - Let :math:`x_i(t),y_i(t)` be the unregistered and registered functions - respectively. The total mean square error measure (see [RGS09-8-5]_) is - defined as - - - .. math:: - \text{MSE}_{total}= - \frac{1}{N}\sum_{i=1}^{N}\int[x_i(t)-\overline x(t)]^2dt - - We define the constant :math:`C_R` as - - .. math:: - - C_R = 1 + \frac{\frac{1}{N}\sum_{i}^{N}\int [Dh_i(t)-\overline{Dh}(t)] - [ y_i^2(t)- \overline{y^2}(t) ]dt} - {\frac{1}{N} \sum_{i}^{N} \int y_i^2(t)dt} - - Whose structure is related to the covariation between the deformation - functions :math:`Dh_i(t)` and the squared registered functions - :math:`y_i^2(t)`. When these two sets of functions are independents - :math:`C_R=1`, as in the case of shift registration. - - The measures of amplitude and phase mean square error are - - .. math:: - \text{MSE}_{amp} = C_R \frac{1}{N} - \sum_{i=1}^{N} \int \left [ y_i(t) - \overline{y}(t) \right ]^2 dt - - .. math:: - \text{MSE}_{phase}= - \int \left [C_R \overline{y}^2(t) - \overline{x}^2(t) \right]dt - - It can be shown that - - .. math:: - \text{MSE}_{total} = \text{MSE}_{amp} + \text{MSE}_{phase} - - The squared multiple correlation index of the proportion of the total - variation due to phase is defined as: - - .. math:: - R^2 = \frac{\text{MSE}_{phase}}{\text{MSE}_{total}} - - See [KR08-3]_ for a detailed explanation. - - - Args: - original_fdata (:class:`FData`): Unregistered functions. - regfd (:class:`FData`): Registered functions. - warping_function (:class:`FData`): Warping functions. - eval_points: (array_like, optional): Set of points where the - functions are evaluated to obtain a discrete representation. - - - Returns: - :class:`collections.namedtuple`: Tuple with amplitude mean square error - :math:`\text{MSE}_{amp}`, phase mean square error - :math:`\text{MSE}_{phase}`, squared correlation index :math:`R^2` - and constant :math:`C_R`. - - Raises: - ValueError: If the curves do not have the same number of samples. - - References: - .. [KR08-3] Kneip, Alois & Ramsay, James. (2008). Quantifying - amplitude and phase variation. In *Combining Registration and - Fitting for Functional Models* (pp. 14-15). Journal of the American - Statistical Association. - .. [RGS09-8-5] Ramsay J.O., Giles Hooker & Spencer Graves (2009). In - *Functional Data Analysis with R and Matlab* (pp. 125-126). - Springer. - - Examples: - - >>> from skfda.datasets import make_multimodal_landmarks - >>> from skfda.datasets import make_multimodal_samples - >>> from skfda.preprocessing.registration import ( - ... landmark_registration_warping, mse_decomposition) - - - We will create and register data. - - >>> fd = make_multimodal_samples(n_samples=3, random_state=1) - >>> landmarks = make_multimodal_landmarks(n_samples=3, random_state=1) - >>> landmarks = landmarks.squeeze() - >>> warping = landmark_registration_warping(fd, landmarks) - >>> fd_registered = fd.compose(warping) - >>> mse_amp, mse_pha, rsq, cr = mse_decomposition(fd, fd_registered, - ... warping) - - Mean square error produced by the amplitude variation. - - >>> f'{mse_amp:.6f}' - '0.000987' - - In this example we can observe that the main part of the mean square - error is due to the phase variation. - - >>> f'{mse_pha:.6f}' - '0.115769' - - Nearly 99% of the variation is due to phase. - - >>> f'{rsq:.6f}' - '0.991549' - - """ - - if registered_fdata.dim_domain != 1 or registered_fdata.dim_codomain != 1: - raise NotImplementedError - - if original_fdata.n_samples != registered_fdata.n_samples: - raise ValueError(f"the registered and unregistered curves must have " - f"the same number of samples " - f"({registered_fdata.n_samples})!= " - f"({original_fdata.n_samples})") - - if warping_function is not None and (warping_function.n_samples - != original_fdata.n_samples): - raise ValueError(f"the registered curves and the warping functions " - f"must have the same number of samples " - f"({registered_fdata.n_samples})" - f"!=({warping_function.n_samples})") - - # Creates the mesh to discretize the functions - if eval_points is None: - try: - eval_points = registered_fdata.sample_points[0] - - except AttributeError: - nfine = max(registered_fdata.basis.n_basis * 10 + 1, 201) - domain_range = registered_fdata.domain_range[0] - eval_points = np.linspace(*domain_range, nfine) - else: - eval_points = np.asarray(eval_points) - - x_fine = original_fdata.evaluate(eval_points, keepdims=False) - y_fine = registered_fdata.evaluate(eval_points, keepdims=False) - mu_fine = x_fine.mean(axis=0) # Mean unregistered function - eta_fine = y_fine.mean(axis=0) # Mean registered function - mu_fine_sq = np.square(mu_fine) - eta_fine_sq = np.square(eta_fine) - - # Total mean square error of the original funtions - # mse_total = scipy.integrate.simps( - # np.mean(np.square(x_fine - mu_fine), axis=0), - # eval_points) - - cr = 1. # Constant related to the covariation between the deformation - # functions and y^2 - - # If the warping functions are not provided, are suppose to be independent - if warping_function is not None: - # Derivates warping functions - dh_fine = warping_function.evaluate(eval_points, derivative=1, - keepdims=False) - dh_fine_mean = dh_fine.mean(axis=0) - dh_fine_center = dh_fine - dh_fine_mean - - y_fine_sq = np.square(y_fine) # y^2 - y_fine_sq_center = np.subtract( - y_fine_sq, eta_fine_sq) # y^2 - E[y^2] - - covariate = np.inner(dh_fine_center.T, y_fine_sq_center.T) - covariate = covariate.mean(axis=0) - cr += np.divide(scipy.integrate.simps(covariate, - eval_points), - scipy.integrate.simps(eta_fine_sq, - eval_points)) - - # mse due to phase variation - mse_pha = scipy.integrate.simps(cr * eta_fine_sq - mu_fine_sq, eval_points) - - # mse due to amplitude variation - # mse_amp = mse_total - mse_pha - y_fine_center = np.subtract(y_fine, eta_fine) - y_fine_center_sq = np.square(y_fine_center, out=y_fine_center) - y_fine_center_sq_mean = y_fine_center_sq.mean(axis=0) - - mse_amp = scipy.integrate.simps(y_fine_center_sq_mean, eval_points) - - # Total mean square error of the original funtions - mse_total = mse_pha + mse_amp - - # squared correlation measure of proportion of phase variation - rsq = mse_pha / (mse_total) - - mse_decomp = collections.namedtuple('mse_decomposition', - 'mse_amp mse_pha rsq cr') - - return mse_decomp(mse_amp, mse_pha, rsq, cr) - - def invert_warping(fdatagrid, *, eval_points=None): r"""Compute the inverse of a diffeomorphism. diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/shift_registration.py index 49f17bfb2..a23f839a7 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -97,7 +97,7 @@ class ShiftRegistration(RegistrationTransformer): Shifts applied during the transformation >>> reg.deltas_.round(3) - array([ -0.126, 0.19 , 0.029, 0.036, -0.104, 0.116, ..., -0.058]) + array([-0.126, 0.19 , 0.029, 0.036, -0.104, 0.116, ..., -0.058]) Registration and creation of a dataset in basis form using the From e63bf10b295bc2bd5ceda6f2ac2e6114ce17bc0d Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 23:46:52 +0200 Subject: [PATCH 023/624] ShiftRegistration accepts a callable as a template --- .../registration/shift_registration.py | 12 ++++ tests/test_registration.py | 55 ++++++++++++++++++- 2 files changed, 66 insertions(+), 1 deletion(-) diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/shift_registration.py index a23f839a7..c1aa5f5a6 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -4,6 +4,7 @@ # pablo.marcosm@protonmail.com from scipy.integrate import simps +from sklearn.utils.validation import check_is_fitted import numpy as np @@ -244,6 +245,10 @@ def _compute_deltas(self, fd, template): x.mean(axis=0, out=tfine_aux) elif template == "fixed" and self.restrict_domain: tfine_aux = template_points_aux[domain] + elif callable(template): # Callable + fd_x = FDataGrid(x, sample_points=output_points) + fd_tfine = template(fd_x) + tfine_aux = fd_tfine.data_matrix.flatten() # Calculates x - mean np.subtract(x, tfine_aux, out=x) @@ -339,6 +344,13 @@ def transform(self, X: FData, y=None): "an extrapolation method with " "restrict_domain=False or fit_predict") + # If the template is an FData, fit doesnt learn nothing + if not hasattr(self, 'template_') and isinstance(self.template, FData): + self.template_ = self.template + + # Check is fitted + check_is_fitted(self, 'template_') + deltas, template = self._compute_deltas(X, self.template_) self.template_ = template self.deltas_ = deltas diff --git a/tests/test_registration.py b/tests/test_registration.py index b2b9cadcc..ebb69d677 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -10,7 +10,8 @@ from skfda.preprocessing.registration import ( normalize_warping, invert_warping, landmark_shift_deltas, landmark_shift, landmark_registration_warping, landmark_registration, ShiftRegistration) - +from skfda.exploratory.stats import mean +from sklearn.exceptions import NotFittedError class TestWarping(unittest.TestCase): """Test warpings functions""" @@ -218,6 +219,58 @@ def test_inverse_transform(self): np.testing.assert_array_almost_equal(fd.data_matrix, self.fd.data_matrix, decimal=3) + def test_raises(self): + + reg = ShiftRegistration() + + # Test not fitted + with np.testing.assert_raises(NotFittedError): + reg.transform(self.fd) + + reg.fit(self.fd) + reg.set_params(restrict_domain=True) + + # Test use fit or transform with restrict_domain=True + with np.testing.assert_raises(AttributeError): + reg.transform(self.fd) + + with np.testing.assert_raises(AttributeError): + reg.fit(self.fd) + + # Test inverse_transform without previous transformation + with np.testing.assert_raises(AttributeError): + reg.inverse_transform(self.fd) + + reg.fit_transform(self.fd) + + # Test inverse transform with different number of sample + with np.testing.assert_raises(ValueError): + reg.inverse_transform(self.fd[:1]) + + reg.set_params(initial=[0.]) + + # Wrong initial estimation + with np.testing.assert_raises(ValueError): + reg.fit_transform(self.fd) + + def test_template(self): + + reg = ShiftRegistration() + fd_registered_1 = reg.fit_transform(self.fd) + + reg_2 = ShiftRegistration(template=reg.template_) + fd_registered_2 = reg_2.fit_transform(self.fd) + + reg_3= ShiftRegistration(template=mean) + fd_registered_3 = reg_3.fit_transform(self.fd) + + np.testing.assert_array_almost_equal(fd_registered_1.data_matrix, + fd_registered_3.data_matrix) + + # With the template fixed could vary the convergence + np.testing.assert_array_almost_equal(fd_registered_1.data_matrix, + fd_registered_2.data_matrix, + decimal=3) From 4b36ae30e9005dbffc25c54827ee5540bcc96b0e Mon Sep 17 00:00:00 2001 From: pablomm Date: Thu, 3 Oct 2019 23:58:55 +0200 Subject: [PATCH 024/624] Update citation label --- .../preprocessing/registration/shift_registration.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/shift_registration.py index c1aa5f5a6..e6cd1746c 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -17,7 +17,7 @@ class ShiftRegistration(RegistrationTransformer): r"""Register a functional dataset using shift alignment. Realizes the registration of a set of curves using a shift aligment - [RS05-7-2-1]_. Let :math:`\{x_i(t)\}_{i=1}^{N}` be a functional dataset, + [RaSi2005-7-2]_. Let :math:`\{x_i(t)\}_{i=1}^{N}` be a functional dataset, calculates :math:`\delta_{i}` for each sample such that :math:`x_i(t + \delta_{i})` minimizes the least squares criterion: @@ -27,7 +27,8 @@ class ShiftRegistration(RegistrationTransformer): Estimates each shift parameter :math:`\delta_i` iteratively by using a modified Newton-Raphson algorithm, updating the template - :math:`\mu` in each iteration as is described in detail in [RS05-7-9-1-1]_. + :math:`\mu` in each iteration as is described in detail in + [RaSi2005-7-9-1]_. Method only implemented for univariate functional data. @@ -52,7 +53,7 @@ class ShiftRegistration(RegistrationTransformer): By default uses the method defined in the data to be transformed. See the `extrapolation` documentation to obtain more information. step_size (int or float, optional): Parameter to adjust the rate of - convergence in the Newton-Raphson algorithm, see [RS05-7-9-1-1]_. + convergence in the Newton-Raphson algorithm, see [RaSi2005-7-9-1]_. Defaults to 1. restrict_domain (bool, optional): If True restricts the domain to avoid evaluate points outside the domain using extrapolation, in which @@ -112,10 +113,10 @@ class ShiftRegistration(RegistrationTransformer): References: - .. [RS05-7-2-1] Ramsay, J., Silverman, B. W. (2005). Shift + .. [RaSi2005-7-2] Ramsay, J., Silverman, B. W. (2005). Shift registration. In *Functional Data Analysis* (pp. 129-132). Springer. - .. [RS05-7-9-1-1] Ramsay, J., Silverman, B. W. (2005). Shift + .. [RaSi2005-7-9-1] Ramsay, J., Silverman, B. W. (2005). Shift registration by the Newton-Raphson algorithm. In *Functional Data Analysis* (pp. 142-144). Springer. """ From 2b3a93d5e711d659bc405e102ce53af2c6713ef0 Mon Sep 17 00:00:00 2001 From: pablomm Date: Fri, 4 Oct 2019 00:56:52 +0200 Subject: [PATCH 025/624] Test coverage and PEP 8 --- .../registration/shift_registration.py | 4 +- skfda/representation/grid.py | 2 + tests/test_registration.py | 60 ++++++++++++++++--- 3 files changed, 55 insertions(+), 11 deletions(-) diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/shift_registration.py index e6cd1746c..4ddd3ff1e 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -195,7 +195,7 @@ def _compute_deltas(self, fd, template): # Case template fixed if isinstance(template, FData): original_template = template - tfine_aux = template.evaluate(output_points, keepdims=False) + tfine_aux = template.evaluate(output_points, keepdims=False)[0] if self.restrict_domain: template_points_aux = tfine_aux @@ -246,7 +246,7 @@ def _compute_deltas(self, fd, template): x.mean(axis=0, out=tfine_aux) elif template == "fixed" and self.restrict_domain: tfine_aux = template_points_aux[domain] - elif callable(template): # Callable + elif callable(template): # Callable fd_x = FDataGrid(x, sample_points=output_points) fd_tfine = template(fd_x) tfine_aux = fd_tfine.data_matrix.flatten() diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 7adfdbb69..4e5749ebe 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -971,6 +971,8 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, if eval_points is None: eval_points = self.sample_points + else: + eval_points = np.atleast_2d(eval_points) if restrict_domain: domain = np.asarray(self.domain_range) diff --git a/tests/test_registration.py b/tests/test_registration.py index ebb69d677..f517b0415 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -13,6 +13,7 @@ from skfda.exploratory.stats import mean from sklearn.exceptions import NotFittedError + class TestWarping(unittest.TestCase): """Test warpings functions""" @@ -86,7 +87,7 @@ def test_landmark_shift(self): aligned_evaluation=False) # Test default location fd_registered = landmark_shift(fd, landmarks) - center = (landmarks.max() + landmarks.min())/2 + center = (landmarks.max() + landmarks.min()) / 2 reg_modes = fd_registered(center) # Test callable location @@ -161,16 +162,16 @@ def _test_mse_decomposition(self): np.testing.assert_almost_equal(ret.rsq, 0.9915489952877273) np.testing.assert_almost_equal(ret.cr, 0.9999963424653829) + class TestShiftRegistration(unittest.TestCase): """Test shift registration""" def setUp(self): """Initialization of samples""" - self.fd = fd = make_sinusoidal_process(n_samples=2, error_std=0, - random_state=1) + self.fd = make_sinusoidal_process(n_samples=2, error_std=0, + random_state=1) self.fd.extrapolation = "periodic" - def test_fit_transform(self): reg = ShiftRegistration() @@ -185,14 +186,13 @@ def test_fit_transform(self): self.assertTrue(isinstance(fd_reg, FDataGrid)) deltas = reg.deltas_.round(3) - np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) + np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) # Test with Basis fd = self.fd.to_basis(Fourier()) reg.fit_transform(fd) deltas = reg.deltas_.round(3) - np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) - + np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) def test_fit_and_transform(self): """Test wrapper of shift_registration_deltas""" @@ -208,7 +208,7 @@ def test_fit_and_transform(self): fd_registered = reg.transform(fd) deltas = reg.deltas_.round(3) - np.testing.assert_array_almost_equal(deltas, [ 0.071, -0.071]) + np.testing.assert_array_almost_equal(deltas, [0.071, -0.071]) def test_inverse_transform(self): @@ -247,6 +247,11 @@ def test_raises(self): with np.testing.assert_raises(ValueError): reg.inverse_transform(self.fd[:1]) + fd = make_multimodal_samples(dim_domain=2, random_state=0) + + with np.testing.assert_raises(NotImplementedError): + reg.fit_transform(fd) + reg.set_params(initial=[0.]) # Wrong initial estimation @@ -261,9 +266,12 @@ def test_template(self): reg_2 = ShiftRegistration(template=reg.template_) fd_registered_2 = reg_2.fit_transform(self.fd) - reg_3= ShiftRegistration(template=mean) + reg_3 = ShiftRegistration(template=mean) fd_registered_3 = reg_3.fit_transform(self.fd) + reg_4 = ShiftRegistration(template=reg.template_) + fd_registered_4 = reg_4.transform(self.fd) + np.testing.assert_array_almost_equal(fd_registered_1.data_matrix, fd_registered_3.data_matrix) @@ -272,6 +280,40 @@ def test_template(self): fd_registered_2.data_matrix, decimal=3) + np.testing.assert_array_almost_equal(fd_registered_2.data_matrix, + fd_registered_4.data_matrix) + + def test_restrict_domain(self): + reg = ShiftRegistration(restrict_domain=True) + fd_registered_1 = reg.fit_transform(self.fd) + + np.testing.assert_array_almost_equal( + fd_registered_1.domain_range.round(3), [[0.022, 0.969]]) + + reg2 = ShiftRegistration(restrict_domain=True, template=reg.template_) + fd_registered_2 = reg2.fit_transform(self.fd) + + np.testing.assert_array_almost_equal( + fd_registered_2.data_matrix, fd_registered_1.data_matrix, + decimal=3) + + reg3 = ShiftRegistration(restrict_domain=True, template=mean) + fd_registered_3 = reg3.fit_transform(self.fd) + + np.testing.assert_array_almost_equal( + fd_registered_3.data_matrix, fd_registered_1.data_matrix) + + def test_initial_estimation(self): + reg = ShiftRegistration(initial=[-0.02161235, 0.03032652]) + reg.fit_transform(self.fd) + + # Only needed 1 iteration until convergence + self.assertEqual(reg.n_iter_, 1) + + def test_custom_output_points(self): + reg = ShiftRegistration(output_points=np.linspace(0, 1, 50)) + reg.fit_transform(self.fd) + print(reg.deltas_) if __name__ == '__main__': From 1dc1a5915bf8c2e24ddb91d933a22bd62a98ec10 Mon Sep 17 00:00:00 2001 From: Pablo Marcos Date: Mon, 7 Oct 2019 16:56:34 +0200 Subject: [PATCH 026/624] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Carlos Ramos Carreño --- skfda/preprocessing/registration/shift_registration.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/shift_registration.py index 4ddd3ff1e..69dd104a7 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/shift_registration.py @@ -43,7 +43,7 @@ class ShiftRegistration(RegistrationTransformer): least squares criterion. If template="mean" it is use the functional mean as in the original paper. The template can be a callable that will receive an FDataGrid with the samples and will - return another FDataGrid with template, such as any of the means or + return another FDataGrid as a template, such as any of the means or medians of the module `skfda.explotatory.stats`. If the template is an FData it is used directly as the final template to the registration and it is not necessary to fit the @@ -268,7 +268,7 @@ def _compute_deltas(self, fd, template): if template == "fixed": - # Stores the original template instead of build it again + # Stores the original template instead of building it again template = original_template else: @@ -345,7 +345,7 @@ def transform(self, X: FData, y=None): "an extrapolation method with " "restrict_domain=False or fit_predict") - # If the template is an FData, fit doesnt learn nothing + # If the template is an FData, fit doesnt learn anything if not hasattr(self, 'template_') and isinstance(self.template, FData): self.template_ = self.template From 7be332d0e761b3ceb349ff8a737182d7dd1647a6 Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 9 Oct 2019 10:35:39 +0200 Subject: [PATCH 027/624] Review changes in ShiftRegistration --- skfda/preprocessing/registration/__init__.py | 2 +- ...registration.py => _shift_registration.py} | 22 +++++++++++-------- tests/test_registration.py | 3 +-- 3 files changed, 15 insertions(+), 12 deletions(-) rename skfda/preprocessing/registration/{shift_registration.py => _shift_registration.py} (96%) diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index f3311a4ab..b6a406ac9 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -9,7 +9,7 @@ landmark_registration_warping, landmark_registration) -from .shift_registration import ShiftRegistration +from ._shift_registration import ShiftRegistration from ._registration_utils import (invert_warping, normalize_warping, diff --git a/skfda/preprocessing/registration/shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py similarity index 96% rename from skfda/preprocessing/registration/shift_registration.py rename to skfda/preprocessing/registration/_shift_registration.py index 69dd104a7..055a514a2 100644 --- a/skfda/preprocessing/registration/shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -45,9 +45,11 @@ class ShiftRegistration(RegistrationTransformer): callable that will receive an FDataGrid with the samples and will return another FDataGrid as a template, such as any of the means or medians of the module `skfda.explotatory.stats`. - If the template is an FData it is used directly as the final - template to the registration and it is not necessary to fit the - estimator. Defaults to "mean". + If the template is an FData is used directly as the final + template to the registration, if it is a callable or "mean" the + template is computed iteratively constructing a temporal template + in each iteration. In [RaSi2005-7-9-1] is described in detail this + procedure. Defaults to "mean". extrapolation (str or :class:`Extrapolation`, optional): Controls the extrapolation mode for points outside the domain range. By default uses the method defined in the data to be transformed. @@ -249,7 +251,7 @@ def _compute_deltas(self, fd, template): elif callable(template): # Callable fd_x = FDataGrid(x, sample_points=output_points) fd_tfine = template(fd_x) - tfine_aux = fd_tfine.data_matrix.flatten() + tfine_aux = fd_tfine.data_matrix.ravel() # Calculates x - mean np.subtract(x, tfine_aux, out=x) @@ -316,7 +318,13 @@ def fit(self, X: FData, y=None): "an extrapolation method with " "restrict_domain=False or fit_predict") - _, self.template_ = self._compute_deltas(X, self.template) + + # If the template is an FData, fit doesnt learn anything + if isinstance(self.template, FData): + self.template_ = self.template + + else: + _, self.template_ = self._compute_deltas(X, self.template) return self @@ -345,10 +353,6 @@ def transform(self, X: FData, y=None): "an extrapolation method with " "restrict_domain=False or fit_predict") - # If the template is an FData, fit doesnt learn anything - if not hasattr(self, 'template_') and isinstance(self.template, FData): - self.template_ = self.template - # Check is fitted check_is_fitted(self, 'template_') diff --git a/tests/test_registration.py b/tests/test_registration.py index f517b0415..291859e1d 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -270,7 +270,7 @@ def test_template(self): fd_registered_3 = reg_3.fit_transform(self.fd) reg_4 = ShiftRegistration(template=reg.template_) - fd_registered_4 = reg_4.transform(self.fd) + fd_registered_4 = reg_4.fit(self.fd).transform(self.fd) np.testing.assert_array_almost_equal(fd_registered_1.data_matrix, fd_registered_3.data_matrix) @@ -313,7 +313,6 @@ def test_initial_estimation(self): def test_custom_output_points(self): reg = ShiftRegistration(output_points=np.linspace(0, 1, 50)) reg.fit_transform(self.fd) - print(reg.deltas_) if __name__ == '__main__': From e2ff4a63c7e657c312d65922702ddb851e15604d Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 9 Oct 2019 11:34:45 +0200 Subject: [PATCH 028/624] Refacor registration validation --- docs/modules/preprocessing/registration.rst | 10 +- skfda/_utils/__init__.py | 3 +- skfda/_utils/_utils.py | 31 ++ skfda/preprocessing/registration/__init__.py | 4 +- .../registration/_shift_registration.py | 2 +- .../preprocessing/registration/validation.py | 416 ++++++++++-------- 6 files changed, 277 insertions(+), 189 deletions(-) diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 8b1fa283a..11738ac2a 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -92,11 +92,11 @@ validation of the registration procedure. .. autosummary:: :toctree: autosummary - skfda.preprocessing.registration.validation.RegistrationScorer - skfda.preprocessing.registration.validation.mse_r_squared - skfda.preprocessing.registration.validation.least_squares - skfda.preprocessing.registration.validation.sobolev_least_squares - skfda.preprocessing.registration.validation.pairwise_correlation + + skfda.preprocessing.registration.validation.AmplitudePhaseDecomposition + skfda.preprocessing.registration.validation.LeastSquares + skfda.preprocessing.registration.validation.SobolevLeastSquares + skfda.preprocessing.registration.validation.PairwiseCorrelation Utility functions diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 6d7d7e221..2cfcb3d13 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -1,4 +1,5 @@ from . import constants from ._utils import (_list_of_arrays, _coordinate_list, - _check_estimator, parameter_aliases) + _check_estimator, parameter_aliases, + _to_grid, _check_univariate) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 29142d6fb..394fac042 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -6,6 +6,37 @@ import numpy as np +def _check_univariate(fd): + """Checks if an FData is univariate and raises an error""" + + if fd.dim_domain != 1 or fd.dim_codomain != 1: + raise ValueError(f"The functional data must be univariate, i.e.," + f"with dim_domain=1 ({fd.dim_domain}) and " + f"dim_codomain=1 ({fd.dim_codomain})") + + + + +def _to_grid(X, y, eval_points=None): + """Transforms the functional data in grids to perform calculations.""" + + from .. import FDataGrid + x_is_grid = isinstance(X, FDataGrid) + y_is_grid = isinstance(y, FDataGrid) + + if eval_points is not None: + X = X.to_grid(eval_points) + y = y.to_grid(eval_points) + elif x_is_grid and not y_is_grid: + y = y.to_grid(X.sample_points[0]) + elif not x_is_grid and y_is_grid: + X = X.to_grid(y.sample_points[0]) + elif not x_is_grid and not y_is_grid: + X = X.to_grid() + y = y.to_grid() + + return X, y + def _list_of_arrays(original_array): """Convert to a list of arrays. diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index b6a406ac9..2b8968322 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -15,9 +15,7 @@ normalize_warping, _normalize_scale) -from .validation import (RegistrationScorer, AmplitudePhaseDecomposition, - mse_r_squared, least_squares, sobolev_least_squares, - pairwise_correlation) +from . import validation from ._elastic import (to_srsf, from_srsf, elastic_registration, diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 055a514a2..412033f22 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -48,7 +48,7 @@ class ShiftRegistration(RegistrationTransformer): If the template is an FData is used directly as the final template to the registration, if it is a callable or "mean" the template is computed iteratively constructing a temporal template - in each iteration. In [RaSi2005-7-9-1] is described in detail this + in each iteration. In [RaSi2005-7-9-1]_ is described in detail this procedure. Defaults to "mean". extrapolation (str or :class:`Extrapolation`, optional): Controls the extrapolation mode for points outside the domain range. diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 0fb01d456..c73159fe2 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -3,26 +3,7 @@ import numpy as np from typing import NamedTuple - -def _to_grid(X, y, eval_points=None): - """Transforms the functional data in grids to perform calculations.""" - - from ... import FDataGrid - x_is_grid = isinstance(X, FDataGrid) - y_is_grid = isinstance(y, FDataGrid) - - if eval_points is not None: - X = X.to_grid(eval_points) - y = y.to_grid(eval_points) - elif x_is_grid and not y_is_grid: - y = y.to_grid(X.sample_points[0]) - elif not x_is_grid and y_is_grid: - X = X.to_grid(y.sample_points[0]) - elif not x_is_grid and not y_is_grid: - X = X.to_grid() - y = y.to_grid() - - return X, y +from ..._utils import _check_univariate, _to_grid class RegistrationScorer(): @@ -32,10 +13,9 @@ class RegistrationScorer(): model validation or parameter selection. Attributes: - score_function (callable): Function to compute the score. By default - it is used :func:`mse_r_squared`. See other - available metrics in the module :mod:`registration.validation - `. + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain a discrete representation and + perform the calculation. Args: estimator (Estimator): Registration method estimator. The estimator @@ -52,23 +32,18 @@ class RegistrationScorer(): to the `score_function` when necessary. See also: - :func:`mse_r_squared ` - :func:`least_squares ` - :func:`sobolev_least_squares ` - :func:`pairwise_correlation ` + :class:`~AmplitudePhaseDecomposition` + :class:`~LeastSquares` + :class:`~SobolevLeastSquares` + :class:`~PairwiseCorrelation` """ - - def __init__(self, score_function=None): - self.score_function = score_function + def __init__(self, eval_points=None): + """Initialize the transformer""" + self.eval_points = eval_points def __call__(self, estimator, X, y=None): - - # By default it is used the R^2 coefficient of Ramsay - if self.score_function is None: - score_function = mse_r_squared - else: - score_function = self.score_function + """Compute the score of the transformation""" if y is None: y = X @@ -76,17 +51,10 @@ def __call__(self, estimator, X, y=None): # Register the data X_reg = estimator.transform(X) - # Pass the warpings if needed in the score function - # and the estimator generates warpings - # By the moment only used in the mse_r_squared - if (hasattr(estimator, 'warping_') and - 'warping' in score_function.__kwdefaults__): - return score_function(y, X_reg, warping=estimator.warping_) - else: - return score_function(y, X_reg) + return self.score_function(y, X_reg) -class AmplitudePhaseDecomposition(NamedTuple): +class AmplitudePhaseDecompositionStats(NamedTuple): r"""Named tuple to store the values of the amplitude-phase decomposition. Values of the amplitude phase decomposition computed in @@ -106,7 +74,7 @@ class AmplitudePhaseDecomposition(NamedTuple): c_r: float -def mse_r_squared(X, y, *, warping=None, return_stats=False, eval_points=None): +class AmplitudePhaseDecomposition(RegistrationScorer): r"""Compute mean square error measures for amplitude and phase variation. Once the registration has taken place, this function computes two mean @@ -160,19 +128,24 @@ def mse_r_squared(X, y, *, warping=None, return_stats=False, eval_points=None): See [KR08-3]_ for a detailed explanation. - - Args: - X (:class:`FData`): Unregistered functions. - y (:class:`FData`, optional): Target data, generally the same as X. By - default 'None', which uses `X` as target. + Attributes: return_stats (boolean, optional): If `true` returns a named tuple with four values: :math:`R^2`, :math:`MSE_{amp}`, :math:`MSE_{pha}` and :math:`C_R`. Otherwise the squared correlation index :math:`R^2` is returned. Default `False`. - eval_points: (array_like, optional): Set of points where the + + eval_points (array_like, optional): Set of points where the functions are evaluated to obtain a discrete representation and perform the calculation. + + Args: + estimator (RegistrationTransformer): Registration transformer. + X (:class:`FData`): Unregistered functions. + y (:class:`FData`, optional): Target data, generally the same as X. By + default 'None', which uses `X` as target. + + Returns: (float or :class:`NamedTuple `): squared correlation index :math:`R^2` if `return_stats` is `False`. Otherwise a named @@ -198,93 +171,123 @@ def mse_r_squared(X, y, *, warping=None, return_stats=False, eval_points=None): Springer. See also: - :class:`RegistrationScorer ` - :func:`least_squares ` - :func:`sobolev_least_squares ` - :func:`pairwise_correlation ` + :class:`~AmplitudePhaseDecomposition` + :class:`~LeastSquares` + :class:`~SobolevLeastSquares` + :class:`~PairwiseCorrelation` """ - from scipy.integrate import simps + def __init__(self, return_stats=False, eval_points=None): + """Initialize the transformer""" + super().__init__(eval_points) + self.return_stats=return_stats - # Parameter checks - if not X._univariate or not y._univariate: - raise ValueError("Scorer only valid for univariate data.") + def __call__(self, estimator, X, y=None): + """Compute the score of the transformation""" - if len(y) != len(X): - raise ValueError(f"the registered and unregistered curves must have " - f"the same number of samples ({len(y)})!=({len(X)})") + if y is None: + y = X - if warping is not None and len(warping) != len(X): - raise ValueError(f"The registered curves and the warping functions " - f"must have the same number of samples " - f"({len(X)})!=({len(warping)})") + # Register the data + X_reg = estimator.transform(X) + + # Pass the warpings if are generated in the transformer + if hasattr(estimator, 'warping_'): + return self.score_function(y, X_reg, warping=estimator.warping_) + else: + return self.score_function(y, X_reg) + + def score_function(self, X, y, *, warping=None): + """Compute the score of the transformation performed. - # Creates the mesh to discretize the functions - if eval_points is None: - try: - eval_points = y.sample_points[0] + Args: + X (FData): Original functional data. + y (Fdata): Functional data registered. - except AttributeError: - nfine = max(y.basis.nbasis * 10 + 1, 201) - eval_points = np.linspace(*y.domain_range[0], nfine) - else: - eval_points = np.asarray(eval_points) + Returns: + float: Score of the transformation. - x_fine = X.evaluate(eval_points, keepdims=False) - y_fine = y.evaluate(eval_points, keepdims=False) - mu_fine = x_fine.mean(axis=0) # Mean unregistered function - eta_fine = y_fine.mean(axis=0) # Mean registered function - mu_fine_sq = np.square(mu_fine) - eta_fine_sq = np.square(eta_fine) + """ + from scipy.integrate import simps - # Total mean square error of the original funtions - # mse_total = scipy.integrate.simps( - # np.mean(np.square(x_fine - mu_fine), axis=0), - # eval_points) + _check_univariate(X) + _check_univariate(y) - cr = 1. # Constant related to the covariation between the deformation - # functions and y^2 + if len(y) != len(X): + raise ValueError(f"the registered and unregistered curves must have " + f"the same number of samples ({len(y)})!=({len(X)})") + + if warping is not None and len(warping) != len(X): + raise ValueError(f"The registered curves and the warping functions " + f"must have the same number of samples " + f"({len(X)})!=({len(warping)})") + + # Creates the mesh to discretize the functions + if self.eval_points is None: + try: + eval_points = y.sample_points[0] + + except AttributeError: + nfine = max(y.basis.nbasis * 10 + 1, 201) + eval_points = np.linspace(*y.domain_range[0], nfine) + else: + eval_points = np.asarray(self.eval_points) - # If the warping functions are not provided, are suppose to be independent - if warping is not None: - # Derivates warping functions - dh_fine = warping.evaluate(eval_points, derivative=1, keepdims=False) - dh_fine_mean = dh_fine.mean(axis=0) - dh_fine_center = dh_fine - dh_fine_mean + x_fine = X.evaluate(eval_points, keepdims=False) + y_fine = y.evaluate(eval_points, keepdims=False) + mu_fine = x_fine.mean(axis=0) # Mean unregistered function + eta_fine = y_fine.mean(axis=0) # Mean registered function + mu_fine_sq = np.square(mu_fine) + eta_fine_sq = np.square(eta_fine) - y_fine_sq = np.square(y_fine) # y^2 - y_fine_sq_center = np.subtract(y_fine_sq, eta_fine_sq) # y^2-E[y2] + # Total mean square error of the original funtions + # mse_total = scipy.integrate.simps( + # np.mean(np.square(x_fine - mu_fine), axis=0), + # eval_points) - covariate = np.inner(dh_fine_center.T, y_fine_sq_center.T) - covariate = covariate.mean(axis=0) - cr += np.divide(simps(covariate, eval_points), - simps(eta_fine_sq, eval_points)) + cr = 1. # Constant related to the covariation between the deformation + # functions and y^2 - # mse due to phase variation - mse_pha = simps(cr * eta_fine_sq - mu_fine_sq, eval_points) + # If the warping functions are not provided, are suppose to be independent + if warping is not None: + # Derivates warping functions + dh_fine = warping.evaluate(eval_points, derivative=1, keepdims=False) + dh_fine_mean = dh_fine.mean(axis=0) + dh_fine_center = dh_fine - dh_fine_mean - # mse due to amplitude variation - # mse_amp = mse_total - mse_pha - y_fine_center = np.subtract(y_fine, eta_fine) - y_fine_center_sq = np.square(y_fine_center, out=y_fine_center) - y_fine_center_sq_mean = y_fine_center_sq.mean(axis=0) + y_fine_sq = np.square(y_fine) # y^2 + y_fine_sq_center = np.subtract(y_fine_sq, eta_fine_sq) # y^2-E[y2] - mse_amp = simps(y_fine_center_sq_mean, eval_points) + covariate = np.inner(dh_fine_center.T, y_fine_sq_center.T) + covariate = covariate.mean(axis=0) + cr += np.divide(simps(covariate, eval_points), + simps(eta_fine_sq, eval_points)) - # Total mean square error of the original funtions - mse_total = mse_pha + mse_amp + # mse due to phase variation + mse_pha = simps(cr * eta_fine_sq - mu_fine_sq, eval_points) - # squared correlation measure of proportion of phase variation - rsq = mse_pha / (mse_total) + # mse due to amplitude variation + # mse_amp = mse_total - mse_pha + y_fine_center = np.subtract(y_fine, eta_fine) + y_fine_center_sq = np.square(y_fine_center, out=y_fine_center) + y_fine_center_sq_mean = y_fine_center_sq.mean(axis=0) - if return_stats is True: - stats = AmplitudePhaseDecomposition(rsq, mse_amp, mse_pha, cr) - return stats + mse_amp = simps(y_fine_center_sq_mean, eval_points) - return rsq + # Total mean square error of the original funtions + mse_total = mse_pha + mse_amp + # squared correlation measure of proportion of phase variation + rsq = mse_pha / (mse_total) -def least_squares(X, y, *, eval_points=None): + if return_stats is True: + stats = AmplitudePhaseDecompositionStats(rsq, mse_amp, mse_pha, cr) + return stats + + return rsq + + +class LeastSquares(AmplitudePhaseDecomposition): r"""Cross-validated measure of the registration procedure. Computes a cross-validated measure of the level of synchronization @@ -304,7 +307,13 @@ def least_squares(X, y, *, eval_points=None): curves, while zero corresponds to no improvement in the synchronization. It can be negative because the model can be arbitrarily worse. + Attributes: + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain a discrete representation and + perform the calculation. + Args: + estimator (RegistrationTransformer): Registration transformer. X (:class:`FData `): Original functional data. y (:class:`FData `): Registered functional data. @@ -324,41 +333,55 @@ def least_squares(X, y, *, eval_points=None): (p. 18). arXiv:1103.3817v2. See also: - :class:`RegistrationScorer ` - :func:`mse_r_squared ` - :func:`sobolev_least_squares ` - :func:`pairwise_correlation ` + :class:`~AmplitudePhaseDecomposition` + :class:`~LeastSquares` + :class:`~SobolevLeastSquares` + :class:`~PairwiseCorrelation` """ - from ...misc.metrics import pairwise_distance, lp_distance + def score_function(self, X, y): + """Compute the score of the transformation performed. + + Args: + X (FData): Original functional data. + y (Fdata): Functional data registered. + + Returns: + float: Score of the transformation. - X, y = _to_grid(X, y, eval_points=eval_points) + """ + from ...misc.metrics import pairwise_distance, lp_distance - # Instead of compute f_i - 1/(N-1) sum(j!=i)f_j for each i = 1 ... N - # It is used (1 + 1/(N-1))f_i - 1/(N-1) sum(j=1 ... N) f_j = - # (1 + 1/(N-1))f_i - N/(N-1) mean(f) = - # C1 * f_1 - C2 mean(f) for each i= 1 ... N - N = len(X) - C1 = 1 + 1 / (N - 1) - C2 = N / (N - 1) + _check_univariate(X) + _check_univariate(y) - X = C1 * X - y = C1 * y - mean_X = C2 * X.mean() - mean_y = C2 * y.mean() + X, y = _to_grid(X, y, eval_points=self.eval_points) - # Compute distance to mean - distance = pairwise_distance(lp_distance) - ls_x = distance(X, mean_X).flatten() - ls_y = distance(y, mean_y).flatten() + # Instead of compute f_i - 1/(N-1) sum(j!=i)f_j for each i = 1 ... N + # It is used (1 + 1/(N-1))f_i - 1/(N-1) sum(j=1 ... N) f_j = + # (1 + 1/(N-1))f_i - N/(N-1) mean(f) = + # C1 * f_1 - C2 mean(f) for each i= 1 ... N + N = len(X) + C1 = 1 + 1 / (N - 1) + C2 = N / (N - 1) - # Quotient of distance - quotient = ls_y / ls_x + X = C1 * X + y = C1 * y + mean_X = C2 * X.mean() + mean_y = C2 * y.mean() - return 1 - 1. / N * quotient.sum() + # Compute distance to mean + distance = pairwise_distance(lp_distance) + ls_x = distance(X, mean_X).flatten() + ls_y = distance(y, mean_y).flatten() + # Quotient of distance + quotient = ls_y / ls_x -def sobolev_least_squares(X, y, *, eval_points=None): + return 1 - 1. / N * quotient.sum() + + +class SobolevLeastSquares(RegistrationScorer): r"""Cross-validated measure of the registration procedure. Computes a cross-validated measure of the level of synchronization @@ -379,13 +402,16 @@ def sobolev_least_squares(X, y, *, eval_points=None): curves, while zero corresponds to no improvement in the registration. It can be negative because the model can be arbitrarily worse. - Args: - X (:class:`FData `): Original functional data. - y (:class:`FData `): Registered functional data. + Attributes: eval_points (array_like, optional): Set of points where the functions are evaluated to obtain a discrete representation and perform the calculation. + Args: + estimator (RegistrationTransformer): Registration transformer. + X (:class:`FData `): Original functional data. + y (:class:`FData `): Registered functional data. + Note: The original sobolev least square measure used in [S11-5-2-3]_ is defined as :math:`1 - sls`, but has been modified according to the @@ -399,31 +425,45 @@ def sobolev_least_squares(X, y, *, eval_points=None): (p. 18). arXiv:1103.3817v2. See also: - :class:`RegistrationScorer ` - :func:`mse_r_squared ` - :func:`least_squares ` - :func:`pairwise_correlation ` + :class:`~AmplitudePhaseDecomposition` + :class:`~LeastSquares` + :class:`~SobolevLeastSquares` + :class:`~PairwiseCorrelation` """ - from ...misc.metrics import pairwise_distance, lp_distance + def score_function(self, X, y): + """Compute the score of the transformation performed. + + Args: + X (FData): Original functional data. + y (Fdata): Functional data registered. + + Returns: + float: Score of the transformation. + + """ + from ...misc.metrics import pairwise_distance, lp_distance + + _check_univariate(X) + _check_univariate(y) - # Compute derivative - X = X.derivative() - y = y.derivative() + # Compute derivative + X = X.derivative() + y = y.derivative() - # Discretize if needed - X, y = _to_grid(X, y, eval_points=eval_points) + # Discretize if needed + X, y = _to_grid(X, y, eval_points=self.eval_points) - # L2 distance to mean - distance = pairwise_distance(lp_distance) + # L2 distance to mean + distance = pairwise_distance(lp_distance) - sls_x = distance(X, X.mean()) - sls_y = distance(y, y.mean()) + sls_x = distance(X, X.mean()) + sls_y = distance(y, y.mean()) - return 1 - sls_y.sum() / sls_x.sum() + return 1 - sls_y.sum() / sls_x.sum() -def pairwise_correlation(X, y, *, eval_points=None): +class PairwiseCorrelation(RegistrationScorer): r"""Cross-validated measure of pairwise correlation between functions. Computes a cross-validated pairwise correlation between functions @@ -441,13 +481,16 @@ def pairwise_correlation(X, y, *, eval_points=None): The larger the value of :math:`pc`, the better the alignment between functions in general. - Args: - X (:class:`FData `): Original functional data. - y (:class:`FData `): Registered functional data. + Attributes: eval_points (array_like, optional): Set of points where the functions are evaluated to obtain a discrete representation and perform the calculation. + Args: + estimator (RegistrationTransformer): Registration transformer. + X (:class:`FData `): Original functional data. + y (:class:`FData `): Registered functional data. + Note: Pearson’s correlation between functions is calculated assuming the samples are equiespaciated. @@ -458,22 +501,37 @@ def pairwise_correlation(X, y, *, eval_points=None): (p. 18). arXiv:1103.3817v2. See also: - :class:`RegistrationScorer ` - :func:`mse_r_squared ` - :func:`least_squares ` - :func:`sobolev_least_squares ` + :class:`~AmplitudePhaseDecomposition` + :class:`~LeastSquares` + :class:`~SobolevLeastSquares` + :class:`~PairwiseCorrelation` """ - # Discretize functional data if needed - X, y = _to_grid(X, y, eval_points=eval_points) - # Compute correlation matrices with zeros in diagonal - # corrcoefs computes the correlation between vector, without weights - # due to the sample points - X_corr = np.corrcoef(X.data_matrix[..., 0]) - np.fill_diagonal(X_corr, 0.) + def score_function(self, X, y): + """Compute the score of the transformation performed. + + Args: + X (FData): Original functional data. + y (Fdata): Functional data registered. + + Returns: + float: Score of the transformation. + + """ + _check_univariate(X) + _check_univariate(y) + + # Discretize functional data if needed + X, y = _to_grid(X, y, eval_points=self.eval_points) + + # Compute correlation matrices with zeros in diagonal + # corrcoefs computes the correlation between vector, without weights + # due to the sample points + X_corr = np.corrcoef(X.data_matrix[..., 0]) + np.fill_diagonal(X_corr, 0.) - y_corr = np.corrcoef(y.data_matrix[..., 0]) - np.fill_diagonal(y_corr, 0.) + y_corr = np.corrcoef(y.data_matrix[..., 0]) + np.fill_diagonal(y_corr, 0.) - return y_corr.sum() / X_corr.sum() + return y_corr.sum() / X_corr.sum() From 2d715479aa316da0016767615739fd2a1dab0e0e Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 9 Oct 2019 17:25:57 +0200 Subject: [PATCH 029/624] Validation tests --- skfda/_utils/_utils.py | 13 +- .../registration/_shift_registration.py | 3 +- .../preprocessing/registration/validation.py | 155 ++++++++++++++++-- tests/test_registration.py | 33 ++++ 4 files changed, 181 insertions(+), 23 deletions(-) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 394fac042..561be0715 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -10,12 +10,13 @@ def _check_univariate(fd): """Checks if an FData is univariate and raises an error""" if fd.dim_domain != 1 or fd.dim_codomain != 1: - raise ValueError(f"The functional data must be univariate, i.e.," - f"with dim_domain=1 ({fd.dim_domain}) and " - f"dim_codomain=1 ({fd.dim_codomain})") - - - + raise ValueError(f"The functional data must be univariate, i.e., " + + f"with dim_domain=1 " + + (f"" if fd.dim_domain==1 + else f"(currently is {fd.dim_domain}) ") + + f"and dim_codomain=1 " + + (f"" if fd.dim_codomain==1 else + f"(currently is {fd.dim_codomain})")) def _to_grid(X, y, eval_points=None): """Transforms the functional data in grids to perform calculations.""" diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 412033f22..753cf93c4 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -318,9 +318,8 @@ def fit(self, X: FData, y=None): "an extrapolation method with " "restrict_domain=False or fit_predict") - # If the template is an FData, fit doesnt learn anything - if isinstance(self.template, FData): + if isinstance(self.template, FData): self.template_ = self.template else: diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index c73159fe2..9d4e1301b 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -43,8 +43,18 @@ def __init__(self, eval_points=None): self.eval_points = eval_points def __call__(self, estimator, X, y=None): - """Compute the score of the transformation""" + """Compute the score of the transformation. + Args: + estimator (Estimator): Registration method estimator. The estimator + should be fitted. + X (:class:`FData `): Functional data to be registered. + y (:class:`FData `, optional): Functional data target. + If provided should be the same as `X` in general. + + Returns: + float: Cross validation score. + """ if y is None: y = X @@ -170,8 +180,45 @@ class AmplitudePhaseDecomposition(RegistrationScorer): *Functional Data Analysis with R and Matlab* (pp. 125-126). Springer. + Examples: + + Calculate the score of the shift registration of a sinusoidal process + synthetically generated. + + >>> from skfda.preprocessing.registration.validation import \ + ... AmplitudePhaseDecomposition + >>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> X = make_sinusoidal_process(error_std=0, random_state=0) + + Fit the registration procedure. + + >>> shift_registration = ShiftRegistration() + >>> shift_registration.fit(X) + ShiftRegistration(...) + + Compute the :math:`R^2` correlation index + + >>> scorer = AmplitudePhaseDecomposition() + >>> score = scorer(shift_registration, X) + >>> round(score, 3) + 0.972 + + Also it is possible to get all the values of the decomposition. + + >>> scorer = AmplitudePhaseDecomposition(return_stats=True) + >>> stats = scorer(shift_registration, X) + >>> round(stats.r_squared, 3) + 0.972 + >>> round(stats.mse_amp, 3) + 0.07 + >>> round(stats.mse_pha, 3) + 0.227 + >>> round(stats.c_r, 3) + 1.0 + + See also: - :class:`~AmplitudePhaseDecomposition` :class:`~LeastSquares` :class:`~SobolevLeastSquares` :class:`~PairwiseCorrelation` @@ -183,8 +230,18 @@ def __init__(self, return_stats=False, eval_points=None): self.return_stats=return_stats def __call__(self, estimator, X, y=None): - """Compute the score of the transformation""" + """Compute the score of the transformation. + + Args: + estimator (Estimator): Registration method estimator. The estimator + should be fitted. + X (:class:`FData `): Functional data to be registered. + y (:class:`FData `, optional): Functional data target. + If provided should be the same as `X` in general. + Returns: + float: Cross validation score. + """ if y is None: y = X @@ -202,7 +259,7 @@ def score_function(self, X, y, *, warping=None): Args: X (FData): Original functional data. - y (Fdata): Functional data registered. + y (FData): Functional data registered. Returns: float: Score of the transformation. @@ -248,10 +305,11 @@ def score_function(self, X, y, *, warping=None): cr = 1. # Constant related to the covariation between the deformation # functions and y^2 - # If the warping functions are not provided, are suppose to be independent + # If the warping functions are not provided, are suppose independent if warping is not None: # Derivates warping functions - dh_fine = warping.evaluate(eval_points, derivative=1, keepdims=False) + dh_fine = warping.evaluate(eval_points, derivative=1, + keepdims=False) dh_fine_mean = dh_fine.mean(axis=0) dh_fine_center = dh_fine - dh_fine_mean @@ -280,7 +338,7 @@ def score_function(self, X, y, *, warping=None): # squared correlation measure of proportion of phase variation rsq = mse_pha / (mse_total) - if return_stats is True: + if self.return_stats is True: stats = AmplitudePhaseDecompositionStats(rsq, mse_amp, mse_pha, cr) return stats @@ -332,9 +390,32 @@ class LeastSquares(AmplitudePhaseDecomposition): Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* (p. 18). arXiv:1103.3817v2. + Examples: + + Calculate the score of the shift registration of a sinusoidal process + synthetically generated. + + >>> from skfda.preprocessing.registration.validation import \ + ... LeastSquares + >>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> X = make_sinusoidal_process(error_std=0, random_state=0) + + Fit the registration procedure. + + >>> shift_registration = ShiftRegistration() + >>> shift_registration.fit(X) + ShiftRegistration(...) + + Compute the least squares score. + >>> scorer = LeastSquares() + >>> score = scorer(shift_registration, X) + >>> round(score, 3) + 0.796 + + See also: :class:`~AmplitudePhaseDecomposition` - :class:`~LeastSquares` :class:`~SobolevLeastSquares` :class:`~PairwiseCorrelation` @@ -344,7 +425,7 @@ def score_function(self, X, y): Args: X (FData): Original functional data. - y (Fdata): Functional data registered. + y (FData): Functional data registered. Returns: float: Score of the transformation. @@ -393,8 +474,8 @@ class SobolevLeastSquares(RegistrationScorer): {\sum_{i=1}^{N} \int\left(\dot{f}_{i}(t)-\frac{1}{N} \sum_{j=1}^{N} \dot{f}_{j}\right)^{2} dt} - where :math:`f_i` and :math:`\tilde f_i` are the derivatives of the - original and the registered data respectively. + where :math:`\dot f_i` and :math:`\dot \tilde f_i` are the derivatives of + the original and the registered data respectively. This criterion measures the total cross-sectional variance of the derivatives of the aligned functions, relative to the original value. @@ -424,10 +505,32 @@ class SobolevLeastSquares(RegistrationScorer): Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* (p. 18). arXiv:1103.3817v2. + Examples: + + Calculate the score of the shift registration of a sinusoidal process + synthetically generated. + + >>> from skfda.preprocessing.registration.validation import \ + ... SobolevLeastSquares + >>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> X = make_sinusoidal_process(error_std=0, random_state=0) + + Fit the registration procedure. + + >>> shift_registration = ShiftRegistration() + >>> shift_registration.fit(X) + ShiftRegistration(...) + + Compute the sobolev least squares score. + >>> scorer = SobolevLeastSquares() + >>> score = scorer(shift_registration, X) + >>> round(score, 3) + 0.762 + See also: :class:`~AmplitudePhaseDecomposition` :class:`~LeastSquares` - :class:`~SobolevLeastSquares` :class:`~PairwiseCorrelation` """ @@ -436,7 +539,7 @@ def score_function(self, X, y): Args: X (FData): Original functional data. - y (Fdata): Functional data registered. + y (FData): Functional data registered. Returns: float: Score of the transformation. @@ -500,11 +603,33 @@ class PairwiseCorrelation(RegistrationScorer): Using Fisher-Rao Metric (2011). In *Comparisons with other Methods* (p. 18). arXiv:1103.3817v2. + Examples: + + Calculate the score of the shift registration of a sinusoidal process + synthetically generated. + + >>> from skfda.preprocessing.registration.validation import \ + ... PairwiseCorrelation + >>> from skfda.preprocessing.registration import ShiftRegistration + >>> from skfda.datasets import make_sinusoidal_process + >>> X = make_sinusoidal_process(error_std=0, random_state=0) + + Fit the registration procedure. + + >>> shift_registration = ShiftRegistration() + >>> shift_registration.fit(X) + ShiftRegistration(...) + + Compute the pairwise correlation score. + >>> scorer = PairwiseCorrelation() + >>> score = scorer(shift_registration, X) + >>> round(score, 3) + 1.816 + See also: :class:`~AmplitudePhaseDecomposition` :class:`~LeastSquares` :class:`~SobolevLeastSquares` - :class:`~PairwiseCorrelation` """ @@ -513,7 +638,7 @@ def score_function(self, X, y): Args: X (FData): Original functional data. - y (Fdata): Functional data registered. + y (FData): Functional data registered. Returns: float: Score of the transformation. diff --git a/tests/test_registration.py b/tests/test_registration.py index 291859e1d..b1faa8db3 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -12,6 +12,10 @@ landmark_registration_warping, landmark_registration, ShiftRegistration) from skfda.exploratory.stats import mean from sklearn.exceptions import NotFittedError +from skfda._utils import _check_estimator +from skfda.preprocessing.registration.validation import \ + (AmplitudePhaseDecomposition, LeastSquares, + SobolevLeastSquares, PairwiseCorrelation) class TestWarping(unittest.TestCase): @@ -315,6 +319,35 @@ def test_custom_output_points(self): reg.fit_transform(self.fd) +class TestRegistrationValidation(unittest.TestCase): + """Test shift registration""" + + def setUp(self): + """Initialization of samples""" + self.X = make_sinusoidal_process(error_std=0, random_state=0) + self.shift_registration = ShiftRegistration().fit(self.X) + + def test_amplitude_phase_score(self): + scorer = AmplitudePhaseDecomposition() + score = scorer(self.shift_registration, self.X) + np.testing.assert_almost_equal(score, 0.972000160) + + def test_least_squares_score(self): + scorer = LeastSquares() + score = scorer(self.shift_registration, self.X) + np.testing.assert_almost_equal(score, 0.795742349) + + def test_sobolev_least_squares_score(self): + scorer = SobolevLeastSquares() + score = scorer(self.shift_registration, self.X) + np.testing.assert_almost_equal(score, 0.762240135) + + def test_pairwise_correlation(self): + scorer = PairwiseCorrelation() + score = scorer(self.shift_registration, self.X) + np.testing.assert_almost_equal(score, 1.816298653) + + if __name__ == '__main__': print() unittest.main() From 695c74a23a9a0e8e0d70a91d7ce3f562ca027e64 Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 9 Oct 2019 17:29:38 +0200 Subject: [PATCH 030/624] Update old test --- tests/test_registration.py | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/tests/test_registration.py b/tests/test_registration.py index b1faa8db3..72c38a941 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -152,21 +152,6 @@ def test_landmark_registration(self): np.testing.assert_array_almost_equal(fd_reg(center), original_values, decimal=2) - def _test_mse_decomposition(self): - # Test disabled - fd = make_multimodal_samples(n_samples=3, random_state=1) - landmarks = make_multimodal_landmarks(n_samples=3, random_state=1) - landmarks = landmarks.squeeze() - warping = landmark_registration_warping(fd, landmarks) - fd_registered = fd.compose(warping) - ret = mse_decomposition(fd, fd_registered, warping) - - np.testing.assert_almost_equal(ret.mse_amp, 0.0009866997121476962) - np.testing.assert_almost_equal(ret.mse_pha, 0.11576861468435257) - np.testing.assert_almost_equal(ret.rsq, 0.9915489952877273) - np.testing.assert_almost_equal(ret.cr, 0.9999963424653829) - - class TestShiftRegistration(unittest.TestCase): """Test shift registration""" @@ -347,6 +332,20 @@ def test_pairwise_correlation(self): score = scorer(self.shift_registration, self.X) np.testing.assert_almost_equal(score, 1.816298653) + def test_mse_decomposition(self): + + fd = make_multimodal_samples(n_samples=3, random_state=1) + landmarks = make_multimodal_landmarks(n_samples=3, random_state=1) + landmarks = landmarks.squeeze() + warping = landmark_registration_warping(fd, landmarks) + fd_registered = fd.compose(warping) + scorer = AmplitudePhaseDecomposition(return_stats=True) + ret = scorer.score_function(fd, fd_registered, warping=warping) + np.testing.assert_almost_equal(ret.mse_amp, 0.0009866997121476962) + np.testing.assert_almost_equal(ret.mse_pha, 0.11576861468435257) + np.testing.assert_almost_equal(ret.r_squared, 0.9915489952877273) + np.testing.assert_almost_equal(ret.c_r, 0.9999963424653829) + if __name__ == '__main__': print() From c79396118851b30f81fb21822f59dba1abb2fd20 Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 9 Oct 2019 17:36:52 +0200 Subject: [PATCH 031/624] Typo in doctest --- skfda/preprocessing/registration/validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 9d4e1301b..bca8acb45 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -211,7 +211,7 @@ class AmplitudePhaseDecomposition(RegistrationScorer): >>> round(stats.r_squared, 3) 0.972 >>> round(stats.mse_amp, 3) - 0.07 + 0.007 >>> round(stats.mse_pha, 3) 0.227 >>> round(stats.c_r, 3) From b55b7470c0b580076b62d72a9a2175e22357a71b Mon Sep 17 00:00:00 2001 From: pablomm Date: Fri, 11 Oct 2019 18:56:28 +0200 Subject: [PATCH 032/624] Fix score method of the registration transformers --- skfda/preprocessing/registration/base.py | 18 +++++++++--------- tests/test_registration.py | 5 +++++ 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py index 0ae75c3b4..ff2ab2837 100644 --- a/skfda/preprocessing/registration/base.py +++ b/skfda/preprocessing/registration/base.py @@ -20,8 +20,9 @@ def score(self, X: FData, y=None): where :math:`\text{MSE}_{total}` is the mean squared error and :math:`\text{MSE}_{phase}` is the mean squared error due to the phase - explained by the registration procedure. See :func:`mse_decomposition` - for a detailed explanation. + explained by the registration procedure. See + :class:`~.validation.AmplitudePhaseDecomposition` for a detailed + explanation. Args: X (FData): Functional data to be registered @@ -31,13 +32,12 @@ def score(self, X: FData, y=None): float. See also: - :class:`RegistrationScorer ` - :func:`mse_r_squared ` - :func:`least_squares ` - :func:`sobolev_least_squares ` - :func:`pairwise_correlation ` + :class:`~.validation.AmplitudePhaseDecomposition` + :class:`~.validation.LeastSquares` + :class:`~.validation.SobolevLeastSquares` + :class:`~.validation.PairwiseCorrelation` """ - from .validation import RegistrationScorer + from .validation import AmplitudePhaseDecomposition - return RegistrationScorer()(self, X, y) + return AmplitudePhaseDecomposition()(self, X, y) diff --git a/tests/test_registration.py b/tests/test_registration.py index 72c38a941..1067c2d7c 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -317,6 +317,11 @@ def test_amplitude_phase_score(self): score = scorer(self.shift_registration, self.X) np.testing.assert_almost_equal(score, 0.972000160) + def test_default_score(self): + + score = self.shift_registration.score(self.X) + np.testing.assert_almost_equal(score, 0.972000160) + def test_least_squares_score(self): scorer = LeastSquares() score = scorer(self.shift_registration, self.X) From 7e4896f58ed2bbad6f0cc4fe449286020fbe73a2 Mon Sep 17 00:00:00 2001 From: pablomm Date: Sat, 12 Oct 2019 18:36:40 +0200 Subject: [PATCH 033/624] Refactor elastic registration class --- skfda/misc/metrics.py | 2 +- skfda/preprocessing/registration/__init__.py | 4 +- skfda/preprocessing/registration/_elastic.py | 276 ++++++++++--------- skfda/preprocessing/registration/base.py | 3 +- 4 files changed, 144 insertions(+), 141 deletions(-) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 9ab25b97c..56f3bb91f 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -4,7 +4,7 @@ from ..preprocessing.registration import ( normalize_warping, _normalize_scale, to_srsf, - elastic_registration_warping) + ElasticRegistration) from ..representation import FData from ..representation import FDataGrid diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 2b8968322..df1c783da 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -17,7 +17,5 @@ from . import validation -from ._elastic import (to_srsf, from_srsf, - elastic_registration, - elastic_registration_warping, +from ._elastic import (ElasticRegistration, to_srsf, from_srsf, elastic_mean, warping_mean) diff --git a/skfda/preprocessing/registration/_elastic.py b/skfda/preprocessing/registration/_elastic.py index 1af90a60f..bf970f313 100644 --- a/skfda/preprocessing/registration/_elastic.py +++ b/skfda/preprocessing/registration/_elastic.py @@ -1,16 +1,19 @@ import scipy.integrate +from sklearn.utils.validation import check_is_fitted + import numpy as np import optimum_reparam + from . import invert_warping -from ... import FDataGrid +from .base import RegistrationTransformer from ._registration_utils import _normalize_scale - - -from...representation.interpolation import SplineInterpolator +from ... import FDataGrid +from ..._utils import _check_univariate +from ...representation.interpolation import SplineInterpolator __author__ = "Pablo Marcos Manchón" @@ -185,10 +188,9 @@ def _elastic_alignment_array(template_data, q_data, lam, grid_dim).T -def elastic_registration_warping(fdatagrid, template=None, *, lam=0., - eval_points=None, fdatagrid_srsf=None, - template_srsf=None, grid_dim=7, **kwargs): - r"""Calculate the warping to align a FDatagrid using the SRSF framework. + +class ElasticRegistration(RegistrationTransformer): + r"""Align a FDatagrid using the SRSF framework. Let :math:`f` be a function of the functional data object wich will be aligned to the template :math:`g`. Calculates the warping wich minimises @@ -199,7 +201,7 @@ def elastic_registration_warping(fdatagrid, template=None, *, lam=0., \gamma^* = argmin_{\gamma \in \Gamma} d_{\lambda}(f \circ \gamma, g) - Where :math:`d_{\lambda}` denotes the extended amplitude distance with a + Where :math:`d_{\lambda}` denotes the extended Fisher-Rao distance with a penalty term, used to control the amount of warping. .. math:: @@ -218,176 +220,178 @@ def elastic_registration_warping(fdatagrid, template=None, *, lam=0., composition :math:`f^*(t)=f(\gamma^*(t))`. If the template is not specified it is used the Karcher mean of the set of - functions under the Fisher-Rao metric to perform the alignment, wich is - the local minimum of the sum of squares of elastic distances. - See :func:`elastic_mean`. + functions under the elastic metric to perform the alignment, also known as + `elastic mean`, wich is the local minimum of the sum of squares of elastic + distances. See :func:`elastic_mean`. - In [SK16-4-3]_ are described extensively the algorithms employed and + In [SK16-4-2]_ are described extensively the algorithms employed and the SRSF framework. Args: - fdatagrid (:class:`FDataGrid`): Functional data object to be aligned. - template (:class:`FDataGrid`, optional): Template to align the curves. - Can contain 1 sample to align all the curves to it or the same - number of samples than the fdatagrid. By default it is used the - elastic mean. - lam (float, optional): Controls the amount of elasticity. + template (str, :class:`FDataGrid` or callable, optional): Template to + align the curves. Can contain 1 sample to align all the curves to + it or the same number of samples than the fdatagrid. By default + `elastic mean`, in which case :func:`elastic_mean` is called. + penalty_term (float, optional): Controls the amount of elasticity. Defaults to 0. - eval_points (array_like, optional): Set of points where the + output_points (array_like, optional): Set of points where the functions are evaluated, by default uses the sample points of the - fdatagrid. - fdatagrid_srsf (:class:`FDataGrid`, optional): SRSF of the fdatagrid, - may be passed to avoid repeated calculation. - template_srsf (:class:`FDataGrid`, optional): SRSF of the template, - may be passed to avoid repeated calculation. - grid_dim (int, optional): Dimension of the grid used in the alignment - algorithm. Defaults 7. - **kwargs: Named arguments to be passed to :func:`elastic_mean`. - - Returns: - (:class:`FDataGrid`): Warping to align the given fdatagrid to the - template. - - Raises: - ValueError: If functions are multidimensional or the number of samples - are different. + fdatagrid which will be transformed. + grid_dim (int, optional): Dimension of the grid used in the DP + alignment algorithm. Defaults 7. + **kwargs: Named arguments to be passed to be passed to the callable + which constructs the template or to :func:`elastic_mean` by + default. + + Attributes: + template_ (:class:`FDataGrid`): Template learned during fitting, + used for alignment in :meth:`transform`. + warping_ (:class:`FDataGrid`): Warping applied during the last + transformation. References: - .. [SK16-4-3] Srivastava, Anuj & Klassen, Eric P. (2016). Functional + .. [SK16-4-2] Srivastava, Anuj & Klassen, Eric P. (2016). Functional and shape data analysis. In *Functional Data and Elastic Registration* (pp. 73-122). Springer. """ + def __init__(self, template="elastic mean", penalty=0., output_points=None, + grid_dim=7, **kwargs): + """Initializes the registration transformer""" - # Check of params - if fdatagrid.dim_domain != 1 or fdatagrid.dim_codomain != 1: + self.template = template + self.penalty = penalty + self.output_points = output_points + self.grid_dim = grid_dim + self.kwargs = kwargs - raise ValueError("Not supported multidimensional functional objects.") + def fit(self, X: FDataGrid=None, y=None): + """Fit the transformer. - if template is None: - template = elastic_mean(fdatagrid, lam=lam, eval_points=eval_points, - **kwargs) + Learns the template used during the transformation. - elif ((template.n_samples != 1 and template.n_samples != fdatagrid.n_samples) - or template.dim_domain != 1 or template.dim_codomain != 1): + Args: + X (FDataGrid, optionl): Functional samples used as training + samples. If the template provided it is an FDataGrid this + samples are it is not need to construct the template from the + samples and this argument is ignored. + y (Ignored): Present for API conventions. - raise ValueError("The template should contain one sample to align all" - "the curves to the same function or the same number " - "of samples than the fdatagrid") + Returns: + RegistrationTransformer: self. - # Construction of srsfs - if fdatagrid_srsf is None: - fdatagrid_srsf = to_srsf(fdatagrid, eval_points=eval_points) + """ + if isinstance(self.template, FDataGrid): + self.template_ = self.template # Template already constructed + elif X is None: + raise ValueError("Must be provided a dataset X to construct the " + "template.") + elif self.template == "elastic mean": + self.template_ = elastic_mean(X, **self.kwargs) + else: + self.template_ = self.template(X, **self.kwargs) - if template_srsf is None: - template_srsf = to_srsf(template, eval_points=eval_points) + # Constructs the SRSF of the template + self._template_srsf = to_srsf(self.template_, + eval_points=self.output_points) + return self - if eval_points is None: - eval_points = fdatagrid_srsf.sample_points[0] - # Discretizacion in evaluation points - q_data = fdatagrid_srsf(eval_points, keepdims=False).squeeze() - template_data = template_srsf(eval_points, keepdims=False).squeeze() + def transform(self, X: FDataGrid, y=None): + """Apply elastic registration to the data. - # Values of the warping - gamma = _elastic_alignment_array(template_data, q_data, - _normalize_scale(eval_points), - lam, grid_dim) + Args: + X (:class:`FDataGrid`): Functional data to be registered. + y (ignored): - # Normalize warping to original interval - gamma = _normalize_scale(gamma, a=eval_points[0], b=eval_points[-1]) + Returns: + :class:`FDataGrid`: Registered samples. - # Interpolator - interpolator = SplineInterpolator(interpolation_order=3, monotone=True) + """ + check_is_fitted(self, '_template_srsf') + _check_univariate(X) - return FDataGrid(gamma, eval_points, interpolator=interpolator) + if (len(self._template_srsf) != 1 and + len(fdatagrid) != len(self._template_srsf)): + raise ValueError("The template should contain one sample to align " + "all the curves to the same function or the " + "same number of samples than X.") -def elastic_registration(fdatagrid, template=None, *, lam=0., eval_points=None, - fdatagrid_srsf=None, template_srsf=None, grid_dim=7, - **kwargs): - r"""Align a FDatagrid using the SRSF framework. - Let :math:`f` be a function of the functional data object wich will be - aligned to the template :math:`g`. Calculates the warping wich minimises - the Fisher-Rao distance between :math:`g` and the registered function - :math:`f^*(t)=f(\gamma^*(t))=f \circ \gamma^*`. + fdatagrid_srsf = to_srsf(X, eval_points=self.output_points) - .. math:: - \gamma^* = argmin_{\gamma \in \Gamma} d_{\lambda}(f \circ - \gamma, g) + # Points of discretization + if self.output_points is None: + output_points = fdatagrid_srsf.sample_points[0] + else: + output_points = self.output_points - Where :math:`d_{\lambda}` denotes the extended Fisher-Rao distance with a - penalty term, used to control the amount of warping. + # Discretizacion in evaluation points + q_data = fdatagrid_srsf(output_points, keepdims=False).squeeze() + template_data = self._template_srsf(output_points, keepdims=False).squeeze() - .. math:: - d_{\lambda}^2(f \circ \gamma, g) = \| SRSF(f \circ \gamma) - \sqrt{\dot{\gamma}} - SRSF(g)\|_{\mathbb{L}^2}^2 + \lambda - \mathcal{R}(\gamma) + if q_data.shape[0] == 1: + q_data = q_data[0] - In the implementation it is used as penalty term + if template_data.shape[0] == 1: + template_data = template_data[0] - .. math:: - \mathcal{R}(\gamma) = \|\sqrt{\dot{\gamma}}- 1 \|_{\mathbb{L}^2}^2 + # Values of the warping + gamma = _elastic_alignment_array(template_data, q_data, + _normalize_scale(output_points), + self.penalty, self.grid_dim) - Wich restrict the amount of elasticity employed in the alignment. + # Normalize warping to original interval + gamma = _normalize_scale( + gamma, a=output_points[0], b=output_points[-1]) - The registered function :math:`f^*(t)` can be calculated using the - composition :math:`f^*(t)=f(\gamma^*(t))`. + # Interpolator + interpolator = SplineInterpolator(interpolation_order=3, monotone=True) - If the template is not specified it is used the Karcher mean of the set of - functions under the elastic metric to perform the alignment, wich is - the local minimum of the sum of squares of elastic distances. - See :func:`elastic_mean`. + self.warping_ = FDataGrid(gamma, output_points, + interpolator=interpolator) - In [SK16-4-2]_ are described extensively the algorithms employed and - the SRSF framework. - Args: - fdatagrid (:class:`FDataGrid`): Functional data object to be aligned. - template (:class:`FDataGrid`, optional): Template to align the curves. - Can contain 1 sample to align all the curves to it or the same - number of samples than the fdatagrid. By default it is used the - elastic mean. - lam (float, optional): Controls the amount of elasticity. - Defaults to 0. - eval_points (array_like, optional): Set of points where the - functions are evaluated, by default uses the sample points of the - fdatagrid. - fdatagrid_srsf (:class:`FDataGrid`, optional): SRSF of the fdatagrid, - may be passed to avoid repeated calculation. - template_srsf (:class:`FDataGrid`, optional): SRSF of the template, - may be passed to avoid repeated calculation. - grid_dim (int, optional): Dimension of the grid used in the alignment - algorithm. Defaults 7. - **kwargs: Named arguments to be passed to :func:`elastic_mean`. + return X.compose(self.warping_, eval_points=output_points) - Returns: - (:class:`FDataGrid`): FDatagrid with the samples aligned to the - template. + def inverse_transform(self, X: FDataGrid): + r"""Reverse the registration procedure previosly applied. - Raises: - ValueError: If functions are multidimensional or the number of samples - are different. + Let :math:`gamma(t)` the warping applied to construct a registered + functional datum :math:`f^*(t)=f(\gamma(t))`. - References: - .. [SK16-4-2] Srivastava, Anuj & Klassen, Eric P. (2016). Functional - and shape data analysis. In *Functional Data and Elastic - Registration* (pp. 73-122). Springer. + Given a functional datum :math:`f^*(t) it is computed + :math:`\gamma^{-1}(t)` to reverse the registration procedure + :math:`f(t)=f^*(\gamma^{-1}(t))`. - """ + Args: + X (:class:`FDataGrid`): Functional data to apply the reverse + transform. + + Returns: + :class:`FDataGrid`: Functional data compose by the inverse warping. + + Raises: + ValueError: If the warpings :math:`\gamma` were not build via + :meth:`transform` or if the number of samples of `X` os different + than the number of samples of the dataset previosly transformed. + + See also: + :func:`invert_warping` + + """ + if not hasattr(self, 'warping_'): + raise ValueError("Data must be previosly transformed to apply the " + "inverse transform") + elif len(X) != len(self.warping_): + raise ValueError("Data must contain the same number of samples " + "than the dataset previously transformed") + + inverse_warping = invert_warping(self.warping_) + + return X.compose(inverse_warping, eval_points=self.output_points) - # Calculates corresponding set of warpings - warping = elastic_registration_warping(fdatagrid, - template=template, - lam=lam, - eval_points=eval_points, - fdatagrid_srsf=fdatagrid_srsf, - template_srsf=template_srsf, - grid_dim=grid_dim, - **kwargs) - - return fdatagrid.compose(warping, eval_points=eval_points) def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py index ff2ab2837..a705c52a0 100644 --- a/skfda/preprocessing/registration/base.py +++ b/skfda/preprocessing/registration/base.py @@ -8,6 +8,7 @@ from ... import FData class RegistrationTransformer(ABC, BaseEstimator, TransformerMixin): + """Base class for the registration methods.""" def score(self, X: FData, y=None): r"""Returns the percentage of total variation removed. @@ -26,7 +27,7 @@ def score(self, X: FData, y=None): Args: X (FData): Functional data to be registered - y : Ignored + y (Ignored): Ignored, only for API conventions. Returns: float. From dba72d311f1af249bc7543085ca58f46b9abeec5 Mon Sep 17 00:00:00 2001 From: pablomm Date: Sun, 13 Oct 2019 16:37:42 +0200 Subject: [PATCH 034/624] Update elastic_registration references --- skfda/misc/metrics.py | 36 ++++++------ skfda/preprocessing/registration/_elastic.py | 2 +- tests/test_elastic.py | 62 +++++++++++--------- 3 files changed, 54 insertions(+), 46 deletions(-) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 56f3bb91f..34e9f988f 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -482,23 +482,22 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - fdata1_srsf = to_srsf(fdata1) - fdata2_srsf = to_srsf(fdata2) + #fdata1_srsf = to_srsf(fdata1) + #fdata2_srsf = to_srsf(fdata2) + + elastic_registration = ElasticRegistration(template=fdata2, + penalty=lam, + output_points=eval_points_normalized, + **kwargs) - warping = elastic_registration_warping(fdata1, - template=fdata2, - lam=lam, - val_points=eval_points_normalized, - fdatagrid_srsf=fdata1_srsf, - template_srsf=fdata2_srsf, - **kwargs) - fdata1_reg = fdata1.compose(warping) + fdata1_reg = elastic_registration.fit_transform(fdata1) - distance = lp_distance(to_srsf(fdata1_reg), fdata2_srsf) + distance = lp_distance(to_srsf(fdata1_reg), to_srsf(fdata2)) if lam != 0.0: # L2 norm || sqrt(Dh) - 1 ||^2 + warping = elastic_registration.warping_ penalty = warping(eval_points_normalized, derivative=1, keepdims=False)[0] penalty = np.sqrt(penalty, out=penalty) @@ -564,14 +563,15 @@ def phase_distance(fdata1, fdata2, *, lam=0., eval_points=None, _check=True, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - warping = elastic_registration_warping(fdata1, - template=fdata2, - lam=lam, - eval_points=eval_points_normalized, - **kwargs) + elastic_registration = ElasticRegistration(penalty=lam, template=fdata2, + output_points=eval_points_normalized) + + elastic_registration.fit_transform(fdata1) + - derivative_warping = warping(eval_points_normalized, keepdims=False, - derivative=1)[0] + derivative_warping = elastic_registration.warping_(eval_points_normalized, + keepdims=False, + derivative=1)[0] derivative_warping = np.sqrt(derivative_warping, out=derivative_warping) diff --git a/skfda/preprocessing/registration/_elastic.py b/skfda/preprocessing/registration/_elastic.py index bf970f313..fb9c2bd68 100644 --- a/skfda/preprocessing/registration/_elastic.py +++ b/skfda/preprocessing/registration/_elastic.py @@ -312,7 +312,7 @@ def transform(self, X: FDataGrid, y=None): _check_univariate(X) if (len(self._template_srsf) != 1 and - len(fdatagrid) != len(self._template_srsf)): + len(X) != len(self._template_srsf)): raise ValueError("The template should contain one sample to align " "all the curves to the same function or the " diff --git a/tests/test_elastic.py b/tests/test_elastic.py index 5552aaf44..e111ba7bc 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -8,8 +8,8 @@ phase_distance, pairwise_distance, lp_distance, warping_distance) from skfda.preprocessing.registration import ( - elastic_registration, to_srsf, from_srsf, - elastic_registration_warping, invert_warping, normalize_warping) + to_srsf, from_srsf, ElasticRegistration, + invert_warping, normalize_warping, elastic_mean) metric = pairwise_distance(lp_distance) pairwise_fisher_rao = pairwise_distance(fisher_rao_distance) @@ -18,44 +18,41 @@ class TestElasticRegistration(unittest.TestCase): """Test elastic registration""" - def setUp(self): """Initialization of samples""" template = make_multimodal_samples(n_samples=1, std=0, random_state=1) self.template = template - self.template_rep = template.concatenate(template).concatenate(template) + self.template_rep = template.concatenate( + template).concatenate(template) self.unimodal_samples = make_multimodal_samples(n_samples=3, random_state=1) t = np.linspace(-3, 3, 9) self.dummy_sample = FDataGrid([np.sin(t)], t) - def test_to_srsf(self): """Test to srsf""" # Checks SRSF conversion srsf = to_srsf(self.dummy_sample) - data_matrix = [[[-0.92155896], [-0.75559027], [ 0.25355399], - [ 0.81547327], [ 0.95333713], [ 0.81547327], - [ 0.25355399], [-0.75559027], [-0.92155896]]] + data_matrix = [[[-0.92155896], [-0.75559027], [0.25355399], + [0.81547327], [0.95333713], [0.81547327], + [0.25355399], [-0.75559027], [-0.92155896]]] np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) - def test_from_srsf(self): """Test from srsf""" # Checks SRSF conversion srsf = from_srsf(self.dummy_sample) - data_matrix = [[[ 0. ], [-0.23449228], [-0.83464009], + data_matrix = [[[0.], [-0.23449228], [-0.83464009], [-1.38200046], [-1.55623723], [-1.38200046], - [-0.83464009], [-0.23449228], [ 0. ]]] + [-0.83464009], [-0.23449228], [0.]]] np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) - def test_srsf_conversion(self): """Converts to srsf and pull backs""" initial = self.unimodal_samples(-1) @@ -66,42 +63,56 @@ def test_srsf_conversion(self): np.testing.assert_allclose(distances, 0, atol=8e-3) - def test_template_alignment(self): """Test alignment to 1 template""" - register = elastic_registration(self.unimodal_samples, self.template) + reg = ElasticRegistration(template=self.template) + register = reg.fit_transform(self.unimodal_samples) distances = metric(self.template, register) np.testing.assert_allclose(distances, 0, atol=12e-3) def test_one_to_one_alignment(self): """Test alignment to 1 sample to a template""" - register = elastic_registration(self.unimodal_samples[0], self.template) + reg = ElasticRegistration(template=self.template) + register = reg.fit_transform(self.unimodal_samples[0]) distances = metric(self.template, register) np.testing.assert_allclose(distances, 0, atol=12e-3) - def test_set_alignment(self): """Test alignment 3 curves to set with 3 templates""" # Should give same result than test_template_alignment - register = elastic_registration(self.unimodal_samples, - self.template_rep) + reg = ElasticRegistration(template=self.template_rep) + register = reg.fit_transform(self.unimodal_samples) distances = metric(self.template, register) np.testing.assert_allclose(distances, 0, atol=12e-3) + def test_default_alignment(self): + """Test alignment by default""" + # Should give same result than test_template_alignment + reg = ElasticRegistration() + register = reg.fit_transform(self.unimodal_samples) + + values = register([-.25, -.1, 0, .1, .25]) + expected = [[0.6607, 0.9974, 0.7722, 0.3636, 0.0459], + [0.6407, 0.9982, 0.7916, 0.3822, 0.0501], + [0.6472, 0.9976, 0.7859, 0.3766, 0.0488]] + + np.testing.assert_allclose(values, expected, atol=1e-4) def test_simetry_of_aligment(self): """Check registration using inverse composition""" - warping = elastic_registration_warping(self.unimodal_samples, - self.template) + reg = ElasticRegistration(template=self.template) + reg.fit_transform(self.unimodal_samples) + warping = reg.warping_ inverse = invert_warping(warping) register = self.template_rep.compose(inverse) distances = np.diag(metric(self.unimodal_samples, register)) np.testing.assert_allclose(distances, 0, atol=12e-3) + class TestElasticDistances(unittest.TestCase): """Test elastic distances""" @@ -109,7 +120,7 @@ def test_fisher_rao(self): """Test fisher rao distance""" t = np.linspace(0, 1, 100) - sample = FDataGrid([t, 1-t], t) + sample = FDataGrid([t, 1 - t], t) f = np.square(sample) g = np.power(sample, 0.5) @@ -134,7 +145,7 @@ def test_fisher_rao_invariance(self): distance_warping = fisher_rao_distance(cos.compose(gamma), sin.compose(gamma)) distance_warping2 = fisher_rao_distance(cos.compose(gamma2), - sin.compose(gamma2)) + sin.compose(gamma2)) # The error ~0.001 due to the derivation np.testing.assert_almost_equal(distance_original, distance_warping, @@ -154,12 +165,11 @@ def test_amplitude_distance_limit(self): np.testing.assert_almost_equal(amplitude_limit, fr_distance) - def test_phase_distance_id(self): """Test of phase distance invariance""" f = make_multimodal_samples(n_samples=1, random_state=1) - phase = phase_distance(f, 2*f) + phase = phase_distance(f, 2 * f) np.testing.assert_allclose(phase, 0, atol=1e-7) @@ -170,14 +180,12 @@ def test_warping_distance(self): w2 = FDataGrid([t**3], t) d = warping_distance(w1, w2) - np.testing.assert_allclose(d, np.arccos(np.sqrt(15)/4), atol=1e-3) + np.testing.assert_allclose(d, np.arccos(np.sqrt(15) / 4), atol=1e-3) d = warping_distance(w2, w2) np.testing.assert_allclose(d, 0, atol=2e-2) - - if __name__ == '__main__': print() unittest.main() From 1cb3206c167490a4a17b809d603d26a4bb3a2620 Mon Sep 17 00:00:00 2001 From: pablomm Date: Sun, 13 Oct 2019 17:46:06 +0200 Subject: [PATCH 035/624] Rename _check_univariate --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 15 +++++++-- skfda/preprocessing/registration/_elastic.py | 33 ++++++++++++++++--- .../registration/_shift_registration.py | 5 ++- .../preprocessing/registration/validation.py | 18 +++++----- 5 files changed, 52 insertions(+), 21 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 2cfcb3d13..8e2219e47 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -2,4 +2,4 @@ from ._utils import (_list_of_arrays, _coordinate_list, _check_estimator, parameter_aliases, - _to_grid, _check_univariate) + _to_grid, check_is_univariate) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 561be0715..c6beb356f 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -6,9 +6,18 @@ import numpy as np -def _check_univariate(fd): - """Checks if an FData is univariate and raises an error""" +def check_is_univariate(fd): + """Checks if an FData is univariate and raises an error + Args: + fd (:class:`~skfda.FData`): Functional object to check if is + univariate. + + Raises: + ValueError: If it is not univariate, i.e., `fd.dim_domain != 1` or + `fd.dim_codomain != 1`. + + """ if fd.dim_domain != 1 or fd.dim_codomain != 1: raise ValueError(f"The functional data must be univariate, i.e., " + f"with dim_domain=1 " + @@ -19,7 +28,7 @@ def _check_univariate(fd): f"(currently is {fd.dim_codomain})")) def _to_grid(X, y, eval_points=None): - """Transforms the functional data in grids to perform calculations.""" + """Transform a pair of FDatas in grids to perform calculations.""" from .. import FDataGrid x_is_grid = isinstance(X, FDataGrid) diff --git a/skfda/preprocessing/registration/_elastic.py b/skfda/preprocessing/registration/_elastic.py index fb9c2bd68..843bd1fb5 100644 --- a/skfda/preprocessing/registration/_elastic.py +++ b/skfda/preprocessing/registration/_elastic.py @@ -12,7 +12,7 @@ from .base import RegistrationTransformer from ._registration_utils import _normalize_scale from ... import FDataGrid -from ..._utils import _check_univariate +from ..._utils import check_is_univariate from ...representation.interpolation import SplineInterpolator @@ -309,7 +309,7 @@ def transform(self, X: FDataGrid, y=None): """ check_is_fitted(self, '_template_srsf') - _check_univariate(X) + check_is_univariate(X) if (len(self._template_srsf) != 1 and len(X) != len(self._template_srsf)): @@ -328,8 +328,8 @@ def transform(self, X: FDataGrid, y=None): output_points = self.output_points # Discretizacion in evaluation points - q_data = fdatagrid_srsf(output_points, keepdims=False).squeeze() - template_data = self._template_srsf(output_points, keepdims=False).squeeze() + q_data = fdatagrid_srsf(output_points, keepdims=False) + template_data = self._template_srsf(output_points, keepdims=False) if q_data.shape[0] == 1: q_data = q_data[0] @@ -374,9 +374,32 @@ def inverse_transform(self, X: FDataGrid): Raises: ValueError: If the warpings :math:`\gamma` were not build via - :meth:`transform` or if the number of samples of `X` os different + :meth:`transform` or if the number of samples of `X` is different than the number of samples of the dataset previosly transformed. + Examples: + + Center the datasets taking into account the misalignment. + + >>> from skfda.preprocessing.registration import \ + ... ElasticRegistration + >>> from skfda.datasets import make_multimodal_samples + >>> X = make_multimodal_samples(random_state=0) + + Registration of the dataset. + + >>> elastic_registration = ElasticRegistration() + >>> X = elastic_registration.fit_transform(X) + + Substract the elastic mean build as template during the + registration and reverse the transformation. + + >>> X = X - elastic_registration.template_ + >>> X_center = elastic_registration.inverse_transform(X) + >>> X_center + FDataGrid(...) + + See also: :func:`invert_warping` diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 753cf93c4..affe41c75 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -8,7 +8,7 @@ import numpy as np -from ..._utils import constants +from ..._utils import constants, check_is_univariate from .base import RegistrationTransformer from ... import FData, FDataGrid @@ -150,8 +150,7 @@ def _compute_deltas(self, fd, template): template. """ - if fd.dim_codomain > 1 or fd.dim_domain > 1: - raise NotImplementedError("Method for unidimensional data.") + check_is_univariate(fd) domain_range = fd.domain_range[0] diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index bca8acb45..2df4d99cc 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -3,7 +3,7 @@ import numpy as np from typing import NamedTuple -from ..._utils import _check_univariate, _to_grid +from ..._utils import check_is_univariate, _to_grid class RegistrationScorer(): @@ -267,8 +267,8 @@ def score_function(self, X, y, *, warping=None): """ from scipy.integrate import simps - _check_univariate(X) - _check_univariate(y) + check_is_univariate(X) + check_is_univariate(y) if len(y) != len(X): raise ValueError(f"the registered and unregistered curves must have " @@ -433,8 +433,8 @@ def score_function(self, X, y): """ from ...misc.metrics import pairwise_distance, lp_distance - _check_univariate(X) - _check_univariate(y) + check_is_univariate(X) + check_is_univariate(y) X, y = _to_grid(X, y, eval_points=self.eval_points) @@ -547,8 +547,8 @@ def score_function(self, X, y): """ from ...misc.metrics import pairwise_distance, lp_distance - _check_univariate(X) - _check_univariate(y) + check_is_univariate(X) + check_is_univariate(y) # Compute derivative X = X.derivative() @@ -644,8 +644,8 @@ def score_function(self, X, y): float: Score of the transformation. """ - _check_univariate(X) - _check_univariate(y) + check_is_univariate(X) + check_is_univariate(y) # Discretize functional data if needed X, y = _to_grid(X, y, eval_points=self.eval_points) From 11e2c10e888b8f167692d4e1bcde4788e6ed5812 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 12:04:45 +0200 Subject: [PATCH 036/624] Refactor SRSF --- skfda/misc/metrics.py | 19 +- skfda/preprocessing/registration/__init__.py | 6 +- .../registration/{_elastic.py => elastic.py} | 267 +++++++++++------- tests/test_elastic.py | 16 +- 4 files changed, 194 insertions(+), 114 deletions(-) rename skfda/preprocessing/registration/{_elastic.py => elastic.py} (73%) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 34e9f988f..49f6c08ad 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -3,8 +3,9 @@ import numpy as np from ..preprocessing.registration import ( - normalize_warping, _normalize_scale, to_srsf, + normalize_warping, _normalize_scale, ElasticRegistration) +from ..preprocessing.registration.elastic import SRSF from ..representation import FData from ..representation import FDataGrid @@ -369,7 +370,7 @@ def fisher_rao_distance(fdata1, fdata2, *, eval_points=None, _check=True): Let :math:`f_i` and :math:`f_j` be two functional observations, and let :math:`q_i` and :math:`q_j` be the corresponding SRSF - (see :func:`to_srsf`), the fisher rao distance is defined as + (see :class:`SRSF`), the fisher rao distance is defined as .. math:: d_{FR}(f_i, f_j) = \| q_i - q_j \|_2 = @@ -413,8 +414,9 @@ def fisher_rao_distance(fdata1, fdata2, *, eval_points=None, _check=True): fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - fdata1_srsf = to_srsf(fdata1) - fdata2_srsf = to_srsf(fdata2) + srsf = SRSF(store_initial=False) + fdata1_srsf = srsf.fit_transform(fdata1) + fdata2_srsf = srsf.transform(fdata2) # Return the L2 distance of the SRSF return lp_distance(fdata1_srsf, fdata2_srsf, p=2) @@ -426,7 +428,7 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, Let :math:`f_i` and :math:`f_j` be two functional observations, and let :math:`q_i` and :math:`q_j` be the corresponding SRSF - (see :func:`to_srsf`), the amplitude distance is defined as + (see :class:`SRSF`), the amplitude distance is defined as .. math:: d_{A}(f_i, f_j)=min_{\gamma \in \Gamma}d_{FR}(f_i \circ \gamma,f_j) @@ -482,8 +484,6 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - #fdata1_srsf = to_srsf(fdata1) - #fdata2_srsf = to_srsf(fdata2) elastic_registration = ElasticRegistration(template=fdata2, penalty=lam, @@ -493,7 +493,10 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata1_reg = elastic_registration.fit_transform(fdata1) - distance = lp_distance(to_srsf(fdata1_reg), to_srsf(fdata2)) + srsf = SRSF(store_initial=False) + fdata1_reg_srsf = srsf.fit_transform(fdata1_reg) + fdata2_srsf = srsf.transform(fdata2) + distance = lp_distance(fdata1_reg_srsf, fdata2_srsf) if lam != 0.0: # L2 norm || sqrt(Dh) - 1 ||^2 diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index df1c783da..34bad5393 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -15,7 +15,7 @@ normalize_warping, _normalize_scale) -from . import validation -from ._elastic import (ElasticRegistration, to_srsf, from_srsf, - elastic_mean, warping_mean) +from .elastic import ElasticRegistration, elastic_mean, warping_mean + +from . import validation, elastic diff --git a/skfda/preprocessing/registration/_elastic.py b/skfda/preprocessing/registration/elastic.py similarity index 73% rename from skfda/preprocessing/registration/_elastic.py rename to skfda/preprocessing/registration/elastic.py index 843bd1fb5..e87556277 100644 --- a/skfda/preprocessing/registration/_elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -2,6 +2,7 @@ import scipy.integrate from sklearn.utils.validation import check_is_fitted +from sklearn.base import BaseEstimator, TransformerMixin import numpy as np @@ -25,131 +26,194 @@ # and *ElasticFDA.jl* (https://github.com/jdtuck/ElasticFDA.jl). # ############################################################################### +class SRSF(BaseEstimator, TransformerMixin): + r"""Square-Root Slope Function (SRSF) transform. -def to_srsf(fdatagrid, eval_points=None): - r"""Calculate the square-root slope function (SRSF) transform. - - Let :math:`f_i : [a,b] \rightarrow \mathbb{R}` be an absolutely continuous + Let :math:`f : [a,b] \rightarrow \mathbb{R}` be an absolutely continuous function, the SRSF transform is defined as .. math:: - SRSF(f_i(t)) = sgn(f_i(t)) \sqrt{|Df_i(t)|} = q_i(t) + SRSF(f(t)) = sgn(f(t)) \sqrt{|\dot f(t)|} = q(t) This representation it is used to compute the extended non-parametric Fisher-Rao distance between functions, wich under the SRSF representation becomes the usual :math:`\mathbb{L}^2` distance between functions. - See [SK16-4-6-1]_ . + See [SK16-4-6]_ . - Args: - fdatagrid (:class:`FDataGrid`): Functions to be transformed. - eval_points: (array_like, optional): Set of points where the + The inverse SRSF transform is defined as + + .. math:: + f(t) = f(a) + \int_{a}^t q(t)|q(t)|dt . + + This transformation is a mapping up to constant. Given the SRSF and the + initial value :math:`f(a)` the original function can be obtained, for this + reason it is necessary to store the value :math:`f(a)` during the fit, + which is dropped due to derivation. If it is applied the inverse + transformation without fit the estimator it is assumed that :math:`f(a)=0`. + + Attributes: + eval_points (array_like, optional): Set of points where the functions are evaluated, by default uses the sample points of the fdatagrid. + store_initial (bool): If true stores the value :math:`f(a)` of the + samples during fitting to apply the inverse transform. + Defaults True. - Returns: - :class:`FDataGrid`: SRSF functions. - - Raises: - ValueError: If functions are multidimensional. + Note: + Due to the use of derivatives it is recommended that the samples are + sufficiently smooth, or have passed a smoothing preprocessing before, + in order to achieve good results. References: - .. [SK16-4-6-1] Srivastava, Anuj & Klassen, Eric P. (2016). Functional + .. [SK16-4-6] Srivastava, Anuj & Klassen, Eric P. (2016). Functional and shape data analysis. In *Square-Root Slope Function Representation* (pp. 91-93). Springer. - """ + Examples: - if fdatagrid.dim_domain > 1: - raise ValueError("Only support functional objects with unidimensional " - "domain.") + Create a toy dataset and apply the transformation and its inverse. - elif fdatagrid.dim_codomain > 1: - raise ValueError("Only support functional objects with unidimensional " - "codomain.") + >>> from skfda.datasets import make_sinusoidal_process + >>> from skfda.preprocessing.registration import SRSF + >>> fd = make_sinusoidal_process(error_std=0, random_state=0) + >>> srsf = SRSF() + >>> srsf + SRSF(eval_points=None) - elif eval_points is None: - eval_points = fdatagrid.sample_points[0] + Fits the estimator (to apply the inverse transform) and apply the SRSF - g = fdatagrid.derivative() + >>> q = srsf.fit_transform(fd) - # Evaluation with the corresponding interpolation - g_data_matrix = g(eval_points, keepdims=False) + Apply the inverse transform. - # SRSF(f) = sign(f) * sqrt|Df| - q_data_matrix = np.sign(g_data_matrix) * np.sqrt(np.abs(g_data_matrix)) + >>> fd_pull_back = srsf.inverse_transform(q) - return fdatagrid.copy(data_matrix=q_data_matrix, sample_points=eval_points) + The original and the pull back `fd` are almost equal + >>> zero = fd - fd_pull_back + >>> zero.data_matrix.flatten().round(3) + array([ 0., 0., 0., ...]) -def from_srsf(fdatagrid, initial=None, *, eval_points=None): - r"""Given a SRSF calculate the corresponding function in the original space. + """ + def __init__(self, output_points=None, store_initial=True): + """Initializes the transformer. + Args: + eval_points: (array_like, optional): Set of points where the + functions are evaluated, by default uses the sample points of + the :class:`FDataGrid ` transformed. + store_initial (bool): If true stores the value :math:`f(a)` of the + samples during fitting to apply the inverse transform. + Defaults True. - Let :math:`f_i : [a,b]\rightarrow \mathbb{R}` be an absolutely continuous - function, the SRSF transform is defined as + """ + self.output_points = output_points + self.store_initial = store_initial - .. math:: - SRSF(f_i(t)) = sgn(f_i(t)) \sqrt{|Df_i(t)|} = q_i(t) - This transformation is a mapping up to constant. Given the srsf and the - initial value the original function can be obtained as + def fit(self, X: FDataGrid): + """Fits the transformer. + Stores the initial value of the functions to be transformed, in order + to apply its inverse transform. + Args: + X (:class:`FDataGrid 1: - raise ValueError("Only support functional objects with " - "unidimensional domain.") + # Evaluation with the corresponding interpolation + data_matrix = g(output_points, keepdims=False) - elif fdatagrid.dim_codomain > 1: - raise ValueError("Only support functional objects with unidimensional " - "image.") + # SRSF(f) = sign(f) * sqrt|Df| (avoiding multiple allocation) + sign_g = np.sign(data_matrix) + data_matrix = np.abs(data_matrix, out=data_matrix) + data_matrix = np.sqrt(data_matrix, out=data_matrix) + data_matrix *= sign_g - elif eval_points is None: - eval_points = fdatagrid.sample_points[0] - q_data_matrix = fdatagrid(eval_points, keepdims=True) + return X.copy(data_matrix=data_matrix, sample_points=output_points) + + + def inverse_transform(self, X: FDataGrid): + r"""Computes the inverse SRSF transform. + Given the srsf and the initial value the original function can be + obtained as [SK16-4-6-2]_ : + .. math:: + f(t) = f(a) + \int_{a}^t q(t)|q(t)|dt + where :math:`q(t)=SRSF(f(t))`. + If it is applied this inverse transformation without fitting the + estimator it is assumed that :math:`f(a)=0`. + Args: + X (:class:`FDataGrid`): SRSF to be transformed. + Returns: + :class:`FDataGrid`: Functions in the original space. + Raises: + ValueError: If functions are multidimensional. + References: + .. [SK16-4-6-2] Srivastava, Anuj & Klassen, Eric P. (2016). + Functional and shape data analysis. In *Square-Root Slope + Function Representation* (pp. 91-93). Springer. + + """ + check_is_univariate(X) + + if self.store_initial: + check_is_fitted(self, 'initial_') + + if self.output_points is None: + output_points = X.sample_points[0] + else: + output_points = self.output_points + + data_matrix = X(output_points, keepdims=True) - f_data_matrix = q_data_matrix * np.abs(q_data_matrix) + data_matrix *= np.abs(data_matrix) - f_data_matrix = scipy.integrate.cumtrapz(f_data_matrix, - x=eval_points, - axis=1, - initial=0) + f_data_matrix = scipy.integrate.cumtrapz(data_matrix, x=output_points, + axis=1, initial=0) - if initial is not None: - initial = np.atleast_1d(initial) - initial = initial.reshape( - fdatagrid.n_samples, 1, fdatagrid.dim_codomain) - initial = np.repeat(initial, len(eval_points), axis=1) - f_data_matrix += initial + # If the transformer was fitted, sum the initial value + if hasattr(self, 'initial_'): + f_data_matrix += self.initial_ - return fdatagrid.copy(data_matrix=f_data_matrix, sample_points=eval_points) + return X.copy(data_matrix=f_data_matrix, sample_points=output_points) def _elastic_alignment_array(template_data, q_data, @@ -292,8 +356,9 @@ def fit(self, X: FDataGrid=None, y=None): self.template_ = self.template(X, **self.kwargs) # Constructs the SRSF of the template - self._template_srsf = to_srsf(self.template_, - eval_points=self.output_points) + srsf = SRSF(output_points=self.output_points, store_initial=False) + self._template_srsf = srsf.fit_transform(self.template_) + return self @@ -318,8 +383,8 @@ def transform(self, X: FDataGrid, y=None): "all the curves to the same function or the " "same number of samples than X.") - - fdatagrid_srsf = to_srsf(X, eval_points=self.output_points) + srsf = SRSF(output_points=self.output_points, store_initial=False) + fdatagrid_srsf = srsf.fit_transform(X) # Points of discretization if self.output_points is None: @@ -475,8 +540,10 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, warping = FDataGrid(_normalize_scale(warping.data_matrix[..., 0]), _normalize_scale(warping.sample_points[0])) - psi = to_srsf(warping, eval_points=eval_points).data_matrix[..., 0].T - mu = to_srsf(warping.mean(), eval_points=eval_points).data_matrix[0] + srsf = SRSF(output_points=eval_points, store_initial=False) + psi = srsf.fit_transform(warping).data_matrix[..., 0].T + mu = srsf.fit_transform(warping.mean()).data_matrix[0] + dot_aux = np.empty(psi.shape) n_points = mu.shape[0] @@ -600,15 +667,14 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, """ - if fdatagrid.dim_domain != 1 or fdatagrid.dim_codomain != 1: - raise ValueError("Not supported multidimensional functional objects.") + check_is_univariate(fdatagrid) + srsf_transformer = SRSF(store_initial=False, output_points=eval_points) - if fdatagrid_srsf is not None and (fdatagrid_srsf.dim_domain != 1 or - fdatagrid_srsf.dim_codomain != 1): - raise ValueError("Not supported multidimensional functional objects.") + if fdatagrid_srsf is not None: + check_is_univariate(fdatagrid_srsf) - elif fdatagrid_srsf is None: - fdatagrid_srsf = to_srsf(fdatagrid, eval_points=eval_points) + else: + fdatagrid_srsf = srsf_transformer.fit_transform(fdatagrid) if eval_points is not None: eval_points = np.asarray(eval_points) @@ -646,7 +712,8 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, interpolator=interpolator) fdatagrid_normalized = fdatagrid_normalized.compose(gammas) - srsf = to_srsf(fdatagrid_normalized).data_matrix[..., 0] + srsf = srsf_transformer.fit_transform( + fdatagrid_normalized).data_matrix[..., 0] # Next iteration mu_1 = srsf.mean(axis=0, out=mu_1) @@ -664,13 +731,17 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, mu = mu_1 + if initial is None: initial = fdatagrid.data_matrix[:, 0].mean() + srsf_transformer.set_params(store_initial=True) + srsf_transformer.initial_ = initial + + # Karcher mean orbit in space L2/Gamma - karcher_mean = from_srsf(fdatagrid.copy(data_matrix=[mu], - sample_points=eval_points), - initial=initial) + karcher_mean = srsf_transformer.inverse_transform( + fdatagrid.copy(data_matrix=[mu], sample_points=eval_points)) if center: # Gamma mean in Hilbert Sphere diff --git a/tests/test_elastic.py b/tests/test_elastic.py index e111ba7bc..8c1131861 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -8,9 +8,11 @@ phase_distance, pairwise_distance, lp_distance, warping_distance) from skfda.preprocessing.registration import ( - to_srsf, from_srsf, ElasticRegistration, + ElasticRegistration, invert_warping, normalize_warping, elastic_mean) +from skfda.preprocessing.registration.elastic import SRSF + metric = pairwise_distance(lp_distance) pairwise_fisher_rao = pairwise_distance(fisher_rao_distance) @@ -33,7 +35,8 @@ def setUp(self): def test_to_srsf(self): """Test to srsf""" # Checks SRSF conversion - srsf = to_srsf(self.dummy_sample) + + srsf = SRSF().fit_transform(self.dummy_sample) data_matrix = [[[-0.92155896], [-0.75559027], [0.25355399], [0.81547327], [0.95333713], [0.81547327], @@ -45,7 +48,7 @@ def test_from_srsf(self): """Test from srsf""" # Checks SRSF conversion - srsf = from_srsf(self.dummy_sample) + srsf = SRSF(store_initial=False).inverse_transform(self.dummy_sample) data_matrix = [[[0.], [-0.23449228], [-0.83464009], [-1.38200046], [-1.55623723], [-1.38200046], @@ -55,8 +58,11 @@ def test_from_srsf(self): def test_srsf_conversion(self): """Converts to srsf and pull backs""" - initial = self.unimodal_samples(-1) - converted = from_srsf(to_srsf(self.unimodal_samples), initial=initial) + + srsf = SRSF() + + converted = srsf.fit_transform(self.unimodal_samples) + converted = srsf.inverse_transform(converted) # Distances between original samples and s -> to_srsf -> from_srsf distances = np.diag(metric(converted, self.unimodal_samples)) From 02709fe46b8d75de50154fedaba5e401be4110c7 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 12:14:23 +0200 Subject: [PATCH 037/624] Update tests --- skfda/preprocessing/registration/elastic.py | 4 ++-- tests/test_registration.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index e87556277..42e08506c 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -74,11 +74,11 @@ class SRSF(BaseEstimator, TransformerMixin): Create a toy dataset and apply the transformation and its inverse. >>> from skfda.datasets import make_sinusoidal_process - >>> from skfda.preprocessing.registration import SRSF + >>> from skfda.preprocessing.registration.elastic import SRSF >>> fd = make_sinusoidal_process(error_std=0, random_state=0) >>> srsf = SRSF() >>> srsf - SRSF(eval_points=None) + SRSF(output_points=None, store_initial=True) Fits the estimator (to apply the inverse transform) and apply the SRSF diff --git a/tests/test_registration.py b/tests/test_registration.py index 1067c2d7c..360fefabf 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -238,7 +238,7 @@ def test_raises(self): fd = make_multimodal_samples(dim_domain=2, random_state=0) - with np.testing.assert_raises(NotImplementedError): + with np.testing.assert_raises(ValueError): reg.fit_transform(fd) reg.set_params(initial=[0.]) @@ -318,7 +318,7 @@ def test_amplitude_phase_score(self): np.testing.assert_almost_equal(score, 0.972000160) def test_default_score(self): - + score = self.shift_registration.score(self.X) np.testing.assert_almost_equal(score, 0.972000160) From a3822e38ce9e7de6d1169a0c759e62a8f0e98632 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 12:25:23 +0200 Subject: [PATCH 038/624] Simplify API of elastic mean and warping mean --- skfda/preprocessing/registration/elastic.py | 40 +++++++-------------- 1 file changed, 13 insertions(+), 27 deletions(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 42e08506c..53a42b75f 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -217,7 +217,7 @@ def inverse_transform(self, X: FDataGrid): def _elastic_alignment_array(template_data, q_data, - eval_points, lam, grid_dim): + eval_points, penalty, grid_dim): r"""Wrapper between the cython interface and python. Selects the corresponding routine depending on the dimensions of the @@ -228,7 +228,7 @@ def _elastic_alignment_array(template_data, q_data, q_data (numpy.ndarray): Array with the srsf of the curves to be aligned. eval_points (numpy.ndarray): Discretisation points of the functions. - lam (float): Penalisation term. + penalty (float): Penalisation term. grid_dim (int): Dimension of the grid used in the alignment algorithm. Return: @@ -249,7 +249,7 @@ def _elastic_alignment_array(template_data, q_data, return reparam(np.ascontiguousarray(template_data.T), np.ascontiguousarray(eval_points), np.ascontiguousarray(q_data.T), - lam, grid_dim).T + penalty, grid_dim).T @@ -482,7 +482,7 @@ def inverse_transform(self, X: FDataGrid): -def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, +def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., return_shooting=False): r"""Compute the karcher mean of a set of warpings. @@ -510,7 +510,6 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, Defaults to 1e-5. step_size (float): Step size :math:`\epsilon` used to update the mean. Default to 1. - eval_points (array_like): Discretisation points of the warpings. shooting (boolean): If true it is returned a tuple with the mean and the shooting vectors, otherwise only the mean is returned. @@ -529,9 +528,8 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, arXiv:1103.3817v2. """ - if eval_points is None: - eval_points = warping.sample_points[0] + eval_points = warping.sample_points[0] original_eval_points = eval_points if warping.sample_points[0][0] != 0 or warping.sample_points[0][-1] != 1: @@ -607,9 +605,8 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points=None, return mean -def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, - initial=None, eval_points=None, fdatagrid_srsf=None, - grid_dim=7, **kwargs): +def elastic_mean(fdatagrid, *, penalty=0., center=True, iter=20, tol=1e-3, + initial=None, grid_dim=7, **kwargs): r"""Compute the karcher mean under the elastic metric. Calculates the karcher mean of a set of functional samples in the amplitude @@ -633,7 +630,7 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, Args: fdatagrid (:class:`FDataGrid`): Set of functions to compute the mean. - lam (float): Penalisation term. Defaults to 0. + penalty (float): Penalisation term. Defaults to 0. center (boolean): If true it is computed the mean of the warpings and used to select a central mean. Defaults True. iter (int): Maximun number of iterations. Defaults to 20. @@ -642,9 +639,6 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, < tol´. initial (float): Value of the mean at the starting point. By default takes the average of the initial points of the samples. - eval_points (array_like): Points of discretization of the fdatagrid. - fdatagrid_srsf (:class:`FDataGrid`): SRSF if the fdatagrid, if it is - passed it is not computed in the algorithm. grid_dim (int, optional): Dimension of the grid used in the alignment algorithm. Defaults 7. ** kwargs : Named options to be pased to :func:`warping_mean`. @@ -668,18 +662,10 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, """ check_is_univariate(fdatagrid) - srsf_transformer = SRSF(store_initial=False, output_points=eval_points) - - if fdatagrid_srsf is not None: - check_is_univariate(fdatagrid_srsf) - else: - fdatagrid_srsf = srsf_transformer.fit_transform(fdatagrid) - - if eval_points is not None: - eval_points = np.asarray(eval_points) - else: - eval_points = fdatagrid.sample_points[0] + srsf_transformer = SRSF(store_initial=False) + fdatagrid_srsf = srsf_transformer.fit_transform(fdatagrid) + eval_points = fdatagrid.sample_points[0] eval_points_normalized = _normalize_scale(eval_points) y_scale = eval_points[-1] - eval_points[0] @@ -707,12 +693,12 @@ def elastic_mean(fdatagrid, *, lam=0., center=True, iter=20, tol=1e-3, for _ in range(iter): gammas = _elastic_alignment_array( - mu, srsf, eval_points_normalized, lam, grid_dim) + mu, srsf, eval_points_normalized, penalty, grid_dim) gammas = FDataGrid(gammas, sample_points=eval_points_normalized, interpolator=interpolator) fdatagrid_normalized = fdatagrid_normalized.compose(gammas) - srsf = srsf_transformer.fit_transform( + srsf = srsf_transformer.transform( fdatagrid_normalized).data_matrix[..., 0] # Next iteration From 5b6b89d5791e03dece72352ccd17d1d09fc3c3c2 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 12:32:58 +0200 Subject: [PATCH 039/624] Update doctest --- skfda/preprocessing/registration/elastic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 53a42b75f..007b09722 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -92,7 +92,7 @@ class SRSF(BaseEstimator, TransformerMixin): >>> zero = fd - fd_pull_back >>> zero.data_matrix.flatten().round(3) - array([ 0., 0., 0., ...]) + array([ 0. , 0. , 0. , ... ]) """ def __init__(self, output_points=None, store_initial=True): From aef25446aca769eb02ac36f728a37b8a18afee1f Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 13:35:31 +0200 Subject: [PATCH 040/624] Increase coverage and fix little bug --- skfda/preprocessing/registration/__init__.py | 2 +- .../registration/_registration_utils.py | 5 +- .../preprocessing/registration/validation.py | 2 +- tests/test_elastic.py | 66 +++++++++++++++++-- tests/test_registration.py | 39 +++++++++++ 5 files changed, 105 insertions(+), 9 deletions(-) diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 34bad5393..7373370b0 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -16,6 +16,6 @@ _normalize_scale) -from .elastic import ElasticRegistration, elastic_mean, warping_mean +from .elastic import ElasticRegistration from . import validation, elastic diff --git a/skfda/preprocessing/registration/_registration_utils.py b/skfda/preprocessing/registration/_registration_utils.py index 4f13e42a8..957e4ac43 100644 --- a/skfda/preprocessing/registration/_registration_utils.py +++ b/skfda/preprocessing/registration/_registration_utils.py @@ -9,6 +9,8 @@ import numpy as np +from ..._utils import check_is_univariate + __author__ = "Pablo Marcos Manchón" __email__ = "pablo.marcosm@estudiante.uam.es" @@ -66,8 +68,7 @@ def invert_warping(fdatagrid, *, eval_points=None): """ - if fdatagrid.dim_codomain != 1 or fdatagrid.dim_domain != 1: - raise ValueError("Multidimensional object not supported.") + check_is_univariate(fdatagrid) if eval_points is None: eval_points = fdatagrid.sample_points[0] diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 2df4d99cc..2739494d8 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -285,7 +285,7 @@ def score_function(self, X, y, *, warping=None): eval_points = y.sample_points[0] except AttributeError: - nfine = max(y.basis.nbasis * 10 + 1, 201) + nfine = max(y.basis.n_basis * 10 + 1, 201) eval_points = np.linspace(*y.domain_range[0], nfine) else: eval_points = np.asarray(self.eval_points) diff --git a/tests/test_elastic.py b/tests/test_elastic.py index 8c1131861..c71397687 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -7,11 +7,10 @@ from skfda.misc.metrics import (fisher_rao_distance, amplitude_distance, phase_distance, pairwise_distance, lp_distance, warping_distance) -from skfda.preprocessing.registration import ( - ElasticRegistration, - invert_warping, normalize_warping, elastic_mean) - -from skfda.preprocessing.registration.elastic import SRSF +from skfda.preprocessing.registration import (ElasticRegistration, + invert_warping, + normalize_warping) +from skfda.preprocessing.registration.elastic import SRSF, elastic_mean metric = pairwise_distance(lp_distance) pairwise_fisher_rao = pairwise_distance(fisher_rao_distance) @@ -56,6 +55,22 @@ def test_from_srsf(self): np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) + def test_from_srsf_with_output_points(self): + """Test from srsf""" + + # Checks SRSF conversion + srsf_transformer = SRSF( + store_initial=False, + output_points=self.dummy_sample.sample_points[0]) + srsf = srsf_transformer.inverse_transform(self.dummy_sample) + + data_matrix = [[[0.], [-0.23449228], [-0.83464009], + [-1.38200046], [-1.55623723], [-1.38200046], + [-0.83464009], [-0.23449228], [0.]]] + + np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) + + def test_srsf_conversion(self): """Converts to srsf and pull backs""" @@ -107,6 +122,19 @@ def test_default_alignment(self): np.testing.assert_allclose(values, expected, atol=1e-4) + def test_callable_alignment(self): + """Test alignment by default""" + # Should give same result than test_template_alignment + reg = ElasticRegistration(template=elastic_mean) + register = reg.fit_transform(self.unimodal_samples) + + values = register([-.25, -.1, 0, .1, .25]) + expected = [[0.6607, 0.9974, 0.7722, 0.3636, 0.0459], + [0.6407, 0.9982, 0.7916, 0.3822, 0.0501], + [0.6472, 0.9976, 0.7859, 0.3766, 0.0488]] + + np.testing.assert_allclose(values, expected, atol=1e-4) + def test_simetry_of_aligment(self): """Check registration using inverse composition""" reg = ElasticRegistration(template=self.template) @@ -118,6 +146,34 @@ def test_simetry_of_aligment(self): np.testing.assert_allclose(distances, 0, atol=12e-3) + def test_raises(self): + reg = ElasticRegistration() + + # X not in fit, but template is not an FDataGrid + with np.testing.assert_raises(ValueError): + reg.fit() + + # Inverse transform without previous transform + with np.testing.assert_raises(ValueError): + reg.inverse_transform(self.unimodal_samples) + + # Inverse transform with different number of samples than transform + reg.fit_transform(self.unimodal_samples) + with np.testing.assert_raises(ValueError): + reg.inverse_transform(self.unimodal_samples[0]) + + # FDataGrid as template with n != 1 and n!= n_samples to transform + reg = ElasticRegistration(template=self.unimodal_samples) + with np.testing.assert_raises(ValueError): + reg.transform(self.unimodal_samples[0]) + + def test_score(self): + """Test score method of the transformer""" + reg = ElasticRegistration() + reg.fit(self.unimodal_samples) + score =reg.score(self.unimodal_samples) + np.testing.assert_almost_equal(score, 0.9997604452) + class TestElasticDistances(unittest.TestCase): """Test elastic distances""" diff --git a/tests/test_registration.py b/tests/test_registration.py index 360fefabf..d81676d3c 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -55,6 +55,21 @@ def test_standard_normalize_warping(self): np.testing.assert_array_almost_equal(normalized(1), [[1.], [1.]]) + def test_standard_normalize_warping_default_value(self): + """Test normalization """ + + normalized = normalize_warping(self.polynomial) + + # Test new domain range (0, 1) + np.testing.assert_array_equal(normalized.domain_range, [(-1, 1)]) + + np.testing.assert_array_almost_equal(normalized.sample_points[0], + np.linspace(-1, 1, 50)) + + np.testing.assert_array_almost_equal(normalized(-1), [[-1], [-1]]) + + np.testing.assert_array_almost_equal(normalized(1), [[1.], [1.]]) + def test_normalize_warpig(self): """Test normalization to (a, b)""" a = -4 @@ -317,6 +332,18 @@ def test_amplitude_phase_score(self): score = scorer(self.shift_registration, self.X) np.testing.assert_almost_equal(score, 0.972000160) + def test_amplitude_phase_score_with_output_points(self): + eval_points = self.X.sample_points[0] + scorer = AmplitudePhaseDecomposition(eval_points=eval_points) + score = scorer(self.shift_registration, self.X) + np.testing.assert_almost_equal(score, 0.972000160) + + def test_amplitude_phase_score_with_basis(self): + scorer = AmplitudePhaseDecomposition() + X = self.X.to_basis(Fourier()) + score = scorer(self.shift_registration, X) + np.testing.assert_almost_equal(score, 0.9950259588) + def test_default_score(self): score = self.shift_registration.score(self.X) @@ -351,6 +378,18 @@ def test_mse_decomposition(self): np.testing.assert_almost_equal(ret.r_squared, 0.9915489952877273) np.testing.assert_almost_equal(ret.c_r, 0.9999963424653829) + def test_raises_amplitude_phase(self): + scorer = AmplitudePhaseDecomposition() + + # Inconsistent number of functions registered + with np.testing.assert_raises(ValueError): + scorer.score_function(self.X, self.X[:2]) + + # Inconsistent number of functions registered + with np.testing.assert_raises(ValueError): + scorer.score_function(self.X, self.X, warping=self.X[:2]) + + if __name__ == '__main__': print() From 4274c37bf40e530de4ef0c19d00a9142e97e9e09 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 14 Oct 2019 17:58:50 +0200 Subject: [PATCH 041/624] Add CoefficientsTransformer --- skfda/representation/basis.py | 42 +++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 9709ae469..257850c65 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -15,6 +15,8 @@ import scipy.interpolate import scipy.linalg from scipy.special import binom +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.utils.validation import check_is_fitted import numpy as np @@ -2452,3 +2454,43 @@ def construct_from_string(cls, string): @classmethod def construct_array_type(cls): return FDataBasis + + +class CoefficientsTransformer(BaseEstimator, TransformerMixin): + """ + Transformer returning the coefficients of FDataBasis objects as a matrix. + + Attributes: + shape_ (tuple): original shape of coefficients per sample. + + Examples: + >>> from skfda.representation.basis import (Monomial, + ... CoefficientsTransformer) + >>> + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> fd = FDataBasis(basis, coefficients) + >>> + >>> transformer = CoefficientsTransformer() + >>> transformer.fit_transform(fd) + array([[ 0.5, 1. , 2. , 0.5], + [ 1.5, 1. , 4. , 0.5]]) + + """ + + def fit(self, X: FDataBasis, y=None): + + self.shape_ = X.coefficients.shape[1:] + + return self + + def transform(self, X, y=None): + + check_is_fitted(self, 'shape_') + + assert X.coefficients.shape[1:] == self.shape_ + + coefficients = X.coefficients.copy() + coefficients = coefficients.reshape((X.n_samples, -1)) + + return coefficients From 14015f38563044745cafadc396ee36c09b0e8a27 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 18:49:58 +0200 Subject: [PATCH 042/624] Fix bug in warping mean due to the vectorization --- skfda/misc/metrics.py | 5 +- skfda/preprocessing/registration/__init__.py | 5 +- .../{_registration_utils.py => _warping.py} | 14 +-- skfda/preprocessing/registration/elastic.py | 86 +++++++++---------- 4 files changed, 51 insertions(+), 59 deletions(-) rename skfda/preprocessing/registration/{_registration_utils.py => _warping.py} (91%) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 49f6c08ad..d0eb5a586 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -2,9 +2,8 @@ import numpy as np -from ..preprocessing.registration import ( - normalize_warping, _normalize_scale, - ElasticRegistration) +from ..preprocessing.registration import normalize_warping, ElasticRegistration +from ..preprocessing.registration._warping import _normalize_scale from ..preprocessing.registration.elastic import SRSF from ..representation import FData from ..representation import FDataGrid diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 7373370b0..ce4a52cae 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -11,10 +11,7 @@ from ._shift_registration import ShiftRegistration -from ._registration_utils import (invert_warping, - normalize_warping, - _normalize_scale) - +from ._warping import invert_warping, normalize_warping from .elastic import ElasticRegistration diff --git a/skfda/preprocessing/registration/_registration_utils.py b/skfda/preprocessing/registration/_warping.py similarity index 91% rename from skfda/preprocessing/registration/_registration_utils.py rename to skfda/preprocessing/registration/_warping.py index 957e4ac43..ff03622ea 100644 --- a/skfda/preprocessing/registration/_registration_utils.py +++ b/skfda/preprocessing/registration/_warping.py @@ -16,7 +16,7 @@ __email__ = "pablo.marcosm@estudiante.uam.es" -def invert_warping(fdatagrid, *, eval_points=None): +def invert_warping(fdatagrid, *, output_points=None): r"""Compute the inverse of a diffeomorphism. Let :math:`\gamma : [a,b] \rightarrow [a,b]` be a function strictly @@ -70,17 +70,17 @@ def invert_warping(fdatagrid, *, eval_points=None): check_is_univariate(fdatagrid) - if eval_points is None: - eval_points = fdatagrid.sample_points[0] + if output_points is None: + output_points = fdatagrid.sample_points[0] - y = fdatagrid(eval_points, keepdims=False) + y = fdatagrid(output_points, keepdims=False) - data_matrix = np.empty((fdatagrid.n_samples, len(eval_points))) + data_matrix = np.empty((fdatagrid.n_samples, len(output_points))) for i in range(fdatagrid.n_samples): - data_matrix[i] = PchipInterpolator(y[i], eval_points)(eval_points) + data_matrix[i] = PchipInterpolator(y[i], output_points)(output_points) - return fdatagrid.copy(data_matrix=data_matrix, sample_points=eval_points) + return fdatagrid.copy(data_matrix=data_matrix, sample_points=output_points) def _normalize_scale(t, a=0, b=1): diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 007b09722..94cdbaecc 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -11,7 +11,7 @@ from . import invert_warping from .base import RegistrationTransformer -from ._registration_utils import _normalize_scale +from ._warping import _normalize_scale from ... import FDataGrid from ..._utils import check_is_univariate from ...representation.interpolation import SplineInterpolator @@ -481,9 +481,7 @@ def inverse_transform(self, X: FDataGrid): return X.compose(inverse_warping, eval_points=self.output_points) - -def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., - return_shooting=False): +def warping_mean(warping, *, iter=100, tol=1e-5, step_size=.3): r"""Compute the karcher mean of a set of warpings. Let :math:`\gamma_i i=1...n` be a set of warping functions @@ -510,8 +508,6 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., Defaults to 1e-5. step_size (float): Step size :math:`\epsilon` used to update the mean. Default to 1. - shooting (boolean): If true it is returned a tuple with the mean and - the shooting vectors, otherwise only the mean is returned. Return: (:class:`FDataGrid`) Fdatagrid with the mean of the warpings. If @@ -532,61 +528,66 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., eval_points = warping.sample_points[0] original_eval_points = eval_points + # Rescale warping to (0, 1) if warping.sample_points[0][0] != 0 or warping.sample_points[0][-1] != 1: eval_points = _normalize_scale(eval_points) warping = FDataGrid(_normalize_scale(warping.data_matrix[..., 0]), _normalize_scale(warping.sample_points[0])) + # Compute srsf of warpings and their mean srsf = SRSF(output_points=eval_points, store_initial=False) - psi = srsf.fit_transform(warping).data_matrix[..., 0].T - mu = srsf.fit_transform(warping.mean()).data_matrix[0] - - dot_aux = np.empty(psi.shape) + psi = srsf.fit_transform(warping) - n_points = mu.shape[0] + # Find psi closest to the mean + psi_centered = psi - srsf.fit_transform(warping.mean()) + psi_data = psi_centered.data_matrix[..., 0] + np.square(psi_data, out=psi_data) + d = psi_data.sum(axis=1).argmin() - sine = np.empty((warping.n_samples, 1)) + # Get raw values to calculate + mu = psi[d].data_matrix[0,..., 0] + psi = psi.data_matrix[..., 0] + vmean = np.empty((1, len(eval_points))) + # Construction of shooting vectors for _ in range(iter): - # Dot product - # = S psi(t) mu(t) dt - dot = scipy.integrate.simps(np.multiply(psi, mu, out=dot_aux), - eval_points, axis=0) - - # Theorically is not possible (Cauchy–Schwarz inequallity), but due to - # numerical approximation could be greater than 1 - dot[dot < -1] = -1 - dot[dot > 1] = 1 - theta = np.arccos(dot)[:, np.newaxis] - - # Be carefully with tangent vectors and division by 0 - idx = theta[:, 0] > tol - sine[idx] = theta[idx] / np.sin(theta[idx]) - sine[~idx] = 0. - - # compute shooting vector - cos_theta = np.repeat(np.cos(theta), n_points, axis=1) - shooting = np.multiply(sine, (psi - np.multiply(cos_theta.T, mu)).T) + + vmean[0] = 0. + # Compute shooting vectors + for i in range(len(warping)): + psi_i = psi[i] + #print(Psi.shape, psi.shape) + inner = scipy.integrate.simps(mu*psi_i, x=eval_points) + #print(tmp) + if inner > 1: + inner = 1 + if inner < -1: + inner = -1 + + theta = np.arccos(inner) + + if theta > 1e-10: + vmean += theta / np.sin(theta) * (psi_i - np.cos(theta)*mu) # Mean of shooting vectors - vmean = shooting.mean(axis=0, keepdims=True) - v_norm = scipy.integrate.simps(np.square(vmean[0]))**(.5) + vmean /= warping.n_samples + v_norm = np.sqrt(scipy.integrate.simps(np.square(vmean))) # Convergence criterion if v_norm < tol: break - # Update of mu - mu *= np.cos(step_size * v_norm) - vmean += np.sin(step_size * v_norm) / v_norm - mu += vmean.T + # Calculate exponential map of mu + a = np.cos(step_size*v_norm) + b = np.sin(step_size*v_norm) / v_norm + mu = a * mu + b * vmean # Recover mean in original gamma space - warping_mean = scipy.integrate.cumtrapz(np.square(mu, out=mu)[:, 0], + warping_mean = scipy.integrate.cumtrapz(np.square(mu, out=mu)[0], x=eval_points, initial=0) - # Affine traslation + # Affine traslation to original scale warping_mean = _normalize_scale(warping_mean, a=original_eval_points[0], b=original_eval_points[-1]) @@ -597,11 +598,6 @@ def warping_mean(warping, *, iter=20, tol=1e-5, step_size=1., mean = FDataGrid([warping_mean], sample_points=original_eval_points, interpolator=monotone_interpolator) - # Shooting vectors are used in models based in the amplitude-phase - # decomposition under this metric. - if return_shooting: - return mean, shooting - return mean @@ -731,7 +727,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, iter=20, tol=1e-3, if center: # Gamma mean in Hilbert Sphere - mean_normalized = warping_mean(gammas, return_shooting=False, **kwargs) + mean_normalized = warping_mean(gammas, **kwargs) gamma_mean = FDataGrid(_normalize_scale( mean_normalized.data_matrix[..., 0], From ce9da2fb4e4d0ea7a9255e9949d41642b9a31070 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 19:00:39 +0200 Subject: [PATCH 043/624] Update tests --- skfda/preprocessing/registration/elastic.py | 8 ++++---- tests/test_elastic.py | 14 +++++++------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 94cdbaecc..4822ed8d3 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -481,7 +481,7 @@ def inverse_transform(self, X: FDataGrid): return X.compose(inverse_warping, eval_points=self.output_points) -def warping_mean(warping, *, iter=100, tol=1e-5, step_size=.3): +def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): r"""Compute the karcher mean of a set of warpings. Let :math:`\gamma_i i=1...n` be a set of warping functions @@ -557,12 +557,12 @@ def warping_mean(warping, *, iter=100, tol=1e-5, step_size=.3): # Compute shooting vectors for i in range(len(warping)): psi_i = psi[i] - #print(Psi.shape, psi.shape) + inner = scipy.integrate.simps(mu*psi_i, x=eval_points) - #print(tmp) + if inner > 1: inner = 1 - if inner < -1: + elif inner < -1: inner = -1 theta = np.arccos(inner) diff --git a/tests/test_elastic.py b/tests/test_elastic.py index c71397687..18044f575 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -116,9 +116,9 @@ def test_default_alignment(self): register = reg.fit_transform(self.unimodal_samples) values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.6607, 0.9974, 0.7722, 0.3636, 0.0459], - [0.6407, 0.9982, 0.7916, 0.3822, 0.0501], - [0.6472, 0.9976, 0.7859, 0.3766, 0.0488]] + expected = [[0.623701, 0.997427, 0.772248, 0.390317, 0.064725], + [0.639201, 0.997155, 0.791649, 0.382181, 0.050098], + [0.63332 , 0.997369, 0.785886, 0.376556, 0.048804]] np.testing.assert_allclose(values, expected, atol=1e-4) @@ -129,9 +129,9 @@ def test_callable_alignment(self): register = reg.fit_transform(self.unimodal_samples) values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.6607, 0.9974, 0.7722, 0.3636, 0.0459], - [0.6407, 0.9982, 0.7916, 0.3822, 0.0501], - [0.6472, 0.9976, 0.7859, 0.3766, 0.0488]] + expected = [[0.623701, 0.997427, 0.772248, 0.390317, 0.064725], + [0.639201, 0.997155, 0.791649, 0.382181, 0.050098], + [0.63332 , 0.997369, 0.785886, 0.376556, 0.048804]] np.testing.assert_allclose(values, expected, atol=1e-4) @@ -172,7 +172,7 @@ def test_score(self): reg = ElasticRegistration() reg.fit(self.unimodal_samples) score =reg.score(self.unimodal_samples) - np.testing.assert_almost_equal(score, 0.9997604452) + np.testing.assert_almost_equal(score, 0.999666175) class TestElasticDistances(unittest.TestCase): From 763e5af315414d64848ddc664de2689364619a3d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 14 Oct 2019 19:19:19 +0200 Subject: [PATCH 044/624] Evaluation transformer. --- skfda/representation/__init__.py | 10 +- .../representation/_evaluation_trasformer.py | 140 ++++++++++++++++++ skfda/representation/basis.py | 5 +- skfda/representation/grid.py | 2 +- 4 files changed, 148 insertions(+), 9 deletions(-) create mode 100644 skfda/representation/_evaluation_trasformer.py diff --git a/skfda/representation/__init__.py b/skfda/representation/__init__.py index da6e4fa05..d6a18fc9b 100644 --- a/skfda/representation/__init__.py +++ b/skfda/representation/__init__.py @@ -1,8 +1,8 @@ -from ._functional_data import FData -from .basis import FDataBasis -from .grid import FDataGrid - from . import basis from . import extrapolation -from . import interpolation from . import grid +from . import interpolation +from ._evaluation_trasformer import EvaluationTransformer +from ._functional_data import FData +from .basis import FDataBasis +from .grid import FDataGrid diff --git a/skfda/representation/_evaluation_trasformer.py b/skfda/representation/_evaluation_trasformer.py new file mode 100644 index 000000000..271b80b03 --- /dev/null +++ b/skfda/representation/_evaluation_trasformer.py @@ -0,0 +1,140 @@ +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.utils.validation import check_is_fitted +from ._functional_data import FData +from .grid import FDataGrid + + +class EvaluationTransformer(BaseEstimator, TransformerMixin): + r""" + Transformer returning the coefficients of FDataBasis objects as a matrix. + + Args: + eval_points (array_like): List of points where the functions are + evaluated. If `None`, the functions must be `FDatagrid` objects + and all points will be returned. + derivative (int, optional): Order of the derivative. Defaults to 0. + extrapolation (str or Extrapolation, optional): Controls the + extrapolation mode for elements outside the domain range. By + default it is used the mode defined during the instance of the + object. + grid (bool, optional): Whether to evaluate the results on a grid + spanned by the input arrays, or at points specified by the + input arrays. If true the eval_points should be a list of size + dim_domain with the corresponding times for each axis. The + return matrix has shape n_samples x len(t1) x len(t2) x ... x + len(t_dim_domain) x dim_codomain. If the domain dimension is 1 + the parameter has no efect. Defaults to False. + + Attributes: + shape_ (tuple): original shape of coefficients per sample. + + Examples: + + >>> from skfda.representation import (FDataGrid, FDataBasis, + ... EvaluationTransformer) + >>> from skfda.representation.basis import Monomial + + Functional data object with 2 samples + representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. + + >>> data_matrix = [[1, 2], [2, 3]] + >>> sample_points = [2, 4] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[1, 2], + [2, 3]]) + + Functional data object with 2 samples + representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}^2`. + + >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] + >>> sample_points = [2, 4] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[ 1. , 0.3, 2. , 0.4], + [ 2. , 0.5, 3. , 0.6]]) + + Representation of a functional data object with 2 samples + representing a function :math:`f : \mathbb{R}^2\longmapsto\mathbb{R}`. + + >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] + >>> sample_points = [[2, 4], [3, 6]] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[ 1. , 0.3, 2. , 0.4], + [ 2. , 0.5, 3. , 0.6]]) + + Evaluation of a functional data object at several points. + + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> fd = FDataBasis(basis, coefficients) + >>> + >>> transformer = EvaluationTransformer([0, 0.2, 0.5, 0.7, 1]) + >>> transformer.fit_transform(fd) + array([[ 0.5 , 0.784 , 1.5625, 2.3515, 4. ], + [ 1.5 , 1.864 , 3.0625, 4.3315, 7. ]]) + + Evaluating derivative of a FDataGrid at all points. + + >>> data_matrix = [[1, 2], [2, 3]] + >>> sample_points = [2, 4] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> + >>> transformer = EvaluationTransformer(derivative=1) + >>> transformer.fit_transform(fd) + array([[ 0.5, 0.5], + [ 0.5, 0.5]]) + + Evaluation of the derivative of a functional data object at several + points. + + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> fd = FDataBasis(basis, coefficients) + >>> + >>> transformer = EvaluationTransformer([0, 0.2, 0.5, 0.7, 1], + ... derivative=1) + >>> transformer.fit_transform(fd) + array([[ 1. , 1.86 , 3.375, 4.535, 6.5 ], + [ 1. , 2.66 , 5.375, 7.335, 10.5 ]]) + """ + + def __init__(self, eval_points=None, *, derivative=0, + extrapolation=None, grid=False): + self.eval_points = eval_points + self.derivative = derivative + self.extrapolation = extrapolation + self.grid = grid + + def fit(self, X: FData, y=None): + + if self.eval_points is None and not isinstance(X, FDataGrid): + raise ValueError("If no eval_points are passed, the functions " + "should be FDataGrid objects.") + + self._is_fitted = True + + return self + + def transform(self, X, y=None): + + check_is_fitted(self, '_is_fitted') + + if self.eval_points is None: + if self.derivative != 0: + X = X.derivative(self.derivative) + evaluation = X.data_matrix.copy() + else: + evaluation = X(self.eval_points, derivative=self.derivative, + extrapolation=self.extrapolation, grid=self.grid) + + evaluation = evaluation.reshape((X.n_samples, -1)) + + return evaluation diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 257850c65..473cef2bf 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -13,16 +13,15 @@ from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly import scipy.interpolate -import scipy.linalg from scipy.special import binom from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted import numpy as np -from . import FData from . import grid from .._utils import _list_of_arrays, constants +from ._functional_data import FData __author__ = "Miguel Carbajo Berrocal" @@ -2464,7 +2463,7 @@ class CoefficientsTransformer(BaseEstimator, TransformerMixin): shape_ (tuple): original shape of coefficients per sample. Examples: - >>> from skfda.representation.basis import (Monomial, + >>> from skfda.representation.basis import (FDataBasis, Monomial, ... CoefficientsTransformer) >>> >>> basis = Monomial(n_basis=4) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 7adfdbb69..bb76c46b7 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -14,9 +14,9 @@ import numpy as np -from . import FData from . import basis as fdbasis from .._utils import _list_of_arrays, constants +from ._functional_data import FData from .interpolation import SplineInterpolator From 33aae351ce237924608465e2cc11be2b5008d211 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 14 Oct 2019 19:55:41 +0200 Subject: [PATCH 045/624] Fix doc error. --- skfda/representation/_evaluation_trasformer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/representation/_evaluation_trasformer.py b/skfda/representation/_evaluation_trasformer.py index 271b80b03..f09f16c83 100644 --- a/skfda/representation/_evaluation_trasformer.py +++ b/skfda/representation/_evaluation_trasformer.py @@ -6,7 +6,7 @@ class EvaluationTransformer(BaseEstimator, TransformerMixin): r""" - Transformer returning the coefficients of FDataBasis objects as a matrix. + Transformer returning the evaluations of FData objects as a matrix. Args: eval_points (array_like): List of points where the functions are From 9cc7b0246cb33c001c9530a2ad12856ddb8fcab8 Mon Sep 17 00:00:00 2001 From: pablomm Date: Mon, 14 Oct 2019 19:56:51 +0200 Subject: [PATCH 046/624] Update documentation --- docs/modules/preprocessing/registration.rst | 28 +++++----- examples/plot_elastic_registration.py | 24 +++++---- examples/plot_pairwise_alignment.py | 58 ++++++++++---------- skfda/preprocessing/registration/elastic.py | 59 +++++++++++++++++---- 4 files changed, 106 insertions(+), 63 deletions(-) diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 11738ac2a..127f05947 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -31,7 +31,7 @@ takes all the times of a given feature into a common value. The simplest case in which each sample presents a unique landmark can be solved by performing a translation in the time scale. See the -`Landmark Shift Example <../auto_examples/plot_landmark_shift.html>`_. +:ref:`sphx_glr_auto_examples_plot_landmark_shift.py` example.. .. autosummary:: :toctree: autosummary @@ -42,8 +42,7 @@ by performing a translation in the time scale. See the The general case of landmark registration may present multiple landmarks for each sample and a non-linear transformation in the time scale should be applied. -See the `Landmark Registration Example -<../auto_examples/plot_landmark_registration.html>`_ +See the :ref:`sphx_glr_auto_examples_plot_landmark_registration.py` example. .. autosummary:: :toctree: autosummary @@ -57,16 +56,15 @@ Elastic Registration The elastic registration is a novel approach to this problem that uses the properties of the Fisher-Rao metric to perform the alignment of the curves. -In the examples of `pairwise alignment -<../auto_examples/plot_pairwise_alignment.html>`_ and `elastic registration -<../auto_examples/plot_elastic_registration.html>`_ is shown a brief +In the examples of +:ref:`sphx_glr_auto_examples_plot_pairwise_alignment.py` and +:ref:`sphx_glr_auto_examples_plot_elastic_registration.py` is shown a brief introduction to this topic along the usage of the corresponding functions. .. autosummary:: :toctree: autosummary - skfda.preprocessing.registration.elastic_registration - skfda.preprocessing.registration.elastic_registration_warping + skfda.preprocessing.registration.ElasticRegistration The module contains some routines related with the elastic registration, making @@ -76,11 +74,9 @@ on the elastic framework. .. autosummary:: :toctree: autosummary - skfda.preprocessing.registration.elastic_mean - skfda.preprocessing.registration.warping_mean - skfda.preprocessing.registration.to_srsf - skfda.preprocessing.registration.from_srsf - + skfda.preprocessing.registration.elastic.elastic_mean + skfda.preprocessing.registration.elastic.warping_mean + skfda.preprocessing.registration.elastic.SRSF Validation @@ -99,11 +95,11 @@ validation of the registration procedure. skfda.preprocessing.registration.validation.PairwiseCorrelation -Utility functions +Warping utils ----------------- -There are some other method related with the registration problem in this -module. +There module contains some functions related with the warping of functional +data. .. autosummary:: :toctree: autosummary diff --git a/examples/plot_elastic_registration.py b/examples/plot_elastic_registration.py index 222c6be65..5e69bb46b 100644 --- a/examples/plot_elastic_registration.py +++ b/examples/plot_elastic_registration.py @@ -13,10 +13,14 @@ import numpy as np import skfda +from skfda.datasets import make_multimodal_samples, fetch_growth +from skfda.preprocessing.registration import ElasticRegistration +from skfda.preprocessing.registration.elastic import elastic_mean + ############################################################################## # In the example of pairwise alignment was shown the usage of -# :func:`~skfda.preprocessing.registration.elastic_registration` to align +# :func:`~skfda.preprocessing.registration.ElasticRegistration` to align # a set of functional observations to a given template or a set of templates. # # In the groupwise alignment all the samples are aligned to the same template, @@ -28,12 +32,12 @@ # We will create a synthetic dataset to show the basic usage of the # registration. # -fd = skfda.datasets.make_multimodal_samples(n_modes=2, stop=4, random_state=1) +fd = make_multimodal_samples(n_modes=2, stop=4, random_state=1) fd.plot() ############################################################################### # The following figure shows the -# :func:`~skfda.preprocessing.registration.elastic_mean` of the +# :func:`~skfda.preprocessing.registration.elastic.elastic_mean` of the # dataset and the cross-sectional mean, which correspond to the karcher-mean # under the :math:`\mathbb{L}^2` distance. # @@ -41,17 +45,19 @@ # curves compared to the standard mean, since it is not affected by the # deformations of the curves. + + fig = fd.mean().plot(label="L2 mean") -skfda.preprocessing.registration.elastic_mean( - fd).plot(fig=fig, label="Elastic mean") +elastic_mean(fd).plot(fig=fig, label="Elastic mean") fig.legend() -fig ############################################################################## # In this case, the alignment completely reduces the amplitude variability # between the samples, aligning the maximum points correctly. -fd_align = skfda.preprocessing.registration.elastic_registration(fd) +elastic_registration = ElasticRegistration() + +fd_align = elastic_registration.fit_transform(fd) fd_align.plot() @@ -66,7 +72,7 @@ # # First we show the original curves: -growth = skfda.datasets.fetch_growth() +growth = fetch_growth() # Select only one sex fd = growth['data'][growth['target'] == 0] @@ -80,7 +86,7 @@ ############################################################################## # We now show the aligned curves: -fd_align = skfda.preprocessing.registration.elastic_registration(fd) +fd_align = elastic_registration.fit_transform(fd) fd_align.dataset_label += " - aligned" fd_align.plot() diff --git a/examples/plot_pairwise_alignment.py b/examples/plot_pairwise_alignment.py index 1bf27c482..63f919b98 100644 --- a/examples/plot_pairwise_alignment.py +++ b/examples/plot_pairwise_alignment.py @@ -16,6 +16,8 @@ import numpy as np import skfda +from skfda.preprocessing.registration import ElasticRegistration, invert_warping +from skfda.datasets import make_multimodal_samples ############################################################################## # Given any two functions :math:`f` and :math:`g`, we define their @@ -38,47 +40,45 @@ # Due to the similarity of these curves can be aligned almost perfectly # between them. # + # Samples with modes in 1/3 and 2/3 -fd = skfda.datasets.make_multimodal_samples( - n_samples=2, modes_location=[1 / 3, 2 / 3], - random_state=1, start=0, mode_std=.01) +fd = make_multimodal_samples(n_samples=2, modes_location=[1 / 3, 2 / 3], + random_state=1, start=0, mode_std=.01) fig = fd.plot() fig.axes[0].legend(['$f$', '$g$']) -fig ############################################################################## # In this example :math:`g` will be used as template and :math:`f` will be # aligned to it. In the following figure it is shown the result of the # registration process, wich can be computed using -# :func:`~skfda.preprocessing.registration.elastic_registration`. +# :class:`~skfda.preprocessing.registration.ElasticRegistration`. # f, g = fd[0], fd[1] +elastic_registration = ElasticRegistration(template=g) + + # Aligns f to g -fd_align = skfda.preprocessing.registration.elastic_registration(f, g) +f_align = elastic_registration.fit_transform(f) fig = fd.plot() -fd_align.plot(fig=fig, color='C0', linestyle='--') +f_align.plot(fig=fig, color='C0', linestyle='--') # Legend fig.axes[0].legend(['$f$', '$g$', '$f \\circ \\gamma $']) -fig ############################################################################## # The non-linear transformation :math:`\gamma` applied to :math:`f` in -# the alignment can be obtained using -# :func:`~skfda.preprocessing.registration.elastic_registration_warping`. +# the alignment is stored in the attribute `warping_`. # -# Warping to align f to g -warping = skfda.preprocessing.registration.elastic_registration_warping(f, g) - -# Warping used +# Warping used in the last transformation +warping = elastic_registration.warping_ fig = warping.plot() # Plot identity @@ -97,7 +97,7 @@ # function. # -warping_inverse = skfda.preprocessing.registration.invert_warping(warping) +warping_inverse = invert_warping(warping) fig = fd.plot(label='$f$') g.compose(warping_inverse).plot(fig=fig, color='C1', linestyle='--') @@ -106,9 +106,6 @@ # Legend fig.axes[0].legend(['$f$', '$g$', '$g \\circ \\gamma^{-1} $']) -fig - - ############################################################################## # The amount of deformation used in the registration can be controlled by # using a variation of the metric with a penalty term @@ -120,19 +117,20 @@ # # Values of lambda -lambdas = np.linspace(0, .2, 20) +penalties = np.linspace(0, .2, 20) # Creation of a color gradient cmap = clr.LinearSegmentedColormap.from_list('custom cmap', ['C1', 'C0']) -color = cmap(.2 + 3 * lambdas) +color = cmap(.2 + 3 * penalties) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) -for lam, c in zip(lambdas, color): - # Plots result of alignment - skfda.preprocessing.registration.elastic_registration( - f, g, lam=lam).plot(fig=fig, color=c) + +for penalty, c in zip(penalties, color): + + elastic_registration.set_params(penalty=penalty) + elastic_registration.transform(f).plot(fig, color=c) f.plot(fig=fig, color='C0', linewidth=2., label='$f$') @@ -142,7 +140,6 @@ fig.axes[0].legend() - ############################################################################## # This phenomenon of loss of elasticity is clearly observed in # the warpings used, since as the term of penalty increases, the functions @@ -152,9 +149,10 @@ fig = plt.figure() ax = fig.add_subplot(1, 1, 1) -for lam, c in zip(lambdas, color): - skfda.preprocessing.registration.elastic_registration_warping( - f, g, lam=lam).plot(fig=fig, color=c) +for penalty, c in zip(penalties, color): + elastic_registration.set_params(penalty=penalty) + elastic_registration.transform(f) + elastic_registration.warping_.plot(fig, color=c) # Plots identity fig.axes[0].plot(t, t, color='C0', linestyle="--") @@ -198,7 +196,9 @@ # # Registration of the sets -fd_registered = skfda.preprocessing.registration.elastic_registration(fd, g) +elastic_registration = ElasticRegistration(template=g) + +fd_registered = elastic_registration.fit_transform(fd) # Plot of the curves fig = fd.plot(color="C0", label="$f_i$") diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 4822ed8d3..590ed87ec 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -97,6 +97,7 @@ class SRSF(BaseEstimator, TransformerMixin): """ def __init__(self, output_points=None, store_initial=True): """Initializes the transformer. + Args: eval_points: (array_like, optional): Set of points where the functions are evaluated, by default uses the sample points of @@ -112,13 +113,17 @@ def __init__(self, output_points=None, store_initial=True): def fit(self, X: FDataGrid): """Fits the transformer. + Stores the initial value of the functions to be transformed, in order to apply its inverse transform. + Args: X (:class:`FDataGrid >> from skfda.preprocessing.registration import \ + ... ElasticRegistration + >>> from skfda.datasets import make_multimodal_samples + >>> X_train = make_multimodal_samples(n_samples=15, random_state=0) + >>> X_test = make_multimodal_samples(n_samples=3, random_state=1) + + Fit the transformer, which learns the elastic mean of the train + set as template. + + >>> elastic_registration = ElasticRegistration() + >>> elastic_registration.fit(X_train) + ElasticRegistration(...) + + Registration of the test set. + + >>> elastic_registration.transform(X_test) + FDataGrid(...) + """ def __init__(self, template="elastic mean", penalty=0., output_points=None, grid_dim=7, **kwargs): @@ -501,7 +543,7 @@ def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): after a transformation of the warpings, see [S11-3-3]_. Args: - warping (:class:`FDataGrid`): Set of warpings. + warping (:class:`~skfda.FDataGrid`): Set of warpings. iter (int): Maximun number of interations. Defaults to 20. tol (float): Convergence criterion, if the norm of the mean of the shooting vectors, :math:`| \bar v | Date: Mon, 14 Oct 2019 20:22:22 +0200 Subject: [PATCH 047/624] Coverage --- skfda/preprocessing/registration/elastic.py | 6 +----- tests/test_elastic.py | 13 ++++++++++--- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 590ed87ec..5e3b8048c 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -601,11 +601,7 @@ def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): psi_i = psi[i] inner = scipy.integrate.simps(mu*psi_i, x=eval_points) - - if inner > 1: - inner = 1 - elif inner < -1: - inner = -1 + inner = max(min(inner, 1), -1) theta = np.arccos(inner) diff --git a/tests/test_elastic.py b/tests/test_elastic.py index 18044f575..179c0133a 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -3,14 +3,15 @@ import numpy as np from skfda import FDataGrid -from skfda.datasets import make_multimodal_samples +from skfda.datasets import make_multimodal_samples, make_random_warping from skfda.misc.metrics import (fisher_rao_distance, amplitude_distance, phase_distance, pairwise_distance, lp_distance, warping_distance) from skfda.preprocessing.registration import (ElasticRegistration, invert_warping, normalize_warping) -from skfda.preprocessing.registration.elastic import SRSF, elastic_mean +from skfda.preprocessing.registration.elastic import (SRSF, elastic_mean, + warping_mean) metric = pairwise_distance(lp_distance) pairwise_fisher_rao = pairwise_distance(fisher_rao_distance) @@ -163,7 +164,7 @@ def test_raises(self): reg.inverse_transform(self.unimodal_samples[0]) # FDataGrid as template with n != 1 and n!= n_samples to transform - reg = ElasticRegistration(template=self.unimodal_samples) + reg = ElasticRegistration(template=self.unimodal_samples).fit() with np.testing.assert_raises(ValueError): reg.transform(self.unimodal_samples[0]) @@ -174,6 +175,12 @@ def test_score(self): score =reg.score(self.unimodal_samples) np.testing.assert_almost_equal(score, 0.999666175) + def test_warping_mean(self): + warping = make_random_warping(start=-1, random_state=0) + mean = warping_mean(warping) + values = mean([-1, -.5, 0, .5, 1]) + expected = [[-1., -0.3762928 , 0.13613892, 0.59923733, 1. ]] + np.testing.assert_array_almost_equal(values, expected) class TestElasticDistances(unittest.TestCase): """Test elastic distances""" From ab21b4d73ce24da63e75db1dbbb581b223744404 Mon Sep 17 00:00:00 2001 From: Pablo Marcos Date: Tue, 15 Oct 2019 15:02:01 +0200 Subject: [PATCH 048/624] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Carlos Ramos Carreño --- docs/modules/preprocessing/registration.rst | 2 +- examples/plot_elastic_registration.py | 2 +- skfda/preprocessing/registration/elastic.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/modules/preprocessing/registration.rst b/docs/modules/preprocessing/registration.rst index 127f05947..abf7db45a 100644 --- a/docs/modules/preprocessing/registration.rst +++ b/docs/modules/preprocessing/registration.rst @@ -98,7 +98,7 @@ validation of the registration procedure. Warping utils ----------------- -There module contains some functions related with the warping of functional +This module contains some functions related with the warping of functional data. .. autosummary:: diff --git a/examples/plot_elastic_registration.py b/examples/plot_elastic_registration.py index 5e69bb46b..4678ee6da 100644 --- a/examples/plot_elastic_registration.py +++ b/examples/plot_elastic_registration.py @@ -20,7 +20,7 @@ ############################################################################## # In the example of pairwise alignment was shown the usage of -# :func:`~skfda.preprocessing.registration.ElasticRegistration` to align +# :class:`~skfda.preprocessing.registration.ElasticRegistration` to align # a set of functional observations to a given template or a set of templates. # # In the groupwise alignment all the samples are aligned to the same template, diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 5e3b8048c..14a633571 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -544,7 +544,7 @@ def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): Args: warping (:class:`~skfda.FDataGrid`): Set of warpings. - iter (int): Maximun number of interations. Defaults to 20. + iter (int): Maximum number of interations. Defaults to 20. tol (float): Convergence criterion, if the norm of the mean of the shooting vectors, :math:`| \bar v | Date: Tue, 15 Oct 2019 15:38:30 +0200 Subject: [PATCH 049/624] Rename iter as max_iter, remove **kwargs in creation of transformers --- .../registration/_shift_registration.py | 2 +- skfda/preprocessing/registration/elastic.py | 24 ++++++++----------- 2 files changed, 11 insertions(+), 15 deletions(-) diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index affe41c75..81f20be79 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -125,7 +125,7 @@ class ShiftRegistration(RegistrationTransformer): def __init__(self, max_iter=5, tol=1e-2, template="mean", extrapolation=None, step_size=1, restrict_domain=False, - initial="zeros", output_points=None, **kwargs): + initial="zeros", output_points=None): self.max_iter = max_iter self.tol = tol self.template = template diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 14a633571..1bd6c6498 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -1,4 +1,4 @@ - +max_iter import scipy.integrate from sklearn.utils.validation import check_is_fitted @@ -323,9 +323,6 @@ class ElasticRegistration(RegistrationTransformer): fdatagrid which will be transformed. grid_dim (int, optional): Dimension of the grid used in the DP alignment algorithm. Defaults 7. - **kwargs: Named arguments to be passed to be passed to the callable - which constructs the template or to :func:`~elastic_mean` by - default. Attributes: template_ (:class:`FDataGrid`): Template learned during fitting, @@ -362,14 +359,13 @@ class ElasticRegistration(RegistrationTransformer): """ def __init__(self, template="elastic mean", penalty=0., output_points=None, - grid_dim=7, **kwargs): + grid_dim=7): """Initializes the registration transformer""" self.template = template self.penalty = penalty self.output_points = output_points self.grid_dim = grid_dim - self.kwargs = kwargs def fit(self, X: FDataGrid=None, y=None): """Fit the transformer. @@ -393,9 +389,9 @@ def fit(self, X: FDataGrid=None, y=None): raise ValueError("Must be provided a dataset X to construct the " "template.") elif self.template == "elastic mean": - self.template_ = elastic_mean(X, **self.kwargs) + self.template_ = elastic_mean(X) else: - self.template_ = self.template(X, **self.kwargs) + self.template_ = self.template(X) # Constructs the SRSF of the template srsf = SRSF(output_points=self.output_points, store_initial=False) @@ -523,7 +519,7 @@ def inverse_transform(self, X: FDataGrid): return X.compose(inverse_warping, eval_points=self.output_points) -def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): +def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): r"""Compute the karcher mean of a set of warpings. Let :math:`\gamma_i i=1...n` be a set of warping functions @@ -544,7 +540,7 @@ def warping_mean(warping, *, iter=100, tol=1e-6, step_size=.3): Args: warping (:class:`~skfda.FDataGrid`): Set of warpings. - iter (int): Maximum number of interations. Defaults to 20. + max_iter (int): Maximum number of interations. Defaults to 100. tol (float): Convergence criterion, if the norm of the mean of the shooting vectors, :math:`| \bar v | Date: Tue, 15 Oct 2019 15:40:26 +0200 Subject: [PATCH 050/624] Add 'y' argument for API conventions --- skfda/preprocessing/registration/elastic.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 1bd6c6498..ace195fd9 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -111,7 +111,7 @@ def __init__(self, output_points=None, store_initial=True): self.store_initial = store_initial - def fit(self, X: FDataGrid): + def fit(self, X: FDataGrid, y=None): """Fits the transformer. Stores the initial value of the functions to be transformed, in order @@ -120,6 +120,7 @@ def fit(self, X: FDataGrid): Args: X (:class:`FDataGrid Date: Tue, 15 Oct 2019 15:49:24 +0200 Subject: [PATCH 051/624] Delete typo --- skfda/preprocessing/registration/elastic.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index ace195fd9..caf0adfa5 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -1,4 +1,3 @@ -max_iter import scipy.integrate from sklearn.utils.validation import check_is_fitted From 60df77d11611a71acb4b40e9fcff66a3a7c634f1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 15 Oct 2019 18:29:53 +0200 Subject: [PATCH 052/624] Fix bug when adding a FDataGrid and a matrix without the codomain dim. --- skfda/representation/grid.py | 73 ++++++++++++++++++------------------ tests/test_grid.py | 31 +++++++++++++++ 2 files changed, 67 insertions(+), 37 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index df3ea98eb..6eb5a5be9 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -496,7 +496,7 @@ def derivative(self, order=1): dataset_label=dataset_label) def __check_same_dimensions(self, other): - if self.data_matrix.shape[1] != other.data_matrix.shape[1]: + if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: raise ValueError("Error in columns dimensions") if not np.array_equal(self.sample_points, other.sample_points): raise ValueError("Sample points for both objects must be equal") @@ -610,18 +610,37 @@ def __eq__(self, other): return True + def _get_op_matrix(self, other): + if isinstance(other, numbers.Number): + return other + elif isinstance(other, np.ndarray): + # Product by number or matrix with equal dimensions, or + # matrix with same shape but only one sample + if(other.shape == () or other.shape == (1) + or other.shape == self.data_matrix.shape + or other.shape == self.data_matrix.shape[1:]): + return other + # Missing last dimension (codomain dimension) + elif (other.shape == self.data_matrix.shape[:-1] + or other.shape == self.data_matrix.shape[1:-1]): + return other[..., np.newaxis] + else: + return None + elif isinstance(other, FDataGrid): + self.__check_same_dimensions(other) + return other.data_matrix + else: + return None + def __add__(self, other): """Addition for FDataGrid object. It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=self.data_matrix + data_matrix) @@ -641,12 +660,8 @@ def __sub__(self, other): It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=self.data_matrix - data_matrix) @@ -657,12 +672,8 @@ def __rsub__(self, other): It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=data_matrix - self.data_matrix) @@ -673,12 +684,8 @@ def __mul__(self, other): It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=self.data_matrix * data_matrix) @@ -697,12 +704,8 @@ def __truediv__(self, other): It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=self.data_matrix / data_matrix) @@ -713,12 +716,8 @@ def __rtruediv__(self, other): It supports other FDataGrid objects, numpy.ndarray and numbers. """ - if isinstance(other, (np.ndarray, numbers.Number)): - data_matrix = other - elif isinstance(other, FDataGrid): - self.__check_same_dimensions(other) - data_matrix = other.data_matrix - else: + data_matrix = self._get_op_matrix(other) + if data_matrix is None: return NotImplemented return self.copy(data_matrix=data_matrix / self.data_matrix) diff --git a/tests/test_grid.py b/tests/test_grid.py index daaeb054e..2026cefa2 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -127,6 +127,37 @@ def test_coordinates(self): fd3.coordinates[(False, False, True, False, True)].data_matrix, fd.data_matrix) + def test_add(self): + fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) + + fd2 = fd1 + fd1 + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[2, 4, 6, 8], [4, 6, 8, 10]]) + + fd2 = fd1 + 2 + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]]) + + fd2 = fd1 + np.array(2) + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]]) + + fd2 = fd1 + np.array([2]) + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]]) + + fd2 = fd1 + np.array([1, 2, 3, 4]) + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[2, 4, 6, 8], [3, 5, 7, 9]]) + + fd2 = fd1 + fd1.data_matrix + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[2, 4, 6, 8], [4, 6, 8, 10]]) + + fd2 = fd1 + fd1.data_matrix[..., 0] + np.testing.assert_array_equal(fd2.data_matrix[..., 0], + [[2, 4, 6, 8], [4, 6, 8, 10]]) + if __name__ == '__main__': print() From f650210566d8c13c971c286a1036f3d06104c8cd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 16 Oct 2019 11:55:33 +0200 Subject: [PATCH 053/624] Try to fix Travis builds. --- .travis.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 8b445a1df..7d034ed31 100644 --- a/.travis.yml +++ b/.travis.yml @@ -24,11 +24,11 @@ matrix: env: - PEP8COVERAGE=true # coverage test are only install: - - pip3 install --upgrade pip cython numpy || pip3 install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' + - pip install --upgrade pip cython numpy || pip install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' - | if [[ $PEP8COVERAGE == true ]]; then - pip3 install flake8 || pip3 install --user flake8 - pip3 install codecov pytest-cov || pip3 install --user codecov pytest-cov + pip install flake8 || pip install --user flake8 + pip install codecov pytest-cov || pip install --user codecov pytest-cov fi # 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows From c8a26236225906e169339931d938dc6b46d33b12 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 16 Oct 2019 12:11:50 +0200 Subject: [PATCH 054/624] Second try --- .travis.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 7d034ed31..6f498110d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,9 @@ matrix: - name: "Python 3.7.3 on Windows" os: windows # Windows 10.0.17134 N/A Build 17134 language: shell # 'language: python' is an error on Travis CI Windows - before_install: choco install python + before_install: + - choco install python + - python -m ensurepip env: PATH=/c/Python37:/c/Python37/Scripts:$PATH - name: "Coverage and pep 8 tests on Python 3.7.1 on Xenial Linux" python: 3.7 # this works for Linux but is ignored on macOS or Windows @@ -24,11 +26,11 @@ matrix: env: - PEP8COVERAGE=true # coverage test are only install: - - pip install --upgrade pip cython numpy || pip install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' + - pip3 install --upgrade pip cython numpy || pip3 install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' - | if [[ $PEP8COVERAGE == true ]]; then - pip install flake8 || pip install --user flake8 - pip install codecov pytest-cov || pip install --user codecov pytest-cov + pip3 install flake8 || pip3 install --user flake8 + pip3 install codecov pytest-cov || pip3 install --user codecov pytest-cov fi # 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows From 8e29698a41b81ddf13f2ac30db7616ef52e58bba Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 16 Oct 2019 12:23:00 +0200 Subject: [PATCH 055/624] Third try --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 6f498110d..52df399c6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,7 +18,7 @@ matrix: language: shell # 'language: python' is an error on Travis CI Windows before_install: - choco install python - - python -m ensurepip + - python -m pip install -U --force-reinstall pip env: PATH=/c/Python37:/c/Python37/Scripts:$PATH - name: "Coverage and pep 8 tests on Python 3.7.1 on Xenial Linux" python: 3.7 # this works for Linux but is ignored on macOS or Windows From 1ab8126a48b6b3f5c49b122fc27b085a292bbcfd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 16 Oct 2019 12:30:34 +0200 Subject: [PATCH 056/624] Change choco version of python to 3.7.3 The environment variable PATH was pointing to a Python 3.7 install, but the installed version by default is now 3.8. --- .travis.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 52df399c6..e48c60eda 100644 --- a/.travis.yml +++ b/.travis.yml @@ -17,8 +17,7 @@ matrix: os: windows # Windows 10.0.17134 N/A Build 17134 language: shell # 'language: python' is an error on Travis CI Windows before_install: - - choco install python - - python -m pip install -U --force-reinstall pip + - choco install python3 --version=3.7.3 env: PATH=/c/Python37:/c/Python37/Scripts:$PATH - name: "Coverage and pep 8 tests on Python 3.7.1 on Xenial Linux" python: 3.7 # this works for Linux but is ignored on macOS or Windows From 07eebf3a60e0a88f6367a3f23e5034699a535561 Mon Sep 17 00:00:00 2001 From: pablomm Date: Wed, 16 Oct 2019 21:21:13 +0200 Subject: [PATCH 057/624] Change initial values of SRSF --- skfda/misc/metrics.py | 4 +- skfda/preprocessing/registration/elastic.py | 70 +++++++++++---------- tests/test_elastic.py | 4 +- 3 files changed, 40 insertions(+), 38 deletions(-) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index d0eb5a586..6aba2a115 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -413,7 +413,7 @@ def fisher_rao_distance(fdata1, fdata2, *, eval_points=None, _check=True): fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - srsf = SRSF(store_initial=False) + srsf = SRSF(initial_value=0) fdata1_srsf = srsf.fit_transform(fdata1) fdata2_srsf = srsf.transform(fdata2) @@ -492,7 +492,7 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata1_reg = elastic_registration.fit_transform(fdata1) - srsf = SRSF(store_initial=False) + srsf = SRSF(initial_value=0) fdata1_reg_srsf = srsf.fit_transform(fdata1_reg) fdata2_srsf = srsf.transform(fdata2) distance = lp_distance(fdata1_reg_srsf, fdata2_srsf) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index caf0adfa5..92ca3bb80 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -54,9 +54,10 @@ class SRSF(BaseEstimator, TransformerMixin): eval_points (array_like, optional): Set of points where the functions are evaluated, by default uses the sample points of the fdatagrid. - store_initial (bool): If true stores the value :math:`f(a)` of the - samples during fitting to apply the inverse transform. - Defaults True. + initial_value (float, optional): Initial value to apply in the + inverse transformation. If `None` there are stored the initial + values of the functions during the transformation to apply + during the inverse transformation. Defaults None. Note: Due to the use of derivatives it is recommended that the samples are @@ -77,7 +78,7 @@ class SRSF(BaseEstimator, TransformerMixin): >>> fd = make_sinusoidal_process(error_std=0, random_state=0) >>> srsf = SRSF() >>> srsf - SRSF(output_points=None, store_initial=True) + SRSF(intial_value=None, output_points=None) Fits the estimator (to apply the inverse transform) and apply the SRSF @@ -94,44 +95,37 @@ class SRSF(BaseEstimator, TransformerMixin): array([ 0. , 0. , 0. , ... ]) """ - def __init__(self, output_points=None, store_initial=True): + def __init__(self, output_points=None, initial_value=None): """Initializes the transformer. Args: eval_points: (array_like, optional): Set of points where the functions are evaluated, by default uses the sample points of the :class:`FDataGrid ` transformed. - store_initial (bool): If true stores the value :math:`f(a)` of the - samples during fitting to apply the inverse transform. - Defaults True. + initial_value (float, optional): Initial value to apply in the + inverse transformation. If `None` there are stored the initial + values of the functions during the transformation to apply + during the inverse transformation. Defaults None. """ self.output_points = output_points - self.store_initial = store_initial + self.initial_value = initial_value - def fit(self, X: FDataGrid, y=None): - """Fits the transformer. - - Stores the initial value of the functions to be transformed, in order - to apply its inverse transform. + def fit(self, X=None, y=None): + """This transformer do not need to be fitted. Args: - X (:class:`FDataGrid Date: Wed, 16 Oct 2019 22:09:42 +0200 Subject: [PATCH 058/624] Typo in doctest --- skfda/preprocessing/registration/elastic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 92ca3bb80..9aea4aae5 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -78,7 +78,7 @@ class SRSF(BaseEstimator, TransformerMixin): >>> fd = make_sinusoidal_process(error_std=0, random_state=0) >>> srsf = SRSF() >>> srsf - SRSF(intial_value=None, output_points=None) + SRSF(initial_value=None, output_points=None) Fits the estimator (to apply the inverse transform) and apply the SRSF From 6c6b68adf03d25e325b5ab03e2d81219b33994a5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 31 Oct 2019 12:54:42 +0100 Subject: [PATCH 059/624] Change plot names related with groups. Rename sample_labels to group. Rename label_colors to group_colors. Rename label_names to group_names. --- examples/plot_boxplot.py | 12 +-- examples/plot_clustering.py | 4 +- examples/plot_explore.py | 8 +- examples/plot_k_neighbors_classification.py | 2 +- examples/plot_magnitude_shape.py | 16 ++-- examples/plot_magnitude_shape_synthetic.py | 8 +- .../plot_radius_neighbors_classification.py | 4 +- examples/plot_representation.py | 2 +- .../visualization/representation.py | 82 +++++++++---------- skfda/representation/_functional_data.py | 6 +- 10 files changed, 71 insertions(+), 73 deletions(-) diff --git a/examples/plot_boxplot.py b/examples/plot_boxplot.py index 0e37b0186..2130824d2 100644 --- a/examples/plot_boxplot.py +++ b/examples/plot_boxplot.py @@ -38,9 +38,9 @@ nlabels = len(label_names) label_colors = colormap(np.arange(nlabels) / (nlabels - 1)) -fd_temperatures.plot(sample_labels=dataset["target"], - label_colors=label_colors, - label_names=label_names) +fd_temperatures.plot(group=dataset["target"], + group_colors=label_colors, + group_names=label_names) ############################################################################## @@ -70,9 +70,9 @@ color = 0.3 outliercol = 0.7 -fd_temperatures.plot(sample_labels=fdBoxplot.outliers.astype(int), - label_colors=colormap([color, outliercol]), - label_names=["nonoutliers", "outliers"]) +fd_temperatures.plot(group=fdBoxplot.outliers.astype(int), + group_colors=colormap([color, outliercol]), + group_names=["nonoutliers", "outliers"]) ############################################################################## # The curves pointed as outliers are are those curves with significantly lower diff --git a/examples/plot_clustering.py b/examples/plot_clustering.py index 537c1e264..af83395f3 100644 --- a/examples/plot_clustering.py +++ b/examples/plot_clustering.py @@ -58,8 +58,8 @@ n_climates = len(climates) climate_colors = colormap(np.arange(n_climates) / (n_climates - 1)) -fd.plot(sample_labels=indexer, label_colors=climate_colors, - label_names=climates) +fd.plot(group=indexer, group_colors=climate_colors, + group_names=climates) ############################################################################## # The number of clusters is set with the number of climates, in order to see diff --git a/examples/plot_explore.py b/examples/plot_explore.py index 0b86c21db..175e50d65 100644 --- a/examples/plot_explore.py +++ b/examples/plot_explore.py @@ -36,7 +36,7 @@ labels[low_fat] = 1 colors = ['red', 'blue'] -fig = fd.plot(sample_labels=labels, label_colors=colors, +fig = fd.plot(group=labels, group_colors=colors, linewidth=0.5, alpha=0.7) ############################################################################## @@ -48,7 +48,7 @@ means = mean_high.concatenate(mean_low) means.dataset_label = fd.dataset_label + ' - means' -means.plot(sample_labels=[0, 1], label_colors=colors, +means.plot(group=[0, 1], group_colors=colors, linewidth=0.5) ############################################################################## @@ -60,11 +60,11 @@ # The first derivative is shown below: fdd = fd.derivative(1) -fig = fdd.plot(sample_labels=labels, label_colors=colors, +fig = fdd.plot(group=labels, group_colors=colors, linewidth=0.5, alpha=0.7) ############################################################################## # We now show the second derivative: fdd = fd.derivative(2) -fig = fdd.plot(sample_labels=labels, label_colors=colors, +fig = fdd.plot(group=labels, group_colors=colors, linewidth=0.5, alpha=0.7) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 1b4d2d415..39a8e057e 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -37,7 +37,7 @@ class_names = data['target_names'] # Plot samples grouped by sex -X.plot(sample_labels=y, label_names=class_names, label_colors=['C0', 'C1']) +X.plot(group=y, group_names=class_names, group_colors=['C0', 'C1']) ############################################################################## diff --git a/examples/plot_magnitude_shape.py b/examples/plot_magnitude_shape.py index 15c791c61..bda1e6f04 100644 --- a/examples/plot_magnitude_shape.py +++ b/examples/plot_magnitude_shape.py @@ -37,9 +37,9 @@ nlabels = len(label_names) label_colors = colormap(np.arange(nlabels) / (nlabels - 1)) -fd_temperatures.plot(sample_labels=dataset["target"], - label_colors=label_colors, - label_names=label_names) +fd_temperatures.plot(group=dataset["target"], + group_colors=label_colors, + group_names=label_names) ############################################################################## # The MS-Plot is generated. In order to show the results, the @@ -62,9 +62,9 @@ # To show the utility of the plot, the curves are plotted according to the # distinction made by the MS-Plot (outliers or not) with the same colors. -fd_temperatures.plot(sample_labels=msplot.outliers.astype(int), - label_colors=msplot.colormap([color, outliercol]), - label_names=['nonoutliers', 'outliers']) +fd_temperatures.plot(group=msplot.outliers.astype(int), + group_colors=msplot.colormap([color, outliercol]), + group_names=['nonoutliers', 'outliers']) ############################################################################## # We can observe that most of the curves pointed as outliers belong either to @@ -118,5 +118,5 @@ ############################################################################## # We now plot the curves with their corresponding color: -fd_temperatures.plot(sample_labels=labels, - label_colors=colormap([color, outliercol, 0.9])) +fd_temperatures.plot(group=labels, + group_colors=colormap([color, outliercol, 0.9])) diff --git a/examples/plot_magnitude_shape_synthetic.py b/examples/plot_magnitude_shape_synthetic.py index 7f2b18725..4242bc9f5 100644 --- a/examples/plot_magnitude_shape_synthetic.py +++ b/examples/plot_magnitude_shape_synthetic.py @@ -76,8 +76,8 @@ # The data is plotted to show the curves we are working with. labels = [0] * n_samples + [1] * 6 -fd.plot(sample_labels=labels, - label_colors=['lightgrey', 'black']) +fd.plot(group=labels, + group_colors=['lightgrey', 'black']) ############################################################################## # The MS-Plot is generated. In order to show the results, the @@ -96,8 +96,8 @@ colors = ['lightgrey', 'orange', 'blue', 'black', 'green', 'brown', 'lightblue'] -fd.plot(sample_labels=labels, - label_colors=colors) +fd.plot(group=labels, + group_colors=colors) ############################################################################## # We now show the points in the MS-plot using the same colors diff --git a/examples/plot_radius_neighbors_classification.py b/examples/plot_radius_neighbors_classification.py index 8591fe39f..4c07289d1 100644 --- a/examples/plot_radius_neighbors_classification.py +++ b/examples/plot_radius_neighbors_classification.py @@ -40,7 +40,7 @@ y = np.array(15 * [0] + 15 * [1]) # Plot toy dataset -X.plot(sample_labels=y, label_colors=['C0', 'C1']) +X.plot(group=y, group_colors=['C0', 'C1']) ############################################################################## # @@ -69,7 +69,7 @@ radius = 0.3 sample = X_test[0] # Center of the ball -fig = X_train.plot(sample_labels=y_train, label_colors=['C0', 'C1']) +fig = X_train.plot(group=y_train, group_colors=['C0', 'C1']) # Plot ball sample.plot(fig=fig, color='red', linewidth=3) diff --git a/examples/plot_representation.py b/examples/plot_representation.py index 1aa2de55f..bdeccc7f7 100644 --- a/examples/plot_representation.py +++ b/examples/plot_representation.py @@ -28,7 +28,7 @@ print(repr(fd)) -fd.plot(sample_labels=y, label_colors=['red', 'blue']) +fd.plot(group=y, group_colors=['red', 'blue']) ############################################################################## # This kind of representation is a discretized representation, in which the diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 18a6c6772..ac3bb4be8 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -9,50 +9,50 @@ _set_labels) -def _get_label_colors(n_labels, label_colors=None): +def _get_label_colors(n_labels, group_colors=None): """Get the colors of each label""" - if label_colors is not None: - if len(label_colors) != n_labels: - raise ValueError("There must be a color in label_colors " + if group_colors is not None: + if len(group_colors) != n_labels: + raise ValueError("There must be a color in group_colors " "for each of the labels that appear in " - "sample_labels.") + "group.") else: colormap = matplotlib.cm.get_cmap() - label_colors = colormap(np.arange(n_labels) / (n_labels - 1)) + group_colors = colormap(np.arange(n_labels) / (n_labels - 1)) - return label_colors + return group_colors -def _get_color_info(fdata, sample_labels, label_names, label_colors, kwargs): +def _get_color_info(fdata, group, group_names, group_colors, kwargs): patches = None - if sample_labels is not None: + if group is not None: # In this case, each curve has a label, and all curves with the same # label should have the same color - sample_labels = np.asarray(sample_labels) + group = np.asarray(group) - n_labels = np.max(sample_labels) + 1 + n_labels = np.max(group) + 1 - if np.any((sample_labels < 0) | (sample_labels >= n_labels)) or \ - not np.all(np.isin(range(n_labels), sample_labels)): - raise ValueError("Sample_labels must contain at least an " + if np.any((group < 0) | (group >= n_labels)) or \ + not np.all(np.isin(range(n_labels), group)): + raise ValueError("group must contain at least an " "occurence of numbers between 0 and number " "of distint sample labels.") - label_colors = _get_label_colors(n_labels, label_colors) - sample_colors = np.asarray(label_colors)[sample_labels] + group_colors = _get_label_colors(n_labels, group_colors) + sample_colors = np.asarray(group_colors)[group] - if label_names is not None: - if len(label_names) != n_labels: - raise ValueError("There must be a name in label_names " + if group_names is not None: + if len(group_names) != n_labels: + raise ValueError("There must be a name in group_names " "for each of the labels that appear in " - "sample_labels.") + "group.") patches = [matplotlib.patches.Patch(color=c, label=l) - for c, l in zip(label_colors, label_names)] + for c, l in zip(group_colors, group_names)] else: # In this case, each curve has a different color unless specified @@ -75,7 +75,7 @@ def _get_color_info(fdata, sample_labels, label_names, label_colors, kwargs): def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, n_rows=None, n_cols=None, n_points=None, domain_range=None, - sample_labels=None, label_colors=None, label_names=None, + group=None, group_colors=None, group_names=None, **kwargs): """Plot the FDatGrid object graph as hypersurfaces. @@ -115,15 +115,15 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, interval; in the case of surfaces a list with 2 tuples with the ranges for each dimension. Default uses the domain range of the functional object. - sample_labels (list of int): contains integers from [0 to number of + group (list of int): contains integers from [0 to number of labels) indicating to which group each sample belongs to. Then, the samples with the same label are plotted in the same color. If None, the default value, each sample is plotted in the color assigned by matplotlib.pyplot.rcParams['axes.prop_cycle']. - label_colors (list of colors): colors in which groups are + group_colors (list of colors): colors in which groups are represented, there must be one for each group. If None, each group is shown with distict colors in the "Greys" colormap. - label_names (list of str): name of each of the groups which appear + group_names (list of str): name of each of the groups which appear in a legend, there must be one for each one. Defaults to None and the legend is not shown. **kwargs: if dim_domain is 1, keyword arguments to be passed to @@ -145,7 +145,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, domain_range = _list_of_arrays(domain_range) sample_colors, patches = _get_color_info( - fdata, sample_labels, label_names, label_colors, kwargs) + fdata, group, group_names, group_colors, kwargs) if fdata.dim_domain == 1: @@ -205,8 +205,8 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, fig=None, axes=None, - n_rows=None, n_cols=None, n_points=None, domain_range=None, - sample_labels=None, label_colors=None, label_names=None, + n_rows=None, n_cols=None, domain_range=None, + group=None, group_colors=None, group_names=None, **kwargs): """Plot the FDatGrid object. @@ -231,28 +231,21 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, n_cols(int, optional): designates the number of columns of the figure to plot the different dimensions of the image. Only specified if fig and ax are None. - n_points (int or tuple, optional): Number of points to evaluate in - the plot. In case of surfaces a tuple of length 2 can be pased - with the number of points to plot in each axis, otherwise the - same number of points will be used in the two axes. By default - in unidimensional plots will be used 501 points; in surfaces - will be used 30 points per axis, wich makes a grid with 900 - points. domain_range (tuple or list of tuples, optional): Range where the function will be plotted. In objects with unidimensional domain the domain range should be a tuple with the bounds of the interval; in the case of surfaces a list with 2 tuples with the ranges for each dimension. Default uses the domain range of the functional object. - sample_labels (list of int): contains integers from [0 to number of + group (list of int): contains integers from [0 to number of labels) indicating to which group each sample belongs to. Then, the samples with the same label are plotted in the same color. If None, the default value, each sample is plotted in the color assigned by matplotlib.pyplot.rcParams['axes.prop_cycle']. - label_colors (list of colors): colors in which groups are + group_colors (list of colors): colors in which groups are represented, there must be one for each group. If None, each group is shown with distict colors in the "Greys" colormap. - label_names (list of str): name of each of the groups which appear + group_names (list of str): name of each of the groups which appear in a legend, there must be one for each one. Defaults to None and the legend is not shown. **kwargs: if dim_domain is 1, keyword arguments to be passed to @@ -265,12 +258,17 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, """ + evaluated_points = None + if sample_points is None: # This can only be done for FDataGrid sample_points = fdata.sample_points - evaluated_points = fdata.data_matrix - else: - evaluated_points = fdata(sample_points, grid=True) + if derivative == 0: + evaluated_points = fdata.data_matrix + + if evaluated_points is None: + evaluated_points = fdata( + sample_points, grid=True, derivative=derivative) fig, axes = _get_figure_and_axes(chart, fig, axes) fig, axes = _set_figure_layout_for_fdata(fdata, fig, axes, n_rows, n_cols) @@ -281,7 +279,7 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, domain_range = _list_of_arrays(domain_range) sample_colors, patches = _get_color_info( - fdata, sample_labels, label_names, label_colors, kwargs) + fdata, group, group_names, group_colors, kwargs) if fdata.dim_domain == 1: diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index e3d0af9da..5a1a0294c 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -685,15 +685,15 @@ def plot(self, *args, **kwargs): interval; in the case of surfaces a list with 2 tuples with the ranges for each dimension. Default uses the domain range of the functional object. - sample_labels (list of int): contains integers from [0 to number of + group (list of int): contains integers from [0 to number of labels) indicating to which group each sample belongs to. Then, the samples with the same label are plotted in the same color. If None, the default value, each sample is plotted in the color assigned by matplotlib.pyplot.rcParams['axes.prop_cycle']. - label_colors (list of colors): colors in which groups are + group_colors (list of colors): colors in which groups are represented, there must be one for each group. If None, each group is shown with distict colors in the "Greys" colormap. - label_names (list of str): name of each of the groups which appear + group_names (list of str): name of each of the groups which appear in a legend, there must be one for each one. Defaults to None and the legend is not shown. **kwargs: if dim_domain is 1, keyword arguments to be passed to From c26be8b4fb1352848323d78806a10b996800bfd5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 31 Oct 2019 17:55:27 +0100 Subject: [PATCH 060/624] Modify group, group_colors and group_labels * `group` is now not required to be an array of integers. * `group_colors` and `group_names` are now objects that index the color/name per group (so if `group` is an array of integers, they can still be arrays, but they can be dict-like objects for other types) * A new parameter `legend` allows to plot a legend even if no `group_names` is passed (it will use the objects passed to `group`) * The *explore* example is changed to make use of some of these features in order to test them --- examples/plot_explore.py | 17 ++++--- .../visualization/representation.py | 51 ++++++++++++------- 2 files changed, 41 insertions(+), 27 deletions(-) diff --git a/examples/plot_explore.py b/examples/plot_explore.py index 175e50d65..ddd73ba4d 100644 --- a/examples/plot_explore.py +++ b/examples/plot_explore.py @@ -32,12 +32,13 @@ # the rest. low_fat = fat < 20 -labels = np.zeros(fd.n_samples, dtype=int) -labels[low_fat] = 1 -colors = ['red', 'blue'] +labels = np.full(fd.n_samples, 'high fat') +labels[low_fat] = 'low fat' +colors = {'high fat': 'red', + 'low fat': 'blue'} fig = fd.plot(group=labels, group_colors=colors, - linewidth=0.5, alpha=0.7) + linewidth=0.5, alpha=0.7, legend=True) ############################################################################## # The means of each group are the following ones. @@ -48,8 +49,8 @@ means = mean_high.concatenate(mean_low) means.dataset_label = fd.dataset_label + ' - means' -means.plot(group=[0, 1], group_colors=colors, - linewidth=0.5) +means.plot(group=['high fat', 'low fat'], group_colors=colors, + linewidth=0.5, legend=True) ############################################################################## # In this dataset, the vertical shift in the original trajectories is not @@ -61,10 +62,10 @@ fdd = fd.derivative(1) fig = fdd.plot(group=labels, group_colors=colors, - linewidth=0.5, alpha=0.7) + linewidth=0.5, alpha=0.7, legend=True) ############################################################################## # We now show the second derivative: fdd = fd.derivative(2) fig = fdd.plot(group=labels, group_colors=colors, - linewidth=0.5, alpha=0.7) + linewidth=0.5, alpha=0.7, legend=True) diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index ac3bb4be8..824ef348e 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -24,7 +24,7 @@ def _get_label_colors(n_labels, group_colors=None): return group_colors -def _get_color_info(fdata, group, group_names, group_colors, kwargs): +def _get_color_info(fdata, group, group_names, group_colors, legend, kwargs): patches = None @@ -32,27 +32,30 @@ def _get_color_info(fdata, group, group_names, group_colors, kwargs): # In this case, each curve has a label, and all curves with the same # label should have the same color - group = np.asarray(group) + group_unique, group_indexes = np.unique(group, return_inverse=True) + n_labels = len(group_unique) - n_labels = np.max(group) + 1 + if group_colors is not None: + group_colors_array = np.array( + [group_colors[g] for g in group_unique]) + else: + colormap = matplotlib.cm.get_cmap() + group_colors_array = np.asarray( + colormap(np.arange(n_labels) / (n_labels - 1))) - if np.any((group < 0) | (group >= n_labels)) or \ - not np.all(np.isin(range(n_labels), group)): - raise ValueError("group must contain at least an " - "occurence of numbers between 0 and number " - "of distint sample labels.") + sample_colors = group_colors_array[group_indexes] - group_colors = _get_label_colors(n_labels, group_colors) - sample_colors = np.asarray(group_colors)[group] + group_names_array = None if group_names is not None: - if len(group_names) != n_labels: - raise ValueError("There must be a name in group_names " - "for each of the labels that appear in " - "group.") + group_names_array = np.array( + [group_names[g] for g in group_unique]) + elif legend is True: + group_names_array = group_unique + if group_names_array is not None: patches = [matplotlib.patches.Patch(color=c, label=l) - for c, l in zip(group_colors, group_names)] + for c, l in zip(group_colors_array, group_names_array)] else: # In this case, each curve has a different color unless specified @@ -76,6 +79,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, n_rows=None, n_cols=None, n_points=None, domain_range=None, group=None, group_colors=None, group_names=None, + legend: bool = False, **kwargs): """Plot the FDatGrid object graph as hypersurfaces. @@ -125,7 +129,11 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, group is shown with distict colors in the "Greys" colormap. group_names (list of str): name of each of the groups which appear in a legend, there must be one for each one. Defaults to None - and the legend is not shown. + and the legend is not shown. Implies `legend=True`. + legend (bool): if `True`, show a legend with the groups. If + `group_names` is passed, it will be used for finding the names + to display in the legend. Otherwise, the values passed to + `group` will be used. **kwargs: if dim_domain is 1, keyword arguments to be passed to the matplotlib.pyplot.plot function; if dim_domain is 2, keyword arguments to be passed to the @@ -145,7 +153,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, domain_range = _list_of_arrays(domain_range) sample_colors, patches = _get_color_info( - fdata, group, group_names, group_colors, kwargs) + fdata, group, group_names, group_colors, legend, kwargs) if fdata.dim_domain == 1: @@ -207,6 +215,7 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, fig=None, axes=None, n_rows=None, n_cols=None, domain_range=None, group=None, group_colors=None, group_names=None, + legend: bool = False, **kwargs): """Plot the FDatGrid object. @@ -247,7 +256,11 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, group is shown with distict colors in the "Greys" colormap. group_names (list of str): name of each of the groups which appear in a legend, there must be one for each one. Defaults to None - and the legend is not shown. + and the legend is not shown. Implies `legend=True`. + legend (bool): if `True`, show a legend with the groups. If + `group_names` is passed, it will be used for finding the names + to display in the legend. Otherwise, the values passed to + `group` will be used. **kwargs: if dim_domain is 1, keyword arguments to be passed to the matplotlib.pyplot.plot function; if dim_domain is 2, keyword arguments to be passed to the @@ -279,7 +292,7 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, domain_range = _list_of_arrays(domain_range) sample_colors, patches = _get_color_info( - fdata, group, group_names, group_colors, kwargs) + fdata, group, group_names, group_colors, legend, kwargs) if fdata.dim_domain == 1: From 750318fd3554522c9045cfb350ba6e5296593dc4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 4 Nov 2019 13:40:37 +0100 Subject: [PATCH 061/624] Plot of groups now uses the default matplotlib cycle if no colors are provided. --- examples/plot_k_neighbors_classification.py | 2 +- skfda/exploratory/visualization/representation.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 39a8e057e..296273928 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -37,7 +37,7 @@ class_names = data['target_names'] # Plot samples grouped by sex -X.plot(group=y, group_names=class_names, group_colors=['C0', 'C1']) +X.plot(group=y, group_names=class_names) ############################################################################## diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 824ef348e..54d65e045 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -39,9 +39,11 @@ def _get_color_info(fdata, group, group_names, group_colors, legend, kwargs): group_colors_array = np.array( [group_colors[g] for g in group_unique]) else: - colormap = matplotlib.cm.get_cmap() - group_colors_array = np.asarray( - colormap(np.arange(n_labels) / (n_labels - 1))) + prop_cycle = matplotlib.rcParams['axes.prop_cycle'] + cycle_colors = prop_cycle.by_key()['color'] + + group_colors_array = np.take( + cycle_colors, np.arange(n_labels), mode='wrap') sample_colors = group_colors_array[group_indexes] From 5eee5ac210ea8fe2d99642978d2cf95005f1c9bd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 5 Nov 2019 17:19:47 +0100 Subject: [PATCH 062/624] Fixed grammar error: interquartilic* -> interquartile --- docs/modules/exploratory/outliers.rst | 4 ++-- skfda/exploratory/outliers/_iqr.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/modules/exploratory/outliers.rst b/docs/modules/exploratory/outliers.rst index ef79c6367..0adba3291 100644 --- a/docs/modules/exploratory/outliers.rst +++ b/docs/modules/exploratory/outliers.rst @@ -10,12 +10,12 @@ identify the outliers. Each of the outlier detection methods in scikit-fda has the same API as the outlier detection methods of `scikit-learn `_. -Interquartilic Range Outlier Detector +Interquartile Range Outlier Detector ------------------------------------ One of the most common ways of outlier detection is given by the functional data boxplot. An observation is marked as an outlier if it has points :math:`1.5 \cdot IQR` times outside the region containing the deepest 50% of the curves -(the central region), where :math:`IQR` is the interquartilic range. +(the central region), where :math:`IQR` is the interquartile range. .. autosummary:: :toctree: autosummary diff --git a/skfda/exploratory/outliers/_iqr.py b/skfda/exploratory/outliers/_iqr.py index d5a7ac6da..d48d41cf1 100644 --- a/skfda/exploratory/outliers/_iqr.py +++ b/skfda/exploratory/outliers/_iqr.py @@ -5,10 +5,10 @@ class IQROutlierDetector(BaseEstimator, OutlierMixin): - r"""Outlier detector using the interquartilic range. + r"""Outlier detector using the interquartile range. Detects as outliers functions that have one or more points outside - ``factor`` times the interquartilic range plus or minus the central + ``factor`` times the interquartile range plus or minus the central envelope, given a functional depth measure. This corresponds to the points selected as outliers by the functional boxplot. From cca094cfb0a0918a88acaefcabe01d885957245e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Sun, 17 Nov 2019 16:23:45 +0100 Subject: [PATCH 063/624] Fix KMeans documentation not properly generated. --- skfda/ml/clustering/base_kmeans.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/ml/clustering/base_kmeans.py b/skfda/ml/clustering/base_kmeans.py index 6ef1efabe..3c6bc6748 100644 --- a/skfda/ml/clustering/base_kmeans.py +++ b/skfda/ml/clustering/base_kmeans.py @@ -362,7 +362,7 @@ class KMeans(BaseKMeans): metric=.pairwise at 0x7faf3aa061e0>, # doctest:+ELLIPSIS n_clusters=2, random_state=0, tol=0.0001) - """.replace('+IGNORE_RESULT', '+ELLIPSIS\n<...>') + """ def __init__(self, n_clusters=2, init=None, metric=pairwise_distance(lp_distance), @@ -613,8 +613,8 @@ class FuzzyKMeans(BaseKMeans): metric=.pairwise at 0x7faf3aa06488>, # doctest:+ELLIPSIS n_clusters=2, n_dec=3, random_state=0, tol=0.0001) - """.replace('+IGNORE_RESULT', '+ELLIPSIS\n<...>') - + """ + def __init__(self, n_clusters=2, init=None, metric=pairwise_distance(lp_distance), n_init=1, max_iter=100, tol=1e-4, random_state=0, fuzzifier=2, n_dec=3): From a1cc8bee7005be20c3381e6277ca42da3f8e4fc1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Sun, 17 Nov 2019 16:25:29 +0100 Subject: [PATCH 064/624] Remove space. --- skfda/ml/clustering/base_kmeans.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/clustering/base_kmeans.py b/skfda/ml/clustering/base_kmeans.py index 3c6bc6748..e11efebdd 100644 --- a/skfda/ml/clustering/base_kmeans.py +++ b/skfda/ml/clustering/base_kmeans.py @@ -614,7 +614,7 @@ class FuzzyKMeans(BaseKMeans): 0x7faf3aa06488>, # doctest:+ELLIPSIS n_clusters=2, n_dec=3, random_state=0, tol=0.0001) """ - + def __init__(self, n_clusters=2, init=None, metric=pairwise_distance(lp_distance), n_init=1, max_iter=100, tol=1e-4, random_state=0, fuzzifier=2, n_dec=3): From 9792962e0eff62efdd10c2407c793f4dbd01640e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 17 Nov 2019 17:17:35 +0100 Subject: [PATCH 065/624] Update examples Rename KMeans module --- skfda/exploratory/visualization/clustering.py | 2 +- skfda/ml/clustering/__init__.py | 4 +- .../clustering/{base_kmeans.py => kmeans.py} | 51 ++++++++++++------- tests/test_clustering.py | 4 +- 4 files changed, 37 insertions(+), 24 deletions(-) rename skfda/ml/clustering/{base_kmeans.py => kmeans.py} (96%) diff --git a/skfda/exploratory/visualization/clustering.py b/skfda/exploratory/visualization/clustering.py index a266da294..a786ac128 100644 --- a/skfda/exploratory/visualization/clustering.py +++ b/skfda/exploratory/visualization/clustering.py @@ -8,7 +8,7 @@ import matplotlib.pyplot as plt import numpy as np -from ...ml.clustering.base_kmeans import FuzzyKMeans +from ...ml.clustering import FuzzyKMeans from ._utils import (_darken, _get_figure_and_axes, _set_figure_layout_for_fdata, _set_figure_layout, _set_labels) diff --git a/skfda/ml/clustering/__init__.py b/skfda/ml/clustering/__init__.py index 96b818792..8553c2616 100644 --- a/skfda/ml/clustering/__init__.py +++ b/skfda/ml/clustering/__init__.py @@ -1,5 +1,5 @@ -from . import base_kmeans -from .base_kmeans import KMeans, FuzzyKMeans +from . import kmeans from ..._neighbors import NearestNeighbors +from .kmeans import KMeans, FuzzyKMeans diff --git a/skfda/ml/clustering/base_kmeans.py b/skfda/ml/clustering/kmeans.py similarity index 96% rename from skfda/ml/clustering/base_kmeans.py rename to skfda/ml/clustering/kmeans.py index e11efebdd..f9aaaf780 100644 --- a/skfda/ml/clustering/base_kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -353,15 +353,24 @@ class KMeans(BaseKMeans): ... [-0.5, -0.5, -0.5, -1, -1, -1]] >>> sample_points = [0, 2, 4, 6, 8, 10] >>> fd = FDataGrid(data_matrix, sample_points) - >>> kmeans = KMeans() - >>> init= np.array([[0, 0, 0, 0, 0, 0], [2, 1, -1, 0.5, 0, -0.5]]) - >>> init_fd = FDataGrid(init, sample_points) - >>> kmeans.fit(fd, init=init_fd) - >>> kmeans - KMeans(max_iter=100, - metric=.pairwise at - 0x7faf3aa061e0>, # doctest:+ELLIPSIS - n_clusters=2, random_state=0, tol=0.0001) + >>> kmeans = KMeans(random_state=0) + >>> kmeans.fit(fd) # doctest:+ELLIPSIS + KMeans(...) + >>> kmeans.cluster_centers_.data_matrix + ... # doctest:+NORMALIZE_WHITESPACE + array([[[ 0.16666667], + [ 0.16666667], + [ 0.83333333], + [ 2. ], + [ 1.66666667], + [ 1.16666667]], + [[-0.5 ], + [-0.5 ], + [-0.5 ], + [-1. ], + [-1. ], + [-1. ]]]) + """ def __init__(self, n_clusters=2, init=None, @@ -603,16 +612,20 @@ class FuzzyKMeans(BaseKMeans): ... [[3, 0.2], [4, 0.3], [5, 0.4], [6, 0.5]]] >>> sample_points = [2, 4, 6, 8] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fuzzy_kmeans = FuzzyKMeans() - >>> init=np.array([[[3, 0], [5, 0], [2, 0], [4, 0]], - ... [[0, 0], [0, 1], [0, 0], [0, 1]]]) - >>> init_fd = FDataGrid(init, sample_points) - >>> fuzzy_kmeans.fit(fd, init=init_fd) - >>> fuzzy_kmeans - FuzzyKMeans(fuzzifier=2, max_iter=100, - metric=.pairwise at - 0x7faf3aa06488>, # doctest:+ELLIPSIS - n_clusters=2, n_dec=3, random_state=0, tol=0.0001) + >>> fuzzy_kmeans = FuzzyKMeans(random_state=0) + >>> fuzzy_kmeans.fit(fd) # doctest:+ELLIPSIS + FuzzyKMeans(...) + >>> fuzzy_kmeans.cluster_centers_.data_matrix + ... # doctest:+NORMALIZE_WHITESPACE + array([[[ 2.84075812, 0.2476166 ], + [ 3.84075812, 0.3476166 ], + [ 4.84075812, 0.4476166 ], + [ 5.84075812, 0.53175479]], + [[ 1.25224668, 0.35041906], + [ 2.25224668, 0.45041906], + [ 3.25224668, 0.55041906], + [ 4.25224668, 0.6252065 ]]]) + """ def __init__(self, n_clusters=2, init=None, diff --git a/tests/test_clustering.py b/tests/test_clustering.py index 8de97af24..bcc9fe344 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -1,8 +1,8 @@ import unittest -import numpy as np +import numpy as np +from skfda.ml.clustering import KMeans, FuzzyKMeans from skfda.representation.grid import FDataGrid -from skfda.ml.clustering.base_kmeans import KMeans, FuzzyKMeans class TestClustering(unittest.TestCase): From 5f440fbba71a056a5c8b65b769d9d17014f22417 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 17 Nov 2019 21:49:53 +0100 Subject: [PATCH 066/624] Fix clustering example --- examples/plot_clustering.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_clustering.py b/examples/plot_clustering.py index af83395f3..b412ef68d 100644 --- a/examples/plot_clustering.py +++ b/examples/plot_clustering.py @@ -17,7 +17,7 @@ from skfda import datasets from skfda.exploratory.visualization.clustering import ( plot_clusters, plot_cluster_lines, plot_cluster_bars) -from skfda.ml.clustering.base_kmeans import KMeans, FuzzyKMeans +from skfda.ml.clustering import KMeans, FuzzyKMeans ############################################################################## From 14dcd0271d49fc400f869e26847b6b347d219ae8 Mon Sep 17 00:00:00 2001 From: davidgarciafer Date: Sun, 24 Nov 2019 20:56:18 +0100 Subject: [PATCH 067/624] Initial commit for functional ANOVA feature. --- skfda/inference/anova/__init__.py | 0 skfda/inference/anova/anova.py | 5 ++ skfda/inference/anova/anova_simulation.py | 81 +++++++++++++++++++++++ skfda/misc/covariances.py | 2 + 4 files changed, 88 insertions(+) create mode 100644 skfda/inference/anova/__init__.py create mode 100644 skfda/inference/anova/anova.py create mode 100644 skfda/inference/anova/anova_simulation.py diff --git a/skfda/inference/anova/__init__.py b/skfda/inference/anova/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/inference/anova/anova.py b/skfda/inference/anova/anova.py new file mode 100644 index 000000000..8c3fb79cc --- /dev/null +++ b/skfda/inference/anova/anova.py @@ -0,0 +1,5 @@ +from skfda.misc.metrics import norm_lp + + +def v_n_statistic(means, sizes): + print(means) diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py new file mode 100644 index 000000000..75e0bcdd0 --- /dev/null +++ b/skfda/inference/anova/anova_simulation.py @@ -0,0 +1,81 @@ +from skfda import FDataGrid +from skfda.datasets import make_gaussian_process +import matplotlib.pyplot as plt +import numpy as np +import sklearn +from skfda.misc.metrics import lp_distance +from statsmodels.distributions.empirical_distribution import ECDF + +def generate_samples_independent(mean, sigma, n_samples): + return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] + + +# Cuevas simulation study +grid = np.linspace(0, 1, 25) +n_levels = 3 + +# Case M2 +mean1 = np.vectorize(lambda t: t*(1-t)**5)(grid) +mean2 = np.vectorize(lambda t: t**2*(1-t)**4)(grid) +mean3 = np.vectorize(lambda t: t**3*(1-t)**3)(grid) + +fd_means = FDataGrid([mean1, mean2, mean3]) + +samples1 = generate_samples_independent(mean1, 0.2/25, 10) +samples2 = generate_samples_independent(mean2, 0.2/25, 10) +samples3 = generate_samples_independent(mean3, 0.2/25, 10) + +# Storing in FDataGrid +fd_1 = FDataGrid(samples1, sample_points=grid, dataset_label="Process 1") +fd_2 = FDataGrid(samples2, sample_points=grid, dataset_label="Process 2") +fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") +fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) + +# Anova + + +def f_oneway(*args): + + if len(args) < 1: + return + # fd_total = args[0].concatenate(*args[1:]) + N = 2000 + alpha = 0.05 + + simulations = [np.squeeze(make_gaussian_process(N, len(p.sample_points[0]), cov=np.squeeze(p.cov().data_matrix[0])).data_matrix) for p in args] + + ecdf = np.array([]) + for l in range(N): + for i in range(len(simulations)): + for j in range(i + 1, len(simulations)): + ecdf = np.append(ecdf, np.linalg.norm(simulations[i][l] - (np.sqrt(1)) * simulations[j][l])) + + v_alpha = np.quantile(ecdf, 1 - alpha) + F = ECDF(ecdf) + print(v_alpha) + + +def v_n_statistic(means, sizes): + lp_distance(means, means) + + +# f_oneway(fd_2, fd_1, fd_3) +v_n_statistic(fd_means, [1, 2, 3]) + + + + + + + + + + + + + + + + + + diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index f433a38a3..e6cf1410d 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -37,6 +37,8 @@ def _execute_covariance(covariance, x, y): else: if callable(covariance): result = covariance(x, y) + elif hasattr(covariance, "shape"): + result = covariance else: # GPy kernel result = covariance.K(x, y) From 4cedc3c9c782def0dc28a6d6796abc5deb77d93b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 26 Nov 2019 20:08:10 +0100 Subject: [PATCH 068/624] Alternative inputs for oneway anova function. --- skfda/inference/anova/__init__.py | 1 + skfda/inference/anova/anova.py | 5 --- skfda/inference/anova/anova_oneway.py | 38 +++++++++++++++++++++++ skfda/inference/anova/anova_simulation.py | 11 +++---- 4 files changed, 44 insertions(+), 11 deletions(-) delete mode 100644 skfda/inference/anova/anova.py create mode 100644 skfda/inference/anova/anova_oneway.py diff --git a/skfda/inference/anova/__init__.py b/skfda/inference/anova/__init__.py index e69de29bb..dd64b01a1 100644 --- a/skfda/inference/anova/__init__.py +++ b/skfda/inference/anova/__init__.py @@ -0,0 +1 @@ +from . import anova_oneway diff --git a/skfda/inference/anova/anova.py b/skfda/inference/anova/anova.py deleted file mode 100644 index 8c3fb79cc..000000000 --- a/skfda/inference/anova/anova.py +++ /dev/null @@ -1,5 +0,0 @@ -from skfda.misc.metrics import norm_lp - - -def v_n_statistic(means, sizes): - print(means) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py new file mode 100644 index 000000000..bd834df97 --- /dev/null +++ b/skfda/inference/anova/anova_oneway.py @@ -0,0 +1,38 @@ +import numpy as np +from skfda.misc.metrics import lp_distance +from skfda.representation import FDataGrid +import matplotlib.pyplot as plt + + +def vn_statistic(fd_means, sizes): + # Calculating weighted sum of L2 distances between means + distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected + # Calculating square of the distances and summing by groups + distances_group = np.sum(np.multiply(distances_m, distances_m), axis=1) + # Weighted sum + return sum(distances_group * sizes) + + +def func_oneway(fdata, groups, n_sim): + + # Obtaining the different group labels + group_set = np.unique(groups) + + fd_groups = [] + means = None + for group in group_set: + # Creating an independent FDataGrid for each group + indices = np.where(groups == group)[0] + fd = FDataGrid(np.squeeze(np.take(fdata.data_matrix, indices, axis=0)), + sample_points=fdata.sample_points) + fd_groups.append(fd) + # Creating FDataGrid with the means of each group + if not means: + means = fd.mean() + else: + means = means.concatenate(fd.mean()) + + # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) + +# func_oneway(None, np.array(['a', 'b', 'a', 'a']), 1000) + diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 75e0bcdd0..849b666f8 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -5,6 +5,7 @@ import sklearn from skfda.misc.metrics import lp_distance from statsmodels.distributions.empirical_distribution import ECDF +from skfda.inference.anova.anova_oneway import func_oneway def generate_samples_independent(mean, sigma, n_samples): return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] @@ -31,6 +32,9 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) +# print(fd_total.data_matrix[0]) +# print(np.squeeze(np.take(fd_total.data_matrix, np.array([0, 3]), axis=0))) + # Anova @@ -55,12 +59,7 @@ def f_oneway(*args): print(v_alpha) -def v_n_statistic(means, sizes): - lp_distance(means, means) - - -# f_oneway(fd_2, fd_1, fd_3) -v_n_statistic(fd_means, [1, 2, 3]) +func_oneway(fd_total, np.array(['a' for _ in range(10)] + [ 'b' for _ in range(10)] + ['c' for _ in range(10)]), 100) From d4741ab77b3aea03d40626c2b6ac1ede67f20a65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 27 Nov 2019 00:01:19 +0100 Subject: [PATCH 069/624] Basic one way ANOVA structure. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 45 ++++++++++++++++++-- skfda/inference/anova/anova_simulation.py | 52 +---------------------- 2 files changed, 43 insertions(+), 54 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index bd834df97..4e27978cf 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,20 +1,53 @@ import numpy as np from skfda.misc.metrics import lp_distance from skfda.representation import FDataGrid -import matplotlib.pyplot as plt +from skfda.datasets import make_gaussian_process def vn_statistic(fd_means, sizes): # Calculating weighted sum of L2 distances between means - distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected + distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected # Calculating square of the distances and summing by groups distances_group = np.sum(np.multiply(distances_m, distances_m), axis=1) # Weighted sum return sum(distances_group * sizes) -def func_oneway(fdata, groups, n_sim): +def anova_bootstrap(fd_grouped, n_sim): + if len(fd_grouped) < 1: + return + + m = fd_grouped[0].ncol + k = len(fd_grouped) + start, stop = fd_grouped[0].domain_range[0] + + # Estimating covariances + k_est = [np.squeeze(fd.cov().data_matrix[0]) for fd in fd_grouped] + + # Simulation + simulation = np.empty((0, k, m)) + for l in range(n_sim): + sim_l = np.empty((0, m)) + for i, fd in enumerate(fd_grouped): + process = make_gaussian_process(n_samples=1, n_features=m, start=start, + stop=stop, cov=k_est[i]) + sim_l = np.append(sim_l, [np.squeeze(process.data_matrix)], axis=0) + simulation = np.append(simulation, [sim_l], axis=0) + return simulation + +def v_gorros(simulaciones, sizes): + distr = [] + for s in simulaciones: + v = 0 + for i in range(len(s)): + for j in range(i + 1, len(s)): + v += np.linalg.norm(s[i] - s[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 + distr.append(v) + return np.array(distr) + + +def func_oneway(fdata, groups, n_sim): # Obtaining the different group labels group_set = np.unique(groups) @@ -33,6 +66,10 @@ def func_oneway(fdata, groups, n_sim): means = means.concatenate(fd.mean()) # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) + vn = 0.01 # Temporal -# func_oneway(None, np.array(['a', 'b', 'a', 'a']), 1000) + simulation = anova_bootstrap(fd_groups, n_sim) + v = v_gorros(simulation, [10, 10, 10]) + p_value = len(np.where(v >= vn)[0]) / len(v) + return p_value, vn, v diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 849b666f8..d4aee8d53 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -1,12 +1,9 @@ from skfda import FDataGrid from skfda.datasets import make_gaussian_process -import matplotlib.pyplot as plt import numpy as np -import sklearn -from skfda.misc.metrics import lp_distance -from statsmodels.distributions.empirical_distribution import ECDF from skfda.inference.anova.anova_oneway import func_oneway + def generate_samples_independent(mean, sigma, n_samples): return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] @@ -32,49 +29,4 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) -# print(fd_total.data_matrix[0]) -# print(np.squeeze(np.take(fd_total.data_matrix, np.array([0, 3]), axis=0))) - -# Anova - - -def f_oneway(*args): - - if len(args) < 1: - return - # fd_total = args[0].concatenate(*args[1:]) - N = 2000 - alpha = 0.05 - - simulations = [np.squeeze(make_gaussian_process(N, len(p.sample_points[0]), cov=np.squeeze(p.cov().data_matrix[0])).data_matrix) for p in args] - - ecdf = np.array([]) - for l in range(N): - for i in range(len(simulations)): - for j in range(i + 1, len(simulations)): - ecdf = np.append(ecdf, np.linalg.norm(simulations[i][l] - (np.sqrt(1)) * simulations[j][l])) - - v_alpha = np.quantile(ecdf, 1 - alpha) - F = ECDF(ecdf) - print(v_alpha) - - -func_oneway(fd_total, np.array(['a' for _ in range(10)] + [ 'b' for _ in range(10)] + ['c' for _ in range(10)]), 100) - - - - - - - - - - - - - - - - - - +func_oneway(fd_total, np.array(['a' for _ in range(10)] + ['b' for _ in range(10)] + ['c' for _ in range(10)]), 2000) From 7db55504fbadee6f8d7cf50e961a03e8ea442b54 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 070/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From d257f6fabcc9b6399e1190175d59831593245bc0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 071/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": 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-1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 622130b5084b7b5c2488aedc5623713ebe0064e6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 072/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, 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-1.16847828e-02, 8.27650439e-03, -4.07520249e-03,\n", + " -6.95629737e-03, -8.21706210e-02, 1.73518348e-01,\n", + " -1.84427223e-01, 2.41338888e-01, -2.77715008e-01,\n", + " 2.68570100e-01, -2.80085226e-01, 3.11853865e-01,\n", + " -2.27113287e-01, 5.83895482e-02, 8.24289689e-02,\n", + " -2.17798167e-01, 2.99927824e-01, -2.31185365e-01,\n", + " 1.90290075e-02, 2.29696679e-01, -3.61920633e-01,\n", + " 2.40831472e-01, -9.15337522e-02, 1.10142033e-01,\n", + " -6.92704402e-02],\n", + " [-2.68762463e-03, -1.72901441e-02, 4.81603671e-02,\n", + " -4.51696594e-02, 2.18321361e-03, -3.77910377e-03,\n", + " 6.01433208e-03, -2.87812954e-03, 3.13700942e-03,\n", + " 2.62878591e-02, -3.19781435e-03, -5.63379740e-02,\n", + " 6.08448909e-02, -7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From 8b05075f7e784ac39e60f120819b5506f5322956 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 073/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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cnQI7uvnR5WITodNwe6t53t71NmnUJI/1znyQEVHQpq9NHP0m2cQRjpZNgy/vs9cgnPxfbkfjG1mDIb6VTX6aKEKKJgrlH5Xl8M6v7PTXg6+Ds//Z8EnrIiJsM0ur7jDoKruvbL/TVOUkjyVTYcGTMObu8PkirbFtIcz4jV3TYey94dP0FhEJ3c6211McOQzRzdyOSDWQJgrle4eLYdrl8P0XcNafYNgtjf+yi0uDnNH2BnaZzXd/BR/ebkdSjbk7uGZPPVFFW22Hf1ImXPRiaI5w8qbHePh2qv1s5IxyOxrVQDo8VvlWyS54fqydtO7cJ+C03/rnF3GzJLjwRThlip0e4rVL7QihULb+Y/veVZbDpdNscgw3HU+3o93WznQ7EnUcNFEo38lfC8+OhMItdpROTWe1v0REwOi/wTn3wYaP4YWxNlGFmtI98Pov4JULIToOrngb0ru5HZV/RMXajvl1s/Q6mhCiiUL5Rt48eG6U/TV89fvQ5azAvfbg6+CSabB3IzwzAvasCtxrN0Z1tR3d9ehg28F75h1ww5ehM4fTieoxDg4W2L4YFRI0UajGWz0DXpwI8elw3WzI7B/4GHJGwTUfgqmCZ0fDxjmBj+F4FKyDF86BmTdB6z7wX9/AGb+zv7jDXZeREBljk6MKCZooVOMseAqmXwkZfeGaj+0FdW7J6AvXzYHU9vDyhT9eixFMKsvhs7vh8VMhfzVMeASufg9adnU7ssBplgQdz7CJQtfTDgmaKNSJqa6G2X+CWf9thzxeOSM4ppdIbmtrFp3PtL/WZ//Jxuq26mpY9Y5NEHPvgV7nwZTFMPCK8Bn+ejy6n2P7svJXux2JagBNFOr4VVbAOzfA1w/AoF/YieqC6Wrp2ETbZ5F7jY3xjV/AkUPuxFJVCcteg8dOhtevAgxc/iac/zQkpLsTUzDoNhYQnXo8ROh1FOr4HC6B6VfA5s/hZ/8Lp90WnL+II6PsdNypHWH2H+2KcJe8CvEtA/P6leWw9BWbqAq3QKtecMFz0PPc8Ljeo7ESW9ur69e+B8N/73Y06hg0UaiGK90NL18Ae1bDxMdgwGVuR+SdCJx6o+2zeGsyPHMWXPaGf/sDKsrsBWVfPwSlO6HtIDtXU84YO5xX/aj7OJvEC/Psv5EKWvrJVQ2zdQE8NRz2bbYXgwV7kvDUcyJc9Z69IO+ZEbDla9+/xoF8+PweeKCPvVo8rZO9HuK6OdB9rCaJunQ/x97rGhVBT2sUyjtjYOFT8NH/QHIWXPsRtOnjdlTHL3swXPcJvHIRvHQuTHwU+l50/OeproL9m2HPSnu9xm7nvnirPd51tJ2ypP0pvo0/HLXoDK162n6KcJuvK8xoolD1qzgIM260E/vljIHznoDmqW5HdeLSOsK1H8O0K+Ct6+0cUWf8rv4+lrL9PyaEmvv8NXYRIQCJtM1Y2YMh92roPh7ScwL254SF7ufYWXIP7guOUXOqTpooVN32brSd1vlrbKf1sFvDo/mkeSpc/padnfXzv9uO5nPute3knglhzyrbx1AjriW06W2vAm/dy95adtMZUBur+zi7cNX6WTDgcrejUfXQRKGOtmamnSI8IsoO5QzkdByBEBVja0dpHeHzu2HZq4Bz4VdENKR3t5PX1SSE1r0hoVVwju4KdRn9IDkb1r6viSKIuZooRGQM8CAQCTxjjLmn1vHTgQeAvsAkY8wbgY+yCamqhE//Yod0Zg6w01yntHM7Kv8QgeG32/6W7YtsW3nr3rYpKTLa7eiaDhHb/LTkBdvUGRPvdkSqDq4lChGJBB4FRgLbgUUiMsMY43mp5lbgauC2wEfYxBwogDevsesEDLoaxvyjaTSrdD/nx9E3yh3dz4EFT9j5uXpOcDsaVQc3G52HABuNMZuNMRXAa8BEzwLGmC3GmOVAEMzBEMa2LYInT7dDYCc+CuMfbBpJQgWHdkNt35FOEhi03EwUbYFtHo+3O/uOm4hMFpHFIrK4oKDAJ8E1CcbAwqfh+bPtlczXfqztxCrwIqMg52xY/yFUHXE7GlWHMBjGAsaYp4wxucaY3PT0Jjx/zvGoKIO3b4APbrMT6E2e68704EqBXaPicDFs+crtSFQd3EwUO4Bsj8dZzj7lb/s325Xolk+D4f9jJ9ALx2U3VejodCZENdfmpyDlZqJYBHQVkY4iEgNMAma4GE/TsG4WPDkcirfbeY+G/z48ro9QoS0mzg7DXvtBcEwLr37CtW8IY0wlMAX4CFgDTDfGrBKRP4vIBAARGSwi24ELgSdFJETWuAxC1VUw5y/w6iRI6wC/nAtdR7gdlVI/6jHeXuS48zu3I1G1uHodhTHmA+CDWvvu9NhehG2SUo1RugfevBa2fGk7q8fep6OaVPDpOspOi7J2JmQNcjsa5UHbHMJZdRWsfBOeGAbbF9upwSc+qklCBae4NGg/FNZ/5HYkqhZNFOGosgK+fQkeGQxvXGMX67n+09CaGlw1Td3OtsujFua5HYnyoIkinFQchPmPw0P9YcYUiE2w03Dc8BW07ul2dEodW84Ye7/hY3fjUD+hkwKGg0NFsOhpmyTK9kH7U2HCQ9D5LJ3IToWWFp2hRRd78d2Q692ORjk0UYSyA/kw/zFY+AxUlNrOwGG/1UVzVGjLGWMXyyo/YGvFynWaKEJR0Vb45mH49kWoLIde59oEkdHX7ciUaryc0TDvEdj8ub1iW7lOE0UoKVgPX90PK6YDAv0uhlNvgZZd3I5MKd9pdwrEJtnmJ00UQUETRSjY+R18+W+7oFBUMxh8PQydYtewVircREbbq7Q3fGyv0taZA1ynicIfFj4NW+dBZKz90EfFQmTMj/f1bdfed7jEztO/aQ7EJsNpt9pF6ONbuv0XKuVfOWNg1duwaym0Heh2NE2eJgpfW/u+nZE1qS1IhO1DqCq30ydXloOpOr7zxbWEs/4Eg6+FZsn+iVmpYNNlJCD24jtNFK7TROFr3/3HJombltt59murrjo6eVRV/Hj/w3a5Xca5/VA7YZpSTUl8C8geYvspzvyD29E0eZoofOlwiV3OcfC1dScJgIhI54tfv/yV8ipnNMz5M5TsgqQMt6Np0rSXyJfWf2hrAj3PdTsSpUKfXqUdNDRR+NKqdyAxE7IGux2JUqGvVU9IztZJAoOAJgpfKS+FjZ9Azwk6nE8pXxCxzU+bP4Mjh92OpknTbzRfWf+RNjsp5Ws5Y+BIma6l7TJNFL6y6m1IzIDsk9yORKnw0eE0iI6z/X/KNZoofOFwCWyYbWsT2uyklO9EN4NOw22N3Ri3o2my9FvNF2pGO/U6z+1IlAo/OaOheCvkr3E7kiZLE4UvrHrbXmSno52U8r2uo+29Nj+5RhNFYx0udkY7abOTUn6RlAEZ/XWYrIv0m62x1s2y025os5NS/pMzBrYvhIP73I6kSdJEcSLKS2HFGzDtcph5MyS3g6xct6NSKnzljAZTDRtnux1Jk6RzPR2PgvUw72FYPh0qD0NCGxh4BQy+TtemVsqfMvpDQmvbT9FvktvRNDmaKBpi63z4+kFY94FdOKjfJdD3YnvNhPZLKOV/ERF2TfjV79pZlyOj3Y6oSWnQt5yIvNSQfcdLRMaIyDoR2Sgit9dxPFZEpjnHF4hIh8a+5nHZ8S08OwqeG22TxRm/h1tWwfgHoP0pmiSUCqScMVBeYhcFUwHV0BpFL88HIhIJDGrMCzvneBQYCWwHFonIDGPMao9i1wKFxpguIjIJ+AdwcWNet0EqyuCzv8H8xyC+FYy9F/pfputCKOWmTsPt6o/rP4KOp7sdTZPi9SexiPxBREqBviJS4txKgXzg3Ua+9hBgozFmszGmAngNmFirzERgqrP9BnCWiJ87AzbPhcdPgXmPwMArYcpCGHK9Jgml3BabYKf00OspAs5rojDG3G2MSQT+ZYxJcm6JxpgWxpjGLjvVFtjm8Xi7s6/OMsaYSqAYaFH7RCIyWUQWi8jigoKCE4vmUBG8OwVenGCXML36fRj/oC4/qlQwyRkD+zbC3o1uR9KkNKiR3RjzBxFpKyJDReT0mpu/g2soY8xTxphcY0xuenr6iZ2kqsJeE3HqzfBf30CHYb4NUinVeDmj7P0GvfgukBrURyEi9wCTgNVAlbPbAF804rV3ANkej7OcfXWV2S4iUUAy4J8rbhJawU1LITbRL6dXSvlAagdI72Gbn075tdvRNBkN7cw+D+hmjCn34WsvArqKSEdsQpgEXFqrzAzgKmAecAHwqTF+nEJSk4RSwS9ntO1DPFysTcMB0tDxnZsBnw5cdvocpgAfAWuA6caYVSLyZxGZ4BR7FmghIhuB3wJHDaFVSjUxOWOguhI2fep2JE2G1xqFiDyMbWIqA5aKyBzgh1qFMebGxry4MeYD4INa++702D4MXNiY11BKhZmswdA81Q6T1TnWAuJYTU+Lnfsl2GYgpZRyV2QUdBkJGz6G6iqIiHQ7orDnNVEYY6Z6O66UUq7IGQ0rpsOOJZA9xO1owl5DRz2twDZBeSrG1jj+aozRuX+VUoHT5SyQSDukXROF3zW0M3sW8D5wmXObiU0Su4EX/BKZUkrVp3kqtDtFFzMKkIYOjx1hjBno8XiFiHxrjBkoIpf7IzCllPIqZzTM/iMUbYWUdm5HE9YaWqOIFJEf6nciMhio6UGq9HlUSil1LN3Otvdaq/C7hiaK64BnReR7EdmCvb7hehGJB+72V3BKKVWvFl0grZMmigBoUNOTMWYR0EdEkp3HxR6Hp/sjsECrqKzm5mnfkZ0WRzvn1j4tnoyUZkRH6roTSgUdEXvx3aJnoeIgxMS7HVHYOtYFd5cbY/4jIr+ttR8AY8y//RhbQBWWVbB2VymzV+/hSNWPA7wiI4T2LeLon5XCgHYpDGiXSrc2iZo8lAoGOaPtujGb50L3sW5HE7aOVaOoSdFhPwlS66RmfHrbcKqqDXtKDpO3r4xt+8vYur+MtbtL+WJDAW99Z+csbBYdQZ+2yQxol0r/7BSGdm5BSlyMy3+BUk1Qu6EQk2gnCdRE4TfHuuDuSef+/wITjvsiI4TMlOZkpjTnlM4/Ln1hjGF74SGWbiviu61FfLetkBe+3kJFVTV9s5KZMUWnJVcq4KJioMvPbD+FMbY5SvlcQy+4ywEeB1obY3qLSF9ggjHmr36NLoiICNlpcWSnxTG+XyYA5ZVV3PvROp756nuKyiq0VqGUG3LGwOp3YdcyyOzvdjRhqaEN7U8DfwCOABhjlmOnBW/SYqMiGdWrDcbAgu/3ux2OUk1Tl5GA6OgnP2pooogzxiystU+vnwD6ZiUTGxXB/M06i4lSrkhIh6xcXUvbjxqaKPaKSGec+Z5E5AJgl9+iCiGxUZEMap/Kgs1ao1DKNTmjYee3ULrH7UjCUkMTxa+BJ4HuIrIDuBm4wW9RhZiTO7Vgze4SisuOuB2KUk1Tzhh7v+Fjd+MIUw1NFDuA54G/Aa8Bs7FLlCrgpI5pGAMLt2itQilXtO4NSW21+clPGpoo3gXGYzuzdwIHgIP+CirU9MtO0X4KpdwkYpufNn0GleXHLq+OS0Nnj80yxozxayQhrFl0JAPapbDge00USrkmZwwsfg62fAldRrgdTVhpaI3iGxHp49dIQtzJnVqwamcJxYe0n0IpV3Q8HaKa6zBZP/CaKERkhYgsB4YB34rIOhFZ7rFfOU7q2AJjYJFeT6GUO6KbQ6czbD+Fqb0gp2qMYzU9jQtIFGFgQLsUYiIjWLhlPyN6tnY7HKWappzRNlEUrIVWPdyOJmwca66nvEAFEuqaRUfSPzuFBdqhrZR7uo629+s/1EThQzpXtg+d1CmNlTtLOFCuF60r5YrkttCmL6yZ6XYkYcWVRCEiaSIyW0Q2OPep9ZT7UESKROS9QMd4IoZ0TKOq2rAkr9DtUJRqunqfDzuWwP7NbkcSNtyqUdwOzDHGdAXmOI/r8i/gioBF1UiD2qcSFSHa/KSUm/pcYO9XvOFuHGHErUQxEZjqbE8Fzq2rkDFmDlAaqKAaKy4mij5ZyTqTrFJuSs6C9qfC8uk6+slH3EoUrY0xNZMK7gYaNUxIRCaLyGIRWVxQUND46BrhpI4tWL69iKKyClfjUKpJ63MB7NsAu3UUvy/4LdD+PjMAABnlSURBVFGIyCcisrKO20TPcsYYgzMr7YkyxjxljMk1xuSmp6c3Ku7GOndAJpXVhhtfW8rn6/I5UlXtajxKNUk9z4WIaFurUI3W0Ck8jpsxpt5r6EVkj4hkGGN2iUgGkO+vOAKte5skbhvVjSfmbuLq9QWkxcdwdu82TOiXyeAOaURE6FKNSvldXJqdxmPlmzDyzxAR6XZEIc1vieIYZmBnn73HuX/XpTj84tdnduG60zoyd10BM5fv4q1vd/Dygq20SWrGuL4ZjO+XSd+sZETX91XKf/pcAOtnQd430PE0t6MJaWJc6OwRkRbAdKAdkAdcZIzZLyK5wA3GmOuccl8C3YEEYB9wrTHG60Quubm5ZvHixX6N/3iVVVTyyZp8Zizdydz1+RypMrRvEcf4vplM6J9JTutEt0NUKvxUlMG/ukCf82HCw25HE/REZIkxJrfOY24kCn8KxkThqbjsCB+t2s3M5Tv5euNeqg10a53IhP6ZjOubQfsW8W6HqFT4eGuyvUr7tg0QFet2NEFNE0WQKigtZ9bKXcxYupPFzkV6/bJTGN83g3F9M2mT3MzlCJUKcRtmw8sXwKRXoPs5bkcT1DRRhIAdRYd4b9lOZi7fycodJYjAkA5pjO+Xydg+GaTFx7gdolKhp+oI3NcdOgyDi6Yeu3wTpokixGwqOMB7y3YxY9kONhUcJCpCGNa1JeP7ZjKqV2sSm0W7HaJSoeP92+C7l2zzU7Mkt6MJWpooQpQxhjW7SpmxbCczl+1kR9EhYqIiOLNbOmP7ZPCz7q00aSh1LNsWwrMj4dwnoP8lbkcTtDRRhAFjDN9uLWLmsp3MWrmLPSXlxERGcFrXlozp3YaRPVuTEqfNU0odxRh4sC+06AJXvO12NEHLW6Jw6zoKdZxEhEHtUxnUPpU7x/Xku22FzFqxm1krdzNnbT5REcIpnVswtk8Go3q2pkWCjvBQCgAR6HMhfHU/HMiHhFZuRxRytEYR4owxrNhRzAcrdjNr5S7y9pURIXbOqbF92jC6VxtaJenoKdXE5a+Fx06Cs/8JJ/3S7WiCkjY9NRE1fRqzVu5i1srdbMw/gAjktk9laOeW9MpMonfbZDKSm+lV4Y5DFVXsL6sgU9+T8Pf4MIhuBtd94nYkQUmbnpoIEaFnZhI9M5O4dVQ3NuwpZdbK3Xy4cjcPfbrhhxmXU+Oi6ZWZTK/MJHq1tfcdW8Q3uXmoFmzex02vLWV3yWHS4mPom5VM36wU+jn36YnafBdW+lwAn/zJLmiU1sntaEKK1iiaiLKKStbsKmXVzmJW7Shh1a5i1u8+QIUzu21cTCQ9MpJsrSMzmZ6ZSeS0TiQmKvxWy62qNjz62UYe+GQ97VvEc8XJ7Vm7u4Tl24tZv6eUaud/ibYpzemblUy/7BT6ZiXTp22yjjILZcXb4f5ecOb/whn/7XY0QUebnlSdKiqr2Zh/gJU7i1m9s4RVzv3BiioAoiOFrq0Sf2iy6pWZRI+MJOJjQ7ciml9ymJunLeWbTfs4t38mfz2vDwkef09ZRSUrd5SwfHsRS7cVsXx7MVv3lwG2T7RTy3j6ZafQL8smjx4ZSTSL1plJQ8bzY+FgAfx6of0HVT/QRKEarLrakLe/jFU7i1m548fkse+gXYhJBDq2iP+hycrekkPiyvEv1hfw2+lLOVBeyZ8n9ubCQVkN6pcoPFjB8h3FLNtW5CSQYvYeKAdsMu3eJumHGkfX1gl0Tk/QocrBavHz8N7N8MsvIKOf29EEFU0UqlGMMewpKf9J8li1s4QdRYd+KNM6KZbubZLonpFID+e+U8uEoGi6qqyq5t+z1/PY55vIaZ3Ao5cOpGsjZuw1xrCr+DDLtxexbLtNICu2F1NaXvlDmbT4GDqnx9M53SaOzq3sdlZqHJFNrC8oqJTth3tz4OQbYNRf3Y4mqGiiUH5RVFbhNFmVsGZ3CWt3lbIx/8d+j+hIoXN6Aj0ykujeJpHuGUn0aJNIemJswEYY7Sg6xI2vfseSvEIuGZLNneN60TzG901F1dWGbYVlbCo4wOaCg2wqOMCmfHtfUxsDiI2KoF92CoM7pJLbIY2B7VJJbq79HgH1yiTYtQxuWQUR7v+QCRaaKFTAHKmq5vu9B1mzq4S1u0tZ69zvKj78Q5ms1OZcOCibiwZnkZHc3G+xzF69h9teX0ZlVTV//3kfJvZv67fX8qaorIJNTvJYt7uUxXmFrNpRTGW1QcSuiliTOIZ0SNNZg/1t5ZvwxjVw6euQM8rtaIKGJgrluqKyCtbuLmXNrhI+XZvPlxv2EiEwvFsrJg3O5mfdWxEV6Ztfd+WVVdwzay3Pf72F3m2TeOSSgXRoGVzrfJRVVLJ0axGLthSyOG8/S/IKKXMGEbRNaU7X1gm0T4ujfYt4OrSMo11aPNlpzYmN0o7zRqusgIcHQlJbuOZD7dR2aKJQQWfb/jKmLdrG9MXbyC8tp1ViLBfmZnFxbjvatYg74fPm7TvIlFe+Y8WOYq4e2oE/jO0eEl+ulVXVrNlVyqIt+1mytZDvCw6ydX8ZBzz6PUQgM7k57VvYBJKd1pxWic1omRBDy4RYWiXGkhYf47OEG9YWPg0f3AZXv2+nIFeaKFTwqqyq5rN1Bby2cCufrcun2sCwLi2ZNCSbkT1bH9eX/IxlO/mft1YQIfCvC/sxulcbP0buf8YY9h2sIG9fGXn7Dv54v7+MvH1l7Pfo+6ghAqlxMbRMiCE9MZZ2aXF0Tk+ga+tEurZK0Kvyaxw5BA/0hda94Mp33I4mKGiiUCFhV/EhXl+8nWmLtrGj6BBp8TGcP7Atk4a0o3N6Qr3PO3ykiv+buZpXF25lYLsUHrpkAFmpJ14rCRVlFZXsLa2g4MBhCkor2HugnILScvYesLf80vKjEkp8TCRdWiXQpVUiXVsn0K11IrkdUpvmhYRfPwiz74TrPoWsQW5H4zpNFCqkVFUbvtq4l9cWbmX26j1UVhuGdEzjkiHZnN074ycXuG3YU8qUV75j3Z5SbjijM7eOyiFam15+Yt+BcjbmH2BD/gE2OrcN+aXsKbHXgkRGCP2ykhnWpSVDu7RkQLuUkGiua7TyUnigD7Q7BS551e1oXKeJQoWsgtJy3liynWmLtrJlXxnJzaM5b0BbLh6czfLtRdw1YzVxMZHcd1E/hnfT6aOPR/GhI6zaWcy8Tfv4auNelm0rotpA8+hIBndMY1iXFpzapSU92iSF7zxgn/8DPv873PA1tOntdjSu0kShQl51tWH+5n28umgbH63c/cO1Gid3SuPBSQNorVOpN1rJ4SMs2Lyfrzfu5euNe9mQfwCAFvExjO7dhgn9MhnSIS28ksahQri/D3QdCRc+73Y0rtJEocLK/oMVvL9iF0nNohjXN1OvdPaTPSWH+XrjXj5bV8Anq/dw6EgVGcnNGNc3g4n929IrMyk8OsY/uQu+egCmLIaWXdyOxjWaKJRSjVJWUckna/KZsXQHc9cXcKTK0KllPBP6ZzKhXyadvAw2CHoHCmxfRe/z4dxH3Y7GNZoolFI+U1RWwayVu5mxdCfzv9+HMTC4Qyo3nZXDqV1ahGYtY9bvYdEzcON3kNLO7Whc4S1RuDI8RETSRGS2iGxw7lPrKNNfROaJyCoRWS4iF7sRq1Lqp1LiYrhkSDtenXwy824/izvG9mB74SEuf3YBFz85n3mb9rkd4vEbeiMgdsisOopb4whvB+YYY7oCc5zHtZUBVxpjegFjgAdEJCWAMSqljqFNcjOuP70Tn902nP+b0Iu8/Qe55On5THpqHgs2h1DCSG4L/S+Fb1+C0t1uRxN03EoUE4GpzvZU4NzaBYwx640xG5ztnUA+kB6wCJVSDdYsOpKrhnZg7n+fyZ3jerKp4CAXPzWfy56Zz5K8/W6H1zDDbobqIzDvEbcjCTqu9FGISJExJsXZFqCw5nE95YdgE0ovY0x1HccnA5MB2rVrNygvL88/gSulGuRQRRUvL8jjibmb2HuggtNz0rllRFcGtDuqlTm4vHk9rH0fblkJcWluRxNQrnRmi8gnQF2T7dwBTPVMDCJSaIyp8xMkIhnA58BVxpj5x3pd7cxWKniUVVTy0rw8nvxiM/sPVnBmt3RuGZlD36wgbUXOXwOPnQyn/w5+dofb0QRU0I16EpF1wHBjzK6aRGCM6VZHuSRskvi7MeaNhpxbE4VSwedgeSVT523hqS82U1R2hBE9WnHziBx6t012O7SjTbscvv8Cbl4JzZLcjiZggm7UEzADuMrZvgp4t3YBEYkB3gZebGiSUEoFp/jYKH41vAtf/u5Mbh2Zw8Lv9zPu4a+44aUlrNtd6nZ4P3XarXC42A6XVYB7NYoWwHSgHZAHXGSM2S8iucANxpjrRORy4HlglcdTrzbGLPV2bq1RKBX8ig8d4dmvvue5r77nYEUl4/pmctNZXenSKkgu3PvP+bBzKdy8AmLCfyZiCMKmJ3/SRKFU6Cgqq+CpLzbzwjdbOHykign9MrlyaAcGZKe4e+He1vnw3GgY/gcYXtfo/fCjiUIpFdT2HijnybmbeHnBVsoqqujeJpHLTmrHxAFtSXJrrYw3roU1M+CGryD9qC7UsKOJQikVEg6UV/Lu0h28smArq3aW0Dw6kgn9Mrn0pHb0zUoObC3jQD48Mhha9bRLpkaE9zonmiiUUiHFGMPy7cW8smArM5bt5NCRKnplJnHpSe0Y06sNLRJiAxPId/+Bd38N4x6A3F8E5jVdoolCKRWySg4f4d3vdvDygq2sdUZIdWwZz6D2qeS2TyW3Qyqd0xP8U9swBqaOh13LYcpCSAztddi90UShlAp5xhhW7Cjm6437WJK3nyV5hRSWHQEgJS6aQe1SGdQhldz2aXRsGU9cTCTNoyMbv9DSvk3w2CnQbQxc9KIP/pLg5C1RRAU6GKWUOhEiQt+sFOeq7s4YY9hUcJBv8wpZnLefxXmFzFmbf9Tz4mIinVvUD9vxsVFH7YuLiSI+NpLmMVHEexxrHpNKRr/fkPXtvaybO43C7BFUGwP2P+ymce5tQvvh3lDHfsCjfLX56XNxyojYZWnjYqJoHhNB8+gomjvJr+Y+Jiow/SZao1BKhY39BytYklfI7uJDlFVUcbCiirLySsqOOPcVVc7+Sg4592Xldt+hI1X1njeKSt6LuYMkOcio8n9ygOC4tiIqQmgeE0mCk/j6ZqVw/8X9T+hcWqNQSjUJafExjOzZ+oSeW1VtOHSkijInedQkk5oEcnjvA3T76AJm9/uCLUPuQgQEW9P5cRtAiBBnv7NPEGq6UDwfR9Tx3JrH1QYOH6lyYqriUEUVh4/8mNQOVVT+sH2wvJKDFVW0SvRPJ78mCqWUAiIjhITYKBJioyCxjgJdR0DhZDIWPkXGsCshe3DAY3RLeA8MVkopXzrrj5CUCTNvhKojbkcTMJoolFKqoWIT4Zz7IH91k1o2VROFUkodj25nQ8+JMPefduhsE6CJQimljtfZ/4SoZvDOr6Cy3O1o/E4ThVJKHa/ENjDu37BtPrx5HVTXP7Q2HGiiUEqpE9HnAhh9t51h9r1baq6kC0s6PFYppU7UKb+Csr3w5X3QPAVG/B+4uY6Gn2iiUEqpxvjZH+FQoR0FFZsEp9/mdkQ+p4lCKaUaQwTG3gflB+DTv9hkcdJkt6PyKU0USinVWBERcO5jUHEQZv03xCZA/0vdjspntDNbKaV8ITIaLngOOp5hFzta/a7bEfmMJgqllPKV6GYw6RVom2vX3N74idsR+YQmCqWU8qXYBLjsdUjvDq9dDnnz3I6o0TRRKKWUrzVPgSvehuQs+M/PYd5jIX1RniYKpZTyh4R0uPo96DAMPvoDPDMCdq90O6oT4kqiEJE0EZktIhuc+9Q6yrQXkW9FZKmIrBKRG9yIVSmlTlhiG7h0Opz/LBRthafOgDl/gSOH3Y7suLhVo7gdmGOM6QrMcR7Xtgs4xRjTHzgJuF1EMgMYo1JKNZ6Ine5jyiLocxF8eS88cSps+crtyBrMrUQxEZjqbE8Fzq1dwBhTYYypmZYxFm0mU0qFsrg0OO9x23dRdQReOAdm3gSHityO7Jjc+vJtbYzZ5WzvBupc5FZEskVkObAN+IcxZmc95SaLyGIRWVxQUOCfiJVSyhc6/wx+NQ+G/ga+fREePclecxHEkwqK8VNwIvIJ0KaOQ3cAU40xKR5lC40xR/VTeBzPBN4Bxhtj9nh73dzcXLN48eITjFoppQJo53cw4zewewW07m2TR+/z7cV7ASYiS4wxuXUd81uNwhgzwhjTu47bu8AeEclwgssA8o9xrp3ASuA0f8WrlFIBlzkArv8MJjrDZ9/+JTzYD755GA6XuB3dD9xqepoBXOVsXwUcda27iGSJSHNnOxUYBqwLWIRKKRUIkdEw4DLbHHXZG5DWCT7+X7i/F3z8Ryips8U9oNxKFPcAI0VkAzDCeYyI5IrIM06ZHsACEVkGzAXuNcascCVapZTyNxHoOtJeezH5c7s97xF4oC+8/V+wZ7V7ofmrj8It2kehlAobhXkw/zHb6X2kDDqdCQMuh+7nQHRzn76Utz4KTRRKKRXsyvbD4udgyVQo3gqxydDnfOh/GbQd5JNV9TRRKKVUOKiuhi1fwtJX7JDaykPQsptd+6LvxZCUccKn1kShlFLh5nAJrH7HJo2t80AioOe5cOHzJ3Q6b4lCV7hTSqlQ1CwJBl5pb/s22YSBf374a6JQSqlQ16IznPVHv51e509SSinllSYKpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXoXdFB4iUgDkuR1HA7UE9rodxHEItXhBYw6UUIs51OIF/8fc3hiTXteBsEsUoUREFtc3t0owCrV4QWMOlFCLOdTiBXdj1qYnpZRSXmmiUEop5ZUmCnc95XYAxynU4gWNOVBCLeZQixdcjFn7KJRSSnmlNQqllFJeaaLwIxHJFpHPRGS1iKwSkZvqKDNcRIpFZKlzu9ONWGvFtEVEVjjxHLVcoFgPichGEVkuIgPdiNMjnm4e799SESkRkZtrlXH9fRaR50QkX0RWeuxLE5HZIrLBuU+t57lXOWU2iMhVLsb7LxFZ6/y7vy0iKfU81+tnKMAx3yUiOzz+7cfW89wxIrLO+Vzf7nLM0zzi3SIiS+t5bmDeZ2OM3vx0AzKAgc52IrAe6FmrzHDgPbdjrRXTFqCll+NjgVmAACcDC9yO2SO2SGA3dkx4UL3PwOnAQGClx75/Arc727cD/6jjeWnAZuc+1dlOdSneUUCUs/2PuuJtyGcowDHfBdzWgM/NJqATEAMsq/3/aiBjrnX8PuBON99nrVH4kTFmlzHmW2e7FFgDtHU3Kp+YCLxorPlAioic+KruvnUWsMkYE3QXXRpjvgD219o9EZjqbE8Fzq3jqaOB2caY/caYQmA2MMZvgTrqitcY87ExptJ5OB/I8nccx6Oe97ghhgAbjTGbjTEVwGvYfxu/8xaziAhwEfBqIGKpjyaKABGRDsAAYEEdh08RkWUiMktEegU0sLoZ4GMRWSIik+s43hbY5vF4O8GTACdR//9UwfY+A7Q2xuxytncDresoE6zv9zXYmmVdjvUZCrQpTnPZc/U07wXre3wasMcYs6Ge4wF5nzVRBICIJABvAjcbY0pqHf4W20zSD3gYeCfQ8dVhmDFmIHA28GsROd3tgBpCRGKACcDrdRwOxvf5J4xtSwiJYYgicgdQCbxcT5Fg+gw9DnQG+gO7sE05oeISvNcmAvI+a6LwMxGJxiaJl40xb9U+bowpMcYccLY/AKJFpGWAw6wd0w7nPh94G1st97QDyPZ4nOXsc9vZwLfGmD21DwTj++zYU9Ns59zn11EmqN5vEbkaGAdc5iS3ozTgMxQwxpg9xpgqY0w18HQ9sQTVewwgIlHAz4Fp9ZUJ1PusicKPnPbFZ4E1xph/11OmjVMOERmC/TfZF7goj4onXkQSa7axnZcraxWbAVzpjH46GSj2aD5xU72/voLtffYwA6gZxXQV8G4dZT4CRolIqtNsMsrZF3AiMgb4HTDBGFNWT5mGfIYCplb/2Xn1xLII6CoiHZ2a6STsv42bRgBrjTHb6zoY0Pc5EL36TfUGDMM2JSwHljq3scANwA1OmSnAKuwoi/nAUJdj7uTEssyJ6w5nv2fMAjyKHSWyAsgNgvc6HvvFn+yxL6jeZ2wS2wUcwbaBXwu0AOYAG4BPgDSnbC7wjMdzrwE2OrdfuBjvRmxbfs3n+QmnbCbwgbfPkIsxv+R8Tpdjv/wzasfsPB6LHZm4ye2Ynf0v1Hx+Pcq68j7rldlKKaW80qYnpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXmmiUMqHROQdZ4K2VTWTtInItSKyXkQWisjTIvKIsz9dRN4UkUXO7VR3o1eqbnrBnVI+JCJpxpj9ItIcOy3EaOBr7HoDpcCnwDJjzBQReQV4zBjzlYi0Az4yxvRwLXil6hHldgBKhZkbReQ8ZzsbuAKYa4zZDyAirwM5zvERQE9nCiqAJBFJMM7khUoFC00USvmIiAzHfvmfYowpE5HPgbVAfbWECOBkY8zhwESo1InRPgqlfCcZKHSSRHfsMrHxwBnOzK9RwPke5T8GflPzQET6BzRapRpIE4VSvvMhECUia4B7sLPU7gD+DizE9lVsAYqd8jcCuc7Ka6uxs90qFXS0M1spP6vpd3BqFG8Dzxlj3nY7LqUaSmsUSvnfXSKyFLuozPcE4TKsSnmjNQqllFJeaY1CKaWUV5oolFJKeaWJQimllFeaKJRSSnmliUIppZRXmiiUUkp59f8rJFgbFDPVyAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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Ir0LdyVOhTFlzKKgiJaeAW6ugoaHpFuFoilSojWaznQQpLCwMrVEiKJAgKgIqILEf9r30ujwn5yII7oFA4ClRrpxicPAfyOfvoeCu4BP734Iyt/DZPz2PC3uTcP/n4Pb3o2p5Ble9kP/T9mpG3TTdc5L3phI8OxGnXdORkw7+6BxlqVBSUZirMnO6QG66DAoG0iY7+mZpLt+BLAvq9Z1MnPLRwhlUcjPm9X+EZcRJCI1mwMenVitT9uZZ9KuYVoR4JENYRtGlhqmFwBAIo4YsjLOUHWLSzDKSrjLTZFA3dJI1jfZCjLZqnIiKEtd70FqiWNEkMREn7kWxpPWw4+EKDxcPS5kY6GfmKxSObpO351gsTdG0YoAeLn7Kz8fjNmISQnwOuAGYV0ptXp53PvApIAx4wBuVUntEo5/NjwPPA6rAa5VS+x5vJ4JGTIHAby/PKzEy8i9MTH4BSZjvnLqB/SOX8ufn9/H8gWbE+BD+8X34dUFN76JECzFPx/gNtp9USqGcErI8hyzNoapZZG0JZRdQ9SVUvYjy6o1OwR5rO0JHmmE8M0QtHKcUSZCNpxlPtzLUsRqvrZ8mWcJwjjId3oNlxthR3cgzKxeQcRIoAyKXtdHy3LPriuBXaqEqhLgcKANffEhwvwX4qFLqh0KI5wFvV0pdufz6zTSC+8XAx5VSj3tJCoJ7IPDbRynF3PjNTOz/H/R8Ejt/IbVsPwNYWA8LKz5oS0xEwoxEUyRTIda1JehoiqJFDbRQ44GksHRy81VO7ptn9EgOz5O09idZv7OdHjFI9abvUy+ugMQGhNGopy55S8zrRWyxRDY/SLE0j2mF6Vu7lbbMJhaLUUZzVfKVeYziOPHcGB35CTqLM0Td2pk9lELgmCE8y8IxTVzDxNU1PE3g6AZ2KEzVNKgjkUJDKYHluUQdh4hjE3FsEvUaMdsm7D3YKlUCuWic0WQ7+zvXcU//JmbaTELGUXq1Ya6trqatfy2v/cPXntU5+JVaqCqlfi6EGPjF2TQeG0BjvPAHarBupHERUMBuIURaCNGplJo5qz0PBAJPG0op/MU69liR6tAMldPjGMUMnfw5ADkkkZYImbUZzPK9aKe+BGqKT/dezedXvZKX9/Xw2u4WOkIPb7Tj+5LhfVkO/myCuZEiZlhn/c4O1q9OY45MU715P3mRAf3FyNACC5WTnAyXKBmL2AvjuLUqRiRKePMzqCRvZHBecvvYEB17f8x52dNszY0RW+7F0RMaC/EIY21R7FAT1WicUiJONZkglMogEdRtB8cK4VlhpKajSR9D+uhPsKsWzfeJl8skl5ZILy2RyS+xOT/BjtkhXrf/ZmqhEGNtXRztXM2BzlYWmOe1T+mZajjbOve3AD8WQvxfQAMeGCeqG3ho0ubk8rxfCu5CiNcDrwfo6zu7TnMCgcC5Jasu9dNL1Afz2Kfy+IXlDA+jip2e4nhvns9PtBLqSvL3L7+AVcU91L7/50TyQ/yo+TI+su7/csO6bezqbiFh6A/bdr3icvTOKQ7fPkVlySbdEuZZV/fQhsI5vohzKIvtu/jZcdzqrdyfkIzFfGJ+GXs6z1KklfLqa5mN9TE7scCmPUe4cO52nrU4QnQ5mFdbWin2dTMYDzPf2koh04wyfjnsCaAufWpmiFK6naoVAqET8zTCFaDuUNezREN1Vvlp+u0OdDRmrAVmEwW8jE5LooWmaJpEKEGIEFJKPM+j6nkslIo4Q6exTg+Rmp5mYG6K9RMjvEgI7t9y/jk5d2cb3P8CeKtS6ptCiJcBnwWe/WQ2oJT6NPBpaFTLnOV+BAKBp5ibrVI7skD9WA5nsgQKRFhH79dYWnkL+cjt2E0DfOCe6xmbTvLW69by+m1xSj/8SzjxbWYi3Xz4/A+zZfsL+XZX85lWow/IzVQ4dNskJ++ZwXMla1cmWbcmhT5ZQu2do65c3OkDeFP78K0T3Nm3lkI4hFsoMCG7yHY8k+GOLprnJrj8vr28YPZLdBbmAagkEsz0dzHX1k62rZV6JILwHIRjo5TCcHUifgfUMuC4uPZJjvUa7N1yCQtN7RieZMOUy6Zxh9RCgfHMQcabjrAxkuYVS1cyUOqiKmyOagtMVSzE4haSy7euElgAihGDZEuYTEeMlo4o6c4ozRfESf9RFE2AV8oycs/PGP3+j6gfGqS/N31OzuPZBvfXAP/P8uuvA/+x/HoK6H3Iej3L8wKBwNOUUgp3pkLtyAK1I4t481UAzJ44iav6MFeFmXQ/w8TUFzDNZu5dfA3/9v1eNnam+N6bttA69g2cf/17ol6df1n5Z4Se+Vb+qa+b6ENSGZVSTBzPcfCnE4wfzREzNS5ekaDV8VG5GhRqKHuc6t6bkAuHMQdc7lqzkRl3A8NeC8Opdcyl29hSGuHa43eydeIEiUoZKQQLLS0cPH8rlQ0bqVlRajMT+LUc2uwoSdmMbqxBN1djRROk22JEEw4Ti7u5JWFwYONzqYWjrNY0LnEMDuydZtg5zlzLPhI9p7gufwlvW3g1aTeO16qRvnwlXVvbWGvp+J7EqXnUKy7VgkN5yaayZFPO1Sks1JgdXuLUfQ92ZWBoDq3mMG36IO3mKXamj2FcXkReePk5Oa9nG9yngSuA24GrgFPL878H/KUQ4is0HqgWgvr2QODpySvYVPfPU9033wjoAkIrUsQvXkl4cwtGKsTCwm0cPvkubHsGLfZi3nPH5Ywv6fyvq1bxyvUO1W+/iJbsPu5Jb+PwFe/jNVsuIfmQO3XpS07vnWffLeMsTpbpS5pctyJBKF+H+Sp6u4ln30fpJ/+FEFVCa2zuXL2OXWoj4+EBZEuCtbVp/nTiDlaNDZMqFpFCkG/vZuriq3C27qRQ8lk4cT/Osf2Ai6ancJIdFHuTtHa0093aiRUCpz7D+Imj3OZ3cODSZ+IaJmuKi6wbGcSan2QhNkoqNU6PDHH50oVsrT4HlOBEaI75zBjlkIK9R9D2aWiahq4JdE0nbBpELZOoaRLxS4TtcToZYm3oGKH2Cq6XoioGyBvbWLBXcKS4loPVxoUv2WyyLdbN5nNwfp9ItsyXgSuBFmAOeA9wkkbKowHUaaRC7l1OhfxX4DoaqZB/opR63DSYIFsmEPj1kI5P7cgC1X3z2ENLoMDqTxLd3kZkUzN6vJGr7bpLDA7+v8zOfYdodA178q/jo3eEWdUa40MvWoc8+M9sPfRpKnqUWy54O1c+6w20hx/M83Ztn2N3TXPw1gnsfJ31zSH6TQ2t5qElLEIrNKp3f4Pyrd9DWBpijc33W3ewL3weKpagiwIbpodZOTxMx3JHXrWWXmoD57G4ZivDtk+9MIxeHkKrF1BCw0014aZakJHYw0Zi0pREVooc6+hnqmcVUcdhRWGBpsISyrcRwsWSGhqP3ZWvQjWaNwmFEAqW/1ZKQ6kn1g2wruuYpoVlWOhYKMdgzZo1XP/Sq57MaTzjV82WefmjLPqlXvKXs2Te9OR2LxAInGvuXIXy7hmq++ZRto/eFCZxVR+x7W0YzQ8fwWg++2NOnnw3rrtEqvUNvPf2HRyaqvHKS/q4oXec1m9cQ191gl1919P1gg/xhy09Z95bLTocvn2Sw3dMEqp5nNcapjljITxJqD9BaECj+L3PMP+lW1ARi/ELWvlJx6XUIxma9To7CvOsOLyHFeNjWPU6dizJ2KaLOLWih8W4Cb6Hld2FmZ/H9FwwLIqtMU73gwrXuWL1Gra1bMOxHYrFIiODJ5kuFDHjaTaWC2w80Wh242gaFdOhbi6AkKz0VrFWmRixBUqts+ixcSJ6Dl130XUPTVcoK4MtYpSlTrFepuaUqUiHmpIYCAwEITTiaMQwiUgTIS08N4RvxynV05Rrzbh2glpVgl/FwyU3GXT5GwgEngTlSWrHFinfM4MzUgBdED2vldhFHVgDScQvjDPqOIucHPx75udvJh7fwLT2j7zxaw5Ry+f9L11N2+EPcsmBbzIZ7WH/jf/DM7Zdf+a9S/NVDt46wfF7ZsgoxaUtYZKmQChF7JJOQit18l/+DAsf/Q6F5ib2XHs5U8kuIoYiKSUrp0bYPHyatpkpfE1jqqeb4ZUrybZ1EJIxDMJ01OaoTx/Hd2yaUykGtdPcfb5Bu9vBhbGLSdhJ5u+f52b/5jP7VbVilNNd2EaEaiSJY0iEfRDLOciAFuVF+iqSeg4ndi8IBQi6ZJpQ7jxE0aCer2Nn6zgFF08pokIjKgRthoEIWeihMCISRYvF0cJxzHAcS0XQnAha3cKyNcKe8Yj/KrDNIot6iTt1+5eWPRWCYfYCgd8xsupS3j1D+Z5pZMlFbwoTv7iD6AXtZ6pdHqrRH8wPODn4D3heiY7uN/Kx3Rdxy7FFdq5t4Zr+Ia7b827a7UUObflTNt3wXqxQoyfDudEi+28ZZ3j/PF2WxuamEOGahxY3iV/WRXh9lPwXPsvUt77FSE8PR9avR1kmSkGpbrB+dIhtp46Q8AS1TAf5FVvQ2jYQ0zIYepRwKoSq5nEKJUxh4YUMZkWBGb3InFagttz5lq40WlWSVpmkRSZpUjFSKvq4VS1PBSV98B2U76B8F19JbKkoayaLZoyCDiVLYidNZFsUkTyFH76FNusgbUaZk/4m3njN987qs4Nh9gKB3wNerk551xSV+2dRjiS0NkP8JV2E12QQmnjE99h2lpOD7yabvYVk4jxqsXfw2q8XWarmedWz27hw+CO84M6bmUmuovhH/8W2FZeglGLsyCL7bxljanCJvpjBczsimDUPI2YSv26AyPo4c//1Re762E8Y6u4me911REWIuN9MsmCxpViiXRroLesQ/a9BaDpxoPUh++Yrn9J8lgltgWyyxqxaoqo1ugPQhaAn3UUk0UXOTDIsDfSJU0ybOkO9fRhI+jCZPjFPInEvW9NHWRl2scISfIk5JjCHFdaUjoxozGRshuIwGlEsJoBQhpbiOlpLq8hUOxBATMuR0OeIaAVCWgFLLGGJJUStjqjUURUPWfQhX0MrVYlLSUxoFFIryDadx3z7Nliq0LzqJE19UcxYLzX7JM/q3XZOfg9BcA8Efss5U2VKP5+kdjgLQhDd2kri8h7MjkfvJ1wpxezcdxkcfC9S1hhY8Td85dhlfObOcfq7E/zB1hFes+dNtLhLTF/4Zrqe8y58YXJy9wz7fzLO4lSFFWmL63uiGGUXI2GRfNFqwhsyDH/tqxz8j3upt6wls/klbJNJMk6C5APhJgQqY+M4ZVw9QkToSAHGyhSRC9Psu/fH7D24ByeWwI8nUYBULrPxLJVwhTXbXs7p5AY+ky/jSMmFY8foGD/Nd7dfSSme4nJNY/y+cVrMz/H8zUdItPjggX5cEDoUJ7q0ivK6OQ5ePMfPzDgHfYknJC2VbtZkt7Nltpf1TNNqnCCu/QwzmUf5FepKUDbiVPUoVc3ARqJJn7BRJxqrEZF1dCSaLtFNH00HW4AyS+jpoyT6voKMNi6yjmdRmFiBV7qWjLYd1j/1v4ugWiYQ+C1ljxcp/XSc+sk8IqQTu7iD+GXdGKnQY77PcRY4fuKdLCzcSiq5jVj7e/jrby9xZLrIFTvj/MH0v3Dj3E9YyKwj9ZJ/RzVv4diuaQ7+dIJy3mZ1W4T1YQ296GC0Rkhc1YvWGmboprsojRTIaC3EafT94qBYcEvEc4NYc6dxawtMx1Yi+i6mv1VHWSWMLTr1Ppv77z/E5LRHXTYa9UQjHtHwGLXmUci4RCJd1AghUUQ0Qad00RYXmAynWIo1EUGnubhEi3eSWLKKMECUwJqNEs4PIBItFHtOclpWmKlIYk6Znpqis5ogLuPoQmHrGhUjStGIU9JjFI0YJSNGXbOoahGqWoi6FsLVLWzNxNFMXM1AIh7IpQHEmb+lEHBmmUJTEs1TCKnQNYUmJFfWxnjrK951Vr+BoFomEPgd8tCgrkUNktcNEL+kEy38+P87Z7O3cPzEO/H9MqtXv4N9C9fwzs8cR0tZvGLHMG898k9kvBJLl/0N0R1vYe/P5zhyx93YVY+1KxKsbw0j5qvo8RDhZ3ThVh3mv30c09FJk8DQLE4Im93SJT23l6tPfodEvooXDzNxQSveM3Wspp/ihb7OkK+Ry/YxP7SC/L5OFHGS4UUGUvvoTDOVXugAACAASURBVExg6jlcPYRPGL+mo5wJwoZORNfxqxXyUlGKx+jQ51lrD2HqdURSgQIlQUqNQjxJpbuZckucvB+nWL+avMiwGMuQTWdYsDLkzdRjHjOhJBYOBi7mmeI85LWLhqSRGqkaoV2BriQhX2K5PqYvEb4ApaEQ+LqGRMMXGvF8MMxeIPB77RGD+s4utJD+uO/1vBKDg+9lZvZbJOKbWLnmg3zopw5f3XuEteeF+F+5j/P8I7ez1LKZ6rP/jUMHoxx/9/14nmTDpibWhXTU0BIipKO3RfAW61R2TePiMaXlOKUX+Jkuqaemef7cLl6+ewIrq9D6PNRzHSI9S6y1FwnPaiyMt3LEu4gTcgUuJmkKPIP72MIJ2uq5RsuZucf9SmcsmClORfs5ERvgZGSA4Wg/k+FOpsKt2JoFMRoFiHkVmpwCGbtAa2GeAWecZq9AT9imbcUa5twYJycXmc9VMZVioNlhZeo4XeIUKfKE9BqaVOiORbjcQbTURKhuoTugPBtNVgmTJ0KOkMqfGUBboWOLdgpWG6OJFg40dzKqRYgsKiz/kZ+H/KqC4B4IPM25cxUKPxqlfjz3pIM6QC5/D8ePvZ26PcvAwJvwIq/l5Z8/zKBtc/32Cd5z6oM0e0Vmtr2XY/lnM/jP80CeDTvaWGcJ/KOLjRAlQNk+paUSp5hiKjRLvmkIP7NIf3KUvzo5T+ten7jhEbrQJRT30ERjFKL6uMUBNnO/toUFmUTHI6EvYcYVdLRxZ/g6/su7gaLW6InRFxo+GlLoKMBQPqZycYVBTQtT0SOUjBhlPYqtP1gNFfWrrK5OsKk8yNWLd9PhLNDhzNNbm2VldZIWv/joB2roEeYtPdaRnX/YX57QKVhxpkJNjIUGGDa3MRxq52RsNcfjG1EiTdRxiDl14naNVKlMu1NBU4kndB6frCC4BwJPU17BpviTMap75xCWTvI5/cQv7X7CQd336wwN/xMTE58jEhnggu1f5ZZTrbzrpnsJr7T4/+wv8qqjNzEauYpbY29j6EcOhpFl8+WdrNUE7oEsvly+84zrTIRzHKodRGs7RLJ5mrXhadqW6jSPeDTVbQxTwVqwvQjZxHYWUms4WO9g0I8iKhUMKZmPpjnWNcDpth5MTSet6eD6KFcSNhTJeol1mPSF43iezZTvMuzWmQyHWIhH8fTGdzd9l97KHOeVB+muZumsZknVfaQXYtpMMBUKM6ZnmJJtaO5mhBFGaw9jqDK6rKDpAk/Xkb6LJR0i1AnjYCoPIUBD0ag4kctVLgJbW66cEQYejVLRIhS1OCUtjisNLN/D8l3Crkuk4hIpuGx1bC707kOohw+npymIO7DaX3xKfzcPCB6oBgJPM7LqUrxjkvJd06AU8Z1dJJ7Vix574i0Zi8XDHD3211Srp+npfhUdvX/Fe743xLdGslyyaoYPDn0Ao9jEXvMtTM03Y0UMtu3soN/zsY8sglSgCfyVYfb7R5mVt9DaNky3MU37gk3TvE/KbuSYu1WN/EKa3ZnL+e7Ol3Bnuod4cYHzJ0/Tl5vD13Sq8U6SRif9bprWuk2iXsX3a5RFjaKoURF1siEYbEoxnkozk2pmKda4o9V9n9byEq2lRmkpL5Gulh6WwS4UiOX/oNFVgAKU+PXGNw2BpXRMBBY+Bh4WNhZldDOPEcljxJbQkyXsWI1pTSPkp/mLF953Vp8XPFANBH4LKE9Svnua4m0TqLpH9Pw2ktf0YzSFn/A2pHQZHfsUo6P/imW1cP7W/2TWPo8bP7mP8bTkb1q/zfX7TnFf/a/J1lcQSZpcfkUbbSUbd99cY+hmQ+BsiXA/3wH9TrrD42zN1mgZ9EjYLlIJ8osxZsfDTFS7+NQVL+eH1z4TpQl2Tkzy0sHbMJ0iJiYr/C7CdclSZYlqJMekcBhVHujgG4K5ZBPTyWYmmtcwl2oCIGLXWT01xnPnBtlRPsoOcYykWcXwXfyswM5qlPMGLjpENFREg4iGjAhUWKAMAaaGMkCZAqULpKE3/jYEUhNIXaCWp2gCqTUuZlIAGkgNlJAoTSI1idAlLP+t6T5C9xC6RBNyua8ZiaZJfqHRL0pCyYdFXyPrC2ZcjWlHY9YRFJaiAFwrC0/ND+gXBME9EPgNU0pRP55j6QfD+It1QmszpK4bwOqKP6ntVCrDHDv2NoqlQ3S038iaNe/mq/sK/P3te1i9Isdnj/6Y2dwz+aH3UlIZk2dvbyaZreIfnMcFEFDf5nEs/GVC2l1sLi7RMeHQVHLwERxzViMnNCKHlpiNtPKf1/8B9297JpfkfN55YIxa6RRlUSOsWZi6QdmpM6hPgw6G55FazNFNntOdbRzoHuBESw+OJtFliW7nZ6xdGKXdGyElFpAJhZeE+xDsRqCEgRAGAtBEY4SgRmaKj4aHTmOeTqO6Q0ehiwde85DlCk2AsbwNXTSKponGVCg0odB1GqmKy+toy5/rKKhLgavAVuBIgS2hIgVl36DsC8pSUPQFOV+Q8wQOD0Z8SylWOS5XOg5rHZe1VZfwQudT8Cv6ZUFwDwR+g9y5CkvfH8Y+tYTRFqHlTzcTXpt5UttQSjI5+SVOD30QTYuwefO/EElew1u+fpibC0u8STtKx8/bOOa/kraM5Lmr2whNFFHHFvENgUJR3jzOeMvXSbgH2TZTo2PWwZSSrGznZv1yqoNVNuw7QC0c5TtXvwJn3eW8uruDl88Osnf8EFnNpzH6psAmB8YwWmyWYqzGfBimzBg5dGy/jiaPABBdgOjydygulxMANLpI0JXCoBGgWd66BKR48LX6xVvl3zBdKdJS0uz7bHQ9ur1G6XU92n1JxtCpJiMsNkeYD6cZQsPSLuJcjMUUBPdA4DdAVl2Kt45T3j2NsAxSz19J/JJOhP7k+kKp16c5dvxvyefvprn5Sjasfz8nsxZ/8cldrJQV3jli43jnkYznuXx1Gn2yCqfzaOkQXt2l0Lufmf7v0lw5zbYTNm1LNr7SGRPP4EeZaygeG+Z5u25FCY37tl+HWnkeL9qSYP/J3fx8qkbd8CjGF3Dio5Sjc0zrPjnJQxr0QESZZOo2m3yPXrdKj1unzXVJSklSShJSkpSKqJSElMJEYSzfcT8eBfiAL8BD4J2ZikeYB/7y1BYCR4ArNBzAFQJHCFzRGGdVLW/7wYsISAQhpYgoSUQqImq5SEnGl0SVwtZMFs0Qi2aIKSvOdMTiPkvjZkth6w6WsrEEhIUg4nlEhEab+ZgpOWctCO6BwK+RkorKnlmKt4wiax6xizpIXtP/iB16PeZ2lGJ29jsMnvoHlPJZv+59dHa+jM/fOcr3bhvlZfk6hh2lM5TjvP4kWq4NJqpY/Umc2RzZxM0sbL2Z9vwUl+xXJOwKrkpyf+QlfKj/Bnr37+Hl3/k8EdvmVP/FaJkMyTbJUf8gu8cKzKVyLEbnWDLLZ/apw/fZXPdZX6+zynUYcF36XQ9LCerKRJUUqqTh1zR8R8O3TTwL7HYodUC+SeCkBTLRqPOWmsD3Ie8LclJQ8KDgaxQ9yPsaFb9Rp20qMJVqFCBKI9CaCiwa8y0FBjq6L1BYKBFCaBYCrVFto8BAEsInho8SjYexSojGvixfGGxNI6vrVDSDiqZT1jTKQmNB1yki8JTE9cDxTFzPxPMspG3iSxMpQyg/gvIjIMON1zLCzkyQ5x4I/FZzpsrkv3Mad6KEtSJF+vkrn3S9OoDj5Dhx8l1ksz8ilbqAjRs+TM1t528/vJuWqSrXOhorQnNsaougOysQJZ3IBS042SVmva+xeOH36ZqrcemeKmFZpq76+XbmDbx79TVsObafN378H2lfWiSb7mNsRQuH19Y4vWKacaNA3qwAEFKKrY7DRfkaW2yH9bZD1WrmaGwNp+J9nKwJqqcmkfuG0CoKhCDU4qPW2tS2aBSbBHa7QC5/feVDqSSYkBpjrsaUL5h1NXJ+o2Y9KgRpXZI2JClL0qQrVvoxUk6GlN1MS7WTdGUl6XI/ISdzpo5dnEWvkB6KopKNqiKlKKIooygtT8uwPF0uQlFBUVme7z5OrNZRhJBYeIRkHY36k97HJyII7oHAOSbrHsVbxijfM40WM2n6w3VEzm/9pf7Un4iFhZ9x/MQ7cN0iq1e9na6OP+HHN41w7Pa7GfCgLzTO1rSLxsZGT4tXd+N7VSbHvshS9630jYdZf28BixxlVvHP7X/Jh1ddxZqJEf7+Y+9j4+Qw85k0X3neCo6tLzNhHaYiFJqC8+sOryhXubBms9o3yQqTg62b+GTX89jVdBGJQokb77iV6+65A18rUmpXcImDta5GsUNnJmWhjEbIWapbTDoGp3M+Iy5Muxr4Jr0qQbsBK8IVLkiXaDMkrZpOstKLWexGz3diV1qp1pqRvokpFRUtzYRrcRyQSKSo4VgWKmrgGwLHV9RdRb3uYbsS25M4KGoPFAE2irpQ2IJGcH6wm5hfoikIKQgrQUhBSAliSqPpF+aFz0wfPs/4hY2OhB+jYdWvIAjugcA5opSidniBpZuGkWWH2MWdpK7tR4s++ZF3PK/MqVPvY3rma8Tj69m04fOM7o3x/Y/eDXVJd2yWK+KLwHkISyf+zF7M/ggjuz9F0dpLTznGhr05TDHHEmv4YNdf8ImVV9OeW+D/fOJj7Bzcx53np/n6jRlOJ0rYokxcSi6v1LiyWmN7zSDRcRFTrXUO1gp81Hoht3dejmtYbD9xmLff/Dk2+GXGY2Xsl0hau8rozRqzYR2IsGRbnKroHHV9Tts6nhtmjd1Dvwjz7EiZ1vQ0rfECuiginSjVQjtLs2soLjYxWUjh+j74HppfRVMjCH8EVJSqSlElT0VLUBERKppFWTMpuZJSTVF9pBt3o5EXHwZCQEhASAiimkDXBJquIXSBMjSUqaFMHQyBLiQmHkJ4ILRGSqUAKQSOplE1QkjdACGW8+6B5amQCkP66L5PplSgY3aa9pkp2rLTxIyNv8Kv7NEFwT0QOAe8xRr57w5hD+Yxu2K0vHojVu/ZNTPP5/dw7PjfUK9P09P9RqoTL+Zb75/ELs/ipKq8IDpKSG4GvYvEZT3ELuti/K7/YemOA3TMxlnjjmNpw+S0Pj7U/Xf854rnEK9WeOtn/53V83fxoyuifP5Gg4rWCOjPK1d5TrlKW72VBXkxqU1rGbHuZehEme96L2LXuoswPY8r9u9h69Ap8lYY57wKqvMgfc1VfENjRpoMVkMcyClO1DXydgq/OnCmSLuDLDp3A4bw0IWPrnws38XyHEK+TcivE/VdwiKCLsL4WoSaFqFomBRNjYKmGnnpD2ECIU1gGDphS8cKa3gRAzei40Y06lEdGdYROvieg1XIEV1aIFPKkSwtEa1VCNs1wnaNkFPHcmx030WX8pFOzYOUQpcKw5cYCgwJIV8RcX0irsRyXUzXJVmtEHG9M2+rhGC2bQ5461n9Nh5LENwDgaeQ8iWlOyYp/mwcoWukblhJfGcXQn/yVTC+bzM88hHGxz9LyOwn4XyBez4nqRRGWEgontU5xsraAMiNxM8Pkbj+ArInfs7Uf36bpmyULnGAsH6Iot7CBzrfyCdXv5SQ7fC6r36GuP1zfnqlzpfCGiFZ5VnVGtdWa/T7fRypb+YAa1ndUyE5fwt7brf5+pZXsPeSLURrVbbvP0RxHnJNNs2XHeOC9mGUCXUP9tdCHKxrnK6ZRKoryZRX0+OmWGlViMeKRFsW0LU56iWLSj5KtRBGlTTitoPhGSg9jqOFKVhRsuFmxuMJHP3BMBXxbNqrc6yr5Giv5uioLNJeXaTJLtJULxGWLkoTjQGyl8sD3QmgJEpKUIpGy3zFA+1XlVju01E0Gjk9MJWi0dhJLZ8+XSo0KdGlQpcSbTmgW66P/iit/V1NUDcN6qbBbCpONp1morWVwb5exrr72Bzxn/Rv44kIgnsg8BRxJkvkv3EKd7ZCZEsL6RtWoj9O3+qPplQ6ytFjb6NcGkIrvJWhvVsp5+rIZhOrv8KfFk1EbRVW+xSZVz6P0uwoI5/+HyLZFP3mLmLGbVSJ8a+tr+TDa1+NknDjLZ9AWffwg0sFrtDYbNf5s4Uyl2gxFptexg+zFveisSoyxsZjP2Dq4Ar+6bp3cmjnBiKVKk2Hp9HyRS7o2c2lG3+OGbbxpeJA1eDeJYPJSpQtpU1syXWy0y4Q7zhG55rvE045KAecQ1Hqe+PUpuOomkXeypANZxhKd3EgvY5s9MH8/ky9SG95nk2LJ2izsyT9LFE5R4gyvi7xNYmng59SLGUgt9xgSSgIOxoRWydq64RtjZCrLefKC0DH0yWeIfE1tVwkUlNITSFUYzuNbSm0RgNUNNXI4HF1gauDa4Crg2doOIZBJRKmGLEoR2IUI0mK0WbysXbmMt2UY+0oPQahGH4oSsiRdOR9uhYddoxMoeuzT8nv7xcFwT0Q+BUp16d46zilOyfRYhbNr9pAZFPLWW1LSo/x8U8zNPQvVKavIH/i7ZQXoanbpLZKcUOhTLyQxjCPkrlhA3X9IqY+93OMfJxmczeJ8DcBn281Xc//XvsGirrFZXs/RS22h7s2aCR8+INSmas9n3a9G7nzI3zzzhMURsoknUU27j5EJZTkQ8//K/Zv2IxZs0mfmmVb6CQvWf0DUtYQQoPxmuCORYuRcoT1S1tYt9DKtoUFOrsHyay7j6RmY53S8L4RQR+L4lcMhjIrONyyksMrVjGeaD/TAKnZrtLqL9Frj+OkshSacjgxk5xIUCTMoOgA+kFYICykbuLpBr4u8DQN5RsIqSN8A6SGEhpSa9SJ+7qOp2v4mo6ra3iafqY7sDODaCzftT/Yd8Cjv27cwS/P+//Ze+8oya7q3v9zQ+Vc1TnnMD0zPd0TpQka5YASEiAy2PhhMM9pOYf3e35eDi9hP/kZ7IeNwAJJoIQQQtJIQmFy7p7UMz0dpnOsrq4cbjq/P6olBIKRRuAA9Gets0716apbt2+d/ta5e++ztyKD/GOicYTApQlqE2k2LWSoiMcpiV3CGU9CPoFlzCPMBcCgpLzhXc2Vt+NtE4dJkvQAcDuwIIRY+6bxXwc+R3EPwXeFEL+/Mv5HwKdWxn9DCLHn7U5iNXHYKj+rFEYTLD85hBHN4d5UTvC2xnflMAXIZi9x9tzvMX1eIn7hI2RjfiLVXkpbvdgvzNKYt2OTLqE2X8Le9mHieyeQ0jZU1/MEpIdwWXGOuzfwO+2/yZAnTO/gPxJ3nCFhk2nVNO5Lp2mRQui5dtbe/BEeO5FgfHocuaCx+cgxNEXlC+/9KKc7u7AXCnQvDnCb+igNgVHsToOCCYeyKodSKhXxRprmm4lcmsMRWaSsfpnyTBrnkIQyqGBmHPSVtnKksovTZa3MO4vVlWxCUC5kSpAoEzLVmozflFGNdxO0WEQgMGUwZd5YgQtlJS+MIrDk13PECKyVcSF/P2+MkC0MGUxZYLxxDOsNo41sFdf8qmWiYqFaArsJHt3AbRjYDYGig5Q3MQsCUxPouollpRDmMsJaRljfj4iRZYVwaRlVHV3UrOumqn0NgbLyd/W3Xy5x2DsR911AGnjwdXGXJOla4E+A9wghCpIklQkhFiRJWgM8AmwBqoCXgDYhxGWNSqvivsrPGlbBIPHcGJnDsyghB6F7WnG2XlnagNcRwmJy6iH69z3N4pk7yC/XEKpws/naGiZPTtE2ryNLURyOp1G6PkT6ghMyEobvEAH5AUKFWWaUcv5ry6/zbKiBtZP/wII6gSnBrmyOO6w8itlIYm47a3tLeCrVQHrsHAqCzvPnCc0tcP99v8SJrm48epZPzjxOu7qXYGkCxWYxXZB4OW1jMeFh/fw6qsYsjPwCJZUparUYgQsm6rjCcOla9tdu4ERpIxMOH5YkYRdQY8jUGDJ1hkJQksg5FLIOiYLdwpTyyEYSVUuimBlUPYMpTExhoFg6iqnjsTKoBR3ZMIppc1dMLKrdgc3hxO5wYLfbi7Z0s2hXF5bAMgWGAboBhiUjhAKSjISMJBUzzggUVuJZVsJbXo+DtECYCMxiEP5KX/y5AKKAWGmIPAjtLZ+roqj4/AHCleWUNbcRbmjG7vJgahpL05PMXxphbuQiG256D9vuue9dzZ2fKCukEGKvJEkNPzT8WeC/CyEKK895PWv9XcA3VsYvSZI0TFHoD72rM19llf+A5AZjxJ8cxkwW8G6vwn9zA7L9neVY/2Hy+RmOvvp5hg+0kFv8LL6IjR0fa8Ibz1N4fpQWYeBVniIXqiWf/mXECcgGT+Oq/Co1y8Po2Pi7mk/xd5WbaJp/gLA2z7Jq8b5Mhl12wZS5nsFLN6GUpDlQ2s6hE+fwOs9RvrRI44VBHr7tbl7r3oaMxQdmn2Sd+hoNNVNIEpzNybwSs1O+VE7veAdiKorDsUC9OkvJtIY21MSZiuvZ29zOwLoA8ZVLEDEl2iUVt9cBXguHPoc9NU48uYCRWcaTyBA0NCRhFG3bV3C93rwU1fPFln3zE143ofyoRevlfvdjkKRiaKQiKyiKjKwo2B0OHC4XTk8Ql9eD0x/EEYjg8AWxOZ0oioplWeTTKVJLiySji4yc6ufY89/F1PU3ziVUWU3d2m5K6xuv4Aq8c96tzb0N2ClJ0l9SLIr1u0KIY0A1cPhNz5taGXsLkiR9Gvg0QF1d3bs8jVVW+bfDyhvEnxkle3wetcxF6We6cdT739WxhBAMnX2aI09fIjn5Hhwek133tdIYcTL39DAibeBWjqFI86SleyCqkC45jVH/CA1Tl/Dm07zm3cIfN9yEN/U0ruiLZDD5VDrDJrfKgLWFb52/jlmHndOOENfPnaE92I8qG7SdPc8rG7fyt3d+hKzNyYalPm6RnqKrfABDwIGMyomESs9CI7eOVJGMTVJhnqEkJ2Hk13Cq4g6Oratj0A4JpeiEDNpVKvwKERaJpMbxRucITUcJaolipMrr2FWWXXliDp0qt5ugJThSuoXBUCOuTIb6sUu0xoaRC4ViGjKvl85tO1m/cze+SCl6PoeWy6Hlsmi5LIVslkImTSYRZ274ItHJcfLpVPGtXC4U1YZlWWi5bDFS5jJIsozd7cHhdCKrCpKsoqgqkiwjywqSLGEZJqahkzd0MtE45twiWj6HUSj8yGM6PB78kVJ8JaXUd/dSUluPt6qWyUCE/pzBY8ksN5f4aX5Xs+jyvFtxV4EwsA3YDDwqSVLTlRxACPEl4EtQNMu8y/NYZZV/E/LDyyw/PoSZKODbXYP/hnok9d1ZiZcX53j5kWeYO1+PrHbQc0uIDT1NpPeME9+TAHURj3ycjLgGw9xCpuIc8eqvUz+xRMX4DHNyhM813sms1Ucy8yB2y+QzhQxdPgcDmR18eWAL56QqlmwO7pw/zMZQjnQ4QMXsNKPVNfz5L/8Wi64ANfFLfMp6jK2RIxQseDGpcnHZwbWza7lj2I2xPEG5NEaV3sWlyCa+U13CWbvJslIsBO1yyzQSpz05QtnUKP7s4hur8KzqIuozGK/RaWzdRKC2i+8k+5gWBWpc9fiMCN9xNeOJJ2gfOsM1h18gmF7GkiQWymqYrm3G6N5EqKqeS7LMi0jYYwU8ig2PzYHXFcYtSShDA2QHjpA8fQJhGATqGlh79wfo2rGbSDD4xi5gIQR6IU8hm0HLZslnMmjZDIU3WrbYZzJo+RyWaSIsC8s0sSxzxdRjIatFwVdUW7HZVGxOFy6vD6fXi9Prw+Hx4g2FUQJhFhQbw9k8g5k8z2eK/fB0FmOqeL/R6LKzM3TlKSjeCe+oEtOKWeaZN9ncnwf+hxDilZWfRygK/a8ACCH+emV8D/BnQojLmmVWbe6r/EfF0kwSz10ic2gWtcRF6ANtOOre3Wq9kNXZ/9ReBg/oCCHT0Jti1x03YRyeJ314loJk4JaOY5ntQAitcoaZun+kLL5A/fgiMjr3l2zlJfcS00qeKt3gXpGlKehiKLqRkxPrOGi1ksPOx+afpV1fYqi5A7uhYSoyL2y7mYvuAKHsIndpT3Bj4EVyJryatjGz5GLXVA/qiMBKxvDam0k7N9AfrOCs3WRCLXoVXQ6L1vwM6xf7CWYmkQBNsrHoKCPlrWWx0s/5difpQDWoZZjSW53LvlScNUP9dA32E0lEsSSJ2cp6RmvbmaptQgmH8PhCGELCEAJdCAwhKFgWWdNCzqRYd/443QPHCKQTZJ1uzrd0c7ajl4WS7+dGlwGvKuNVlGJTZXwrfXFMxqt+v/cpMi5FRpUkFElClXjjMaagoJnkNROtYKDpFlrBJFMwSOsmGc0kbRT7uGYQKxjkDfP7oZSWoERVqbCpVNps1DhsVDvsuGWJ8gY/1VeY5vl1/jUqMT0FXAu8IklSG8UEzFHgaeBhSZL+hqJDtRU4+i7fY5VV/l0pjCdZfnQQYyn/E9nWTd3i1MsjHHtuBCNvJ9w8wjXvv4ZgvIz4P53DTOskHZOECk4stiHKE0w3/A3CdpY1pwVhbZGn3TV8Jexg2DZNhWHwG0aWxoiTkYWr+c6pLl4zOzAtk88sP8668XFOtW/iYngNNlPn7NU38bIriGpqvCf5JB/0PULebvFM3EZq0cPuqaupm0qzbAaoFG1cqm6l32EwYDfQJR2bYtGVn6Jn4QghLYpAYtZRzsWSzUxXNTG2phUz4CjatIWBy4jRGwhi6tMMLR5GlVSqpFaUGZ2Oi6epmxlFAmLOShwdnSRk8As395amuOPGrQSDbw0jFUIwc/EC/Xue4eLhA1imQfma9dTuvhHv2l6ulWUypkXaMEmbFinDJGNapEyTtGGRMgy0tIGWzpPMGCQzOuQspLyJXRM4dYFTt3DoAqcmcOgChyFQTbCZAuUyFh37SrsSeY6uNICO60rftbhfjrcVd0mSHgF2AyWSJE0B/xV4AHhAkqSzgAZ8QhRvAc5JkvQoMAAYwOfeLlJmlVX+oyF0i8RL46T3TqEEHJT8p3U4m4NXfhwhGDm5yP7Hz5FZj2pSaQAAIABJREFUFngqhthyk0xXy0dJPD1BbHiQuEMjKCUJFGrRvFHiPY+y5PgujaNB6mZinLLb+c3KWvqdEhEjz6fNLO2lNsYXtvBifxf79VZUK8fvpR9izbmLjAbbeW3rDUhYxDs38XSolKxqY0PmGL/q/iIOT5o9CRvJ+SC3TF3LQEIwlavAq7Qx6XXwjL3AtK2AJAlqjUU2Lh6nOjeOJdkZdddzONzLpcY2svUhHE6F0mSOiuwBUvoZwnqG317/IWS7yf/t/0sWrEra8ldRNrpI2+hT2A2dhM3P6eAmNrRUEWSUaMZJJJLlPbfdTlPTZgDyRp6UliKlpYilowz0H+T8qUMsx+aRHDZKb2qhtLWVlNfFKfMMxmgfZFWktP0Hmjttx52xUZFTIa8iiR/julUtcFgIu4VlF+C1EA6BKOYJxlTAVEGxSSgqSDZQVFBlgd0m4bbbsKkKsiphSDoFUUBHo2DlKYgCeStHQk+Q0OMktDhxPU5cWyahJRAIPlL1Ua5n3U8yZX8kqwWyV1nlTWjTaWKPDmLMZ/FsqSDwnkZkx5Xf4M5fSrLvsUHmR1M4ApPUbNnLtt2/hnSyhNSrk5gSFEjgNvwIeZbstmGmPF/Bl3XRcipPTIrxv8MRDrjtBE2Lu6Q868oFc4trGJtYz4l8I6ZZ4CPZA6y9cJZc3MWh7duIB8LkSmr4XlM7Uy4flflJfs12P3VcYl9GZWHez62TN3M8FyKcCJGz1XHapnHGbpBTFFxSnp7ls3QlTuEQgll3E32eRkZrWjBrPLTo03RM+fHks4z4v85w+QgOy8XHWj7B5qYePn/yfgaTftrnGukYvEg4EcVQ7Qy7WzjvaeOaihI83gNMZFJo7iXCDT7UQBmLuUUWsgssZBfIGtkfeU3thotgrqzY8uUEc2WEcuX48yUo4gc/o7yaIe1YJmtPkLWnyKkpsrYUWVuSnK34OG9Loyk5hCRwGS7chhuXWezdhhu7acdurbSVx4pQ3ijC/eOwsNBlHV3W0WQNQzGKu2oVC0uWwFIQhg0578StBdi1YSvvv/f6K55j8BPGuf9bsCruq/x7I0xB6pUJki9PIntshN7Xiqs9fMXHScXyHH5qhItH51GdaUrWPkHX9kbqpM+Q/M4kxlKetN3Aq6nIxIjVnSS+9nsU9FkaL5TjXBrkH0IBnva5cQnBHWj0VpnElxoYn+jhYqaGmCazPTPJzTOvopzPcr6njXOt6ynY3Zxs66a/pBK3kebj0pe5WtrL0YzC5IKf2ybu4WymAk/az7wa4IQtx0WHjCRBjT7PxuhRavLT5Fz19Lvb6Qs1oNX4CZWmuOniS3Se72IhEuBk5be4WD6Aatm5o+QePnj1e/nC6X+hfyhLx4SD1ktDqKZBPhSmz1vBQIlOiX8OyTVFXEpiSd+3cdhkG2XuMkpdpZS5y/AaDrIj0+SGEni1CioD6wi4WjATTvKJ7xsBJFkiWOYiWO4mWO7GH3HiDTvxRZz4wk7szrd+IWuaxvz8PIuLiyxGF4lGo0SjUeLLcX5YBx1OBy63C4fTgcPlwOF0oKgqwpQwDBNTFxi6QCsY6AUDraCjawZWHoQBkrRSi0oyEZKJpWhYslY0wL8Jt9NL74ZN3HDL7iuea7Aq7qusclmMpRyxbw6iTaRwbSgldGfzFe8y1fIGfS9M0PfiOMIyCLU9T9WGM3Q1/TfYFyR3Oopll0EzUChguPcwv3OClDhJKF1Bef8MTwRMvub3YUgSNwqTXZV5CulyLo1vYjZRxXjOg1mw80cLD+E9FSVR4mP/zh2k7QFGKhp4pWENll3lFuM73Ks+ysWszvkFH7eO38doqg5b1s+QTaVPzTLtsGEXGmtT5+mOn8IrFBZ9Xez1tDAf9JJv8tPgneDakZeoP93BYqiRU5XPcr78JIpQ2e24ld+48dN8bei7nDxwkc6ROCXxKLoqMV8l6KtKsRSOvXF9vLoHv+6j2q2wa+2NrKvZTb2/nogzgp43OPXSCc7tPUMypiCrFUiSEyia8YPlbkpqfZTUeAlVuAlVePCVOFEuU5JQ0zTm5uaYmZlhdnaWmZkZotHoGyIuyzKRSISSkhJCgTAuuxeb5EI2HaDZKaRMMvEc2aUUmUSBbFpgmG99P1XWcduzeOw53M4CbpeOxwPuoANP2I+7NIynug5nZT0WgnQ6TTweJ5FIEIvFiEajtLa20t3dfUXz7XVWxX2VVX4EQgiyfQvEvz0CEoTubsG9oeyKjmFZgguHZjny7VGySY1Q4xnCa75OY+vtVEQ/TvrFWYRuYgoLWQg8yvOc7RzErBlCsiwa+v0cFJP8v1CAmKKwyZJ4T3kau+FjeGwzsaUaZtNu+qwq/tvCwzSfvoQQgpPXdTMS6mDZ7WNPcw/xSIQW4wK/ovwjaFPsX3Rz7aUPs7zcTi7n4pxDol/NEbfZ8Jspepf76EgPYlPrGQ2s5zV3OR4lSaKzhMrgDNunD1BxPMKyv4O+6pcZLu1HEjKbC9fxOzd8ju/OnuDsS3tpH57EbphEAxoX6pKMVWbRrAC2XANb3OV4UjlcqQDVkUV2X7uNNZ2fRM8LpofiTJxbZOz0DOllQTG2ReDymtR2VVLZFKKk1kek2ovN8fZO7Gw2y8TEBBMTE4yPjzM7O4u1EtfudrkJB0rxOSM48CFyTnJJmURSJ5HUKJjWSp1VsVJOD2RZwyYlUeUsqpRBkbMocg5F1pHtErINJMVEyBI6KroFmikwTIFumugmaKjoKBioaLID3eZHV73oqg+f5GGNZmN9HqS2ELd/fP2VT2BWxX2VVd6ClTNYfmqY3KlF7A1+wve1o4acV3SM2eE4e795kehkmkBVgmDnFwhXm7QF/wrze0706TSoEhgCp3yI5cgzTPWCJaapXKhnbGKYL4bdjNtsNFsqd5ZkqHHC+fGNxKdbyGRVjuWquT1xgJvPHkHJCKY3hzjSdA1Zxc2h6nbON7TgIsuHpQfpNl7htWUbXUP3oi5tZSlv56TD4pRNo6AoVGhzbIqdpCE3i+Rcy9nAOo46HaxfukC8qwyrzaA3eprIUYOMfQ3H6w8wFj6LatjYEL+GT2z5JV5aPEBy32vUzaSxJMFYZYaphgIptZPJZAPubCO/VFmCpPUTjxfw+RbZ2OulqfKzzI8IJs/HmBtNICxAGJjGDC5Plo6rO9h421W4/a53dO11Xef80Ch950cYGJthNp6lIFQ0bAjJg2Y5yRsqBVMh/3ox7JUSeOZPuWSpKkvYFBmbImFXZVRZxqaATTKxY6KaBSqMAi0atBsqzcJHmOLfuSwXyLTBtk/e8K7ee1XcV1nlTRTGEsS+MYiZLOC/vh7ftbVI8jv/j88kChx6coTBI3O4AxKl6x/HWfE8teW/TOnwvWQPL4JSjI0W0iRhxxfZ1wW2yBxuI4DUn+WLAYM+p5NKU+bakMJmf4KRxVYWR9eh5dzEohIZHX594Fu4YhrZFokDvTuJSRWMhcp5pa2HgsvFDusV3i8epC+ZIzJ8PWWztzKVd3DCYXDabmBK0Jwbo3f5JOWGhuTaSL+vjX67zs0j+1BqbIzsrqRLm0Tvi4HRyYn6w0wHhnDoDtbPX0dPyw4GFl4icmqYUEomZzcZqytQWx1iNHU7+5dLsSPx0VoHpbYBZmZiOO0ZGktd+LmJmQuCfKa47d7pzpGND2AURqnrKmfTHXdTt7b7LSUHhRDEMhoTsSwTsSzj0QwD41FG5uJEsxppU0bnR6/o7YBHUfDaFPxOlaDLRlDJEDBm8GVGceWmcZPHJRk4w1W4SupxldTjKGtCDVRjsymosoyqSNgVGVWR3xBwVZGwyTI2VVoRcekHzt3KGxjRHEY0hz6bQZtOo02nEbligQ7JpeKolnF6pnCYB1BnvoO07Vdh1+9e4Swusiruq6xC0Wma/N44qVcmUUJOwh9sv6INSaZpcfrlKY599xKmYVHXO4695n/g8VbSyl+hfw+sFRHTpTylyj8xVn2K2TY3qpWkYjjMQ2aUp70eAhZs9Ea4LTLFcjbMxeGtmIkSbEspjuh1/Nro49RPRNHLBQM7W7kgdZNxeXm5qZvp8koqxRS/xD+RSZ+nMNpD68THGSnYOWYvxqcLIWjPDrFp+SRBoSI7t3DK18wpe45bh15lrT7Gybs7KS81uXRuAiVbz+nqfmKeWbwFF+1z1yCXumG2j+ZRHaeuEPUL4nV57inbwROLO3khXcAGvL/KTXt4jNGLk7j0IGHJhytdj92S8DgUwiEZkVvATESxK3b8oTJ8gQiqZMPSTUzNxDAFhmlhWkXThmFZCIrx1BoCDTCxMLGwXs/WqKg4HHacbhsenx1f0Ikv6MDmVJGsHNLSWaTF00gL/UhGAkk2kSrbkarXI9VtQKpdj+TyINnk4pcxFJPXCEGxtocASyA0E6tgIgpv6rM6ZkrDTGpYKQ0zpWMs5bDS+vcnjCJhq/Bgr/Ziq/Zir/Fhq/T84ELCssDUwHZld42vsyruq/zC82anqbu3jOBdzVcU4jg5EGPfoxdZnstS2Q7BjvsRjnPU+T9LoO96tJFUMS+4EAjHi0QcX2HvmgocvhhVC372zy3zz0EvBUlikxTglsoYbknQN7YVbboOtWAyG7PTtXCe3YOnEQ7B3LVeDgV2k7c8nKtu5mRjO5YicRdPsK7wNEPjlWwY+S0G806O20wu2Iql6takz9OT6MePD8W5mTOeevpcWW4eepXbJo4wcHML1o46Dg2eRaTLGCw/R86epizpoyy1iaQvQdXEBK1TbmQLxqpsmFUxPhC+j4em2vleroAT+Fi1j153lNxwgRI9hB8nHlnG+SPugkxhIBygBjzkZZmkYRIrGMzndZKG+Ub2GRWwC4EdA5uk4ZRMXJLAqzjxOd14XW4cdhuSKO5HEIaF0M3iY90C899WzySniuK3ofjsKCEntlIXasSFutK/2xQV7/j9V8V9lV9UhBBkT644TWUIvbcVd3fpO359cinHwceHGelbxF9ip27rXgzPl/E4O2iM/Sn6QfONlV7aNUaT9decazCI1kqUxwxmJ03uDziZtNnYUJC5usJJmzfKwEInseE1CMONa26JaNLGR86/gD1vkLpK5njbVmbSdSSCQfa2rmPBX8Ja0c8HzH9iaC5Nx9nfYyxXxhHV5KLdwm5qrEudozt5Go8cxubYxoCnimPuLFdPH+ETfc8RWxtm+r4NPD13FiPrYyI8giWbtMxHQGkipUzQccmkfs6NkCUu1jnI185xp/MzPDdeyYyusxGV29xOKnQLp/594SoIgelVcVV6SGuzDA8eZWl5GisSxOjZxbi7imOTSYYW0m8kZSy326jQJMI5iyBZ/K4oimMBS9JRFRuNDU2s37CW1tZWnM4fs7LNLMHAU8U2th9hgQh3ItrvQjTdggi2IHTxli+BH2iGiTBEMWmktJL6d6WXZAnJriA7FKSVJtsVZI8NxWdDsr27bKA/LVbFfZVfSKycwfK3hsidjmJvXHGaBt/Z7a+pW/S9OM6J58YBaNuRhrI/R5CiUf19nAfWYMaLObx1l4ab/4PqO8zR9irKCsvYJ2T+zqly2OWiQTPY5o6wrXKWhUwJFwY3I6VLcKXTLC2q3Db8CuVLSQotFoM7GjiV3opps9PX0MbZ2ma8pPiI+AokDuM/9UniiY0cly3O2Qwclk5vvJ+u9FkcSgiHYwfjrir2ewrUFy7y+y99HcUnGPzIBr7kGMbSXSx55lBNG+0zpcSCIaT8OGtH3VQtudBsMgMNgkxFhjsLv04y6qdayKxDwb2yeSdjmSwbEgnTQJTGab52DaUtEU69+Ax9L+1hTASJ1vQy661nZCU23aXKNDsclGYEkbRFhSnh8WnIpcss69PktSw2m42Ojg7Wrl1LU1MTNtuPCUc1dRh6Efofgot7wNIh0gpdd0PXe6FszZuqJ/18syruq/zCURhNEHt0xWl6Yz2+a96503R6cJlXHx4kPp+lodtNuOsBssb3CDuuoWr4s+jn88Un2iDpeYEW/YscbS1H9WQoHzN4RNh41O/FbQlu1RxsqssgbBLHRq9CmqpESCru2SiVE5NsHB/GCAtmb/ZwVL2GeKGUWCTAofY1LDuCXCteZFv+IRJn1iEvfJQTluCU3UAWJr3Lp1ifPoVd8eK07yTqqmOvS0fxzfAnLzxARSzO8HV1fH7dEmmbhKbm8eQD1MWqmCrJEYkusG40QCRpp+CUGarXafau46r0jZRodiIrtZGSdokUOjNxi5gukXPEqN0wzTV3vIdCAvY+/W2+d3aaUVcdk94mckLBJku0+9zUaBKRRY1yQ8btsVHW4kbzzDMdG2E5HkOWZVpaWli3bh3t7e3Fohs/jrkz0P8wnH4UslHwlML6+6D7g1C+9hdG0N/Mqriv8guDMK1iPdNXJ1HCTiIf7MBe63tHr82lNQ4+PsyFw3P4Ig46rhsho/x3JOy05P4c6WCkaNcFUhVzVCf+mFhZlrEaD81TCfZnVb4QCpCSZe5MFegtdeEvy3B6fg2Zc80YahBPIo5rIsHuC0eQFIvE9RJ99RsZinajOnWOtnVysbSRKjHFfeb/IzsSQ730h5wt2DnhMLCw2LB8lu50P25JxeHYSdrRzD6XQapilt898TXaT82xUOvm/usMhiqL/9818Q5sVpCp4Ai1cxnWD4cIZhTw2qGmhg1iG81aDSoSKQQXFYG31M3MXJxkXEFIJiIwRsfVUa669gOMDUR5+Jn9HIo7GXfXYkkKQYdCt9dDVcykbNnEjkRZvY+aNSGkYJLR6UGGhi4ihKC2tpbu7m7WrFmD2+2+zIcSh9PfhL6vFcVdtkH7rbDhw9ByAyjvrqThzwur4r7KLwTGUo7YNwbRJlO4N5YTvLPpHTlNhShuRDr4xAhazqBjlw1H3f8ilz9LhfwhwifuwFwsmmCsEGS5n1pe5lhzOQ3xZWaWBP8zHGTEbmdrLs8dloNAS4ZZrYyhUxuQckUbf3Bijq1nj+PLFshuMhnaVs+x6C4U02Ciuo4jLZ3oso07xBNULj6LdebXGcrUcdxhUECwfvkC3ZmTBMw8qnMnpquLQw7BUs04vxp9hLXPzmMAj+yUeG6ThMP0UhvfTcplMO89RsukxrqRMKGcjbpIO+FQJw1GIyoyi1i8ik4qYKer3Mfi6ShmXsFQssil51l/TZ616z7EE88P8HT/DINyBbpsI6QKtgYD1EQtAnEDVZGpXROmuacUX7XM+YtnOHXqFOl0Go/Hw4YNG+jp6aGk5G0KiM/0wbEvw9knQM9CZTds+Cisex+4rzwtxM8rq+K+ys81QgiyJxaIPz0CskTonhbc69+Z0zQ2m+G1hweZGYpT3uSm7qoXSOn/gktupGH6T7DOrHw52CWWSw7SHvtrLtYHsEs6ylyB+/0BXvG4qdYNPpPIUVankAjaODG0GcdwmIw/hG9piY6zF2ianUKrs5i71c0R7VpiiQCK386+NeuZ8FXTLga4Ifslsqd7mI7ewnHVICtDe3KUjYljRPRlZFcPivMq+hwK4+UTfDDwDbqeXCQ8o3GsReKBm2VUmvGbtzEdHCfDPjomDNaNRGgQ1bSW9FJpb8WGjRgWL6FzRDZZ2xBmrZCZPb2MMGU0+zL2yjNsus6F7LqJB56/wGuLNrKKCxc6G70u2nMuQks6qqpQ1xWmubeM2jVBLk2McOzYMcbGxpAkiba2Nnp6emhtbUVRLuOA1LJw7smiqM+cBNUF698Pm34Zqnp+CjPl549VcV/l5xYrqxd3mp6OYm8MrDhNHW/7OkMzOf7cGH0vTGBzKHReG0X3/wWWmaGh8Ac4DrcjCkUTTKF2meDyH2B5FpmLeKicSfE1l5evBfyoQvDp5SSbXDaWW+B0dA3icAVJfyWyaVJ3cYyN5/oQHkH8dsGZsl7OT3XQoETZ27KJ47VrsKNxl/k17IOjzE7+Fv0CkrKgNjfH9uh+SvUFFHs9qvtmLtnd9AcX2Nj4bTr3DrPlSI5lH/zLDTaWym4g797NpdAxbNmX6BiT2DpWR6d9Lc3BHjyyH00IXpV0nkYn77XxkXXVeC7Fmb2QRWCRd83jrT/Lpmva6Btv5JvHZhk2/UjColPV2WwPE5k1UZCoag3ScVUFzT1lFIwcJ06c4MSJE6RSKQKBAJs2bWLDhg34fG9jFosOwfEHig7SfAJK2mHzp4r2dNeVp1r+RWJV3Ff5uaQwGif2zYuYKQ3/TfX4dtW8I6fp5PkYrz48SHIxR1OvA3/H35M3jlIi30LZqY9hzq5sRAlLZJX/S11hD8OVQWqjSV6WnNwfDrKkKNyZSvPxjEayXWFYrWDqSCdyJkLK76dsbpHNxw/jyWbIXGNxaWMdZ6Y20VQYo6+0l1fX9LDgKGWr2E/n3COMD36OwWyQmCIoMVJcN/M9ysxZbHhRvHcSd5Sy35vBUf8cNTPH+fCeLME0vNrj5tDGX+FSxVpmvXvxLT9D5yWZG6fW0+nupcbThizJXBQGj0o6+9HY2RTm/Z3VLBwYZmlMwpIM8p4pwq1DtG/cxreOKjw7rpOT7ATMLNucTtqSHpwF8Je66NhWQfvWCnwRJxMTExw7doyBgQEsy6K5uZktW7bQ2tqKLF8mxtvU4cJ34fiX4dLeoi29846iqNdv/4V0jr4bVsV9lZ8r3uw0VSMuwve1vyOnaSGrc+DxYc4fnCVQ6qBp12Fyyj/gUKponP8viD5ncYeiXSZefojWxb9iusJFMFdgIi/xVyVhztvtrMtp/EFsGU+ZjZFaL8eHNlF20sZ0XQOufIENp89SNzpMoc1i9lYX51NbqFmaIe6t4smOa+gv6SIiFrkh+89MDaxjItrLoizwC4Mbp/dQqU9gMyVs7uvJu7s45DSYrNmHS/ken3opxcYRwUBDGd+66XMca62nIPYRWHqKDZds3Dm3nQ5vLwF7KXlh8oxk8BgaObvG+zdXc0tZmFPPnicz78aSNPK+CarXz+Op3sbDh+IcSzgRQLtIs81WQsmSjKoqtGwsY83OKiqbAxiGwZkzZzhy5Ajz8/M4HA56enrYtGnT29vSE1Nw4l/g5IOQnoNALWz8JPR+HLxXlrRtlVVxX+XnCCOaY+kbF9Cn0rg3lRO8oxn5HWQNHO1f5LVHBsmlNFqvymGr+QsslqgzfxvXwW5EthiPnatZIpj4IyTPIqYsYyUMPh8JssfjIawLfm95iassk5EOF4fy63C/5CUWqiPnctE6ucC6o/uRfDrLd1sM+dYTmshRZU/wQNU9vNqykZTsZbf1LMbwKOPj72dBCDwCrl7aR2f8DEjgUZrQArdzxiFxPHIOvfwp7jgV5X0HBP1t63n8pg/R11SNPXeQyMJjbJrw8f7o9bR4urHJDiatPF+XBa+Qp7RkgV+9rpuurMbRZxbQ4gFMuYAeGKdlm2DOaOPhk1HGTS92S2OzYrEhH8KdK67S1+6spuPqClxeO5lMhuPHj3P06FEymQzl5eVs2bKFdevWXT6E0bJg9GU49gBcfK646av1Rtj0qWIv//tuBPpZZlXcV/mZp+g0nS86TRWZ0D2tuNe9zSoRyCY19n7jIiMnFwhVylRueQjL8TIh224qz/wnzImiCcYMmljy5ymz9pP0OPAv5/lKwM9XA34sIfGJeIpPJRMs1Dk5VlLD9NEufDMq07U1BNJ5eo8coGR5kcxNFiPrqvGMutgoLvCU92YeW3stFzxt1ItRmmaeYmTwbhZ0By4L1urD7Bzbg26X8RhOzOD7mHSF2edbIFb3KJ2xUT6218uJjt08vetmFkJB/MnDhKPfYMtUhHuXbqDR3QXAISvNg4rCmDpPae0wf7irG+9EjLMv+TFTZZhKHlEyQdf2co6MwePDOsuyh6CZYYfDTXPMhUNINKwvYe2uamo7w0iyRDQa5fDhw/T392MYBi0tLVx99dU0Nja+JeHXD178GPR9vWhPX74E7hLo/VhxpR5q+MknxSqr4r7KzzZWVmf5W8PkzkRxNAUI3deOGri801QIwcUjc+x7bAg9b1K3eQBHzf04HeU0Lv4p4rgbLMAmkQp+l5b0F1kKuYgk8uxxuvjbSJgFRWZX0uBPEvM43ArnmgO8Nr+LNS/luNDSiKmotA8OsvbMGQrdJjM3OZBny9maO8dFtZUvtNzLvqrNgMTG+HeYONdINF2JA2gTaa4f/TqmIrAbAodjJ7FgL/tcBS7VPEHQeYLbz7fTX3cDBzZsxlQU6ub68SYfZstsOe+NX0+1sxnN0nnRSvJ1m8Ji4BjNNcP85toq8oMFJo9ug0wlplzAXjNHW285z/QvsGfJTUbxUC1y7LYFqIzKOJwqa3ZUse7aGvwRF0IIxsfHOXToEIODgyiKQnd3N9u2baOs7DLmEyFg6lgx4uXct8AsQN3VRVt65x2gvr2ze5V3zqq4r/IzS34kzvKjg5gp/R07TVOxPK8+NMjEuSWCVVki3X+LIzBLvfLbOF9bi5Uupl9NRUaoz/wxmaCOP2MwJKn8ZVmYszY7dXmVP4tNs94wGGl28YJtI5V7SonaHSyWlRFeirHt8CGc7hTL95hkRQUbo2Ogqvyf0AfZ07mDaVs1bdl+cgNLRJc6UAR0WhK9S98glIxiSRIBs5JU6d0cdaoMlR7CVvEi6zO7OFa5m+nSCjzZPOtmRnDoT9C+GOae5E2U2CrJmFmeMZPsKcsx5/0u11SPc2uJj9hgCcvnb0fOVGLJGoGWHCXVJo/1zXDQrKagOGlRdLabfkrjAl/YSfd1tazZXoXdpWJZFgMDAxw8eJCZmRncbjebN29m8+bNeL3eH3/RC2k481jRQTp3Buw+6L6vaHopX/NTnBGrvJlVcV/lZw5hWCRfGif12lTRafrBduw1l3eaCktwbt80B58cwRIGFd3P4a1/iorg3ZT2fxB9OAeA5sngt/4M1XMRuwFpHT5fGuJZlxuPYeNzS3E+nF1ioczB4eoaRs7tpOHkLAPfw2TcAAAgAElEQVSd7ciWxYa+fhqmR0nfbpCo9rJuNkaZkuIx+Vq+3v0eTvh7CWmLBAZPszDTDkKiS1eo5xCdY0fI21R8BQUr8F7O+Cu44J3B1r4Ph3c3JwNr0FUbnZcu0TuzRN59kJKUwj3Jm4nYyonry7wgZenrjKG7H2B7IEOzojA3sonk8M3YsuUI2aC0zcTmmODRgRgnXB3osp31TolNaReRtKCs3seGG+to7ilFVmQMw+D06dPs37+fWCxGJBLhqquuoru7+8fneAGYHyiaXU5/EwrJYhqAzZ+Cde8HxzvbGbzKu2dV3Ff5mUJfzBL75iD6VBrP5goCdzQh2y/vdIvPZ3n5a+eZHU7gr56iZMPfEy4rp2H5j9D3FVPBWoqF7PgKIdu3MRUZR87gy+EAX/EF0ZG4JS7zp4lLSA6ZgWY/z+Ru5qrH5jnf3EAiGKR6coqNJ04gejJkt0HDjEattMxZvYHPt3+IfTXbyRpOai/1szhehmWqrNEV6u0LtI49ihBg1028Sg9jZTs56TIwN8yyUNbOlC2AJ5vhhmOH2DKdoq8hRamR4b2pmwirZSS0JQ4qWWLbz4H6JGudGmYuyMz4VWRHr8aeLUdSBCUNOfKZkzw7a9EX6EaTHWz02tkYUwlkBfVrI/TeXE9lSwBJktA0jZMnT3Lw4EGSySSVlZXs3LmTjo6OHx/KmE8WNxudfBCmT4BiLybs2vQpqN2yGsb4b8hPJO6SJD0A3A4sCCHW/tDvfgf430CpECIqFb0r9wO3AVngk0KIk293gqvivgqsOE2PF52mkq3oNHWtvbzT1DIt+l+a5Ogzo0iyRsn6hyhpPU+z9w9RXqjFjBcQCHTnYarUz5N3mgTSOs/63fxtqIR5GbrSbv5ieYxGM89EjYunAtuI7AkgZwyGW1tw5vJsOn6cEsc02i0FShMWjVacqO7lb0rv4dU1uxlVmiibGCUzImEaDlp0mfVC4M4+TGk0hilLhPMBlivv5YDHw3y3jfHaIJqk0DY+zl2vPc+GqWUe2tZAvZLivenrCaulJLQo5+VZjN17cTqO45FgcamBpfmryI03485WI0kyoYo40ZkXOWSV0h/sISc76PW56FmAsAbNPaVsvKWB0rriajqXy3H06FEOHz5MLpejvr6enTt30tzc/KOdpELA5BE4+bWisOtZKO0sOkjXfxA8kX+NKbHK23A5cX8n1Qq+Cvw98OAPHbQWuAmYeNPwrUDrStsK/MNKv8oql8XM6MSfHCJ3bglHc4DwB9pR3sZpGp1K8fKDAyxOZPDVnKa892Ga6u8leOJ3KQwkMCmgq/NUKH9CzreMI1VgxLDzudpqTqsKpQU3/zO2xK35CZZCNp6pbuXEyHY2PTnCwNoK8lVOWoaG6Rw/Dbdk8Ms6TctJsobK/cr17N+6mwPuq3HOJvANjZDUXNQYMtsLKnn3XhpGTqArMm4N8N3Ea1XtnOh0s9Dqw2kY7Dp+hg+88E1qo3Hu372NaH2E38xsJqKUkbAWGdBewLjxBUKOKOmch/GJ7WQXWyBWhjfTgMe04fIusjT3LAf1Gk6V3EJa2FjvddGzIChPSrRtKaf35nrClR4AUqkUhw8f5tixY2iaRltbGzt27KCuru5HX+T0Ipx6pJi4K3oR7N5ifpfeT0D1xtVV+n9g3lbchRB7JUlq+BG/+lvg94Fvv2nsLuBBUbwdOCxJUlCSpEohxOxP42RX+fkkP7RM7LGLWBmdwG2NeHdUX9ZpauoWx54d5eSecRR7hqqrvkZjd5i65JfJPpKkYCQwJY2w+jfogaPYsxrprMwfVpfxnM2Jw7LzyajgN1MX0B0yx9ojPGzcwfavDlNRq3Fy8yZCsRjbD+3Ds2meyNoc9VqGvKbwjWwPL22+hn2l15BacOHpn0TP2gkJD9dlVPDO4F98irJ5DckSlOkNnKq/iT2tfhY7/dSlk3z0xRO8f88/48mn+WrvZgo7mvnP6S1U5GtImktcLHwD87Y9SCoMzteQXroLc8mPQ4sQzLdh5WzI8gLZ5Iuclcs53HAvy4bCGpeTnkVBTVqi8+pqem+qw19SLMScSCTYv38/J0+exLIsurq62LFjBxUVFW+9wJYJw9+Dvgdh8DmwDKjdCnd9AdbcDY7LOFZX+Q/DO68z9iYkSboLmBZCnPqhW7hqYPJNP0+tjL1F3CVJ+jTwaeDHrxpW+blGGBaJPWOk902jlrko+UQX9urLC8fcaIIXv3KC5CL46w/RvHOAtvLfQf+2SjYaRyBwKs+jBh/AWcjjTAv+sTzIg64geQQ7kl7+PH6RkDAYr3PxiP9aXC852Bqb5VzPRlTToPf4carDFym5OU2tlsHIy7yYaOGJrl2catzO+HINrsOL2FM5HLKT2zIqZYpOXHqYmuF5DFmiLGNnoeouvthcx9RaH1uji3z4OxNcf+Cr+DKz7K9r4cyOrdyX7KE+30yWJKPxb6Ld9ALTqo9zo904453ImopPLSNQaKIQt2Nay+jZ10jWlvJa9b2MpgSNqoObE9CYUejaVc2GG+rwhop3PfF4/A1RB9iwYQPbt28nEvkRZpT5gaJj9PSjkJopxqVv+yz0fAxK23/aH/8q/8pcsbhLkuQG/piiSeZdI4T4EvAlKNrcf5JjrfKzhz6fIfaNQfTZDJ5tlQRua7ys01QvmOx7/Djn96VR3TGart9Dz9V3Y9v/PrLPLCKhI0vjuPx/hlPEcGZMno14uN9bxqxs0pT18v8tz7BRG2cxYuexyi4Oje3kusfPcHFNJxcqvNRfGqMz3kf5xih1ZBAFOByv4asN25m7qpdj2R6cJ2LY40soisK1BRtdBYlL/r14R0/ilMCtWdhcW3msdzsj7V6uWVziQ08nWDf4JBXzx5nzBXn2o7dwTbyDW7NrKEhZxha+TX7jfg77ypgbuYZIpgo3Eg2VTchTIVJzHvJWFss4iH9DJS8q93Dg/2fvvuPjuu47739umd4HAwx6LwRAgiQAEqRYVChSvVqWZNlxrBQ7iTdOnuzmlSfJs3GSzWad9SZOHttx3GRbtixb1VSvJEVSbGIDC4jeOzCYXu/ce/YPKLIdO5YTSZZszfsvcHhJXpzD1/d18DttKklRRuGmlExbQmHtzgo6r6nB4flhqB86dIgzZ84A0NnZyfbt2/F6/81BXLG51SWM5x6ChfMgq9CwC677O2i+FtSfsfO04D3t51ot83pZ5ikhxFpJktYBL7M6YQpQCcwCm4G/Ag4IIR58/c8NAFe8WVmmMKH6/iGEIHl0jsgzY8gWBd8dTdhaf/Zk3EjvMAce6CcTs+NvepWemysojl9D+PExZE0AGRyOz6Cae3GlNc67LHzWG+SMCr6cnU+upLgrPUnKqnC2pohv5z/Ald+/QLS0lJnKStzRKOsHT1G9ZpxaRwxZgrORMu4LbCDZ0cYB43KM4SxyKIeswCbdwraIxKxnBvfCD7Cmsqi6QVGunMNNN3KxpogdSwnKF2WqZl+hdvwZJGFw+LZN1Gc20KqsRxcaM6GDxIoH2VfhRoq6ceSdmKwm1lY1Eh+AyIIPALN5iLorqnnZqODh0/OYJYmetEJXTqVjewVd19a+MVIPh8McOnSIs2fPIknSG6Hu8Xh+2KDZOFx6cnWUPvoKIKCie/VGo/bbwPHmO38L3hve6oTqjxFCnAfe2KImSdI40P36apkngP8iSdL3WJ1IjRbq7QX/So/nCD8ySGYgjLXFh++OZhTXvz8yTMbCvPTtF5k+H8DsjLPprjHWtn6cuW+PEg2NIiOwmh5G9jyCL5FiXqh8pryUp8wWVEPlQ8sK/zU+gKRAf52Tb9uvJfhslquiQ/Rv2ADA2r5ztPnO0tgZRpUNLsSC3OfqILejhldMVxMbMaMsxlFkQZPVyvXzEmlLjmnzI1SOzKLJMsVJlamyG3ihppFtIYO1l/L4oiM0jjyMK7bEhR2VyJ4dXJfbhqTITIRfZcmY5mijA1u2GWdIxhN00+YpYfZMlKlRJ0hWnN4QXbc0cSBTxx8cGCGbm2O9prItY6LzsnK6r6vF5V+9E/bfhnpXV9ePh7quwch+OPc96H8G8mnw1cHlfwIdd0JRwzve/wW/WG8a7pIkPQhcAQQkSZoGPi2E+Pq/8/gzrC6DHGZ1ZH/v2/SeBb/k0v0rhB8exMjqeG9pwLGl7N89l8QwNE7t38vpp1TyGR9VXUPsvH0Pk89GCD0/gAUJk3wO2f85ihJLpDMSXw76+IbFS0rWuSzu4K/CIxQbWWbKrTxX1MHwxbVsOzbAYGsrF6tdlM9M02kcp71xBptZpy8W4Jv2taQ2l3HCcTVzo36U+TSKnKbUbeGWOQVHXjDiPUTzyAnMsow1J7DbNvLs2m10xs1cNwO2zAJrZh/BN9nPRJ2Vge3X0CPvwa66mEqcZyIxzMU6O4oowZrXqawLUJ1TGX1thkFRg6yU4ynW2PnhNZxMC377uX7m47M05xV2ZKxctnk11D3FqxOlKysrHDp0iN7eXiRJoru7m23btq2GuhAwfWp1hH7h0dV7R21+2Pjh1bPSKzcVVrv8CitsYip4Rxk5negzYySPzWEqc+C/uwVT0PFTnxVCMD3+Ige/P0hkvA17UYid99Qzu2yj8vk5TIYZmWVU32cJZC4iJHje7+QL1hKmTHnq0g7+MjRPpxZmyW/mtYpy9kZu4vqHTzJXXcN8eRmuaJSNkZP0BPtwWPMMxv183dpCYk0Jl7xXMThegzqbQkJg95m5MWSmOmow5Z6kbHYvei6Hqhv4tSBHam+kLufFjozJSNAeexjfhVOEHDL9PV30KDfit5SypE3TlzrHUIkZJIm0I0VbIIhjNspk3xwm205ktQKnT+bye9qZtUn8zVN99M3HKTNkLk+p7OoqZ9P1dXiDq/eNrqyscPDgQXp7e5Fl+Y1Qd7vdsDL2eh39+xAaBsWyeu/o+rtX6+mFOvqvjMIO1YJ3RW4mwcr3+8kvpnHurMCzpxZJ/em7HiORMxx97lHGj2xG5K20XiGTXFNN7eOX8Kb9QAaL46t4eQFVF/T6rHzBUsJxG3g1K/9PKMlt6RnidpX+OhcPipvpeGgWs2piuLkJNZ+nbf4CV/qP4XVmGUn4+KqthlRDMeO+yzkz2Y4ymwYEis/EDs3KpimdmCWNyDyKe2mOrEmhKGVirHQ3NqUOFxZkcrTzOP4zB8mkFE5d1sx6yw1U2puJG1FOG+cZcmTRZI2MJ8YGcwnpCyMkwhpWz1UI6rA5VXpuacDc5OJvn+ln/+ASbiGxI61y47oyem6sf2OdeigU4uDBg5w7dw5FUejq6loNdTW/urno3EOrm42QoHb76gi97Wawen5quxf8ciuEe8EvlNAF8YNTxF6aRHaY8H+wGWuT76c+m05PcuHsF7j4fAXJ+XX4KnJwTQ1lB4/StFwPgNX8JE7zt7DmNaZdKl+3FvGYY7WufndY4g/jI+RVhbE6G0/bekgeKWdj/wSX2tvIWK3UzY9xjfUVSn0RRhI+vmyrJNNQxLx3O8enNsJsFgmB8Ki0OexcPaij6gaLlgPUjZ0gajHhyBqkXZtIuzrxGC4kdGrsL1N5cS9iRuHUxmpqXFfT7OokT57XlD4GTRHC6gqyI057ykukbxjDMOGvvJF0qhpFldm4u5qqrUE+/8oI33ttCpOAnozKHWtK2XZzA0WvLw39t6He3d3Nts3duOYOrwb60AtgaKu7RtfftXq2i6fyF9bnBe+OQrgX/MJoy2nCDw2Qm4xj6wjgvaURxfGTB09pWpjRsS9y/sAkS+duRZJMqDu9OEIH2DHeBriwKMdx2L6APR8halN41O7iKw4vKdngiriFvwiP4REG01UWjhXVcGB6N7c/eoyB9lZWiorwR5bZkz/AmsAUQ0kfX7EESTcUsezbybHJLsRcDgmB4VYpKXdwfZ9BMKozbx+jYeJxwrJAEgKrVMVy8VX4jGJAUGQ/R/3SfVhOG5xtKcVXvJ129zZMsoVe0yB9zDFpGsdlylI9q5BeWsHq8lLadCsrcwG0rMGay8pYf20ND/bO8MX9w2TyBhtyCnfVB9l1S+MbxwT8aPlFURQ2dXdzWbUJ19Dj0PcEZKPgLF3dNbr+7tWDuwp19PeNQrgXvOOEIUgenyP6zBioMr5bG7Cv/8lzv3U9y/T0txg4/zDTx+4gvdyEXKOSK3qVOyb8CL0VVRrCafsCTmOUjFnmoN3K5xwlTJsNWtJm/sfyLM35NDNlVvorvOyN38ieb5wjXFrKeH091kyaHYmjbC3qpT/r5cuWEnK1PmKenRyZ3owxpyEh0L0mHHUudg3maZvOkzAlKV7+PunMMhmziktzEA3swS7XAWA1LdLI/fgOT3Oh1A3l3Wz0XonL5GdYmeRSdoh+9SJ+WcE9kcHI5ylrbqO85VomL1mIh7JUt/vZclsDB+cjfOapSyxlNBo1mbvKA9x8ewvBOjfww4nSs2fPro7U2+vZZhnE1f8wxKZXjwFovXl1pUvdzsJtRu9ThXAveEflo1nCjwySHYpgafbhv6MJxf3j58IIYbCw8CTDw59jtred5b5bEapCqvoS90SGEdrNKNIyLvPXcErHyKkyAzYTn7cXcdSuEtBM/FkoxNXpCNN+J1MNJl7MbCfwFASjafpb12AoMusjF7jGe4iLhoOvWAOolX4Sru0cmt6CPpdHkgWGzwxNbraNa2wZ1BDoWJMvYlk+zYrTiiWvkndtw2TtAgSypFHr3k/Fwf0MOFSiFWvo9O6ixFbNohxiMHqKs/JZvLoZUyiL2WajdcdVlLdcQd+rSRbGYhRVONn2gUamTDp/8fB5hmNpgnmJOwI+PnxHK+WNq5uLwuHwGyN1SZLorjCxPf0CrqVTICnQuGu1jt5yPZjt70JvF7yXFMK94B0hhCB1donI3mEwBJ4b6nFsLv2JJY7h8DGGhv8XS5NRFk59gvRKkGxgidvUB7FkPgaoONUHcarPIGTBnM3MtywOHnI7MRsyH4+k+FhskUWnm5kmmXNSAwPn13P5oX761q0l5XBQFxvnevsB+kzwdYePohI/UedlHJjahj6nI8kgAmayLR7Wz2W48oKOKyPIG72UjT3DtNeMhIRsXYdi34UsGQhUSn3D1J15grFcmPmyKjq8O6l1tpOU0oyuHOFM/iQWzYSU0ymuqWPDnhsobdrEqWdnGT27hMNjpueWBpRaO59+6DxH56O4DIkbXW5+945Wql/fwBWJRDh48ODqOnUEXc5Ftsf24iYO5Z2vbzC6HZzFv/iOLnjPKoR7wdtOT2pEHh8ifSGEucaN/85m1CLbjz2TTA4zPPx3LC4eJDxwN0sXd6KrGltc91MnNpMTbTiUJ3GZvo9MlojNztMmmS96/SRluDmu80fhOTTVyXSzxKTTx77Z3dzwnTMMtrexUlREILXMdep++hxJ7vd6qfUEmLdezqtTm9AXVkNdD1rQWnxURxJcfQ4qVnRyzFM//D2mXVkyZhOqUonivAGTopAXVtyuOI2TzzG3eI6pYIAWz1ZavJuQJJmxyDF6EyfQNQ1JUWjZsp0Ne27AW9bAyafHuXh4FtUs07mnhorNJXzmsQs8ObqEImCX1cEf3dZO0/oAkiStHhNw8CBnzp5BEgadXGSHOILbG1gdoXfcCYGmd6mXC97rCuFe8LZK94UIPzaEkc7j2VODc8ePX32XzS0zNvqPzM49RDrUxvhrvw1RG1WOI2y3TJIy7sSuHMSt3o8qhYlbXZySNf7eV8S4WWFDCv77yhxB3cpQg5mVEpmDoZ1sfHCOcFEZ09XV2HNJrhCHGPAv8JDHy3pnMYPqVbw2uRGxnAcF8uV28g1uAuk4O/pk1k7l0UlSN/oIIXWKkMuOIjlRnDdgNznICheqCWrTx4mPPsdEsYca5zraAjtwSW4mk72cCx8hqcWw+3xs2H09HbuuxWxzc/alSc68MImuGbTvKGftniq+9PwQ95+bIScEm01W/vjGVrp6VjdvRaNRDj33OKf7x0AYdHKBHZYBPOuuWT0fvXDpRcHPoRDuBW8LPakReWKEdO8SpjIHvjtbMJf9cEOSrqeYnPw6E5NfJZdRGL3wKfShGmxKiKtdD6MoH8YiRnCr38QsT5Ayu5iUBP/ktXHYbqM8B3+6ssTmtMK5Sjep2ix98RbkF914QzDc3IyMwcb8SYaLR3jR7WWzvZjT8m7OjrdBRAcTaNVO9BoXZekoa8YlegYNVD1PxfRLGKljTATcIKkoth14bGXkZA+abqNEv4QYe4Qpv41iWy1twSsolctYzE3Su/wKK9lZylvb6Lr2Zhq6tyBJMv1H5zn+5CipaI76jcVsvqmOx16b5p+PjREWBmtkE//t6mZ2XVGDJEvEpvo49NxjnJ7JIoCNUj87Gp14u+6Axt2FDUYF/yGFcC94y1Lnl4jsHcFI53FfWYXriqo3NiQJoTM39xijo58jk11gau4jxE9sQcmZ2GB/kgZPK2RMeNVvYlXOkVXtLMluvuPM8qDbic2A341E+GBUcLo4iNYSYUnzM9y7jtajy/S3tZO1WKjNX2AseJFeh5/NjiIO69dwcbwRKa4jWSBX50GU2miPxrBHdLZeknFlwL98Bt/8kwwFneRUUMzt+Jz1CHMxyawXhz6Beeoh5twSTnMRTeU7aJZbiRlhLiwfZCY3zNrLd7PxmhsIVNcihGDifIgjj48QnktSWu9m622NHB8J8fcHh5k28pSh8Afb6rjzhmbkbJTY6Uc5fPQ1TiUCCCQ2OEPs7OnE230H2Lxv0voFBT9dIdwL/tP0eI7I3mHSF0KYKpz4P9iMqfSHo/VQ6CDDw58hkRxgObOZmcO3YlkppkQdYmtwGim9Dp/yHezKK+QlKyG1mhdtc3zJ6yEhS9wRT/A7Kxoj9joia5dAhTNjnazbu8Rg8zpiHg8OMcxkoJclq5/17mJeyuxheKIaKaWDXUKr92B2mdm1nGDFSLBuyEFJDByJaSrHH2I0IBG3CCSlmIC7BbOzklCiHFN+Bsv8Yyzbc6iKlarKHrqUzeSFxqXwMWZMY2y68TbaL78Ki331e14Yj3H0sWFmBiN4SmxsuaWBiUiKv395iD4jhxuZj3dW8zs3N6JO7CN+8mEOD0c4KdowkNlQZmbnNbfiq1377zV5QcHPrRDuBf9hQgjSvUtEnhjByOq4d9fg2lGJpKzWgePxSwwPf4aV8GGSVDB4+gM4RlpQ0NkcOIJX6cKffxan8gOQDEKmNs6q03zBb2HUbKInleGPQ2l00Ur/uhU87ggD881U/CDLTFELC8ESssok0/5ezBY/1Z5Kno/vYnYigJQ1wKWQq3cTlGQ+NJfmnGOJqjE3lWEz5myYmrG9zDujLDl0kKwUOWrxljQwG2lGaHOYl58gZk6CLOOtWst29QpshpWRRC+h4DKbbr2d6nXr31j5szKb5PiTo4yeWcLmMtF9XS0JSfCPLw7ymp7FLEl8qK2MP96ex9H/MLFzz/BqpoFTrENHYX1zNTuvvQ2/3/9udmvBr5hCuBf8h+ixLOHHh8lcWsFc7cJ3RzOmktU11ZnMHKOjn2Nu/jFyOBkY34Pn7HpSuTLqnBdoKi6lKNaLR/0OihQhZGpnwEjz7UCcw3YbNTmNP1pJ0JTsYv+aBBWl4ywmAijPOUika5ioqWbJOsa0p59q1Y/DX8/zK1cRnrQjaQLhU8lXu1ifhk9OZ3kyOIl3zElV2IOiZ6mcepm4eY55exoDHa+tjNLKOibDm8hnZlGiz5FWYuRlA7mmmqvkPQSNYubSoySacnTeeTOekuAbbRFdSvPaU2MMnJjHZFFYf1UVikPhS/tGOJhPo0twU42L/6/hFMWD3yWyPMdhaQtnaEcg09Gxjh07L//pNx8VFLxFhXAv+LkIQ5B8bZ7os+OIvIHnmhqc21bvM83nk0xMfpmJya+jGzrDS5spOdPCfKQHhxqjqzFFcHERr/otzPI4cbmGi/kgrxQN8T2PE5sh+HgkwfUrPTxfrlHSfJG8rhI7WoIxXMpAfR0T7knmHWN0mAJkfGt4aXEHqWkFSReIYjPmUgvXRBQ+MZvjW6UXsEzYqYiVAyoliycwpEnmrRE0I4nD7KGiqo7p6A60zAxSfB85KUJO1YnUutgtX8carZGYFkLrUGi962rM1h8u5UxGspx8Zpy+w7NIikT79nLMThMPHBpjn5EmIcN2X56/8nyfhvmnCeHhsONGelMlIMls3LiR7du34/P99DN1CgreDoVwL3hT2nyS8OPD5CZiWOo9eG9vwhSwIYTB3PxjDI/8H7TcEhPxtZScrWBxYRdpw8PG+iUqE1GKxcPYlJPkCHAqt5Fh/xm+5rcQlmVuj6e4d7mLC2YXmc4zeC0xli6VYjrm50xdC4P+CSLmRXosAaZ9HRya3Yw2YyAJMEotVHpkPrJk4roVnW8Vv4IybqUk2ULe7MYdGcBqjLNsWSCVW8asWCgrr2IxvZt8egojeRCdGGlznsl6iaukq9me2UzWSCN1Oqj9wBZk0w+37qcTOU4/P8n5A9MIXdDcE8RkVXjmxAwvyRmWFUGbJcFfyl9gszjHkmc9h6y7Ob9ooCgKnZ2dPzxPvaDgHVYI94J/l9B0YvumiL8yjWxV8NxQj72z5PUNNicZGPwfJBIXWElXY79UQnZiO3NaOw1FCept85Rn9uFQnsfAyvn8TiadA3y3KMVFi4UNmSx/sNiClFzL+Z7jVHkmiC55MO/zcqSkjQvFk+hqis32Yi66uzgx2YGY11aXd5dZ2GI2+N15C7UZwXedj2MZU/DlOkk5yjFn5nHpU6TVUVZSc8gSBIqDxI1r0FKz6OlXESJJ3KbR35Bhh9jB9cmrkJFRN3gpu20tsvWHd9Vkkhq9L0/Ru28KLatT1xHAZFY4dG6eA6Yck6pBpRzlz5RvcJ31EotNH+Rgeg0Xx+YwmUx0d3dz2WWX4XK53r3OLHjfKYR7wU+VGQoTfkw0dbQAACAASURBVHwYfSWDvbMEzw31KA4T6fQMwyOfYXHxGbI5D9JIKY7RNVxIXke5VdAYnKIieQaP8igSGSb0nVy0ZDjkG+Jpl4OSfJ7fWy5jfehG9rcfobbyLLmsGeUVN6+orZwtn8eGxHp3kGPWTVycaEJezoEC5lIzd+pw76IVXc+y13Q//kEDG1sJ+1uR83Ec0gwW6QIzsSWESOPyeslJu9AzIfKZYyAyrLhynG+IsSnfxZ3xG3DILtQ1bgI3t6C+fjUdrI7Uz740xfn902hZnYpmL7Iqc3pgmcNWjWGTgY8En1If5cMNGZbrbufgrIn+wSHMZjObN29m69atOBw//QKSgoJ3UiHcC36MnsgReWqU9Nkl1IAN722NWBu8q3X1iX9hfPJriJwgP1VG5bCX12Ifwav4aC5ZpEg/R8B4BFVeIGJs4KipkTHnfr7ltaFJEveE7dw6fy8ny85iW3MEu5pGnHHwcryZ06URSgwHdb5yDqk9jI2XI4dzYIJAkYk/SpnZEVOYIcqx3FeouZjFsO5kvrQHQR6reYFS/QQj0QT5fAiT1YakXoaRT6NlX0MSGvO+DBfqonSkm/lw/Hb8phLUCge+mxux1LjfaIN0PMeZFyc5/8oM+ZxOsMaNltMZmY9z1JajTzVwSmk+4TjIxzaXsRC8nMO9w4yNjWG1WtmyZQs9PT3YbLaf0dIFBe+sQrgXAKuXaCSPzxF9YQKh6biuqMJ9RRWoMD//OIPDn8XILqLPlNE0qnF65deQWEuzN4nDcp5g9hmsyjlyRjWnLLuZVZ/hviKYNJnYmZD4zfl7icgrzG58mTL3AtqUhX2TVZwK5KnTivCWVrM/v4WlMTdyXEOyQKvLxKejNso0g1PSKAvR+2k5myLp2clk1dUYsgK2EC35gwxGDTLZKSTFhGrqIG8IdK0XWehMFafor4nTEi3nnsQdVFhrkT0WvNfXYesIvLGkMRXLceaFCS4cnCGvGfjLHaQiWRZTOU45EpxWFCySxr2lY/zmrg7mCPLqkaPMzc3hdDrZunUrXV1dWK3WN2ntgoJ3XiHcC8iMRIg8MUJ+IYWl0Yv35gZMJXai0dNcGvxrkrFzSEt+GoZzjIeuJZa/lka7wOS5SDB9FKf8HAZ2pk230i+d4HF/mFftNmpzBr87fxNlsWqOrv8BLeUjpGMK+0aKOW+z05QvQ5TXcSC9meSYipzKo9jgapOF/xYzo5PhlHEIY+kF1pxNE/FtYajhZoRiJ2ePsV7fz2BUIZUeQkigqg3kJROG1o8kDMbKUoxUJGheLOL29J00OJuQzQquq6pwbatAMq3uok1Gs5x5YZKLB2fI5w3cRVbiKxkSQtDnmOWIujqqv6chw8ev3870zAxHjx4lHA5TVFTEtm3b6OjoQFXf9E75goJfmEK4v4/lIxmiT4+RPr+M4rPgvaEea3sRmhZiaPh/Mz//KErUTumIRHphI/O5D1NvtqK6JvFqp/DKjyCTICpdzSUpxSHvRR70OLEagl8PrWfL3A0caPwuLQ0jZITGvlEvw6KYFqOGUGU9r0Y60Sd0pIyO1Q4f0+3clZWZkZaYyP8A++R5Gs7nCPs7uNB6NygeMvYMHWI/kzGVWOoihmGgKGXkFTPkJjAkwXBlgplAmpZZH7v1u2nzNqEYAsfmUty7a1Ccq2e0RJfSnH1xkktHZjF0gcWukknmySkZhuzj7FdKyWLmjlYbH9+9kZmh8xw/fpxUKkVlZSXbtm2jpaUFWf7pd78WFLybCuH+PiQ0nfjBGeIHpgBwXVGFa2cFQhHMzDzA8OjnUBNx7GMufNOljGU+SZXZg9kSxyT3UiwewiyPkTHamZCrOec4whf9TlYUmRuiAW6Z/S2Ouh+hun0c7AlenrYxkaqmRW5krKKJ04ttMJlF0gy8DviDrJ2t+TyD6hjZ1CP4+qepGBUs+Ro40/FRVClAxqLRoh5hKS6Ipc6T1zRkyYOmmlG0JTTFYKAqQcSZo3W6mC7lDjqLmzBldSzNPrw31GEKrk5sLk8nOP38BMMnFwCQZDB0UEzz9NnmeEFpICdUbl5Xwq/3VLIwfI5Tp06haRpNTU1s27aNmpqanzibvqDgveQthbskSfcBNwKLQoi1r3/2WeAmIAeMAPcKISKv/96fAr8J6MCnhBDPv9kLFsL97SOEIHMxROTpUfRwFtu6AJ7r61B9ViKRk1wa+DTZaB/OSRflY1ZG039IUK3EKuto1gFK9aewK4fIiwARupg0H+NzAStnrRbWphU+MvtR+vPHKW0ex1Qe4qUlK/Mr9dRaW7lU0sqluVqUqRSSLqiyy/xxykqxFGHY2odr5WlKzobxLcCir5pjXffizJegqQaV1lNkUmlWUhfR0mkkrGiqippPkDHpDFTHyZoM2qdrabDcSE95PdaEhhq0r/400uxDCMHccJRTz44z2beCJIEQIKETsL7GSZvGXjaioXDLhnJuabIyN3CWwcFBANatW8e2bdsIBoNv0soFBe8NbzXcdwIJ4P4fCfc9wD4hRF6SpL8DEEL8iSRJbcCDwGagHHgJaBZC6D/r3yiE+9tDW0wReWKE7HBkNfRubsDa4CWbXWR4+O+Yn38cz4KZ4KCdufjH8Spt2GWJpHmOoL4Pt/ooAGmjm6g6yLd8Ot9zO/Ho8JHFy4ksxwmWD2FqXeblqIXQUhPFzo2cL2pjejqAOpMEQ7DWqvKppERWnWfOfpLqmaMUn0xgT0hMF9dwePO9+NPFSAj89j7Qwqyk+tFiMUAhryioeo6kNc9AVRxVl2idW0/QdhVb66pxrmSQnSbcu2twdJeCBOMXQpx4cpTlqcQb7eFSFimzHeCAu4KHMxvRhcQtG8q5ojjD1KXTLC0tYbfb6erqoru7u7DxqOCXzs8K9zedHRJCHJQkqfbffPbCj/zyGHDH61/fAnxPCJEFxiRJGmY16I/+J9674OdkZPLEXpokcWQWyazgvbkBR08ZQsozOXkfI2P/iC2SoOJSEYmlD5OUN1JukkmQxqSeoZ77MJnmyRqt5InwsqeXv/d5iSgy10cqcMwGkcxnKN0e4uWMhcRQJ3bfJkaq2zg1aUUZSmMiwXZV5SOpCDMsMek+RuPIBWpOZTFpEsOVa9h/za9RHvdTnDSwOcaxiWmW48NokQhgoMsKiqGTtGYYrkjgypjpnLkSl20L29aX41lOQSyH64pKXFdUIUwyl47NceLJMZKRLAASBrXmE5Q4j7O36Gr+JnQTRkbihvYAmxwrTPc/z+lLGcrKyrj11ltpb2/HZDK9ux1YUPAOeDum/n8D+P7rX1ewGvb/avr1zwreAcIQpE4tEH1+HCOp4dhUinvP6mRiNHqW/v4/JxvpwzMUgPF7MKStlJpkErqBJk9QrTyAXTlCXhSRMyoYtwzzP4v8nLEV0Zo2sW28ibLIPEVbjvO8bCY+1YVadBnnqlrJjIPSl8EsJ7lWkrhxeYzeQJSQ/zhrLoziOW9gSBL9tZ28uO0eqlecNIQMZNsCLscQ4eQosXAYgzyGBLKAsDPFZDBFMOFh8/QtWO3ruawrSNFyCrGYxL6xBPeeWnSzzKt7R7l0ZA4tu/pDoU2Js9b6BPbAPN9y3cMPZjcgQnBNs5tWplgeOsGoLNPW1kZPTw+VlZWFenrBr7S3FO6SJP05kAce+E/82Y8DHweorq5+K6/xvpSdjBF5YgRtOoG5xo333rWYK5zk83EGBv6W6elv45uyo/Tdi8XYgUdVSekGMZGg3PwcbvVBkHTyRjFpZZnP+/w85C7FpcPls5XUjGao7+zl2XaFxeVuJP/lnC9rRhvPo4QyWBWd23Nprpo5zaFGQSZ4kl2nZ3GOSGRMCuead/DM9g9QGbayfkpHWKI4nX0kIgOsxOJo0upNRBKw4Muw6MtQEy9ly+xNqLY2enpKKI1kMKbjmJu8eK6rI5zTeeJrF5gbiYIAEFSYL9Bt/z4rtS18SXyA5yYlzEmZXbVmqlJDaOPzpB0Odu7cSXd3N263+2c3bEHBr4j/dLhLkvQxVidad4kfFu5ngKofeazy9c9+ghDiK8BXYLXm/p99j/cbPZ4j+tw4qVMLyC4zvjubsW8sAWBx8XkGBv8K08ICjtMfRMnupkQ1k8JgSdMpt1zAZ/pnzNIMunAjE+MpV55/9JezosisjXhY029hY804z1wpcyCyCZ1d9BU3wFgGOZLEqea5OzLHFYMvcXBrAFHTywdeDWOdl4jaLby2dhdPb7+J4rjKlhEN3ZTC5riIvnyOeDJNVkkjpNVQnw6kiDk1mtON1CxuRbE00L21hKqUhj4eRSm14/31Ni5NxDn3T2dJRXMAWJUk7dan2eh9kVP19/L/Rv+Gw+NpnBaFa6sMisPnkWdTFJeX03PlbbS3txfWpxe87/xcSyFfr7k/9SMTqtcC/wBcLoRY+pHn2oHv8sMJ1ZeBpsKE6lsndIPEkTliL00g8gbO7RW4r6pCtqhkMrMMDP4lK7P7UE9ej3XlRsrMNnLCIKQJSswRAqb7cCivYGBGJkef6uFvA056bQrlGYW1g272mJd5oc3gYmoLGe81DOerkMeSyIk8LlOWj0xc4spzezm9u5qW3Cj+oynMMYkFn5Oxmut5duvVODSJjSM5JEnHZu3HPPMaiXyOlJoABAKYCqbImA3WaR1IqW5kUxUbuoPUGwb54Qiy24zUFeTUQJjJ/gjCEIAgaBllq/0+ysryvFD1+3xpupbemTg+m8JmT4KicB9WBdrb29m8eXOh9FLwK++trpZ5ELgCCAALwKeBPwUsQOj1x44JIX7n9ef/nNU6fB74QyHEs2/2goVw/9kyQ2EiT46QX0yvrue+qR5TsR0hdKam72dk5HOI3g1YJ+6iwrR6KuGilsdtkihRn8Vt+gYyGghBUrLyD94qHvNmsBjQOuXioysxjnVkeJkdpNw3MpkuRR1PIKV0/OYkdw+d4NoLzzC0q4SSXAjvEQ01IzFa5mem4nZe3LwFIUtsHchg0QQmyyjeiUOsqDpJJcq/hvpEaRIhSaxXetASG5DVIGs3BWmxyuTOLSGpCvEKJyfGosQjGgCqrNFieZktrgdQ1lzBXt/H+JdLZoYXEwQdMutMC5SmJ/A47XR3d9Pd3V04mbHgfaOwiemXVH4lQ+TpUTIXQyh+K94b67G2+pEkiVRqjIt9f8Jybx5b30epVvyYJAjlUyiqnRJ5HLfl/2BjAoEEQuYpaxf/VDzHgkmiNmThkxMZFptjfNOxjajzg8wnSjCNxSArKLWucNPIMW66sI/lnRZs6RyuYwZSTmKwupS5yrs5uH4tUYfM9r4s3pSBbJqhdGwfs7Y8aTmMBBiSYLIkhdlQWe/cSSK6Fkny0rqllHa/mdyJeQzNYNlu4uRcipyx+r27zStssn6bZl8vqQ0f43vKTXzjdJSZSJoKB7QYE1Qai1RVVtDT00NbW1uh9FLwvlMI918yIm8QPzRD7OVJJInVc1K2VyKZ5NXR+tQ3OXfkB6in76FOVOBQJOJ6hKThotSUx2b6Jn7lydf/MolRaTufDYR51RXDk5H5rXGdCv8Knw1exqLtbpYSQUxjccgJqm1z7Jk4xp6hE+Q3aahRgeMYCEPiUm0li6Uf4mjHGqYCsL1PozysI8khKsf2MeZNkxPLyGI11Gf9aZzYaQ/uIrLUjKHbaN4cZH2Ni9yrs4ikxrwhuJDIkzQABFW2PjbbvkVpucRcxyf5ZrST756aI57JU2PXaNTGqFbjrFu39o3SS0HB+1Uh3H+JZMejhB8fJr+QwtZehOemBlSvBYBkcpSTh/+O0KGN1KVbKTbJZEWUkKYQNDuxyKfxWz6DWaQAiOudfMNdw3cCJ8khce2c4B59iU/XbGbI/lHCsVJM46uhXmef5JqpY3QvD2JfE8I8K2F7TcJA4mJ9LaGSD3OyrY5LVRKbB3RaZjWQklRMHGAosIKhLaEaq6G+5M7iUz201OwhNFdHLqPSsDFAR4sP7fAMSkJjJW9wMW2wogtUOU+77Xk67Htxt6ynr+kTfG28mCd659ANQZM1QZMxSZ1boru7m66urkLppaCAQrj/UjBSGtFnx0m+No/iteC9pQFb6+qlykLoXOq9n96nVqgMdVFrlhHkiOrL2NQK7EQxWf+GYi4BkDWqOaZ8kH8qfYYha5LmuOAvVpb5auU6XvL8BvFI5Wqoa4Im1yjXTRymIbeIv34W65jAflwmL0tcrG8iEriH0y2VnGmS6BiBztEMkshTOnuMgcAUSmYekw4GgrAzR5GliObm61icqiKThOp2Pw1VLpTeRRzpPAld0J8zmMkaOM1xNlgeotV1BNPGW3kl+Gt87ZzG4eFlLDI0qUu0MEtrdQk9PT20trYWSi8FBT+iEO7vYUIIUmeXiD41ipHWVlfBXF2DbF691zM0P8q+B5/GMd7GGosJkyTQpX6SehMeRSVveogq5QEUDPJYmcn/Hl8uGucp30kcuuBPlqJMusr5QtknSa7UYhpPQF7Q6h3kxtlXCGoJfA0zOAd0HEcUDOB8QyPRwEc53xDkeJtE3azKjr4Uqi7wL59j2DeIKTWFOS8hEMRteUocQerXXsfCRDnJiE5JnYsSrxXnWIQgkBWCwZzBWNqgxDbOBstD1Aem0Lp/iyfM1/HV4wsMLiRwqQbNzNBqXqFz7Rp6enqoqCjsgyso+GkK4f4epceyhB8bJtO/grnKhfe2RszlTgByaY2Dj73A8nETHRYLbkVGYoiY7sapBBGmQbzKX+MmggGEjRt40dLNl0q/y4qS4QPRDF15M39W8/uEV9pQJpNIeUF70SWujxyiLBnF3rSM50IO1yEFDDjfUEu4+Nfpry3nyDoDT9TGNWfjODIy9tgoE+5ezPERLHkZgSBlyVPqqaR+4/XMjgaJL+dwFVmxmyRKozmqzRKGJDGc0RnJ6NTYX2OD9TFK65xENv4eD8TW8c0jkywlcgTULGukGda5s2zZvImuri6cTue720EFBe9xhXB/jxFCkDq9SOTJUdAN3NfU4rysHEmWEIbg4qsjnHx8kCbhoMYiA8sYzGCIdahyDt36WarFcQDipnLGU/+dfy7dy6vuPpqyeT4Ry/A3Vf+FsWgXymQKKS9YV3KRa/TD1C0sILWlcJ/N4t6voOThYl0VoeBHGaqu5kiHQU44ufFUhGBYRsmGmLGfwBK9iE1bDfWMWac0UE3zppuZGvQTmc9gtqlIOZ0Gk0S9VUECxrM6o1qWRstzdNifxd2xlcnWT3DfqIvvnZgkkzeoUGK0yXP01HjYsmW19KIoyrvaPwUFvyze0sFhBW+vHxut17rx3dGMKbB6D+fsUIQDD57BtQw7bTbMksAsHyWpr8ckrcfw7qM4+3ksQiOnKszmP8V+2c6XG/6enJTldyIpjnh/nd/07US5kEbVkrSX9HOb+1nK+8KIzVls0RzeLyjYUgp9NWUsl36U0ap6jnTkmXc4uf5MmObpGELPsWB9DVPqGN5lBYFEVtUpL6unbeftjJ5zcP5gClnJIgN1sqDRraIImM4ZTOpRms0P86HiE1g23cWZir185XSC5x+YRxLL1MohOmxL7OxooKfnbsrLy9/djiko+BVTGLn/AqXOLhL+wchPjNYT4SyHHx5g8ewyG5wCv2zGxCAaEtCE4VjEzF9Tqo8jgHl3PVORP+GLwYc57RxkQyZLmXw5Dyp3I4/nkHIGjUWj3FX9A4Ln4qhrE0jTGp7HVXwrMF7qZ6bqo4xVruFIh8ZQoIirLkboGtBRDFgxnUfEX8aZWd1+pCmCyqomOq7+IAOvmVmaXD1WVwLaK+xUZfKYdcGCZjBvzNBsfoCG0mnElt/lJdu1fPnVaU5PRbFIOk3yIps8Sa7cspHOzs5C6aWg4C0olGXeZUYmT2TvCKkzi5hr3Pg+uDpaN3SD86/McHLvCPWyQaNFQSKJVT5HSu9ByHksJd+nJPoIMoKURWFC+Tj7hYNvlDyOTI6rMkEetvxXtEkZKaNT6ZnlnqaHqJ1YRnZpZEQW+yMqVVOw6LExUXcXI9U9HO3QOFceYNNYjMvPZrFpKjFljFzySZzpPAJBXhZU1q1h7ZV3039MEJpJAmCyKHQ0uAkspbBqBuG8QVj002j5BmU1NtI9v88jyQ189dAoU5EsTilLm7LAlbVWdmzZVCi9FBS8TQrh/i7KTcUJPdiPHs7g3lWN68pqJEViYTzGK98dQMwm2OjSsQsLFvk1ckY1giA5/3lKs5/BrkcxgMmyciYXf58vBp6nzz7KuozCoPkPWZwuQ07mKXJE+OiaB2jNjaONW0k0xpAfNrH2AsRtKqN11zNUu4cjHYKz1X7ql1JcdyKOL2khJS+TSj+KM7Ua3nnZoLK+jfquuxk6mSO+kgHA6bfQ1ujFNRbBqRkkdIOEOEWj5ct413aysP6TfHOiiG8fHSeRMwhICdaZF7lxQxWXbemhrKzs3euIgoJfQYVwfxcIQxB/ZZrYixMobjP+u1uw1HrQsjrHfjBC34Fp1jmhWjGBtIBFmiFrdJKUIwRK76NoZR8AMbuZMfe17I/VcH/x01iEgUtcx6XQVSgrGjZLlo80f49NRWfIH/EQaQ+z8oqFbYcEiiExUruNgYYPcHi9hTO1bnzJLDcdX6Yy5CRLkrj2GI7EIhISedmgvLqdspY7GO3NvHFWelGFg8Y6N6a+EEWGIGMY5MSr1Dm+jq3rJvoafouv9OZ48twcuhDUyGG63XFu3tpOd3c3Dofj3eyKgoJfWYVw/wXTkxor3x8gOxjGti6A77ZGZLuJ2eEIL3/rEtZwhk6PgVlXsSin0PRmDOFksfgI69Kfx5RPISSJ4Ro/S3O/zed9x7loH6EsX8VA8rdh1oyi6lxXuZ+bG59GGjUTiQnOJ+GqZw1KIzATbKSv5aO83F3C6XoHiqFz08lpGia9yEInajyFLTaKBOiSIFDega/iJuaGM/zrf4lApYPKMgfWgTBBGTShIzhMte+7yJt/jVf8d/ClowucmIiiotOkLHNlpcR127tpa2srlF4KCt5hhdUyv0C56Tih71xCj+fw3tqIo6cUXTM48vAQffun2OBWKHeqCHkWi7FCTt9ETJrGV/oPdEVOAZC02Bkta+bo4g6+WvbIav07+yEGxjuQBKwv7uM32u7HJnJoz9t5OZiha7/MPSOCqNPLsc6P8OT2Dk41W8lLMrsvDtI0EMCT9xI3DmKKncaOQJcEnsAGbO49xFd0Mq8HuydoJ+g1456OUx7PYkgGsI/q0pfQtv4mD+l7+fKhcSYig9jJ0W1a5Ja1Aa7ctqdw1ktBwXtEIdzfRskT84T3DqM4zZT8znrMVS6WpuK88LWLmENpdvsUVF2gmE9h5NaQFcVMuF6gR3wFNZxFSBITQR8r0ev5fGqJk6V7MesNLM/ejZRwUeSe51PN36bKP4Xe6+DskkJmJcfHnpEQkkJf0w08sus6jrXZyKgql49epPpCgOp0CRn9POnEK5iFhiEJbJ4NKOZd5DXIrh5Fg9VloshtpjiUoSqTQ6gGZvkFiusvsdL1W3xu8W7uf3aCWHYQv5RklyPMXVsb2bJ5V+GGo4KC95hCuL8NhG4Q2TtC8sQ8liYv/rvXINtVzu2f5vijw6x1KFQ5VfLqNGYRQ8t1kRLjaMXfYXviGEKAJnsYK/dwenkX/3/wKClZIx2/nfh0N7I1ywdrf8Cepv3oKRPJR+w87dK586CgPAyzpRt5aPdHeKkrQNJiZtvMGYp7fbTGyxH5cdLJF5CNJAIwudqRld3IqoLTZyW6mEbXBQGfhfKMRk08i2QW2NTn8LWFGFt7L//70h3sfWSevDFGpRzhhmCWD16+gXXr1hUuly4oeI8qhPtbZKQ0Qg9cIjsSxXVFJe49tWTTefb9y3kiF0Jc6VWx6ALDcQJzcg0aQWbUF1hnvQ/b/23vzuOjqu/9j7++58w+k2Qm+0oWSAgQCIRNFhFkC4vgWitWsVq91qUu99rlWrWtvVZbf1q3ar1W61b3pVhp1eKCRUFZwh4gCRCyb5N19pnv74/Ex4NLQRE0k4Tv8/GYx5w553vgzTeHz5z5nm/OdPfOTmm2ZNItMnnYG8+H6WuIhLPx7D8fGUogO2kjPxqxGmdMG6EyB+uqAqQ3hbnxfUmP1cXLCy/j+YXj6bSaOK2pDNd2AwWtmTiCTfi8r6OF2hCAZsvHZFyE1WEhMdNBw4EOOpq9xNgMZMsIuZEwwgQ24z+JK5FsyF3BIxs6+OSFNnQiDNdbOKvQxlmzZ5Cdna2+4UhRBjhV3E9CsNlD69O7CLl9uL5TgL0khcYDnbzz2DYy/GFOjzEQ1tvQDfuJ9EwlJA/S7HicqeGPkEFBBBv1sXEc9I7m56lNtBh24WsvJVh/OgZnDVekvM6krF2E/Sa6X7HxmQxy7qc6loDks7Gl3HfReTS67Exw7yJ9k5v4xnyG+dwEfK8RCNYgkEhzFmbLclwpTtLynRza1Urt3nYsBsFIs0aeUaIhsFv+hfU0B2/HreDRdTVUflaNhSATTc18pySdeacvIyEhIdpdrijKcVLF/QT5KttpfW43QoOkK8dizomjfH09nz+/h0l2nVizhs+5FVtnCpHIBHrkh8Q7nqMk3ABATyQVv8XKy4ziz5k7CUsX3gNXIyOpjMh4g+uytxHjaCdQ7qRig4fcao3v1YeoT8zm9ot/wKbCXAo7q5jxWTmddRMY5zES9q4mEKxAAmFTPDbLOWQWZpM9NoG9nzVQ/kk9RgGFFo0RZtCEwGbbgj4jhZe083h87X5avZU4hYc5djcrZhQwfep8NZVRUQYhNRXyBHi2NtP28h4MCVYSLxuDFmfik9cqafu4hmKHAWEIEXSsw+yegYabJv1txhrfRJMRQNIqk+nWY/mv1FT2WA4S6J6Av3Y5RudeVqR8wMyM/YSDBuRbVqqbw0zfGSasG3l+0Xd5buF8hnkbObPiYzYfmsKs7hB2z0bCXEJuqwAAHyRJREFUgZ2AJGi0YrWcTcGkcYyemcbudfVUbmnGgCTPrFFgAV0YsMbsJjQzmz91ZvDchmo8IUma1sk0Vw8r5oynuLhYjacrygCnpkJ+g7o/raN9VSWm7FgSV44hEJa890AZiTVdlNgNBOOb0L21mN1nIPgMn+UtxrMFKQWhsJWAwc6n1kx+kdqDnwZ8dRcgfYUkZzzPTWl1JDub8R5y0f2Wn5Q6A7PavGzLH8+vLr8SzaJz7e7nWFc9lp6eySzq2kzYX0aYCCFdx2Sdz8QZcxi/YBiVG5t4+9HtaOEII8wahRaJLgxY4g7QOX0Ev2+YxJv/qCcsD5CtuZk3THL+mVMoKChA07Rod7OiKCdJFffjJKWk85/VdK2pxjIqnoQVhXR1BHj/gTIK/UHsJg3PsDLstWnI8Fgi8lUSrG9hk20gwOOLAUuY2xLH8F5MFSF/Fr5DF2KKOcTpeQ9yXkobBj2I//0kvBsjjKmWBIxh7lp5Df8qmcLlB1/nQGUcm32zmddeBr5VhAkSEQLsJUyefSETS/Oo2+vmzXs3E/KFyDNBoSOCQZgwOxuomzKCBw7k88HfW9GIMEJv4awCG2fPnaPmpyvKEKOK+3GQEUn7qkp61tdjm5iC69x8mg51su0P25ggJJpdx5P4Afbq6Wi04xNPkG3+B0JGAEFP0EmrI8xlGeNo1qvwt80k1DoHW+rLXJNSS2FCE97OGIKvxJJQoZHb0cGnRSXct+IK5nRv4YqP/8Jq7xwWt+1itPcZkD7AiN+WytQ5P2TqWUW01nXzxr2b8HT4yTFJRseFMQgbJqebfRNSuXefZMu7dZgIMc7QxDlF8Syas5iUlJRod6+iKN+CryzuQogngaVAk5SyqG9dPPASkAMcAL4jpXSL3vlxDwCLAQ9wmZRy87cTvX/IiMT92j48mxpxnJFJXGkO1WXNNP2lnFG6QGbo+IPrsB86A6PYiNT/Qa5hfe8wTNCANNpYHZ/BrxNDhCOt+Gq+h46FEcMf4JqkbmJs3XSVpyHe1MmrbcNrMvM/37+WnuwEbt7xFI90LWJWu+S8rhdBdoOw4zObGTv5cuZeOo9ut59Vv99Me6OXbFOYMXEhDMKB0dXDttHx3LU7yP4PqrELP1ONjZxfksHcM84jPj4+2l2rKMq36CsvqAohZgHdwDOHFfffAm1SyruFED8FXFLKnwghFgPX01vcpwIPSCmnflWIgXpBVUYk7lf34tncROy8YcTOy6ZiTTWhfxzArgvEREmofA+GnkLM2qvYDGuxa1UAeP0mNFOEazPPZIOpnLA/HW/NRZhd61iaUsaCpA4iYQP+v6cQ+7kkta2RfxVP4uWl53NFw8u81jyJSLeJae5PEJEO0OII4SVx5CwuuO4qwmHJ+0/voqGqk2xTiCJrCIOIxeAMsKUwjTt31lLXFcAlPBSbmrhgah4zZ0wnLi4uyr2qKMo35aQuqEop1wohco5YvRyY3bf8NPAh8JO+9c/I3neM9UIIpxAiTUpZf2LRo0dGJO5X9uLZclhhf3Uvhs8b0A0azOpErvNiCOVi0R/BaViPTjtSgi9io86u871hE+mW5QTapxBunUVM+gtck9RKvquN7tYEIi86ya5oRgL3X3Qlw+PdzNvxLg/657K45VOswXrQ4hBaMjLJwsqb7sMeF8e6l3dTWdZKhjHC4jg/RuFEd4bZNDyeX5XX0rR+P4mim4XWZi6YUci0aYvVdEZFOcWc6Jh7ymEFuwH4YuA2Azh0WLuavnWDqrj/n8I+P5uY2ZlUPLYVy4FOuk065qm1iLUuNBnArD+Iy/ApgiDhCEjNzpuJI7jLBTJyCF/9BWhhG3l5j3B1oo84Ww/uncPQV8dSuH8vO/IKeH/BYha1/pP7Dy5gcksP53lfB2FFMxbg0w5RetkPKZg4mY1v7WXXuu0k6ZLSWC9mzYUWa2ZjjpNf7qujddMBUkQni6xNnDdjDNOnn4XNZot2dyqKEgUnfUFVSimFEF97srwQ4irgKoBhw4adbIxvjJQS9+v7egv7gmzsk1M58LuNWNr9NDtMOAv3YvzXMIxiHxbDa8TqnyAE9IQ1DLqJ67MX8Im2FRmMwVt9NYbY7SzM+BdL4z1IqdHw3hiy17QQ113JK/PPYVRqPb66Zl5zj2JZ5996bxVgHksk1EzyxCTOvvxOtr1bzbM/W4tTCObGdGPTEsBmZmN2LL+oqqd9WzXpWgdLrU0snz6G6dOXqTN1RTnFnWhxb/xiuEUIkQY09a2vBbIOa5fZt+7fSCkfBx6H3jH3E8zxjZJS0rF6P56NjcScmYUl30XtvRsRvhDViTbSk3Zj2JiDRfsUq/Y2dkMZAJ0YabUmsCJ3Dt2BdUQ8uXjrLsCWsoor0/cx1tlFV3sCre8UMP2jTRxKTuOz+QvIDu3lmbpiprdtxBTxoplGIkQsIXsFK/7zdpqrdF64bR2WsM5sezcOPRFpNlGWE8Nt+xtw7+4iS3Mzy9rEktPGMGPGMvWdpIqiACde3FcBK4G7+57/etj664QQL9J7QbVjMI23d31YQ/fHtdinpWFIstL46Fa8oQg1qXZGGMvRy3Ow6n/Doa3BrO9DSvALE+8kTuKOhFj0wDoCbdMItk0nY9hTXJfSTJLDQ0NVPvY3DEyv3MTaiWcwLLuDjR6drBYrswMfI/RUNPscQoHNlJwznqzMK1jzx53IHiNTbV5chngiejy7hsdwW3UjjXu6GKa3c4alnoVTxzBz5nJiYmKi3X2KogwgxzNb5gV6L54mAo3AHcCbwMvAMOAgvVMh2/qmQj4MlNI7FfL7UsqvnAYzEGbLdG+op/2NCqzjk9CdZro/rKElJKlLtlEkq9DcadiNT2FnAya9hiAQxMqtuefzjr4bLdSKr/4cZDCeybnPcmlSFwY9QmXZZCb+ZTcRTWPH9Dl0W91sa3MyuqschBmj9QxkxIchcR/zL7iR7X8/REeTkWKblxRDDFKT7MuO4ZfNrVT3BMgydFKs1zC/pIDZs2er2S+KcgpTX7P3Fbw7W2l9bhfmfBfoAv/uNg4GIjQnWSmJHETrSiLG/CB2uQVda8OLwG1MZmXBpdR7V6GFNXoOrkQztbIs71UWJXbj8zmo/ngCc/66nsqsAgxj7bzszyG/bS+miB+DuRjNPJag9yMmLJmOvz6N6r2C0dYAOSYzUmhUpFj5TU8n+7r9ZBh7GCeqOWN0JnPnziUpKSlq/aUoysCg7i3zJQI1XbS9WI4x1U64w0+oycPOYISuOBNTgzUITwJ2y7045EZ0zUu3EGyKncg1OXMRnS+hh1x07b8SW9xWrsx9l7HxXbS2pCNeieeMHRsoL55GbUaE6hadIv82pDEFs30BMtyEIfYjik9bRvnHGjkGSWmcRBd2qmN07tcDfN7YTLrRy0LjQabmupg37zsD6uKzoigD1yld3ENuHy1P70RYdEIdfmRYslkKeow6MyPNaP4YHJa7iJFb0EQID4I/Zl7Moy4H1s4XkL5hdB68nKTk97glbx0JDg8H948l/4lGDMEmamaU8JFuJ6P2ICnCgG6bh2YcTtC7hvzJBbTtL6WtzMw8RxCzZqZRlzzlgr+1uEk0Bplr3M+EFBPz5y+loKBAfUGGoijH7ZQt7hFfiJY/70T6wkgp0WJMbPRF6PAFmR3Tg+Y3YbffSUx4O0JE6BQmrh11KxvYhrVrLeHOIjy1F5Cb9Ro/Hr4JTYO9G6cx/dlt1KcMp36kjaoOGBbcT8iSjcVcSiTchKa/RVbWdDr3ZDPZFiLWbKAjInjeBU+6u4jpkEw3HGB8rI95c89k/Pjx6i6NiqJ8badkcZcRSetfygk1eUCCMdPBpqCkrbqDuS4velDgsN9ObHgPAAdMiVxadActPa9hDlQRaJ2Jv3kBJbnP88O8rfgDdhr+Ucj097ZSN7qYj50W4lsaiNVsyNiF2LVCQt61uJIiBD1nk+kzkenQCUh4wyJ5yO9B65RMNNUxxtDEGTNOY+bMmZjN5ij3lKIog9UpWdw7/rEf/143AJbRCewxG6j/sIa58X70kIbN/jNiw1VI4MPYsVxTeBN62yMYwy14688i1DGFJXnPcu7wMtrdKegvOCk8UE3VlFFsDYRJ6GzA58gl1rAIIh5CvleJsY8hOTSO0bESDY3PRITfmgI0+0OMsbQzOrKfSUUjmTfvfFwuV3Q7SFGUQe+UK+49m5voXtv7e1X2aWk0JdvY/Uw5ZzqDGMNgtf8nrnANEeDR9GXcM+xc4pp/B5Eeeg5dRKSnkMtGPMXM3B3U144g808eAkJjQ3EOoe4uHJoVX8JsnJESQv4dGA3lJNuXM95uwa7rVIdCPGgPsd7vY5juZ6nYx5hUJ6Wll5CdnR3dzlEUZcg4pYq7f3877ld6h1piS3Pw5cax7t7NzIqNYJYhrI4biA81E0JwXcHNvO3Kx9n0a4iE8FavRHpzuGHU/1KUsZeDu4spevIgtcNGssMRwdbdid+eidU0n9iwjUDPamLNiYyNuYB0k4GeSJBHtQDPG3y4gNnGSkbb/Myfv4Di4mI1rq4oyjfqlCnuwSYPzU/sAAnOs0egjU7g3bs+Y6o1gl14sNuvwxnqwKOZOHfcb9llDONsuhspJYGDPyDiy+LmcX8kP+EA1etKKHyrmt2F+TSGurEGTbSljiXdP59wuImQ51XynUsocsSjIVgTCnKv0YdfSCZZmxklDzFt2kTmzJmD1WqNdtcoijIEnRLFPdjqpfGhLRCWxJ2Vh21KKm/dv5kxoRAugxu77UfEhrtpNsaxqORhWoP7iGv5M1IK5P4fEvKnc8P4x8mJqaf5rbGkb+1kfeEwZKCbsCWJrviJpHtHE/JvJ0bWMyltBfEGE/UhP3ebJZtEgBFWP8WhckZlJLN48RWkp6dHu1sURRnChnxxDzb20PSHrRCMYJ+ZTsyMDDa+VUFKbTdp5gZiLDdhjXiosGaxtOQhIp61ONpfRUQ0DFXX0R5M4ZriJ8m2NOP/SzaRTgtlmWEMAR/NyRkkRhaS4rET9rzHmNjRFDgmECbE85EAfzT4cegwW6+k0ORlwdJSNbVRUZR+MaSLu7+6k5Y/7UD6w5iHx+FckkdteQue9w8x0lJFnPlnGKWfT+OKuWjcb7F2voGlczWmsAlj5bU0hJO4auwzZOtuDE/Gc8CWQsjWgIaNA3lZjHQvhUgXMcGPmJoyF4fBzK6gl1+bIxyKhBlrbacoUsW0SeOZO3euure6oij9ZsgWd98+Ny3P7IQwaLEmEi4ehb/Tx97HN1FkLcdlupMIQV5LWcANBT8l1v0Upp6PiPXHYq2+lIpwCpeMeonsUCfm5+xscyVjDtfTE5NAR8JoRrknIwOVFJqDjHSVEsDH7yM+XjUGSTNHWBQuZ3SClWXLVqpbBiiK0u+GZHH37uq9EZgw6shwhITvjUKYNTb8ahXjLHtxme7Dj+TxrIv5Te6VxLU8gMm7iVRPCjH1S9kcymRx7ruM6O5AvGNnh8uEOdzKgcxYEiNzyOvIwh4sY7KrgDhjHLtDXdxm0miWYSZbmhjDIc6Yczqnn346BsOQ7GJFUQa4IVd5vDtbaP1LOXqchbDbR9yiHMxZMWz61W8p0twkmB6lS9P5Tc61PJl5HnFNv8Hk301uVw7xrdP5MJDPlNSNTG6twbPFSaetA6MMsq3QTnHrhViCJkZo+xiVOJEwPh7BywsGSarJz+JIOePS41m27D9ISUn56rCKoijfkiFV3D3bW2h7oRxjqo1Qiw9Tbhz2GWlU3H0J2f4EEkx/xm0wc2P+T3gn6Uycjb/EGKiksL2AtK7RrPaNJt9ZSWnDTpprEtC1OnwWG/uGxzK19kJssoeJNg+J5jEcCLfwE5OV+kiIEmMD4w2NLJw/l8mTJ6sLpoqiRN2QKe6ebc29t+7NjEEIQEDMkhSafjMFp3cCiaY/02x1sGLkr9geW0J8/c/RQ9UUtY8iz5fJG75xJFpbOa9hAzVtcdiDdRxKMhFwjWFa7emkai1McCSgC8lLdPCQbiLJ4GeJ3MOU/DTOOusanE5ntLtBURQFGCLF3VPWRNtLezDlxGLJd9H57kHMZ9rwPTEJc3ASLtOrHHLGsSz/XuotuaTX/TfBcA1FHYUUh5y86S9CQ3J+y3oa2w3Y/fVszRFkhUspbB7BGFMbubY0uiLN/NzsYFNAMNrUwlRjLUtKFzBx4kR1O15FUQaUQV/cezY34n5lL+bcOGIX5dD82DZID+JYt5BIZDh24/uUJ7tYmvcA3cY0Cmtuo5VaijpGMl2aeU8Op8mbyHm+dfhbOzGHfXxSaGRq+8WkBZ1MsvuIMSSzlVpuMcQiwn7mGiuYmetk+fKriY+Pj3YXKIqi/JtBXdw921t6C/twJ/EXj6Llie1ILUBK60qEdGDUy1mfFs8FOQ8TMiQw7eAdVOi1FHUUcKYOZYZ4ttUXMS28k8SGvfiFkc9HuZjT9B2yNQPjHTphqfG41c2zvhgytS5ON1axbMFspk6dqsbWFUUZsAZ1cTfnxGKfmoZzSS5d/6ohWNtNgvF3aMKPQfhZlZbM1bkPgx7Dkoo72GCpY2znCEpNPupiLby9bSG51DOh+mPcJhcVOWnMr59PkVky3GqhLVzHz2Li2O3RmWQ4xPwsjXPP/YH6/lJFUQa8QV3c9RgTrrNH4DnUQMc7lVi1z9CNGzBEwjyVnM6teQ9hEBYu2/Uz3ohtY1znCJYYu4hk9PDkpz/GJboorVpFgzkVd1oRpS1jmWSPkGC0sFvu5XpjGrrPT6mpggvmlDBr1ix0XY/2P1tRFOUrDeriDtBWs5eex9aikYY55veY/WEeTszkrvyHsGDgv3b8J390ehnTlcMyUysxeU388uOfISOCs6vfoNo6DN05nQXdaUx2gC4Er+mV3B9OJY0OlsQ3cckF56t7rSuKMqgM6uJ+cOtHWF95AiKXYU24gpgeL/clZPK7wj9gjwj+Z+f13OOEPE8a5xpbSchv5OFPr6YxkMDyhr9RY8nAaZvD6WEHxQ6N7lAn91i9rA0lMU6vY8W4OJYvu0rdlldRlEHnpIq7EOIm4AeABLYD3wfSgBeBBGATcImUMnCSOY+qqz1MRJyFNfkK4jvc3JeQwe9GPYZVwgM7ruGXTp0Uv5MLje0kDG/hr1vOYUvXaCa7N9JudJJpnM1c3UKBVafGX8fNVgstYQsLrVX8x1kzmDBhgpriqCjKoHTC0z2EEBnAj4BJUsoiQAe+C9wD3C+lHAG4gSu+iaBHYzaHkIk3kdrh5r6EdO4d9QQmqfH49uu5O07DEbKxwtxD0vAWPtl1Jn9rnkWmtwYTQQqMc1lusVJg1dni388lZjvdMshl6U38+prvUlJSogq7oiiD1snO5TMAViGEAbAB9cCZwKt9258Gzj7Jv+OYDpXfTZ67m/sTs3mg8GkMUuNPW27kntgQkYiJS8wB0rPbKKs4nZdq5mMJ+cnxNzJRn81ym4lUI7zpr+B6cwJJWic/n2rhlqtXqtkwiqIMeic8LCOlrBVC3AtUA17gXXqHYdqllKG+ZjVAxtH2F0JcBVwFnPAtcf+a+H1eisTyUfrPAclj62/h0ZQu2oWFyy2SYelutlTNYVXVFLqxM9m7lxn6DM60GTCJMPeFannTnEyRsYk7zythwvjiE8qhKIoy0JzMsIwLWA7kAumAHSg93v2llI9LKSdJKSed6JnyuLrtbE6+FZ+ucdf7t7EqsZ4Ko5ELzQby09x8vn8Bn1SMoJoMRniaWUIxC+xGNPzcEmlhldHJQlczT16/RBV2RVGGlJO5oDoP2C+lbAYQQrwOzACcQghD39l7JlB78jGPrjqYRYtZ5+bVv2Ff7h7W2Rws101MSHezsWoe2w+kUB4aThxeLo1kMcthoCfcww3CS7XBzNWFQW646CLMZvO3FVFRFCUqTqa4VwOnCSFs9A7LzAU2Ah8A59M7Y2Yl8NeTDXksDe0bueXZVzAW1fNanIOZmok56e1sqZrNtpoM6jri8dksXOu1MdtupDnUwQ/1CB4N7pmfzNlnnqYumiqKMiSd8LCMlHIDvRdON9M7DVIDHgd+AtwshKigdzrkn76BnEe1dEsrqTk9PJhiYYxm4Nz0drZVzWRjUz7WQx3st+dydtDMcpuJA8FWLjVAxODnue9P4Jy501RhVxRlyDqpee5SyjuAO45YXQVMOZk/97jl+LgzJ0CW0LksvZM9VdP4tHUcY3Z8xvOZFzEirHGjycKOQAvXm0xkGjp44cZFpCa6+iWeoihKtAzq2xp+pseRYTTwH2ldHKiYytr2yUzZ8gFrkhcghZFf6za2Blq51mSiyNzE6tvOV4VdUZRTwqC+/UD2pB5mJ3RSuWc6H3dPZPKmNexwTaLaksqPMFMbcPNjk4mZ5oM8efvV6Pqgfi9TFEU5boO6uIs9E9llKGYbCRRtX4vblsZncZMZi05KsIdbTEaW6GU8cPtP0VRhVxTlFDKoK54vSVIZiiWrfBMR3cKuhOWEBMwO+vm50cCKyBru/e8b0dRtehVFOcUM6uKe1TaZ2IN7MAXD6PEXU6ZLzghLHjboXB16jZt/dANmuz3aMRVFUfrdoC7uH9tqcHR3MsL1PV63CFIlrNEk1wdf5MLvXkF8proHu6Iop6ZBPeaeFT+DnMR81tl1mgigE+F7obeZPWMuOZNmRDueoihK1AzqM/cJvoPYbWZelgE0KZkd3MAZyRYmnHtZtKMpiqJE1aA+c++2BLiTHiRQ6NvHOVoZM659Uf3mqaIop7xBXdyf2R+mTgiSfY1cGv47k65/GIvDEe1YiqIoUTeoh2VKR5pw+Vs5V/uIkYuvI3VEQbQjKYqiDAiDurjPykvmfPERY9LzmbDorGjHURRFGTAGdXG3JWYwMiGdBdfcpMbZFUVRDjOox9xTcodz/q13RjuGoijKgDOoz9wVRVGUo1PFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQgSUspoZ0AI0QwcjHaO45AItEQ7xNekMvePwZZ5sOUFlflosqWUSUfbMCCK+2AhhNgopZwU7Rxfh8rcPwZb5sGWF1Tmr0sNyyiKogxBqrgriqIMQaq4fz2PRzvACVCZ+8dgyzzY8oLK/LWoMXdFUZQhSJ25K4qiDEGquCuKogxBqrgfQQiRJYT4QAixSwixUwhxw1HazBZCdAghyvoet0cj6xGZDgghtvfl2XiU7UII8aAQokIIsU0IURKNnIflGXlY/5UJITqFEDce0Sbq/SyEeFII0SSE2HHYunghxHtCiH19z65j7Luyr80+IcTKKOb9nRCivO/n/oYQwnmMfb/0GOrnzL8QQtQe9rNffIx9S4UQe/qO659GOfNLh+U9IIQoO8a+/dPPUkr1OOwBpAElfcsxwF5g9BFtZgN/i3bWIzIdABK/ZPti4O+AAE4DNkQ782HZdKCB3l/IGFD9DMwCSoAdh637LfDTvuWfAvccZb94oKrv2dW37IpS3gWAoW/5nqPlPZ5jqJ8z/wL4r+M4biqBPMAEbD3y/2p/Zj5i+/8Dbo9mP6sz9yNIKeullJv7lruA3UBGdFN9I5YDz8he6wGnECIt2qH6zAUqpZQD7reUpZRrgbYjVi8Hnu5bfho4+yi7LgTek1K2SSndwHtA6bcWtM/R8kop35VShvpergcyv+0cX8cx+vh4TAEqpJRVUsoA8CK9P5tv3ZdlFr1f6Pwd4IX+yHIsqrh/CSFEDjAB2HCUzdOEEFuFEH8XQozp12BHJ4F3hRCbhBBXHWV7BnDosNc1DJw3re9y7P8IA62fAVKklPV9yw1AylHaDNT+vpzeT3BH81XHUH+7rm8o6cljDH0N1D4+HWiUUu47xvZ+6WdV3I9BCOEAXgNulFJ2HrF5M71DCMXAQ8Cb/Z3vKGZKKUuARcC1QohZ0Q50PIQQJmAZ8MpRNg/Efv4/ZO/n7EExn1gIcSsQAp4/RpOBdAw9CgwHxgP19A5zDBYX8eVn7f3Sz6q4H4UQwkhvYX9eSvn6kdullJ1Syu6+5dWAUQiR2M8xj8xU2/fcBLxB70fWw9UCWYe9zuxbF22LgM1SysYjNwzEfu7T+MWQVt9z01HaDKj+FkJcBiwFLu57Q/o3x3EM9RspZaOUMiyljAD/e4wsA6qPAYQQBuBc4KVjtemvflbF/Qh942V/AnZLKe87RpvUvnYIIabQ24+t/Zfy3/LYhRAxXyzTewFtxxHNVgGX9s2aOQ3oOGxoIZqOeZYz0Pr5MKuAL2a/rAT+epQ27wALhBCuviGFBX3r+p0QohT4MbBMSuk5RpvjOYb6zRHXg845RpbPgXwhRG7fJ8Dv0vuziaZ5QLmUsuZoG/u1n/vjyvJgegAz6f2YvQ0o63ssBq4Gru5rcx2wk96r8+uB6VHOnNeXZWtfrlv71h+eWQCP0Du7YDswaQD0tZ3eYh132LoB1c/0vvHUA0F6x3SvABKANcA+4J9AfF/bScATh+17OVDR9/h+FPNW0Ds2/cXx/Fhf23Rg9ZcdQ1HM/GzfcbqN3oKddmTmvteL6Z3RVhntzH3r//zF8XtY26j0s7r9gKIoyhCkhmUURVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGoP8P/9diBtPqFfMAAAAASUVORK5CYII=\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From 16fdd04641374458940662b375130933c5f7f1d7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 074/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 60da65b12538d4f0b64e4de388c1d2c415c76bef Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 075/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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HoTCranxOfrodsJnQwz9xKnUYNLmowJKzxW5be7WJ+DK40cPTltLuCLj+Fwhqos2KHZ0vi9sXQddjYW8efHgx7Fhi25iOvRtG/8ndGFWz1kT/z1ONVs4WiEyEsMg/9nzfM2ybygXvNt3EAna1TAmGRW/As0PgiV42sXQaCSX5tlOEUi5qwv/3qUYpZ0vNPcF81WmY7bLcugF7g7khLBKOOB/WfWsHZ47+s1018+LJMPwGuwBaZQXszbezOJeXuB2xama0WkwFlpytVVU+qm4n/sN2Xuh18v7r0CQdAQsK7ZQyX9wCaQvsTM7dxsKFH0BYS7ciVs2IllxU4DAGCtKrxn2ourVKtON3qi9w1mmE3a75ynZ59tg0w5Z0vFVW2OWdtdeo8jNNLipwFGXZsSDe83OpQxffw1YtTv8nYODcN+zg06ikA9eXWfUZvHKcncVAKT/S5KICR/4Ou9XkcnhE7IBRU2mPe46Htv3txJ47V9hSiqekkr7Ubn/5t13Vc/da+Phq2LHUndhVk6FtLipwFDjrzUdpcjlsY+6w/54Dz7Nr0ICd+HL1F/DPtnb1zGPvhV2r7bU9O2HuczDrKSjOtpNonjXJvfhVo6fJRQUOLbn4T2gLmPjc/uc8c69VlNixMFPvBAQGnGvHy0z7G7RqC0lHwsbptnSjMzCrP0irxVTgyNkCQaH2A075X5fREBFjl3Qe/w87n1lZoZ0O5/x34Ki/wNXfwag/2ZLMhh/djlg1YlpyUYEja4NdlbGhJ5VsLqLbw73b7L4xMO4BW1rsfrwzc/MRzn0d4ce/25LNtdMPbYYEpRxaclHuW/+jXYMlc31V1Y2qXyIw5n/gtCcOrPoKCYPz3rRjjmY/XePjSh2Mq8lFRE4WkbUiskFE7q3heriITHauzxeRZOd8sogUi8hS5+dFr2eGisgK55lnRLTSOKAtnwLvnWNn+c1ca7vRKvd1Gga9T4U5z8CCV6rO/3A/PNYVvr7d9ixTqhauJRcRCQaeB04B+gEXiUi/arddA+QYY3oATwGPeV3baIwZ5Pzc6HV+EnAd0NP5Obm+fgflB1ucKeNjO9v2gH4T3Y1HVTlrEnQcBj8/CmXFkLrQLr5WUWpXx3zleDsLgLe5L8Abp9mlDzb/6k7cKiC4WXIZDmwwxmwyxpQCHwLVP1kmAm85+x8D4+oqiYhIEhBtjJln7EI1bwNn+j905TfZm+1ki7cuhDvWHDjaXLknIgbG3Gm7Jm9fAtMegMg28D9r7TLTAD89VHV/aZEt2WydBfnb4dMboGyvO7Er17mZXDoAqV7Hac65Gu8xxpQDeYCndbGriPwmIr+IyBiv+9MO8poqkGRvchrxQ3XOq0DUabjdrvkGts2BkTfacTOtk2HYtfD7VzDjMbvdMtMu4XzZZ3DWS3bFzNR5roav3NNYu+WkA52NMVkiMhT4XET6H8oLiMj1wPUAnTt3rocQVa0qyuGlY6DXePsNN66b2xGp2rSMs+1g8563x12Oqro29Ao78HLGo1XnImLsPRVlEBRi5zPrNrYBA1aBws2Sy3agk9dxR+dcjfeISAgQA2QZY0qMMVkAxpjFwEagl3O/9/q1Nb0mznMvG2NSjDEpiYmJfvh1lM/WfmNXnJz1lD3WqrDA5pkIMyjEDrD0iOsGt62Ae7ZAl6PtucGXQUi4Ld10HG6Ti2qW3EwuC4GeItJVRMKAC4Evq93zJXCFs38uMN0YY0Qk0ekQgIh0wzbcbzLGpAP5IjLSaZu5HKg2U59ynecDp2UCtB9ix1mowNV+sN22TrYj/71Ft4cWreHSj+HST+wszR7dxto5yoqyGyhQFUhcSy5OG8qtwPfA78AUY8wqEXlYRM5wbnsNiBeRDcAdgKe78jHAchFZim3ov9EY4/kLvhl4FdiALdFUm2NcuS431X4DvuN3uGqqTjES6DzJf9yDtd8T2gJ6nLD/CqLdxgKmqkegalZcbXMxxkwFplY794DX/l7gvBqe+wT4pJbXXAQM8G+kyq9yt9nBkiFhbkeifBHfHf6WaTtdHIoOQyAsypZUtYt5s6Mj9FXDMgbyUiG2iS9D3NQcamLxPJN8tB0Ts22+/2NSAU2Ti2pYRVl2wsTYTge/VzV+45yKiDnPuBuHanCaXFTDynUmTozV7t/NQtt+MOhSWPM1PNkfNvzkdkSqgWhyUQ0rzxk3G6Mll2ajz6l2m58GU++y+ys+huJc92JS9U6Ti2pYWnJpfvqcBlf/AEMuh+yNsGUWfHINTL7U7chUPdLkohpWbqpdBbFFrNuRqIbUeQQMOMfuL3rdbrfMhPJS92JS9UqTi2pYudu0Sqy5ajvQbld6jSLYtdKdWFS90+SiGtbuNXbchGp+IuOrvlh0HGa3aYvci0fVK00uquEU50DO5qrpRFTzM/gyuz3mbohqD9/eBY8lay+yJkiTi2o4O5barSaX5uuYO+Gq7+yM2MnODMvFOfDLY7U/U1poB9+qRkWTi2o46U5y8Z5ZVzUvQcHQZZTd9/wdxPeE1PmwcfqB92+bD4+2h3XfN1yMyi80uaiGs+M3O7Nuyzi3I1GBYPgNcMazcMMvdvr+qXdX9R4ryrZLK3/1Z3u8dmrtr6MCUmNdLEw1JvNftotIbf8NOg51OxoVKELC7NgXgPGPwIcXwbrvoLwEPr3OzrBcusdez9roXpzqDzlochGRNsBRQHugGFgJLDLGVNZzbKopyNxgG209jr2r9ntV89XzRAhrBRt/grXfAsb2KBt+na0uWzbZrmAarN+HG4ta/0uJyHHY9VPigN+ADCACOBPoLiIfA08YY/IbIlDVSC19125bd4XIRDjyInfjUYEpOBS6jIbFb9rj89+umqa/vAQWvmqrVTsNq/t1ti+242l0OQfX1fU14FTgOmPMtuoXnCWHJwAnUsu6KkoBsGYqdD0Wrqi+yKhS1XQcBut/sPs9Tqw6320sIPD7F3Unl9SF8NoJkDwGrvy6HgNVvqi1Qd8Yc1dNicW5Vm6M+dxZtEupmuWnQ+Za6Dne7UhUY+DdizCsZdV+yzgYeC7MfQGyN9X+vGf57C0zobSoXkJUvqs1uYjIHSJyTQ3nrxGR2+o3LNUkpC2w284j3Y1DNQ7tjrDbqPYHXht7H5gK2Phz7c97d2Xetcq/salDVldX5EuAt2s4/w5wdf2Eo5qU1AUQHF71oaFUXaLawYkPw2WfHXgtrptNOltm1vzs9sWwbQ6kON+Hdy6r+b6KMphyOWyd45+YVa3qSi4hxpiy6ieNMaWA1F9IqsnIXA8JvbRxVflGBI76C7TpU/O17sfZaWLK9h54feaTtrv7CX+H8BjI+L3m90idD6u/gDcn+DNyVYO6kkuQiLStfrKmc3+UiJwsImtFZIOI3FvD9XARmexcny8iyc75E0VksYiscLbHez0zw3nNpc5PG3/Fq3yw5htY9qHdz90Krbu4G49qOgacAyX5Bw6o3Jtv/+6GXgUR0RDfrfZxMWucZ02Fjp2pZ3Ull38D34jIsSIS5fyMBb4G/nO4bywiwcDzwClAP+AiEelX7bZrgBxjTA/gKcAzAVEmcLoxZiBwBbaqztslxphBzk/G4caqfJS+HD68GD67wc4FlbsNYjW5KD/pNtZWj816Ciq9htntXgsY6DTCHsd1t4uSVbfqc5j3AnQ5CiQY3jwNfrgfSvYceO/0R3QyzcNUV2+xt4G/AQ8DW4DNwEPAA8aYt/zw3sOBDcaYTU5V24fAxGr3TAQ87/UxME5ExBjzmzFmh3N+FdBCRML9EJM6HFtnV+3vXgNlRbripPKfoGAY+1fYuRx+fRzevxC2zoXdThWYpzotvjvkpdnxMR6lhfDln6FjClzyMQy+FArSYc6zMOvJ/d8nfZl9/XfPbpjfq4mqc7irMeZb4Nt6eu8OQKrXcRoworZ7jDHlIpIHxGNLLh7nAEuMMV5/SbwhIhXYMTj/NObAKVVF5HrgeoDOnfUD0C92ei38tOYbu9Xkovxp4Lmw5C2Y8X/2OL47VJRCSAuITbbn4rqBqYScrZDYy55b8TGU5NkOA2Et4eR/2dm5F79hZwQY90DVeyyfUrVfWminoVGHrK6uyFeKyCwRmSkiVzjn/tFwoR2ciPTHVpXd4HX6Eqe6bIzzc1lNzxpjXjbGpBhjUhITE+s/2OZg1wpo56w2uPQ9u21bvaZTqcMgAqP/DBJkE8rW2bD0A+h5AgQ5H2dxzmJ02Rtt6WbeizDnGfu32dmZkTmsJaRcZdtxMlbDHq/a87SFVftbZjXM79UE1dXmcoox5mhjzBjgDOdcDz++93bAe73bjs65Gu9xZgWIAbKc447AZ8Dlxph9FazGmO3OtgB4H1v9pupbRTlkrLGj8Vsn28FuLeK0zUX5X6/xcNdG6HOqnRKmrBCO9yp5eFY63fAjrPsWvrsHsjbYKjWp1tG17QC7zVxntxVltlos5RqbvGpaBkD5pK7kEi4ibUQkCaiP9oyFQE8R6SoiYcCFQPU5Qr7ENtgDnAtMN8YYEYkFvgHuNcbsq+gXkRARSXD2Q7FT1Ogi3Q0haz1UlNhvhx1S7Ln2gw78n1kpf2gZZ9eBARh2XVX1l+daRKydj8xj1K02GVWX4LxG5nq7TV0A5Xuh6xi7mJk26v9hdbW5/AN4DjCA52vBV/56Y6cN5VbgeyAYeN0Ys0pEHsbOuvwl8BrwjohsALKxCQjgVmwp6gER8cQ2HigEvncSSzDwI/CKv2JWdfCMiG47wJZYdq2C4/7X3ZhU0zbqFkg6AnqdcuC1+B6wfZEdZ3XLgtq/5ER3tAN9szbY0vfs/9oSS48T7fRF39/n9HrUtsNDJTW0dTc7KSkpZtGiRW6H0bh9cQus/BTu2aqDJpX7NvwIU66EM1+AfmfUfe8Lo6FVG+h/Jnz1FzuQ88SHbRfn54fDhKdt+4w6gIgsNsak1HStrgb9r0RkglMKqH6tm4g8LCI6DYyCkgJY8QkMPE8TiwoMPU6Ae7YcPLEA9DjeNtwv/cCWUE54yJ5P6GVLNhu1auyPqKvN5TrgGGCNiCwUkakiMl1ENgEvAYuNMa83SJQqsKUvg/Ji6Hu625EoVcXXhcUGnAOVZZA6D3qfVlWF5plyZtOvtspMHZJa//WNMTuBu4G7nWlXkrArUa4zxuh81s3R7P/aqTaO+6sd0OaRvtxudYJK1Ri1H2zHvWyZBWPv2f9aj3Hw2zu2/UZn9z4kPqV2Y8wW7Ch91ZxNc/pOdBha1fMmd5udjbZVW4jy27RzSjWskTfZn+q6jbVjat6cYNtdTv13Q0fWaNVVLaZUlaLsqn1Pz7D8HfD0QPj9Ky21qKapRWs7F1llGSx4GSor3I6o0dDkonyze03VvmfA2bxJVeeSNLmoJuq0J6r261oJU+3Hp+QiIi1EpHd9B6MCmCehJPSySxcDbP6l6npct4aPSamGkNgbbvjV7u9c4W4sjchBk4uInA4sBb5zjgeJSPWR9Kqpy99h6567HmvXwSjOtQ353Y+3izR1P/7gr6FUY5XYxw623L7Y7UgaDV8a9P+OnZ9rBoAxZqmIdK3HmFQgKkiHyEQ78rl0D6z5GjBw9B12qgylmrKQcOgwBLbNdTuSRsOXarEyY0xetXM6rL+5Kdhl1zhvnWyPf3sXgsPs+hhKNQedR9kxXd6dW1StfEkuq0TkYiBYRHqKyLPAnHqOSwUSY2zJpZVXctk213ZJDm3hamhKNZiB50FlOTzeFbI3ux1NwPMlufwJ6A+UYKewzwNuq8+gVADZOhf+r5Nd/S+qHbT2mkI/6Uj34lKqobXtB4Mutfs6Ff9B1dnm4qxz/7Ax5k5Ap7htjr67B0oL7H5Uki2phEbaNTQ8a2Eo1VxMfA7WfKW9xnxQZ8nFGFMBHN1AsahAlL+jan/AOXab7PxJJGrvdNXMiNgBw5pcDsqX3mK/OV2PP8KulwKAMebTeotKBYbKStt42ftUGHpl1YJMZ06yyxh30MZ81Qy1GwiL3rCj9b3n2FP78SW5RGCXFvYeyGAATS5N3d5cMBXQ9RjodVLV+ch4OOrP7sWllJvaDbSzgGdt3H8FTLWfgyYXY4yuktNcFe6225YJ7sahVCDxzKO3c7kmlzocNLmIyBvUMK7FGKMLhTV1hWZYdvcAACAASURBVJl2GxnvbhxKBZLE3rZTy5pvoLTQtkWGt3I7qoDjS7XY1177EcBZwI5a7lVNSZEnuSS6G4dSgSQ4FPpNhGXvw6pP7bx7Jz3idlQB56DjXIwxn3j9vAecD/ilJVdEThaRtSKyQUTureF6uIhMdq7PdxYt81y7zzm/VkRO8vU11SHQajGlajbmDhh8mZ1Tb8nbdqCx2s8fmXK/J9DmcN/YGUPzPHAK0A+4SET6VbvtGiDHGNMDeAp4zHm2H3AhdnDnycALIhLs42sqXxVm2W1LrRZTaj8JPe2Yl54nQUl+VRWy2seXWZELRCTf8wN8BdxzsOd8MBzYYIzZZIwpBT4EJla7ZyLwlrP/MTBORMQ5/6ExpsQYsxnY4LyeL6+pfFWUaWc8DglzOxKlAlOcM4dvjk4HU50vvcWi6um9OwCpXsdpwIja7jHGlItIHhDvnJ9X7dkOzv7BXhMAEbkeuB6gc+fOf+w3aOoKM7VKTKm6tHaSS/Zm6DTc3VgCjC8ll598OdfYGGNeNsakGGNSEhO1wXqfXashP93uF+6GSE0uStWqdRdAIGu925EEnFqTi4hEiEgckCAirUUkzvlJpqqUcDi2A528jjs652q8R0RCgBjsgM7anvXlNVVt8nfApFHwZB+oKIOiLO0pplRdQsJtieX3r7RRv5q6Si43AIuBPs7W8/MF8Jwf3nsh0FNEuopIGLaBvvoKl18CVzj75wLTjTHGOX+h05usK7aTwQIfX1PVZtfqqv2crU61mDbmK1WngefB7jWQvcntSAJKrW0uxpj/Av8VkT8ZY5719xs7bSi3At8DwcDrxphVIvIwsMgY8yXwGvCOiGwAsrHJAue+KcBqoBy4xZlkk5pe09+xN1mZ6/bfL8rSajGlDqbDELvN+B3iu7sbSwDxpUH/WREZgO3aG+F1/u3DfXNjzFRgarVzD3jt7wXOq+XZR4ADRi7V9JrKR5nrQILAVMK2OXZescjD7nWuVNMW39Nuvb+cKZ+mf3kQGItNLlOxY0hmAYedXFSAyVwHHYdD9kZY6cxL2qaPuzEpFegioiGqvS257M2z3feVT4MozwXGATudSSyPxDasq6Ymc50dHNZpBOQ7/SB0QTClDi6hJ6yYAs8MgbK9bkcTEHxJLsXGmEqgXESigQz275GlmoKibNv1OLE3JI+x50IitM1FKV94Fs4ryoRNM1wNJVD4MnHlIhGJBV7B9hbbA8yt16hUw8t0+ukn9IL2gyFtAfQ6xd2YlGosErym3v/9K+h9snuxBIg6k4sz1cr/GWNygRdF5Dsg2hizvEGiUw0nw+mGnNgbWrWBc193Nx6lGpNor6F/S9+FY++CoFCY9jfI2QLnvQmxzWsmkDqTizHGiMhUYKBzvKUhglINqDjXrgu+cwWEx0BsF7cjUqrx6TIa2vSDvmfAL/+C/x4JCPuWwvrhfji/efWB8qVabImIDDPGLKz3aFTDmzQa9uyC9kPs8q0ibkekVOPTIhZudloLuoyCKZfbFSvH/8MuKvbrv2Hd9/svF97E+ZJcRgCXiMhWoBAnHRtjjqjXyFT9Ksy0PcI8vcLSFsCoW92NSammoNtYuGdr1Re1xD6wfAoseFmTSzXN51+jOXl2KOzN3f/c4EvdiUWppsa7BiC0BXQYCtsXuxePC3xZiXIrtuvx8c5+kS/PqQBWmLV/YjnnNTjrZWjT172YlGrKEnpC7rZmNQbG1xH6KUBv4A0gFHgXOKp+Q1P1Zt13+x/3OxOCfSnEKqX+kPiegLGLijWTL3G+lEDOAs7AtrdgjNkB1NcCYqohrK029ZomFqXqV3w3u81uPitW+pJcSp1p7g2AiETWb0iqXpXthY3TYehV9njMne7Go3xSsLeMvWUVboeh/qio9nZbkO5uHA3Il6+sU0TkJSBWRK4DrsaO1leN0ZaZUFYEfU6D0592Oxrlg6WpuVz95kJCgoS3rxlOn3bRboekDlVkop1xvGCn25E0GF+m3P+PiJwI5AO9gAeMMdPqPTJVP3Ystdsuo92NQ9Vpb1kF09dksCw1lzfnbCEsJIjCkkpuencJU/88htLySmasy2B1ej79kqJJimlBv/bRtArXKs6AFBxil6/QkssBVgAtsFVjK+ovHFXvsjfZInqY1m4GkuzCUhZsziYluTW78vdy24dLWZ+xB4Dx/dry6NkDWbergItfmc8t7y9ha1YhG3cX7vcaCa3COePI9tx8XHcSWoW78WuoukS105KLNxG5FngAmI4dQPmsiDxsjNHJpxqTDT/ZOY52r4G4bm5Ho7zM2ZDJrR/8RnZhKcFBgjGGhFbhvHjpUPomRdE5riUiQkKrcC4d2Zl3520jKiKESZcM4eieCbw/fxttosP5ZvlO3pm3hcVbs/n05qN4fdZmlmzL4c/jetI3SavSXBeVBHlpbkfRYMS21ddxg8haYLQxJss5jgfmGGN6N0B8DSIlJcUsWrTI7TDq15P9Id/5wx58GUx8zt14FBsyCkjNLuaGdxfTOa4ld53UmwWbswkNDuKGY7rROjLsgGcqKg3zN2fRs00UiVEHlk4+XZLGHVOWcWTHGJal5QEQHRHCu9eO4IiOsQCk5xUzY+1u2sVE0L99NG2iIg54HVUPvvqLnQrmrg1uR+I3IrLYGJNS0zVfqsWygAKv4wLnnGpM9uZV7Scd6V4czUxJeQUbMwrZkVtMQUkZZw3uCEBqdhETn5tNYantAfbaFSl0iY/kpP7t6ny94CBhdPfa19g548j2PDt9A8vS8jihbxsePL0/F7w0lzOem83ZgzsQ3SKU9xdso7S8EoCwkCBuHtud4/u0YWCHGETnlqs/kYlQlAWVlRDU9Meh+5JcNgDzReQLbJvLRGC5iNwBYIx58lDfVETigMlAMrAFON8Yk1PDfVcA9zuH/zTGvCUiLYGPgO5ABfCVMeZe5/4rgX8DzoRZPGeMefVQ42tyykuhtACO+18Ydi20aO12RM1CRv5ern5rISu35+87Fx4SzIaMPTw5za63fn5KR07o25Yu8f5pAwsJDuKzm0czZ2MWJ/RtS1hIEJ/efBQv/rKRt+duAeDsIR25dkxXCvaW88qvm3j6x/U8/eN6ThuYRL/20fyens8dJ/aiW2IrKisNqTlFdGrdkqAgTTyHJTIRTCUU50BkvNvR1DtfkstG58fjC2d7OAMp7wV+Msb8S0TudY7v8b7BSUCe2QEMsFhEvgRKgP8YY34WkTDgJxE5xRjzrfPoZGOMzsDordjJ2y1aQ8s4d2NpJtbtKuCqNxaSU1TKg6f3wxiYsiiVm99bAsBpRyRx3ZhuDOoU6/f3jm0ZxqkDk/Ydt4uJ4O9n9OfGY7sTFMR+1WApXVqzJauITxan8dzPG/hmhe3NtD23mE9vGs3fvljJe/O3MaJrHK9dOYzcolIy95TSp10UT/ywluzCMu4+uTchQcKXy3ZQaeDyUV0IDW7638wPmWdV18LdtkNNSQG0SnQ3pnrkS1fkh+rhfScCY539t4AZVEsu2AkzpxljsgFEZBpwsjHmA+BnJ7ZSEVkCdKyHGJuO4my71cTSIL5bmc5tk5cSHRHKlBtGMaBDDAATB7XnyWnraB/bghuP7U5wA5cE2sUc2LYiInRNiOTOk3qTktya8JBgUrOLuPuT5dw+eSmfL91Br7atWLQ1h5R/TqOkvBJjoHXLUHKKygD4ZMn+jdQLN2fzwiVDtKRTXaSTSAp3w8JXYM1UuGN1k13mwpfeYinA/wJdvO8/zCn32xpjPB2+dwJta7inA5DqdZzmnPOOLRY4Hfiv1+lzROQYYB1wuzHG+zWapyJPcmn6RXG3Ze4p4d5PV9CjTSteuTyFpJgW+67FtwrnkbMGuhhd3cb2bgPAiK5xTFmUyudLd5AUE8EXtxzNnI2Z/OPr1YzoGs/gzrG8Nmszl43swskDkvh2ZTrhIUG0iY4gr6iMR6b+zudLt3P2EP3Otx9PcsnfASs+tpPH5u+AmA51P9dI+VIt9h5wF3Z8S6WvLywiPwI1tU7+r/eBs9pl3V3Wan79EOAD4BljzCbn9FfAB8aYEhG5AVsqOr6W568Hrgfo3LmJLz/qKbm00JKLP+UVl5GaXUREaBBfL08nMSqcqSvSKSwp5+kLBu2XWBqToCDhzauH88niNMb3b0uLsGDG9W3LuL5V3wEvHF71/0y/9lXdnI0xfLw4jRd/2ciZgzpo6cWbJ7ms+KhqVvJdq5p1ctltjPnyUF/YGHNCbddEZJeIJBlj0kUkCcio4bbtVFWdga36muF1/DKw3hizbw4TT3dpx6vA43XE97LzGqSkpBxycmtUirRazN/mbcri6jcXUlS6/3xfocHCQ2cMoEebxj23a6vwEK4YnXzIz4kIN43tzm2Tl/LTmgxO7FdTpUQz1aI1hLWCDdMgLMp2sslYBb3Gux1ZvfAluTwoIq8CP2Eb0wEwxnx6GO/7JXAF8C9n+0UN93wPPCoinq5N44H7AETkn0AMcK33A56E5RyeAfx+GDE2HXuc3K3VYn6xLauIm99bQruYCG4/oRc5RaUc2TGWqIgQYluGEVfD+JTmZMIRSTzz03oe+WY1w5PjiGkZ6nZIgSEoGHqOh1WfwpDL4PevbMmlifIluVwF9MGu4+KpFjPA4SSXf2EnxLwG2AqcD/vad240xlxrjMkWkX8AC51nHnbOdcRWra0Bljj98j1djv8sImcA5UA2cOVhxNh0ZK6DmE52RTx1yN6cvZnXZm/mnCEdiYsM46lp6zDAa1cMo2uCTqNTXUhwEI+ePZDLXpvPxa/O495T+jB1xU4iQoP4y7iexLZsxsn3xIcgvjuM+R/I2tikk4tPI/Sb0mj8mjT5EfovjrH1vZcdzveB5mnG2gyufGMhYSFB+wYe9kuK5h9n9mdoF61mrMuMtRnc9O4SissqCAsOorTC/vt1bN2CZy4aTK+2UXy3cienDUyiRVgwADvz9rJuVwG92kYxe0MmR/dMoG10E51B4MeHYM4zcPM8iOtuB1ZunA4zn4TTnoDEwP/YPdwR+nNEpJ8xZrWf41INobISMtdD8hi3I2l0ikrLufOjZfRpF8WnN4/mjdlbiGkRysXDO2tDtQ/G9m7D9DuP5dd1uxndPYEl23L4ZMl21u0s4NJX55MUE8HG3YV8sXQ7b101nK3ZRZzx7CwKSsr3vUZ8ZBif33IUneJauvib1JP2g6CyHJ5LgQlPQcrVsOozuyzGzCfg7JfdjvCw+JJcRgJLRWQzts1FsJ28DqcrsmoomeugvBja9nM7kkbn/fnbyNxTyouXDqVlWAi3HNfD7ZAanaSYFlwwzPYs6xTXkomDOrB+VwEXvDyP7MJSjuudyM9rd/POvK18uiSNoCDhrpN6s3ZnAcf0SuTBL1by9y9X8dqVw1z+TepBD68+T6kLbHLJ2WqPm8AEl74kl5PrPQpVf7bNsdvOo9yNo5HZW1bBy79uYlS3eFKStfrLn3q2jWL+X8ch2LnSLnl1Pg9+adseJl0yhFO8ZhdIzy3miWnrGPP4dB4+YwDH9WnjUtT1ICwSTn4MvrunavnjbGdURf722p9rJA46R4MxZivQCTje2S/y5TkVIFIX2EWKdJr9Q/LR4jQyCkr40/FaWqkPocFBhAQHISI8ef4gRnaL49qju+6XWAAuH5XMmJ4JlJZX8ucPfmNn3l6XIq4nI2+EETfCzuV2ctk8Z8x3/g44SHt4oDtokhCRB7FTs9znnAoF3q3PoJQfZa6zVWJNdIqJ+lBWUcmLMzYypHMso7pr9+361i4mgg+vH8X9Ew6suo1pGco714zgoxtGU1JRyb+/X+tChPWs67F26fGfH7XHHYdDRamdQbkR86UEchZ2zEghgDFmB4c3aaVqSNmboXVXt6MIeJ5ek+UVlfz9y1Vszy3m1uN76BT0AaJzfEuuPqornyxJY8rCVD5alEpJecXBH2wMuh1rt/NftNuuTueb/B3uxOMnviSXUmP/zzMAIqId+xuLvXl26pc4TS512ZpVyOh/TefeT5bzz29+573527jhmG4c17sJ1e83ATcf150u8S25+5Pl3PXxcu7/bKXbIflHWCSc9VLVcXLTSC6+NOhPEZGXgFgRuQ64Gju1igp0nkZCbW85QHFpBavT82gf24Jr3lpEblEZHy609d3np3TkvlP7uhyhqi46IpTPbz6KeZuy+H7VTj5anMblo5Ipr6wkJCiIfu2ja51p2hhDUWkFkeG+fOS54MgL4bMb7H5iH7tt5I36vky5/x8RORHIB3oDDxhjptV7ZOrwrfgIJAjaNe9e43vLKggSISwkiJ/XZDBjbQY//p7B9txiwK7G+OZVwygureDXdbu5RRvxA1bryDBOGZjE6B4J/LJuN+dMmrNvcGZsy1CO7ZXIg6f3J6eolFveW8Lgzq25/7S+/PWzFXy3ciePn3sEEwcF6ESRw65z1nhpAxLc9EsuIvKYMeYeYFoN51Sg2jQD5r0Agy6B1l3cjsY163cVcM6kOURFhPKn43tw76crAEhoFc5NY7uzLauI64/pxpHOol3eM/+qwBXTIpTHzz2SF2Zs4KJhnQkPDWLm+kw++2077WIiWLA5mzU7C1izs4APFmzb99xfPlxKkAinH9nexehrcdp/qvaj2jX65OLL9C9LjDFDqp1b3pQGUTa56V8qyuC5YXaivOt/gfBWbkfkinW7Crjro2UsS8vbd65PuygeP/cI2se2IKFVuIvRqfpw83uLmbpiJwB/P70foSFBbM0qYlyfNgzu3JrzX5rLzry9zL3v+MDurPHqCbYt5vKa5vQNHH9o+hcRuQm4GegmIsu9LkUBs/0bovKrncshZzOc/WqzTSyPf7eGF2ZsJDwkiBcvHcqGjAL+88M6/jahH0d09P/Swiow/G1CP9akF5DQKpxzhnYkKmL/GZkvG9mF//loGb+l5jKkc+taXiUARLeHjMY9qXtd1WLvA98C/4dd496jwLP0sAowJQV2ev0dS+1xpyY4ZYYPFm/NYdIvGzlzUHvun9DPKaG04/xhnfZbP141PUkxLZh+59harx/Xpw2RYcFc+PI8/nRcj8Dtbh7VHtb/aAdSFuy01WSBGGcdau2KbIzJM8ZsMcZcZIzZ6vWjiSVQVJTbH48PLoJnh8COJRARC7HNr62lpLyCez9ZTlJ0BP88a+B+VV+aWFRcZBhf/eloTuzbliemreOjxWnsLatgb1mAjZmJbg9lhbDxJ3iyD0y+1O2IDplO49KYPZcCb3hN/bZlpt2u/tLOuNrIvun4w/M/b2R9xh4eOWsgrQK126lyVbfEVjx70WCGJ8dx36cr6P/g99z6/m9uh7W/aKfDwZxn7XbtVPdi+YM0uTRWlRW2XSVtIZRVm2+pJB+SBrkTl0s+XpzG7ZOX8tz09Zw5qH3TmuBQ+V1QkPDSZUO5YFgnKioNP/6+i9TsIrfDquJJLptm2K2phPKSWm8PRJpcGivP7KlQNfNxqNfkCUlHNmw8DezntRnc//kKduQW883ydO78aBmfL93OWYM78shZA90OTzUCrSPDePSsgcy8+zgiQoM46elfufvjZfsWhXNVtFdX6QRn0bDCTHdi+YO03qCx2rmiaj9tsZ0yosz55tWqLXQZ7U5cDWBHbjHXvrWIikrD3I1ZVFQaereN4ps/H01IsH5fUoemU1xLPrhuJE/8sI4pi9I47Yj2HNsr0d2gor0GevY9HWauhcLdEBOgA0BroP8nBoKN0+GlY2D3Ot+f2TITwlrZRvsdv9n1uDFw+jNw5zrbu6SJ+un3XVRUGh6e2J+NuwvZklXEjWO7aWJRf9jgzq155fIUwoKDmLV+t9vhQHAoXPkNHHEhdD/enivSkstBiUgcMBlIBrYA5xtjcmq47wrgfufwn8aYt5zzM4AkoNi5Nt4YkyEi4cDbwFAgC7jAGLOl3n4Rf6isgHfOsvvpSyGx18GfMQbW/QDdxkJoS1gxBTJWQ0gL6DGuPqMNCN+sSKdLfEsuG2l7w6XlFHNmoE7poRqNFmHBjOwez5fLdjCyWzxDu7QmtmWYewElH21/sjba40ZWLebWV717gZ+MMT2Bn9h/HA2wLwE9CIwAhgMPioj3qKdLjDGDnJ8M59w1QI4xpgfwFPBYff4SfrFlVtV+kY+9vPO3Q36aTS4dnMkTcjbDWZMgpqO/Iwwoy1Jzmbcpm4uGd0ZEuHxUMn89tW9gjlVQjc7NY7uzK7+Ea95axMTnZ5NbVOp2SBCZYLe/vQsZa2q+Z28epAXWLCNuJZeJwFvO/lvAmTXccxIwzRiT7ZRqpnHwJZe9X/djYJwE+qfOhh/tJHVgp8f3xU5nqvF2A6H94Krz/c/yb2wBpqS8gvs/X0lCq3AuHtHZ7XBUEzSyWzxf/+loJl0yhNTsIp7+cT3GGFak5bE9t5iyChca+8Ojodtxtip86p013/P17fDqOMhLa9jY6uBWg35bY0y6s78TqGm2wA5AqtdxmnPO4w0RqQA+wVaZGe9njDHlIpIHxAOBW55MnQ8dhtoVI30tuexykkubfhDk/Cf0TjJNkDGGBz5fxYrtebx46VCiq03roZS/DOgQw4AOMZw3tBMfLNhGSXnlvskvB3aI4aMbRxERGtxwAYnAJR/DW6fD7lpKLp4OPis+gqNvb7jY6lBvJRcR+VFEVtbwM9H7Pu+FyA7BJcaYgcAY5+eyPxDf9SKySEQW7d7tUgNeeYltjO88AlrG+V5y2b0WYjpBRDSEtYRrfoRLP63fWF1ijOGhr1Zx2WsLmLwolVuP68HJA5puZwUVOK4Z03VfYumaEMlFwzuzYnser8/e3PDBBIdA75Ntj7Hi3AOvlxTY7eZfGzauOtRbycUYc0Jt10Rkl4gkGWPSRSQJyKjhtu3AWK/jjsAM57W3O9sCEXkf2ybztvNMJyBNREKAGGzDfk3xvQy8DHZW5EP65fwlc71dKztpEGydA8UH9GmoWV4axHpVCzXhOcSWpeXxxuwtAJw6sB13nOhDhwel/KBX2yhevmwoCzZnc/fJfQgLCSIjfy+Tft7IhcM6ExfZwI39Cc7f/rd325UrPTX+hZlQ4FQEpS+zHX4CoDXArTaXL4ErnP0rgJrmlf4eGC8irZ2G/PHA9yISIiIJACISCkwAPOuder/uucB0c7A1BdyUsdpu2/SDFnGH1qAf3Tx6R320KJXQYOGrW4/m+YuHEFTLSoNK1Yfx/dtx/4R+hIXYj8p7T+lDYWk5z/y0vuGD6TAUwmNg+WRY+l7VeU+VWJ8JUJRVtYJl5gY7zMElbiWXfwEnish64ATnGBFJEZFXAZwJMv8BLHR+HnbOhWOTzHJgKba08orzuq8B8SKyAbiDGnqhBZRdqyAoFBJ6+l4tVllpFxGKDsDFjvwsI38vHy9O4+zBHRnYMUZ7hCnX9WwbxYXDO/POvK2s3VnQsG/eqg3cu9Uug7x8ctV5TxvskRc5x6vsdvIldphDzpYGDdPDlQZ9Y0wWcMCADGPMIuBar+PXgder3VOIHcdS0+vuBc7za7D1KXO9Xd8+OBRatK65LrW6okyoLGuyXY5/XpvBsz+tp3XLMLY6cz3dNLa7y1EpVeWu8b35dkU693++gg+vH0VwQ5amRaDneJg3CUr22PWadq6AqCToPMrek7keep1kFw0EWPoBHHdfw8Xo0CHNbsrdCnFd7X5ErG2UqzxIV0dPV8MmWHLJKyrj1veWkJpTzPbcYkrLK3nqgkEkJ0Qe/GGlGkjryDD+97R+LNySw6NTXVjQq/Mo+wVz91p7vHOlHZYQGW+/pGY5VXbifLxnuVCFh84t5h5jIGcrdDnKHkfEAAZK8uwfSG0862o3wTaXt+ZuobC0go9uHE2/9tFuh6NUrc4d2pGV2/N4bdZmduQWc8eJvejZNqph3tzzhTRnM7QbAJlrbUkFIL6nbWsB2LPLbj0j/BuYJhe3FOdAaQG0dhb0ioix270HSy5OY10TSy5FpeW8MXszx/dpo4lFNQp/m9CP3XtK+GZ5OjPXZ/LIWQMIDhIWbM6ma0IkVx3VtX7e2LMIYM4WO+6lstyWXMD2KFv/A5QW2aU3ALI3u9KDTJOLW3KcvvKtk+3WO7l47M2Df3WGc16Dgefac/nbITisakqIJiAtp4iXftlETlEZN2v7imokgoOE5y8ewl3jC7nx3cX85cOl+85XVBo2ZOxha1YR/zO+F4M71/GF8VCFtbQzn2dvhrnP24HUHZ3hCAk9YOm7dlA22KSzc4XtRdbAnxmaXNySs9VuY2souVS/5/u/ViWXvO22vaWJ9JyasjCV//18BWUVhtOOSCIlOc7tkJQ6JMkJkXx+y1FMX5NB+9gW9GjTilvfX8J78+2o/pU78ph2+7EkRoUf5JUOQXxPm0QAjr0XYjtVnYeqOQs7jbDJJXebJpdmI9dJHDVVi3l4BlV66k7B6YbcNHqKbcks5L7PVjCyWxyPnjWQLvHacK8ap4jQYE4dmLTv+M2rhlNYUk56XjGn/Hcm//5+DY+f68cF/E75F/xwv51PcMgVVecTnOTiGd/SeRQsfBXyUqsmuW0g2lvMLTlb7cDJcKcRsEWs3XonF+/1G35+1PZf3zYH4rs1XJz16I3ZmwkSeOr8QZpYVJMTGR5CjzZRXD4qmY8Xp7FmZ77/XrzdQLj8Cxh65f61GK272olwN/5kjz2LBnpPaLnuB/jiVtszNWsjlBVTHzS5uCVnS1V7C9RccvGM2E8eA788BpOcP5SUqxsiQr9avSN/v+Vjc4tKmbIojdOPbE+b6AgXI1Oqfv3p+B60Cg/hzo+WUVhSXr9vFhJW9bkSmWjHv4RGQoZXl+n3z4Pf3rFLpT87xE7lXw80ubgld2tVlRhAWBQg+ycXz+JAl31u61V7jocLP2h0MyAv3prDqc/M5OT//kpecRlTFqZy83tLKC6r4LoxTaMUplRtYluG8dQFg1i5PZ9XmPCOTAAAFPZJREFUZm7adz6vqIx6mZ3KUzUW08mWaqLa2WSy+ov9p5hKnWe3rdr4Pwa0zeXwGGO7/WVthN2/w7BrIcmHetXKCshNhb5nVJ0LCrKzHHuP0i/KtN2Sg0NcGWHrLz+s3gnApt2FHPnQD/vOH9Ujnr5J2u1YNX3j+rblxH5teXPOFq4cncwt7y9h9oYsjumVyKuXp+ybu8wvPLN3xPew29P+Y6eB+fHvMP6Rqvu2zbXbVjWteHL4NLkcjl8ehxmPVh2HtvQtuRSk2xG23tViYKvGqpdcWsb7JdSGZozhmxXpbM8p5pPFaRzdI4HgIOGXdbv5x8T+tImOYGCHGLfDVKrBnDOkI9NW72LM4z9TWFLOhCOS+Hp5Ou/O28rVR/txTMyAc2yV14kP2+Pux8OZk+Dzm2D+pKr7tmnJJXAdeaFtiB9wDrw5oWo6hoPxTCTnXS0GByaXoixo2TjHs7wzbysPfGEn0IttGcrfJvSjS3xLlqbmMqJrnE5CqZqdY3slEh0RQv7ecv4yrie3ndCTzD0lvDZrM1eOTvbfjN9dRvPj0BcJ3xXEGE/FQO9T7CS5m3+FDin2C26WM5I/sn6Si7a5HI7WXWDEDbb/eNKRVQOXDqb6GBePiNgDk0sjGyyZUbCXt+du4fHv1jKyWxxz7j2eefeNo3e7KCJCgxnZLV4Ti2qWWoQF8/3tx/D8xUO47YSeiAgXDe/M9txiHv56td/aX35em8G1by/istcWsHirM5yhRWto08fu9zoJ2vYHoCIk0k5+WQ80ufhLYi87et6XmY2zN9nugjGd9j9/iNVie8sq/mCw9aOsopJLXpnPA1+sIioihCfOH0T72BYNuySsUgEsKaYFpx2RtO8L1kn92zGuTxvenLOFb1ak77uvotIwfc0usvaUHPJ7TJqxkZZh9v+5ORu8hjMMvtxu+0wgPcLOhJFe9v/t3Xl0VdW9wPHvjyRkIiMECBmQMBYZgomAPkEFB4T3jPNCUcCqODzL81VbofS9tta5C63WqRQVeSrOVpTlAIgF1BAGGcKUxDCGkEBCEgIkZNjvj3NCDuEmQLi55yq/z1pZOcO+l182Ofndvc8+e4dSVFHVyp+mZZpcvCVpuPX9VJYZ3Z9jTT4X2GQlO2fLpb7e7hbznFxmL8sn/dFF5BS1bk2JzPwSnlmYQ9nho6dUfuOecsqP1DR7fm95FTO/yiG3uJIXbhnCtw+PIiE6tFWxKXW2CAkKYNbEdFI6hTMvy3qiv6qmjtvnrOSXc1Yx5rll7C0/9T/+OUUHydpWytTRvenVuQNrdzk+7A69i20TV3Lx3CIe/8GaLWBNfW8WrC9s5t3OjN5zOUP19cbqK00aZq0S98Ob0G8ctGvh0/r+3MYlS52cLZfqcjB1HrvFKqpqeHSBNW59ytxVvDPlArpGnfqzIqWHjjJl7ioqqmr5aM1u5t01nKTYsGbLb9t/iHHPLyciOJDM340mPDiQveVV3P/2GkLbBxAd1p7P1u/BGLikbxzjBsZr15dSpyignXD5uV2YvWwbb63YwTtZu9hQUM7NQ5P4+IcCJr+exe/G/oKRfeJafJ/aunpmfrWV9gHtuDEtkbziSpZsKcYYY12PIsxYXMqOksPURY0ga1gqqQMvIzmubWZz1pbLGfhq416ufelbiiuqrOHC/zYVcr9seWnRuloo/bFxLLpTSJQ1U3JdLRwqsY55uKG/aJM1HcyMsb+gqKKaGR9vOK24X1qSR2V1LU9fP4iDVbVMej2LzPwSCso8P6n76nJrbP7B6lo+XbeHqpo6fv3eWlbtOMB3P5bw6bo9XDckkdkT03nl1jRNLEqdpmuHJBDYTpjxcTalh44y88bBPHHdIF6+NY3C8iomvpbF/HV7WnyPJz7fwpcbi/jvy/vQsUMwqUnRlBw6yu4D1nW972A13+eXMHV0b5ZPv4yho65ts8QC2nI5I4EBQm5xJde+9B3z7hpOctrt8PWfrVEYvS/3/KKyHVB3lN0BSTw063seyRhAn4Z1IBqe0q+uaJz6JfzEbrGFm4qIjwrhzhE9OFJTxzMLc9hZcpjkjs23PhoUlh9hbuYOrj8vkZvOTyI+OoTJr69k/KxMAtoJb94xjAt6Nv6b+yur+WD1bm5KT2T1jgN89EMBy/L2831+CTNvHMzIPnHsPnCY1KRoTSpKtVK/rpF88cBItpcc4qJenQgKsD73X9q3M1kzRpPxwrfMWvojVw/2vEhgUUUVc77bzs1Dk46t3JqaZE0ptWbnAWLC2/Pg++swBsY55kBrS9pyOQOj+nXhvbsvoLK6lslzspj0Th7VEkLV/u3Nv2i/tSrcI5k1ZOaXcsWzS/lkrb1GS8P9lcqixqfzm9xzMcawYlspF/bshIhwfZr1wNRnG1r+VNPgvZW7qamrZ+poq+U0onccXz94MX+/LY1OHdrzly+3UFdvWLSpiD1lR/iff2ZTW2eYMrInYwZ0JWtbKQvWF/LwmH5cn5ZIXEQwQ5JjNLEodYZ6dArn0r6djyWWBsGBAdwyLJnsggqyC8qPO1dfb6irN/zt61zq6q3rtEG/rhFEhATy5ca9/HLOSr7N28+T1w2kb1ffLGqmLZczNCAhir/flsZtr66guKKanaYjgdu3kpm1k/YB7aw//v+8D7YsgPFvUbZrI9HAioqO/ObKvjy3KJdnFubw74O6EdAwVLBoY2NyaTIDcm5xJaWHjjIsxZqaPiE6lJS4cNbuPIVRaljDFAcnRh93j6V7x3C6dwwnZ+9BnlmUw3OLc3l+cePSqNOv6kevzh248tyuvLjkR8YNjOfukTpti1K+kjE4gccWbOaRTzdxy7BkUuLCCQ0KYPLrKyksP0K9gckXnkMPx5LggQHtyEjtxpuZ1kCB58ankpHqu0UGXUkuIhILvAucA2wHbjLGHPBQbhLwe3v3UWPMGyISASxzFEsE3jTGPCAik4G/AHZTgBeMMbPb5IdwGJ7SkeUPjyI6LIgNT3YluHgb0z+y7oN8tnwVrx94yyr4w5us3FpCqonk3qvSuefinnSNDOHB99exaU8FA7v2tR50KsqG2qPWE/9Nbuiv2GbNDTSsR+O6J+d2i2LNjhOq7wTFFVWs213GA6M9DCYALunbmZkLc3h+cS5xEcHcmJbIkOQYLu9vTQ8xKDGa76aNIj4qRFsqSvlQVFgQv7myL099sYWs7Y3zg0WGBHLr8O4MTIjiuvNOXIpj6uje1NUbhvXo6NPEAu61XKYBi40xT4rINHv/YWcBOwH9AUgHDLBaRObbSSjVUW418JHjpe8aY+5v6x+gqS72zL6x3VKI2ZnHr0b1Iio0CJY8QT2CJA+nfsvnxB6JoyamF/dcbDVfL+ptJY/M/BIGJqZAXD/YNN9abTI6+YRFwVbkl9A1MoRkR8tjQLdIPl23h8z8EoaneB66XHroKG9n7bT6XAd57nMdkBDJ0HNiydpeyuyJ6Qy2+2yduunwYqVcceeIFCYM605B2WG++7GE4opqbh3evcWRop0jQnjiukE+jLKRW/dcMoA37O03gGs8lLkSWGiMKbUTykJgjLOAiPQBOnN8S8ZVKcmJxEglD17ehztHpHBL8DKW1g0iN+4KAqrLSGuXS2RS/2Plu0SGkNIpnK+3FANgLrjPGk22b7O1fKlDZXUt/8rZx4U9j3/KfdygeGLCghg/K5O532+33scYPt9QSF5xJfX1howXl/PXRbkM7RFLr86en8gVEebeMZTFD17sMbEopdwV2j7g2BoxD13Z97QeQfA1t5JLF2NMw5M7ewFP03ImALsc+7vtY07jsVoqznkTrheR9SLygYg0eQS+kYhMEZFVIrJq3759rfgRmhESbT2fcvQQVFcSdqSQDYED+KKgcYnTDgn9j3vJDemJfJ9fQnZBOb/a1I/Hov4AQE3HPmTml/Dh6t38+t21jHx6CQerapl44TnHvT4xJowFU0cwICGSpz7fQkHZEV75Vz73vrWG+99ew/f5JewqPULniGBeuLnl6fpDggLoGdc200Eopc4ebdYtJiKLgK4eTs1w7hhjjIi0dlKd8cBtjv1PgXnGmGoRuRurVTTK0wuNMbOAWQDp6eneW1Th2IqSZcceiEzq2Y8XNgYytSG/NHmAcsLQ7ry6bBu3/COTiqpaoC8bIl9gx6YOFK6xZi4NbCfW2tyX9jo2xNCpW3QoL09I44pnlzJ65jdU1VgLc23Ze5AJs1fQqUN7lv72Up2KRSnlE22WXIwxlzV3TkSKRCTeGFMoIvFAsYdiBcAljv1E4BvHewwGAo0xqx3/Zomj/Gzg6dZFfwZC7D/8R8qgzBqlMWzIEKZlO1pHCWnHvSQqLIjnxg/hrrmrCAoQLu7TmUWbYXBSNI9f1pvusWGnNEdXUmwY7949nHlZu9hcWMFvx/Tl6S+2snFPOY9kDNDEopTyGbdu6M8HJgFP2t8/8VDmS+BxEYmx968AnCtm3QzMc76gIWHZu1cDm/E1Z8vFTi7x3fsx/eok+ApMRDwSFnvCyy7q3YmVv7+M6po6YsPbs73kMEkxoQQGnF7P5aDEaAYlNrZs3pkSQ3VNPVFhQa3/mZRS6jS5lVyeBN4TkTuAHcBNACKSDtxjjLnTGFMqIn8GVtqvecQY41ijk5uAsU3ed6qIXA3UAqXA5Db8GTxztlzKd0FgKIR3YtKFcdB/AxLS/AJZHYID6RBs/Zc4x6ufUThBAdpiUUr5nCvJxe6+Gu3h+CrgTsf+a8BrzbzHCU/xGWOmc3zrxvecLZeDhdb61Q0ju6KT3YtLKaV8SKd/8TZny+VgkZVclFLqLKPJxduCI0HaWS2Xyr3QwdMoa6WU+nnT5OJt7dpZS4oeLtGWi1LqrKXJpS2EdbJGih09qC0XpdRZSZNLWwiPs2Y2Bm25KKXOSppc2kJ4R2ukGGjLRSl1VtLk0hacSxNH+GbVN6WU8ieaXNpCeFzjtnaLKaXOQppc2oJzga/QmObLKaXUz5Qml7YQ169xW1dsVEqdhTS5tIXkC9yOQCmlXOXWxJU/bwGBcP2rEBh88rJKKfUzpMmlrQy8we0IlFLKNdotppRSyus0uSillPI6TS5KKaW8TpOLUkopr9PkopRSyus0uSillPI6TS5KKaW8TpOLUkoprxNjjNsxuE5E9gE7WvHSTsB+L4fTFjRO79I4veenECNonM3pboyJ83RCk8sZEJFVxph0t+M4GY3TuzRO7/kpxAgaZ2tot5hSSimv0+SilFLK6zS5nJlZbgdwijRO79I4veenECNonKdN77kopZTyOm25KKWU8jpNLq0kImNEZKuI5InINLfjcRKR7SKyQUTWisgq+1isiCwUkVz7e4wLcb0mIsUiku045jEusTxv1+96ETnP5Tj/KCIFdp2uFZGxjnPT7Ti3isiVPooxSUSWiMgmEdkoIv9lH/er+mwhTn+rzxARyRKRdXacf7KP9xCRFXY874pIe/t4sL2fZ58/x8UY54jINkddptrHXbuGADDG6NdpfgEBwI9ACtAeWAf0dzsuR3zbgU5Njj0NTLO3pwFPuRDXSOA8IPtkcQFjgc8BAYYDK1yO84/AQx7K9rf//4OBHvbvRYAPYowHzrO3I4AcOxa/qs8W4vS3+hSgg70dBKyw6+k9YLx9/BXgXnv7PuAVe3s88K6LMc4BbvBQ3rVryBijLZdWGgrkGWPyjTFHgXeADJdjOpkM4A17+w3gGl8HYIxZCpQ2OdxcXBnAXGPJBKJFJN7FOJuTAbxjjKk2xmwD8rB+P9qUMabQGLPG3j4IbAYS8LP6bCHO5rhVn8YYU2nvBtlfBhgFfGAfb1qfDfX8ATBaRMSlGJvj2jUE2i3WWgnALsf+blq+YHzNAF+JyGoRmWIf62KMKbS39wJd3AntBM3F5Y91fL/dvfCao1vR9TjtLpkhWJ9k/bY+m8QJflafIhIgImuBYmAhVqupzBhT6yGWY3Ha58uBjr6O0RjTUJeP2XX5rIgEN43RQ/xtTpPLz9NFxpjzgKuA/xSRkc6Txmoz+90wQX+Ny/Yy0BNIBQqBme6GYxGRDsCHwAPGmArnOX+qTw9x+l19GmPqjDGpQCJWa6mfyyGdoGmMIjIAmI4V6/lALPCwiyEeo8mldQqAJMd+on3MLxhjCuzvxcDHWBdKUUOT2P5e7F6Ex2kuLr+qY2NMkX1h1wP/oLGrxrU4RSQI6w/2W8aYj+zDflefnuL0x/psYIwpA5YAF2B1JQV6iOVYnPb5KKDEhRjH2F2PxhhTDbyOn9SlJpfWWQn0tkeStMe6oTff5ZgAEJFwEYlo2AauALKx4ptkF5sEfOJOhCdoLq75wER7xMtwoNzR3eNzTfqqr8WqU7DiHG+PHuoB9AayfBCPAK8Cm40xzzhO+VV9NhenH9ZnnIhE29uhwOVY94eWADfYxZrWZ0M93wB8bbcUfR3jFseHCcG6J+SsS/euIV+OHvg5fWGNxMjB6ped4XY8jrhSsEbbrAM2NsSG1R+8GMgFFgGxLsQ2D6sLpAar//eO5uLCGuHyol2/G4B0l+P8PzuO9VgXbbyj/Aw7zq3AVT6K8SKsLq/1wFr7a6y/1WcLcfpbfQ4CfrDjyQb+1z6egpXc8oD3gWD7eIi9n2efT3Exxq/tuswG3qRxRJlr15AxRp/QV0op5X3aLaaUUsrrNLkopZTyOk0uSimlvE6Ti1JKKa/T5KKUUsrrNLko5Ufs2YIfcjsOpc6UJhellFJep8lFKZeJyAwRyRGR5UBf+9hdIrLSXrvjQxEJE5EIe92OILtMpHNfKX+iyUUpF4lIGtb0QalYT66fb5/6yBhzvjFmMNY0JHcYa8r6b4Bxdpnxdrka30at1MlpclHKXSOAj40xh401W3DDHHUDRGSZiGwAJgDn2sdnA7fb27djTVSolN/R5KKUf5oD3G+MGQj8CWsuK4wx3wLniMglWCs0Zjf7Dkq5SJOLUu5aClwjIqH2bNb/YR+PAArt+ykTmrxmLvA22mpRfkwnrlTKZSIyA2v69mJgJ7AGOAT8FtiHtXJjhDFmsl2+K7ANaybhMjdiVupkNLko9RMjIjcAGcaY29yORanmBJ68iFLKX4jI37CWrx7rdixKtURbLkoppbxOb+grpZTyOk0uSimlvE6Ti1JKKa/T5KKUUsrrNLkopZTyOk0uSimlvO7/AY0c1tSlnH5sAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From cf48671defb64d539198fd0e477c42454db7c0ef Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 076/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 2987ea52f015579cc0a5fbd514f02a91c060780e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Tue, 10 Dec 2019 14:10:05 +0100 Subject: [PATCH 077/624] Add scikit-learn version dependence OutlierMixin does not exist in scikit-learn versions previous to 0.20. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 82ebce2ac..619d281c2 100644 --- a/setup.py +++ b/setup.py @@ -82,7 +82,7 @@ ], install_requires=['numpy', 'scipy>=1.3.0', - 'scikit-learn', + 'scikit-learn>=0.20', 'matplotlib', 'scikit-datasets[cran]>=0.1.24', 'rdata', From 84509b15c98b8491d17c566048ce07b0a04b8955 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 10 Dec 2019 18:48:30 +0100 Subject: [PATCH 078/624] Temporal statistic definitions. --- skfda/inference/anova/anova_oneway.py | 17 ++++++++++++++++- skfda/inference/anova/anova_simulation.py | 6 +++--- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 4e27978cf..b0f8bdba7 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -36,6 +36,21 @@ def anova_bootstrap(fd_grouped, n_sim): return simulation +def vn_temp(fd_means, sizes): + + means = [] + for f in fd_means.data_matrix: + means.append(FDataGrid(np.squeeze(f), sample_points=np.squeeze(fd_means.sample_points[0]))) + + v = 0 + + for i in range(len(means)): + for j in range(i + 1, len(means)): + v += sizes[i] * lp_distance(means[i], means[j]) ** 2 + + return v + + def v_gorros(simulaciones, sizes): distr = [] for s in simulaciones: @@ -66,7 +81,7 @@ def func_oneway(fdata, groups, n_sim): means = means.concatenate(fd.mean()) # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) - vn = 0.01 # Temporal + vn = vn_temp(means, [fd.n_samples for fd in fd_groups]) simulation = anova_bootstrap(fd_groups, n_sim) v = v_gorros(simulation, [10, 10, 10]) diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index d4aee8d53..54d4f64b1 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -1,7 +1,6 @@ from skfda import FDataGrid -from skfda.datasets import make_gaussian_process import numpy as np -from skfda.inference.anova.anova_oneway import func_oneway +from skfda.inference.anova.anova_oneway import func_oneway def generate_samples_independent(mean, sigma, n_samples): @@ -29,4 +28,5 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) -func_oneway(fd_total, np.array(['a' for _ in range(10)] + ['b' for _ in range(10)] + ['c' for _ in range(10)]), 2000) +p_v, vn, v = func_oneway(fd_total, np.array(['a' for _ in range(10)] + ['b' for _ in range(10)] + ['c' for _ in range(10)]), 2000) +print(p_v, vn) \ No newline at end of file From 7beda97ce4b1bdb568d5d2562a4a9a02543834a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 11 Dec 2019 00:21:57 +0100 Subject: [PATCH 079/624] Implemented fda.usc version. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 121 ++++++++++------------ skfda/inference/anova/anova_oneway_aux.py | 89 ++++++++++++++++ skfda/inference/anova/anova_simulation.py | 27 +++-- 3 files changed, 161 insertions(+), 76 deletions(-) create mode 100644 skfda/inference/anova/anova_oneway_aux.py diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index b0f8bdba7..062932420 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,90 +1,77 @@ import numpy as np -from skfda.misc.metrics import lp_distance +from skfda.misc.metrics import norm_lp, lp_distance from skfda.representation import FDataGrid from skfda.datasets import make_gaussian_process def vn_statistic(fd_means, sizes): - # Calculating weighted sum of L2 distances between means - distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected - # Calculating square of the distances and summing by groups - distances_group = np.sum(np.multiply(distances_m, distances_m), axis=1) - # Weighted sum - return sum(distances_group * sizes) + k = fd_means.data_matrix.shape[0] + v_n = 0 + for i in range(k): + for j in range(i + 1, k): + # v1 = np.squeeze(fd_means[i].data_matrix[0]) + # v2 = np.squeeze(fd_means[j].data_matrix[0]) + # v_n += sizes[i] * np.linalg.norm(v1 - v2) ** 2 + v_n += sizes[i] * norm_lp(fd_means[i] - fd_means[j]) ** 2 + return v_n def anova_bootstrap(fd_grouped, n_sim): - if len(fd_grouped) < 1: - return + assert len(fd_grouped) > 0 m = fd_grouped[0].ncol + samples = fd_grouped[0].sample_points k = len(fd_grouped) start, stop = fd_grouped[0].domain_range[0] + sizes = [fd.n_samples for fd in fd_grouped] - # Estimating covariances + # Estimating covariances for each group k_est = [np.squeeze(fd.cov().data_matrix[0]) for fd in fd_grouped] - # Simulation - simulation = np.empty((0, k, m)) + l_vector = [] for l in range(n_sim): - sim_l = np.empty((0, m)) + sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(n_samples=1, n_features=m, start=start, - stop=stop, cov=k_est[i]) - sim_l = np.append(sim_l, [np.squeeze(process.data_matrix)], axis=0) - simulation = np.append(simulation, [sim_l], axis=0) - return simulation - + process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) + sim = sim.concatenate(process.mean()) + # l_vector.append(v_hat_statistic(sim, sizes)) + l_vector.append(v_usc(sim)) + return l_vector + + +def v_hat_statistic(values, sizes): + k = len(values) + v_hat = 0 + for i in range(k): + for j in range(i + 1, k): + # v1 = np.squeeze(values[i].data_matrix[0]) + # v2 = np.squeeze(values[j].data_matrix[0]) + # v_hat += np.linalg.norm(v1 - v2 * np.sqrt(sizes[i] / sizes[j]))**2 + v_hat += norm_lp(values[i] - values[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 + return v_hat + + +def func_oneway(*args, n_sim=2000): + # TODO Check grids + assert len(args) > 0 + + fd_groups = args + fd_means = fd_groups[0].mean() + for fd in fd_groups[1:]: + fd_means = fd_means.concatenate(fd.mean()) + + # vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) + vn = v_usc(fd_means) + simulation = anova_bootstrap(fd_groups, n_sim) + p_value = len(np.where(simulation >= vn)[0]) / len(simulation) -def vn_temp(fd_means, sizes): + return p_value, vn, simulation - means = [] - for f in fd_means.data_matrix: - means.append(FDataGrid(np.squeeze(f), sample_points=np.squeeze(fd_means.sample_points[0]))) +def v_usc(values): + k = len(values) v = 0 - - for i in range(len(means)): - for j in range(i + 1, len(means)): - v += sizes[i] * lp_distance(means[i], means[j]) ** 2 - + for i in range(k): + for j in range(i + 1, k): + v += norm_lp(values[i] - values[j]) return v - - -def v_gorros(simulaciones, sizes): - distr = [] - for s in simulaciones: - v = 0 - for i in range(len(s)): - for j in range(i + 1, len(s)): - v += np.linalg.norm(s[i] - s[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 - distr.append(v) - return np.array(distr) - - -def func_oneway(fdata, groups, n_sim): - # Obtaining the different group labels - group_set = np.unique(groups) - - fd_groups = [] - means = None - for group in group_set: - # Creating an independent FDataGrid for each group - indices = np.where(groups == group)[0] - fd = FDataGrid(np.squeeze(np.take(fdata.data_matrix, indices, axis=0)), - sample_points=fdata.sample_points) - fd_groups.append(fd) - # Creating FDataGrid with the means of each group - if not means: - means = fd.mean() - else: - means = means.concatenate(fd.mean()) - - # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) - vn = vn_temp(means, [fd.n_samples for fd in fd_groups]) - - simulation = anova_bootstrap(fd_groups, n_sim) - v = v_gorros(simulation, [10, 10, 10]) - p_value = len(np.where(v >= vn)[0]) / len(v) - - return p_value, vn, v diff --git a/skfda/inference/anova/anova_oneway_aux.py b/skfda/inference/anova/anova_oneway_aux.py new file mode 100644 index 000000000..e4193b6f6 --- /dev/null +++ b/skfda/inference/anova/anova_oneway_aux.py @@ -0,0 +1,89 @@ +import numpy as np +from skfda.misc.metrics import lp_distance +from skfda.representation import FDataGrid +from skfda.datasets import make_gaussian_process + + +def vn_statistic(fd_means, sizes): + # Calculating weighted sum of L2 distances between means + distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected + # Calculating square of the distances and summing by groups + distances_group = np.sum(np.multiply(distances_m, distances_m), axis=1) + # Weighted sum + return sum(distances_group * sizes) + + +def anova_bootstrap(fd_grouped, n_sim): + if len(fd_grouped) < 1: + return + + m = fd_grouped[0].ncol + k = len(fd_grouped) + start, stop = fd_grouped[0].domain_range[0] + + # Estimating covariances + k_est = [np.squeeze(fd.cov().data_matrix[0]) for fd in fd_grouped] + + # Simulation + simulation = np.empty((0, k, m)) + for l in range(n_sim): + sim_l = np.empty((0, m)) + for i, fd in enumerate(fd_grouped): + process = make_gaussian_process(n_samples=1, n_features=m, start=start, + stop=stop, cov=k_est[i]) + sim_l = np.append(sim_l, [np.squeeze(process.data_matrix)], axis=0) + simulation = np.append(simulation, [sim_l], axis=0) + return simulation + + +def vn_temp(fd_means, sizes): + means = [] + for f in fd_means.data_matrix: + means.append(FDataGrid(np.squeeze(f), sample_points=np.squeeze(fd_means.sample_points[0]))) + + v = 0 + + for i in range(len(means)): + for j in range(i + 1, len(means)): + v += sizes[i] * lp_distance(means[i], means[j]) ** 2 + + return v + + +def v_gorros(simulaciones, sizes): + distr = [] + for s in simulaciones: + v = 0 + for i in range(len(s)): + for j in range(i + 1, len(s)): + v += np.linalg.norm(s[i] - s[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 + distr.append(v) + return np.array(distr) + + +def func_oneway(fdata, groups, n_sim): + # Obtaining the different group labels + group_set = np.unique(groups) + + fd_groups = [] + means = None + for group in group_set: + # Creating an independent FDataGrid for each group + indices = np.where(groups == group)[0] + fd = FDataGrid(np.squeeze(np.take(fdata.data_matrix, indices, axis=0)), + sample_points=fdata.sample_points) + fd_groups.append(fd) + # Creating FDataGrid with the means of each group + if not means: + means = fd.mean() + else: + means = means.concatenate(fd.mean()) + + # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) + vn = vn_temp(means, [fd.n_samples for fd in fd_groups]) + + simulation = anova_bootstrap(fd_groups, n_sim) + v = v_gorros(simulation, [10, 10, 10]) + p_value = len(np.where(v >= vn)[0]) / len(v) + + return p_value, vn, v diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 54d4f64b1..fa2d82ee1 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -10,17 +10,18 @@ def generate_samples_independent(mean, sigma, n_samples): # Cuevas simulation study grid = np.linspace(0, 1, 25) n_levels = 3 - +sigmas = np.array([0, 0.2, 1, 1.8, 2.6, 3.4, 4.2, 5]) +sigmas_star = sigmas / 25 # Case M2 -mean1 = np.vectorize(lambda t: t*(1-t)**5)(grid) -mean2 = np.vectorize(lambda t: t**2*(1-t)**4)(grid) -mean3 = np.vectorize(lambda t: t**3*(1-t)**3)(grid) +mean1 = np.vectorize(lambda t: t * (1 - t) ** 5)(grid) +mean2 = np.vectorize(lambda t: t ** 2 * (1 - t) ** 4)(grid) +mean3 = np.vectorize(lambda t: t ** 3 * (1 - t) ** 3)(grid) fd_means = FDataGrid([mean1, mean2, mean3]) -samples1 = generate_samples_independent(mean1, 0.2/25, 10) -samples2 = generate_samples_independent(mean2, 0.2/25, 10) -samples3 = generate_samples_independent(mean3, 0.2/25, 10) +samples1 = generate_samples_independent(mean1, sigmas_star[4], 10) +samples2 = generate_samples_independent(mean2, sigmas_star[4], 10) +samples3 = generate_samples_independent(mean3, sigmas_star[4], 10) # Storing in FDataGrid fd_1 = FDataGrid(samples1, sample_points=grid, dataset_label="Process 1") @@ -28,5 +29,13 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) -p_v, vn, v = func_oneway(fd_total, np.array(['a' for _ in range(10)] + ['b' for _ in range(10)] + ['c' for _ in range(10)]), 2000) -print(p_v, vn) \ No newline at end of file +print(func_oneway(fd_1, fd_2, fd_3, n_sim=10000)[:-1]) + +# pr1 = FDataGrid([[1, 1], [1.5, 1.5]]) +# pr2 = FDataGrid([[2, 2], [2.5, 2.5]]) +# # print(pr1.concatenate(pr2)) +# +# def cosa(*args): +# print(args[0]) +# +# cosa(pr1, pr2) From cf001fd49aaceeec9425b3b37091e6fcce570f20 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 27 Dec 2019 14:37:21 +0100 Subject: [PATCH 080/624] Fixed crisp K-means with its tests passing --- skfda/ml/clustering/kmeans.py | 187 +++++++++++++++++----------------- 1 file changed, 94 insertions(+), 93 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index f9aaaf780..a1d91155d 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -4,8 +4,8 @@ import warnings from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin -from sklearn.exceptions import NotFittedError from sklearn.utils import check_random_state +from sklearn.utils.validation import check_is_fitted import numpy as np @@ -39,11 +39,13 @@ def __init__(self, n_clusters, init, metric, n_init, max_iter, tol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. metric (optional): metric that acceps two FDataGrid objects and - returns a matrix with shape (fdatagrid1.n_samples, - fdatagrid2.n_samples). Defaults to *pairwise_distance(lp_distance)*. + returns a matrix with shape (fdatagrid1.n_samples, + fdatagrid2.n_samples). Defaults to + *pairwise_distance(lp_distance)*. n_init (int, optional): Number of time the k-means algorithm will - be run with different centroid seeds. The final results will be the - best output of n_init consecutive runs in terms of inertia. + be run with different centroid seeds. The final results will + be the best output of n_init consecutive runs in terms of + inertia. max_iter (int, optional): Maximum number of iterations of the clustering algorithm for a single run. Defaults to 100. tol (float, optional): tolerance used to compare the centroids @@ -96,8 +98,8 @@ def _generic_clustering_checks(self, fdatagrid): if self.init is not None and self.init.data_matrix.shape != ( self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain): raise ValueError("The init FDataGrid data_matrix should be of " - "shape (n_clusters, n_features, dim_codomain) and " - "gives the initial centers.") + "shape (n_clusters, n_features, dim_codomain) " + "and gives the initial centers.") if self.max_iter < 1: raise ValueError( @@ -121,17 +123,26 @@ def _init_centroids(self, fdatagrid, random_state): centroid initialization. Returns: - centers (ndarray): initial centers + centroids (ndarray): initial centroids """ - comparison = True - while comparison: - indices = random_state.permutation(fdatagrid.n_samples)[ + + if self.init is None: + _, idx = np.unique(fdatagrid.data_matrix, + axis=0, return_index=True) + unique_data = fdatagrid.data_matrix[np.sort(idx)] + + if len(unique_data) < self.n_clusters: + return ValueError("Not enough unique data points to " + "initialize the requested number of " + "clusters") + + indices = random_state.permutation(len(unique_data))[ :self.n_clusters] - centers = fdatagrid.data_matrix[indices] - unique_centers = np.unique(centers, axis=0) - comparison = len(unique_centers) != self.n_clusters + centroids = unique_data[indices] - return centers + return fdatagrid.copy(data_matrix=centroids) + else: + return self.init.copy() @abstractmethod def fit(self, X, y=None, sample_weight=None): @@ -146,20 +157,6 @@ def fit(self, X, y=None, sample_weight=None): """ pass - def _check_is_fitted(self): - """Perform is_fitted validation for estimator. - - Checks if the estimator is fitted by verifying the presence of - of the calculated attributes "labels_" and "cluster_centers_", and - raises a NotFittedError if that is not the case. - """ - msg = ("This %(name)s instance is not fitted yet. Call 'fit' with " - "appropriate arguments before using this method.") - - if not hasattr(self, "labels_") or \ - not hasattr(self, "cluster_centers_"): - raise NotFittedError(msg % {'name': type(self).__name__}) - def _check_test_data(self, fdatagrid): """Checks that the FDataGrid object and the calculated centroids have compatible shapes. @@ -182,7 +179,7 @@ def predict(self, X, sample_weight=None): Returns: labels_ """ - self._check_is_fitted() + check_is_fitted(self) self._check_test_data(X) return self.labels_ @@ -216,7 +213,7 @@ def transform(self, X): distances_to_centers (numpy.ndarray: (n_samples, n_clusters)): distances of each sample to each cluster. """ - self._check_is_fitted() + check_is_fitted(self) self._check_test_data(X) return self._distances_to_centers @@ -252,7 +249,7 @@ def score(self, X, y=None, sample_weight=None): attribute. """ - self._check_is_fitted() + check_is_fitted(self) self._check_test_data(X) return -self.inertia_ @@ -315,8 +312,9 @@ class KMeans(BaseKMeans): classified. Defaults to 2. init (FDataGrid, optional): Contains the initial centers of the different clusters the algorithm starts with. Its data_marix must - be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). - Defaults to None, and the centers are initialized randomly. + be of the shape (n_clusters, fdatagrid.ncol, + fdatagrid.dim_codomain). Defaults to None, and the centers are + initialized randomly. metric (optional): metric that acceps two FDataGrid objects and returns a matrix with shape (fdatagrid1.n_samples, fdatagrid2.n_samples). Defaults to *pairwise_distance(lp_distance)*. @@ -334,8 +332,9 @@ class KMeans(BaseKMeans): See :term:`Glossary `. Attributes: - labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional matrix - in which each row contains the cluster that observation belongs to. + labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional + matrix in which each row contains the cluster that observation + belongs to. cluster_centers_ (FDataGrid object): data_matrix of shape (n_clusters, ncol, dim_codomain) and contains the centroids for each cluster. @@ -435,29 +434,34 @@ def _kmeans_implementation(self, fdatagrid, random_state): repetitions(int): number of iterations the algorithm was run. """ repetitions = 0 - centers_old = np.zeros( + centroids_old_matrix = np.zeros( (self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain)) - if self.init is None: - centers = self._init_centroids(fdatagrid, random_state) - else: - centers = np.copy(self.init.data_matrix) + centroids = self._init_centroids(fdatagrid, random_state) + centroids_old = centroids.copy(data_matrix=centroids_old_matrix) - while not np.allclose(centers, centers_old, rtol=self.tol, + while not np.allclose(centroids.data_matrix, + centroids_old.data_matrix, + rtol=self.tol, atol=self.tol) and repetitions < self.max_iter: - centers_old = np.copy(centers) - centers_fd = FDataGrid(centers, fdatagrid.sample_points) + centroids_old.data_matrix[...] = centroids.data_matrix + distances_to_centers = self.metric(fdata1=fdatagrid, - fdata2=centers_fd) + fdata2=centroids) + clustering_values = np.argmin(distances_to_centers, axis=1) + for i in range(self.n_clusters): + indices, = np.where(clustering_values == i) - if indices.size != 0: - centers[i] = np.average( + + if len(indices) != 0: + centroids.data_matrix[i] = np.average( fdatagrid.data_matrix[indices, ...], axis=0) + repetitions += 1 - return clustering_values, centers, distances_to_centers, repetitions + return clustering_values, centroids, distances_to_centers, repetitions def fit(self, X, y=None, sample_weight=None): """ Computes K-Means clustering calculating the attributes @@ -475,8 +479,7 @@ def fit(self, X, y=None, sample_weight=None): clustering_values = np.empty( (self.n_init, fdatagrid.n_samples)).astype(int) - centers = np.empty((self.n_init, self.n_clusters, - fdatagrid.ncol, fdatagrid.dim_codomain)) + centroids = np.empty(self.n_init, dtype=object) distances_to_centers = np.empty( (self.n_init, fdatagrid.n_samples, self.n_clusters)) distances_to_their_center = np.empty( @@ -484,7 +487,7 @@ def fit(self, X, y=None, sample_weight=None): n_iter = np.empty((self.n_init)) for j in range(self.n_init): - (clustering_values[j, :], centers[j, :, :, :], + (clustering_values[j, :], centroids[j], distances_to_centers[j, :, :], n_iter[j]) = ( self._kmeans_implementation(fdatagrid=fdatagrid, random_state=random_state)) @@ -496,9 +499,7 @@ def fit(self, X, y=None, sample_weight=None): index_best_iter = np.argmin(inertia) self.labels_ = clustering_values[index_best_iter] - self.cluster_centers_ = FDataGrid(data_matrix=centers[index_best_iter], - sample_points=fdatagrid.sample_points - ) + self.cluster_centers_ = centroids[index_best_iter] self._distances_to_centers = distances_to_centers[index_best_iter] self.inertia_ = inertia[index_best_iter] self.n_iter_ = n_iter[index_best_iter] @@ -630,7 +631,7 @@ class FuzzyKMeans(BaseKMeans): def __init__(self, n_clusters=2, init=None, metric=pairwise_distance(lp_distance), n_init=1, max_iter=100, - tol=1e-4, random_state=0, fuzzifier=2, n_dec=3): + tol=1e-4, random_state=0, fuzzifier=2): """Initialization of the FuzzyKMeans class. Args: @@ -659,15 +660,13 @@ def __init__(self, n_clusters=2, init=None, deterministic. Defaults to 0. fuzzifier (int, optional): Scalar parameter used to specify the degree of fuzziness in the fuzzy algorithm. Defaults to 2. - n_dec (int, optional): designates the number of decimals of the - labels returned in the fuzzy algorithm. Defaults to 3. + """ super().__init__(n_clusters=n_clusters, init=init, metric=metric, n_init=n_init, max_iter=max_iter, tol=tol, random_state=random_state) self.fuzzifier = fuzzifier - self.n_dec = n_dec def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): """ Implementation of the Fuzzy K-Means algorithm for FDataGrid objects @@ -696,46 +695,53 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): """ repetitions = 0 - centers_old = np.zeros( + centroids_old_matrix = np.zeros( (self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain)) - U = np.empty((fdatagrid.n_samples, self.n_clusters)) - distances_to_centers = np.empty((fdatagrid.n_samples, self.n_clusters)) + membership_matrix = np.empty((fdatagrid.n_samples, self.n_clusters)) - if self.init is None: - centers = self._init_centroids(fdatagrid, random_state) - else: - centers = np.copy(self.init.data_matrix) + centroids = self._init_centroids(fdatagrid, random_state) + centroids_old = centroids.copy(data_matrix=centroids_old_matrix) - while not np.allclose(centers, centers_old, rtol=self.tol, + while not np.allclose(centroids.data_matrix, + centroids_old.data_matrix, + rtol=self.tol, atol=self.tol) and repetitions < self.max_iter: - centers_old = np.copy(centers) - centers_fd = FDataGrid(centers, fdatagrid.sample_points) - distances_to_centers = self.metric( - fdata1=fdatagrid, - fdata2=centers_fd) + centroids_old.data_matrix[...] = centroids.data_matrix + + distances_to_centers = self.metric(fdata1=fdatagrid, + fdata2=centroids) + distances_to_centers_raised = (distances_to_centers ** ( 2 / (self.fuzzifier - 1))) - for i in range(fdatagrid.n_samples): - comparison = (fdatagrid.data_matrix[i] == centers).all( - axis=tuple(np.arange(fdatagrid.data_matrix.ndim)[1:])) - if comparison.sum() >= 1: - U[i, np.where(comparison == True)] = 1 - U[i, np.where(comparison == False)] = 0 - else: - for j in range(self.n_clusters): - U[i, j] = 1 / np.sum( - distances_to_centers_raised[i, j] / - distances_to_centers_raised[i]) - - U = np.power(U, self.fuzzifier) - for i in range(self.n_clusters): - centers[i] = np.sum((U[:, i] * fdatagrid.data_matrix.T).T, - axis=0) / np.sum(U[:, i]) + membership_matrix[:, :] = 1 / np.sum( + distances_to_centers_raised[:, :] / + distances_to_centers_raised[:]) + +# for i in range(fdatagrid.n_samples): +# +# comparison = (fdatagrid.data_matrix[i] == centers).all( +# axis=tuple(np.arange(fdatagrid.data_matrix.ndim)[1:])) +# +# if comparison.sum() >= 1: +# U[i, np.where(comparison == True)] = 1 +# U[i, np.where(comparison == False)] = 0 +# else: +# for j in range(self.n_clusters): +# U[i, j] = 1 / np.sum( +# distances_to_centers_raised[i, j] / +# distances_to_centers_raised[i]) + + membership_matrix_raised = np.power( + membership_matrix, self.fuzzifier) + + centroids.data_matrix[:] = np.sum( + (membership_matrix_raised[:, :] * fdatagrid.data_matrix.T).T, + axis=0) / np.sum(membership_matrix_raised[:, :]) repetitions += 1 - return (np.round(np.power(U, 1 / self.fuzzifier), self.n_dec), centers, + return (membership_matrix, centroids, distances_to_centers, repetitions) def fit(self, X, y=None, sample_weight=None): @@ -755,11 +761,6 @@ def fit(self, X, y=None, sample_weight=None): if self.fuzzifier < 2: raise ValueError("The fuzzifier parameter must be greater than 1.") - if self.n_dec < 1: - raise ValueError( - "The number of decimals should be greater than 0 in order to " - "obtain a rational result.") - membership_values = np.empty( (self.n_init, fdatagrid.n_samples, self.n_clusters)) centers = np.empty( From 4db4937f555a338cc477f4bf0a56496be0d8cb1c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 28 Dec 2019 03:01:07 +0100 Subject: [PATCH 081/624] Update of membership matrix vectorized and tested --- skfda/ml/clustering/kmeans.py | 44 ++++++++++++----------------------- 1 file changed, 15 insertions(+), 29 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index a1d91155d..9f9352487 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -713,32 +713,22 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): fdata2=centroids) distances_to_centers_raised = (distances_to_centers ** ( - 2 / (self.fuzzifier - 1))) - - membership_matrix[:, :] = 1 / np.sum( - distances_to_centers_raised[:, :] / - distances_to_centers_raised[:]) - -# for i in range(fdatagrid.n_samples): -# -# comparison = (fdatagrid.data_matrix[i] == centers).all( -# axis=tuple(np.arange(fdatagrid.data_matrix.ndim)[1:])) -# -# if comparison.sum() >= 1: -# U[i, np.where(comparison == True)] = 1 -# U[i, np.where(comparison == False)] = 0 -# else: -# for j in range(self.n_clusters): -# U[i, j] = 1 / np.sum( -# distances_to_centers_raised[i, j] / -# distances_to_centers_raised[i]) + 2 / (1 - self.fuzzifier))) + + membership_matrix[:, :] = (distances_to_centers_raised + / np.sum(distances_to_centers_raised, + axis=1, keepdims=True)) + + # 0 / 0 divisions should be 1 in this context + membership_matrix[np.isnan(membership_matrix)] = 1 membership_matrix_raised = np.power( membership_matrix, self.fuzzifier) - centroids.data_matrix[:] = np.sum( - (membership_matrix_raised[:, :] * fdatagrid.data_matrix.T).T, - axis=0) / np.sum(membership_matrix_raised[:, :]) + centroids.data_matrix[:] = ( + np.einsum('ij,i...->j...', membership_matrix_raised, + fdatagrid.data_matrix) + / np.sum(membership_matrix_raised, axis=0)) repetitions += 1 return (membership_matrix, centroids, @@ -763,9 +753,7 @@ def fit(self, X, y=None, sample_weight=None): membership_values = np.empty( (self.n_init, fdatagrid.n_samples, self.n_clusters)) - centers = np.empty( - (self.n_init, self.n_clusters, fdatagrid.ncol, - fdatagrid.dim_codomain)) + centroids = np.empty(self.n_init, dtype=object) distances_to_centers = np.empty( (self.n_init, fdatagrid.n_samples, self.n_clusters)) distances_to_their_center = np.empty( @@ -773,7 +761,7 @@ def fit(self, X, y=None, sample_weight=None): n_iter = np.empty((self.n_init)) for j in range(self.n_init): - (membership_values[j, :, :], centers[j, :, :, :], + (membership_values[j, :, :], centroids[j], distances_to_centers[j, :, :], n_iter[j]) = ( self._fuzzy_kmeans_implementation(fdatagrid=fdatagrid, random_state=random_state)) @@ -785,9 +773,7 @@ def fit(self, X, y=None, sample_weight=None): index_best_iter = np.argmin(inertia) self.labels_ = membership_values[index_best_iter] - self.cluster_centers_ = FDataGrid(data_matrix=centers[index_best_iter], - sample_points=fdatagrid.sample_points - ) + self.cluster_centers_ = centroids[index_best_iter] self._distances_to_centers = distances_to_centers[index_best_iter] self.inertia_ = inertia[index_best_iter] self.n_iter_ = n_iter[index_best_iter] From eb1180e20ec0eb1134f19ba9081a2285b6666ec0 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 30 Dec 2019 14:26:15 +0100 Subject: [PATCH 082/624] Vectorized fuzzy k-means --- skfda/ml/clustering/kmeans.py | 43 ++++++++++++++++++++++------------- tests/test_clustering.py | 2 +- 2 files changed, 28 insertions(+), 17 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index 9f9352487..204c5a610 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -10,7 +10,6 @@ import numpy as np from ...misc.metrics import pairwise_distance, lp_distance -from ...representation.grid import FDataGrid __author__ = "Amanda Hernando Bernabé" @@ -346,13 +345,14 @@ class KMeans(BaseKMeans): Example: + >>> import skfda >>> data_matrix = [[1, 1, 2, 3, 2.5, 2], ... [0.5, 0.5, 1, 2, 1.5, 1], ... [-1, -1, -0.5, 1, 1, 0.5], ... [-0.5, -0.5, -0.5, -1, -1, -1]] >>> sample_points = [0, 2, 4, 6, 8, 10] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> kmeans = KMeans(random_state=0) + >>> fd = skfda.FDataGrid(data_matrix, sample_points) + >>> kmeans = skfda.ml.clustering.KMeans(random_state=0) >>> kmeans.fit(fd) # doctest:+ELLIPSIS KMeans(...) >>> kmeans.cluster_centers_.data_matrix @@ -571,8 +571,9 @@ class FuzzyKMeans(BaseKMeans): classified. Defaults to 2. init (FDataGrid, optional): Contains the initial centers of the different clusters the algorithm starts with. Its data_marix must - be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). - Defaults to None, and the centers are initialized randomly. + be of the shape (n_clusters, fdatagrid.ncol, + fdatagrid.dim_codomain). Defaults to None, and the centers are + initialized randomly. metric (optional): metric that acceps two FDataGrid objects and returns a matrix with shape (fdatagrid1.n_samples, fdatagrid2.n_samples). Defaults to *pairwise_distance(lp_distance)*. @@ -594,8 +595,9 @@ class FuzzyKMeans(BaseKMeans): returned in the fuzzy algorithm. Defaults to 3. Attributes: - labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional matrix - in which each row contains the cluster that observation belongs to. + labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional + matrix in which each row contains the cluster that observation + belongs to. cluster_centers_ (FDataGrid object): data_matrix of shape (n_clusters, ncol, dim_codomain) and contains the centroids for each cluster. @@ -608,12 +610,13 @@ class FuzzyKMeans(BaseKMeans): Example: + >>> import skfda >>> data_matrix = [[[1, 0.3], [2, 0.4], [3, 0.5], [4, 0.6]], ... [[2, 0.5], [3, 0.6], [4, 0.7], [5, 0.7]], ... [[3, 0.2], [4, 0.3], [5, 0.4], [6, 0.5]]] >>> sample_points = [2, 4, 6, 8] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fuzzy_kmeans = FuzzyKMeans(random_state=0) + >>> fd = skfda.FDataGrid(data_matrix, sample_points) + >>> fuzzy_kmeans = skfda.ml.clustering.FuzzyKMeans(random_state=0) >>> fuzzy_kmeans.fit(fd) # doctest:+ELLIPSIS FuzzyKMeans(...) >>> fuzzy_kmeans.cluster_centers_.data_matrix @@ -712,23 +715,31 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): distances_to_centers = self.metric(fdata1=fdatagrid, fdata2=centroids) - distances_to_centers_raised = (distances_to_centers ** ( - 2 / (1 - self.fuzzifier))) + # Divisions by zero allowed + with np.errstate(divide='ignore'): + distances_to_centers_raised = (distances_to_centers ** ( + 2 / (1 - self.fuzzifier))) - membership_matrix[:, :] = (distances_to_centers_raised - / np.sum(distances_to_centers_raised, - axis=1, keepdims=True)) + # Divisions infinity by infinity allowed + with np.errstate(invalid='ignore'): + membership_matrix[:, :] = (distances_to_centers_raised + / np.sum( + distances_to_centers_raised, + axis=1, keepdims=True)) - # 0 / 0 divisions should be 1 in this context + # inf / inf divisions should be 1 in this context membership_matrix[np.isnan(membership_matrix)] = 1 membership_matrix_raised = np.power( membership_matrix, self.fuzzifier) + slice_denominator = ((slice(None),) + (np.newaxis,) * + (fdatagrid.data_matrix.ndim - 1)) centroids.data_matrix[:] = ( np.einsum('ij,i...->j...', membership_matrix_raised, fdatagrid.data_matrix) - / np.sum(membership_matrix_raised, axis=0)) + / np.sum(membership_matrix_raised, axis=0)[slice_denominator]) + repetitions += 1 return (membership_matrix, centroids, diff --git a/tests/test_clustering.py b/tests/test_clustering.py index bcc9fe344..779ac545a 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -72,7 +72,7 @@ def test_fuzzy_kmeans_univariate(self): fd = FDataGrid(data_matrix, sample_points) fuzzy_kmeans = FuzzyKMeans() fuzzy_kmeans.fit(fd) - np.testing.assert_array_equal(fuzzy_kmeans.predict(fd), + np.testing.assert_array_equal(fuzzy_kmeans.predict(fd).round(3), np.array([[0.965, 0.035], [0.94, 0.06], [0.227, 0.773], From 3ed73572eb2e5e22d45277956560fdb186b2e78c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 30 Dec 2019 14:54:03 +0100 Subject: [PATCH 083/624] Renamed FuzzyKMeans to FuzzyCMeans, which is more standard. --- docs/modules/ml/clustering.rst | 2 +- examples/plot_clustering.py | 12 ++++++------ skfda/exploratory/visualization/clustering.py | 19 ++++++++++--------- skfda/ml/clustering/__init__.py | 2 +- skfda/ml/clustering/kmeans.py | 8 ++++---- tests/test_clustering.py | 4 ++-- 6 files changed, 24 insertions(+), 23 deletions(-) diff --git a/docs/modules/ml/clustering.rst b/docs/modules/ml/clustering.rst index ce07f534b..3bdb59647 100644 --- a/docs/modules/ml/clustering.rst +++ b/docs/modules/ml/clustering.rst @@ -20,7 +20,7 @@ detailed explanation. :toctree: autosummary skfda.ml.clustering.KMeans - skfda.ml.clustering.FuzzyKMeans + skfda.ml.clustering.FuzzyCMeans Nearest Neighbors diff --git a/examples/plot_clustering.py b/examples/plot_clustering.py index b412ef68d..a4f87b57c 100644 --- a/examples/plot_clustering.py +++ b/examples/plot_clustering.py @@ -17,7 +17,7 @@ from skfda import datasets from skfda.exploratory.visualization.clustering import ( plot_clusters, plot_cluster_lines, plot_cluster_bars) -from skfda.ml.clustering import KMeans, FuzzyKMeans +from skfda.ml.clustering import KMeans, FuzzyCMeans ############################################################################## @@ -96,17 +96,17 @@ ############################################################################## # Other clustering algorithm implemented is the Fuzzy K-Means found in the -# class :class:`~skfda.ml.clustering.FuzzyKMeans`. Following the +# class :class:`~skfda.ml.clustering.FuzzyCMeans`. Following the # above procedure, an object of this type is instantiated with the desired # data and then, the -# :func:`~skfda.ml.clustering.FuzzyKMeans.fit` method is called. +# :func:`~skfda.ml.clustering.FuzzyCMeans.fit` method is called. # Internally, the attribute ``labels_`` is calculated, which contains # ´n_clusters´ elements for each sample and dimension, denoting the degree of # membership of each sample to each cluster. They are obtained calling the -# method :func:`~skfda.ml.clustering.FuzzyKMeans.predict`. Also, the centroids +# method :func:`~skfda.ml.clustering.FuzzyCMeans.predict`. Also, the centroids # of each cluster are obtained. -fuzzy_kmeans = FuzzyKMeans(n_clusters=n_clusters, random_state=seed) +fuzzy_kmeans = FuzzyCMeans(n_clusters=n_clusters, random_state=seed) fuzzy_kmeans.fit(fd) print(fuzzy_kmeans.predict(fd)) @@ -121,7 +121,7 @@ ############################################################################## # Another plot implemented to show the results in the class -# :class:`~skfda.ml.clustering.FuzzyKMeans` is +# :class:`~skfda.ml.clustering.FuzzyCMeans` is # :func:`~skfda.exploratory.visualization.clustering_plots.plot_cluster_lines` # which is similar to parallel coordinates. It is recommended to assign colors # to each of the samples in order to identify them. In this example, the diff --git a/skfda/exploratory/visualization/clustering.py b/skfda/exploratory/visualization/clustering.py index a786ac128..c945e02ef 100644 --- a/skfda/exploratory/visualization/clustering.py +++ b/skfda/exploratory/visualization/clustering.py @@ -4,11 +4,12 @@ from mpldatacursor import datacursor from sklearn.exceptions import NotFittedError +from sklearn.utils.validation import check_is_fitted import matplotlib.patches as mpatches import matplotlib.pyplot as plt import numpy as np -from ...ml.clustering import FuzzyKMeans +from ...ml.clustering import FuzzyCMeans from ._utils import (_darken, _get_figure_and_axes, _set_figure_layout_for_fdata, _set_figure_layout, _set_labels) @@ -249,12 +250,12 @@ def plot_clusters(estimator, X, chart=None, fig=None, axes=None, """ _check_if_estimator(estimator) try: - estimator._check_is_fitted() + check_is_fitted(estimator) estimator._check_test_data(X) except NotFittedError: estimator.fit(X) - if isinstance(estimator, FuzzyKMeans): + if isinstance(estimator, FuzzyCMeans): labels = np.argmax(estimator.labels_, axis=1) else: labels = estimator.labels_ @@ -355,11 +356,11 @@ def plot_cluster_lines(estimator, X, chart=None, fig=None, axes=None, fdata = X _check_if_estimator(estimator) - if not isinstance(estimator, FuzzyKMeans): - raise ValueError("The estimator must be a FuzzyKMeans object.") + if not isinstance(estimator, FuzzyCMeans): + raise ValueError("The estimator must be a FuzzyCMeans object.") try: - estimator._check_is_fitted() + check_is_fitted(estimator) estimator._check_test_data(X) except NotFittedError: estimator.fit(X) @@ -456,11 +457,11 @@ def plot_cluster_bars(estimator, X, chart=None, fig=None, axes=None, sort=-1, fdata = X _check_if_estimator(estimator) - if not isinstance(estimator, FuzzyKMeans): - raise ValueError("The estimator must be a FuzzyKMeans object.") + if not isinstance(estimator, FuzzyCMeans): + raise ValueError("The estimator must be a FuzzyCMeans object.") try: - estimator._check_is_fitted() + check_is_fitted(estimator) estimator._check_test_data(X) except NotFittedError: estimator.fit(X) diff --git a/skfda/ml/clustering/__init__.py b/skfda/ml/clustering/__init__.py index 8553c2616..01e2be6af 100644 --- a/skfda/ml/clustering/__init__.py +++ b/skfda/ml/clustering/__init__.py @@ -2,4 +2,4 @@ from . import kmeans from ..._neighbors import NearestNeighbors -from .kmeans import KMeans, FuzzyKMeans +from .kmeans import KMeans, FuzzyCMeans diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index 204c5a610..ebe09e94c 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -507,8 +507,8 @@ def fit(self, X, y=None, sample_weight=None): return self -class FuzzyKMeans(BaseKMeans): - r""" Representation and implementation of the Fuzzy K-Means clustering +class FuzzyCMeans(BaseKMeans): + r""" Representation and implementation of the Fuzzy c-Means clustering algorithm for the FDataGrid object. Let :math:`\mathbf{X = \left\{ x_{1}, x_{2}, ..., x_{n}\right\}}` be a @@ -616,9 +616,9 @@ class FuzzyKMeans(BaseKMeans): ... [[3, 0.2], [4, 0.3], [5, 0.4], [6, 0.5]]] >>> sample_points = [2, 4, 6, 8] >>> fd = skfda.FDataGrid(data_matrix, sample_points) - >>> fuzzy_kmeans = skfda.ml.clustering.FuzzyKMeans(random_state=0) + >>> fuzzy_kmeans = skfda.ml.clustering.FuzzyCMeans(random_state=0) >>> fuzzy_kmeans.fit(fd) # doctest:+ELLIPSIS - FuzzyKMeans(...) + FuzzyCMeans(...) >>> fuzzy_kmeans.cluster_centers_.data_matrix ... # doctest:+NORMALIZE_WHITESPACE array([[[ 2.84075812, 0.2476166 ], diff --git a/tests/test_clustering.py b/tests/test_clustering.py index 779ac545a..50a23cd95 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -1,7 +1,7 @@ import unittest import numpy as np -from skfda.ml.clustering import KMeans, FuzzyKMeans +from skfda.ml.clustering import KMeans, FuzzyCMeans from skfda.representation.grid import FDataGrid @@ -70,7 +70,7 @@ def test_fuzzy_kmeans_univariate(self): [-0.5, -0.5, -0.5, -1, -1, -1]] sample_points = [0, 2, 4, 6, 8, 10] fd = FDataGrid(data_matrix, sample_points) - fuzzy_kmeans = FuzzyKMeans() + fuzzy_kmeans = FuzzyCMeans() fuzzy_kmeans.fit(fd) np.testing.assert_array_equal(fuzzy_kmeans.predict(fd).round(3), np.array([[0.965, 0.035], From 3684164b9354b65b2e628d02f9e6ce7105cc30fe Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 9 Jan 2020 14:55:27 +0100 Subject: [PATCH 084/624] Some small fixes. Fix docs typos. The algorithms accept now a functional metric, not the pairwise metric directly. --- skfda/ml/clustering/kmeans.py | 59 +++++++++++++++-------------------- 1 file changed, 25 insertions(+), 34 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index ebe09e94c..b310582f1 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -37,10 +37,8 @@ def __init__(self, n_clusters, init, metric, n_init, max_iter, tol, must be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. - metric (optional): metric that acceps two FDataGrid objects and - returns a matrix with shape (fdatagrid1.n_samples, - fdatagrid2.n_samples). Defaults to - *pairwise_distance(lp_distance)*. + metric (optional): functional data metric. Defaults to + *lp_distance*. n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of @@ -314,9 +312,8 @@ class KMeans(BaseKMeans): be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. - metric (optional): metric that acceps two FDataGrid objects and returns - a matrix with shape (fdatagrid1.n_samples, fdatagrid2.n_samples). - Defaults to *pairwise_distance(lp_distance)*. + metric (optional): functional data metric. Defaults to + *lp_distance*. n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. @@ -331,9 +328,8 @@ class KMeans(BaseKMeans): See :term:`Glossary `. Attributes: - labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional - matrix in which each row contains the cluster that observation - belongs to. + labels_ (numpy.ndarray: n_samples): vector in which each entry contains + the cluster each observation belongs to. cluster_centers_ (FDataGrid object): data_matrix of shape (n_clusters, ncol, dim_codomain) and contains the centroids for each cluster. @@ -353,10 +349,9 @@ class KMeans(BaseKMeans): >>> sample_points = [0, 2, 4, 6, 8, 10] >>> fd = skfda.FDataGrid(data_matrix, sample_points) >>> kmeans = skfda.ml.clustering.KMeans(random_state=0) - >>> kmeans.fit(fd) # doctest:+ELLIPSIS + >>> kmeans.fit(fd) KMeans(...) >>> kmeans.cluster_centers_.data_matrix - ... # doctest:+NORMALIZE_WHITESPACE array([[[ 0.16666667], [ 0.16666667], [ 0.83333333], @@ -373,7 +368,7 @@ class KMeans(BaseKMeans): """ def __init__(self, n_clusters=2, init=None, - metric=pairwise_distance(lp_distance), + metric=lp_distance, n_init=1, max_iter=100, tol=1e-4, random_state=0): """Initialization of the KMeans class. @@ -385,10 +380,8 @@ def __init__(self, n_clusters=2, init=None, must be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. - metric (optional): metric that acceps two FDataGrid objects and - returns a matrix with shape (fdatagrid1.n_samples, - fdatagrid2.n_samples). - Defaults to *pairwise_distance(lp_distance)*. + metric (optional): functional data metric. Defaults to + *lp_distance*. n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms @@ -440,14 +433,16 @@ def _kmeans_implementation(self, fdatagrid, random_state): centroids = self._init_centroids(fdatagrid, random_state) centroids_old = centroids.copy(data_matrix=centroids_old_matrix) + pairwise_metric = pairwise_distance(self.metric) + while not np.allclose(centroids.data_matrix, centroids_old.data_matrix, rtol=self.tol, atol=self.tol) and repetitions < self.max_iter: centroids_old.data_matrix[...] = centroids.data_matrix - distances_to_centers = self.metric(fdata1=fdatagrid, - fdata2=centroids) + distances_to_centers = pairwise_metric(fdata1=fdatagrid, + fdata2=centroids) clustering_values = np.argmin(distances_to_centers, axis=1) @@ -574,9 +569,8 @@ class FuzzyCMeans(BaseKMeans): be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. - metric (optional): metric that acceps two FDataGrid objects and returns - a matrix with shape (fdatagrid1.n_samples, fdatagrid2.n_samples). - Defaults to *pairwise_distance(lp_distance)*. + metric (optional): functional data metric. Defaults to + *lp_distance*. n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. @@ -591,11 +585,9 @@ class FuzzyCMeans(BaseKMeans): See :term:`Glossary `. fuzzifier (int, optional): Scalar parameter used to specify the degree of fuzziness in the fuzzy algorithm. Defaults to 2. - n_dec (int, optional): designates the number of decimals of the labels - returned in the fuzzy algorithm. Defaults to 3. Attributes: - labels_ (numpy.ndarray: (n_samples, dim_codomain)): 2-dimensional + labels_ (numpy.ndarray: (n_samples, n_clusters)): 2-dimensional matrix in which each row contains the cluster that observation belongs to. cluster_centers_ (FDataGrid object): data_matrix of shape @@ -617,10 +609,9 @@ class FuzzyCMeans(BaseKMeans): >>> sample_points = [2, 4, 6, 8] >>> fd = skfda.FDataGrid(data_matrix, sample_points) >>> fuzzy_kmeans = skfda.ml.clustering.FuzzyCMeans(random_state=0) - >>> fuzzy_kmeans.fit(fd) # doctest:+ELLIPSIS + >>> fuzzy_kmeans.fit(fd) FuzzyCMeans(...) >>> fuzzy_kmeans.cluster_centers_.data_matrix - ... # doctest:+NORMALIZE_WHITESPACE array([[[ 2.84075812, 0.2476166 ], [ 3.84075812, 0.3476166 ], [ 4.84075812, 0.4476166 ], @@ -633,7 +624,7 @@ class FuzzyCMeans(BaseKMeans): """ def __init__(self, n_clusters=2, init=None, - metric=pairwise_distance(lp_distance), n_init=1, max_iter=100, + metric=lp_distance, n_init=1, max_iter=100, tol=1e-4, random_state=0, fuzzifier=2): """Initialization of the FuzzyKMeans class. @@ -645,10 +636,8 @@ def __init__(self, n_clusters=2, init=None, must be of the shape (n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain). Defaults to None, and the centers are initialized randomly. - metric (optional): metric that acceps two FDataGrid objects and - returns a matrix with shape (fdatagrid1.n_samples, - fdatagrid2.n_samples). - Defaults to *pairwise_distance(lp_distance)*. + metric (optional): functional data metric. Defaults to + *lp_distance*. n_init (int, optional): Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. @@ -705,6 +694,8 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): centroids = self._init_centroids(fdatagrid, random_state) centroids_old = centroids.copy(data_matrix=centroids_old_matrix) + pairwise_metric = pairwise_distance(self.metric) + while not np.allclose(centroids.data_matrix, centroids_old.data_matrix, rtol=self.tol, @@ -712,8 +703,8 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): centroids_old.data_matrix[...] = centroids.data_matrix - distances_to_centers = self.metric(fdata1=fdatagrid, - fdata2=centroids) + distances_to_centers = pairwise_metric(fdata1=fdatagrid, + fdata2=centroids) # Divisions by zero allowed with np.errstate(divide='ignore'): From f88be20a974665166963f2a4c4e6d846b54ed67e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 9 Jan 2020 17:21:10 +0100 Subject: [PATCH 085/624] fit moved to BaseKMeans --- skfda/ml/clustering/kmeans.py | 152 ++++++++++++++-------------------- 1 file changed, 63 insertions(+), 89 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index b310582f1..69f84c253 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -62,7 +62,7 @@ def __init__(self, n_clusters, init, metric, n_init, max_iter, tol, self.tol = tol self.random_state = random_state - def _generic_clustering_checks(self, fdatagrid): + def _check_clustering(self, fdatagrid): """Checks the arguments used in the :func:`fit method `. @@ -141,18 +141,63 @@ def _init_centroids(self, fdatagrid, random_state): else: return self.init.copy() + def _check_params(self): + pass + + @abstractmethod + def _algorithm(self): + pass + @abstractmethod + def _compute_inertia(self, membership, centroids, + distances_to_centroids): + pass + def fit(self, X, y=None, sample_weight=None): - """ Computes clustering. + """ Computes Fuzzy K-Means clustering calculating the attributes + *labels_*, *cluster_centers_*, *inertia_* and *n_iter_*. Args: X (FDataGrid object): Object whose samples are clusered, classified into different groups. - y (Ignored): present here for API consistency by convention. + y (Ignored): present here for API consistency by convention. sample_weight (Ignored): present here for API consistency by convention. """ - pass + fdatagrid = self._check_clustering(fdatagrid=X) + random_state = check_random_state(self.random_state) + + self._check_params() + + best_inertia = None + best_membership = None + best_centroids = None + best_distances_to_centroids = None + best_n_iter = None + + for _ in range(self.n_init): + (membership, centroids, + distances_to_centroids, n_iter) = ( + self._algorithm(fdatagrid=fdatagrid, + random_state=random_state)) + + inertia = self._compute_inertia(membership, centroids, + distances_to_centroids) + + if best_inertia is None or inertia < best_inertia: + best_inertia = inertia + best_membership = membership + best_centroids = centroids + best_distances_to_centroids = distances_to_centroids + best_n_iter = n_iter + + self.labels_ = best_membership + self.cluster_centers_ = best_centroids + self._distances_to_centers = best_distances_to_centroids + self.inertia_ = best_inertia + self.n_iter_ = best_n_iter + + return self def _check_test_data(self, fdatagrid): """Checks that the FDataGrid object and the calculated centroids have @@ -401,7 +446,7 @@ def __init__(self, n_clusters=2, init=None, n_init=n_init, max_iter=max_iter, tol=tol, random_state=random_state) - def _kmeans_implementation(self, fdatagrid, random_state): + def _algorithm(self, fdatagrid, random_state): """ Implementation of the K-Means algorithm for FDataGrid objects of any dimension. @@ -458,48 +503,12 @@ def _kmeans_implementation(self, fdatagrid, random_state): return clustering_values, centroids, distances_to_centers, repetitions - def fit(self, X, y=None, sample_weight=None): - """ Computes K-Means clustering calculating the attributes - *labels_*, *cluster_centers_*, *inertia_* and *n_iter_*. - - Args: - X (FDataGrid object): Object whose samples are clusered, - classified into different groups. - y (Ignored): present here for API consistency by convention. - sample_weight (Ignored): present here for API consistency by - convention. - """ - random_state = check_random_state(self.random_state) - fdatagrid = super()._generic_clustering_checks(fdatagrid=X) - - clustering_values = np.empty( - (self.n_init, fdatagrid.n_samples)).astype(int) - centroids = np.empty(self.n_init, dtype=object) - distances_to_centers = np.empty( - (self.n_init, fdatagrid.n_samples, self.n_clusters)) - distances_to_their_center = np.empty( - (self.n_init, fdatagrid.n_samples)) - n_iter = np.empty((self.n_init)) - - for j in range(self.n_init): - (clustering_values[j, :], centroids[j], - distances_to_centers[j, :, :], n_iter[j]) = ( - self._kmeans_implementation(fdatagrid=fdatagrid, - random_state=random_state)) - distances_to_their_center[j, :] = distances_to_centers[ - j, np.arange(fdatagrid.n_samples), - clustering_values[j, :]] - - inertia = np.sum(distances_to_their_center ** 2, axis=1) - index_best_iter = np.argmin(inertia) - - self.labels_ = clustering_values[index_best_iter] - self.cluster_centers_ = centroids[index_best_iter] - self._distances_to_centers = distances_to_centers[index_best_iter] - self.inertia_ = inertia[index_best_iter] - self.n_iter_ = n_iter[index_best_iter] + def _compute_inertia(self, membership, centroids, + distances_to_centroids): + distances_to_their_center = np.choose(membership, + distances_to_centroids.T) - return self + return np.sum(distances_to_their_center ** 2) class FuzzyCMeans(BaseKMeans): @@ -660,7 +669,7 @@ def __init__(self, n_clusters=2, init=None, self.fuzzifier = fuzzifier - def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): + def _algorithm(self, fdatagrid, random_state): """ Implementation of the Fuzzy K-Means algorithm for FDataGrid objects of any dimension. @@ -736,48 +745,13 @@ def _fuzzy_kmeans_implementation(self, fdatagrid, random_state): return (membership_matrix, centroids, distances_to_centers, repetitions) - def fit(self, X, y=None, sample_weight=None): - """ Computes Fuzzy K-Means clustering calculating the attributes - *labels_*, *cluster_centers_*, *inertia_* and *n_iter_*. - - Args: - X (FDataGrid object): Object whose samples are clusered, - classified into different groups. - y (Ignored): present here for API consistency by convention. - sample_weight (Ignored): present here for API consistency by - convention. - """ - fdatagrid = super()._generic_clustering_checks(fdatagrid=X) - random_state = check_random_state(self.random_state) - - if self.fuzzifier < 2: + def _check_params(self): + if self.fuzzifier <= 1: raise ValueError("The fuzzifier parameter must be greater than 1.") - membership_values = np.empty( - (self.n_init, fdatagrid.n_samples, self.n_clusters)) - centroids = np.empty(self.n_init, dtype=object) - distances_to_centers = np.empty( - (self.n_init, fdatagrid.n_samples, self.n_clusters)) - distances_to_their_center = np.empty( - (self.n_init, fdatagrid.n_samples)) - n_iter = np.empty((self.n_init)) - - for j in range(self.n_init): - (membership_values[j, :, :], centroids[j], - distances_to_centers[j, :, :], n_iter[j]) = ( - self._fuzzy_kmeans_implementation(fdatagrid=fdatagrid, - random_state=random_state)) - distances_to_their_center[j, :] = distances_to_centers[ - j, np.arange(fdatagrid.n_samples), - np.argmax(membership_values[j, :, :], axis=-1)] - - inertia = np.sum(distances_to_their_center ** 2, axis=1) - index_best_iter = np.argmin(inertia) - - self.labels_ = membership_values[index_best_iter] - self.cluster_centers_ = centroids[index_best_iter] - self._distances_to_centers = distances_to_centers[index_best_iter] - self.inertia_ = inertia[index_best_iter] - self.n_iter_ = n_iter[index_best_iter] + def _compute_inertia(self, membership, centroids, + distances_to_centroids): + distances_to_their_center = distances_to_centroids[ + np.arange(len(membership)), np.argmax(membership, axis=-1)] - return self + return np.sum(distances_to_their_center ** 2) From 1d32c59aa2ed3e6c2b456995e3498582f5869bc1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 9 Jan 2020 17:59:13 +0100 Subject: [PATCH 086/624] All common logic for KMeans and FuzzyCMeans is now in the base class --- skfda/ml/clustering/kmeans.py | 256 +++++++++++++++------------------- 1 file changed, 115 insertions(+), 141 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index 69f84c253..e6f6c4b1a 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -62,20 +62,20 @@ def __init__(self, n_clusters, init, metric, n_init, max_iter, tol, self.tol = tol self.random_state = random_state - def _check_clustering(self, fdatagrid): + def _check_clustering(self, fdata): """Checks the arguments used in the :func:`fit method `. Args: - fdatagrid (FDataGrid object): Object whose samples + fdata (FDataGrid object): Object whose samples are classified into different groups. """ - if fdatagrid.dim_domain > 1: + if fdata.dim_domain > 1: raise NotImplementedError( "Only support 1 dimension on the domain.") - if fdatagrid.n_samples < 2: + if fdata.n_samples < 2: raise ValueError( "The number of observations must be greater than 1.") @@ -93,7 +93,7 @@ def _check_clustering(self, fdatagrid): "because the init parameter is set.") if self.init is not None and self.init.data_matrix.shape != ( - self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain): + self.n_clusters, fdata.ncol, fdata.dim_codomain): raise ValueError("The init FDataGrid data_matrix should be of " "shape (n_clusters, n_features, dim_codomain) " "and gives the initial centers.") @@ -105,7 +105,7 @@ def _check_clustering(self, fdatagrid): if self.tol < 0: raise ValueError("The tolerance must be positive.") - return fdatagrid + return fdata def _init_centroids(self, fdatagrid, random_state): """Compute the initial centroids @@ -145,9 +145,70 @@ def _check_params(self): pass @abstractmethod - def _algorithm(self): + def _create_membership(self, n_samples): pass + @abstractmethod + def _update(self, fdata, membership_matrix, distances_to_centroids, + centroids): + pass + + def _algorithm(self, fdata, random_state): + """ Implementation of the Fuzzy K-Means algorithm for FDataGrid objects + of any dimension. + + Args: + fdata (FDataGrid object): Object whose samples are clustered, + classified into different groups. + random_state (RandomState object): random number generation for + centroid initialization. + + Returns: + (tuple): tuple containing: + + membership values (numpy.ndarray): + membership value that observation has to each cluster. + + centroids (numpy.ndarray: (n_clusters, ncol, dim_codomain)): + centroids for each cluster. + + distances_to_centroids (numpy.ndarray: (n_samples, + n_clusters)): distances of each sample to each cluster. + + repetitions(int): number of iterations the algorithm was run. + + """ + repetitions = 0 + centroids_old_matrix = np.zeros( + (self.n_clusters, fdata.ncol, fdata.dim_codomain)) + membership_matrix = self._create_membership(fdata.n_samples) + + centroids = self._init_centroids(fdata, random_state) + centroids_old = centroids.copy(data_matrix=centroids_old_matrix) + + pairwise_metric = pairwise_distance(self.metric) + + while not np.allclose(centroids.data_matrix, + centroids_old.data_matrix, + rtol=self.tol, + atol=self.tol) and repetitions < self.max_iter: + + centroids_old.data_matrix[...] = centroids.data_matrix + + distances_to_centroids = pairwise_metric(fdata1=fdata, + fdata2=centroids) + + self._update( + fdata=fdata, + membership_matrix=membership_matrix, + distances_to_centroids=distances_to_centroids, + centroids=centroids) + + repetitions += 1 + + return (membership_matrix, centroids, + distances_to_centroids, repetitions) + @abstractmethod def _compute_inertia(self, membership, centroids, distances_to_centroids): @@ -164,7 +225,7 @@ def fit(self, X, y=None, sample_weight=None): sample_weight (Ignored): present here for API consistency by convention. """ - fdatagrid = self._check_clustering(fdatagrid=X) + fdata = self._check_clustering(X) random_state = check_random_state(self.random_state) self._check_params() @@ -178,7 +239,7 @@ def fit(self, X, y=None, sample_weight=None): for _ in range(self.n_init): (membership, centroids, distances_to_centroids, n_iter) = ( - self._algorithm(fdatagrid=fdatagrid, + self._algorithm(fdata=fdata, random_state=random_state)) inertia = self._compute_inertia(membership, centroids, @@ -446,69 +507,28 @@ def __init__(self, n_clusters=2, init=None, n_init=n_init, max_iter=max_iter, tol=tol, random_state=random_state) - def _algorithm(self, fdatagrid, random_state): - """ Implementation of the K-Means algorithm for FDataGrid objects - of any dimension. - - Args: - fdatagrid (FDataGrid object): Object whose samples are clusered, - classified into different groups. - random_state (RandomState object): random number generation for - centroid initialization. - - Returns: - (tuple): tuple containing: - - clustering_values (numpy.ndarray: (n_samples,)): 1-dimensional - array where each row contains the cluster that observation - belongs to. - - centers (numpy.ndarray: (n_clusters, ncol, dim_codomain)): - Contains the centroids for each cluster. - - distances_to_centers (numpy.ndarray: (n_samples, n_clusters)): - distances of each sample to each cluster. - - repetitions(int): number of iterations the algorithm was run. - """ - repetitions = 0 - centroids_old_matrix = np.zeros( - (self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain)) - - centroids = self._init_centroids(fdatagrid, random_state) - centroids_old = centroids.copy(data_matrix=centroids_old_matrix) - - pairwise_metric = pairwise_distance(self.metric) - - while not np.allclose(centroids.data_matrix, - centroids_old.data_matrix, - rtol=self.tol, - atol=self.tol) and repetitions < self.max_iter: - centroids_old.data_matrix[...] = centroids.data_matrix + def _compute_inertia(self, membership, centroids, + distances_to_centroids): + distances_to_their_center = np.choose(membership, + distances_to_centroids.T) - distances_to_centers = pairwise_metric(fdata1=fdatagrid, - fdata2=centroids) + return np.sum(distances_to_their_center ** 2) - clustering_values = np.argmin(distances_to_centers, axis=1) + def _create_membership(self, n_samples): + return np.empty(n_samples, dtype=int) - for i in range(self.n_clusters): + def _update(self, fdata, membership_matrix, distances_to_centroids, + centroids): - indices, = np.where(clustering_values == i) + membership_matrix[:] = np.argmin(distances_to_centroids, axis=1) - if len(indices) != 0: - centroids.data_matrix[i] = np.average( - fdatagrid.data_matrix[indices, ...], axis=0) + for i in range(self.n_clusters): - repetitions += 1 + indices, = np.where(membership_matrix == i) - return clustering_values, centroids, distances_to_centers, repetitions - - def _compute_inertia(self, membership, centroids, - distances_to_centroids): - distances_to_their_center = np.choose(membership, - distances_to_centroids.T) - - return np.sum(distances_to_their_center ** 2) + if len(indices) != 0: + centroids.data_matrix[i] = np.average( + fdata.data_matrix[indices, ...], axis=0) class FuzzyCMeans(BaseKMeans): @@ -669,82 +689,6 @@ def __init__(self, n_clusters=2, init=None, self.fuzzifier = fuzzifier - def _algorithm(self, fdatagrid, random_state): - """ Implementation of the Fuzzy K-Means algorithm for FDataGrid objects - of any dimension. - - Args: - fdatagrid (FDataGrid object): Object whose samples are clusered, - classified into different groups. - random_state (RandomState object): random number generation for - centroid initialization. - - Returns: - (tuple): tuple containing: - - membership values (numpy.ndarray: (n_samples, n_clusters)): - 2-dimensional matrix where each row contains the membership - value that observation has to each cluster. - - centers (numpy.ndarray: (n_clusters, ncol, dim_codomain)): - Contains the centroids for each cluster. - - distances_to_centers (numpy.ndarray: (n_samples, n_clusters)): - distances of each sample to each cluster. - - repetitions(int): number of iterations the algorithm was run. - - """ - repetitions = 0 - centroids_old_matrix = np.zeros( - (self.n_clusters, fdatagrid.ncol, fdatagrid.dim_codomain)) - membership_matrix = np.empty((fdatagrid.n_samples, self.n_clusters)) - - centroids = self._init_centroids(fdatagrid, random_state) - centroids_old = centroids.copy(data_matrix=centroids_old_matrix) - - pairwise_metric = pairwise_distance(self.metric) - - while not np.allclose(centroids.data_matrix, - centroids_old.data_matrix, - rtol=self.tol, - atol=self.tol) and repetitions < self.max_iter: - - centroids_old.data_matrix[...] = centroids.data_matrix - - distances_to_centers = pairwise_metric(fdata1=fdatagrid, - fdata2=centroids) - - # Divisions by zero allowed - with np.errstate(divide='ignore'): - distances_to_centers_raised = (distances_to_centers ** ( - 2 / (1 - self.fuzzifier))) - - # Divisions infinity by infinity allowed - with np.errstate(invalid='ignore'): - membership_matrix[:, :] = (distances_to_centers_raised - / np.sum( - distances_to_centers_raised, - axis=1, keepdims=True)) - - # inf / inf divisions should be 1 in this context - membership_matrix[np.isnan(membership_matrix)] = 1 - - membership_matrix_raised = np.power( - membership_matrix, self.fuzzifier) - - slice_denominator = ((slice(None),) + (np.newaxis,) * - (fdatagrid.data_matrix.ndim - 1)) - centroids.data_matrix[:] = ( - np.einsum('ij,i...->j...', membership_matrix_raised, - fdatagrid.data_matrix) - / np.sum(membership_matrix_raised, axis=0)[slice_denominator]) - - repetitions += 1 - - return (membership_matrix, centroids, - distances_to_centers, repetitions) - def _check_params(self): if self.fuzzifier <= 1: raise ValueError("The fuzzifier parameter must be greater than 1.") @@ -755,3 +699,33 @@ def _compute_inertia(self, membership, centroids, np.arange(len(membership)), np.argmax(membership, axis=-1)] return np.sum(distances_to_their_center ** 2) + + def _create_membership(self, n_samples): + return np.empty((n_samples, self.n_clusters)) + + def _update(self, fdata, membership_matrix, distances_to_centroids, + centroids): + # Divisions by zero allowed + with np.errstate(divide='ignore'): + distances_to_centers_raised = (distances_to_centroids ** ( + 2 / (1 - self.fuzzifier))) + + # Divisions infinity by infinity allowed + with np.errstate(invalid='ignore'): + membership_matrix[:, :] = (distances_to_centers_raised + / np.sum( + distances_to_centers_raised, + axis=1, keepdims=True)) + + # inf / inf divisions should be 1 in this context + membership_matrix[np.isnan(membership_matrix)] = 1 + + membership_matrix_raised = np.power( + membership_matrix, self.fuzzifier) + + slice_denominator = ((slice(None),) + (np.newaxis,) * + (fdata.data_matrix.ndim - 1)) + centroids.data_matrix[:] = ( + np.einsum('ij,i...->j...', membership_matrix_raised, + fdata.data_matrix) + / np.sum(membership_matrix_raised, axis=0)[slice_denominator]) From 186bc776df70648561c10525852fa23099a8ac27 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 9 Jan 2020 18:09:34 +0100 Subject: [PATCH 087/624] Using the proper inertia for FuzzyCMeans --- skfda/ml/clustering/kmeans.py | 5 +---- tests/test_clustering.py | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index e6f6c4b1a..e021257df 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -695,10 +695,7 @@ def _check_params(self): def _compute_inertia(self, membership, centroids, distances_to_centroids): - distances_to_their_center = distances_to_centroids[ - np.arange(len(membership)), np.argmax(membership, axis=-1)] - - return np.sum(distances_to_their_center ** 2) + return np.sum(membership**self.fuzzifier * distances_to_centroids**2) def _create_membership(self, n_samples): return np.empty((n_samples, self.n_clusters)) diff --git a/tests/test_clustering.py b/tests/test_clustering.py index 50a23cd95..5945f5113 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -90,7 +90,7 @@ def test_fuzzy_kmeans_univariate(self): np.testing.assert_allclose(fuzzy_kmeans.cluster_centers_.data_matrix, centers.data_matrix) np.testing.assert_allclose(fuzzy_kmeans.score(fd), - np.array([-13.928868250627902])) + np.array([-12.025179])) np.testing.assert_array_equal(fuzzy_kmeans.n_iter_, np.array([18.])) # def test_fuzzy_kmeans_multivariate(self): From bbd2a0bb452a1f4e40a24888b032f6bd690c2feb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 16 Jan 2020 17:35:01 +0100 Subject: [PATCH 088/624] USC bootstrap implemented. --- skfda/inference/anova/anova_oneway.py | 64 +++++++++++--- skfda/inference/anova/anova_oneway_aux.py | 103 ++++++++-------------- skfda/inference/anova/anova_simulation.py | 18 ++-- 3 files changed, 93 insertions(+), 92 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 062932420..431fb83c3 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -9,9 +9,6 @@ def vn_statistic(fd_means, sizes): v_n = 0 for i in range(k): for j in range(i + 1, k): - # v1 = np.squeeze(fd_means[i].data_matrix[0]) - # v2 = np.squeeze(fd_means[j].data_matrix[0]) - # v_n += sizes[i] * np.linalg.norm(v1 - v2) ** 2 v_n += sizes[i] * norm_lp(fd_means[i] - fd_means[j]) ** 2 return v_n @@ -21,12 +18,12 @@ def anova_bootstrap(fd_grouped, n_sim): m = fd_grouped[0].ncol samples = fd_grouped[0].sample_points - k = len(fd_grouped) start, stop = fd_grouped[0].domain_range[0] sizes = [fd.n_samples for fd in fd_grouped] # Estimating covariances for each group - k_est = [np.squeeze(fd.cov().data_matrix[0]) for fd in fd_grouped] + k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] + print(fd_grouped[0]) l_vector = [] for l in range(n_sim): @@ -34,8 +31,9 @@ def anova_bootstrap(fd_grouped, n_sim): for i, fd in enumerate(fd_grouped): process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) sim = sim.concatenate(process.mean()) - # l_vector.append(v_hat_statistic(sim, sizes)) - l_vector.append(v_usc(sim)) + # l_vector.append(v_usc(sim)) + l_vector.append(v_hat_statistic(sim, sizes)) + return l_vector @@ -52,7 +50,9 @@ def v_hat_statistic(values, sizes): def func_oneway(*args, n_sim=2000): + # TODO Check grids + assert len(args) > 0 fd_groups = args @@ -60,10 +60,11 @@ def func_oneway(*args, n_sim=2000): for fd in fd_groups[1:]: fd_means = fd_means.concatenate(fd.mean()) - # vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) - vn = v_usc(fd_means) - simulation = anova_bootstrap(fd_groups, n_sim) - p_value = len(np.where(simulation >= vn)[0]) / len(simulation) + vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) + # vn = v_usc(fd_means) + + simulation = anova_bootstrap(fd_groups, n_sim=n_sim) + p_value = np.sum(simulation >= vn) / len(simulation) return p_value, vn, simulation @@ -75,3 +76,44 @@ def v_usc(values): for j in range(i + 1, k): v += norm_lp(values[i] - values[j]) return v + + +def anova_bootstrap_usc(fd_grouped, n_sim): + assert len(fd_grouped) > 0 + + m = fd_grouped[0].ncol + samples = fd_grouped[0].sample_points + start, stop = fd_grouped[0].domain_range[0] + sizes = [fd.n_samples for fd in fd_grouped] + + # Estimating covariances for each group + k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] + + l_vector = [] + for l in range(n_sim): + sim = FDataGrid(np.empty((0, m)), sample_points=samples) + for i, fd in enumerate(fd_grouped): + process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) + sim = sim.concatenate(process.mean()) + l_vector.append(v_usc(sim)) + + return l_vector + + +def func_oneway_usc(*args, n_sim=2000): + + # TODO Check grids + + assert len(args) > 0 + + fd_groups = args + fd_means = fd_groups[0].mean() + for fd in fd_groups[1:]: + fd_means = fd_means.concatenate(fd.mean()) + + vn = v_usc(fd_means) + + simulation = anova_bootstrap_usc(fd_groups, n_sim=n_sim) + p_value = len(np.where(simulation >= vn)[0]) / len(simulation) + + return p_value, vn, simulation diff --git a/skfda/inference/anova/anova_oneway_aux.py b/skfda/inference/anova/anova_oneway_aux.py index e4193b6f6..9fc755b2a 100644 --- a/skfda/inference/anova/anova_oneway_aux.py +++ b/skfda/inference/anova/anova_oneway_aux.py @@ -1,89 +1,56 @@ import numpy as np -from skfda.misc.metrics import lp_distance +from skfda.misc.metrics import norm_lp, lp_distance from skfda.representation import FDataGrid from skfda.datasets import make_gaussian_process -def vn_statistic(fd_means, sizes): - # Calculating weighted sum of L2 distances between means - distances_m = np.tril(lp_distance(fd_means, fd_means)) # lp_distance not working as expected - # Calculating square of the distances and summing by groups - distances_group = np.sum(np.multiply(distances_m, distances_m), axis=1) - # Weighted sum - return sum(distances_group * sizes) +def v_usc(values): + k = len(values) + v = 0 + for i in range(k): + for j in range(i + 1, k): + v += norm_lp(values[i] - values[j]) + return v -def anova_bootstrap(fd_grouped, n_sim): - if len(fd_grouped) < 1: - return +def anova_bootstrap_usc(fd_grouped, n_sim): + assert len(fd_grouped) > 0 m = fd_grouped[0].ncol - k = len(fd_grouped) + samples = fd_grouped[0].sample_points start, stop = fd_grouped[0].domain_range[0] + sizes = [fd.n_samples for fd in fd_grouped] - # Estimating covariances - k_est = [np.squeeze(fd.cov().data_matrix[0]) for fd in fd_grouped] + # Estimating covariances for each group + k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - # Simulation - simulation = np.empty((0, k, m)) + l_vector = [] for l in range(n_sim): - sim_l = np.empty((0, m)) + sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(n_samples=1, n_features=m, start=start, - stop=stop, cov=k_est[i]) - sim_l = np.append(sim_l, [np.squeeze(process.data_matrix)], axis=0) - simulation = np.append(simulation, [sim_l], axis=0) - return simulation + process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) + sim = sim.concatenate(process.mean()) + l_vector.append(v_usc(sim)) + return l_vector -def vn_temp(fd_means, sizes): - means = [] - for f in fd_means.data_matrix: - means.append(FDataGrid(np.squeeze(f), sample_points=np.squeeze(fd_means.sample_points[0]))) - v = 0 +def oneway(*args, n_sim=2000): - for i in range(len(means)): - for j in range(i + 1, len(means)): - v += sizes[i] * lp_distance(means[i], means[j]) ** 2 + # TODO Check grids - return v + assert len(args) > 0 + + fd_groups = args + fd_means = fd_groups[0].mean() + for fd in fd_groups[1:]: + fd_means = fd_means.concatenate(fd.mean()) + + vn = v_usc(fd_means) + + simulation = anova_bootstrap_usc(fd_groups, n_sim=n_sim) + p_value = len(np.where(simulation >= vn)[0]) / len(simulation) + + return p_value, vn, simulation -def v_gorros(simulaciones, sizes): - distr = [] - for s in simulaciones: - v = 0 - for i in range(len(s)): - for j in range(i + 1, len(s)): - v += np.linalg.norm(s[i] - s[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 - distr.append(v) - return np.array(distr) - - -def func_oneway(fdata, groups, n_sim): - # Obtaining the different group labels - group_set = np.unique(groups) - - fd_groups = [] - means = None - for group in group_set: - # Creating an independent FDataGrid for each group - indices = np.where(groups == group)[0] - fd = FDataGrid(np.squeeze(np.take(fdata.data_matrix, indices, axis=0)), - sample_points=fdata.sample_points) - fd_groups.append(fd) - # Creating FDataGrid with the means of each group - if not means: - means = fd.mean() - else: - means = means.concatenate(fd.mean()) - - # vn = vn_statistic(means, [fd.n_samples for fd in fd_groups]) - vn = vn_temp(means, [fd.n_samples for fd in fd_groups]) - - simulation = anova_bootstrap(fd_groups, n_sim) - v = v_gorros(simulation, [10, 10, 10]) - p_value = len(np.where(v >= vn)[0]) / len(v) - - return p_value, vn, v diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index fa2d82ee1..457449052 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -1,17 +1,17 @@ from skfda import FDataGrid import numpy as np -from skfda.inference.anova.anova_oneway import func_oneway +from skfda.inference.anova.anova_oneway import func_oneway, func_oneway_usc +from skfda.datasets import make_gaussian_process def generate_samples_independent(mean, sigma, n_samples): return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] -# Cuevas simulation study grid = np.linspace(0, 1, 25) n_levels = 3 sigmas = np.array([0, 0.2, 1, 1.8, 2.6, 3.4, 4.2, 5]) -sigmas_star = sigmas / 25 +sigmas_star = sigmas * 25 # Case M2 mean1 = np.vectorize(lambda t: t * (1 - t) ** 5)(grid) mean2 = np.vectorize(lambda t: t ** 2 * (1 - t) ** 4)(grid) @@ -29,13 +29,5 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) -print(func_oneway(fd_1, fd_2, fd_3, n_sim=10000)[:-1]) - -# pr1 = FDataGrid([[1, 1], [1.5, 1.5]]) -# pr2 = FDataGrid([[2, 2], [2.5, 2.5]]) -# # print(pr1.concatenate(pr2)) -# -# def cosa(*args): -# print(args[0]) -# -# cosa(pr1, pr2) +# print(func_oneway_usc(fd_1, fd_2, fd_3, n_sim=2000)[:-1]) +print(func_oneway(fd_1, fd_2, fd_3, n_sim=2000)[:-1]) From 27637a247dff9da3f8db9b623de41f66ee587047 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 089/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From 08c1673b8bfc0db97d03931d5f77a8357ffe4c89 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 090/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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y1wHvhtohIhOBiQDt2/v0S8oYvxzaC69dD03awJl/9SWEbXsP8d9Za3n2iw30Lh3Gi/U+ZsborbQ97Txf4qntTu7SnKxmDZg8J4/x/dpG/Xw1YtSTiFwB5ACnhtqvqo8Cj4KbFDCGoRnjv/d/AzvWuRldIziFxuGUB5Q563YwdcEmXv8qn5KyAGf1bcMtI06GKVNou+wpGPHD6F3oV4clJAgX52Rx/wcrWb9tHx0zGkX1fH4migIgeHxXO++57xCRkcBtwKmqeihGsRlTMyx7E+Y/7abO6Dgk6qdTVb7K28WbCzbx9sLNFO45RIPkRM7u24abhnf59iK5QTfBGz+CtTOhy4iox1UXXZiTxd8+XMWLuXn875jjonouPxPFXCBbRDrhEsQlwGXBBUTkBOARYIyqFsY+RGPi2J4t7sK61sfD8N9G7TSqyrLNe3hz4SbeXLCJ/J0HSElKYET3TM4+vg2nHdfi+xPz9b4APvwDzP6PJYooadmkPiO6t+Dl3Hx+MapbVC9U9C1RqGqZiNwCvA8kAk+o6hIR+SOQq6pTgfuAVOBlbxjdRlU9x6+YTXxQVeZt2EnBrgO0a9qALpmppDdM8Tus2AoEYMqPoPSAG+WUFNn3X3yglC/WbOOTVduYtaqIvB0HSEwQhnTN4GcjuzG6V0ua1A8z/1Jyfci5Dj6+201v3rxLROMzziUDsvhw2VZmLC/kjF7RWw/E1z4KVX0HeKfSc7cHbY+MeVAmbhXuOcir8wp4ce5G1m/f/519zRql0DmjEV0yU+mc2YjOmal0yWxEVrOGtXNKiDmPwprprvM6s9sxH66sPMDXebu+SQwL8nYRUEitl8RJXZpz46ldGNu7Nc0aHUFCyrkWZv3V1SrG3XfMMZrvG949k5ZN6jF5zsbamyiMOZzygPLJqiImz9nI9GWFlAWUgZ2a8dOR2fRpm8bGHftZU7iPtdv2sqZwH9OXb+XF3JJvXp+UILRv3vCbBNIlI5UuLRrROSOVpkfypRdPCpfBtNvd/Eo51x3VIQ6UlLNy6x4W5u9i1qptfLFmO3sOlZEg0LddOreM6MrQbpn0y0o/+kTbuCX0mQBfPQcjbotpR3tdkZSYwEU5WTz80Wo27TpAm/ToTP5oicLEpU27DvBSbh4v5+ZTsOsAzRulcN2QTlw8IOs7s4p2bdGY0yr14xXvL2XNtr2sLdrH2qK9rCly2zNXFH5nCcqmDZPpnJnKGb1acsPQzjXjKuEDu+DFK91V0OMfOuyIovKAsmH7PlZs2cPyLXtYvmU3K7bsYcOO/VSsgtw2vQFnHd+GYdkZnNwlg7SGEZzSe9CNsOAF+OpZOPmWyB3XfOOinCz+OWM1L+fm89OR2VE5hyUKEzdKywNMX1bIi3M38vHKIhQY0jWD287swcgeLUlJqt4v27SGyfRv35T+7Zt+5/my8gD5Ow98U/tYu20vSzft5s53llO4+xC3ndkjvpNFeRm88gNvVtg30EaZ7Nh7iC27D1K4291vKT7I1t0Hv9lev30fB0sDgFs2tGPzRvRo3YRzT2jLca0a06N1E9o3axi9992mn5t3as4jMPim2E8rUgdkNWvI0OwMXsrN45bTun5nCdVIsURhfLd+2z5ezM3jlXn5FO05RMsm9bh5RFcuyskiq1nDiJ0nKTGBjhmN6JjR6JtaiKryf28u5bFP1xFQ+P1Z8ZUsVJUd+0pYu20faTN/R7f1M5iU8Uueea2Mjdvfo6Q88J3yItC8UT1apdWjXdMGDOmaQfdWjTmuVROyW6bGZLqH7xl0I7x0Jaz+ELqdEfvz1wGXDGjPzc/PZ9aqIoZ3bxHx41uiML44WFrO+0u2MHlOHl+s3U5igjCiewsuHZjFqd0yY7bcpYjwh7N7IgJPfLaOgKr3OPbJ4mBpOQvzi8ndsIPVha65bN22fRQfKOXSxOnclfwcT5aP5fmSU+mc0YjTj2tBq7T6tGpSn5Zp9WnZpD4tGteLv877bmMgpTEsf8sSRZSM7NmCZo1SeHFuniUKU7Pt2r2Hkik/pcHGmcwv68iS0m40Su3Pr0YNY8KAjrRsUt+XuESE28/qSYIIj3/qksX/ndMr6sli36Ey5m/cyZx1O5i9bgdf5+2ipMzVEFo1qU/nzEac1bc1pyQuY8xXkziQNYKrrnqGHyTVsGVBk1IgeySseM8N602Is0RWC9RLSuS6IZ04UFKOqkb8b9cShYk4VSV/5wGWbt7Nkk27WbppNxs2beEP++9kSOISpgVy6FtvK6cyDw69AHPSoPAUt+5yx6HQomfMv0xEhN+d2YMEgf/Ocsnij+f0jtg6CYGAUrDrAMs27yZ3w05mr9vB4oJiygNKYoLQu00Trj6pAwM7NSenQ9NvR2RtXwOP/Rqad6HBZZOgpiWJCt3HuenHN82Hdjl+R1Mr3Tyia9SObYnCHJPygLK6cC+LC4q9xFDM0k272X2wDHAdqCc2L+UJ/kybpHWsGHwfA4f+wI2s2bMF1n8K6z5xtxXeJTUNm7vpKDoNg47DICM7JvMFiQi/HdeDBBEe+WQtAYU/jz+yZFFaHmDD9n2sLtzL6sK9rPLu1xTt/aZTOSUxgX5Z6dx0ahcGdmpG/w5NSQ21/vHBYnjhUrd92WSonxaJt+mPriNBEt2/sSWKGscSham28oCypmgvi/KLWVTgbks37eZAaTkA9ZISOK51E846vg09WzehV5sm9Egpov7kCXCoCC57ke7ZQddQNm7lxtn3meAe78qD9bNg3SyXOJa+4Z5PbQWdhrrE0WkYNO0YtfcoItw69jgSEoR/z1yDqvKXc/uETBaHyspZsWUPiwqKWex9Hiu27PnOENy26Q3o0iKVQZ2ak90ylewWqfRum3b4TuVAObxyHexYA1dOgWadI/1WY6thM+hwMqx41y3RamoUSxQmpPKAsrZoL4sKilmY774IlwQlhQbJifRu24RLBmbRp20afdqm0Smj0Xc7oQvmw9MXAgpXv1XlOsrfSM+Cfpe5myrsXOfVNmbB2o9h0cuuXFp7lzg6D4fjzoSUyM6cKSL8+ozuJAg8/NEaAgG445xerC7cy8KCXSGTQlqDZPq0TePaIZ3o3rIxXVuk0iUzlUahagrV8cHvYfU0OOvv7r3WBt3HfTvTbbNOfkdjjoCo1q5ZuXNycjQ3N9fvMGqU8oCybtv3k8L+km+TQq82TejtJYS+7dLonJkafrz26unuwrBGzeGK1yHjGNtPVWHbym+bqdZ/Cgd2uDWa+14MOT+Alr2O7RzfO6XywLSV/HPG6u8836R+En3apdGnbfo3STKrWYPIdSDOfxqm/hgG/hDG3RuZY8aDHWvhHyfAmLvdNRUmrojIPFUN2S5oiaKOCQSUtdv2sahgF4vyd7O4oJjFm4q/SQr1kxPo1Sbtmy/APu3S6HK4pFDZwpdgyk2Q2QOueMU1MUX+jbhlP+c9CUumQPkhyBrk5hfqOR6SIzOVgarywdKtLMovpkfrJpFPCpWtmwXPnOdqEZe9DIm1rNL/8GBIzYSr3/Q7ElOJJYo6bH9JGR+vKCJ3w04WFRSzpKCYfUFJoWfrJvRtl/5NbaFLZqNju4bh84fgg9vc6KVLnotNB+z+HfD18y5pbF8N9dOh3+Vw4jURmTAvJkoPwMf3wuf/gKad4PoPa+fcSB/+H3z2IPx6DTRoevjyJmYsUdQx+0vKmLG8kHcWbWbG8kIOlgaol5RAzzZN6Ns2zSWFdml0zUyN3IVtgQB8eDt8/k/3i/78/0JSvcgcu7pUXWd47pNuQZ9AqUtYJ14DPc6OfTzVtWYGvPVzNzVHv8th1J9ck11tlDcXHh/ppkb3cW1v833hEkUtq9fWXaGSQ0ZqPS48MYtxfVozoGPT6F3tXF4Kb9wMC1+EATfA2Hv8mdNH5NuRUXuL4OtnXdJ49TpomAEneLWMeBlBtLfIde4uehmad3XNMZ2G+R1VdLU9ERplumGylihqDEsUNdjhksPATs2iMkHYdxzaCy9d5dZGOO33MPSX8bFGcmqmWx705J/C2o8g9wnXLPbZg9B5hOvL6D4WEn24gC0QgK+ecVOFl+6HU291sSb7c2V6TCUkuCk9lr4BZSURX3DJRIclihomLpJDhb1F8PyFsHkhnPMQ9L8yNuc9EgkJ0PV0d9u92X1Bz5vkJqlLbQn9r4ITfwBpbWMTT+FyeOtnsPEL6DAEzvpbzelHiZTu49y/w4bPbJnUGsL6KGqAqpLD2N6tOLNvawZ0jGFyqLBzvRuds3sTXPiU+3VeUwTKYdU01/m98n13Hca4++H4S6JXG9qxFj79u+t0r5cKo//s+iPiofYVayX74d7OLknXpuG/NZz1UdRAqsr7S7YwdcGm79UcfEsOFTYvhOcmQNkhuGoqtB/kTxxHKyERuo9xtx3rXP/KlBu/XVo0kiO1CpfBrAdg8SuQkOy+HEf8FhplRO4cNU1KQ1eTWPGu68+qi8myhrFEEYeK95fyy5cX8OGyrfGTHCqs/RgmX+6+TK97EzK7+xvPsWrWyXUif/oAfHSXuzbjgscha+CxHbdgvlsvevlbkNwIBv8ITv5xdK4pqYm6j3Ud2luXQKvefkdjDsMSRZxZlF/MTc/NY0vxQX5/Vk+uObmj/8mhwqJX3IV0zbrAFa/Grl0/2hISYdivoNOpboTUE2Ng+K2uY/5IRm+pwobPYdb9bshr/TQY9mt3FXLDZtGLvybqNgYQlywsUcQ9SxRxQlV59ssN/OmtZWSkpvDSjSd9bylP35SXwrQ/wJcPQ/uT4dLna+fFUlkD4cZP4e1fwkd/gTUfwfmPujmoqlJWAttXuRpE7uOw6Ss3/HPkHZBzHdRvEqvoa5bUFm4W2RXvwKm/9jsacxiWKOLA3kNl/Oa1Rby5YBPDu2fyt4v6fbsegd/2FsLL17gRKoNudBeD1eYhjfXT4ILH3LTYb/8S/nMKnP0g9DwXivNg61IoXOLdL3XzTwXclOo0z3Z9HMdf5trhTXjdx8L0P7oBEU3a+B2NCcMShc9WbNnDTc/NY/22ffzqjO7cdGqXiC2Wc8zy5rhrJA7sclda973I74hi5/hLXA3j1etdokys5+aTqpCW5RZY6nYGtOgFLXu6ua1s9bbq6z7OJYqV77nrWkzc8jVRiMgY4EEgEXhMVe+utH8Y8HegL3CJqr4S+yij55V5+fxuyiJS6yXz3PWDOalLnEzboApzH4P3fgNp7dy8Q3WxHblZZ7j2fZj3FGxb5TruW/aCFj1q9iJC8SLzOLe2yIp3LVHEOd8ShYgkAg8Do4B8YK6ITFXVpUHFNgLXAP8T+wij52BpOX94Ywkv5uYxuHMz/nHpCbRoHCdX5Zbsd/MOLZwM2WfA+Y/Uzv6I6kpMhoE3+B1F7SQC3c90P0oO7XXXl5i45Gc9eSCwWlXXqmoJMBkYH1xAVder6kIg4EeA0bBu2z7OffgzXszN4+YRXXj2ukHxkyR2rIPHR7s5m4b/Fi6dXLeThIm+7mNdk97aj/yOxIThZ9NTWyAv6HE+UMOu3Doy7yzazK9fWUhSovDkDwYwonsLv0P61qpprj0ehctegm6j/Y7I1AXtB7tp4Ze/42b4NXGpVnRmi8hEYCJA+/btfY7m+0rKAtz5zjKe+nw9J7RP56HL+tM2PTIL6xyzQAA+uQ9m3gUte8PFT8fP7Kqm9ktMhuzRrkM7UO7PrMPmsPxseioAggeot/OeO2Kq+qiq5qhqTmZmZkSCi5T8nfu58JEveOrz9Vx7SidenHhS/CSJA7tg8qUw8043oum6DyxJmNjrPtYta5s3x+9ITBX8rFHMBbJFpBMuQVwCXOZjPBE3Y/lWfv7iAgIB5d+X92dsn9Z+h/StLYvhxSvctQHj7ocB19ucO8YfXU9382CteAc6nOR3NCYE32oUqloG3AK8DywDXlLVJSLyRxE5B0BEBohIPnAh8IiILPEr3iNRVh7g3veWc+1TubRJb8CbPx4SX0li4Uvw2Ei3/OY177hRPZYkjF/qp0HHIW6YrIlLvvZRqOo7wDuVnrs9aHsurkmqxggElBufnceHywq5dGAWfzi7F/WT46TdtbwUPvgdzP6Pm4rjwqegcUu/ozLGXXz37q/c9SoZ2X5HYyqxy0gj7GCgVw4AABs0SURBVLWvCvhwWSG/O7MHd53fN36SxO7NMOlslyQG/wiunmpJwsSPivVMrFYRlyxRRNikz9fTvWVjrhvSye9QvrV2Jjwy1K0jccHjMOYuf5YANaYq6VnQqo8lijhliSKCFubvYlFBMVcMbo/EQ5v/zvXuKuunz4WGzWHiR9Bngt9RGRNa93GQ9yXs2+53JKYSSxQR9NyXG2mQnMj4E3xep6FwGbw2Ef7RH756FgZcBzfMqPmLDJnarftY0ACset/vSEwlteKCu3iw+2ApUxdsYny/NjSp71OzTn6uW3Zzxdveqmo3wUk32xTOpmZo3Q8at3HDZPvVqpHyNZ4ligh5bV4+B0rLuXxQh9ieWNX1QXz6AKz7xE2HMPw3MHCirapmahYRyB4Fi19zI/SsHy1uWKKIAFXl2dkbOb5dGn3axWj66UDA1Rxm/dWtqpbaCkb/GU68Buo1jk0MxkRa9miYPwk2fgmdhvodjfFYooiA2et2sLpwL/dO6Bv9k5WXurWrP/0bbFsBTTu5FdiOvxSS6kX//MZEU+dT3VXaqz6wRBFHLFFEwLNfbqBJ/STO7hvFvoDSA65j+rN/QPFGN4HfBY+7JToT7Z/R1BL1GkPHU1yiGP0nv6MxHvuGOUaFew7y3uItXH1yRxqkROHiuoPFMPdx+PJfsK8IsgbBmfe7Kno8DME1JtKyR8P7v4WdG6BpjPv8TEiWKI7S3kNlLMzbxeS5eZQFlMsHRWh680C5u/6hcBnkzXbLcB7aDV1Oh6G/hA4nW4IwtVtFolg9zU1WaXxniaIaVJW12/Yxf8NO5m/cxVcbd7Jy6x4C6vZfnJNF58wjXMaxvNStKFe0DIpWQNFyd79tlVvxCwCBnufAkF9Am34RfU/GxK3mXd1a2qssUcQLSxRhzNuwg3/PXMPc9TspPlAKQOP6SfTLSueMXq3o36Ep/dqlk9YwzDC+shLYscbVEIITwvbVECj9tlx6B7fYfJfToEUPd3FcRjcbwWTqHhFv9NMzUHoQkuNkqeA6zBJFFWatKuK6p3JJb5jMmF6t6N8hnf7tm9IlM5WEhMM0/Xz9ghu6WrQCtq8BLfd2CDTr5BJC9zGQWZEQsiGlUdTfkzE1RvZomPMobPgUuo70O5o6zxJFCHPW7eCGp3PpnNmIyRMHk94wpfov/voFmHKjqyG06gM9znGJocVxrkqdHCer2xkTzzoOgaT6rvnJEoXvLFFU8nXeLq59ai5t0xvw7PWDjixJlJe6ZUXb9IfrP7T1f405WskNoNMwN0x27D1+R1Pn2aSAQZZt3s3VT8yhWaMUnrt+MBmpR3gB28IXYddGGH6rJQljjlX2aNix1jXfGl9ZovDk7djPlY/PpmFKIs9dP4hWaUfYgVZeBp/cD62Pd3/gxphjkz3K3a+02WT9ZonC06JJPUb2aMmz1w8iq1nDIz/Aopdh5zoY9mu7zsGYSGjaETK6u+Yn4ytLFJ56SYncfUFfuhzp9RDg1Sbuc53Xx50Z+eCMqauyR8GGz+DQXr8jqdMsUUTC4lfctRKn3mq1CWMiKXs0lJe4KfSNbyxRHKvyMvj4XqtNGBMN7U+ClFRrfvKZDY89FuWlMPsRV5u4+FmrTRgTaUkp0Hm4u55C1f6P+cQSxZEqL4M1M2DpG+7q6wM7oW0OdLfahDFRkT0alr/lpsFp2dPvaOokXxOFiIwBHgQSgcdU9e5K++sBTwMnAtuBi1V1fazjBODALpj/tJtWoDgP6qW5aTh6nANdT4cEa8UzJioqhpuv+sAShU98SxQikgg8DIwC8oG5IjJVVZcGFbsO2KmqXUXkEuAe4OKYBrpvO3z+IMx5DEr3QcehMOYuyD7DVYuNMdHVpLXrA1z1AQz5md/R1El+1igGAqtVdS2AiEwGxgPBiWI8cIe3/QrwkIiIqmrUozuwC754CL78N5Tsgz4T4OQfuwvqjDGxlT0aPv27+3/ZIN3vaOqcarWXiMgz1XnuCLUF8oIe53vPhSyjqmVAMdD8GM8b3qE98PF98GBfd21E15Hwoy/hgscsSRjjl+zRbhbmtR/5HUmdVN0aRa/gB16z0YmRD+foiMhEYCJA+/ZHudJcyX7X//DZg3BgB3QfB8N/A637RjBSY8xRaZsD9dPd6Kde5/kdTZ0TtkYhIr8RkT1AXxHZ7d32AIXAG8d47gIgK+hxO++5kGVEJAlIw3Vqf4eqPqqqOaqak5mZeXTRHNwFH90JbfvDDTPg0hcsSRgTLxKT3KCRVdMgEPA7mjonbKJQ1btUtTFwn6o28W6NVbW5qv7mGM89F8gWkU4ikgJcAkytVGYqcLW3PQGYEbX+iSZt4Me5cMWr0DZuKkvGmArZo2FfIWxZ4HckdU61mp5U9Tci0hboEPwaVT3q6+pVtUxEbgHexw2PfUJVl4jIH4FcVZ0KPA48IyKrgR24ZBI96UfZbGWMib6uIwFxtYo2J/gdTZ1SrUQhInfjvqSXAhXreipwTBOwqOo7wDuVnrs9aPsgcOGxnMMYU0s0ynC1/VUfwKm/9juaOqW6ndnnAd1V9VA0gzHGmLCyR8PMu2DfNpc4TExU93LitUByNAMxxpjDyh4FKKye7nckdUrYGoWI/BPXxLQf+FpEpgPf1CpU9SfRDc8YY4K07geNMl3z0/GxnaShLjtc01Oudz+P749IMsaY2EpIgK6jYMU7ECi3teljJGyiUNVJsQrEGGOqJXsULHge8nOh/SC/o6kTqjvqaRGuCSpYMa7G8WdV/d5FcMYYExVdRoAkuuYnSxQxUd3O7HeBt4HLvdubuCSxBXgqKpEZY0woDZpC1iBb9S6Gqjs8dqSq9g96vEhE5qtqfxG5IhqBGWNMlbqNhg/vgN2b3TTkJqqqW6NIFJGBFQ9EZADuamqAsohHZYwx4VQsZrT6Q3/jqCOqmyiuBx4XkXUish43tcYNItIIuCtawRljTEgtekKTtrDqfb8jqROqO9fTXKCPiKR5j4uDdr8UjcCMMaZKIm7006JXoazEVpuMssNdcHeFqj4rIr+o9DwAqvpAFGMzxpiqZY+GeU9B3pfQaZjf0dRqh2t6auTdN67iZowx/uh0KiQk2+inGDjcBXePePf/F5twjDGmmuqlQsdT3LTjo//sdzS1WnXXzO4mItNFZLH3uK+I/C66oRljzGFkj4ai5bBzg9+R1GrVHfX0X+A3QCmAqi4k2osIGWPM4WSf4e5XT/M3jlquuomioarOqfScXT9hjPFX8y7QtJNrfjJRU91EsU1EuuDN9yQiE4DNUYvKGGOqQ8Q1P639GEoP+B1NrVXdRHEz8AhwnIgUAD8DboxaVMYYU13Zo6HsAKz/zO9Iaq3qJooC4EngL8BkYBpwdbSCMsaYaut4CiQ1sGGyUVTdRPEGcDauM3sTsBfYF62gjDGm2pIbuAvuVr0PWnk1BBMJ1Z09tp2qjolqJMYYc7SyR7lEsX0NZHT1O5pap7o1is9FpE9UIzHGmKNVMZusNT9FRdhEISKLRGQhMASYLyIrRGRh0PPGGOO/ph0g8zhLFFFyuKans2IShTHGHKvsUTD7ETi0103vYSImbI1CVTeEux3tSUWkmYhME5FV3n3TKsq9JyK7ROStoz2XMaaOyB4N5SWw7hO/I6l1qttHEWm3AtNVNRuY7j0O5T7gyphFZYypubIGQ0pjW8woCvxKFOOBSd72JODcUIVUdTqwJ1ZBGWNqsKQU6DLcTedhw2Qjyq9E0VJVK6YA2QK0PJaDichEEckVkdyioqJjj84YUzN1GwO7C2DTV35HUqtELVGIyIcisjjEbXxwOVVVvDmkjpaqPqqqOaqak5mZeUxxG2NqsOPOdIsZLX7V70hqlepecHfEVHVkVftEZKuItFbVzSLSGiiMVhzGmDqkQVM3+mnxqzDqj5CQ6HdEtYJfTU9T+XauqKtxU4QYY8yx6zMB9myGDZ/7HUmt4VeiuBsYJSKrgJHeY0QkR0QeqygkIrOAl4HTRSRfRM7wJVpjTM3RbSwkN4LFr/gdSa0RtaancFR1O3B6iOdzgeuDHg+NZVzGmFogpSEcNw6WTIGx97nRUOaY+FWjMMaY6OlzIRzcBWtm+B1JrWCJwhhT+3Qe4Tq2rfkpIixRGGNqn6QU6Dkelr8NJbZ0zrGyRGGMqZ16T4DS/bDiXb8jqfEsURhjaqcOJ0PjNnbxXQRYojDG1E4JidD7fDf30/4dfkdTo1miMMbUXr0vgEApLHvT70hqNEsUxpjaq80J0KwLLHrZ70hqNEsUxpjaS8RN6bH+U9i9+fDlTUiWKIwxtVvvCYDCktf9jqTGskRhjKndMrtBq77W/HQMLFEYY2q/PhNg03zYvsbvSGokSxTGmNqv1/nufvFr/sZRQ1miMMbUfulZ0P4k1/xk62kfMUsUxpi6oc8E2LYCti72O5IaxxKFMaZu6HkuSCIsshllj5QlCmNM3dAoA7qMcHM/BQJ+R1OjWKIwxtQdfS6E4jzIn+N3JDWKJQpjTN1x3JmQVB8WvuR3JDWKJQpjTN1Rr7FLFotfhbJDfkdTY1iiMMbULSdc4dbTtiu1q80ShTGmbuk8Alr1gVkPQKDc72hqBEsUxpi6RQSG/g/sWANLp/gdTY1gicIYU/f0OAcyusMn99tQ2WrwJVGISDMRmSYiq7z7piHK9BORL0RkiYgsFJGL/YjVGFMLJSTA0F9C4VJY+a7f0cQ9v2oUtwLTVTUbmO49rmw/cJWq9gLGAH8XkfQYxmiMqc16XwBNO7pahc3/FJZfiWI8MMnbngScW7mAqq5U1VXe9iagEMiMWYTGmNotMQmG/NxNP75mht/RxDW/EkVLVa1Yl3AL0DJcYREZCKQAISeTF5GJIpIrIrlFRUWRjdQYU3sdfyk0buNqFaZKUUsUIvKhiCwOcRsfXE5VFaiy3icirYFngB+oasheJ1V9VFVzVDUnM9MqHcaYakqqB6f8FDZ+Dus/8zuauJUUrQOr6siq9onIVhFpraqbvURQWEW5JsDbwG2q+mWUQjXG1GX9r4JZ97tbx1P8jiYu+dX0NBW42tu+GnijcgERSQFeB55WVZsX2BgTHSkN4aSbXT9FwTy/o4lLfiWKu4FRIrIKGOk9RkRyROQxr8xFwDDgGhH52rv18ydcY0ytlnMd1E+HT/7qdyRxKWpNT+Go6nbg9BDP5wLXe9vPAs/GODRjTF1UvwkMvglm3gVbFkOr3n5HFFfsymxjjAEYOBFSUmGW1Soqs0RhjDEADZvBgOthyeuwbZXf0cQVSxTGGFPhpFvcwkaf/s3vSOKKJQpjjKmQmgknXg0LJsPODX5HEzcsURhjTLCTfwKSAJ896HckccMShTHGBEtrC/0ug6+egd2b/I4mLliiMMaYyob83N1/8Dt/44gTliiMMaayZp3cKniLX4WV7/sdTfWUHozaoS1RGGNMKEN+Dpk94K1fwKE9fkdzeFNuhKfOisqhLVEYY0woSSlwzj9gdwFM/5Pf0YRXXgqrp7uFmKLAEoUxxlQlayAMvAHmPAp5c/2Opmobv4BDu6HbmKgc3hKFMcaEc/rt0KQNTP0xlJX4HU1oK9+HxBToPDwqh7dEYYwx4dRrDGc+AEXL4LO/+x1NaCvfg45DoV5qVA5vicIYYw6n+xjodT58ch8UrfA7mu/avga2r45asxNYojDGmOoZew8kN4Q3fwqBkKsy+6Ni+G630VE7hSUKY4ypjtQWcMadruN43pN+R/Otle+5YbxRGvEEliiMMab6+l0GnU6FaX+Ij+k9Du6GDZ9BtzOiehpLFMYYU10icPbfIVAGb/8PqPobz5oZLpYo9k+AJQpjjDkyzTrDiN/Airdh2VR/Y1n5PjRoCu0GRPU0liiMMeZIDb4ZWvV1Hdt+jYIKlMOqD6DrSEhMiuqpLFEYY8yRSkyCiya5i9yePhd2bYx9DAXzYf+2qDc7gSUKY4w5Os06wxWvQek+lyz2Fsb2/CvfA0mELqdF/VSWKIwx5mi16g2XvQx7NsMz58H+HbE798r3of1gaNgs6qeyRGGMMcei/SC45HnYthKem+CGrEZbcT5sXRT1YbEVfEkUItJMRKaJyCrvvmmIMh1EZL6IfC0iS0TkRj9iNcaYw+oyAi6cBJu+hhcugZL90T3fN1djR79/AvyrUdwKTFfVbGC697iyzcBJqtoPGATcKiJtYhijMcZU33Hj4PxHYcPn8OIVUHYoeuda+b67EjujW/TOEcSvRDEemORtTwLOrVxAVUtUteKTroc1kxlj4l2fCXDOP2HNdHjlWigvi/w5SvbDuo9dbUIk8scPwa8v35aqutnb3gK0DFVIRLJEZCGQB9yjqiGvmReRiSKSKyK5RUVF0YnYGGOqo/+VMOYeWP4WTLkp8hMIrp8FZQdj1j8BELWrNETkQ6BViF23BT9QVRWRkNfBq2oe0NdrcpoiIq+o6tYQ5R4FHgXIycnx+Zp6Y0ydN/hGKNkLM/7kllQd91dIrh+ZY698D1JSocMpkTleNUQtUajqyKr2ichWEWmtqptFpDUQdgCyqm4SkcXAUOCVCIdqjDGRN+x/3C//T+6DDV/A2Q9Cp6HHdkxV1z/ReTgk1YtElNXiV9PTVOBqb/tq4I3KBUSknYg08LabAkOAOFsxxBhjwjjtd3DlFNBymHQWvHHzsV1rsXUx7C6I2WinCn4liruBUSKyChjpPUZEckTkMa9MD2C2iCwAPgbuV9VFvkRrjDFHq8sIuOkLGPJz+PoFeGgALHz56GaeXfmeu8+O3iJFoYj6PU1uhOXk5Ghubq7fYRhjzPdtWeQmEiyYB11Oh7MeOLIFhx4b6SYDnPhRxEMTkXmqmhNqnw05NcaYWGnVB66bBmPvhbzZ8K+T4LN/VG8Y7d4iyM+NebMTWKIwxpjYSkiEQT+Em2e7Tulpv4dHh8PSqa62UJXV0wCN6bDYCpYojDHGD2nt3BxRFz0NJXvgpStd/0Xuk1B68PvlV74HjVtD6+NjHqolCmOM8YsI9BwPt8yDCU9Cvcbw1s/g733gk/vhwE5XrqwEVs9wndgxuho7WHSXRTLGGHN4iUnQ+3zodZ678vqzB93FerMegBOvgYyurtbhQ/8EWKIwxpj4IQKdhrnblkXw+T9h9n/cdRiN20D2KF/CskRhjDHxqFUfNxvtab9z/RbtT4LEZF9CsURhjDHxLL09jPyDryFYZ7YxxpiwLFEYY4wJyxKFMcaYsCxRGGOMCcsShTHGmLAsURhjjAnLEoUxxpiwLFEYY4wJq9YtXCQiRcAGv+Oopgxgm99BHIGaFi9YzLFS02KuafFC9GPuoKqZoXbUukRRk4hIblUrSsWjmhYvWMyxUtNirmnxgr8xW9OTMcaYsCxRGGOMCcsShb8e9TuAI1TT4gWLOVZqWsw1LV7wMWbrozDGGBOW1SiMMcaEZYkiikQkS0Q+EpGlIrJERH4aosxwESkWka+92+1+xFoppvUissiLJzfEfhGRf4jIahFZKCL9/YgzKJ7uQZ/f1yKyW0R+VqmM75+ziDwhIoUisjjouWYiMk1EVnn3Tat47dVemVUicrWP8d4nIsu9f/fXRSS9iteG/RuKccx3iEhB0L/9uCpeO0ZEVnh/17f6HPOLQfGuF5Gvq3htbD5nVbVblG5Aa6C/t90YWAn0rFRmOPCW37FWimk9kBFm/zjgXUCAwcBsv2MOii0R2IIbEx5XnzMwDOgPLA567l7gVm/7VuCeEK9rBqz17pt62019inc0kORt3xMq3ur8DcU45juA/6nG380aoDOQAiyo/H81ljFX2v9X4HY/P2erUUSRqm5W1fne9h5gGdDW36giYjzwtDpfAuki0trvoDynA2tUNe4uulTVT4AdlZ4eD0zyticB54Z46RnANFXdoao7gWnAmKgF6gkVr6p+oKpl3sMvgXbRjuNIVPEZV8dAYLWqrlXVEmAy7t8m6sLFLCICXAS8EItYqmKJIkZEpCNwAjA7xO6TRGSBiLwrIr1iGlhoCnwgIvNEZGKI/W2BvKDH+cRPAryEqv9TxdvnDNBSVTd721uAliHKxOvnfS2uZhnK4f6GYu0Wr7nsiSqa9+L1Mx4KbFXVVVXsj8nnbIkiBkQkFXgV+Jmq7q60ez6umeR44J/AlFjHF8IQVe0PjAVuFpFhfgdUHSKSApwDvBxidzx+zt+hri2hRgxDFJHbgDLguSqKxNPf0L+BLkA/YDOuKaemuJTwtYmYfM6WKKJMRJJxSeI5VX2t8n5V3a2qe73td4BkEcmIcZiVYyrw7guB13HV8mAFQFbQ43bec34bC8xX1a2Vd8Tj5+zZWtFs590XhigTV5+3iFwDnAVc7iW376nG31DMqOpWVS1X1QDw3ypiiavPGEBEkoDzgRerKhOrz9kSRRR57YuPA8tU9YEqyrTyyiEiA3H/JttjF+X34mkkIo0rtnGdl4srFZsKXOWNfhoMFAc1n/ipyl9f8fY5B5kKVIxiuhp4I0SZ94HRItLUazYZ7T0XcyIyBvg1cI6q7q+iTHX+hmKmUv/ZeVXEMhfIFpFOXs30Ety/jZ9GAstVNT/Uzph+zrHo1a+rN2AIrilhIfC1dxsH3Ajc6JW5BViCG2XxJXCyzzF39mJZ4MV1m/d8cMwCPIwbJbIIyImDz7oR7os/Lei5uPqccUlsM1CKawO/DmgOTAdWAR8CzbyyOcBjQa+9Fljt3X7gY7yrcW35FX/P//HKtgHeCfc35GPMz3h/pwtxX/6tK8fsPR6HG5m4xu+Yveefqvj7DSrry+dsV2YbY4wJy5qejDHGhGWJwhhjTFiWKIwxxoRlicIYY0xYliiMMcaEZYnCGGNMWJYojDHGhGWJwpgIEpEp3gRtSyomaROR60RkpYjMEZH/ishD3vOZIvKqiMz1bqf4G70xodkFd8ZEkIg0U9UdItIANy3EGcBnuPUG9gAzgAWqeouIPA/8S1U/FZH2wPuq2sO34I2pQpLfARhTy/xERM7ztrOAK4GPVXUHgIi8DHTz9o8EenpTUAE0EZFU9SYvNCZeWKIwJkJEZDjuy/8kVd0vIjOB5UBVtYQEYLCqHoxNhMYcHeujMCZy0oCdXpI4DrdMbCPgVG/m1yTggqDyHwA/rnggIv1iGq0x1WSJwpjIeQ9IEpFlwN24WWoLgDuBObi+ivVAsVf+J0COt/LaUtxst8bEHevMNibKKvodvBrF68ATqvq633EZU11WozAm+u4Qka9xi8qsIw6XYTUmHKtRGGOMCctqFMYYY8KyRGGMMSYsSxTGGGPCskRhjDEmLEsUxhhjwrJEYYwxJqz/B/d9rXoe+brjAAAAAElFTkSuQmCC\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From 58a884fce1cf67b369cd89a85843c2ab3985d678 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jan 2020 23:06:22 +0100 Subject: [PATCH 091/624] Improve tolerance check - Tolerance is now relative. - Te centers difference is compared using the appropiate metric. --- skfda/ml/clustering/kmeans.py | 31 +++++++++++++++++++------------ tests/test_clustering.py | 24 +++++++++++------------- 2 files changed, 30 insertions(+), 25 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index e021257df..b9caf62f6 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -107,6 +107,12 @@ def _check_clustering(self, fdata): return fdata + def _tolerance(self, fdata): + variance = fdata.var() + mean_variance = np.mean(variance[0].data_matrix) + + return mean_variance * self.tol + def _init_centroids(self, fdatagrid, random_state): """Compute the initial centroids @@ -188,10 +194,10 @@ def _algorithm(self, fdata, random_state): pairwise_metric = pairwise_distance(self.metric) - while not np.allclose(centroids.data_matrix, - centroids_old.data_matrix, - rtol=self.tol, - atol=self.tol) and repetitions < self.max_iter: + tolerance = self._tolerance(fdata) + + while (not np.all(self.metric(centroids, centroids_old) < tolerance) + and repetitions < self.max_iter): centroids_old.data_matrix[...] = centroids.data_matrix @@ -641,14 +647,15 @@ class FuzzyCMeans(BaseKMeans): >>> fuzzy_kmeans.fit(fd) FuzzyCMeans(...) >>> fuzzy_kmeans.cluster_centers_.data_matrix - array([[[ 2.84075812, 0.2476166 ], - [ 3.84075812, 0.3476166 ], - [ 4.84075812, 0.4476166 ], - [ 5.84075812, 0.53175479]], - [[ 1.25224668, 0.35041906], - [ 2.25224668, 0.45041906], - [ 3.25224668, 0.55041906], - [ 4.25224668, 0.6252065 ]]]) + array([[[ 2.83994301, 0.24786354], + [ 3.83994301, 0.34786354], + [ 4.83994301, 0.44786354], + [ 5.83994301, 0.53191927]], + [[ 1.25134384, 0.35023779], + [ 2.25134384, 0.45023779], + [ 3.25134384, 0.55023779], + [ 4.25134384, 0.6251158 ]]]) + """ diff --git a/tests/test_clustering.py b/tests/test_clustering.py index 5945f5113..cd35b50ab 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -77,21 +77,19 @@ def test_fuzzy_kmeans_univariate(self): [0.94, 0.06], [0.227, 0.773], [0.049, 0.951]])) - np.testing.assert_allclose(fuzzy_kmeans.transform(fd), - np.array([[1.49228858, 7.87898791], - [1.29380155, 5.12696975], - [4.85542339, 2.63309793], - [7.77455633, 1.75920889]])) - centers = FDataGrid(data_matrix=np.array( - [[0.7065078, 0.7065078, 1.45508111, 2.46698825, - 1.98143302, 1.48206743], - [-0.69456401, -0.69456401, -0.49444239, -0.19713489, - -0.19872214, -0.39844583]]), sample_points=sample_points) - np.testing.assert_allclose(fuzzy_kmeans.cluster_centers_.data_matrix, - centers.data_matrix) + np.testing.assert_allclose(fuzzy_kmeans.transform(fd).round(3), + np.array([[1.492, 7.879], + [1.294, 5.127], + [4.856, 2.633], + [7.775, 1.759]])) + centers = np.array([[0.707, 0.707, 1.455, 2.467, 1.981, 1.482], + [-0.695, -0.695, -0.494, -0.197, -0.199, -0.398]]) + np.testing.assert_allclose( + fuzzy_kmeans.cluster_centers_.data_matrix[..., 0].round(3), + centers) np.testing.assert_allclose(fuzzy_kmeans.score(fd), np.array([-12.025179])) - np.testing.assert_array_equal(fuzzy_kmeans.n_iter_, np.array([18.])) + self.assertEquals(fuzzy_kmeans.n_iter_, 19) # def test_fuzzy_kmeans_multivariate(self): # data_matrix = [[[1, 0.3], [2, 0.4], [3, 0.5], [4, 0.6]], From 56c243f0901f8e08ad8f6f007661f2d04f2183c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 19 Jan 2020 23:43:52 +0100 Subject: [PATCH 092/624] New simulation for oneway anova. Waiting to test. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 66 ++++++++++++++--------- skfda/inference/anova/anova_simulation.py | 54 +++++++++++++------ 2 files changed, 80 insertions(+), 40 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 431fb83c3..027fd7497 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -5,6 +5,7 @@ def vn_statistic(fd_means, sizes): + # fd_means es un FDataGrid k = fd_means.data_matrix.shape[0] v_n = 0 for i in range(k): @@ -13,42 +14,60 @@ def vn_statistic(fd_means, sizes): return v_n +def v_statistic(values, sizes): + k = values.data_matrix.shape[0] + v_hat = 0 + + for i in range(k): + for j in range(i + 1, k): + v_hat += norm_lp(values[i] - values[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 + + return v_hat + + +# def v_statistic_2(values, sizes, std=False): +# +# if std: +# m = values.mean() +# +# k = values.data_matrix.shape[0] +# v_hat = 0 +# for i in range(k): +# for j in range(i + 1, k): +# if std: +# v_hat += norm_lp(np.sqrt(sizes[i]) * (values[i] - m) - np.sqrt(sizes[j]) * (values[j] - m) * np.sqrt( +# sizes[i] / sizes[j])) ** 2 +# else: +# v_hat += norm_lp(values[i] - values[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 +# return v_hat + + def anova_bootstrap(fd_grouped, n_sim): + # fd_grouped es una lista de fdatagrids assert len(fd_grouped) > 0 - m = fd_grouped[0].ncol - samples = fd_grouped[0].sample_points - start, stop = fd_grouped[0].domain_range[0] - sizes = [fd.n_samples for fd in fd_grouped] + m = fd_grouped[0].ncol # Number of points in the grid + samples = fd_grouped[0].sample_points # Sample points + start, stop = fd_grouped[0].domain_range[0] # Domain range + + sizes = [fd.n_samples for fd in fd_grouped] # List of sizes of each group # Estimating covariances for each group k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - print(fd_grouped[0]) l_vector = [] for l in range(n_sim): sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) - sim = sim.concatenate(process.mean()) - # l_vector.append(v_usc(sim)) - l_vector.append(v_hat_statistic(sim, sizes)) + process = make_gaussian_process(1, n_features=m, start=start, stop=stop, cov=k_est[i]) + sim = sim.concatenate(process) + # process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) + # sim = sim.concatenate(process.mean()) + l_vector.append(v_statistic(sim, sizes)) return l_vector -def v_hat_statistic(values, sizes): - k = len(values) - v_hat = 0 - for i in range(k): - for j in range(i + 1, k): - # v1 = np.squeeze(values[i].data_matrix[0]) - # v2 = np.squeeze(values[j].data_matrix[0]) - # v_hat += np.linalg.norm(v1 - v2 * np.sqrt(sizes[i] / sizes[j]))**2 - v_hat += norm_lp(values[i] - values[j] * np.sqrt(sizes[i] / sizes[j])) ** 2 - return v_hat - - def func_oneway(*args, n_sim=2000): # TODO Check grids @@ -61,7 +80,6 @@ def func_oneway(*args, n_sim=2000): fd_means = fd_means.concatenate(fd.mean()) vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) - # vn = v_usc(fd_means) simulation = anova_bootstrap(fd_groups, n_sim=n_sim) p_value = np.sum(simulation >= vn) / len(simulation) @@ -93,8 +111,8 @@ def anova_bootstrap_usc(fd_grouped, n_sim): for l in range(n_sim): sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) - sim = sim.concatenate(process.mean()) + process = make_gaussian_process(1, n_features=m, start=start, stop=stop, cov=k_est[i]) + sim = sim.concatenate(process) l_vector.append(v_usc(sim)) return l_vector diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 457449052..984a0e544 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -2,32 +2,54 @@ import numpy as np from skfda.inference.anova.anova_oneway import func_oneway, func_oneway_usc from skfda.datasets import make_gaussian_process +from matplotlib import pyplot as plt def generate_samples_independent(mean, sigma, n_samples): return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] -grid = np.linspace(0, 1, 25) +scale = 25 + +start = 0 +stop = 1 + n_levels = 3 +n_samples = 100 + +t = np.linspace(start, stop, scale) + sigmas = np.array([0, 0.2, 1, 1.8, 2.6, 3.4, 4.2, 5]) -sigmas_star = sigmas * 25 -# Case M2 -mean1 = np.vectorize(lambda t: t * (1 - t) ** 5)(grid) -mean2 = np.vectorize(lambda t: t ** 2 * (1 - t) ** 4)(grid) -mean3 = np.vectorize(lambda t: t ** 3 * (1 - t) ** 3)(grid) +sigmas_star = sigmas * scale + +# Case M1 +mean1 = t * (1 - t) +mean2 = t * (1 - t) +mean3 = t * (1 - t) fd_means = FDataGrid([mean1, mean2, mean3]) +fd_means.plot() +plt.show() + +p = [] +reps = 500 + +for i in range(reps): + if i % 100 == 1 and i != 1: + print(np.mean(p)) + p = [] + + print('Simulation {}...'.format(i + 1)) + samples1 = generate_samples_independent(mean1, sigmas_star[1], n_samples) + samples2 = generate_samples_independent(mean2, sigmas_star[1], n_samples) + samples3 = generate_samples_independent(mean3, sigmas_star[1], n_samples) -samples1 = generate_samples_independent(mean1, sigmas_star[4], 10) -samples2 = generate_samples_independent(mean2, sigmas_star[4], 10) -samples3 = generate_samples_independent(mean3, sigmas_star[4], 10) + # Storing in FDataGrid + fd_1 = FDataGrid(samples1, sample_points=t, dataset_label="Process 1") + fd_2 = FDataGrid(samples2, sample_points=t, dataset_label="Process 2") + fd_3 = FDataGrid(samples3, sample_points=t, dataset_label="Process 3") + fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) -# Storing in FDataGrid -fd_1 = FDataGrid(samples1, sample_points=grid, dataset_label="Process 1") -fd_2 = FDataGrid(samples2, sample_points=grid, dataset_label="Process 2") -fd_3 = FDataGrid(samples3, sample_points=grid, dataset_label="Process 3") -fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) + p.append(func_oneway(fd_1, fd_2, fd_3, n_sim=2000)[0]) -# print(func_oneway_usc(fd_1, fd_2, fd_3, n_sim=2000)[:-1]) -print(func_oneway(fd_1, fd_2, fd_3, n_sim=2000)[:-1]) +print(np.mean(p)) From 69a2a05d4ea1b5755189f0a4705c8ba327daf3df Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 093/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", 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[15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 3f4ac36d5fc3eb20c64c6a86c09440f4cc03231f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 21 Jan 2020 11:52:15 +0100 Subject: [PATCH 094/624] Anova bootstrap working. --- skfda/inference/anova/anova_oneway.py | 13 +++-- skfda/inference/anova/anova_oneway_aux.py | 59 +++-------------------- skfda/inference/anova/anova_simulation.py | 32 ++++++------ 3 files changed, 32 insertions(+), 72 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 027fd7497..083b0a2ac 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -42,7 +42,7 @@ def v_statistic(values, sizes): # return v_hat -def anova_bootstrap(fd_grouped, n_sim): +def anova_bootstrap(fd_grouped, n_sim, f): # fd_grouped es una lista de fdatagrids assert len(fd_grouped) > 0 @@ -60,11 +60,14 @@ def anova_bootstrap(fd_grouped, n_sim): sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): process = make_gaussian_process(1, n_features=m, start=start, stop=stop, cov=k_est[i]) + #  sim = sim.concatenate(process) + # process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, + # cov=k_est[i]) + # process = (f[i].mean()) * np.sqrt(f[i].n_samples) sim = sim.concatenate(process) - # process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) - # sim = sim.concatenate(process.mean()) l_vector.append(v_statistic(sim, sizes)) + return l_vector @@ -81,8 +84,8 @@ def func_oneway(*args, n_sim=2000): vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) - simulation = anova_bootstrap(fd_groups, n_sim=n_sim) - p_value = np.sum(simulation >= vn) / len(simulation) + simulation = anova_bootstrap(fd_groups, n_sim, fd_groups) + p_value = np.sum(simulation > vn) / len(simulation) return p_value, vn, simulation diff --git a/skfda/inference/anova/anova_oneway_aux.py b/skfda/inference/anova/anova_oneway_aux.py index 9fc755b2a..34fe4f074 100644 --- a/skfda/inference/anova/anova_oneway_aux.py +++ b/skfda/inference/anova/anova_oneway_aux.py @@ -1,56 +1,11 @@ +from skfda import FDataGrid import numpy as np -from skfda.misc.metrics import norm_lp, lp_distance -from skfda.representation import FDataGrid +from skfda.inference.anova.anova_oneway import func_oneway, func_oneway_usc from skfda.datasets import make_gaussian_process +from matplotlib import pyplot as plt +m = 25 +n = 1 -def v_usc(values): - k = len(values) - v = 0 - for i in range(k): - for j in range(i + 1, k): - v += norm_lp(values[i] - values[j]) - return v - - -def anova_bootstrap_usc(fd_grouped, n_sim): - assert len(fd_grouped) > 0 - - m = fd_grouped[0].ncol - samples = fd_grouped[0].sample_points - start, stop = fd_grouped[0].domain_range[0] - sizes = [fd.n_samples for fd in fd_grouped] - - # Estimating covariances for each group - k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - - l_vector = [] - for l in range(n_sim): - sim = FDataGrid(np.empty((0, m)), sample_points=samples) - for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, cov=k_est[i]) - sim = sim.concatenate(process.mean()) - l_vector.append(v_usc(sim)) - - return l_vector - - -def oneway(*args, n_sim=2000): - - # TODO Check grids - - assert len(args) > 0 - - fd_groups = args - fd_means = fd_groups[0].mean() - for fd in fd_groups[1:]: - fd_means = fd_means.concatenate(fd.mean()) - - vn = v_usc(fd_means) - - simulation = anova_bootstrap_usc(fd_groups, n_sim=n_sim) - p_value = len(np.where(simulation >= vn)[0]) / len(simulation) - - return p_value, vn, simulation - - +process = make_gaussian_process(n, n_features=m) +print(process.mean().data_matrix) diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 984a0e544..818c01f92 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -15,34 +15,33 @@ def generate_samples_independent(mean, sigma, n_samples): stop = 1 n_levels = 3 -n_samples = 100 +n_samples = 10 t = np.linspace(start, stop, scale) sigmas = np.array([0, 0.2, 1, 1.8, 2.6, 3.4, 4.2, 5]) -sigmas_star = sigmas * scale +sigmas_star = sigmas / scale # Case M1 -mean1 = t * (1 - t) -mean2 = t * (1 - t) -mean3 = t * (1 - t) +# mean1 = t * (1 - t) +# mean2 = t * (1 - t) +# mean3 = t * (1 - t) + +mean1 = t * (1 - t) ** 5 +mean2 = t ** 2 * (1 - t) ** 4 +mean3 = t ** 3 * (1 - t) ** 3 fd_means = FDataGrid([mean1, mean2, mean3]) -fd_means.plot() -plt.show() p = [] -reps = 500 +reps = 20 for i in range(reps): - if i % 100 == 1 and i != 1: - print(np.mean(p)) - p = [] print('Simulation {}...'.format(i + 1)) - samples1 = generate_samples_independent(mean1, sigmas_star[1], n_samples) - samples2 = generate_samples_independent(mean2, sigmas_star[1], n_samples) - samples3 = generate_samples_independent(mean3, sigmas_star[1], n_samples) + samples1 = generate_samples_independent(mean1, sigmas_star[3], n_samples) + samples2 = generate_samples_independent(mean2, sigmas_star[3], n_samples) + samples3 = generate_samples_independent(mean3, sigmas_star[3], n_samples) # Storing in FDataGrid fd_1 = FDataGrid(samples1, sample_points=t, dataset_label="Process 1") @@ -50,6 +49,9 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=t, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) - p.append(func_oneway(fd_1, fd_2, fd_3, n_sim=2000)[0]) + anova = func_oneway(fd_1, fd_2, fd_3, n_sim=2000) + print(anova) + p.append(anova[0]) print(np.mean(p)) + From c3fd27c65a55166164cea5d22dc208c34a33482f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 23 Jan 2020 02:18:05 +0100 Subject: [PATCH 095/624] Kmeans does at least one iteration. --- skfda/ml/clustering/kmeans.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index b9caf62f6..85306d0b8 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -3,15 +3,13 @@ from abc import abstractmethod import warnings +import numpy as np from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted -import numpy as np - from ...misc.metrics import pairwise_distance, lp_distance - __author__ = "Amanda Hernando Bernabé" __email__ = "amanda.hernando@estudiante.uam.es" @@ -196,8 +194,9 @@ def _algorithm(self, fdata, random_state): tolerance = self._tolerance(fdata) - while (not np.all(self.metric(centroids, centroids_old) < tolerance) - and repetitions < self.max_iter): + while (repetitions == 0 or + (not np.all(self.metric(centroids, centroids_old) < tolerance) + and repetitions < self.max_iter)): centroids_old.data_matrix[...] = centroids.data_matrix @@ -702,7 +701,7 @@ def _check_params(self): def _compute_inertia(self, membership, centroids, distances_to_centroids): - return np.sum(membership**self.fuzzifier * distances_to_centroids**2) + return np.sum(membership ** self.fuzzifier * distances_to_centroids ** 2) def _create_membership(self, n_samples): return np.empty((n_samples, self.n_clusters)) @@ -727,7 +726,7 @@ def _update(self, fdata, membership_matrix, distances_to_centroids, membership_matrix_raised = np.power( membership_matrix, self.fuzzifier) - slice_denominator = ((slice(None),) + (np.newaxis,) * + slice_denominator = ((slice(None),) + (np.newaxis,) * (fdata.data_matrix.ndim - 1)) centroids.data_matrix[:] = ( np.einsum('ij,i...->j...', membership_matrix_raised, From b6e26c52f88c6f844ad9d22b292c6f614fb96a7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 31 Jan 2020 17:27:00 +0100 Subject: [PATCH 096/624] Adding example and some documentation for ANOVA functionality. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/apilist.rst | 1 + docs/modules/inference.rst | 16 ++ docs/modules/inference/anova.rst | 16 ++ examples/plot_oneway.py | 55 +++++++ skfda/inference/__init__.py | 1 + skfda/inference/anova/__init__.py | 1 + skfda/inference/anova/anova_oneway.py | 173 +++++++++++++++------- skfda/inference/anova/anova_simulation.py | 17 +-- 8 files changed, 216 insertions(+), 64 deletions(-) create mode 100644 docs/modules/inference.rst create mode 100644 docs/modules/inference/anova.rst create mode 100644 examples/plot_oneway.py diff --git a/docs/apilist.rst b/docs/apilist.rst index e443d49ce..b7cc7d15a 100644 --- a/docs/apilist.rst +++ b/docs/apilist.rst @@ -13,3 +13,4 @@ API Reference modules/datasets modules/misc modules/ml + modules/inference diff --git a/docs/modules/inference.rst b/docs/modules/inference.rst new file mode 100644 index 000000000..d94580159 --- /dev/null +++ b/docs/modules/inference.rst @@ -0,0 +1,16 @@ +Inference +============= + +TODO - Description + +.. toctree:: + :maxdepth: 3 + :caption: Modules: + + inference/anova + + +ANOVA +----- + +TODO - Description, ANOVA :doc:`here ` \ No newline at end of file diff --git a/docs/modules/inference/anova.rst b/docs/modules/inference/anova.rst new file mode 100644 index 000000000..8e51d1443 --- /dev/null +++ b/docs/modules/inference/anova.rst @@ -0,0 +1,16 @@ +ANOVA +============== + +TODO - Description + + +Statistic +----------------- +TODO - Description + +.. autosummary:: + :toctree: autosummary + + skfda.inference.anova.v_sample_stat + skfda.inference.anova.v_asymptotic_stat + diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py new file mode 100644 index 000000000..25b76de92 --- /dev/null +++ b/examples/plot_oneway.py @@ -0,0 +1,55 @@ +""" +One-way functional ANOVA +======================== + +This example shows how to perform a functional one-way ANOVA test. +""" + +# Author: David García Fernández +# License: MIT + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import skfda +from skfda.inference.anova import func_oneway + +################################################################################ +# TODO +# + +dataset = skfda.datasets.fetch_aemet() + +y = dataset['meta'] +fd = dataset['data'][0] +meta_names = dataset['meta_names'] + +province = y[:, np.asarray(meta_names) == 'province'].ravel() + +fig = fd.plot(group=province) + +################################################################################ +# TODO + +sel_prov = ['A CORUÑA', 'BALEARES', 'LAS PALMAS'] + +filt = np.logical_or.reduce([np.asarray(province) == p for p in sel_prov]) + +province = province[filt] +fd = fd[filt] + +fig = fd.plot(group=province, legend=True) + +############################################################################## +# TODO + + +fd_groups = [fd.copy(data_matrix=fd.data_matrix[province == label], + dataset_label=fd.dataset_label + ' in ' + label) + for label in sel_prov] + +############################################################################### +# ANOVA +# + +func_oneway(*fd_groups) diff --git a/skfda/inference/__init__.py b/skfda/inference/__init__.py index e69de29bb..23b76f4d2 100644 --- a/skfda/inference/__init__.py +++ b/skfda/inference/__init__.py @@ -0,0 +1 @@ +from . import anova diff --git a/skfda/inference/anova/__init__.py b/skfda/inference/anova/__init__.py index dd64b01a1..02695de8f 100644 --- a/skfda/inference/anova/__init__.py +++ b/skfda/inference/anova/__init__.py @@ -1 +1,2 @@ from . import anova_oneway +from .anova_oneway import v_sample_stat, v_asymptotic_stat, func_oneway diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 083b0a2ac..93f5c3b11 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,79 +1,144 @@ import numpy as np -from skfda.misc.metrics import norm_lp, lp_distance +from skfda.misc.metrics import norm_lp from skfda.representation import FDataGrid from skfda.datasets import make_gaussian_process -def vn_statistic(fd_means, sizes): - # fd_means es un FDataGrid - k = fd_means.data_matrix.shape[0] +def v_sample_stat(fd, weights, p=2): + """ + Calculates a statistic that measures the variability between groups of + samples in a FDataGrid object. + + The statistic defined as below is calculated between all the samples in a + FDataGrid object with a given set of weights, and the desired Lp norm. + + Let :math:`\{f_i\}_{i=1}^k` be a set of samples in a FDataGrid object. + Let :math:`\{w_j\}_{j=1}^k` be a set of weights, where :math:`w_i` is + related to the sample :math:`f_i` for :math:`i=1,\dots,k`. + The statistic is defined as: + + .. math:: + V_n = \sum_{i 0 - m = fd_grouped[0].ncol # Number of points in the grid - samples = fd_grouped[0].sample_points # Sample points - start, stop = fd_grouped[0].domain_range[0] # Domain range + n_groups = len(fd_grouped) + sample_points = fd_grouped[0].sample_points + m = len(sample_points[0]) # Number of points in the grid + start, stop = fd_grouped[0].domain_range[0] - sizes = [fd.n_samples for fd in fd_grouped] # List of sizes of each group + sizes = [fd.n_samples for fd in fd_grouped] # List with sizes of each group # Estimating covariances for each group k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - l_vector = [] - for l in range(n_sim): - sim = FDataGrid(np.empty((0, m)), sample_points=samples) - for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(1, n_features=m, start=start, stop=stop, cov=k_est[i]) - #  sim = sim.concatenate(process) - # process = make_gaussian_process(fd.n_samples, n_features=m, start=start, stop=stop, - # cov=k_est[i]) - # process = (f[i].mean()) * np.sqrt(f[i].n_samples) - sim = sim.concatenate(process) - l_vector.append(v_statistic(sim, sizes)) + # Simulating n_sim observations for each of the n_groups gaussian processes + sim = [make_gaussian_process(n_sim, n_features=m, start=start, stop=stop, + cov=k_est[i]) for i in range(n_groups)] + v_samples = [] + for i in range(n_sim): + fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) + v_samples.append(v_asymptotic_stat(fd, sizes, p=p)) + return v_samples - return l_vector +def func_oneway(*args, n_sim=2000, p=2): + """ + Perform one-way functional ANOVA. + Args: + n_sim (int, optional): Number of simulations for the bootstrap + procedure. + -def func_oneway(*args, n_sim=2000): + Returns: - # TODO Check grids + + Raises: + TODO + + References: + Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. An + anova test for functional data. *Computational Statistics Data + Analysis*, 47:111-112, 02 2004 + """ assert len(args) > 0 @@ -82,9 +147,9 @@ def func_oneway(*args, n_sim=2000): for fd in fd_groups[1:]: fd_means = fd_means.concatenate(fd.mean()) - vn = vn_statistic(fd_means, [fd.n_samples for fd in fd_groups]) + vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) - simulation = anova_bootstrap(fd_groups, n_sim, fd_groups) + simulation = _anova_bootstrap(fd_groups, n_sim, p=p) p_value = np.sum(simulation > vn) / len(simulation) return p_value, vn, simulation @@ -114,7 +179,8 @@ def anova_bootstrap_usc(fd_grouped, n_sim): for l in range(n_sim): sim = FDataGrid(np.empty((0, m)), sample_points=samples) for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(1, n_features=m, start=start, stop=stop, cov=k_est[i]) + process = make_gaussian_process(1, n_features=m, start=start, + stop=stop, cov=k_est[i]) sim = sim.concatenate(process) l_vector.append(v_usc(sim)) @@ -122,7 +188,6 @@ def anova_bootstrap_usc(fd_grouped, n_sim): def func_oneway_usc(*args, n_sim=2000): - # TODO Check grids assert len(args) > 0 diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py index 818c01f92..14ce203b9 100644 --- a/skfda/inference/anova/anova_simulation.py +++ b/skfda/inference/anova/anova_simulation.py @@ -1,12 +1,11 @@ from skfda import FDataGrid import numpy as np -from skfda.inference.anova.anova_oneway import func_oneway, func_oneway_usc -from skfda.datasets import make_gaussian_process -from matplotlib import pyplot as plt +from skfda.inference.anova.anova_oneway import func_oneway def generate_samples_independent(mean, sigma, n_samples): - return [mean + np.random.normal(0, sigma, len(mean)) for _ in range(n_samples)] + return [mean + np.random.normal(0, sigma, len(mean)) for _ in + range(n_samples)] scale = 25 @@ -37,11 +36,10 @@ def generate_samples_independent(mean, sigma, n_samples): reps = 20 for i in range(reps): - print('Simulation {}...'.format(i + 1)) - samples1 = generate_samples_independent(mean1, sigmas_star[3], n_samples) - samples2 = generate_samples_independent(mean2, sigmas_star[3], n_samples) - samples3 = generate_samples_independent(mean3, sigmas_star[3], n_samples) + samples1 = generate_samples_independent(mean1, sigmas_star[2], n_samples) + samples2 = generate_samples_independent(mean2, sigmas_star[2], n_samples) + samples3 = generate_samples_independent(mean3, sigmas_star[2], n_samples) # Storing in FDataGrid fd_1 = FDataGrid(samples1, sample_points=t, dataset_label="Process 1") @@ -49,9 +47,8 @@ def generate_samples_independent(mean, sigma, n_samples): fd_3 = FDataGrid(samples3, sample_points=t, dataset_label="Process 3") fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) - anova = func_oneway(fd_1, fd_2, fd_3, n_sim=2000) + anova = func_oneway(fd_1, fd_2, fd_3) print(anova) p.append(anova[0]) print(np.mean(p)) - From 7653f01907f8309122cf5cc4cee7039cf853fa2f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 31 Jan 2020 17:55:46 +0100 Subject: [PATCH 097/624] Changing data in example for ANOVA. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 25b76de92..59668fc7e 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -31,7 +31,7 @@ ################################################################################ # TODO -sel_prov = ['A CORUÑA', 'BALEARES', 'LAS PALMAS'] +sel_prov = ['BARCELONA', 'TARRAGONA', 'VALENCIA', 'ALICANTE', 'MURCIA'] filt = np.logical_or.reduce([np.asarray(province) == p for p in sel_prov]) From 6d37c93c8bb010cbfd6a81c920a936e9b29d07b4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 098/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- skfda/representation/basis.py | 11 ++ tests/test_fpca.py | 26 ++++ 5 files changed, 304 insertions(+), 69 deletions(-) create mode 100644 tests/test_fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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mnphiXEvyvK2YnNdPrT1CfVEBx1eez+buXqr72mnqPEEgPApIOBQfZ/nbOevcBWxpuoCvHL4FOZ/hX09VMjr9T6gmF0gyxVUu3vbxRTg8llf3QBleU0bIG96SxvtibP3dSaaGMgx5OvFKGq7oPCqyYyzwuTFZvbSsLeaL3hy9GRWAUHiC9R1HUTUXB6llPJEHBBKCOm2YsxKnKIhOIGlxJEkGBEIIAg2zaSs8Rl22i015heJMElnLvqw+ulAQWAEFiSwSaSTp5d+vXNZDJhEkHfWSmvKgphzITifmgA9zUQilsBhTqBilZBZh2UlbX5ThoRimtEYBErMQFGBGemGlHF3oRHNhOoVKv1VDlocIykl+uXYTrSYLBeFxrtl/gMKhNpL5CCZJoik4SfHGt/FV0UzLdBvvmdCob/8Qg6aFIHQcboVLb1xCcJbRT/9mYYS84S1FTefZ/2g3J7YPkDXHaS3Yz6KJc5ByVpanuwmV1JMpsPLzdQXcEY/PtJt1jeWdxykdmuCIuYGR9Exr2iWSrEqdoDbRhTmVAMDq8JPPJdFyKktWzKFI3k7FdDfOmYUIULN+svm55MwLyFODJkKoWRNqdoyUmCRpCxOzTZO2TCLLCZzEcWsRArk4ASmHx6ShmGbKSqs2BqLFjAz7kfpVgskI8gvvk1WshEuqSc2eR2TeQg57K3jmdJScqtNAjlWWFCtcGewTEn5KsZlmuovSaITzIwxqk/xw7XI63QqzUlm+uqub2PgBhlOnAUGZX6NvRTW3s4t6Nc/1J5dwMv1edNmMySSx8WMLjYXO3iSMkDe8JQghOH1kgl33dpCKZmkP7cWmmamcWo4/M8Zyu47VW8Gh5UG+GhIMqzODoqHoJMtPnaA5Wcqo7gagQh/inNgRgtERJF1Dkn0o9iIkaYRsIsJFC0xUaMfx6FGEkMhkZ5OWNpAVZ5PTvUzl+jlt6eGYp4f9BT1MKHGcmhNnzolNt2HVrSiagkmfmXP/h5Z3XsqTk1T8xGnSxlmuTrBQTWBFMGWycdC9mBPaWiwjZnyDvRQMdVMx1Y+ia+QkE52BCjpqFrK3uJ5mUxA7KoscYS4oep7c8QSBTBke5xyKHDUUSjNdLhPmHE+VOdgRMnPeUJyr+rMcmNzKVLaDnJbH5rOwpzpMT2iKL3fJjI98jpi1FBCsfEcNizdWGfPp3+CMkDe86UUn0uy8u4P+U1Po7gm2Fj3F6oGN2LKFNEwdYXbVAtIOJz/dUMi9uTQ2WSKjCxacPoXck6FdK0ISOk3ZdpbFjmBPxkECu62InLQQk7WNIdpYHJriAi1MQImh6V4S2iUktQ1MqUm6RSu9tKH5JJzVi8lYSglP5QjHckRSeXQhIZDQX4h0s6SD0LFIOg5ZxWPKo6CBrr9s2xRyzKWd+dIp5ohhBLDX5mVL2WyU2nUsdDcQ6tBQ97UgHzlIYKgbgD53EQdmzWdz2VLGXX6WeyZZ636I6GEQaRj2LsXnb2KDYmVW3o2MzIRV4rhHsHwakvEBDqZasWT2E1EtqHY4Uh3mXMco81qupdNyLkgStfO9XPixxcgmY0D2jcoIecOblpbXOfpsP4ee7EWSBIOlj9ElVM7pvxRbLs3yaDP+2jW0VTj4wkI7vWoORQI5l2P+oeN0RAtB01iUPMXS+GFMah5dsVDq0ZkwXUS7ey+j7tNcPl3E5aYe3NYe8qKQiHolfdkS8vE+oq3NHKlqor22iX4cTKgm4sKK+CsHUGUJSn125oRczC6wU+VXqHHpSNk409PTjA72EhvuYJHezFJO4CBDjxzkDkcZT3ly2Lx2Vs5ayXmWRqqPRolu3om99TiyELQFKthccTb7ZzWytGSYZanHiLU4ycg2dgXOweP1cf2cCibHFRZOS1h1SMsCc07lQLQHp2MX8WgvwykPSVueROUU7xov4XntY+gmK/6AzJVfXD1zdSvDG44R8oY3peHOabbf2c70aIqSeTIPmr5J6dDbqYw0EYq0sMQhYQot4K6VPn7q1bDJMglNp3hqHPlIBFXVWBRtZkGyBZOWJ+/0YClSSHjG2enUmaN6eOfAIhY4T+NRHkFgoS+9ks0pJ+kxN3v9s2n3VRCTZy7gIaNTYBVUBmzMLfExp6yAkMdO90SSJ5tHODkcw2ExcXFTCW9rKqbc7yCpaoRTKiORDCPRNL1TKdpHY3RPJMnrM9+9mgIny2uCbGgIsaomSCoRY+TQE+SP3ElVphkvCQYpZqvlbJ5zSXTbeohZYzQVNrHRdTbLjudIPfQMtuF+0mYLz5YvY0vt2SytO0FpZxfqhJkuRzUHClawqcZEwfIltDdPc/FwjgVRHSEE0VyUdjnMfPFdnomXo0bNpOw5ltojjKRvJG0LYVV03vmV1bgCtlc6bIYzwAh5w5tKOqGy94Eu2vaN4g7aKD67n1t7f8+qrvfjVD3M63uKusbVTDtDfGmtj31SniKLmTE1T8WxLsRAgkWRE8xJdiFLOprPxUCxlfFQG3mpkEvHz2Z1fAkuZZyA8h0UeYRBqY6b4/MZzq6ix1GKkCRs5Ci3Zlhc7mXt/ArWLqrDYZvp5xZCsL19gh8820HzUJTygJ0PrKrm6qVluG3/+zIB2bxG60icAz1T7O8Os78nTCKbx2kxsXZeiCvPKuPcugLMk22kHvkMpuGDWMnRRQWbWUvYGWLEN8Ih+RBpc5qzChfzD/kmqp/pQ966HZOucbBoHtvrl9BQdRDpuE5GsrE1uAb8Qa57+3y+poIvkuPjHWnOnxBIQCSfwapsZUB6iu2TxXijZqzWND79XKKuNcjoXHLDIsrqjQHZNxIj5A1vCkIXtO4bYe+DXeTSGos2lDPs+A0PH4mxvO9SHLkoi3sfp3D5tTQH7PzrMhdhoeM1m5hKZGh89nnqp05RmR5AmBXsVSoHPXYG/BOsSC7g0olzmKXPQmgZnJlf4A88S9rk5t+zV/FQfh1CkglISWqUOOsbS9i0cj4V5eV/Muh4sDfMt55s5Uh/hPKAnRvWz+GyRaWY/4Y+62xe4/nuMJtPjbL55ChTSZUij5UrzirjfSsrKckNwcMfQQzOfD9aLIt5TF1BVrJjCVlodbZyVD+KYlK4xLeaTcftyA9sxZVOcCJYQ/OyCkK5fnKTEu3OOnYXrOKSOhuti+dwIJaiLKXx2+fTuNQcZlkhp6tYlPv5oasbRwd4Ugoe2UnGeQWyKciqS8pYfPErXujN8DoyQt7whjfWG2Pn3R2M98YoqfWy5p113Pv8x+k8tojZU2dRON3MwuQpHAvey31zHPyg0kzAYiapqtQePMji5n2E1EkyZjtlK0vZaj6GrhVyYXQl58YWY0ZBm+5FG91KqO4pXK40z+lL+Iz6YXJYqDVNcW4gx6aN5zF37lzM5j/tex6NZvjWU608cmyYIo+Nq9dVM7vaz1AkwlTfAOlIhGwsTlpVycgmUmaFsNtLMhBEd7twms0UWswUWRRKrAqzHVbmOm3MdtiwveQHQs3rbG0b575DA2xrH0eWJC5dVMqH19QwL98JD/wjTPegY6Kl5Eo2x+cQTyRw+9ykS9NsVjcT0SLMdVTz7lNllD58EG8mQWtBBaOLFdITkLR4eSy4AbfXyfJLFvDbWBpFE/zsQIK50wkS+SyF1iBCT3PCt50HM4eY12XCpJmQLU2YbauY01TIhuuXGzNv3gCMkDe8YaXjKs8/fJqWvSPY3RbO+Ydaqhc5+ebvrsPccgX+dIja7keYWxYkN2s931ri4imfRKNFwnp4L8sO7sSjxplWvOgN89HqDuDur+HCyDmU5grR8yny/c+THdiNqB2moDaBjzhfy7+PbWIZjQyzplRh47uuJRgM/tk6qnmdn+06zU+PDyBcEudmhijraWNOdwc1QwMEY5E/+7qXylmtjFdU0VdexamyKnbVzmWgsHjm8n9ArcPKcq+LFT4nK3wuyl7oFhoIp/jV7h7uOThAOqfx9gUlfGbDHGoGH4EnPwu5FMLmp23Zt9jdnWBoaAir1Yp3tpdd5l2cjJ2kxFzAJQdCLN7ZQzAdp7W8jJFCK2ldZo9/Bac8DVy8opAHAy6yms4XTqV520CaE9MHKXaeRanVhi6pPOnZw3D4efwDMgIrZvtqQsVNXPHldcaA7BlmhLzhDUfXdE7uHObAY93kMhpN55dx9turUbPDfOVXX6D09DU4cnnmN/+KyrWXM2qbzadXuhjSEmzqOEzRkd3Ys2mGrcWc9M9hzYoMZb121sSWoAiFTLoTvWUX6vARojUW2pqquULZjY7EN7TrMOXMhOzDvP+Gr1Lo//MLc41nc9zaMcLvu0Zp6D3BBYf2svzUMZyZNLosk62uwdrYiL+mGldFOSafD9ntRjKZEPk8ejpDfnKC/MQEuaFhMm0tZFrbEIkkAFqRn4kFtTQvXciO2fM5nguSEDP9+bNMcc6xDrHaPsJ8S5SM5uahllLuP+FGzcNlC6x8am0ps3Z9G7nl0ZkKz93EwMqvs+/AYVpaWrBYLMxqmMUeZQ/7JvfhF042bi3goiMDWLQ8nVUFDDoc9PuqeTywnrKQlbHllUxoGu/tVbm+Lc3Byc3ETPUslq0UeENk5TzPubZj6zrEZMqMZCrC6V7DO79+Fd5C46LhZ4oR8oY3lOHOaXbe3cHUUJKyeX7OvWYOgRInY0OHueWXD1A5cgGuVD8LW/6boqtu5pju4qtz09Sf3Mu89iPI+TzdjipOeBewqjDF2/KF1KUryEhZRtMdFBx+ADExzGDQw+6Vy4laHHxP+QVDFPJQ6kJi2dMEr1rHx1d/Bll6eT96Iq/x+ESE+0amOT4yzjt2bObSnc8Rikyh+f1416/Hd8F6HEuXYXL96dWVdD1PKtVNItFGKt1HOt1HOj1AOt2Pqo6DANM4WNtkrK0y1nYJOSuheSCz1ETP2mqOlSzksFbPSTGXPGY8xFjBXtaILfizkzzRfSE7BldjNWW5ou4x3l54mMbWCdzxLHGPndTaT5IJruPAgQFOnWpDURRqFtSwW9nNzrGdFCWcXLLZz/qOPrJWM+2hAAPFRTxYcBERZwHBdRV0y7BmPMf3jmY4Gd5BZ1aiWKqh0TKFP7CQuJxkIv8MxwaGyekJzJb5vP2TH2L28trX62NkeAkj5A1vCInpLHsf7KLz4BiugJXVV9ZRs7gQSZI43fIsv/1dK8WR+ZSO7qEhuhvf5V/modgQxxMHqO1pRTeZaCmoZ9C8gPUOM5cKF17NxahljF61h+IDmwkODxGz23hmwSIeKdnABabDfNP8K/r0Ep6ZquP+hkFu2PRVLq65+GV1O53K8OvBSe4ZDZNPpnjnM4/zD1ufwp1NYV2xkoL3vAv32rVIyh9nzuh6nkSyjWj0CPH4KRKJVpLJTnRdffE5FnMIm1yGVSrBZpqFRS7EYi3A6irE6i/GYvGQ3n2A+BNPkti+A5HL4Vy1Cv+170OsOodtkQRPTETZPBElKwT1DjNXFMg05cP8+NlpDg/KzClI8Mkle1jU/wTlPZNkbDIn57lJeJ1YbXVMT3vo65PIqRXMalzEw5lHOD51nKpBD+9/2kLjxChjHietswp5rmwtB53zKV4WoC9gZ34kz20H0vREj3As3onFfgH1Y1vR58+jXmsiLU1zOtxJS2QHQrIyd/klbLrhvciyceLU68kIecMZpeV0jm8d4OCTvQhNsPjCCs7aWIlimTnl/+jue3nqARVPqoi5nfdRW5hkqvHtPDe0CyUyQM5qp63hbDLxuVys2TgXKwDHva1M+fbi3Jqi8VQLmCR2lM/m4YaNdJgr+JT5QT5lvp/RTBGfLfAwWO7iR+f/iIWFC1+s297pBD/pH2NbOI4CXHNwP5fe/RsKU1HUFauZe9ON2BoaZrZDyxKNHmI6sp9o9Aix2HE0LQWAogRwyLOxpSpRxktQRoswTxUi66+8mqPsNGMOObFUuDEHdNIHNhO57x7y4+NY582j8PpP4jr/fKJ5jYfGI9wzEuZYPIXLJPOu4gCVEY1bN3cQz+T57EVzuK74NKb734+kppgs9tG/cCFxtQ9Nm1mXJ5t1kE5XYA42cPv4QTqSk6zbV8r79o5j1VS6Qz521a/gQc9alDoP0VofFUmNO/alGI23sX9qKzbH5YRSXfQVHWCh99U2uC4AACAASURBVDIa07Wk8lEOhA8wljyC01vFlV+4mYLyslf7o2T4C4yQN5wxfSen2HVvB9HxNFULClh9VR3eQvuLj29/+DaOPFuELSfRdOKXcFYprak08fQkUZeXzPL16GIOl/TkqcVEVE6y2b+H3Jweatv7mfWAii8aZSrk4Zd169hduBIZ+JJyJ+8xbWbIWsulJTlqgvP4yfk/odhZjBCC3dMJbukd5flokkKLmXdrKvVf/yZz+08xWVJF/be/RvDsJSSS7YTDuwiH9xCJHEDXs4CM21WP13MW9ngdSucstFYz5ATIEpYyF0qJE3OhA7Pfhuw0I9tfGJgUoGfyaPEcWiRLfjxFbjSJOpwATYBJwlrtQo8fJ/7k78kN9GNrbCR00004l58NwPF4il8MTPDI+DS6gAvdLtTmMHvbJzi7OsAP31FN6ePvhcGDIMmIdf9GavGlTEcP0j+whXj8EGbzzI9TmgB7ogl6JhXWPRRiTfcQEbuVffX13DHrEibLi1Hn+ynMCu7em2Q6O8D+gftQ7BsxO3xM6rcwWl/Px0Yvw68VMJyJcHTyYZL5KZZf/k5WXnkVssn0On/q/v4YIW943UXGUuy+v5O+5im8ITvnXj2Hyvkvn73y8C//k4FDc7BmhigYuJPRUi9pNUPCW8TxRet4r3s+c9vieDXoUiZ5tOBJBooneW9hHu9vxgkdjKHaLeydPYc7Kt/OsLmQWXKE72v/xUr7CU4VzOY9rixrKy/gm6u/iUNxcCSa5Cunh9kfTVJsUfhkRSHFjz5L6Oc/QNHyxK/9R+qvm89keAuTk1tR1QkAnM46Av5zCARW4zYtJLM/RvLgKHoyh+xWsDcWYG8MYqn0IFv+GGq6niOZ7CSR7CCZaCeV7kNVp1DVSfL5l6xTL5kxCw+mjAt52okyXYQlWYJ9Ik1uzxby46O4N26k6KbPoZSWAjCSVfnV4CS/GZoklddYlpDoOjSK1Szzo2sWsmbgVtj9HzNvULUarr4dHAHy+TwHDjxCW/v9+Hx9eDwTSJJOXIORQReLHtVwtWucnFXC3Qsu58isBvILA3jzgnv3pEiIaQ50/oq8fQGKfSV5963cU9fPtZPruHT6EsBMV6KLk+EncJeUsOmTn6KoZvZr+4H7O2eEvOF1o2byHHqyl+PPDWBSZJZtqmbB+WUvu9qQruncecsviHQWYYptQdV70WSJoLeGkw3raBTFrB/PI4RgjynCo8X30OI6ybsqL2Xd8Has/xnFNp1juKaY+ypX8Jx3BQKJS82HuTn/MCFbL0f9JXzQa+aDCz/CJxZ9gqFsnm+eHuah8QiFFjOfrirmUred567/N5r2b2aytAD7Z2eTcB5B0xKYTC6CwTUEg+cRCKzGZi1Gi2WJbx8kcWAENIGtPohrRQnW2T4k+Y9zxVOpHiYntxKe3kckcgBNm5lNI0kKdnslVksBiiWIYvbACwO/Qs+Ry02j5qbIZsfIZIZeLM8Ud+N7JoB15ziSyUzh9dcT+MAHkF5oIU+pef5rYJxfDk6SjWcpOBUjHslww/o6/rm8B/m+90M+Dc4QvPtumLUEgFgsxjPPPENb22HKyqI4i7pwKqexyqBFTXj3C9ItTh4t38SDleeROasQnwZ37UmRUTLsP/UzMrYgLusVRMu38ljoSUKai2/0XoePelShcTy8jd7EMZZcfBmrrno3itVYEuG1YIS84TUndEH7/lH2PXSaVExl3spiVlxWi9Nrfdnzctk8v/zCj8gMD6GrHUgI5i44m5C+goTsoi6hk1UkHpCzPO7azHToKZw5H19b/E8E7v4P7I9nSdkdHFvayIPelbSYKiiWotyi/BfLSoIoY9s47PLyyVCQL6z+GmsrN/Kj3jF+PjiBBHysPMQnKkIMdPYwcMOHKe8fIb5OEP+HHIotSGHhBRQWbCAQWIUsz9Rdz2rEt/UT3z0Muo7jrCI851dgfskaLtnsBKNjjzA29hjx+EkAHI5q/P6V+LzLcLnrcdirkOX/fckDgHw+SSp1mtj0SSb7dhNNHULEp/DcZ8Z+QkaeV0zpt7+He94fv9cTao4f943xm/5xlJYoYijFBfVF/OQiD/a7LofoIEgmeNt3YNk/wQsnMXV3d/PEE08wNTVFRW0RR6U7qfOPUW/VkWUwDUic7q3lJ44PMbqoBm8efv98irRNsP/Uj9HMJuzKVUwUD7On7BfEFME3T6+iKPseAmaZyWyYw5OPIHwyGz78SSqbFv0tHzXDn2GEvOE1NdYbY9c9HYz1xAhVeTj3mjqKq70ve46ua7Tu3MMzv/4NenYCWZeYZ/Vz1ttvJH0ygVmDAadM6zw3321rQyq4HbOjl9pUA99pvIjsN7+P0is4XVPNvgWL2SwtYFK4WC+a+YntFmwrPoQ4dBttiolPV87hexf8lHG5ips7BhjM5LiyyM/N1SHs6cMc3vFrin6wD2VaEHmvnYIrriBUuBGvdzGS9MeuFiEE6eZJok90o0VVHItDeC6owBz845hCLNbMwMBvGRt/AiFyuN3zKSq6hKLQJmy20ldtH+uazvTxwwy3PES6axvOx6aRsqC/dw4lH7qJQOCcF888PZ3K8OXOIbYeHUFpi1JV5OLed9cReuz9MHhgpsCma+DSH4My80OVz+fZuXMnu3btwuFw0K4M0BzYzrq8zIXWJKJER8/KHI4t4eHAlUyp1dy9P03YLXOi57cQm0CyX0Ta6WNP4w8YUlJ8prsW//QNNNhNWCSJnlQLxyafZe5553He+z6IzWlceerVYoS84TWRjGZ5/pFu2vaO4PBYWHl5LXOXF7+s6yKXzdCycysHHnmQ2MQoSG7mZTw0zT0P2VwBmmB/wMRTtTb0IjvbdzyDvfguZFnl3NQaPo0V9b8eIW8ys2P1Wlp85WzNz0UImS9KD/Ae+6NI679Edud3GdOzfKl+FTeuu5WfDOd5bCLCHIeNr1VKlMYfYGT0UUTfJIGfKIisCcd3bqT2wg+8LNj/QIupTD/YSaYtjFLixHfZbKyVnhcfj0aPcLr7B0xP78NkclJSciVls96L01nzmu5zoQtSxycYf+Jpkvt+gen0FOmFOrmP1lDV8ElCoY0vbs/OcJwbd7YzeXAcu9XEb69dwIrDX4Tme2cKK10C77oL3EUvlj88PMzDDz/M+Pg4ukPmcd9WZMLcdMBEWVWC1FIdyQJtYh5b1Yv5/L5Ghj0KrcnNmFoPk/MsxmpawdZFP6bXNsRH+6uxDn2CeRaJSrsNVeQ4Ft7ChHmI9f/4MeqWrXxN99ffi9c85CVJ+jVwMTAuhJj/wn0B4B6gCugFrhZCTL9SOUbIvzloeZ0TWwc5+GQPWk5n4fpylm6qetmp7dHxUY5ufoKT254hm0wim0OUu9awSFKw+SqQFJlxh8yn6i1kg1ZmJfKcOHkn1sLNyGqQd6bXcUXzYfTdHYzMLWT3gvPopJA9uWo8msYdwduZn9mCtvE7xHZ8C5GN8eOzLqV60bf4Rs8kWU3wTwXTbMj8gmRsP0hmhk/V0XDbALLNyZz//g3OeX9+ga1U8wSRh7rQVR3vxipcq0pf/OGKJ9o4ffr7TE1tQ1GCVFV+hNLSqzGb3a/Lvv8DXdWI7xhg8le/InviQXQfhP8pi7m+kprqGygquhhJksnqOl843MM9j3cg6fCxy+Zx0/TtSLtvmem6cRXNBH3pH7tQXtqql00yO53tjPqPcdkBH1c9P0X8HJi8yILLkWBML6a280L602s47e5G2fIQWqAcm34JWxt/x2nvKd4zWIVv4BP4tSz1Hi8BxUREn2D/yOOEzprD+dd9BKfP/7ruv7ea1yPk1wAJ4HcvCfnvAmEhxLclSboZ8Ash/uWVyjFC/o1voDXMzrs7iIylqGoKcs6VdfiKZk5nF0LQ13yMo08/RveRg0iSRNXs5bgjjVTbfNhMJiSrhvu8GrZ1T/LPVRIVVgXLUISh8V+geI9ijtVzY6SBZc89QX4yyeELzqLHU8sxrZTjWhmVeY176x+gaOBhMhd+g979P6Y6Nsbtyz/ErtKPs3kqxkLLFB/I30JIa8duryAmbeK2e238y5bf4vB5mPP7O7CUzfqTbdNVjcgjp0kdHkMpcxG4ei5KaGbb8vk4p7v/g8HB2zGbXVRWfITy8msxmc7sqfz5SJaJW58met/3EGqE1Af9RBYN43Y3UTf7Zvz+FQDsGJzmQ789SDadp+m8cu62bcG19ctgsgISXPELaHjHy8oeHh7mgQceYGpqitOWOMdLn6VhwMlN98Ux6zrPvmsx7hUT1EqnkVUXsakNNHvOwX7nrzH5AghxMftrtnKqeDfvGC2nrOd6lEyCIoeHepcNiyzRFT9GR+YQq699Pw1rzjcWO/v/9Lp010iSVAU8/pKQbwfWCiFGJEkqAbYLIV5xbVIj5N+4kpEse+7vpPPQON5CO6uvrnvxIs9qOsWpHc9xdPMTTA8PYvd4Ofvsy/BGZmGdzCNLEpmpdoKXzMd70VLuve8kny0RzLFYSLT1kMjfisnRj238HL7aa6Jy+zZGq4IcWLKClORkb7aCLopYZjbzu6YHsbfeTWTtTew6+XsumRzkgSUf4Qvea4hpgqvFHWySnqaocAOhoqv52V4v2zYf5JZ9P8ftdlBz5+1Yysv/ZPvyk2mm7mwlN5rEva4cz/oKJJOMEIKx8cfp7Pw6qjrFrFnvprbm0yiK7/U+BH+REILEni5GPv85tPF25IuWM3FlJ9ncKKHQJubU/TtWaxGj0TSX/Hwf49NpbEsLebDwKI3bbgbFAWoCNn4HVnz0ZWWrqsrTTz/NkSNHiEk6e0q24cik+dqdKsFojq1nn8e9157L5flHaLIcRtfsdCob0e/qRs8pZOzn0VE0wP7Kx1k/XkZ99ycxZ1KYhaDGV0iVzUyODEcmtkC1hQ0f/iTeUNFf2FLDX3KmQj4ihPC98H8JmP7D7f/xug8DHwaoqKhY0tfX96rUx/Dq0DWd5u1D7H+sGz0vWPK2ShZfWIFZMTHZ38uJ5zZzascW1HSaWTX1LKl/O64JJ9pEBlUXjMfGMJ3+NU23/QxreSW3P3CKfw3maTRbGD1xBM3+CyRTkuLeC/jasTbsPX0cXreQAX8NeSGxM1NJv1TIO4I+ftD0NKbnf8zwsuv47cAWPj86wJMV6/lg9RepED3caLmXleVrKSm5gsmUk4/feYSBjj5u238rbjNU3XE7lqqqP9nGdOsU4XvakWSJwDVzsc0NAKCqYdo7vsT4+JN43AuYO/ereDxNr/MR+L/ToikGPnYz6SPPosxehvj6QgYjv0SSLNTWfpayWe8mmta46rbn6RyLoy4K8m3/Kd637yYkiwsyEVj1z3DBV+B/LEvQ0tLCQw89RCaX45C/mQlHN1++D2r7M3Q2NPGJj3yGxfE+Pp97gGTRIVTsJLpKGNlnQfUvp8+psKP2Ls4Ol3J25ycw57KYUzFkfxUr/TLWvInJ7BDHottYcNXFLLpoE7JsnET1f3XGQ/6F29NCiFfseDNa8m8so91RdtzVzuRAgoqGAOe+cw5Or0zH83s4vuUpRjrakE1mFi25iDmBpdCjIlSdjMNMy0SG/MDzBLMPs+rOpzD7/Nz5SAs3eVUWSQq9R3cg+36D0K0sbF7NTft3MBzwc3TZQnJYyAkTW7O1jEh+PlhTzBcWHER6+l/onnM+v8+d5ObeCQ57G7hiwY+40nGKm2fXURRciSTJ7OyY4Ia7j2LKZPjl0duwjw1Teecd2OrrX7Z9My3gYaJPdKOUugi+tx6zf2a2yeTUdlpbbyaXi1BT/SkqKz/0Zwdo32iEEIx+4ydE7rgVU6ge/1duZMB3G9PTe/B4FtHY8H1ylPHO2/bRMZ4gvTjABy3H+Nqxf0OyByA5Dk1Xwzv+E8wvX5IhGo1y++23Mzk5yWnHCCcK9vPpZ2XOPppmtLKKD13/b1TGTNza3cuhuY8QCh5CaGbGj3lJRM+lQyvluTm3My9WzPltH8Os57BPDRIuaGKxeZyKQBlC1eiIHmIyOMEFH/0EwbI//avL8KeM7hrDXyWbzrPvwS5O7RrG6bNy7tV1uAMJmrc+Q+uubWRTSUIl1Syp34Q/WYA2nkFSZGwLCmgZS3LiZITygeew2ray/o7nMNns3PtkO5+2pZkvTPQefwIleA96toBLd83jsq7DHF62iIlgEZZsnLjZw5bcXCaFi88ureYTjV0kH7uOltpSxh1JVh9LE5W9vHPZf/KNpnmsC82skaLrgp9s7eKHz3Uwr8DBD5vvRD/4POX/dSuuc8992TYKTRB5/DTJfSPYG4P4r5mLbDEhhEZ393/Q23crLudcGhpuwe2u/3O76Q0tfNf9jH31S8ieMvwf/BLapjE6er6Ormepq/s8du8VvPu2/fSEk0hLCzg/9zz/2fJlZFcxxAahZi1ccydYXz7NUdM07rvvPtra2gibk+wv3sm7jmS5YFuWaEEhH/3Ul2iMW/heG9wa7CV41jMskvaST5uI9dZzaHgVW2ruZ1aqgItbP4JZ1/GPnGS4ZDVF0XZWNpWjxzxktRTHprdRtnExyy67EtOfuYiL4Y/OVMh/D5h6ycBrQAhx0yuVYYT8mdd7YpLtv28nFc3SeF4Ib2CQlh3PMtLVjslsZvHCTdR4FiAN5EETKGUunEuLsTQEeOa3J+lriVLT/SiZkkNc8sutmBULD27p5Hopwdy8xODJ+7AEH0OKV/Dxp10U2PK0NtYjmfO4xkeZ8FXybL6eKeHiGxfMZl3JEwx1/IiIz0xSt1Lc4mHhdBdfWPMbPnfORgotMycXTSdVbrz3GNvbJ/iHxbP4dNujxO+6i+KvfAX/NVe/bBv1rEb4961k2qdxrSnDu7EKSZbIqpOcOvUppqf3UVp6DXPqvoTJZP1zu+lNIb5tO0P//CkkWwD3xf+K+wO1dIW/Qnh6N8HgOgrLv8a1v2lnJJqheE0pVeHt3Nb6FSR/JXK4G8rPhvfcBzbvn5S9ZcsWduzaQ17OczC0j/P6J3jHI3nSDic33vBvrEj6uKFX4gumDIPLp/is6w6stJFL2ugaWMntyin8WS9XtHwYkw7FQwfoLV2PMzHESlMLrvnvQA/rjKX76LG0sPqj11FcW3cG9uKbw+sxu+YuYC1QAIwBXwIeBu4FKoA+ZqZQhl+pHCPkz5x0XGXXvZ10HBjF5ZvEG+xj4OQBctkMs0rnsahuA56oFz2aQ7KbcS4O4VhahKXURTad54kfH2akO87cznsYa+jg6h9tQTEpPL63j4+mw9RkBKMdv8Ua2IZnZDaf2CUxMreKuMdDwN2H1JJgrHgez2iNTOLi8+f2MMf1G3JaFCWjc3e2itrkYj7T9SueW/5F1m78f+ydd3hVVfa/39tLbkvvvZGQEHrooKAgKCoiiiIKiA3rqOPYRcWGo2AHsaOI0pWO9BpCSYeEJIT0enN7v+f3RxwcBixYvj/H4X2e++R5knP2XWefk3X2Xnvtz/obku8zMQpqO7nr88O0Wlw8PT6TcY1HafzHPwi65RbC/3FmQpff7qHtoxLc9RYMV6agyY0EwGQ6SlHRXXi8naSnP0tU5MT/83vwR2DLy6N25u2IlEGohj5I0HW9MEZs5ETly0ilBsLj5zHtcxsur4/BlyVhr1zDgtLZeCN6IG8uhohsuGklqIPOajs/P5+vv9mAUuSh1FBKuv0EE5Z4QCzlsTsfZqQ7mmsaxDyAnUOZGuamVaGrm4syyI7DEcxXRoEGUyDXld6G2CchoXEHFWGjkXrt9CxdSMzEGXjMkQhuP+Xmg8gHBDPw+skXpBHOwYXNUBf4UQRB4ER+C9sWH8BhKkQiLsdl60CjDqJ3xhgiJQnQ6gMRKJINBPQLR5UZgkjWtTBnN7tZ83o+HQ1WMko/4fjgBma8sAmZWMamgnpubW0h1uGjvWohMsM+MoszGdWmoT4+FhUWokKP0nHQQHt0Glsl6TS4g7k95yP6hRcT2CkQVG/ilvDLCVRezFdH7qUzeQwhN34OIhGCIPD5gVM8+00poVoF707pTZqtmZPXXY+qRw/iPvwA0b9N830WN20fFOFpdRB8Qwaq7l2Cac0t6yktfRC5PIwe2e/+V4ZnfgpbXh61t92OWB2Msv/9aIakIRvloajsHpzOeqRBj3PPqgiCA+TcfE0GZfsX8tLxVzHHDUNXtx9CUmDqatCEndV2SUkJC5d+S6DYQV1AHYGKQiZ+5ELl9vHsbQ8wwZdKrxY/M3FwMk7FzN4hdNv9JNqUMhQ6DxU2BVubQhl19G7EfhkZTd9SahiHX6Ykq3ABUbEa1KNm4an2YveaKfcdJmfGeGKzcs5xpf+7XHDyFzgnxiYj695eTnNlHoKvAYlYTk76SBJ12UjbROAHWUQA6l6hqHLCkBrODF3YTC5WzT2IucVKZslC8i81cs8TG5FL5Ow60cqUk3VE2710nnwXiTaPKw71QaONwiOTEactQS09RWtFMOo+Ur5uG8kxYwqzen/DNX2zkWz5lPCGCq7q9TDVqqHsODKTYLkC6Z27QKnH7vby+MpiVh6pZ0R6KK9P6onO56R64rUITieJK5YjDQk5bavX6KRtURE+s5vgqZkoUwMRBIFTpxZyovIV9Pre9Mh+D7n83HVe/9ux5eVRe/sdSAzhKHvdhzwxHP0NMRyvf4K2ti20MYUntw4gK0rHQ5OyyVv/PPdXLqA+5QqiT24BfTTc/A3ozpZqKC0t5fUlG4iSmOiUdyIKLuTa9zvR2d38c/rdTPFkEWhyMw0XxmA5vbsHc1PhCuze74jMNYLYzaFOLSG770NwBtGzdQklynHYAyLJqFtJRM1Ogm65H7c3HVGnQKO9GnuakwHTb0ShPrs61/8iF5z8BU7jdbupPprPoXWbqT92BAkQF9KLrISBqE1q8AhI9ArUPUNR9wpDFnHufyKr0cnKV/KwtVlJL3uXHeOtPPrgBhQSBUcbOplYUk2g04Pr5NsESssZXZGLW2fAYOwgtc9OHDYxUq2HgEgH7xdOJa+5D0+PkXPDkCEc/nQM2fXHubrPqxQpu7Ouag69GrYjmrERovtwosXCrM+PUN5i4YFRadx9UQoiEdTdcw/W7TuI//RT1L17nbbV02qnbVExfpePkGndUcTr8Pu9HC9/ioaGpYSFjSMzY+7vHn/3C35a7C3UWmppd7ZjdpkxuUx4/d7Tx6ikKvQKPTqFjjBVGDHaGAwKwx+yKci2bx+1t92OPLkb8u53IlapCJrSjSaWUFn1GiXmcbx+4BIuzQznsQlZHFj2AJOqv+BI5lR6nVgB2ki4Ze0ZMgj/4ujRo7y4bA8p0mZ8Eje+iFIu/6CBEJOdhVNmcounH1aXlemCD5dagiZJxzOWozTsX0PiaB/q8CpcfhG2ksswlV9M345FHPOPxRiUQTdZOZGb56NMTUM35TFsBQ7wwUlXCWHjM+l20Yj/+U1UF5z8/zh+n49TxQUc27OTiry9+J0eItTdSDT0IVIVjsgHYrUUVfcQ1L1CkSfoz9Cf+U/MbQ5WvZKHvcNKSvnbrLvawvN3b0QtU3Oiw8b4/HKkHg/y6jfIsrlJtHVD6vGQaC8lfEwxPrEYicyPw6bl05IbyOvsziNjujGxv47VX01gZFM71/d+jTpZBIuFPC7a+TCMegZh8P0sO1THU6tLUMslzLu+J0NTQwEwfvUVTU89Tdjf/07w9GmnbfW2OWhZWAg+gZAZWcijNPj9LopLHqC1dSPx8XeSnPQ3RKLfVq5OEAROmk9ytOUohW2FFLUWcdJ8EpfPdd5taWQakvRJZARnkBmcSU5oDkn6pN/FkZk3bKT+gQdQ5w5CljEDv9lH4FUpOBJLKS65j801I/m89BLuHZnKbSMSOfzZNIad+oa1vR5mbPHbiAzxcMu3EBByVtv7DxzgmdVFdFecRC2IEEXXM/zTUqJaTXw1cSo3+odQKWrnLkGKVyzGF6/hHncN8j3L0KapEOWWkaCw4rYG0370amKrt9BmHElTxACSo90kbn4Jf1srQdNuxxOYi3DChdvnpF5RRcaM0YQmJv7m/vlv5YKT/x9E8PtpKD/Gsb07KN+/B4/ZTqw+k2h1DuHyMCQiMeIAGaqsYFRZISiS9IgkP+/oOlvsrHolD1enhcSKt/h6golX79iIXqGnwebi8j1l2P1uEo4toFenAbVEQar4CPqUWuShDvw+MFXraGkbynf2TA55Y5k+OJGrcr18uPZWrjQFcHvWHCQKLZ/ESum/5FII74518mqeXFPGyiP1DEwKZt71PQnXdS3AuaqrqZ5wDepePYldtAjR9xt5vB1OWhcUIHj9hM7sgSwiAJ/PTmHRXXR07CI19QniYqf91OX+JD6/j4PNB9leu50dtTuos9YBoJVryQ7JJtWQSpwujhhtDGGqsNMjdrm4K/9cQMDhdWBymeh0dZ4e9ddaajnReYKy9jKsnq6yfaGqUAZEDmBA1ACGRg8lUPnrtV6MS7+i6emn0V0xHlnajbgrTWgGRyEe5qSg6DYWHLmU3fV9efuG3ozOCKZq0RUktOTzQe5L3Jb/FJLgZLh5zTkXY7/bvpOnN9WQrS4h3KtEFmUiZ+lhkutb+G7cJMbLRnFU1cgjPgV2txRvXAADOxoYWrkav0yg9jIj/VSnCJN7sTen4Si2oznSj+q4scTESelt/w7rsq+QJyYS8uBsOg7bkHVIsHg6sCbayZ42HmXA/14I54KT/x9BEARaTlZxfO9Oju3didDpJUabRmJoT7QePSJEOAFlRhChQ6N/dsT+nxibbKx6JQ+PyUJc1Zt8MsHI/OlrCQsIp8Pt4YodpRjdNoYWf0WmykpUUDmBoQ2IZAJ+r4iWoyF0HA/DpRlFgQ52eZK5okckg3uf4MN9zzPRm8OzyfeRrFbxaY8k4pdOhMajlE/YwO3ftlPTbuO+kWncfXEKku/tFjweTk6+AU9tLYlrViML7woleDudtL5XyCd6zwAAIABJREFUiN/lI3RmNvIoDV6vhaMFMzCZjpDR7QWioq79Vf180nSS1ZWrWVO5hhZ7CwqJgtzIXIZFD6NfZD8SdAmIf+PMALrCPafMpzjccpj9DfvZ37gfo8uIRCQhNzKX0QmjGRk3Er3i7BTHn6P1rbdpe+stQu+/H2n0KKx7GlCkGNBMDOJw2Sye2n4J9bZ4lt81lMxAP+3vjURia2HugHk8tf9elKGpXYuxqrPlHVav38QzOzvoocsj0aVDGeElae1hsiuqOXTJREYEXMqB4Drm2OV0OJQIUWo0DS1MbVuPyN3B4XFiIqhhrN6DWOqkvS6QoNWZlIfdREiohJEXyeic8zSexkaCpk9HMXwi7d9UoPAoafc0oBwWRtoVw0+/7P8XuODk/8IIgkBLdSXlB/Zw4sBepEYJUQGpxBkyUPm7RjQOqZg6qwdpsoF+M7qjVP+ywhX/Tnu9lVWv5OG3mImueYP3runkrRtXEqePx+b1cfXOYoJadzPavZaI0Brkcid+u4TW9kh0MiMV62KQyvS4A0ZSGWRkkyeDXvEGkjM3sKFqNRcrr+GL8Ku4KEDEgt5Z6PLegU1PsCdrNtOOpBMYIGP+9b0YkHTmwmjLvHm0v7eA6Dfmo7v0UgB8JhctCwrx2z2E3pqNPEaLx2PmyNGpWK3H6N79NcLDxp53Px9qPsTHJR+zo24HYpGYIdFDuDL5SobGDEUlVf18I78Rv+CnrKOMLTVb2FC9gTprHTKxjFHxo5iUNok+4X1+cUhHEAQaHnoY89q1RL8xH0lQD4wrTyANVhJ4UyK7Kx7loY3Dkcu0rLt/NMGeRhwLLqIJFc/kzuOtvbeijczsSq+UnXntfr+fT5au5NUCH1lBO8mwh6AOlRK59TB9S8qoHD6RnmFj2Bd9ivmtIupsWiRhKqRGC1fVrSXQVUfeWBFGVwd3+5LQJ+Xh84sQ7YqksuVhAgIUXHF/H5zvz6Pz669RpKYS+dJLGKs8OPe0IkdBO40EXpZK3PBeP9IDfy0uOPm/GILfT+OJ45Qf2EtdXiEah4ZwVSKRAUlIkXUVg0424ApUsmt/Iya7j6GTUskcEvWr4rqttRZWz81DsJiIrJvH/Akm3pr0JenBGZhtNby9dyFpns0YlO34fWJkpVLKK1IpCc1gaPB2anaHoNSE4ZFfSkPoKb71ZGDQKQlN/ZgqWznpwfezW53FzYpO5gwYjrS1FGHhCAoUfbmqYxYXpYfx6rU5BGvOXBh1lJRwctJ16MePJ+rFF4AuHfjWhYX4LG5CZmShiNPh9Vo4cvQWLJYSemS/Q0jIxed1/fsb9/PmkTcpbC0kUBHI5G6TmZg2kVB16Hn35e+FIAiUdpTyTeU3rDmxBovHQrI+mcndJnNlypUopT+fS+53uTg19Wacx48Tv3gxIlU07Z+VIZKKCLoplbXV/+SRTf3pEenmq7smIm3Ix/fxOPI16czp/QKLd05GlzKsq3as5MwdqV6vl1cXLWFRTQDZ4evI7oxCY1ARtL+QwUeP0jbgWpK7Xc7+uBreq3Zw3BKMLFBBslZKQv4qEp3l7Brppk2wc3vVREIytxMQVYLPKKOlcCqu9t6M//sgVNVHaHziSbxGI6F3z8Jw483ULDuA6JgXuUhBh6yFyIk9Cc1J/qNuxZ+CC07+L4Df76P+WCmVe/djKqhD7wsmQpWI5ns1RLFWhjI9CFVGELIkA0e21nLw22r0YWpGz8wiJObXVeFpq7Ow8uUDiCydhDfN55UJnbx+6QuEihpoal6H1dpV6s5kCsFfKCP1mw4WJl9NW0YIl8tX0FKgR2WIxyseTWdMOavtSdjFCjSJ7yLR+FCGPsJxQcczniPcdul0RH4vlreG4TbWM9bzMreOzmXGkETE/xFWEjweqiddh6+tjaS13yLR6fA7vLQuKMDb4SRkehaKBD1er42jBdMwmwvIznqL0NBLfvG1H+84zuuHXmdPwx4iAyKZkTWD8Snj/09G7eeDw+tgQ/UGlh5fSkl7CcHKYKZ2n8qktElo5D99371tbVRPmgReHwlffwUiLW0fl+Azuwm6LoUFVUuYvy+Vm3rV8+ykmYhKVsCy6XwReTmfdLuTpbuuw9BjAlzxxulygqftcjh4/J0vWdGupXvMcnq1JqNRBxB4tIxhhw7i7n0dkSMmcDCqlkWFjRyxRiJWSpkyII5Tq5bQrTOfzUOsWOQebi26A0VgO6G9F6PQmHA0ptBaOIlRU8cTGSWl+bnnMa9bh7JHD6JeeglCwqj8dCeqU3KkIhkWrYmoa3phyIj5I2/F/zcuOPn/UnxeD7WFRdTvLsBdaSZIHEGQPBKRSIQgFVAkGVCnB6NIDUQaqkIkEuGwutn8QQm1ZUbScsMZPjn9jGIe50N7vZWVL+4Dq5kI5ytsm9DJuKhoBHeXUqjZFkZbcwyNLaEM/+YAaqebOb2nYY4OZLL3c8yVStRBmSC5BE9yOcvbAznl06GM+ZDkhCjqddPpdLl5t2UJYybNxeGXsPujR7mkcQGz1f/g2imzyIzSndO2tvcW0DpvHjFvvYl21Cj8bh9tHxbjrrUQckt3lKmB+HwOjhbMoLPzIFlZ839xiMbitvDG4TdYenwpOoWOmdkzub7b9Sj+5BIHgiCQ35zP+4Xvs69xH1q5lmndpzElc8pPvpicx8s5OXkyyvR04j/9BL9LoP3TUty1FrSj43nwxA6+O2HgxdHlXDfib4i+exZ2v8Y/0h7iUMQwlu6dQtDA22Dkk2e1bTKZmPXGcva4VKTEf0G/pmzUUg2BZSe4OG8f0pzJhNx4A4X6et7fU8JuRwIg4qlrsqhavx510Wo2DujAHiBwW9HteFwRhGcsR5OyA4lcwFQ1hIzMv5E5pAfm9etpemY2fqeTsAcfJHDKjdhajFR9uhNtmxaZWIEtwEb4Fd3R58T8pdIuLzj5/yLsHSbqth/FUtKI1CjGIAtDIpIgIOAPEqHNjiQgIxR5rPasbJjWUxbWv1eEzexi+PXpZAyO/NUPcnuDhbXvfok6OB995HbEoV253RpNFmZjMvlHRBh9IQQYK5mwdg/NwWrm9LqDTl0gNzk/Q6jzoQrqg0IzEllmLSsqbRz1RqMIX8Pwfjls8w9G7TTyafnz5Nz0CUUWDa9+8Q0LbfdTFTSExLuWo5SdW/XRVVlJ9VVXoxk1kpjXX0fw+Wn/rAzn8Q6CJndD3SMUv99FQcFMOox76Z75GhER43/RdW+u2cxLB16i1dHK5G6TmdVrFjr5uV80f2ZK2kp4r/A9ttduJ0wVxl097+LKlCuRis/9wjetXUvDgw8RdPNUwh99FMHjo+OrchxFbYj6hnLDiRKMdjfvTahiYPYjiL6YhL96JxNy3sAcEMFXB24hZNTjkHv7WW03NjYy/Z1NHBf5iYtfzMCm/qgIIKiihpH7d6PoOZWwh6ZxjDoWbNrNFk83BJefpyZmEd5YzpElb7K5bxM2DdxRchtuRxyBmkr8yQsJT7Xg9yoI8F/PwDF/x9fWSeOTT2LbsRN1bi5RL8xBFh2NsaaOyiW70LcZUEk1OJVOgi9NxdA/DpH0v3+B9oKT/xPjs3kwHq2h43A1/kYXAT4tIpEYv+DDqXQij9cS0i8FdWow4p8YkR8/0MS2xcdQaWSMuS2b8MTzd0w+nxOjcR/1pzbQ1LgZqcqE4Icqtxitpi99Iu5m3YrddDodlIfFMGTfOi46WMaRVAPvpN9BiyyQG2xL0bZ0ogwcTHDUSPS9Oll+oIQtnjQUhgImXtaPT43hJHnb+SL/DsInLuC9UzHM33KcZYrnyJQ1Irvn4Dk33EDXekTNlJtwV1Z2hWmCgjEuK8d+uAXDVcloBkQhCD6Ki++jpXU9GRkv/yIdGovbwvP7n2dd9Tq6BXXj6YFPkxWSdd59+GfjcPNhXjv0GgWtBSTrk3ks9zH6R/Y/57FNz8/BuHgx0fNeRzdmDIJfwLypBsv2WuoTAphaV0e8tpp5VxrJiLsX0cIRuDxOhuS8hw4Py/NmYLh2EaSPOavt4tIybl18lA5lOyGxSxjRPBy5R0VYxUlGHNyHst8MouZM51h7DYvWb2WtvzuC1cvfrsjg6lAPi196ko3Z1Zg1ImaVzMTlSEQvacKpWUVkt3YU8afwu8PI6f0sISEjMS1fTsuLL4FIRPhjj6GfcDUikYiOujrKP9+KvkWPVhaEV+JF1TuU4ItSkAb992riXHDyfxIEn4Cn2YarxoSptB73KQtyV1fOtNfvwUwHokg5Qb0SiBiQgUT581kwPp+ffcsrKdhaS1SqgdEzs1Dr5D97HnRN7+32Kjo6dtHesRujcT9+vwO/R4G9IR1teQOPd7NySfhI+liHcaS4GInLxXcZ/bl3yVtkVDewZlAoGyJuodofznjLOhKMJ1EGjSA2/RIi+nv5cu0mVnu6IVVauH5ib95r8TFAaufjHdfi6TGTaTUjKa43Mzd2H9e2vglXvQc9J/+ozZ0rV9H46KNEznke/YQJmNZWY91dj25UHLpR8QiCQHn5bOrqPyMl5VHi42792X443HyYR3c9SrO9mdtzbmdm9swfHfH+NyIIAltPbeXV/Feps9YxNnEsD/V96KyFY8HtpuamqbgqKkhY9jWKpK6i5Na8RjpXnmBzoJjZHZ1clbyWO4dHkKa9CtEHl9IZlk3vlJfo5qzlq4IH0Uxb1SVs9h98u2UHj2xpRx5cjiR4GWM7xiKySYitqGLQ4UPIBk8j4fU7OFZdxftrN/Et2fg7Pcy8NJVZ3XV8+sIjLE8pwaISM6t4Oi5nClpJE0pZNTLhFPIRZSj0jRgMA0lPexJ5ZwCNjz6G/eBBtJdcQsSzs5EGdu0vaK+vpezLTchqxESqujaaiWIVBA9PRtkt6L9udH/Byf9/wmdx4z5lwV1rxlFlxFNvReTrCp84fXY6XA14dD60mRHEDOtF0Dnqjv4UdrObje8X01DRSY+LYxh0TQqSn9nQ5PF00tGxh46O3bR37MLlagRApUpAoxpI8TfBWGviyOxcyL1j6xiiGEBcTTI2lwtdcxOr+17CMx++SqDJxPuXR3BKeS2FrngGWg+Qa85HEXgJmQPGoutpZ/nS1azxpOIQaRh7VTeWWhyM00l5a+M42pWJjGx7EK1ayaujDIz47kqIHwg3LjtrAe90f5rNVI65DHlcHPFffI5lZz3mDScJGBiJYXwyIpGI6uq3qKp+nbi4W0lNefQn+0IQBD4q+Yj5h+cTFRDFS8NeIif0ryt85fQ6+aD4Az4o+gCFRMG9ve/luvTrzsjp9zQ1UX31BCTBQSR+/TViVVcs31HaTvsXx5gtcfCd28mj/f/JyJwrSbJGw4pbqc6ZwRDDVHItJXxe+QqqWzeCNuKM7xcEgfmfreSNUhmxCbuxyDczwXYNnnYPCeWV5BYUIIycRPprf6O8/CTvr1nHalkO/lY3k4bE89TgKBa/8iiLYw7ikEuZVXwzDlc6GnEbAQovkqo87EN1hGStQip3ERlxFQnx9+D4cgst8+YhDQwk8sUX0AwefNoma0c7hWvW4chvJU6ZgUqqQZAKqHPC0PSJOO+9JP+/uODk/2AEQcBvduNusOKpt+JusOGpt+AzuYGu/Gaju4l2ZwNWiQlNahhRfboTn9MLlfbXxXvb6618+3YBDouHi6Z0Iz034pzH+Xx2Ok2H6TQeoMO4B7O5EBCQSrUEBg4mOGgIQUFD8NhCWD57J267i562D3lwZDV9bAMJ7IxAazITbGxhY0ouD329CJfUzWvXRiB3X8Feexqp9kout65HaRjHgHFX0RhRws4VO9nnTqTaE0WfEbHsUfi5JTKQJzbfiL/jJKMdL5DbM5unL88kcMV1UHcQ7toPhh+vBNT03PMYlywhcdnX+N0hdHx5HFVOKEHXpSMSi6iv/5Jjxx8nIuIqMjPm/qRUgd1j58k9T7KpZhOXxl/K7EGzfzYT5a9CjbmGOfvnsK9xH7kRucwePJtozQ8DDOuePdTOuBXD5OuJfPrp0793nTRR/XExN7vMiAOsPNH/KXpkPEJsUSnkLWDfmPeZ4EjjIuNBPm77Evkt34D8zELnHo+HB95YyretBjKyVtPoOcj17ptxNppIOV5Bn6JSbOMvpuezs6msrGfhqm9YpeyJr9HFmF5RvDYulc9ff4IPQ3bgl8uZVXgjFlcWaqkRuVROUOVqTiZcTmDmMkLSDyIWiYiOnkyk+xJaH3kBd2UlQTffTOjfHkCs+GEh3e2wU7x1M6c2HybEG0GMOh2pWAZqCQFZoSgzg1EmG06rr/7ZuODkf0cErx9vmwNPsx1Po63LsTdY8Vs9XX9HwCm202ato91RT4enCVVCIHE9e5HYsw+hcQm/eSdeTXE7GxcVI1dIGHtXD8Lif3hReL0WOk2H6DTmYezMw2IpQhC8iEQSdNoeBAUPIzhoKFptNuLvQxKWDifLn96Oy+6lr2cJz/dvoJuxPwqvkvTycmQhOo54tNywYx014SJemxhCrG0Mezsz0bmM3GD7Cl3w5QyfNIGV9s9o3NZIkyuOve5UIjOCqI5T8XB8OEM3zaZ/0xIekjzC6GtmcElmOBz9AlbdCWNfhf4zf/SaHSUlnLx2EoGTJxM45R5aFxUhj9MSOiMbkVRMW9tWCgpvJzh4KD2yFyAW/3ioq85Sxz1b76HKVMV9ve9jWvdpv1umhd/np7PFQXu9FUuHE7vJjcvuwe8XEPwgU0pQqqWodQoM4WoM4Wp0Icr/80wPQRBYUbGCuflzEQSBh/o9xMTUiaftaH75FTo++oiYd95Be/FFp8/zNNvYtPAId9tMjIw/yeT018hMe5HItW+BsYaVE77hzno/l7duZ4FwCMm1n5xVL9ZsNnPja6spcmnJ7PU5Ha5axjun4q5vIaPkGD2OV9J2fRYDHp7PyZPNLFi+ipW6PvhOORiSGcbCa7NY+s4zvKfZiEymZFbhJDqcPZFKTCCoSK//mrKwaxECTqEcs5R4eS0SiYKYiJtQLbNg/mwZirQ0ol6dizIt7cx+8fs5VVJI8ebNOErbiValEBWQjAQpSEUoUwNRJBtQJBuQhav/NKP8C07+V+B3evG2O/G22rsceosdb7Mdb4cD/N8fJAKP2kunp4WGtnLarHV0ulvQR0cS2z2buKwc4rJ6olCrf/K7zofCbbXs/qqC4BgN4+7KQaFxYTLlY+w8QKcxD7OlGPAjEsnQ6bIxGHIJNPRHr++NVHr2SNVidLLiqW04HH76itfxcbKZEEcyKquJ4U2tNMSFYyyqZVBFEfszFCwcqybTNpbCljQsHglT7V8QG3EJva4dyZyKp9CV6giwJ7DOl4PIoMDcJ4iZhkDsG77mFdfz7A66huyZC9CrZGDvgDf7QEgaTFt/ljP4F4LfT83kG3DX1RH32XI6FlchDpARdmcOYrUMi6WUQ4evQ61Ook/vJUgkP97fJe0lzNoyC7ffzavDXmVQ9KDffE/a663UFLdTW9ZBY6UJn8d/+m9SuRhlgAyxRIRIJMLt8uGyefD7fvi/UwbIiEjWE5MeSEKPEPSh/3d5+A3WBp7a8xQHmg4wInYEzw16DoPSgN/t5uR11+NtaiJpzWqkoT/E770mF8/O38endhsP9dlGZsgaesY+SdBX/4Dw7rw/6iOerGplev0K5sRoEI04uyDciapqbliUR6fET0zWh4gRyDVdi6yhkeyiMjIra2i4JZJhsz7kxIkmPlj9DauC++GttDMwPZSPbujF0o/m8KZ4JRqJmrtLJtBs74NLbEXhVdGjczWl2kvxSvzk5b7PlCw1YtthpFI9Eb6LEJ7fB602wh56kMApU8458LKbOinduZXyPXsQmtxEq1OI0XVDSdfzJVZLkSfqkcdokUdrkEVrkASc/27y34MLTv4/EDx+fFY3Posbv8WDz+LCa3Th63Di/f4jOH6Qg0UMIoMMt9yN2dNGc8dJ6hpKMbla8Qs+gqJiiO3eg9ju2cRmZqPWn63n8Vvx+/zs+rqC43kFxPZuIb53CxbrEazWMkBAJJKj1+VgCOxPoCEXvb7XTzo7AGunk+VPbcNh95Oi2cWOIB9iv5qwhuNckTOUvJPHCdhbSEJ7M18P07F6gJ8s59U014ZzzB/Ota4V9E/sg+qiZOYUzya1LZX49mS2qwZQZ/MjDA6jt01E/cESNqoeQ2aIJuCu7SD7Poth9d1QsARu3wXhmT9qZ+eKlTQ+9hgRzzyPszYOwekl7K6eSINVuFzNHMyfAEC/vitRKM4ubPEvdtXt4sEdDxKoCOTdUe+SZEg639twGofVTdneRsoPNNNe3yUiFhwdQHR6IKFxWoKjNehDVMiUkrNG6YIg4LB4MLXY6Wi00VRtpvFEJ6YWx+l2ug2MJD03ApX2ly2i/xb8gp/Pyz7ntUOvEawM5pVhr9A7vHdXquqEa1D360fswgVnOEKnxcVVc3fQ6Pbw0oiPUSpL6K+6hYD1c2DI35idcCvv1rbyeNUC7hl6FaSNPut7v926mwc3tRMc1I4vegGJ2iRCG0YS3FRHj6IS0qvqqb1Tz8hpSzh2rJaP165nVWh/fBU2BqSG8PFNfVi29FVec31BIAHcW3oldfb+mCQ29F41KaYt1Cr74Jar2JD2AX37BjFW78Vk3IVEHIC+OBTZx/Xoeg4l8oU5yMJ+/NkxNtZzfO8uju3diaOpkzBlLDGB3QhVxSH3/HCPJAYF0jA1shAV0mAl0lA1kkAFEq0ckeLsZ+H34i/v5N31Vmz5TSAAfgHBL4AAgs+P4PLhd/oQXF78Lh9+u/dMB/4vJCKkgUrEehlumRu7z4zR2kRzezW1dSU4bRYApAoFEcmpRKZ2IzI1najUbgQYfr0i4E/h97uxWErpaMunvHAbgrwMmcrUZa5EjU7XE4O+L4bA/uh1vZBIfnkKmM3kYvmT32FzCGgD93NCLcEsM5NsquTGKc+y9Z15RO8vQOb38+74EPJT2sn0TkJSIWenqBvDvLuZ1D2M/CQLy09+zWDxYCIqIygN7kdePYh6BqOosyF0OtkS/Box9jJEt++AkO/rdNbsg4/GwOD74JJnf7wP7HYqR49BGhmJesSjeBqshM7sgSJeh89n59Dh67HbT9Kn99KfrOj0TeU3PLnnSdIC03h75Nu/WpKgs8XO0S21HN/XiNfjJzxRR3puBEk9Qwkw/LbNUqZWOycL26nIb6a52oxYIiIxJ5Teo+POCMn9UZS0l/DwjodpsDYwq+csZmTPwPTlUppmP0v4Y48SNHXqGcefaDAx7s099EHgzpEvIyjsDGrNRlq4Av+UFdztSGBFm4U3T7zGtROehuAzpQUEQeC5D1bw4QklAzOqKWYBo+Ovor00nviWOnoUlpB0qp7WBwK56LovKS6u5OONm1kTnov/mIV+yUF8dkt/lq+ax6vmT4j0a5hVNpaT9kGYAzzobDICjftxy2Kxq8PZF/85Dal1PN/vDjS2nbS0rEckSFHvEqHbpyX24RfQjhr1s/3U2dxE9dF8qo/kU1tciMgnIlARQUxYN8J08WjEBiQOMXjO9K0imRixVo44QIZYIUEklyCWixHJJSDpCgWpMn9d0Zq/vJN3lLTR8XUFIgldmRnirqkxElFXZyoliBVSRAoxPokfn8SDCxcuvw2zrZ1OUyNtrbV0tjRiN3WeblemVBEal0BofAKh8YlEpnYjJDYeseTcm3R+K253GybTEUymw3SaDmOxFOL3dy3eum0h6DS9iE8dgl7fm4CAtNMx9fPFbnax/IktGD0OPIEFWOQSTuhO0E3r4/4Jb7Lt7tuJLzhGuz6IuddFUmc4Rpb/WsJLXKyW9ieJau7rbeJDxVEqTSe4Oe5mHHsc1ASnsbkmAH+0GjrdDI8yMD9uD0F7ZsP4t6D3TV0G+DywYBi4LDDrAMh/XBq29c23aHv7bQKnvYDXGELQDV2bnQTBR2HRXbS1bSWnx0JCQi760TZWVqzk6b1P0y+iH29c/AYBsvOXorWZXOSvPUnp7gZEYhHpueH0GBlLcNQfs1jb3mClbE8jx/Y14rJ7icsMos/YBKJSfv9Z4r9jdVt5dv+zrK9ez8WxFzNnyByM9z6Mbd8+klavQp6QcMbxC3dU8sL6Yzwmd5E27DmUSj25BTZEdiPuO/Zy47EW9pmdLK59ixGT3wLFmf3lcrmYMncZB60GxgzOZ0/HMq7p/hAVeR5SW+rILiwiur4e+6MRDLt8MUeOlPHp9l18G94PodRM74RAFk/vz9JvX+N102ekunXcdmwUJxzDUcTJsZ1yEdBZilKkwKRLojZ8LeuSv2NG9gxuSR1Dfe0iGptWgd+P8oiYCNFIEu78J5JfKFfscTlprCin/ngJ9cdKaaw4htvRNSNTybRER6QTEhKHVh2MSqZFIVIi9csRC2JEXvC7fQguH/gFNIOi0I2K/1X37S/v5GsKj7Jrycddjl0kOj0l8no8eF1OPE4nHpcLl8MO/3m9IhGaoGAM4REYwqMwhEcQHBtPaFw8upCwP0yuVBB82GwnMJkOn3bqDsfJ702SodVmIaM7x3YYsLcmccnUocRmnq3ffb44LG6WPb6RZkkrNm0NMpGD7eGHSAkP5Pkhb3Bo2i3EVNVSmJDGK5PjcbONXp4rSChysFw2ELnUzUP98pjn2IdapmZ239kcWXOEUoWBTU0RCFIRgUoZz43O4IoII6KFIyD1Urhu8Q+pkXvmw+anYPKXkH7Zj9rqaW6hcswYFOl9kCVMRX9ZAtrhXdk3FSde5NSpRaSlPkVs7M0/2sZXx7/iuf3PMThqMPMumveLhLv+Hb9foGhbHfvXVOH3+MkcGkXfsQkE6P9vJA7cDi/FO+s5uuUUDouHxJwQBl2TgiHs91vn+U8EQWBx2WL+mf9PEvWJvJ71FK7Jd6BISSH+s08R/dsgx+cXmPjuXqoaLSxQN2HPfYlwcRKZuw8iSr0UyzWfcNWBo5x0uFlpWUEHcfdPAAAgAElEQVSPq+eelSLb2NTMVW/uwCgoGDj4Wwo78hne/RWaD5wivbmWHgUFBDfXIXoqnkGjPmH//iN8sT+fdRF9odhEr3gDn03P5ZP1L/GeaSm9bXpuqhjOccdIEnoHU1nYjqLzFHp3J+1BPRB0O1mQuZzs0GxeHvoyITIxdac+pq7mc/xSF/ImBXGptxPT+47zrhbm9/tor6ul7dTJrk9tDW21NVja2hAE/xnHikRiFGo1Co0GhSqArItG0WvMFb/qnv3lnXxdWTF5q5eBICD820cqlyNTKLs+SgXKAA1qfSABegNqvQG1wYA2KASp/I+PezqdjZjNBZjNBZjMBVgsRfh8dgBksiD0+t4Y9L3R6/ug1WZTfdTElo9LCdDLGXdXDkFRv70QgsPqZukT31KvOolHYSbKdZLPkwoI0QfzRs/XqZ5+G8FtnazrNZR3Jyagsi6hr3M4iSU+Noh606IK5daclXzuPsTAyEE8N+g51n69ju02L9uciYjbXYztFcXL47PQSf3w/kiwNnWlRv6rklDnKXg7F5Iugslf/KS9DY89jmnNNwRc9Azai7IxXJ2CSCSioXEZZWWPEBN9E+npz/zo+Z+Xfc5LeS8xPGY4/xzxz/PWnmmrs7L10zJaT1mI6x7M0EmpGML/OOf6U3jcPgq31pK/vga/z0/OxbH0G5eITPHHzCqhS33zoR0P4Rf8vOGcQMDLH5w7bNNiZewbuxgapOFvHKCp57tkGhOILMqHK9+mKfM6Lt97CI/LynptJVGDz5Y+2Lr/CHeuOkWIzoMu/UOcPicBcS8QdLiYpJY6eh4+gsrcgOapJHKHfcSWLTtZWVrOhoi+iIqM9Igx8MWtuby94WkWd65hhEnPhOrBlDpGkzE4gsqSdvxNTQSaK2gNG4BBms87A1bjE/t4PPdxrki+Ap/PTs2++dTVfYInzIPEpyAi5moioyeh0/b4TfF0v8+HtaMdc2sL5rYW7GYTLpsVp82Gy2bF5bCT2n8QWSN+Plx0Lv7yTv7PhtdrxWwpwmwqwGw+itlciMvdDIBIJEerzUCn64FO1xO9LgeVKuH0AyQIAoc31rB/VRURSXrG3pn9uyy+OW1uPnlyCU2aU4j9bga4i3g99Rg2lZZ3EmbjuPdxVHYn7w+fwIorEtG3vUGOI5uMMgOH3REcMvRmXMpK9soPMqvXPUzPms6HS1ayxWRilyYdWUkn04Ym8vS47xdQNz8Ne+bB5KVnbnNfMhmqtsOsvJ/MiXeWlVE94RpkKaPQX30bITd3RyQRYTIXcPjw9ej1feiZ8/GPhqyWlS9j9r7ZjIwbydxhc5FJfnnWgyAIFO+oZ8+yE8jVUoZOSiWlT9ifQtDK1uli/6pKju1vQhei5OKpGUSn/TFrQgC1llru23YflcYTLNicgKG4titsE39mWOHd7ZW8vOEYc/skkNb+KW2pXzOoXIvK2AZ37KZMFs4VeUUk2k+xqkcCAfH9zvquFz5ew8JjEi7JtFMgmUuSIY0Szd8YUnSI8LZ6eucfQuxpIezpTHr3f49Vq75hQ10zGyN6IynooE9cIJ/N6M+c9Q+zpnMLV7drGFU3mCL7WNJzw+lottNR0Uhw+yFaIoYT7Cti/WX7OGwp4fKky3k893E0cg3ezk6q3r6XNsk+nL0FBKlAQEAqEeFXEhY2BrX6z1dm8IKT/wPxei1YLKVYrKVYLMVYLCXYbCfoWgUGlSoeva4nOl0OOl0OWm0GYvG5R5Q+r5/ti49xbH8Tqf3CuXhqN6Q/ItJ1PlhMdhY9twiTpgOt2cHVCXaelW+gSKXiDflMNHMWgCDwyqhb2DEmkaCWF0lxRDH4eDdqjFbWRFxOTtgBXHH7eGnYXIIkaby2ZAM1Chv7ojNQ722hT4yBpbcN7JIErtkLH42FPjfDFfN/MOTYWvjyBrjkORh874/aKwgCNTfegrO4FP2N/yT8vkGIlVJcrlYO5l+FSCSlX9+VyOXnDl9tqN7A33f+naExQ5l30TxkP5Ez/584bR62fXaMqqOtxGcFM/LmjP+TDJfzpaHCyHefHsPc6iB7RAwDJyQjk/8xo3q7x84jux6hoHQbb30oQZeR3RW2+bdQptfn55p391JrdLDq4m40Vz+OK2gng444EUfkwLR1fNfUzE3HmrjMdIj3R1+D+D/KB7rdbia/soxDVj13jDHyec3LDEuYwCrvldxUkIfS3ES/vDzcMiOJT/Wne4+5LFnyJVtNDrYEZyMtMjIgMZiPbunL39feyXbzfm5tVpDb3J9Dtokk9gxB8AmcOtpIWPs+mkKHEuipouLSUla4txEVEMXc4XNPaxaZvvmWhpefxp7twnN1GDZpl/qqJiCd0LAxhIZeiiYg/U/x8r/g5H8HBMGP09mIzV6B1VLa5dgtJTicp04fI5eHodVmotPloNfloNP1QCb7ZaMsp9XD+gVFNFR00u/yRPqNS/hdHp7aI8Us/moNLoWbuCYr1988gJcOP8JKjZpXTw0h5ovt2BUyXrzkdg6MSCKw/TnCXXImVYyiqa6QpbET0QSYGDSwkEcGzOaLfa0s21mAIVngSHwakYUmXB1ONtw3jLhgNTjN8N5gEEngjt0/LLS5bV1hGoUWbt8JPzGyNm/cRv19d6HsdyOxbz6M1KDA73dz+MgULJYS+vZZ9qOZNDvrdnLf1vvICcvhvVHvnVcM3thkY+3bhVg6nAy8Opmci2P/NJtdzoXH5WP/6koKt9URFBnAmNuyCIz4Y+qb+vw+Xsx7kaavlzBrrZ+QJx4jdMpNZxxT3mzh8jd2Mzorgpeywjh64hYC7RVkVhhh5FMw9EHeLzrMk21i7rXu47HL7zgrPl9T18BVb+/FI1Fw3dhSvqpYzIDUv7HW0YN79+/F6W4jd/8BrEEmuj85mqSkf/DJJ5+wU6xkmzoVeZGRYakhvDMlh3vWziDfXMg/msV0b8tmj2U60emB6EKUlO2uJ860nzpdP9TeVjq6H+Sb2BKMrnYe7vswk7tNRiQS4amvp+Efj2I/eBDl+CGIZvSk3bqLTlM+ICCXhxIUOJigoEEEBg5CqYz8Q/r/57jg5M8Dn8+J01mH3V6NzXYCm/0ENtsJ7Paq0zF0AJUqDq2mO1ptJlptdzTa7ijkZ1ew/yV0Ntv59u0CLB1ORk7NIK3/uSUKzgfB72fve+/zXWMTgkhMz1Y3V8y+ic+WjuP1AAVzdkeRtLeWdp2S+WPuZW/POPSOF9G5rNxTNZmGii18kzCORkkY94zrIFR6Ca9tqcBisRCdLaIkKoF+Rh9FeU3MuTqLG3O/n76vuqsr9336Roj9N7XDrXNg5ytdm57if3wDks/hofLS8fjtVhKXr0GR0PWSPHb8SerrvyCr+3zCwy8/57n5TfncseUOkg3JfHDpB+clU1B/3Mj6BUWIJSIuu6MHkcnnXzf1/xenStvZ/GEpXo+fETek/6jExW9FEAQ+LPoAzd9fI71JTNy3qwmKOTMtct6WcuZtqeCT6f3p6TdypOo6Mk+0EdppR3TbdoTwLB7ZtZFPfRHMl1Vw3ZCz6+wu27yXv3/XTk64lJDuKzjcfJjQpBepcoZw984dGOmk/759WOPN9PnHjYSFT2PRokXsDo5mH1HISjq5uFsYr1+fyfQ1N3DCVsXLjT7iTelsM99NSJyO+O7B5K+tJsFRQJ08DYngQhzyLav6+Kl2HD5D6kLw+ej46CNa5r+B1GAg8sUXkfVLo719Ox3GPXR07MHj6QBAoYj8fpDXA50uB40m/RcP9H4L/9NOXhAE/H4Xfr8Dn8+Bx2PE7W7H7enA427H5W7F6aw//XG72844X6GIIECdQkBACuqAZAICUtFquiGVan8X+/7lXERiEWPvyCbyd0iRs1dVserNtynXa5G6tQx1wbBnb2HHJyN5VORh9hoFsdUO6iM0fHnlI6wPCUYrfROlq5rHamZSX7aZvNgs9kkGMKmvg8K6MI41Wegdq8Ma5aDQEMYEmYxtG2vpmxDIp9P7d806StfAVzfBsIfh4id+MMh4Et7qD5nj4ZpFP2q34BdoeGwR5lWvEXLfE4TeeWNXH9Uv4djxJ4iPu52UlLN3TwJUdVYxZf0UQlQhfDzmY4KUvzwT6fj+RrZ+egx9mIrL785BF/Lnqvz0S7AaXWz6oJjGEyZ6XBTD4IkpiH9GrO7Xsmnnx4Tf+TLHs3SM/HgdwaofcrtdXh+XzduF1y+w6YFhWCr3UFY9gwGHjMiCuiG6fTsexNyweQ37ZdEsixOTm9rnjPYFQeCet1bwbb2Su4frWG99BolYzqngp4lzyblq1zZaxSZy9+7D0tPEwPvvQy6/hEUffMCB1Bzy7XpkpZ2M7h7O89ckMXX19bTYmnivyUGII4WNpofRBqvJGh7N3uUniHIeo8Ufjl8iI16zmJfT4ukI2ESsLoZ/Dv8n6UHpQNc6Uf3DD+M+UUngTTcR9uDfECuVCIIfq/U4RuO+75MrCs+Y4ctkgahVCajVSahUscgVYSjkocjlocjlIUilGiSSgJ/UWvo5/vJOvrllPSUl9wESRCIxIlHXT7/fjd/v+slzRSI5SmUUKmUMSmUUSlUMKmUMKlU8AQHJv5szPxeluxvY8cVx9GEqxs3K+c3b2QWfj5MffsjqklI6DXpU1miGyzzkPjud8sWX84+2Bh5cLiLQ5KcqMYi8ybP5yCxGHboYheMgf6+bjrOskLJgEStVVxMfaOekUUNskIqHRnfjw5aTHJCqmaKCk0ftHG+ysOmBYUTqVWBthXdyQR8Lt245Mxzz5Y1QuQ3uyQdd1LltFwSMK8tpfeF2xDolKZvXIpJI6DQd4vDhGwkKHEhOziJEorPjzm2ONqasm4LT6+TzcZ+fIbb1cxTvqGPHknJiugUy5rYsFL+iyPmfBf//Y++8w6sosz/+uT03N733HkghgZCEAIKE3kILSBMLHbsrIoKi6KIooKjoooBIVQHpvUnoPYEQQnrvPbnJze3z+yOsiqCrWHZ/7n6fh4eHO8y8M+edOXPmvOd8vyYz527TTvuEOzBgWgcUyj+GMjl58cso1+9mzWR35jz9JW6q778ezufWMmH1BZ7qHcicgSEU3thAY/p8Im+pMXV/BcmAl2hQ1zDkzGWaJJYc7doRd5s7g5vmlhaGvLufMoOSZZNseePqc7R36UaSfDqPlunxyzpFNWrizp2j5cF6es56m6amQDZt3sylmF5cr5Yiy2gkIdKdl4e5MXHPWAzqZjZV1WNhDuZAwyvIlXJiE/w5vTUbR00+ar0CrcKBThZrme0Ujt7vGIJIw/y4eSQGJyISiTBrtVS9/z71GzYiDwrEc+lSLELvTh3q9XWo1TduZwPy0Gjy0Wjy0Ourf9Kmvr6zCAqcc1/z8W918iKRaBDwISAB1giC8M5P/d/7dfLNzZlUVu5FEMwImNr+FkyIxXIkYgvEEiUSsQKxRIlcZo9M5oBc7ohM5ohUav2nL5yYzQLnd+Zy7WgR3mEODJz+2x9GXXY2lxcv5pSrK0apBVaNofSwrCP27RnU7pzGoqvneXQfiAQx2SHu1D7+Dotv1CNrdxRL9UGmVibinw2p0lS2OY3FKEiRSKx5tm87xnf15vHLNzmrh4eMajopvfj7vnTeH9uRxM63NTO3PgqZB9soClxCvj+x3BOwcST0WQAPvviT568+U0rNJxvQpmzAc8VH2PTvj05fw6VLw5BILIiN2YVMdncKpdXYytTDU8muz+aLQV/8KqGPa8eKOPtNDn6RTgycHv67LHL/J+Dm6VJOfZX1h36ZmPV6biUMokZdybLnPPnH0M/xtv6+Wmr21uvsvlbKged60s7VmvSrr+B4fhXONSbMjyQhDYwkM+scQwqhPc3s7NsPxY96Ui6nZTJp0y1crGRMHVnLe1eXEuI7ldNCPO9fbaKu9SJ1+iZiz51DP6KB+Mc+JyvLwJ4DBzjfYxAZhXqkWU1M6OLDIw9KeOzAo1g1mdhWW4kgC2Vv3WuYzSK6jgzk4u48lM2lmDRamiy9iJVsZJlTELc8UkCZTUJAAgu6LsBS1lZC23zmLOXz5mFsaMDluWdxmDz5jv6Bn7SbWYdOV4NeX337Tw1GUwsmYwu2tlE4Oj54X/Pxb3PyorawKwvoD5QAl4EJgiCk3+v//yfk5P9o6LVGjq5NpyC1hohenvQYG/ybPqsFg4HqVas4feIEN8LDURgVqOojiLMppcvimehOvMPnX64j/oyYBisZ+WH+qGYt4W+H85BE3cCycSP9Gx5kQl4c37Zs4dvAfuQ0BZLQ0ZuFw8JRWkh5JDmT0806EmqLWRAfz6APz9It0JHPH4tpe0He3AnbHoe+r0PPF74/OZMBPu0BRi08efF7zpofofVmDTXrU9EkvY7c1x2/rVsAMykpj9LYlEJM9PZ7LrSaBTOzk2ZzvOg4y3svp69P319st+TDhZzfmUtglDP9p4Yj+X8mEvGvUJJZz6HPbiCViRn2bCccPX//ztyWCxcpevxx9vdUcrC/PasHrP6OD6iuRU/f95IIdLZi68xugJHrJxMJP3sGs+CP9InTSJ1U7D+2iqmSLkxSNrOsa4+7xli88QCf3RQYF2mPweMbvi36Fiuf16kzB7AuqZaLFldo0DQRfeE8PKKh99gtJCXd5MzlK5zuNZSirGbIVTOrVyA9Imt46thT+NRK+LqpGINNNAcaFtBYq6fL8ADSkkqgvhpFcxW1VkFEGreTGubMh+ZGFE7H8bLy5eN+HxBo17YOYayvp+L1haiPHEHZuTPuby1C4f/vKa/8OSf/R9/ZXYAcQRDyBEHQA18DI/7gMf9joa7TsmNZMoU3aug5rh0PTmj/mxx8a9pNMseOY3dyCjc6dMDWYId1bTRdbMrosngm5stf8u37X9DnjJh8NwXZMeH4zvmIuQfzkEcWo2zcRJA2glklwzjfdJCSKH8yG9vxt/6hfDg+CrmFhIev53BaraV/4S3eH9CLV3enIxbBopEd2hx8czXsnw0enaH7j8oiL6+B6gwYuPgnHby+WE3d15mY6y9gbqrB5YW/IRKJyMtbTn3DBdq3f/MnK2k+TvmYY0XHmBM751c5+LRTpZzfmUtwjAsDpv31HDyAV3t7Rs3ujADsfC+ZirzG330MVdc4bEeMYOh5A86VOqYcnkJeYx4ADio584eEcqWwnq1XihGLZYR2X01uO2csTLk0f7YAY20rQ+Mf57naI2xqtWJjbu5dY7w4vj+Rqia2pNYxxPlJvKy9EFd+hJYm3op1YKC+CzYqFclxXTFvsuDc4an06RNHWGAA3c8cxrm9LRIfKz49mcuNbHde7/46BY4GZln4YNV0kVEe7+AeYM2FnbkEdnZB4uKK2soTd00GqdLRBN3Qs8fZjLRyGsWNNYzePY4dmXsAkNrb4/nhB3i8+853usO1n69FMJl+d1v/FvzRd7cnUPyDf5fc/u2/DhX5jXzzzhXUNa0Mfbojkb297vtY5pYWKt9dQuq0aRwICqTM2wv3VndkdZFE2xYSt3gGhot7SHn+TXxzJZzsoKQ6ojMxc5bx7M5MJO0akLd8hkrwYWnBVK42HEeIb2J/yUBGdHTjqd5BqI0mJl7P41xDC32yUljUuzvHcpo4nV3D3MEheNjdTgEceLGNg2bkSpD8IOXUXA0nFkNg35+kLjDWaalZfxORwojuxj4su3ZF1a0bNTXfUlC4Eg/3sT+pz3q08Cirb6xmdPBoJoVO+sW2y75SycmvMvGLcKTv5LA/bHHyPwGOnlaMnhONhUrG7g9SKE6v+93HcJn7EhKVioXnPUAQmHZ4GoVNbfXkY6K9iPN34O0Dt6hW67BQuOHady3VDnKsjeuo/ewAxiYzLz04gt71l5lf2MCVevUdx5fJZLz3SA+sRHpe3nqLt7svpdXQTFjzai6qTGzxsmS4Q18slUqux3ZDs9JI8vmZJCYOx9vOhn7JJ1F0sEPhqeLdQxno6mOYGTmTa+56XhT7Y1FxiqGuywnp6sr148U4eVlh6+NMpTIIX306OYp+3PrWgzOuZxlouQCdxp3XL7zCrIPz0Jl0iEQibEeMIGDvHlQ9e1C1dCkFEyaiy8n53W19v/i33+EikWiGSCS6IhKJrlRX//SixP9npJ8tY+d7yUjlYhJfisY3/P6Y5gRBoOnoUXKHJnDj8GGODRyA0dEJ30Y/jI3BRDsU0PWd6bTs2UjWzLkIGjEb+ymRe8fQZ+5intyehca+CTEfIxLZsCprJuXqbCT9ktlcOAF3WwsWjYqk0Whi7LVcrjQ20/fWZZ7tFIbK0Y0396XT2ceOSf8sl7y5E9J3Qfy8O/PwAN++CYYWGPTOPeX8zK1GatbdRDCakShvYGqox+Vvz9PaWszN9NlYW4XTrt3Ce9ohuz6bV868QqRzJPPj5v/iNZWim7Uc+yId90BbBk7v8C+lEv8KsHFSkjgnGltnS/avTKU44/d19FIHB5yffw4h+QarpJMxCSamHJ5CcVMxIpGIt0ZFoDWYWXzwFgAOjj3Q9H0es9iMFa9Rvfo6KPxY6SXDQ1vB1Gu3qNQZ7hgj2M+bp2NtqdGJWX2gile7vkphfQpRhv18Gignvd7AuK5jkCsUpEXFUf1eFVkZ8xk3bhw2rS2MzktF18EOlZsl83fewFc8iuGBwznua+Bdkx+ynH3E268ibpg/ucnVSGQSXIIdKZSFEEgmZcpY9p6IZ1H5u2zo+jpKTT/OVu0jftNorpW3OXOZiwteK1bg8d4yDEVF5I9KpObTzxAMhrts9mfjj77LS4Ef9q573f7tOwiCsEoQhBhBEGKcne+P/vU/FSajmaQvMzmxMQPPYDseejn2vlkL9SUllMx6gpJnniW9XTBnHuyJk7s7HqXetGi8iHUrJm7RZOqWvkrRy29TZi1i+WgFgbIYhs9/m2d351Cqr0fssAoEI+9kTcJCL6K1xwn2VPWkptWeD8dHoxOLGHMtlzS1hn5pFxnp6kBcXBx/35dOi87Iu6Mj27paW2p+Ok1TlgLJGyFuFji3u+taBKOZ2s23MNa0Yj/Gn8ZvvkTVsyfyDiHcSHsKEIiI+Pie5FCNukaeO/EcKpmK5fHLkUt+WTdqTUkzh1alYe+uYuiTkUj/oO7Q/0RY2sgZ8XwnbJ2VHPgklZLf2dHbjR2LIiQE0T828FnPFehMOqYemUppcylBLlZM6+nPjuRSrha2jesTNo+yiE6o9CVITBuoXnMD65AJrGs+SJPJzPRrt9Cb7yTzmj4inljbFvZmNqPSdGJ08GhKKrbhbkxjQbSKpjO1PDpxGiK5nPSwrhS+fY2mxq8YPXo0koJcHlOXU9/BFmsnJS9svU4fp6eIc4/j6yD4Qu+FJGUdnS230H9KGFWFTbTU6fAKcSSX9gQr8qlTBLItdSrtd03nzIBR9Hd4GbWpikcOTuD1o1swm4W2qH7oUAL278Oqb1+qP/iA/DEPoUlO/l3t/WvxRzv5y0CwSCTyF4lEcmA8sOcPHvM/Ai2NOna9n8LNU6VEDfAh4emOWFj9+vI8s05Hzaefkjc0AfXVq1yfOoUUDw9C24dgdd2eJr0zXf2qiHnpIcqnjKZq7Q5SgkW8O07GA+poxr36Di/uzeVaaTVy/y+RGKuZWDycSH0wle3OkmI2cqY0lqd7B+HrYc3oazlkt2hJyLxCnMTM8OHDScqsZve1Mp7qHUSw6+2S0v2zb6dp/nFnmkYQ4MBLbYRkve6uaW8rlcxBl9OA/ehgWi8dwlRfj9OTT5CV/SZq9U3CQpehVPrcta/JbGLuqbmUt5SzPH45LpY/LfLwQ2ia9Oz/x3XkFhISnur4/7pM8n6htJYz4vkobJyV7P8kldLM+t/t2CKJBLdX5mMsK8dx+ylW919Ns6GZqYenUtlSyVO9g3CzseC13TcxmQVEIjHug7+h0c4Se/NXGNUF1KxNo338XJbnreCSxsxbOXfEgkgkEpY/Ho+NSMecbdd5OnI2IQ4hSKtXUiOqZnGYAuFINZOffAqzXM4t/27c/PtX2Nnn07t3b0TJF5mqMFIVYYfKVsHTm1N5LPA1AuwD+UeIkgNaV8QnFxMkPkTi7GjMZoGKvEY829uRrfMn0LYajcyJbcWv0LjyGd73MLK67yaUIld2lC2i1+ezSStre4lJHR3x+mA5nis+wtTYSOHEhyl75RWM9b+fzX8N/lAnLwiCEXgaOAzcArYKgnDzjxzzPwFlOQ1sffsyNSVqBkwLp3vir29MEQSBxv37yRs8hOoPPkQcH8/ZqVPIbGmhR9cHMCSJaTQ50L19PRHj4ygY0oumC7c42sPM8hESBpZ3YtyL7zDpqxSSsqqxCt2H1JhJePNAHlM/SKVdJpVeR9iU+Rgdve0Y39OPxJQcilp1TChOx6e+mnHjxmEQxLy6K41gFyueiL/d3fhdmuZlcPnRomjqVii5BP0WgsXdJY/qE8VorlZi3ccbZbgttWvXYtmtK41u+ZSVfY2vz0ycne/NxPfJtU84W3aW+XHz6eTS6RfZ0WgwcWBlKlq1gSFPRmJl/+dQBP8noi2ij8LaScm+T67/rouxlrGx2AwZQu2aNQS22rC6/2oadA3MOjYLIy28MjSUm2VNfH25rUlIrnBCNPwTJCYTlh6L0NdoqN5aw/DOQ5lSuoPPSms5UN1wxxhe7i7M7uFMvUHMK19d4f1e7yNGIKjpU444CWw3t6LM0PP4rFkY5XIynLpzZdEbREU5ExISgvTbg0xwtKAi0g4LSylPbbzF7Mgl2ChteSfMictaB9j3PM66M4ydH4uLnw2lmQ24+FmTo3bHx1GDWSRjZ/3fKVr3D7qlr+XUw18T6zCUBvlRxu5+jAX7zqLRt4kS2fTvT+D+fThOm0rj7j3kDRpM/bZtCD/6Svmj8YcnJQVBOCAIQjtBEAIFQXjrjx7v3wmzWeDy/nx2vZeMTC5hzNwYgmNcf/VxWq9fp3DCRMpmvx1Wx5MAACAASURBVIjY1haLjz5kn68PVXV1DB+YQPmORpoEW3pGNhMcLqNg1DAMtU0cHG5kTQ85g4vC8Rv0Mr0/O0BqsRabgJOIzRdRSQawKL8XzeIGGmLeZ1PWs5gEOa8ldmBcah4lWgPPttZgkZvJyJEjcXR05INjWZQ2tPLO6AgUUsntNM2L4BEF3Z+788R16jaeeI/O0HHiXdeluVZF05FCLDs5Y9Pfl4at2zDV1KB6PIGMzAXY2cUREPDCXfsBnC09y+obqxkVNIqH2t3dCn8vCILAiU0ZVOY30W9K2J+irvSfjn+mbixtFez75Dp15S2/27Fd5rwIYjFVS5YQ7hTOR70/orCpkCePP0mfUFu6Bjiw9HAm9S1tQjg2AYk0RvTGoSwfec9DGMpaqLkUxmuiXDqpM3kuvYCC1jubGR8Z/ADd7DQcztWQnm/m7z3+TrU6i6CWLSwLU5J6tghniR2PTJmM1sKCW5KuXHj/CRISeuHo6Ijb8X0MdLWmvKMdIqmYv20u4LWY99FLzSwI8yNfZ4Xw9aMoG1IY/nwnInp7UVWgxsbJgvwGB1xcxUgFPfu1C8nYkYzFlkdZ238eC7osQm5Zzo7K2fT5ZDUnMqsAEKtUuLz4IgE7d6AIDqZiwWsUjHmIlgsXfje7/yv89Vee/iS0NOjY82EKl/bmExzrythXYn91bbIuL4/SF2ZTMG48+tIS3N9ahG7R3/ny8mUEQWDMgBGkri2iWbAiPkaLe8Npip6fh1Rh5NRDWr4IV9K12Idrqom8fmo76hYnHFxTEBSHMFs8wNJLocglSuq6fUZSVSLXK5z429AQni8qo0Rr4G07CY0XThMXF0doaCgZFU2sPVvAhC7eRPvepgk48CLomu6upgE4/V4bf/zgJXeJcuvyG6nbloXc3wb7Me0QDAZqP/8cZXQU2YpVSKVWdAj/8J7UwZUtlcw7PY8guyDmxc37xfa8eaqUrIuVxA33JzDql6V2/hugslUw/NmOiCVi9n50DXWd9nc5rszdHacZ01EfOULL+fN0ce/CkgeXkFaTxuyTs3kloR1qrZFlRzK/28cuYRNalSXW1z5FOVqKvlhNi2Y6q3KWIjFomJ6Wj9b0feQrFotZ9ng8tiItL32TSoxzTyaHT6ax7jBK3XleiVRS8U0W3r7+jH3oITSWlqQ1RJHyxUzGjh2N2WQi9nISsa42NHSyR2Mw8do3dbzW5V0qaWBeaAS1egmGdSMR1+Xw4Lh29JscRqvagFQupqRehbWbNVbmRr4Vz+HyUQuE1X0Z6xzOjhFb8bR2osV+JTP3vMOTm69Q1dRmW0VwMD4bN+CxdCnGhnqKHp9M8cxZf0oVzl/GyQvGe+i2/kkoSK3h60WXqMxvos+jofSbHIbc4pd3sOoLCymbO5e8hGGok5JwfGIWgQcPcsPFhS3btuHi4sKo7gM5/VkuOkFOv26tWB9eTvWmg1gHyrg5qpWPfG0ILHfgrHYglZLj6NRROFjnoHfYhkHRgWfPuuFv2R51+ClyRSa2pD9ArwhX1tNKidbAqkAXCg/uxd3dnf79+yMIAgt2pWFjIeWlgbcrZ27tbUvV3CtNU5sL5z9pi+C97+QKN1RrqN2YjtTBAqdHwhBJxTTu2IGxshJNggUaTS7hYe+jUNy98G40G5l7ei5ak5b3er2HUvrLujcrC5o4vS0b3w6ORA/y+8Vz8d8CW2dLhj3dEV2rkb0rrqNt+X2qQBymTEHm5UXl228jGI308+3H691e52zZWTZmv8ukOG++vFREWmlbqkgkt0I8bAWWrUa0t2ZgO8qP1lwJDqpJrEh/gxvNWhb8KD/v6ebC7AecaDKImL3xHM92fpZo12gsaj8nR17KcpUBdVIJ7Tt2ZNjA/jRbW3M1x5/iY68zcuRIasrKeLg8B08nFYbOjlQ2aVmxX8xLMa9ySyjh1XZdMegNaD8dgNBUTvs4N8a+EouDuwoEqG6QIXJxxVFUyyXLaRy50A/jyj4EVNxi16itDPYfjMLlKCcb36Hv8gOsOZ2H3mhuW5gdlkDgwYO4vDgbzdWr5A0fQdmrr2IoLb2XOX8XSBYuXPiHHfzXYtWqVQtnzJjxq/fTJCdTOOkRRKK2N6boT1B6gjbu8ZNfZnJ+Zy62zpYMf64TPmEOv7ikT5eTQ9XSZZS/9jr6/AIcHn0Erw+WY9GjB3sOHuTChQt06NCB7u4RHNtUjNhsZGBUBea1i9GWqHEd1o6CsFJedLHGuV5FnSgOO8tCaqqGYWlRidFvLSaZG4NuhPKwOB6jcy35gR/xSdoCUCho7ORAud7Ipg5+5OzbSUtLC48++igqlYrtyaWsO1fAm8M7EOPnAK0NsHksOPjDyE/vitTZ9QQ0lbWpPf1Ax9PUrKd6zQ0wmXGeHonEVoFgMFD6txcQedtT2jsFP78n8PQcf08bfXLtE/bl7eON7m/Q1aPrL56XPR9cQ66QMuzZTn+oetL/Z6hsFbj625J6opjy7AbadXFDLPltFB8iqRSpqxv1m79E6uaGMjycUMdQFBIFm25tItRLQlGJDylFDTwU7Y1IJELiFIa27Cx2OalUhhix8x5I4xUlHVSXMRgqWSMOxE8pJ8zq+xd8RJAPl6+mcLJcRKizksnRCezJ3Y2lNpkz7g8SdF1NOz8HPMODkaobuVVXT3W2nnZOZVj7xpFy8QIPh7fjmFSOwl5BRVY9jQ0uJHb2YnfVUSrsoulTl4E2eTuymEko7awJ6eaOyWCmPLcRnQ5MljZ4KarIF0VSVBGMX+5cVBId/XotxMnShfM1e5HYJHM0Rcm+ZA3eDkr8HFWIpVIsO3fG7qGHEHQ6Grdvp27TZsQKOZadO9+X3d94443yhQsXrrrXtr9EJC+SSpF7elK5+B2y+/Sl6sMPMdbW/qFjFtyo4es3L5J5qZLowb489HJM25v+X0AQBJrPnqVo+gzyEobRdPAgDpMeJujYUVznzKFVJmP9+vWkpqbSp08fOog8OLK1ArlRw0CXE7SuXI5gMKKcM4l8+yxmO1lgqZUht/fEzWigtjIRqawJIXg9ZrGSoJpePKaJQCIXUxL6DocrXyFXLYJurlQYjGyODKDl6gVKSkoYPnw4Dg4ONGj0LD5wi2hfe8ZE327aOrYQWqpg+Iq70zTZRyHrUFs1jfX3RFWCwUztxluYGnU4PhqO1LHtIW3cswdDWRk1fUqxtYvG3//5e9rqXNk5VqeuZmTQSIYHDv9F8yKYBY6tS6elUcfAGR2wUP33VdL8Gni1t6ff42GU5zZyYlMGvwfNifWA/ig7d6b6oxWYmtty/lM6TOGxsMfYkbOVXjHpJBc1sDPl++jVYvgXCDIFNqfWYogowKq7J9UVU5lb9BVdW3OZk1lMZsv3aSWxWMySR+OxE7Uyb3sqCpEty3otQ6urwKNhDQvDFGTvyEQwmXlgxCi6+HlRb+/Akf3FhFk24OPjw9UD+/jQ254GOxlusa5cLqzj2o1YEoNGs0+SxUee/VDqymlc0R/BoEUiFdN9dBDDn+2EpbUcncZEidaFcJ8WapQhbKtcStWerYg2j2asV182DdmIi7Ul1gGfoVEmMWXdZR7/4jI5VW0NX1J7e9xemU/gkcPYjRmN3D/gN9v+XvhLRPIyV1fsEhOx6tEDQ2UFjdu+oW7DRnTZWUhsbZF5ev5uJGTqOi0nNmZwcU8e1o4WJDwVSfs4938ZARlraqj/6mvKF7xG/br1mDUanGZMx2PpUmz690dsaUlFRQXr16+noaGBMWPGwKVazpwzYqOtIL5lDS2nrmPhpeCrcfOxK13LYg8DDSIZUrEFwSXxZNc/SLNchzJ8A0ZTAyrZZF64pCbQOpKK8C9IV0byWWooyl7utIjhy8gAHKvKOHDgADExMfTo0cYd8vd96VwuqGPNo7G42Fi0KT0deBG6PQVRD//owvTw9QSwdLgd4bdFzYJZoG5rJrqsehzGh6Bs35bTF4xGSv/2AgZHHc2jJXSO2nhP4rFqTTUzj87E08rzV6k7pRwtIu1kKT3HtSOg01+r7+KPgqOHFWIxXP+2BLFUjEfwb6O7FolEKIKCqN+wAZFUgiouDpFIRFePruQ15HG07Bu8rfw5lipiYpwPcqkY5CqwsMXyxmGKNEk4D34Cc70SfamMIXVL+co7kaP1Gsa5OSC//RVpY22FvKWSw4UGCsuqmd6zG0qpktMF29DLLEm1CGBwhQGLADuCo6KpvHSGIpmSkgtpDOnTlfTSKpoK8hjXoxsbm5sJs1dx+XolAaoYPF3r2KdNQSnrTLfGK9SmJmEZ9wiIRNg6Kwnv6UFznZaa4maqG+W0byelpk5Ehr4f1sVncCz8CJf2wxgWNZPcxlyytQcJ92vlZq4b686UUq/R09HLDqVcgsTaGuv4+N/Ee/Nzkfxfwsn/EzI3N2yHDMFmyGAQgfrYcRq2bKVx7z6MdXVIbG2RODndl8M3GkwkHy7iyJo0Gio1xA71o9/jYVg7/LTykEmtRn30GNUffkTFwoW0nDmD3M8P52efxf2tRaji4hAr26LbjIwMNm/ejEwm4+GHJ5G7NoXUAis81dfpnPsxuiI19VHejAl5kcmtq9jiVcUtuQKpQc6Qkme4qg6iVGbEteM3tOqzMdg9yfTD14h3GILG4wYlgcm8kzKJ1hgnBIWULzsGECoysWnTJhwdHRk7diwSiYTrxQ28siuNyd39GRPjBUYdfDm27SEcux5+3Hx0/hNI2w6Jq+5ofGo6XEDLxQpsB/thFfe9Wk7T3r00bt9Bw1gtIf0+wta24112MwtmXkh6gWJ1MasGrPrF9fDVxWqOfn6TgE7OdB8d9B8hy/b/Be5BdjRWt5L6bZvK1G8Vjpe5uaHPy6Nhx05sR45EYmWFSCSil1cvLlVcoth4jPo6XwSDLQ8EtYntiNw7YcrYg21pIVk2Jfj0fpyWQids6s4S1XyWVfbxlOgMDHGy/W5uI4N9OH/lGifLIMpDxfCwHuQ05FBctZd8+3BEBTLi3O2Q2MgJ69aD3CN7KLdzovzcRQb26cOljExcDFpiO4SzRdtCjK2KpJQKeng8iMgym0NCPm6mMKIaL1B+8yrWsWNBJEIiFRMY5YKjp4q869XU1AjYOFuCRk2WuA/aEh3eeS+jtHRg8AMLUMosOVS0HRf3m3R2j2DnJQ2bLhRhMAl08LRte9H9Bvyck/9L8MnrjCbSShu/rwC5DbNOh/rIERq+2Y7m8mUwm5F5e2Pdrx+qbl1Rdo5GYvXzN7PZLJB1qYLL+/JpqtESGOVM9zFB2DjevQAomM3ocnLQXL5M87cnaLl0CQwGJM5O2I0YgW1iIoqAOz/JBEHg3LlzHD16FA8PDxKHDifprSQqzS50qPgGt9zjCIjYFD2AA4HD+MTua45ynG021ti0WvNw0QIOaUWkyo0ERh2gSnsatf1kxhzPZ4rFIFSWEvK7vsKn5cs45WSN3ErG150CibVWsn79esrLy5k5cyZOTk6YzAIjPzlLZZOW47N7YW0hgxNvw8l3YdIOCPoRCZi6AlZEg18PmLjlu5+bL5XTsCMHVRc37EZ972wFk4nswX3QGiuQ/2MiISGv39PmG9M3suTyEhZ0XcDY9mN/dn7+CaPexNbFV9BpDExYEHdfjWf/7TAaTOxefo3qYjWJL3b+zSWn+pIS8gYPwSYhAY/Fb3/3e522jkkHJlGhbqC54EmOP5OIt0MbhS8lV2BNXwq9lMiGfoybw0jqPj2IY90UlndawBK7B3mvvTcPe3xPDZJfVMrwlRdQKBScnj8YE62M3z+eMk0T1c5vsD7fht5ToxBJxbQ2q1kz72/UOnrhVV5Ku/g+fJuZydChCeyydePzkmp6lhu5fKOKZ/q7cbxxAWq9msVFEnpoUij2Gof3tDt9qaZJxzfvXkVdqwURqMSttJiUuLakMdj9XVQRcTByJTe0Vbx8+mWK1cWM8p9EWcGDHE2vxVEl58neQTwc54PFfdJd/+VFQ7ZdKWbON6nE+tnzZHwQ8e2d74rijLW1qI8fR33kKC0XL4LBABIJFuHhWISGYhHSHkW7dsg8PZE6OyMgIie5isv7Cmio1ODkbUX3xCC8Qx3a1KbUagzlFehzc9BmZaHLyKQ1JQVTY1vVgNzXF+v+/bDq2xdlx453iB5/d05GI/v37yclJYXw8HB6hsVy+IPLtAqWdMl+H2VlGRp7BUvin2dwQi96Va/ndN4aljra42XyJjH9Jc5g4KRUT2jHs5To99Jim0iXmwqmV9vT3qIDxdFLOW0zleV6D2RWMrZFBRFnZ8Xx48c5ffo0iYmJREZGArDxfAELdt9kxYQohnX0gKpb8GlP6JDYFqn/GDufgLRv4MkL4NjWKKXNqqdmXRqKIHucHgtH9IM0Vs3uzVTPXUTr0250evLQPQXNs+qzmLBvAt09uvNRn49+cTR+eksWqSdKGPZsR3zC7o8b6H+AVrWebYuvIAgCY+fH/mYR88olS6n74gv8d2y/Q1yjoLGAifsfpqlFTnfLN1j18Pc86sLup+HaJi7HuhPR+xhyozMtH76MyvgFE/rs55Kg4kB0uzsWYldsOcx7KUbGRTry7sSuZNdnM3H/RHRSP+SqOexWOOM5oC0dUlNcyMa3XqfRzQ/fslKs2rUno7WVx6dM4dU6HUerG+lRZOBSRjUvJTiypXQuSqmSpZl1ROgzKQh6Ar9Jd8piGPUmDnx64zsSOJFIQDALWOgbGaB6D2/fKhi5Eo3fAyy5vITt2dsJdQhlcvArbD6j5UxODZO6+rBoZMR92fkv7+Rb9Sa2XC5i9el8ShtaCXGz5on4QIZGuCO9R6epWaOh9do1Wi5eovXqVbSZmZjVbYshBqmKUs8elHr1QiezxcpYS3tdMi6tOaDTYdZqMdbUILS2fn9AiQS5nx/KTh2xjI7BMiYambf3zzoojUbDli1bKCwspFevXrhVyzl1pAGlpoKYjA8QaYxkRoRjnL2EhGgfTu9+CUnulzzv6kSgKJgBl58mx1bCdlMLYWGpFAtf0qrqjVtTV6afv0pfx5HU+xwlO8qSp2v6gqWU7VFBdHOwJjc3l40bNxIVFcWIEW3Mz9VqHX3eS6Kjlx0bp3ZBJAiwdiDU5cJTl0H1I8d5O+Ligeeh/xtttqtooWrldaT2CpxndUT8gzJSk1FPxqAumE06AvbsRWUddJdNdCYdE/ZPoLa1lh3Dd9whK/dzKLpZy94V14ns40XPsXdz5fwPvw7VRWq2L7mKe5Atw57p+JuYOk1NTeQOGIgiJASfL9be8UxcrbzKlEPT0Gu8WDtwNd0Cbi/at9QgfBRFg1JPXs8+dO68GWN5E3zWizqFmAEPbsBKKuVwTDuspG2Rr16vJ3HxN9xstWHbzK7E+DuxL28f807Po9V6CL1ax7CyVwhyrzZajuzLZ9m9eg3NHr54l5VhdnSkxdWVR6ZNZ2JGCZlqLZ2zNVzLr2PeSBWrc17Cz9qHpdcz8DKWUNhhPgEP3UnbYTa1cVXdOluOjZMF6lotglkABNobk+jtvgLJA7Og72scr7jAwnMLaTW28kzUMwTKB+Flb4Wf0/2lyX7Oyf8lcvIao5rkxh0sGTaEQGc7LhXU8eXFIrZeKaFZZ8TfSYWV4nuHI5LJkHt7o+rWFbvERGwfn0xDhwFkO/bipn0/6uxCcFBoiJDeoINwDSuhAYmVCqmTEzIvL1SxsVgPGIBt4iicnpiF68sv4/joo1j37YtFaCgSW9ufdfDV1dWsX7+e6upqhgwZRtnOAtIyLPApOUSHzA0gFqF/eQG9/v4mYstK1myaTETRYZ5zd8YNL/peegqNlzVf6tQEBeRRIlmPQRmNRD6WcUd3MMRzLCaLSoq6XeTZhofRW0hYFeJDX1c71Go1GzduxM7OjnHjxiG5rWazcM9N0soa+fzxWBxUijYu+KtfQMKHd9W9YzbDlkltPDVj14FUgalJR/WqGyARtZVKWt0ZAeZ++TzCvmysnhuPc2ziPe2y/OpyThSfYFmvZYQ63ptD/sfQthjY89E1rB0tGDitw1+aOvjPgspWgcpOwfXjxZiMZrxDf7le7o8hVigQKSxo+OorLCI6oPDz+26bh5UHbpaenKzczoncbCZ3TGgjv5NbIlJYo7xxgFpJBTpbBxy8umG0aI9N5meEtFrxuW07in+Qn5dIJIS7Kth9vYKkjEomPRBAmGMI9dp6Mst2ku7gi32agqgOrojEIhw9fRB0dVSlXKfG0xub6mpobKRcq+WFB7uzq6aBSnspQa0i9l5t4anuPTlcsp384Bi6l1XhXH6M/GY7HNp/71dFYhF+kU5IZGJyk6tx9rXGK9CKugoNNZIArjWPRJefhk3Gx4SF9GZY9NPkNebxZcaXlGivE+/XBXuL+xP9/svn5Pfm7mX+mfm4q9x5ucvL9PKM50RmNRsvFHIquxqxSES/UBdGdvKkd4gLMpGIurIWSrPqKcmopyy7AYPOhNJGTlC0C+E9PP4QJR2A3Nxctm7diiASY7QOx/ViHYLckc43P8S6vhhZeBC+Kz9HY6vg45SPyT69jbmacmZ4OyEWHEm49gKeMYG8nVeGg2Mh9bYrMSgCUDvO5uFd6xjvnoBziw1Z3f/BC8qXqRSJmG3vwJzOvpjNZjZs2EBJSQkzZszAxaVtQfN6cQMjPjnLzAcDmDckFBpL4ZM48O4Ck7bfTRWcsgl2PwWjVkHHcZh1JqpXpWKs1uA8syPyH9mutvYMJeOnITNaEXr0AiLp3Y1iF8ovMP3IdMa1H8erXV+9a/tP4fi6dLIuVTLm5Ricff44Pd7/Rpz8MpO0U6UMnN6BoOj77xgW9Hryhg0HqZSA3bvumv9nDr5DUtVmBrpNZ9nA24ymZhPCql4YGnO4EG1HdLf9qFSBGL54GmnBJpZE7WC5rQPL2nsz6Qf5+UVrd7EmS8bUOHcWjOqM3qTn8UOPk1abg8Z5IbtE7ekwqC21KAgCu957mdyMCpq9/HGrqMAMRD32GI6RUQxLzsYREcrLtVQ0tDJzaA2rM95luFc/5p3ehsiopaznRwQPvJvCI/tKJcfX3cLKQcHAaeFcWHuBonIx3BbrdpTm4+Vlwr33AG6qbrIkbTGJQYm8EHNvWo9/hb98uqY4o45jX6VSaM6lUlyCq6MT8f49cbCyp7ZRR3JBHXnFTUj1Ag6CGEeTCNHty7ZztcSrvT0BUc54trP7wyJBjc7IjiMnyb56BjVKTKXWhAo+2DQV0DHjM8RGI85PzcJ2xkx25e/h45SP8Sps5u/qUp7ytadOZMWoW3PokdCVF89lY5KVoHP9BIPUgQbXBfQ/fZRRZhdi9REUBe9ibvB4sg1S+rSI+XJ4WwVLUlISSUlJjBgxgqioKKBtYXn0p+cormvlxIu9sFZI4asJkH8SnjwP9n53Xoi2sW2x1d4fph5BEKB2YzrajDocHwtHGXJn1KfT15CyZiC2H2lwXfQ6DmPubnpq1DWSuCcRS6klW4dt/cVdrYU3a9m34jrRg33pOiLw10/K//CzMBnN7HwvmdqyFsbMjb5vmmwA9bFjlDz9DG5vvIH9uDsX040mEw98MRmN9BofxK+gr1+vtg1FF2HtAIp87ano0ImY6G2IDVrM70Vj0CqZ2HszV8QmDkS3I/x2fr6lpYVR7+4iR2/L3md6EO5pR0VLBaP3jKHBbIWL5QL2RUeg8mlbVDbodWycN5W6Zmh2C8SlqgqF3sCAha9TZGXH+Ot5dJTJqT9dTqvBRGKfm2zJ+ZxHfRN4JulzWvViagesJih+2F3XXJ7TwIGVNwAY/EQE4qZaDq9MoVnigMKkxihRYKLti1dpJyOyjycxA+6vVv7fKf/3p0BiUOMiqSFEFkqHhgdwTg/l5v4aTm/JJv1QERYZzXQyy4mwscTSVk6KpYm9lnrW2GnZ4yGQ5i6hTCHQavz92OEaNQbO5tTw/pFMxq08y4y3PiPn6mlqTNZEFNvSXhRASPbXRKV+jMLJAd9Nm7meEE7ivjG8ef5NulQ78VZ9BfO9bKmUKBhV+hwPzejHomsFNJsqMbqvwSi2pMlpDqHZWcQ2aoghjAb7HN5sP5xsoxSP/BZWDwwHID8/n5MnTxIZGUmnTt8zOO66VkpKUQNzB7Vvq6ZJ3wVZB6H3/LsdPMDJJW0kZUOWgEhE4748tLfqsBseeJeDFwQz6Tdno9yjQeLhgv2I0XcdThAE3jz/JnWtdbzz4Du/2MHrtUaSNmdg72ZJ7JB/j67mXx0SqZhBMyKQKSQcXpWGQXf/snZWffuijIqi5uOPMf9wPQuQSiS833sxJp0bL516ifzG/LYNPnHQcSLexU2YKlMoKPwUFNaIRr2PQlzAklObsEPMjLQCmo1t56ZSqXh1aAhyjDyz8SJGkxk3lRvLei1FYiyn1LCGRWeyEAxtz7pMrmDMvOUojE3Y1GRT7eKCxsKCC6+9Rie5mOUh3lzR6wiO90IEHDgdQYL/aDYU7mN9/HSsZQasDs0i9+zRu67ZPciO0XOjsbBqU+aq0yh5eMVwwtwb0IlViPVaOnCErqr1eEuuY83vr9wFf5GcvHXlEQJSZxCp3EuXQf74TOjKYYdt7LT8nJyAC8Qm+DNp0iC69PPngb6+DOrrR7tgB6ys5WRVNrP7WhnfXC3h05O5HLhRwcX8WtLLmihr1FLTrEOtNWIwC+iNZnQGM3qTmSatgcomLcX1Gm6UNHI2p4YDaeWsO1vAuwczWHokkx3JpaQVVBBjvImbUIcn1riUBiFv1RF3fQk2DQXYjR1LyauP8nrhP1ifvh57C3uelyYy/OYOlvkquKpUMl7zLDOmjeO5vWncqi7FJngtWqEVrfNL2DSLSTh/kDEBiRg0RuY8qCDZ6IwirYGvEzri42BJc3MzGzduxNramgkTJiC9/bncrDMyfcMVglyteWN4OCLtbeoCp+C2ztYfVwRVZ7XRF0RNgpgpqM+UQ16t8QAAIABJREFUoj5ehFUPT2z63s3/Xli0itpjX2N9RILriy+hvF3F80McLjjMp6mf8nTU0wzyH/SL5/zMtmxKMusZ+mQkNk6/7MXwP/x6yJVSnLytuH68mJZGPQEd76/BTCQSIffzpX7TJsQqFZbR0Xds93GwITnTlUJ9EmfKkhgeNAyFRAHeXRBdWYed3op0yQUcHeOx8OqJUJKCXd0ufOr7stFJSZFWz1Dntvy8j6c7xZnXOVMlQywY6Rrkgre1N1ZyKy4WbiPFSkz7Ym+Cg9rSPApLFR7t2pN++CAqpZoGW2+0IhG67dsZMm4MMomE9dX1jAh142Z6DU11gXQPga+L9mMfOZG4kvPobh6k2qYz9l5+d1yXhUpGuy6uVBepuX68GG2LkQdnPYCXq4nSa6UUiyPQNtsSJ1+Hb7AAAfH3Zd+/fE6+cP8Fjm8voZP1aTrZrEds6wI9/ka6bxc+TFvFubJzuKnceDz8cUYFjcJSZnnH/jXNOlJLGrhW3MiNkgbyalooqW/FZP51tpFJRPg5qgh1tyHU3QZPWQuZ5w6ja23FpdIeoyGEdrlb8Sw/h8TBnopnR/Ox8jy36m7haunKk52exDfTjNWJl9joL7Db2opJlrN4fsRMZm1OJimnEPfwdTQZKxE5z6FB7MfE/esZ32k49tkqnn+wgQsW3kjT6pkb5sUzfYMxm81s3ryZgoICpk+fjpvb97QD7x7KYGVSLjuf7E6Ujz3seQZSNsOME+D+oyYlQYBNiVByFZ65SmuhmNpN6ViEOeL4cCgi8Z15+8bGZK5eHY/bR47IGiwIPHIY8Y84hWpaaxi5eyQ+1j5sGLwB6T0YKO+Fsux6dr6X8r9qmj8RF/fkceVAAf0mh9E+zu1f7/ATKJ45C01yMkFHjyCxu7OztrhOQ79/rEPhvYruHl35pO8nSMQSuLASDr1Meicfmtw96RK7G3FjOcLHcWhNsbwX/AYfe0juyM/X1taS+P4hSs22HH4hnkBnKwRBYN6ZV9mftwfB5hmOdHwIj4DvFzqTj2zmxOdfYRdpR7k+EFlrK90rKui+ciUvFtfyVXkdT1vZsGF3JqHuKjzbb+V06Un+7j2S4SdXUKa1QZ+4gYC4+Luu22wWuLg7j+TDhbgF2DBoRgQW1jKufLiPa7ekmEQSOnrV8cBrE+7Lrn/5nHzhsRRObcmhSeKIla6CWLtvCbHehtjSFqInc8E/lpXZ20iuSsZOYcf4kPGMDh6Nm+qnb1a90UxpQys1zTrqWvTUt+jRm8yYzAIms4BCJsFaIUWlkOJsrcDD1gInK0VbdQBw5eI5Dhw6gkwvwrKhE0615XTM34iksY66bu35oI+WDHMpfjZ+TOkwhaH+Q7m4dRPOV95il5+JzbbWPOw5mZf6/I0Xtl5jV2o+PhEbqdMXonB5gVKLDgw9sZ1hnu3oWOTNvGgDSfbOWGU1ESVI+XpGNyRiEadPn+b48eMkJCQQE/P9PVBQ08KA5adI6OjO+2M7QcEZWDcUHngO+r95t0Fu7YMtD8Ogd9B7TaL6s1Skbiqcp0cg/pGUnsHQyKVLCUgzTdgsqcf11VdxmHQnHYIgCDx34jnOlp5l27BtBNj9slykyWDm60WXMJvMjF8Q9z/ysT8JZpOZXctTqC5uZtz8WOxcLf/1TveANjOL/JEjcZgyGdc5c+7avuRQBquvfYWF+w4eDXuUObFzwGSAld0xGdScjNDjG/A0gQEvwMmlcGIRlYY3eeKBeK5awoGY7/Pze4+dYvaxeoJdrNj7fB/EYhE6k47x+x4juzEHf8sF7Bo2FMkPKu8OfDafW9+mEjAgmPRCa0QGAz1z84hetozJdXrONqh5QWnLx7vS6RZki9xzLSlVySzxGkm/kx9QorGlZcinhPa+t3h9ztUqjm+4hVwhof/UcLza29NUVM2p5ccIiPUgbGKv+7LrX97JQ9tNmLYpiaunG9BI7bDWVxBhf4UIyw1IpSYI7MO1oB583pxDUukpxCIxPT17khicSA/PHr9YK/TnT8KMPu8MO3cc4pZGjlxrh321D1HF27ApSUWrkrKmH5xub6KzazQTQyfSz6cfIgEOfrycgIJPOe6r5zN7WyYEPszL3V/izX23WHc+G7+Ir6gxZKJ0fY5iRRSdbl4iobmOIaKOvOFjw2E3FQFVetTp9Rx4rifeDpYUFRXxxRdfEBYWxpgxY+4o65y2/grnc2s48WI8LkpgZXcQTPDEeZD/6AE2tMInXUCmwjjuKFWf3kQkE+PyZCckP2qWEQSBG2lPUVNzHN/PIzAVVhJ09ChiizvpH/5Zw/xC9AtM7jD5F5v4yoECLu7JI+GZjvctiP4/3B+a67VsWXQZKwcFo1+KRnqf3Zllc+fSdOgwgYcPIXO7M9Bq1hmJX5qE0m0PDbITvNXjrTZyuuxjsHk0FZFxpNsXEBuzE2tlEPyjG2atkbTmD5kU74CVpZwjt+vnTSYTz7y/mQO1jrw6uB3TegUDbbxIw3Y+RJMgYrLDu8wZ0uW78c1mE5tee4TqnEa6PDaYU+erMAkCcTfS6Dx7NmMVTpRo9TwlVvHBvgwGRdjRZPcJGXUZrPBOpHvSUgqb7aiPX0ZUwr3FbWpL2/SGG6o0RA/0JXaY/28Wlf/L18lDW42qayd/IgYFISvJorLCRJ6pEzcb+qM2hWHTkox/7laGVOQxzKkTFvaBnK5PZ0fOTjalbyKrPguTYMLewv6udM7PoqUGco5hPvMRyV+sZ1NqKxUmMZbNnnTIL6Bd/kZsKopJ6gAfT7CmQ8+RvPnAm0yLmEaQXRAmvZ6vX19ISPUGrvhoWeFgx6igUSx44P/Ye8/4qKrt//99pqT33ia9k0CoofeOCIgQQDpSRFFU4FqwUGyoCCqCIKDSQu/Se09CIKSQ3hPS+2T6zP9BFOQmXoF77+//vcr79eIB2efsM7PPmXX2Xnutz1rMt+eyWHshDZ/wvVRok5A6z6VK3AaHinsMzrjNc4Gd+czCnqNupvQxSLl7pZjPng8n0teexsZGfv75Z8zMzJgwYQJS6YM0/wvp5aw8lc4bA4LoHeQE5z+GtKMw5scWC29zeRWkHkb/7AbK98jRK7Q4zgxHYtfcF15UtI38gh/wUU1EvfkcjvPmYd7x4WevvLGcV868QpBdEB92+RCR8GgPeF2FghM/JOMb4UCHId6Pfo+e8h/ByFSCnas5CWcKUDdq8Qp3eKJ+jENCqd66FV1dLZZ9+z58DYkIa1MJe6+YEeJTyZGcvfRw74GjRyQU3cI8O54yN3sq6q7j5j4ewSEI4eZ6LJ1t8c7xZauz6L5/XiQS0cbLnhNx6ZzKkjOyrQfWplLMpeZ0cm3HgfSdxOlSiRR3w92xKfxWEEQEdupL8uUDFMSn0X/cSDIyi8h3c0V+9AhzjEXsd/EiQaRjqsyR6OvFdHXpg840iV0VsYS3nkZ48VmU6ZdIrbLCo1VEs5wZMysjQrq60lin5s65QgruVuERbPtv1R7+y/vkq+7JOb81lR7jAnGUNd0svU5P2q7L3DlXQIXgDIIIe00BPrY5BFicwY47aIDrdq6csXXiHAqq9E1Spp5mrkQ4hOFv44+PtQ9eJo7YCRIsNCrENflQnQsVaegK4sgoMCajqidZFkHU2BUj0otwKK3FqugE7bM0lNuISJjelYihk+ns2hmp+MGNLMsrYe/yD+hmdp5UzwaWOdgx0GsgK3quYMu1fD48nIh3q4NU6mPA8UVMxR0p00PU+f1MGTCI74os2eNpzARbCw7tSmdIuCtfj2uKnNmxYwdZWVnMmDEDNze3+9fU6PQMXnURnd7Aidd7YlxxF9b3gvCxMGpt88GtKYBvO2IIGEhF/UJU2bU4TA/DxL+5UmF9fQpxN0dja9sVu29FKJNT8D99CpHZg5emwWDg1bOvcu3eNXYP342P9aNFxhgMBo5+d4ei9Bpe+DASC9s/FoZ7yn+Xy3sySDhdwJDZ4fi2fbKN2JKPP6Z66zZ8Dx/C2O/h8Fed3sCwry9Rr6nGxPsbpCIJ0cOisWmogO8iUQT34qpDAr4+8/HxmQe7JmNIP0ml9WbWmtqzxs+Iz4M8mOTW9BLatv8XPryhpbWHNXte7nnf6G5L3sencR8gMenDxeFfYGn2YFVamnebHe+9g5G5QMSE+Zw6dQmDSIR7QSGdgJnPTUFi70Dfch1br+Qxs7cTcaqPKagv4GuP4XQ5/xWFcisy/F6l14vzEbeQGwJN8fTnt6VhMBjoNT7oifc7/vLumoK7VZzalIxSriWin4yOz/g85KutTMohcVcMOYViGo2awvwsNBU4mlTgZFaMm8ltbIUYMkwNxBsbE29iTIKJMVXih5ejpkoTwkrd8Kxwx17uj0QIRCs1osEyHZVZBajlWNbE0P9KJRIdiMaPIOD1d5CYPZyqrNfpubrnOnEHVjPIJZ47Ho0sd7Cjh3sPVvdZTXRsMe8dSMAr5DBVXEdrN4EIs96cE5ky/MJBJnTpyKEyS3Z4WDLdwYSrJ0pQa3Qcm98Ta1MpV69e5eTJkwwZMoTIyMiHrv3DpWyWH73LD5M70D/YATYOgOo8eCW2SS74n9k9FUPacWp9dtKQKMJ2TCDm7ZvXrdVq5cTGjUCnbaS16QqKXpiJ45tv4DBz5kPHHco6xLuX32VBhwVMaTXlke9x9u1yjq1LpOtof9oOaB7J85T/d+i0evauuEldpYLx70VibvP4xdG1VVVkDRiIedcueHzzTbP2yxkVTNx4g+l9RRwofZeOLh35rt93iE+9D9fWkNnvGfK1cXTqeBALnUXTRMSzB0X5b/BqKyk3bcT3/fMqlYo5X2zlXL0Ly0eEMrHLg4nF6yc+4nRJNL4WEzg4+uHSkklXt3Limx04+JpjHvE8SUnJCIKAZV0dXe4ksXLkBKo6dSY8V8mB+CJe7ufMNcXH5Nfl87XPGDqf+pQyhRkxluMZ9MYSjM1aliyoq1Rw9qe7BHdxJbiLa4vH/Bl/eSMPTent1/ZlknLlHpb2JvQcF4hXmP1DSyWDwUDZjWQyT6dQlKuiGlu0kgezTKlWjgkKJKiRCFp0BtAaBNSYohZZoBc/mD0aq6owmGRT5qRAIxgIcbKizcETGPLysejTB+e338LIs7kxKs6o5sT6I9QW7OZZ2V1uuDTysYMdvTx6sbL3SvbeLOHtfbfxCj5ElXADlW0UY5yGsVEh0DHxGuPtLUgwduYneydeMNUgLTZiZ2whO2Z2prOvPYWFhWzatInAwECioqIe+v4VDSr6fH6etl62/DStI8KN7+H4P2D0Rgh/vvmg5lyEn4aj9JpLRdpQLPvKsB7o3eL4J6csoKTkIO3abqXhnZ9QxMfjd+bMQyqfpfJSRh0ahb+NP5sHbW6KnHgE1EotO5bcwMhUwth3O/7b/sun/PtUl8jZ9VEsbgE2PDOvzRPJOpevWUPFN9/iHb0D09/lbvzG9B9jic2pYsHz1XwR/xEzw2fyashk+KY9ensfLgfUYGLqQYf2exBdWwOn3kfTdxOpZ5x5oYcFFhZGnOgQhKVETGpqKtN+vkW1yIozC/ribtPkatQb9Azd9RJFyquM8XmH93s+HN1yJnoRt/enENAjkHx8aWhoQNDr0SgUtIuNI9HTn5sTZ+BcZOBwQjEv9XEhRvUxubW5fB04mS7Hl1GllHJGM5jBiz7F2qn5BAma6i8g8MTy2H/5ZChoikftMymEUW+2RSwRcXTNHQ6tvk15fv39YwRBwLlzGN0Wj2XsD5OY+d1Axkx1olvrRsLsinA3qcQMOSK9DrVWgkErIDWArVCPt0kJrR2K6dNOznPTHHAdbUKRcyOWFmY8k5NL6683IBVEyNZ/j2ztd80MfEVhPYe/ucWu5WuoL9jOWL80rroq+NjBjt6y3qzsvZKDt0p5Z/9tZEFNBl5uE8UrvlH82KDDqyib/loFac5NBn6kso7+5h5ExxQyp5cfnX3tUSgU7NmzB0tLS0aMGNHsgfniRBoKjY73nwlFqC2AM0vBfwCENU9SQqeFY/9Ab+ZBRVo/zCIcsRrg1eLY37u3j5KS/fj4zMO0xIqGc+ewnTL5IQNvMBhYcm0JGp2GZd2WPbKBh6bN1oZqFb0mBD018P9HsHUxp9vz/uSnVJF4/snqk9pPnYrY3p6yL1e2WJHqnaHBNGp0ZGaF8VzAc2xI3MDZ8njouxhRQSytJUOor08kv2AjdJ4LjsFI4z/Eu7cLH8U1kqtQsyCtAIPBQHBwMFNCJGi1OhbuvHn/eiJBxK5nViKVBLIr53OOZ19/6DP0jfoUj/amZFxKJ8zZgFarxd7JCXdvb2I6R+KsqmPO26/iIE9hZFt31p4roYPR2/hY+zAv/SfODnkPOzMDg42PcfSD2RSmJLU4FoJI+K/VP/jLzOR/j06rJ+liEXFHc1HKNQR0dKbdIC8cPP59PZrU1FR++eUX6urqaFVfT/DxE5g4OuLw8lxsRo1qpstRllfHrZP5ZMTmolMex1iXxrjgfHZZKvjG1pp+nv34vOfnHLpdyoI98bgHHqRWFIPcJor3wqfxaWoOaDWMu30Zk8F9WC23YEhlPcv6tOOZNVfwtDNjz5yuSMUCu3btIi0tjWnTpiGTyR76HImFtTy75jLTu/nw3rCQpkIguVfg5etg04L748b3cGwRFdp30csG4TgjHKGFwgZyeTaxcSOwtAynXdstFL32BvKrV/E/ewax1QM98v0Z+3n/6vu81ektXgh5oVk/f0R1iZzopTEERjrTb0roI5/3lP8+BoOBo2vuUJhWzdi3Oz5RoZGqrdsoXb4c2frvsejZs1n7+weT2HYjn0OvdGJZ/Cvk1eWxY+g2vHdMxqCsJqlPNypqLtGp4xHMy4rhp2cw9FxEVdEovlPUsybAmBWBHkx2d6Curo6XVkZzRenO58+3ZkyHB7+R+JQsJsXPQaxvYO+z2wmweeDSUSkr2bL4BeqKxESMf4GL8en06NEDkUjEhQsXMFMqibx6lQYvf+K6jmFznp7ZvZ1J1q8isSKR94Im89y5b9A0yjlQGILvqPl0eGbUf9So/y1m8r9HLBHRpq+Mics6026QF7l3Kti5PIbD39ymILXqV/nPx6OmpoYdO3YQHR2NqKKCvqfP0ObKVdzefBO/48ewHTPmvoHX6fTkJJRzYGU8uz+JI/vWHQzqHdiLU5nSKos1dnq+sbVmmO8wPu/1OdGxxby5Ox73gAPUimJosIliSftZbE5MQS41ZtCtS5gOHcBquQX9SlWs7hbEon2JqDR6VkVFYCQRERMTw927d+nXr18zA28wGFhyOBk7MyNe7RfQVMkp4yT0XdyygZdXYDj7EUraobXug8Ok0BYNvE6nIil5HiKRCa1arUSdmU39yZPYTpr4kIEvkZewInYF7Z3bMz748ZI9Lu/ORGIkosuo5tLET/n/F0EQ6DMpGKmxmFObk9E9gSyI7dgxSGUyylZ+hUHf/PzX+gVgZiTmixPZfNX7K6QiKfPPv0HjwA8RagsJqXJDJDLj7t1/YPDuCmHPI1xZjW1/KTPqxHSt1vNeRhFJ9Y1YWVnx6tAInIV6PjyYSGndg5qx7UL9mGezGJ0g8MIvMylrLLvfZmxiz4g3l2JkqSFp/w7CAn25dOkSMpmMadOmYeLqyrm+fSlHy4h1b/NF6Wl2nMgmRFhAV7euLEn9kY19XkLiIGOMZyLlhz7h0JcfoWqUP9nAPyZ/mRDKlpBIxchC7GjV0x0jEwk5CeUkXywm9XoJqkYN5rbGf1roWalUcu7UKfbv20d1aRnhdxLompGJ99SpuH32aVP9SokEg95AeX49t07lc/anu9y9cg+dVoudcyKVuYcIcW5kiNsd3nW04pCZlCmhU1jceTEbL+Wx9GgCsuA91IriabCJ4sP2szkXF8cNaxf6xV/Eo0s3vlWb06NMy1ceRuypgB0xBXw0KoweAY4UFxezZ88e/P39GTJkSLMZwqGEYjZdyeWD4aF0cAJ2RIFjMDz79X1VvN+jP7IIofgm1aIPsZ/dA7F1yxtr6RnLqKw8S3jYN1hZhVP68SdoiopwX/nl/bKGBoOBhRcWUiwvZl3/ddiYPHr90NzECuKO5hI5wg/P0CeXu33Kfw8jEwk2TmbcOVuIXmd4bFliQSxGbGtLzY4dGHl7YxIU9FC7mZEEiUhgy/V8evnLGBzYgS13t5An6Blo6oH4zm5Mu7xFQdkuJBIrrFvNhriNCDWZmIx4kdbHCjnqKuVEXT1jXezwdnenJvMmN6pMSC+pZ0TbB/Wf2/vISLhrS5b2DMdzzzDCdygmkqZ9OHNLdyw9FGRcSUFXmY+JeyB3EpPo3r07Xbp0Qa1Wk9zYSGpgIF7piUxN+IXU9BIsPMbi4ytha+Yeqls9Sxe9hGD9LWrvFXL6dAJOPn5YOT65wudv/G1qvP4REiMxbgE2hPf2wM7VnIZqJXevlZB4rpD0mBJqyhTotHqkxhKkJmIEQUClUnH10GF2791LTlERHrl59C4sIGLiJNyXL8O0XXtqq7XkJ1eRcDqf8zvSSThTQHl+PbIQO0K7GVGWtY2StFhGdBAIEF/lVQ8PLksNLOiwgJfavMRXpzL48kwiHiE7qCWJetspLG4/g+rEW/xk4UKbjAQCZDI2mdjTqULLF/UN1HQIYf7O2wxq5cKiwcGoVCp+/vlnxGIxEydOxNj4YYPcqNYy6+ebeNmbsXxkOKJfFkJRHEzYBZbNw7UM+fEIx9+kQT8C82mvYuTasourrOw4mVmf4un5IjKPSaiycyhZuhS7KZMfin0+kHmAn1N+ZmGHhXT36P7I90yn1XNsXSLG5hL6Tw29n0n8lP972Lo0/aYSzxfiEWSLpf3jhbcaB/hTf/Yc8ouXsB03DuGfotrC3K05eLuIG9lVLOzXFROJMdvubsM8cCgRGRcxN5hT5+5N8b1dOMuikBo7QuwGxAGdMPdphd/lMrY6ichVqhnuZEOglzu342K4Ui7Fx8GcYNemVacgFtHXyp1DJbaUKU5ysfgqw32H3k+UdHBph8b4GjnXy7A1UlEjtiA/v4B27doRGBiIv78/twqLKLKzocDPjy6Zt2hzah8m+Y60bhXOpvJDJLgG08u+NX51V3CWVHLocCyNCg0ewa0QiZ88e/svHycvr6nm2p4ddHl+POY2jya6X1+lJCehnPzkKorSqtFq9GDQY0MhWnE6RTYCaiMpzqVlhCqlGIf1ReEciKJeQ12lkrpyxX1VPmNzCZ4hdniG2ePmZ0b8sd3EHzuErbUxUWEVFNYm8qqnD+XoWdJtCYO8hvL+wSR23EzFPXgbdfpc6uxn8U6bMXgUZjGnXoxTTTmdFXUc8AwmvFbBN0kKXF7uyoiNN1BodBx7rQfWplL27NlDSkoKU6dOxcur+cboylPpfH0mg12zu9DJcAd+HgHd34D+zeurGnQ6tJ/3QqQoQPXsOczatyw1oFAUEBM7HDMzX9q3i0YkMqL4H29Rd+IE/mdOI7FvykQtlZcy6uAoAu0C2TRo0yMnPQHcPp3PlT2ZDHu5Nd5PmHTzlP93qJVadi6PwaCHce91wsj00XSIfqPh0iUKZs7C+d13sZs0sVn7L4n3mLstnk+fCyeqo4w3L7zJ2fyzbLDvQcfYn1FNiuZa4VtYWraiXevNCOt6gE4Fc29QfbiANeVVfBtozGeBHkxxd+D0mTO8fboCpZEVZxb0wcnywYsp81gWI1XnEWq/pq1zO9b3X3t/Rq/TKTn68/NkHAfX8FDSNaZ07daNgQMHAqDX6/no9EXq4q5jrlZipdISdv0aHveKUXq7sMe/koIIF5aEDsXn7Ceo9Ebsz/ZGaRfGgFmv4BHc6onG/7/mkxcEYYwgCMmCIOgFQejwT21vC4KQKQhCmiAIg/6d6/wZhSl3KLuym03zZxFzcA9ajeZPz7G0MyGsmzP9+kgYHVlMhPgQZtqdZDtmkuNkhE2dgqBsMUaGYSSbjCAh34acO5XUliuwsDUmuIsrfScHM+69Tkz/vAf9p4eg16Sy/b153PzlIH27yJjqG88VZSYTPT1RG1uyefBmerkNYvqPsUTHJ+Eespl6QwF1Dq+xvF0UXRoqeL1CjYlaSeuaUg55BhOgqGZ1rA6PsRF8fC6DnEo5K8dGYGNmxM2bN0lOTqZPnz4tGviiGgXfX8hiWGtXOnmYwuH5YOcLvRa1MCKg2PYtUmUi6pAFf2jg9XoNScnzAQhrtRqRyAh1QQG1R45gGxV138AbDAaWXl+KRq9hadelj2XgFfVqYo/m4tnKDq+wp9IF/wsYmUjoP60VDdVKruzLfOzzzbt3x6xTJyrWrkXX0NxXPSTMhQ5etnxxMh25WseybsuQWcpYUJ9AqZULxmdWEOD3D2pqblBUuheGft6UtHhlNTbDfZmhkNCtSsf7GUUk1jfSq2dPhjlU0ajSsnh/4kPRPX4DfPi8vC0NdrO4VXqTNy+8iUbXZFPEYhP6R63BtWMd9xJT8DY2cPXqVZKSmqJmRCIRiwf0wmb0BC77h1NjY8XVXj3YPnIsiTYyoi7AO18UkPnWBmLqBqGpsuF5l2TaiG9SlHT7yQb/T3i8121zkoDngO9//0dBEEKBcUArwA04LQhCoMFgeHJB6n+BR9YljFLzUVtUkvvFCk5u3IhXx864hbdBbGKKQaVEr1Cil8vRlJSgKS5GWVREUV0dxc5OFLm7I3e0QWqwJtTBgS6DB+MWEPBI1zbo9WTEXOHq7u1UFubj4evJ5K46RDnb+Njdj2ipmtb2IXzV+yu0akvGrLtGZm06ziFbqNcrqHFcyMq2g+ihVzI0KReFtQNdsxI5E9IOmaac766a4hhpzwW1hh0xBbzU248ufvaUlJRw/PhxfH196d69ZTfIp8dSAXh7SDCc/xSqc2DKYZA2lyOQX07FJOsLtOZtMBk79w+/b1b2l9TV3SYs7FtMTZs2eCvWrUOQSLCbMf2zzsXIAAAgAElEQVT+cUeyj3Cx8CILOyzE0+rxkpduHMpGo9LR7fmA/1pY2VP+87j6WRPR35Nbp/Lxi3DE8zG0hQRBwGnBm+SOjaJq82Yc573SrH3xM6GMXHOFdeezWDAoiFV9VjH+6HgWePqyKekqbhVQatuFzMzPcIg8hkmrUXB5JUKbcThMCGHJultM6GzOrORcTnYIYspzg0nceIKTKSJ+SSxhWOumRCRBIqL/iGDm7New1lXBxcKfeP3866zsvRIjsRFmZl70n/weR2qXUJkQj3NYew4cOIC9vT2urq4IgsA7gTKkRlJWZ3vzXEMFnrkZ5JgIZAb54SYRYZEWi/2J2xSqtIAzVubV2Ir2wPOPHnn2yGP7n3DXCIJwHlhgMBjifv3/2wAGg+GTX/9/AvjQYDBc+1f9PKm7pvTsKe7+vB6T4kKMqpVIFFrEuqb3iVYiQSOV0mhmRoOFBXUODlQ7O1Fpbo5WJEIsCHi5udG6Y0dCQ0MxMno0oTKNUsndy+e5dfwwFQV52Lm5M7ibKy4528jU1LDIK4hMbS2TQyfzWrvXuJ3fwMvb41GKkzBy345GZEat4wLWRHSnt1TPc4dPc0cWSGR6AgmBrXESqvj+khpnc2uEKREMWXMZma0Ze1/qikGnYf369U2ZfHPmYGHR3G8em1vFmHXXeLWvP2+Eq2B9b4iYACO+bXasMqMa7c9zMBefglkXENxarhhfUXmehIQZuLtPIDhoGQDqwkKyBg/Bdvx4XN59p+k4RQUjDozAx9qHnwb/9Fgx8RWF9ez6KJbwPk9lhP8X0Wp07PooFrVSx/j3Oz22Hkvhq68hv3wZv1Mn768Kf8/86FscSyrh7ILeuNuYcjz3OAsvLGSCzpS3q2pRvHiA67fHYGsbSRvvJQjfdgS/vjBuG/K4Us6dzmJ2JzOecbZhXagX+w8e4uMbKnQm1px+szf2Fg/2tOqvFjEvp5jzlhexrP6R7u7dWdVnVZPOPZCetoJz644hL7HAENgGkY09s2bNwtz8QSjpD4XlLM4ooqu1OfMlBrYeOoetpgJToWlloKAGa3UDret0hLaPxHfanCcZ9n/prvl3Z/J/hDvw+6yCwl//9l8h3kTLDe9W4P3n/iyxWIyLiwtt3dzw8/PD19f3kQ27XqcjP/kOaVcvkXHjCqpGOY5e3jw/oT+eZYfQJEWzThbCBqkFlhIxa3uvpZtbNzZezuGTY6k4ut5EZLUHtUSGymUh2yLa0cFIYPbWaO4EdiAsL5XEgHDsxY2supmHo8ofuxdbM33fHVQaPavHNYVL7j/8C5WVlUyZMqVFA6/XG1h6OAUXKxPm9PCCLYPBzB4GLmt2rKZETt3WfTiKT0DHl/7QwKtUpaSkLMTCIpgA/3fv/73y+/UIgoD9izOAJjfN8uvLUWqVLO229LEMvMFg4PLuDIzNpHQc9rTa0/8iEqmYflND2bviJpd3Zzx2boPj/PnUnzlDxdp1uCx+t1n7wsHBHEsq4fPjqawa15bB3oO5U36HLSlbaG2oY1jcLvyC3iQjYzklznG49lwIZ5ZAxmnM2veja1YNL2XU8K1QQ1cbC8YMHMCNlA3sqTPj7X2JfD+p/f3Vo0UXN5anVzFZ35tCOzFXijYx78w8VvddjanEFP+AN6gZm0Dsz2VospORywLZtWsXkydPRvzrJuqLHo7YSsS8lprPMnNTvntlEkv33CEtK5eBngKu4hIqSsqJM5KQZabitX//FjTjTx2lgiCcFgQhqYV/I/4TH0AQhFmCIMQJghBXXl7+RH04ejmSHJjMFecrNAbIiQy2oK9xEr25Sn/zDNqa12Fdmod5ZiKWGQk4VBVjr6zHWClHUVONTtvch6/X66irKCPn9k1iD+1l/2dLWDNjPHs/eo+0a5fwbdeBKbOfZVJwJl63PiBWUcLo4Ai+k8jp7zWAvc/uJcy2E69sv8Xyo0n4BZ1BbrULtWlrpLIPOdSpE5FmUj7ctJkTgR3wKi0gR+aPtVTLJ1nH8CwNwHZkIOtT7nE1q5Ilz7bC19GCW7dukZCQQK9evfDxadkQ7okvJLGolreGBGN26wcovgVDPgPThzeldXUqKjbdwUb0HZg7I/R/p8X+DAYdyclvoNMpCGv1NeJf5R00RUXU7N+PzZgxSJ2b0rVP5J7gTP4ZXm77Mr7Wj1evMi+xkqK0GjoN9/nT0Nan/N/F2duKdgM9Sb1WQu6disc619jXB5vRo6neuRN1QUGzdncbU2b28OXA7WJuF9QA8Hr712nn1I4lTk6kx61DZt4Ta6u2pKcvQ9VhHNj7w7GFCDo1NiP9mV4vplu1jvcyikjTwgvP9KGtpJCTKaXsuVl4/1qCIOA6Ooiv0jQYG/dC7Dyb6/euM+fUHGpVtYhEEiLaryZkZCMSUxUWRVkUpKVy/Pjxhz7zaBc7fgr3JbNRRVRKDm+OCWN0rwi25lnwizyCkVNn0di5kYDwR3MRPy5/CXeNwWAgu7yWc6V7WH9nPQaDgXGBY5imMcYu9geoycdg7UmNc09S6525m1pMdfGDm4kgYGJugVgqRSQSo1Y2opI/vPlj6+aBLDSMAD8nZEI24oRtUJNPmo0rX8sCuNiQi4eFB4s7L6abezcupJezaE8ClYoq/Frtp1iVhMJiAD6yF9naJgAHscCqtWtYFdQF68YGFOaWmJuI+bBiFR1jZmDWxon0jo688MMNnm3jxldREZSXl7Nhwwbc3d2ZPHkyon8uz0eTHnefL87jYWvKvihXhLVdm0qKjd8Bv/Nv61U6ytffwbg8GhvRuiaZ4VajWhzf7JxvyMlZRUjIZ7i5PtC4uffhh9Tu3YffqZNIXVyoUlYx8sBI3C3c2TJ0yyNXeoIm0bboZTEYDDDu/U5P5Qv+x9Fp9Oz+NBZFg4bx70c+1ktbU1pG1qBBWPbvj/sXnzdr/01z3tvejN1zuiAIAuWN5Yw99DxmDeVEm7dG9OxybsQMx9GxP+EmI5uqmvV9D3ouQF3cQPqG20zpYoHeXMqx9gGc2LWTdWlG1ImtOT6/JzK7B5pWiruVXNyfyqzO5gRwi6qib5BZyljXfx2uFq7U1N7k2vnJZB7yQ683ocbVj8EjRjYTB0yqb2RyYg7VGh3fhXpiVq3hjZ23kau1vDsslImRnv8V7Zr/lpFvBWwHOtG08XoGCPizjdcnNfKHEop5Y+dtXuzhy9jO5mxI+o5fcn7BWGzM8wGjGSdxxDPpEORcAIMe7PzQeXaj2siHKp0llY1S5A0K9FoNep0OqYkpppaWWFhZ4GQtwVZUiXFFUpNgV/ldDECcVwe22tlztuYulkaWTA+bzsSQiajUYj47kcr2G/l4u1ZicP6JanUNtbbTGOL7DF8GyzAVYPP6dSz3jEBsAJ2RFAsTIxYrlxF5ZTrG5o6Ip7fimXXXsDCRcPiV7kgFPRs2bKCxsZE5c+ZgaWnZ4lj8VtLvwNyuRJyfBoVx8PINsH7gLTPoDVRuSUGdmomrxcsInh1h4r6HXgK/UV19g/hbE3FxfpbQ0C/uP4Sae/fIHDgIm9HP4fprrsWCCws4m3+WXc/swt/28TJUky8VcX5bGkPmhOMb8WTytU/5v0V5fj17Po3Dv4MTA6Y/Xmhg2cqvqFy/Hp99ezEJbe7y2RGTz9v7EvnuhXYMDW/aMI0vjWfG8an0kMtZNXA9eZJUsrNXEh7+HU5ntzQVHnklFmxkNFwrJvZMDjO6mRNiZcZGbwe+WrOBA8pWtPa0u19Z7Teq92ewN6+CxW1MGWRWSEbWx5hITFjbfy1BdkEUFPxIwo3PyD4ShF4woc7dj6hJkwn6p+SuMpWGqUk5xNc1Mt/LmUl2Nry19w6XMiqY1s2bD4b/50Mo/y0jLwjCKOAbwBGoAW4bDIZBv7a9C0wHtMB8g8Fw7M/6e1IjX9mg4tNjqey+WYi7jSkfDA/F372R9YnrOZFzAq1BS1e3rgxx7Uqf2mqsc69C3hVQN/z6RURgYgOmNiA2boqvVcuhofTBRaRmFHq04biDjKPqEjLrcrExtiEqKIrJrSZjIbFkV1wBK06kUdOooke7dO4ot6ETWVHj8BqLQ7sy08MRg0FP9PdrWOIWjsrEFEEQYWFqzAeiFYRf74l5VSgOcyOYeTyFmJwqDrzcjRBXK/bv309CQgKTJ0/G17dlN0h+ZSP9V17gmdaurAy6CwfmwNAvoNPDcr81h7JouFqMs886pGUnYe51sPdr1p9aXUlMzHBEYlM6dTyIRPLA/1+ydCnVu/fgf+I4Ujc3Tued5vXzrzOv7TxmtX68hDa1Usu2969j7WjKqAXtnkbU/IWIOZxN7NHcx9ae19XVkTlgIKbh4Xj+sKF5+6+a83K1ltNv9MJY0uQD35q4mc/iV/KaSsr06VeIjR+LWl1O5+BNSNf1hYABELUFg8FA1ba7HK2oZVEbU6Jc7BhTWcC6E/Fc1vjy9pBgZvd68JvQq3WUfXuLNfawwVPCdMdGYu6+T4OmgRU9V9DDvQd3U98iM+EIOUcD0EtMaPQKYurMWbi7P7wdqdDpeSejkB33quhsbc7aUC/O3L5HJx87Ap1bnrz9GX8LqeE6rY60wloW708irbSeTj52LBwUhLeTjj0ZeziQcYBieTESQUKYQxhtHdvQ2sgWT5USj/pKzBQ1oKwBnRqD2Jg6iZR8U0typFISDHJi63LIqcsBoI1jG0b6j+QZ32cwFhtzPr2clSfTSSyqpa23BEvZPm5VXEVj2gZT15dZExZOV1sL9Hod+9d+zRKnIKpsHJEYDJibGPOJ+Y/IEnQ4po/BZoQfPykb+fxEGh+PCmdCpCe3bt3i4MGD9OrViz59+vzhGMzeEseljArOzgnHZUs3cAiEacfhd26d+stF1B7JxqZ1IRbpc6DXP6BPc1+8waAn4c5Mqqqu0rHDXiwtH8ymNCUlZA0YiPXIkbguW0qNsoYRB0fgbObMtmHbkIoez58ecySH2CM5jF7UHhdf68c69yn/t9Hp9Oz5NA55jYrxH0RiavHoZTYrN22mbMUKPH/cjHnnzs3aL2WUM2ljDO8MDWZWzyaDbDAYWHRkIicrE/jecxStOo4jNm4ULi6jCC13gLPLYdJ+8OuLXqGldHU86zzEfO8u5gNfV/hlP3vu2ZCvs+LQK90JcX2gwaQplVP67W2WtzfngDV84GXMueQPSK1KZW7EXF4Mm0pCwnQK794l57g3OrEUfUBrXpz7Mra2zZM0d5dUsSitEDOxiK+CZQx0ePJn/y9v5M9W1jEnJZd3fN0Y52zLrtgCvjmbSXm9ii6+9kzv7kOfIEfSqu9yKu8UcaVxJFcmo9Vr7/chEUkwFTfFjzdoGjDwYFzMpea0c2pHJ5dODPAegLuFO2qtnlMppay/mEVCYS3uNqaM6trAweKV1KhrabAZxyDfsXwaJMNGKkGv13Hku1Ust/amwM0HI50Wc2NjVtoewjrtBrL4RZiGOZLZxYnxG64zrLUbX4+LoKysjA0bNiCTyZg0aVKLfniAq1kVTNhwgwUDA3ml6lO4ewhmXwKn4PvHKJIrqdyagkmIJfa1LyLoNU2z+Bbi5vPyfyAz8xMCAz9A5jH5obaS5R9RHR2N3/HjGHm489altziRc4LoZ6IJsgtq1te/Ql6rYuv71/FqZcfgWS1H9jzlf5vKogZ2fRyLb4Qjg2aGPfJ5epWKrMFDkDg44L1rZ4srvGmbY4jLreb8wgfhj41qORN29KBKp2LXs3tpqD1CXt5aIsI2YB/9JghieOkqSIxQ5ddRui6Bd7pbc9ZUz9fu1tzcvp0juja421tx8JVu91cJAPKbpZTvSWdhf1uuirVsCHXnctpXHM4+TB9ZHz6MXEha4jSq8uRkHnFFI4gxat2J6XNeajESLl2u5KWUXJIblLzh7cwin6dFQ1okq1HJW+mFXKpuoK2lGSuCPPA3Nmbr9Tw2XcnhXq0SmZ0pIyPcGd7GjUBnS5RaJVk1WRTUF1DYUEiDugGlTonBYMDCyAIrIytkljK8rb3xtPREIpI0FakuquVYUgm74wqpaFAhszNleg9nUtTbOJ57GJ3UDZxfZXlYV55zbnp7azUaDq/5ii+tPMj0CcVIq8HcSMq3zteQZG/CN+YzpFZWiKeGMnzdNUykIg7P647Rr354hULxL/3w98ulKbWceVaNya4o6P0O9P7H/WPUhfWUf38HibMZjiHHEV36FF7Y07R8/Sdqam8SHz8BB4e+hId999CPS1NaRtaAAVg9Oxy35cs5X3CeeWfn8VKbl5gb8cdJVH/E+W2p3L16j/EfRGLj9Bi1dZ/yP0XcsVxuHMxm4IutCOjQcuGMlqjZt59777yD+6pVWA1unjifUVrP4NWXeCHSk6UjHrxAcrJOM/7ia/ga2bBxzHFux49Gr1fS2W4h4uiJ0P9D6P46AHXnCyg5lcvMAbaUiA38o7GEK5eTOKMJZGYPH94d9vCeQNXudCoSSpk72J4snZZdbXxJv3eQz2M/x8nMiQ86voo27wOU5fakHrREbRAwa9eV6XNewtS0+YRKrdfzZW4p/e2t6Gj9+HLN8Dcw8tC0TNtfVsP7GUVUabSMdrFlgbcLbkZSTiSXEB1TwNWsCvQGkNmZ0sXXng7edgQ4WeDnZIGVibRZf1VyNQXVCpKLa7mZV831rEqKa5WIRQK9Ah15IVJGvTSej2M+RaGpo9FqKCODp7PY3xtbaVNkiVrRyL4vP2GtcyBp/uFItRrMpVLWyzLRZi/GL+FzJPX22M1tzYuHk4jNrWbfS11p5dbkh09MTGTy5Ml/GC4JsO1GHu/uT+K7sSEMPT8cjC1h9kWQNC2NtdVKytbcRpCIcBpnhnhLHwgZDs9vataXWl1JTOyziAQjOnY8iFRq9VB7yUcfU719O37Hj6FwsmLUwVHYmtgSPSz6ofq1j0LVPTnRy2II7+VOj6iniU9/ZfS6X0sGVigZ/0EkZlaP5rYx6HTkjByJQaPF9/AhBGnzZ+y9A0lsj8nnxPwe+Ds9mAid3j+J1+tuEyXrzyvtJ3LzZhQeHhMJupkO2eebNmGt3THoDVRsTiLnXj0zelpiLBHx3O1LXCu3JFFhw+ZpHekT9EApUq/WUbbmNmUqNbN6WlGl07Enwh+Umbx96W0K6guYEDCEdsqDSBrCSdyjR60zYNWxO1NfermZiOB/gr+Fkf+NWo2Wr/JK+bGoAq3BwBgXO2Z6OBJqYUpZvZLjSSVczqjgRk4VtYoH8fFGEhFWJlKMxAIqrR65WotS80Df2sHCiPZetvQPcaZ/iDNZ8kzeu76CwuqbaIx88POax0dh3YiwejAbbayrZe+nS9js3YaUgDZIdFqsJRI2+pUjz3gJr6x3MMkKwH5SCKvzK1h3IYsVo1sztqOM+Ph4Dh06RO/evendu/cff1+Fhj5fnMffyYKdHvsRYtfDjJMg6wSAXqmlbG0CuloVTnPCkR59HirS4OVYsHh4I8xg0HM7YTo1NTdo3343VpYPL6019+6RNXBQ0yz+o49YfHkxR7KPsH3YdkLtH7+gx9Hv7lCcXs3E5V0ey1f7lP9Nqorl7Pw4Bu8wBwbPDnvkDfb6s+conDsXlw8/xHZcVLP2ygYVvT8/T0cfOzZN7figobGKlZs7s9nCmI+6LSdIn0Bh4U909FuN1ZYZEDSkKXQY0NWrKV0dT6qDlBdDpMgkApFnDnGZCJSCEcde64Gz1QMRM01ZI2Xf3qLCw5wZYVLkOj372vrjZWzgs9jP2JexD09zJ4abFRAm6crtnSqUjY1Yd+rB1FdeQ9rCy+rf4W9l5H/jnkrN13llRN+rRKE30NnanNEutgx1sMHeSIJebyC3Uk5WuZzs8gaqGtXUKbRodHpMpCJMpWJcrU2R2ZkR5GyJzM4UrQGO3stmbcJaispPYhCZ4eg8jqUdptLT7mGd9MrCfPauWMbuVl1JDGyLSK/DUSzix8BaqtPm4FwehXV8fyz7yLjiZszcbfG8EOnJR6PCKS0tZcOGDXh6ejJx4sQ/9MMDLDuSwqYrORweY0PYoWFNkTRDm2KLDTo9FZuTUWXX4jA9DJPqPXD0TRjxHbRtrpGRk/MN2TmrCApahof7hOZj+v4H1Ozfj//xY1w3ZDP3zNymupvtXn3s+1OUXs2BlbfoPNKX9oO9H/v8p/xvEn8ij2v7sxgwI5TAjs2lrlvCYDCQ98JENAUF+J04jsisuVvv+wtZfHIslS0zOtEj4MHkRXvje2bf+oIEMwt+GryJmsz5iERSIhV9EF1YAZMPNuWR0CTvUbExibiuDrxspSJEpyL04nlO6sJpI7Nl+8zOD4VVNt4pp2p7KpWRTkxx0qAxwIG2/gSYm3Cp8BIf3fiIooYiOphpGe/Uk6xoFfLqSqwiIpny+sL/6Iz+b2nkf6Nao2X7vSq2FVeSrVAhEaCdlTldbCzoYGVGgLkJHsZGSP5Jr9xgMFCh0ZLdqOJ2fSMXS7O5lR+NqP4CYMDbZTjvdXyZSLvmD2p2fCyHvvmCw71GkuwVgkivx10q4seABkpTZ2Kn6IfDlXEY+9lQPcSTkWuvEuRiSfSszug1ajZs2IBarf5DXZrfyCpvYNBXFxnTzo1PSmeDqr4pJt7YEoPBQPXeDBrjSrEdE4h5gA6+7QTu7Zoe7H+aRVVVXeHW7Sk4Ow+nVejKZrMsdX4+WUOHYTt2LGZvvcZzB5/DXGrO7uG77+ttPyoGvYE9n8XRWKfmhSWdkRg9uY72U/630OsN7Pv8JjVljYx/PxLzPyhI8880xseTN+EFHOfPx2HO7GbtSo2OAV9dwNxIwtFXezwwxjotFd93J8pUjpGVO+u6vUFmykt4u8/A7/jOppDpOZfvuzZrj+dQf76QU8/JeFteQ5vqEjxTCzlV58pr/QJ4fcDDbsXaE7nUnyugargXL1CHAOyK8CPY3BSFVsGGOxvYnLQRAR0D7QNwP++AOv8exj6BTHtvKebm/35JUvibG/nfMBgMJDUoOFxWw+WaBhLqG9H9+tUlAlhLJFiImwTLGnV6arU6FDodUlUqJg3nMGmMQRAEImXDeKvdLPysmysr6vU6Yg/u5dyeHRwfPo1UZ08Egx5fIwk/BjZSmDIDK11bXC7PRWxphMm0Vjy38QZ1Si1H5nXHydKI6OhoMjMzmTp1Kp6e/1q98bfIgnNdE3G4tgzG74SgwQDUncun7kQeln1lWA/wgugXIOsszL3aJDf8O1SqUm7EDEcqtaVjh31IJM03f4oWLaL+5Cn8Tp7gg7RV/JLzC9uGbqOVw+Mnb2TElnJyYzL9poQQ3OXJogme8r9LdYmcnR/F4hlqx5A54Y/stimY+zKNMTFN4mUthCQevXOPl7c3ac6P6/S73072BW7vfJ5pbm509ejOXFdLSkv30dnmdcwOvgUDlkG3ptWoQaen/Ps7aEob2RIlY1VpJW3zM7CoMCWuQmDbjEi6+j+ob2DQG6j8OQVlejXVk4OYWFmKWm9gexs/2v7qui2oK2DF5blcKM/BWCSlTZ0v7nEN2Ji4M3nJJ9g7PfpG9B/xt6vx2hKCIBBuacY7fm780j6Q9O7hHGrrz1fBMubKnBjmaE17a3PCLU2JNKslUn+awMrF2JR9jIMmiUmhEzg5+hgb+ixv0cA3VFWy96P3OL1vF4fHziXVSQYGA6EmUrYEySm6OxNzUSBuN+chiARsJ4Xy5sEk8qsa+e6FdrhYm3Dp0iXS09MZNGjQnxr4M3dLOZdWzqudrXGI+QzCRt838I23y6g7kYdZhCNWA7yawinTjkKft5sZeL1eS1LyfHS6RsLDv23RwKsyM6k7fATbFyZwUZHI4ezDzGw984kMvE6j5/rBLOzdLQiMfLTl+lP+Wti6mNN5hC85CRWkx5T++Qm/4vT6fPSNjVSsXdti+9BwF9r/qjnfoHoQHo1vLyJ8B7Gouo6LhRe5qHLDSOpAouYIhoABcOEzqLsHNFWHshsXDAJMPVfJJFc7bnkGILdtwMPKiNd23qbsd7VhBZGA3bggJPYm2O/OZK+vJ5YSMWNuZ3K1uinZUmYl4+shB/my9QBCTJTEWWawv3cxJ32T+PCzycRcPP0Eo/jo/CWMfFZ1Ju+ffIszeWeoVFQ+0jnmEjGdbCwY72rPbDdjBpmk4yvfTWnGfK7emU1y/mY8zW1Z1m0ZZ8eeYVHHRbiYt2yUMmKu8tOieSSVlLFz4ptkWTqAINDTypRNfiXkJU/HROqJV/K76Oo02E9uxRcxuZxJLeOD4aF08rEjIyODc+fOER4eTqdOnf7lZ1dqdCw5nIK/ozlTCz8AqRkM/hQAVW4tVbvTMfKxwvb5QARlDfyyEFxaQ+eXm/WVnb2SmpoYgoOXY2HeskBS+dffIDIzQzRxNEuvLyXELoRZ4U9WpjHxQiF1FUq6jvZ7WtLvb0zrvjJc/ay5tDMdeY3qkc4xDgjAZswYqrfvQJWT06xdEAQWDwuhokHFuvNZDzcOXM64ejnDJA6svfMDdXYTaGhIpTAsDHQaOLn4/qESOxNsRwegLajn7RwtzztZE+8TjLVbAw1KDS9ti0f9u6LlIhMJ9pNDMejAYns6+0O8cTM2YvydLPaVVv/62UT0j1jJu23G875rI897hFDvKuV862JmZ7zBuB+GcyHv/OMP5CPw35Ia/n/K4ZNbOSg/yv57RwFwMXfBx8oHmaUMO1M7bIxt7mdhqnVqalQ1VCmryK/PJ68ujxJ5CQBSkZQIpwjGBI2hn2e/PzTqv1FfWcHZzevIjL1OfZvO/NxpMGpBAEFgrKM1C+1ukZHyFpYWYfikf4gyrxa7cUHsL61hw6UcpnTxYlIXb6qqqti7dy/Ozs4MHz78T5evGy5mk1/VyNYu95Deug6j1oOFE5oKBZU/pyCxNcFhUiiCRAS/vA/yiqaaruKHb3dFxVny8r/HzS0KV5eRLV5LkZxM/cmT2L88l49Tv6FeXc8PA3947HBJAKVcQzMLlxAAACAASURBVNwvuchC7fAMfVrx6e+MSCTQd3IIO5fHcG5bKsPmtn4kt43jvFeoO3KEsi++RLameW2Etp62jIhwY8OlbMZHeuJu82tcuq03Qtd5vH/5S9JbdebjO9EsC+hHRlU0zh0nYnR9I7SfCj49ADALd0QVWYP8QhGfTmtFraWSU54+dDYu5+btapYeSWb5yAfJe1JHMxwmh1K+MRGj6Az2Tw5hRmoec1PyyGpUssDbBUEQCAx4D0EQY12wiZFtn+Ue/dh4fDXZFgXsOriWXq/2/k8M70P8JYz8c52moF1TTll9OhWOWiStnKlV1ZJSlUKdqu6h7FUAAQFrY2tkljI6OHcgwDaAtk5tCbUPvV8Q4F+hVjQSd2Q/cUcOoNfruRc1i23WHogNenQiMa97OjFG2Ed66lfY2nTFO+9dGhPLsRrkzW1LEYs3JtEz0JH3nglFrVaza9cuAKKiov5U276wupE15zMZGmhB98R3IXAItB6LTq6hcnMSCOAwrRUiM2mTIFP8z9DtNXCLeKgfhaKQ5JQFWFiEEhjQvN7rb5SvXo3I2prYXs6cvrWe19u/ToDtk0mixh/PQ6XQ0vW55jo5T/n7YeNsRudRflzelUHqtRJCuv75/ozEwQH72bMpX7kS+fUbmHeObHbMosHBHP+d5vx9ur+O2e1trKqqZ5yFnrXFlcy2siDRNpt21jKEXxbCnEvw6wTG5hlfVLl11O1KZ8OrbRlzK5Hrzo607ahg6/V8wt2tier4wK1q7GuNXVQQVdtTMd2fxc6oQBZlFPFlbimZjSpWBskwl4gJ8H8HqcSK7JxVONpW8NPcnexav5nQyObf5T/BX2bjVaVScSB6O7kXTiGR12FqZU3n56II7tkbpViLTt8kgCkVSbE0snysYha/oaiv486ZE9w8sh9FfR2unbuzJaQbt4wtkeq06MUSPglwoV3NJ5SWHcHFeSTuRXNpOFuMxf/H3nmHR1V1ffuemt57TyAhJAFC6B1EEGmCdFARFEGKoGBviF3RR0FEFEEQFKRDpLcQeklIICRAICEhddIzk8n08/0xtBAIMcDzPS/OfV1cJLP32adkzjr7rL3Wb3X1o7itB0N/OoqngxXrp3TCwUrK+vXrSUlJYcyYMTRpcu+EoMkrE4i7UMQev8X4lZ+EKccRbDwpWnwWXZ4Kj5daYBXkCNXlsLAjWDvCxAMguxnjazRqSUgcgVp9hXZtN2NrG3zHfVUdO072uHHYTJ/IGOe1NHZuzLInlzXo2lWWVPPn7OOEtvGk17h/HlNv4dFEMAls+u40xVeVjJ7dHnsX63tuY9JqyejbD7GTEyHr1iKS1P4+fr3jPAvjLrNpamdaBtwS3nx2Hax/kf1dpzA952/6+rWhjzieFrKBeOz9Dfp8AR1vZm7rC6tQLEhCHuSIw/MRDNh1iBRbZ4KKtJQml7JmUgdiAmsuAisP5lCxNRP7zr449g9h4dUiPs/IJ8TGip+jgmjmYF6Qzc9fT9r5d7GzbUx09K9YW/s28Cr+CxZeNZp8Ll16h6dHP0X/me9iCo9GZTCyf9kv/Dr5BRJXrkKXXYSbtSvO1s7/yEiZjEayziSxc9F8fpkynkOrluMZ0hjb8dOZE9GdJJkdMkHA3sqKlRFONM1/mULFVho3eoPA0tdQ7cvDtrUXmi4+PL/0JFKxiCXPt8XRWkZ8fDwpKSn07NmzXgb+YHoR21MKmBZagl/BHnjyKwR7b0r/uoDuqhLXkU3NBh5gxztmFc3BC2sYeEEQuHDxQ5TKFKIiv7mrgRdMJhRz5yL18WZuUCoGwcBnXT5rkIEHOLHF7ENt/9Q/KyRi4dFGdM1tYxJg/4rz1GfSKbaywmPWTLRpaVRs2nzHPpN7NMbdXs5nW1NrjtlsKIR057ETf/BS+Bi2554iRdSMFMMujCGdIe4LUN5cDJZ52eH8VGO0l8rRHcpnTZcYIotyyfKwQhLlzITfE8gpU9fYt30XP+w7+6I6nIdyTzbTgrxY1zKUKqOJ/onpLM0pQhAEfHyGEh29hGpNLidODqKs7HjDLuK9rtdDGfW/jFJ5FkXRdo6fGICvbyXT3nmfiCHPoA6OQGPrwJl9u1g9+00WTRrL9gXfcnpHLHkX01CVlmAy3pS4v14NKicthcRtm4n97ksWTXqOdZ+9z4Uj8YR36kr0hOn81rQjc6w80UmkiMRiGtvbsDIkD/HFp1GrM2nR/CfcsgdQsTUTmyg3JH2DGffbScrVOpaNb0egmy2pqak3Flq7du16z3PUGUx8tOUcwc4yJmS/BWF9IHoUFdsyqU4pwalfCLbNr4V2XdgOyX+atTn8WtcYJzdvFfn56wgOnoqHR23dmutUbtuO5tw5Mkd2Ir74GDNbz/zHBbmvU5St5MKJAqIf98fB9d4zNQv/Lpw8bOj0dGOyU0tJO5xfr20c+/XDJjoaxfffYbqtwA+Ag7WMmb3DOXmljB0pBTcbRCLo/y3o1UzNzaSDTwd+z7nKVb2UtBAZgkEDuz+sMZZtGy9soj2o3H0Fu3IRC5o3olluBuU+NhSH2fP8spM1sudFIhFO/Rth19Yb5b6rVO7NppOLPXvahtPF2YF303MZmnSZDLUWN9cutG2zEZnMmYqK0w27gPfgkXHXKJVppJybgVqdQWDACzRq9CqlpVUcOHCAlOQkZGolrhgxlpegVSlvbigSIZXLEYxGjEYj3HI9HNw98AuPxC2sKeUiKavySjno2xi13ApviYgCE/Rzs2WK+DcqFGtwdIymWdT36I+LqNxxBZvm7tgODeX5Zac4fbWM38a1o0uYO/n5+SxduhRPT0/GjRtXrxTnX+Iv8/m28/zmG8tjVdtg6jFUZwXKYzOw7+SL08BG5oUrdSks7AB2HvDS/htJHgAVFYkkJI7B1bUT0S0WIxLdeVZu0unI6NsPg50Vz49Q0MKzJT/3/hmxqGFzgi3zTqPIVvLcJx3/cWFnC/8OBJPA5nmnUWQpGfVBOxzdagt53Y769GmyRo/BfcpkPKbXzro2GE30n3+Iar2R3TO71VCTZO/HcPBbSsesZuSZ/yAYq5numk+PqjY4Ju0wS3QHdbzR3aQxUDj/NJgEvKbHsHXfTpYUlHO8cRSiCj2dSk38ObYdcunNe0QwCZStu4g6UYFjn2AcHwtAEAT+zC9lzuVcdCaBKYGeTA3wxAoNEont/25lqAdFQ428xmgitaqaaDsR6emfk5u3Cmtrf8KbfISbWw+Ki4tJTEwkKSmJarUaKQLeDnbYy2XIRQJSkQipTI5UJkNmZ4/E1g69VEaJsorsq1c5Y2VPYlA4xfbOBEtFqEQSKg1GZnkqaFn8LkZjJUGBEwkOfgXVvnyUe7OxifbAYWgYU1efZk9aIfNHxTAw2helUsnixeYiCC+99NJdlSVvpbBSQ89v4ujoquLX8hdh0EKq5U9ekw12w+3ZCETXwxHXT4BzG80G3qfFjTG02iJOnHwKidiatm03IZPdXbu65LdlKL76it8nhnDQt5L1A9fjZdewhI3s1BJi5yfTZXgY0Y8HNGgMC/8OKourWf3JCbxCHHlqRst6GbzcmTNR7ttP421bkfnW9mlf15x//YkmTOt5S8CATg0/tge5LWeHLeL5XS/S1M6aiU5KuifrEdm4mdeybolI011VovgpGZsIVxxGhrJ06VJOiazY2SQGfbWBPhopy56OrnHcgkmgbM0F1ElFOPTwx7FPMCKRiEKtng8v5bJZUY6HXMrMYG/G+LhiVYeESV088j75LUXl9EtIZ8DpHM45z6RZy9WIxVYkn5nA6dPPIpdfpU+fPsycOZNnnn2W1h06orWx52K5ijMlShKLKzmRX8yR7HwOpKWzLyGJ3QlJxGphRYsu7Ipqj62bO0+4OZJtELAVafnKagHNCl7GzjaIdm230Cj4NSo2XkG5NxvbVp44Dg3jtbXJ7E4t5KOBUQyM9kWn07Fq1Sqqq6sZPXp0vQw8wBfb0tAbTXyg/BhCe6NzG0jp6vPI/B1wHRV+08CnboGza6HbmzUMvMmk42zKNAwGJc1b/FSngTdWVFC8aBFFLfz52+0qczrOabCBN5kEjmy4jKO7Nc26+d17Awv/ahzdbeg0NJSc82WcO5hXr208Zs4CQaDw69q1YAG6hnnQt5k3C/Zf4mrpLb5zuS30+xqKztP8Ujxvt3ubs0olsZUCV5oGQWEKnFpSYyx5gANOTwZTfa4EXWIxI0eOJLSskHFXzuJgLWWnk8CoXecwmW7G0IvEIlxGhGPX3htlXA5l69MRjAJeVjJ+jgpmW6swGttY8c7FHD5Iz/3nF60ePBIhlP3cnVCG+bE0p5ipadk4SKx4wm0R7ZzOUF00n7KE4Tg7t8PPbwyNG/chLMz8RDcYDJSWlqJSqdDpdBToDJwWJJzQweEqHTpBoIW9Dc+7O7GhsIRdJZX0lp5khHYebjaeNIr8Di+vAQg6E8XLU9FeLMOhZwC2PQN4bU0yW8/m837/CJ7vFIzRaGTt2rXk5+czcuRIfHzql85/5FIxm5LymO52kiBDGYauX1G8PBWxgxz35yMRX9d9qcyH2BngEw1dZ9YYI/3SF1RUnCIq6nsc7JveYS83KV74E8bKSr5uW8XQsKE8HvT4P/+DXOPiiQJKclQ88WIUEtkjMZ+w8JCJ6urL5UQFh9dfIjDSFUf3ut02cn8/3Ca+RPEPC6g6Mhy7Tp1q9flgQCRxF4r45O9Ufhl7y2Q3vC+E94O4Lxk+9QSpYUNZn74eL9cMZvhFYbXvM3Nxe/ubMsP2XfzQXi6n/O8MPINiGDJkCH/++Scfujgx3zaQA3IDj8WdY0OXCNzkZvMqEotwHhyK2F6Ocm82pio9rqOaIraS0MrJjo0xoRwsU+Fr/XBcmY+Eu+Y6JkHgYJmKTYoydhRVUGYwL6oGyDR4G9NxNWbjIq7GyS4IB7vG6KQ+KAU7rmj0nFNVo9CZU6F9rGT0dXegu52SzYWFbKpwxoVSJggL6WxvwN//Oby9ByMWy9AXqSlZmYahSI3L4DDkrT2ZtSaZLcl5N+pECoLAli1bOH36NAMGDKBNmzu+VdVCozfSb95BjOoydhpfQt7vOxQHIzCp9XhMjkbmcU2Nz2SClU9D9nFznK/7zdfS/PyNpKa9TmDAi4SF1S7zdyvay5fJGDSIIy2s2DTcl78G/IWtrGGFPAx6I398eAxbRznD3mpz823DgoV7oCzVsOrj43gGOjDo1Zh7fndMWi0ZAwYikslotGkjojvkmvwUd5mvdpznt3FteazpTaNNWZbZbRPWG/2wpby0+yWSFYm85SwwMikfUYsR5gi1WzCqdBTOO43YWoLnKzHEHTpAfHw8T/brzxyFjNM2ArZiMd9GBjLY07mG+0Z1JI/y2MvIvGxxey4SaT3WHurDI++TvxN6k8DpyipOVqpJqKgis1pLVnU1alPNL4wdKjxFFYRIigmVltBCdB53YyZbdc1YLwxDixV9pYeY4qmisU9/HB1v+gqrU4opXXsRkUSE6+imEOzI1D8S2XtewZtPhjOlRygA+/fv58CBA3Tr1o2ePXvW+xy+232ReXvTWWH1NV0iAigqex1drgqPCc2xCr7F5XJkAex6DwZ8D23G3/i4UplCQsIIHB1bEtPyd8Tiu7+4CYLA1QkvUZp4nBmTJCwavooIt4h6H+vtJO7K4uiGywx6LQb/8NpiUhYs1EXq4Tz2rzhPlxFhRPe891qOcv9+ciZPwfON13F78cVa7TqDiSfnxWM0Cex8tRvWslsWYeO/gX2fwJg1lAW2Y/TfI1Bq8lmEFc0vXYIXd9+oz3AdzaVyipecxTbGE6ehoaxatYqMjAxGj3mGjxMr2CMzIDjL6eXmyJxQXxrb3owq01wso2TVeQBcR4Zj09S1gVfpJv9KI38nBEFAaxLQm4yUqc6D+gIa9Xm0mgIMRhVVBh17DO1Zq21DicmOTnbVzG7kTgu30BpPY5PGQHlsBuqEQmQBDrg905QqKwkTlp/kVFYZHw9qxnMdggA4ceIE27Zto2XLlgwaNKjeq+eXi1T0/T6evlZn+d52CaVuy6i+aMR1VFNso28p9pF/Bn59HMKegJErb0gIa7UKTp56GhDRru0m5HL3O+/oGsq9e8mZOo3feomJfPkNxjUb94+u7a1oVHpWfHAUn1AnBkyNbvA4Fv69CILA1oVnyEkrY/i7bXDzvbck79WXJ1N14gSNt29D5lV7HenwpWKe+fU4r/VqwoxetyzCGnTwc7drUt3HuKQuZMzWkbiJqlmTW4WDYwC8FFdLFqRidxbKvdk4D2qMNMaVJUuWoFQqeX7ceD7Zl8uWikpE4c4IYnjGx42Zwd54WZldMoaSakpWpKIvUGPX0QfnfiGIZA2X3H7kF16NlTrKNqZjVOrq7CcSibCWiHGQyQh0aU6g3zCahL2PU+i37HL8gpc177KoujtNHD1ZE92Y9W07EO0edsMwC4JAdWoJhd8lok4sxKFHAJ6TWlCIwMifj5J0tZwfRsfcMPAJCQls27aN8PDwemnSXEcQBN7fmIK1SM/7xgWo3N6h+oIR5wGNahp4ndocTWPjCgPn3zDwRqOWM2cno9dXEN1i8T0NvEmrJeezT8hxF1HRvwNjo8bW2f9enNpxBb3GQMfBFvkCCw1DJBLR87kI5DYSdi9JxXhLlba74fXeu2AwoPjq6zu2dw51Z0ALHxbGXSK75JZFWKkcnpoPlbmwZw6hLqF80/07cg0SXvWxxVRwFo7+UGs8x8cDsW7qSnlsBqICLWPGjEEsFrPmr9V8ObAJz3q6Io7Lp4lWxMq8EtoeTeW189mkqaqRutngOTUG+y5+VB3Np3D+abRXKhp8verikTDy2isVVJ0qpGDuKSrjriLU4wuRq9Hxa04RQ09fov2xNOZlFdLS0ZZNMaFsjAmjm6tDzQLWBVUUL02h5PdURFZiPKe0xOnJYE5eLeepHw6RW1bN0nFtGdDCHMaVnJxMbGwsoaGhDB8+HMkdUq/vxobEXI5mlPAWy3H0fpKKC6E49AzAvvNtESq73jeX8nt6EdiZBb8EQeD8+XeorEwiKupbHBzu7XIp+PVnyCtkfX9nPuvxVYPj4cEcBnc2LoemnXxw83swBREs/DuxdZTT87kISnJVHNt8+Z795QEBuE2YQOW2bVQdu3P26Pv9I5GIRcyJPVczEzagHbSfBCd/hexjdAvoxoyWL3PCJOVbfx+E/V9AcXqNsURiEa4jw5G6WFHyRxqOEltGjx5NZWUla9f8xZwB4bzcIZiM/Tl0zzcwwsuFTYVlPHbyAk+cusAvBcVUPO6H+4vNEPQmtBkPx8g/Eu6a81XVrMpQ4H2xEu/LStxkEjxaeuHcyguNTIzSaCRHoyOzWkeqqpqTFVVka8yz/jBbK57ydGa0jxv+1rUXbLRZlSgP5KBJLUFkI8WxVyD2HXwQScSsOpHNh5tTCHCx5ZexbQj1NBu1lJQU1q9fT3BwMGPGjPlH9RzLqnQ8/u1+gnWXWOOwmMKyudi2C8b56ZouI1I2wLrx0OkVeOLTGx9fubKIyxlzadRoJiHBtaWFb0eXm8v5vn1IDDER9fNy2nq3vec2dbFryTkyk4p45uOO2Ls8+ILFFv59HFh1gZQDuTw1oyUBEXX7r00ajXkRViolZPMmxHcosbc4PoPPtqXx0zOt6Nv8lig3rcqcSCizhZcPIkjkvLVvAttzTvBRWSVDHcJh/Ha4TdpDX1CFYmESMh97PF5qTuqFNNatW0ejRo0YNWoUK47n8OnWVJp4OTB3TAxHtNWsLyzjjLIagABrOR0d7HjK04lenjXLiNaXR94nH6soZ1paFlrTvc/FSy6ljZMdbR3t6OXuSKhtzTR7QRAwlGioTimmOkmBvkCNyEaKfUcf7Dv7IbGTUanR8+GmFDYl5dGtiQc/jI7BycZsyJOTk9m0aRMBAQE8++yz91SVvJ031yWzISGbWPl7uOqmI43shOszETUjDIovwS89wDMCxm+7oZpXVLSbM2cn4+U1gKjI7+7pHhIEgRNjByNPusjZ715kbK/X/9Gx3k5RtpI1n5+kdd8gOgyyuGosPBj0OiNrPz+JrtrAqA/aY21f96RJdfgwV1+cgNvLk/B89dVa7QajiUE/Hkah1LJnZvcb9y5gVm79Y6g516Tne+iNesZt6c25ymIWFhTRqfsc6PByrTGv13u16+CDy+BQEhMT2bJlCxEREQwbNowjGaVM/SMRiVjEvFExdGviwWW1hrhSJYfKVByvUPGSvwevBTeskM4jb+QBjIJAvlbPlWotpXojlcVqlJnlSLNV2FQb8dIKBDvb4u5jj8TZComT1TXDKWCqNmIs12IoVqPNUmK65tuXBzpgG+OJbSsvxFbmp/eJzFJmrU0ir1zDjMfDmPpY6I16ksePH2f79u2EhIQwatSof1yo93p23suSLUwWydAFTsR9fDNEt8aY66vh115QmWcOl3TyB0CpOk9CwnDsbENp1WoVEsm9NWJS1y1B9P43xD/diJc+j70vN40gCGz+PomSXBXPfdIRuc0jkYJh4X+Eomwl6746RUgLd/pMbHbPCUze2+9Q8fffhKxfh3V4eK32szkVDPrxECPbBvLFkOY1GzdMhJT1MCkevKIory5g9KY+lGiNLFeUEfHSYXAJrjVm+bZMVPE5uAwNw66tN0ePHmXnzp1ER0czaNAgskqrmbTiFBcLVYzvHMxbTza9EeUjCAJ6QUD+EDJeHxkjfzcEownt5Qq0mRVor1Sgz1cjaAy1O4pFSFyssApwQB7shHUTF6S3iGmVq3V8uf08q09eJcDVhu9HtqR1kPnVURAEDhw4QFxcHE2bNmXo0KH/yEUDoNIa6PPtXqyUV9ko/RON+1d4TIpBbH2bsdw8DU6vgGfWQZhZYEyrLeTUqWEIgpG2bTdiZXXvDNWyohwu9HuSSnsxbWL34mrvcc9t6iLzTDHbFp6h68gwWjxmkS+w8OBJ3JnF0Y2X6Tm2KRGd6pblNZSVkdF/ADI/P4JXr7qjHPFnW1NZfDCTvyZ2oH2jW4rYVJXAj+3A0Rcm7AWpnPSCPby4ewZSvcBKSTC+Y7feCHS4jmAUKF6WgvZyBe4vNsO6sTMHDhxg//79REZGMmTIEAyCiC+3n2fZkSuEetrz6eBmdGh0/wV06jLyCILQ4H/AXOA8cAbYCDjf0vYOcAm4APSpz3itW7cWGorRaKp/X41e0CmqBF2BStAVVgn6co1gusv21TqD8OvBDCHm411Co3e2Cp9vTRWqtPob7Xq9XtiwYYMwe/ZsYcOGDYLBYGjQ8b+//rQQ/FascPz9HkLhN7sEg0pXu9PpPwRhtqMg7Jlzy/5VwvHjA4X9cc2Eisqz9dqX3qgXVr/4mJDStKmQFL++Qcd7Kwa9UVj54VHhj9lHBYPBeN/jWbBwJ4xGk7Dx2wRh0fQ4oTRfdc/+5X//LaSGNxVKli+/Y3uVVi90/nKv8Ng3+4Vq3W33beoW872295MbH8WlfCK0WxYpDP6liVBxdMGdj7FaL+R/e0rI+eiIoFNUCYIgCIcPHxZmz54trFy5UtDpzPd13AWF0PnLvULQW38LM1YlCrll6vpcgrsCnBLuYlfvN7pmN9BMEIQWwMVrhh2RSBQJjAKigCeBhaK7SR4+ABKyynjs2ziWH7mCWneHWfptiK2kyDxskXnZIfO0RXrDdXMTpUbPb4czefzbA3zydyqRPo7ETuvCO/0isL2WrqxSqVi+fDnJycn06NGDwYMH/6MomuscyyhhxYlcXpBsJ9RuBG4TuyOxu+1NoOAsbJ0FQV2ghzlz1VyE+xVUVedpFjUfR4dm9drf73++Q4tD+ZQ91YnorkP+8fHeTsqBXMoL1XQaGopE8kgEbFn4H0QsFtFrfBRSmZidi1Mw6Ix19nfs1w/77t1RfD8P3dWrtdpt5VI+f7o5GUVVLNx/qWZjxECIHgMHv4WrJwHoFvku00OacEUmY0byfDSFKbWP0VqK+7goRBIRxcvOYazS06lTJwYMGEB6ejorVqygqqqK7k082DOzO9N7hrLtbAE9vonj14MZDb84dXBfd6QgCLsEQbhuVY8B/td+HgSsFgRBKwhCJuYZfd3Vqe8TNzs5s7eco8Pne5m9OYUTmaWY6rEQeysGo4lD6cW8s+EsHT7fy5zYVLydrPljQntWTmhPpK/jjb45OTksXryY/Px8hg8fTo8ePRokE1qtM/LmysMEiQp42dqE0+QXkDjctlhbVQKrxoC1EwxbAhLpteIfsykpOUB4kzm4uz9Wr/1tPbeeRgv+RuVhT6cP5//j470djUrPya2ZBES6EtTMUrfVwsPF3sWKXuMjKcmt4uBfF+vsKxKJ8P5oNiKJhLy330Ew1n4odGviwdMxfvx04DLnCyprNvb9Ehz9YOMk0KkRicQM67CYF9ykJFjJmLV1LHq9utaYUldr3MZGYqzQUrIiFUFvok2bNgwbNozc3FwWL15MYWEh1jIJM58IZ9/r3Rnc0hd/l4ZJiNyLB+aTF4lEscBfgiCsFIlEC4BjgiCsvNa2BNguCMK6usa4X598QlYZSw9nsie1EK3BhLu9nDZBrrQKcqaRuz2+zja42skRicw6N8VKHQWVGi4UVJJ0tZxTWWWUq/XYyCT0bebN852CiQ6oGdJkMpk4cuQI+/btw8HBgZEjR+J7B4nT+vLRsjiWna9ipXw5HaYvQep+mzKlUQ8rnoarJ+CF7TeKgFwPlQwKmkxo4/pFxZwrOcf+V0bR87QB/9+X4dj2/mtKxv91kZS4HEZ+0K5eWYkWLDwIjm26TMKOLHqNjyS8fd0RKRVbtpD35lt4vj4LtwkTarWXVuno/Z8DeDtZs2lqZ2S3vo1mxsPygdBuIvQzK12Wlh5m+faxLDXY0tc2kC+GbrljxbTrETfWkW64PROBSCIiJyeH1atXo9PpGDhwIM2bsfwAUwAAIABJREFUN6+1XUOoyyd/zxAIkUi0B7jTVXxPEITN1/q8BxiAPxpwcBOBiQCBgQ2rPHSd1kEutA5yQaU1sDetkP3nFSRml7PjXME9t23sYUfvCC8ej/CiexMPbOS1/2jFxcXExsaSlZVFZGQkAwcOxMam4QJDhw5eZPl5Jc9KDtLhhc9rG3iAne/ClYPw9M83DHxBwRYuZ8zFy2sgjRvNrL3NHSiuLubnn19mcqIB27GjH4iBL82vIuVALlFd/SwG3sJ/lXYDQ8i7VE7cnxfwDHLAxdvurn0dBw5EuXcfinnzsevSBeumNZVYXe3kfD6kOZNWJLBg3yVe631LKc6QbtBhChxbaFatbNwTV9fOPNVhCqJ9P7CEbOz2zuDDXj/UepO3beGBSaWnfMtlytZfxGVYE/z9/Zk4cSJr165l/fr1pKen069fP6ytH17FtPueyYtEonHAJOBxQRDU1z57B0AQhC+u/b4T+EgQhKN1jdXQmbwgCFRVVWFvf2dDU6LSkl2qJr9CQ7laj4CACBHu9nK8nawJcrOrGSt7GzqdjqNHjxIfH49UKqVPnz7ExMQ0uIoLQHFaEQOX78VKVMWW4YE4trpDKb6E5RA7HTpOgz6fmbcr3s+Zsy/j5NSamJa/IRbfO0xTrVczdePzTJh7Dhd3f5puir1jksg/5e8FyeRfruDZjztgc7uLyYKFh4yqTMuaz09g4yBn2NttkN1hYnYdQ1kZGQOfQurqSvC6tYjvkL8y868kNifnsWlKZ5r73yIAqK+Gn7uDpgImHwY7d0wmA2dPPceek0dZZu/A+KZjeK3d23e0CZV7s6ncnYV9Z1+cBpgruBmNRuLj44mPj8fBwYF+/frRtGndMuB18dBCKEUi0ZPAf4DugiAU3fJ5FPAnZj+8L7AXCBMEoc6VkoYa+QsXLrB27VratWtH586dsbO7+1P9n2A0GklKSiIuLg6lUklkZCR9+/atd7GPu6G5UMqs5evZYfJibbtcWg2ZXLtT1lHza2JIVxizFiRSystPcTrpeexsG9Oq1R9Ipfc+DqPJyKv7Z9B23j7aZIgJ+esvbKKi7uv4AbLPlRD7QzKdhoYS0/v+3sAsWGgo2anm72HTjj70fK5pnRMvZVwcOS9PxvWFF/B6841a7RVqPU98fwBHaxmxr3SpqVRZcBYWPw7BXczhy2IxOl0pabseJzazjL8cHZjaciovR9dOlBIEgYq/M1AdzsOxVyCOvYJutF29epXY2FgUCgWdO3emd++7112ui4cpULYAcAB2i0SiJJFItAhAEIRzwBogFdgBTL2Xgb8fPDw8iIyM5MiRI8ybN4+dO3dSXFzc4PFUKhXx8fF8//33xMbG4uTkxLhx4xgxYsR9G/iqkwWsX7aRrSZfpnml0erp2l8Kii/B6tHgHAjDloJEilKZRvKZCVhb+9Cy5dJ6GXhBEPj65NfYbNxP24sC3m+88UAMvNFo4tC6Szh62NCih/+9N7Bg4SERGOlGm77BnD+Sz7n4uisrOfTogfOokZQuXYrqwIFa7U62Mr4a2oJ0hYrvdt+2qOvdHJ78Ai7vhSPzAJDLXQnp9jsjnIw8pVTxY9KP/JT8U61xrxf2tm3tReWebCp2XbmhmxMQEMCkSZPo3bs3ERENl/aui0cqGUqhUHDgwAHS0tIwmUwEBAQQFhZG48aN8fb2vmt4o9FoRKFQkJ2dzfnz57lyxfxHaNSoER06dCAsLOy+XDNgNrjKvdlc2XOEZ5HQSF7GurfHILW9zVirimBJL7OOxoTd4NoItfoKCYkjEYmktGm9Fmvr+i30/n7udzZs+ZrPVgo4du+B/48L7vs8AE7vzubI+kv0m9KCkBZ1K1xasPCwMZkEtv10hqvnShn0Wgy+YXfXfzFpNFwZNRpDQQEhGzcgu0OFtnc2nGX1yWz+eLE9nUJv+X4LglkvKnWLWU4ksAMAeXlrEa+fwg9iB7bY2zE5ejKToyfXutcEk0DZhnTUpwqx7+6P05PBD+R+hH9hxqtKpSIpKYmUlBQKCsyLrmKxGBcXF5ycnJDJZIjFYjQaDSqVirKyMgwGcySou7s7ERERNG/eHE9Pz7p2U28Eg4myTZeoOnWR1yWXOWsMYNuk5gSHhNXsqFPD8gFQmArj/gb/NlRX55J4ejRGYzWtW63Gzq5+mjCbL23m873vMX+FHFeZM402bkDi3DDxo1upKtfyx+xj+DZxtmjFW/ifQVttYN2Xp9Cq9Qx/py0OrndfyNRmZnJl6DCswsMJ+n05otuy09U6AwN/OIRSY2D7jK642d+yfqWpNGvPG3Xw8iGwNWe9p599G5+tP/OlsxebbWRMbDGRaS2n3dHQl2+5TNWxfOw7XfPRP4Cqaf86I38rKpWKzMxMFAoFxcXFKJVKDAYDBoMBGxsb7OzscHFxwdfXFz8/P1xd779Ky60YlTpK/khDd6WYVbb7+FHdibmPOzK8d9fbOhpgzVi4sA1G/QFN+6PR5JGQOAaDoYKYlr/j6Fi/cKvdWbt5Y/8sPt9sR0i6iqAVv2MbE/NAzmfXknNknC5i9Ox2OHk8nLheCxYaQllBFWu/PIWzpy1DXm+FtI6F2Mpt28idOeuu/vnUvEoGLzxMl1B3ljzfpqaxzkuCJb0hqLPZPy+RYjIZuBg/lNADcXwcGMZGsZYXm73IjFYzaht6QaBiayaqQ7nYRLnhMjL8Zq3mBnJfIZT/17G3t39gsaj/FG12JSUr0xCq9aT77mBhXjeGNzbWNvAmE2x5BS5shb5zbzPw5f/IwB/OPcyb8W8y46gLIWkKvOfMeWAGPvdCGeknC2nTP9hi4C38z+HibUfvF6LYtvAM+/84T69xkXd1hzj264f61ClKly7FOqIpTgMH1miP9HXkvX4RzN5yjqWHr/Bil5Cbjb4tof9/YMs02DMb+nyGWCwltMtKskq689HZdMThbViSsoQKXQXvt3+/Rhy9SCTCeUAjJM5WVGzNwLD4LO5jI2snQT4gHokcdKNKR/mWy5i095Y0+G8gmASUB3Mo+vkMIokIonYwK68VTex1fPx8/9s6C7D9TUj+Ex57D9pPRKPJJzHxGfT6smsGvkW99ptQmMCr+19lWLobHQ4ocHnmGVxGjngg52Q0moj/6yIObta07hN07w0sWPj/QEgLd9oNDOHi8UIStmfV2dfrnXewbduW/Pfep/rMmVrtYzsG0SvCiy+3p5GSe1tBj1bPmROkji6A5L8AkEod8O23hdxAF2ZfOMWLrtGsu7iOWQdmoTVqa43v0MUPt2cjMBRUofgxCV2uquEnXgePhJHXXq5AdTQPxQ8P70LVF0O5luIlZ6nYmol1uCtureJ4LdkZjdiOH196onaS1d6P4eRic/GPbm+g0eSReHoMOn0pMS2X19vAH8s/xuQ9k+lY5MyQjQpsO3bA6523H9h5nd2fQ2leFV2Gh9X5GmzBwv9v2vQLpkl7L45vyeDC8bsnQopkMvzmz0Pq6UnO1GnoCwtrtotEzB3WAnd7Kyb/kUC5+rbyon0+N2tJbXkFchMAsLHxx/Hp9ZS4WjEjMZY3g59ib/ZeJu2eRKXuNtkEwCbKHY9JLcAkUJ3S8IjAungkjLxttAceLzVH0BlRLExCGZ+DYPzvrjUIJgHViXwKv09Ed1WJy9Aw3Joe4au4LE4IEXw+LIZQr9siaQ7MhUP/gdbjoPcnVKkzOJUw/NoMfhlOTi3rte/4nHim7plKG6U7U/4oR+7vj/933yGSPhhvnKpMy4m/Mwlq5kZItCWaxsL/NiKRiJ7PRuDXxJl9K9LIvVh2175SFxf8F/6IqaqKnClTMaqqarS72MlZ+EwrCiu0vLLqNMZb9bAkMhixHOy94M+RUHYFACeX1oiG/UaVrZQx8T/xZdRLJBclM37HeAqqaj905P4OeM5ohWPvh/OG/EgYeQCrRs54zmiFdbgrFdsyUSz8783qdbkqihYlU77hEjIfW7ymt8JOspv1sZtZYuzHuI6BDG51i8a6IMC+z2D/p9BiJPT/D5XKFBISRyEIBlrFrMLJqX5+9L1Ze5mxfwZtDf7MWFGBxN6ewCW/PpBImuvEr76AYBToOvL+Q0ktWPhvIJGJeXJSc5zcbdi+6CxlBVV37WvdpAm+//kWzfnz5E5/BUFXc8YeE+jCx4OiOJhezDe7LtTc2M4dnl1n1phaOQzUpQC4+fZHPeQ/6KQCfXZ9woLWb5GrymX01tGcKartGpLYyR5IlM2deGSMPJgvlNtzEbiOaYqxQotiwWlK11zAUKZ5KPvTF6kp+TMNxQ+nMZRU4zK8CR4TWyDN/JPETd/zruElOjdy4f0BtyQgCQLs/gDiv4aYZ2HwT5RVnCLx9LNIJDa0brW6XsW3ATamb2TWgVm0k4Yyc6UKkdFE4JJfkd2HYNrtXD6tIDO5mLYDQiyLrRb+T2FtJ2PAtGjEEhGx85NRlt7dDjj06IHPp59SdeQoeW+/jWAy1Wgf1S6Q0e0C+SnuMtvP5tfc2CMcRq+C8mxYNcosgwB4Nn6eikGzMQl62v49k9+7fI2VxIrxO8azNWPrAz/fu/HIhlCa1Hoq466iOpIHAtjGeGLfyRf5fQppCYKANqMC1eE8NGkliGRi7Lv44dDVH7GNFI4tomD7lww0zsXGwY3N07rgYndt1dxkgh1vwYlfoM2L0O8b8hVbSEt7BxubQGJilmNtde8aj4IgsDB5IYuSF9HbuhWTl+RjKi4hcPkybB5gJJFWrefPOcexdZQz/O02iC1a8Rb+D1KUrWTTfxKxdbJiyOut6tRZKlmyBMXcb3AZMxqvDz6o8eaqNRgZ9csxLhQoWT+5ExE+jjU3PrcR1o6DJk/CiBUgNe8nL3E2nlvnYbSxR/XsVl5P+p5Thad4sdmLTIuZhlR8/27Vf3WcvKFci3J/NupEBYLehDzQAZtm7thEuSF1q5+CpGAU0OepqD5XjPpsMcYSDWJbKXbtfLDv7GsOfRIEiPuCyrj5jBB9Q47gzoYpnWly3Q9v0MHmqXB2DXSchtD7YzKuzOPKlR9xdm5Pi+YLkcnu7WLRG/V8dPQjtlzewjNOvRn6QzKmikoCFv/ywEIlrxP3x3lSD+Ux7O02eAY53nsDCxb+R8lLLyd2fhLO3rYMfi0GK9u7CxIWzp1L6ZKlZkP//vuIbqm7WlChYfCPhwHYOLUTPk632ZCTv5qL+zQdAMOXmf32QP6p9/HYvgCjlS28sIev0tex9uJa2ni14etuX+Nhe3/lN/8VRl5v1COT3P0PZ1LrqTpViPq0An2+2T8ndpQj93dA5mmL2EF2oxqTYBQwVekxlGkwKNTorioRdCYQg1VjZ2yjPbCN9kB0XcDIoIUt09Emr2Oc1X84qfJg2fh2dAm7tkipqYC/njVrUz/2PsbO00g9/yYKxTZ8fIbTNPxjxOJ7x8gWVhUy68AskouSedN5BB2+3YNJqyXw11+xaV6/qlD1JS+9nI3fJhLdK4Auw8LuvYEFC//jZJ0rYdvCM3gFOzJwektkVneOEhMEAcXcbyhduhTn4cPxnvNRDUOfll/J8EVH8XexYc3LHXG0vs3uHPsJdrwNUUNgyGKQmGfqhac/w3XrXASZNeLntrKtOpdPj32KjdSGL7t+SUffjg0+t0feyJ9WnObN+Df5sMOHdPXves/+hpJqNOdL0eWo0OUoMZRo4A5VpETWUqRu1sgDHbAKdsQq1KV2WT51KawZiynzEDNcFxKb78R3I6N5OuaacFf5VfhzBBRfhEE/Ut2kE2fPTkOpSiW08RsEBk6s12JmQmECs+JmoTaomWv9DN6fr0Ds4EDAzz9jHd7kntv/E/Q6I399egKTUWD0h+3vejNYsPB/jUsJCnb9moJPqDP9p7ZAbn1nV4kgCBR9P4+Sn3/GafBgfD75uIb8QfzFIl5YdpIOjdxYOq4tcultrszD881rb5GDYcgvIDVLIyjOfodT7MdITCJMw37lqnc0s+JmkVGRwaw2s3g+6vkGndcjn/FqLbHGXmbPlL1TGBI2hDfavIG9/O6+d6mbDfad/W78LpgETNUGTFV6EIFILEJsJ0N8ly/ADa6egLXjEVQKPgpcRmy6jLeebHrTwGfGm310Rj08s44iRyOpJ58CRES3+AV39573PDejyciyc8tYcHoBfva+/Fz+FKbPFyMLCyPg50XIvLzqcYX+GUc3XKZCUc2g12IsBt7CI0Voa09Mpkj2/JZG7PxkBrwSjZVN7ftcJBLh8eoMRFZyiuf/gEGhwG/e90iuqdB2a+LB50Oa8+a6M7z2VxLzRrVEeuuaVefpIBLBrvehuswsVWLlgGfz1yh18Ee+djK2f43H77HX+bPfH3xx8kvCnB/OG/MjMZMH0Bl1LExayG/nfsPT1pM5HefQya/TAz7Ca5iMcOQH2PcJgoMfH/vM57ekKl7qGsK7/SIQgbmSzK4PwC0U4/DfyKjcTPbVJTg4RNG82Y/Y2ATcay/kqfJ499C7JBQm0NfrMabsEFG9Yxf2PXvi+/VXSO5SJOV+uJpWypZ5SUT3DKDLCIubxsKjyeVEBbt+PYd7gD0Dp7fE+vY39FsoX7+e/NkfYRUSTMCiRcj8bk4Qfz2Ywadb03g6xo9vhkcjuT0MMmmVeS3Ou7lZ58be7HtXFp9Cv3owrsVKNI07YD18LVg3fN3rkXfXCJV56He/ibzPfzhTXcD7h98nsyKT3kG9eb3N6/jaP7iQQhTnYfMUyE1AaDqQz21msfhoHuM7B/PhgEhE1WXmak5psdB0AJW9ppN6eTZVVen4+z1HaOg7SCR1V2UyCSY2pG/g21PfIiAwx/k5Quf/jS47G48ZM3B7aUINH+GDQqvWs/qTE8isJIx4t60ls9XCI03mmWJ2/HIWVx87BkyLxs7p7vdl1dGj5EyfgUgux2/u19h1ujmB/HH/JebuvMCotgF8/nRzxLcb+os7Yc3zYOcBo1aCj1m9VaPJo3jTQHzPX8Jg74R02ErEwd0adC4Ps2jI/wRlZ+YhTYnFOC+KqMsHWdt/NdNjpnMw5yCDNg3ip+Sf0BjuM1ZeXQo734NFXaA0E9OQJXxi9w6Lj+bxfMcgs4HPiIOfOsGFHZh6fcjlNjGcOjsWg76SltG/ER7+0T0N/IXSC4zdPpY5R+cQ5dCEP3IGEPjGT+YF1qVLcZ808aEYeICDa9KpqtDx+LhIi4G38MgT0sKd/pNbUF6oZv3XCXUmTNl17Ejw6lVIXJzJfnECinnzEK7Jk099LJRXeoay+uRV3lh3Br2xZow9TfrA+K0gGGFJHzizFgBra198Rxwlp/dojHolJQmfP5TzfCRm8kZjNdmn38Pp0O+4lusxugQgeexD8oM78k3i9+zK2oWHjQcTmk9gWJNhyCX/QO2tqgQSlsKRBeYomZhn0Hb/kNe35xGbnMcLnUP4oJcvon2fwMlfEdybUN5zAqnlf6DR5OLtPZgmYR8ikznVuZsidRE/n/mZdRfX4Sh3ZLbQn6Df9qLPvorToEF4vf/eDX/gw+BSgoKdi1No2z+YdgMbPbT9WLDwv4Yiq5K/FyRjMgn0n9wCn9A6io6o1RR89hkV6zdg07o1Pp9+glVICIIgMH/vJb7bc5HHm3ry4zOtapYPBFApzDP67CPmRMgnvwQr8z1dlLMJB9dWWNs2rJTmI++uuU5Z6XEUcZPwu5SNvdqIyTkAccxYTvlF8kP6WhIViXjbeTM+ajyDQwdjK7tLBqfRAFmHIWUdnFkDBg2EPQGPz0bpHM7LKxM4fKmEt58MZ5JbEqKd70JVEdqWg0n1raZUeRI7uyaEN/kIF5f2dR5zhbaC5eeWszJtJXqjnhese9J/VznaQ0eQN2qE13vvYt+5c4OvSX2oKKpmzWcncPa2Y8gbrZBYkp4s/MuoKKrm7wXJKEs0PD4ugrA2dQc0VMTGUvDxJwhaLe5TJuP2wguI5HJWHMviw80ptAly4dexbXG6PR7fqIe4L82aVc5BMOhHCL7/+/vfYeQFAUQijEYN2Vk/o0qYh3+uEpdysw6F4BnBUZ+mLDQUkKzOxUFmx7CQ/gwPGUAAMlAVmCsy5SbAlUOgLgaZLTQbCh2ngmcEGUUqJq1IIKO4iiU99PTIWQTZRzB4NeFShC+5phRkMjeCgyfj7/csYvHdF3OuVFxhZdpKtlzeQrWhmmfFHXn6oAFj/FHE9va4T5mC67PPILpDVfkHidFgYsPcBMoV1Yx8ry2O7vVLELNg4VFDo9Kz7acz5F+uIOaJQDoMalRnlrdeoaDwiy9Qbt+BPLQxnjNnYf9YD7aezee1v5LwdbZh8dg2NxMibyXrKGycBOVZED0aen9yY1G2ITz6Rr7gLGyeBt1eh/D+IBaj0eSReWUBZZlr8FRo8FY7YldagkivIdlKzgpHB/bY2WIUiYjRaBioqqJ3VTXOjgEQ0B4iBkBob5CbZ/t7Ugt57a/TtBKn853PblzzDmC0dSI7xIMM1zJkcjeCAl/C3/9ZJJI7vyEodUr2ZO1ha8ZWjhccx8YkZVJZNJ1OKCEpFbGjI65jx+I69jkkjv+dDNNDa9NJ3nuVJyc1o3HMgyl3aMHC/1WMehMH16ZzLj4X/6YuPDEhChv7uidayv37KfzyS/RZ2djExODx6qukeTbm5T9OU60z8M3waPo2r11LFp0aDn4Lh+eBzAae+MSsSNsAHn0jnxkPsTOgNAM8I80z78jBYGVPdXUu2VcXk5+/EZNeibPghadVFM4iP5QiO7YqLxNbmU6GpggxYlp4tKCbfzfa+bQjwjUCg1HMN7EJlCVs5GWb3YQbL2GQybjib8VVXytsnSLx93sWb++nahl3QRDIrMjkSN4RjuYf5VjeMfQGLd2LPRiU7YH/yWyE8gpkgYG4jByB84gRD9XvfjsZSUVsX3SW5j386TbqwSZUWbDwf5nUw3nEr7qIraOcJyZE4d2o7jU1Qa+nfMNGin/8EYNCgXWzZoiHjeI1hRuJeVWMahvABwMisbO6Q+5Ncbo53DrqaYge2aDjffSNPJj96Oc2mp+MRWkgtzcb+vAnoVEPjFIpCsU2ChVbKS09iiDoEIkk2Nk1wc42jFyjFYnlJZwszSK90qwyJ0GEn1ZKjLaCYIMOT5ERwU2K3scPP59e+Hg8gZVtKAaTgUp9JUXqIhRqBVmVWZwvPc/50vNU6ipxqxDoUehKpzx7/M+XIiqrQGRtjX2PHjgPG4Zdp44PLWLmbpTkqVj/VQIu3rY8/XorpLcvElmw8C+n8EolO39JQVWupU2/YNr0DbqnSJ9Jo6Fi40ZKV6xEl5GBxNWNi1EdWCgKQRMWwadDWtCp8V1qMlxzOTeEf4eRv44gwNXjkLgC0raAthLEMvCKBO8W4BmB0dYFpaCgqjqTKnUG+qocRFUlyLV67KuMVKshQxBzxkpOopUtmdYyKupz7QUBRzU0LpHRttyVJgoJ3ldVyAvMRQskrq7YdeiAwxO9se/WDbHt/x/pXk2VnrVfnkKvNTLinTbYu9y9sr0FC/9mtNUG4ldf4OLxQrxCHOn9QmS9JLcFQaDqyBHK/1qDKi4OQaej1M6F4+5NELdtz/CJg/ELvIMLp4E88kbeUFqKLjMTmY8PUk/PmxWRjHqzwb+0B3IToeCMOcW4DspkXiRq/UgWQvFq2Ych/Z/CxlpOlb6K7LJMynMz0Sjy0RcWIioux7pCjVVhOVZ5pchyixBVVd8YS+bri3VUFDatW2HXsSNWYWH/9Rn77ZiMJv7+8Qy5F8oYPLMVPo3rfg21YMECpJ8s5MCqCxgNJtoNbER0T/96S28bVSpUe/dSvmMXFUePIdOoAVC6eeMa0wKXls2Rh4ZiHRHRYJmSR97IF8X+TfEbb5h/EYuRenoi9fBA4uCA2NHR/L+dndn4m3SITBowVAMCJr2BwnINl4sNZJfpwAiNnWQ0sRch16gxKZUYlUpMSiWmqjskS4jFyHx8kAcFIQ8ORh4chLxRY6yjIpG6uNzfBXnACILAwTXpnN2fQ49nwonq6nfvjSxYsACAslRD/OqLXDlTjJu/PT2eCcc75J9NkgSDgazDpzi8bieac+cILcvB89rE02X8eLzferNBx/bIG/kdh9L4Zcl2IqVqWsq1BBgqcaxWYqWtRlApMVVWYqqqQjAaEYxGuJapBmBChF4swSiRIpHLsba1QmptbX4wODggcXRAbH/tfwdHpB4eSD09bjxIpG5uiCT/N/zZiTuzOLrxskU+2IKFBiIIAplJxcT/dZGqCi2RnX1pNzCkTkmEu3G1VM3yI1fYeewi9oW59GwfxhsTnmjQcT3yRj6vvJodKQXEpxdx9HIJWsPNtGIbmQQHayl6o4kqrRGd0QSCgBgBX0crOjXx5PFIbx4L96wtF/oIceFYPnuWpRHW1ove4yMfWj1JCxb+Deg0Bk7EZnJ2fw5imZiYXgG07B14V+niOscymNibVkigmy1Rvg1znz40Iy8SiT4BBgEmQAGMEwQhT2QWSJ8H9APU1z5PvNd4D2LhVWswcllRRbpCSVaJmspqPSqtAZlEjJ2VFDc7OaGe9oR62uPvYvOvKEydlWIuluAT5szAadFIZI/uw8yChf8m5Qo1xzZlcDlRgY2jnNZ9gojs6ovsv6z99DCNvKMgCJXXfp4ORAqC8LJIJOoHvILZyLcH5gmCUHd+Pw+n/N+/naxzJWz/6SwuPrY8PbMV8jtoZ1uwYOH+KMio4Nimy+ReLMfGQUbLXoE06+7XoJl9Q3hoRUOuG/hr2AHXnxiDgN8F8xPkmEgkchaJRD6CIOTXGsTCQyMrpYRti/5fe/ceU2d9x3H8/YXSA+VSOEhPKZcWLAXb2guus9t0q1vmbclwSZMZ/9Asi2YXF82yRI3J5v4wcUu2JUuWuZl52UXr5mZsnG6zWqtLZqvdCqW1UCxQoFxKgVKQAuV898fzox4pF7Vwnuecfl/JCc/5PU/TT78r5DSuAAAJrUlEQVTlfDnP7/nxnDrChZnU3LvZGrwxC2R5+VJu+X41J5oG2P9yC/95/j32/6OVKz5byJXbij7SssuFctGvehF5GLgdOA1c54aLgLaYw9rdmDX5OJm8V3b+iiy+es/sH4pgjJkfK1bnsuJ7m+huGaR213EO7m6n9rU2Vq7PZ8O2YkquCMf9etic0zUisgtYPs2uB1X1hZjjHgDSVfVHIvIi8Iiq/tvtexW4T1UvmIsRkbuAuwBKS0uvam1t/cT/GOM59GYHe55uoKA0e85PvTHGLJzhgVHq3+jg0JsdjJwZJyscovLq5VRtLSQ3Mn/v7uOyukZESoGXVHW9iPwGeF1Vn3H7GoBtc03X2Jz8xdGosu/FZt55qYXSdfnccOe6uM0JGmNmNjEe5diBkxx5q5O2w32oelM8lVuXc3l1wZw3QZvLgs3Ji0iFqh51T2uAI257J3C3iOzAu/B6eiHn4/u7hql9tY2yjQUUV+ZdkqtHRkfOseuJw7TU9XLF5wr5wm2Vdl94YwIiNS2Fii0RKrZEGB4YpWFvF0fe6mLP0w28saORkqo8rtxWzKoNM9zX5iJc7Nu8R0SkEm8JZSvwLTf+Et7Kmia8JZTfuMi/Z1Z9ncM07Ovm0JsnSAulUroun7KNl7Fyff4lMVXR2z7EPx+rZ/DkCNd+vYIrtxVfEktDjUlEmbkhqm9YyebrS+ltG6Jpfw9N+7vp6xxekCafFL8MBXBufIL2I/001/bSXNfLyOAYKSnCijW5lK7Lp3RdmHBhZlI1v2hUOfDKcfbuPEYoM40b71zHiopg3UrBGDM3VSU6oaR+wl/ITPrfeJ1Ko0p3yyDNtSdpru2lv8u7IVBWXoiStWFK1+ZTXJWX0O/ye9vPsOfpRrqOnaZ8cwHbbqskI3thP0XKGBNMl1yTn+pM31naDvdx/NAp2o70MzZyDhGIlOVQsjaf4so8IqtyEmIuf2RojLf/3kL96+2EMtO4Zvtq1ly9PKnOUIwxH88l3+RjRSeidLec4fihUxw/3EdP6yCod2FkeflSitbkUrQmeE3/7NA4B3Ydp253O+NjE6y/toira8oT+mzEGDM/rMnP4uzwOJ1NA3Q0DNBxtJ/e9qELmn6kLIdlK3Pi3lBVld62IQ7uaadxXzcT41FWX7WMLV8pI7wiM65ZjDHBtWBLKJNBemYaZRsLKNvofVL62eFxThwd4ESj1/T3vdh8/mYNuZElLFuVTWTVUpatyiZcmDnv69CjUaW37QzNdb28t7+H/q73WbQ4hcqty9mwrZj8oqx5/fuMMcntkm/yU6VnplG+qYDyTV7THx05R0/rID0tg3Q3D9L+bj+Ne7vPH58VDhEuzCSvMJO8yBKywulk5YbIyguxOGPRjHPlGlXOvj/O8MAY/Z3DnOoYordjiM6m04yNnAOBoopcNlxXTMWWCKElNi1jjPn4rMnPIZSxiJKqMCVVYcCbQhnqH+Vk6xn6Oofp6xymv2uYjsYBJsajH/qzkiKkhVJJC6WyaHEKqt41gYlzyujQONGofujY3MgSVlcXUFSVR3FlmCU5tlrGGHNxrMl/TCJCdjid7HA65ZsLzo9Ho8pQ/1mGB8YY6j/LUP8oo++PMz46cf4hIqQuElIWpZCRmcaSpYtZkhMiN5JBXiQzUBd6jTHJwZr8PElJEXLyM8jJzwDsw7GNMcFgbx2NMSaJWZM3xpgkZk3eGGOSmDV5Y4xJYtbkjTEmiVmTN8aYJGZN3hhjkpg1eWOMSWKBuguliJzE+xjBT+IyoHce4yyERMgIlnO+Wc75kwgZIf45V6pqwXQ7AtXkL4aIvDPTrTaDIhEyguWcb5Zz/iRCRghWTpuuMcaYJGZN3hhjklgyNfnf+h3gI0iEjGA555vlnD+JkBEClDNp5uSNMcZcKJneyRtjjJnCmrwxxiSxhG/yInKjiDSISJOI3O93nlgi0iIiB0XkgIi848bCIvKKiBx1X/N8yPW4iPSISH3M2LS5xPNLV986Ean2OedDItLhanpARG6O2feAy9kgIjfEKWOJiOwWkcMickhE7nHjgarnLDmDVs90EdknIrUu54/deJmI7HV5nhWRxW485J43uf2rfM75pIg0x9Rzkxv37XWEqibsA0gF3gPKgcVALbDW71wx+VqAy6aM/RS4323fD/zEh1yfB6qB+rlyATcDLwMCbAX2+pzzIeAH0xy71v3/h4Ay932RGoeMhUC1284GGl2WQNVzlpxBq6cAWW47Ddjr6vRn4FY3/ijwbbf9HeBRt30r8Gyc6jlTzieB7dMc79vrKNHfyX8aaFLVY6o6BuwAanzONJca4Cm3/RRwS7wDqOobQN+U4Zly1QC/V89bQK6IFPqYcyY1wA5VHVXVZqAJ7/tjQalqp6r+122fAd4FighYPWfJORO/6qmqOuSeprmHAl8EnnPjU+s5WefngC+JiPiYcya+vY4SvckXAW0xz9uZ/Rs33hT4l4jsF5G73FhEVTvddhcQ8SfaBWbKFcQa3+1OeR+Pme7yPaebKtiM964usPWckhMCVk8RSRWRA0AP8AreWcSAqp6bJsv5nG7/aSDfj5yqOlnPh109fyEioak5nbjVM9GbfNBdo6rVwE3Ad0Xk87E71TuPC9wa1qDmcn4NXA5sAjqBn/kbxyMiWcBfgXtVdTB2X5DqOU3OwNVTVSdUdRNQjHf2UOVzpGlNzSki64EH8PJuAcLAfT5GBBK/yXcAJTHPi91YIKhqh/vaAzyP9w3bPXma5r72+JfwQ2bKFagaq2q3e3FFgcf4YArBt5wikobXOP+kqn9zw4Gr53Q5g1jPSao6AOwGPoM3vbFomiznc7r9S4FTPuW80U2LqaqOAk8QgHomepN/G6hwV94X41142elzJgBEJFNEsie3geuBerx8d7jD7gBe8CfhBWbKtRO43a0O2AqcjpmGiLsp85hfw6speDlvdastyoAKYF8c8gjwO+BdVf15zK5A1XOmnAGsZ4GI5LrtDODLeNcPdgPb3WFT6zlZ5+3Aa+7MyY+cR2J+sAvedYPYevrzOorXFd6FeuBdtW7Em7d70O88MbnK8VYn1AKHJrPhzRe+ChwFdgFhH7I9g3dqPo43N/jNmXLhrQb4lavvQeBTPuf8g8tRh/fCKYw5/kGXswG4KU4Zr8GbiqkDDrjHzUGr5yw5g1bPDcD/XJ564IduvBzvh0wT8Bcg5MbT3fMmt7/c55yvuXrWA3/kgxU4vr2O7LYGxhiTxBJ9usYYY8wsrMkbY0wSsyZvjDFJzJq8McYkMWvyxhiTxKzJG2NMErMmb4wxSez/ovW8NBpBJwkAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 473cef2bf..e77fd928b 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1482,6 +1482,17 @@ def penalty(self, derivative_degree=None, coefficients=None): # implement using inner product return self._numerical_penalty(coefficients) + def gram_matrix(self): + r"""Return the Gram Matrix of a fourier basis + We already know that a fourier basis is orthonormal, so the matrix is + an identity matrix of dimension n_basis*n_basis + + Returns: + numpy.array: Gram Matrix of the fourier basis. + + """ + return np.identity(self.n_basis) + def basis_of_product(self, other): """Multiplication of two Fourier Basis""" if not _same_domain(self.domain_range, other.domain_range): diff --git a/tests/test_fpca.py b/tests/test_fpca.py new file mode 100644 index 000000000..fff7be7d4 --- /dev/null +++ b/tests/test_fpca.py @@ -0,0 +1,26 @@ +import unittest + +import numpy as np +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather + + +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data + +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): + fpca = FPCABasis() + with self.assertRaises(AttributeError): + fpca.fit(None) + + + + +if __name__ == '__main__': + unittest.main() From e8e54c0d3c474ee5231faa8d9a1720f27cfe965e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sat, 1 Feb 2020 17:06:17 +0100 Subject: [PATCH 099/624] Adding explanation to ANOVA example. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/modules/inference/anova.rst | 2 +- examples/plot_oneway.py | 55 ++++++++++++++++++++++++++++---- 2 files changed, 50 insertions(+), 7 deletions(-) diff --git a/docs/modules/inference/anova.rst b/docs/modules/inference/anova.rst index 8e51d1443..c1fed862f 100644 --- a/docs/modules/inference/anova.rst +++ b/docs/modules/inference/anova.rst @@ -13,4 +13,4 @@ TODO - Description skfda.inference.anova.v_sample_stat skfda.inference.anova.v_asymptotic_stat - + skfda.inference.anova.func_oneway diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 59668fc7e..bca249e49 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -15,8 +15,18 @@ from skfda.inference.anova import func_oneway ################################################################################ -# TODO +# *One-way ANOVA* (analysis of variance) is a test that can be used to +# compare the means of different samples of data. +# Let :math:`X_{ij}(t), j=1, \dots, n_i` be trajectories corresponding to +# :math:`k` independent samples :math:`(i=1,\dots,k)` and let :math:`E(X_i(t)) = +# m_i(t)`. Thus, the null hypotesis in the statistical test is: # +# .. math:: +# H_0: m_0(t) = m_1(t) = \dots = m_k(t) +# +# In this particular example we are going to use the Spanish Weather dataset, +# with information about the average temperature for the period 1980-2009 in +# meteorological stations of different provinces of Spain. dataset = skfda.datasets.fetch_aemet() @@ -29,10 +39,18 @@ fig = fd.plot(group=province) ################################################################################ -# TODO +# In the figure above we can see different trajectories that represent the +# average temperature in an specific meteorological station. The measurements +# of stations in the same province are represented in the same color. +# +# For this example we will study only five provinces located in the +# mediterranean coast, and we will try to test the average temperatures +# equality using the *ANOVA* test. + sel_prov = ['BARCELONA', 'TARRAGONA', 'VALENCIA', 'ALICANTE', 'MURCIA'] +# Creating a filter with only the selected provinces in sel_prov filt = np.logical_or.reduce([np.asarray(province) == p for p in sel_prov]) province = province[filt] @@ -40,8 +58,10 @@ fig = fd.plot(group=province, legend=True) -############################################################################## -# TODO +############################################################################### +# Now it is necessary to prepare the data. Each independent sample of data +# has to be stored in different :class:`~skfda.representation.grid.FDataGrid` +# objects. So, we need to group the measurements of the same provinces. fd_groups = [fd.copy(data_matrix=fd.data_matrix[province == label], @@ -49,7 +69,30 @@ for label in sel_prov] ############################################################################### -# ANOVA +# At this point is time to perform the *ANOVA* test. This functionality is +# implemented in the function :func:`~skfda.inference.anova.func_oneway`. As +# it consists in an asymptotic method it is possible to set the number of +# simulations necessary to approximate the result of the statistic. It is +# possible to set the :math:`p` of the :math:`L_p` norm used in the +# calculations (defaults 2). # +p_val, v_n, dist = func_oneway(*fd_groups, n_sim=1500) -func_oneway(*fd_groups) +################################################################################ +# The function returns first the *p-value* for the test, second the value of +# the statistic :func:`~skfda.inference.anova.v_sample_stat` used to measure +# the variability between groups. The third return value corresponds to the +# sampling distribution of the statistic which is compared with the previous +# one to get the *p-value*. For further information visit +# :func:`~skfda.inference.anova.func_oneway` and [1]. + +print('p-value: ', p_val) +print('Statistic: ', v_n) +print('Distribution: ', dist) + +################################################################################ +# **References:** +# +# [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. An anova test +# for functional data. *Computational Statistics Data Analysis*, +# 47:111-112, 02 2004 From cafa5f7525f490bac625ac667cb57d3d4168168f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sat, 1 Feb 2020 17:06:17 +0100 Subject: [PATCH 100/624] Docstring for func_oneway with references. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/modules/inference/anova.rst | 2 +- examples/plot_oneway.py | 55 ++++++++++++++++--- skfda/inference/anova/anova_oneway.py | 78 ++++++++++++++++++++------- 3 files changed, 109 insertions(+), 26 deletions(-) diff --git a/docs/modules/inference/anova.rst b/docs/modules/inference/anova.rst index 8e51d1443..c1fed862f 100644 --- a/docs/modules/inference/anova.rst +++ b/docs/modules/inference/anova.rst @@ -13,4 +13,4 @@ TODO - Description skfda.inference.anova.v_sample_stat skfda.inference.anova.v_asymptotic_stat - + skfda.inference.anova.func_oneway diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 59668fc7e..daaa238c0 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -15,8 +15,18 @@ from skfda.inference.anova import func_oneway ################################################################################ -# TODO +# *One-way ANOVA* (analysis of variance) is a test that can be used to +# compare the means of different samples of data. +# Let :math:`X_{ij}(t), j=1, \dots, n_i` be trajectories corresponding to +# :math:`k` independent samples :math:`(i=1,\dots,k)` and let :math:`E(X_i(t)) = +# m_i(t)`. Thus, the null hypothesis in the statistical test is: # +# .. math:: +# H_0: m_1(t) = \dots = m_k(t) +# +# In this particular example we are going to use the Spanish Weather dataset, +# with information about the average temperature for the period 1980-2009 in +# meteorological stations of different provinces of Spain. dataset = skfda.datasets.fetch_aemet() @@ -29,10 +39,18 @@ fig = fd.plot(group=province) ################################################################################ -# TODO +# In the figure above we can see different trajectories that represent the +# average temperature in an specific meteorological station. The measurements +# of stations in the same province are represented in the same color. +# +# For this example we will study only five provinces located in the +# mediterranean coast, and we will try to test the average temperatures +# equality using the *ANOVA* test. + sel_prov = ['BARCELONA', 'TARRAGONA', 'VALENCIA', 'ALICANTE', 'MURCIA'] +# Creating a filter with only the selected provinces in sel_prov filt = np.logical_or.reduce([np.asarray(province) == p for p in sel_prov]) province = province[filt] @@ -40,8 +58,10 @@ fig = fd.plot(group=province, legend=True) -############################################################################## -# TODO +############################################################################### +# Now it is necessary to prepare the data. Each independent sample of data +# has to be stored in different :class:`~skfda.representation.grid.FDataGrid` +# objects. So, we need to group the measurements of the same provinces. fd_groups = [fd.copy(data_matrix=fd.data_matrix[province == label], @@ -49,7 +69,30 @@ for label in sel_prov] ############################################################################### -# ANOVA +# At this point is time to perform the *ANOVA* test. This functionality is +# implemented in the function :func:`~skfda.inference.anova.func_oneway`. As +# it consists in an asymptotic method it is possible to set the number of +# simulations necessary to approximate the result of the statistic. It is +# possible to set the :math:`p` of the :math:`L_p` norm used in the +# calculations (defaults 2). # +v_n, p_val, dist = func_oneway(*fd_groups, n_sim=1500) -func_oneway(*fd_groups) +################################################################################ +# The function returns first the statistic :func:`~skfda.inference.anova +# .v_sample_stat` used to measure the variability between groups the test, +# second the *p-value* of the test . The third return value corresponds to the +# sampling distribution of the statistic which is compared with the previous +# one to get the *p-value*. For further information visit +# :func:`~skfda.inference.anova.func_oneway` and [1]. + +print('Statistic: ', v_n) +print('p-value: ', p_val) +print('Distribution: ', dist) + +################################################################################ +# **References:** +# +# [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An anova test +# for functional data". *Computational Statistics Data Analysis*, +# 47:111-112, 02 2004 diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 93f5c3b11..b26019f76 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -20,6 +20,8 @@ def v_sample_stat(fd, weights, p=2): .. math:: V_n = \sum_{i 0 @@ -152,7 +192,7 @@ def func_oneway(*args, n_sim=2000, p=2): simulation = _anova_bootstrap(fd_groups, n_sim, p=p) p_value = np.sum(simulation > vn) / len(simulation) - return p_value, vn, simulation + return vn, p_value, simulation def v_usc(values): From 8024a90b2599b90b70a120d914f2d084c84f87d8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 101/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From 691ef1c674fa5ef80823e05fa0dac5af49969568 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 102/624] Add docstring and references for fpca module --- docs/modules/exploratory.rst | 3 +- docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 5 files changed, 119 insertions(+), 30 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index 45f048bfa..edc2c8d73 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -10,4 +10,5 @@ and visualize functional data. exploratory/visualization exploratory/depth - exploratory/outliers \ No newline at end of file + exploratory/outliers + exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 027501bba4f4cbc05043539221036c6d20ee0d0d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 103/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 4e3b54ceb22dbe079c53a81ede5facd9334a9302 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 104/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 ++- examples/plot_fpca.py | 122 ++++++++++++++++++++++++++++++ skfda/exploratory/fpca/_fpca.py | 93 ++++++++++++++++++++--- 3 files changed, 214 insertions(+), 13 deletions(-) create mode 100644 examples/plot_fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py new file mode 100644 index 000000000..135b4bf2a --- /dev/null +++ b/examples/plot_fpca.py @@ -0,0 +1,122 @@ +""" +Functional Principal Component Analysis +======================================= + +Explores the two possible ways to do functional principal component analysis. +""" + +# Author: Yujian Hong +# License: MIT + +import numpy as np +import skfda +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.representation.basis import BSpline, Fourier +from skfda.datasets import fetch_growth +from matplotlib import pyplot + + +############################################################################## +# In this example we are going to use functional principal component analysis to +# explore datasets and obtain conclusions about said dataset using this +# technique. +# +# First we are going to fetch the Berkeley Growth Study data. This dataset +# correspond to the height of several boys and girls measured from birth to +# when they are 18 years old. The number and time of the measurements are the +# same for each individual. To better understand the data we plot it. +dataset = skfda.datasets.fetch_growth() +fd = dataset['data'] +y = dataset['target'] +fd.plot() +pyplot.show() + +############################################################################## +# FPCA can be done in two ways. The first way is to operate directly with the +# raw data. We call it discretized FPCA as the functional data in this case +# consists in finite values dispersed over points in a domain range. +# We initialize and setup the FPCADiscretized object and run the fit method to +# obtain the first two components. By default, if we do not specify the number +# of components, it's 3. Other parameters are weights and centering. For more +# information please visit the documentation. +fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized.fit(fd) +fpca_discretized.components.plot() +pyplot.show() + +############################################################################## +# In the second case, the data is first converted to use a basis representation +# and the FPCA is done with the basis representation of the original data. +# We obtain the same dataset again and transform the data to a basis +# representation. This is because the FPCA module modifies the original data. +# We also plot the data for better visual representation. +dataset = fetch_growth() +fd = dataset['data'] +basis = skfda.representation.basis.BSpline(n_basis=7) +basis_fd = fd.to_basis(basis) +basis_fd.plot() +pyplot.show() + +############################################################################## +# We initialize the FPCABasis object and run the fit function to obtain the +# first 2 principal components. By default the principal components are +# expressed in the same basis as the data. We can see that the obtained result +# is similar to the discretized case. +fpca = FPCABasis(n_components=2) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() + +############################################################################## +# To better illustrate the effects of the obtained two principal components, +# we add and subtract a multiple of the components to the mean function. +# As the module modifies the original data, we have to fetch the data again. +# And then we get the mean function and plot it. +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +mean_fd = basis_fd.mean() +mean_fd.plot() +pyplot.show() + +############################################################################## +# Now we add and subtract a multiple of the first principal component. We can +# then observe now that this principal component represents the variation in +# growth between the children. +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[0, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[0, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# The second component is more interesting. The most appropriate explanation is +# that it represents the differences between girls and boys. Girls tend to grow +# faster at an early age and boys tend to start puberty later, therefore, their +# growth is more significant later. Girls also stop growing early +mean_fd = basis_fd.mean() +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[1, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[1, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# We can also specify another basis for the principal components as argument +# when creating the FPCABasis object. For example, if we use the Fourier basis +# for the obtained principal components we can see that the components are +# periodic. This example is only to illustrate the effect. In this dataset, as +# the functions are not periodic it does not make sense to use the Fourier basis +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From b8eba656548e4065e821984f10b5c681ea8fd30a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 3 Feb 2020 01:47:24 +0100 Subject: [PATCH 105/624] Fix assertEqual warning. --- tests/test_clustering.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/test_clustering.py b/tests/test_clustering.py index cd35b50ab..3bdc5bbbd 100644 --- a/tests/test_clustering.py +++ b/tests/test_clustering.py @@ -1,8 +1,8 @@ +from skfda.ml.clustering import KMeans, FuzzyCMeans +from skfda.representation.grid import FDataGrid import unittest import numpy as np -from skfda.ml.clustering import KMeans, FuzzyCMeans -from skfda.representation.grid import FDataGrid class TestClustering(unittest.TestCase): @@ -78,10 +78,10 @@ def test_fuzzy_kmeans_univariate(self): [0.227, 0.773], [0.049, 0.951]])) np.testing.assert_allclose(fuzzy_kmeans.transform(fd).round(3), - np.array([[1.492, 7.879], - [1.294, 5.127], - [4.856, 2.633], - [7.775, 1.759]])) + np.array([[1.492, 7.879], + [1.294, 5.127], + [4.856, 2.633], + [7.775, 1.759]])) centers = np.array([[0.707, 0.707, 1.455, 2.467, 1.981, 1.482], [-0.695, -0.695, -0.494, -0.197, -0.199, -0.398]]) np.testing.assert_allclose( @@ -89,7 +89,7 @@ def test_fuzzy_kmeans_univariate(self): centers) np.testing.assert_allclose(fuzzy_kmeans.score(fd), np.array([-12.025179])) - self.assertEquals(fuzzy_kmeans.n_iter_, 19) + self.assertEqual(fuzzy_kmeans.n_iter_, 19) # def test_fuzzy_kmeans_multivariate(self): # data_matrix = [[[1, 0.3], [2, 0.4], [3, 0.5], [4, 0.6]], From 06edee221f5c5a95cb45bf4617f50fc7f21ed705 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 106/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From b41a14f3d64397f67055f2c318c5280d6c9c5a7b Mon Sep 17 00:00:00 2001 From: VNMabus Date: Tue, 4 Feb 2020 17:24:33 +0100 Subject: [PATCH 107/624] Add cython as dependency --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 619d281c2..67e10792f 100644 --- a/setup.py +++ b/setup.py @@ -86,6 +86,7 @@ 'matplotlib', 'scikit-datasets[cran]>=0.1.24', 'rdata', + 'cython', 'mpldatacursor'], setup_requires=pytest_runner, tests_require=['pytest', From ed1dfadb92a9b9c93291e9d82962fa8191a8f574 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 5 Feb 2020 19:24:58 +0100 Subject: [PATCH 108/624] Fix duplicated references. --- examples/plot_landmark_shift.py | 4 ++-- skfda/preprocessing/smoothing/_basis.py | 8 ++++---- skfda/representation/basis.py | 10 +++++----- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/plot_landmark_shift.py b/examples/plot_landmark_shift.py index c38961244..bc1d47f84 100644 --- a/examples/plot_landmark_shift.py +++ b/examples/plot_landmark_shift.py @@ -34,7 +34,7 @@ # associate with a specific argument value t. These are typically maxima, # minima, or zero crossings of curves, and may be identified at the level of # some derivatives as well as at the level of the curves themselves -# [RaSi2005]_. +# [RaSi2005-2]_. # # For alignment we need to know in advance the location of the landmark of # each of the samples, in our case it will correspond to the maxima of each @@ -126,6 +126,6 @@ plt.show() ############################################################################### -# .. [RaSi2005] Ramsay, J., Silverman, B. W. (2005). Functional Data Analysis. +# .. [RaSi2005-2] Ramsay, J., Silverman, B. W. (2005). Functional Data Analysis. # Springer. # diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index f86623de7..88efb777f 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -133,7 +133,7 @@ class BasisSmoother(_LinearSmoother): to the closest function that can be generated by the basis.a. The fit is made so as to reduce the penalized sum of squared errors - [RS05-5-2-5]_: + [RS05-5-2-6]_: .. math:: @@ -163,7 +163,7 @@ class BasisSmoother(_LinearSmoother): method for the resolution of a LS problem. If this method throughs a rounding error warning you may want to use the QR factorisation that is more numerically stable despite being more expensive to compute. - [RS05-5-2-7]_ + [RS05-5-2-8]_ Args: basis: (Basis): Basis used. @@ -291,11 +291,11 @@ class BasisSmoother(_LinearSmoother): array([[ 0.18, 0.07, 0.09]]) References: - .. [RS05-5-2-5] Ramsay, J., Silverman, B. W. (2005). How spline + .. [RS05-5-2-6] Ramsay, J., Silverman, B. W. (2005). How spline smooths are computed. In *Functional Data Analysis* (pp. 86-87). Springer. - .. [RS05-5-2-7] Ramsay, J., Silverman, B. W. (2005). HSpline + .. [RS05-5-2-8] Ramsay, J., Silverman, B. W. (2005). HSpline smoothing as an augmented least squares problem. In *Functional Data Analysis* (pp. 86-87). Springer. diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 473cef2bf..fac0e387e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -514,7 +514,7 @@ def penalty(self, derivative_degree=None, coefficients=None): The differential operator can be either a derivative of a certain degree or a more complex operator. - The penalty matrix is defined as [RS05-5-6-2-1]_: + The penalty matrix is defined as [RS05-5-6-2-2]_: .. math:: R_{ij} = \int L\phi_i(s) L\phi_j(s) ds @@ -543,7 +543,7 @@ def penalty(self, derivative_degree=None, coefficients=None): array([[ 0.]]) References: - .. [RS05-5-6-2-1] Ramsay, J., Silverman, B. W. (2005). Specifying + .. [RS05-5-6-2-2] Ramsay, J., Silverman, B. W. (2005). Specifying the roughness penalty. In *Functional Data Analysis* (pp. 106-107). Springer. @@ -670,7 +670,7 @@ def penalty(self, derivative_degree=None, coefficients=None): The differential operator can be either a derivative of a certain degree or a more complex operator. - The penalty matrix is defined as [RS05-5-6-2-2]_: + The penalty matrix is defined as [RS05-5-6-2-1]_: .. math:: R_{ij} = \int L\phi_i(s) L\phi_j(s) ds @@ -1011,7 +1011,7 @@ def penalty(self, derivative_degree=None, coefficients=None): numpy.array: Penalty matrix. References: - .. [RS05-5-6-2-1] Ramsay, J., Silverman, B. W. (2005). Specifying + .. [RS05-5-6-2-3] Ramsay, J., Silverman, B. W. (2005). Specifying the roughness penalty. In *Functional Data Analysis* (pp. 106-107). Springer. @@ -1452,7 +1452,7 @@ def penalty(self, derivative_degree=None, coefficients=None): numpy.array: Penalty matrix. References: - .. [RS05-5-6-2-1] Ramsay, J., Silverman, B. W. (2005). Specifying + .. [RS05-5-6-2-4] Ramsay, J., Silverman, B. W. (2005). Specifying the roughness penalty. In *Functional Data Analysis* (pp. 106-107). Springer. From 33e2ec21e4fb2653d0660154d48401ff4a5349ce Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 5 Feb 2020 19:25:34 +0100 Subject: [PATCH 109/624] Move init position in docs. --- docs/_templates/autosummary/class.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst index 5d4fff393..4aeb4de6b 100644 --- a/docs/_templates/autosummary/class.rst +++ b/docs/_templates/autosummary/class.rst @@ -5,8 +5,6 @@ .. autoclass:: {{ objname }} {% block methods %} - .. automethod:: __init__ - {% if methods %} .. rubric:: Methods @@ -15,6 +13,8 @@ ~{{ name }}.{{ item }} {%- endfor %} {% endif %} + + .. automethod:: __init__ {% endblock %} {% block attributes %} From 52522996451481f18304c9c942f672f4f629baf0 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 5 Feb 2020 20:04:54 +0100 Subject: [PATCH 110/624] Fix some warnings * Fix some docstring errors * Fix KMeans and FuzzyCMeans predict --- skfda/_neighbors/base.py | 18 +++++++++--------- skfda/ml/clustering/kmeans.py | 35 +++++++++++++++++------------------ skfda/representation/basis.py | 2 +- skfda/representation/grid.py | 4 +++- 4 files changed, 30 insertions(+), 29 deletions(-) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 499d18cb8..0ca33638b 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -543,15 +543,15 @@ def _weighted_local_regression(self, neighbors, distance): def predict(self, X): """Predict the target for the provided data - Parameters - ---------- - X (:class:`FDataGrid` or array-like): FDataGrid with the test - samples or array (n_query, n_indexed) if metric == - 'precomputed'. - Returns - ------- - y : array of shape = [n_samples] or [n_samples, n_outputs] - or :class:`FData` containing as many samples as X. + + Args: + X (:class:`FDataGrid` or array-like): FDataGrid with the test + samples or array (n_query, n_indexed) if metric == + 'precomputed'. + + Returns: + y : array of shape = [n_samples] or [n_samples, n_outputs] + or :class:`FData` containing as many samples as X. """ self._check_is_fitted() diff --git a/skfda/ml/clustering/kmeans.py b/skfda/ml/clustering/kmeans.py index 85306d0b8..aed86ce2c 100644 --- a/skfda/ml/clustering/kmeans.py +++ b/skfda/ml/clustering/kmeans.py @@ -285,27 +285,26 @@ def predict(self, X, sample_weight=None): convention. Returns: - labels_ + Label of each sample. """ check_is_fitted(self) self._check_test_data(X) - return self.labels_ - - def fit_predict(self, X, y=None, sample_weight=None): - """Compute cluster centers and predict cluster index for each sample. - - Args: - X (FDataGrid object): Object whose samples are classified into - different groups. - y (Ignored): present here for API consistency by convention. - sample_weight (Ignored): present here for API consistency by - convention. - - Returns: - labels_ - """ - self.fit(X) - return self.labels_ + + membership_matrix = self._create_membership(X.n_samples) + centroids = self.cluster_centers_.copy() + + pairwise_metric = pairwise_distance(self.metric) + + distances_to_centroids = pairwise_metric(fdata1=X, + fdata2=centroids) + + self._update( + fdata=X, + membership_matrix=membership_matrix, + distances_to_centroids=distances_to_centroids, + centroids=centroids) + + return membership_matrix def transform(self, X): """Transform X to a cluster-distance space. diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index fac0e387e..5c7e10f87 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -165,7 +165,7 @@ def plot(self, chart=None, *, derivative=0, **kwargs): Args: chart (figure object, axe or list of axes, optional): figure over with the graphs are plotted or axis over where the graphs are - plotted. + plotted. derivative (int or tuple, optional): Order of derivative to be plotted. Defaults 0. **kwargs: keyword arguments to be passed to the diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 6eb5a5be9..38fdb388e 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -504,7 +504,9 @@ def __check_same_dimensions(self, other): def mean(self, weights=None): """Compute the mean of all the samples. - weights (array-like, optional): List of weights. + Args: + weights (array-like, optional): List of weights. + Returns: FDataGrid : A FDataGrid object with just one sample representing the mean of all the samples in the original object. From 6ad34e3687a0074dd915a8b18bf418258d466686 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 6 Feb 2020 09:17:21 +0100 Subject: [PATCH 111/624] Fix docstring. --- skfda/preprocessing/registration/elastic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 9aea4aae5..420cac766 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -672,7 +672,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, used to select a central mean. Defaults True. max_iter (int): Maximum number of iterations. Defaults to 20. tol (float): Convergence criterion, the algorithm will stop if - :math:´|mu_{(\nu)} - mu_{(\nu - 1)}|_2 / | mu_{(\nu-1)} |_2 < tol´. + :math:`|mu_{(\nu)} - mu_{(\nu - 1)}|_2 / | mu_{(\nu-1)} |_2 < tol`. initial (float): Value of the mean at the starting point. By default takes the average of the initial points of the samples. grid_dim (int, optional): Dimension of the grid used in the alignment From 8d1d68e94a6e39fa3ed1bc5373982a1828c0f7bd Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 6 Feb 2020 16:59:35 +0100 Subject: [PATCH 112/624] Use short names in titles and toctrees. --- docs/_templates/autosummary/base.rst | 2 +- docs/_templates/autosummary/class.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/_templates/autosummary/base.rst b/docs/_templates/autosummary/base.rst index 27f71e506..38fba4a8b 100644 --- a/docs/_templates/autosummary/base.rst +++ b/docs/_templates/autosummary/base.rst @@ -1,4 +1,4 @@ -{{ fullname | escape | underline}} +{{ objname | escape | underline}} .. currentmodule:: {{ module }} diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst index 4aeb4de6b..c97621a73 100644 --- a/docs/_templates/autosummary/class.rst +++ b/docs/_templates/autosummary/class.rst @@ -1,4 +1,4 @@ -{{ fullname | escape | underline}} +{{ objname | escape | underline}} .. currentmodule:: {{ module }} From b5238abd35c5c2a2f1d09b90acb6b04c4c8a5907 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 113/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 3aee860c69f5ad0ab4a246a7c20f624d7ecee903 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 114/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- skfda/representation/basis.py | 5 +- tests/test_fpca.py | 28 +- 4 files changed, 1160 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index e77fd928b..8aaa5a1f3 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,8 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) + #return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 @@ -2170,7 +2171,7 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, .. math:: = \int_a^b x(t)y(t) dt - When we talk abaout FDataBasis objects, they have many samples, so we + When we talk about FDataBasis objects, they have many samples, so we talk about inner product matrix instead. So, for two FDataBasis objects we define the inner product matrix as diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From d3f774365e2ec48d27813f419c721778baad6e99 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:22:29 +0100 Subject: [PATCH 115/624] Finilized Module testing --- skfda/representation/basis.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 8aaa5a1f3..f160b8fb2 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,8 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return self.to_basis().inner_product(other) - #return np.transpose(other.inner_product(self.to_basis())) + return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From f0ac0e9c1bad189ca8650604bd9c8dcb66db7cec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 18 Feb 2020 21:40:09 +0100 Subject: [PATCH 116/624] Documentation of Inference module and ANOVA --- docs/modules/datasets.rst | 1 + docs/modules/inference.rst | 10 +++------- docs/modules/inference/anova.rst | 11 +++++++---- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/modules/datasets.rst b/docs/modules/datasets.rst index 4121e988d..fc09bb486 100644 --- a/docs/modules/datasets.rst +++ b/docs/modules/datasets.rst @@ -18,6 +18,7 @@ The following functions are used to retrieve specific functional datasets: skfda.datasets.fetch_weather skfda.datasets.fetch_aemet skfda.datasets.fetch_octane + skfda.datasets.fetch_gait Those functions return a dictionary with at least a "data" field containing the instance data, and a "target" field containing the class labels or regression values, diff --git a/docs/modules/inference.rst b/docs/modules/inference.rst index d94580159..a06ebfba8 100644 --- a/docs/modules/inference.rst +++ b/docs/modules/inference.rst @@ -1,16 +1,12 @@ Inference ============= -TODO - Description +This module provides functions and utilities to analyze functional data in +order to draw conclusions from a sampled population and the degree of +reliability of this results. .. toctree:: :maxdepth: 3 :caption: Modules: inference/anova - - -ANOVA ------ - -TODO - Description, ANOVA :doc:`here ` \ No newline at end of file diff --git a/docs/modules/inference/anova.rst b/docs/modules/inference/anova.rst index d454ea1e6..9aad0fc3f 100644 --- a/docs/modules/inference/anova.rst +++ b/docs/modules/inference/anova.rst @@ -1,11 +1,13 @@ ANOVA ============== - -TODO - Description +This package groups a collection of statistical models, useful for analyzing +equality of means for different subsets of a sample. One-way functional ANOVA ------------------------ -TODO - Description +Functionality to perform One-way ANOVA analysis, to compare means among +different samples. One-way stands for one functional response variable and +one unique variable of input. .. autosummary:: :toctree: autosummary @@ -14,7 +16,8 @@ TODO - Description Statistics ---------- -TODO - Description +Statistics that measure the internal and external variability between +groups, used in the models above. .. autosummary:: :toctree: autosummary From 324d0631a4444779785786804ffe5f8e8a020917 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 19 Feb 2020 23:00:46 +0100 Subject: [PATCH 117/624] Adding ANOVA example with synthetic data --- examples/plot_oneway.py | 7 +- examples/plot_oneway_synthetic.py | 145 ++++++++++++++++++++++++++++++ 2 files changed, 149 insertions(+), 3 deletions(-) create mode 100644 examples/plot_oneway_synthetic.py diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index c56566d41..b2e821664 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -1,8 +1,9 @@ """ -One-way functional ANOVA -======================== +One-way functional ANOVA with real data +======================================= -This example shows how to perform a functional one-way ANOVA test. +This example shows how to perform a functional one-way ANOVA test usign a +real dataset. """ # Author: David García Fernández diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py new file mode 100644 index 000000000..8cb01cb2d --- /dev/null +++ b/examples/plot_oneway_synthetic.py @@ -0,0 +1,145 @@ +""" +One-way functional ANOVA with synthetic data +============================================ + +This example shows how to perform a functional one-way ANOVA test with +synthetic data. +""" + +# Author: David García Fernández +# License: MIT + +import skfda +from skfda.inference.anova import oneway_anova +from skfda.representation import FDataGrid + +################################################################################ +# *One-way ANOVA* (analysis of variance) is a test that can be used to +# compare the means of different samples of data. +# Let :math:`X_{ij}(t), j=1, \dots, n_i` be trajectories corresponding to +# :math:`k` independent samples :math:`(i=1,\dots,k)` and let :math:`E(X_i(t)) = +# m_i(t)`. Thus, the null hypothesis in the statistical test is: +# +# .. math:: +# H_0: m_1(t) = \dots = m_k(t) +# +# In this example we will explain the nature of ANOVA method and its behavior +# under certain conditions simulating data. Specifically, we will generate +# three different trajectories, for each one we will simulate a stochastic +# process by adding to them brownian processes. The main objective of the +# test is to illustrate the differences in the results of the ANOVA method +# when the covariance function of the brownian processes changes. + +import numpy as np + +import skfda +from skfda.representation import FDataGrid +from skfda.inference.anova import oneway_anova +from skfda.datasets import make_gaussian_process + +################################################################################ +# First, the means for the future processes are drawn. + +n_samples = 100 +n_features = 50 +n_groups = 3 + +t = np.linspace(-np.pi, np.pi, n_features) + +m1 = np.sin(t) +m2 = 1.1 * np.sin(t) +m3 = 1.2 * np.sin(t) + +_ = FDataGrid([m1, m2, m3], + dataset_label="Means to be used in the simulation").plot() + + +############################################################################### +# Now, a function to simulate processes as described above is implemented, +# to make code clearer. + +def make_process_b_noise(mean, cov, random_state): + return FDataGrid([mean for _ in range(n_samples)]) \ + + make_gaussian_process(n_samples, n_features=mean.shape[0], + cov=cov, random_state=random_state) + + +################################################################################ +# A total of `n_samples` trajectories will be created for each mean, so a array +# of labels is created to identify them when plotting. + +groups = np.full(n_samples * n_groups, 'Sample 1') +groups[100:200] = 'Sample 2' +groups[200:] = 'Sample 3' + +############################################################################### +# First simulation uses a low :math:`\sigma = 0.1` value. In this case the +# differences between the means of each group should be clear, and the +# p-value for the test should be near to zero. + +sigma = 0.1 +cov = np.identity(n_features) * sigma + +fd1 = make_process_b_noise(m1, cov, random_state=1) +fd2 = make_process_b_noise(m2, cov, random_state=2) +fd3 = make_process_b_noise(m3, cov, random_state=3) + +stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) +print("Statistic: ", stat) +print("p-value: ", p_val) + +################################################################################ +# In the plot below we can see the simulated trajectories for each mean, +# and the averages for each group. + +fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) +fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" +fd.plot(group=groups, legend=True) +fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() + +################################################################################ +# In the following, the same process will be followed incrementing sigma +# value, this way the differences between the averages of each group will be +# lower and the p-values will increase (the null hypothesis will be harder to +# refuse). + +################################################################################ +# Plot for :math:`\sigma = 1`: + +sigma = 1 +cov = np.identity(n_features) * sigma + +fd1 = make_process_b_noise(m1, cov, random_state=1) +fd2 = make_process_b_noise(m2, cov, random_state=2) +fd3 = make_process_b_noise(m3, cov, random_state=3) + +_, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) + +fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) +fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" +fd.plot(group=groups, legend=True) +fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() + +################################################################################ +# Plot for :math:`\sigma = 10`: + +sigma = 10 +cov = np.identity(n_features) * sigma + +fd1 = make_process_b_noise(m1, cov, random_state=1) +fd2 = make_process_b_noise(m2, cov, random_state=2) +fd3 = make_process_b_noise(m3, cov, random_state=3) + +_, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) + +fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) +fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" +fd.plot(group=groups, legend=True) +fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() + +################################################################################ +# **References:** +# +# [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An anova test +# for functional data". *Computational Statistics Data Analysis*, +# 47:111-112, 02 2004 From 22dc7832417c471dadcb1f425cde50240b76e450 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 118/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From 4c96cc3ed272ade769695e8cc1cc5c8d459ef36c Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 21 Feb 2020 15:07:14 +0100 Subject: [PATCH 119/624] Renamed NearestCentroids to NearestCentroid, as in scikit-learn --- docs/modules/ml/classification.rst | 2 +- skfda/_neighbors/__init__.py | 4 ++-- skfda/_neighbors/classification.py | 12 ++++++------ skfda/ml/classification/__init__.py | 2 +- tests/test_neighbors.py | 10 +++++----- 5 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/modules/ml/classification.rst b/docs/modules/ml/classification.rst index 9524a4aea..e4c2d0a77 100644 --- a/docs/modules/ml/classification.rst +++ b/docs/modules/ml/classification.rst @@ -21,4 +21,4 @@ it is explained the basic usage of these estimators. skfda.ml.classification.KNeighborsClassifier skfda.ml.classification.RadiusNeighborsClassifier - skfda.ml.classification.NearestCentroids + skfda.ml.classification.NearestCentroid diff --git a/skfda/_neighbors/__init__.py b/skfda/_neighbors/__init__.py index 58316566d..22047b996 100644 --- a/skfda/_neighbors/__init__.py +++ b/skfda/_neighbors/__init__.py @@ -3,7 +3,7 @@ - NearestNeighbors - KNeighborsClassifier - RadiusNeighborsClassifier - - NearestCentroids + - NearestCentroid - KNeighborsRegressor - RadiusNeighborsRegressor @@ -11,4 +11,4 @@ from .unsupervised import NearestNeighbors from .regression import KNeighborsRegressor, RadiusNeighborsRegressor from .classification import (KNeighborsClassifier, RadiusNeighborsClassifier, - NearestCentroids) + NearestCentroid) diff --git a/skfda/_neighbors/classification.py b/skfda/_neighbors/classification.py index 228ea4e2a..e914660f4 100644 --- a/skfda/_neighbors/classification.py +++ b/skfda/_neighbors/classification.py @@ -93,7 +93,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, See also -------- :class:`~skfda.ml.classification.RadiusNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.classification.NearestCentroid` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` @@ -251,7 +251,7 @@ class RadiusNeighborsClassifier(NeighborsBase, NeighborsMixin, See also -------- :class:`~skfda.ml.classification.KNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.classification.NearestCentroid` :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` @@ -303,7 +303,7 @@ def _init_estimator(self, sklearn_metric): outlier_label=self.outlier_label, n_jobs=self.n_jobs) -class NearestCentroids(BaseEstimator, ClassifierMixin): +class NearestCentroid(BaseEstimator, ClassifierMixin): """Nearest centroid classifier for functional data. Each class is represented by its centroid, with test samples classified to @@ -343,10 +343,10 @@ class and return a :class:`FData` object with only one sample We will fit a Nearest centroids classifier - >>> from skfda.ml.classification import NearestCentroids - >>> neigh = NearestCentroids() + >>> from skfda.ml.classification import NearestCentroid + >>> neigh = NearestCentroid() >>> neigh.fit(fd, y) - NearestCentroids(...) + NearestCentroid(...) We can predict the class of new samples diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index 6f69cb3a8..7a2b9e3bb 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -1,4 +1,4 @@ from ..._neighbors import (KNeighborsClassifier, RadiusNeighborsClassifier, - NearestCentroids) + NearestCentroid) diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index 60dffc190..22cded6c3 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -8,7 +8,7 @@ from skfda.misc.metrics import lp_distance, pairwise_distance from skfda.ml.classification import (KNeighborsClassifier, RadiusNeighborsClassifier, - NearestCentroids) + NearestCentroid) from skfda.ml.clustering import NearestNeighbors from skfda.ml.regression import KNeighborsRegressor, RadiusNeighborsRegressor #from skfda.exploratory.outliers import LocalOutlierFactor @@ -55,8 +55,8 @@ def test_predict_classifier(self): for neigh in (KNeighborsClassifier(), RadiusNeighborsClassifier(radius=.1), - NearestCentroids(), - NearestCentroids(metric=lp_distance, mean=l2_mean)): + NearestCentroid(), + NearestCentroid(metric=lp_distance, mean=l2_mean)): neigh.fit(self.X, self.y) pred = neigh.predict(self.X) @@ -255,12 +255,12 @@ def test_radius_outlier_functional_response(self): def test_nearest_centroids_exceptions(self): # Test more than one class - nn = NearestCentroids() + nn = NearestCentroid() with np.testing.assert_raises(ValueError): nn.fit(self.X[0:3], 3 * [0]) # Precomputed not supported - nn = NearestCentroids(metric='precomputed') + nn = NearestCentroid(metric='precomputed') with np.testing.assert_raises(ValueError): nn.fit(self.X[0:3], 3 * [0]) From d5c1bc2c35f2e1ae79b468962edf9880e38d6673 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 23 Feb 2020 22:35:57 +0100 Subject: [PATCH 120/624] Asymptotic tests --- ANOVA notebooks/ANOVA synthetic.ipynb | 288 ++++++++++ ANOVA notebooks/Pruebas con ANOVA.ipynb | 490 ++++++++++++++++++ .../Resultados pruebas ANOVA.ipynb | 258 +++++++++ ANOVA notebooks/anova_data_100k.csv | 201 +++++++ ANOVA notebooks/anova_data_500.csv | 51 ++ ANOVA notebooks/anova_data_50k_p1.csv | 81 +++ ANOVA notebooks/anova_data_80000.csv | 161 ++++++ ANOVA notebooks/csv/anova_50k_p1.csv | 81 +++ ANOVA notebooks/csv/anova_50k_p1_sigma10.csv | 101 ++++ ANOVA notebooks/csv/anova_50k_p1_sigma50.csv | 101 ++++ ANOVA notebooks/csv/anova_50k_p2_sigma10.csv | 101 ++++ ANOVA notebooks/csv/anova_50k_p2_sigma50.csv | 101 ++++ ANOVA notebooks/means_p1.csv | 10 + ANOVA notebooks/means_p2.csv | 10 + examples/plot_oneway.py | 2 +- skfda/inference/anova/anova_oneway.py | 56 -- 16 files changed, 2036 insertions(+), 57 deletions(-) create mode 100644 ANOVA notebooks/ANOVA synthetic.ipynb create mode 100644 ANOVA notebooks/Pruebas con ANOVA.ipynb create mode 100644 ANOVA notebooks/Resultados pruebas ANOVA.ipynb create mode 100644 ANOVA notebooks/anova_data_100k.csv create mode 100644 ANOVA notebooks/anova_data_500.csv create mode 100644 ANOVA notebooks/anova_data_50k_p1.csv create mode 100644 ANOVA notebooks/anova_data_80000.csv create mode 100644 ANOVA notebooks/csv/anova_50k_p1.csv create mode 100644 ANOVA notebooks/csv/anova_50k_p1_sigma10.csv create mode 100644 ANOVA notebooks/csv/anova_50k_p1_sigma50.csv create mode 100644 ANOVA notebooks/csv/anova_50k_p2_sigma10.csv create mode 100644 ANOVA notebooks/csv/anova_50k_p2_sigma50.csv create mode 100644 ANOVA notebooks/means_p1.csv create mode 100644 ANOVA notebooks/means_p2.csv diff --git a/ANOVA notebooks/ANOVA synthetic.ipynb b/ANOVA notebooks/ANOVA synthetic.ipynb new file mode 100644 index 000000000..1a6188503 --- /dev/null +++ b/ANOVA notebooks/ANOVA synthetic.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "import skfda\n", + "from skfda.inference.anova import oneway_anova\n", + "from skfda.representation import FDataGrid\n", + "from skfda.datasets import make_gaussian_process\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 100\n", + "n_features = 50\n", + "n_groups = 3\n", + "\n", + "t = np.linspace(-np.pi, np.pi, n_features)\n", + "\n", + "m1 = np.sin(t)\n", + "m2 = 1.1 * np.sin(t)\n", + "m3 = 1.2 * np.sin(t)\n", + "\n", + "_ = FDataGrid([m1, m2, m3], dataset_label=\"Means to be used in the simulation\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "groups = np.full(n_samples * n_groups, 'Sample 1')\n", + "groups[100:200] = 'Sample 2'\n", + "groups[200:] = 'Sample 3'" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def make_process_b_noise(mean, cov, random_state):\n", + " return FDataGrid([mean for _ in range(n_samples)]) + make_gaussian_process(n_samples, n_features=mean.shape[0], cov=cov, random_state=random_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3.5251341441516106, 0.0)" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigma = 0.1\n", + "cov = np.identity(50) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", + "\n", + "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", + "stat, p_val" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", + "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", + "fd.plot(group=groups, legend=True)\n", + "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9.966812874778942, 0.0195)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigma = 1\n", + "cov = np.identity(50) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", + "\n", + "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", + "stat, p_val" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wXRxzmYODg4PDMcMRMg4ODg7HmW9+85usXbuWdevWsX79ejZt2nRMrzfdUv+f+cxnOgrLELz61a9edfjwYXe15zohzA4ODg42LrjtAkLp0Ky11+Jr4eF3PTzh/qeeeor77ruP5557Dq/XSzAYJJs9saxiX/3qVwM/+tGP+gGuuuqquV/+8pfnV5uM6WgyDg4ODjZmU8BU097AwACtra14vaqaS2trKx0dHQB8/etf59xzz+X000/nsssuo5A8/0KX+m9ubi6WkkkkElplDbTJcISMg4ODw3Hkoosuore3l9WrV3P55ZfzyCOPFPd96lOf4tlnn6Wzs5NUKsVDDz1UXJHyhS71/+lPf3pBe3v7ujvvvLPlmmuu6a/2/hwh4+Dg4HAcqaurY8uWLdxwww20tbXxrne9i5tvvhmAhx56iJe//OWcccYZPPjggxw4cKA4Zr/Qpf5//OMf9wUCge3veMc7Qtdcc83cau/P8ck4ODg4HGd0XeeCCy7gggsu4IwzzuCWW27h3e9+N5dffjmbN29m0aJFfO1rXyMQCBTPeaFL/Rf48Ic/PPKWt7xl1Q9+8IOqtBlHk3FwcDihkVLyxwN/JJGbdhHiFwV79+5l//79xb+3bt3KkiVLSKfTgPLRxONx7rzzzmm3PVul/u1LEdx+++1NK1asSFXbB0eTcXBwOKHZNryNrzzxFbYMbuHrr/768e7OrBOPx/n0pz9NOBzG5XKxcuVKbrjhBpqamvjYxz7G6aefTnt7O+eee+60256tUv+f//znFx48eNAnhJALFy7M3nTTTd3V9uG4lPo/55xzpJPx7+DgUA0P9TzEFQ9dwdlzz+aWN98y6+1XZvy/0CHM06GzszN5+umn767mWKfUv4ODw0nLaHqUOb7qFnocSAwAYEjjWHapyGwJBAeF45NxcHB4QXlm4BkuuP0CeqO9VR3fH1f+5ZeqT+ZY4ZT6d3BwOCnZNrwNU5p0x6oz6/cnlJDpi/cdy245HCMcIePg4PCCciB8AIBQqjq/R0GTSeVTZIzMFEcfHc4y9DPHNE0BmJXbHSHj4ODwgtIV7gIgmApWdXxByADEsrFZ74/P5yMUCjmCZgaYpimGh4cbgc7KfY7j38HB4QXDMA0ORVTeXzVCJplLMpoZZWXTSg6EDxDLxmj1t85qnxYuXMiRI0cYHh6e+uDjTCAQcBmGMbsPYHYwgc58Pv/Ryh2OkHFweAnz88cP8fX7drHnG2/C59anPuEY0xvrJWuqCsPVmMsKWsypzacWhcxs43a7WbZs2ay3eyxYs2bNDinlOce7H9PBMZc5OJxodD0IX2uE4IEZN/X1+3YBEIwfG1/GdCmYympcNQTTU2syBaf/KXNOAY6NuQwgksrxD9c/we6B6DFp/2Rm1oSMEEIXQjwvhLhvttp0cDgp2Xab+r/nyVlrMpLKzVpbM6ErooTM2fPOrspcVtBkTmm2hEzu2AiZ53pG2dYb5j//OMal4DBDZlOT+QxQVSaqg4PDJAjrZynHBOpMi3SulLwYTp4YQuZA+AAdtR0srl9ctZDxaB6WNSpzVrWaTN7Ms3Voa9X9Mk3l9D847OTizDazImSEEAuBtwI3zkZ7Dg4nNQUhk67OdGOakp883EUgki7bPhwrmcgqhYyUknA6PLN+HgVd4S5WNK2gxd9CLBsbG5I8tAdsUV598T466jpo8DQA1QuZn27/Ke+///3sDFaXi1h4PqHEibUi5UuB2dJkfgj8O+PESBcQQlwmhNgshNj8YojicDg52D+6n6ufuRpzhlrDrJJLqv/jg1UdvncwxtV/2cNrrn6wbLt9wAynygfPJ/qf4HV3vI6h5NDM+joN8maeQ5FDrGxaWYwQK3P+7/sr/PfLofOu4qaBxADza+fjd/lxCVfVQqYgXAolacowx5anCdvMiYlMvqprOFTHjIWMEGIDMCSl3DLZcVLKG6SU50gpz2lra5vsUAeHF4zL/3Y5v97966pzNl4QEtYkLF6dAChoMHlTkjdKwjIUn1iTeXrgafJmnj0je2bY2erpjfWSM3OsaFpRFDJlz73PKpob2F7aZGkyQgjqPHVVCxnN0gbHRLAN7YZvtkNwf9nmSLIkhAciVVexd6iC2dBkXg1cIoQ4DPwOeL0Q4tez0K6DwzEnaWkN6Xx6iiNfQBLWwFulJjNgM5PZZ+SheGngrHT87w4p9+n24e1Uy7STFePD8NR/F81fhciylU0rafG3ABVCpnDfedXvVD7FSHqEBXULAKj31BPNVmdCzJnqfgvRaUVCXWBkYWBb2Wb7c+sPn0DfhZcAMxYyUsr/kFIulFIuBd4NPCilfN+Me+bg8AJQGIziufgUR76AJCwNJlGddhWwzbxHbSayYEJpMg0+F+FkubmsMLh3R6urH9Yd7WbdL9fxSO8jUx9c4K6PwAP/AcN7gVI5GZ/Lx/c3f7+sH0DxOMKqTwNxZerqqOsAlJCpVpMpmAHt1QLUDhXSTf/zZZtHkzlcmlo10tFkZhcnT8bhpCZrqME3nj1BhIyRh+SI+pwaqeoUuyYzajOLjcSz1Hh0Opr8Y8xlodQoAIfD1VVC7gyq0N67999d1fGAMk1B8T4Ohg+yoG4BO0M7eSbwjNUPy5wlZUkAjKiKAAUtxC5kKt9Tf7yfVH6sUBhMDJa1UWTUEqoV5rJwMssp7fUI4Wgys82sChkp5cNSyg2z2aaDw7GksEbJCaPJJEOABHctWIJgKgLRNG5dzcJHbRpLKJGludZDU417jJApDNYHRgbIGVMHPUQyEQAy5jSSOpOWAImpdekPRA6womlFUbDUumpLmkx8qCRUo6rackEL6ahVQqbB01CmySRzSS6+62LO/935RY0UIGfkivk0BW2oSEGoRcuFTySVo63eS2ud95hpMul8mkORQyeWafYFwNFkHBw4gdYqKZjK2k6BfBqyySlPCUTSnDZfhfiWmcviGVrqvDT5PWOiywzUoJyTcb5z/9TO/0ORHgAG41VGhpomFBYZiw+SN/McjhwuEzI+l68kZIYtrWf5BZCJQjZBX7wPl+airUYFClWaywpCKGNkyhz8kawSiC2+FoZTw0VtFYCUFbadKL+PcDJHk99NR5P/mGkye0f3cskfLilqcScLjpBxOGmxO7KPVbmSaVMY/NpUhns1JrNAJM1p7ZaQSZY7/lvH0WQS2ZJAFXqOmx4/xF86xwn1tfH0ERUgcCCyf9Ljitj7HQvQE+shZ+ZY2bSyuLSxR/eUSssUTGsrXl88pz/eT0dtRzFSrN5dX5bxH0gGip9H0yWtr6B1ndaillQuC2POWOdXaInhZJamGg8djT76j5EmU+hXk7fpmLR/ouIIGYeTFrv2csJoMgVNoXW1+n8Kk1ksnSOWybOsrRavSytz8IcSGVZ4Rnlj5C7CqWxRqBYc8ArJGQu9fOGO7RwOTvwMgqmh4vGZfBUmM3v4dXywGFlm12QEoqSBDO0CfzO0n1E8pz/Rz/y6+cVm6j31pPIpcoYSmIFESciEM+Exn09rVkKmzPlfEDJGpqjVGKYkms7T6Hczv9HPQDh9TMr+F4RMo7dx1ts+kXGEjMNJiz0c9oTxyVRqMsnJNZnBqDLtzG/0MafGw4hlLpNSMpLI8vGeL/DGnh/SlB8hnVO+l32j+8ra+Lc3N6Npgstvfa6sFE1Zt4xSPw5FD1VxHzYhEwsUBduyhmVFTcaUJqGUtY7L0B6YuwbqLaESG6A/3l8MXwao89SpXZY2M5gshXiPJ2TWtKwBKjSZnM38GFFBD1ErfLmpxk1Hk49Uzjgmtd4cTcbB4STDbiI7YaLLEkOge6Bpifp7Ck2mEFnW3uBjTq2naC6LpvPkDElL+jAAc8Vo0S9zOHq4rI2wcZjv/9OZ7BqI8tV7tvFQz0NlFRAi6QhSlAbdAyNVVIcuaGQtq4qazIK6BdS4a4raS9bMkjbSJLJxGNpNvHUV/3fXTYxqGulIH8FUsOj0B4qlZQrvKpAI4BJqtRK7kIlm1ORh9ZzVaEIr12TsTncrsi5cJmT8wLGJMAtnwggEde66WW/7RMYRMg4nLWWaTLq6cOGjYtc98Ou3l9XkAtg/GOPfbttKNm+L7koEobYNalSy4lQ+mYKQmd/oZ06Nu2guC8UzCFuVp3YxymhCDaZHYkfK2jgQPsAbTpvHv752Bb8/+FuueOgKtgyWCnhs7usqO3536ODU91zQZNrPgFiArnAXK5tWYpgGI5b/JG75hoLDnZCN8Xt3jnu6H+CmOXMYiKhrFMKXQZnLoDQ5CCQCrJqzCqCsDltB4PxhS5S5/rnlmkzeFgQQUc+h8Mya/B7mN/oA6A/Pvl8mkonQ4G1A147/uj4vJI6QcTgpkFJiVNSsKtNk0tWFCx8Vt78fDmyEdKRs88bdQ/z++T72BmxBB/Ehcv5WvvAnFc01pbnMEjJzG7zKXFYQMoksrUR41O/j7R3ttGjBoiZjd5iDKvcC8OG/a8Hf9jAAWwMlk9rTvcrZLw0PAIfCPVPfc9zSyNpOIZca4XBURZZFshEkJmaugawVDh0MqOx7s24uADlPLQPxUo6MKU1u2nETmwOq7ExhchBIBFhYv5B6T/0Yc5lA5/sPHKbB3UZfvK/Ur2KoswArYq6gyTTaNJljEcYcyURo9Jxc/hhwhIzDScJVT1/F+l+tL9tWGKw6cnnimWO0WJVde6nQSgr+lL2DNiGTGOZwqoY7tgXJ676pzWXRNC21HnxunTm1pSiyUDzDPDHKFp+XfV4PsqaXiLUvmLRl2UtZdKD/dPv/ILQcUups6StFke0cOgzAvLwS0gOJciEVTAXHFhhNDCuNrL6dXreLvJlXkWWWqcxMlzSUYFBFluUL2punhj4r0GBB3QK6wl388LkfcsuuWwA1OZBSMpgcZJ6niSbhYTRTEV1m1ACCJs+8Uq6MlKXlEzRXyVxmCeY5NR5a67y4NEF/ZPbNZZFs5KTzx4AjZBxOEm7fdztQqlUGJU1mfj5P4lg5/u1JfxUCozBb3l8hZFKeZsAkqjeWnZPJG/xo437itirBgUiaeQ3KxDOnxkM4mcU0JcF4lrkiTMClfBYRf7A4Yy/kkRQIpoJ0hbu4c/+dvGP1O5HZVrqjJW2lJ9oLElYY6hmNZEo5Kc8NPseFd1zIXfvvKmuT+JASMnXtHHC7AVjetLzo9BeeUhuh8CGoa2fUUAN72u2jPxPBJVy0+dvKoshAvbdoNkoqn6J9z1+YE+knEi8dE8lEMPJKI6nV2hhMqjwdbBMJCTZzmeWT8bvRNcG8Bh8DMzSXjRdAEc6EafA2zKjdFyOOkHF4yWO314/YfC8FITPPMIgfqxBme5HLZLmQ6R1RAm9PwBr8pITEML16DbUr/h8fafORttUvu+25Hfy06xN87f5SSf+BSLroR2iq8WBKiKZzjCSyzBOjBFzK/t9fE2PjrgA5I1e+hosQxLJRvrf5e9S6avnk+sup1eYRzCjhmM2bjGQH0RDMNQyQEM8pIRXNRvnSY18iL/P85dBfyu4tmRjkej8cDO+ny+NGIFjeuLyoyeheFRigoRFMDMDc0whZwiQkBH1GkvbadnRNH2Pei2VjRcEzL3SIRtNkNFryM4UzYUyjBgAPLRjSYDg5XKw8EJc+MPNgmf0KQqbBr4Thgib/jDSZ+3cMcOqVfymfPKCEn6PJODgcS9IRGKnCaTzLFHwOMFbI1JmSRsMkbszcPHJv1738cMsPyzfaM8srzGU9lpDZ1W8NRukIGFk2kUHzjHDAI/lOtlTAcvvo4+i+QZ4cubW4LRBJ0d7og4OPcP7hawGVkBmKZ1jkjjKoK02mx5vnwX1HePTg2GTKjJnlsb7HuGzdZXQPCyKxOtJShRbvCUTBFUIiaTZMPFKSk0lM0+Sqp65iKDnEqxe8ms2Dm8kYGfJmnjv33cnFnlH+R47w3p3/wwG3mwXuBvwu/5jS+zXuGoLpUZi7hpBlNgtmIwxoko6aeeoeEwE0odHsbQaUcCuELz/v9eAxTcI27WwkHUZaQkYz1Dn9if6i5hKSDQikCk7IpYmkcsz1Gej3fBKi/cxv8s3I8X/bZvV966pYZTOSiZx0OTLgCBmHF5KfvQGuPesFv6y90q9dyEQzERpMkzppEjdzM9aO7SQAACAASURBVErAOxI7wpcf/zI3dd5U3o49KdHmxM8ZJvGMMqkMxzPE0rmiQBqwTn9FtIa7tCT3dt0LQH9CDZIJU/kY0jmD0WSORXXALy9h9YGf00Cc0WSWYCLLAj3MoEtnbSaDKQSa/xCfufOhcfs/v3Y+7zntPTxxIIiZawItzcGREM/3jKK5R5ECmg2DOtNEYnD7vtu5//D9XL7+ct689M2Y0uS+rvt4xz3v4L+e+i+yyiBFXNfo9HpYqdcCEEqHkFLVWUMKvMJFUAPmnkrQUEI3KEz6XTod1qw/kAgwxzuHM9rOQBMa8Vy8qMn8prGBB2trCJu2BdpsQsbIqjb64/0QHUACmzw2k1W0j3Ayy5s822HrrfDXK5nf6Gcwmi4uyTxdgvGxyao5M0c8F3eEjIPDMSVkzaKryRifRYZTJW2iTMikR6k3DGpNEwNJegbajD0XI5m3JfzZkxJt/pXhWIbFYpArXb9Cx2D/ULwoZAYNkFLwmqH5nJ01+MbT3+Bg+CD9SfX8DNcgeSNfXKxslVYyFZ0ijjCayBKKZ5jjGiIvBBcmkrilpLbhAFkx/vIBb1vxNjy6hxqPjsypgfnp7gM809uD0JQPqNk0aTCV4/x7m7/Hy+a9jI+c/hE8uoo6+9pTXyNn5njXireR1DTWpdR5AZeLFSiNKpgKgVSfJQJdmoR0XWkylskygsmQy0WHJZh6o72E0iEGk4OY0mQ0Paq0G5SwkkKQEqqGmZSSaDYCpht382NkUnWl9xMP8JzXyzcWZXnO61U3HuklnMrR7MkV31FHk4+cIccVFtVQWPY6akvoLOTuONFlDg6zwXhCJGcbwKtc8XG2mEiTiWVGqTdN6q0Z60xKy9hDaO3XIBHkF82tfLijAzNZMhUNRNL8vfYUH3Hdz2mim9390eJzCZp5hPQTkw1cPTiE3+Xjsw9/llFDZdoLYXI4MlDMkVlASYieovVa5rIsXqH6MS9vcGY6g7/uALjL/RsFCjkoQ7EIep0qmLktcJjtgZJ587qGFnRLyCDgO+d/B13T2di9EYC3Lnsrv7j4F/ylZyNnp9P8U1SZAU0hWJFVAmcwPoxmaTJCmJhGlqCuYbSsJGwoE1XaUnQWoPxJR+JKiBZW8QwkAgwmB6nRPOXvIBkibaTJyxx67T588/5EIN1Li69F5crEhzngUX6XLT7r3HAv4WSORZr1zrIJOhqthMyj9MsU8pHsVQMKwRaOT8bBYaYMbIOr5sLzt5ZvD9l8AcdByDT7mql115b5BGKZKPWmSa1lFZlJkUx7xJa9WONQ9Ajfb6zhWa+LWLIkDAKRNMs1pf2cpvXw9MGRoiaTEAZm3s+orKM9n+PbL7+SrkgXJqVBq3PoQDEEurDIl4ngFNFLOJllJJHFQN1Pk2lyXjpNUg/Q1Dz++jGHrOTHzeF78TQ9B8C+YC8DtvVYBrzQ5VMawEWLL6a9tp2cmePJ3kcBWNawhOu3XU8in+QrwVHWZkt+jRUpFZk2mAiiYSAsk2LazDGiuwiZWUxgWbZ0j/MNEynlGD9OMBUkkAgov4qNcO+TxdItutdaTiDRSUddh9JkksNs9yrhssmngiWI9BJJ5egoaHjhbuY3qX1HE2GWN0yy1tIJkfE0Gcdc5uBQzkN7hnj99x4uz0qfjMJiUPd8qny73QFe5bLCE14iFeQ/n/jPsnDkyRhODdPib6HZ11xuLsvFlE/GKsI4E01mNFXSZOxC5mCqlG0esWlUh4JxVgi1b43oZkdfGBLDSAQ5PYc0aoigTD2valzFGxa/oex624c6i5rMcO8BNnob2WSu5BTtCMF4hnAyTVoojXK32815qQwIkO7xS/X3jKjky6Q1yEsp6I32obnHydOR4Baqb88HdhM309SaJs/3PcPd++/m/W2vYFUux+K8FWotJcui6rqhdAhTGEih1JW0NDBEqdRNSC8NSQsyaSKZCKatcoGOTiQbIZAIkDbL64uFux8r0ygBlmSfZX7tfKXJpEYJ6ko7Oux2g6/J0mSyJOnjI+1zOZQO0lGnzHl9RyFkRpLjL3ld6JejyTg4VPDVe3ZycDjBkdHqBvRiVvuY5DzbbDQxM03mlzt/ye8P/H5sbsYEBJNB2vQamvP5cnNZLkm9aVI3qkJZY5nIRE1MyYFQSXDar9FtEzj2AfDgcJzllpA53dWramUlhsl5m0BPIY0aYprloE6OsLh+MfaJ+77wXgKRFHVene5kF//W0chPWnycInoJhFM0yyhRa8C+tmUO83I5dFMjbYz/HvdGVD2yRMZ6b6aHhBlCuEfwyIoyKAIOW9nyj3er8jPtuRzPhbbTXtvOvzauBcADICUuwB8fRhp5EvkwBb+/MCFvfT4UUabAqCUENCmZm4yOCV/2urwkc0kCiQC5Ck1mdHhXUZMpMF/so6NmHv3xfsx0mFHrmQy7dHK+BmSkl5js4itNIzzj9/Gsz0uTGcLn1pQQlxK23AJ9z4373CoJxccXMoV+OXkyDg4V1Hoth208O8WRFvaEQ3uUlc0fMVNzWZNPzQarXZ8+mA7SeugJmoNdRQGQN/MkzGwxugwgkZhawxqMpvmPu3eoaDAbQ4lRpKHMLHZN5rBR0o7CtmTAaLCfBpHE1L2sEd1kDYNMJEDS3YLQk0ijhozLMq2kRtgV3IvH9jgDySMMRNI01bgZ9qjrddYnaBQJhge6mSvCDOsl4fDrpgbaMt7xb0pKwvkkO0M7GbFyinQpEO4wNTUR3ONEWQ0kVKmWPYHN1BsmAkFKZvnSuV+iJjlaHP5F8QyTaOQwJkZxo8vW7K7QrrL250iBKx4Yk4gphMCQBllz7PcxEh8Yo8mk9Dzz4mGyZpaRTISIVTfMFIJD3ho2xXvwLbqROYaBR8IRlwsRC9DR6FfJsuEeuPcK+NnrqlpAbiIh42gyDg4TUG8JmUC0SieoXcjYzVnJECDA2zhjc1neVGYYe/7LREgpCaaCtBoGzYbJqGXfL1TyrTcldZYzO15Fv/7r3p389pkenjlUnvMykg5j5pqQpotAQWsz8nSLPH6rUnAkXxI4fssHklv2emplggUEiQ73M0JDUcgkdOWMJxVmz+ieUsFLKYnkggSiaZIZg26/Gthyep4c4B/dw1wxymG3q3i92xrq8crxy/gXuPKJK3EJJTzqRIJWdy8udxAYayodySjzV09sN8tzWQ553GgSXr/49RAfQgJpobwmhbNDofIcHc2miTzW93jZvgZTQnxwjJAZU77GRl8+WabJaFLSr3uZ16dqo/UbKeJaSezd4zH5VK2JlqvnloEAC/Qaet0uiPaVVsiM2L5jwfIlEsYjlFAmyvmNPqLpck1GF/pJV4EZHCHjMAX1PkvIVFsw0C5kbH4KkiGoaYb69hkLmYKDfrBC83iyK8h539xYyqBHzSDzZp42w6TZNBjNhDGlWWyjwTSps2bq8eTkGtZj+4f58w416B0Olc9qo5kI0qhBGrUMxi3fSzJEt9vFmTXK5xM2s2AtuNWc6aHPpXOd38BAOf/NxDBHjHqEnqHe3cioVAPSULSHSHaErCgNkBkzzpHRFGYyyHZfSZiM6DrLzB7mijD9rtJ2KaHfWzH7L6obAiTsH92P9CvtMK7pRF158iJETphUWKZImzHi2TiDZpAetxtDCExhrcszchAN+J/GBhBKNErgif4nytowS7fDcKr82dflcxDtHyNkcnY/TEVeU7cLIpYp1m1KTsnmGNQ9LD6iTHr9ZoaUphXP/aWeZmUuy/qeV9NmmCysm88RlwuiA8xvtBIyB7bbOrmXqShoMsvbaseYyxq9jQjbOzxZcISMw6QUfhSBSJU5A2VCxvY5GVLl6+vmltYaOUoKhS3tppHekSSfvPU5hmIZ/rqzJHwK4cuthkGLYZCXhqp9lVNt1JuSmoImUxHFZCebN/nqPTtZ0lJDvddFd6g8SCBhRC0hU8OwlXSZi/XT53JxRsNyNARhTYNUGNOULDT6eKCmlpvje+h2u1ijdVOXG6Err0xay5rbCOZVQuHWkPKXFIdUIZAYjKTCtHsOMexy0RRX2fH79Tmcoh1hEUPKXGYNxLWmSa7CteKzV6UWcOac15DX1cBooExKeSEs4TbWZLZpYBN5IRnVtOJ1nup5CHP0MAC3NtYX+xvRBHcOPFZ2fm6c8dZlteOVJmSiBOwVlKGskra3Qsj0u1yM9G9W/pycoNUwyOg5FmSVFt4r7PF5UAvcODDEegZICcGm0IjSZGL9zG/yMxzPkB/cUzpheA9TEUpk0DXB4uaasjyZSDZSXA/nZMMRMg6TkrCKMQ4ejbksXanJtEDdvFnTZCKZCFJKktk8H/vlZgxTsnCOnye7SlFchUTMRsNAt8akUDpUbKPelLgBv2mSSJfb8+3c9PghDg4n+Nola1nSWjNGkzHMKJfyHO1GplgR+EhwN4YQLG1cToPuI6JrkBohlMiyTPSzyaeEyIH6Vs5y9VBLikOmEjJn5XuIZk2isobOmDXQVsyCNc8w9XVKAPnTytneqc3lFNHDUjFA2BapFdPH/tTb8+Xms+bYwuLnsksJMebaAFsCzxT3vyqlNN1vPXs1o6kQhhSktdI1762t5WCmYtkCS4MCQILXNMlb1ym4gfrD3eoYKUFKZOEESXFyUPg7pOuMBveiAXPzAm/eTUjXcbsbqZeCwy7bfQiBDtRJyUrRx3NeL1n3KAlNIxk5QkejDynBCOwoXWNoaiEzksgyp8ZDU42HSKpURSKcCZ+U4cvgCBmHKShU/B2uNvs5NWpb1dEuZEZsQmZmjv9CzkFe5oln43zhju3sG4zx4/eczWtXt7F7oJTvUtBkftrUyDdbVR2rkdRIsY16a6CqNeWE0WX94RTX/m0/F66Zx+tOmcvi5hqOjJSEjJQSQyRoN1OcK3uJZdV991jr2i9sWkW9u1ZpMskRlSMjBghYPpPdXh9rNRVd1S9UHsf+5J95s/4Qo7KOfdkQteO4InR/D1nfERoMg0bvywHYqzWwSvSxQAwT07TioFoIGbabmJbnyoMXAuHtjKexFM+p0Bx2dj+KZj2/Z6y8k3A2xr+3NhIV5UPLbxrrqZVjmighSloMKLMfQF+0pxgoUCnmjArBF9U0YpkwBtBq5Oj0+hnWdVzZKAuyGY7YfFQAEQFBXWORGObpmpKWMRjvY761royI2BZ4G949QedLBONZzvX38Zbe76MbaVJWNeZoJnpSOv3BETLVs38j3PuZ492LF5yCJlN1iY3UKDQvV5/LNJmg8snUtUEuAZmjL61vX9Hyuke38acdA3zxTafy2tVtdDT5iaRypX5bQmaL31c8ZyQ9UtRkGg11XJ2UE5b7/+afdmNKyVcvXgoP/F++d/AS6mKlTPjgaB+mgHpTo9kwSOXVfR+OqTDfuwYepTcdVANnaoSe4VEWiyEi1q/vsGYy17TySDSVkb7Z76en4xHC1NEl44hxnPa6r59R/whnpA3aW1eotvRafCJHixbEtA/CcuwIn7KZuQAG9UNjNRbJuFoMEnrivUXhldc0hJS8Mm3wjN/Hd1vLZ+1H3G5WZXIIOVbYFdrL2K7T59bZ53YxVIjOE6KYwKn+hpS9X0KdPyxMpBD0eU0C3hxSKC1ufj5PQC8XMgA7/E3MFWG63P7itp7kIAushEzNPvEYPVxeuWIcwrEEP4ldwRl9t3Getqfol3E0GYep2fV72HIzRAemPPSlREGTCcaqEDJSKo2lIGQKmoyU5eYymFGuTDQ+QL2VVX3jk538w/oOLvs7dc0FFSsbDieH8Qt32fl2IVPQZOokxHJjgxse3x/kTzsG+ObZURbediE8dR1+I84HjbuKa4YMbP4lAOHFb2eOYZIlQ9bI0p0M0GSY3NOtyuD3u3RIjTLUvReXMElav74BWVofJmwzMfX40wzoNQR0g6Q29qeqeQcZ9WRZkvawlqfxGIJBq9TK/zZWCIaCycs2MD9V4y87JOItv38pGas+2Ahp5XqP35Q05VO8LxLlvvryKCqfaVJv5EBYwk6MVU/ytr7lNI13Lphftt+sEHa5SuEnBMPWcxqxPa/Hml9HR94gOI7JcLuvkSYRp1crCaDd+SjzG3yARDft33sJln8MYPuRMJffuoWMzey4PPpM6bMYKAqZk7UCM5zEQuaPW/u4/qED1VdaLSw5219dUtZLhYJGEE3ny35M45JNqOVtmxaB0EqaTCaq1u8oOP5hRiazWDbGorz68S5tg6vfvq4YoFBao13NOEOpEK1aeX7ISHqEaDaKLiU1Bce4lCTM8llqNm/yrT8+y/frfs07tl8G0sQ46wMA/J2+XWl3UpLdc6c6Yf5ZDGm1xWt0Z0ZZbAuhGtF1SI6QHVJRSoWZe9DmkI/rtswSTbCpXg1+phBjZv+Fhb9kvp7rMzfiJU/elWZQ0/npnOqczEUTlQSjYjQQUH7Nsj5IpcXYBvq8gICu87mRMHVG+XdlTt6k1+1WjZZpWLaLVQiNMb/MSs1sHAoJqIV1dADurTuP+fk82QpB7TdNdrs8+EWWkO0dHHBp1JoxFvjSiMpL2Zz/l1z3BH/eEaCzr6RZn59+mJRWhyl0Vog+IskcOSNHMp88KYtjwkksZH79dDfXPLCXz9+xjZxRRcmUgpCpMvP3pYBpShJZg7n1apAOTZWQWXD617SAz7aqYyERs6a1pMkcpfPflCYxabA4p4TfB8+fi89dGiEq12gfTg3TYPMP1EpLyKRHqDdNNZD6m6k3JXFbeOwj+4Z575U/4IbYp7g0fz+84nL4xJN0hdWI00xMaXfdT5BJKcf8L/ZfzW1NSmsaTY9y2EhQZyvimBYCUiP4IgfJUpqZRzUNac2kE7pZNqrtqpk4AVBoGTymZLeVyxTTdfyuYb7Y1l5mepqMYoTWmNFUbbMPEF7TpM0wyjURG1kh6HO5cDHWX5LVhBr4x5jtKs1mJf9P7SQTQA0m7AMV+/aah+hoWj7mki4pOeg20JCkNLO4r8ftgmg/r6vpIS0EV8yby3avBxMY6VaBAKOJ0m9hV78yqaUTUV7HsyR889CkwbnaXqLp/EldHBNOYiEzFMvQWufh7uf7+PivtpDKTjJLN43igkf0bXlhOngCkLAq5y5tUbPzKf0yBaHin6PqQhXMZYV1VGpaoHZmmkwil0AKWGTVxdL08kF4XoMPIUqaTDAVpM42VtVYQiaWChVNZbSspNY0iNuSDn//+DZu8HwfobsRH34A3vRt8NTii6k8El1I4oEueOZnBN21Y/o5MLyTIfKYrpKpTgpBKj5Ec7qHvVrJnJQWAunyYUiBoduesZT0udN4TDnuwC6A07IZdteVfElpT5QtNToL8nmqwT2FIm/fvSiXV6tj2voH0JTPF/s36NI54tJL+SjWcRFNU0K2knFMXoX/V2Qn/r65JtBkEAJXxZwxKbfTMW998W+3dW5OCAb1LGkBec26LwGDug6xAV6h72VjjZ+Ha31cNq+NvNR5fsvT7A3EOGQLYd81oDSZ1I57qRUZTEuYzBOjRFK54sqsjrnsJEJKyVA0wz+sX8C3Lj2Dh/YO8b6bNhFJ5sY/ITagzD0uvzKXzWBxqxcTCWtRrSUtKtS2ak3GPwf8TSVzWVGTaYHaVmVKO0ohU3D6L8jlEVISTpcXcPS4NNrqvMWVDYOpILW2gdFrGoRSIWLpcEnItK6iLp8nAZBX93h2bCNRd5o7lnwNFr+8eH5NvFTKxnfof2HPfWytWTumnwd6VeJhslDGxMp56Uz2syB/hE5vSchIIRjBRCDLhaYQRHSp6nuNcchLVUDBlGRcOS4bVbPlpC45PaGTENX9tIVNcxgPabtuUhPUFLQ/Sn0Ku1x4LA3FFII/1Y4VusV7mCAcerw+bPP5JuyXmOQ3qNsrFEhJWo+SSZWEQqsV7JG1Eki3eD1lvp+opkG0n1PNfdxdp+4loesEXbBO7uXdNzzFY/tKuV5be9X33LXzTvpkC8Kj3vUckSARixQ1GUfInETEM3lSOYN5DV7e8/LFXP+es9lxJMI//fSp8fNBwlZpidUXHbclhI8HBaf/0lb1Q5syjLlMyMyxaTIFIdMMmq7MZkdpLis47JtMk0bTJJwYuz7K/CY/A5E0qXyKeC6O1yhNHnTTZCQdIpaNFteRoWUFdaZJXBOYVkBCgAfYsKgDTdpKoZgGjel+usx2ABb2/BHMPFv0pWP6cMhaSjhi6QJGajEAj2aHWEI/vZ7yzMjDQqIJ8LpC5YOwEGMc3oXtAAfdbuoMeF2iFIX18WCckXGc3EVsJquixjHZ4G/R73bzrN9fbuCy2snazr+7fpzSKeNGqFWYzsYJsx73HMZx+tsomwpZxz0deLJ47sJcHk3Koij6bYNKGvVZk468EOSiR/AaR8qiEm+rq6eVUV6mH+T6hw6wvGEjcxb9hAPDIXLRIeqOPMK9xquIZAf4xLw2oppAH+0qJg07QubFwMavwfWvmHEzg1E1WM6tV1+gt5wxn1986FyOjCZ54/ceYcO15ZnJRX/M2kvV/yeJyazg9C9oMlObyyyzWMFcVtBkElZyZE2L+n8GuTKF/JYG06TJMAnHx0b7dTT66I+kCCbVdV2GGnbcQkcAI6kQ0awq84/mgsbFallhIUhFlVl0k08JpmjKVq8q2oebHI+aZwLQmtgPK9/IQF6iS6VdLcnmQEK/VT04InMIU/Cx9E4AnjHitIgYw+UBbzxQq/xemsuWsFjFYBt06bw1HuOjHe3FXWlPcnzNZxwy2uSCZVxsbRdMT/Zt/a7Kqs1jr6FXcW/jCr5CsmZltJ2tPbv2pRI54U++0v5V2SwNhglC4DElz/qUH0+zXbdzaC+PeTJlAv7h2hoE8D+eH7DANcDwgo3k67qp9XUy8sztCGnwB+NV3KhHeLzGz19ra/BFukpryeQypP74ORiaOt/mpcSLS8gg1OJXZhWO+kkYiiltpeDQBnj1ylZ+9dGXE8vk6eyPlq/TXhAyKy9UJrOTxPlf0GTa6rzUeHSCsWmay+yajOYGr1VmpK7t6DUZK2+hxsDSZMa2o4obporZ/npO9Xupvw1TQDQXJ5pPKiHja4TalmIl5rglZAatEsEho6TJGMMqfPVJU5nHXOQxz/koiXwUAczNw+pcDgEEZZp5+TxRM0uTmeMUUz2bbqFMdyOu8kH2f2trMKBY1gWYfGZv8128NpkkYRt0D3j18Y8fp50xW+zHVGEWriztUta3SZD24yraGJvNUgUV2p/9ShLocZek+lafj/mWz6rBMEhaWl/SVrX6ufBB/lzro95mau126xiAKxXkiprrittX1T6Ga+cdjNSupFfORbN0JAnUxQ8XNZnIoS78z9/Is7vKC4W+1HlxCZn6duUbSU5cY6oaRqzIkJa68tDWsxfPKX4uMw2Fu9Xs21sHHetPGk2mIGRqvS5a67zVOf5dfnD7S5pMIUemtrX046+bV76I2TSIWiVWDFnHHMMYU9odVBhzOmfSHVbahDSzeNBo8bQWo49iRpp6wySuN/Ivtx0qFcmMBwinw0StUOJhVykZb+fOrQDsMJdhSlVhONT2CqSWQCLRso2szmaRqHyX+fk8JpKM0PiPea0AJHSNiKYxqpebikIuFw/76pBi+oP2rZa5p8A+r3tKwVT2eaJrTSCY7IyXv1ONcDInuj4gmUS4juNDqjcmCdoRonQJ68Munxd/YXkHXRtXsG41R9jm8xa/FwCGJtju9WKuvJCItfjbymwWartpGd3K9jkXMl+LFMOnu11u5iQPE8lEcGkuevfvYJPPS9vyFRP39yXIjIWMEGKREOIhIcQuIcROIcSxS4uvt0wC8XI7fDKbZ2d/9QtOxdLWLMY/8Zypb9SWmBbugcZF6vOCl0Fge7Ga7kuZgrmszuuitc5TnZDxW4La36QmBNl4qaRMgbq5SpM5igCKqLXUcFQsUJqMUXpPqazB1X/ZQ4NPzVoPjipTWh5wm262dJvFCKeMNKg3TUZlHXuibmpt5f7v2Ht70eQy6AZpVSfo2rudtHTTKOJoQg2FI727cOkxDCEIyzpaM8qJHNc0ZZKB4ky5wGaPj7DdTCUlupRcP6ehKi2gkicrkioPudwTHDkF4wUBVGobFe9sXH/RdKkQZmaFUJiKpdkpfotShV/byViCLFVpkrM+73ZLhLQqpdnu/b6GuWTwsMlbz/xcjnl5gz0eN2FN46BYxHxXhN0eFbZ+0ONiXkZpMo2eRrqCO/jo/Hnszc+sdt+LjdnQZPLA56SUa4BXAJ8UQqyZhXbHcPMOyykfKxcy/++BfVz6308WM7ALRLNR/nL4L+WmLyguOFXvK/8xGrZZy6GgrcpupBealOOWjrMgn4ah8kWWTiiySXj2JhV6PQMS09ZkwiUhYy0sRipcKvNfoG4eGNnysjNVEo32oknJqG8VTaZJpCK35ScPd3Hzk4cBOBIdQhcaWU1D5txk8k1lZqUGCRHqGKW+GGkWSQzx2923Fo8Z0TSivTvp7ItQG++hR87lW66fY1rlUTJ9O9Bdyube1TTIXpfSKkwhMCvS5Qt+iL/5G8vqioEyO+33TdNQVCwaWU6/S5+esKr0q4yX9DiJ5jHhsRPtHw/bOZNOPcZpe+5kmgyAsPmOLHrdrklNg8MunfPSGeKahgYqjFwI/uZzYQ7tYY9PkBaCJ2r8IARPemv5p75vs9bcT8KaQAQ1nXajj2gmQr2nkWS+H4BQ5ORaU2bGQkZKOSClfM76HAN2Awtm2u54zDt4NwC5cH9xm2FK7tveTzZvcnC4vPz6tZu+yxce+QLPDmwq2x5KRvC0PkjGiJZtH7Wtz31gyMo9ME0VXVYQMgtepv4/kU1mT10Pf/osbL+t6lNC8QxbusvDgWOWkKn3uWit9069OmalJgNKkBRKyhQoJmRO3/kfSw5RZ5pE61fTZJikBaTySps5GFTvbN+geq+BxBAt7gaimoYwfWTzjWVRSfWm5EjaRwYPHqFMuJooqQAAIABJREFUp88kuhm2qgU3W1niwb3/y8+fOMRSbZCE9PEyfX+xMHCyfzuGywo7lvBgvSg6kGN6+YBYGOi2+l1jkiVzQuCapq9RwLjO8crM9hkxlbAaU4OsOkEyaTvVCEibhlWsMD3JteN6uZ8qqo/jt7JhCsGb4wkSmoruy1qXC7kMdqR7Cbp0RgtLRZsmP/edSlq6+bh+d1ELHnHp+MgRTg4hzBqES/2+XsHMzP0vNmbVJyOEWAqcBWya/MijQ/OrEMDQQClXYfPhEVYnNnOd+1q6hmJlx4eOPA3An7feULZ9Z/RBvG1/5bOP/FvZ9qFoaaZe1GTiAatUiiVk5ixVA+mJ7PwvFFMc2Fb1Ke+9cRNv/8mT5G3VDxKZPLom8Lo0Wms9jCazZfvHkBwpCZeCsEmFreKYNiFT26b+PwohE02H8UrJd7y3glXwsLAa4v5BJWTyJrh1QSgdpMVVS1TTMK0FxezUmwbhDLxTf5islcfySHaYeutnsS6jhOqe3r9x37YjLBFDpPCQFoKILkhpgh+ZT6p6XAAChrz5ouktqJUPZAJASo54KmbrQpAr7B+P6eSKTJCNP2MmyqepNDVN99ozzDnzSDhQcOpP59oTmMnsDOpaKVLNZtL7Q52v7BwXsNdv8MHclwnalNGoJewjySCplJt+Tw6PaZJyl49TL3VmTcgIIeqAu4D/I6WMjrP/MiHEZiHE5uHho3P6CssvkhgsFam7b/sAv/Z8mw360wT6DpUd32eoGea+ivXDBzIqnPRI7EjZ9hFbqYg+K5mvGFlWPx8SVg5Dx1nQv/Wo7uEFwVpaeDqhknsC6os/ECnlCSUyBrUeHSEErfVepISR5CTaTGq0ZBYrmMuSISVoxtVkpm+bjuYTRS1gyG2Vl7ec/7uHjuBf8hOEO0RLrZdYbgSREkQ1jZbaZqShzBSaNa41GHk2iCe4xn0DeWuxsMNkWZwzmGMYnJtSz2JX5git5ghesnhIcfGiDt6yqIOPt7ex06u0Pd1mavVanwcrSsvnURn2lTW/ACVopjlAm7bZ/FFTEEpTtXGsBFc1ZrhJmJMXjLgmMTNOlmw6hUD+eVNj+bEAEp72+xC2950VArxBdsl67veWytekrMlDOBshHc3R63GR1TQS3ppqbu0lw6wIGSGEGyVgbpVS3j3eMVLKG6SU50gpz2lrazuq69S3WYsqWQO/YUru7yzlSWQCpZwGwzToMtRgO2CUr/0xYqoid8OpYXI2B344VRpAi0mZBSFz6BG47mWqCGTH2conk524rtRxpaAhHMUg3mNbJyWeyVNn1cVqtSLxJgxjlnJ8c1lsAJBjHf/2fk5AJBPhDwf+ULYtamQpRP+6XUrIjKZCGKakO70JV0033rb/panGTcoIk43kiWoa7a1uattUFn6hlEqDaVKnKe3Vb2kfupToRo5luRzLrDDXAx7J2xap78NgbYQRXSehaWz3evn30KjVZmmwKjiu8xWDtyEE7sk8DhMNeFNtn4n2MN02ZlPYVCPcptg/6Jl096T9naraQa5C0yn8FdJ1PNIs2weg13YRsS9xLQQBzU0kn8KXyTJkmdeam1dO0emXFrMRXSaAm4DdUsrvz7xLE7MovoMc4EurwWnToVCZn6AmrKrbpnMG39n4OFkki3I5gkKSMdRg0hXuIk8cv7ECiWQwWRqIw8kcda4ALd49jCSyKmDAEjJmLKAG0X1/gQVnK5OUfdW8E4lCYES6+og7l+Ws7Lat+JjI5KnzVQiZiZz/uRQYmbGO/8LSCHYh45+j8mamEIL3dt3LlU9cSf//Z++94yS7rvvO732pcuc002HyDDDAIBMERBOESJEgJTFZtC3a1NqkZXklU7JXtmWutSuttFYwLVuWFZy0opWsLFIMMDNBEABBpEEYAJNT5+lUXV3xpbt/3FdVr1LH6pnuEX6fz0x3V713333pnHvO+Z1zstUY3AoeeiCoc5ZiVS0vX2ViKY+Hsj6FsUzMEnhihZQjyWqC5xaeRUZVfkJZQKR8n/Omya91dxLTFN16v+MyYersd1wGg6rTF02TD/epBcyT8WDVLCWHbIfb7eD5k9VXqRQq6RIWcX7wb000FJGs+261v9czdju328h+zc5rLZLAdlhQAcq5Mq2OUe+OVBao2r5UF/cSPujxi8wbtQvP18wIJXz6vFKlEZtlvmHJbBRvAX4IeLsQ4sXg3/e2YdwG/Fj+67xp/yhJT7lHPv/yNH1W1RI5knuBv3xhgu/+lcf41LPfBuChvFqBzqwoQfXc7HMAvLOoTn06V7WElgsO2uFfxT74P3A8yVLeUUom0c/UpFI28uU/U5YM7Nyy/2ULYZ1KRkqJFrxo9ZZMomLJqCVjSyUTTsQElXgp9CrdPKxkhFDWzBq5MjNByZhr+eB8fJ8VAXYgxJcNpfjSSxc4O5tFs9R4mrWAJ1ZASLp8tzYDnCrLK+VLfnhogN/u6uQPekwQgr2uw6Kuc8B2SAbsvFlDJzH/Lf6v3l4+16kU2x7H51zE4jVLxQPCq95Cqyx6IdZdHXldJVY2go2Os94g/UbH3IzFtJ7jbcLdl1uDIOGt9n1oPCElmpAYiQtcCczk7kCBfS2I3wyb+cpzeGn5En+d0A522RNSSiGlvENKeVfw79F2TK4eWSHxhCAnCmQLJb54aoa/t69Kgx1zL/GTf/oSfckIf/tBHSEld+fV2nF6Ua1En5t9jqRr8sNL3wSqggyUkik/CAaOypVJX4WuMYzydue/CmZUxWh2avC/LNid/LryeTJFFzsI6F9drDL0atxlqTUsmXolI4TKpq8vKVNGOVdmFZSVy0IhYOPkF1jRRKUjYrkJ1VL6CmdnV9AsdSyh55kOxu6Qte49zQ9Wo1IyressBIlzX0gliPo+xUCBFTTB940OY/iqb8qHrQyfTcXpCOiyrlC5F48mVJzHCyVRVmMljXRc2cxFtIbbpgatkifXWvk3S8TcirLYzHcbwXrYZqFtVi1T0wItGWar3A+tyWcx38dDLW5mg+dpKYgTPRdTi5AevRrsH73wxIbnupuxqzL+jaA17RdTCf7XM6+xmLN5f7TKoOoTy/z8+27jr/7JWyh6FxlzXXxfkQWmF88ipeT52ee5u2AzFLhCwpbMYq6a2DeozzKxlK8kYna6Czzt34rwHXjts8qaWcuScYprtmttO9ySEvhlBlexysGwXZ/feuw8f/58LeFhbqU6x7Alkyu5JCz1sqQiBpah1dKYnWK1jXK4blkZsa6K8slonfz4H51kvDx+crCiZHIll1OTjVZX2ZVZbqFspy9T1DTswFJIC5+k77Ocm+Hc7Ap6RCkjobkslZSC6hW1SlaT1Rq9n0sl0KTkSMkmr2kMuF6lsGRa03CFUNWFgYwm+O8z1yrxoCVD4z25PKeiQVBANHGEBa6Vxs9bKIpm8ZF1Cu0yc21DaDZ2uyynjWItN9oa5Wvq+9es65BrbdBkzIiUtbEcKYmGCAS5Oit2LmC+GX71+R7o3ZY0wh2LXaVkCr6a7rfiMZ499RoJS2df9mTl+Ytjc+dwAk0TnEuf57DtkDR6EFIynbnClcwV5gvzfHdpEcuHLs9jaqXq71/IX6783q9PMrmUg+VxStF+YpT4incPC5FReOXPYPhu1Yp1eRImnmuc7Px5+IVB+It/uP4TdIrV3iubRdlV1ndU/QwlPD53eZFPflE1agtjMacE8YG+BFdrYjIeiYhO0fEQQtCfjFTbMK/MqvP7k4+ovyuWTCjpMtZdcdn97ovLXHj5KZ78i/+kvksOVOb6sf/xLN//609gu7WCuqxkyjXIMksXKt91RjrJ+A6dnk+6lOb03CzoOWK2oikLU13Hbq3WkjHRcFGr+c8lEzxYKFZegpiUZDQNQ0pORZTyyAdlR96ZK9CV7a0IERf4W8srVeG2ERm32bjIKvs1ZaxtBtsYA9nScbdjXq0UWStXnZS4VJWTHiiX8jMgpGxwzVarVKv3xpCSZ7IjbTqB3YFdpWTKGeznTJPFmau88/gg2kKVUSYErIyfouSVuJqf5bDt8Nb84/R7HpfSE5V4zL0FG11I9rgeV5YuV/Z3C9XWqt3mNOm5SfBs5hwlcOZEL9+03gpXnoTugKr46X8Mv/2ORqbU+a+qn6c/33AaZxbP8OzMs43n9/gn4b8+1ChMXvlz+M9/A7x1NKIqu6D6jqifobjM+FIty66MMnX7rtEuMkWXdEBTXik6XFp5nfv/+0fJFIuqtEyZ5n3uy+rnxW8o5Rhyl+XtYIxoF5RWkGaCP3phjkcj/5ofnPwltX1iQLnSfI/vXFqsmUd5fnOBu6xsyZRLyoCiIWecHF2+x5Kb43Lw3fcEFkwkiM/EUVaaLhUJJIqNF8iBGcPg/kKRMxGLmO+T1jRymsaY43I6KA2S8jxMCVcsgx5tpabzYoenVVhpNVgtcL8atoNGvJF5XE8rpt3Huh4KVgiccqWGkELJC0HC85taRuUE3LSmuoLGfZ8H73zDktmx6JBB+XVdo8tf4AcOegg7V/NsaFef5tLyJXwkhx21/V7X48XZcX7nua/S40k0W7l09rguU6FcGd+p/q5FprHnLwMwF8jmvSMH+IOV+0D61R4z5Z7fS1UBCEDIQiJbG+D+0Oc+xMe+9LHGE5w/p0rYlGnTZVx5EmZfUYU610JFyRxTP0NKJlyPrRAqwVOudHDXqGKEXV3MI6VqvTzvP4tMnOTp8dP0hi2ZcEmYyedrlMyP/eELfNcvf51LORPp5CmanUwtV48tZ15W7jLp4eWq2c/heE+6lMYOSsbMZ9T1WMlU78+SvYwrXVKeJO3ZuLpSLneU1Bgd5jTSi+FLpWTKrZYlnqIWo3zpF4PA/buyeeYMnZIQ7HecijIxpGoadsaySIqVGkEyZ4qamljlJMw1A9zrpSpvlpq80ez5Vttuh9JZD2tsJym7Ve6VLwSaVKSP20p2023Ln1wK3Gadno984j9uYcK7D7tKyYzYitkjhSASmeABqdw+Vw2DTwcd7FLzL3BuSVFVDwWF8wZcDzdSYjr/IvcV8izQyYKm0en7LNgLlVW9JqvNyGRkoSLUyxWZjx09xgvFIZzeY3DuS9B9AL9cH2ypjjGSCSmZUJ0zJ1Rry6kPypcVxHRdomdZ6awnubJMX+7Yq36WqjGZiXRV0KdDXUDLFsQdIyr57OpinpLr4/mSolDC+7mpV2uLZBZCSubqU0rJ6BFen3d47Mwc3XGLJyZcXLvIhJ3g3o7qPIqXn67kykxPXuG7tZN8xvq/sS8/XdmmwiiTkvl84C7LKMWuheRCXAiWhY9mzSMkDAcLi6ixiPCS5IK2umld56ppUhCCsnq9s1jiXKBk7ikV8YPvjJCPPa3rSCHIaRovRq0aQTJtaDW5MH31LY83a9FsFltMbGw5XjO0izTQDubYVvffiPuyzn0mJSAED+eb58w5wfNx1TRACPo8j9mlNzL+dyxGQh0Ic4kJSq9+AQf4yYE+fqa/lxUh6M2e5Vz6HCaCIcflrGmy13VJazaumePuokNRc3h43wjfiMco4VZa+rp61eIomjlieVVWfmlFBbcP7FcussuDj8DVb8PALXhFxcbKTYeaWwH+8iSF1AH1R0jJnF2qbjeZnaw9wYqSqSsHU7Ga1qFkym67P/v7wYlULZmJkCWzXKgqmaWcTTy+zKSjXHhXFvKVMv+OUHM6s3SavmSEhZyN70s1bqwb+m+FK9+uJGI+G9Q/+9P//UHuPXYAQ7pMlmK8t6NapcG//HQl639++ioPay9yl3aBu776YXjiV8H3q/lLQjAVxGRWAsUT7ksfFQZpTaBZ83R4Gj+2J6gmoBeIS1VSJmx+lEKFKW+1bRaD0i8loZH0PBCiWqYEanzs34zX5je8EIlSDNFcr1ihzMB2CrZ2jNNubEUBhS2u9RAjtop2xXuaWJrl9CivfMplRRScoxSCiJSV6t9xX+J07VnnxG8O7Colc0ehSjeeimcQV57k9ztTnAmCtFdNg357nPNL59nva3yyt5sfGNnDiiZwA4qpnz/AlYRSKumAwjiTm6HkehSMKssqY7oMetfwY73I3AIFLcFtl/8Huib4VuStKMklMKVa2X/rO8/w7798htenVcOz/MI4X0kPkdE6a5TM+Mp409+RsqogwiVrpFQuNIC5M2tfpOwMWKEqr3XusnKjthpLJm9jjPwG/+cT/5zepM74Yp5s0QX8iuIdz12gLxnB8yXpgqPcZdFO2PcgjH9HldyJdfPqZIbuuMnezijHD46pOBlxSjPVeJcx+1LFklmen2KvWOCiP8SrHQ+p7qd/+CFmF6tB/iU3hy99lgMXXfhVtzSLnKYRic0TD7muSrpLUkYr9aOaIelLVQ0ZOBWxGA4skUtWqDp3ICyElLwSPGdmcJxXohYto2TtYm6tR0Bud4LlRrGeBMt6BbPa3LaTjFA/hw24z94csl6eitfWM0uExkr5fuXzJxMxfm/55m8TEsauUjJ/Xi7ZICWTEY9F0+W3ujorwuWCaWLicX7xNN2lEp/pUML2O0Gf7qTncapwH0tmbWnw6dw0ywWHrB4KhuswLObIxvbS6y+gawLz8V/mRJ/giaVuGDpB6Vp1dd4rF/nNb5znPb/2Ld7+K49h5WaYlj2ckaM1bq7FQpU9VqNk7KzKa0Eod1n5Yc8vBJ8D16qCuiVWZlXTsDKCWInr+cxkity2twOotWQWczYy4PHv7XW5uqgsGWGmQXhI32TJuUxvOCGzuKwC+/veouaemYB4D69OL3P7nhRCCPK6OtZgbw/v2FONt0SyExAkUhaWphgR81yUe/jF+E/B9/8qXHmSa098suaFv7B0noyjrMZSSC7oeqAQzDn8EI04q2n02UUWdL0p80tIyaKuVRInX41Y9AY5MDW5LIFwkKjMf1BtlgEmDGN1gVofm9kENXlTuTNb3W67UH/+m40dbReEWB9JMJj3s9Fo5aNXIqEGiAERoHxO9UL2VHZrLTh2G3aVkukNWtgiBBld42d6uzGpdud7JWKxIgTThTkumjDguvyj9DITgXDY53gsRsaUfzSE84vj5K5dUaXZgwcoq2mMiVmm6WdILGD4SsE93LOoGqTd9kEiS2f5SizGQ2PD7Imu8MxPfw+/+METHOtwsHDIyDivOsPIa69XWkYvFBcQUmL5slbJlK2YkfuUYskErrRyPKbnEMyfXbtHTHZW9a0vI4jRzK6U8HzJbXtV3CVT5y4rozOV58pCnlzJrSQ3utlbcMUKpqUU0fxKScVkop0w9mBwnFn8aBf6zCl+f/LdcP5rPDWuLMNDe/uIFOrYd/PnwYzjLs+wV8yxKDuYWC7CfR+Df/R1rplWJUcF4PNn/5z5QCGEyoSRD15kXzhkQ0+zLwQj9jLzLRLuYr7PjK5XEicvmmY1G7/JKtsAskEOTTnWly/nRGyny2eriZM7ETv4XKwNzM0PWcnZug6b4bbU2TprerBva+3jdxt2lZJ52aptMvZcPMY/XagGoC9YZsWfPm8Y/KuFJd6drZq0fZ7Lv9D/iGdDqw5dSs7Mj+NdebraJS+gJ5rGEpfcHvaKRbRAydwTnWE2U2Jh//cD8J96uljSdSa8ZfqSEf7um8f4L+9XPtd/af4Z94nTCDtbcXktZKfp9n3GXIeJsJIpl1g5/D3qZ9llVnaVHXmXqg22uEZJiuwsNUv3FRXbmAiSII8Hlky4GGi4srJhrjC9XCBdcCpKhtwdaiquIkLM52xymQW+cL7AlOxRbRCKaTIkeROqnpv8wk/y2Fl1Tl2pBDJ7DQfIlYXm1POQHMDMTfFEUvBH+86QzgaJnYO3Mbv3RE2dr29OPF7Jpg5jIdSwqr6V7lFvqVHJBN/HpGQ2yMqO+j5SCK6Uq/nWKQhNSsZsp/L515LxptttK673Sv96KIKdYL3UobRZpV5ntYYdYoU6q+0DnUNbmOHuw65SMqZf+1D2uR7vy1XLoFwxTV6IKgXypkKRd+YLHHEcOgNBZPiCI+6ZSqE6UP7S8fQEpamnK0He8kV5ImFxrthJt6iyQYYdJWifWe7gZf8AIogqX7RCWeshZtltWkA7Dlxmi+lL9Hoeo47LeCZESS5TgA88BEKrMszKlsyRd6qf89W4zOWp5/mFz/wgjhcoCd9XFpHvqL43oHq5UG1dcGwoha6JusB/9XebRXwJE0t5NGse6UU41qFqtV1aUXGS+ZUSbm6JjEzw1IUF5TJzi8w6MfYLZTm52YUKa+3qkk1KZvnxwX4e2D9KRqRg8gXsaD/93hyfGOjjvGUR88cp2EG9sOx0jeviQm6S6SYl3WdlnX879KIf8RZZrmt9XP7ekrJSlqYcvF9sYfX4QlBT7Hej8ZZ2Cuw6dtO24XrFQW4UWl279TL01rj25QVMecEqyvtISXo2stquNx12lZIpUNu2tMP3OBuybhY1jb/oSIKU/Nz8AgL4UiJO1Fc3t6TDKeMgXqj0gyElC/lxSgvVEjFlF8qXEwlmSiYaUMTiNX8fsbSiR3/x1Axf9u7lWvAwfSdu8W+/8IqiQ2eqrLHKs3pN9bBZKCzS4/mMui4T2Sn8csnwcqb/pceh/5Yqwyw9DpEO5UaDmuD/J7/yT/jj5Vd5/nTQXaGwpBSMk6928AyUVzlH5tX0N0kOfbUS+C+5Hlm7qqgzjlJK44sFNGse3+7jTWMj+E4HF9LnMTTBfLZE3M+yTIILc9nKsRazBW7R1bmbzgp7o+oYp2dWMCmqVrXAFZlCTr7AS0sWA6LajfNQ9CVen1GkjGvFhYaKxTNNlMByQFEWTd75ouFVWD0VBMLBkLAcairW7Xk17o/wtgjB0lYYSu0UqpuJ67TCDnZbNaAdc91ABYVVsZa1IyUy7EqVoRbZQvBSZ5NE7JsYu0rJdOu1YmfCMPhskB+DlLhCMG6aDLkeo66HB/yb3m7lZhGCC6aFT21RRgfw7YssFxsTHV+OWGSChfJZf5jTcpRE5hxjPXGev7LEGeNopY7RyUiU1y9e5bGzc/jLUzXPX0HEK5bMgpOpWDIl32YuyAGp1P567Jdg8A7lLiszy7rGVPwjtUclbAaIB8rhzExQ1qZcGNPOqbIymgElZYVNpgv0Ji1++qlP4Hd+pWLJpPMOwqhaahlHJUfOLBcrSuaWPSn80hBXVi7Qk7BIZzKY0gaJst4GVAazk7nGMW0S31SLgXeMqoswvbjM+Wj1UbtgRBErU1zJajVWYip+jlMTaYpukQxeTVOuuG+oNrt1DKBcMKzRhBl02rJaltbXkTXVkluU/axgrt5VdzMI6BtpUWzGHbVVrKagt8r8a0ZiqP8s+HxAtujJdJNiVymZWbPWNWJrGv8rEa9h8ggpebBYREp4MRJhWdcrq4lrus5UpFbslDSNeV1w3mp0xZQ0jUJcFdB80T/EOX+ETmeO+wYFU+kCd/ZrlXLhS7rGoc4sv/zoaUpzl2qeP1/KqpJxC0rJBHTZSvB/OZQz0zEMuWuq4Vf6Kk7HsFJGfUdr3GXpIE50cvak+iBc1Tg1BFZC9XlB5cgM9FTpzOmg6+NizkbooXpl3gKWoTGzsoIwl+h1dPjcTyBKA8yVxulNGuQzgfXR/ziLpX/DdOCy6iyMk5IrvJZ8MwB3dapxO8gpJlaAieDXlAVFvRpTKUQWOHl1mWtTSmmGV38ebmMRRClxgnvuCxpe+mei0ZZZ9A61PvR8M6pz6PsGK6edAnq7FdZalOIbgZ3gMtsKzbxewaxHaQWf3bu08bbjuxm7SsnkZJXCXEa2rEQCSCE4WrJBwC/0dtfs72nwrSBmUx7DEao8xFmzSYs9Kcl1KpryPjHDxw3llrrFmMaXcKt1rUYQdu09xZnZFZbHX60ZxpIlmDtDvpShiEfUNRl16pRMyEKhnJM+9SKkx/m9iMd7P/Ne8r0HYe5sZe6vBYrxdCkI0K+ElExySFk/QbO2yXSBzo6qklksKkWxFFIyw46LzSKj3THmi9MIIfle9zwf4uu8jTQ+DqnUMqWVBS6aBr/V1cnVxGXe+8RP8htdnQwIpZB/f14V54wU1Hz2x20mQoy+8aiHKzWGo3aN8pm3XNyr32H2zz5S+axsKZbKT2r9alSoqFiDAhKCVyOt2yautIjVrIntEMw3SuC2yrHZ7Dm2c6x2j7EW1huLWW3fVkmmdWM/n73OldlvMHaVkqmsbdd4EAwp+VRHinOh5LnbgppWTwRxgfIY5XX0eat5MO5ccoW8EJSSF/mZgRQAqYxSCFp+ouYBe1F+m3v2pYjklOIo20ym8MF3WJhWFofnpUg5JhqiqmRCdbly6WuAUEmOpWVOUiTn5Hgl0QH2StDSuMpgmcfj8bNzjZZMrBekj++6TKYLJGJViyVdUkpmMW8T1xVDL6cJ0JcZ6Ymx4qpj7HUkPvBhW83djM1SWFngO9EoUgg+NTnLg/Ej/NfuTj4y2snnEnEec27FRSe/MIknBd0s12TRz1oe58QYCXuOk9GqIlgwBP8+96+ZpVoDrELGWC1RrtXzsMpz0tCwKjz+RhID15PAt5PRTvr1dlG5d4LV0wSaV5tOoAfPQmW2Leb9Nf/I9k5sh2FXKRnTW99K6fc6O/iPPV2VvzXgh9MqoLxcHzwOHoRreqO7DCEoafBYPMbnUwm+nEwwp2nEF14GYMYer9k2L4o8dPc4HTLPY7EYdx4YYzy0Ul+YCRhjbpJF2UWfrzNRLtCZm6ucymuvvkS+4yBMPAPA6yUVJ3mBwJKbP4uzPFkJapc0wY/8wbeZnboCeqAsv/2bELDOFmbHsV0fzazSvVccFQNaytkMGWoOaV0HzSMZsykF5WTc6AIPjw1zhz0LUuAb04jiMjOGjiEl99gljp6M83tTM/R7Hv96oI+VfX/CRdFLKTOHi4FZXORySMnMmT4n3QP0O9O8EEpoKwjBebmH2bqn0vR9ZdGstdrcQGC3xvKR61u8NEUddXVXYTcpxx3O44S8AAAgAElEQVQ4V79OlhhQsaxXQ0/nG6X+dyyGS6FM9laCQUrGLVPRBoMEyJIQ3OqsHmzLt7gSnZ7Ho4k4LwRW0WnL4s32s8QtnWtabaOtlKPx2NT/RAifzwW5FF8IfkrgWlBexnU7mJE9DNnVhExZzFROZUhe44tLe8iNv8K8rnHNVQH+k0EtNebOMn/hqzXnPtSV54VXT+PqUSQCXvlTZFAheilw32VlVSmWmMb3JYs5hx6j1kcstTTSnAcvwZxVYknXuWh10+9o5OQ4KfJMGwaDrocGPJia5e6Sze9PXeOn0iX0+Difj3dgOCvYwqJD5LkWCpwv6hovy4MkRYGz4cRYIfiy1cucrmGFSsQYrKMp1Xr84qtZQmXX0VaEWbsE4fUaZ4s03Q1vt5X9d4Eirzyxa8x1Nndq2+eyk7CrlEynVi3wuNaN1KVEll0iQnDatGpap8ZCpm7E91X73PocBCnZ7zg8EY+xHAjJp60OhrQ0H+i5yql4reI6bue4sHKVx2OxisX0nWCl7kmNqTkV3ym6XczSzYhdYjw7DsVl1XEzOGyvyNB58D4S/gqvlYsuSslLS6dxIx0wf4bZyadrjv2v3mExqC2TtiGPOqaQ6hwLQfHOnFctaSOseVaKLtdWiiT0pZqxCv4CmjVH1E4xGSiBb3bdz532CgvFC/SIDLOGzp6AvHBYTOJgIBB8cDmH7yZ5LmYQo0TWtzCwWSrfCynJaoILUtUumwvlEwB8c+Aas4ZekzHtbtZaaBH0r0ELBlBLbMZtt1G0ipVsZpzrsf/1Os71wBauu7PO83hvX8/aG91E2FVKZtBdi2hKNdZS96I+mkzQFVTZBSiETF2zzGOve0gsKUn4smYV/XygND6e/03ORmrN5UXdotMx+P+6UrwW5O+UA9s6Pks5laSZ8/q4Jrs54uRYLi2TCdxi5SnEZIkHHnwrAKci1cJ7ea/IM8kRViZeZXIhTBSATPY0x5J5fAlFWev68xZUC4N8qWqxCDNNumAzvpjH0GtLj6ftBTRrgR7HrLj7nonv55DjkPHnOSQuMW0YDAWKOpKfZlnGMfBJ+lnuLZS4EnUw8cgRZc6sXtsR11V5JxGwpab6tkhJRzDW+YjBedOs+LUjnl/78m5ECKzHrbbROML1co/tJMG7k9HuRNd2KfjyeE3+Hq3vF3WTY1cpmclmcZN6BDdSq7vB34zHiPjNYzq5ULe7CoTAFoKrhkHEq77wE0HIY499mXRodQ4wZwi+xxvlxWiUlXKFZ0P1lBcC8hRIeD7fGH2OPxm+zH5XWULjVx6vmZIQ8LvPKsbYmWgUMzTvTxct8lOvc25homafJy+exM/MYAqfSRMeGdnLPx3oA2Bp+hKdMZOsvayabEmJaSyxXHBYWF6hpIfYLhLmS1NoZoZh22EyUDIzXpZicRQEpKJXuaZXLZkIJYqyGnN50J1jxbApaYLuri6mTHUtIr5fiSON9KS5JFKVfTJ6dZtx06iQGkrrZYFtRCivZ9sdGAOoYCfPbTuwESLGVrAVhll5v7XyZYBPueuopn4TYVcpmXPRdSiZ4Mb6we9lVlJJ05g2mwf3Wz3CGjBhGoTydVnWJXkheMwcxK8rkJjVBR9aThMPs06EYEbXkBIWDB0hJflohvn4ciVX5upsXf8Y4Mzpl/H1CK+bGm5wmLiv44x1MCjSZK1aV92lzDgpUaDT9PhqymTKNPh6Is550yTpzBHRBQXpkfAlOmAZyywXHFLZS6SN6mMQkZKMpyyuMS+HHZzjijfNM4WHAJiLZfCEoD/orqmhLDUATwoOOG6lFliX6VUUVUnTmA9+LxTP8ZkOq2oZBC9o3PfxAgVfwVpCNfx9s3bI60G9G2w9eQ9rzWUr2G7BupsU1W6y6po9N3XK6/3e4HWe1I3FrlIyXf76XnwR+twPuzc26Esvd0i0tVrh85KZ4Mm40TCeCwwUJjhm1yaNPh6LIYQqe1MKZZnHPSUQryxfAeBr8Rj/LmDFneAic9Jn2jAqNN5Ox+d1X5ENpozaOdumoidrTp4nYxaHbRvLl/xxR5J+MhzMP4MvBIuGjicEQiuSzjuMORdryqtYUuIKZUXtRcVqfDdJSczxivddWL7kXOAm/MX+HqYDCySplXBNVXxzf9Cd8pJloi+cZcIwau4JQCpyic91KrMw/F05KbJloH8N4djneuvargFrCbLrFZ/Y7Dg3IoP+DayNJtf5MS3VZMObF7tKyXS7dQyxFi9Ks/gKAHXurQbU7WOHiANhPGX18kKiruR+WSGJFYpa7faPB7k587peE184ZVnE/AhTnoqJ/LPBfn6vUwnq49plLtTFfGLSZcpe4pquM1PnOswFJXdmdZ2zEYv3ZnM8ksvxuWSCmJbhI/HP1GzvaJLlgs0ReZlsMN8B10VDQhCj6dDUTy+/H6mnKXgeCTvJhVC9uC8kE0gJcQqsiCS6kOwLEk0vmwYuGpOG3vCgTcUKLAcWlAyt9ErhVZ+U3F0s1roh1rBw3M0Iz61QmHcKbvTcd5NlBDd0vndYzQux3qzYVUpGW4uBvpaw2KgwaZHB/GJMqETPJuPMGkFzKykruaNnIha+hGt11Qm+mojRa2tM1NXFKgrBQTFdZZYF8IMqkC9Fo8yH95GSXPDnE0GDtrfmi/zgSpa8pvHNpMA0x2vHAibTWY6Ky6olMXCr7eAKgaYX0Jwk84ZqXWwmX0MIiWYuYZeGmQwd+5loBCHUvUnZKoHz08kECd/nsmmiST9wOdZi3GzxooWvqRCcjEab05PD24a+S5tG6+1D16vlMbdL+GynUNsJAn4ryv2vGb6c28WLmU1gVymZW0trsMtWcYuJtQRPq/HKvvqQgjoVb9I4LBj7hVik2kc+ONy8rlMUUKgLYj8bizFsO4ybBsuhDPRlodEnMrwWsUh5XuXYeQ2QBifjyZrtQa3gV4TgiXiMQdflsONwomRza8nmTzqTXK7rxYMQnJ59nb2mIhBoUnLQdlTvC80n5USYMA0sCWjKSoqNfopi8iK5EDPvZFCmRwRjTGs6v9TXQ07TOBsol0nDqBS7NOpp4uvBRhcF7XJ9tRPbeczrdT4bSHZdF2609XWDsGzO3egpXFfsKiXzxXLF5U1gS6+DEKpkRABXE4ql1QRficcbDuoLweOxWMO2aV1jv1tkVtc5HSIlZHQNQ/i8bpkkQsyyFV3DK4zwvFXt6FieH8AV3eTbsShvzRcQKMH/4cwK5y2Lk5HGsjm9S3+JHtQtS/k+g55bGbfHlYwbBn7oyhkiylgxUFbB9ShqGtcC5ekheD5aPc5l0yQrRLXKgpTEAnbbqlTg8rW+EYmRmw34bxU7cVW/1WTOnYwbNXcp+XBpee3tbiLsKiXTtFJuM6zmWlnPy1xnuQC1Qh0qLqZ6hOMV4a5bf9GRbNhWCgGajRSC3+7urHz+QsRiWRNMmCaOqM6hIAReYYSzLVxNTyYS5DSNtxSK/GFHki8k4rw7lyfl+bweMRu2T/EK6SDo3+H7NZq43y9y1bAqzDZNSnT7CJ+Yy1avS3CN/mdKBTJNIbkSuKv6XRdH0xp6n6+06FS56mfrzcRvonh3DW7kfLeSXLoZy/SvM4Tgm33HbvQsrit2lZI5XHK3Psh6Xpwmq2xZp6SiYZpy6OWyQ8JXurHK9y9GIg3bIiWngz4rT4dqeH0pEed0EI9Jh5WZEPTaomVw+ztRC0NKSkLwy709fGKgD92XfCCbVV0f64RA2iyQDqyQKcPgl0OZyN0yRzrEYBNA0b/GXf61ai5S8N0XknHKBtfj8RgIQSKw9B5LVM8rfB5Nf6//bCcoip0wh+3G9WbOtQO7WKHdnvo7N3oK1xW7Ssk4q832ej10gRIRITOlp1k1VkCWM++FoKg3YaoJwcuB8rFC8z8dsSpBf6/OYjourrSc2pmIyT3FEudC1tQLsQh/J5NtQVLQmRJKEdZThjW9UDNfTwji1iSa5jZYdzOmyctRi3lN43RQ461cEPPpJm5CYOOZ++sNzq/HMtrIvu3Ytp3YxcK1rdgKeedGQkoO5b99o2dxXbGrlEynuwFhsRVhs459wjTlemujLHw1c6V+t8bhA6WVCMV4VjSNlyMWKa8x7nOtc1x93mReGV3joXyB05bFPsch6vt8Ix5nzHGbEh/mdJ2lZu4rKVk2Gl9iz1ypaYFcyW+Rkj9NJfnVnq7aIoFSMm4amM2u4WpCYq24yEb2XWv7zWy30W23gmbuqDY+x5vGRu/pZsZrB3aS9QUgBBevPXqjZ3FdsauUjCW91g9jvQsmnFvRbJuNoJ5hRi2RoL5rotzgcQSQrVNUL0YjlJoMcykma6yeepwolThtWZwo2jxQKPL1RAyvbr5lpHWNvFabe1QmDMzWKx/A1eCVUGxnKKhYgBA8mkzw2VSy4Rr7QlCbmlqHjcZathO7Ib6wGmGiHqtVMGjHPNYad7PXcKde+60iOK+S33+DJ3J9sauUzEuxSPOHeiuBy7UQfpma/d4Kq6w6tXAZeymRQjRUcJ03DJUMWre/IaWqaNziOvy7nm4WDJ1vxmMcL9nMGAZPtbhuRU1jvC5HRwrlCFww9KZC95sh9ly5qCVSFRGN1zHuYmXFfL0ZW+1glm2HYtvMvNYzp+18/teLVnPYDE19p1kf0J5ntszc3FVSd+vYVacrYWtm+vVMiKtf6YW+H3Xdyt+G77ekQzeDLoN+9i2OfyqgEK/oGk/GY2hS8qmgikAzvBqtC8xLiS9ErQswhNdC8R5VvNOvxmjqLkGhVYWFeoHS7vvSDiG1Hc/KThD624VW57aRc253Hk470cZ7Z+uNTNObGW1RMkKIdwshzgghzgshPtGOMZvBpYUF0e6kvvqx1yMINzD2FataLcDWNG4r2U3315pYATXU6Xp3iBDcl6/23Bk3De4slVTWfAvM11kyCFE9bvi8y0F+o7acTdkCs3xfkRuaKZS16OM7ceV6PebULiG63jjUVtxXG923mQW7UewEWvc2KLqlaHHtjW4ibFnJCCF04DeB9wDHgQ8LIY5vddymx2o9ifUNsBV3xXrZTeXvW63epeT+QlUR+NBoyQT7tzxKg5VUnZMemt+iruMh8LSNvaz1OUFhOPUWTrDt+1aytfNv9nMnKpN2YqPstK3ECLey7Ubfg2bu4bWs7/r3ZbOxt40yCduFbXpWhZR8OPNG7bKN4n7gvJTyopTSBv4YeH8bxm1AoqmfaANo14OzhXEsKXndqk1QPBWty8YPxpdhS2KdczprmjXze7l+7DDasdosHx5RbZm83jyX9bhHbqTLZDPCeL37bUXBbGDfnjBDsV3Pv5SIDTLx2i6yr2fKQhshheAZYa294U2EdiiZYSBcfXEi+Kzt8LR1WBA7AasQAyQqXhJGQxZ8eYzwz8oAzWJS1V+X6txf3a7XaFHUH2M1rHNF+nI0oqyoZhbfdlCIN4rVVtPtduNt53lsMMaxrDdxr7bBlWZu0IW2KuNyE4sLq40LpC1hEzLHL2a2YSI7F9ct8C+E+BEhxHNCiOfm5jZXIK7fWcPM3K6HbjPU1hbbNbibNjLmeiBETf7Kkq5Vb/JG2UnUMuEqY9QLGCm5ZBrVQP8aY9aM1Y5twlhNYaw3OL2V+7FdC51Nunpb9uXZyDGaLE7s0L1O1Fe/2GgAv1VMaRVFEtlMnGg7sAmZ80RPfhsmsnPRDiUzCYyG/h4JPquBlPK/SSnvk1Le19+/OZ64udmuh42T2dj2m6FXbtCd0IDVhOUa86+MFgjWNa/aKnOtMexbXQdR18lyA3NdN9pF7tgOV1Y9AWO9x9kI2rCAanjZmxBHmimIZkLiE/OLqkI4qPbloXHqk28TTZKKG7CR2IuU1b5Duw1S0mG+0Rlzo3gWOCKEOCCEsIAfBD7bhnEbMLOO7ss7Huu1ilYT0KsJsoCCXNnU31qAubiZoqThFej1joNtJCAdxlbosxshhbQDa7mXmrlHy8/FWgqx7voIKRuURpfr8fdWsrwrl6/uE9omWXcP9jvOpqyZDq9JS41gW7tVrlgr7JR4nxA8OH/5+h/3BmLLSkZK6QIfB74EvA78qZTy1a2O2wyaaJMlc6NWQdfDj1w3vtzs4aSkZQhsLUG9Uf97O7FepViP+jlvNR6z3XGZ1eJrTY7d0oqpeya1unGlENSXpT3mqCoR9xVD/Z1CY1QqQQS4Z60+UPVzClBxia3XnbnKs9W0tNEa+6yKVnHOdez3V6lDmzvmLkVbYjJSykellEellIeklL/QjjGbYa+7aoGSnY+NCrKNft/swd+CsJSCtV+ijcaodqubY6chrGhaWWFC8P9cm8eQkrjfIoYRtmaC7+8oFmu+r4/rnCgqJXO7XdcOHWX5HLTdmnkcLdqtLfO6ZzVc3XzBMJo/45t4hlrWz7veiwkh6DE6197uJsKuyvi3W+V77JQgILR/Hpt127Tj0PVjbiaA+4Zy2T60EnShvy9aFh4QlX6tUG1xP3xg2KmzXeoUxAMFpYRG3cZagknf59aQ8nkwX2DRqlYjb3oOoTGKAdNS1Ll9G85tNYXZBMVNkF62tO0q6NNm2jLObsGuUjIrrVwhW1mRtBPb4Q7bzHhtoKg2HWczbrB2BOJ3ygLiRqLuGojw36tcn68lYkghMNe6hOV3SAjO1rfqBnpDFsYdgRLxsRoEiCUlJ8ruMSH45NwC34iv3u5Bq/sboMPzGapXdvXzbTJWw+8BWiYYtyA7rOuYm8TI0oW2jLNbsKuUzF3Fdfp2V8N2Cqz1PITXS2DWu1K2K06ylttgs77rZse43tgKGWCjY6+FumvQF457rLK6nwz6+ghkrWJaBVfNUIttKen0PO4uKcUiZFAFXOjM995H1K8V7p4Q7LOrsdOXIlZtd9Tak4IwsSB0jgOey9uyLQL/qJp/LbGaBbSe7bf5eft64o3aZTsWs4ax9Re93Q/QRs3t6yUwd4JlF8ZqK8kwdpJlsx3Cp03uw+5mpYhWuZa2qMu6X+UaluvRCSkxgeO2zRFbxUOlEHw2mYCuMawDD9DjhxSBEKwIjazfVRn/v3R1ts7VEWqf+grkAPttl49kF1rOMbLRQP56P283QaAJ7nJ61t7oJsKuUjLfnVulsNyNcqlcL2G+mfNbr1LbzDmsdz6bCIy2fcydhLJ7ZiOU2iaf20LUWiZh2nq9GwrVWK/GZdQs1tagABWz7JaSw3BgOaU8j9/o7qQw9gAd/SOM1Lm0PE3wil7NAzkVjWCs8aw0UKuBYc+l042rc2miCHL1VTLWcp+t95lpNU4bn7meXGsL7WbErlIyf9DZ2BRr07hBHPnruu9Wr9Vq12g3CfqdGNPZiEunyefjptmyVEtcSjRZ6x5zK20XVhm/wXJTlsutts09AePsH6UzXDMM/jCmY3YOcdgJMT6D412IdVRakNccU7ZgmNFYJPavUkn+fa+hzrGJQtSkrNbKW+tc1nKVrUUcajOx6H91zrdtrN2AXaVkcqsxSzaK6y0kd6KgWwvrvUabdTFcL9fY9ViYbMgyWedYrYLZQeyj1Ridnke35ze4qQxfKhdVE8sg5XkNCqisxI6VbIZdySuXxvloZoW35Qv8zsJzpCNJjpaqSqYjEPozloEVStByg2NKadCqMFSsrhlRp+fzVx3JBgGvQaV766ijLILOcn+mVe6BvoqiWNONWFZ0G7nHq2DAf6MK845F5bndyCrwegr3zaz8d6PyqUerF3At4b7TXGPtIies5WJp9nEz11do/0pBypDr50ShCRFGSro8yajjNMypHJVp9tIfLrmYTaqcm75kv+sG+kf9/88W0+S8Iv9t8qvcYVfnMOq4WL6kT5yiy1dutHsKBZX/ItX+9Qm+ZWukXFmiM3DLvTOoJrDHdWvOwxeCEdfFC84JwBMq+79lW3IplcuuxXNqbiVWuglCzf3DP7C5Y+1S7Cols1YR5qbY6W6dGzG/9QrTnRSEvx7HaTc7sI3uxv3l2EcoFmNoTY4hBB5GTa6KCISoE7zt8TpGGEIg0HFEo+W0x9EC66H63WHH4f0H38cfX/pCTYWAI7bNqOvwncgwrqMSDs8GbS360RGaW9vVVQZ10aRUDe+gEmv5i1SSe4pF/sViuub0ul2XewslEIKLEcWCywbN8qKrEM5K9TEsqJQgqrDb6pXNZpXPGouM9MouTyrfIHaVkhmyV+HNbxa71ZK4EUK3GRNnLQrzds3lRmCtfKytBInX2Cfl+5VcFSkEEd/njGWxz2kUWDl0DoQC8vWxmyGntsdM1PdZ0E0lDerccqOOottqdb60Hxt7D5rQ+M89ffxvy6p0/e0lmzHH5YquM+vuAwIFAIxZXQANLjxXCGK+qFCh3eC4i4bOjy+kK4SDMt5cKHIkUKBhy0WjTlmEzq/q7qq9f72BkrGbPcPNFMxmyQN1uMs+vb5xbhLsKiUz6rXBx7+d2GoMYyPbXW8SQbP9doLgbxc2Gm9pgvq6Xy2TYle5bq0qjc/rOm/NV9mVD+SL5DWNO0ulhvlldY2BuuKSpldNsByzqdnnqO0waam/q7wyNf8hbS9+EzExtHCRj9z6Eb4Yj9AdVFk+4LiMuS4lI4PrdtVs3xXtbjwpodhrXZ7FO3KlmmvzcK6AVhpjr1vbRmCvK3k1omqDH7ftCsFAMeeqpIAGSImsOw3dSYCUTSnUfe72McC+WpretrF3InaVkpk2ygyZJitquDEMrM1glaBtw3ZvYHuxmWenxb2rUIRXCdivNU5DSRcg5vuMmwb3h2qKvSeXA8ClMf9lxfAYqBOSRbuamzHk1Sq6e4tFXAHS1xroxvvj+5EddT0I9QhMPMvHTnyMFBq/1qMUyqgNY46Hp/lIL1p7qmY8xDKrtTT6GOBvZpdrrOJ/uOyiRbvp8v2q4hWCvAaPppR1tSJ0fmKp6k7L6RpIqD1y6Hh1lzuh5VrGar4/m6sfpW3IeW+UldmxmLYCVsZuE76bCYq32m8z2+x2tDOGVI9W1sZq22yAalz/mdbi9/JxmwWvj5ZspBAkfMmxks2PL6ZV8UjgXMSqKa0fkRJPQLRuHOkmsbQIMTQGvVpFdnexRFLqSBmpxGskKpZzqOsQ+sAx3PBsew/BxHN0WB18zNgLwH7boUPq7BVKxHeatWeX0QBPWTP1imwkdif3FUvEgvMYtR36zBEOMY4rNQZDVtkXk3H22w5ImDFMPrq8wn+YVU0Qi5qG4UUai2EKoSye0K3QpGTeELTqHvLeXL6Whl25kOtcNKyy3QPFN5Ixdyz0VjJkpyudrbqntsttdiOxWeW42kvermux2nVvRoVd9zwEw/YqQV8hmDUa6a1vzxcAmDIN/nxqhh9ZzvBiNEKH53HOsmqsn65AUJc0DSHBzR1EelE6ExpxM0avHmPYq12l97sef8OLIIRNQasu5Po9j4H+Eeg5iAAmCJIsB2+Ha69BaYWP9N7JD6Wz/Nq1OXRNMJYYAWCfNltzfa6W0njFfgzXqgj2ciD+lpHvQwOOB6VrDroOerKXVGmG03K0JuEzo2n8v3OLRD2dnKYKei6FEjM1p6NpKtDHlmtbHvd6Hiu63rJf0pArqy0LQve8xp252uJjlTydQmJ/02PerNhVSsbyV2nvu5bQullX/KvQNm/o8dfCRhTCOl/etqOVZbKR2FTd9dGEqLht6t1rpi9ZCWeyB5+/rViiz/V41bIoSAsJnIxEuLukVvjl5Euo9nKZj8TpwUQ63WTP/gx6JI2hGfRGe2pLwQBJT+Nd+RWE5mFrMBCMcch2GBjaCz2H0PHpl0GZl+F7QPq8+PTX+fJl+KmlRQ46LiYOw3vuxPIlPdp4zTHGs5N4dh+601HJlREoi+LOoWFKqX18KLMCwEO5Ah2aUsZ/Lt7FWCj4/3czK4yULLo9FcefNAxmDL2isPpLVm2nTiDm+Xw8ncHwqnZL2Z1olZl2oXtoSIlv9HEiUHrhPJkRd4Pkoybvx8nexgKkNzN2lZLJ1guYZrkFrbBbV/ywegznRp/XjT5+u9GMobQV1F2fZV1DE6oGn+HXBtv3u3XdKwM3zwHb4bht81rEIiZsJg2deUPnrq47OFEsMWEaFYW1P2BgzsRSDEiBMDLELEnGmcOXPr3JIXrqSAG6F+Mti9MIqYTwWwKCwR2lEl29Q8o9BkSEGvvXzyl3z5e//HmemqkqRd230bv30eNZaGK+chqdvkDig3DA6a60A/eBHs/n2OI3mIkf5T25Il8an+RvZ3PEsldhz12kxx5hb0iwf3wpjSEkY0HjtIumyayuEwm40e+ypxoYbG9dTCAQDNjVatA5qX5/S1aih5Q0UtLh+WT0Th7I1VqdGrC3WSvpDS5wi0tvJGPuWNha3Q3eLgF3o6yedrrF1lpd36yWXSts1PLdJFatdCwgret0WX0gBCnf47ZiSRk2UnLY7wVq2VH9roeG5HjJ5rJpkBeCk0FV4/v6budduTzXjOoK/ZDtYkjJTDRJr+sjjAyP3KXYWCWvRE9qlI4QSzPu+yyKQeLSZ684CMBTcRVXub9QQsR7oedgzWn88TnBjDXGR/fN83MfeUfNd7JzhKQ2yKwpEYF42YNauVsdr+G63ZRCAf5BR5A4+5ec8vehC1lhk2mZcTj6bk4cOcigU51vQkIXWe5wsgA8GjvGjGFgSuh1fd7pNDK3+jOHKcYGOW5XhfuUoeb0pkw/Hb5fQ57o8T1kMUOiMBSclDp+p+cpGvlasZowhGgQsqPRw823vUmxq5TMe7L5m1s4Xi+rYK18j/XiRt6LjbpH13Jx1bPM6nMmVrNwQt/317PDmmx/ou8OAHKaRkZX2eoRKSnZSnmEg9Hl2mDHbRtfqF4vL0YjJH2f280I78zna8beb/sMeJJZK0JvqYRmLrNkTwGQd/L0xvuJadVS8/2ex5XoLQB8l6lyW1S1czjsaGBGoWuMsKj41sh/YcjI0z/zONbXf77m+K+spEgkjzNuGCRddSYJPaG+1O1KbL8AACAASURBVPJ40cWaQp1JfxRx+QlezsQbr+veu3jgUB/CVfsPOT4+GkudxzkQXJfnzQTTQRyrz9HpdKI1111IyVX3EE7HPr7LqVYnsI0SQkqODdzOXleV4PnHi0tK8bkee+Q1LjlHAsJAYCU6Lr3NLBlWiRdLSf0eV+yLLTa+ObGrlEw993/bsFNdQO1cjbdDQdzI69Ru9+hqiaetlHKTa7gQsiqkbBxXR/ChW94PqJL687oOUtLp+1xSqYjV5EDg1iAucEugu16NWJyMRrijWMIoLLHX9SvJiQCjrku/nmAKnyGniNCLXFlRTbIkkt5YL4YQdAbvUo/ncaH3IQAeMZOVeJElQTeDXBfdhO59wbXQ0WKdEOsG3wVZK0J/62SRke6jlDSNPl+53aQRQ/oWR+PvgJiaS1dwfDP5MACJ7NXG63vuKxzf00HS7uVoQfBLs0tkZIxfcX6APcH+jjHLrG7gaJIuN8oT8Q/Q77rEgnuT9H3OyBFEzwFudWtbBwy5Ln3DRziMsvS8IJYz7HhYwuOUPESnW73Hd5RKytXYZMESl34DRbr8Xf32OX2VavI3IXaVkhmPdKyPcXWzoVUux2aE/Gp5IX+drmkzhCySDXnNQ4LE06rPZ7NLfMjq5d7Bu9UmQCHYqNfzmIlnMdBq7s0dQW5Mv5Gkx/N4JhrlvGlyV6kEKzPIeK+y8APs94oMxAeZcEuMeWrfefc1DFQM4le+MIFmZysJlD2ej7PnPoj1cNibx3eUYumzdbxoiGobxGXoOwo/9Gn4W59Sf3/Xx9Xfe+5CIvjqhEZEKhaaEcRwFqWPX+rnh479KFowj7IiHdnzZjK9d/JO/QVKAf1ZAvQehpN/gJadwUwO8+vTGe51cugde3ihOKxqmgFLlo2tCYqaoEN28Nrw32K/42IEj3Kv5zMuB7H6DzJkL1ZOR/MijLoen3rV49aoygMqd/DsL6mFwkXzGEOlasO1e4olesrssrp3Jeq3eHfqiR9ScjSfaL7tTYpdpWSejIRe/bUSGm+EwNyuY15PN9pWsVMVVX0caq24VMg9lvRqG3OtCy2U+P29t5GyUkEiSrXkScrzKRhFeoxUzbz2BMpAjyQ5XrL5eiKOFILjRQnpK4jeQ7w7SMz8saU0BjDce4QFWaS/HDCPXcS1UwA8PBDFEi7dAcOs05MM9nRBz0E6C+N4hVEAjpZ89ERIyfQESqbngPo5cBzMBEw8C4feDoO3QWqIwa4U3zmjNnkkl+MDK1kW/SK+3c+b941iLr8XgLymEifv2XOYFzrfya3aVexYn7p0AA/8mLKUnvp1Ev372CuWEEBq71H+4P/4AF2O1mA5pLR++gaGsEqd5ANl3+95aGYEq/8QHcG19O1eDKmKb76U7eSVq2MAXAo6gh6RBsT7sJPD9BaqlQruK5ZIuM3vq1nOxalH3aJYAPfqbyiZHQvfDfy2q63Gywi+a3rj67FNTKJNH3M7BXU7FdZqL9VWx2nHGKuM2yzpUferisXTNBWAFwLLq+3IOlCuF7bGuQ75da+XEHR1DPPTT/w09ZWYy3GKdF0OS68UoBkITeeWUFn9ozIBi5cQg7ex1/V56dJVfjSdwUPQ3XM7nvAr3SOF8LlzeA8AP35cWQtdgcCNeSZ7u2LQewgjfYnI8odwVo7xowsFop391YmUlYsVxHM0XVGZJ55Vfy9PIDpH+PjbD3N6Ukf6BhfiR/mE301GLhNjiM+/PMX89F0YgTGQ9DSODXbzl/b9eGgkrRC1946/rf499zvsGaw2QWPoBL2pGNe0IRJ1VaM7IyOM9cSZLN1SYZiNOJK+ZATRcwAL0HwdN3sM2yixx/X41R95H1P6/Sp2Euxzv7Rh+B72dsdJ5dR167BNOqRkyh2jAULgCA2jqb8MwuVyYr7PPXKp+XY3KXaVkgmzUtaLdUVx6sYTq/UPXw0bEZbrUJBtP+aNGG89WMsq3cq4zX6HajmREPp9Twnm4PMyy2tRC/W8hxo2V9M6WQASvmdlGWTtMX7jzP/ka1e/RpTavvcLmoYhdDRNr1n9dnWMQOcoSI9jQSLnQdslYqagmIaeQyyJ7sqLPKENM7mUKE+hgrihFmi9i1eRQqczYJhJN8HerqhikC1PcLS7m+LERxkVK1ipkJKJpII5hd6NkTfBzCvgFCAzCR3DfOjeEUa6E+D28JQ5wsRH/giAvsgIv/zFMxwdTDFkq+vZW4ox0h3jW1OCc8k3IXJBAqduqeO99Z+DW2Qg82rtMYEFay+DTu172pU4xL7eOIulKhtu0DHoT0WgWynJiK+jRWZBwJAW5cCePtyeYySDBYYmJaniAuy9h329cYqlUf7ttXl+d0rFc1707qEZCprW0sVaKW4tJQVN45IYabHlzYldpWRu947UfrCe1esmyre06ji4JlrttxEBulELpx2Ww/VIqGz3WPVsr/XsH9qm0CTTe5/j1pQkqXSTNGwQoRphISWQ8vwW9wVuK5UwwkpGSj5w6AN85Qe+wl3dx2s2v2yZSCQ/fPsPA3BLyabb9zFG3wzd+8Ep8nAhzz9eWuY/TGXRzYBF1XOASetAZZyL0dt4+Yo65nIosdPUTSzNIjl7GvqPYXpKyWXdPoa7YoE7THJ3Ko2BS4oCItFXneCRR1Sc5K0/Wf1s5E3KpTX1IixPQOcIpq7xE28/glvqJeNOc3pBBfonriXZ3xvnI2/eR3dJKbwev4Pp5SJLeYeFg+8HJ4gt3fI+9bPvCNz2QTj35eoxR9+s5h0f46BbwncT9OT66fA8OjoPMdaTIFeq1lozvBh9yYgiKkQ6SfgCPaJozsPxQX7/6Ss8dWm5QipK+IEbdfgeDvcnmaWH783lOSxVjbTTztHGew1kRSOLrPxcSK26OPaA13tj9Vve1NhVSiYup6p/bEYurjehsX5lvVkhvBEB2Gou6/1uI2gHgeB6YJVFRMWW2OTcZRPr6Yhtk9U09XGT2x+rt3CFYMDzGpIby/jZ/j7cUG6XheAzFz7Dx778MXqTvQ3z+ejxj/K2sYcBOB2xVHD+yLuUkilliErJx9PL9PsORhA/oOcgU4lbK+NcTN7LyYAhO6PrRIQRnINilolrryIGbycnBwCY8g/SGTMruTDHI/N0kw1OOFQ5OdkPP/68ir2UMXKf+nnuS+AWoVOt0D94zzCm3480FnjiirJCpN3Hf/2h+xjqjBJxlJIZNAd5cVwJ7557Pwhm4A4frJ4PD/0LpXyEDh3DEO0AoNixj1HXRtOKWMJhzHWxeobpS1p4WlXJ+G5CKRkhoGc/3b6HMJQy64zs4RcffZ2Hj/UzEtymeNlS23sPt+zpYFrW1hk7745h+o1WMECx3iMS+j1c5FP2vq1h35sZu0rJ+EaIgihob4IiNNJW17vfeo+3Xa6odlKZd5LCWWUu7lbnKRvCIqTckPBocvt7myiTUcetNhSrw/enDvOubLby970yzicf+iTpUpovjH+1ZlshJf/wjh9mNDVa+azf82DPnUrJ2NVYjUBild/c7v2c7X9X5bsXteO4TgxDmMwYBo8k9wMqEbPX6lRuraETuIHLRkSOIISAXqVkugrjdAtV3oV4rSJsQHIAuvbBq59WfwdKxtQ1TgwcQmgOj557At/p4ufeew+HB5J0xS2MoKHZcGIfJ6+miZk6R0aG4Nj3BuOGYjCDt8Et3w/Sg4MPVz6W3QdUoU/NYyGaYa/j09HVixCC4Z4O7gu6hi46A8pdFlyrXlmNa700lSBu6XzyB+7gAaFiTbeWHOgYgWQ/B/sTjMsB0lK5H6dlNyUsEnVPTkf5uah7Jstvli70mioEI71vNC3bsbgzV6j8HvZz1qBd/v3tyEHZLgG+lXF3klK5joiL+jZc8HSQ6S4EIXec+qEhGK2vWyUle2zBqBttsHw/dPgH+bmHf4UHQy2S79RivOfAe/jsBz7LPzj+D2q2v9vXSVpJEmaCPqncXIOer4R49/7Kpv/A/ikSlDD8EqT2ghnD672Fn3X+Pp9yH+GVlSSjPQmGEoPMmhY/m7iVT7/v02TsDL2BVcPQCbLRv8v7xo8iu75LfRbrhlg393Uscd9A+SKtoWQARu+Hpcvq91BLgHcfu11dt/hFhNPP37lfKc/uuEl8ZR8/P7fAHb138uJ4mhMjnRi6Bnd+WO3cVRdcf+hfKkvmcLW6gNl/mD1BdQBH8+m2TfpS6v6N9cT5uWs2/2ZugZXSPvqTKg+G7gP0eUqG9Lker2S6+MUPnmCgI8poTJ3rQccGXV2n/mQEicYHjN/iPCNcZpjehEWHViUoxDyvQgcH0EPWbvjNCiegfu3yt9e8rDcTdpWSOR/pDqin0CHbKBybubW2Q3DvFHrvWnGftei9NxD1ZeI3M09NCrr1WMN9eiVqVf8IvvODVfeA2cmYU2fJCEHMj5NK3NLw7Dxy4B3Qe4jRjqrAvM1WeSsxI8ZP3PsTNUM9IFKV30dN9fug1akEXkjJHBTTaEIi7FzFxdWdsPhd7xF+zv37XF3M830n9jKUGGLGimAVlzncfZiFwgI9brCCHjqB0bGHP8x+jKHuavY/PYdIZq/wi48oRhXxdZSkDwLxQMWSAXjzyLHK70Px6jXojJuc8w/w8Iqgf/8JXpvKcPdokPR55HvgRx6DAw/VHmPvXfCTr8Ntf7PyUXLwAP2hcjNxN05PQt2/fb1xHDfK+7M5lmVKucsAeg5UklD3ui77Dt7Ce06oc33b3r/Bz88t8KPpjFKaZ76IEIKoqZP24wyIZa6wh7vHuujSqh1rCrpOIkSGCPfx6fAFAoEnvZqKALOFidbX8ybErlIyy9HRIK8AIr56YRp6RwCVNUS7Au7bsV+79t/M8daT3b4TLRwp+d6Q+wlWSZpc5bomtG6y0muwPsqB/ohd7eoovQRIONKxj7EmrY5N0ceefW9vOPZdA3cBMHrk+yofDy1NQNDLxdT+//bePD6Ssz7w/j5V1fetllrdrVtz3zOew4Nvx8bGNtjYxuEygZCENZBjs2GTAPuGJEtIsvvZ5N0cnw1ks8lL3jcHCSQhAZZsCJDg2BCb4DE2Nr7GnkOj0Uga3X1U1fP+8VRXVUstqWc0mvF46vv56COp+6mq5ym1nl/97pBnTxGCa6IFd1zCiQTrbpjOfEJmozgBQG32DNIJK87FPeFoS3jj7hLFRJFRXYP5CWxpM1GZIF+ZhVQJEp10OMf0ZH0tvjqGYeJFmHfM0u1oMg2/DEDCi0bbmPcEzoasF5iQjYV5Wg7yiZ1fxEx0U7Ns9vb5umiW97X+7KW6m17vzibBeQAAMGppV8j05xPMOEmfUySazGVpR9MomSbveMP17vEiXebe2TnVhyc3CJ//cZgdIxU1MGpnScsZnjML7OvPkQ97DwQAEScyzZAw4PtELgiBRCIQXFfPuZ+3jZnWwQOvVS4rIfPynGPOAMadyJklH0fZwti+HOfjmF/MahUIfK+vy7Z9rkKq1VpfrVUUWphCdf9dlHL5NrkrmE0H07uYsitLHfkOs9P73cz3hvNmY8cWBluUeddjGxgYbH7yzlgaUUN9Vkt7HnRf76rMwcgT7u+GsxRdSnYkPV9MxHkyTqYcjSKWhWgGKTSuzyoBEDZn+cLxGHNVk1zCEzLd6Qg7ymm6492MCok9P850dRpLWuRnz6heMOBuyOWsL9Ipv0FFiU07RSZjbWgyxT0qcfL9jzR9tnRNB1tt7nuLXlRo2NBIhHXSsZDr9N/b39yquR0KqQgTVjc5509i1/KMzSjT5EBHnDHpBS24mkxuyBUyZdMiWfCEH0lPyHPf70NlCv72J+lMhOm11f04Kovs7cvSGW2+Lxqg29Cd7KUn1uWGtdc0SSFWQCI5nLrKvT93bt17zuu9nLmshMxVKVUVNY+GJQSavcgBvPgpfbXNc73yM5ahZWj0WjWGSxlGvJ4sE933jbhvUxSCaa19Id/g9gHlYG7qH+9be21mO9aCY+LRlIlrML+tqXpxAz1/iB2FoaZotILtPWGLjiE+vucn2JfZRM624eg/ue/FnKfekmliJLxN7urhOwDo2eaZh8gNIrQQfZZX4+tLJ6Pc/TvfYGbB07Bu3daNEIJioogpYKIywXhFCab81AgUdwHLCJmOYUDCicdV0mVoSSPjpegGvOFXoXv7kreu61ahyDcP72p6PRsPMzlf4zvHztKdjlDKnHtIbyJicEIU+fyJ4/zm6BgzZoE/+1d1bwbycT5Sfy//07yDb9lb6WxoMple0s7fqaTHIOS7rqu1CeVnuuVj8OwXeUD/RwaFapd8VBbZ3ZuhO15smosF2MKgL91LKTvU5H+5tudaAIpdRZUYLiUHuwIh86rlBN8DIF83EUDMTjT9QZfdIv3CZL2c8ud7/IUUchdTE1nva/lNdr57O2Y0Z98vtGjy5T9HZLF/SUru33YLfYmeJlu68I3bU9iO7ZRXEYaK6hrKbSBEc6VgISWb4lHyiRgp05tHVm82h9yz9318+s2fQ+vaBi/9s/t6Tleb+OZarck09baDP81f3/PX7B32osZck9mCly3+vntuZWrB5Gf+4jvua/fuU873YkJthKeqU4wvOELGrENRaTLby2kysRCbCs0+GQCOP9aeFrMKv3vHf+KL932RrV19Ta9n4yGm5ut859jZZlPZOXI22kvWtrl1foHZUCefeew4dcumnI0xRo6Pm+9CN8IkGm3bNZ204+AvxbqaT1bcBZvfAB9wnPKHPwBDN/DOyd/jNv0xalJHyw2RioYoZQbUGOdjNSd0hJCUE2Uet1V49EansOn2vBK+M1HVKgAheOTJT5/3mi9HLishU48o9VraJhsjedKUm95vaAruhtFO8uJayru0c+xq17tcNZHzTZ5cK4u01by/xEuLe21Ilrz+U392hOmzg7wQ9sxMedOm8ZjywMECtbMHqIzegaarzWIwPYimextv0rLosixufOzHEX/9fmKW9142dU3ruQ9dD688Cpaac4ehQmP3VmrgS3wUQrAhu6H52NwgWLWml/bs3scXf+o6DgyqayciOvsHlJmoO67CgEetWcbnx9QaLQuKqs3AwcEOnvjYbeSTvsoDjdIx1SmVF7NGNKE1hWS7S4mHeenMHC+Pz7O3L9fiyPaYSXgBBVqmzNhMla98b5SQrrHREZ65RFiFaDvsSw5y78ws+9NDzSeLpOAdfw4FJ0dH0+DN/wOpGdypf4vH7S3sHVLaZqfjY9KkRtYUTGs6NhbfGfsOj0w+DcAW53N5VUFVCDglK3Q7vWhuTgf9ZF613JNQf9xjIYN93QcoxvYsHSQlHfVzcFz7TWpSNjWdCi9XWbUx/nx5tfg+1iJg2x13rmtdbbxoLkT4ExNTKw6vaM0m0bCEU9MV0mJbU5vekmm5qvCk/o+ERBxrTm0GcSNOLpqDuOfMzlk2XZaFFS/Akb/gmppnxurKLapM0WDweqjPwYlvA7CjQ/Vx2Vivrx7JlRvEFykA8U6Ipimkonz6vVfzB+8+wLc+cqu7obqajK4xPquCBfJaZEkDsibiHV4CZrK4/Lg1komHePGM0hD3nYc/poGVGXR/zhb6KGWi/Mm3VNvnz33gGjYVkpTSzSa/TH4jv3xmglRuhfvgDu5l5NqPA/BP9m5XgOedYw0pyJgak4b6HL049SIP7XkIgEdiUQyhM5wdphArcLI+qyLPhODlM8+d95ovR9YkZIQQ/1UI8YwQ4ogQ4q+EEOf/iWmHmlJFFzSNff03sbXU2rY5WAk3JuifbOtzOq93WhaC5kzwnbVa62NWOl8711tPjeNcNvULUV3gfO7DSnNsOd47Lm7bbmJb2rIQ2srXT9l2k1AK2YIv/OT1fPod7wSgkZBvCm/M/375Cwzk42jhMwDe03iqj7hTRkZD0mnaqMtLCraXO9Gf9iUT+hlQ9vmGXyZfVJ/fvGUpobESvggzNKNJWOia4JZt3SQiXk21bCRLRBic0g3GZ09iSEh3blWFLVeiYTJLLbOGC0Auru6VJmBXT2aV0csjOoY4JTs4LjsJdfTx1oN9/PNzYxybmCcdDaFrwosscy8+qL4vzsVZhs5rHuTttY/yR9Zt7OtXQiaXUPdGFzAatqg5u+i/v+rf877d70MTGhO6zkB6EEMzKCVLjFQn3PYEj1WqLa/1WmWtmsz/AXZKKXcD3wc+vPYpLc+JcISoEx2yr3sfNwwscjY6ppRbq+PuRqYv1kaW2eA2+LO2HUFzVWWV5kKrbLCZ5SKf2uV8NJ5zqWqwOLpqpVI6a9FI/CauViHU53C+omkSd+Z9zUKFkUU+miaE4Kyus6nmOcYbzXA7Y530ywjCES4nnaKXiVCCo9NH2VrW0SJKyGzOKR+Lnu0hZ9u8cW6eKV0nY+mIH/8WXPUutlY9X0kpvcyzViKvorscv0xPsgdDaEqLWi1c2C9kYGWNBGVy645kGTV0xudG6bBtNMdUtiKNXJdGZNs6kI2ph8DN3akmwXiu5LMpDld/m+uqv0UmleIHD/QhwA0AGJupek7/Bo37lh1o6xrJiMHb3/ogV2/p80xwESVsBDYV3w56z8Z7CGkhSgl174YyyvJSTpQ5MTdC2fkYPnt29DxWe/myJiEjpfx7KWVjd34UWNfyom8bvpud1RqFUJpyoswBY8atCZT0Rf7sqc2hO8ma1qIn3SWOYFTl1W3VanMGuBBM6ss89bWZrDijr1GGt7MZt3Bst33Oc6lhttJ7bQog0a6AWeK3As3xrYSkZKMjNPZVqpw09GVypRR7KhXSPqf8vCaRzvgD0aKrFU05LXx3dapIqJ1bvs/2AZUd3vCPRDv6SNs2Z3SNs7pOSkaVeelN/53nhz4GgDG5h67kClFZg9fDsW+CWeX2wdv5u557VdTZakIm04drz7PrqwoZgGKsk1OGzvjMcfKm6Tr9V6RRyj92/r6S1cg6msxaTGUA3ekojXvSmQxTzsa4eUuBzzx2nErdYmK+5oUvN9j4erjrv8HQjW1f5+49Zf7ohw+hO3tJSA9hALqQXFVR219YC5OPqr9hQ7g0vpeSJU7Nn2KTpaLZZo02/g6vIS6kT+a9wJeWe1MI8T4hxGNCiMfGxsbO6wIbTz3D8ZDBvuIBlY177J/pdVTQWd0xQ0kYMm2Spu/J2UduUfvUsG3TbVpsrtebN0Ap+b7PMdwyYXEV7EXnW5a1+GhWExTnappq571zxTlXyxDuNn06moCobTOl63x4fJLBWp1b5xcYNQySrSIHnZ8/ODnFi+E4yUbDKk1ydPooAAczXlZ6g1v6VOmSLxz9a0RIha42NotUVz8Z2+IFpzhlXPM24kz/Xcw+92EmT72NfDLMsgxdr4pJHn8MXdPpqVfbCxfWQ81+m4aTfgWKiRKnDENpMj6n/4pEHCFjrp9JJ+skgq4lsgxoMoU1QrLffqifsZkqf/H4caTEKynTwAjDwR91S8ecLxF06kjeNT0NQCFecP1hMUMJk3JSBSb1JHswbZOuWAf/9wtpHth775qufbmxqpARQvyDEOK7Lb7u8Y35KGAC/99y55FSfkpKeUBKeaCr6/wiV0ZyZU4ZBvtKh9QLL36NDbXmTSpMhun0Xvob5i+fmSZi2269owZRKSlZJsO15mzulG3zdGRpmZG2Wc8osnPhfDWQNVRAWLbHSpuUG0mPvsgwC+ivSU7rOltqNf72xAgFy2LE0FtXQXaO21KtcSZksbPqbZpHxo4AcE3Zy1bPO9e8tvdaQlqIo9NHeWn6JQAGHQdzutBP2rJVGDWQjHgmpa5UBGlmALGykBm4BhBw1Allnh9vL7MeoMtXnbgNTaY71cuYrjNmzpG3bNXNcjWu/xnYchfsfUd7czoPtnSnSIR1rtmwih9qFbp9Tv18Qgmcm7Z0UcpE+a2vKOf6Ep/MBSKuhagDZ5wdtDflGXFu7rsZgK1OYEfDfHY2lWVTrEo8vIpf7DXGqkJGSnmrlHJni6+/ARBCvAd4I/BOKde4u6zCH86oJ4QNqZ1Qr8DLj5CoNf+D9iaHOdt7M1t8HQYbEWM9pk3PoqxtiaCnbjFcN5s21s21ujKlXOgltTK1rcFHcd7HL3euxedsZ/3++ySE6qtxvoEDUrLDyTFoSu4XguFKFFsITjumLRs4pRt0t/B96bZNwraZNKIgLJ61BxASQiLmCpmO/CbX1Fa01L9CMVFkX2EfAJa0EAj6nGx8LVMm4/NjZTJeKGp32tvMIsYKm0gsB6XdXr7M3Jn2hUzeJ1jaMZelBzCF4LSuqVIokeSqx5AswNv/pL26ZefJrt4M3/2l2+nriK8+eAWKfiHjCHZD1/jBA31u9v8Sc9kFIqlHsYTgtGNSH0h7Pp43bXgTX3ngK+zsVGaxhkZzJh6jP7LAzjUEO1yOrDW67A3AzwJ3SynnL8yUlufbo99GWmHe+6nj/Onn/hLMBcywzwQgJYfK+5CbXs9wzRMmjS6I2+omA4vKss9pgpJpErVlk5Zz49w8PXWTFZVqv8Bo009zwaLLVtNQWvlpzsV30xjTyuS3Wv5RKwElZVPZ+2URQoX0LiJi22x1SoU0nPQTukZNE25NMf8d6bIsSqbFSzFVZ2pMRDGr3fQntvHE2BPunB48q8wdWS1GR7SDkBbipr6bvPPEuwjpTuSYEWlq+dtd2uf+vKsnw6/fv4u/+eC1q69x8Ho4/i3VUXJ+vClHZkUazn/NaMtnUkx6mlY+sX6O/PNBXID/gUTE4NPvPcRP37qZgk9jeetBLzdnIJ9Y83VakXbql73ifBYX5wMV4l4Fh4YmM6LrXl24K4i1GSbhd4AI8H+cD82jUsqH1jyrZfiVW3+MR49dx+OxMuNPfgZT1/iefS3wL2qAEBws7yCT3kmm6j0lNXwjV1eqpBdtYLYQ9JgmEsHmep2RkLolt8wvMGkY/GHGVwzPt+k2tKNmP0MbIdN+zjfS6lxpkTnf8vcGq82robmcY/jyGaO9j9tgQ6v0Hb+zWmMgkDxI1wAAIABJREFUmgdOO0KmyohjV2/Vz8VARaK9kkgBJkZ0kvrsRrbmdvKl4/8v8/V54tMnuW92jjDwb7kBupws8NeVXueeZ2O2OXEuJj1TWO/QYd9SBW892F5YLEM3wCO/A8e+pTaddsxY4OWudAy39blpJGQCdORW9+FcjtywuYsbNjeb38vZGH/8I4foTEbWzVyWi3bA3DFeCavPYE+yZ9mx8VCcXCTHSc0Gc0H1Bgqvj/B7NbLW6LKNUso+KeVe52vdBAzAlo4tvHvPm/mtt+/jR3te5pXYdo6cyDYlUG7p2EIhHeOF+lb3tYZ+cnB+jq0tcl9KpolAssH3Xp9lcdvc/BJTEABSesLF/96i//u7pqZXjv46l3DjVr8vh19bale7anFtYyXN5zyE4wt6dOX5OO+ZYun5b1hYIGOpDX7EMUeddL5vcB4c/Gee1jRKpskr0RjCToA+g1UpsadrN7a0eWr8KTj9FAOmyY+fnWJS1+iKq81qQ3YDKedJdXdXs7M8KpTJVpOSUvE8c0n6XwdCV36Z+fH2TVP9jlC747+0NbyRkAmQ72xTkL1GuH5TF9tK6XU7f4eTK3PMUFruSkIGVITZSdvZX64wbeayyvh3WThL9PQTDB+6k//xroOEnc1JSOhN9hI2NJ7RVDmHsG27UWdF01SlHYCBWp33TaoyNQVTDel0IpB2VyoIYKupkbCW5pJEpGSoVleb2gqbbW5xSZNWT/8rmNlaOtBXe+1CZNg7Qirdot3wOeMmqQumjPaE5vdC4SX35fCcydzUFHnTcs1lje+bzEUPAcC0rlM0LV4yNGKasoHbC30cdBJ4nxh7AkaOYDn/AhPUXBOHEIJCTP28KducvS+cMvzbavWVfS8rEU2rHinf/7JqLdyuuSy/AT52Fjbc3NbwTMSz/ed9pr2AtdPt9AmaddIUelIrC5lyosxJ0zEXB0LmMuDoN0DaMHwTt27tdnMdDLNDlRgHTqdfB1JSc94LCxXbbqBKq89pwrUVSrOD0yLEn6aVY/SuWeVeMo0EO2rVJZtr1rbJznWtWrr/iciiygMtNumVckeWhPwK0fqavnH6uQoZ92JLBUC0Xb/NSsMWT7hhbrRZum4hEFLySDxKxyLhnoj2EK2MUbAEJx0z2ahhkLBtOkLJJm025wR3lCyTl+wqsVAIaRtYlT76s10MpgeVkDn1JGgGFjBpzdEZ8zb7d21/F+AVOGwwkNhGl2nyzsk15pEMXg+nVABC245/OGdB3+P4A/KdW1cZGXAuFHxCJayFSYdX1ppKyRIjtSn1rzIXCJlXPy9+DUJx6D2IoWtI08nEDe1wh+SzebW/Of+UecPbQGK2ZEbTOGkY5EzJMTvHQ90qpwApmXKeTqS0eN38oqx/ISiaFp2FQ64QeMfUdPMYZ797JtIilNVJ/Gxs6kvMbv6hLTaU1bb96GLNaKXyNqu8f9ZX22vlMjktXneDBFpcE5Cy9Ucvadu8EA6zsWq743UpmYlvo5sJugkxElIPEiOGrkyd8TwR5zq/c+o0HxhXkUVJy2bSrmCLOayFAVLhKGFDY3fXbo6c/g5y+jh6eQ8TuoaNdLUXgPs3388jb3+kKTQVYCg7zD8eO8nO4hrLtQ95DbNWLSmzBv7ojk/z4UMfbhKgAWvHbx7LRlfP9+lJ9lCxa0y+5Q+UFnsFcXkKmZe+rupAGWoTnzujOhO+oeeH3CHlbAzh28g25tQTqUTlwFQ1jeOGQcm0+K2ixXNR+LXTZ+gxLV50ku3C9Wk2VpcKisF6nTdf63U8fDbcOi+iri26vc7G+8mR00s3+UWbuFhJi1jBxCbAC7NdHA22nI9HShItGnjN6/ry5rcl52o9TFRbP+FJzW5qSdsg7Ly2e957M29ZPF7poaxN0h9OMWIY2MCIYVA0LUh1E3Pm8zn7IM+gNoC6c08na2e4c9N1fP1nlZlpT9ceJqpnOW7oiBt/ltMP/gWA65NpkAwvDfkd2n2d+n7DGvNI+r3ggnPSZM6RYqLIO7atX87LlUpv0nv48D+cLIcbYVbc1r559DXC5Sdkpk7Ame/D8E3uS/Wz1zLzvY/zAxu9Ph7FTBTT8iLMtnbtgEgGieYm7x0LGZzV4fnEAndN9nLb/AIb63UejsX4hc4O/jIV51RoqVlkMxH6fZE7zzWEzGpqhuPrOBJpEfGyyK/ScGa3rAHW+GohAOY0bUliadP1l9Fy3IrEi8OyV1pL0+8sFX5SYk1ct+zhg43S+b5jagI2VyUbzJD7Xsm0+MZ4kgg1+uIF6kLwiOjnFSOqig6me0k7NermwxVqoTmElE2a2Dt23+xmhe/pUtW7j0QiUL7KTa70h50uy/CN8POvsKQP/bnijy5aRyETsD6UfCHhjTyYlWiMOeFUxL6SuPyEzEtfV9+Hb3RfevuhPgQGu3s9tbWYjiJtr/PdpvygKv6n6fQ5Ia+jus7JkMG+6S283UkAfPfUNLurVb4aj/Pxzg5+pau2ZLPtiRfp+ey/c7WNaV+NspCILjUfNQ53xn8nGm76vZXWsqdabyuUOLcofFcKwTPhUNNroWUEiysUFufuuGYqcW4fEKHGCyfgISIlc1PXLiur7tj4riWvzeg6h6pReoXmnrPbNJm01QNDOa3yEX5Tv5E53aZsA9k+cpa6DzI0w7xRodOyORYKoaFs5o2aZKCix2JoPJHqgEQnYwuqzFHbJqXoBUqme+gbsOM+yA2sPjbgVYXhK0vjT8RcDleTmRtZtzm9Wrn8hMyLX1M27ILnf/nV+3bz0q/dRcjwllPMRJE+TWY42w+ZHpXBbXolZw4uVDgUvYU+6zgABytVfm90jC+NWXz52Al+cTpCfNEuWTh7nNDo06p9b9MGDluNReGoUnpCx9m8n3E0n8bLS/ZgIRg0rZaZ7Iup+guAOieq+c10UqrrtIhA6zatFhqJz28ipMoxWiUAQDrFSDUp0aXE+ZWMBaCzWFRJCdI2eMPW+5ZcE6B/JkufVXWDGLKWoC+hzHmlvKo3Vo+pJ8JiKA3JblUuH6gYFWYMk5Jp8lI4REjC3sJewrpn0jQ0g50mHImpz8fY/BgCQT52kTWK4i544A9VXbKAy45GHtLO/OoFL9PhNMlQkpOzJ9d7Wq86Li8hI6USMsM3qs51K1DKxLAXPOdcX6pPaTLSJuuLXHrP1DT58kYy9TNNe2ktlKNsWrx58hibFuX6dS9Mwabb6DOtphuYMy3uPPOiN1doqYm86GgaAohaLTQJYKBaVeVVWvg+wj7/ybxr5vKGNKLt+usqNrsmmht9Na5VMlub1XRb9UyxF41fDmmp8h4F08L0je2qhdhZTjOYai6BIgRY80P0ZnJoi9S+7rpJpdJJtDqpmjwBYTPCjSU113JJ1Rsz8qpZWSlegEQnnc6Dw4xuctrQKJomL4RCVJEcKB5ougb1BfbMTvEsVSpmhdPzp91s/4CAdmnUtCslV6+mIIRQuTJzgZB5dTP2LMyONvljlqOYjiIt5XQWdox4KA6ZXoQ06bA9DaHXNNncGUHHbNpLTSmYknF0u0IvRpNpK3/NT8PMCAUSTVpIf01n8/zSTo0aBphenaVx3au9FTdbBw0MmHW21WpNfpSCs5E2ZWe4gQO4qtGg45MxfRqUpWktNBKBIZdm4Vs+v09frU7METrQoj8PIO0ISBiqmU0RcV2VJD963RA39C9tRxytHkDXBGGtOfN534Lkpn3bSYgqXY5wr5gd7MuqsvuJ7t1kLJsXher1Uk72QKKgAgCAGUNyytDpsiyVrCkEBzt2NF2D00+zu1rBRPL0+NOMLYy1548JCPDxies+wQf3fpAtHUurebeinCgHmsyrnhe/pr4P37Tq0FhYJ4p6whAN+35aRYQM+0rLxOthtp/+AgAL0nuSNaqTfI9hJIKi9DZOHdA33Ayj36Uj3u9uqlLCrlqdnLV0E7alyZ55Tx2q+iLKajK+1CcjJb01k03+/vWoXjjZeogFJ7l0uSTMomnyofFJZnzaXtSyl2gkE7pO0mjxFOYbF7UFC77zpGULrUbqIKCvEm6qEpCY76K3I8415aVCplNXPpJcuDlrfu9MlL5e5XfpNpUuNSEH6NXPQqILQhHKNpioas9dmUFIFig75rIJXaOiaUSlMvUZts0uEWu6BqeeZLfTnfDI2BHG5seCEN+Ac6YQL/DQnofQRHvbaDlZZmQ28Mm8upk6BvlNbbdOLYUOUBm9i37bcTA7Xf82+6KvZkQXsaf+HIAjtlffKV07zcn4FkRpN8V6zd14TSF4YeL7YJsUOve744WAQXOGXKMZUpMjHe5upNsscuZXjAVV2t63OSdtGx3hNudqMKbrbK9YXLdQaZ3k6JzihVCMH5qecSPGcqaXy6L5kiePhwx6koNL7ps/tPi4E86NEGgSEoTcZTTQNKVlCLuTzb7SPHa1zOnpKtvyvhL1ALZOMak0h4GUrzaYlPTJAkZSbfiGpgRgqHgz+uwIpFWETslJoy1YFka2DxJdFBwh0zAV2s5He0utTuTsK83XP/Uk+VCS3mQvT4w9EWgyAReFcqLMTH2GmdrMpZ7KReXyEjK3/wq8/1/aHl5Mx6hPXM/OrBNumlE+GgMv4soIRaAyiUTwrPQqqRqYzOW2wdANFOdU+ZlGNNZnj/5vAAaGbmq63ozZxdf7PuL+3mgVbdcTJN/wd4jFWoCUmEZNPVX7BE/RtNCFpNc0myLDKrrOTHiBO2adNgatwoiBMwac1nW3MOjhhTkqjc3XSZ4UzpP+rhaZ4JbvtAu6dDWtkLSJ6wmk1N2LSQnCUBUS5o2NHFrwklfHrW6eGZ2hI9qBv1ZBygy7Jdj9kTkRCSJRckN6o8nrkWac2w+9HmZGIKWETFlXmslAXYUvE82QW5TcedaxKV6zsADjLzQvcOQIdO9kd9duvn3624wvjC/JkQkIuNA0fDdXmsns8hIy4CZgtkMj8GpbyamknPLi2b/6ynG+fHSUsqmiykSqyFm9eaPRSzth6EZKZqO/iaDfgs9PfY9qbpAd/Vc1jf925A4+9MUREoTQpKSiaSAhUtvH7TvKhEWzYzntmNbidnME13C9zmS4Bx0oOiajPsd09mQ0wt+klukL0khvEfDNWNR97arK0tI4jZIxh3tbZx9HfMEFjZnpQCKcU45+qXZxacVd4abnDvLDE1UOzZm8ZXqGU7KD755QPqpM2Av7PbBQodPp/9Gb9sx13ZZJKFt2M+B/fscOPnHgr7h1az9MnwBnbNjpPLitWlPajRBko81RfS85hQuv0dLNQsa2YPQpKO5SSZmVCSTSrcAcELBelBNq/wmEzGuIyXm1MW/ocjZlIwypksr6tyRlWSViO21wSnuoORvNXGE/VWmQH9gB/Ycp+vwsB7QkU1h8pXuYfCJJxPIc5w/c9UMcHOhgtpYm2tijBdy7+U50TZDQfW0DgKS0SNqSI6EsCZ+Q2VmtYe9+GwD9jivnakdDeGB6hm9Fl7bqlXaImPA2yo+HVcXemG0zWltqXozaNnFbZ3tncwHIkKk+Etc55XT8tdUsIBHrBiuGtNUmbtc8X0ax72pOyCJ/cPokHxufZEx08OSJKaSUTSazd8+OMGBMAjCc9SIAh2t1Ul19riYTN6e4e08ZYVZgYdJ9SNgeVeu8eX7BNYFmFpVmeSUUQgC7MoMw/rz3xsRLUJ+D0m43KRMIhEzAurOlYwtfvv/LXN97/eqDX0O8poXM+2/aANDciS7dA77AWavxBDx4A6RUafTwxDM8L3sY7s5BJEW6a6uKsAIOxnroqZt8VlebcF142fsH+gf4g/ccIKpnsJ0cHd0W/OS1twGQj3Q1aRRn9BQ7q1WORi32L3gRb/11yJeUGWlzVV3nSacO2g9PTfNzmeuak2sk6LU0m1JOW2opmUsoDS0jJS8JtQH7AwxsIeik263U+/rZOQ4vLGBItZ77Z6uwqPZlTQgy6R6kHUPWVSUEa075VKI2bC6WeEkqbaMmdRZCWcZmqoxOV9lb8DSmTfU6u6e+qtaa8TSZwbpJodQPsSwgvGq1M46z1PHJ3JbexFdeOcFVluY27zISBQypot9umF/grK7RFcoQyW+GCZ8mc8ppWFbcxeYOr0JE4JMJWG/CephysoyhrbWN1+XFa1rI3L6jyNFfu4tMzGemyvSqar8NR3hjzy/tQnd8NiFzjmfkgNtVTwxc54bIdusR7p+Z5Vvzx3l5+mUvcs2OkIrESUVDHOjtw9SyvO1slZ+bjZJ57Hfha7/Gpmqzw6+mVeit16lrNvtmvA9eVi/ByW+D0NhVVZrW8+EwupQUTIt/No+hJIBbSoBbrGP83CtfBpRZSzOUM74r3kMkOqHWAeyqVECqAqH50AZiRgykTr9p8vunxtBQwnFhoVeFSjclZwqi5oyTF6Mz871PILQampR0yBAD+QQvOkLmLCk6ksqs9eSJKbfvOcDL1gCDo38PNJfk6DVNJVw1XQmPORWmzLRjXnDMZSKeV47+eIc3v2RB1S8T8B8mJrCFYEtmEDo2KC1oXt2DRuVlurYS0kJubkwQXRYQsD68poVMSzK9COlpDWLB2Xy6dxLq8pIGR2MbCTcqCAzfTNEpW9JdW+DNCya60Pnsc5+lS1cml6jmlbTpTubJJOu8O7aPt48/C1/5Jfjar7Lz9FNLptPYwtML3iZXLu2HE49DLOf2SrGEahP9yVyGf5l/mZ1V03e04OXqFoZiheYkSmCo9yB2aIqorRz985oGQmALQSG9ByEEmowx7USiCcep/qy5oTlZ0xFoR099B2nHENoCoBEOnyYkJVk9R3cqykmpTF1H7CH6cjE0oYTMtvw2Pva6j3FX+X38nfU60uNPwOTRpkz8ommiOdoK8bynyUw7mkzDpxZyQpL9fe4TXSRsG0sIvpRQ5tGri1dD3olea5jMRo5A1zYwlMb2mTd+hndvf3egyQQErBNXpJBZQqIT4h10dXjFMBf8YbdDN7iaTGF6lK7iHm7svZG/ef5v6Kj+INZCuWmTykVyTNXO0vMjf6KKKX50FH5hgtrrf7npshqCU4ZBf6oftG7+9MQpfnFsnMSGa2D0acj00iu9JErD1vj9bIb7FyzO1m/2yxiOVA9SfefnCQu9KSFyY3qISaNCvt7c6AugP6tyVcJakn8T6r5ozvuV8h4O+iLFGh+UV2qn0WUcHHNhJHxa3dZokXTM4O+tA/yxeSsfqj9EORtnQ1fSdf6/ZfNb2JF8E1+wnQ6PT/1V0/3otPC6RCY6feayhibjCJmUY2I7/AHv4EQXaSeM+etZJbB/YPgun5BxTGannlTlXBr3J7eRDx38EBei53xAQMBSAiGDAKdbYjHjJe0ZJV/b3XCcW+er3DczS/TM89B7gPs3389EZYItgyMIvcKmvGf2yUVzmLbJrDWviimGoqDpnJg9BoDmJNBvtMMcicXY372fQqHIzlqN+2fniGcLIC3o2oJRn3d7pRwLa+yuVPnIW/4WI+5tlACG3Uk+ESYbao4868XguGEQrmWJ27abWBm1JcNZ5feJG0meEQUGK39C3WkRO7h/BzurXs5LIxx7LARpMedoMjZmaJqaEHRkNiCEYFZL83+Z7+UsKcrZKLt6Mjx5wquCMDZb4wRdyJ4D8N3PqfuB0mayes4zf8Xznolr+iSEk6qjJMD2e+Anvg1b7/QWmiyQdeb4jKijSUlvdkgVnxS60mRmRmHuNJSaWyoHBASsH1eekEn72qQKJ5miqDadwXyc520lLHp6moXRDTLCL01VwKpC70GuLV9LMVHkpPWPhCJTqgCnQy6qNKLJymTTOZ4efxpQIcZIg+FalSkB+7v3M9zny9GZcEw7TsvcsJMDEpHwm9Mm4c4N9CTUeNuJ8irG+xBC0L0olLdjfoIRQ2ey2segr9JBT1Ujn1BRatlIBqErH07FVDk4uWKEvWGvYGTK16Mmqb+C0CyEcRZLs5FCUHZMV4mIV/SmvyPOzp6M4/xXms/YTJWOeBix417VGXL8Ba6J/wKvnwyTSviy/+MdMO/zyaR8lQmEUK2I/SS66PIVFM1LobQTPaQEzfjzXifKYrOADggIWD+uPCGT8TZzpAVI6Fa1rXpzce6r/RKvq/w2G7qaa2qRLqt+7AC9h9A1nfs23sc3T30TS1qqAKdDLuIImaonZKaqU3xv4nvKXy9AaCbxmur5vb97P/09Svg9+QOfVv6YTB8UVKKkYavw2vsXMhQcR/mQo4VoWh2kQX9GRcb5+1wAyImj2EIwVhtmm+mZhHKVtNtfpZDIOkJGYmtKyIzMjbChfLVrqks537eaOgsRpZlo0VPu+QZzal7+IIuhzgS7elX02pPH1TFnZqsqEXPHm9Wgpz7HcHoLD00sEMn5+nI0fDJSquiytO+9ViS6GK57mtdGfPlUHRtUhFlDyHSvXjU3ICDgwnDlCZlEJ+iLEjqdJ1tdE0yTYIQ8GwqLEh4jjqkmO+BGOd276V63bpG/TW8rTebRkUexpU1Sek/6k7pGIZRWrVwdX8SuDb1KyPRc5Tq6e1Ab9Y1mzb12bzaDXXdCs+sd9OWUUOxJDzQlds5OqqrQ9VoXr7M9Lceq9JCLO1pQMgfaAmgLCM3EEAYjcyPofYcIN4SMprSe2wt7mXLcOnp41D1fI9+lI+7d23I2xvZS2nX+gyNkUmFltuw7DN/9K96yv4+B8AwRX84M8TzYJlSnlSazmpBJFuj0VdfeG/K1xM1vhPEXYeQJ9feLrd4uNyAg4MJw5QkZIZr9MkZUPek6FNNqM01HF5V9b/gDfG1zi4ki1/Wozo/+nt+thMy/nPwXUqEUBRlz81WeioTZ37lLmXWcfA/OPA9nX4ae/W7eTkGo8ZsWxt3NtpiOuomQ9WonvTkVepxPlly/Rj6aZ2RG1e2y63k6Y16uyuTCJrKOQOhMZBF6BS007c5/ZHYEeg+4rZwbrYjftO/9qv4ZkI4cdwVaMalMXZ0pL28oFw+TiBhNzn9XkwHYcS+cfoqh2nNEzGl3vYDX9352DGZONZvLWhHPk/VViN7ob4mb36ASMJ/7h8BUFhBwkbnyhAwoIRPNQCgBXVvB1+Xuiz91PV/70E1Lj4k42fr9h5te/uDeD/LWLW+lmPA2yMXmMiklD594mMPlw+T0FH2myQPH+jltGOzvc+qqNVrwPqdyXejZ78wxzpv0DNGzd9KxMO5qN93pKDhl+q35IfocIZOJedpKKV7gWH2aqNBJhTo4KrwCoLNsc0O00+E0QthoYdUhshgvqg5+xV0UGqVvYjliRozuvsNsrSshFg2NkpASITU6HF/Q1lLavUbj/Lt6MhxpCJmZmidktt8DCPjm76nfm4SMcz/GnlFmzdU0GU2n0xf0MJTwPUg0/Df1Odf/FhAQcHG4MoVMulcJmFAMis32+Y5EmMHOxNJjIks1GYDt+e38p8P/qancd8yIEdEjribzwtkXGJ0f5ZryNeTDOcZ1nXxERZrtLzpZ+gmnrMlz/wBCUxFvQkCqxC1xyaPver8zd/VEX8pEXXOZNT9Eb05FxvlrhO1L9HHMMOiNdtKXi/OYOcQnR07z8+MTWHFPM0iH1dq0iPKx9KR6lJDRQ9wRyhO2bUKxvDtuV1hlys/HxonakhAZd/2bHDOjPyK44fx/6cwcC3XLEzLpEgxcC0/8qfq9lZA59aQzdvU+6tsjXqBCf8YTqHT5wtGDyLKAgIvKlSlkMr0q92L+TPtO4PI+tfF3bl51qBCCXDTnCpmHTz4MwLXla+lPF5nTNF6Mz5JBZzjrJBRGUmDEoDqlNsWI81SeLiNmTqHPnnJ/ByUM5fgbMUfejV3pdYVMNqr8DV3oPBgf5HjIoC89SF8uzr/OFrjm+o/QY91ALuH5TtKRZiEznBlmojJBxazwYPlm/uHYSeaMsDtue98DANQ1pcXEdG9z31FOU0hF+OSDXhuEhvP/q8+onJpGcUwAdt7r/ew3iTXyZRpCZjVzGaq0zBvna/TW60RSvkg1XxHOwFwWEHBxuUKFjM/B3K6Quepd8O++vmrb5wa5SM41lz184mGGM8OUkiU2dKqosL9PJrgqVvY0ICEg6WgzPb7qzqmSEojTJ5zfy85wQXcqw8LZbcRCuhsp1qhF9jO1KMWJ4xwLhejr2ExvLsbxyXnkNT/Jb8Q+2CxkHA1lqDxLR7TDDWIYmRtBP/wQuTf8F6Zl3R3X3Xeje2wFg0zIEzIbCym+9dFbuW2Hp5VsL6URAr76rCNkfH4btt3j/Zz0aTIJxycz2tBkfH+z5UgW+MToKb54fMTTDBvc/dswdEN75wkICLhgXJlCxi9YuncsP24NNDSZBXOBx0cf59qeawHo7d7njtnfuUjAJRxndY+nBZAuKcf3ovpd4AUp9OZibsZ6w1w2VZ9jbOxJqkLQl+6nNxejUrc5M1tjcr7WFAWWCit/0/HZl+mOd7th0CNzI0rrO/RjTNemXSFTSCcxbDCkZF6HfHTlkiwN5/8/P6fyXrqSPiGT7IL7/idsut3TXkAlX+phOPuKqjW2WGi0vFCX14V68firfgje/bdLe/AEBASsK1emkCn7NIV4x/Lj1kBDyDw++jg1u8a1ZSVktvZ4gQP7+29uPijZQsikymDV4NR3IRSHqBd+26hQ0N8Rd19raDJT5jzHJlVSZ1+qjz5nzPHJeSbn6k2aTOMYUNWIG82V/K1ip6vT7rhcIoxlJdlVz2JrFt2J5hbKrdjlq4Td6RcyALsfgHd+ZlE3UeH5ZZLF9jRIv2BpRygFBASsO1emkNE09fT8pv++bpdomMsePvEwET3C/m4lOKIhr3TN1sEfaD4oXVYBCQWfo7rhDD/xuDKd+Tbixk8HhzxBaWgGSRHiLBbHTJXs2Zfqc0OcXxybY7Zqujkyjbk2KMQLFOIFNKEpTcbBr8lkYyFMO85Thjpnj9/MtQz+dgv5ZHiFkT4aQqYNpz/gCWnwQsIDAgIuKVdWYwN86zJWAAAJmUlEQVQ/ux9Y19Pnojnm6nN89dhXOdB9gKjhNRr7zehmRqdewjAWPdFf/yHY/TZVCqVBY4Mdfw4Gm5sdxUIqsfPAQPOGmjFiTOkax0IGOhqlZIlaRImkRlKkX5OJh+IYwsCUJoV4gZAWoivW5QqZulVnwVxwhYyha+gyTk07CsBgdnWn/N4+JWSEgJDe5rNNQ8vMtOlH8Wsvmr78uICAgIvGlStk1plGQuaJ2RO8Y+s7mt679a2fbX1QutQcCQXNUVWLIqw+fOdWrh7uYP8iIZMNpzirjWIbBkVHaIQiKiKtIWT8PhkAUzqtDOLK9FVOll0hM1VTxzSiywBCIkGjEtqm/OpC4Kr+HJ981362+/JoVqWhjeQG2xtf2quixw78SPvXCAgIWFcCIbNObMltcX9uOP3Pi1QRZRiTS8xG2XiY+65a2rogk+phauJFpnStKV+kNxdza4j5NRk/jZYFxUSRI2Oq1td0TVUCaGgyAFHdEzLDudXNWUIIbt+xulmtCbOqvrcrZFLd8NA3zu0aAQEB68oF8ckIIX5GCCGFEEF7QYedvsix4czwCiNXQQ95ZqA2fROZWJ6peI5Xoommwp29uRg1p75XLt5ayLiaTKLM6Pwolm0xXV0qZOKGUwHBjhPz+ZkuKI1Ooq16AAUEBFwWrFmTEUL0AbcBr6x9Oq8dNKHxGzf9Bhra2htipUuqD0obCYkA2UiWY5aqGO0XMo3SMwC5RGjJcQAFJ4y6lChh2iZnFs64mkw24kW2JY0koyaE5DoWm8wNwcsPB7ktAQGXMRdCk/lN4GcBudrAK43XD7yeWwZuWfuJGm2H29xs/SHJizWZBos1mfs23acuFVIaihvGPDfCVHVqyXmTEVV6J2Wso/J6x6/DOz7THG0XEBBwWbEmISOEuAc4IaV84gLNJ6AVjTDmxUEBy+DXOPwtCHp9msziCK9fOPwL/Os7/9XVuvwJma2ETM1S/pJduevaXcW5E0nC5tvX7/wBAQHrzqrmMiHEPwCtPLYfBT6CMpWtihDifcD7APr7+1cZHdBEaTcku72KAKuwnCZz9XAHO8rppcmQgK7p6L6wX7+QWTAXEAiSvirHW2J38m9Ha9x94J4l5woICAhosKqQkVLe2up1IcQuYAh4wnn67QW+LYQ4JKU8tXi8lPJTwKcADhw4EJjWzoWr3gN739nUkmAldnV6RSDjIU97iYcNvvCT17c6ZAnJcJJUOMXI7AhCCFLhVJMQ+o+37WNLoZtbt7WnXQUEBFyZnLfjX0r5JOA+WgshjgIHpJRnLsC8AvxoGmhLtY/lGEgPcFPvTU0JoOdDOaFyZRKhRJN2BKqp24OHB9Z0/oCAgNc+QZ7Ma5TfvuW313yOUqLEybmTdMW7mvrUBAQEBLTLBatdJqUcDLSY1xalZImR2ZGm4pgBAQEB58KVWSAzoC1KiRIz9RlOzJ5oKikTEBAQ0C6BkAlYlkauzERlIjCXBQQEnBeBkAlYlkYYM3htnQMCAgLOhUDIBCyLX8gEmkxAQMD5EAiZgGXpjHViaCoAMXD8BwQEnA+BkAlYFk1oFOOq2EMgZAICAs6HQMgErEg5qYpz+sv8BwQEBLRLIGQCVqSYCDSZgICA8ycQMgErEmgyAQEBayEoKxOwIm8afhO60OmIdlzqqQQEBFyGBEImYEX60/08tOehSz2NgICAy5TAXBYQEBAQsG4EQiYgICAgYN0IhExAQEBAwLoRCJmAgICAgHUjEDIBAQEBAetGIGQCAgICAtaNQMgEBAQEBKwbgZAJCAgICFg3hJTy4l9UiDHg5fM8vBM4cwGnczkQrPnKIFjzlcFa1jwgpey6kJNZby6JkFkLQojHpJQHLvU8LibBmq8MgjVfGVxpaw7MZQEBAQEB60YgZAICAgIC1o3LUch86lJP4BIQrPnKIFjzlcEVtebLzicTEBAQEHD5cDlqMgEBAQEBlwmvWiEjhHiDEOJZIcTzQoifb/F+RAjx58773xRCDF78WV5Y2ljzfxBCPC2EOCKE+IoQYuBSzPNCstqafePuF0JIIcRlHZXTznqFED/o/J2fEkL8ycWe44Wmjc91vxDiq0KIf3M+23deinleSIQQ/0sIcVoI8d1l3hdCiN9y7skRIcRVF3uOFw0p5avuC9CBF4BhIAw8AWxfNOYDwO85P78N+PNLPe+LsOabgbjz8/uvhDU741LAPwGPAgcu9bzX+W+8Cfg3IOf8XrjU874Ia/4U8H7n5+3A0Us97wuw7huAq4DvLvP+ncCXAAEcBr55qee8Xl+vVk3mEPC8lPJFKWUN+DPgnkVj7gH+H+fnvwRuEUKIizjHC82qa5ZSflVKOe/8+ijQe5HneKFp5+8M8J+BXwcqF3Ny60A76/0x4HellJMAUsrTF3mOF5p21iyBtPNzBjh5Eee3Lkgp/wmYWGHIPcCnpeJRICuEKF2c2V1cXq1Cpgc45vv9uPNayzFSShOYAvIXZXbrQztr9vMjqCehy5lV1+yYEfqklF+4mBNbJ9r5G28GNgshHhZCPCqEeMNFm9360M6afxF4UAhxHPgi8BMXZ2qXlHP9f79sMS71BALOHSHEg8AB4MZLPZf1RAihAb8BvOcST+ViYqBMZjehNNV/EkLsklKevaSzWl/eDvyRlPK/CSFeB/yxEGKnlNK+1BMLWDuvVk3mBNDn+73Xea3lGCGEgVKzxy/K7NaHdtaMEOJW4KPA3VLK6kWa23qx2ppTwE7ga0KIoyjb9ecvY+d/O3/j48DnpZR1KeVLwPdRQudypZ01/wjwGQAp5SNAFFXf67VMW//vrwVerULmX4FNQoghIUQY5dj//KIxnwfe7fz8FuAfpeNRu0xZdc1CiH3AJ1EC5nK31cMqa5ZSTkkpO6WUg1LKQZQf6m4p5WOXZrprpp3P9V+jtBiEEJ0o89mLF3OSF5h21vwKcAuAEGIbSsiMXdRZXnw+D/yQE2V2GJiSUo5c6kmtB69Kc5mU0hRC/DjwZVR0yv+SUj4lhPhl4DEp5eeBP0Cp1c+jHGxvu3QzXjttrvm/AkngL5wYh1eklHdfskmvkTbX/JqhzfV+GbhNCPE0YAH/UUp52Wroba75Z4DfF0L8NCoI4D2X+QMjQog/RT0sdDq+po8BIQAp5e+hfE93As8D88APX5qZrj9Bxn9AQEBAwLrxajWXBQQEBAS8BgiETEBAQEDAuhEImYCAgICAdSMQMgEBAQEB60YgZAICAgIC1o1AyAQEBAQErBuBkAkICAgIWDcCIRMQEBAQsG78/3BYeDXQ5V1hAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", + "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", + "fd.plot(group=groups, legend=True)\n", + "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(78.09942021013121, 0.1415)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigma = 10\n", + "cov = np.identity(50) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", + "\n", + "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", + "stat, p_val" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", + "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", + "fd.plot(group=groups, legend=True)\n", + "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ANOVA notebooks/Pruebas con ANOVA.ipynb b/ANOVA notebooks/Pruebas con ANOVA.ipynb new file mode 100644 index 000000000..a47088809 --- /dev/null +++ b/ANOVA notebooks/Pruebas con ANOVA.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import skfda\n", + "from skfda.representation import FDataGrid\n", + "from skfda.inference.anova import oneway_anova\n", + "from skfda.datasets import make_gaussian_process" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 10\n", + "n_features = 50\n", + "n_groups = 3\n", + "\n", + "t = np.linspace(-np.pi, np.pi, n_features)\n", + "\n", + "m1 = np.sin(t)\n", + "m2 = 1.1 * np.sin(t)\n", + "m3 = 1.2 * np.sin(t)\n", + "\n", + "_ = FDataGrid([m1, m2, m3],\n", + " dataset_label=\"Means to be used in the simulation\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def make_process_b_noise(mean, cov, random_state=None):\n", + " return FDataGrid([mean for _ in range(n_samples)]) \\\n", + " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", + " cov=cov, random_state=random_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 1\n", + "cov = np.identity(n_features) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4.616968659709636, 0.80733)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oneway_anova(fd1, fd2, fd3, n_sim=100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8088749999999999" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean([oneway_anova(fd1, fd2, fd3)[1] for _ in range(20)])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/50000\n", + "0.998\n", + "0.874\n", + "1000/50000\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{i}/{x[-1]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moneway_anova\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_sim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m 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x,\n", + " \"y\": z\n", + " }).to_csv('anova_data_50k_p2.csv')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/50000\n", + "0.002\n", + "0.826\n", + "1000/50000\n", + "0.0\n", + "0.794\n", + "1500/50000\n", + "0.0006666666666666666\n", + "0.8033333333333333\n", + "2000/50000\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m 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244\u001b[0;31m random_state=random_state)\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0mp_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mvn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/inference/anova/anova_oneway.py\u001b[0m in \u001b[0;36m_anova_bootstrap\u001b[0;34m(fd_grouped, n_sim, p, random_state)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_sim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m 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list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list_of_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mdata_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim_domain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/_utils/_utils.py\u001b[0m in \u001b[0;36m_list_of_arrays\u001b[0;34m(original_array)\u001b[0m\n\u001b[1;32m 63\u001b[0m \"\"\"\n\u001b[1;32m 64\u001b[0m new_array = np.array([np.asarray(i) for i in\n\u001b[0;32m---> 65\u001b[0;31m np.atleast_1d(original_array)])\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;31m# Special case: Only one array, expand dimension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/_utils/_utils.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \"\"\"\n\u001b[0;32m---> 64\u001b[0;31m new_array = np.array([np.asarray(i) for i in\n\u001b[0m\u001b[1;32m 65\u001b[0m np.atleast_1d(original_array)])\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "sigma = 1\n", + "cov = np.identity(n_features) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", + "\n", + "x = [_ for _ in range(500, 50001, 500)]\n", + "y = []\n", + "z = []\n", + "for i in x:\n", + " print(f'{i}/{x[-1]}')\n", + " y.append(oneway_anova(fd1, fd2, fd3, n_sim=i, p=1)[1])\n", + " z.append(oneway_anova(fd1, fd2, fd3, n_sim=i, p=2)[1])\n", + " print(y[-1])\n", + " print(z[-1])\n", + " if i % 5000 == 0:\n", + " '''print('Saving')\n", + " pd.DataFrame({\n", + " \"x\": x[:len(y)] if len(x) != len(y) else x,\n", + " \"y\": y\n", + " }).to_csv('csv/anova_50k_p1_sigma10.csv')\n", + " pd.DataFrame({\n", + " \"x\": x[:len(y)] if len(x) != len(y) else x,\n", + " \"y\": z\n", + " }).to_csv('csv/anova_50k_p2_sigma10.csv')'''\n", + " continue\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "means_p1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.05322\n" + ] + } + ], + "source": [ + "n_samples = 10\n", + "n_features = 50\n", + "n_groups = 3\n", + "\n", + "t = np.linspace(-np.pi, np.pi, n_features)\n", + "\n", + "m1 = np.sin(t)\n", + "m2 = 1.1 * np.sin(t)\n", + "m3 = 1.2 * np.sin(t)\n", + "\n", + "_ = FDataGrid([m1, m2, m3],\n", + " dataset_label=\"Means to be used in the simulation\").plot()\n", + "\n", + "def make_process_b_noise(mean, cov, random_state=None):\n", + " return FDataGrid([mean for _ in range(n_samples)]) \\\n", + " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", + " cov=cov, random_state=random_state)\n", + "\n", + "sigma = 100\n", + "cov = np.identity(n_features) * sigma\n", + "n_samples = 100\n", + "\n", + "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", + "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", + "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", + "\n", + "p = oneway_anova(fd1, fd2, fd3, p=2, n_sim=50000)[1]\n", + "print(p)\n", + "p = oneway_anova(fd1, fd2, fd3, p=1, n_sim=50000)[1]\n", + "print(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import skfda\n", + "from skfda.representation import FDataGrid\n", + "from skfda.inference.anova import oneway_anova\n", + "from skfda.datasets import make_gaussian_process" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 100\n", + "n_features = 50\n", + "n_groups = 3\n", + "\n", + "t = np.linspace(-np.pi, np.pi, n_features)\n", + "\n", + "m1 = np.sin(t)\n", + "m2 = 1.1 * np.sin(t)\n", + "m3 = 1.2 * np.sin(t)\n", + "\n", + "_ = FDataGrid([m1, m2, m3],\n", + " dataset_label=\"Means to be used in the simulation\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def make_process_b_noise(mean, cov):\n", + " return FDataGrid([mean for _ in range(n_samples)]) \\\n", + " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", + " cov=cov)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "groups = np.full(n_samples * n_groups, 'Sample 1')\n", + "groups[100:200] = 'Sample 2'\n", + "groups[200:] = 'Sample 3'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistic: 3.415040947599544\n", + "p-value: 0.0\n" + ] + }, + { + "data": { + "text/plain": [ + "(100,)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sigma = 0.1\n", + "cov = np.identity(n_features) * sigma\n", + "\n", + "fd1 = make_process_b_noise(m1, cov)\n", + "fd2 = make_process_b_noise(m2, cov)\n", + "fd3 = make_process_b_noise(m3, cov)\n", + "\n", + "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", + "print(\"Statistic: \", stat)\n", + "print(\"p-value: \", p_val)\n", + "fd1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd1.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ANOVA notebooks/Resultados pruebas ANOVA.ipynb b/ANOVA notebooks/Resultados pruebas ANOVA.ipynb new file mode 100644 index 000000000..eecbd2553 --- /dev/null +++ b/ANOVA notebooks/Resultados pruebas ANOVA.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resultados pruebas ANOVA" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Los siguientes datasets han sido generados tomando un número de samples *n_samples=100*." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "df_p2 = pd.read_csv('anova_data_100k.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "El siguiente ejemplo ha sido generado tomando los valores: $\\sigma = 1$ y utilizando la norma de $L_2$." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df_p2.x, df_p2.y);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.045477719628741135\n", + "Var: 3.6307290867741124e-06\n" + ] + } + ], + "source": [ + "print('Mean: ', np.mean(df_p2.y))\n", + "print('Var: ', np.var(df_p2.y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "El siguiente ejemplo ha sido generado utilizando la norma de $L_1$. A la vista de que el $p-valor$ siempre era nulo se ha escogido una $\\sigma$ superior, en este caso $\\sigma=50$." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df_p1.x, df_p1.y);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.011401661187528415\n", + "Var: 8.065091451821023e-07\n" + ] + } + ], + "source": [ + "print('Mean: ', np.mean(df_p1.y))\n", + "print('Var: ', np.var(df_p1.y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En el siguiente gráfico puede observarse que la norma 1 es mejor en términos de convergencia hacia el $p-valor$ que la norma 2." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df_p2.x, df_p2.y - np.mean(df_p2.y), alpha=0.4)\n", + "plt.scatter(df_p2.x, df_p1.y - np.mean(df_p1.y), alpha=0.4);" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "df_p1_10 = pd.read_csv('csv/anova_50k_p1_sigma10.csv')\n", + "df_p2_10 = pd.read_csv('csv/anova_50k_p2_sigma10.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parece que la separación entre los $p-valores$ para diferentes normas depende del número de trayectorias que le damos a cada grupo. En este caso probamos con 10 para cada uno, y comprobamos que para ambas el resultado es mucho más cercano que en el caso anterior." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df_p2_10.x, df_p2_10.y, alpha=0.4)\n", + "plt.scatter(df_p1_10.x, df_p1_10.y, alpha=0.4);" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "df_p1_50 = pd.read_csv('csv/anova_50k_p1_sigma50.csv')\n", + "df_p2_50 = pd.read_csv('csv/anova_50k_p2_sigma50.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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-0,0 +1,10 @@ +,x,y +0,10,0.0 +1,20,0.0 +2,30,0.0 +3,40,0.0 +4,50,0.0 +5,60,0.0 +6,70,0.0 +7,80,0.0 +8,90,0.0 diff --git a/ANOVA notebooks/means_p2.csv b/ANOVA notebooks/means_p2.csv new file mode 100644 index 000000000..529298086 --- /dev/null +++ b/ANOVA notebooks/means_p2.csv @@ -0,0 +1,10 @@ +,x,y +0,10,0.001265 +1,20,0.001265 +2,30,0.001265 +3,40,0.001265 +4,50,0.001265 +5,60,0.001265 +6,70,0.001265 +7,80,0.001265 +8,90,0.001265 diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index b2e821664..7978c586a 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -2,7 +2,7 @@ One-way functional ANOVA with real data ======================================= -This example shows how to perform a functional one-way ANOVA test usign a +This example shows how to perform a functional one-way ANOVA test using a real dataset. """ diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index cab55cd74..f21189cff 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -248,59 +248,3 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): return vn, p_value, simulation return vn, p_value - - -''' -def v_usc(values): - """ - - :rtype: object - """ - k = len(values) - v = 0 - for i in range(k): - for j in range(i + 1, k): - v += norm_lp(values[i] - values[j]) - return v - - -def anova_bootstrap_usc(fd_grouped, n_sim): - assert len(fd_grouped) > 0 - - m = fd_grouped[0].ncol - samples = fd_grouped[0].sample_points - start, stop = fd_grouped[0].domain_range[0] - sizes = [fd.n_samples for fd in fd_grouped] - - # Estimating covariances for each group - k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - - l_vector = [] - for l in range(n_sim): - sim = FDataGrid(np.empty((0, m)), sample_points=samples) - for i, fd in enumerate(fd_grouped): - process = make_gaussian_process(1, n_features=m, start=start, - stop=stop, cov=k_est[i]) - sim = sim.concatenate(process) - l_vector.append(v_usc(sim)) - - return l_vector - - -def anova_oneway_usc(*args, n_sim=2000): - # TODO Check grids - - assert len(args) > 0 - - fd_groups = args - fd_means = fd_groups[0].mean() - for fd in fd_groups[1:]: - fd_means = fd_means.concatenate(fd.mean()) - - vn = v_usc(fd_means) - - simulation = anova_bootstrap_usc(fd_groups, n_sim=n_sim) - p_value = len(np.where(simulation >= vn)[0]) / len(simulation) - - return p_value, vn, simulation -''' From e1405441dde4ef93b448056b3fca765deb3367ff Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 11 Mar 2020 19:56:52 +0100 Subject: [PATCH 121/624] Begin working in simplify the basis. --- skfda/representation/basis.py | 104 ++++++++++++--------------------- tests/test_basis_evaluation.py | 20 ++++++- 2 files changed, 56 insertions(+), 68 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 5c7e10f87..4b61b905e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -6,6 +6,7 @@ """ from abc import ABC, abstractmethod import copy +from scipy.special import binom from numpy import polyder, polyint, polymul, polyval import pandas.api.extensions @@ -13,7 +14,6 @@ from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly import scipy.interpolate -from scipy.special import binom from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted @@ -100,8 +100,27 @@ def domain_range(self): def domain_range(self, value): self._domain_range = value + def basis_functions_derivatives(self, derivative=0): + """Return a list with the basis functions of the derivatives. + + The functions should accept an array of points at which the evaluation + will be performed. + + Args: + derivative (int, optional): Order of the derivative. Defaults to 0. + Returns: + functions: Iterable of callables, one per basis. + """ + pass + + def _evaluate_default(self, eval_points, derivative=0): + """Default implementation of _evaluate""" + basis = self.basis_functions_derivatives(derivative=derivative) + + return np.array([b(eval_points) for b in basis]) + @abstractmethod - def _compute_matrix(self, eval_points, derivative=0): + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. Args: @@ -157,7 +176,7 @@ def evaluate(self, eval_points, derivative=0): raise ValueError("The list of points where the function is " "evaluated can not contain nan values.") - return self._compute_matrix(eval_points, derivative) + return self._evaluate(eval_points, derivative) def plot(self, chart=None, *, derivative=0, **kwargs): """Plot the basis object or its derivatives. @@ -207,9 +226,9 @@ def _evaluate_single_basis_coefficients(self, coefficients, basis_index, x, res = np.zeros(self.n_basis) for i, k in enumerate(coefficients): if callable(k): - res += k(x) * self._compute_matrix([x], i)[:, 0] + res += k(x) * self._evaluate([x], i)[:, 0] else: - res += k * self._compute_matrix([x], i)[:, 0] + res += k * self._evaluate([x], i)[:, 0] cache[x] = res return cache[x][basis_index] @@ -471,6 +490,16 @@ def __init__(self, domain_range=None): """ super().__init__(domain_range, 1) + def basis_functions_derivatives(self, derivative=0): + if derivative == 0: + return (lambda x: np.ones(len(x)),) + else: + return (lambda x: np.zeros(len(x)),) + + def _evaluate(self, eval_points, derivative=0): + return (np.ones((1, len(eval_points))) if derivative == 0 + else np.zeros((1, len(eval_points)))) + def _ndegenerated(self, penalty_degree): """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. @@ -488,66 +517,7 @@ def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) - def _compute_matrix(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - - For each of the basis computes its value for each of the points in - the list passed as argument to the method. - - Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - """ - return np.ones((1, len(eval_points))) if derivative == 0\ - else np.zeros((1, len(eval_points))) - def penalty(self, derivative_degree=None, coefficients=None): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2-2]_: - - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds - - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - derivative_degree (int): Integer indicating the order of the - derivative or . For instance 2 means that the differential - operator is :math:`f''(x)`. - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. Only used if derivative degree is None. - - - Returns: - numpy.array: Penalty matrix. - - Examples: - >>> Constant((0,5)).penalty(0) - array([[5]]) - >>> Constant().penalty(1) - array([[ 0.]]) - - References: - .. [RS05-5-6-2-2] Ramsay, J., Silverman, B. W. (2005). Specifying - the roughness penalty. In *Functional Data Analysis* - (pp. 106-107). Springer. - - """ if derivative_degree is None: return self._numerical_penalty(coefficients) @@ -625,7 +595,7 @@ def _ndegenerated(self, penalty_degree): """ return penalty_degree - def _compute_matrix(self, eval_points, derivative=0): + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. For each of the basis computes its value for each of the points in @@ -924,7 +894,7 @@ def _ndegenerated(self, penalty_degree): """ return penalty_degree - def _compute_matrix(self, eval_points, derivative=0): + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. It uses the scipy implementation of BSplines to compute the values @@ -1326,7 +1296,7 @@ def period(self): def period(self, value): self._period = value - def _compute_matrix(self, eval_points, derivative=0): + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. Args: diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 05a95edf5..4bbc86fbf 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -1,8 +1,26 @@ +from skfda.representation.basis import FDataBasis, Monomial, BSpline, Fourier, Constant import unittest import numpy as np -from skfda.representation.basis import FDataBasis, Monomial, BSpline, Fourier + + +class TestDerivativeFunctions(unittest.TestCase): + + def _apply_test(self, basis): + t = np.linspace(basis.domain_range[0][0], + basis.domain_range[0][1], + 100) + + for derivative in [0, 1, 2, 3]: + np.testing.assert_allclose( + basis.evaluate(t, derivative=derivative), + basis._evaluate_default(t, derivative=derivative)) + + def test_derivative_function_constant(self): + constant = Constant(domain_range=(0, 1)) + + self._apply_test(constant) class TestBasisEvaluationFourier(unittest.TestCase): From 44548d36068cbd9652d5d63715a6dc0c29f4cd98 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 11 Mar 2020 22:42:11 +0100 Subject: [PATCH 122/624] Improve LinearDifferentialOperator code, tests and docs. --- skfda/misc/_lfd.py | 113 +++++++++++++++++++++++++++++++++++++++------ tests/test_lfd.py | 75 ++++++++++++++++++++++-------- 2 files changed, 156 insertions(+), 32 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 80e1d2308..672828d1e 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -18,9 +18,82 @@ class LinearDifferentialOperator: weights (list): A FDataBasis objects list of length order + 1 + Examples: + + Create a linear differential operator that penalizes the second + derivative (acceleration) + + >>> from skfda.misc import LinearDifferentialOperator + >>> from skfda.representation.basis import (FDataBasis, + ... Monomial, Constant) + >>> + >>> LinearDifferentialOperator(2) + LinearDifferentialOperator( + nderiv=2, + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[1]], + ...)] + ) + + Create a linear differential operator that penalizes three times + the second derivative (acceleration) and twice the first (velocity). + + >>> LinearDifferentialOperator(weights=[0, 2, 3]) + LinearDifferentialOperator( + nderiv=2, + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[2]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[3]], + ...)] + ) + + Create a linear differential operator with non-constant weights. + + >>> constant = Constant() + >>> monomial = Monomial((0, 1), n_basis=3) + >>> fdlist = [FDataBasis(constant, [0]), + ... FDataBasis(constant, [0]), + ... FDataBasis(monomial, [1, 2, 3])] + >>> LinearDifferentialOperator(weights=fdlist) + LinearDifferentialOperator( + nderiv=2, + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Monomial(domain_range=[array([0, 1])], n_basis=3), + coefficients=[[1 2 3]], + ...)] + ) + """ - def __init__(self, order=None, weights=None, domain_range=(0, 1)): + def __init__(self, order=None, *, weights=None, domain_range=None): """Lfd Constructor. You have to provide one of the two first parameters. It both are provided, it will raise an error @@ -33,7 +106,9 @@ def __init__(self, order=None, weights=None, domain_range=(0, 1)): domain_range (tuple or list of tuples, optional): Definition of the interval where the weight functions are - defined. Defaults to (0,1). + defined. If the functional weights are specified + and this is not, takes the domain range from them. + Otherwise, defaults to (0,1). """ from ..representation.basis import (FDataBasis, Constant, @@ -43,28 +118,27 @@ def __init__(self, order=None, weights=None, domain_range=(0, 1)): raise ValueError("You have to provide the order or the weights, " "not both") - self.domain_range = domain_range + real_domain_range = (domain_range if domain_range is not None + else (0, 1)) if order is None and weights is None: - self.order = 0 - self.weights = [] + self.weights = (FDataBasis(Constant(real_domain_range), 0),) elif weights is None: if order < 0: raise ValueError("Order should be an non-negative integer") - self.order = order self.weights = [ - FDataBasis(Constant(domain_range), 0 if (i < order) else 1) for - i in range(order + 1)] + FDataBasis(Constant(real_domain_range), + 0 if (i < order) else 1) + for i in range(order + 1)] else: if len(weights) == 0: raise ValueError("You have to provide one weight at least") if all(isinstance(n, int) for n in weights): - self.order = len(weights) - 1 - self.weights = (FDataBasis(Constant(domain_range), + self.weights = (FDataBasis(Constant(real_domain_range), np.array(weights) .reshape(-1, 1)).to_list()) @@ -72,18 +146,29 @@ def __init__(self, order=None, weights=None, domain_range=(0, 1)): if all([_same_domain(weights[0].domain_range, x.domain_range) and x.n_samples == 1 for x in weights]): - self.order = len(weights) - 1 self.weights = weights - self.domain_range = weights[0].domain_range + + real_domain_range = weights[0].domain_range + if (domain_range is not None + and real_domain_range != domain_range): + raise ValueError("The domain range provided for the " + "linear operator does not match the " + "domain range of the weights") else: - raise ValueError("FDataBasis objects in the list has " + raise ValueError("FDataBasis objects in the list have " "not the same domain_range") else: raise ValueError("The elements of the list are neither " "integers or FDataBasis objects") + self.domain_range = real_domain_range + + @property + def order(self): + return len(self.weights) - 1 + def __repr__(self): """Representation of Lfd object.""" @@ -93,7 +178,7 @@ def __repr__(self): return (f"{self.__class__.__name__}(" f"\nnderiv={self.order}," - f"\nbwtlist=[{bwtliststr[:-1]}]" + f"\nweights=[{bwtliststr[:-1]}]" f"\n)").replace('\n', '\n ') def __eq__(self, other): diff --git a/tests/test_lfd.py b/tests/test_lfd.py index 3a0f6e920..64497d6db 100644 --- a/tests/test_lfd.py +++ b/tests/test_lfd.py @@ -1,13 +1,26 @@ +from skfda.misc import LinearDifferentialOperator +from skfda.representation.basis import FDataBasis, Constant, Monomial import unittest import numpy as np -from skfda.misc import LinearDifferentialOperator -from skfda.representation.basis import FDataBasis, Constant, Monomial class TestBasis(unittest.TestCase): + def test_init_default(self): + """Tests default initialization (do not penalize).""" + lfd = LinearDifferentialOperator() + weightfd = [FDataBasis(Constant((0, 1)), 0)] + + np.testing.assert_equal(lfd.order, 0, + "Wrong deriv order of the linear operator") + np.testing.assert_equal( + lfd.weights, weightfd, + "Wrong list of weight functions of the linear operator") + def test_init_integer(self): + """Tests initializations which only specify the order.""" + # Checks for a zero order Lfd object lfd_0 = LinearDifferentialOperator(order=0) weightfd = [FDataBasis(Constant((0, 1)), 1)] @@ -20,7 +33,7 @@ def test_init_integer(self): # Checks for a non zero order Lfd object lfd_3 = LinearDifferentialOperator(3) - consfd = FDataBasis(Constant((0, 1)), np.identity(4)[3].reshape(-1, 1)) + consfd = FDataBasis(Constant((0, 1)), [[0], [0], [0], [1]]) bwtlist3 = consfd.to_list() np.testing.assert_equal(lfd_3.order, 3, @@ -29,13 +42,18 @@ def test_init_integer(self): lfd_3.weights, bwtlist3, "Wrong list of weight functions of the linear operator") - np.testing.assert_raises(ValueError, LinearDifferentialOperator, -1) + # Negative order must fail + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(-1) def test_init_list_int(self): + """Tests initializations with integer weights.""" + coefficients = [1, 3, 4, 5, 6, 7] constant = Constant((0, 1)) fd = FDataBasis(constant, np.array(coefficients).reshape(-1, 1)) + lfd = LinearDifferentialOperator(weights=coefficients) np.testing.assert_equal(lfd.order, 5, @@ -45,31 +63,52 @@ def test_init_list_int(self): "Wrong list of weight functions of the linear operator") def test_init_list_fdatabasis(self): - weights = np.arange(4 * 5).reshape((5, 4)) - monomial = Monomial((0, 1), n_basis=4) - fd = FDataBasis(monomial, weights) + """Test initialization with functional weights.""" + + n_basis = 4 + n_weights = 6 - fdlist = [FDataBasis(monomial, weights[i]) - for i in range(len(weights))] + monomial = Monomial((0, 1), n_basis=n_basis) + + weights = np.arange(n_basis * n_weights).reshape((n_weights, n_basis)) + + fd = FDataBasis(monomial, weights) + fdlist = [FDataBasis(monomial, w) for w in weights] lfd = LinearDifferentialOperator(weights=fdlist) - np.testing.assert_equal(lfd.order, 4, + np.testing.assert_equal(lfd.order, n_weights - 1, "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd.weights, fd.to_list(), "Wrong list of weight functions of the linear operator") - contant = Constant((0, 2)) - fdlist.append(FDataBasis(contant, 1)) - np.testing.assert_raises(ValueError, LinearDifferentialOperator, - None, fdlist) + # Check failure if intervals do not match + constant = Constant((0, 2)) + fdlist.append(FDataBasis(constant, 1)) + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(weights=fdlist) def test_init_wrong_params(self): - np.testing.assert_raises(ValueError, - LinearDifferentialOperator, 0, ['a']) - np.testing.assert_raises(ValueError, - LinearDifferentialOperator, 0, 'a') + + # Check specifying both arguments fail + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(1, weights=[1, 1]) + + # Check invalid domain range + monomial = Monomial((0, 1), n_basis=3) + fdlist = [FDataBasis(monomial, [1, 2, 3])] + + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(weights=fdlist, + domain_range=(0, 2)) + + # Check wrong types fail + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(weights=['a']) + + with np.testing.assert_raises(ValueError): + LinearDifferentialOperator(weights='a') if __name__ == '__main__': From 2b22cc1a10990896981b8844158d274cccfed49f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 12 Mar 2020 21:04:25 +0100 Subject: [PATCH 123/624] Monomial evaluation --- skfda/representation/basis.py | 100 ++++++++++++++++++--------------- tests/test_basis_evaluation.py | 14 +++-- 2 files changed, 64 insertions(+), 50 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 4b61b905e..f0aa69f01 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -6,7 +6,6 @@ """ from abc import ABC, abstractmethod import copy -from scipy.special import binom from numpy import polyder, polyint, polymul, polyval import pandas.api.extensions @@ -14,6 +13,7 @@ from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly import scipy.interpolate +from scipy.special import binom from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted @@ -492,9 +492,9 @@ def __init__(self, domain_range=None): def basis_functions_derivatives(self, derivative=0): if derivative == 0: - return (lambda x: np.ones(len(x)),) + return (lambda x: np.ones_like(x),) else: - return (lambda x: np.zeros(len(x)),) + return (lambda x: np.zeros_like(x),) def _evaluate(self, eval_points, derivative=0): return (np.ones((1, len(eval_points))) if derivative == 0 @@ -565,69 +565,77 @@ class Monomial(Basis): values. >>> bs_mon.evaluate([0, 1, 2]) - array([[ 1., 1., 1.], - [ 0., 1., 2.], - [ 0., 1., 4.]]) + array([[1, 1, 1], + [0, 1, 2], + [0, 1, 4]]) And also evaluates its derivatives >>> bs_mon.evaluate([0, 1, 2], derivative=1) - array([[ 0., 0., 0.], - [ 1., 1., 1.], - [ 0., 2., 4.]]) + array([[0, 0, 0], + [1, 1, 1], + [0, 2, 4]]) >>> bs_mon.evaluate([0, 1, 2], derivative=2) - array([[ 0., 0., 0.], - [ 0., 0., 0.], - [ 2., 2., 2.]]) + array([[0, 0, 0], + [0, 0, 0], + [2, 2, 2]]) """ - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. + def _coefs_exps_derivatives(self, derivative): + """ + Return coefficients and exponents of the derivatives. - Returns: - int: number of close to 0 eigenvalues. + This function is used for computing the basis functions and evaluate. + When the exponent would be negative (the coefficient in that case + is zero) returns 0 as the exponent (to prevent division by zero). """ - return penalty_degree + + seq = np.arange(self.n_basis) + + # Each column of coef_mat contains the numbers that must be multiplied + # together in order to obtain the coefficient of each basis function + # Thus, column i will contain i, i - 1, ..., i - derivative + 1 + coef_mat = np.linspace(seq, seq - derivative + 1, + derivative, dtype=int) + coefs = np.prod(coef_mat, axis=0) + + exps = np.maximum(seq - derivative, 0) + + return coefs, exps + + def basis_functions_derivatives(self, derivative=0): + + coefs, exps = self._coefs_exps_derivatives(derivative) + + # Necessary to create closures by value + def monomial_basis(c, e): + return lambda x: (c * x**e) + + return tuple([monomial_basis(c, e) + for c, e in zip(coefs, exps)]) def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - For each of the basis computes its value for each of the points in - the list passed as argument to the method. + coefs, exps = self._coefs_exps_derivatives(derivative) + + raised = np.power.outer(eval_points, exps) + + return (coefs * raised).T + + def _ndegenerated(self, penalty_degree): + """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. + penalty_degree (int): Degree of the derivative used in the + calculation of the penalty matrix. Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. + int: number of close to 0 eigenvalues. """ - # Initialise empty matrix - mat = np.zeros((self.n_basis, len(eval_points))) - - # For each basis computes its value for each evaluation - if derivative == 0: - for i in range(self.n_basis): - mat[i] = eval_points ** i - else: - for i in range(self.n_basis): - if derivative <= i: - factor = i - for j in range(2, derivative + 1): - factor *= (i - j + 1) - mat[i] = factor * eval_points ** (i - derivative) - - return mat + return penalty_degree def _derivative(self, coefs, order=1): return (Monomial(self.domain_range, self.n_basis - order), diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 4bbc86fbf..8afd24044 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -12,16 +12,22 @@ def _apply_test(self, basis): basis.domain_range[0][1], 100) - for derivative in [0, 1, 2, 3]: - np.testing.assert_allclose( - basis.evaluate(t, derivative=derivative), - basis._evaluate_default(t, derivative=derivative)) + for derivative in range(6): + with self.subTest(derivative=derivative): + np.testing.assert_allclose( + basis.evaluate(t, derivative=derivative), + basis._evaluate_default(t, derivative=derivative)) def test_derivative_function_constant(self): constant = Constant(domain_range=(0, 1)) self._apply_test(constant) + def test_derivative_function_monomial(self): + monomial = Monomial(n_basis=6, domain_range=(0, 1)) + + self._apply_test(monomial) + class TestBasisEvaluationFourier(unittest.TestCase): From f7cb8e97d9c6ebb1d0568a24411d2ba3afd59c1a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 124/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- skfda/representation/basis.py | 2 +- 5 files changed, 175 insertions(+), 97 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index f160b8fb2..5d777cacc 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From 9819c2712161f8aa4793158e831fde00e0f26333 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 14 Mar 2020 20:33:39 +0100 Subject: [PATCH 125/624] BSplines and Fourier evaluate. --- skfda/preprocessing/smoothing/_basis.py | 2 +- skfda/representation/basis.py | 164 ++++++++++++++++-------- tests/test_basis_evaluation.py | 10 ++ 3 files changed, 125 insertions(+), 51 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 88efb777f..d4df2bf72 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -236,7 +236,7 @@ class BasisSmoother(_LinearSmoother): ... basis, method='qr', return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[-0. , 0.71, 0.71]]) + array([[ 0. , 0.71, 0.71]]) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', return_basis=True) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index f0aa69f01..ede44a053 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -30,6 +30,13 @@ # aux functions +def _constant_basis(derivative=0, constant=1): + if derivative == 0: + return (lambda x: constant * np.ones_like(x),) + else: + return (lambda x: np.zeros_like(x),) + + def _polypow(p, n=2): if n > 2: return polymul(p, _polypow(p, n - 1)) @@ -491,10 +498,7 @@ def __init__(self, domain_range=None): super().__init__(domain_range, 1) def basis_functions_derivatives(self, derivative=0): - if derivative == 0: - return (lambda x: np.ones_like(x),) - else: - return (lambda x: np.zeros_like(x),) + return _constant_basis(derivative=derivative) def _evaluate(self, eval_points, derivative=0): return (np.ones((1, len(eval_points))) if derivative == 0 @@ -863,9 +867,9 @@ def __init__(self, domain_range=None, n_basis=None, order=4, knots=None): n_basis = len(knots) + order - 2 if (n_basis - order + 2) < 2: - raise ValueError(f"The number of basis ({n_basis}) minus the order " - f"of the bspline ({order}) should be greater " - f"than 3.") + raise ValueError(f"The number of basis ({n_basis}) minus the " + f"order of the bspline ({order}) should be " + f"greater than 3.") self.order = order self.knots = None if knots is None else list(knots) @@ -889,6 +893,43 @@ def knots(self): def knots(self, value): self._knots = value + def basis_functions_derivatives(self, derivative=0): + """Return a list with the basis functions of the derivatives. + + The functions should accept an array of points at which the evaluation + will be performed. + + Args: + derivative (int, optional): Order of the derivative. Defaults to 0. + Returns: + functions: Iterable of callables, one per basis. + + Implementation details: In order to allow a discontinuous behaviour at + the boundaries of the domain it is necessary to placing m knots at the + boundaries [RS05]_. This is automatically done so that the user only + has to specify a single knot at the boundaries. + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + + """ + # Places m knots at the boundaries + knots = np.array([self.knots[0]] * (self.order - 1) + self.knots + + [self.knots[-1]] * (self.order - 1)) + + identity = np.identity(self.n_basis) + + # Necessary to create closures by value + def bspline_basis(c): + return lambda x: (scipy.interpolate.splev( + x, (knots, c, self.order - 1), der=derivative) + if derivative <= (self.order - 1) + else np.zeros_like(x)) + + return tuple([bspline_basis(c) + for c in identity]) + def _ndegenerated(self, penalty_degree): """Return number of 0 or nearly to 0 eigenvalues of the penalty matrix. @@ -928,6 +969,9 @@ def _evaluate(self, eval_points, derivative=0): Analysis*. Springer. 50-51. """ + if derivative > (self.order - 1): + return np.zeros((self.n_basis, len(eval_points))) + # Places m knots at the boundaries knots = np.array([self.knots[0]] * (self.order - 1) + self.knots + [self.knots[-1]] * (self.order - 1)) @@ -1304,6 +1348,45 @@ def period(self): def period(self, value): self._period = value + def _functions_pairs_coefs_derivatives(self, derivative=0): + """ + Compute functions to use, amplitudes and phase of a derivative. + """ + functions = [np.sin, np.cos] + signs = [1, 1, -1, -1] + omega = 2 * np.pi / self.period + + deriv_functions = (functions[derivative % len(functions)], + functions[(derivative + 1) % len(functions)]) + + deriv_signs = (signs[derivative % len(signs)], + signs[(derivative + 1) % len(signs)]) + + seq = 1 + np.arange((self.n_basis - 1) // 2) + seq_pairs = np.array([seq, seq]).T + power_pairs = (omega * seq_pairs)**derivative + amplitude_coefs_pairs = deriv_signs * power_pairs + phase_coef_pairs = omega * seq_pairs + + return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs + + def basis_functions_derivatives(self, derivative=0): + + functions, amplitude, phase = self._functions_pairs_coefs_derivatives( + derivative) + + normalization_denominator = np.sqrt(self.period / 2) + + # Necessary to create closures by value + def fourier_basis(f, a, p): + return lambda x: a * f(p * x) / normalization_denominator + + return (_constant_basis(derivative, 1 / (np.sqrt(2) * normalization_denominator)) + + sum([ + (fourier_basis(functions[0], a[0], p[0]), + fourier_basis(functions[1], a[1], p[1])) + for a, p in zip(amplitude, phase)], ())) + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. @@ -1321,53 +1404,34 @@ def _evaluate(self, eval_points, derivative=0): if derivative < 0: raise ValueError("derivative only takes non-negative values.") - omega = 2 * np.pi / self.period - omega_t = omega * eval_points - n_basis = self.n_basis if self.n_basis % 2 != 0 else self.n_basis + 1 + functions, amplitude_coefs, phase_coefs = self._functions_pairs_coefs_derivatives( + derivative) - # Initialise empty matrix - mat = np.empty((self.n_basis, len(eval_points))) + normalization_denominator = np.sqrt(self.period / 2) + + # Multiply the phase coefficients elementwise + res = np.einsum('ij,k->ijk', phase_coefs, eval_points) + + # Apply odd and even functions + for i in [0, 1]: + functions[i](res[:, i, :], out=res[:, i, :]) + + # Multiply the amplitude and ravel the result + res *= amplitude_coefs[..., np.newaxis] + res = res.reshape(-1, len(eval_points)) + res /= normalization_denominator + + # Add constant basis if derivative == 0: - # First base function is a constant - # The division by numpy.sqrt(2) is so that it has the same norm as - # the sine and cosine: sqrt(period / 2) - mat[0] = np.ones(len(eval_points)) / np.sqrt(2) - if n_basis > 1: - # 2*pi*n*x / period - args = np.outer(range(1, n_basis // 2 + 1), omega_t) - index = range(1, n_basis - 1, 2) - # odd indexes are sine functions - mat[index] = np.sin(args) - index = range(2, n_basis, 2) - # even indexes are cosine functions - mat[index] = np.cos(args) - # evaluates the derivatives + constant_basis = np.full( + shape=(1, len(eval_points)), + fill_value=1 / (np.sqrt(2) * normalization_denominator)) else: - # First base function is a constant, so its derivative is 0. - mat[0] = np.zeros(len(eval_points)) - if n_basis > 1: - # (2*pi*n / period) ^ n_derivative - factor = np.outer( - (-1) ** (derivative // 2) * - (np.array(range(1, n_basis // 2 + 1)) * omega) ** - derivative, - np.ones(len(eval_points))) - # 2*pi*n*x / period - args = np.outer(range(1, n_basis // 2 + 1), omega_t) - # even indexes - index_e = range(2, n_basis, 2) - # odd indexes - index_o = range(1, n_basis - 1, 2) - if derivative % 2 == 0: - mat[index_o] = factor * np.sin(args) - mat[index_e] = factor * np.cos(args) - else: - mat[index_o] = factor * np.cos(args) - mat[index_e] = -factor * np.sin(args) + constant_basis = np.zeros(shape=(1, len(eval_points))) - # normalise - mat = mat / np.sqrt(self.period / 2) - return mat + res = np.concatenate((constant_basis, res)) + + return res def _ndegenerated(self, penalty_degree): """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 8afd24044..ea158ba02 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -28,6 +28,16 @@ def test_derivative_function_monomial(self): self._apply_test(monomial) + def test_derivative_function_bspline(self): + bspline = BSpline(n_basis=6, order=3, domain_range=(0, 1)) + + self._apply_test(bspline) + + def test_derivative_function_fourier(self): + fourier = Fourier(n_basis=6, domain_range=(0, 1)) + + self._apply_test(fourier) + class TestBasisEvaluationFourier(unittest.TestCase): From 77ba0ae5605cc8405bfc10d6759ce069bdb712f7 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 14 Mar 2020 21:53:57 +0100 Subject: [PATCH 126/624] Remove basis_function_derivatives --- skfda/representation/basis.py | 94 ---------------------------------- tests/test_basis_evaluation.py | 34 ------------ 2 files changed, 128 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index ede44a053..bf7141f38 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -30,13 +30,6 @@ # aux functions -def _constant_basis(derivative=0, constant=1): - if derivative == 0: - return (lambda x: constant * np.ones_like(x),) - else: - return (lambda x: np.zeros_like(x),) - - def _polypow(p, n=2): if n > 2: return polymul(p, _polypow(p, n - 1)) @@ -107,25 +100,6 @@ def domain_range(self): def domain_range(self, value): self._domain_range = value - def basis_functions_derivatives(self, derivative=0): - """Return a list with the basis functions of the derivatives. - - The functions should accept an array of points at which the evaluation - will be performed. - - Args: - derivative (int, optional): Order of the derivative. Defaults to 0. - Returns: - functions: Iterable of callables, one per basis. - """ - pass - - def _evaluate_default(self, eval_points, derivative=0): - """Default implementation of _evaluate""" - basis = self.basis_functions_derivatives(derivative=derivative) - - return np.array([b(eval_points) for b in basis]) - @abstractmethod def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. @@ -497,9 +471,6 @@ def __init__(self, domain_range=None): """ super().__init__(domain_range, 1) - def basis_functions_derivatives(self, derivative=0): - return _constant_basis(derivative=derivative) - def _evaluate(self, eval_points, derivative=0): return (np.ones((1, len(eval_points))) if derivative == 0 else np.zeros((1, len(eval_points)))) @@ -609,17 +580,6 @@ def _coefs_exps_derivatives(self, derivative): return coefs, exps - def basis_functions_derivatives(self, derivative=0): - - coefs, exps = self._coefs_exps_derivatives(derivative) - - # Necessary to create closures by value - def monomial_basis(c, e): - return lambda x: (c * x**e) - - return tuple([monomial_basis(c, e) - for c, e in zip(coefs, exps)]) - def _evaluate(self, eval_points, derivative=0): coefs, exps = self._coefs_exps_derivatives(derivative) @@ -893,43 +853,6 @@ def knots(self): def knots(self, value): self._knots = value - def basis_functions_derivatives(self, derivative=0): - """Return a list with the basis functions of the derivatives. - - The functions should accept an array of points at which the evaluation - will be performed. - - Args: - derivative (int, optional): Order of the derivative. Defaults to 0. - Returns: - functions: Iterable of callables, one per basis. - - Implementation details: In order to allow a discontinuous behaviour at - the boundaries of the domain it is necessary to placing m knots at the - boundaries [RS05]_. This is automatically done so that the user only - has to specify a single knot at the boundaries. - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - - """ - # Places m knots at the boundaries - knots = np.array([self.knots[0]] * (self.order - 1) + self.knots + - [self.knots[-1]] * (self.order - 1)) - - identity = np.identity(self.n_basis) - - # Necessary to create closures by value - def bspline_basis(c): - return lambda x: (scipy.interpolate.splev( - x, (knots, c, self.order - 1), der=derivative) - if derivative <= (self.order - 1) - else np.zeros_like(x)) - - return tuple([bspline_basis(c) - for c in identity]) - def _ndegenerated(self, penalty_degree): """Return number of 0 or nearly to 0 eigenvalues of the penalty matrix. @@ -1370,23 +1293,6 @@ def _functions_pairs_coefs_derivatives(self, derivative=0): return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs - def basis_functions_derivatives(self, derivative=0): - - functions, amplitude, phase = self._functions_pairs_coefs_derivatives( - derivative) - - normalization_denominator = np.sqrt(self.period / 2) - - # Necessary to create closures by value - def fourier_basis(f, a, p): - return lambda x: a * f(p * x) / normalization_denominator - - return (_constant_basis(derivative, 1 / (np.sqrt(2) * normalization_denominator)) - + sum([ - (fourier_basis(functions[0], a[0], p[0]), - fourier_basis(functions[1], a[1], p[1])) - for a, p in zip(amplitude, phase)], ())) - def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index ea158ba02..d888d31b8 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -5,40 +5,6 @@ import numpy as np -class TestDerivativeFunctions(unittest.TestCase): - - def _apply_test(self, basis): - t = np.linspace(basis.domain_range[0][0], - basis.domain_range[0][1], - 100) - - for derivative in range(6): - with self.subTest(derivative=derivative): - np.testing.assert_allclose( - basis.evaluate(t, derivative=derivative), - basis._evaluate_default(t, derivative=derivative)) - - def test_derivative_function_constant(self): - constant = Constant(domain_range=(0, 1)) - - self._apply_test(constant) - - def test_derivative_function_monomial(self): - monomial = Monomial(n_basis=6, domain_range=(0, 1)) - - self._apply_test(monomial) - - def test_derivative_function_bspline(self): - bspline = BSpline(n_basis=6, order=3, domain_range=(0, 1)) - - self._apply_test(bspline) - - def test_derivative_function_fourier(self): - fourier = Fourier(n_basis=6, domain_range=(0, 1)) - - self._apply_test(fourier) - - class TestBasisEvaluationFourier(unittest.TestCase): def test_evaluation_simple_fourier(self): From 4038b08a1f7d0250c01e1c0d96a44bb60676d91f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 15 Mar 2020 03:09:27 +0100 Subject: [PATCH 127/624] Split FdataBasis in another file --- skfda/ml/regression/linear_model.py | 4 +- skfda/representation/_fdatabasis.py | 937 ++++++++++++++++++++++++++++ skfda/representation/basis.py | 921 +-------------------------- 3 files changed, 942 insertions(+), 920 deletions(-) create mode 100644 skfda/representation/_fdatabasis.py diff --git a/skfda/ml/regression/linear_model.py b/skfda/ml/regression/linear_model.py index 49014b114..3e16562d0 100644 --- a/skfda/ml/regression/linear_model.py +++ b/skfda/ml/regression/linear_model.py @@ -1,8 +1,10 @@ +from skfda.representation import FData +from skfda.representation.basis import FDataBasis, Constant, Basis + from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted import numpy as np -from skfda.representation.basis import FDataBasis, Constant, Basis, FData class LinearScalarRegression(BaseEstimator, RegressorMixin): diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/_fdatabasis.py new file mode 100644 index 000000000..d59a5f41a --- /dev/null +++ b/skfda/representation/_fdatabasis.py @@ -0,0 +1,937 @@ +import copy + +import pandas.api.extensions +import scipy.integrate + +import numpy as np + +from . import grid +from .._utils import constants +from ._functional_data import FData + + +def _same_domain(one_domain_range, other_domain_range): + return np.array_equal(one_domain_range, other_domain_range) + + +class FDataBasis(FData): + r"""Basis representation of functional data. + + Class representation for functional data in the form of a set of basis + functions multplied by a set of coefficients. + + .. math:: + f(x) = \sum_{k=1}{K}c_k\phi_k + + Where n is the number of basis functions, :math:`c = (c_1, c_2, ..., + c_K)` the vector of coefficients and :math:`\phi = (\phi_1, \phi_2, + ..., \phi_K)` the basis function system. + + Attributes: + basis (:obj:`Basis`): Basis function system. + coefficients (numpy.darray): List or matrix of coefficients. Has to + have the same length or number of columns as the number of basis + function in the basis. If a matrix, each row contains the + coefficients that multiplied by the basis functions produce each + functional datum. + + Examples: + >>> from skfda.representation.basis import FDataBasis, Monomial + >>> + >>> basis = Monomial(n_basis=4) + >>> coefficients = [1, 1, 3, .5] + >>> FDataBasis(basis, coefficients) + FDataBasis( + basis=Monomial(domain_range=[array([0, 1])], n_basis=4), + coefficients=[[ 1. 1. 3. 0.5]], + ...) + + """ + class _CoordinateIterator: + """Internal class to iterate through the image coordinates. + + Dummy object. Should be change to support multidimensional objects. + + """ + + def __init__(self, fdatabasis): + """Create an iterator through the image coordinates.""" + self._fdatabasis = fdatabasis + + def __iter__(self): + """Return an iterator through the image coordinates.""" + yield self._fdatabasis.copy() + + def __getitem__(self, key): + """Get a specific coordinate.""" + + if key != 0: + return NotImplemented + + return self._fdatabasis.copy() + + def __len__(self): + """Return the number of coordinates.""" + return self._fdatabasis.dim_codomain + + def __init__(self, basis, coefficients, *, dataset_label=None, + axes_labels=None, extrapolation=None, keepdims=False): + """Construct a FDataBasis object. + + Args: + basis (:obj:`Basis`): Basis function system. + coefficients (array_like): List or matrix of coefficients. Has to + have the same length or number of columns as the number of + basis function in the basis. + """ + coefficients = np.atleast_2d(coefficients) + if coefficients.shape[1] != basis.n_basis: + raise ValueError("The length or number of columns of coefficients " + "has to be the same equal to the number of " + "elements of the basis.") + self.basis = basis + self.coefficients = coefficients + + super().__init__(extrapolation, dataset_label, axes_labels, keepdims) + + @classmethod + def from_data(cls, data_matrix, sample_points, basis, + method='cholesky', keepdims=False): + r"""Transform raw data to a smooth functional form. + + Takes functional data in a discrete form and makes an approximates it + to the closest function that can be generated by the basis. This + function does not attempt to smooth the original data. If smoothing + is desired, it is better to use :class:`BasisSmoother`. + + The fit is made so as to reduce the sum of squared errors + [RS05-5-2-5]_: + + .. math:: + + SSE(c) = (y - \Phi c)' (y - \Phi c) + + where :math:`y` is the vector or matrix of observations, :math:`\Phi` + the matrix whose columns are the basis functions evaluated at the + sampling points and :math:`c` the coefficient vector or matrix to be + estimated. + + By deriving the first formula we obtain the closed formed of the + estimated coefficients matrix: + + .. math:: + + \hat{c} = \left( \Phi' \Phi \right)^{-1} \Phi' y + + The solution of this matrix equation is done using the cholesky + method for the resolution of a LS problem. If this method throughs a + rounding error warning you may want to use the QR factorisation that + is more numerically stable despite being more expensive to compute. + [RS05-5-2-7]_ + + Args: + data_matrix (array_like): List or matrix containing the + observations. If a matrix each row represents a single + functional datum and the columns the different observations. + sample_points (array_like): Values of the domain where the previous + data were taken. + basis: (Basis): Basis used. + method (str): Algorithm used for calculating the coefficients using + the least squares method. The values admitted are 'cholesky' + and 'qr' for Cholesky and QR factorisation methods + respectively. + + Returns: + FDataBasis: Represention of the data in a functional form as + product of coefficients by basis functions. + + Examples: + >>> import numpy as np + >>> t = np.linspace(0, 1, 5) + >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + >>> x + array([ 1., 1., -1., -1., 1.]) + + >>> from skfda.representation.basis import FDataBasis, Fourier + >>> basis = Fourier((0, 1), n_basis=3) + >>> fd = FDataBasis.from_data(x, t, basis) + >>> fd.coefficients.round(2) + array([[ 0. , 0.71, 0.71]]) + + References: + .. [RS05-5-2-5] Ramsay, J., Silverman, B. W. (2005). How spline + smooths are computed. In *Functional Data Analysis* + (pp. 86-87). Springer. + + .. [RS05-5-2-7] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (pp. 86-87). Springer. + + """ + from ..preprocessing.smoothing import BasisSmoother + from .grid import FDataGrid + + # n is the samples + # m is the observations + # k is the number of elements of the basis + + # Each sample in a column (m x n) + data_matrix = np.atleast_2d(data_matrix) + + fd = FDataGrid(data_matrix=data_matrix, sample_points=sample_points) + + smoother = BasisSmoother( + basis=basis, + method=method, + return_basis=True) + + return smoother.fit_transform(fd) + + @property + def n_samples(self): + """Return number of samples.""" + return self.coefficients.shape[0] + + @property + def dim_domain(self): + """Return number of dimensions of the domain.""" + + # Only domain dimension equal to 1 is supported + return 1 + + @property + def dim_codomain(self): + """Return number of dimensions of the image.""" + + # Only image dimension equal to 1 is supported + return 1 + + @property + def coordinates(self): + r"""Return a component of the FDataBasis. + + If the functional object contains samples + :math:`f: \mathbb{R}^n \rightarrow \mathbb{R}^d`, this object allows + a component of the vector :math:`f = (f_1, ..., f_d)`. + + + Todo: + By the moment, only unidimensional objects are supported in basis + form. + + """ + + return FDataBasis._CoordinateIterator(self) + + @property + def n_basis(self): + """Return number of basis.""" + return self.basis.n_basis + + @property + def domain_range(self): + """Definition range.""" + return self.basis.domain_range + + def _evaluate(self, eval_points, *, derivative=0): + """"Evaluate the object or its derivatives at a list of values. + + Args: + eval_points (array_like): List of points where the functions are + evaluated. If a matrix of shape `n_samples` x eval_points is + given each sample is evaluated at the values in the + corresponding row. + derivative (int, optional): Order of the derivative. Defaults to 0. + + + Returns: + (numpy.darray): Matrix whose rows are the values of the each + function at the values specified in eval_points. + + """ + #  Only suported 1D objects + eval_points = eval_points[:, 0] + + # each row contains the values of one element of the basis + basis_values = self.basis.evaluate(eval_points, derivative) + + res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) + + return res.reshape((self.n_samples, len(eval_points), 1)) + + def _evaluate_composed(self, eval_points, *, derivative=0): + r"""Evaluate the object or its derivatives at a list of values with a + different time for each sample. + + Returns a numpy array with the component (i,j) equal to :math:`f_i(t_j + + \delta_i)`. + + This method has to evaluate the basis values once per sample + instead of reuse the same evaluation for all the samples + as :func:`evaluate`. + + Args: + eval_points (numpy.ndarray): Matrix of size `n_samples`x n_points + derivative (int, optional): Order of the derivative. Defaults to 0. + extrapolation (str or Extrapolation, optional): Controls the + extrapolation mode for elements outside the domain range. + By default uses the method defined in fd. See extrapolation to + more information. + Returns: + (numpy.darray): Matrix whose rows are the values of the each + function at the values specified in eval_points with the + corresponding shift. + """ + + eval_points = eval_points[..., 0] + + res_matrix = np.empty((self.n_samples, eval_points.shape[1])) + + _matrix = np.empty((eval_points.shape[1], self.n_basis)) + + for i in range(self.n_samples): + basis_values = self.basis.evaluate(eval_points[i], derivative).T + + np.multiply(basis_values, self.coefficients[i], out=_matrix) + np.sum(_matrix, axis=1, out=res_matrix[i]) + + return res_matrix.reshape((self.n_samples, eval_points.shape[1], 1)) + + def shift(self, shifts, *, restrict_domain=False, extrapolation=None, + eval_points=None, **kwargs): + r"""Perform a shift of the curves. + + Args: + shifts (array_like or numeric): List with the the shift + corresponding for each sample or numeric with the shift to + apply to all samples. + restrict_domain (bool, optional): If True restricts the domain to + avoid evaluate points outside the domain using extrapolation. + Defaults uses extrapolation. + extrapolation (str or Extrapolation, optional): Controls the + extrapolation mode for elements outside the domain range. + By default uses the method defined in fd. See extrapolation to + more information. + eval_points (array_like, optional): Set of points where + the functions are evaluated to obtain the discrete + representation of the object to operate. If an empty list is + passed it calls numpy.linspace with bounds equal to the ones + defined in fd.domain_range and the number of points the maximum + between 201 and 10 times the number of basis plus 1. + **kwargs: Keyword arguments to be passed to :meth:`from_data`. + + Returns: + :obj:`FDataBasis` with the shifted data. + """ + + if self.dim_codomain > 1 or self.dim_domain > 1: + raise ValueError + + domain_range = self.domain_range[0] + + if eval_points is None: # Grid to discretize the function + nfine = max(self.n_basis * 10 + 1, constants.N_POINTS_COARSE_MESH) + eval_points = np.linspace(*domain_range, nfine) + else: + eval_points = np.asarray(eval_points) + + if np.isscalar(shifts): # Special case, all curves with same shift + + _basis = self.basis.rescale((domain_range[0] + shifts, + domain_range[1] + shifts)) + + return FDataBasis.from_data(self.evaluate(eval_points, + keepdims=False), + eval_points + shifts, + _basis, **kwargs) + + elif len(shifts) != self.n_samples: + raise ValueError(f"shifts vector ({len(shifts)}) must have the " + f"same length than the number of samples " + f"({self.n_samples})") + + if restrict_domain: + a = domain_range[0] - min(np.min(shifts), 0) + b = domain_range[1] - max(np.max(shifts), 0) + domain = (a, b) + eval_points = eval_points[ + np.logical_and(eval_points >= a, + eval_points <= b)] + else: + domain = domain_range + + points_shifted = np.outer(np.ones(self.n_samples), + eval_points) + + points_shifted += np.atleast_2d(shifts).T + + # Matrix of shifted values + _data_matrix = self.evaluate(points_shifted, + aligned_evaluation=False, + extrapolation=extrapolation, + keepdims=False) + + _basis = self.basis.rescale(domain) + + return FDataBasis.from_data(_data_matrix, eval_points, + _basis, **kwargs) + + def derivative(self, order=1): + r"""Differentiate a FDataBasis object. + + + Args: + order (int, optional): Order of the derivative. Defaults to one. + """ + + if order < 0: + raise ValueError("order only takes non-negative integer values.") + + if order == 0: + return self.copy() + + basis, coefficients = self.basis._derivative(self.coefficients, order) + + return FDataBasis(basis, coefficients) + + def mean(self, weights=None): + """Compute the mean of all the samples in a FDataBasis object. + + Returns: + :obj:`FDataBasis`: A FDataBais object with just one sample + representing the mean of all the samples in the original + FDataBasis object. + + Examples: + + >>> from skfda.representation.basis import FDataBasis, Monomial + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> FDataBasis(basis, coefficients).mean() + FDataBasis( + basis=Monomial(domain_range=[array([0, 1])], n_basis=4), + coefficients=[[ 1. 1. 3. 0.5]], + ...) + + """ + + if weights is not None: + return self.copy(coefficients=np.average(self.coefficients, + weights=weights, + axis=0 + )[np.newaxis, ...] + ) + + return self.copy(coefficients=np.mean(self.coefficients, axis=0)) + + def gmean(self, eval_points=None): + """Compute the geometric mean of the functional data object. + + A numerical approach its used. The object its transformed into its + discrete representation and then the geometric mean is computed and + then the object is taken back to the basis representation. + + Args: + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain the discrete + representation of the object. If none are passed it calls + numpy.linspace with bounds equal to the ones defined in + self.domain_range and the number of points the maximum + between 501 and 10 times the number of basis. + + Returns: + FDataBasis: Geometric mean of the original object. + + """ + return self.to_grid(eval_points).gmean().to_basis(self.basis) + + def var(self, eval_points=None): + """Compute the variance of the functional data object. + + A numerical approach its used. The object its transformed into its + discrete representation and then the variance is computed and + then the object is taken back to the basis representation. + + Args: + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain the discrete + representation of the object. If none are passed it calls + numpy.linspace with bounds equal to the ones defined in + self.domain_range and the number of points the maximum + between 501 and 10 times the number of basis. + + Returns: + FDataBasis: Variance of the original object. + + """ + return self.to_grid(eval_points).var().to_basis(self.basis) + + def cov(self, eval_points=None): + """Compute the covariance of the functional data object. + + A numerical approach its used. The object its transformed into its + discrete representation and then the covariance matrix is computed. + + Args: + eval_points (array_like, optional): Set of points where the + functions are evaluated to obtain the discrete + representation of the object. If none are passed it calls + numpy.linspace with bounds equal to the ones defined in + self.domain_range and the number of points the maximum + between 501 and 10 times the number of basis. + + Returns: + numpy.darray: Matrix of covariances. + + """ + return self.to_grid(eval_points).cov() + + def to_grid(self, eval_points=None): + """Return the discrete representation of the object. + + Args: + eval_points (array_like, optional): Set of points where the + functions are evaluated. If none are passed it calls + numpy.linspace with bounds equal to the ones defined in + self.domain_range and the number of points the maximum + between 501 and 10 times the number of basis. + + Returns: + FDataGrid: Discrete representation of the functional data + object. + + Examples: + + >>> from skfda.representation.basis import FDataBasis, Monomial + >>> fd = FDataBasis(coefficients=[[1, 1, 1], [1, 0, 1]], + ... basis=Monomial((0,5), n_basis=3)) + >>> fd.to_grid([0, 1, 2]) + FDataGrid( + array([[[ 1.], + [ 3.], + [ 7.]], + + [[ 1.], + [ 2.], + [ 5.]]]), + sample_points=[array([0, 1, 2])], + domain_range=array([[0, 5]]), + ...) + + """ + + if self.dim_codomain > 1 or self.dim_domain > 1: + raise NotImplementedError + + if eval_points is None: + npoints = max(constants.N_POINTS_FINE_MESH, + constants.BASIS_MIN_FACTOR * self.n_basis) + eval_points = np.linspace(*self.domain_range[0], npoints) + + return grid.FDataGrid(self.evaluate(eval_points, keepdims=False), + sample_points=eval_points, + domain_range=self.domain_range, + keepdims=self.keepdims) + + def to_basis(self, basis, eval_points=None, **kwargs): + """Return the basis representation of the object. + + Args: + basis(Basis): basis object in which the functional data are + going to be represented. + **kwargs: keyword arguments to be passed to + FDataBasis.from_data(). + + Returns: + FDataBasis: Basis representation of the funtional data + object. + """ + + return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) + + def to_list(self): + """Splits FDataBasis samples into a list""" + return [self[i] for i in range(self.n_samples)] + + def copy(self, *, basis=None, coefficients=None, dataset_label=None, + axes_labels=None, extrapolation=None, keepdims=None): + """FDataBasis copy""" + + if basis is None: + basis = copy.deepcopy(self.basis) + + if coefficients is None: + coefficients = self.coefficients + + if dataset_label is None: + dataset_label = copy.deepcopy(dataset_label) + + if axes_labels is None: + axes_labels = copy.deepcopy(axes_labels) + + if extrapolation is None: + extrapolation = self.extrapolation + + if keepdims is None: + keepdims = self.keepdims + + return FDataBasis(basis, coefficients, dataset_label=dataset_label, + axes_labels=axes_labels, extrapolation=extrapolation, + keepdims=keepdims) + + def times(self, other): + """"Provides a numerical approximation of the multiplication between + an FDataObject to other object + + Args: + other (int, list, FDataBasis): Object to multiply with the + FDataBasis object. + + * int: Multiplies all samples with the value + * list: multiply each values with the samples respectively. + Length should match with FDataBasis samples + * FDataBasis: if there is one sample it multiplies this with + all the samples in the object. If not, it multiplies each + sample respectively. Samples should match + + Returns: + (FDataBasis): FDataBasis object containing the multiplication + + """ + if isinstance(other, FDataBasis): + + if not _same_domain(self.domain_range, other.domain_range): + raise ValueError("The functions domains are different.") + + basisobj = self.basis.basis_of_product(other.basis) + neval = max(constants.BASIS_MIN_FACTOR * + max(self.n_basis, other.n_basis) + 1, + constants.N_POINTS_COARSE_MESH) + (left, right) = self.domain_range[0] + evalarg = np.linspace(left, right, neval) + + first = self.copy(coefficients=(np.repeat(self.coefficients, + other.n_samples, axis=0) + if (self.n_samples == 1 and + other.n_samples > 1) + else self.coefficients.copy())) + second = other.copy(coefficients=(np.repeat(other.coefficients, + self.n_samples, axis=0) + if (other.n_samples == 1 and + self.n_samples > 1) + else other.coefficients.copy())) + + fdarray = first.evaluate(evalarg) * second.evaluate(evalarg) + + return FDataBasis.from_data(fdarray, evalarg, basisobj) + + if isinstance(other, int): + other = [other for _ in range(self.n_samples)] + + coefs = np.transpose(np.atleast_2d(other)) + return self.copy(coefficients=self.coefficients * coefs) + + def inner_product(self, other, lfd_self=None, lfd_other=None, + weights=None): + r"""Return an inner product matrix given a FDataBasis object. + + The inner product of two functions is defined as + + .. math:: + = \int_a^b x(t)y(t) dt + + When we talk abaout FDataBasis objects, they have many samples, so we + talk about inner product matrix instead. So, for two FDataBasis objects + we define the inner product matrix as + + .. math:: + a_{ij} = = \int_a^b x_i(s) y_j(s) ds + + where :math:`f_i(s), g_j(s)` are the :math:`i^{th} j^{th}` sample of + each object. The return matrix has a shape of :math:`IxJ` where I and + J are the number of samples of each object respectively. + + Args: + other (FDataBasis, Basis): FDataBasis object containing the second + object to make the inner product + + lfd_self (Lfd): LinearDifferentialOperator object for the first + function evaluation + + lfd_other (Lfd): LinearDifferentialOperator object for the second + function evaluation + + weights(FDataBasis): a FDataBasis object with only one sample that + defines the weight to calculate the inner product + + Returns: + numpy.array: Inner Product matrix. + + """ + from ..misc import LinearDifferentialOperator + from .basis import Basis + + if not _same_domain(self.domain_range, other.domain_range): + raise ValueError("Both Objects should have the same domain_range") + if isinstance(other, Basis): + other = other.to_basis() + + # TODO this will be used when lfd evaluation is ready + lfd_self = (LinearDifferentialOperator(0) if lfd_self is None + else lfd_self) + lfd_other = (LinearDifferentialOperator(0) if (lfd_other is None) + else lfd_other) + + if weights is not None: + other = other.times(weights) + + if self.n_samples * other.n_samples > self.n_basis * other.n_basis: + return (self.coefficients @ + self.basis._inner_matrix(other.basis) @ + other.coefficients.T) + else: + return self._inner_product_integrate(other, lfd_self, lfd_other) + + def _inner_product_integrate(self, other, lfd_self, lfd_other): + + matrix = np.empty((self.n_samples, other.n_samples)) + (left, right) = self.domain_range[0] + + for i in range(self.n_samples): + for j in range(other.n_samples): + fd = self[i].times(other[j]) + matrix[i, j] = scipy.integrate.quad( + lambda x: fd.evaluate([x])[0], left, right)[0] + + return matrix + + def _to_R(self): + """Gives the code to build the object on fda package on R""" + return ("fd(coef = " + self._array_to_R(self.coefficients, True) + + ", basisobj = " + self.basis._to_R() + ")") + + def _array_to_R(self, coefficients, transpose=False): + if len(coefficients.shape) == 1: + coefficients = coefficients.reshape((1, coefficients.shape[0])) + + if len(coefficients.shape) > 2: + return NotImplementedError + + if transpose is True: + coefficients = np.transpose(coefficients) + + (rows, cols) = coefficients.shape + retstring = "matrix(c(" + for j in range(cols): + for i in range(rows): + retstring = retstring + str(coefficients[i, j]) + ", " + + return (retstring[0:len(retstring) - 2] + "), nrow = " + str(rows) + + ", ncol = " + str(cols) + ")") + + def __repr__(self): + """Representation of FDataBasis object.""" + if self.axes_labels is None: + axes_labels = None + else: + axes_labels = self.axes_labels.tolist() + + return (f"{self.__class__.__name__}(" + f"\nbasis={self.basis}," + f"\ncoefficients={self.coefficients}," + f"\ndataset_label={self.dataset_label}," + f"\naxes_labels={axes_labels}," + f"\nextrapolation={self.extrapolation}," + f"\nkeepdims={self.keepdims})").replace('\n', '\n ') + + def __str__(self): + """Return str(self).""" + + return (f"{self.__class__.__name__}(" + f"\n_basis={self.basis}," + f"\ncoefficients={self.coefficients})").replace('\n', '\n ') + + def __eq__(self, other): + """Equality of FDataBasis""" + # TODO check all other params + return (self.basis == other.basis and + np.all(self.coefficients == other.coefficients)) + + def concatenate(self, *others, as_coordinates=False): + """Join samples from a similar FDataBasis object. + + Joins samples from another FDataBasis object if they have the same + basis. + + Args: + others (:class:`FDataBasis`): Objects to be concatenated. + as_coordinates (boolean, optional): If False concatenates as + new samples, else, concatenates the other functions as + new components of the image. Defaults to False. + + Returns: + :class:`FDataBasis`: FDataBasis object with the samples from the + original objects. + + Todo: + By the moment, only unidimensional objects are supported in basis + representation. + """ + + # TODO: Change to support multivariate functions + # in basis representation + if as_coordinates: + return NotImplemented + + for other in others: + if other.basis != self.basis: + raise ValueError("The objects should have the same basis.") + + data = [self.coefficients] + [other.coefficients for other in others] + + return self.copy(coefficients=np.concatenate(data, axis=0)) + + def compose(self, fd, *, eval_points=None, **kwargs): + """Composition of functions. + + Performs the composition of functions. The basis is discretized to + compute the composition. + + Args: + fd (:class:`FData`): FData object to make the composition. Should + have the same number of samples and image dimension equal to 1. + eval_points (array_like): Points to perform the evaluation. + kwargs: Named arguments to be passed to :func:`from_data`. + """ + + grid = self.to_grid().compose(fd, eval_points=eval_points) + + if fd.dim_domain == 1: + basis = self.basis.rescale(fd.domain_range[0]) + composition = grid.to_basis(basis, **kwargs) + else: + #  Cant be convertered to basis due to the dimensions + composition = grid + + return composition + + def __getitem__(self, key): + """Return self[key].""" + + if isinstance(key, int): + return self.copy(coefficients=self.coefficients[key:key + 1]) + else: + return self.copy(coefficients=self.coefficients[key]) + + def __add__(self, other): + """Addition for FDataBasis object.""" + if isinstance(other, FDataBasis): + if self.basis != other.basis: + raise NotImplementedError + else: + basis, coefs = self.basis._add_same_basis(self.coefficients, + other.coefficients) + else: + try: + basis, coefs = self.basis._add_constant(self.coefficients, + other) + except TypeError: + return NotImplemented + + return self.copy(basis=basis, coefficients=coefs) + + def __radd__(self, other): + """Addition for FDataBasis object.""" + + return self.__add__(other) + + def __sub__(self, other): + """Subtraction for FDataBasis object.""" + if isinstance(other, FDataBasis): + if self.basis != other.basis: + raise NotImplementedError + else: + basis, coefs = self.basis._sub_same_basis(self.coefficients, + other.coefficients) + else: + try: + basis, coefs = self.basis._sub_constant(self.coefficients, + other) + except TypeError: + return NotImplemented + + return self.copy(basis=basis, coefficients=coefs) + + def __rsub__(self, other): + """Right subtraction for FDataBasis object.""" + return (self * -1).__add__(other) + + def __mul__(self, other): + """Multiplication for FDataBasis object.""" + if isinstance(other, FDataBasis): + raise NotImplementedError + + try: + basis, coefs = self.basis._mul_constant(self.coefficients, other) + except TypeError: + return NotImplemented + + return self.copy(basis=basis, coefficients=coefs) + + def __rmul__(self, other): + """Multiplication for FDataBasis object.""" + return self.__mul__(other) + + def __truediv__(self, other): + """Division for FDataBasis object.""" + + other = np.array(other) + + try: + other = 1 / other + except TypeError: + return NotImplemented + + return self * other + + def __rtruediv__(self, other): + """Right division for FDataBasis object.""" + + raise NotImplementedError + + ##################################################################### + # Pandas ExtensionArray methods + ##################################################################### + @property + def dtype(self): + """The dtype for this extension array, FDataGridDType""" + return FDataBasisDType + + @property + def nbytes(self) -> int: + """ + The number of bytes needed to store this object in memory. + """ + return self.coefficients.nbytes() + + +class FDataBasisDType(pandas.api.extensions.ExtensionDtype): + """ + DType corresponding to FDataBasis in Pandas + """ + name = 'functional data (basis)' + kind = 'O' + type = FDataBasis + na_value = None + + @classmethod + def construct_from_string(cls, string): + if string == cls.name: + return cls() + else: + raise TypeError("Cannot construct a '{}' from " + "'{}'".format(cls, string)) + + @classmethod + def construct_array_type(cls): + return FDataBasis diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index bf7141f38..c41fe12ec 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -8,7 +8,6 @@ import copy from numpy import polyder, polyint, polymul, polyval -import pandas.api.extensions import scipy.integrate from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly @@ -19,9 +18,8 @@ import numpy as np -from . import grid -from .._utils import _list_of_arrays, constants -from ._functional_data import FData +from .._utils import _list_of_arrays +from ._fdatabasis import FDataBasis, FDataBasisDType __author__ = "Miguel Carbajo Berrocal" @@ -1488,921 +1486,6 @@ def __eq__(self, other): return super().__eq__(other) and self.period == other.period -class FDataBasis(FData): - r"""Basis representation of functional data. - - Class representation for functional data in the form of a set of basis - functions multplied by a set of coefficients. - - .. math:: - f(x) = \sum_{k=1}{K}c_k\phi_k - - Where n is the number of basis functions, :math:`c = (c_1, c_2, ..., - c_K)` the vector of coefficients and :math:`\phi = (\phi_1, \phi_2, - ..., \phi_K)` the basis function system. - - Attributes: - basis (:obj:`Basis`): Basis function system. - coefficients (numpy.darray): List or matrix of coefficients. Has to - have the same length or number of columns as the number of basis - function in the basis. If a matrix, each row contains the - coefficients that multiplied by the basis functions produce each - functional datum. - - Examples: - >>> basis = Monomial(n_basis=4) - >>> coefficients = [1, 1, 3, .5] - >>> FDataBasis(basis, coefficients) - FDataBasis( - basis=Monomial(domain_range=[array([0, 1])], n_basis=4), - coefficients=[[ 1. 1. 3. 0.5]], - ...) - - """ - class _CoordinateIterator: - """Internal class to iterate through the image coordinates. - - Dummy object. Should be change to support multidimensional objects. - - """ - - def __init__(self, fdatabasis): - """Create an iterator through the image coordinates.""" - self._fdatabasis = fdatabasis - - def __iter__(self): - """Return an iterator through the image coordinates.""" - yield self._fdatabasis.copy() - - def __getitem__(self, key): - """Get a specific coordinate.""" - - if key != 0: - return NotImplemented - - return self._fdatabasis.copy() - - def __len__(self): - """Return the number of coordinates.""" - return self._fdatabasis.dim_codomain - - def __init__(self, basis, coefficients, *, dataset_label=None, - axes_labels=None, extrapolation=None, keepdims=False): - """Construct a FDataBasis object. - - Args: - basis (:obj:`Basis`): Basis function system. - coefficients (array_like): List or matrix of coefficients. Has to - have the same length or number of columns as the number of - basis function in the basis. - """ - coefficients = np.atleast_2d(coefficients) - if coefficients.shape[1] != basis.n_basis: - raise ValueError("The length or number of columns of coefficients " - "has to be the same equal to the number of " - "elements of the basis.") - self.basis = basis - self.coefficients = coefficients - - super().__init__(extrapolation, dataset_label, axes_labels, keepdims) - - @classmethod - def from_data(cls, data_matrix, sample_points, basis, - method='cholesky', keepdims=False): - r"""Transform raw data to a smooth functional form. - - Takes functional data in a discrete form and makes an approximates it - to the closest function that can be generated by the basis. This - function does not attempt to smooth the original data. If smoothing - is desired, it is better to use :class:`BasisSmoother`. - - The fit is made so as to reduce the sum of squared errors - [RS05-5-2-5]_: - - .. math:: - - SSE(c) = (y - \Phi c)' (y - \Phi c) - - where :math:`y` is the vector or matrix of observations, :math:`\Phi` - the matrix whose columns are the basis functions evaluated at the - sampling points and :math:`c` the coefficient vector or matrix to be - estimated. - - By deriving the first formula we obtain the closed formed of the - estimated coefficients matrix: - - .. math:: - - \hat{c} = \left( \Phi' \Phi \right)^{-1} \Phi' y - - The solution of this matrix equation is done using the cholesky - method for the resolution of a LS problem. If this method throughs a - rounding error warning you may want to use the QR factorisation that - is more numerically stable despite being more expensive to compute. - [RS05-5-2-7]_ - - Args: - data_matrix (array_like): List or matrix containing the - observations. If a matrix each row represents a single - functional datum and the columns the different observations. - sample_points (array_like): Values of the domain where the previous - data were taken. - basis: (Basis): Basis used. - method (str): Algorithm used for calculating the coefficients using - the least squares method. The values admitted are 'cholesky' - and 'qr' for Cholesky and QR factorisation methods - respectively. - - Returns: - FDataBasis: Represention of the data in a functional form as - product of coefficients by basis functions. - - Examples: - >>> import numpy as np - >>> t = np.linspace(0, 1, 5) - >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - >>> x - array([ 1., 1., -1., -1., 1.]) - - >>> basis = Fourier((0, 1), n_basis=3) - >>> fd = FDataBasis.from_data(x, t, basis) - >>> fd.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) - - References: - .. [RS05-5-2-5] Ramsay, J., Silverman, B. W. (2005). How spline - smooths are computed. In *Functional Data Analysis* - (pp. 86-87). Springer. - - .. [RS05-5-2-7] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (pp. 86-87). Springer. - - """ - from ..preprocessing.smoothing import BasisSmoother - from .grid import FDataGrid - - # n is the samples - # m is the observations - # k is the number of elements of the basis - - # Each sample in a column (m x n) - data_matrix = np.atleast_2d(data_matrix) - - fd = FDataGrid(data_matrix=data_matrix, sample_points=sample_points) - - smoother = BasisSmoother( - basis=basis, - method=method, - return_basis=True) - - return smoother.fit_transform(fd) - - @property - def n_samples(self): - """Return number of samples.""" - return self.coefficients.shape[0] - - @property - def dim_domain(self): - """Return number of dimensions of the domain.""" - - # Only domain dimension equal to 1 is supported - return 1 - - @property - def dim_codomain(self): - """Return number of dimensions of the image.""" - - # Only image dimension equal to 1 is supported - return 1 - - @property - def coordinates(self): - r"""Return a component of the FDataBasis. - - If the functional object contains samples - :math:`f: \mathbb{R}^n \rightarrow \mathbb{R}^d`, this object allows - a component of the vector :math:`f = (f_1, ..., f_d)`. - - - Todo: - By the moment, only unidimensional objects are supported in basis - form. - - """ - - return FDataBasis._CoordinateIterator(self) - - @property - def n_basis(self): - """Return number of basis.""" - return self.basis.n_basis - - @property - def domain_range(self): - """Definition range.""" - return self.basis.domain_range - - def _evaluate(self, eval_points, *, derivative=0): - """"Evaluate the object or its derivatives at a list of values. - - Args: - eval_points (array_like): List of points where the functions are - evaluated. If a matrix of shape `n_samples` x eval_points is - given each sample is evaluated at the values in the - corresponding row. - derivative (int, optional): Order of the derivative. Defaults to 0. - - - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points. - - """ - #  Only suported 1D objects - eval_points = eval_points[:, 0] - - # each row contains the values of one element of the basis - basis_values = self.basis.evaluate(eval_points, derivative) - - res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) - - return res.reshape((self.n_samples, len(eval_points), 1)) - - def _evaluate_composed(self, eval_points, *, derivative=0): - r"""Evaluate the object or its derivatives at a list of values with a - different time for each sample. - - Returns a numpy array with the component (i,j) equal to :math:`f_i(t_j - + \delta_i)`. - - This method has to evaluate the basis values once per sample - instead of reuse the same evaluation for all the samples - as :func:`evaluate`. - - Args: - eval_points (numpy.ndarray): Matrix of size `n_samples`x n_points - derivative (int, optional): Order of the derivative. Defaults to 0. - extrapolation (str or Extrapolation, optional): Controls the - extrapolation mode for elements outside the domain range. - By default uses the method defined in fd. See extrapolation to - more information. - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points with the - corresponding shift. - """ - - eval_points = eval_points[..., 0] - - res_matrix = np.empty((self.n_samples, eval_points.shape[1])) - - _matrix = np.empty((eval_points.shape[1], self.n_basis)) - - for i in range(self.n_samples): - basis_values = self.basis.evaluate(eval_points[i], derivative).T - - np.multiply(basis_values, self.coefficients[i], out=_matrix) - np.sum(_matrix, axis=1, out=res_matrix[i]) - - return res_matrix.reshape((self.n_samples, eval_points.shape[1], 1)) - - def shift(self, shifts, *, restrict_domain=False, extrapolation=None, - eval_points=None, **kwargs): - r"""Perform a shift of the curves. - - Args: - shifts (array_like or numeric): List with the the shift - corresponding for each sample or numeric with the shift to - apply to all samples. - restrict_domain (bool, optional): If True restricts the domain to - avoid evaluate points outside the domain using extrapolation. - Defaults uses extrapolation. - extrapolation (str or Extrapolation, optional): Controls the - extrapolation mode for elements outside the domain range. - By default uses the method defined in fd. See extrapolation to - more information. - eval_points (array_like, optional): Set of points where - the functions are evaluated to obtain the discrete - representation of the object to operate. If an empty list is - passed it calls numpy.linspace with bounds equal to the ones - defined in fd.domain_range and the number of points the maximum - between 201 and 10 times the number of basis plus 1. - **kwargs: Keyword arguments to be passed to :meth:`from_data`. - - Returns: - :obj:`FDataBasis` with the shifted data. - """ - - if self.dim_codomain > 1 or self.dim_domain > 1: - raise ValueError - - domain_range = self.domain_range[0] - - if eval_points is None: # Grid to discretize the function - nfine = max(self.n_basis * 10 + 1, constants.N_POINTS_COARSE_MESH) - eval_points = np.linspace(*domain_range, nfine) - else: - eval_points = np.asarray(eval_points) - - if np.isscalar(shifts): # Special case, all curves with same shift - - _basis = self.basis.rescale((domain_range[0] + shifts, - domain_range[1] + shifts)) - - return FDataBasis.from_data(self.evaluate(eval_points, - keepdims=False), - eval_points + shifts, - _basis, **kwargs) - - elif len(shifts) != self.n_samples: - raise ValueError(f"shifts vector ({len(shifts)}) must have the " - f"same length than the number of samples " - f"({self.n_samples})") - - if restrict_domain: - a = domain_range[0] - min(np.min(shifts), 0) - b = domain_range[1] - max(np.max(shifts), 0) - domain = (a, b) - eval_points = eval_points[ - np.logical_and(eval_points >= a, - eval_points <= b)] - else: - domain = domain_range - - points_shifted = np.outer(np.ones(self.n_samples), - eval_points) - - points_shifted += np.atleast_2d(shifts).T - - # Matrix of shifted values - _data_matrix = self.evaluate(points_shifted, - aligned_evaluation=False, - extrapolation=extrapolation, - keepdims=False) - - _basis = self.basis.rescale(domain) - - return FDataBasis.from_data(_data_matrix, eval_points, - _basis, **kwargs) - - def derivative(self, order=1): - r"""Differentiate a FDataBasis object. - - - Args: - order (int, optional): Order of the derivative. Defaults to one. - """ - - if order < 0: - raise ValueError("order only takes non-negative integer values.") - - if order == 0: - return self.copy() - - basis, coefficients = self.basis._derivative(self.coefficients, order) - - return FDataBasis(basis, coefficients) - - def mean(self, weights=None): - """Compute the mean of all the samples in a FDataBasis object. - - Returns: - :obj:`FDataBasis`: A FDataBais object with just one sample - representing the mean of all the samples in the original - FDataBasis object. - - Examples: - >>> basis = Monomial(n_basis=4) - >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] - >>> FDataBasis(basis, coefficients).mean() - FDataBasis( - basis=Monomial(domain_range=[array([0, 1])], n_basis=4), - coefficients=[[ 1. 1. 3. 0.5]], - ...) - - """ - - if weights is not None: - return self.copy(coefficients=np.average(self.coefficients, - weights=weights, - axis=0 - )[np.newaxis, ...] - ) - - return self.copy(coefficients=np.mean(self.coefficients, axis=0)) - - def gmean(self, eval_points=None): - """Compute the geometric mean of the functional data object. - - A numerical approach its used. The object its transformed into its - discrete representation and then the geometric mean is computed and - then the object is taken back to the basis representation. - - Args: - eval_points (array_like, optional): Set of points where the - functions are evaluated to obtain the discrete - representation of the object. If none are passed it calls - numpy.linspace with bounds equal to the ones defined in - self.domain_range and the number of points the maximum - between 501 and 10 times the number of basis. - - Returns: - FDataBasis: Geometric mean of the original object. - - """ - return self.to_grid(eval_points).gmean().to_basis(self.basis) - - def var(self, eval_points=None): - """Compute the variance of the functional data object. - - A numerical approach its used. The object its transformed into its - discrete representation and then the variance is computed and - then the object is taken back to the basis representation. - - Args: - eval_points (array_like, optional): Set of points where the - functions are evaluated to obtain the discrete - representation of the object. If none are passed it calls - numpy.linspace with bounds equal to the ones defined in - self.domain_range and the number of points the maximum - between 501 and 10 times the number of basis. - - Returns: - FDataBasis: Variance of the original object. - - """ - return self.to_grid(eval_points).var().to_basis(self.basis) - - def cov(self, eval_points=None): - """Compute the covariance of the functional data object. - - A numerical approach its used. The object its transformed into its - discrete representation and then the covariance matrix is computed. - - Args: - eval_points (array_like, optional): Set of points where the - functions are evaluated to obtain the discrete - representation of the object. If none are passed it calls - numpy.linspace with bounds equal to the ones defined in - self.domain_range and the number of points the maximum - between 501 and 10 times the number of basis. - - Returns: - numpy.darray: Matrix of covariances. - - """ - return self.to_grid(eval_points).cov() - - def to_grid(self, eval_points=None): - """Return the discrete representation of the object. - - Args: - eval_points (array_like, optional): Set of points where the - functions are evaluated. If none are passed it calls - numpy.linspace with bounds equal to the ones defined in - self.domain_range and the number of points the maximum - between 501 and 10 times the number of basis. - - Returns: - FDataGrid: Discrete representation of the functional data - object. - - Examples: - >>> fd = FDataBasis(coefficients=[[1, 1, 1], [1, 0, 1]], - ... basis=Monomial((0,5), n_basis=3)) - >>> fd.to_grid([0, 1, 2]) - FDataGrid( - array([[[ 1.], - [ 3.], - [ 7.]], - - [[ 1.], - [ 2.], - [ 5.]]]), - sample_points=[array([0, 1, 2])], - domain_range=array([[0, 5]]), - ...) - - """ - - if self.dim_codomain > 1 or self.dim_domain > 1: - raise NotImplementedError - - if eval_points is None: - npoints = max(constants.N_POINTS_FINE_MESH, - constants.BASIS_MIN_FACTOR * self.n_basis) - eval_points = np.linspace(*self.domain_range[0], npoints) - - return grid.FDataGrid(self.evaluate(eval_points, keepdims=False), - sample_points=eval_points, - domain_range=self.domain_range, - keepdims=self.keepdims) - - def to_basis(self, basis, eval_points=None, **kwargs): - """Return the basis representation of the object. - - Args: - basis(Basis): basis object in which the functional data are - going to be represented. - **kwargs: keyword arguments to be passed to - FDataBasis.from_data(). - - Returns: - FDataBasis: Basis representation of the funtional data - object. - """ - - return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) - - def to_list(self): - """Splits FDataBasis samples into a list""" - return [self[i] for i in range(self.n_samples)] - - def copy(self, *, basis=None, coefficients=None, dataset_label=None, - axes_labels=None, extrapolation=None, keepdims=None): - """FDataBasis copy""" - - if basis is None: - basis = copy.deepcopy(self.basis) - - if coefficients is None: - coefficients = self.coefficients - - if dataset_label is None: - dataset_label = copy.deepcopy(dataset_label) - - if axes_labels is None: - axes_labels = copy.deepcopy(axes_labels) - - if extrapolation is None: - extrapolation = self.extrapolation - - if keepdims is None: - keepdims = self.keepdims - - return FDataBasis(basis, coefficients, dataset_label=dataset_label, - axes_labels=axes_labels, extrapolation=extrapolation, - keepdims=keepdims) - - def times(self, other): - """"Provides a numerical approximation of the multiplication between - an FDataObject to other object - - Args: - other (int, list, FDataBasis): Object to multiply with the - FDataBasis object. - - * int: Multiplies all samples with the value - * list: multiply each values with the samples respectively. - Length should match with FDataBasis samples - * FDataBasis: if there is one sample it multiplies this with - all the samples in the object. If not, it multiplies each - sample respectively. Samples should match - - Returns: - (FDataBasis): FDataBasis object containing the multiplication - - """ - if isinstance(other, FDataBasis): - - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("The functions domains are different.") - - basisobj = self.basis.basis_of_product(other.basis) - neval = max(constants.BASIS_MIN_FACTOR * - max(self.n_basis, other.n_basis) + 1, - constants.N_POINTS_COARSE_MESH) - (left, right) = self.domain_range[0] - evalarg = np.linspace(left, right, neval) - - first = self.copy(coefficients=(np.repeat(self.coefficients, - other.n_samples, axis=0) - if (self.n_samples == 1 and - other.n_samples > 1) - else self.coefficients.copy())) - second = other.copy(coefficients=(np.repeat(other.coefficients, - self.n_samples, axis=0) - if (other.n_samples == 1 and - self.n_samples > 1) - else other.coefficients.copy())) - - fdarray = first.evaluate(evalarg) * second.evaluate(evalarg) - - return FDataBasis.from_data(fdarray, evalarg, basisobj) - - if isinstance(other, int): - other = [other for _ in range(self.n_samples)] - - coefs = np.transpose(np.atleast_2d(other)) - return self.copy(coefficients=self.coefficients * coefs) - - def inner_product(self, other, lfd_self=None, lfd_other=None, - weights=None): - r"""Return an inner product matrix given a FDataBasis object. - - The inner product of two functions is defined as - - .. math:: - = \int_a^b x(t)y(t) dt - - When we talk abaout FDataBasis objects, they have many samples, so we - talk about inner product matrix instead. So, for two FDataBasis objects - we define the inner product matrix as - - .. math:: - a_{ij} = = \int_a^b x_i(s) y_j(s) ds - - where :math:`f_i(s), g_j(s)` are the :math:`i^{th} j^{th}` sample of - each object. The return matrix has a shape of :math:`IxJ` where I and - J are the number of samples of each object respectively. - - Args: - other (FDataBasis, Basis): FDataBasis object containing the second - object to make the inner product - - lfd_self (Lfd): LinearDifferentialOperator object for the first - function evaluation - - lfd_other (Lfd): LinearDifferentialOperator object for the second - function evaluation - - weights(FDataBasis): a FDataBasis object with only one sample that - defines the weight to calculate the inner product - - Returns: - numpy.array: Inner Product matrix. - - """ - from ..misc import LinearDifferentialOperator - - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Both Objects should have the same domain_range") - if isinstance(other, Basis): - other = other.to_basis() - - # TODO this will be used when lfd evaluation is ready - lfd_self = (LinearDifferentialOperator(0) if lfd_self is None - else lfd_self) - lfd_other = (LinearDifferentialOperator(0) if (lfd_other is None) - else lfd_other) - - if weights is not None: - other = other.times(weights) - - if self.n_samples * other.n_samples > self.n_basis * other.n_basis: - return (self.coefficients @ - self.basis._inner_matrix(other.basis) @ - other.coefficients.T) - else: - return self._inner_product_integrate(other, lfd_self, lfd_other) - - def _inner_product_integrate(self, other, lfd_self, lfd_other): - - matrix = np.empty((self.n_samples, other.n_samples)) - (left, right) = self.domain_range[0] - - for i in range(self.n_samples): - for j in range(other.n_samples): - fd = self[i].times(other[j]) - matrix[i, j] = scipy.integrate.quad( - lambda x: fd.evaluate([x])[0], left, right)[0] - - return matrix - - def _to_R(self): - """Gives the code to build the object on fda package on R""" - return ("fd(coef = " + self._array_to_R(self.coefficients, True) + - ", basisobj = " + self.basis._to_R() + ")") - - def _array_to_R(self, coefficients, transpose=False): - if len(coefficients.shape) == 1: - coefficients = coefficients.reshape((1, coefficients.shape[0])) - - if len(coefficients.shape) > 2: - return NotImplementedError - - if transpose is True: - coefficients = np.transpose(coefficients) - - (rows, cols) = coefficients.shape - retstring = "matrix(c(" - for j in range(cols): - for i in range(rows): - retstring = retstring + str(coefficients[i, j]) + ", " - - return (retstring[0:len(retstring) - 2] + "), nrow = " + str(rows) + - ", ncol = " + str(cols) + ")") - - def __repr__(self): - """Representation of FDataBasis object.""" - if self.axes_labels is None: - axes_labels = None - else: - axes_labels = self.axes_labels.tolist() - - return (f"{self.__class__.__name__}(" - f"\nbasis={self.basis}," - f"\ncoefficients={self.coefficients}," - f"\ndataset_label={self.dataset_label}," - f"\naxes_labels={axes_labels}," - f"\nextrapolation={self.extrapolation}," - f"\nkeepdims={self.keepdims})").replace('\n', '\n ') - - def __str__(self): - """Return str(self).""" - - return (f"{self.__class__.__name__}(" - f"\n_basis={self.basis}," - f"\ncoefficients={self.coefficients})").replace('\n', '\n ') - - def __eq__(self, other): - """Equality of FDataBasis""" - # TODO check all other params - return (self.basis == other.basis and - np.all(self.coefficients == other.coefficients)) - - def concatenate(self, *others, as_coordinates=False): - """Join samples from a similar FDataBasis object. - - Joins samples from another FDataBasis object if they have the same - basis. - - Args: - others (:class:`FDataBasis`): Objects to be concatenated. - as_coordinates (boolean, optional): If False concatenates as - new samples, else, concatenates the other functions as - new components of the image. Defaults to False. - - Returns: - :class:`FDataBasis`: FDataBasis object with the samples from the - original objects. - - Todo: - By the moment, only unidimensional objects are supported in basis - representation. - """ - - # TODO: Change to support multivariate functions - # in basis representation - if as_coordinates: - return NotImplemented - - for other in others: - if other.basis != self.basis: - raise ValueError("The objects should have the same basis.") - - data = [self.coefficients] + [other.coefficients for other in others] - - return self.copy(coefficients=np.concatenate(data, axis=0)) - - def compose(self, fd, *, eval_points=None, **kwargs): - """Composition of functions. - - Performs the composition of functions. The basis is discretized to - compute the composition. - - Args: - fd (:class:`FData`): FData object to make the composition. Should - have the same number of samples and image dimension equal to 1. - eval_points (array_like): Points to perform the evaluation. - kwargs: Named arguments to be passed to :func:`from_data`. - """ - - grid = self.to_grid().compose(fd, eval_points=eval_points) - - if fd.dim_domain == 1: - basis = self.basis.rescale(fd.domain_range[0]) - composition = grid.to_basis(basis, **kwargs) - else: - #  Cant be convertered to basis due to the dimensions - composition = grid - - return composition - - def __getitem__(self, key): - """Return self[key].""" - - if isinstance(key, int): - return self.copy(coefficients=self.coefficients[key:key + 1]) - else: - return self.copy(coefficients=self.coefficients[key]) - - def __add__(self, other): - """Addition for FDataBasis object.""" - if isinstance(other, FDataBasis): - if self.basis != other.basis: - raise NotImplementedError - else: - basis, coefs = self.basis._add_same_basis(self.coefficients, - other.coefficients) - else: - try: - basis, coefs = self.basis._add_constant(self.coefficients, - other) - except TypeError: - return NotImplemented - - return self.copy(basis=basis, coefficients=coefs) - - def __radd__(self, other): - """Addition for FDataBasis object.""" - - return self.__add__(other) - - def __sub__(self, other): - """Subtraction for FDataBasis object.""" - if isinstance(other, FDataBasis): - if self.basis != other.basis: - raise NotImplementedError - else: - basis, coefs = self.basis._sub_same_basis(self.coefficients, - other.coefficients) - else: - try: - basis, coefs = self.basis._sub_constant(self.coefficients, - other) - except TypeError: - return NotImplemented - - return self.copy(basis=basis, coefficients=coefs) - - def __rsub__(self, other): - """Right subtraction for FDataBasis object.""" - return (self * -1).__add__(other) - - def __mul__(self, other): - """Multiplication for FDataBasis object.""" - if isinstance(other, FDataBasis): - raise NotImplementedError - - try: - basis, coefs = self.basis._mul_constant(self.coefficients, other) - except TypeError: - return NotImplemented - - return self.copy(basis=basis, coefficients=coefs) - - def __rmul__(self, other): - """Multiplication for FDataBasis object.""" - return self.__mul__(other) - - def __truediv__(self, other): - """Division for FDataBasis object.""" - - other = np.array(other) - - try: - other = 1 / other - except TypeError: - return NotImplemented - - return self * other - - def __rtruediv__(self, other): - """Right division for FDataBasis object.""" - - raise NotImplementedError - - ##################################################################### - # Pandas ExtensionArray methods - ##################################################################### - @property - def dtype(self): - """The dtype for this extension array, FDataGridDType""" - return FDataBasisDType - - @property - def nbytes(self) -> int: - """ - The number of bytes needed to store this object in memory. - """ - return self.coefficients.nbytes() - - -class FDataBasisDType(pandas.api.extensions.ExtensionDtype): - """ - DType corresponding to FDataBasis in Pandas - """ - name = 'functional data (basis)' - kind = 'O' - type = FDataBasis - na_value = None - - @classmethod - def construct_from_string(cls, string): - if string == cls.name: - return cls() - else: - raise TypeError("Cannot construct a '{}' from " - "'{}'".format(cls, string)) - - @classmethod - def construct_array_type(cls): - return FDataBasis - - class CoefficientsTransformer(BaseEstimator, TransformerMixin): """ Transformer returning the coefficients of FDataBasis objects as a matrix. From f3eefd2ed3b5d8de4f6938592f66c57d43fa1afd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 15 Mar 2020 13:50:55 +0100 Subject: [PATCH 128/624] Change smoothing basis example --- skfda/preprocessing/smoothing/_basis.py | 24 ++++++++++++------------ skfda/representation/basis.py | 11 ++++++----- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index d4df2bf72..c8854b6bf 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -205,9 +205,9 @@ class BasisSmoother(_LinearSmoother): >>> import numpy as np >>> import skfda >>> t = np.linspace(0, 1, 5) - >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + 2 >>> x - array([ 1., 1., -1., -1., 1.]) + array([ 3., 3., 1., 1., 3.]) >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) @@ -215,11 +215,11 @@ class BasisSmoother(_LinearSmoother): ... basis, method='cholesky') >>> fd_smooth = smoother.fit_transform(fd) >>> fd_smooth.data_matrix.round(2) - array([[[ 1.], + array([[[ 3.], + [ 3.], [ 1.], - [-1.], - [-1.], - [ 1.]]]) + [ 1.], + [ 3.]]]) However, the parameter ``return_basis`` can be used to return the data in basis form, by default, without extra smoothing: @@ -230,19 +230,19 @@ class BasisSmoother(_LinearSmoother): ... basis, method='cholesky', return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) + array([[ 2. , 0.71, 0.71]]) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) + array([[ 2. , 0.71, 0.71]]) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) + array([[ 2. , 0.71, 0.71]]) >>> smoother.hat_matrix().round(2) array([[ 0.43, 0.14, -0.14, 0.14, 0.43], [ 0.14, 0.71, 0.29, -0.29, 0.14], @@ -264,7 +264,7 @@ class BasisSmoother(_LinearSmoother): ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0.18, 0.07, 0.09]]) + array([[ 2.18, 0.07, 0.09]]) >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) @@ -276,7 +276,7 @@ class BasisSmoother(_LinearSmoother): ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0.18, 0.07, 0.09]]) + array([[ 2.18, 0.07, 0.09]]) >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) @@ -288,7 +288,7 @@ class BasisSmoother(_LinearSmoother): ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 0.18, 0.07, 0.09]]) + array([[ 2.18, 0.07, 0.09]]) References: .. [RS05-5-2-6] Ramsay, J., Silverman, B. W. (2005). How spline diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index c41fe12ec..f699da555 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -150,6 +150,9 @@ def evaluate(self, eval_points, derivative=0): eval_points. """ + if derivative < 0: + raise ValueError("derivative only takes non-negative values.") + eval_points = np.asarray(eval_points) if np.any(np.isnan(eval_points)): raise ValueError("The list of points where the function is " @@ -1305,11 +1308,9 @@ def _evaluate(self, eval_points, derivative=0): eval_points. """ - if derivative < 0: - raise ValueError("derivative only takes non-negative values.") - - functions, amplitude_coefs, phase_coefs = self._functions_pairs_coefs_derivatives( - derivative) + (functions, + amplitude_coefs, + phase_coefs) = self._functions_pairs_coefs_derivatives(derivative) normalization_denominator = np.sqrt(self.period / 2) From ff1c31cf42eb37fb5b9a375da7f7637462acb773 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 15 Mar 2020 15:53:21 +0100 Subject: [PATCH 129/624] Change conversion to basis examples. --- skfda/representation/_fdatabasis.py | 6 +++--- skfda/representation/grid.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/_fdatabasis.py index d59a5f41a..172ac9d4b 100644 --- a/skfda/representation/_fdatabasis.py +++ b/skfda/representation/_fdatabasis.py @@ -148,15 +148,15 @@ def from_data(cls, data_matrix, sample_points, basis, Examples: >>> import numpy as np >>> t = np.linspace(0, 1, 5) - >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + 2 >>> x - array([ 1., 1., -1., -1., 1.]) + array([ 3., 3., 1., 1., 3.]) >>> from skfda.representation.basis import FDataBasis, Fourier >>> basis = Fourier((0, 1), n_basis=3) >>> fd = FDataBasis.from_data(x, t, basis) >>> fd.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) + array([[ 2. , 0.71, 0.71]]) References: .. [RS05-5-2-5] Ramsay, J., Silverman, B. W. (2005). How spline diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 38fdb388e..1f1c9b006 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -831,15 +831,15 @@ def to_basis(self, basis, **kwargs): >>> import numpy as np >>> import skfda >>> t = np.linspace(0, 1, 5) - >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) + 2 >>> x - array([ 1., 1., -1., -1., 1.]) + array([ 3., 3., 1., 1., 3.]) >>> fd = FDataGrid(x, t) >>> basis = skfda.representation.basis.Fourier(n_basis=3) >>> fd_b = fd.to_basis(basis) >>> fd_b.coefficients.round(2) - array([[ 0. , 0.71, 0.71]]) + array([[ 2. , 0.71, 0.71]]) """ if self.dim_domain > 1: From c8efbb63336f948f23fa832211b30685e81eae6b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 15 Mar 2020 19:09:18 +0100 Subject: [PATCH 130/624] Change simple tests to match R. --- skfda/representation/basis.py | 6 +- tests/test_basis_evaluation.py | 110 ++++++++++++++++----------------- 2 files changed, 57 insertions(+), 59 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index f699da555..c2f9053a6 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -717,7 +717,7 @@ def rbasis_of_product(self, other): def _to_R(self): drange = self.domain_range[0] return "create.monomial.basis(rangeval = c(" + str(drange[0]) + "," +\ - str(drange[1]) + "), n_basis = " + str(self.n_basis) + ")" + str(drange[1]) + "), nbasis = " + str(self.n_basis) + ")" class BSpline(Basis): @@ -1158,7 +1158,7 @@ def rbasis_of_product(self, other): def _to_R(self): drange = self.domain_range[0] return ("create.bspline.basis(rangeval = c(" + str(drange[0]) + "," + - str(drange[1]) + "), n_basis = " + str(self.n_basis) + + str(drange[1]) + "), nbasis = " + str(self.n_basis) + ", norder = " + str(self.order) + ", breaks = " + self._list_to_R(self.knots) + ")") @@ -1474,7 +1474,7 @@ def rescale(self, domain_range=None, *, rescale_period=False): def _to_R(self): drange = self.domain_range[0] return ("create.fourier.basis(rangeval = c(" + str(drange[0]) + "," + - str(drange[1]) + "), n_basis = " + str(self.n_basis) + + str(drange[1]) + "), nbasis = " + str(self.n_basis) + ", period = " + str(self.period) + ")") def __repr__(self): diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index d888d31b8..9c77dad92 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -1,5 +1,5 @@ -from skfda.representation.basis import FDataBasis, Monomial, BSpline, Fourier, Constant +from skfda.representation.basis import FDataBasis, Monomial, BSpline, Fourier import unittest import numpy as np @@ -9,22 +9,23 @@ class TestBasisEvaluationFourier(unittest.TestCase): def test_evaluation_simple_fourier(self): """Test the evaluation of FDataBasis""" - fourier = Fourier(domain_range=(0, 1), n_basis=3) + fourier = Fourier(domain_range=(0, 2), n_basis=5) - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) + coefficients = np.array([[1, 2, 3, 4, 5], + [6, 7, 8, 9, 10]]) f = FDataBasis(fourier, coefficients) - t = np.linspace(0, 1, 4) + t = np.linspace(0, 2, 11) - res = np.array([0.905482867989282, 0.146814813180645, -1.04995054116993, - 0.905482867989282, 0.302725561229459, - 0.774764356993855, -1.02414754822331, 0.302725561229459] - ).reshape((2, 4)).round(3) + # Results in R package fda + res = np.array([[8.71, 9.66, 1.84, -4.71, -2.80, 2.71, + 2.45, -3.82, -6.66, -0.30, 8.71], + [22.24, 26.48, 10.57, -4.95, -3.58, 6.24, + 5.31, -7.69, -13.32, 1.13, 22.24]]) - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) + np.testing.assert_array_almost_equal(f(t).round(2), res) + np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) def test_evaluation_point_fourier(self): """Test the evaluation of a single point FDataBasis""" @@ -103,11 +104,10 @@ def test_evaluation_composed_fourier(self): f = FDataBasis(fourier, coefficients) t = np.linspace(0, 1, 4) - res_test = f(t) - # Test same result than evaluation standart - np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], - aligned_evaluation=False)) + np.testing.assert_array_almost_equal(f([1]), + f([[1], [1]], + aligned_evaluation=False)) np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), aligned_evaluation=False)) @@ -135,9 +135,10 @@ def test_evaluation_keepdims_fourier(self): t = np.linspace(0, 1, 4) - res = np.array([0.905482867989282, 0.146814813180645, -1.04995054116993, - 0.905482867989282, 0.302725561229459, - 0.774764356993855, -1.02414754822331, 0.302725561229459] + res = np.array([0.905482867989282, 0.146814813180645, + -1.04995054116993, 0.905482867989282, + 0.302725561229459, 0.774764356993855, + -1.02414754822331, 0.302725561229459] ).reshape((2, 4)).round(3) res_keepdims = res.reshape((2, 4, 1)) @@ -171,9 +172,6 @@ def test_evaluation_composed_keepdims_fourier(self): t = [[0, 0.5, 0.6], [0.2, 0.7, 0.1]] - res = np.array([[0.69173518, -0.69017042, -1.08997978], - [0.60972512, -0.57416354, 1.02551401]]).round(3) - res = np.array([0.905482867989282, -0.903918107989282, -1.13726755517372, 1.09360302608278, -1.05804144608278, 0.85878105128844] @@ -192,10 +190,8 @@ def test_evaluation_composed_keepdims_fourier(self): res_keepdims) # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims(t, - aligned_evaluation=False - ).round(3), - res_keepdims) + np.testing.assert_array_almost_equal(f_keepdims( + t, aligned_evaluation=False).round(3), res_keepdims) np.testing.assert_array_almost_equal( f_keepdims(t, aligned_evaluation=False, keepdims=False).round(3), res) @@ -219,9 +215,10 @@ def test_evaluation_grid_keepdims_fourier(self): t = np.linspace(0, 1, 4) - res = np.array([0.905482867989282, 0.146814813180645, -1.04995054116993, - 0.905482867989282, 0.302725561229459, - 0.774764356993855, -1.02414754822331, 0.302725561229459] + res = np.array([0.905482867989282, 0.146814813180645, + -1.04995054116993, 0.905482867989282, + 0.302725561229459, 0.774764356993855, + -1.02414754822331, 0.302725561229459] ).reshape((2, 4)).round(3) res_keepdims = res.reshape((2, 4, 1)) @@ -243,9 +240,8 @@ def test_evaluation_grid_keepdims_fourier(self): np.testing.assert_array_almost_equal(f_keepdims(t, grid=True, keepdims=False ).round(3), res) - np.testing.assert_array_almost_equal(f_keepdims(t, grid=True, - keepdims=True).round(3), - res_keepdims) + np.testing.assert_array_almost_equal( + f_keepdims(t, grid=True, keepdims=True).round(3), res_keepdims) def test_domain_in_list_fourier(self): """Test the evaluation of FDataBasis""" @@ -272,20 +268,23 @@ class TestBasisEvaluationBSpline(unittest.TestCase): def test_evaluation_simple_bspline(self): """Test the evaluation of FDataBasis""" - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) + bspline = BSpline(domain_range=(0, 2), n_basis=5) - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] + coefficients = np.array([[1, 2, 3, 4, 5], + [6, 7, 8, 9, 10]]) f = FDataBasis(bspline, coefficients) - t = np.linspace(0, 1, 4) + t = np.linspace(0, 2, 11) - res = np.array([[0.001, 0.564, 0.435, 0.33], - [0.018, 0.468, 0.371, 0.12]]) + # Results in R package fda + res = np.array([[1, 1.54, 1.99, 2.37, 2.7, 3, + 3.3, 3.63, 4.01, 4.46, 5], + [6, 6.54, 6.99, 7.37, 7.7, 8, + 8.3, 8.63, 9.01, 9.46, 10]]) - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) + np.testing.assert_array_almost_equal(f(t).round(2), res) + np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) def test_evaluation_point_bspline(self): """Test the evaluation of a single point FDataBasis""" @@ -360,8 +359,6 @@ def test_evaluation_composed_bspline(self): f = FDataBasis(bspline, coefficients) t = np.linspace(0, 1, 4) - res_test = f(t) - # Test same result than evaluation standart np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], @@ -444,10 +441,8 @@ def test_evaluation_composed_keepdims_bspline(self): res_keepdims) # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims(t, - aligned_evaluation=False - ).round(3), - res_keepdims) + np.testing.assert_array_almost_equal( + f_keepdims(t, aligned_evaluation=False).round(3), res_keepdims) np.testing.assert_array_almost_equal( f_keepdims(t, aligned_evaluation=False, keepdims=False).round(3), res) @@ -528,19 +523,23 @@ class TestBasisEvaluationMonomial(unittest.TestCase): def test_evaluation_simple_monomial(self): """Test the evaluation of FDataBasis""" - monomial = Monomial(domain_range=(0, 1), n_basis=3) + monomial = Monomial(domain_range=(0, 2), n_basis=5) - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] + coefficients = np.array([[1, 2, 3, 4, 5], + [6, 7, 8, 9, 10]]) f = FDataBasis(monomial, coefficients) - t = np.linspace(0, 1, 4) + t = np.linspace(0, 2, 11) - res = np.array([[1., 2., 3.667, 6.], - [0.5, 1.111, 2.011, 3.2]]) + # Results in R package fda + res = np.array([[1.00, 1.56, 2.66, 4.79, 8.62, 15.00, + 25.00, 39.86, 61.03, 90.14, 129.00], + [6.00, 7.81, 10.91, 16.32, 25.42, 40.00, + 62.21, 94.59, 140.08, 201.98, 284.00]]) - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) + np.testing.assert_array_almost_equal(f(t).round(2), res) + np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) def test_evaluation_point_monomial(self): """Test the evaluation of a single point FDataBasis""" @@ -611,11 +610,10 @@ def test_evaluation_composed_monomial(self): f = FDataBasis(monomial, coefficients) t = np.linspace(0, 1, 4) - res_test = f(t) - # Test same result than evaluation standart - np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], - aligned_evaluation=False)) + np.testing.assert_array_almost_equal(f([1]), + f([[1], [1]], + aligned_evaluation=False)) np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), aligned_evaluation=False)) From 4a8e57de3bb5130648147ce873dc316e5430c6ae Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 16 Mar 2020 00:25:42 +0100 Subject: [PATCH 131/624] Add linear differential operator evaluation. --- skfda/misc/_lfd.py | 28 +++++++++++++++++++++++++--- skfda/representation/basis.py | 23 +++++++++++++---------- tests/test_lfd.py | 2 +- 3 files changed, 39 insertions(+), 14 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 672828d1e..c6d4067e3 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -1,3 +1,5 @@ +import numbers + import numpy as np @@ -93,9 +95,12 @@ class LinearDifferentialOperator: """ - def __init__(self, order=None, *, weights=None, domain_range=None): + def __init__(self, order_or_weights=None, *, order=None, weights=None, + domain_range=None): """Lfd Constructor. You have to provide one of the two first - parameters. It both are provided, it will raise an error + parameters. It both are provided, it will raise an error. + If a positional argument is supplied it will be considered the + order if it is an integral type and the weights otherwise. Args: order (int, optional): the order of the operator. It's the highest @@ -114,13 +119,22 @@ def __init__(self, order=None, *, weights=None, domain_range=None): from ..representation.basis import (FDataBasis, Constant, _same_domain) - if order is not None and weights is not None: + num_args = sum( + [a is not None for a in [order_or_weights, order, weights]]) + + if num_args > 1: raise ValueError("You have to provide the order or the weights, " "not both") real_domain_range = (domain_range if domain_range is not None else (0, 1)) + if order_or_weights is not None: + if isinstance(order_or_weights, numbers.Integral): + order = order_or_weights + else: + weights = order_or_weights + if order is None and weights is None: self.weights = (FDataBasis(Constant(real_domain_range), 0),) @@ -186,3 +200,11 @@ def __eq__(self, other): return (self.order == other.nderic and all(self.weights[i] == other.bwtlist[i] for i in range(self.order))) + + def __call__(self, f): + """Return the function that results of applying the operator.""" + def applied_lfd(t): + return sum(w(t) * f(t, derivative=i) + for i, w in enumerate(self.weights)) + + return applied_lfd diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index c2f9053a6..22ea7fade 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -160,6 +160,9 @@ def evaluate(self, eval_points, derivative=0): return self._evaluate(eval_points, derivative) + def __call__(self, *args, **kwargs): + return self.evaluate(*args, **kwargs) + def plot(self, chart=None, *, derivative=0, **kwargs): """Plot the basis object or its derivatives. @@ -178,7 +181,7 @@ def plot(self, chart=None, *, derivative=0, **kwargs): """ self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) - def _evaluate_single_basis_coefficients(self, coefficients, basis_index, x, + def _evaluate_single_basis_coefficients(self, lfd, basis_index, x, cache): """Evaluate a differential operator over one of the basis. @@ -206,15 +209,11 @@ def _evaluate_single_basis_coefficients(self, coefficients, basis_index, x, """ if x not in cache: res = np.zeros(self.n_basis) - for i, k in enumerate(coefficients): - if callable(k): - res += k(x) * self._evaluate([x], i)[:, 0] - else: - res += k * self._evaluate([x], i)[:, 0] + res = lfd(self)([x])[:, 0] cache[x] = res return cache[x][basis_index] - def _numerical_penalty(self, coefficients): + def _numerical_penalty(self, lfd): """Return a penalty matrix using a numerical approach. See :func:`~basis.Basis.penalty`. @@ -226,6 +225,10 @@ def _numerical_penalty(self, coefficients): instance the tuple (1, 0, numpy.sin) means :math:`1 + sin(x)D^{2}`. """ + from skfda.misc import LinearDifferentialOperator + + if not isinstance(lfd, LinearDifferentialOperator): + lfd = LinearDifferentialOperator(lfd) # Range of first dimension domain_range = self.domain_range[0] @@ -234,15 +237,15 @@ def _numerical_penalty(self, coefficients): for i in range(self.n_basis): penalty_matrix[i, i] = scipy.integrate.quad( lambda x: (self._evaluate_single_basis_coefficients( - coefficients, i, x, cache) ** 2), + lfd, i, x, cache) ** 2), domain_range[0], domain_range[1] )[0] for j in range(i + 1, self.n_basis): penalty_matrix[i, j] = scipy.integrate.quad( (lambda x: (self._evaluate_single_basis_coefficients( - coefficients, i, x, cache) * + lfd, i, x, cache) * self._evaluate_single_basis_coefficients( - coefficients, j, x, cache))), + lfd, j, x, cache))), domain_range[0], domain_range[1] )[0] penalty_matrix[j, i] = penalty_matrix[i, j] diff --git a/tests/test_lfd.py b/tests/test_lfd.py index 64497d6db..c787cf192 100644 --- a/tests/test_lfd.py +++ b/tests/test_lfd.py @@ -5,7 +5,7 @@ import numpy as np -class TestBasis(unittest.TestCase): +class TestLfd(unittest.TestCase): def test_init_default(self): """Tests default initialization (do not penalize).""" From 707758561299dde827c2a521c08a1fe3ce828059 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 16 Mar 2020 11:54:58 +0100 Subject: [PATCH 132/624] Refactor numerical penalty to use vectorized integration. --- skfda/representation/basis.py | 70 +++++++++++------------------------ tests/test_basis.py | 67 ++++++++++++++++++++------------- 2 files changed, 62 insertions(+), 75 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 22ea7fade..53ec8fd07 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -181,38 +181,6 @@ def plot(self, chart=None, *, derivative=0, **kwargs): """ self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) - def _evaluate_single_basis_coefficients(self, lfd, basis_index, x, - cache): - """Evaluate a differential operator over one of the basis. - - Computes the result of evaluating a the result of applying a - differential operator over one of the basis functions. It also admits a - "cache" dictionary to store the results for the other basis not - returned because they are evaluated by the function and may be needed - later. - - Args: - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. - basis_index (int): index in self.basis of the basis that is - evaluated. - x (number): Point of evaluation. - cache (dict): Dictionary with the values of previous evaluation - for all the basis function and where the results of the - evalaution are stored. This is done because later evaluation - of the same differential operator and same x may be needed - for other of the basis functions. - - """ - if x not in cache: - res = np.zeros(self.n_basis) - res = lfd(self)([x])[:, 0] - cache[x] = res - return cache[x][basis_index] - def _numerical_penalty(self, lfd): """Return a penalty matrix using a numerical approach. @@ -230,25 +198,29 @@ def _numerical_penalty(self, lfd): if not isinstance(lfd, LinearDifferentialOperator): lfd = LinearDifferentialOperator(lfd) + indices = np.triu_indices(self.n_basis, 0) + + def _cross_product(x): + """Multiply the two lfds""" + res = lfd(self)([x])[:, 0] + + return res[indices[0]] * res[indices[1]] + # Range of first dimension domain_range = self.domain_range[0] - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - cache = {} - for i in range(self.n_basis): - penalty_matrix[i, i] = scipy.integrate.quad( - lambda x: (self._evaluate_single_basis_coefficients( - lfd, i, x, cache) ** 2), - domain_range[0], domain_range[1] - )[0] - for j in range(i + 1, self.n_basis): - penalty_matrix[i, j] = scipy.integrate.quad( - (lambda x: (self._evaluate_single_basis_coefficients( - lfd, i, x, cache) * - self._evaluate_single_basis_coefficients( - lfd, j, x, cache))), - domain_range[0], domain_range[1] - )[0] - penalty_matrix[j, i] = penalty_matrix[i, j] + + penalty_matrix = np.empty((self.n_basis, self.n_basis)) + + # Obtain the integrals for the upper matrix + triang_vec = scipy.integrate.quad_vec( + _cross_product, domain_range[0], domain_range[1])[0] + + # Set upper matrix + penalty_matrix[indices] = triang_vec + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = triang_vec + return penalty_matrix @abstractmethod diff --git a/tests/test_basis.py b/tests/test_basis.py index 28cef06c5..b6fe89e81 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,8 +1,8 @@ +from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, + BSpline, Fourier) import unittest import numpy as np -from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, - BSpline, Fourier) class TestBasis(unittest.TestCase): @@ -31,43 +31,58 @@ def test_from_data_qr(self): def test_bspline_penalty_special_case(self): basis = BSpline(n_basis=5) - np.testing.assert_array_almost_equal( + + res = np.array([[1152., -2016., 1152., -288., 0.], + [-2016., 3600., -2304., 1008., -288.], + [1152., -2304., 2304., -2304., 1152.], + [-288., 1008., -2304., 3600., -2016.], + [0., -288., 1152., -2016., 1152.]]) + + np.testing.assert_allclose( basis.penalty(basis.order - 1), - np.array([[1152., -2016., 1152., -288., 0.], - [-2016., 3600., -2304., 1008., -288.], - [1152., -2304., 2304., -2304., 1152.], - [-288., 1008., -2304., 3600., -2016.], - [0., -288., 1152., -2016., 1152.]])) + res + ) + + np.testing.assert_allclose( + basis._numerical_penalty(basis.order - 1), + res + ) def test_fourier_penalty(self): basis = Fourier(n_basis=5) + + res = np.array([[0., 0., 0., 0., 0.], + [0., 1558.55, 0., 0., 0.], + [0., 0., 1558.55, 0., 0.], + [0., 0., 0., 24936.73, 0.], + [0., 0., 0., 0., 24936.73]]) + np.testing.assert_array_almost_equal( basis.penalty(2).round(2), - np.array([[0., 0., 0., 0., 0.], - [0., 1558.55, 0., 0., 0.], - [0., 0., 1558.55, 0., 0.], - [0., 0., 0., 24936.73, 0.], - [0., 0., 0., 0., 24936.73]])) + res + ) + + np.testing.assert_array_almost_equal( + basis._numerical_penalty(2).round(2), + res + ) def test_bspline_penalty(self): basis = BSpline(n_basis=5) + + res = np.array([[96., -132., 24., 12., 0.], + [-132., 192., -48., -24., 12.], + [24., -48., 48., -48., 24.], + [12., -24., -48., 192., -132.], + [0., 12., 24., -132., 96.]]) + np.testing.assert_array_almost_equal( basis.penalty(2).round(2), - np.array([[96., -132., 24., 12., 0.], - [-132., 192., -48., -24., 12.], - [24., -48., 48., -48., 24.], - [12., -24., -48., 192., -132.], - [0., 12., 24., -132., 96.]])) + res) - def test_bspline_penalty_numerical(self): - basis = BSpline(n_basis=5) np.testing.assert_array_almost_equal( - basis.penalty(coefficients=[0, 0, 1]).round(2), - np.array([[96., -132., 24., 12., 0.], - [-132., 192., -48., -24., 12.], - [24., -48., 48., -48., 24.], - [12., -24., -48., 192., -132.], - [0., 12., 24., -132., 96.]])) + basis._numerical_penalty(2).round(2), + res) def test_basis_product_generic(self): monomial = Monomial(n_basis=5) From c710230e5c2024e58574f669396e28468b1041f9 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 16 Mar 2020 20:03:06 +0100 Subject: [PATCH 133/624] Change penalty --- skfda/misc/_lfd.py | 35 +- skfda/preprocessing/smoothing/_basis.py | 14 +- skfda/representation/basis.py | 420 +++++++++--------------- tests/test_basis.py | 45 ++- tests/test_lfd.py | 10 - 5 files changed, 217 insertions(+), 307 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index c6d4067e3..2a2415140 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -31,7 +31,6 @@ class LinearDifferentialOperator: >>> >>> LinearDifferentialOperator(2) LinearDifferentialOperator( - nderiv=2, weights=[ FDataBasis( basis=Constant(domain_range=[array([0, 1])], n_basis=1), @@ -52,7 +51,6 @@ class LinearDifferentialOperator: >>> LinearDifferentialOperator(weights=[0, 2, 3]) LinearDifferentialOperator( - nderiv=2, weights=[ FDataBasis( basis=Constant(domain_range=[array([0, 1])], n_basis=1), @@ -77,7 +75,6 @@ class LinearDifferentialOperator: ... FDataBasis(monomial, [1, 2, 3])] >>> LinearDifferentialOperator(weights=fdlist) LinearDifferentialOperator( - nderiv=2, weights=[ FDataBasis( basis=Constant(domain_range=[array([0, 1])], n_basis=1), @@ -151,7 +148,7 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, if len(weights) == 0: raise ValueError("You have to provide one weight at least") - if all(isinstance(n, int) for n in weights): + if all(isinstance(n, numbers.Integral) for n in weights): self.weights = (FDataBasis(Constant(real_domain_range), np.array(weights) .reshape(-1, 1)).to_list()) @@ -179,27 +176,37 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, self.domain_range = real_domain_range - @property - def order(self): - return len(self.weights) - 1 - def __repr__(self): """Representation of Lfd object.""" bwtliststr = "" - for i in range(self.order + 1): - bwtliststr = bwtliststr + "\n" + self.weights[i].__repr__() + "," + for w in self.weights: + bwtliststr = bwtliststr + "\n" + repr(w) + "," return (f"{self.__class__.__name__}(" - f"\nnderiv={self.order}," f"\nweights=[{bwtliststr[:-1]}]" f"\n)").replace('\n', '\n ') def __eq__(self, other): """Equality of Lfd objects""" - return (self.order == other.nderic and - all(self.weights[i] == other.bwtlist[i] - for i in range(self.order))) + return (self.weights == other.weights) + + def constant_weights(self): + """ + Return the weights of the weights if they are constant basis. + Otherwise, return None. + + This function is mostly useful for basis which want to override + the _penalty method in order to use an analytical expression + for constant weights. + """ + from ..representation.basis import Constant + + coefs = [w.coefficients[0, 0] if isinstance(w.basis, Constant) + else None + for w in self.weights] + + return np.array(coefs) if coefs.count(None) == 0 else None def __call__(self, f): """Return the function that results of applying the operator.""" diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index c8854b6bf..116ed319c 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -343,12 +343,10 @@ def _penalty(self): """Get the penalty differential operator.""" if self.penalty is None: penalty = LinearDifferentialOperator(order=2) - elif isinstance(self.penalty, int): - penalty = LinearDifferentialOperator(order=self.penalty) - elif isinstance(self.penalty, collections.abc.Iterable): - penalty = LinearDifferentialOperator(weights=self.penalty) - else: + elif isinstance(self.penalty, LinearDifferentialOperator): penalty = self.penalty + else: + penalty = LinearDifferentialOperator(self.penalty) return penalty @@ -365,8 +363,7 @@ def _penalty_matrix(self): penalty = self._penalty() if self.smoothing_parameter > 0: - penalty_matrix = self.basis.penalty(penalty.order, - penalty.weights) + penalty_matrix = self.basis.penalty(penalty) else: penalty_matrix = None @@ -471,7 +468,8 @@ def fit_transform(self, X: FDataGrid, y=None): or self.smoothing_parameter > 0): # TODO: The penalty could be None (if the matrix is passed) - ndegenerated = self.basis._ndegenerated(self._penalty().order) + ndegenerated = self.basis._ndegenerated( + len(self._penalty().weights) - 1) method = self._method_function() diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 53ec8fd07..5505c8011 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -100,19 +100,7 @@ def domain_range(self, value): @abstractmethod def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - - Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - """ + """Subclasses must override this to provide basis evaluation.""" pass @abstractmethod @@ -187,11 +175,9 @@ def _numerical_penalty(self, lfd): See :func:`~basis.Basis.penalty`. Args: - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. + lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. """ from skfda.misc import LinearDifferentialOperator @@ -200,7 +186,7 @@ def _numerical_penalty(self, lfd): indices = np.triu_indices(self.n_basis, 0) - def _cross_product(x): + def cross_product(x): """Multiply the two lfds""" res = lfd(self)([x])[:, 0] @@ -213,7 +199,7 @@ def _cross_product(x): # Obtain the integrals for the upper matrix triang_vec = scipy.integrate.quad_vec( - _cross_product, domain_range[0], domain_range[1])[0] + cross_product, domain_range[0], domain_range[1])[0] # Set upper matrix penalty_matrix[indices] = triang_vec @@ -223,8 +209,17 @@ def _cross_product(x): return penalty_matrix - @abstractmethod - def penalty(self, derivative_degree=None, coefficients=None): + def _penalty(self, lfd): + """ + Subclasses may override this for computing analytically + the penalty matrix in the cases when that is possible. + + Returning NotImplemented will use numerical computation + of the penalty matrix. + """ + return NotImplemented + + def penalty(self, lfd): r"""Return a penalty matrix given a differential operator. The differential operator can be either a derivative of a certain @@ -239,14 +234,9 @@ def penalty(self, derivative_degree=None, coefficients=None): functions and :math:`L` is a differential operator. Args: - derivative_degree (int): Integer indicating the order of the - derivative or . For instance 2 means that the differential - operator is :math:`f''(x)`. - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. Only used if derivative degree is None. + lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. Returns: numpy.array: Penalty matrix. @@ -257,7 +247,17 @@ def penalty(self, derivative_degree=None, coefficients=None): Springer. """ - pass + from skfda.misc import LinearDifferentialOperator + + if not isinstance(lfd, LinearDifferentialOperator): + lfd = LinearDifferentialOperator(lfd) + + matrix = self._penalty(lfd) + + if matrix is NotImplemented: + return self._numerical_penalty(lfd) + else: + return matrix @abstractmethod def basis_of_product(self, other): @@ -468,13 +468,14 @@ def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) - def penalty(self, derivative_degree=None, coefficients=None): - if derivative_degree is None: - return self._numerical_penalty(coefficients) + def _penalty(self, lfd): + coefs = lfd.constant_weights() + if coefs is None: + return NotImplemented - return (np.full((1, 1), - (self.domain_range[0][1] - self.domain_range[0][0])) - if derivative_degree == 0 else np.zeros((1, 1))) + return np.array([[coefs[0] ** 2 * + (self.domain_range[0][1] - + self.domain_range[0][0])]]) def basis_of_product(self, other): """Multiplication of a Constant Basis with other Basis""" @@ -582,50 +583,17 @@ def _derivative(self, coefs, order=1): np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])) - def penalty(self, derivative_degree=None, coefficients=None): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2-1]_: + def _penalty(self, lfd): - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds + coefs = lfd.constant_weights() + if coefs is None: + return NotImplemented - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - derivative_degree (int): Integer indicating the order of the - derivative or . For instance 2 means that the differential - operator is :math:`f''(x)`. - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. Only used if derivative degree is None. - - - Returns: - numpy.array: Penalty matrix. - - Examples: - >>> Monomial(n_basis=4).penalty(2) - array([[ 0., 0., 0., 0.], - [ 0., 0., 0., 0.], - [ 0., 0., 4., 6.], - [ 0., 0., 6., 12.]]) - - References: - .. [RS05-5-6-2-1] Ramsay, J., Silverman, B. W. (2005). Specifying - the roughness penalty. In *Functional Data Analysis* - (pp. 106-107). Springer. - - """ + nonzero = np.flatnonzero(coefs) + if len(nonzero) != 1: + return NotImplemented - if derivative_degree is None: - return self._numerical_penalty(coefficients) + derivative_degree = nonzero[0] integration_domain = self.domain_range[0] @@ -904,145 +872,113 @@ def _derivative(self, coefs, order=1): return deriv_basis, np.array(deriv_coefs)[:, 0:deriv_basis.n_basis] - def penalty(self, derivative_degree=None, coefficients=None): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2-3]_: - - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds - - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - derivative_degree (int): Integer indicating the order of the - derivative or . For instance 2 means that the differential - operator is :math:`f''(x)`. - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. Only used if derivative degree is None. - - Returns: - numpy.array: Penalty matrix. - - References: - .. [RS05-5-6-2-3] Ramsay, J., Silverman, B. W. (2005). Specifying - the roughness penalty. In *Functional Data Analysis* - (pp. 106-107). Springer. - - """ - if derivative_degree is not None: - if derivative_degree >= self.order: - raise ValueError(f"Penalty matrix cannot be evaluated for " - f"derivative of order {derivative_degree} for" - f" B-splines of order {self.order}") - if derivative_degree == self.order - 1: - # The derivative of the bsplines are constant in the intervals - # defined between knots - knots = np.array(self.knots) - mid_inter = (knots[1:] + knots[:-1]) / 2 - constants = self.evaluate(mid_inter, - derivative=derivative_degree).T - knots_intervals = np.diff(self.knots) - # Integration of product of constants - return constants.T @ np.diag(knots_intervals) @ constants - - if np.all(np.diff(self.knots) != 0): - # Compute exactly using the piecewise polynomial - # representation of splines - - # Places m knots at the boundaries - knots = np.array( - [self.knots[0]] * (self.order - 1) + self.knots - + [self.knots[-1]] * (self.order - 1)) - # c is used the select which spline the function - # PPoly.from_spline below computes - c = np.zeros(len(knots)) - - # Initialise empty list to store the piecewise polynomials - ppoly_lst = [] - - no_0_intervals = np.where(np.diff(knots) > 0)[0] - - # For each basis gets its piecewise polynomial representation + def _penalty(self, lfd): + + coefs = lfd.constant_weights() + if coefs is None: + return NotImplemented + + nonzero = np.flatnonzero(coefs) + if len(nonzero) != 1: + return NotImplemented + + derivative_degree = nonzero[0] + + if derivative_degree >= self.order: + raise ValueError(f"Penalty matrix cannot be evaluated for " + f"derivative of order {derivative_degree} for" + f" B-splines of order {self.order}") + if derivative_degree == self.order - 1: + # The derivative of the bsplines are constant in the intervals + # defined between knots + knots = np.array(self.knots) + mid_inter = (knots[1:] + knots[:-1]) / 2 + constants = self.evaluate(mid_inter, + derivative=derivative_degree).T + knots_intervals = np.diff(self.knots) + # Integration of product of constants + return constants.T @ np.diag(knots_intervals) @ constants + + if np.all(np.diff(self.knots) != 0): + # Compute exactly using the piecewise polynomial + # representation of splines + + # Places m knots at the boundaries + knots = np.array( + [self.knots[0]] * (self.order - 1) + self.knots + + [self.knots[-1]] * (self.order - 1)) + # c is used the select which spline the function + # PPoly.from_spline below computes + c = np.zeros(len(knots)) + + # Initialise empty list to store the piecewise polynomials + ppoly_lst = [] + + no_0_intervals = np.where(np.diff(knots) > 0)[0] + + # For each basis gets its piecewise polynomial representation + for i in range(self.n_basis): + # write a 1 in c in the position of the spline + # transformed in each iteration + c[i] = 1 + # gets the piecewise polynomial representation and gets + # only the positions for no zero length intervals + # This polynomial are defined relatively to the knots + # meaning that the column i corresponds to the ith knot. + # Let the ith not be a + # Then f(x) = pp(x - a) + pp = (PPoly.from_spline( + (knots, c, self.order - 1)).c[:, no_0_intervals]) # We need the actual coefficients of f, not pp. So we + # just recursively calculate the new coefficients + coeffs = pp.copy() + for j in range(self.order - 1): + coeffs[j + 1:] += ( + (binom(self.order - j - 1, + range(1, self.order - j)) * + np.vstack([(-a) ** + np.array(range(1, self.order - j)) + for a in self.knots[:-1]])).T * + pp[j]) + ppoly_lst.append(coeffs) + c[i] = 0 + + # Now for each pair of basis computes the inner product after + # applying the linear differential operator + penalty_matrix = np.zeros((self.n_basis, self.n_basis)) + for interval in range(len(no_0_intervals)): for i in range(self.n_basis): - # write a 1 in c in the position of the spline - # transformed in each iteration - c[i] = 1 - # gets the piecewise polynomial representation and gets - # only the positions for no zero length intervals - # This polynomial are defined relatively to the knots - # meaning that the column i corresponds to the ith knot. - # Let the ith not be a - # Then f(x) = pp(x - a) - pp = (PPoly.from_spline( - (knots, c, self.order - 1)).c[:, no_0_intervals]) # We need the actual coefficients of f, not pp. So we - # just recursively calculate the new coefficients - coeffs = pp.copy() - for j in range(self.order - 1): - coeffs[j + 1:] += ( - (binom(self.order - j - 1, - range(1, self.order - j)) * - np.vstack([(-a) ** - np.array(range(1, self.order - j)) - for a in self.knots[:-1]])).T * - pp[j]) - ppoly_lst.append(coeffs) - c[i] = 0 - - # Now for each pair of basis computes the inner product after - # applying the linear differential operator - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - for interval in range(len(no_0_intervals)): - for i in range(self.n_basis): - poly_i = np.trim_zeros(ppoly_lst[i][:, + poly_i = np.trim_zeros(ppoly_lst[i][:, + interval], 'f') + if len(poly_i) <= derivative_degree: + # if the order of the polynomial is lesser or + # equal to the derivative the result of the + # integral will be 0 + continue + # indefinite integral + integral = polyint(_polypow(polyder( + poly_i, derivative_degree), 2)) + # definite integral + penalty_matrix[i, i] += np.diff(polyval( + integral, self.knots[interval: interval + 2]))[0] + + for j in range(i + 1, self.n_basis): + poly_j = np.trim_zeros(ppoly_lst[j][:, interval], 'f') - if len(poly_i) <= derivative_degree: - # if the order of the polynomial is lesser or - # equal to the derivative the result of the - # integral will be 0 + if len(poly_j) <= derivative_degree: + # if the order of the polynomial is lesser + # or equal to the derivative the result of + # the integral will be 0 continue - # indefinite integral - integral = polyint(_polypow(polyder( - poly_i, derivative_degree), 2)) + # indefinite integral + integral = polyint( + polymul(polyder(poly_i, derivative_degree), + polyder(poly_j, derivative_degree))) # definite integral - penalty_matrix[i, i] += np.diff(polyval( - integral, self.knots[interval: interval + 2]))[0] - - for j in range(i + 1, self.n_basis): - poly_j = np.trim_zeros(ppoly_lst[j][:, - interval], 'f') - if len(poly_j) <= derivative_degree: - # if the order of the polynomial is lesser - # or equal to the derivative the result of - # the integral will be 0 - continue - # indefinite integral - integral = polyint( - polymul(polyder(poly_i, derivative_degree), - polyder(poly_j, derivative_degree))) - # definite integral - penalty_matrix[i, j] += np.diff(polyval( - integral, self.knots[interval: interval + 2]) - )[0] - penalty_matrix[j, i] = penalty_matrix[i, j] - return penalty_matrix - else: - # if the order of the derivative is greater or equal to the order - # of the bspline minus 1 - if len(coefficients) >= self.order: - raise ValueError(f"Penalty matrix cannot be evaluated for " - f"derivative of order {len(coefficients) - 1}" - f" for B-splines of order {self.order}") - - # compute using the inner product - return self._numerical_penalty(coefficients) + penalty_matrix[i, j] += np.diff(polyval( + integral, self.knots[interval: interval + 2]) + )[0] + penalty_matrix[j, i] = penalty_matrix[i, j] + return penalty_matrix def rescale(self, domain_range=None): r"""Return a copy of the basis with a new domain range, with the @@ -1346,64 +1282,6 @@ def _derivative(self, coefs, order=1): # normalise return self.copy(), deriv_coefs - def penalty(self, derivative_degree=None, coefficients=None): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2-4]_: - - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds - - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - derivative_degree (int): Integer indicating the order of the - derivative or . For instance 2 means that the differential - operator is :math:`f''(x)`. - coefficients (list): List of coefficients representing a - differential operator. An iterable indicating - coefficients of derivatives (which can be functions). For - instance the tuple (1, 0, numpy.sin) means :math:`1 - + sin(x)D^{2}`. Only used if derivative degree is None. - - Returns: - numpy.array: Penalty matrix. - - References: - .. [RS05-5-6-2-4] Ramsay, J., Silverman, B. W. (2005). Specifying - the roughness penalty. In *Functional Data Analysis* - (pp. 106-107). Springer. - - """ - if isinstance(derivative_degree, int): - omega = 2 * np.pi / self.period - # the derivatives of the functions of the basis are also orthogonal - # so only the diagonal is different from 0. - penalty_matrix = np.zeros(self.n_basis) - if derivative_degree == 0: - penalty_matrix[0] = 1 - else: - # the derivative of a constant is 0 - # the first basis function is a constant - penalty_matrix[0] = 0 - index_even = np.array(range(2, self.n_basis, 2)) - exponents = index_even / 2 - # factor resulting of deriving the basis function the times - # indcated in the derivative_degree - factor = (exponents * omega) ** (2 * derivative_degree) - # the norm of the basis functions is 1 so only the result of the - # integral is just the factor - penalty_matrix[index_even - 1] = factor - penalty_matrix[index_even] = factor - return np.diag(penalty_matrix) - else: - # implement using inner product - return self._numerical_penalty(coefficients) - def basis_of_product(self, other): """Multiplication of two Fourier Basis""" if not _same_domain(self.domain_range, other.domain_range): diff --git a/tests/test_basis.py b/tests/test_basis.py index b6fe89e81..1b1e3d233 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -48,6 +48,43 @@ def test_bspline_penalty_special_case(self): res ) + def test_constant_penalty(self): + basis = Constant(domain_range=(0, 3)) + + res = np.array([[12]]) + + lfd = [2, 3, 4] + + np.testing.assert_allclose( + basis.penalty(lfd).round(2), + res + ) + + np.testing.assert_allclose( + basis._numerical_penalty(lfd).round(2), + res + ) + + def test_monomial_penalty(self): + basis = Monomial(n_basis=5, domain_range=(0, 3)) + + # Theorethical result + res = np.array([[0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0.], + [0., 0., 12., 54., 216.], + [0., 0., 54., 324., 1458.], + [0., 0., 216., 1458., 6998.4]]) + + np.testing.assert_allclose( + basis.penalty(2).round(2), + res + ) + + np.testing.assert_allclose( + basis._numerical_penalty(2).round(2), + res + ) + def test_fourier_penalty(self): basis = Fourier(n_basis=5) @@ -57,12 +94,12 @@ def test_fourier_penalty(self): [0., 0., 0., 24936.73, 0.], [0., 0., 0., 0., 24936.73]]) - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( basis.penalty(2).round(2), res ) - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( basis._numerical_penalty(2).round(2), res ) @@ -76,11 +113,11 @@ def test_bspline_penalty(self): [12., -24., -48., 192., -132.], [0., 12., 24., -132., 96.]]) - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( basis.penalty(2).round(2), res) - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( basis._numerical_penalty(2).round(2), res) diff --git a/tests/test_lfd.py b/tests/test_lfd.py index c787cf192..77de990cb 100644 --- a/tests/test_lfd.py +++ b/tests/test_lfd.py @@ -12,8 +12,6 @@ def test_init_default(self): lfd = LinearDifferentialOperator() weightfd = [FDataBasis(Constant((0, 1)), 0)] - np.testing.assert_equal(lfd.order, 0, - "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd.weights, weightfd, "Wrong list of weight functions of the linear operator") @@ -25,8 +23,6 @@ def test_init_integer(self): lfd_0 = LinearDifferentialOperator(order=0) weightfd = [FDataBasis(Constant((0, 1)), 1)] - np.testing.assert_equal(lfd_0.order, 0, - "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd_0.weights, weightfd, "Wrong list of weight functions of the linear operator") @@ -36,8 +32,6 @@ def test_init_integer(self): consfd = FDataBasis(Constant((0, 1)), [[0], [0], [0], [1]]) bwtlist3 = consfd.to_list() - np.testing.assert_equal(lfd_3.order, 3, - "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd_3.weights, bwtlist3, "Wrong list of weight functions of the linear operator") @@ -56,8 +50,6 @@ def test_init_list_int(self): lfd = LinearDifferentialOperator(weights=coefficients) - np.testing.assert_equal(lfd.order, 5, - "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd.weights, fd.to_list(), "Wrong list of weight functions of the linear operator") @@ -77,8 +69,6 @@ def test_init_list_fdatabasis(self): fdlist = [FDataBasis(monomial, w) for w in weights] lfd = LinearDifferentialOperator(weights=fdlist) - np.testing.assert_equal(lfd.order, n_weights - 1, - "Wrong deriv order of the linear operator") np.testing.assert_equal( lfd.weights, fd.to_list(), "Wrong list of weight functions of the linear operator") From 0667ff5926d10bba95aedfe582f1b2741fda5e2d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 16 Mar 2020 23:04:26 +0100 Subject: [PATCH 134/624] Fixes bug in penalized basis smoothing when coefficients where used. --- skfda/misc/_lfd.py | 2 +- skfda/preprocessing/smoothing/_basis.py | 15 +++++++++------ 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 2a2415140..5976ea7c0 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -148,7 +148,7 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, if len(weights) == 0: raise ValueError("You have to provide one weight at least") - if all(isinstance(n, numbers.Integral) for n in weights): + if all(isinstance(n, numbers.Real) for n in weights): self.weights = (FDataBasis(Constant(real_domain_range), np.array(weights) .reshape(-1, 1)).to_list()) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 116ed319c..3d13751df 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -260,11 +260,12 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='cholesky', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator(weights=[3, 5]), + ... penalty=LinearDifferentialOperator( + ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 2.18, 0.07, 0.09]]) + array([[ 2.04, 0.51, 0.55]]) >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) @@ -272,11 +273,12 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator(weights=[3, 5]), + ... penalty=LinearDifferentialOperator( + ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 2.18, 0.07, 0.09]]) + array([[ 2.04, 0.51, 0.55]]) >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) @@ -284,11 +286,12 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator(weights=[3, 5]), + ... penalty=LinearDifferentialOperator( + ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) - array([[ 2.18, 0.07, 0.09]]) + array([[ 2.04, 0.51, 0.55]]) References: .. [RS05-5-2-6] Ramsay, J., Silverman, B. W. (2005). How spline From 967c5b09dc005c6e61065ac10e64ad4a7ec5c30a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 17 Mar 2020 21:13:45 +0100 Subject: [PATCH 135/624] Add evaluation of lfd in monomial basis. --- skfda/representation/basis.py | 86 ++++++++++++++++++++++++++++++----- tests/test_basis.py | 37 +++++++++++++++ 2 files changed, 112 insertions(+), 11 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 5505c8011..a324a8c1b 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -6,12 +6,14 @@ """ from abc import ABC, abstractmethod import copy +from scipy.misc.common import derivative from numpy import polyder, polyint, polymul, polyval import scipy.integrate from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly import scipy.interpolate +from scipy.odr.models import polynomial from scipy.special import binom from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted @@ -534,6 +536,21 @@ class Monomial(Basis): """ + def _coef_mat(self, derivative): + """ + Obtain the matrix of coefficients. + + Each column of coef_mat contains the numbers that must be multiplied + together in order to obtain the coefficient of each basis function + Thus, column i will contain i, i - 1, ..., i - derivative + 1. + """ + + seq = np.arange(self.n_basis) + coef_mat = np.linspace(seq, seq - derivative + 1, + derivative, dtype=int) + + return seq, coef_mat + def _coefs_exps_derivatives(self, derivative): """ Return coefficients and exponents of the derivatives. @@ -543,14 +560,7 @@ def _coefs_exps_derivatives(self, derivative): When the exponent would be negative (the coefficient in that case is zero) returns 0 as the exponent (to prevent division by zero). """ - - seq = np.arange(self.n_basis) - - # Each column of coef_mat contains the numbers that must be multiplied - # together in order to obtain the coefficient of each basis function - # Thus, column i will contain i, i - 1, ..., i - derivative + 1 - coef_mat = np.linspace(seq, seq - derivative + 1, - derivative, dtype=int) + seq, coef_mat = self._coef_mat(derivative) coefs = np.prod(coef_mat, axis=0) exps = np.maximum(seq - derivative, 0) @@ -583,18 +593,72 @@ def _derivative(self, coefs, order=1): np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])) + def _evaluate_constant_lfd(self, weights): + """ + Evaluate constant weights of a linear differential operator + over the basis functions. + """ + + max_derivative = len(weights) - 1 + + _, coef_mat = self._coef_mat(max_derivative) + + # Compute coefficients for each derivative + coefs = np.cumprod(coef_mat, axis=0) + + # Add derivative 0 row + coefs = np.concatenate((np.ones((1, self.n_basis)), coefs)) + + # Now each row correspond to each basis and each column to + # each derivative + coefs_t = coefs.T + + # Multiply by the weights + weighted_coefs = coefs_t * weights + assert len(weighted_coefs) == self.n_basis + + # Now each row has the right weight, but the polynomials are in a + # decreasing order and with different exponents + + # Resize the coefs so that there are as many rows as the number of + # basis + # The matrix is now triangular + # refcheck is False to prevent exceptions while debugging + weighted_coefs = np.copy(weighted_coefs.T) + weighted_coefs.resize(self.n_basis, + self.n_basis, refcheck=False) + weighted_coefs = weighted_coefs.T + + # Shift the coefficients so that they correspond to the right + # exponent + indexes = np.tril_indices(self.n_basis) + coefs_shifted = np.zeros_like(weighted_coefs) + coefs_shifted[indexes[0], indexes[1] - + indexes[0] - 1] = weighted_coefs[indexes] + + # Now flip the matrix so that the exponents are in increasing order + polynomials = np.fliplr(coefs_shifted) + + # At this point, each row of the matrix correspond to a polynomial + # that is the result of applying the linear differential operator + # to each element of the basis + + return polynomials + def _penalty(self, lfd): - coefs = lfd.constant_weights() - if coefs is None: + weights = lfd.constant_weights() + if weights is None: return NotImplemented - nonzero = np.flatnonzero(coefs) + nonzero = np.flatnonzero(weights) if len(nonzero) != 1: return NotImplemented derivative_degree = nonzero[0] + polynomials = self._evaluate_constant_lfd(weights) + integration_domain = self.domain_range[0] # initialize penalty matrix as all zeros diff --git a/tests/test_basis.py b/tests/test_basis.py index 1b1e3d233..948481af3 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -65,6 +65,43 @@ def test_constant_penalty(self): res ) + def test_monomial_lfd(self): + n_basis = 5 + + basis = Monomial(n_basis=n_basis) + + lfd = [3] + res = 3 * np.identity(n_basis) + + np.testing.assert_allclose( + basis._evaluate_constant_lfd(lfd), + res + ) + + lfd = [3, 2] + res = np.array([[3., 0., 0., 0., 0.], + [2., 3., 0., 0., 0.], + [0., 4., 3., 0., 0.], + [0., 0., 6., 3., 0.], + [0., 0., 0., 8., 3.]]) + + np.testing.assert_allclose( + basis._evaluate_constant_lfd(lfd), + res + ) + + lfd = [3, 0, 5] + res = np.array([[3., 0., 0., 0., 0.], + [0., 3., 0., 0., 0.], + [10., 0., 3., 0., 0.], + [0., 30., 0., 3., 0.], + [0., 0., 60., 0., 3.]]) + + np.testing.assert_allclose( + basis._evaluate_constant_lfd(lfd), + res + ) + def test_monomial_penalty(self): basis = Monomial(n_basis=5, domain_range=(0, 3)) From 86dd46778425a6f50648b54a7f6df704b224dd65 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 18 Mar 2020 17:15:20 +0100 Subject: [PATCH 136/624] Add analytical monomial penalty. --- skfda/representation/basis.py | 95 +++++++++++++++-------------------- tests/test_basis.py | 26 ++++++---- 2 files changed, 56 insertions(+), 65 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index a324a8c1b..3e167a883 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -6,14 +6,13 @@ """ from abc import ABC, abstractmethod import copy -from scipy.misc.common import derivative +import scipy.signal from numpy import polyder, polyint, polymul, polyval import scipy.integrate from scipy.interpolate import BSpline as SciBSpline from scipy.interpolate import PPoly import scipy.interpolate -from scipy.odr.models import polynomial from scipy.special import binom from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted @@ -186,7 +185,7 @@ def _numerical_penalty(self, lfd): if not isinstance(lfd, LinearDifferentialOperator): lfd = LinearDifferentialOperator(lfd) - indices = np.triu_indices(self.n_basis, 0) + indices = np.triu_indices(self.n_basis) def cross_product(x): """Multiply the two lfds""" @@ -632,12 +631,9 @@ def _evaluate_constant_lfd(self, weights): # Shift the coefficients so that they correspond to the right # exponent indexes = np.tril_indices(self.n_basis) - coefs_shifted = np.zeros_like(weighted_coefs) - coefs_shifted[indexes[0], indexes[1] - - indexes[0] - 1] = weighted_coefs[indexes] - - # Now flip the matrix so that the exponents are in increasing order - polynomials = np.fliplr(coefs_shifted) + polynomials = np.zeros_like(weighted_coefs) + polynomials[indexes[0], indexes[1] - + indexes[0] - 1] = weighted_coefs[indexes] # At this point, each row of the matrix correspond to a polynomial # that is the result of applying the linear differential operator @@ -655,55 +651,46 @@ def _penalty(self, lfd): if len(nonzero) != 1: return NotImplemented - derivative_degree = nonzero[0] - polynomials = self._evaluate_constant_lfd(weights) + # Expand the polinomials with 0, so that the multiplication fits + # inside. It will need the double of the degree + length_with_padding = polynomials.shape[1] * 2 - 1 + + # Multiplication of polynomials is a convolution. + # The convolution can be performed in parallel applying a Fourier + # transform and then doing a normal multiplication in that + # space, coverting back with the inverse Fourier transform + fft = np.fft.rfft(polynomials, length_with_padding) + + # We compute only the upper matrix, as the penalty matrix is + # symmetrical + indices = np.triu_indices(self.n_basis) + fft_mul = fft[indices[0]] * fft[indices[1]] + + integrand = np.fft.irfft(fft_mul, length_with_padding) + integration_domain = self.domain_range[0] - # initialize penalty matrix as all zeros - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - # iterate over the cartesion product of the basis system with itself - for ibasis in range(self.n_basis): - # notice that the index ibasis it is also the exponent of the - # monomial - # ifac is the factor resulting of deriving the monomial as many - # times as indicates de differential operator - if derivative_degree > 0: - ifac = ibasis - for k in range(2, derivative_degree + 1): - ifac *= ibasis - k + 1 - else: - ifac = 1 - - for jbasis in range(self.n_basis): - # notice that the index jbasis it is also the exponent of the - # monomial - # jfac is the factor resulting of deriving the monomial as - # many times as indicates de differential operator - if derivative_degree > 0: - jfac = jbasis - for k in range(2, derivative_degree + 1): - jfac *= jbasis - k + 1 - else: - jfac = 1 - - # if any of the two monomial has lower degree than the order of - # the derivative indicated by the differential operator that - # factor equals 0, so no calculation are needed - if (ibasis >= derivative_degree - and jbasis >= derivative_degree): - # Calculates exactly the result of the integral - # Exponent after applying the differential operator and - # integrating - ipow = ibasis + jbasis - 2 * derivative_degree + 1 - # coefficient after integrating - penalty_matrix[ibasis, jbasis] = ( - ((integration_domain[1] ** ipow) - - (integration_domain[0] ** ipow)) * - ifac * jfac / ipow) - penalty_matrix[jbasis, ibasis] = penalty_matrix[ibasis, - jbasis] + # To integrate, divide by the position and increase the exponent + # in the evaluation + denom = np.arange(integrand.shape[1], 0, -1) + integrand /= denom + + # Now, apply Barrow's rule + powers = denom + x_right = integration_domain[1]**powers * integrand + x_left = integration_domain[0]**powers * integrand + + integral = np.sum(x_right - x_left, axis=-1) + + penalty_matrix = np.empty((self.n_basis, self.n_basis)) + + # Set upper matrix + penalty_matrix[indices] = integral + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = integral return penalty_matrix diff --git a/tests/test_basis.py b/tests/test_basis.py index 948481af3..99cb7e922 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -71,7 +71,11 @@ def test_monomial_lfd(self): basis = Monomial(n_basis=n_basis) lfd = [3] - res = 3 * np.identity(n_basis) + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 0.], + [0., 0., 3., 0., 0.], + [0., 3., 0., 0., 0.], + [3., 0., 0., 0., 0.]]) np.testing.assert_allclose( basis._evaluate_constant_lfd(lfd), @@ -79,11 +83,11 @@ def test_monomial_lfd(self): ) lfd = [3, 2] - res = np.array([[3., 0., 0., 0., 0.], - [2., 3., 0., 0., 0.], - [0., 4., 3., 0., 0.], - [0., 0., 6., 3., 0.], - [0., 0., 0., 8., 3.]]) + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 2.], + [0., 0., 3., 4., 0.], + [0., 3., 6., 0., 0.], + [3., 8., 0., 0., 0.]]) np.testing.assert_allclose( basis._evaluate_constant_lfd(lfd), @@ -91,11 +95,11 @@ def test_monomial_lfd(self): ) lfd = [3, 0, 5] - res = np.array([[3., 0., 0., 0., 0.], - [0., 3., 0., 0., 0.], - [10., 0., 3., 0., 0.], - [0., 30., 0., 3., 0.], - [0., 0., 60., 0., 3.]]) + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 0.], + [0., 0., 3., 0., 10.], + [0., 3., 0., 30., 0.], + [3., 0., 60., 0., 0.]]) np.testing.assert_allclose( basis._evaluate_constant_lfd(lfd), From 622b2372472ee3494fdfe88f99eee397c4ae350a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 18 Mar 2020 20:04:26 +0100 Subject: [PATCH 137/624] Evaluate integral using Horner's method in Barrow's rule. --- skfda/representation/basis.py | 19 ++++++----- tests/test_basis.py | 61 +++++++++++++++-------------------- 2 files changed, 37 insertions(+), 43 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 3e167a883..44f456cdd 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -647,10 +647,6 @@ def _penalty(self, lfd): if weights is None: return NotImplemented - nonzero = np.flatnonzero(weights) - if len(nonzero) != 1: - return NotImplemented - polynomials = self._evaluate_constant_lfd(weights) # Expand the polinomials with 0, so that the multiplication fits @@ -677,12 +673,19 @@ def _penalty(self, lfd): denom = np.arange(integrand.shape[1], 0, -1) integrand /= denom + # Add column of zeros at the right to increase exponent + integrand = np.pad(integrand, + pad_width=((0, 0), + (0, 1)), + mode='constant') + # Now, apply Barrow's rule - powers = denom - x_right = integration_domain[1]**powers * integrand - x_left = integration_domain[0]**powers * integrand + # polyval applies Horner method over the first dimension, + # so we need to transpose + x_right = np.polyval(integrand.T, integration_domain[1]) + x_left = np.polyval(integrand.T, integration_domain[0]) - integral = np.sum(x_right - x_left, axis=-1) + integral = x_right - x_left penalty_matrix = np.empty((self.n_basis, self.n_basis)) diff --git a/tests/test_basis.py b/tests/test_basis.py index 99cb7e922..9c9c2c680 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -9,6 +9,22 @@ class TestBasis(unittest.TestCase): # def setUp(self): could be defined for set up before any test + def _test_penalty(self, basis, lfd, result=None): + + penalty = basis.penalty(lfd).round(2) + numerical_penalty = basis._numerical_penalty(lfd).round(2) + + np.testing.assert_allclose( + penalty, + numerical_penalty + ) + + if result is not None: + np.testing.assert_allclose( + penalty, + result + ) + def test_from_data_cholesky(self): t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) @@ -53,17 +69,7 @@ def test_constant_penalty(self): res = np.array([[12]]) - lfd = [2, 3, 4] - - np.testing.assert_allclose( - basis.penalty(lfd).round(2), - res - ) - - np.testing.assert_allclose( - basis._numerical_penalty(lfd).round(2), - res - ) + self._test_penalty(basis, lfd=[2, 3, 4], result=res) def test_monomial_lfd(self): n_basis = 5 @@ -116,15 +122,14 @@ def test_monomial_penalty(self): [0., 0., 54., 324., 1458.], [0., 0., 216., 1458., 6998.4]]) - np.testing.assert_allclose( - basis.penalty(2).round(2), - res - ) + self._test_penalty(basis, lfd=2, result=res) - np.testing.assert_allclose( - basis._numerical_penalty(2).round(2), - res - ) + basis = Monomial(n_basis=8, domain_range=(1, 5)) + + self._test_penalty(basis, lfd=[1, 2, 3]) + self._test_penalty(basis, lfd=7) + self._test_penalty(basis, lfd=1) + self._test_penalty(basis, lfd=27) def test_fourier_penalty(self): basis = Fourier(n_basis=5) @@ -135,15 +140,7 @@ def test_fourier_penalty(self): [0., 0., 0., 24936.73, 0.], [0., 0., 0., 0., 24936.73]]) - np.testing.assert_allclose( - basis.penalty(2).round(2), - res - ) - - np.testing.assert_allclose( - basis._numerical_penalty(2).round(2), - res - ) + self._test_penalty(basis, lfd=2, result=res) def test_bspline_penalty(self): basis = BSpline(n_basis=5) @@ -154,13 +151,7 @@ def test_bspline_penalty(self): [12., -24., -48., 192., -132.], [0., 12., 24., -132., 96.]]) - np.testing.assert_allclose( - basis.penalty(2).round(2), - res) - - np.testing.assert_allclose( - basis._numerical_penalty(2).round(2), - res) + self._test_penalty(basis, lfd=2, result=res) def test_basis_product_generic(self): monomial = Monomial(n_basis=5) From d05cd395d8486b10366e2619a25b89a49db52031 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 19 Mar 2020 13:47:42 +0100 Subject: [PATCH 138/624] Remove n_degenerated --- skfda/preprocessing/smoothing/_basis.py | 25 +++------- skfda/representation/basis.py | 66 ------------------------- 2 files changed, 8 insertions(+), 83 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 3d13751df..556b32d72 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -44,7 +44,7 @@ class _QR(): """Solve the linear equation using qr factorization""" def __call__(self, *, basis_values, weight_matrix, data_matrix, - penalty_matrix, ndegenerated, **_): + penalty_matrix, **_): if weight_matrix is not None: # Decompose W in U'U and calculate UW and Uy @@ -54,15 +54,11 @@ def __call__(self, *, basis_values, weight_matrix, data_matrix, if penalty_matrix is not None: w, v = np.linalg.eigh(penalty_matrix) - # Reduction of the penalty matrix taking away 0 or almost - # zeros eigenvalues - if ndegenerated: - index = ndegenerated - 1 - else: - index = None - w = w[:index:-1] - v = v[:, :index:-1] + w = w[::-1] + v = v[:, ::-1] + + w = np.maximum(w, 0) penalty_matrix = v @ np.diag(np.sqrt(w)) # Augment the basis matrix with the square root of the @@ -71,9 +67,9 @@ def __call__(self, *, basis_values, weight_matrix, data_matrix, basis_values, penalty_matrix.T], axis=0) - # Augment data matrix by n - ndegenerated zeros + # Augment data matrix by n zeros data_matrix = np.pad(data_matrix, - ((0, len(v) - ndegenerated), + ((0, len(v)), (0, 0)), mode='constant') @@ -470,10 +466,6 @@ def fit_transform(self, X: FDataGrid, y=None): if(data_matrix.shape[0] > self.basis.n_basis or self.smoothing_parameter > 0): - # TODO: The penalty could be None (if the matrix is passed) - ndegenerated = self.basis._ndegenerated( - len(self._penalty().weights) - 1) - method = self._method_function() # If the method provides the complete transformation use it @@ -486,8 +478,7 @@ def fit_transform(self, X: FDataGrid, y=None): basis_values=basis_values, weight_matrix=weight_matrix, data_matrix=data_matrix, - penalty_matrix=penalty_matrix, - ndegenerated=ndegenerated) + penalty_matrix=penalty_matrix) elif data_matrix.shape[0] == self.basis.n_basis: # If the number of basis equals the number of points and no diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 44f456cdd..fb580fb12 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -104,20 +104,6 @@ def _evaluate(self, eval_points, derivative=0): """Subclasses must override this to provide basis evaluation.""" pass - @abstractmethod - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. - - Returns: - int: number of close to 0 eigenvalues. - - """ - pass - @abstractmethod def _derivative(self, coefs, order=1): pass @@ -452,19 +438,6 @@ def _evaluate(self, eval_points, derivative=0): return (np.ones((1, len(eval_points))) if derivative == 0 else np.zeros((1, len(eval_points)))) - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. - - Returns: - int: number of close to 0 eigenvalues. - - """ - return penalty_degree - def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) @@ -574,19 +547,6 @@ def _evaluate(self, eval_points, derivative=0): return (coefs * raised).T - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. - - Returns: - int: number of close to 0 eigenvalues. - - """ - return penalty_degree - def _derivative(self, coefs, order=1): return (Monomial(self.domain_range, self.n_basis - order), np.array([np.polyder(x[::-1], order)[::-1] @@ -851,19 +811,6 @@ def knots(self): def knots(self, value): self._knots = value - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly to 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. - - Returns: - int: number of close to 0 eigenvalues. - - """ - return penalty_degree - def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. @@ -1303,19 +1250,6 @@ def _evaluate(self, eval_points, derivative=0): return res - def _ndegenerated(self, penalty_degree): - """Return number of 0 or nearly 0 eigenvalues of the penalty matrix. - - Args: - penalty_degree (int): Degree of the derivative used in the - calculation of the penalty matrix. - - Returns: - int: number of close to 0 eigenvalues. - - """ - return 0 if penalty_degree == 0 else 1 - def _derivative(self, coefs, order=1): omega = 2 * np.pi / self.period From cc99b7f809285f08d910c07ad68a1da04d10f9ce Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 139/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3ni4FWgU5RqVUOPN4YMk/IaErdB7jdjQhy81EkQikez3PcLZV5Gbg8/J2iMgkEVkpIiuzs7P9GKJSKqxt/Qyyt8CQ3+ucTj5Ui09GRCYC/YByJ4U3xkw2xvQzxvRLSEgIbnBKqerJGFj8NDRqD90vczuakObmdRSZgPe8uK2cbT8jIiOAh4FzjTEnghSbUircpX4Je9fB2BchUi8p88XNGsUKIFlE2olIDDAemO1dQETOBF4DxhpjslyIUSkVjkprE3GtoNd4t6MJea4lCmNMMXAH8AWwGZhpjNkkIo+LyFin2NNALPC+iKwVkdkVHE4ppSpv5zeQvgwG3wNRMW5HE/JcrW8ZY+YAc8pse9Tr8YigB6WUCn+Ln4Z6TeHMiW5HUi1Ui85spZTym8zVsGMRnHMnRNdxO5pqQROFUqpmWT0VoupA3xvcjqTa0EShlKo5ik/AplnQ9WKoHed2NNWGJgqlVM2R+iUU5EKvX7kdSbWiiUIpVXNs+ADqNLKLE6lK00ShlKoZThyDrZ9D90shMtrtaKoVTRRKqZph6xwoPg49r3I7kmpHE4VSqmbY8AHEJdpV7NQp0UShlAp/+Qdh+wLocbnOEnsa9BNTSoW/Hz4GT7E2O50mTRRKqfC34UNonAzNe7kdSbWkiUIpFd5yM2HXt9DzSl3q9DRpolBKhbdNswADPa50O5JqSxOFUiq8bfgAWvSGJh3djqTa0kShlApfB1Jh71rtxK4iTRRKqfC18QNA7LBYddo0USilwpMxttmpzSCIa+l2NNWaJgqlVHjatx5yUuxoJ1UlmiiUUuFpw/sQEQXdxrkdSbWniUIpFX48Htg4CzoMh7qN3I6m2tNEoZQKP+lL4UimjnbyE00USqnws+F9uy5259FuRxIWNFEopcJLSRFs+tgmiVqxbkcTFjRRKKXCS9pCOH5Qm538yNVEISKjRGSriKSKyIPl7B8qIqtFpFhEdIybUurkNrwPteOh43C3IwkbriUKEYkEXgZGA92ACSLSrUyx3cANwHvBjU4pVS0V5sOWz6DrWIiq5XY0YSPKxXMPAFKNMWkAIjIdGAf8UFrAGLPT2edxI0ClVDWTMg8Kj2mzk5+52fSUCKR7Pc9wtp0yEZkkIitFZGV2drZfglNKVUPb5kKdRtB2sNuRhJWw6Mw2xkw2xvQzxvRLSEhwOxyllBs8HkiZDx1HQESk29GEFTcTRSaQ5PW8lbNNKaVO3Z41kH8Aki9wO5Kw42aiWAEki0g7EYkBxgOzXYxHKVWdpcwDREc7BYBricIYUwzcAXwBbAZmGmM2icjjIjIWQET6i0gGcBXwmohscitepVSIS5kHrfrr3E4B4OaoJ4wxc4A5ZbY96vV4BbZJSimlKnYsC/ashvMecTuSsBQWndlKqRoudYG9Tx7pbhxhShOFUqr6S5kHsc2geS+3IwlLlUoUIvJ2ZbYppVTQlRTD9gXQcSRE6G/fQKjsp9rd+4kz/UZf/4ejlFKnKGMFFORqs1MA+UwUIvKQiBwFeonIEed2FMgCPglKhEop5UvKF3bJ0w7nuR1J2PKZKIwxTxhj6gNPG2PinFt9Y0xjY8xDQYpRKaUqljIfWp9tZ4xVAVGp4bHGmIdEJBFo4/0aY8ziQAWmlFInlZsJ+zfCyMfdjiSsVSpRiMiT2CunfwBKnM0G0EShlHJP6nx7r9N2BFRlL7i7DOhsjDkRyGCUUuqUpMyH+CRI6OJ2JGGtsqOe0oDoQAailFKnpPiEXfY0eSSIuB1NWPNZoxCRF7FNTPnAWhFZAPxYqzDG3BXY8JRSqgK7v7eLFGmzU8CdrOlppXO/Cp3ZVSkVSlLmQ2QtaDfU7UjCns9EYYyZEqxAlFLqlGz7wq5kF1PP7UjCXmVHPW3ANkF5y8XWOP5qjMnxd2BKKVWhg2mQkwL9b3E7khqhsqOePscOi33PeT4eqAvsA/4LXOL3yJRSqiIpX9p7nbYjKCqbKEYYY/p4Pd8gIquNMX1EZGIgAlNKqQqlzINGHaBxB7cjqREqOzw2UkQGlD4Rkf5A6erlxX6PSimlKlKYDzuX6GinIKpsjeIW4E0RiQUEOALcIiL1gCcCFZxSSv3Czm+guECbnYKosnM9rQB6iki88zzXa/fMQASmlFLlSpkH0XWhzSC3I6kxTnbB3URjzDsicl+Z7QAYY54JYGxKKfVzxthpxdudC9G13Y6mxjhZH0XpAOX6FdyUUip4DqTA4d3QSfsngulkF9y95tz/OTjhKKWUDylf2PuO2j8RTJVdM7uTiCwQkY3O814i8khgQ1NKqTJS5kHTbtAgye1IapTKDo99HXgIKAIwxqzHXnSnlFLBUXAEdn2vo51cUNlEUdcYs7zMtipfPyEio0Rkq4ikisiD5eyvJSIznP3LRKRtVc/pU7Eut6FUyNqxCDxFev2ECyqbKA6ISAec+Z5E5Epgb1VOLCKRwMvAaKAbMEFEupUpdjNwyBjTEXgW+EdVzunT8cPwXC+Y+xAcywrYaZRSpyllHtSKg6Sz3I6kxqlsorgdeA3oIiKZwD3ArVU89wAg1RiTZowpBKYD48qUGQeUzmD7ATBcJEArlJQUQccRsOw1eP4MmP8o5B8MyKmUUqfIGDuteIfzIFLXUAu2yiaKTOAt4G/YL/T5wPVVPHcikO71PMPZVm4ZY0wxdsbaxmUPJCKTRGSliKzMzs4+vWhiE+DSl+H25dBlDHz7AjzXE776m61tKKXcs38jHN2rzU4uqWyi+AQ7Q2wRsAc4BuQFKqhTZYyZbIzpZ4zpl5CQULWDNekIV7wBv/seOg6HxU/ZJqlFT2kNQym3pMyz9zos1hWVneuplTFmlJ/PnQl4j3Fr5Wwrr0yGiEQB8UBw1r5o2hWungp718PCJ+Drv8GSZ+CM8TDwNkjoHJQwlFLAtnnQojfUb+Z2JDVSZWsU34lITz+fewWQLCLtRCQGO9y27HKrs/mpietK4CtjTNkFlAKrRS+YMA1u+w56Xglr34OXB8Crg2Hx05C9LajhKFXj5B+EjOXa7OQin4lCRDaIyHpgMLDaGcq63mv7aXP6HO4AvgA2AzONMZtE5HERGesU+w/QWERSgfuAXwyhDZpm3WHcS3DvJrjgrxBVB776K7zcHz64ybWwlAp7278C49FE4SLx9QNdRNr4erExZpffI6qifv36mZUrVwbnZLmZ8M2zsOJ1uPFzaHNOcM6rVE0y67e2j+IPqRARefLy6rSIyCpjTL/y9vmsURhjdvm6BSbcaiQ+EUY+DvWawsIn3Y5GqfDj8UDqfDt0XZOEayrbR6EqElMXBt1trxrd9b3b0SgVXvasgfwcbXZymSYKf+h3E9RLgEVaq1DKr1LmAWKHqivXaKJwGGN4fXEaB/MKT/3FMXVh0D2QtlBrFUr5U8o8aNUf6jZyO5IaTROFI+1AHv+ct5Xr3lxG7vGiUz9Aaa1i8VP+D06pmuhYFuxZrYsUhQBNFI4OCbG8em1ftu47yo1vLSfvxClOjhtTF8650w7lS18RmCCVqklSv7T32j/hOk0UXs7r3JQXJ5zJuoxcbpmykoKiklM7QL+boU4jWBS4SW6VqjFS5kFsc2jey+1IajxNFGWM6tGCf17Vi6U7crjtnVUUFnsq/+JasTDoLjuc7+3L4cs/w7oZsGctFOYHLmilwk1JMaR+BckjIEATRqvKq+xcTzXKZWe24nihhz99tIGrXvueMT2b06d1Q3okxlM7+iRjuc+5CwpyYevnzkIrpU1YAg3bQEIXO09UQhd7a9LJJhil1E8ylsOJXG12ChGaKCpwzVmtqRMTwfNfpvD3OVsAiI4UurWM58ykBvRp05A+rRuQ2KAOP1siIyISRjxmbyVFcDANsrdA9lbI2mzvt38FJV6jq+JbO8nDSSBNu9oEUjsuiO9YqRCSMg8ioqD9MLcjUZxkCo/qKBBTeGQdLWDt7sOs3n2Y1bsPsT7jMAVFtkkqoX4tbhrUjtuGdaj8AUuK4dBOJ4F43Q6kQHHBT+Xik+CMCXDWrVDvF8twKBW+/j0I6jSEG/7ndiQ1hq8pPDRRnIaiEg9b9x1lze5DzF63hzW7D/Ptg+fTLK521Q7sKYHDu36qfaQvg21zIbou9LkezrkD4lv5500oFapyM+HZbnZ6nEF3ux1NjeErUWjT02mIjoygR2I8PRLjGZKcwLB/LmT68nTuHpFctQNHREKj9vbWebTdlrUFvn0Olk+GFW/Y9TAG3WMXWFIqHKXOt/faPxEydNRTFbVtUo8hyU2Ytnw3xSWnMEKqspp2gctehbvXQr8bYcP78FI/mHk97F3n//Mp5bZt85x+uy5uR6Icmij8YOLANuw7UsCCLVmBO0mD1nDR03DPBhh8r+0Qf20ovHMF7PoucOdVKpiKT9ipcJJH6rDYEKKJwg+Gd2lKi/javLM0CDOvxzaFEf8P7t0Iwx+112i8NRr+c6H9JRZmfU6qhtn1HRTlabNTiNFE4QdRkRFMGNCaJSkH2HkgLzgnrR0PQ35vaxijn4YjmfDeVfDqENjwge0YV6q6SZkPkbWg3RC3I1FeNFH4yfj+SURFCNOW7w7uiWPqwlmT4K41cOm/oeQEfHiz7cdYNcVW5ZWqLlK+gLaDIKae25EoL5oo/KRpXG1GdmvGzJXpnCh24dd8ZDT0vgZ+twyufhtqxcGnd8HzZ8CyyXalMKVC2YEUyEmFzhe5HYkqQxOFH11zVmsO5Rcxd+M+94KIiIBuY2HSQrj2I2jcET7/A7x3NeQfdC8upU5m6xx732mUu3GoX9BE4UeDOjShTeO6vLs0yM1P5RGBDufD9Z/CmGfsvFOvDoGMwF6MqNRp2/o5NO8JDZLcjkSVoYnCjyIihIlntWH5zoPM2+RircKbCPS/GW6eZ2sbb42GlW/q6CgVWvIO2JkItNkpJGmi8LPrzmlDtxZx/O7d1byxJI2QmSKl5ZkwaRG0Gwr/uxc+uQOKjrsdlVJWyjwwnp9mJFAhRROFn9WKimTapIEM79qUv362mVvfWXV6S6sGQt1GcM1MGPpHWPsOvHkhHArCtR9KnczWOVC/JbTo7XYkqhyuJAoRaSQi80UkxblvWEG5uSJyWESq1RSS8XWieXViXx4Z05UFm7O45MVv2JiZ63ZYVkQknP8wTJgOB3fC5HMhdYHbUamarKjALlLUebRejR2i3KpRPAgsMMYkAwuc5+V5Grg2aFH5kYhwy5D2zPjtQIpKPFz+7+94d9mu0GmK6jwaJn0N9VvYaUAW/1OH0Cp37Fxir8bW/omQ5VaiGAdMcR5PAS4tr5AxZgFwNFhBBULfNo347K4hDGzfmIc/2si9M9aSd6L45C8MhsYd4JYvoccV8NVfYMZEuzqfUsG05TOIidWrsUOYW4mimTFmr/N4H9DMpTiColG9GP57Q39+P7ITs9ftYdzL35KyP0TyX0w9uOINGPWkvSp28nmw/we3o1I1hcdj11zpcD5E1XI7GlWBgCUKEflSRDaWcxvnXc7YtpgqtceIyCQRWSkiK7Ozs6sUd6BERAh3Dk/mnZvP4nB+IWNf+paP1mS4HZYlAgNvs9dcnDgKbwyHjR+6HZWqCfauhaN7tdkpxAUsURhjRhhjepRz+wTYLyItAJz7Ks3PbYyZbIzpZ4zpl5CQ4I/wA+acjk2Yc9cQeraK594Z63ho1gYKikJkAr8258BvF9uLnj64Ceb+ya77rVSgbP0cJEJniw1xbjU9zQaudx5fD3ziUhyuaBpXm/duOYvbhnVg2vLdXP7Kd+zKCdKssycT1wKu/x8MmARLX4apl8KxAK6zEeqMgR2L7eqCO5boNCj+tvVzSBqoa8KHOFfWzBaRxsBMoDWwC7jaGHNQRPoBtxpjbnHKLQG6ALFADnCzMeYLX8cOxprZ/vTVlv3cO2MdHo/h6at6MapHC7dD+sm6GfDp3VCngZ1oMKm/2xEFV0EufPZ7u6qgt9jm0KwbNO0Gzbrb+4QuEF3FNdNrmsO74bmeMPIvMOgut6Op8Xytme1Kogik6pYoADIO5XP7e2tYl36Ymwa148HRXYiJCpFrIfdtsKOhcjNh9JPQ7+aaMdY9fbmdrj03E859AM74lZ3ZdP8PkPUD7N8E2VvttO4AEgmdLv5b8ncAABqMSURBVLR9PW2H1IzPqKqWTbYTVt652o7AU67SRFENFBZ7+Puczfz3u52c2boBL1/Th5YN6rgdlnX8EMyaZKdZ6D0RLnnOTmsejjwlsOQZWPgExCfC5W9A67PKL1tSDAfTIGsTZK6Cte9Bfg607AND74dOo+38Wqp8Uy+1C27dscLtSBSaKKqVz9bv5YEP1xMdKTzzq96c17mp2yFZHg8sehIW/QM6DIerp0KtWLej8q/D6TYh7v4OelwJFz9jVxKsrKLjsG4afPMcHN5lm6QG3wfdL4PIqMDFXR0V5MJTHeDs38HIx92ORqGJotpJyz7G795dzZZ9RxnQthFjerVgdI/mNI0LgTbw1VNtv0WL3vDr96FeE7cj8o9NH9uFnjwlMOZf0OtXp998VFJshxd/8wxkb4GG7WDwvXDGBIiK8W/c1dXGD+3Iupu+gNYD3Y5GoYmiWiooKuGNJWl8um4vW/cftbOFtwmRpLFlDnxwI8QlwrWzoGFb92KpqsI8mPugTYCJfe3Fh43a++fYHg9s/cxOj7J3rf28Bt0NfW/UhPHhLbD9K7g/xc4/plyniaKaS806ymfr9zFnQwgljd1L4b1f2atpJ35or72oTnK2w6q3YM27tg9m8L1w3p8C0/diDGxfAIv/ZZu1Errafp6a+ku6pAie7gBdLoZLX3E7GuXQRBFGKkoaF/VszuieLWgWzKSRtQXeudxezT3+XbvWRSgrLrS/8Fe+ZVf8i4iCLmNg4O0Vd1j729a5MOd+yE2HvjfAiMegTrmTJ4evHYthyiXwq3eg6yVuR6McmijCVEgkjdxMmywOpsHlk23Hbag5tBNWTYE170BeFsS3hr7Xw5nXQn0Xphk7ccyOqlr6CtRtAqOesBMz1pQhtXMfghX/gQd22LnGVEjQRFEDuJo08g/CtAl2KcuLnoYBvwncuSqrpNhOcrjyTbvehgh0GgX9brIT0IVCu/jedXZgwJ41diTZxc9U7/6eyjAGnj/DXqD465luR6O8aKKoYcpLGp2b1adHYjy9WsXTIzGebi3iqB3txy/LouN2FMvWOTDkfjj/EXd+Iedm2o7p1VPh6B67alqf66DPtRDfKvjxnIynxE4PsuBx+3jYA3D2HeF7nUrWZnhlIFz8HPS70e1olBdNFDVYatZRPt+wj1W7D7EhI5ecvEIAIiOE5Kax9EyMp2ereHomxtO1qsmjpBg+uw9WT4EzJ8LFzwfn+gFPia01rHrLTlltDHQcYb+Iki+sHtcw5GbC53+ELf+zV3ZPnBWeI6MW/9OufXLfFjuvmAoZmigUAMYY9uYWsCEzlw0ZuWzIzGVj5s+TR6dm9emZGOckkAZ0aV7/1JKHMbb9fdE/bFPPlW9BTN3AvKGj+2HN27b/IXc31Gtqaw59roeGbfx2mm37j7JpTy5dmseR3DSWqMgAXm29eirMvtPWgi55Ifz6LV4fDsZjV1dUIcVXoqgGP7WUv4gILRvUoWWDOlzYvTlgk8ee3AI2ZNiksT4zly83ZzFzpV0rIypCSG5Wn16J8fRoFU+vxHg6+0oeInaYaWxT+Ox+mDoOrpkBdRtV/Q0UHbdzMO1YbJfPzFgJpgTanQsX/MWuaeDnX+GzVmdw//vr8Di/p2pHR9C1hZNIndpYxwQ/Jo8+19nO9yX/sld2D7zNP8cNBUf3Q+ZKOO8RtyNRp0hrFOoXjDFkHj7Oxkxb61jvJJFD+XZtiiin5lHa39EzMZ4uLepTK6pM8vhhtr2wqmFbe61Fg6RTC6S40H6x7Fhik0PGcigptBPwJfaB9sPs1c4BmlDug1UZ/OGDdZzdvjEPjOrCzpw8NmTYZLopM5e8QruOSGny6JVoP49OzerToWkssbVO83eYxwMzr7X9Pde8D8kj/PiuXLRqir36/dZvoXkPt6NRZWjTk6qy0uRR2mRVejvslTw6No2lW8s4ure0neXdWsYRv38ZTLvGDoOc+KGdnrsiJcX2CuYdi+0tfRkU5QMCLXrZ6zTanWsvVKtVP6Dvd+bKdB74cD2DOjTh9ev6USfm50nQ4zHscBJH6WfhnTwAWsbXpkPTWDo6t+Sm9UluGkvDepWo9Zw4Bm+OsnNG3fIlJHT291sMvvfG21l371kffk1qYUAThQoIYwwZh47/2Nfxw94jbNpzhOyjJ34s06phHUY2PsDvsx6ilikk99KpNO42DBGxv5z3b3ASwxLY9R0UOmuJN+0O7YbYjt22g4J6UdqMFbt5cNYGBne0SaKyfTQej2FnTh4pWcdILXM77rWKYfuEepzVrhED2jViQLvGJFY0S/DhdHj9PIiJhd985Z/mO7cU5sNT7exFhqP/4XY0qhyaKFRQZR0t4Ic9R/hh7xF7v+cIhTk7mRL9JIlygPejLubs+IO0z1tHxInD9kWNOzo1hqHQZjDEurOk7bTlu3lo1gaGdkpg8rV9/TKE2OMx7Mk9TmrWMTbvPcrKnQdZvvMgRwuKAUhsUMcrcTSiXZN6NpGC7ZP57xhIOguu/aj6DpvdMgemT4DrPrFNhirkaKJQrss7UUzKzp0kzrmRhNz17PA0Z4XpzKGmA+k08CIG9+lFdCBHE1XCu8t28fBHGxnWOYFXJ/onSVSkxGPYuu8oy3fksHznQZbvOMiBY3b0WZPYWnRuHku7JvVo3ySWs4/Np+vSP+DpeyMRFz9bPZttPrnD9ln9cXv1TXYhbtbqDEo8hiv7tvrph8Yp0EShQocxUJBL+vEY3l+ZzsyVGew7UkCT2Biu6NuK8f1b065J8Kd1eHvpLv7v442c36Up/57Y55cd8wFmjCHtQB7Ldxxk5c5DpGYfIy372I+1jgeipnFb1Ke8VHsSGxPH0y6hHu0a16N5fG2ax9emWVxt4mpHndYXRMB5SuBfnW1t8co33Y4mLHk8hqFPf02bxnV595bTm2xSh8eq0CECdRqQVAfuu6Azd4/oxKJtWUxfns4bS3bw2qI0zmrXiAkDWjOqR/OA/qovNfX7nTz6ySZGdG3Ky78OfpIAO3S5Q0IsHRJimTCgNWCTR05eIWnZeaRldWfb94e57fAbPLgnkdc3d6LY8/MfeXWiI2kWV4tmcTZ5NI+rTdsm9ejcvD6dmtU//VFYVbXrW8jLtsOXVUB8u/0AGYeO88dRXQJyfK1RqJCRdaSA91dlMGNFOrsP5hNfJ5rLzkxkwoDWdG7un1FOJR5DxqF8tmcfIy07j017jvDRmkxGdmvGy9f0CZ21ystz4ij850LIzaD4pvnsiUpi35EC9h0pIOtIAfty7eP9R0rvT1BY7Pnx5UmN6tC5WRxdmtenc/P6dGlen3ZN6gX2AkKA6b+2AxXu+wGiQ2R53zBz+7ur+W77AZb+afhp/9DRpidVrXg8hqVpOUxbkc4XG/dRWOLhzNYNmNC/NRef0YK6MSf/ZZx7vIg0Jxls97rflZNPYclPX54N6kZzQbdm/PXSnqGdJEod3g2Tz7OjwH67yOfsqx6PHdK8Zd9Rtu47wpZ9R9my7yg7DuRR4tRGakdHcFa7xgxJbsLQTgkkN431b/PVwR3wwpkw5Pcw/P/8d1z1o5xjJxj4xAKuO7st/3exj+HnJ6GJQlVbB/MKmbU6g+kr0knNOkZsrSguOaMlEwYk0b1l/M9qB9uzj7E9O4+07DwOHPtpiG5UhNC6cV3aN4mlQ0I9OiTE0j6hHu0TYmlUmWsaQk3aInvFe78b4eJnT/nlBUUlbM8+xtZ9R1mfkcuSlGy2Z+cB0CyuFoM7JjC0UxMGdWxCk9haVYt17kOwfDLcs1HndgqQ1xen8bc5m5l/71CSm51+zVsThar2jDGs2nWI6SvS+d/6PRQUeRCxfeOlGtaN/lkSKH3culFd10dU+d28R+C7F2HCdOg8usqHyzx8nG9SslmccoBvUw/8eCFl95ZxDElO4LzOCfRv24iIiFOobRQcgWe6QedRdolZ5XfGGIY/s4iGdWP48LZzqnQsTRQqrBwpKGLO+r1kHj5OUsO6dGhqh5FW6orncFF8At4YDkf2wu++t3Nr+UmJx7AxM5dvUg+weFs2q3YdothjaBZXizE9W3LJGS3ondTg5E1US1+FuQ/ALV9Bq75+i0/9ZPmOg1z92vc8fWUvrup3ilPklBFyiUJEGgEzgLbATuBqY8yhMmV6A/8G4oAS4G/GmBknO7YmClVjZG2ByefaYafXzAzY9RXHThTz9ZYsPl23h4Vbsyks8ZDUqA6X9GrJJWe0pEvz+r9MGp4SeLGvTWA3zwtIXArum7mW+Zv2s+zh4ZXqu/PFV6Jwqz7+ILDAGJMMLHCel5UPXGeM6Q6MAp4TkQZBjFGp0Na0C4z8C6TMs4sfBUhpv9Dk6/qx4pERPH1lL9o1ieW1xWmMfn4JI59dzKuLtnPsRPFPL9r2BRzaEV6z34aY3ONFzNmwl7G9W1Y5SZyMW9dRjAOGOY+nAAuBB7wLGGO2eT3eIyJZQAJwODghKlUNDPiNXfJ13iO2ZhHgyQPj60RzVb8kruqXRM6xE3y+cR+z1+7hyc+38O+F27lpUDtuGNSW+GX/hrhW0OWSgMZTk32yNpOCIs+P190Ekls1imbGmL3O432AzxXuRWQAEANsD3RgSlUrIjDuFTtM9sNb7NTsQdI4thYTB7Zh5q1n88ntg+jfthHPfrmNm558C3YsJr/3TdVjdcFqyBjDtOXpdG8ZR4/E+ICfL2CJQkS+FJGN5dzGeZcztpOkwo4SEWkBvA3caIzxVFBmkoisFJGV2dnZfn0fSoW8+s1g7Euwbz18/VdXQjgjqQFvXN+Pz+4azO/jvyLf1GL4wjY8MWfzz2YTVv6xITOXzXuPMD4ItQkIYNOTMabC1VZEZL+ItDDG7HUSQVYF5eKAz4CHjTFLfZxrMjAZbGd21SJXqhrqcpGdwvvbF6DjSDtFuwu6xxdB3lcc7n41AzwdeH1JGlO+38mEAa357dAONI+v7Upc4Wba8nRqR0cwrnfLoJzPraan2cD1zuPrgU/KFhCRGOAjYKox5oMgxqZU9XTh36FRe/joVjh+6OTlA2HlW1Byggbn3cnz48/ky/vO5eJeLZn6/S6GPvU1j3y8gYxD+e7EFibyThQze20mY3q2JK52cGbidStRPAmMFJEUYITzHBHpJyKlwzeuBoYCN4jIWufW251wlaoGYurBFa/DsX3w2e9/fjViMBQXworXoeOIHzvV2yfE8s+rzmDh/cO4om8rZqxIZ9jTC3ngg/XsyskLbnxh4rP1e8krLGHCgKpdN3Eq9II7pcLN4qfhq7/CZZPhjF8F77zrZsBHk+yStx3Lb3nec/g4ry3azrQV6ZR4DOPOaMltwzpUaeqJmubyV77lSEEx8+8d6td5uULxOgqlVKAMvg+SBtpaxYGU4JzTGFj2b2jSCToMr7BYywZ1+PO4Hnzzx/O4aVBbPt+4j5HPLmbS1JWsTdeR7yezbf9RVu8+zPj+SUFde0QThVLhJiLSzq0UVQumTYCC3MCfM30Z7FkDZ91aqSvEm8bV5uEx3fj2wfO56/yOLE3L4dKXv2X85O/5aE0G+YXFJz1GTTRt+W5iIiO4vE+roJ5XE4VS4ahBElw9BQ6mwazfgqfckeX+s/QVqN0Azhh/Si9rVC+G+y7ozHcPDedPF3Uh49Bx7p2xjv5//ZI/vL+OpWk5eDzh1Tx+ugqKSvhoTSYXdG8W9FmP9WoYpcJV28Ew6gn4/I+w6B9w3kOBOc+hXbD5UzjnTp/rY/gSWyuKSUM7cMvg9izfeZAPV2UwZ8Ne3l+VQVKjOlx+Ziuu6NOK1o3r+jn46uOLTfs4nF/E+P7BuXbCmyYKpcLZgEmwZy0sehJa9IIuY/x/ju9fAomEAb+t8qEiIoSB7RszsH1j/jyuO3M37uPD1Rm88FUKzy9IYUDbRgzrkkCPlvH0TIyvUTMGT1+eTlKjOpzToXHQz62JQqlwJmIXN8rebJugfrPAv/NBHcuC1VPt6Kr4RP8dF6gbE8XlfVpxeZ9WZB4+zsdrMvloTSZPzd36Y5nEBnXomRhPj0Q7lUXPxHgaV3WxpRC0PfsY36fl8IcLO5/amiB+osNjlaoJcjNg8jCoHQ+/+creV1X+QXj7UsjaDLd9B02Sq37MSsjNL2Ljnlw2ZOay0bntzPnpIr6W8bVpHl+bqMgIoiOFyIgIoiOEyAghOjKCyAghKlKIihBbJsIpEynOvghnn1MmIoKoSKG4xFDs8VBUYigq8VBU4qG4xFDo3Ntt9r7Y46Gw+KfHRcWGIo/v14hAdGQEMU7c0ZER9hYVwYGjJziUX8iiP5xHQv3AJEJfw2O1RqFUTRDfCq6eClMugVmTYPw0iKjCWJb8g3Y51uytMP69oCUJgPi60QzqaJdqLZV7vIgf9hxhY6ZNIIfyCykq8XCiyEOxp4Rij8f5ojcUl3ice+e5x0NJif0iL91WGTbxCNER9ss8KqL0y93rS955HBUpxEZH/bgtykkIURFCdJRNVgCFXkmosNi5LzHUjY7knhHJAUsSJ6OJQqmaos05MOpJmHM/LHwCzn/49I5TNkkkVzitW9DE14nm7A6NOdsP7ffGGEo8pUnkp8RSmgiinOTgRhOQWzRRKFWT9L/Fdm4vfsp2bnc9xfUi8g/C1LGQvQ0mvFfhFdjVmYjT7BTpdiShQ6+jUKomEYEx/4LEvnbywLRFlZ8TqgYkCVU+TRRK1TTRteFX79gO7alj4bUhsOYdKCqo+DV5OTBFk0RNpYlCqZooriXcsQIufg5KiuGT2+HZbvDln+0IKW95OTah5KTAhGmaJGogHR6rVE1nDOxcAsteg61zAIGuF9t5m5p0sh3XOak2SXQ43+1oVYDo8FilVMVEoN1Qezu0C1a8YS+i++ETQOzkghOmQ4fz3I5UuUQThVLqJw3bwAV/gWEPwYaZkLkaev8aWp/ldmTKRZoolFK/FFPXrsHd9wa3I1EhQDuzlVJK+aSJQimllE+aKJRSSvmkiUIppZRPmiiUUkr5pIlCKaWUT5oolFJK+aSJQimllE9hN9eTiGQDu9yOo5KaAAfcDuIUVLd4QWMOluoWc3WLFwIfcxtjTEJ5O8IuUVQnIrKyokm4QlF1ixc05mCpbjFXt3jB3Zi16UkppZRPmiiUUkr5pInCXZPdDuAUVbd4QWMOluoWc3WLF1yMWfsolFJK+aQ1CqWUUj5pogggEUkSka9F5AcR2SQid5dTZpiI5IrIWuf2qBuxlolpp4hscOL5xbqyYr0gIqkisl5E+rgRp1c8nb0+v7UickRE7ilTxvXPWUTeFJEsEdnota2RiMwXkRTnvmEFr73eKZMiIte7GO/TIrLF+Xf/SEQaVPBan39DQY75MRHJ9Pq3v6iC144Ska3O3/WDLsc8wyvenSKytoLXBudzNsboLUA3oAXQx3lcH9gGdCtTZhjwP7djLRPTTqCJj/0XAZ8DAgwElrkds1dskcA+7JjwkPqcgaFAH2Cj17angAedxw8C/yjndY2ANOe+ofO4oUvxXgBEOY//UV68lfkbCnLMjwH3V+LvZjvQHogB1pX9vxrMmMvs/xfwqJufs9YoAsgYs9cYs9p5fBTYDCS6G5VfjAOmGmsp0EBEWrgdlGM4sN0YE3IXXRpjFgMHy2weB0xxHk8BLi3npRcC840xB40xh4D5wKiABeooL15jzDxjTLHzdCnQKtBxnIoKPuPKGACkGmPSjDGFwHTsv03A+YpZRAS4GpgWjFgqookiSESkLXAmsKyc3WeLyDoR+VxEugc1sPIZYJ6IrBKRSeXsTwTSvZ5nEDoJcDwV/6cKtc8ZoJkxZq/zeB/QrJwyofp534StWZbnZH9DwXaH01z2ZgXNe6H6GQ8B9htjUirYH5TPWRNFEIhILPAhcI8x5kiZ3auxzSRnAC8CHwc7vnIMNsb0AUYDt4vIULcDqgwRiQHGAu+XszsUP+efMbYtoVoMQxSRh4Fi4N0KioTS39C/gQ5Ab2AvtimnupiA79pEUD5nTRQBJiLR2CTxrjFmVtn9xpgjxphjzuM5QLSINAlymGVjynTus4CPsNVyb5lAktfzVs42t40GVhtj9pfdEYqfs2N/abOdc59VTpmQ+rxF5AbgYuDXTnL7hUr8DQWNMWa/MabEGOMBXq8glpD6jAFEJAq4HJhRUZlgfc6aKALIaV/8D7DZGPNMBWWaO+UQkQHYf5Oc4EX5i3jqiUj90sfYzsuNZYrNBq5zRj8NBHK9mk/cVOGvr1D7nL3MBkpHMV0PfFJOmS+AC0SkodNscoGzLehEZBTwR2CsMSa/gjKV+RsKmjL9Z5dVEMsKIFlE2jk10/HYfxs3jQC2GGMyytsZ1M85GL36NfUGDMY2JawH1jq3i4BbgVudMncAm7CjLJYC57gcc3snlnVOXA87271jFuBl7CiRDUC/EPis62G/+OO9toXU54xNYnuBImwb+M1AY2ABkAJ8CTRyyvYD3vB67U1AqnO70cV4U7Ft+aV/z686ZVsCc3z9DbkY89vO3+l67Jd/i7IxO88vwo5M3O52zM72/5b+/XqVdeVz1iuzlVJK+aRNT0oppXzSRKGUUsonTRRKKaV80kShlFLKJ00USimlfNJEoZRSyidNFEoppXzSRKGUH4nIx84EbZtKJ2kTkZtFZJuILBeR10XkJWd7goh8KCIrnNsgd6NXqnx6wZ1SfiQijYwxB0WkDnZaiAuBb7HrDRwFvgLWGWPuEJH3gFeMMd+ISGvgC2NMV9eCV6oCUW4HoFSYuUtELnMeJwHXAouMMQcBROR9oJOzfwTQzZmCCiBORGKNM3mhUqFCE4VSfiIiw7Bf/mcbY/JFZCGwBaiolhABDDTGFAQnQqVOj/ZRKOU/8cAhJ0l0wS4TWw8415n5NQq4wqv8PODO0ici0juo0SpVSZoolPKfuUCUiGwGnsTOUpsJ/B1Yju2r2AnkOuXvAvo5K6/9gJ3tVqmQo53ZSgVYab+DU6P4CHjTGPOR23EpVVlao1Aq8B4TkbXYRWV2EILLsCrli9YolFJK+aQ1CqWUUj5polBKKeWTJgqllFI+aaJQSinlkyYKpZRSPmmiUEop5dP/B2ncmmLrQ3uLAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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81wcScC6Ohh4OfPZsG7o0qFw4d5TGX4SjX8LRr4x3idq6GOdTaTkEqvre/euLHBU03H8CugJ/KqUaAFZADLAe+EopNQ/jBdX6wP7CKFQIcauDUQeZtHsSEdciGOYzjJdbvoy1ef5vHkpIyWD+lrN8HnARR1tL3u3flMFtvTG/2zVIDQYI/QP2L4Pg343b6naDntOgUV/j3CuiSOVnKOTXQBfAXSkVDkwBVgIrlVIngQxgqOksPlAp9R1wCsgCRslIGSEKX1pWGouOLOLzU59Tzb4aq3qvopVHq3zvn23QfHsgjDmbT5OYmsmQ9jV5s2cDnCtZ3WVhicYz9P2fQlwo2FWB+96Glk+Dc43b7y8KjdK65HtE/Pz89MGDB0u6DCHKhNNxpxm7cyznEs/xeMPHebP1m1SyzP/FzoMX4piyPpDAy0m0re2K/4M+d79ARkwIBHwCx76BzOtQvS20HQ5NHjZObyuKhFLqkNY6x+k75Q5VIcoIgzawJnANC48sxMXahaU9ltKxWsd87381OZ2ZG4P48UgEnk42LBrckn7NPO+uXz38EOxeAEG/GOcm9x0IbV8Ar5YFf01RKCTchSgDrly/woTdE9gXuY/u3t3x7+CPs41zvvY1GDRfH7jEe5tOk5qZzctd6zGya10qWRXwn7/WxqkAdi80zuti42S8Y7Tti+DgUbDXFIVOwl2IUm7LxS347/En05DJ1I5TeaTeI/k+2z51OYkJP53gyKUEOtRxY3r/ptSrYl+wQgzZcHKd8Uz9yklwrAa9ZkDroWBdsMnHRNGRcBeilErJTGH2/tn8GPIjTd2aMvve2dR0rJmvfa+nZ7Fg61lW7r6As60l8x9vTv8W1QrWBWPINt5wtOM944pFlRtB/8XQdKD0p5diEu5ClEInrp5g3F/jCEsO4wXfF3ipxUv5nj5gc2AU/usDiUxMY3Bbb8b2bliwUTD/hPr7EBtsnD530OfQqB+Ymd3564liJeEuRCmSbchm+YnlLD62mCqVqvBZ789o7dE6X/teTkhl8s+BbA26QqOqDnz0ZEta1yzAykJ/d7/seE9CvQyTcBeilIi4FsE7f73DkegjPFD7ASa0n4Cj1e2HKGqt+e5gGO9uCCLLoBn/QCOe7VQbS/M7DGKtjdPr/jEdrp42Tg0goV5mSbgLUQpsOLeBGQEzAJjVeRb96vTL134RCamMW3ucv4JjaF/HlfcfbY63WwEm+LqwG7b6Q/h+45qij62Cxg9LqJdhEu5ClKCkjCTeDXiXTec30apKK2Z2nkk1+9tPpKq15uv9YczcGIRBa6b3b8pTbb0xu9NpA6JOwNapELLFuETdgx9Ci6fAXKKhrJO/QSFKyMGog4zfNZ7olGheafkKzzd9HvN8LEQRFpfCO+tOsCskho513Xjv0WZ3Ph1v/AX4Ywac+B5sHI1zp7d7ESxtC3YwotSRcBeimGUaMll8dDHLTyynukN11vRZQ7PKzW67n8Gg+XL/JWZvDAJg5iO+DG5b486GN6Ylws45sG8pKDPo9Brc87pxpkZRrki4C1GMLiZdZNzOcZyMPcmA+gMY22ZsvuaFCYtL4e0fjrP3XCyd67sza4Av1V3u4Gw9OwsOr4Y/Z0JKLLR4ErpNlEUxyjEJdyGKgdaadcHreO/Ae1iaWTKvyzx61ux52/0MBs0X+y4ye9NpzJRi9gBfHm9zh2frIdtg8wS4GgQ1O8H9M8GrxV0cjSgLJNyFKGIJaQn47/Vn26VttPNsx4xOM/Cwu/0cLBdjr/P2D8fZdz6O+xpUZtYAX7yc76BP/OpZ+H2CcT51l1rGYY2NH4TCWIBDlHoS7kIUoT2X9zBx10QS0hN4y+8tnm7yNGYq7+GFBoNm1Z4LzNl8BgtzxfsDm/FY6+r5P1tPiYPts+HAcrCyMy6Q0W6ELJBRwUi4C1EE0rPTWXh4IZ+f+pw6TnX4pMcnNHJtdNv9zsdc5+0fjnHgQjxdG1Zm5gBfPJ3yebZuyIbDa2DbNEhLgNbDoMt4sK98dwcjyiQJdyEKWUh8CGP/GsvZ+LM80fAJRvuNxsbCJs99sg2az3afZ87mM1hbmPHBY80Z0OoOJvoKPwQbR8PlI8Z+9T7vQ9WmhXA0oqyScBeikGit+er0V8w7OA97K3s+7v4x91a/97b7hV69xpjvj3H4UgLdG1Vh5gBfPBzz/mXwj+sxsG0qHP4c7D1gwHLjghnSr17hSbgLUQhiUmOYuHsiuyN207laZ6Z1moa7rXue+2QbNCt2neOD389iY2l+Z9PyGrLh4Er4413IuAYdRsF9Y403JAmBhLsQd2172HYm755MSlYKE9pN4PGGj982oEOikxnzw3GOXEqgZxMPZvRvSpX8nq2H7YdfR0PUcah9L/SZA1Vu358vKhYJdyEKKDUrlbkH5vLd2e9o5NqI2Z1nU9e5bp77ZGUb+PSv88zfepZKVuYsfKIFDzX3yt/Z+rVo2DIFjn1lXAXpsVXQpL90wYgcSbgLUQCnYk8xdudYLiRdYJjPMF5p+QpW5nkviHH2SjJjvj/GsfBEevtUZXr/plR2yMfwRIPBeHfp1imQkQL3vAGd3wLrAi6XJyoECXch7kC2IZvVp1az6MgiXG1c+bTXp7T3bJ/nPlnZBpbuPMfCrcHY21jw0ZMt6evrmb+z9SunYMPrELYPanWGfvPBvX4hHY0ozyTchcinqOtRjN81ngNRB+hZsydTOkzBydopz31ORyUx5vvjnIhIpK+vJ1Mf9sHdPh9n6xkpsPN92LMIrB2h/xJo/oR0wYh8k3AXIh9+O/8b0wKmkWXIYlrHafSv1z/PM+/MbANLtofy4R/BONpY8vGTrejbzDN/bxay1XjBNP4CtBhivMPUzq1wDkRUGBLuQuQhMT2RGQEz2HRhE83cmzGr8yy8Hb3z3CcoMom3vj9G4OUkHmzuhf+DTXDLz9l68hXY/I5xUWq3+jB0A9TuXEhHIioaCXchcrErYheTd08mPi2el1u8zPO+z2Nhlvs/mYwsA59sD+GjP0JwrmTJkiGt6N00H2frBgMcXgVb/CEr1ThlwD2vy1ww4q5IuAtxk5TMFOYenMv3Z7+nnnM9Pur+EU3cmuS5z8mIRMb8cJygyCQebuGF/4M+uNjlPXoGyOGC6QJwr1dIRyIqMgl3IW5wJPoI4/8aT8S1CIb5DOPlli9jbZ77GXRGloGP/gjmk+2huNhZsezp1vTyqXr7N5ILpqKI3TbclVIrgX5AtNa66U3PjQbmApW11jHKeIVpIfAAkAIM01ofLvyyhShcGdkZfHT0I1adXIWXvRcr71+JX1W/PPc5EZ7ImB+OcToqmQEtqzH5wSY4V8rH2XrwVvj1TUi4KBdMRZHJz5n7KuAjYM2NG5VSNYBewKUbNvcB6pu+2gGLTd+FKLVOx51m/K7xBMcH82j9RxnTZgx2lna5tk/PymbRthAW7wjFzc6K5c/40aPJ7RffkAumojjdNty11juVUrVyeGo+8Dbw8w3bHgbWaK01EKCUclZKeWqtIwujWCEKU5Yhi1WBq/j46Mc4WzvnaxbHI5fiefuH4wRHX2NAq2pM6eeDUyXLvN9ILpiKElCgPnel1MNAhNb62E1jfasBYTf8Ody07ZZwV0oNB4YDeHvnPbRMiMJ2PvE8k3ZP4tjVY/Sq2YtJ7SfhbOOca/vUjGzmbTnDil3n8XC04bNn29C1YZXbv9GVQPjldQjfLxdMRbG643BXSlUCxmPskikwrfUyYBmAn5+fvpvXEiK/sgxZrA5czSdHP8HGwob3Or9Hn9p98rwhad+5WMauPc6F2BSebOfNO30a4WBzm7N1uWAqSlhBztzrArWBv8/aqwOHlVJtgQigxg1tq5u2CVHizsafZdLuSZyKPUV37+5MbD8xzznXr6dn8d5vp1mz9yI1XG356n/t6Fgv7znaAblgKkqFOw53rfUJ4J//H1VKXQD8TKNl1gMvK6W+wXghNVH620VJy8zOZPmJ5Sw7sQxHK0fm3jeXXjV75Xm2vis4hrFrj3M5MZVnO9VizP0NqWR1m38ucsFUlCL5GQr5NdAFcFdKhQNTtNYrcmm+EeMwyBCMQyGfLaQ6hSiQwJhAJu2ZRHB8MH3r9GVsm7G42Ljk2j4pLZOZvwbxzYEw6rjb8f2LHfCr5Zr3mxgMcOgz2DpVLpiKUiM/o2UG3+b5Wjc81sCouy9LiLuTlpXG4mOLWRW4CncbdxZ1W0SXGl3y3Gdb0BUm/HiS6OQ0RtxXl9d71MfG0jzvN5ILpqKUkjtURblzJPoIk3dP5kLSBQbUH8Bov9E4WuW+tmjc9QymbzjFj0ciaFTVgWXPtKZZ9dxHzgCQcR12vAd7PgJbZ3hkKTR7XC6YilJDwl2UGymZKXx45EO+CvoKTztPlvZcSkevjrm211rzw6FwZm4MIjkti9e612dU13pYWZjl/UZnf4eNoyHhErQcAj2nQ6XbdN0IUcwk3EW5sC9yH1P2TCHiWgSDGw3m9VavU8myUq7tQ69eY8KPJwg4F4dfTRdmDvClgYdD3m+SFAm/jYNTP4F7Qxi2EWp1KuQjEaJwSLiLMi05I5l5h+bxw9kf8HbwZlXvVbT2aJ1r+/SsbJZsP8fHf4ZgY2nGrAG+PO5XAzOzPLpTDNlwcCVsmwZZ6dBtInR8DSzyMY+MECVEwl2UWTvDdzJ171RiUmN41udZRrYYiY2FTa7tA87FMv7HE5y7ep2HmnsxsV9jqjjk3h6AyOPGKXkjDkGdLtB3HrjVLdTjEKIoSLiLMicxPZH39r/HL+d+oZ5zPRZ0WYBvZd9c28dfz2DWpiC+OxhODVdbVj3bhi63mzog/RpsnwUBi4396QOWg+9AuWAqygwJd1GmbLm4hRkBM0hMT+TFZi8yvNlwrMxz7h7RWvPT0QimbwgiKTWTl7rU5dVu9bG1us3wxjO/wca3IDEMWg2FHv5ywVSUORLuokyISY1h5r6ZbLm4hcaujVnacykNXRvm2v58zHUm/nSC3SGxtPR2ZtYAXxpVzX04JABx5+G3d+DsJqjcGJ7bDN7tC/lIhCgeEu6iVNNa8+v5X5m9fzYpmSm81uo1hvoMxdIs54m7MrIMLNsZyod/hGBtbsb0/k15qq133hdMM1Nh1wLYNR/MLKDHVGg/Ui6YijJNwl2UWleuX2F6wHR2hO+gWeVmTO84nTrOdXJtf+BCHO+sO0FI9DX6NvNkSr8mVHHM44Kp1nBmk3F4Y8JF8BkAvd4Fp2pFcDRCFC8Jd1HqaK1ZF7yOuQfnkmXI4u02b/NkoycxN8u5rzwxJZPZvwXx9f4wqjnb8tmwNnRtdJsLprGhxlAP/h0qN4Khv0DtvBfqEKIskXAXpUp4cjhT904lIDKANlXb4N/BH2/HnBdz0Vqz/thlpm84RXxKJsPvrcPrPernPXtjRgrsmge7F4K5NfSaAe1eBPPbzM8uRBkj4S5KBYM28M3pb1hweAFmyoxJ7ScxsMFAzFTOUwFcik1h4s8n2Xn2Ks2rO7H6ubb4eDnl/gZaw+kN8Nt4SLwEvoOg13RwqFpERyREyZJwFyXuQuIFpuyZwuHow3Ty6sSUDlPwtPfMsW1mtoFP/zrHwq3BWJqbMfUhH4a0r4l5XhdMY0Jg09sQug2q+Mi0AaJCkHAXJSbLkMXnpz7n46MfY2Vuxbud3uWhug/luojGoYvxjF93gjNXkuntUxX/h3yo6pTHBdOM67BzrnGpO0tb6D0b2rwA5vJjL8o/+SkXJSI4PpjJuydzMvYkXWt0ZVL7SVSuVDnHtompmbz/22m+2n8JT0cbPn3Gj55NPHJ/ca3h1M+weQIkhUPzwcbhjQ557CNEOSPhLopVZnYmy08uZ9nxZThYOjDn3jncX+v+HM/Wtdb8eiKSqb+cIvZaOs91qs2bPRtgZ53Hj+3Vs7BpDJzbDh6+MHCF3IgkKiQJd1FsAmMDmbx7Mmfjz9Kndh/GtR2Hq03Ot/WHxaUw+eeT/HnmKk2rObJyaBt8q+dxwTQ9GXa8DwGfgKUd9JkDfs9JF4yosOQnXxS59Ox0lhxbwmcnP8PVxpWFXRfSzbtbjm0zsw18tvs887cEoxRM6teEoR1qYmGeywIaWhsXpP59IiRHGhfP6JCnv9QAAB8NSURBVO4P9jl38QhRUUi4iyJ1NPook/dM5nzieR6p9wij/UbjZJ3zGfjRsATeWXeCoMgkejT2YNrDPng52+b+4tFBsHEMXPgLPJvDoM+hRpsiOhIhyhYJd1EkUrNS+fDwh3wZ9CVV7aqytMdSOlbLecm75LRM5m4+w5qAi3g42LBkSGt6N81j/HlaknH90n1LwMreOMd662GQyx2sQlREEu6i0B2IOsCUPVMISw7j8YaP80brN7CztLulndaazYFRTFkfSHRyOkM71GJ0rwY42ORyt6jWcOJ7+H0SXLsCrZ6B7lPAzq2Ij0iIskfCXRSalMwU5h2ax7dnvqW6fXVW3r+SNlVz7iaJSEhlys8n2RoUTRNPR5Y97UfzGs65v/iVQGMXzMXd4NUKnvgKque+nJ4QFZ2EuygUey/vxX+PP5HXIxnSeAivtHwlxwWqs7INrNpzgXlbzqI1THigMc92qpX7BdO0RPhzFuxfBjZO8OBCaPkMmOXSXggBSLiLu5SckcwHBz9gbfBaajnWYnWf1bSs0jLHtifCE3nnx+OcjEiiW6MqTHvYh+out/4CAIxdMMe+gS2T4fpV8HsWuk2SFZGEyCcJd1FguyJ24b/Hn6upVxnmM4xRLUbluED1tfQsPvj9DKv3XMDd3ppPnmpFn6ZVc51mgMjjxi6YsACo5gdPfQdeOf/CEELkTMJd3LHE9ETmHJjDz6E/U9epLvO6zKNZ5WY5tv3ddME0KimNIe1qMqZ3Qxxzu2CamgB/zoADy8HWBR76CFo8JV0wQhSAhLu4I9vDtjNt7zTi0uJ4wfcFRjQfkeMC1VGJaUxZf5LNgVdoVNWBj59qRStvl5xf1GCAo1/CVn9IjYM2/4Ou440BL4QoEAl3kS8JaQnM2j+Ljec30sClAYu6L8LHzeeWdtkGzZf7LvL+b2fIzDYwtncj/te5Npa5XTC9fBQ2vgXhB6BGe3hgDnjm/H8BQoj8u224K6VWAv2AaK11U9O2OcCDQAYQCjyrtU4wPfcO8DyQDbyqtd5cRLWLYrLl4hbeDXiXpPQkRjYfyf98/4dlDisXBUUm8c66ExwNS6BzfXfe7d+Umm63jm8HICUO/pgOBz8Du8rQfwk0fwJy64cXQtyR/Jy5rwI+AtbcsG0L8I7WOksp9R7wDjBWKdUEeALwAbyArUqpBlrr7MItWxSHhLQEZuybwW8XfqOxa2OW9VxGQ9eGt7RLy8xm4bZgPt15DkdbSxY83oKHW3jlfMHUYIAja2DrVOMwx3YjoOs7xmGOQohCc9tw11rvVErVumnb7zf8MQAYaHr8MPCN1jodOK+UCgHaAnsLpVpRbLaHbWfq3qkkpCfwcouXec73OSzNbj1b/yv4KhN+PMmluBQea12d8Q80xsXu1j54ACIOwa9vweXD4N3R2AVTtWkRH4kQFVNh9Lk/B3xrelwNY9j/Ldy07RZKqeHAcABv75wXQBbFLzkjmfcPvM9PIT/RwKUBi3ssppFro1vaxV5L591fg/jxSAS13e346oV2dKzrnvOLpsQZL5YeXgP2VWDAp+D7mHTBCFGE7irclVITgCzgyzvdV2u9DFgG4Ofnp++mDlE4AiIDmLR7EtEp0bmOhNFa88OhcGZsDOJ6ehavdqvHyK71sLHMYdIuQ7Yx0LdNNU721WEU3DcWbByL6YiEqLgKHO5KqWEYL7R211r/Hc4RQI0bmlU3bROlWEpmCvMPzeebM99Qy7EWn/f5PMdx6+euXmPCjyfZey4Wv5ouzBrgS30Ph5xfNOIQ/DoaLh+BmvcYu2A8mhTxkQgh/lagcFdK9QbeBu7TWqfc8NR64Cul1DyMF1TrA/vvukpRZI5EH2HCrgmEJ4fzdJOnebXlq7fcZZqRZWDpjlAW/RmCtYUZMx5pyuA23piZ5dCtkhJnPFM/tBrsPeDRFdD0UemCEaKY5Wco5NdAF8BdKRUOTME4OsYa2GIaERGgtR6htQ5USn0HnMLYXTNKRsqUTunZ6Xx85GNWBa7Cy96LFfevyHEGx0MX4xm39jjB0dfo28yTKf2aUMXx1ikGMBjg8GrpghGilFD/9qiUHD8/P33w4MGSLqPCCIwNZMJfEwhNDOWxBo8x2m/0LfOtX0/PYu7vZ1i15wJeTrZM7+9Dt0YeOb/gjaNgpAtGiGKjlDqktfbL6Tm5Q7UCyTJkseLECpYcW4KrrSuLeyzmnmr33NLur+CrvLPuBOHxqQztUJMxvRthb53Dj0pKHGybBodWmUbBLAffgdIFI0QpIOFeQYQlhzH+r/EcvXqUPrX7MKHdhFvWMk1MyWTGxlN8dzCcOpXt+H5EB9rUymGKXYMBjnxuHN6YlgjtR0KXcdIFI0QpIuFezmmtWR+6nln7Z2GGGbM7z6Zvnb63tPvtZBSTfj5J3PUMRnapy6vd6+c8vPHyEeMomIhDxhuR+s4Fj1vnmBFClCwJ93IsIS2BaQHT2HJxC34efsy4ZwZe9l7/aXM1OR3/9YH8eiKSJp6OfDasDU2r5TAVwM1zwTyyDJoNki4YIUopCfdyas/lPUzaNYm49DjeaP0GQ5sMxdzs3zNxrTXrDkcwbcMpUjOyGXN/Q4bfW+fW2Ru1hqNfwZZJxvnW279k6oKRuWCEKM0k3MuZ9Ox0FhxawBdBX1DHqQ4fdf+Ixm6N/9MmIiGV8etOsOPsVVrXdOG9R5tRr4r9rS8WfRp+fdO4KHWN9tD3A5kLRogyQsK9HAmJD2HMzjGEJIQwuNFg3mz95n9uSDIYNF/uv8TsjUFowP/BJjzTodatNyNlpMDOObDnQ7B2gIcWQYshsiKSEGWIhHs5oLVmbfBaZu+fjZ2lHZ90/4TO1Tv/p014fApj1x5nd0gsneu7M/MRX2q45rA4dfAW4wXThIvGJe56TgO7XCYEE0KUWhLuZVxyRjJT905l84XNdPDswMzOM3G3/TeMtdZ8eyCMd38NQmvNzEd8Gdy2xq1zrSddht/Gwamfwb0hDPsVat06Bl4IUTZIuJdhJ66eYMzOMURdj+K1Vq/xXNPnMFP/dp1EJqYybq2xb71DHTfeH9js1rN1Qzbs/xT+eBcMmdBtEnR8FSxymZNdCFEmSLiXQQZtYE3gGhYeXkiVSlVY1XsVLaq0+Of5v0fC+P8SSFa2ZupDPjzdvuatfesRh2HD6xB5DOr1gAfmgmvtYj4aIURRkHAvY2JTY5mwewK7I3bTw7sH/h39/3OnaXRyGuPXnWBrUDRtarkwZ2BzarnftI5pWqLxTH3/p8aZGx9bBU36y5h1IcoRCfcy5NCVQ4zZMYbE9EQmtpvIoIaD/tN3vv7YZSb/fJLUjGwm9m3Ms51qY37z2fqp9bBxDFyPhrbDodtEmTZAiHJIwr0M0Fqz5tQa5h+aT3WH6izusfg/C1UnpmYy5eeT/HT0Mi29nZn7WHPqVr5p3HrSZWOon94AVZvB4K+hWqtiPhIhRHGRcC/lrmVcY/KeyWy5uIXu3t2Z3mk6Dlb/rn4UcC6W0d8dIyopjTd7NmBkl7pY3HiXqcEAh1fBlimQnWEc2th+FJjLX70Q5Zn8Cy/FguODeXP7m4QlhzG69WiG+gz9pxsmI8vAvC1nWbozlJqulVj7Ukda1HD+7wvEBMP6V+HSHqh9L/RbAG51S+BIhBDFTcK9lNpwbgPT9k6jkkUlPu316X9WSQqJTua1b44SeDmJwW1rMLFvE+xunG89KwP2LIQd74OlLTz0EbQcIhdMhahAJNxLmUxDJnMOzOHr01/Tqkor5t43l8qVKgPGvvfPAy4y49cg7KwtWPZ0a3r5VP3vC4QfhPWvQPQp8HkEer8HDrmsoCSEKLck3EuR+LR4Ru8YzYGoAzzd5GneaP0GlmaWAMRdz+Ct74/xx+loujSszPsDm1HF4Ya1TDNSjMMbAz4BB0944mto9EAJHYkQoqRJuJcSZ+LO8Nqfr3E15Soz75nJg3Uf/Oe5fediee2bo8Rdz2DqQz4806Hmf6cPuBQAP42EuFDwex56+MvwRiEqOAn3UmDLxS1M2DUBB0sHVvdZTVN347S62QbNJ3+GMH/rWWq62bFuaMf/LqSRmWo8W9/7MTjXgKG/GC+cCiEqPAn3EmTQBhYfW8ySY0toVrkZC7os+Kd/PTo5jTe+PcrukFgebuHFjEd8/7tIddh++OkliA0Bv+eMQxytHXJ5JyFERSPhXkJSMlMYv2s82y5to3+9/kxqPwkrc+NkXX8FX+WNb49yLT2L9x9txmN+1f/thslMgz9nwN6PwLEaPP0T1O1agkcihCiNJNxLQExqDC9ve5mguCDebvM2QxoPQSmFwaBZuC2YD/8Ipl5le756oT0NPG44G484BD++BDFnoNVQ6PWu9K0LIXIk4V7MguODGbVtFAnpCSzsupAuNboAkJCSwevfHmX7masMaFWNGf19sbUyrXmanQW75sH22eBQFYasNc7iKIQQuZBwL0Z7Lu9h9PbR2FrYsqr3Kpq4NQHgZEQiL315iKjENN7t35Sn2nn/2w0TfwHWvQhhAdD0UeM6prYuJXcQQogyQcK9mKw9u5bpAdOp41yHT7p/QlU7481HPxwKZ8KPJ3CpZMV3L3agpbcpuLWGY98YJ/tSCgZ8Cs0GleARCCHKEgn3Iqa15sMjH7L8xHI6eXVi7n1zsbeyJz0rm+kbTvFFwCU61HFj0ZMtcbe3Nu6UGg8b3oDAH8G7IzyyBFxqluyBCCHKFAn3IpRlyMJ/jz8/h/7MwAYDGd9uPJZmlsRcS2fE54c4eDGeF++rw5heDf+dyfHCblj3Aly7At0nQ6fXwcy8ZA9ECFHm3DbclVIrgX5AtNa6qWmbK/AtUAu4AAzSWscrY0fxQuABIAUYprU+XDSll26pWamM2TGGHeE7GNl8JCOaj0ApxanLSbyw5iAx19JZNLglDzb3Mu5gyIa/5sH2meBSG57fIvOtCyEKzOz2TVgF9L5p2zhgm9a6PrDN9GeAPkB909dwYHHhlFm2JKYn8uKWF9kZvpOJ7SbyUouXUEqxOTCKgUv2kGUw8P2IDv8G+7Vo+GIA/Pmu8aLpizsk2IUQd+W2Z+5a651KqVo3bX4Y6GJ6vBrYDow1bV+jtdZAgFLKWSnlqbWOLKyCS7sr168wYusILiZdZM59c7i/1v1orflkeyhzNp+heQ1nlj3dGg9H06Rf53bA2v9BehI8+CG0ekam5hVC3LWC9rl73BDYUcDfc8pWA8JuaBdu2nZLuCulhmM8u8fb27uAZZQuFxIvMHzLcBLTE1ncYzHtPNuRlpnN2z8cZ/2xy/Rv4cXsR5thY2lu7IbZ8Z5xznX3+vDMT+DhU9KHIIQoJ+76gqrWWiuldAH2WwYsA/Dz87vj/Uub4PhgXvj9BTSalb1X4uPmQ+y1dP635iBHLiUw5v6GjOxS1zh+/XosrH0Ozm2H5k9C37lgZVfShyCEKEcKGu5X/u5uUUp5AtGm7RFAjRvaVTdtK9eCYoMYvmU4lmaWLO+1nDrOdbgQc51hn+0nMjGNxU+1oo+vp7Hx5aPw7dNwLQoeWmTshhFCiEKWnwuqOVkPDDU9Hgr8fMP2Z5RReyCxvPe3H796nOd/f/6fu07rONfhyKV4BizeQ2JqJl+90O7fYD/6Nay8H3Q2PPebBLsQosjkZyjk1xgvnrorpcKBKcBs4Dul1PPAReDvWyc3YhwGGYJxKOSzRVBzqXHoyiFGbh2Jq40rK+5fgZe9F78HRvHqN0eo4mDDqmfbUKeyvXFN083j4cCnUKszDPwM7CuXdPlCiHIsP6NlBufyVPcc2mpg1N0WVRbsvbyX1/58DY9KHizvtRwPOw8+33uBKesD8a3uzIqhfsY7TpOvwHfPGOeG6fAy9JgK5nLvmBCiaEnKFEBAZACv/PEK3o7eLOu5DDcbN+ZvOcvCbcH0aFyFDwe3pJKVBUQeg68HG6cTeHQF+A4s6dKFEBWEhPsdOhB1gFe2vUINhxqs6LUCJytnpv5yilV7LvBY6+rMGuBrnEogaINxGgFbF3huM3g2K+nShRAViIT7HTgSfYRR20bhZe/F8l7LcbB04q0fjrHucATP31ObCQ80xkwBuxbAVn/jXaZPfGWcg10IIYqRhHs+Hb96nJe2vkSVSlVY3ms5dhbOjPjiMFuDrvBWrwaM6loPlZ1hnM3x6JfgMwD6fwKWtiVduhCiApJwz4fA2EBGbBmBi7ULy3stx9bchWc/O8Dec7FMe9iHZzrUgpQ4+OYpuLQH7hsHXcbJNAJCiBIj4X4bZ+LOMPz34ThaO7Ly/pVUMnfj6RX7OB6eyILHW9C/ZTVICIMvHoX483LhVAhRKki45yEsOYwRW0dgY2Fj6opx55kV+zkZkcjHT7aid9OqEHUSvhwIGSkwZB3U7lzSZQshhIR7bmJSYxj++3AyDZms7r0aBwsPnl6+j1ORSXzyVCt6+VSF8zuNXTFW9vDcJpn4SwhRaki45yApI4kRW0YQmxbL8l7Lcbfy5ukV+wiKTGLxU63p0cQDTq6FH0eAax0Yshacqpd02UII8Q8J95ukZaXxyrZXCE0M5eNuH+Nt14inVgRwNuoaS59uTbdGHrBvKWx627i+6eCvjGPZhRCiFJFwv0GmIZMxO8ZwJPoI79/3Pj4ubXhyeQDB0ddY+kxrujasAjvnwh/ToVE/48VTS5uSLlsIIW4h4W6itcZ/jz/bw7czsd1E7vHswdMr9hF85RrLnmlNlwaVYetU2DUPfAdB/8UyR4wQotSSdDJZfGwx60PXM7L5SB6uO5BnPzvA8fBEPnmqFV3qu8OmsbB/KbQeBn3ng1lBZ0sWQoiiJ+EO/BzyM4uPLaZ/vf485zOcEV8cIuB8LPMHteD+xpVh/cvGu047vAy93pWbk4QQpV6FD/eAyAD89/jT3rM949tO5I3vjvLnmavMGuBL/2ZVjItXB66DLu/AfWMl2IUQZUKFDveQ+BDe/PNNajnV4oP7PmDST6fZeCKKiX0bM7i1F6z7HwT+CD2nQafXSrpcIYTItwob7jGpMYzcNhJrC2s+6f4Jn/xxmR8OhfN6j/r8r6M3/DjcGOy93oWOr5R0uUIIcUcqZLj/PZY9IT2BVb1XseloGkt2hDKkvTevda0DP40w3qTUY6oEuxCiTKpw4a61xn+vP4GxgSzouoDQcGem/3qE3j5VmdqvMernUXDie+g+Be55vaTLFUKIAqlw4b7y5Ep+Pfcrr7R8BZuMZrz43X7a1HRlwePNMN/wKhz/BrpNhM5vlnSpQghRYBUq3HeE7WDh4YX0rtWbjm6DeGLZPmq72/Hp062x2TbRONzxvnFw75iSLlUIIe5KhQn30IRQxv41lkaujRjZdDyPLzmIg40Fq59ri9P+D2DfEmg/yrjIhhBClHEV4jbLxPREXvnjFazNrZndaR6jvjhJSkY2nz3bBs+gVbBjNrQYAvfPkHHsQohyodyfuWcbsnl759tEXo9kec+VzPwlitNRSawY1oZGURvgt3HQ+EF4cKEEuxCi3Cj3Z+5Lji9hz+U9vNP2HX47ZM3WoCtM7teErob98PPLUKeLcXZHmQRMCFGOlOtw3xm+kyXHlvBQ3YfISmjHp3+d55kONRlWIxrWPg9eLeHxL8HCuqRLFUKIQlVuT1fDk8N55693aOjSkB6VX+KF1ce5r0FlJnewgs8eBMdq8OR3YG1f0qUKIUShK5fhnp6dzpvb30RrzdstZzL8s1PUqWzHxw9Xx+KLPqDMYMgPYOdW0qUKIUSRuKtuGaXUG0qpQKXUSaXU10opG6VUbaXUPqVUiFLqW6WUVWEVm1+z9s0iKC6Iye2nMXltFAaDZvngJtivfQqSrxjP2F3rFHdZQghRbAoc7kqpasCrgJ/WuilgDjwBvAfM11rXA+KB5wuj0Pz6KeQn1gav5fmmz7MhwJUzV5JZ9Lgv3n+8ApFHYeBKqN66OEsSQohid7cXVC0AW6WUBVAJiAS6AT+Ynl8N9L/L98i3c4nnmLlvJm2qtsE6+QE2HI9kzP0Nue/8fDi7Cfq8D40eKK5yhBCixBQ43LXWEcBc4BLGUE8EDgEJWussU7NwoFpO+yulhiulDiqlDl69erWgZfwjPTudMTvGYGNuQ/9qY5izOZi+vp68ZLcD9i8zrqLU9oW7fh8hhCgL7qZbxgV4GKgNeAF2QO/87q+1Xqa19tNa+1WuXLmgZfzjg4MfcDb+LK82m8iktWE08HBgbptE1Ka3oX4v44IbQghRQdzNaJkewHmt9VUApdQ6oBPgrJSyMJ29Vwci7r7MvG27tI2vT3/Nkw2H8NlWW7ROYcVDbth+3xfc6hlvUjIzL+oyhBCi1LibPvdLQHulVCWllAK6A6eAP4GBpjZDgZ/vrsS8RV2PYvLuyTRxa0JSZE8CLyex8JF6VNv4rLHB4K/BxrEoSxBCiFLnbvrc92G8cHoYOGF6rWXAWOBNpVQI4AasKIQ6c5RlyGLszrFkGbLoXeUtvt4XyYjONel6chzEhcKgNTLkUQhRId3VTUxa6ynAlJs2nwPa3s3r5tdPIT9xOPowbzT3Z+66WFrXdGGM5XcQ/Dv0mw+17y2OMoQQotQp03eo9q/Xn0oWjny43gYrizQ+bRuJ+S8LofUw8HuupMsTQogSU6YnDrMws2DH4aqcjkpmSR8nXDe/Bl6tjOPZhRCiAivTZ+7rj13m24NhvH6vF+32vwQWVsZ+dpnlUQhRwZXpM/d76rkzqksdXr2+CGLOGKcWcK5R0mUJIUSJK9Ph7mpnxRjnHZgFroVuE40LbwghhCjb4c6lAPh9AjTsC53eKOlqhBCi1Cjb4W5ZCWrfB48sBrOyfShCCFGYyvQFVTybwdPrSroKIYQodeR0VwghyiEJdyGEKIck3IUQohyScBdCiHJIwl0IIcohCXchhCiHJNyFEKIcknAXQohySGmtS7oGlFJXgYslXUc+uAMxJV3EHZKai0dZq7ms1QtSc05qaq0r5/REqQj3skIpdVBr7VfSddwJqbl4lLWay1q9IDXfKemWEUKIckjCXQghyiEJ9zuzrKQLKACpuXiUtZrLWr0gNd8R6XMXQohySM7chRCiHJJwF0KIckjC/SZKqRpKqT+VUqeUUoFKqddyaNNFKZWolDpq+ppcErXeVNMFpdQJUz0Hc3heKaU+VEqFKKWOK6ValUSdN9TT8IbP76hSKkkp9fpNbUr8c1ZKrVRKRSulTt6wzVUptUUpFWz67pLLvkNNbYKVUkNLsN45SqnTpr/3H5VSzrnsm+fPUDHX7K+Uirjh7/6BXPbtrZQ6Y/q5HlfCNX97Q70XlFJHc9m3eD5nrbV83fAFeAKtTI8dgLNAk5vadAE2lHStN9V0AXDP4/kHgE2AAtoD+0q65htqMweiMN6QUao+Z+BeoBVw8oZt7wPjTI/HAe/lsJ8rcM703cX02KWE6u0FWJgev5dTvfn5GSrmmv2Bt/LxcxMK1AGsgGM3/1stzppvev4DYHJJfs5y5n4TrXWk1vqw6XEyEARUK9mqCsXDwBptFAA4K6U8S7ook+5AqNa61N2lrLXeCcTdtPlhYLXp8Wqgfw673g9s0VrHaa3jgS1A7yIr1CSnerXWv2uts0x/DACqF3UddyKXzzg/2gIhWutzWusM4BuMfzdFLq+alVIKGAR8XRy15EbCPQ9KqVpAS2BfDk93UEodU0ptUkr5FGthOdPA70qpQ0qp4Tk8Xw0Iu+HP4ZSeX1pPkPs/hNL2OQN4aK0jTY+jAI8c2pTWz/s5jP8Hl5Pb/QwVt5dNXUkrc+n6Kq2fcWfgitY6OJfni+VzlnDPhVLKHlgLvK61Trrp6cMYuxCaA4uAn4q7vhzco7VuBfQBRiml7i3pgvJDKWUFPAR8n8PTpfFz/g9t/P/sMjGeWCk1AcgCvsylSWn6GVoM1AVaAJEYuznKisHkfdZeLJ+zhHsOlFKWGIP9S631upuf11onaa2vmR5vBCyVUu7FXObNNUWYvkcDP2L8X9YbRQA1bvhzddO2ktYHOKy1vnLzE6Xxcza58neXlul7dA5tStXnrZQaBvQDnjL9QrpFPn6Gio3W+orWOltrbQA+zaWWUvUZAyilLIABwLe5tSmuz1nC/Sam/rIVQJDWel4ubaqa2qGUaovxc4wtvipvqcdOKeXw92OMF9BO3tRsPfCMadRMeyDxhq6FkpTrWU5p+5xvsB74e/TLUODnHNpsBnoppVxMXQq9TNuKnVKqN/A28JDWOiWXNvn5GSo2N10PeiSXWg4A/2/n/lEaCKIAjH9bWwix0k4hN0glllY5Qdpok8Ib5BwBCwvBO1hpb2kiAcHYCR7CYlO8F1iCWGbi8P1gip2dhcfs8Jb5w/abpjnNGeCIeDclXQLvbdt+/XZzp/28i53l/1SAC2KavQBeswyBCTDJNjfAktidfwHOC8d8lrHMM65p1ndjboAZcbrgDRjsQV8fEMn6sFO3V/1MfHi+gR9iTfcaOAKegQ/gCehl2wFw13n2ClhlGReMd0WsTW/G8222PQEe/xpDBWN+yHG6IBL28XbMeT0kTrR9lo456+8347fTtkg/+/sBSaqQyzKSVCGTuyRVyOQuSRUyuUtShUzuklQhk7skVcjkLkkVWgPZVyRMqvMjjwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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EyJvWRu+XTZONZoHaHeHBZUZ/8CLaGXaR6b8cY39EAgFernz44C3c2dwHmyumokvNTuXVP15la+RWhjQcwqvtX8XepoJdUWprD27exq0cKe53hYbArUqpqUAGME5rvRuoBezItV6kZdlVlFIjgBEAtWvXLmYZQggA4kJh/ctw6jeoWh/u/9rozljEI8nTcalMW3+UXw+fp0ZlJ6YPbs69bXyxs726C2NkciQv/PYC4Ynh/K/9/3ig8QMltTeiBBQ33O0AT6AD0Bb4XilVtygb0FovBBaC0VummHUIcXPLSoM/34dtc43ufn2mQ9snizweS2JaNnN/O8kX209jb2vDSz0b8uStdXF2yHtkxj0xexi7dSw5OocFdyygY82Oea4nrKe44R4J/KiNfpS7lFJmwAuIAnKfRfG1LBNClCSt4djP8MsESIyAFg9AzylQqXqRNpOVY+arHWeY+9tJEtOzuT/Ij7E9G1Ktcv7DC6w4sYK3d76Nr5sv83rMo07lOte7N+IGKG64rwK6A1uUUg0BByAOWAN8o5SahXFCtQGwqyQKFUJYJEbBz2ONy++9m8CwdeDfuUib0Fqz6Wgs76w7SnhcKl3qezGxXxOa1qyc73MyTZlM2zmNFSdX0KlmJ2beNpPKDvmvL6yrMF0hlwHdAC+lVCQwGVgCLFFKhQBZwFDLUfxhpdT3wBEgB3hOesoIUUK0Ni5Z3/i60Wuj19vQfmSRm2DC41J586fDbD1+gXrernw2rC3dGv23B8yVolOiGbN1DIcvHuap5k/xXKvnsLWRyTTKMrlCVYjyID4M1owyxoDxvxUGzAXPIp3mIi0rh4+2hPLpH+E42Nnw4h0NGNrJH/s8TpbmtiN6B+N/H0+2OZu3u7xNj9o9rmdPRAmSK1SFKK/MJmO4gM1TjKsi+8+BNsOK1AtGa8264Bje/vkI0YkZDG5di1f7NqZapfzb1S8/b0nIEubun0tA5QDmdJ+Dv7v/9e2PKDUS7kKUVfFhsHKkMSZKg17Qfza4+xZpE6GxyUxec5i/Qy/S1KcyHz54C0H+ngU+LyUrhdf+fo1NEZvo7d+bKZ2m4GLvUtw9EVYg4S5EWaM17PvC6AljYwd3fwIt7i/S0XpqZg4fbD7Jkr/CcXGw5a2BgTzUvg62NgVv48jFI7z8+8tEpUQxLmgcjzV97Jrt8aJsknAXoixJuQA/jYLj64y29bs/LvLR+obDMbyx5jDRSRncH+THy70bUdWtgAkyMJphlh1bxnt73qOKUxUW915Mm+pFG1xMlB0S7kKUFcd/gTXPQ0YS9H4H2j8DNoWf3CI6MZ3Jqw+z4ch5GteoxLyHW9O6dpVCPTcxM5HJ2yazOWIzXX278nbnt6niVLjnirJJwl0Ia8tKhV8nGt0cqzeDx9YUaeRGk1nzxfbTvPfrcUxa82rfxjzRJaDAXjCXHbpwiPF/jOd86nlphqlAJNyFsKZz++GHJ4yTp51Gwe2TwK7gJpTLQqISmfBjMMFRidzW0Ju3BzXDz7NwJz7N2syXR75kzt45VHOpxtK+S2nh3aK4eyLKGAl3IaxBa6OL44ZJxmw8w9aCf5dCPz01M4dZG0/w2d/hVHVzZN5DxqiNhT3ijk2LZdJfk9gevZ07at/BG53eKF/D9IoCSbgLUdrS4mH183D8Z2jYFwbNN2b7KaSNR84zeXUI0UkZPNy+Ni/3boy7c+GvUt14ZiNvbn+TLFMWr3d8nXsb3CvNMBWQhLsQpensLvjhcUiOgd7TjDlMC3u0nZzB5NWHWR8SQ+Malfjwoda0qVP4k56p2alM2zmN1adWE1g1kOm3TpeLkiowCXchSoPZDNs+gM1vGdOwPbEBarUu1FO11qzYF8Vba4+Qnm1ifJ9GPHVr3UKfMAU4EHuACX9O4FzqOUa0GMHIliMr3qQa4j8k3IW40VIuwMqn4dRmCLwb7voAnArXvh15KY2JK0P448QF2vpXYfo9Lajn7Vbol842Z/PJwU/4NPhTfFx9+LzP59xS7Zbi7okoRyTchbiRwv+EFU9C+iVj+IA2wwvVDGM2a77aeYZ31x9DA1MGBvJI+zpXTXN3LScunWDSX5M4Gn+UAfUGMKHdBNwcCv+HQZRvEu5C3AhmE/wxE35/FzzrwSMroEazQj017EIKr6w4xO7Tl7i1gRfTBjfHt0rhx3XJMeewJGQJCw4uoLJDZWZ1m0XPOj2LuyeinJJwF6KkJUXDj08Zw/O2fAj6zQTHgo+Yc0xmPv0znNmbTuBkZ8PMe1twbxvfIvVkOXnpJJP+nsSRi0fo49+Hie0nypWmNykJdyFK0slNsHIEZKfDoAXQ6qFCPe3IuSTGrzhISFQSfQJrMGVQYIFD8uZ25dH6+7e9Ty//XsXdC1EBSLgLURJM2fDb2/D3HKgWCPd9Dt4NC3xaZo6Jeb+FsmDrKTxcHFjwcGv6Nvcp0kvnPlrv7d+bie0n4ulU+H7zomKScBfieiVEGEMIRO6CoMeNQb/snQt82r6IS4z/4RChsSkMbl2L1/s3xcPFodAvm2XKYnHIYj499CmVHCrJ0br4Dwl3Ia7H0bWw+lljOIF7P4Nmgwt8SlpWDu/9eoLPtoXjU9mJz4a3pXujakV62X3n9/Hm9jcJSwyjj38fJrSfIEfr4j8k3IUojpxMY6LqnR+DTyu477NCzWm6LTSOV38MJiI+jUc71OGVvo1xcyz8f8OkrCTm7J3D8hPLqelak496fERX367XsyeigpJwF6KoLp6CH4ZD9EHo8Czc8UaBIzkmZWQzbd0xlu2KIMDLle9GdKB93aqFfkmtNRvPbGTarmnEZ8TzWNPHeK7VczL1nciXhLsQRXFoOax9EWzt4YFl0LhfgU/ZciyWiSuDOZ+UwdNd6zKmZ0Oc7G0L/ZIxqTFM3TGVrZFbaeLZhHk95hFYNfB69kLcBCTchSiMrFRYNx4OfAW1O8I9iwqc/i4hLYspa4/w474oGlZ34+NHOtPSz6PQL5ljzuHbY9/y4f4P0WjGBY3j4SYPY2cj/21FweRTIkRBzh+G5cMh7gR0fRluexVsr/1f55eQGCatCiEhLYtRPRrwXPd6ONoV/mh9f+x+pu6YyvFLx+lcszOTOkzCt1LR5lIVNzcJdyHyozXs/Qx+mWAM9PXYaqh72zWfEpeSyeTVh/k5OJrAmpVZ+nhbAmsWfhKMi+kXmb13NqtPraa6S3VmdZvFHbXvkPHWRZFJuAuRl/QE+Gk0HFkF9XrA3Z+Am3e+q2utWXPwHG+sOUxqpomXezdiRNfCD8trMpv4/sT3fLjvQ9JN6TzR7AlGtBghJ0xFsUm4C3GlyD1Gb5ikc3DHm8bcpjb5h3RMYgaTVgWz6Wgsrfw8mHlvCxpUr1TolzsQe4B3dr7D0fijtPdpz8T2E6nrXnC3SiGuRcJdiMvMJmP4gC3vQKWaMPwX8Gub7+paa5bvieStn4+QlWNm0p1NGN45ANtCDssbnxHPnL1zWBm6kmou1Zh520x61+ktTTCiRBQY7kqpJUB/IFZr3eyKx14C3gO8tdZxyvhUfgD0A9KAYVrrfSVfthAlLCECVo6EM38bE2r0nw3O+Y+mGHkpjQk/BvPnyTjaBXjy7j0tCPByLdRL5ZhzWH5iOfP2zyMtO43hgcMZ2XKkNMGIElWYI/fPgXnAF7kXKqX8gF5ARK7FfYEGllt7YIHlpxBlV/APsHYsaDMM+hhaPpDvhBpms+brnWeYbplE462BgTxchEk0tp/bzozdMwhNCKVdjXZMbD+Reh71SnBnhDAUGO5a6z+UUv55PDQbGA+szrVsIPCF1loDO5RSHkopH611dEkUK0SJykiEn8dB8Pfg1x4GL4Qq/vmufjoulfErDrErPJ5bG3jxzt3N8fMs3NF2RFIEM/fMZOvZrdRyq8XsbrPpUbuHNMGIG6ZYbe5KqYFAlNb64BUfzlrA2Vy/R1qWXRXuSqkRwAiA2rVrF6cMIYrvzDb48WlIioJuE+HWl/Ltu55jMrPor3DmbDqBva0NM+5pwX1BhZtEIyUrhYWHFvLl0S9xsHFgdOvRPNr0URxtrz1cgRDXq8jhrpRyASZiNMkUm9Z6IbAQICgoSF/PtoQoNFM2bJ0Of80Cj9rw+K/XPGkaHJnIKysOcSQ6iZ5Nq/PWwGbUcC94Eg2T2cSq0FXM3T+X+Ix4BtYbyOjWo/F2yb87pRAlqThH7vWAAODyUbsvsE8p1Q6IAvxyretrWSaE9V08ZUxWfW4ftHoE+k4Hx7y7LKZl5TB74wkW/xWOl5sjHz/Smj7NCjeJxp6YPczYPYOj8Udp5d2K+T3mE+glY8GI0lXkcNdaBwP/DD6tlDoNBFl6y6wBnldKfYtxIjVR2tuF1WkNez+HXyeCrQPctxQCB+W7+h8nLjBxZTCRl9J5qH1tXunTGHdn+wJfJiolivf3vM/GMxup7lKdGV1n0Me/j7SrC6soTFfIZUA3wEspFQlM1lovzmf1dRjdIEMxukIOL6E6hSiepGhY8wKEboSArkZvGPdaea56MSWTt38+ysr9UdTzduX7pzvSLqDgCTCSs5JZFLyIr458hY2y4dmWzzKs2TCc7QqejUmIG6UwvWUeLOBx/1z3NfDc9ZclxHXS2ujiuG6cMbFG35nQ9sk8rzTVWrNyfxRvrT1CSmZOoQf6yjZl8/2J7/n44MckZCbQv25/RrceTQ3XGjdqr4QoNLlCVVQ8qXGwdgwcXQO+bY2jda/6ea4acTGN/60yLkZqXduD6fe0oGEBQwdordkUsYk5e+cQkRxBuxrteCnoJZpWbXoj9kaIYpFwFxXLsXXw0yhj4K8ek6HzaLC5+gg8x2Rmyd/hzNp4Ajsbm0JfjHTwwkHe2/0eBy4coJ57PT7q8RG31rpV2tVFmSPhLiqGjERjaN4DX0P15vDoKqjRLM9VD5xN4H8rgzl8zujeOGVgID7u124fP5t0ljn75rDhzAaqOlVlcsfJDKo/SCbOEGWWfDJF+Re2FVY9B8nn4NZxcNsrYOdw1WqJadnM+PUY3+yKoFolRxY83Jo+zWpc86g7ISOBTw59wrfHv8Xexp5nWj7DsMBhMg6MKPMk3EX5lZUGmybDroVQtQE8sRF8g65a7fIJ03fWHeVSWjaPdw5gTM+GuDnm//HPNGXyzdFv+PTQp6TmpHJ3/bt5ttWzVHOplu9zhChLJNxF+XR2lzGKY/wpaP8M9HgdHK4+mj55PplJq0LYGR5P69oefPF4c5rWrJzvZs3azPrw9czdN5dzqefoUqsLY9uMpUGVBjdyb4QocRLuonzJSoMtU2H7R+DuB0N/MvqvXyEtK4e5m0NZ9GcYbk52TB/cnCFBftc8Ybo7Zjfv7XmPIxeP0NizMW92fpMOPh1u5N4IccNIuIvy48w2WP0cxIdB0OPQc0qewwdsPHKeN9YcJiohnfva+PJq38ZUdct/oK6whDBm753N1sitVHepztQuU+lftz82qnBT5AlRFkm4i7IvKxU2vWm0rXvUhsfW5DlRdeSlNN5Yc4RNR8/TqHollo/sSFv//K8wjUuPY8GBBaw4uQInOydGtx7NI00ewcmu4IHBhCjrJNxF2Rb+B6x+HhLOQLunjbZ1R7f/rJKVY2bRX2HM3XwSG6WY2K8xwzsH5Ds5dXpOOl8c/oIlIUvIMmUxpNEQRrYciadTwUMNCFFeSLiLsikzGTa+DnuWgGddGL4e6nS6arUdYRd5bVUIJ2NT6B1Yncl3BVLTI+8+6yaziTWn1jBv/zxi02PpUbsHL7Z+EX93/xu8M0KUPgl3UfaEboafRkNiJHR8Hrr/76qeMHEpmbyz7ig/7ovCt4ozS4YFcXvj6vlu8u+ov3l/7/ucvHSSFl4tmHnbTFpXb32j90QIq5FwF2VHRiL8+j/Y/yV4NYQnNoBfu/+sYjZrvtkVwYxfjpGebeL57vV5rnt9nB3yHuTrePxxZu2dxbZz2/B182XmbTPpXae3DBcgKjwJd1E2nNhgHK2nxEDnF6HbBLD/74nNkKhE/rcqhINnE+hYtypvDWpG/WpueW4uJjWGefvnsebUGio5VOLloJd5oPEDONhefeWqEBWRhLuwrvRLxpgwB5eBdxN44Cuo1eY/qyRlZDNrwwm+2H6BsnwAABwpSURBVH4aT1dH5tzfioGtauZ59J2ancri4MV8eeRLTNrE0MChPNn8Sdwd3Utph4QoGyTchfUc+9kYmjc1Drq+bNzs/u2PrrXmp0PRvL32CBdSMnm0Qx1e6tUoz1mRss3Z/HjiR+YfnE98Rjx9A/oy6pZR+FbyLc09EqLMkHAXpS/1IqwfDyE/GCM4PrwcfFr+Z5WwCym8vvowf4XG0byWO4uGBtHC1+OqTWmt2Xp2K7P2zuJ00mnaVG/DRz0+oplX3iNCCnGzkHAXpevIavj5JaM5ptsE6DL2PyM4ZmSbmL8llI9/D8PR3hhn/aH2dbDNY9iAkLgQ3tvzHnvP78W/sj9zu8+lm183OVkqBBLuorSkXIB1Lxnh7tMyz/HWtx6PZfKaw5y5mMagVjWZeGcTqlW6+mrRqJQoPtj3AevD1+Pp5Mmk9pMY3HAw9jYFT2ItxM1Cwl3cWFpDyApY9zJkpcDtrxmzI9n+G8QxiRlMWXuYdcEx1PV25Zsn29OpvtdVm0rMTGRR8CK+Pvo1tsqWp5o/xePNHsfNIe8eM0LczCTcxY2THANrx8Lxn40eMAM/gmpN/nk4x2Tm822nmb3xBDlmzcu9G/HkrQFXTUydbcrm2+Pf8vHBj0nOSmZg/YE81+o5mYhaiGuQcBclT2s48A38OgFyMqHnW9DhWbD99+O290w8/1sZwrGYZG5vXI03BwTi5+lyxWY0v575lQ/2fkBkSiSdanZibJuxNPJsVNp7JES5I+EuSlZipHExUugmqN0RBswDr/r/PHwpNYt3fznGt7vP4uPuxMePtKF3YPWrToLuj93Pe3ve49CFQzSo0oCP7/iYzrU6l/beCFFuSbiLkqE17P0cNrwG2gR9Z0Dbp8DGGJnRbNb8sDeSaeuPkpyRw9Nd6zKqRwNcr5jq7kzSGebsncOmiE1Uc67GlE5TGFBvALY2eQ8vIITIm4S7uH7x4fDTKGN43oCucNdc8Az45+HjMclMWhXM7tOXCKpThal3N6dRjf9OshGfEc/HBz9m+fHlONg68Hyr53m06aMyEbUQxSThLorPbIbdn8KmN0DZQv850GYYWJpY0rJy+GDzSRb/GU4lJztm3NOCe9v4/mequ4ycDL46+hWLgxeTnpPOPQ3u4ZlWz+DlfHVvGSFE4Um4i+KJC4U1z0PEdqh/B9z1Abj/e6l/7qnuhgT58mrfJni6/nuxktaa9eHrmbNvDtGp0XTz7caYNmOo61HXGnsjRIUj4S6KxmyC7fNgyzvGODCDFkDLB/85Wi/MVHcHLxxkxu4ZHLpwiCaeTZjaZSpta7S1xt4IUWEVGO5KqSVAfyBWa93MsmwmcBeQBZwChmutEyyPTQCeAEzAKK31rzeodlHaYo8aE1RH7YVGd0L/WVDJ6GuebTKz+K9wPth0EoAJfRvzeJf/TnUXnRLN7H2zWR++Hi9nLzlZKsQNVJgj98+BecAXuZZtBCZorXOUUu8CE4BXlFJNgQeAQKAmsEkp1VBrbSrZskWpMmXDX3Pg93fBsRLcsxia3fPP0fqu8HgmrQrmxPkUejatzhsDAqmVa6q7tOw0FgUv4osjxkdoRIsRPNHsCTlZKsQNVGC4a63/UEr5X7FsQ65fdwD3Wu4PBL7VWmcC4UqpUKAdsL1EqhWlL/oQrH4WYoIhcLDRxdHNG4D41CymrTvK8r2R1PJw5tPHgujZ9N+p7i7PWTp3/1zi0uPoF9CPF1u/iI+bj7X2RoibRkm0uT8OfGe5Xwsj7C+LtCy7ilJqBDACoHbt2iVQhihROZnwx3vw1yxw9oT7v4ImdwFGn/Xle88ybf0xUjJyGHlbPUb1qI+Lw78fp90xu5mxewbH4o/R0rslH3T/gBbeLay1N0LcdK4r3JVS/wNygK+L+lyt9UJgIUBQUJC+njpECYs+CCufgdjD0OIB6DMNXIyTosdikpi0MoQ9Zy7Rzt+Tt+9uRsPq//ZZj0iKYNbeWWyO2IyPqw8zus6gj38fGYZXiFJW7HBXSg3DONHaQ2t9OZyjAL9cq/lalonywJQNf74Pf8wEl6rw4HfQqA9g6bO+6SSL/gqnspMdM+81+qxfDu2krCQWHlzI18e+xsHGgVG3jOLRpo/iZHf1kL1CiBuvWOGulOoDjAdu01qn5XpoDfCNUmoWxgnVBsCu665S3HjnD8PKkRBzCJoPgb7v/nO0vvV4LJNWhRB5KZ37g/x4tW9jqlj6rJvMJlacXMG8/fNIyEzg7gZ388ItL8hFSEJYWWG6Qi4DugFeSqlIYDJG7xhHYKPlyG2H1nqk1vqwUup74AhGc81z0lOmjDPlwN9zYOt0cHL/T9v6heRM3lp7hDUHz1HP25Xvn+5Iu4B/+6zvPb+X6bumcyz+GEHVg3il3Ss09mxsrT0RQuSi/m1RsZ6goCC9Z88ea5dx87lw3DhaP7cPmg6CO98HVy+01izfE8nUdUdJzzLxbPd6PNOt3j/jrMekxjBr7yzWh6/Hx9WHcUHj6Fmnp7SrC1HKlFJ7tdZBeT0mV6jejC5fZfrbVHBwhXs/g2aDAWNi6okrg9kRFk87f0/eGdyM+tWME6aZpkyWHl7KouBFmLWZZ1o+w/Bmw3G2c77WqwkhrEDC/WYTFwqrnoHIXdC4P/SfDW7VyMox88nvp/hwSyiOdjZMG9yc+4P8sLFRaK3ZcnYLM3bPIColip51evJS0EvUcsuzl6sQogyQcL9ZmM2w6xPY9CbYOcDgT6H5faAUe8/E8+qKYE7GptC/hQ+v39X0n4mpwxLCeHf3u2w7t436HvX5tNendPDpYOWdEUIURML9ZpBw1jhaP/0nNOhljLde2YekjGxm/HKMr3ZEUMvDmSXDgri9sXGFaXJWMgsOLmDZ0WU42znzartXGdJoCPY29gW8mBCiLJBwr8i0huDl8PM4Y3akAR/CLY+CUmw4HMOkVSHEpWTyRJcAxvZsiKujHWZtZnXoaubsm8OljEsMbjCYUa1H4enkWfDrCSHKDAn3iiotHn4eC4dXgl97uPsT8AzgYkomk9ccZu2haJr4VGbR0CBa+HoAcDz+OFN3TmV/7H5aerdk/h3zCawaaOUdEUIUh4R7RXRqC6x6FlJj4fbXoMsYtLJhzYEo3lhzmNRME+N6NeTp2+phb2tDSlYK8w/O55uj31DZoTJTOk1hYP2B2Cibgl9LCFEmSbhXJNnpxgnTnQvAqxE8uAxqtiImMYNJq4LZdDSWVn4ezLy3BQ2qV/pnNqSZu2cSlx7HvQ3vZXTr0bg7ult7T4QQ10nCvaKIPgg/joALx6Dd09DzTbSdE9/timDquqNkm8xMurMJwzsHYGujCE8MZ+rOqeyM3kkTzyZ80P0Dmns3t/ZeCCFKiIR7eWc2w7a58NvbxmBfj6yA+ndwNj6NCT/u4q/QONoHePLuPS3w93IlPSedTw98ymeHP8PZ1pmJ7ScypOEQmQ1JiApGwr08Sz4PK5+GsC3QZADc9QFmpyp8ue007/5yDAW8PagZD7WrjY2NYkvEFqbvms651HMMqDeAMW3GyABfQlRQEu7lVegmY1yYzGToPwfaDCMyIZ2Xv9rJ9rCLdG3ozbTBzanl4UxMagzv7HyHLWe3UN+jPp/1/oygGnkORyGEqCAk3MubnCz4bQps+xCqNYWhP6G9G7N8TyRT1h5Ba830wc25v60fZm3m66NfM3ffXMzazJg2Y3i06aNyIZIQNwEJ9/IkPgx+eMIYxTHoCeg9ldh0xYSle9h8LJb2AZ68d19L/DxdOB5/nDe3v0lwXDCda3ZmUodJ+FbytfYeCCFKiYR7eRH8A/z0ItjYwJAvoekA1h46x6RVIaRnmXi9f1OGdfIny5zJnL1zWHp4KZUdKzP91un0C+gnw/EKcZORcC/rcjLhlwmwZzH4dYB7FnHJvjqvfbOPtYeiaennwfv3taR+NTe2n9vOWzve4mzyWQbVH8RLbV7Cw8nD2nsghLACCfeyLCECvh9qNMN0GgU9JvNXWAJjv/+DS2lZjOvVkJG31SM5O5GJf07kp7CfqFO5Dot7LaadTztrVy+EsCIJ97Lq5Cb48UljYo37vyKrwZ28/+txPvkjjHreriwZ1pZmtdzZcHoDU3dOJSkziREtRjCixQgcbR2tXb0Qwsok3Msaswl+nwG/vwvVA2HIF4SZqzN6wTaCoxJ5uH1tJt3ZlDRTAi9tfYkNZzbQtGpTPu31KQ2rNLR29UKIMkLCvSxJvWgcrZ/6DVo+hL7zPZYfjGfymr9wtLfhk0fb0KtpdX498yvv7HiHlOwURrcezbDAYdjZyD+lEOJfkghlRUwwLHsIUs7DXR+Q2PghJiwPZl1wDB3rVmX2/a2wc0jhpd9fYuOZjTSr2oy3Or9F/Sr1rV25EKIMknAvC46sNq42dXKHx9ezN6cuL8z9k9jkTF7p05inbg1gw5lfmLZrGmnZaYxpM4bHmj4mR+tCiHxJOliT2Qxbp8EfM8C3LXrIlyw+mM709dup6eHMimc6Uccbxv85jo1nNtLCqwVvdX6Luh51rV25EKKMk3C3lsxk42j92Fpo9QiJPd5l/Kpj/Hr4PL2aVmfmfS0Jid/F4DWvcSnzEi+2fpFhgcNk9EYhRKFIuFtDfDgsexDiTkCf6YT4PsizC3ZzLiGdSXc24aEONZizbybLji2jvkd95t8xn8aeja1dtRCiHJFwL20RO+HbB8FsQj+ygm/i6vLmx9up6urAd093wNkthgd+foDwxHAeafIIL7Z5UfqtCyGKTMK9NIX8aDTFuNciY8h3TPg9jZX7Q+ja0Jv3hzRndfjXfPT7R3g6e7Kw50I61uxo7YqFEOVUgeGulFoC9AditdbNLMs8ge8Af+A0MERrfUkZo1N9APQD0oBhWut9N6b0ckRr+HsObHoD/DoQ3W8xT34fzpHoJMb2bMj97d2Z8PcL7IzZSW//3rzW4TWZx1QIcV0KM73950CfK5a9CmzWWjcANlt+B+gLNLDcRgALSqbMcsyUDT+NNoK92T3s7PoZ/RcdJeJiGouHBtGmcSxDfr6PQ3GHmNJpCjO7zpRgF0JctwLDXWv9BxB/xeKBwFLL/aXAoFzLv9CGHYCHUsqnpIotdzKT4ZshsG8pustLfFFzEg9/dgB3F3t+eLY9h1KXMXLTSDydPFl25zLubnC3DM0rhCgRxW1zr661jrbcjwGqW+7XAs7mWi/Ssiyam01qHHx9L0QfIvvOOfzvTGu+33SUO5pUY3z/6kzZ+TwHLxzk3ob38krbV3Cyc7J2xUKICuS6T6hqrbVSShf1eUqpERhNN9SuXft6yyhbEiLgy7shMZLEgZ8zbFtV9kdE8sLt9WnVKIrhG17EpE3M7DqTPgFXtngJIcT1K0ybe17OX25usfyMtSyPAvxyredrWXYVrfVCrXWQ1jrI29u7mGWUQbFHYXFvSLlARP9vuPNXN45GJ/HRQy2xrbqeF7eOxreSL8v7L5dgF0LcMMUN9zXAUMv9ocDqXMsfU4YOQGKu5puK7+xuWNIHtIm9Pb7mzpU5ZOaYWTy8Cati3mRxyGLua3gfX/b9Er/KfgVvTwghiqkwXSGXAd0AL6VUJDAZmA58r5R6AjgDDLGsvg6jG2QoRlfI4Teg5rIpdBN89yi4VWdNy/mMWZVAg2pujB/oypt7R3Ax/SJTOk3h7gZ3W7tSIcRNoMBw11o/mM9DPfJYVwPPXW9R5c7x9fD9Y2ivhsyt+S6zf7nEbQ296dX+NOP+mo63szdf9PuCwKqB1q5UCHGTkCtUr9eRNfDDcMw1WjDe+Q1+2J7Iw+1rYVdtFdP3rKCjT0fe7fouVZyqWLtSIcRNRML9eoSsgBVPYarZmhHmCWw+nMKY3j7sy5jNvtB9PNn8SZ5v9byM5CiEKHUS7sV18FtY9QxZtdrzUOoYDpzP5JUB7qyOnkhcehwzus6gb0Bfa1cphLhJSbgXx74vYc0LpPt2ZlD880Qka14ckMPnp17C1d6Vz3p/RnPv5tauUghxE5NwL6r9X8Ga50n2vY0+0U+Tqm14tHcYC48voLFnY+bePpcarjWsXaUQ4iYn4V4UwT/A6udJqtmF7mefwtHZnm5tt7Ds1Fp61unJ1C5TcbZztnaVQggh4V5oR9bAjyNIrN6O7pEjqOxuR50m37M5cidPt3iaZ1s9i40q7jVhQghRsiTcC+PEBvjhcRKrtqB71EiqeEEl/085FBfOW53fYlD9QQVvQwghSpGEe0FObYHvHiGxckO6Rz+Hl08OpmrziU5NZl6PeXSu1dnaFQohxFUk3K8lcg98+xBJrrW5PXY03n6pJLkvxAlHPu/zOU2qNrF2hUIIkScJ9/xcOA5f30uqfVXuuDAG77oXiHVagq+LLwvuWEAtt1rWrlAIIfIl4Z6XxCj4cjAZZlvuTBqLZ71ooh2+pKVXSz68/UOZBk8IUeZJ944rpcXDV4PJSbvEfSljsa0XSZT9Ujr6dOSTnp9IsAshygU5cs8tKw2WPYD54imGZo0nyT+CePt19KrTi+m3Tsfe1t7aFQohRKFIuF9mNsEPj6PP7uKFnNGcrnOWJIetDG4wmNc7vC6DfwkhyhUJ98t+mQAn1vOmaTh76pwjzWEHjzV9jHFB41BKWbs6IYQoEgl3gB0fw65PWGzuxzq/RNId9vBsq2cZ2WKkBLsQolyScD/+C/rXCWwmiAU1Hch03MPo1qN5svmT1q5MCCGK7eYO9+iDmH8YzhH8GV+tBtnO+3ix9Ys80fwJa1cmhBDX5eYN98QozF8PISbHhaFeDch2PcTYNmMZ3uzmmdNbCFFx3Zz93LPSMC17kJTURAZXbU6W21FeavOSBLsQosK4+cJda8xrXsAcc4gBnm1IrXSKcUHjGNZsmLUrE0KIEnPThbve9iEq5AeGeLbhYuUIxrQZw9DAodYuSwghStTNFe6nfsO8cTLPVmlMqHssI1qM4PFmj1u7KiGEKHE3zwnV+HCyvh3KNPda/OWRxkONH+L5Vs9buyohhLghbo5wz0wh7Yv7Wepszw+eigH1BvFKu1fkAiUhRIVV8ZtltCZl+TP8ZIpiflVXuvvewZROb8h8p0KICq3CJ1z635+w49xG3q7qSVC1TrzfbYYMAiaEqPCuK9yVUmOUUoeVUiFKqWVKKSelVIBSaqdSKlQp9Z1SyqGkii0q09m9hPz5Ji97e1PPPZD5PefIsL1CiJtCscNdKVULGAUEaa2bAbbAA8C7wGytdX3gEmCda/nTLxG87GFGVa+Ku6MPn/f9GGc7Z6uUIoQQpe16m2XsAGellB3gAkQDtwM/WB5fCgy6ztcoOq0JXvooL3sqzLZufHXXEjycPEq9DCGEsJZih7vWOgp4D4jACPVEYC+QoLXOsawWCeQ5k7RSaoRSao9Sas+FCxeKW0aejq+dyhs2J7lo58infRfjW8m3RLcvhBBl3fU0y1QBBgIBQE3AFehT2OdrrRdqrYO01kHe3t7FLeMqMYe3MCNqKaEODky79QNaVAsssW0LIUR5cT3NMncA4VrrC1rrbOBHoDPgYWmmAfAFoq6zxkLLTI5nxtbn2OXsxPNNxtG73m2l9dJCCFGmXE+4RwAdlFIuyrgaqAdwBNgC3GtZZyiw+vpKLLyZ39zLRjdb+rv35Kn2Ml6MEOLmdT1t7jsxTpzuA4It21oIvAKMVUqFAlWBxSVQZ4EWrhjPd04XaGOqwTsD3y+NlxRCiDLruoYf0FpPBiZfsTgMaHc92y2qTQfW8mnSOhpk2/LhoytlWAEhxE2v3I8tczr+LFP2TsQDM+/cvphKzm7WLkkIIayuXA8/kJadxshV95NpY2Kcz1Aa129v7ZKEEKJMKNfhPnfdDM7ZJDEqy5/e/V6xdjlCCFFmlOtwH93qLuamVOfBx76xdilCCFGmlOs2d+c6bej2/GZrlyGEEGVOuT5yF0IIkTcJdyGEqIAk3IUQogKScBdCiApIwl0IISogCXchhKiAJNyFEKICknAXQogKSGmtrV0DSqkLwBlr11EIXkCctYsoIqm5dJS3mstbvSA156WO1jrPqezKRLiXF0qpPVrrIGvXURRSc+kobzWXt3pBai4qaZYRQogKSMJdCCEqIAn3ollo7QKKQWouHeWt5vJWL0jNRSJt7kIIUQHJkbsQQlRAEu5CCFEBSbhfQSnlp5TaopQ6opQ6rJQancc63ZRSiUqpA5bb69ao9YqaTiulgi317MnjcaWUmquUClVKHVJKtbZGnbnqaZTr/TuglEpSSr14xTpWf5+VUkuUUrFKqZBcyzyVUhuVUictP6vk89yhlnVOKqWGWrHemUqpY5Z/95VKKY98nnvNz1Ap1/yGUioq1799v3ye20cpddzyuX7VyjV/l6ve00qpA/k8t3TeZ6213HLdAB+gteV+JeAE0PSKdboBa61d6xU1nQa8rvF4P2A9oIAOwE5r15yrNlsgBuOCjDL1PgNdgdZASK5lM4BXLfdfBd7N43meQJjlZxXL/SpWqrcXYGe5/25e9RbmM1TKNb8BjCvE5+YUUBdwAA5e+X+1NGu+4vH3gdet+T7LkfsVtNbRWut9lvvJwFGglnWrKhEDgS+0YQfgoZTysXZRFj2AU1rrMneVstb6DyD+isUDgaWW+0uBQXk8tTewUWsdr7W+BGwE+tywQi3yqldrvUFrnWP5dQfge6PrKIp83uPCaAeEaq3DtNZZwLcY/zY33LVqVkopYAiwrDRqyY+E+zUopfyBW4CdeTzcUSl1UCm1XikVWKqF5U0DG5RSe5VSI/J4vBZwNtfvkZSdP1oPkP9/hLL2PgNU11pHW+7HANXzWKesvt+PY3yDy0tBn6HS9rylKWlJPk1fZfU9vhU4r7U+mc/jpfI+S7jnQynlBqwAXtRaJ13x8D6MJoSWwIfAqtKuLw9dtNatgb7Ac0qprtYuqDCUUg7AAGB5Hg+Xxff5P7TxPbtc9CdWSv0PyAG+zmeVsvQZWgDUA1oB0RjNHOXFg1z7qL1U3mcJ9zwopewxgv1rrfWPVz6utU7SWqdY7q8D7JVSXqVc5pU1RVl+xgIrMb6y5hYF+OX63deyzNr6Avu01uevfKAsvs8W5y83aVl+xuaxTpl6v5VSw4D+wMOWP0hXKcRnqNRorc9rrU1aazPwaT61lKn3GEApZQcMBr7Lb53Sep8l3K9gaS9bDBzVWs/KZ50alvVQSrXDeB8vll6VV9XjqpSqdPk+xgm0kCtWWwM8Zuk10wFIzNW0YE35HuWUtfc5lzXA5d4vQ4HVeazzK9BLKVXF0qTQy7Ks1Cml+gDjgQFa67R81inMZ6jUXHE+6O58atkNNFBKBVi+AT6A8W9jTXcAx7TWkXk9WKrvc2mcWS5PN6ALxtfsQ8ABy60fMBIYaVnneeAwxtn5HUAnK9dc11LLQUtd/7Msz12zAj7C6F0QDASVgffaFSOs3XMtK1PvM8YfnmggG6NN9wmgKrAZOAlsAjwt6wYBi3I993Eg1HIbbsV6QzHapi9/nj+2rFsTWHetz5AVa/7S8jk9hBHYPlfWbPm9H0aPtlPWrtmy/PPLn99c61rlfZbhB4QQogKSZhkhhKiAJNyFEKICknAXQogKSMJdCCEqIAl3IYSogCTchRCiApJwF0KICuj/hkQuW6a35OIAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From a803c03c2bc18b76aed460155267e01c0dfbf8e0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 140/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 2dc56500604d081451dd952e10dd9c41318cb025 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 141/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From 57c25fb535e5f7075010f85f060cc2b6e35ce997 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 19 Mar 2020 20:47:51 +0100 Subject: [PATCH 142/624] ANOVA ready to pull request --- ANOVA notebooks/ANOVA synthetic.ipynb | 288 ---------- ANOVA notebooks/Pruebas con ANOVA.ipynb | 490 ------------------ .../Resultados pruebas ANOVA.ipynb | 258 --------- ANOVA notebooks/anova_data_100k.csv | 201 ------- ANOVA notebooks/anova_data_500.csv | 51 -- ANOVA notebooks/anova_data_50k_p1.csv | 81 --- ANOVA notebooks/anova_data_80000.csv | 161 ------ ANOVA notebooks/csv/anova_50k_p1.csv | 81 --- ANOVA notebooks/csv/anova_50k_p1_sigma10.csv | 101 ---- ANOVA notebooks/csv/anova_50k_p1_sigma50.csv | 101 ---- ANOVA notebooks/csv/anova_50k_p2_sigma10.csv | 101 ---- ANOVA notebooks/csv/anova_50k_p2_sigma50.csv | 101 ---- ANOVA notebooks/means_p1.csv | 10 - ANOVA notebooks/means_p2.csv | 10 - examples/plot_oneway.py | 2 +- examples/plot_oneway_synthetic.py | 68 +-- .../anova/AEMET vs. Canadian Weather.ipynb | 394 -------------- skfda/inference/anova/anova_oneway.py | 66 ++- skfda/inference/anova/anova_oneway_aux.py | 11 - skfda/inference/anova/anova_simulation.py | 54 -- tests/test_oneway_anova.py | 72 +++ 21 files changed, 163 insertions(+), 2539 deletions(-) delete mode 100644 ANOVA notebooks/ANOVA synthetic.ipynb delete mode 100644 ANOVA notebooks/Pruebas con ANOVA.ipynb delete mode 100644 ANOVA notebooks/Resultados pruebas ANOVA.ipynb delete mode 100644 ANOVA notebooks/anova_data_100k.csv delete mode 100644 ANOVA notebooks/anova_data_500.csv delete mode 100644 ANOVA notebooks/anova_data_50k_p1.csv delete mode 100644 ANOVA notebooks/anova_data_80000.csv delete mode 100644 ANOVA notebooks/csv/anova_50k_p1.csv delete mode 100644 ANOVA notebooks/csv/anova_50k_p1_sigma10.csv delete mode 100644 ANOVA notebooks/csv/anova_50k_p1_sigma50.csv delete mode 100644 ANOVA notebooks/csv/anova_50k_p2_sigma10.csv delete mode 100644 ANOVA notebooks/csv/anova_50k_p2_sigma50.csv delete mode 100644 ANOVA notebooks/means_p1.csv delete mode 100644 ANOVA notebooks/means_p2.csv delete mode 100644 skfda/inference/anova/AEMET vs. Canadian Weather.ipynb delete mode 100644 skfda/inference/anova/anova_oneway_aux.py delete mode 100644 skfda/inference/anova/anova_simulation.py create mode 100644 tests/test_oneway_anova.py diff --git a/ANOVA notebooks/ANOVA synthetic.ipynb b/ANOVA notebooks/ANOVA synthetic.ipynb deleted file mode 100644 index 1a6188503..000000000 --- a/ANOVA notebooks/ANOVA synthetic.ipynb +++ /dev/null @@ -1,288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "import skfda\n", - "from skfda.inference.anova import oneway_anova\n", - "from skfda.representation import FDataGrid\n", - "from skfda.datasets import make_gaussian_process\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_samples = 100\n", - "n_features = 50\n", - "n_groups = 3\n", - "\n", - "t = np.linspace(-np.pi, np.pi, n_features)\n", - "\n", - "m1 = np.sin(t)\n", - "m2 = 1.1 * np.sin(t)\n", - "m3 = 1.2 * np.sin(t)\n", - "\n", - "_ = FDataGrid([m1, m2, m3], dataset_label=\"Means to be used in the simulation\").plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "groups = np.full(n_samples * n_groups, 'Sample 1')\n", - "groups[100:200] = 'Sample 2'\n", - "groups[200:] = 'Sample 3'" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "def make_process_b_noise(mean, cov, random_state):\n", - " return FDataGrid([mean for _ in range(n_samples)]) + make_gaussian_process(n_samples, n_features=mean.shape[0], cov=cov, random_state=random_state)" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3.5251341441516106, 0.0)" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sigma = 0.1\n", - "cov = np.identity(50) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", - "\n", - "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", - "stat, p_val" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", - "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", - "fd.plot(group=groups, legend=True)\n", - "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.966812874778942, 0.0195)" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sigma = 1\n", - "cov = np.identity(50) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", - "\n", - "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", - "stat, p_val" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", - "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", - "fd.plot(group=groups, legend=True)\n", - "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(78.09942021013121, 0.1415)" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sigma = 10\n", - "cov = np.identity(50) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", - "\n", - "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", - "stat, p_val" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd = fd1.concatenate(fd2.concatenate(fd3.concatenate()))\n", - "fd.dataset_label = f\"Sample with $\\sigma$ = {sigma}, p-value = {p_val}\"\n", - "fd.plot(group=groups, legend=True)\n", - "_ = fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ANOVA notebooks/Pruebas con ANOVA.ipynb b/ANOVA notebooks/Pruebas con ANOVA.ipynb deleted file mode 100644 index a47088809..000000000 --- a/ANOVA notebooks/Pruebas con ANOVA.ipynb +++ /dev/null @@ -1,490 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import skfda\n", - "from skfda.representation import FDataGrid\n", - "from skfda.inference.anova import oneway_anova\n", - "from skfda.datasets import make_gaussian_process" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_samples = 10\n", - "n_features = 50\n", - "n_groups = 3\n", - "\n", - "t = np.linspace(-np.pi, np.pi, n_features)\n", - "\n", - "m1 = np.sin(t)\n", - "m2 = 1.1 * np.sin(t)\n", - "m3 = 1.2 * np.sin(t)\n", - "\n", - "_ = FDataGrid([m1, m2, m3],\n", - " dataset_label=\"Means to be used in the simulation\").plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def make_process_b_noise(mean, cov, random_state=None):\n", - " return FDataGrid([mean for _ in range(n_samples)]) \\\n", - " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", - " cov=cov, random_state=random_state)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "sigma = 1\n", - "cov = np.identity(n_features) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.616968659709636, 0.80733)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oneway_anova(fd1, fd2, fd3, n_sim=100000)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8088749999999999" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean([oneway_anova(fd1, fd2, fd3)[1] for _ in range(20)])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "500/50000\n", - "0.998\n", - "0.874\n", - "1000/50000\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{i}/{x[-1]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moneway_anova\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_sim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m 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2\n\u001b[0m\u001b[1;32m 144\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/representation/grid.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 665\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_matrix\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdata_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 668\u001b[0m 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x,\n", - " \"y\": z\n", - " }).to_csv('anova_data_50k_p2.csv')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "500/50000\n", - "0.002\n", - "0.826\n", - "1000/50000\n", - "0.0\n", - "0.794\n", - "1500/50000\n", - "0.0006666666666666666\n", - "0.8033333333333333\n", - "2000/50000\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m 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244\u001b[0;31m random_state=random_state)\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0mp_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mvn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/inference/anova/anova_oneway.py\u001b[0m in \u001b[0;36m_anova_bootstrap\u001b[0;34m(fd_grouped, n_sim, p, random_state)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_sim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m 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list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list_of_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mdata_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim_domain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/_utils/_utils.py\u001b[0m in \u001b[0;36m_list_of_arrays\u001b[0;34m(original_array)\u001b[0m\n\u001b[1;32m 63\u001b[0m \"\"\"\n\u001b[1;32m 64\u001b[0m new_array = np.array([np.asarray(i) for i in\n\u001b[0;32m---> 65\u001b[0;31m np.atleast_1d(original_array)])\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;31m# Special case: Only one array, expand dimension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/_utils/_utils.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \"\"\"\n\u001b[0;32m---> 64\u001b[0;31m new_array = np.array([np.asarray(i) for i in\n\u001b[0m\u001b[1;32m 65\u001b[0m np.atleast_1d(original_array)])\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "sigma = 1\n", - "cov = np.identity(n_features) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", - "\n", - "x = [_ for _ in range(500, 50001, 500)]\n", - "y = []\n", - "z = []\n", - "for i in x:\n", - " print(f'{i}/{x[-1]}')\n", - " y.append(oneway_anova(fd1, fd2, fd3, n_sim=i, p=1)[1])\n", - " z.append(oneway_anova(fd1, fd2, fd3, n_sim=i, p=2)[1])\n", - " print(y[-1])\n", - " print(z[-1])\n", - " if i % 5000 == 0:\n", - " '''print('Saving')\n", - " pd.DataFrame({\n", - " \"x\": x[:len(y)] if len(x) != len(y) else x,\n", - " \"y\": y\n", - " }).to_csv('csv/anova_50k_p1_sigma10.csv')\n", - " pd.DataFrame({\n", - " \"x\": x[:len(y)] if len(x) != len(y) else x,\n", - " \"y\": z\n", - " }).to_csv('csv/anova_50k_p2_sigma10.csv')'''\n", - " continue\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "means_p1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.05322\n" - ] - } - ], - "source": [ - "n_samples = 10\n", - "n_features = 50\n", - "n_groups = 3\n", - "\n", - "t = np.linspace(-np.pi, np.pi, n_features)\n", - "\n", - "m1 = np.sin(t)\n", - "m2 = 1.1 * np.sin(t)\n", - "m3 = 1.2 * np.sin(t)\n", - "\n", - "_ = FDataGrid([m1, m2, m3],\n", - " dataset_label=\"Means to be used in the simulation\").plot()\n", - "\n", - "def make_process_b_noise(mean, cov, random_state=None):\n", - " return FDataGrid([mean for _ in range(n_samples)]) \\\n", - " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", - " cov=cov, random_state=random_state)\n", - "\n", - "sigma = 100\n", - "cov = np.identity(n_features) * sigma\n", - "n_samples = 100\n", - "\n", - "fd1 = make_process_b_noise(m1, cov, random_state=1)\n", - "fd2 = make_process_b_noise(m2, cov, random_state=2)\n", - "fd3 = make_process_b_noise(m3, cov, random_state=3)\n", - "\n", - "p = oneway_anova(fd1, fd2, fd3, p=2, n_sim=50000)[1]\n", - "print(p)\n", - "p = oneway_anova(fd1, fd2, fd3, p=1, n_sim=50000)[1]\n", - "print(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import skfda\n", - "from skfda.representation import FDataGrid\n", - "from skfda.inference.anova import oneway_anova\n", - "from skfda.datasets import make_gaussian_process" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_samples = 100\n", - "n_features = 50\n", - "n_groups = 3\n", - "\n", - "t = np.linspace(-np.pi, np.pi, n_features)\n", - "\n", - "m1 = np.sin(t)\n", - "m2 = 1.1 * np.sin(t)\n", - "m3 = 1.2 * np.sin(t)\n", - "\n", - "_ = FDataGrid([m1, m2, m3],\n", - " dataset_label=\"Means to be used in the simulation\").plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def make_process_b_noise(mean, cov):\n", - " return FDataGrid([mean for _ in range(n_samples)]) \\\n", - " + make_gaussian_process(n_samples, n_features=mean.shape[0],\n", - " cov=cov)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "groups = np.full(n_samples * n_groups, 'Sample 1')\n", - "groups[100:200] = 'Sample 2'\n", - "groups[200:] = 'Sample 3'" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistic: 3.415040947599544\n", - "p-value: 0.0\n" - ] - }, - { - "data": { - "text/plain": [ - "(100,)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sigma = 0.1\n", - "cov = np.identity(n_features) * sigma\n", - "\n", - "fd1 = make_process_b_noise(m1, cov)\n", - "fd2 = make_process_b_noise(m2, cov)\n", - "fd3 = make_process_b_noise(m3, cov)\n", - "\n", - "stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1)\n", - "print(\"Statistic: \", stat)\n", - "print(\"p-value: \", p_val)\n", - "fd1.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd1.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ANOVA notebooks/Resultados pruebas ANOVA.ipynb b/ANOVA notebooks/Resultados pruebas ANOVA.ipynb deleted file mode 100644 index eecbd2553..000000000 --- a/ANOVA notebooks/Resultados pruebas ANOVA.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Resultados pruebas ANOVA" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Los siguientes datasets han sido generados tomando un número de samples *n_samples=100*." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "df_p2 = pd.read_csv('anova_data_100k.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "El siguiente ejemplo ha sido generado tomando los valores: $\\sigma = 1$ y utilizando la norma de $L_2$." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(df_p2.x, df_p2.y);" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.045477719628741135\n", - "Var: 3.6307290867741124e-06\n" - ] - } - ], - "source": [ - "print('Mean: ', np.mean(df_p2.y))\n", - "print('Var: ', np.var(df_p2.y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "El siguiente ejemplo ha sido generado utilizando la norma de $L_1$. A la vista de que el $p-valor$ siempre era nulo se ha escogido una $\\sigma$ superior, en este caso $\\sigma=50$." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(df_p1.x, df_p1.y);" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.011401661187528415\n", - "Var: 8.065091451821023e-07\n" - ] - } - ], - "source": [ - "print('Mean: ', np.mean(df_p1.y))\n", - "print('Var: ', np.var(df_p1.y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "En el siguiente gráfico puede observarse que la norma 1 es mejor en términos de convergencia hacia el $p-valor$ que la norma 2." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(df_p2.x, df_p2.y - np.mean(df_p2.y), alpha=0.4)\n", - "plt.scatter(df_p2.x, df_p1.y - np.mean(df_p1.y), alpha=0.4);" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "df_p1_10 = pd.read_csv('csv/anova_50k_p1_sigma10.csv')\n", - "df_p2_10 = pd.read_csv('csv/anova_50k_p2_sigma10.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parece que la separación entre los $p-valores$ para diferentes normas depende del número de trayectorias que le damos a cada grupo. En este caso probamos con 10 para cada uno, y comprobamos que para ambas el resultado es mucho más cercano que en el caso anterior." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(df_p2_10.x, df_p2_10.y, alpha=0.4)\n", - "plt.scatter(df_p1_10.x, df_p1_10.y, alpha=0.4);" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "df_p1_50 = pd.read_csv('csv/anova_50k_p1_sigma50.csv')\n", - "df_p2_50 = pd.read_csv('csv/anova_50k_p2_sigma50.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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@@ -1,10 +0,0 @@ -,x,y -0,10,0.0 -1,20,0.0 -2,30,0.0 -3,40,0.0 -4,50,0.0 -5,60,0.0 -6,70,0.0 -7,80,0.0 -8,90,0.0 diff --git a/ANOVA notebooks/means_p2.csv b/ANOVA notebooks/means_p2.csv deleted file mode 100644 index 529298086..000000000 --- a/ANOVA notebooks/means_p2.csv +++ /dev/null @@ -1,10 +0,0 @@ -,x,y -0,10,0.001265 -1,20,0.001265 -2,30,0.001265 -3,40,0.001265 -4,50,0.001265 -5,60,0.001265 -6,70,0.001265 -7,80,0.001265 -8,90,0.001265 diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 7978c586a..0a868f4b8 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -104,7 +104,7 @@ # sampling distribution of the statistic which is compared with the first # return to get the *p-value*. -v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_sim=1500, p=1, +v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_sim=1500, p=2, return_dist=True) print('Statistic: ', v_n) diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 8cb01cb2d..3fd902f62 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -9,6 +9,8 @@ # Author: David García Fernández # License: MIT +# sphinx_gallery_thumbnail_number = 2 + import skfda from skfda.inference.anova import oneway_anova from skfda.representation import FDataGrid @@ -26,7 +28,7 @@ # In this example we will explain the nature of ANOVA method and its behavior # under certain conditions simulating data. Specifically, we will generate # three different trajectories, for each one we will simulate a stochastic -# process by adding to them brownian processes. The main objective of the +# process by adding to them white noise. The main objective of the # test is to illustrate the differences in the results of the ANOVA method # when the covariance function of the brownian processes changes. @@ -40,11 +42,11 @@ ################################################################################ # First, the means for the future processes are drawn. -n_samples = 100 +n_samples = 10 n_features = 50 n_groups = 3 -t = np.linspace(-np.pi, np.pi, n_features) +t = np.linspace(0, np.pi, n_features) m1 = np.sin(t) m2 = 1.1 * np.sin(t) @@ -58,10 +60,11 @@ # Now, a function to simulate processes as described above is implemented, # to make code clearer. -def make_process_b_noise(mean, cov, random_state): - return FDataGrid([mean for _ in range(n_samples)]) \ +def make_process_w_noise(mean, cov, t, random_state): + return FDataGrid([mean for _ in range(n_samples)], sample_points=t) \ + make_gaussian_process(n_samples, n_features=mean.shape[0], - cov=cov, random_state=random_state) + cov=cov, random_state=random_state, + start=t[0], stop=t[-1]) ################################################################################ @@ -69,32 +72,33 @@ def make_process_b_noise(mean, cov, random_state): # of labels is created to identify them when plotting. groups = np.full(n_samples * n_groups, 'Sample 1') -groups[100:200] = 'Sample 2' -groups[200:] = 'Sample 3' +groups[10:20] = 'Sample 2' +groups[20:] = 'Sample 3' ############################################################################### -# First simulation uses a low :math:`\sigma = 0.1` value. In this case the +# First simulation uses a low :math:`\sigma = 0.01` value. In this case the # differences between the means of each group should be clear, and the # p-value for the test should be near to zero. -sigma = 0.1 +sigma = 0.01 cov = np.identity(n_features) * sigma -fd1 = make_process_b_noise(m1, cov, random_state=1) -fd2 = make_process_b_noise(m2, cov, random_state=2) -fd3 = make_process_b_noise(m3, cov, random_state=3) +fd1 = make_process_w_noise(m1, cov, t, random_state=1) +fd2 = make_process_w_noise(m2, cov, t, random_state=2) +fd3 = make_process_w_noise(m3, cov, t, random_state=3) stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) -print("Statistic: ", stat) -print("p-value: ", p_val) +print("Statistic: {:.3f}".format(stat)) +print("p-value: {:.3f}".format(p_val)) ################################################################################ # In the plot below we can see the simulated trajectories for each mean, # and the averages for each group. fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) -fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" -fd.plot(group=groups, legend=True) +fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( + sigma, p_val) +fd.plot(group=groups, legend=True, alpha=0.6) fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() ################################################################################ @@ -104,37 +108,39 @@ def make_process_b_noise(mean, cov, random_state): # refuse). ################################################################################ -# Plot for :math:`\sigma = 1`: +# Plot for :math:`\sigma = 0.1`: -sigma = 1 +sigma = 0.1 cov = np.identity(n_features) * sigma -fd1 = make_process_b_noise(m1, cov, random_state=1) -fd2 = make_process_b_noise(m2, cov, random_state=2) -fd3 = make_process_b_noise(m3, cov, random_state=3) +fd1 = make_process_w_noise(m1, cov, t, random_state=1) +fd2 = make_process_w_noise(m2, cov, t, random_state=2) +fd3 = make_process_w_noise(m3, cov, t, random_state=3) _, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) -fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" -fd.plot(group=groups, legend=True) +fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( + sigma, p_val) +fd.plot(group=groups, legend=True, alpha=0.6) fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() ################################################################################ -# Plot for :math:`\sigma = 10`: +# Plot for :math:`\sigma = 1`: -sigma = 10 +sigma = 1 cov = np.identity(n_features) * sigma -fd1 = make_process_b_noise(m1, cov, random_state=1) -fd2 = make_process_b_noise(m2, cov, random_state=2) -fd3 = make_process_b_noise(m3, cov, random_state=3) +fd1 = make_process_w_noise(m1, cov, t, random_state=1) +fd2 = make_process_w_noise(m2, cov, t, random_state=2) +fd3 = make_process_w_noise(m3, cov, t, random_state=3) _, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) -fd.dataset_label = f"Sample with $\sigma$ = {sigma}, p-value = {p_val}" -fd.plot(group=groups, legend=True) +fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( + sigma, p_val) +fd.plot(group=groups, legend=True, alpha=0.6) fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() ################################################################################ diff --git a/skfda/inference/anova/AEMET vs. Canadian Weather.ipynb b/skfda/inference/anova/AEMET vs. Canadian Weather.ipynb deleted file mode 100644 index d0bb02f2c..000000000 --- a/skfda/inference/anova/AEMET vs. Canadian Weather.ipynb +++ /dev/null @@ -1,394 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "import skfda\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comparativa datasets: AEMET y Canadian Weather\n", - "### Canadian Weather\n", - "Descargamos el dataset de Canadian Weather y comprobamos los campos que posee." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['data', 'target', 'target_names', 'target_feature_names', 'DESCR'])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = skfda.datasets.fetch_weather()\n", - "data.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "El dataset se compone de 35 observaciones diferentes, muestreadas a lo largo de 365 días, siendo cada una de ellas bidimensional (temperatura y precipitaciones)." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "skfda.representation.grid.FDataGrid" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(data['data'])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(35, 365, 2)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['data'].data_matrix.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cada observación corresponde a un tipo de clima de entre cuatro distintos." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 0, 0, 0])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Arctic', 'Atlantic', 'Continental', 'Pacific'], dtype='" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = data['data'].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['data', 'meta', 'meta_names', 'meta_feature_names', 'DESCR'])" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_aemet = skfda.datasets.fetch_aemet()\n", - "data_aemet.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo de data: \n", - "Longitud de data: 3\n", - "Tipo de data[0]: \n", - "Shape de data[0]: (73, 365, 1)\n", - "Shape de data[1]: (73, 365, 1)\n", - "Shape de data[2]: (73, 365, 1)\n" - ] - } - ], - "source": [ - "print('Tipo de data:', type(data_aemet['data']))\n", - "print('Longitud de data: ', len(data_aemet['data']))\n", - "print('Tipo de data[0]: ', type(data_aemet['data'][0]))\n", - "print('Shape de data[0]: ', data_aemet['data'][0].data_matrix.shape)\n", - "print('Shape de data[1]: ', data_aemet['data'][1].data_matrix.shape)\n", - "print('Shape de data[2]: ', data_aemet['data'][2].data_matrix.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(73, 6)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_aemet['meta'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['1387', 'A CORUÑA', 'A CORUÑA', 58, -8.419444444444444,\n", - " 43.367222222222225], dtype=object)" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_aemet['meta'][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ind', 'name', 'province', 'altitude', 'longitude', 'latitude']" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_aemet['meta_names']" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['location']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_aemet['meta_feature_names']" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['day', 'm/s'], dtype='" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_temp = data_aemet['data'][0]\n", - "fd_logprec = data_aemet['data'][1]\n", - "fd_wind = data_aemet['data'][2]\n", - "\n", - "data_matrix = np.empty((73, 365, 3))\n", - "data_matrix[:, :, 0] = fd_temp.data_matrix[:, :, 0]\n", - "data_matrix[:, :, 1] = fd_logprec.data_matrix[:, :, 0]\n", - "data_matrix[:, :, 2] = fd_wind.data_matrix[:, :, 0]\n", - "\n", - "fd = fd_temp.copy(data_matrix=data_matrix, axes_labels=['day', 'ºC', 'mm', 'm/s'], dataset_label='AEMET')\n", - "fig = fd.plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index f21189cff..b87f134e0 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,6 +1,6 @@ import numpy as np from skfda.misc.metrics import norm_lp -from skfda.representation import FDataGrid +from skfda.representation import FData, FDataGrid, FDataBasis from skfda.datasets import make_gaussian_process @@ -37,7 +37,7 @@ def v_sample_stat(fd, weights, p=2): The value of the statistic. Raises: - TODO + ValueError Examples: @@ -65,10 +65,18 @@ def v_sample_stat(fd, weights, p=2): anova test for functional data". *Computational Statistics Data Analysis*, 47:111-112, 02 2004 """ - k = fd.n_samples + + if not isinstance(fd, FData): + raise ValueError("Argument type must inherit FData.") + if len(weights) != fd.n_samples: + raise ValueError("Number of weights must match number of samples.") + if isinstance(fd, FDataBasis): + raise NotImplementedError("Not implemented for FDataBasis objects.") + + n = fd.n_samples v_n = 0 - for i in range(k): - for j in range(i + 1, k): + for j in range(n): + for i in range(j): v_n += weights[i] * norm_lp(fd[i] - fd[j], p=p) ** p return v_n @@ -106,7 +114,7 @@ def v_asymptotic_stat(fd, weights, p=2): The value of the statistic. Raises: - TODO + ValueError. Examples: @@ -134,13 +142,19 @@ def v_asymptotic_stat(fd, weights, p=2): anova test for functional data". *Computational Statistics Data Analysis*, 47:111-112, 02 2004 """ - - k = fd.n_samples + if not isinstance(fd, FData): + raise ValueError("Argument type must inherit FData.") + if len(weights) != fd.n_samples: + raise ValueError("Number of weights must match number of samples.") + if isinstance(fd, FDataBasis): + raise NotImplementedError("Not implemented for FDataBasis objects.") + + n = fd.n_samples v = 0 - for i in range(k): - for j in range(i + 1, k): + for j in range(n): + for i in range(j): v += norm_lp( - fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]), p=p) ** 2 + fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]), p=p) ** p return v @@ -223,7 +237,26 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): (float, float, numpy.array) Raises: - TODO + ValueError: In case of bad arguments. + + Examples: + >>> from skfda.inference.anova import oneway_anova + >>> from skfda.datasets import fetch_gait + >>> from numpy.random import RandomState + + >>> fd = fetch_gait()["data"].coordinates[1] + >>> fd1, fd2, fd3 = fd[:13], fd[13:26], fd[26:] + >>> oneway_anova(fd1, fd2, fd3, random_state=RandomState(42)) + (179.52499999999998, 0.602) + >>> oneway_anova(fd1, fd2, fd3, p=1, random_state=RandomState(42)) + (67.27499999999999, 0.0) + >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_sim=3, + ... random_state=RandomState(42), + ... return_dist=True) + >>> print(dist) + [163.35765183 208.59495097 229.76780354] + + References: [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An @@ -231,7 +264,14 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): Analysis*, 47:111-112, 02 2004 """ - assert len(args) > 0 + if len(args) < 1: + raise ValueError("At least one sample must be passed as parameter.") + if not all(isinstance(fd, FData) for fd in args): + raise ValueError("Argument type must inherit FData.") + if n_sim < 1: + raise ValueError("Number of simulations must be positive.") + if any(isinstance(fd, FDataBasis) for fd in args): + raise NotImplementedError("Not implemented for FDataBasis objects.") fd_groups = args fd_means = fd_groups[0].mean() diff --git a/skfda/inference/anova/anova_oneway_aux.py b/skfda/inference/anova/anova_oneway_aux.py deleted file mode 100644 index 34fe4f074..000000000 --- a/skfda/inference/anova/anova_oneway_aux.py +++ /dev/null @@ -1,11 +0,0 @@ -from skfda import FDataGrid -import numpy as np -from skfda.inference.anova.anova_oneway import func_oneway, func_oneway_usc -from skfda.datasets import make_gaussian_process -from matplotlib import pyplot as plt - -m = 25 -n = 1 - -process = make_gaussian_process(n, n_features=m) -print(process.mean().data_matrix) diff --git a/skfda/inference/anova/anova_simulation.py b/skfda/inference/anova/anova_simulation.py deleted file mode 100644 index 5a14b55a1..000000000 --- a/skfda/inference/anova/anova_simulation.py +++ /dev/null @@ -1,54 +0,0 @@ -from skfda import FDataGrid -import numpy as np -from skfda.inference.anova.anova_oneway import oneway_anova - - -def generate_samples_independent(mean, sigma, n_samples): - return [mean + np.random.normal(0, sigma, len(mean)) for _ in - range(n_samples)] - - -scale = 25 - -start = 0 -stop = 1 - -n_levels = 3 -n_samples = 10 - -t = np.linspace(start, stop, scale) - -sigmas = np.array([0, 0.2, 1, 1.8, 2.6, 3.4, 4.2, 5]) -sigmas_star = sigmas / scale - -# Case M1 -# mean1 = t * (1 - t) -# mean2 = t * (1 - t) -# mean3 = t * (1 - t) - -mean1 = t * (1 - t) ** 5 -mean2 = t ** 2 * (1 - t) ** 4 -mean3 = t ** 3 * (1 - t) ** 3 - -fd_means = FDataGrid([mean1, mean2, mean3]) - -p = [] -reps = 20 - -for i in range(reps): - print('Simulation {}...'.format(i + 1)) - samples1 = generate_samples_independent(mean1, sigmas_star[2], n_samples) - samples2 = generate_samples_independent(mean2, sigmas_star[2], n_samples) - samples3 = generate_samples_independent(mean3, sigmas_star[2], n_samples) - - # Storing in FDataGrid - fd_1 = FDataGrid(samples1, sample_points=t, dataset_label="Process 1") - fd_2 = FDataGrid(samples2, sample_points=t, dataset_label="Process 2") - fd_3 = FDataGrid(samples3, sample_points=t, dataset_label="Process 3") - fd_total = fd_1.concatenate(fd_2.concatenate(fd_3)) - - anova = oneway_anova(fd_1, fd_2, fd_3) - print(anova) - p.append(anova[0]) - -print(np.mean(p)) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py new file mode 100644 index 000000000..e8f2c0122 --- /dev/null +++ b/tests/test_oneway_anova.py @@ -0,0 +1,72 @@ +import unittest +import numpy as np + +from skfda.representation import FDataGrid +from skfda.datasets import make_gaussian_process, fetch_gait +from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ + v_sample_stat + + +def make_process_w_noise(mean, cov, n_samples, t, random_state): + return FDataGrid([mean for _ in range(n_samples)], sample_points=t) \ + + make_gaussian_process(n_samples, n_features=mean.shape[0], + cov=cov, random_state=random_state, + start=t[0], stop=t[-1]) + + +class MyTestCase(unittest.TestCase): + + def test_oneway_anova_args(self): + with self.assertRaises(ValueError): + oneway_anova() + with self.assertRaises(ValueError): + oneway_anova(1, '2') + with self.assertRaises(ValueError): + oneway_anova(FDataGrid([0]), n_sim=-2) + + def test_v_stats_args(self): + with self.assertRaises(ValueError): + v_sample_stat(1, [1]) + with self.assertRaises(ValueError): + v_sample_stat(FDataGrid([0]), [0, 1]) + with self.assertRaises(ValueError): + v_asymptotic_stat(1, [1]) + with self.assertRaises(ValueError): + v_asymptotic_stat(FDataGrid([0]), [0, 1]) + + def test_v_stats(self): + n_features = 50 + weights = [1, 2, 3] + t = np.linspace(0, 1, n_features) + m1 = [1 for _ in range(n_features)] + m2 = [2 for _ in range(n_features)] + m3 = [3 for _ in range(n_features)] + fd = FDataGrid([m1, m2, m3], sample_points=t) + self.assertEqual(v_sample_stat(fd, weights), 7.0) + self.assertEqual(v_sample_stat(fd, weights, p=1), 5.0) + res = (1 - 2 * np.sqrt(1 / 2)) ** 2 + (1 - 3 * np.sqrt(1 / 3)) ** 2 \ + + (2 - 3 * np.sqrt(2 / 3)) ** 2 + self.assertAlmostEqual(v_asymptotic_stat(fd, weights), res) + res = abs(1 - 2 * np.sqrt(1 / 2)) + abs(1 - 3 * np.sqrt(1 / 3))\ + + abs(2 - 3 * np.sqrt(2 / 3)) + self.assertAlmostEqual(v_asymptotic_stat(fd, weights, p=1), res) + + def test_asymptotic_behaviour(self): + dataset = fetch_gait() + fd = dataset['data'].coordinates[1] + fd1 = fd[0:13] + fd2 = fd[13:26] + fd3 = fd[26:39] + + n_little_sim = 50 + + sims = np.array([oneway_anova(fd1, fd2, fd3, n_sim=2000)[1] for _ in + range(n_little_sim)]) + little_sim = np.mean(sims) + big_sim = oneway_anova(fd1, fd2, fd3, n_sim=50000)[1] + print(little_sim, big_sim) + self.assertAlmostEqual(little_sim, big_sim, delta=0.01) + +if __name__ == '__main__': + print() + unittest.main() \ No newline at end of file From 68a8c08d148f4594d36a96e12c06954aceb3be93 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 19 Mar 2020 22:46:49 +0100 Subject: [PATCH 143/624] Add Fourier penalty --- skfda/representation/basis.py | 67 +++++++++++++++++++++++++++++++++++ tests/test_basis.py | 8 +++++ 2 files changed, 75 insertions(+) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index fb580fb12..d0a4b8069 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1250,6 +1250,73 @@ def _evaluate(self, eval_points, derivative=0): return res + def _penalty_orthonormal(self, weights): + """ + Return the penalty when the basis is orthonormal. + """ + + signs = np.array([1, 1, -1, -1]) + signs_expanded = np.tile(signs, len(weights) // 4 + 1) + + signs_odd = signs_expanded[:len(weights)] + signs_even = signs_expanded[1:len(weights) + 1] + + phases = (np.arange(1, (self.n_basis - 1) // 2 + 1) * + 2 * np.pi / self.period) + seq_derivs = np.arange(len(weights)) + + coefs_no_sign = np.power.outer(phases, seq_derivs) + + coefs_no_sign *= weights + + coefs_odd = signs_odd * coefs_no_sign + coefs_even = signs_even * coefs_no_sign + + # After applying the linear differential operator to a sinusoidal + # element of the basis e, the result can be expressed as + # A e + B e*, where e* is the other basis element in the pair + # with the same phase + + odd_sin_coefs = np.sum(coefs_odd[:, ::2], axis=1) + odd_cos_coefs = np.sum(coefs_odd[:, 1::2], axis=1) + + even_cos_coefs = np.sum(coefs_even[:, ::2], axis=1) + even_sin_coefs = np.sum(coefs_even[:, 1::2], axis=1) + + # The diagonal is the inner product of A e + B e* + # with itself. As the basis is orthonormal, the cross products e e* + # are 0, and the products e e and e* e* are one. + # Thus, the diagonal is A^2 + B^2 + # All elements outside the main diagonal are 0 + main_diag_odd = odd_sin_coefs**2 + odd_cos_coefs**2 + main_diag_even = even_sin_coefs**2 + even_cos_coefs**2 + + # The main diagonal should intercalate both diagonals + main_diag = np.array((main_diag_odd, main_diag_even)).T.ravel() + + penalty_matrix = np.diag(main_diag) + + # Add row and column for the constant + penalty_matrix = np.pad(penalty_matrix, pad_width=((1, 0), (1, 0)), + mode='constant') + + penalty_matrix[0, 0] = weights[0]**2 + + return penalty_matrix + + def _penalty(self, lfd): + + weights = lfd.constant_weights() + if weights is None: + return NotImplemented + + # If the period and domain range are not the same, the basis functions + # are not orthogonal + if self.period != (self.domain_range[0][1] - self.domain_range[0][0]): + return NotImplemented + + return self._penalty_orthonormal(weights) + def _derivative(self, coefs, order=1): omega = 2 * np.pi / self.period diff --git a/tests/test_basis.py b/tests/test_basis.py index 9c9c2c680..ff7830941 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -128,6 +128,7 @@ def test_monomial_penalty(self): self._test_penalty(basis, lfd=[1, 2, 3]) self._test_penalty(basis, lfd=7) + self._test_penalty(basis, lfd=0) self._test_penalty(basis, lfd=1) self._test_penalty(basis, lfd=27) @@ -142,6 +143,13 @@ def test_fourier_penalty(self): self._test_penalty(basis, lfd=2, result=res) + basis = Fourier(n_basis=9, domain_range=(1, 5)) + self._test_penalty(basis, lfd=[1, 2, 3]) + self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) + self._test_penalty(basis, lfd=0) + self._test_penalty(basis, lfd=1) + self._test_penalty(basis, lfd=3) + def test_bspline_penalty(self): basis = BSpline(n_basis=5) From 73181646c327937a696709d8a67139a58b759d57 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 144/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From 8b8d2c114a9e06946c88e4d89501e066a2d9c481 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 20 Mar 2020 20:17:27 +0100 Subject: [PATCH 145/624] Raw doctrings + new line --- skfda/inference/anova/anova_oneway.py | 6 +++--- tests/test_oneway_anova.py | 3 ++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index b87f134e0..2763683f8 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -5,7 +5,7 @@ def v_sample_stat(fd, weights, p=2): - """ + r""" Calculates a statistic that measures the variability between groups of samples in a :class:`skfda.representation.grid.FDataGrid` object. @@ -82,7 +82,7 @@ def v_sample_stat(fd, weights, p=2): def v_asymptotic_stat(fd, weights, p=2): - """ + r""" Calculates a statistic that measures the variability between groups of samples in a :class:`skfda.representation.grid.FDataGrid` object. @@ -183,7 +183,7 @@ def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): - """ + r""" Performs one-way functional ANOVA. This function implements an asymptotic method to test the following diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index e8f2c0122..c46a926e2 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -67,6 +67,7 @@ def test_asymptotic_behaviour(self): print(little_sim, big_sim) self.assertAlmostEqual(little_sim, big_sim, delta=0.01) + if __name__ == '__main__': print() - unittest.main() \ No newline at end of file + unittest.main() From 1dc1a0679f3549e00f1c977fb1ad1b8878738bd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 20 Mar 2020 22:34:14 +0100 Subject: [PATCH 146/624] Adding warning. Number of simulations in oneway_anova may change --- skfda/inference/anova/anova_oneway.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 2763683f8..60d3e9ea2 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -159,9 +159,11 @@ def v_asymptotic_stat(fd, weights, p=2): def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): - assert len(fd_grouped) > 0 - n_groups = len(fd_grouped) + sample_p_list = [fd.sample_points[0] for fd in fd_grouped] + print(sample_p_list) + assert n_groups > 0 + assert sample_p_list.count(sample_p_list[0]) == n_groups sample_points = fd_grouped[0].sample_points m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] @@ -217,7 +219,8 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): fd1,fd2,.... (FDataGrid): The sample measurements for each each group. n_sim (int, optional): Number of simulations for the bootstrap - procedure. Defaults to 2000. + procedure. Defaults to 2000 (This value may change in future + versions). p (int, optional): p of the lp norm. Must be greater or equal than 1. If p='inf' or p=np.inf it is used the L infinity metric. From e8208af2f63e5612cbe742acbb6eb2a837b7e291 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 147/624] transfer files to new location and modify documentation --- docs/modules/exploratory.rst | 1 - docs/modules/preprocessing.rst | 13 +- docs/modules/preprocessing/dim_reduction.rst | 18 + .../dim_reduction}/fpca.rst | 10 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ----------------- skfda/preprocessing/dim_reduction/__init__.py | 1 + .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 437 +++++++++++++++++- tests/test_fpca.py | 6 +- 12 files changed, 456 insertions(+), 463 deletions(-) create mode 100644 docs/modules/preprocessing/dim_reduction.rst rename docs/modules/{exploratory => preprocessing/dim_reduction}/fpca.rst (75%) delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index edc2c8d73..832b93193 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,4 +11,3 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers - exploratory/fpca \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index 06f3eb6da..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -12,6 +12,7 @@ this category deal with this problem. preprocessing/smoothing preprocessing/registration + preprocessing/dim_reduction Smoothing --------- @@ -28,4 +29,14 @@ Sometimes, the functional data may be misaligned, or the phase variation should be ignored in the analysis. To align the data and eliminate the phase variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the -registration methods available in the library. \ No newline at end of file +registration methods available in the library. + +Dimension Reduction +------------------- + +The functional data may have too many samples so we cannot analyse +the data with clarity. To better understand the data, we need to use +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. +:doc:`Here ` you can learn more about the +dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst new file mode 100644 index 000000000..9da0452b7 --- /dev/null +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -0,0 +1,18 @@ +Dimension Reduction +=================== + +When dealing with data samples with high dimensionality, we often need to +reduce the dimensions so we can better observe the data. + +Projection +---------- +One way to reduce the dimension is through projection. For example, in +functional principal component analysis, we project the data samples +into a smaller sample of functions that preserve the maximum sample +variance. + +.. toctree:: + :maxdepth: 4 + :caption: Modules: + + dim_reduction/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst similarity index 75% rename from docs/modules/exploratory/fpca.rst rename to docs/modules/preprocessing/dim_reduction/fpca.rst index b80519747..7af947b89 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -9,9 +9,9 @@ of FPCA are orthogonal functions (usually a much smaller sample than the input data sample) that represent the most important modes of variation in the original data sample. -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. +For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, +where the process is applied to several datasets in both discretized and basis +forms. FPCA for functional data in a basis representation ---------------------------------------------------------------- @@ -19,7 +19,7 @@ FPCA for functional data in a basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis + skfda.preprocessing.dim_reduction.projection.FPCABasis FPCA for functional data in a discretized representation ---------------------------------------------------------------- @@ -27,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index e69de29bb..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -0,0 +1 @@ +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd4b4dadc..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import fpca +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index f966cce17..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,33 +1,426 @@ -"""Functional principal component analysis. -""" +"""Functional Principal Component Analysis Module.""" import numpy as np +import skfda +from abc import ABC, abstractmethod +from skfda.representation.basis import FDataBasis +from skfda.representation.grid import FDataGrid +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut -from ....exploratory.stats import mean +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" -def fpca(fdatagrid, n=2): - """Compute Functional Principal Components Analysis. +class FPCA(ABC, BaseEstimator, TransformerMixin): + """Defines the common structure shared between classes that do functional + principal component analysis - Performs Functional Principal Components Analysis to reduce - dimensionality and obtain the principal modes of variation for a - functional data object. + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + + def __init__(self, n_components=3, centering=True): + """FPCA constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + self.n_components = n_components + self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) + + @abstractmethod + def fit(self, X, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + pass + + def fit_transform(self, X, y=None, **fit_params): + """Computes the n_components first principal components and their scores + and returns them. + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + """Funcional principal component analysis for functional data represented + in basis form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + + """ + + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization_derivative_degree=2, + regularization_coefficients=None, + regularization_parameter=0): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + # basis that we want to use for the principal components + self.components_basis = components_basis + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients + + def fit(self, X: FDataBasis, y=None): + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + """ + + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # setup principal component basis if not given + if self.components_basis: + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range + g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. + j_matrix = X.basis.inner_product(self.components_basis) + else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object + self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix + + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 + + # Apply regularization / penalty if applicable + if self.regularization_parameter > 0: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix - It uses SVD numpy implementation to compute PCA. + # obtain triangulation using cholesky + l_matrix = np.linalg.cholesky(g_matrix) - Args: - fdatagrid (FDataGrid): functional data object. - n (int, optional): Number of principal components. Defaults to 2. + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - Returns: - tuple: (scores, principal directions, eigenvalues) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) + self.pca.fit(final_matrix) + + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) + + component_coefficients = np.transpose(component_coefficients) + + # the singular values obtained using SVD are the squares of eigenvalues + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case it is the inner product of our data with the components + return X.inner_product(self.components) + + +class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ - fdatagrid = fdatagrid - mean(fdatagrid) # centers the data - # singular value decomposition - u, s, v = np.linalg.svd(fdatagrid.data_matrix) - principal_directions = v.T # obtain the eigenvectors matrix - eigenvalues = (np.diag(s) ** 2) / (fdatagrid.n_samples - 1) - scores = u @ s # functional principal scores - - return scores, principal_directions, eigenvalues + + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + self.weights = weights + + def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book, chapter 8. + + Args: + X (FDataGrid): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # get the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + fd_data -= np.squeeze(meanfd.data_matrix) + + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) + + weights_matrix = np.diag(self.weights) + + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From 3a5a07e9599da29f4d55e268345acf54a2f46d24 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:50:18 +0100 Subject: [PATCH 148/624] fix gram matrix in Fourier basis --- skfda/representation/basis.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index ed13bf9d8..71ec3f77e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1484,14 +1484,18 @@ def penalty(self, derivative_degree=None, coefficients=None): def gram_matrix(self): r"""Return the Gram Matrix of a fourier basis - We already know that a fourier basis is orthonormal, so the matrix is - an identity matrix of dimension n_basis*n_basis + We already know that a fourier basis is orthonormal when the period is + the same as the domain range so the matrix is an identity matrix of + dimension n_basis*n_basis. Else we compute the matrix. Returns: numpy.array: Gram Matrix of the fourier basis. """ - return np.identity(self.n_basis) + if self.domain_range[1] - self.domain_range[0] == self.period: + return np.identity(self.n_basis) + else: + return super.gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 7eb94b3f3553f3f522127638d9f21502e44e0431 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:58:09 +0100 Subject: [PATCH 149/624] fix gram matrix method in Fourier basis --- skfda/representation/basis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 71ec3f77e..aee9584be 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1492,10 +1492,10 @@ def gram_matrix(self): numpy.array: Gram Matrix of the fourier basis. """ - if self.domain_range[1] - self.domain_range[0] == self.period: + if self.domain_range[0][1] - self.domain_range[0][0] == self.period: return np.identity(self.n_basis) else: - return super.gram_matrix() + return super().gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 3f237ce938d341762f9d0deca43e2d5da1885380 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 20 Mar 2020 23:01:56 +0100 Subject: [PATCH 150/624] Adding warning. Number of simulations in oneway_anova may change --- skfda/inference/anova/anova_oneway.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 60d3e9ea2..2a1d17fb9 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -161,9 +161,9 @@ def v_asymptotic_stat(fd, weights, p=2): def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): n_groups = len(fd_grouped) sample_p_list = [fd.sample_points[0] for fd in fd_grouped] - print(sample_p_list) + # print(sample_p_list) assert n_groups > 0 - assert sample_p_list.count(sample_p_list[0]) == n_groups + # assert sample_p_list.count(sample_p_list[0]) == n_groups sample_points = fd_grouped[0].sample_points m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] From 48e75857cce08fbdb7f21cbbe0230c41429bb2de Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 20 Mar 2020 23:30:23 +0100 Subject: [PATCH 151/624] Adding warning. Number of simulations in oneway_anova may change --- skfda/inference/anova/anova_oneway.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 2a1d17fb9..474f6c8b2 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -159,11 +159,9 @@ def v_asymptotic_stat(fd, weights, p=2): def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): + assert len(fd_grouped) > 0 + n_groups = len(fd_grouped) - sample_p_list = [fd.sample_points[0] for fd in fd_grouped] - # print(sample_p_list) - assert n_groups > 0 - # assert sample_p_list.count(sample_p_list[0]) == n_groups sample_points = fd_grouped[0].sample_points m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] From 48c28d17a7f8c2f2f78ecc9908c49f1457648f30 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 21 Mar 2020 17:55:10 +0100 Subject: [PATCH 152/624] BSpline penalty computed in knots coordinates. --- skfda/representation/basis.py | 179 +++++++++++++++++++--------------- tests/test_basis.py | 36 ++++--- 2 files changed, 123 insertions(+), 92 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index d0a4b8069..01064115e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -128,7 +128,7 @@ def evaluate(self, eval_points, derivative=0): if derivative < 0: raise ValueError("derivative only takes non-negative values.") - eval_points = np.asarray(eval_points) + eval_points = np.atleast_1d(eval_points) if np.any(np.isnan(eval_points)): raise ValueError("The list of points where the function is " "evaluated can not contain nan values.") @@ -811,6 +811,18 @@ def knots(self): def knots(self, value): self._knots = value + def _evaluation_knots(self): + """ + Get the knots adding m knots to the boundary in order to allow a + discontinuous behaviour at the boundaries of the domain [RS05]_. + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + """ + return np.array([self.knots[0]] * (self.order - 1) + self.knots + + [self.knots[-1]] * (self.order - 1)) + def _evaluate(self, eval_points, derivative=0): """Compute the basis or its derivatives given a list of values. @@ -841,8 +853,8 @@ def _evaluate(self, eval_points, derivative=0): return np.zeros((self.n_basis, len(eval_points))) # Places m knots at the boundaries - knots = np.array([self.knots[0]] * (self.order - 1) + self.knots + - [self.knots[-1]] * (self.order - 1)) + knots = self._evaluation_knots() + # c is used the select which spline the function splev below computes c = np.zeros(len(knots)) @@ -880,15 +892,20 @@ def _penalty(self, lfd): return NotImplemented nonzero = np.flatnonzero(coefs) + + # All derivatives above the order of the spline are effectively + # zero + nonzero = nonzero[nonzero < self.order] + + if len(nonzero) == 0: + return np.zeros((self.n_basis, self.n_basis)) + + # We will only deal with one nonzero coefficient right now if len(nonzero) != 1: return NotImplemented derivative_degree = nonzero[0] - if derivative_degree >= self.order: - raise ValueError(f"Penalty matrix cannot be evaluated for " - f"derivative of order {derivative_degree} for" - f" B-splines of order {self.order}") if derivative_degree == self.order - 1: # The derivative of the bsplines are constant in the intervals # defined between knots @@ -900,86 +917,86 @@ def _penalty(self, lfd): # Integration of product of constants return constants.T @ np.diag(knots_intervals) @ constants - if np.all(np.diff(self.knots) != 0): - # Compute exactly using the piecewise polynomial - # representation of splines + # We only deal with the case without zero length intervals + # for now + if np.any(np.diff(self.knots) == 0): + return NotImplemented - # Places m knots at the boundaries - knots = np.array( - [self.knots[0]] * (self.order - 1) + self.knots - + [self.knots[-1]] * (self.order - 1)) - # c is used the select which spline the function - # PPoly.from_spline below computes - c = np.zeros(len(knots)) + # Compute exactly using the piecewise polynomial + # representation of splines - # Initialise empty list to store the piecewise polynomials - ppoly_lst = [] + # Places m knots at the boundaries + knots = self._evaluation_knots() + + # c is used the select which spline the function + # PPoly.from_spline below computes + c = np.zeros(len(knots)) - no_0_intervals = np.where(np.diff(knots) > 0)[0] + # Initialise empty list to store the piecewise polynomials + ppoly_lst = [] + + no_0_intervals = np.where(np.diff(knots) > 0)[0] + + # For each basis gets its piecewise polynomial representation + for i in range(self.n_basis): - # For each basis gets its piecewise polynomial representation + # Write a 1 in c in the position of the spline + # transformed in each iteration + c[i] = 1 + + # Gets the piecewise polynomial representation and gets + # only the positions for no zero length intervals + # This polynomial are defined relatively to the knots + # meaning that the column i corresponds to the ith knot. + # Let the ith knot be a + # Then f(x) = pp(x - a) + pp = PPoly.from_spline((knots, c, self.order - 1)) + pp_coefs = pp.c[:, no_0_intervals] + + # We have the coefficients for each interval in coordinates + # (x - a), so we will need to subtract a when computing the + # definite integral + coefs = pp_coefs.copy() + ppoly_lst.append(coefs) + c[i] = 0 + + # Now for each pair of basis computes the inner product after + # applying the linear differential operator + penalty_matrix = np.zeros((self.n_basis, self.n_basis)) + for interval in range(len(no_0_intervals)): for i in range(self.n_basis): - # write a 1 in c in the position of the spline - # transformed in each iteration - c[i] = 1 - # gets the piecewise polynomial representation and gets - # only the positions for no zero length intervals - # This polynomial are defined relatively to the knots - # meaning that the column i corresponds to the ith knot. - # Let the ith not be a - # Then f(x) = pp(x - a) - pp = (PPoly.from_spline( - (knots, c, self.order - 1)).c[:, no_0_intervals]) # We need the actual coefficients of f, not pp. So we - # just recursively calculate the new coefficients - coeffs = pp.copy() - for j in range(self.order - 1): - coeffs[j + 1:] += ( - (binom(self.order - j - 1, - range(1, self.order - j)) * - np.vstack([(-a) ** - np.array(range(1, self.order - j)) - for a in self.knots[:-1]])).T * - pp[j]) - ppoly_lst.append(coeffs) - c[i] = 0 - - # Now for each pair of basis computes the inner product after - # applying the linear differential operator - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - for interval in range(len(no_0_intervals)): - for i in range(self.n_basis): - poly_i = np.trim_zeros(ppoly_lst[i][:, + poly_i = np.trim_zeros(ppoly_lst[i][:, + interval], 'f') + if len(poly_i) <= derivative_degree: + # if the order of the polynomial is lesser or + # equal to the derivative the result of the + # integral will be 0 + continue + # indefinite integral + integral = polyint(_polypow(polyder( + poly_i, derivative_degree), 2)) + # definite integral + penalty_matrix[i, i] += np.diff(polyval( + integral, self.knots[interval: interval + 2] - self.knots[interval]))[0] + + for j in range(i + 1, self.n_basis): + poly_j = np.trim_zeros(ppoly_lst[j][:, interval], 'f') - if len(poly_i) <= derivative_degree: - # if the order of the polynomial is lesser or - # equal to the derivative the result of the - # integral will be 0 + if len(poly_j) <= derivative_degree: + # if the order of the polynomial is lesser + # or equal to the derivative the result of + # the integral will be 0 continue - # indefinite integral - integral = polyint(_polypow(polyder( - poly_i, derivative_degree), 2)) + # indefinite integral + integral = polyint( + polymul(polyder(poly_i, derivative_degree), + polyder(poly_j, derivative_degree))) # definite integral - penalty_matrix[i, i] += np.diff(polyval( - integral, self.knots[interval: interval + 2]))[0] - - for j in range(i + 1, self.n_basis): - poly_j = np.trim_zeros(ppoly_lst[j][:, - interval], 'f') - if len(poly_j) <= derivative_degree: - # if the order of the polynomial is lesser - # or equal to the derivative the result of - # the integral will be 0 - continue - # indefinite integral - integral = polyint( - polymul(polyder(poly_i, derivative_degree), - polyder(poly_j, derivative_degree))) - # definite integral - penalty_matrix[i, j] += np.diff(polyval( - integral, self.knots[interval: interval + 2]) - )[0] - penalty_matrix[j, i] = penalty_matrix[i, j] - return penalty_matrix + penalty_matrix[i, j] += np.diff(polyval( + integral, self.knots[interval: interval + 2] - self.knots[interval]) + )[0] + penalty_matrix[j, i] = penalty_matrix[i, j] + return penalty_matrix def rescale(self, domain_range=None): r"""Return a copy of the basis with a new domain range, with the @@ -1263,9 +1280,9 @@ def _penalty_orthonormal(self, weights): phases = (np.arange(1, (self.n_basis - 1) // 2 + 1) * 2 * np.pi / self.period) - seq_derivs = np.arange(len(weights)) - coefs_no_sign = np.power.outer(phases, seq_derivs) + # Compute increasing powers + coefs_no_sign = np.vander(phases, len(weights), increasing=True) coefs_no_sign *= weights diff --git a/tests/test_basis.py b/tests/test_basis.py index ff7830941..6c6eae607 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -9,20 +9,22 @@ class TestBasis(unittest.TestCase): # def setUp(self): could be defined for set up before any test - def _test_penalty(self, basis, lfd, result=None): + def _test_penalty(self, basis, lfd, atol=0, result=None): - penalty = basis.penalty(lfd).round(2) - numerical_penalty = basis._numerical_penalty(lfd).round(2) + penalty = basis.penalty(lfd) + numerical_penalty = basis._numerical_penalty(lfd) np.testing.assert_allclose( penalty, - numerical_penalty + numerical_penalty, + atol=atol ) if result is not None: np.testing.assert_allclose( penalty, - result + result, + atol=atol ) def test_from_data_cholesky(self): @@ -141,14 +143,15 @@ def test_fourier_penalty(self): [0., 0., 0., 24936.73, 0.], [0., 0., 0., 0., 24936.73]]) - self._test_penalty(basis, lfd=2, result=res) + # Those comparisons require atol as there are zeros involved + self._test_penalty(basis, lfd=2, atol=0.01, result=res) basis = Fourier(n_basis=9, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3]) - self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) - self._test_penalty(basis, lfd=0) - self._test_penalty(basis, lfd=1) - self._test_penalty(basis, lfd=3) + self._test_penalty(basis, lfd=[1, 2, 3], atol=1e-7) + self._test_penalty(basis, lfd=[2, 3, 0.1, 1], atol=1e-7) + self._test_penalty(basis, lfd=0, atol=1e-7) + self._test_penalty(basis, lfd=1, atol=1e-7) + self._test_penalty(basis, lfd=3, atol=1e-7) def test_bspline_penalty(self): basis = BSpline(n_basis=5) @@ -161,6 +164,17 @@ def test_bspline_penalty(self): self._test_penalty(basis, lfd=2, result=res) + basis = BSpline(n_basis=9, domain_range=(1, 5)) + self._test_penalty(basis, lfd=[1, 2, 3]) + self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) + self._test_penalty(basis, lfd=0) + self._test_penalty(basis, lfd=1) + self._test_penalty(basis, lfd=3) + self._test_penalty(basis, lfd=4) + + basis = BSpline(n_basis=16, order=8) + self._test_penalty(basis, lfd=0, atol=1e-7) + def test_basis_product_generic(self): monomial = Monomial(n_basis=5) fourier = Fourier(n_basis=3) From c902482827ad568c6d7d333b227deb662ae6e00c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 21 Mar 2020 18:09:45 +0100 Subject: [PATCH 153/624] Prevent copy --- skfda/representation/basis.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 01064115e..7e2294ad9 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -956,8 +956,7 @@ def _penalty(self, lfd): # We have the coefficients for each interval in coordinates # (x - a), so we will need to subtract a when computing the # definite integral - coefs = pp_coefs.copy() - ppoly_lst.append(coefs) + ppoly_lst.append(pp_coefs) c[i] = 0 # Now for each pair of basis computes the inner product after From 6ad13de270c21cb498832260a2375c3e5e5fcac0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 154/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 620abf4047c8f79d5d349249c83f7123b1cd6053 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 155/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From d6d19fa5ecfa70b3261abe8bc016887296fcb18b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 20 Mar 2020 23:30:23 +0100 Subject: [PATCH 156/624] Editing assertion over the number of samples passed in oneway_anova. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 2a1d17fb9..1d0861db4 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -159,11 +159,9 @@ def v_asymptotic_stat(fd, weights, p=2): def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): + assert len(fd_grouped) > 0 + n_groups = len(fd_grouped) - sample_p_list = [fd.sample_points[0] for fd in fd_grouped] - # print(sample_p_list) - assert n_groups > 0 - # assert sample_p_list.count(sample_p_list[0]) == n_groups sample_points = fd_grouped[0].sample_points m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] @@ -267,8 +265,8 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): Analysis*, 47:111-112, 02 2004 """ - if len(args) < 1: - raise ValueError("At least one sample must be passed as parameter.") + if len(args) < 2: + raise ValueError("At least two samples must be passed as parameter.") if not all(isinstance(fd, FData) for fd in args): raise ValueError("Argument type must inherit FData.") if n_sim < 1: From 59257223f0e07abf0d098b15daf9bbfaef70ef95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 22 Mar 2020 20:29:03 +0100 Subject: [PATCH 157/624] Removing unused import MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- tests/test_oneway_anova.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index 6235782f7..0f34fe7c9 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -2,8 +2,8 @@ import numpy as np from skfda.representation import FDataGrid -from skfda.datasets import make_gaussian_process, fetch_gait -from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ +from skfda.datasets import fetch_gait +from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ v_sample_stat From 706e2f040a9b31beea63253d005899a7c0000bd0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 22 Mar 2020 22:36:43 +0100 Subject: [PATCH 158/624] Updating synthetic example MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway_synthetic.py | 78 +++++++++++++++++-------------- 1 file changed, 42 insertions(+), 36 deletions(-) diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 3fd902f62..4c5583c61 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -34,7 +34,6 @@ import numpy as np -import skfda from skfda.representation import FDataGrid from skfda.inference.anova import oneway_anova from skfda.datasets import make_gaussian_process @@ -43,30 +42,20 @@ # First, the means for the future processes are drawn. n_samples = 10 -n_features = 50 +n_features = 100 n_groups = 3 +start = 0 +stop = 1 -t = np.linspace(0, np.pi, n_features) +t = np.linspace(start, stop, n_features) -m1 = np.sin(t) -m2 = 1.1 * np.sin(t) -m3 = 1.2 * np.sin(t) +m1 = t * (1 - t) ** 5 +m2 = t ** 2 * (1 - t) ** 4 +m3 = t ** 3 * (1 - t) ** 3 _ = FDataGrid([m1, m2, m3], dataset_label="Means to be used in the simulation").plot() - -############################################################################### -# Now, a function to simulate processes as described above is implemented, -# to make code clearer. - -def make_process_w_noise(mean, cov, t, random_state): - return FDataGrid([mean for _ in range(n_samples)], sample_points=t) \ - + make_gaussian_process(n_samples, n_features=mean.shape[0], - cov=cov, random_state=random_state, - start=t[0], stop=t[-1]) - - ################################################################################ # A total of `n_samples` trajectories will be created for each mean, so a array # of labels is created to identify them when plotting. @@ -83,11 +72,16 @@ def make_process_w_noise(mean, cov, t, random_state): sigma = 0.01 cov = np.identity(n_features) * sigma -fd1 = make_process_w_noise(m1, cov, t, random_state=1) -fd2 = make_process_w_noise(m2, cov, t, random_state=2) -fd3 = make_process_w_noise(m3, cov, t, random_state=3) - -stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) +fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, + n_features=n_features, random_state=1, start=start, + stop=stop) +fd2 = make_gaussian_process(n_samples, mean=m2, cov=cov, + n_features=n_features, random_state=2, start=start, + stop=stop) +fd3 = make_gaussian_process(n_samples, mean=m3, cov=cov, + n_features=n_features, random_state=3, start=start, + stop=stop) +stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) print("Statistic: {:.3f}".format(stat)) print("p-value: {:.3f}".format(p_val)) @@ -108,16 +102,22 @@ def make_process_w_noise(mean, cov, t, random_state): # refuse). ################################################################################ -# Plot for :math:`\sigma = 0.1`: +# Plot for :math:`\sigma = 1`: -sigma = 0.1 +sigma = 1 cov = np.identity(n_features) * sigma -fd1 = make_process_w_noise(m1, cov, t, random_state=1) -fd2 = make_process_w_noise(m2, cov, t, random_state=2) -fd3 = make_process_w_noise(m3, cov, t, random_state=3) +fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, + n_features=n_features, random_state=1, start=t[0], + stop=t[-1]) +fd2 = make_gaussian_process(n_samples, mean=m2, cov=cov, + n_features=n_features, random_state=2, start=t[0], + stop=t[-1]) +fd3 = make_gaussian_process(n_samples, mean=m3, cov=cov, + n_features=n_features, random_state=3, start=t[0], + stop=t[-1]) -_, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) +_, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( @@ -126,16 +126,22 @@ def make_process_w_noise(mean, cov, t, random_state): fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() ################################################################################ -# Plot for :math:`\sigma = 1`: +# Plot for :math:`\sigma = 10`: -sigma = 1 +sigma = 10 cov = np.identity(n_features) * sigma -fd1 = make_process_w_noise(m1, cov, t, random_state=1) -fd2 = make_process_w_noise(m2, cov, t, random_state=2) -fd3 = make_process_w_noise(m3, cov, t, random_state=3) - -_, p_val = oneway_anova(fd1, fd2, fd3, random_state=1) +fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, + n_features=n_features, random_state=1, start=t[0], + stop=t[-1]) +fd2 = make_gaussian_process(n_samples, mean=m2, cov=cov, + n_features=n_features, random_state=2, start=t[0], + stop=t[-1]) +fd3 = make_gaussian_process(n_samples, mean=m3, cov=cov, + n_features=n_features, random_state=3, start=t[0], + stop=t[-1]) + +_, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( From f79c8d5616d03ae41deb347a31737f2b5c1f5481 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 22 Mar 2020 23:06:00 +0100 Subject: [PATCH 159/624] Checks over sample points in anova. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 8981f9100..60ac27c49 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -161,9 +161,17 @@ def v_asymptotic_stat(fd, weights, p=2): def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): - assert len(fd_grouped) > 0 n_groups = len(fd_grouped) + assert n_groups > 0 + + # Creating list with all the sample points + list_sample = [fd.sample_points[0].tolist() for fd in fd_grouped] + # Checking that the all the entries in the list are the same + if not list_sample.count(list_sample[0]) == len(list_sample): + raise ValueError("All FDataGrid passed must have the same sample " + "points.") + sample_points = fd_grouped[0].sample_points m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] @@ -280,6 +288,13 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): raise NotImplementedError("Not implemented for FDataBasis objects.") fd_groups = args + # Creating list with all the sample points + list_sample = [fd.sample_points[0].tolist() for fd in fd_groups] + # Checking that the all the entries in the list are the same + if not list_sample.count(list_sample[0]) == len(list_sample): + raise ValueError("All FDataGrid passed must have the same sample " + "points.") + fd_means = fd_groups[0].mean() for fd in fd_groups[1:]: fd_means = fd_means.concatenate(fd.mean()) From 4fb0d589437d061c4ad723cfd503b9e24b663a0a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 23 Mar 2020 20:52:19 +0100 Subject: [PATCH 160/624] Fixed error in docstring. --- skfda/misc/_lfd.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 5976ea7c0..8855bbba4 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -193,7 +193,8 @@ def __eq__(self, other): def constant_weights(self): """ - Return the weights of the weights if they are constant basis. + Return the scalar weights of the linear differential operator if they + are constant basis. Otherwise, return None. This function is mostly useful for basis which want to override From d9c4d876bf4d2f120f34d294aee4aea42f7d0974 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 161/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 021c5b99f7bac59b181fa9821f0b615a762cc197 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 162/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 8f7e646b3b10eaa36de1ac459e7247e06720a316 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Wed, 25 Mar 2020 03:53:10 +0100 Subject: [PATCH 163/624] Fix docstring in make_gaussian_process --- skfda/datasets/_samples_generators.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index ac24b104d..69c7260d6 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -18,7 +18,7 @@ def make_gaussian_process(n_samples: int = 100, n_features: int = 100, *, Args: n_samples: The total number of trajectories. - n_features: The total number of trajectories. + n_features: The total number of features (points of evaluation). start: Starting point of the trajectories. stop: Ending point of the trajectories. mean: The mean function of the process. Can be a callable accepting From b330949a7236dc85dcfb94ce9fed780286f402f4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 26 Mar 2020 00:51:52 +0100 Subject: [PATCH 164/624] Refactor LinearScalarRegression --- skfda/ml/regression/linear_model.py | 136 ++++++++++++++++------------ tests/test_regression.py | 64 ++++++------- 2 files changed, 107 insertions(+), 93 deletions(-) diff --git a/skfda/ml/regression/linear_model.py b/skfda/ml/regression/linear_model.py index 3e16562d0..9f892e9a2 100644 --- a/skfda/ml/regression/linear_model.py +++ b/skfda/ml/regression/linear_model.py @@ -9,90 +9,112 @@ class LinearScalarRegression(BaseEstimator, RegressorMixin): - def __init__(self, beta_basis): - self.beta_basis = beta_basis + def __init__(self, coef_basis): + self.coef_basis = coef_basis def fit(self, X, y=None, sample_weight=None): - y, X, weights = self._argcheck(y, X, sample_weight) + X, y, sample_weight = self._argcheck_X_y(X, y, sample_weight) - nbeta = len(self.beta_basis) - n_samples = X[0].n_samples + # X is a list of covariates + n_covariates = len(X) - y = np.asarray(y).reshape((n_samples, 1)) + inner_products = [None] * n_covariates - for j in range(nbeta): - xcoef = X[j].coefficients - inner_basis_x_beta_j = X[j].basis.inner_product(self.beta_basis[j]) - inner_x_beta = (xcoef @ inner_basis_x_beta_j - if j == 0 - else np.concatenate((inner_x_beta, - xcoef @ inner_basis_x_beta_j), - axis=1)) + for i, (x, w_basis) in enumerate(zip(X, self.coef_basis)): + xcoef = x.coefficients + inner_basis = x.basis.inner_product(w_basis) + inner_products[i] = xcoef @ inner_basis - if any(w != 1 for w in weights): - inner_x_beta = inner_x_beta * np.sqrt(weights) - y = y * np.sqrt(weights) + # This is C @ J + inner_products = np.concatenate(inner_products, axis=1) - gram_inner_x_beta = inner_x_beta.T @ inner_x_beta - inner_x_beta_y = inner_x_beta.T @ y + if any(w != 1 for w in sample_weight): + inner_products = inner_products * np.sqrt(sample_weight) + y = y * np.sqrt(sample_weight) - gram_inner_x_beta_inv = np.linalg.inv(gram_inner_x_beta) - betacoefs = gram_inner_x_beta_inv @ inner_x_beta_y + gram_inner_x_coef = inner_products.T @ inner_products + inner_x_coef_y = inner_products.T @ y + coef_basiscoefs = np.linalg.solve(gram_inner_x_coef, inner_x_coef_y) + + # Express the coefficients in functional form + coefs = [None] * n_covariates idx = 0 - for j in range(0, nbeta): - self.beta_basis[j] = FDataBasis( - self.beta_basis[j], - betacoefs[idx:idx + self.beta_basis[j].n_basis].T) - idx = idx + self.beta_basis[j].n_basis + for i, basis in enumerate(self.coef_basis): + coefs[i] = FDataBasis( + basis, + coef_basiscoefs[idx:idx + basis.n_basis].T) + idx = idx + basis.n_basis + + self.coef_ = coefs + self._target_ndim = y.ndim - self.beta_ = self.beta_basis return self def predict(self, X): - check_is_fitted(self, "beta_") - return [sum(self.beta[i].inner_product(X[i][j])[0, 0] for i in - range(len(self.beta))) for j in range(X[0].n_samples)] + check_is_fitted(self) + X = self._argcheck_X(X) + + inner_products = np.sum([covariate.inner_product( + x) for covariate, x in zip(self.coef_, X)], axis=0) + + if self._target_ndim == 1: + inner_products = inner_products.ravel() + + return inner_products + + def _argcheck_X(self, X): + if isinstance(X, FData): + X = [X] + + if all(not isinstance(i, FData) for i in X): + raise ValueError("All the covariates are scalar.") + + domain_ranges = [x.domain_range for x in X if isinstance(x, FData)] + domain_range = domain_ranges[0] + + for i, x in enumerate(X): + if not isinstance(x, FData): + # TODO: Support multivariate data + coefs = np.asarray(x) + X[i] = FDataBasis(Constant(domain_range), coefs) - def _argcheck(self, y, x, weights=None): + return X + + def _argcheck_X_y(self, X, y, sample_weight=None): """Do some checks to types and shapes""" - if all(not isinstance(i, FData) for i in x): - raise ValueError("All the dependent variable are scalar.") + + # TODO: Add support for Dataframes + + X = self._argcheck_X(X) + + y = np.asarray(y) + if any(isinstance(i, FData) for i in y): raise ValueError( - "Some of the independent variables are not scalar") - - ylen = len(y) - xlen = len(x) - blen = len(self.beta_basis) - domain_range = ([i for i in x if isinstance(i, FData)][0] - .domain_range) + "Some of the response variables are not scalar") - if blen != xlen: + if len(self.coef_basis) != len(X): raise ValueError("Number of regression coefficients does" " not match number of independent variables.") - for j in range(xlen): - if isinstance(x[j], list): - xjcoefs = np.array(x[j]).reshape((-1, 1)) - x[j] = FDataBasis(Constant(domain_range), xjcoefs) - - if any(ylen != xfd.n_samples for xfd in x): + if any(len(y) != len(x) for x in X): raise ValueError("The number of samples on independent and " "dependent variables should be the same") - if any(not isinstance(b, Basis) for b in self.beta_basis): - raise ValueError("Betas should be a list of Basis.") + if any(not isinstance(b, Basis) for b in self.coef_basis): + raise ValueError("coefs should be a list of Basis.") - if weights is None: - weights = [1 for _ in range(ylen)] + if sample_weight is None: + sample_weight = np.ones(len(y)) - if len(weights) != ylen: - raise ValueError("The number of weights should be equal to the " - "independent samples.") + if len(sample_weight) != len(y): + raise ValueError("The number of sample weights should be equal to" + "the number of samples.") - if np.any(np.array(weights) < 0): - raise ValueError("The weights should be non negative values") + if np.any(np.array(sample_weight) < 0): + raise ValueError( + "The sample weights should be non negative values") - return y, x, weights + return X, y, sample_weight diff --git a/tests/test_regression.py b/tests/test_regression.py index 3531df513..eb99f0d10 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,14 +1,14 @@ -import unittest - -import numpy as np from skfda.ml.regression import LinearScalarRegression from skfda.representation.basis import (FDataBasis, Constant, Monomial, Fourier, BSpline) +import unittest + +import numpy as np class TestLinearScalarRegression(unittest.TestCase): - def test_regression_fit(self): + def test_regression_single_explanatory(self): x_basis = Monomial(n_basis=7) x_fd = FDataBasis(x_basis, np.identity(7)) @@ -24,29 +24,12 @@ def test_regression_fit(self): 0.11384314859153018] scalar = LinearScalarRegression([beta_basis]) - scalar.fit([x_fd], y) - np.testing.assert_array_almost_equal(scalar.beta_[0].coefficients, - beta_fd.coefficients) + scalar.fit(x_fd, y) + np.testing.assert_allclose(scalar.coef_[0].coefficients, + beta_fd.coefficients) - def test_regression_predict_single_explanatory(self): - - x_basis = Monomial(n_basis=7) - x_fd = FDataBasis(x_basis, np.identity(7)) - - beta_basis = Fourier(n_basis=5) - beta_fd = FDataBasis(beta_basis, [1, 1, 1, 1, 1]) - y = [1.0000684777229512, - 0.1623672257830915, - 0.08521053851548224, - 0.08514200869281137, - 0.09529138749665378, - 0.10549625973303875, - 0.11384314859153018] - - scalar = LinearScalarRegression([beta_basis]) - scalar.fit([x_fd], y) - np.testing.assert_array_almost_equal(scalar.beta_[0].coefficients, - beta_fd.coefficients) + y_pred = scalar.predict(x_fd) + np.testing.assert_allclose(y_pred, y) def test_regression_predict_multiple_explanatory(self): y = [1, 2, 3, 4, 5, 6, 7] @@ -61,7 +44,7 @@ def test_regression_predict_multiple_explanatory(self): scalar.fit([x0, x1], y) - betas = scalar.beta_ + betas = scalar.coef_ np.testing.assert_array_almost_equal(betas[0].coefficients.round(4), np.array([[32.6518]])) @@ -82,7 +65,8 @@ def test_error_X_not_FData(self): scalar = LinearScalarRegression([Fourier(n_basis=5)]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) def test_error_y_is_FData(self): """Tests that none of the explained variables is an FData object @@ -92,7 +76,8 @@ def test_error_y_is_FData(self): scalar = LinearScalarRegression([Fourier(n_basis=5)]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) def test_error_X_beta_len_distinct(self): """ Test that the number of beta bases and explanatory variables @@ -103,10 +88,12 @@ def test_error_X_beta_len_distinct(self): beta = Fourier(n_basis=5) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd, x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd, x_fd], y) scalar = LinearScalarRegression([beta, beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) def test_error_y_X_samples_different(self): """ Test that the number of response samples and explanatory samples @@ -117,14 +104,16 @@ def test_error_y_X_samples_different(self): beta = Fourier(n_basis=5) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) x_fd = FDataBasis(Monomial(n_basis=8), np.identity(8)) y = [1 for _ in range(7)] beta = Fourier(n_basis=5) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) def test_error_beta_not_basis(self): """ Test that all beta are Basis objects. """ @@ -134,7 +123,8 @@ def test_error_beta_not_basis(self): beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y) def test_error_weights_lenght(self): """ Test that the number of weights is equal to the @@ -146,7 +136,8 @@ def test_error_weights_lenght(self): beta = Monomial(n_basis=7) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y, weights) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y, weights) def test_error_weights_negative(self): """ Test that none of the weights are negative. """ @@ -157,7 +148,8 @@ def test_error_weights_negative(self): beta = Monomial(n_basis=7) scalar = LinearScalarRegression([beta]) - np.testing.assert_raises(ValueError, scalar.fit, [x_fd], y, weights) + with np.testing.assert_raises(ValueError): + scalar.fit([x_fd], y, weights) if __name__ == '__main__': From 6b138665fae6d4eadb556ff0c31118c49b70c74f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 26 Mar 2020 03:12:54 +0100 Subject: [PATCH 165/624] Add `fit_intercept` parameter. --- skfda/ml/regression/linear_model.py | 27 ++++++++++++---- tests/test_regression.py | 50 +++++++++++++++-------------- 2 files changed, 47 insertions(+), 30 deletions(-) diff --git a/skfda/ml/regression/linear_model.py b/skfda/ml/regression/linear_model.py index 9f892e9a2..1077f1401 100644 --- a/skfda/ml/regression/linear_model.py +++ b/skfda/ml/regression/linear_model.py @@ -9,19 +9,26 @@ class LinearScalarRegression(BaseEstimator, RegressorMixin): - def __init__(self, coef_basis): + def __init__(self, *, coef_basis, fit_intercept=True): self.coef_basis = coef_basis + self.fit_intercept = fit_intercept def fit(self, X, y=None, sample_weight=None): X, y, sample_weight = self._argcheck_X_y(X, y, sample_weight) + coef_basis = self.coef_basis + + if self.fit_intercept: + X = [FDataBasis(Constant(X[0].domain_range), + np.ones((len(y), 1)))] + X + coef_basis = [Constant()] + coef_basis # X is a list of covariates n_covariates = len(X) inner_products = [None] * n_covariates - for i, (x, w_basis) in enumerate(zip(X, self.coef_basis)): + for i, (x, w_basis) in enumerate(zip(X, coef_basis)): xcoef = x.coefficients inner_basis = x.basis.inner_product(w_basis) inner_products[i] = xcoef @ inner_basis @@ -41,12 +48,18 @@ def fit(self, X, y=None, sample_weight=None): # Express the coefficients in functional form coefs = [None] * n_covariates idx = 0 - for i, basis in enumerate(self.coef_basis): + for i, basis in enumerate(coef_basis): coefs[i] = FDataBasis( basis, coef_basiscoefs[idx:idx + basis.n_basis].T) idx = idx + basis.n_basis + if self.fit_intercept: + self.intercept_ = coefs[0].coefficients[0] + coefs = coefs[1:] + else: + self.intercept_ = 0.0 + self.coef_ = coefs self._target_ndim = y.ndim @@ -56,13 +69,15 @@ def predict(self, X): check_is_fitted(self) X = self._argcheck_X(X) - inner_products = np.sum([covariate.inner_product( + result = np.sum([covariate.inner_product( x) for covariate, x in zip(self.coef_, X)], axis=0) + result += self.intercept_ + if self._target_ndim == 1: - inner_products = inner_products.ravel() + result = result.ravel() - return inner_products + return result def _argcheck_X(self, X): if isinstance(X, FData): diff --git a/tests/test_regression.py b/tests/test_regression.py index eb99f0d10..229a41705 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -23,10 +23,12 @@ def test_regression_single_explanatory(self): 0.10549625973303875, 0.11384314859153018] - scalar = LinearScalarRegression([beta_basis]) + scalar = LinearScalarRegression(coef_basis=[beta_basis]) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) + np.testing.assert_allclose(scalar.intercept_, + 0.0, atol=1e-6) y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) @@ -34,27 +36,27 @@ def test_regression_single_explanatory(self): def test_regression_predict_multiple_explanatory(self): y = [1, 2, 3, 4, 5, 6, 7] - x0 = FDataBasis(Constant(domain_range=(0, 1)), np.ones((7, 1))) - x1 = FDataBasis(Monomial(n_basis=7), np.identity(7)) + X = FDataBasis(Monomial(n_basis=7), np.identity(7)) - beta0 = Constant(domain_range=(0, 1)) beta1 = BSpline(domain_range=(0, 1), n_basis=5) - scalar = LinearScalarRegression([beta0, beta1]) + scalar = LinearScalarRegression(coef_basis=[beta1]) - scalar.fit([x0, x1], y) + scalar.fit(X, y) - betas = scalar.coef_ + np.testing.assert_allclose(scalar.intercept_.round(4), + np.array([32.6518])) - np.testing.assert_array_almost_equal(betas[0].coefficients.round(4), - np.array([[32.6518]])) + np.testing.assert_allclose( + scalar.coef_[0].coefficients.round(4), + np.array([[-28.6443, + 80.3996, + -188.587, + 236.5832, + -481.3449]])) - np.testing.assert_array_almost_equal(betas[1].coefficients.round(4), - np.array([[-28.6443, - 80.3996, - -188.587, - 236.5832, - -481.3449]])) + y_pred = scalar.predict(X) + np.testing.assert_allclose(y_pred, y, atol=0.01) def test_error_X_not_FData(self): """Tests that at least one of the explanatory variables @@ -63,7 +65,7 @@ def test_error_X_not_FData(self): x_fd = np.identity(7) y = np.zeros(7) - scalar = LinearScalarRegression([Fourier(n_basis=5)]) + scalar = LinearScalarRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -74,7 +76,7 @@ def test_error_y_is_FData(self): x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) y = list(FDataBasis(Monomial(n_basis=7), np.identity(7))) - scalar = LinearScalarRegression([Fourier(n_basis=5)]) + scalar = LinearScalarRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -87,11 +89,11 @@ def test_error_X_beta_len_distinct(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd, x_fd], y) - scalar = LinearScalarRegression([beta, beta]) + scalar = LinearScalarRegression(coef_basis=[beta, beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -103,7 +105,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(8)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -111,7 +113,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -122,7 +124,7 @@ def test_error_beta_not_basis(self): y = [1 for _ in range(7)] beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -135,7 +137,7 @@ def test_error_weights_lenght(self): weights = [1 for _ in range(8)] beta = Monomial(n_basis=7) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) @@ -147,7 +149,7 @@ def test_error_weights_negative(self): weights = [-1 for _ in range(7)] beta = Monomial(n_basis=7) - scalar = LinearScalarRegression([beta]) + scalar = LinearScalarRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) From 59c30328bf3f8adbf16b04dca48317eeeceafa4f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 26 Mar 2020 03:20:17 +0100 Subject: [PATCH 166/624] Add no `fit_intercept` test case. --- tests/test_regression.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/test_regression.py b/tests/test_regression.py index 229a41705..e7f27c4b8 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -33,6 +33,17 @@ def test_regression_single_explanatory(self): y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) + scalar = LinearScalarRegression(coef_basis=[beta_basis], + fit_intercept=False) + scalar.fit(x_fd, y) + np.testing.assert_allclose(scalar.coef_[0].coefficients, + beta_fd.coefficients) + np.testing.assert_equal(scalar.intercept_, + 0.0) + + y_pred = scalar.predict(x_fd) + np.testing.assert_allclose(y_pred, y) + def test_regression_predict_multiple_explanatory(self): y = [1, 2, 3, 4, 5, 6, 7] From 8cc4dc1ad727b7d81c3c5504c4a56e58e8f85f39 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 28 Mar 2020 00:54:51 +0100 Subject: [PATCH 167/624] Rename linear module --- skfda/ml/regression/__init__.py | 2 +- skfda/ml/regression/{linear_model.py => linear.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename skfda/ml/regression/{linear_model.py => linear.py} (100%) diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index c2a67127a..8371124e6 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,4 +1,4 @@ from ..._neighbors import KNeighborsRegressor, RadiusNeighborsRegressor -from .linear_model import LinearScalarRegression +from skfda.ml.regression.linear import LinearScalarRegression diff --git a/skfda/ml/regression/linear_model.py b/skfda/ml/regression/linear.py similarity index 100% rename from skfda/ml/regression/linear_model.py rename to skfda/ml/regression/linear.py From 300bb5121861cc9face4f25917b6ea0ae084e99e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 28 Mar 2020 19:31:33 +0100 Subject: [PATCH 168/624] Improve support for mixed data. --- skfda/ml/regression/linear.py | 87 ++++++++++++++++++++++------------- tests/test_regression.py | 38 ++++++++++++++- 2 files changed, 92 insertions(+), 33 deletions(-) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 1077f1401..3e2882ae6 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -1,3 +1,4 @@ +from skfda.misc._math import inner_product from skfda.representation import FData from skfda.representation.basis import FDataBasis, Constant, Basis @@ -9,19 +10,18 @@ class LinearScalarRegression(BaseEstimator, RegressorMixin): - def __init__(self, *, coef_basis, fit_intercept=True): + def __init__(self, *, coef_basis=None, fit_intercept=True): self.coef_basis = coef_basis self.fit_intercept = fit_intercept def fit(self, X, y=None, sample_weight=None): - X, y, sample_weight = self._argcheck_X_y(X, y, sample_weight) - coef_basis = self.coef_basis + X, y, sample_weight, coef_basis = self._argcheck_X_y( + X, y, sample_weight, self.coef_basis) if self.fit_intercept: - X = [FDataBasis(Constant(X[0].domain_range), - np.ones((len(y), 1)))] + X - coef_basis = [Constant()] + coef_basis + X = [np.ones((len(y), 1))] + X + coef_basis = [None] + coef_basis # X is a list of covariates n_covariates = len(X) @@ -29,9 +29,18 @@ def fit(self, X, y=None, sample_weight=None): inner_products = [None] * n_covariates for i, (x, w_basis) in enumerate(zip(X, coef_basis)): - xcoef = x.coefficients - inner_basis = x.basis.inner_product(w_basis) - inner_products[i] = xcoef @ inner_basis + if isinstance(x, FDataBasis): + if w_basis is None: + w_basis = x.basis + xcoef = x.coefficients + inner_basis = x.basis.inner_product(w_basis) + inner = xcoef @ inner_basis + else: + if w_basis is not None: + raise ValueError("Multivariate data coefficients " + "should not have a basis") + inner = np.atleast_2d(x) + inner_products[i] = inner # This is C @ J inner_products = np.concatenate(inner_products, axis=1) @@ -48,14 +57,24 @@ def fit(self, X, y=None, sample_weight=None): # Express the coefficients in functional form coefs = [None] * n_covariates idx = 0 - for i, basis in enumerate(coef_basis): - coefs[i] = FDataBasis( - basis, - coef_basiscoefs[idx:idx + basis.n_basis].T) - idx = idx + basis.n_basis + for i, (x, basis) in enumerate(zip(X, coef_basis)): + if isinstance(x, FDataBasis): + if basis is None: + basis = x.basis + + # Functional coefs + used_coefs = basis.n_basis + coefs[i] = FDataBasis( + basis, + coef_basiscoefs[idx:idx + used_coefs].T) + else: + # Multivariate coefs + used_coefs = x.shape[1] + coefs[i] = coef_basiscoefs[idx:idx + used_coefs] + idx = idx + used_coefs if self.fit_intercept: - self.intercept_ = coefs[0].coefficients[0] + self.intercept_ = coefs[0] coefs = coefs[1:] else: self.intercept_ = 0.0 @@ -69,8 +88,8 @@ def predict(self, X): check_is_fitted(self) X = self._argcheck_X(X) - result = np.sum([covariate.inner_product( - x) for covariate, x in zip(self.coef_, X)], axis=0) + result = np.sum([self._inner_product_mixed( + coef, x) for coef, x in zip(self.coef_, X)], axis=0) result += self.intercept_ @@ -79,25 +98,24 @@ def predict(self, X): return result + def _inner_product_mixed(self, x, y): + inner_product = getattr(x, "inner_product", None) + + if inner_product is None: + return y @ x + else: + return inner_product(y) + def _argcheck_X(self, X): - if isinstance(X, FData): + if isinstance(X, FData) or isinstance(X, np.ndarray): X = [X] if all(not isinstance(i, FData) for i in X): raise ValueError("All the covariates are scalar.") - domain_ranges = [x.domain_range for x in X if isinstance(x, FData)] - domain_range = domain_ranges[0] - - for i, x in enumerate(X): - if not isinstance(x, FData): - # TODO: Support multivariate data - coefs = np.asarray(x) - X[i] = FDataBasis(Constant(domain_range), coefs) - return X - def _argcheck_X_y(self, X, y, sample_weight=None): + def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): """Do some checks to types and shapes""" # TODO: Add support for Dataframes @@ -106,11 +124,15 @@ def _argcheck_X_y(self, X, y, sample_weight=None): y = np.asarray(y) - if any(isinstance(i, FData) for i in y): + if (np.issubdtype(y.dtype, np.object_) + and any(isinstance(i, FData) for i in y)): raise ValueError( "Some of the response variables are not scalar") - if len(self.coef_basis) != len(X): + if coef_basis is None: + coef_basis = [None] * len(X) + + if len(coef_basis) != len(X): raise ValueError("Number of regression coefficients does" " not match number of independent variables.") @@ -118,7 +140,8 @@ def _argcheck_X_y(self, X, y, sample_weight=None): raise ValueError("The number of samples on independent and " "dependent variables should be the same") - if any(not isinstance(b, Basis) for b in self.coef_basis): + if any(b is not None and not isinstance(b, Basis) + for b in coef_basis): raise ValueError("coefs should be a list of Basis.") if sample_weight is None: @@ -132,4 +155,4 @@ def _argcheck_X_y(self, X, y, sample_weight=None): raise ValueError( "The sample weights should be non negative values") - return X, y, sample_weight + return X, y, sample_weight, coef_basis diff --git a/tests/test_regression.py b/tests/test_regression.py index e7f27c4b8..c5cf8a39a 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -44,7 +44,7 @@ def test_regression_single_explanatory(self): y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) - def test_regression_predict_multiple_explanatory(self): + def test_regression_multiple_explanatory(self): y = [1, 2, 3, 4, 5, 6, 7] X = FDataBasis(Monomial(n_basis=7), np.identity(7)) @@ -69,6 +69,42 @@ def test_regression_predict_multiple_explanatory(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_regression_mixed(self): + + multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5]]) + + X = [multivariate, + FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1]])] + + # y = 2 + sum([3, 1] * array) + int(3 * function) + intercept = 2 + coefs_multivariate = np.array([3, 1]) + coefs_functions = FDataBasis( + Monomial(n_basis=3), [[3, 0, 0]]) + y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) + y_sum = multivariate @ coefs_multivariate + y = 2 + y_sum + y_integral + + scalar = LinearScalarRegression() + scalar.fit(X, y) + + np.testing.assert_allclose(scalar.intercept_, + intercept, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[0], + coefs_multivariate, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[1].coefficients, + coefs_functions.coefficients, atol=0.01) + + y_pred = scalar.predict(X) + np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_error_X_not_FData(self): """Tests that at least one of the explanatory variables is an FData object. """ From a68e9e1c9e181f7203ed7ccbbd1d1b99ccf504f3 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 169/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 67cee71387bcd5b90363fb01869251839506d9eb Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 29 Mar 2020 00:43:40 +0100 Subject: [PATCH 170/624] Add documentation. --- skfda/ml/regression/__init__.py | 3 +- skfda/ml/regression/linear.py | 93 ++++++++++++++++++++++++++++++++- tests/test_regression.py | 32 ++++++------ 3 files changed, 110 insertions(+), 18 deletions(-) diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index 8371124e6..03dd84e82 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,4 +1,5 @@ +from skfda.ml.regression.linear import MultivariateLinearRegression + from ..._neighbors import KNeighborsRegressor, RadiusNeighborsRegressor -from skfda.ml.regression.linear import LinearScalarRegression diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 3e2882ae6..ef07c4e5b 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -8,7 +8,96 @@ import numpy as np -class LinearScalarRegression(BaseEstimator, RegressorMixin): +class MultivariateLinearRegression(BaseEstimator, RegressorMixin): + r"""Linear regression with multivariate response. + + This is a regression algorithm equivalent to multivariate linear + regression, but accepting also functional data expressed in a basis + expansion. + + The model assumed by this method is: + + .. math:: + y = w_0 + w_1 x_1 + \ldots + w_p x_p + \int w_{p+1}(t) x_{p+1}(t) dt \ + + \ldots + \int w_r(t) x_r(t) dt + + where the covariates can be either multivariate or functional and the + response is multivariate. + + .. warning:: + For now, only scalar responses are supported. + + Args: + coef_basis (iterable): Basis of the coefficient functions of the + functional covariates. If multivariate data is supplied, their + corresponding entries should be ``None``. If ``None`` is provided + for a functional covariate, the same basis is assumed. If this + parameter is ``None`` (the default), it is assumed that ``None`` + is provided for all covariates. + fit_intercept (bool): Whether to calculate the intercept for this + model. If set to False, no intercept will be used in calculations + (i.e. data is expected to be centered). + + Attributes: + coef_ (iterable): A list containing the weight coefficient for each + covariate. For multivariate data, the covariate is a Numpy array. + For functional data, the covariate is a FDataBasis object. + intercept_ (float): Independent term in the linear model. Set to 0.0 + if `fit_intercept = False`. + + Examples: + + >>> from skfda.ml.regression import MultivariateLinearRegression + >>> from skfda.representation.basis import FDataBasis, Monomial + + Multivariate linear regression can be used with functions expressed in + a basis. Also, a functional basis for the weights can be specified: + + >>> x_basis = Monomial(n_basis=3) + >>> x_fd = FDataBasis(x_basis, [[0, 0, 1], + ... [0, 1, 0], + ... [0, 1, 1], + ... [1, 0, 1]]) + >>> y = [2, 3, 4, 5] + >>> linear = MultivariateLinearRegression() + >>> _ = linear.fit(x_fd, y) + >>> linear.coef_[0] + FDataBasis( + basis=Monomial(domain_range=[array([0, 1])], n_basis=3), + coefficients=[[-15. 96. -90.]], + ...) + >>> linear.intercept_ + array([ 1.]) + >>> linear.predict(x_fd) + array([ 2., 3., 4., 5.]) + + Covariates can include also multivariate data: + + >>> x_basis = Monomial(n_basis=2) + >>> x_fd = FDataBasis(x_basis, [[0, 2], + ... [0, 4], + ... [1, 0], + ... [2, 0], + ... [1, 2], + ... [2, 2]]) + >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] + >>> y = [11, 10, 12, 6, 10, 13] + >>> linear = MultivariateLinearRegression( + ... coef_basis=[None, Constant()]) + >>> _ = linear.fit([x, x_fd], y) + >>> linear.coef_[0] + array([ 2., 1.]) + >>> linear.coef_[1] + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[ 1.]], + ...) + >>> linear.intercept_ + array([ 1.]) + >>> linear.predict([x, x_fd]) + array([ 11., 10., 12., 6., 10., 13.]) + + """ def __init__(self, *, coef_basis=None, fit_intercept=True): self.coef_basis = coef_basis @@ -110,6 +199,8 @@ def _argcheck_X(self, X): if isinstance(X, FData) or isinstance(X, np.ndarray): X = [X] + X = [x if isinstance(x, FData) else np.asarray(x) for x in X] + if all(not isinstance(i, FData) for i in X): raise ValueError("All the covariates are scalar.") diff --git a/tests/test_regression.py b/tests/test_regression.py index c5cf8a39a..5dffa7a24 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,4 +1,4 @@ -from skfda.ml.regression import LinearScalarRegression +from skfda.ml.regression import MultivariateLinearRegression from skfda.representation.basis import (FDataBasis, Constant, Monomial, Fourier, BSpline) import unittest @@ -6,7 +6,7 @@ import numpy as np -class TestLinearScalarRegression(unittest.TestCase): +class TestMultivariateLinearRegression(unittest.TestCase): def test_regression_single_explanatory(self): @@ -23,7 +23,7 @@ def test_regression_single_explanatory(self): 0.10549625973303875, 0.11384314859153018] - scalar = LinearScalarRegression(coef_basis=[beta_basis]) + scalar = MultivariateLinearRegression(coef_basis=[beta_basis]) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -33,8 +33,8 @@ def test_regression_single_explanatory(self): y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) - scalar = LinearScalarRegression(coef_basis=[beta_basis], - fit_intercept=False) + scalar = MultivariateLinearRegression(coef_basis=[beta_basis], + fit_intercept=False) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -51,7 +51,7 @@ def test_regression_multiple_explanatory(self): beta1 = BSpline(domain_range=(0, 1), n_basis=5) - scalar = LinearScalarRegression(coef_basis=[beta1]) + scalar = MultivariateLinearRegression(coef_basis=[beta1]) scalar.fit(X, y) @@ -88,7 +88,7 @@ def test_regression_mixed(self): y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral - scalar = LinearScalarRegression() + scalar = MultivariateLinearRegression() scalar.fit(X, y) np.testing.assert_allclose(scalar.intercept_, @@ -112,7 +112,7 @@ def test_error_X_not_FData(self): x_fd = np.identity(7) y = np.zeros(7) - scalar = LinearScalarRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = MultivariateLinearRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -123,7 +123,7 @@ def test_error_y_is_FData(self): x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) y = list(FDataBasis(Monomial(n_basis=7), np.identity(7))) - scalar = LinearScalarRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = MultivariateLinearRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -136,11 +136,11 @@ def test_error_X_beta_len_distinct(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd, x_fd], y) - scalar = LinearScalarRegression(coef_basis=[beta, beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta, beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -152,7 +152,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(8)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -160,7 +160,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -171,7 +171,7 @@ def test_error_beta_not_basis(self): y = [1 for _ in range(7)] beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -184,7 +184,7 @@ def test_error_weights_lenght(self): weights = [1 for _ in range(8)] beta = Monomial(n_basis=7) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) @@ -196,7 +196,7 @@ def test_error_weights_negative(self): weights = [-1 for _ in range(7)] beta = Monomial(n_basis=7) - scalar = LinearScalarRegression(coef_basis=[beta]) + scalar = MultivariateLinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) From 478a6e15ddc160228fac5452576af054113d8a5c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 29 Mar 2020 19:47:48 +0200 Subject: [PATCH 171/624] Changing n_sim for n_reps in Oneway ANOVA. Fixing doctests. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway.py | 4 ++-- skfda/inference/anova/anova_oneway.py | 24 ++++++++++++------------ tests/test_oneway_anova.py | 6 +++--- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 0a868f4b8..40ca6e47b 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -92,7 +92,7 @@ ################################################################################ # In this case the optional arguments of the function are going to be set. -# First, there is a `n_sim` parameter, which allows the user to select the +# First, there is a `n_reps` parameter, which allows the user to select the # number of simulations to perform in the asymptotic procedure of the test ( # see :func:`~skfda.inference.anova.oneway_anova`), defaults to 2000. # @@ -104,7 +104,7 @@ # sampling distribution of the statistic which is compared with the first # return to get the *p-value*. -v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_sim=1500, p=2, +v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_reps=1500, p=2, return_dist=True) print('Statistic: ', v_n) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 60ac27c49..55f678dce 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -160,7 +160,7 @@ def v_asymptotic_stat(fd, weights, p=2): return v -def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): +def _anova_bootstrap(fd_grouped, n_reps, p=2, random_state=None): n_groups = len(fd_grouped) assert n_groups > 0 @@ -184,18 +184,18 @@ def _anova_bootstrap(fd_grouped, n_sim, p=2, random_state=None): # Instance a random state object in case random_state is an int random_state = check_random_state(random_state) - # Simulating n_sim observations for each of the n_groups gaussian processes - sim = [make_gaussian_process(n_sim, n_features=m, start=start, stop=stop, + # Simulating n_reps observations for each of the n_groups gaussian processes + sim = [make_gaussian_process(n_reps, n_features=m, start=start, stop=stop, cov=k_est[i], random_state=random_state) for i in range(n_groups)] - v_samples = np.empty(n_sim) - for i in range(n_sim): + v_samples = np.empty(n_reps) + for i in range(n_reps): fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) v_samples[i] = v_asymptotic_stat(fd, sizes, p=p) return v_samples -def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): +def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): r""" Performs one-way functional ANOVA. @@ -221,7 +221,7 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): implemented using a bootstrap procedure. One observation of the :math:`k` different gaussian processes defined above is simulated, and the value of :func:`~skfda.inference.anova.v_asymptotic_stat` is - calculated. This procedure is repeated `n_sim` times, creating a + calculated. This procedure is repeated `n_reps` times, creating a sampling distribution of the statistic. This procedure is from Cuevas[1]. @@ -229,7 +229,7 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): Args: fd1,fd2,.... (FDataGrid): The sample measurements for each each group. - n_sim (int, optional): Number of simulations for the bootstrap + n_reps (int, optional): Number of simulations for the bootstrap procedure. Defaults to 2000 (This value may change in future versions). @@ -264,11 +264,11 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): (179.52499999999998, 0.602) >>> oneway_anova(fd1, fd2, fd3, p=1, random_state=RandomState(42)) (67.27499999999999, 0.0) - >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_sim=3, + >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) >>> print(dist) - [163.35765183 208.59495097 229.76780354] + [ 163.35765183 208.59495097 229.76780354] @@ -282,7 +282,7 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): raise ValueError("At least two samples must be passed as parameter.") if not all(isinstance(fd, FData) for fd in args): raise ValueError("Argument type must inherit FData.") - if n_sim < 1: + if n_reps < 1: raise ValueError("Number of simulations must be positive.") if any(isinstance(fd, FDataBasis) for fd in args): raise NotImplementedError("Not implemented for FDataBasis objects.") @@ -301,7 +301,7 @@ def oneway_anova(*args, n_sim=2000, p=2, return_dist=False, random_state=None): vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) - simulation = _anova_bootstrap(fd_groups, n_sim, p=p, + simulation = _anova_bootstrap(fd_groups, n_reps, p=p, random_state=random_state) p_value = np.sum(simulation > vn) / len(simulation) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index 0f34fe7c9..4e34d3295 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -15,7 +15,7 @@ def test_oneway_anova_args(self): with self.assertRaises(ValueError): oneway_anova(1, '2') with self.assertRaises(ValueError): - oneway_anova(FDataGrid([0]), n_sim=-2) + oneway_anova(FDataGrid([0]), n_reps=-2) def test_v_stats_args(self): with self.assertRaises(ValueError): @@ -53,10 +53,10 @@ def test_asymptotic_behaviour(self): n_little_sim = 50 - sims = np.array([oneway_anova(fd1, fd2, fd3, n_sim=2000)[1] for _ in + sims = np.array([oneway_anova(fd1, fd2, fd3, n_reps=2000)[1] for _ in range(n_little_sim)]) little_sim = np.mean(sims) - big_sim = oneway_anova(fd1, fd2, fd3, n_sim=50000)[1] + big_sim = oneway_anova(fd1, fd2, fd3, n_reps=50000)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.01) From e1c4bc311a91f12ef2f3261e1a74de933b6ef883 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 30 Mar 2020 22:21:57 +0200 Subject: [PATCH 172/624] Fixing doctest array format --- skfda/inference/anova/anova_oneway.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 55f678dce..376670d53 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -267,8 +267,9 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) - >>> print(dist) - [ 163.35765183 208.59495097 229.76780354] + >>> np.set_printoptions(precision=6) + >>> dist + array([163.357652, 208.594951, 229.767803]) From e23af4d38bf62788e6551d2a7fdb412c8a8ffa52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 30 Mar 2020 22:48:56 +0200 Subject: [PATCH 173/624] Fixing doctests formatting numpy output string (blank space) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 97ceec73d..fa96eac57 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -269,9 +269,7 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): ... random_state=RandomState(42), ... return_dist=True) >>> dist - array([163.357652, 208.594951, 229.767803]) - - + array([ 163.357652, 208.594951, 229.767803]) References: [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An From 977e973e7ed66c5e7fd19c2b114f4a8fbe25f7d1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 1 Apr 2020 20:31:57 +0200 Subject: [PATCH 174/624] Simplify linear regression and linear differential operator penalty. --- skfda/misc/_lfd.py | 49 ++++++++++++ skfda/ml/regression/linear.py | 99 ++++++++++++++++--------- skfda/preprocessing/smoothing/_basis.py | 58 +++++---------- 3 files changed, 129 insertions(+), 77 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 8855bbba4..8ec0f940e 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -1,5 +1,7 @@ import numbers +import scipy.linalg + import numpy as np @@ -216,3 +218,50 @@ def applied_lfd(t): for i, w in enumerate(self.weights)) return applied_lfd + + +def _apply_lfd(X, basis, penalty): + """ + Apply the lfd to a single data type. + """ + penalty_method = getattr(basis, "penalty") + + if penalty_method: + return penalty_method(penalty) + else: + # Multivariate objects have no penalty + return np.zeros((X.shape[1], X.shape[1])) + + +def compute_lfd_matrix(X, basis, regularization_parameter, + penalty, penalty_matrix): + """ + Computes the regularization matrix for a linear differential operator. + + X can be a list of mixed data. + """ + from skfda.representation.basis import Basis + + # If there is no regularization, return 0 and rely on broadcasting + if regularization_parameter == 0: + return 0 + + # Compute penalty matrix if not provided + if penalty_matrix is None: + + # Convert the linear differential operator if necessary + if penalty is None: + penalty = LinearDifferentialOperator(order=2) + elif not isinstance(penalty, LinearDifferentialOperator): + penalty = LinearDifferentialOperator(penalty) + + if isinstance(basis, Basis): + penalty_matrix = _apply_lfd(X, basis, penalty) + else: + # If X and basis are lists + + penalty_blocks = [_apply_lfd(x, b, penalty) + for x, b in zip(X, basis)] + penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) + + return regularization_parameter * penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index ef07c4e5b..150c6a6ce 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -37,6 +37,23 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): fit_intercept (bool): Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). + regularization_parameter (int or float, optional): Regularization + parameter. Trying with several factors in a logarithm scale is + suggested. If 0 no regularization is performed. Defaults to 0. + penalty (int, iterable or :class:`LinearDifferentialOperator`): If it + is an integer, it indicates the order of the + derivative used in the computing of the penalty matrix. For + instance 2 means that the differential operator is + :math:`f''(x)`. If it is an iterable, it consists on coefficients + representing the differential operator used in the computing of + the penalty matrix. For instance the tuple (1, 0, + numpy.sin) means :math:`1 + sin(x)D^{2}`. It is possible to + supply directly the LinearDifferentialOperator object. + If not supplied this defaults to 2. Only used if penalty_matrix is + ``None``. + penalty_matrix (array_like, optional): Penalty matrix. If + supplied the differential operator is not used and instead + the matrix supplied by this argument is used. Attributes: coef_ (iterable): A list containing the weight coefficient for each @@ -103,6 +120,43 @@ def __init__(self, *, coef_basis=None, fit_intercept=True): self.coef_basis = coef_basis self.fit_intercept = fit_intercept + def _inner_product_matrix(self, x, basis): + """ + Compute the inner product matrix of a variable. + + The variable can be multivariate or functional. + + """ + if isinstance(x, FDataBasis): + # Functional inner product + if basis is None: + basis = x.basis + xcoef = x.coefficients + inner_basis = x.basis.inner_product(basis) + return xcoef @ inner_basis + else: + # Multivariate inner product + if basis is not None: + raise ValueError("Multivariate data coefficients " + "should not have a basis") + return np.atleast_2d(x) + + def _convert_coefs(self, x, basis, coefs): + """ + Convert to original form. + """ + if isinstance(x, FDataBasis): + if basis is None: + basis = x.basis + + # Functional coefs + return FDataBasis( + basis, + coefs.T) + else: + # Multivariate coefs + return coefs + def fit(self, X, y=None, sample_weight=None): X, y, sample_weight, coef_basis = self._argcheck_X_y( @@ -112,24 +166,11 @@ def fit(self, X, y=None, sample_weight=None): X = [np.ones((len(y), 1))] + X coef_basis = [None] + coef_basis - # X is a list of covariates - n_covariates = len(X) - - inner_products = [None] * n_covariates - - for i, (x, w_basis) in enumerate(zip(X, coef_basis)): - if isinstance(x, FDataBasis): - if w_basis is None: - w_basis = x.basis - xcoef = x.coefficients - inner_basis = x.basis.inner_product(w_basis) - inner = xcoef @ inner_basis - else: - if w_basis is not None: - raise ValueError("Multivariate data coefficients " - "should not have a basis") - inner = np.atleast_2d(x) - inner_products[i] = inner + inner_products = [self._inner_product_matrix(x, basis) + for x, basis in zip(X, coef_basis)] + + coef_lengths = np.array([i.shape[1] for i in inner_products]) + coef_start = np.cumsum(coef_lengths) # This is C @ J inner_products = np.concatenate(inner_products, axis=1) @@ -141,26 +182,12 @@ def fit(self, X, y=None, sample_weight=None): gram_inner_x_coef = inner_products.T @ inner_products inner_x_coef_y = inner_products.T @ y - coef_basiscoefs = np.linalg.solve(gram_inner_x_coef, inner_x_coef_y) + basiscoefs = np.linalg.solve(gram_inner_x_coef, inner_x_coef_y) + basiscoef_list = np.split(basiscoefs, coef_start) # Express the coefficients in functional form - coefs = [None] * n_covariates - idx = 0 - for i, (x, basis) in enumerate(zip(X, coef_basis)): - if isinstance(x, FDataBasis): - if basis is None: - basis = x.basis - - # Functional coefs - used_coefs = basis.n_basis - coefs[i] = FDataBasis( - basis, - coef_basiscoefs[idx:idx + used_coefs].T) - else: - # Multivariate coefs - used_coefs = x.shape[1] - coefs[i] = coef_basiscoefs[idx:idx + used_coefs] - idx = idx + used_coefs + coefs = [self._convert_coefs(x, basis, bcoefs) + for x, basis, bcoefs in zip(X, coef_basis, basiscoef_list)] if self.fit_intercept: self.intercept_ = coefs[0] diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 556b32d72..0097db79f 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -52,7 +52,7 @@ def __call__(self, *, basis_values, weight_matrix, data_matrix, basis_values = upper @ basis_values data_matrix = upper @ data_matrix - if penalty_matrix is not None: + if not np.all(penalty_matrix == 0): w, v = np.linalg.eigh(penalty_matrix) w = w[::-1] @@ -166,7 +166,7 @@ class BasisSmoother(_LinearSmoother): weights (array_like, optional): Matrix to weight the observations. Defaults to the identity matrix. smoothing_parameter (int or float, optional): Smoothing - parameter. Trying with several factors in a logarythm scale is + parameter. Trying with several factors in a logarithm scale is suggested. If 0 no smoothing is performed. Defaults to 0. penalty (int, iterable or :class:`LinearDifferentialOperator`): If it is an integer, it indicates the order of the @@ -336,43 +336,10 @@ def _method_function(self): return method_function - def _penalty(self): - from ...misc import LinearDifferentialOperator - - """Get the penalty differential operator.""" - if self.penalty is None: - penalty = LinearDifferentialOperator(order=2) - elif isinstance(self.penalty, LinearDifferentialOperator): - penalty = self.penalty - else: - penalty = LinearDifferentialOperator(self.penalty) - - return penalty - - def _penalty_matrix(self): - """Get the final penalty matrix. - - The smoothing parameter is already multiplied by it. - - """ - - if self.penalty_matrix is not None: - penalty_matrix = self.penalty_matrix - else: - penalty = self._penalty() - - if self.smoothing_parameter > 0: - penalty_matrix = self.basis.penalty(penalty) - else: - penalty_matrix = None - - if penalty_matrix is not None: - penalty_matrix *= self.smoothing_parameter - - return penalty_matrix - def _coef_matrix(self, input_points): """Get the matrix that gives the coefficients""" + from ...misc._lfd import compute_lfd_matrix + basis_values_input = self.basis.evaluate(input_points).T # If no weight matrix is given all the weights are one @@ -381,9 +348,13 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input - penalty_matrix = self._penalty_matrix() - if penalty_matrix is not None: - inv += penalty_matrix + penalty_matrix = compute_lfd_matrix( + X=None, basis=self.basis, + regularization_parameter=self.smoothing_parameter, + penalty=self.penalty, + penalty_matrix=self.penalty_matrix) + + inv += penalty_matrix inv = np.linalg.inv(inv) @@ -430,6 +401,7 @@ def fit_transform(self, X: FDataGrid, y=None): self (object) """ + from ...misc._lfd import compute_lfd_matrix _check_r_to_r(X) @@ -438,7 +410,11 @@ def fit_transform(self, X: FDataGrid, y=None): if self.output_points is not None else self.input_points_) - penalty_matrix = self._penalty_matrix() + penalty_matrix = compute_lfd_matrix( + X=X, basis=self.basis, + regularization_parameter=self.smoothing_parameter, + penalty=self.penalty, + penalty_matrix=self.penalty_matrix) # n is the samples # m is the observations From ac1c25cd9e450138a853d3823d9eae3a13d22aa6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 2 Apr 2020 02:39:23 +0200 Subject: [PATCH 175/624] Add regularization. --- skfda/misc/_lfd.py | 2 +- skfda/ml/regression/linear.py | 40 ++++++++++++++------- tests/test_regression.py | 68 +++++++++++++++++++++++++++++++++++ 3 files changed, 97 insertions(+), 13 deletions(-) diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 8ec0f940e..23cfb2746 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -224,7 +224,7 @@ def _apply_lfd(X, basis, penalty): """ Apply the lfd to a single data type. """ - penalty_method = getattr(basis, "penalty") + penalty_method = getattr(basis, "penalty", None) if penalty_method: return penalty_method(penalty) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 150c6a6ce..59e2b0230 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -116,9 +116,15 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): """ - def __init__(self, *, coef_basis=None, fit_intercept=True): + def __init__(self, *, coef_basis=None, fit_intercept=True, + regularization_parameter=0, + penalty=None, + penalty_matrix=None): self.coef_basis = coef_basis self.fit_intercept = fit_intercept + self.regularization_parameter = regularization_parameter + self.penalty = penalty + self.penalty_matrix = penalty_matrix def _inner_product_matrix(self, x, basis): """ @@ -129,8 +135,6 @@ def _inner_product_matrix(self, x, basis): """ if isinstance(x, FDataBasis): # Functional inner product - if basis is None: - basis = x.basis xcoef = x.coefficients inner_basis = x.basis.inner_product(basis) return xcoef @ inner_basis @@ -146,9 +150,6 @@ def _convert_coefs(self, x, basis, coefs): Convert to original form. """ if isinstance(x, FDataBasis): - if basis is None: - basis = x.basis - # Functional coefs return FDataBasis( basis, @@ -158,6 +159,7 @@ def _convert_coefs(self, x, basis, coefs): return coefs def fit(self, X, y=None, sample_weight=None): + from ...misc._lfd import compute_lfd_matrix X, y, sample_weight, coef_basis = self._argcheck_X_y( X, y, sample_weight, self.coef_basis) @@ -179,7 +181,13 @@ def fit(self, X, y=None, sample_weight=None): inner_products = inner_products * np.sqrt(sample_weight) y = y * np.sqrt(sample_weight) - gram_inner_x_coef = inner_products.T @ inner_products + penalty_matrix = compute_lfd_matrix( + X=X, basis=coef_basis, + regularization_parameter=self.regularization_parameter, + penalty=self.penalty, + penalty_matrix=self.penalty_matrix) + + gram_inner_x_coef = inner_products.T @ inner_products + penalty_matrix inner_x_coef_y = inner_products.T @ y basiscoefs = np.linalg.solve(gram_inner_x_coef, inner_x_coef_y) @@ -233,6 +241,15 @@ def _argcheck_X(self, X): return X + def _get_coef_basis(self, x, basis): + if basis is None: + basis = getattr(x, 'basis', None) + return basis + else: + if not isinstance(basis, Basis): + raise ValueError("coef_basis should be a list of Basis.") + return basis + def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): """Do some checks to types and shapes""" @@ -251,16 +268,15 @@ def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): coef_basis = [None] * len(X) if len(coef_basis) != len(X): - raise ValueError("Number of regression coefficients does" - " not match number of independent variables.") + raise ValueError("Number of regression coefficients does " + "not match number of independent variables.") if any(len(y) != len(x) for x in X): raise ValueError("The number of samples on independent and " "dependent variables should be the same") - if any(b is not None and not isinstance(b, Basis) - for b in coef_basis): - raise ValueError("coefs should be a list of Basis.") + coef_basis = [self._get_coef_basis(x, b) + for x, b in zip(X, coef_basis)] if sample_weight is None: sample_weight = np.ones(len(y)) diff --git a/tests/test_regression.py b/tests/test_regression.py index 5dffa7a24..f336d0b98 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -105,6 +105,74 @@ def test_regression_mixed(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_regression_regularization(self): + + x_basis = Monomial(n_basis=7) + x_fd = FDataBasis(x_basis, np.identity(7)) + + beta_basis = Fourier(n_basis=5) + beta_fd = FDataBasis(beta_basis, [1.0403, 0, 0, 0, 0]) + y = [1.0000684777229512, + 0.1623672257830915, + 0.08521053851548224, + 0.08514200869281137, + 0.09529138749665378, + 0.10549625973303875, + 0.11384314859153018] + + y_pred_compare = [0.890341, + 0.370162, + 0.196773, + 0.110079, + 0.058063, + 0.023385, + -0.001384] + + scalar = MultivariateLinearRegression(coef_basis=[beta_basis], + regularization_parameter=1) + scalar.fit(x_fd, y) + np.testing.assert_allclose(scalar.coef_[0].coefficients, + beta_fd.coefficients, atol=1e-3) + np.testing.assert_allclose(scalar.intercept_, + -0.15, atol=1e-4) + + y_pred = scalar.predict(x_fd) + np.testing.assert_allclose(y_pred, y_pred_compare, atol=1e-4) + + x_basis = Monomial(n_basis=3) + x_fd = FDataBasis(x_basis, [[1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [2, 0, 1]]) + + beta_fd = FDataBasis(x_basis, [3, 2, 1]) + y = [1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30] + + # Non regularized + scalar = MultivariateLinearRegression(regularization_parameter=0) + scalar.fit(x_fd, y) + np.testing.assert_allclose(scalar.coef_[0].coefficients, + beta_fd.coefficients) + np.testing.assert_allclose(scalar.intercept_, + 1) + + y_pred = scalar.predict(x_fd) + np.testing.assert_allclose(y_pred, y) + + # Regularized + beta_fd_reg = FDataBasis(x_basis, [2.812, 3.043, 0]) + y_reg = [5.333, 3.419, 2.697, 11.366] + + scalar_reg = MultivariateLinearRegression(regularization_parameter=1) + scalar_reg.fit(x_fd, y) + np.testing.assert_allclose(scalar_reg.coef_[0].coefficients, + beta_fd_reg.coefficients, atol=0.001) + np.testing.assert_allclose(scalar_reg.intercept_, + 0.998, atol=0.001) + + y_pred = scalar_reg.predict(x_fd) + np.testing.assert_allclose(y_pred, y_reg, atol=0.001) + def test_error_X_not_FData(self): """Tests that at least one of the explanatory variables is an FData object. """ From 6414e1db4eaee16d79bdd37b4fc389ff44a75803 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 176/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From ca8dbd1f3cf7d07b8651c5e1054cb656a75086a1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 177/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e82c572bc7c90128fbfe2639dc1abe5e292ef65d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 178/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, 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4.81603671e-02,\n", + " -4.51696594e-02, 2.18321361e-03, -3.77910377e-03,\n", + " 6.01433208e-03, -2.87812954e-03, 3.13700942e-03,\n", + " 2.62878591e-02, -3.19781435e-03, -5.63379740e-02,\n", + " 6.08448909e-02, -7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From 5c2c40cf95982ec8660744f3f7a2e5d81f2b03d1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 179/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From b6a6f3844aa56f8e501eeb49eefbce9c6ef776cc Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 180/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 12cfbdf7ce6df39d366c13e82cd48e4103b117b3 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 181/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 001a6b6f3cbaeee969731dcdd7df12c6b617ce5d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 182/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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EyJvWRu+XTZONZoHaHeHBZUZ/8CLaGXaR6b8cY39EAgFernz44C3c2dwHmyumokvNTuXVP15la+RWhjQcwqvtX8XepoJdUWprD27exq0cKe53hYbArUqpqUAGME5rvRuoBezItV6kZdlVlFIjgBEAtWvXLmYZQggA4kJh/ctw6jeoWh/u/9rozljEI8nTcalMW3+UXw+fp0ZlJ6YPbs69bXyxs726C2NkciQv/PYC4Ynh/K/9/3ig8QMltTeiBBQ33O0AT6AD0Bb4XilVtygb0FovBBaC0VummHUIcXPLSoM/34dtc43ufn2mQ9snizweS2JaNnN/O8kX209jb2vDSz0b8uStdXF2yHtkxj0xexi7dSw5OocFdyygY82Oea4nrKe44R4J/KiNfpS7lFJmwAuIAnKfRfG1LBNClCSt4djP8MsESIyAFg9AzylQqXqRNpOVY+arHWeY+9tJEtOzuT/Ij7E9G1Ktcv7DC6w4sYK3d76Nr5sv83rMo07lOte7N+IGKG64rwK6A1uUUg0BByAOWAN8o5SahXFCtQGwqyQKFUJYJEbBz2ONy++9m8CwdeDfuUib0Fqz6Wgs76w7SnhcKl3qezGxXxOa1qyc73MyTZlM2zmNFSdX0KlmJ2beNpPKDvmvL6yrMF0hlwHdAC+lVCQwGVgCLFFKhQBZwFDLUfxhpdT3wBEgB3hOesoIUUK0Ni5Z3/i60Wuj19vQfmSRm2DC41J586fDbD1+gXrernw2rC3dGv23B8yVolOiGbN1DIcvHuap5k/xXKvnsLWRyTTKMrlCVYjyID4M1owyxoDxvxUGzAXPIp3mIi0rh4+2hPLpH+E42Nnw4h0NGNrJH/s8TpbmtiN6B+N/H0+2OZu3u7xNj9o9rmdPRAmSK1SFKK/MJmO4gM1TjKsi+8+BNsOK1AtGa8264Bje/vkI0YkZDG5di1f7NqZapfzb1S8/b0nIEubun0tA5QDmdJ+Dv7v/9e2PKDUS7kKUVfFhsHKkMSZKg17Qfza4+xZpE6GxyUxec5i/Qy/S1KcyHz54C0H+ngU+LyUrhdf+fo1NEZvo7d+bKZ2m4GLvUtw9EVYg4S5EWaM17PvC6AljYwd3fwIt7i/S0XpqZg4fbD7Jkr/CcXGw5a2BgTzUvg62NgVv48jFI7z8+8tEpUQxLmgcjzV97Jrt8aJsknAXoixJuQA/jYLj64y29bs/LvLR+obDMbyx5jDRSRncH+THy70bUdWtgAkyMJphlh1bxnt73qOKUxUW915Mm+pFG1xMlB0S7kKUFcd/gTXPQ0YS9H4H2j8DNoWf3CI6MZ3Jqw+z4ch5GteoxLyHW9O6dpVCPTcxM5HJ2yazOWIzXX278nbnt6niVLjnirJJwl0Ia8tKhV8nGt0cqzeDx9YUaeRGk1nzxfbTvPfrcUxa82rfxjzRJaDAXjCXHbpwiPF/jOd86nlphqlAJNyFsKZz++GHJ4yTp51Gwe2TwK7gJpTLQqISmfBjMMFRidzW0Ju3BzXDz7NwJz7N2syXR75kzt45VHOpxtK+S2nh3aK4eyLKGAl3IaxBa6OL44ZJxmw8w9aCf5dCPz01M4dZG0/w2d/hVHVzZN5DxqiNhT3ijk2LZdJfk9gevZ07at/BG53eKF/D9IoCSbgLUdrS4mH183D8Z2jYFwbNN2b7KaSNR84zeXUI0UkZPNy+Ni/3boy7c+GvUt14ZiNvbn+TLFMWr3d8nXsb3CvNMBWQhLsQpensLvjhcUiOgd7TjDlMC3u0nZzB5NWHWR8SQ+Malfjwoda0qVP4k56p2alM2zmN1adWE1g1kOm3TpeLkiowCXchSoPZDNs+gM1vGdOwPbEBarUu1FO11qzYF8Vba4+Qnm1ifJ9GPHVr3UKfMAU4EHuACX9O4FzqOUa0GMHIliMr3qQa4j8k3IW40VIuwMqn4dRmCLwb7voAnArXvh15KY2JK0P448QF2vpXYfo9Lajn7Vbol842Z/PJwU/4NPhTfFx9+LzP59xS7Zbi7okoRyTchbiRwv+EFU9C+iVj+IA2wwvVDGM2a77aeYZ31x9DA1MGBvJI+zpXTXN3LScunWDSX5M4Gn+UAfUGMKHdBNwcCv+HQZRvEu5C3AhmE/wxE35/FzzrwSMroEazQj017EIKr6w4xO7Tl7i1gRfTBjfHt0rhx3XJMeewJGQJCw4uoLJDZWZ1m0XPOj2LuyeinJJwF6KkJUXDj08Zw/O2fAj6zQTHgo+Yc0xmPv0znNmbTuBkZ8PMe1twbxvfIvVkOXnpJJP+nsSRi0fo49+Hie0nypWmNykJdyFK0slNsHIEZKfDoAXQ6qFCPe3IuSTGrzhISFQSfQJrMGVQYIFD8uZ25dH6+7e9Ty//XsXdC1EBSLgLURJM2fDb2/D3HKgWCPd9Dt4NC3xaZo6Jeb+FsmDrKTxcHFjwcGv6Nvcp0kvnPlrv7d+bie0n4ulU+H7zomKScBfieiVEGEMIRO6CoMeNQb/snQt82r6IS4z/4RChsSkMbl2L1/s3xcPFodAvm2XKYnHIYj499CmVHCrJ0br4Dwl3Ia7H0bWw+lljOIF7P4Nmgwt8SlpWDu/9eoLPtoXjU9mJz4a3pXujakV62X3n9/Hm9jcJSwyjj38fJrSfIEfr4j8k3IUojpxMY6LqnR+DTyu477NCzWm6LTSOV38MJiI+jUc71OGVvo1xcyz8f8OkrCTm7J3D8hPLqelak496fERX367XsyeigpJwF6KoLp6CH4ZD9EHo8Czc8UaBIzkmZWQzbd0xlu2KIMDLle9GdKB93aqFfkmtNRvPbGTarmnEZ8TzWNPHeK7VczL1nciXhLsQRXFoOax9EWzt4YFl0LhfgU/ZciyWiSuDOZ+UwdNd6zKmZ0Oc7G0L/ZIxqTFM3TGVrZFbaeLZhHk95hFYNfB69kLcBCTchSiMrFRYNx4OfAW1O8I9iwqc/i4hLYspa4/w474oGlZ34+NHOtPSz6PQL5ljzuHbY9/y4f4P0WjGBY3j4SYPY2cj/21FweRTIkRBzh+G5cMh7gR0fRluexVsr/1f55eQGCatCiEhLYtRPRrwXPd6ONoV/mh9f+x+pu6YyvFLx+lcszOTOkzCt1LR5lIVNzcJdyHyozXs/Qx+mWAM9PXYaqh72zWfEpeSyeTVh/k5OJrAmpVZ+nhbAmsWfhKMi+kXmb13NqtPraa6S3VmdZvFHbXvkPHWRZFJuAuRl/QE+Gk0HFkF9XrA3Z+Am3e+q2utWXPwHG+sOUxqpomXezdiRNfCD8trMpv4/sT3fLjvQ9JN6TzR7AlGtBghJ0xFsUm4C3GlyD1Gb5ikc3DHm8bcpjb5h3RMYgaTVgWz6Wgsrfw8mHlvCxpUr1TolzsQe4B3dr7D0fijtPdpz8T2E6nrXnC3SiGuRcJdiMvMJmP4gC3vQKWaMPwX8Gub7+paa5bvieStn4+QlWNm0p1NGN45ANtCDssbnxHPnL1zWBm6kmou1Zh520x61+ktTTCiRBQY7kqpJUB/IFZr3eyKx14C3gO8tdZxyvhUfgD0A9KAYVrrfSVfthAlLCECVo6EM38bE2r0nw3O+Y+mGHkpjQk/BvPnyTjaBXjy7j0tCPByLdRL5ZhzWH5iOfP2zyMtO43hgcMZ2XKkNMGIElWYI/fPgXnAF7kXKqX8gF5ARK7FfYEGllt7YIHlpxBlV/APsHYsaDMM+hhaPpDvhBpms+brnWeYbplE462BgTxchEk0tp/bzozdMwhNCKVdjXZMbD+Reh71SnBnhDAUGO5a6z+UUv55PDQbGA+szrVsIPCF1loDO5RSHkopH611dEkUK0SJykiEn8dB8Pfg1x4GL4Qq/vmufjoulfErDrErPJ5bG3jxzt3N8fMs3NF2RFIEM/fMZOvZrdRyq8XsbrPpUbuHNMGIG6ZYbe5KqYFAlNb64BUfzlrA2Vy/R1qWXRXuSqkRwAiA2rVrF6cMIYrvzDb48WlIioJuE+HWl/Ltu55jMrPor3DmbDqBva0NM+5pwX1BhZtEIyUrhYWHFvLl0S9xsHFgdOvRPNr0URxtrz1cgRDXq8jhrpRyASZiNMkUm9Z6IbAQICgoSF/PtoQoNFM2bJ0Of80Cj9rw+K/XPGkaHJnIKysOcSQ6iZ5Nq/PWwGbUcC94Eg2T2cSq0FXM3T+X+Ix4BtYbyOjWo/F2yb87pRAlqThH7vWAAODyUbsvsE8p1Q6IAvxyretrWSaE9V08ZUxWfW4ftHoE+k4Hx7y7LKZl5TB74wkW/xWOl5sjHz/Smj7NCjeJxp6YPczYPYOj8Udp5d2K+T3mE+glY8GI0lXkcNdaBwP/DD6tlDoNBFl6y6wBnldKfYtxIjVR2tuF1WkNez+HXyeCrQPctxQCB+W7+h8nLjBxZTCRl9J5qH1tXunTGHdn+wJfJiolivf3vM/GMxup7lKdGV1n0Me/j7SrC6soTFfIZUA3wEspFQlM1lovzmf1dRjdIEMxukIOL6E6hSiepGhY8wKEboSArkZvGPdaea56MSWTt38+ysr9UdTzduX7pzvSLqDgCTCSs5JZFLyIr458hY2y4dmWzzKs2TCc7QqejUmIG6UwvWUeLOBx/1z3NfDc9ZclxHXS2ujiuG6cMbFG35nQ9sk8rzTVWrNyfxRvrT1CSmZOoQf6yjZl8/2J7/n44MckZCbQv25/RrceTQ3XGjdqr4QoNLlCVVQ8qXGwdgwcXQO+bY2jda/6ea4acTGN/60yLkZqXduD6fe0oGEBQwdordkUsYk5e+cQkRxBuxrteCnoJZpWbXoj9kaIYpFwFxXLsXXw0yhj4K8ek6HzaLC5+gg8x2Rmyd/hzNp4Ajsbm0JfjHTwwkHe2/0eBy4coJ57PT7q8RG31rpV2tVFmSPhLiqGjERjaN4DX0P15vDoKqjRLM9VD5xN4H8rgzl8zujeOGVgID7u124fP5t0ljn75rDhzAaqOlVlcsfJDKo/SCbOEGWWfDJF+Re2FVY9B8nn4NZxcNsrYOdw1WqJadnM+PUY3+yKoFolRxY83Jo+zWpc86g7ISOBTw59wrfHv8Xexp5nWj7DsMBhMg6MKPMk3EX5lZUGmybDroVQtQE8sRF8g65a7fIJ03fWHeVSWjaPdw5gTM+GuDnm//HPNGXyzdFv+PTQp6TmpHJ3/bt5ttWzVHOplu9zhChLJNxF+XR2lzGKY/wpaP8M9HgdHK4+mj55PplJq0LYGR5P69oefPF4c5rWrJzvZs3azPrw9czdN5dzqefoUqsLY9uMpUGVBjdyb4QocRLuonzJSoMtU2H7R+DuB0N/MvqvXyEtK4e5m0NZ9GcYbk52TB/cnCFBftc8Ybo7Zjfv7XmPIxeP0NizMW92fpMOPh1u5N4IccNIuIvy48w2WP0cxIdB0OPQc0qewwdsPHKeN9YcJiohnfva+PJq38ZUdct/oK6whDBm753N1sitVHepztQuU+lftz82qnBT5AlRFkm4i7IvKxU2vWm0rXvUhsfW5DlRdeSlNN5Yc4RNR8/TqHollo/sSFv//K8wjUuPY8GBBaw4uQInOydGtx7NI00ewcmu4IHBhCjrJNxF2Rb+B6x+HhLOQLunjbZ1R7f/rJKVY2bRX2HM3XwSG6WY2K8xwzsH5Ds5dXpOOl8c/oIlIUvIMmUxpNEQRrYciadTwUMNCFFeSLiLsikzGTa+DnuWgGddGL4e6nS6arUdYRd5bVUIJ2NT6B1Yncl3BVLTI+8+6yaziTWn1jBv/zxi02PpUbsHL7Z+EX93/xu8M0KUPgl3UfaEboafRkNiJHR8Hrr/76qeMHEpmbyz7ig/7ovCt4ozS4YFcXvj6vlu8u+ov3l/7/ucvHSSFl4tmHnbTFpXb32j90QIq5FwF2VHRiL8+j/Y/yV4NYQnNoBfu/+sYjZrvtkVwYxfjpGebeL57vV5rnt9nB3yHuTrePxxZu2dxbZz2/B182XmbTPpXae3DBcgKjwJd1E2nNhgHK2nxEDnF6HbBLD/74nNkKhE/rcqhINnE+hYtypvDWpG/WpueW4uJjWGefvnsebUGio5VOLloJd5oPEDONhefeWqEBWRhLuwrvRLxpgwB5eBdxN44Cuo1eY/qyRlZDNrwwm+2H6BsnwAABwpSURBVH4aT1dH5tzfioGtauZ59J2ancri4MV8eeRLTNrE0MChPNn8Sdwd3Utph4QoGyTchfUc+9kYmjc1Drq+bNzs/u2PrrXmp0PRvL32CBdSMnm0Qx1e6tUoz1mRss3Z/HjiR+YfnE98Rjx9A/oy6pZR+FbyLc09EqLMkHAXpS/1IqwfDyE/GCM4PrwcfFr+Z5WwCym8vvowf4XG0byWO4uGBtHC1+OqTWmt2Xp2K7P2zuJ00mnaVG/DRz0+oplX3iNCCnGzkHAXpevIavj5JaM5ptsE6DL2PyM4ZmSbmL8llI9/D8PR3hhn/aH2dbDNY9iAkLgQ3tvzHnvP78W/sj9zu8+lm183OVkqBBLuorSkXIB1Lxnh7tMyz/HWtx6PZfKaw5y5mMagVjWZeGcTqlW6+mrRqJQoPtj3AevD1+Pp5Mmk9pMY3HAw9jYFT2ItxM1Cwl3cWFpDyApY9zJkpcDtrxmzI9n+G8QxiRlMWXuYdcEx1PV25Zsn29OpvtdVm0rMTGRR8CK+Pvo1tsqWp5o/xePNHsfNIe8eM0LczCTcxY2THANrx8Lxn40eMAM/gmpN/nk4x2Tm822nmb3xBDlmzcu9G/HkrQFXTUydbcrm2+Pf8vHBj0nOSmZg/YE81+o5mYhaiGuQcBclT2s48A38OgFyMqHnW9DhWbD99+O290w8/1sZwrGYZG5vXI03BwTi5+lyxWY0v575lQ/2fkBkSiSdanZibJuxNPJsVNp7JES5I+EuSlZipHExUugmqN0RBswDr/r/PHwpNYt3fznGt7vP4uPuxMePtKF3YPWrToLuj93Pe3ve49CFQzSo0oCP7/iYzrU6l/beCFFuSbiLkqE17P0cNrwG2gR9Z0Dbp8DGGJnRbNb8sDeSaeuPkpyRw9Nd6zKqRwNcr5jq7kzSGebsncOmiE1Uc67GlE5TGFBvALY2eQ8vIITIm4S7uH7x4fDTKGN43oCucNdc8Az45+HjMclMWhXM7tOXCKpThal3N6dRjf9OshGfEc/HBz9m+fHlONg68Hyr53m06aMyEbUQxSThLorPbIbdn8KmN0DZQv850GYYWJpY0rJy+GDzSRb/GU4lJztm3NOCe9v4/mequ4ycDL46+hWLgxeTnpPOPQ3u4ZlWz+DlfHVvGSFE4Um4i+KJC4U1z0PEdqh/B9z1Abj/e6l/7qnuhgT58mrfJni6/nuxktaa9eHrmbNvDtGp0XTz7caYNmOo61HXGnsjRIUj4S6KxmyC7fNgyzvGODCDFkDLB/85Wi/MVHcHLxxkxu4ZHLpwiCaeTZjaZSpta7S1xt4IUWEVGO5KqSVAfyBWa93MsmwmcBeQBZwChmutEyyPTQCeAEzAKK31rzeodlHaYo8aE1RH7YVGd0L/WVDJ6GuebTKz+K9wPth0EoAJfRvzeJf/TnUXnRLN7H2zWR++Hi9nLzlZKsQNVJgj98+BecAXuZZtBCZorXOUUu8CE4BXlFJNgQeAQKAmsEkp1VBrbSrZskWpMmXDX3Pg93fBsRLcsxia3fPP0fqu8HgmrQrmxPkUejatzhsDAqmVa6q7tOw0FgUv4osjxkdoRIsRPNHsCTlZKsQNVGC4a63/UEr5X7FsQ65fdwD3Wu4PBL7VWmcC4UqpUKAdsL1EqhWlL/oQrH4WYoIhcLDRxdHNG4D41CymrTvK8r2R1PJw5tPHgujZ9N+p7i7PWTp3/1zi0uPoF9CPF1u/iI+bj7X2RoibRkm0uT8OfGe5Xwsj7C+LtCy7ilJqBDACoHbt2iVQhihROZnwx3vw1yxw9oT7v4ImdwFGn/Xle88ybf0xUjJyGHlbPUb1qI+Lw78fp90xu5mxewbH4o/R0rslH3T/gBbeLay1N0LcdK4r3JVS/wNygK+L+lyt9UJgIUBQUJC+njpECYs+CCufgdjD0OIB6DMNXIyTosdikpi0MoQ9Zy7Rzt+Tt+9uRsPq//ZZj0iKYNbeWWyO2IyPqw8zus6gj38fGYZXiFJW7HBXSg3DONHaQ2t9OZyjAL9cq/lalonywJQNf74Pf8wEl6rw4HfQqA9g6bO+6SSL/gqnspMdM+81+qxfDu2krCQWHlzI18e+xsHGgVG3jOLRpo/iZHf1kL1CiBuvWOGulOoDjAdu01qn5XpoDfCNUmoWxgnVBsCu665S3HjnD8PKkRBzCJoPgb7v/nO0vvV4LJNWhRB5KZ37g/x4tW9jqlj6rJvMJlacXMG8/fNIyEzg7gZ388ItL8hFSEJYWWG6Qi4DugFeSqlIYDJG7xhHYKPlyG2H1nqk1vqwUup74AhGc81z0lOmjDPlwN9zYOt0cHL/T9v6heRM3lp7hDUHz1HP25Xvn+5Iu4B/+6zvPb+X6bumcyz+GEHVg3il3Ss09mxsrT0RQuSi/m1RsZ6goCC9Z88ea5dx87lw3DhaP7cPmg6CO98HVy+01izfE8nUdUdJzzLxbPd6PNOt3j/jrMekxjBr7yzWh6/Hx9WHcUHj6Fmnp7SrC1HKlFJ7tdZBeT0mV6jejC5fZfrbVHBwhXs/g2aDAWNi6okrg9kRFk87f0/eGdyM+tWME6aZpkyWHl7KouBFmLWZZ1o+w/Bmw3G2c77WqwkhrEDC/WYTFwqrnoHIXdC4P/SfDW7VyMox88nvp/hwSyiOdjZMG9yc+4P8sLFRaK3ZcnYLM3bPIColip51evJS0EvUcsuzl6sQogyQcL9ZmM2w6xPY9CbYOcDgT6H5faAUe8/E8+qKYE7GptC/hQ+v39X0n4mpwxLCeHf3u2w7t436HvX5tNendPDpYOWdEUIURML9ZpBw1jhaP/0nNOhljLde2YekjGxm/HKMr3ZEUMvDmSXDgri9sXGFaXJWMgsOLmDZ0WU42znzartXGdJoCPY29gW8mBCiLJBwr8i0huDl8PM4Y3akAR/CLY+CUmw4HMOkVSHEpWTyRJcAxvZsiKujHWZtZnXoaubsm8OljEsMbjCYUa1H4enkWfDrCSHKDAn3iiotHn4eC4dXgl97uPsT8AzgYkomk9ccZu2haJr4VGbR0CBa+HoAcDz+OFN3TmV/7H5aerdk/h3zCawaaOUdEUIUh4R7RXRqC6x6FlJj4fbXoMsYtLJhzYEo3lhzmNRME+N6NeTp2+phb2tDSlYK8w/O55uj31DZoTJTOk1hYP2B2Cibgl9LCFEmSbhXJNnpxgnTnQvAqxE8uAxqtiImMYNJq4LZdDSWVn4ezLy3BQ2qV/pnNqSZu2cSlx7HvQ3vZXTr0bg7ult7T4QQ10nCvaKIPgg/joALx6Dd09DzTbSdE9/timDquqNkm8xMurMJwzsHYGujCE8MZ+rOqeyM3kkTzyZ80P0Dmns3t/ZeCCFKiIR7eWc2w7a58NvbxmBfj6yA+ndwNj6NCT/u4q/QONoHePLuPS3w93IlPSedTw98ymeHP8PZ1pmJ7ScypOEQmQ1JiApGwr08Sz4PK5+GsC3QZADc9QFmpyp8ue007/5yDAW8PagZD7WrjY2NYkvEFqbvms651HMMqDeAMW3GyABfQlRQEu7lVegmY1yYzGToPwfaDCMyIZ2Xv9rJ9rCLdG3ozbTBzanl4UxMagzv7HyHLWe3UN+jPp/1/oygGnkORyGEqCAk3MubnCz4bQps+xCqNYWhP6G9G7N8TyRT1h5Ba830wc25v60fZm3m66NfM3ffXMzazJg2Y3i06aNyIZIQNwEJ9/IkPgx+eMIYxTHoCeg9ldh0xYSle9h8LJb2AZ68d19L/DxdOB5/nDe3v0lwXDCda3ZmUodJ+FbytfYeCCFKiYR7eRH8A/z0ItjYwJAvoekA1h46x6RVIaRnmXi9f1OGdfIny5zJnL1zWHp4KZUdKzP91un0C+gnw/EKcZORcC/rcjLhlwmwZzH4dYB7FnHJvjqvfbOPtYeiaennwfv3taR+NTe2n9vOWzve4mzyWQbVH8RLbV7Cw8nD2nsghLACCfeyLCECvh9qNMN0GgU9JvNXWAJjv/+DS2lZjOvVkJG31SM5O5GJf07kp7CfqFO5Dot7LaadTztrVy+EsCIJ97Lq5Cb48UljYo37vyKrwZ28/+txPvkjjHreriwZ1pZmtdzZcHoDU3dOJSkziREtRjCixQgcbR2tXb0Qwsok3Msaswl+nwG/vwvVA2HIF4SZqzN6wTaCoxJ5uH1tJt3ZlDRTAi9tfYkNZzbQtGpTPu31KQ2rNLR29UKIMkLCvSxJvWgcrZ/6DVo+hL7zPZYfjGfymr9wtLfhk0fb0KtpdX498yvv7HiHlOwURrcezbDAYdjZyD+lEOJfkghlRUwwLHsIUs7DXR+Q2PghJiwPZl1wDB3rVmX2/a2wc0jhpd9fYuOZjTSr2oy3Or9F/Sr1rV25EKIMknAvC46sNq42dXKHx9ezN6cuL8z9k9jkTF7p05inbg1gw5lfmLZrGmnZaYxpM4bHmj4mR+tCiHxJOliT2Qxbp8EfM8C3LXrIlyw+mM709dup6eHMimc6Uccbxv85jo1nNtLCqwVvdX6Luh51rV25EKKMk3C3lsxk42j92Fpo9QiJPd5l/Kpj/Hr4PL2aVmfmfS0Jid/F4DWvcSnzEi+2fpFhgcNk9EYhRKFIuFtDfDgsexDiTkCf6YT4PsizC3ZzLiGdSXc24aEONZizbybLji2jvkd95t8xn8aeja1dtRCiHJFwL20RO+HbB8FsQj+ygm/i6vLmx9up6urAd093wNkthgd+foDwxHAeafIIL7Z5UfqtCyGKTMK9NIX8aDTFuNciY8h3TPg9jZX7Q+ja0Jv3hzRndfjXfPT7R3g6e7Kw50I61uxo7YqFEOVUgeGulFoC9AditdbNLMs8ge8Af+A0MERrfUkZo1N9APQD0oBhWut9N6b0ckRr+HsObHoD/DoQ3W8xT34fzpHoJMb2bMj97d2Z8PcL7IzZSW//3rzW4TWZx1QIcV0KM73950CfK5a9CmzWWjcANlt+B+gLNLDcRgALSqbMcsyUDT+NNoK92T3s7PoZ/RcdJeJiGouHBtGmcSxDfr6PQ3GHmNJpCjO7zpRgF0JctwLDXWv9BxB/xeKBwFLL/aXAoFzLv9CGHYCHUsqnpIotdzKT4ZshsG8pustLfFFzEg9/dgB3F3t+eLY9h1KXMXLTSDydPFl25zLubnC3DM0rhCgRxW1zr661jrbcjwGqW+7XAs7mWi/Ssiyam01qHHx9L0QfIvvOOfzvTGu+33SUO5pUY3z/6kzZ+TwHLxzk3ob38krbV3Cyc7J2xUKICuS6T6hqrbVSShf1eUqpERhNN9SuXft6yyhbEiLgy7shMZLEgZ8zbFtV9kdE8sLt9WnVKIrhG17EpE3M7DqTPgFXtngJIcT1K0ybe17OX25usfyMtSyPAvxyredrWXYVrfVCrXWQ1jrI29u7mGWUQbFHYXFvSLlARP9vuPNXN45GJ/HRQy2xrbqeF7eOxreSL8v7L5dgF0LcMMUN9zXAUMv9ocDqXMsfU4YOQGKu5puK7+xuWNIHtIm9Pb7mzpU5ZOaYWTy8Cati3mRxyGLua3gfX/b9Er/KfgVvTwghiqkwXSGXAd0AL6VUJDAZmA58r5R6AjgDDLGsvg6jG2QoRlfI4Teg5rIpdBN89yi4VWdNy/mMWZVAg2pujB/oypt7R3Ax/SJTOk3h7gZ3W7tSIcRNoMBw11o/mM9DPfJYVwPPXW9R5c7x9fD9Y2ivhsyt+S6zf7nEbQ296dX+NOP+mo63szdf9PuCwKqB1q5UCHGTkCtUr9eRNfDDcMw1WjDe+Q1+2J7Iw+1rYVdtFdP3rKCjT0fe7fouVZyqWLtSIcRNRML9eoSsgBVPYarZmhHmCWw+nMKY3j7sy5jNvtB9PNn8SZ5v9byM5CiEKHUS7sV18FtY9QxZtdrzUOoYDpzP5JUB7qyOnkhcehwzus6gb0Bfa1cphLhJSbgXx74vYc0LpPt2ZlD880Qka14ckMPnp17C1d6Vz3p/RnPv5tauUghxE5NwL6r9X8Ga50n2vY0+0U+Tqm14tHcYC48voLFnY+bePpcarjWsXaUQ4iYn4V4UwT/A6udJqtmF7mefwtHZnm5tt7Ds1Fp61unJ1C5TcbZztnaVQggh4V5oR9bAjyNIrN6O7pEjqOxuR50m37M5cidPt3iaZ1s9i40q7jVhQghRsiTcC+PEBvjhcRKrtqB71EiqeEEl/085FBfOW53fYlD9QQVvQwghSpGEe0FObYHvHiGxckO6Rz+Hl08OpmrziU5NZl6PeXSu1dnaFQohxFUk3K8lcg98+xBJrrW5PXY03n6pJLkvxAlHPu/zOU2qNrF2hUIIkScJ9/xcOA5f30uqfVXuuDAG77oXiHVagq+LLwvuWEAtt1rWrlAIIfIl4Z6XxCj4cjAZZlvuTBqLZ71ooh2+pKVXSz68/UOZBk8IUeZJ944rpcXDV4PJSbvEfSljsa0XSZT9Ujr6dOSTnp9IsAshygU5cs8tKw2WPYD54imGZo0nyT+CePt19KrTi+m3Tsfe1t7aFQohRKFIuF9mNsEPj6PP7uKFnNGcrnOWJIetDG4wmNc7vC6DfwkhyhUJ98t+mQAn1vOmaTh76pwjzWEHjzV9jHFB41BKWbs6IYQoEgl3gB0fw65PWGzuxzq/RNId9vBsq2cZ2WKkBLsQolyScD/+C/rXCWwmiAU1Hch03MPo1qN5svmT1q5MCCGK7eYO9+iDmH8YzhH8GV+tBtnO+3ix9Ys80fwJa1cmhBDX5eYN98QozF8PISbHhaFeDch2PcTYNmMZ3uzmmdNbCFFx3Zz93LPSMC17kJTURAZXbU6W21FeavOSBLsQosK4+cJda8xrXsAcc4gBnm1IrXSKcUHjGNZsmLUrE0KIEnPThbve9iEq5AeGeLbhYuUIxrQZw9DAodYuSwghStTNFe6nfsO8cTLPVmlMqHssI1qM4PFmj1u7KiGEKHE3zwnV+HCyvh3KNPda/OWRxkONH+L5Vs9buyohhLghbo5wz0wh7Yv7Wepszw+eigH1BvFKu1fkAiUhRIVV8ZtltCZl+TP8ZIpiflVXuvvewZROb8h8p0KICq3CJ1z635+w49xG3q7qSVC1TrzfbYYMAiaEqPCuK9yVUmOUUoeVUiFKqWVKKSelVIBSaqdSKlQp9Z1SyqGkii0q09m9hPz5Ji97e1PPPZD5PefIsL1CiJtCscNdKVULGAUEaa2bAbbAA8C7wGytdX3gEmCda/nTLxG87GFGVa+Ku6MPn/f9GGc7Z6uUIoQQpe16m2XsAGellB3gAkQDtwM/WB5fCgy6ztcoOq0JXvooL3sqzLZufHXXEjycPEq9DCGEsJZih7vWOgp4D4jACPVEYC+QoLXOsawWCeQ5k7RSaoRSao9Sas+FCxeKW0aejq+dyhs2J7lo58infRfjW8m3RLcvhBBl3fU0y1QBBgIBQE3AFehT2OdrrRdqrYO01kHe3t7FLeMqMYe3MCNqKaEODky79QNaVAsssW0LIUR5cT3NMncA4VrrC1rrbOBHoDPgYWmmAfAFoq6zxkLLTI5nxtbn2OXsxPNNxtG73m2l9dJCCFGmXE+4RwAdlFIuyrgaqAdwBNgC3GtZZyiw+vpKLLyZ39zLRjdb+rv35Kn2Ml6MEOLmdT1t7jsxTpzuA4It21oIvAKMVUqFAlWBxSVQZ4EWrhjPd04XaGOqwTsD3y+NlxRCiDLruoYf0FpPBiZfsTgMaHc92y2qTQfW8mnSOhpk2/LhoytlWAEhxE2v3I8tczr+LFP2TsQDM+/cvphKzm7WLkkIIayuXA8/kJadxshV95NpY2Kcz1Aa129v7ZKEEKJMKNfhPnfdDM7ZJDEqy5/e/V6xdjlCCFFmlOtwH93qLuamVOfBx76xdilCCFGmlOs2d+c6bej2/GZrlyGEEGVOuT5yF0IIkTcJdyGEqIAk3IUQogKScBdCiApIwl0IISogCXchhKiAJNyFEKICknAXQogKSGmtrV0DSqkLwBlr11EIXkCctYsoIqm5dJS3mstbvSA156WO1jrPqezKRLiXF0qpPVrrIGvXURRSc+kobzWXt3pBai4qaZYRQogKSMJdCCEqIAn3ollo7QKKQWouHeWt5vJWL0jNRSJt7kIIUQHJkbsQQlRAEu5CCFEBSbhfQSnlp5TaopQ6opQ6rJQancc63ZRSiUqpA5bb69ao9YqaTiulgi317MnjcaWUmquUClVKHVJKtbZGnbnqaZTr/TuglEpSSr14xTpWf5+VUkuUUrFKqZBcyzyVUhuVUictP6vk89yhlnVOKqWGWrHemUqpY5Z/95VKKY98nnvNz1Ap1/yGUioq1799v3ye20cpddzyuX7VyjV/l6ve00qpA/k8t3TeZ6213HLdAB+gteV+JeAE0PSKdboBa61d6xU1nQa8rvF4P2A9oIAOwE5r15yrNlsgBuOCjDL1PgNdgdZASK5lM4BXLfdfBd7N43meQJjlZxXL/SpWqrcXYGe5/25e9RbmM1TKNb8BjCvE5+YUUBdwAA5e+X+1NGu+4vH3gdet+T7LkfsVtNbRWut9lvvJwFGglnWrKhEDgS+0YQfgoZTysXZRFj2AU1rrMneVstb6DyD+isUDgaWW+0uBQXk8tTewUWsdr7W+BGwE+tywQi3yqldrvUFrnWP5dQfge6PrKIp83uPCaAeEaq3DtNZZwLcY/zY33LVqVkopYAiwrDRqyY+E+zUopfyBW4CdeTzcUSl1UCm1XikVWKqF5U0DG5RSe5VSI/J4vBZwNtfvkZSdP1oPkP9/hLL2PgNU11pHW+7HANXzWKesvt+PY3yDy0tBn6HS9rylKWlJPk1fZfU9vhU4r7U+mc/jpfI+S7jnQynlBqwAXtRaJ13x8D6MJoSWwIfAqtKuLw9dtNatgb7Ac0qprtYuqDCUUg7AAGB5Hg+Xxff5P7TxPbtc9CdWSv0PyAG+zmeVsvQZWgDUA1oB0RjNHOXFg1z7qL1U3mcJ9zwopewxgv1rrfWPVz6utU7SWqdY7q8D7JVSXqVc5pU1RVl+xgIrMb6y5hYF+OX63deyzNr6Avu01uevfKAsvs8W5y83aVl+xuaxTpl6v5VSw4D+wMOWP0hXKcRnqNRorc9rrU1aazPwaT61lKn3GEApZQcMBr7Lb53Sep8l3K9gaS9bDBzVWs/KZ50alvVQSrXDeB8vll6VV9XjqpSqdPk+xgm0kCtWWwM8Zuk10wFIzNW0YE35HuWUtfc5lzXA5d4vQ4HVeazzK9BLKVXF0qTQy7Ks1Cml+gDjgQFa67R81inMZ6jUXHE+6O58atkNNFBKBVi+AT6A8W9jTXcAx7TWkXk9WKrvc2mcWS5PN6ALxtfsQ8ABy60fMBIYaVnneeAwxtn5HUAnK9dc11LLQUtd/7Msz12zAj7C6F0QDASVgffaFSOs3XMtK1PvM8YfnmggG6NN9wmgKrAZOAlsAjwt6wYBi3I993Eg1HIbbsV6QzHapi9/nj+2rFsTWHetz5AVa/7S8jk9hBHYPlfWbPm9H0aPtlPWrtmy/PPLn99c61rlfZbhB4QQogKSZhkhhKiAJNyFEKICknAXQogKSMJdCCEqIAl3IYSogCTchRCiApJwF0KICuj/hkQuW6a35OIAAAAASUVORK5CYII=\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 5c2afc93e29875095233e6b810afa970dd787da9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 183/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From d462bf1ae2e6ed5352ba43391549f55d76399f32 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 184/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From 0ad5e8803929bd0e06d6bf254fad2821ddbe0ad9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 185/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From b9de26909bc967ba3098591b0885127d1eaa9ad7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 186/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- skfda/representation/basis.py | 11 ++ tests/test_fpca.py | 26 ++++ 5 files changed, 304 insertions(+), 69 deletions(-) create mode 100644 tests/test_fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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mnphiXEvyvK2YnNdPrT1CfVEBx1eez+buXqr72mnqPEEgPApIOBQfZ/nbOevcBWxpuoCvHL4FOZ/hX09VMjr9T6gmF0gyxVUu3vbxRTg8llf3QBleU0bIG96SxvtibP3dSaaGMgx5OvFKGq7oPCqyYyzwuTFZvbSsLeaL3hy9GRWAUHiC9R1HUTUXB6llPJEHBBKCOm2YsxKnKIhOIGlxJEkGBEIIAg2zaSs8Rl22i015heJMElnLvqw+ulAQWAEFiSwSaSTp5d+vXNZDJhEkHfWSmvKgphzITifmgA9zUQilsBhTqBilZBZh2UlbX5ThoRimtEYBErMQFGBGemGlHF3oRHNhOoVKv1VDlocIykl+uXYTrSYLBeFxrtl/gMKhNpL5CCZJoik4SfHGt/FV0UzLdBvvmdCob/8Qg6aFIHQcboVLb1xCcJbRT/9mYYS84S1FTefZ/2g3J7YPkDXHaS3Yz6KJc5ByVpanuwmV1JMpsPLzdQXcEY/PtJt1jeWdxykdmuCIuYGR9Exr2iWSrEqdoDbRhTmVAMDq8JPPJdFyKktWzKFI3k7FdDfOmYUIULN+svm55MwLyFODJkKoWRNqdoyUmCRpCxOzTZO2TCLLCZzEcWsRArk4ASmHx6ShmGbKSqs2BqLFjAz7kfpVgskI8gvvk1WshEuqSc2eR2TeQg57K3jmdJScqtNAjlWWFCtcGewTEn5KsZlmuovSaITzIwxqk/xw7XI63QqzUlm+uqub2PgBhlOnAUGZX6NvRTW3s4t6Nc/1J5dwMv1edNmMySSx8WMLjYXO3iSMkDe8JQghOH1kgl33dpCKZmkP7cWmmamcWo4/M8Zyu47VW8Gh5UG+GhIMqzODoqHoJMtPnaA5Wcqo7gagQh/inNgRgtERJF1Dkn0o9iIkaYRsIsJFC0xUaMfx6FGEkMhkZ5OWNpAVZ5PTvUzl+jlt6eGYp4f9BT1MKHGcmhNnzolNt2HVrSiagkmfmXP/h5Z3XsqTk1T8xGnSxlmuTrBQTWBFMGWycdC9mBPaWiwjZnyDvRQMdVMx1Y+ia+QkE52BCjpqFrK3uJ5mUxA7KoscYS4oep7c8QSBTBke5xyKHDUUSjNdLhPmHE+VOdgRMnPeUJyr+rMcmNzKVLaDnJbH5rOwpzpMT2iKL3fJjI98jpi1FBCsfEcNizdWGfPp3+CMkDe86UUn0uy8u4P+U1Po7gm2Fj3F6oGN2LKFNEwdYXbVAtIOJz/dUMi9uTQ2WSKjCxacPoXck6FdK0ISOk3ZdpbFjmBPxkECu62InLQQk7WNIdpYHJriAi1MQImh6V4S2iUktQ1MqUm6RSu9tKH5JJzVi8lYSglP5QjHckRSeXQhIZDQX4h0s6SD0LFIOg5ZxWPKo6CBrr9s2xRyzKWd+dIp5ohhBLDX5mVL2WyU2nUsdDcQ6tBQ97UgHzlIYKgbgD53EQdmzWdz2VLGXX6WeyZZ636I6GEQaRj2LsXnb2KDYmVW3o2MzIRV4rhHsHwakvEBDqZasWT2E1EtqHY4Uh3mXMco81qupdNyLkgStfO9XPixxcgmY0D2jcoIecOblpbXOfpsP4ee7EWSBIOlj9ElVM7pvxRbLs3yaDP+2jW0VTj4wkI7vWoORQI5l2P+oeN0RAtB01iUPMXS+GFMah5dsVDq0ZkwXUS7ey+j7tNcPl3E5aYe3NYe8qKQiHolfdkS8vE+oq3NHKlqor22iX4cTKgm4sKK+CsHUGUJSn125oRczC6wU+VXqHHpSNk409PTjA72EhvuYJHezFJO4CBDjxzkDkcZT3ly2Lx2Vs5ayXmWRqqPRolu3om99TiyELQFKthccTb7ZzWytGSYZanHiLU4ycg2dgXOweP1cf2cCibHFRZOS1h1SMsCc07lQLQHp2MX8WgvwykPSVueROUU7xov4XntY+gmK/6AzJVfXD1zdSvDG44R8oY3peHOabbf2c70aIqSeTIPmr5J6dDbqYw0EYq0sMQhYQot4K6VPn7q1bDJMglNp3hqHPlIBFXVWBRtZkGyBZOWJ+/0YClSSHjG2enUmaN6eOfAIhY4T+NRHkFgoS+9ks0pJ+kxN3v9s2n3VRCTZy7gIaNTYBVUBmzMLfExp6yAkMdO90SSJ5tHODkcw2ExcXFTCW9rKqbc7yCpaoRTKiORDCPRNL1TKdpHY3RPJMnrM9+9mgIny2uCbGgIsaomSCoRY+TQE+SP3ElVphkvCQYpZqvlbJ5zSXTbeohZYzQVNrHRdTbLjudIPfQMtuF+0mYLz5YvY0vt2SytO0FpZxfqhJkuRzUHClawqcZEwfIltDdPc/FwjgVRHSEE0VyUdjnMfPFdnomXo0bNpOw5ltojjKRvJG0LYVV03vmV1bgCtlc6bIYzwAh5w5tKOqGy94Eu2vaN4g7aKD67n1t7f8+qrvfjVD3M63uKusbVTDtDfGmtj31SniKLmTE1T8WxLsRAgkWRE8xJdiFLOprPxUCxlfFQG3mpkEvHz2Z1fAkuZZyA8h0UeYRBqY6b4/MZzq6ix1GKkCRs5Ci3Zlhc7mXt/ArWLqrDYZvp5xZCsL19gh8820HzUJTygJ0PrKrm6qVluG3/+zIB2bxG60icAz1T7O8Os78nTCKbx2kxsXZeiCvPKuPcugLMk22kHvkMpuGDWMnRRQWbWUvYGWLEN8Ih+RBpc5qzChfzD/kmqp/pQ966HZOucbBoHtvrl9BQdRDpuE5GsrE1uAb8Qa57+3y+poIvkuPjHWnOnxBIQCSfwapsZUB6iu2TxXijZqzWND79XKKuNcjoXHLDIsrqjQHZNxIj5A1vCkIXtO4bYe+DXeTSGos2lDPs+A0PH4mxvO9SHLkoi3sfp3D5tTQH7PzrMhdhoeM1m5hKZGh89nnqp05RmR5AmBXsVSoHPXYG/BOsSC7g0olzmKXPQmgZnJlf4A88S9rk5t+zV/FQfh1CkglISWqUOOsbS9i0cj4V5eV/Muh4sDfMt55s5Uh/hPKAnRvWz+GyRaWY/4Y+62xe4/nuMJtPjbL55ChTSZUij5UrzirjfSsrKckNwcMfQQzOfD9aLIt5TF1BVrJjCVlodbZyVD+KYlK4xLeaTcftyA9sxZVOcCJYQ/OyCkK5fnKTEu3OOnYXrOKSOhuti+dwIJaiLKXx2+fTuNQcZlkhp6tYlPv5oasbRwd4Ugoe2UnGeQWyKciqS8pYfPErXujN8DoyQt7whjfWG2Pn3R2M98YoqfWy5p113Pv8x+k8tojZU2dRON3MwuQpHAvey31zHPyg0kzAYiapqtQePMji5n2E1EkyZjtlK0vZaj6GrhVyYXQl58YWY0ZBm+5FG91KqO4pXK40z+lL+Iz6YXJYqDVNcW4gx6aN5zF37lzM5j/tex6NZvjWU608cmyYIo+Nq9dVM7vaz1AkwlTfAOlIhGwsTlpVycgmUmaFsNtLMhBEd7twms0UWswUWRRKrAqzHVbmOm3MdtiwveQHQs3rbG0b575DA2xrH0eWJC5dVMqH19QwL98JD/wjTPegY6Kl5Eo2x+cQTyRw+9ykS9NsVjcT0SLMdVTz7lNllD58EG8mQWtBBaOLFdITkLR4eSy4AbfXyfJLFvDbWBpFE/zsQIK50wkS+SyF1iBCT3PCt50HM4eY12XCpJmQLU2YbauY01TIhuuXGzNv3gCMkDe8YaXjKs8/fJqWvSPY3RbO+Ydaqhc5+ebvrsPccgX+dIja7keYWxYkN2s931ri4imfRKNFwnp4L8sO7sSjxplWvOgN89HqDuDur+HCyDmU5grR8yny/c+THdiNqB2moDaBjzhfy7+PbWIZjQyzplRh47uuJRgM/tk6qnmdn+06zU+PDyBcEudmhijraWNOdwc1QwMEY5E/+7qXylmtjFdU0VdexamyKnbVzmWgsHjm8n9ArcPKcq+LFT4nK3wuyl7oFhoIp/jV7h7uOThAOqfx9gUlfGbDHGoGH4EnPwu5FMLmp23Zt9jdnWBoaAir1Yp3tpdd5l2cjJ2kxFzAJQdCLN7ZQzAdp7W8jJFCK2ldZo9/Bac8DVy8opAHAy6yms4XTqV520CaE9MHKXaeRanVhi6pPOnZw3D4efwDMgIrZvtqQsVNXPHldcaA7BlmhLzhDUfXdE7uHObAY93kMhpN55dx9turUbPDfOVXX6D09DU4cnnmN/+KyrWXM2qbzadXuhjSEmzqOEzRkd3Ys2mGrcWc9M9hzYoMZb121sSWoAiFTLoTvWUX6vARojUW2pqquULZjY7EN7TrMOXMhOzDvP+Gr1Lo//MLc41nc9zaMcLvu0Zp6D3BBYf2svzUMZyZNLosk62uwdrYiL+mGldFOSafD9ntRjKZEPk8ejpDfnKC/MQEuaFhMm0tZFrbEIkkAFqRn4kFtTQvXciO2fM5nguSEDP9+bNMcc6xDrHaPsJ8S5SM5uahllLuP+FGzcNlC6x8am0ps3Z9G7nl0ZkKz93EwMqvs+/AYVpaWrBYLMxqmMUeZQ/7JvfhF042bi3goiMDWLQ8nVUFDDoc9PuqeTywnrKQlbHllUxoGu/tVbm+Lc3Byc3ETPUslq0UeENk5TzPubZj6zrEZMqMZCrC6V7DO79+Fd5C46LhZ4oR8oY3lOHOaXbe3cHUUJKyeX7OvWYOgRInY0OHueWXD1A5cgGuVD8LW/6boqtu5pju4qtz09Sf3Mu89iPI+TzdjipOeBewqjDF2/KF1KUryEhZRtMdFBx+ADExzGDQw+6Vy4laHHxP+QVDFPJQ6kJi2dMEr1rHx1d/Bll6eT96Iq/x+ESE+0amOT4yzjt2bObSnc8Rikyh+f1416/Hd8F6HEuXYXL96dWVdD1PKtVNItFGKt1HOt1HOj1AOt2Pqo6DANM4WNtkrK0y1nYJOSuheSCz1ETP2mqOlSzksFbPSTGXPGY8xFjBXtaILfizkzzRfSE7BldjNWW5ou4x3l54mMbWCdzxLHGPndTaT5IJruPAgQFOnWpDURRqFtSwW9nNzrGdFCWcXLLZz/qOPrJWM+2hAAPFRTxYcBERZwHBdRV0y7BmPMf3jmY4Gd5BZ1aiWKqh0TKFP7CQuJxkIv8MxwaGyekJzJb5vP2TH2L28trX62NkeAkj5A1vCInpLHsf7KLz4BiugJXVV9ZRs7gQSZI43fIsv/1dK8WR+ZSO7qEhuhvf5V/modgQxxMHqO1pRTeZaCmoZ9C8gPUOM5cKF17NxahljF61h+IDmwkODxGz23hmwSIeKdnABabDfNP8K/r0Ep6ZquP+hkFu2PRVLq65+GV1O53K8OvBSe4ZDZNPpnjnM4/zD1ufwp1NYV2xkoL3vAv32rVIyh9nzuh6nkSyjWj0CPH4KRKJVpLJTnRdffE5FnMIm1yGVSrBZpqFRS7EYi3A6irE6i/GYvGQ3n2A+BNPkti+A5HL4Vy1Cv+170OsOodtkQRPTETZPBElKwT1DjNXFMg05cP8+NlpDg/KzClI8Mkle1jU/wTlPZNkbDIn57lJeJ1YbXVMT3vo65PIqRXMalzEw5lHOD51nKpBD+9/2kLjxChjHietswp5rmwtB53zKV4WoC9gZ34kz20H0vREj3As3onFfgH1Y1vR58+jXmsiLU1zOtxJS2QHQrIyd/klbLrhvciyceLU68kIecMZpeV0jm8d4OCTvQhNsPjCCs7aWIlimTnl/+jue3nqARVPqoi5nfdRW5hkqvHtPDe0CyUyQM5qp63hbDLxuVys2TgXKwDHva1M+fbi3Jqi8VQLmCR2lM/m4YaNdJgr+JT5QT5lvp/RTBGfLfAwWO7iR+f/iIWFC1+s297pBD/pH2NbOI4CXHNwP5fe/RsKU1HUFauZe9ON2BoaZrZDyxKNHmI6sp9o9Aix2HE0LQWAogRwyLOxpSpRxktQRoswTxUi66+8mqPsNGMOObFUuDEHdNIHNhO57x7y4+NY582j8PpP4jr/fKJ5jYfGI9wzEuZYPIXLJPOu4gCVEY1bN3cQz+T57EVzuK74NKb734+kppgs9tG/cCFxtQ9Nm1mXJ5t1kE5XYA42cPv4QTqSk6zbV8r79o5j1VS6Qz521a/gQc9alDoP0VofFUmNO/alGI23sX9qKzbH5YRSXfQVHWCh99U2uC4AACAASURBVDIa07Wk8lEOhA8wljyC01vFlV+4mYLyslf7o2T4C4yQN5wxfSen2HVvB9HxNFULClh9VR3eQvuLj29/+DaOPFuELSfRdOKXcFYprak08fQkUZeXzPL16GIOl/TkqcVEVE6y2b+H3Jweatv7mfWAii8aZSrk4Zd169hduBIZ+JJyJ+8xbWbIWsulJTlqgvP4yfk/odhZjBCC3dMJbukd5flokkKLmXdrKvVf/yZz+08xWVJF/be/RvDsJSSS7YTDuwiH9xCJHEDXs4CM21WP13MW9ngdSucstFYz5ATIEpYyF0qJE3OhA7Pfhuw0I9tfGJgUoGfyaPEcWiRLfjxFbjSJOpwATYBJwlrtQo8fJ/7k78kN9GNrbCR00004l58NwPF4il8MTPDI+DS6gAvdLtTmMHvbJzi7OsAP31FN6ePvhcGDIMmIdf9GavGlTEcP0j+whXj8EGbzzI9TmgB7ogl6JhXWPRRiTfcQEbuVffX13DHrEibLi1Hn+ynMCu7em2Q6O8D+gftQ7BsxO3xM6rcwWl/Px0Yvw68VMJyJcHTyYZL5KZZf/k5WXnkVssn0On/q/v4YIW943UXGUuy+v5O+5im8ITvnXj2Hyvkvn73y8C//k4FDc7BmhigYuJPRUi9pNUPCW8TxRet4r3s+c9vieDXoUiZ5tOBJBooneW9hHu9vxgkdjKHaLeydPYc7Kt/OsLmQWXKE72v/xUr7CU4VzOY9rixrKy/gm6u/iUNxcCSa5Cunh9kfTVJsUfhkRSHFjz5L6Oc/QNHyxK/9R+qvm89keAuTk1tR1QkAnM46Av5zCARW4zYtJLM/RvLgKHoyh+xWsDcWYG8MYqn0IFv+GGq6niOZ7CSR7CCZaCeV7kNVp1DVSfL5l6xTL5kxCw+mjAt52okyXYQlWYJ9Ik1uzxby46O4N26k6KbPoZSWAjCSVfnV4CS/GZoklddYlpDoOjSK1Szzo2sWsmbgVtj9HzNvULUarr4dHAHy+TwHDjxCW/v9+Hx9eDwTSJJOXIORQReLHtVwtWucnFXC3Qsu58isBvILA3jzgnv3pEiIaQ50/oq8fQGKfSV5963cU9fPtZPruHT6EsBMV6KLk+EncJeUsOmTn6KoZvZr+4H7O2eEvOF1o2byHHqyl+PPDWBSZJZtqmbB+WUvu9qQruncecsviHQWYYptQdV70WSJoLeGkw3raBTFrB/PI4RgjynCo8X30OI6ybsqL2Xd8Has/xnFNp1juKaY+ypX8Jx3BQKJS82HuTn/MCFbL0f9JXzQa+aDCz/CJxZ9gqFsnm+eHuah8QiFFjOfrirmUred567/N5r2b2aytAD7Z2eTcB5B0xKYTC6CwTUEg+cRCKzGZi1Gi2WJbx8kcWAENIGtPohrRQnW2T4k+Y9zxVOpHiYntxKe3kckcgBNm5lNI0kKdnslVksBiiWIYvbACwO/Qs+Ry02j5qbIZsfIZIZeLM8Ud+N7JoB15ziSyUzh9dcT+MAHkF5oIU+pef5rYJxfDk6SjWcpOBUjHslww/o6/rm8B/m+90M+Dc4QvPtumLUEgFgsxjPPPENb22HKyqI4i7pwKqexyqBFTXj3C9ItTh4t38SDleeROasQnwZ37UmRUTLsP/UzMrYgLusVRMu38ljoSUKai2/0XoePelShcTy8jd7EMZZcfBmrrno3itVYEuG1YIS84TUndEH7/lH2PXSaVExl3spiVlxWi9Nrfdnzctk8v/zCj8gMD6GrHUgI5i44m5C+goTsoi6hk1UkHpCzPO7azHToKZw5H19b/E8E7v4P7I9nSdkdHFvayIPelbSYKiiWotyi/BfLSoIoY9s47PLyyVCQL6z+GmsrN/Kj3jF+PjiBBHysPMQnKkIMdPYwcMOHKe8fIb5OEP+HHIotSGHhBRQWbCAQWIUsz9Rdz2rEt/UT3z0Muo7jrCI851dgfskaLtnsBKNjjzA29hjx+EkAHI5q/P6V+LzLcLnrcdirkOX/fckDgHw+SSp1mtj0SSb7dhNNHULEp/DcZ8Z+QkaeV0zpt7+He94fv9cTao4f943xm/5xlJYoYijFBfVF/OQiD/a7LofoIEgmeNt3YNk/wQsnMXV3d/PEE08wNTVFRW0RR6U7qfOPUW/VkWUwDUic7q3lJ44PMbqoBm8efv98irRNsP/Uj9HMJuzKVUwUD7On7BfEFME3T6+iKPseAmaZyWyYw5OPIHwyGz78SSqbFv0tHzXDn2GEvOE1NdYbY9c9HYz1xAhVeTj3mjqKq70ve46ua7Tu3MMzv/4NenYCWZeYZ/Vz1ttvJH0ygVmDAadM6zw3321rQyq4HbOjl9pUA99pvIjsN7+P0is4XVPNvgWL2SwtYFK4WC+a+YntFmwrPoQ4dBttiolPV87hexf8lHG5ips7BhjM5LiyyM/N1SHs6cMc3vFrin6wD2VaEHmvnYIrriBUuBGvdzGS9MeuFiEE6eZJok90o0VVHItDeC6owBz845hCLNbMwMBvGRt/AiFyuN3zKSq6hKLQJmy20ldtH+uazvTxwwy3PES6axvOx6aRsqC/dw4lH7qJQOCcF888PZ3K8OXOIbYeHUFpi1JV5OLed9cReuz9MHhgpsCma+DSH4My80OVz+fZuXMnu3btwuFw0K4M0BzYzrq8zIXWJKJER8/KHI4t4eHAlUyp1dy9P03YLXOi57cQm0CyX0Ta6WNP4w8YUlJ8prsW//QNNNhNWCSJnlQLxyafZe5553He+z6IzWlceerVYoS84TWRjGZ5/pFu2vaO4PBYWHl5LXOXF7+s6yKXzdCycysHHnmQ2MQoSG7mZTw0zT0P2VwBmmB/wMRTtTb0IjvbdzyDvfguZFnl3NQaPo0V9b8eIW8ys2P1Wlp85WzNz0UImS9KD/Ae+6NI679Edud3GdOzfKl+FTeuu5WfDOd5bCLCHIeNr1VKlMYfYGT0UUTfJIGfKIisCcd3bqT2wg+8LNj/QIupTD/YSaYtjFLixHfZbKyVnhcfj0aPcLr7B0xP78NkclJSciVls96L01nzmu5zoQtSxycYf+Jpkvt+gen0FOmFOrmP1lDV8ElCoY0vbs/OcJwbd7YzeXAcu9XEb69dwIrDX4Tme2cKK10C77oL3EUvlj88PMzDDz/M+Pg4ukPmcd9WZMLcdMBEWVWC1FIdyQJtYh5b1Yv5/L5Ghj0KrcnNmFoPk/MsxmpawdZFP6bXNsRH+6uxDn2CeRaJSrsNVeQ4Ft7ChHmI9f/4MeqWrXxN99ffi9c85CVJ+jVwMTAuhJj/wn0B4B6gCugFrhZCTL9SOUbIvzloeZ0TWwc5+GQPWk5n4fpylm6qetmp7dHxUY5ufoKT254hm0wim0OUu9awSFKw+SqQFJlxh8yn6i1kg1ZmJfKcOHkn1sLNyGqQd6bXcUXzYfTdHYzMLWT3gvPopJA9uWo8msYdwduZn9mCtvE7xHZ8C5GN8eOzLqV60bf4Rs8kWU3wTwXTbMj8gmRsP0hmhk/V0XDbALLNyZz//g3OeX9+ga1U8wSRh7rQVR3vxipcq0pf/OGKJ9o4ffr7TE1tQ1GCVFV+hNLSqzGb3a/Lvv8DXdWI7xhg8le/InviQXQfhP8pi7m+kprqGygquhhJksnqOl843MM9j3cg6fCxy+Zx0/TtSLtvmem6cRXNBH3pH7tQXtqql00yO53tjPqPcdkBH1c9P0X8HJi8yILLkWBML6a280L602s47e5G2fIQWqAcm34JWxt/x2nvKd4zWIVv4BP4tSz1Hi8BxUREn2D/yOOEzprD+dd9BKfP/7ruv7ea1yPk1wAJ4HcvCfnvAmEhxLclSboZ8Ash/uWVyjFC/o1voDXMzrs7iIylqGoKcs6VdfiKZk5nF0LQ13yMo08/RveRg0iSRNXs5bgjjVTbfNhMJiSrhvu8GrZ1T/LPVRIVVgXLUISh8V+geI9ijtVzY6SBZc89QX4yyeELzqLHU8sxrZTjWhmVeY176x+gaOBhMhd+g979P6Y6Nsbtyz/ErtKPs3kqxkLLFB/I30JIa8duryAmbeK2e238y5bf4vB5mPP7O7CUzfqTbdNVjcgjp0kdHkMpcxG4ei5KaGbb8vk4p7v/g8HB2zGbXVRWfITy8msxmc7sqfz5SJaJW58met/3EGqE1Af9RBYN43Y3UTf7Zvz+FQDsGJzmQ789SDadp+m8cu62bcG19ctgsgISXPELaHjHy8oeHh7mgQceYGpqitOWOMdLn6VhwMlN98Ux6zrPvmsx7hUT1EqnkVUXsakNNHvOwX7nrzH5AghxMftrtnKqeDfvGC2nrOd6lEyCIoeHepcNiyzRFT9GR+YQq699Pw1rzjcWO/v/9Lp010iSVAU8/pKQbwfWCiFGJEkqAbYLIV5xbVIj5N+4kpEse+7vpPPQON5CO6uvrnvxIs9qOsWpHc9xdPMTTA8PYvd4Ofvsy/BGZmGdzCNLEpmpdoKXzMd70VLuve8kny0RzLFYSLT1kMjfisnRj238HL7aa6Jy+zZGq4IcWLKClORkb7aCLopYZjbzu6YHsbfeTWTtTew6+XsumRzkgSUf4Qvea4hpgqvFHWySnqaocAOhoqv52V4v2zYf5JZ9P8ftdlBz5+1Yysv/ZPvyk2mm7mwlN5rEva4cz/oKJJOMEIKx8cfp7Pw6qjrFrFnvprbm0yiK7/U+BH+REILEni5GPv85tPF25IuWM3FlJ9ncKKHQJubU/TtWaxGj0TSX/Hwf49NpbEsLebDwKI3bbgbFAWoCNn4HVnz0ZWWrqsrTTz/NkSNHiEk6e0q24cik+dqdKsFojq1nn8e9157L5flHaLIcRtfsdCob0e/qRs8pZOzn0VE0wP7Kx1k/XkZ99ycxZ1KYhaDGV0iVzUyODEcmtkC1hQ0f/iTeUNFf2FLDX3KmQj4ihPC98H8JmP7D7f/xug8DHwaoqKhY0tfX96rUx/Dq0DWd5u1D7H+sGz0vWPK2ShZfWIFZMTHZ38uJ5zZzascW1HSaWTX1LKl/O64JJ9pEBlUXjMfGMJ3+NU23/QxreSW3P3CKfw3maTRbGD1xBM3+CyRTkuLeC/jasTbsPX0cXreQAX8NeSGxM1NJv1TIO4I+ftD0NKbnf8zwsuv47cAWPj86wJMV6/lg9RepED3caLmXleVrKSm5gsmUk4/feYSBjj5u238rbjNU3XE7lqqqP9nGdOsU4XvakWSJwDVzsc0NAKCqYdo7vsT4+JN43AuYO/ereDxNr/MR+L/ToikGPnYz6SPPosxehvj6QgYjv0SSLNTWfpayWe8mmta46rbn6RyLoy4K8m3/Kd637yYkiwsyEVj1z3DBV+B/LEvQ0tLCQw89RCaX45C/mQlHN1++D2r7M3Q2NPGJj3yGxfE+Pp97gGTRIVTsJLpKGNlnQfUvp8+psKP2Ls4Ol3J25ycw57KYUzFkfxUr/TLWvInJ7BDHottYcNXFLLpoE7JsnET1f3XGQ/6F29NCiFfseDNa8m8so91RdtzVzuRAgoqGAOe+cw5Or0zH83s4vuUpRjrakE1mFi25iDmBpdCjIlSdjMNMy0SG/MDzBLMPs+rOpzD7/Nz5SAs3eVUWSQq9R3cg+36D0K0sbF7NTft3MBzwc3TZQnJYyAkTW7O1jEh+PlhTzBcWHER6+l/onnM+v8+d5ObeCQ57G7hiwY+40nGKm2fXURRciSTJ7OyY4Ia7j2LKZPjl0duwjw1Teecd2OrrX7Z9My3gYaJPdKOUugi+tx6zf2a2yeTUdlpbbyaXi1BT/SkqKz/0Zwdo32iEEIx+4ydE7rgVU6ge/1duZMB3G9PTe/B4FtHY8H1ylPHO2/bRMZ4gvTjABy3H+Nqxf0OyByA5Dk1Xwzv+E8wvX5IhGo1y++23Mzk5yWnHCCcK9vPpZ2XOPppmtLKKD13/b1TGTNza3cuhuY8QCh5CaGbGj3lJRM+lQyvluTm3My9WzPltH8Os57BPDRIuaGKxeZyKQBlC1eiIHmIyOMEFH/0EwbI//avL8KeM7hrDXyWbzrPvwS5O7RrG6bNy7tV1uAMJmrc+Q+uubWRTSUIl1Syp34Q/WYA2nkFSZGwLCmgZS3LiZITygeew2ray/o7nMNns3PtkO5+2pZkvTPQefwIleA96toBLd83jsq7DHF62iIlgEZZsnLjZw5bcXCaFi88ureYTjV0kH7uOltpSxh1JVh9LE5W9vHPZf/KNpnmsC82skaLrgp9s7eKHz3Uwr8DBD5vvRD/4POX/dSuuc8992TYKTRB5/DTJfSPYG4P4r5mLbDEhhEZ393/Q23crLudcGhpuwe2u/3O76Q0tfNf9jH31S8ieMvwf/BLapjE6er6Ormepq/s8du8VvPu2/fSEk0hLCzg/9zz/2fJlZFcxxAahZi1ccydYXz7NUdM07rvvPtra2gibk+wv3sm7jmS5YFuWaEEhH/3Ul2iMW/heG9wa7CV41jMskvaST5uI9dZzaHgVW2ruZ1aqgItbP4JZ1/GPnGS4ZDVF0XZWNpWjxzxktRTHprdRtnExyy67EtOfuYiL4Y/OVMh/D5h6ycBrQAhx0yuVYYT8mdd7YpLtv28nFc3SeF4Ib2CQlh3PMtLVjslsZvHCTdR4FiAN5EETKGUunEuLsTQEeOa3J+lriVLT/SiZkkNc8sutmBULD27p5Hopwdy8xODJ+7AEH0OKV/Dxp10U2PK0NtYjmfO4xkeZ8FXybL6eKeHiGxfMZl3JEwx1/IiIz0xSt1Lc4mHhdBdfWPMbPnfORgotMycXTSdVbrz3GNvbJ/iHxbP4dNujxO+6i+KvfAX/NVe/bBv1rEb4961k2qdxrSnDu7EKSZbIqpOcOvUppqf3UVp6DXPqvoTJZP1zu+lNIb5tO0P//CkkWwD3xf+K+wO1dIW/Qnh6N8HgOgrLv8a1v2lnJJqheE0pVeHt3Nb6FSR/JXK4G8rPhvfcBzbvn5S9ZcsWduzaQ17OczC0j/P6J3jHI3nSDic33vBvrEj6uKFX4gumDIPLp/is6w6stJFL2ugaWMntyin8WS9XtHwYkw7FQwfoLV2PMzHESlMLrvnvQA/rjKX76LG0sPqj11FcW3cG9uKbw+sxu+YuYC1QAIwBXwIeBu4FKoA+ZqZQhl+pHCPkz5x0XGXXvZ10HBjF5ZvEG+xj4OQBctkMs0rnsahuA56oFz2aQ7KbcS4O4VhahKXURTad54kfH2akO87cznsYa+jg6h9tQTEpPL63j4+mw9RkBKMdv8Ua2IZnZDaf2CUxMreKuMdDwN2H1JJgrHgez2iNTOLi8+f2MMf1G3JaFCWjc3e2itrkYj7T9SueW/5F1m78f+ydd3hVVfa/39tLbkvvvZGQEHrooKAgKCoiiiIKiA3rqOPYRcWGo2AHsaOI0pWO9BpCSYeEJIT0enN7v+f3RxwcBixYvj/H4X2e++R5knP2XWefk3X2Xnvtz/obku8zMQpqO7nr88O0Wlw8PT6TcY1HafzHPwi65RbC/3FmQpff7qHtoxLc9RYMV6agyY0EwGQ6SlHRXXi8naSnP0tU5MT/83vwR2DLy6N25u2IlEGohj5I0HW9MEZs5ETly0ilBsLj5zHtcxsur4/BlyVhr1zDgtLZeCN6IG8uhohsuGklqIPOajs/P5+vv9mAUuSh1FBKuv0EE5Z4QCzlsTsfZqQ7mmsaxDyAnUOZGuamVaGrm4syyI7DEcxXRoEGUyDXld6G2CchoXEHFWGjkXrt9CxdSMzEGXjMkQhuP+Xmg8gHBDPw+skXpBHOwYXNUBf4UQRB4ER+C9sWH8BhKkQiLsdl60CjDqJ3xhgiJQnQ6gMRKJINBPQLR5UZgkjWtTBnN7tZ83o+HQ1WMko/4fjgBma8sAmZWMamgnpubW0h1uGjvWohMsM+MoszGdWmoT4+FhUWokKP0nHQQHt0Glsl6TS4g7k95yP6hRcT2CkQVG/ilvDLCVRezFdH7qUzeQwhN34OIhGCIPD5gVM8+00poVoF707pTZqtmZPXXY+qRw/iPvwA0b9N830WN20fFOFpdRB8Qwaq7l2Cac0t6yktfRC5PIwe2e/+V4ZnfgpbXh61t92OWB2Msv/9aIakIRvloajsHpzOeqRBj3PPqgiCA+TcfE0GZfsX8tLxVzHHDUNXtx9CUmDqatCEndV2SUkJC5d+S6DYQV1AHYGKQiZ+5ELl9vHsbQ8wwZdKrxY/M3FwMk7FzN4hdNv9JNqUMhQ6DxU2BVubQhl19G7EfhkZTd9SahiHX6Ykq3ABUbEa1KNm4an2YveaKfcdJmfGeGKzcs5xpf+7XHDyFzgnxiYj695eTnNlHoKvAYlYTk76SBJ12UjbROAHWUQA6l6hqHLCkBrODF3YTC5WzT2IucVKZslC8i81cs8TG5FL5Ow60cqUk3VE2710nnwXiTaPKw71QaONwiOTEactQS09RWtFMOo+Ur5uG8kxYwqzen/DNX2zkWz5lPCGCq7q9TDVqqHsODKTYLkC6Z27QKnH7vby+MpiVh6pZ0R6KK9P6onO56R64rUITieJK5YjDQk5bavX6KRtURE+s5vgqZkoUwMRBIFTpxZyovIV9Pre9Mh+D7n83HVe/9ux5eVRe/sdSAzhKHvdhzwxHP0NMRyvf4K2ti20MYUntw4gK0rHQ5OyyVv/PPdXLqA+5QqiT24BfTTc/A3ozpZqKC0t5fUlG4iSmOiUdyIKLuTa9zvR2d38c/rdTPFkEWhyMw0XxmA5vbsHc1PhCuze74jMNYLYzaFOLSG770NwBtGzdQklynHYAyLJqFtJRM1Ogm65H7c3HVGnQKO9GnuakwHTb0ShPrs61/8iF5z8BU7jdbupPprPoXWbqT92BAkQF9KLrISBqE1q8AhI9ArUPUNR9wpDFnHufyKr0cnKV/KwtVlJL3uXHeOtPPrgBhQSBUcbOplYUk2g04Pr5NsESssZXZGLW2fAYOwgtc9OHDYxUq2HgEgH7xdOJa+5D0+PkXPDkCEc/nQM2fXHubrPqxQpu7Ouag69GrYjmrERovtwosXCrM+PUN5i4YFRadx9UQoiEdTdcw/W7TuI//RT1L17nbbV02qnbVExfpePkGndUcTr8Pu9HC9/ioaGpYSFjSMzY+7vHn/3C35a7C3UWmppd7ZjdpkxuUx4/d7Tx6ikKvQKPTqFjjBVGDHaGAwKwx+yKci2bx+1t92OPLkb8u53IlapCJrSjSaWUFn1GiXmcbx+4BIuzQznsQlZHFj2AJOqv+BI5lR6nVgB2ki4Ze0ZMgj/4ujRo7y4bA8p0mZ8Eje+iFIu/6CBEJOdhVNmcounH1aXlemCD5dagiZJxzOWozTsX0PiaB/q8CpcfhG2ksswlV9M345FHPOPxRiUQTdZOZGb56NMTUM35TFsBQ7wwUlXCWHjM+l20Yj/+U1UF5z8/zh+n49TxQUc27OTiry9+J0eItTdSDT0IVIVjsgHYrUUVfcQ1L1CkSfoz9Cf+U/MbQ5WvZKHvcNKSvnbrLvawvN3b0QtU3Oiw8b4/HKkHg/y6jfIsrlJtHVD6vGQaC8lfEwxPrEYicyPw6bl05IbyOvsziNjujGxv47VX01gZFM71/d+jTpZBIuFPC7a+TCMegZh8P0sO1THU6tLUMslzLu+J0NTQwEwfvUVTU89Tdjf/07w9GmnbfW2OWhZWAg+gZAZWcijNPj9LopLHqC1dSPx8XeSnPQ3RKLfVq5OEAROmk9ytOUohW2FFLUWcdJ8EpfPdd5taWQakvRJZARnkBmcSU5oDkn6pN/FkZk3bKT+gQdQ5w5CljEDv9lH4FUpOBJLKS65j801I/m89BLuHZnKbSMSOfzZNIad+oa1vR5mbPHbiAzxcMu3EBByVtv7DxzgmdVFdFecRC2IEEXXM/zTUqJaTXw1cSo3+odQKWrnLkGKVyzGF6/hHncN8j3L0KapEOWWkaCw4rYG0370amKrt9BmHElTxACSo90kbn4Jf1srQdNuxxOYi3DChdvnpF5RRcaM0YQmJv7m/vlv5YKT/x9E8PtpKD/Gsb07KN+/B4/ZTqw+k2h1DuHyMCQiMeIAGaqsYFRZISiS9IgkP+/oOlvsrHolD1enhcSKt/h6golX79iIXqGnwebi8j1l2P1uEo4toFenAbVEQar4CPqUWuShDvw+MFXraGkbynf2TA55Y5k+OJGrcr18uPZWrjQFcHvWHCQKLZ/ESum/5FII74518mqeXFPGyiP1DEwKZt71PQnXdS3AuaqrqZ5wDepePYldtAjR9xt5vB1OWhcUIHj9hM7sgSwiAJ/PTmHRXXR07CI19QniYqf91OX+JD6/j4PNB9leu50dtTuos9YBoJVryQ7JJtWQSpwujhhtDGGqsNMjdrm4K/9cQMDhdWBymeh0dZ4e9ddaajnReYKy9jKsnq6yfaGqUAZEDmBA1ACGRg8lUPnrtV6MS7+i6emn0V0xHlnajbgrTWgGRyEe5qSg6DYWHLmU3fV9efuG3ozOCKZq0RUktOTzQe5L3Jb/FJLgZLh5zTkXY7/bvpOnN9WQrS4h3KtEFmUiZ+lhkutb+G7cJMbLRnFU1cgjPgV2txRvXAADOxoYWrkav0yg9jIj/VSnCJN7sTen4Si2oznSj+q4scTESelt/w7rsq+QJyYS8uBsOg7bkHVIsHg6sCbayZ42HmXA/14I54KT/x9BEARaTlZxfO9Oju3didDpJUabRmJoT7QePSJEOAFlRhChQ6N/dsT+nxibbKx6JQ+PyUJc1Zt8MsHI/OlrCQsIp8Pt4YodpRjdNoYWf0WmykpUUDmBoQ2IZAJ+r4iWoyF0HA/DpRlFgQ52eZK5okckg3uf4MN9zzPRm8OzyfeRrFbxaY8k4pdOhMajlE/YwO3ftlPTbuO+kWncfXEKku/tFjweTk6+AU9tLYlrViML7woleDudtL5XyCd6zwAAIABJREFUiN/lI3RmNvIoDV6vhaMFMzCZjpDR7QWioq79Vf180nSS1ZWrWVO5hhZ7CwqJgtzIXIZFD6NfZD8SdAmIf+PMALrCPafMpzjccpj9DfvZ37gfo8uIRCQhNzKX0QmjGRk3Er3i7BTHn6P1rbdpe+stQu+/H2n0KKx7GlCkGNBMDOJw2Sye2n4J9bZ4lt81lMxAP+3vjURia2HugHk8tf9elKGpXYuxqrPlHVav38QzOzvoocsj0aVDGeElae1hsiuqOXTJREYEXMqB4Drm2OV0OJQIUWo0DS1MbVuPyN3B4XFiIqhhrN6DWOqkvS6QoNWZlIfdREiohJEXyeic8zSexkaCpk9HMXwi7d9UoPAoafc0oBwWRtoVw0+/7P8XuODk/8IIgkBLdSXlB/Zw4sBepEYJUQGpxBkyUPm7RjQOqZg6qwdpsoF+M7qjVP+ywhX/Tnu9lVWv5OG3mImueYP3runkrRtXEqePx+b1cfXOYoJadzPavZaI0Brkcid+u4TW9kh0MiMV62KQyvS4A0ZSGWRkkyeDXvEGkjM3sKFqNRcrr+GL8Ku4KEDEgt5Z6PLegU1PsCdrNtOOpBMYIGP+9b0YkHTmwmjLvHm0v7eA6Dfmo7v0UgB8JhctCwrx2z2E3pqNPEaLx2PmyNGpWK3H6N79NcLDxp53Px9qPsTHJR+zo24HYpGYIdFDuDL5SobGDEUlVf18I78Rv+CnrKOMLTVb2FC9gTprHTKxjFHxo5iUNok+4X1+cUhHEAQaHnoY89q1RL8xH0lQD4wrTyANVhJ4UyK7Kx7loY3Dkcu0rLt/NMGeRhwLLqIJFc/kzuOtvbeijczsSq+UnXntfr+fT5au5NUCH1lBO8mwh6AOlRK59TB9S8qoHD6RnmFj2Bd9ivmtIupsWiRhKqRGC1fVrSXQVUfeWBFGVwd3+5LQJ+Xh84sQ7YqksuVhAgIUXHF/H5zvz6Pz669RpKYS+dJLGKs8OPe0IkdBO40EXpZK3PBeP9IDfy0uOPm/GILfT+OJ45Qf2EtdXiEah4ZwVSKRAUlIkXUVg0424ApUsmt/Iya7j6GTUskcEvWr4rqttRZWz81DsJiIrJvH/Akm3pr0JenBGZhtNby9dyFpns0YlO34fWJkpVLKK1IpCc1gaPB2anaHoNSE4ZFfSkPoKb71ZGDQKQlN/ZgqWznpwfezW53FzYpO5gwYjrS1FGHhCAoUfbmqYxYXpYfx6rU5BGvOXBh1lJRwctJ16MePJ+rFF4AuHfjWhYX4LG5CZmShiNPh9Vo4cvQWLJYSemS/Q0jIxed1/fsb9/PmkTcpbC0kUBHI5G6TmZg2kVB16Hn35e+FIAiUdpTyTeU3rDmxBovHQrI+mcndJnNlypUopT+fS+53uTg19Wacx48Tv3gxIlU07Z+VIZKKCLoplbXV/+SRTf3pEenmq7smIm3Ix/fxOPI16czp/QKLd05GlzKsq3as5MwdqV6vl1cXLWFRTQDZ4evI7oxCY1ARtL+QwUeP0jbgWpK7Xc7+uBreq3Zw3BKMLFBBslZKQv4qEp3l7Brppk2wc3vVREIytxMQVYLPKKOlcCqu9t6M//sgVNVHaHziSbxGI6F3z8Jw483ULDuA6JgXuUhBh6yFyIk9Cc1J/qNuxZ+CC07+L4Df76P+WCmVe/djKqhD7wsmQpWI5ns1RLFWhjI9CFVGELIkA0e21nLw22r0YWpGz8wiJObXVeFpq7Ow8uUDiCydhDfN55UJnbx+6QuEihpoal6H1dpV6s5kCsFfKCP1mw4WJl9NW0YIl8tX0FKgR2WIxyseTWdMOavtSdjFCjSJ7yLR+FCGPsJxQcczniPcdul0RH4vlreG4TbWM9bzMreOzmXGkETE/xFWEjweqiddh6+tjaS13yLR6fA7vLQuKMDb4SRkehaKBD1er42jBdMwmwvIznqL0NBLfvG1H+84zuuHXmdPwx4iAyKZkTWD8Snj/09G7eeDw+tgQ/UGlh5fSkl7CcHKYKZ2n8qktElo5D99371tbVRPmgReHwlffwUiLW0fl+Azuwm6LoUFVUuYvy+Vm3rV8+ykmYhKVsCy6XwReTmfdLuTpbuuw9BjAlzxxulygqftcjh4/J0vWdGupXvMcnq1JqNRBxB4tIxhhw7i7n0dkSMmcDCqlkWFjRyxRiJWSpkyII5Tq5bQrTOfzUOsWOQebi26A0VgO6G9F6PQmHA0ptBaOIlRU8cTGSWl+bnnMa9bh7JHD6JeeglCwqj8dCeqU3KkIhkWrYmoa3phyIj5I2/F/zcuOPn/UnxeD7WFRdTvLsBdaSZIHEGQPBKRSIQgFVAkGVCnB6NIDUQaqkIkEuGwutn8QQm1ZUbScsMZPjn9jGIe50N7vZWVL+4Dq5kI5ytsm9DJuKhoBHeXUqjZFkZbcwyNLaEM/+YAaqebOb2nYY4OZLL3c8yVStRBmSC5BE9yOcvbAznl06GM+ZDkhCjqddPpdLl5t2UJYybNxeGXsPujR7mkcQGz1f/g2imzyIzSndO2tvcW0DpvHjFvvYl21Cj8bh9tHxbjrrUQckt3lKmB+HwOjhbMoLPzIFlZ839xiMbitvDG4TdYenwpOoWOmdkzub7b9Sj+5BIHgiCQ35zP+4Xvs69xH1q5lmndpzElc8pPvpicx8s5OXkyyvR04j/9BL9LoP3TUty1FrSj43nwxA6+O2HgxdHlXDfib4i+exZ2v8Y/0h7iUMQwlu6dQtDA22Dkk2e1bTKZmPXGcva4VKTEf0G/pmzUUg2BZSe4OG8f0pzJhNx4A4X6et7fU8JuRwIg4qlrsqhavx510Wo2DujAHiBwW9HteFwRhGcsR5OyA4lcwFQ1hIzMv5E5pAfm9etpemY2fqeTsAcfJHDKjdhajFR9uhNtmxaZWIEtwEb4Fd3R58T8pdIuLzj5/yLsHSbqth/FUtKI1CjGIAtDIpIgIOAPEqHNjiQgIxR5rPasbJjWUxbWv1eEzexi+PXpZAyO/NUPcnuDhbXvfok6OB995HbEoV253RpNFmZjMvlHRBh9IQQYK5mwdg/NwWrm9LqDTl0gNzk/Q6jzoQrqg0IzEllmLSsqbRz1RqMIX8Pwfjls8w9G7TTyafnz5Nz0CUUWDa9+8Q0LbfdTFTSExLuWo5SdW/XRVVlJ9VVXoxk1kpjXX0fw+Wn/rAzn8Q6CJndD3SMUv99FQcFMOox76Z75GhER43/RdW+u2cxLB16i1dHK5G6TmdVrFjr5uV80f2ZK2kp4r/A9ttduJ0wVxl097+LKlCuRis/9wjetXUvDgw8RdPNUwh99FMHjo+OrchxFbYj6hnLDiRKMdjfvTahiYPYjiL6YhL96JxNy3sAcEMFXB24hZNTjkHv7WW03NjYy/Z1NHBf5iYtfzMCm/qgIIKiihpH7d6PoOZWwh6ZxjDoWbNrNFk83BJefpyZmEd5YzpElb7K5bxM2DdxRchtuRxyBmkr8yQsJT7Xg9yoI8F/PwDF/x9fWSeOTT2LbsRN1bi5RL8xBFh2NsaaOyiW70LcZUEk1OJVOgi9NxdA/DpH0v3+B9oKT/xPjs3kwHq2h43A1/kYXAT4tIpEYv+DDqXQij9cS0i8FdWow4p8YkR8/0MS2xcdQaWSMuS2b8MTzd0w+nxOjcR/1pzbQ1LgZqcqE4Icqtxitpi99Iu5m3YrddDodlIfFMGTfOi46WMaRVAPvpN9BiyyQG2xL0bZ0ogwcTHDUSPS9Oll+oIQtnjQUhgImXtaPT43hJHnb+SL/DsInLuC9UzHM33KcZYrnyJQ1Irvn4Dk33EDXekTNlJtwV1Z2hWmCgjEuK8d+uAXDVcloBkQhCD6Ki++jpXU9GRkv/yIdGovbwvP7n2dd9Tq6BXXj6YFPkxWSdd59+GfjcPNhXjv0GgWtBSTrk3ks9zH6R/Y/57FNz8/BuHgx0fNeRzdmDIJfwLypBsv2WuoTAphaV0e8tpp5VxrJiLsX0cIRuDxOhuS8hw4Py/NmYLh2EaSPOavt4tIybl18lA5lOyGxSxjRPBy5R0VYxUlGHNyHst8MouZM51h7DYvWb2WtvzuC1cvfrsjg6lAPi196ko3Z1Zg1ImaVzMTlSEQvacKpWUVkt3YU8afwu8PI6f0sISEjMS1fTsuLL4FIRPhjj6GfcDUikYiOujrKP9+KvkWPVhaEV+JF1TuU4ItSkAb992riXHDyfxIEn4Cn2YarxoSptB73KQtyV1fOtNfvwUwHokg5Qb0SiBiQgUT581kwPp+ffcsrKdhaS1SqgdEzs1Dr5D97HnRN7+32Kjo6dtHesRujcT9+vwO/R4G9IR1teQOPd7NySfhI+liHcaS4GInLxXcZ/bl3yVtkVDewZlAoGyJuodofznjLOhKMJ1EGjSA2/RIi+nv5cu0mVnu6IVVauH5ib95r8TFAaufjHdfi6TGTaTUjKa43Mzd2H9e2vglXvQc9J/+ozZ0rV9H46KNEznke/YQJmNZWY91dj25UHLpR8QiCQHn5bOrqPyMl5VHi42792X443HyYR3c9SrO9mdtzbmdm9swfHfH+NyIIAltPbeXV/Feps9YxNnEsD/V96KyFY8HtpuamqbgqKkhY9jWKpK6i5Na8RjpXnmBzoJjZHZ1clbyWO4dHkKa9CtEHl9IZlk3vlJfo5qzlq4IH0Uxb1SVs9h98u2UHj2xpRx5cjiR4GWM7xiKySYitqGLQ4UPIBk8j4fU7OFZdxftrN/Et2fg7Pcy8NJVZ3XV8+sIjLE8pwaISM6t4Oi5nClpJE0pZNTLhFPIRZSj0jRgMA0lPexJ5ZwCNjz6G/eBBtJdcQsSzs5EGdu0vaK+vpezLTchqxESqujaaiWIVBA9PRtkt6L9udH/Byf9/wmdx4z5lwV1rxlFlxFNvReTrCp84fXY6XA14dD60mRHEDOtF0Dnqjv4UdrObje8X01DRSY+LYxh0TQqSn9nQ5PF00tGxh46O3bR37MLlagRApUpAoxpI8TfBWGviyOxcyL1j6xiiGEBcTTI2lwtdcxOr+17CMx++SqDJxPuXR3BKeS2FrngGWg+Qa85HEXgJmQPGoutpZ/nS1azxpOIQaRh7VTeWWhyM00l5a+M42pWJjGx7EK1ayaujDIz47kqIHwg3LjtrAe90f5rNVI65DHlcHPFffI5lZz3mDScJGBiJYXwyIpGI6uq3qKp+nbi4W0lNefQn+0IQBD4q+Yj5h+cTFRDFS8NeIif0ryt85fQ6+aD4Az4o+gCFRMG9ve/luvTrzsjp9zQ1UX31BCTBQSR+/TViVVcs31HaTvsXx5gtcfCd28mj/f/JyJwrSbJGw4pbqc6ZwRDDVHItJXxe+QqqWzeCNuKM7xcEgfmfreSNUhmxCbuxyDczwXYNnnYPCeWV5BYUIIycRPprf6O8/CTvr1nHalkO/lY3k4bE89TgKBa/8iiLYw7ikEuZVXwzDlc6GnEbAQovkqo87EN1hGStQip3ERlxFQnx9+D4cgst8+YhDQwk8sUX0AwefNoma0c7hWvW4chvJU6ZgUqqQZAKqHPC0PSJOO+9JP+/uODk/2AEQcBvduNusOKpt+JusOGpt+AzuYGu/Gaju4l2ZwNWiQlNahhRfboTn9MLlfbXxXvb6618+3YBDouHi6Z0Iz034pzH+Xx2Ok2H6TQeoMO4B7O5EBCQSrUEBg4mOGgIQUFD8NhCWD57J267i562D3lwZDV9bAMJ7IxAazITbGxhY0ouD329CJfUzWvXRiB3X8Feexqp9kout65HaRjHgHFX0RhRws4VO9nnTqTaE0WfEbHsUfi5JTKQJzbfiL/jJKMdL5DbM5unL88kcMV1UHcQ7toPhh+vBNT03PMYlywhcdnX+N0hdHx5HFVOKEHXpSMSi6iv/5Jjxx8nIuIqMjPm/qRUgd1j58k9T7KpZhOXxl/K7EGzfzYT5a9CjbmGOfvnsK9xH7kRucwePJtozQ8DDOuePdTOuBXD5OuJfPrp0793nTRR/XExN7vMiAOsPNH/KXpkPEJsUSnkLWDfmPeZ4EjjIuNBPm77Evkt34D8zELnHo+HB95YyretBjKyVtPoOcj17ptxNppIOV5Bn6JSbOMvpuezs6msrGfhqm9YpeyJr9HFmF5RvDYulc9ff4IPQ3bgl8uZVXgjFlcWaqkRuVROUOVqTiZcTmDmMkLSDyIWiYiOnkyk+xJaH3kBd2UlQTffTOjfHkCs+GEh3e2wU7x1M6c2HybEG0GMOh2pWAZqCQFZoSgzg1EmG06rr/7ZuODkf0cErx9vmwNPsx1Po63LsTdY8Vs9XX9HwCm202ato91RT4enCVVCIHE9e5HYsw+hcQm/eSdeTXE7GxcVI1dIGHtXD8Lif3hReL0WOk2H6DTmYezMw2IpQhC8iEQSdNoeBAUPIzhoKFptNuLvQxKWDifLn96Oy+6lr2cJz/dvoJuxPwqvkvTycmQhOo54tNywYx014SJemxhCrG0Mezsz0bmM3GD7Cl3w5QyfNIGV9s9o3NZIkyuOve5UIjOCqI5T8XB8OEM3zaZ/0xIekjzC6GtmcElmOBz9AlbdCWNfhf4zf/SaHSUlnLx2EoGTJxM45R5aFxUhj9MSOiMbkVRMW9tWCgpvJzh4KD2yFyAW/3ioq85Sxz1b76HKVMV9ve9jWvdpv1umhd/np7PFQXu9FUuHE7vJjcvuwe8XEPwgU0pQqqWodQoM4WoM4Wp0Icr/80wPQRBYUbGCuflzEQSBh/o9xMTUiaftaH75FTo++oiYd95Be/FFp8/zNNvYtPAId9tMjIw/yeT018hMe5HItW+BsYaVE77hzno/l7duZ4FwCMm1n5xVL9ZsNnPja6spcmnJ7PU5Ha5axjun4q5vIaPkGD2OV9J2fRYDHp7PyZPNLFi+ipW6PvhOORiSGcbCa7NY+s4zvKfZiEymZFbhJDqcPZFKTCCoSK//mrKwaxECTqEcs5R4eS0SiYKYiJtQLbNg/mwZirQ0ol6dizIt7cx+8fs5VVJI8ebNOErbiValEBWQjAQpSEUoUwNRJBtQJBuQhav/NKP8C07+V+B3evG2O/G22rsceosdb7Mdb4cD/N8fJAKP2kunp4WGtnLarHV0ulvQR0cS2z2buKwc4rJ6olCrf/K7zofCbbXs/qqC4BgN4+7KQaFxYTLlY+w8QKcxD7OlGPAjEsnQ6bIxGHIJNPRHr++NVHr2SNVidLLiqW04HH76itfxcbKZEEcyKquJ4U2tNMSFYyyqZVBFEfszFCwcqybTNpbCljQsHglT7V8QG3EJva4dyZyKp9CV6giwJ7DOl4PIoMDcJ4iZhkDsG77mFdfz7A66huyZC9CrZGDvgDf7QEgaTFt/ljP4F4LfT83kG3DX1RH32XI6FlchDpARdmcOYrUMi6WUQ4evQ61Ook/vJUgkP97fJe0lzNoyC7ffzavDXmVQ9KDffE/a663UFLdTW9ZBY6UJn8d/+m9SuRhlgAyxRIRIJMLt8uGyefD7fvi/UwbIiEjWE5MeSEKPEPSh/3d5+A3WBp7a8xQHmg4wInYEzw16DoPSgN/t5uR11+NtaiJpzWqkoT/E770mF8/O38endhsP9dlGZsgaesY+SdBX/4Dw7rw/6iOerGplev0K5sRoEI04uyDciapqbliUR6fET0zWh4gRyDVdi6yhkeyiMjIra2i4JZJhsz7kxIkmPlj9DauC++GttDMwPZSPbujF0o/m8KZ4JRqJmrtLJtBs74NLbEXhVdGjczWl2kvxSvzk5b7PlCw1YtthpFI9Eb6LEJ7fB602wh56kMApU8458LKbOinduZXyPXsQmtxEq1OI0XVDSdfzJVZLkSfqkcdokUdrkEVrkASc/27y34MLTv4/EDx+fFY3Posbv8WDz+LCa3Th63Di/f4jOH6Qg0UMIoMMt9yN2dNGc8dJ6hpKMbla8Qs+gqJiiO3eg9ju2cRmZqPWn63n8Vvx+/zs+rqC43kFxPZuIb53CxbrEazWMkBAJJKj1+VgCOxPoCEXvb7XTzo7AGunk+VPbcNh95Oi2cWOIB9iv5qwhuNckTOUvJPHCdhbSEJ7M18P07F6gJ8s59U014ZzzB/Ota4V9E/sg+qiZOYUzya1LZX49mS2qwZQZ/MjDA6jt01E/cESNqoeQ2aIJuCu7SD7Poth9d1QsARu3wXhmT9qZ+eKlTQ+9hgRzzyPszYOwekl7K6eSINVuFzNHMyfAEC/vitRKM4ubPEvdtXt4sEdDxKoCOTdUe+SZEg639twGofVTdneRsoPNNNe3yUiFhwdQHR6IKFxWoKjNehDVMiUkrNG6YIg4LB4MLXY6Wi00VRtpvFEJ6YWx+l2ug2MJD03ApX2ly2i/xb8gp/Pyz7ntUOvEawM5pVhr9A7vHdXquqEa1D360fswgVnOEKnxcVVc3fQ6Pbw0oiPUSpL6K+6hYD1c2DI35idcCvv1rbyeNUC7hl6FaSNPut7v926mwc3tRMc1I4vegGJ2iRCG0YS3FRHj6IS0qvqqb1Tz8hpSzh2rJaP165nVWh/fBU2BqSG8PFNfVi29FVec31BIAHcW3oldfb+mCQ29F41KaYt1Cr74Jar2JD2AX37BjFW78Vk3IVEHIC+OBTZx/Xoeg4l8oU5yMJ+/NkxNtZzfO8uju3diaOpkzBlLDGB3QhVxSH3/HCPJAYF0jA1shAV0mAl0lA1kkAFEq0ckeLsZ+H34i/v5N31Vmz5TSAAfgHBL4AAgs+P4PLhd/oQXF78Lh9+u/dMB/4vJCKkgUrEehlumRu7z4zR2kRzezW1dSU4bRYApAoFEcmpRKZ2IzI1najUbgQYfr0i4E/h97uxWErpaMunvHAbgrwMmcrUZa5EjU7XE4O+L4bA/uh1vZBIfnkKmM3kYvmT32FzCGgD93NCLcEsM5NsquTGKc+y9Z15RO8vQOb38+74EPJT2sn0TkJSIWenqBvDvLuZ1D2M/CQLy09+zWDxYCIqIygN7kdePYh6BqOosyF0OtkS/Box9jJEt++AkO/rdNbsg4/GwOD74JJnf7wP7HYqR49BGhmJesSjeBqshM7sgSJeh89n59Dh67HbT9Kn99KfrOj0TeU3PLnnSdIC03h75Nu/WpKgs8XO0S21HN/XiNfjJzxRR3puBEk9Qwkw/LbNUqZWOycL26nIb6a52oxYIiIxJ5Teo+POCMn9UZS0l/DwjodpsDYwq+csZmTPwPTlUppmP0v4Y48SNHXqGcefaDAx7s099EHgzpEvIyjsDGrNRlq4Av+UFdztSGBFm4U3T7zGtROehuAzpQUEQeC5D1bw4QklAzOqKWYBo+Ovor00nviWOnoUlpB0qp7WBwK56LovKS6u5OONm1kTnov/mIV+yUF8dkt/lq+ax6vmT4j0a5hVNpaT9kGYAzzobDICjftxy2Kxq8PZF/85Dal1PN/vDjS2nbS0rEckSFHvEqHbpyX24RfQjhr1s/3U2dxE9dF8qo/kU1tciMgnIlARQUxYN8J08WjEBiQOMXjO9K0imRixVo44QIZYIUEklyCWixHJJSDpCgWpMn9d0Zq/vJN3lLTR8XUFIgldmRnirqkxElFXZyoliBVSRAoxPokfn8SDCxcuvw2zrZ1OUyNtrbV0tjRiN3WeblemVBEal0BofAKh8YlEpnYjJDYeseTcm3R+K253GybTEUymw3SaDmOxFOL3dy3eum0h6DS9iE8dgl7fm4CAtNMx9fPFbnax/IktGD0OPIEFWOQSTuhO0E3r4/4Jb7Lt7tuJLzhGuz6IuddFUmc4Rpb/WsJLXKyW9ieJau7rbeJDxVEqTSe4Oe5mHHsc1ASnsbkmAH+0GjrdDI8yMD9uD0F7ZsP4t6D3TV0G+DywYBi4LDDrAMh/XBq29c23aHv7bQKnvYDXGELQDV2bnQTBR2HRXbS1bSWnx0JCQi760TZWVqzk6b1P0y+iH29c/AYBsvOXorWZXOSvPUnp7gZEYhHpueH0GBlLcNQfs1jb3mClbE8jx/Y14rJ7icsMos/YBKJSfv9Z4r9jdVt5dv+zrK9ez8WxFzNnyByM9z6Mbd8+klavQp6QcMbxC3dU8sL6Yzwmd5E27DmUSj25BTZEdiPuO/Zy47EW9pmdLK59ixGT3wLFmf3lcrmYMncZB60GxgzOZ0/HMq7p/hAVeR5SW+rILiwiur4e+6MRDLt8MUeOlPHp9l18G94PodRM74RAFk/vz9JvX+N102ekunXcdmwUJxzDUcTJsZ1yEdBZilKkwKRLojZ8LeuSv2NG9gxuSR1Dfe0iGptWgd+P8oiYCNFIEu78J5JfKFfscTlprCin/ngJ9cdKaaw4htvRNSNTybRER6QTEhKHVh2MSqZFIVIi9csRC2JEXvC7fQguH/gFNIOi0I2K/1X37S/v5GsKj7Jrycddjl0kOj0l8no8eF1OPE4nHpcLl8MO/3m9IhGaoGAM4REYwqMwhEcQHBtPaFw8upCwP0yuVBB82GwnMJkOn3bqDsfJ702SodVmIaM7x3YYsLcmccnUocRmnq3ffb44LG6WPb6RZkkrNm0NMpGD7eGHSAkP5Pkhb3Bo2i3EVNVSmJDGK5PjcbONXp4rSChysFw2ELnUzUP98pjn2IdapmZ239kcWXOEUoWBTU0RCFIRgUoZz43O4IoII6KFIyD1Urhu8Q+pkXvmw+anYPKXkH7Zj9rqaW6hcswYFOl9kCVMRX9ZAtrhXdk3FSde5NSpRaSlPkVs7M0/2sZXx7/iuf3PMThqMPMumveLhLv+Hb9foGhbHfvXVOH3+MkcGkXfsQkE6P9vJA7cDi/FO+s5uuUUDouHxJwQBl2TgiHs91vn+U8EQWBx2WL+mf9PEvWJvJ71FK7Jd6BISSH+s08R/dsgx+cXmPjuXqoaLSxQN2HPfYlwcRKZuw8iSr0UyzWfcNWBo5x0uFlpWUEHcfdPAAAgAElEQVSPq+eelSLb2NTMVW/uwCgoGDj4Wwo78hne/RWaD5wivbmWHgUFBDfXIXoqnkGjPmH//iN8sT+fdRF9odhEr3gDn03P5ZP1L/GeaSm9bXpuqhjOccdIEnoHU1nYjqLzFHp3J+1BPRB0O1mQuZzs0GxeHvoyITIxdac+pq7mc/xSF/ImBXGptxPT+47zrhbm9/tor6ul7dTJrk9tDW21NVja2hAE/xnHikRiFGo1Co0GhSqArItG0WvMFb/qnv3lnXxdWTF5q5eBICD820cqlyNTKLs+SgXKAA1qfSABegNqvQG1wYA2KASp/I+PezqdjZjNBZjNBZjMBVgsRfh8dgBksiD0+t4Y9L3R6/ug1WZTfdTElo9LCdDLGXdXDkFRv70QgsPqZukT31KvOolHYSbKdZLPkwoI0QfzRs/XqZ5+G8FtnazrNZR3Jyagsi6hr3M4iSU+Noh606IK5daclXzuPsTAyEE8N+g51n69ju02L9uciYjbXYztFcXL47PQSf3w/kiwNnWlRv6rklDnKXg7F5Iugslf/KS9DY89jmnNNwRc9Azai7IxXJ2CSCSioXEZZWWPEBN9E+npz/zo+Z+Xfc5LeS8xPGY4/xzxz/PWnmmrs7L10zJaT1mI6x7M0EmpGML/OOf6U3jcPgq31pK/vga/z0/OxbH0G5eITPHHzCqhS33zoR0P4Rf8vOGcQMDLH5w7bNNiZewbuxgapOFvHKCp57tkGhOILMqHK9+mKfM6Lt97CI/LynptJVGDz5Y+2Lr/CHeuOkWIzoMu/UOcPicBcS8QdLiYpJY6eh4+gsrcgOapJHKHfcSWLTtZWVrOhoi+iIqM9Igx8MWtuby94WkWd65hhEnPhOrBlDpGkzE4gsqSdvxNTQSaK2gNG4BBms87A1bjE/t4PPdxrki+Ap/PTs2++dTVfYInzIPEpyAi5moioyeh0/b4TfF0v8+HtaMdc2sL5rYW7GYTLpsVp82Gy2bF5bCT2n8QWSN+Plx0Lv7yTv7PhtdrxWwpwmwqwGw+itlciMvdDIBIJEerzUCn64FO1xO9LgeVKuH0AyQIAoc31rB/VRURSXrG3pn9uyy+OW1uPnlyCU2aU4j9bga4i3g99Rg2lZZ3EmbjuPdxVHYn7w+fwIorEtG3vUGOI5uMMgOH3REcMvRmXMpK9soPMqvXPUzPms6HS1ayxWRilyYdWUkn04Ym8vS47xdQNz8Ne+bB5KVnbnNfMhmqtsOsvJ/MiXeWlVE94RpkKaPQX30bITd3RyQRYTIXcPjw9ej1feiZ8/GPhqyWlS9j9r7ZjIwbydxhc5FJfnnWgyAIFO+oZ8+yE8jVUoZOSiWlT9ifQtDK1uli/6pKju1vQhei5OKpGUSn/TFrQgC1llru23YflcYTLNicgKG4titsE39mWOHd7ZW8vOEYc/skkNb+KW2pXzOoXIvK2AZ37KZMFs4VeUUk2k+xqkcCAfH9zvquFz5ew8JjEi7JtFMgmUuSIY0Szd8YUnSI8LZ6eucfQuxpIezpTHr3f49Vq75hQ10zGyN6IynooE9cIJ/N6M+c9Q+zpnMLV7drGFU3mCL7WNJzw+lottNR0Uhw+yFaIoYT7Cti/WX7OGwp4fKky3k893E0cg3ezk6q3r6XNsk+nL0FBKlAQEAqEeFXEhY2BrX6z1dm8IKT/wPxei1YLKVYrKVYLMVYLCXYbCfoWgUGlSoeva4nOl0OOl0OWm0GYvG5R5Q+r5/ti49xbH8Tqf3CuXhqN6Q/ItJ1PlhMdhY9twiTpgOt2cHVCXaelW+gSKXiDflMNHMWgCDwyqhb2DEmkaCWF0lxRDH4eDdqjFbWRFxOTtgBXHH7eGnYXIIkaby2ZAM1Chv7ojNQ722hT4yBpbcN7JIErtkLH42FPjfDFfN/MOTYWvjyBrjkORh874/aKwgCNTfegrO4FP2N/yT8vkGIlVJcrlYO5l+FSCSlX9+VyOXnDl9tqN7A33f+naExQ5l30TxkP5Ez/584bR62fXaMqqOtxGcFM/LmjP+TDJfzpaHCyHefHsPc6iB7RAwDJyQjk/8xo3q7x84jux6hoHQbb30oQZeR3RW2+bdQptfn55p391JrdLDq4m40Vz+OK2gng444EUfkwLR1fNfUzE3HmrjMdIj3R1+D+D/KB7rdbia/soxDVj13jDHyec3LDEuYwCrvldxUkIfS3ES/vDzcMiOJT/Wne4+5LFnyJVtNDrYEZyMtMjIgMZiPbunL39feyXbzfm5tVpDb3J9Dtokk9gxB8AmcOtpIWPs+mkKHEuipouLSUla4txEVEMXc4XNPaxaZvvmWhpefxp7twnN1GDZpl/qqJiCd0LAxhIZeiiYg/U/x8r/g5H8HBMGP09mIzV6B1VLa5dgtJTicp04fI5eHodVmotPloNfloNP1QCb7ZaMsp9XD+gVFNFR00u/yRPqNS/hdHp7aI8Us/moNLoWbuCYr1988gJcOP8JKjZpXTw0h5ovt2BUyXrzkdg6MSCKw/TnCXXImVYyiqa6QpbET0QSYGDSwkEcGzOaLfa0s21mAIVngSHwakYUmXB1ONtw3jLhgNTjN8N5gEEngjt0/LLS5bV1hGoUWbt8JPzGyNm/cRv19d6HsdyOxbz6M1KDA73dz+MgULJYS+vZZ9qOZNDvrdnLf1vvICcvhvVHvnVcM3thkY+3bhVg6nAy8Opmci2P/NJtdzoXH5WP/6koKt9URFBnAmNuyCIz4Y+qb+vw+Xsx7kaavlzBrrZ+QJx4jdMpNZxxT3mzh8jd2Mzorgpeywjh64hYC7RVkVhhh5FMw9EHeLzrMk21i7rXu47HL7zgrPl9T18BVb+/FI1Fw3dhSvqpYzIDUv7HW0YN79+/F6W4jd/8BrEEmuj85mqSkf/DJJ5+wU6xkmzoVeZGRYakhvDMlh3vWziDfXMg/msV0b8tmj2U60emB6EKUlO2uJ860nzpdP9TeVjq6H+Sb2BKMrnYe7vswk7tNRiQS4amvp+Efj2I/eBDl+CGIZvSk3bqLTlM+ICCXhxIUOJigoEEEBg5CqYz8Q/r/57jg5M8Dn8+J01mH3V6NzXYCm/0ENtsJ7Paq0zF0AJUqDq2mO1ptJlptdzTa7ijkZ1ew/yV0Ntv59u0CLB1ORk7NIK3/uSUKzgfB72fve+/zXWMTgkhMz1Y3V8y+ic+WjuP1AAVzdkeRtLeWdp2S+WPuZW/POPSOF9G5rNxTNZmGii18kzCORkkY94zrIFR6Ca9tqcBisRCdLaIkKoF+Rh9FeU3MuTqLG3O/n76vuqsr9336Roj9N7XDrXNg5ytdm57if3wDks/hofLS8fjtVhKXr0GR0PWSPHb8SerrvyCr+3zCwy8/57n5TfncseUOkg3JfHDpB+clU1B/3Mj6BUWIJSIuu6MHkcnnXzf1/xenStvZ/GEpXo+fETek/6jExW9FEAQ+LPoAzd9fI71JTNy3qwmKOTMtct6WcuZtqeCT6f3p6TdypOo6Mk+0EdppR3TbdoTwLB7ZtZFPfRHMl1Vw3ZCz6+wu27yXv3/XTk64lJDuKzjcfJjQpBepcoZw984dGOmk/759WOPN9PnHjYSFT2PRokXsDo5mH1HISjq5uFsYr1+fyfQ1N3DCVsXLjT7iTelsM99NSJyO+O7B5K+tJsFRQJ08DYngQhzyLav6+Kl2HD5D6kLw+ej46CNa5r+B1GAg8sUXkfVLo719Ox3GPXR07MHj6QBAoYj8fpDXA50uB40m/RcP9H4L/9NOXhAE/H4Xfr8Dn8+Bx2PE7W7H7enA427H5W7F6aw//XG72844X6GIIECdQkBACuqAZAICUtFquiGVan8X+/7lXERiEWPvyCbyd0iRs1dVserNtynXa5G6tQx1wbBnb2HHJyN5VORh9hoFsdUO6iM0fHnlI6wPCUYrfROlq5rHamZSX7aZvNgs9kkGMKmvg8K6MI41Wegdq8Ma5aDQEMYEmYxtG2vpmxDIp9P7d806StfAVzfBsIfh4id+MMh4Et7qD5nj4ZpFP2q34BdoeGwR5lWvEXLfE4TeeWNXH9Uv4djxJ4iPu52UlLN3TwJUdVYxZf0UQlQhfDzmY4KUvzwT6fj+RrZ+egx9mIrL785BF/Lnqvz0S7AaXWz6oJjGEyZ6XBTD4IkpiH9GrO7Xsmnnx4Tf+TLHs3SM/HgdwaofcrtdXh+XzduF1y+w6YFhWCr3UFY9gwGHjMiCuiG6fTsexNyweQ37ZdEsixOTm9rnjPYFQeCet1bwbb2Su4frWG99BolYzqngp4lzyblq1zZaxSZy9+7D0tPEwPvvQy6/hEUffMCB1Bzy7XpkpZ2M7h7O89ckMXX19bTYmnivyUGII4WNpofRBqvJGh7N3uUniHIeo8Ufjl8iI16zmJfT4ukI2ESsLoZ/Dv8n6UHpQNc6Uf3DD+M+UUngTTcR9uDfECuVCIIfq/U4RuO+75MrCs+Y4ctkgahVCajVSahUscgVYSjkocjlocjlIUilGiSSgJ/UWvo5/vJOvrllPSUl9wESRCIxIlHXT7/fjd/v+slzRSI5SmUUKmUMSmUUSlUMKmUMKlU8AQHJv5szPxeluxvY8cVx9GEqxs3K+c3b2QWfj5MffsjqklI6DXpU1miGyzzkPjud8sWX84+2Bh5cLiLQ5KcqMYi8ybP5yCxGHboYheMgf6+bjrOskLJgEStVVxMfaOekUUNskIqHRnfjw5aTHJCqmaKCk0ftHG+ysOmBYUTqVWBthXdyQR8Lt245Mxzz5Y1QuQ3uyQdd1LltFwSMK8tpfeF2xDolKZvXIpJI6DQd4vDhGwkKHEhOziJEorPjzm2ONqasm4LT6+TzcZ+fIbb1cxTvqGPHknJiugUy5rYsFL+iyPmfBf//Y++8w6sosz/+uT03N733HkghgZCEAIKE3kILSBMLHbsrIoKi6KIooKjoooBIVQHpvUnoPYEQQnrvPbnJze3z+yOsiqCrWHZ/7n6fh4eHO8y8M+edOXPmvOd8vyYz527TTvuEOzBgWgcUyj+GMjl58cso1+9mzWR35jz9JW6q778ezufWMmH1BZ7qHcicgSEU3thAY/p8Im+pMXV/BcmAl2hQ1zDkzGWaJJYc7doRd5s7g5vmlhaGvLufMoOSZZNseePqc7R36UaSfDqPlunxyzpFNWrizp2j5cF6es56m6amQDZt3sylmF5cr5Yiy2gkIdKdl4e5MXHPWAzqZjZV1WNhDuZAwyvIlXJiE/w5vTUbR00+ar0CrcKBThZrme0Ujt7vGIJIw/y4eSQGJyISiTBrtVS9/z71GzYiDwrEc+lSLELvTh3q9XWo1TduZwPy0Gjy0Wjy0Ourf9Kmvr6zCAqcc1/z8W918iKRaBDwISAB1giC8M5P/d/7dfLNzZlUVu5FEMwImNr+FkyIxXIkYgvEEiUSsQKxRIlcZo9M5oBc7ohM5ohUav2nL5yYzQLnd+Zy7WgR3mEODJz+2x9GXXY2lxcv5pSrK0apBVaNofSwrCP27RnU7pzGoqvneXQfiAQx2SHu1D7+Dotv1CNrdxRL9UGmVibinw2p0lS2OY3FKEiRSKx5tm87xnf15vHLNzmrh4eMajopvfj7vnTeH9uRxM63NTO3PgqZB9soClxCvj+x3BOwcST0WQAPvviT568+U0rNJxvQpmzAc8VH2PTvj05fw6VLw5BILIiN2YVMdncKpdXYytTDU8muz+aLQV/8KqGPa8eKOPtNDn6RTgycHv67LHL/J+Dm6VJOfZX1h36ZmPV6biUMokZdybLnPPnH0M/xtv6+Wmr21uvsvlbKged60s7VmvSrr+B4fhXONSbMjyQhDYwkM+scQwqhPc3s7NsPxY96Ui6nZTJp0y1crGRMHVnLe1eXEuI7ldNCPO9fbaKu9SJ1+iZiz51DP6KB+Mc+JyvLwJ4DBzjfYxAZhXqkWU1M6OLDIw9KeOzAo1g1mdhWW4kgC2Vv3WuYzSK6jgzk4u48lM2lmDRamiy9iJVsZJlTELc8UkCZTUJAAgu6LsBS1lZC23zmLOXz5mFsaMDluWdxmDz5jv6Bn7SbWYdOV4NeX337Tw1GUwsmYwu2tlE4Oj54X/Pxb3PyorawKwvoD5QAl4EJgiCk3+v//yfk5P9o6LVGjq5NpyC1hohenvQYG/ybPqsFg4HqVas4feIEN8LDURgVqOojiLMppcvimehOvMPnX64j/oyYBisZ+WH+qGYt4W+H85BE3cCycSP9Gx5kQl4c37Zs4dvAfuQ0BZLQ0ZuFw8JRWkh5JDmT0806EmqLWRAfz6APz9It0JHPH4tpe0He3AnbHoe+r0PPF74/OZMBPu0BRi08efF7zpofofVmDTXrU9EkvY7c1x2/rVsAMykpj9LYlEJM9PZ7LrSaBTOzk2ZzvOg4y3svp69P319st+TDhZzfmUtglDP9p4Yj+X8mEvGvUJJZz6HPbiCViRn2bCccPX//ztyWCxcpevxx9vdUcrC/PasHrP6OD6iuRU/f95IIdLZi68xugJHrJxMJP3sGs+CP9InTSJ1U7D+2iqmSLkxSNrOsa4+7xli88QCf3RQYF2mPweMbvi36Fiuf16kzB7AuqZaLFldo0DQRfeE8PKKh99gtJCXd5MzlK5zuNZSirGbIVTOrVyA9Imt46thT+NRK+LqpGINNNAcaFtBYq6fL8ADSkkqgvhpFcxW1VkFEGreTGubMh+ZGFE7H8bLy5eN+HxBo17YOYayvp+L1haiPHEHZuTPuby1C4f/vKa/8OSf/R9/ZXYAcQRDyBEHQA18DI/7gMf9joa7TsmNZMoU3aug5rh0PTmj/mxx8a9pNMseOY3dyCjc6dMDWYId1bTRdbMrosngm5stf8u37X9DnjJh8NwXZMeH4zvmIuQfzkEcWo2zcRJA2glklwzjfdJCSKH8yG9vxt/6hfDg+CrmFhIev53BaraV/4S3eH9CLV3enIxbBopEd2hx8czXsnw0enaH7j8oiL6+B6gwYuPgnHby+WE3d15mY6y9gbqrB5YW/IRKJyMtbTn3DBdq3f/MnK2k+TvmYY0XHmBM751c5+LRTpZzfmUtwjAsDpv31HDyAV3t7Rs3ujADsfC+ZirzG330MVdc4bEeMYOh5A86VOqYcnkJeYx4ADio584eEcqWwnq1XihGLZYR2X01uO2csTLk0f7YAY20rQ+Mf57naI2xqtWJjbu5dY7w4vj+Rqia2pNYxxPlJvKy9EFd+hJYm3op1YKC+CzYqFclxXTFvsuDc4an06RNHWGAA3c8cxrm9LRIfKz49mcuNbHde7/46BY4GZln4YNV0kVEe7+AeYM2FnbkEdnZB4uKK2soTd00GqdLRBN3Qs8fZjLRyGsWNNYzePY4dmXsAkNrb4/nhB3i8+853usO1n69FMJl+d1v/FvzRd7cnUPyDf5fc/u2/DhX5jXzzzhXUNa0Mfbojkb297vtY5pYWKt9dQuq0aRwICqTM2wv3VndkdZFE2xYSt3gGhot7SHn+TXxzJZzsoKQ6ojMxc5bx7M5MJO0akLd8hkrwYWnBVK42HEeIb2J/yUBGdHTjqd5BqI0mJl7P41xDC32yUljUuzvHcpo4nV3D3MEheNjdTgEceLGNg2bkSpD8IOXUXA0nFkNg35+kLjDWaalZfxORwojuxj4su3ZF1a0bNTXfUlC4Eg/3sT+pz3q08Cirb6xmdPBoJoVO+sW2y75SycmvMvGLcKTv5LA/bHHyPwGOnlaMnhONhUrG7g9SKE6v+93HcJn7EhKVioXnPUAQmHZ4GoVNbfXkY6K9iPN34O0Dt6hW67BQuOHady3VDnKsjeuo/ewAxiYzLz04gt71l5lf2MCVevUdx5fJZLz3SA+sRHpe3nqLt7svpdXQTFjzai6qTGzxsmS4Q18slUqux3ZDs9JI8vmZJCYOx9vOhn7JJ1F0sEPhqeLdQxno6mOYGTmTa+56XhT7Y1FxiqGuywnp6sr148U4eVlh6+NMpTIIX306OYp+3PrWgzOuZxlouQCdxp3XL7zCrIPz0Jl0iEQibEeMIGDvHlQ9e1C1dCkFEyaiy8n53W19v/i33+EikWiGSCS6IhKJrlRX//SixP9npJ8tY+d7yUjlYhJfisY3/P6Y5gRBoOnoUXKHJnDj8GGODRyA0dEJ30Y/jI3BRDsU0PWd6bTs2UjWzLkIGjEb+ymRe8fQZ+5intyehca+CTEfIxLZsCprJuXqbCT9ktlcOAF3WwsWjYqk0Whi7LVcrjQ20/fWZZ7tFIbK0Y0396XT2ceOSf8sl7y5E9J3Qfy8O/PwAN++CYYWGPTOPeX8zK1GatbdRDCakShvYGqox+Vvz9PaWszN9NlYW4XTrt3Ce9ohuz6bV868QqRzJPPj5v/iNZWim7Uc+yId90BbBk7v8C+lEv8KsHFSkjgnGltnS/avTKU44/d19FIHB5yffw4h+QarpJMxCSamHJ5CcVMxIpGIt0ZFoDWYWXzwFgAOjj3Q9H0es9iMFa9Rvfo6KPxY6SXDQ1vB1Gu3qNQZ7hgj2M+bp2NtqdGJWX2gile7vkphfQpRhv18Gignvd7AuK5jkCsUpEXFUf1eFVkZ8xk3bhw2rS2MzktF18EOlZsl83fewFc8iuGBwznua+Bdkx+ynH3E268ibpg/ucnVSGQSXIIdKZSFEEgmZcpY9p6IZ1H5u2zo+jpKTT/OVu0jftNorpW3OXOZiwteK1bg8d4yDEVF5I9KpObTzxAMhrts9mfjj77LS4Ef9q573f7tOwiCsEoQhBhBEGKcne+P/vU/FSajmaQvMzmxMQPPYDseejn2vlkL9SUllMx6gpJnniW9XTBnHuyJk7s7HqXetGi8iHUrJm7RZOqWvkrRy29TZi1i+WgFgbIYhs9/m2d351Cqr0fssAoEI+9kTcJCL6K1xwn2VPWkptWeD8dHoxOLGHMtlzS1hn5pFxnp6kBcXBx/35dOi87Iu6Mj27paW2p+Ok1TlgLJGyFuFji3u+taBKOZ2s23MNa0Yj/Gn8ZvvkTVsyfyDiHcSHsKEIiI+Pie5FCNukaeO/EcKpmK5fHLkUt+WTdqTUkzh1alYe+uYuiTkUj/oO7Q/0RY2sgZ8XwnbJ2VHPgklZLf2dHbjR2LIiQE0T828FnPFehMOqYemUppcylBLlZM6+nPjuRSrha2jesTNo+yiE6o9CVITBuoXnMD65AJrGs+SJPJzPRrt9Cb7yTzmj4inljbFvZmNqPSdGJ08GhKKrbhbkxjQbSKpjO1PDpxGiK5nPSwrhS+fY2mxq8YPXo0koJcHlOXU9/BFmsnJS9svU4fp6eIc4/j6yD4Qu+FJGUdnS230H9KGFWFTbTU6fAKcSSX9gQr8qlTBLItdSrtd03nzIBR9Hd4GbWpikcOTuD1o1swm4W2qH7oUAL278Oqb1+qP/iA/DEPoUlO/l3t/WvxRzv5y0CwSCTyF4lEcmA8sOcPHvM/Ai2NOna9n8LNU6VEDfAh4emOWFj9+vI8s05Hzaefkjc0AfXVq1yfOoUUDw9C24dgdd2eJr0zXf2qiHnpIcqnjKZq7Q5SgkW8O07GA+poxr36Di/uzeVaaTVy/y+RGKuZWDycSH0wle3OkmI2cqY0lqd7B+HrYc3oazlkt2hJyLxCnMTM8OHDScqsZve1Mp7qHUSw6+2S0v2zb6dp/nFnmkYQ4MBLbYRkve6uaW8rlcxBl9OA/ehgWi8dwlRfj9OTT5CV/SZq9U3CQpehVPrcta/JbGLuqbmUt5SzPH45LpY/LfLwQ2ia9Oz/x3XkFhISnur4/7pM8n6htJYz4vkobJyV7P8kldLM+t/t2CKJBLdX5mMsK8dx+ylW919Ns6GZqYenUtlSyVO9g3CzseC13TcxmQVEIjHug7+h0c4Se/NXGNUF1KxNo338XJbnreCSxsxbOXfEgkgkEpY/Ho+NSMecbdd5OnI2IQ4hSKtXUiOqZnGYAuFINZOffAqzXM4t/27c/PtX2Nnn07t3b0TJF5mqMFIVYYfKVsHTm1N5LPA1AuwD+UeIkgNaV8QnFxMkPkTi7GjMZoGKvEY829uRrfMn0LYajcyJbcWv0LjyGd73MLK67yaUIld2lC2i1+ezSStre4lJHR3x+mA5nis+wtTYSOHEhyl75RWM9b+fzX8N/lAnLwiCEXgaOAzcArYKgnDzjxzzPwFlOQ1sffsyNSVqBkwLp3vir29MEQSBxv37yRs8hOoPPkQcH8/ZqVPIbGmhR9cHMCSJaTQ50L19PRHj4ygY0oumC7c42sPM8hESBpZ3YtyL7zDpqxSSsqqxCt2H1JhJePNAHlM/SKVdJpVeR9iU+Rgdve0Y39OPxJQcilp1TChOx6e+mnHjxmEQxLy6K41gFyueiL/d3fhdmuZlcPnRomjqVii5BP0WgsXdJY/qE8VorlZi3ccbZbgttWvXYtmtK41u+ZSVfY2vz0ycne/NxPfJtU84W3aW+XHz6eTS6RfZ0WgwcWBlKlq1gSFPRmJl/+dQBP8noi2ij8LaScm+T67/rouxlrGx2AwZQu2aNQS22rC6/2oadA3MOjYLIy28MjSUm2VNfH25rUlIrnBCNPwTJCYTlh6L0NdoqN5aw/DOQ5lSuoPPSms5UN1wxxhe7i7M7uFMvUHMK19d4f1e7yNGIKjpU444CWw3t6LM0PP4rFkY5XIynLpzZdEbREU5ExISgvTbg0xwtKAi0g4LSylPbbzF7Mgl2ChteSfMictaB9j3PM66M4ydH4uLnw2lmQ24+FmTo3bHx1GDWSRjZ/3fKVr3D7qlr+XUw18T6zCUBvlRxu5+jAX7zqLRt4kS2fTvT+D+fThOm0rj7j3kDRpM/bZtCD/6Svmj8YcnJQVBOCAIQjtBEAIFQXjrjx7v3wmzWeDy/nx2vZeMTC5hzNwYgmNcf/VxWq9fp3DCRMpmvx1Wx5MAACAASURBVIjY1haLjz5kn68PVXV1DB+YQPmORpoEW3pGNhMcLqNg1DAMtU0cHG5kTQ85g4vC8Rv0Mr0/O0BqsRabgJOIzRdRSQawKL8XzeIGGmLeZ1PWs5gEOa8ldmBcah4lWgPPttZgkZvJyJEjcXR05INjWZQ2tPLO6AgUUsntNM2L4BEF3Z+788R16jaeeI/O0HHiXdeluVZF05FCLDs5Y9Pfl4at2zDV1KB6PIGMzAXY2cUREPDCXfsBnC09y+obqxkVNIqH2t3dCn8vCILAiU0ZVOY30W9K2J+irvSfjn+mbixtFez75Dp15S2/27Fd5rwIYjFVS5YQ7hTOR70/orCpkCePP0mfUFu6Bjiw9HAm9S1tQjg2AYk0RvTGoSwfec9DGMpaqLkUxmuiXDqpM3kuvYCC1jubGR8Z/ADd7DQcztWQnm/m7z3+TrU6i6CWLSwLU5J6tghniR2PTJmM1sKCW5KuXHj/CRISeuHo6Ijb8X0MdLWmvKMdIqmYv20u4LWY99FLzSwI8yNfZ4Xw9aMoG1IY/nwnInp7UVWgxsbJgvwGB1xcxUgFPfu1C8nYkYzFlkdZ238eC7osQm5Zzo7K2fT5ZDUnMqsAEKtUuLz4IgE7d6AIDqZiwWsUjHmIlgsXfje7/yv89Vee/iS0NOjY82EKl/bmExzrythXYn91bbIuL4/SF2ZTMG48+tIS3N9ahG7R3/ny8mUEQWDMgBGkri2iWbAiPkaLe8Npip6fh1Rh5NRDWr4IV9K12Idrqom8fmo76hYnHFxTEBSHMFs8wNJLocglSuq6fUZSVSLXK5z429AQni8qo0Rr4G07CY0XThMXF0doaCgZFU2sPVvAhC7eRPvepgk48CLomu6upgE4/V4bf/zgJXeJcuvyG6nbloXc3wb7Me0QDAZqP/8cZXQU2YpVSKVWdAj/8J7UwZUtlcw7PY8guyDmxc37xfa8eaqUrIuVxA33JzDql6V2/hugslUw/NmOiCVi9n50DXWd9nc5rszdHacZ01EfOULL+fN0ce/CkgeXkFaTxuyTs3kloR1qrZFlRzK/28cuYRNalSXW1z5FOVqKvlhNi2Y6q3KWIjFomJ6Wj9b0feQrFotZ9ng8tiItL32TSoxzTyaHT6ax7jBK3XleiVRS8U0W3r7+jH3oITSWlqQ1RJHyxUzGjh2N2WQi9nISsa42NHSyR2Mw8do3dbzW5V0qaWBeaAS1egmGdSMR1+Xw4Lh29JscRqvagFQupqRehbWbNVbmRr4Vz+HyUQuE1X0Z6xzOjhFb8bR2osV+JTP3vMOTm69Q1dRmW0VwMD4bN+CxdCnGhnqKHp9M8cxZf0oVzl/GyQvGe+i2/kkoSK3h60WXqMxvos+jofSbHIbc4pd3sOoLCymbO5e8hGGok5JwfGIWgQcPcsPFhS3btuHi4sKo7gM5/VkuOkFOv26tWB9eTvWmg1gHyrg5qpWPfG0ILHfgrHYglZLj6NRROFjnoHfYhkHRgWfPuuFv2R51+ClyRSa2pD9ArwhX1tNKidbAqkAXCg/uxd3dnf79+yMIAgt2pWFjIeWlgbcrZ27tbUvV3CtNU5sL5z9pi+C97+QKN1RrqN2YjtTBAqdHwhBJxTTu2IGxshJNggUaTS7hYe+jUNy98G40G5l7ei5ak5b3er2HUvrLujcrC5o4vS0b3w6ORA/y+8Vz8d8CW2dLhj3dEV2rkb0rrqNt+X2qQBymTEHm5UXl228jGI308+3H691e52zZWTZmv8ukOG++vFREWmlbqkgkt0I8bAWWrUa0t2ZgO8qP1lwJDqpJrEh/gxvNWhb8KD/v6ebC7AecaDKImL3xHM92fpZo12gsaj8nR17KcpUBdVIJ7Tt2ZNjA/jRbW3M1x5/iY68zcuRIasrKeLg8B08nFYbOjlQ2aVmxX8xLMa9ySyjh1XZdMegNaD8dgNBUTvs4N8a+EouDuwoEqG6QIXJxxVFUyyXLaRy50A/jyj4EVNxi16itDPYfjMLlKCcb36Hv8gOsOZ2H3mhuW5gdlkDgwYO4vDgbzdWr5A0fQdmrr2IoLb2XOX8XSBYuXPiHHfzXYtWqVQtnzJjxq/fTJCdTOOkRRKK2N6boT1B6gjbu8ZNfZnJ+Zy62zpYMf64TPmEOv7ikT5eTQ9XSZZS/9jr6/AIcHn0Erw+WY9GjB3sOHuTChQt06NCB7u4RHNtUjNhsZGBUBea1i9GWqHEd1o6CsFJedLHGuV5FnSgOO8tCaqqGYWlRidFvLSaZG4NuhPKwOB6jcy35gR/xSdoCUCho7ORAud7Ipg5+5OzbSUtLC48++igqlYrtyaWsO1fAm8M7EOPnAK0NsHksOPjDyE/vitTZ9QQ0lbWpPf1Ax9PUrKd6zQ0wmXGeHonEVoFgMFD6txcQedtT2jsFP78n8PQcf08bfXLtE/bl7eON7m/Q1aPrL56XPR9cQ66QMuzZTn+oetL/Z6hsFbj625J6opjy7AbadXFDLPltFB8iqRSpqxv1m79E6uaGMjycUMdQFBIFm25tItRLQlGJDylFDTwU7Y1IJELiFIa27Cx2OalUhhix8x5I4xUlHVSXMRgqWSMOxE8pJ8zq+xd8RJAPl6+mcLJcRKizksnRCezJ3Y2lNpkz7g8SdF1NOz8HPMODkaobuVVXT3W2nnZOZVj7xpFy8QIPh7fjmFSOwl5BRVY9jQ0uJHb2YnfVUSrsoulTl4E2eTuymEko7awJ6eaOyWCmPLcRnQ5MljZ4KarIF0VSVBGMX+5cVBId/XotxMnShfM1e5HYJHM0Rcm+ZA3eDkr8HFWIpVIsO3fG7qGHEHQ6Grdvp27TZsQKOZadO9+X3d94443yhQsXrrrXtr9EJC+SSpF7elK5+B2y+/Sl6sMPMdbW/qFjFtyo4es3L5J5qZLowb489HJM25v+X0AQBJrPnqVo+gzyEobRdPAgDpMeJujYUVznzKFVJmP9+vWkpqbSp08fOog8OLK1ArlRw0CXE7SuXI5gMKKcM4l8+yxmO1lgqZUht/fEzWigtjIRqawJIXg9ZrGSoJpePKaJQCIXUxL6DocrXyFXLYJurlQYjGyODKDl6gVKSkoYPnw4Dg4ONGj0LD5wi2hfe8ZE327aOrYQWqpg+Iq70zTZRyHrUFs1jfX3RFWCwUztxluYGnU4PhqO1LHtIW3cswdDWRk1fUqxtYvG3//5e9rqXNk5VqeuZmTQSIYHDv9F8yKYBY6tS6elUcfAGR2wUP33VdL8Gni1t6ff42GU5zZyYlMGvwfNifWA/ig7d6b6oxWYmtty/lM6TOGxsMfYkbOVXjHpJBc1sDPl++jVYvgXCDIFNqfWYogowKq7J9UVU5lb9BVdW3OZk1lMZsv3aSWxWMySR+OxE7Uyb3sqCpEty3otQ6urwKNhDQvDFGTvyEQwmXlgxCi6+HlRb+/Akf3FhFk24OPjw9UD+/jQ254GOxlusa5cLqzj2o1YEoNGs0+SxUee/VDqymlc0R/BoEUiFdN9dBDDn+2EpbUcncZEidaFcJ8WapQhbKtcStWerYg2j2asV182DdmIi7Ul1gGfoVEmMWXdZR7/4jI5VW0NX1J7e9xemU/gkcPYjRmN3D/gN9v+XvhLRPIyV1fsEhOx6tEDQ2UFjdu+oW7DRnTZWUhsbZF5ev5uJGTqOi0nNmZwcU8e1o4WJDwVSfs4938ZARlraqj/6mvKF7xG/br1mDUanGZMx2PpUmz690dsaUlFRQXr16+noaGBMWPGwKVazpwzYqOtIL5lDS2nrmPhpeCrcfOxK13LYg8DDSIZUrEFwSXxZNc/SLNchzJ8A0ZTAyrZZF64pCbQOpKK8C9IV0byWWooyl7utIjhy8gAHKvKOHDgADExMfTo0cYd8vd96VwuqGPNo7G42Fi0KT0deBG6PQVRD//owvTw9QSwdLgd4bdFzYJZoG5rJrqsehzGh6Bs35bTF4xGSv/2AgZHHc2jJXSO2nhP4rFqTTUzj87E08rzV6k7pRwtIu1kKT3HtSOg01+r7+KPgqOHFWIxXP+2BLFUjEfwb6O7FolEKIKCqN+wAZFUgiouDpFIRFePruQ15HG07Bu8rfw5lipiYpwPcqkY5CqwsMXyxmGKNEk4D34Cc70SfamMIXVL+co7kaP1Gsa5OSC//RVpY22FvKWSw4UGCsuqmd6zG0qpktMF29DLLEm1CGBwhQGLADuCo6KpvHSGIpmSkgtpDOnTlfTSKpoK8hjXoxsbm5sJs1dx+XolAaoYPF3r2KdNQSnrTLfGK9SmJmEZ9wiIRNg6Kwnv6UFznZaa4maqG+W0byelpk5Ehr4f1sVncCz8CJf2wxgWNZPcxlyytQcJ92vlZq4b686UUq/R09HLDqVcgsTaGuv4+N/Ee/Nzkfxfwsn/EzI3N2yHDMFmyGAQgfrYcRq2bKVx7z6MdXVIbG2RODndl8M3GkwkHy7iyJo0Gio1xA71o9/jYVg7/LTykEmtRn30GNUffkTFwoW0nDmD3M8P52efxf2tRaji4hAr26LbjIwMNm/ejEwm4+GHJ5G7NoXUAis81dfpnPsxuiI19VHejAl5kcmtq9jiVcUtuQKpQc6Qkme4qg6iVGbEteM3tOqzMdg9yfTD14h3GILG4wYlgcm8kzKJ1hgnBIWULzsGECoysWnTJhwdHRk7diwSiYTrxQ28siuNyd39GRPjBUYdfDm27SEcux5+3Hx0/hNI2w6Jq+5ofGo6XEDLxQpsB/thFfe9Wk7T3r00bt9Bw1gtIf0+wta24112MwtmXkh6gWJ1MasGrPrF9fDVxWqOfn6TgE7OdB8d9B8hy/b/Be5BdjRWt5L6bZvK1G8Vjpe5uaHPy6Nhx05sR45EYmWFSCSil1cvLlVcoth4jPo6XwSDLQ8EtYntiNw7YcrYg21pIVk2Jfj0fpyWQids6s4S1XyWVfbxlOgMDHGy/W5uI4N9OH/lGifLIMpDxfCwHuQ05FBctZd8+3BEBTLi3O2Q2MgJ69aD3CN7KLdzovzcRQb26cOljExcDFpiO4SzRdtCjK2KpJQKeng8iMgym0NCPm6mMKIaL1B+8yrWsWNBJEIiFRMY5YKjp4q869XU1AjYOFuCRk2WuA/aEh3eeS+jtHRg8AMLUMosOVS0HRf3m3R2j2DnJQ2bLhRhMAl08LRte9H9Bvyck/9L8MnrjCbSShu/rwC5DbNOh/rIERq+2Y7m8mUwm5F5e2Pdrx+qbl1Rdo5GYvXzN7PZLJB1qYLL+/JpqtESGOVM9zFB2DjevQAomM3ocnLQXL5M87cnaLl0CQwGJM5O2I0YgW1iIoqAOz/JBEHg3LlzHD16FA8PDxKHDifprSQqzS50qPgGt9zjCIjYFD2AA4HD+MTua45ynG021ti0WvNw0QIOaUWkyo0ERh2gSnsatf1kxhzPZ4rFIFSWEvK7vsKn5cs45WSN3ErG150CibVWsn79esrLy5k5cyZOTk6YzAIjPzlLZZOW47N7YW0hgxNvw8l3YdIOCPoRCZi6AlZEg18PmLjlu5+bL5XTsCMHVRc37EZ972wFk4nswX3QGiuQ/2MiISGv39PmG9M3suTyEhZ0XcDY9mN/dn7+CaPexNbFV9BpDExYEHdfjWf/7TAaTOxefo3qYjWJL3b+zSWn+pIS8gYPwSYhAY/Fb3/3e522jkkHJlGhbqC54EmOP5OIt0MbhS8lV2BNXwq9lMiGfoybw0jqPj2IY90UlndawBK7B3mvvTcPe3xPDZJfVMrwlRdQKBScnj8YE62M3z+eMk0T1c5vsD7fht5ToxBJxbQ2q1kz72/UOnrhVV5Ku/g+fJuZydChCeyydePzkmp6lhu5fKOKZ/q7cbxxAWq9msVFEnpoUij2Gof3tDt9qaZJxzfvXkVdqwURqMSttJiUuLakMdj9XVQRcTByJTe0Vbx8+mWK1cWM8p9EWcGDHE2vxVEl58neQTwc54PFfdJd/+VFQ7ZdKWbON6nE+tnzZHwQ8e2d74rijLW1qI8fR33kKC0XL4LBABIJFuHhWISGYhHSHkW7dsg8PZE6OyMgIie5isv7Cmio1ODkbUX3xCC8Qx3a1KbUagzlFehzc9BmZaHLyKQ1JQVTY1vVgNzXF+v+/bDq2xdlx453iB5/d05GI/v37yclJYXw8HB6hsVy+IPLtAqWdMl+H2VlGRp7BUvin2dwQi96Va/ndN4aljra42XyJjH9Jc5g4KRUT2jHs5To99Jim0iXmwqmV9vT3qIDxdFLOW0zleV6D2RWMrZFBRFnZ8Xx48c5ffo0iYmJREZGArDxfAELdt9kxYQohnX0gKpb8GlP6JDYFqn/GDufgLRv4MkL4NjWKKXNqqdmXRqKIHucHgtH9IM0Vs3uzVTPXUTr0250evLQPQXNs+qzmLBvAt09uvNRn49+cTR+eksWqSdKGPZsR3zC7o8b6H+AVrWebYuvIAgCY+fH/mYR88olS6n74gv8d2y/Q1yjoLGAifsfpqlFTnfLN1j18Pc86sLup+HaJi7HuhPR+xhyozMtH76MyvgFE/rs55Kg4kB0uzsWYldsOcx7KUbGRTry7sSuZNdnM3H/RHRSP+SqOexWOOM5oC0dUlNcyMa3XqfRzQ/fslKs2rUno7WVx6dM4dU6HUerG+lRZOBSRjUvJTiypXQuSqmSpZl1ROgzKQh6Ar9Jd8piGPUmDnx64zsSOJFIQDALWOgbGaB6D2/fKhi5Eo3fAyy5vITt2dsJdQhlcvArbD6j5UxODZO6+rBoZMR92fkv7+Rb9Sa2XC5i9el8ShtaCXGz5on4QIZGuCO9R6epWaOh9do1Wi5eovXqVbSZmZjVbYshBqmKUs8elHr1QiezxcpYS3tdMi6tOaDTYdZqMdbUILS2fn9AiQS5nx/KTh2xjI7BMiYambf3zzoojUbDli1bKCwspFevXrhVyzl1pAGlpoKYjA8QaYxkRoRjnL2EhGgfTu9+CUnulzzv6kSgKJgBl58mx1bCdlMLYWGpFAtf0qrqjVtTV6afv0pfx5HU+xwlO8qSp2v6gqWU7VFBdHOwJjc3l40bNxIVFcWIEW3Mz9VqHX3eS6Kjlx0bp3ZBJAiwdiDU5cJTl0H1I8d5O+Ligeeh/xtttqtooWrldaT2CpxndUT8gzJSk1FPxqAumE06AvbsRWUddJdNdCYdE/ZPoLa1lh3Dd9whK/dzKLpZy94V14ns40XPsXdz5fwPvw7VRWq2L7mKe5Atw57p+JuYOk1NTeQOGIgiJASfL9be8UxcrbzKlEPT0Gu8WDtwNd0Cbi/at9QgfBRFg1JPXs8+dO68GWN5E3zWizqFmAEPbsBKKuVwTDuspG2Rr16vJ3HxN9xstWHbzK7E+DuxL28f807Po9V6CL1ax7CyVwhyrzZajuzLZ9m9eg3NHr54l5VhdnSkxdWVR6ZNZ2JGCZlqLZ2zNVzLr2PeSBWrc17Cz9qHpdcz8DKWUNhhPgEP3UnbYTa1cVXdOluOjZMF6lotglkABNobk+jtvgLJA7Og72scr7jAwnMLaTW28kzUMwTKB+Flb4Wf0/2lyX7Oyf8lcvIao5rkxh0sGTaEQGc7LhXU8eXFIrZeKaFZZ8TfSYWV4nuHI5LJkHt7o+rWFbvERGwfn0xDhwFkO/bipn0/6uxCcFBoiJDeoINwDSuhAYmVCqmTEzIvL1SxsVgPGIBt4iicnpiF68sv4/joo1j37YtFaCgSW9ufdfDV1dWsX7+e6upqhgwZRtnOAtIyLPApOUSHzA0gFqF/eQG9/v4mYstK1myaTETRYZ5zd8YNL/peegqNlzVf6tQEBeRRIlmPQRmNRD6WcUd3MMRzLCaLSoq6XeTZhofRW0hYFeJDX1c71Go1GzduxM7OjnHjxiG5rWazcM9N0soa+fzxWBxUijYu+KtfQMKHd9W9YzbDlkltPDVj14FUgalJR/WqGyARtZVKWt0ZAeZ++TzCvmysnhuPc2ziPe2y/OpyThSfYFmvZYQ63ptD/sfQthjY89E1rB0tGDitw1+aOvjPgspWgcpOwfXjxZiMZrxDf7le7o8hVigQKSxo+OorLCI6oPDz+26bh5UHbpaenKzczoncbCZ3TGgjv5NbIlJYo7xxgFpJBTpbBxy8umG0aI9N5meEtFrxuW07in+Qn5dIJIS7Kth9vYKkjEomPRBAmGMI9dp6Mst2ku7gi32agqgOrojEIhw9fRB0dVSlXKfG0xub6mpobKRcq+WFB7uzq6aBSnspQa0i9l5t4anuPTlcsp384Bi6l1XhXH6M/GY7HNp/71dFYhF+kU5IZGJyk6tx9rXGK9CKugoNNZIArjWPRJefhk3Gx4SF9GZY9NPkNebxZcaXlGivE+/XBXuL+xP9/svn5Pfm7mX+mfm4q9x5ucvL9PKM50RmNRsvFHIquxqxSES/UBdGdvKkd4gLMpGIurIWSrPqKcmopyy7AYPOhNJGTlC0C+E9PP4QJR2A3Nxctm7diiASY7QOx/ViHYLckc43P8S6vhhZeBC+Kz9HY6vg45SPyT69jbmacmZ4OyEWHEm49gKeMYG8nVeGg2Mh9bYrMSgCUDvO5uFd6xjvnoBziw1Z3f/BC8qXqRSJmG3vwJzOvpjNZjZs2EBJSQkzZszAxaVtQfN6cQMjPjnLzAcDmDckFBpL4ZM48O4Ck7bfTRWcsgl2PwWjVkHHcZh1JqpXpWKs1uA8syPyH9mutvYMJeOnITNaEXr0AiLp3Y1iF8ovMP3IdMa1H8erXV+9a/tP4fi6dLIuVTLm5Ricff44Pd7/Rpz8MpO0U6UMnN6BoOj77xgW9Hryhg0HqZSA3bvumv9nDr5DUtVmBrpNZ9nA24ymZhPCql4YGnO4EG1HdLf9qFSBGL54GmnBJpZE7WC5rQPL2nsz6Qf5+UVrd7EmS8bUOHcWjOqM3qTn8UOPk1abg8Z5IbtE7ekwqC21KAgCu957mdyMCpq9/HGrqMAMRD32GI6RUQxLzsYREcrLtVQ0tDJzaA2rM95luFc/5p3ehsiopaznRwQPvJvCI/tKJcfX3cLKQcHAaeFcWHuBonIx3BbrdpTm4+Vlwr33AG6qbrIkbTGJQYm8EHNvWo9/hb98uqY4o45jX6VSaM6lUlyCq6MT8f49cbCyp7ZRR3JBHXnFTUj1Ag6CGEeTCNHty7ZztcSrvT0BUc54trP7wyJBjc7IjiMnyb56BjVKTKXWhAo+2DQV0DHjM8RGI85PzcJ2xkx25e/h45SP8Sps5u/qUp7ytadOZMWoW3PokdCVF89lY5KVoHP9BIPUgQbXBfQ/fZRRZhdi9REUBe9ibvB4sg1S+rSI+XJ4WwVLUlISSUlJjBgxgqioKKBtYXn0p+cormvlxIu9sFZI4asJkH8SnjwP9n53Xoi2sW2x1d4fph5BEKB2YzrajDocHwtHGXJn1KfT15CyZiC2H2lwXfQ6DmPubnpq1DWSuCcRS6klW4dt/cVdrYU3a9m34jrRg33pOiLw10/K//CzMBnN7HwvmdqyFsbMjb5vmmwA9bFjlDz9DG5vvIH9uDsX040mEw98MRmN9BofxK+gr1+vtg1FF2HtAIp87ano0ImY6G2IDVrM70Vj0CqZ2HszV8QmDkS3I/x2fr6lpYVR7+4iR2/L3md6EO5pR0VLBaP3jKHBbIWL5QL2RUeg8mlbVDbodWycN5W6Zmh2C8SlqgqF3sCAha9TZGXH+Ot5dJTJqT9dTqvBRGKfm2zJ+ZxHfRN4JulzWvViagesJih+2F3XXJ7TwIGVNwAY/EQE4qZaDq9MoVnigMKkxihRYKLti1dpJyOyjycxA+6vVv7fKf/3p0BiUOMiqSFEFkqHhgdwTg/l5v4aTm/JJv1QERYZzXQyy4mwscTSVk6KpYm9lnrW2GnZ4yGQ5i6hTCHQavz92OEaNQbO5tTw/pFMxq08y4y3PiPn6mlqTNZEFNvSXhRASPbXRKV+jMLJAd9Nm7meEE7ivjG8ef5NulQ78VZ9BfO9bKmUKBhV+hwPzejHomsFNJsqMbqvwSi2pMlpDqHZWcQ2aoghjAb7HN5sP5xsoxSP/BZWDwwHID8/n5MnTxIZGUmnTt8zOO66VkpKUQNzB7Vvq6ZJ3wVZB6H3/LsdPMDJJW0kZUOWgEhE4748tLfqsBseeJeDFwQz6Tdno9yjQeLhgv2I0XcdThAE3jz/JnWtdbzz4Du/2MHrtUaSNmdg72ZJ7JB/j67mXx0SqZhBMyKQKSQcXpWGQXf/snZWffuijIqi5uOPMf9wPQuQSiS833sxJp0bL516ifzG/LYNPnHQcSLexU2YKlMoKPwUFNaIRr2PQlzAklObsEPMjLQCmo1t56ZSqXh1aAhyjDyz8SJGkxk3lRvLei1FYiyn1LCGRWeyEAxtz7pMrmDMvOUojE3Y1GRT7eKCxsKCC6+9Rie5mOUh3lzR6wiO90IEHDgdQYL/aDYU7mN9/HSsZQasDs0i9+zRu67ZPciO0XOjsbBqU+aq0yh5eMVwwtwb0IlViPVaOnCErqr1eEuuY83vr9wFf5GcvHXlEQJSZxCp3EuXQf74TOjKYYdt7LT8nJyAC8Qm+DNp0iC69PPngb6+DOrrR7tgB6ys5WRVNrP7WhnfXC3h05O5HLhRwcX8WtLLmihr1FLTrEOtNWIwC+iNZnQGM3qTmSatgcomLcX1Gm6UNHI2p4YDaeWsO1vAuwczWHokkx3JpaQVVBBjvImbUIcn1riUBiFv1RF3fQk2DQXYjR1LyauP8nrhP1ifvh57C3uelyYy/OYOlvkquKpUMl7zLDOmjeO5vWncqi7FJngtWqEVrfNL2DSLSTh/kDEBiRg0RuY8qCDZ6IwirYGvEzri42BJc3MzGzduxNramgkTJiC9/bncrDMyfcMVglyteWN4OCLtbeoCp+C2ztYfVwRVZ7XRF0RNgpgpqM+UQ16t8QAAIABJREFUoj5ehFUPT2z63s3/Xli0itpjX2N9RILriy+hvF3F80McLjjMp6mf8nTU0wzyH/SL5/zMtmxKMusZ+mQkNk6/7MXwP/x6yJVSnLytuH68mJZGPQEd76/BTCQSIffzpX7TJsQqFZbR0Xds93GwITnTlUJ9EmfKkhgeNAyFRAHeXRBdWYed3op0yQUcHeOx8OqJUJKCXd0ufOr7stFJSZFWz1Dntvy8j6c7xZnXOVMlQywY6Rrkgre1N1ZyKy4WbiPFSkz7Ym+Cg9rSPApLFR7t2pN++CAqpZoGW2+0IhG67dsZMm4MMomE9dX1jAh142Z6DU11gXQPga+L9mMfOZG4kvPobh6k2qYz9l5+d1yXhUpGuy6uVBepuX68GG2LkQdnPYCXq4nSa6UUiyPQNtsSJ1+Hb7AAAfH3Zd+/fE6+cP8Fjm8voZP1aTrZrEds6wI9/ka6bxc+TFvFubJzuKnceDz8cUYFjcJSZnnH/jXNOlJLGrhW3MiNkgbyalooqW/FZP51tpFJRPg5qgh1tyHU3QZPWQuZ5w6ja23FpdIeoyGEdrlb8Sw/h8TBnopnR/Ox8jy36m7haunKk52exDfTjNWJl9joL7Db2opJlrN4fsRMZm1OJimnEPfwdTQZKxE5z6FB7MfE/esZ32k49tkqnn+wgQsW3kjT6pkb5sUzfYMxm81s3ryZgoICpk+fjpvb97QD7x7KYGVSLjuf7E6Ujz3seQZSNsOME+D+oyYlQYBNiVByFZ65SmuhmNpN6ViEOeL4cCgi8Z15+8bGZK5eHY/bR47IGiwIPHIY8Y84hWpaaxi5eyQ+1j5sGLwB6T0YKO+Fsux6dr6X8r9qmj8RF/fkceVAAf0mh9E+zu1f7/ATKJ45C01yMkFHjyCxu7OztrhOQ79/rEPhvYruHl35pO8nSMQSuLASDr1Meicfmtw96RK7G3FjOcLHcWhNsbwX/AYfe0juyM/X1taS+P4hSs22HH4hnkBnKwRBYN6ZV9mftwfB5hmOdHwIj4DvFzqTj2zmxOdfYRdpR7k+EFlrK90rKui+ciUvFtfyVXkdT1vZsGF3JqHuKjzbb+V06Un+7j2S4SdXUKa1QZ+4gYC4+Luu22wWuLg7j+TDhbgF2DBoRgQW1jKufLiPa7ekmEQSOnrV8cBrE+7Lrn/5nHzhsRRObcmhSeKIla6CWLtvCbHehtjSFqInc8E/lpXZ20iuSsZOYcf4kPGMDh6Nm+qnb1a90UxpQys1zTrqWvTUt+jRm8yYzAIms4BCJsFaIUWlkOJsrcDD1gInK0VbdQBw5eI5Dhw6gkwvwrKhE0615XTM34iksY66bu35oI+WDHMpfjZ+TOkwhaH+Q7m4dRPOV95il5+JzbbWPOw5mZf6/I0Xtl5jV2o+PhEbqdMXonB5gVKLDgw9sZ1hnu3oWOTNvGgDSfbOWGU1ESVI+XpGNyRiEadPn+b48eMkJCQQE/P9PVBQ08KA5adI6OjO+2M7QcEZWDcUHngO+r95t0Fu7YMtD8Ogd9B7TaL6s1Skbiqcp0cg/pGUnsHQyKVLCUgzTdgsqcf11VdxmHQnHYIgCDx34jnOlp5l27BtBNj9slykyWDm60WXMJvMjF8Q9z/ysT8JZpOZXctTqC5uZtz8WOxcLf/1TveANjOL/JEjcZgyGdc5c+7avuRQBquvfYWF+w4eDXuUObFzwGSAld0xGdScjNDjG/A0gQEvwMmlcGIRlYY3eeKBeK5awoGY7/Pze4+dYvaxeoJdrNj7fB/EYhE6k47x+x4juzEHf8sF7Bo2FMkPKu8OfDafW9+mEjAgmPRCa0QGAz1z84hetozJdXrONqh5QWnLx7vS6RZki9xzLSlVySzxGkm/kx9QorGlZcinhPa+t3h9ztUqjm+4hVwhof/UcLza29NUVM2p5ccIiPUgbGKv+7LrX97JQ9tNmLYpiaunG9BI7bDWVxBhf4UIyw1IpSYI7MO1oB583pxDUukpxCIxPT17khicSA/PHr9YK/TnT8KMPu8MO3cc4pZGjlxrh321D1HF27ApSUWrkrKmH5xub6KzazQTQyfSz6cfIgEOfrycgIJPOe6r5zN7WyYEPszL3V/izX23WHc+G7+Ir6gxZKJ0fY5iRRSdbl4iobmOIaKOvOFjw2E3FQFVetTp9Rx4rifeDpYUFRXxxRdfEBYWxpgxY+4o65y2/grnc2s48WI8LkpgZXcQTPDEeZD/6AE2tMInXUCmwjjuKFWf3kQkE+PyZCckP2qWEQSBG2lPUVNzHN/PIzAVVhJ09ChiizvpH/5Zw/xC9AtM7jD5F5v4yoECLu7JI+GZjvctiP4/3B+a67VsWXQZKwcFo1+KRnqf3Zllc+fSdOgwgYcPIXO7M9Bq1hmJX5qE0m0PDbITvNXjrTZyuuxjsHk0FZFxpNsXEBuzE2tlEPyjG2atkbTmD5kU74CVpZwjt+vnTSYTz7y/mQO1jrw6uB3TegUDbbxIw3Y+RJMgYrLDu8wZ0uW78c1mE5tee4TqnEa6PDaYU+erMAkCcTfS6Dx7NmMVTpRo9TwlVvHBvgwGRdjRZPcJGXUZrPBOpHvSUgqb7aiPX0ZUwr3FbWpL2/SGG6o0RA/0JXaY/28Wlf/L18lDW42qayd/IgYFISvJorLCRJ6pEzcb+qM2hWHTkox/7laGVOQxzKkTFvaBnK5PZ0fOTjalbyKrPguTYMLewv6udM7PoqUGco5hPvMRyV+sZ1NqKxUmMZbNnnTIL6Bd/kZsKopJ6gAfT7CmQ8+RvPnAm0yLmEaQXRAmvZ6vX19ISPUGrvhoWeFgx6igUSx44P/Ye8/4qKrt//99pqT33ia9k0CoofeOCIgQQDpSRFFU4FqwUGyoCCqCIKDSQu/Se09CIKSQ3hPS+2T6zP9BFOQmXoF77+//vcr79eIB2efsM7PPmXX2Xnutz1rMt+eyWHshDZ/wvVRok5A6z6VK3AaHinsMzrjNc4Gd+czCnqNupvQxSLl7pZjPng8n0teexsZGfv75Z8zMzJgwYQJS6YM0/wvp5aw8lc4bA4LoHeQE5z+GtKMw5scWC29zeRWkHkb/7AbK98jRK7Q4zgxHYtfcF15UtI38gh/wUU1EvfkcjvPmYd7x4WevvLGcV868QpBdEB92+RCR8GgPeF2FghM/JOMb4UCHId6Pfo+e8h/ByFSCnas5CWcKUDdq8Qp3eKJ+jENCqd66FV1dLZZ9+z58DYkIa1MJe6+YEeJTyZGcvfRw74GjRyQU3cI8O54yN3sq6q7j5j4ewSEI4eZ6LJ1t8c7xZauz6L5/XiQS0cbLnhNx6ZzKkjOyrQfWplLMpeZ0cm3HgfSdxOlSiRR3w92xKfxWEEQEdupL8uUDFMSn0X/cSDIyi8h3c0V+9AhzjEXsd/EiQaRjqsyR6OvFdHXpg840iV0VsYS3nkZ48VmU6ZdIrbLCo1VEs5wZMysjQrq60lin5s65QgruVuERbPtv1R7+y/vkq+7JOb81lR7jAnGUNd0svU5P2q7L3DlXQIXgDIIIe00BPrY5BFicwY47aIDrdq6csXXiHAqq9E1Spp5mrkQ4hOFv44+PtQ9eJo7YCRIsNCrENflQnQsVaegK4sgoMCajqidZFkHU2BUj0otwKK3FqugE7bM0lNuISJjelYihk+ns2hmp+MGNLMsrYe/yD+hmdp5UzwaWOdgx0GsgK3quYMu1fD48nIh3q4NU6mPA8UVMxR0p00PU+f1MGTCI74os2eNpzARbCw7tSmdIuCtfj2uKnNmxYwdZWVnMmDEDNze3+9fU6PQMXnURnd7Aidd7YlxxF9b3gvCxMGpt88GtKYBvO2IIGEhF/UJU2bU4TA/DxL+5UmF9fQpxN0dja9sVu29FKJNT8D99CpHZg5emwWDg1bOvcu3eNXYP342P9aNFxhgMBo5+d4ei9Bpe+DASC9s/FoZ7yn+Xy3sySDhdwJDZ4fi2fbKN2JKPP6Z66zZ8Dx/C2O/h8Fed3sCwry9Rr6nGxPsbpCIJ0cOisWmogO8iUQT34qpDAr4+8/HxmQe7JmNIP0ml9WbWmtqzxs+Iz4M8mOTW9BLatv8XPryhpbWHNXte7nnf6G5L3sencR8gMenDxeFfYGn2YFVamnebHe+9g5G5QMSE+Zw6dQmDSIR7QSGdgJnPTUFi70Dfch1br+Qxs7cTcaqPKagv4GuP4XQ5/xWFcisy/F6l14vzEbeQGwJN8fTnt6VhMBjoNT7oifc7/vLumoK7VZzalIxSriWin4yOz/g85KutTMohcVcMOYViGo2awvwsNBU4mlTgZFaMm8ltbIUYMkwNxBsbE29iTIKJMVXih5ejpkoTwkrd8Kxwx17uj0QIRCs1osEyHZVZBajlWNbE0P9KJRIdiMaPIOD1d5CYPZyqrNfpubrnOnEHVjPIJZ47Ho0sd7Cjh3sPVvdZTXRsMe8dSMAr5DBVXEdrN4EIs96cE5ky/MJBJnTpyKEyS3Z4WDLdwYSrJ0pQa3Qcm98Ta1MpV69e5eTJkwwZMoTIyMiHrv3DpWyWH73LD5M70D/YATYOgOo8eCW2SS74n9k9FUPacWp9dtKQKMJ2TCDm7ZvXrdVq5cTGjUCnbaS16QqKXpiJ45tv4DBz5kPHHco6xLuX32VBhwVMaTXlke9x9u1yjq1LpOtof9oOaB7J85T/d+i0evauuEldpYLx70VibvP4xdG1VVVkDRiIedcueHzzTbP2yxkVTNx4g+l9RRwofZeOLh35rt93iE+9D9fWkNnvGfK1cXTqeBALnUXTRMSzB0X5b/BqKyk3bcT3/fMqlYo5X2zlXL0Ly0eEMrHLg4nF6yc+4nRJNL4WEzg4+uHSkklXt3Limx04+JpjHvE8SUnJCIKAZV0dXe4ksXLkBKo6dSY8V8mB+CJe7ufMNcXH5Nfl87XPGDqf+pQyhRkxluMZ9MYSjM1aliyoq1Rw9qe7BHdxJbiLa4vH/Bl/eSMPTent1/ZlknLlHpb2JvQcF4hXmP1DSyWDwUDZjWQyT6dQlKuiGlu0kgezTKlWjgkKJKiRCFp0BtAaBNSYohZZoBc/mD0aq6owmGRT5qRAIxgIcbKizcETGPLysejTB+e338LIs7kxKs6o5sT6I9QW7OZZ2V1uuDTysYMdvTx6sbL3SvbeLOHtfbfxCj5ElXADlW0UY5yGsVEh0DHxGuPtLUgwduYneydeMNUgLTZiZ2whO2Z2prOvPYWFhWzatInAwECioqIe+v4VDSr6fH6etl62/DStI8KN7+H4P2D0Rgh/vvmg5lyEn4aj9JpLRdpQLPvKsB7o3eL4J6csoKTkIO3abqXhnZ9QxMfjd+bMQyqfpfJSRh0ahb+NP5sHbW6KnHgE1EotO5bcwMhUwth3O/7b/sun/PtUl8jZ9VEsbgE2PDOvzRPJOpevWUPFN9/iHb0D09/lbvzG9B9jic2pYsHz1XwR/xEzw2fyashk+KY9ensfLgfUYGLqQYf2exBdWwOn3kfTdxOpZ5x5oYcFFhZGnOgQhKVETGpqKtN+vkW1yIozC/ribtPkatQb9Azd9RJFyquM8XmH93s+HN1yJnoRt/enENAjkHx8aWhoQNDr0SgUtIuNI9HTn5sTZ+BcZOBwQjEv9XEhRvUxubW5fB04mS7Hl1GllHJGM5jBiz7F2qn5BAma6i8g8MTy2H/5ZChoikftMymEUW+2RSwRcXTNHQ6tvk15fv39YwRBwLlzGN0Wj2XsD5OY+d1Axkx1olvrRsLsinA3qcQMOSK9DrVWgkErIDWArVCPt0kJrR2K6dNOznPTHHAdbUKRcyOWFmY8k5NL6683IBVEyNZ/j2ztd80MfEVhPYe/ucWu5WuoL9jOWL80rroq+NjBjt6y3qzsvZKDt0p5Z/9tZEFNBl5uE8UrvlH82KDDqyib/loFac5NBn6kso7+5h5ExxQyp5cfnX3tUSgU7NmzB0tLS0aMGNHsgfniRBoKjY73nwlFqC2AM0vBfwCENU9SQqeFY/9Ab+ZBRVo/zCIcsRrg1eLY37u3j5KS/fj4zMO0xIqGc+ewnTL5IQNvMBhYcm0JGp2GZd2WPbKBh6bN1oZqFb0mBD018P9HsHUxp9vz/uSnVJF4/snqk9pPnYrY3p6yL1e2WJHqnaHBNGp0ZGaF8VzAc2xI3MDZ8njouxhRQSytJUOor08kv2AjdJ4LjsFI4z/Eu7cLH8U1kqtQsyCtAIPBQHBwMFNCJGi1OhbuvHn/eiJBxK5nViKVBLIr53OOZ19/6DP0jfoUj/amZFxKJ8zZgFarxd7JCXdvb2I6R+KsqmPO26/iIE9hZFt31p4roYPR2/hY+zAv/SfODnkPOzMDg42PcfSD2RSmJLU4FoJI+K/VP/jLzOR/j06rJ+liEXFHc1HKNQR0dKbdIC8cPP59PZrU1FR++eUX6urqaFVfT/DxE5g4OuLw8lxsRo1qpstRllfHrZP5ZMTmolMex1iXxrjgfHZZKvjG1pp+nv34vOfnHLpdyoI98bgHHqRWFIPcJor3wqfxaWoOaDWMu30Zk8F9WC23YEhlPcv6tOOZNVfwtDNjz5yuSMUCu3btIi0tjWnTpiGTyR76HImFtTy75jLTu/nw3rCQpkIguVfg5etg04L748b3cGwRFdp30csG4TgjHKGFwgZyeTaxcSOwtAynXdstFL32BvKrV/E/ewax1QM98v0Z+3n/6vu81ektXgh5oVk/f0R1iZzopTEERjrTb0roI5/3lP8+BoOBo2vuUJhWzdi3Oz5RoZGqrdsoXb4c2frvsejZs1n7+weT2HYjn0OvdGJZ/Cvk1eWxY+g2vHdMxqCsJqlPNypqLtGp4xHMy4rhp2cw9FxEVdEovlPUsybAmBWBHkx2d6Curo6XVkZzRenO58+3ZkyHB7+R+JQsJsXPQaxvYO+z2wmweeDSUSkr2bL4BeqKxESMf4GL8en06NEDkUjEhQsXMFMqibx6lQYvf+K6jmFznp7ZvZ1J1q8isSKR94Im89y5b9A0yjlQGILvqPl0eGbUf9So/y1m8r9HLBHRpq+Mics6026QF7l3Kti5PIbD39ymILXqV/nPx6OmpoYdO3YQHR2NqKKCvqfP0ObKVdzefBO/48ewHTPmvoHX6fTkJJRzYGU8uz+JI/vWHQzqHdiLU5nSKos1dnq+sbVmmO8wPu/1OdGxxby5Ox73gAPUimJosIliSftZbE5MQS41ZtCtS5gOHcBquQX9SlWs7hbEon2JqDR6VkVFYCQRERMTw927d+nXr18zA28wGFhyOBk7MyNe7RfQVMkp4yT0XdyygZdXYDj7EUraobXug8Ok0BYNvE6nIil5HiKRCa1arUSdmU39yZPYTpr4kIEvkZewInYF7Z3bMz748ZI9Lu/ORGIkosuo5tLET/n/F0EQ6DMpGKmxmFObk9E9gSyI7dgxSGUyylZ+hUHf/PzX+gVgZiTmixPZfNX7K6QiKfPPv0HjwA8RagsJqXJDJDLj7t1/YPDuCmHPI1xZjW1/KTPqxHSt1vNeRhFJ9Y1YWVnx6tAInIV6PjyYSGndg5qx7UL9mGezGJ0g8MIvMylrLLvfZmxiz4g3l2JkqSFp/w7CAn25dOkSMpmMadOmYeLqyrm+fSlHy4h1b/NF6Wl2nMgmRFhAV7euLEn9kY19XkLiIGOMZyLlhz7h0JcfoWqUP9nAPyZ/mRDKlpBIxchC7GjV0x0jEwk5CeUkXywm9XoJqkYN5rbGf1roWalUcu7UKfbv20d1aRnhdxLompGJ99SpuH32aVP9SokEg95AeX49t07lc/anu9y9cg+dVoudcyKVuYcIcW5kiNsd3nW04pCZlCmhU1jceTEbL+Wx9GgCsuA91IriabCJ4sP2szkXF8cNaxf6xV/Eo0s3vlWb06NMy1ceRuypgB0xBXw0KoweAY4UFxezZ88e/P39GTJkSLMZwqGEYjZdyeWD4aF0cAJ2RIFjMDz79X1VvN+jP7IIofgm1aIPsZ/dA7F1yxtr6RnLqKw8S3jYN1hZhVP68SdoiopwX/nl/bKGBoOBhRcWUiwvZl3/ddiYPHr90NzECuKO5hI5wg/P0CeXu33Kfw8jEwk2TmbcOVuIXmd4bFliQSxGbGtLzY4dGHl7YxIU9FC7mZEEiUhgy/V8evnLGBzYgS13t5An6Blo6oH4zm5Mu7xFQdkuJBIrrFvNhriNCDWZmIx4kdbHCjnqKuVEXT1jXezwdnenJvMmN6pMSC+pZ0TbB/Wf2/vISLhrS5b2DMdzzzDCdygmkqZ9OHNLdyw9FGRcSUFXmY+JeyB3EpPo3r07Xbp0Qa1Wk9zYSGpgIF7piUxN+IXU9BIsPMbi4ytha+Yeqls9Sxe9hGD9LWrvFXL6dAJOPn5YOT65wudv/G1qvP4REiMxbgE2hPf2wM7VnIZqJXevlZB4rpD0mBJqyhTotHqkxhKkJmIEQUClUnH10GF2791LTlERHrl59C4sIGLiJNyXL8O0XXtqq7XkJ1eRcDqf8zvSSThTQHl+PbIQO0K7GVGWtY2StFhGdBAIEF/lVQ8PLksNLOiwgJfavMRXpzL48kwiHiE7qCWJetspLG4/g+rEW/xk4UKbjAQCZDI2mdjTqULLF/UN1HQIYf7O2wxq5cKiwcGoVCp+/vlnxGIxEydOxNj4YYPcqNYy6+ebeNmbsXxkOKJfFkJRHEzYBZbNw7UM+fEIx9+kQT8C82mvYuTasourrOw4mVmf4un5IjKPSaiycyhZuhS7KZMfin0+kHmAn1N+ZmGHhXT36P7I90yn1XNsXSLG5hL6Tw29n0n8lP972Lo0/aYSzxfiEWSLpf3jhbcaB/hTf/Yc8ouXsB03DuGfotrC3K05eLuIG9lVLOzXFROJMdvubsM8cCgRGRcxN5hT5+5N8b1dOMuikBo7QuwGxAGdMPdphd/lMrY6ichVqhnuZEOglzu342K4Ui7Fx8GcYNemVacgFtHXyp1DJbaUKU5ysfgqw32H3k+UdHBph8b4GjnXy7A1UlEjtiA/v4B27doRGBiIv78/twqLKLKzocDPjy6Zt2hzah8m+Y60bhXOpvJDJLgG08u+NX51V3CWVHLocCyNCg0ewa0QiZ88e/svHycvr6nm2p4ddHl+POY2jya6X1+lJCehnPzkKorSqtFq9GDQY0MhWnE6RTYCaiMpzqVlhCqlGIf1ReEciKJeQ12lkrpyxX1VPmNzCZ4hdniG2ePmZ0b8sd3EHzuErbUxUWEVFNYm8qqnD+XoWdJtCYO8hvL+wSR23EzFPXgbdfpc6uxn8U6bMXgUZjGnXoxTTTmdFXUc8AwmvFbBN0kKXF7uyoiNN1BodBx7rQfWplL27NlDSkoKU6dOxcur+cboylPpfH0mg12zu9DJcAd+HgHd34D+zeurGnQ6tJ/3QqQoQPXsOczatyw1oFAUEBM7HDMzX9q3i0YkMqL4H29Rd+IE/mdOI7FvykQtlZcy6uAoAu0C2TRo0yMnPQHcPp3PlT2ZDHu5Nd5PmHTzlP93qJVadi6PwaCHce91wsj00XSIfqPh0iUKZs7C+d13sZs0sVn7L4n3mLstnk+fCyeqo4w3L7zJ2fyzbLDvQcfYn1FNiuZa4VtYWraiXevNCOt6gE4Fc29QfbiANeVVfBtozGeBHkxxd+D0mTO8fboCpZEVZxb0wcnywYsp81gWI1XnEWq/pq1zO9b3X3t/Rq/TKTn68/NkHAfX8FDSNaZ07daNgQMHAqDX6/no9EXq4q5jrlZipdISdv0aHveKUXq7sMe/koIIF5aEDsXn7Ceo9Ebsz/ZGaRfGgFmv4BHc6onG/7/mkxcEYYwgCMmCIOgFQejwT21vC4KQKQhCmiAIg/6d6/wZhSl3KLuym03zZxFzcA9ajeZPz7G0MyGsmzP9+kgYHVlMhPgQZtqdZDtmkuNkhE2dgqBsMUaGYSSbjCAh34acO5XUliuwsDUmuIsrfScHM+69Tkz/vAf9p4eg16Sy/b153PzlIH27yJjqG88VZSYTPT1RG1uyefBmerkNYvqPsUTHJ+Eespl6QwF1Dq+xvF0UXRoqeL1CjYlaSeuaUg55BhOgqGZ1rA6PsRF8fC6DnEo5K8dGYGNmxM2bN0lOTqZPnz4tGviiGgXfX8hiWGtXOnmYwuH5YOcLvRa1MCKg2PYtUmUi6pAFf2jg9XoNScnzAQhrtRqRyAh1QQG1R45gGxV138AbDAaWXl+KRq9hadelj2XgFfVqYo/m4tnKDq+wp9IF/wsYmUjoP60VDdVKruzLfOzzzbt3x6xTJyrWrkXX0NxXPSTMhQ5etnxxMh25WseybsuQWcpYUJ9AqZULxmdWEOD3D2pqblBUuheGft6UtHhlNTbDfZmhkNCtSsf7GUUk1jfSq2dPhjlU0ajSsnh/4kPRPX4DfPi8vC0NdrO4VXqTNy+8iUbXZFPEYhP6R63BtWMd9xJT8DY2cPXqVZKSmqJmRCIRiwf0wmb0BC77h1NjY8XVXj3YPnIsiTYyoi7AO18UkPnWBmLqBqGpsuF5l2TaiG9SlHT7yQb/T3i8121zkoDngO9//0dBEEKBcUArwA04LQhCoMFgeHJB6n+BR9YljFLzUVtUkvvFCk5u3IhXx864hbdBbGKKQaVEr1Cil8vRlJSgKS5GWVREUV0dxc5OFLm7I3e0QWqwJtTBgS6DB+MWEPBI1zbo9WTEXOHq7u1UFubj4evJ5K46RDnb+Njdj2ipmtb2IXzV+yu0akvGrLtGZm06ziFbqNcrqHFcyMq2g+ihVzI0KReFtQNdsxI5E9IOmaac766a4hhpzwW1hh0xBbzU248ufvaUlJRw/PhxfH196d69ZTfIp8dSAXh7SDCc/xSqc2DKYZA2lyOQX07FJOsLtOZtMBk79w+/b1b2l9TV3SYs7FtMTZs2eCvWrUOQSLCbMf2zzsXIAAAgAElEQVT+cUeyj3Cx8CILOyzE0+rxkpduHMpGo9LR7fmA/1pY2VP+87j6WRPR35Nbp/Lxi3DE8zG0hQRBwGnBm+SOjaJq82Yc573SrH3xM6GMXHOFdeezWDAoiFV9VjH+6HgWePqyKekqbhVQatuFzMzPcIg8hkmrUXB5JUKbcThMCGHJultM6GzOrORcTnYIYspzg0nceIKTKSJ+SSxhWOumRCRBIqL/iGDm7New1lXBxcKfeP3866zsvRIjsRFmZl70n/weR2qXUJkQj3NYew4cOIC9vT2urq4IgsA7gTKkRlJWZ3vzXEMFnrkZ5JgIZAb54SYRYZEWi/2J2xSqtIAzVubV2Ir2wPOPHnn2yGP7n3DXCIJwHlhgMBjifv3/2wAGg+GTX/9/AvjQYDBc+1f9PKm7pvTsKe7+vB6T4kKMqpVIFFrEuqb3iVYiQSOV0mhmRoOFBXUODlQ7O1Fpbo5WJEIsCHi5udG6Y0dCQ0MxMno0oTKNUsndy+e5dfwwFQV52Lm5M7ibKy4528jU1LDIK4hMbS2TQyfzWrvXuJ3fwMvb41GKkzBy345GZEat4wLWRHSnt1TPc4dPc0cWSGR6AgmBrXESqvj+khpnc2uEKREMWXMZma0Ze1/qikGnYf369U2ZfHPmYGHR3G8em1vFmHXXeLWvP2+Eq2B9b4iYACO+bXasMqMa7c9zMBefglkXENxarhhfUXmehIQZuLtPIDhoGQDqwkKyBg/Bdvx4XN59p+k4RQUjDozAx9qHnwb/9Fgx8RWF9ez6KJbwPk9lhP8X0Wp07PooFrVSx/j3Oz22Hkvhq68hv3wZv1Mn768Kf8/86FscSyrh7ILeuNuYcjz3OAsvLGSCzpS3q2pRvHiA67fHYGsbSRvvJQjfdgS/vjBuG/K4Us6dzmJ2JzOecbZhXagX+w8e4uMbKnQm1px+szf2Fg/2tOqvFjEvp5jzlhexrP6R7u7dWdVnVZPOPZCetoJz644hL7HAENgGkY09s2bNwtz8QSjpD4XlLM4ooqu1OfMlBrYeOoetpgJToWlloKAGa3UDret0hLaPxHfanCcZ9n/prvl3Z/J/hDvw+6yCwl//9l8h3kTLDe9W4P3n/iyxWIyLiwtt3dzw8/PD19f3kQ27XqcjP/kOaVcvkXHjCqpGOY5e3jw/oT+eZYfQJEWzThbCBqkFlhIxa3uvpZtbNzZezuGTY6k4ut5EZLUHtUSGymUh2yLa0cFIYPbWaO4EdiAsL5XEgHDsxY2supmHo8ofuxdbM33fHVQaPavHNYVL7j/8C5WVlUyZMqVFA6/XG1h6OAUXKxPm9PCCLYPBzB4GLmt2rKZETt3WfTiKT0DHl/7QwKtUpaSkLMTCIpgA/3fv/73y+/UIgoD9izOAJjfN8uvLUWqVLO229LEMvMFg4PLuDIzNpHQc9rTa0/8iEqmYflND2bviJpd3Zzx2boPj/PnUnzlDxdp1uCx+t1n7wsHBHEsq4fPjqawa15bB3oO5U36HLSlbaG2oY1jcLvyC3iQjYzklznG49lwIZ5ZAxmnM2veja1YNL2XU8K1QQ1cbC8YMHMCNlA3sqTPj7X2JfD+p/f3Vo0UXN5anVzFZ35tCOzFXijYx78w8VvddjanEFP+AN6gZm0Dsz2VospORywLZtWsXkydPRvzrJuqLHo7YSsS8lprPMnNTvntlEkv33CEtK5eBngKu4hIqSsqJM5KQZabitX//FjTjTx2lgiCcFgQhqYV/I/4TH0AQhFmCIMQJghBXXl7+RH04ejmSHJjMFecrNAbIiQy2oK9xEr25Sn/zDNqa12Fdmod5ZiKWGQk4VBVjr6zHWClHUVONTtvch6/X66irKCPn9k1iD+1l/2dLWDNjPHs/eo+0a5fwbdeBKbOfZVJwJl63PiBWUcLo4Ai+k8jp7zWAvc/uJcy2E69sv8Xyo0n4BZ1BbrULtWlrpLIPOdSpE5FmUj7ctJkTgR3wKi0gR+aPtVTLJ1nH8CwNwHZkIOtT7nE1q5Ilz7bC19GCW7dukZCQQK9evfDxadkQ7okvJLGolreGBGN26wcovgVDPgPThzeldXUqKjbdwUb0HZg7I/R/p8X+DAYdyclvoNMpCGv1NeJf5R00RUXU7N+PzZgxSJ2b0rVP5J7gTP4ZXm77Mr7Wj1evMi+xkqK0GjoN9/nT0Nan/N/F2duKdgM9Sb1WQu6disc619jXB5vRo6neuRN1QUGzdncbU2b28OXA7WJuF9QA8Hr712nn1I4lTk6kx61DZt4Ta6u2pKcvQ9VhHNj7w7GFCDo1NiP9mV4vplu1jvcyikjTwgvP9KGtpJCTKaXsuVl4/1qCIOA6Ooiv0jQYG/dC7Dyb6/euM+fUHGpVtYhEEiLaryZkZCMSUxUWRVkUpKVy/Pjxhz7zaBc7fgr3JbNRRVRKDm+OCWN0rwi25lnwizyCkVNn0di5kYDwR3MRPy5/CXeNwWAgu7yWc6V7WH9nPQaDgXGBY5imMcYu9geoycdg7UmNc09S6525m1pMdfGDm4kgYGJugVgqRSQSo1Y2opI/vPlj6+aBLDSMAD8nZEI24oRtUJNPmo0rX8sCuNiQi4eFB4s7L6abezcupJezaE8ClYoq/Frtp1iVhMJiAD6yF9naJgAHscCqtWtYFdQF68YGFOaWmJuI+bBiFR1jZmDWxon0jo688MMNnm3jxldREZSXl7Nhwwbc3d2ZPHkyon8uz0eTHnefL87jYWvKvihXhLVdm0qKjd8Bv/Nv61U6ytffwbg8GhvRuiaZ4VajWhzf7JxvyMlZRUjIZ7i5PtC4uffhh9Tu3YffqZNIXVyoUlYx8sBI3C3c2TJ0yyNXeoIm0bboZTEYDDDu/U5P5Qv+x9Fp9Oz+NBZFg4bx70c+1ktbU1pG1qBBWPbvj/sXnzdr/01z3tvejN1zuiAIAuWN5Yw99DxmDeVEm7dG9OxybsQMx9GxP+EmI5uqmvV9D3ouQF3cQPqG20zpYoHeXMqx9gGc2LWTdWlG1ImtOT6/JzK7B5pWiruVXNyfyqzO5gRwi6qib5BZyljXfx2uFq7U1N7k2vnJZB7yQ683ocbVj8EjRjYTB0yqb2RyYg7VGh3fhXpiVq3hjZ23kau1vDsslImRnv8V7Zr/lpFvBWwHOtG08XoGCPizjdcnNfKHEop5Y+dtXuzhy9jO5mxI+o5fcn7BWGzM8wGjGSdxxDPpEORcAIMe7PzQeXaj2siHKp0llY1S5A0K9FoNep0OqYkpppaWWFhZ4GQtwVZUiXFFUpNgV/ldDECcVwe22tlztuYulkaWTA+bzsSQiajUYj47kcr2G/l4u1ZicP6JanUNtbbTGOL7DF8GyzAVYPP6dSz3jEBsAJ2RFAsTIxYrlxF5ZTrG5o6Ip7fimXXXsDCRcPiV7kgFPRs2bKCxsZE5c+ZgaWnZ4lj8VtLvwNyuRJyfBoVx8PINsH7gLTPoDVRuSUGdmomrxcsInh1h4r6HXgK/UV19g/hbE3FxfpbQ0C/uP4Sae/fIHDgIm9HP4fprrsWCCws4m3+WXc/swt/28TJUky8VcX5bGkPmhOMb8WTytU/5v0V5fj17Po3Dv4MTA6Y/Xmhg2cqvqFy/Hp99ezEJbe7y2RGTz9v7EvnuhXYMDW/aMI0vjWfG8an0kMtZNXA9eZJUsrNXEh7+HU5ntzQVHnklFmxkNFwrJvZMDjO6mRNiZcZGbwe+WrOBA8pWtPa0u19Z7Teq92ewN6+CxW1MGWRWSEbWx5hITFjbfy1BdkEUFPxIwo3PyD4ShF4woc7dj6hJkwn6p+SuMpWGqUk5xNc1Mt/LmUl2Nry19w6XMiqY1s2bD4b/50Mo/y0jLwjCKOAbwBGoAW4bDIZBv7a9C0wHtMB8g8Fw7M/6e1IjX9mg4tNjqey+WYi7jSkfDA/F372R9YnrOZFzAq1BS1e3rgxx7Uqf2mqsc69C3hVQN/z6RURgYgOmNiA2boqvVcuhofTBRaRmFHq04biDjKPqEjLrcrExtiEqKIrJrSZjIbFkV1wBK06kUdOooke7dO4ot6ETWVHj8BqLQ7sy08MRg0FP9PdrWOIWjsrEFEEQYWFqzAeiFYRf74l5VSgOcyOYeTyFmJwqDrzcjRBXK/bv309CQgKTJ0/G17dlN0h+ZSP9V17gmdaurAy6CwfmwNAvoNPDcr81h7JouFqMs886pGUnYe51sPdr1p9aXUlMzHBEYlM6dTyIRPLA/1+ydCnVu/fgf+I4Ujc3Tued5vXzrzOv7TxmtX68hDa1Usu2969j7WjKqAXtnkbU/IWIOZxN7NHcx9ae19XVkTlgIKbh4Xj+sKF5+6+a83K1ltNv9MJY0uQD35q4mc/iV/KaSsr06VeIjR+LWl1O5+BNSNf1hYABELUFg8FA1ba7HK2oZVEbU6Jc7BhTWcC6E/Fc1vjy9pBgZvd68JvQq3WUfXuLNfawwVPCdMdGYu6+T4OmgRU9V9DDvQd3U98iM+EIOUcD0EtMaPQKYurMWbi7P7wdqdDpeSejkB33quhsbc7aUC/O3L5HJx87Ap1bnrz9GX8LqeE6rY60wloW708irbSeTj52LBwUhLeTjj0ZeziQcYBieTESQUKYQxhtHdvQ2sgWT5USj/pKzBQ1oKwBnRqD2Jg6iZR8U0typFISDHJi63LIqcsBoI1jG0b6j+QZ32cwFhtzPr2clSfTSSyqpa23BEvZPm5VXEVj2gZT15dZExZOV1sL9Hod+9d+zRKnIKpsHJEYDJibGPOJ+Y/IEnQ4po/BZoQfPykb+fxEGh+PCmdCpCe3bt3i4MGD9OrViz59+vzhGMzeEseljArOzgnHZUs3cAiEacfhd26d+stF1B7JxqZ1IRbpc6DXP6BPc1+8waAn4c5Mqqqu0rHDXiwtH8ymNCUlZA0YiPXIkbguW0qNsoYRB0fgbObMtmHbkIoez58ecySH2CM5jF7UHhdf68c69yn/t9Hp9Oz5NA55jYrxH0RiavHoZTYrN22mbMUKPH/cjHnnzs3aL2WUM2ljDO8MDWZWzyaDbDAYWHRkIicrE/jecxStOo4jNm4ULi6jCC13gLPLYdJ+8OuLXqGldHU86zzEfO8u5gNfV/hlP3vu2ZCvs+LQK90JcX2gwaQplVP67W2WtzfngDV84GXMueQPSK1KZW7EXF4Mm0pCwnQK794l57g3OrEUfUBrXpz7Mra2zZM0d5dUsSitEDOxiK+CZQx0ePJn/y9v5M9W1jEnJZd3fN0Y52zLrtgCvjmbSXm9ii6+9kzv7kOfIEfSqu9yKu8UcaVxJFcmo9Vr7/chEUkwFTfFjzdoGjDwYFzMpea0c2pHJ5dODPAegLuFO2qtnlMppay/mEVCYS3uNqaM6trAweKV1KhrabAZxyDfsXwaJMNGKkGv13Hku1Ust/amwM0HI50Wc2NjVtoewjrtBrL4RZiGOZLZxYnxG64zrLUbX4+LoKysjA0bNiCTyZg0aVKLfniAq1kVTNhwgwUDA3ml6lO4ewhmXwKn4PvHKJIrqdyagkmIJfa1LyLoNU2z+Bbi5vPyfyAz8xMCAz9A5jH5obaS5R9RHR2N3/HjGHm489altziRc4LoZ6IJsgtq1te/Ql6rYuv71/FqZcfgWS1H9jzlf5vKogZ2fRyLb4Qjg2aGPfJ5epWKrMFDkDg44L1rZ4srvGmbY4jLreb8wgfhj41qORN29KBKp2LXs3tpqD1CXt5aIsI2YB/9JghieOkqSIxQ5ddRui6Bd7pbc9ZUz9fu1tzcvp0juja421tx8JVu91cJAPKbpZTvSWdhf1uuirVsCHXnctpXHM4+TB9ZHz6MXEha4jSq8uRkHnFFI4gxat2J6XNeajESLl2u5KWUXJIblLzh7cwin6dFQ1okq1HJW+mFXKpuoK2lGSuCPPA3Nmbr9Tw2XcnhXq0SmZ0pIyPcGd7GjUBnS5RaJVk1WRTUF1DYUEiDugGlTonBYMDCyAIrIytkljK8rb3xtPREIpI0FakuquVYUgm74wqpaFAhszNleg9nUtTbOJ57GJ3UDZxfZXlYV55zbnp7azUaDq/5ii+tPMj0CcVIq8HcSMq3zteQZG/CN+YzpFZWiKeGMnzdNUykIg7P647Rr354hULxL/3w98ulKbWceVaNya4o6P0O9P7H/WPUhfWUf38HibMZjiHHEV36FF7Y07R8/Sdqam8SHz8BB4e+hId999CPS1NaRtaAAVg9Oxy35cs5X3CeeWfn8VKbl5gb8cdJVH/E+W2p3L16j/EfRGLj9Bi1dZ/yP0XcsVxuHMxm4IutCOjQcuGMlqjZt59777yD+6pVWA1unjifUVrP4NWXeCHSk6UjHrxAcrJOM/7ia/ga2bBxzHFux49Gr1fS2W4h4uiJ0P9D6P46AHXnCyg5lcvMAbaUiA38o7GEK5eTOKMJZGYPH94d9vCeQNXudCoSSpk72J4snZZdbXxJv3eQz2M/x8nMiQ86voo27wOU5fakHrREbRAwa9eV6XNewtS0+YRKrdfzZW4p/e2t6Gj9+HLN8Dcw8tC0TNtfVsP7GUVUabSMdrFlgbcLbkZSTiSXEB1TwNWsCvQGkNmZ0sXXng7edgQ4WeDnZIGVibRZf1VyNQXVCpKLa7mZV831rEqKa5WIRQK9Ah15IVJGvTSej2M+RaGpo9FqKCODp7PY3xtbaVNkiVrRyL4vP2GtcyBp/uFItRrMpVLWyzLRZi/GL+FzJPX22M1tzYuHk4jNrWbfS11p5dbkh09MTGTy5Ml/GC4JsO1GHu/uT+K7sSEMPT8cjC1h9kWQNC2NtdVKytbcRpCIcBpnhnhLHwgZDs9vataXWl1JTOyziAQjOnY8iFRq9VB7yUcfU719O37Hj6FwsmLUwVHYmtgSPSz6ofq1j0LVPTnRy2II7+VOj6iniU9/ZfS6X0sGVigZ/0EkZlaP5rYx6HTkjByJQaPF9/AhBGnzZ+y9A0lsj8nnxPwe+Ds9mAid3j+J1+tuEyXrzyvtJ3LzZhQeHhMJupkO2eebNmGt3THoDVRsTiLnXj0zelpiLBHx3O1LXCu3JFFhw+ZpHekT9EApUq/WUbbmNmUqNbN6WlGl07Enwh+Umbx96W0K6guYEDCEdsqDSBrCSdyjR60zYNWxO1NfermZiOB/gr+Fkf+NWo2Wr/JK+bGoAq3BwBgXO2Z6OBJqYUpZvZLjSSVczqjgRk4VtYoH8fFGEhFWJlKMxAIqrR65WotS80Df2sHCiPZetvQPcaZ/iDNZ8kzeu76CwuqbaIx88POax0dh3YiwejAbbayrZe+nS9js3YaUgDZIdFqsJRI2+pUjz3gJr6x3MMkKwH5SCKvzK1h3IYsVo1sztqOM+Ph4Dh06RO/evendu/cff1+Fhj5fnMffyYKdHvsRYtfDjJMg6wSAXqmlbG0CuloVTnPCkR59HirS4OVYsHh4I8xg0HM7YTo1NTdo3343VpYPL6019+6RNXBQ0yz+o49YfHkxR7KPsH3YdkLtH7+gx9Hv7lCcXs3E5V0ey1f7lP9Nqorl7Pw4Bu8wBwbPDnvkDfb6s+conDsXlw8/xHZcVLP2ygYVvT8/T0cfOzZN7figobGKlZs7s9nCmI+6LSdIn0Bh4U909FuN1ZYZEDSkKXQY0NWrKV0dT6qDlBdDpMgkApFnDnGZCJSCEcde64Gz1QMRM01ZI2Xf3qLCw5wZYVLkOj372vrjZWzgs9jP2JexD09zJ4abFRAm6crtnSqUjY1Yd+rB1FdeQ9rCy+rf4W9l5H/jnkrN13llRN+rRKE30NnanNEutgx1sMHeSIJebyC3Uk5WuZzs8gaqGtXUKbRodHpMpCJMpWJcrU2R2ZkR5GyJzM4UrQGO3stmbcJaispPYhCZ4eg8jqUdptLT7mGd9MrCfPauWMbuVl1JDGyLSK/DUSzix8BaqtPm4FwehXV8fyz7yLjiZszcbfG8EOnJR6PCKS0tZcOGDXh6ejJx4sQ/9MMDLDuSwqYrORweY0PYoWFNkTRDm2KLDTo9FZuTUWXX4jA9DJPqPXD0TRjxHbRtrpGRk/MN2TmrCApahof7hOZj+v4H1Ozfj//xY1w3ZDP3zNymupvtXn3s+1OUXs2BlbfoPNKX9oO9H/v8p/xvEn8ij2v7sxgwI5TAjs2lrlvCYDCQ98JENAUF+J04jsisuVvv+wtZfHIslS0zOtEj4MHkRXvje2bf+oIEMwt+GryJmsz5iERSIhV9EF1YAZMPNuWR0CTvUbExibiuDrxspSJEpyL04nlO6sJpI7Nl+8zOD4VVNt4pp2p7KpWRTkxx0qAxwIG2/gSYm3Cp8BIf3fiIooYiOphpGe/Uk6xoFfLqSqwiIpny+sL/6Iz+b2nkf6Nao2X7vSq2FVeSrVAhEaCdlTldbCzoYGVGgLkJHsZGSP5Jr9xgMFCh0ZLdqOJ2fSMXS7O5lR+NqP4CYMDbZTjvdXyZSLvmD2p2fCyHvvmCw71GkuwVgkivx10q4seABkpTZ2Kn6IfDlXEY+9lQPcSTkWuvEuRiSfSszug1ajZs2IBarf5DXZrfyCpvYNBXFxnTzo1PSmeDqr4pJt7YEoPBQPXeDBrjSrEdE4h5gA6+7QTu7Zoe7H+aRVVVXeHW7Sk4Ow+nVejKZrMsdX4+WUOHYTt2LGZvvcZzB5/DXGrO7uG77+ttPyoGvYE9n8XRWKfmhSWdkRg9uY72U/630OsN7Pv8JjVljYx/PxLzPyhI8880xseTN+EFHOfPx2HO7GbtSo2OAV9dwNxIwtFXezwwxjotFd93J8pUjpGVO+u6vUFmykt4u8/A7/jOppDpOZfvuzZrj+dQf76QU8/JeFteQ5vqEjxTCzlV58pr/QJ4fcDDbsXaE7nUnyugargXL1CHAOyK8CPY3BSFVsGGOxvYnLQRAR0D7QNwP++AOv8exj6BTHtvKebm/35JUvibG/nfMBgMJDUoOFxWw+WaBhLqG9H9+tUlAlhLJFiImwTLGnV6arU6FDodUlUqJg3nMGmMQRAEImXDeKvdLPysmysr6vU6Yg/u5dyeHRwfPo1UZ08Egx5fIwk/BjZSmDIDK11bXC7PRWxphMm0Vjy38QZ1Si1H5nXHydKI6OhoMjMzmTp1Kp6e/1q98bfIgnNdE3G4tgzG74SgwQDUncun7kQeln1lWA/wgugXIOsszL3aJDf8O1SqUm7EDEcqtaVjh31IJM03f4oWLaL+5Cn8Tp7gg7RV/JLzC9uGbqOVw+Mnb2TElnJyYzL9poQQ3OXJogme8r9LdYmcnR/F4hlqx5A54Y/stimY+zKNMTFN4mUthCQevXOPl7c3ac6P6/S73072BW7vfJ5pbm509ejOXFdLSkv30dnmdcwOvgUDlkG3ptWoQaen/Ps7aEob2RIlY1VpJW3zM7CoMCWuQmDbjEi6+j+ob2DQG6j8OQVlejXVk4OYWFmKWm9gexs/2v7qui2oK2DF5blcKM/BWCSlTZ0v7nEN2Ji4M3nJJ9g7PfpG9B/xt6vx2hKCIBBuacY7fm780j6Q9O7hHGrrz1fBMubKnBjmaE17a3PCLU2JNKslUn+awMrF2JR9jIMmiUmhEzg5+hgb+ixv0cA3VFWy96P3OL1vF4fHziXVSQYGA6EmUrYEySm6OxNzUSBuN+chiARsJ4Xy5sEk8qsa+e6FdrhYm3Dp0iXS09MZNGjQnxr4M3dLOZdWzqudrXGI+QzCRt838I23y6g7kYdZhCNWA7yawinTjkKft5sZeL1eS1LyfHS6RsLDv23RwKsyM6k7fATbFyZwUZHI4ezDzGw984kMvE6j5/rBLOzdLQiMfLTl+lP+Wti6mNN5hC85CRWkx5T++Qm/4vT6fPSNjVSsXdti+9BwF9r/qjnfoHoQHo1vLyJ8B7Gouo6LhRe5qHLDSOpAouYIhoABcOEzqLsHNFWHshsXDAJMPVfJJFc7bnkGILdtwMPKiNd23qbsd7VhBZGA3bggJPYm2O/OZK+vJ5YSMWNuZ3K1uinZUmYl4+shB/my9QBCTJTEWWawv3cxJ32T+PCzycRcPP0Eo/jo/CWMfFZ1Ju+ffIszeWeoVFQ+0jnmEjGdbCwY72rPbDdjBpmk4yvfTWnGfK7emU1y/mY8zW1Z1m0ZZ8eeYVHHRbiYt2yUMmKu8tOieSSVlLFz4ptkWTqAINDTypRNfiXkJU/HROqJV/K76Oo02E9uxRcxuZxJLeOD4aF08rEjIyODc+fOER4eTqdOnf7lZ1dqdCw5nIK/ozlTCz8AqRkM/hQAVW4tVbvTMfKxwvb5QARlDfyyEFxaQ+eXm/WVnb2SmpoYgoOXY2HeskBS+dffIDIzQzRxNEuvLyXELoRZ4U9WpjHxQiF1FUq6jvZ7WtLvb0zrvjJc/ay5tDMdeY3qkc4xDgjAZswYqrfvQJWT06xdEAQWDwuhokHFuvNZDzcOXM64ejnDJA6svfMDdXYTaGhIpTAsDHQaOLn4/qESOxNsRwegLajn7RwtzztZE+8TjLVbAw1KDS9ti0f9u6LlIhMJ9pNDMejAYns6+0O8cTM2YvydLPaVVv/62UT0j1jJu23G875rI897hFDvKuV862JmZ7zBuB+GcyHv/OMP5CPw35Ia/n/K4ZNbOSg/yv57RwFwMXfBx8oHmaUMO1M7bIxt7mdhqnVqalQ1VCmryK/PJ68ujxJ5CQBSkZQIpwjGBI2hn2e/PzTqv1FfWcHZzevIjL1OfZvO/NxpMGpBAEFgrKM1C+1ukZHyFpYWYfikf4gyrxa7cUHsL61hw6UcpnTxYlIXb6qqqti7dy/Ozs4MHz78T5evGy5mk1/VyNYu95Deug6j1oOFE5oKBZU/pyCxNcFhUiiCRAS/vA/yiqaaruKHb3dFxVny8r/HzS0KV5eRLV5LkZxM/cmT2L88l49Tv6FeXc8PA3947HBJAKVcQzMLlxAAACAASURBVNwvuchC7fAMfVrx6e+MSCTQd3IIO5fHcG5bKsPmtn4kt43jvFeoO3KEsi++RLameW2Etp62jIhwY8OlbMZHeuJu82tcuq03Qtd5vH/5S9JbdebjO9EsC+hHRlU0zh0nYnR9I7SfCj49ADALd0QVWYP8QhGfTmtFraWSU54+dDYu5+btapYeSWb5yAfJe1JHMxwmh1K+MRGj6Az2Tw5hRmoec1PyyGpUssDbBUEQCAx4D0EQY12wiZFtn+Ue/dh4fDXZFgXsOriWXq/2/k8M70P8JYz8c52moF1TTll9OhWOWiStnKlV1ZJSlUKdqu6h7FUAAQFrY2tkljI6OHcgwDaAtk5tCbUPvV8Q4F+hVjQSd2Q/cUcOoNfruRc1i23WHogNenQiMa97OjFG2Ed66lfY2nTFO+9dGhPLsRrkzW1LEYs3JtEz0JH3nglFrVaza9cuAKKiov5U276wupE15zMZGmhB98R3IXAItB6LTq6hcnMSCOAwrRUiM2mTIFP8z9DtNXCLeKgfhaKQ5JQFWFiEEhjQvN7rb5SvXo3I2prYXs6cvrWe19u/ToDtk0mixh/PQ6XQ0vW55jo5T/n7YeNsRudRflzelUHqtRJCuv75/ozEwQH72bMpX7kS+fUbmHeObHbMosHBHP+d5vx9ur+O2e1trKqqZ5yFnrXFlcy2siDRNpt21jKEXxbCnEvw6wTG5hlfVLl11O1KZ8OrbRlzK5Hrzo607ahg6/V8wt2tier4wK1q7GuNXVQQVdtTMd2fxc6oQBZlFPFlbimZjSpWBskwl4gJ8H8HqcSK7JxVONpW8NPcnexav5nQyObf5T/BX2bjVaVScSB6O7kXTiGR12FqZU3n56II7tkbpViLTt8kgCkVSbE0snysYha/oaiv486ZE9w8sh9FfR2unbuzJaQbt4wtkeq06MUSPglwoV3NJ5SWHcHFeSTuRXNpOFuMxf/H3nmHR1V1ffuemt57TyAhJAFC6B1EEGmCdFARFEGKoGBviF3RR0FEFEEQFKRDpLcQeklIICRAICEhddIzk8n08/0xtBAIMcDzPS/OfV1cJLP32adkzjr7rL3Wb3X1o7itB0N/OoqngxXrp3TCwUrK+vXrSUlJYcyYMTRpcu+EoMkrE4i7UMQev8X4lZ+EKccRbDwpWnwWXZ4Kj5daYBXkCNXlsLAjWDvCxAMguxnjazRqSUgcgVp9hXZtN2NrG3zHfVUdO072uHHYTJ/IGOe1NHZuzLInlzXo2lWWVPPn7OOEtvGk17h/HlNv4dFEMAls+u40xVeVjJ7dHnsX63tuY9JqyejbD7GTEyHr1iKS1P4+fr3jPAvjLrNpamdaBtwS3nx2Hax/kf1dpzA952/6+rWhjzieFrKBeOz9Dfp8AR1vZm7rC6tQLEhCHuSIw/MRDNh1iBRbZ4KKtJQml7JmUgdiAmsuAisP5lCxNRP7zr449g9h4dUiPs/IJ8TGip+jgmjmYF6Qzc9fT9r5d7GzbUx09K9YW/s28Cr+CxZeNZp8Ll16h6dHP0X/me9iCo9GZTCyf9kv/Dr5BRJXrkKXXYSbtSvO1s7/yEiZjEayziSxc9F8fpkynkOrluMZ0hjb8dOZE9GdJJkdMkHA3sqKlRFONM1/mULFVho3eoPA0tdQ7cvDtrUXmi4+PL/0JFKxiCXPt8XRWkZ8fDwpKSn07NmzXgb+YHoR21MKmBZagl/BHnjyKwR7b0r/uoDuqhLXkU3NBh5gxztmFc3BC2sYeEEQuHDxQ5TKFKIiv7mrgRdMJhRz5yL18WZuUCoGwcBnXT5rkIEHOLHF7ENt/9Q/KyRi4dFGdM1tYxJg/4rz1GfSKbaywmPWTLRpaVRs2nzHPpN7NMbdXs5nW1NrjtlsKIR057ETf/BS+Bi2554iRdSMFMMujCGdIe4LUN5cDJZ52eH8VGO0l8rRHcpnTZcYIotyyfKwQhLlzITfE8gpU9fYt30XP+w7+6I6nIdyTzbTgrxY1zKUKqOJ/onpLM0pQhAEfHyGEh29hGpNLidODqKs7HjDLuK9rtdDGfW/jFJ5FkXRdo6fGICvbyXT3nmfiCHPoA6OQGPrwJl9u1g9+00WTRrL9gXfcnpHLHkX01CVlmAy3pS4v14NKicthcRtm4n97ksWTXqOdZ+9z4Uj8YR36kr0hOn81rQjc6w80UmkiMRiGtvbsDIkD/HFp1GrM2nR/CfcsgdQsTUTmyg3JH2DGffbScrVOpaNb0egmy2pqak3Flq7du16z3PUGUx8tOUcwc4yJmS/BWF9IHoUFdsyqU4pwalfCLbNr4V2XdgOyX+atTn8WtcYJzdvFfn56wgOnoqHR23dmutUbtuO5tw5Mkd2Ir74GDNbz/zHBbmvU5St5MKJAqIf98fB9d4zNQv/Lpw8bOj0dGOyU0tJO5xfr20c+/XDJjoaxfffYbqtwA+Ag7WMmb3DOXmljB0pBTcbRCLo/y3o1UzNzaSDTwd+z7nKVb2UtBAZgkEDuz+sMZZtGy9soj2o3H0Fu3IRC5o3olluBuU+NhSH2fP8spM1sudFIhFO/Rth19Yb5b6rVO7NppOLPXvahtPF2YF303MZmnSZDLUWN9cutG2zEZnMmYqK0w27gPfgkXHXKJVppJybgVqdQWDACzRq9CqlpVUcOHCAlOQkZGolrhgxlpegVSlvbigSIZXLEYxGjEYj3HI9HNw98AuPxC2sKeUiKavySjno2xi13ApviYgCE/Rzs2WK+DcqFGtwdIymWdT36I+LqNxxBZvm7tgODeX5Zac4fbWM38a1o0uYO/n5+SxduhRPT0/GjRtXrxTnX+Iv8/m28/zmG8tjVdtg6jFUZwXKYzOw7+SL08BG5oUrdSks7AB2HvDS/htJHgAVFYkkJI7B1bUT0S0WIxLdeVZu0unI6NsPg50Vz49Q0MKzJT/3/hmxqGFzgi3zTqPIVvLcJx3/cWFnC/8OBJPA5nmnUWQpGfVBOxzdagt53Y769GmyRo/BfcpkPKbXzro2GE30n3+Iar2R3TO71VCTZO/HcPBbSsesZuSZ/yAYq5numk+PqjY4Ju0wS3QHdbzR3aQxUDj/NJgEvKbHsHXfTpYUlHO8cRSiCj2dSk38ObYdcunNe0QwCZStu4g6UYFjn2AcHwtAEAT+zC9lzuVcdCaBKYGeTA3wxAoNEont/25lqAdFQ428xmgitaqaaDsR6emfk5u3Cmtrf8KbfISbWw+Ki4tJTEwkKSmJarUaKQLeDnbYy2XIRQJSkQipTI5UJkNmZ4/E1g69VEaJsorsq1c5Y2VPYlA4xfbOBEtFqEQSKg1GZnkqaFn8LkZjJUGBEwkOfgXVvnyUe7OxifbAYWgYU1efZk9aIfNHxTAw2helUsnixeYiCC+99NJdlSVvpbBSQ89v4ujoquLX8hdh0EKq5U9ekw12w+3ZCETXwxHXT4BzG80G3qfFjTG02iJOnHwKidiatm03IZPdXbu65LdlKL76it8nhnDQt5L1A9fjZdewhI3s1BJi5yfTZXgY0Y8HNGgMC/8OKourWf3JCbxCHHlqRst6GbzcmTNR7ttP421bkfnW9mlf15x//YkmTOt5S8CATg0/tge5LWeHLeL5XS/S1M6aiU5KuifrEdm4mdeybolI011VovgpGZsIVxxGhrJ06VJOiazY2SQGfbWBPhopy56OrnHcgkmgbM0F1ElFOPTwx7FPMCKRiEKtng8v5bJZUY6HXMrMYG/G+LhiVYeESV088j75LUXl9EtIZ8DpHM45z6RZy9WIxVYkn5nA6dPPIpdfpU+fPsycOZNnnn2W1h06orWx52K5ijMlShKLKzmRX8yR7HwOpKWzLyGJ3QlJxGphRYsu7Ipqj62bO0+4OZJtELAVafnKagHNCl7GzjaIdm230Cj4NSo2XkG5NxvbVp44Dg3jtbXJ7E4t5KOBUQyM9kWn07Fq1Sqqq6sZPXp0vQw8wBfb0tAbTXyg/BhCe6NzG0jp6vPI/B1wHRV+08CnboGza6HbmzUMvMmk42zKNAwGJc1b/FSngTdWVFC8aBFFLfz52+0qczrOabCBN5kEjmy4jKO7Nc26+d17Awv/ahzdbeg0NJSc82WcO5hXr208Zs4CQaDw69q1YAG6hnnQt5k3C/Zf4mrpLb5zuS30+xqKztP8Ujxvt3ubs0olsZUCV5oGQWEKnFpSYyx5gANOTwZTfa4EXWIxI0eOJLSskHFXzuJgLWWnk8CoXecwmW7G0IvEIlxGhGPX3htlXA5l69MRjAJeVjJ+jgpmW6swGttY8c7FHD5Iz/3nF60ePBIhlP3cnVCG+bE0p5ipadk4SKx4wm0R7ZzOUF00n7KE4Tg7t8PPbwyNG/chLMz8RDcYDJSWlqJSqdDpdBToDJwWJJzQweEqHTpBoIW9Dc+7O7GhsIRdJZX0lp5khHYebjaeNIr8Di+vAQg6E8XLU9FeLMOhZwC2PQN4bU0yW8/m837/CJ7vFIzRaGTt2rXk5+czcuRIfHzql85/5FIxm5LymO52kiBDGYauX1G8PBWxgxz35yMRX9d9qcyH2BngEw1dZ9YYI/3SF1RUnCIq6nsc7JveYS83KV74E8bKSr5uW8XQsKE8HvT4P/+DXOPiiQJKclQ88WIUEtkjMZ+w8JCJ6urL5UQFh9dfIjDSFUf3ut02cn8/3Ca+RPEPC6g6Mhy7Tp1q9flgQCRxF4r45O9Ufhl7y2Q3vC+E94O4Lxk+9QSpYUNZn74eL9cMZvhFYbXvM3Nxe/ubMsP2XfzQXi6n/O8MPINiGDJkCH/++Scfujgx3zaQA3IDj8WdY0OXCNzkZvMqEotwHhyK2F6Ocm82pio9rqOaIraS0MrJjo0xoRwsU+Fr/XBcmY+Eu+Y6JkHgYJmKTYoydhRVUGYwL6oGyDR4G9NxNWbjIq7GyS4IB7vG6KQ+KAU7rmj0nFNVo9CZU6F9rGT0dXegu52SzYWFbKpwxoVSJggL6WxvwN//Oby9ByMWy9AXqSlZmYahSI3L4DDkrT2ZtSaZLcl5N+pECoLAli1bOH36NAMGDKBNmzu+VdVCozfSb95BjOoydhpfQt7vOxQHIzCp9XhMjkbmcU2Nz2SClU9D9nFznK/7zdfS/PyNpKa9TmDAi4SF1S7zdyvay5fJGDSIIy2s2DTcl78G/IWtrGGFPAx6I398eAxbRznD3mpz823DgoV7oCzVsOrj43gGOjDo1Zh7fndMWi0ZAwYikslotGkjojvkmvwUd5mvdpznt3FteazpTaNNWZbZbRPWG/2wpby0+yWSFYm85SwwMikfUYsR5gi1WzCqdBTOO43YWoLnKzHEHTpAfHw8T/brzxyFjNM2ArZiMd9GBjLY07mG+0Z1JI/y2MvIvGxxey4SaT3WHurDI++TvxN6k8DpyipOVqpJqKgis1pLVnU1alPNL4wdKjxFFYRIigmVltBCdB53YyZbdc1YLwxDixV9pYeY4qmisU9/HB1v+gqrU4opXXsRkUSE6+imEOzI1D8S2XtewZtPhjOlRygA+/fv58CBA3Tr1o2ePXvW+xy+232ReXvTWWH1NV0iAigqex1drgqPCc2xCr7F5XJkAex6DwZ8D23G3/i4UplCQsIIHB1bEtPyd8Tiu7+4CYLA1QkvUZp4nBmTJCwavooIt4h6H+vtJO7K4uiGywx6LQb/8NpiUhYs1EXq4Tz2rzhPlxFhRPe891qOcv9+ciZPwfON13F78cVa7TqDiSfnxWM0Cex8tRvWslsWYeO/gX2fwJg1lAW2Y/TfI1Bq8lmEFc0vXYIXd9+oz3AdzaVyipecxTbGE6ehoaxatYqMjAxGj3mGjxMr2CMzIDjL6eXmyJxQXxrb3owq01wso2TVeQBcR4Zj09S1gVfpJv9KI38nBEFAaxLQm4yUqc6D+gIa9Xm0mgIMRhVVBh17DO1Zq21DicmOTnbVzG7kTgu30BpPY5PGQHlsBuqEQmQBDrg905QqKwkTlp/kVFYZHw9qxnMdggA4ceIE27Zto2XLlgwaNKjeq+eXi1T0/T6evlZn+d52CaVuy6i+aMR1VFNso28p9pF/Bn59HMKegJErb0gIa7UKTp56GhDRru0m5HL3O+/oGsq9e8mZOo3feomJfPkNxjUb94+u7a1oVHpWfHAUn1AnBkyNbvA4Fv69CILA1oVnyEkrY/i7bXDzvbck79WXJ1N14gSNt29D5lV7HenwpWKe+fU4r/VqwoxetyzCGnTwc7drUt3HuKQuZMzWkbiJqlmTW4WDYwC8FFdLFqRidxbKvdk4D2qMNMaVJUuWoFQqeX7ceD7Zl8uWikpE4c4IYnjGx42Zwd54WZldMoaSakpWpKIvUGPX0QfnfiGIZA2X3H7kF16NlTrKNqZjVOrq7CcSibCWiHGQyQh0aU6g3zCahL2PU+i37HL8gpc177KoujtNHD1ZE92Y9W07EO0edsMwC4JAdWoJhd8lok4sxKFHAJ6TWlCIwMifj5J0tZwfRsfcMPAJCQls27aN8PDwemnSXEcQBN7fmIK1SM/7xgWo3N6h+oIR5wGNahp4ndocTWPjCgPn3zDwRqOWM2cno9dXEN1i8T0NvEmrJeezT8hxF1HRvwNjo8bW2f9enNpxBb3GQMfBFvkCCw1DJBLR87kI5DYSdi9JxXhLlba74fXeu2AwoPjq6zu2dw51Z0ALHxbGXSK75JZFWKkcnpoPlbmwZw6hLqF80/07cg0SXvWxxVRwFo7+UGs8x8cDsW7qSnlsBqICLWPGjEEsFrPmr9V8ObAJz3q6Io7Lp4lWxMq8EtoeTeW189mkqaqRutngOTUG+y5+VB3Np3D+abRXKhp8verikTDy2isVVJ0qpGDuKSrjriLU4wuRq9Hxa04RQ09fov2xNOZlFdLS0ZZNMaFsjAmjm6tDzQLWBVUUL02h5PdURFZiPKe0xOnJYE5eLeepHw6RW1bN0nFtGdDCHMaVnJxMbGwsoaGhDB8+HMkdUq/vxobEXI5mlPAWy3H0fpKKC6E49AzAvvNtESq73jeX8nt6EdiZBb8EQeD8+XeorEwiKupbHBzu7XIp+PVnyCtkfX9nPuvxVYPj4cEcBnc2LoemnXxw83swBREs/DuxdZTT87kISnJVHNt8+Z795QEBuE2YQOW2bVQdu3P26Pv9I5GIRcyJPVczEzagHbSfBCd/hexjdAvoxoyWL3PCJOVbfx+E/V9AcXqNsURiEa4jw5G6WFHyRxqOEltGjx5NZWUla9f8xZwB4bzcIZiM/Tl0zzcwwsuFTYVlPHbyAk+cusAvBcVUPO6H+4vNEPQmtBkPx8g/Eu6a81XVrMpQ4H2xEu/LStxkEjxaeuHcyguNTIzSaCRHoyOzWkeqqpqTFVVka8yz/jBbK57ydGa0jxv+1rUXbLRZlSgP5KBJLUFkI8WxVyD2HXwQScSsOpHNh5tTCHCx5ZexbQj1NBu1lJQU1q9fT3BwMGPGjPlH9RzLqnQ8/u1+gnWXWOOwmMKyudi2C8b56ZouI1I2wLrx0OkVeOLTGx9fubKIyxlzadRoJiHBtaWFb0eXm8v5vn1IDDER9fNy2nq3vec2dbFryTkyk4p45uOO2Ls8+ILFFv59HFh1gZQDuTw1oyUBEXX7r00ajXkRViolZPMmxHcosbc4PoPPtqXx0zOt6Nv8lig3rcqcSCizhZcPIkjkvLVvAttzTvBRWSVDHcJh/Ha4TdpDX1CFYmESMh97PF5qTuqFNNatW0ejRo0YNWoUK47n8OnWVJp4OTB3TAxHtNWsLyzjjLIagABrOR0d7HjK04lenjXLiNaXR94nH6soZ1paFlrTvc/FSy6ljZMdbR3t6OXuSKhtzTR7QRAwlGioTimmOkmBvkCNyEaKfUcf7Dv7IbGTUanR8+GmFDYl5dGtiQc/jI7BycZsyJOTk9m0aRMBAQE8++yz91SVvJ031yWzISGbWPl7uOqmI43shOszETUjDIovwS89wDMCxm+7oZpXVLSbM2cn4+U1gKjI7+7pHhIEgRNjByNPusjZ715kbK/X/9Gx3k5RtpI1n5+kdd8gOgyyuGosPBj0OiNrPz+JrtrAqA/aY21f96RJdfgwV1+cgNvLk/B89dVa7QajiUE/Hkah1LJnZvcb9y5gVm79Y6g516Tne+iNesZt6c25ymIWFhTRqfsc6PByrTGv13u16+CDy+BQEhMT2bJlCxEREQwbNowjGaVM/SMRiVjEvFExdGviwWW1hrhSJYfKVByvUPGSvwevBTeskM4jb+QBjIJAvlbPlWotpXojlcVqlJnlSLNV2FQb8dIKBDvb4u5jj8TZComT1TXDKWCqNmIs12IoVqPNUmK65tuXBzpgG+OJbSsvxFbmp/eJzFJmrU0ir1zDjMfDmPpY6I16ksePH2f79u2EhIQwatSof1yo93p23suSLUwWydAFTsR9fDNEt8aY66vh115QmWcOl3TyB0CpOk9CwnDsbENp1WoVEsm9NWJS1y1B9P43xD/diJc+j70vN40gCGz+PomSXBXPfdIRuc0jkYJh4X+Eomwl6746RUgLd/pMbHbPCUze2+9Q8fffhKxfh3V4eK32szkVDPrxECPbBvLFkOY1GzdMhJT1MCkevKIory5g9KY+lGiNLFeUEfHSYXAJrjVm+bZMVPE5uAwNw66tN0ePHmXnzp1ER0czaNAgskqrmbTiFBcLVYzvHMxbTza9EeUjCAJ6QUD+EDJeHxkjfzcEownt5Qq0mRVor1Sgz1cjaAy1O4pFSFyssApwQB7shHUTF6S3iGmVq3V8uf08q09eJcDVhu9HtqR1kPnVURAEDhw4QFxcHE2bNmXo0KH/yEUDoNIa6PPtXqyUV9ko/RON+1d4TIpBbH2bsdw8DU6vgGfWQZhZYEyrLeTUqWEIgpG2bTdiZXXvDNWyohwu9HuSSnsxbWL34mrvcc9t6iLzTDHbFp6h68gwWjxmkS+w8OBJ3JnF0Y2X6Tm2KRGd6pblNZSVkdF/ADI/P4JXr7qjHPFnW1NZfDCTvyZ2oH2jW4rYVJXAj+3A0Rcm7AWpnPSCPby4ewZSvcBKSTC+Y7feCHS4jmAUKF6WgvZyBe4vNsO6sTMHDhxg//79REZGMmTIEAyCiC+3n2fZkSuEetrz6eBmdGh0/wV06jLyCILQ4H/AXOA8cAbYCDjf0vYOcAm4APSpz3itW7cWGorRaKp/X41e0CmqBF2BStAVVgn6co1gusv21TqD8OvBDCHm411Co3e2Cp9vTRWqtPob7Xq9XtiwYYMwe/ZsYcOGDYLBYGjQ8b+//rQQ/FascPz9HkLhN7sEg0pXu9PpPwRhtqMg7Jlzy/5VwvHjA4X9cc2Eisqz9dqX3qgXVr/4mJDStKmQFL++Qcd7Kwa9UVj54VHhj9lHBYPBeN/jWbBwJ4xGk7Dx2wRh0fQ4oTRfdc/+5X//LaSGNxVKli+/Y3uVVi90/nKv8Ng3+4Vq3W33beoW872295MbH8WlfCK0WxYpDP6liVBxdMGdj7FaL+R/e0rI+eiIoFNUCYIgCIcPHxZmz54trFy5UtDpzPd13AWF0PnLvULQW38LM1YlCrll6vpcgrsCnBLuYlfvN7pmN9BMEIQWwMVrhh2RSBQJjAKigCeBhaK7SR4+ABKyynjs2ziWH7mCWneHWfptiK2kyDxskXnZIfO0RXrDdXMTpUbPb4czefzbA3zydyqRPo7ETuvCO/0isL2WrqxSqVi+fDnJycn06NGDwYMH/6MomuscyyhhxYlcXpBsJ9RuBG4TuyOxu+1NoOAsbJ0FQV2ghzlz1VyE+xVUVedpFjUfR4dm9drf73++Q4tD+ZQ91YnorkP+8fHeTsqBXMoL1XQaGopE8kgEbFn4H0QsFtFrfBRSmZidi1Mw6Ix19nfs1w/77t1RfD8P3dWrtdpt5VI+f7o5GUVVLNx/qWZjxECIHgMHv4WrJwHoFvku00OacEUmY0byfDSFKbWP0VqK+7goRBIRxcvOYazS06lTJwYMGEB6ejorVqygqqqK7k082DOzO9N7hrLtbAE9vonj14MZDb84dXBfd6QgCLsEQbhuVY8B/td+HgSsFgRBKwhCJuYZfd3Vqe8TNzs5s7eco8Pne5m9OYUTmaWY6rEQeysGo4lD6cW8s+EsHT7fy5zYVLydrPljQntWTmhPpK/jjb45OTksXryY/Px8hg8fTo8ePRokE1qtM/LmysMEiQp42dqE0+QXkDjctlhbVQKrxoC1EwxbAhLpteIfsykpOUB4kzm4uz9Wr/1tPbeeRgv+RuVhT6cP5//j470djUrPya2ZBES6EtTMUrfVwsPF3sWKXuMjKcmt4uBfF+vsKxKJ8P5oNiKJhLy330Ew1n4odGviwdMxfvx04DLnCyprNvb9Ehz9YOMk0KkRicQM67CYF9ykJFjJmLV1LHq9utaYUldr3MZGYqzQUrIiFUFvok2bNgwbNozc3FwWL15MYWEh1jIJM58IZ9/r3Rnc0hd/l4ZJiNyLB+aTF4lEscBfgiCsFIlEC4BjgiCsvNa2BNguCMK6usa4X598QlYZSw9nsie1EK3BhLu9nDZBrrQKcqaRuz2+zja42skRicw6N8VKHQWVGi4UVJJ0tZxTWWWUq/XYyCT0bebN852CiQ6oGdJkMpk4cuQI+/btw8HBgZEjR+J7B4nT+vLRsjiWna9ipXw5HaYvQep+mzKlUQ8rnoarJ+CF7TeKgFwPlQwKmkxo4/pFxZwrOcf+V0bR87QB/9+X4dj2/mtKxv91kZS4HEZ+0K5eWYkWLDwIjm26TMKOLHqNjyS8fd0RKRVbtpD35lt4vj4LtwkTarWXVuno/Z8DeDtZs2lqZ2S3vo1mxsPygdBuIvQzK12Wlh5m+faxLDXY0tc2kC+GbrljxbTrETfWkW64PROBSCIiJyeH1atXo9PpGDhwIM2bsfwAUwAAIABJREFUN6+1XUOoyyd/zxAIkUi0B7jTVXxPEITN1/q8BxiAPxpwcBOBiQCBgQ2rPHSd1kEutA5yQaU1sDetkP3nFSRml7PjXME9t23sYUfvCC8ej/CiexMPbOS1/2jFxcXExsaSlZVFZGQkAwcOxMam4QJDhw5eZPl5Jc9KDtLhhc9rG3iAne/ClYPw9M83DHxBwRYuZ8zFy2sgjRvNrL3NHSiuLubnn19mcqIB27GjH4iBL82vIuVALlFd/SwG3sJ/lXYDQ8i7VE7cnxfwDHLAxdvurn0dBw5EuXcfinnzsevSBeumNZVYXe3kfD6kOZNWJLBg3yVe631LKc6QbtBhChxbaFatbNwTV9fOPNVhCqJ9P7CEbOz2zuDDXj/UepO3beGBSaWnfMtlytZfxGVYE/z9/Zk4cSJr165l/fr1pKen069fP6ytH17FtPueyYtEonHAJOBxQRDU1z57B0AQhC+u/b4T+EgQhKN1jdXQmbwgCFRVVWFvf2dDU6LSkl2qJr9CQ7laj4CACBHu9nK8nawJcrOrGSt7GzqdjqNHjxIfH49UKqVPnz7ExMQ0uIoLQHFaEQOX78VKVMWW4YE4trpDKb6E5RA7HTpOgz6fmbcr3s+Zsy/j5NSamJa/IRbfO0xTrVczdePzTJh7Dhd3f5puir1jksg/5e8FyeRfruDZjztgc7uLyYKFh4yqTMuaz09g4yBn2NttkN1hYnYdQ1kZGQOfQurqSvC6tYjvkL8y868kNifnsWlKZ5r73yIAqK+Gn7uDpgImHwY7d0wmA2dPPceek0dZZu/A+KZjeK3d23e0CZV7s6ncnYV9Z1+cBpgruBmNRuLj44mPj8fBwYF+/frRtGndMuB18dBCKEUi0ZPAf4DugiAU3fJ5FPAnZj+8L7AXCBMEoc6VkoYa+QsXLrB27VratWtH586dsbO7+1P9n2A0GklKSiIuLg6lUklkZCR9+/atd7GPu6G5UMqs5evZYfJibbtcWg2ZXLtT1lHza2JIVxizFiRSystPcTrpeexsG9Oq1R9Ipfc+DqPJyKv7Z9B23j7aZIgJ+esvbKKi7uv4AbLPlRD7QzKdhoYS0/v+3sAsWGgo2anm72HTjj70fK5pnRMvZVwcOS9PxvWFF/B6841a7RVqPU98fwBHaxmxr3SpqVRZcBYWPw7BXczhy2IxOl0pabseJzazjL8cHZjaciovR9dOlBIEgYq/M1AdzsOxVyCOvYJutF29epXY2FgUCgWdO3emd++7112ui4cpULYAcAB2i0SiJJFItAhAEIRzwBogFdgBTL2Xgb8fPDw8iIyM5MiRI8ybN4+dO3dSXFzc4PFUKhXx8fF8//33xMbG4uTkxLhx4xgxYsR9G/iqkwWsX7aRrSZfpnml0erp2l8Kii/B6tHgHAjDloJEilKZRvKZCVhb+9Cy5dJ6GXhBEPj65NfYbNxP24sC3m+88UAMvNFo4tC6Szh62NCih/+9N7Bg4SERGOlGm77BnD+Sz7n4uisrOfTogfOokZQuXYrqwIFa7U62Mr4a2oJ0hYrvdt+2qOvdHJ78Ai7vhSPzAJDLXQnp9jsjnIw8pVTxY9KP/JT8U61xrxf2tm3tReWebCp2XbmhmxMQEMCkSZPo3bs3ERENl/aui0cqGUqhUHDgwAHS0tIwmUwEBAQQFhZG48aN8fb2vmt4o9FoRKFQkJ2dzfnz57lyxfxHaNSoER06dCAsLOy+XDNgNrjKvdlc2XOEZ5HQSF7GurfHILW9zVirimBJL7OOxoTd4NoItfoKCYkjEYmktGm9Fmvr+i30/n7udzZs+ZrPVgo4du+B/48L7vs8AE7vzubI+kv0m9KCkBZ1K1xasPCwMZkEtv10hqvnShn0Wgy+YXfXfzFpNFwZNRpDQQEhGzcgu0OFtnc2nGX1yWz+eLE9nUJv+X4LglkvKnWLWU4ksAMAeXlrEa+fwg9iB7bY2zE5ejKToyfXutcEk0DZhnTUpwqx7+6P05PBD+R+hH9hxqtKpSIpKYmUlBQKCsyLrmKxGBcXF5ycnJDJZIjFYjQaDSqVirKyMgwGcySou7s7ERERNG/eHE9Pz7p2U28Eg4myTZeoOnWR1yWXOWsMYNuk5gSHhNXsqFPD8gFQmArj/gb/NlRX55J4ejRGYzWtW63Gzq5+mjCbL23m873vMX+FHFeZM402bkDi3DDxo1upKtfyx+xj+DZxtmjFW/ifQVttYN2Xp9Cq9Qx/py0OrndfyNRmZnJl6DCswsMJ+n05otuy09U6AwN/OIRSY2D7jK642d+yfqWpNGvPG3Xw8iGwNWe9p599G5+tP/OlsxebbWRMbDGRaS2n3dHQl2+5TNWxfOw7XfPRP4Cqaf86I38rKpWKzMxMFAoFxcXFKJVKDAYDBoMBGxsb7OzscHFxwdfXFz8/P1xd779Ky60YlTpK/khDd6WYVbb7+FHdibmPOzK8d9fbOhpgzVi4sA1G/QFN+6PR5JGQOAaDoYKYlr/j6Fi/cKvdWbt5Y/8sPt9sR0i6iqAVv2MbE/NAzmfXknNknC5i9Ox2OHk8nLheCxYaQllBFWu/PIWzpy1DXm+FtI6F2Mpt28idOeuu/vnUvEoGLzxMl1B3ljzfpqaxzkuCJb0hqLPZPy+RYjIZuBg/lNADcXwcGMZGsZYXm73IjFYzaht6QaBiayaqQ7nYRLnhMjL8Zq3mBnJfIZT/17G3t39gsaj/FG12JSUr0xCq9aT77mBhXjeGNzbWNvAmE2x5BS5shb5zbzPw5f/IwB/OPcyb8W8y46gLIWkKvOfMeWAGPvdCGeknC2nTP9hi4C38z+HibUfvF6LYtvAM+/84T69xkXd1hzj264f61ClKly7FOqIpTgMH1miP9HXkvX4RzN5yjqWHr/Bil5Cbjb4tof9/YMs02DMb+nyGWCwltMtKskq689HZdMThbViSsoQKXQXvt3+/Rhy9SCTCeUAjJM5WVGzNwLD4LO5jI2snQT4gHokcdKNKR/mWy5i095Y0+G8gmASUB3Mo+vkMIokIonYwK68VTex1fPx8/9s6C7D9TUj+Ex57D9pPRKPJJzHxGfT6smsGvkW99ptQmMCr+19lWLobHQ4ocHnmGVxGjngg52Q0moj/6yIObta07hN07w0sWPj/QEgLd9oNDOHi8UIStmfV2dfrnXewbduW/Pfep/rMmVrtYzsG0SvCiy+3p5GSe1tBj1bPmROkji6A5L8AkEod8O23hdxAF2ZfOMWLrtGsu7iOWQdmoTVqa43v0MUPt2cjMBRUofgxCV2uquEnXgePhJHXXq5AdTQPxQ8P70LVF0O5luIlZ6nYmol1uCtureJ4LdkZjdiOH196onaS1d6P4eRic/GPbm+g0eSReHoMOn0pMS2X19vAH8s/xuQ9k+lY5MyQjQpsO3bA6523H9h5nd2fQ2leFV2Gh9X5GmzBwv9v2vQLpkl7L45vyeDC8bsnQopkMvzmz0Pq6UnO1GnoCwtrtotEzB3WAnd7Kyb/kUC5+rbyon0+N2tJbXkFchMAsLHxx/Hp9ZS4WjEjMZY3g59ib/ZeJu2eRKXuNtkEwCbKHY9JLcAkUJ3S8IjAungkjLxttAceLzVH0BlRLExCGZ+DYPzvrjUIJgHViXwKv09Ed1WJy9Aw3Joe4au4LE4IEXw+LIZQr9siaQ7MhUP/gdbjoPcnVKkzOJUw/NoMfhlOTi3rte/4nHim7plKG6U7U/4oR+7vj/933yGSPhhvnKpMy4m/Mwlq5kZItCWaxsL/NiKRiJ7PRuDXxJl9K9LIvVh2175SFxf8F/6IqaqKnClTMaqqarS72MlZ+EwrCiu0vLLqNMZb9bAkMhixHOy94M+RUHYFACeX1oiG/UaVrZQx8T/xZdRLJBclM37HeAqqaj905P4OeM5ohWPvh/OG/EgYeQCrRs54zmiFdbgrFdsyUSz8783qdbkqihYlU77hEjIfW7ymt8JOspv1sZtZYuzHuI6BDG51i8a6IMC+z2D/p9BiJPT/D5XKFBISRyEIBlrFrMLJqX5+9L1Ze5mxfwZtDf7MWFGBxN6ewCW/PpBImuvEr76AYBToOvL+Q0ktWPhvIJGJeXJSc5zcbdi+6CxlBVV37WvdpAm+//kWzfnz5E5/BUFXc8YeE+jCx4OiOJhezDe7LtTc2M4dnl1n1phaOQzUpQC4+fZHPeQ/6KQCfXZ9woLWb5GrymX01tGcKartGpLYyR5IlM2deGSMPJgvlNtzEbiOaYqxQotiwWlK11zAUKZ5KPvTF6kp+TMNxQ+nMZRU4zK8CR4TWyDN/JPETd/zruElOjdy4f0BtyQgCQLs/gDiv4aYZ2HwT5RVnCLx9LNIJDa0brW6XsW3ATamb2TWgVm0k4Yyc6UKkdFE4JJfkd2HYNrtXD6tIDO5mLYDQiyLrRb+T2FtJ2PAtGjEEhGx85NRlt7dDjj06IHPp59SdeQoeW+/jWAy1Wgf1S6Q0e0C+SnuMtvP5tfc2CMcRq+C8mxYNcosgwB4Nn6eikGzMQl62v49k9+7fI2VxIrxO8azNWPrAz/fu/HIhlCa1Hoq466iOpIHAtjGeGLfyRf5fQppCYKANqMC1eE8NGkliGRi7Lv44dDVH7GNFI4tomD7lww0zsXGwY3N07rgYndt1dxkgh1vwYlfoM2L0O8b8hVbSEt7BxubQGJilmNtde8aj4IgsDB5IYuSF9HbuhWTl+RjKi4hcPkybB5gJJFWrefPOcexdZQz/O02iC1a8Rb+D1KUrWTTfxKxdbJiyOut6tRZKlmyBMXcb3AZMxqvDz6o8eaqNRgZ9csxLhQoWT+5ExE+jjU3PrcR1o6DJk/CiBUgNe8nL3E2nlvnYbSxR/XsVl5P+p5Thad4sdmLTIuZhlR8/27Vf3WcvKFci3J/NupEBYLehDzQAZtm7thEuSF1q5+CpGAU0OepqD5XjPpsMcYSDWJbKXbtfLDv7GsOfRIEiPuCyrj5jBB9Q47gzoYpnWly3Q9v0MHmqXB2DXSchtD7YzKuzOPKlR9xdm5Pi+YLkcnu7WLRG/V8dPQjtlzewjNOvRn6QzKmikoCFv/ywEIlrxP3x3lSD+Ux7O02eAY53nsDCxb+R8lLLyd2fhLO3rYMfi0GK9u7CxIWzp1L6ZKlZkP//vuIbqm7WlChYfCPhwHYOLUTPk632ZCTv5qL+zQdAMOXmf32QP6p9/HYvgCjlS28sIev0tex9uJa2ni14etuX+Nhe3/lN/8VRl5v1COT3P0PZ1LrqTpViPq0An2+2T8ndpQj93dA5mmL2EF2oxqTYBQwVekxlGkwKNTorioRdCYQg1VjZ2yjPbCN9kB0XcDIoIUt09Emr2Oc1X84qfJg2fh2dAm7tkipqYC/njVrUz/2PsbO00g9/yYKxTZ8fIbTNPxjxOJ7x8gWVhUy68AskouSedN5BB2+3YNJqyXw11+xaV6/qlD1JS+9nI3fJhLdK4Auw8LuvYEFC//jZJ0rYdvCM3gFOzJwektkVneOEhMEAcXcbyhduhTn4cPxnvNRDUOfll/J8EVH8XexYc3LHXG0vs3uHPsJdrwNUUNgyGKQmGfqhac/w3XrXASZNeLntrKtOpdPj32KjdSGL7t+SUffjg0+t0feyJ9WnObN+Df5sMOHdPXves/+hpJqNOdL0eWo0OUoMZRo4A5VpETWUqRu1sgDHbAKdsQq1KV2WT51KawZiynzEDNcFxKb78R3I6N5OuaacFf5VfhzBBRfhEE/Ut2kE2fPTkOpSiW08RsEBk6s12JmQmECs+JmoTaomWv9DN6fr0Ds4EDAzz9jHd7kntv/E/Q6I399egKTUWD0h+3vejNYsPB/jUsJCnb9moJPqDP9p7ZAbn1nV4kgCBR9P4+Sn3/GafBgfD75uIb8QfzFIl5YdpIOjdxYOq4tcultrszD881rb5GDYcgvIDVLIyjOfodT7MdITCJMw37lqnc0s+JmkVGRwaw2s3g+6vkGndcjn/FqLbHGXmbPlL1TGBI2hDfavIG9/O6+d6mbDfad/W78LpgETNUGTFV6EIFILEJsJ0N8ly/ADa6egLXjEVQKPgpcRmy6jLeebHrTwGfGm310Rj08s44iRyOpJ58CRES3+AV39573PDejyciyc8tYcHoBfva+/Fz+FKbPFyMLCyPg50XIvLzqcYX+GUc3XKZCUc2g12IsBt7CI0Voa09Mpkj2/JZG7PxkBrwSjZVN7ftcJBLh8eoMRFZyiuf/gEGhwG/e90iuqdB2a+LB50Oa8+a6M7z2VxLzRrVEeuuaVefpIBLBrvehuswsVWLlgGfz1yh18Ee+djK2f43H77HX+bPfH3xx8kvCnB/OG/MjMZMH0Bl1LExayG/nfsPT1pM5HefQya/TAz7Ca5iMcOQH2PcJgoMfH/vM57ekKl7qGsK7/SIQgbmSzK4PwC0U4/DfyKjcTPbVJTg4RNG82Y/Y2ATcay/kqfJ499C7JBQm0NfrMabsEFG9Yxf2PXvi+/VXSO5SJOV+uJpWypZ5SUT3DKDLCIubxsKjyeVEBbt+PYd7gD0Dp7fE+vY39FsoX7+e/NkfYRUSTMCiRcj8bk4Qfz2Ywadb03g6xo9vhkcjuT0MMmmVeS3Ou7lZ58be7HtXFp9Cv3owrsVKNI07YD18LVg3fN3rkXfXCJV56He/ibzPfzhTXcD7h98nsyKT3kG9eb3N6/jaP7iQQhTnYfMUyE1AaDqQz21msfhoHuM7B/PhgEhE1WXmak5psdB0AJW9ppN6eTZVVen4+z1HaOg7SCR1V2UyCSY2pG/g21PfIiAwx/k5Quf/jS47G48ZM3B7aUINH+GDQqvWs/qTE8isJIx4t60ls9XCI03mmWJ2/HIWVx87BkyLxs7p7vdl1dGj5EyfgUgux2/u19h1ujmB/HH/JebuvMCotgF8/nRzxLcb+os7Yc3zYOcBo1aCj1m9VaPJo3jTQHzPX8Jg74R02ErEwd0adC4Ps2jI/wRlZ+YhTYnFOC+KqMsHWdt/NdNjpnMw5yCDNg3ip+Sf0BjuM1ZeXQo734NFXaA0E9OQJXxi9w6Lj+bxfMcgs4HPiIOfOsGFHZh6fcjlNjGcOjsWg76SltG/ER7+0T0N/IXSC4zdPpY5R+cQ5dCEP3IGEPjGT+YF1qVLcZ808aEYeICDa9KpqtDx+LhIi4G38MgT0sKd/pNbUF6oZv3XCXUmTNl17Ejw6lVIXJzJfnECinnzEK7Jk099LJRXeoay+uRV3lh3Br2xZow9TfrA+K0gGGFJHzizFgBra198Rxwlp/dojHolJQmfP5TzfCRm8kZjNdmn38Pp0O+4lusxugQgeexD8oM78k3i9+zK2oWHjQcTmk9gWJNhyCX/QO2tqgQSlsKRBeYomZhn0Hb/kNe35xGbnMcLnUP4oJcvon2fwMlfEdybUN5zAqnlf6DR5OLtPZgmYR8ikznVuZsidRE/n/mZdRfX4Sh3ZLbQn6Df9qLPvorToEF4vf/eDX/gw+BSgoKdi1No2z+YdgMbPbT9WLDwv4Yiq5K/FyRjMgn0n9wCn9A6io6o1RR89hkV6zdg07o1Pp9+glVICIIgMH/vJb7bc5HHm3ry4zOtapYPBFApzDP67CPmRMgnvwQr8z1dlLMJB9dWWNs2rJTmI++uuU5Z6XEUcZPwu5SNvdqIyTkAccxYTvlF8kP6WhIViXjbeTM+ajyDQwdjK7tLBqfRAFmHIWUdnFkDBg2EPQGPz0bpHM7LKxM4fKmEt58MZ5JbEqKd70JVEdqWg0n1raZUeRI7uyaEN/kIF5f2dR5zhbaC5eeWszJtJXqjnhese9J/VznaQ0eQN2qE13vvYt+5c4OvSX2oKKpmzWcncPa2Y8gbrZBYkp4s/MuoKKrm7wXJKEs0PD4ugrA2dQc0VMTGUvDxJwhaLe5TJuP2wguI5HJWHMviw80ptAly4dexbXG6PR7fqIe4L82aVc5BMOhHCL7/+/vfYeQFAUQijEYN2Vk/o0qYh3+uEpdysw6F4BnBUZ+mLDQUkKzOxUFmx7CQ/gwPGUAAMlAVmCsy5SbAlUOgLgaZLTQbCh2ngmcEGUUqJq1IIKO4iiU99PTIWQTZRzB4NeFShC+5phRkMjeCgyfj7/csYvHdF3OuVFxhZdpKtlzeQrWhmmfFHXn6oAFj/FHE9va4T5mC67PPILpDVfkHidFgYsPcBMoV1Yx8ry2O7vVLELNg4VFDo9Kz7acz5F+uIOaJQDoMalRnlrdeoaDwiy9Qbt+BPLQxnjNnYf9YD7aezee1v5LwdbZh8dg2NxMibyXrKGycBOVZED0aen9yY1G2ITz6Rr7gLGyeBt1eh/D+IBaj0eSReWUBZZlr8FRo8FY7YldagkivIdlKzgpHB/bY2WIUiYjRaBioqqJ3VTXOjgEQ0B4iBkBob5CbZ/t7Ugt57a/TtBKn853PblzzDmC0dSI7xIMM1zJkcjeCAl/C3/9ZJJI7vyEodUr2ZO1ha8ZWjhccx8YkZVJZNJ1OKCEpFbGjI65jx+I69jkkjv+dDNNDa9NJ3nuVJyc1o3HMgyl3aMHC/1WMehMH16ZzLj4X/6YuPDEhChv7uidayv37KfzyS/RZ2djExODx6qukeTbm5T9OU60z8M3waPo2r11LFp0aDn4Lh+eBzAae+MSsSNsAHn0jnxkPsTOgNAM8I80z78jBYGVPdXUu2VcXk5+/EZNeibPghadVFM4iP5QiO7YqLxNbmU6GpggxYlp4tKCbfzfa+bQjwjUCg1HMN7EJlCVs5GWb3YQbL2GQybjib8VVXytsnSLx93sWb++nahl3QRDIrMjkSN4RjuYf5VjeMfQGLd2LPRiU7YH/yWyE8gpkgYG4jByB84gRD9XvfjsZSUVsX3SW5j386TbqwSZUWbDwf5nUw3nEr7qIraOcJyZE4d2o7jU1Qa+nfMNGin/8EYNCgXWzZoiHjeI1hRuJeVWMahvABwMisbO6Q+5Ncbo53DrqaYge2aDjffSNPJj96Oc2mp+MRWkgtzcb+vAnoVEPjFIpCsU2ChVbKS09iiDoEIkk2Nk1wc42jFyjFYnlJZwszSK90qwyJ0GEn1ZKjLaCYIMOT5ERwU2K3scPP59e+Hg8gZVtKAaTgUp9JUXqIhRqBVmVWZwvPc/50vNU6ipxqxDoUehKpzx7/M+XIiqrQGRtjX2PHjgPG4Zdp44PLWLmbpTkqVj/VQIu3rY8/XorpLcvElmw8C+n8EolO39JQVWupU2/YNr0DbqnSJ9Jo6Fi40ZKV6xEl5GBxNWNi1EdWCgKQRMWwadDWtCp8V1qMlxzOTeEf4eRv44gwNXjkLgC0raAthLEMvCKBO8W4BmB0dYFpaCgqjqTKnUG+qocRFUlyLV67KuMVKshQxBzxkpOopUtmdYyKupz7QUBRzU0LpHRttyVJgoJ3ldVyAvMRQskrq7YdeiAwxO9se/WDbHt/x/pXk2VnrVfnkKvNTLinTbYu9y9sr0FC/9mtNUG4ldf4OLxQrxCHOn9QmS9JLcFQaDqyBHK/1qDKi4OQaej1M6F4+5NELdtz/CJg/ELvIMLp4E88kbeUFqKLjMTmY8PUk/PmxWRjHqzwb+0B3IToeCMOcW4DspkXiRq/UgWQvFq2Ych/Z/CxlpOlb6K7LJMynMz0Sjy0RcWIioux7pCjVVhOVZ5pchyixBVVd8YS+bri3VUFDatW2HXsSNWYWH/9Rn77ZiMJv7+8Qy5F8oYPLMVPo3rfg21YMECpJ8s5MCqCxgNJtoNbER0T/96S28bVSpUe/dSvmMXFUePIdOoAVC6eeMa0wKXls2Rh4ZiHRHRYJmSR97IF8X+TfEbb5h/EYuRenoi9fBA4uCA2NHR/L+dndn4m3SITBowVAMCJr2BwnINl4sNZJfpwAiNnWQ0sRch16gxKZUYlUpMSiWmqjskS4jFyHx8kAcFIQ8ORh4chLxRY6yjIpG6uNzfBXnACILAwTXpnN2fQ49nwonq6nfvjSxYsACAslRD/OqLXDlTjJu/PT2eCcc75J9NkgSDgazDpzi8bieac+cILcvB89rE02X8eLzferNBx/bIG/kdh9L4Zcl2IqVqWsq1BBgqcaxWYqWtRlApMVVWYqqqQjAaEYxGuJapBmBChF4swSiRIpHLsba1QmptbX4wODggcXRAbH/tfwdHpB4eSD09bjxIpG5uiCT/N/zZiTuzOLrxskU+2IKFBiIIAplJxcT/dZGqCi2RnX1pNzCkTkmEu3G1VM3yI1fYeewi9oW59GwfxhsTnmjQcT3yRj6vvJodKQXEpxdx9HIJWsPNtGIbmQQHayl6o4kqrRGd0QSCgBgBX0crOjXx5PFIbx4L96wtF/oIceFYPnuWpRHW1ove4yMfWj1JCxb+Deg0Bk7EZnJ2fw5imZiYXgG07B14V+niOscymNibVkigmy1Rvg1znz40Iy8SiT4BBgEmQAGMEwQhT2QWSJ8H9APU1z5PvNd4D2LhVWswcllRRbpCSVaJmspqPSqtAZlEjJ2VFDc7OaGe9oR62uPvYvOvKEydlWIuluAT5szAadFIZI/uw8yChf8m5Qo1xzZlcDlRgY2jnNZ9gojs6ovsv6z99DCNvKMgCJXXfp4ORAqC8LJIJOoHvILZyLcH5gmCUHd+Pw+n/N+/naxzJWz/6SwuPrY8PbMV8jtoZ1uwYOH+KMio4Nimy+ReLMfGQUbLXoE06+7XoJl9Q3hoRUOuG/hr2AHXnxiDgN8F8xPkmEgkchaJRD6CIOTXGsTCQyMrpYRti/5fe/ceU2d9x3H8/YXSA+VSOEhPKZcWLAXb2guus9t0q1vmbclwSZMZ/9Asi2YXF82yRI3J5v4wcUu2JUuWuZl52UXr5mZsnG6zWqtLZqvdCqW1UCxQoFxKgVKQAuV898fzox4pF7Vwnuecfl/JCc/5PU/TT78r5DSuAAAJrUlEQVTlfDnP7/nxnDrChZnU3LvZGrwxC2R5+VJu+X41J5oG2P9yC/95/j32/6OVKz5byJXbij7SssuFctGvehF5GLgdOA1c54aLgLaYw9rdmDX5OJm8V3b+iiy+es/sH4pgjJkfK1bnsuJ7m+huGaR213EO7m6n9rU2Vq7PZ8O2YkquCMf9etic0zUisgtYPs2uB1X1hZjjHgDSVfVHIvIi8Iiq/tvtexW4T1UvmIsRkbuAuwBKS0uvam1t/cT/GOM59GYHe55uoKA0e85PvTHGLJzhgVHq3+jg0JsdjJwZJyscovLq5VRtLSQ3Mn/v7uOyukZESoGXVHW9iPwGeF1Vn3H7GoBtc03X2Jz8xdGosu/FZt55qYXSdfnccOe6uM0JGmNmNjEe5diBkxx5q5O2w32oelM8lVuXc3l1wZw3QZvLgs3Ji0iFqh51T2uAI257J3C3iOzAu/B6eiHn4/u7hql9tY2yjQUUV+ZdkqtHRkfOseuJw7TU9XLF5wr5wm2Vdl94YwIiNS2Fii0RKrZEGB4YpWFvF0fe6mLP0w28saORkqo8rtxWzKoNM9zX5iJc7Nu8R0SkEm8JZSvwLTf+Et7Kmia8JZTfuMi/Z1Z9ncM07Ovm0JsnSAulUroun7KNl7Fyff4lMVXR2z7EPx+rZ/DkCNd+vYIrtxVfEktDjUlEmbkhqm9YyebrS+ltG6Jpfw9N+7vp6xxekCafFL8MBXBufIL2I/001/bSXNfLyOAYKSnCijW5lK7Lp3RdmHBhZlI1v2hUOfDKcfbuPEYoM40b71zHiopg3UrBGDM3VSU6oaR+wl/ITPrfeJ1Ko0p3yyDNtSdpru2lv8u7IVBWXoiStWFK1+ZTXJWX0O/ye9vPsOfpRrqOnaZ8cwHbbqskI3thP0XKGBNMl1yTn+pM31naDvdx/NAp2o70MzZyDhGIlOVQsjaf4so8IqtyEmIuf2RojLf/3kL96+2EMtO4Zvtq1ly9PKnOUIwxH88l3+RjRSeidLec4fihUxw/3EdP6yCod2FkeflSitbkUrQmeE3/7NA4B3Ydp253O+NjE6y/toira8oT+mzEGDM/rMnP4uzwOJ1NA3Q0DNBxtJ/e9qELmn6kLIdlK3Pi3lBVld62IQ7uaadxXzcT41FWX7WMLV8pI7wiM65ZjDHBtWBLKJNBemYaZRsLKNvofVL62eFxThwd4ESj1/T3vdh8/mYNuZElLFuVTWTVUpatyiZcmDnv69CjUaW37QzNdb28t7+H/q73WbQ4hcqty9mwrZj8oqx5/fuMMcntkm/yU6VnplG+qYDyTV7THx05R0/rID0tg3Q3D9L+bj+Ne7vPH58VDhEuzCSvMJO8yBKywulk5YbIyguxOGPRjHPlGlXOvj/O8MAY/Z3DnOoYordjiM6m04yNnAOBoopcNlxXTMWWCKElNi1jjPn4rMnPIZSxiJKqMCVVYcCbQhnqH+Vk6xn6Oofp6xymv2uYjsYBJsajH/qzkiKkhVJJC6WyaHEKqt41gYlzyujQONGofujY3MgSVlcXUFSVR3FlmCU5tlrGGHNxrMl/TCJCdjid7HA65ZsLzo9Ho8pQ/1mGB8YY6j/LUP8oo++PMz46cf4hIqQuElIWpZCRmcaSpYtZkhMiN5JBXiQzUBd6jTHJwZr8PElJEXLyM8jJzwDsw7GNMcFgbx2NMSaJWZM3xpgkZk3eGGOSmDV5Y4xJYtbkjTEmiVmTN8aYJGZN3hhjkpg1eWOMSWKBuguliJzE+xjBT+IyoHce4yyERMgIlnO+Wc75kwgZIf45V6pqwXQ7AtXkL4aIvDPTrTaDIhEyguWcb5Zz/iRCRghWTpuuMcaYJGZN3hhjklgyNfnf+h3gI0iEjGA555vlnD+JkBEClDNp5uSNMcZcKJneyRtjjJnCmrwxxiSxhG/yInKjiDSISJOI3O93nlgi0iIiB0XkgIi848bCIvKKiBx1X/N8yPW4iPSISH3M2LS5xPNLV986Ean2OedDItLhanpARG6O2feAy9kgIjfEKWOJiOwWkcMickhE7nHjgarnLDmDVs90EdknIrUu54/deJmI7HV5nhWRxW485J43uf2rfM75pIg0x9Rzkxv37XWEqibsA0gF3gPKgcVALbDW71wx+VqAy6aM/RS4323fD/zEh1yfB6qB+rlyATcDLwMCbAX2+pzzIeAH0xy71v3/h4Ay932RGoeMhUC1284GGl2WQNVzlpxBq6cAWW47Ddjr6vRn4FY3/ijwbbf9HeBRt30r8Gyc6jlTzieB7dMc79vrKNHfyX8aaFLVY6o6BuwAanzONJca4Cm3/RRwS7wDqOobQN+U4Zly1QC/V89bQK6IFPqYcyY1wA5VHVXVZqAJ7/tjQalqp6r+122fAd4FighYPWfJORO/6qmqOuSeprmHAl8EnnPjU+s5WefngC+JiPiYcya+vY4SvckXAW0xz9uZ/Rs33hT4l4jsF5G73FhEVTvddhcQ8SfaBWbKFcQa3+1OeR+Pme7yPaebKtiM964usPWckhMCVk8RSRWRA0AP8AreWcSAqp6bJsv5nG7/aSDfj5yqOlnPh109fyEioak5nbjVM9GbfNBdo6rVwE3Ad0Xk87E71TuPC9wa1qDmcn4NXA5sAjqBn/kbxyMiWcBfgXtVdTB2X5DqOU3OwNVTVSdUdRNQjHf2UOVzpGlNzSki64EH8PJuAcLAfT5GBBK/yXcAJTHPi91YIKhqh/vaAzyP9w3bPXma5r72+JfwQ2bKFagaq2q3e3FFgcf4YArBt5wikobXOP+kqn9zw4Gr53Q5g1jPSao6AOwGPoM3vbFomiznc7r9S4FTPuW80U2LqaqOAk8QgHomepN/G6hwV94X41142elzJgBEJFNEsie3geuBerx8d7jD7gBe8CfhBWbKtRO43a0O2AqcjpmGiLsp85hfw6speDlvdastyoAKYF8c8gjwO+BdVf15zK5A1XOmnAGsZ4GI5LrtDODLeNcPdgPb3WFT6zlZ5+3Aa+7MyY+cR2J+sAvedYPYevrzOorXFd6FeuBdtW7Em7d70O88MbnK8VYn1AKHJrPhzRe+ChwFdgFhH7I9g3dqPo43N/jNmXLhrQb4lavvQeBTPuf8g8tRh/fCKYw5/kGXswG4KU4Zr8GbiqkDDrjHzUGr5yw5g1bPDcD/XJ564IduvBzvh0wT8Bcg5MbT3fMmt7/c55yvuXrWA3/kgxU4vr2O7LYGxhiTxBJ9usYYY8wsrMkbY0wSsyZvjDFJzJq8McYkMWvyxhiTxKzJG2NMErMmb4wxSez/ovW8NBpBJwkAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 5c7e10f87..32372a329 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1482,6 +1482,17 @@ def penalty(self, derivative_degree=None, coefficients=None): # implement using inner product return self._numerical_penalty(coefficients) + def gram_matrix(self): + r"""Return the Gram Matrix of a fourier basis + We already know that a fourier basis is orthonormal, so the matrix is + an identity matrix of dimension n_basis*n_basis + + Returns: + numpy.array: Gram Matrix of the fourier basis. + + """ + return np.identity(self.n_basis) + def basis_of_product(self, other): """Multiplication of two Fourier Basis""" if not _same_domain(self.domain_range, other.domain_range): diff --git a/tests/test_fpca.py b/tests/test_fpca.py new file mode 100644 index 000000000..fff7be7d4 --- /dev/null +++ b/tests/test_fpca.py @@ -0,0 +1,26 @@ +import unittest + +import numpy as np +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather + + +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data + +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): + fpca = FPCABasis() + with self.assertRaises(AttributeError): + fpca.fit(None) + + + + +if __name__ == '__main__': + unittest.main() From a78556edcc7526d2bce4cb9a96ce9bfe45e56f88 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 187/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 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109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From 3cb2c8f1a29fe92cd65c66dae6affb96b5dd61ee Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 188/624] Add docstring and references for fpca module --- docs/modules/exploratory.rst | 3 +- docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 5 files changed, 119 insertions(+), 30 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index 45f048bfa..edc2c8d73 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -10,4 +10,5 @@ and visualize functional data. exploratory/visualization exploratory/depth - exploratory/outliers \ No newline at end of file + exploratory/outliers + exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 7036f75fefc82316bbbcd84da742f73b1215ea99 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 189/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From b6b8b2fb86c868629c40ecd81674572964d174bd Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 190/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 ++- examples/plot_fpca.py | 122 ++++++++++++++++++++++++++++++ skfda/exploratory/fpca/_fpca.py | 93 ++++++++++++++++++++--- 3 files changed, 214 insertions(+), 13 deletions(-) create mode 100644 examples/plot_fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py new file mode 100644 index 000000000..135b4bf2a --- /dev/null +++ b/examples/plot_fpca.py @@ -0,0 +1,122 @@ +""" +Functional Principal Component Analysis +======================================= + +Explores the two possible ways to do functional principal component analysis. +""" + +# Author: Yujian Hong +# License: MIT + +import numpy as np +import skfda +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.representation.basis import BSpline, Fourier +from skfda.datasets import fetch_growth +from matplotlib import pyplot + + +############################################################################## +# In this example we are going to use functional principal component analysis to +# explore datasets and obtain conclusions about said dataset using this +# technique. +# +# First we are going to fetch the Berkeley Growth Study data. This dataset +# correspond to the height of several boys and girls measured from birth to +# when they are 18 years old. The number and time of the measurements are the +# same for each individual. To better understand the data we plot it. +dataset = skfda.datasets.fetch_growth() +fd = dataset['data'] +y = dataset['target'] +fd.plot() +pyplot.show() + +############################################################################## +# FPCA can be done in two ways. The first way is to operate directly with the +# raw data. We call it discretized FPCA as the functional data in this case +# consists in finite values dispersed over points in a domain range. +# We initialize and setup the FPCADiscretized object and run the fit method to +# obtain the first two components. By default, if we do not specify the number +# of components, it's 3. Other parameters are weights and centering. For more +# information please visit the documentation. +fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized.fit(fd) +fpca_discretized.components.plot() +pyplot.show() + +############################################################################## +# In the second case, the data is first converted to use a basis representation +# and the FPCA is done with the basis representation of the original data. +# We obtain the same dataset again and transform the data to a basis +# representation. This is because the FPCA module modifies the original data. +# We also plot the data for better visual representation. +dataset = fetch_growth() +fd = dataset['data'] +basis = skfda.representation.basis.BSpline(n_basis=7) +basis_fd = fd.to_basis(basis) +basis_fd.plot() +pyplot.show() + +############################################################################## +# We initialize the FPCABasis object and run the fit function to obtain the +# first 2 principal components. By default the principal components are +# expressed in the same basis as the data. We can see that the obtained result +# is similar to the discretized case. +fpca = FPCABasis(n_components=2) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() + +############################################################################## +# To better illustrate the effects of the obtained two principal components, +# we add and subtract a multiple of the components to the mean function. +# As the module modifies the original data, we have to fetch the data again. +# And then we get the mean function and plot it. +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +mean_fd = basis_fd.mean() +mean_fd.plot() +pyplot.show() + +############################################################################## +# Now we add and subtract a multiple of the first principal component. We can +# then observe now that this principal component represents the variation in +# growth between the children. +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[0, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[0, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# The second component is more interesting. The most appropriate explanation is +# that it represents the differences between girls and boys. Girls tend to grow +# faster at an early age and boys tend to start puberty later, therefore, their +# growth is more significant later. Girls also stop growing early +mean_fd = basis_fd.mean() +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[1, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[1, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# We can also specify another basis for the principal components as argument +# when creating the FPCABasis object. For example, if we use the Fourier basis +# for the obtained principal components we can see that the components are +# periodic. This example is only to illustrate the effect. In this dataset, as +# the functions are not periodic it does not make sense to use the Fourier basis +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 18dfa032d2337249e19f9b7df0929cb870868ca4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 191/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From 7daa37856c568152c27fedb87c1153c6159912e4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 192/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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1rSJj9DBGqmfh96/BlPWep7LoTlN6yOs+1rrq/ax7uJ54KMXks0qZvmIUxv+xTowsp7j3uct4LNbGN45fxWRxLt+rtbIl38h5Xjs/Hz8KCbjuhR3sPRpDSCoUW8PMsgZwxAeJaiYirgxnZWm80JDkgFBBTDuxnrtH66Iy2U5lpJfsWBCPIc54zwDVrmGyzXEA/EkbA2En/rCFSNREImaCjICoaQRNTlpcpdR5K2lwV5AwWqhQelic2MscdT8l9hGKrVHMokJGFWmOZHE0mEdn3INVknGLAnZrDt6sXLJKShmSRP67WWGPVIlZUJhlqiev5BDbBpbQqZahTHahOK2M621DEm18tc1BTVhhR2wXlpwC+gaKMNuNmBf7+U333Vy5ezmiNAdHoo9FFxVTtnLhR72Ldf8mPeR1H0tyWmHH8y0cXt+NN9/GWavHkVvm+qfHhYIdfPWFyzickrjz2JeQcgr52mQLQzaJ71QV89nibF5r6+aLaxuQe9MYTSqzzCPkZfqwaylGTFDjCfJKu4NmcQIpwYEvPcLYaCNV0SZ8ahinwUSZKUqNrYmCrBAAsWEzkWEvvQkvrUKamJbEltLITZrxxgWM0STCOz5egsmEajJzwDeKN/MmsjV/PLIoMavvGJd3bmVK3gBqcYh8xzA2QWFAM7I7kUv/SBapuI2U+lZZStNwpDLIBhvbsifRYK8k2yxzUX4jgex2nm89h56yMSgldnyhASb0D7AyWsn8YZUthm783btwZZ9DOGomb4qZ+50/oLzBxbSeVWQkJ+M9Xcz73lVIVstHsZt1HwA95HUfO8PdEd54oA5/b4wJi4uZc1ElhpOs8tjavoFb1t+CdbCKm3tvomOUkzsmmMmyGLl/QgWTHFZufnkrrx1OIEZkCswRagwhShMdCLEwJvo4nBrDQec0kpKFkkQHM5L7OStyAGcAbAMKXmuE3Elh7LlpVMzEPHPZkjOaN3qO4WoPMXrAQG7UjGawIRgsSB4fxpx8LL4sbN5c7Fl52HPzcbjdCKIIIgiiwGA4zF8PtvN4V5yQKjAr3M6n9zxFeawfZ6WKozaO2xxhRBR51u3GmtA4I1nMkGE6/X1RhgPDJJUT5aGEaGHAmofXIDOxqJe9xTk8lVxFrDoHSU6zuP4A87QxLB80sdmVoPX4oxRY55ASRmN1m9g7/gX2xTfz+V2XEjVNIzvZzjlfmY9nQtVHvet174Me8rqPDU3TOPhmFzufb8HiMHLmtWMpHZ910sfuOvgAX97/C84+uowl6nmsH2fld1VmprtsPDBhFKFAgMvW7MffrmLQZKbK7VSFG3DHh5BSCVpso9icNZuowU2+2s7qwV2c3bKd1JABELBWeMibkcZKM2mjl/22hewKFROXJdKSiPYvXrfDoIk4NStOzYpHs5OlOsnWnBg1C8+S4c+kSAArDAZukOPYB9owpg/gyd6F3TuAXzXwW5+LbC3DlaVLcS37MeGYTOeh/exZv4muzjYccgw4cWkSZ1GUHbkzeH38RShWM5Maj7Ay4mZ5IIstWbCn92lKR1zYHTPJaFZCE1p5zPorbjw4HTFxOQY5wfxZItWfW4GgX6TktKaHvO5jIRnLsO7hetoPD1MxOYfF19T808yZv3ll83e5q+51rtx/HeNdNTx4ho3XCoxcluflJzUlPLZzL3fu8VPa0sLYeBNlsU6MahoN6POZWe+dS0CtJkvp54aW3Sw4vhkU0HIcxJZegst8gNHxN8ggsY3p7GQKombDo9lwqBacmgG3xY7d58XqdmJ12DA5LAhWA6LZACJkUEgqKZKZFPFEnGA4SCAUIhgK4g8FUNQTi96YDSYKPXl4bfls8FtZM5zAJYr8p9nOkgQICJiEI3iMf8QkthCTy9gkLsRgjzN33iXYZi5DMIgEYmlu+f2r+JsbqE00kRPvQwA0CVoLqqirnkxC8fKZwQjnJ0rZ5RN5kr1M378Tq+tSEL2QF+SR4p+yYNjC+MbPEDPlUW1pY9E912CwWT+6N4PuX6KHvO60198W4vX7jxELpZh76WgmLCo++dGjpvHwS5/l2foRzm1cTZXXxY9nOTnoNfDNigKuz7LxX396hGhDO5XBFsxqmoxoRHbZac0PsMuVS3x4OaIqcVXrZla17UcrqKCvsoROjxtJq+cC4XWyCNKoTqQpvhR7QMMRGoIiG6OWnYt38RxEi+nfer2yLDM0NERvby+9vb20t7czMjICQMLsY4dSQWdUYPGYbO5aMgZPTCaxrwGh4Qnc0nOIYpKwvIqIchmqAOYCF+ZRHgxlLu5v6eOXu7rI1UKcG9pITsqPrMZRkiIZyUBHTgWjhSxWmmbS4LNyb34356x7ErthEQbzWARjmldGP4LR0si1O1fSY55HdqqTc29fjKtKvzjJ6UgPed1pS9M0Dq/vZvuzzdg9Zs75bC15o/55cBVAVTL89KmVtB0ppXb4Yirc8P15PjqdEj91aZi3raNu9w5MmRQp0USHvQzZ6SJV1MRRTxvxvhWkY5OYGB3iy2E/cr6HJkuIASGEKCicI+xjhradpOygf08W0a40TePcZF+2ijkrP4/B/PZApKppjGRk+lIZ+lMZwrJCStVIqCoCYBVFLJKIxyBRYDZSYDbiNkjvWfYIhUK0trbS3NzM8cYmDiW8HJCLsRgFvnvOKC6ZOw5BEFB6W8j8+XosiQOkoi7ah1YQy5tKjrEKQTnRftJpZEM0xgEtiYfDZPXspbBW4kDCjb0jgiMeRRMkyuw1aPmTuGt6Lue/8ihZcSdm23IEwcDhkjdoKXyFb+4eR73yaSyZMGddnEfpivkf6HtA9+/TQ153WkolZNY/XE/rwSFGTcpmybVjsdhPXp7JpGJ864kVOHefhU+eRYk3xV3THeR3H+WcjqPEuzuQRYlW2ygaHaPptRQyzb2X7qytZEXH0TGwlIBg4ioBqg09dEgDyIKKz2hkakU+Z7T8DKvcTbDFxsE2F4dmFDJt9deYU3suGlAfS7IjGOVoJEF9LEFjLEVCVU+6re/GKYmUGAx4ZAFHSsEckokFk/ijaaIpmWhKJpaSUVQNEZVCMUS2EKVFySakWak2DFPhMWIrqCQvy830+DbmHb0DMRNjYI+bpqCL2FlzmbnoK2g9aRKtIYSUgopGvxDDHzpK0jXIprkyW8ILGHd0P7Xtx5DUNEaTm53jJuEd6aG8pwuL41OIkptedwO7qv7M9xsEjgW/TkawMKNihDNuu0qv059G9JDXnXYC/TFe/u8jhIYSzLm4kklnlrxraKQSAb7x8OVU7LsSUSzD62plu9hMWWcDkqogerNZXziJ49EyFMmIV0uy0nuQikw2gchYfiEouFFZKrRiMgcwIVMrtTFh+lLcuzbhTj0HaOxs9/L8xAqWrvo6UwqX8KY/zGvDYbYHo4TkE/XzbKOBsQ4LNXYL5VYzBWYj+WYjnrcu+WcWRTSgK5RgX3eQfb0hGoJxupMp4gYB1WlEcxjBeGIJBmNGJS8No2SR0YKEx2zEIL7dD7KqEY4m2NnYTUtYJF8MM9/YSq/q4aich0VLcJ/x18yUGujrsxHa6mbAJaLe+DkWXHMLg00BnnrqCFVxGI+EiEBaTdKaM8D9JVUcMGZYtuUA0wMNDCZPXJUt4vTgiAQRHGdiNk4kZgqwsfqPfGuoha7ubzFiKGG0oYUz79Xr9KcLPeR1p5X2w8O88cAxJKPIOZ+rpWjMu59lGY/28+37P0/FsStR0+1owhEyyQApi43q2fPZH0/xsHsG7qYIC5E4W5SZojoAkfuUEM9KIsUEmW9uxSvILGQTk7wpQsMzEeqeJac2xEjSyJ155cw+/5vgnMfTAyF2BKOoQJHZyCKfk1keB7M8DkrepRYfiKXZ3DTEpuND7Grz0xNMAGA2iNQWuRmT56Qq10FVnoOKbDsRg8CucIztwSib/RECsoJFFFjic3FFgY8lPtc/hD3As/u7ue2ZwzgMKovEBlxalKzCUixFY6nqfIxFAw8zlLHRuN2Br8/A8QIXR8+7hcqF83hmTwfHuyJcZohwdkYmT83DJFmIS7DTJzIQSLI8EWdP+gitsaM4Y2E0QDBkYbRfjCZZ2FbxBDcor6F0fYVmZTK56Q7Ov/NcbMX5H9A7Q/d+6SGvOy1oqsa+V9vZ9WIbOSVOzr1pAk7fu59wEw128MOffJPsjhKUdAOQYSC7iI7a2dx+5hzue3kdmjqeswdlZmLAhMCwOMLwwAF+Yi+lwZbPOKmf2WqIs3K7mRl6gLRWRueLGXInB/CUxllvtrNh4W2EPOfz0nCMmKIyympiZa6Xc3PcTHBY3/U/jJahKC8f7mP98UEOdQVRNfDZTcyuyGJqmZepZV7GFrgwGcSTPv9vZFVjZyjKK0MhXhgMMpyRyTUZWJXv4zPF2RSY3/7DcrAryA2P7CWSzHDzJDOJ1r3E43EqKys5t1wma/PtKEi82Sfh3mHDE4ONpWP4w7grEDwe/EmVWkM/n8qJkDnQTG7JNPKESrLSkEbDhECzJcOdRX3M2r+BLP+J1TEF02iM5mnUFTWzwn4/OUOr2R1ehDM1yHlfnET2tPH/+htC94HRQ153yqWTMuserqf1wBBjZuax+Oqak57cBCcGYxt2vMKaPzyEIREHRFxF4/jj1LlIuSX8JpOiad8QU+IurJrAEDK7XPvIdG3C02/hvjEXMyw4mUc/C405XFX6KLae1/A3Oxiqc+NcGqTQEuMvZQt4esLd7IwoWEWBC3O9XFngY4bb/q7B3h2Is/ZwHy8e6uVYbxhBgInFHhaNyWFxTS4TitxI4vuvVWdUjfX+MI/1jfD6cBhRgIvyvHy+JJdxjhOlkYFwks88tIeG/gh3XTiOErmHbdu2EY/HmVZs4tzAw0jJAMcReKU1myW7VVSDkddmXcb9nonIgoFqaZAlWRlM+19huKSEY1NXsaxP5cweDSsCaTSeLzHRYh6mdP2fMMhpEAQEqZCQr4Aziv7CuPQy1nUvR1KSnH2+m/JLlrzv16379+ghrzulwsMJXvrtYQL98fesv6uKQtPu7ex49lFGOrtBsOHS8sm99DLutti4vFfhor4EpqRITNBYp2V4w9qEhd+zcr+PusqZPGeeSkwzcV5K4cKxRcwb/g8s6XoGDrmpK66lJHc35YrMD2q/zu+yziPHZOD6omyuLcrGZzScdPtTssLrxwZ4bHcn21tOTHOcXOJhxaRCzp9QQL775P+NaJkM8vAwajyOlkqBICDabIhOJ5LX+/8duOxIpPhj9xCP9vmJKypnZ7n4r4oCxjmsRFMyN/15H1ubh/nGshpWzypi3759bN26FSkxzGetr+FOdpMwmPiRIYtxGzQmtmtQU839Uy/n2ZibSnEYhxxhZv82Aj4vTy+/BreQ5svbYUlMQkFDQqDbITIc2Ed7z2bSmopGGlVykpPfxWJfDus6riOFhTljw0z6sr5G/amgh7zulBloD/PSbw6hKhrn3FB70pUjM8kkRze+wb6Xnic0OACiDYNlDuWpMAUXrqS3Pcq0wImBz0P2NM9KIlvDUSyeV7iufj9W62SO55bzenoMqmbkkoSFSypD1LTeitUTpScwgfvGaNwabMKrwurxd9FcMIcvledzaZ4Xi3TyckrHSIy/7Ozgmf09+GNpijxWrphewsopRZT4bH9/nCxHifc2EN2xjdSxOjKN7SidwxCIv+uFoTSjgJptRC0yoY12QrUXoToXyWJDkuwYjR5MxiyMJh9xwcdTQTd/6leJKCqX5Hn52qh8CoxGvvLUIV481MvNiyr52jnVJJNJtmzZwoGdm1mlvUC51olsdPKE08a2HgvXrlNwJaD1zIv5knkaRcYQRRaF/OZtCC6Jpy+8DoeU5Jb9EucNi+yQ4jidDmqDKioq3dFGekMtdKgDIA8hShrV2TFGktcTFouY6O1k3l3Xnli+QfeR0UNed0q0HRri9T8ew+oyseI/JuHNt//D/al4jP2vrGH/y2tIRiNkl5YRGi7GYZ7GFEM3vuxqpKTCgEUgaOjmnlInjYNmpICfqTzIOT0eekqrCCtmXpHHIWomVmNlaecjjM5eizVLZmPufO619PNQ/wAG4PNTfsrZExZxbVEW5ncJon0dAe7f3Mprdf1IgsDZ461r7hgAACAASURBVPK4Ylouk/L9xBNNJBKdJBJdJNubYVMnpn1JjD0n2tKMGpkiDbkAVK8RzW0CgxFEE4IsQFqGVAYxKCP5FQx9CtLIiamYqhnSY0RSUwTik5Noln/8bEZx8LJ4Ba9oZ6EissrZwY35Gvdvy+KpAzFuXFjBbctqEAQBv9/P+tdfYVzDzxhHM0PGYgbNcb6VU845Lw2x8LBCtKiM26ouRvNZuHFBGQ3r1xON9/HUBZ/GKUW4Y5+Z2X6Jn0sh6qbnc86AygWdcUyKREKJ0pgO0xHYSyJVjyioOCxjSZkWUmUa5OyffArRYv7g31S6k9JDXveRO7yhm61PNpJT6uT8L0zC5np78DAVj3PglTXse+l5krEoFVNnUDp+Mm0vCZSb7eSZDCAKbM+SeKPAyLj29fypehKRVglruIlrh17E4KolYbMRj8NacQqCZuSWVJR5G++mYskQFneSe8rGsd5czaPtG5A0hReWPcKqSfNwGP55LEBVNV6vG+D+La0c7+2iNqeb88eGqckeRE41EY+3AxrIYN0r4dhmwdiSAQHE8YUYpoxFKqpG0Eqgy4yQMJH0tJDIaSSZ3UHC1owihk/aV0JEwNQGlmMi5qMihoCAatZITpKQZxZhKq3C5ivGmOMkrQ3QHQ/yp8gkNijTyNKGuFp7iMb6EjZ2z+fCmiZumQ9u9yRcron0dgdJP7GaMalDHBQnkGPy8/0JZ5DYcYBb3zRjDqf5a9US9o6bxE+vmcXgkUOs3b6Fp1Z8mlxhkDu2WZiYMHO7EKJ+UREJk8TPuobJ29dLvrUcRVPpTadojb3GQKgBDRHJPJFCYx4rf/ppTJ6Tn9im+2DpIa/7yGiqxvZnmzn4ZhflE7NZev14jOYToZpOxDnw6lr2rn2OZDRC5bSZzFq+isQ+P6lDSRyShEIKdWYJnxGjjJgFrt79Bg/UnkHyuMb4kfUsiw/jzy3GnIgxJDh4VZuMoBn4essuZjQ/Q+UlIgatm8+UzKAz6xIeO3YPFgEiVz9Pcemkf9peRVF47dB2NhxZh1M6TrWvnWzL4N/vt1pLcThqsAsVGNYNk3pmG8rgCKbKSpzLlmMomEamWyDTH0cVMiTH1BMt2k/YuBdFiwICdnsVbtdkHI4arNYSLJYiTKYcJMmGKJ442lXVBLISJ50cIrxzI9E1b6JsOY6QUkjWqESXKiij7LhSs8gtWEb+5PPYG09yW2MHDXGVmZYRsutaWddUyNllG1k15lkEASyWYpz28eRt20PeYCPrmY3FKLJj3mSeql/DV7d4GL9vhDpfGU/PXMZ3bzoXZbCX37+whqeXXU2p2sXdm53kyUb+gzCheQUMOozcaU6h/P73VGVNp8hYjRkDISXKcOw19vvbUTUVm1TKBV+9nqIzpnwk773/y/SQ130k5LTCmw/V0bJ/iAmLi5l3WRWiKJBOJt4O90iYijOmM3vZKiydRiI7exEUCKRTpD3HyPniZ7lgbxMpTeWa7et5aNo0MgfjXDrwPB53NqooYokG6bfl82pmAiIG7tj3DDNLMuTN6Iahw1ww9gYijjmsOXgrTknEvPpFhNyxf9/ORKKLEf9W6tvXk4ztxmaIAqAKHnJ8U/G4z8DtnoLTOR5JMxN48kmGf/NbFL8f26yZOM9ZhZIuJdXgP/G8qjDhyi2M8AayEsRozCI7ezE52Wfi9c7GYHC+r/5UQiECTzyJ/88PowyNoFS7CVwQIT0qiSHlJZtzKZx4HU+rbu5p60PUNGb0yWw/2M+Nc21cNamZcPgw4dBBUskexjVEyB9KczC3kCbjKKSa2dzd8TxnNVq58oUYaQWem7aU6799A8ZEjHuffIJnFl5MTaaVX2zNJiULfJ4I8pRs/Hk2fuQzErzvB2gGA9ayOczyj8UliahqjKZIHY2h/cSVEKPGTWbBZz5Ldom+7s2HRQ953YcuEU3z8m8P098WZu4lo5l0ZgmZVJKDr73EnhefJRkJM2rKNGYvvAxTm0jiyPCJM0OTCr3BPrKnHWH89d9j+eZj+NFYvW0LD06djHNvD5eHtpL05uAID5CSDIQsObyamkhGk7i3/lkWfv5iUv2/YNjv57LaOxAFF+uP/Ac+NYG4+hWUrDL8/q0Mj2wk4N9OItkJQCDppis2njGli1hYexZ2W+k/zHiJbt7MwN33kG5vxzp9Ou6V15PudpLpiyHajWgzRhjKeo6R8AYEwUhOztkUFlyGzzcXQTj59ND3Q02nCT7xJMO/+Q1KMIhx/mSCS1OEfIcB8CQWYqj8It9JONgZjFLaHGOwNcR3Voxj9dxRACSTvQRGtmN79W7cXS3UVznoLbCgak4OJVW6h0VWPO6jsK2PvRUTWPLrH2IyiNzxl0d5bvb5TIkf59fbC2hVZL5IHLnKSbLCzc8LHATu+wGxdJS6afksqltOjVEiz3CiPNedGKQ+8CaBdC81cxcw65IryCoq+cD6RneCHvK6D1VwMM7aXx0iGkxx9upxlI5zcfD1l9iz5hkSkTDlk6Yye+YlGFsg1RpCMEtEnCrbWtIYg43kL21n7rV3sXJjHY2azOe37eWBKTVM3bKL8cYRVFHCHT9CyDiKhNnL64lJRASJX1maWfTl66h/eiWHlVJuH30rxUKKN459BUe4k6Hz/5M+sQW/fyuqmkIQ7XREx7Klo5yAMpFr5izgwilFGP7H7BrZ72fg7nsIr12LadQoPKtuJD1ciDKcxJBtRZgfo8d4P4HgDgwGDyUl11FcdDUm08nXvf+gKJEII3+4H/9DDyHYbHhuXs1QQSuDwotoUgpnfDY7S7/Oz0ZUpIN+lIEEP181iYumFL/jxaXh8SvRWtaxw11NZ4ENn68fSUoja5Dud5O3NUqy1UvNPb/DWJjH1x59nLVnLGFhqI4f7yxmt5Dia1oapcRKZqyX21xWnH/5OcOhAQ7MdzL54IXkyXZmWDuxC5VIBjNDKT9N4c10x5oYO28hsy69Em9+4YfaX/+X6CGv+9D0tYR4+bcnjijP+Vw1/U1b2fPis8RDQUZNnMrMSSuRGhXkwQSS24x9biGH69o5dDCN238Ay4oOVl79S67Z1sDmTJJbdzfz50ofK3aux+w04gz6kXx7iEZmkLT6WJeYyKBo4HfzfYxdWMaGx69gi+ti1uQu4Uy7zO/3fBbbSDsHx7sIeI1YLEXYXYt4o3UMf9jpxGqy8B9Lqrh2ThnmkwzAhl99lf7vfg8lFsOz6jpEz0Iy3QkMuVbMi030GB9kYHANRqOP8rKbKCy8AoPB/k/tfJhSra303XEHib37sM2eRfa3vkZnx5P0y0+hihkS6au413UJbdsHMARS/P5T01g6Lu/tBtIxePgCtL5DNKplPCsuw+ELYyitw2XuIN94IhMMHSLZ+eeRO2k1t67ZzfrqGVzad5jbDo/iRUOEH8kaFFlJjvUwz5/ivD1/pX+ojV0LJaYevZisWBE15i1k95iwjZqLVZJIEqMusIO2yBGq5y9g1sVX4MnTl0X4d33oIS8IwgPAcmBQ07Tat27zAU8A5UA7cLmmaYH3akcP+Y+Xlv2DvPFAHXa3yKgJ/RxZv4Z4KEjFhOlMH7McsTGDGs1gLLDjXFCMpTaLDf+9nYa6DL6hrUQubOVzn3qQ/9zXyhORCDcf7me9M8mSxt1oJgPevnrU2mbi7bNIufLYHK+lXbLwq8snYslqZO26n7G94At0m/O5zryBL+/7Edn+NM1njMMw4Wo83iU8dsDIbza0kJJVrplVxq1nVuG1//P6M2oiwcDddxN86mkstbXYl9xEutOE6DDiOKsIf97LtHXcB2iUlHyG8rIb33et/YOgqSrBJ59k4N4fIxqNFNxzN4YzxtG0+4cMSWuRZRdPSXfz+m4RKSbzp+tnsKQi++0G4n54YBmEOglnDDxpuYrupA25QGaHbQ1ztVzm0YtUnALAZq3mjf4ynnZfyOWtAT7dXMz9xmEezpgwF9sI1bhxHR3hpva1RAONbF2SYWrDSooD4yg2HUKs349UcSmlTjceg4QsyRz376Y5coCqBXOZddEqXDm5p6g3P/4+ipBfAESBR94R8vcCfk3TfigIwm2AV9O0b7xXO3rIfzxomsahdV1sfboem72RVGQX8VCQqnGzOKN8KUJzBi2jYh7jxbmgCHOlB03VeP1nW2hpUcgaeJ32C5v5xnVP8aOGHn7RP8y1jSEGo8cZFerGmkgQj+zFOTFB5tg4kjmlHIxVcVDy8K3za4hanuBgdwtvum/CTZAv8nNWdrZS0N5H+uzbMc39Btubh/n2C0dpGYpx1tg8bj+vhoocx0lfT6qpie4vfYl0SyuuC68G20K0pIZjXhHMCHG89dtEo/XkZJ/NmDF3YLGcPmWGVFsbPV/5Cqm6erxXX03eN75OaKCBhsPfImY9xtbw1Tx4cA6CBr++fhrnl74j6EPd8KelkI6iJMNsKPgCW/uMiA6R19yvkU6W86mtMrOzDpBaYCOVGwGgUasmq6+WGc2L+IkWYW3KgaPEzkiNC9P+YS5vWos93cjGJTEmty6lqn8uPqmDVHAvCLOwufI5w53GorlQBZX2yBGOh/cxasF0Zl50Oc6s7Hd5tbp385GUawRBKAfWviPkjwOLNE3rEwShANioaVr1e7Whh/zpT1U1Nj9Wx6E3XgFlH3I6wtia+UzMXwidGRAFbJNycC4oxvjWyU+KrPLKjzbR0aWRNfAiB1Yc565Pr+HRbj/faOnlwvYwvs7tmLUkeb3d7PHVM77IjHykgETBaNpSpWwinwsnwricu9kuLuM1cQUT1CPcWzTM2OEEljd/BLNuZnDud7j7pXqeP9hLqc/G9y4Yz+Kadz9CjLz5Jj1f/waixYrj7JtRUyUYixx4LhpFT+bPtLf/CpMxmzHV3yE355yPqpv/JWo6zdBPf4r/4UewTp1K8X2/RPR66Nj7AO3B+9gbG8tv912HajFw+9WTuHHUO8oj/UdOHNEbrRAbomX693muPk00HuWA9wCtkon5W6v4wuEXESs8KF9fwOHEJvKkPjTVgDMwiZcGRvPXnhlYCj2ExrkpbAwzZfcz5AjHeX1BgDP651Lbfj42MYjGUaRQKTFHCZMyexh77sXEDw6jKSq98RYaI3sonDeRmRddhsP34Y5xfJKcqpAPaprmeet7AQj87ed3o4f86S0RSfDMDx9hoGU9aDEmjFnCWN8shAEFwSLhmFmAY04hkvvtMx3ltMLauzfS0y/g63+GTRfU8YtrX2FTIMnqI22c1T7A6LYdgEpZ4yH+MjnIEqMbQ52FWPFYQkYHa+LjqMlq5frJD/Jr4VbqxMlcG3iVu876NMaBo/DXy9FGn8Wfy+/hx683k5JVblpUyc2LKrEY330RtJHf/Y6hX96HqWos5gmfA9GJ+5xypDNU6hq+Qii0n/y8lVRXf/eUlmb+t0IvvUTfN7+F5PVS8tvfYBk7lnigm6M7v8yGiMx/H/gMSpaFy84bwz01xW+f8Xv8VXj8SrDnQHSQ6MV/4fnDQZqbm+myd7HHNkJl3Vy+f2gNhkgE8x3f5iZbjCnSLs5Wt4MpTDJjYWvvTHYmF9NWPYErBTPBR+/HIx7h1TnDTA9M4ozjqxAFBbO5A21IJOaspqrzOebeeQNywEV0WzdaUmU41UtTdB/Zc6uYsfJS7J53X4pad8IpD/m3fg5omvZPe0sQhBuAGwBKS0undnR0fCDbo/vgyJkM+19+me1PPY4mxxlbtJjxWdMRwiqS24xjXhH2GXknLmD9DumkzJrvb2BgWMA38Dhrlh/jd596la60keU765nf2kxlbx2OaBRX105+P9fBuZFsfC0RLFMdSL4wv6y7Do85xJLJT/K49aukRB8/avkVV634KggS/GkpSWcJn9LuZE9vmnmjs7nzwvHvWpoB0NJpem//JuG1a7FMWYSh+FJMRR58V9QQlHZwrO4rANRUf5/8/As+1L79oCWOHaP7C19ECQYp+tnPcC5ZjKaptO7+LQ/V7+XPDZchl9mZPquIB2tH4fnbomw7fguv/deJoM8kUFe/yrbmIOvWryNsCLPV3YqvcxE/b94CdXVw/fWsqJ2DPZ3igdY65NHbCFn3IokKrbEKttjP5dqqS9n0898TT+7kjenDzImMZlr9p8hoVhzWYdIjwyRtE6hofYEzLhxD9urPkjg0QmhjJ1ooQyQToDl2ANfsEqavvBib+z2PEf9P08s1uvdFkTMc3fAGO55+gkw4RpVnIWOzJyFlBIyFJwZTrROyEU6ywFcqnuH5721gOCjiHXyEx847xh+veglN8nHOhv3MOH6QomA/hd09DCm7+ev0Ipb6C5luP4x3bBRZEPnB7m8QydgwjX6OkeIv4NI0Hjr0VaafezuUzkb7w0Ji8QTnxr5LwpbPd1aMZ/nEgvdc3VGNxei+5VZi27ZhnXk5Uv6ZOOcV4TqnjPbuX9PW/iuczlom1P4Gq7X4Xds5ncnDw3Td9HmS9fUU3Pk9PJdcAkCo7xBfffp53uiZhlrrpmx0Fo9OqqDUagZNg5e+DHsfAIsbTE64YSMtA2Eef+px4qk4uz1NWEfm8otIO6lXXiW9ZAkXL7+KkkiKBw8aSc2I8+zgE5QUNFDoGCCBlazs82neqLC7ZS8bp4wwP5zH7OOfIazk4rDEUYP1xC3TKe18nRprI6U//gmm8lEkjo0QXNeK2p8iqcRpjR/GNiOXMy5aic3lPrUdfBo6VSH/Y2DkHQOvPk3Tvv5ebeghf3pQ5AzHNq5j53NPoAbT1HgXUW4fg0EQsVR7ccwvxlzpftcwTUTTPPedDQQjAp7hB/nTeXXcf/HT5LkqWLl2IxPq9+FKxhh39Biby+rpmpjH+RaNiuxOEGB4uISnBm5hf58RqWoTiVFXUkKKx3ddT/mc62Hel4j88QLMvbu4NHUHlZMXcMfycSedNfNOciBA1403kTx6FOv06zCOmofv8moMVUaO1X2JkZGNFORfQnX1nUjSu1/M5OPgnX/Mcv7zVrJuvBFBEIjFA1x233M0hrPQpmdhy3bz50mVTHbZQMnAIyuhe/eJRopnwLXPE4zEePDRBwkNhah3dGFKT+Rer4ngfb8iXjWG61bfwqSQyj0NBoKLDfxoywEGHBoLK3czLWsfJtKoyRz2NmV42ptiQcDBnObPMJipxGxSsIV3ErDMo7B3E9XtL1Bw+214Vq0CIN0Rxv9aM0pbHFnN0JlowDjVw+RLlmN16uvi/M1HMbvmMWARkA0MAN8BngeeBEqBDk5MofS/Vzt6yJ9aJ47c32TX809iihipzVtEnliMJgiYx2fhO7vs74Op7yYWTPHsdzcQjYE78Ad+vayJ353/ALX507ju8ecpbTyCLZVi0uFd7Luwm8pikQJzCjkpMdRVQpd/Ct05C3mpWcRQ1kSsZiFTjBke2XQp2dX/j73zjo6q2v74Z/pMyqT3HhIgIaGF3qUrPAtNqSKogILYfVh4YsOCYkFQFEFBpIj03kIvCZCEkkp6TyaZTKaXe39/xIfPZwF8/l5RPmvdxcrK5Mw5516+d5999tl7CNY7P+X8yifoVfUVr8kepdfYuQxsG/SrfQJwVFdT+sA07OUVaFIeRN2hF36TE3G615OROR2LpZTW8S8RFvbHKVAt2u1UvvAihu3b8XvoQQKefBKJREJds4Xb39uDQ7Tg6h6ESePHp0kxDPX3AmMtfNofXDYw66D7LLj9TRwOB19tWk1ZTik1ykbcPdrwYmJrap59DpNaw+MPP8kAsw8PVFmp7Kdm6ZFLnHDG4h4kMKBTNuPVqdjMudgdEk5aZbiVy2mXP5UyawoymYi/6Qg16gH41Z0m+crXaAf0J+T115D7tqSmdtSZqd+di+OKAYkoocpWiDTZjaR770Dt8cuuuT8Ltw5D3eJXcTpa3DJnt3yLl8WbpKB+eOGHXRDRuStJnJGM+3XEHVqKg2xekIrFClr9Ut4bXsT7t71L75ihPLPiS9zLiwlylhHnkY4zxYhKBmVNodgvyjDUhmH0j8cUHM3GYn/kgQaMHRMY5qVg2YG7cfMO5fzgdWzduIoF1rc443sXCQ+vQKtWXLdfjupqSiZPwVmrQ931ETwH9sJndGuMtstkZj2EINhpn/wpPj7dfo/p/FVMDhNFTUUUG4opMZRQZ67DYDdgsBtwuBzIpDKkEinucnf8NH74a/wJ9Qgl3jueGK8Y3BRu1/+Sf0AUBKpfeQX9uvX4TptG4DNPI5FIyCrXM2bZcSK1xZg7RVCiiOSDhCjGBPtC+TlYORw8AlvCLO/5FDrcB8D6g5u4dCwDi8xOWFQCs7r3pGzmLCwNDcyf/hh3O+Lpba/hagc31py6yglHLPipCOsZwto2zRSceR2n+goyKTQbwC2nL/klk5AgJcyaSrlqAB5N5+l86StUXlpCFy7Eo2/fa+NxNdup3ZuN7ZwOuahAZ69CaCun7fghaDz/+zfH/7+4JfK3+FmcdjsXD+/j3NYtBDpCSfDrhQZ37AopOU0OFMn+DJyaiExx/QIQjdUmNr96FIfNhbfxY14fVsYb3eZxe9v7eG3px/iSQYxvJpoQPU4BLhh8OJw3kW4FaXhjxxzZAZOHG+vqo5C5KTD1iOD+MB/eSJ2M1FDOisRVrD+Vx1blS7j82+I5cx/Ir5+v3FFTQ8mkKThr6tD0mIvPuAF4DopEp0vl4qU5KJW+dOzwBe7ucb/HlP4Eg93AyYqTpNekk1GbQb4+H0FsyR8vlUjxUfngpfJCq9SikClwCS4EUcDoMNJgbaDB+sPiV4KEGK8YugZ3pWtwV7oHd8dbff3NSFEUqXn1VRrXfoPv1KkEPvcsEomETefKeWpjJoMjT1HUJpHL0kRejwtjekQAnPsStj8G2nAw18O0vRDaEYCNp3eTvv8IckFOQvsk7u09kNKZs7Dk5PDRuKmMVfQizDePywEebMms5oQjFsFPRbt+4WxMieP0Nx9yWlhJ2xAL3nIRzBpq829HX9SXSFMmxYreqIwXSajcgG9lAz6TJxP49FNIVT/cb8HuonbfFUwnq1AJGoxOPfZWIq0nDETt9eez7G+J/C1+hNNu5+KhvWRu20WoEEO8dwoKlMjDPcgzu7hYaKDz8Ch63BmL5AbqldaXG9jyxkkEqw1f28e8MqSSp9tN5+424/hu2zME+F5CrTYhGOTsdkpIq4+jvORB7tHvIsxcjbNNLwxYWWeNQBCCsPYMZm7bUJ7LegUy1/Gq1wI21IRySPsK/nIL0hlHwCvsuv1y1NRSMnkKjqoa3Ho9jv/Dw3FPCaKqahNXsv+Kp2cCHdqvQKUK+D2m9RpNtiZ2F+3mYOlB0qvTcYpO3ORutA9oT6fATrTxbUOMNoZwz3CUsl/fR3AIDiqNleQ35pPfmE9WfRbna85jdpqRSWR0Ce7C0KihDI4ajK/6p1W3/o4oitS8/gaNa9bge//9BP71OSQSCS9tucTq0yXM6fAdhwJ7cE7alWejgngiJhjJjsfh3CrQ+IHSDR4+Au4tsetfHN9DxtE9+Ni9SOyYyJjBIyia+ziOEyfYOPgvjPT9C+oOuZwxyDhcZOaoPRrBV0XPQVGs7tiKI6s/5b36FQREmJmitiDxlCK45DSXdkWdF0yRfjhKSz4a+SZSTlagio8ndNEi1G1a/3hcgkjN4Ss0HSrG3aXFJliwhTuJvq8nboF/ntDLWyJ/CwDsVgsXD+4lb9dRwokjyrMdEokUTTs/FJ0C2bOlEF2Fif7jW9Ou7/VFFKCmsJGt75xBYjUT5FrKCwOreSS6Nz28VdTX70YqFRCK3bDlhPJCQgVKSyR1ZQ/T13SWTroMZJ0H0mBs5lu5ClNzB+yd/ZjXPYbZtTuQ7HySJcIYlkvHsiv8K8IrdsP92yG6z3X75WxooPjeCS0C3+9xgp68E3WcD+XlX5ObNx9fn94kJy/73fLOiKLI2eqzbMrbxMHSg9gFO9HaaG6LvI2BEQNJ9k9GJv19MlM6BAdXdFc4UnaE/SX7KTYUI5fKGRw5mHFtxtElqMvP7iuIokjNGwtpXL0a/zmzCXj0UWxOF6OXnaRUZ+Llriv5Wt2bE9J+zAj15+VYfyQrb4f63JbEZrH9YcJG+D6+/uVd2yi9tJ1IcxitWrfivnvGkL/gVSRbNnOqU096RoxDMqKeAxf1nGuQc9gcgctfxV+GteKjdlHsXbWEd5q/pMnLyadNtRh8onEFNSFT2HDVB1NTcDvW/ECuRmzg/gMmJEYzgU8/hc+kST8pLyiKItUnr1C/JxcfRwAu0YnJz0T4mC5oY//4uXFuifyfHLOhiQu7dlB/JJcoVSL+6jBEOXh0C8GzdxhNVhc7lmRiNTsZ/lASUUk3dtKwMqee7e+fQ241EK5axvrBVYz0UeMlNeByyamtiiF0q4EGz0QWDDiLhzWU6pKHaW0vZWjVftx7DqBaZ+Kgu4ky3QCcMR68OrIdY61XUa0ZwXFnIp+ELeSTpBx8DjwJA1+Efs9ct18uo4mSCZOxXS3AY8jTBP91NIpgd0pKP6egYCH+/oNIavcRMtm/Xp7OKTjZV7yPlZdXktOQg1apZUTsCO6Ju4cEv4TrN/AvIooieY15bL26la0FWzHYDcR5xzEtaRq3x9yOXCr/yeernn+Bps2bCXrxRXwnTaREZ2Lkh8eJCdDwTMJiltOd/dLhTAzw4Z1QAemn/UGthaYyGDQf+j51ra3JX36FVb+LJH0CQSFBTBw/kSvLV+D1+XKKohOIajMG+f3ubDuYR47Dj4OGIFyBaqaOaMOC+FC2fbGYd+xrENxgdUUZtYphFMiiCIg7jNKzFqdFS3NeB7bJCnjyfBiq01m49+lDyBuvowj8+ZPM1edzqN5+CR+zHzKJHJNbM/5D2+Db7cZWpv+L3BL5PymG+loyN+/AmtFAjFsSapk7aGV49Y/CPSUIqVpOeU4Duz+5iFwlY+SjHQiIvLHNq9KLNexakoFGWkhE2bzr2QAAIABJREFU8gqa4xvxkIFMFkZebhj6ohB6HzpJTrfuvJ+SipcjmLrCB9E6LYwq30Rg/06UVUO2VzNpukE4PRS8OakTrQ2NhG8YjlOEvX02MDVZjezzQRDZHSZ9B9exhkW7neL7H8KakY774LmEvjIFuY+aoqKPKCx6n8DAO2iX+B5S6fU3bH8NQRTYWbiTjzM+psJYQbQ2mgeSHmBE7AhUv8PL47dgdVrZU7yHLy9/SYG+gHCPcB5q/xB3trrzR2IvOp2UPzYX46FDhL7zDl5/Gcnui1XM+vo8U3uFcYf3a3zuTGSbZBTj/bx5V56JdMMU8IuDhkK4fwdE9275ToeLYR9/gkqxk+71KXh5eDFxwkSO7dxHwnvvYPIJQdp+GNpHO7Np6ymKlNEc0HnjCtbw9F2JPBYVyIbP3+JdcT3uSinflBfTFDyR7efvxD8wm4D43ahC8kGUkGNQ0qq2E9GfXEKmcSPk9dfwHDjwF+dDl19CyaYzeNZ7opF7YJfZcOseRMCgtsjc/7X7/9/GLZH/k1FXWkz2t/tRFEsI08QjkUiQRbvhO7AVqjjva9ZM9skqUtfk4B3sxsjZHfD0vbHY8MK0co5u+w6/2L24ReYgAGV2d6I1MzmaWoN3QyPdTp3h0PDBbGy1E60QTPPVSVidGsaVbyKitxfFdb4Y3WGvcwAWo4OXJneioVBPyslZ9JVdpOTuLcQnpsBnt7VkTJx5HDx/PVRSFATKZszFdOwAbv0fIvyd2ci0SoqLl3G1cBHBwXeT0PYtpP9k3d4sx8qP8f7598lrzCPBN4EZHWZwW8RtSCXX36D+dyCIAqllqSzPWs5l3WXivON4MuVJ+oT1uebGEWw2yh58CPOFC0Qs/RiPfv14edtlVp0sZumEZAKb/8pntli2SsYw3teL98o/RpL2GXh87/qYeRw8WvYyagxWBi/9GC/fLfSr7Yu71J2xY8ayPu0St7/zGkqZmvqUbgQ8Oobvthykwqsd+6o1OEPdeHN0MhNDfFn12QI+km0mRKrg6/JChI5P8sWB3mjsoNHk4xmXjlfsSWQqK2anlsgjnsi31eI76j6CnnsWqdsvRx0ZauvIXX8QeaFIgCocAQFJKzWBwxNQRfwxYu1vifyfAFEQKDyVRvXei/gY/dEq/XBJXWhS/PG9LQ75Pwi4KIqc3V5E+q5iwtv6MHxGMirN9YXP6Wwm6+QKqhs2otJWI7HLSbWIXLX6cY/6KS5lXiK0rIyEi1f4atxdpPmtRysNhvxRlDpCuLNmJwkdqsgzx6CWerM3YCANBQbGDm5Fbk49KVXr+JtiNbYhC1H1fgS2zoYLa2DyZmh123X7V/7U32jeuQFNr/FEfDQPmbuC0rKV5Oe/RlDQnbRLXPQvVWyqNFay8OxCUstSifCMYE6nOQyLHvZfI+7/jCiKHCg9wOJziylrLqNXaC9e6P4CkdpIAFxGY8vGdEkJUWu/RhIXz9hPTlFcb2LnnB7U5M7ic2ssWyRjmOilYVHaQ0gaCsFha7HkJ313zT+fVlTPxLWf4xX0HYN0g9BY1QwaPpxPyxqY+fYreFvtlPSIx/eBWezZe5jqwK7sKQVXhDufju3A7f5eLF0+j+XKXbQRlawqv4pi8Jss35kEDXZwNSOXOPGKPoOk/Tb83RxInUo0qS68r0YQ9dKHaJLa/ep8mA1NXNq8B9v5BsJVrVFIlTi9BHz7tcIjJRip+l97+f8nuSXyf2CsRhMFW45gy2wkQBqOVCLF5mnHt38sXt0ikSp/LGouh8DBr7LJT6shoXcI/Se0QfYzaQn+EZOpgPLyNVRUfIuIBXttKMFGH56W5iOXejNGnEZFSQVtrlwhsLyKRQ+Mp06+AjdpAL5Xh5Nhj6dnw2n6RJ0kXRVJqDmW1KShVJytJz7Gm/LyZjrIi1nLi0hbD4H71sLFjfDdQ9D3aRj00nXnoea9FTQsX4S643AiV76NTKOgvGItubkvERAwjKR2H/5mC94hOFh9ZTWfZH4CwKwOs5iUMAmF7H9jye9wOViXu46lGUtxCA5mdZjFlHZTUEgVOGpqKB53L0gkRK9fT4XMnTs+PEb7cC++eqA9GWn3s9LSls3SMTyqaOLFoxORuPtDYxHc9iL0/2GP5Mtjebx6bCMewRsZ3jwcdaOadl26ssSmYsHi1whsbKCkfzTSu2Zx8uQZasP7sPuqDaI9+ObeznT3cmPR8idYoz5MD4eCjyuuIh+1gnX7Y2jIb0JwWVG5HCARudxpMa3j9LRVmAEB1WUpYT5jiBr/ClL5r98Xh81KdmoqtQeyCXZF4q0MRJAKqBN90faMQBX7y6e5/1u5JfJ/QOovFlK5JxNNrRqNzAM7VmilImxkJ1QhP+9Xtxod7Poki6qCJnrcHUvnYVG/+DALgoO6+gNUlK+hUX8aRDlNxV1xZsbRva2VWcot2PFlhHkMBl0TXc6eRWqx88KMaSgsH6KQuhNXMogj5g60Ml3lHq/NHA7xpk1DCse6DqHkZCMahRSr2cmgWA2fmp9C7rLCrBNg1cMnfSE4ucX/K/t1cW5Yt5eaBU+iiOpAzHerkLkpvw+TfBY/vwG0T16GVPrroYq/RH5jPvOOzSO3MZfbIm5jXrd5hHiE/Ka2/tPUmGp48+ybHCg9QBufNizovYB2fu2wZmdTPHESyugoolev5tvsBp79NovnhrfloT6BpJ+ZyFfW9nwnHcPbxuNMOfcCBCVB7ZUf+ecBHll1gn1V+9GEbGKEYwTqCjUBsa341COUxR+9Q0h1GdWD4qjvN5HLl69QEzOIPTkGZK20bL8vhbbuKl5ePpPNmtPcbpXxVk0ZkvHrOXA6jJwTVYiCA7XDhCBTcK7VFxTHlDMvthsS3SFcKjuKJjWR8TMIb/vAdTOHiqJISeZ5crYfRl2lJMojEYVUBZ5StD0jcOsciNz7fyO9xS2R/4NgrTVQvvM8zjwjHqJXy6EZdRPefaMJuS3pZxOF/R19rZkdSzIxNtgYNDWB+C4/79+22qqprFhPReU67PZa1OpwJPqBZO3tiHdNBQNGNPOY5QsaxWBuaxiKYDLT5+hxmlRqnpr9CAG6d5BKJKRUDmCfoTPujmYmS9ZwIllK2/K+HOzcj/LLNqQNNuQSeG54Ag/Wv4nk4sYWwYjoDitvh7rcFsH3/vWiz4aDF6h4/EFkWj9itm5A4e9Nbd1eLl6cjY9PDzq0//w3RdEIosDqK6v54PwHeCo9md9zPoMiB910O/+NHCw9yBun36DB2sCcznOY2m4qpiNHKX/kUTz69yfsow+Zsz6LvZer2fxIb9oGiaSfvY/V1q58JxnFnsI36VB5AIl7YMtG+MzjoGk5lGV1uBj+zj6qZUdRBO/gbsXdyPPlyP0C+Do8gY+XfkRwaTb6IUlcaj+UmppaiiMGciC7AXUbb/aP70qYQsZTn93PAU0mU0zwTGM9TNlKWlYAZ7cXIYouVHYDLrmGoogv2R2VzVOdH6NPSQmVdeuxR7mQiipCwscSET75hg666crLyNi1HcP5SiJVbQnSRAEgj3DHo3MwmmR/ZB6/zVD4d3BL5P+HcRns1B3LxZBegZulJaZbL9RBrIqov3TDM+z6B3kqC/TsXnYRgDtmJRMS9+NTkqIo0th4ivKKr6mv348oCvj59Sc8bBJXD2hJT20ioPEyQyZoeKb6bSqdMXSv74naaGJA6hGq/HyYO/dpQqoXgmimf+0gDjcm0SiqmWL+mtK+ZlpVDmJrbEdKG1UocpoI8FCxalpX2tXugi0zYcA8GPBXOPoOHHoNRn0O7cf+6rhM54opm3E/uKzEbFyPKi4avT6dCxmT8fBIpHOn1chkN5cGAFos3heOv8CZ6jMMiBjAyz1fxk/zxypg0WRrYsGpBewv2U/34O683ud1FJsPUPPaa/jNmIFyxiPc/sExNAoZOx7rgww96afHsco+kCNif9IuPIhWLkfSXAWJd8GYL+D7VWFJvZFh7x1G5bsPISCV8b7jEbNEbAolW+JTWPrZF/gVnsM6oBOprbvgEkSyAvpwNLseryRfDt/bFS8JzFgxjjPqfJ5ocjLNbIZpe7icpyV1TS6IIkq7HqfcHWPQGr6Kz2RU/CieC5tK6aK5NARnY+0OolTAx6cXEeGT8fcfdN09GbvVQs6JI+TtP4pbgxtRnu3wUviDBFRx3rh1CETTzg/pDexh/Tu5JfL/YzjqzDSllWG4UIGyucW/aHDoMPmaCRqYSESPDjfsM8xLq+bgl9lo/TSMeLQ93oE/iJ7D0UR19WbKK9ZiNl9FofAhNGQsYWHjUasjOPVlOhdONxPUmMWwmVEsyP4rRY4kkhuT8dHp6Hf0GKVhfjzy+N8IrlqIVKhjRN1I0hpCuUwYI5t34T+0gqjmUXym8qdIHYDyRC1h3moOPNEft+Zi+LQfhHaC+7dBVSasGPKDaPwKlrx6yqY/hEtXQMTnX+DRqytGUz7nzo1DqfQjpfMGlMpfPgH6S5ytOsszR5/B4rTwXNfnGBU/6n/OP3ujiKLIloItLDy7EKVMyZt936TVJ/vQb9xI2AcfcDkuhQmfn+a+rhEsHNUei6WMs2dGs8IxHqPRm02ZTyAJ7QSV5+HuZdBxwrW2d2eUMmtdFiGhmzF6neXh6IexpdnQW22ktu7Mu6s3oc0/gaNHB3bGJeHl50+qqiNn83SEdArgwJgUFC4nU1bdzWVVOW/ozPxFVML0vRQWa9jz6UVEUURh0+NUeKLSrub95AxSglJ4r887uL7cQM1XS7ENUWMeJMMu6lCrwwgLm0hIyGhUyuuXGKwpLCDrwB4qzlwiVBFLtDYJN6knSEEV640m0Q91oh9y7/9MyOw/ckvk/8sRHQK24iaMl2swXqxBbmpxuzTYqmhSN6JNCSN+aN+byqMtCiJnthVybk8JofHe3D4jGbWHAlEUaGw8TWXVRurq9iAIdrTaToSHTSQw8A5kMhWiKHL00zNcyjAT1niBoU914r302eRbuhLTHENYWRk9T52mKMaXmXMW4lu/CLmjhPF1Y8nWqzgstKWjKZM7hmYQr32Ulyv15AZGojlagzsSDj3Vn0A3KawYDPqy75f7PrC8P9iM8MjJlp9/AXuFkbJHXsCeu4/gl1/D577RWG3VpKePQRSddEnZiEbz626en8yXKLLy8ko+OP8BUdoo3h/wPrHesTfVxv8qRU1FPHPkGfIa83gsaRaD3j6CLT+fmPXreP+qi2WpV/lkUgrDk4IxNF8iLX0Cy+1z6Ft+jtll30BAAuhLYeYx8Gt1rd2XN6WzKq2K1q3WU6XM5OmkpzGcMVFXV8fFyGTmbd2NZ85xHMkJbGvbjqi2iWwwRXGxsJH47iHsuqsTDoeJe7+6k1J5HUvqDfRV+8O0vVRVydmy+AKCS0Rub8apcCdQsZo3u18k0C2IJYOWEFrcTMWzz2GvLEPx5EAMHRrQN51BIpHj7z+Q0JBx+Pr2ve6GvN1qoeDsKa4cPYwxv5YwTRxR3u1wo8XnrwjzQJPgiyreB2W4JxLZv98ouCXy/2WIgoiz1ow1X4/xUjXOMhMSQYIguqizltOoqEPbKYz4QX3wCb75otF2q5MDK69QlFlPYu8Q+o1vg8NZTVXVd1RWfYvVWoZcriU46C5CQ8fi6flD6JkgiBz88Dh5OQ4i9ekMnT+IL1IfJMvQgyBrEG1yc2l/IYPiOC8em/kWiuZPUNouM71mEiXNdnbY2+HjaOTxvntITHidp9KzuBTeioAsPc1VJj6Z1JnhSSFw8FU4tgju/RoSRsKuZ+Dscpi85VfDJZ31FsrnfYbl2DK8xt5H6Kt/w+EwcP78fVisFaR0Xvuj8dwIZoeZF46/wIHSAwyNGsorvV/BXfH7pDv4X8HitPC3k39jd9Fu7tT2Ycq7F5G5uxO2dh3j1l6mQm9h3xP98PdQodMd5Xzmwyy3/pWXriyhtaMONS7wi4fp++D7qCOXIHL34v1crjPTof1GChxZvNbjNSpO6aktLqLavxUTDuzDPycdR2w0Ozp2omPf/rxX6EZBmYEu/cL59vb2NFkaGbv2TnSSJlbV1dPetw3cvx2dTsKmt87hsLmQOS245BqihTW81T8Hh0Tk7X5v08u7MzUL36Bp03eok5Pxfu0R6mWnqKr6DoejAZUyiJCQ0YSGjkWjibzuPBkbG8g5cYTsY6lYKhoJc4sj2rc9WnyRIEGikqFq5Y063htVnDdyf82/ZSV4S+T/wwh2F/ayZuzFBmzFTdhKmsDeMu8Gu45qSxEmdyM+HSOJ79WbwJhWv/nBMNRb2Lk0i8ZqM73HRBLcLpeqqo3oGo4DAj4+PQkNGUdAwNCfFMZwOQV2v32UklKB2KbTDFo4ho3bp5Ju6I7W7knXzCyic/MojvfgpakLMTrXorac5eHq8dQZ7eyyxNIscefZpG8J7/QG754/R1ZUHB0aneSn1TCqcziLxnaA0jMtqWw7ToC7PoaCA7Bm9LXc5b+Ey2CnauEumre/jLptW6LXfoUol5CZOZ1G/Vk6dliBr2/vX/z7n6PaVM2cQ3PIa8zjyZQnmZI45Q/rnrkeoiiy+spq3jv3Hv0bApn5eRXuvXpiffltRi49xYDWAXw6OQWJREJV1Saysp9ns34Oiy8tQO+fREjt+ZaUB4PmX2tTZ7Qx6K19OF1m2nXdSm7zFRb3X0zGaR1N2ZdwuAXT4+ReYnNzcAQGsqdHD/qNGsvTp01UVBsZOjiGzwYlUmWoZNzGe7ALFtbVVBET0QMmfouhSeDbN9OxNNuRCk4EqYLW9nUsvi2PCrGRp7s8zaSESTTv20/V/PmIdjtBzz2Hduzd6HSpVFZtQKc7Cgj4ePcgNHQcAQFDbmgvp6GygoK0UxScPYWusJQgTRSRfokEqaNQOFo2aaWeCpSRWlRRWpSRnijDPJHcQFbXm+WWyP8bcZkcOKpMOKqMOCpN2KuMOGvM8P00Gxw66ixl1NsqkYariOjSnlZduuMTcmMJwX6NitxG9izPQuWbQ5uBuZgdh3A6m1GpgluslZAxv2it2K1Odiw8QlWNhLaGY/Rb9ADb1kwhzdoDlUvKgNPn8Csrp6iNJ0vv/RtXFbvRGA/zQM09iM1uHDApyVa2YVrwFoiZzsGaCnLjW9FfJqHqdAOCKLJ7bl88JVb4pE9LublZJ1qqES3t2RKd8XAqKDQ/2z/B4qT2o9M0bXwJidxOzJbNKIICycn9GxUVa0hIeIvQkDE3NV9XdFeYc3AOJqeJRf0X0Sfs+onP/gycqTrDE6lPMOickwk7mvF/9FE2d7idN3blsPjeDtzTqaUsYlHRR+QULaWoaCiPlK3iYvRIkot3wtQdP0oid7aghvs+TyNc1UhwynYKDYUsG7SMradrkWemoZB7EXHhIJ1zinC6e3CoX1+GTH+Y+3dVoKs3M35Ea97sE89VXQETtt6LyuFgU20ZAa1HwthVmI0uNi06h6HW3PJcSaS0tW5mea/LXFHVMTp+NC90fwHqG6iaNw/TyVO49+5NyGuvoggJwWqtoqpq07VVrkzmRoD/EIKC78TXp88Nna9obqjnatoZ8tNOUZF9CTUehLrHEhGQiI88CLnt+zZkEhQh7ihDPVAEu7dcIe7/8kbuH17krQWN6LcXIvdRI/dVI/NVI/dRI/NRIfNQInWX/2p44c0g2Jy4DHZcBjuCwY5TZ8Gps+Kst+DUWRDMzmuftUusNFiqabBWUm+twOktEJqUQGRSByLatf/dypcJgkDm0VQKcjbiFZWGTNWITOZOQMAQgoPuwte3969GFVhNDra+0lKPtb35KClP3Mnuzc+RIe+JRLBxx6HTqBobKUz0YteoF0hVHsOteQf31g8jvLEVh40VHFH3pJ/mNFb/DhSipDwxkp4SB20bVKxLK2PDjJ50jfaFbXPg/Gp4YDdE9YRvp8GVbfDQQQjp8LP9Ex0ualdcxLDhHZzVmUSu+gL3bt0oK/+KvLwFREU+TFzcczc1Z6llqTx79Fm8Vd4sGbSE1j6tr/9HfyIK9YU8cmAWozZU0ueii4jPP+eBLMiraWbfE/0J9lIjiiKXrzxBcfU+wi94E2sp5mpACp0sxS0vcPUPz/eSPZksSi1nQEgDDVEbqTPXsXzoChYdryAi6xTuyPG6eJI+eaWIEhmnBg9i4GNPMuqbbJqbrMwe1Y5nukSTWXWBB/Y8QIDFxbd1ZXh2ngojF2OzONn6fgZ1JYZrUT5xlt3s6HiaVF89XYK6sHjAYryUWvTr11PzziIkUilBf30Or9GjkUgkiKKAXp9Gdc1Wamt343QaUCh8CQoaQXDQXWi1HW9oleewWanIvkxx1gVKLmZQX1qMSupGgHskkYHt8NeEona4IbH/8DcyLyUefcLw7Pvb6gr/4UW+Ni0f/YFCVIIamUUKzp9+RuomR+quQOqmQKKUIlHIkCikLSdCZZIWC0Bs8ZcjgugUEK1OBJsL0eZCsLkQjHZEu/CTth1yOybBgN5ci95Ujd5eR5OzHm14ECHxrQmJb0tEu/Zo/X+/vOWiKNLcfInqmr2UFW4HRTmiKMPPtx+hoXfj7z8ImeznreJ/xNhoY8srhzGYJKQ4jhPdwZ/U8r1c8eiKIDRyz66TCA47RQleZIx+kbWys3g0b2Covg9Davqx13KW7Yr+hMqr0Hg50Xm2obxtAG2tRubHtmbaqnQe7hfL83ckQM4uWDce+jwBg19uEfcNk39ycvJH4xREGtZm07R1A7aL6wl89ln8pj2ATneMjMxp+PsPpH3yMiQ3kVpga8FW5p+cT6JvIh8N+gh/zfUjLf6M1FvqeWLXLCYvvkyw0x3PL7/ljjXZdIvxZdUDXZFIJLhcVs6fn4C+poje5ys555WEv62BhDa9Wlxx3yOKIpOWHOBEhY25PQV2O5Zgc9l4f9AXPH2ijG4XT+LucuCZl8WA3GKkVjuZw4fR8+l5jPjiPBaTg/n3dWB6chjHSo4w+/Ac4o0CX+vKUPV9Bga+iMPmYueyLCqydfD98xBpOkZu4k5WhzoI9Qjh40FLiPWOxV5WRtXzL2BOS8O9b19CXn0FRfAPKYkFwYZOd4Tq6m3U6w4iCHbU6ggCA4YSEDgML22nG37mTPpGKvOyqczLoTI3m5rCfFxOJ2qZB/4e4YQGxOPrFoJHcjBRd/626mR/eJHPPXWMnR+8g/h9xR2lVIOXWwB+3uF4uPngptKilnmgkqqRi0qkohSJKEHikiARJCAAkh/mQaQlvlaQuHBKnDhFBw6XDYvDgMGko6m5FouzGYvLhNnZhFSlwD88Er+ISPzCIwluFU9QbBwK1e97Wk4UXej16dTV7aOubh9WWyWiKMVcF4+v13C6DZyEUnXjYYP6aiObXzuGzSrSjeNo67M5HquiWNsWwVHOmK1nMCllFLfzpWbsy7xvS8PTvIouxhRml41lm/0A2+iMXaGidUAektAhnAlSEGQ28F339kz8IgOVXMquuX1R23QtbhltCDx4COxG+LgbeIbAQ4eubdb9pI+7imjadgLzsbfw6N+P8I+XYDZfJS19NBpNBCmd199UTvjVV1bzdtrb9Azpyfu3vX/T5fT+bJgdZhauf4RRb53B2jqC/CeXMX97DgtHJTO+W4vrz2arJS3tHtyumulcVMiCuMeYVraeiFEfQJvbr7XVbLEz6M09GGwC748PZGHOPFQyFX/t8wlzzlUxMuskbjYj7qUFDM4rQqHTc3XEHbR+bj4jPzmN0+bk3SkpjG4dxPbcrTx/+kW66gU+ayxHNvwt6DETl0Ng34pLFF6oBSQgkRBkykQRt5KXI91RKkXeG/AuvcN6IwoCjWu/ofbdd5HI5QTNm4fXPXf/xFp3Opuprd1Lbd1uGhpOIIoOlMpAAgKGEBgwDG/vbjeV1dTpcFBbdJW6kqIfrtJiuoy8m15jJ/6m+/SHF3loKUJtqKtFX1ONvqaKppoqDHV1mA16zE1NmA16bCbTTbcrkUpRu3ug9vDA3ccXrX8gWv8APL//1zcsHE+/gP+3zTqbvZ4G3TF0DUdpaDiOw9GAVKpELe9O8ZnWWGo7Mmhy9xvOAf936ooa2fr2aQS7g66mvUhzTnJqWDeqNSFIjTmM2ZGJzlNDaVIgjomv83LtKTwcy4mxJfJO4XR2uo6w1+pHnls8fUPP0LH7Aywx61GZTXzTJoRNly18faaUb2f2JCXSB74ZD1cPwYwjEJgAmx6Ey5tbqg0FJ/1sH42nq2jceAnL6YVIFCIxm79D9IC0tFG4BDNdu2xGrb6x6CNRFFmSsYTlWcsZEjWEN/u+ed2qTL8HJr2N+gojDRUmGqpNmPU2zM12rEYHLtcP//dUGjkaTwUaTyVafw2+Ie74hbnjF+pxQ+UX/z9xCA5Wvj2FvqsyKLinM2tjZ5NV1sSex/sR4dvykmxuvkx6+jgSzlvxMJmY0v4dlhctxv/hfeD+w0opq7iW0Z+cIUBp56MZscw+8ggBbgGMab+Ylwp0TLpwGoWlAXVNGUMKinErq6R6xAi8n3mJ0Z+eQnCKfDG9GwOj/ViV+QXvZixmqM7BIkMVklGfQftxCC6B1K9zyT5RiVQiICBDaymhfeRCpkeEYFfpebbrM0xMmIhEIsFeWkrl889jST+HR//+BL/8NxQhP5+6wulspr7+MLV1e9HpjiAIFuRyT3x9+uDn1w9f376o1Tef9kIUBFxOJ3Llb3sm/xQifyM4HQ7sZhNOux2H3YbTbsdptyMKLqQyOVKZrOWSSlGoNag9PFFq/j0hUH/H5bJhMGSgazhGg+4ozcbLACgUfvj59sXPbyDFZ6M5t6sG/wgPbp+RjNb/+m6Zf6TkQiV7PrmIzGakc+lqBH0Zx0cOolGqxqP6HCNTC6n0cacqKQzFlDd5NvconopleAkxfJk3m+NiBqkNtaT69qN3cCaPjp3FwwUlmC0W3lBYiYxKZsLnZ3iwTwwvjkz8oV7osIXQ8xHI3gHDk3FpAAAgAElEQVTrJ8KA52HAz/vSLbkN1K+8hCNvDbacE0R99SWalE5kZDyAvimNzp3W4uXV6YbGK4gCb559k29yvmFU/Cjm95j/u1Vp+mfsFicll3SU5zZSntuIoc5y7XduWiUePio0WiUaDwUyeYt4i4Dd7MRitGM2ODDUWXA5W1alMoWUkFZehLX2ISrJD/8Ij/9I9I9LcLH34b8QdbyIg4/3Z3nVXSSFebP2wR5Iv09dXVu7l/z0GXRLN3LWM4lXY2fwrW0/HmNXXPOTA6zYn8GrBysYEOxi9n2hzDwwkzjvOGJC5/Nlo4XHMtKxNlehaKxjYFEZ3nn5mIcPx/TUi9z/eRoSYP3DPega5s27Z95hVc5XTKy18pxZh2TCeogfgiiKnNhUQOaBMpQSO3ZRidJhYJjvc9wfFUm9ZzV/iRnFgj4vopAqWqz6NWuoXfw+EomEgMfn4jNxIhLZLz8nLpeFhoZj1NcfRtdwFJutGgB399bXBN/bq/NvOnV9s9wS+f9inM5m9E3n0OvT0evTMBiyEEU7EokcL6/O+Pn2xdevH54eiZj0dvZ/cYXKfD0JvULod19r5MqbE6vLBwo4srEIN1M1HS4uxRbmy9GeXTAKDkLyTjLgXA1FgV7UJ0XiOXUhT54+jLv3MuQE8k3O4xSLNRyqOcp3AXcS513BqjmTGJ1VQrnJwoyqPOaMGcMdH51AIZOy67G+aIwlsKwPhKfA5K0tycc+7t6SG/6hwz/rprFXGqn7JAtX3VlMhz/F/7E5BDzyCAUFb1NS+ikJbd8iNPTGImkEUeD106+zIW8DU9tN5cmUJ393kXQ5BAoz6yhIr6Xkkg6XU0CpkRPW2puw1j4ERHriG+qO+gYLVQgugaY6Cw2VJqoKmijPbURXYQTAK1BDXEogbboH4xP8743ld5lMnLtzME69ni+euI0Dl27n1buSmdwz+tpnCos+xHbyLRLyjTwf9xgFblGsaaVF2WHcj9qa9vFeDpU5+WsfPxI62Jh7eC6dAzvTqH6MdKfAS/npVNZUIjMZ6FVaQWhmFsJtt3H1yRd5bHUmcpmE7bN60zbAgxeOPs/24h3MrTIxzW5E+sAOiOiGKIqc213MmW1FeMrNNDvdkAoOhmleY0G0ivO+NbTWduKLOz7CS9Vy0NBeXkH1KwswHT2GOimJkFcWoE5MvO7ciKKIyZSHruEoOt0R9Pp0RNGBRCLH0zMJb+8ueHt3w9urCwrFjR9qvFFuifx/CU6nCaMxm+bmSzQ3X8bQfAmTqQAQfngYvFLw9u6Kj0+PH2XRK8qs4+BX2bicIv3va03bnje3JBRFkdPfZHH+qA6fxhySL39O87h7OCQRsIvNtEk/Ruc8AzmhvjQntcJnysvMPXgUTcgnIHHn87y5KJ0SdlSuY1PACKQaKZsf78ecYiMX9EbuyUnjtYn38t6RclafLmmJpon0hlV3QM2VllOsXuHw3Qy49G2LwIe0/+kcNdmo+zgDl6EK454FaNq3J3LlF9Tp9nPx0qOEhU2gbZtXb2jM/yjwDyY/yGOdHvtdBd7YaOXS0QquHK/E0uzAzUtJXOdA4lICCYr1umbh/h6YDXaKMusoOFdLRW4jogjhbX1IHhBOdHv/3/W7fg1LTg6FY0ZzLkbk83v6oCu+m71PDCDcp8VaFUWBrMyHCT+yFa0eenf9is6mPJYOGY3U+4fIEbPNweCFO6mzSlk/vTOVsgzmHZtH77ABpIkPYHfC/PpDXC4wIHPYSK6sps3ps8h69ODM4/N5cVM2aqWcfbN7E+alYs7+RzledZJXKpu4UxSQPbS/xS0IZB0u59j6PHzVJhosLf3sJNvAqbjLrPQx4ikPYOXty2jrH/f9GESad++m+o2FuBob8b3/fgJmP/qrhUn+GafThL4p7SfGG4CbWwyenkl4eiah9UzG0zPxuhkzr8cfXuQbG09TUPAWbm4xaNxicHeLwe3769+xVPpn7PYGzOZCzOai769CTOarmM1F/D1gXqn0//4mt8fbuwteXp1+tq9Oh4uTm65yMbUc/wgPhj2YhHfQzY3J5RLY//YhrpZICa4+TbL1BOXTH+Do5UsgraNL6gliqm1cigjAmhBH4JSXmL3jBKqYT5Eg4YWSmXQzBrOj7hu2uben2C2KL6ZEssqlZW9dE4OvpPH8oL7olYGM/+w003rHMP8viXBqKeydB3cthU4TIXc3fHMf9H8Obnv+J/0U7C7qlmXiqGvGduE9XA11xGzZgs2jifT00bi7tyal89obShssiAJvnHmD9bnrmZ40nbmd5/5uAt9UZ+Hc7mJyTlcjiiLRyf4kDwgjvK3vv0VszQY7V05UcvloBcZGG9oADV3viKZ1tyCkv1Oo8K+hW7WK2jffYvlwKfta9aCz+0N89UD3a/PrcBjIPHY7HU9coVSeSK/uS5huPMtrIx/6UQHu7NIa7l52Gq1c4PDzd7CteBMLzy6kX/gd7BTG0cYsYbZjFeeztEhEiK6to8uRoyjbt2f33JdYtLsUdzcFh2f3ResmMm33A2TXX+HDSh29ZCrkMw6DT0s2ydwz1Rz6MhsflZFGkxJBqiBIvIy21Rqe9gOkIs92eo1JHYde65+rqYnad99Dv2EDitBQAuf9Fc/Bg3/Tc+RyWTEYMlsEv/kizc2Xrrl3ADSaKCIiphIRPuU33ZM/gcifobh4KWZzIVZb5Y9+p1D4olIFo1IFfX8Fo1T4IJdrkSu0KORa5HItUqkSiUSBRKpAKpEjkcgQRReC4EAUHQiCHUGw4XAacDqacDoNOJxNOOwN2Gw12GzVWG3V2Gw1uFzGa98vkSjQaKJwd4vBw7MdWs92eHomoVL9fBHif0RXYWT/yivoyo10GBhBz3ta3fQmnLWhme0v7qRWCCS6ZDedhoRxJjqSzKwsVBTRc086viYXGVGh0CaekMnzmPndaVRxy5FIzNxdN51ZtW05ZNjDPofIcb/ePD5ARUVMG1ZX6uiTn8m08AAGDB7KsPePIpVI2DO3H5rmYljWG2L6wYT137tpeoCbX8uhJ/mPhVoURRrW5WLJqkMipmLYspbwZUvR9O1CWvo9OJ3NdO26FbUq+OeG+ZO2Xj/z+u8u8MZGK2d3FJF7qhqJVEK7vqF0GBRx03sivxeCS6Awo55ze4qpLzPiFaCh219iiO8a9P/qtxcFgbIHH8KQfpanpooUS3vwep+/MabLD/mCjMZcKrYOpU2+no1+U5iTNJ15inLm9hn5o7bWHDzPi/ur6BUMax8fwbLMZSzNWEqPkFFsl9/N2CYY6nyFCxltEOVKAhoa6X/wEKroaNbNmc9nx3V4a1UcfrQPUrmFKbsmU6kv5YuKatqqfFHOPAjals35kss69iy/hDtGrCYXNpk7Kox0CfuQGSFOTAo93bRTWfqXOagVPxxOMp87R/XLC7Dl5+PeqydBzz+PKu766Yuvh91ej6H5Es2GSzQbswnwH0hIyOjf1NYfXuT/EZfLgsVSislc2CL61srvRbhFiB2Oht+pt39HikoV+P2LJBi1Khi1OhQ3t1jc3GJQq8NvuiKR4BI4v6+UtB1FqNzkDJySQHTyzcVyi4JAxfrtHNzdjEkTRNuybXRc+BBbz5+jpKQEf2cW3XZkI5eInI8MR52QSPikZ3h4w1lkrZYjk+iIt03lo6sduGy9xP6GdL4LvYs+sS46DOnBO8U1dK0sZERzLdOnT+f13Xl8eaqY9Q/3pFuUF6y8A+qy4ZEzLWGTm2dB1vqWcMnQjj/pb/Oxcpp2FqGKaaL+/WfxHjeO4Jfnk3VxFjpdKp06rcHHu+v1xy2KLEpfxFdXvmJa0jQe7/z4vyx4ToeLjP1lnNtTjCCItOsbRsqwKNz/C7IPQsuYizLrSdtZRH2ZkeBYLX3vbU1g1P9f/VJHTS1Fd91Fg7ecmeMacZn6cOD+xQRpf3jhVVdvRb12Gh4mKc9Hz+Or8IG8F65mQnzbH7X1yLJd7CoReaJ3AI+N7MrbaW+zJnsNib7jOeJxB3+zSwipm8vFi11wqd3xMDYz7MAh1D4+rJj5PN9k2QnydWP/o32wCDom7pyIpbmBNeXlhGsCUM46fK0+cG2JgR1LMhFtVlSWBppkASCKJHls4eOEfLIVFWhs3Vg6/HW6RP5gUIhOJ43r1lP30UcIRiM+EycQMHs2Mu1/R43Y/6jISySS4cAHgAz4XBTFX0xO8u/wyQuCDYfDgNNpuGaNOx0GBNGOKDgRRSeC6EAUXS0WvVSJ9O8WvlSJXP53698LhcILudzzX6ob+s80VJk4uOoKtSXNtOocSP8JrdHcZLEC8/kLXHn7C867DUaUyujsPEHU67NZt2kTTU16IpqP0WlXOWZ3uBAaiXe7ZKImPsG0tWlIY1Ygk5WjkE/j24vJNLka2FW+jg3R9+DuoWLaxN68VFRFZ6OOXlmnmTljBkVGKWM/OcXUXtG8fGc7OPUx7H0e7v4EOo6H/APw9Wjo13Jo5Z+xFjRSv+ISqjgN+q+fQ6KQE/vdd5TUrqSwaDGt4+cTEXH/DY3908xPWZKxhIkJE3mu63P/ssCXXNZx9JtcDPVWYjsF0Ht03H/Mcr8eoiCSc7qKU1sKsRjsJPYOodfoOFRu/z9lCg3791Mx5zEuD2/Hgk65hEtGsnvKwh99pujck0TuWIFJFc9D8bM47tORlcmtGBrwQ00Dm93B0De2UW5V8s20znSJD+GlEy+x7eo2ArVTydUOZKWXi6aMR8i50gOn1hel1cqIQ4dQS6R8NvUpNpa6ERXswe6ZvakyF3P/7vtRmWx8U1GEl3sI6kdSr4VyNtVZ2P5hBs0NFsIop8zVslfgLS2nOmE/qzwzEeyBjA5/nheHDkAp/2H17GxspO6DD9Cv34DM25uAx+bgPWYMEsV/thTkf0zkJS3qlwcMAcqBNGC8KIpXfu7zf/SN119DcAlkHCzj7LYiFCoZ/ca3/sXqTb+EraiIug8/JPf/2Dvv8KjK/It/pk8mk957hySkEAgBQg29VxVQiiALKDbUta5d13UtK1gREUWQ3pEqvUOAJBAS0nsvM8n0dn9/hEVZK6z+dpfd8zzzR57c5M5937ln3vt9z/eci1qudpqK0tRM/9gmxFNGsX7DBsQIRFduo/NBLQ0+kOMbhn9iVyLufoQZK88hDv0SibwQk/M8vs6Ow9MisKfyczaHplMpDuWJmd14ubaJJKz0OLqLyePHE5eQyKjFxzDbHOxb1B9VWxl80gciB8K0tWDRdzRByZw6rGilN65+bS0mGj64iFgtx163gbadOwj/ejXGUAMXs2bi7zeO+Ph3fhVZr8lfw5/P/JlxUeN4tc+r/1TAtklv5cTGQvJP1eHhr6Lf1E6ExN68P/2/AhajjXO7ysg+UInKRcaAe2KJSPp9unprn38BzcaNfDEnjV2+5xkTNI83hjx0/fcOh5WaDX0IzrtKg3wwM7rcxVXXGDZ2iyXV7Tt1UGFFHeM/Po1SKuLg08NRO0lZdHgRRyqPIHWdj0WVzurwWvL2vkBxYSpWL38kNhtjTp1G2dDAF3fOZ60xnM7Bbmyf15v81kvM3TcXX4OEVTWFODkH47TwMKg65tDYbmHnhzk0lrfRya2Oqy0+HSpPEfj4HuXtiEPoBSNepul8OH4OCUE3KmJMeXnUv/5nDJmZyMPC8Fn0KC7Dh//LzO1+juR/712aNKBIEIQSoWNreS0w/nc+538c6kq1rH8jk1Obiwnt4sm0F3veFMFbq6upee45isaM50KxC/mx0/FoK2b0SDna4emsWr0aFycZ3S6tJe6AlpIwMRf8wglK6UHMjEXM+OIc4sC1SOUF6F1n80FNIoFWOWcbdnA2MIISIpg5KoY36puJlUtIPb6XlMREunbtyoeHiilu1PP6xERUUhFse6CDyMe816GNPvRn0FbA2MU/IHiHxU7zV1cQHAKKsDratm/Da/48xHFB5F5ZhEoVRWzsa7/qxtlZspM/n/kzGSEZvJz+8j9F8OW5zax5+QxXz9TTfWQYU55L+48heAC5k5Q+k6O546nuKNVydn2Uw/4VuViMP+L38U/C75mnkYeGMnd3JZ66ZHZWf8qaK5uu/14sluEzbhs6tQJ3jrA8ZwUBxjpmZBdSoDddPy4m1J8XBgfRapVw39JDSEQS3h7wNqn+qTjal2G2ZPN4cSDJo+4lLCgLRW05dqmEbX3SaevShXu//oA/6M9QUKFh6udniPVI5N2B71LrZOHewE5YdJUYPswAYysATi5yJixKITTBi6saf+KCdEhsRnAINDRksPDCAnqautPi/Dl3bPgjL+3IRm/+bvyUcXGEfrWS4I8+QiSXUf3oIsrumoL+9OnffIz/WfzeK/k7gBGCIMy99vMMoKcgCA9+75h5wDyA0NDQ7uXl5b/b+/l3g0lv5fS2EnKPVePspqDflBgiu/767llbYyNNSz9Fs24dVokTeb0eppEAQppPM+DJYRxvqOfcuXNE+bkRvfNT3IvhbLKMelEwMd164TH+HuZ+eQlpwHZkrufQu9/N25IRpB3Xkd92lv3SfNarxtM/xYszgWrcJWJGnzvQEc02fz7lGgujlxxjdGIA701NgZMfwL7nYOJSSJ4K1efhsyHQ/V4Y87cb3rsgCLSuu4ohuxG3cf7UPjYLWUAAYWtXk5U7F632Ij1SN6NWd/7FcThceZhHDz1Kd7/ufDTkIxS3kOkKHSqkM9tKuLivAs9AZ4bcG49P6D8nbftXw25zcH53GZm7ynD1dmLY3C6/ea3ecOEi5dOnYx8xmruiC5E6l7A44z0yQr/LBdBeXYnLmofQ+oSiaZMyrvti5GovdnaPIUDxXTnykY+2s61Cwv29fHlqQg90Fh337buPgpYimnyeZJwskQV+y8jccI6GymBMYZ0REJGh0eC7Zy8Hu/Xn3eDRJEf78PXsNE7UHObxI48TYXJiZXU+Iucw1A8fAWXHytxhd3BkTQFXjtcQGWShoVSLTuaJXGTCgjNWv6usCV5Fu90Nt/bZvDamP4PjblyACXY72u07aFyyBFttLaqePfG+/35UPdP+31b2/8pyzS+S/Pfx31KucTgE8k/WcnpbMSadlaRBIaSNjUCu/HUbtJbKSpo//xztps0IdjuOMTM5q0vAaJXSxXiC5FfuZcvhw5SXl9M7UI7fyi+QNUnYPUiFrdWX+B79qE7ryTs725H670fmcRS963ieDZ3F4A1VaMz17NNvZZXfRNy9PNH1CMSOwNzKK2gLrzJ37lz8/Py5c+kpShp1fPvYALxMFR0WwpEZMG0NOGzwaQYYmmDhmes31d/Rfqwa7TcluAwNpW39G+hPnyZi00aqxd9QWrbkVzc8ZTVkMXffXGLcY/hs+Ge3HPbR1mRk3/Jc6kvb6NI/iL53RN90o9m/M2qKNOxfnouhzUL6pGiSBgX/pgRU/9ZbtCz/nJPznuId1XoUqjo+HbaUHv7fbZZrNo7F/fJRWn37UGVoYGK3TwhWq9maEo37NTWL2Wxm9BvbKDGp+GJWCv3jgmgxtTBr9yyq2utp9HuWP3kn0c3+JNkbWtDWeWCITMAhEpEmlRK+ajXZkV14NW4q8Z2C+Gp2Gkdq9vPU0afoZHZmRXUeDqdQXB4+jOha+pggCGR9W8nJzUX4+ooR11VQJwpGbW/EIHEHsZ0zYd+S5XMCXd1YhoWN5MWxXfBzvdGbymE2o1m7lubPlmNrbMSpWze871+Ac9++vzvZ/ytJvjfwkiAIw6/9/AyAIAhv/Njx/w0kX5HbzIlNRbTU6PGPdKP/1E6/erVoys+nedlntO3ejUgiwXX8BOq7jOb04TZkZi29fIrweHAa6zdvRq/XM1JVjWL1AawWCWtHq3Gv9CG5z1BWOgtk5kUj9TmOzHs3RvVg7oh5iOlrC3Gyitnf9AUbonpTaYvCe1g4DTYbL0sMFO7fw8iRI+nZsydfnSrj+W25vHNnMpNTAuDzEdBU0EHoLv5w/G/w7UswZRXEjb3xOoo0NH1+CWWcFxJFLnUvvIDfs8/CmEguZs0iwH8i8fFv/eJ4lGnLmLF7Bm4KN74a+RUeyp+ODPw5VFxpZt9nuQgOgYwZcUR3/2V5638iTDorB1bmUZbTRFSKD4PvjUem+G2+yBxmM6WTJ2Nv17Fo5INUeX+Ck0rPypFfXrdxFiw6zIs7IdhN4IjjnJMz93T9K93dnFmbHIXymsa/sKyKSUvPIJLI+fbJIfi6OlGnr2PGrhk0Gg20+P6JL2O7ICmfyZVNCoytzrRHJuAQiens6krCii+o8vDlha73EtYlkpWz0zhYtYdnjz1LF6sby6suYZMFoH70GOLv+eqUZjey7/MrKJQiQijnqsYfpU2Lq1MT9bZOGFUtHA3eSpFUgqRlEo8NTWJ6rzBk/9Cb4DCb0WzaRPOyz7DV1qJMSMBrzmxchg793TZo/5UkL6Vj43UwUE3HxuvdgiDk/tjxtzPJN1a2c3pLMRVXWnD1VtJ7YjRR3X65NCPYbOgOH6b16zXoT55E7OyM+9QpuEydwdF1hRQXWfFqzWPAKE8aU7qwfft2VEo54xp3Yd5VS5tczPKJaiLzvfHvMZBP7DW01A9F6nUOmd9mzKreJEU+zkPfXCHK4M2xxg18G6/g2+aBhI4Io8xhZ0mQK5fWfEVMTAxTp06lrs3E0HePkhLqzso5aYhOvg/7n4drBlE0F8PH6RA9BKauvuF6bK3XNlqdZXhM8qNs8kSUCQn4ffIm586PQyp1J63Hll9sYms2NjN913QMNgOrRq4ixPXmMl2hYwWXc7CKExsL8Qx0ZuSCRNx8bm9XSkEQyNpfyaktRXgGqRl1fyKuXr+NWsh46TJlU6diyxjKOPdUvDstxV2lYPWo1fiqOr44LUW7kK+aRn2QNz7VBjb73sWDcbMZ7ePGp13CkVy7H9bvP8nTB5qJ95az7bFhSMQiSrWlzPxmJlq7FKvP82xNDaQicxoFWwKxmVVoQzrjkEjxd3UldeMmTBY7r3SfhWtKMl/OTmN/5U6eP/E8XR0+LC29gFXqjfPDR5G6f2dy11jZzq6PcjAZbKSEa8m+LGAVK4hzOkutI5RWWyh1LqVkhh7maktvoly68MLYePrF/NBGXLBY0G7fTtOyZVjLK5D6+eExbRruU+5C6nFrC5Kfwr9aQjkKeI8OCeXngiC8/lPH3o4k31jRztmdpZTlNKFQSUkdFU7igOBfbGqyNTai2bSJ1nXrsdXWIvX3x2PaNDymTaVVC3veOYnWKCW69QR9/jSRk3V1nDx5klB3KcMufEHbOSmV3iI+n+BK0kVPaqOjueBso6FpNDLPHCT+a7E7JeMS/ARPZ5WQVuFNnuY0p+LPs7xmBgEZQZTJYXFMIHWb1mC321mwYAFOTk78YeV5jhc1su/RAYQ6qjrKNN8n9JXjoeYiLDzboZG/BsFqp+HjbGzNJnwWJlP37KMYLlwgYttmchufRduWRY/ULajVPx/gYbQZuW/vfRS2FrJ8+HKSfH5oj/BLsFsdHFlzlbyTtUQkezNkdvyvLpfdDijP7Xh6kUhFjJifSGC0+y//0a9A45IlNH30MYdnPsk7Fhse0cuIcAvnixFfXLd1Nm+6G/mlb6iPSsK/OIf3oz/g9aBEZgZ68Wan4GsBHgJPfLyZTRVKZvfw48XJHfx1pfkK9+6ajV7sjq/PC6zqJpBzYh5F26IRS1xo9AtHLFeglMnpfuYM3oVFvNf1Ttr6DuHLOWnsLt/CK6deoYcokPeLzmIXu6FceAi5T8T1a9Brzez6KIeGinZS05QUHy+hVRZImJBLiMsJzpimYLW5UeF+hUueWq60JDA0Pog/jY4jzOuH5ULBbkd39CitX61Cf/IkIrkc17Fj8LjzTpTJyb9JKee2b4YS7HZwOP7lWtXvo760jczdZdfJPXlwCEkZwT+rWXaYTLQfOIB2+3b0x0+A3Y5zejoed09DPXAgiCVc/OYqZ76pRGrR0905l8hn57Blzx7Ky8tJdW4g+fAudMVOXIiCdcNc6XnOi7NRMoyKOEq0/ZB75CIOWA3KWAy+T/BcYzODz8tpMddxNvILFjfejzQ5iEYvOS9FBeJ99ii5ubnce++9hIWFsetSLQ+svsCzo2KZ1zccPh8OzUUdTU8ufnBxdYfCZvS70OO+69cmCAKt6wswZDXgNTMe85Uj1D73HH5/+hOa9EZKy97/VRF+doedRYcXcbjyMO9lvMeg0EE3PTdmg5VdH1+iplBD6qhw0sZEIPp/8n75d0JrnZ5vPsqhvdnE4Hvj6NTjl7uJfwmCxULplKlYGxpYMPiPCH61aFw+oU9QHxZnLEYqloJRg21JAkaxAYkiEmVzJS/22s4ypYI/hvvzeETH+zAYDEz+61byTK4suyeFoYkdEZnn6s4xb+98jLIg+oa9wotheWSdfI2SndHI1F40eAXhpHbBZDLRub6BpIMH2RgzkKzhd/Pl3F58U7aJ18+8TndpCEsKToOgQjZvH07B3xmRWS12Dn6ZR9H5BiIT3ZGVXeZqWyAqm4YM3y+psfmSaZqIxKqiwa2SUzIpZQ5P5vSL4P6BUbg5/fh9bi4qomXVKrTbtiMYjcgjInCbOBG38eOQ+d2cZPr7+DmSl7z00ku3/I9/a3z66acvzZs376b/Tn/qFGVTpmIpKgKxCFlgICLp//+qzG53UHyhgcOr8jm7sxRju4XUkWEMvS+B0HgvpLIf1j8dJhO6Y8do/nQZtc89R9vObxDMZjymTiHgtdfwuncWishIDG1Wdv7lKHkX2/FqucKQwXKkU0ewat06NC1NjLUfJmzPRQzVCraki9jaT036WW9OddGhNI8jz5CMk2cBooBViJXRNHo/zoM2C4OOGRA5BHKDP+cL4120BgWjCVJxf4gPgzV1HDt2jIyMDJKTk9EarMz58hyRPs68OTkJ8ekPIGs1jPsAQtNA1whrp0JgCox6+wZ7WQsldZwAACAASURBVN2JGnRHq3AdGoYiTEzVAwtxSkpC/uAg8q8+R4D/JCIjH/3Z8RUEgb+c+ws7SnbwTNozjIsed9NzpGs1s33xRZoqdQyZHU/yoJD/2uBuJ7WcTmn+1BVrOyx5lRL8I/85h0SRRIJTSldav1rFQBcrn9pTGdo5miP1m9CYNfQL6odI5oTIIxxF1haqfSy46QTSyzOp6jSJz1o0+CmkJLuokMlkpIW4sPNiOTtzmxjfLRhXJxlB6iDi3Duxv2Q9pboc1J6z6RUkwio/TtNlBW5i0EoUBAQGUiY4qI+LZ9jp/XiUXuWvWm8eHTySMLcA1lbsINMvnhFNZZD5NcbA3ii9O8p+EomYqG4+SBUSLh+txe4ZQFqsnqpKG/mW/vhKDYx1/RM17jKMbbEktHsTJzFwvFjLB5kVIIKEIDek/1Cvl3p64jJwIB7TpyMPC8VSXo5202ZaVq4EAZzTbi0Z6uWXX6596aWXPv3RObkdVvKmvDxavviS9kOHcLS1IVKpUPfrh7p/P5x790YW+OuCJW4V2kYjV0/Xkney9rphVFJGMHHpAT9aArDW1aE/cZL2gwfRnziBYDIhVqtxGTYMt3HjUKX1uG7kJAgCBccrOfr1FWw2iNMdJ+2F6WQ2NnDo0CG8JAbGN23GdEyGxexgyRgJRQFK+l7wIzPBiH/TgxxCiZt3KVbf5UiVYdR7P8k9CjkTt+QTIg3hkvtKNrqFsd/WD1uiB5P9PHjB24nPli0jODiYGTNmIBaLeWZzDuszq9i2sA8J8jr4pB/EDO3YXBWJYON9cGVbR76nz3fSR1Oxhqbll1DGeuF5TyzVCxeiP32akI1fcKFuPlKp26+qw6/MXclbmW8xK34WT/R44qbnqbVOz/YlWZj1NkYuSCQk7j9H+/57wma1s//zK5RcbKTr0FDSJ0b90082TZ98QuN7i9k++RFWiMOYOvwSm4q/4onUJ5jVZRYIAo6vJyMUH6Q6KpjQwkoaHA/y6JhZHNYbWJ4QzshrXbHrdx/muSNthHsq+eaxIdc7UPfkfMMfLz6LVRHD4gFL8G98icLTmZQfCEQZGEKjqy8p3bqRnZ2NTCSiz+69mC1iVoxayLuPj+dc036eP/E8sYpA3r9yDmcHtI/4BL8+N/rHVOa3sO+zXOw2B/2HeZC3+Sw10kg87TUMDfwUsaSAj11m46hIxNPoj1Xq4ILETq2nlDkjY7ize/APyP77sJSXo922DaeUbqj73VqY/G1frvk7BKsV/dmztO/fT/uBA9gbmwCQhYXi3Ks3qtRUlAldkIeF3eCGdysw6ayUZDdy9XQdNYUaEEFIrAdJGSGEJXhdv0kEQcBaVYXxwgX0585hOHsOa0UFANKAAFwyMlAPHoRzjx6I/iEVRtdq5uBHp6mstOPSVk56khH/BXezbecOCouKSRAV0L8yk5bTUixKO8/dKcWoUNIj15OSGG/c6u9ln9SGX0A1es+lyOQB1Hk/zWBnVyZvOEQPeTeKFQc51qmIpbWzsHXzpp+nmhVxIXy5fDk6nY4FCxbg6urK6ZJmpn56uiOvdUQnWD4MWoq/K9MU7ofVd8CApyHjmevXYNOYaHj/ImKVDN+FXWnfv5uaPz6J71NPUdXtBC2tx0lN3YKL+kY/k3/E0aqjPHTwIQaFDOKdge/cdLNTfVkbO97PQiwWMfahrv/x+vffGg6HwLG1BVw+Wk3nXv4MmhH7TzlaClYrpXdNwdLQyKz+jxET7Y9f1Eb2le/j3YHvMjRsKGgqET5MpVltRyn1Q9XYRIXyM+4f1Ik8o4l1yVH0dFfjcDh45qP1rKtyYUqKL29O+U6WueHIOl4pex2HIo61I5ZgKrqf4hO1VJ/0Qh4SicbVm+EjRnD8+HHa29uJzb5EZEExKwbcy1OvzKVAd5InjzxJlCqQxZcv4uMwUdv9BcLG3/hU2d5iYs/SSzSUt9NtaDCy0hzO5zvhEEtJdjtHT6d3OOYbyifiJIKruhHekoQIETUSB/UeYgYPC2dyevgNFgm/Jf5rSP77EAQBc2EhhtOn0Z88heHcORzX4v/EajXK+HiUcXHIw8OQh4UhCw1DFuD/k0kwgiCgbTRSfqmZ0uxGago1CEJHiENs7wA6pXqjNGuxVlViqazEfLUAU34e5vyrOHQdrpRiNzdUqamoeqTi3LMnitjYHy0VCA6BS/uLOLWlFIfdQUzrMXo9PYl6D3e2bFyH0WhiuP0QgUUC7Reb0QbaeOxOBa4GJXHFrvhETya/IpqjShthoU00qT9AIfem1fcZYpQeTNqyjVHifjRJirjU4wteLXweQ3cfuriq2JISzeE9u8nMzOSee+4hJiYGk9XOqMXHsDoc7Ht0AE7nPuxQ00xeDol3gFkHH/UCmeoG6wLBaqfhkxxsTUZ8H+yKCD0lY8Yij4hA/OYICotfoVPM84SE3Puzc1msKWb6rumEuITcsIH3a1FXomXHkiyUahnjHul62ytobhWCIJC5q4yzO0qJTvVlyOx4JP8E0Rtzcym7awqN6YOZ6TWMv94Zy7b6FyhsLWTlyJXEesZet6O+GuVMTLkdi6UzFaFLmJ2goMlqY1u3aGKdnWhvb2f6O1vINnnx3l2JTOgWev08H29Zzodti5E4JbFzxF+ozJ1JyWGB+ixnJKHRGD39mDp1KsePH6e4uBi35hYyjhxhf0x/xix+mUYus+jQIkJVAbyTe4UwewvFofcRM+ftG+5Pm9XO0bUF5J2oJTDGnR49FZz+/DT18gjcHI0MDvwKF9FxPozqxlajlYTWPiS0DESqkeNAoEkBQfGejB0ZhX+Iy29aJrztSV4QhF8lRTQXF2O6fBnj5cuYLudiLihAMJu/O0gqRerpicTLC7GHF3qXQLRib5oc3jTZ3DHaO8jLRawjQFyDr6EIdUsxjtZWbM3NYPuu7VmkUqHs3BllXCyK2FickpJQdOr0i08QDRVtHP74NI2tUtw1BfROthH84CwOHdzNqYtX8KGZMWShOSxCXtdIUTcrfxqqJLTOmc5N/sS5LGSvRuCs0kZcJx0V0ndRyNywBT6PVOLOnfu2cqexB3aJmcI+L/Na0WtUdPEnwFnB7tRONBUXsn79etLT0xk2rMNb+519V3n/YBFf3ZdGP/fWDjXN98s0e56B0x/B7D0Q1vv6nLRuKMBwoWOjVRnnSfXDj6A7cgS/r98lq+lBPDx6k5y0/GfnTmPSMO2baRhtRtaOWYu/881tDtYWadjxQTYqFznjF6Xg4vnbhqvfjriwr5xTm4uJTPFh2H1drscU3goa3nmX5mXLWD7hMQ46h7PugS7cf3AmIpGINaPX4K3wQPhsELamXMpDXYkuaqbVupDq9Dnc425CIhKxo1sMQUo5eVcLmfnFBdpEzuxaNJAoHzUAgl3gtVVLWM9nqNSp7Bj6LHlZd1N+0JPGPBmERCH4hzBnzhyuXr3K3v37wWJlwLFjaBzOhC3+G2b/Zh459AjeCg/+UlRDorGCK87D6fTISmSKGz8z+adrObKmAIlExMBp0egOH+FsvgqbVEWMUz793N+lXGXntdAYsswNpCsy6K6dREu+gIuxg28FJwmhndwJj/UkIModzyDnf+oL9bYn+bJLTRz4Mg93XxXufk64+6lw8VTipJbj5CpD6SxHKhcjFouul1GsZjsWgwVjTSNtpbW0VTTS3qBHpxfQWpxoF1wQrrlLyq3teLSX4KEvw9tUjrPQjkguR+LujsTDA4mHB1IfH+QhwciCr70CA2+qJGRst3DiywtcvaRHZtURa71Aj+fuRqeWsmnNSur0IlJFuajanXDbk4NSbOHYMDMfJqroVO5Mf1EGsqZh7JVZyJbaSEowUiy8jVyiwj38FcqtLkw9sYc7G0LxUPhRmvYin2oe5rB/BC5qOXt6dMbDbOCTTz7B29ub2bNnI5VKya9rY8yS44xLDuTdOxO/1/R0tqNM8xPWBe0nqtHuKMF1SCiuQ8Jo272b6kWP4f3YIxQnb8VqbaVn2jfI5T9tnGV1WJm/fz7ZDdl8PuJzkn2Sb+pzUVPYyo4PclC7Kxj/aApqj38Pa+D/BGQfqOT4hkLCk7wZ8YeEWw4Td5hMlE6YiMVk5o60hxiRGsncwXJm7p5JrGcsy4cvR96Qh/BpBrX+StRWJS4tRuoMH1BzRy+mGprxV8jY3i0GD5mUdTv28cIJA35uKvY8PgiVvGPPy66z8Nja9zgo+wpv93Q29J9HzsVZVB6OoOmqgD04EmVYNPfddx9tbW0sW7seh7aVyIJCovOKcDz9Ai6DgnnwwIOIEPFmvY3erblcscUT9OAm3Pxv3NfT1BvYtzyXxop2EvoHkZyi4PjiA5QLYcgdRlJ9T5Eo/ZgdvoH8zc2ZVruRURGjSFfezdED7ZhqDITYJbg4OvhIIhXTfWQYPUZH/GAMfw1ue5K/mF3P3m1FBEikONqsGLSWWzq/SCzC2V2OZ4Aa7+BrrxA17n6q302BYbPaydlVQOaeCmx2MSFNp+gxIQafSSM4u+1jDhQakGMhWlxL1SkYWpoJ3g6+GmNjR4CK5BJ3xjg9QE2ZL3u8HRTYLKR2M5NnehO51JnOnV7jaLuKSZdPMjHPTGfXVKriPmafSx8+lvRA6ipne/dOJDkrWLFiBY2NjSxYsAAPDw/sDoHJH5+kosXAt48NwDP70w5vmr83PdmtP2pdYC7R0PjZJZSdPfGaEY9d00rJ6DHIgoMxvxxLdd1quiavwMur/0+OiyAIvHr6VTYUbODPff/M2KixP3nsj6H6ais7P8zGxVPJ+EUpOLv9j+BvFpePVHFkTUEH0c9PuOWVpiEzk/LpMygZMJaFHgNYPbcnOul5njjyBBOiJ/BK+iuI9j8PJ9/nUpyaLoUWbOIkGo0vUzSrEzOqakl2UbGuaxQKBP704WrW1HgyuosPH0z/zh/GUtnO7P2LyZGsI9xnAMtSx3M5+wGqjyTSVGjBEhSJV2wCs2bNQiQS8c7mbZjzc1G16ehz8iTmAaMIf3IqDxx+kBZTC6/aghheepAyoy+SqSsJSel9w3XZbQ5Obysha/93Xkf6s2c5saMKjVMIbo4m+gTswFu8gxVB0axUOHAAd8fezSD/aWw+38K+c9V4GiFJ5UTvXoFMHBNzS2N820sos5t0vHWpgn1GPUUeYqL7BpAxPILu/YIIT/QmqLMHIbGehMR7EtzZg5A4TyK7+hCT6kvnXgEkDgwmbUwE6ROjSBkaRuee/oTEeeIVpMZJLf9dCN5ud5D7bQm7l2RSWmDAvbWAftF1pL0yC5PuLOvWr+disxJ3oZW8JjdSj16ia10B4lgDr0yQcsxDSZ+yEPq0P0Jtswc7AwTKrVb6pFu5pPsLSpmaQYlvsV2jYHBpLsPPFdDVcyDNQfu5GqXiL8YhCO5yViZFku7hwsGDB7l8+TKTJk0iNLSj3rnyZBlrz1XyxqREUlTNsGEWxAyDwS90lGlOLoFL62HiJ9fzWm0aE02fXUbipsB7dgJimYTaF1/EdOUK6jfnUNKyhNCQ+wgOnv6z47Mmfw1Lc5YyJ2FOhxrjJlBXqmXH+9m4eDkx4bFu/yP4W4RvuCtOahnZByrRNhiIuAnzvO9DFhiIvaUFp282UxOTzMZKC38aloFEDKvyVuEsc6Zr9/kIOetx01opCRLj11SGIPfGNd+P5AFhfFrbTL7eyDhfT3p0DiMzM5MjtWJ81DKSQjq6RyVuCoZZotnbYqFCv4vMdgNT4mdiVa/Drg3HXNFAm9lKWUMTSUlJDExOIkemor6+loqIMJyvXkZYu4fZM17hlP4S600leIWPoHfjWazZWyjWuuIb1+36GIjFIkLjPfGPcKUws56cg1W4xoYz+P4eKAoyqWxSkG/qTa2+H8PtZUxvu4TG2ZP1zRfYV7WFnlFqXhgzDKWnC3ubtURGutM97NYUX7e9hFIQBPRmKwfyG9l0oZrjhY04BIj1d2FYF3+GxfvRJdD130IPbbPayT9aTua2AvQWOa7aEhI8a4heMIaGvM2U513khJCCSBCowp2eWh1p+zdiUTpQ9tLwUIoHzWIJw0u7EdwwFau/K2tlRnQWG2n9LJytfQ0nmRtTU9/l7So7qQ3lTPhmJ0MD7sbkXkJp70M80PAgBh8lb0QGMjvMl6KiIlatWkX37t0ZO7ZjxVyjMTL03SN0D/fky1ndEH0xuiPpaeHZDm+alpIOn/jvdbresNG6sCsyXxW64yeonDsX9/mzKEzdiEIRQI/UjYjFP028J2tO8sC3D9AvqB+LBy2+KSVNU5WOre9eQOEsY9IT/yP43wJ/r9HHpQeQMT32luSVdp2OkrHjMEvlTOx6P3MHd+aPwzvxxJEnOFBxgA8GfUA/owlW30FFpB+uGj1ubQ7qTB8iDY9hx3B/ni2q5p4AT97uHMKly7ksWJNDA25sXdj3Br/3uo1XucO2Ba15Ncl+fXguOoqq4qXUHOpFc1kbhsAIwlJSmTZtGlKplOXFlXy7dy+dG6pQa9tIPX+B6PlzeMn7CGfqzjIvcAjzTqzCZhO46DqF1IVvonRW33B9Jr2V4xsKuXq6Dq8gNYNnxeEiN3Hub9vJa/TGKnchQFxJD++NmBSnWOofyj6pDaVUyZTOU5gVPwtXuectq29u+5V8VsNF5h+4jwgfBY8N7Muc9E74uSqpajWyNaua1Wcq2Hi+iuJGHQaLHU9nOc6K/99mKZPeyvmtV9i3NJviy+0oNdXESrOR9nPD4Xwe2cUP+bbJh8t0RuKkJjyxG332rqTz6TNowyzYhrSxINYbi13K2CujCGibjKyXP0u1LUglYlL6mzhb/SrOcg8eTv+AV8otxGobGbN1FUMCpiJy0lPbZx2Pt/yRVm8Fc709ebxTIFqtllWrVuHh4cGUKVOQSCQIgsCidVlUtBj5YnYP3C59Dhe+gLFLILQXCAJsnAPt9XDPBlC4dGy0birEXNCK191xKCLccBgMVM5fgNTLi+YZ7RgtlaR0/RKF4oc+H39HeVs58/fPJ9gl+KZtgzX1Bra+dxGpTMyE/22y/mYIiHK/7vNjNtgI7eJ50wsmsVyOIjKC9tWriA105e0GF4Z38WdS7BCOVh1lc+FmBqXMxUPXhGtRFvnRSnwbjai869GWpZLspEQa7c6yqiYcAkyOjULVVs6JKit78xq4MzUU5bVmQ+cYT/qcVLPFw43a1h0UWZwYGpqKyGM/1pZo7NX1NOqN1Gm0xMfH093LHUtQGKtMIgJ0WirDQtCfOM2kMjWi7vGsrNtHaewQ+jbXE208Tea+b5FG9MbF67v9JKlcQmRXH3xCXSg637GqF2QK0uYNpEuiEsuFs1Qa/cgz9ae1rT9jTBruNmbRKlewqSWHr/O/RiVT0tX3h9GYvwY/t5K/LUi+oeQguSW72Vx/mtV5q2i21DA8thML+3dnRq8won3VtBltHMhrYFtWDcuOlbAzp5b8unbq28w4BAF3lexnGxZuBYIgUFus4eQX5zm8ppDqUhNuzVdxNWbS4FdKtPcherRv5oo5gD2iDMxyT6LSo5EU7aHrR2twatGh6mvnUpqBZwK8UOudGHPpPqJ8BtPW15t3s8oJ93YmsKeWrIpXcVF48/KAj3ms2ECAvo3x6z9hiP8k1DIXGtJX8IrtWYpcZQxTOLGkeyQOh4Ovv/6a9vZ2ZsyYgYtLh3b8m0u1fHiomKdHxpLh0w7rZ3as2Ie81FGmyVkPp96H4X/uSIAC9KdqaT9chcvgUNS9OvxqGt57D/2RI0j/NJRadtG508s/W4c3WA3M2z8Pk83E8uHL8Xb69WlG7S0mtv7tAoJdYMKiFNx9/yeT/C0R1Mkdi9lOzsEqHA6B4FsIUZGHhWGtrMTj251cCk/m23ob96RFMiC4P1uKtnCo4hBjB72B4uJq3G2elPia8K0uQRIRQdtFdwZEe9PkIWdZVRPecilTU7pQf+UUZ1qcyKvRMD6lw/dGJBHhHuNBtwMKtgb6UN+8k1KbKwMCY5F6H8HWFI29ppaGdj1NOgOxsbEkuTrj4+3NOwpPPHQmbGo5JWIx3fcWMjCsF5+ZD3M6NI6eEj8SzZlUn9hOudGbgE5dbvjC8/BTEZcegLHdwqXD1RScqcMzJoCU2f2JS1AiXDpHXZuKAmsfajTDSNe6M9OQhV1qIkHuTmTU8Fuan9u+XNN8/Fuy159B7bybfQHV7HB1wSASiHIJY3T0eEZFjiJIHYTN7uByTRunS5o5VdzMhYpW2k0dskeJWESUjzPhXs6EeakI9VQR6O6Ep7P8+ksllyL5h0dVQRCw2gV0ZhstejPNOgu11ToazlVgL9TiEFRIbCa8mi5ipBjfyHIGuF5CgZkz8gEcFbpjsgnIwuVcat/PnVvriKsCfawn0QmV/DlAzm61E6GN3oysfpTeY1L4urmFTReqGRLvR21IIeUVb+GuCuDdjKXMymtFYtBx19rFDHLLIFwZT33Kl3wa8ADb7GLirWIODE1EJBKxd+9eTp06xeTJk0lMTARAa7Ay+N0jBLgp2bKgF9KvxkJ9Liw83ZF6b2iBD1LBMxLm7AWxBHOJtmOjtZMHXjPjEYlFmK5cofTOu1CNHUjx8P14ew8hMeGDn1wBCoLAk0efZF/5Pj4e8jHpgem/ev5NOiub3jqPQWtmwmPd/tfo9DtBEAQOr8rnyola+k/tROLA4Jv+H7bWjk14vbs3kxPn8sK4BO7tE0FmXSZ/2PcH+gb3ZbFrd8Q7H6EsORG3igLcdRKa3VZgrnfBY0ESC5ob2d/cxrIu4fQUWXn4g82cMgfz1IjO3D8w+vq5zGVaDq3L5aG4iyhbPyXZJ4kH/EW015+m7lBfmiubMASEkzhgMGPHjkUsFnOkpZ05l0sJrW2kx+UzqCQW/OrqSLC280ZqIQZPFW+7dKP72c9pMSvJdJ5M7wUv4er9w6fT2mItR9ZcpblKR2i8J33visHD3xlzbT25n+/lap6FFnUkCA4CxKWk9FcTMW3aLc3Nba+uyf5oOyeyFAhiGQqHjiBxNq3ep9gXWMF55w5S6eoWTUbkaPqHDCTKPeq6011li5HcGi25NW3k17VR3mygosWA2eb40XOJRSCXipGKxVjsDiw2ByIB/O0ikgwmOlvEKKQdyTtu2mLc9TkE+10hNuAKSrEBh5MXJcGT2NnohUZjRqfSkelyioyLWiadFBArlQROiKdd2MVDvr6UKKR0L+3G3eFP0Hl4OI9uzeFihYZ5A6PYIz1GS/UH+LvG8MmQj7j7ciPNOj1TN3xEH2VnklR9aYnay87USSzRiPHR2ckc2RWFVEJeXh7r1q2jR48ejB49+vr1Pb0phw3nr1kXVK2F3U/C+I8g5Z6OA7Y+ADnrYP5R8OuCTWPusA5WSvF9sCtipRTBZqNsylSsdXU0vyzD7mShZ9o3yGQ/7XT4d8uCR7o9wtzEub967m0WO9sXZ9FQ3s64R7oSGPPbuCn+Dz8Oh93B7qWXKbvUxMh5iUSm/HTp7aeg3fkNNU88wf7B97DUK5X9jw0g0N2J1Xmr+cvZv7Aw+QEWXNiO0JDL+QQ1KRfrEAX2pr7ueZBJcFmQxNTCcnLajaxNjsK5opiH1+VQKXiy5g+96Bnpdf1c+nN17DxSwtOxOaibP6GTRxQP+IFDc4W6I/1pKKnBGBBBypDhjBw5ErFYTFabgXtyShCMVlIvXCFcV4BEJBBVVka5XwWbu2h4JHIcU4+tQGTWcbw1Fp+JL9MlY+gPFjEOu4NLR6o5u70Eq8VBfJ8AeoyJwNlNgWC1UrP9IJd351NhDyHGX8fAN2be0rzc9iQvOBxoz16kaOd5ykotNKmisEsUiAQ7ro5qrPLLZPtVkenbgFbZRJDMmd5eCXQL7k/3sEEEqANvmBxBEGhsN1OjNdGqt9Cst9CiN2O0OLDa7FgNNmi14NyoRVmnw2ZUIIikIDhw05bgb7pMtPMFAv2vIHex43ALpSyiJyeUoeQXmJG1yNBL9OR75JFuVDBqZy3y+lZcMtLxibjMaaGMJ719sAtSJmnmMGfSLEoEKw+vyUJvtvHMhHjeb9yEqf5zory68cng95l5uZa8Nh13bVtOmsOFdJdR6L0vc2pMGs9US3Fqs3FyUAIBrk60tLSwdOlSvLy8mDNnDtJrZm5/ty6Y3z+SZ3opOzzhw/p01N1FIig5AivHQd/HYMiLCFYHDUuzsTV0dLTKrpVImld8QcObbyI8kUpt5Cm6pazGw6PnT87fubpz/GHfHxgYMpC/Dfzbr673Cg6BvZ9dpvhiI8PnJty2YR//brBa7Gz7W4fJ27hHu960TbEgCFTOm4/+/HnmZjxOXFIMy2Z28NNzx59jZ8lOPkh9mv6bHsYU1ZMy4SKxRXps6W9QdyQJRaQb4ntimZBdRL3FyraUGHIPHOSV0yYkSmf2LhqI7/dSmzQ7S1hbUs/r0YV4Ni8hSOXDQj8BhbGK+iMDqCuqwBgQTvKgYYwePRqxWEyJwcyU7GKaTFb6luuQ5Z8hVKFFarMRWl3C7ohc/Hqn80JlOZ6VJylu9yTf+076z38SF88flhkNbRYyd5WRe7QasVRE8uAQUoaFoXC6lohVVY1DkOAUcmtOoLc9yX8fgiCgy7pE+b4LVBdoaDSqaVOHIYg7NmXEDjMioZZ2qYY2hQaNSotDacBLIcHb2ZlANx/85D64iD2QCc6YdQ50LWbatXb0OhFtRgVmvgtZUOlr8WwvxM+eS4jrJSTBjVQF+lLhHUGZixe5YhtljQ2ENYURog/BJrYhjhLTJyCCmK+OYDl1FkV0ND4TuiCpXsbb7m5scHfG2+DF693fo2f3JD46XMzfvi0gwtuZB8bF8mL+p4haNtDVvz/vZ7zNvZcrOavRMX7PanobrAzwGINNriF3SiDzq5wRt1nZ0i2GtFAPrFYry5cvR6PRMH/+fDyuhRf83brA5hDY+3BfnNZOhNpseOA0uAWB1dRB7VZFiAAAIABJREFU+oIDHjiFIFXSurEQw/l6vGbE4dSl44NtqaqmZOxYpF3DKZ+eTXj4A0RFPf6T81Wnr2PKzim4yl1ZM3oNarn6J4/9RxzfUEj2gUr63BFN1yGhv/wH/8NvBqPOwqa/nsekszLpj93xDLi52EVLVRUlY8bS1CmJ6RF38uE93RmdFIDJZmLm7plUtVexxnsAYSc/oqL/WJxz9uCpl2Lsu5OW3UbUA4LRZQQx9kIhDkFgS3IkX3+xli+qfIgNcGXjA32vb8QKdoGmL3NZatPzSWgFvs3v4iZTsNAPPKwa6o/2p/ZqCcaAcBIzhl4v3dSbrUzPKSG33cAYg5TMk1cZ7shHobShNBpRNV/ldJqB52L6EHf0fUxWEQeauxA07nG6DhuF+EcsUrSNBs5sK6EwswGFSkriwGCSBgXjpJb/4NibwW1P8ia9lbKcJqK7+/4gl9NhMtF+PpuGC8U0ljTR0mSlzeKEWe6GWeGOXfrLqTgihxWFWYPSrEFubkRMFVZFFRr3Oioj7BT5y2mWQKvDgkX4ztrAy+pFN303XFtdEUvFxCXHMSy+G/oVK9Bu2YpYpcJz5h3I9ZtotBfziJcvFU4ShqmG8/qE19GbYdH6bI4WNDK+ayDdewXw2vk3Uei+ZWDYaP7a91X+kFvBgeY2Rh3YSLq2mUG+oxFZJRRPUXBvgxc2nZU3Any5t0cYADt27OD8+fNMmzaNzp2/c4q8wbqgdSvseqLDQrjbjI4DDr4GR9+CGVshKgPdqRo024pxGRSC27Bw4LsVmuF8Jo0v2FEGx9C92xrE4h/31rbYLczeO5ui1iK+Hv01Ue5Rv3rO/96RmZQRTN+7Yv4t5LH/bdA2Gtn01nkkUhF3PJV603LV5uWf0/DWW3w5fD7f+iRw4LEBuKlkVOuqmbpzKt5KT1ZXVuFkt3AxwZ2kU5cRB/dC674Y/Zl6PKfFUh6lZuLFIlykElZG+fLe0g3s1YcyPrkjXP7vnwuHyUb9h1m86QcbfOoIbnkbiWDmfn8JQQ4dDScGUn2lCJNvMHEZw5gwYQJisRi93c6DVyrY3aRltMyJqyeqEWnqmCgtpE0iIDebQF+E37A47indg7K5gHytDznyDPre9ziBnX7ceK+hvI3MXWWUZjchlYvp0jeIrkNDUHvcmiLstif5KydqOPRVPgqVlNj0ABL6BeHu99PqCsFux9bUjK2uFkNFHcbmNqwGM2aDCa2mAZ1Vg0nQoRd0tCh0tKgNtKgFGp0dNKgciCUypFIFUrEUhUSBh9IDD4UHnkpPfFW+qLVqmq42UVVWhUKhoGfPnqR26oTxq69o/XoNAE5jJ2BXVxNs3syXLi585OWGk9iZv2T8lf6h/Tl0tYGnNuagMVp5cWw8V92trM16GYUpi6lxs3gqdREPXqlga6OWIUe3k6GtY6B/f2StvhRPEpij88ZksDJTcOLNMV0AyMnJYfPmzfTp04ehQ4deH4+rde2MXnKsw7pgmDt8lN4hlZy+qaNM05DX4VeTcAdMWoq5VEvjsksoY9zxmtXlum7677VW84xANH2b6Jm2Ayenn15hv3rqVdYXrOedAe8wLHzYr57v4gsN7Fl2mchkH4bPS0D8Xxj48e+ChvI2trx9Aa9gNRMeS/nRzISfgmCzUXrnXZgaGpmS/iije8Xwl8kdTXWnak6x4NsFDPHuyttnt2LpMZ1SzQ5iC7U4RrxDU1YPrNU6fB7oyhVnEXdmFeEtl/Kuu4Q3Vx0hyxbE0yNjWTDgu4WDtclI7YdZvJCgYJ9rM5Gt72CyNDLLR0GC3EDruRGUnr+M2cufmIzhTJo0CYlEgkMQeKOklvcrGkh3dsItr41DeQ1M8jHQuSWLWpkMqcWC0lbH8FQvuuR9jsUu4khdOELy3aRPmY6r94+XEptrdFzcW0HBuXoSBwTRb8rPp6L9FG57khcEgZoCDZeOVFOa1YjDIRDUyZ2YHn5EpfiiVP/+iVEGg4Hs7GzOnj1La2srarWatLQ0UkJDMaxZQ+uGjQgmE45eQ2nzdyFO9CVNKgNPePhR7CymX0BfXun3KiqJO69/k8fqMxV08lPz6uREFjeVcCH/ZWTWCp5Je4ZpsVP4Y145q+o19Duzj1HaGvoEdUFVFUvJCAv3iXzQ6S30a3Lw9T2pSCViGhoaWLZsGQEBAcyaNQvJtUdJh0Ng8icnKW828O2j/fDcdAfUZHWoadyCweGAFSOgqRAePIfN5tJhHayU4ruwK+JrNUW7RkPxqNE4fKRUP1RFfMI7BPhP+Mnx2lq0ledPPM/shNk81v2xXz3OjRXtbH7rfAepLEr5wZPb//D/j6LzDexddpmYHn4MnRN/U09VxkuXKJsylZLew1joM4Sv/9CT9KiO0t+Kyyt49/y7LFJGMCf/OLXjFiE/shgPvRhh1knqVzYjkorxe7Ar560WpmQXE6yQs0hXw+ID1ZQ7PFl+byqDYr9LXDIVa6hbcZlne6o55NRGsv5jqjWXmejlRIazBUPeZPKPnMHi5k34wGHcceedyK4lzq2tbeaPV6sIUkgZb5Sx4kAxaqWUF7oI1J/ZQ51CjSAS4enQM9itlDjdt9QZXTnaFENAxnR6TrgLpfrHy5FtTUYkMvEtN+/d9iT/fei1ZvJO1JB/ug5tgxGxWERIvCcRyd6ExHv+ZqHFADabjYKCArKzsyksLMThcBAaGkpaWhoRgGblStq+2YUgQHt0b+q9Qujutw4vp2o+dPbkK281Lor/Y+89w6sqs/f/z+k1vfdGQu8dKUqV3rsUQVCBEbuijmIfu44oXVERBOlVkN5LaCEJSQjpvef0uvf/RWgR1OjM/P7fcbivKy/gPPs5+zx7n3WevdZ938uTl7q8zIPRD3I+r4Zn1l8kt8rCrB6xDOgazmOXTmAueBc1Zj7p9SE9w3vyWlouy0pq6HThCBOsZbSN8MEjtRO53Z084hVArdlBXKaFnY90wVOtwGq1snz5cux2O48++iienp43P8O3J3N4dWsKn4xvzUjXHtj5NAz9rM5wDODsyrr/G7EEsfl4ypYl4Sq1EDi3NYqgW3nYopdepnbrVspfdODXbgjNm3/8q+uWWpnKlF1TaBvYliX9ltS1hGsAzLV2Nvyj7v4Yu6AjWs9/LY95D/8+JO7O4fTWLDoNjfnDJlsl77xD9XereW/wM+QExfLTkz1RK+pEec8deY6fc35mcbWVrtpQUpsH0vjAXghrj7vPRsqXJaOK9cL/4RacrDUzOekaMRoVI9LO8U2aEqtcz9a53YkPukWrtVwoo2R9Os/38uK40sb9zu9JLjnAfZ5KxvoISAumcn77fpx6bwK73s+khx5Cra5Lo5ytNTMrOYcal4un/PzYdzCHy4W1jGgTysPBFZz6cTVVHiHY1Wo0bgetyKCd7DxVtVLOmJoT3388bQcORaP/99J8/6eC/A2IokhFvomriaVkJpZhrLIB4B2kJeK630RgtCdeAZo/tvOwWsnMzCQ9PZ2rV69it9vR6/W0bNmSlgkJcPAUtRvWQ1YabrmKwuBuGCPDaRu4mVh1BjtVHnwUGEiF3MnA6IG82PlFFHjw0d4MvjmZQ6iXhg/HtuKaRsKrl3aiq/gCL4WGpf2+oJlvM15NzmR5hZnWKWd4TKwmPtSE99n+5LdyMTsygGqLA+9L1eyc0YUoPx2CILB27VquXbvGtGnTiIqKuvlZimut9Pv4CG0jvfl2RACSJd0homNd3l0iAUMxfNEJQtsiTtlC9aZMLIml+D3UFE2LWwwC86lT5E1/GOsgLdYxejp32oFcfvebuMZWw/gd4xEQWDdkHb7qholqXE43Wz6+QGWhiVHPtScg4h4X/v8SRFFk/6orpJ8uof8jzYnv0PB+pW6TmawhQ7CptIxsPZtZvRvzwoN1uWyL08LkXZMpNxayLieToPtfJKtgBQlppQgD38MqHUH1xqt49ArHa2AMh6oMTE3KprFWSYej+9laGYGflwfb5nXHR3drU2A4mEfZz7k83ceHc1IHYxT7OXDtW5po5MwMFPCsfpSTP+zGrdXh0bYbU2bMQH99F17ucPJYSi7Ha0xMDvYltMDK4oPX8NMrWTi0OfKqvaSu3YBKCKY0OARRKsXPUUVj+TWkxlpya31o1Hsk7QcNR+/rd9c1+aP4nwzyt0MURapLLOSnVpGXWknR1RpcjjoevEorxy9Mj1egBq8ADV4BWjQeCtQ6BSqtHFHiprCogLz8PPLz8yksKkAQBNQqDaGBUQRoQtFl5KM4fwR91hnkLhtmbTDFET1Qt1DRRLqBCFkWSTI1/wiK4LLKTiPvRizotICOwR3Zk1LCa9tSKDPamdolijl94nkzr5idGd+hr/2RWO9GLO6ziBBdCAsSk1llctMq9SxPezgJ8UnD98RIimOkPNrcjwqrE/npctZMaE/XuLqb58CBAxw5coTBgwfTsWPHemsy69tzHMssZ+/87kRuG1cneppzoi5NA3VK14w98PgJTFc11Gy5hscDEXgNiL45j2CzkTV8OA57BSULjLTv8gNeXu3ueh3cgpvH9z1OYmki3w78lhb+LRp8/W4EkAcfbUFc23tUyf+LcDsFtnxygfJ8IyOfbkdQjOfvH3QdxgMHKZgzh8S+41no2Ynt87rTLLTu+DxDHhN2TCDc6eTbggIsEz9B3DYPHxNI5yZSfciJ+XQJvpOaoG0VwN6KWmYkZ9NMJSdm3372WeLoEO3HdzM73/SGEUWRms2ZlJ0rYX5/X1JEF496pbIx+X38ZCIzA1zEM5+jq3bikspQNG/P1NmP4etbtylxCSLvZdfl6VvqNTzl48M/d6SRVmKkd5NAnn8wil3JX2DYsJ2EqnCq/cOp8PdHlErRCFb8nGWYjG5Co5vQecAgIlu0/pfIA3/5IJ+ceJafd+8kNDyCuBYtCQ4JxdvbG61Wi/Qunu6CW6Cq2EJZjoHSHANVRSaqKgyYLUYEmR2X3IJbbsalMOOWWUACiCB3eqBweKM1awksLcC/Ko2AiksoXGbcCg2Opl0Qu3VBLTuDf+kuguSVJMvVfB4UxwmlEU+lJ/PazmNswliyyq28s+sKh9LLaRriybujWuLwkDMvJYOaoi9RWU7TP2oAb973Bhq5hicPn2adqKbVlUQWxnqgEncTcGwyJcFq5rT3pdzmRDxZynv9mjKxU12x84bgqW3btgwbNqzeTbT7cjGPf3+elwY1YbZ8F+x9BUYsgTbXFXfpu2HtBOj9d+wRs+5aaAUo++RTKpcupeIJJ2ED5xMb87dfvU6fnf+MFZdXsLDrQkYnjP7Vcb/EDYOsP5MKuIf/t7AYHGx4LxG3S2DcSx3/UI654In5GA8d4pmBLyCPiGTT491uWo0cKTjC3P1zGWax85auGdkt44ncsRwhuAWK6YcoX5GCs8hUZ4wXrGNHWQ2PpuaQIBUJPnCSk7YoRrUN46Nxt4Kp6Bap/DaF8qxq5g/w44rLyfMh1Wy48CpmZw2TfV3093+eg0t/wmazIcY1Y8Lsx4mIiLh5znsqankqLQ+rW+CV2FCEbAOf7ruKSxCZ+0AjercSWHLxn1QdP8SDqXoCjL4UBwVTHhSAVVNHDpG6nSgEgTZt2jBw7Pg/te5/+SB/bMkHnM4uxqTWI/7CsVCr1aLV1nV+l8lkSKVSJBIJTqcTh8OBw+HAYrHguq2rE4BO44GXzhe92ht/iRK/yiqUeVeRZV5GkpsBooBEq0Pbqxe0b0OtKw/p1R3ESDLQyl2cVviyOjyWw5SjVWiZ0mwKU5pNwW5X8cm+DH44k4dOJWd+n3gmdYnk07wyvsxKxrfyn2DPY367+cxoMQOA2Tv2sV0fQKv0C3zaMQxjxVKCjz1Csa8Hczp5U+1w4T5ZxiMtw3l1aDMAysvLWb58OQEBAUyfPv1m8Qig1uqk38eHCfBQsXWcH/Ll999yk5RIwG6EL7qA2hPX+L2ULU6pK7TOaY1Ue2seW3o62aNHY+0oIsxrTru2a5D+Sn59f95+njz4JKPjR7Ow28IGX9ucpAp2Lk6iUbtA+j/S/B5V8r8AFQUmNr6fiH+4ByOebtvgzlLO0jKyBg/GFJ3A6LiJvDKkGY/0iL35+uKLi/ny0pe8XFHFuD7/ICfzU2KTM3H2X4i0xVxKF124WYiVahXsKKvhsdQcItwO/I8mcdkWwhN94nm63y0Gi2B3U74sieoKC88M8OWiw847MRp+uvwqyZWp9PV08Wij5zm+8hQ1JUXYQ6IZOmP2TRsQgFK7kyfT8jhYZaSvnycvBAew+OcMdl0uIdhTzdP9EogKL+HTcx9ztfgyvQu8GJ7tgyq9jHIfXyoC/Knx9yFGJzLkrUV/as3/8kHe+P3HlHy8HIdFgkmvx+jhgdFbh0GjxerhheDtjVSnQ6bRItWoQSZHqVCglCtQKORo5HL0Uik6UUTndKKvrERSUIgjPx9HVhbumpq6N1IoUDVritgkAYOfJxZTOpqKRGLVxXgrbdhEKTuDmrExwJvLljy0ci2Tmk5ievPpCC4NXx3P5uvjOdicbh7qEsUTfeLJcTl5Nj2fa2WH8K35Go1Mxvs936d7WHecTicTN2znWHAsrTMvs/T+cIpy3iXkxFwKtf7M7eKJ2S3gPFHKoEg/Fk1qh0wqwWazsXz5cmw2G7Nnz8bLy6veer20+TI/nMlj6+NdaLl7JNQW1DXk1l+XqO9+EU4vQZy2m7Id6nrWwTcgut3kTJyENTuF8oVSOvXehUZzdy+T7NpsJu6cSIxnDN8M/AalrGEF06piMxveS8Q7UMvIZ9uhuMek+a/B1cRS9q5IoUXPMHpNavz7B1xH9dq1lLz+BrsGz2a5tgl7n+xFpF/dfSeIAn/bP48TBUf5uspM/OQ12H8YhpfBhXRuIg6TP+VLk1DFeeM/ve6Jc09FLbOSc/C3mfE6lUW2zYcPxrRibIdbu3G3yUH50iQMZgfP9fflrNXGh42Dycj+go2ZW0hQuVnQYhZZmyvIT76EwzuAzuMm80DvPrd4+KLIV4UVvHmtCLVUymtxocTZ4N3daVzMr6FxkAdP9YtH55XJypSVnCs9R4DUi+muTnRLLkeWeAnPPt3we3XJn1rvv3yQzzfk8/2V1fS3e9Po1AlsF87iKLfhMMlxmOSIrj+++xO9vXD7+ODy9sTsqcestIGkFC+hhBC1kVCtAYVUwI2MiwHN+Dk8hj22PCpsVUR4RDCpySSGNxqO3aFk+dEsVp/MxexwM7BFMM8NaIy/j4Z3s4pZlV9IgGENouEArQJa8X7P9wnTh1FWWsrk3Qe5HNWEbrmp/LOPD1lXXif87LPkyUOZ19UDFyLO46W08dbx/SOdUStk9QqtU6dOJTo6ut7nOpNdxbilJ5nVI4aXtVvh8D9g3HfQbFjdgMLzsKIPYvsZVJtmYblYjt/UZmia1S8QVa3+ntK33qJ6movYGR8THHT3zk1mp5lJOydRbatm/dD1De7R6rC6+PEfidgtTsYu6HjPNvi/ECc2ZnLh5zwemNKEZveF/v4B1FmU5E5+CFtWNtPvf4b4hAi+nXGr+1OtvZaJ20ZjMxSx3rMDzladCFj/Ci6/aNSPncOUWEbNpkw87o/A68FoAA5UGph+ORtPixGPxFLKbRpWPdyJ7vG3yAOuWjvliy9hcbt5oZ8fx80W3ksIR2vazzun30ElcfO3Rt0JzezAuR1bcKu1RPYZxKgJk1Aqb21aMi02nk3L51StmW7eej5ICCc9q5r3f0ojp9JCQpCeOfc3IjyklG9TV3G44DCCKNAttBvTmk6hW3j3P7XW/7EgL5FIxgILgaZAJ1EUE297bQEwE3ADT4iiuOf35vuzQX5Pzh5eOvoSDsFBoCaQPpF96K6PpEN1KZqc47iuncddY0RwSHE7pNicGqyCEptTgsMFTmS45RJQgKgQkWsE1EoXapkLL4UNL6UdmaSuUCsixeIZzcXIlpzw9OCIKYccYx5yqZyeYT0ZGT+SHmE9SCky8u3JXLZdKsLpFhjaKpR5vRvRKFDP5tJq3rhWRKUph8jaJRitucxoMYN5beehkCq4ePYM81KyyYxqzPDKfF7uVMu1Kx8Sff7vZElCmNdVj0wqQTxZRpBUxsbHu91kDtxwlhw0aBCdOnWqt052V511gd0lsHe8J9pv+kPLMTDqukOpywHLHwBLJca2m6ndW4Fnvyg8+9QXNDlLSrg2aCDWKAvKt4bQvPlHd70uoijyzOFn2J+3n2X9ltE55Nf9a3553J5lyWRdqmD4/DaENfb5I7fDPfwfgSCI7Pj8IoVXaxj5TDuCY7x+/yDAlpFB9qjRVHTqxZTAgXw8rjWj2t16SkyvSmfKjvE0tZpZ3uszSjI/IzLxFPbuj6Hq+x7Vm6/WFWInN0Hbsu7p9EiVkalJ19CYjGgu1uBwKtnweFeaBN8qDjsrrJQvuYRdLuGVvr7sN5p5NjqY4V61PLl/JnmWWgYEhDDN61n2LV6CSxBQN2/HpMfm4ud3axMkiCJriqt441ohdkHk0fAAHg8P4HBqGV8eyiSj1ESEr4apXaLp0VTBwaIdbLq6ifGNx/8hc77b8Z8M8k0BAVgKPHsjyEskkmbAWqATEArsAxJEUXT/1nx/2tbA6aaotoY0w2n25u7lWOEx7G47comcVgGtaOHfgqYqP5o5XISbq1FW50F1DlirEK3VYK0GwQ1c76Su0CGqvZFofXB5hVHgGUi2WstlqZtL1hKSK1OxuW0opAo6BnekT2QfBkQPQHRr+Cm5hLVn87mUX4NWKWNk2zBmdI8hLkDP8Wojr18rIslgItb+M9aK9XgqPXin+zvcF3Yfoiiy4euVfKD0IS8sjjlSK5PCjpB/7TtiLr1JMkE83UmPTiFFcaYCwexk85z7iPCte5w9d+4c27dvp1OnTgwaNOiOdfpwTzqLDmbyzdTW9Dowqi73PucEaK4H0cMfwMG3cPRcTtnPIWia++E7qekdnYDy5jyG6dhhDG/402HIbuTyuws8ViWv4qNzH/F0+6d5uMXDDb6eNwqt3UY3om2/e540/82wmZysf/csgltk7IKGWx/cKOivHPkM+zSR7Hu6F/76W8fuytzKC8df4SGryNOTd1Gz+j78yoyIM/cgDe5I+bIknCVmAufUFWIBTlSbmHQxE4XFjOqiAZ1EwYbHut38/gA4ikyUL7uMWy/nw37+rK+qZWqoHwvjAnj78KNsKzhPhErJKy3eIHnpZgxlJQhB4QyePY/mLeqzxUrtTt68VsSG0mr8FXJejA1hfJAPB9PKWHYki8TcapQyKYNaBjO+YxitIzzQKv+cjuc/nq6RSCSHqB/kFwCIovju9X/vARaKonjyt+b5s0H+p+RiHlt9njYR3jzYIpj7m3hTI1zlVNEpzpacJb06HbvbXneuSAjQBhCqC8VL5YVeqUcnr7sJ3KIbt+im1l5Lla2KSmslxeZi3Nd/m+QSOU18m9A6sDWdgjvRJaQLdoecQxllbL9UzJGMclyCSFyAjildohjVPhxPtYKLBgsf5ZTwc6WBUEkZgTUrKaxNpW9kX17u8jL+Gn+qy0pZ8skHfN++L1U+AbwerKaT61MqS08Qd/kfHJP681IbLaFqBdoLVRQVm1j3aBdahdc5AGZlZbF69WpiY2OZOHHiTUXrDSQX1jL8i+OMahvGB57r4eSiOtuCRn3rBpSlwdIeCLEDKc58HLmXkoDH2yBV1Z/HsPdnCp94AsNIgSYvrsPL6+6dbE4Xn2b2z7PpE9mHj3p91OCCaUFaFds+u0hs20AGzLpXaP0roKLAyMb3zhEQ5cHwJxtWiL1BzXW6RMZ0nEvfNpF8NqFtvTHv7X+K1QX7eNezNfd1mYrm6/FI1F6o/nYFt1VK6ecXkChlBM1tc5MwkFhrZvz5dNx2J6pLNQRLlfz4WFcCPW6lA+15BipWJiPRK/hqcDCLSisZ6O/Fl82i2Je2iHfPL8MsSHgobihxiXqyT57ArdbRZMgoBo0cfdPV9QbOG8wszCziTK2ZBK2ap6ODGBroTWapiTWnc9l0vhCj3cX0btEsHNb8T63x/x9BfhFwShTF1df/vRLYLYriht+a588G+YJqC1svFrEnpYSkgloAYgN0dIn1o3OML20jPbFSREZ1BgXGAgpNhRSbizE6jBgdRiwuCwAyiQyZVIan0hMftQ++Kl/CPcKJ8Yoh2jOaeJ94LHYplwpqOJdTzdGr5SQV1iKKEOqlZmjrUIa2DqX5dX7vqVozn+WUcqjaiKfUxX0cJilvDRqFhpc716lcJRIJZ/bu4sedO9jUbyJutYbFTbR4FzyBzVhGoysfsU2q553mGlroNaguVHIlt4YV0zrSK6HuUbSiooIVK1bg4eHBzJkzb6rzbsDpFhi26DgVJjv7xqjwWjsUOjwMQz6pGyC44asHESszKZcvx2XWETi3DfJfqIPdRiNXB/bFoa5Bv2QusY2euOv1KDGXMG77OHzUPqwZvAadomEOhcYqGz++exa1TsGYFzugVP+/bdF4D/85ZJwt4eeVqbTuHUH3cfENOsZ88iR5D8/gWv8xzNN24evpHXmgyS2NhFNwMmttH1Iclazu/Ab6yv2E7f8eS6uBaEf9gD3XQPmyJNSN6lN/00xWRpxKwSSKqC7WEq9Qsu7RrnhpbjHHbgR6qU7B9pHhvFZQSgdPHV+1jMZmSOT1I3M5bXQSpQtgts80rq3aitvpQpXQnLFz5hMcXL/2JIoi28tr+SC7mKsWO420KuZHBTEy0AeHy82elBJi/fW0jvhz/RD+pSAvkUj2AXerlr0siuLW62MO8SeDvEQimQ3MBoiMjGyfm5vb0M91E4IoIgIyiYTCGit7U0o4erWCs9lVGO111EgvjYKmIR7EB3oQ5qMh1FtDkIcKvVqOXiVHrZDhFkTcgojdJVBldlBldlButJFdYSGn0szVMiP5VVagrpNU2whvesQH0DPBn9bh3ki1evusAAAgAElEQVSlEqxuge3lNawqrOC8wYK/Qs4gbTaXcxZTYMynf1R/FnRegL/GH2NVJRs+/gennSI7+4zDVylnSVwN9qynkLs9iUl+l6/VChbFq+jlo0d6vpLTVyv4YlI7Brasa7FnsVhYsWIFNpuNWbNm3bQOvh2f77/KRz9nsGx8E/ofGg5SOTx2DFTX0yynFsNPL2IMXEhtfgf8Z7RAHX/nPAWvPY9h/XbsbyTQZswmJJI72S52t53pu6eTbchm7eC1xHg1jNfudgps+ug81SVmxr7YAZ/gP2Zdew//93FkXQaXDxbw4OwWxLVrmKCt6MUF1O7YwbvDXyRTH8zep3uhv60/c0VNDuM3D0GJlLXj9mD/sS9BOQU4JqxE2WQMptPF1GzOvEPEl2uyMOT4JSrlKpRJNXRQq/luZmc0tzG4bg/0p8dG8VReMb4KOd+0jCFBZeX7kzP4Kj+TGreUweEDiDhgxpaZj1vnSadxU+jZf8AdOh1BFNlRXsunOSWkmm2EqBRMCfXjoRA/AlV/3mPrL5+uOV5tZN6VPIYHejMqyIeW+jqrApdbILXYwKX8GlKLjVwpNnCt3HSz5V9DoVHIiPbXERugo1WYF60jvGkR5nXzZhNFkXMGC1vKqtlQUk2Ny00jrYrh3hbyCr/lUP4Boj2jWdBpAd3CuiGKIie2beLk+tUcb9ODkx1601Kr5E3P7ZiLl+Gl7Ehw4lN86CPhh0glwwO8ES5U8HNKKe+PacW46/Qvp9PJ6tWrKSgoYNq0aURG3pm/ziitc5h8sHkwnysXQcoWmPkzhLevG1CdA192xalrT2nJ83gNjsOjR9gd85jPJ5I7eQrWB2Q0/3gvavXd2RILTyxk49WNfHr/p/SJ6tPgNT60Jp2UI4X3FK1/YbhdAps+PE9NiZmxCzr+plPsDbiqq8kaNBhHUAjDm0xnYpdo3hrRst6YS4lLmZ78OV20YXzQ/2Mky3qiEOQonkhFovWjetNVzGdK8J3cFG3LW4yaghoDw45dpEjrgSK1hj46HcumdLipigVw5BspX3kZqVZBycRGzMwvotrpZlGzSAb66bmc8S5fpX7PYZMCtUzLYEUP1NuuInGJaBOaM3rOfIKC79wjC6LIvkoDXxVUcKjaiEIi4bmYYJ6IargdxO34/yPINwfWcKvwuh+I/08VXi8YLHyaW8KBSiNOUaSRVkU/P0/6+HnSyUuH8he/pkabk+JaG2UGOya7C7Pdhc3lRi6VIJVIUMqlN/u6+utVBHqo7sgNW9wCp2pMHK4ysqO8hkK7E5VUQn8/L4b7CiTmfMuWzC0oZUpmtZzFtObTUMqUFKRfYceXn1JZVcnO/hPJCo9jtL+CCda/4zBdIsJ3FrJDfXkhDI75y5kV5o/pYgWbzxfy6pBmzOhetzMWBIENGzaQmppar0fr7XC5BUYvPkF+tZWf+1fht3s29H4Fej5XN0AU4bsRiLlnKTEvQtW+BT5j7vRmF51O0of0wlVbhf/atwmOubtaddPVTbx24jUeafkI89vNb/D1Sz9VzL5VV2g3IJKuIxv9/gH38F8LQ6WV9e+cRe+tZswL7RvkIlq7fTtFzz3PhREzeYmmrHmkM90a1e++tH7NYN505vFYo7GM89Djt/l1LNGt0E87iugSbhVi57apZ6yXV1bGuGMXyfEJRJZjYohcw6JJbVHI6gf6iq+TQSZBmNqUR8tKOWewMD8qiOeig6ms2M2hpBfZVCWSaoVQbQitsnwJvGBEVGpp0n8IA8dPqidIvB2ZFhvfFFbQw8eD/v4NYyD9Ev9Jds1I4HMgAKgBLoqiOOD6ay8DMwAX8KQoirt/b75/1bumyuliZ3kN28pqOFVjximKaGVS2nloaeeppZ2njgSdmgi1EsUf8CB3CAL5NgfJJiuXDFYuGM2cq7XgEEVUUgk9fDwYHuhNW42VTRnfsz59PW7RzfjG45nVchZ+Gj8MlRXsWPJPipPOUxIQxq7BUzFodDwbUEar8meRShU0CXqPwt1ePBEnI1sv5c34MFJPFrHhXAFP9U1gft+6XKYoiuzevZszZ84wYMAAunbtetfzXnbkGu/sSuPzoWEMPTwEglvC9B1wvUsW57+DbfOods/BGT6RgJktkNylKJb/6cuYlmyCBZ1pOm3VXd8ruSKZqbun0iGoA4v7LkYmbZhwqbrEzPp3EwmM9GD4k22QyhqmjryH/17kJleyY9Elmt4XQu8pTX93vCiK5M+ajeX8eV4a9jJlGm/2PNkT3W1pG9FYyqure7JFq+Sf939Ks7NvEZR8HsvAv6Pt/Cxug53Szy8gVcoInNf2pkU2QGZ2NnNOXCApNBZpmY1BLgWLJ9QP9M5SMxUrkxEcbvRTmvK608ia4iq6eutY3CwaL6GYy8lPcbosib2WQHIsBkLkgTROUhKZI0XmG0Svh2bQrtt9/xEywV9eDHU3mFxujlWbOFxt5JzBTKrJiuv6R5VLIFKtIlApx0chx0chQyGRIJFIkABmtxuDy02N002B3UGRzcmNtt5KiYSmejVdvfX08vGgs7eeQsM1VqWsYlfWLkREBscO5vHWjxPuEY7FUMuB71eRfvQAglvkUs/BHG7WGV+FlOfVawk2rMfbuzMJ+rc4sbOMp5upcKplLG0ezfb9WWy6UMiTfeN5su8tKfbRo0fZv38/Xbt2ZcCAAXf9/FnlJgZ+dpRe8f4sdbyEpCIdHj8G3tdTOoZixEWdcDiiqNZ9TMCctsh0d+40TJkXyRsxEVcbLc2/OYZMdifFq9Jayfgd45FJZKwbsg5vdcOKRy6Hmw3vncNca2f8y53Q+/w5L+17+O/DqS3XOPdTLr2nNqVpt5DfHX+jraSzVTuGhYzgoS7RvDmiPmXRduE7pp59iwKNnu8GfU3Adw+iNdngsWPI/Zthz6lrdqOK88Z/WnMkslvBNiUlhdeOJ3K8USswuehvlrJiXP1A76q2UbEyGVeNHb9JTdjhK+H59AK0MilfNouih7eG7JzPyc7+kjS3P3tMHuQYi/ERPWiUrqBRnh7P4Fj6TX+Exi1b//sWk//RIP9LWN0CKSYr1yx2sqx2sq12Kh0uqpx1fy4RREREEbQyKV5yGZ5yGWFqJVEaJZFqJc30Gpro1CilUixOC3tz97L56mbOl51HI9cwOn40U5pNIVQfirmmmgNrviHj2EFEtxtTaDSnhk7lokRJL52RyZYX8ZQYiIt7Hr/qB/n6SBbvNVESolbyTatYluxMY8vFIp7ul8ATfW6xES5evMiWLVto2bIlI0eOvLsBmyAyftlJ0kuM7OtymcBTb8LolXXCJwBRRFwzCa7uo4wv8Z07GEXAnflRQXCTOq4bkkwD4Zu/wivmzicGl+Bi9s+zSSpP4ruB39HU7/d3ZjdweE06yUcKGTy3FdEt/X//gHv4y0BwC2z750VKswyMebEDfmG/39v3RoP4Uw89zeum0DvTNqJI0ZrRjLen4+8dzeI2M/H/fgYO7wC0c1JBpsB0ppiaTZnouoTgPTyu3q767NmzLDlxhr3Nu+J0iXQ3wNrR9QO92+SgYlUKzkITXoNjKWrjy+zUXNLNNmaF+7MgNhSH8TxX0hZgMmdRoOrGAYPAxfIklIKc2DwNcYV6onya0nvywzRudXcK8h/FvSD/b4Ldbed08Wn25e5jT84eLC4L0Z7RjGg0gjEJY/BUelJ0NY2jP66l8PIFRFFE8A3EMmwS6/RBWN1uZip30NX2Nb4+XWnS+B0sZyW8nFfM1nAlPTx1fNE8ire3pLD1YhHP9k9gXu9bAT41NZUff/yR6OhoJk+efAcf9wZWHc9m4fZUPuytZ8zJEdByHIxaevN1MWkjkk0zqHE9jPrhhajj7r7zzlwxH+eHe1HPH0TM43dXtX5w9gO+Tf2Wd7q/w9C4u1sb3HXu692E2vaLpNvoe3n4/0WYa+2sf/ssSo2csS92QKn5bcqs6HKRM34CjpISnhq0AINcc0faBmMJJ5Z343FfHf2jB/Csu5ago+sxtR2Cfvj3ANTszsZ0uACvQTF49Kzvt3T48GE2nT7L7tb3Y1DIaGYQ2DmoNRrlrfcQHG6qfkjHllqJrnMwysExvJNTwsrCCmI1Kj5rGkk7vZyc3C/IzV2KXO6J4P8Qu8sL+Tl3H07BiZdJQWyhjnhHFH37TqBzv4F3bfzdUPzlg/zR03v4bv8iusT2YvTAh/Hy+vcY8QuiQGZNJokliZwpOcOJohNYXVb0Cj19IvswKn4UbQPbYq6u4tKBvSQd2oelvBRRKkUSEELIg8PZEhLPkRozTRUVzHC8QbTSQaO4Fwj0HULy1qvMV1pJ85IxPzyAJ6KCmL/2IvuulPL8g42Zc/+t4JeRkcEPP/xAaGgoU6ZMQaW6e2oju8LMwM+O0DnKk1XGx+p2Ko8dA/V1+baxFOHTjricgTgHbUHX5e6mYlW5hyge9RiScG+abDp+1xtwd/Zunj/yPBObTOSlzi81eF1ry62sf/sMPiE6Rj7bDtm9PPz/LAozqtn6yQXiGugyaktNJXvsOJz9BjFc04spXaJ4Y/gv+hJc+oEVB57jM19vnuvwDANPf4B/XgGO8ctQNR2PKIhUrU3DmlyB76T6jJsb9a5j585zqF0/srQq/E1u9j7QnFD9Lf2JKIgY9uZgPFSAqpE3fpOacNJu48m0fApsDmaFB/BcTDASWyZpaS9Ta7iAXt+M4OgnOVtTzeYrG0mqSQbAwywnosqDfrFDeGTS839qHf/yQf7DHxfyjWUjAFIBAm2exHrG0q5RF5qGtSRQG4i/xh9vlfcdreZcgguz00y5pZwSSwnF5mIyqzPJqM4gvTodo8MIQIguhO5h3ekT2YeOQR0xFBeTfvoEaadPUJOXDYBbrUUX3YiOQ0dz0i+UT/LKkIpOxour6cMeoiOmEx09Fwxy1m1N5fUwCSikLGoRTWe9llnfJHI2t4rXhzVnatfom+eYlZXFmjVrCAgIYOrUqWg0d5c+u9wC45aeJLPMxN64DQRnb4KHf6rr9gQgiji/GIm8/BjGVj/gObrvXedxOmtIefR+lKftRG1ci67JnY+U6VXpTNk9haa+TVnRfwUKWcM4vm6XwKYPzlFbbmXcSx3x9P/3tWO8h/9OnPsph1Nbsug1qTEtet5J3/0lSj/4gKqVX3HwsTd4v0Rbry8scD0dOYGnDOc5pNPyRffXabPhERRuCfK5F5B6hiM63ZQvv4yjyEzA7JaoIm952AiCwObNm0m6fJm0tv04rNeicoqsahPLA8H1n3rNiaVUb76KzFOJ3+SmOIK1vHmtiG+LKvFXynklNpQxQd5UlO8i89r72GyF+Pv3ISbmCUwSX/Zl/cy2CxvIdOfSQ2zL5zO/+VNr+JcP8gDFtUVsPLia01lHKJKWUqt1YFcJd4xTSBUopApkUhkOt+Om3cHt0Mg1JPgkEO8TT2v/1jTXJaCudlGQfoWc5CTKc67hNJsAcKs0yPyDaXJfTzr06MVxUcHbmQUUOtx0JJEp4jKaB3UnJuZvaLUxVCSX8/LlXLYGy2mpULK8fRxap8jUr85wrdzEx+PaMLT1LQ56Xl4e3333Hd7e3kyfPh2d7tdFQl8czOSDPel81qGK4cnzoP9b0O1WEw/79mWozj2Hye8JdHPfuMOTBup2MsnfT0D+VhIeD48k/IV37hhTa69l4s6J2Fw21g9dj7+m4fn0Yz9e5dL+fAY+2pLYtgENPu4e/roQBZEdiy5RmFHDmBc74B/+2/l5wWola9hwRKmMx+9/CrtMzk/z70zbmL7szKQgH2rVnixpOoKEra9jC4pBN+s8SKW4TQ7KvryEaHfXKbxvczp1u91s2rSJlJQUbO378o1cg6iS8niIP680CUd22xOHPc9A1fdpuE0OvIfEousSwiWjlZeuFnDeYKGDp5bXGoXRTi8nP/9rcvOW4HIZ8fPrRXT0XLy92mNxWrA5bPjqGtYO85f4ywd5t9uK1ZqHXl/nW221Wrl08jjnzx4kp/QKFsGAXeHEqnIjSEUEuQypWolKrkYj16BVaPFEh5dbi5dbi8okYDOasJuMWGuqEJzOm+8lKFS4NTr0oREkdOxC87btCAoOZm+lkY+yc0ixQDTZTBK/4YHACGJinkCvi0d0ujn8UybPScwUaCXMCfTjhWbhZJeZmbHqLNUWB0untKdH/K3Al5+fz+rVq9HpdDz88MN4ePx6X9PUIgPDvzhG/xgVi4omIGnUGyb+UNcEBLBfvoJiQx9cyjgUz+5Horq7p3vetZXUTv8AhdKXhF0Hkf4iLSSIAvP2z+Nk8Um+HvA1bQIbXjjKTqpg15dJtLw/nJ4TEn7/gHv4n4HF4GDd22dQaeSMXdARheq389Om48fJn/kIjonTGGFryYSOEbw7qlX9QZfWkbVjDpMiooj1a8I/RCuRiQcwdnsIj/5fAOAst1D25SVkegWBj9dviuN2u9m4cSOpqakEderLP4xy7IFqWmhUfNU6lkjNre+G2+yken06tvRqNK388RnRCDRy1pdU8da1YiqcLvr4evJ8bDDNNW4KClaTl/8VTmcVXl4diAifSkBAf6TSP6d6/csH+ZKSbaSkPoVe34yQ4JEEBQ1FpaoLlqIoUllZSWZqMjkpl6kuLsJSXYnLbELidiFxu0FwI0FE5Pqvs0yGKJMjyuSg0qDx8cUrKISw+MbEJDQmLCwMtVqNXRDYXJTH57lFXHNoCRKLGS3dxtiQEKIipqHV1jXNrik08vaxq3wfICFYlLKoTQzd/Dw5mFbG39ZeQKOUsXJah5tmYwA5OTmsWbMGvV7PtGnT7mj8cTvsLjfDFx2nwmhjr/41fEUDPHYUtHW7AmexCffSoShJhVlHkYbdPcAaDJdJ//s49HsgYtXX6Lt0uWPMoguLWJq0lFc6v8L4Jg1vVWassrHu7TN4+KoZ/Xx75Ip7DUDuoT4K0qrY+tlFmnQJps+0Zr87vuiFF6nduZMDT3/IBxkulk/tQL9mtylGRRF+mMS+wmM8FeDNmPhR/C3pe7xLy3FMXo26UR1RwJ5VQ/nKZJThHvjPbIH0NoGW2+3mxx9/JC0tjeb39efNHIGyaC0quYy/x4fycJj/zV29KIgYjxRg2JuDVK/Ed3Q86sa+mN1uviqo4Mu8Mqpdbh7w9WB2eAA9vGQUFa+nIP9brLY8wsIm06TxG39q7f7yQd7hqKK0dDvFJZsxGi8DUrw8W+Pn/wD+fvej1ze5w2fF6XRisVhu/gnCrdSOSqVCo9Gg0Wju6BMrCC6uVKayKj+XbQZfakUdoWI+EzXnGBuRQFjQEBSKuvye6BTYdegaf3caKdJImaTVs7B9DB4yKV8fz+Gtnak0CfZk5fQOhHjdyk1fu3aNtWvX4u3tzdSpU/H0/O2GyO/9lMbiQ9dYGXeEPkXL4eHdEFnn3e6qsWP6/G283Z/hfuBdZL3m3HUOp7OGcxsG4flmNZ7DBhP+jw/vGHMw7yBPHHyCEY1G8Ea3Nxos6hDcAls+vkBFgYlxLzVMzn4P/5s4vS2LxF059J3elMZdfps/f8PyQB4Zybwuj1NqcrDnqZ71LIkxlsAXnfk0MJiVMgsvt36UET+9hgQZ8rkXkXnUpUYtlyuoWnMFdWNf/KY0RXI7P97l4scffyQ9PZ2OPXrzxTU5F72lCAFq2nlo+ahJBE31t76/jgIjVeszcJVZ0HUMxmtwDFK1HKPLzcqCcr4qrKDM4SJBq2ZKqB8jAj2RGE+gVofezEb8Ufzlg3yWxc43hRX09fOkuaKEmvJdVFQevB7wQSbT4+nZCi/P1mh1jdBqY9BqopDLvX41UImigMtlwGLNxWLOItNQyJ4qFwdtEVwjDonoppPiKhP8XAyJ7IKHvj4NMCujkjcu5/KTr5QYl4QPW0RyX4gPVoebV7cm8+O5Avo3C+KT8W3q5RIzMjJYt24d/v7+TJkyBb3+t/OT53KrGLvkJGMjzbxXOgv6vg7dnwRAsDipXLwXP8NMCGuP9JHtcBdevSgKXDr/CNIXTqCy+tBo10/IfvHkkFObw8SdE4nyjOKbgd+gkjVcuHRD+NJvZjMSOjasM9Q9/G9CcAts/fQiZXlGxi34faO62m3bKHr+BSTzn2VYYSg94/1ZPrVD/e918kbcG2bwWNNOnHdUsajJSDrt+gBbYAS6Ry/dVIDfMDPTtg3EZ2xCvZqV2+1m8+bNJCcn07nrfeyu9mdzaTWSFj4IMgmPhAfwVFQQXorrflZOAcO+XIxHCpB5KPEaHIumlT8SiQS7ILC1rIYVBeUkGa3IJNDb15OZ4f7c7/vbG7pfw18+yG8rq2Feai4OUUQnk9LdR09HTx0tNE4inIk4TRcwGC5gMqVxu32ORCJHLve6vvOWUdc0RMDmMJHv1pAjRnGF5qTSglJJ3a4iQVHDQG+RCZFNiPG8c6dRW2bmo5PX+EbnBgk86uXN022jUMukZJWbmPP9edJKjPytdyOe6puA9LYb6cKFC2zbto3g4GCmTJmCVvvbO16Lw8Wgz47ictjY7ZqFR6OuMHEdSKUIdhcVKy7hVTofpSobydyTt9Suv0BOzpeUfvEJntvlhC/6HI++9Vk3BoeByTsnU2uvZd2QdYTof1+heAP5qVVs+/wizbqF8EADJOz3cA+majvr3jqDzkdV52/zG6m9G5YH1vPnObNwMX8/Uc67o1oysdMv7vVNs6lO2cT4+BaIcgVfaLxJOLsXU7th6Id9d3OYYX8ehp9z0XcPw2twTL0fC0EQ2LVrF4mJibRv34E8fWM+PJiJqqUvtf4qfBQynosJYUqIH/Lr32t7noGarddwFppQxXrhPSzuZhMTgCsmKxtKq9lYUs3McH/+9n/VoOzfhX+FXWN2uzlebWJfpYHDVUZybY6br4WoFESplUSq5eixoBJqUQhVuNxWbG4HNreTGreaSlFLuVtHvtsbJ3U3ll4q0NlTSU8/f/r7+xCjvfsO1lRr46uT2SzFSqVKylBRyd87xhDpUfcYtzOpmBc2JqGQSfhkfBvub3zLaVEURY4ePcqBAweIjY1l3Lhxd3jC3w0vbEhifWI+a72+oIu2EGYdBK0votNNxdcpKPNW4CX/BoZ/AW0fuuscVVXHubx7OgHvKvDsO4DwTz+p97pbcDP3wFxOF51mef/ldAi+6310V5hr676sGg8lY17scK8R9z00GDmXK9j5RRIte4XRc+JvpzAcBQVkDR2GtksXFrSbwoX8WnY90YNo/9ueAmy1sPg+UhQypnrJaRvYhrcLThGUX4Bt1CeoW80A6r6LtduzMJ0owmtgNB69Iuq9lyiK7Nu3j+PHj9O0aVPC2t7PUz9eplouEtI1lKtuJ/FaFc9EBzM00BuZRIIoiJjPlmDYk4NgdaFpHYBn3ygUt9GH3aKIQxDR/EnNyP9EkP8lKh0uLhgtJBkt5Fjt5Fkd5Nsc1LrcmNz1qZUSwF8pJ1ipIFiloJFWRXO9hqZ6DY216pu/yneDscrKitPZrJDYqFRJ6eiU8mrLSDqG1BVRa61OXt+ewqbzhbSL9GbRpHaEet92cd1udu3axblz52jVqhXDhg37VSXr7diZVMzcNeeZ45vI847F8MjPENwS0SVQufoK7ozTBKqeQ9J0CIxddZNlczts9hLOnBqCzwdOlOUa4nbuQO5fnw75UeJHrEpZxatdX2VswtjfPa8bEASRbZ9dpDSrlrELOuIbes8f/h7+GI5tuMqlffkNsp+u/Opryt5/H+2b7zI0RUNcoJ4fH+2K/PagmXMcVg1mS/N+/N2SxkPxI5lzcgkaqwsePYY8oK7YKwoiVevSsV4qx3t4HPqud9pqnzhxgr179xIWFka/oaNZsC2DE1mVtOsYQmmomiybg3itiiejghge6INcKkGwODEeKcB0vAjRLaBtG4S+exjKkH/9u/GXD/KCw427xo4isGEFPUEUsbiFOlthiQSZhD/kDCcKIplpFay6WswGtYtapZQuDinPNA6jR/Qtte3hjHJe2JBEucnOnPvj+Fvv+Hpe1VarlY0bN5KZmUn37t3p06dPg86joNrCwM+OEqeo4kfHXBSjl0KrsXU35w9p2JIKCPF7FqnMWad21d7JvRUEJ+cvTMK9NRXP9QKh77+H17Bh9cZszdzKK8df+cOKVoDEXdmc3pbNA1Oa0Oy+u3vP38M9/Bb+iHBOdLvJmTQJZ24eV99bztzdufWcW2/i59fg+Ke83WU8P5SeZEHCcMbuX4RL5416bgoSZV3AFV0Cld9fwXalCp9R8eg63VlLunLlChs3bkSv1zN+wkQ2XTHy6b4MPDVKRgyMY7/bTprZRoRaydRQPyaG+OGvlOM2OjAeysd8pgTRKaCK9UJ/XyjqJr71Cr5/BH/5IG+5VE7V2jQUwTo0rQPQtvK/o3XdvwpREKnNq2VXWilbLGaOedc5VvYRFDzeOJSuEbcCaZXZwfs/pfHD2XwaBer5aGzrO9p6lZWV8cMPP1BTU8PgwYNp3759g87D5RaYsOwUaYVV7JI+RWS3MfDgu4iCSPWmq1gSSwmM/QZl0QaYtg1iet51noyMNym6uIqgd3ToOnclYsmSej8wl8ov8fBPD9MusB2L+y1G8Qf4u0VXq9ny8QXiOwbR9+Fm9/q03sOfxh+xwLBfu0b2yFHoe/Xiw27T2ZZUzA+zu9Ip5rZNjssBK3rjNBQxp2UPEiuS+DCkDb2PbcIc0xr91MM3n3pFl0Dld6nYMqrxGZOArv2d+fLCwkLWrFmDy+Vi1KhRCJ4hPLP+EqnFBoa3CaVLt3DWV9ZwssaMUiJhWKA344J96eatR2pzYT5biulEEe5aO7ouIXX8+j+Bv3yQdxsdWJLKsV4qx5FXZ0Mg81OjjvNGFeuFIkyP3E9zV4Xnr0F0CTjLLJTn1nCwpJYDDhuHfKVY5BICXTBGr2dGq3DCdbep5ASRtWfy+HBvOkabi0e6x/BUvwTUv+ZIYUsAACAASURBVCgcpaWlsWnTJhQKBePHj79rR6dfwyc/Z/DZ/qt8olrGyGgXTN2CKJFTsyUT85kSfNpkokt7Eu57Evq9ftc5ioo3cCX1BUKXRSLNNBK7YzuKkFvF1BJzCRN3TkQj17Bm0JoGWwcDWI0O1r11BrlKxriXOt7r03oP/zJumNk1pKlMxfLllH/0MX7vf8C4dB02p8Cu+T3w1d0m/itLg2W9qI3qxkN6J7V2A5/JJLRNuYi5y2R0D355c6joFKj4NgV7Zg2+4xujbXNn2qimpoZ169ZRXFxMjx49uK9HL748nMXiQ5mo5TKe7JdA51ZBrCmpYn1JFSa3gL9CzuAALwb4e9HZQ4c0swaZj/pPp27+8kE+2WhhaUE5rT20tJTIickyI82qxZ5Vi2i/zqaRS1EEaJB5qZB5KJHqFXV+0td/tQWbC5fZQaHdySW7g0syN5e9ZCR7SxEkErwE6K/WMjYukPuCvOrJmkVR5GB6GR/syeBKsYEusb68MbwFCUH1Faoul4v9+/dz8uRJQkNDGT9+/G+KnH6JM9lVTFh2kuHKRD7x2QCPHEDU+lO98SqWc6V43qfCI3U8Eu8ImLkP5HeqWmtrL3Lu/ET8LkShXJZL8MLX8Jkw4ebrVpeV6T9NJ9eQy/eDvifOO67B5ycKIju/TKIgrZrRL7QnIOLXFbr3cA9/BIe+TyPlaBFD/9aayOa/bkAoulzkTJyEs6AAx4o1jFpzhR7x/qyY9gta5ellsPs5cnu/yKTCHQRo/PmsLIWoonLsIz5G1WbmzaGCw03lqhTs2bX4TmyCttWddhxOp5Ndu3Zx4cIFYmNjGT16NKUWkYXbU/n/2jvv8KiqrQ+/J1PSe0ghjYRAKAmhhCIgIAJSBKSIKNeGioh69WLB9tkFLKiIiF1QmlIEEaRKEemBACGkEdJ7L5PJtP39MYMkJAEJCSWc93nmyZnT5jc7c9bZZ+2119qTkE+IpwPPD2vPgA6e/FlUzvq8ErYXllJlEqglid4u9tzf2oMxntegkPfVpLFGfmtBKc/Fp5OvM9dulYDWlogaX2GFU5UJh0oDtuV60BowVhvR642UKSVK1BJFaokMewXpdhLVlt6+WkC4UkV/dyeG+LrS3dm+lmEHs3Hfd6aQeVvjOZpWQoCbHc/fEcroLj51XBQFBQWsXr2anJwcevbsybBhwxosB1YfJRodo+bvQVGZw0bbN3F8bAPCowPFqxPQHM3DcbAvTtnPIKUfhsf3QKu6s1qrq3M5dPguFKVK3N/UYtOhIwFLFiNZYudNwsQLu19gW+o2FgxewED/gZfzb+DYtjT2rUliwOT2hA+qP7uljExjMBeYOYKmTMc9r/bC3qXheRrahARSJkzEYcjtbJ/4X97cEMtrozry6K3B53eyzIYlcRsHx33C48fm0cerK3NObcKpUo94+A+UfudnfJt0Rgq+j0GXVobbpPp79ABHjx5l48aN2NraMnbsWEJCQtgWm8vcP+JILqgk3NeZmUPbMyi0FVUmwcGSCnYVl7OnqJy7vd2YEdC4+sYt3siD2eDm6PQcL6viZIWG1CodaVpzRE2J3kiVqW6yMivAVaXEXaUg0NaaYDtr2tpa08XRjk4ONnVqw55DZzCx4XgW3+09S2x2Gd5ONjx9ewiTIv1rFRg4pysqKootW7agVCoZO3YsHTp0uKzvZjIJHl1ymL8Sclilfouu/3kfEXw7RaviqYrOx2loIE6K5bBrToPhkiZTNVFHp1BZEUfgjxFUH40leN2vqNu0+WefT6I+4fuY73mux3M8FPbQZWnMOVvKrx8epU2EB8Onhcl+eJkmpyi7klVzDuMV5MSYZ7rVmmNyIQVffkX+p5/S+tNPeCHPg53xeaye3rf22JimCL4aAJIVv9w+k3eOzuMe//48f2AlVgobVDOikRzP++FN1UYKl5h79C53heDQu/75ItnZ2axdu5b8/HwiIyMZOnQoCqWKX49lMn9HIhnFVbT3cmBqvyDu6ub7jztXCNHo66bFG3mt3kiVzoirff1Jt8Bcp7XcYDb0VhIoJAkHhRVW/7JRhRDEZJax9lgGG45nUVCho52nA4/0r/2PqklhYSEbNmwgJSWFoKAgxo0bd8kUBfXxxa4kPtgcz1vKxTw4egii+2MUroxDe6oQp+FtcAo4Az/eBRGT4a5FdcIlhRCcjnuJ7OzVtEu5n8oPfsbrtddw+8+Uf/ZZnbCat/a/xaT2k3itz2uX9WPTVur55b3DIME9r/bE2q5xSZZkZC5F3P5sdiw5Ta/RQfQcFdTgfkKvJ+WeyehzcvBYtZbRP53Cygo2/vdWnGxq/D7TD8EPIyB0JHPadGJ53HL+59udB/etw+Dqi/Xjh0FtX+O8RgqXxaGNK6q36Mg59Ho9O3fuZN++fbi6ujJmzBiCgoLQGUz8Zukgns4uw9VOxdiuvozv7ku4b8Mz8C9FizfyW0/lMH1pFD0CXbmtgyeDO3jS3tPxonf6f4NWb+TQ2SJ2J+SzMz6P5PxK1Aorbu/oyeReAQxo51HvP0Wv17N//352796NUqlk2LBhdOvWrd5SfZdi/5lCpnyzn5FWB1jQtwpx+1wKfjyN7mwpzqODceyihC/7g60rTNtZ6wd5jrT0H0hMfJdA2wcxPrkemy5dCPj+u3/cNPsy9zFjxwz6tO7D54M/r5Nz/2IIIdj8dQwpxwsY90J3vIMaV21eRubfsn1xLAkHcxj7bDd8Q10b3E8bH8/ZiXfjNGwYOf99hUlfHWBoRy8W/ad77ev27/mw7XUMIz7gqbJjHMw+yOuu3twVtQ9dQFesH9wBihpFww0mcxz9yQIcbw/AaUhAg8Y5JSWF9evXU1xcTHh4OMOGDcPR0REhBAeSi1h6MJVtsbnoDCYe6R/E/9156cRs9dHijXxKQSVrj2awIy6PU1llADjaKInwcyHC35kgDwcC3Ozwc7XFyVaFnUrxzw3AYDSh0RvJK9OSUVxFRnEVsdllxGSWEpddjs5oQq20oneQGyPCfBgV7oNzAz1VIQQxMTFs376d0tJSOnbsyIgRIxrVewfIK9My8uPtOFVn81vHP7Ed8x0FS+LQ52lwu7s9dl3c4cexkHHEbOA966YNyM/fzomT02nlPgTneRq0p2IJ/m09Kl9zcYaE4gQe+OMBfB18WTJ8CQ7qS9farMnJXRnsWZlA3wkhdBv676OEZGQai05rYNWcI+i0Bia/1gtbx4af4PO/+IKCzxbgu+AzfrFpy7sbTzNreAeeGFQjoMBkghX3QPIuKh78jYei55Fens5sleD22Hh0YSNRT1he6wlZmMQ/AQ/2vb1xGRNSqzB4TfR6PXv37mXv3r0olUr69+9P7969UavNukur9Gw6mU17L0d6BDZ807oYLd7I1ySnVMuexHyi00uITishPrcco6nud7RRWWEwCgz1bHO0URLu60y4rzN92rrTJ8gd24tMyRdCkJCQwO7du8nKysLb25thw4YRHBzc4DGXwmA0cd/nWzmZrWG9/88EjV9IwU9nMVXocP9PJ2zau8LO2bD7fRj7BXSbUuccZeUxREVNxt4+hODYMeTP+RCf997FZcIEAPI0eUzZNAWTycSyUcvwtr+85GH56eWsfv8I/h3dGPVEl8sKUZWRuRIKMspZPTcK31AX7nwyosHfntDrOTvpHgx5eQStX8f/tqax6WQ2P07tTf92NWZ3Vxaan4iV1uTdv5opfz6B3qjjI20OkWdz0Pd7AtXQubXPbRKUbUmhfHcGNqGuuN3XEauL5MEvLCxky5YtJCQk4ODgwIABA+jevfu/muF+KVq8kT/3Hep7ZNIZTGSWVJFepCGjuIqKaj2V1Uaq9EaUVhI2KgU2Kiu8nGxo7WJLaxdbfJxs/pWrx2AwcPr0afbu3Utubi4uLi4MHDiQiIiIRrlmavLOL3/x3dEyPnZdy6jRr1G4Ogck8HgoDLW/I8RvhhWToet9cNcXdY7XarM4fGQCVpKSLh6fkjnpEez79MHvy0VIkkSlvpKHNz9MSlkKS4YvoaP75SUP02kN/DL7MAadiXte64mtQ8O9KRmZ5iBmTya7l8dzy/i2dB8W2OB+2oQEUibejX2/frh+Mp/xi/aRX17Nhqf74+daY5Z82gFYPApChpA0/B0e2PIQ7tbOzMuPoX12GcYRc1H0fqLO+SsOZlOyLgmVjz0eD3VG4XTxDK2pqans2LGDtLQ0HBwc6NWrF5GRkZdMSHgxWryRT0tLY926dURERNClSxdcXRv3yPNvKSws5OjRoxw7dgyNRoOHhwe33norYWFhKK6g4vo5Vu8+yvN/ZPOQ7V+8OGgSRVs0KN1t8HiwM0oPWyhIhG8Gg1swTN0Mqtqzew2GCqKO3kNVVQY9uq6gcNrbVKekELzhN1SenuiMOmbsmMGRnCN8NvgzBvjVPyu2IYQQbPs+lqQjudw1sxut2zVve8vI1IcQgi3fnOJsdD7jnu+Od3DD40GFixeTN/d9vN95m5LbRjJmwV4CPexYPb1v7aCJQ9/Apudh4CwOdxzK49sep5NLIB+c/RufomrEuEVYRdxX5/xVcUUULT+Nla0K94c6X3JSkxCC5ORk9u3bx5kzZ1AqlQwePJi+ffs2qi1uCiP/559/kpKSAoC/vz+hoaGEhITg5eV1xeF8JpOJnJwc4uPjOX36NHl5eUiSRGhoKJGRkQQHB19xz/0cR2Nimbw0kZ7KMywI74zmmB3W7V1xv68DVjZK0JbBt7ebw7+m7QKX2lnyTCY9J05Mo6j4byK6fIdYE0f+xx/T+qOPcL5zFEaTkVl/zWJLyhbe7fcuY0PGXrbG2L1Z7FwaR+8xQUSObDjCQUamuamuMvDLe4cwmQT3vNoLG/sGxstMJtKmPkLViRME/7qWPZU2PPrjESZ09+Oju7uctxFCwPqnIHopTF7OH9ZWvLjnRQZ4duCtuJ24lxlg4mKkznfV+QxdZgUFS04hqgy4TmyHXcS/i3nPzc3lwIEDhISE0Llz50a1Q4s38ucoKSnhxIkTnDp1itzcXADs7e3x8/PD29sbHx8fXFxccHJywtbWto7xF0Kg1WopLi6msLCQgoICMjIySE9PR6czpy4OCAigY8eOdO7cudEDqg2Rk57M6EUHsaWaxd5OqLNa4dCvNc4jg82DOiYT/PwfSNgMD6yHoFsv0G8iNvZ5cnLX06HDbNyKwkiZPBnHIUPw/eRjAGYfnM3K+JWNioUHKMysYNXcI/i0dWb0f7tecQSTjMyVkptSxtoPowgMc2fE9PAGO3X67GySx96FdXAwgUt/4tOdyczfkchLIzowfWCNgVi9Fn4YDgVJ8NifLM7dx7yoeYz0as9LsbtwKTchTV4OoSPqfIaxTEfhstPoUstwuNUX5+FBDQ7INiXNZuQlSfoQGA3ogDPAw0KIEsu2l4FHACPwXyHElkudrylTDZeVlXHmzBmSk5PJzs6moKCg1nalUolKpUKhUGBlZYVOp6O6upoL28PT05OAgAD8/f1p27btJSs1NRZtYQaTPt3IGb0H39lW428IxOWukNpJkXa9D7tmw/D3oc/0WscLIUhMfJf0jMW0DX6eAM8HOTt+AiatluB1v6JwcWHR8UV8Ef0FD3d+mJmRMy9b4z9RDVUG7nmtF3ZOsh9e5vogensaf69O4tZ72tHlNv8G9yv9fSNZzz9Pq2f+i/v06Ty94hgbT2azaEoPhofVCDwozYCvBoKtK+LR7XwS+z0/xPzAeK+2PBfzF44agTR5JbQfVuczhMFEycZkKvdnYx3sjNvkDiia+Vq5mJG/0mHdbcDLQgiDJEnvAy8DsyRJ6gRMBjoDrYHtkiS1FzXLMjUzTk5OdOvWjW7dugFQXV1Nfn4+paWllJWVUV5ejsFgwGAwYDKZUKvV2NjYYGNjg4uLC+7u7ri5uV1W6oHGYio6ywsLVnBSH84HUgltHDriPqVjrQoyxK43G/iIe6H343XOkZq6iPSMxfj7P0xg4HRyXn8dXWoqAYsXo3Bx4ee4n/ki+gvGth3L/3r877I1CiHYsyKB0jwNY57tJht4meuKiNv9yYwv5u81Sfi0daFVQP15k5zvHEXFzp3kL/wC+/638tHdEWSWVPHsz8dY5dKXcD+LX9/ZDyYtgR/HIq1+mP/d+zPlunJWJ6zGJqwPT8bsx3HlZKSJP0Cn2i5PSWmF69gQ1H6OlKxLInd+FK4T2mPbqeGcO82KEKJJXsA4YJll+WXMxv/cti3ALZc6R48ePcRNR+5pMfuNmSJw1u/ig1nLRcHy08KoNdTeJ/2wEO94CvHtUCF0VXVOkZGxQmzfESxiYmYKk8koSjdvEbGhHUTuR/OEEEKsS1wnwheHiye3Pyn0Rn2jZMb+nSk+f3yHOLghuVHHy8g0N1XlOrH4pb3ip9f2iWpNw79zQ0mJSBg4SCQNHyGMGo3IK9OKvnN2iJ7vbhNZJZraO0ctEeINJyHWPy0MBr14YdcLImxxmJi3eagonucuTG+6CHFseYOfpcutFDnzo0T6rD2iaG2CMFYbGtz3SgCOiAbsatOMFpqZCvxhWfYF0mtsy7Csq4MkSdMkSToiSdKR/Pz8JpRz/SMyjrJk4Xt8pR3MBIWOGfcMxm1yaO1Y2+JUc6ikozdMXg6q2mUBs3PWERf/Gu7uA+nYcS6G3DyyX38dm7AwWj39FJuSN/H6vtfp49OHeYPmXdZs1nMUZlWwZ0UCvqGuRI5sc4XfWkamebBxUDH0kc6UFWrZtSyujuv1HApnZ1rPnYMuJYWc996jlaM13z/UE43OyCOLj1BRbTi/c/cHoP9MOLoExYGFvHfrewzwG8DinByWdg6n2FkJ66abo3LqQeVph+eMrjgM8KPyYA55nx1De6akOb5+g1zSyEuStF2SpJh6XmNr7PMqYACWXa4AIcTXQohIIURkq1Z1U3i2VAyHN7H161d5q3oSA+1gzgsjsO92QSRQVQksnwRGHdy3Cuxrl+bLyd1AbOwLuLr0JjxsIZKwImvWSwi9Ht+PPmRb1i5e2fsKPbx6MH/wfKwVF4/frQ99tZEtX8egslUydGoneaBV5rqmdYgLvUYHkXgkj9N/Zze4n32fPrhPm0bp6jWUbvidUG9HFtzXjfjccqb9eIRqQw3P8uD/g87jYdvrqE5vZN7AeUR6R/JtbjErOnWmwN3GHHa5/S1zcMQFSEorXEYG4fFoOMIkKPjmJEWrEzBp9M3RBHW4pJEXQgwRQoTV81oPIEnSQ8CdwBRx/taZCdQc/fCzrLvpMWkNaL7/gOjf3uQZ3TQ6u1izaNYdqF0uKNyt15ojaQqT4J6ldVIH5+ZtIjb2OVxcIomI+AaFwpbC775Hc/Ag3q++wl6rZGbtmUWXVl34fPDn2CobVylrz4p4inM1DJ3aCXvny79JyMhcbXrcEYh/R1f2/JxAYWZFg/u1evopbLt3J+eNN9ClpHBbqCcfTuzCvjOFPLsy+vxMeSsrc+I//97w6+PYZJ9kweAFRLSK4JucElaGhpLpYw97P4Y1U0FfVe/n2YS44PVsdxwH+aE5mkvOvCgqD+cg6pl135RckbtGkqThwIvAGCGEpsam34DJkiRZS5IUBLQDDl3JZ10MIQTGCl1znb5JEAYT5X+loXl/Gskpy3jI8BI+bk788NRA7KwvcKEYDbDmEUj5y5w6+IISfnl5mzl16lmcnLoS0eVbFAo7NIcPkz9/Po7DhxMV6cxzu5+jo3tHvrj9C+xUjZtJd3pfNnEHcogc2Qb/DnXrxMrIXI9IVhJDHu6M2lbJlm9i0FfXH+8hKZX4zvsISaUic+ZzmHQ6xnf347VRHfkjJofX1sWcd/mobMzuUkcfWH439kWpfDHkC4uhL2V1UFuSgp0Rp9bBktFQUb/r2UqtwHl4EJ5PdUPpbkPxmkTyPjuKNqG4uZrjin3ynwOOwDZJkqIlSfoSQAhxCvgFiAU2A0+KZoys0Z4uInvuYYrXJ2Eo0TbXxzQKYTBReTiH3A93oNr6IFnGA9xvfBNnZ2eWTeuLh8MFvWMh4PdnIO53GD7XnD64BtnZa4k59V+cHLvQNeJ7lEp7DIWFZM58DpWfLycfvZXndj9PJ7dOLBqy6LITjp2jKKuSPSvj8W3vctGUrjIy1yN2TmqGPtyJ4lwNu1fEN+ifV/n44DNnNtrYWPI+/AiAR28NZsagtqw4lMaHW2oca+8BD6wDhTX8NA778jwWDVlEd8/ufJ9Txu/egcR0dkPknIBvboOMqAb1qVs70OqJCNzu64BJZ6Lg+xhK/jjb5O0ALWQylKGwirKd6WiO5oEEdt08cejbGnXr5olp/zeYNHoqDuVQ8XcWioqTeNh+QJpJxSTmIKnsWDX9FgLdL5j6LARs+z/YtwAGvAiDX621OT19CQmJb+Pq2pcu4V+iVNojjEbSH3sMzZEoznz4GC/nfE0Prx58fvvn2KsaVy9SpzWw+v0otBWXrsIjI3M9c2hDMoc3pjBoSiidb6039gOA3DlzKFryI36fL8BxyBCEELy6LoblB9N4enAIM4e2Pz9elnfanIPexhke3ozG1pmn/nyKqNwoHvB2ZYA2ne6JEorKEhg+B3o+WqfGQ02EwUTF/izUgU5YBzRuguVNM+PVUKKlfHcGlYdzwWBC7e+IfW9vbMM8zCkBmhlhElQnl6I5koMmphAMBly8t2Jf9hUptp35j+5lNEYrfnn8Ftp51RPHe26yU8/HYOSH56vGC0FKykKSz36Ch8cQwjp/hsIyiJq/cCEFCz4n9YmRvOCylX6+/fhk0CeN9sELIdj63SnOROUx+pmusptG5oZGmAS/LzxORnwx45/vgVeb+o2oSacj9d770KWnE7R2DWo/P0wmwSu/nmTl4fS6hj4zCpaMAWd/eGgjVdZ2PLvzWfZl7eNur1YMIJU+mT7YpJ80D9qOmgd2zXct3TRG/hwmjZ7Ko3lUHszGkF8FCgmb9q7Yhnlg0861SWefCYOJ6uRSqmIL0Z4uxFiqQ7JR4NDRgGPJbKyy95MYeC9TMsdjEBI/PdKLzq0vSKQkhLl03+73IeI+sx/+XN1Vk4HExHfJyPwJb++76NjhfawsYZCV+/eTNvURcvq157/9k7g9cAgfDPgAtaLx3+/4jnT2rkqkz13B9BjeptHnkZG5XtBW6Pl5tnlI8J5XemHjUP8ER116OmcnTETl60ub5cuwsrW9uKE/uweW3Q1ubeGB9ehtXXh176v8kfIHI1p5MVSdTKSmJy7R25HsW8HYzyFkSLN8x5vOyJ9DCIEurZyqkwVUnSzAWFoNgLKVLdZBzqh8HVB52aHyssfK9tI9faE3YijUos+vQp9RTnVqGbqMCjCYkFRWWLd3xS7MFVvtBqSdbwMQ0+cjHvjbHYWVxPJHe9ftwQsBO9+DPR+aa7OOXvCPgTcYKog59QyFhbsI8H+EkJCXkCTzNn1WFmcnTqTI2sBT92m4o+NY3ur7VqPi4M+RnVTCuo+PERDmzsjp4XJ+eJkWQ25KGWs/isKvvSujnopoMBS4Ys8e0h+fjvOY0fjMnYskSbUM/WO3BvHyiI7nj0/ebZ7H4uwPD/6GycGTuYfmsiJuBQPcvRlrm0yIzSCCok8i5cdD9wdhyJtN3qu/aY18TYQQ6LMqqT5TQnVyKdUppQjt+bFgSa1A4ajCykGNpLICyTxKb6o2YqoyYNLoMZXXiGtVSKh9HVAHOGEd4oJNWxeknCjYOBNyTkDwIKJ6zOWhVWk4WitZ9lgfgjzq8cHveNscetX9Abhz/j8GXqvN5viJx6isTKB9+zfx8z2f3tSk1ZJ8771UnE3kpfth9O1PMCNixhVl26wsreaX2YdRqhVMejlSrtMq0+I4l3++56g29BrdcEGfc9WkvF59Fbf7/wOAySR4a8MpluxPZVw3Xz6Y2AWVwhK3kvK3uUfv5AMPbkA4+vDliS/5IvoLurn6MskuEV/nnnQpDEBx8FuzL3/Im9Dt/n+u9ytFNvL1IITAWFKNPleDIVeDsawaY4UeU7kOYRRgskwJViuwslUi2SpRutqg9LBB6W6LyssO6Vwe6oIk2D0XTq4yh1jdMZvN4hae+Tkab2cblj3au3ZxAgCTETY+B1E/QI+HYNQn//zDS0qOcDLmaYxGDeFhC3B3H1BLd/LM/6LdvJ15E5WMuv9NJrSfcEVtYTKaWP9pNHkpZUyYFYmH37UbsJaRaS6EEPy55DRxB3O488kIAsPqzyUjTCYynnqaij17CFz8A3aRkf8cv3BnEh9tTWBA+1YsmtId+3Phz2kHYOlEcw/9/l/BvS2rE1bz3oH38LVz5QHnbPwdfYnweg67nfMhbT/4dDVPtAq5/aIDs/+Glm/kjQbQFJin/l9NCs+Ye+HRK0BpDb0fR/SfyXeHC3hv02m6+rvw7QORuF8YJqmvgjWPmsMk+8+E218HSUIIQXrGYpKS5mJj05ou4V/i4BBa69ATn72D6ovlrB1kza2vXX7Bj/rYtyaJY9vSGPJQR0L7+Fzx+WRkrlf0OiNr3o+iokTLpJd74uRRf4CCsbyclIl3Y6ysJGjNalRe57PBrjyUxiu/niTc15lvHojE08kykTEjCpbfbV6+7xfwi+Rg9kFm7pqJhImpHjqC1To6tH8X7zwt/PkulKaBfx+47WUIGthoY38xI9+UuWuuHQmb4eNOsHIKJG2vd2pxk2EyQcJWWDoBFnSHE6vMWSGfOU71oP/j1T9SeXfjaYZ39mbFY33qGvjKQvhpHMRthBEfwJA3QJIs/vf/kpj4Lh7ut9Ezcn0dA79p5WwUi5ZzsrM9k2evbhIDf+ZYHse2pRE2wFc28DItHpVawfDHwxBGweavYzDo65++o3B0xO/zBQiNhownZmDSnJ/rOblXAF/+pwcJuRWM/nwv0emWXDR+PeCRbWDtCIvvhPg/6O3Tm2Ujl+Fq48GCHBOHqr2JiZ1JnE0s8b6dpQAAHEpJREFUpqf2waiPoSQNfhwLW15plu/cMnryxalw+FuIXgaaQnAJMKf/DB0F/r3A6gpL8pmM5sex2HUQ+xtU5ICDN0RONbtaHL3ILq3iiaVHiU4v4fGBwcy6o0PdwZ2cGFh5L5TnwrgvIWw8YHbPxMa+QJU2g5C2LxAQ8Fgt/3qVoYqFv73GwLc3oXW1J/zX33FxufKnlsLMCtZ8EIWrjz3jn+uOQtUy7vkyMpciOTqfP748SYc+3gx+sGOD41nlu3aRMeNJHG67Db/P5iPVKO95OruMx348Ql55NbPHhTOxh595Q0WeOedU9nHzU3q/ZynVlfHSXy+xN3Mvt3oEcqf1aTycO9Op0zwc1P5w4mfwCjPfKBpBy3fXnMNQDac3QPRyc3iTSQ927ubHIb8e4NsD3NuZ/eYNDXiYTFCebZ7wkBtj9p2l7ofqUlDaQLuhEDbBfANRmkMV/04q4L8rjqHVG/nw7ghGhtfTIz69AdY+DjZOMHkZ+PbAZKom+exnpKZ+jY2NL507fYSLS+3/U1JxEm9u+h+PLEjCzWhD+9W/YhvYpvFtZEFboWfVXHMh7rtf7omDqzzhSebm4txEqX4TQ+g6JKDB/Yp+/Inc2bNxmzoVrxdfqL2tUseTy46yP7mQSZF+vDmmM3ZqJegqzWUET62FzuNg7EJMKlu+PfktC6MX4m/fivtdivFUaAgOnkmA/1QkqfGd0RZv5PX6EtLTlxAQ8ChKpSWCRVtqdt0kboOMw+ZEX+dQqMHBC9QOoLYz99RNBnPd1Mo88/I53EMgsB8ED4R2d4D1+UFJrd7Ih1vi+W7vWdq2suer+3sQ4nlBiKSh2pyd7sBC8I00G3hHb4qLDxKf8AaVlYm0bn0P7UJeQak8f24hBKsSVvHJvvd5dbmO4FyJNkuWYGcpgnIlmIwmNiw4TlZSCeNmXrwAsoxMS0WYzC6bs8fzufPpCAIaKOohhCD3nXcpXr4c77ffwnXSpFrb9UYT87cnsnBXEkEe9nw2uRthvs7m6Lm/PzVf/56dYOL34NmBg9kHeXHPi1TqK5nk3Zpu0ilcXbrTocNsHOzbNeq7tHgjn539K7Gnn8da7UVIyEt4eY2u+/ilKYLsaCg6CyWpUFlgvhHoNWClBCsV2LqCoxc4tYZWHcGzY4PxrDGZpcz8JZqE3Aru7xPIyyM7mO/gNSk8A6sfNj+29ZoGQ9+hWlSQlDSHnJx12Nj4Edr+TTw8bqt1WE5lDu8ceIe/0nfzzmZX2h8vxPfTT3G6o26pscbw188JnNiZweAHOtKxr+yHl7l50WkNrP0wioriaia+FImLZ/3J/ITBQPoTM6jcv5+Ar7/Cvm/fOvvsO1PA/36OpqhSx4xBIcy4rS3WSoW5s7l2mrl3f8dsiJxKflUBb+x7g78y/6KrWxDj7TMIC5hEu5CXGvU9WryRTy5N5o2/XmSMUwVuhkScnXvQtu0LuLr0bHKNZVo9H29N4Mf9Kbg7WPPBxC7cFnpBVXaTEQ5/B9vfNLt0xi7EEDKAtLTvSEv/HpNJR2DAo7Rp8yQKxfnRfZMwsTphNR9HfYzJZOT9E53x/u0gnrNm4f7wQ02iP/bvLHb+FEfEYH/6T2pcr0FGpiVRVlDFL3MOY+eoZuKsSNQNTIw0VlSQet8U9BkZBCxZgm14WJ19iip1vPnbKX47nkWIpwNzxofTs40blOfAuifgzJ8QOhJGfYxw9GZN4ho+PPwhkiTxaq9ZjA4Z16jv0OKja3Iqc0irzOfdlGx2MIDCihSOHp3MsWMPUFLacCa4y8FgNLHqSDqDP9rNkv0p3Nc7gO3/G1jXwOedhu/vgD9egIDeGB7bQqptJvv2D+ZsygLc3QbQu9cm2rZ9vpaBjy+KZ+qWqbxz4B3CPMJYUXQ33r8dxHXKFNweerBJvkP2mVJ2L4/Hv6MrfSe0vfQBMjI3AU4etgx/LIySvCq2/RDbYH53hYMD/t98g8LVlfRp06hOTq6zj5u9ms/u7cYPD/ekSmfk7i/38/SKY6TpnGDKGnNPPmkHLOyFdPhbJoaMY82YNYS6hmJopv52i+jJA5Tpylh4bCEr41firHbm/jY9CNHtxWQowsmpK35+9+PlOQIrq8sbYDSaBL+fyGL+jkSS8yuJ8Hfh3bFh5wv+nqOy0Jya4PC3YO2I7vbnSXEqIit7FUZjJW6u/Wnb9jmcnLrUOqywqpDPoz9nbeJaHNWOzOwxk0EHq8h9912cxoym9dy5SE0wK668SMuquUdQWSu4+6VIbOzlGa0yMjU5sTODv35OoPvwQG65q+FOkC41lZT7piCp1bRZvgyVT/0uz8pqA4t2neHbvckYTYIpvQN5fGAwPoYs88z45F3QujvcMRtTQG8kpEbPWm/x7hoqC+HoEoh8mLiqXN478B7R+dEEOgYwOaALQfpDaKtSUKnc8PIahafnKFyce/yTB6Y+Sqv0rInKYOmBVJILKgn1cuR/Q9tzR+cLSvRVl8Ohr2HvpwhdBZr2t5AYaE2h9gSSpMTLcxT+AVNxcqz9aFdaXcqSU0tYHrecakM1kztMZnrEdNiym6wXZ+EweDB+8z9FUl25MdZVGVj7URTlhVomvBiJW+vGpSCWkWnJCCHYtTye2L+yuO3+DnTq17rBfbWnT5N6/wMoPT0JXPoTSreGc9Hklmn5dHsCPx9Ox0qSGB3Rmkf6tSGsaCts/T9zSHboSHOqg1ahDZ7nYrR8Ix+9wlxMV2UPPR5E9J7On+VJfH7sc5JKkghxCWFC4C10lM5SVrwbk6katdoTN7d+uLr0wdW1FzY2/uiNgn1nCth4IpvfT2RTpTfSLcCFqf2CGBXuUzvuvTQT04HPIWoJVrpKir1aEe9npNLeCju7tvh434W39zhsbGrf5fM0eayMW8nyuOVU6isZFjiMJ7s9SbBzMGVbtpI5cyZ2kZH4f/0VVtZXHtZoNJrYuPAEmXHF3Pl0BP4d5dTBMjINUet6eSoC/04NXy+aI0dIe/Qx1P7+BCz+AaV7/dE550gv0vD932f5+XA6Gp2Rzq2dmBDuyt36DTgeWQg9p8LQtxulu8Ub+ZMZpaz9YzP3GtYRkrcVCRNS8G0Yw+9ms62a7+KXkViciLuNOxPb3UV/VzcUlYfJzj9KarE1Z0rbkFDSgbiiEDR6a+xURga1rebublZ08LRCCCNC6DFU5aFMOYTDmWM45mQiCchrpSbN1w58u+PmPoBWHkNwdAyr1dsXQnA07ygr41ayPXU7RmFkaOBQpkdMp52refCzdONGsl6chW14OP7ffovC4cp720IIdi2NI/bv7Ev2TGRkZMzUfPId/0IP3H0bzuVUeeAA6dOfQO3vR8DixZc09HDeS7D+eBbHLbNl+/nAPX2CGdO7Y6M0t3gjvzM+j7c3xHK2oBIfCrlPuYMJyr9pTT7Vkg1JdhFsdmrDNus8MqUzACiqQ6gs6oKhogPC4ISPo5ZOHplEeEQR6nIIpaRDMgnsNUacy/S4F+lxLdGhNIHOWkVpQAja8BHYtr4VJ6euqNW17/hCCBKKE/jj7B9sTtlMZkUmjmpHxoWM457QewhwOj/5onT9erJefgW77t3x+/LLJjHwAFGbUziwLpkeIwLpM1YeaJWR+beUF2lZ8/4RJCuJiS9FXrSIfeXBQ6RPn47KtzWBixej9PD415+TUlDJxpPZ7IzLY3REax7s26ZRelu8kT9HYUU1R9NKSMwrJzW/HLvcI3Qv30lX3VH8RTYAqUprfnbyZpu9FTlKc96KUKUrt9r70kXhRDhq3CvyofgsFCYjGc0Fwk3OvtBuKFYdxkDwoDqpEowmI6nlqZwqOMWB7AMcyDpAXlUeCklBH58+jAgawdDAoXWKapesWUP2a/+HXe/e+H+xECu7xhXdvpCEwzls+y6Wdj29GDq10xWlIZaRuRnJTytn7byjuHrZcdfMbqgvUl2u8tAh0h+fjsrHh4Bvv0HV+uo+Nd80Rv6ilKRBxhFzrvfcWERZBgmaHP5SGPnLVs1xa2uMFkPoaZLwV9jib+OOj0swDq5tsXf2x0Zpi96kR2fUUWWoIk+TR64ml6yKLBKLE9EazUXEXaxd6O3Tm1t8bmGQ/yDcbes+wgkhKPz2W/LnfYx9//74fb4AKxubJvmqGfHFbFgQjVcbJ8Y+003OSSMj00hSThaw6YsT+HdyZ+SMcBSKhq8lzZEjpD8xAytbW/y/+Qab0PZXTads5C+F0UCVvpK4smRO5J8gviiezIpM0svTya/Kb/AwG4UNXvZeeNt50861HR3cOtDBrQPtXNthdZHIHWE0kvvebIqXL8dp5Eh85s7BSt00JQnzUstY9/ExHNxsGP9c9wZLncnIyPw7YvdmsXNpnPmp+OFOF62Ypo1PIH3aNEwaDX4LP8e+V6+rolE28leAwWRAY9Cg0WuoMlShslKhVqixUdrgqHK8bDeISaMha9Ysyrdtx23qVDyff65J4uABinMqWfvRUVRqBeNf6CEnHZORaSKObkll/69nCB/kx633tLvoda/PyiLtsWno09LwmTsH51Gjml3fxYx84wuC3iQorZQ4qZ1wUtdf5f1y0KWnk/HkU1QnJeH1yiu4PXB/Eyg0U16k5bf50UgSjHmmq2zgZWSakG7DAqiq0BO9LQ0be+VFyweqWremzbKlpD/1FFnPPU91XBytnn22Vpriq4nsrL1KVPy1l7MT70afm4v/1183qYGvKtex4bNodFUGRj/dFRevphm8lZGRMSNJEn3Ht6VDXx8Ob0zh+J/pF91f4eJC4Pff43LvZAq/+Zb0x6djLCm5SmprIxv5Zkbo9eR98inp06ah8vIiaNUvOPTv12Tnr6rQsf7TaMoKtYx6sgutAhwvfZCMjMxlI0kSt00JJbhrK/b+kkjMnsyL769W4/PGG3i//RaVBw+SPG48mmvgjr4iIy9J0juSJJ2QJClakqStkiS1tqyXJEn6TJKkJMv27k0j98ZCl5JCyn1TKPzqK5zHj6PNiuWoAxouTnC5aCv0rP80mpI8DaOe6ELrdq5Ndm4ZGZm6WCmsGPZIZ9qEu7N7eTyn/rq4oQdwnTSJNsuXIalVpD7wIHnz5yP0+qug1syV9uQ/FEJ0EUJ0BX4HXresHwG0s7ymAYuu8HNuKITBQOHixSSPn4AuLQ3fTz+l9XvvYWXfdDljtBV61n16jJIcDSOfCL/o9GsZGZmmQ6GyYvi0cALD3dm17N8ZetvwcILWrMV57FgKF31JyuR7qYo5dRXUXqGRF0KU1XhrD5wL1RkL/CjMHABcJEm6KapTVJ08ydm7J5E3933sekYSvH4dTsPvaNLPqGXgZ4Q3WNFGRkameVCorBgxLZzAMLOhj92bdeljHOxpPWc2vvPno8/LJWXSJHLnzMFYUdmsWq/YJy9J0nuSJKUDUzjfk/cFao5MZFjWNQvCYEAbn9Bcp/9X6DIyyJo1i5RJ92AsNFdy8v/yS1TeV15wuyaVpdWs++ToPz142cDLyFwbFCorhj8eRkBnN3YujbvkYOw5nO4YRtuNG3G5ZxJFP/7EmRHDKf7lF4TBcOmDG8EljbwkSdslSYqp5zUWQAjxqhDCH1gGPHW5AiRJmiZJ0hFJko7k5zc88ehilG3axNmxY0l/fDqao8cadY7Gos/MJOftdzgzYiRlm7fg/shUgjdtxGn4HU2eSqA0v4q1H0ZRWqBl1IwuBHSWDbyMzLVEqVIwYno4QREe7P0lkUMbkvk3c48UTk74vPEGbVauQO3nT87rb5A7e3azaGyyyVCSJAUAm4QQYZIkfQXsEkKssGyLBwYJYUkg0wCNnQxlLC2laNkyin/8CWNJCXaRkbhMnozjkNubLFVATYQQaA4fpvinpZTv2AFWVrhMmIDHjCdQeXk1+ecBFGZW8Ntn0Rj1Ju58OgLvILn4tozM9YLJaGLn0jji9ucQfpsft97d7qIzY2sihKB8+3asg4Oxbtu4RILNNuNVkqR2QohEy/LTwEAhxERJkkZh7tWPBHoDnwkhLjm/90pnvJo0GkpWraJoyY/os7KwcnTEaeRIHIcMwa5XzyvKzy5MJrSxpynfupWyTZvQZ2SgcHHBZdIkXO+d3GB1mKYgK6mETV+cQKmyYvQzXXFv3XDqUxkZmWuDMAn+XpvE8e3ptOvpxeAHOqBUXZ0JUM1p5NcAoYAJSAWmCyEyJbOf4nNgOKABHhZCXNJ6N1VaA2EyoTl4kJK1v1K+bRtCq0WytcWuWzdsuoRj07kz6sBA1P7+WNna1jnepNNhyM1Fl5KKNu402phTaA4eNE9mUCiwv+UWnEaNwmnE8GZ5UqhJ/IFs/lwah5O7LaOfjsDJo65eGRmZ6wMhBEe3pHJgXTI+bZ0ZMT0cW8emyUt1MW7q3DUmrRbNoUNU7N6D5uhRqhMSwGj8Z7tka4uVvT1WajVCr8ek02EqLa11DpWvL3a9emHf9xbs+/W7aKmvpkKYBAc3JBP1Ryq+oa4MnxYm12WVkblBSDySy44lp7F3VnPnUxG4ejdvyc2b2shfiKmqiurERHTp6egzMjGWlGCqqEDoqpHUaiS1NUoPd5SeXqj8/bDp0AGF05XnrbkcdFoDf/4Yx5mjeXTs58PA+0IvmuJURkbm+iPnbCmbFp3EqDcx7NHOBDZjoIRs5G8girIq2fz1SUpyNfQZ15ZuQwPkgh8yMjcoZYVVbFp0ksLMCiJHtqHnqKDataKbCDkL5Q1CwqEcdi6NQ2WtYMyz3fALldMUyMjcyDi52zLxxR7sXpnAkY0p5JwpZdgjna+Kn/4csg/gOkCnNbDzp9Ns+z6WVgGO3PNqL9nAy8i0EJRqBbc/0JHb7u9A9plSfn73EGmnCq/e51+1T5Kpl6zEYnYsOU15oZbudwTSe0wQVrL/XUamxdGpX2taBTiy7ftYNiw4TucBvvQd3/aitWObAtnIXyN0VQYObTjL8Z3pOHnYMu657viEuFxrWTIyMs1IK39HJr0SycHfzhK9PY302EIG398R32Z8cm8RRl6vM3ImKo/2vb2bZVCjKRFCkBSVx9+rEqks0xE2wJdbxjX/3VxGRub6QKlS0G9CCEERHuxYHMu6T44R2sebvuNDsHNqel99i7AsiYdy2bk0jmPb0rhlXFsCw9yvy4iU3LNl7F+XRGZ8CR7+DgyfHi6nJ5CRuUlpHeLC5Nd7E7UphWPb0lBZKxh4b2iTf06LCKEUQnDmaD77152hLL8K31BXet3ZBp8Ql+vC2BdlVXJoQzJnjuVj66ii56ggOg/wve6fOmRkZK4ORdmV2DqqsHVoXE/+pomTNxpMnPorkyObUqgq1+Md7Ez34YG0CXP/18mCmgohBFmJJURvSyPlZCEqawXdhgUQcbu/7JqRkZFpUm4aI38Ovc5I3L5sjm1No7xIi4ObNR37tqZjXx8c3Zo314ymTEfi4VxO78+mMKMCW0cV4YP8CBvo2+i7tIyMjMzFuOmM/DmMRhPJx/I5/XcW6aeLQQKvNk4ERXgQ1KUVrj52TeLOKc3XkBpTRGpMAemnixEmgWegI536tya0tzdK9dXJRCcjI3NzctMa+ZqUFVQRfzCHs8cLyE8rB8DWUYVXkDPewU64+djj1MoWZw/bBo2yQW+koqia8kItBRkV5KWVkZdSRlmBFgBnT1uCu7aiQx8f3Fo3b0IiGRkZmXPIRv4CKoq1pMYUkn2mlJzkUkrzqmptV6qtUNsoUVkrEEJg0Jsw6k1Ua2qX53J0s8GzjSM+IS4Ehrnj4mnX7NplZGRkLkTOXXMBDq42dL7Vl863msvOaiv1lOZVUVqgoSy/Cq3GgF5rRK81IFlJKFVWKFQK7JzUOLpZ4+Bmg5uP/VXNPyEjIyPTGG5KI38hNvYqbIJUeAVd3ZTCMjIyMs2NnCRFRkZGpgUjG3kZGRmZFoxs5GVkZGRaMLKRl5GRkWnByEZeRkZGpgUjG3kZGRmZFoxs5GVkZGRaMLKRl5GRkWnBXFdpDSRJygdSG3m4B1DQhHKagxtBI8g6mxpZZ9NxI2iEq68zUAjRqr4N15WRvxIkSTrSUO6G64UbQSPIOpsaWWfTcSNohOtLp+yukZGRkWnByEZeRkZGpgXTkoz819dawL/gRtAIss6mRtbZdNwIGuE60tlifPIyMjIyMnVpST15GRkZGZkLkI28jIyMTAvmhjfykiQNlyQpXpKkJEmSXrrWemoiSVKKJEknJUmKliTpiGWdmyRJ2yRJSrT8db0Gur6XJClPkqSYGuvq1SWZ+czSvickSep+jXW+KUlSpqVNoyVJGllj28sWnfGSJN1xlTT6S5K0U5KkWEmSTkmS9Ixl/XXVnhfReb21p40kSYckSTpu0fmWZX2QJEkHLXp+liRJbVlvbXmfZNne5hrrXCxJ0tka7dnVsv6aXUcIIW7YF6AAzgDBgBo4DnS61rpq6EsBPC5Y9wHwkmX5JeD9a6BrANAdiLmULmAk8AcgAX2Ag9dY55vA8/Xs28ny/7cGgiy/C8VV0OgDdLcsOwIJFi3XVXteROf11p4S4GBZVgEHLe30CzDZsv5L4AnL8gzgS8vyZODnq9SeDelcDEysZ/9rdh3d6D35XkCSECJZCKEDVgJjr7GmSzEWWGJZXgLcdbUFCCH2AEUXrG5I11jgR2HmAOAiSZLPNdTZEGOBlUKIaiHEWSAJ8++jWRFCZAshjlqWy4HTgC/XWXteRGdDXKv2FEKICstbleUlgMHAasv6C9vzXDuvBm6XJEm6hjob4ppdRze6kfcF0mu8z+DiP9yrjQC2SpIUJUnSNMs6LyFEtmU5B/C6NtLq0JCu67GNn7I88n5fw911zXVaXAXdMPfqrtv2vEAnXGftKUmSQpKkaCAP2Ib5KaJECGGoR8s/Oi3bSwH3a6FTCHGuPd+ztOcnkiRZX6jTwlVrzxvdyF/v9BdCdAdGAE9KkjSg5kZhfo677mJYr1ddFhYBbYGuQDYw79rKMSNJkgOwBnhWCFFWc9v11J716Lzu2lMIYRRCdAX8MD89dLjGkurlQp2SJIUBL2PW2xNwA2ZdQ4nAjW/kMwH/Gu/9LOuuC4QQmZa/ecCvmH+wuece0yx/866dwlo0pOu6amMhRK7l4jIB33DehXDNdEqSpMJsOJcJIdZaVl937VmfzuuxPc8hhCgBdgK3YHZvKOvR8o9Oy3ZnoPAa6RxucYsJIUQ18APXQXve6Eb+MNDOMvKuxjzw8ts11gSAJEn2kiQ5nlsGhgExmPU9aNntQWD9tVFYh4Z0/QY8YIkO6AOU1nBDXHUu8GOOw9ymYNY52RJtEQS0Aw5dBT0S8B1wWgjxcY1N11V7NqTzOmzPVpIkuViWbYGhmMcPdgITLbtd2J7n2nki8Kflyela6IyrcWOXMI8b1GzPa3MdXa0R3uZ6YR61TsDst3v1WuupoSsYc3TCceDUOW2Y/YU7gERgO+B2DbStwPxorsfsG3ykIV2YowEWWtr3JBB5jXX+ZNFxAvOF41Nj/1ctOuOBEVdJY3/MrpgTQLTlNfJ6a8+L6Lze2rMLcMyiJwZ43bI+GPNNJglYBVhb1ttY3idZtgdfY51/WtozBljK+Qica3YdyWkNZGRkZFowN7q7RkZGRkbmIshGXkZGRqYFIxt5GRkZmRaMbORlZGRkWjCykZeRkZFpwchGXkZGRqYFIxt5GRkZmRbM/wNh8iyY1obflgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 065ec91a8dd7c697863c139e3d88d26183857876 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 193/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- skfda/representation/basis.py | 5 +- tests/test_fpca.py | 28 +- 4 files changed, 1160 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 32372a329..886f90e79 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,8 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) + #return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 @@ -2170,7 +2171,7 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, .. math:: = \int_a^b x(t)y(t) dt - When we talk abaout FDataBasis objects, they have many samples, so we + When we talk about FDataBasis objects, they have many samples, so we talk about inner product matrix instead. So, for two FDataBasis objects we define the inner product matrix as diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 8dc87ec5d8a38b45d20be5c04121bdb5f6de6add Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:22:29 +0100 Subject: [PATCH 194/624] Finilized Module testing --- skfda/representation/basis.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 886f90e79..d1fb95a0e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,8 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return self.to_basis().inner_product(other) - #return np.transpose(other.inner_product(self.to_basis())) + return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From eaedbee7b9980e87a75dcec113a2c9f5fdf155e3 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 195/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From e797fb4edd33de358f9c42d14056b3d7937e5508 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 196/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- skfda/representation/basis.py | 2 +- 5 files changed, 175 insertions(+), 97 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index d1fb95a0e..ed13bf9d8 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From e8da96a44d7e555590ef1de9f813667d8932a158 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 197/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From acc2a00d416fdd2e04002d5f76c41879c0774346 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 198/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 90fa6d474860677edd74808d16803a30e1d2378e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 199/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From a2ad833c8907d6626370088f460ba0de01909d83 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 200/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From de716f60f5d5e90b867ee66397f8ba5410770adc Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 201/624] transfer files to new location and modify documentation --- docs/modules/exploratory.rst | 1 - docs/modules/preprocessing.rst | 13 +- docs/modules/preprocessing/dim_reduction.rst | 18 + .../dim_reduction}/fpca.rst | 10 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ----------------- skfda/preprocessing/dim_reduction/__init__.py | 1 + .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 437 +++++++++++++++++- tests/test_fpca.py | 6 +- 12 files changed, 456 insertions(+), 463 deletions(-) create mode 100644 docs/modules/preprocessing/dim_reduction.rst rename docs/modules/{exploratory => preprocessing/dim_reduction}/fpca.rst (75%) delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index edc2c8d73..832b93193 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,4 +11,3 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers - exploratory/fpca \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index 06f3eb6da..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -12,6 +12,7 @@ this category deal with this problem. preprocessing/smoothing preprocessing/registration + preprocessing/dim_reduction Smoothing --------- @@ -28,4 +29,14 @@ Sometimes, the functional data may be misaligned, or the phase variation should be ignored in the analysis. To align the data and eliminate the phase variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the -registration methods available in the library. \ No newline at end of file +registration methods available in the library. + +Dimension Reduction +------------------- + +The functional data may have too many samples so we cannot analyse +the data with clarity. To better understand the data, we need to use +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. +:doc:`Here ` you can learn more about the +dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst new file mode 100644 index 000000000..9da0452b7 --- /dev/null +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -0,0 +1,18 @@ +Dimension Reduction +=================== + +When dealing with data samples with high dimensionality, we often need to +reduce the dimensions so we can better observe the data. + +Projection +---------- +One way to reduce the dimension is through projection. For example, in +functional principal component analysis, we project the data samples +into a smaller sample of functions that preserve the maximum sample +variance. + +.. toctree:: + :maxdepth: 4 + :caption: Modules: + + dim_reduction/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst similarity index 75% rename from docs/modules/exploratory/fpca.rst rename to docs/modules/preprocessing/dim_reduction/fpca.rst index b80519747..7af947b89 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -9,9 +9,9 @@ of FPCA are orthogonal functions (usually a much smaller sample than the input data sample) that represent the most important modes of variation in the original data sample. -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. +For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, +where the process is applied to several datasets in both discretized and basis +forms. FPCA for functional data in a basis representation ---------------------------------------------------------------- @@ -19,7 +19,7 @@ FPCA for functional data in a basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis + skfda.preprocessing.dim_reduction.projection.FPCABasis FPCA for functional data in a discretized representation ---------------------------------------------------------------- @@ -27,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index e69de29bb..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -0,0 +1 @@ +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd4b4dadc..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import fpca +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index f966cce17..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,33 +1,426 @@ -"""Functional principal component analysis. -""" +"""Functional Principal Component Analysis Module.""" import numpy as np +import skfda +from abc import ABC, abstractmethod +from skfda.representation.basis import FDataBasis +from skfda.representation.grid import FDataGrid +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut -from ....exploratory.stats import mean +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" -def fpca(fdatagrid, n=2): - """Compute Functional Principal Components Analysis. +class FPCA(ABC, BaseEstimator, TransformerMixin): + """Defines the common structure shared between classes that do functional + principal component analysis - Performs Functional Principal Components Analysis to reduce - dimensionality and obtain the principal modes of variation for a - functional data object. + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + + def __init__(self, n_components=3, centering=True): + """FPCA constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + self.n_components = n_components + self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) + + @abstractmethod + def fit(self, X, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + pass + + def fit_transform(self, X, y=None, **fit_params): + """Computes the n_components first principal components and their scores + and returns them. + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + """Funcional principal component analysis for functional data represented + in basis form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + + """ + + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization_derivative_degree=2, + regularization_coefficients=None, + regularization_parameter=0): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + # basis that we want to use for the principal components + self.components_basis = components_basis + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients + + def fit(self, X: FDataBasis, y=None): + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + """ + + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # setup principal component basis if not given + if self.components_basis: + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range + g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. + j_matrix = X.basis.inner_product(self.components_basis) + else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object + self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix + + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 + + # Apply regularization / penalty if applicable + if self.regularization_parameter > 0: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix - It uses SVD numpy implementation to compute PCA. + # obtain triangulation using cholesky + l_matrix = np.linalg.cholesky(g_matrix) - Args: - fdatagrid (FDataGrid): functional data object. - n (int, optional): Number of principal components. Defaults to 2. + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - Returns: - tuple: (scores, principal directions, eigenvalues) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) + self.pca.fit(final_matrix) + + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) + + component_coefficients = np.transpose(component_coefficients) + + # the singular values obtained using SVD are the squares of eigenvalues + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case it is the inner product of our data with the components + return X.inner_product(self.components) + + +class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ - fdatagrid = fdatagrid - mean(fdatagrid) # centers the data - # singular value decomposition - u, s, v = np.linalg.svd(fdatagrid.data_matrix) - principal_directions = v.T # obtain the eigenvectors matrix - eigenvalues = (np.diag(s) ** 2) / (fdatagrid.n_samples - 1) - scores = u @ s # functional principal scores - - return scores, principal_directions, eigenvalues + + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + self.weights = weights + + def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book, chapter 8. + + Args: + X (FDataGrid): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # get the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + fd_data -= np.squeeze(meanfd.data_matrix) + + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) + + weights_matrix = np.diag(self.weights) + + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From 4e76b8b08732d90e49531df6bd9e5c3a41f3b7b4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:50:18 +0100 Subject: [PATCH 202/624] fix gram matrix in Fourier basis --- skfda/representation/basis.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index ed13bf9d8..71ec3f77e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1484,14 +1484,18 @@ def penalty(self, derivative_degree=None, coefficients=None): def gram_matrix(self): r"""Return the Gram Matrix of a fourier basis - We already know that a fourier basis is orthonormal, so the matrix is - an identity matrix of dimension n_basis*n_basis + We already know that a fourier basis is orthonormal when the period is + the same as the domain range so the matrix is an identity matrix of + dimension n_basis*n_basis. Else we compute the matrix. Returns: numpy.array: Gram Matrix of the fourier basis. """ - return np.identity(self.n_basis) + if self.domain_range[1] - self.domain_range[0] == self.period: + return np.identity(self.n_basis) + else: + return super.gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 4545bc43f08668e077bb14b913abf549eeb3a373 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:58:09 +0100 Subject: [PATCH 203/624] fix gram matrix method in Fourier basis --- skfda/representation/basis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 71ec3f77e..aee9584be 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1492,10 +1492,10 @@ def gram_matrix(self): numpy.array: Gram Matrix of the fourier basis. """ - if self.domain_range[1] - self.domain_range[0] == self.period: + if self.domain_range[0][1] - self.domain_range[0][0] == self.period: return np.identity(self.n_basis) else: - return super.gram_matrix() + return super().gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 823a97cddcadbe1463b04ebf26ef1f0def8b1fb0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 204/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 85263dbd62140748e65d5fb510c53efa53cc49c6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 205/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From 08fd1054a9b92c8bc5b745bb52c964ed952b4b00 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 206/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From c03c8f9780825781eb3b68a60ac162f5ce7a7a71 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 207/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 761f6e28942416b47d0b1037265db3d6f99b6493 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 208/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 49e5d4a530474920b9fce2a13ec0b77429af040f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 209/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From 01fa1c458744269e6847f7bbfca7a9667025c293 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 210/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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-1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d1833ea25d2b73ca6a9810b29b0e88b1d69ffb4c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 211/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From d952896849379a1cdae44b02d24435d12ed3adb8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 212/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From 5613ab9c9eec96e6672d73cc4adea614c865c658 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 213/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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Pgbs3wJKH5AMmhNXGXgn2Tij6wOpKhrxBB75Syg94ElgGjANuVEqN67XZ14F6rXUW8BjwyGD3O2D2Ltj4G/jtHCjfDisehdtXyegbITzFiFwITzLdOsKlnNGlkwcUa61LAZRSLwNXAwd6bHM18EPHz68DTyillNZaO2H//asvg7fuNnPe5KyAFb+AyOEu3aUQ4gLZbGYK8d2vmOHRstqbyzijS2cEcKzH7+WOx/rcRmvdCTQCcb1fSCl1l1IqXymVX11dffEVaW2mKn5qLlTthZW/gxtekrAXwlONucJc0V66zupKhjSPGpaptX5aa52rtc5NSEi4uBc5Uwev3Axv3wPJk+GejTDlRpnFUghPlnaJWRHuoHTruJIzunQqgJ4LsqY4Hutrm3KllD8QBdQ6Yd//TGuo3GNmspx1r/m6KITwbP6BkL0YCt4zy4P6+dYAQndxRhpuA0YrpdKVUoHADUDvyTHeAW51/Pwl4COX9d+HxcF922DONyXshfAmY64w05oc3WR1JUPWoBPR0Sd/H7AaOAi8qrXer5T6sVLqKsdmzwBxSqli4LvAPw3ddCpZUlAI75O1CPyCZLSOCznle5PW+j3gvV6PPdjj51bgOmfsSwgxRAWFQ8ZCKFgFSx+W824uIH0eQgjPkbMUGsqg+pDVlQxJEvhCCM+RvdTcF75vbR1DlAS+EMJzRA43w6kLJPBdQQJfCOFZspdC+VazzKhwKgl8IYRnyV4K2i6TqbmABL4QwrMkTzGTqUk/vtNJ4AshPIvNZq66LV4Lne1WVzOkSOALITxP9jJob4ayDVZXMqRI4AshPE/GQvAPhsLVVlcypEjgCyE8T2AopC+AwlVmQkThFBL4QgjPlL0E6o9AdYHVlQwZEvhCCM/UfdXtKmvrGEIk8IUQnilqBCRNkn58JxqSgX+mvdPqEoQQzpCzDI5tMSvZiUEbcoHf3NrBlB+t4eon1vPI+4dYX1RDa0eX1WUJIS5G9hK56taJhtw6Yp1dmrsXZLCxpJY/fFrKU+tKCPSzMW1UNHMz45mTFceklGgC/IbcsU6IoSd5KoQnmqtuJ99gdTVeT7lqpcHBys3N1fn5+YN6jVNtnWw7UsfG4ho2FNdyoLIJgLBAP/LSY5mbFc+czHjGJEVgs8liC0J4pLfvgwPvwA9KwC/A6mo8nlJqu9Y6t6/nhlwLv6fwIH8uzRnGpTnDAKg73c7m0lo2ltSwsbiWjwsOAhAbFsjsjDjmZMUxJzOetLhQlKy2I4RnyF4CO1+Eo5sh/RKrq/FqQzrwe4sNC2T5xGSWT0wGoLKxhY3FtWxwHADe3VsJwPCoYOZkxTMn0xwAkqJkjVwhLJOxEPwCoWi1BP4gDekunQuhteZwzWk2lNSyqaSGTSW11J/pACAjIYy5mfHMzYpjVkYc0aGBbqtLCAG8sBKaKuC+bVZX4vF8tkvnQiilyEgIJyMhnFtmjcJu1xyobGJTifkG8MaOcl7cXIZSMH54JHMy45mdEUduWgwRwdKvKIRLZS+B9++HulKIzbC6Gq8lLfwB6uiys/tYAxuKzTmAnUcbaO+yY1MwYUQUM9NjmZURR25aLFEhcgAQwqnqSuHxqbD0EZh1t9XVeLRztfAl8C9SS3sXO4/Ws/lwHZtLa9nlOACc/QYwMz2Omemx5KXHSheQEM7wm1yIHgm3vGV1JR5NunRcICTQz5zYzYoHoLWji51HG9hyuJYtpXX8eXMZz6w/jFIwJimSWRmx3QeBmDA5AAhxwbKXwNanoe0UBIVbXY1XksB3kuAAP2ZnxjE7Mw6Ats4udh9rZHNpLVsO1/LXrUd5bsMRAMYkRXR3AeWlxxIXHmRh5UJ4iewlsOkJKF0HY6+wuhqvJIHvIkH+5uKuvPRYYDTtnXb2lDewxdEF9Gp+Oc9vKgPMKKDcUTHkpsUyIy1WrgMQoi+psyEo0lx1K4F/USTw3STQ30ZuWiy5abHce2kWHV129lY0sqW0ju1ldXxw4ASv5pcDEBcWSG5aDLmjYslNi2H88CgC/WUqCOHj/AIg8zIoWgN2u1n7VlwQCXyLBPjZmJYaw7TUGCATu11TWnOKbUfqyT9ST35ZHav3nwAgyN/GlJHRzEiLZXqa+RsZCSR8UvZSOPA3qNoNw6daXY3XkcD3EDabImtYBFnDIrgxLxWAk82tbD9Sbw4CZXU89UkJXR9rlIKcxAimjzLhPyU1mvS4MJkPSAx9o78AKCj8QAL/IsiwTC9ypr2TXUcbyC+rZ9uROnYebeBUm5n7PyokgMkjo5kyMpqpqdFMSYmW0UBiaPrjIrB3wV0fW12JR5JhmUNEaKD/54aCdtk1xSdPsetYPbuONbDzaANPfFSE3XEMT4sLZWpqTPdBYExSpJwLEN5v9BL4+H/g1EkIH2Z1NV5FWvhDzOm2TvaUN7LzWD27jjaw81gD1c1tgDlxPGF4JJNSopk4IoqJKVFkJoTjJ11BwptU7oHfXwJXPwlTb7a6Go8jV9r6MK01xxtb2XW0gV3H6tl5tIH9x5tocawCFhLgx7jhkUwcEcX44ZFMTIkiKyEcf1kgRngqreHRcZCSC19+0epqPI506fgwpRQjokMYER3CiklmWuguu6ak+hR7yxvZd7yRfRWNvJp/jDPt5iAQHGBjbLI5CEwYHsWEEVGMTgyXVcKEZ1AKshfD3jegsx385VzVQEkLXwDmIHC45hR7KxrZW97EvopG9h9v5LTjIBDgpxg9LIKxyZGMTY5gXHIkY5Mj5cSwsMah9+DlG+Grb5v58kU3aeGL8/LrMSz0GsdoN7tdc7j2NPsqGjlwvIkDlU18UljNGzvKu/8uKTKYsclnDwTmlh4fJucFhGtlLAC/IChcLYF/ASTwRb9sNkVmQjiZCeFcPWVE9+PVzW0crGzqcWvm06IauhzDg4IDbOQkRTKux4FgTFKErBsgnCcwzKx+Vbgalv7U6mq8hgS+uGAJEUEkRCQwPzuh+7G2zi6KTpzqPgAcrGxi1b4q/rr1WPc2I2NDGJv02TeBccmRjIwNkXmDxMXJXgrvfQ9qiiE+y+pqvIIEvnCKIH8/JowwJ3jP0lpT1dTafRA4cNx8I1hz8ARnTx1FBPkzpleXUE5iBCGBfhb9lwivMXqxuS9aLYE/QIMKfKVULPAKkAYcAa7XWtf32mYK8BQQCXQBD2mtXxnMfoV3UEqRHBVCclQIl41J7H78THsnBVXN3d8EDlY28eaOCk61mdlDbQrS48M+901gbHIkiZFB8m1AfCZmFCSMNbNnzr7X6mq8wmBb+PcDa7XWDyul7nf8/m+9tjkDfFVrXaSUGg5sV0qt1lo3DHLfwkuFBvozNTWGqakx3Y/Z7Zpj9Wc4WNnEAceBYNexBv6+p7J7m5jQgM8dAMYmR5I1LFyuHvZl2Yth05PQ2gTBkVZX4/EGNSxTKVUALNRaVyqlkoF1Wuuc8/zNbuBLWuuic20nwzIFQFNrB4cqmzlwvNF8I6hqoqCqmbZOO2CGi2YmhH/uIDA2OUIWlfEVZRvhuWVw3fMwfqXV1XgEVw7LTNRan22CVQGJ59pYKZUHBAIl/Tx/F3AXQGpq6iBLE0NBZHBAj4VkjM4uO0dqT3d/EzhY2cT64hre3FnRvU1SZDATU6KY5JhCYuKIKDkIDEUpeRAcDUUfSOAPwHkDXyn1IZDUx1MP9PxFa62VUv1+XXB8A3gRuFVrbe9rG63108DTYFr456tN+CZ/P1v3NQNXTR7e/Xjtqbbu8wL7jzeyp6KRNQdOdD8/IjqESSlRjgOBmU8oKlSGino1P3/IWmQCXxZFOa/zBr7WelF/zymlTiilknt06ZzsZ7tI4F3gAa315ouuVohziAsPYt7oIOaNju9+rKm1g/0VTeytaGBPeSN7KxpZta+q+/lRcaFMHBHFpJQopqXGMGFEFMEBMkLIq2QvgX2vw/GdkDLd6mo82mC7dN4BbgUedty/3XsDpVQg8Bbwgtb69UHuT4gLEhkc8LnF5QEazrSzr6KJPRUN7C1vZOfRz04OB/gpxg834T9tVDTTR8WQHBViVfliILIWgbKZ0ToS+Oc02JO2ccCrQCpQhhmWWaeUygXu1lrfoZS6GXgO2N/jT2/TWu8612vLSVvhTtXNbew8Ws/2o/XsLGtgd3lD94nh5KhgpqXGMDXVHAAmjIiSieQ8zTNLoLMF/uVTqyuxnEyPLMQFau+0c7CyiR1H69lxtIEdZfVUNLQAZkrp6aNimJkey8yMOCaPjCLIX7qBLPWPR2Htj+C7hyAy2epqLCWBL4QTnGhqZXtZPVtKa9lyuI5DVc2AWWR+amo0M9PjmJkRy7TUGDkP4G4n9sNTc+DKx2H6rVZXYykJfCFcoP50O1uP1LGltI4th2s5UNmE1hDoZ2PKyGjmZsUzb3Qck1OiZUEZV9MafjURkifDDS9ZXY2lZHpkIVwgJiyQJeOTWDLejFpubOkg/0gdWw7Xsamkll+tLeSxDyE8yJ9ZGXHMy4pj3uh4MhPCZYoIZ1PKzK2z+2XobAN/ueaiLxL4QjhJVEgAl49N5PKx5vrDhjPtbCypZX1xDRuKa/jwoLkmICkyuLv1PzcrnmERwVaWPXRkL4X8Z+DIesi63OpqPJIEvhAuEh0ayPKJySyfaE4iHqs7w/riGtYX1/DRoRPdC8nkJEZwyeh4FuQkkJceKyeAL1b6JeAfYubIl8Dvk/ThC2EBu11zwDElxPqiGrYeqaO9005IgB+zM+NYmJPAguwERsWFWV2qd/nLl+HkQfjWbtPN44OkD18ID2Ozqe71A+5ekMmZ9k42l9bySUE16wqr+eiQuWg9PT6MBdkJLMhJYFZ6nKwTcD6jF5sLsGoKIeGc8zj6JAl8ITxAaKA/l41J7F434EjNadYVnOSTwmpe3naUP208QpC/jZkZcSx0HAAy4sPk5G9v2UvMJC6FqyXw+yBdOkJ4uNaOLrYermNdQTWfFJ6kpPo0YJaMXJCdwILsYczJjCMsSNpvADw118ygefu7VldiCenSEcKLBQf4MT/77BrC4zhWd4ZPCqtZV1DNmzsq+PPmowT62chLj2VhTgILcxJ8e+hn9hJY/ytoaYCQaKur8SjSwhfCi7V1drH9SD3rCqv5+NBJik6eAsxU0Cb8fbD1f3QLPLsYvvQsTPii1dW4nVxpK4SPqGhoYV3BSdYVVLOxuIbT7V0E+tmYkR7DwuxhLMxJIGvYEG/927vg51nmBO61v7e6GreTwBfCB7V32sk/Use6wmrWFZyk8MRnrf8FOQkszE5gblb80Gz9v3kXFK2B7xeDzbdGNkngCyGoaGgxwz4LTrLB0foP8FPMSIvt7v4ZPVRa//vegNe/Bl9fAyPzrK7GrSTwhRCf095pJ7+sznEAqKbghJn5c0R0CPOzzYnfuVnxhHtr67+lAX6WAfO+DZc/aHU1biWBL4Q4p+MNLazro/WfO+qz1n92ope1/p9bAa2N8I31VlfiVhL4QogB66/1PzwqmAU5Ztz/vNFe0Prf8GtY8yB8Zz9EpVhdjdtI4AshLtrxhhbHuP+TbCiu5VRbJ/42RW5aDAtzzMifnMQIz2v9VxfAk3lwxWOQ+zWrq3EbCXwhhFO0d9rZXlbfPfTzbOs/ISKIOZlxzM2MZ05WHCkxoRZXilkU5deTIWEMfOVVq6txG7nSVgjhFIH+NmZnxjE7M45/Xz6W4w0t/KOomg3FtWworuHtXccBGBUXypzMeOZlxTM7M47YsED3F6sUjFkB2/4Ibc0QFOH+GjyMtPCFEE6htabgRDMbimvZWFzDlsN1nGrrBGBcciRzs+KYkxVPXlqs+8b+l22C55b61FW30qUjhHC7ji47e8ob2Vhcw4aSGnaUNdDeZcfPppgwPJIZabHMSI9lRlqs674B2Lvgl2Ng1By4/nnX7MPDSOALISzX0t7FtiNmwfdth+vZVd5Ae6cdgNHDwpmRHkue4yAwIjrEeTv+v2/DnlfhByUQ4MTX9VDShy+EsFxIYM9ZP820z3srGtl6uI6th+t4Z9dx/rLlKGCGgE4eGW1uKdFMTIm6+GGg466C7c9ByUemT9+HSeALISwRHOBnunXSYrn3Uuiyaw5WNrH1cB07jzWw+1gDq/ZVAW7b9R0AAA0NSURBVOb86+hh4UxKMQeBiSOiyE4MJzRwABGWdgkER8HB//OawO+ya/xszh/mKoEvhPAIfj2WfTyr7nQ7u8tN+O8pb+TjQyd5fbtZ/F0pGBUbypikSHKSIhibHEFOUiSpsaGfD0u/AMhZDgXvQVeH+d2DdNk1JdWn2FPeyJ7yBrYdqScxMog/3e78OYAk8IUQHis2LJBLc4Zxac4wwIwEKq9vYf/xJgqqmjlU1cShqmZWH6ji7OnIQD8bI2NDSI8PIy0ujPSEMKZFL2Bs619pL15HYM4XLPlv0VpT1dRKafVpSqtPUVJ9mgPHm9h3vJEz7V0AhAX6MSU1mjmZcS6pQQJfCOE1lFKMjA1lZGwoSyckdT9+pr2TohOnOFTVRGnNaY7UnOZIzRn+UVRDW6edIILZERTE3158ikeDICkqmOSoYBIjg4kPDyI6NIDo0ACiQgKICgkkKsSfIH8/gvxt5j7ARqCfDaWg067psuvu+/ZOO82tHTS3dtLkuK873c7JplaqmlqpamrjRGMr5fVnOO0IdjDhnp0UwfW5I5k4IorJI6NIjw93SVfOWRL4QgivFxro332Stye7XVPZ1MqRmtPUrV3Iypp8DoxNoLKpg4qGVraX1VN/psMlNdkUxIcHkRQVTGpcKLMz48hMCCMzIZyMhHASI4PcPh2FBL4QYsiy2RQjokPMMM+W6+GN1Tw0/YwZl+/QZdc0tXTQ0NJBY0sHDWfaaWzpoK3TTnunvce9aZ372xQ2m8LfpvCz2Qj0U0QEBxAR7N99Hx0aQEJ4EP5+Nqv+0/skgS+E8A3ZS8A/GPa/9bnA97MpYsICibFi+gc386zDjxBCuEpQhFnndv9b0NVpdTWWkMAXQviOCV+E09VQ5luLopwlgS+E8B3ZSyAw3Kx564Mk8IUQviMgxFxte+Ad6Gy3uhq3k8AXQviWCV+E1gYzt46PkcAXQviWjEshONonu3Uk8IUQvsU/EMZdbebWaT9jdTVuJYEvhPA9E74I7aegaLXVlbjVoAJfKRWrlFqjlCpy3MecY9tIpVS5UuqJwexTCCEGLW0eRCTD7petrsStBtvCvx9Yq7UeDax1/N6fnwCfDnJ/QggxeDY/mHwDFK2B5hNWV+M2gw38q4GzC0U+D6zsayOl1HQgEfhgkPsTQgjnmHwT6C7Y+6rVlbjNYAM/UWtd6fi5ChPqn6OUsgG/BL43yH0JIYTzJGRDygzY9Rfw0LW9ne28ga+U+lApta+P29U9t9NmNfS+3rV7gPe01uUD2NddSql8pVR+dXX1gP8jhBDioky5CU4egMrdVlfiFuedLVNrvai/55RSJ5RSyVrrSqVUMnCyj81mA5cope4BwoFApdQprfU/9fdrrZ8GngbIzc31jUOuEMI646+FVfebVv7wKVZX43KD7dJ5B7jV8fOtwNu9N9Baf0Vrnaq1TsN067zQV9gLIYTbhUTD2CtMP35nm9XVuNxgA/9h4AtKqSJgkeN3lFK5Sqk/DrY4IYRwuSk3QUs9FL5vdSUup7SHnqzIzc3V+fn5VpchhBjq7F3wq4mQkAO3vGV1NfDeD8xFYSt/e1F/rpTarrXO7es5udJWCOHbbH4w/XYzmVptibW12O2w/03obHXJy0vgCyHEtK+CzR/yn7W2jortZoGW7GUueXkJfCGEiEiEsVfCzj9DR4t1dRx8B2wBMLrfwZGDIoEvhBAAM+4w8+Tve9Oa/WsNB96GjIUQ0u+0ZIMigS+EEACj5kLCGNj2B2uuvK3cDQ1lZupmF5HAF0IIAKUg7044vhPKNrp//wfeBuVnlmB0EQl8IYQ4a8pXIDQeNj7u3v1qDQf+BunzITTWZbuRwBdCiLMCQiDvLnMR1slD7ttv5S6oK3Vpdw5I4AshxOfl3QkBobDxN+7b586XwD8Yxl/j0t1I4AshRE+hsTD1ZtjzCjQdd/3+OlrMXD5jrzRz+7iQBL4QQvQ2+17Qdlj/K9fv69C70NpoDjIuJoEvhBC9xaSZAN7+HDQcc+2+dr4I0amQNt+1+0ECXwgh+jb/++b+H79w3T6qC6B0HUy9BWyuj2MJfCGE6Ev0SJh+m5luwVWTqm38jTlZm/s117x+LxL4QgjRn0u+B/4hsPo/nP/azVXmxPCUr0BYvPNfvw8S+EII0Z+IRFjwfTMuv+hD5772lt9DV4c5QewmEvhCCHEuM78BsZnw/v3Q4aR56k/XwNY/wLirIC7TOa85ABL4QghxLv6BsPznUFsEnzzsnNf89BfQcRoufcA5rzdAEvhCCHE+WZebRVI2/BrKB7n0anUhbPuj6btPyHFOfQMkgS+EEAOx+CGIGA5v3gktDRf3GnY7/P3bEBgGl/+3c+sbAAl8IYQYiOBI+NIz5kKsN+4wi59fqC2/g7INsPgnEJ7g/BrPQwJfCCEGKnUWLP8ZFK+BD/7rwhZKKc+HNQ9CzgpzoZUF/C3ZqxBCeKvcr5mpkzc/aU7oXv7fZvGUc6kuhJeug8jhcPUT59/eRSTwhRDiQi19GLraYf1j0HAUrngMgqP63vboZnj5JrD5wS1vuXSBk/ORwBdCiAtls5mQj06Fj34CZZtg/vdg4pc+C/66w7D5t2ZETkwa3PSaW8fc90VpKxbrHYDc3Fydnz/I4U9CCOFq5dth1fehYjsoG0SmQFcbnDphfp9+m+n2cfFc92cppbZrrXP7ek5a+EIIMRgp0+GOtVC+DYo/NF08yg8Sx8G4lRA1wuoKu0ngCyHEYCkFI/PMzYPJsEwhhPAREvhCCOEjJPCFEMJHSOALIYSPkMAXQggfIYEvhBA+QgJfCCF8hAS+EEL4CI+dWkEpVQ2UWV3HAMUDNVYXcQG8rV6Qmt3F22r2tnrB9TWP0lr3Odm+xwa+N1FK5fc3d4Un8rZ6QWp2F2+r2dvqBWtrli4dIYTwERL4QgjhIyTwneNpqwu4QN5WL0jN7uJtNXtbvWBhzdKHL4QQPkJa+EII4SMk8IUQwkdI4A+AUmqkUupjpdQBpdR+pdS3+thmoVKqUSm1y3F70Ipae9V0RCm111HPP60XqYzHlVLFSqk9SqlpVtTZo56cHu/fLqVUk1Lq2722sfx9Vko9q5Q6qZTa1+OxWKXUGqVUkeM+pp+/vdWxTZFS6lYL6/25UuqQ4//7W0qpPtffO99nyM01/1ApVdHj//3yfv52qVKqwPG5vt/iml/pUe8RpdSufv7WPe+z1lpu57kBycA0x88RQCEwrtc2C4G/W11rr5qOAPHneH45sApQwCxgi9U196jND6jCXETiUe8zMB+YBuzr8djPgPsdP98PPNLH38UCpY77GMfPMRbVuxjwd/z8SF/1DuQz5Oaafwh8bwCfmxIgAwgEdvf+t+rOmns9/0vgQSvfZ2nhD4DWulJrvcPxczNwEPCchSov3tXAC9rYDEQrpZKtLsrhcqBEa+1xV1trrT8F6no9fDXwvOPn54GVffzpEmCN1rpOa10PrAGWuqxQh77q1Vp/oLXudPy6GUhxdR0Xop/3eCDygGKtdanWuh14GfP/xuXOVbNSSgHXA391Ry39kcC/QEqpNGAqsKWPp2crpXYrpVYppca7tbC+aeADpdR2pdRdfTw/AjjW4/dyPOdAdgP9/+PwtPcZIFFrXen4uQpI7GMbT32/v4b5pteX832G3O0+RzfUs/10m3nqe3wJcEJrXdTP8255nyXwL4BSKhx4A/i21rqp19M7MN0Pk4HfAH9zd319mKe1ngYsA+5VSs23uqCBUEoFAlcBr/XxtCe+z5+jzXd0rxjvrJR6AOgEXupnE0/6DD0FZAJTgEpMF4m3uJFzt+7d8j5L4A+QUioAE/Yvaa3f7P281rpJa33K8fN7QIBSKt7NZfauqcJxfxJ4C/N1t6cKYGSP31Mcj1ltGbBDa32i9xOe+D47nDjbHea4P9nHNh71fiulbgOuAL7iOEj9kwF8htxGa31Ca92ltbYDf+inFo96jwGUUv7AtcAr/W3jrvdZAn8AHP1vzwAHtdaP9rNNkmM7lFJ5mPe21n1V/lM9YUqpiLM/Y07S7eu12TvAVx2jdWYBjT26JazUb2vI097nHt4Bzo66uRV4u49tVgOLlVIxju6IxY7H3E4ptRT4AXCV1vpMP9sM5DPkNr3OL13TTy3bgNFKqXTHN8UbMP9vrLQIOKS1Lu/rSbe+z+44e+3tN2Ae5iv6HmCX47YcuBu427HNfcB+zKiAzcAci2vOcNSy21HXA47He9asgCcxoxr2Arke8F6HYQI8qsdjHvU+Yw5GlUAHpo/460AcsBYoAj4EYh3b5gJ/7PG3XwOKHbfbLay3GNPXffbz/DvHtsOB9871GbKw5hcdn9M9mBBP7l2z4/flmJF0JVbX7Hj8T2c/vz22teR9lqkVhBDCR0iXjhBC+AgJfCGE8BES+EII4SMk8IUQwkdI4AshhI+QwBdCCB8hgS+EED7i/wO9gnhaWPSDlQAAAABJRU5ErkJggg==\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 2cf36e08025d68f4db5868bf0c0e5cbef3e4ec53 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 214/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Bvj1ITk05L48dzBNFTnwHG3hpegZ+rZeFX3oIRHeSMSScNn0v3pVhhHnaueiFcynZ2s7at8sACBOSPumnx1hIpGcy49o/YPAfryJi1IhjcOSV/6V/1SB9LIPDFGA9sAcI9iffRqjdYTGQBtQQepW181+tSwUH5X+pqdLOkoeLAHDatrIkcwXHVZ5HUm86fXonFm+oeskgwKaF1GQNmWeNwKwVvP3IDswWLU+ODeeE3U7WDjVTF6cnwuPGYTQR1dMJeis9Ri26gBfcfgz4GLNnE2smnYKtpZPsvmb2pWXg1phJ8EhazBpGNBaxO2kUUmgw+QK49aEqqjEtXhq1QVqjjOQ5gmT0elieGvrQ7pX1PZhb9vDFxAz8Ni1J65Zh6DmdSQm7GfHnRexeVc+ODypwSWiL2svSgW9w2bYHAUjxljP/5V8dg6Ov/C/9KIPDD0kFB+V/xd/Xxlt3rsbRG4sr6XO8naOJ9MSxdsp9lAZayJNRTCw7jeO84zjol7R4goeWtWkF9oDklZOs6OPNXJQczZ1VzegdbqyFjXRNyyDYf1O3aAR52zbS6rRQO3UYAOEd3STsaKAzGE6Bdj9fHT+LnKpSBvQ0kdPTR5vRjK6tHq+hg88yrkLEh2Hd38B0bSXl1mhyynYQ0OnxJQ5le0oCSUST7tXxQVqogXnh8vs5zXMJ7V5IzDdRuV9ygkXHRkeAroAkcoqTZfUrmFIdanO48i+jMMREofx8/KvgcMwbpBXlx+zBd6/H1nspXWGVrI7ZxxlNJwFwsedBqpM/JucVKzkZY+mWQT7MEQyvEDSlGMip9NAdkOzOM+Gw6rirYQ/vOZIZtLsMc00P+8NzySmsoGxyqAF5+J5VFLXnsqBvCVurtHRExTN633o2BscB0OCN5TevP0iY2wlAMMKAzech4BH4NDoMwXYK9pbi05uIMbYxYNd63DojWiSGunKydhp47sKb2G40kGP3UR6pp2vgJeRVRZBslqws9ZOqE/QaBLmR3dT0WWneEMZI0wx8mh70QSsrn9jIqffMoaehg56aNlInDTo2J0U5KlTJQVEIdWFhb3URFmnAYAo9MzV1HeTp+5aR0jsEgCAONESQ0LKNloSxpEd0M1DEsmhCGC5PkKpEPX4hQEquK3JRFKVhXZaRWIedrnAL0zZ/RsGezQAUZw2lSI7ljOy1dEdqWLpjFickbiC9ZD9dejvRDgPtmkyKbGMY6dhDkqMeg/QgELhiUzDa2wki8FgiiOhoQ2o1iMDXpRavzkhl0ijWiCHkxDYwc8dn+HU6duWPpX3wSey1abmi0stJzX5+O8aMJyiJ9vupthoYX7uUJ0tn0DIinl1NlVS0VpNmD/XllGzpoaXbQEBr4tRLs8gYn3F0T5Tyg1LVSoryLZ2NfbidPpIH2vB5A6x6tZSKHa1odYIB+dHMujSf1/7+Jt796Wj9TkBDQGfC6qpH07UUU9xChkTHUR+p4YLJEd9Y9xhXOZMiEnjWF05QaJi8bTtG6Sd9fyGdWgvWMB01ljQKgzkAhOFkcKyJJPcK+lwDyajfSbinm6AUaAgigPLwbIIpA4jSSvoq7NiN4bgSIglIGFu/BqPXiScQidXpYmdiFjttI5gZXkWJPQxjlomg0JBRXUpqYxWNqdnsGjSW6gF59BmPHBjI5Gtl/QYzwgt6cymfz3awfOVmptZdDVICEoQGjfRx4ilWomO0RE4YjUanKiJ+alRwUJTDtNb08OEjO/D7gkxbmEvlzlYayroBCOJDgx6hkWiDAr+UHJdWQVlPPI32SGqSW9mXEc2fy7S8lBvBhlgNnSYdQ+o6qUi2YNC48Gl02Oq7aEtKQl/RTXhNJ0/rnyaIhqt81xNEg0SQIZp4Xv8Y7TKShwMXYiecuI6DjOsuYmnCbCSCeZ3L8E/SsqTrV9h623mVO/FtBUdbJLfMuIGuoIvri9+kzxzJ6PoKAA5GR/PltDs4gSBmjYvGYDhVohenfh+elk5S7AcRUtJhi2VP3hgK9u/CMWQ8y3KHktu9ni2JJ1HQ2MXf9oRu9tHD3uLisEZsu4cxvHkS+mg7gY5EdP4+/LpwAJI1Dcx77Gw0JuOxOanK/4kKDorSL+AP8smTu2ivd2AwaXF0efqnSFIaNpBT/i6bJtxOnCUJVxCSXaV47juHq4uqOb7By58P+gG4ZoyZLbE6kJJUaqkXoT6QhnZsY2/M2NAq3QFMa5u4Xvcei3QfAXCz53JK7Akkepq5NulLhuhCI7jd1zqXD4wzWdj8ES6zldfiz2RupuR3EytIijybLc9uYaR+A1H6V3C5YmlpuQFtwhD6OvYSPLASj6cUv7BQHelkpL8A89graSAABAlHixnBU9p9WGo28GXscYT7+5jetZFwv/PQ/g/MqCH/rMeY1RaB22gmr93HWzs8FLYtxzJWz77hel46sBgEzCq7lMyuXHR+LWG+bhymBMZoC5nw7C1H6UwqPwTVIK0ohNoVvnqtlMbybmacn0d8upXda+ppaCwnosGCwbGK6FF2Rrd8QkLib0MLWYdw4b569DrBoloPoKXEqmFLrA5NMMA5HS+xo30k5KaRdaCKzrpI4mNrGWZsodVupBYLF4iVVDmi0JsFg1v3Eu84wIT4JoboavmyKZtJcTVcbv2KyNpW5qTWkGgUXCfdmBp2Iha7aXVGkKNPIUK7GAC9SYMuMZ9wz3NYYy2446YSa1iHW9jZYDyRMS2nE7DXsbR5M/m+Groj8pibMZdL/Jl8Gusj09/MyK5C3o0/k5TUXnosBk776hMqqjOoePgprtZo6Y6MoT0slgrTccREDmb76sVcc/5rrKhaTqPfgTNhJ5rOkQw8oYfk+LEULi6lzJ7MOKcTTVjY958E5SdDlRyUX4zGim4+fGQHQ4+PZOL8QeikkfbqBurersOlhZ6uJ5h97995cnUZ+7t0TFnzITPiTuHpgXoya9sZqovk1kwnzfFxODVwTt2LLDlwKgAGi0T0+vEQqsMfpq3lRLmB3M4yyu0xTEqswRbp5sPSoZjjO/l19D7Ke2PZ0DOBuYmSON0y+rzhhOld+MjGIMrxemIwGDtwuvNwN3YTldlCRWcSOTFNtLtTiTXVH9o3F0Y0BNHKIM3+q9i3fz9p+8q+3vmUAryjLiRGZ2YJXkodm6nQWPCG2dDlGiHcxKylb+M3GWiNiEJ6IcVejzYQINmUjkunY+oELcZTr6JNdpGjj+b5e3YT3v+NhxYvAQzMOUVP+typR++kKv8VVa2kKMCXr5ZwcGcrJfoXiI1u5neJv6P3QAxjT4xACsHuva1sWziOy/dWY/ZLvBrJX3Z7sHa58PsFd0yNpEcfZFjZDhr1CTgOhtoOkBDj6wSDEYvWS4LU8YD+LjJFqP9Id0CHSRuqjvqwZjAnGKvQxQaZ23cXSbokojVm7tHdiFVfxwr/eHoCyYz4YiO74nTUneLkps5Qe0hpbyIfWhZwAy9gxMsOsqknnUnsoFCMJjdQjl4ESRFNnB+VzbDGSdjDJAQ6yWoKZ3i9l/QRlwPQSJDHcBMBmKnk46ljuKhoFfqAB31LDZ4eF5umHEd0ZycT9m1Dhw6Nxo/H72Xo2OMZMGE0H214GWv1JZTHbienvQARDJBo6kRoNEyanUDCSdOOyXlW/n2qWkn5xfB5AuiNX3doJ6WkrrQTs8VAXUknhB0gp+ty6HGw01tPqiYG2d/ZY2NXBG/Xt5PilrzYpmN2qp8HBxv422YfX6Xp6dFLFi19GH1zH16didVRk0jzt5Lqa8HiaKMjIpEwj4/xcdVkGpr4pH4wWdYubImClkrJoLgOTk8vBeAe3wVUGNOpBbwEcMsz+TuPsyJYwMrAGO5M6WHZuB6qw5uZ4PQy2h1kmWUuXgyskWNBBikSIzl9VAIxI29kdmoB7Q1VtBR+hG7f/ZzVlY3RXM/xchNfaKZRnJJMkbED2/JrMcQOImbslTwiQtU/3Qxh9HoH05nMPnopsrlIDhi4/aCF38wcy97BY7hw5cfYervRCtizbRV7tq0ib4iVB8bcjkvfS4QniuTuZJq8oX7OVr1YzDnTx6ExmY7uBaD8YFTJQfnZ6O108/ZdWxk4Oo5pC/PQG7XsW9/Amre+rl7xBXeg14wGYHJMH2sseh4YnwjAwhov76QbmF/v5XSblYsj3Oj9QSYfKKNN6yW7voK88p34rSm4+rqxBPqQgOj/tiFEcmHmTgyaAF84ktg/YDLDiw1s7ROIrCB3RrzOfjmQjc7fU2Cy0RPs4KbkF3EGkpnd7GHSWhc3TftdKH+6KlK07QwQDdhJIFVUEK0J8JL/RIo9yVhwcuVx+Vx53CDM/WNDB3wevPdn0RfUEyn60EsvdmHgwugCRneMJ/HgTqYXHqA3LApTziwMtixMtkzcQlKOh1GY2axpZWIwNFxpo8HP2kQzn6boye0JcHxVC2MdUOLfSnnDLmZklJA37zpuL2omZ98Jh46zkAHOPCVA/LyT/8dnXflvqGol5Rdh4wcV7FoZevtHZ5WcddM4lj62G61Ogy4YoKPDS3ygi26/QOjNOIdH8XKanl6DxGY10+DxAfDnPS4+zTVQ7u8jc88+Tty17NA2qixD8ccez55AO7/OqWbi1PnEfHQnbYUNfGGKYECYnXkDSqkOxnOw7WK2RE7geUJvRA0Ouhmma2Ocu4dx+vFs0ZSw19BGrwZKSCAYZeKPS15gRex43s+aTkCj5TTDPmJFD8nBWuqDkdRlnM2qyh7m6ArpiR3NuuZQ4X/uiGSeOGckGo1AFr6AWH4jaI0weRGse5g/J6bQ0XU8xqAROr/iimWeQ/u0JXUkkbRQHWtjUvo1pAo9nVo3KaOScWxvQ8vXJTGAHukiXOr5uPkVfN5O4q2SbkcAj+UMLP5EcqI7Ke1NY3RUJRPvv5KAL8DB4naEEGSPjuPwbvmVY0sFB+Vnz+vy89qtG0kbGkNNfCHuFan4w/swOiIZNz8dx+bNmHsHkKgDb8DBrkgdi6aGxpHKbu6lR9/HpG4jkZowzHVOFsf3MdheSl7JXsL1Lg5osxnhc3Jy/HwihY79ws72zM2c2ZVNpL2PWP2dyGAABLSSzBTv/RjQ4QTyels4BQOa6FY6tKFR10aXHiSneBtLLv4VHS4XNn8fc5wuzJ8tB2DrpFO5L+kEsuLC2d/c+419HZJs5c0rRvNpl4vmim7Kqrv4srSVBxcM45yxaaFSTNU6MEVCdCY8kEarOZJfhxcwvH04u9N2kFmhp9fg4KTtteSHPo+gyRbJxzPOZAAj6dPvJTHVTMEwC0XPfUZn4nSy9XrqUnM5rcGHOQh+gqxuWopdliC9Btx6P+FhVxEVF4OjrQ9D0MWwucPYvrz6UMFq5qX55PWX1JRj70c5TKii/JB2fVWH1x1gWNhH/KP9McriCjE6IgHYuXoHOZ503EFJl99PmN7KsoHRhPsCXLW8gaTarVzy3jNkrXqKyD2fUFu3nCvW/I3JO9cS4+lgVPoUrNFTmZl8KmG+PrwHV5MXjOCCgydj6sohUvcKQSJoZyKbgtOY772FAUKPCAaw+h3MjqiB+Eo6tD3MP20eQb2VnOJtAMwbN4q+pPGcsHYNps+WU52Rji8nh2ltBzh7bCqVbQ5OHZYEwKWTM0ixmfntcQO5pryFmw7U83jQwZmn5DA8NZIX1lchpQQhIGs6JI8MBYiIeOJddl6ZcAZhOBleO5pEQzYL8fHMPA1X/1bL2+OHk9Rt54zPP+BVbyetuliGv/oaJYWt9GrsHOgqpThW8tc0D2eP6OGlZAc6NMxKmsfgsJMZFjUVayCcFrEYe2sn/mAvbq2FbctCgcHg6cbgsbPv0z3H7BpR/jOq5KD8pMmgZNeXdWxaUoE5uYnVsffj6c5kRu11SCkxerpItsYwPEzHqh4fLefH8rJb0iWCjCndQXbJTtLbqzkYn02Yo5VEZ+gpvcyaR50hmXkDspjTHXtoe64tz+IXxfQNs7KnYwS57nomD9vICt1QNvbeyptCcHnp54y0rySxw0TJH66l+mA16enp5Obm8kFHgFPuvY1kezsAL849h4VWI+FvvcHGyZPouuI38O5i5qxcRtSa9Zz14mbqu4MUpEex+FcT0WgENS4PE7aUcm5SNMW9Thz+IDcZLLKFMXMAACAASURBVNz43m5iwg0YdRrmjkzh0nF+Ym05aN+/HEqXAtCiTearwBhms5YoelhhSuT2+Ai8IkDB7tHcsGILu4eOpa/TxZTGPXwydDq+bAPm6oMAFA2byKrJpxLd3sop7RFcWxM4dGw6/S2srHv10N8m0tEZxxLj66AlchSJ3iqa9emcd8twojLj/9eXhvJvUG8rKT9bRW+vZusGAElbu2Rk652M1mpJjdLxVK6emY1ahvdq2RYhKLJ08q43muza/Zy9finWPjsObRj22Jnow3OoStlIo38LzdiY6rVQ6YuluTuCW+il0VzJ9GYdWTEu5g3txCoaSY9qpDiYBcBH+mmsDXqZbA1wSsNm2qMHoB0TQ3NjM0NzcjgxPp6t2YNIeeE2Yvu6uWXRbfzqvdc5fc3nGFxOIufM4ZTbbmPMrmpOikviNL+fG9av4bLRT5Jgu4zmxLHM2FbGSbFWJBDd2831cXq2pSVz3c4yrjd6scaY6O72EGPT8/e1FSwtauLmKY9w2ohzEf3BISHQyHkGO3j7AJjpbmdb7dk0hZezYeQ2DjaFMWpX4aHja+nu4V1xEnMJBYfuVhMZJSW4Yq28mWdmdoeTPEeQRpMg2Z1ATnIKUUTg12RR3rKVPu+H6G0dCJlGsyETgC0vbWL2vfOP2jWi/N+okoPykyWl5G+LPqTJ2E2UZRWGpssI18AMi447hun4IiUMXVBy7243t4w0Y/S4yN+7g+O2fU6H0UpZipdO0zjedo0/tM495nJ2h5WzsONkVuDiAQKE4WWq2MUN+vfI1TTQKSOokKkUiDI0QrIxMIQLfLcywKrnrV9PY81fn6DS5z20zvnNrRjXrOaluQu5YPkHbJl4HHeedym3Fq3nxBefA+Cy+55Gk5xCpcvDc9LB4N9cidNo4vnLziR9dDerS8dgsERQlJhKRkMdzzz9F8J77IiJmQS21vL55Wfz/IhpdMtI0AoGte+icXckBCXnDvfzx96/YtCFQf58WHkHBAO0j7ia2B1PUMxgdpPH8vhSuvQOXmk5l66ly6mWJhJd3Vxw4h9Z4PkUf6KBgpNv5Z63N3JhwzusnDKH6oFjeXGLnUSPjlajIM3zzXPklN2sqHsNj3CTZhlNBzMQ0s8F903FGmM+KteJ8v1Ug7Tyk9bR6GDP6noyhscSl2bB5w5VZXjb6nnvmXo2pr1NQm8SuR1TSNe3c/+Jgyi3apkeZeHg3mK8egO6+j7m7v6QCLeDLksqHUMi2WNdx5UtZ3BCzwTWRhQSLsMp6At1z/05B7ifeFK0dl7SPcBAUUetTGBzcCib5UhsuiA2etALH1t92aRr7WiF5MILL+T+Jcvw+YL02iK55+mH0Xzr/5jhuOvZe8cCptrC4JNPWOzXsG7oKFZ19jIzxsrr+WnUXXElzi1bcBmMLPzLU3x8U2gUttYhwwmrqsQknPgzA5j2hpoNg2ZJ48N+ghoNH3MGS8RCcPnRl/egbXIRG9vFpRNaOHfEZQj3Hhyd2wg3phH96hWH8rU6bCKL4uuZLebwfslEBnoKefKzJWyPzyPW1U1SXwevnXQ6p57VSfXOPRSXDef9eZeSZe/inlIThb1OIsxtDN5VSEb0UIxxobEq3rMup6+yhPH6Kdi9HTR72tCFj+CsW89n4yvbOfEPMzBFqA77jgUVHJSfrKriNr56vRRPn/87pwcJENlTQ681i7CujdRkBfho9Hj+NHU8Uz123vhD6JsBCfToLKyLm8XdlgFk+b7+OMtT9ikl3Z/w+OkG7ik8ky5HJ7dkTUIrvNwa/iYLNZsocWRSZMgns64ZX1IuL9qmMH7MSC6fkslnn31GbGws27dvpwIdI7cVMW3XNpqjY0nsbMdlNPLWZb/jir8+AkDEac8Sc8FQwkaE6t09NT1oIw1019qJzLRRv/81mt2LCe6qJfpZLWUFqeRtD3WVIQ3gTQvSfb6fmAHzsZJE89IXsH6swTDrd2iu0BA/cCb1pHJHeQNru3rRVvWiP9CDBCKi3QxLKWV3aR5jE4p5KbGaQFoBzWvfJ925h/3WWJ6xprHZNR6vdRkXbNQxvCaBioEDGVNUxD7zALy/7mVqYiKvrjXSIlJZM/FkhNDyt/U93JHo5NxNnxBrOhmd1U9seC95njS6dHYS/KG2G3fAxcqGV3BjAk0UUyZOY+xvVTXTsaCCg/KT5PMEeOP2TRhMOmZdPoS6kk40WoHBrMPj9LF19SakPRGDt4eA8OPueZWE8Gya/a3MWfQ7ileuoGb3TupMKfjMKVwUOZUUEWpm66paismSTZ2/GnfVZlYkTCQuysvg9hqWWsewLSaXv656lIwZXcRYuikvtlE5+GROv/shzn6hkB213ei1gnV/OI6kyFD1iN1uZ/bGvTz6p+uIdDoQUrI3K5ff3XgneyYPhVfex15lpzt/G96Eemxpw7GZJxL4ewxS46czYwXOuH24Ig9g9g3Epa8g5jEdxopQ6aD5Pi/BcLCKAuLXXYbWH4Z1ZhoiykX9+adjyD8D6/yFxF0UGmbUEwxS6/LS4fZxzwd7aHU6aGv2feMY/3bkC4yO34MWC1kVLaQ1OtkxJJI32kdjCZqJSCwjR6vH5bJQUzqIUz79irK4gQyrqWD/lCH81TSZ7qgYao8fzq373Hi7PTgrXyRKexLS30KyMY+scDN6jYMvTZ+zM7GVe+oW0efrIigDaIWGLe0riRkxiaS8weQWJGFLTDqKV9kvm2qQVn6S9qypx9XrY/avhpGQYSUhwwqAc+UdeNp284XvdBL7qpgY42RjXzW2iBymJJzBPud+Pnn0PgAOxs6i2DKQM/RaUnw6Po/cRJerhnx7FftS/PRZrWxNuYjSQAJGfJycEE+hJ5cBvna2zpzJGPML1MpEvhhn45bbHqfJ7mJHbTenjUhmaXEjO2u7SRoWCg67/IJgZye2vl6ibryJQmeQ2zNyOU5rIs6o52C2g5qBjyGEDpM9nba2lTQFlqCdYUFoNfi1dvTOOJKbr8DpqcGVVknqvU/RfPPN+IaHk1J+CUZS0botdAz/CKduP7HbzsRsz0aEx+EMrqE6+T3Slp9FVF06lpNOIicjHX3bqzwwqYTM7Du5+7MG6ntdFKcYca1vZEvbOSycsoCurs20WztJsW8n52AXJ43Yj97wdSCxWttJSKiibTpkvH0AajQk76ijc040g+LKaPNkc/+QcJ7aHmDlyJN5LSedP2zoY2Iwkhqdi/dc9aRU2TB5D1Dc28gIazJeCSYBBTHHs3rbW7TstLBlsYsL7n+M6JTUo3/BKd+ggoPyo9TZ1Mf25dWkDYkmaaAN6XWy7rPfUzD5Fq6tfIfd2jgucg7A0v42b1sMZHn8FCSfC4DWPJC9lnxyNCZutYS6ysAHO8JKeSbxHeYePInCCaFGaOF2UieiCMOLEwMfe4eCgKzwHqK0rYjGAJp1bjpvDfW+WtXWR1Z3A/PyR6B//20Mt73Kfb+/Fp+I5SO9l5O2bkIKQczcOcyNjyeispA45/Ps3Sdp0Swj3JHP0GFP0f18LdZTM2iq/ZD2qI9wG2pIjD+D3IS78IV3sGXLCUQ2TEXnzUX76Km0NXxC3OZ8uuJW0pOzGbct9OVa49hnyFhzLzLOBu0HEAEtnjs+oM0haH/2BSy3/4Fq7WMId4Du7q2cnZbG0CFPss4RzmUV3eyoDmKNOZEBqReGjpPpQyzvX8KIii7K8sIJ9J5F/LBzGRCn5avVv8JqbaX3fD/1JyeR+3A743xBStwzGdlYzubMkdw+3Myfdmew3BjGNE0+BCHdb8aUmIa3eSc59WFszFhDQ+fZSCBVv5cx4aNYkHEdAHZvBx/88XHOvP16ogamHL0LTjmCCg7Kj4YMSjZ9WIkpXMfBnW1o9RqOuyA0iH1x8av8rmMz6W9dRbx9AVn6HoKBTmotLYS5opiSfAldJj17dEFOcGgZmprPyd4k+HpYZT6PWMWC4mH4bSYyDh6gPTMZnacHh9HAqd4+ND4HjZ5mArZwhmprOEP3OfbGUKlg+mtfMC9mJgsaqnh2zeNs793EvPoK4u0d5Dz2EItu+DODDlRw1prP0I3JoaLzCVLN56Nr/C3tgdBro+Hd+WTLewjPTMOV00PPsmrCGUXSvNMRQ3sIDx9MIGBnX/G1SCSJ3gvwHLRjtuQQ1DsJnHmA1tY3ABic+xARkXls2zaPzsxlaN0e9J8KBm78A32Oh/CNS0Zf1Ebb0kdI2KYFtHS/asNu30FL63JmDbicYVlRlFY5eGFnLTdOyg4dpCHzoeVGEtc/SkKHB01WOeSMBEBvuoVNG7aTnFJKRsYuWu4MMvDLz7j4lNMwddt57pM3+fjkC7AEYNFXnViDBm5OFVxXHyCjz8IXY0dij4wls/5xDsQk449eR2GvCVPXcHK1bbgHZmGu9jM+agIf3/wY5//9VvSxX39johxdqs1BOaYCviBlhc0kD7RRtbudTR/09+Ug4MTLhpAzNtTFxbuvXUbXiijcxkhksAehsYK3noQwG1PjTgPg/IlhhBd3cpPXQ44/Eq/w8YR5HSMb7BQPaWbYxmbqB00jraaGSfWFpE7u4HLvjZTIDB7zbcTijmVDzl76smz8ccd76PCzaUMeUfWhD+P2ZuVicThIb208lP+DSalkNdXz/pgTmLp/B1qNBvlHOz5raIQ1rTaC0aPewt5aDC+lEDUvl4gJyQTdfro/rkQToSfy5Ex6nfvYvftXeDzNACQlnUly9a9xbGiAZA9lg68CQKMxMGXyVvT6UBVbSektNDW9h75KEPewHuPg0XhKd9C1KIrksmG4Pl93KK9JD79LaeT1RHwqSZ91O01j0jn5qQNId4Dk2DDeuHgs2XH942HvXQLvXwZIuGQZZEzB7Xbz+cY1PL/tS6aJIMOyvsAY4zq0/qbdcbw5/AqG1KfSqothSLuPF4NOHq7vYoAuhjknRQNw3NYn2JtUBIDBK7ii8EGSnVtxxQ7D6A1nbGQ4pd1baDEf4ILHX/jBrjXlSKrNQfnRKvqsmm3LqgEQGkHWyDiGH5eKKUJPTEoEfq+X0o1raPi8EylbwR0aPwEh0QkDo+PPpVkPj3qd6IvdNLrt3Jr8KPeusGBzmjlN00dqQwMxZYn8cfw1+N0a/PGjiY0/nl63GScmbtCsZGr4EtaffycjvnyG7D0+NPi5Mu/PXPPhq4hZA9lVa2VwxW4AHjzvCq4uWknEgVp2LRxK4voaztz+FQC11xnQWZ0kd1+JcVQEcbHHU9/wFo2N7xCfchEJKWMA0Jh0mE4zsKv4cmxlBTj6DhwKDEOHPElCwhzc2i4ca+uhzkBk/jjsspDUlAsPBQaAvNw7AfDYGjAMsuMp3QGAK7uF7pws9GtA0//tQdd7q7Ho9Gg3VdPwyW9wjgvwREESd1juo6G8m3PeXMOzU95n9MjH0Q89A8w2eON0ePVUWPASpmFnMu+Ek6kMZnFw60vIAxOYkLuaiJVavFmSpHFt3MT9OFIjKCcXb6IRF7/nr7F+Xt6rJ2tNHS3RRvYNPpeUA/vQZyRwwegr6Vt7kHrLNPBAXvU/6MieSm7kWCrqdlC8bBlhUTbqyvaSP/U4Egfm/m8vSOUQVXJQjrrOxj66WvrIHhXP0qd3Ubsv9BZSTEoE864bhdEcembx+3y8f+/tNOzfh9BEYDLMYN4pIwhs6aTZrGNtrOTc1nCWBRu4X2MBJMa4FVyzbTUzd319XTdkpfBs1lx2RmSRFuxAow1gwMsBOYDLp8Rxk7UQ17r7MeAn3O+mzRDNzQOvw+eI5baH7qHzSh/uURLpMAAaRIQbJAhf6NVSU3Ay1iWtNOTpMQ6rJ+rgDGIrzsD2m3TMKQms31BAIOBE67ExasLL7C+/HYtlCEJoaGx891A+09KuwGYbR1xsqOtrKSU9X9QQ7PNhmZOKvbeQ6OgpCPHNXlL/yVVcTO2VV2GaPZOK6e8AkKJbQHbejRw89VQMWSNwl+7Ak+3BuD9U3ya1Esd7T3L6ljD0B3qwhDnIKYjinmlTGRJhhncugP1LwRwFC16CzGn0+uDyx58i39PL8etWUxOIJrOnHjHETMmJerJSqw/l6dPWObwTfwmrVjlYbqqgOXkTByxDSCtuJq2iBKdGT5wmFhlxDuGOBlz6B6hMiOf38i5a3HUkmtOwe9uo7dtPB02ceOt1xGdk/YBX4y+bepVV+dHweQM8v2gtAOfdOZ6lTxWTmB3JjPPz0Bu09Ha0seuLZYRF2tjz1eckOgeQHz8Zh0sgPe3YLAkEZRCNDHX73EKQ9tlWnvvgK7wprVg9y7jz7SClg/Jwm830RkRQmTiAJb6RDPJ1cIO5l7HZ6wjvKuORCTdQVvwX/trShhZYHjuVtxNPZVXMBK42tHHKZ+8Q/sFGfK//iuLGD4iNTCUrMY+IiDyQQQ4cuBtL00TyUu/GOj0d94Eu2l/bhy7aRIvlPdoHvs+AlEupa3iFuNYFtMV/cMTxMJszcLmqAZgxfS9a7X/31bAMBhEaDfvL/oTTWcXQIU9gMMTSdNdddP8jFDASHnyM5v3LEXVu+HID7nPjaZsVweV1t6IpdSDcAcLyI9l0ZiY2Qxw8ORx6Q6PacfyfYNqN2HvsPPbYYzTGNeKPimLwx1uYtbebty6bQ3rlNkwWD+nHNxJEy4v6qzm3eSdRcVuR2tAbUE3+ZFrfsVATSMMXs5ULNyRj0rbwm4vcADxT9jgJ3zFQUE1fCXHnDCF3ypT/6jgpIT/aaiUhxMvAHKBVSjm0Py0aeBfIAKqBs6WUXccqj8oPJ+APsmFx+aG/lz23m95ON/lTkvB7+yhb/AzrV27G1d/zRKJtIMOipoEPtEISHhaLKcfGAuwkmPRMq3TyisPJcbVF7I2XJFu2c0bJCCqyujmYFIEvMQa/P5ytvjQkMME1gK3GFxnVuJ6eqEy27r2fv3TZ6dFFcPHQB9gfnolDF86VSSbO1xfj3LIJmWdlX2ABd2zKZeV108hJsAChp3rxRibSHqRnby3mwXHYV1ShjTQSe9VQSgrPB6Cu4RU0ARO24pOxnJaHPWwDA9IuZ9euiwHISL+alpalpKSc+18HBgChCX0TMSjvnm+kx11zDb76BkyD8oieNxtT+hg63ynDHr0D9jSjnern5uRX2J11FSVr91BfAuMeLOTX8wZw1bnvE/H8ZDBYoGotTLuRSGsk8WnxiFoBbdCTV4Cm5EuKKjIZlb2Plu0m2lZ5iZrYzdX6p/El6DB2DKGvaSyFzfsZO2MLlnOgtmU8m1pcHDyniJ4wcJgF2Q3h1FW8Q13mBXic3YwMtNAblUqiMZL08Hxq3y2lvbGGCQsWotF+dylK+e8d05KDEGIa4ABePyw4PAR0SikfEELcAkRJKW/+V+tRJYefhpWv7OPA1haGTk8hfUgMn7+4F2/ffoymHfS2hxp5U20ZTE6ahWZGFo2fdRAjNXzZ40fnaEIfvZURf7ibMzaWIfZ04gsEMRtcWIyfkO0pIis4G50m1KAqRIBMWc5XgUms9WVzitfBtMYW9g97ipt0HYS7ggQJ9Vl/Xe4f2JOcw0NhX7GWTAY7XsVoD5B4q46oRVdxW9gUypp72XDzcYcGqnFXdtP+wh4iZ2fS82UN0heqpolemIdmcJCNGydj0CTgDbYQ5spjsHiGyJMyEdrQ8tuLzsFu386kieswm4/+K5syEKTr/XI6Xn0IX81mkha/SmT+CDQaI/XNW5j9iYPegx4C0UZiJiexqvBCoqQb4eyEW2pBZ6Cnp4ePP/2Y6Jhotm3eRv6+fWwxpGEYnMvQlUsZ01JJc6qN4qsH82LUQk4sE1y5cxusewPX8CBdvw599V7nSCQ5rJkAsKPZTMemODI6W4gNXkNX9KBDeTb7ejj9nGH0rWqkrq+MsBHxDLvoVDQGFSD+r3604zlIKdcBnd9Knvf/2DvrwLqqbI3/znW/N+7u0qZtUte0lJYaFOsAgxcbdIB5g9sw2AxanBYoTtGWQt0l9TZtGnfXm3uT63beHxfCdPAZ3sBj+uWPJEf2WWfvc/Y6e8m3gOVf/r0cOJlX/xtAc3kf1Xu7KJqbTOHsCDSGAS5+ZDxhmioi3BFodBpkWcOZGLII0a0nsNZCNFLq3QE8IkR37afWPZwvXjyIt7OUgGYv8tANRGjLuGVbGZfuiEEm0TH8SClq8wCCT0U92VT7wkikiwcjb2Rk8WOkJaehdorsjMinM1RLtyIUc7jA3ZJHsDp2UeT5lIzkK8ntuwGAttzJbK7s5qzC+CHF4Ld56F9RhTREiXZsNKaF6QhqGbpJcagLInA5g1QXiamXBX+PuBDTnNQhxQAwfNjzDMt/7hdRDACCVELooiwi//A7hIBI5zmXYt91GNEfQHcgkge1kYipGqRmF4kSKW8Yx8NAB/ic0H4YAIPBwIXnX8jcWXNJzEikPC8PQ4Yela8Dd2oYR+J1RLdaiHvTwlWffcjGaDn9PU0MaHXslY1H/5IaMSAhQddJ3WAati4T42KdDEzv5LVTBAIpj2IMVA/J7JTpCcg6MJySSII2i7DaEHbesxOf1/9dt3kS/wZ+jdFKUaIofmngpBOI+raDBEG4ErgSIDEx8T8k2kn8GIiiyME1TRgj1WQURWHpdrDtnSoM4SoyRql4+44/4rLZmHHp1eTLxhMaHkOAmUg8UmqxsC7uHq7VvUDXMTf+ik8Y01uGTyHSP+58Gt0foVd9AcE6PuS2jaZ83AIAlC4XPRHhTBqdj6b6Yz7159IhmrhD9QZHR6kxy0JobclBwh5uT7qVem0ikkCAv0XsZmzmaixuHf/zYRkPZeVj++h3KHPzuKzESkKomiunBJ2gYkDE/F4VfruXyGtGIFHJ0I6KQjvq68fU6WwBIDxsOtFRp6NQfDNWX6EIIzLyl6+vbJw/ExRLab9xMebX30MWkYl11Q5GGeN5JGkbtzOWytV12JKncBNvBU9q2gWxI0EqDxYWAhadsYgnnrgPfdcgXsFOS1IaXZlyQjc3o/J6OGXNDlr18awviEGWMYOKMCXTXttL3xfX8H6Ck0WfbiCx28Hhh1WcE+JijEbOEZmGOvcbzK07HY1ZQr+skE3vNTL3WiO+YgWeDX1EItBwsIOMccGMao/dgdfrRmsK+aW69DeDX6NyGIIoiqIgCN9q9xJF8WXgZQialf6jgp3E9+L4jnb2rgry/1fu7qC9xoIoQuFsePvOmwARn8dN9dtbGBNxGk6JA3VAQ6c3QIVdS0mSifCmO1HZ72R4ohqTJ5Lbfn8hu+OdhLWtJ78siVmVoPe2UFEUg//Lp1hwuxiIMeDrfYh3A2ewwj8VI16S8+rZK5/EK94r2d16EY7YIl6cMpPH67qo7ncwM+1qVColn++rY3u1mY33P8XYhgZ673iIjnIXyy4uQqeU4WmzYV5Rha/LQchZGSjigiYsv99FTe1fkUrUZGTcgdMVXDmoVHFIpb9+tlHjrIn0pI7DuX87nffehat0C5px45i18M/c1VaFxy+jvk7HFzHTmd2/lbbq7STsexlyFsC020ATilar5Yqbb+GPm+5GIzcxjzGU7oWSiV9O2v4+ogfMWE0mfBoVyKqwK+X09TSgkc8guSuoeKKWqzl0mZcslZ+0xADQBRkvY6qHAyVP0SOP5c0lzai9ZgxqH6NN6exf/hGpRX+gcuUW9PuUtDqrqVOXYYyMIiYji8xxkzBGfus35kl8D36NyqFLEIQYURQ7BEGIAbp/aYFO4ocR8AfwegIo1TIqSzoIidGQkBtK3YFuYjM1JGQPsv2tF4mITGRa/u+xdnaiMivo9Q6yy64izNGEqWMvgbSFnFN6N+36zxgpE2iYdQbXKiTYvJ8T1raSOHsUpzXqKKw7RntCKH6ZnJi6SgaMRXhMUq6SvoLE4uSKwAS0cjt3jnsMh9rL89zI5W2vEek1w6y3yTPokJdV0nq8izvbPDwzNYJhty/mBY9InKMP3fTpPOSNJUI/yNTMCES/SO/y44guH4ZTk9CO/roOcmfnp7S1vQNAePgpDA6WoVLF/79QDF/BsOBi+pc/iqt0CwCOPXuIedDIX7UiL0uaaXdF85KlmDls5jHNWC61dDJq30uw7yWY/zQUXkK0Npp3F3ydtBZpMvL03qfJsmRxeGQ+MoWRvLIy1o4JJWMwiz3jpMQ3NlKkCq60qjJHkFVxhOUH5LwTncIUfT/R7lD00ZXkpvhJP/AsNb4b8cvUOAklofIDfGOSCBWkvPeHG8lSj8eoTiZRk00i2Ryv3kVz+X46j1Ux/847fpF+/f+MX6NyWAVcDDzy5e+Vv6w4J/FjsOG1cmoPdFN8YTbtVfsxcZymTgF9YijNR6qp3jlITuIECjRT8VfY0KEDAY44BQz9u5gy3oBp4kVYHiuhLyyfU6Rn0Jwq437JPkJ6NjK9O4II52yUooqGPPClyhhUCyCK+AwXowyIyKVrkcqc3Bt6Lp4mJZcNf5+JBfexpE+N2yzjgo6PcMWPhtixuJ1etlT1ALC+vJPmQ++i7+tEJZFSa4zni6zT2VHTyx1zspFJJbhqLQQGPIRekINm2Ilmoq7uz1Eqo/H5bLR3vI/ZvIPY2N/9rP3b7ehmRdUKko3JqGVqRkaOJFQV+rO1H3LmGLw9dyMO1qIt0tJ5z104Sw8xKzeP4R3VrMjcxZsHzsWq0jCzt4Sbcu5iY/crdHRWkrj+bgSJDIb/DqRfTykTx0/EGmVl//YDiI1B38rq4T0odSnI9HZ6xVQKD62lOi240rrn92fxzBNlXLDFx+2LXByuv5wIiRWLx0JYcgcxC+tIef9FWqXFuCjAl6BgcPA4acYRpFGAKIroZyXRU1aLqk1OXshEANq76mirLCcuO/dn66//BvzSoazvAtOAcEEQWoF7CSqFFYIgXA40Aef+chKexHehpbwBhVpJVEoslm4HNftqQdCzefl+0uR9FIQsAqCtq5Y+aRPZI88hyxKB6A4QekE2czHfIgAAIABJREFU7hoLhzYexusU6Jiyidhrt7J8dyOtmm4KAsd5tGg8VbIv0Fk+YHjXZOKdwdoH8j4bCf1d1KenAyB1eSmc72dL42csbZrDy/7JBFolJBmbOSwfwOAI4XWzluiBXaR5nPQnnsrMR7fQawumDT93/ihufmMPg+vXsyl5HKF33cOasg72N/YzKT2ciyckA+As60WQS1BlhSCK/qFENFEMMDBwhJiYs3E6Guns/BSA6Kj5/1b/iqLIXbvuot3WzoK0BTy872Gcvq+pKpIMSayYtwKNXPNvXecryCM1xN42DmTjwe+n+9GHcR48iPHMsRj2jeeGmTOpkvtYt380p5t3s980jAeURRzJPoer+7cwb+W10FwCpz+HJxCgz+sjRqlgTuocTok/hXuff4ZKyS5cei1jNjRjNTnQRIyiNSmR+ZtXcThNjui8nzemB7jlY7jn004OjQ9hafEopJ4MXuv+M7dEu1AvOkazsxLfvivxS85gV8jrXOEfgScgcqzpKGPqmhhMGEXoqRFoZHK6lmwmWp3C6gcfYvr1fyBj7ISfpb/+G/CLKgdRFM/7jl0z/qOCnMSPQvPxPjRGBTKZhxX3B4vonHvv02x85mWifSLtXilSXOTFzEcar0aTFUHcZkhIGo6tw45PgD02H8V6JboRSqrXGhlU7CQneywtZgcPrC7HHzOejEio0bmZcfAoc3ZFc6wogszqKlLr6zEMDGBXqahPS0WQBMjOP4hN/h7rzbfgF2VIAm7UahWzJ8oorWwjofVhElOv5SHfcQDe7ogbUgwzc6OYOzyG56xtqPxeZBMmcdH4JM4fm0hFxwAF8SYkEoGAy4fzaA+qzBD8gp1d2ycTHX0G2Vn343K14fc70GmzUCmj6TNvJzz8FIzGkT+qTz+r+4w6Sx03Fd50wvaq/ipW1a0C4EDXAeJ18TxV/BSf1QVrQS8vX86m5k3MT/v3lNA/QpB/Gbwok6HMysJdXYMiKUjV4XrPxd9lEm6WncKCwG7+Uvfs0HnLxzwIISoqaw/w2NFa9g646PP6uFtr5toRY1EotPx+3jksfMOEpz2SikQpIViZpCyhIiMBUWnio8JOhvUZKEurZckCOzeu8hMZto+3JyfjVMQj6xvDEtleLlS7mKT30zXpVQbXPEazKpm3+u5mRPNs2sNHsnlDPZaQLqo+LmFKWgfObWvQnvIgo8KmsvaFp5ArlbStKSVl4mhipwz72frut4hfo1npJH5lcDsctNf08Pnz1QQ81cRoejg98XrM7nY++cv/MDvucjQyPdXWAzgDdmQSOeHn5CCP0iKPUNP3XhUaiUBgUjT9a1rZ/OIBdKKZgCSUxrhSbix8lsd3NxEQRaSxB+k0rWXS8QISvPlUTHCTnryXlJY2Wk8xkrFSSsdZatKS9yNXuAiNaGLT8Tk0DCRxvXI5l8b3Ybx8I0e6D3LmbhtZrh0UeRuJGewkkDaDZfVhLCiI4tKJyQyPNwHwQLYUdsC1V81HJpUgk8LIxGC0i7O8j743ygHQjIrE6WzE77fR1vYWKcnXYrMHQy11ukx0uly0uixCvzRn/BDabG3csTNoC58UN4mi6K/DzT+r+wyZRMboqNEc6TnCXePuIis0i6zQLAJigI9rP+aOnXdgUBiYmjD1hHZ3t+9md9turhlxDVq5FoCWwRZitbFIJT8uJ0CZlsrgps1I9Qpk4Wp8vcFVyxSDlmEDy1DjQoubN9R/45TSpzkr+252jboCk3mACSF6Gnq6+NuAjvM+/AOh5y8nJzWBSTIf5T47BsFFrT+cdqlAmNJISaaLYQPBe4+3x7MzdRuHUwfJqdrAs080s0cZyqazFuF0HOQhj5bRdinnR9uQFr3OqD1X8nrRVoa714EsH0tIMHu9T5NKyeFuco1q8FYRo84ncqCS7U8uZXrM+QS+sHC05jMGfL1kz5lOeEJSMLFREL6vW/6rcFI5nMT3wu/z8d6tt4ItgEEWg1HqI18zGaVUTawmnXkJf0AhUSCLVJNJ8AVXppuQRwUnJc2ISGoq+ynb0U7Bn88hKv0sOmImYiZoL78kqp8WTyiv7T6OLKaTSY7jBFzpJDnCCOvtJWbsIUJiu3BcCCbRQcdEGVp1F1q6ARHzQD4f9uYjlVu5VrKFgyn3kOb1keSTEu7qwyNIibG2gETOoeH30X+8hTnDoocmf4CYtlocERHIoyK/cf+2nW0gFZCf56VT8y5aV8bQvu7uNfT0rEcQFGi1mUilKsLDpv3ovn2vMkhnIREkXL/5ep4qforR0aMZcA/wRcMXTI6bzNPFTyMiIhG+TkmSCBKKE4pZVbeK6zZfx5joMbQOtpJiSiFWG8sH1R8EZffauG/CfbxV/haP7n+U87LP4/YxtwP84CSoSE3D/8GHmN9+G+eelYRdfRP64nFcUBaJov5qwg0Xsbs0lFt6rmC5+Bgfld7ArRm3cE3Le6Q422hVRbMxbDzrfU5+13oASXwRp6QbSKyuIjcrC22olgdLUkiTdqL3R9Ah7cBiTyZH7WdqSxEfTdjGg2/ZSW04RCqwr6aI4k4964qczFphwDpNQWThYZw1DZwtxqM4u5Q07200bf4zgs1IbPwmWnXj2d4/jNyDb5KYo2V8xHz8+BABd8BBaI2JUExsf+B5pt91I/0vVSAfayL+9MIfPYa/ZZzkVjqJ70X52k3INrrRyPRD25wBEcncVIxNA7jK+5An6YlcPAzzimoCdi8h52YhM30dqbPhlaM0ldQxcfcdBAQJWwtP4d3TLiDaYubd0e2csi2FlgEHp2rXkSQqiIuvQCW3Ey52IEb5qDmQiiHUSlRqH3arjs6ebKbN+TNfNL/J0sNd2Fou4L6pOiTNr/A/mbeiEASu6vyUOyufoGXin0jY9TcYeSG3+6/ik8OtbB9mQxMViW7qVAJ2O9WTp2CcO4eYv5xIORFw+Wi/vwTd9HgOSk8BIDXlj9Q3PHnCcVmZDxAff8FP6tcGawPnfX4eE2Mn8qfRf+KajddQa6k94Zhnpz/7jVXBV3B4HfS5+lh6bCkra1dSGFVIWW8ZDp+DhekLkQgSVtWt4rEpj3HLtlsIiMEM7lhtLDavjUcmP8Lk+MnfKZ+9pITmSy87YVvS22+hKSxkx86xGI1FHGxo5v7dV6PHwQrtErIDZXhQoBS/9o3UqBPJcDbDvKfoSZpHS0sLw4cPRyKRcPHDb1DitTF3RC8r9w4HUclc014iXBJ8gg+P382pDTKiStcj8wQp0Lsyi4iqPkBALdJ0LygNQa4mR28aCn0XbkscHlsUIWnb8XvUNG+9FY85jszmNcSHxaFS6tBeUEzV9naS3cFVlMM3QIu9iizjaPoD3Qx77KyfNJb/n3GSeO8kvhMBf4DerdXoosLo8jQiV6uJz8kHwNFrpfnvO1AFNHSoBJKKI1FGRWPzBEgaEYHo9TO4qx11TujQSuGEtj0O3lm2GGvpYkL6K1mZ9xxznHY2F1zGuqhg4trlPa3Yqg6Tn3aAcE0nBkMvok/AMahFG2Kjzx5K23sRBHwC6gQ/3doi/GFePjV+gcQfirPhBpIFG89GvcPM7AeY5W3id03vk9pfjkkuxXTdHsS1t3OPdR5vlvu4MEvH+Y9eDUDcU09i37MHy3vvk/TuO2hGnugncNdb6Hn5GPLzvZT1XgGAyTSWwcFjGI2FmM07iI5eSF7u33+wnw92HcTmsTE1YSp1ljrO+zzobvtg/gckGZIY9Axyx847qOirIDcsl5zQHK4Zcc2PGsOvzCHdjm76Xf1khWZR3V/NWauCk1yyIZlXZ73KxWsvpmUwGDYaq41l9cLVyKXyb28zEKDpoovw1NaR+MZyGs8+B+PChcTcfx+Hj1yC2bwDgFb3TDbURFLenseamJeR91fzincOIyU1tIoRXCZbS79Uz5rY05DP+gvzYiJRS4OroI9Wr+OOnS7cyAjRyDH5+mjw6EmUWEgzbCfOHYkoyDmnO4my+hIK67+uTeGTynj/zJvxJ+xhhFtFX+Vs+sc8yLiEIO25rysBpbGdQEBG9YaH8bv1SAIepuy4herTH6PDoiZaLqCx1JMfF6QB94s+JEjxTVRgTI0hJC/xN29m+tUS753EL4P9n31M09HD5BfPpHXDYbKdozDTg8XVwlHbdhY8eA+Hl31CnDkJjUzHLksN465bSOzwYAhn2JftCHIphmkJ32hfFEWay81s2r4M9/5iUIBNKCEqVspZ8zfw4NEqpro3UGRrItZXTejoNhQKFx6PEufOKB7gCk6rKkWW7yTeVI0/IEdhcGMWsxAC8IV2I8PCC7A2XE5NwMZTssd5LOIyFH4Pz1Q9hmyglYAuEvkZz4FSR0n+vbz5yl4uGJvITb6qYOKMINB2U7A0ZciFF35DMQB42mwADCoODW2zWPai0aSjVARNUFptxjfO+2d0O7q5ZO0lADww4QE+qf0ElVTF66e9TpIhCQC9Qs+S6UuCjLPCT2O1+WoCi9REEqkJypUZkskVw65gVd0qni5+mghNBB8t+Air20p1fzXXbrqWdU3rmJc6D4A6Sx0Huw5yTuY5CIKAIJGQ+OqriB4PUp0O3Yzp2DZvRrzvXiIjZmE27yAsbCrj0v8HPWfS1JPH2I6bUcklrLhmPFWdgyxZU4kgwqX+tZiR8mBNJ8+19bNpTDZSQWBMQS6n7nsXdcZ4cjSDbDrSSwMGmgMhtLnziI3+jCmdY1kR38el+vOw5E0gpGwv1c4OUjoruXIwkuL6BQRsCmK9blZiRe2Q4gxATU8IwwY6GJ7mJnL4B5hLpuCWpdE7Mxmb8Qip1jKIt9KmPoth+h58zTZkqS6cfYmodgs4djfTpj2MZJKevOL/zviYk8rhNw4xEMBlt6HS6TG3tbB/1Uc07TpIhqGQzreOEK9NxSfxUuHcS45uLONl8zn64Edk6kYwKOlnW+9KvCHzSMr78TH1e1bWc2htE1AECnB5PuSlWYf5dPYbrAqEknL8Gc6I7SIy3DZ0TsXhePJXB3gyeyGSMDlzMpooqXBhlamxpQ8bip+XhHvwuLMYqL+C4202PMPCWGx8hHptLJc3fo6qv4b/8V7BGefdxoSUoDL7+FAbBpWMu+fl0nfHm0gjwklctozmSy4lbPFiQi+9BF+fk4GNzajzw1HnBdWfp2UQqVGBxb4XnS4Xmy3omNbrc0hOvgabvZLo6NO/cf/lfeV4/B5GRI5gT8ce/rrnr8gkMsJUYdyz+x4A7h53N6nGb9Yl+KmK4ftww6gbuH7k9UPKQy1To5apidREkqBP4PYdt/PIvkcYGTGSXe278Aa89Dp7+cOIPwRlUShAoQBAM3o0g2vW4m1rJzbud2i06ei0mcjlRtJjJ3DXxAdpN1/N1BlnkBplYni8CbPNwv1rL0QSL+O6lndJczRzaf5D7GhtYFpCKnFxcSQbpQzWbaIJSNCYwAphGilmRypR1knUGmrJtsq4OP0JBmUN3BFyG1sVXm5aWkm52MrI+Aw+aHMilQiMrDqV3r5OPh9XTsBYy0EULLTDlKQ92I4XoY7ejq+oklgqIVhCnHjXEjZ+nkyxKoqB17ajufM+Gptb8bTbyKSQlk+qMGe04vW4CPj9xGRk/Wzj82vHSeXwG4a9p5+KJ75g0NrDoHqAnt4mUvTDOC3xChBFvH43SrkW42nJnDZ5Ou6mAXpeKCVNN4LBcJGttTr8sjkUTkxCIv1xk5bd4ubQ2kaiO/fREz4Mv0zDW5NKECUCqTGjeHHDw/wpoQlJiI/a8kzeNLZQ0BvDFcs6ORaVRl+cjgXh69ix3Y/gl+JVhYFUhtynISEzjk8cS7E13kIZNnyJGgKxGurRgNOHrylYq/lQIIPqdVXcKZOQGalnR00PUzIjUMmlOEtL0YwYgSozk4ydO4Yori2f1eOqNONpGUSVG1SE7sYBFClaBgePEx/3e0TRi91eQ3j4DDSaFMaMXnXCvYuiyIulL/J86fMA3Fp0K38/8HciNZG8MvMVko3JLPpsEekh6ZyZcebPNczfi28zi0gECS/PfJmPaz7G7DKzpWULcomccHU4L5S+gEwi49K8S08wOX21sup98QVsW7cRddttyOeNBiAj804sHWeTGP0ErdWv4+ydjEIRTlJgHRr5dbzvO48LNXuZ1beLMI+Zd46WMS0uGYlEwpQpU/j888/Jy8tj1qxZtDzxLqfmxPDqYStHzSNZqKrAJ/gY25nOoXArf417DUUgaMKs8LexqMtISVos8tpBSsLmkNe+lNABKb0mPzMOROBV+Amc1o42fy+66AocvWl4jueSatnBpxOkTInoQ2EIZ0X6cc44Fotkzw4mPRcM0+1bX0PCZmh5vgS3w45TsBH9ROZv3tT0FU76HH6D6KitQnCBZWUdukH9N/ZrRkaiKY4hoBJR6wyUbW+j+Xgfar0Cg8tHcqyGjze04nUH2S4vuH8cpqgfl2zVeLSHz58/Rljr4zSdo+Caue9wyLmX8O4ArT27kclfw+XSMdAbSdbjndx/bhg3f+xCKfEiO9eCJy+H2lIrPaVGQiZCc1cBloCWBN8EOPcA29e5OWYvwDU6ApPWQcn+33O9/3quyvAzQtOPruwt3p9Zwp8/KT9BrkfPGsZZaTpqJkwk8tZbCFu8eGiff8BNx0P7kOjlBAa9mE5PQ5FspPvpQ8jn+ylzX05e3lOYjIU0NS8lPe1P31p74YXSF3j+yPPkhOZQYa4AYGzMWJ6d/iwqWbBwjdPnRClV/qwrhH8X/oAf8cufm7fezNaWrUyOm8yTxU+i/JICRPT5qB43noAtuNqT6PVk7t0zpFzNa6rpqPuQztxlJ7S9t2MUS8su5trwI9wy+Dcqw0dxecofmasY5I6ZQSe+xWLBYDAgkUj4+OOPOXr0KN0BHV94cjg7TcLUkH3sPiagFAJUG2ooC6ni6ZecVKUUMTb5fF6ObeSIMwRvm5fwaAsjatbRd1oml8eexYOflDIv9x0yktsAaNl+AzE7qzgwegOZ4Q5MOSZcLRm87bHyZHsRlk9WY1q0COO8uSgzMihfsoUQ+9cZ8fILo7Ef7CRyVi66qDD+v+Pf8jkIghAJTARiASdQBhwQxS/DH07iF4Otu4/K1zYQOTaTxGmjAOhvbqPhqe0k6rLRoafT0ELOrBk4DnYhi9SgSNCjLfyahKzxWC/b36s+od2jNXK8bj9zrx2OzxP40YoBoOtoEwAlGZ1cP+oSOmxr0Lep6HLehFzhx2YLoXz7CFrteeT7lvHwW91IEFmXk43/YAwJ0lSsdVUIqWG0mFPYLsulwa0FqR3h80hEVxjyOCnZ8lam1x7m2KzVLNlxFabqqqAAKVM4Z3QyAUHK/gYzHx8OTgpzhsXgLNkJgLqgAAB3vRV5jBZXjQWAsAtysH7RgGVl3dD9uCOaoRUM+uGoVLFkZd7zrfd9uPswL5a+yNzUuTw86WHeqngLi9vC5fmXDykGCJp2fm34x9yHZ4qfYUXVCv6696+c+9m5vDzzZaK0UQgyGfpZp2L96GMM8+YxsHo13uZmFMnJAGhSwzFsm0SgqBeDYTgyo4qOzo+ZG2ZEo3yfF46cR7ThAs43v8/r9js5u+BJbvf7EaRSTCbT0PUnTJhAbW0tBUYDVZ1OPq5TETH1bD7y1DBS2k7OAIR7c6iJe5+CmjJePdVAboONCVXbKCo9zL7U0dw/4goCByUcrbHTJYtHUXE6ocqNqKUQaIhA7i0hiSnUyTeR0RmJLrEew57xCNlSFKmpWN5/H8v77yMNCSFj3VqqP9uBQReGrMSL841mFIKChmNbyX3kdKTS367x5TvvTBCEYuA2IBQ4TJAAT0WwvkKaIAgfAo+LojjwnxD0twzLmgYEAcRwKWZ/J8kjR+Gq6keZbMS6pwXbkQ7Cfp+D3KTG63bTU19PR201ikMBoknE/XkP/kk+pDIZTW/uJkGbRWPgOO5wHxOvvQSVVneCQvgKHpePzW9UEJ6gI3VEBPs+ayAkRkt/hx25SkrysG9STYuiH7/fhUwWXNr7fIM4HA1IJCp0ukx6GzqJyf8At8ZOa/8b+HuC5TpcqGitKcTeGs4eQYtdomR/VBYTOsrpMhrxKbwIQOvBNkS/HH+anka/hAa7lkCSlnTpUerr00AKc3u3crQ9jdyzrmJyYTxs6vxawBEXIJEInDcmkfPGJHLRhGR0Sil6lZzu0lKQSlHl5eHtddLz8lGQgCxcjUQrR5FoIOKqAtwNVnqXHkOZZqTXX4FMZkKt/m5a+IAY4ME9DxKjjeGusXchCAIX5l747z0UvxAEQWBR9iIiNZHcuu1WHtv/GI9PexyA6DvuwDB7NrKICAZWr8Z57NiQclCkGJGo5YR8uhAA7ZR48pJn0qtey7DQe7g6K5Q7j88lY9Ioxhy4hVRnK1XNx8hOGXHC9aOjo7npppuQSqUkbdvFfRvbeGFbIyCnN6BliyeNYuqoyylkVMMxNGWfIxrSSW8MsgCPqd/PJTGjeTUila4BNyBwSJpN8vadJEguQqE2UpFzJUqLjRXppcwfkDEl2c4Un8AO73bO+WI3Fdk5APj7+/EeOMjwC+Yi+kUa92xGjgJvwEOINJLGDftJmz3+PzEsvwi+T+3NAa4QRbH5n3cIgiAjWN5zJvDNwrgn8aPhrDRj29Y69L/Ha6H5ix3I3DI8Cg8KjwIJUP7w5/S4WkjS5WBURBATiEQmUeDCgUqqYd2jT1B49pmYrGEMmqxMuuPq77xm9b5O9q6qR66U4Rz0Muea4USlGMifEocowqqnD5M7KfYb59lsVRwvvxWbrZyEhMtQKsKpb3iGQCBY9zc393Ec6j2E5u6kAPB5zXT0ZCAJH6S7IhvFcQmRdfspLbyDcd4DyBRWAHwSP4nxIQwGxmFpX4NXKeGYM4sSXxIBjRRPppEqXyr+xGguaf6EhoZwLj39VM4sjIf+RnBZYcTvIXYE5J8Yoz4i4euvUmdpKaqsLCQaDa6qL8l+A+DrdmI8LQVBErQlq9JNxN4/AQSoP3IUg2HY99qZ1zeup7q/mkcmP4JOofu+4f5/g+LEYq4cfiXPHnmWhSsXMipyFNeNvI6+/BiW7H+SxVolnffdj6+3j7BLL0GikKLK9GL9ZBMBWy+Dq3cjDU1DNmcRjIH82M8JqZnIO93pjEZgguUQ0xsLmGyt4/ncJELlUmz+ABv6BpgeqscklVI4LIcp27bTEDaWFKEHS3cv673ZHPU5GC6D1QuSmbljB4J/PyarFWvBDIylm5gv9PL8jIlIm2wIPhFZo40uIYfynGXMrT0blV+D2x9Oel8ha00HGW8PIXLsbqpcShoq3yL5gxW4ysroee55ep5dgiIhHmVGBqoF0XRuPk7UGcPwLG/HfqAXfqAkR98HVfh6nURcnIdE8+1hw79WnPQ5/AQ4K/pABHXuz2NrFH0Bup46RCDgZ2fnJwgDAbL0oxEQEARJsLg74Ehxo2kI2n4dajsBk4DOGEqvv5W06ROwvlTNoNdMo+04w0Imo70gkZBhSd96zd5WGx88vJ+APzjuRXOSGT0vDonk66S1r+LmRVGkru5vWKz7iY46g+qaBxFFD4KgQBSDhZ7DwqYSHXUGzS1Lsdmqg9TdnTrE/REcj8/Cbg86dyV+L6euWcvdYy+ndkI2l659ib7wSBY276AzbQqyEZPp2Kanx/0G62Jm0SWGYwhT0Z+pQ+l9E6dpEcnuft4/eguVp67ilMLg1x0Hl8NnN8C1+yDiuyNJRL+f6jFjMZ6+gOh77sGyuh7bng6irhuBf8CDMt00pByczlb6LXswGkaxZ++ppKbcSErK9d/artvv5syVZ6KQKvhowUf/li/h2NZWKks6yJsch1QuYfdHtQwrjqfotOR/uc1/Bx6/h8XrF3O4O1j5LUYbw4BnALvXTkabyF+3REFLO9KwMBSJiTiPHAFRBIkEWXgCvu4mVGOuxLm4l8HAcV4qy2FX2yQqEh+me6CHBaNepFkWzFTXSCXIBBjwBbguMZK70mIRRZElS5bg9/uxWq2kpGVw7/Ggsg+RdzJXVo+pp4W5G/cD8NDiP3LJyveIVUbxyu23UWJup7iphrW2FMwuCRFxzyPqGhFEWHj0HjRuI4IgotOXEzP6U5Sh7Ticek6ZtgMkFjy7j9Nx6x2ITichF15I1B23Y9u8GWV6OhWv7cfgDMFUnEzvgVr00xOJnpAz1HdiQKTttf1QE+TxEkZriTtr1Hf2td/mQaKRDz2D/yn8Sz4HQRBuBqyiKC77p+2XA3pRFJ/6ecX8dcPb66RvedDJGXZpHuqsn06XHHD7cNdZUWWFErB7sK5vwtfr5IBrA53mWs68/T4kUinHt20iZ1wxXR8cRRqupOCKhfSuq0L0B4ibO2noKzae0YiiiL2wC91hGCafjE/vx5T/TROIx+WjqayP3R/XotTKOe+eMcgUUtyeOrZtH0F83IWkp/8ZQZAOtd/Z+QlNzS8BYLUeQiYzkJZ2N7Ex5zIwWAqA0TAKQRAwmYrYt+s6LD1uBtc5qEkbD3ZIamigKzqagiOlvJI1G5dazuJ1LyD3eYge04NsQj5VZZGI1cfxq4bxUcRpuH1GYnQ2GkbGIG11YLDoOdZ+A2pzPYcD6YzOTf/6xhq2gS4KwjO/s989LS0EHA4CdvuQv8HTMshA7jZqq/7A5En7hl5Kv9/B7pJgVrJOl41EoiIu7tv5IQc9gzxQ8gDNg828dMpLP1oxBAIiNrMLfagKr9vPnpX19LYO0tU4QMAn0t1UOXTs/s8a8PuCdTIKpif8RycPhVTBslnL6LJ30WHv4MbNNyIg8Mqpr3Dtxmv59PaJXLzei/PoUZyHD6OfPZuo229HajQgyOXUzjwView4uVOW0te3g1E9f2Vz6yT2SVSMc3ayb88irl9Ywgc9gzj8AcY5ailVxrOhrYW70mIRBIGsrCxKSkoAOH3+XNZWv0u9GI7GL6dfLscZaUQEyvLzCcjGBLw+AAAgAElEQVRslKWkElJWxtX33MMVfUFf2uUZs7k071Q67IWoDEFDiOC3IRcjQASXdSQNG0eijSklftJzbN86DZl6AJOpkGGb1tH39HP0v/km3rY2bJs3o506BeW8RUh3SrBvbUeNBvuqFhymSHyddvTFCTS8tgNFjUC3qwWjPAzXMTMxc4eDRMDTOIAiXje0kvC02ehechjdhFhMC9L+Y+P7Q/g+s9IFwLhv2f4mcAD4r1EOoihi+bQWpAJIYGBNA6qMkJ/0oor+AB3PHEDs8yJEyPGZXUj8UjqEepp6yzj33oeHYqhjM4NfIPHD8obOjzgt+1vbFQSB6HOG4x5txbK6nsjT008wgbidPra+XUntgaAZxRChZtYVuah1wfj1ltY1BAIemluW4XS1kJ+3BIlEFkxka3kVnS6HUSPfoanpRaKjT0enC8potYQjl8sxKN2gUCDpDdD58e+wDfoRZesQAgF2x0eiaN3D1E37KI/RcyA2j2m9O5AqlaiyleRFldB44I+IQi8OUcYqtZyA34AsU0NHahImn0iaHZK6ZagV9dzkvwFvzkKec7bCgAPCMqBmI+TMHypX+c9wVVbScMbCr/vLmILlszo8LYN0ZC0DP/SZtxIZMQuA5uZXh4612SpJTLj8hDKfVreVF0tfxOwyU2Opoc5Sx42jbmRC3A9TQfc0D1K2o43Wyn4GepxEpRhwO3xYe5zEpBnJGhvN2AWp9LbYsPW7MISp+eLFoxz4vBGAxmN9DJ8WT+rIiB+81s8FuUROvD6eeH08m8/djIiIWqZmbupc3qn9hNaZU7j51idIarajHjnyhGfPcNpszG+8iX9ggNDQSZyafhovHnHzpmMkY9mP4HfzdOtSFlfsIM/Vgsxr56WYM7g3/TqaHC6SNCqys7MpKSlh5MiRmEwmpifKyGo7TqM/hC5PBDlyP31hYZTn55FhGaA7KRL5QRsNYQZkmhTsSilxzhZ+L2nnYUsB8rB1mOx+VAPtoE7BohnA5DBgdFRj7Sigdee1hKWvR5C6sFj3s7fjBSbc/Ecs77+PbfNmAHwdnWTOmcr6/U9g7zSjjjYx3DMB85cEjfbmXuTV0OqvJfR3GTjX92JyhNF2Xwlf9Y5fGSBsXiaa4RH0rw3Spth2t6ObEofMpDphDHwWN6Lb960sBP+X+D7lIBNF0fvPG0VR9Aj/LYG+X8JxpAd3rYWDvevxBFyM9y7AuroeWZgKX58LebwOf78b57Fe9NMTUOeF4+t1IIvUBM0zfhHLunrEPi9Onw11jw4pUta0LmXQ18/8W27/t5NrlMlGoq47Mct33xd76Oh9CW9ARu7cMCJicsgZcT5SmRyzeRd9fdvo7FqF0VhERPgMausepbT0ciRSJSpVLDZbBVkZD9FV5yMt609DL77f7+e1114DYPKWWrQZGRwRxuCXhqN1fkx/nAl3oId25Se8OxneK4rD0fF7rq58B2NuJMnjJdgdlXi9Cg55ojiUGU9dnwFJuxPf2HD8RiWj9GoeyohHUG2hoOcZzIoYVg6MYfXYMHh1Jti6QB0KbmtQOXzX2O3bF+yfjAxUI4sZWGsFrASkLkACBOjqWk1kxCw6Oj+lofEZIiPn0N39BQBJSVchiiJ7O/fydvnb7GzfSUAMEK4Ox+w089S0pyhOLP7B8dm7qp4DXzQiV0oJj9eRlB9Gzb4u5EopC24oID7765Wo1vi1ie/yv08mEBA5trWV4zvaWfPSMUbMTGT8wjQk//Bx0lppZvt71cy6Ip+wuP8bv8c/Rl0tHraYjc0b2d66ncq+Sh6b+hiF/zQtGGbOxLzsVWzbtmGcP5+M4dcz5YNNrOueTGfEu0QPOpDseZ4CQwJkzYLkyZxqG+ReP2xoqGBx3kiecAjsnHEmzSYdcwMiKSkpdHd3c9bYHB7f0YlU7OFgUZAob0A+gB49e8aPoy0+fkgOa2MrI21y0mgDz18Yc3AZn4QmkCv62KWyc6VDh1WTicTvRnvUhnKzjJqcZ9DPuhWT/w02W/eivV7AuCyAYfQ07Hv3Ivj9jMlJQDZrAurRRWy59RkyVYXIJHL8lXYGvH2EnZ1JxriJ9Err6fqglAFnHzHqFHpcbRh94Vg+qqXzi2MonEp6nM2EKGPoWXqU6BuL8Flc+PpcBJw+LCtrwS9iOj0N+75OQs/NQhb+fx/19p0+B0EQjgGniKLY9U/bo4CNoij+asjQ/y98Dq5aC36rG2W6ia4nDmJxdbHbvpqY9Cwi6iOI0wRNGyIiAl+/FBKDAnV+OPbd7QSipRgyY3BW9OLvcdFir4LRGqLLInFkunFGuQlPSiF5+I/j/v9HiKKI19uPQvHt5i1L9wC7dixAFdKCIKgRvyRDk0q1xMWdR0fHx3i9ZuTyEEYUvIpeP4z9B05ncLAcCD4T0dFn0LjpPBqPDzDj/DSypwT9GIeWLWNVS5CjR+YxoBtIQ+7TIyLSr1uFXxdCla6MsohgeKm74nbi3f3MN39O9qIGunvTaKgvoiI0lpL+REStDGmvG32IA3eiluLQRF6alQ/2PgJLCpG4+vmb/zxeCZxOxdQ9SHc/DVP+BB1HIKYApt0O30FF3XbzzTiOHCF08XM4j/YiKKUo00xYwjfTpHoSnS4Hu72O0aM/4eDBc9DpchlRsAy7vRaJVIVel83SY0t5+tDTKCQKzss+j7mpc8kJy8Hr934nN9E/YqDXydv37CFlRATFF2ajVMuGxhB+mCH1K/j9AXZ9UMuxra0oNTJ83gAh0Royx0RTWdKBud2OIVzFObeNRqX7v3d+ttvaqe6v5rH9j9Fua+f12a8zIvLr6CMxEKB2WjHqggLilzwDwJYXD3FpYwcLtZt50r+UgESHIA0geIPEevypnklbdxOvkPLmjNNI2FY61N5fO9/jonm34kKJ1+vloadfQBQC6PGjt/azLC7AWTYTUqSo7Q7q0wuJaS8nurOT4abTeMZxhMPxE3H3exCAYaEisUY52sYq4q0pKAJaslWbKPcNoHDNwmDcS+icd7D5BUwykTjNWTiPhqF64nVCLrqQ/jfeBCD2b4/RVnqY8vYmopXheFzRVFv3sHj5W8jkwXHw+7yU79hC1Y7tjJp/OpaOTnyHrMTagu/UMfVezA1NTI0+96tvliHIY7R47S4YCOYd6afGYzwt5WcZw3+JeE8QhIuAG4BbgK/IZQqBvwHPiqK4/GeR7mfAz60cvD0Ouh4/CIA8XoenzcbnzS8y4bKLyBw7kaU3XI7EFXQaO32D6OWhiEBMVDoj5dO+0Z7F00NZ/06kqRrOvusv+Dxe5ErFj5Klt3cLPT3rSUq6Crk8BJnMgCAI1NY+SlPzy4wdswad7kR7e2e9lcMlz6OIWUp68lMkJM8mEHDR37+HtvZ36evbBggUDH+Z0NDJSCTBB9jnswEigYAXv9+GVIjllZuCx4bIB0gpjKVqfw+OwFr6Q4yo7XEMGHtAEAl3WTkcEUqCzYfO3MHbmT30t1/AtPCjbO0ew5ntnzJm9GGanZNwOEx4pDLeUY+B3uDiVIaHRVMGeHt7OMsuLmKGbTV8fjMiAjcoH2C1NYXCWDUf2i+FtOlw7o97/Opmn4Y8IQVJ2Hko04wY56SiiNOxf/9C/AEnebmPs//A2UMO9tFFn2AwDB86v8PWwdxP5jI+djz3T7ifcPU3w3u/Dz0tg+x4r5qeVhvn3zsWfajqh0/6AVTt7aR6XyemSA2dDQN0NwajyfOnxlG+q53YdBPzry9AIpX8ZAX0r2DQM8jClQtx+V0k6ZOYmzqX83POB6DzgQewfPIpmSW7kahUeNpsPPfuXp7udbE1/kmSe/fjj5uBNDQMjq2ACz7kvto2XlXk8V5BKmcea+HF7DgeP7KfJHsjb43IhPQgQ+6WLVt4emMVKomX/FGjWHagjRnGjcQ7YqnIHcW2iER+t3sdcf09ZElHst2yjw9CZ3JeupcPqwQkBQaizGa6PNtRhm/hhoOL0Qf0ZKs3U1uTT1f0GHL7lvPu6H2kZUso0rmw2pXE7Pdi/FCKIiYeUQzga+84oT+qY01IkTF3/RYE2XcbZ3wuD5337UUkgP6WdA5+tgrZQT+p+uH0G3oZ1FhRSNQ4DDaOr99EprGIbOMYpBFqYm751vn8J+NfckiLoviGIAg9wANAPsHPyePAPaIorvlZJPuF4TO7GNjQhGlhOvhFBIUEQSoJcvh/CW+rjS5lM6JWIHdyMTKFgov/9hzNZaV01lWTO2U6G5c+j8fhYFBlobb/EEZFJDW6UpydFlxOO6IOpl5+ORljJiAIAnKlArenF6v1IBHhpwyVnPxnuFztHCu7lkDATXvHCgCMxiLy856iqfllAOrq/07B8ODfbnc35v7dbHi7huiid/Ba80lMmYcgCEgkciIiZhIefgpW60GkUi16fc4J15PJ/tEcEUrL8R5AwGSpod+UQfeBTpy6VtzqMLQD8WQ0NBKX7GSfv4vOsGQSbD6iOzqoUu/E7pwNARVbu8cQq+hg/MQDDPgLcDhMqKOq2OO8DLo95Gq7eM13LxJge+kM3hHOZozBCituBUGCMPsRFFVZRFpbuDKyESoHYPRifgxErxdPSwu2TAd6XTcx549DqpUzOFjOwOBRMjPuQa/PIy/vCcrKrkOjST1BMQC8XfF2sGTn2Lt+smKoLOlg0/IKBInAjItzfhbFAJA1NpqssdFD//c0D+L3BYhONRKVbGDT8gr2fdbAqNlJfPrEYexWNwtvHvWTkhl/CvQKPc/NeI4lh5ewrXUbFeYKko3JTIidgK54Ov3vvIu9pAR9cTGKOB0XXj2JFx9ewx99i1jCn1BrEwk7PQ3qNsM7i/hD4jQ+jYnjyYpKQEth3UcU9ffxbsxcHm5rRC7t5JbkKIqLi3GoIrh2ZRN79lrQCTIOSaWUx27g4qwYtF1rkWkkONyhDPa3k+73kmGrJ2L9eorCCtkacwaDRgP/y955h0dVpv3/c6bXzKT3XggJEDpILyoqIjbABmJ3Lbv27q6964prRVfXiiKKgqiANCkBQiAJKaT3XmYyvZ/fH4NBpKr47u77/r7XlSsz5zztPOfMuZ/nLt9buV1OSNRuakIsJPXkYpS1Eh4TSacYoCL0cq5c3cVbPiPpYWo00dU4prWBDDKj56ObOoX+0m0YppxJ9UVnYp/sRz+jB91qAXddDaqso9sKAaRKOaaLVtJjW4uiOpzJl69hG5+wruADvObgYsXtsONzuzFGx1LauxWHz8JITsfb7RhQLf1Rgv+44X0HhcD/CkFwNHi7HDiKuvB2OfD1OJFHaQi7fDD2wk7qrCWUmbczZdZCtqxZxmkXX4bsIAmZPjyC3KkzyZ0aZGu8/KmXCPgDyORyqgvy6aqv5YK5j9BaUcbaN5cw7oL5DJ40baBfp7OJ3QXn4/P1k552Nykph2ISPJ4eqmueJjHhSrq6vkMUfYwc8TE9PRvx+e20tX3G9h2TAAgPn05Pz0aczlbU6njKyu/EZNpB/ETwufREGe454sH5ybPoZNC6pwGA1EAxW3UynNpmBBEUrlBGlG4lvmk3VME4uYBULMZsNBLW18dHtwio2mLQCXb6RC2zB60jLNZC9Q95eLTdrFCXYXEGkMpgluY5oq0W+tFykXsl0phQ9KX5IEjgtlKQynl20xgkKhO0J4IhCZJPLtOau7ke/H7s0W0IuduRaoMxEJ1d3yII0gHSvKjIs8jMeIDIg0bpn/Bm8Zu8X/4+ZySfQawu9qT6/AlOm4ety6uJSgnhjKty/rAXM0Bk0iGKlOzTYmmtMlH4fSMlmw5RoOxb18j0hYOP1cTvxqCwQbw681V6nD1ct+46btt0GyvmrCBh3FgkBgP9K79CPz1om4nQKbl2TDuv7UziVr2UC8s7mefPRDX1XvjubqIaN/C6uZcLhy8hQyUjcfOTnDboOpYBSwIp0NDBdz1mvhiewemjBsHXwaj8WIkFb38uvRHbeLXoVbRyLeGyUMZIInC5uwhHzfi+3SCKZDgauC1Bz9LWXjaKMqx9g9mWuIELzFmsMT0MgEwOPp+Eoty7GdcEvTVWzAEvxlHfw6RNMGQQjaZPaQn7EEqfhkcPzYd1LjRUvU521iuHzZPNVonL1Up4+DS6u9fTZf0amcyAy9VKZ9cXzLj6Brj6hoHygYAfp8WC1hiKy25j2Z13AtDzz1JEv4ggEwi/bDCKxCNpcn4vjul7JwjCYkEQtgmCsFUQhCsPHnv8WOX/G6EaFAqn6QjYPCAG3Ru73ywGn0hV/x4cPgvfr3kNgLwzzj5mOxKJdEC3mDnmNCbOvwK5UkXK8FHc8OYHjJh17kDZQMBLefk9gIhanUJd/RIslhLc7k66u9dxoPKvdHR8xZ7CBbS0fkxkxJmEho4nM/MBBmc/SWLiVQBER89hUNajgEBb2zKs1nJMph2Eai+ladNdRKk+ZsT035dMvae2CyHQxIZBYTh1zSQ1NjGvrJh5TcuDguGnefSKPH/F9YT329kzJByzUk2sWWCh0sn1BgPjYwtxuzS4FGbseiVK8RyEbh+S0AYut3fREjaeldM3UqocwVzTe5D/WtDIHBILu95C5upDotAi6W+CvAUgOfTYigER8WDMhrvJgmVzM35bcNXVWbQCAH+EBHPIdvx+B16vma6uNRiN45DLgz7zgiCQlHQNavUhI+aPLT/yWtFrTEucxgPjHjjpObP2ufC6/RSsacDr9jNz0eA/VDAcDZPmZZI9IZak3DDOv30EQ6fGU769ndIfW09c+XciQh3BG6e/gT/gZ9mBZUgUCkIXLMD6ww84Cgpw1wapSRZPTOe8tO/YZ3XzsM/O5W/ksy92Hqe7n2Nn8o1M6C8i21bLrP5CcNu4SGVlY+sSCvZezcQQNWU2F8s7+lAq5Aw+mPBnpNHDtJRkXJ1nIxU1XBLzIvHJQUp5n9+GXhrBtNpKZpQ3Em/pYmhUKIsjdPijVPj6h2NR9fLhqKcxq4LCZsHEbxk2IwGJP9i+ge2ks5yuinn4PFqKS6+mpfVDYmMuJj3tTlKM15LivYy3bSOR1kroEjbxE8tQR8cqduTPZNfucyguuY7y8ruorn4CnS6byZN2o1Il0N+/74j5lEikaI3BWBCVVkfK+FHU2orxm91ItTJEv4jzQN8fci+Pt3M4WxTFSQCCIHwBvA9kHKf8fx1KN61n3cevcNrFlzLmvIuwftuEY1cnZqEbeayWq+94grVvvkJ8ds7ADQIQxQCtrZ9g7t9DYsIiQkJGAIFjqod+jvKKuzH3F5Az+HnCw6eyu2AuewoXIAgQCARfakplDG53B36/h4TEKw+rn552JyH6oURGzkIqVRIRMYPmlg+xO2oRBCkB83wc3T0MHvfb2SPddfX0vvMOfV15WMNa0UiczOtbTXhyBKqcbPa/WIZNL+WBq4fw1pJSvhs/FUX8GJidwstZD+OzDicxtgtFy3BqM9bzWUMSYe0jkQl+NvXG0OfJQCZ6mEQBEQE/zRNvZ/GobMh4Ed6eERzEuBvBbYXdSyH7XDj3Zdj+Moy94bCx9i07EIxAvWEYfR9X4O/34CjpRD9exFSyFiUQ2XsDrZmvsXnLIR+KzMyHjnrtoiiy7MAyXtjzAskhybw09aWTMjo3H+hj/6YW6ot7UGpkeJw+cibHExb3P+t+CKDUyJm56NAuITbDgKnTwZZPKind0srpVw0mIuHUrzR/Qow2hnGx49jcvJm7x9xNyJULMX/+OY0LFwGQXVaK0TiWuRk3cdnowRQtz+bZjn4ueH0HkMA99QZ+lMDfat9gjLUUEkYj3b2UnIPtf7HpPGaNfZ8vO81cnxjFksvH8NGyz7h41unEJ6fzr6clmPsm8vIBM6eNDUXqtuBRSTCIalwqLb2aENK7LLz9lys4866b8Y5IwN86CrlpNzFGF8vzlpBgV3NtVTkjT88hMGkk1ZtrMSln0usHY281psqpRA79ls7yeCyva2m4pJ7EVWm4vBEw6Es6nRIi0h30m/eiUERSXnEPWm0GmRkPYrdXD6iJR+a+hEQiQ6vNwO4ICk6v14zVWobROHbAHmgy7aSs/C5CBo1my3e1xM8diVVtI+DyETvj2NQuvwfHEw7Kg6R7UkB5nHL/tRg8eTqtB8rJX7GM/BXLGDRmMqMyz6BgywfkXjSL0Nh4Lnn0WQCs1gqamt5BoQinq/t7XK5WQKCr6zsUikgkEgXD8/6JRnNsLwKzeQ+dnatJSbmZ2NggZfPo0Stoanwbr89MR8dXAGRlPkxd/cuEh00h1DjmsDakUhUxMcEsan5/gAjtnfT1nU939zrCwibTsUvAEKlGpf1t3iqi10vT4sV4u7qxTBuDU+Ml3lOBJ3EEFtd6bPnVKC0RvHNuIlXZD7Lgr58hercwp28zjyfvxC8JEOg5jblZTTQ1S6jSFTKhfSouv4uNGT56Qseg6XLh6/HwnGUjJbJkho44M9h53EgYdglEZUPSeNjxKrjMMOl20EXCrCcPG6vf4sG5vweAnn+V4e/3oB4STu/bj2J6vQS5QkQM0RI/aR6yOB8ebx9qVQL6kCFHzfXs9Xt5JP8RVtWuYlL8JO4fe/8JBUMgILJ3bSO7vq5DrpQybEYC1l4XHqePCRf+ZwQ0SaQSpl+RzYpn99DbamPVkiKmXDKI4g3N2EwuZi7OIWFQ6Ikb+hWYlTKLh7Y/xLiPxyGVSFl0/SimPxuME3BXVqLKycFgGEm/+SUyz4Q/dQ6jrDcbgyaEVVXDcMYOYropGPmMywwIgBi0N+37iMmta3krdi7uQICs5Hgeuec2JAd3lEsXjuIf3xfT1dNHUa2cKFk1/shh2LZ9w/Zp0wC44KvVlDqNVOx4jsu9Z7A95xw6Kq+godVPrvI9mtKqeM4Vxb3r72Xo5fmU5h+cHyloXZW0l59DhUlGVMccTBEgX7WcTk3QHX3KgWh+TOvgQq+PvTsvAb8IKimZ/psQtvcTP+9RdLpsECQDv2+tJh2TaQfl5XfT2bWGQMBNWNhkhue9h81Wzv7SW/B6TfikG1Fos9nw5VvY+noBEKUBhkw/45TePzi+cHgceDV4R/iJinL1KR/BvxEyuZxZf/oLyXkj2L1yOZUFW6lkKwjCYTaCQMBNyf4bcbmCHEiCICc+/grS026nsuoR7PYa7PYa9u69nOHD3xsIFPs5eno2UbL/RhSKKFKSD9kYVMoYsrKCOs6YmAuorX2e8PApREbOOu7KXxRFVr1cRFu1mRHn341g/JSszL9R/GkbcZnGY9Y7Eez5+fi6upDf/hiOqiBb6fC1XXjt9cz/cxyzawLMEUR2ZkUTYtmM6P0GgNXGj5H5FXjb5pJidtBTnUl96H7OzphJf3s/vpREGnWjCahluLtdRMkslOomoTz78QHaZwQBLnzrp0mHXW9B8iRIONJGIgZEej4oA4kAARFPgwVpmArteCNdT5QAIPEIKAcPI2RKIiHcc9zr9vg9PLLjEVbXreamvJu4Ie+Gk4p43rUqmNgoZVgEs67LRSY/8e7x34GQCDWX/W08VpOLb14tZu3bpciVUhDgx2WVXPrXcac0+vrctHP5svpLavtrSTOk8XbPDpbfJOWN1/049hSiyskhMeFK+vsLMYoTONMazcUzEyhpXMrqqmHsJYLThEoc469Al/8RTH8Itv09aIta8DHD1y7BGzOXMpuTkSHaAcEAcGZuDFPSjTzx8pt86gxnR2Id5zblsH3SpIEyrTFRxHm9mBtFZk5ejso8lH9OTyZ6Ux0NvsXk2t5ieWQjKrmGK767k7D4B+hrtWMMDdDnm4zRVEVAOBRfI2jmD3wOd+awLbOLK7+OIzDCiSQgQ7nSRGfLveDzISiUJF50uEZAp88hEPDQ1b2O2JgLQZDQ2voxPb0baWx8E4mgID3tbmrrnidtQhwV62tIzBmKXKVCpvxj1u7H81YqBOb/4tgnf8go/o0QBIHBE6eSPWEKa155nsodP5J3xjnoww95prS1f4HL1UJuzt8xGEahUsUN1B2SGwwUt9kq2bvvCnYXzGHIkH+gUadQVHQVKnU8qSm3Ul//CqLoIzfnBaTSo+ugw8MmER426ajnRFHE7w0gUwRfPo2lvbQdpJrevyaVq55fQ3+3E7u5/jcLB1dVFR1PPokkNJSifSacUZ3YhU509mASnaWvBI2bFfEK3MoilOYiFAE5Z3ZcwE7vATJaVawPHU+SciO99lBqk/KZ5LmMfezlS1MW/mQlU5wS9vY6uFi9h5l3fDCQ4e0wBPyw7BLob4IZR1f/uA704W2xEXpxJs79Pdh3FeGpWov3wOEuwoZZp5/wup0+J4u/X0x5bzm3DL+FG/JuOGEdCNoXin9oJn1kFLOuzf0f58X5tVDp5Kh0ci59eBz1Jd3Ephvpbray7p0yavd1k5QbRlu1mbgMIwr176OilkqkvH/2IXdjp8/JxGUTsYcLOAoLCVu0kOjo2RhDxyFz6WnfVIBQ6EAfZyNZ38o3tnQmitvxHDiYUCn3AmjcBtXr4KxnGKV8CYDP2/tIUimx+/0kqw+9JFUqFddecj4ln6ylXulnd6Ka4Z0B7Imga/XSnpDEiKYG8rtjUCjaUJdtQ54Xj2NYBEKRjbLOmzgtZQsf8C1ntuxg3Ew/hZu1WPvcOGURZLCOPoJqSr3/AFZpNlGde+iLSEIiTULphU/PnsVLcx7Ctm07zX+/FvAB4Covh4sOT/YUHXUuanUiet0QpFIlgYCP7u511Na+gN1eRXra3SQkLKS+YQlROQE6yhI46+bb0YdH/s97KwmCsBp4C1j7y0hpQRDSgMVAgyiK7x6l+n8dBEFg9p/vZsycC4lIOpy0rrX1E/T6XKKj5xzzRuh0gxg3dg0lJTdQWvpnQEQU/bg9nRQVB43IGRn3ExZ2cp42P0fZ1lYKv2/E2usiPF7L0GkJ/PhZFVqjkulXZPPNq8WUbmnF1OlAkAik/wZ6BTEQoOXWP+M390g9hkYAACAASURBVCO59E/UtjoRBT9W95FGsrJkHy7NREb0epnQMZ6v3OFMaj7AlvAxSEUfg5MsWPsayLVmsWNXKc3KGALDo0iSSpngaGc3UuaMH3J0wQBQtjL4EhCkkH3OUYvYCzqQhCjQjIhGFimn980bCfT34gYErZ6uG/sIrxhL2FWXHfOafQEfW5q3sLxqOeW95bw49UXOTDnzxHMliuz5toGK7UH/9okXZ/zHC4afQ6WTM3hCcIETEqkmNKae/JU17F4twdThIDJJz7z7Rp/Sa1LL1EyMn0hF/A5CCgsHyB2VighQgOHsVPpX1xFhu5ihhgZWtUzhSfUnhJksmMQ4FJokXEnJhNdthvKviB23mGuLV/AOF/NeWy8KQaBpWt5hfaakpPDUwnlcsv5tnJI6Vs24FWoXMlI1EmVsMuO3byXaH46kREZOupqlaVHcWt+FbKRAbKNIUfFktEkbWWbUc9uGm8mwjWCH7QoA9ofNJ8RcgyiXMjfhBQqrzyFdXE+BcD1WXQLziqezWR6MvVJlH9IkSAwGPM1HEF0jkcgwGkYd9j06+lyam99DIlETEzMXmUxLaOhE7PZirnpp8x+eke54++brgCnAAUEQCgRB+FYQhI2CINQRFBqFf6RgEAThLEEQKgVBqBEE4b4/qp9f9El0WgZS2SE9s9Vagc1WQWzsxSe8GUplFMOGvYlSGY0o+hk69HWG5/2L+LhLSU7+E4kJv57jv/lAH5s/rkRrUDDm3FQcFg+bP65Eo1cw/4ExJOWEET8olPyVtRzY0c6QqfGo9ScXYAfg8/loa2ujf9Mm9mlnUjzsZnbvbsOl6UAu1JDYYcEvCFxzxj1En2tmf2oS60cKeLQzeK7xaspcKbT7YL9+CM3qRGYmbsNtiSRAgANR6Xwy6Uw2jR+PRi7hxdQ43tlrZ7KsjCHTFxx7UAX/DBLpPdwDyiMNpwGHF1eVCc2wSASpgGPnBgL9vcQ99yzSsDDkV0/Gmy4SdfVNx71nz+x+hts230Z5bzl/GfmXkxIMALV7u9m9uh6ZQsKZ1+aesviFfwckEoHR56Rg6XFh6nCQmhdBd5OVqoLOE1f+lZidNpvCeBf+nh76/nV4EKN+YjyRNwwjbeifuWTMTdjRkC9eQQ96/uxdxEVLtrLPuwaLTob41a349Vnc3/cdQw6qej2iiDtwZP6xnPhQBG8UZl8HT0UG8350ajrxSyVsnTKZzkFDCflQR16qjrPTk7lVK8EUFoJvkI4ei8Ag/ems02qQ0Mg/4+t+1rIMizEDqzaVz53vkvXEY0R/Xk6pKgOHOoqkjjA6xEY8Pg+yiKAWQpGcjHb8eOw/bqXm9DPoevn49HRpqbeRlno7ecOWolIFXakjI07H5WrBZjtw3LqnAsdTK3UA9wD3CIKQAsQSzARXJYqi448clBB0+3mNYL6IFqBAEIRVoiiWH7/mr0N//z7q6l5GrUkie9DRvXQ7OlYiCHJioo/N3/NzKJXRjB+3FperFa026NwVHj75V49NDIggQMW2NtQhCubePgKZXEp8lpGNH1RwxtW5aEKCQmDOLXnUFHYiSAQyRkWdVPuOggLK9hSysa0Vp1SK3mLBm5GKwq3EExqKIHbQErmPmTtFGvWxtGmjOF/7Fp1nfoNc30OkPw1BdFGo9eO2qyk2DCNJ1cFZSZso6TmXA/EC29OGkuxzoa9zkxcdwpN7SlGKLp4d0g6Kn6nWemuh5LOg4dnnguZdwc+So69dnKW94BfRDA/ukGwbNyJPTiJkzhz0585m1+6z0UuGEJF75C6t1lzL1pataOQallcuZ8GgBdw39j5kkuOrUbxuP06rB7lSyo+fVmKM1nDJX8cdxm/034qM0dG4HT60RiWpwyJY/nQBu76uIyk3bICg8VTg9KTTeXdyFnuqKxn+wnNsS/Nw1qQrB1KRKlMNKFMNjPH50aws5knXWZRzBvEqO639Lg6YM1HlHuC03Q58Xz2PdtrtrP16IcuH38PthrOpc7gZrDucc0giEYhQJNDtL+W7A6vQyXX4IrQEukQ6YoMv3PLcHCK//BhFeDRXDb2YpwtqOKCWoAiRU1Kdiy/yGy6Ni6ZL2ku0ugPUtYSZJpI7OY6yH9twuwJ8/XIRWWOjiRf0eASQe2VIAkr+WfQlo+LT6PrwQc7InsPav/2JTMDb0kLvm2+hmzIFzcijU3nLZDpSU2857FhE5OlQ+RBd3d8fEcR6qnFSHMOiKDaIopgvimLRHy0YDmIsUCOKYp0Y5DX4FJh7qjsJiD76TNtobf2E3r5tA8f7+rbT2vYZfr+Lru51hIdPQS4/eW8OqVQ9IBhOFg6Lhw3vl9NZb8Hj8vH+AzvYt66J1mozCYNCBwyd8VmhLHxiAjFphkP9ySUMGh9L1tgYJNLj39KAx4M9P5/iW27l2/Y2VP39jLDbkWsMuJUWLKHluDQd5FJFkRaSu6XUGeN5aPZget1yVNpavKpspnd4qXY0Yrb5yHLVcLnrUx6Y9BxuR3Bc+wJjESUSXs3LpbrOzIr8Jg50WHlB9gZxOT/LnhUIBN1XtzwLT8bA8itB9EPW0bOoiKKIvbATWbgKebwO0ePBXlCAbuJEBEHAZNqOw1FLYtJVR901PLnrSV4sfJHHdz5OujGdO0bdcULBALDpwwo+fCifd+/ehtPqZei0hP8VggGCL9Ch0xJIGx6JIBGYcskgHBYPHz2UT9EPR6pAfitkEhmvn/02njuvxi8TML/4Mgu/XYjDe/grRSGTMk6jppwAIHDvhBUoJV6Ku0/DJzFgUYciMRcTyL0U6cyHGFb9OQBV7bXBfBK/QG50FoLcyjbzNqYlTmNGykTMCvPA+aakJKw7jBSs/zsd7S8zwdoDgoA2PQRTTygR6mF0yWTE9CmpDX+JVZmfEq7pob/Dxvi5qeQYdxKiMlO1uxNPMH0DoiAwzBzL62VPcs26a7i//FnO+XYB70YGnSW0EycijYyg64UXEf3+k55DpSKCsNCJdLR/id/vpL+/CIej4dfdiJPEf06W88MRDzT/7HvLwWMDEAThekEQ9giCsKe7u/s3dWI0jGba1DIUighaWoIkWl1d37OvaBEHDjxA4d4FuFzNhBqPxlx+avHjp5UcyO9g9+o6yn5sw252k7+yFke/5zcbmPfs2cOGDRvwH3z4AnY7ey+6gR//+jZbp01FIQhcdv75zH3+eTI1E4nozkPntSLDw+qYA8gcAjq7n8awEMzNpVxpcxJQduFR5XBWF3wqcxMQpIztKSAvqwy5xIfTkQiClEB0BKlKBSU1wQCd5HANTw3rYbq0CFJ+tpPqKj/oqngQ9VuCbKtH8VACcNea8TRa0E2MRxAE3DU1iA4HmtHB8p2d3yCThRAddbitYn/3fgo6CijoKGBRziKemfwMH53zERr5iQPUXHYv1Qcpz5UaGbOuG8LQqfEnqPXfi9h0AxfdM4rIJD35K2txHgwqPBWIUEdwzfS7SLj5L4ytEjnz3VJ2P3obfpvtsHJ3T00nVa3gzxO70cr2khjSREtXFsrebPoi7ciFZuzF5TD2etLdHSgCHorzP4SNR2oAJicHqe9FRFKdqWQJSeyO2kWLQcNUyTB8cjn+sCgcm8NoKn+fxzJ6uLlgHVd2FIBSSnf7XEZ50jmrPpP4kCm4JBJEyT46anrIFJYzXfUs8ydv44K7RnLxNVrkAQdOdSSLKg1c2S/hISGaUD90uVqpjRO46y8jiV+yhMibb8G5dy8HcofQ9tBDJy0kEhMX43K3sXnLMPYUXkRT8x+j3f+vzY4tiuJSYCkEifd+SxuCICCVqoiNnUdj41tYrRU0Nr6FRpNOWNhEWlo+AMBgOHYGp1MBj8tHQ0nQZ7ml0oS523nY+czRJ6cq+gmi10vLO+/wTWdQb5ycnExGRgZtqzezK34hVn0Nbk071113HTpBzvc3v02dP53kzl1UZH7JamMYLkmA8wuGA4W0ZDSyzvsY+sEGRKTIJUPZanXwgyKWQUIL8QkWQtMtpKTcTF2dhp5BUdgNcs6OMvLV93XkxoWw5s+TYcU1EJIQjHz+CfU/Bv//uQj0sbDzNUgYe9T8DKIoYlnXiNSgQDs2yC3kqgwmdFFmD8Zs3kN7xxfERM9FIjmkDmmztXHZt0HDtFqm5vph12NQGo5o/4j+AiLr3yuncX8PgkTg/NuHE5kcglzxn+mueioRmaRn8oIsPn18NxXb21GoZcRlGgmLPTVBfWFXXollzbdMrKiEiq10Bp4m7qlDcSw5U5PZNDWZ3t4fKSqGRH0bm8zpXFlyBXckOUlnCx2V96Eb9Q2qjOmMtFaRbxgOxY/DjIfBaQKJDFQhTE7Kgz0g8Rhp2d5Kg9CEM83FpuRuJjVMQt+5l8qcIQAI3x0gPOk7Fl/6BO+++y5pMRLqGrXcdef71I628JfP9mLQbKUowkSSVYF360cgB2XHduIuMsJrfyFOcQGNxjHQL2Fh+2ai+xqYLpVikUhYEmqgWFmPVKfFeNGF9H30IZ6aWvpXfIEsLJyoO24/Yq4CLheiy4XUGFwkRkRMZ8SIj+jt3YJGk0p01OxTck9+iZPaOQiCoBYE4fclHPh1aAUSf/Y94eCxPwTJSdchlxvZXXAuFmsJCQkLiYk+b+C8Xp97nNq/Hy0VJvy+ABMvziDgF7F0Oxk3N43z7xjBRfeMQvkrcs+6a2upnjqNsq++GjjW0NCA22ZjTcF+rCGVuDTtRMjjiYuLY81Tm2hwxxHXtYu61BV8EKEjwh3BwsJHya7VEQDqkhoQcWPSiljDb2DhXgcfiB5GRe3j9hkvkXVmGbEJ55IQfzNlZicrotMAOEOppqSlnwtHHqSlaNsXzPP8c3SWgi4GwlJBroLJd0LqkTYab4cd64YmPE1W9DOSEGTBR9ddWYmgVCLE6SnZfyNSqY7ExMWH1V1RtWLg87VDrz0pwQDQcsBEdUEnHpefceelEpcZ+n9CMPyE8HgdUcnB3cOWTypZ8cweXPYjUrz8JkiUSlJXfE7V0jtYPVbAvHIl3tYjf+KhoaeRnnY3UwzB7HweJLzRcgUiIPUU0fzoBsQ5r3Ha4NPYFzKYOWn3s6m+nLu+eh3Le0E7YZw+junqV+mvvYetuqms8+SRLc1BY9vIE7ku9qYOGeivJSQJa0U+MTE6xo0bR5IpuPh4c18rL66vAWSIvSPZZgjS0X9s/RN3i3ex2dIO7SWI3Qc4K/x5clRb6Dek81Hj3cxwv0CU30+G10uq14dFasPt9SDI5aR+9hnZJcUY58+nd+lSul58EfEXhvX2Bx6kavxptD3wIC1/uQ1fdzdhoaeRmXEf8XELfkGYeepwQuEgCMIcoAj4/uD34YIgrPpDRnMIBUCmIAipgiAogEuAP6xPudxAZuaDAGg0GcTHzUevH0pE+Azyhr0zEML+R6Gt2oxULmHo1ARCY7UgEDRuZYUeZls4EQIuF42LF+MxW+nMyEDhdhMFVGzcyPKbbqZd78Kl7gRExNZIuqu7iNz3JVO33cWY8EI+HCIwtRvSqxZB3RqGN25hc2Y8TqVAsvI2+uJfIsSZjN+mJSDAn6dJWCq9ma+0T5I06EVaWlqoC4sGYP3oLLaUdiKVCJyXFwf7V0BfLSSOO3zQpgYISzv+dTl9dC8twfJDE7IoDdpR0QPn3NXVKDMyaO/8Aq/XxKiRy45gVi3pKSE3PJdvLviG64ddf9LzWbyxGZVOzuJnJjLqrJSTrve/CXmnJyKVS4jNMOD1+Fn9j2J2ra7D2uf63W0LcjlnnHY5m0/TISKy9ZUHqTPXHVZGIpGTknIjC8+dzpdj03nz0hH0B7TY9ckYrG7shv14+6RcfpBDqcAwlEsbvXwUey6LYxZjNwfdjW+aMgKFVEZ9r5OegAZaxoHoJdq9ho2ZmcSphpEgglOjoWtnIt3dGxg3bhzJ7h4kCgnfbKynqc+BqJJi6z8Dj9CHW+pA6skmpXMiO8030f3qlZQ4Z1KpFMjTv4/e3oIqZDBz1IMoc51Nuflscv0G/ILI5M8mU9lXiUSrRVAoiHn4IYwLFtD79jv0vPrqwPUHnE4s69cD0P/ll1jXrsW+azfO/ftxV1f/7ntwPJzMzuERggZiM4AoikXAqck0cQyIougDbgHWAhXAclEUy051P36Lha4lSwh4PMTGnM/ECVsZO+YrJBIlEomMvLy3iYg4cZav34vWahMxqSFI5RIuvHMk1700hZDwX5/pyfL99+wPO4utU16gIyGRBK+X2H1F9CoU1KalonLEcmaDi9n6JKQeJRWLbyWqpxipDqxbyxhSKzBsr5FhZT8wu24r6xNH886ZIUilseyJHkWIIHLrj+vIj9cTHSKwy3gVO4QpfO7IZtrOcm5r7GFvUhaj9GqG6NR8X9rOhPRwIt3NsPLGYLTz2F+8nPvqITTluNdlL+wk4PBhPD+d6FuHD+waADyNjShSUmhr/wKDYSR6fc5hdUVRpKqvikFhg0gOSf5l08dE84E+Gkt7GX56Ilrj/0r2mJNC1pgYbvzHNC68axRZY6LparCwZ00Dq18pIhD4Tdrcw6CRa5gw4jy25QhEfLOLW96/iB1tO44oJzOqGHlhNnkpQceQhrAphJu8ZNieQrJyPrrvK/gyOYFcf1A9O9RaxQ7jCN7ZH6TgyI4JYdWtE9ly9zQS9FKa+qOYHDMJi2kdod1PURgbw5wJl6C3WKjUjqVw1+MEAuWoVEoSjUEVZ0Ajw5emJxBQsyB+EVsyPqVLX0+Lbi/6/rEs732Rbf03c4duMr0qG9E54QAo2nxsNl/PJtf12LcuYERfAk6/g+vXX4/dGwwwFeRyYh75GyFz5tDz9tv4rVYAbNu2gddL0nvvEvvM0wB4W5ppmDefujmHtBt/BE5GOHhFUez/xbHf/1ScAKIofiuKYpYoiumiKD554hq/HrYtW+h9403a7r0XAJUqDqn0t6ff8zQ1Yd+xY4B58mTQ3+2gp9lG0pDgg6TSyX91dKp91276V6+m9Z+f0BF7GhZ9I063i4ywMJKaDnmbhJg1ZE7LJnfWGCbsepjI3hLao8fSN1zAoRO554sAk/f2MLF5JwD/mDQPb0gTNnXwhTvz+2XUJI2lOM9I15gY3mvtYajbxgV7tyBaLewPSMl2Wlg6JJWqThsNvQ7OzI0JBrQFvHDBG0HV0U/wusDaFlQpHQPuOjP939QhDVehGx+HcNBrSxRFqiuextvWSiBKjsNRS2zsxUfU73H2YHKbyArNOuLcsSAGRH5cVoUxWsOwGYknrvB/BJPnZzF9YTYzFg3G1OGgqaz3lLR7x6g7GPf4q8iVam5cL+GvPz6ExWM5atmYEBWxBhWf+4KqR2nAg6xvB/6SF0j+VyWrpkxiW2gz66dPYbqtnPdchoGER9kxISSHa5maFUFnQMc84yKuyr0KqbuK7eHN0OxhpsGAKJFQUTKZ0rK7iYzUM7u7kCu8O4lIglRvFyLweUkec8bdzm2PnM+MG4bSpW0cGON55bewoesZahpVKAIOJH4PEr8HpbObXsNgbv4Obtw2A59J4NXCd1hX0YTPH0AQBBQXXgxeHx8u+RQA2w8/IDUY0IwejWHuXCR6PaaPDxFVBA6yF/wROBnhUCYIwmWAVBCETEEQ/gEcKdr/C2GYM4fw667F+v1avG1tv6st29Zt1J4zm6arr6FhwSX4LUd/uH+JA/nBwJzjxSfYd++m9Y47aH/4YZr/dBOi55D3iLe9nabFi2m7+x66+pX4pA6cmla01jCy4uPRuFxckDsUrS2JhJYyVKp+2m64EqXEQm9oNip1DR3o2TQ8uDpamTOLbrWB986+COc4LwJuvMpsZuWvZIq6mD3pmQA4ZRJMPj/JNWUkuGzM3bWexdu+4X51gPjyT3nqmxJ0Shln5cZA43YwJoPxF+yRnaXB/z9TK4miiGNfF5YNTdj3dtK9dD/AEcnVzebdtBa/AwGRLtlWpFLdER5KAGW9wQ1ndtixk678Es0VfZg7HYw+J+X/lI3hRFDp5ORMjCNrbDS6UCU7v67D7z0y8OzXQiPXMDxnBjH33kdmjYMR+d3MWzWPG9bfwNO7nqbd1s6Gxg0MfX8ova5e5uTF8XG9nrb5n1AwdhK9Rjk66beIHjcql0DG8Dmgj2G2QUKHzEBt+8HFWkcp2HuYPz6dABK+LuzgmqHXICDB4S0lv89G1pz5jCosxCFGUF6STndTMw6lF5leyuLi70hx9eBP02N2+XhiVQdVndDfn0PzmL30atqojQoGp9kkkBOxgwV35jK48iMCUgVeRQgelZE9qXei8E3jsqK/8t3eb3lg+zxevn8Nn325jvMqH8aikmLdspmC2i6sm7fQO2wst31RyswXt9CrDcX3M+9Mx76i3z3/x8LJCIdbgVzADXwC9AO3/WEj+h+GccECEEW6l7yCp6HhN7fT969/IQ0JIfSKKwjYbJhXfHFEGVEUaSztZcMHFXQ3W+luslK8oZn0EZHHVCOJXi9Ni67E8u13mD9fgW3TJkwrDhlYLd9+C6JI7BOP45pwHl5FOwigcmbR/dnXIIro8veisaUQ11VAy2Nv4DM7+MfpOt6b3EFnkkhGWh0fTJTy8ux0lmadzl9ueArx2kXEtn0LgFeVzXXxs5mc8iQHQmRIGw+5Hcb1tHPxxYdW7CNC7TR+9ThbaszcODWNyJoVcOCbo8ctFC8DmWog7SOA5Ycm+j6rxLK+EdPyKmSRarRjYgg5uIL3es1U1zzD3n2XIesKCjS30cbQIf9AJjsymrq4uxiZICMnPOeIc0eDz+Nn6/Jq9GEq0kf+ehqS/wuQyiRMuSSL3hYb3y/dj8NyalxdjfPnocobxmVlRuweK32uPr4u+5ylj17Ei5uCmXTKe8u5ZEwiIvB6VRLDkt6nX5iJ0utDJd+Apym4KKusegRDIMgTuqu6ALxOeHMiPJ/OsCg5qSHwdZOMV7+vJzc8D61zJ0/kKKgo8xGZnkpyQwOdPem4JcHfpcLtximTk9zVSmyihP4pUajVMhYs3cldK0pobL+Qh1+8nKqIIbyftoplw5YwUv4aIZlJjF1yJxFCF3K1IhjDI0jwHHSKmFZ7GTNqrkBt0dKzTobT46Q8OcAw0wG2fLmRQH8/b0qtfF1SS12PnWqCxmd5chJIpVi++xbR5zsl8/9LHFc4HIxUfkwUxQdFURxz8O8hURR/vzXqPwSKhATUI0fS//XX1J47B2dZGZ7GRnwm00m34bdYsOfnY5w/j5iHHkSVk4P1hx+OKFe6pZVvXi3mwI52lj9ZwPKnCpArpUy46NgBc87i4sO+KzMz6Hzscfo++AC/1Ur/mjWohg7FePHF9AcMiJIeQvV6FB4f3pZgqIhz3bforM1obW1oY92UX3U6ac2x5DSGMT2+nJV6LQgS6gyXAgJTYlehKp+PS7obVSAbNQa6On0sb3MQkApITG7ezU3hPp8JmURCamoql156KWPGjCG0t5CvAhMRELkwzgRr7oDUqXDm40H3wrKV4LFDayHs+xhyLwR10EXPtrMN64YmNCOjiLgqF3VeJJHXDSX0okwUCXpEUaSs/A6amt4GIMYe9GAZc9EPhIdPOer8lXSXkBWWhVp2curCXavrMXc6mLEo+z+WYfU/Aal5kUy9NIvG0l6WPbrr1BioBYGwRYtQtfTwted6Pp/zOf+smsT81SYWfhakZm+0NJIWqWPh+GQ+2tnEXpWIasbl2NVSlCFvUdJ2NQ5HAy0tH6EO7MTgs1LYbQnuXn/qp6WA968aQ7Lcytv5rTQ1DAVvB1bJAe7Teci55wky+3pIrK8j29CCoXEX0W0tODUa4sRy5pXlc3akAcfgQ84itZ0OqrtsVHfZ6XWl4JHAHjWITbvQjBjBgjcu4epnJzB1xz2E24M7GZ21hUh7IsnmXOySoHpoket5urKjiLI5iFi3Er9EQsWIApKGvMMLlyRQERrcfUu0WhQpKfR/8SXdJ6Dh+K04rnAQRdEPHJ0m9H8Rou+/H8PcueDz0fnU09TOOovm64/OzNlU3sv2FdUDekwIuo8SCKDOCxJ/6WbMwLlvH76+QxmaAgGRXavrSMgO5bJHxjFkajzj5qax4KGxhEQc+8Vl37EDJBKiH3qImMcexXhwld751NM0Lb4Kd3kFIbPPIeAPYLJI8CjdJKakkOovR+6x0RE9BqnPxei9z4EAT5wr56mITegNJoTIPhbGxbA8RI/WnkaNOxRfuo5Bmr185/AT3avGnHgbdqnATWM0bMrWQUDkjTOzGWTtRSgvISUlBXdAICYxjdmzZ0NjPl/5JzFeUk7cp2eAygAXvQMyJWx5Dj5fDE/FBaOifc6g6yoQcPno/74RZYaR0IuzUA0KI/zSbKQhh4zBJtMOenu3kJnxIGNGr0RRL0ORkY7cePTczr6Aj/09+8mLzDvq+V+io66f4h+ayJ0ST0J22EnV+b+MIVMTmHf/GHy+APkrT97OdjyEnHMOuhkz6H7p77irq5GuDTIXDK8XiTKJ1JqD/dx3djbxRjUvra8mPGs8TQlq9HYvMlsR+TtnktxsJ7ldRbqngUqZHv+Ojw510lVBUmwUr10yjCnyWlpbs9DJjAz2bWG/UcoPrVZi//EqZlcfpkobAYcUqcwHgoC31YvcXU7kN8vRRilxTYvBPSEKUQIL3tqJ2xcgVjsUfGrWajQ4dn040K1Erab/kc8xGTIYlClg0x/KPBiFm4hY0FY7EEZMIyDAuOYSymK1eJQ6/IKVT5oepWXqGTgUasIWLSL0ssuCeTHOP/+UzP0vcTJqpX2CIKwSBGGhIAgX/vT3h4zm3wCX1093XBqxzzyNbuZMnIWFweP79+OuO9ytzlVTw+pXiin6oZmuRuvAcU99AwDK1KBhVTd9Gogitk2bBsr0tthw230MnhBLaIyWqZcOYvTZKQP8SD+HY88eet5+m4Ddjn37DlRDh6CbPw/N3LkY588n+sEHQmrCmQAAIABJREFUMVxwAa6yoD7dcN55tNf24xP8eBUQo3AwxPImAIGD2ekkYoBApIf8cCVuiQRrbgvGlD4aFEE33Y6OC/HGa/BlGHjOeR5uoCL3IazSQ7r+AqMEpdWHsq2cDz/8EJPJRNbgIZz/2nbGPPkDe+p7Ke50US/GcoHkIB3JdRtBd9Ce0nIweYsgBU0EXPopRAR3Ta4DfYguHyGnJx2TDbS+/h8oFdHEx1+ONpCEc+9e1MOHH7UsQI25BqfPybDIYccs8xN8Hj8b3q9AG6r8j0nS89+AyCQ9uZPiqC3swmZy/+72BEEg5uGHED0e2h/+K6LTSfQjfwNgXlsCX1R/wXf136GUSbhyQjKFjSa6HTrCp7+BX6FhSKmElCYXGfUOMqtbGOItpVKbjKRuJevGP0pVWF4wKh/Izs5miMGLUgStfQy1pp1E2Oq53m3mvGYbOTPPxmZ2IIkWMUYHI+RFv5pASTchPW1c01LBORqRoVjwDTJgcgTVa/VGBR7rEDZpNPgqvsBjPbRIDI3WEPCJdHUcUsVpbS3YA2EIxXuReK0YSwexa1DwN7BnkI/skLE8N+U5qk3V7A99gHnn30LhoNOY3RTNvoeWoMz4YxJ0noxwUAG9wAxgzsG/c49b478Eoiiy+L3dTHl+E+9tb6Aq/vA4v+7dhQOf/f39lM6/duB78Q9NrH+vjK/+vhdXXQPI5cgTDq4EMgfhjUqg5eFH6F+zBoDWqqCa6nhUGAGPh64lS2hcfBXdL75E5zPP4Ny/H+2ECSxfvpx33nmHgFxO2MIriHnkb3ivWoz88cdwuSWse30v0kDQuK3f8hpeswwQGZ3yLbqEIHdN/oygbjLequRfxhD+HqkgxB/AUX8jhtAExEFBnb3CtR+/NBK/IriF/XOVmyujw5D6RIbZRNoOGu9TUlJY266gttuOxx/gw21VrHSOQCkJcNb5C+GGH8FwcE48dmjdG9wp/LUX7qmFQYfycjvLepGEKFAkhRw2J31926mrXsKewgX0dxeQGHoFvsZWGhcuIuBwEHb55cecz31dQbrxk9k57P5JnbRwMArVfy1xwL8Fw6YnIIoi+ze3nJL25LGxqIYOxVlUhCwyEuMFF6BIS2NCV/C3c8+P9/D07qeZkhm0Ce2o7SUq7jwkl61AFgiQ3mAjIA0+y9P7d2GTafk48mYWKacxZegrdPcG1a1SqZSZ06cRLrFjahpOqCqUaNObEHDT6/XRETmNlbc+yQvnP06rYj5Snw9NUnAxpe5pg442cnZvZmzRdmSJh2hYAtFqfNahuKQCezUSml6/gp7mRrB2kppoQRDAZJGR2LSOkfte4uzcfISAn+6okYS6mvD2RPPurHDevuJC1o9yc/X6duLue56HMy8jgB+ptoZrP9qMSfMBd3+18TAtxqnECX8Foihe9Yf0/B+AHbXdFDnfRBkj5bFvILVfxeuAKWsouppy9v64j4RL5gHQv2o1Fn0KADEp2gGuHYCNYjwxg89jsCw4nQ+u2E9czt0keNqQ3nc/ipQU6otcGCOU6EKPTe9s+uhjet94E93pM/G2tWH+fAUWfRJNNSI1phoA3n1yGdc8eDkShYIvnU4oK2N4gQmHM43YznV0xWbi3OxHZpMiiQhgSHHhnjydPTX95EfVkegSyatdjHn4x3iwYZFKUKcsxRZ2KxIxj1xfAW2uMiTKCdx2wMXL2SrSI/RU19mQbW/jnFmDaMtvY8SIEfRH5vHeqjIuH5dEQIRlu5uAWZyXLiFk9LzDL66zLGiMix8NgoAYEPG225HHahEkAp5GC6o0w2G7BqezmaL8RUQ9KkceKhLbF4LD+QZ1gdeQGo0kvLIE1eDBdNo70Sv0R/AkbW/dToIugQRdAseCz+Onek8n+9Y3kTMpjsTB/1+d9GsREqEmfVQUReubUOvlDJuecEICyBMh8ra/0PXsc4TfcD0SpRL18OF4vvySldf/nU8le1h2YBlbW7YSGjmdHTVxzB+diJAyEdf0b3CuXYc7MIxo4/2M6W9AQoC7cg7lLfvBb+TSQAAkEkaNGsWwQjMb6uw8PvQ+7i24l1uLvuST3AXcb/SCB9Ksdt4aksR1m/bjjo7FOKEf0w43zq52/Bo9MmB8UyVbR6Yi6fcQ6TXT781EipZVIXKebd3JW/fdwq2ZP6IGRp29D6VGxr61CmRx5xN9+3gu3biVtSu76fEGY3Hm7X8QWUCBss9LrW8GdQofl6y5nVr7o2xN3EZd1HfIjXtB6qTVfDEJoSfmCPu1OJkI6fcEQXj3l3+nfCT/BuzpW4vcuBdFaAGCopv6kFjeGDqXH86/CYdMSebGlXS/9hoAjt27seqTkPpc5LSuIrx3P9kHPiS0vxqTEEFF2Aw+eWQndcXdNBR0okZGryIJd1Q65X+6n7YaM8Y9X9L77nsD/ZuWLaPt3nvxdnXR9/HH2PPzkcfFkfjqqxhmB/lSygdfSYk8+MKSeXW0Bep54dnnefTRRwfaqfXaUNlb8cb4EMQA2oO+z9ZIH80yKUtLnRT2CTQoJcg9EXyrSOSaEdfgBaS9lyIJJKI0v43S/AHtba8hiG5urR9JXY6BSLmMx4qbeGdbPeEaBer2fbjdbtRRSTyxpoLpgyJ55Lxcrp6YMjCeayZnHjnZ7QcN67FBFY/5qxq6/rGPvo8r8Fs9+C0e5L9Iev//2Dvv6Diqs43/ZntfaSXtqvdqWbIs25J7BwM2GGNKgukQEgKh904K+QIkpJCEUBIIHTtgbDDFFffem3rvZbWr7WXm+2MUywYCjoG04+ecPZJm986M7szOvfd93+d5uro+wLhOgdItoGlSoLbGY51/Hpa5c8lashjz7Nksrl7M7CWzOetvZ9HsbiYYDbK2eS17u/eyrWMbk1Mm/0NPh/52L2/+dDtr/iqXH1ae9+VM7dP4x5ixqJDUIhubltTy6etVX3t/pkmTyF723rHvgaFC9lqO3vwQ942+k8cmPoZRbSQa/xobG4dzgIapJRguuZL4H45HOWYecc4BLpI+QidK/LpDQYIQ5lNLKbiGdT1HZScRRomnxUKRcQp/s33ClTWylEdJbyv03YXC91NiBpz0izpanGVEYuMwR/txOBzMnj2bosYqEjReIrkWZjcfRbTqEX3FbDUpQRml1DJ8vPxxGspmp1M4MZnGGj99bR48ycUMCjFE1UaQRAQgKkRI9Z2FqNQQVRtY4f0FlkAiYztHE2uSV8UqYw1HOo4TrvwGcTLr5/eP+10HLAC+HingPwQ/HHMJtoGdPNGxBrX5AKHQdJblTMHQ6CUjwUZFm4/e3z1DNH80uzqSaE2tJHawDja+wyhAmRBP0p5tDMTkcqTyVpydPlb++QDjQioGFCIWScHhCbfj6vKiCQ2S0r6Jnt+uwTJvLmq7nZ4//IFoTy+u94aVQcxz5gAQc+GFDKxZj18XT1B3AFXYhLWvCFfcXvzCsDBfJAges5+YQBMhh57YgJ+oPsrLE1WosyA9ZOVe86fsV9l4T2WEQCYuQcWnrZtQ63LoskxGYUonxv8L9N51mCQrl7fMID3Bzcs6iPNBYyjKc5ePwY6LpYvlqo/VrQII8IuFpaiVCvIcZv6Qt4v4ro2MKvj4853dsVdWW7WkEO7y4t0uh8D8h/pQD4m5aVJO1IgZePUtLMtVaPNySX7ql2jS01Doh5P3g6FBfrXzV2Rbs+nx93DDqhuw6Wzs7Rmu/b4w//PEOAB3r583frINJCg/K4OUvJgvzP+cxslBo1cx76ZSNr5dw4F1rYyalY4t+ZsR6QOZkxTp7KLn17/Gu249F8y5gInJEzlzyRwGNZ/yUU02M3NK0Cq1GEYNlSBLc2DTr7mu5w88bDwX/2EXE3J07DYX4XrzYwwLL0NtN1CcngA0cP+6AaLKycTkb2UgawOf5N/Ew40P0qn0ggQ613DuQIqzou6v5pprvks0qmHt2rXM278ZVaybGWW7WFr1AN7OUiTDdl6yxXOV2ESDWoVHUMC2lcSefzWjZqVxeGM7i3++E1GUiLEb0LlcuP1qHJH1vJ11gDk11xLWyKFWZ0AOq8V5Ulm0/cdsKf4J+61BdrbVccaI5G+sr/+Or1w5SJL0t+NeryH7Sn+xnvJ/GdT73uTyzS9RGAyhTViNueh+FLpWorHv8acLO7hx7vkQa2PdcztpjZc1gRRhmXEcc/HF5G/YQOKDD1B448VcfauWhbZ7iATBIikwjInjiCqCszeMQq2kovEvZD52L1IwiPPV15CiUUT3IIJGg6DXIxjkZaFx0kQAgnoj6pseIqB3EdEMovfaGF3/Kmd4BqnYUsOM1WtIb2yi/FA1kiJMsW0rrYp0Yjo6WZ+n5MNxCppNSsIaiafjrYhxMjehLSAbhOzrqcKtTCWaYyEcX0xYLS9nz+tfgBhMJzx+GrW+IP5eP4WJZs4sTiQakPdROnosyw90ct6oZOwWnTxCBdycE/yIinTzFyqq0rxV1lUSBLy7ukEp4LhjDChgYE0tg8k7UR83OESjQdTvdiKMSiLjtdfQFeSfMDAAvF//Pp6wh8enPM6vp/+aHn8P+3v3c2PZjdw59k4eqHyAAtsX60XW7OwCCebeWMqE83NIL447pXvoNIYhCAJj52ai0irZ8HY17TVOPM5vpupdUCqJu/YaBK0W/245F5hoTGRqykw0ceu5e8vV3LL2lhMbpVYg6q0kdQYIpjZBRCJPMtOsT6azbze3b63FHYkyJkOW5IhIAlLESrZmNsv7PmTRqrnUK5p5fPLjKAQlA7ow4zdvIc7lIqKw4u9XsfgXV7Fjx0yuuiqfB+68g7njm4lGm8iydBD15WIRR/OixUiLVsF5qclcmpJIpFvWRDJYNJx/x2hGTEmmdEYq828dzZRrx6D3ddOrmM3MptsIayxMTfgAbWgADUEsunpUkhZBMHPOgaFnkqabbwOnknnLA/45Den/VJRcDE2bmdD8IUe18qxRE7celekogwqBHkeUT0rOQk0RSd3bWVpQTjMZfG/OpZx7803sbRlg1KJL5bDFh/eQqKlmuuX3rFHnUms8yFrj9RQaEpi7IJ/MkvcA2bXM+eabmOeciRQM0vmDO3lKyGNmkYPzR9rRxZt5dWsTDy49yD0aN+6YanRigLmHfo3UEobmvViAlsxK0g/Xk+Bzc6CsiHXWCoI+iO/uZfsI+eF8UKthr04uBa3SyP9fOJiKwxbGxyCikIzR68ZrtDCtewzbYjqY6ipk4awUcEqoBYGeaifnl8sENJfLJfv+Zo7Gt2UvC0anyOYqL82D1u1yn5Z99/P9PNgFfbVQfgUgezJoMyyoEwyok000W5/GnbKJRO9EDC3xDLz9NuJUBwov6GdPQmmxfH6fwIr6FeTG5FIcJ6vmrrxwJaFoiATDl5PXomGRg5+2kZRjJbPki8tgT+PUoDdpGD8/hw1vVdN61IlGp2ThPWO/EalvQa1GV1yMf9/+Y9uuH3U1n7bJnKJNbZtoGWwhzTwkeaJUwfgbiV/7OC0D7yEoryRpUz+U6/lzZiqLzSITuwf4bnIc5xQnsOJQDwpEOuqmcP50LcFIkBvKbiDLmsVzB16kzeZm8sE2RIWC7eMryS1NpW5/K5q4MAbjW6SmXkwo0IqqAybYdlMTczYdjZMxZu/hNvvwPSk6G+XvjSAQl2xi6iXD0i5qrRJV2EtO/buEVUbMcW6EMWPJGujiaLiAypiDbOiUw5/R0Fgm1EnYkr8d/7WTyTkMCoLg/vsLWA7c862czb8YPr+CzeIdXFirZ9IhWQZAZTqMoJDLzArTfSw3liEplDguncF6KUxjTBI/sYxlwcv7OP/3m7h7yX56GnvY+asjLKtK49ECN8/lr2B9sJmKnAbe0gSOzUqX7GrFNf8SRLebxoVyuOOxKpFDHYP8bk0tF724kxte282DS2VZib6ebhAkpgU+Itoapss+nCx1WnJRGFLQRAI4Aj4CPkASSW5rJ0bQo2j6LpHjZvDb9DrUURVhnY2OTDkqGOPUcOnS53lo6TIe6pzNO1VPUh0jH2NCjJEHHQmIg2F0ng5WrFiBy+XCbDazu8WFVqWgMssGtavlgSF5NIz/IVTe8PmOXvkQCArInY0YiBBu96DJkglEhrEO3MmyGouzbxtNl19O/8t/pe+WJwCwlk//wmu3vnU9e3v2siB3wbFtVq31KwcG70CQlx/YjMcZPK2b9C2hdEYq591cxpRL8hAl2L6s/qsbnST0JSMJHDlyTNa6NKGUKdbbifTLhMi1zWtP+Lxi4s0ETGbsm5bQMeFHGLJlxdO/OWRBzVUNMrnuqUvKWXtLJTNjnbQPKpgZdxNPTHuCLKtcnp4Xk82mYjmvYRviL8UZC7GlJuM9nIrHVU173TY0b/Rg/4mGM1auIVRmozCzFClgp0EzrOycEfqQ8E9TOfDak5/7/zR6FYrUZBQ5Vs6+v4ik5FY+3ZVJs9OMKuxhb8OYY58d1GcwqvtCYpd9MxpXn8XJhJXMkiRZjnvlS5L0eW2I/0Js+cs29qxsYW3fPZx9+AKuF4oQFMNUdJOpj9sny6P697b3orZ9ytQxNQTCInU9XnISjCze1crrT21iW86tiJ6L0DR1MiOhHJUk4bDspW3Az5xfr+e3q2u4c/E+5q4awHiJPLv2TZhGozWZ2UUOrpqQQb83xLqqHrIUfUxUNaDWRhFEgeZ2H0jwwNQKnOMs1BYuwK/Loib7YkRBgatnFT2GHvJ7ahCiIWq05+Dyj0AxlKTTBuQHvuS1k5jwIFb/H4kqYylrdDLGWMp8/QwCui7E4gaWl5lJUKtYUpaLokcOCXQc3ML27dtpa2vDarVyoNXFiGQLKqUCOocSzVe+D2f9XJ6tHY/+Btj/Nky8GRzFhJrcIIE2S14NqEZFQJDP07X5E6L9TqJWCaVHALMWy8jPczCf3/88t629jRxrDt8p/M4/dc0/faOKsD/CzCsKT8tjfItIG2GjdEYapdNTqN/X+41JbGgyM5GCQSLdw6GUqcmz8HedTaY5l9XNq09soNYTWfgHBCRK9zdgtWwhThHAo5JXMhu8Htz7ezBoVGQlxXP59JHoCHPDX7fR2DPIo8sO8fq2ZhINSXTFulk8/xqyvvcIqmiUju4etMm5hMIOxJdULL73MXobctg1ppy2zkLy+5vYnalD8MlOkgrk1XunUoU66kHc/gJd9bUQHJTVA4YQG6+iX5mNqnYxjcFxqNQKShZNxhTsYVAvOxBqA8P5j7ET/008B0EQVp/Mtv9GlIwAQYrisWTQmjqD4pc03Lj2Bkpa8vgOFg72HSTsHkDQKNDF9qN3fER7dPmx9itvm8Z9Zxei8ssPt7aUKUzufJKHi35JRSDMruAB7BYlNd0efrWy+li77/aksiR/BjfbZ2HQKLm7woRu3zuMEDs4T9rENE09+ape/DFu9CEFOYcV7MkRcOet4r5ZUYK2AbyGJCIaC902PQXNHmInxpJRu4dOG2xX5IGkRl13DecdKMLZfT6SJKARAvh0IUTBQMC0kHnuBMbq5RvXk7yVhilZbCZCpMZFNCrx1s4WsqxKDIJs8NLb24s1JpZD7S5KUoakA5xNMqFN+w8MR/a/Jf+slBnnwQY3KIRjfIbBQZmQFBs7kcjmakS1RPfDYQw/nE/2W++i0A4zpHd27uTi5Rfz2z2/ZWLKRF6Y8wIa5cknkXtaBmnY18uYczIpmpj8D6uYTuObQ35FIpIoceDTb4gDkSZzb0LHqQ2nx8n5uuKYiezp3kOf/8SZtCnrPKJnP442JJGjnkaBuBMAR7AXt1rJprX1SBF5JTJlXBnfLzcREJVM/+V6XtrcyP3vHmDVfh+SFGLx9EqU6gziIhG6QyGqB310JCfT489FbYhhe2UltXl5VOcXMveA7HLoCk0jZvA2PM0yJ+fNvlzWBxLIM/dxZPlL8EQ2PFMBITk8ZE2zMxBJQmzYRGukjJwxdsaenYnNPlwGH1abiO0/isHXRXpW1zfSt5/FPxwcBEHQCYJgA+IFQYgVBME29MrkM37O/63oNeQgDTGINWE3R/KvIaorZFLrTdi33s0Fh25lb/VhzHYlUyq2IyHRFexGUHqIN2kgEubybC2C0ojV+TG9lsWICg07n3qXRbEldEshfjJxK7U/O5vFP5jA29+fwIQ0PWfrq0i0h1gY2sytPWv5+J13CIgiEzSN3Nx+4qIss7cdo09gZbkSvUpPvxDkYPLBY+8fSA2Q4VQx7UgsCq9Er0VACstVDb3hPD7UjCOsKWbA+zNa827Hmfh/9KX9iVlCITnmUl4sHGBjwU4CeVX8pS8NQlF8tS5+8dFRars9lOj6sNvt2Gzy6sOaXog3FKUsbYjMN9AMsV/ik1D9MaSOA4tcTRFscKFOMaEYUjsd9BxGEJTkZN+GtlYgnK1g/IzVZNz8f2izT5Ty/tWuX9Hn7+P2Mbfz9PSnidf/c/mCqi2dKFQCI6f+T9y+/xWISzGRN9bOzg8aqd319ROnmvQhAcaW4cEm3SYPDnblWCQk1rWs+1w7/YjLQKEi1Z/APGkpl1paeLfpVyilKOvNTsIdcvm3QqHghgUz0CnkCd+Z6qNUqpqo75IfzE5FP85mNw6TiV7TcB7FGRdHJDYGs8vFj66+AovLhTYa4eFwHwqzhpZWB1G/fO7vlXq4sUjPXrOSmIal9EYNHPJakYYmUrFp8UTR0hEqIhAxHjP8isuKPXY8UaHGGZNHTlI1CunrM9O/CF+2cvg+sAsoHPr599d7wDNf0u6/BkUTkyiaKPsZm9NODDGEJDN2dxbxzgw2RD9hTes6pvrkEtK7z9Oy/EeT6XvhBZrOOAOLuxG3ph/zPBvJlkFa/AmMTLgRqySw/uhbqIgyLtNGRZaNBQl9RMx62pOTcVuttDv09IVDpDU3E1WpWKedJJ/PQCEmdw7ZDTvwGSRuN8WzZcxjnG8bx4pcDU7Vc3ycdT/9ZtD5IgR3vIp2UIHTYOXvl1XreJ9wwV/RZzyHOmUXUU0GUU0KC3atY0Z9N7/P1PPHjDRuzZzBb2KfYo3Li7IrgBCVeHFjAzaDGutALcXFxVx++eWcffbZtIXkiqFxmUP5D2fj56W4Qa5gWv8ktO+GvDPkPm33yGS3vJhjdelebx16fTomRR7qNiVJs67FYMg8YVeuoItf7vwlB3oPcNmIy7h65NWoFP9cLUU0KlK9o5Osknh0xm/X2e80TsTMK4tIyrGy6i+H6aj9ejX56qQkUCoJNQ37JyRb9Vj1ajq7bSQbk1ndvJpez2cemDoLJJejat3HSLORRcLrZI+9hCmufaxKBO/eZgZWNBDu9KJTK3n3+jFcYT3KqEQdP7/mLGxR+X5TRPq5p1BNeuLwxMXR3k5ffDxeYyzWASf93V04/L24dbEED+1ngiFMZcchEA2EnH8PMSlZY9BTFNPJs3yXxczjoyeXEentJW6oau+QJBNJHUkSeLpJnCjbmWoIyhWBCiW14kwaDJ8hnH5D+IeDgyRJv5EkKQu4U5KkbEmSsoZeoyRJ+p8YHARBYNw8+SJ73FEKMrvIc8m0jiLjOjK1O1BpIhxKXs9FfRU82h5BJUmEw3s57NzI+hV/AiCj+WOcul5KtNMZMX8MIY2FPS+u5yzVCNZrBKI1nwAg1ayisaaKjMZGbiktZdFAK8agl3hnH+fNqETn99Oamo4gKlEP9tEXXYKu30MkMYw2eoD9717B5fuXYYi289a4QzQkeqn0yA/pdQ4f+hDUq0oJViYQHq9CHbsFAEV0ABhWGFWLUT7oNrA8YXjbWqc88Bl9Uf581Vjy7Ca+X6ZDKUhk5BYQVuoZO66C5fs7SInRkxqrh31vgbMBEr7AK2H/W7Dmp/LvxQsIdXjp/q1M3NEVxrJ125nU1P4cn68egyEb/+7dsrx4hZxj2Ny2mes+vg5f2McPV/+QVw6/wtmZZ3NR/ql9EVoO9+MfDFMwPvGU2p/GqUOlVnL2DSWYbFo+fO4gIf+pS0wLajW6kcV41q49NsFQKATGZsSys9nJlJTpbG7fwtjH3+f1bc0nNk6vhPbd2CzjcLn2Ei5dwEVpKbToE1lWV4dnfSvuVfKgU5SZxP233ci1115LdnY249PkuL4y1MXmBBW/NmVSfPAgFVu3ERdjJqJWE9Jo0HsG6aqrIcEIokLJoLOXB5+8jUe3/YV0dwfBzvPIGbgXrbOEdXozn4j5RJBXJbUJKay9/mq6G7ahUArUDIxCqVJg3Hkvgy/NJa08jbgUEyGGQ63+wfAJOm/fJE4mIf07QRBGCoJwsSAIV/z99a2czb8BplgtGr2KoC9CS7cdrUZOwqqkEHNjH+f62It4MeEx4qoXsaL7CQqCId7e/wEPrLwDjS/KgNVK7EAtXq2T+tfUrHm9BoPgpdpQwdi/JnDe5uks3fky7Pwz7//tR/jCEexd3ZhmzSJvTB9S0tu8W7IKV/0jpDUPLZUFHc9PfZYdWbUo/QJv5+q4KCWJK5ITWZiaxMJ++UG+5aJNbM+RyWSFNfJsuFWbg9Wmw6bchiBIDOgfx237KVH1sIm6Ia2YTqUOyapB3+7GrlSSLMm3QrFagdR2kJW3TyPG34FTl8z03+2i4vHVXP/XnexqcnLTzFwEgA2/BHsxTLjxc/0qtckDQThmAlJMDs4l1US0Tnxn7iAY24bPV09z8wt4vdUY9Jn4du4EtRr9KJlB/UHDB2zr3MYjmx9hf89+7q24lyemPYFJc2pm6lXbOtEZ1af5DP8m6E0azrimGL87xO6Pm766wZcgZsEFBGtq8Kxdd2zbhJw46nu8/OUTM1EpgspYxStb5eNIkiQPJOkTIBoiScxEkkIcOHADZycYyfE180BpGkfH2PAf7SfUMkig2olOp0Onkx/ck/LzESMGVH75O/ppvJ4S5UgqF13Dwp8/gTA0UMWFQ3TUVpOSnQlAoqebqCCwdXwlMxNauFS7lzJnDWcOZHPNMiNH/JNesi8fAAAgAElEQVSIEqL44EH64uKwtray5sXfE5cqh8qSso0s8u5mojmA54PFJKo+n1+IcXzz0hlwcgnpR4DfDb1mAE8A36556b8QgiCQlCvH9Hw+gVZtBUhRutWT6Cu6i6ZQOfuXy7pGHiGesa4Ynv+th8feyGYgpZSPzz6LxsxUvrO2F1NfHXEpJnySEVGppS57AQbVhQhvl7D2oQ95NyoTsiwRN6rYWOr6a3jFaqFbrWJJ149IbfZStP8wl5Vn89L2EeQO8dAbHCcmTtUKFS+c+QKfrHqalclyQnbS3ij9Zhu7skcwm3ehfzHKcCpSjB37QDcaRRIzO8M4/CIvJSZyaIacA4h0hnGvaKbvkxY0m7pQNRxgw4YNtLe309rayt7w8Ex79dFuBAHmlyVDTxX0VsG4a0B7nOzF6h/DiruQajYSiI6mq/MBOv5vO+E2D+Fzqmjh9+zYceLtYzDm4N2+HX1x8TGi264umej0UeNHAMxMn3nK1zjoC9Owt5e8cQ6Uqq+n+XMapw5HpoW8cQ72rm75Wv4P1gsWoC0ooP3uuwlUyYUeiyozyEkwEvVnIEbMqKz7qO4aZDAQ5PqV13P9yusJp8hloIa+XuJsU3EObKWq42ne2XcLMUKYZzNUEJXo/v1eev98kEjfsBLBhMI0xKADVbgNo1JBWCGwdfwcdIVTUCqVXOwPcO6yZcTGxNJVX0NK+Sz0Ph8ewcSGqVNoysjA4najEaIAqBCoLj8Ln9GIvauTxI5OEARcljhiXIPkjJK/17nJ7dSqlRQ1S7Te+Qj2V++XORLAhAU5GKwaXN3/Jp4DcCEwC+gcEuEbBVi/vMl/D6KRMEk5ZjQ6OcQyoEhHExqky2XkzbUT+cD5ED3qbEyDsjbKmY1yGCq9vYpOh/yA3Vkxju3jKxlR/xYX3juWKx6XWc75lQ5suhY6kidxOOMGCpwTMXi9hH0ioaZq1oqyNffN2ssxixOICkHy6w4x+MxrGFbv50fL5QoKySwiRowQ1ZPmVbHMqqTr+XdYXrcYpTYKM2bRWjKeR8svR3Ic4tNG2SkueSCdiFpLR2I6Hp2Oig43NzS3Yg5LMCRwJ3jlJX5llg2FJ0KiQnbS+vTTT+lx+2nyKrnzzHx+f2k5ALkJJgwaFbTLKwMypwx3pqtNXk1sfw6Fu5qAUAGA6AljPTebgPHv+jISWm0i4ys/IT/vIeK1UwgcOIhx4gQA6gbqaPO0sTBvIQ6DgwlJE7AbTp13Wbe7h2hEPB1S+g/A+POzQYI3fryNroaTs9L9LBQaDWnP/hFBqaTn6acB0GuUfHLbNOaVphBxlaE1VyEKbl7ev5StHVvZ2rGVD7u3Q1wuwpFllI38Ezk5d9Pv24tdJXBtYD8bvX78c9JRGOX8gmdrx7FjptmMCCE7Kuq40biGBKWP99M1BBvl/yHnhzdgscZgnD2LoNeLS6GjZP8B3GYrPQkJaD1uZq5ajdbjw1o1rD5UtmcPs9ZuQT/oIkqUHnsCuZ5+rAlB5t9aRt6Q7ejEI/KzQIhGMLsOY7BqKJ+TwRU/nUjFud+OJtjJDA5+SZJEICIIggXoBv4n2EOSKPLynTdSu/U5rnpiMjEOedZq8siiW9rIIPnVb2L0tFHok/MG/gZ5EPHp9XhNJmL64smuq6M1LQ2/Ts4TmW067BlmOutcTLxsEhp1Gz5tG36TD3tXN72JC1nxwBI+1eopNiSTuTsZQYywobADXVBE1SoLlykk8BgkFGo13rq7GKy9C93ARFrUapo9KzlslRjnFvnogpv5Xs6F1MSmYTQcxioaWbhzDFGL/LBN6+sn3x0lKG4nL/82Ho4+f6wPll1dyarbp/Ly1RXcN9FKnMKPzWajqqqKhugQIS4njrmlSbx8TQV/vEweJOg6KFt82o7zPjgyrBElSjoovRjHbeVY52ZhmpCM11tNXNw0RhQ9xZjytzAac0hLu4rgroMgihgnycn4Vw6/glqh5ubym1l10SqeO/O5U7q+YlRk98dNrH31KLGJBuwZn7cRPY1/LSxxes64ZgTRiMiaV44QCpxa/kGdlETs5ZfjWbfuWHJaqRD4+QUlvHbJLYhEMeX/jOeP/By9So9NZ2Nj20ao+L7sQrj1D9gTZOvaoNXGuR3yCvXTXAPJD01AmxtDoHqYeyAIAnFRWXrmLwdfxNH/BzbFKunu9lDnCxAyJpDzwccUXHQJlgQ7q157kWxnD3M+/IjK4MdoWmvoN+o4//3lnLVnAyP3HyCmt5+wNR4BqIvLoF/rpC05ntiBIP1tLaQW2uhslFkDDm88a2ZPozUlhQxrC+PPNdLT3IhS/e2thE9mzzsFQYgBnkeuVtoNbPnWzuhfiMMb1uLsaKf1yEEQw2SMlEsjY51HKTryMpVbHqV0komzXPdTsKAURTSMvqWaAUs2zZlyElYVTqBc7EYRFanKSGbHn35PyO+jaFYsTucATXUi33u4AntCI8pIhNIjR2iI76bNNJ5Z687lTO8UmnoN2NV9FE6WWdNKSeTvtu3tcQLqqJ5KeywOUywHhqwx12b78CkV5CeMY2t9HwUOM6ZyG0rpEI5WAVN3L3XJ2SBJPFsV4OW9PtyWOl45egMPfToXXZOHyVYjZWkx5NrN6DVK7NEejEYjo0aNwiep2RFJI8GspTRVLludlp9Art0MrbtgyzNgH3Ei6a1xI8RmErqmjs7g82hy01E7jJinpBIVffh8DZhNI0hKWoBen0rg8GEGlizBs2YtCqMRfWkpNc4a3ql5h4vyL8Km+3ry2XtXtxxzKMsb5zjNa/gPQU65nTnXjaS/3cvzt64/5RVEzEUXgkLBwLvvHttm1qkZm1LALeW3IETiEYliVpuZkDyBre1biYy7Rl7t7ngRgy4VozGPTlUrOa1rKFSLvN8pcyS0uTFEunxEB4fJeyXWckId5zEmfixdrn0IkS7WSSEmbTvKDSsP0/dmFWqtjnNuupOB7k6cl1yEPSsJt0GeUPaY5fyFJAiQP4IV0SI+QJ71J44dSzcaPMZYolEVg4cPQzhAfe8h1CGJ9pyx9MQncqBkJFapnxW/e4i/3nXTt+blAF/tIS0AP5ckaUCSpGeBM4Ar/1c8HvIqJpBTPApVNEpb9ZFjJWToDCR1bSc07QIcP/kViW/sJjrhChI7t6COeGnIOJvQ/DloVDoWPToP0z2vYRiQaMrIoOqDD/j196/knRWvImU2cWBtK+0uB92BICltbaTdfheN87twKbfgtc0l6fUegpoYCmbkUpw4XIlzJCWe2swinpmnQAibuW56Ho+cW0w4asQRhGq9nICWCsayp3mAgHKAkVVvE5WCqMRktpRPw2O04Aj6sHk0vBdq47X6BaxrLiIlRs89mUksKZeltYPBIAcPHuTIkSOkpaVRUlKCKjEPEQW/uaQM9We1+bf8Tv55fCJaFOXBIXMyofYwIrEnGPf09HyMJEWIi5t+bFv7Aw/S8eBDuJYuxVBRgaBWs7FtIxIS3yv93te6tqIocWBtK/ZMC5MuzGXUaamM/yhkjYpn3NxMALYsrT2lfagdDoyTJ+F6dylSNHrCe9eVXMdo1QMAjE0cyxkZZ+AMOtnUtomO0oUsFvsIbPglJUe8hJUgiFHm1bzENrefR2vb+OtQ3ULguNLbKfl2ggMTqVDKCgcm/05eSm7B1nYb6y27CNY4ae5v5kXnEsyVhew+uIfMV98mFJZVFnpNcuK4v2wU2tvv4EhOHuuTSnl2zEWEb7mBpMRSBEFBX1wc6n3boWUbRzQKsvvjGLRYECJ9uGNiGOjsPHZO7VVHTqnvTgZf5SEtASuO+7tRkqT9X9LkvwrRXRsoWvoBE2vaaNy1HXOcnATqyp1Nd/6Z9ObL9fmYEli7fj9ecwuDxiQ85ihHGhsoGVVMbKIRV4+fcmkDmnCI7qKRhLOKEIHBYB8ag4IDGxvwqlRo/WqW70ninvGP0TW/F4EI9dnzUUTDqJJTGOgZXsaKhQW0pEBnjIJwJIZVQpRnBuT3FcbhB92qtkOkuOs4b9NzJPTKUtW7Rl/AljFyAneyaw9qSctHmgBxuj623KZl070z+d7kDJ5//nlWrFjBO++8w5IlSwiFQmRnZ2Oz2TBnlqBWCozJHCbeAASOdCAd+oBI/uVQcpwcdtdBCAxA5hRCzW4UJjXKWLnkbtfuSzl85C4MhiysVjkpKEWjhOqGfYdjLlwIyO5t6eb0f5rg9ln0NA3K+kkzUimbnY5Gf9rd7T8JgiBQcW42ledl0VY1cMoJ6pgLFhLp6sK7+fPBjIr0TDx1d3Br2X1MTZ2KTWdjae1SHuxez4/j43hq/x8wNu4no1OiO8HItW3vkBnu4dmWHn7a20dTnAJh6zMylweYOyYHFVG214qkmdNwRBvoj7yPMtqL1reFEPDzDT9lcfViPkmtIeD10HRgLwOtHowOHwGNitpLNewqL2SMxYAYp0MUFHxUNo3LajogpRwRkcZMB+FuPx//5I9UiVpSB7MQRJE6rfz9dkpwfuohxsc30bh/z6legq/EyYSVdguCMO5bO4N/J/rrEH1BTMEwXa+/yso/3ockRUktS8M16ypqj/rYt6aFPavXcLBlC7X5aWycUkl7mvyQrqiQE67O9i5G2g6RE9jLoMGIhEBGXS2RSARzikRri5yA0gbV+N0hoo16fnPm05jiTCiUAqJSzZq/HkXa9QdSJvWTNq2PWY6tKGLlWGpXOIuX+5zsDgeR9FE6Qj1oFFomJU/iqHsHKRFZ2MxpDoMk4DHmISrlpexUfwdvEuSoP4HpaZtIiJNlfpubm2lra2P79u1UVQ2bs+QO+dHuaR5gRJIFrWqYCwEQ3PQpAiEC4dEn9mWDLBUQMo2mLfgKQkYIQRAIh90MDGwDYFTpC8dCO6HmZqRQiNjLL8dx/32YZ81CkiT29eyjzP6PfaFPFi1H+kCA9BGnnd3+k5E71gFA/Z6eU2pvmjkDZUwMA+98Xu6tNNWKFEqgriuCSlAxL3seq5pXsb1Lls94y2Lm9yOmowkEaExVYRm3iNU7rmJJXiwq4IWCIPqu33Dk+fO5cNcRJI2KbH2AnZ1hSuNHIYZqUQflCY4q1MKWWDcbB+RBqiXUgVqvY+uSNwn5/NgKB1BoovRJJsJCD2JvN5kFsYRKYnEXyCvst6Uw3QqR+qw8NkybypaMQmLax2BVpJHS2ka9zYlSDOMzm8k29TMhvoXWIwdOqd9OBiczOFQCWwRBqBMEYb8gCAcEQfhaqwdBEC4SBOGQIAiiIAhjP/PefYIg1AqCUCUIwpyvc5yvgn7quWRc0oGoEShQt5OpXcekC7RMW1RA4cQklCoF217bzZYlizG73WQ0NuKO0RIXlii2nMF7jx9lw1vVRJs282CylcdHtjJ2x1bOXracUQcOARAxuhhwylUPqmArgrTvmMVo0OdFq11N3pgw0XAzMYpGajMF6i6/B5vYgpjag0KSaBbHgyAgiG5UBRtAEcCquoMdussIGMYhWesx5xWhK89GpbCBYlhvyNeTxDPIbNGKxF2oVHJStqam5oS+uOuuu7j11ltBa2JzbS97mweozP48J0A5KAvt+b3HVUiIUdj1F8KqfJrf2EV3yhs0pMgEOLdbntmMLvvrCczn4NDxreedh+0KmTbT5G6iP9DPaPtnBp5TQN2eHuwZFvTm0wY+/8mIsRuITTTQfOjUlEUVGg2W887Fs2o1EafzhPdKU+Rc2eqjXZT9eCXLNw7LvNwx5g4AnvXXU6VWYx2M0BiXgDriZfILpdzV8Dwfmm28njiXX2VcyUZ3kMPeABVJajwRBXGqAtzBPhSii9iIDWW0hw8S5EHnkoJL8EV8WEvy6aitAkFgziXLSc4rwt1kwqTzsWbNGs62xyAmG5DMau7NSgSNkgYxE+WQk4I6HAJTEQJQeOgQLq0BZcSJM9bG0YiWJrWKMYNvE9j60in13Vf27Ul8Zg6QA8wEzgXmDf38OjgIXACsP36jIAgjgO8AxcBZwB8EQVB+vvk3g4/6DzIpKxUhVcDVp+GBAjUhZzWdNUdISI1y9ZOTsUqd9MbHk9LWxvit25i5cifG1gK6q4NY4nXsX9vKgHsjW/R6RCUcTWkizuJFaTdj6+2ly1lNVNGH2e3GG3XhH1hN475mBvsDePs242zby4FVvyPsWcLB2FauTE/gsrqXuCQrn1ctZhIDOkSthWsSgiR33IEh+BEqZTaHHYW0RUwMxt+I29pNfn4+TZFOAroM8iQ5Dvk96Rk+GpBDUL+YGyDJfD+NjY0A1NbWkp2dzW233cZdd92F0WjEaLZwwR82cekL2whFRSYcPzjseQ1euxitZxUR0UGoSzucDNvxAvTV4vYtJGwccnijDlEM43LtARRYLKNO6PvwkHCaJitz+BDd8kBSlvD1Vg7OTi+9LR7yxzm+1n5O41+D9OI42qoHCPrCp9Q+ZuFCpHAY5xtvnLDdalCTbjPwl02NuPxhmruM5Kjnc1nRZSwqWsS9FfciIPCOLZ7AoMjFR17lZZsso/Oj5teY6tzJHQV3s9wuy3t39Q0wOcGEApGujuHQ7qW9cvj5oPIjbFIMczLkOa2YJ4dG0wtLMIixTLzoCsJ+FaaBIPX19Uwe7GG81cgP0hL4fpqdOzMTqTHKfucuTR/pHXtRRiKM27GLNpuDkGckvWoXLquVxzQJXJVsJ9PUR/e+DafUb1+Fk2FINyGXrs4c+t13Mu2+Yp9HJEn6IqPZ+cCbkiQFJUlqAGqBiq9zrC9DVkw2EUFgY3IYuwuiYYHe3a+y9hc38edbv49KLZFdqUdUKkmKl2vku8Z/H78hkdTCWObeKD/wumPksE54YAwvT1IRma8hM7ednPp6BoM+fEYf8b29OI1ytULI18C+VS2I0RPdVg9b5JDLA5UP0KQS8CgUNPfPQ2ddzs7DjxAJB0kPjKY/8Y4T3Naak5OxlxTT6e8gqMlmWpN8g1eaJ9AiqpnjMJOMnTVr9vPSSy+xfv16enp6yM3NxWq1YjTKAmIrDnTQ2CcTai4em8qk3KG4f8c+eO+HUPMx6mg1IakQKRhlYGktuNth9Y+JOqbgFyeinjZcPeF278Pl2o3JVIhKdSKzOdTaitJqRWka3r6zaydWrZXsmK9Xt129owsEyB37v+FJ9b+O/AoH0YjIh386wLrXqxjo+udIXbqCAsxz5tD37J8It7Wd8N6IJDlkYzNqOHtkIp2NM7h73N2olWoWFS1ietp0Vhn0LMZISIzyqSOJUPpoFN99g2eSQH9cMVDjym3YdvhJUbhYuSdAmbWMuenn8J0rvw8ISJIHRzST7QE5T7fLpuHMH9xMadY5vPPcK6TklJA4UkX3YRexWjX1e3aztDyPR3NT0CsV3JmViCnezDK1kk8TN7E8s5mZHy6lOyGFN++8HyFUSqfRQ1ijIWlAR79Syaz0FN60fzv5tJNlSN8D3De0SQ28+q2cjaz22nLc3618iwqwQjgRDTo2J8qVP8l9ICVUcXnWHvTKME0H9gIyS9Iy70z2lBcR1MuVFePn52C26SisdNBodmOMKJhh/w6IGhbFxKKO7SO7uQGbqw8EgfieHpLGVaIzmVEILexb00Jxwk6mj+pAozeQpHNTr1Pi8FkYFzePlRet4oWxb4LlEDrtGnr8nUw4FMf4rRGCCjNIEroBN0gSg5YRLDpwOwBlnixsPvmBm76plC4kMhMtbNu2DYfDQWxsLGvWrEGn01FcXHysLzpcfp74qIqseCP1j5/DExeOQvN3NvH6J0FrhYJz5H5LLURfGo93RyfihmeRwn66m68ABALqNhQKPaCgr389LvderNbyz/V9uLUNdWrqsb8lSWJrx1YqEytRCKc+95AkiZodXaTkx2K0ar+6wWn822HPsJBf4aC9eoBD69vYuLiGfatb+PBPB/AOnJziqONe2X+s78UXT9ieEitzl6bnJzA1P4FOd4C6Hu+x9+fnzqebCO8ZDKiROBR0sj7bjZg3C/uE69g9YQRL9vSiEiM4NT6SxBjGKp24IuDZOZlHxj9KTGICDoO8ShVI44+tfkSFhSOuekpmnMlVwjP8NPUZ6pvrGH3uZJSaCMrGnbS31uL3+RCPq7SqyLTR7xmJUq3mSIbA1ZfNY+99D1GUlkLQmEWnWS77HdtoZmSjyLlroVD8ElXkr4GT+RYuQJbL8AJIktQOfCWbSBCEVYIgHPyC1/yvd8rH9n+9IAg7BUHY2dNzasms9n4/hp5y2mPlWXh2H2zRy7P7RN0g3Q31eN2yqFVnSwMd0RBBz1EWPTYeR5YFPD3MGrWbRi0Yg1YmZhayKOsRfEonvyiYgq1IpGLTdrLr6vCGfdgzskgvKUMUW5AkiYdHBbgqJcQP77qc83IOUa3R0Osv4OY39vDuzn60NgtKUxUq1TzuqJlJXquJ2vQ82V5QgtnbV4AUJKQrJiJ58dt+yI9rkhiIUSNIEp5OH1HAblfj8XioqKjgpptu4rvf/S7XXnstVusw0f2D/R20Dfh58sJSFEPsaXz9sOm3cGQ54azL6Ou7jJCYSSRlPtazs4Ao7HuTsK6CqJBMzPk5BMMdWMwjsVhKaG5+kWjUS4x1DJ9FuLX1hMGhwd1At6+b8cnjT+la/l0/p6d5EFe3n/yK0yGl/yaccU0xP3hmOpXzs2k62MfGxTXU7+lh14eNJ9VenZSEafp0BteuO6H2f2F5KpNy47h/bhGj0+UcxME217H3Z6bN5ALHBKb6/Fyn9RGWBLYPeujrkyPesXoNZeNHEB8eoN8gYhhl56JIKpmKPvZGUqhtkcOoD094CAQtOxLL6I1EQZNCj6eR1kAIl0KOECyuXUZe0XXknhklOBAm1bCFxT99kPd++TMA3CE3Z+UlIEkaio23YPItwB+dTrnFQIXVSNAaj1stV3WZIhYefkNk7g6JrPpvxqf7sziZwSE0VNIqAQiCcFJmsJIkzZYkaeQXvN77kmZtnMi+Th3a9kX7f06SpLGSJI1NSDg1Ry9LoINZniQGXbcRERSkdJhZZjZxVaKdtPgom956hUG3fCO5avYwLq4FT38PVrue5oP7aP7ZJKRlN9GoVuMJppISq+fuqediClfyjtTA2/fcTP7sKNMN6wmnQIHiIAVZMYT9LsrPhAF9MRLwq1U3MSk9iYBCYHCwnEPtbh5Zdoi39u5CECSSrCPZiI7aypk0puYiIJHX3cUPfCMx9niJaPMImufxg/YCtLYW3HFarGGJziEqnSok/w8ZGRkolUoKCgr4bJ/V9XiwGTWMzTyuumfFXbDyIaSEIrr3TcbfaqE79AxCfBaqWB2W7GYUoS4GXVOxnJGBaXwyoVAvGk08ttiJiKJ808bEDkcGowMDtN58C6GmJmotPsJROc68rUOuaBqf9M8PDtGwyDtP7uLZH63j3V/uRq1Vkl122uXtvw0KpYLyORlM+24+Ey/IJbUwlqZDfSdN9DJOnEiko4NQQwNiKIQUDjMi2cJr140n3qQlN8GEVqXgUPvw4CAIAo9Nf4rfd/VypeUscq1ZrBtU43LvG97v+CTsUoB+oxpTZRLpUjy3pGYjIbB5nzzRm5o6lREjX8dnyCfZJ1IUTSUabGVpZzcK0QPA0u6/4YnCWZesQGtWEWgV6Kmrpn7Xdp5f9xsmvTGJbJ0byaKmtjmF7pbx6DVRRmmVTLWZwarBFzGjiARxWyysLxZYPFmg/7zrvtkLMYSTGRzeFgThT0CMIAjfA1Yhs6W/DSwDviMIglYQhCwgD9j+LR2LAyb5QWiLQrspHlu3HI7Zpdch2OQHW293F+pIhOmKj5hqbySWfrzOfj587kl+liayKMmBT6GgP5TNH7v72TTg4akZjxHx5vLcoWe5vkhLbU6QRVl7sR99nryDj6FA5PfBWlyO+wlrC/hrjAVxKIeQm6jix+cXIwjw9l7Z1KcYBUtnXcy7o2fSkJaHhECF00SiPoNFtUdRSgL5+oXMb1bjtAfo0wnEhiQakZer/q4GrFYrcXH/WJG0tttDbsJxeYHBTjj0DpRcRHjuB0jS8GJR0MoxTpPqQ0TJiF+sxDRe9sUIhfrQaOKx288GwGjMQ6cd1jRqv+9+Bteu5aOJOu5O28zPtsmzpq3tW0kxpQybw58kolGRda8fpbPeTUZxHHljHZx/++jTng3/pVAoBEZOS2X0melkjYrH3Rs4aQ6Eaaqs8zW4chUNF1xA89XXnPC+SqmgMNHMvhbXiQ11FrAXYezv4sqR19ARVrC1Y5g3ISgEHCqJTkGPNttKyk8mMuOCMgQkduzuoPuZvYj+CGfYY9FIcEVjiMz+eBSSn79UfYwghfFaFxKQ/Pxm929QKtVkji7C2yGT4hQqNa8cfAWAxr69GOL1tHZ6iEZFrtz+F9becjWmaJjRCUrEUBwuzSDNifE8XbaI14qnsL/1X2/2A4AkSU8BS4C/AfnAw5Ik/e7rHFQQhAWCILQCE4APBEH4eOhYh4C3gcPAR8CNkiRF//Gevh7mZ6bgNJgZp25h56RKsnvdGJVyqKVX66agYjxBUUQTDhMwB3g0zoZR62ffqg9psR5ms1HPAZ0c1xaDCWwI+LnpSBNjMpOYYL0WKaqlSnRziyOBd9NGIiaPRgh7KS23ssYhJ7NFpZz0/WHxNSAaaFbs5v8ig2RmWLFJMs/B/PZbx845pJXDXrluuVvG+608cjDAbq2S28s0vF/Vw0G/C1tIosGmId6koauplpKSkn8oHyFJErXdHnLsxy0KmzaBJNLffgbdz8pWnqZJstCgQqOAjv0oGj8kknc5jtvGo9CrEMUQkYgLtSYOs7mYSZM2Mbrs5WO79B88hGftWsJXLeDPU8PorXEsrV1K62ArOzp3/NOrBnevnw+e2cfRLZ2MnZvJOTeUMvOKIuwZlq9ufBr/8fj7dexr837FJ2Wok5PRl5XR8/TThGrr8O3cSail5YTPTC+ws6Opn+a+zyS9MyZBwwbOsXXlGXYAACAASURBVI7AqtKwrO0IR47cx6HDd1BX9xTpGgVNWjuSrx9BrSQm0USsIcqasWkcHvAwuLmN61ITaJxcwnXTcylPkFnRXucKQMBvmk2OMI336t6jz99HzlmzUKijKGxqPI40fFp5Bb142TOM0coluXHhfnThAGIkwqN/vJuWqqsR1AG6BBtuSzw+aSzBrvO4ZNy3w/4/2czfAWADcunp12ZdSJL0riRJqZIkaSVJckiSNOe4934mSVKOJEkFkiR9+HWP9WWIUauQKiYRFQTCMQaUeiVjXA+iQKBGiDJv8EnQKtGEvFyW7OBvFhM+a4DdK5ZxJDmA8rjlrsb+CeX6NrpCEVb0unjx0rn8eeYy9F55Bv2wys0obR8/S0rlA9N2JIX8kLeZZTGvMY5zifjSEdT1pGg1VFkUWIVWNGEF/bbUE867sHovZX1BwkiMVCRzTkeEu+o62RGv5d3RU+nQGZhmt1CrgXSzAkmSKCz8AkOeIbT0+3H6wscqOwCkpi3/z955h8dRnW3/d2Z7l1a9y+qyLLngjo07mOYE001CDZ28aZAXUoD0QkghJCG0QAgkxiQxLdgU2xjcu2TZlqze+0rb68z3xwivHdtghzi85NN9XXtJu3PO7NlpzzlPuW8UYcLfGffdOy4sIPnmSozFZnj5TrAko7/0XnRpqlEJR1TRc71eXaEYDekYDPH+I6tXIwwGtsxKRBISjy9RCfX+d+P/4ol4Tsk4dDUM8/Iv9/DKr/bwp/u30lk/zILPlzHjDDFTjuGTgzNTva4GO7yn3Cfp1luOed/97fuRfXHjctX0HCQh+POOfxICmvtVQEG/+1kuLriAuqBES+eL9PSspqX1dySLHrxaC4P9LUe6iNJE/E4zX6gS/HmP6hatj4SpHOhkTbZ6v+lC9aTE8rEJK1H9IqJylPkvzueK97+Cb3krjhkRBpMlQnrVBdxh9zB5w/PIFQ4Kk+MrnF1SNQoyWnMDQ1EnJiXK1dJOrq0IkWqPa0v/O3Eq2UpfQHXtLEel794qhLjxw3t9ejC7tJg/zVRtU096Onf9+itMqzFTm5CJUnE5fp0ZOz5GRiuOw7YIMXmIBpOEXtGhxNRsCK2pDZ3rBTINOv7cPYgCTM/P5JaqL+BvjfsE/2KUWGPPOPI+EJzOA9Mf4gtPNUIwD43o5Q5xgAU5BxGGEcwBDbunzEY/qhM7eXAHy9b9nb9IggV4iES0BByNzJn8JOeGRui3JSBcIf6ysYW6Xg8pkg+TyURmZuZJj8GOFvWhPm2cE0VRcL/dSqx2M6FoEfbF4zBPTsVW1IN45U6MuQbE1t9CTzUsexRMcXqNSHiUtEx/PPWFoih43nkHy9lns3lkD+XOckqdpSzJW0L1QDWJhkRmZc760HMVi8i8/ptqOg65aD/oomphNp/73izGn33y3zaGTy/0Ri32ZCOt+wfY82Ybg50fbSRsCxaQ+4enKXxzLbbzzsO/dSsDj8e94BkOE4vKUnlxRzuh6FFOCXumKgZU+3emps9BBtInvEBq5WrWBXOQ/GqAunUwHgJ1O1Xj5bNZ+N2wRDAU5m89LgKywuaYjEVW3dZX+M5lqdHM9rRcgs7ryE8oI8WUwkqPhs26biLZalZ/haeAYWMQD8M4jB6sgV4Ui42oRqHH7iLNr7pnGw0jdKYV4TNZkRqr6eo6NiX+34VTWTncA0xWFOV6RVGuA85CTW39r8A8p42A3siwyUp9njr7vPktH1tlLw+EZLxWK2jirJEeYwQpqx9ZCAIiRtg1ndDAfAD6fW3ckOnkPZeXLx5sYzgS5Q2T4IsXfZYvTvofRMyBXklC1sYfngMxiZC7nJDPx9QR9eF6f+39VDf9ku5kD4o1n/qc8RRSj1BiXBH6E1ZdAk8LlS2yHRlP4RZKih9Av78L41ud6LcPMOSJICuQHe0iJycHSTr+VCuKwpf/soevrdpHgllHSaqNUNMI7rdbkXxNxIyF2Bbm4ryyFIf3u7D3edj0K9j1DBTMh9Klx+wvHB4AQKc/PrYRrD1AtLsbw4K5VPdXMyNDpfH41sxvccekO3hk4SM4DB8uE9JR5yIciDLrkkKu+vZ05lxWjM15ZmZNY/i/gUmLc+lpcrP5bw389ae7CHjDH9nHMmsW+txc0h98AIDgoWPJ6a6ZmcegL8za2n9SVStaBO5Oxv9VJZTcte4Bvvz6NbzSP8h2jxr/WzkC32vsoi8UwaPVIIZUPeehVCfXvV3DBpea3eiNyXwr/F2+03YHl6cv4f7CDD7fHMZjXUxx8c/4+fyfk6Azsi0aplZqJ1MnMy9JnQC2pPvJ7G0ndaAbW34B3iQNsqSQ680BBNGiMD1pydTOmE9Iq+PlLds+ziE+KU7FOAwCR4uUekY/+9QjJsewihDFZgMus43+1DR+Nv1qRlLSyRyE7gP7kTUaNiaEMAotekXgNgv6U/xoZEAoFDsLCPcvJWS8BU/YTWmghi/npfHXXhfn7qxnk9vHBsLcMvFmEqSbCOFCq1dXDmI4jGzT8fSONjKtA7w1//bjxuixTydPaWRYk8dZhh5yMzpYeE2c8G5n2R+pWv4wDcPZvD1gJ9sY5SrDHubnmbj/wjJiIz0nXTWsO9TH6r3qrOPBiyuQJEGguh8N/UgigPHsWQhJwFAzDI8uw7c/DiPtMPHq4/YXCqkpxQb98ZlCnrffAkmifryDqBI9YhwcBge3T7z9lPiUWmsG0Bo0VC3MjjPojuG/GhPmZXH5fVM5/7ZKIqHYadF7axMTsV9wAeGGxmM+n1uUTHaiib/u6mBHyxDD/lGDM+1m+OzvSA8HKQyH+VWkk0Fk8tFRH5QRcoA/ksNv2vr4er0ay9B0+UFWiFQ6edesodoTQBNSVyTvpCRwduJMHEvySUqz8qX6EAt7IqzrclGSVEVLsmq8BmOCchMUpwZICSRzoNCDJfwSO0r2oc/T4U1SJ13OWCJaTSKSGMDZ2Uqab4TVk+Zimn72xz3MJ8SpGIcGYJsQ4sHRgritQL0Q4qtCiK+ekVH9h7CpaxOLVi3iRutuzivMwxbxo880sHnO2ZT0VvI5g+pu6kjwoImVYg0Z6dXKbHZoiUng1OXw4jW3krAkCyfZSIrEY289xkKvejLbgupF1x2K0B30MyR+A7KWvMTFwOiFpZM47A4wVJIJQk/QPBN7xMqchlJSXQ56ExagI0a3bOeyXJVz6EBLvFx+u2cGO1pD7GlR7fU9sxMxiSjXlQnmpKkXaXr6iRXQ1tb2YDdqqfv+Uj47Wa01jPT6MWWo+9LkV6oNWzerf9MrwT+IWn685Lj9hUIqh9QHcYZofz+ulS8S7ujA/drrWGbOYKu/Fp2k+5f4k7qbRkgfZ0erO2OMKmP4PwYhBKl5drJLVfflwGnEHwD0RYVEurqQ/fEAtCQJFpSm8m59P5c/toWFD79LrzsIejNMWoFY8iDfGRhiiqLnx+kLeKC7g6AcZfbA8wAUmgysGVCN1DzfISbtq2VGc0d8zM3qXHqbJ0DaXZPRJhgQQmA/L5+FvVF6NDDp/f1EtFkoqho75427CCE6uDgBhKSh2dHDgCPEu5r3cdvDCAXOGpeMw6cl29fMuMFuZjQfYM2iGVyfc2aYAE7FODQCqxmtcwBeBppRC+E+1dJa2dZsMiwZ/KHm91RkZSAUhTSjWhGdKBXQdbgVFIWAPcZg9zykiJ2DOh19Gg26qJ5fzP0D3VENPRLkDI+QEkihy9xF04FaflySzdfy07gnP53WYJhp776JIESg51L6AwUgKxCJgaIgpWnpS83E7nHhSb6TKY1ziUrjGcj5NkLoaBAl5EcFU59pJl//ZboDcZ6i7Z3ZXP3EVrY09GEgSkl2KlarFZfLxauvvorFYiE3NxeAmBwPoCuKwqaGQWYXJh9hXlUUBaW3GUtkldooVQ2W07xRjS2UXTR64KaC5QSuo1APOl0SkqTyLrXdeis9DzxA4+IlRDo6sH/2s2zs3Mik1EmYtKbTOleRUIzBDi/pBf81CrVjOA3oTWr8YaDd89GNj4KxpAQUheChQ8d8fn6lOmG6cmoO3lCUa57cxpDvgxXEF5j4+Td49sp3WLrop0zWJ5OgCMyB9/nH3hv4dbmaHeTUafjlrVfx1JUL+HJfFKlPfXZoOtTVRFs0QjASj2vYF+Rw/fWTuagzgluWQQj8afeRn345Rbk30WWYgzbVxnWhUh5Le4A59mnUyr30WRXsPi2lBSFsYTPeSB+Whmq8Pd1k688cFf2ppLJ+58NeZ2xk/wEUJBRwfcX1uEIuOEq2wBAIoph11BUV4AtpmKz7NRF3LkHZSr9OC0JgclUhrBb+52AboJDiGWaSbRI+nY/dLbu5OtnGPeMyWJHp5LrMJLQhlX9J0hfSOeSHUIz0vAQciiBY4ESRJG6Mqhd+R1IGr53zWXqdaShC4mrjTqZvHODHPi+W7bNo8WRiEIKXbpvFhZWqi+qdw8MkCD/Jyck4HA6qq6vxeDx85jOfwWw2s76uj8nffZNNDeqqps8TonM4wIyCeNGb7I2QJH8NnXc7aAxgdqpV0gdWQ/nFkKTSeVN8LFluMNjFvupbcA1txmhUbzrf5s2EDhwk6ZZbsM6fT+KKFRyekkrzSDMXF5w+b+OBTV0oCmSVJJx23zH8dyBtnIPuhpHTUj8zTVZXqIHdu4/5fHZhMru+tZifXFbFjy6ppKHPy5u1cREdsqeCKQF0JjTzvs6KYRd7RYzfGz3ohzfwUI6HTTPKSUlMJCsrixk3LSKpphfD+m5ETCHRF0G26Wjo8xKKxmgfUlcu2gQjP3Tr+GsTrJ5cxOTUiRw2f4a5+/zcG/4KPxDf5WBePuPnncvS8kUEFWgxhEl1Gelt2o4jYiKojxGTg+AZZnh4mDOFU8lWmiqE+LsQYvcoZXf1x6Xs/r+EqWkqY/jarX8mJ0edEcw1OxnX1IQUjtCaUM6r+1S//IgxHgzzRSs5f28Du91+QFCXkcdN56sxg15tL/X19QBkGPT8pDSHSxOGsOgTuH7hDGSLamC+NCmdkaPOQJESwRIJsbdyFookMat2KwCzrIJXIkHeJ0qkz8+BQIhSu5Gp+U4eXTEZ46iObJbGQ2JiIgUFamBdCEFhYSGyrPCdV2pxB6N87zW1ZuFQj2qIyo9KX430uNEK1XgQGy2sObAaokHVH5s7E9IqYcLyY45hbe1XGRh4h0CwDYNBNVYjL7+MZLeTfNed5Dz2O9Lv/zZ/aViFXW/n/HHnn9Y58gwF2fZyE7kVTrJKEz+6wxj+K5FdlojfHWao69TqHgC0SUno8/PxbT++ljbJqtYoLZ+SRaJZx65W13FtACiYz+3Dbr5uyGWz2cTVG+7jp5vuYFvH20eamFNs5CSYEGEZB1Ecvn5km45tdZ2c89P1zP3p+iOrCEOBg7xGL9PNJjKNZoajqrEzCkGZMsyrxmW0DbRR45KP7D9tyMBQ+wCJIbWtxxwl2XCYB9c/yMbWY8it/204FbfS88AfgEtRqbo/eH3qEfL7CB3uRh+W2Fb3LtMKcrnnnns465ZrES1uXvOUMTi3Al2pg8j4BIQubqVHNOnMT4x71Wozx/HqiBGbzsZB50Huq76PoeDQke1NrlqmpFRy7+Q8cjNsTEm1sXLDe6Ao6MNBinSCgN9PJl0E9GoGToFeDSQXjMRTX1uK7OwnxvgMM+vWrSMUClEkq+l1VQkRtFotEyeqbqcLLrgAjUbDlqZBWgb9FKRYqOv1MBKIHJkllaXHf4PcuDd+cD6IKdStgYQ8SK8kZDSwa0YuQYs53kcO4fbE5wpGQway34/n7Xewn3cukl7VU+jz97GubR2XFF2CUXvqGUahQJR1fzyIIivMu7p0TAf6/2PklKur3Ka9p8elZl24EN/mLURPwsEmhGBKbiJ72k8yC0/IA4OdFcLJXH+ASr16/b/b8e4xzQrS1FVtkjRMSdgPRg2PbW+l161OtJoHVKOmz3eArBBu96A7imTS5o/xza06QsLIS73N/D5cynDqN5mQcR4TlFwC/UYyZdXARLI99A6F2ODbwF/fOTM8qKdiHPoVRXlFUZRmRVFaP3idkdH8h7F17Ru8/NPvkuwz4kqIsv1vf8FoMGDKSGfn3d+jtyiLdjmKJ9+KI1+HJMX9nRFTKjvdx85g9roGKE8qJ6AJ0Bpt5bd7f8sz+59hV+8uGocbqUyuRCMEekmQZdBRn5ZNdk8rRcN96PQGYnIdRm38Am1KzEQjK5hfj2fmfL6hkxjg661l48aNbNu2jSnaDpbrqylMU1cBycnJ3HvvvUybNg1/OMoDr9SSajPwrQvLURSY9v23eX5bG1likARzXAxH7lb9ssqKVXDFsxANQ8t7UHIeCEF390sMD2+jrT3OfOnx1CLL8fL91LQL8axfj+L3Y78oPodYVb+KqBLlytIrT+scrX28ho5DLmYtL8SefHpxijH8d8HmNJJVmsChLd0M9/kJB6Kn1C/hsstAUWi/866TuqRK0220DPiIxuTjNwoBaRPQuDv5bW8/D0mwKHcRGzs2HuEGA/jiknLGa3qYoO3h8rJCALqt8YnQ4T41mK4fzbTzvNtBtDF+v/cbBOO8MjkhNxsCWmJIRIxlRFNvIzd/AvVSJQftpQC8Mc7L6un9SDLMjhad0nE4XZyKcXhACPGkEOJqIcTyD15nZDT/YeSOL4eiHNIshQzZQgwMdrPhj2rBzCPLKtl3xznsmV3BfflJLNetByA8OIMpu6pIMvvxjl5IV7/+LHkdDexp72d2MJVZ5lmkBFNYWbeSh3c9zPVrrkdBYXqGSkDX5w+yo64et8nKxNrtFKWm0B4IodE00U0W4w1BhKKwJVlLWijGkHLsBbu0Ih2HX82OWL9+PZIAuxRiwoQJR9oYjepF+cDLtTT2e/nFlZOYMS6JFJsBo07ibu1KntH9GEJxgycGDqMgIQrmgd6i6jhE/JA/BwBFUW/GaNQ7+j5GW/szCKFn4sSnKCz8OokJ0/Cu34AmKQnzNNVlt617G8/sf4bFuYvJsZ96qX9XwzDtB13MuqSQqgVnhiJgDJ8uTDgnG/dAkOfv38qfHtiKeyDwkX0MBeNI++Y3CFZXE9iz94Rt8pMsRGWFzuGT7C8xDzw9RAxG7F1tTB3qxh12890t8bBrabqdR26YR3GKmQUVJRiCEeQUI0WafgQKDb3qvSZZVd6vUL2L8ChvVJYsiEmCP0y1U+rScJB0okJLntLJ1mEfL5bP4o/n3sybE64kplFdq7nGGNcZL+DKW75+ysfvdHAqxuEGYBKqMtsHLqWLzsho/sMwJzQzadGbVJh6iAmFvul97F37Ooe3q6mbCTotFo0GV/dTvFyvLt0SuitwigQ8aXHft8EmKI0dwOVIZuBvh7ja7mBa5Nj0sjJ7GZOSJ9F1uI4RBXqcaZQ37iff7cIiy3hlhWgwgwGRyvkOJxUjqkFIs3UQvWE8AM/eOJ03v3IOP7xwHFoRnwFVVFQwc+ZMqqqqjvnO9w73s2pXB19cWMzZRclYdIJtU99l723Z3KV9mWKpE5reheeWozyxCMl7CNmQBdpRHYTDa9W/ubMBCATUWge/v4lo1Meu3VfT1/c6WZlXkpw0n/y8W1FkGd+WLVhmzUKMFt59e9O3EULwtalfO63zc2hLN1qDhsr52R/deAz/X6Bgcgr5VckUTU0l7I+y8xQpvR3LliEMBtyvv37C7fnJarXzB66f42DLAE8Prqp52Eb8XF2zhjn+AG80vYo/4icSUVNbi4qK+OIXv4jVaqUqEkVONaHP0mMTQfa0qPG8o12jUZ2EIyyz8h03C1wy/0iWMMbipJG3epuJKApv6WwUNR8gq7sFRaj35zx7hGsWzT+l3/+v4FTyoKYpilJ6xkbwCSIhYQaJSecxRVnLul4jB1IDZGu1vPSHp/ji+CqsVit1+7fzct3fmZMyg4sm383K957hz9fcDIDT1c9QYgqt+XmMz9nNm8CI1UHThm0sXtRFSduVjNT20a8VSKZknux7klDrYVjyOdIlOKejgVhSGt7mRiisYjB8OQDlUQs9w8PsT9DwucwMttYPoRHgiA5TklbMzp2jWrVXXsnAwADTp0/HYDhe2Ob16m5sBi13LRhddtavRdryCOw4ilR35TUACMAkIOocjTX4h2DLb9T0Vata1OYPtACqK6ml9beMjOyitOS7ZGZecWR3ka5uYoODmKdNU/tE/HT7urlr0l1k2079IR8Nx2jc1UfR5BR0hrG6hjGokCTBhXeok6D1zx+ibmsPsy4pxGT9cK1wjdWKdf583GvWkHbfvQjtsY++/GQ1jlDTMcLmxkHmlaTElRBBpddQYkSL5/OetANjKMbnD3l432zi7zvvwujZyHppEffO+PaR6/z+KXmsONDB3rJyxrnr2duhZloJIUhcXkyo1c1AoaCgw4tRhtmKlvWxGAMWdeInKTEWbZ7IhRMidJXaWfDWSiRZ5rEVS9AOP0ehQcbna/h3HdrjcCorh82j2s7/dTAYUnhc+hL3iV9wllVDZ0TiTwub6TH3sW2bWpL+wl8eJirJVHQnMz+9GIslTh8syVEMwQD708pJRp0V2GdNY6TFgbvVwvDafSgd3SS3dDG9qJTuxjpWFaiVwHM1MbKzayks3E5yv1p09rNy1RWUe3CEWxpCPJyo44r8abxe00WaGGH1i2oRTl1dHXa7nbKyMubOnXtCw3Cox80/aro5pzRFVXSrWwNr7lU3Ro9dOivLfousV1c6onSh+mHrZtWlNEulElAUBZ+vEb0+FVkO0tr6GMnJi8nOvgZJis90ov19AOgy1SB6u0etJM1znJ5aVUvNIOFgjJKZJy7gG8MYqhZkE4vIPH//Vl57dB9rHt9PS83ASdvbL7qQ2OAgvq3H002kWA1kJZj4xdv1PL6xiWue3MbLe4+SkrGp17MlZiJskHDbtFRo9UiKwt7OTTw9qOfdzs2sb19/pMu07Ex2LTwLnSIgy4knrPCHdw+xuWEAy/R0nJeX0B4Mk+dUDdMMp5ocsjVhlOlZaOg3Ch7cH+Q5exINdz5AzaRp2EzllBT/iBRLFi7XFmT51GIvp4tTMQ4zgb1CiLrRNNaa/5ZU1sFwlNUDATpELn7bVSyxRYhoZToSh2hoaCAYCNAVUrN6/Pua0EWjNBWrdvLSuh0MJaSQMzhAvyGVAaHSQRjHz0ORFVrezkbSyTgX6tFotdDXxe6SQg4XqnGBHI+LgoLdJJHIHSNncUNjiBK5i+/UDePcO4glBlfmFvKPmm7ahwKUaNRMiyeeeILDhw8zadKkD83c+eVbh9FIgrvPLQXfIKz8HAy3wuXPHGkjL/8TffyKoYNT8EbV9FLNhHPVjW1b1FqHLFXFLRTqJhodISf7uiP901IvPO57owPqzRmwqRd4q1vNXciznZ5x6KxzoTdqyCoZS10dw4mRlGml/OwMQv4orh4f3Q3DvP6bavrbTlwoZz3nHCSrFfdrrx23TQjBxRMzkRX42pISytJtPPlec7zBKFmmXaSQmXkVqWkXYrvkWYoiEfa7JdrC6uq23dMOG3+mvgC7VsNMtHidThSt4DtvNrHiyW0c6HITkRW6QxHGZdrJfHAWZ83NwzSqwpjlH2VpNUkI4Ol+F69EJNbOXIZTH2JIk4vBkMbw8Daamh7+dx3SY3AqbqWlH93k04m1g3FK3A7rZ7lNfpr6QQOD9iBDvXVseectPOYImphg5cyr+Mv71XRVqEHlfnsSskbLin47f7HF+JtlCXotuG2JOPR6ouEwjiI3mcUN9MnLOfTeNnbd+C0KXHU0JZYiu5ppMeXwvdoVXEaIsoYIG7q7sSXZUUjj+wS5vNPFOwf7SLXqyIuoOdidowLq06dPP/4HjSIQjrGhvo8rpuYwLtkCm54COQK3b4a0CnC1QNiPp6uCcLATagYIcBmGK76AIXnUBdW6CbKnHYk/eL1qJlNC4jRKS79HS8tvSUqaf9x3x0aNwyWbbuRqbmdz52a0Qkue/fSMQ1+rm5Q8W1yydAxjOAEWXFNG5bxsknOseF0h/viNzXQdHiYl93jyBslgwLpwAd4NG4h5PGhsx7b54sIiqrIdnD8hHZNew/dfP0jboJ/cJDPY1LRy4emhfNoPjvQZv8nM6tFEjQStRNNIExzcoW48524ArihO587GDliUiRSOIh328LmaJn6gy0cGckx6JKOWmKzwAYnBtc1hflRhpCdFD+4gb4WC5Jr1DEejDGvKGQnFSMv6LLFYgPSMM5MfdCoV0q2o0p0LR//3n0q/TwOuSnfyjynFXJeZxJ5AFF8gkXxLFJctjF2uY8fG9bgtUbQ46cwqoEvSoQuraZsbMwrQxqLM6YN5HSG6NSbS9XraQxFmLr8KgE55Bq/qv8Qvyq5i4/QlxDRasg1qlpHJV8M/Di8D4CXC/Jgg3b50nveacaOwlgiPbmziQLebfIeGoxcJd911F1bryYnnGvu9BCMyswqSQJZh1x9UOuK0CrXBnK+gnHMfvt1H5X1rJfSTRrmUQh41UylvNooSo7HpF+yrvhkhNFgtpWRnrWDO2e+j0x0vqhMZGEAW4DHDY/seo3qgmmVFyzDrzMe1PRmikRgDHd4x0Z4xfCSEJEjJtSGEwOY0Yrbr6f8Qig3rnDnEhoepnzadoT89f8w2i0HLBZUZCCGYU6zGG3a2jtYqWVNBaMDdfUyfGwvUmN10eyolhhjNw03galVfYbUqenlOEvdnp3F+3wgmJUK0IpEuPdywvwWAXKMaL7nrhd0k1rlZ4rDymQEZSVHocujwJxupkWJcleHkxqwUukIRvDEZg/NSXnL8lg2+MyOJeyoV0g+gUnTfN/qRDjgzVRf/YUhCMMVhocpmxheT8RpnMc4eQZagsz8IvZ0M2yJE9Zk43YNcv/IRitrqMI/OZi+ORbhV8fHsoHoxGjSC9mCY6Z+5jMl33MM5V1zLP6Kqu2nXRJU5qKUfKgAAIABJREFU0WLyIRSFixov44CriNRR4q0oMLcAaoeg8WyVuG5v+zANfV6cwo/BYGD+/PnMmTPnQ+U+AQZHOWJSbAbVPTTUBGddD4ASlRl4tpbuH29HdodJvLwEfZ6d1FsrEbufgV+fBY/NBUWGvFl0d/+dlpZHATAas9FqP5wNta+jHrcJylNU99nUtKmnnaXUWTeMHFPILB6jyhjD6SE5x/ah/EuWs89GjMbohletItzRgRI93mdfnGrDZtDGq6YlDVjTwHOscRiXOY1Vnd3cnz4FpyZCf2CACAqgwICq0yCE4I7iDO5NTuDife8zuXcQTVN8jDlGPf5wlDf29zDc7KZxbSvDaSbSggrdFomWNHW8lSYjc23mIyR3j+xr57muQZoDn5BMKHAJsAzwASiK0sWnnHDvn1FiUQPBhoLLyB1VZBpKCBHEjcccJWgqI080k+LqI2x3UGDQsnzXBm6L6uhGQfijEJEJesK0B8MMRGN8XZ/MlUMyfkXLldbDCGBeopWgkkhCROZ5wgQUDVcTz7L4wnzVVbQlEjlmfJZAL7m5ucyfP5/Fixd/ZJXwkE+9WJwWPVSvBJ1F5UYCfNt7CB4cQvZGMI5PwjwlldTbJ6LvWw2vfRkGG8Cl+lqV3Bm0tT+J0ZCJ1VpOSfG3j/uu4eAwETlCIBpAURT6Ww/htgp+v+T3/GbRb3jqvKew609vBdC4uw+tQUN22Vi8YQynh5QcK0PdfqKRE6sLa5OSKNm+jZQvf5lQXR2Ni5fQefc9x7XTSIKqHAc1nUfpTdszwP1PwjpJhZSFIzhDAodGQUFhYFQYjL5jyf5yJheSEPAx5eAmEgfiiS17DvXz1gFVW0KnEXSNBHk07CPLL9MuKXSWqvdPVp2b8R1BtLKCLqbwZFCtN8o2nhm99FMxDmFFLStUAIQQlo9o/6lCzBdhXFR92D4xlMoW2704dGZCFS7609SHtNdaRZG1ngnX1eFPsJKIQqE4TE+vymkiAOGJ0OcPMxiJ8lBzDx1Bte/8RBuvB0pRgNZAmLBSjjGk8DQh5qBluS0+Ey9JU//fUKdm/CwqclCic2H1dlBcXPzRP2akA9q3MzgqiJJkUKB2NXLhBYR7VaMX7vIiWXVk/3guydeOjxuag6+AIxfuVWsZ5IJ5rHuvCp/vMAUFX2HG9NdITl5wzNdFYhHmrpzL9Wuu5+w/n81ta27BVtdJoCADh8HBOdnnIIlT90DKssKeN9s4uKWbspnpY9TcYzhtJOfYUGTlQ/mXJIMB27nnos/PB0nCs2YNke7u49oVp9po7PPGq6rtmcetHLBlgiUFU2cDCRq1Xa9WC5IO+o8VGTI7rVybeB5WScPFDjfjPTKpe1387M167n+5loJEHd+ZoYaBe4RCntlAp1HQYtdilBWS6oeJ7RugIKCQKzRUDqsG8H3XqXNNnQ5O5c59UQjxeyBBCHEz8Dbw5BkZzSeAoT8fIvjQLvK0Wmq9QV4ITiUtYSKdaIjM6EcvBFFdLjm0ojXKuDUGrLEIpWXv098fTyGVvBEiJvVh9seuQaY7VBu6weXBG5P5QnYyLcEwB2NOQsEwaaYAd6Yp7BvXzRMrJvDmV84hzWbEpNPQPRLETJicjreZrWlAp4vzJZ0UIS/8ogKeWsKgJ4BOI7B3boTQCIPVE+j7zV5kf4ToQABtyj/RULi7VVru8cvA6IAvVeNachcAQmhJSztxzeOhIXVmVN1fTSwaJunVzVgCCs6F5/4rp4JNqw6z+W8NZJcmMmPZmCb0GE4fKbnqBOtkGUsfwFAwjsI1b5D3x2cBCB0+fFybolQrvnCM7pHRWb4t87iYA/0HIXcW2sZ3ybOoyRw99jRILjlu5QCQVJpObjSZRHcj71w8mVsn5dDhCjASiDApVkf97s0syZXY3+WmNhimPxbjzb5hCtEQaXYTrHdRbjDg0QtGdIJiJL5ZmHHc9/w7cCoB6Z8BLwF/BUqB+xVFeeSMjOY/jLd7h7kwPcKQXvDnbi21Z0/ArJGQjEX0RBRq/DESjU4QEmUWB7LIw6exovW0YjL6cLnVDJwfLn6f9AjI2vjhlACzRmKJrOPHewPMHc36GdJLeLwxpmYmsHZkK7vr9zFQu5nCZDOSJChIUY1KuuRGkiQWL17MPffcc8JahmPQuunIvwPd7SRIEkrDJhShJSSrgeZg4zDR/gC6lKOCw4FhtRBOjsG0Ua3rxDy8ETUFde6crUjSiQuM9vbHqQi+WVfO59fJtOdbmL78to8++P+E/jYP1es7qFyQzbIvTcJoOTNL5TH8d8OeZEJn1DDQfmqiQIbRFXlolEX5aBSnqoZm5wdxh4QcCI2oBaIAnh743Wx11S1HqVDyAeixOSG17LiVA4A+20aa7MDv9zMwMMCsQjV+WJlhwT6qwe7vUpXrmurV72kOR+gbCPDLWIABOcbEDAd90RhtFolr6oMknJkyh1MKSP9EUZS3FEW5R1GUuxVFeUsI8ZMzM5z/LMxDIdosEntTdRgPubBLGorMBvy6QhTALUs4nIswayQumv57IiZ19u50d2Dqn0hvzIBGxFhYuZCl2Spj5M0RPXflprJ1xMeXctP46W4/i3uj5A4fdQY9EUJtNUfe1tXV8b3vfY9nn32WW+aOAyBPM8yVV17JnDlzjjcMARe4u/nb7g4W//xdDvd6oCv+oB7saMMRUYgd2kxUW4I21YEwaPC+14nsi6grh/q1sO4HsOY+6NoDn/kNOMcd2YfXW4dBn4ZOl4isyDx34DkaXMdWY+7r3weATW9jYl0YXWkJi1/fhtF6+oI8+9a1ozNqmLGsYIx5dQz/MoQkSM62MtBxaqJAGocDbXo6gZr9x22ryk5gXLKFu1/cx7pDvXHxqz6V9p49zx21IwMZLg9GWaZbr1VVE4fbwHssE6wu3UKqrN4fDQ0NSEOt/P2OWXxztmqIbrjhBsYZ1Swn4Y5LBAz3+PgrYW6T/BTqdJyXbOdKu42lrSHcb50ZHtRTcSsdrwcJp0fI/38UE3R6jLLCc9lauvxhXH+tpygq0UWc5M1nqKRYlhh6ppaoVc3AqWqfQtrB6+nWgdOg4B7JoyrJChGZFk+Qg94g2UYdX8xLRfaqsYekg3GueOGOkCipVcrnnht3wTQ3N5MSaOOZS3PJlVzYbCeJ+79wFaGHJ3DPqn009Hl5emO9mnqaXIIy5QYagxqyRSNa3wFC4RIMBQlYZqQTbvOAAHNVCqy6ATb+FPa9ALP/ByYdqwnt9dZhtaqsKfsH9vPTHT/lklcuYTg4TMzrZfill+ip3ckjq+y8Gr4Nua4Bx6JFSJrTjxMoskJrzSAFk1IwmM6cstUY/v9ASo6NgQ4vsnxiBtZ/hm3xYjzr1hFuPfYha9Jr+NvtsylIsfDAK7VEkkeNw6ob4NmLYd33442dBRgbNpEejdElIvhSVFeP8nAxbIo7WjROIwlaKwLB2rVrWb16NSZ/H8ODA2i1WnJycphcXsj59k6+MS+L5d4+Zht1rLp0Ms/MK6VHlrn7z3t5anw+vzqrkJTlxVjPyfp4B+wkOKlxEELcLoSoAUqPFvkRQjQD/xUV0vaiRGwhhRojPK4N49/dR2b1EN3RuBulVc5gXLufYJ2LQIv6sEwKK+jCdnrsWrRhHy+88AKZCSY0vQHeckq8M+RmWUoi0S4fyqjYeHN1P7qdA+h2DCD5oiSZNMybN49p06axdOlSrrrqKgoLC9m0aROGqBchOLFxGG6D9q1slccTU8AmQry16xB074WMidSWfolWJZ3zNG8jCBGKlKDPtmHPqydhUjuJlxajkXsg4oOsqXDJ47BIFTqX5QiKoiDLEXy+BiyjxmFD+4YjX//DbT+k75e/pPtb3+abj/SS3jBE349+BLKMdcHCf+k89Ld7CPoiR/j6xzCGj4P0QgfRsExn/UnEe/4Jzms/j6TX03jeUlo/fy3h1lYURaH3pw/RO2cm96R6aR8KsGfICDoz+PrUGB3AhaPVyXoLwj9EWixGf2iEBt+bPGu3sU+vg7cfPBKrEJLAkG5DIW64tm3bRn9/PykpKUiSRHp6OmnhLiz9+0ndtZmlNZuYkZXAvPOLeHTFZDzBKPs6VKpvy9R0tAmnro9yOviwadoLwBvAj4B7j/rcoyjK0Im7fLqgKArKoRGYmMCa8RZ+UlpA5duqnOfE9Pk0Du6iWTZQ5FEDUr0tGqiA16QW0qc6aK+WSBPq0s8iRdE0eYhlqzGDC1/rpK+nkZe1UdYoYabFNGgG1RTTMmuItLRUFixQs39mzpwJQCwWY9WqVTQ0NCCEwGI5KjGs9wAER6DlfQD+opyLTQpxk/Qqv4xehntkCIt9PK/scaNBZqlGrdIMy2XYcyxIv7sSK8CUF2HfHkDAZU9BYj6xWJD21mdpavo5yckLKRj3FRQljNVaBsD69vVMS5/GjPQZPLH913zuRXA7tRCJklxcic7lxTx9OqbKOGX46aBmfQdanURuxZhxGMPHx7iqZAwWLW8+UYuQoOisNNLG2SmYnIJOf/zKVp+bS/6LK3G//jqu51+g9drrsJ17Lq7nVLdRQeNeYCL7u9xMv+5VUBQQAu/WpwmXrcD57kNqXRCQFo3SFgmzp+5dHk5KxCnBu41t+Hb8HMuihwDQpZkR/QIFhRkzZrBt2zaEEEco97OzVeK+hgbVjdvV1UVXVxdZWVlMy1EnjFsaBzkr78zeLyc1DoqijAAjwNUna/NpR687hLfPT57HTmuGmZ02iUpJi1aBssL/5cYpFlbsb2V8mg2DQaYmpga5Xh1OQBPJwB3qpkg7WoDidyEFYqzYNsJkvZ4MvyA2KYWH9qrBpVqiZOm0vP/dc/nFrx7BZjt+KZiZqZboNzY24nA4kKSjFnYrPwdD6r4aMi5mTfNk7tKsplyoqac/iH6OmzaYeV3Tw3R8JAp1rLIhHZ3+KDKy1berF3fhQkjMx+9vZdv2C5DlIJJkor//TSIRNbfbZi2nw9NBw3AD90y9h2srriW0fiO68G4ePU9ieEIeay598WOdg+E+P3Xbe6lamP2RzJpjGMOpQKvXcO6NFexa08pQl4+aDR3UbICimlSKp6aRlGXBkXJsxb6hsJCU//kfbOedR/NnPovrueewX3ghkY4OOFhLSsV0arvcMGfqkT4TdvYi7VpPY3k5wtUCoK4cNBq2+CTQgUtWGDDpiR18Bv0530ans6NLt7Bs11Ri0+1kJGexjW0oikJ+fj7AEbligCVLlrBu3TpqampIS0vjT08/joVx1HePcKbxiTh4hRAPoepChIFG4AZFUYZHt90H3ATEgP9RFGXtmRrHgdEDfPm4FH42NMSrTf3cn2ym3B9hpztAYkQNjE7ISUQTcXM4BIRjCAVe3tuNQGF8kgZGIOgZxqTTYLboWezX4PxcKbuiERg1DjJQbJBwuVz4PG7s9vLjxpOQkIDBYCAUCh25UI7Aq9Y+ULSEJ7gZo8bNDdo3iChaDIRZGVvASoBYlJuSE8AL0WkPkD5nKnS/o/Y9+0uw6Vfq/4vuB2BgcB2yHKS87Eekpy9n374bGXJtwmBIx2Ip4a2G1WpXTQkjL7/Mpb25uC2H+NpNj5Js+/iMqTUbOpAkweQluR97X2MYwwfIrUgityIJRVEY7PRyYFM3Nes7aNip3kfn31pJweTjaSeMpaVkPvQQvi1bSH/wAfoffhjXyhepWGyltiv+QP5AFEhWoMtQQJZnM6RXkeprIioEm3VGsi2pdPj62OrQs3jIj8ezH6dzNvpsKymKHbaBsq2TpfMX0eHtZdIklbFZkiSWL1+Oy+Vi5syZdHR0UF1djdPpxOPxYJdC1Lb3s6NliH3tw3xh7plJ+/6kOJLeAiYoilIF1DNKzTFKDX4VUIFK+PdbIcQZq4RKt5u4fnYeS3PUStxt/R60qWaqBiLscfvZ5/KSFJJJz7RhPiuNToPALKsGY1yyhStt9cwsTsdgMOByuUh3GHElGcj43+kY8h1HNGkrRnWa/eEWHnnkEWKxGHb78VXDQogjxW4FBUed8OAIhD2w+DuEr3qRNxqCnF9ixSm8pEnDPKx7jByN/0jzC5eeBfe2ob3wq2gcBugYJQKbezcs+zV8YR1kqhfi4OC7mEz5ZGZegSRpKSv7ERqNldSUpQghqB6oJj1qIXbT3XT97724/74a64yZTMuexThHPLvpX0X7gSGyShKwOD4iVXcMY/gXIIQgOdvG3CuK+fwPZnH5fVOxOg3sWtNyUslQx8UXkfnDHyDp9ejy8lCCQc6yqTKfwdHK6x3Ncc96g8iFaAgkLUWauCv41srbAagxmzCGZfzd7wGgz7NjPy8f86QUhEFD8WAyl112GZqjkjmqqqqYN28eGo2GWbNm4ff7+cc//kFycjKpZkGXJ8rlj23h+68fPLl63cfEJ2IcFEV5U/lAcxK2Ah+owHwG+IuiKCFFUZqBBuDk9KMfE+Mz7VSEDvL2c0+jU6AjFsVfksAkj0JIUXglFKDQK6NLM9Ph0OI1SWQYdez+9hJeumkSxoibpKQknE4nfX19pNkN9LnjZfHv1vdTlm7juZtnclGZnTyhprUlJiYyfvyJJTIuueQSrrnmGiorK+MfDqqrD5KK2Nk6hDsYZelZcf2l86WDvHHR2Txx9WR+u2w8aRNS1GI2gJ4a2PEUlF4ARjtMuRayVRpul2sbQ0PvkZ7+mSP7MpmymDXrHQoLVenBmv4arq5xEBscRONUfZzG8uNXPaeLXWtaWP/cQVw9frLHAtFjOMMQQmBPMpGaZ+espfn0tXqo3977kf30War7t0J4ickKdT1qimx1xwhGnYTTomdreJTJuGs3lQlxJoPF9hJyrNn0Jp/NCzYru/e8CNGwOpYFOTivKsM8MYVgvQvlRNrVo8hOy2Lx4sWkpaVx0UUXMS7ZQiAWf3S/tq/rpH0/Dv4vsKveiBr4BsgC2o/a1jH62XEQQtwihNgphNjZ399/oiYfiWg0Sm1tLcMuF+OUKHKinqfrephxVvwrS6IStX1elv76fRSrjtnJNpwWPYcO1AKqLGBubi6tra0YlRA97iChaIw/b29jR8sQFY4oYc8QF6X7cOhkvvGNb3DXXXedcOUAoNFoKC4uPjbecMQ4FNLUr5bKl5mMRGR1nBJeTBVOlrj+yAXvLYy7oLqr4anzVD3oc79/9NegKDHq67+D0ZhFXu7Nx2wz6JPRaAz4I346euuZ+l4v1sWLyPvTn9AXFuJYdvG/dLw/QHfjCFtXN3FgUzcpuTbKZ52ZCs8xjOFEKJ+dQWKGhbefOUB344f77nWjweH8qNpuT5uaAXWge4SydDvlGTY2uxxgUic4hqRisoxJZESjWD09VCZXsdvdxI+SnXwzFqb3z5ces39jSSJKKKammZ8AoVY3XQ9sZnxnCrfffjv5+fnMGx9XVJyda6Yg5cPJMP9VnDHjIIR4Wwix/wSvzxzV5puohKTPn3xPJ4aiKI8rijJVUZSpKSn/GmVte3vcDuW7+1Hsen5/uJuvxtxHPp+i0/PgGweITXSCJFiSkYiiKOzevZucnBxSUlJYsmQJCQkJxDyDtA8FuPR3m7nvbzWUpFqRWrbw2GOPMTQ0RFJSEnq9/pjl46kg2nIQBUFMn0WHS6XGcHQNoRW9KJIJIWJoND7Y8SQEhuCdUdHz9T8ArR5uXgdJhUf25/XWsW59CV5fHUVF96LRmE74vQeHDjKnJobOFyL51ltVyoHXX0Ofd3raDP+MvW+3YTBruenhuVx+31SM1rFq6DH856DRSiy/ewp6g4aaDR0f2lY3miRiG+6nIMXCWwd7URSFA11uxmfaKU+3c7DXizwqioUlhb9fuJKXuwfgpRuZUb0aV2j4yP529O6BWLwg1lCUAJIgeJK02+BB1X3l39NHzKNmRi47O06lU+HazIKSD2dp/ldxxoyDoiiLFUWZcILXywBCiOuBi4BrlLjzrxOOqkBT3U2dnCFoNBpKSkqYOnUqjiaVXjcyKYmd0XhlYrHewGazQiDJQK5RT6Veor29ncHBQc46S70gtFot5eXlhEfUFcz+Tje/WTGF31+Sh0WoRXCtra0kJPwTBXXdGnjtqzDczocheqCamJKCf7+HdpefrAQTomkHQkRhwugs3t2p8isBVL8IQ81w+E2Yct0RFavOrpU0NPyEjs4XALDbqkhNObmW077+fZxTI6MtKcJ0tJvrY2Cwy0vT3n4q5mZhtOjGqqHH8InAaNFRPD2d5n39J2VwBZBMJjRJSUQ7O7moMoNNDYPc8fxu3MEoU3ITmZKXSDgqsz7rVrVD6VJM1jRMBQsh7GHW8LFejQN6zZGsQwDJqEWfa4sbh54a2P83iATgmYuI1W1F6NTHdOCgSq+h0Uisum0Wv744m0gkcswk99+JT8StJIRYCnwdWKYoiv+oTa8AVwkhDEKIcUAxsP1MjSM3N5cVK1Ywb948MsMBxofj7IYTXVHuqw3yjhRBTjfxuZRE/ppt4/Ff/Jynn34agLKysiPtCwoK0CqqIRifYWNOb5jOvXGZwVAoRGLiP1FQr/8+7HwKdjxx0jEqsoIUaCUqZxI8OEjHkJ8cpxnRuxMAUa4KBrHmXlUbev59EAvD6jvU3Ov8OQCEw4McOvQNWtsep7PzT6SlXcy0aX9HfAhr6ub9b1DcDYkXnJh473Qx1O3j9UerMZp1TFyU89EdxjCGM4j8yiSiYZmu+uEPbafLziLS2ckdC4pYWJbKG/t7SDTruKgqg8m56oTvprVh3rmijsPaEm7+405GznkQLnyYjIJFXB6I4tA7KDbaqDXokXtreGT3I9z2tspBZixNJNLpJdbZBI/NgZduIPrm76DlPSyDP8c6JwuN00hg/+CRMVUJLfOzspEkicbGxhMN+2Pjk+IqeBQwAG+Nzhy3Kopym6IotUKIF4EDqO6mOxVFOblZ/zfBZrNRVVVFaOe7JOcuZr8JGpx6Ljksc43XB2Yddxaks+GvLx6T4WA0xisTc3JySNf40MTgbosdzzttdJib0Ol0REb1GY4xDsER6FXjFjSrWQyKrBCoHUCXbjlCjhcbCqClk4B2AYGmYZp0fpboDGjDe4lZxqFJGQ0Ot24iWDiDvfq3mZCagbVts/p5tpqXPTCw7pjfnJF+cmnBgd8/zkFNL5rDKoeM5ezZp3lEj8VgpxejVcd7K+sJBaJceGcVZvtYTcMYPllklyai1Um07B8kt+Lkrhl9VhaB/bUYdRoeXTGZV/Z2MSUvEaNOQ4bDxHeWVfDAK7X87t0mClOsvHWgl5/YcvnhJV8AUyL3v3Qj9533ON9tfpa1/i3UHVzJU4E6ZEXhsOsweSUZuNe2Envvj3zgcJa2/xgEKIoZQ4EDJabg3dSJHFRdUv2/U3nNll21jIyMMxOz+0SMg6IoRR+y7QfAD062/UyhsrKSHTt2kCkH2dkZw5+kp/2WctrWHcDgD9GybTPt7e1MmDCB/fv3H1OoAqqhKM5I4Fv0kN6oZYu2g4NyOyVFJWi1WlpbW4/VZGjfoc7sc2aoqaZhP573BnC/1YrQS6TeOQldmoVIeycm4UNbMoHmfTKecIzycBSD4SBy1sWqfCGgSFo2ZTagl1PpMY9QBMj2TCSTapCGR3ai0yUysepxurpW4XSefcLjEPP66P/FL0gGrksyINn1GE+SWXUq6Gt1s+pHO4+8P/uyIjKLxhTexvDJQ6vXkFWW+P/aO+/wuIqrD7+zXbuSVlr13qzqXuSGG7gALkDAOKa3QAjwJV9CjwOEAPmSkEpIIIQSuukYbAi4G2zj3mQVq1m997Laer8/7lqykWUItpCJ5n0ePb479+7ot2PtPXfmnDmHskONKMtSB1zi1MfE0r52HYrHg9mgY/nkE/fkXDc9kbKmbl7dUUZ9h7optjfUNXUBaI3o81czNnEm71fs4KftOej1WpwINpRv4JbRt6Dx1yMKP8LhzQS9H0bPXgAUoceYEIjQaejcUklPYQtee5/PYmRUKvqIwSmxczZEK50V6N6ow6wz4dfThKfejlkjeL6iEYfVgK2xmc2bN+N0OklJSeGWW25h+fLl/fqIj4+nqq6a/cYyDuvUdcCk6ASWLVvGXXfdhc3WF7LpqjnEKs90vKOXgeJFaS6la1ctWqsRxaPQtbMWAG+ZmvZXP3osORbBSHGUC3VvoxFd6CZcoIanLn2B6u8/CEKQPeldLBlXA9AWoTrq7fYKGhvXY7VOwGqdQGbm/zHQ9pHjUxcHNzkwj5+A+AbJ9I6Ru7Uv/73FamDU7MFJEiaRfBNGTAynvbGHXatLB7xGHxMDLhfOo0dRvCcPOR0XH4TD7aW8uRujTkNZUzcerwLGABgxF/I+ZGzENACqdDou19gJN5opai1CaATmuAZ07gJchrEYJvXN1HXGdjQGLYb4QISfjp7DTbhrfSvxAroPNp5Mzhlh2BsHb7cLT5cLb1MPMY5gDM0lCI9CmlfLmqY20AqmWUz4+6vhYjExMURHR5+Y98hHfHw8bsXDEapJjkkk25VCZqgaJfTlp5KnD7j5iesO3s9Vp4SuvMN4Wh1YL0zElBaM/ZB6U1Xq1CIkmsg0Cv1dvGF4hFjdSghNh2P+hlGX0uwqxmiMwmSKJmrK7yifdi6HottwuVrYt/86FMVLYuLtXzkePXmqMVq3RL2J+8/9Zsn0jlFV0EL8yBAW3T6G7z8wWVZ3k5xVpE+JJGNqJLvWHGXHB2peNWePm9a6PleoMT0NgJJFiylZvATF03+le2pS34PfTTOScHq8VB/bnDZiHrRXkYKeebZgftjRxq01LYRrXRS1FoHXi7X0al6xBvCzxEIqYkb29qUV6gxEaAXmMaF05zThKG9HH+OPMdlK55ZKug98s1D+r2JYG4fuAw1U/3on9kOq9Y3yBCMcHaSG+iEq+pzTC6JDuOOOO7jiiisIDw8/aV+KVyGmu2+5ZPbMmYz1JKLrOPmTRk6TaiwePg6NAAAgAElEQVSK81UfhuvwYYRJh9/IUAJ7niDccQ1KRzOiuQgFLQQnENK1CX9hp2f8n+Hm9WrRc0BRvLS0fEFQULbauRD4Z9+NS+lk796rsNvLGDP671gDv6KaHFC6YRUtFgi54QZS1q0l6LLLvvI9A9Hd7qS1rpuY9CASR4fK3EmSsw4hBOddm0nmOVHs/ugouZ9Xs+ZvB3n1oS8o9d10/caM6b3eWVKC8+jRfv2EB5p45OKRLBodxZx09R5R0ui7h8RMAEBTc4D/HXsN59k8WLs8JDg7ONpWSlfuGwjglcAAtnsa+EhpZ92yZ/h9QhJOdyPrSv/Nx6UfY8mOBLcXV2Un+jA/rAuT0UVa8HY6++k5Ewxr42BMCgQUWt9Xsx9GedWbe0YwFBY0kdnSjWF7PZPTEzGZTKSnpw/YV8fGCrreLWWOcSzTJk0hPi0ZtAJ3q6P/xW4HxQ51E9xWdHg1FpTaIvynRiG6qzHUrEQrmvFu+AM6+z48lkxqO9wsdb5PqyUZ00XXoxgsvc7xxqaNuFxNhIb01XgODp6G0RhJZ1cBIbZZBAdPPUHCq3mvct9nfcl2u13dvLP5KQzbD5I/IZTLM5ZhiI09rSWlhgp1Y09E4sk3/EkkZwNCI5h9ZTox6cFsfCWf6kI1emn/OnVpWGi1hN97L/5z5gDQk5t70n6umZbI366aQFKouqpQ2uALLQ/PAq0BKncRH3cjKd/bjTc0lfGN3bgVDwXbH8QF1OnU79qW8vXcv/d3vKjxcEd4KD/dcjf3bLmHhqInKDW/h4KCPsqCIcafiPObsEwYoO7LaTKsjYPXoiFveoM62NEW/DFhMpiI03fh9ii07GvC0tlDdMiJN7fugw246vumnV6nh47NFZhGhjD7nks4f/GFCI1AG2TEcxLjoNTnU66o/oD9ePhjz3J0xkYCZsVA4VoAXN54tPuexKjJwxs9i/o9q8jUVNA8/jZc7na2vzCJA9eMw9FZy+HDP0OvDyYkZE7v7xBCQ0jIbAAiI7934u9XFH6z8zesKVnDwYaDHGo4xE1PzSf0nidwG7UsuvtJ9JrT35jWUKYah9C4wfnjlUjOFFqthgtvHc30S0ew8EejyV6cRHVRK12+72/IDdcT+9cnEAYD9oOHTtlXqL+BAKOO0mMzB51RdUzv+Rei+ShGvyg0M+8mya5GMbZ2dVKh1+ERggCPl0PN+fR41NnADr++iMgLylZyW8Ja3gpdy0PuP9JVnwOvX4FY/9AgjMgwNw6rc17iZw0P0nxFJ2E/HKMm6TIF4apX1x5bXRqiLSf6CrwOD82v5VP3xz29be4GO4rTqybS0vZdrwsy4mlRcy3Zcxppei0Pr9NDW0UuTgzcaIWJaPmHdw5Ocw8asx5KNqH4R9Hs+llvP5rsZYQfeJJKJZTQaVdRV7ca86oujLudlD1xLx5PJ1mZj6PXn2jERqTcR2rqLwgPP7FwX0lbSe/x/Z/dz1+euYl7nm0hTBdE+ouvEZH21ctPX4eaolasYX6yupvkO4HRT8f4BfEkjQ1jxMRwUGDPJ2W9M3Sh12OZNZO2VavwtLcP2I8QgqQwS9+yEsCCR1Qj8dr31XrtmYuJ0arh6o1eDVv1EQDc2Nn30DnLTzUQD9lGMyVwBFafr+OFsPdZW7mO59bfDRodzLr7jI7DMYa1cThPMaFTFD7duQKNQYsuxI9g/HF1tmBCteqpESc+9Tor+nKgdHymbr13N6mOJ13IiWkotEEm3K0OFK9C08oC7AcbqfvzXqpzVadvVlIsVyeG4kJLfo/vSb1iB97EmXQZ0ql1PEmD8Vl0JieR7YdYqb+UQIuZ+vqPQFGNkOelnQSsNWKz9d+LoNcHEh93AxqNHkdhIe5m1bmV06jWy70y40qUkjJ+/FoHfnHxpL77Pubj1ldPh/YmO+V5zaRmR5yR/iSSbxNblIXgSDOHNlZSsr/P4Rt22214u7qo/vnPB4xcAjVrc+nxxsGWDIv/DE2FkLsKDBbCZ69AqyhUa/Rs0oeDomFZWDZhHg8BnaOJ1t/LK7V1LOhu4VnLKD4vryLbrj5sCgQvOiqoHLkYAqMHZQyGtXGwjruK2YEj+EDroLt8G/poC4l2G6mpqeh8ZfwWjOjbz+Cq7aJjg1pcx5AYSNuaUhylbQMaB12wEW+HUw1LdXuxZEeiuLxUF5Whwcvs9t9yod/vAMizB0NLGW3tbSzIX8T1mi6cSgLGaVNxFW3Ai6Ap+WI8HjutrXsx1pronubFPsaL/0cCny3rR/0f/kheRiYlSy6ieP4CPB0dlFYf5tZ/w+UPbOLxdyzoDCZGvPwa+ogzdyPP84WwZp4jk+pJvpssul2dQdcf7ZslmLKyCL/7LjrXraf1nXcGfO+IMH+qWu10Ovr2JJCxGCzhkL8GAN3km4nUmqnSayjyc6M4YjEnz2dDeRW6ynns3OcmWa9B01AMNQdQosbxw852JrscPBQ7B63Xy590PQMoOH2GtXFAUbg+7fu0arU8s/cJjClBRHQEcPmsi/g5Fm5EwwKz2XepQuNLuThK2rBkRxJ64ygQ0HOkhZ4jrWgCDGiMJzpvtUEmUKD1/SIMCYEEXTqCgBnRNAk7CzU7CKteg6n0E6YaSzmkJEHBxzzrXkRxp55yu5PaixIJmB1Ly5HtFHmjuXBSOjmHf4Jod0K7g4AxM3Gca0Y4vHR/8UW/j+duaaH5xRd7X3u7uuhYuw7Tx1s5b58bV3k5gWPGE/2LB9DZzlzabI/HS+7n1SSMCiEw5ORJ/SSSsx1rmB8hMRaaqrtOaLdddx2mkSNpfvHFAWtCZEUHoiiQX3Pc8pNGAynnQslGdWkJmBQ6hn/7W2j2a8PZmcIej7o/+MPa+3lq12PY7cH4tTRA9X5cofHEBMA/auqZV7OfX33iZtkDuyhvKxuUzz+8jcP+1xj39q18z6nh+fZccoLVtfiOzRXMwo8b8cfum1I6KzrwNPdgnBbG9dq7eLnwFfwD1qD57GGcpW1YTrJ8og3uK2ATtDgZIQSmJIVGTFym3YJi8AetgVvMm9njTaM1dx0veM5nelIQOo3gyfxavF4Fv/p95GvTSAs6QGPjeiId6t6DmGk/YMpNOxAGA107TkxB5ayooGbFL1CcTpJWrSJt9250UVF0rFtH8vZyuoNMJK1aRdw/nibosoFTafwndDT3sOPDEvK21tDd7mTUTLnhTfLdxhbtT1Nl5wltQgiCr7wSZ1Ex3Tt3nfR9WdGq/2//5t2UXXMt3Xv3qScyFkF3ExR8BIrCtbo0MhxOgnXh+Nvn8WKRBbvXSO3uINpKzXhyHWg9XnLtVm4tmURhYBQ6j0Jg4QESDmgJtMO6T/4xKJ99WBuHznD15nW310y028NfC59EG+JLcKUVBMyJw1HSRk9xK85SNZ/7rowiStpKWHV4NVPbJ7NbFBMwSYs1/1IoXHdC/7qgPuNg8EXs6EUtPYqOmZpDiMm3wMhLmeXYhJUunioOpRMzD148mvsuzGDLkQbWbn6XAG87nVYjh3N+iFZrIbRb3c9gTE1FYzBgTE3FkZ/X+7vcDQ0ULVlM54YNGDMyMKWnofW34D9rFp0bNhBX66ZxyVRMvs09ZwJFUVjz94PsXnOUza8VYA40ED9qcFIJSyTfFpHJgXS2ODh6sJH3/rCXVx7YTnluE4GLFqK1Wml59cRqA927dnFk2nQ6L1nIzdXbiPj77+jetYvGJ/8KQJFtFt6gRLUm/JOTsJXt4LmqNn43dSXfGzeCj3IbuKfqJrwu9dYs2tRlqQjRwqamZB4+eh92RY+jrS/IY3rx4AR8DGvj4PDT0eGvR9tWzDVtbRxszKEiSU2da0wJIuC8OLTBRto/OYqrthut1cCbpW8DUNRVioLCTm8GARH7oLmYLZ/+jMONh3v71/pKX2ptfeFotJaTKqrRCS9lwZ3Yp16D0Oq5Q/ce//AsYYSxhYzIQC7OasGi72LTpq0AKDY1ncbYMc/gKi5Ha7OhC1FvvqasTHpy83qnuLUHdiB6nOTEC7bfOJGXDr/E3DfncjShT0fQzDlndCxb67ppquwkLlPN5ZQ+NRKNRqbjlny3SRkfDgLW/P0gTdWdOB0eNr6Sj6IzYF16GR3r1+Oq6UsR07ZmDZ6WFhCCS3e+y4i2Krp0Jrq2baeysp55f97Gdc57UEbMh6YiQivXUaJEExNs4fvZqn/T1tCBotVSExBGqTeTdsWPENFBielqbul6g62WGfS0qAEswaldRFsLB+WzD2vjEGKbgXfEufh1O7jAF0K2J+YIAefFEXxpKhqDFv+p0TjLO+g+1EhdZDt76vaQYk1F0Th4znI/9+rfYNWeIzznns/t/grL1yzH5VW9w0KnIfTm0YTfdlxoaEsps7QH2SeSKGp7l23519MY5GKkRl03nBGqOrebm9aQFZJPhreSLsWILaKOyZPXEBw8FUdhIcbjkviZsrLwtLZSkrcdRVGoOKKG2a5cGsZvWt7g8d2PU2+v5zmNmqm1zQxJE/s2zJ0JKvJUozrnqgy+d+cEpiwZnKLnEsm3iSXIyLlXZ5AxNZLL78tm7nWZdDY7yN9eg+3KK2kPTODAn97qvb5r23b858xhxCefYL30UgyTsnl+opplYOd6den3s+YgVo38C/xgA4WtE9lQMZaIQBNpEQH87coJzPPU4JeVRUdUPD3NTvaf9wpeX3TilboNWM0BOFr1CK2XiPFtOMbG9hd+BhjWxgFAN2o5AggyBjBSmNjWsgPrgsTeJSHzBF+6DLeXvVa1INC0IDWxXZOfuhb5Zn0MvzbM7u3zl9t+2XtsSglCe3zaiNocgkQXaw0TSR3xc0JsM+ky64gUzVwTs4bKhM109dRRV/cBo2xdTNccpkDEEB46jgD/DBSvF2dhEca0viUhc7a6zPSXZ2/mk7JPaC8rxK2Bhy9+gkhLJGnBafxwzA/ZIUp565Z0HvofG2Hmb1Y9byBqi1vxDzYSGOpHdGoQWv2w/9OS/JeQdU40c6/PwhrmR3yWjfDEQPZ8XIYmIordY+9kR/soOg8X4G5pwVVejjl7EkKvJ/rXj5HyykukzFfvDRvWbCMlzEJ6RAB/31SEEjMB979ruHD7F7h2q76LCxIthJUXYs7OxhQXR3B7I5+1R3Kx8xG6lr2NGy0Taz+ks96KMcKM0IA9esAk16fFsP8Gm2IuwKXT4NYJZrS3cbDxIG2ONrZUbmFt6Uc02tdBrLpL8oClgBitBfva9eg8Wg4Z1Zv+uWIfWrOa1XF+/Dw+Kv0Ih8e3M/rtG+HIJ32/0FcFqtUcTXz8TYwb9wI1aT/hiF7PuqBt7Gg5wOebbyehoJJ7K18nTVNFpTGa9PRfAWDftw9vdzfG1BFc9/F1PLj1QQwpKXT4azl/j5e8T97EXVlNS5COURFj+eSyT3hj8RtcnnY5AG+FFBMbnXHGK7DVl3UQLtNkSP7LEUIweXESHc09fPF+X5Gdkrc34vAlrfxyivubL5tCl9FCemctj14ymltmJXOkrpPNh/pKlFb86EfY9++nbfVqFJeLwAvOJ3Z0Gkavm/fX7qfOPxNL1nwO2c6npciMs8WD7ccPwl1FxCf9cFA+67A2Du2ffkrRjNm0GUPROLqZ2dGCV/Ey/+353L7+dlZsXcHBQ/9D4/Q3CL99HMUdhWS1N5DjTSHZIThkMuE2WfiB7iO0hgYsLgOLY2bj9ro52HCQ1flv4sx5B15bpv5CRUFpVPM4OQNVZ7jb6+bWoldZGhtFpy+8rTVnOwmVdkxO1Qk+bfFP8fdPw1FaStlVvnTcSaHsrd/Le0XvkduUy6YsL8l1cMGfthORV0d3lBUAjdCg0+iIsEQwPnw8AImBiWd0HHu6XLQ12AlPkGkyJP/9xI+0EZMW1Jt7CeBohejNaGw8rkIkQKCfgbCxI1ls6WRaSghLxkYTEWjkV3//GICdi29AZ7NRfvMt1D36GH7jxmEaPZrE8Wp21rjOeuZnqdGQ5ef8hvVV4yApBesl3wP/M7sCcDzD2jgYU1LwdnVhb4xC73Qy2uFktn8iLq+LqVGTsXvcHO7R8vuSPZRaqqnpriHK5SZXSWCCs5Mjej3tWQsQAgL8ajB2B5G2MReTQ+Gnm37K/Tse4Y3A426YTcXgslOvWHH7K1z6waWsK1/XT1eeb0ZC4kxa7z3KA7Vv81reazQ88QTCYiby2afZHlDXe/1vd/2W96dpaE5QncGWbi/dU0b263fFlBVk2DK4IGngutH/KZ0tDnatUWdNcZlnbq+ERHK2ciyTa3CkmVGzYkg2V1GpTaZ1fz766Gh0Xy4HDBgz0nEUFqJ4PBh0Gh5aMpIF/qp/8Xs3XkTUY4+hj4wg5Ac/IO6fzyCEwJii+u3mme3clLuGkosvYWRdEelNFdSfoRQ3p2JYJ70xpqRgSErCUwKMBJdO8Hi3C+eyTRSUv8IXNTt5rtEIuLj+39fiVLz4eY0YcDHV0cFKYaI0dQHWfe8hdPVM3x9Jx7aXuTvTj0cuUZ/6t/mZuKa9A7Y8DhseRUGQ740n17MO2/4Ctq++E8NkQWrkSEaV76RMoyXXYKB5/m3YJt7Pk/ufYGv1VnIKP2fcp7B6opdW5RP8WvwINAQSYAhgX/0+AoICCXjlad68/womH/FiWTCv3+dNt6Xz1pK3+rV/U9wuD68//AXOHg/xI22EJ8hlJcnwIDDUjysemgJAob2Akl1aqvaWkXLOxJNeb0rPQOnpwVlWhjE5mYWjo5gQ1Emrnx8BqSkIvZ7kDz884T3akBC0VisXVOzCsU71d/KzOzAAD7iSSX5hJ3+7cgIWowxlHRQC5s3DnluGxyHowoBzTx5djz2Od92/GBdgRiM0TLW46XSp0Uw9pkTGaYoZ43AwrthLTlUVOfE2ujUKc/PUAuBpxT2gKBiFlj0mo5qIY8OjAGiEwp9MY6lxHOHKjV6Wb/FyT34qKxe/wS1BkWQ6XBQa9GjGXItiDGBj+UZGBI1gQpGC8HjYMkrDhyUfUtBSQKI1kXnxqhGI9Y8l3ZbO2zM13HOTjhGJEwZ97GqL23D2eBg3P56Ft56ZnEwSyXcFIQRCCOKXnIPwummzJuM3KZv6snZyNlfy/N2fseUNtbKi35jRgM9naLfT8MQTdGxYj9+4sQj9yTMgCyEwT87GUVAAej0p//4Y2/XXU7z8h5QHRrKpoIHpv9nAs5+VnPT9p8uwnjkABCyYT9M//0lrqT+N+f54ewTwDgEWhT+8/Rc8wXEU597BF4XqTmlXzBWMq9pCYKvg52u88Obz/OTnQVi6XcS0dOPy12Ds9BLcCZdHJvFMVxF1Wi0hFhv69gY2WGMoDNpKsNZKanMn4GBCvpp9UTf9TkI23olbCKodCnWuI9Tb67lj/B20Pb+CxgCwZGZBcx4HGg5wUcpF3DzmZrZWb+XGUTdi1Bp5Y/EbVHVWkRKUckbHye30oDOcmB6kMr8FoRFkL0yU0UmSYYspPASrsQd74gQqrBP5/Lia6Yc2VpI8LoyYtBFoQ0Lo2rEDEDT+/SkAgpd9/5R9+583l46167AuWYIhMZGI++5lkaKQ3eEgp6qNd/ZWEjJIRbSGvXFoiE7GkJhI/f6jvhYF44JwHJ82oP/744THNhLl6GCVq5W3UibTzURmaJ7H7okD1Ju6tdxFhFPdgFaVGkvivnJ+KXow2tW8KkfnPwBlbxPZ3sCG+FsRnf/k/+J/DI6HMGZm4sjLw93QgC1xKenTSmDHyxxsPIhAjSiaGD6B1iojR8eE8cyCfzJj5QwAEgITsBqtvHfxe72fJyski6yQE6MlTpf2Jjuv/2onE89PYNLCxN72ivwWIhIDMciU3JJhTti4FGpL2ijYo64enLN0BKNmxfDSim3s+6SM2PRgLNOn07lxE54mNTuy/9y52K67tl9fVUdaCAz1I8BmwnrREnQ29b3HEEIQEWgiItDE3MzBy3o8rB/33tlTyazHN+FZ3FcMJ2N5DQnBB9CYoftAIbSUQlA8yeNv4t4FT9HTVMV4TTEuT9/Gk0s6RrCgwQpCoTRe3ZxmbtCR0K7unNxrsNGW20q9fxQHutUqUylH1T0StquvAvpqN09Jv5sIcwS763ZzoOEAIaYQwmsd6Dt7mLX4R1iNVuYnzAdgcfLiQRubbe8U8fqvdlBT1Mr+dRW4HR52fFCCo1vd4Oewu2koayc2o7/zTSIZbljD/eho6qGhvIPpl45g3Lx4dAYt4+cnUJ7bzNa3CylKXUqVZSSdW7diXXoZcX97Eo3fiYkpXU4P7/9xH2//Vp19CK0W/9mzB1x6GkyG9SPfpET1xrZjzBxmjt2AbmIMiv1ZFA0ERHTTUWnC6xZo7C0QORr+Npnf+vYvtHYFoE+IRxsQyPQiBa05Abe1nlKtidkmP8wtZoIcRURlhPPUod/TENqFu+kCqizFLCj0o6f8Y/RxcQQsWEDNil/Qk5uL/6xZCCHIjsxmffl6PF4Pc+PnYj+wHwDzRNWP8NiMx3j0nEcx682DMi5NVZ3sW1cOCqx7MQ9Htwv/YCOdLQ7KD6s1Go4ebERRID5LRihJJEFhfTf5xDF9OcXGnBdLZUFzX9hrxjV4tEaSr7qq9xqXw4Pel9G5xleitLvNicftRasbuuf3YT1zSAixEGfzY3NpG4lvrCTkx7+g0WZA54WgpG68Lg1tVcHq7OGDO8BnGNp1NpyNdoyJSViXLKbn8GG6du3HGOphvKaIYqMN/SdNVLwXzJ357QS4dbwdGMAHQZ2kVefyg4866MnJIWDePLQBqpHpye1LnLc0bSl2tx2n18nStKX05OWh8fdHH6fmXvHT+Q2aYQAoPdAICpx7TQbtDXYcXW6mXZqCyV9PWU4TiqKQ+3k1gaEmIpOtg6ZDIvmuEBSh1o02WfQERfR9N7U6DYvvGMv1vz2HW5+cQ1RyABVjliES1F3NTdWdPPOTzexcrYaDH81p6n1vnS/Z51AxrI0DwNyMCD4rbOSv6wuZ9OsdFKXdhlunxTR+DKZRWTQWxeBd9iac/3/0/PgwI3ue47VJ7+KsrESfEI/1ssswZmUi/PwIGhXAudr9tJssvf3b96WyqryJC7qNeIMPMLNAzQ0fdPnlhP3vTwB1R+XxRcsnRkzk3ux7+fWMX5MdmY0jNw9TRgZC8+38d1UXtmCLtpA5PYrUSeFknRNFyvhwYtKCqTrSwtFDTVQXtjJ2bhxCJteTSAhPDGDhbWO4+tFp/bIPCCGwWI1odRrOWZaOwyH48K8HaCjvoHhPPQC7VpfS0+WiaHcdMWlBANQUD0PjIIR4RAhxUAixXwjxqRAi2tcuhBBPCCGKfOcHPR5z8ZgoHG4vf1h7hC6nh6cqF6K76yiaGz4i4ucrcDc00by5BKbdRo03mC78iNRrUbq7McQnoPX3J2nlStK2fo5lVCpRooXZ2oMAuI0asmrLCBNtXJd2A3qniSkFYFmyiKhHfoXGqOZvMmVl4aqsxNPW98dwddbVLElZAopCT2EhxszMwR4KQPUl1BS1EZMejBCCBT8YxbnXZKLVaYhODaKzxcGOVSVYrAZGzZL1GiQS8NWNHhP6lfXSIxIDWfCDkTRVd7Hqz/so2Nm3mXXL6wXYO1yMnRdPcKSZmiL1fuDscdPZcmLFtw0v5/Hi/VtpKO9gsBiqmcPjiqKMURRlHLAaeNDXfiGQ6vu5BXhqsIVMTAhmcqK6bn7R2Gi+KGmiR2sBnRHzhAlYpk+j7cPVANS0qjsao7obATDEq8s8wmBAYzZDuBolFDGhDVOWi/CMdjw9WqrDFjJq5vV8ELMCi0PBdtElJ2jwG63uESiaO4/2tWtPOOeqrkbx5VL6Njiyoxa3y0vG1Mh+5+JHquPUVNXJiEkRaLTDfuIpkfzHpEwIZ9FtY3B0u2lvsDNreRp+AXoKd9fjbzOSMCqEqBFB1BS3oXgVtrx+hBfv30bu59W4nR48bi8F22vpbHGQs6Vq0HQOybdbUZTjaudhAY7V2rsYeElR+QIIEkIMahFiIQSv3TyFL+6fy8XjonF5FPaVt/aJmzkLZ0kJrpoaqnzGIbRJjUIyJCSc2NmUWyFyDM0TriBpTAOWINVHETTlTjBYEOu2oLXZsEybesLbzFMmE3733Xg7O6n//e9RPJ7ec44iNReTcUQqg42iKORsqSIsPuCku52Dws1kTI3EEmRkwvkJJ+lBIpF8HWLSghh9biwRSYGkT4kkcUwoAKNmxaDRCKJGWHHa3TTXdFHnq2G98ZV8/vHjzTx9xya8XgWhERTvrUfxnrxU6ekyZI9+QojHhBAVwFX0zRxigIrjLqv0tQ0qOq2GSKuJSYk2DDoNb+3uk+A/ayYAbatXU92qTu1M5SVozOZeB3Ev1hi49TMi590BgDFQreLkKCpW6zdv2EjgBecjdCdOPYUQhNx0I1GPPYqrrBxnaWnvOWevcTizm9pORm1xG83VXadcLjr32kyueWQa5sDB2XgjkQwHhBDM+n4aS++dhMFPx8gZMYQnBJB1TjQAUSlqoEfVkVbaG+y9s/bjmbQwEUe3m5a67kHROGihrEKIdUD/tQlYoSjKKkVRVgArhBD3A3cAD/2H/d+CuvREfHz8N9Z5rHqaEAKrn54bz0niH1uKuXFGEqNirBhTUjBPnUrzC/+i44ZEQv2NuPIKMJ7KQRyaDoDO7EETGIg95xAaixmlp4fARYsG1GIaNQqAnvwCjCPUZSRHUTG68HC0gYOTt0hRFKoKWghPDOTwZ9UYTFpGTAof8HqNRoB0QkskZ5SIpEAuvz+793VgqB8GPx3Fe+vxehXSsiNIGBWCf7CJ2IxgOpp60GgFu1aXUlfahi3KcorevxmDZhwURemf+e3kvAp8hGocqoDjH8djfW0n6/8Z4BmASZMmfaN5VVlOE5tezScowsxFPxmHEILbzk3h4+3l/HVNPv+4RU2spfnxnXhuvp4lf7mL2ot/hiMvD+sllwzcsUYDC7pTV/gAAA4QSURBVB5FmEOxNG+na9s2PA2N6KKi8Bs/fsC3GZOSQK+n5dVXcdfWYLvpJhxFRYM6a8jbVsPGl/NBAAqMnh2DwTSst79IJEOOEILQWH+qffseQuMCCInx7z0fEuOP4lUwmnW01H7HZg6nQgiRqijKscKnFwP5vuMPgDuEECuBKUCboig1J+vjTHBsY1dni4OK3Gbismwc/OAoy+p1uOo7OXiwHnOkH/PfKiNi+o/5zdanuf2d3+AFzJMnn7rz6f8DgGWmlo61a+msrsF2042nDEcVBgOm1FTs+/Zh37cPjdWKo6SEoKWXncFPrc4WhBDUlrTx2RtH0Bm1mMw6OlsdjJQRSBLJWUFonGoc/G1GbNH9ZwZCI7jmselfGSH1TRkqn8NvhBA5QoiDwALgJ772j4ASoAj4J3DbYIoIifHn1r/NwWjRkf9FLQU7ajmwoYKYsSF4gS3vFLGztAVFAV1sLK9l9dVBsEyd8rV+h/WSSzBlZaGPicF25ZVfeX3kww/3Li81P/+CGqmUcuYilY7squWVB7ZTU9TK6r8dwGI1cs0j07j60Wlc+9j0E55OJBLJ0DH2vDjiR9rIXpg0YOXGwTIMAOLYmvt3mUmTJim7d+/+6gsHYOPLeeRurUGjEYQnBnDpXRO585HPSKlxU58dxHsVDex7cAHN7V2Ila+iCwsl6LKBn+Z7ulwYzbre/1Cv04nQaPo5ok9FzcMP0/r6SgCSV3/Y64M4HRSvwuuP7KSlRt2Ip9EKlj8wmeDIM79eKZFIzn6EEHsURZl0snMyUB0YNVtNouf1Ksy5KgOhESRNjaBLKHgOtTAmNgitRhAW5E/orT88pWHoaO7huTs/Y/2/+tJhaAwGhE5H3dF2Gis7v5amwPnze48NKWfG57D9/WJaaroYPScWa5gfc6/PlIZBIpGcFOl5BMLiA5ixLJXgSHPvssr45BD+ZCzlvB4D8davv9RyaJNaNLxgRy1TLk4mwGYCwOPx8s7v9qB4FZb9PJuw+ABa67pZ+0IuI2dG94awHcMyfToxf/ojGotlwCnlQORtq2bfp+XMuyGrd79C7udq26hZMcz8fipCpP1HfUokkuGFNA4+xp534p6FsXFW9hs9THJ4SSrpHw3Q3mTni/dLSJ8SSWxmMFrfbuH6snZ0eg1ul5fqIy2kT1X38DVXd/VuVsn5rAq8Cnnba1G8CvVH20keG4bJvy8tr6IoNEZNwmjW8Z94AVxOD5tfO4LH7eXIzjrCEwLxerxsfaeImPRgn2GQoagSieTUyGWlAQgw6Vl/9xwmzI2jqbSDT587TOFuNQ+K4lX4+OlDFO6qY/WTB1j5q510t6uFf9rq7SSPD8No1vWGoQE0lKk5UMITA8n9rJrcrTWMmhXD/BvVlBvVRa0n/P6CL2r5+OlDvP/HfVQXtpxSa1N1J83Vqh+hZF8DHrcXoRFU5KlFRRoqOnHa3YycES1TXkgkkq+FvFOcgsRQC7MvTCYuy0bxvno+ffYw1UWtFO2tp7Gik8lLkph4QQLtDXZ2fFCCy+mhs8VBUISZqBQr1UVtuBweKvKbqSlqxWjWMfWSZACiRliZtTyNlAnh6PQaKn03clCNz95PytAbtZgselb9eT9NVX2+isLddeRsqUJRFDa8lMfKX+1k5aM7aW+yc3BDBUERZrIXJdJc3YWzx01VgWpcon3ZHiUSieSrkMtKX4HJoueiH4/D2ePmX/dtZePL+Ti6XQRHWZh4YSIajcBhd5O7tZpU385ia7gfOr2Wo4eaePPXu2j1bW9PzY4gNj2YuddnkjhazaWi1WlIGhtK3he1tDf1kDQ2lIIvammp7WbeDVnEZdp4/Vc7eO+Pe/HzN9DeaMfrUZenKnKbKdnfQMKoEMpymtj2TjH1ZR3MWp6Gf7Ca8bW5uouqIy0ER5qxWI1DMIISieS7iJw5fE0MJh2zl6fhtLsJCPFj7rWZaioJYNTsGLxuhVV/3o/QCCKTrMRmqlXmWo/Le5IyPgwhBBlTozBZ+vwLkxYlYQk0+HZsF1BT3IY1zI8Rk8IxBxqYfUU6ji43Xa0OwhMCGDsvjoAQEyX7G4hICmTRbWMIifWneG89BpOW9KmRvY71hvKO3hTcEolE8nWRM4f/gPSpUb0O5uMJifYne1Eiez4pY+L5CQSGqiUDZy1Po7Gqk5nLUulucxIQYjppv7YoC1c/Mo0dH5awe81RLrtnIuEJAb3+gRETwwmNm4pGKwgMUfs2mHTs/ugos69IR2gECaNCaKrsJDIlCINJh96gRW/Skr+9BpfDQ0yaNA4SieTrI43DGWLykmSyFyWdUBlt9JzY3uNjBuNUZC9MJGNqJNaw/iVAg8JPbJt4fgJp2RG9JQkzp0dx+LMqshclAurW+pBoC7UlarrfGOlvkEgk/wHSOJxBTrdkpkarOalhOBlaveaEWrVB4WZ+8IdZJ1wTEuNPbUk7tmgLfgEyxbZEIvn6SJ/DfzHHai4kjw8bYiUSieS7hpw5/BczanYsToeHCQtk1TaJRPKfIY3DfzHmQAMzlg5+eVGJRPLfh1xWkkgkEkk/pHGQSCQSST+kcZBIJBJJP6RxkEgkEkk/pHGQSCQSST+kcZBIJBJJP6RxkEgkEkk/pHGQSCQSST+EoihDreG0EUI0AGXf8O2hQOMZlDMYfBc0gtR5ppE6zxzfBY3w7etMUBTlpPl1/iuMw+kghNitKMqkodZxKr4LGkHqPNNInWeO74JGOLt0ymUliUQikfRDGgeJRCKR9EMaB3hmqAV8Db4LGkHqPNNInWeO74JGOIt0Dnufg0QikUj6I2cOEolEIunHsDUOQogLhBAFQogiIcR9Q63neIQQR4UQh4QQ+4UQu31tNiHEWiFEoe/f4CHQ9bwQol4IkXNc20l1CZUnfON7UAgxYYh1/lIIUeUb0/1CiIXHnbvfp7NACHH+t6QxTgixUQiRK4Q4LIT4ia/9rBrPU+g828bTJITYKYQ44NP5sK89SQixw6fnDSGEwddu9L0u8p1PHGKd/xJClB43nuN87UP2PUJRlGH3A2iBYiAZMAAHgKyh1nWcvqNA6Jfafgfc5zu+D/jtEOiaBUwAcr5KF7AQ+BgQwFRgxxDr/CVw10muzfL9/xuBJN/fhfZb0BgFTPAdBwBHfFrOqvE8hc6zbTwF4O871gM7fOP0JrDc1/408CPf8W3A077j5cAb39J4DqTzX8DSk1w/ZN+j4TpzmAwUKYpSoiiKE1gJXDzEmr6Ki4EXfccvApd82wIURdkCNH+peSBdFwMvKSpfAEFCiKgh1DkQFwMrFUVxKIpSChSh/n0MKoqi1CiKstd33AHkATGcZeN5Cp0DMVTjqSiK0ul7qff9KMB5wNu+9i+P57FxfhuYK4QQQ6hzIIbsezRcjUMMUHHc60pO/Qf/baMAnwoh9gghbvG1RSiKUuM7rgUihkZaPwbSdTaO8R2+qfnzxy3LDblO35LGeNSnyLN2PL+kE86y8RRCaIUQ+4F6YC3qrKVVURT3SbT06vSdbwNChkKnoijHxvMx33j+SQhh/LJOH9/aeA5X43C2M0NRlAnAhcDtQohZx59U1PnmWRdmdrbq8vEUkAKMA2qAPwytHBUhhD/wDvC/iqK0H3/ubBrPk+g868ZTURSPoijjgFjU2UrGEEs6KV/WKYQYBdyPqjcbsAH3DqFEYPgahyog7rjXsb62swJFUap8/9YD76H+odcdm076/q0fOoUnMJCus2qMFUWp830pvcA/6VvqGDKdQgg96g33VUVR3vU1n3XjeTKdZ+N4HkNRlFZgIzANdRlGdxItvTp9561A0xDpvMC3fKcoiuIAXuAsGM/hahx2Aam+SAYDqkPqgyHWBIAQwiKECDh2DCwAclD1Xee77Dpg1dAo7MdAuj4ArvVFW0wF2o5bLvnW+dI67fdQxxRUnct90StJQCqw81vQI4DngDxFUf543KmzajwH0nkWjmeYECLId+wHzEf1j2wElvou+/J4HhvnpcAG30xtKHTmH/dAIFD9IseP59B8j74tz/fZ9oMaBXAEdV1yxVDrOU5XMmq0xwHg8DFtqOuh64FCYB1gGwJtr6MuIbhQ1z5vGkgXanTF33zjewiYNMQ6X/bpOIj6hYs67voVPp0FwIXfksYZqEtGB4H9vp+FZ9t4nkLn2TaeY4B9Pj05wIO+9mRU41QEvAUYfe0m3+si3/nkIda5wTeeOcAr9EU0Ddn3SO6QlkgkEkk/huuykkQikUhOgTQOEolEIumHNA4SiUQi6Yc0DhKJRCLphzQOEolEIumHNA4SyRnEl630rqHWIZGcLtI4SCQSiaQf0jhIJKeJEGKFEOKIEOJzIN3XdrMQYpcvb/87QgizECLAl7Nf77sm8PjXEsnZhDQOEslpIISYiJp+ZRzqzuFs36l3FUXJVhRlLGoah5sUNeX1JmCR75rlvutc365qieSrkcZBIjk9ZgLvKYrSrajZSo/l6BolhPhMCHEIuAoY6Wt/FrjBd3wDapI1ieSsQxoHiWRw+Bdwh6Ioo4GHUXP5oCjKViBRCDEHtUJazoA9SCRDiDQOEsnpsQW4RAjh58umu8TXHgDU+PwJV33pPS8BryFnDZKzGJl4TyI5TYQQK1DTP9cD5cBeoAu4B2hArZwWoCjK9b7rI4FS1EymrUOhWSL5KqRxkEi+ZYQQS4GLFUW5Zqi1SCQDofvqSyQSyZlCCPFX1PKvC4dai0RyKuTMQSKRSCT9kA5piUQikfRDGgeJRCKR9EMaB4lEIpH0QxoHiUQikfRDGgeJRCKR9EMaB4lEIpH04/8BiARKdGzxyh8AAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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mnphiXEvyvK2YnNdPrT1CfVEBx1eez+buXqr72mnqPEEgPApIOBQfZ/nbOevcBWxpuoCvHL4FOZ/hX09VMjr9T6gmF0gyxVUu3vbxRTg8llf3QBleU0bIG96SxvtibP3dSaaGMgx5OvFKGq7oPCqyYyzwuTFZvbSsLeaL3hy9GRWAUHiC9R1HUTUXB6llPJEHBBKCOm2YsxKnKIhOIGlxJEkGBEIIAg2zaSs8Rl22i015heJMElnLvqw+ulAQWAEFiSwSaSTp5d+vXNZDJhEkHfWSmvKgphzITifmgA9zUQilsBhTqBilZBZh2UlbX5ThoRimtEYBErMQFGBGemGlHF3oRHNhOoVKv1VDlocIykl+uXYTrSYLBeFxrtl/gMKhNpL5CCZJoik4SfHGt/FV0UzLdBvvmdCob/8Qg6aFIHQcboVLb1xCcJbRT/9mYYS84S1FTefZ/2g3J7YPkDXHaS3Yz6KJc5ByVpanuwmV1JMpsPLzdQXcEY/PtJt1jeWdxykdmuCIuYGR9Exr2iWSrEqdoDbRhTmVAMDq8JPPJdFyKktWzKFI3k7FdDfOmYUIULN+svm55MwLyFODJkKoWRNqdoyUmCRpCxOzTZO2TCLLCZzEcWsRArk4ASmHx6ShmGbKSqs2BqLFjAz7kfpVgskI8gvvk1WshEuqSc2eR2TeQg57K3jmdJScqtNAjlWWFCtcGewTEn5KsZlmuovSaITzIwxqk/xw7XI63QqzUlm+uqub2PgBhlOnAUGZX6NvRTW3s4t6Nc/1J5dwMv1edNmMySSx8WMLjYXO3iSMkDe8JQghOH1kgl33dpCKZmkP7cWmmamcWo4/M8Zyu47VW8Gh5UG+GhIMqzODoqHoJMtPnaA5Wcqo7gagQh/inNgRgtERJF1Dkn0o9iIkaYRsIsJFC0xUaMfx6FGEkMhkZ5OWNpAVZ5PTvUzl+jlt6eGYp4f9BT1MKHGcmhNnzolNt2HVrSiagkmfmXP/h5Z3XsqTk1T8xGnSxlmuTrBQTWBFMGWycdC9mBPaWiwjZnyDvRQMdVMx1Y+ia+QkE52BCjpqFrK3uJ5mUxA7KoscYS4oep7c8QSBTBke5xyKHDUUSjNdLhPmHE+VOdgRMnPeUJyr+rMcmNzKVLaDnJbH5rOwpzpMT2iKL3fJjI98jpi1FBCsfEcNizdWGfPp3+CMkDe86UUn0uy8u4P+U1Po7gm2Fj3F6oGN2LKFNEwdYXbVAtIOJz/dUMi9uTQ2WSKjCxacPoXck6FdK0ISOk3ZdpbFjmBPxkECu62InLQQk7WNIdpYHJriAi1MQImh6V4S2iUktQ1MqUm6RSu9tKH5JJzVi8lYSglP5QjHckRSeXQhIZDQX4h0s6SD0LFIOg5ZxWPKo6CBrr9s2xRyzKWd+dIp5ohhBLDX5mVL2WyU2nUsdDcQ6tBQ97UgHzlIYKgbgD53EQdmzWdz2VLGXX6WeyZZ636I6GEQaRj2LsXnb2KDYmVW3o2MzIRV4rhHsHwakvEBDqZasWT2E1EtqHY4Uh3mXMco81qupdNyLkgStfO9XPixxcgmY0D2jcoIecOblpbXOfpsP4ee7EWSBIOlj9ElVM7pvxRbLs3yaDP+2jW0VTj4wkI7vWoORQI5l2P+oeN0RAtB01iUPMXS+GFMah5dsVDq0ZkwXUS7ey+j7tNcPl3E5aYe3NYe8qKQiHolfdkS8vE+oq3NHKlqor22iX4cTKgm4sKK+CsHUGUJSn125oRczC6wU+VXqHHpSNk409PTjA72EhvuYJHezFJO4CBDjxzkDkcZT3ly2Lx2Vs5ayXmWRqqPRolu3om99TiyELQFKthccTb7ZzWytGSYZanHiLU4ycg2dgXOweP1cf2cCibHFRZOS1h1SMsCc07lQLQHp2MX8WgvwykPSVueROUU7xov4XntY+gmK/6AzJVfXD1zdSvDG44R8oY3peHOabbf2c70aIqSeTIPmr5J6dDbqYw0EYq0sMQhYQot4K6VPn7q1bDJMglNp3hqHPlIBFXVWBRtZkGyBZOWJ+/0YClSSHjG2enUmaN6eOfAIhY4T+NRHkFgoS+9ks0pJ+kxN3v9s2n3VRCTZy7gIaNTYBVUBmzMLfExp6yAkMdO90SSJ5tHODkcw2ExcXFTCW9rKqbc7yCpaoRTKiORDCPRNL1TKdpHY3RPJMnrM9+9mgIny2uCbGgIsaomSCoRY+TQE+SP3ElVphkvCQYpZqvlbJ5zSXTbeohZYzQVNrHRdTbLjudIPfQMtuF+0mYLz5YvY0vt2SytO0FpZxfqhJkuRzUHClawqcZEwfIltDdPc/FwjgVRHSEE0VyUdjnMfPFdnomXo0bNpOw5ltojjKRvJG0LYVV03vmV1bgCtlc6bIYzwAh5w5tKOqGy94Eu2vaN4g7aKD67n1t7f8+qrvfjVD3M63uKusbVTDtDfGmtj31SniKLmTE1T8WxLsRAgkWRE8xJdiFLOprPxUCxlfFQG3mpkEvHz2Z1fAkuZZyA8h0UeYRBqY6b4/MZzq6ix1GKkCRs5Ci3Zlhc7mXt/ArWLqrDYZvp5xZCsL19gh8820HzUJTygJ0PrKrm6qVluG3/+zIB2bxG60icAz1T7O8Os78nTCKbx2kxsXZeiCvPKuPcugLMk22kHvkMpuGDWMnRRQWbWUvYGWLEN8Ih+RBpc5qzChfzD/kmqp/pQ966HZOucbBoHtvrl9BQdRDpuE5GsrE1uAb8Qa57+3y+poIvkuPjHWnOnxBIQCSfwapsZUB6iu2TxXijZqzWND79XKKuNcjoXHLDIsrqjQHZNxIj5A1vCkIXtO4bYe+DXeTSGos2lDPs+A0PH4mxvO9SHLkoi3sfp3D5tTQH7PzrMhdhoeM1m5hKZGh89nnqp05RmR5AmBXsVSoHPXYG/BOsSC7g0olzmKXPQmgZnJlf4A88S9rk5t+zV/FQfh1CkglISWqUOOsbS9i0cj4V5eV/Muh4sDfMt55s5Uh/hPKAnRvWz+GyRaWY/4Y+62xe4/nuMJtPjbL55ChTSZUij5UrzirjfSsrKckNwcMfQQzOfD9aLIt5TF1BVrJjCVlodbZyVD+KYlK4xLeaTcftyA9sxZVOcCJYQ/OyCkK5fnKTEu3OOnYXrOKSOhuti+dwIJaiLKXx2+fTuNQcZlkhp6tYlPv5oasbRwd4Ugoe2UnGeQWyKciqS8pYfPErXujN8DoyQt7whjfWG2Pn3R2M98YoqfWy5p113Pv8x+k8tojZU2dRON3MwuQpHAvey31zHPyg0kzAYiapqtQePMji5n2E1EkyZjtlK0vZaj6GrhVyYXQl58YWY0ZBm+5FG91KqO4pXK40z+lL+Iz6YXJYqDVNcW4gx6aN5zF37lzM5j/tex6NZvjWU608cmyYIo+Nq9dVM7vaz1AkwlTfAOlIhGwsTlpVycgmUmaFsNtLMhBEd7twms0UWswUWRRKrAqzHVbmOm3MdtiwveQHQs3rbG0b575DA2xrH0eWJC5dVMqH19QwL98JD/wjTPegY6Kl5Eo2x+cQTyRw+9ykS9NsVjcT0SLMdVTz7lNllD58EG8mQWtBBaOLFdITkLR4eSy4AbfXyfJLFvDbWBpFE/zsQIK50wkS+SyF1iBCT3PCt50HM4eY12XCpJmQLU2YbauY01TIhuuXGzNv3gCMkDe8YaXjKs8/fJqWvSPY3RbO+Ydaqhc5+ebvrsPccgX+dIja7keYWxYkN2s931ri4imfRKNFwnp4L8sO7sSjxplWvOgN89HqDuDur+HCyDmU5grR8yny/c+THdiNqB2moDaBjzhfy7+PbWIZjQyzplRh47uuJRgM/tk6qnmdn+06zU+PDyBcEudmhijraWNOdwc1QwMEY5E/+7qXylmtjFdU0VdexamyKnbVzmWgsHjm8n9ArcPKcq+LFT4nK3wuyl7oFhoIp/jV7h7uOThAOqfx9gUlfGbDHGoGH4EnPwu5FMLmp23Zt9jdnWBoaAir1Yp3tpdd5l2cjJ2kxFzAJQdCLN7ZQzAdp7W8jJFCK2ldZo9/Bac8DVy8opAHAy6yms4XTqV520CaE9MHKXaeRanVhi6pPOnZw3D4efwDMgIrZvtqQsVNXPHldcaA7BlmhLzhDUfXdE7uHObAY93kMhpN55dx9turUbPDfOVXX6D09DU4cnnmN/+KyrWXM2qbzadXuhjSEmzqOEzRkd3Ys2mGrcWc9M9hzYoMZb121sSWoAiFTLoTvWUX6vARojUW2pqquULZjY7EN7TrMOXMhOzDvP+Gr1Lo//MLc41nc9zaMcLvu0Zp6D3BBYf2svzUMZyZNLosk62uwdrYiL+mGldFOSafD9ntRjKZEPk8ejpDfnKC/MQEuaFhMm0tZFrbEIkkAFqRn4kFtTQvXciO2fM5nguSEDP9+bNMcc6xDrHaPsJ8S5SM5uahllLuP+FGzcNlC6x8am0ps3Z9G7nl0ZkKz93EwMqvs+/AYVpaWrBYLMxqmMUeZQ/7JvfhF042bi3goiMDWLQ8nVUFDDoc9PuqeTywnrKQlbHllUxoGu/tVbm+Lc3Byc3ETPUslq0UeENk5TzPubZj6zrEZMqMZCrC6V7DO79+Fd5C46LhZ4oR8oY3lOHOaXbe3cHUUJKyeX7OvWYOgRInY0OHueWXD1A5cgGuVD8LW/6boqtu5pju4qtz09Sf3Mu89iPI+TzdjipOeBewqjDF2/KF1KUryEhZRtMdFBx+ADExzGDQw+6Vy4laHHxP+QVDFPJQ6kJi2dMEr1rHx1d/Bll6eT96Iq/x+ESE+0amOT4yzjt2bObSnc8Rikyh+f1416/Hd8F6HEuXYXL96dWVdD1PKtVNItFGKt1HOt1HOj1AOt2Pqo6DANM4WNtkrK0y1nYJOSuheSCz1ETP2mqOlSzksFbPSTGXPGY8xFjBXtaILfizkzzRfSE7BldjNWW5ou4x3l54mMbWCdzxLHGPndTaT5IJruPAgQFOnWpDURRqFtSwW9nNzrGdFCWcXLLZz/qOPrJWM+2hAAPFRTxYcBERZwHBdRV0y7BmPMf3jmY4Gd5BZ1aiWKqh0TKFP7CQuJxkIv8MxwaGyekJzJb5vP2TH2L28trX62NkeAkj5A1vCInpLHsf7KLz4BiugJXVV9ZRs7gQSZI43fIsv/1dK8WR+ZSO7qEhuhvf5V/modgQxxMHqO1pRTeZaCmoZ9C8gPUOM5cKF17NxahljF61h+IDmwkODxGz23hmwSIeKdnABabDfNP8K/r0Ep6ZquP+hkFu2PRVLq65+GV1O53K8OvBSe4ZDZNPpnjnM4/zD1ufwp1NYV2xkoL3vAv32rVIyh9nzuh6nkSyjWj0CPH4KRKJVpLJTnRdffE5FnMIm1yGVSrBZpqFRS7EYi3A6irE6i/GYvGQ3n2A+BNPkti+A5HL4Vy1Cv+170OsOodtkQRPTETZPBElKwT1DjNXFMg05cP8+NlpDg/KzClI8Mkle1jU/wTlPZNkbDIn57lJeJ1YbXVMT3vo65PIqRXMalzEw5lHOD51nKpBD+9/2kLjxChjHietswp5rmwtB53zKV4WoC9gZ34kz20H0vREj3As3onFfgH1Y1vR58+jXmsiLU1zOtxJS2QHQrIyd/klbLrhvciyceLU68kIecMZpeV0jm8d4OCTvQhNsPjCCs7aWIlimTnl/+jue3nqARVPqoi5nfdRW5hkqvHtPDe0CyUyQM5qp63hbDLxuVys2TgXKwDHva1M+fbi3Jqi8VQLmCR2lM/m4YaNdJgr+JT5QT5lvp/RTBGfLfAwWO7iR+f/iIWFC1+s297pBD/pH2NbOI4CXHNwP5fe/RsKU1HUFauZe9ON2BoaZrZDyxKNHmI6sp9o9Aix2HE0LQWAogRwyLOxpSpRxktQRoswTxUi66+8mqPsNGMOObFUuDEHdNIHNhO57x7y4+NY582j8PpP4jr/fKJ5jYfGI9wzEuZYPIXLJPOu4gCVEY1bN3cQz+T57EVzuK74NKb734+kppgs9tG/cCFxtQ9Nm1mXJ5t1kE5XYA42cPv4QTqSk6zbV8r79o5j1VS6Qz521a/gQc9alDoP0VofFUmNO/alGI23sX9qKzbH5YRSXfQVHWCh99U2uC4AACAASURBVDIa07Wk8lEOhA8wljyC01vFlV+4mYLyslf7o2T4C4yQN5wxfSen2HVvB9HxNFULClh9VR3eQvuLj29/+DaOPFuELSfRdOKXcFYprak08fQkUZeXzPL16GIOl/TkqcVEVE6y2b+H3Jweatv7mfWAii8aZSrk4Zd169hduBIZ+JJyJ+8xbWbIWsulJTlqgvP4yfk/odhZjBCC3dMJbukd5flokkKLmXdrKvVf/yZz+08xWVJF/be/RvDsJSSS7YTDuwiH9xCJHEDXs4CM21WP13MW9ngdSucstFYz5ATIEpYyF0qJE3OhA7Pfhuw0I9tfGJgUoGfyaPEcWiRLfjxFbjSJOpwATYBJwlrtQo8fJ/7k78kN9GNrbCR00004l58NwPF4il8MTPDI+DS6gAvdLtTmMHvbJzi7OsAP31FN6ePvhcGDIMmIdf9GavGlTEcP0j+whXj8EGbzzI9TmgB7ogl6JhXWPRRiTfcQEbuVffX13DHrEibLi1Hn+ynMCu7em2Q6O8D+gftQ7BsxO3xM6rcwWl/Px0Yvw68VMJyJcHTyYZL5KZZf/k5WXnkVssn0On/q/v4YIW943UXGUuy+v5O+5im8ITvnXj2Hyvkvn73y8C//k4FDc7BmhigYuJPRUi9pNUPCW8TxRet4r3s+c9vieDXoUiZ5tOBJBooneW9hHu9vxgkdjKHaLeydPYc7Kt/OsLmQWXKE72v/xUr7CU4VzOY9rixrKy/gm6u/iUNxcCSa5Cunh9kfTVJsUfhkRSHFjz5L6Oc/QNHyxK/9R+qvm89keAuTk1tR1QkAnM46Av5zCARW4zYtJLM/RvLgKHoyh+xWsDcWYG8MYqn0IFv+GGq6niOZ7CSR7CCZaCeV7kNVp1DVSfL5l6xTL5kxCw+mjAt52okyXYQlWYJ9Ik1uzxby46O4N26k6KbPoZSWAjCSVfnV4CS/GZoklddYlpDoOjSK1Szzo2sWsmbgVtj9HzNvULUarr4dHAHy+TwHDjxCW/v9+Hx9eDwTSJJOXIORQReLHtVwtWucnFXC3Qsu58isBvILA3jzgnv3pEiIaQ50/oq8fQGKfSV5963cU9fPtZPruHT6EsBMV6KLk+EncJeUsOmTn6KoZvZr+4H7O2eEvOF1o2byHHqyl+PPDWBSZJZtqmbB+WUvu9qQruncecsviHQWYYptQdV70WSJoLeGkw3raBTFrB/PI4RgjynCo8X30OI6ybsqL2Xd8Has/xnFNp1juKaY+ypX8Jx3BQKJS82HuTn/MCFbL0f9JXzQa+aDCz/CJxZ9gqFsnm+eHuah8QiFFjOfrirmUred567/N5r2b2aytAD7Z2eTcB5B0xKYTC6CwTUEg+cRCKzGZi1Gi2WJbx8kcWAENIGtPohrRQnW2T4k+Y9zxVOpHiYntxKe3kckcgBNm5lNI0kKdnslVksBiiWIYvbACwO/Qs+Ry02j5qbIZsfIZIZeLM8Ud+N7JoB15ziSyUzh9dcT+MAHkF5oIU+pef5rYJxfDk6SjWcpOBUjHslww/o6/rm8B/m+90M+Dc4QvPtumLUEgFgsxjPPPENb22HKyqI4i7pwKqexyqBFTXj3C9ItTh4t38SDleeROasQnwZ37UmRUTLsP/UzMrYgLusVRMu38ljoSUKai2/0XoePelShcTy8jd7EMZZcfBmrrno3itVYEuG1YIS84TUndEH7/lH2PXSaVExl3spiVlxWi9Nrfdnzctk8v/zCj8gMD6GrHUgI5i44m5C+goTsoi6hk1UkHpCzPO7azHToKZw5H19b/E8E7v4P7I9nSdkdHFvayIPelbSYKiiWotyi/BfLSoIoY9s47PLyyVCQL6z+GmsrN/Kj3jF+PjiBBHysPMQnKkIMdPYwcMOHKe8fIb5OEP+HHIotSGHhBRQWbCAQWIUsz9Rdz2rEt/UT3z0Muo7jrCI851dgfskaLtnsBKNjjzA29hjx+EkAHI5q/P6V+LzLcLnrcdirkOX/fckDgHw+SSp1mtj0SSb7dhNNHULEp/DcZ8Z+QkaeV0zpt7+He94fv9cTao4f943xm/5xlJYoYijFBfVF/OQiD/a7LofoIEgmeNt3YNk/wQsnMXV3d/PEE08wNTVFRW0RR6U7qfOPUW/VkWUwDUic7q3lJ44PMbqoBm8efv98irRNsP/Uj9HMJuzKVUwUD7On7BfEFME3T6+iKPseAmaZyWyYw5OPIHwyGz78SSqbFv0tHzXDn2GEvOE1NdYbY9c9HYz1xAhVeTj3mjqKq70ve46ua7Tu3MMzv/4NenYCWZeYZ/Vz1ttvJH0ygVmDAadM6zw3321rQyq4HbOjl9pUA99pvIjsN7+P0is4XVPNvgWL2SwtYFK4WC+a+YntFmwrPoQ4dBttiolPV87hexf8lHG5ips7BhjM5LiyyM/N1SHs6cMc3vFrin6wD2VaEHmvnYIrriBUuBGvdzGS9MeuFiEE6eZJok90o0VVHItDeC6owBz845hCLNbMwMBvGRt/AiFyuN3zKSq6hKLQJmy20ldtH+uazvTxwwy3PES6axvOx6aRsqC/dw4lH7qJQOCcF888PZ3K8OXOIbYeHUFpi1JV5OLed9cReuz9MHhgpsCma+DSH4My80OVz+fZuXMnu3btwuFw0K4M0BzYzrq8zIXWJKJER8/KHI4t4eHAlUyp1dy9P03YLXOi57cQm0CyX0Ta6WNP4w8YUlJ8prsW//QNNNhNWCSJnlQLxyafZe5553He+z6IzWlceerVYoS84TWRjGZ5/pFu2vaO4PBYWHl5LXOXF7+s6yKXzdCycysHHnmQ2MQoSG7mZTw0zT0P2VwBmmB/wMRTtTb0IjvbdzyDvfguZFnl3NQaPo0V9b8eIW8ys2P1Wlp85WzNz0UImS9KD/Ae+6NI679Edud3GdOzfKl+FTeuu5WfDOd5bCLCHIeNr1VKlMYfYGT0UUTfJIGfKIisCcd3bqT2wg+8LNj/QIupTD/YSaYtjFLixHfZbKyVnhcfj0aPcLr7B0xP78NkclJSciVls96L01nzmu5zoQtSxycYf+Jpkvt+gen0FOmFOrmP1lDV8ElCoY0vbs/OcJwbd7YzeXAcu9XEb69dwIrDX4Tme2cKK10C77oL3EUvlj88PMzDDz/M+Pg4ukPmcd9WZMLcdMBEWVWC1FIdyQJtYh5b1Yv5/L5Ghj0KrcnNmFoPk/MsxmpawdZFP6bXNsRH+6uxDn2CeRaJSrsNVeQ4Ft7ChHmI9f/4MeqWrXxN99ffi9c85CVJ+jVwMTAuhJj/wn0B4B6gCugFrhZCTL9SOUbIvzloeZ0TWwc5+GQPWk5n4fpylm6qetmp7dHxUY5ufoKT254hm0wim0OUu9awSFKw+SqQFJlxh8yn6i1kg1ZmJfKcOHkn1sLNyGqQd6bXcUXzYfTdHYzMLWT3gvPopJA9uWo8msYdwduZn9mCtvE7xHZ8C5GN8eOzLqV60bf4Rs8kWU3wTwXTbMj8gmRsP0hmhk/V0XDbALLNyZz//g3OeX9+ga1U8wSRh7rQVR3vxipcq0pf/OGKJ9o4ffr7TE1tQ1GCVFV+hNLSqzGb3a/Lvv8DXdWI7xhg8le/InviQXQfhP8pi7m+kprqGygquhhJksnqOl843MM9j3cg6fCxy+Zx0/TtSLtvmem6cRXNBH3pH7tQXtqql00yO53tjPqPcdkBH1c9P0X8HJi8yILLkWBML6a280L602s47e5G2fIQWqAcm34JWxt/x2nvKd4zWIVv4BP4tSz1Hi8BxUREn2D/yOOEzprD+dd9BKfP/7ruv7ea1yPk1wAJ4HcvCfnvAmEhxLclSboZ8Ash/uWVyjFC/o1voDXMzrs7iIylqGoKcs6VdfiKZk5nF0LQ13yMo08/RveRg0iSRNXs5bgjjVTbfNhMJiSrhvu8GrZ1T/LPVRIVVgXLUISh8V+geI9ijtVzY6SBZc89QX4yyeELzqLHU8sxrZTjWhmVeY176x+gaOBhMhd+g979P6Y6Nsbtyz/ErtKPs3kqxkLLFB/I30JIa8duryAmbeK2e238y5bf4vB5mPP7O7CUzfqTbdNVjcgjp0kdHkMpcxG4ei5KaGbb8vk4p7v/g8HB2zGbXVRWfITy8msxmc7sqfz5SJaJW58met/3EGqE1Af9RBYN43Y3UTf7Zvz+FQDsGJzmQ789SDadp+m8cu62bcG19ctgsgISXPELaHjHy8oeHh7mgQceYGpqitOWOMdLn6VhwMlN98Ux6zrPvmsx7hUT1EqnkVUXsakNNHvOwX7nrzH5AghxMftrtnKqeDfvGC2nrOd6lEyCIoeHepcNiyzRFT9GR+YQq699Pw1rzjcWO/v/9Lp010iSVAU8/pKQbwfWCiFGJEkqAbYLIV5xbVIj5N+4kpEse+7vpPPQON5CO6uvrnvxIs9qOsWpHc9xdPMTTA8PYvd4Ofvsy/BGZmGdzCNLEpmpdoKXzMd70VLuve8kny0RzLFYSLT1kMjfisnRj238HL7aa6Jy+zZGq4IcWLKClORkb7aCLopYZjbzu6YHsbfeTWTtTew6+XsumRzkgSUf4Qvea4hpgqvFHWySnqaocAOhoqv52V4v2zYf5JZ9P8ftdlBz5+1Yysv/ZPvyk2mm7mwlN5rEva4cz/oKJJOMEIKx8cfp7Pw6qjrFrFnvprbm0yiK7/U+BH+REILEni5GPv85tPF25IuWM3FlJ9ncKKHQJubU/TtWaxGj0TSX/Hwf49NpbEsLebDwKI3bbgbFAWoCNn4HVnz0ZWWrqsrTTz/NkSNHiEk6e0q24cik+dqdKsFojq1nn8e9157L5flHaLIcRtfsdCob0e/qRs8pZOzn0VE0wP7Kx1k/XkZ99ycxZ1KYhaDGV0iVzUyODEcmtkC1hQ0f/iTeUNFf2FLDX3KmQj4ihPC98H8JmP7D7f/xug8DHwaoqKhY0tfX96rUx/Dq0DWd5u1D7H+sGz0vWPK2ShZfWIFZMTHZ38uJ5zZzascW1HSaWTX1LKl/O64JJ9pEBlUXjMfGMJ3+NU23/QxreSW3P3CKfw3maTRbGD1xBM3+CyRTkuLeC/jasTbsPX0cXreQAX8NeSGxM1NJv1TIO4I+ftD0NKbnf8zwsuv47cAWPj86wJMV6/lg9RepED3caLmXleVrKSm5gsmUk4/feYSBjj5u238rbjNU3XE7lqqqP9nGdOsU4XvakWSJwDVzsc0NAKCqYdo7vsT4+JN43AuYO/ereDxNr/MR+L/ToikGPnYz6SPPosxehvj6QgYjv0SSLNTWfpayWe8mmta46rbn6RyLoy4K8m3/Kd637yYkiwsyEVj1z3DBV+B/LEvQ0tLCQw89RCaX45C/mQlHN1++D2r7M3Q2NPGJj3yGxfE+Pp97gGTRIVTsJLpKGNlnQfUvp8+psKP2Ls4Ol3J25ycw57KYUzFkfxUr/TLWvInJ7BDHottYcNXFLLpoE7JsnET1f3XGQ/6F29NCiFfseDNa8m8so91RdtzVzuRAgoqGAOe+cw5Or0zH83s4vuUpRjrakE1mFi25iDmBpdCjIlSdjMNMy0SG/MDzBLMPs+rOpzD7/Nz5SAs3eVUWSQq9R3cg+36D0K0sbF7NTft3MBzwc3TZQnJYyAkTW7O1jEh+PlhTzBcWHER6+l/onnM+v8+d5ObeCQ57G7hiwY+40nGKm2fXURRciSTJ7OyY4Ia7j2LKZPjl0duwjw1Teecd2OrrX7Z9My3gYaJPdKOUugi+tx6zf2a2yeTUdlpbbyaXi1BT/SkqKz/0Zwdo32iEEIx+4ydE7rgVU6ge/1duZMB3G9PTe/B4FtHY8H1ylPHO2/bRMZ4gvTjABy3H+Nqxf0OyByA5Dk1Xwzv+E8wvX5IhGo1y++23Mzk5yWnHCCcK9vPpZ2XOPppmtLKKD13/b1TGTNza3cuhuY8QCh5CaGbGj3lJRM+lQyvluTm3My9WzPltH8Os57BPDRIuaGKxeZyKQBlC1eiIHmIyOMEFH/0EwbI//avL8KeM7hrDXyWbzrPvwS5O7RrG6bNy7tV1uAMJmrc+Q+uubWRTSUIl1Syp34Q/WYA2nkFSZGwLCmgZS3LiZITygeew2ray/o7nMNns3PtkO5+2pZkvTPQefwIleA96toBLd83jsq7DHF62iIlgEZZsnLjZw5bcXCaFi88ureYTjV0kH7uOltpSxh1JVh9LE5W9vHPZf/KNpnmsC82skaLrgp9s7eKHz3Uwr8DBD5vvRD/4POX/dSuuc8992TYKTRB5/DTJfSPYG4P4r5mLbDEhhEZ393/Q23crLudcGhpuwe2u/3O76Q0tfNf9jH31S8ieMvwf/BLapjE6er6Ormepq/s8du8VvPu2/fSEk0hLCzg/9zz/2fJlZFcxxAahZi1ccydYXz7NUdM07rvvPtra2gibk+wv3sm7jmS5YFuWaEEhH/3Ul2iMW/heG9wa7CV41jMskvaST5uI9dZzaHgVW2ruZ1aqgItbP4JZ1/GPnGS4ZDVF0XZWNpWjxzxktRTHprdRtnExyy67EtOfuYiL4Y/OVMh/D5h6ycBrQAhx0yuVYYT8mdd7YpLtv28nFc3SeF4Ib2CQlh3PMtLVjslsZvHCTdR4FiAN5EETKGUunEuLsTQEeOa3J+lriVLT/SiZkkNc8sutmBULD27p5Hopwdy8xODJ+7AEH0OKV/Dxp10U2PK0NtYjmfO4xkeZ8FXybL6eKeHiGxfMZl3JEwx1/IiIz0xSt1Lc4mHhdBdfWPMbPnfORgotMycXTSdVbrz3GNvbJ/iHxbP4dNujxO+6i+KvfAX/NVe/bBv1rEb4961k2qdxrSnDu7EKSZbIqpOcOvUppqf3UVp6DXPqvoTJZP1zu+lNIb5tO0P//CkkWwD3xf+K+wO1dIW/Qnh6N8HgOgrLv8a1v2lnJJqheE0pVeHt3Nb6FSR/JXK4G8rPhvfcBzbvn5S9ZcsWduzaQ17OczC0j/P6J3jHI3nSDic33vBvrEj6uKFX4gumDIPLp/is6w6stJFL2ugaWMntyin8WS9XtHwYkw7FQwfoLV2PMzHESlMLrvnvQA/rjKX76LG0sPqj11FcW3cG9uKbw+sxu+YuYC1QAIwBXwIeBu4FKoA+ZqZQhl+pHCPkz5x0XGXXvZ10HBjF5ZvEG+xj4OQBctkMs0rnsahuA56oFz2aQ7KbcS4O4VhahKXURTad54kfH2akO87cznsYa+jg6h9tQTEpPL63j4+mw9RkBKMdv8Ua2IZnZDaf2CUxMreKuMdDwN2H1JJgrHgez2iNTOLi8+f2MMf1G3JaFCWjc3e2itrkYj7T9SueW/5F1m78f+ydd3hVVfa/39tLbkvvvZGQEHrooKAgKCoiiiIKiA3rqOPYRcWGo2AHsaOI0pWO9BpCSYeEJIT0enN7v+f3RxwcBixYvj/H4X2e++R5knP2XWefk3X2Xnvtz/obku8zMQpqO7nr88O0Wlw8PT6TcY1HafzHPwi65RbC/3FmQpff7qHtoxLc9RYMV6agyY0EwGQ6SlHRXXi8naSnP0tU5MT/83vwR2DLy6N25u2IlEGohj5I0HW9MEZs5ETly0ilBsLj5zHtcxsur4/BlyVhr1zDgtLZeCN6IG8uhohsuGklqIPOajs/P5+vv9mAUuSh1FBKuv0EE5Z4QCzlsTsfZqQ7mmsaxDyAnUOZGuamVaGrm4syyI7DEcxXRoEGUyDXld6G2CchoXEHFWGjkXrt9CxdSMzEGXjMkQhuP+Xmg8gHBDPw+skXpBHOwYXNUBf4UQRB4ER+C9sWH8BhKkQiLsdl60CjDqJ3xhgiJQnQ6gMRKJINBPQLR5UZgkjWtTBnN7tZ83o+HQ1WMko/4fjgBma8sAmZWMamgnpubW0h1uGjvWohMsM+MoszGdWmoT4+FhUWokKP0nHQQHt0Glsl6TS4g7k95yP6hRcT2CkQVG/ilvDLCVRezFdH7qUzeQwhN34OIhGCIPD5gVM8+00poVoF707pTZqtmZPXXY+qRw/iPvwA0b9N830WN20fFOFpdRB8Qwaq7l2Cac0t6yktfRC5PIwe2e/+V4ZnfgpbXh61t92OWB2Msv/9aIakIRvloajsHpzOeqRBj3PPqgiCA+TcfE0GZfsX8tLxVzHHDUNXtx9CUmDqatCEndV2SUkJC5d+S6DYQV1AHYGKQiZ+5ELl9vHsbQ8wwZdKrxY/M3FwMk7FzN4hdNv9JNqUMhQ6DxU2BVubQhl19G7EfhkZTd9SahiHX6Ykq3ABUbEa1KNm4an2YveaKfcdJmfGeGKzcs5xpf+7XHDyFzgnxiYj695eTnNlHoKvAYlYTk76SBJ12UjbROAHWUQA6l6hqHLCkBrODF3YTC5WzT2IucVKZslC8i81cs8TG5FL5Ow60cqUk3VE2710nnwXiTaPKw71QaONwiOTEactQS09RWtFMOo+Ur5uG8kxYwqzen/DNX2zkWz5lPCGCq7q9TDVqqHsODKTYLkC6Z27QKnH7vby+MpiVh6pZ0R6KK9P6onO56R64rUITieJK5YjDQk5bavX6KRtURE+s5vgqZkoUwMRBIFTpxZyovIV9Pre9Mh+D7n83HVe/9ux5eVRe/sdSAzhKHvdhzwxHP0NMRyvf4K2ti20MYUntw4gK0rHQ5OyyVv/PPdXLqA+5QqiT24BfTTc/A3ozpZqKC0t5fUlG4iSmOiUdyIKLuTa9zvR2d38c/rdTPFkEWhyMw0XxmA5vbsHc1PhCuze74jMNYLYzaFOLSG770NwBtGzdQklynHYAyLJqFtJRM1Ogm65H7c3HVGnQKO9GnuakwHTb0ShPrs61/8iF5z8BU7jdbupPprPoXWbqT92BAkQF9KLrISBqE1q8AhI9ArUPUNR9wpDFnHufyKr0cnKV/KwtVlJL3uXHeOtPPrgBhQSBUcbOplYUk2g04Pr5NsESssZXZGLW2fAYOwgtc9OHDYxUq2HgEgH7xdOJa+5D0+PkXPDkCEc/nQM2fXHubrPqxQpu7Ouag69GrYjmrERovtwosXCrM+PUN5i4YFRadx9UQoiEdTdcw/W7TuI//RT1L17nbbV02qnbVExfpePkGndUcTr8Pu9HC9/ioaGpYSFjSMzY+7vHn/3C35a7C3UWmppd7ZjdpkxuUx4/d7Tx6ikKvQKPTqFjjBVGDHaGAwKwx+yKci2bx+1t92OPLkb8u53IlapCJrSjSaWUFn1GiXmcbx+4BIuzQznsQlZHFj2AJOqv+BI5lR6nVgB2ki4Ze0ZMgj/4ujRo7y4bA8p0mZ8Eje+iFIu/6CBEJOdhVNmcounH1aXlemCD5dagiZJxzOWozTsX0PiaB/q8CpcfhG2ksswlV9M345FHPOPxRiUQTdZOZGb56NMTUM35TFsBQ7wwUlXCWHjM+l20Yj/+U1UF5z8/zh+n49TxQUc27OTiry9+J0eItTdSDT0IVIVjsgHYrUUVfcQ1L1CkSfoz9Cf+U/MbQ5WvZKHvcNKSvnbrLvawvN3b0QtU3Oiw8b4/HKkHg/y6jfIsrlJtHVD6vGQaC8lfEwxPrEYicyPw6bl05IbyOvsziNjujGxv47VX01gZFM71/d+jTpZBIuFPC7a+TCMegZh8P0sO1THU6tLUMslzLu+J0NTQwEwfvUVTU89Tdjf/07w9GmnbfW2OWhZWAg+gZAZWcijNPj9LopLHqC1dSPx8XeSnPQ3RKLfVq5OEAROmk9ytOUohW2FFLUWcdJ8EpfPdd5taWQakvRJZARnkBmcSU5oDkn6pN/FkZk3bKT+gQdQ5w5CljEDv9lH4FUpOBJLKS65j801I/m89BLuHZnKbSMSOfzZNIad+oa1vR5mbPHbiAzxcMu3EBByVtv7DxzgmdVFdFecRC2IEEXXM/zTUqJaTXw1cSo3+odQKWrnLkGKVyzGF6/hHncN8j3L0KapEOWWkaCw4rYG0370amKrt9BmHElTxACSo90kbn4Jf1srQdNuxxOYi3DChdvnpF5RRcaM0YQmJv7m/vlv5YKT/x9E8PtpKD/Gsb07KN+/B4/ZTqw+k2h1DuHyMCQiMeIAGaqsYFRZISiS9IgkP+/oOlvsrHolD1enhcSKt/h6golX79iIXqGnwebi8j1l2P1uEo4toFenAbVEQar4CPqUWuShDvw+MFXraGkbynf2TA55Y5k+OJGrcr18uPZWrjQFcHvWHCQKLZ/ESum/5FII74518mqeXFPGyiP1DEwKZt71PQnXdS3AuaqrqZ5wDepePYldtAjR9xt5vB1OWhcUIHj9hM7sgSwiAJ/PTmHRXXR07CI19QniYqf91OX+JD6/j4PNB9leu50dtTuos9YBoJVryQ7JJtWQSpwujhhtDGGqsNMjdrm4K/9cQMDhdWBymeh0dZ4e9ddaajnReYKy9jKsnq6yfaGqUAZEDmBA1ACGRg8lUPnrtV6MS7+i6emn0V0xHlnajbgrTWgGRyEe5qSg6DYWHLmU3fV9efuG3ozOCKZq0RUktOTzQe5L3Jb/FJLgZLh5zTkXY7/bvpOnN9WQrS4h3KtEFmUiZ+lhkutb+G7cJMbLRnFU1cgjPgV2txRvXAADOxoYWrkav0yg9jIj/VSnCJN7sTen4Si2oznSj+q4scTESelt/w7rsq+QJyYS8uBsOg7bkHVIsHg6sCbayZ42HmXA/14I54KT/x9BEARaTlZxfO9Oju3didDpJUabRmJoT7QePSJEOAFlRhChQ6N/dsT+nxibbKx6JQ+PyUJc1Zt8MsHI/OlrCQsIp8Pt4YodpRjdNoYWf0WmykpUUDmBoQ2IZAJ+r4iWoyF0HA/DpRlFgQ52eZK5okckg3uf4MN9zzPRm8OzyfeRrFbxaY8k4pdOhMajlE/YwO3ftlPTbuO+kWncfXEKku/tFjweTk6+AU9tLYlrViML7woleDudtL5XyCd6zwAAIABJREFUiN/lI3RmNvIoDV6vhaMFMzCZjpDR7QWioq79Vf180nSS1ZWrWVO5hhZ7CwqJgtzIXIZFD6NfZD8SdAmIf+PMALrCPafMpzjccpj9DfvZ37gfo8uIRCQhNzKX0QmjGRk3Er3i7BTHn6P1rbdpe+stQu+/H2n0KKx7GlCkGNBMDOJw2Sye2n4J9bZ4lt81lMxAP+3vjURia2HugHk8tf9elKGpXYuxqrPlHVav38QzOzvoocsj0aVDGeElae1hsiuqOXTJREYEXMqB4Drm2OV0OJQIUWo0DS1MbVuPyN3B4XFiIqhhrN6DWOqkvS6QoNWZlIfdREiohJEXyeic8zSexkaCpk9HMXwi7d9UoPAoafc0oBwWRtoVw0+/7P8XuODk/8IIgkBLdSXlB/Zw4sBepEYJUQGpxBkyUPm7RjQOqZg6qwdpsoF+M7qjVP+ywhX/Tnu9lVWv5OG3mImueYP3runkrRtXEqePx+b1cfXOYoJadzPavZaI0Brkcid+u4TW9kh0MiMV62KQyvS4A0ZSGWRkkyeDXvEGkjM3sKFqNRcrr+GL8Ku4KEDEgt5Z6PLegU1PsCdrNtOOpBMYIGP+9b0YkHTmwmjLvHm0v7eA6Dfmo7v0UgB8JhctCwrx2z2E3pqNPEaLx2PmyNGpWK3H6N79NcLDxp53Px9qPsTHJR+zo24HYpGYIdFDuDL5SobGDEUlVf18I78Rv+CnrKOMLTVb2FC9gTprHTKxjFHxo5iUNok+4X1+cUhHEAQaHnoY89q1RL8xH0lQD4wrTyANVhJ4UyK7Kx7loY3Dkcu0rLt/NMGeRhwLLqIJFc/kzuOtvbeijczsSq+UnXntfr+fT5au5NUCH1lBO8mwh6AOlRK59TB9S8qoHD6RnmFj2Bd9ivmtIupsWiRhKqRGC1fVrSXQVUfeWBFGVwd3+5LQJ+Xh84sQ7YqksuVhAgIUXHF/H5zvz6Pz669RpKYS+dJLGKs8OPe0IkdBO40EXpZK3PBeP9IDfy0uOPm/GILfT+OJ45Qf2EtdXiEah4ZwVSKRAUlIkXUVg0424ApUsmt/Iya7j6GTUskcEvWr4rqttRZWz81DsJiIrJvH/Akm3pr0JenBGZhtNby9dyFpns0YlO34fWJkpVLKK1IpCc1gaPB2anaHoNSE4ZFfSkPoKb71ZGDQKQlN/ZgqWznpwfezW53FzYpO5gwYjrS1FGHhCAoUfbmqYxYXpYfx6rU5BGvOXBh1lJRwctJ16MePJ+rFF4AuHfjWhYX4LG5CZmShiNPh9Vo4cvQWLJYSemS/Q0jIxed1/fsb9/PmkTcpbC0kUBHI5G6TmZg2kVB16Hn35e+FIAiUdpTyTeU3rDmxBovHQrI+mcndJnNlypUopT+fS+53uTg19Wacx48Tv3gxIlU07Z+VIZKKCLoplbXV/+SRTf3pEenmq7smIm3Ix/fxOPI16czp/QKLd05GlzKsq3as5MwdqV6vl1cXLWFRTQDZ4evI7oxCY1ARtL+QwUeP0jbgWpK7Xc7+uBreq3Zw3BKMLFBBslZKQv4qEp3l7Brppk2wc3vVREIytxMQVYLPKKOlcCqu9t6M//sgVNVHaHziSbxGI6F3z8Jw483ULDuA6JgXuUhBh6yFyIk9Cc1J/qNuxZ+CC07+L4Df76P+WCmVe/djKqhD7wsmQpWI5ns1RLFWhjI9CFVGELIkA0e21nLw22r0YWpGz8wiJObXVeFpq7Ow8uUDiCydhDfN55UJnbx+6QuEihpoal6H1dpV6s5kCsFfKCP1mw4WJl9NW0YIl8tX0FKgR2WIxyseTWdMOavtSdjFCjSJ7yLR+FCGPsJxQcczniPcdul0RH4vlreG4TbWM9bzMreOzmXGkETE/xFWEjweqiddh6+tjaS13yLR6fA7vLQuKMDb4SRkehaKBD1er42jBdMwmwvIznqL0NBLfvG1H+84zuuHXmdPwx4iAyKZkTWD8Snj/09G7eeDw+tgQ/UGlh5fSkl7CcHKYKZ2n8qktElo5D99371tbVRPmgReHwlffwUiLW0fl+Azuwm6LoUFVUuYvy+Vm3rV8+ykmYhKVsCy6XwReTmfdLuTpbuuw9BjAlzxxulygqftcjh4/J0vWdGupXvMcnq1JqNRBxB4tIxhhw7i7n0dkSMmcDCqlkWFjRyxRiJWSpkyII5Tq5bQrTOfzUOsWOQebi26A0VgO6G9F6PQmHA0ptBaOIlRU8cTGSWl+bnnMa9bh7JHD6JeeglCwqj8dCeqU3KkIhkWrYmoa3phyIj5I2/F/zcuOPn/UnxeD7WFRdTvLsBdaSZIHEGQPBKRSIQgFVAkGVCnB6NIDUQaqkIkEuGwutn8QQm1ZUbScsMZPjn9jGIe50N7vZWVL+4Dq5kI5ytsm9DJuKhoBHeXUqjZFkZbcwyNLaEM/+YAaqebOb2nYY4OZLL3c8yVStRBmSC5BE9yOcvbAznl06GM+ZDkhCjqddPpdLl5t2UJYybNxeGXsPujR7mkcQGz1f/g2imzyIzSndO2tvcW0DpvHjFvvYl21Cj8bh9tHxbjrrUQckt3lKmB+HwOjhbMoLPzIFlZ839xiMbitvDG4TdYenwpOoWOmdkzub7b9Sj+5BIHgiCQ35zP+4Xvs69xH1q5lmndpzElc8pPvpicx8s5OXkyyvR04j/9BL9LoP3TUty1FrSj43nwxA6+O2HgxdHlXDfib4i+exZ2v8Y/0h7iUMQwlu6dQtDA22Dkk2e1bTKZmPXGcva4VKTEf0G/pmzUUg2BZSe4OG8f0pzJhNx4A4X6et7fU8JuRwIg4qlrsqhavx510Wo2DujAHiBwW9HteFwRhGcsR5OyA4lcwFQ1hIzMv5E5pAfm9etpemY2fqeTsAcfJHDKjdhajFR9uhNtmxaZWIEtwEb4Fd3R58T8pdIuLzj5/yLsHSbqth/FUtKI1CjGIAtDIpIgIOAPEqHNjiQgIxR5rPasbJjWUxbWv1eEzexi+PXpZAyO/NUPcnuDhbXvfok6OB995HbEoV253RpNFmZjMvlHRBh9IQQYK5mwdg/NwWrm9LqDTl0gNzk/Q6jzoQrqg0IzEllmLSsqbRz1RqMIX8Pwfjls8w9G7TTyafnz5Nz0CUUWDa9+8Q0LbfdTFTSExLuWo5SdW/XRVVlJ9VVXoxk1kpjXX0fw+Wn/rAzn8Q6CJndD3SMUv99FQcFMOox76Z75GhER43/RdW+u2cxLB16i1dHK5G6TmdVrFjr5uV80f2ZK2kp4r/A9ttduJ0wVxl097+LKlCuRis/9wjetXUvDgw8RdPNUwh99FMHjo+OrchxFbYj6hnLDiRKMdjfvTahiYPYjiL6YhL96JxNy3sAcEMFXB24hZNTjkHv7WW03NjYy/Z1NHBf5iYtfzMCm/qgIIKiihpH7d6PoOZWwh6ZxjDoWbNrNFk83BJefpyZmEd5YzpElb7K5bxM2DdxRchtuRxyBmkr8yQsJT7Xg9yoI8F/PwDF/x9fWSeOTT2LbsRN1bi5RL8xBFh2NsaaOyiW70LcZUEk1OJVOgi9NxdA/DpH0v3+B9oKT/xPjs3kwHq2h43A1/kYXAT4tIpEYv+DDqXQij9cS0i8FdWow4p8YkR8/0MS2xcdQaWSMuS2b8MTzd0w+nxOjcR/1pzbQ1LgZqcqE4Icqtxitpi99Iu5m3YrddDodlIfFMGTfOi46WMaRVAPvpN9BiyyQG2xL0bZ0ogwcTHDUSPS9Oll+oIQtnjQUhgImXtaPT43hJHnb+SL/DsInLuC9UzHM33KcZYrnyJQ1Irvn4Dk33EDXekTNlJtwV1Z2hWmCgjEuK8d+uAXDVcloBkQhCD6Ki++jpXU9GRkv/yIdGovbwvP7n2dd9Tq6BXXj6YFPkxWSdd59+GfjcPNhXjv0GgWtBSTrk3ks9zH6R/Y/57FNz8/BuHgx0fNeRzdmDIJfwLypBsv2WuoTAphaV0e8tpp5VxrJiLsX0cIRuDxOhuS8hw4Py/NmYLh2EaSPOavt4tIybl18lA5lOyGxSxjRPBy5R0VYxUlGHNyHst8MouZM51h7DYvWb2WtvzuC1cvfrsjg6lAPi196ko3Z1Zg1ImaVzMTlSEQvacKpWUVkt3YU8afwu8PI6f0sISEjMS1fTsuLL4FIRPhjj6GfcDUikYiOujrKP9+KvkWPVhaEV+JF1TuU4ItSkAb992riXHDyfxIEn4Cn2YarxoSptB73KQtyV1fOtNfvwUwHokg5Qb0SiBiQgUT581kwPp+ffcsrKdhaS1SqgdEzs1Dr5D97HnRN7+32Kjo6dtHesRujcT9+vwO/R4G9IR1teQOPd7NySfhI+liHcaS4GInLxXcZ/bl3yVtkVDewZlAoGyJuodofznjLOhKMJ1EGjSA2/RIi+nv5cu0mVnu6IVVauH5ib95r8TFAaufjHdfi6TGTaTUjKa43Mzd2H9e2vglXvQc9J/+ozZ0rV9H46KNEznke/YQJmNZWY91dj25UHLpR8QiCQHn5bOrqPyMl5VHi42792X443HyYR3c9SrO9mdtzbmdm9swfHfH+NyIIAltPbeXV/Feps9YxNnEsD/V96KyFY8HtpuamqbgqKkhY9jWKpK6i5Na8RjpXnmBzoJjZHZ1clbyWO4dHkKa9CtEHl9IZlk3vlJfo5qzlq4IH0Uxb1SVs9h98u2UHj2xpRx5cjiR4GWM7xiKySYitqGLQ4UPIBk8j4fU7OFZdxftrN/Et2fg7Pcy8NJVZ3XV8+sIjLE8pwaISM6t4Oi5nClpJE0pZNTLhFPIRZSj0jRgMA0lPexJ5ZwCNjz6G/eBBtJdcQsSzs5EGdu0vaK+vpezLTchqxESqujaaiWIVBA9PRtkt6L9udH/Byf9/wmdx4z5lwV1rxlFlxFNvReTrCp84fXY6XA14dD60mRHEDOtF0Dnqjv4UdrObje8X01DRSY+LYxh0TQqSn9nQ5PF00tGxh46O3bR37MLlagRApUpAoxpI8TfBWGviyOxcyL1j6xiiGEBcTTI2lwtdcxOr+17CMx++SqDJxPuXR3BKeS2FrngGWg+Qa85HEXgJmQPGoutpZ/nS1azxpOIQaRh7VTeWWhyM00l5a+M42pWJjGx7EK1ayaujDIz47kqIHwg3LjtrAe90f5rNVI65DHlcHPFffI5lZz3mDScJGBiJYXwyIpGI6uq3qKp+nbi4W0lNefQn+0IQBD4q+Yj5h+cTFRDFS8NeIif0ryt85fQ6+aD4Az4o+gCFRMG9ve/luvTrzsjp9zQ1UX31BCTBQSR+/TViVVcs31HaTvsXx5gtcfCd28mj/f/JyJwrSbJGw4pbqc6ZwRDDVHItJXxe+QqqWzeCNuKM7xcEgfmfreSNUhmxCbuxyDczwXYNnnYPCeWV5BYUIIycRPprf6O8/CTvr1nHalkO/lY3k4bE89TgKBa/8iiLYw7ikEuZVXwzDlc6GnEbAQovkqo87EN1hGStQip3ERlxFQnx9+D4cgst8+YhDQwk8sUX0AwefNoma0c7hWvW4chvJU6ZgUqqQZAKqHPC0PSJOO+9JP+/uODk/2AEQcBvduNusOKpt+JusOGpt+AzuYGu/Gaju4l2ZwNWiQlNahhRfboTn9MLlfbXxXvb6618+3YBDouHi6Z0Iz034pzH+Xx2Ok2H6TQeoMO4B7O5EBCQSrUEBg4mOGgIQUFD8NhCWD57J267i562D3lwZDV9bAMJ7IxAazITbGxhY0ouD329CJfUzWvXRiB3X8Feexqp9kout65HaRjHgHFX0RhRws4VO9nnTqTaE0WfEbHsUfi5JTKQJzbfiL/jJKMdL5DbM5unL88kcMV1UHcQ7toPhh+vBNT03PMYlywhcdnX+N0hdHx5HFVOKEHXpSMSi6iv/5Jjxx8nIuIqMjPm/qRUgd1j58k9T7KpZhOXxl/K7EGzfzYT5a9CjbmGOfvnsK9xH7kRucwePJtozQ8DDOuePdTOuBXD5OuJfPrp0793nTRR/XExN7vMiAOsPNH/KXpkPEJsUSnkLWDfmPeZ4EjjIuNBPm77Evkt34D8zELnHo+HB95YyretBjKyVtPoOcj17ptxNppIOV5Bn6JSbOMvpuezs6msrGfhqm9YpeyJr9HFmF5RvDYulc9ff4IPQ3bgl8uZVXgjFlcWaqkRuVROUOVqTiZcTmDmMkLSDyIWiYiOnkyk+xJaH3kBd2UlQTffTOjfHkCs+GEh3e2wU7x1M6c2HybEG0GMOh2pWAZqCQFZoSgzg1EmG06rr/7ZuODkf0cErx9vmwNPsx1Po63LsTdY8Vs9XX9HwCm202ato91RT4enCVVCIHE9e5HYsw+hcQm/eSdeTXE7GxcVI1dIGHtXD8Lif3hReL0WOk2H6DTmYezMw2IpQhC8iEQSdNoeBAUPIzhoKFptNuLvQxKWDifLn96Oy+6lr2cJz/dvoJuxPwqvkvTycmQhOo54tNywYx014SJemxhCrG0Mezsz0bmM3GD7Cl3w5QyfNIGV9s9o3NZIkyuOve5UIjOCqI5T8XB8OEM3zaZ/0xIekjzC6GtmcElmOBz9AlbdCWNfhf4zf/SaHSUlnLx2EoGTJxM45R5aFxUhj9MSOiMbkVRMW9tWCgpvJzh4KD2yFyAW/3ioq85Sxz1b76HKVMV9ve9jWvdpv1umhd/np7PFQXu9FUuHE7vJjcvuwe8XEPwgU0pQqqWodQoM4WoM4Wp0Icr/80wPQRBYUbGCuflzEQSBh/o9xMTUiaftaH75FTo++oiYd95Be/FFp8/zNNvYtPAId9tMjIw/yeT018hMe5HItW+BsYaVE77hzno/l7duZ4FwCMm1n5xVL9ZsNnPja6spcmnJ7PU5Ha5axjun4q5vIaPkGD2OV9J2fRYDHp7PyZPNLFi+ipW6PvhOORiSGcbCa7NY+s4zvKfZiEymZFbhJDqcPZFKTCCoSK//mrKwaxECTqEcs5R4eS0SiYKYiJtQLbNg/mwZirQ0ol6dizIt7cx+8fs5VVJI8ebNOErbiValEBWQjAQpSEUoUwNRJBtQJBuQhav/NKP8C07+V+B3evG2O/G22rsceosdb7Mdb4cD/N8fJAKP2kunp4WGtnLarHV0ulvQR0cS2z2buKwc4rJ6olCrf/K7zofCbbXs/qqC4BgN4+7KQaFxYTLlY+w8QKcxD7OlGPAjEsnQ6bIxGHIJNPRHr++NVHr2SNVidLLiqW04HH76itfxcbKZEEcyKquJ4U2tNMSFYyyqZVBFEfszFCwcqybTNpbCljQsHglT7V8QG3EJva4dyZyKp9CV6giwJ7DOl4PIoMDcJ4iZhkDsG77mFdfz7A66huyZC9CrZGDvgDf7QEgaTFt/ljP4F4LfT83kG3DX1RH32XI6FlchDpARdmcOYrUMi6WUQ4evQ61Ook/vJUgkP97fJe0lzNoyC7ffzavDXmVQ9KDffE/a663UFLdTW9ZBY6UJn8d/+m9SuRhlgAyxRIRIJMLt8uGyefD7fvi/UwbIiEjWE5MeSEKPEPSh/3d5+A3WBp7a8xQHmg4wInYEzw16DoPSgN/t5uR11+NtaiJpzWqkoT/E770mF8/O38endhsP9dlGZsgaesY+SdBX/4Dw7rw/6iOerGplev0K5sRoEI04uyDciapqbliUR6fET0zWh4gRyDVdi6yhkeyiMjIra2i4JZJhsz7kxIkmPlj9DauC++GttDMwPZSPbujF0o/m8KZ4JRqJmrtLJtBs74NLbEXhVdGjczWl2kvxSvzk5b7PlCw1YtthpFI9Eb6LEJ7fB602wh56kMApU8458LKbOinduZXyPXsQmtxEq1OI0XVDSdfzJVZLkSfqkcdokUdrkEVrkASc/27y34MLTv4/EDx+fFY3Posbv8WDz+LCa3Th63Di/f4jOH6Qg0UMIoMMt9yN2dNGc8dJ6hpKMbla8Qs+gqJiiO3eg9ju2cRmZqPWn63n8Vvx+/zs+rqC43kFxPZuIb53CxbrEazWMkBAJJKj1+VgCOxPoCEXvb7XTzo7AGunk+VPbcNh95Oi2cWOIB9iv5qwhuNckTOUvJPHCdhbSEJ7M18P07F6gJ8s59U014ZzzB/Ota4V9E/sg+qiZOYUzya1LZX49mS2qwZQZ/MjDA6jt01E/cESNqoeQ2aIJuCu7SD7Poth9d1QsARu3wXhmT9qZ+eKlTQ+9hgRzzyPszYOwekl7K6eSINVuFzNHMyfAEC/vitRKM4ubPEvdtXt4sEdDxKoCOTdUe+SZEg639twGofVTdneRsoPNNNe3yUiFhwdQHR6IKFxWoKjNehDVMiUkrNG6YIg4LB4MLXY6Wi00VRtpvFEJ6YWx+l2ug2MJD03ApX2ly2i/xb8gp/Pyz7ntUOvEawM5pVhr9A7vHdXquqEa1D360fswgVnOEKnxcVVc3fQ6Pbw0oiPUSpL6K+6hYD1c2DI35idcCvv1rbyeNUC7hl6FaSNPut7v926mwc3tRMc1I4vegGJ2iRCG0YS3FRHj6IS0qvqqb1Tz8hpSzh2rJaP165nVWh/fBU2BqSG8PFNfVi29FVec31BIAHcW3oldfb+mCQ29F41KaYt1Cr74Jar2JD2AX37BjFW78Vk3IVEHIC+OBTZx/Xoeg4l8oU5yMJ+/NkxNtZzfO8uju3diaOpkzBlLDGB3QhVxSH3/HCPJAYF0jA1shAV0mAl0lA1kkAFEq0ckeLsZ+H34i/v5N31Vmz5TSAAfgHBL4AAgs+P4PLhd/oQXF78Lh9+u/dMB/4vJCKkgUrEehlumRu7z4zR2kRzezW1dSU4bRYApAoFEcmpRKZ2IzI1najUbgQYfr0i4E/h97uxWErpaMunvHAbgrwMmcrUZa5EjU7XE4O+L4bA/uh1vZBIfnkKmM3kYvmT32FzCGgD93NCLcEsM5NsquTGKc+y9Z15RO8vQOb38+74EPJT2sn0TkJSIWenqBvDvLuZ1D2M/CQLy09+zWDxYCIqIygN7kdePYh6BqOosyF0OtkS/Box9jJEt++AkO/rdNbsg4/GwOD74JJnf7wP7HYqR49BGhmJesSjeBqshM7sgSJeh89n59Dh67HbT9Kn99KfrOj0TeU3PLnnSdIC03h75Nu/WpKgs8XO0S21HN/XiNfjJzxRR3puBEk9Qwkw/LbNUqZWOycL26nIb6a52oxYIiIxJ5Teo+POCMn9UZS0l/DwjodpsDYwq+csZmTPwPTlUppmP0v4Y48SNHXqGcefaDAx7s099EHgzpEvIyjsDGrNRlq4Av+UFdztSGBFm4U3T7zGtROehuAzpQUEQeC5D1bw4QklAzOqKWYBo+Ovor00nviWOnoUlpB0qp7WBwK56LovKS6u5OONm1kTnov/mIV+yUF8dkt/lq+ax6vmT4j0a5hVNpaT9kGYAzzobDICjftxy2Kxq8PZF/85Dal1PN/vDjS2nbS0rEckSFHvEqHbpyX24RfQjhr1s/3U2dxE9dF8qo/kU1tciMgnIlARQUxYN8J08WjEBiQOMXjO9K0imRixVo44QIZYIUEklyCWixHJJSDpCgWpMn9d0Zq/vJN3lLTR8XUFIgldmRnirqkxElFXZyoliBVSRAoxPokfn8SDCxcuvw2zrZ1OUyNtrbV0tjRiN3WeblemVBEal0BofAKh8YlEpnYjJDYeseTcm3R+K253GybTEUymw3SaDmOxFOL3dy3eum0h6DS9iE8dgl7fm4CAtNMx9fPFbnax/IktGD0OPIEFWOQSTuhO0E3r4/4Jb7Lt7tuJLzhGuz6IuddFUmc4Rpb/WsJLXKyW9ieJau7rbeJDxVEqTSe4Oe5mHHsc1ASnsbkmAH+0GjrdDI8yMD9uD0F7ZsP4t6D3TV0G+DywYBi4LDDrAMh/XBq29c23aHv7bQKnvYDXGELQDV2bnQTBR2HRXbS1bSWnx0JCQi760TZWVqzk6b1P0y+iH29c/AYBsvOXorWZXOSvPUnp7gZEYhHpueH0GBlLcNQfs1jb3mClbE8jx/Y14rJ7icsMos/YBKJSfv9Z4r9jdVt5dv+zrK9ez8WxFzNnyByM9z6Mbd8+klavQp6QcMbxC3dU8sL6Yzwmd5E27DmUSj25BTZEdiPuO/Zy47EW9pmdLK59ixGT3wLFmf3lcrmYMncZB60GxgzOZ0/HMq7p/hAVeR5SW+rILiwiur4e+6MRDLt8MUeOlPHp9l18G94PodRM74RAFk/vz9JvX+N102ekunXcdmwUJxzDUcTJsZ1yEdBZilKkwKRLojZ8LeuSv2NG9gxuSR1Dfe0iGptWgd+P8oiYCNFIEu78J5JfKFfscTlprCin/ngJ9cdKaaw4htvRNSNTybRER6QTEhKHVh2MSqZFIVIi9csRC2JEXvC7fQguH/gFNIOi0I2K/1X37S/v5GsKj7Jrycddjl0kOj0l8no8eF1OPE4nHpcLl8MO/3m9IhGaoGAM4REYwqMwhEcQHBtPaFw8upCwP0yuVBB82GwnMJkOn3bqDsfJ702SodVmIaM7x3YYsLcmccnUocRmnq3ffb44LG6WPb6RZkkrNm0NMpGD7eGHSAkP5Pkhb3Bo2i3EVNVSmJDGK5PjcbONXp4rSChysFw2ELnUzUP98pjn2IdapmZ239kcWXOEUoWBTU0RCFIRgUoZz43O4IoII6KFIyD1Urhu8Q+pkXvmw+anYPKXkH7Zj9rqaW6hcswYFOl9kCVMRX9ZAtrhXdk3FSde5NSpRaSlPkVs7M0/2sZXx7/iuf3PMThqMPMumveLhLv+Hb9foGhbHfvXVOH3+MkcGkXfsQkE6P9vJA7cDi/FO+s5uuUUDouHxJwQBl2TgiHs91vn+U8EQWBx2WL+mf9PEvWJvJ71FK7Jd6BISSH+s08R/dsgx+cXmPjuXqoaLSxQN2HPfYlwcRKZuw8iSr0UyzWfcNWBo5x0uFlpWUEHcfdPAAAgAElEQVSPq+eelSLb2NTMVW/uwCgoGDj4Wwo78hne/RWaD5wivbmWHgUFBDfXIXoqnkGjPmH//iN8sT+fdRF9odhEr3gDn03P5ZP1L/GeaSm9bXpuqhjOccdIEnoHU1nYjqLzFHp3J+1BPRB0O1mQuZzs0GxeHvoyITIxdac+pq7mc/xSF/ImBXGptxPT+47zrhbm9/tor6ul7dTJrk9tDW21NVja2hAE/xnHikRiFGo1Co0GhSqArItG0WvMFb/qnv3lnXxdWTF5q5eBICD820cqlyNTKLs+SgXKAA1qfSABegNqvQG1wYA2KASp/I+PezqdjZjNBZjNBZjMBVgsRfh8dgBksiD0+t4Y9L3R6/ug1WZTfdTElo9LCdDLGXdXDkFRv70QgsPqZukT31KvOolHYSbKdZLPkwoI0QfzRs/XqZ5+G8FtnazrNZR3Jyagsi6hr3M4iSU+Noh606IK5daclXzuPsTAyEE8N+g51n69ju02L9uciYjbXYztFcXL47PQSf3w/kiwNnWlRv6rklDnKXg7F5Iugslf/KS9DY89jmnNNwRc9Azai7IxXJ2CSCSioXEZZWWPEBN9E+npz/zo+Z+Xfc5LeS8xPGY4/xzxz/PWnmmrs7L10zJaT1mI6x7M0EmpGML/OOf6U3jcPgq31pK/vga/z0/OxbH0G5eITPHHzCqhS33zoR0P4Rf8vOGcQMDLH5w7bNNiZewbuxgapOFvHKCp57tkGhOILMqHK9+mKfM6Lt97CI/LynptJVGDz5Y+2Lr/CHeuOkWIzoMu/UOcPicBcS8QdLiYpJY6eh4+gsrcgOapJHKHfcSWLTtZWVrOhoi+iIqM9Igx8MWtuby94WkWd65hhEnPhOrBlDpGkzE4gsqSdvxNTQSaK2gNG4BBms87A1bjE/t4PPdxrki+Ap/PTs2++dTVfYInzIPEpyAi5moioyeh0/b4TfF0v8+HtaMdc2sL5rYW7GYTLpsVp82Gy2bF5bCT2n8QWSN+Plx0Lv7yTv7PhtdrxWwpwmwqwGw+itlciMvdDIBIJEerzUCn64FO1xO9LgeVKuH0AyQIAoc31rB/VRURSXrG3pn9uyy+OW1uPnlyCU2aU4j9bga4i3g99Rg2lZZ3EmbjuPdxVHYn7w+fwIorEtG3vUGOI5uMMgOH3REcMvRmXMpK9soPMqvXPUzPms6HS1ayxWRilyYdWUkn04Ym8vS47xdQNz8Ne+bB5KVnbnNfMhmqtsOsvJ/MiXeWlVE94RpkKaPQX30bITd3RyQRYTIXcPjw9ej1feiZ8/GPhqyWlS9j9r7ZjIwbydxhc5FJfnnWgyAIFO+oZ8+yE8jVUoZOSiWlT9ifQtDK1uli/6pKju1vQhei5OKpGUSn/TFrQgC1llru23YflcYTLNicgKG4titsE39mWOHd7ZW8vOEYc/skkNb+KW2pXzOoXIvK2AZ37KZMFs4VeUUk2k+xqkcCAfH9zvquFz5ew8JjEi7JtFMgmUuSIY0Szd8YUnSI8LZ6eucfQuxpIezpTHr3f49Vq75hQ10zGyN6IynooE9cIJ/N6M+c9Q+zpnMLV7drGFU3mCL7WNJzw+lottNR0Uhw+yFaIoYT7Cti/WX7OGwp4fKky3k893E0cg3ezk6q3r6XNsk+nL0FBKlAQEAqEeFXEhY2BrX6z1dm8IKT/wPxei1YLKVYrKVYLMVYLCXYbCfoWgUGlSoeva4nOl0OOl0OWm0GYvG5R5Q+r5/ti49xbH8Tqf3CuXhqN6Q/ItJ1PlhMdhY9twiTpgOt2cHVCXaelW+gSKXiDflMNHMWgCDwyqhb2DEmkaCWF0lxRDH4eDdqjFbWRFxOTtgBXHH7eGnYXIIkaby2ZAM1Chv7ojNQ722hT4yBpbcN7JIErtkLH42FPjfDFfN/MOTYWvjyBrjkORh874/aKwgCNTfegrO4FP2N/yT8vkGIlVJcrlYO5l+FSCSlX9+VyOXnDl9tqN7A33f+naExQ5l30TxkP5Ez/584bR62fXaMqqOtxGcFM/LmjP+TDJfzpaHCyHefHsPc6iB7RAwDJyQjk/8xo3q7x84jux6hoHQbb30oQZeR3RW2+bdQptfn55p391JrdLDq4m40Vz+OK2gng444EUfkwLR1fNfUzE3HmrjMdIj3R1+D+D/KB7rdbia/soxDVj13jDHyec3LDEuYwCrvldxUkIfS3ES/vDzcMiOJT/Wne4+5LFnyJVtNDrYEZyMtMjIgMZiPbunL39feyXbzfm5tVpDb3J9Dtokk9gxB8AmcOtpIWPs+mkKHEuipouLSUla4txEVEMXc4XNPaxaZvvmWhpefxp7twnN1GDZpl/qqJiCd0LAxhIZeiiYg/U/x8r/g5H8HBMGP09mIzV6B1VLa5dgtJTicp04fI5eHodVmotPloNfloNP1QCb7ZaMsp9XD+gVFNFR00u/yRPqNS/hdHp7aI8Us/moNLoWbuCYr1988gJcOP8JKjZpXTw0h5ovt2BUyXrzkdg6MSCKw/TnCXXImVYyiqa6QpbET0QSYGDSwkEcGzOaLfa0s21mAIVngSHwakYUmXB1ONtw3jLhgNTjN8N5gEEngjt0/LLS5bV1hGoUWbt8JPzGyNm/cRv19d6HsdyOxbz6M1KDA73dz+MgULJYS+vZZ9qOZNDvrdnLf1vvICcvhvVHvnVcM3thkY+3bhVg6nAy8Opmci2P/NJtdzoXH5WP/6koKt9URFBnAmNuyCIz4Y+qb+vw+Xsx7kaavlzBrrZ+QJx4jdMpNZxxT3mzh8jd2Mzorgpeywjh64hYC7RVkVhhh5FMw9EHeLzrMk21i7rXu47HL7zgrPl9T18BVb+/FI1Fw3dhSvqpYzIDUv7HW0YN79+/F6W4jd/8BrEEmuj85mqSkf/DJJ5+wU6xkmzoVeZGRYakhvDMlh3vWziDfXMg/msV0b8tmj2U60emB6EKUlO2uJ860nzpdP9TeVjq6H+Sb2BKMrnYe7vswk7tNRiQS4amvp+Efj2I/eBDl+CGIZvSk3bqLTlM+ICCXhxIUOJigoEEEBg5CqYz8Q/r/57jg5M8Dn8+J01mH3V6NzXYCm/0ENtsJ7Paq0zF0AJUqDq2mO1ptJlptdzTa7ijkZ1ew/yV0Ntv59u0CLB1ORk7NIK3/uSUKzgfB72fve+/zXWMTgkhMz1Y3V8y+ic+WjuP1AAVzdkeRtLeWdp2S+WPuZW/POPSOF9G5rNxTNZmGii18kzCORkkY94zrIFR6Ca9tqcBisRCdLaIkKoF+Rh9FeU3MuTqLG3O/n76vuqsr9336Roj9N7XDrXNg5ytdm57if3wDks/hofLS8fjtVhKXr0GR0PWSPHb8SerrvyCr+3zCwy8/57n5TfncseUOkg3JfHDpB+clU1B/3Mj6BUWIJSIuu6MHkcnnXzf1/xenStvZ/GEpXo+fETek/6jExW9FEAQ+LPoAzd9fI71JTNy3qwmKOTMtct6WcuZtqeCT6f3p6TdypOo6Mk+0EdppR3TbdoTwLB7ZtZFPfRHMl1Vw3ZCz6+wu27yXv3/XTk64lJDuKzjcfJjQpBepcoZw984dGOmk/759WOPN9PnHjYSFT2PRokXsDo5mH1HISjq5uFsYr1+fyfQ1N3DCVsXLjT7iTelsM99NSJyO+O7B5K+tJsFRQJ08DYngQhzyLav6+Kl2HD5D6kLw+ej46CNa5r+B1GAg8sUXkfVLo719Ox3GPXR07MHj6QBAoYj8fpDXA50uB40m/RcP9H4L/9NOXhAE/H4Xfr8Dn8+Bx2PE7W7H7enA427H5W7F6aw//XG72844X6GIIECdQkBACuqAZAICUtFquiGVan8X+/7lXERiEWPvyCbyd0iRs1dVserNtynXa5G6tQx1wbBnb2HHJyN5VORh9hoFsdUO6iM0fHnlI6wPCUYrfROlq5rHamZSX7aZvNgs9kkGMKmvg8K6MI41Wegdq8Ma5aDQEMYEmYxtG2vpmxDIp9P7d806StfAVzfBsIfh4id+MMh4Et7qD5nj4ZpFP2q34BdoeGwR5lWvEXLfE4TeeWNXH9Uv4djxJ4iPu52UlLN3TwJUdVYxZf0UQlQhfDzmY4KUvzwT6fj+RrZ+egx9mIrL785BF/Lnqvz0S7AaXWz6oJjGEyZ6XBTD4IkpiH9GrO7Xsmnnx4Tf+TLHs3SM/HgdwaofcrtdXh+XzduF1y+w6YFhWCr3UFY9gwGHjMiCuiG6fTsexNyweQ37ZdEsixOTm9rnjPYFQeCet1bwbb2Su4frWG99BolYzqngp4lzyblq1zZaxSZy9+7D0tPEwPvvQy6/hEUffMCB1Bzy7XpkpZ2M7h7O89ckMXX19bTYmnivyUGII4WNpofRBqvJGh7N3uUniHIeo8Ufjl8iI16zmJfT4ukI2ESsLoZ/Dv8n6UHpQNc6Uf3DD+M+UUngTTcR9uDfECuVCIIfq/U4RuO+75MrCs+Y4ctkgahVCajVSahUscgVYSjkocjlocjlIUilGiSSgJ/UWvo5/vJOvrllPSUl9wESRCIxIlHXT7/fjd/v+slzRSI5SmUUKmUMSmUUSlUMKmUMKlU8AQHJv5szPxeluxvY8cVx9GEqxs3K+c3b2QWfj5MffsjqklI6DXpU1miGyzzkPjud8sWX84+2Bh5cLiLQ5KcqMYi8ybP5yCxGHboYheMgf6+bjrOskLJgEStVVxMfaOekUUNskIqHRnfjw5aTHJCqmaKCk0ftHG+ysOmBYUTqVWBthXdyQR8Lt245Mxzz5Y1QuQ3uyQdd1LltFwSMK8tpfeF2xDolKZvXIpJI6DQd4vDhGwkKHEhOziJEorPjzm2ONqasm4LT6+TzcZ+fIbb1cxTvqGPHknJiugUy5rYsFL+iyPmfBf//Y++8w6sosz/+uT03N733HkghgZCEAIKE3kILSBMLHbsrIoKi6KIooKjoooBIVQHpvUnoPYEQQnrvPbnJze3z+yOsiqCrWHZ/7n6fh4eHO8y8M+edOXPmvOd8vyYz527TTvuEOzBgWgcUyj+GMjl58cso1+9mzWR35jz9JW6q778ezufWMmH1BZ7qHcicgSEU3thAY/p8Im+pMXV/BcmAl2hQ1zDkzGWaJJYc7doRd5s7g5vmlhaGvLufMoOSZZNseePqc7R36UaSfDqPlunxyzpFNWrizp2j5cF6es56m6amQDZt3sylmF5cr5Yiy2gkIdKdl4e5MXHPWAzqZjZV1WNhDuZAwyvIlXJiE/w5vTUbR00+ar0CrcKBThZrme0Ujt7vGIJIw/y4eSQGJyISiTBrtVS9/z71GzYiDwrEc+lSLELvTh3q9XWo1TduZwPy0Gjy0Wjy0Ourf9Kmvr6zCAqcc1/z8W918iKRaBDwISAB1giC8M5P/d/7dfLNzZlUVu5FEMwImNr+FkyIxXIkYgvEEiUSsQKxRIlcZo9M5oBc7ohM5ohUav2nL5yYzQLnd+Zy7WgR3mEODJz+2x9GXXY2lxcv5pSrK0apBVaNofSwrCP27RnU7pzGoqvneXQfiAQx2SHu1D7+Dotv1CNrdxRL9UGmVibinw2p0lS2OY3FKEiRSKx5tm87xnf15vHLNzmrh4eMajopvfj7vnTeH9uRxM63NTO3PgqZB9soClxCvj+x3BOwcST0WQAPvviT568+U0rNJxvQpmzAc8VH2PTvj05fw6VLw5BILIiN2YVMdncKpdXYytTDU8muz+aLQV/8KqGPa8eKOPtNDn6RTgycHv67LHL/J+Dm6VJOfZX1h36ZmPV6biUMokZdybLnPPnH0M/xtv6+Wmr21uvsvlbKged60s7VmvSrr+B4fhXONSbMjyQhDYwkM+scQwqhPc3s7NsPxY96Ui6nZTJp0y1crGRMHVnLe1eXEuI7ldNCPO9fbaKu9SJ1+iZiz51DP6KB+Mc+JyvLwJ4DBzjfYxAZhXqkWU1M6OLDIw9KeOzAo1g1mdhWW4kgC2Vv3WuYzSK6jgzk4u48lM2lmDRamiy9iJVsZJlTELc8UkCZTUJAAgu6LsBS1lZC23zmLOXz5mFsaMDluWdxmDz5jv6Bn7SbWYdOV4NeX337Tw1GUwsmYwu2tlE4Oj54X/Pxb3PyorawKwvoD5QAl4EJgiCk3+v//yfk5P9o6LVGjq5NpyC1hohenvQYG/ybPqsFg4HqVas4feIEN8LDURgVqOojiLMppcvimehOvMPnX64j/oyYBisZ+WH+qGYt4W+H85BE3cCycSP9Gx5kQl4c37Zs4dvAfuQ0BZLQ0ZuFw8JRWkh5JDmT0806EmqLWRAfz6APz9It0JHPH4tpe0He3AnbHoe+r0PPF74/OZMBPu0BRi08efF7zpofofVmDTXrU9EkvY7c1x2/rVsAMykpj9LYlEJM9PZ7LrSaBTOzk2ZzvOg4y3svp69P319st+TDhZzfmUtglDP9p4Yj+X8mEvGvUJJZz6HPbiCViRn2bCccPX//ztyWCxcpevxx9vdUcrC/PasHrP6OD6iuRU/f95IIdLZi68xugJHrJxMJP3sGs+CP9InTSJ1U7D+2iqmSLkxSNrOsa4+7xli88QCf3RQYF2mPweMbvi36Fiuf16kzB7AuqZaLFldo0DQRfeE8PKKh99gtJCXd5MzlK5zuNZSirGbIVTOrVyA9Imt46thT+NRK+LqpGINNNAcaFtBYq6fL8ADSkkqgvhpFcxW1VkFEGreTGubMh+ZGFE7H8bLy5eN+HxBo17YOYayvp+L1haiPHEHZuTPuby1C4f/vKa/8OSf/R9/ZXYAcQRDyBEHQA18DI/7gMf9joa7TsmNZMoU3aug5rh0PTmj/mxx8a9pNMseOY3dyCjc6dMDWYId1bTRdbMrosngm5stf8u37X9DnjJh8NwXZMeH4zvmIuQfzkEcWo2zcRJA2glklwzjfdJCSKH8yG9vxt/6hfDg+CrmFhIev53BaraV/4S3eH9CLV3enIxbBopEd2hx8czXsnw0enaH7j8oiL6+B6gwYuPgnHby+WE3d15mY6y9gbqrB5YW/IRKJyMtbTn3DBdq3f/MnK2k+TvmYY0XHmBM751c5+LRTpZzfmUtwjAsDpv31HDyAV3t7Rs3ujADsfC+ZirzG330MVdc4bEeMYOh5A86VOqYcnkJeYx4ADio584eEcqWwnq1XihGLZYR2X01uO2csTLk0f7YAY20rQ+Mf57naI2xqtWJjbu5dY7w4vj+Rqia2pNYxxPlJvKy9EFd+hJYm3op1YKC+CzYqFclxXTFvsuDc4an06RNHWGAA3c8cxrm9LRIfKz49mcuNbHde7/46BY4GZln4YNV0kVEe7+AeYM2FnbkEdnZB4uKK2soTd00GqdLRBN3Qs8fZjLRyGsWNNYzePY4dmXsAkNrb4/nhB3i8+853usO1n69FMJl+d1v/FvzRd7cnUPyDf5fc/u2/DhX5jXzzzhXUNa0Mfbojkb297vtY5pYWKt9dQuq0aRwICqTM2wv3VndkdZFE2xYSt3gGhot7SHn+TXxzJZzsoKQ6ojMxc5bx7M5MJO0akLd8hkrwYWnBVK42HEeIb2J/yUBGdHTjqd5BqI0mJl7P41xDC32yUljUuzvHcpo4nV3D3MEheNjdTgEceLGNg2bkSpD8IOXUXA0nFkNg35+kLjDWaalZfxORwojuxj4su3ZF1a0bNTXfUlC4Eg/3sT+pz3q08Cirb6xmdPBoJoVO+sW2y75SycmvMvGLcKTv5LA/bHHyPwGOnlaMnhONhUrG7g9SKE6v+93HcJn7EhKVioXnPUAQmHZ4GoVNbfXkY6K9iPN34O0Dt6hW67BQuOHady3VDnKsjeuo/ewAxiYzLz04gt71l5lf2MCVevUdx5fJZLz3SA+sRHpe3nqLt7svpdXQTFjzai6qTGzxsmS4Q18slUqux3ZDs9JI8vmZJCYOx9vOhn7JJ1F0sEPhqeLdQxno6mOYGTmTa+56XhT7Y1FxiqGuywnp6sr148U4eVlh6+NMpTIIX306OYp+3PrWgzOuZxlouQCdxp3XL7zCrIPz0Jl0iEQibEeMIGDvHlQ9e1C1dCkFEyaiy8n53W19v/i33+EikWiGSCS6IhKJrlRX//SixP9npJ8tY+d7yUjlYhJfisY3/P6Y5gRBoOnoUXKHJnDj8GGODRyA0dEJ30Y/jI3BRDsU0PWd6bTs2UjWzLkIGjEb+ymRe8fQZ+5intyehca+CTEfIxLZsCprJuXqbCT9ktlcOAF3WwsWjYqk0Whi7LVcrjQ20/fWZZ7tFIbK0Y0396XT2ceOSf8sl7y5E9J3Qfy8O/PwAN++CYYWGPTOPeX8zK1GatbdRDCakShvYGqox+Vvz9PaWszN9NlYW4XTrt3Ce9ohuz6bV868QqRzJPPj5v/iNZWim7Uc+yId90BbBk7v8C+lEv8KsHFSkjgnGltnS/avTKU44/d19FIHB5yffw4h+QarpJMxCSamHJ5CcVMxIpGIt0ZFoDWYWXzwFgAOjj3Q9H0es9iMFa9Rvfo6KPxY6SXDQ1vB1Gu3qNQZ7hgj2M+bp2NtqdGJWX2gile7vkphfQpRhv18Gignvd7AuK5jkCsUpEXFUf1eFVkZ8xk3bhw2rS2MzktF18EOlZsl83fewFc8iuGBwznua+Bdkx+ynH3E268ibpg/ucnVSGQSXIIdKZSFEEgmZcpY9p6IZ1H5u2zo+jpKTT/OVu0jftNorpW3OXOZiwteK1bg8d4yDEVF5I9KpObTzxAMhrts9mfjj77LS4Ef9q573f7tOwiCsEoQhBhBEGKcne+P/vU/FSajmaQvMzmxMQPPYDseejn2vlkL9SUllMx6gpJnniW9XTBnHuyJk7s7HqXetGi8iHUrJm7RZOqWvkrRy29TZi1i+WgFgbIYhs9/m2d351Cqr0fssAoEI+9kTcJCL6K1xwn2VPWkptWeD8dHoxOLGHMtlzS1hn5pFxnp6kBcXBx/35dOi87Iu6Mj27paW2p+Ok1TlgLJGyFuFji3u+taBKOZ2s23MNa0Yj/Gn8ZvvkTVsyfyDiHcSHsKEIiI+Pie5FCNukaeO/EcKpmK5fHLkUt+WTdqTUkzh1alYe+uYuiTkUj/oO7Q/0RY2sgZ8XwnbJ2VHPgklZLf2dHbjR2LIiQE0T828FnPFehMOqYemUppcylBLlZM6+nPjuRSrha2jesTNo+yiE6o9CVITBuoXnMD65AJrGs+SJPJzPRrt9Cb7yTzmj4inljbFvZmNqPSdGJ08GhKKrbhbkxjQbSKpjO1PDpxGiK5nPSwrhS+fY2mxq8YPXo0koJcHlOXU9/BFmsnJS9svU4fp6eIc4/j6yD4Qu+FJGUdnS230H9KGFWFTbTU6fAKcSSX9gQr8qlTBLItdSrtd03nzIBR9Hd4GbWpikcOTuD1o1swm4W2qH7oUAL278Oqb1+qP/iA/DEPoUlO/l3t/WvxRzv5y0CwSCTyF4lEcmA8sOcPHvM/Ai2NOna9n8LNU6VEDfAh4emOWFj9+vI8s05Hzaefkjc0AfXVq1yfOoUUDw9C24dgdd2eJr0zXf2qiHnpIcqnjKZq7Q5SgkW8O07GA+poxr36Di/uzeVaaTVy/y+RGKuZWDycSH0wle3OkmI2cqY0lqd7B+HrYc3oazlkt2hJyLxCnMTM8OHDScqsZve1Mp7qHUSw6+2S0v2zb6dp/nFnmkYQ4MBLbYRkve6uaW8rlcxBl9OA/ehgWi8dwlRfj9OTT5CV/SZq9U3CQpehVPrcta/JbGLuqbmUt5SzPH45LpY/LfLwQ2ia9Oz/x3XkFhISnur4/7pM8n6htJYz4vkobJyV7P8kldLM+t/t2CKJBLdX5mMsK8dx+ylW919Ns6GZqYenUtlSyVO9g3CzseC13TcxmQVEIjHug7+h0c4Se/NXGNUF1KxNo338XJbnreCSxsxbOXfEgkgkEpY/Ho+NSMecbdd5OnI2IQ4hSKtXUiOqZnGYAuFINZOffAqzXM4t/27c/PtX2Nnn07t3b0TJF5mqMFIVYYfKVsHTm1N5LPA1AuwD+UeIkgNaV8QnFxMkPkTi7GjMZoGKvEY829uRrfMn0LYajcyJbcWv0LjyGd73MLK67yaUIld2lC2i1+ezSStre4lJHR3x+mA5nis+wtTYSOHEhyl75RWM9b+fzX8N/lAnLwiCEXgaOAzcArYKgnDzjxzzPwFlOQ1sffsyNSVqBkwLp3vir29MEQSBxv37yRs8hOoPPkQcH8/ZqVPIbGmhR9cHMCSJaTQ50L19PRHj4ygY0oumC7c42sPM8hESBpZ3YtyL7zDpqxSSsqqxCt2H1JhJePNAHlM/SKVdJpVeR9iU+Rgdve0Y39OPxJQcilp1TChOx6e+mnHjxmEQxLy6K41gFyueiL/d3fhdmuZlcPnRomjqVii5BP0WgsXdJY/qE8VorlZi3ccbZbgttWvXYtmtK41u+ZSVfY2vz0ycne/NxPfJtU84W3aW+XHz6eTS6RfZ0WgwcWBlKlq1gSFPRmJl/+dQBP8noi2ij8LaScm+T67/rouxlrGx2AwZQu2aNQS22rC6/2oadA3MOjYLIy28MjSUm2VNfH25rUlIrnBCNPwTJCYTlh6L0NdoqN5aw/DOQ5lSuoPPSms5UN1wxxhe7i7M7uFMvUHMK19d4f1e7yNGIKjpU444CWw3t6LM0PP4rFkY5XIynLpzZdEbREU5ExISgvTbg0xwtKAi0g4LSylPbbzF7Mgl2ChteSfMictaB9j3PM66M4ydH4uLnw2lmQ24+FmTo3bHx1GDWSRjZ/3fKVr3D7qlr+XUw18T6zCUBvlRxu5+jAX7zqLRt4kS2fTvT+D+fThOm0rj7j3kDRpM/bZtCD/6Svmj8YcnJQVBOCAIQjtBEAIFQXjrjx7v3wmzWeDy/nx2vZeMTC5hzNwYgmNcf/VxWq9fp3DCRMpmvx1Wx5MAACAASURBVIjY1haLjz5kn68PVXV1DB+YQPmORpoEW3pGNhMcLqNg1DAMtU0cHG5kTQ85g4vC8Rv0Mr0/O0BqsRabgJOIzRdRSQawKL8XzeIGGmLeZ1PWs5gEOa8ldmBcah4lWgPPttZgkZvJyJEjcXR05INjWZQ2tPLO6AgUUsntNM2L4BEF3Z+788R16jaeeI/O0HHiXdeluVZF05FCLDs5Y9Pfl4at2zDV1KB6PIGMzAXY2cUREPDCXfsBnC09y+obqxkVNIqH2t3dCn8vCILAiU0ZVOY30W9K2J+irvSfjn+mbixtFez75Dp15S2/27Fd5rwIYjFVS5YQ7hTOR70/orCpkCePP0mfUFu6Bjiw9HAm9S1tQjg2AYk0RvTGoSwfec9DGMpaqLkUxmuiXDqpM3kuvYCC1jubGR8Z/ADd7DQcztWQnm/m7z3+TrU6i6CWLSwLU5J6tghniR2PTJmM1sKCW5KuXHj/CRISeuHo6Ijb8X0MdLWmvKMdIqmYv20u4LWY99FLzSwI8yNfZ4Xw9aMoG1IY/nwnInp7UVWgxsbJgvwGB1xcxUgFPfu1C8nYkYzFlkdZ238eC7osQm5Zzo7K2fT5ZDUnMqsAEKtUuLz4IgE7d6AIDqZiwWsUjHmIlgsXfje7/yv89Vee/iS0NOjY82EKl/bmExzrythXYn91bbIuL4/SF2ZTMG48+tIS3N9ahG7R3/ny8mUEQWDMgBGkri2iWbAiPkaLe8Npip6fh1Rh5NRDWr4IV9K12Idrqom8fmo76hYnHFxTEBSHMFs8wNJLocglSuq6fUZSVSLXK5z429AQni8qo0Rr4G07CY0XThMXF0doaCgZFU2sPVvAhC7eRPvepgk48CLomu6upgE4/V4bf/zgJXeJcuvyG6nbloXc3wb7Me0QDAZqP/8cZXQU2YpVSKVWdAj/8J7UwZUtlcw7PY8guyDmxc37xfa8eaqUrIuVxA33JzDql6V2/hugslUw/NmOiCVi9n50DXWd9nc5rszdHacZ01EfOULL+fN0ce/CkgeXkFaTxuyTs3kloR1qrZFlRzK/28cuYRNalSXW1z5FOVqKvlhNi2Y6q3KWIjFomJ6Wj9b0feQrFotZ9ng8tiItL32TSoxzTyaHT6ax7jBK3XleiVRS8U0W3r7+jH3oITSWlqQ1RJHyxUzGjh2N2WQi9nISsa42NHSyR2Mw8do3dbzW5V0qaWBeaAS1egmGdSMR1+Xw4Lh29JscRqvagFQupqRehbWbNVbmRr4Vz+HyUQuE1X0Z6xzOjhFb8bR2osV+JTP3vMOTm69Q1dRmW0VwMD4bN+CxdCnGhnqKHp9M8cxZf0oVzl/GyQvGe+i2/kkoSK3h60WXqMxvos+jofSbHIbc4pd3sOoLCymbO5e8hGGok5JwfGIWgQcPcsPFhS3btuHi4sKo7gM5/VkuOkFOv26tWB9eTvWmg1gHyrg5qpWPfG0ILHfgrHYglZLj6NRROFjnoHfYhkHRgWfPuuFv2R51+ClyRSa2pD9ArwhX1tNKidbAqkAXCg/uxd3dnf79+yMIAgt2pWFjIeWlgbcrZ27tbUvV3CtNU5sL5z9pi+C97+QKN1RrqN2YjtTBAqdHwhBJxTTu2IGxshJNggUaTS7hYe+jUNy98G40G5l7ei5ak5b3er2HUvrLujcrC5o4vS0b3w6ORA/y+8Vz8d8CW2dLhj3dEV2rkb0rrqNt+X2qQBymTEHm5UXl228jGI308+3H691e52zZWTZmv8ukOG++vFREWmlbqkgkt0I8bAWWrUa0t2ZgO8qP1lwJDqpJrEh/gxvNWhb8KD/v6ebC7AecaDKImL3xHM92fpZo12gsaj8nR17KcpUBdVIJ7Tt2ZNjA/jRbW3M1x5/iY68zcuRIasrKeLg8B08nFYbOjlQ2aVmxX8xLMa9ySyjh1XZdMegNaD8dgNBUTvs4N8a+EouDuwoEqG6QIXJxxVFUyyXLaRy50A/jyj4EVNxi16itDPYfjMLlKCcb36Hv8gOsOZ2H3mhuW5gdlkDgwYO4vDgbzdWr5A0fQdmrr2IoLb2XOX8XSBYuXPiHHfzXYtWqVQtnzJjxq/fTJCdTOOkRRKK2N6boT1B6gjbu8ZNfZnJ+Zy62zpYMf64TPmEOv7ikT5eTQ9XSZZS/9jr6/AIcHn0Erw+WY9GjB3sOHuTChQt06NCB7u4RHNtUjNhsZGBUBea1i9GWqHEd1o6CsFJedLHGuV5FnSgOO8tCaqqGYWlRidFvLSaZG4NuhPKwOB6jcy35gR/xSdoCUCho7ORAud7Ipg5+5OzbSUtLC48++igqlYrtyaWsO1fAm8M7EOPnAK0NsHksOPjDyE/vitTZ9QQ0lbWpPf1Ax9PUrKd6zQ0wmXGeHonEVoFgMFD6txcQedtT2jsFP78n8PQcf08bfXLtE/bl7eON7m/Q1aPrL56XPR9cQ66QMuzZTn+oetL/Z6hsFbj625J6opjy7AbadXFDLPltFB8iqRSpqxv1m79E6uaGMjycUMdQFBIFm25tItRLQlGJDylFDTwU7Y1IJELiFIa27Cx2OalUhhix8x5I4xUlHVSXMRgqWSMOxE8pJ8zq+xd8RJAPl6+mcLJcRKizksnRCezJ3Y2lNpkz7g8SdF1NOz8HPMODkaobuVVXT3W2nnZOZVj7xpFy8QIPh7fjmFSOwl5BRVY9jQ0uJHb2YnfVUSrsoulTl4E2eTuymEko7awJ6eaOyWCmPLcRnQ5MljZ4KarIF0VSVBGMX+5cVBId/XotxMnShfM1e5HYJHM0Rcm+ZA3eDkr8HFWIpVIsO3fG7qGHEHQ6Grdvp27TZsQKOZadO9+X3d94443yhQsXrrrXtr9EJC+SSpF7elK5+B2y+/Sl6sMPMdbW/qFjFtyo4es3L5J5qZLowb489HJM25v+X0AQBJrPnqVo+gzyEobRdPAgDpMeJujYUVznzKFVJmP9+vWkpqbSp08fOog8OLK1ArlRw0CXE7SuXI5gMKKcM4l8+yxmO1lgqZUht/fEzWigtjIRqawJIXg9ZrGSoJpePKaJQCIXUxL6DocrXyFXLYJurlQYjGyODKDl6gVKSkoYPnw4Dg4ONGj0LD5wi2hfe8ZE327aOrYQWqpg+Iq70zTZRyHrUFs1jfX3RFWCwUztxluYGnU4PhqO1LHtIW3cswdDWRk1fUqxtYvG3//5e9rqXNk5VqeuZmTQSIYHDv9F8yKYBY6tS6elUcfAGR2wUP33VdL8Gni1t6ff42GU5zZyYlMGvwfNifWA/ig7d6b6oxWYmtty/lM6TOGxsMfYkbOVXjHpJBc1sDPl++jVYvgXCDIFNqfWYogowKq7J9UVU5lb9BVdW3OZk1lMZsv3aSWxWMySR+OxE7Uyb3sqCpEty3otQ6urwKNhDQvDFGTvyEQwmXlgxCi6+HlRb+/Akf3FhFk24OPjw9UD+/jQ254GOxlusa5cLqzj2o1YEoNGs0+SxUee/VDqymlc0R/BoEUiFdN9dBDDn+2EpbUcncZEidaFcJ8WapQhbKtcStWerYg2j2asV182DdmIi7Ul1gGfoVEmMWXdZR7/4jI5VW0NX1J7e9xemU/gkcPYjRmN3D/gN9v+XvhLRPIyV1fsEhOx6tEDQ2UFjdu+oW7DRnTZWUhsbZF5ev5uJGTqOi0nNmZwcU8e1o4WJDwVSfs4938ZARlraqj/6mvKF7xG/br1mDUanGZMx2PpUmz690dsaUlFRQXr16+noaGBMWPGwKVazpwzYqOtIL5lDS2nrmPhpeCrcfOxK13LYg8DDSIZUrEFwSXxZNc/SLNchzJ8A0ZTAyrZZF64pCbQOpKK8C9IV0byWWooyl7utIjhy8gAHKvKOHDgADExMfTo0cYd8vd96VwuqGPNo7G42Fi0KT0deBG6PQVRD//owvTw9QSwdLgd4bdFzYJZoG5rJrqsehzGh6Bs35bTF4xGSv/2AgZHHc2jJXSO2nhP4rFqTTUzj87E08rzV6k7pRwtIu1kKT3HtSOg01+r7+KPgqOHFWIxXP+2BLFUjEfwb6O7FolEKIKCqN+wAZFUgiouDpFIRFePruQ15HG07Bu8rfw5lipiYpwPcqkY5CqwsMXyxmGKNEk4D34Cc70SfamMIXVL+co7kaP1Gsa5OSC//RVpY22FvKWSw4UGCsuqmd6zG0qpktMF29DLLEm1CGBwhQGLADuCo6KpvHSGIpmSkgtpDOnTlfTSKpoK8hjXoxsbm5sJs1dx+XolAaoYPF3r2KdNQSnrTLfGK9SmJmEZ9wiIRNg6Kwnv6UFznZaa4maqG+W0byelpk5Ehr4f1sVncCz8CJf2wxgWNZPcxlyytQcJ92vlZq4b686UUq/R09HLDqVcgsTaGuv4+N/Ee/Nzkfxfwsn/EzI3N2yHDMFmyGAQgfrYcRq2bKVx7z6MdXVIbG2RODndl8M3GkwkHy7iyJo0Gio1xA71o9/jYVg7/LTykEmtRn30GNUffkTFwoW0nDmD3M8P52efxf2tRaji4hAr26LbjIwMNm/ejEwm4+GHJ5G7NoXUAis81dfpnPsxuiI19VHejAl5kcmtq9jiVcUtuQKpQc6Qkme4qg6iVGbEteM3tOqzMdg9yfTD14h3GILG4wYlgcm8kzKJ1hgnBIWULzsGECoysWnTJhwdHRk7diwSiYTrxQ28siuNyd39GRPjBUYdfDm27SEcux5+3Hx0/hNI2w6Jq+5ofGo6XEDLxQpsB/thFfe9Wk7T3r00bt9Bw1gtIf0+wta24112MwtmXkh6gWJ1MasGrPrF9fDVxWqOfn6TgE7OdB8d9B8hy/b/Be5BdjRWt5L6bZvK1G8Vjpe5uaHPy6Nhx05sR45EYmWFSCSil1cvLlVcoth4jPo6XwSDLQ8EtYntiNw7YcrYg21pIVk2Jfj0fpyWQids6s4S1XyWVfbxlOgMDHGy/W5uI4N9OH/lGifLIMpDxfCwHuQ05FBctZd8+3BEBTLi3O2Q2MgJ69aD3CN7KLdzovzcRQb26cOljExcDFpiO4SzRdtCjK2KpJQKeng8iMgym0NCPm6mMKIaL1B+8yrWsWNBJEIiFRMY5YKjp4q869XU1AjYOFuCRk2WuA/aEh3eeS+jtHRg8AMLUMosOVS0HRf3m3R2j2DnJQ2bLhRhMAl08LRte9H9Bvyck/9L8MnrjCbSShu/rwC5DbNOh/rIERq+2Y7m8mUwm5F5e2Pdrx+qbl1Rdo5GYvXzN7PZLJB1qYLL+/JpqtESGOVM9zFB2DjevQAomM3ocnLQXL5M87cnaLl0CQwGJM5O2I0YgW1iIoqAOz/JBEHg3LlzHD16FA8PDxKHDifprSQqzS50qPgGt9zjCIjYFD2AA4HD+MTua45ynG021ti0WvNw0QIOaUWkyo0ERh2gSnsatf1kxhzPZ4rFIFSWEvK7vsKn5cs45WSN3ErG150CibVWsn79esrLy5k5cyZOTk6YzAIjPzlLZZOW47N7YW0hgxNvw8l3YdIOCPoRCZi6AlZEg18PmLjlu5+bL5XTsCMHVRc37EZ972wFk4nswX3QGiuQ/2MiISGv39PmG9M3suTyEhZ0XcDY9mN/dn7+CaPexNbFV9BpDExYEHdfjWf/7TAaTOxefo3qYjWJL3b+zSWn+pIS8gYPwSYhAY/Fb3/3e522jkkHJlGhbqC54EmOP5OIt0MbhS8lV2BNXwq9lMiGfoybw0jqPj2IY90UlndawBK7B3mvvTcPe3xPDZJfVMrwlRdQKBScnj8YE62M3z+eMk0T1c5vsD7fht5ToxBJxbQ2q1kz72/UOnrhVV5Ku/g+fJuZydChCeyydePzkmp6lhu5fKOKZ/q7cbxxAWq9msVFEnpoUij2Gof3tDt9qaZJxzfvXkVdqwURqMSttJiUuLakMdj9XVQRcTByJTe0Vbx8+mWK1cWM8p9EWcGDHE2vxVEl58neQTwc54PFfdJd/+VFQ7ZdKWbON6nE+tnzZHwQ8e2d74rijLW1qI8fR33kKC0XL4LBABIJFuHhWISGYhHSHkW7dsg8PZE6OyMgIie5isv7Cmio1ODkbUX3xCC8Qx3a1KbUagzlFehzc9BmZaHLyKQ1JQVTY1vVgNzXF+v+/bDq2xdlx453iB5/d05GI/v37yclJYXw8HB6hsVy+IPLtAqWdMl+H2VlGRp7BUvin2dwQi96Va/ndN4aljra42XyJjH9Jc5g4KRUT2jHs5To99Jim0iXmwqmV9vT3qIDxdFLOW0zleV6D2RWMrZFBRFnZ8Xx48c5ffo0iYmJREZGArDxfAELdt9kxYQohnX0gKpb8GlP6JDYFqn/GDufgLRv4MkL4NjWKKXNqqdmXRqKIHucHgtH9IM0Vs3uzVTPXUTr0250evLQPQXNs+qzmLBvAt09uvNRn49+cTR+eksWqSdKGPZsR3zC7o8b6H+AVrWebYuvIAgCY+fH/mYR88olS6n74gv8d2y/Q1yjoLGAifsfpqlFTnfLN1j18Pc86sLup+HaJi7HuhPR+xhyozMtH76MyvgFE/rs55Kg4kB0uzsWYldsOcx7KUbGRTry7sSuZNdnM3H/RHRSP+SqOexWOOM5oC0dUlNcyMa3XqfRzQ/fslKs2rUno7WVx6dM4dU6HUerG+lRZOBSRjUvJTiypXQuSqmSpZl1ROgzKQh6Ar9Jd8piGPUmDnx64zsSOJFIQDALWOgbGaB6D2/fKhi5Eo3fAyy5vITt2dsJdQhlcvArbD6j5UxODZO6+rBoZMR92fkv7+Rb9Sa2XC5i9el8ShtaCXGz5on4QIZGuCO9R6epWaOh9do1Wi5eovXqVbSZmZjVbYshBqmKUs8elHr1QiezxcpYS3tdMi6tOaDTYdZqMdbUILS2fn9AiQS5nx/KTh2xjI7BMiYambf3zzoojUbDli1bKCwspFevXrhVyzl1pAGlpoKYjA8QaYxkRoRjnL2EhGgfTu9+CUnulzzv6kSgKJgBl58mx1bCdlMLYWGpFAtf0qrqjVtTV6afv0pfx5HU+xwlO8qSp2v6gqWU7VFBdHOwJjc3l40bNxIVFcWIEW3Mz9VqHX3eS6Kjlx0bp3ZBJAiwdiDU5cJTl0H1I8d5O+Ligeeh/xtttqtooWrldaT2CpxndUT8gzJSk1FPxqAumE06AvbsRWUddJdNdCYdE/ZPoLa1lh3Dd9whK/dzKLpZy94V14ns40XPsXdz5fwPvw7VRWq2L7mKe5Atw57p+JuYOk1NTeQOGIgiJASfL9be8UxcrbzKlEPT0Gu8WDtwNd0Cbi/at9QgfBRFg1JPXs8+dO68GWN5E3zWizqFmAEPbsBKKuVwTDuspG2Rr16vJ3HxN9xstWHbzK7E+DuxL28f807Po9V6CL1ax7CyVwhyrzZajuzLZ9m9eg3NHr54l5VhdnSkxdWVR6ZNZ2JGCZlqLZ2zNVzLr2PeSBWrc17Cz9qHpdcz8DKWUNhhPgEP3UnbYTa1cVXdOluOjZMF6lotglkABNobk+jtvgLJA7Og72scr7jAwnMLaTW28kzUMwTKB+Flb4Wf0/2lyX7Oyf8lcvIao5rkxh0sGTaEQGc7LhXU8eXFIrZeKaFZZ8TfSYWV4nuHI5LJkHt7o+rWFbvERGwfn0xDhwFkO/bipn0/6uxCcFBoiJDeoINwDSuhAYmVCqmTEzIvL1SxsVgPGIBt4iicnpiF68sv4/joo1j37YtFaCgSW9ufdfDV1dWsX7+e6upqhgwZRtnOAtIyLPApOUSHzA0gFqF/eQG9/v4mYstK1myaTETRYZ5zd8YNL/peegqNlzVf6tQEBeRRIlmPQRmNRD6WcUd3MMRzLCaLSoq6XeTZhofRW0hYFeJDX1c71Go1GzduxM7OjnHjxiG5rWazcM9N0soa+fzxWBxUijYu+KtfQMKHd9W9YzbDlkltPDVj14FUgalJR/WqGyARtZVKWt0ZAeZ++TzCvmysnhuPc2ziPe2y/OpyThSfYFmvZYQ63ptD/sfQthjY89E1rB0tGDitw1+aOvjPgspWgcpOwfXjxZiMZrxDf7le7o8hVigQKSxo+OorLCI6oPDz+26bh5UHbpaenKzczoncbCZ3TGgjv5NbIlJYo7xxgFpJBTpbBxy8umG0aI9N5meEtFrxuW07in+Qn5dIJIS7Kth9vYKkjEomPRBAmGMI9dp6Mst2ku7gi32agqgOrojEIhw9fRB0dVSlXKfG0xub6mpobKRcq+WFB7uzq6aBSnspQa0i9l5t4anuPTlcsp384Bi6l1XhXH6M/GY7HNp/71dFYhF+kU5IZGJyk6tx9rXGK9CKugoNNZIArjWPRJefhk3Gx4SF9GZY9NPkNebxZcaXlGivE+/XBXuL+xP9/svn5Pfm7mX+mfm4q9x5ucvL9PKM50RmNRsvFHIquxqxSES/UBdGdvKkd4gLMpGIurIWSrPqKcmopyy7AYPOhNJGTlC0C+E9PP4QJR2A3Nxctm7diiASY7QOx/ViHYLckc43P8S6vhhZeBC+Kz9HY6vg45SPyT69jbmacmZ4OyEWHEm49gKeMYG8nVeGg2Mh9bYrMSgCUDvO5uFd6xjvnoBziw1Z3f/BC8qXqRSJmG3vwJzOvpjNZjZs2EBJSQkzZszAxaVtQfN6cQMjPjnLzAcDmDckFBpL4ZM48O4Ck7bfTRWcsgl2PwWjVkHHcZh1JqpXpWKs1uA8syPyH9mutvYMJeOnITNaEXr0AiLp3Y1iF8ovMP3IdMa1H8erXV+9a/tP4fi6dLIuVTLm5Ricff44Pd7/Rpz8MpO0U6UMnN6BoOj77xgW9Hryhg0HqZSA3bvumv9nDr5DUtVmBrpNZ9nA24ymZhPCql4YGnO4EG1HdLf9qFSBGL54GmnBJpZE7WC5rQPL2nsz6Qf5+UVrd7EmS8bUOHcWjOqM3qTn8UOPk1abg8Z5IbtE7ekwqC21KAgCu957mdyMCpq9/HGrqMAMRD32GI6RUQxLzsYREcrLtVQ0tDJzaA2rM95luFc/5p3ehsiopaznRwQPvJvCI/tKJcfX3cLKQcHAaeFcWHuBonIx3BbrdpTm4+Vlwr33AG6qbrIkbTGJQYm8EHNvWo9/hb98uqY4o45jX6VSaM6lUlyCq6MT8f49cbCyp7ZRR3JBHXnFTUj1Ag6CGEeTCNHty7ZztcSrvT0BUc54trP7wyJBjc7IjiMnyb56BjVKTKXWhAo+2DQV0DHjM8RGI85PzcJ2xkx25e/h45SP8Sps5u/qUp7ytadOZMWoW3PokdCVF89lY5KVoHP9BIPUgQbXBfQ/fZRRZhdi9REUBe9ibvB4sg1S+rSI+XJ4WwVLUlISSUlJjBgxgqioKKBtYXn0p+cormvlxIu9sFZI4asJkH8SnjwP9n53Xoi2sW2x1d4fph5BEKB2YzrajDocHwtHGXJn1KfT15CyZiC2H2lwXfQ6DmPubnpq1DWSuCcRS6klW4dt/cVdrYU3a9m34jrRg33pOiLw10/K//CzMBnN7HwvmdqyFsbMjb5vmmwA9bFjlDz9DG5vvIH9uDsX040mEw98MRmN9BofxK+gr1+vtg1FF2HtAIp87ano0ImY6G2IDVrM70Vj0CqZ2HszV8QmDkS3I/x2fr6lpYVR7+4iR2/L3md6EO5pR0VLBaP3jKHBbIWL5QL2RUeg8mlbVDbodWycN5W6Zmh2C8SlqgqF3sCAha9TZGXH+Ot5dJTJqT9dTqvBRGKfm2zJ+ZxHfRN4JulzWvViagesJih+2F3XXJ7TwIGVNwAY/EQE4qZaDq9MoVnigMKkxihRYKLti1dpJyOyjycxA+6vVv7fKf/3p0BiUOMiqSFEFkqHhgdwTg/l5v4aTm/JJv1QERYZzXQyy4mwscTSVk6KpYm9lnrW2GnZ4yGQ5i6hTCHQavz92OEaNQbO5tTw/pFMxq08y4y3PiPn6mlqTNZEFNvSXhRASPbXRKV+jMLJAd9Nm7meEE7ivjG8ef5NulQ78VZ9BfO9bKmUKBhV+hwPzejHomsFNJsqMbqvwSi2pMlpDqHZWcQ2aoghjAb7HN5sP5xsoxSP/BZWDwwHID8/n5MnTxIZGUmnTt8zOO66VkpKUQNzB7Vvq6ZJ3wVZB6H3/LsdPMDJJW0kZUOWgEhE4748tLfqsBseeJeDFwQz6Tdno9yjQeLhgv2I0XcdThAE3jz/JnWtdbzz4Du/2MHrtUaSNmdg72ZJ7JB/j67mXx0SqZhBMyKQKSQcXpWGQXf/snZWffuijIqi5uOPMf9wPQuQSiS833sxJp0bL516ifzG/LYNPnHQcSLexU2YKlMoKPwUFNaIRr2PQlzAklObsEPMjLQCmo1t56ZSqXh1aAhyjDyz8SJGkxk3lRvLei1FYiyn1LCGRWeyEAxtz7pMrmDMvOUojE3Y1GRT7eKCxsKCC6+9Rie5mOUh3lzR6wiO90IEHDgdQYL/aDYU7mN9/HSsZQasDs0i9+zRu67ZPciO0XOjsbBqU+aq0yh5eMVwwtwb0IlViPVaOnCErqr1eEuuY83vr9wFf5GcvHXlEQJSZxCp3EuXQf74TOjKYYdt7LT8nJyAC8Qm+DNp0iC69PPngb6+DOrrR7tgB6ys5WRVNrP7WhnfXC3h05O5HLhRwcX8WtLLmihr1FLTrEOtNWIwC+iNZnQGM3qTmSatgcomLcX1Gm6UNHI2p4YDaeWsO1vAuwczWHokkx3JpaQVVBBjvImbUIcn1riUBiFv1RF3fQk2DQXYjR1LyauP8nrhP1ifvh57C3uelyYy/OYOlvkquKpUMl7zLDOmjeO5vWncqi7FJngtWqEVrfNL2DSLSTh/kDEBiRg0RuY8qCDZ6IwirYGvEzri42BJc3MzGzduxNramgkTJiC9/bncrDMyfcMVglyteWN4OCLtbeoCp+C2ztYfVwRVZ7XRF0RNgpgpqM+UQ16t8QAAIABJREFUoj5ehFUPT2z63s3/Xli0itpjX2N9RILriy+hvF3F80McLjjMp6mf8nTU0wzyH/SL5/zMtmxKMusZ+mQkNk6/7MXwP/x6yJVSnLytuH68mJZGPQEd76/BTCQSIffzpX7TJsQqFZbR0Xds93GwITnTlUJ9EmfKkhgeNAyFRAHeXRBdWYed3op0yQUcHeOx8OqJUJKCXd0ufOr7stFJSZFWz1Dntvy8j6c7xZnXOVMlQywY6Rrkgre1N1ZyKy4WbiPFSkz7Ym+Cg9rSPApLFR7t2pN++CAqpZoGW2+0IhG67dsZMm4MMomE9dX1jAh142Z6DU11gXQPga+L9mMfOZG4kvPobh6k2qYz9l5+d1yXhUpGuy6uVBepuX68GG2LkQdnPYCXq4nSa6UUiyPQNtsSJ1+Hb7AAAfH3Zd+/fE6+cP8Fjm8voZP1aTrZrEds6wI9/ka6bxc+TFvFubJzuKnceDz8cUYFjcJSZnnH/jXNOlJLGrhW3MiNkgbyalooqW/FZP51tpFJRPg5qgh1tyHU3QZPWQuZ5w6ja23FpdIeoyGEdrlb8Sw/h8TBnopnR/Ox8jy36m7haunKk52exDfTjNWJl9joL7Db2opJlrN4fsRMZm1OJimnEPfwdTQZKxE5z6FB7MfE/esZ32k49tkqnn+wgQsW3kjT6pkb5sUzfYMxm81s3ryZgoICpk+fjpvb97QD7x7KYGVSLjuf7E6Ujz3seQZSNsOME+D+oyYlQYBNiVByFZ65SmuhmNpN6ViEOeL4cCgi8Z15+8bGZK5eHY/bR47IGiwIPHIY8Y84hWpaaxi5eyQ+1j5sGLwB6T0YKO+Fsux6dr6X8r9qmj8RF/fkceVAAf0mh9E+zu1f7/ATKJ45C01yMkFHjyCxu7OztrhOQ79/rEPhvYruHl35pO8nSMQSuLASDr1Meicfmtw96RK7G3FjOcLHcWhNsbwX/AYfe0juyM/X1taS+P4hSs22HH4hnkBnKwRBYN6ZV9mftwfB5hmOdHwIj4DvFzqTj2zmxOdfYRdpR7k+EFlrK90rKui+ciUvFtfyVXkdT1vZsGF3JqHuKjzbb+V06Un+7j2S4SdXUKa1QZ+4gYC4+Luu22wWuLg7j+TDhbgF2DBoRgQW1jKufLiPa7ekmEQSOnrV8cBrE+7Lrn/5nHzhsRRObcmhSeKIla6CWLtvCbHehtjSFqInc8E/lpXZ20iuSsZOYcf4kPGMDh6Nm+qnb1a90UxpQys1zTrqWvTUt+jRm8yYzAIms4BCJsFaIUWlkOJsrcDD1gInK0VbdQBw5eI5Dhw6gkwvwrKhE0615XTM34iksY66bu35oI+WDHMpfjZ+TOkwhaH+Q7m4dRPOV95il5+JzbbWPOw5mZf6/I0Xtl5jV2o+PhEbqdMXonB5gVKLDgw9sZ1hnu3oWOTNvGgDSfbOWGU1ESVI+XpGNyRiEadPn+b48eMkJCQQE/P9PVBQ08KA5adI6OjO+2M7QcEZWDcUHngO+r95t0Fu7YMtD8Ogd9B7TaL6s1Skbiqcp0cg/pGUnsHQyKVLCUgzTdgsqcf11VdxmHQnHYIgCDx34jnOlp5l27BtBNj9slykyWDm60WXMJvMjF8Q9z/ysT8JZpOZXctTqC5uZtz8WOxcLf/1TveANjOL/JEjcZgyGdc5c+7avuRQBquvfYWF+w4eDXuUObFzwGSAld0xGdScjNDjG/A0gQEvwMmlcGIRlYY3eeKBeK5awoGY7/Pze4+dYvaxeoJdrNj7fB/EYhE6k47x+x4juzEHf8sF7Bo2FMkPKu8OfDafW9+mEjAgmPRCa0QGAz1z84hetozJdXrONqh5QWnLx7vS6RZki9xzLSlVySzxGkm/kx9QorGlZcinhPa+t3h9ztUqjm+4hVwhof/UcLza29NUVM2p5ccIiPUgbGKv+7LrX97JQ9tNmLYpiaunG9BI7bDWVxBhf4UIyw1IpSYI7MO1oB583pxDUukpxCIxPT17khicSA/PHr9YK/TnT8KMPu8MO3cc4pZGjlxrh321D1HF27ApSUWrkrKmH5xub6KzazQTQyfSz6cfIgEOfrycgIJPOe6r5zN7WyYEPszL3V/izX23WHc+G7+Ir6gxZKJ0fY5iRRSdbl4iobmOIaKOvOFjw2E3FQFVetTp9Rx4rifeDpYUFRXxxRdfEBYWxpgxY+4o65y2/grnc2s48WI8LkpgZXcQTPDEeZD/6AE2tMInXUCmwjjuKFWf3kQkE+PyZCckP2qWEQSBG2lPUVNzHN/PIzAVVhJ09ChiizvpH/5Zw/xC9AtM7jD5F5v4yoECLu7JI+GZjvctiP4/3B+a67VsWXQZKwcFo1+KRnqf3Zllc+fSdOgwgYcPIXO7M9Bq1hmJX5qE0m0PDbITvNXjrTZyuuxjsHk0FZFxpNsXEBuzE2tlEPyjG2atkbTmD5kU74CVpZwjt+vnTSYTz7y/mQO1jrw6uB3TegUDbbxIw3Y+RJMgYrLDu8wZ0uW78c1mE5tee4TqnEa6PDaYU+erMAkCcTfS6Dx7NmMVTpRo9TwlVvHBvgwGRdjRZPcJGXUZrPBOpHvSUgqb7aiPX0ZUwr3FbWpL2/SGG6o0RA/0JXaY/28Wlf/L18lDW42qayd/IgYFISvJorLCRJ6pEzcb+qM2hWHTkox/7laGVOQxzKkTFvaBnK5PZ0fOTjalbyKrPguTYMLewv6udM7PoqUGco5hPvMRyV+sZ1NqKxUmMZbNnnTIL6Bd/kZsKopJ6gAfT7CmQ8+RvPnAm0yLmEaQXRAmvZ6vX19ISPUGrvhoWeFgx6igUSx44P/Ye8/4qKrt//99pqT33ia9k0CoofeOCIgQQDpSRFFU4FqwUGyoCCqCIKDSQu/Se09CIKSQ3hPS+2T6zP9BFOQmXoF77+//vcr79eIB2efsM7PPmXX2Xnutz1rMt+eyWHshDZ/wvVRok5A6z6VK3AaHinsMzrjNc4Gd+czCnqNupvQxSLl7pZjPng8n0teexsZGfv75Z8zMzJgwYQJS6YM0/wvp5aw8lc4bA4LoHeQE5z+GtKMw5scWC29zeRWkHkb/7AbK98jRK7Q4zgxHYtfcF15UtI38gh/wUU1EvfkcjvPmYd7x4WevvLGcV868QpBdEB92+RCR8GgPeF2FghM/JOMb4UCHId6Pfo+e8h/ByFSCnas5CWcKUDdq8Qp3eKJ+jENCqd66FV1dLZZ9+z58DYkIa1MJe6+YEeJTyZGcvfRw74GjRyQU3cI8O54yN3sq6q7j5j4ewSEI4eZ6LJ1t8c7xZauz6L5/XiQS0cbLnhNx6ZzKkjOyrQfWplLMpeZ0cm3HgfSdxOlSiRR3w92xKfxWEEQEdupL8uUDFMSn0X/cSDIyi8h3c0V+9AhzjEXsd/EiQaRjqsyR6OvFdHXpg840iV0VsYS3nkZ48VmU6ZdIrbLCo1VEs5wZMysjQrq60lin5s65QgruVuERbPtv1R7+y/vkq+7JOb81lR7jAnGUNd0svU5P2q7L3DlXQIXgDIIIe00BPrY5BFicwY47aIDrdq6csXXiHAqq9E1Spp5mrkQ4hOFv44+PtQ9eJo7YCRIsNCrENflQnQsVaegK4sgoMCajqidZFkHU2BUj0otwKK3FqugE7bM0lNuISJjelYihk+ns2hmp+MGNLMsrYe/yD+hmdp5UzwaWOdgx0GsgK3quYMu1fD48nIh3q4NU6mPA8UVMxR0p00PU+f1MGTCI74os2eNpzARbCw7tSmdIuCtfj2uKnNmxYwdZWVnMmDEDNze3+9fU6PQMXnURnd7Aidd7YlxxF9b3gvCxMGpt88GtKYBvO2IIGEhF/UJU2bU4TA/DxL+5UmF9fQpxN0dja9sVu29FKJNT8D99CpHZg5emwWDg1bOvcu3eNXYP342P9aNFxhgMBo5+d4ei9Bpe+DASC9s/FoZ7yn+Xy3sySDhdwJDZ4fi2fbKN2JKPP6Z66zZ8Dx/C2O/h8Fed3sCwry9Rr6nGxPsbpCIJ0cOisWmogO8iUQT34qpDAr4+8/HxmQe7JmNIP0ml9WbWmtqzxs+Iz4M8mOTW9BLatv8XPryhpbWHNXte7nnf6G5L3sencR8gMenDxeFfYGn2YFVamnebHe+9g5G5QMSE+Zw6dQmDSIR7QSGdgJnPTUFi70Dfch1br+Qxs7cTcaqPKagv4GuP4XQ5/xWFcisy/F6l14vzEbeQGwJN8fTnt6VhMBjoNT7oifc7/vLumoK7VZzalIxSriWin4yOz/g85KutTMohcVcMOYViGo2awvwsNBU4mlTgZFaMm8ltbIUYMkwNxBsbE29iTIKJMVXih5ejpkoTwkrd8Kxwx17uj0QIRCs1osEyHZVZBajlWNbE0P9KJRIdiMaPIOD1d5CYPZyqrNfpubrnOnEHVjPIJZ47Ho0sd7Cjh3sPVvdZTXRsMe8dSMAr5DBVXEdrN4EIs96cE5ky/MJBJnTpyKEyS3Z4WDLdwYSrJ0pQa3Qcm98Ta1MpV69e5eTJkwwZMoTIyMiHrv3DpWyWH73LD5M70D/YATYOgOo8eCW2SS74n9k9FUPacWp9dtKQKMJ2TCDm7ZvXrdVq5cTGjUCnbaS16QqKXpiJ45tv4DBz5kPHHco6xLuX32VBhwVMaTXlke9x9u1yjq1LpOtof9oOaB7J85T/d+i0evauuEldpYLx70VibvP4xdG1VVVkDRiIedcueHzzTbP2yxkVTNx4g+l9RRwofZeOLh35rt93iE+9D9fWkNnvGfK1cXTqeBALnUXTRMSzB0X5b/BqKyk3bcT3/fMqlYo5X2zlXL0Ly0eEMrHLg4nF6yc+4nRJNL4WEzg4+uHSkklXt3Limx04+JpjHvE8SUnJCIKAZV0dXe4ksXLkBKo6dSY8V8mB+CJe7ufMNcXH5Nfl87XPGDqf+pQyhRkxluMZ9MYSjM1aliyoq1Rw9qe7BHdxJbiLa4vH/Bl/eSMPTent1/ZlknLlHpb2JvQcF4hXmP1DSyWDwUDZjWQyT6dQlKuiGlu0kgezTKlWjgkKJKiRCFp0BtAaBNSYohZZoBc/mD0aq6owmGRT5qRAIxgIcbKizcETGPLysejTB+e338LIs7kxKs6o5sT6I9QW7OZZ2V1uuDTysYMdvTx6sbL3SvbeLOHtfbfxCj5ElXADlW0UY5yGsVEh0DHxGuPtLUgwduYneydeMNUgLTZiZ2whO2Z2prOvPYWFhWzatInAwECioqIe+v4VDSr6fH6etl62/DStI8KN7+H4P2D0Rgh/vvmg5lyEn4aj9JpLRdpQLPvKsB7o3eL4J6csoKTkIO3abqXhnZ9QxMfjd+bMQyqfpfJSRh0ahb+NP5sHbW6KnHgE1EotO5bcwMhUwth3O/7b/sun/PtUl8jZ9VEsbgE2PDOvzRPJOpevWUPFN9/iHb0D09/lbvzG9B9jic2pYsHz1XwR/xEzw2fyashk+KY9ensfLgfUYGLqQYf2exBdWwOn3kfTdxOpZ5x5oYcFFhZGnOgQhKVETGpqKtN+vkW1yIozC/ribtPkatQb9Azd9RJFyquM8XmH93s+HN1yJnoRt/enENAjkHx8aWhoQNDr0SgUtIuNI9HTn5sTZ+BcZOBwQjEv9XEhRvUxubW5fB04mS7Hl1GllHJGM5jBiz7F2qn5BAma6i8g8MTy2H/5ZChoikftMymEUW+2RSwRcXTNHQ6tvk15fv39YwRBwLlzGN0Wj2XsD5OY+d1Axkx1olvrRsLsinA3qcQMOSK9DrVWgkErIDWArVCPt0kJrR2K6dNOznPTHHAdbUKRcyOWFmY8k5NL6683IBVEyNZ/j2ztd80MfEVhPYe/ucWu5WuoL9jOWL80rroq+NjBjt6y3qzsvZKDt0p5Z/9tZEFNBl5uE8UrvlH82KDDqyib/loFac5NBn6kso7+5h5ExxQyp5cfnX3tUSgU7NmzB0tLS0aMGNHsgfniRBoKjY73nwlFqC2AM0vBfwCENU9SQqeFY/9Ab+ZBRVo/zCIcsRrg1eLY37u3j5KS/fj4zMO0xIqGc+ewnTL5IQNvMBhYcm0JGp2GZd2WPbKBh6bN1oZqFb0mBD018P9HsHUxp9vz/uSnVJF4/snqk9pPnYrY3p6yL1e2WJHqnaHBNGp0ZGaF8VzAc2xI3MDZ8njouxhRQSytJUOor08kv2AjdJ4LjsFI4z/Eu7cLH8U1kqtQsyCtAIPBQHBwMFNCJGi1OhbuvHn/eiJBxK5nViKVBLIr53OOZ19/6DP0jfoUj/amZFxKJ8zZgFarxd7JCXdvb2I6R+KsqmPO26/iIE9hZFt31p4roYPR2/hY+zAv/SfODnkPOzMDg42PcfSD2RSmJLU4FoJI+K/VP/jLzOR/j06rJ+liEXFHc1HKNQR0dKbdIC8cPP59PZrU1FR++eUX6urqaFVfT/DxE5g4OuLw8lxsRo1qpstRllfHrZP5ZMTmolMex1iXxrjgfHZZKvjG1pp+nv34vOfnHLpdyoI98bgHHqRWFIPcJor3wqfxaWoOaDWMu30Zk8F9WC23YEhlPcv6tOOZNVfwtDNjz5yuSMUCu3btIi0tjWnTpiGTyR76HImFtTy75jLTu/nw3rCQpkIguVfg5etg04L748b3cGwRFdp30csG4TgjHKGFwgZyeTaxcSOwtAynXdstFL32BvKrV/E/ewax1QM98v0Z+3n/6vu81ektXgh5oVk/f0R1iZzopTEERjrTb0roI5/3lP8+BoOBo2vuUJhWzdi3Oz5RoZGqrdsoXb4c2frvsejZs1n7+weT2HYjn0OvdGJZ/Cvk1eWxY+g2vHdMxqCsJqlPNypqLtGp4xHMy4rhp2cw9FxEVdEovlPUsybAmBWBHkx2d6Curo6XVkZzRenO58+3ZkyHB7+R+JQsJsXPQaxvYO+z2wmweeDSUSkr2bL4BeqKxESMf4GL8en06NEDkUjEhQsXMFMqibx6lQYvf+K6jmFznp7ZvZ1J1q8isSKR94Im89y5b9A0yjlQGILvqPl0eGbUf9So/y1m8r9HLBHRpq+Mics6026QF7l3Kti5PIbD39ymILXqV/nPx6OmpoYdO3YQHR2NqKKCvqfP0ObKVdzefBO/48ewHTPmvoHX6fTkJJRzYGU8uz+JI/vWHQzqHdiLU5nSKos1dnq+sbVmmO8wPu/1OdGxxby5Ox73gAPUimJosIliSftZbE5MQS41ZtCtS5gOHcBquQX9SlWs7hbEon2JqDR6VkVFYCQRERMTw927d+nXr18zA28wGFhyOBk7MyNe7RfQVMkp4yT0XdyygZdXYDj7EUraobXug8Ok0BYNvE6nIil5HiKRCa1arUSdmU39yZPYTpr4kIEvkZewInYF7Z3bMz748ZI9Lu/ORGIkosuo5tLET/n/F0EQ6DMpGKmxmFObk9E9gSyI7dgxSGUyylZ+hUHf/PzX+gVgZiTmixPZfNX7K6QiKfPPv0HjwA8RagsJqXJDJDLj7t1/YPDuCmHPI1xZjW1/KTPqxHSt1vNeRhFJ9Y1YWVnx6tAInIV6PjyYSGndg5qx7UL9mGezGJ0g8MIvMylrLLvfZmxiz4g3l2JkqSFp/w7CAn25dOkSMpmMadOmYeLqyrm+fSlHy4h1b/NF6Wl2nMgmRFhAV7euLEn9kY19XkLiIGOMZyLlhz7h0JcfoWqUP9nAPyZ/mRDKlpBIxchC7GjV0x0jEwk5CeUkXywm9XoJqkYN5rbGf1roWalUcu7UKfbv20d1aRnhdxLompGJ99SpuH32aVP9SokEg95AeX49t07lc/anu9y9cg+dVoudcyKVuYcIcW5kiNsd3nW04pCZlCmhU1jceTEbL+Wx9GgCsuA91IriabCJ4sP2szkXF8cNaxf6xV/Eo0s3vlWb06NMy1ceRuypgB0xBXw0KoweAY4UFxezZ88e/P39GTJkSLMZwqGEYjZdyeWD4aF0cAJ2RIFjMDz79X1VvN+jP7IIofgm1aIPsZ/dA7F1yxtr6RnLqKw8S3jYN1hZhVP68SdoiopwX/nl/bKGBoOBhRcWUiwvZl3/ddiYPHr90NzECuKO5hI5wg/P0CeXu33Kfw8jEwk2TmbcOVuIXmd4bFliQSxGbGtLzY4dGHl7YxIU9FC7mZEEiUhgy/V8evnLGBzYgS13t5An6Blo6oH4zm5Mu7xFQdkuJBIrrFvNhriNCDWZmIx4kdbHCjnqKuVEXT1jXezwdnenJvMmN6pMSC+pZ0TbB/Wf2/vISLhrS5b2DMdzzzDCdygmkqZ9OHNLdyw9FGRcSUFXmY+JeyB3EpPo3r07Xbp0Qa1Wk9zYSGpgIF7piUxN+IXU9BIsPMbi4ytha+Yeqls9Sxe9hGD9LWrvFXL6dAJOPn5YOT65wudv/G1qvP4REiMxbgE2hPf2wM7VnIZqJXevlZB4rpD0mBJqyhTotHqkxhKkJmIEQUClUnH10GF2791LTlERHrl59C4sIGLiJNyXL8O0XXtqq7XkJ1eRcDqf8zvSSThTQHl+PbIQO0K7GVGWtY2StFhGdBAIEF/lVQ8PLksNLOiwgJfavMRXpzL48kwiHiE7qCWJetspLG4/g+rEW/xk4UKbjAQCZDI2mdjTqULLF/UN1HQIYf7O2wxq5cKiwcGoVCp+/vlnxGIxEydOxNj4YYPcqNYy6+ebeNmbsXxkOKJfFkJRHEzYBZbNw7UM+fEIx9+kQT8C82mvYuTasourrOw4mVmf4un5IjKPSaiycyhZuhS7KZMfin0+kHmAn1N+ZmGHhXT36P7I90yn1XNsXSLG5hL6Tw29n0n8lP972Lo0/aYSzxfiEWSLpf3jhbcaB/hTf/Yc8ouXsB03DuGfotrC3K05eLuIG9lVLOzXFROJMdvubsM8cCgRGRcxN5hT5+5N8b1dOMuikBo7QuwGxAGdMPdphd/lMrY6ichVqhnuZEOglzu342K4Ui7Fx8GcYNemVacgFtHXyp1DJbaUKU5ysfgqw32H3k+UdHBph8b4GjnXy7A1UlEjtiA/v4B27doRGBiIv78/twqLKLKzocDPjy6Zt2hzah8m+Y60bhXOpvJDJLgG08u+NX51V3CWVHLocCyNCg0ewa0QiZ88e/svHycvr6nm2p4ddHl+POY2jya6X1+lJCehnPzkKorSqtFq9GDQY0MhWnE6RTYCaiMpzqVlhCqlGIf1ReEciKJeQ12lkrpyxX1VPmNzCZ4hdniG2ePmZ0b8sd3EHzuErbUxUWEVFNYm8qqnD+XoWdJtCYO8hvL+wSR23EzFPXgbdfpc6uxn8U6bMXgUZjGnXoxTTTmdFXUc8AwmvFbBN0kKXF7uyoiNN1BodBx7rQfWplL27NlDSkoKU6dOxcur+cboylPpfH0mg12zu9DJcAd+HgHd34D+zeurGnQ6tJ/3QqQoQPXsOczatyw1oFAUEBM7HDMzX9q3i0YkMqL4H29Rd+IE/mdOI7FvykQtlZcy6uAoAu0C2TRo0yMnPQHcPp3PlT2ZDHu5Nd5PmHTzlP93qJVadi6PwaCHce91wsj00XSIfqPh0iUKZs7C+d13sZs0sVn7L4n3mLstnk+fCyeqo4w3L7zJ2fyzbLDvQcfYn1FNiuZa4VtYWraiXevNCOt6gE4Fc29QfbiANeVVfBtozGeBHkxxd+D0mTO8fboCpZEVZxb0wcnywYsp81gWI1XnEWq/pq1zO9b3X3t/Rq/TKTn68/NkHAfX8FDSNaZ07daNgQMHAqDX6/no9EXq4q5jrlZipdISdv0aHveKUXq7sMe/koIIF5aEDsXn7Ceo9Ebsz/ZGaRfGgFmv4BHc6onG/7/mkxcEYYwgCMmCIOgFQejwT21vC4KQKQhCmiAIg/6d6/wZhSl3KLuym03zZxFzcA9ajeZPz7G0MyGsmzP9+kgYHVlMhPgQZtqdZDtmkuNkhE2dgqBsMUaGYSSbjCAh34acO5XUliuwsDUmuIsrfScHM+69Tkz/vAf9p4eg16Sy/b153PzlIH27yJjqG88VZSYTPT1RG1uyefBmerkNYvqPsUTHJ+Eespl6QwF1Dq+xvF0UXRoqeL1CjYlaSeuaUg55BhOgqGZ1rA6PsRF8fC6DnEo5K8dGYGNmxM2bN0lOTqZPnz4tGviiGgXfX8hiWGtXOnmYwuH5YOcLvRa1MCKg2PYtUmUi6pAFf2jg9XoNScnzAQhrtRqRyAh1QQG1R45gGxV138AbDAaWXl+KRq9hadelj2XgFfVqYo/m4tnKDq+wp9IF/wsYmUjoP60VDdVKruzLfOzzzbt3x6xTJyrWrkXX0NxXPSTMhQ5etnxxMh25WseybsuQWcpYUJ9AqZULxmdWEOD3D2pqblBUuheGft6UtHhlNTbDfZmhkNCtSsf7GUUk1jfSq2dPhjlU0ajSsnh/4kPRPX4DfPi8vC0NdrO4VXqTNy+8iUbXZFPEYhP6R63BtWMd9xJT8DY2cPXqVZKSmqJmRCIRiwf0wmb0BC77h1NjY8XVXj3YPnIsiTYyoi7AO18UkPnWBmLqBqGpsuF5l2TaiG9SlHT7yQb/T3i8121zkoDngO9//0dBEEKBcUArwA04LQhCoMFgeHJB6n+BR9YljFLzUVtUkvvFCk5u3IhXx864hbdBbGKKQaVEr1Cil8vRlJSgKS5GWVREUV0dxc5OFLm7I3e0QWqwJtTBgS6DB+MWEPBI1zbo9WTEXOHq7u1UFubj4evJ5K46RDnb+Njdj2ipmtb2IXzV+yu0akvGrLtGZm06ziFbqNcrqHFcyMq2g+ihVzI0KReFtQNdsxI5E9IOmaac766a4hhpzwW1hh0xBbzU248ufvaUlJRw/PhxfH196d69ZTfIp8dSAXh7SDCc/xSqc2DKYZA2lyOQX07FJOsLtOZtMBk79w+/b1b2l9TV3SYs7FtMTZs2eCvWrUOQSLCbMf2zzsXIAAAgAElEQVT+cUeyj3Cx8CILOyzE0+rxkpduHMpGo9LR7fmA/1pY2VP+87j6WRPR35Nbp/Lxi3DE8zG0hQRBwGnBm+SOjaJq82Yc573SrH3xM6GMXHOFdeezWDAoiFV9VjH+6HgWePqyKekqbhVQatuFzMzPcIg8hkmrUXB5JUKbcThMCGHJultM6GzOrORcTnYIYspzg0nceIKTKSJ+SSxhWOumRCRBIqL/iGDm7New1lXBxcKfeP3866zsvRIjsRFmZl70n/weR2qXUJkQj3NYew4cOIC9vT2urq4IgsA7gTKkRlJWZ3vzXEMFnrkZ5JgIZAb54SYRYZEWi/2J2xSqtIAzVubV2Ir2wPOPHnn2yGP7n3DXCIJwHlhgMBjifv3/2wAGg+GTX/9/AvjQYDBc+1f9PKm7pvTsKe7+vB6T4kKMqpVIFFrEuqb3iVYiQSOV0mhmRoOFBXUODlQ7O1Fpbo5WJEIsCHi5udG6Y0dCQ0MxMno0oTKNUsndy+e5dfwwFQV52Lm5M7ibKy4528jU1LDIK4hMbS2TQyfzWrvXuJ3fwMvb41GKkzBy345GZEat4wLWRHSnt1TPc4dPc0cWSGR6AgmBrXESqvj+khpnc2uEKREMWXMZma0Ze1/qikGnYf369U2ZfHPmYGHR3G8em1vFmHXXeLWvP2+Eq2B9b4iYACO+bXasMqMa7c9zMBefglkXENxarhhfUXmehIQZuLtPIDhoGQDqwkKyBg/Bdvx4XN59p+k4RQUjDozAx9qHnwb/9Fgx8RWF9ez6KJbwPk9lhP8X0Wp07PooFrVSx/j3Oz22Hkvhq68hv3wZv1Mn768Kf8/86FscSyrh7ILeuNuYcjz3OAsvLGSCzpS3q2pRvHiA67fHYGsbSRvvJQjfdgS/vjBuG/K4Us6dzmJ2JzOecbZhXagX+w8e4uMbKnQm1px+szf2Fg/2tOqvFjEvp5jzlhexrP6R7u7dWdVnVZPOPZCetoJz644hL7HAENgGkY09s2bNwtz8QSjpD4XlLM4ooqu1OfMlBrYeOoetpgJToWlloKAGa3UDret0hLaPxHfanCcZ9n/prvl3Z/J/hDvw+6yCwl//9l8h3kTLDe9W4P3n/iyxWIyLiwtt3dzw8/PD19f3kQ27XqcjP/kOaVcvkXHjCqpGOY5e3jw/oT+eZYfQJEWzThbCBqkFlhIxa3uvpZtbNzZezuGTY6k4ut5EZLUHtUSGymUh2yLa0cFIYPbWaO4EdiAsL5XEgHDsxY2supmHo8ofuxdbM33fHVQaPavHNYVL7j/8C5WVlUyZMqVFA6/XG1h6OAUXKxPm9PCCLYPBzB4GLmt2rKZETt3WfTiKT0DHl/7QwKtUpaSkLMTCIpgA/3fv/73y+/UIgoD9izOAJjfN8uvLUWqVLO229LEMvMFg4PLuDIzNpHQc9rTa0/8iEqmYflND2bviJpd3Zzx2boPj/PnUnzlDxdp1uCx+t1n7wsHBHEsq4fPjqawa15bB3oO5U36HLSlbaG2oY1jcLvyC3iQjYzklznG49lwIZ5ZAxmnM2veja1YNL2XU8K1QQ1cbC8YMHMCNlA3sqTPj7X2JfD+p/f3Vo0UXN5anVzFZ35tCOzFXijYx78w8VvddjanEFP+AN6gZm0Dsz2VospORywLZtWsXkydPRvzrJuqLHo7YSsS8lprPMnNTvntlEkv33CEtK5eBngKu4hIqSsqJM5KQZabitX//FjTjTx2lgiCcFgQhqYV/I/4TH0AQhFmCIMQJghBXXl7+RH04ejmSHJjMFecrNAbIiQy2oK9xEr25Sn/zDNqa12Fdmod5ZiKWGQk4VBVjr6zHWClHUVONTtvch6/X66irKCPn9k1iD+1l/2dLWDNjPHs/eo+0a5fwbdeBKbOfZVJwJl63PiBWUcLo4Ai+k8jp7zWAvc/uJcy2E69sv8Xyo0n4BZ1BbrULtWlrpLIPOdSpE5FmUj7ctJkTgR3wKi0gR+aPtVTLJ1nH8CwNwHZkIOtT7nE1q5Ilz7bC19GCW7dukZCQQK9evfDxadkQ7okvJLGolreGBGN26wcovgVDPgPThzeldXUqKjbdwUb0HZg7I/R/p8X+DAYdyclvoNMpCGv1NeJf5R00RUXU7N+PzZgxSJ2b0rVP5J7gTP4ZXm77Mr7Wj1evMi+xkqK0GjoN9/nT0Nan/N/F2duKdgM9Sb1WQu6disc619jXB5vRo6neuRN1QUGzdncbU2b28OXA7WJuF9QA8Hr712nn1I4lTk6kx61DZt4Ta6u2pKcvQ9VhHNj7w7GFCDo1NiP9mV4vplu1jvcyikjTwgvP9KGtpJCTKaXsuVl4/1qCIOA6Ooiv0jQYG/dC7Dyb6/euM+fUHGpVtYhEEiLaryZkZCMSUxUWRVkUpKVy/Pjxhz7zaBc7fgr3JbNRRVRKDm+OCWN0rwi25lnwizyCkVNn0di5kYDwR3MRPy5/CXeNwWAgu7yWc6V7WH9nPQaDgXGBY5imMcYu9geoycdg7UmNc09S6525m1pMdfGDm4kgYGJugVgqRSQSo1Y2opI/vPlj6+aBLDSMAD8nZEI24oRtUJNPmo0rX8sCuNiQi4eFB4s7L6abezcupJezaE8ClYoq/Frtp1iVhMJiAD6yF9naJgAHscCqtWtYFdQF68YGFOaWmJuI+bBiFR1jZmDWxon0jo688MMNnm3jxldREZSXl7Nhwwbc3d2ZPHkyon8uz0eTHnefL87jYWvKvihXhLVdm0qKjd8Bv/Nv61U6ytffwbg8GhvRuiaZ4VajWhzf7JxvyMlZRUjIZ7i5PtC4uffhh9Tu3YffqZNIXVyoUlYx8sBI3C3c2TJ0yyNXeoIm0bboZTEYDDDu/U5P5Qv+x9Fp9Oz+NBZFg4bx70c+1ktbU1pG1qBBWPbvj/sXnzdr/01z3tvejN1zuiAIAuWN5Yw99DxmDeVEm7dG9OxybsQMx9GxP+EmI5uqmvV9D3ouQF3cQPqG20zpYoHeXMqx9gGc2LWTdWlG1ImtOT6/JzK7B5pWiruVXNyfyqzO5gRwi6qib5BZyljXfx2uFq7U1N7k2vnJZB7yQ683ocbVj8EjRjYTB0yqb2RyYg7VGh3fhXpiVq3hjZ23kau1vDsslImRnv8V7Zr/lpFvBWwHOtG08XoGCPizjdcnNfKHEop5Y+dtXuzhy9jO5mxI+o5fcn7BWGzM8wGjGSdxxDPpEORcAIMe7PzQeXaj2siHKp0llY1S5A0K9FoNep0OqYkpppaWWFhZ4GQtwVZUiXFFUpNgV/ldDECcVwe22tlztuYulkaWTA+bzsSQiajUYj47kcr2G/l4u1ZicP6JanUNtbbTGOL7DF8GyzAVYPP6dSz3jEBsAJ2RFAsTIxYrlxF5ZTrG5o6Ip7fimXXXsDCRcPiV7kgFPRs2bKCxsZE5c+ZgaWnZ4lj8VtLvwNyuRJyfBoVx8PINsH7gLTPoDVRuSUGdmomrxcsInh1h4r6HXgK/UV19g/hbE3FxfpbQ0C/uP4Sae/fIHDgIm9HP4fprrsWCCws4m3+WXc/swt/28TJUky8VcX5bGkPmhOMb8WTytU/5v0V5fj17Po3Dv4MTA6Y/Xmhg2cqvqFy/Hp99ezEJbe7y2RGTz9v7EvnuhXYMDW/aMI0vjWfG8an0kMtZNXA9eZJUsrNXEh7+HU5ntzQVHnklFmxkNFwrJvZMDjO6mRNiZcZGbwe+WrOBA8pWtPa0u19Z7Teq92ewN6+CxW1MGWRWSEbWx5hITFjbfy1BdkEUFPxIwo3PyD4ShF4woc7dj6hJkwn6p+SuMpWGqUk5xNc1Mt/LmUl2Nry19w6XMiqY1s2bD4b/50Mo/y0jLwjCKOAbwBGoAW4bDIZBv7a9C0wHtMB8g8Fw7M/6e1IjX9mg4tNjqey+WYi7jSkfDA/F372R9YnrOZFzAq1BS1e3rgxx7Uqf2mqsc69C3hVQN/z6RURgYgOmNiA2boqvVcuhofTBRaRmFHq04biDjKPqEjLrcrExtiEqKIrJrSZjIbFkV1wBK06kUdOooke7dO4ot6ETWVHj8BqLQ7sy08MRg0FP9PdrWOIWjsrEFEEQYWFqzAeiFYRf74l5VSgOcyOYeTyFmJwqDrzcjRBXK/bv309CQgKTJ0/G17dlN0h+ZSP9V17gmdaurAy6CwfmwNAvoNPDcr81h7JouFqMs886pGUnYe51sPdr1p9aXUlMzHBEYlM6dTyIRPLA/1+ydCnVu/fgf+I4Ujc3Tued5vXzrzOv7TxmtX68hDa1Usu2969j7WjKqAXtnkbU/IWIOZxN7NHcx9ae19XVkTlgIKbh4Xj+sKF5+6+a83K1ltNv9MJY0uQD35q4mc/iV/KaSsr06VeIjR+LWl1O5+BNSNf1hYABELUFg8FA1ba7HK2oZVEbU6Jc7BhTWcC6E/Fc1vjy9pBgZvd68JvQq3WUfXuLNfawwVPCdMdGYu6+T4OmgRU9V9DDvQd3U98iM+EIOUcD0EtMaPQKYurMWbi7P7wdqdDpeSejkB33quhsbc7aUC/O3L5HJx87Ap1bnrz9GX8LqeE6rY60wloW708irbSeTj52LBwUhLeTjj0ZeziQcYBieTESQUKYQxhtHdvQ2sgWT5USj/pKzBQ1oKwBnRqD2Jg6iZR8U0typFISDHJi63LIqcsBoI1jG0b6j+QZ32cwFhtzPr2clSfTSSyqpa23BEvZPm5VXEVj2gZT15dZExZOV1sL9Hod+9d+zRKnIKpsHJEYDJibGPOJ+Y/IEnQ4po/BZoQfPykb+fxEGh+PCmdCpCe3bt3i4MGD9OrViz59+vzhGMzeEseljArOzgnHZUs3cAiEacfhd26d+stF1B7JxqZ1IRbpc6DXP6BPc1+8waAn4c5Mqqqu0rHDXiwtH8ymNCUlZA0YiPXIkbguW0qNsoYRB0fgbObMtmHbkIoez58ecySH2CM5jF7UHhdf68c69yn/t9Hp9Oz5NA55jYrxH0RiavHoZTYrN22mbMUKPH/cjHnnzs3aL2WUM2ljDO8MDWZWzyaDbDAYWHRkIicrE/jecxStOo4jNm4ULi6jCC13gLPLYdJ+8OuLXqGldHU86zzEfO8u5gNfV/hlP3vu2ZCvs+LQK90JcX2gwaQplVP67W2WtzfngDV84GXMueQPSK1KZW7EXF4Mm0pCwnQK794l57g3OrEUfUBrXpz7Mra2zZM0d5dUsSitEDOxiK+CZQx0ePJn/y9v5M9W1jEnJZd3fN0Y52zLrtgCvjmbSXm9ii6+9kzv7kOfIEfSqu9yKu8UcaVxJFcmo9Vr7/chEUkwFTfFjzdoGjDwYFzMpea0c2pHJ5dODPAegLuFO2qtnlMppay/mEVCYS3uNqaM6trAweKV1KhrabAZxyDfsXwaJMNGKkGv13Hku1Ust/amwM0HI50Wc2NjVtoewjrtBrL4RZiGOZLZxYnxG64zrLUbX4+LoKysjA0bNiCTyZg0aVKLfniAq1kVTNhwgwUDA3ml6lO4ewhmXwKn4PvHKJIrqdyagkmIJfa1LyLoNU2z+Bbi5vPyfyAz8xMCAz9A5jH5obaS5R9RHR2N3/HjGHm489altziRc4LoZ6IJsgtq1te/Ql6rYuv71/FqZcfgWS1H9jzlf5vKogZ2fRyLb4Qjg2aGPfJ5epWKrMFDkDg44L1rZ4srvGmbY4jLreb8wgfhj41qORN29KBKp2LXs3tpqD1CXt5aIsI2YB/9JghieOkqSIxQ5ddRui6Bd7pbc9ZUz9fu1tzcvp0juja421tx8JVu91cJAPKbpZTvSWdhf1uuirVsCHXnctpXHM4+TB9ZHz6MXEha4jSq8uRkHnFFI4gxat2J6XNeajESLl2u5KWUXJIblLzh7cwin6dFQ1okq1HJW+mFXKpuoK2lGSuCPPA3Nmbr9Tw2XcnhXq0SmZ0pIyPcGd7GjUBnS5RaJVk1WRTUF1DYUEiDugGlTonBYMDCyAIrIytkljK8rb3xtPREIpI0FakuquVYUgm74wqpaFAhszNleg9nUtTbOJ57GJ3UDZxfZXlYV55zbnp7azUaDq/5ii+tPMj0CcVIq8HcSMq3zteQZG/CN+YzpFZWiKeGMnzdNUykIg7P647Rr354hULxL/3w98ulKbWceVaNya4o6P0O9P7H/WPUhfWUf38HibMZjiHHEV36FF7Y07R8/Sdqam8SHz8BB4e+hId999CPS1NaRtaAAVg9Oxy35cs5X3CeeWfn8VKbl5gb8cdJVH/E+W2p3L16j/EfRGLj9Bi1dZ/yP0XcsVxuHMxm4IutCOjQcuGMlqjZt59777yD+6pVWA1unjifUVrP4NWXeCHSk6UjHrxAcrJOM/7ia/ga2bBxzHFux49Gr1fS2W4h4uiJ0P9D6P46AHXnCyg5lcvMAbaUiA38o7GEK5eTOKMJZGYPH94d9vCeQNXudCoSSpk72J4snZZdbXxJv3eQz2M/x8nMiQ86voo27wOU5fakHrREbRAwa9eV6XNewtS0+YRKrdfzZW4p/e2t6Gj9+HLN8Dcw8tC0TNtfVsP7GUVUabSMdrFlgbcLbkZSTiSXEB1TwNWsCvQGkNmZ0sXXng7edgQ4WeDnZIGVibRZf1VyNQXVCpKLa7mZV831rEqKa5WIRQK9Ah15IVJGvTSej2M+RaGpo9FqKCODp7PY3xtbaVNkiVrRyL4vP2GtcyBp/uFItRrMpVLWyzLRZi/GL+FzJPX22M1tzYuHk4jNrWbfS11p5dbkh09MTGTy5Ml/GC4JsO1GHu/uT+K7sSEMPT8cjC1h9kWQNC2NtdVKytbcRpCIcBpnhnhLHwgZDs9vataXWl1JTOyziAQjOnY8iFRq9VB7yUcfU719O37Hj6FwsmLUwVHYmtgSPSz6ofq1j0LVPTnRy2II7+VOj6iniU9/ZfS6X0sGVigZ/0EkZlaP5rYx6HTkjByJQaPF9/AhBGnzZ+y9A0lsj8nnxPwe+Ds9mAid3j+J1+tuEyXrzyvtJ3LzZhQeHhMJupkO2eebNmGt3THoDVRsTiLnXj0zelpiLBHx3O1LXCu3JFFhw+ZpHekT9EApUq/WUbbmNmUqNbN6WlGl07Enwh+Umbx96W0K6guYEDCEdsqDSBrCSdyjR60zYNWxO1NfermZiOB/gr+Fkf+NWo2Wr/JK+bGoAq3BwBgXO2Z6OBJqYUpZvZLjSSVczqjgRk4VtYoH8fFGEhFWJlKMxAIqrR65WotS80Df2sHCiPZetvQPcaZ/iDNZ8kzeu76CwuqbaIx88POax0dh3YiwejAbbayrZe+nS9js3YaUgDZIdFqsJRI2+pUjz3gJr6x3MMkKwH5SCKvzK1h3IYsVo1sztqOM+Ph4Dh06RO/evendu/cff1+Fhj5fnMffyYKdHvsRYtfDjJMg6wSAXqmlbG0CuloVTnPCkR59HirS4OVYsHh4I8xg0HM7YTo1NTdo3343VpYPL6019+6RNXBQ0yz+o49YfHkxR7KPsH3YdkLtH7+gx9Hv7lCcXs3E5V0ey1f7lP9Nqorl7Pw4Bu8wBwbPDnvkDfb6s+conDsXlw8/xHZcVLP2ygYVvT8/T0cfOzZN7figobGKlZs7s9nCmI+6LSdIn0Bh4U909FuN1ZYZEDSkKXQY0NWrKV0dT6qDlBdDpMgkApFnDnGZCJSCEcde64Gz1QMRM01ZI2Xf3qLCw5wZYVLkOj372vrjZWzgs9jP2JexD09zJ4abFRAm6crtnSqUjY1Yd+rB1FdeQ9rCy+rf4W9l5H/jnkrN13llRN+rRKE30NnanNEutgx1sMHeSIJebyC3Uk5WuZzs8gaqGtXUKbRodHpMpCJMpWJcrU2R2ZkR5GyJzM4UrQGO3stmbcJaispPYhCZ4eg8jqUdptLT7mGd9MrCfPauWMbuVl1JDGyLSK/DUSzix8BaqtPm4FwehXV8fyz7yLjiZszcbfG8EOnJR6PCKS0tZcOGDXh6ejJx4sQ/9MMDLDuSwqYrORweY0PYoWFNkTRDm2KLDTo9FZuTUWXX4jA9DJPqPXD0TRjxHbRtrpGRk/MN2TmrCApahof7hOZj+v4H1Ozfj//xY1w3ZDP3zNymupvtXn3s+1OUXs2BlbfoPNKX9oO9H/v8p/xvEn8ij2v7sxgwI5TAjs2lrlvCYDCQ98JENAUF+J04jsisuVvv+wtZfHIslS0zOtEj4MHkRXvje2bf+oIEMwt+GryJmsz5iERSIhV9EF1YAZMPNuWR0CTvUbExibiuDrxspSJEpyL04nlO6sJpI7Nl+8zOD4VVNt4pp2p7KpWRTkxx0qAxwIG2/gSYm3Cp8BIf3fiIooYiOphpGe/Uk6xoFfLqSqwiIpny+sL/6Iz+b2nkf6Nao2X7vSq2FVeSrVAhEaCdlTldbCzoYGVGgLkJHsZGSP5Jr9xgMFCh0ZLdqOJ2fSMXS7O5lR+NqP4CYMDbZTjvdXyZSLvmD2p2fCyHvvmCw71GkuwVgkivx10q4seABkpTZ2Kn6IfDlXEY+9lQPcSTkWuvEuRiSfSszug1ajZs2IBarf5DXZrfyCpvYNBXFxnTzo1PSmeDqr4pJt7YEoPBQPXeDBrjSrEdE4h5gA6+7QTu7Zoe7H+aRVVVXeHW7Sk4Ow+nVejKZrMsdX4+WUOHYTt2LGZvvcZzB5/DXGrO7uG77+ttPyoGvYE9n8XRWKfmhSWdkRg9uY72U/630OsN7Pv8JjVljYx/PxLzPyhI8880xseTN+EFHOfPx2HO7GbtSo2OAV9dwNxIwtFXezwwxjotFd93J8pUjpGVO+u6vUFmykt4u8/A7/jOppDpOZfvuzZrj+dQf76QU8/JeFteQ5vqEjxTCzlV58pr/QJ4fcDDbsXaE7nUnyugargXL1CHAOyK8CPY3BSFVsGGOxvYnLQRAR0D7QNwP++AOv8exj6BTHtvKebm/35JUvibG/nfMBgMJDUoOFxWw+WaBhLqG9H9+tUlAlhLJFiImwTLGnV6arU6FDodUlUqJg3nMGmMQRAEImXDeKvdLPysmysr6vU6Yg/u5dyeHRwfPo1UZ08Egx5fIwk/BjZSmDIDK11bXC7PRWxphMm0Vjy38QZ1Si1H5nXHydKI6OhoMjMzmTp1Kp6e/1q98bfIgnNdE3G4tgzG74SgwQDUncun7kQeln1lWA/wgugXIOsszL3aJDf8O1SqUm7EDEcqtaVjh31IJM03f4oWLaL+5Cn8Tp7gg7RV/JLzC9uGbqOVw+Mnb2TElnJyYzL9poQQ3OXJogme8r9LdYmcnR/F4hlqx5A54Y/stimY+zKNMTFN4mUthCQevXOPl7c3ac6P6/S73072BW7vfJ5pbm509ejOXFdLSkv30dnmdcwOvgUDlkG3ptWoQaen/Ps7aEob2RIlY1VpJW3zM7CoMCWuQmDbjEi6+j+ob2DQG6j8OQVlejXVk4OYWFmKWm9gexs/2v7qui2oK2DF5blcKM/BWCSlTZ0v7nEN2Ji4M3nJJ9g7PfpG9B/xt6vx2hKCIBBuacY7fm780j6Q9O7hHGrrz1fBMubKnBjmaE17a3PCLU2JNKslUn+awMrF2JR9jIMmiUmhEzg5+hgb+ixv0cA3VFWy96P3OL1vF4fHziXVSQYGA6EmUrYEySm6OxNzUSBuN+chiARsJ4Xy5sEk8qsa+e6FdrhYm3Dp0iXS09MZNGjQnxr4M3dLOZdWzqudrXGI+QzCRt838I23y6g7kYdZhCNWA7yawinTjkKft5sZeL1eS1LyfHS6RsLDv23RwKsyM6k7fATbFyZwUZHI4ezDzGw984kMvE6j5/rBLOzdLQiMfLTl+lP+Wti6mNN5hC85CRWkx5T++Qm/4vT6fPSNjVSsXdti+9BwF9r/qjnfoHoQHo1vLyJ8B7Gouo6LhRe5qHLDSOpAouYIhoABcOEzqLsHNFWHshsXDAJMPVfJJFc7bnkGILdtwMPKiNd23qbsd7VhBZGA3bggJPYm2O/OZK+vJ5YSMWNuZ3K1uinZUmYl4+shB/my9QBCTJTEWWawv3cxJ32T+PCzycRcPP0Eo/jo/CWMfFZ1Ju+ffIszeWeoVFQ+0jnmEjGdbCwY72rPbDdjBpmk4yvfTWnGfK7emU1y/mY8zW1Z1m0ZZ8eeYVHHRbiYt2yUMmKu8tOieSSVlLFz4ptkWTqAINDTypRNfiXkJU/HROqJV/K76Oo02E9uxRcxuZxJLeOD4aF08rEjIyODc+fOER4eTqdOnf7lZ1dqdCw5nIK/ozlTCz8AqRkM/hQAVW4tVbvTMfKxwvb5QARlDfyyEFxaQ+eXm/WVnb2SmpoYgoOXY2HeskBS+dffIDIzQzRxNEuvLyXELoRZ4U9WpjHxQiF1FUq6jvZ7WtLvb0zrvjJc/ay5tDMdeY3qkc4xDgjAZswYqrfvQJWT06xdEAQWDwuhokHFuvNZDzcOXM64ejnDJA6svfMDdXYTaGhIpTAsDHQaOLn4/qESOxNsRwegLajn7RwtzztZE+8TjLVbAw1KDS9ti0f9u6LlIhMJ9pNDMejAYns6+0O8cTM2YvydLPaVVv/62UT0j1jJu23G875rI897hFDvKuV862JmZ7zBuB+GcyHv/OMP5CPw35Ia/n/K4ZNbOSg/yv57RwFwMXfBx8oHmaUMO1M7bIxt7mdhqnVqalQ1VCmryK/PJ68ujxJ5CQBSkZQIpwjGBI2hn2e/PzTqv1FfWcHZzevIjL1OfZvO/NxpMGpBAEFgrKM1C+1ukZHyFpYWYfikf4gyrxa7cUHsL61hw6UcpnTxYlIXb6qqqti7dy/Ozs4MHz78T5evGy5mk1/VyNYu95Deug6j1oOFE5oKBZU/pyCxNcFhUiiCRAS/vA/yiqaaruKHb3dFxVny8r/HzS0KV5eRLV5LkZxM/cmT2L88l49Tv6FeXc8PA3947HBJAKVcQzMLlxAAACAASURBVNwvuchC7fAMfVrx6e+MSCTQd3IIO5fHcG5bKsPmtn4kt43jvFeoO3KEsi++RLameW2Etp62jIhwY8OlbMZHeuJu82tcuq03Qtd5vH/5S9JbdebjO9EsC+hHRlU0zh0nYnR9I7SfCj49ADALd0QVWYP8QhGfTmtFraWSU54+dDYu5+btapYeSWb5yAfJe1JHMxwmh1K+MRGj6Az2Tw5hRmoec1PyyGpUssDbBUEQCAx4D0EQY12wiZFtn+Ue/dh4fDXZFgXsOriWXq/2/k8M70P8JYz8c52moF1TTll9OhWOWiStnKlV1ZJSlUKdqu6h7FUAAQFrY2tkljI6OHcgwDaAtk5tCbUPvV8Q4F+hVjQSd2Q/cUcOoNfruRc1i23WHogNenQiMa97OjFG2Ed66lfY2nTFO+9dGhPLsRrkzW1LEYs3JtEz0JH3nglFrVaza9cuAKKiov5U276wupE15zMZGmhB98R3IXAItB6LTq6hcnMSCOAwrRUiM2mTIFP8z9DtNXCLeKgfhaKQ5JQFWFiEEhjQvN7rb5SvXo3I2prYXs6cvrWe19u/ToDtk0mixh/PQ6XQ0vW55jo5T/n7YeNsRudRflzelUHqtRJCuv75/ozEwQH72bMpX7kS+fUbmHeObHbMosHBHP+d5vx9ur+O2e1trKqqZ5yFnrXFlcy2siDRNpt21jKEXxbCnEvw6wTG5hlfVLl11O1KZ8OrbRlzK5Hrzo607ahg6/V8wt2tier4wK1q7GuNXVQQVdtTMd2fxc6oQBZlFPFlbimZjSpWBskwl4gJ8H8HqcSK7JxVONpW8NPcnexav5nQyObf5T/BX2bjVaVScSB6O7kXTiGR12FqZU3n56II7tkbpViLTt8kgCkVSbE0snysYha/oaiv486ZE9w8sh9FfR2unbuzJaQbt4wtkeq06MUSPglwoV3NJ5SWHcHFeSTuRXNpOFuMxf/H3nmHR1V1ffuemt57TyAhJAFC6B1EEGmCdFARFEGKoGBviF3RR0FEFEEQFKRDpLcQeklIICRAICEhddIzk8n08/0xtBAIMcDzPS/OfV1cJLP32adkzjr7rL3Wb3X1o7itB0N/OoqngxXrp3TCwUrK+vXrSUlJYcyYMTRpcu+EoMkrE4i7UMQev8X4lZ+EKccRbDwpWnwWXZ4Kj5daYBXkCNXlsLAjWDvCxAMguxnjazRqSUgcgVp9hXZtN2NrG3zHfVUdO072uHHYTJ/IGOe1NHZuzLInlzXo2lWWVPPn7OOEtvGk17h/HlNv4dFEMAls+u40xVeVjJ7dHnsX63tuY9JqyejbD7GTEyHr1iKS1P4+fr3jPAvjLrNpamdaBtwS3nx2Hax/kf1dpzA952/6+rWhjzieFrKBeOz9Dfp8AR1vZm7rC6tQLEhCHuSIw/MRDNh1iBRbZ4KKtJQml7JmUgdiAmsuAisP5lCxNRP7zr449g9h4dUiPs/IJ8TGip+jgmjmYF6Qzc9fT9r5d7GzbUx09K9YW/s28Cr+CxZeNZp8Ll16h6dHP0X/me9iCo9GZTCyf9kv/Dr5BRJXrkKXXYSbtSvO1s7/yEiZjEayziSxc9F8fpkynkOrluMZ0hjb8dOZE9GdJJkdMkHA3sqKlRFONM1/mULFVho3eoPA0tdQ7cvDtrUXmi4+PL/0JFKxiCXPt8XRWkZ8fDwpKSn07NmzXgb+YHoR21MKmBZagl/BHnjyKwR7b0r/uoDuqhLXkU3NBh5gxztmFc3BC2sYeEEQuHDxQ5TKFKIiv7mrgRdMJhRz5yL18WZuUCoGwcBnXT5rkIEHOLHF7ENt/9Q/KyRi4dFGdM1tYxJg/4rz1GfSKbaywmPWTLRpaVRs2nzHPpN7NMbdXs5nW1NrjtlsKIR057ETf/BS+Bi2554iRdSMFMMujCGdIe4LUN5cDJZ52eH8VGO0l8rRHcpnTZcYIotyyfKwQhLlzITfE8gpU9fYt30XP+w7+6I6nIdyTzbTgrxY1zKUKqOJ/onpLM0pQhAEfHyGEh29hGpNLidODqKs7HjDLuK9rtdDGfW/jFJ5FkXRdo6fGICvbyXT3nmfiCHPoA6OQGPrwJl9u1g9+00WTRrL9gXfcnpHLHkX01CVlmAy3pS4v14NKicthcRtm4n97ksWTXqOdZ+9z4Uj8YR36kr0hOn81rQjc6w80UmkiMRiGtvbsDIkD/HFp1GrM2nR/CfcsgdQsTUTmyg3JH2DGffbScrVOpaNb0egmy2pqak3Flq7du16z3PUGUx8tOUcwc4yJmS/BWF9IHoUFdsyqU4pwalfCLbNr4V2XdgOyX+atTn8WtcYJzdvFfn56wgOnoqHR23dmutUbtuO5tw5Mkd2Ir74GDNbz/zHBbmvU5St5MKJAqIf98fB9d4zNQv/Lpw8bOj0dGOyU0tJO5xfr20c+/XDJjoaxfffYbqtwA+Ag7WMmb3DOXmljB0pBTcbRCLo/y3o1UzNzaSDTwd+z7nKVb2UtBAZgkEDuz+sMZZtGy9soj2o3H0Fu3IRC5o3olluBuU+NhSH2fP8spM1sudFIhFO/Rth19Yb5b6rVO7NppOLPXvahtPF2YF303MZmnSZDLUWN9cutG2zEZnMmYqK0w27gPfgkXHXKJVppJybgVqdQWDACzRq9CqlpVUcOHCAlOQkZGolrhgxlpegVSlvbigSIZXLEYxGjEYj3HI9HNw98AuPxC2sKeUiKavySjno2xi13ApviYgCE/Rzs2WK+DcqFGtwdIymWdT36I+LqNxxBZvm7tgODeX5Zac4fbWM38a1o0uYO/n5+SxduhRPT0/GjRtXrxTnX+Iv8/m28/zmG8tjVdtg6jFUZwXKYzOw7+SL08BG5oUrdSks7AB2HvDS/htJHgAVFYkkJI7B1bUT0S0WIxLdeVZu0unI6NsPg50Vz49Q0MKzJT/3/hmxqGFzgi3zTqPIVvLcJx3/cWFnC/8OBJPA5nmnUWQpGfVBOxzdagt53Y769GmyRo/BfcpkPKbXzro2GE30n3+Iar2R3TO71VCTZO/HcPBbSsesZuSZ/yAYq5numk+PqjY4Ju0wS3QHdbzR3aQxUDj/NJgEvKbHsHXfTpYUlHO8cRSiCj2dSk38ObYdcunNe0QwCZStu4g6UYFjn2AcHwtAEAT+zC9lzuVcdCaBKYGeTA3wxAoNEont/25lqAdFQ428xmgitaqaaDsR6emfk5u3Cmtrf8KbfISbWw+Ki4tJTEwkKSmJarUaKQLeDnbYy2XIRQJSkQipTI5UJkNmZ4/E1g69VEaJsorsq1c5Y2VPYlA4xfbOBEtFqEQSKg1GZnkqaFn8LkZjJUGBEwkOfgXVvnyUe7OxifbAYWgYU1efZk9aIfNHxTAw2helUsnixeYiCC+99NJdlSVvpbBSQ89v4ujoquLX8hdh0EKq5U9ekw12w+3ZCETXwxHXT4BzG80G3qfFjTG02iJOnHwKidiatm03IZPdXbu65LdlKL76it8nhnDQt5L1A9fjZdewhI3s1BJi5yfTZXgY0Y8HNGgMC/8OKourWf3JCbxCHHlqRst6GbzcmTNR7ttP421bkfnW9mlf15x//YkmTOt5S8CATg0/tge5LWeHLeL5XS/S1M6aiU5KuifrEdm4mdeybolI011VovgpGZsIVxxGhrJ06VJOiazY2SQGfbWBPhopy56OrnHcgkmgbM0F1ElFOPTwx7FPMCKRiEKtng8v5bJZUY6HXMrMYG/G+LhiVYeESV088j75LUXl9EtIZ8DpHM45z6RZy9WIxVYkn5nA6dPPIpdfpU+fPsycOZNnnn2W1h06orWx52K5ijMlShKLKzmRX8yR7HwOpKWzLyGJ3QlJxGphRYsu7Ipqj62bO0+4OZJtELAVafnKagHNCl7GzjaIdm230Cj4NSo2XkG5NxvbVp44Dg3jtbXJ7E4t5KOBUQyM9kWn07Fq1Sqqq6sZPXp0vQw8wBfb0tAbTXyg/BhCe6NzG0jp6vPI/B1wHRV+08CnboGza6HbmzUMvMmk42zKNAwGJc1b/FSngTdWVFC8aBFFLfz52+0qczrOabCBN5kEjmy4jKO7Nc26+d17Awv/ahzdbeg0NJSc82WcO5hXr208Zs4CQaDw69q1YAG6hnnQt5k3C/Zf4mrpLb5zuS30+xqKztP8Ujxvt3ubs0olsZUCV5oGQWEKnFpSYyx5gANOTwZTfa4EXWIxI0eOJLSskHFXzuJgLWWnk8CoXecwmW7G0IvEIlxGhGPX3htlXA5l69MRjAJeVjJ+jgpmW6swGttY8c7FHD5Iz/3nF60ePBIhlP3cnVCG+bE0p5ipadk4SKx4wm0R7ZzOUF00n7KE4Tg7t8PPbwyNG/chLMz8RDcYDJSWlqJSqdDpdBToDJwWJJzQweEqHTpBoIW9Dc+7O7GhsIRdJZX0lp5khHYebjaeNIr8Di+vAQg6E8XLU9FeLMOhZwC2PQN4bU0yW8/m837/CJ7vFIzRaGTt2rXk5+czcuRIfHzql85/5FIxm5LymO52kiBDGYauX1G8PBWxgxz35yMRX9d9qcyH2BngEw1dZ9YYI/3SF1RUnCIq6nsc7JveYS83KV74E8bKSr5uW8XQsKE8HvT4P/+DXOPiiQJKclQ88WIUEtkjMZ+w8JCJ6urL5UQFh9dfIjDSFUf3ut02cn8/3Ca+RPEPC6g6Mhy7Tp1q9flgQCRxF4r45O9Ufhl7y2Q3vC+E94O4Lxk+9QSpYUNZn74eL9cMZvhFYbXvM3Nxe/ubMsP2XfzQXi6n/O8MPINiGDJkCH/++Scfujgx3zaQA3IDj8WdY0OXCNzkZvMqEotwHhyK2F6Ocm82pio9rqOaIraS0MrJjo0xoRwsU+Fr/XBcmY+Eu+Y6JkHgYJmKTYoydhRVUGYwL6oGyDR4G9NxNWbjIq7GyS4IB7vG6KQ+KAU7rmj0nFNVo9CZU6F9rGT0dXegu52SzYWFbKpwxoVSJggL6WxvwN//Oby9ByMWy9AXqSlZmYahSI3L4DDkrT2ZtSaZLcl5N+pECoLAli1bOH36NAMGDKBNmzu+VdVCozfSb95BjOoydhpfQt7vOxQHIzCp9XhMjkbmcU2Nz2SClU9D9nFznK/7zdfS/PyNpKa9TmDAi4SF1S7zdyvay5fJGDSIIy2s2DTcl78G/IWtrGGFPAx6I398eAxbRznD3mpz823DgoV7oCzVsOrj43gGOjDo1Zh7fndMWi0ZAwYikslotGkjojvkmvwUd5mvdpznt3FteazpTaNNWZbZbRPWG/2wpby0+yWSFYm85SwwMikfUYsR5gi1WzCqdBTOO43YWoLnKzHEHTpAfHw8T/brzxyFjNM2ArZiMd9GBjLY07mG+0Z1JI/y2MvIvGxxey4SaT3WHurDI++TvxN6k8DpyipOVqpJqKgis1pLVnU1alPNL4wdKjxFFYRIigmVltBCdB53YyZbdc1YLwxDixV9pYeY4qmisU9/HB1v+gqrU4opXXsRkUSE6+imEOzI1D8S2XtewZtPhjOlRygA+/fv58CBA3Tr1o2ePXvW+xy+232ReXvTWWH1NV0iAigqex1drgqPCc2xCr7F5XJkAex6DwZ8D23G3/i4UplCQsIIHB1bEtPyd8Tiu7+4CYLA1QkvUZp4nBmTJCwavooIt4h6H+vtJO7K4uiGywx6LQb/8NpiUhYs1EXq4Tz2rzhPlxFhRPe891qOcv9+ciZPwfON13F78cVa7TqDiSfnxWM0Cex8tRvWslsWYeO/gX2fwJg1lAW2Y/TfI1Bq8lmEFc0vXYIXd9+oz3AdzaVyipecxTbGE6ehoaxatYqMjAxGj3mGjxMr2CMzIDjL6eXmyJxQXxrb3owq01wso2TVeQBcR4Zj09S1gVfpJv9KI38nBEFAaxLQm4yUqc6D+gIa9Xm0mgIMRhVVBh17DO1Zq21DicmOTnbVzG7kTgu30BpPY5PGQHlsBuqEQmQBDrg905QqKwkTlp/kVFYZHw9qxnMdggA4ceIE27Zto2XLlgwaNKjeq+eXi1T0/T6evlZn+d52CaVuy6i+aMR1VFNso28p9pF/Bn59HMKegJErb0gIa7UKTp56GhDRru0m5HL3O+/oGsq9e8mZOo3feomJfPkNxjUb94+u7a1oVHpWfHAUn1AnBkyNbvA4Fv69CILA1oVnyEkrY/i7bXDzvbck79WXJ1N14gSNt29D5lV7HenwpWKe+fU4r/VqwoxetyzCGnTwc7drUt3HuKQuZMzWkbiJqlmTW4WDYwC8FFdLFqRidxbKvdk4D2qMNMaVJUuWoFQqeX7ceD7Zl8uWikpE4c4IYnjGx42Zwd54WZldMoaSakpWpKIvUGPX0QfnfiGIZA2X3H7kF16NlTrKNqZjVOrq7CcSibCWiHGQyQh0aU6g3zCahL2PU+i37HL8gpc177KoujtNHD1ZE92Y9W07EO0edsMwC4JAdWoJhd8lok4sxKFHAJ6TWlCIwMifj5J0tZwfRsfcMPAJCQls27aN8PDwemnSXEcQBN7fmIK1SM/7xgWo3N6h+oIR5wGNahp4ndocTWPjCgPn3zDwRqOWM2cno9dXEN1i8T0NvEmrJeezT8hxF1HRvwNjo8bW2f9enNpxBb3GQMfBFvkCCw1DJBLR87kI5DYSdi9JxXhLlba74fXeu2AwoPjq6zu2dw51Z0ALHxbGXSK75JZFWKkcnpoPlbmwZw6hLqF80/07cg0SXvWxxVRwFo7+UGs8x8cDsW7qSnlsBqICLWPGjEEsFrPmr9V8ObAJz3q6Io7Lp4lWxMq8EtoeTeW189mkqaqRutngOTUG+y5+VB3Np3D+abRXKhp8verikTDy2isVVJ0qpGDuKSrjriLU4wuRq9Hxa04RQ09fov2xNOZlFdLS0ZZNMaFsjAmjm6tDzQLWBVUUL02h5PdURFZiPKe0xOnJYE5eLeepHw6RW1bN0nFtGdDCHMaVnJxMbGwsoaGhDB8+HMkdUq/vxobEXI5mlPAWy3H0fpKKC6E49AzAvvNtESq73jeX8nt6EdiZBb8EQeD8+XeorEwiKupbHBzu7XIp+PVnyCtkfX9nPuvxVYPj4cEcBnc2LoemnXxw83swBREs/DuxdZTT87kISnJVHNt8+Z795QEBuE2YQOW2bVQdu3P26Pv9I5GIRcyJPVczEzagHbSfBCd/hexjdAvoxoyWL3PCJOVbfx+E/V9AcXqNsURiEa4jw5G6WFHyRxqOEltGjx5NZWUla9f8xZwB4bzcIZiM/Tl0zzcwwsuFTYVlPHbyAk+cusAvBcVUPO6H+4vNEPQmtBkPx8g/Eu6a81XVrMpQ4H2xEu/LStxkEjxaeuHcyguNTIzSaCRHoyOzWkeqqpqTFVVka8yz/jBbK57ydGa0jxv+1rUXbLRZlSgP5KBJLUFkI8WxVyD2HXwQScSsOpHNh5tTCHCx5ZexbQj1NBu1lJQU1q9fT3BwMGPGjPlH9RzLqnQ8/u1+gnWXWOOwmMKyudi2C8b56ZouI1I2wLrx0OkVeOLTGx9fubKIyxlzadRoJiHBtaWFb0eXm8v5vn1IDDER9fNy2nq3vec2dbFryTkyk4p45uOO2Ls8+ILFFv59HFh1gZQDuTw1oyUBEXX7r00ajXkRViolZPMmxHcosbc4PoPPtqXx0zOt6Nv8lig3rcqcSCizhZcPIkjkvLVvAttzTvBRWSVDHcJh/Ha4TdpDX1CFYmESMh97PF5qTuqFNNatW0ejRo0YNWoUK47n8OnWVJp4OTB3TAxHtNWsLyzjjLIagABrOR0d7HjK04lenjXLiNaXR94nH6soZ1paFlrTvc/FSy6ljZMdbR3t6OXuSKhtzTR7QRAwlGioTimmOkmBvkCNyEaKfUcf7Dv7IbGTUanR8+GmFDYl5dGtiQc/jI7BycZsyJOTk9m0aRMBAQE8++yz91SVvJ031yWzISGbWPl7uOqmI43shOszETUjDIovwS89wDMCxm+7oZpXVLSbM2cn4+U1gKjI7+7pHhIEgRNjByNPusjZ715kbK/X/9Gx3k5RtpI1n5+kdd8gOgyyuGosPBj0OiNrPz+JrtrAqA/aY21f96RJdfgwV1+cgNvLk/B89dVa7QajiUE/Hkah1LJnZvcb9y5gVm79Y6g516Tne+iNesZt6c25ymIWFhTRqfsc6PByrTGv13u16+CDy+BQEhMT2bJlCxEREQwbNowjGaVM/SMRiVjEvFExdGviwWW1hrhSJYfKVByvUPGSvwevBTeskM4jb+QBjIJAvlbPlWotpXojlcVqlJnlSLNV2FQb8dIKBDvb4u5jj8TZComT1TXDKWCqNmIs12IoVqPNUmK65tuXBzpgG+OJbSsvxFbmp/eJzFJmrU0ir1zDjMfDmPpY6I16ksePH2f79u2EhIQwatSof1yo93p23suSLUwWydAFTsR9fDNEt8aY66vh115QmWcOl3TyB0CpOk9CwnDsbENp1WoVEsm9NWJS1y1B9P43xD/diJc+j70vN40gCGz+PomSXBXPfdIRuc0jkYJh4X+Eomwl6746RUgLd/pMbHbPCUze2+9Q8fffhKxfh3V4eK32szkVDPrxECPbBvLFkOY1GzdMhJT1MCkevKIory5g9KY+lGiNLFeUEfHSYXAJrjVm+bZMVPE5uAwNw66tN0ePHmXnzp1ER0czaNAgskqrmbTiFBcLVYzvHMxbTza9EeUjCAJ6QUD+EDJeHxkjfzcEownt5Qq0mRVor1Sgz1cjaAy1O4pFSFyssApwQB7shHUTF6S3iGmVq3V8uf08q09eJcDVhu9HtqR1kPnVURAEDhw4QFxcHE2bNmXo0KH/yEUDoNIa6PPtXqyUV9ko/RON+1d4TIpBbH2bsdw8DU6vgGfWQZhZYEyrLeTUqWEIgpG2bTdiZXXvDNWyohwu9HuSSnsxbWL34mrvcc9t6iLzTDHbFp6h68gwWjxmkS+w8OBJ3JnF0Y2X6Tm2KRGd6pblNZSVkdF/ADI/P4JXr7qjHPFnW1NZfDCTvyZ2oH2jW4rYVJXAj+3A0Rcm7AWpnPSCPby4ewZSvcBKSTC+Y7feCHS4jmAUKF6WgvZyBe4vNsO6sTMHDhxg//79REZGMmTIEAyCiC+3n2fZkSuEetrz6eBmdGh0/wV06jLyCILQ4H/AXOA8cAbYCDjf0vYOcAm4APSpz3itW7cWGorRaKp/X41e0CmqBF2BStAVVgn6co1gusv21TqD8OvBDCHm411Co3e2Cp9vTRWqtPob7Xq9XtiwYYMwe/ZsYcOGDYLBYGjQ8b+//rQQ/FascPz9HkLhN7sEg0pXu9PpPwRhtqMg7Jlzy/5VwvHjA4X9cc2Eisqz9dqX3qgXVr/4mJDStKmQFL++Qcd7Kwa9UVj54VHhj9lHBYPBeN/jWbBwJ4xGk7Dx2wRh0fQ4oTRfdc/+5X//LaSGNxVKli+/Y3uVVi90/nKv8Ng3+4Vq3W33beoW872295MbH8WlfCK0WxYpDP6liVBxdMGdj7FaL+R/e0rI+eiIoFNUCYIgCIcPHxZmz54trFy5UtDpzPd13AWF0PnLvULQW38LM1YlCrll6vpcgrsCnBLuYlfvN7pmN9BMEIQWwMVrhh2RSBQJjAKigCeBhaK7SR4+ABKyynjs2ziWH7mCWneHWfptiK2kyDxskXnZIfO0RXrDdXMTpUbPb4czefzbA3zydyqRPo7ETuvCO/0isL2WrqxSqVi+fDnJycn06NGDwYMH/6MomuscyyhhxYlcXpBsJ9RuBG4TuyOxu+1NoOAsbJ0FQV2ghzlz1VyE+xVUVedpFjUfR4dm9drf73++Q4tD+ZQ91YnorkP+8fHeTsqBXMoL1XQaGopE8kgEbFn4H0QsFtFrfBRSmZidi1Mw6Ix19nfs1w/77t1RfD8P3dWrtdpt5VI+f7o5GUVVLNx/qWZjxECIHgMHv4WrJwHoFvku00OacEUmY0byfDSFKbWP0VqK+7goRBIRxcvOYazS06lTJwYMGEB6ejorVqygqqqK7k082DOzO9N7hrLtbAE9vonj14MZDb84dXBfd6QgCLsEQbhuVY8B/td+HgSsFgRBKwhCJuYZfd3Vqe8TNzs5s7eco8Pne5m9OYUTmaWY6rEQeysGo4lD6cW8s+EsHT7fy5zYVLydrPljQntWTmhPpK/jjb45OTksXryY/Px8hg8fTo8ePRokE1qtM/LmysMEiQp42dqE0+QXkDjctlhbVQKrxoC1EwxbAhLpteIfsykpOUB4kzm4uz9Wr/1tPbeeRgv+RuVhT6cP5//j470djUrPya2ZBES6EtTMUrfVwsPF3sWKXuMjKcmt4uBfF+vsKxKJ8P5oNiKJhLy330Ew1n4odGviwdMxfvx04DLnCyprNvb9Ehz9YOMk0KkRicQM67CYF9ykJFjJmLV1LHq9utaYUldr3MZGYqzQUrIiFUFvok2bNgwbNozc3FwWL15MYWEh1jIJM58IZ9/r3Rnc0hd/l4ZJiNyLB+aTF4lEscBfgiCsFIlEC4BjgiCsvNa2BNguCMK6usa4X598QlYZSw9nsie1EK3BhLu9nDZBrrQKcqaRuz2+zja42skRicw6N8VKHQWVGi4UVJJ0tZxTWWWUq/XYyCT0bebN852CiQ6oGdJkMpk4cuQI+/btw8HBgZEjR+J7B4nT+vLRsjiWna9ipXw5HaYvQep+mzKlUQ8rnoarJ+CF7TeKgFwPlQwKmkxo4/pFxZwrOcf+V0bR87QB/9+X4dj2/mtKxv91kZS4HEZ+0K5eWYkWLDwIjm26TMKOLHqNjyS8fd0RKRVbtpD35lt4vj4LtwkTarWXVuno/Z8DeDtZs2lqZ2S3vo1mxsPygdBuIvQzK12Wlh5m+faxLDXY0tc2kC+GbrljxbTrETfWkW64PROBSCIiJyeH1atXo9PpGDhwIM2bsfwAUwAAIABJREFUN6+1XUOoyyd/zxAIkUi0B7jTVXxPEITN1/q8BxiAPxpwcBOBiQCBgQ2rPHSd1kEutA5yQaU1sDetkP3nFSRml7PjXME9t23sYUfvCC8ej/CiexMPbOS1/2jFxcXExsaSlZVFZGQkAwcOxMam4QJDhw5eZPl5Jc9KDtLhhc9rG3iAne/ClYPw9M83DHxBwRYuZ8zFy2sgjRvNrL3NHSiuLubnn19mcqIB27GjH4iBL82vIuVALlFd/SwG3sJ/lXYDQ8i7VE7cnxfwDHLAxdvurn0dBw5EuXcfinnzsevSBeumNZVYXe3kfD6kOZNWJLBg3yVe631LKc6QbtBhChxbaFatbNwTV9fOPNVhCqJ9P7CEbOz2zuDDXj/UepO3beGBSaWnfMtlytZfxGVYE/z9/Zk4cSJr165l/fr1pKen069fP6ytH17FtPueyYtEonHAJOBxQRDU1z57B0AQhC+u/b4T+EgQhKN1jdXQmbwgCFRVVWFvf2dDU6LSkl2qJr9CQ7laj4CACBHu9nK8nawJcrOrGSt7GzqdjqNHjxIfH49UKqVPnz7ExMQ0uIoLQHFaEQOX78VKVMWW4YE4trpDKb6E5RA7HTpOgz6fmbcr3s+Zsy/j5NSamJa/IRbfO0xTrVczdePzTJh7Dhd3f5puir1jksg/5e8FyeRfruDZjztgc7uLyYKFh4yqTMuaz09g4yBn2NttkN1hYnYdQ1kZGQOfQurqSvC6tYjvkL8y868kNifnsWlKZ5r73yIAqK+Gn7uDpgImHwY7d0wmA2dPPceek0dZZu/A+KZjeK3d23e0CZV7s6ncnYV9Z1+cBpgruBmNRuLj44mPj8fBwYF+/frRtGndMuB18dBCKEUi0ZPAf4DugiAU3fJ5FPAnZj+8L7AXCBMEoc6VkoYa+QsXLrB27VratWtH586dsbO7+1P9n2A0GklKSiIuLg6lUklkZCR9+/atd7GPu6G5UMqs5evZYfJibbtcWg2ZXLtT1lHza2JIVxizFiRSystPcTrpeexsG9Oq1R9Ipfc+DqPJyKv7Z9B23j7aZIgJ+esvbKKi7uv4AbLPlRD7QzKdhoYS0/v+3sAsWGgo2anm72HTjj70fK5pnRMvZVwcOS9PxvWFF/B6841a7RVqPU98fwBHaxmxr3SpqVRZcBYWPw7BXczhy2IxOl0pabseJzazjL8cHZjaciovR9dOlBIEgYq/M1AdzsOxVyCOvYJutF29epXY2FgUCgWdO3emd++7112ui4cpULYAcAB2i0SiJJFItAhAEIRzwBogFdgBTL2Xgb8fPDw8iIyM5MiRI8ybN4+dO3dSXFzc4PFUKhXx8fF8//33xMbG4uTkxLhx4xgxYsR9G/iqkwWsX7aRrSZfpnml0erp2l8Kii/B6tHgHAjDloJEilKZRvKZCVhb+9Cy5dJ6GXhBEPj65NfYbNxP24sC3m+88UAMvNFo4tC6Szh62NCih/+9N7Bg4SERGOlGm77BnD+Sz7n4uisrOfTogfOokZQuXYrqwIFa7U62Mr4a2oJ0hYrvdt+2qOvdHJ78Ai7vhSPzAJDLXQnp9jsjnIw8pVTxY9KP/JT8U61xrxf2tm3tReWebCp2XbmhmxMQEMCkSZPo3bs3ERENl/aui0cqGUqhUHDgwAHS0tIwmUwEBAQQFhZG48aN8fb2vmt4o9FoRKFQkJ2dzfnz57lyxfxHaNSoER06dCAsLOy+XDNgNrjKvdlc2XOEZ5HQSF7GurfHILW9zVirimBJL7OOxoTd4NoItfoKCYkjEYmktGm9Fmvr+i30/n7udzZs+ZrPVgo4du+B/48L7vs8AE7vzubI+kv0m9KCkBZ1K1xasPCwMZkEtv10hqvnShn0Wgy+YXfXfzFpNFwZNRpDQQEhGzcgu0OFtnc2nGX1yWz+eLE9nUJv+X4LglkvKnWLWU4ksAMAeXlrEa+fwg9iB7bY2zE5ejKToyfXutcEk0DZhnTUpwqx7+6P05PBD+R+hH9hxqtKpSIpKYmUlBQKCsyLrmKxGBcXF5ycnJDJZIjFYjQaDSqVirKyMgwGcySou7s7ERERNG/eHE9Pz7p2U28Eg4myTZeoOnWR1yWXOWsMYNuk5gSHhNXsqFPD8gFQmArj/gb/NlRX55J4ejRGYzWtW63Gzq5+mjCbL23m873vMX+FHFeZM402bkDi3DDxo1upKtfyx+xj+DZxtmjFW/ifQVttYN2Xp9Cq9Qx/py0OrndfyNRmZnJl6DCswsMJ+n05otuy09U6AwN/OIRSY2D7jK642d+yfqWpNGvPG3Xw8iGwNWe9p599G5+tP/OlsxebbWRMbDGRaS2n3dHQl2+5TNWxfOw7XfPRP4Cqaf86I38rKpWKzMxMFAoFxcXFKJVKDAYDBoMBGxsb7OzscHFxwdfXFz8/P1xd779Ky60YlTpK/khDd6WYVbb7+FHdibmPOzK8d9fbOhpgzVi4sA1G/QFN+6PR5JGQOAaDoYKYlr/j6Fi/cKvdWbt5Y/8sPt9sR0i6iqAVv2MbE/NAzmfXknNknC5i9Ox2OHk8nLheCxYaQllBFWu/PIWzpy1DXm+FtI6F2Mpt28idOeuu/vnUvEoGLzxMl1B3ljzfpqaxzkuCJb0hqLPZPy+RYjIZuBg/lNADcXwcGMZGsZYXm73IjFYzaht6QaBiayaqQ7nYRLnhMjL8Zq3mBnJfIZT/17G3t39gsaj/FG12JSUr0xCq9aT77mBhXjeGNzbWNvAmE2x5BS5shb5zbzPw5f/IwB/OPcyb8W8y46gLIWkKvOfMeWAGPvdCGeknC2nTP9hi4C38z+HibUfvF6LYtvAM+/84T69xkXd1hzj264f61ClKly7FOqIpTgMH1miP9HXkvX4RzN5yjqWHr/Bil5Cbjb4tof9/YMs02DMb+nyGWCwltMtKskq689HZdMThbViSsoQKXQXvt3+/Rhy9SCTCeUAjJM5WVGzNwLD4LO5jI2snQT4gHokcdKNKR/mWy5i095Y0+G8gmASUB3Mo+vkMIokIonYwK68VTex1fPx8/9s6C7D9TUj+Ex57D9pPRKPJJzHxGfT6smsGvkW99ptQmMCr+19lWLobHQ4ocHnmGVxGjngg52Q0moj/6yIObta07hN07w0sWPj/QEgLd9oNDOHi8UIStmfV2dfrnXewbduW/Pfep/rMmVrtYzsG0SvCiy+3p5GSe1tBj1bPmROkji6A5L8AkEod8O23hdxAF2ZfOMWLrtGsu7iOWQdmoTVqa43v0MUPt2cjMBRUofgxCV2uquEnXgePhJHXXq5AdTQPxQ8P70LVF0O5luIlZ6nYmol1uCtureJ4LdkZjdiOH196onaS1d6P4eRic/GPbm+g0eSReHoMOn0pMS2X19vAH8s/xuQ9k+lY5MyQjQpsO3bA6523H9h5nd2fQ2leFV2Gh9X5GmzBwv9v2vQLpkl7L45vyeDC8bsnQopkMvzmz0Pq6UnO1GnoCwtrtotEzB3WAnd7Kyb/kUC5+rbyon0+N2tJbXkFchMAsLHxx/Hp9ZS4WjEjMZY3g59ib/ZeJu2eRKXuNtkEwCbKHY9JLcAkUJ3S8IjAungkjLxttAceLzVH0BlRLExCGZ+DYPzvrjUIJgHViXwKv09Ed1WJy9Aw3Joe4au4LE4IEXw+LIZQr9siaQ7MhUP/gdbjoPcnVKkzOJUw/NoMfhlOTi3rte/4nHim7plKG6U7U/4oR+7vj/933yGSPhhvnKpMy4m/Mwlq5kZItCWaxsL/NiKRiJ7PRuDXxJl9K9LIvVh2175SFxf8F/6IqaqKnClTMaqqarS72MlZ+EwrCiu0vLLqNMZb9bAkMhixHOy94M+RUHYFACeX1oiG/UaVrZQx8T/xZdRLJBclM37HeAqqaj905P4OeM5ohWPvh/OG/EgYeQCrRs54zmiFdbgrFdsyUSz8783qdbkqihYlU77hEjIfW7ymt8JOspv1sZtZYuzHuI6BDG51i8a6IMC+z2D/p9BiJPT/D5XKFBISRyEIBlrFrMLJqX5+9L1Ze5mxfwZtDf7MWFGBxN6ewCW/PpBImuvEr76AYBToOvL+Q0ktWPhvIJGJeXJSc5zcbdi+6CxlBVV37WvdpAm+//kWzfnz5E5/BUFXc8YeE+jCx4OiOJhezDe7LtTc2M4dnl1n1phaOQzUpQC4+fZHPeQ/6KQCfXZ9woLWb5GrymX01tGcKartGpLYyR5IlM2deGSMPJgvlNtzEbiOaYqxQotiwWlK11zAUKZ5KPvTF6kp+TMNxQ+nMZRU4zK8CR4TWyDN/JPETd/zruElOjdy4f0BtyQgCQLs/gDiv4aYZ2HwT5RVnCLx9LNIJDa0brW6XsW3ATamb2TWgVm0k4Yyc6UKkdFE4JJfkd2HYNrtXD6tIDO5mLYDQiyLrRb+T2FtJ2PAtGjEEhGx85NRlt7dDjj06IHPp59SdeQoeW+/jWAy1Wgf1S6Q0e0C+SnuMtvP5tfc2CMcRq+C8mxYNcosgwB4Nn6eikGzMQl62v49k9+7fI2VxIrxO8azNWPrAz/fu/HIhlCa1Hoq466iOpIHAtjGeGLfyRf5fQppCYKANqMC1eE8NGkliGRi7Lv44dDVH7GNFI4tomD7lww0zsXGwY3N07rgYndt1dxkgh1vwYlfoM2L0O8b8hVbSEt7BxubQGJilmNtde8aj4IgsDB5IYuSF9HbuhWTl+RjKi4hcPkybB5gJJFWrefPOcexdZQz/O02iC1a8Rb+D1KUrWTTfxKxdbJiyOut6tRZKlmyBMXcb3AZMxqvDz6o8eaqNRgZ9csxLhQoWT+5ExE+jjU3PrcR1o6DJk/CiBUgNe8nL3E2nlvnYbSxR/XsVl5P+p5Thad4sdmLTIuZhlR8/27Vf3WcvKFci3J/NupEBYLehDzQAZtm7thEuSF1q5+CpGAU0OepqD5XjPpsMcYSDWJbKXbtfLDv7GsOfRIEiPuCyrj5jBB9Q47gzoYpnWly3Q9v0MHmqXB2DXSchtD7YzKuzOPKlR9xdm5Pi+YLkcnu7WLRG/V8dPQjtlzewjNOvRn6QzKmikoCFv/ywEIlrxP3x3lSD+Ux7O02eAY53nsDCxb+R8lLLyd2fhLO3rYMfi0GK9u7CxIWzp1L6ZKlZkP//vuIbqm7WlChYfCPhwHYOLUTPk632ZCTv5qL+zQdAMOXmf32QP6p9/HYvgCjlS28sIev0tex9uJa2ni14etuX+Nhe3/lN/8VRl5v1COT3P0PZ1LrqTpViPq0An2+2T8ndpQj93dA5mmL2EF2oxqTYBQwVekxlGkwKNTorioRdCYQg1VjZ2yjPbCN9kB0XcDIoIUt09Emr2Oc1X84qfJg2fh2dAm7tkipqYC/njVrUz/2PsbO00g9/yYKxTZ8fIbTNPxjxOJ7x8gWVhUy68AskouSedN5BB2+3YNJqyXw11+xaV6/qlD1JS+9nI3fJhLdK4Auw8LuvYEFC//jZJ0rYdvCM3gFOzJwektkVneOEhMEAcXcbyhduhTn4cPxnvNRDUOfll/J8EVH8XexYc3LHXG0vs3uHPsJdrwNUUNgyGKQmGfqhac/w3XrXASZNeLntrKtOpdPj32KjdSGL7t+SUffjg0+t0feyJ9WnObN+Df5sMOHdPXves/+hpJqNOdL0eWo0OUoMZRo4A5VpETWUqRu1sgDHbAKdsQq1KV2WT51KawZiynzEDNcFxKb78R3I6N5OuaacFf5VfhzBBRfhEE/Ut2kE2fPTkOpSiW08RsEBk6s12JmQmECs+JmoTaomWv9DN6fr0Ds4EDAzz9jHd7kntv/E/Q6I399egKTUWD0h+3vejNYsPB/jUsJCnb9moJPqDP9p7ZAbn1nV4kgCBR9P4+Sn3/GafBgfD75uIb8QfzFIl5YdpIOjdxYOq4tcultrszD881rb5GDYcgvIDVLIyjOfodT7MdITCJMw37lqnc0s+JmkVGRwaw2s3g+6vkGndcjn/FqLbHGXmbPlL1TGBI2hDfavIG9/O6+d6mbDfad/W78LpgETNUGTFV6EIFILEJsJ0N8ly/ADa6egLXjEVQKPgpcRmy6jLeebHrTwGfGm310Rj08s44iRyOpJ58CRES3+AV39573PDejyciyc8tYcHoBfva+/Fz+FKbPFyMLCyPg50XIvLzqcYX+GUc3XKZCUc2g12IsBt7CI0Voa09Mpkj2/JZG7PxkBrwSjZVN7ftcJBLh8eoMRFZyiuf/gEGhwG/e90iuqdB2a+LB50Oa8+a6M7z2VxLzRrVEeuuaVefpIBLBrvehuswsVWLlgGfz1yh18Ee+djK2f43H77HX+bPfH3xx8kvCnB/OG/MjMZMH0Bl1LExayG/nfsPT1pM5HefQya/TAz7Ca5iMcOQH2PcJgoMfH/vM57ekKl7qGsK7/SIQgbmSzK4PwC0U4/DfyKjcTPbVJTg4RNG82Y/Y2ATcay/kqfJ499C7JBQm0NfrMabsEFG9Yxf2PXvi+/VXSO5SJOV+uJpWypZ5SUT3DKDLCIubxsKjyeVEBbt+PYd7gD0Dp7fE+vY39FsoX7+e/NkfYRUSTMCiRcj8bk4Qfz2Ywadb03g6xo9vhkcjuT0MMmmVeS3Ou7lZ58be7HtXFp9Cv3owrsVKNI07YD18LVg3fN3rkXfXCJV56He/ibzPfzhTXcD7h98nsyKT3kG9eb3N6/jaP7iQQhTnYfMUyE1AaDqQz21msfhoHuM7B/PhgEhE1WXmak5psdB0AJW9ppN6eTZVVen4+z1HaOg7SCR1V2UyCSY2pG/g21PfIiAwx/k5Quf/jS47G48ZM3B7aUINH+GDQqvWs/qTE8isJIx4t60ls9XCI03mmWJ2/HIWVx87BkyLxs7p7vdl1dGj5EyfgUgux2/u19h1ujmB/HH/JebuvMCotgF8/nRzxLcb+os7Yc3zYOcBo1aCj1m9VaPJo3jTQHzPX8Jg74R02ErEwd0adC4Ps2jI/wRlZ+YhTYnFOC+KqMsHWdt/NdNjpnMw5yCDNg3ip+Sf0BjuM1ZeXQo734NFXaA0E9OQJXxi9w6Lj+bxfMcgs4HPiIOfOsGFHZh6fcjlNjGcOjsWg76SltG/ER7+0T0N/IXSC4zdPpY5R+cQ5dCEP3IGEPjGT+YF1qVLcZ808aEYeICDa9KpqtDx+LhIi4G38MgT0sKd/pNbUF6oZv3XCXUmTNl17Ejw6lVIXJzJfnECinnzEK7Jk099LJRXeoay+uRV3lh3Br2xZow9TfrA+K0gGGFJHzizFgBra198Rxwlp/dojHolJQmfP5TzfCRm8kZjNdmn38Pp0O+4lusxugQgeexD8oM78k3i9+zK2oWHjQcTmk9gWJNhyCX/QO2tqgQSlsKRBeYomZhn0Hb/kNe35xGbnMcLnUP4oJcvon2fwMlfEdybUN5zAqnlf6DR5OLtPZgmYR8ikznVuZsidRE/n/mZdRfX4Sh3ZLbQn6Df9qLPvorToEF4vf/eDX/gw+BSgoKdi1No2z+YdgMbPbT9WLDwv4Yiq5K/FyRjMgn0n9wCn9A6io6o1RR89hkV6zdg07o1Pp9+glVICIIgMH/vJb7bc5HHm3ry4zOtapYPBFApzDP67CPmRMgnvwQr8z1dlLMJB9dWWNs2rJTmI++uuU5Z6XEUcZPwu5SNvdqIyTkAccxYTvlF8kP6WhIViXjbeTM+ajyDQwdjK7tLBqfRAFmHIWUdnFkDBg2EPQGPz0bpHM7LKxM4fKmEt58MZ5JbEqKd70JVEdqWg0n1raZUeRI7uyaEN/kIF5f2dR5zhbaC5eeWszJtJXqjnhese9J/VznaQ0eQN2qE13vvYt+5c4OvSX2oKKpmzWcncPa2Y8gbrZBYkp4s/MuoKKrm7wXJKEs0PD4ugrA2dQc0VMTGUvDxJwhaLe5TJuP2wguI5HJWHMviw80ptAly4dexbXG6PR7fqIe4L82aVc5BMOhHCL7/+/vfYeQFAUQijEYN2Vk/o0qYh3+uEpdysw6F4BnBUZ+mLDQUkKzOxUFmx7CQ/gwPGUAAMlAVmCsy5SbAlUOgLgaZLTQbCh2ngmcEGUUqJq1IIKO4iiU99PTIWQTZRzB4NeFShC+5phRkMjeCgyfj7/csYvHdF3OuVFxhZdpKtlzeQrWhmmfFHXn6oAFj/FHE9va4T5mC67PPILpDVfkHidFgYsPcBMoV1Yx8ry2O7vVLELNg4VFDo9Kz7acz5F+uIOaJQDoMalRnlrdeoaDwiy9Qbt+BPLQxnjNnYf9YD7aezee1v5LwdbZh8dg2NxMibyXrKGycBOVZED0aen9yY1G2ITz6Rr7gLGyeBt1eh/D+IBaj0eSReWUBZZlr8FRo8FY7YldagkivIdlKzgpHB/bY2WIUiYjRaBioqqJ3VTXOjgEQ0B4iBkBob5CbZ/t7Ugt57a/TtBKn853PblzzDmC0dSI7xIMM1zJkcjeCAl/C3/9ZJJI7vyEodUr2ZO1ha8ZWjhccx8YkZVJZNJ1OKCEpFbGjI65jx+I69jkkjv+dDNNDa9NJ3nuVJyc1o3HMgyl3aMHC/1WMehMH16ZzLj4X/6YuPDEhChv7uidayv37KfzyS/RZ2djExODx6qukeTbm5T9OU60z8M3waPo2r11LFp0aDn4Lh+eBzAae+MSsSNsAHn0jnxkPsTOgNAM8I80z78jBYGVPdXUu2VcXk5+/EZNeibPghadVFM4iP5QiO7YqLxNbmU6GpggxYlp4tKCbfzfa+bQjwjUCg1HMN7EJlCVs5GWb3YQbL2GQybjib8VVXytsnSLx93sWb++nahl3QRDIrMjkSN4RjuYf5VjeMfQGLd2LPRiU7YH/yWyE8gpkgYG4jByB84gRD9XvfjsZSUVsX3SW5j386TbqwSZUWbDwf5nUw3nEr7qIraOcJyZE4d2o7jU1Qa+nfMNGin/8EYNCgXWzZoiHjeI1hRuJeVWMahvABwMisbO6Q+5Ncbo53DrqaYge2aDjffSNPJj96Oc2mp+MRWkgtzcb+vAnoVEPjFIpCsU2ChVbKS09iiDoEIkk2Nk1wc42jFyjFYnlJZwszSK90qwyJ0GEn1ZKjLaCYIMOT5ERwU2K3scPP59e+Hg8gZVtKAaTgUp9JUXqIhRqBVmVWZwvPc/50vNU6ipxqxDoUehKpzx7/M+XIiqrQGRtjX2PHjgPG4Zdp44PLWLmbpTkqVj/VQIu3rY8/XorpLcvElmw8C+n8EolO39JQVWupU2/YNr0DbqnSJ9Jo6Fi40ZKV6xEl5GBxNWNi1EdWCgKQRMWwadDWtCp8V1qMlxzOTeEf4eRv44gwNXjkLgC0raAthLEMvCKBO8W4BmB0dYFpaCgqjqTKnUG+qocRFUlyLV67KuMVKshQxBzxkpOopUtmdYyKupz7QUBRzU0LpHRttyVJgoJ3ldVyAvMRQskrq7YdeiAwxO9se/WDbHt/x/pXk2VnrVfnkKvNTLinTbYu9y9sr0FC/9mtNUG4ldf4OLxQrxCHOn9QmS9JLcFQaDqyBHK/1qDKi4OQaej1M6F4+5NELdtz/CJg/ELvIMLp4E88kbeUFqKLjMTmY8PUk/PmxWRjHqzwb+0B3IToeCMOcW4DspkXiRq/UgWQvFq2Ych/Z/CxlpOlb6K7LJMynMz0Sjy0RcWIioux7pCjVVhOVZ5pchyixBVVd8YS+bri3VUFDatW2HXsSNWYWH/9Rn77ZiMJv7+8Qy5F8oYPLMVPo3rfg21YMECpJ8s5MCqCxgNJtoNbER0T/96S28bVSpUe/dSvmMXFUePIdOoAVC6eeMa0wKXls2Rh4ZiHRHRYJmSR97IF8X+TfEbb5h/EYuRenoi9fBA4uCA2NHR/L+dndn4m3SITBowVAMCJr2BwnINl4sNZJfpwAiNnWQ0sRch16gxKZUYlUpMSiWmqjskS4jFyHx8kAcFIQ8ORh4chLxRY6yjIpG6uNzfBXnACILAwTXpnN2fQ49nwonq6nfvjSxYsACAslRD/OqLXDlTjJu/PT2eCcc75J9NkgSDgazDpzi8bieac+cILcvB89rE02X8eLzferNBx/bIG/kdh9L4Zcl2IqVqWsq1BBgqcaxWYqWtRlApMVVWYqqqQjAaEYxGuJapBmBChF4swSiRIpHLsba1QmptbX4wODggcXRAbH/tfwdHpB4eSD09bjxIpG5uiCT/N/zZiTuzOLrxskU+2IKFBiIIAplJxcT/dZGqCi2RnX1pNzCkTkmEu3G1VM3yI1fYeewi9oW59GwfxhsTnmjQcT3yRj6vvJodKQXEpxdx9HIJWsPNtGIbmQQHayl6o4kqrRGd0QSCgBgBX0crOjXx5PFIbx4L96wtF/oIceFYPnuWpRHW1ove4yMfWj1JCxb+Deg0Bk7EZnJ2fw5imZiYXgG07B14V+niOscymNibVkigmy1Rvg1znz40Iy8SiT4BBgEmQAGMEwQhT2QWSJ8H9APU1z5PvNd4D2LhVWswcllRRbpCSVaJmspqPSqtAZlEjJ2VFDc7OaGe9oR62uPvYvOvKEydlWIuluAT5szAadFIZI/uw8yChf8m5Qo1xzZlcDlRgY2jnNZ9gojs6ovsv6z99DCNvKMgCJXXfp4ORAqC8LJIJOoHvILZyLcH5gmCUHd+Pw+n/N+/naxzJWz/6SwuPrY8PbMV8jtoZ1uwYOH+KMio4Nimy+ReLMfGQUbLXoE06+7XoJl9Q3hoRUOuG/hr2AHXnxiDgN8F8xPkmEgkchaJRD6CIOTXGsTCQyMrpYRti/5fe/ceU2d9x3H8/YXSA+VSOEhPKZcWLAXb2guus9t0q1vmbclwSZMZ/9Asi2YXF82yRI3J5v4wcUu2JUuWuZl52UXr5mZsnG6zWqtLZqvdCqW1UCxQoFxKgVKQAuV898fzox4pF7Vwnuecfl/JCc/5PU/TT78r5DSuAAAJrUlEQVTlfDnP7/nxnDrChZnU3LvZGrwxC2R5+VJu+X41J5oG2P9yC/95/j32/6OVKz5byJXbij7SssuFctGvehF5GLgdOA1c54aLgLaYw9rdmDX5OJm8V3b+iiy+es/sH4pgjJkfK1bnsuJ7m+huGaR213EO7m6n9rU2Vq7PZ8O2YkquCMf9etic0zUisgtYPs2uB1X1hZjjHgDSVfVHIvIi8Iiq/tvtexW4T1UvmIsRkbuAuwBKS0uvam1t/cT/GOM59GYHe55uoKA0e85PvTHGLJzhgVHq3+jg0JsdjJwZJyscovLq5VRtLSQ3Mn/v7uOyukZESoGXVHW9iPwGeF1Vn3H7GoBtc03X2Jz8xdGosu/FZt55qYXSdfnccOe6uM0JGmNmNjEe5diBkxx5q5O2w32oelM8lVuXc3l1wZw3QZvLgs3Ji0iFqh51T2uAI257J3C3iOzAu/B6eiHn4/u7hql9tY2yjQUUV+ZdkqtHRkfOseuJw7TU9XLF5wr5wm2Vdl94YwIiNS2Fii0RKrZEGB4YpWFvF0fe6mLP0w28saORkqo8rtxWzKoNM9zX5iJc7Nu8R0SkEm8JZSvwLTf+Et7Kmia8JZTfuMi/Z1Z9ncM07Ovm0JsnSAulUroun7KNl7Fyff4lMVXR2z7EPx+rZ/DkCNd+vYIrtxVfEktDjUlEmbkhqm9YyebrS+ltG6Jpfw9N+7vp6xxekCafFL8MBXBufIL2I/001/bSXNfLyOAYKSnCijW5lK7Lp3RdmHBhZlI1v2hUOfDKcfbuPEYoM40b71zHiopg3UrBGDM3VSU6oaR+wl/ITPrfeJ1Ko0p3yyDNtSdpru2lv8u7IVBWXoiStWFK1+ZTXJWX0O/ye9vPsOfpRrqOnaZ8cwHbbqskI3thP0XKGBNMl1yTn+pM31naDvdx/NAp2o70MzZyDhGIlOVQsjaf4so8IqtyEmIuf2RojLf/3kL96+2EMtO4Zvtq1ly9PKnOUIwxH88l3+RjRSeidLec4fihUxw/3EdP6yCod2FkeflSitbkUrQmeE3/7NA4B3Ydp253O+NjE6y/toira8oT+mzEGDM/rMnP4uzwOJ1NA3Q0DNBxtJ/e9qELmn6kLIdlK3Pi3lBVld62IQ7uaadxXzcT41FWX7WMLV8pI7wiM65ZjDHBtWBLKJNBemYaZRsLKNvofVL62eFxThwd4ESj1/T3vdh8/mYNuZElLFuVTWTVUpatyiZcmDnv69CjUaW37QzNdb28t7+H/q73WbQ4hcqty9mwrZj8oqx5/fuMMcntkm/yU6VnplG+qYDyTV7THx05R0/rID0tg3Q3D9L+bj+Ne7vPH58VDhEuzCSvMJO8yBKywulk5YbIyguxOGPRjHPlGlXOvj/O8MAY/Z3DnOoYordjiM6m04yNnAOBoopcNlxXTMWWCKElNi1jjPn4rMnPIZSxiJKqMCVVYcCbQhnqH+Vk6xn6Oofp6xymv2uYjsYBJsajH/qzkiKkhVJJC6WyaHEKqt41gYlzyujQONGofujY3MgSVlcXUFSVR3FlmCU5tlrGGHNxrMl/TCJCdjid7HA65ZsLzo9Ho8pQ/1mGB8YY6j/LUP8oo++PMz46cf4hIqQuElIWpZCRmcaSpYtZkhMiN5JBXiQzUBd6jTHJwZr8PElJEXLyM8jJzwDsw7GNMcFgbx2NMSaJWZM3xpgkZk3eGGOSmDV5Y4xJYtbkjTEmiVmTN8aYJGZN3hhjkpg1eWOMSWKBuguliJzE+xjBT+IyoHce4yyERMgIlnO+Wc75kwgZIf45V6pqwXQ7AtXkL4aIvDPTrTaDIhEyguWcb5Zz/iRCRghWTpuuMcaYJGZN3hhjklgyNfnf+h3gI0iEjGA555vlnD+JkBEClDNp5uSNMcZcKJneyRtjjJnCmrwxxiSxhG/yInKjiDSISJOI3O93nlgi0iIiB0XkgIi848bCIvKKiBx1X/N8yPW4iPSISH3M2LS5xPNLV986Ean2OedDItLhanpARG6O2feAy9kgIjfEKWOJiOwWkcMickhE7nHjgarnLDmDVs90EdknIrUu54/deJmI7HV5nhWRxW485J43uf2rfM75pIg0x9Rzkxv37XWEqibsA0gF3gPKgcVALbDW71wx+VqAy6aM/RS4323fD/zEh1yfB6qB+rlyATcDLwMCbAX2+pzzIeAH0xy71v3/h4Ay932RGoeMhUC1284GGl2WQNVzlpxBq6cAWW47Ddjr6vRn4FY3/ijwbbf9HeBRt30r8Gyc6jlTzieB7dMc79vrKNHfyX8aaFLVY6o6BuwAanzONJca4Cm3/RRwS7wDqOobQN+U4Zly1QC/V89bQK6IFPqYcyY1wA5VHVXVZqAJ7/tjQalqp6r+122fAd4FighYPWfJORO/6qmqOuSeprmHAl8EnnPjU+s5WefngC+JiPiYcya+vY4SvckXAW0xz9uZ/Rs33hT4l4jsF5G73FhEVTvddhcQ8SfaBWbKFcQa3+1OeR+Pme7yPaebKtiM964usPWckhMCVk8RSRWRA0AP8AreWcSAqp6bJsv5nG7/aSDfj5yqOlnPh109fyEioak5nbjVM9GbfNBdo6rVwE3Ad0Xk87E71TuPC9wa1qDmcn4NXA5sAjqBn/kbxyMiWcBfgXtVdTB2X5DqOU3OwNVTVSdUdRNQjHf2UOVzpGlNzSki64EH8PJuAcLAfT5GBBK/yXcAJTHPi91YIKhqh/vaAzyP9w3bPXma5r72+JfwQ2bKFagaq2q3e3FFgcf4YArBt5wikobXOP+kqn9zw4Gr53Q5g1jPSao6AOwGPoM3vbFomiznc7r9S4FTPuW80U2LqaqOAk8QgHomepN/G6hwV94X41142elzJgBEJFNEsie3geuBerx8d7jD7gBe8CfhBWbKtRO43a0O2AqcjpmGiLsp85hfw6speDlvdastyoAKYF8c8gjwO+BdVf15zK5A1XOmnAGsZ4GI5LrtDODLeNcPdgPb3WFT6zlZ5+3Aa+7MyY+cR2J+sAvedYPYevrzOorXFd6FeuBdtW7Em7d70O88MbnK8VYn1AKHJrPhzRe+ChwFdgFhH7I9g3dqPo43N/jNmXLhrQb4lavvQeBTPuf8g8tRh/fCKYw5/kGXswG4KU4Zr8GbiqkDDrjHzUGr5yw5g1bPDcD/XJ564IduvBzvh0wT8Bcg5MbT3fMmt7/c55yvuXrWA3/kgxU4vr2O7LYGxhiTxBJ9usYYY8wsrMkbY0wSsyZvjDFJzJq8McYkMWvyxhiTxKzJG2NMErMmb4wxSez/ovW8NBpBJwkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From c0271b2b82266d1a41ddf652d4edc6741d34fd93 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 215/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 6f31640e2ca1e494ed05b5daba3f05732c89b244 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 216/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From 09df2375b2e0774c8e0f533e2991226106b21cd5 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 217/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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cnQI7uvnR5WITodNwe6t53t71NmnUJI/1znyQEVHQpq9NHP0m2cQRjpZNgy/vs9cgnPxfbkfjG1mDIb6VTX6aKEKKJgrlH5Xl8M6v7PTXg6+Ds//Z8EnrIiJsM0ur7jDoKruvbL/TVOUkjyVTYcGTMObu8PkirbFtIcz4jV3TYey94dP0FhEJ3c6211McOQzRzdyOSDWQJgrle4eLYdrl8P0XcNafYNgtjf+yi0uDnNH2BnaZzXd/BR/ebkdSjbk7uGZPPVFFW22Hf1ImXPRiaI5w8qbHePh2qv1s5IxyOxrVQDo8VvlWyS54fqydtO7cJ+C03/rnF3GzJLjwRThlip0e4rVL7QihULb+Y/veVZbDpdNscgw3HU+3o93WznQ7EnUcNFEo38lfC8+OhMItdpROTWe1v0REwOi/wTn3wYaP4YWxNlGFmtI98Pov4JULIToOrngb0ru5HZV/RMXajvl1s/Q6mhCiiUL5Rt48eG6U/TV89fvQ5azAvfbg6+CSabB3IzwzAvasCtxrN0Z1tR3d9ehg28F75h1ww5ehM4fTieoxDg4W2L4YFRI0UajGWz0DXpwI8elw3WzI7B/4GHJGwTUfgqmCZ0fDxjmBj+F4FKyDF86BmTdB6z7wX9/AGb+zv7jDXZeREBljk6MKCZooVOMseAqmXwkZfeGaj+0FdW7J6AvXzYHU9vDyhT9eixFMKsvhs7vh8VMhfzVMeASufg9adnU7ssBplgQdz7CJQtfTDgmaKNSJqa6G2X+CWf9thzxeOSM4ppdIbmtrFp3PtL/WZ//Jxuq26mpY9Y5NEHPvgV7nwZTFMPCK8Bn+ejy6n2P7svJXux2JagBNFOr4VVbAOzfA1w/AoF/YieqC6Wrp2ETbZ5F7jY3xjV/AkUPuxFJVCcteg8dOhtevAgxc/iac/zQkpLsTUzDoNhYQnXo8ROh1FOr4HC6B6VfA5s/hZ/8Lp90WnL+II6PsdNypHWH2H+2KcJe8CvEtA/P6leWw9BWbqAq3QKtecMFz0PPc8Ljeo7ESW9ur69e+B8N/73Y06hg0UaiGK90NL18Ae1bDxMdgwGVuR+SdCJx6o+2zeGsyPHMWXPaGf/sDKsrsBWVfPwSlO6HtIDtXU84YO5xX/aj7OJvEC/Psv5EKWvrJVQ2zdQE8NRz2bbYXgwV7kvDUcyJc9Z69IO+ZEbDla9+/xoF8+PweeKCPvVo8rZO9HuK6OdB9rCaJunQ/x97rGhVBT2sUyjtjYOFT8NH/QHIWXPsRtOnjdlTHL3swXPcJvHIRvHQuTHwU+l50/OeproL9m2HPSnu9xm7nvnirPd51tJ2ypP0pvo0/HLXoDK162n6KcJuvK8xoolD1qzgIM260E/vljIHznoDmqW5HdeLSOsK1H8O0K+Ct6+0cUWf8rv4+lrL9PyaEmvv8NXYRIQCJtM1Y2YMh92roPh7ScwL254SF7ufYWXIP7guOUXOqTpooVN32brSd1vlrbKf1sFvDo/mkeSpc/padnfXzv9uO5nPute3knglhzyrbx1AjriW06W2vAm/dy95adtMZUBur+zi7cNX6WTDgcrejUfXQRKGOtmamnSI8IsoO5QzkdByBEBVja0dpHeHzu2HZq4Bz4VdENKR3t5PX1SSE1r0hoVVwju4KdRn9IDkb1r6viSKIuZooRGQM8CAQCTxjjLmn1vHTgQeAvsAkY8wbgY+yCamqhE//Yod0Zg6w01yntHM7Kv8QgeG32/6W7YtsW3nr3rYpKTLa7eiaDhHb/LTkBdvUGRPvdkSqDq4lChGJBB4FRgLbgUUiMsMY43mp5lbgauC2wEfYxBwogDevsesEDLoaxvyjaTSrdD/nx9E3yh3dz4EFT9j5uXpOcDsaVQc3G52HABuNMZuNMRXAa8BEzwLGmC3GmOVAEMzBEMa2LYInT7dDYCc+CuMfbBpJQgWHdkNt35FOEhi03EwUbYFtHo+3O/uOm4hMFpHFIrK4oKDAJ8E1CcbAwqfh+bPtlczXfqztxCrwIqMg52xY/yFUHXE7GlWHMBjGAsaYp4wxucaY3PT0Jjx/zvGoKIO3b4APbrMT6E2e68704EqBXaPicDFs+crtSFQd3EwUO4Bsj8dZzj7lb/s325Xolk+D4f9jJ9ALx2U3VejodCZENdfmpyDlZqJYBHQVkY4iEgNMAma4GE/TsG4WPDkcirfbeY+G/z48ro9QoS0mzg7DXvtBcEwLr37CtW8IY0wlMAX4CFgDTDfGrBKRP4vIBAARGSwi24ELgSdFJETWuAxC1VUw5y/w6iRI6wC/nAtdR7gdlVI/6jHeXuS48zu3I1G1uHodhTHmA+CDWvvu9NhehG2SUo1RugfevBa2fGk7q8fep6OaVPDpOspOi7J2JmQNcjsa5UHbHMJZdRWsfBOeGAbbF9upwSc+qklCBae4NGg/FNZ/5HYkqhZNFOGosgK+fQkeGQxvXGMX67n+09CaGlw1Td3OtsujFua5HYnyoIkinFQchPmPw0P9YcYUiE2w03Dc8BW07ul2dEodW84Ye7/hY3fjUD+hkwKGg0NFsOhpmyTK9kH7U2HCQ9D5LJ3IToWWFp2hRRd78d2Q692ORjk0UYSyA/kw/zFY+AxUlNrOwGG/1UVzVGjLGWMXyyo/YGvFynWaKEJR0Vb45mH49kWoLIde59oEkdHX7ciUaryc0TDvEdj8ub1iW7lOE0UoKVgPX90PK6YDAv0uhlNvgZZd3I5MKd9pdwrEJtnmJ00UQUETRSjY+R18+W+7oFBUMxh8PQydYtewVircREbbq7Q3fGyv0taZA1ynicIfFj4NW+dBZKz90EfFQmTMj/f1bdfed7jEztO/aQ7EJsNpt9pF6ONbuv0XKuVfOWNg1duwaym0Heh2NE2eJgpfW/u+nZE1qS1IhO1DqCq30ydXloOpOr7zxbWEs/4Eg6+FZsn+iVmpYNNlJCD24jtNFK7TROFr3/3HJombltt59murrjo6eVRV/Hj/w3a5Xca5/VA7YZpSTUl8C8geYvspzvyD29E0eZoofOlwiV3OcfC1dScJgIhI54tfv/yV8ipnNMz5M5TsgqQMt6Np0rSXyJfWf2hrAj3PdTsSpUKfXqUdNDRR+NKqdyAxE7IGux2JUqGvVU9IztZJAoOAJgpfKS+FjZ9Azwk6nE8pXxCxzU+bP4Mjh92OpknTbzRfWf+RNjsp5Ws5Y+BIma6l7TJNFL6y6m1IzIDsk9yORKnw0eE0iI6z/X/KNZoofOFwCWyYbWsT2uyklO9EN4NOw22N3Ri3o2my9FvNF2pGO/U6z+1IlAo/OaOheCvkr3E7kiZLE4UvrHrbXmSno52U8r2uo+29Nj+5RhNFYx0udkY7abOTUn6RlAEZ/XWYrIv0m62x1s2y025os5NS/pMzBrYvhIP73I6kSdJEcSLKS2HFGzDtcph5MyS3g6xct6NSKnzljAZTDRtnux1Jk6RzPR2PgvUw72FYPh0qD0NCGxh4BQy+TtemVsqfMvpDQmvbT9FvktvRNDmaKBpi63z4+kFY94FdOKjfJdD3YnvNhPZLKOV/ERF2TfjV79pZlyOj3Y6oSWnQt5yIvNSQfcdLRMaIyDoR2Sgit9dxPFZEpjnHF4hIh8a+5nHZ8S08OwqeG22TxRm/h1tWwfgHoP0pmiSUCqScMVBeYhcFUwHV0BpFL88HIhIJDGrMCzvneBQYCWwHFonIDGPMao9i1wKFxpguIjIJ+AdwcWNet0EqyuCzv8H8xyC+FYy9F/pfputCKOWmTsPt6o/rP4KOp7sdTZPi9SexiPxBREqBviJS4txKgXzg3Ua+9hBgozFmszGmAngNmFirzERgqrP9BnCWiJ87AzbPhcdPgXmPwMArYcpCGHK9Jgml3BabYKf00OspAs5rojDG3G2MSQT+ZYxJcm6JxpgWxpjGLjvVFtjm8Xi7s6/OMsaYSqAYaFH7RCIyWUQWi8jigoKCE4vmUBG8OwVenGCXML36fRj/oC4/qlQwyRkD+zbC3o1uR9KkNKiR3RjzBxFpKyJDReT0mpu/g2soY8xTxphcY0xuenr6iZ2kqsJeE3HqzfBf30CHYb4NUinVeDmj7P0GvfgukBrURyEi9wCTgNVAlbPbAF804rV3ANkej7OcfXWV2S4iUUAy4J8rbhJawU1LITbRL6dXSvlAagdI72Gbn075tdvRNBkN7cw+D+hmjCn34WsvArqKSEdsQpgEXFqrzAzgKmAecAHwqTF+nEJSk4RSwS9ntO1DPFysTcMB0tDxnZsBnw5cdvocpgAfAWuA6caYVSLyZxGZ4BR7FmghIhuB3wJHDaFVSjUxOWOguhI2fep2JE2G1xqFiDyMbWIqA5aKyBzgh1qFMebGxry4MeYD4INa++702D4MXNiY11BKhZmswdA81Q6T1TnWAuJYTU+Lnfsl2GYgpZRyV2QUdBkJGz6G6iqIiHQ7orDnNVEYY6Z6O66UUq7IGQ0rpsOOJZA9xO1owl5DRz2twDZBeSrG1jj+aozRuX+VUoHT5SyQSDukXROF3zW0M3sW8D5wmXObiU0Su4EX/BKZUkrVp3kqtDtFFzMKkIYOjx1hjBno8XiFiHxrjBkoIpf7IzCllPIqZzTM/iMUbYWUdm5HE9YaWqOIFJEf6nciMhio6UGq9HlUSil1LN3Otvdaq/C7hiaK64BnReR7EdmCvb7hehGJB+72V3BKKVWvFl0grZMmigBoUNOTMWYR0EdEkp3HxR6Hp/sjsECrqKzm5mnfkZ0WRzvn1j4tnoyUZkRH6roTSgUdEXvx3aJnoeIgxMS7HVHYOtYFd5cbY/4jIr+ttR8AY8y//RhbQBWWVbB2VymzV+/hSNWPA7wiI4T2LeLon5XCgHYpDGiXSrc2iZo8lAoGOaPtujGb50L3sW5HE7aOVaOoSdFhPwlS66RmfHrbcKqqDXtKDpO3r4xt+8vYur+MtbtL+WJDAW99Z+csbBYdQZ+2yQxol0r/7BSGdm5BSlyMy3+BUk1Qu6EQk2gnCdRE4TfHuuDuSef+/wITjvsiI4TMlOZkpjTnlM4/Ln1hjGF74SGWbiviu61FfLetkBe+3kJFVTV9s5KZMUWnJVcq4KJioMvPbD+FMbY5SvlcQy+4ywEeB1obY3qLSF9ggjHmr36NLoiICNlpcWSnxTG+XyYA5ZVV3PvROp756nuKyiq0VqGUG3LGwOp3YdcyyOzvdjRhqaEN7U8DfwCOABhjlmOnBW/SYqMiGdWrDcbAgu/3ux2OUk1Tl5GA6OgnP2pooogzxiystU+vnwD6ZiUTGxXB/M06i4lSrkhIh6xcXUvbjxqaKPaKSGec+Z5E5AJgl9+iCiGxUZEMap/Kgs1ao1DKNTmjYee3ULrH7UjCUkMTxa+BJ4HuIrIDuBm4wW9RhZiTO7Vgze4SisuOuB2KUk1Tzhh7v+Fjd+MIUw1NFDuA54G/Aa8Bs7FLlCrgpI5pGAMLt2itQilXtO4NSW21+clPGpoo3gXGYzuzdwIHgIP+CirU9MtO0X4KpdwkYpufNn0GleXHLq+OS0Nnj80yxozxayQhrFl0JAPapbDge00USrkmZwwsfg62fAldRrgdTVhpaI3iGxHp49dIQtzJnVqwamcJxYe0n0IpV3Q8HaKa6zBZP/CaKERkhYgsB4YB34rIOhFZ7rFfOU7q2AJjYJFeT6GUO6KbQ6czbD+Fqb0gp2qMYzU9jQtIFGFgQLsUYiIjWLhlPyN6tnY7HKWappzRNlEUrIVWPdyOJmwca66nvEAFEuqaRUfSPzuFBdqhrZR7uo629+s/1EThQzpXtg+d1CmNlTtLOFCuF60r5YrkttCmL6yZ6XYkYcWVRCEiaSIyW0Q2OPep9ZT7UESKROS9QMd4IoZ0TKOq2rAkr9DtUJRqunqfDzuWwP7NbkcSNtyqUdwOzDHGdAXmOI/r8i/gioBF1UiD2qcSFSHa/KSUm/pcYO9XvOFuHGHErUQxEZjqbE8Fzq2rkDFmDlAaqKAaKy4mij5ZyTqTrFJuSs6C9qfC8uk6+slH3EoUrY0xNZMK7gYaNUxIRCaLyGIRWVxQUND46BrhpI4tWL69iKKyClfjUKpJ63MB7NsAu3UUvy/4LdD+PjMAABnlSURBVFGIyCcisrKO20TPcsYYgzMr7YkyxjxljMk1xuSmp6c3Ku7GOndAJpXVhhtfW8rn6/I5UlXtajxKNUk9z4WIaFurUI3W0Ck8jpsxpt5r6EVkj4hkGGN2iUgGkO+vOAKte5skbhvVjSfmbuLq9QWkxcdwdu82TOiXyeAOaURE6FKNSvldXJqdxmPlmzDyzxAR6XZEIc1vieIYZmBnn73HuX/XpTj84tdnduG60zoyd10BM5fv4q1vd/Dygq20SWrGuL4ZjO+XSd+sZETX91XKf/pcAOtnQd430PE0t6MJaWJc6OwRkRbAdKAdkAdcZIzZLyK5wA3GmOuccl8C3YEEYB9wrTHG60Quubm5ZvHixX6N/3iVVVTyyZp8Zizdydz1+RypMrRvEcf4vplM6J9JTutEt0NUKvxUlMG/ukCf82HCw25HE/REZIkxJrfOY24kCn8KxkThqbjsCB+t2s3M5Tv5euNeqg10a53IhP6ZjOubQfsW8W6HqFT4eGuyvUr7tg0QFet2NEFNE0WQKigtZ9bKXcxYupPFzkV6/bJTGN83g3F9M2mT3MzlCJUKcRtmw8sXwKRXoPs5bkcT1DRRhIAdRYd4b9lOZi7fycodJYjAkA5pjO+Xydg+GaTFx7gdolKhp+oI3NcdOgyDi6Yeu3wTpokixGwqOMB7y3YxY9kONhUcJCpCGNa1JeP7ZjKqV2sSm0W7HaJSoeP92+C7l2zzU7Mkt6MJWpooQpQxhjW7SpmxbCczl+1kR9EhYqIiOLNbOmP7ZPCz7q00aSh1LNsWwrMj4dwnoP8lbkcTtDRRhAFjDN9uLWLmsp3MWrmLPSXlxERGcFrXlozp3YaRPVuTEqfNU0odxRh4sC+06AJXvO12NEHLW6Jw6zoKdZxEhEHtUxnUPpU7x/Xku22FzFqxm1krdzNnbT5REcIpnVswtk8Go3q2pkWCjvBQCgAR6HMhfHU/HMiHhFZuRxRytEYR4owxrNhRzAcrdjNr5S7y9pURIXbOqbF92jC6VxtaJenoKdXE5a+Fx06Cs/8JJ/3S7WiCkjY9NRE1fRqzVu5i1srdbMw/gAjktk9laOeW9MpMonfbZDKSm+lV4Y5DFVXsL6sgU9+T8Pf4MIhuBtd94nYkQUmbnpoIEaFnZhI9M5O4dVQ3NuwpZdbK3Xy4cjcPfbrhhxmXU+Oi6ZWZTK/MJHq1tfcdW8Q3uXmoFmzex02vLWV3yWHS4mPom5VM36wU+jn36YnafBdW+lwAn/zJLmiU1sntaEKK1iiaiLKKStbsKmXVzmJW7Shh1a5i1u8+QIUzu21cTCQ9MpJsrSMzmZ6ZSeS0TiQmKvxWy62qNjz62UYe+GQ97VvEc8XJ7Vm7u4Tl24tZv6eUaud/ibYpzemblUy/7BT6ZiXTp22yjjILZcXb4f5ecOb/whn/7XY0QUebnlSdKiqr2Zh/gJU7i1m9s4RVzv3BiioAoiOFrq0Sf2iy6pWZRI+MJOJjQ7ciml9ymJunLeWbTfs4t38mfz2vDwkef09ZRSUrd5SwfHsRS7cVsXx7MVv3lwG2T7RTy3j6ZafQL8smjx4ZSTSL1plJQ8bzY+FgAfx6of0HVT/QRKEarLrakLe/jFU7i1m548fkse+gXYhJBDq2iP+hycrekkPiyvEv1hfw2+lLOVBeyZ8n9ubCQVkN6pcoPFjB8h3FLNtW5CSQYvYeKAdsMu3eJumHGkfX1gl0Tk/QocrBavHz8N7N8MsvIKOf29EEFU0UqlGMMewpKf9J8li1s4QdRYd+KNM6KZbubZLonpFID+e+U8uEoGi6qqyq5t+z1/PY55vIaZ3Ao5cOpGsjZuw1xrCr+DDLtxexbLtNICu2F1NaXvlDmbT4GDqnx9M53SaOzq3sdlZqHJFNrC8oqJTth3tz4OQbYNRf3Y4mqGiiUH5RVFbhNFmVsGZ3CWt3lbIx/8d+j+hIoXN6Aj0ykujeJpHuGUn0aJNIemJswEYY7Sg6xI2vfseSvEIuGZLNneN60TzG901F1dWGbYVlbCo4wOaCg2wqOMCmfHtfUxsDiI2KoF92CoM7pJLbIY2B7VJJbq79HgH1yiTYtQxuWQUR7v+QCRaaKFTAHKmq5vu9B1mzq4S1u0tZ69zvKj78Q5ms1OZcOCibiwZnkZHc3G+xzF69h9teX0ZlVTV//3kfJvZv67fX8qaorIJNTvJYt7uUxXmFrNpRTGW1QcSuiliTOIZ0SNNZg/1t5ZvwxjVw6euQM8rtaIKGJgrluqKyCtbuLmXNrhI+XZvPlxv2EiEwvFsrJg3O5mfdWxEV6Ztfd+WVVdwzay3Pf72F3m2TeOSSgXRoGVzrfJRVVLJ0axGLthSyOG8/S/IKKXMGEbRNaU7X1gm0T4ujfYt4OrSMo11aPNlpzYmN0o7zRqusgIcHQlJbuOZD7dR2aKJQQWfb/jKmLdrG9MXbyC8tp1ViLBfmZnFxbjvatYg74fPm7TvIlFe+Y8WOYq4e2oE/jO0eEl+ulVXVrNlVyqIt+1mytZDvCw6ydX8ZBzz6PUQgM7k57VvYBJKd1pxWic1omRBDy4RYWiXGkhYf47OEG9YWPg0f3AZXv2+nIFeaKFTwqqyq5rN1Bby2cCufrcun2sCwLi2ZNCSbkT1bH9eX/IxlO/mft1YQIfCvC/sxulcbP0buf8YY9h2sIG9fGXn7Dv54v7+MvH1l7Pfo+6ghAqlxMbRMiCE9MZZ2aXF0Tk+ga+tEurZK0Kvyaxw5BA/0hda94Mp33I4mKGiiUCFhV/EhXl+8nWmLtrGj6BBp8TGcP7Atk4a0o3N6Qr3PO3ykiv+buZpXF25lYLsUHrpkAFmpJ14rCRVlFZXsLa2g4MBhCkor2HugnILScvYesLf80vKjEkp8TCRdWiXQpVUiXVsn0K11IrkdUpvmhYRfPwiz74TrPoWsQW5H4zpNFCqkVFUbvtq4l9cWbmX26j1UVhuGdEzjkiHZnN074ycXuG3YU8qUV75j3Z5SbjijM7eOyiFam15+Yt+BcjbmH2BD/gE2OrcN+aXsKbHXgkRGCP2ykhnWpSVDu7RkQLuUkGiua7TyUnigD7Q7BS551e1oXKeJQoWsgtJy3liynWmLtrJlXxnJzaM5b0BbLh6czfLtRdw1YzVxMZHcd1E/hnfT6aOPR/GhI6zaWcy8Tfv4auNelm0rotpA8+hIBndMY1iXFpzapSU92iSF7zxgn/8DPv873PA1tOntdjSu0kShQl51tWH+5n28umgbH63c/cO1Gid3SuPBSQNorVOpN1rJ4SMs2Lyfrzfu5euNe9mQfwCAFvExjO7dhgn9MhnSIS28ksahQri/D3QdCRc+73Y0rtJEocLK/oMVvL9iF0nNohjXN1OvdPaTPSWH+XrjXj5bV8Anq/dw6EgVGcnNGNc3g4n929IrMyk8OsY/uQu+egCmLIaWXdyOxjWaKJRSjVJWUckna/KZsXQHc9cXcKTK0KllPBP6ZzKhXyadvAw2CHoHCmxfRe/z4dxH3Y7GNZoolFI+U1RWwayVu5mxdCfzv9+HMTC4Qyo3nZXDqV1ahGYtY9bvYdEzcON3kNLO7Whc4S1RuDI8RETSRGS2iGxw7lPrKNNfROaJyCoRWS4iF7sRq1Lqp1LiYrhkSDtenXwy824/izvG9mB74SEuf3YBFz85n3mb9rkd4vEbeiMgdsisOopb4whvB+YYY7oCc5zHtZUBVxpjegFjgAdEJCWAMSqljqFNcjOuP70Tn902nP+b0Iu8/Qe55On5THpqHgs2h1DCSG4L/S+Fb1+C0t1uRxN03EoUE4GpzvZU4NzaBYwx640xG5ztnUA+kB6wCJVSDdYsOpKrhnZg7n+fyZ3jerKp4CAXPzWfy56Zz5K8/W6H1zDDbobqIzDvEbcjCTqu9FGISJExJsXZFqCw5nE95YdgE0ovY0x1HccnA5MB2rVrNygvL88/gSulGuRQRRUvL8jjibmb2HuggtNz0rllRFcGtDuqlTm4vHk9rH0fblkJcWluRxNQrnRmi8gnQF2T7dwBTPVMDCJSaIyp8xMkIhnA58BVxpj5x3pd7cxWKniUVVTy0rw8nvxiM/sPVnBmt3RuGZlD36wgbUXOXwOPnQyn/w5+dofb0QRU0I16EpF1wHBjzK6aRGCM6VZHuSRskvi7MeaNhpxbE4VSwedgeSVT523hqS82U1R2hBE9WnHziBx6t012O7SjTbscvv8Cbl4JzZLcjiZggm7UEzADuMrZvgp4t3YBEYkB3gZebGiSUEoFp/jYKH41vAtf/u5Mbh2Zw8Lv9zPu4a+44aUlrNtd6nZ4P3XarXC42A6XVYB7NYoWwHSgHZAHXGSM2S8iucANxpjrRORy4HlglcdTrzbGLPV2bq1RKBX8ig8d4dmvvue5r77nYEUl4/pmctNZXenSKkgu3PvP+bBzKdy8AmLCfyZiCMKmJ3/SRKFU6Cgqq+CpLzbzwjdbOHykign9MrlyaAcGZKe4e+He1vnw3GgY/gcYXtfo/fCjiUIpFdT2HijnybmbeHnBVsoqqujeJpHLTmrHxAFtSXJrrYw3roU1M+CGryD9qC7UsKOJQikVEg6UV/Lu0h28smArq3aW0Dw6kgn9Mrn0pHb0zUoObC3jQD48Mhha9bRLpkaE9zonmiiUUiHFGMPy7cW8smArM5bt5NCRKnplJnHpSe0Y06sNLRJiAxPId/+Bd38N4x6A3F8E5jVdoolCKRWySg4f4d3vdvDygq2sdUZIdWwZz6D2qeS2TyW3Qyqd0xP8U9swBqaOh13LYcpCSAztddi90UShlAp5xhhW7Cjm6437WJK3nyV5hRSWHQEgJS6aQe1SGdQhldz2aXRsGU9cTCTNoyMbv9DSvk3w2CnQbQxc9KIP/pLg5C1RRAU6GKWUOhEiQt+sFOeq7s4YY9hUcJBv8wpZnLefxXmFzFmbf9Tz4mIinVvUD9vxsVFH7YuLiSI+NpLmMVHEexxrHpNKRr/fkPXtvaybO43C7BFUGwP2P+ymce5tQvvh3lDHfsCjfLX56XNxyojYZWnjYqJoHhNB8+gomjvJr+Y+Jiow/SZao1BKhY39BytYklfI7uJDlFVUcbCiirLySsqOOPcVVc7+Sg4592Xldt+hI1X1njeKSt6LuYMkOcio8n9ygOC4tiIqQmgeE0mCk/j6ZqVw/8X9T+hcWqNQSjUJafExjOzZ+oSeW1VtOHSkijInedQkk5oEcnjvA3T76AJm9/uCLUPuQgQEW9P5cRtAiBBnv7NPEGq6UDwfR9Tx3JrH1QYOH6lyYqriUEUVh4/8mNQOVVT+sH2wvJKDFVW0SvRPJ78mCqWUAiIjhITYKBJioyCxjgJdR0DhZDIWPkXGsCshe3DAY3RLeA8MVkopXzrrj5CUCTNvhKojbkcTMJoolFKqoWIT4Zz7IH91k1o2VROFUkodj25nQ8+JMPefduhsE6CJQimljtfZ/4SoZvDOr6Cy3O1o/E4ThVJKHa/ENjDu37BtPrx5HVTXP7Q2HGiiUEqpE9HnAhh9t51h9r1baq6kC0s6PFYppU7UKb+Csr3w5X3QPAVG/B+4uY6Gn2iiUEqpxvjZH+FQoR0FFZsEp9/mdkQ+p4lCKaUaQwTG3gflB+DTv9hkcdJkt6PyKU0USinVWBERcO5jUHEQZv03xCZA/0vdjspntDNbKaV8ITIaLngOOp5hFzta/a7bEfmMJgqllPKV6GYw6RVom2vX3N74idsR+YQmCqWU8qXYBLjsdUjvDq9dDnnz3I6o0TRRKKWUrzVPgSvehuQs+M/PYd5jIX1RniYKpZTyh4R0uPo96DAMPvoDPDMCdq90O6oT4kqiEJE0EZktIhuc+9Q6yrQXkW9FZKmIrBKRG9yIVSmlTlhiG7h0Opz/LBRthafOgDl/gSOH3Y7suLhVo7gdmGOM6QrMcR7Xtgs4xRjTHzgJuF1EMgMYo1JKNZ6Ine5jyiLocxF8eS88cSps+crtyBrMrUQxEZjqbE8Fzq1dwBhTYYypmZYxFm0mU0qFsrg0OO9x23dRdQReOAdm3gSHityO7Jjc+vJtbYzZ5WzvBupc5FZEskVkObAN+IcxZmc95SaLyGIRWVxQUOCfiJVSyhc6/wx+NQ+G/ga+fREePclecxHEkwqK8VNwIvIJ0KaOQ3cAU40xKR5lC40xR/VTeBzPBN4Bxhtj9nh73dzcXLN48eITjFoppQJo53cw4zewewW07m2TR+/z7cV7ASYiS4wxuXUd81uNwhgzwhjTu47bu8AeEclwgssA8o9xrp3ASuA0f8WrlFIBlzkArv8MJjrDZ9/+JTzYD755GA6XuB3dD9xqepoBXOVsXwUcda27iGSJSHNnOxUYBqwLWIRKKRUIkdEw4DLbHHXZG5DWCT7+X7i/F3z8Ryips8U9oNxKFPcAI0VkAzDCeYyI5IrIM06ZHsACEVkGzAXuNcascCVapZTyNxHoOtJeezH5c7s97xF4oC+8/V+wZ7V7ofmrj8It2kehlAobhXkw/zHb6X2kDDqdCQMuh+7nQHRzn76Utz4KTRRKKRXsyvbD4udgyVQo3gqxydDnfOh/GbQd5JNV9TRRKKVUOKiuhi1fwtJX7JDaykPQsptd+6LvxZCUccKn1kShlFLh5nAJrH7HJo2t80AioOe5cOHzJ3Q6b4lCV7hTSqlQ1CwJBl5pb/s22YSBf374a6JQSqlQ16IznPVHv51e509SSinllSYKpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXoXdFB4iUgDkuR1HA7UE9rodxHEItXhBYw6UUIs51OIF/8fc3hiTXteBsEsUoUREFtc3t0owCrV4QWMOlFCLOdTiBXdj1qYnpZRSXmmiUEop5ZUmCnc95XYAxynU4gWNOVBCLeZQixdcjFn7KJRSSnmlNQqllFJeaaLwIxHJFpHPRGS1iKwSkZvqKDNcRIpFZKlzu9ONWGvFtEVEVjjxHLVcoFgPichGEVkuIgPdiNMjnm4e799SESkRkZtrlXH9fRaR50QkX0RWeuxLE5HZIrLBuU+t57lXOWU2iMhVLsb7LxFZ6/y7vy0iKfU81+tnKMAx3yUiOzz+7cfW89wxIrLO+Vzf7nLM0zzi3SIiS+t5bmDeZ2OM3vx0AzKAgc52IrAe6FmrzHDgPbdjrRXTFqCll+NjgVmAACcDC9yO2SO2SGA3dkx4UL3PwOnAQGClx75/Arc727cD/6jjeWnAZuc+1dlOdSneUUCUs/2PuuJtyGcowDHfBdzWgM/NJqATEAMsq/3/aiBjrnX8PuBON99nrVH4kTFmlzHmW2e7FFgDtHU3Kp+YCLxorPlAioic+KruvnUWsMkYE3QXXRpjvgD219o9EZjqbE8Fzq3jqaOB2caY/caYQmA2MMZvgTrqitcY87ExptJ5OB/I8nccx6Oe97ghhgAbjTGbjTEVwGvYfxu/8xaziAhwEfBqIGKpjyaKABGRDsAAYEEdh08RkWUiMktEegU0sLoZ4GMRWSIik+s43hbY5vF4O8GTACdR//9UwfY+A7Q2xuxytncDresoE6zv9zXYmmVdjvUZCrQpTnPZc/U07wXre3wasMcYs6Ge4wF5nzVRBICIJABvAjcbY0pqHf4W20zSD3gYeCfQ8dVhmDFmIHA28GsROd3tgBpCRGKACcDrdRwOxvf5J4xtSwiJYYgicgdQCbxcT5Fg+gw9DnQG+gO7sE05oeISvNcmAvI+a6LwMxGJxiaJl40xb9U+bowpMcYccLY/AKJFpGWAw6wd0w7nPh94G1st97QDyPZ4nOXsc9vZwLfGmD21DwTj++zYU9Ns59zn11EmqN5vEbkaGAdc5iS3ozTgMxQwxpg9xpgqY0w18HQ9sQTVewwgIlHAz4Fp9ZUJ1PusicKPnPbFZ4E1xph/11OmjVMOERmC/TfZF7goj4onXkQSa7axnZcraxWbAVzpjH46GSj2aD5xU72/voLtffYwA6gZxXQV8G4dZT4CRolIqtNsMsrZF3AiMgb4HTDBGFNWT5mGfIYCplb/2Xn1xLII6CoiHZ2a6STsv42bRgBrjTHb6zoY0Pc5EL36TfUGDMM2JSwHljq3scANwA1OmSnAKuwoi/nAUJdj7uTEssyJ6w5nv2fMAjyKHSWyAsgNgvc6HvvFn+yxL6jeZ2wS2wUcwbaBXwu0AOYAG4BPgDSnbC7wjMdzrwE2OrdfuBjvRmxbfs3n+QmnbCbwgbfPkIsxv+R8Tpdjv/wzasfsPB6LHZm4ye2Ynf0v1Hx+Pcq68j7rldlKKaW80qYnpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXmmiUMqHROQdZ4K2VTWTtInItSKyXkQWisjTIvKIsz9dRN4UkUXO7VR3o1eqbnrBnVI+JCJpxpj9ItIcOy3EaOBr7HoDpcCnwDJjzBQReQV4zBjzlYi0Az4yxvRwLXil6hHldgBKhZkbReQ8ZzsbuAKYa4zZDyAirwM5zvERQE9nCiqAJBFJMM7khUoFC00USvmIiAzHfvmfYowpE5HPgbVAfbWECOBkY8zhwESo1InRPgqlfCcZKHSSRHfsMrHxwBnOzK9RwPke5T8GflPzQET6BzRapRpIE4VSvvMhECUia4B7sLPU7gD+DizE9lVsAYqd8jcCuc7Ka6uxs90qFXS0M1spP6vpd3BqFG8Dzxlj3nY7LqUaSmsUSvnfXSKyFLuozPcE4TKsSnmjNQqllFJeaY1CKaWUV5oolFJKeaWJQimllFeaKJRSSnmliUIppZRXmiiUUkp59f8rJFgbFDPVyAAAAABJRU5ErkJggg==\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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9G2Z/plfrxkovGQMXfgf+9zJ490FY9BWvK5JuKPDlI2obW070Y3+/rIH3y1x/9pb2DuCDC6gzM+P5ZF4mOSmud0xmwghCB3uysOq9rg35vcehthgiomDKMphxvTub16CgwZG10I28Xf0jmLMShsd7XZF0QYEfxDo6LPuqjp4I9R1l9bxfVk+ZbwUmcP3ZJ6fGsHhiElNSY/rnAmpf1RTDjmdg+zPuAiwGxp0L598Nk69w7coy+C64B361CN7+qTvjF7+jwA8Sbe0d7K44ypaSWraV1rGltO5DUwyEhhjGJ49kfnYCk1NjTtySoweonb23aophx7NusM/Bje65tFlw4Xdh+jUQm+FtfeK6tU67BtY96HpAaQplv6PAH4KOTw62paSOraXutv1gHU2trklmZEQoU9Ni+eTcTKakxTAl1U0xEBnuR13qrIVDW2DXS1DwIhzc5J4/HvJTl0N8lqclSheWfB22PeUGtH3sbq+rkZMo8ANcR4dlT+VRtpbWsrWknq2lblrf4/3ah4eHMi09huvnjeGsjFimp8eRnTRy8Nvae6L1GOx9EwpegoKXob4UMJCR55oIpix3g33Ef42a7HpErfs1nHMnRMZ6XZF0osAPMJVHmtm8v5ZNB2p887bXcaS5DXC9ZKamxXJtXibT02M5KyOWcclR/hnu4M7iq3bDntdh9z9gzz+htdFdeB1/vmuTz7kYopK9rlR6Y/G/ulHM6x9yj8VvKPD9WEtbBzvK6tm034X7pv217K9uBFyb++TUaJbPSmNGRhxnZcQxPnkkYaF+3ivlaBXs/Sfsft0FfJ1v3r24sTDzRpi0FLIWQ5ifXDuQ3kubBRMuhHfuh/m36yK6H1Hg+wlrLaW1x9i0v9YX7jVsO1hPS5trdx8dE8msMXF8asEYZo2JZ1paLMMj/KjNvTtNdbB/HRSvdgFftgWw7k/97CWw6KvubD5hnNeVSn9a/HV4ZCls/qOmT/YjCnyPtLR1sLW0jg3F1eTvq2HTgVoqfKNTI8Pdiks3nZPFrMw4Zo6JIzU2QNZLPVIB+9e4QVDFa+DQVsC60a4Zc10zzfjzIXUmhOrjN2SNWQBps93F27xbNB7CT+h/3CCpa2xlw34X7vn7anivpJZm39l7VuIIFk9IYtaYOGaNiWfS6OgPFuXwZx0dUL0HSjd8EPKVBW5b2HDInAvn3QVjz4H0PA25DybGuBlIn74FilbBxEu8rkhQ4A8Ia910v+v3VZNfXEP+vmoKyo8AbpTqtPRYPr1gLHlZCcwZG+8/fd1Pp77MhfvBjb77Ta7JBtzsk2PPdu3wYxdC6gwIi/C2XvHWlGXwyrddW74C3y8o8PtBW3sH75c1sH5fNRuKa1i/r/rE5GHRw8KYPTaeK2ekkZeVwIyMuMBoez9W6wL9eLCXboCGMrctJAxSpsLUqyB9NqTPgeRcTY0rHxYa7trvX/sulG93nxnxlAL/DDS2tLGxuJZ391WzobiaTftrT/R7T48bztnjE8nLSiBvbDwTU6L9t1vkca3HXFt76QYo9Z29V+/+YHviBHeBNc0X7qOnQXiAXFMQb825Cd64z7XlL/uF19UEPQV+DzQ0tZJfXMO6PdW8u7eKLSV1tHVYQgzkjo5hxZwM5vgCPi3Oz4OwvQ0qdn7QLFO6EQ7vgA7Xl5/oNHfWPutGF/Bps2B4nLc1S+AakQBnXQtb/gwXf1+fJY8p8LtQ29jC+n01rNtTxbv7qtlWWkeHde3vZ2XE8rkl45iX7QK+Xxfr6G/WunVbOzfLlL3nBjeB6xqZNhsWfsWFfNpsLUYt/S/vs25tgi1/hvm3eV1NUFPg40avrt9bzbq91azdU8Wu8gashYiwEGZmxnHn+ROYPy6RWWPiGBHhx2/Z0SoozfedufvO3o9Vu21hke5C6uyVrlkmfbbr+67FQGSgpc1y3XA3POLa9PWZ84wfp9fAKa9vYu2eKt71hXzRYdeDJjI8hDlj4/nq9InMz05gRmacf00o1llrk6/dPd8tyl2a787mwS3MPWqKmyr4eLv7qMnuIpqIF/Juhr99CQ6sc330xRNBEfglNY2+9vdq1u2tYl+Va9KIGhZGXlY8V81OZ352ItPTY72d5/1U6stcX/f9a13AH9oKHb71YWPSXajn3ez6u6fN1HB28S/TroaX74b8RxT4HhpygW+tpbiqkXV7q1i3x53Bl9a6Zfhih4czNyuBTy0Yy7zsBKakxvjn3DPWugFNxWtg/ztQ/PYHZ+/hI11zzNlfcLNIpuep3V3837Aod/F20+9h6X9prnyPDLnAL6k5xnn/808AEkdGMC87gc8tzmb+uEQmpUQT4q9dJOsPugnFdv/DLbx9pNw9PzzBjVSddxuMORtGn6UpCSQwzVkJ+Q/D9r/A3Fu9riYoDbnkyIgfzn3XnMXsMXGMT44a+MWzz1TzEXfmfjzkK3e550eOcn3esxbCmHMgeZIucsnQMPosGDXVTaimwPfEkAt8YwzX5mV6XUbX6kph1wvutvct1wYfFunO4Gd/Gsad70YjKuBlKDIGZt4Ar9wNFbvcyYwMqiEX+H7FWijfBjtfgF1/d33gwY1cXXA7jL/ANdOER3pbp8hgOetaWHWPO8u/6LteVxN0FPgDoaLAreu57WmoKsIt0zfXLdM36XJInuhxgSIeiRoFORfBlj/BBfdo/qVBpsDvL3UlsPUpF/SHtgIGshbB2XdC7uXugy4irlmn4CW3tOWEC72uJqgo8PuirQUKXoSNv4Oi1wDrukkuvdctuK3ukiIfNXEpRMbB5scV+INMgX8mKgtdyL/3OBytcAOfzv0GzLhOS/WJnE7YMJi+AjY95tZTiIz1uqKgocDvqY4Ot3LP2gfc2qwhYe5MZfZKmHCB2iJFemPGdbD+N/D+825mVhkUCvzTaT7izuTX/tLNER+dBh/7Nsz6NESneF2dSGBKnwPxWe6alwJ/0Cjwu9NY7UL+3V+7PzvT58DVD7tl2zQJmUjfGOPm11n9E7fwfVSy1xUFBQX+yY4chnd+AesfhpYjkHsFLPwyZM7zujKRoWXa1fDWD2HHM27aZBlwCvzjjlTA6h+52fzam916rYv/FVKmeF2ZyNCUMhWSJ7vxKgr8QaHAb26Ad+6HNT93a7vOuA4WfQ2SJnhdmcjQN/1q+Mf33TiW2Ayvqxny/HBu4EHS1gLv/gZ+Ngv++V8w/mPwhXWw/AGFvchgmXqVu9/2F2/rCBLBeYZf8Aq89E035/zYRXD9E25ueREZXInj3aps256ChV/yupohL7jO8Kv3wh+vgz+uABMKNzwJNz2vsBfx0rSr3cSClUVeVzLk9UvgG2OWGmN2GWOKjDF3dbF9mDHmT77t64wxWf1x3B5rPQav/yfcPx/2vgkXfhfuWAMTL9ZUxCJem3YVYNzFWxlQfQ58Y0wocD9wKTAFuN4Yc3LXlluAGmvtBODHwA/6etwe2/MGPLAA3viBW9T7i/mw6CsQFjFoJYjIKcSkwdiFCvxB0B9n+POAImvtHmttC/AEsOykfZYBj/oePwVcYAZ6KaqmOvjbl+F3V4IJgZV/g2t+6z5cIuJfpi53q74d3ul1JUNafwR+OnCg09clvue63Mda2wbUAYknv5Ax5jZjTL4xJr+iouLMKyp4Ge5f4CY4O+eLcPvbbtlAEfFPuVe4+/ef87aOIc6vLtpaax+01uZZa/OSk89wqHVlIfzxk24GvltehYu/DxEj+rdQEelfMamQOR92KPAHUn8EfinQeRHZDN9zXe5jjAkDYoGqfjj2RyXlwA1/gn95AzLmDMghRGQATFkG5VuharfXlQxZ/RH464EcY0y2MSYCuA44+df0c8BK3+NrgH9Ya20/HLtrEy9xc26LSOCY/HF3r2adAdPnwPe1yd8JvAy8D/zZWrvdGPPvxpgrfbs9DCQaY4qArwEf6bopIkEubgykzVKzzgDql5G21toXgBdOeu6eTo+bgBX9cSwRGcKmLINXvwO1+90vAOlXfnXRVkSC3GRfo8D7f/O2jiFKgS8i/iNxPKRMU7POAFHgi4h/mbIMDqyF+jKvKxlyFPgi4l+ON+vsfN7bOoYgBb6I+JdRuZA0CXY863UlQ44CX0T8z5QrofhtOFrpdSVDigJfRPxP7hVgO6DgJa8rGVIU+CLif1JnQEwG7Py715UMKQp8EfE/xkDu5bD7H9By1OtqhgwFvoj4p9zLoK0Jdr/udSVDhgJfRPzT2IVumnM16/QbBb6I+KfQcJi4FApehPY2r6sZEhT4IuK/ci+HYzWw/x2vKxkSFPgi4r/GXwChw2DXC6ffV05LgS8i/mtYFIw7z02zMIBrJgULBb6I+Lfcy938+OXbvK4k4CnwRcS/TboUMOqt0w8U+CLi36JGQeY8BX4/UOCLiP/LvRwObXFNO3LGFPgi4v9yr3D3O9Vbpy8U+CLi/xLHQ3KuFkXpIwW+iASGSZdB8RporPa6koClwBeRwJB7Bdh2KHzF60oClgJfRAJD2iyITlWzTh8o8EUkMISEuGadoteg9ZjX1QQkBb6IBI7cy6C1Efa84XUlAUmBLyKBI2sJDItRs84ZUuCLSOAIi4Cci2DXi9DR7nU1AUeBLyKBJfdyaKyEA+96XUnAUeCLSGCZcBGEhMMuza3TWwp8EQkskTGQvQTe1xz5vaXAF5HAk3s51OyFip1eVxJQFPgiEngmXebu1VunVxT4IhJ4YlIhfY5mz+wlBb6IBKbcy+HgRqgr9bqSgKHAF5HAdHyO/F06y++pPgW+MSbBGLPKGFPou4/vYp+Zxph3jDHbjTFbjDGf7MsxRUQASJoIiRO09GEv9PUM/y7gNWttDvCa7+uTNQKfsdZOBZYCPzHGxPXxuCIS7IxxzTr73oJjtV5XExD6GvjLgEd9jx8Flp+8g7W2wFpb6Ht8EDgMJPfxuCIiMOly6GiDole9riQg9DXwU6y1Zb7Hh4CUU+1sjJkHRAC7u9l+mzEm3xiTX1FR0cfSRGTIy8iDkaPUPbOHwk63gzHmVWB0F5vu7vyFtdYaY7od9maMSQUeA1Zaazu62sda+yDwIEBeXp6G0InIqYWEwqRLYdvT0NYMYcO8rsivnTbwrbUXdrfNGFNujEm11pb5Av1wN/vFAH8H7rbWrj3jakVETpZ7BWx8FPa+6WbSlG71tUnnOWCl7/FK4NmTdzDGRAB/BX5nrX2qj8cTEfmw7CUQPlK9dXqgr4F/L3CRMaYQuND3NcaYPGPMQ759rgWWADcZYzb7bjP7eFwRESc8EnIudP3xO7psLRaf0zbpnIq1tgq4oIvn84FbfY9/D/y+L8cRETml3Ctgx7NQugEy53pdjd/SSFsRCXw5F0FImHrrnIYCX0QC3/B4yFqkaRZOQ4EvIkPDpMuhsgAqCryuxG8p8EVkaMj1zZGvpQ+7pcAXkaEhNgNSZ6p75iko8EVk6Mi9AkryoeGQ15X4JQW+iAwduZcBFna96HUlfkmBLyJDx6gpEJ+lZp1uKPBFZOgwxjXr7H0Dmhu8rsbvKPBFZGjJvRzaW6DwFa8r8TsKfBEZWjLnuznyd3xkLsegp8AXkaElJBSmXAkFr0DLUa+r8SsKfBEZeqYsg7ZjULjK60r8igJfRIaesQthRBLseMbrSvyKAl9Ehp6QUJj8cSh4GVoava7GbyjwRWRomrocWhuh6FWvK/EbCnwRGZrGLoIRiWrW6USBLyJDU2iYG4S16yVoPeZ1NX5BgS8iQ9fU5dB6FIpe87oSv6DAF5GhK2uxWw1LzTqAAl9EhrLQ8E7NOk1eV+M5Bb6IDG1TlkNLA+z+h9eVeE6BLyJD27hzITIOtv/F60o8p8AXkaEtNNxdvN3596CfW0eBLyJD3/QVbhDWzhe8rsRTCnwRGfrGnAMxGbD1Sa8r8ZQCX0SGvpAQmH417H4NjlZ5XY1nFPgiEhymr4CONtjxV68r8YwCX0SCQ8o0SJ4MW4K3WUeBLyLBwRg4awUcWAs1xV5X4wkFvogEj2nXuPttT3lbh0cU+CISPOLHQuYC16xjrdfVDDoFvogEl7NWQMX7cGir15UMOgW+iASXqVdBaARs/oPXlQw6Bb6IBJcRCW4GzS1/grZmr6sZVAp8EQk+sz4Fx2pgV/RU8l4AAAnnSURBVHBNtdCnwDfGJBhjVhljCn338afYN8YYU2KM+UVfjiki0mfjznNTLWx8zOtKBlVfz/DvAl6z1uYAr/m+7s73gDf7eDwRkb4LCYVZN7o58utKvK5m0PQ18JcBj/oePwos72onY8wcIAV4pY/HExHpHzNvACxsftzrSgZNXwM/xVpb5nt8CBfqH2KMCQF+CHz9dC9mjLnNGJNvjMmvqKjoY2kiIqcQnwXZS2Dz76Gjw+tqBsVpA98Y86oxZlsXt2Wd97PWWqCrkQyfB16w1p727yZr7YPW2jxrbV5ycnKPfwgRkTMy69NQsw/2BUdrc9jpdrDWXtjdNmNMuTEm1VpbZoxJBQ53sdvZwGJjzOeBKCDCGHPEWnuq9n4RkYE3+UoY/k1Y/5C7kDvE9bVJ5zlgpe/xSuDZk3ew1t5orR1jrc3CNev8TmEvIn4hPBJmf8YtfxgEF2/7Gvj3AhcZYwqBC31fY4zJM8Y81NfiREQGXN7Nbl6d/Ee8rsQp2QAVBQPy0sb66QRCeXl5Nj8/3+syRCQYPH49lKyHr26HsGHe1vLbpdB8BO5YfUbfbozZYK3N62qbRtqKiMy9FY5WwI7nvK2joRz2r4XJHx+Ql1fgi4iMOx8SJ8DaB7ydNvn95wALk68YkJdX4IuIhITA2V+Agxuh+G3v6tj2tFuGcdSUAXl5Bb6ICMCM62FEErz9U2+OX3sA9r8D069xyzEOAAW+iAhA+HCYfzsUvgLl2wf/+NuedvfTrh6wQyjwRUSOm3sLhI+ANT8f/GNvfQoy5kJC9oAdQoEvInLciASYvRK2Pgk1xYN33PLtUL71g0XWB4gCX0Sks3O+CCYU3rhv8I6Z/wiEDoPpKwb0MAp8EZHOYtNd0857f4TKwoE/XvMReO8JmLocRiYO6KEU+CIiJ1v0NQgbDq//58Afa9vT0NIAebcM+KEU+CIiJ4tKhgW3w/a/wKGtA3ccayH/YRg1FTLnDdxxfBT4IiJdOeeLEBkLr3x74Ebf7n0Dyt6DebcOWN/7zhT4IiJdGR4P530L9rwOO58fmGO8+T8QnQozbhiY1z+JAl9EpDtzb3XTHLz0LWhp7N/XLn4H9r0F53zJzcs/CBT4IiLdCQ2Dy/4b6vbDG/f23+ta615vRBLMuan/Xvc0FPgiIqeStcitirXm53Dg3f55zcJVsOefsPhrEDGif16zBxT4IiKnc/F/QEw6/PV2aDnat9dqa4aXv+WmY577uf6pr4cU+CIipxMZA8sfgOo98Oydfeu18897oaoQlv4AwiL6r8YeUOCLiPRE9hK44B7XN3/Nz87sNfavddMvz/wU5FzYv/X1QNigH1FEJFAt+qrrN7/q32DkKJh5fc+/t74M/vwZiB8LSwdhBG8XFPgiIj1lDHziV9BUC8/cAViY2YM+9Ecr4bHlbt6cTz/jBnR5QE06IiK9ET4crnvcNfE8c4cbidvW3P3+h3fCwxdDzT644U+QMjDLF/aEAl9EpLciRsCNT7oJz9b8DH55Dmx+3J3BH1d7AF79Lvx6CTQ3uDP77MXe1QwY6+UK7aeQl5dn8/PzvS5DROTUil5z3Swrdrp59GPToa0Fjhxy26evgIu+BzGpg1KOMWaDtTavq21qwxcR6YsJF8D4tVD8thtMVXsAQkIhORcmf3xAlyzsLQW+iEhfGeNG5GYt8rqSU1IbvohIkFDgi4gECQW+iEiQUOCLiAQJBb6ISJBQ4IuIBAkFvohIkFDgi4gECb+dWsEYUwEUe11HDyUBlV4X0QuBVi+o5sESaDUHWr0w8DWPtdYmd7XBbwM/kBhj8rubu8IfBVq9oJoHS6DVHGj1grc1q0lHRCRIKPBFRIKEAr9/POh1Ab0UaPWCah4sgVZzoNULHtasNnwRkSChM3wRkSChwBcRCRIK/B4wxmQaY143xuwwxmw3xny5i33OM8bUGWM2+273eFHrSTXtM8Zs9dXzkfUijfMzY0yRMWaLMWa2F3V2qmdSp/dvszGm3hjzlZP28fx9Nsb81hhz2BizrdNzCcaYVcaYQt99fDffu9K3T6ExZqWH9f63MWan79/9r8aYuG6+95SfoUGu+TvGmNJO//aXdfO9S40xu3yf67s8rvlPnerdZ4zZ3M33Ds77bK3V7TQ3IBWY7XscDRQAU07a5zzgea9rPammfUDSKbZfBrwIGGABsM7rmjvVFgocwg0i8av3GVgCzAa2dXruPuAu3+O7gB908X0JwB7ffbzvcbxH9V4MhPke/6CrenvyGRrkmr8DfL0Hn5vdwDggAnjv5P+rg1nzSdt/CNzj5fusM/wesNaWWWs3+h43AO8D6d5W1S+WAb+zzlogzhgzOCstn94FwG5rrd+NtrbWvglUn/T0MuBR3+NHgeVdfOslwCprbbW1tgZYBSwdsEJ9uqrXWvuKtbbN9+VaIGOg6+iNbt7jnpgHFFlr91hrW4AncP82A+5UNRtjDHAt8Phg1NIdBX4vGWOygFnAui42n22Mec8Y86IxZuqgFtY1C7xijNlgjLmti+3pwIFOX5fgP7/IrqP7/xz+9j4DpFhry3yPDwEpXezjr+/3zbi/9Lpyus/QYLvT1wz1226azfz1PV4MlFtrC7vZPijvswK/F4wxUcDTwFestfUnbd6Ia36YAfwceGaw6+vCImvtbOBS4AvGmCVeF9QTxpgI4ErgyS42++P7/CHW/Y0eEP2djTF3A23AH7rZxZ8+Q78ExgMzgTJcE0mguJ5Tn90PyvuswO8hY0w4Luz/YK39y8nbrbX11tojvscvAOHGmKRBLvPkmkp994eBv+L+3O2sFMjs9HWG7zmvXQpstNaWn7zBH99nn/LjzWG++8Nd7ONX77cx5ibgCuBG3y+pj+jBZ2jQWGvLrbXt1toO4Dfd1OJX7zGAMSYMuAr4U3f7DNb7rMDvAV/728PA+9baH3Wzz2jffhhj5uHe26rBq/Ij9Yw0xkQff4y7SLftpN2eAz7j662zAKjr1CzhpW7Phvztfe7kOeB4r5uVwLNd7PMycLExJt7XHHGx77lBZ4xZCnwDuNJa29jNPj35DA2ak64vfaKbWtYDOcaYbN9fitfh/m28dCGw01pb0tXGQX2fB+PqdaDfgEW4P9G3AJt9t8uA24HbffvcCWzH9QpYC5zjcc3jfLW856vrbt/znWs2wP24Xg1bgTw/eK9H4gI8ttNzfvU+434ZlQGtuDbiW4BE4DWgEHgVSPDtmwc81Ol7bwaKfLfPelhvEa6t+/jn+Ve+fdOAF071GfKw5sd8n9MtuBBPPblm39eX4XrS7fa6Zt/z/3v889tpX0/eZ02tICISJNSkIyISJBT4IiJBQoEvIhIkFPgiIkFCgS8iEiQU+CIiQUKBLyISJP4Py7qXM1peBkgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From ce6be8bea29c155c6acc3502ffd192c6d384a28a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 218/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 96907c16d5725f7e1406254f7cd3a2ab3b463ccf Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 219/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- skfda/representation/basis.py | 14 +- tests/test_fpca.py | 78 ++--------- 5 files changed, 283 insertions(+), 145 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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5K0lpkwLqRFGFZRdkM231NKJSo1g0ahGt6rUyOySbOX7uOLP3zmZD9AZ83Xy5p+M9jG85HicHJ7NDq7mO/QRfTTK+2Cd+Bg4VqFDISoEvboKze+H696HTzbaL8yrlFxZxPCGDI7FpHD6bxpG4NFIy8yksKvrzC7+weAIo+ltCKCyq2PegCDg5/JlAnCyCo4MDjhYjkTheJsE4Wo9zdhSUgvxCRX5hEfmFRRQUKvKszwuLFFN6NWFKryY2+q2V9Bl0oqh2krKTeHHbi6w/vZ7Zg2czqPEgs0Oyi30J+3hr91vsSdhDsGcwM7rOYHiT4aYPIKxxYvfDJ6PAr6XR1uBsgxJcbgZ8PRlOboI71xvtGHaWlpPPoTNpHI5Nu5gYjidkkFdo9KhzdnSgdYAnDTxdcHRwwGIRHB0Ei8OFnw5/fW25zHbrzwtf6hdKDIVFF77IjdJGfqGRhP58bnyx5xcZzwsK1cXnF5JA8WMLitTF7QJGMnF0wNmaQC48jzmXzYnEDNY+MpAm9T3s8rvViaKaiEqNYn30etafXs+BxAMoFI+GPcrUdjV7vSalFJtjNvP2nrc5fv44beu3ZWbXmfRu2Nvs0GqG1DPw8RAQC9y1DjwDbHfu3HR4uyM0CoNbvrXdeYvJyitgzeF4lu8/y6bwRPKt1UC+dZxpE+hF24ZetA00Hk19PXC01LybjPi0HPq9sp5/9grhmevaXvkN5aATRRVVpIo4mHSQ9afXsyF6A5GpRm+gtvXbck3jaxgSPKRWrSJXWFTIisgVvLfvPWIzY+kV2IuZ3WbSrn7VHS9S5eVmGCOrU6KMhuiA9ra/xq9vwrrnbFqqyCsoYnN4Ij/uP8vaw/Fk5xcS4OXKdZ0C6dvCl7YNvWjg6WqTa1UXM77ay8ZjCWx/agjuzrZfnFQniiokvzCfHXE7LiaHxOxELGIhLCCMwY0Hc03jawisE2h2mKbKLczlm6PfMO+PeZzPPc+IkBHM6DKDJl72q5+tkYoKjaqhiF9g8mIItdNqhLnp8FZ7COkHN39R7tMUFSm2nUxm2b6z/HQwjtTsfOq6OzGqQyDjOjWke0g9HGpAo3F57YpKYcKHW/nfDR2Y3DPY5ucvLVGYtmZ2bZKel85vZ35jw+kN/HrmVzLyM3BzdKNfUD+uaXwNAxoNwNvF2+wwqwwXiwu3truVG0JvYOGhhXx2+DPWnlrLjaE3ck+ne/Bz9zM7xOrh56chfDWMft1+SQLAxRN63AWbX4fEcKMd5Cpk5RWwdHcMn2yJ4mRSJu7OFoa39Wdc5yD6hfriVAOrksqjW5O6tAn0YtHWKCb1aFyp3eR1icJOErIS2Bi9kfWn17M9bjsFRQXUc63HNY2vYXDwYHoG9sTF4mJ2mNVCUnYSc/fPZUn4EpwsTkxpM4Vp7afh5exldmhV1x9LYOkd0PMeGPWK/a+XkQhvt4cOE2Dce2V6S3xaDgt/j+LL7adJzc6nU2Mfbu8bwvC2Abg5W+wccPX01Y7TPPndH3x7T2+6h9Sz6bl11VMle2HrCywOXwxAsGcwg4MHMzh4MB19O2Jx0P8Byut02mnm7J3DT1E/4e3izePdH6/0xZqqhcRwY3K/gPYwbSVYKqnL8cr/g92fwswD4NXwsodFxKfz0eZIfth3hsIixYh2AdzZvyldg+tWu8GklS0rr4Ce/1vHwJZ+zJnc1abn1lVPlSg6LZrF4YsZ3XQ00ztOp5l3M/3HbyPBXsG8OvBVbmt/Gy/veJmnfnuKnMIcbmp5k9mhVR15WfDtVHB0gQkLKi9JAPR+AHZ9Atve/9tgPqUUO06m8NHmSNYdTcDVyYFJPYK5o19Tu3X3rIncnR2ZGNaYT3+PIiEthwZeldOgb2rln4iMFJFjInJcRJ4oYf8jInJYRA6IyDoRqfKtmWtPG0uBPtztYZr7NNdJwg7a1G/DxyM+pn9Qf17c9qJefrW4Vf8yJu0bPw+8gyr32vWaQrvxsGshZBvTsxQWKVb9Ecv17//OPz7axt7o88wcGsrvTwzh+XHtdZIohym9mlBQpPhqR3SlXdO0RCEiFuA9YBTQFpgkIpd2EN4LhCmlOgJLgFcrN8qrF34unAbuDQjwsGFfde1vnByceH3g63Tw7cBjmx9jZ9xOs0My397PYd/nMOBRCB1qTgx9H4K8dPK3f8xnW6MY/MZG7vtiD+ez8njh+vZseXwwM4e2pJ6Hsznx1QBNfT0Y0NKPL3ecIr+wcqbuN7NE0QM4rpSKVErlAV8D44ofoJTaoJTKsr7cBjSq5BivWsS5iFo19sFM7k7uvDfkPYI9g3lw/YMcTTlqdkjmiT8EKx+FkP4w6EnTwkjxak2UTy9SN77Liz/uxcfdmQ9u6cr6/xvEP3s10Y3UNjK1dxPi03L55VB8pVzPzEQRBBQvO8VYt13OHcBPJe0QkekisktEdiUmJtowxKuTX5RPZGokLeteXfdArfy8Xbz5cNiH1HGuwz1r7iE6rfKK41VGbjosngquXnDjfDChw8Sp5Ez+88NB+ry8jicThuDLeVYPjOaH+/owqkNgjZg0ryoZ1KoBjeq6sWhrVKVcr1p0UBaRKUAY8FpJ+5VSHymlwpRSYX5+5vWxP5V6ivyifEJ9dImiMgV4BDB36FwKVAF3r72bpOwks0OqPEoZK9OlnDCShGflLY17LjOPb3dFc/vCnVzz+ka+3nmasZ0a8vyD90DDrjQN/wTRqxrahcVBmNKrCdtPpnAszv6LgZmZKM4AjYu9bmTd9hciMhR4GhirlMqtpNjKJeJ8BIAuUZigmU8z3h/yPknZSdy39j4y8jLMDqly7PoEDi6Fa56Gpv3tfrmE9Bw+23aKKR9vJ2zWWv615ADH4tK5e2Bzfnt8MK9O6ERogBf0mwnnTsLhH+0eU201Mawxzo4OlVKqMLN77E4gVESaYiSIm4HJxQ8QkS7AXGCkUiqh8kO8OhHnIrCIhabeTc0OpVbq6NeRNwe9yYx1M3how0O8P/T9mj2o8ew+Y22JFkOh3yN2uYRSiqjkLNYdiefnQ3HsOnUOpaCZrwd3D2jGyPYBdAjy/nvvvtbXQv0WsOVtaHfD5VfO08qtnoczYzs15Pu9Z3h8VGu8XO3XFdq0RKGUKhCRB4CfAQvwiVLqkIg8D+xSSi3DqGqqA3xr/UM8rZQaa1bMVxJxLoIQrxCcLbpHh70opTh0No0f953hbGoOQT5uhNT3IMTXnaa+HvQJ7MsL/V7gyV+f5InNT/D6wNdr5iDH7PPGeAkPP7jho4qtLXGJmHNZbD2RbDwik4lNzQGgTaAXM4e0ZFSHAEIb1Cm967eDBfo8CMsfhMiN0Pwam8Wn/enW3k1YsjuG73bHMK2v/W5QTR1wp5RaBay6ZNszxZ6b1MevfCLOR9DBt4PZYdRI8Wk5/LD3DEv3xBAen4GzxYGGPq6sORR/cS0CAFcnB0Lqe9G03iTWnv6K21c8yb3tHqOZXx38PF1qxrgWpeDH+yE1xlhbwqN+hU6XkJbD1kgjMfx+IpnTKUZHw/oezvRqXp/ezerTP9T36sc8dLoZNvzPKFXoRGEXHRv50KmxD4u2nWJqnxC7/X3rkdk2kpmfyZmMM4wPHW92KDVGdl4hvxyOY+meM/wWkUiRgi7BPrx4fXuu69gQb3cnCosUZ89nE5WcSVRyFlFJmUQlZXIysQ8FDrHs4Sdu/T6PvKRheDhbaGItfRilEA+a+nrQNtALD5dq9F/h93fh6AoYPguCe5b5bQWFRZxOySI8PoPjCelEJGRw8EwqJxIzAfBydaRns/rc1jeEPs19ael/hVLDlTi6QK97Ye1/jZXwGnYp/7m0y5rauwmPLN7PluPJ9Au1z1LJ1eh/R9UWcc5oyNY9nipGKcXOqHMs3R3Dqj9iSc8tIMjHjfsGtWB81yCa+dX5y/EWB6FxPXca13On/yW/+vyC/jy++T+sYTlj2oXiUzCIqKRMjsSm88uheAqsy18GeLny2R09CPX3rKyPWX6HfoA1z0CbsdD7/r/tzswtICE9l4S0HOLTc4lKyiQ8Pp3jCRlEJmb+pfQV5ONGqwBPJoY1pk9zY40Hm3djDbsNfn0DfnsbJn5q23NrAIzuEMiLK4+waGuUThRV3cUeT/V0j6fyOJWcyXd7zvDd3hiiU7Jxd7Ywqn0gN3YLolfT+uVah8DJ0cKrg57n4Y3prIuey6sDm/PfkJGAsc7ymXPZHI1L5z8/HuSmuVv59LYedGrsY+uPZjMFJ3/D8t100ny7srzh00StPEK8NSkkpueSkJ5LRm7B397XuJ4boQ08GdjKj9AGnoQ2qEOLBnUqpxTl6g1ht8PvsyElEuo1s/81axlXJwv/6N6YuZtOcOZ8NkE+bja/hk4UNhKeEo6HkwcNPS4/a6b2V2k5+aw6EMvSPTHsjDqHCPRt7svDQ1sysn2ATVbxcnRw5LUBr3H3mrt58tcn8Xb2pnfD3jhZHAjxNaqf2gR6MmX+dibP28a8qWH0aW6fu7KyUEpxKjmLk0mZnEzK5JS1So3Eo8zOeoIkVY8bY6ZzPuYErk4O+Hu54u/pSpuGXgz0dKGBpysNPF3w93KlgZcLjeq62WU1tKvS827Y8o4x9fnAx8yNpYa6pWcwczed4Ittp3hsZGubn19PM24jU3+aikKxaNSiSr92daGUInXnYjKO/MLvmUF8eTaAAwWNCfHz5MaujbihSxAN7XA3BJCam8q01dM4m3GWT0Z8Qjvfvy6vGp+Wwz/nbycqOYt3J3VhRLvKmasrO6+Q/THn2X3qHHtOnWP36XOcz8q/uL+OiyNd62XzdvpjuEg+G/t/SYPgVjSp745fnWrUOD9/BORlwr2/mR1JjXXXol0kpOXww/19y/V3odejsDOlFH2/6svoZqP5d69/V+q1q6qCwiJOJmVyODaNw2fTOHL2PIPOzOV29T1ZygV3McZOFjp54tCkF9KkNzTpazR4Otpn7ENCVgL/XPVPcgpzWDRq0d+WVj2flce0BTs5EHOeVyd0YkI3204tVlikiE7J4uDZ1IuJ4dDZtIttJc39POjWpC5dg+sS6l+HkPoe1HPMQRaMMQavTVsJDTvbNKZKs/U9+PkpmLEH6jc3O5oaKSUzD283p3K3M+n1KOwsLjOO9Pz0WtuQnZlbwNE4IyFcSAxH49LJLTAaTr0s+XzgMZe+6neOBd1I2uCX6OidjcvZnVhObYHTW2HdGuNkjq4QFAZNekOTPtCoB7jUKeXqZdfAvQFzh83l1p9u5e41d7No1CIauDe4uN/H3Zkv7uzJ3Z/t5tFv95OWnc/t/a6+b3pmbgEnkzI5npDBiUTrI8GoSrrQmOzq5EDnxj7cPbAZ3ZrUpUvjutS9dEbVgjz48lZIPAKTv6m+SQKMxvefn4Ijy6Dfw2ZHUyPZc0ZeXaKwgc0xm7l/3f0sGrWILg1qbhdApRSJ6bkcsiaDC4khKjmTC39GPu5OtA30Mh4NvejgnU3ztXfhELsPRsyCXveVPEo3M9lIGKe3wqktEHsAVCGIBQI7GqWN4N7Go4LjBg4lHeL2n28nyDOIhSMX/m1J1dyCQh76ah+rD8Xx4OAWPDysZYlF+aIiRcy5bA7Hpl78XRyJTefM+eyLx1gchCb13GnmV4fmDTxo7leH1gGetAn0Kn0taKXg+3vgwNcw7n3ockuFPnOV8JF1LMX0DebGoZVIlyjsLPxcOAAtfFqYHIntFBYpTiZlcKhYKeFIbBpJGXkXjwmu507bQC9u6BJEm0Av2jX0ItDb9c8v1dgD8NXNxijiSV9Bq1GXv6BHfWhzrfEAY0bU6B3WxLEVdsyDrXOMfX6tjdJGk77GOZ2vbiBYO992vH3N29y37j5mrJvB3GFzcXX8c6UwF0cLcyZ34anv/2D2+uOcz87niVGtiUzM/Eup6UhsGunWXkYWB6G5nwdhIXWZ7B9Mcz8jKQTXd8fFsRwjw9c9bySJa/5dM5IEQNuxsPZZOH8afILNjka7CrpEYQOPbXqMA0kHWH3j6kq9rq1k5RVwJDb94hfg4dg0jsWlkZNvVJM4WYSW/p4XSwntGnrTOtCz9Llljq6CpXeCm49RbRJQwRHrBbnGoK1TW4zEEb0dctPAuQ60ux46T4HgXlc1p94jSVAAACAASURBVNDqk6t5bPNjDGo8iDcHvYmjw1/vm5RS/G/VEeb9evIv2z2cLbSx/i4u/E5a+nvi6mSjqUJ2fmysP91tGlz7ds2ZJyn5BLzbFUb8r8QxIJq5dInCzsLPhVebxYoS0nP+cld8ODaNk0l/Vh15uTrSrqE3t/RscvFLsLlfHZwdyziXkFLGnf8v/zHq1Cd9DZ426EHk6GIkguBe0B8oKjSSxb4vjEFoez+Hes2h82ToNKlMy4CObDqSlJwUXtrxEq/ufJWnej71l/0iwlOj29A9pB4Hz6bRyt+Tdg29CK7nXq5xHWVy8DtjOdOWI2H0GzUnSYDRiO3fAQ4v04mimtGJooLyCvOISoticPBgs0P5mwsT6K05HM/e6PMcPptGUsafM7U3qutG20AvxnZqeDEpBPm4lb/LZWG+cSe851NoOw6u/xCc3W30aS7hYLFWP/WBka8Y01nv+wLWvwAbZkHzwdD5Fmg1GpwuvwD95DaTOZNxhkWHF9GqbitubHnjX/aLCMPbBTDc3t1lz0XBz08bU3MEhcGET8BSA/97th1rzP+UHmebGwitUtTAv8TKFZkaSaEqrDIjsvMLi9h5MoVfDsfzy6E4zqbm4CDQ0t+TgS39rFVHXrQJ8MLb3YbTEmefg8W3wsnN0P9RY30EG85oWiqXOkY9fpdbjNG/+76EfV/BktvA1Qc63GTsC+xc4h36w90e5vj547y4/UWa+TSr3A4JeVnw21vGgDQHCwx5Bno/YLcuwqZrM9ZI5EeWQ4+7zI5GKyPdRlFBy04s4+nfnubH63+kmbc50xNk5RWwOTyRXw7Fs+5oAqnZ+bg4OtA/1I/h7fwZ0roB9evY8Ysn+QR8+Q/jrnjsu9B5kv2uVVZFhXByE+z9wvhSKsyFBu2MhNHxH+Dx19HXqbmpTF45mYz8DL659hsCPOx8t6sUHP4Bfv43pMVA+wkw7PkyVZlVe3N6QJ0GMG2F2ZFoxeg2Cjs6knwEF4sLwZ6V24sjOSOXdUcS+OVwHL9GJJFbUIS3mxND2jRgeNsABrT0rZypG6K2wDe3AAJTlxlVQVWBg8Wofmo+2Oh1dXCpUTX181PGpHotRxpVU6HDwOKEt4s37w5+l8mrJvPg+gf5dNSnuDnaZ5Q48Yfhp8cg6lejzv7GeVXn91YZ2o41JgrMTPpbwtaqJl2iKKfTaaeZs3cOP0X9RM/Annw8/GP7XzM5i18Ox/HLoXh2nUqhSBkzgA5r68/wdv70CKmHY2l9821t35ew7EGo19To2VQdJnxLOGI0fB/4BjITwaMBdL/TWLrT0YVN0ZuYsX4GI5uO5JX+r9h2ioz0ePjtTaOrr6sXDP43dLvNSGq1SewBmNsfrpsN3aaaHY1mpafwsKHk7GQ+3P8hS8KX4GRxYkqbKUxtNxVvF2+bX+tCY/Qvh+L45XA8R62LqLcO8DQaWNv6066hV+XP91NUZDQa//YmNB0IExcZ3WCrk8J8iFgDexZB+E9GtdQNH0BgJz7+42Pe2fMOM7vO5I4Od1T8WglHjJ5gBxZDUYGRHAb/G9zrVfzc1ZFSMLuz0Uvtn9+ZHY1mpauebCAzP5NFhxax8NBCcgtzmdByAnd3vBs/dz+7XG9TeCL//uEPolOycRAIC6nHv8e0YXjbAILr26knUVnkZcH30416/263wejXwGK/tXrtxuIErUcbj2OrjSU75w2GAf/ijn6PcCzlGO/seYfQuqEMaDTg6s+vlLEE6NY5cHwtOLpB11uNkem1fa4jEaNX3Nb3jE4QbnXNjki7Al2iuIL8wnyWRCzhw/0fkpKTwrAmw3iwy4OEeIfY7BrF5eQX8urqY3yy5SShDepw14Bm9m+MLqu0WGOkdez+0qfjqI6yUox2gz++hcBOZF/3DlN3v0R0ejRfjP6CZj5lrFYryDPaQ7bOgfiDRtVWz+kQdkftLUGUJGY3fDzY6EJdFTo/VGFFqogFBxcQnxXP1HZTCapjnw4PuuqpnOIy47jzlzs5lXaKMP8wHu72MB39Otrk3CUJj0/nwa/2cjQunam9m/Dk6Da2G+1bUcfXwfd3GyWKCfNLn46jOju8DFY8DLlpxPabwc0J6/B09uTzUZ/j41pK9VpqjNHDavcCSI8FvzbGoLKOE2tuV9eKUAream/M4zXpK7OjqbIKigp4ZsszLI9cDoCboxsPdX2ISa0n4SC2bY/UiaKcnvj1CdaeWsubg96kf1B/u7UFKKX4bNspZq08gqerI69N6MQ1rRtc+Y2VoTAf1r8IW942vvxuWgAN2pgdlX1lJhnJ4sgy9jTqyJ0uGXg6e9E/qD9967Wnt8UTn/NnjFldE49B4lHISjbe23yIUdJqMaTmlLbs5acnYNcn8NgJcKkGy9BWsrzCPB7b/BjrTq/jwS4PMqbZGJ7f9jxbzmyhs19nnuvzXNlLumWgE0U55BXm0fervoxrMc6ua0wkZeTy2JIDrD+awKBWfrw2oRN+nlXkDvTcKVh6B8TsNOYdGvGS/UZaVzVKGVVIqx7lAHks8gtkq+SS5iCIUrTPzaNPvqKvWxAdfNvj2KAttByh2x+uxqmtsGAk3DgfOkwwO5oqJbsgm5kbZvL72d95oscT3NLGmBhSKcWKyBW8svMVsvKzuLfTvUxrPw0nh4q3E5aWKCqxL+XfichIETkmIsdF5IkS9g8QkT0iUiAilfqXtD9xPzmFOfRt2Ndu19h4LIGRb//Kb8eT+O91bVkwrXvVSRKHfzS6MCYegwkL4Lp3ak+SAKM00GEC3Ledjm1v4nUJYHP9IXzeZAL3NhmNQ8MuzPNy51bHZAZk7uHhnAh25CWbHXX10rgn1PE31qjQLkrPS+eeNfewLXYbz/d5/mKSAGNKmeuaX8cP435gUONBzN47m8krJ3Mk+YhdYzKt15OIWID3gGFADLBTRJYppQ4XO+w0MA14tLLj23p2KxaxEBZQYoKtkJz8Ql7+6SgLf4+ilb8nn9/Zg9YBXld+Y2XIzzEGpe2aDw27GnMO1bv6xXtqDE9/I0kCFqCT9XEvxmju7bHb2XJ2C5tjNrP29FoGNRrEv7r/i2AvPY32FTk4QOtrYf9XRttXbboRuYxzOee4Z+09hKeE8+qAVxkRMqLE43zdfHlz0JusPbWWWdtnMWnlJKa1m8a9ne/FxWL7m00zu8f2AI4rpSIBRORrYBxwMVEopaKs+4oqO7jtcdtp59sOT2fb1p0ei0vnoa+NButpfUJ4YlTrqtNgnRhuzI8UfxD6zIDBz4Cj/VbNqu68XbwZHjKc4SHDySnI4YsjXzDvj3lc/+P13N7+du7ocIf9RnfXFG3HGTclx9caI7ZrscSsRO765S5iMmJ4Z/A7ZeqWPbTJULoHdOf1Xa8z/+B8tsVu48sxX9q8odvMRBEERBd7HQP0NCmWv0jPS+dg0kHu7HCnzc6plGLR1lPMWnUEL1dHFtzWnWtaVZEGa6WMUdarHgUnN7hliTG1hVZmro6u3NHhDsY2H8sbu99g7oG5rIhcwePdH2dQ40GVPyiyumjSF9zqGdVPtThRnMk4w12/3EVydjIfDP2A7gHdy/xebxdvXuj7AqOajiIlJ8XmSQJqyIA7EZkOTAcIDq54kX9n3E6KVBG9AntV+FwAiem5PLZkPxuOJXJNKz9eu6kTvlVhXAQYK8mteAT+WAwh/WH8PPAKNDuqasvP3Y+X+7/MjaE38r/t/+PBDQ8yoNEAnuj+BI29GpsdXtVjcYTWY4w1RQpya2VX4ui0aG77+TayCrKYN3xeubvg92lov/nCzGzMPgMU/5/TyLrtqimlPlJKhSmlwvz8Kj5SelvsNlwtrnTy61Thc204msCodzbz+4lknh/Xjk+mda86SeLsPpg7AA4uMaYFv/VHnSRspHtAdxZft5hHwx5lV9wurv/xet7f9z45BTlmh1b1tB0HeelwovatpZ2am8p96+4jtzCXBSMW2HWcVkWYmSh2AqEi0lREnIGbgSrR/WF77Ha6+XfD2VL++vmc/EKeXXaI2xbuxLeOC8tn9OPW3iFVowpCKdj2IcwfZjReT10BAx+rfZPT2ZmTgxNT201l+Q3LGdJkCB/s/4Drf7yezTGbzQ6tamk6EFy8a13vp/yifB7d9CgxGTG8fc3btKrXyuyQLsu0RKGUKgAeAH4GjgCLlVKHROR5ERkLICLdRSQGuAmYKyKH7B1XfGY8kamRFap2OhaXzrg5W1j4exS3923KD/f3paV/FRlQlJUCX02C1Y8bU3DfuwVC7NcFWIMG7g14dcCrzB8+HxeLC/evu59ntjxDel662aFVDY7Oxkj/oyuNAZ61gFKKl7e/zLbYbfy393/p5t/N7JBKZWobhVJqFbDqkm3PFHu+E6NKqtJsj9sOQK+G5UsU8Wk5TJq3DQcRFt7WnUFVpcEa4NTvsPROyEiAkS9Dz3v06OFK1COwB0uuW8IH+z9g/sH5bI3dyvN9nqd3w95mh2a+tmPhwNfGCokthpgdjd19efRLFocv5vb2t3N9i+vNDueKTB1wVxVtO7uNui51aVm3fEubzll/nIycAr6e3qvqJInCAtjwEiwcAxZnuHMN9LpXJwkTOFmceLDrg3w26jNcLa5MXzOdF7e9SFZ+ltmhmav5YHDyqBXVT7/G/MqrO19lcOPBPNT1IbPDKROdKIpRSrE9djs9AnuUq4tZanY+S/fEMLZzQ1o0qGOHCMsh/hAsGAWbXoYOE+HuzdCwEteE1krU0a8j3173Lf9s+08WH1vMhOUT2BO/x+ywzOPkZkyBcnSlsYxtDRVxLoJ/bf4XLeu25KX+L9mlK6s9VI8oK8nJ1JMkZCeUu33i213RZOUVMq1PiG0Du1pKGQOYPrsBPuhjTFp343wYP9dYWU2rElwdXXms+2PMHzGfIlXEtNXTeGPXG+QW5podmjnajjVWHTy91exI7CI5O5kZ62fg7ujOu4Pfxd2p+oxE14mimK2xxh9oeRJFYZFi4e9R9AipR/sg2692Vyb5ObD7U3i/F3x+o7E285Bn4KH9etK1Kqx7QHeWjl3KhJYTWHhoIbf/fHvtrIpqMQwcXY15xmqY3MJcZm6YSVJ2ErMHzybAI8DskK6KThTFbIvdRlCdIBp5Xn37+doj8cScy2Za3xDbB3YlGYlGG8Rb7YyV2ixOcMNcmPkH9P8/vWBONeDh5MEzvZ/htYGvcTDpII9uepT8otrRA+gilzrGNO1HVxml4hpCKcWzvz/LvsR9zOo3i/a+7c0O6arViJHZtlBQVMCuuF2XnYTrSj757SRBPm4Mb+tv48hKkXDEWE7ywGIozIWWI43FckL664bqampkyEjSctN4YdsLPPv7s7zY98WqMfamsrQeDcdWQtwBCKz4gNeq4OM/PmZF5Aoe6PxAub9fzKYThVVSdhIBHgHl6hZ78Ewq20+m8PToNjha7FxIUwpOrDcSxIl1xlrMXW4xFsvxDbXvtbVKMbHVRJKzk3l///v4ufkxs9tMs0OqPC1HgjgYpYoakCg2x2xm9t7ZjGk2hukdp5sdTrnpRGEV4BHA9+O+pzwLOX2y5STuzhYmdrfjXD75OcZ6zlvfM1ZWq+MPg/8N3W4Hj/r2u65mins63UNidiLzD87Hz93vL2sS1GgevsY6FUdXwjVPmh1NhRQWFfLaztdo7t2c5/o8V61LhjpRXOJq/zET0nNYvv8sk3sE4+1W8VWm/iYzCXbOh53zjB4h/u3h+g+g/Y21cgK12kJEeLrn06TkpPDKjleo71qfkU1Hmh1W5Wg1Gtb8x1hhsW4Ts6Mpt1UnVxGVFsWbg960yxoRlUkninIoKlKcTM7kQMx5lu07S0GRYlpfGyzuoxSkx0FSOCRHQMxuYznOwlwIHW60PzQdqNsfagmLg4VXBrzC9F+m8+RvT+Lj6mOzGY2rtNZjjERx7CfodY/Z0ZRLQVEBcw/MpVXdVgwJrv4jzXWiuAKlFLGpORyIOc++6FQOxJznjzOppOcUAODmZGH6gGY09fUo+0nzcyDlBCRFGI/kCCM5JB03ZtG8wLkOdJ4Eve4Hv/KNFNeqNxeLC+8OeZdpq6cxc8NMFoxYQJv6bcwOy77qNwffVkajdjVNFCsjV3Iq7RRvX/N2tRlUVxopT518VRYWFqZ27dplk3Mt2hrF+xtOEJdmTA3tZBFaB3jRsZE3nRr50LGxNy386pTcgK2UUVWUFP5nErhQUjh3Cij2e/dqZDRE+4aCb0uo38L46dVQlx40ABKyEpiyagp5hXl8NvozGnvW8LUt1j4LW2bDYyfAra7Z0VyV/KJ8xn4/Fk9nT7659ptq0zYhIruVUiWu/axLFCVQSvHW2ghmr4ugd7P63DuoOR0bedMm0OvKy5bmZcLymRD+M+Sm/rnd0Q18WxjrUHe8+c/EUL8FOF9FaUSrlRq4N+DDYR8y9aep3L3mbr4c/SU+rj5mh2U/rcbAb29BxBroONHsaK7K8hPLicmIYc7gOdUmSVyJThSXUErx2s/HeH/jCSaGNeKl8R2xOFzFP/b6F43eSV2mGA3PF0oJXkHGYvKaVk7NvJsxZ8gcpq2exks7XuKVAa+YHZL9BHUzevYdXVmtEkV+YT4fHfiI9vXbl2nN6+pCf3MVo5TipZ+O8v7GE0zuGczLV5skzkfDzo+NcQ3j5hj1qy2GgE9jnSQ0m+jk14npHaez6uQqNkZvNDsc+3FwMMZUHF9rLJFaTfxw4gfOZJzhvs731ZjSBOhEcZFSiudXHOajzZHc2rsJs65vj8PVJAmATdY7vIFP2D5ATbO6s8OdhNYNZdb2WTV7TqjWYyAvA07+anYkZZJXmMdHBz6io19H+gX1Mzscm9KJwioyKZOvdpzm9r5NeW5su6u/G0gMh31fQNgdRglC0+zEycGJZ3o9Q1xmHB/u/9DscOyn6UBjjYpjK82OpEy+j/ieuMw47u98f40qTYBOFBc196vDqgf7859r25TvH3nDi+DkDgMetX1wmnaJzg06c2PojSw6vIjwc+Fmh2MfTq7QYrAxnqKoyOxoSpVbmMtHf3xE1wZd6R1Y81YsLFOiEJHPyrKtumvmV6d8SeLMHmNq5N4PGFMQaFolmNl1Jl7OXryw9QWKVNX+Ii23VmMgPRZi95odSamWhC8hISuhxrVNXFDWEkW74i9ExAJU7dXAK9O658G9vjFyWtMqiY+rD/8X9n/sS9zH9xHfmx2OfbQcAWIxJgmsonIKcpj/x3zC/MPoEdDD7HDsotREISJPikg60FFE0qyPdCABqHmri5RHxFqI3GCs+6BXj9Mq2djmYwnzD+PN3W+SkpNidji2514PgnvDsaqbKJaELyExO7HGlibgColCKfWSUsoTeE0p5WV9eCql6iulqvfUjuWlFJzdZywU9GF/+OJG8A42GrE1rZKJCP/p9R+yCrJ4Y9cbZodjH61HQ8JhSDlpdiR/k1uYyycHP6F7QHe6B3Q3Oxy7KVPVk1LqSREJEpE+IjLgwqOiFxeRkSJyTESOi8jf+pSKiIuIfGPdv11EQip6zXLLTTdmcf2gL3w00OgK6+wBw56HO342Gt40zQTNfJpxW7vbWHZiGTvjdpodju21Gm38rIKliguliXs73Wt2KHZVppHZIvIycDNwGCi0blbA5vJe2NrO8R4wDIgBdorIMqXU4WKH3QGcU0q1EJGbgVeAf5T3muWSHgdb58CuhcaEfQEd4Nq3oM1Y3XCtVRl3dbyLVSdX8cK2F1h63VKcLHaY8t4s9ZpCg7ZGO0UVagfMLczlkz8+oWuDroT5lzhFUo1R1ik8bgBaKaVsOUSyB3BcKRUJICJfA+MwktEF44Bnrc+XAHNERFRlzGR4/jRseQf2fAZF+dBuPPS8BxqF6Yn6tCrHzdGNp3o+xf3r7mfhoYXc1fEus0OyrVaj4bc3ISulyqwB/33E9yRkJzCr/6wa2zZxQVl7PUUCtr5FCQKii72OsW4r8RilVAGQCth3ObekCPjhPpjdBXZ/Cp1uhhm7YcJ8aNxdJwmtyhrQaADDmgxj7oG5RKdHX/kN1Unr0aCKjMk2q4jF4YtpX789PQN6mh2K3ZVaohCRdzGqmLKAfSKyDrhYqlBKPWjf8MpGRKYD0wGCg4PLd5L0OFj9BBz6ARxdoftd0GcGeF+auzSt6nq8++NsObOFWdtn8cGQD2rOnW5gF/AMNEZpd55kdjQcSzlGxLkInu75dM35HZfiSlVPFxZ22A0ss/G1zwDF57poZN1W0jExIuIIeAPJl55IKfUR8BEY61GUKxrnOsbAuX4PQ6/7oI5fuU6jaWby9/BnRpcZvLLzFTbFbGJQ40Fmh2QbDg7QahTs/8ZY+MvkziMrI1fiKI6MCBlhahyVpdREoZT61I7X3gmEikhTjIRwMzD5kmOWAVOBrcAEYL3d2idc6sCDe8HhCutNaFoVd3Prm1l0eBGfH/685iQKMEZp7/oETm4yBuKZpLCokJUnV9IvqB91XavXokrlVdYpPP4QkQOXPH4VkbdEpFxtBtY2hweAn4EjwGKl1CEReV5ExloPmw/UF5HjwCOAfadl1UlCqwEcHRyZ2Goi2+O2c+L8CbPDsZ2m/cHZ01ijwkS74neRkJXAmOZjTI2jMpW1MfsnYCVwi/WxHKNaKg5YWN6LK6VWKaVaKqWaK6VmWbc9o5RaZn2eo5S6SSnVQinV40IPKU3TSjc+dDxODk58ffRrs0OxHUcXY32X8NWmThK4InIFHk4eDGo0yLQYKltZE8VQpdSTSqk/rI+ngYFKqVeAEPuFp2laedRzrcfIkJEsO7GMjLwMs8OxndZjICMezuw25fI5BTmsObWGYU2G4epYewbZljVRWETk4mxXItIduFBPU2DzqDRNq7BJrSeRVZDF8sjlZodiO6HDjEkCTVqjYmPMRjLzM7m22bWmXN8sZU0UdwLzReSkiERhtB3cJSIewEv2Ck7TtPLr4NeBdvXb8fXRr6mMMaqVwq0uhPQ1bTbZlSdW0sC9QY0fiX2pss71tFMp1QHoDHRSSnVUSu1QSmUqpRbbN0RN08prUutJRKZGsiNuh9mh2E6rMZB0DJIrt6H+XM45fjvzG2OajsFSyzq+XGma8SnWn4+IyCMYcy/dUey1pmlV2MimI/Fx8alZjdqtrZMEVnLvp5+jfqZAFTCmWe3p7XTBlUoUHtafnpd5aJpWhblYXLgh9AbWR68nLjPO7HBswycY/DtU+myyKyJXEFo3lFb1WlXqdauCK61HMdf687mSHpUToqZpFTGx5USUUiyNWGp2KLbTejREb4fMpEq5XHRaNPsT99e6RuwLyjrgrqWIrBORg9bXHUXk3/YNTdM0W2jk2Yi+QX1ZGr6U/KJ8s8OxjVYXJglcXSmXW3FyBYIwuunoSrleVVPWXk/zgCeBfACl1AGMKTc0TasG/tHqHyRmJ7IxeqPZodhGYCfwalQpvZ+UUqyMXEn3gO4EeATY/XpVUVkThbtS6tJuE3r8hKZVE/2D+hPoEcg3R78xOxTbEDEmCTyxHvKy7Hqpg0kHOZV2qtZWO0HZE0WSiDTHmHIcEZkAxNotKk3TbMriYGFCywlsj9vOydSqt/Z0ubQeDQXZELnRrpdZEbkCZwdnhjYZatfrVGVlTRT3A3OB1iJyBpgJ3GO3qDRNs7nxoeNxFEcWH6shQ5+a9AMXL7uO0s4vymd11GoGNR6Ep3Pt7ehZ1kRxBlgAzAK+BtZgTP+taVo14evmy5AmQ/jxxI9kF2SbHU7FOTobU3ocWw1FhXa5xNazW0nJSanV1U5Q9kTxI3AdRmP2WSADyLRXUJqm2cc/Wv2D9Lx0Vp+snN5CdtdqNGQlQcxOu5x+ReQKvF286RfUzy7nry6utMLdBY2UUiPtGommaXYX5h9Gc+/mLD62mBtCbzA7nIoLHQYOTsYo7eBeNj11Zn4mG05vYFyLcThZnGx67uqmrCWK30Wkg10j0TTN7kSEm1rdxMHkgxxKPmR2OBXn6g0h/ewySnvd6XXkFObU+monuPJcT3+IyAGgH7BHRI5ZV7e7sF3TtGpmbPOxuDm61ZxG7dZjIPk4JIbb9LQrTqwgqE4Qnfw62fS81dGVShTXYrRNjAJaAMOtry9s1zStmvF09mR009GsilxFWl6a2eFUXKtRxk8b9n5Kyk5ie9x2rm12LSJis/NWV1ea6+lUaY/KClLTNNua2GoiOYU5LDu+zOxQKs67kTFS24ajtDdFb6JIFTGsyTCbnbM6K2sbhaZpNUjb+m3p4NuBxeGLa8aiRq3GGD2fMhJscrqNMRsJ9AikZd2WNjlfdacThabVUhNbTeRk6kl2xtmna2mlaj0aUHDspwqfKqcgh21ntzGw0UBd7WSlE4Wm1VIjQ0bi5ezFN8dqwPxP/u3BO9gmvZ92xO0gpzCHQY0HVTyuGsKURCEi9URkjYhEWH/Wvcxxq0XkvIisqOwYNa2mc3V0ZWzzsayPXk9KTorZ4VSMiFGqiNwIeRUbC7wpehNujm50D+hum9hqALNKFE8A65RSocA66+uSvAb8s9Ki0rRaZnzoeAqKClhxogbci7UaDQU5xoyy5aSUYlPMJvo07IOzxdmGwVVvZiWKccCn1uefAteXdJBSah2QXllBaVptE1o3lI6+Hfku4rvq36jdpI8xAK8CvZ+OphwlPiuegY0G2jCw6s+sROGvlLowTXkc4G9SHJpW640PHc+J1BPsT9xvdigVY3GC0BEQ/hMUlm+5nI0xGxGEAY0G2Di46s1uiUJE1orIwRIe44ofp4zbmArdyojIdBHZJSK7EhMTKxS3ptU2I5uOxM3Rje8ivjM7lIprOxayz0HkhnK9fXP0Zjr4daC+W30bB1a92S1RKKWGKqXal/D4EYgXkUAA688KdX5WSn2klApTSoX5+fnZInxNqzU8nDwY1XQUq6NWk5GXYXY4FRM6HNzqwr4vr/qtiVmJHEw+7odhIAAAFyJJREFUqKudSmBW1dMy/lzPYirGNOaapplkfOh4sguyWR1Vzacfd3SBDjcZs8lmn7+qt26O2QygE0UJzEoULwPDRCQCGGp9jYiEicjHFw4SkV+Bb4EhIhIjIiNMiVbTariOvh1p4dOC7yO+NzuUius0CQpz4dDVVaXp0diXZ0qiUEolK6WGKKVCrVVUKdbtu5RSdxY7rr9Syk8p5aaUaqSU+tmMeDWtphMRxoeO50DSAcLP2XYW1krXsAv4tYZ9X5X5LTkFOWyP3a5HY1+GHpmtaRoA1za7FkcHx+pfqhAxShUxOyDpeJnesiNuB9kF2QxsrKudSqIThaZpANR1rcuQ4CEsj1xObmGu2eFUTMd/gDjA/rKVKvRo7NLpRKFp2kXjQ8eTmpvK+tPlH91cJXgFQvPBsP9rKCoq9dDio7FdLC6VFGD1ohOFpmkX9QrsRUOPhiyNWGp2KBXXaRKkxUDU5lIPO3bumB6NfQU6UWiadpGDOHBD6A1sj91OdHq02eFUTOsx4OJ9xUbt/2/v3sOjqO89jr+/uRDCnRAIICEEiAFEgxhB5CJIoggKAl4QT8Vajw9eavv0qI+ttrV3LWrPadUq7VGxR9SqgEjhlATCReViSAG5BBIuIUAIl3BJCCG33/ljJhrC7iYkOzu7nu/refbZ2Z3fznyYLPlmfjPzm1WF1tXYo3uNDlCw0KOFQil1gdv7306YhLEof5HbUVomMhoGT4Wdi+G89yHjVheu5srYK4mNjg1guNCihUIpdYHubbszsudIFuUvorq2eWMmBY2UmVBVDjs83/L166ux9Wwnn7RQKKUuMj1pOkfLj/LF4S/cjtIy8cMgpp/XIT3WHloL6NXYjdFCoZS6yJj4McS0juHj3SF+ULvumoqCz+Dk/otmrypcRfe23fVq7EZooVBKXSQyLJIp/aaw+uBqjp877naclkmZAQhsufCWr+drzrO+SO+N3RRaKJRSHk1NmkqNqeGT/BAfs7NTPCSOti6+q3dzpg1FGzhXfU7vjd0EWiiUUh4ldkxkaLehLMxfGPp3v0uZCSf3wYH1X7+15uAavRq7ibRQKKW8mn75dArOFLCpeJPbUVpm4G0Q2RY2vwt8czX2iB4j9GrsJtBCoZTyKj0hnXaR7UL/7ndR7WDQFNi+CCrL2XVyF0fOHtFupybSQqGU8io6IppJfSexvGA5ZyrPuB2nZYbMhMpSyP0HqwpXAejV2E2khUIp5dO0pGmcrznP0r1L3Y7SMgkjoWNv2DKfrMIsroq9Sq/GbiItFEopnwZ1GcSAmAGh3/0UFgYpMzh0YC07TuxgfMJ4txOFDC0USqlGTUuaxs6Snew4scPtKC2TMoMV0a0BSOud5nKY0KGFQinVqImJE4kKjwr9vYou/ciMiePyGujdrpfbaUKGFgqlVKM6RnUkPSGdpXuXcq76nNtxmu1Y+TE2SyVpZ05B3nK344QMLRRKqSaZljSN0qpSMgsy3Y7SbCsPrMQA6WGdIOs3F1yprbzTQqGUapLUuFR6t+8d0ne/yziQQZ8Ofeg3+mk4shV2fup2pJCghUIp1SQiwrSkaWwq3sT+0/vdjnPJTlWcIvtINmkJachVd0OXJMj6LdTWuB0t6LlSKEQkRkQyRCTPfu7soc0QEVknIttFZKuI3O1GVqXUN6b0n0K4hLMgP/QOamcVZlFjakhLSIPwCBj3Yzi2E7aF3r8l0Nzao3gaWGGMSQJW2K8bKgfuM8ZcAUwA/lNEOgUwo1KqgdjoWMb0GsPi/MVU1Va5HeeSZB7IpGfbngyKGWS9MWgqdLsCVv0OakL8Tn4Oc6tQTAHm2dPzgNsbNjDG7DbG5NnTh4GjQNeAJVRKeTQ9aTonKk6w5uAat6M0WVllGesOr2N8wvhv7j0RFgY3PgMle2Dr++4GDHJuFYo4Y0yRPX0EiPPVWESGAa2APV7mPyQi2SKSfezYMf8mVUpdYORlI+kW3S2krqlYc3ANVbVVpCekXzgjeSL0vBpWvQDVle6ECwGOFQoRyRSRbR4eU+q3M9ZA917PURORHsDfgO8aY2o9tTHGzDXGpBpjUrt21Z0OpZwUERbBlP5T+OzQZxw5e8TtOE2SeSCT2OhYUrqmXDhDBMY9C6cPwL/ecSdcCHCsUBhj0owxgz08PgGK7QJQVwiOelqGiHQA/gE8Y4xZ76mNUirwpiZNpdbUhsTd785Vn+OzQ58xvvd4wsTDr7z+4yH+OljzIlSF7sWETnKr62kxMMuengVc9G0TkVbAQuAdY8xHAcymlGpEfPt4hvcYzsL8hdR63tEPGl8c+oJz1eess508EYEbn4XSIsh+M7DhQoRbheJ5IF1E8oA0+zUikioif7Xb3AWMAe4Xkc32Y4g7cZVSDU1Pms6hskNsPLLR7Sg+ZRzIoGNUR66Ju8Z7o8TRkHgDrH0ZzpcFLlyIcKVQGGNOGGPGG2OS7C6qEvv9bGPMg/b0/xhjIo0xQ+o9NruRVyl1sRt730iHVh1YsDt4D2pX1lSyqnAV43uPJzIs0nfjG5+F8uOwcW5gwoUQvTJbKdUsUeFR3NbvNjIPZHKq4pTbcTxad3gdZ6vOXny2kyfxwyDpZvj8v6DitPPhQogWCqVUs03tP5Wq2iqW7F3idhSPlhcsp32r9gzvPrxpHxj3E6g4BeteczZYiNFCoZRqtuSYZK6MvZKP8z7GBNlIrFU1VWQVZjEufhyR4Y10O9XpOQQG3gbrXoXyEmcDhhAtFEqpFpmWNI38U/l8dfwrt6NcYH3RekorS7kp4aZL++DYn0BlmdUFpQAtFEqpFrol8RaiI6KD7krtjIIM2kW2Y0TPEZf2wbhBcOUdsOENKC12JlyI0UKhlGqRtpFtmdBnAsv2LaOsMjhOLa2qrWJl4UrGxo+lVXirS1/A2B9DbTUse1JvboQWCqWUH9yVfBfl1eV8sic4rtT+8siXnD5/umlnO3nSpZ81DPmOT2BbiNyo6cxhqD7vyKK1UCilWmxw7GCuir2K93PfD4ortTMKMmgT0Ybre17f/IVc/wPodS384z/gTFHj7d326Q/hL+MdWbQWCqWUX9wz8B72n9nPusPrXM1RXVvNygMruaHXDbSOaN38BYVHwO2vW3+lL/5+cHdBnS+FvVmQOMaRxWuhUEr5xc0JN9OldRfm5853Ncem4k2UVJSQ3qeZ3U71xfaHtOcgPwNygnh02bwMqKmEAZMcWbwWCqWUX0SGR3Jn8p2sPbiWwjOFruXIKMggOiKaUZeN8s8Chz0EfUbDP38CJ/f7Z5n+lrsE2sRC7+scWbwWCqWU39x5+Z2ESzjv7XrPlfXX1NaQWZDJqMtGER0R7Z+FhoXB7a8BAosehVr3j8FcoPo87F4OybdAWLgjq9BCoZTym25tupGekM6ivEWUV5UHfP05R3M4UXHi0i+ya0yn3jDhd1DwGWx43b/Lbql9a6Cy1Lqi3CFaKJRSfjVz4ExKq0oDPv6TMYa5W+fSoVUHxvRy4KDu1f9mDRq44hdwbLf/l99cOz+FVu2sYdIdooVCKeVXKV1TGBgzkPdy3wvo+E+rD65mfdF6HhnyCG0i2/h/BSIw+Y8QGQ2LZkNNtf/Xcalqa2DXUkhKh8gWnOHVCC0USim/EhHuGXAP+afy+fLIlwFZZ1VNFS9mv0hix0TuSr7LuRW17w6TXoJDm+DzPzi3nqYq3Ahnj8GAWx1djRYKpZTf3ZJ4C52iOgXsVNn5ufMpOFPAk6lPNn6DopYaPB2umAarXoCirc6uqzG5SyC8FST5+ZhMA1oolFJ+1zqiNdOTppNVmMXhssOOrqukooQ3trzBqMtGMbrXaEfX9bVJL0GbGPjoASg7Fph1NmSMdXwi8QZo3cHRVWmhUEo54u7kuwH4YNcHjq7n1X+9Snl1OU+mPunoei7QJgbueAtOH4R3JsPZ44Fbd53i7XCqAAY62+0EWiiUUg7p0a4HN8bfyMd5H1NRXeHIOnaV7OKjvI+YMWAGfTv1dWQdXvUZCTPfh5K9MG8ynD0R2PXnLgEEkic6viotFEopx8wcOJPT50+zbN8yvy/bGMOc7Dm0b9Weh1Me9vvym6TvWLjnfSjZA/NuC2w31M4lED8c2nVzfFVaKJRSjkmNS6V/p/7Mz53v91Nlswqz2FC0gUdSHqFjVEe/LvuS9BtnF4u9MO9WKDvq/DpP7ofirwLS7QQuFQoRiRGRDBHJs587e2iTICI5IrJZRLaLyGw3siqlmk9EmDlwJrkluX69V0VlTSUvZr9I3459uTP5Tr8tt9n6jYN7/w6nDsDbk6D0iLPr22lfzOjwabF13NqjeBpYYYxJAlbYrxsqAkYYY4YAw4GnRaRnADMqpfxgcr/JXNv9Wn76+U95Z7t/RmCdv3M+haWFPHXtU86fDttUiWPg3o/g9CGrWJxx8Gyv3CUQNxhiEp1bRz1uFYopwDx7eh5we8MGxphKY0zd7Zqi0G4ypUJSVHgUf077M+kJ6czJnsPLm15uUTfUiXMneGPrG4zpNYaRl430Y1I/6DMSvrPA2qN4e5JVNPyt7CgcWB+wvQlw75dvnDGm7pZRR4A4T41EJF5EtgKFwAvGGI8lWkQeEpFsEck+dsylc5qVUl5FhUcxZ8wc7k6+m7e2vcWznz9LVW1Vs5b1yuZXqKiu4InUJ/yc0k96XwffWWgd2H57Ipzy85Dru5YCJmDHJ8DBQiEimSKyzcNjSv12xvrTwuOfF8aYQmPMVUB/YJaIeCwoxpi5xphUY0xq165d/f5vUUq1XHhYOM8Mf4ZHhzzK4j2LeXzl45c8wuyukl0syFvAjAEzSOwYmG6XZokfBvctgvKT8ObN1jEFfx3M37kEOiVYXU8B4lihMMakGWMGe3h8AhSLSA8A+9nnaQL2nsQ2IECXXSqlnCAizE6Zzc9H/JwvDn/Bg8sf5GTFySZ9tqa2huc3Pk+HVh2YnRIC57b0SoX7P4WoDvDBvfDuHXA8v2XLrDgD+1ZbQ4qL+CdnE7jV9bQYmGVPzwIuOh1CRHqJSLQ93RkYBewKWEKllGPuuPwOXh77MrtP7ua+Zfd5Heaj+Gwxy/Yt49frf830xdPJLs7mR9f8yN3TYS9FjxSYvRZu/p01gN9r10HGz+F8WfOWl7fcvuVp4LqdACSQwwB/vVKRLsDfgd5AAXCXMaZERFKB2caYB0UkHXgJq1tKgFeMMXMbW3ZqaqrJzs52ML1Syl9yinN4bOVjtA5vzWtprxEVHkVOcQ45R3PYVLyJQ2XWweDoiGiGdB1CWkKas6PDOqnsKGQ+B5vfhfY94aZfWQMMXsqewYf3w7618MRuv9/NTkQ2GWNSPc5zo1A4SQuFUqEl72QeszNnc7T8mx7omNYxXN3taoZ2G8o1cdeQHJNMRFiEiyn9qHAjLH0CirZAwiiY+HuIu6Lxz1VVwJx+MHgaTP6T32P5KhTfki2vlApVSZ2T+ODWD1iQt4AurbtwddzVJHZIRALYBx9Q8cPg37MgZx6s+CW8PhqufRBGPg4de3n/3L7VUFkGA5y75ak3ukehlFJuKS+Blb+CTW8DYp3yOvxh6xTbhoVy8fdh20J4ag9ERPk9iq89Cr2ITSml3NImBm79A/xgC4x4FPaugrcmwNwbYPN8qLavOa6tgVz7lqcOFInGaKFQSim3deptHdz+0U6rcFSfh0UPwx+ugJW/ga8+hPLjAb3Irj49RqGUUsGiVVtIfQCu+a61d7HhDVgzBzDQLg6SJ7kSSwuFUkoFGxFrRNp+4+DEHsh5BxKuh8jWrsTRQqGUUsGsSz9I/4WrEfQYhVJKKZ+0UCillPJJC4VSSimftFAopZTySQuFUkopn7RQKKWU8kkLhVJKKZ+0UCillPLpWzd6rIgcw7oZUiiIBY67HeIShFpe0MyBEmqZQy0vOJ85wRjT1dOMb12hCCUiku1tWN9gFGp5QTMHSqhlDrW84G5m7XpSSinlkxYKpZRSPmmhcNdctwNcolDLC5o5UEItc6jlBRcz6zEKpZRSPukehVJKKZ+0UDhIROJFJEtEdojIdhH5gYc2Y0XktIhsth8/cyNrg0z7ReQrO0+2h/kiIn8UkXwR2SoiQ93IWS9Pcr3tt1lEzojIDxu0cX07i8ibInJURLbVey9GRDJEJM9+7uzls7PsNnkiMsvFvHNEJNf+uS8UkU5ePuvzOxTgzM+JyKF6P/uJXj47QUR22d/rp13O/EG9vPtFZLOXzwZmOxtj9OHQA+gBDLWn2wO7gUEN2owFlridtUGm/UCsj/kTgWWAANcBG9zOXC9bOHAE65zwoNrOwBhgKLCt3nu/B562p58GXvDwuRhgr/3c2Z7u7FLem4AIe/oFT3mb8h0KcObngCea8L3ZA/QFWgFbGv5fDWTmBvNfAn7m5nbWPQoHGWOKjDE59nQpsBO4zN1UfjEFeMdY1gOdRKSH26Fs44E9xpigu+jSGLMGKGnw9hRgnj09D7jdw0dvBjKMMSXGmJNABjDBsaA2T3mNMcuNMdX2y/VAL6dzXAov27gphgH5xpi9xphK4H2sn43jfGUWEQHuAt4LRBZvtFAEiIj0Aa4GNniYPUJEtojIMhG5IqDBPDPAchHZJCIPeZh/GVBY7/VBgqcAzsD7f6pg284AccaYInv6CBDnoU2wbu8HsPYsPWnsOxRoj9ndZW966d4L1m08Gig2xuR5mR+Q7ayFIgBEpB3wMfBDY8yZBrNzsLpJUoA/AYsCnc+DUcaYocAtwKMiMsbtQE0hIq2AycCHHmYH43a+gLH6EkLiNEQReQaoBt710iSYvkN/BvoBQ4AirK6cUHEPvvcmArKdtVA4TEQisYrEu8aYBQ3nG2POGGPK7OmlQKSIxAY4ZsNMh+zno8BCrN3y+g4B8fVe97Lfc9stQI4xprjhjGDczrbium47+/mohzZBtb1F5H7gVuBeu7hdpAnfoYAxxhQbY2qMMbXAX7xkCaptDCAiEcA04ANvbQK1nbVQOMjuX/xvYKcx5mUvbbrb7RCRYVg/kxOBS3lRnrYi0r5uGuvg5bYGzRYD99lnP10HnK7XfeImr399Bdt2rmcxUHcW0yzgEw9t/gncJCKd7W6Tm+z3Ak5EJgBPAZONMeVe2jTlOxQwDY6fTfWS5UsgSUQS7T3TGVg/GzelAbnGmIOeZgZ0OwfiqP7/1wcwCqsrYSuw2X5MBGYDs+02jwHbsc6yWA9c73LmvnaWLXauZ+z362cW4FWss0S+AlKDYFu3xfrF37Hee0G1nbGKWBFQhdUH/j2gC7ACyAMygRi7bSrw13qffQDItx/fdTFvPlZfft33+XW7bU9gqa/vkIuZ/2Z/T7di/fLv0TCz/Xoi1pmJe9zObL//dt33t15bV7azXpmtlFLKJ+16Ukop5ZMWCqWUUj5poVBKKeWTFgqllFI+aaFQSinlkxYKpZRSPmmhUEop5ZMWCqX8SEQW2QO0ba8bpE1Eviciu0Vko4j8RUResd/vKiIfi8iX9mOku+mV8kwvuFPKj0QkxhhTIiLRWMNC3Ax8jnW/gVJgJbDFGPOYiMwHXjPGfCYivYF/GmMGuhZeKS8i3A6g1LfM4yIy1Z6OB74DrDbGlACIyIfA5fb8NGCQPQQVQAcRaWfswQuVChZaKJTyExEZi/XLf4QxplxEVgG5gLe9hDDgOmNMRWASKtU8eoxCKf/pCJy0i8QArNvEtgVusEd+jQCm12u/HPh+3QsRGRLQtEo1kRYKpfznf4EIEdkJPI81Su0h4LfARqxjFfuB03b7x4FU+85rO7BGu1Uq6OjBbKUcVnfcwd6jWAi8aYxZ6HYupZpK9yiUct5zIrIZ66Yy+wjC27Aq5YvuUSillPJJ9yiUUkr5pIVCKaWUT1oolFJK+aSFQimllE9aKJRSSvmkhUIppZRP/wefUD2sZn3vkgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index aee9584be..32372a329 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return self.to_basis().inner_product(other) + return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 @@ -1484,18 +1484,14 @@ def penalty(self, derivative_degree=None, coefficients=None): def gram_matrix(self): r"""Return the Gram Matrix of a fourier basis - We already know that a fourier basis is orthonormal when the period is - the same as the domain range so the matrix is an identity matrix of - dimension n_basis*n_basis. Else we compute the matrix. + We already know that a fourier basis is orthonormal, so the matrix is + an identity matrix of dimension n_basis*n_basis Returns: numpy.array: Gram Matrix of the fourier basis. """ - if self.domain_range[0][1] - self.domain_range[0][0] == self.period: - return np.identity(self.n_basis) - else: - return super().gram_matrix() + return np.identity(self.n_basis) def basis_of_product(self, other): """Multiplication of two Fourier Basis""" @@ -2174,7 +2170,7 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, .. math:: = \int_a^b x(t)y(t) dt - When we talk about FDataBasis objects, they have many samples, so we + When we talk abaout FDataBasis objects, they have many samples, so we talk about inner product matrix instead. So, for two FDataBasis objects we define the inner product matrix as diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..fff7be7d4 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,81 +1,25 @@ import unittest import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.datasets import fetch_weather +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data - def test_basis_fpca_fit_attributes(self): +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) - basis = Fourier(n_basis=1) - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataBasis(basis, [[0.9]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of elements - # of target basis - fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() - with self.assertRaises(AttributeError): - fpca.fit(None) - - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of attributes - # in the FDataGrid object - fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_basis_fpca_fit_result(self): - - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) - - # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) - fd_basis = fd_data.to_basis(basis) - - fpca = FPCABasis(n_components=n_components) - fpca.fit(fd_basis) - - # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] - results = np.array(results) - # compare results obtained using this library. There are slight - # variations due to the fact that we are in two different packages - for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): - results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) if __name__ == '__main__': From 07d240be7c9524a9801de03f9b9e1522c2e7bd9f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 220/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From e580a749eaa42c14b430a4d31843b832c9b15844 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 221/624] Add docstring and references for fpca module --- docs/modules/exploratory.rst | 1 + docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 5 files changed, 118 insertions(+), 29 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index 832b93193..edc2c8d73 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,3 +11,4 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers + exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 325925ef68e68c7475ff72fc7db935cc4acc37c5 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 222/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From b76b4f85996f4a97d8d6ba252e8ad303adc71d35 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 223/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- skfda/exploratory/fpca/_fpca.py | 93 +++++++++++++++++++++++++++---- 2 files changed, 92 insertions(+), 13 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 3a7aaf15e7bb2f224ed7e300cd811094f9414a95 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 224/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From 07d2e91ea8a6e29d05c7cc6f63db1446003673b1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 225/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 46215a1ea940b49e98aa11f751978dc70cbab109 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 226/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- skfda/representation/basis.py | 5 +- tests/test_fpca.py | 28 +- 4 files changed, 1160 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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WAl8T1QRiO1vNQ0opz1j6BlSsHhDTSbiihcAXtbsRUjfD4U1OJ1HK9x1JtBaA6nyXNXgzAGkh8EVtroOgEJ1yQilPWPYmBIcFzHQSrmgh8EWVa0LTy63rBDojqVIXLisV1k2F9iOsmX4DlBYCX9VuGBw/oDOSKuWOFe9ZY3O6++96xKWhhcBXtRwIoZW0eUipC5VzHFa9b03oWLOZ02kcpYXAV/02I+nXUJDndBqlfM+qD6yJHPs84nQSx2kh8GVxwyDnGOxa4HQSpXxLXjYsG29da6vbwek0jtNC4MuaXgoVo3RMgVLna82nkH1EzwZsWgh8WXCo1ZV0+/eQe8LpNEr5hoI8awBZgx7QsIfTabyCFgJf124YFJyCbXPOva9SCjZ8bvW46/Ow00m8hhYCXxfbBSIbaO8hpUqjsAAWv2pNNd30cqfTeA0tBL4uKAjihsCuRZAVYGs5K3W+tnwN6buh98MBN9X02Wgh8Adxw8AUWj/kSinXiorg15chuhW0+ovTabyKFgJ/ENMaarXR3kNKnc3WWZC6BXo/Yp1Jq9/o0fAXcUMheSWk73E6iVLep6gQfnoRaraEttc7ncbreKQQiMgAEdkuIoki8piL58NF5Av7+RUi0qjYc4/b27eLSH9P5AlIcUOtr5u+cjaHUt5o80xI2wZ9H4OgYKfTeB23C4GIBAPjgYFAa2CEiLQusdudQIYxphnwKvBf+7WtgeFAG2AA8Lb9/dT5qtYAGnSHjdPBGKfTKOU9Cgvgp/9Yzaetr3U6jVfyxBlBFyDRGLPbGJMHfA6UXPRzMDDRvv8lcLmIiL39c2NMrjFmD5Bofz91IeKGWp96UnTBGqV+s+lLa1H6Sx/XawNn4ImjUg9IKvY42d7mch9jTAGQCdQo5WsBEJExIpIgIglpadpN0qXWpxesme50EqW8Q2GBdW2gdjvtKXQWPlMejTETjDHxxpj46Ohop+N4p8o17AVrvtIFa5QCWD8VMvbApU/ouIGz8EQhOADUL/Y41t7mch8RCQEigaOlfK06H3E3wPFkXbBGqfwc62ygbkdoMcDpNF7NE4VgFdBcRBqLSBjWxd/ZJfaZDYy07w8FFhpjjL19uN2rqDHQHFjpgUyBq9VV9oI12jykAtzK96wPRf2e0bOBc3C7ENht/uOAucBWYJoxZrOIPCsig+zdPgRqiEgi8BDwmP3azcA0YAvwA3CfMabQ3UwB7fSCNVt0wRoVwLLTrVHEzfpB4z5Op/F6IZ74JsaYOcCcEtueLHY/B7jhDK99AXjBEzmULe4G64xg1wJrSUulAs3iV6ylKK942ukkPsFnLhar89D0MmvBGm0eUoHoWBKsmAAXj4DabZ1O4xO0EPij0wvWbJujC9aowLPo39bXS59wNocP0ULgr+Ju0AVrVOA5vNHqMtr1bqhW/9z7K0ALgf+q31UXrFGBxRj4/h9QsTr0fsjpND5FC4G/0gVrVKDZPAP2LYHLn7SKgSo1LQT+LO4GXbBGBYa8k/Djk9ZUEh1vczqNz9FC4M9i2uiCNSowLH7NGjx21f/pNNMXQAuBv9MFa5S/y9gLS163zoAbdHM6jU/SQuDvdMEa5e/m/tOadbffs04n8VlaCPydLlij/Nm2ObDtW+jzCFSt63Qan6WFIBDogjXKH+WegDmPQK3W0ON+p9P4NC0EgUAXrFH+aOHzcPwgXPOGNZpeXTAtBIFAF6xR/iZ5Nax4DzrfBfU7O53G52khCBS6YI3yFwV58M0DUKWONXhMuU0LQaDQBWuUv/jlf9b1rqtfhgpVnU7jF7QQBApdsEb5g+TV8OsrcPFN1ocb5RFaCAJJ3A1wKsNasEYpX5N/Cr4eazUJDXzR6TR+RQtBINEFa5QvW/AcHNkBg9+CCpFOp/ErbhUCEYkSkXkistP+6nLKPxEZae+zU0RG2tsqich3IrJNRDaLiJb4sqYL1ihflbgAlo+HzqOh6aVOp/E77p4RPAYsMMY0BxbYj/9ARKKAp4CuQBfgqWIF4yVjTCugA9BTRHSB3bKmC9YoX3MiBWbebQ0cu/I5p9P4JXcLwWBgon1/InCti336A/OMMenGmAxgHjDAGJNtjFkEYIzJA9YAsW7mUefy24I12jykfEBRIcwYDblZMPRjCK3odCK/FOLm62OMMYfs+4eBGBf71AOSij1Otrf9RkSqAdcAr7uZR53L6QVrlrxhLVgTEe10Ir9QUFjE7iMn2XLwOEnp2Rw4dooDx06RejyXrNwCTuYVkJ1bSEFRESFBQQQHCSHBQmTFUKIqh1G9Uhg1I8KpH1WRhjUq0SCqEk2jI6hWKczpf5qzFr8Ke36GQW9CrVZOp/Fb5ywEIjIfqO3iqX8Wf2CMMSJy3rOaiUgIMBV4wxiz+yz7jQHGADRo0OB830YVF3eD9Qu25WvoMtrpND4pK7eAFbuPsjjxCGv2H2PboePkFvw+artmRBj1qll/1KtUCKVyeDCVw0MICRIKigwFhUXkFxqOn8onPTuP9JN5bD98gpQTOX+YG7BuZAVa142kdd2qtK1blfhGUURVDpDisPtnayH6tkOgw61Op/Fr5ywExpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4KdijycAO40xr50jxwR7X+Lj43UaTXecXrBm43QtBOfhcGYO3208xNxNh1mzP4OCIkN4SBDt61fjlm4NaVO3Km3qRtKwRiUqhF7Y4ig5+YUcOHaK/Uez2ZFygi2HjrPl4HEWbkuhyP6pb1Yrgs6NoujaOIqezWoSXSXcg/9KL3FsP3x5O9RoBte8DiJOJ/Jr7jYNzQZGAi/aX2e52Gcu8O9iF4ivBB4HEJHngUjgLjdzqPMVNxQWPGMt6lG9kdNpvNbJ3AJmrTvIrHUHWLk3HWPgojpVuat3E3o3r0mnhtUv+I++KxVCg2kaHUHT6AgubVXrt+05+YVsOpDJyr3prNqTzrcbDjJ15X4A2tarSt8WtejbMpr29asREuzjvcLzT8EXt0BhPgyfAuFVnE7k98S4MUe9iNQApgENgH3AMGNMuojEA2ONMXfZ+90BPGG/7AVjzMciEot17WAbkGs/95Yx5oNzvW98fLxJSEi44NwK6xPXa3Fw2b+sudzVHySmZjFp2V6+WnOArNwCmkZXZtDF9fjLxXVoGh3hdDwKiwxbDx3n5x1p/LQ9lTX7j1FYZIisGEq/1jFcFVebns1qEh7iY8s2GgMzx8KGz2HEF9BygNOJ/IqIrDbGxP9puzuFwClaCDzkowHWSON7l+upt231vgzeXLiTn7anERYcxFVxtbm1eyM6NqiGePExyszOZ3HiEeZvTWH+1hRO5BRQJTyEK1rHMLBtbfq0iPbomUuZ+eUlWPgc9H0C+v7D6TR+50yFwN2mIeXL4obCdw9bE3jVjnM6jaNW7U3njQU7+XXnEapXCuXhfi0Y0bUBNSN8o/09slIoV7erw9Xt6pBbUMjSxKPM2XiIH7ekMHPtAapWCOEvF9dlSMd6dGxQ3TuL2obpVhGIGwaXPOp0moCiZwSB7ORReLkFdB0L/V9wOo0jdqVl8e/vtrJgWyo1Kocxpk8TbunWkMrh/vEZKb+wiKW7jvL12gN8v+kQOflFNKpRies7xnJ9x3rEVq/kdETL3sUw6TqI7QK3zoAQ3yjAvkabhpRrn98M+5fDw9sCapWnjJN5vL5gJ5OX76NCaDD3XdqMUT0aUTHMB5pPLlBWbgHfbzzEV2uSWb47HRG4pEU0N3dtyGWtahEc5NBZQtp2+LAfRMTAnT9CRZcz1SgP0EKgXNv+A0y9EW78DC76i9Npypwxhumrk/n3nK0cP5XPiC4N+Fu/Fj7TBOQpyRnZTEtI5vOV+0k9kUvdyAqM6NKAGzvXp1bVCuUX5FgSfDwQCnLgrvnag62MaSFQrhUWwKutoV4nGDHV6TRlandaFk/M3Mjy3el0blSd56+No2XtwO6amF9YxIKtKUxevp/FiUcICRL6t6nNHb0a0alhVNm++YnDVhE4eRRGfQN1Li7b91N6sVidQXAIXDwClr5pTe5VxdUsIb6tsMjw3i+7eG3+TsJDgvjP9XHcGF+fIKeaQrxIaHAQA9rWYUDbOuw5cpIpK/YxLSGZ7zYeomODaozp04R+rWt7vtkoOx0+vdb6mbt1phYBh+kZgYIjO+GteOj3LPR80Ok0HpWUns1D09axam8GA9vW5plBbcq36cMHncwtYHpCEh8u2UNS+ika1qjEnb0aM7RTLJXCPPDZMScTJg6C1K1w83Rocon731OVijYNqbP7sD+cSof7VvrFmAJjDF+tOcDTszcjwDOD23Bdh3re2W3SSxUWGeZuPsyEX3azLunYb72qbu3e8MILQnY6TL4eDm+0Rg236O/Z0OqstBCos1szCWaPgzvnQf0uTqdxy/GcfB7/aiPfbTxEl8ZRvDLsYu/pJumDjDEk7Mv4bZxFjcphjO7ThFvPt5ttVhpMutZaZWzYJB017AAtBOrsck/ASy2tKaoHvel0mgu25eBx7v1sNUkZp3jkypaM6dPEuW6Rfmj1vgxeX7CTX3akEVU5jNG9m5Su2+3xQ/DpIKuX0Igp1rKpqtydqRD4+OxUymPCq1jLWG6aAXknnU5zQaYlJHHd20vIzivk8zHduKdvUy0CHtapYXU+vaMLM+7tQbvYSP77wzb6vrSIaauSKCw6w4fKY/ut3kHHD8ItX2kR8EJaCNTvOtwCeVmwxdUkst4rJ7+QR79cz6NfbqBTw+p890BvOjcq466PAa5jg+p8cnsXpo/tTt1qFXn0qw0MfP0XFm5L4Q+tDIc3wYdXWtefbpsFjXo6F1qdkRYC9bsG3SCqKaz51OkkpZZ6PIfhE5YzLSGZcZc2Y9KdXf1zfn4v1blRFDPu6cE7N3ckv9BwxycJ3PT+CrYfPmEtLPPxQEBg1ByI/VOLhPISWgjU70Sg0yjYvwxStjid5pw2Hchk8PglbD98gndv6cgj/VtqU5ADRISBcXX48W99eHZwG7YePs47b71I4aTrKaxSF+6aB7XbOh1TnYUWAvVH7W+G4DBY/bHTSc7quw2HGPruUgT48p7uDGhbx+lIAS80OIjbujVkae9NvBbyFqsKm3N5xhN8lQhFZ7p+oLyCFgL1R5VrQOtrYf3nXnnR2BjDq/N2cN+UNbSpG8mscb1oUzfS6VgKoCAXZo+j0s/PQJvriLhzNtWiavLw9PXcOGEZu9KynE6ozkALgfqz+Dsg9zhs+srpJH+QV1DEw9PX8/qCnQzpGMuU0Xo9wGtkpcGng2HtZLjkHzDkI9o2rMWMe3rwvyHt2JGSxcDXf+XtnxLJLyxyOq0qQQuB+rMG3aBWa0j4yOkkvzmRk8+dE1cxY80BHurXgpduaOd7yzD6q8Ob4P3L4OBaGPoRXPoEBFl/WoKChGGd6zPvoT5c3qoW//thO9eOX8KmA5kOh1bFaSFQfyZinRUcXAsH1jidhpTjOQx7bzlLdx3lf0Pb8cDlzXWqCG+x7Ture2hRPtz+PbQd4nK3WlUq8M4tnXjn5o6kHM9l8PglvPzjdj078BJuFQIRiRKReSKy0/7qckUJERlp77NTREa6eH62iGxyJ4vysHbDILSS42cFO1NOcP3bS9l/9CQfjerMsPj6juZRtqIi+Pl/1sJG0S1h9CKo1/GcLxsYV4f5D/VhcPu6vLkwkSHvLGW3XjtwnLtnBI8BC4wxzYEF9uM/EJEo4CmgK9AFeKp4wRCR6wH9SfA2FSKtNY03fQWnjjkSYdXedIa8s5S8wiK+uLs7l7SIdiSHKuFUBkwdDotesD4w3D4Hqpa+11a1SmG8Mqw979zckf3p2Vz9xmKmrNiPL0534y/cLQSDgYn2/YnAtS726Q/MM8akG2MygHnAAAARiQAeAp53M4cqC/F3QH42rC//BWt+3pHGrR+uoGaVcGbc04O29bRnkFc4vBEm9IVdC+Gql+C69yC04gV9q4FxdZj71z7EN6rOEzM3ctfEBI5k5Xo2ryoVdwtBjDHmkH3/MOBqVZN6QFKxx8n2NoDngJeB7HO9kYiMEZEEEUlIS0tzI7IqtbodoH5XWPEeFBWW29t+v/EQd01cRZOaEUy7uzv1o3TmUK+w/gv4oEJqS5cAABnYSURBVJ/VTfT2OdBltNtTlsdUrcDE27vw5F9a82viEa56/VdW7D7qocCqtM5ZCERkvohscnEbXHw/Y53XlfrcTkTaA02NMTNLs78xZoIxJt4YEx8drU0E5abbPZCxB3bMLZe3+3J1MvdNWUO72GpMHdMt4NYS9koFefDdIzBzjLWk6d2/eHSq8qAg4Y5ejZl1X08iwkMY8f5yxi9K1EFo5eichcAYc4Uxpq2L2ywgRUTqANhfU118iwNA8St8sfa27kC8iOwFFgMtROQn9/45yuNaXQNVY2H522X+VhOX7uWR6evp0bQmk+7sQmTF0DJ/T3UOxw/CJ1fBqvehx/3WxHERtcrkrS6qU5XZ9/fi6nZ1+b+527lj4irST+aVyXupP3K3aWg2cLoX0EjA1bSVc4ErRaS6fZH4SmCuMeYdY0xdY0wjoBewwxjT1808ytOCQ6DrGNj7q9U+XEbGL0rkqdmb6dc6hg9GxntmSUTlnj2/wnt9rCUlb5gIVz5v/TyUoYjwEN4Y3p7nrm3L0sSjXP3Gr6zel16m76ncLwQvAv1EZCdwhf0YEYkXkQ8AjDHpWNcCVtm3Z+1tyld0vM3qSrr8XY9/a2MML36/jf+bu53rOtTj7Zs7UiFUB4o5yhhY+qY1UrhidRi9ENq46gdSNkSEW7s1ZMa9PQgNDuLG95bz6bK92quoDOkKZap0vn0I1k6Cv22BCM9coykqMjw5exOTl+/nlm4NeHZQW4J09lBnnToGs+6Dbd/CRYPg2retRYscknkqn4e+WMeCbakMi4/luWvb6ohyN+gKZco9XcdCYZ7HBpgVFFrzBk1evp+xlzTlucFaBBx3cK3VFLTjB+j/Hxj2qaNFACCyYijv3xbP/Zc1Y1pCMje+t5zDmTmOZvJHWghU6US3gGb9YNUHkO/eL2JuQSH3fraGmWsP8Pf+LXlsYCudMsJJxsCqD+2pIgqsqSK63+t211BPCQoSHr6yJe/e0pGdKSe45q3Fet3Aw7QQqNLrMQ5OpsL6KRf8LbLzCrhrYgI/bknhmUFtuO/SZh4MqM5bbhbMGA3fPQSNL4G7f/Vo11BPGtC2DjPv60nlsGCGT1jOlBX7nY7kN7QQqNJrfAnU7QhLXofCgvN+eeapfG79cCVLEo/w0g0XM7JHI89nVKWXsgXev9SaRuSyf8FN06z1KLxYi5gqzLqvFz2a1uSJmRt5YuZG8gp04jp3aSFQpScCvR+CjL2w5evzeunRrFxGTFjOhuRjjL+pI0M7xZZNRlU666ZaU0efOmaNDejzyG9TR3u7yEqhfDSqM/f0bcqUFfu55YMVHNWpKdziG//zynu0vBpqtoRfX7HalkvhUOYphr23jN1HsvhgZGcGxumyko7JPwWzxsHXY63F5McuhsZ9nE513oKDhH8MaMXrw9uzPvkYg95awpaDx52O5bO0EKjzExQEvf4GqZtLNe3EvqMnGfrOMlKP5/LpHV11BlEnHUmED66wugH3fgRu/RqquJoezHcMbl+PL8f2oMgYhryzlDkbD537RepPtBCo8xc3FCIbwOKznxXsSDnBDe8uIzuvgCmju9GlcVQ5hlR/sHmmNWvo8YNw85dw+b/KfJRweYmLjWTWuJ60rluVez9bwyvzdug8RedJC4E6f8Gh0PMBSFoBu39yucu6pGMMe28ZANPu7k5crE4j7YiCXJjzd5g+Cmq1grG/QvN+TqfyuFpVKjBldFeGxcfyxoKd3PPZarJyz79DQ6DSQqAuTMfbrMnoFr3wp7OCpbuOcPP7y6laIZQvx/ageYyzg5ICVsZe+GgArJwA3e6DUXMg0n8v0oeHBPPfIe146prWzN+aypC3l7L/6DlnuFdoIVAXKiQcLvk7JK+CnT/+tnnelhRGfbyKetUr8uXY7jSooWsJOGLzTHi3NxzdBcMmwYB/Q0iY06nKnIhwe8/GTLy9C4eP5zBo/GKW7jridCyvp4VAXbj2N0P1RrDweTCGmWuTGTt5NRfVqcoXY7pTq2oFpxMGnvxT8M2DVlNQzRYw9hdoPcjpVOWuV/OazLqvJ9ER4dz64UqdtO4ctBCoCxccCpc8Boc38NOsD/nbF+vp2jiKz+7qSvXK/v/p0+ukbrPGBqz+BHr+Fe74wSrUAapRzcrMuLcHl7aM5slZm3Xw2VloIVBuMXE3kF6xEXXWvEq/VjX5aFRnIsL9ozeKzzAG1nxq9QrKSoVbvoJ+z1iFOsBVqRDKhFvjue/SpkxdmcTNHyzXdZFd0EKgLlhRkeH573fw/zIH0TIomXfitutaAuUt5zh8dRfMvh/qd4Z7lkCzK5xO5VWCgoS/92/FGyM6sPFAJoPfWsLmg5lOx/IqWgjUBcktKOSBz9fy4eI91Op6Iya2KyGLnofcE05HCxwH1ljTRm+eCZf9P3uAWG2nU3mtQRfX/cPgs283HHQ6ktfQQqDOW+apfEZ+tJJvNxzisYGteGpQG2TAfyArBRa/5nQ8/1dUCL/8H3zYDwrzYdR30OfvEKRnY+fStl4ks8f1ok3dSMZNWctLc7fr4DO0EKjzdCjzFMPeXcbqfRm8Prw9Yy9paq0lENsJ4oZZSxwe0+mBy0zGXvj4Kqun1kWD4J7F0LC706l8SnSVcKaM7sqN8fV5a1Eid0/WwWduFQIRiRKReSKy0/5a/Qz7jbT32SkiI4ttDxORCSKyQ0S2icgQd/KosrX98Amuf3spB4+d4pPbuzC4fb0/7nDFUyBBMP9pR/L5NWNg3RR4pxekboHr34ehH1lrCqvzFh4SzItD4nj6mtYs3JbK9W8vYd/Rk07Hcoy7ZwSPAQuMMc2BBfbjPxCRKOApoCvQBXiqWMH4J5BqjGkBtAZ+djOPKiO/7Ehj6LtLKTKGaWO707NZzT/vFBkLPe635rffu6T8Q/qr7HSYPhK+vgfqXGxdEG43zGtWEPNVIsKono359I4upBzP5S9vLA7Y6wbuFoLBwET7/kTgWhf79AfmGWPSjTEZwDxggP3cHcB/AIwxRcYYHQLoZYwxfLxkD6M+Xkm9ahWZcW9PLqpT9cwv6PU3qNYAvv2rNc+Ncs/OefBOD9g2B654BkbOto6v8piezWry7f29aBYTwbgpa3li5kZy8gudjlWu3C0EMcaY0/O+HgZczWlbD0gq9jgZqCci1ezHz4nIGhGZLiJnnBNXRMaISIKIJKSlpbkZW5VGXkERT8zcyDPfbOGKi2L46p4e1KtW8ewvCqsEV78CR3ZYK5mpC3MqA2beA58NhQrVYPQC6PVXvSBcRupHVWLa3d0Ze4m12M2145eQmBo4PeDOWQhEZL6IbHJxG1x8P2ON3z6fy+8hQCyw1BjTEVgGvHSmnY0xE4wx8caY+OhondO+rKWfzOOWD1cwdWUS4y5txru3dKJyaQeKNe8Hba6DX16y5sBX52f79zC+G2z4wuoNdPfPVpOQKlOhwUE8NrAVn9zembQTuVzz5hKmrNgfEFNTnLMQGGOuMMa0dXGbBaSISB0A+2uqi29xAKhf7HGsve0okA3MsLdPBzq68W9RHrIxOZNBby1mXdIxXh/enkf6tyQo6Dzbowe8CCEVYPY4q7ujOrfsdPhqNEwdDpVrwuiF1viAkHCnkwWUvi1rMefB3nRqWJ0nZm5k5MerOJyZ43SsMuVu09Bs4HQvoJHALBf7zAWuFJHq9kXiK4G59hnEN0Bfe7/LgS1u5lFuMMYwafk+hryzlKIiw7S7u/+5Z1BpVakNA1+E/ctg2VueDepvjLEGhY3vCptnWPM3jV4Edds7nSxgxVStwKd3dOG5wW1YtSedK1/9mZlrk/327EDc+YeJSA1gGtAA2AcMM8aki0g8MNYYc5e93x3AE/bLXjDGfGxvbwhMAqoBacDtxphzdkKPj483CQkJF5xb/dnJ3AKemLmRWesOckmLaF67sb37E8cZA1/cYk1TPeYniGnjiaj+JX0PzHkEEudD7XYweDzUaed0KlXM3iMneWT6ehL2ZXDFRTE8M7jNua+VeSkRWW2Mif/Tdl+scFoIPGvzwUwe/Hwdu9OyeKhfC+7t2+z8m4LO5OQReLsbRMTAXQsgVKemBqAgD5a+YY0QDgqxmoA6j/ab5SP9TWGR1Xvu5R93APDXK5pzR6/GhAb71phcLQTqTwqLDBN+2c0r87ZTrVIYr9/Ynh6uxge4a8dcmDIMOo2Ca7QnEXuXwLd/gyPbrdHBA/8LVes6nUqVQnJGNs98s4V5W1JoGVOF569rS+dGvrMW95kKgW+VM+UxSenZjJiwnP/+sI3LW8Uw9699yqYIALTob40vWP0JrJtaNu/hC44fhBlj4JOroOAU3DQNbpykRcCHxFavxPu3xfP+bfFk5RZww7vLuPez1ew94tujkvWMIMAUFhk+WbqXl3/cTpAIzwxqw/Ud61nzBZXpGxfApGshOQHumg+125bt+3mT/FPWHEyLX7V6UPUYB70fscZcKJ+VnVfAhF92M+GX3eQXFnFz14bcf1kzakR4by8vbRpSbDqQyeMzNrLxQCZ9W0bz/LVtia1ejn+MTqTAhEtAgq1iULVO+b23E073Bpr3FGTut5qBrnwuoFcN80epx3N4df5Ovli1nwqhwdzarSF39W5CdBXvKwhaCAJY+sk8Xp+/g0nL9xFVOZynB7Xm6rg6ZX8W4Mqh9fDRQKjRFG7/HsIjyj9Dedi/AuY/ZXWfjYmDAf+Bxr2dTqXKUGLqCd5amMjs9QcJCwliRJcG3Nmrcfl+2DoHLQQBKCe/kIlL9/LWokSy8wq5qUsDHunfksiKDi9huONHmHojNL0Mhk/xrwFTKVtg4XOwfY7VU6rv49DxNp0aIoDsTsvi7Z92MXPtAYwxXNm6NqN6NqJr4yhnPnwVo4UggOQVFDFjTTJvLUokOeMUl7WqxeMDW9E8porT0X63+hP45kFoeRXcMBFCfHyx+4x98NN/YP3nEF4Fej4I3e6BsMpOJ1MOOXDsFJOX72Pqyv0cy86nZUwVhnaKZXD7utSq6kw3ai0EASAnv5AvViXx7s+7OJSZw8WxkTw6oJXrKaO9wcr3rcFULa+GGz7xzWJwbL+1KtuaT61P/V3GWD2kKvlOl0JVtnLyC/l67QGmrkpifdIxggT6tIjmug71uPyiGCJKO4eXB2gh8GNJ6dl8tmI/0xKSSD+ZR+dG1bn/sub0bl7T8VPRc1oxAb7/OzTuA8MmQcVq536NNzi6Cxa/Yp0BINDhZujzKERe4JQcKiAkpmYxc20yM9cc4GBmDmHBQXRvWoN+rWPo1zqGmDI+U9BC4GdO5RWycFsqX65O4qcdaQSJ0O+iGEb1bES3JjWcjnd+1k2B2Q9YF5Bvnu7d8+0f3mSNCN44HYJCodNIqxkoMtbpZMqHFBUZVu1NZ96WFOZtTWHf0WwAWtWuQrcmNejetAbdGtcgspJnr+dpIcCaT6dSWLD3f0o+gxM5+SxJPMp3Gw+xYGsK2XmFxFQNZ3jnBozo0oDakT48fcOeX+DzWyAoCAa/Da2ucjrR74qKYOdcWDYe9v4KoZUg/g5rNbYqtZ1Op3ycMYadqVnM35rC0sSjJOxLJye/CBFoGVOF9vWr0S62GhfXj6RFTBW3prXQQgBc/cavHDx2iua1qtA8JoIWMVVoXiuCZjERREeEe12ByM4rYNOB4yzffZRfd6axdv8xCooMUZXDGNC2Nn9pV4eujWsQ7Kl5gZx2JBG+vB0Ob7Da2q942tmLraeOWWsCrHgX0ndD1XpWro636TUAVWZyCwpZn5TJsl1HWbM/g/XJxziWnQ9AhdAglj9+OdUqXdj1NC0EwOTl+9h8MJOdKVnsSDnB8ZyC356rEBpEbPVKxFavaN8qUa9aRWpVCadGRDjREeFUrRhSJsUiv7CIpPRs9hw5ye60k+xIOcGG5Ex2pp6gyFhL08bVi6RXs5r0al6TLo2iCPGxya5KrSAX5j8Ny9+GqrHQ/wVoPbj81uctKoI9P8PaybD1GyjMhdjOVg+giwZBsMNdb1XAMcawPz2bdUnHSEzN4uErW17w99JCUIIxhtQTuexMyWJXWhZJ6dkkZ5wi+Zj19XQFLi4sOIgaEWHUjAgnIjyEyuEhVKkQQuXwYCqHh1A5LISQYCFYhCARRKwFsnMLCsnJK+RUvnU7mVvIkaxc0k7kciQrl6Mn8yj+31CjchhxsZHW6WBsJB0bVHd/Smhfs385fPcIpGyE+t2g98PWymdlURCKiuDgGtg6GzbNtEYBV4iEuGHWReC6HTz/nko5QAvBeTqek8/BY6c4ciKPI1nWH+y0rFyOnMjj6MlcsnIKyMot4GReASdzC8nKLSCvoOiM308EKoQEUzEsmIqhwURXCf/tVjMinIZRlWgcXZkmNStf8Gmf3yksgDWfwK+vwvFkqNUa2t8MbYe4Pz1FdjrsWwK7f7IWhj9x0JoOuklfaH+T1aVVp8xWfkYLQTnILyyisMhQZAxFBoqMwRRBeGgQ4SFBXncNwmcU5lu9dFa8B4fWgQRBnfbQqBfU7wo1W1jz97gah1CYDycOQ2YSpGy2prg4tM7q/YOBkIrQ7HKr2afFlVCxenn/65QqN1oIlH84shM2fQW7f4bkVVB0uglPoEJVCI+0CkJBHuRnQ/ZRoNjPeMUoawWwBj2suX/qdfKvKS6UOgstBMr/5GVD6hZrcFf6LjiVATnHreIQHG79gY+Iseb7r1oPYlpDlTrld+FZKS9zpkKg6+Ip3xVWCWLjrZtS6oK51QdRRKJEZJ6I7LS/umxgFZGR9j47RWRkse0jRGSjiGwQkR9ExEsnxVFKKf/lbmf0x4AFxpjmwAL78R+ISBTwFNAV6AI8JSLVRSQEeB241BjTDtgAjHMzj1JKqfPkbiEYDEy0708ErnWxT39gnjEm3RiTAcwDBgBi3yqL1Z2mKnDQzTxKKaXOk7uFIMYYc8i+fxiIcbFPPSCp2ONkoJ4xJh+4B9iIVQBaAx+e6Y1EZIyIJIhIQlpampuxlVJKnXbOQiAi80Vkk4vb4OL7Gav7Uam7IIlIKFYh6ADUxWoaevxM+xtjJhhj4o0x8dHR0aV9G6WUUudwzl5DxpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4Cegvf39d9nfaxourjEopZQqW+42Dc0GTvcCGgnMcrHPXOBK+wJxdeBKe9sBoLWInP543w/Y6mYepZRS58ndcQQvAtNE5E5gHzAMQETigbHGmLuMMeki8hywyn7Ns8aYdHu/Z4BfRCTffv0oN/MopZQ6Tz45slhE0rAKx/mqCRzxcJyyoDk9yxdy+kJG0JyeVt45Gxpj/nSR1ScLwYUSkQRXw6u9jeb0LF/I6QsZQXN6mrfk9NPVTZRSSpWWFgKllApwgVYIJjgdoJQ0p2f5Qk5fyAia09O8ImdAXSNQSin1Z4F2RqCUUqoELQRKKRXgAqYQiMgAEdkuIoki4jVTWYjIXntNhnUikmBvK9U6D2Wc6yMRSRWRTcW2ucwlljfsY7tBRDo6nPNpETlgH9N1InJVsecet3NuF5H+5ZizvogsEpEtIrJZRB60t3vNMT1LRq86niJSQURWish6O+cz9vbGIrLCzvOFiITZ28Ptx4n2840czvmJiOwpdjzb29sd+z3CGOP3NyAY2AU0AcKA9UBrp3PZ2fYCNUts+x/wmH3/MeC/DuTqA3QENp0rF3AV8D3WtOLdgBUO53waeMTFvq3t//twoLH9MxFcTjnrAB3t+1WAHXYerzmmZ8noVcfTPiYR9v1QYIV9jKYBw+3t7wL32PfvBd617w8Hviin//Mz5fwEGOpif8d+jwLljKALkGiM2W2MyQM+x1pLwVuVZp2HMmWM+QVIL7H5TLkGA58ay3Kgmj0JoVM5z2Qw8LkxJtcYswdIxPrZKHPGmEPGmDX2/RNY82rVw4uO6Vkynokjx9M+Jln2w1D7ZoDLgC/t7SWP5elj/CVwuUjZL1x9lpxn4tjvUaAUApdrIjiUpSQD/Cgiq0VkjL2tNOs8OOFMubzx+I6zT68/Kta05hU57aaJDlifEL3ymJbICF52PEUkWETWYc14PA/rbOSYMabARZbfctrPZwI1nMhpjDl9PF+wj+erIhJeMqet3I5noBQCb9bLGNMRGAjcJyJ9ij9prHNGr+vj6625bO8ATbGmOj8EvOxsnN+JSATwFfBXY8zx4s95yzF1kdHrjqcxptAY0x5rWvsuQCuHI7lUMqeItMVad6UV0BmIAv7hYEQgcArBAaB+scex9jbHGWMO2F9TgZlYP9Qpp08J5czrPDjhTLm86vgaY1LsX8Ai4H1+b65wNKdYizF9BXxmjJlhb/aqY+oqo7ceTzvbMWAR0B2rKeX0jMrFs/yW034+EjjqUM4BdhOcMcbkAh/jBcczUArBKqC53asgDOuC0WyHMyEilUWkyun7WGs1bKJ06zw44Uy5ZgO32b0eugGZxZo7yl2JdtXrsI4pWDmH271IGgPNgZXllEmwlmLdaox5pdhTXnNMz5TR246niESLSDX7fkV+X8tkETDU3q3ksTx9jIcCC+2zLydybitW+AXrOkbx4+nM71F5XZV2+oZ1RX4HVlviP53OY2dqgtXrYj2w+XQurPbLBcBOYD4Q5UC2qVjNAPlYbZV3nikXVi+H8fax3QjEO5xzkp1jA9YvV51i+//TzrkdGFiOOXthNftsANbZt6u86ZieJaNXHU+gHbDWzrMJeNLe3gSrECUC04Fwe3sF+3Gi/XwTh3MutI/nJmAyv/cscuz3SKeYUEqpABcoTUNKKaXOQAuBUkoFOC0ESikV4LQQKKVUgNNCoJRSAU4LgVJKBTgtBEopFeD+P51Ronmlh4ZuAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 32372a329..886f90e79 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,8 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) + #return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 @@ -2170,7 +2171,7 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, .. math:: = \int_a^b x(t)y(t) dt - When we talk abaout FDataBasis objects, they have many samples, so we + When we talk about FDataBasis objects, they have many samples, so we talk about inner product matrix instead. So, for two FDataBasis objects we define the inner product matrix as diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 55049a9b269a4e3f5c5c9fb3d43766d11d07744a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:22:29 +0100 Subject: [PATCH 227/624] Finilized Module testing --- skfda/representation/basis.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 886f90e79..d1fb95a0e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,8 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return self.to_basis().inner_product(other) - #return np.transpose(other.inner_product(self.to_basis())) + return np.transpose(other.inner_product(self.to_basis())) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From c8b346c886b19f197620c4502f6795b54cbac81d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 228/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From 706d194b0dd05243c9014702c03ef3209e2accf0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 229/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- skfda/representation/basis.py | 2 +- 5 files changed, 175 insertions(+), 97 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index d1fb95a0e..ed13bf9d8 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -403,7 +403,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From 59abd92e5eedf692efd6da8b9c0569bcae67e95c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 230/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 03ab338ea21f5e470be06a7c7946d8a70ce72745 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 231/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 2df2a32cbf1bfdcfb28d23972038c18657a683ae Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 232/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 20 +++++++++++--------- 2 files changed, 26 insertions(+), 15 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -36,9 +38,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -59,7 +61,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -78,10 +80,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -92,10 +94,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -109,4 +111,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() From fa19f23615599742c9d20b6d34e9b92f68e299f8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 233/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From c3bbab093c6c6bdd5667cf41c9e7d394274e8477 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 234/624] transfer files to new location and modify documentation --- docs/modules/exploratory.rst | 1 - docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 13 files changed, 77 insertions(+), 551 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index edc2c8d73..832b93193 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,4 +11,3 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers - exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From ddb5b79dde9486d5c75db6bf60bcbbb0dfbb5791 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:50:18 +0100 Subject: [PATCH 235/624] fix gram matrix in Fourier basis --- skfda/representation/basis.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index ed13bf9d8..71ec3f77e 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1484,14 +1484,18 @@ def penalty(self, derivative_degree=None, coefficients=None): def gram_matrix(self): r"""Return the Gram Matrix of a fourier basis - We already know that a fourier basis is orthonormal, so the matrix is - an identity matrix of dimension n_basis*n_basis + We already know that a fourier basis is orthonormal when the period is + the same as the domain range so the matrix is an identity matrix of + dimension n_basis*n_basis. Else we compute the matrix. Returns: numpy.array: Gram Matrix of the fourier basis. """ - return np.identity(self.n_basis) + if self.domain_range[1] - self.domain_range[0] == self.period: + return np.identity(self.n_basis) + else: + return super.gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 932bd93d68136c64888f4bdd51a1a7d3dea5cf97 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:58:09 +0100 Subject: [PATCH 236/624] fix gram matrix method in Fourier basis --- skfda/representation/basis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 71ec3f77e..aee9584be 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -1492,10 +1492,10 @@ def gram_matrix(self): numpy.array: Gram Matrix of the fourier basis. """ - if self.domain_range[1] - self.domain_range[0] == self.period: + if self.domain_range[0][1] - self.domain_range[0][0] == self.period: return np.identity(self.n_basis) else: - return super.gram_matrix() + return super().gram_matrix() def basis_of_product(self, other): """Multiplication of two Fourier Basis""" From 3d6e2a62ed5b43327dbd036d723871fabc6146d2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 237/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 1d60a8e149c66eedc9b0467c8f0ae2050164ffd6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 238/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From 3913c7f8d5123f3e38523f7b091e8fb4bc6d2014 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 239/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 1f939a91fccc3b32ffa8362a0e376116e79cbd22 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 240/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From f291a5cd42fb81089134b8397f788f20c837004d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 241/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From e11bf4926dbddff5d7b020c423a70878ea051dbf Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 242/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From 3ede0af102669d46f25043a5a8fa9ac08fbdbf0c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 243/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0d6bb43db8bab30fb8aed550cc2b6dc36c8eb5ee Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 244/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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JCb4lClWtAyYC04GlwIuqulhE7hCRC7zT7gHaAS+JyAIRmdLC5YwxpnUWPu9Wrys4y+9IYoavk5qo6tvA2032/S7g8ZiIB2WMiV8Vm9162Cfdam0TB8HeKWNM2/HVi6ANMPQKvyOJKZYojDFtgyoseB56joK8/n5HE1MsURhj2obNC9zkf8OsNHGwLFEYY9qGBc9DYqpN/ncILFEYY+JfXY2bsuOIsyE9x+9oYo4lCmNM/Fv5LuwqhWFX+h1JTLJEYYyJfwueg8zO0O90vyOJSZYojDHxraoUVkyHIZfZetiHyBKFMSa+ffUyNNTa2InDYInCGBPfFj7nphPverTfkcQsSxTGmPj1zTLY9KWVJg6TJQpjTPxa+DxIIgy+1O9IYpolCmNMfGqoh0WToeAMaNfZ72himiUKY0x8Wv0R7Nhs1U4hYInCGBOfFj4Padm2il0IWKIwxsSf3RWw9E04+mJISvU7mphnicIYE3+WvAZ1u2zKjhCxRGGMiT8LX4CO/aHHCL8jiQuWKIwx8aWsCNZ+6hqxRfyOJi5YojDGxJeFLwACQy/3O5K4YYnCGBNfvnoJ+p4MWT39jiRuWKIwxsSP4kIoWQkDL/A7krhiicIYEz+Wv+3ubexESFmiMMbEj+VToesQq3YKMUsUxpj4UFkM62dZaSIMfE0UIjJWRJaLyEoRua2Z46eIyHwRqRORS/yI0RgTIwrfAW2wRBEGviUKEUkEHgDOBgYBV4jIoCanrQOuA56LbHTGmJiz/G1o3w26DfM7krjjZ4liFLBSVVerag3wAjAu8ARVLVLVRUCDHwEaY2JE7W5Y+YErTdggu5DzM1H0ANYHbG/w9h00EZkgInNFZO62bdtCEpwxJoYUfQK1lXDEOX5HEpfiojFbVSep6khVHdmpUye/wzHGRNrytyE5E/qc7HckccnPRLERyA/Y7untM8aY1lN13WL7fweS0/yOJi61KlGIyDOt2XeQ5gAFItJXRFKAy4Eph3lNY0xbs3kh7NgEA6y3U7i0tkRxVOCG12PpsObvVdU6YCIwHVgKvKiqi0XkDhG5wHudY0VkA3Ap8LCILD6c1zTGxKHlUwGBAWf5HUncSgp2UER+CfwKSBeRisbdQA0w6XBfXFXfBt5usu93AY/n4KqkjDGmecvfhvzjIDPP70jiVtAShar+SVXbA/eoagfv1l5VO6rqLyMUozHGNK98I2xZZIPswixoiaKRqv5SRHoAvQOfo6ofhyswY4w5oBVT3b11iw2rViUKEbkb19i8BKj3ditgicIY45/lUyG3H+QV+B1JXGtVogAuAo5Q1epwBmOMMa1WvQPWfAyjJtho7DBrba+n1UByOAMxxpiDsuoDqK+x9okIOFCvp3/iqpiqgAUi8j6wp1Shqj8Ob3jGGNOC5VMhLRvyj/c7krh3oKqnud79PGwwnDEmWjTUw4rpUHAmJLa2Bt0cqqDvsKo+FalAjDGm1TbOg12lNsguQlrb6+krXBVUoHJcieOPqloS6sCMMaZFK6aDJEL/0/2OpE1obZltKq5bbOMCQpcDGcAW4Eng/JBHZowxLSmc7kZjp+f4HUmb0NpEMUZVhwdsfyUi81V1uIhcHY7AjDGmWRWbYMtXMOZ2vyNpM1rbPTZRREY1bojIsUCit1kX8qiMMaYlhe+4+wJrn4iU1pYobgQeF5F2uEkBK4AbRSQT+FO4gjPGmP2seAc69ITOA/2OpM1o7VxPc4DBIpLlbZcHHH4xHIEZY8x+6qph9Ucw9Hs2GjuCDjTg7mpV/beI3NpkPwCqem8YYzPGmH2t/dStjW3VThF1oBJFpnffPtyBGGPMAa14B5LSoO8pfkfSphxowN3D3v0fIhOOMcYEUTgd+pwMKRl+R9KmtHbN7AEi8r6IfO1tDxGR34Q3NGOMCVC8EkpX22hsH7S2e+wjwC+BWgBVXYQbdGeMMZFRON3dF5zhbxxtUGsTRYaqzm6yz8ZPGGMiZ8V0yDsCcvr4HUmb09pEUSwi/fDmexKRS4DNYYvKGGMCVe+AtZ/BgDP9jqRNau2Aux8Bk4AjRWQjsAa4KmxRGWNMoNUfQUOtdYv1SWsTxUbgCeBDIBc3Mns8cEeY4jLGmL1WTIfULOhlixT5obWJ4nVgOzAf2BS+cIwxpglVKHwX+p0GibYisx9amyh6qurYsEZijDHN2bwQdm6xbrE+am1j9mciMjjULy4iY0VkuYisFJHbmjmeKiKTveOzRKRPqGMwxkS5xtli+1u3WL8caK6nxpXtkoDrRWQ1UI2bQVZVdcihvrCIJAIPAGcAG4A5IjJFVZcEnHYDUKaq/UXkcuDPwPcO9TWNMTFoxXToPhzadfI7kjbrQFVP54XxtUcBK1V1NYCIvACMAwITxTjgdu/xy8D9IiKq2nRZVmNMPKosdutjj96vwsFE0IHmelobxtfuAawP2N4AHNfSOapaJyLlQEegOIxxGWOiReG7gEKBjZ/wU2vbKKKaiEwQkbkiMnfbtm2HdpH6Onj1h7BpQWiDM8YcusLpkNkZug3zO5I2zc9EsRHID9ju6e1r9hwRSQKygJKmF1LVSao6UlVHdup0iPWY29fC6g/h0dNh5t+gof7QrmOMCY36Olj5gStNJMTFd9qY5ee7PwcoEJG+IpKCm2RwSpNzpuAG9gFcAnwQtvaJjv3gh5/BwPPh/TvgyXOhrCgsL2WMaYX1s6C63KbtiAK+JQpVrQMmAtOBpcCLqrpYRO4QkQu80x4DOorISuBWILwtWhm5cMkTcNEk2LoY/nUSfPmsG/BjjImswumQkATfOs3vSNo8ibcORCNHjtS5c+ce/oW2r3NtFms/gc6D4IQfweBLISn18K9tjDmwB46HzDy47k2/I2kTRGSeqo5s7phV/LUkuxeMnwIX/gskAV7/Efz9aJjxFyjf4Hd0xsS37etg21IbjR0lWjuFR9uUkAjDroShV8CaGfD5A/Dhne7W+Si3gMqAs6DnKEi0t9KYkFnRuEiRJYpoYJ9urSEC3xrtbiWrYNlbblqBz++HT/8BR5wDVzzvb4zGxJPCd9wCRXkFfkdisKqng9exH5z4Y1dv+vPVMOJ6WD4VKvfrtWuMORQ1VbDmY1eaEPE7GoMlisOTlgXHXAMorPrA72iMiQ9Fn0DdbusWG0UsURyu7sdARt7eGS6NMYencDokZ0Dvk/yOxHgsURyuhATofzqseh8aGvyOxpjYpgor3oG+p0Jymt/RGI8lilDofwZUlcCmL/2OxJjYtm0ZlK+zaqcoY4kiFPqfDgisfNfvSIyJbcununvrFhtVLFGEQkYu9Bxp7RTGHK4V06DrEMjq4XckJoAlilDpfwZsnO8WWjHGHLzKYlg/G4442+9ITBOWKEKlYAzWTdaYw1D4DqAwYKzfkZgmLFGESrdjIKMjrHzP70iMiU3Lp0K7rrZIURSyRBEqCQnQ73RYad1kjTloddWuND7gLFukKArZbySU+o+BqmLYbMupGnNQ1n4KNTutfSJKWaIIUFpZc3gX2NNN9v3Du07tLiheCeu+cI+NiXfLp0FSmhtoZ6KOzR7rKa+qZcQf36VPx0xG9cnluG/lMqpvLj1zMlp/kcw86D7Mjac49WfNn1NfBzu3QPlGKF8PFRvd+haB21UBEwymZcPQy2HEddB54GH9jMZEJVVYMdXNzpxyEP9vJmIsUTQS+NXZA5m1poSpX29m8tz1APTITue4vo2JoyN9OmYgwWa07D8GZv4NFjwP1TugYkNAItgAOzaD1u/7nNQs1288qyf0GOHus3pCSiYsfhXmPAazHoL841zCGHSh/UOZ+PHNUrdQ0Um3+h2JaYEthdqMhgZl2ZYdzF5Twqw1pcxeU0qJVy2VnZHM4B5ZDOmZxZCe2QztmU3XrIA5aXZshX9927VVACSmQIceez/8Ax83bqd1CB5QZTEsfB7mPQklK11iGfo9GD4euh59WD+rMb6b+Td4/w64dRl06OZ3NG1WsKVQLVG0gqqyalsls9eUsmjDdhZtKGf51h3UN7j37twh3XjgyuF7n1BVCmVrICvfzSwbql4cqrD2M5cwlrwO9dXQY6QrZRz9XVcCMSbWPHoG1NfAD2b4HUmbZokiDHbX1rNkcwX/914h89eWsej2M4NXSYVaVSksfMEljeLlkNIehlwGI8ZDt6GRi8OYw7FzG/y1AEbf5m7GN8EShfV6OkRpyYkM75XDmEFd2FFdx+by3ZENICMXTvgv+NEs+P50GHgeLHgWHj4FJo12CaR6R2RjMuZg2WjsmGCJ4jAN6NwOgBVbffpQFoFex8NFD8H/LoOz/+IGL71xC/ztSHe/eZE/sRlzICumQvvuVgqOcpYoDtOALu0BHxNFoPQcOO4H8MPP4Ib3XO+ohZNdKeP9O6C+1u8IjdmrrhpWfehGY9va2FHNEsVhyslMoVP7VFZs3el3KHuJQP6xcOEDrpRxzNWuZ8ljZ0LpGr+jM8YpmmmjsWOEL4lCRHJF5F0RKfTuc1o4b5qIbBeRNyMd48EY0KVddJQompOeDePuh8uehpJVrnSx+DW/ozLGG42dDn1P8TsScwB+lShuA95X1QLgfW+7OfcA10QsqkM0oEt7CrfupKEhinuQDRoHN38MeQXw0nh481aojXADvDGNVN0iRf1Og+R0v6MxB+BXohgHPOU9fgq4sLmTVPV9IEq/qu81oEt7dtXWs3F7lM/LlNMHrp8G3/5vmPsYPDrGzSllTKRtXeymrLHeTjHBryk8uqjqZu/xFqCLT3GExIAurufT8i07yM+N8qk1klLgzD9Cn5Ph1ZtdVdT5/3BjMNqahnp47/ewegZ06O7dvJHzjY87dLdvvOGwwlsbe4CtjR0LwpYoROQ9oGszh34duKGqKiKHVWcjIhOACQC9evU6nEsdkoLGnk/f7GDMoBjJeQPOgps/gf/cAK/cBGtmwNn3tJ05pOqq3c+95HXo9W03F9f6WbCrbP9zMzp6icNLIFk9vCTSwz1u3x2S0/Z/nmnZ8mnQfTi0b+4jwkSbsCUKVR3T0jER2Soi3VR1s4h0A745zNeaBEwCNzL7cK51KDqkJdMtK40VW6K+lmxfWT1g/Jvw0Z9cr6gNc+HSJ+N/ltrqnTD5alj9IZx5J3x74t5jNVVQscnN4tt4K2+8Xw/rPofd2/e/ZmYn6PcdN/9W729bd89gdn4DG+fBab/yOxLTSn5VPU0BxgN3e/ev+xRHyAzo0j66usi2VmISnP5b6HMivDIBJp0G59zjutTG44ddVSk8ewlsWgDjHoRjrtr3eEoG5PV3t5bUVO5NJuUb3ePSVbDsLVg0GTofBcffDIMvtWqr5qyYjo3Gji1+JYq7gRdF5AZgLXAZgIiMBG5W1Ru97ZnAkUA7EdkA3KCq032KOagBXdrx+eoS6huUxIQY/IDt9x24+VN45UaYMhHWfAzn3Qup7f2OLHQqNsEzF7mxJN97Bo4899Cuk5Lpeo/lFey7v6YSvv4PzHoYpvw3vPt7GHk9HHujq7Iyzopprhqv62C/IzGt5EuvJ1UtUdXTVbVAVceoaqm3f25jkvC2T1bVTqqarqo9ozVJgGunqKlrYG1Jpd+hHLr2XeCa1+C038DXL8PDp8bP9B/FK+Gxs1wJ4Or/HHqSCCYlE4Zf69p+xr8BvU6AmffCPwbDyze4qr22rnb33rWx47HEGqdsZHaI7J3KIwarnwIlJLrV+ca/AbVVrgvt7Edcv/dYtXkhPH4W1FbCdW9A35PD+3oibhDZFc/Bj7+EUT9wk989ejo8crob8NjQEN4YolXRTPd3ZaOxY4olihAp8CYHLIzWEdoHq89J7ptx31Pg7Z+6QXq7mmnEjXZFn8KT57n1mL8/HbofE9nXz+0LY++CW5e4CRt3lbr3ctIpsOKd2E7Ah2L5VEjOdN2zTcywRBEimalJ9MhOp/CbGC9RBMrMgytfhDPucA21D5/ieqvEiuVT4d/fdV0wb5i+f5tCJKW2dxM2TpwLF02C3RXw3KXw+FiXzNoCVdeQ3e80604cYyxRhFBUz/l0qBIS4MRb4PqpoA3w+Nmw6CW/ozqwhS/AC1e5rr7XT3OD6KJBQqJbxnbiXDj3XigrgifPgWe+C5u+9Du68NrylccDQAsAABdqSURBVFtD3no7xRxLFCE0oEt7Vm+rpK4+Duuf80fBDz6GniNdz6gP/xS91SZf/Ate/YHr8jv+Dcjs6HdE+0tKgWNvgFsWwBn/zyWJSaNh8jVu8sZ4tGIaIDYaOwZZogihgi7tqalvYG1pld+hhEdGrusVNewqmHE3/OfG6JpYUBU+uBOm3QZHngdXvhT93XuT0+HEH8MtC2H0L936DI+dGZ/JYvlU6DEC2nX2OxJzkCxRhFDjnE+Fsd7zKZikFBj3AJz+e9eF9qnz3EhbvzU0uEb3j/8Cx1wDlz4VW/XgaR3cmtETPnJVfP++2K0nHS92bIFN8+EIq3aKRZYoQqhfpzjr+dQSETj5VrfGxZavXZfPrUv8i6euxlWHzXkUvv1juOCfbsR5LMrrD1dOhh2b4bnL3CC+eFD4jrsfYN1iY5ElihDKTE2iZ046K+Kp51Mwg8bB9W9DfY2rLil8L/Ix1FTBC1e4EdFj/gBn/r/YH8iVPwoueRw2L4CXroP6Or8jOnzLp0FWPnQ5yu9IzCGwRBFibhGjOC9RBOoxHG76AHL7uO6esyZF7rV3lcEzF7qRvuffByf9JHKvHW5Hngvn/NV9E3/rf6K340Br1O52EzAOGBv7SbyNskQRYgVd2rF6WyW18djzqSVZPVwX1AFjYerP4K2fhv9bcFkRPHGu6y10yRMwYnx4X88Px94AJ/8U5j8NM/7sdzSHbvlbbjR2OKZNMRFhiSLERvTKoaa+gU8Ki/0OJbJS28H3/g0nTIQ5j8Dz33ODykKpphIWvegaeu8bDtvXuQGBRzW7QGJ8+M5vYOiVbir4+U/7Hc2hmTUJcvpC31P9jsQcohht8Yteo4/oTF67FH703HxO+FZHTi7I46SCTvTrlInEe7E7IRHOutONgH7rf127xZWTIaf3oV+zvs5VWyx6EZa96b6ZZuW7QYDH3hA9A+nCRQQuuA92boU3fgLtusKAM/2OqvU2L4T1X8BZd7nBmyYmicZy3WczRo4cqXPn+jtL59cby3lhzjpmFhaztsSNqeielcZJBXmcXNCJE/vnkZuZ4muMYbf6I3jxWkhMgcufcw20raUKG+e7tR0WvwKV2yAt25UchnwP8o9vex861TvgyXOhuBCue9ONR4gFr090HQ1uXQrp2X5HY4IQkXmqOrLZY5YowmtdSRUzV27jk8JiPl1ZTMXuOkTg6O5ZXuLIY0TvHFKTEv0ONfS2rXBdPCs2wYUPwuBLgp9fsgq+eskliNLVkJjq+t0PvgwKzoCk1JCG19Cg3PX2Uj4u3Eav3Ax6d8ykd0d336djBt2z00lOjKKEtGMrPDbG9fS64R3o2M/viIKrKoV7B8LQK9y67CaqWaKIEnX1DXy1sZyZhcV8UljM/HVl1DUo6cmJHPetXE4u6MTJBXkUdG4XP9VUVaVu2dG1n7qRx6f+Yt+eLzu3uVLDohdh41xA3DTggy+DQRdAWlZYwlJV/vDGEp78rIhRfXOp2FVLUUklu2v3dkJITBB65qTTKzeDPk2SSH5uBmnJPiT34kJXpZeR6wbnRfPI80/vg3d/Cz/8zLrFxgBLFFFqZ3UdX6wqYWbhNmYWFrO62A2u6tIhlZP6d+KUAXmc2D+PvHah/SYdcXXVrn594XNw9CUw9m7XpfWrl9y91kOXwTDkMjj6YteLKszumb6MBz5cxU0n9+VX5wxERFBVtu2opqikiqKSStY13pdWsaa4kh279/bkEoGuHdLo3dElkV4d900m7VLD2PxX9Ak8db6rhrvoofC9zuFoqIf7jnHtSde/5Xc0phUsUcSIDWVVfFJYzMyVrppqe1UtAIO6dfAaxV01VUZKDPZBUIVP/g7v/2Hvvqx8Vx01+DLoMihioTz40Ur+Mm05V4zqxV0XHd2q0puqsr2qlrWlVawtqaSouIq1pZWsLXHbxTtr9jk/r12Kq8rap0rLJZPsjOTDLzF+dLfrCXXhQzDsisO7Vjgsn+Z6vl36VHz3SosjlihiUH2DsniTq6aaWbiNeWvLqK1XkhKEo3tkcVzfXEb1zWVkn1yy0pP9Drf1VrzjesEMGAs9Rka8Ufqpz4r4/ZTFjBvWnXsvGxay9c13VtextqQxcXjJxCuVbCrfd+LE9mlJAaWPxuost925fWrrkkhDPTx1gRtH8oMZ/q610ZxnLoJvlsFPFkFiDP19tmGWKOJAZXUdc4pKmVNUyuw1pSxcX05NfQMicGTXDhzXN5fj+uZybN/c2K+qCpOX523gpy8t5IxBXXjwquERa6jeXVvPhrIqior3VmUVeclkQ9ku6hv2/g+mJyfSu2OGaxfJ85JJrrvvnp2+b2Kr2AT/OtFV1d3wXvRMglhcCPePdGuvn/ozv6MxrWSJIg7trq1nwfrtzFpdyuyiEuatLdvTENuvUyaj+nbcU+ronp3uc7T+e/urzUx8bj4n9s/j0fEjo6aXWW19A5u276KopIp1JZV7EsjakirWllZRU7e3cT05UcjP2VsK6d0xgxHVsxny8QTqR95E4nl/9fEnCTD1FzDnMbf8q00pHjMsUbQBNXUNfL2pnNlrSpm1uoS5RWXsqHaNr/m56Yzqszdx9O6YET+9qlrhw2XfMOGZuQztmc3TN4yKmTaehgZlS8XugKqsKtaVeu0jJZVU1tQD8JukZ7gxaSq/TLmNdZ1P29Mzq1duJn3yXOkkYj9z9Q64d5CrWrz4kci8pgkJSxRtUH2DsnRzBbPXuKqq2UWllFa6BtcuHVIZ1bcjo/rkMKJ3Lkd0bR+yuvpo88XqEsY/PpuCLu147qbj6ZAWH/XlqkpJZQ1rSypZt207J3x4Je13beCW7H8yb3smZV5HiEad26fu1y7SNy+TPnkh7qE151E3Kv+G9yD/2NBd14SdJQqDqrLym53M8hLHrDUlbK2oBiAzJZFjeuUwvHcOI3rncEyv7Lj4QF2wfjtXPfIF3bLTmTzheDrGc9tN6Wp46BQ3XuG6tyiv0T3dewMb2YtKKvlmR/U+T81rl0rfPNcjq0+eSyB981wDe3rKQVTRqcKDx0NSmhvj0YZKrfHAEoXZj6qyvnQX89eVMW+tuy3bUkGDuv/vAZ3b70kcI3vnxFx11dLNFVw+6Quy0pN56eYT6NIhShp6w+mrl+E/3oyzp/+2xdOqaur2VGetKa5iTfFOioqrWFNSybYmSaRrhzT65GXsSRyNiaRXcwMOV8+Apy+AC/8Fw64Mx09owsgShWmVndV1LFy/fU/imL+ubM8gs46ZKXsSx4jeOQzukeXPyORWWL1tJ5c9/AVJCcJLN59Afm6G3yFFzusT4ct/w7WvwbdGH/TTd1bXUVTsuvYWFQckkpKqPVWX4L5MdM9K96qvXGnkgmU/p2PJXOp/soSUtDb0nseJqEsUIpILTAb6AEXAZapa1uScYcC/gA5APXCnqk4+0LUtUYROQ4OycttO5q0tY26RSxxrvNHjyYnCUd2z9iSOEb1zouJb+4ayKi576HOq6xqY/IMT6N+5nd8hRVZNJUw6DXZvh5s/CWmvo/JdtXuSyJpiL5GUVLFm207a7d7CzNRbeLj+fP7WcMWeqU/yczPIz8kgPzfdu88gJxQDDk3IRWOi+AtQqqp3i8htQI6q/qLJOQMAVdVCEekOzAMGqur2YNe2RBFeJTurmb/OlTrmry1j4YbtVHtdOHtkp7uqqj45DO+Vw5Fd25MUwUn1vqnYzWUPf05pZQ3PTzieo7qHZ56oqLd1MTzyHej9bbjqZTf9exipKrun/Z602f9k2nems2RXFmuKK1lfWsX6sl37lETAtYnl52bQs0kCyc9Np1tWOh3SkiyR+CAaE8VyYLSqbhaRbsBHqnrEAZ6zELhEVQuDnWeJIrJq6hpYsrliT+KYu7Z0TyN5Rkoiw/KzGdHbNZQPz88hKyM8jeRllTVcPukL1pdV8cwNxzGid05YXidmzHsK3vgxHHcznB3m1fFqd8PfB0GvE+DyZ/c7vLO6ziUNL3GsL61iQ1kV60t3sb6siiqvm2+j9OREunRIpXOHNLp2SKNrVhqd26fSNSuNLt6+zh1So2YsTLwIlij86lDeRVU3e4+3AF2CnSwio4AUYFULxycAEwB69eoVwjDNgaQkJTAsP5th+dnccFJfVJVN5bv3JI55a8t48KNVe0YfF3Rux4jeOQzLz2ZIz2wGdGl32KWOHbtrGf/EbNaUVPLkdcdakgC3NGzxCvj8fre63PE3h++1Fk2GqhIYNaHZw+1SkxjYrQMDu3XY75iqUlpZsyeBbK3YzZby3WzdUc3W8t0sWL+dLYt37zPwsFFORjJdOqR5t1RyM1PJzUwmNzOVjpkp5AbcMlISrZRyGMJWohCR94CuzRz6NfCUqmYHnFumqs3+dzeWOIDxqvrFgV7XShTRp7K6joUbtu9JHPPXbad8l+vnn5acwNHdsxjSM5uh+VkM7Zl9UD2sdtXUM/7x2cxfV8bD14zg9IFBv3O0LQ31bvGo5W+7xaOOODsMr9EADx7nusT+4OOwdIlVVcp31bKlYjdbK1wCcY/dbUvFbr6pqKasqoba+uY/z1KTEshKT6ZDejId0pLokJ5M+7S9jzukJdM+LYmMlETv5h6npySSGfA4IyUpbsccxWzVk4h0wCWJu1T15dZc2xJF9FNVikqqWLRhOwvXl7Nww3YWbyrfMwVJVnoyQ3pmMaSnSxxD87ObbSivrqvnpqfnMbNwG/ddfgznD+0e6R8l+tVUupXxti2H66dC92Ghvf7sR+Dtn8J3H4Uhl4b22gdJVdlRXUdZZQ0llTWU7qyhtKqG0kp3q9hVS8XuWip21bFjdy0Vu+v27GspwTQnNSlhTzJJTUogJSmB1KQEUpMS9zwOvE9KTCA5QUhMSCA5UUhKFJISEkhKEHcsUUhsfJwgJCQISQluX2KCkCh79+051mRforhz26Um0Scv85Dev2hMFPcAJQGN2bmq+vMm56QAU4E3VLXVy2NZoohNdfUNrNi60yUPL4Es37pjT5VVlw6prtTRM4uh+dkc0bU9v3ttMdMWb+HPFw/me8dalWOLdmyFR0+H+lq46f3QrTO+6CV45SboPwaueAESY2NqlKZUleq6Bip21VJVU09VTT27auuorK73tuvcvpp6Kmvq9txX1dRTU9dAdV2Dd7/vdk19A9W1DdQ1NFBbr9Q3KLX1DdQ16D4TQYbS0PxsXv/RiYf03GhMFB2BF4FewFpc99hSERkJ3KyqN4rI1cATwOKAp16nqguCXdsSRfzYXVvP4k0VLFy/nUUbtrNoQ/mexZ0a/e68QXz/pL4+RRhDti6Bx8+C7F6uZJG2f3vBQVn2Fky+xjVgX/0yJNvEkwdDValrUOrqlbqGBurqlVrvvt5LJPWqNDTonsTS4D2ncV+Dd07gvg5pyZzQr+MhxRR1iSKcLFHEt/JdtXy1oZwlm8sZ1M2tO25aadUH8O9LoN9pcMXkQy8BrPrQrYXedTBc+3p0L8dqWi1YooiileONObCs9GROKshjwin9LEkcrH7fgfPuhZXvwdSfubmZDta6L+CFK6FjgRujYUmiTYjNSkVjzKEZcR2UroFP/wG5/eDbE1v/3E0L4NlLoX03N0VIRm7YwjTRxRKFMW3N6b+HsjXwzm9g5xa3ZnnXwcG7tn6zzC1vmpblqptsQaI2xRKFMW1NQgJc9LB7/PmD8Nk/XVXS0d+Fo74LnY/c9/zS1fD0OLf29bWvQ3Z+5GM2vrLGbGPasspiWDoFvn4F1n4K2gCdB7mEcfR33UC6x8dCzQ647m3oMsjviE2YWK8nY8yB7dgKS16Hxa/Aus/dPkmE5AwYPwV6DPc3PhNW0TjXkzEm2rTvAsdNcLfyjS5pFK+AkddDt6F+R2d8ZInCGLO/rB5wwn/5HYWJEjaOwhhjTFCWKIwxxgRlicIYY0xQliiMMcYEZYnCGGNMUJYojDHGBGWJwhhjTFCWKIwxxgQVd1N4iMg23Kp5sSAPKPY7iIMQa/GCxRwpsRZzrMUL4Y+5t6p2au5A3CWKWCIic1uaWyUaxVq8YDFHSqzFHGvxgr8xW9WTMcaYoCxRGGOMCcoShb8m+R3AQYq1eMFijpRYiznW4gUfY7Y2CmOMMUFZicIYY0xQlijCSETyReRDEVkiIotF5JZmzhktIuUissC7/c6PWJvEVCQiX3nx7LdcoDj3ichKEVkkIr4ufSYiRwS8fwtEpEJEftLkHN/fZxF5XES+EZGvA/blisi7IlLo3ee08Nzx3jmFIjLex3jvEZFl3u/9VRHJbuG5Qf+GIhzz7SKyMeB3f04Lzx0rIsu9v+vbfI55ckC8RSKyoIXnRuZ9VlW7hekGdAOGe4/bAyuAQU3OGQ286XesTWIqAvKCHD8HmAoIcDwwy++YA2JLBLbg+oRH1fsMnAIMB74O2PcX4Dbv8W3An5t5Xi6w2rvP8R7n+BTvmUCS9/jPzcXbmr+hCMd8O/DTVvzdrAK+BaQAC5v+r0Yy5ibH/wb8zs/32UoUYaSqm1V1vvd4B7AU6OFvVCExDnhanS+AbBHp5ndQntOBVaoadYMuVfVjoLTJ7nHAU97jp4ALm3nqWcC7qlqqqmXAu8DYsAXqaS5eVX1HVeu8zS+AnuGO42C08B63xihgpaquVtUa4AXc7ybsgsUsIgJcBjwfiVhaYokiQkSkD3AMMKuZwyeIyEIRmSoiR0U0sOYp8I6IzBORCc0c7wGsD9jeQPQkwMtp+Z8q2t5ngC6qutl7vAXo0sw50fp+fx9XsmzOgf6GIm2iV132eAvVe9H6Hp8MbFXVwhaOR+R9tkQRASLSDvgP8BNVrWhyeD6ummQo8E/gtUjH14yTVHU4cDbwIxE5xe+AWkNEUoALgJeaORyN7/M+1NUlxEQ3RBH5NVAHPNvCKdH0N/QvoB8wDNiMq8qJFVcQvDQRkffZEkWYiUgyLkk8q6qvND2uqhWqutN7/DaQLCJ5EQ6zaUwbvftvgFdxxfJAG4H8gO2e3j6/nQ3MV9WtTQ9E4/vs2dpYbefdf9PMOVH1fovIdcB5wFVecttPK/6GIkZVt6pqvao2AI+0EEtUvccAIpIEfBeY3NI5kXqfLVGEkVe/+BiwVFXvbeGcrt55iMgo3O+kJHJR7hdPpoi0b3yMa7z8uslpU4Brvd5PxwPlAdUnfmrx21e0vc8BpgCNvZjGA683c8504EwRyfGqTc709kWciIwFfg5coKpVLZzTmr+hiGnSfnZRC7HMAQpEpK9XMr0c97vx0xhgmapuaO5gRN/nSLTqt9UbcBKuKmERsMC7nQPcDNzsnTMRWIzrZfEF8G2fY/6WF8tCL65fe/sDYxbgAVwvka+AkVHwXmfiPvizAvZF1fuMS2KbgVpcHfgNQEfgfaAQeA/I9c4dCTwa8NzvAyu92/U+xrsSV5ff+Pf8kHdud+DtYH9DPsb8jPd3ugj34d+tacze9jm4nomr/I7Z2/9k499vwLm+vM82MtsYY0xQVvVkjDEmKEsUxhhjgrJEYYwxJihLFMYYY4KyRGGMMSYoSxTGGGOCskRhjDEmKEsUxoSQiLzmTdC2uHGSNhG5QURWiMhsEXlERO739ncSkf+IyBzvdqK/0RvTPBtwZ0wIiUiuqpaKSDpuWoizgE9x6w3sAD4AFqrqRBF5DnhQVT8RkV7AdFUd6FvwxrQgye8AjIkzPxaRi7zH+cA1wAxVLQUQkZeAAd7xMcAgbwoqgA4i0k69yQuNiRaWKIwJEREZjfvwP0FVq0TkI2AZ0FIpIQE4XlV3RyZCYw6NtVEYEzpZQJmXJI7ELRObCZzqzfyaBFwccP47wH83bojIsIhGa0wrWaIwJnSmAUkishS4GzdL7UbgLmA2rq2iCCj3zv8xMNJbeW0JbrZbY6KONWYbE2aN7Q5eieJV4HFVfdXvuIxpLStRGBN+t4vIAtyiMmuIwmVYjQnGShTGGGOCshKFMcaYoCxRGGOMCcoShTHGmKAsURhjjAnKEoUxxpigLFEYY4wJ6v8DXRmeKE09EXUAAAAASUVORK5CYII=\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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X478bFeWam11iZpuvErV3gha3QcfB0KC7dFesIuXpCvkj0BMIUEolA6OAmcBMpVQsUAwMthzFxyml5gH7AAPwrPSUEaJ6MZk082OSGb/iAGdyi7m9XV1evakZ9XzdLryPNvH13q+ZunMqjX0aM6nnJBp6N/z3RumHYdsM2Pm9ecyWwOZw04fQ9j5w86vkdyXOp8yZbF1RUVF6x44d1i5DCJu363gmo5bGsft4Jh3q+zDq1pa0Dbv4VZxZRVm8vfFt1ievZ0DDAYzqOgo3R8svAq3h8DrY+iUcXG4ezjbydnP3xbAu0g+9kimlorXWUWWtkytUhagF0nKKGL/iAL9EJxPo6cyke9pye7vQS05ht+/sPl5e+zKn808zvMtw7m12r7l9vaQA9vxsDvXUfeAWAD1ehajH5ORoNSHhLoQNKzaYmLP5CJP/iqfQYOSp6xvx/A1N8CjHrEcL4xcydstY/Fz9mN1vNm0C20BBJmz/GrZMg/wzUKc1DPzC3ONFLjCqViTchbBR6w+l8e6vcSSm5dGzWSAjb4mkUaDHJffLL8ln7NaxLE1cSteQrozrMQ7fkmL4cyRsnwnFORDRB659wdw/XZpeqiUJdyFszKmsQt77LY5le08R7u/GzEeiuKF5OS4uAvaf3c9r61/jeM5xnm77NE+F9cH+r/dg51wwlZjb07u/BCFtKvldiKsl4S6EjTAYTczefJRJfxzEYNK82rcpT/RohLOD/SX31Vozd/9cJkVPwtfFl6+vGUOnuOWw5B3zSdJ290O3YeaJLUSNIOEuhA3YdTyTtxfuZd/JbHo2C+S921pR3//CXRtLyyjMYOSmkaxNXkvP4M6MybfH58dHzLMSdRkK3Z6Xk6Q1kIS7EDVYVkEJE1YeYO7WYwR5Ol90yICybD+1nTfXv0lGUTpvukZw/7bFKGUPnZ+E7i+CZ51Kfgeiski4C1EDaa1ZsusE7/++n/S8IoZ0a8hLfZrg6VK+OUBLjCV8vutzZsbOpL6dC1OTT9LCkGzuytj9JTlStwES7kLUMIlpuYxYHMvfiWdpW8+bWUM60Sq0/NPYxWfE89a61zmYlcCdufm8nnEKt/YPmcd8kREZbYaEuxA1RJHByLS1iXyxJhFnRzvG3N6K+zvXx/4SFyKdY9Imvts7kym7puJhKGHKmbP0anIHPPAW+Miw27ZGwl2IGiD6aAZvLthDfGout7aty4hbWhDkWf6Lhk5mH+edP4ayLe8YPfPyGe3VFv/BYyC4ZSVWLaxJwl2Iaiy3yMDElQeZvfkIIV4ul9VnHUCbTPz29wd8kPAzJm3iPZMPt/efhmrYvfKKFtWChLsQ1dSag6m8syiWE1kFPHxNA17r17xcwwaccyZxFe+vf4NVdkV0MCre7/A6Ye0elitKawkJdyGqmbO5Rbz32z6W7DpBRJAH84d2pWOD8g+ZqzOP8+uK5xmXf4hCOzteCu7B4N6fYO/oXIlVi+pGwl2IakJrzeJdKbz36z5yiwy80LsJz/RqXK4rTAEozuPUug95L3EeG1ydaecaxHs3TqVhYKvKLVxUSxLuQlQDyRn5DF8Uy7pDabSv78O4O9vQNNizfDubTOjdP7Fw0xgmutthcHXljVaPM6j9M9jblfMXg7A5Eu5CWJHRpJn99xEm/nEQgNG3RvJQ1/Byd2/k2BZSVrzGaNNJtni60smnGe/2+oQwr7BL7ytsmoS7EFZy8FQObyzYw67jmfRsFsj7t7e66FR3/5JxFOOfI/kxeRVT/HxQ9t6M6PQadzW7Bzslc5QKCXchqlyRwcjnaxKZtjYBD2cHPr23HQPb1S3feDAlhfD3FOK2TOY9P0/2+ftybcg1jOz2LnU96lZ+8aLGkHAXogpFH03njQV7SUjN5Y72obxzcwv8PcrZiyX+T/KWvcpUlckPdfzwc/ZlwjVvc1ODm8o9UJioPSTchagCuUUGxq84wHdbjlLX25Vvh3SiV7Og8u2ceQxWvMWqY6v4IDCQNDtv7ml2D8M6DMPLyatyCxc1loS7EJVszYFUhi/ay8nsQgZ3Dee1m5rhXp6LkQxF8PdnnNw0iQ98PVgbHEhTnyZM6jaKtoFtK79wUaNJuAtRSdLzinnv1zgW7zpBkyAP5g/tRscGvuXbOeEvSpa9xg+GND6vGwj2jrzc7lkejHwQR7vyDesrajcJdyEqmNaapbtP8O6v+8gpLLm8i5Eyj8PKt9l6eCUfBgWTaO/LdaHXMvya4YR6yHC8ovwk3IWoQCcyC3hncSyrD6TSNsyH8Xe2oVmdclyMZCiGzVM5teljJnq7sTIkmFCPUKZ0eoOeYT3lhKm4bBLuQlQAk0kzd9sxxi0/gNGkeefmFgy5tmH5LkZKXEPxsleZY0hlRog/JjtHnmnzOENaDsHFofzD+gpRmoS7EFcpKS2XNxfsZduRdK6N8OfDO9qUb3LqrBT4Yzjrk5YzLjCIY/Y+9K5/A691ek2aYMRVu2S4K6VmArcAqVrrVuetewWYCARqrc8o89+Ok4EBQD7wiNY6puLLFsL6SowmvtqQxKd/xePiYMf4u9pwd8d6l25CMRTD1mkc3zCB8T5urK0TRLhnfaZ3eZtrQ6+tmuKFzSvPkfssYCowp/RCpVQY0Bc4Vmpxf6CJ5dYFmGa5F8KmxKZk8fr8Pew7mU3/VnV497aWBHmVowklaR35y17lG8NpZoX4YW/vzEvtnuahFg/haC+9YETFuWS4a63XK6XCy1j1CfA6sKTUsoHAHK21BrYopXyUUiFa65MVUawQ1lZYYuTTv+L5akMSfu5OTH+wA/1ahVx6x+wTmFYOZ8nRlXzm70+anTcDGvbn5Y4vE+xe/pmVhCivK2pzV0oNBFK01rvP+xM0FDhe6udkyzIJd1HjbU06y5sL93L4TB73RNVj+IBIvN0ucbRtLIGt09n+90QmeLuyP9CfNv6t+KTLm3IhkqhUlx3uSik34G3MTTJXTCn1JPAkQP36MvO6qL6yCkoYt+IAP2w9RpifK98/1oXuTQIuvePhDRxb/jKTSGdVoBd1XAIY1+k1+jfsL10bRaW7kiP3xkBD4NxRez0gRinVGUgBSg8kXc+y7D+01jOAGQBRUVH6CuoQolJprfltz0ne/XUf6XlFPN69IS/3bYqb0yX+2+ScInvlW8w4sZq5Xl442vvwfJsnebjlYOnaKKrMZYe71nov8L8Rj5RSR4AoS2+ZpcBzSqmfMJ9IzZL2dlETHU/P553F5pmRWod6M2tIJ1qFel98J6MBw9bpzN/+CV94upDp7cXtjW7l+Y4vEegWWDWFC2FRnq6QPwI9gQClVDIwSmv9zQU2X4a5G2QC5q6QQyqoTiGqRInRxDcbD/PpX4ewV4pRt0bycHlmRjr6NxtXvMhEuywSfdzo5N+K17qOpIV/i6opXIjzlKe3zKBLrA8v9VgDz159WUJUvZ3HMnhr4V4OnMqhT2Qw797Wkro+rhffKec0+1e8zKSzW9ni6kqYcx0+7TqSG+rfIO3qwqrkClVR62UXljBx5UG+23KUYE8XvnyoIze1rHPxnYwGUv6exGex3/C7mxPe7t681vZp7mv5ME72TlVTuBAXIeEuai2tNctjTzF6aRxpuUUM7hrOqzc1w+MSY61nJvzBV2vf4keHIuzcnHms8R082vkVmThDVCsS7qJWSs7IZ9SSOFYdSKVlXS++ejiKtmE+F92nMPMoPyx/hq8LjpDroBgY2JFnr/+IOh7luIhJiCom4S5qlRKjiW83HeaTP+MBeOfmFjzSLRwHe7sL7mMsKeTXVa8xNWU1px3suM69Li/2mkjTILkISVRfEu6i1ticeJaRS2KJT83lxhZBjL6tJfV8Lzx6o9aajTFf8snuL4i317R0cOXDa96hU7Pbq7BqIa6MhLuweanZhYxdtp8lu05Qz9eVrx+O4sbIi4/nEnd0HZ+sf4utphzqARMiBtG36xvY2ZVjNiUhqgEJd2GzDEYTszcf5ZM/D1FsMDHshgie6RWBi+OFAzo58zBTVr3E8txEfI0m3gyI4p6+n+HoIidLRc0i4S5s0vYj6YxYHMuBUzlc3zSQd29rSXiA+wW3zyjMYMaGkfyUshYHbeIJhyCG9JuCZ3DrKqxaiIoj4S5sSlpOER8tP8CCmGTqersw/cGO3NQy+IIXFBUYCpi78wu+2TeHfG3kjmLF011HEdz6niquXIiKJeEubILRpJm79SgTVh6ksMTIMz0b89wNERcc5MtoMrL00Hym7phEqjGfngVFvNj4Lhr3HAmOMriXqPkk3EWNF3MsgxGLY4k7kc21Ef68e1srIoI8ytxWa82G5PV88vd7JBSm0rqwiHGeLYi6bQr4NqjiyoWoPBLuosZKzytm/IoD/LT9OMFezky9vz03tw65YBPM3rS9TNo8hh0Z+6lfUsLHJi/69JmKanR9FVcuROWTcBc1jtGk+Xn7ccavPEBuoYEnezRiWO8mFxw24Hj2cSZvH8/K5LX4GY28nWfgrmtew7HjoyBdG4WNknAXNcqe5ExGLI5ld3IWXRr6Meb2VjQN9ixz2/TCdL7c9QXzDs7D0WTiqexcHmlyNx69hoPrxYcaEKKmk3AXNUJmfjETVh7kh23H8Hd35tN72zGwXd0ym2AKDAV8FzeHmXu/otBQxB05OTzj257AB8ZDQBMrVC9E1ZNwF9WayaSZH53MRysOkJlfzCPdwnmpT1O8XP47MbXBZGBxwmK+iJlCWlEGN+Tl8wI+NOr3FTTpY4XqhbAeCXdRbcWdyGLE4lhijmXSsYEvYwZ2IbLuf68U1Vqz9vhaPt3xMUk5R2lbWMTE3BI6XPs6dHoc7P/7i0AIWyfhLqqdrIISPvnzEHM2H8HXzYkJd7Xhzg71sCtjqrvdabuZtP1jYtJ2Em4w8ml6Bje0GITqNRzc/au+eCGqCQl3UW1orVm0M4UPlh3gbF4RD3ZpwKt9m+Ht9t8j7yNZR5gcM5m/jv2FvwlGpKdzR0AHHB/+CIJbWqF6IaoXCXdRLRw4lc3IxXFsO5JO2zAfvn2kE63ref9nuzMFZ5i+ezrzD/2Ck9Y8k5HJYOWH280zoNkAkHlLhQAk3IWV5RSWMPmveL79+wieLg58+H+tuTcq7D9NMPkl+cyKm8WsuFmUGAq5KyeXobnFBHR/Fa55GhycrfQOhKieJNyFVWit+XXPSd7/bR9puUXc1ymM129qjq/7vyeXLjGVsPDQQqbtnsbZwrP0KTTwQloaDVrfCzeMBM+Lj8suRG0l4S6qXEJqDiOXxPF34llahXrx5UMdaV/f91/baK1ZdWwVk2MmcyT7CB2M9kw+fYq2wR3h0R+gbnsrVS9EzSDhLqpMXpGBKavj+WbDYdyc7Blzeyvu71wf+/OaYGJOxzApehK703bTWLnw2ak0rncKQN06HVr+n7SrC1EOEu6i0mmtWRF7ijG/7eNEViF3dazHm/2bE+Dx73bypMwkPo35lDXH1xBk58K7Z7O4reAMDt1fhm7Pg6Orld6BEDWPhLuoVIfP5DFqaRzrD6XRvI4nUwa1Jyrc71/bpOan8sWuL1iUsAhX5cCwPAMPpsXj2upuuHE0eIdapXYhajIJd1EpCkuMfLEmgenrknBysGPkLZE83LUBDvZ2/9vmXz1gjMUMMjjzZHI8fnXawaOzIayzFd+BEDWbhLuocH/tO83oX+NIzihgYLu6DB/QgiCvf2Y3MpqMLElcwtSdU0krSKOfgz/DjiUS5hIAt30Bbe4FO7uLvIIQ4lIuGe5KqZnALUCq1rqVZdkE4FagGEgEhmitMy3r3gIeA4zAMK31ykqqXVQzx9PzeffXOP7an0pEkAc/PNGFbo0D/rXN3yf+5uMdH3Mo4xBtnQP5JDWTtoWnodsL0P1lcC57BiUhxOUpz5H7LGAqMKfUsj+Bt7TWBqXUOOAt4A2lVCRwH9ASqAv8pZRqqrU2VmzZojopMhiZsS6JqWsSsLdTvNW/OUOubYiTwz9H3wkZCUyMnsimlE2EOvkyMUfT93A0KnIg9HkPfMOt9waEsEGXDHet9XqlVPh5y/4o9eMW4C7L44HAT1rrIuCwUioB6AxsrpBqRbWz7lAao5bEcuRsPgNa1+GdmyOp6/NPr5YzBWf4fKrhwmoAABcYSURBVNfnLIxfiLu9C6+avBl0cDdOwa1h8DRoeJ0VqxfCdlVEm/ujwM+Wx6GYw/6cZMuy/1BKPQk8CVC/fv0KKENUpROZBYz5bR/LY0/RMMCd2Y925vqmgf9bX2AoYE7cHGbGzqTYWMT9TiE8dWgbPi4+cMun0OFhmeJOiEp0VeGulBoOGIC5l7uv1noGMAMgKipKX00douoUG0zM3HSYKaviMZo0r/RpypPXN8LZwRzUJm3it6TfmBIzhdP5p+ntHs5LSXtoUHgcOg+F61+XKe6EqAJXHO5KqUcwn2jtrbU+F84pQFipzepZlgkbsO1wOsMX7SU+NZcbWwQz6tZIwvzc/rd++6ntTNg+gf3p+2npXo+Pcu2JOrwemvSFmz6QKe6EqEJXFO5KqX7A68D1Wuv8UquWAj8opSZhPqHaBNh21VUKq8rIK+aj5Qf4ecdxQn1c+frhKG6M/GfArmPZx5iwYwJrj68lxCWAj1Qw/WP/xs6/CTwwX6a4E8IKytMV8kegJxCglEoGRmHuHeMM/GmZoHiL1nqo1jpOKTUP2Ie5ueZZ6SlTc2mtWRiTwthl+8kqKOGpHo144cYmuDmZvza5xbnM2DOD7/Z/h5OdIy+4N+XBfWtwcfSAmz6Ezk/IFHdCWIn6p0XFeqKiovSOHTusXYYoJTEtl3cWxbI56Szt6/vwwR2taRFinr/03EVIk2Mmk1GYwUDvFgyL305gfgZ0fAR6DQf3gIu/gBDiqimlorXWUWWtkytUxb8UlhiZtjaRaWsTcXa0433LyI3nJs+IPh3NuG3j2J++n3ZeDfkio4CWh1dA+HXQ70Oo09rK70AIARLuopS/E84wfHEsh8/kcVvburxzSwuCPM3DBpzIPcGk6EmsPLKSYJcAxjvUp9/udSif+nDPHGhxmwzFK0Q1IuEuOJNbxNjf97NoZwr1/dyY82hnelj6rOeX5DMzdiaz4mahgKc9IxmybzWuyhFuGAFdnwNHl4u/gBCiykm412Imk2bejuN8uPwA+cUGnusVwXM3RODiaI/Wmt8P/84n0Z+Qmp9Kf7/WvJy4mzqZK6D1PdDnXfCqa+23IIS4AAn3WioxLZe3Fu5l2+F0Oof7MfaOVjQJ9gRg39l9fLD1A3an7SbSqxETsKND9O8Q3BqGfAUNulm5eiHEpUi41zIlRhMz1icxeVU8Lg52jLuzNXd3DMPOTpFVlMVnOz9j3sF5+Dr78J5XOwbuXYadkzsMmAgdh4C9fGWEqAnkf2otsic5kzcW7GX/yWwGtK7D6NtaEuTpgkmbmH9oAZNjJpNdnM39AVE8s38DXrl7zGPA9B4pXRuFqGEk3GuBgmIjn/x1iK83JBHg4cz0BzvSr1UdAGLPxDJ2y1hiz8bSwacpb+c50mz7AgjtCIN+Mt8LIWocCXcbtynhDG8t3Mux9HwGdQ7jzf4t8HZ1JKMwg8kxk1kYvxB/F18+8GjFLbtWoFz9YODn0PZ+mQ1JiBpMwt1GZeWXMHbZPubtSCbc340fn7iGro39MZqMzDs4jyk7p5BbnMtD/h14et86PAr2QOcnoedbMmqjEDZAwt0GrYg9yYglcaTnFTP0+sa8eGMTXBzt2ZO2h/e3vM/+9P108m3B2zknidixCBpcC/3HQ51W1i5dCFFBJNxtSEZeMSOWxPLbnpO0rOvFt490olWoN9nF2UzYPJlfDv1CoKs/4z3b0m/n7yj3QPi/r6H1XXJ1qRA2RsLdRvwRd4q3F8WSVVDMq32b8tT1jXGwUyxLWsb47ePJKMrgwaAuPLtvHe65u6DT43DDO+Dibe3ShRCVQMK9hsvML+bdX/exaGcKkSFefPdYZ1qEeHEs+xjvb3mfzSc308o7gmmFzrTYOg/qtodBP5vvhRA2S8K9Blu1/zRvLdxLel4xL/RuwnM3RKAxMH33dL7a8xWOdo687dORe/b8jr2Dq/lCpKhHZe5SIWoBCfcaKKughDG/7WN+dDLN63gy09K2vv3UdsZsGcPhrMP09W/LG0l7CIpfBG3uhT5jwDP40k8uhLAJEu41zObEs7wybxenc4p4rlcEz/eOoNCYy4hNI1icsJhQtzp84dSI63b8Cv5N4OGl0Oh6a5cthKhiEu41RLHBxMd/HmTG+iQa+ruz4OlutAvz4c+jfzJ2y1gyizJ5LKAzT+39E9eSQvPJ0m7DwMHZ2qULIaxAwr0GSEjN4YWfdhF3IptBnesz4pYW5BszeXnty/x59E9aeDVier4jzbfPN8+IdOtk8G9s7bKFEFYk4V6Naa35fstRxi7bj5uTAzMe6kifyGCWJi5l/PbxFBoKecGvA4N3LcfRwQVu+wzaPyR91oUQEu7VVVpOEW8s2MPqA6n0aBrIxLvaYLTL4OlVT7MpZRPtvZvw7qkTNExcDJEDzVeYetaxdtlCiGpCwr0a2hCfxks/7yK70MCoWyN56Jr6LIifz6ToSWg0b3m25r7dy7HzCIZ750KLW6xdshCimpFwr0YMRhOTV8UzdU0CTYI8mPv4NXh75vHMqqfZfHIzXX1bMurYIULjfzdPnNHnXbnCVAhRJgn3auJ0diHDftzJ1sPp3BNVj9G3tmRNykrGrhmLwVjCCO923L3zN5RXKDy0GBr3snbJQohqTMK9Glh/yNwMk19s5OO729K7pQcjNr/BH0f/oK13BB+cSKZ+wlLzrEh9x4KLl7VLFkJUcxLuVnR+M8zPD3TgRPFO7lg6isyiTF7wbs0ju1fg4BEMDyyAJjdau2QhRA0h4W4lZ3KLeO6HGLYkpXNvVBhvDGjEZ7s/Zv6h+UR4hDEtr4Dmib+bZ0Tq96FMoCGEuCyXDHel1EzgFiBVa93KsswP+BkIB44A92itM5RSCpgMDADygUe01jGVU3rNtft4JkO/jyY9r5iP725Ly4a5DF45iKPZRxni157ndq3AycUL7vsRmg+wdrlCiBqoPJNkzgL6nbfsTWCV1roJsMryM0B/oInl9iQwrWLKtB3zth/n7i83Y6cU84d2pdBtHff/fj95xTl8ZRfKy9FLcGp0PTy9WYJdCHHFLnnkrrVer5QKP2/xQKCn5fFsYC3whmX5HK21BrYopXyUUiFa65MVVXBNVWww8d5vcXy/5RjdIwJ4//8a8vGuEaw9vpbrfSMZcyga34Js6D8BOj8hV5kKIa7Klba5B5cK7FPAubFkQ4HjpbZLtiz7T7grpZ7EfHRP/fr1r7CMmiE1u5Cn58YQfTSDodc3pmfbbB5bNYiMwgzecG/BAzErUEEt4aGlEBxp7XKFEDagPM0yF2U5StdXsN8MrXWU1joqMDDwasuotvYkZ3LLZxvZfzKbKYPa4BWymif/fBw35cDcPEcejF2J6vI0PLFagl0IUWGu9Mj99LnmFqVUCJBqWZ4ChJXarp5lWa20bO9JXp63C393Z2Y9HsnXB8aw+eRmbvNvy/C9a3Czc4L7f4Gmfa1dqhDCxlzpkftSYLDl8WBgSanlDyuza4Cs2tjerrVm6up4npkbQ8u63nw4yIu3tz5K9Olo3vVoydgdv+IW1BKGbpBgF0JUivJ0hfwR88nTAKVUMjAK+AiYp5R6DDgK3GPZfBnmbpAJmLtCDqmEmqu1IoORNxfsZdHOFAa2DaFjm/28sH4iwS7+fFfgQmTicuj6HNw4GuwdrV2uEMJGlae3zKALrOpdxrYaePZqi6qpzuYW8dR30ew4msGw3vU57fw9E3Yso4dPcz44sBVvE3DfD9D8ZmuXKoSwcXKFagU5djafh2du5WRWIe/dWYdFJ0aSdDKJYd5teGznb9iFtIO7Z4FfQ2uXKoSoBSTcK8De5CyGzNqGwaR55y5HvjzwAmjNNPv6dNv1G7R7EG7+GBxdrF2qEKKWkHC/SusPpfH099F4uznycO/jfLznU8Ld6/LZqdOEndkkFyUJIaxCwv0qLIxJ5vX5e4gIdqVduzV8tW8R1/tG8tG+zXjYOcDDS6DhddYuUwhRC0m4X6Hp6xL5aPkBOjd2win0W34/spPHfdvxXMyv2NdpDffNBR/bvvJWCFF9SbhfJq0141ceZNraRHq1NpLiPIH09HTGuUcyIGYpRN4Ot08DJzdrlyqEqMUk3C+DyaR599c4Zm8+Sp8OOcQapuBidGKWMYBWsSvguleg1ztgd9WjOgghxFWRcC8no0nzxoI9zI9Opk+nZHbkT6eBe12+OJVG3TN7YeDn0P5Ba5cphBCAhHu5FBtMvPTzLn7fe4LrO+9mS85PdPZtwSeHYvAyFMNDi6BhD2uXKYQQ/yPhfgmFJUaemRvD6gMn6dJpDTE5f3FrQEfe3bkCR89gGLwMAptau0whhPgXCfeLKCwx8sScHWxMPEHbqCXsy43mqeBreXbrPFSd1vDAfPCw3eGKhRA1l4T7BRSWGHnqu2g2Jh2jebtfOJJ/kFGB13HXlrnQqCfc+z04e1q7TCGEKJOEexmKDEae/j6a9UmJNGr9A6eLUpjo3ZE+2+ZCyzvgji/BwdnaZQohxAVJuJ+n2GDi2bkxrE06QL3IOeQYs/ncpRlddy6ATk9A/3FgZ2/tMoUQ4qIk3EspNph49ocYViftJrjZHLAz8Y0plNZxy6Dn23D96zJGjBCiRpBwtzAYTQz7cSerknbgFzELdyd3virxpFH8aug3Dq4Zau0ShRCi3CTcMV95+ubCvfyRtBXfxrMJdPPlmzwH6iatgVs+gahHrV2iEEJcllof7lpr3v99P4v2bcCn4WxC3AP5OttEnSMb5apTIUSNVevD/bPVCcze+Ree4XOo51GHrzOLCDq+A/5vBrS559JPIIQQ1VCtDvdZmw4zedPveDT4jgZedfnmbC4BJ/bAXTPNXR6FEKKGqrXDFy7emcKY1YtxbzCbxt5hfJtRREDKbvM8pxLsQogarlaG+8b4M7z+20Lcw74jwrsh32SV4Hd8O9z5FbS41drlCSHEVat14b7/ZDZDf1mIS9gsGniF8nWuxvfwJhj4BbS609rlCSFEhahV4X4yq4CHv1+ICvmKEI8gZha54pew2tzdsd0ga5cnhBAVptaEe3ZhCQ/MXkKh/zQC3LyZpQMIPLgS+o+HqCHWLk8IISpUrQj3YoOJR79fRqr7ZLxdXJnj1pyQuKXQexR0ecra5QkhRIWz+XDXWvPygrXsZwLuzvbMCepOWPR3cM2z0P0la5cnhBCVwubD/bO1u1mdNRZnpxJmhw+k0cbPoM290Pd9GQRMCGGzrirclVIvKaXilFKxSqkflVIuSqmGSqmtSqkEpdTPSimniir2ci2PO8b0A+/g4JzO9Gb303zVBxDRxzysgJ3N/14TQtRiV5xwSqlQYBgQpbVuBdgD9wHjgE+01hFABvBYRRR6ufadyOC1da9h73qMD5sNofMfYyG0I9wzG+wdrVGSEEJUmas9fHUAXJVSDoAbcBK4AZhvWT8buP0qX+Oync0t4qElb6Dc9zGsySPcvPZj8G0A988DJ/eqLkcIIarcFYe71joFmAgcwxzqWUA0kKm1Nlg2SwZCy9pfKfWkUmqHUmpHWlralZbxH8UGE3f+PIpit83cXu8entgxB+wc4IFfwM2vwl5HCCGqs6tplvEFBgINgbqAO9CvvPtrrWdoraO01lGBgYFXWsZ/DFkwmbMOvxPl24f3jmyAnJMw6CfwDa+w1xBCiOruapplbgQOa63TtNYlwELgWsDH0kwDUA9Iucoay+2jtUvYXTCLuo7t+aooA3V8K9wxHcI6VVUJQghRLVxNuB8DrlFKuSmlFNAb2AesAe6ybDMYWHJ1JZbP8oM7+T5pDK66LvMDwnGIW2i+SElGeBRC1EJX0+a+FfOJ0xhgr+W5ZgBvAC8rpRIAf+CbCqjzohLTT/DGpheww5kfm9yC59+Tof1DcpGSEKLWuqrJOrTWo4BR5y1OAjpfzfNejvziAu5fOhSTymV8kxeJWPMqhF9nHgxMLlISQtRSNfpKHpM2ce+iF8jjCPcFD2XA1g/BIxjulr7sQojarUZPs/fhhtkcKdxMc8d7GH5yARRkwGN/gLu/tUsTQgirqtHhfl+L/2Nvch7fuR1ExW8xz30a0sbaZQkhhNXV6GaZxoHe/NTEFcddc8wnT2UmJSGEAGp4uHP0b1j+unkwsBtGWLsaIYSoNmp2uDt7QsMecOfXYGdv7WqEEKLaqNFt7tRpDQ8tsnYVQghR7dTsI3chhBBlknAXQggbJOEuhBA2SMJdCCFskIS7EELYIAl3IYSwQRLuQghhgyTchRDCBimttbVrQCmVBhy1dh3lEACcsXYRl0lqrho1reaaVi9IzWVpoLUucxLqahHuNYVSaofWOsradVwOqblq1LSaa1q9IDVfLmmWEUIIGyThLoQQNkjC/fLMsHYBV0Bqrho1reaaVi9IzZdF2tyFEMIGyZG7EELYIAl3IYSwQRLu51FKhSml1iil9iml4pRSL5SxTU+lVJZSapflNtIatZ5X0xGl1F5LPTvKWK+UUlOUUglKqT1KqQ7WqLNUPc1KfX67lFLZSqkXz9vG6p+zUmqmUipVKRVbapmfUupPpVS85d73AvsOtmwTr5QabMV6JyilDlj+3RcppXwusO9Fv0NVXPNopVRKqX/7ARfYt59S6qDle/2mlWv+uVS9R5RSuy6wb9V8zlpruZW6ASFAB8tjT+AQEHneNj2B36xd63k1HQECLrJ+ALAcUMA1wFZr11yqNnvgFOYLMqrV5wz0ADoAsaWWjQfetDx+ExhXxn5+QJLl3tfy2NdK9fYFHCyPx5VVb3m+Q1Vc82jg1XJ8bxKBRoATsPv8/6tVWfN56z8GRlrzc5Yj9/NorU9qrWMsj3OA/UCodauqEAOBOdpsC+CjlAqxdlEWvYFErXW1u0pZa70eSD9v8UBgtuXxbOD2Mna9CfhTa52utc4A/gT6VVqhFmXVq7X+Q2ttsPy4BahX2XVcjgt8xuXRGUjQWidprYuBnzD/21S6i9WslFLAPcCPVVHLhUi4X4RSKhxoD2wtY3VXpdRupdRypVTLKi2sbBr4QykVrZR6soz1ocDxUj8nU31+ad3Hhf8jVLfPGSBYa33S8vgUEFzGNtX1834U819wZbnUd6iqPWdpSpp5gaav6voZXwec1lrHX2B9lXzOEu4XoJTyABYAL2qts89bHYO5CaEt8BmwuKrrK0N3rXUHoD/wrFKqh7ULKg+llBNwG/BLGaur4+f8L9r8d3aN6E+slBoOGIC5F9ikOn2HpgGNgXbASczNHDXFIC5+1F4ln7OEexmUUo6Yg32u1nrh+eu11tla61zL42WAo1IqoIrLPL+mFMt9KrAI85+spaUAYaV+rmdZZm39gRit9enzV1THz9ni9LkmLct9ahnbVKvPWyn1CHAL8IDlF9J/lOM7VGW01qe11kattQn46gK1VKvPGEAp5QD8H/Dzhbapqs9Zwv08lvayb4D9WutJF9imjmU7lFKdMX+OZ6uuyv/U466U8jz3GPMJtNjzNlsKPGzpNXMNkFWqacGaLniUU90+51KWAud6vwwGlpSxzUqgr1LK19Kk0NeyrMoppfoBrwO3aa3zL7BNeb5DVea880F3XKCW7UATpVRDy1+A92H+t7GmG4EDWuvkslZW6edcFWeWa9IN6I75z+w9wC7LbQAwFBhq2eY5IA7z2fktQDcr19zIUstuS13DLctL16yAzzH3LtgLRFWDz9odc1h7l1pWrT5nzL94TgIlmNt0HwP8gVVAPPAX4GfZNgr4utS+jwIJltsQK9abgLlt+tz3ebpl27rAsot9h6xY83eW7+kezIEdcn7Nlp8HYO7Rlmjtmi3LZ537/pba1iqfsww/IIQQNkiaZYQQwgZJuAshhA2ScBdCCBsk4S6EEDZIwl0IIWyQhLsQQtggCXchhLBB/w/aBm8p4DUkbQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From a4e1c55785b02da471a1788d8241504c3354c652 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 245/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From 07bdcefb0134af4c80ae24f1c05bbf992f7ec921 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 246/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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fy7u/n+LQpWvU8XLhw8dac08LX2yun4ouIwl+fgbOrIOgp6D/bChFvVKKgr2NPVWdqlLVqeqtNy5FCvu7QkOgq1LqbSAdeENrvQ+oCezOsV24ZdkNlFLPAs8C1KpVq5BlCCEALiRcYObemey8vJOAKgHM7zGfnv49C3wmeSEmhZnrTvLH8StUr1KJdx9qwaC2ftjZ5tKFMf4CLHsMrp6Gu9+D9s8UzcGIIlHYcLcDPIE7gHbAj0qpugXZgdZ6IbAQjDb3QtYhRIWWlp3GF0e/4KvjX+Fo68jYdmMZ3HhwgcdjSUjN4oO/Qli66wL2tja83qch/+laFyeHPEZmvLADfnwCzNnGPKf1etz+wYgiVdhwDwdWaONq7F6llBnwAiIA/xzb+VmWCSGKkNaav8L+Yvbe2VxOucx9de/jtaDXCty1LjPbzDe7L/LBXyEkpGUxOMif1/o0xKfKTW5gOvA1/PY6eATAkB+gasHa8kXJKGy4rwJ6AJuUUg0BByAGWAN8p5Sai3FBtQGwtygKFUIYolKimLF7BlvCt1DfvT5L7lpCUPVcO0zkSWvNxpPRvLP2JKExKXSp78X4u5vQtEaVvF+UlQ7rRsPBpVCvJwxaAk7ut3k0orjcMtyVUsuA7oCXUiocmAIsBhYrpYKBTGCY5Sz+uFLqR+AEkA28KD1lhCgaWmuWhyxn7v65ZJuzeSPoDYY0GVLgJpjQmBSm/XKczaevUs/bhSXD29G9kffN2+evhRnNMJcPQdfXoccEsJHJNEozuYlJiDIgLDGMqbumsjdqL+2rt2dqx6n4V/G/9QtzSM3M5uNNZ/liaygOdjb8X+8GDOsUgH1uF0tzOr8Zlj8Fpix44FNocm/hD0QUKRlbRogyymQ28d2p7/jg4AfY2tgyueNkBjUYVKBeMFpr1h6LYsZvJ4hMSOehNjUZ178xPq63GBhMa6Pv+p/TwashDP4WvOrf5hGJkiLhLkQpFZYYxvjt4zl89TBda3ZlcsfJBRrYC+BsdBJT1hxnx9lYmvpW4cPHWhMUkI8bjNITYfVIOPkLNHsQ7v8IHCsX8kiENUi4C1HKaK1ZEbKCWftmYafseKfLO9xb994Cna2nZGSz4M8QFm8PxdnBlrcGNGNIh9rYXn8TUm4uH4blIyD+IvR9Gzq+CKXozkuRPxLuQpQisWmxTN01lc1hm2lfvT1vd3m7wGfr649HMXXNcSIT0xkc5M/ouxpRtXLeMyf9Q2vY+wWsnwDOXjD8V6jdqZBHIqxNwl2IUmJL2BYm75xMcmYyo4NGM7TpUGxU/ie3iExIY8rq46w/cYXG1V356PE2tKmVzzHL0+Jh9Utw6ldocJdx4dSlbN1uL/5Nwl0IK0vNSmXO/jksP7Ochh4N+bLvlwUaudFk1izddYH3/jiNSWvG9W/M013q3LoXzN/C9xvNMImXpRmmHJFwF8KKjsceZ+zWsVxKvMSIZiN4qfVLONg65Pv1wREJvLniGMciEujW0JsZDzTH3zOfk02YzbD7Y9g4FVxrwFN/gF/BboYSpZeEuxBWoLXmu1Pf8d7+96haqSqL7lpEu+rt8v36lIxs5m44w5IdoVSt7MhHQ4xRG/N90TUxEla9AOc3QZP74P4Pwankpp0TxU/CXYgSlpCRwKQdk9gUtonuft15q/NbuFfK/238G05cYcrqYCIT03m8Qy1G39UYN6cC3KV6YjX88gpkZ8C986HtcGmGKYck3IUoQYejDzNm6xiupl1lTLsxDG0yNN9n29FJ6UxZfZx1wVE0ru7Kh0Pa0LZ2Ac62M5Jg3Vg4/C3UaA0PfSk3JZVjEu5ClACzNrMkeAkfHvoQXxdfvun/Dc28muXrtVprfj4YwVu/niAty8SYfo14pmvd/F8wBbi0B1Y+C9cuwZ2jodvYcjephvg3CXchillsWiwTtk9gx+Ud3BVwF1M6TsHVwTVfrw2PT2X8ymC2nrlKuwAP3h0YSD3vAtwpasqCLbNh23vg5gcj1kGtOwp5JKIskXAXohjti9rH2K1jjXb2OybxcMOH89UMYzZrvtlzkVnrTqGB6QOaMbRD7RunubuZK8eNi6aRR6DlEOg/CyrdZEhfUa5IuAtRDExmEwuPLuSzo59Ry7UWn/b+lEaejfL12vNXkxn781H2XYinawMvZj7UAj+PfHZvBDBlw455sHkWVHIzJqxuOqCQRyLKKgl3IYpYdGo047aNY1/UPu6vdz8TOkzA2f7W4ZxtMvPFtlDmbTxDJTsb5gwKZFBbv4LNg3rlhOVs/TA0e8iY21TuNK2QJNyFKELbI7Yzftt40k3pzOg8gwH183fGfOJyImN+PkJwRCL9mlVn+gPNbj0kb07Xn60//DU0e6CQRyHKAwl3IYpAljmLjw59xOLgxTTwaMB73d6jrtut54zPyDbx0V9n+XTzOdydHfj08Tb0b+FbsDf/19n6g5az9YLNpSrKHwl3IW7T5eTLjNk6hiNXj/BIw0cY3W40lexufdZ98FI8Y5Yf5Wx0Mg+1qcnke5vi7pz/oQfIzoDt842eMI5V5Gxd/IuEuxC34c9LfzJpxyS01szpNod+Af1u+ZrUzGze++MMS3aG4lulEktGtKNHI5+CvfHFXcZdpjGnLW3rc+RsXfyLhLsQhZBpymTugbl8e/JbmlZtynt3vpevOU13no1h3IpjXIpL5Yk7ajO2f2MqOxbgv2HaNWOgrwNLwK0WDPkJGvYt/IGIckvCXYgCuph4kdFbRnMy7iRDmwzl1bav3nIkx8T0LGauPcWyvZeo4+XCD8/eQYe6BejForUxJsy6MZByFTq+BN3flKnvRJ4k3IUogN/O/8b0XdOxt7Xngx4f0KNWj1u+ZtOpaMavPMaVxHSeu7Mur/ZpSCV72/y/aUI4/PYGnFkH1QNhyA/G2DBC3ISEuxD5kJqVysy9M1l1dhVtfNow685Zt5z+7lpqJtN/PcGKgxE0rFaZz4Z2pqV//kd/xJQN+76Av2aANkPfGdDhBbCV/7bi1uRbIsQtnIk/w+gtowlNCOXZwGd5oeUL2Nnc/L/O78FRTFwVzLXUTEb1asCLPerhaFeAs/VLu42z9SvHoF4vuHcueATc3oGICkXCXYg8aK356cxPzN43G1cHV77o+wUdfDvc9DUxyRlMWX2c345F0qxGFb5+qh3Narjl/02Tr8LGKcawvFVqGkMHNLlfxlsXBSbhLkQuEjMTmbZzGusvrqdzjc683eVtqjrlfQFUa82aI5eZuuY4KRkmRt/ViGfvLMCwvGYT7F8Mf74FWanQ5VVjaF4HlyI6IlHRSLgLcZ2jV48yZusYrqRc4dW2rzK82XBsVN4hHZWQzsRVx9h4MppW/u7MGRRIg2r5G9IXgLC98NvrEHUU6nQz7jD1blgERyIqMgl3ISxMZhNLji/h40Mf4+Psw1f9v6Kld8s8t9da89P+cN767QSZ2WYm3tOEEZ3rYJvfYXlTYowmmEPfGBNUD1piDB8gTTCiCNwy3JVSi4F7gWitdfPr1r0OvAd4a61jlDF83QLgbiAVGK61Plj0ZQtRtC4nX2b89vEcuHKAuwLuYtIdk3BzzLutPDw+lTdXHGNbSAzt63gya2Agdbzy2YRiyjZuQvprBmQmQ6dRxsxI0mddFKH8nLl/BXwELM25UCnlD/QFLuVY3B9oYHl0AD61/ClEqbX2/Fpm7J6BGTNvd3mb++rel+cwu2az5ts9F3nXMonGWwOa8XhBJtE4twl+fxOunoSArkYTjE/jojsYISxuGe5a661KqYBcVs0DxgCrcywbACzVWmtgt1LKXSnlq7WOLIpihShKSZlJvL3nbX47/xutvFsxs+tM/Fz98tz+QkwKY34+yt7QOLo28OKdB1vg75nPSTRiz8H6iXB6LbjXhkf+C03ukyYYUWwK1eaulBoARGitj1x3hlMTCMvx93DLshvCXSn1LPAsQK1atQpThhCFduDKAcZvG8+V1CuMbDWSZ1o8k2ff9WyTmS+3hzJ/4xnsbW2YPTCQh4PyOYlGeiJsnQO7PwU7R+g1Be4YCfYFGKtdiEIocLgrpZyB8RhNMoWmtV4ILAQICgrSt7MvIfIry5zFp4c/ZVHwImq41ODr/l/f9KLpsfAExv58lBORifRpWo23BjSnuls+gtlsMi6U/vWWMRZMq8eh12RwvfldrUIUlcKcudcD6gB/n7X7AQeVUu2BCCDn0Hh+lmVCWN3FxIuM2zqO4NhgHqj/AOPaj8PFPveLoKmZ2czbcIZF20PxquzIZ0Pb0K95PifRuLADfh9ndG307wBDfoSabYrwSIS4tQKHu9b6GPDP4NNKqQtAkKW3zBrgJaXU9xgXUhOkvV1Ym9aa5SHLmbNvDvY29rzf7X36BuT9i+fWM1cZv/IY4fFpDOlQi7H9GuPmZH/rN4q/CBsmGaM3VqkJAxdB84HSri6sIj9dIZcB3QEvpVQ4MEVrvSiPzddidIM8i9EVckQR1SlEoUSnRjNl5xS2R2ynQ/UOzOgyI88Bv2KTM5jx20lWHoqgnrcLPz7XkfZ1PG/9JukJsG2u0a6ubIyheDuNAod8XmwVohjkp7fMY7dYH5DjuQZevP2yhLg9WmvWhq7lnT3vkGnK5M32b/Jo40dzvdNUa83KQxG89esJkjOy8z/QV3am0V9987uQFgeBg412dbe8e9wIUVLkDlVR7sSlxzFj9ww2XNxAoHcgb3d+mwC3gFy3vRSbyoRVxs1IbWq58+7AQBreaugAreHkGmNGpLjzRn/1vjOgRqsiPxYhCkvCXZQrmy5tYuquqSRmJvJKm1cY0WwEtjY3noFnm8ws3hHK3A1nsLOxyf/NSGH7YP0ECNsD3o2Ni6UN+kq7uih1JNxFuZCUmcSsvbNYfW41jTwasbDPQhp5Nsp128Nh15iw8hjHLxvdG6cPaIavm9PN3yDuPGycBidWgYsP3LcAWg2ViTNEqSXfTFHm7Y7czaQdk4hOjeaZFs/wQssXsLe9sXdLQmoWs/84xXd7L+Hj6sinj7ehX/PqN78ZKTXOuAlp7xdgaw/dxkGnl2UcGFHqSbiLMistO415B+ax7NQyAqoE8N/+/yXQO/CG7f6+YPrO2pPEp2bxVOc6vNqnIZUdb/L1z0qHvZ/D1vchMwlaD4Xu46FKPvu6C2FlEu6iTDocfZiJOyZyMfEiQ5sMZVSbUTjZ3di0EnIliYmrgtkTGkebWu4sfaoFTWtUyXvHZjME/wx/ToeES1C/D/SZDtWaFuPRCFH0JNxFmZKWncZHhz7ivyf+i6+LL4v6LqK9b/sbtkvNzOaDP8/y5bbzVK5kx7sPteCRIP+bXzAN3WYM7hV5GKq3gAGroW73YjsWIYqThLsoMw5cOcDkHZO5lHSJRxo+wmtBr+U6fMCGE1eYuuY4EdfSeLitH+P6N6ZqZce8d3z1NGyYAmfWGXeWPvCZ0WfdJp9T5AlRCkm4i1IvNSuVBQcXsOzUMmpUrsGXfb/MdaLq8PhUpq45wcaTV2hUzZWfnu9Iu4Cb3GGaHA2bZ8KBr8He2TJi4wtgf4ueM0KUARLuolTbG7mXyTsnE5EcwZDGQ3ilzSs42//7tv7MbDNfbj/PB3+GYKMU4+9uzIjOdfKenDozFXZ9DDvmQ3Y6tHvamAnJxasEjkiIkiHhLkqllKwU5u6fy49nfqSWay2+6vcVbau1vWG73edjmbQqmJDoZO5qVo0p9zWjhnseZ95mExxZZkxvlxQJje+F3tPAq34xH40QJU/CXZQ6OyN2MnXXVKJSoniy6ZO81PqlG3rCxCRn8M7ak6w4GIGfhxOLhwfRs3G1vHd6diOsnwzRx6FmkDEZde2OxXwkQliPhLsoNZIyk3hv/3usCFlBHbc6LO2/lFY+/x6vxWzWfLf3ErN/P0ValomXetTnxR71cXLIY5CvqGBjGN5zf4FHgBHqzR6U4QJEuSfhLkqFreFbmbZrGjFpMTzV/ClGthqJo+2/e7gERyQwYVUwR8Ku0bFuVd56oDn1ffK4UzQhAja9DYe/g0pucNc70O4/xlR3QlQAEu7CqhIyEpi9bzZrzq2hvnt9FvRYQHOv5v/aJjE9i7nrz7B01wU8XRyZP7gVA1rVyH3YgIwk2D7fuGCqTdDpJTKiK2kAABv7SURBVOj6Ojh5lMwBCVFKSLgLq/nr0l+8tfst4tPjeTbwWZ4LfA4HW4d/1mut+eVoJDN+PcHV5AyeuKM2r/dtlPusSKYsOPg1bJoJqTHQfBD0mmQ0xQhRAUm4ixIXnx7PzL0zWRe6jkYejfik1yc0qdrkX9ucv5rM5NXH2X42hhY13fhyWBCBfu437kxrOL0ONkyG2BCo3Rn6/gg1b+xZI0RFIuEuStSGixuYsXsGiRmJjGw5kv+0+M+/RnBMzzLxyaazfLblPI72xjjrQzrUxja3YQMiDsD6SXBxB1RtAI8ug0b95WKpEEi4ixISmxbL23veZsPFDTTxbJLreOubT0czZc1xLsam8kCrGoy/pwk+rpVu3Fn8RWNgr+Dl4OwF97wPbYYZQ/IKIQAJd1HMtNb8fuF33tnzDilZKYxqPYrhzYdjb/O/II5KSGf6r8dZeyyKut4ufPefDnSqn8vdomnxsO192PM5KFvo+gZ0fgUq3WSURyEqKAl3UWyupl7lrd1vsSlsEy28WjC903Tqe/zvbtBsk5mvdl5g3oYzZJs1o+9qxH+61rlxYursTNj3JWyZBekJ0Opx6DEe3GqW8BEJUXZIuIsip7Vm9bnVzN43m0xTJq+3fZ2hTYdiZ/O/r9uBi3FMWBnMqagkejb2Ydr9zfD3dL5+R3B8Jfw5DeIvQL2extjq1VuU7AEJUQZJuIsiFZUSxdRdU9kRsYM2Pm2Y1mkaAW4B/6yPT8lk1u+n+H5fGL5ulfhsaFvualbtxj7rl3YbY6uH7wOfZjD0Z6jfu2QPRogyTMJdFAmtNctDlvP+/vcxazPj2o/jscaPYaOMkRnNZs3yA+HMXHeSpPRsnruzLqN6NcDl+qnuYs/Bxilw8hdw9YX7P4JWQ8Amj+EFhBC5knAXty0sKYxpO6exJ2oPHap3YEqnKfi7+v+z/nRUEhNXHWPfhXiCanvw9oMtaFTd9d87SYkx2tT3Lwa7StBjInQcCQ43TsYhhLg1CXdRaGZtZtmpZSw4uAAbZcPkjpMZ1GDQP00sqZnZLPgzhEXbQnGtZMfsgYEMauv376nustJg96ewfR5kpkDbYdD9TajsY6WjEqJ8kHAXhXIh4QJTdk7hYPRBOtfszNSOU6nuUv2f9TmnunskyI9x/Zvg6fK/oQXQ2piIeuNUSAiDhv2hzzTwbnTjmwkhCkzCXRSIyWxi6YmlfHz4YxxsHZjReQb317v/n7P1fE11F7YP/njTuFhaPRAe+BTqdLXC0QhRft0y3JVSi4F7gWitdXPLsjnAfUAmcA4YobW+Zln3JvA0YAJGaa3/KKbaRQk7G3+WyTsncyzmGD38ezDpjkl4O3sDkGUys2h7KAs2hgDwZv/GPNXluqnuroUZZ+rBy6FyNRjwMbR8TC6WClEM8nPm/hXwEbA0x7INwJta62yl1CzgTWCsUqop8CjQDKgBbFRKNdRam4q2bFGSssxZLD62mM+OfkZl+8rMvnM2/QL6/XO2vjc0jomrjnHmSjJ9mlZj6v3NqJlzqruMZKNNfddHxt/vHA2d/w8c8xiLXQhx224Z7lrrrUqpgOuWrc/x193AIMvzAcD3WusMIFQpdRZoD+wqkmpFiTsVd4pJOyZxKu4U/QL6Ma79OKo6VQUgLiWTmWtP8tOBcGq6O/HFk0H0aZpjqjuzyZgs46+3IPkKtHgYek0Bd/883k0IUVSKos39KeAHy/OaGGH/t3DLshsopZ4FngWoVatWEZQhilKmKZOFRxey6Ngi3BzdmN99Pr1q9wKMPus/HQhj5rpTJKdn83y3eozqVR9nhxxfp9BtRrt61DHwaw+Pfgd+QVY6GiEqntsKd6XUBCAb+Lagr9VaLwQWAgQFBenbqUMUrZOxJ5mwYwIh8SHcV/c+xrYfi5ujGwCnohKZuDKY/RfjaR/gyYwHm9OwWo4+67HnjLHVT/0Kbv4wcBE0HyjD8ApRwgod7kqp4RgXWntprf8O5wgg5+/cfpZlogzIMmfx5dEvWXh0Ie6V3Pmo50d08+8GWPqsbwzhy+2hVKlkx5xBRp/1f4YNSLsGW+cYIzbaOULPSdDxRbB3usk7CiGKS6HCXSnVDxgDdNNap+ZYtQb4Tik1F+OCagNg721XKYrdmfgzTNw+kZNxJ7mn7j282f7Nf87WN5+OZuKqYMLj0xgc5M+4/o3x+LvPutkEB74yJqNOjYPWQ41gd62W95sJIYpdfrpCLgO6A15KqXBgCkbvGEdgg+XMbbfW+nmt9XGl1I/ACYzmmhelp0zplm3OZknwEj458glVHKr8q239alIGb/16gjVHLlPP24Ufn+tI+zo5+qxf3Anrxhjt6rW7QL+Z4BtopSMRQuSk/teiYj1BQUF6//791i6jwjl/7TwTtk8gODaYvrX7MuGOCXhW8kRrzU/7w3l77UnSMk2M7FGPF7rX+9846wkRRrt68HKjXb3vDGg6QNrVhShhSqkDWutceyrIHaoV0N93mX506COc7Z2Z020O/QL6AcbE1ONXHmP3+TjaB3jyzkPNqe9juWCalQ67PoRtc0Gbods4YyYkB+ebvJsQwhok3CuYCwkXmLhjIkeuHqGnf08mdZyEl5MXmdlmPt9yjg83ncXRzoaZD7VgcJC/MciX1nB6Lfz+Jly7CE3uN87WPWpb+3CEEHmQcK8gzNrMdye/Y8HBBdjb2jOz60zuqXMPSikOXIxj3M/HCIlO5t5AXybf1/R/E1NfPQ2/j4Nzf4F3E3hyNdTtbs1DEULkg4R7BRCZHMmEHRPYF7WPrjW7MrXTVHycfUhMz2L276f4Zvclaro7sXh4ED0bW3q5pCfA5lmw93Owd4F+s6Dd02Brf/M3E0KUChLu5ZjWmt9Cf+Od3e9g0iamdZrGg/UfRCnF+uNRTFwVTExyBk93qcNrfRoasyKZzXDkO2OAr5QYaPMk9JoMLl7WPhwhRAFIuJdTCRkJvLX7Lf648AetvFvxTtd38Hf1JzY5gylrjvPr0Uia+Fbhy2FBBPq5Gy+KCobfXoew3caQAY//BDVaW/dAhBCFIuFeDu26vIuJOyYSlxbHqNajeKr5U9goG1YfjmDqmuOkZJh4o29DnutWzxiSNz0RNr8Lez4DJ3fLvKWPg43Nrd9MCFEqSbiXI+nZ6Sw4uIBvTn5DXbe6fNjzQ5pWbUpUQjoTVx1j48loWvm7M2dQIA2quRq9YI4thz8mGKM2th1uNME4e97yvYQQpZuEezlxMvYkb257k3MJ5xjSeAivtn0VR1tHvt97ibfXniTLZGbiPU0Y0bkOtjYKYkKMJpjQLeDb0jJqY1trH4YQoohIuJdxZm3mq+Nf8eGhD/Fw9OCz3p/RuWZnwuJSeXPFXrafjaFDHU9mDQwkwMsFMlNh23uw4wOwd4a734Ogp2Q2JCHKGQn3MiwmLYbx28azK3IXfWr3YfIdk6ni4MbXOy8w6/dTKGDGA80Z0r6WcTPSqbWwbiwkXDKmt+szHSr7WPswhBDFQMK9jNoRsYPx28eTkpXC5I6TGdRgEBHX0nhh6R52nY/lzobezHyohTHdXUIErB0Np38zbkQavhYCOlv7EIQQxUjCvYzJMmXxwaEP+Or4V9R3r8+ivouo516Pn/aHM/3XE2itefehFgxu54/SZmN89T+nG0Pz9p5mjLEuNyIJUe5JuJchYYlhjNk6huDYYAY3GswbQW+QmAr/+Xo/f56KpkMdT957uCX+ns5Gn/VfRkHEAajXC+6dCx4B1j4EIUQJkXAvI9aeX8v03dOxUTbM6z6P3rV78+vRy0xcFUxaponJ9zZleKcAbEzpxt2lOz+ESu7w0JfQYpAMxytEBSPhXsplmjKZvW82P5z+gdY+rZnVdRaVVFVe+u4gvx6NpKW/O+8/3JL6PpXh3Cb49VWID4VWQ6HvW9JnXYgKSsK9FLucfJnXN79OcGwwI5qNYFSbUew+d43XftxKfGomb/RtyPPd6mGXHg8rXoej34NnPRj2C9S509rlCyGsSMK9lNoesZ1x28ZhMpuY330+XWv2YM7vp/l863nqebuweHg7mtd0g+OrjJuR0q/BnaOh6xtgX8na5QshrEzCvZQxmU18fvRzPjvyGQ08GjC3+1xMGVUZ+OlOjkUk8HiHWky8pylOmXHw4zA4sQp8W8GwNVCtmbXLF0KUEhLupUh8ejzjto1j5+Wd3F/vfiZ0mMCvh2OZsmY7jvY2fP5EW+5qWg2Or4S1b0BGkjEWTKdXwFb+KYUQ/yOJUEqcjjvNqL9GEZMWw5SOU+hd837e+PEYa49F0bFuVeYNbkV120T48Uk4uQZqtIEHPgGfJtYuXQhRCkm4lwIbLm5gwvYJuNq78nX/r8lIqcndH2wjOimDsf0a82zXOtieWGHcZZqZYrkZ6SU5WxdC5EnSwYrM2swnhz/h86OfE+gdyLxu81hzMJl31+2ihrsTP7/QiZZVzbB8mHG2XjPIOFv3bmTt0oUQpZyEu5WkZKUwftt4/gr7iwfqP8ColuOYuOIkfxy/Qt+m1ZjzcEvcIrbAJy9Caiz0ngqdRsnojUKIfJFwt4KwpDBG/TWK0IRQxrYbS8sq9zLwk71cvpbGxHua8HSHaqiN42HvQmOgr8d/At9Aa5cthChDJNxL2OHow4z6axQmbeLTXp9yPrwGA7/bRVUXB3547g7a2l+ChYMh5gzcMRJ6TZF+60KIApNwL0G/X/idCdsmUN2lOnO7fchnGxNZeSiYOxt6M//hFnge/gQ2vQMuPvDEKqjXw9olCyHKqFuGu1JqMXAvEK21bm5Z5gn8AAQAF4BHtNbxSikFLADuBlKB4Vrrg8VTetmhtWZx8GLmH5xPa5/WjGszi9e+PcuJyERe69OQl4IqY7PiEQjdCs0ehHvmypgwQojbkp/p7b8C+l23bBzwp9a6AfCn5e8A/YEGlsezwKdFU2bZlWXOYtquacw/OJ/+Af15psG7PLEwmEuxqSwaFsSo2hex+bwLhO+HAR/DoCUS7EKI23bLM3et9ValVMB1iwcA3S3PvwY2A2Mty5dqrTWwWynlrpTy1VpHFlXBZUlKVgqvbX6NnZd38p8W/8E9/T5GLD5MrarOfPF4S+oFL4Dt88CnqRHqPo2tXbIQopwobJt7tRyBHQVUszyvCYTl2C7csqzChXtcehwjN47kVNwpJnaYzIHghszbf5LeTXyY188L118egfC90HY49HsX7J2sXbIQohy57QuqWmutlNIFfZ1S6lmMphtq1ap1u2WUKpeTL/PchueITIlk2h1z+GqjM4cuhfNyz/q86h+CzZJBxrR3gxZD84HWLlcIUQ7lp809N1eUUr4Alj+jLcsjAP8c2/lZlt1Aa71Qax2ktQ7y9vYuZBmlz9n4szyx7gli02KZFDSfOSttORmZyKdDWvK6zTJsfnjcmO7u+a0S7EKIYlPYcF8DDLM8HwaszrH8SWW4A0ioSO3tR64eYdjvwzBrMy83ncvEZSlkZJv5eVgj+h9+0WhfbzsCnl4PnnWtXa4QohzLT1fIZRgXT72UUuHAFOBd4Eel1NPAReARy+ZrMbpBnsXoCjmiGGoulXZE7ODVza/i5eTFfT5TmfhjDA18KrO0nx0+v9wPydFGb5jWQ61dqhCiAshPb5nH8ljVK5dtNfDi7RZV1mwO28xrm1+jrltdmti8zqxfr9KtoTefNwum0k9joXJ1ePoPqNHa2qUKISoIuUP1Nm28uJHRW0bT0LMRbgkv8t9jcQxvX4PJtkuwWfc11O0BAxeBS1VrlyqEqEAk3G/D76G/M27bOJp4NsMc+TTrzyUyvXc1nrj0JurSLujyGvScKCM5CiFKnIR7If1y7hcm7phI86qtiDs3lJCodL7s50zvwyOM9vWBi6DFIGuXKYSooCTcC2FlyEqm7JxCi6ptCTsxmKuJJlb0TiJw5zPgUBmGrwW/ttYuUwhRgUm4F9DKkJVM3jmZwKrtOXVkIOZsxcY7jlBz6zvGmOuPLgO3mtYuUwhRwUm4F8Da82uZsnMKzTyCOLr/QTwdbfml0Qqq7Psemg6ABz4DB2drlymEEBLu+bXx4kbGbx9PgyqBHD3wIHUr2/Bz1Y9xPLUF7hwD3d8Em8LeEyaEEEVLwj0ftoZvZfTW0dRyaUTwwYG0ds/mv5XexT78NAz4BFo/bu0ShRDiXyTcb2HX5V28uulVqlWqw8nDj9DLM5WPzW9jm5AAQ36A+r2tXaIQQtxAwv0mjl49yiubXsHDoQYhRx5jcNWrvJ3+Dsq+EoxYC74trV2iEELkSsI9D+evnWfknyOpZONO6LHHGekVymtJc1AeAfD4cvCobe0ShRAiTxLuuYhKieK5jc9hMtkQeXooY7xO82zCfJRfe3hsmUyDJ4Qo9STcr5OQkcDzG54nPi2Ra+f/w2S3owy79gXU6wWDv5GujkKIMkHCPYe07DRe+vMlLiReIu3ScN5xOsAjScug6QPw0Bdg52DtEoUQIl8k3C1MZhNjtozhyNUjZF4ewnt2e7kv9Rdo/QTct0AG/xJClCkS7haz981mc/hmTNED+FDvpU/Gn9DxJeg7A5SydnlCCFEgEu7Atye/5btT36Hju/BR5lF6Zm2B7uOh2xgJdiFEmVThw31L2BZm752NSm3G+0mX6GnaDr2mQNfXrF2aEEIUWoUO95OxJ3ljy2hUZg3eunqNvuZd0HsqdHnV2qUJIcRtqbDhHpUSxciNL5KZ6ciEqGzuN++FPtOh8yvWLk0IIW5bhRzG0OjyOIq4tCReuWzPI6a90OctCXYhRLlR4cJda82UHVM5HXeKoZHOPJV9APq+DZ1HWbs0IYQoMhUu3L8+/jXrLqylW6wHozMOQ+9p0Okla5clhBBFqkKF+87LO5l7YB71k6rwYdJhuHM0dPk/a5clhBBFrsKEe1hSGP/31+u4ZzjybexxaP8c9Jhg7bKEEKJYVIjeMqlZqTzz+4uYMtP5NvoSjoFDUP3elRuUhBDlVrkPd601r2+awOWUUD6LjqZavbuxHfChzHcqhCjXyn3CLTn2LdsjN/Jy/DVaV+uMw8OLZBAwIUS5d1vhrpR6VSl1XCkVrJRappSqpJSqo5Tao5Q6q5T6QSlltXFyj0QfY8HBOXRJSWewQz2cHv9Ghu0VQlQIhQ53pVRNYBQQpLVuDtgCjwKzgHla6/pAPPB0URRaUAkZCbzw+4t4Z2cxPtWJKiNWyEQbQogK43abZewAJ6WUHeAMRAI9geWW9V8DD9zmexSY1ppnfnmZNHMcM+Iy8H96jUyNJ4SoUAod7lrrCOA94BJGqCcAB4BrWutsy2bhQM3cXq+UelYptV8ptf/q1auFLSNXM7fO52TKIUbFpdB2yHLwCCjS/QshRGl3O80yHsAAoA5QA3AB+uX39VrrhVrrIK11kLe3d2HLuMGfZ3fwQ+hieqWkMbDPp9j7tS6yfQshRFlxO10hewOhWuurAEqpFUBnwF0pZWc5e/cDIm6/zPyJTbnG9C0v4auzeanpG1Rpke+fNUIIUa7cTpv7JeAOpZSzUkoBvYATwCZgkGWbYcDq2ysx/177YTAJtlm86NKd+t2fL6m3FUKIUud22tz3YFw4PQgcs+xrITAWeE0pdRaoCiwqgjpv6YMVYzloe5kH072479FPSuIthRCi1LqtO1S11lOAKdctPg+0v539FtS+g2v4NuFXGmfZMebJX2RYASFEhVfm71BNjL7A+/vGAYqJvb7EydnV2iUJIYTVlelw15kpfPLDgxyvZMsw/6dpWa+dtUsSQohSoUyH+/dr3mGZaxatbRszsrdMai2EEH8r0+HesPOj1HFoxEeDFlu7FCGEKFXK9JC/bX1bsGrIz9YuQwghSp0yfeYuhBAidxLuQghRDkm4CyFEOSThLoQQ5ZCEuxBClEMS7kIIUQ5JuAshRDkk4S6EEOWQ0lpbuwaUUleBi9auIx+8gBhrF1FAUnPJKGs1l7V6QWrOTW2tda5T2ZWKcC8rlFL7tdZB1q6jIKTmklHWai5r9YLUXFDSLCOEEOWQhLsQQpRDEu4Fs9DaBRSC1FwyylrNZa1ekJoLRNrchRCiHJIzdyGEKIck3IUQohyScL+OUspfKbVJKXVCKXVcKfVKLtt0V0olKKUOWx6TrVHrdTVdUEods9SzP5f1Sin1gVLqrFLqqFKqjTXqzFFPoxyf32GlVKJS6v+u28bqn7NSarFSKlopFZxjmadSaoNSKsTyp0cerx1m2SZEKTXMivXOUUqdsvy7r1RKuefx2pt+h0q45qlKqYgc//Z35/Hafkqp05bv9Tgr1/xDjnovKKUO5/HakvmctdbyyPEAfIE2lueuwBmg6XXbdAd+tXat19V0AfC6yfq7gXWAAu4A9li75hy12QJRGDdklKrPGbgTaAME51g2GxhneT4OmJXL6zyB85Y/PSzPPaxUb1/AzvJ8Vm715uc7VMI1TwXeyMf35hxQF3AAjlz/f7Uka75u/fvAZGt+znLmfh2tdaTW+qDleRJwEqhp3aqKxABgqTbsBtyVUr7WLsqiF3BOa13q7lLWWm8F4q5bPAD42vL8a+CBXF56F7BBax2ntY4HNgD9iq1Qi9zq1Vqv11pnW/66G/Ar7joKIo/POD/aA2e11ue11pnA9xj/NsXuZjUrpRTwCLCsJGrJi4T7TSilAoDWwJ5cVndUSh1RSq1TSjUr0cJyp4H1SqkDSqlnc1lfEwjL8fdwSs8PrUfJ+z9CafucAapprSMtz6OAarlsU1o/76cwfoPLza2+QyXtJUtT0uI8mr5K62fcFbiitQ7JY32JfM4S7nlQSlUGfgb+T2udeN3qgxhNCC2BD4FVJV1fLrpordsA/YEXlVJ3Wrug/FBKOQD3Az/lsro0fs7/oo3fs8tEf2Kl1AQgG/g2j01K03foU6Ae0AqIxGjmKCse4+Zn7SXyOUu450IpZY8R7N9qrVdcv15rnai1TrY8XwvYK6W8SrjM62uKsPwZDazE+JU1pwjAP8ff/SzLrK0/cFBrfeX6FaXxc7a48neTluXP6Fy2KVWft1JqOHAv8LjlB9IN8vEdKjFa6ytaa5PW2gx8kUctpeozBlBK2QEPAT/ktU1Jfc4S7textJctAk5qrefmsU11y3YopdpjfI6xJVflDfW4KKVc/36OcQEt+LrN1gBPWnrN3AEk5GhasKY8z3JK2+ecwxrg794vw4DVuWzzB9BXKeVhaVLoa1lW4pRS/YAxwP1a69Q8tsnPd6jEXHc96ME8atkHNFBK1bH8Bvgoxr+NNfUGTmmtw3NbWaKfc0lcWS5LD6ALxq/ZR4HDlsfdwPPA85ZtXgKOY1yd3w10snLNdS21HLHUNcGyPGfNCvgYo3fBMSCoFHzWLhhh7ZZjWan6nDF+8EQCWRhtuk8DVYE/gRBgI+Bp2TYI+DLHa58CzloeI6xY71mMtum/v8+fWbatAay92XfIijX/1/I9PYoR2L7X12z5+90YPdrOWbtmy/Kv/v7+5tjWKp+zDD8ghBDlkDTLCCFEOSThLoQQ5ZCEuxBClEMS7kIIUQ5JuAshRDkk4S6EEOWQhLsQQpRD/w9XgVMDGqeQ/AAAAABJRU5ErkJggg==\n", 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" ] From cb07fcf1877e531a2d60c1a8432196e245070515 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 247/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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RaMojaSFQVifx3h+h02hdZVS5T0AwDP6vtWbRyQMwuR8kLHI6lSqEFgL1Wydxx5udTqIqouaDYPwSa1jyxyOsPS68cLRiRaaFwNdpJ7EqD5GNYdxCaDXUGmb65R2Qe9bpVMoW4HQA5TDtJFblJSgMRrwHtdvAoqfg1BEY+RGEVnM6mc/TKwJft/FjiIjWTmJVPkTg4gdg2BRIXANTB+lsZA+ghcCXnT4KCT9Auxu0k1iVr3bXwS1fQfpRa+ObJN2OxElaCHzZ5plg8nUrSuWMhr1h7EJroML7V8LhjU4n8llaCHyVMdbmMzFdrTHfSjkh6iK4bR4EV4bp18DBNU4n8klaCHzV4Q2QvAM6jHI6ifJ1kY3gtu+svZI/GAoHVjudyOdoIfBVm2aAfzC0HuZ0EqWs5Shu+w4q14aPr4Mjm5xO5FO0EPii3LOw5XNoeRWEVnU6jVKWyrVh9GyrmejDayF5l9OJfIYWAl+0+3s4k6qdxMrzVK0HY+ZYHcgfDNWhpeVEC4Ev2vgJVK4Djfs7nUSpP6reBEZ/DdkZ8PH1kJXmdKIKTwuBr0lPtvYlbne9zh1QnqtWa7jhAzgeDzNHQ16O04kqNLcUAhEZLCK7RCRBRP6wH7GIBIvIZ/bzq0WkoX38UhFZJyJb7NtL3JFH/YltX4HJg/Y6Wkh5uMb94OrXYO8S+PY+XaiuDLlcCETEH5gEXA60AkaJSKvzThsLpBpjmgIvA8/bx1OAq40xbYExwIeu5lHF2PI51GoDNVs6nUSp4nW8Cfr8HTZ8CKvedDpNheWOK4KuQIIxZq8xJhv4FBhy3jlDgOn2/VnAABERY8wGY8y5jU23AaEiEuyGTKowJ/ZZ67u0HeF0EqVKrv9j0OIqWPAv2L/M6TQVkjsKQTRwsMDjRPtYoecYY3KBNKD6eecMB9YbYwpdm1ZExotInIjEJScnuyG2D9o6y7pto4VAeRERGPqWtZT157dC2iGnE1U4HtFZLCKtsZqL7izqHGPMZGNMrDEmNioqqvzCVRTGwObPoX5Pa4ieUt4kJAJGfgw5Z+DzMbqXgZu5oxAcAgr+zxJjHyv0HBEJAKoAx+3HMcBXwGhjzB435FGFSdoKKbu0WUh5r6iLYOibkLjW2s9AuY07CsFaoJmINBKRIGAkMOe8c+ZgdQYDjAAWG2OMiFQF5gKPGGOWuyGLKsrmmeAXYO0QpZS3ajUEutwBK9+wllBXbuFyIbDb/CcC84EdwExjzDYReUpErrFPmwpUF5EE4H7g3BDTiUBT4N8istH+qulqJnWe/HzY+gU0HWgt7KWUN7vsaYhqCV/dbc2LUS4T44Vjc2NjY01cXJzTMbzH/uXw/hUwfKo2DamKIWkbTO4PjfvCjTOtDmVVLBFZZ4yJPf+4R3QWqzK25XMIrAQXXe50EqXco1ZruOw/EL8A1k5xOo3X00JQ0eVmw/avocWV1ubhSlUUXe+AJpfAwsch9Ren03g1LQQV3Z5F1kqjba93OolS7iViLUEhfjDnXl2CwgVaCCq6LZ9DaCQ00ZVGVQVUtR5c9hTs+wnWve90Gq+lhaAiy86AnfOg9VDwD3Q6jVJlo/Nt0KiPtQTFyYPFn6/+QAtBRbZ7PuSegTbDnU6iVNkRgWvesFbV/e5hp9N4JS0EFdn2ryGsJtTv4XQSpcpWtQbQ7xHYNRd2fed0Gq+jhaCiys6A3Qug1TW6AY3yDd0nQFQLmPcQZGc6ncaraCGoqM41C7W+1ukkSpUP/0C48iVIOwBLX3A6jVfRQlBRabOQ8kUNe1m77y1/DZJ3O53Ga2ghqIi0WUj5skuftmbSz/+H00m8hhaCikibhZQvC4+Cvg9Zq5PGL3Q6jVfQQlARabOQ8nVdx1s7ms1/DPJynE7j8bQQVDTaLKQUBARZi9Kl7NIZxyWghaCi0WYhpSwXXWHNOP7xGWu9LVUkLQQVjTYLKWURgUHPQlYa/KzDSf+MFoKKRJuFlPq92m2s4aRr3oW087dSV+e4pRCIyGAR2SUiCSLySCHPB4vIZ/bzq0WkYYHn/mEf3yUig9yRx2dps5BSf9TvEcDAT885ncRjuVwIRMQfmARcDrQCRolIq/NOGwukGmOaAi8Dz9vf2wprs/vWwGDgTfvnqdLQZiGl/qhqfYi9HTZ8DCnxTqfxSO64IugKJBhj9hpjsoFPgSHnnTMEmG7fnwUMEBGxj39qjDlrjNkHJNg/T10obRZSqmgXPwgBIbD4P04n8UjuKATRQMFFwBPtY4WeY4zJBdKA6iX8XgBEZLyIxIlIXHJyshtiVzDaLKRU0cKjoMc91lXz4Q1Op/E4XtNZbIyZbIyJNcbERkVFOR3H82izkFJ/rudEa7e+RU85ncTjuKMQHALqFXgcYx8r9BwRCQCqAMdL+L2qONospFTxQqrAxffDnsWwf7nTaTyKOwrBWqCZiDQSkSCszt85550zBxhj3x8BLDbGGPv4SHtUUSOgGbDGDZl8izYLKVUyXcZZV84/Pe90Eo/iciGw2/wnAvOBHcBMY8w2EXlKRK6xT5sKVBeRBOB+4BH7e7cBM4HtwPfAPcaYPFcz+RxtFlKqZAJDoddfrc3uD6xyOo3HEOuDuXeJjY01cXFxTsfwDNkZ8L8m0PEmuPJFp9Mo5fmyM+HVdlCrDYz+2uk05UpE1hljYs8/7jWdxaoI2iyk1IUJqgQ974W9P8JBbYkGLQTeT5uFlLpwXcZBpeqwRGcbgxYC76ajhZQqnaAw66pgzyJI1GZmLQTeTJuFlCq9LndY8wp0BJEWAq+mzUJKlV5wOPSYAPEL4OhWp9M4SguBt9JmIaVc12UcBIXD8lecTuIoLQTeSpuFlHJdaDWIvQ22fgEn9jmdxjFaCLyVNgsp5R7dJ4D4w8o3nE7iGC0E3kibhZRyn4i60H4kbPgI0n1zZWMtBN5Im4WUcq9ef4Xcs7D6baeTOEILgTfSZiGl3KtGM2h5Nax9F7JOOZ2m3Gkh8DbaLKRU2ej9N8hKg3XvO52k3Gkh8DbaLKRU2YjuDI36wMpJVjORD9FC4G20WUipstPrb5B+1BpO6kO0EHgTbRZSqmw1uQRqtrKuCrxwif7S0kLgTbRZSKmyJWJtcp+0FfYucTpNudFC4E20WUipstf2Ouv3bOUkp5OUGy0E3kKbhZQqHwHB0PUOSFgIx3Y6naZcuFQIRCRSRBaKSLx9W62I88bY58SLyBj7WCURmSsiO0Vkm4joDhF/RpuFlCo/sWMhIARW+cZVgatXBI8Ai4wxzYBF9uPfEZFI4HGgG9AVeLxAwXjBGNMC6Aj0EpHLXcxTcW37SpuFlCovYdWh/SjY9JlPLDsR4OL3DwH62fenA0uAh887ZxCw0BhzAkBEFgKDjTEzgB8BjDHZIrIeiHExT8WUnQHxC60N6rVZyGNk5eRx+OQZDp08w+GTZ0jNzCHjbC7pZ3M5k50HgIjgJxDo70dEaCARIQFEhARSpVIgtSNCqFMlhBrhwfj5icN/GvUH3SfAumkQNxX6/eEzboXiaiGoZYw5Yt8/CtQq5Jxo4GCBx4n2sV+JSFXgauDVol5IRMYD4wHq16/vQmQvpM1Cjss4m8va/SfYcOAk24+cYseRUySmnvnDeSIQFhRApSB/RCDfgDGGszn5pGfnFjoiMdBfqBURQnTVUBpHhdMkKowmNcNpGhVOdNVQLRJOiWoOzQfDmnettYgCQ51OVGaKLQQi8gNQu5CnHiv4wBhjROSCB96KSAAwA3jNGLO3qPOMMZOByQCxsbG+M8AXtFnIIfFJp/lu61GW7DrG5sQ0cvMNItCoRhgd6lXl+th6xFQLJbpqKHWrhhIZFkRooH+R/3Hn5xvSs3M5dSaHk5k5HE3L4kjaGY6kZXEkLYuDJzKZv+0oJzKyf/2e4AA/LqpdmdZ1q9C6bgRtoqvQonZlQgL1yrBc9LgHpl8Nm2dC5zFOpykzxRYCY8zAop4TkSQRqWOMOSIidYBjhZx2iN+aj8Bq/llS4PFkIN4Y49tbBBVFm4XK1dG0LGbGHWT2xkPsSc4AoH29qtzRpzE9Glenc4NqhAWX7kLaz0+ICAkkIiSQmGrQJrpKoeedyMhmT3I6e46lk3AsnR1HTzFvyxFmrDkAgL+f0KxmOO1iqhDbIJLODavRuEYYInrl4HYNL4ba7ayhpB1vAb+KOdDS1aahOcAY4Dn7dnYh58wH/lugg/gy4B8AIvIfoAowzsUcFZc2C5U5YwzLElKYvuIXFu9MIt9A98aRjOnZkEGta1MrIqRc80SGBREZFkmXhpG/y5iYeoZth9PYdvgUWw6lsWB7EjPjEgGoVimQzg2q0blBJJ0bVKN9vSoEB+gHB5eJQI+J8NV4SPgBml/mdKIyIcaFadQiUh2YCdQHfgGuN8acEJFY4C5jzDj7vNuBR+1ve8YYM01EYrD6DnYC51Z4esMYM6W4142NjTVxcXGlzu1VPrsFDqyCB3bqFYGb5eUb5m87yltL9rDlUBo1woO4LrYeI7vUo0H1MKfjFSs/37A3JZ11v6QStz+VdQdS2WtfxYQE+tG1UXV6NalOr6Y1aFUnQvsaSis3G15tB1EXwejCPut6DxFZZ4yJ/cNxVwqBU3ymEGSdgheaWZekV77gdJoKwxjDkl3JPPvdDnYnpdOweiXu7teEoR2jvf5T9ImMbNbuP8HKPcdZnpBC/LF0wLpi6NGkOv2a16R/i5pEVQ52OKmXWfoSLHoS7l4BtVo7nabUiioErjYNqbK0ax7kZkHbEU4nqTC2HU7jv/N2sDzhOA2rV+K1UR25sm0d/CvIp+XIsCAGta7NoNbW+I6kU1ms2JPC8oTjLItPYd6Wo4hA+5iqDGxZkwEta9GidmXtXyhO51vh5/+DlW/C0Io3yUyvCDzZRyMgeSf8dXOF7aQqLxlnc3lp4W6mLd9HldBA/jqgGTd2a0BQgO+8r8YYth85xaIdx1i0I4lNiWkAxFQL5ap2dbm6fR1a1YnQolCUuQ/A+g/gvm0QXtPpNKWiTUPeJuM4vNjcGr526VNOp/Fqi3cm8c+vtnI4LYubutXnoUEtqFIp0OlYjjt2KovFO4/x3dajLEtIIS/f0DgqjKvb1WVIh7o0jgp3OqJnSUmANzpD34eh/6PFn++BtBB4m7VTYe79cOdSqNPO6TReKTM7l6e/3cGMNQdoXiucZ4e1pXODyOK/0QedyMjmu61H+HbTEVbtO44x0LVhJCO71uPyNnUIDfLuvhO3+WQkJK61rgoCy3c0mTtoIfA2066AjBS4Z7U1hE1dkM2JJ/nbpxvZdzyD8X0ac/+lzb2+I7i8JJ3K4sv1h/hs7QH2H8+kckgAQztEc1P3+rSoHeF0PGft+9maYHbN69BptNNpLpgWAm+Slggvt4b+j0Hfh5xO41WMMUxdto/nvttJVOVgXry+PT2b1HA6llcyxrB63wk+XXOAeVuPkp2bT++mNRh7cSP6NovyzeGoxsDbF0N+LkxY6XUf0nTUkDfZ+qV122a4szm8TMbZXB76YjNzNx9hcOvaPD+8nfYFuEBE6N64Ot0bV+eJzGw+WXOA6Sv2c9u0tTStGc643o0Y1inGpzrcf93B7Ou7YM9iaDrA6URuoVcEnuidPiB+MH6J00m8xt7kdO76aB0Jx9J5aHAL7uzTWEe/lIHs3HzmbTnClGV72XroFNFVQ7mnf1NGdPahgpCbDa+0gVpt4JYvnU5zQYq6IvCRvzkvkpIARzZBG507UFLL4lMYMmk5yafP8uHYbtzVt4kWgTISFODH0I7RfDOxN9Nv70pU5WAe/WoL/V9YwserfyEnL9/piGUvIMjawWzPIji2w+k0bqGFwNNsnQUItBnmdBKvMHPtQW6dtoboqqF8c29vejXV/oDyICL0bR7FVxN6Mv32rtSMCOaxr7Yy6JWfWbg9CW9sabggnW+3dzB70+kkbqGFwJMYA1tmQYNeEFHX6TQezRjDiwt28dAXm+nRpDqf39WDmGqVnI7lc84VhC/v7smU0VaLwx0fxDHq3VVssSesVUgFdzDLSHE6jcu0EHiSIxvheLwuKVGMs7l53PfZRl5fnMANsfV479YuVA7RTmEniQgDW9Vi/t/68PSQ1uxOSufqN5bx8KzNpKQ6mW4AABo/SURBVBbYX6FC6T4B8s5C3HtOJ3GZFgJPsnEG+AdD66FOJ/FYmdm5jJsex9cbD/PgZc15bnhbAv31n7GnCPT345YeDVny936M79OYWesTueTFJcyMO1jxmouimkOzy6wdzHLPFn++B9PfIE+Rm231D1x0OYRWK/58H3QqK4fRU9ewPCGF/w1vx8RLmmmnsIeKCAnk0StaMvcvvWkSFc5DszZzwzuriE867XQ09+o+ATKOWU26XkwLgadIWAiZx6HDjU4n8UjH089y47ur2JR4ktdHdeL6LvWcjqRKoEXtCGbe2YPnh7dl97HTXPnaMt5asoe8/ApyddC4H9Rsbe1g5sVXPFoIPMWmGRAWBU0ucTqJxzmalsUNk1cRn5TO5NGxXNmujtOR1AXw8xNu6FKfH+7vyyUtavL89zsZ8fYK9iSnOx3NdSLQYwIc2wb7fnI6TalpIfAEmSdg1/fQ9nrw107PghJTM7nunRUcTcti+u1d6X+Rdy7/q6BGeDBv3dyJV0d2YG9yBle8upSpy/aR7+1XB22vg7Ca1lWBl3KpEIhIpIgsFJF4+7bQxm0RGWOfEy8iYwp5fo6IbHUli1fb+gXk50D7kU4n8ShH0s4w6t1VpGXm8PG4bnRvXN3pSMpFIsKQDtEsvK8PFzerwdPfbue299eSku7Fna0BwdBlHMQvgOTdTqcpFVevCB4BFhljmgGL7Me/IyKRwONAN6Ar8HjBgiEiw4AKcI3ogk2fWtPVdbnpXx07lcWN767mZEYOH47tRvt6VZ2OpNyoZkQI746O5emhbVi59ziXv7qUZfFePB6/y1hrxN/qt5xOUiquFoIhwHT7/nSgsHGPg4CFxpgTxphUYCEwGEBEwoH7gf+4mMN7pcTDoTi9GiggJf0sN05ZTdKpLN6/vYsWgQpKRLilewPmTOxFldBAbnlvNc9/v9M7l6kIqwHtb7CGgGeecDrNBXO1ENQyxhyx7x8FahVyTjRwsMDjRPsYwNPAi0BmcS8kIuNFJE5E4pKTk12I7GE2zbAWmGt7vdNJPEJqRjY3T1lNYmom027tohvJ+IAWtSP4ZmJvRnapx1tL9nDDOys5mpbldKwL130C5J7xyglmxRYCEflBRLYW8jWk4HnGmi1S4l4fEekANDHGfFWS840xk40xscaY2KioqJK+jGfLy4WNn0DTgVC5sBrqW9LO5HDLe6vZm5LBlNFd6KZ9Aj4jNMifZ4e1440bO7Lr6Gmuen0pq/YedzrWhanZEpoMsCeYedds6mILgTFmoDGmTSFfs4EkEakDYN8eK+RHHAIKDvqOsY/1AGJFZD+wDGguIktc++N4mYSFcPoIdPpD/7nPOZ2Vw+j31rDr6GneuaUzvZvp4nG+6Kp2dZk9sRcRoYHcNGU1U5bu9a4ZyT3ugfSjsM27lqd2tWloDnDuf7ExwOxCzpkPXCYi1exO4suA+caYt4wxdY0xDYHewG5jTD8X83iXddMhvBY0H+R0EkdlnM3ltmlr2XYojUk3dtIhoj6uac3KzL6nFwNb1uQ/c3dw74wNZJzNdTpWyTS5BKJawso3vGqCmauF4DngUhGJBwbajxGRWBGZAmCMOYHVF7DW/nrKPubb0g5B/HzocJNPzx04k53H2OlrWX8glVdHduSy1rWdjqQ8QOWQQN6+uTMPD27BvC1HGDppOftTMpyOVbxzE8yOboH9y5xOU2K6Q5lTfvof/PgM/GUjRDZyOo0jsnLyuOODOJYlpPDKDR0Y0iG6+G9SPmd5QgoTP1lPvoG3bupET0/fcyIny9pzPLoz3DTT6TS/ozuUeZL8PFj/obVOiY8WgezcfCZ8vJ6l8Sk8P7ydFgFVpF5NazD7nt7UrBzMLe+t4cNVvzgd6c8FhkC3u6wr/qPeMU9WC4ET9vwIaQd8tpM4Jy+fiZ+sZ/HOYzxzbRuuj9UF5NSfq1+9El9O6Enf5lH86+ut/OvrrZ4936DrOAiqDMtedjpJiWghcMK6aVCpOrS40ukk5S43L5+/fbaRBduTeOLqVtzUrYHTkZSXqBwSyLujY7mzT2M+XPULY95bw8lMDx2mGVrNmm287Us4vsfpNMXSQlDeTh6EXfOg4y3WGiU+JC/f8PdZm5m7+QiPXtGCW3v5ZrOYKj1/P+EfV7TkhevaE7c/laGTlpNwzENXqOlxD/gHwfJXnE5SLC0E5W3tFOu2yzhnc5Sz/HzDP77czFcbDvHgZc0Z36eJ05GUFxvROYYZ47uRfjaXa99czk+7PXC1gfCa1ge+jTOsUYIeTAtBecrOhPXTocVVUNV32sWNMfx7zlZmxiXyl0uaMvGSZk5HUhVA5waRfH1PL2KqVeK2aWuYtnyf500+6/UXwFjzCjyYFoLytOVzOJNqjSjwEcYYnvxmOx+tOsCdfRtz36XNnY6kKpCYapWYdVcPBrSsxZPfbOfRrzysE7lqfWsdsXXvQ4bnrq6qhaC8GAOr37GWm27Q0+k05cIYw3/m7uD9FfsZ17sRjwxuoXsMK7cLCw7gnZs7c3e/JsxYc4DRU9eQmuFBnci974OcM7DidaeTFEkLQXnZv8zazq7bndbswwrOGMNz3+1k6rJ93NqzIY9d2VKLgCozfn7Cw4Nb8NL17Vn3SypD3/SgTuSo5tB2BKyZDOmFLcfmPC0E5WX129aQsrbXOZ2kzBljeGHBLt75eS83d6/P41e30iKgysWwTlYncobdifyzp3Qi930EcrNgmWeOINJCUB6Sd8POuRB7OwSGOp2mzL3yQzyTftzDqK71eOqaNloEVLk614kcXTWUW6et4X1P6ESu0RTaj4K4qXDqSPHnlzMtBOVhxavWnIFudzudpMy9viieVxfFc13nGJ4Z2hY/Py0CqvzFVKvEF3f35JIWtXjim+085gkzkfv8HfJzYemLzuYohBaCspZ2CDZ9Bp1GQ3gF2VCnEMYYXlywixcX7mZYp2ieG95Oi4ByVFhwAJNv6cxdfZvwyeoDzs9EjmxkrTa8fro1sdSDaCEoa6veBJMPPSY6naTMnBsd9PriBEZ2qcf/jWiPvxYB5QH8/IRHLm/Bi54yE7nP363bJc86l6EQWgjKUuYJiJtmjRioVjHX1MnPN/zz662/jg56dlhbLQLK4wy3ZyKfznK4E7lqPWvk4MZP4MhmZzIUQgtBWVrxGuRkWuOIK6DcvHwenLWJj1cfYEK/Jjo6SHm0zg0imT3R6kS+7f21vPuzQ9tgXvwghFaFBf/0mF3MtBCUldNJ1gSytiOsTa0rmKycPP7y6Qa+XG+tHfSQThZTXiCmWiVm3d2TgS1r8sy8HdzzyXrSy3sbzNCq1nDSfT9B/MLyfe0iuFQIRCRSRBaKSLx9W62I88bY58SLyJgCx4NEZLKI7BaRnSIy3JU8HmXZy5B7Fvr9w+kkbpeWaW00P2/LUf51VStdO0h5lfDgAN6+uTP/uLwF3289yjVvLCM+6XT5hoi9HSKbwMJ/QZ7z+zG7ekXwCLDIGNMMWGQ//h0RiQQeB7oBXYHHCxSMx4BjxpjmQCvgJxfzeIa0RGu8cIcboXrFWmXz8MkzXPfOCjYcSOW1UR0Z21uXklbeR0S4s28TPh7XnVNnchgyaTlzNh0uvwABQXDpk5C809qfxGGuFoIhwHT7/nRgaCHnDAIWGmNOGGNSgYXAYPu524FnAYwx+cYYz12V6UIsec667fuwszncbOfRUwx7cwVHTmYx/bauXNO+rtORlHJJjybVmfuXi2lVJ4K/zNjA47O3kpWTVz4v3uIqaNQHFj/t+NITrhaCWsaYc9PkjgK1CjknGig4aDYRiBaRqvbjp0VkvYh8LiKFfT8AIjJeROJEJC452UOmjRfm8AbY8BF0HV+hlppesusY1729EoNh5l09PH8DcaVKqFZECDPGd+f2Xo2YvvIXhk5azq6j5dBUJAJXvmQtSLfgn2X/en+i2EIgIj+IyNZCvoYUPM9Y3e8X0gUeAMQAK4wxnYCVwAtFnWyMmWyMiTXGxEZFeejELGPgu4chrAb0fcjpNG5hjGHyz3u4/f21xFSrxJcTetGyToTTsZRyq0B/P/59dSum3dqFlPSzXPPGMqav2F/2o4pqNINef4XNn8G+n8v2tf5EsYXAGDPQGNOmkK/ZQJKI1AGwbwu7vjkEFPxoHGMfOw5kAl/axz8HOrnwZ3Hels/h4GoY8G8IqeJ0Gpdl5eRx/8xN/HfeTi5vU4cv7u5BdNWKv1aS8l39W9Tku7/2oWeT6jw+Zxtjp8eRkn62bF/04gegWkP41l6u2gGuNg3NAc6NAhoDzC7knPnAZSJSze4kvgyYb19BfAP0s88bAGx3MY9zsk7Bwn9DnQ7Q4Wan07hsf0oGI95ewVcbDvHApc1548aOVAoKcDqWUmUuqnIw793ahSeubsWyhBQue/lnZm88VHZXB4GhcNUrcDwBFj1VNq9RDFcLwXPApSISDwy0HyMisSIyBcAYcwJ4Glhrfz1lHwN4GHhCRDYDtwAPuJjHOT88DqePwhUvgJ93T8+Ys+kwV72+jIMnzjBldCz3DmimcwSUTxERbu3ViG/v7U39yEr89dONjJ0ex+GTZfSJvUl/q19x1ZuONBGJ48uzlkJsbKyJi4tzOsZv9v0M06+21hMa9IzTaUrtTHYeT327nRlrDtCpflVeG9WRmGqVnI6llKPy8g3vr9jPC/N34e8nPDz4Im7s1sD9S6lkZ8LbvSEvG+5eXibNyyKyzhgTe/5x7/7o6gmy0mD2RIhsDP0fczpNqa3Zd4LLX/2ZGWsOcFffJnx2Zw8tAkoB/n7C2N6NWHBfHzrUq8q/Zm/j6teXsXrvcfe+UFAluPYdOHUYvp5QrstPaCFwhTEw515rAtnQt62/SC+TmZ3LE3O2ccPkleTmGz4Z141HLm9BoL/+01CqoHqRlfhwbFfeuLEjJzOzuWHyKu75ZD2JqZlufJEucNnTsPNba3WCcqK9f65YOwW2z4aBT0L9bk6nuSDGGBZuT+Lpuds5eOIMY3o04KHBLQgL1n8SShVFRLiqXV0GtKjF2z/t4e2f9rBwWxI3dqvPhP5NqFk5xPUX6T4BEuOsiWZ12kPTAa7/zGJoH0Fp7fkRPh4BjfvDjTO9qoM4Puk0T327naXxKTStGc5/hrahe+PqTsdSyuscPnmG1xfHMzMukUB/4daejRh3cSNqhAe79oOzM2DKQGtjq9vmQu22bslbVB+BFoLSSNoO7w2CKjFw+/deM2fgaFoWk35M4JM1BwgL8ue+S5tzc/cG2gyklIv2p2Twyg+7mb3pMIH+fgzvFMO4ixvRJCq89D/05EHr/5n8XLh9vrXDmYu0ELhL0nb44BoQfxj3g1csI5F0Kou3luzhkzUHyM83jOxaj/sGNqe6q59alFK/syc5nSlL9/HF+kRy8vIZ0KImo7rWp2/zKAJK84EreZdVDAJCYfTXEHWRS/m0EID1pkZEQ3Apq3TiOvjkOvAPgjHfWNPDPdiGA6m8v2I/czcfwQAjOsUw8ZKm1Iv0vk5tpbxJSvpZPlixn0/WHCQl/Sy1I0K4LjaGYZ1iaFQj7MJ+2NGt8OG1YPKsZuiYP/w/XmJaCPJy4I1YED8YNgViOpf8e42B9R/AvAehcm245WuPXV46LTOHeVuP8Nnag2w8eJLKwQFcF1uPW3s2pH51LQBKlaecvHwW7Uji07UH+Wl3MsZAyzoRXNm2Nle0rUPjkjYdHd8DHw2zJq1OjCt1S4QWAoD9y+DLO+H0Eeh+t7XGR6XIP/+elAT47iHYswiaXALDpxb/PeXsVFYOS3enMHvjIZbsSiY7L58mUWGM7tGQ4Z1jCNeRQEo57vDJM8zbcoR5W46w/sBJAOpHVqJ3sxr0aVaDHk1qUCU0sOgfkHkCds2DjqVfwkYLwTlnTsKCx6zNowPDoM0waH2tNUzr3H/wmSfglxXWObu/g6Bwa6exbneCn7/7/iCllJOXz44jp1iecJwlu46x7pdUcvMNUZWDuaZ9XYZ2iKZNdIQuC6GUhzp88gwLtyexND6FlXtSyMjOw0/gotoRdKhXhQ71qtK+XlWa1azs1hnMWgiAH7Yn4e8nRFcLJSbnFyqteQ12fAs5GdYJQZXB5P/2uFINq/p2nwCVi9wqoUydyc5jT3I6CcfS2XH0FBt+OcnmQyfJyskHrMvMfhdF0a95FLENI90/7V0pVaZy8vLZcOAkyxJS2HAglY0HT3I6y9q+MijAj8Y1wmhWqzLNaobTrGY4/VvUJCSwdB9ItRAA/V9Ywr6UjF8fVwkNpFEVoXfgLppJIrU4TkhgAHmV65BbqwN5dWMJCQ0lLCiASkH+VAryJyw4gOAAP5c+befnG9KzczmdlcvprBzSs3JJzczh6KksktKyOJKWRdKpLA6cyORgauavM80D/YXWdavQqX41OjWoSpeGkdSKcMMEFqWUx8jPN+w7nsHGAyfZlXSa+KTTxB9LJzH1DCKw46nBWgig9IUg6VQWialnOHTyDIdPnuFQqnWbkn6WE5nZpGbkkH62+I2k/QSCA/wJ8BcC/AR/Pz/7Vgjwl18/leflG3LzDDl5+eTlW7e5+YYzOXlFLiPi7yfUrBxM7SohRFcNpVnNyjSrZX0SaFA9jKAAHfOvlC/KzM7lwIlMWtQu/cZQRRUCn+pFrBURQq2IEDo3qFbkOWdz80jNyOF4xlkyzuaRkZ1L5tk8MrNzyczOs79yOZOdR54x1n/2+Ya8PPs23/rP3gCBdpEI9Be7aFgFo1KQP5VDAqkcEvDrbZXQQGpXCaFGeLA27yil/qBSUIBLReDP+FQhKIngAH9qV/GndhVtclFK+QZtZ1BKKR+nhUAppXycFgKllPJxLhUCEYkUkYUiEm/fFtoLKyJj7HPiRWRMgeOjRGSLiGwWke9FpIYreZRSSl04V68IHgEWGWOaAYvsx78jIpHA40A3oCvwuIhUE5EA4FWgvzGmHbAZmOhiHqWUUhfI1UIwBJhu358ODC3knEHAQmPMCWNMKrAQGAyI/RUm1uysCOCwi3mUUkpdIFcLQS1jzBH7/lGgsHUYooGDBR4nAtHGmBzgbmALVgFoBUwt6oVEZLyIxIlIXHJysouxlVJKnVNsIRCRH0RkayFfQwqeZ6wpyiWepiwigViFoCNQF6tp6B9FnW+MmWyMiTXGxEZFRZX0ZZRSShWj2AllxpiBRT0nIkkiUscYc0RE6gDHCjntENCvwOMYYAnQwf75e+yfNZNC+hgKs27duhQR+aUk556nBpBSiu8rb5rTvbwhpzdkBM3pbuWds0FhB12dWTwHGAM8Z9/OLuSc+cB/C4wougzrk38I0EpEoowxycClwI6SvKgxplSXBCISV9g6G55Gc7qXN+T0hoygOd3NU3K6WgieA2aKyFjgF+B6ABGJBe4yxowzxpwQkaeBtfb3PGWMOWGf9yTws4jk2N9/q4t5lFJKXSCXCoEx5jgwoJDjccC4Ao/fA94r5Ly3gbddyaCUUso1vjazeLLTAUpIc7qXN+T0hoygOd3NI3J65X4ESiml3MfXrgiUUkqdRwuBUkr5OJ8pBCIyWER2iUiCiJRovkJ5EJH99sJ7G0Ukzj5WosX8yjjXeyJyTES2FjhWaC6xvGa/t5tFpJPDOZ8QkUP2e7pRRK4o8Nw/7Jy7RGRQOeasJyI/ish2EdkmIn+1j3vMe/onGT3q/RSREBFZIyKb7JxP2scbichqO89nIhJkHw+2HyfYzzd0OOf7IrKvwPvZwT7u2O8RxpgK/wX4A3uAxkAQsAlo5XQuO9t+oMZ5x/4HPGLffwR43oFcfYBOwNbicgFXAN9hrR3VHVjtcM4ngAcLObeV/XcfDDSy/034l1POOkAn+35lYLedx2Pe0z/J6FHvp/2ehNv3A4HV9ns0ExhpH38buNu+PwF4274/EvisnP7Oi8r5PjCikPMd+z3ylSuCrkCCMWavMSYb+BRrwTxPVZLF/MqUMeZn4MR5h4vKNQT4wFhWAVXtmeZO5SzKEOBTY8xZY8w+IAHr30aZM8YcMcast++fxpo8GY0Hvad/krEojryf9nuSbj8MtL8McAkwyz5+/nt57j2eBQwQkTLfGPxPchbFsd8jXykEhS5851CW8xlggYisE5Hx9rGSLObnhKJyeeL7O9G+vH6vQNOaR+S0myY6Yn1C9Mj39LyM4GHvp4j4i8hGrGVtFmJdjZw0xuQWkuXXnPbzaUB1J3IaY869n8/Y7+fLIhJ8fk5bub2fvlIIPFlvY0wn4HLgHhHpU/BJY10zetwYX0/NZXsLaIK1ntUR4EVn4/xGRMKBL4C/GWNOFXzOU97TQjJ63PtpjMkzxnTAWrusK9DC4UiFOj+niLTBWmKnBdAFiAQedjAi4DuF4BBQr8DjGPuY44wxh+zbY8BXWP+ok85dEkrRi/k5oahcHvX+GmOS7F/AfOBdfmuucDSnWCvufgF8bIz50j7sUe9pYRk99f20s50EfgR6YDWlnFstoWCWX3Paz1cBjjuUc7DdBGeMMWeBaXjA++krhWAt0MweVRCE1WE0x+FMiEiYiFQ+dx9rQb6t/LaYHxS9mJ8Tiso1Bxhtj3roDqQVaO4od+e1q16L9Z6ClXOkPYqkEdAMWFNOmQRrv40dxpiXCjzlMe9pURk97f0UkSgRqWrfD+W3BSt/BEbYp53/Xp57j0cAi+2rLydy7ixQ+AWrH6Pg++nM71F59Uo7/YXVI78bqy3xMafz2JkaY4262ARsO5cLq/1yERAP/ABEOpBtBlYzQA5WW+XYonJhjXKYZL+3W4BYh3N+aOfYjPXLVafA+Y/ZOXcBl5djzt5YzT6bgY321xWe9J7+SUaPej+BdsAGO89W4N/28cZYhSgB+BwIto+H2I8T7OcbO5xzsf1+bgU+4reRRY79HukSE0op5eN8pWlIKaVUEbQQKKWUj9NCoJRSPk4LgVJK+TgtBEop5eO0ECillI/TQqCUUj7u/wEkTM2oT/b1jwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 0eab357b1cbbbf22ee187d94c08fb294e825575e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 248/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 833f28c4a1ce14a915037c5c37b4d6854ee7074c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 249/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From 836adc643f6c3b3d4c8f1dc0d86261c9f3760f56 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 250/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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y1wHvhtohIhOBiQDt2/v0S8oYvxzaC69dD03awJl/9SWEbXsP8d9Za3n2iw30Lh3Gi/U+ZsborbQ97Txf4qntTu7SnKxmDZg8J4/x/dpG/Xw1YtSTiFwB5ACnhtqvqo8Cj4KbFDCGoRnjv/d/AzvWuRldIziFxuGUB5Q563YwdcEmXv8qn5KyAGf1bcMtI06GKVNou+wpGPHD6F3oV4clJAgX52Rx/wcrWb9tHx0zGkX1fH4migIgeHxXO++57xCRkcBtwKmqeihGsRlTMyx7E+Y/7abO6Dgk6qdTVb7K28WbCzbx9sLNFO45RIPkRM7u24abhnf59iK5QTfBGz+CtTOhy4iox1UXXZiTxd8+XMWLuXn875jjonouPxPFXCBbRDrhEsQlwGXBBUTkBOARYIyqFsY+RGPi2J4t7sK61sfD8N9G7TSqyrLNe3hz4SbeXLCJ/J0HSElKYET3TM4+vg2nHdfi+xPz9b4APvwDzP6PJYooadmkPiO6t+Dl3Hx+MapbVC9U9C1RqGqZiNwCvA8kAk+o6hIR+SOQq6pTgfuAVOBlbxjdRlU9x6+YTXxQVeZt2EnBrgO0a9qALpmppDdM8Tus2AoEYMqPoPSAG+WUFNn3X3yglC/WbOOTVduYtaqIvB0HSEwQhnTN4GcjuzG6V0ua1A8z/1Jyfci5Dj6+201v3rxLROMzziUDsvhw2VZmLC/kjF7RWw/E1z4KVX0HeKfSc7cHbY+MeVAmbhXuOcir8wp4ce5G1m/f/519zRql0DmjEV0yU+mc2YjOmal0yWxEVrOGtXNKiDmPwprprvM6s9sxH66sPMDXebu+SQwL8nYRUEitl8RJXZpz46ldGNu7Nc0aHUFCyrkWZv3V1SrG3XfMMZrvG949k5ZN6jF5zsbamyiMOZzygPLJqiImz9nI9GWFlAWUgZ2a8dOR2fRpm8bGHftZU7iPtdv2sqZwH9OXb+XF3JJvXp+UILRv3vCbBNIlI5UuLRrROSOVpkfypRdPCpfBtNvd/Eo51x3VIQ6UlLNy6x4W5u9i1qptfLFmO3sOlZEg0LddOreM6MrQbpn0y0o/+kTbuCX0mQBfPQcjbotpR3tdkZSYwEU5WTz80Wo27TpAm/ToTP5oicLEpU27DvBSbh4v5+ZTsOsAzRulcN2QTlw8IOs7s4p2bdGY0yr14xXvL2XNtr2sLdrH2qK9rCly2zNXFH5nCcqmDZPpnJnKGb1acsPQzjXjKuEDu+DFK91V0OMfOuyIovKAsmH7PlZs2cPyLXtYvmU3K7bsYcOO/VSsgtw2vQFnHd+GYdkZnNwlg7SGEZzSe9CNsOAF+OpZOPmWyB3XfOOinCz+OWM1L+fm89OR2VE5hyUKEzdKywNMX1bIi3M38vHKIhQY0jWD287swcgeLUlJqt4v27SGyfRv35T+7Zt+5/my8gD5Ow98U/tYu20vSzft5s53llO4+xC3ndkjvpNFeRm88gNvVtg30EaZ7Nh7iC27D1K4291vKT7I1t0Hv9lev30fB0sDgFs2tGPzRvRo3YRzT2jLca0a06N1E9o3axi9992mn5t3as4jMPim2E8rUgdkNWvI0OwMXsrN45bTun5nCdVIsURhfLd+2z5ezM3jlXn5FO05RMsm9bh5RFcuyskiq1nDiJ0nKTGBjhmN6JjR6JtaiKryf28u5bFP1xFQ+P1Z8ZUsVJUd+0pYu20faTN/R7f1M5iU8Uueea2Mjdvfo6Q88J3yItC8UT1apdWjXdMGDOmaQfdWjTmuVROyW6bGZLqH7xl0I7x0Jaz+ELqdEfvz1wGXDGjPzc/PZ9aqIoZ3bxHx41uiML44WFrO+0u2MHlOHl+s3U5igjCiewsuHZjFqd0yY7bcpYjwh7N7IgJPfLaOgKr3OPbJ4mBpOQvzi8ndsIPVha65bN22fRQfKOXSxOnclfwcT5aP5fmSU+mc0YjTj2tBq7T6tGpSn5Zp9WnZpD4tGteLv877bmMgpTEsf8sSRZSM7NmCZo1SeHFuniUKU7Pt2r2Hkik/pcHGmcwv68iS0m40Su3Pr0YNY8KAjrRsUt+XuESE28/qSYIIj3/qksX/ndMr6sli36Ey5m/cyZx1O5i9bgdf5+2ipMzVEFo1qU/nzEac1bc1pyQuY8xXkziQNYKrrnqGHyTVsGVBk1IgeySseM8N602Is0RWC9RLSuS6IZ04UFKOqkb8b9cShYk4VSV/5wGWbt7Nkk27WbppNxs2beEP++9kSOISpgVy6FtvK6cyDw69AHPSoPAUt+5yx6HQomfMv0xEhN+d2YMEgf/Ocsnij+f0jtg6CYGAUrDrAMs27yZ3w05mr9vB4oJiygNKYoLQu00Trj6pAwM7NSenQ9NvR2RtXwOP/Rqad6HBZZOgpiWJCt3HuenHN82Hdjl+R1Mr3Tyia9SObYnCHJPygLK6cC+LC4q9xFDM0k272X2wDHAdqCc2L+UJ/kybpHWsGHwfA4f+wI2s2bMF1n8K6z5xtxXeJTUNm7vpKDoNg47DICM7JvMFiQi/HdeDBBEe+WQtAYU/jz+yZFFaHmDD9n2sLtzL6sK9rPLu1xTt/aZTOSUxgX5Z6dx0ahcGdmpG/w5NSQ21/vHBYnjhUrd92WSonxaJt+mPriNBEt2/sSWKGscSham28oCypmgvi/KLWVTgbks37eZAaTkA9ZISOK51E846vg09WzehV5sm9Egpov7kCXCoCC57ke7ZQddQNm7lxtn3meAe78qD9bNg3SyXOJa+4Z5PbQWdhrrE0WkYNO0YtfcoItw69jgSEoR/z1yDqvKXc/uETBaHyspZsWUPiwqKWex9Hiu27PnOENy26Q3o0iKVQZ2ak90ylewWqfRum3b4TuVAObxyHexYA1dOgWadI/1WY6thM+hwMqx41y3RamoUSxQmpPKAsrZoL4sKilmY774IlwQlhQbJifRu24RLBmbRp20afdqm0Smj0Xc7oQvmw9MXAgpXv1XlOsrfSM+Cfpe5myrsXOfVNmbB2o9h0cuuXFp7lzg6D4fjzoSUyM6cKSL8+ozuJAg8/NEaAgG445xerC7cy8KCXSGTQlqDZPq0TePaIZ3o3rIxXVuk0iUzlUahagrV8cHvYfU0OOvv7r3WBt3HfTvTbbNOfkdjjoCo1q5ZuXNycjQ3N9fvMGqU8oCybtv3k8L+km+TQq82TejtJYS+7dLonJkafrz26unuwrBGzeGK1yHjGNtPVWHbym+bqdZ/Cgd2uDWa+14MOT+Alr2O7RzfO6XywLSV/HPG6u8836R+En3apdGnbfo3STKrWYPIdSDOfxqm/hgG/hDG3RuZY8aDHWvhHyfAmLvdNRUmrojIPFUN2S5oiaKOCQSUtdv2sahgF4vyd7O4oJjFm4q/SQr1kxPo1Sbtmy/APu3S6HK4pFDZwpdgyk2Q2QOueMU1MUX+jbhlP+c9CUumQPkhyBrk5hfqOR6SIzOVgarywdKtLMovpkfrJpFPCpWtmwXPnOdqEZe9DIm1rNL/8GBIzYSr3/Q7ElOJJYo6bH9JGR+vKCJ3w04WFRSzpKCYfUFJoWfrJvRtl/5NbaFLZqNju4bh84fgg9vc6KVLnotNB+z+HfD18y5pbF8N9dOh3+Vw4jURmTAvJkoPwMf3wuf/gKad4PoPa+fcSB/+H3z2IPx6DTRoevjyJmYsUdQx+0vKmLG8kHcWbWbG8kIOlgaol5RAzzZN6Ns2zSWFdml0zUyN3IVtgQB8eDt8/k/3i/78/0JSvcgcu7pUXWd47pNuQZ9AqUtYJ14DPc6OfTzVtWYGvPVzNzVHv8th1J9ck11tlDcXHh/ppkb3cW1v833hEkUtq9fWXaGSQ0ZqPS48MYtxfVozoGPT6F3tXF4Kb9wMC1+EATfA2Hv8mdNH5NuRUXuL4OtnXdJ49TpomAEneLWMeBlBtLfIde4uehmad3XNMZ2G+R1VdLU9ERplumGylihqDEsUNdjhksPATs2iMkHYdxzaCy9d5dZGOO33MPSX8bFGcmqmWx705J/C2o8g9wnXLPbZg9B5hOvL6D4WEn24gC0QgK+ecVOFl+6HU291sSb7c2V6TCUkuCk9lr4BZSURX3DJRIclihomLpJDhb1F8PyFsHkhnPMQ9L8yNuc9EgkJ0PV0d9u92X1Bz5vkJqlLbQn9r4ITfwBpbWMTT+FyeOtnsPEL6DAEzvpbzelHiZTu49y/w4bPbJnUGsL6KGqAqpLD2N6tOLNvawZ0jGFyqLBzvRuds3sTXPiU+3VeUwTKYdU01/m98n13Hca4++H4S6JXG9qxFj79u+t0r5cKo//s+iPiofYVayX74d7OLknXpuG/NZz1UdRAqsr7S7YwdcGm79UcfEsOFTYvhOcmQNkhuGoqtB/kTxxHKyERuo9xtx3rXP/KlBu/XVo0kiO1CpfBrAdg8SuQkOy+HEf8FhplRO4cNU1KQ1eTWPGu68+qi8myhrFEEYeK95fyy5cX8OGyrfGTHCqs/RgmX+6+TK97EzK7+xvPsWrWyXUif/oAfHSXuzbjgscha+CxHbdgvlsvevlbkNwIBv8ITv5xdK4pqYm6j3Ud2luXQKvefkdjDsMSRZxZlF/MTc/NY0vxQX5/Vk+uObmj/8mhwqJX3IV0zbrAFa/Grl0/2hISYdivoNOpboTUE2Ng+K2uY/5IRm+pwobPYdb9bshr/TQY9mt3FXLDZtGLvybqNgYQlywsUcQ9SxRxQlV59ssN/OmtZWSkpvDSjSd9bylP35SXwrQ/wJcPQ/uT4dLna+fFUlkD4cZP4e1fwkd/gTUfwfmPujmoqlJWAttXuRpE7uOw6Ss3/HPkHZBzHdRvEqvoa5bUFm4W2RXvwKm/9jsacxiWKOLA3kNl/Oa1Rby5YBPDu2fyt4v6fbsegd/2FsLL17gRKoNudBeD1eYhjfXT4ILH3LTYb/8S/nMKnP0g9DwXivNg61IoXOLdL3XzTwXclOo0z3Z9HMdf5trhTXjdx8L0P7oBEU3a+B2NCcMShc9WbNnDTc/NY/22ffzqjO7cdGqXiC2Wc8zy5rhrJA7sclda973I74hi5/hLXA3j1etdokys5+aTqpCW5RZY6nYGtOgFLXu6ua1s9bbq6z7OJYqV77nrWkzc8jVRiMgY4EEgEXhMVe+utH8Y8HegL3CJqr4S+yij55V5+fxuyiJS6yXz3PWDOalLnEzboApzH4P3fgNp7dy8Q3WxHblZZ7j2fZj3FGxb5TruW/aCFj1q9iJC8SLzOLe2yIp3LVHEOd8ShYgkAg8Do4B8YK6ITFXVpUHFNgLXAP8T+wij52BpOX94Ywkv5uYxuHMz/nHpCbRoHCdX5Zbsd/MOLZwM2WfA+Y/Uzv6I6kpMhoE3+B1F7SQC3c90P0oO7XXXl5i45Gc9eSCwWlXXqmoJMBkYH1xAVder6kIg4EeA0bBu2z7OffgzXszN4+YRXXj2ukHxkyR2rIPHR7s5m4b/Fi6dXLeThIm+7mNdk97aj/yOxIThZ9NTWyAv6HE+UMOu3Doy7yzazK9fWUhSovDkDwYwonsLv0P61qpprj0ehctegm6j/Y7I1AXtB7tp4Ze/42b4NXGpVnRmi8hEYCJA+/btfY7m+0rKAtz5zjKe+nw9J7RP56HL+tM2PTIL6xyzQAA+uQ9m3gUte8PFT8fP7Kqm9ktMhuzRrkM7UO7PrMPmsPxseioAggeot/OeO2Kq+qiq5qhqTmZmZkSCi5T8nfu58JEveOrz9Vx7SidenHhS/CSJA7tg8qUw8043oum6DyxJmNjrPtYta5s3x+9ITBX8rFHMBbJFpBMuQVwCXOZjPBE3Y/lWfv7iAgIB5d+X92dsn9Z+h/StLYvhxSvctQHj7ocB19ucO8YfXU9382CteAc6nOR3NCYE32oUqloG3AK8DywDXlLVJSLyRxE5B0BEBohIPnAh8IiILPEr3iNRVh7g3veWc+1TubRJb8CbPx4SX0li4Uvw2Ei3/OY177hRPZYkjF/qp0HHIW6YrIlLvvZRqOo7wDuVnrs9aHsurkmqxggElBufnceHywq5dGAWfzi7F/WT46TdtbwUPvgdzP6Pm4rjwqegcUu/ozLGXXz37q/c9SoZ2X5HYyqxy0gj7GCgVw4AABs0SURBVLWvCvhwWSG/O7MHd53fN36SxO7NMOlslyQG/wiunmpJwsSPivVMrFYRlyxRRNikz9fTvWVjrhvSye9QvrV2Jjwy1K0jccHjMOYuf5YANaYq6VnQqo8lijhliSKCFubvYlFBMVcMbo/EQ5v/zvXuKuunz4WGzWHiR9Bngt9RGRNa93GQ9yXs2+53JKYSSxQR9NyXG2mQnMj4E3xep6FwGbw2Ef7RH756FgZcBzfMqPmLDJnarftY0ACset/vSEwlteKCu3iw+2ApUxdsYny/NjSp71OzTn6uW3Zzxdveqmo3wUk32xTOpmZo3Q8at3HDZPvVqpHyNZ4ligh5bV4+B0rLuXxQh9ieWNX1QXz6AKz7xE2HMPw3MHCirapmahYRyB4Fi19zI/SsHy1uWKKIAFXl2dkbOb5dGn3axWj66UDA1Rxm/dWtqpbaCkb/GU68Buo1jk0MxkRa9miYPwk2fgmdhvodjfFYooiA2et2sLpwL/dO6Bv9k5WXurWrP/0bbFsBTTu5FdiOvxSS6kX//MZEU+dT3VXaqz6wRBFHLFFEwLNfbqBJ/STO7hvFvoDSA65j+rN/QPFGN4HfBY+7JToT7Z/R1BL1GkPHU1yiGP0nv6MxHvuGOUaFew7y3uItXH1yRxqkROHiuoPFMPdx+PJfsK8IsgbBmfe7Kno8DME1JtKyR8P7v4WdG6BpjPv8TEiWKI7S3kNlLMzbxeS5eZQFlMsHRWh680C5u/6hcBnkzXbLcB7aDV1Oh6G/hA4nW4IwtVtFolg9zU1WaXxniaIaVJW12/Yxf8NO5m/cxVcbd7Jy6x4C6vZfnJNF58wjXMaxvNStKFe0DIpWQNFyd79tlVvxCwCBnufAkF9Am34RfU/GxK3mXd1a2qssUcQLSxRhzNuwg3/PXMPc9TspPlAKQOP6SfTLSueMXq3o36Ep/dqlk9YwzDC+shLYscbVEIITwvbVECj9tlx6B7fYfJfToEUPd3FcRjcbwWTqHhFv9NMzUHoQkuNkqeA6zBJFFWatKuK6p3JJb5jMmF6t6N8hnf7tm9IlM5WEhMM0/Xz9ghu6WrQCtq8BLfd2CDTr5BJC9zGQWZEQsiGlUdTfkzE1RvZomPMobPgUuo70O5o6zxJFCHPW7eCGp3PpnNmIyRMHk94wpfov/voFmHKjqyG06gM9znGJocVxrkqdHCer2xkTzzoOgaT6rvnJEoXvLFFU8nXeLq59ai5t0xvw7PWDjixJlJe6ZUXb9IfrP7T1f405WskNoNMwN0x27D1+R1Pn2aSAQZZt3s3VT8yhWaMUnrt+MBmpR3gB28IXYddGGH6rJQljjlX2aNix1jXfGl9ZovDk7djPlY/PpmFKIs9dP4hWaUfYgVZeBp/cD62Pd3/gxphjkz3K3a+02WT9ZonC06JJPUb2aMmz1w8iq1nDIz/Aopdh5zoY9mu7zsGYSGjaETK6u+Yn4ytLFJ56SYncfUFfuhzp9RDg1Sbuc53Xx50Z+eCMqauyR8GGz+DQXr8jqdMsUUTC4lfctRKn3mq1CWMiKXs0lJe4KfSNbyxRHKvyMvj4XqtNGBMN7U+ClFRrfvKZDY89FuWlMPsRV5u4+FmrTRgTaUkp0Hm4u55C1f6P+cQSxZEqL4M1M2DpG+7q6wM7oW0OdLfahDFRkT0alr/lpsFp2dPvaOokXxOFiIwBHgQSgcdU9e5K++sBTwMnAtuBi1V1fazjBODALpj/tJtWoDgP6qW5aTh6nANdT4cEa8UzJioqhpuv+sAShU98SxQikgg8DIwC8oG5IjJVVZcGFbsO2KmqXUXkEuAe4OKYBrpvO3z+IMx5DEr3QcehMOYuyD7DVYuNMdHVpLXrA1z1AQz5md/R1El+1igGAqtVdS2AiEwGxgPBiWI8cIe3/QrwkIiIqmrUozuwC754CL78N5Tsgz4T4OQfuwvqjDGxlT0aPv27+3/ZIN3vaOqcarWXiMgz1XnuCLUF8oIe53vPhSyjqmVAMdD8GM8b3qE98PF98GBfd21E15Hwoy/hgscsSRjjl+zRbhbmtR/5HUmdVN0aRa/gB16z0YmRD+foiMhEYCJA+/ZHudJcyX7X//DZg3BgB3QfB8N/A637RjBSY8xRaZsD9dPd6Kde5/kdTZ0TtkYhIr8RkT1AXxHZ7d32AIXAG8d47gIgK+hxO++5kGVEJAlIw3Vqf4eqPqqqOaqak5mZeXTRHNwFH90JbfvDDTPg0hcsSRgTLxKT3KCRVdMgEPA7mjonbKJQ1btUtTFwn6o28W6NVbW5qv7mGM89F8gWkU4ikgJcAkytVGYqcLW3PQGYEbX+iSZt4Me5cMWr0DZuKkvGmArZo2FfIWxZ4HckdU61mp5U9Tci0hboEPwaVT3q6+pVtUxEbgHexw2PfUJVl4jIH4FcVZ0KPA48IyKrgR24ZBI96UfZbGWMib6uIwFxtYo2J/gdTZ1SrUQhInfjvqSXAhXreipwTBOwqOo7wDuVnrs9aPsgcOGxnMMYU0s0ynC1/VUfwKm/9juaOqW6ndnnAd1V9VA0gzHGmLCyR8PMu2DfNpc4TExU93LitUByNAMxxpjDyh4FKKye7nckdUrYGoWI/BPXxLQf+FpEpgPf1CpU9SfRDc8YY4K07geNMl3z0/GxnaShLjtc01Oudz+P749IMsaY2EpIgK6jYMU7ECi3teljJGyiUNVJsQrEGGOqJXsULHge8nOh/SC/o6kTqjvqaRGuCSpYMa7G8WdV/d5FcMYYExVdRoAkuuYnSxQxUd3O7HeBt4HLvdubuCSxBXgqKpEZY0woDZpC1iBb9S6Gqjs8dqSq9g96vEhE5qtqfxG5IhqBGWNMlbqNhg/vgN2b3TTkJqqqW6NIFJGBFQ9EZADuamqAsohHZYwx4VQsZrT6Q3/jqCOqmyiuBx4XkXUish43tcYNItIIuCtawRljTEgtekKTtrDqfb8jqROqO9fTXKCPiKR5j4uDdr8UjcCMMaZKIm7006JXoazEVpuMssNdcHeFqj4rIr+o9DwAqvpAFGMzxpiqZY+GeU9B3pfQaZjf0dRqh2t6auTdN67iZowx/uh0KiQk2+inGDjcBXePePf/F5twjDGmmuqlQsdT3LTjo//sdzS1WnXXzO4mItNFZLH3uK+I/C66oRljzGFkj4ai5bBzg9+R1GrVHfX0X+A3QCmAqi4k2osIGWPM4WSf4e5XT/M3jlquuomioarOqfScXT9hjPFX8y7QtJNrfjJRU91EsU1EuuDN9yQiE4DNUYvKGGOqQ8Q1P639GEoP+B1NrVXdRHEz8AhwnIgUAD8DboxaVMYYU13Zo6HsAKz/zO9Iaq3qJooC4EngL8BkYBpwdbSCMsaYaut4CiQ1sGGyUVTdRPEGcDauM3sTsBfYF62gjDGm2pIbuAvuVr0PWnk1BBMJ1Z09tp2qjolqJMYYc7SyR7lEsX0NZHT1O5pap7o1is9FpE9UIzHGmKNVMZusNT9FRdhEISKLRGQhMASYLyIrRGRh0PPGGOO/ph0g8zhLFFFyuKans2IShTHGHKvsUTD7ETi0103vYSImbI1CVTeEux3tSUWkmYhME5FV3n3TKsq9JyK7ROStoz2XMaaOyB4N5SWw7hO/I6l1qttHEWm3AtNVNRuY7j0O5T7gyphFZYypubIGQ0pjW8woCvxKFOOBSd72JODcUIVUdTqwJ1ZBGWNqsKQU6DLcTedhw2Qjyq9E0VJVK6YA2QK0PJaDichEEckVkdyioqJjj84YUzN1GwO7C2DTV35HUqtELVGIyIcisjjEbXxwOVVVvDmkjpaqPqqqOaqak5mZeUxxG2NqsOPOdIsZLX7V70hqlepecHfEVHVkVftEZKuItFbVzSLSGiiMVhzGmDqkQVM3+mnxqzDqj5CQ6HdEtYJfTU9T+XauqKtxU4QYY8yx6zMB9myGDZ/7HUmt4VeiuBsYJSKrgJHeY0QkR0QeqygkIrOAl4HTRSRfRM7wJVpjTM3RbSwkN4LFr/gdSa0RtaancFR1O3B6iOdzgeuDHg+NZVzGmFogpSEcNw6WTIGx97nRUOaY+FWjMMaY6OlzIRzcBWtm+B1JrWCJwhhT+3Qe4Tq2rfkpIixRGGNqn6QU6Dkelr8NJbZ0zrGyRGGMqZ16T4DS/bDiXb8jqfEsURhjaqcOJ0PjNnbxXQRYojDG1E4JidD7fDf30/4dfkdTo1miMMbUXr0vgEApLHvT70hqNEsUxpjaq80J0KwLLHrZ70hqNEsUxpjaS8RN6bH+U9i9+fDlTUiWKIwxtVvvCYDCktf9jqTGskRhjKndMrtBq77W/HQMLFEYY2q/PhNg03zYvsbvSGokSxTGmNqv1/nufvFr/sZRQ1miMMbUfulZ0P4k1/xk62kfMUsUxpi6oc8E2LYCti72O5IaxxKFMaZu6HkuSCIsshllj5QlCmNM3dAoA7qMcHM/BQJ+R1OjWKIwxtQdfS6E4jzIn+N3JDWKJQpjTN1x3JmQVB8WvuR3JDWKJQpjTN1Rr7FLFotfhbJDfkdTY1iiMMbULSdc4dbTtiu1q80ShTGmbuk8Alr1gVkPQKDc72hqBEsUxpi6RQSG/g/sWANLp/gdTY1gicIYU/f0OAcyusMn99tQ2WrwJVGISDMRmSYiq7z7piHK9BORL0RkiYgsFJGL/YjVGFMLJSTA0F9C4VJY+a7f0cQ9v2oUtwLTVTUbmO49rmw/cJWq9gLGAH8XkfQYxmiMqc16XwBNO7pahc3/FJZfiWI8MMnbngScW7mAqq5U1VXe9iagEMiMWYTGmNotMQmG/NxNP75mht/RxDW/EkVLVa1Yl3AL0DJcYREZCKQAISeTF5GJIpIrIrlFRUWRjdQYU3sdfyk0buNqFaZKUUsUIvKhiCwOcRsfXE5VFaiy3icirYFngB+oasheJ1V9VFVzVDUnM9MqHcaYakqqB6f8FDZ+Dus/8zuauJUUrQOr6siq9onIVhFpraqbvURQWEW5JsDbwG2q+mWUQjXG1GX9r4JZ97tbx1P8jiYu+dX0NBW42tu+GnijcgERSQFeB55WVZsX2BgTHSkN4aSbXT9FwTy/o4lLfiWKu4FRIrIKGOk9RkRyROQxr8xFwDDgGhH52rv18ydcY0ytlnMd1E+HT/7qdyRxKWpNT+Go6nbg9BDP5wLXe9vPAs/GODRjTF1UvwkMvglm3gVbFkOr3n5HFFfsymxjjAEYOBFSUmGW1Soqs0RhjDEADZvBgOthyeuwbZXf0cQVSxTGGFPhpFvcwkaf/s3vSOKKJQpjjKmQmgknXg0LJsPODX5HEzcsURhjTLCTfwKSAJ896HckccMShTHGBEtrC/0ug6+egd2b/I4mLliiMMaYyob83N1/8Dt/44gTliiMMaayZp3cKniLX4WV7/sdTfWUHozaoS1RGGNMKEN+Dpk94K1fwKE9fkdzeFNuhKfOisqhLVEYY0woSSlwzj9gdwFM/5Pf0YRXXgqrp7uFmKLAEoUxxlQlayAMvAHmPAp5c/2Opmobv4BDu6HbmKgc3hKFMcaEc/rt0KQNTP0xlJX4HU1oK9+HxBToPDwqh7dEYYwx4dRrDGc+AEXL4LO/+x1NaCvfg45DoV5qVA5vicIYYw6n+xjodT58ch8UrfA7mu/avga2r45asxNYojDGmOoZew8kN4Q3fwqBkKsy+6Ni+G630VE7hSUKY4ypjtQWcMadruN43pN+R/Otle+5YbxRGvEEliiMMab6+l0GnU6FaX+Ij+k9Du6GDZ9BtzOiehpLFMYYU10icPbfIVAGb/8PqPobz5oZLpYo9k+AJQpjjDkyzTrDiN/Airdh2VR/Y1n5PjRoCu0GRPU0liiMMeZIDb4ZWvV1Hdt+jYIKlMOqD6DrSEhMiuqpLFEYY8yRSkyCiya5i9yePhd2bYx9DAXzYf+2qDc7gSUKY4w5Os06wxWvQek+lyz2Fsb2/CvfA0mELqdF/VSWKIwx5mi16g2XvQx7NsMz58H+HbE798r3of1gaNgs6qeyRGGMMcei/SC45HnYthKem+CGrEZbcT5sXRT1YbEVfEkUItJMRKaJyCrvvmmIMh1EZL6IfC0iS0TkRj9iNcaYw+oyAi6cBJu+hhcugZL90T3fN1djR79/AvyrUdwKTFfVbGC697iyzcBJqtoPGATcKiJtYhijMcZU33Hj4PxHYcPn8OIVUHYoeuda+b67EjujW/TOEcSvRDEemORtTwLOrVxAVUtUteKTroc1kxlj4l2fCXDOP2HNdHjlWigvi/w5SvbDuo9dbUIk8scPwa8v35aqutnb3gK0DFVIRLJEZCGQB9yjqiGvmReRiSKSKyK5RUVF0YnYGGOqo/+VMOYeWP4WTLkp8hMIrp8FZQdj1j8BELWrNETkQ6BViF23BT9QVRWRkNfBq2oe0NdrcpoiIq+o6tYQ5R4FHgXIycnx+Zp6Y0ydN/hGKNkLM/7kllQd91dIrh+ZY698D1JSocMpkTleNUQtUajqyKr2ichWEWmtqptFpDUQdgCyqm4SkcXAUOCVCIdqjDGRN+x/3C//T+6DDV/A2Q9Cp6HHdkxV1z/ReTgk1YtElNXiV9PTVOBqb/tq4I3KBUSknYg08LabAkOAOFsxxBhjwjjtd3DlFNBymHQWvHHzsV1rsXUx7C6I2WinCn4liruBUSKyChjpPUZEckTkMa9MD2C2iCwAPgbuV9VFvkRrjDFHq8sIuOkLGPJz+PoFeGgALHz56GaeXfmeu8+O3iJFoYj6PU1uhOXk5Ghubq7fYRhjzPdtWeQmEiyYB11Oh7MeOLIFhx4b6SYDnPhRxEMTkXmqmhNqnw05NcaYWGnVB66bBmPvhbzZ8K+T4LN/VG8Y7d4iyM+NebMTWKIwxpjYSkiEQT+Em2e7Tulpv4dHh8PSqa62UJXV0wCN6bDYCpYojDHGD2nt3BxRFz0NJXvgpStd/0Xuk1B68PvlV74HjVtD6+NjHqolCmOM8YsI9BwPt8yDCU9Cvcbw1s/g733gk/vhwE5XrqwEVs9wndgxuho7WHSXRTLGGHN4iUnQ+3zodZ678vqzB93FerMegBOvgYyurtbhQ/8EWKIwxpj4IQKdhrnblkXw+T9h9n/cdRiN20D2KF/CskRhjDHxqFUfNxvtab9z/RbtT4LEZF9CsURhjDHxLL09jPyDryFYZ7YxxpiwLFEYY4wJyxKFMcaYsCxRGGOMCcsShTHGmLAsURhjjAnLEoUxxpiwLFEYY4wJq9YtXCQiRcAGv+Oopgxgm99BHIGaFi9YzLFS02KuafFC9GPuoKqZoXbUukRRk4hIblUrSsWjmhYvWMyxUtNirmnxgr8xW9OTMcaYsCxRGGOMCcsShb8e9TuAI1TT4gWLOVZqWsw1LV7wMWbrozDGGBOW1SiMMcaEZYkiikQkS0Q+EpGlIrJERH4aosxwESkWka+92+1+xFoppvUissiLJzfEfhGRf4jIahFZKCL9/YgzKJ7uQZ/f1yKyW0R+VqmM75+ziDwhIoUisjjouWYiMk1EVnn3Tat47dVemVUicrWP8d4nIsu9f/fXRSS9iteG/RuKccx3iEhB0L/9uCpeO0ZEVnh/17f6HPOLQfGuF5Gvq3htbD5nVbVblG5Aa6C/t90YWAn0rFRmOPCW37FWimk9kBFm/zjgXUCAwcBsv2MOii0R2IIbEx5XnzMwDOgPLA567l7gVm/7VuCeEK9rBqz17pt62019inc0kORt3xMq3ur8DcU45juA/6nG380aoDOQAiyo/H81ljFX2v9X4HY/P2erUUSRqm5W1fne9h5gGdDW36giYjzwtDpfAuki0trvoDynA2tUNe4uulTVT4AdlZ4eD0zyticB54Z46RnANFXdoao7gWnAmKgF6gkVr6p+oKpl3sMvgXbRjuNIVPEZV8dAYLWqrlXVEmAy7t8m6sLFLCICXAS8EItYqmKJIkZEpCNwAjA7xO6TRGSBiLwrIr1iGlhoCnwgIvNEZGKI/W2BvKDH+cRPAryEqv9TxdvnDNBSVTd721uAliHKxOvnfS2uZhnK4f6GYu0Wr7nsiSqa9+L1Mx4KbFXVVVXsj8nnbIkiBkQkFXgV+Jmq7q60ez6umeR44J/AlFjHF8IQVe0PjAVuFpFhfgdUHSKSApwDvBxidzx+zt+hri2hRgxDFJHbgDLguSqKxNPf0L+BLkA/YDOuKaemuJTwtYmYfM6WKKJMRJJxSeI5VX2t8n5V3a2qe73td4BkEcmIcZiVYyrw7guB13HV8mAFQFbQ43bec34bC8xX1a2Vd8Tj5+zZWtFs590XhigTV5+3iFwDnAVc7iW376nG31DMqOpWVS1X1QDw3ypiiavPGEBEkoDzgRerKhOrz9kSRRR57YuPA8tU9YEqyrTyyiEiA3H/JttjF+X34mkkIo0rtnGdl4srFZsKXOWNfhoMFAc1n/ipyl9f8fY5B5kKVIxiuhp4I0SZ94HRItLUazYZ7T0XcyIyBvg1cI6q7q+iTHX+hmKmUv/ZeVXEMhfIFpFOXs30Ety/jZ9GAstVNT/Uzph+zrHo1a+rN2AIrilhIfC1dxsH3Ajc6JW5BViCG2XxJXCyzzF39mJZ4MV1m/d8cMwCPIwbJbIIyImDz7oR7os/Lei5uPqccUlsM1CKawO/DmgOTAdWAR8CzbyyOcBjQa+9Fljt3X7gY7yrcW35FX/P//HKtgHeCfc35GPMz3h/pwtxX/6tK8fsPR6HG5m4xu+Yveefqvj7DSrry+dsV2YbY4wJy5qejDHGhGWJwhhjTFiWKIwxxoRlicIYY0xYliiMMcaEZYnCGGNMWJYojDHGhGWJwpgIEpEp3gRtSyomaROR60RkpYjMEZH/ishD3vOZIvKqiMz1bqf4G70xodkFd8ZEkIg0U9UdItIANy3EGcBnuPUG9gAzgAWqeouIPA/8S1U/FZH2wPuq2sO34I2pQpLfARhTy/xERM7ztrOAK4GPVXUHgIi8DHTz9o8EenpTUAE0EZFU9SYvNCZeWKIwJkJEZDjuy/8kVd0vIjOB5UBVtYQEYLCqHoxNhMYcHeujMCZy0oCdXpI4DrdMbCPgVG/m1yTggqDyHwA/rnggIv1iGq0x1WSJwpjIeQ9IEpFlwN24WWoLgDuBObi+ivVAsVf+J0COt/LaUtxst8bEHevMNibKKvodvBrF68ATqvq633EZU11WozAm+u4Qka9xi8qsIw6XYTUmHKtRGGOMCctqFMYYY8KyRGGMMSYsSxTGGGPCskRhjDEmLEsUxhhjwrJEYYwxJqz/B/d9rXoe+brjAAAAAElFTkSuQmCC\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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3ni4FWgU5RqVUOPN4YMk/IaErdB7jdjQhy81EkQikez3PcLZV5Gbg8/J2iMgkEVkpIiuzs7P9GKJSKqxt/Qyyt8CQ3+ucTj5Ui09GRCYC/YByJ4U3xkw2xvQzxvRLSEgIbnBKqerJGFj8NDRqD90vczuakObmdRSZgPe8uK2cbT8jIiOAh4FzjTEnghSbUircpX4Je9fB2BchUi8p88XNGsUKIFlE2olIDDAemO1dQETOBF4DxhpjslyIUSkVjkprE3GtoNd4t6MJea4lCmNMMXAH8AWwGZhpjNkkIo+LyFin2NNALPC+iKwVkdkVHE4ppSpv5zeQvgwG3wNRMW5HE/JcrW8ZY+YAc8pse9Tr8YigB6WUCn+Ln4Z6TeHMiW5HUi1Ui85spZTym8zVsGMRnHMnRNdxO5pqQROFUqpmWT0VoupA3xvcjqTa0EShlKo5ik/AplnQ9WKoHed2NNWGJgqlVM2R+iUU5EKvX7kdSbWiiUIpVXNs+ADqNLKLE6lK00ShlKoZThyDrZ9D90shMtrtaKoVTRRKqZph6xwoPg49r3I7kmpHE4VSqmbY8AHEJdpV7NQp0UShlAp/+Qdh+wLocbnOEnsa9BNTSoW/Hz4GT7E2O50mTRRKqfC34UNonAzNe7kdSbWkiUIpFd5yM2HXt9DzSl3q9DRpolBKhbdNswADPa50O5JqSxOFUiq8bfgAWvSGJh3djqTa0kShlApfB1Jh71rtxK4iTRRKqfC18QNA7LBYddo0USilwpMxttmpzSCIa+l2NNWaJgqlVHjatx5yUuxoJ1UlmiiUUuFpw/sQEQXdxrkdSbWniUIpFX48Htg4CzoMh7qN3I6m2tNEoZQKP+lL4UimjnbyE00USqnws+F9uy5259FuRxIWNFEopcJLSRFs+tgmiVqxbkcTFjRRKKXCS9pCOH5Qm538yNVEISKjRGSriKSKyIPl7B8qIqtFpFhEdIybUurkNrwPteOh43C3IwkbriUKEYkEXgZGA92ACSLSrUyx3cANwHvBjU4pVS0V5sOWz6DrWIiq5XY0YSPKxXMPAFKNMWkAIjIdGAf8UFrAGLPT2edxI0ClVDWTMg8Kj2mzk5+52fSUCKR7Pc9wtp0yEZkkIitFZGV2drZfglNKVUPb5kKdRtB2sNuRhJWw6Mw2xkw2xvQzxvRLSEhwOxyllBs8HkiZDx1HQESk29GEFTcTRSaQ5PW8lbNNKaVO3Z41kH8Aki9wO5Kw42aiWAEki0g7EYkBxgOzXYxHKVWdpcwDREc7BYBricIYUwzcAXwBbAZmGmM2icjjIjIWQET6i0gGcBXwmohscitepVSIS5kHrfrr3E4B4OaoJ4wxc4A5ZbY96vV4BbZJSimlKnYsC/ashvMecTuSsBQWndlKqRoudYG9Tx7pbhxhShOFUqr6S5kHsc2geS+3IwlLlUoUIvJ2ZbYppVTQlRTD9gXQcSRE6G/fQKjsp9rd+4kz/UZf/4ejlFKnKGMFFORqs1MA+UwUIvKQiBwFeonIEed2FMgCPglKhEop5UvKF3bJ0w7nuR1J2PKZKIwxTxhj6gNPG2PinFt9Y0xjY8xDQYpRKaUqljIfWp9tZ4xVAVGp4bHGmIdEJBFo4/0aY8ziQAWmlFInlZsJ+zfCyMfdjiSsVSpRiMiT2CunfwBKnM0G0EShlHJP6nx7r9N2BFRlL7i7DOhsjDkRyGCUUuqUpMyH+CRI6OJ2JGGtsqOe0oDoQAailFKnpPiEXfY0eSSIuB1NWPNZoxCRF7FNTPnAWhFZAPxYqzDG3BXY8JRSqgK7v7eLFGmzU8CdrOlppXO/Cp3ZVSkVSlLmQ2QtaDfU7UjCns9EYYyZEqxAlFLqlGz7wq5kF1PP7UjCXmVHPW3ANkF5y8XWOP5qjMnxd2BKKVWhg2mQkwL9b3E7khqhsqOePscOi33PeT4eqAvsA/4LXOL3yJRSqiIpX9p7nbYjKCqbKEYYY/p4Pd8gIquNMX1EZGIgAlNKqQqlzINGHaBxB7cjqREqOzw2UkQGlD4Rkf5A6erlxX6PSimlKlKYDzuX6GinIKpsjeIW4E0RiQUEOALcIiL1gCcCFZxSSv3Czm+guECbnYKosnM9rQB6iki88zzXa/fMQASmlFLlSpkH0XWhzSC3I6kxTnbB3URjzDsicl+Z7QAYY54JYGxKKfVzxthpxdudC9G13Y6mxjhZH0XpAOX6FdyUUip4DqTA4d3QSfsngulkF9y95tz/OTjhKKWUDylf2PuO2j8RTJVdM7uTiCwQkY3O814i8khgQ1NKqTJS5kHTbtAgye1IapTKDo99HXgIKAIwxqzHXnSnlFLBUXAEdn2vo51cUNlEUdcYs7zMtipfPyEio0Rkq4ikisiD5eyvJSIznP3LRKRtVc/pU7Eut6FUyNqxCDxFev2ECyqbKA6ISAec+Z5E5Epgb1VOLCKRwMvAaKAbMEFEupUpdjNwyBjTEXgW+EdVzunT8cPwXC+Y+xAcywrYaZRSpyllHtSKg6Sz3I6kxqlsorgdeA3oIiKZwD3ArVU89wAg1RiTZowpBKYD48qUGQeUzmD7ATBcJEArlJQUQccRsOw1eP4MmP8o5B8MyKmUUqfIGDuteIfzIFLXUAu2yiaKTOAt4G/YL/T5wPVVPHcikO71PMPZVm4ZY0wxdsbaxmUPJCKTRGSliKzMzs4+vWhiE+DSl+H25dBlDHz7AjzXE776m61tKKXcs38jHN2rzU4uqWyi+AQ7Q2wRsAc4BuQFKqhTZYyZbIzpZ4zpl5CQULWDNekIV7wBv/seOg6HxU/ZJqlFT2kNQym3pMyz9zos1hWVneuplTFmlJ/PnQl4j3Fr5Wwrr0yGiEQB8UBw1r5o2hWungp718PCJ+Drv8GSZ+CM8TDwNkjoHJQwlFLAtnnQojfUb+Z2JDVSZWsU34lITz+fewWQLCLtRCQGO9y27HKrs/mpietK4CtjTNkFlAKrRS+YMA1u+w56Xglr34OXB8Crg2Hx05C9LajhKFXj5B+EjOXa7OQin4lCRDaIyHpgMLDaGcq63mv7aXP6HO4AvgA2AzONMZtE5HERGesU+w/QWERSgfuAXwyhDZpm3WHcS3DvJrjgrxBVB776K7zcHz64ybWwlAp7278C49FE4SLx9QNdRNr4erExZpffI6qifv36mZUrVwbnZLmZ8M2zsOJ1uPFzaHNOcM6rVE0y67e2j+IPqRARefLy6rSIyCpjTL/y9vmsURhjdvm6BSbcaiQ+EUY+DvWawsIn3Y5GqfDj8UDqfDt0XZOEayrbR6EqElMXBt1trxrd9b3b0SgVXvasgfwcbXZymSYKf+h3E9RLgEVaq1DKr1LmAWKHqivXaKJwGGN4fXEaB/MKT/3FMXVh0D2QtlBrFUr5U8o8aNUf6jZyO5IaTROFI+1AHv+ct5Xr3lxG7vGiUz9Aaa1i8VP+D06pmuhYFuxZrYsUhQBNFI4OCbG8em1ftu47yo1vLSfvxClOjhtTF8650w7lS18RmCCVqklSv7T32j/hOk0UXs7r3JQXJ5zJuoxcbpmykoKiklM7QL+boU4jWBS4SW6VqjFS5kFsc2jey+1IajxNFGWM6tGCf17Vi6U7crjtnVUUFnsq/+JasTDoLjuc7+3L4cs/w7oZsGctFOYHLmilwk1JMaR+BckjIEATRqvKq+xcTzXKZWe24nihhz99tIGrXvueMT2b06d1Q3okxlM7+iRjuc+5CwpyYevnzkIrpU1YAg3bQEIXO09UQhd7a9LJJhil1E8ylsOJXG12ChGaKCpwzVmtqRMTwfNfpvD3OVsAiI4UurWM58ykBvRp05A+rRuQ2KAOP1siIyISRjxmbyVFcDANsrdA9lbI2mzvt38FJV6jq+JbO8nDSSBNu9oEUjsuiO9YqRCSMg8ioqD9MLcjUZxkCo/qKBBTeGQdLWDt7sOs3n2Y1bsPsT7jMAVFtkkqoX4tbhrUjtuGdaj8AUuK4dBOJ4F43Q6kQHHBT+Xik+CMCXDWrVDvF8twKBW+/j0I6jSEG/7ndiQ1hq8pPDRRnIaiEg9b9x1lze5DzF63hzW7D/Ptg+fTLK521Q7sKYHDu36qfaQvg21zIbou9LkezrkD4lv5500oFapyM+HZbnZ6nEF3ux1NjeErUWjT02mIjoygR2I8PRLjGZKcwLB/LmT68nTuHpFctQNHREKj9vbWebTdlrUFvn0Olk+GFW/Y9TAG3WMXWFIqHKXOt/faPxEydNRTFbVtUo8hyU2Ytnw3xSWnMEKqspp2gctehbvXQr8bYcP78FI/mHk97F3n//Mp5bZt85x+uy5uR6Icmij8YOLANuw7UsCCLVmBO0mD1nDR03DPBhh8r+0Qf20ovHMF7PoucOdVKpiKT9ipcJJH6rDYEKKJwg+Gd2lKi/javLM0CDOvxzaFEf8P7t0Iwx+112i8NRr+c6H9JRZmfU6qhtn1HRTlabNTiNFE4QdRkRFMGNCaJSkH2HkgLzgnrR0PQ35vaxijn4YjmfDeVfDqENjwge0YV6q6SZkPkbWg3RC3I1FeNFH4yfj+SURFCNOW7w7uiWPqwlmT4K41cOm/oeQEfHiz7cdYNcVW5ZWqLlK+gLaDIKae25EoL5oo/KRpXG1GdmvGzJXpnCh24dd8ZDT0vgZ+twyufhtqxcGnd8HzZ8CyyXalMKVC2YEUyEmFzhe5HYkqQxOFH11zVmsO5Rcxd+M+94KIiIBuY2HSQrj2I2jcET7/A7x3NeQfdC8upU5m6xx732mUu3GoX9BE4UeDOjShTeO6vLs0yM1P5RGBDufD9Z/CmGfsvFOvDoGMwF6MqNRp2/o5NO8JDZLcjkSVoYnCjyIihIlntWH5zoPM2+RircKbCPS/GW6eZ2sbb42GlW/q6CgVWvIO2JkItNkpJGmi8LPrzmlDtxZx/O7d1byxJI2QmSKl5ZkwaRG0Gwr/uxc+uQOKjrsdlVJWyjwwnp9mJFAhRROFn9WKimTapIEM79qUv362mVvfWXV6S6sGQt1GcM1MGPpHWPsOvHkhHArCtR9KnczWOVC/JbTo7XYkqhyuJAoRaSQi80UkxblvWEG5uSJyWESq1RSS8XWieXViXx4Z05UFm7O45MVv2JiZ63ZYVkQknP8wTJgOB3fC5HMhdYHbUamarKjALlLUebRejR2i3KpRPAgsMMYkAwuc5+V5Grg2aFH5kYhwy5D2zPjtQIpKPFz+7+94d9mu0GmK6jwaJn0N9VvYaUAW/1OH0Cp37Fxir8bW/omQ5VaiGAdMcR5PAS4tr5AxZgFwNFhBBULfNo347K4hDGzfmIc/2si9M9aSd6L45C8MhsYd4JYvoccV8NVfYMZEuzqfUsG05TOIidWrsUOYW4mimTFmr/N4H9DMpTiColG9GP57Q39+P7ITs9ftYdzL35KyP0TyX0w9uOINGPWkvSp28nmw/we3o1I1hcdj11zpcD5E1XI7GlWBgCUKEflSRDaWcxvnXc7YtpgqtceIyCQRWSkiK7Ozs6sUd6BERAh3Dk/mnZvP4nB+IWNf+paP1mS4HZYlAgNvs9dcnDgKbwyHjR+6HZWqCfauhaN7tdkpxAUsURhjRhhjepRz+wTYLyItAJz7Ks3PbYyZbIzpZ4zpl5CQ4I/wA+acjk2Yc9cQeraK594Z63ho1gYKikJkAr8258BvF9uLnj64Ceb+ya77rVSgbP0cJEJniw1xbjU9zQaudx5fD3ziUhyuaBpXm/duOYvbhnVg2vLdXP7Kd+zKCdKssycT1wKu/x8MmARLX4apl8KxAK6zEeqMgR2L7eqCO5boNCj+tvVzSBqoa8KHOFfWzBaRxsBMoDWwC7jaGHNQRPoBtxpjbnHKLQG6ALFADnCzMeYLX8cOxprZ/vTVlv3cO2MdHo/h6at6MapHC7dD+sm6GfDp3VCngZ1oMKm/2xEFV0EufPZ7u6qgt9jm0KwbNO0Gzbrb+4QuEF3FNdNrmsO74bmeMPIvMOgut6Op8Xytme1Kogik6pYoADIO5XP7e2tYl36Ymwa148HRXYiJCpFrIfdtsKOhcjNh9JPQ7+aaMdY9fbmdrj03E859AM74lZ3ZdP8PkPUD7N8E2VvttO4AEgmdLv5b8ncAABqMSURBVLR9PW2H1IzPqKqWTbYTVt652o7AU67SRFENFBZ7+Puczfz3u52c2boBL1/Th5YN6rgdlnX8EMyaZKdZ6D0RLnnOTmsejjwlsOQZWPgExCfC5W9A67PKL1tSDAfTIGsTZK6Cte9Bfg607AND74dOo+38Wqp8Uy+1C27dscLtSBSaKKqVz9bv5YEP1xMdKTzzq96c17mp2yFZHg8sehIW/QM6DIerp0KtWLej8q/D6TYh7v4OelwJFz9jVxKsrKLjsG4afPMcHN5lm6QG3wfdL4PIqMDFXR0V5MJTHeDs38HIx92ORqGJotpJyz7G795dzZZ9RxnQthFjerVgdI/mNI0LgTbw1VNtv0WL3vDr96FeE7cj8o9NH9uFnjwlMOZf0OtXp998VFJshxd/8wxkb4GG7WDwvXDGBIiK8W/c1dXGD+3Iupu+gNYD3Y5GoYmiWiooKuGNJWl8um4vW/cftbOFtwmRpLFlDnxwI8QlwrWzoGFb92KpqsI8mPugTYCJfe3Fh43a++fYHg9s/cxOj7J3rf28Bt0NfW/UhPHhLbD9K7g/xc4/plyniaKaS806ymfr9zFnQwgljd1L4b1f2atpJ35or72oTnK2w6q3YM27tg9m8L1w3p8C0/diDGxfAIv/ZZu1Errafp6a+ku6pAie7gBdLoZLX3E7GuXQRBFGKkoaF/VszuieLWgWzKSRtQXeudxezT3+XbvWRSgrLrS/8Fe+ZVf8i4iCLmNg4O0Vd1j729a5MOd+yE2HvjfAiMegTrmTJ4evHYthyiXwq3eg6yVuR6McmijCVEgkjdxMmywOpsHlk23Hbag5tBNWTYE170BeFsS3hr7Xw5nXQn0Xphk7ccyOqlr6CtRtAqOesBMz1pQhtXMfghX/gQd22LnGVEjQRFEDuJo08g/CtAl2KcuLnoYBvwncuSqrpNhOcrjyTbvehgh0GgX9brIT0IVCu/jedXZgwJ41diTZxc9U7/6eyjAGnj/DXqD465luR6O8aKKoYcpLGp2b1adHYjy9WsXTIzGebi3iqB3txy/LouN2FMvWOTDkfjj/EXd+Iedm2o7p1VPh6B67alqf66DPtRDfKvjxnIynxE4PsuBx+3jYA3D2HeF7nUrWZnhlIFz8HPS70e1olBdNFDVYatZRPt+wj1W7D7EhI5ecvEIAIiOE5Kax9EyMp2ereHomxtO1qsmjpBg+uw9WT4EzJ8LFzwfn+gFPia01rHrLTlltDHQcYb+Iki+sHtcw5GbC53+ELf+zV3ZPnBWeI6MW/9OufXLfFjuvmAoZmigUAMYY9uYWsCEzlw0ZuWzIzGVj5s+TR6dm9emZGOckkAZ0aV7/1JKHMbb9fdE/bFPPlW9BTN3AvKGj+2HN27b/IXc31Gtqaw59roeGbfx2mm37j7JpTy5dmseR3DSWqMgAXm29eirMvtPWgi55Ifz6LV4fDsZjV1dUIcVXoqgGP7WUv4gILRvUoWWDOlzYvTlgk8ee3AI2ZNiksT4zly83ZzFzpV0rIypCSG5Wn16J8fRoFU+vxHg6+0oeInaYaWxT+Ox+mDoOrpkBdRtV/Q0UHbdzMO1YbJfPzFgJpgTanQsX/MWuaeDnX+GzVmdw//vr8Di/p2pHR9C1hZNIndpYxwQ/Jo8+19nO9yX/sld2D7zNP8cNBUf3Q+ZKOO8RtyNRp0hrFOoXjDFkHj7Oxkxb61jvJJFD+XZtiiin5lHa39EzMZ4uLepTK6pM8vhhtr2wqmFbe61Fg6RTC6S40H6x7Fhik0PGcigptBPwJfaB9sPs1c4BmlDug1UZ/OGDdZzdvjEPjOrCzpw8NmTYZLopM5e8QruOSGny6JVoP49OzerToWkssbVO83eYxwMzr7X9Pde8D8kj/PiuXLRqir36/dZvoXkPt6NRZWjTk6qy0uRR2mRVejvslTw6No2lW8s4ure0neXdWsYRv38ZTLvGDoOc+KGdnrsiJcX2CuYdi+0tfRkU5QMCLXrZ6zTanWsvVKtVP6Dvd+bKdB74cD2DOjTh9ev6USfm50nQ4zHscBJH6WfhnTwAWsbXpkPTWDo6t+Sm9UluGkvDepWo9Zw4Bm+OsnNG3fIlJHT291sMvvfG21l371kffk1qYUAThQoIYwwZh47/2Nfxw94jbNpzhOyjJ34s06phHUY2PsDvsx6ilikk99KpNO42DBGxv5z3b3ASwxLY9R0UOmuJN+0O7YbYjt22g4J6UdqMFbt5cNYGBne0SaKyfTQej2FnTh4pWcdILXM77rWKYfuEepzVrhED2jViQLvGJFY0S/DhdHj9PIiJhd985Z/mO7cU5sNT7exFhqP/4XY0qhyaKFRQZR0t4Ic9R/hh7xF7v+cIhTk7mRL9JIlygPejLubs+IO0z1tHxInD9kWNOzo1hqHQZjDEurOk7bTlu3lo1gaGdkpg8rV9/TKE2OMx7Mk9TmrWMTbvPcrKnQdZvvMgRwuKAUhsUMcrcTSiXZN6NpGC7ZP57xhIOguu/aj6DpvdMgemT4DrPrFNhirkaKJQrss7UUzKzp0kzrmRhNz17PA0Z4XpzKGmA+k08CIG9+lFdCBHE1XCu8t28fBHGxnWOYFXJ/onSVSkxGPYuu8oy3fksHznQZbvOMiBY3b0WZPYWnRuHku7JvVo3ySWs4/Np+vSP+DpeyMRFz9bPZttPrnD9ln9cXv1TXYhbtbqDEo8hiv7tvrph8Yp0EShQocxUJBL+vEY3l+ZzsyVGew7UkCT2Biu6NuK8f1b065J8Kd1eHvpLv7v442c36Up/57Y55cd8wFmjCHtQB7Ldxxk5c5DpGYfIy372I+1jgeipnFb1Ke8VHsSGxPH0y6hHu0a16N5fG2ax9emWVxt4mpHndYXRMB5SuBfnW1t8co33Y4mLHk8hqFPf02bxnV595bTm2xSh8eq0CECdRqQVAfuu6Azd4/oxKJtWUxfns4bS3bw2qI0zmrXiAkDWjOqR/OA/qovNfX7nTz6ySZGdG3Ky78OfpIAO3S5Q0IsHRJimTCgNWCTR05eIWnZeaRldWfb94e57fAbPLgnkdc3d6LY8/MfeXWiI2kWV4tmcTZ5NI+rTdsm9ejcvD6dmtU//VFYVbXrW8jLtsOXVUB8u/0AGYeO88dRXQJyfK1RqJCRdaSA91dlMGNFOrsP5hNfJ5rLzkxkwoDWdG7un1FOJR5DxqF8tmcfIy07j017jvDRmkxGdmvGy9f0CZ21ystz4ij850LIzaD4pvnsiUpi35EC9h0pIOtIAfty7eP9R0rvT1BY7Pnx5UmN6tC5WRxdmtenc/P6dGlen3ZN6gX2AkKA6b+2AxXu+wGiQ2R53zBz+7ur+W77AZb+afhp/9DRpidVrXg8hqVpOUxbkc4XG/dRWOLhzNYNmNC/NRef0YK6MSf/ZZx7vIg0Jxls97rflZNPYclPX54N6kZzQbdm/PXSnqGdJEod3g2Tz7OjwH67yOfsqx6PHdK8Zd9Rtu47wpZ9R9my7yg7DuRR4tRGakdHcFa7xgxJbsLQTgkkN431b/PVwR3wwpkw5Pcw/P/8d1z1o5xjJxj4xAKuO7st/3exj+HnJ6GJQlVbB/MKmbU6g+kr0knNOkZsrSguOaMlEwYk0b1l/M9qB9uzj7E9O4+07DwOHPtpiG5UhNC6cV3aN4mlQ0I9OiTE0j6hHu0TYmlUmWsaQk3aInvFe78b4eJnT/nlBUUlbM8+xtZ9R1mfkcuSlGy2Z+cB0CyuFoM7JjC0UxMGdWxCk9haVYt17kOwfDLcs1HndgqQ1xen8bc5m5l/71CSm51+zVsThar2jDGs2nWI6SvS+d/6PRQUeRCxfeOlGtaN/lkSKH3culFd10dU+d28R+C7F2HCdOg8usqHyzx8nG9SslmccoBvUw/8eCFl95ZxDElO4LzOCfRv24iIiFOobRQcgWe6QedRdolZ5XfGGIY/s4iGdWP48LZzqnQsTRQqrBwpKGLO+r1kHj5OUsO6dGhqh5FW6orncFF8At4YDkf2wu++t3Nr+UmJx7AxM5dvUg+weFs2q3YdothjaBZXizE9W3LJGS3ondTg5E1US1+FuQ/ALV9Bq75+i0/9ZPmOg1z92vc8fWUvrup3ilPklBFyiUJEGgEzgLbATuBqY8yhMmV6A/8G4oAS4G/GmBknO7YmClVjZG2ByefaYafXzAzY9RXHThTz9ZYsPl23h4Vbsyks8ZDUqA6X9GrJJWe0pEvz+r9MGp4SeLGvTWA3zwtIXArum7mW+Zv2s+zh4ZXqu/PFV6Jwqz7+ILDAGJMMLHCel5UPXGeM6Q6MAp4TkQZBjFGp0Na0C4z8C6TMs4sfBUhpv9Dk6/qx4pERPH1lL9o1ieW1xWmMfn4JI59dzKuLtnPsRPFPL9r2BRzaEV6z34aY3ONFzNmwl7G9W1Y5SZyMW9dRjAOGOY+nAAuBB7wLGGO2eT3eIyJZQAJwODghKlUNDPiNXfJ13iO2ZhHgyQPj60RzVb8kruqXRM6xE3y+cR+z1+7hyc+38O+F27lpUDtuGNSW+GX/hrhW0OWSgMZTk32yNpOCIs+P190Ekls1imbGmL3O432AzxXuRWQAEANsD3RgSlUrIjDuFTtM9sNb7NTsQdI4thYTB7Zh5q1n88ntg+jfthHPfrmNm558C3YsJr/3TdVjdcFqyBjDtOXpdG8ZR4/E+ICfL2CJQkS+FJGN5dzGeZcztpOkwo4SEWkBvA3caIzxVFBmkoisFJGV2dnZfn0fSoW8+s1g7Euwbz18/VdXQjgjqQFvXN+Pz+4azO/jvyLf1GL4wjY8MWfzz2YTVv6xITOXzXuPMD4ItQkIYNOTMabC1VZEZL+ItDDG7HUSQVYF5eKAz4CHjTFLfZxrMjAZbGd21SJXqhrqcpGdwvvbF6DjSDtFuwu6xxdB3lcc7n41AzwdeH1JGlO+38mEAa357dAONI+v7Upc4Wba8nRqR0cwrnfLoJzPraan2cD1zuPrgU/KFhCRGOAjYKox5oMgxqZU9XTh36FRe/joVjh+6OTlA2HlW1Byggbn3cnz48/ky/vO5eJeLZn6/S6GPvU1j3y8gYxD+e7EFibyThQze20mY3q2JK52cGbidStRPAmMFJEUYITzHBHpJyKlwzeuBoYCN4jIWufW251wlaoGYurBFa/DsX3w2e9/fjViMBQXworXoeOIHzvV2yfE8s+rzmDh/cO4om8rZqxIZ9jTC3ngg/XsyskLbnxh4rP1e8krLGHCgKpdN3Eq9II7pcLN4qfhq7/CZZPhjF8F77zrZsBHk+yStx3Lb3nec/g4ry3azrQV6ZR4DOPOaMltwzpUaeqJmubyV77lSEEx8+8d6td5uULxOgqlVKAMvg+SBtpaxYGU4JzTGFj2b2jSCToMr7BYywZ1+PO4Hnzzx/O4aVBbPt+4j5HPLmbS1JWsTdeR7yezbf9RVu8+zPj+SUFde0QThVLhJiLSzq0UVQumTYCC3MCfM30Z7FkDZ91aqSvEm8bV5uEx3fj2wfO56/yOLE3L4dKXv2X85O/5aE0G+YXFJz1GTTRt+W5iIiO4vE+roJ5XE4VS4ahBElw9BQ6mwazfgqfckeX+s/QVqN0Azhh/Si9rVC+G+y7ozHcPDedPF3Uh49Bx7p2xjv5//ZI/vL+OpWk5eDzh1Tx+ugqKSvhoTSYXdG8W9FmP9WoYpcJV28Ew6gn4/I+w6B9w3kOBOc+hXbD5UzjnTp/rY/gSWyuKSUM7cMvg9izfeZAPV2UwZ8Ne3l+VQVKjOlx+Ziuu6NOK1o3r+jn46uOLTfs4nF/E+P7BuXbCmyYKpcLZgEmwZy0sehJa9IIuY/x/ju9fAomEAb+t8qEiIoSB7RszsH1j/jyuO3M37uPD1Rm88FUKzy9IYUDbRgzrkkCPlvH0TIyvUTMGT1+eTlKjOpzToXHQz62JQqlwJmIXN8rebJugfrPAv/NBHcuC1VPt6Kr4RP8dF6gbE8XlfVpxeZ9WZB4+zsdrMvloTSZPzd36Y5nEBnXomRhPj0Q7lUXPxHgaV3WxpRC0PfsY36fl8IcLO5/amiB+osNjlaoJcjNg8jCoHQ+/+creV1X+QXj7UsjaDLd9B02Sq37MSsjNL2Ljnlw2ZOay0bntzPnpIr6W8bVpHl+bqMgIoiOFyIgIoiOEyAghOjKCyAghKlKIihBbJsIpEynOvghnn1MmIoKoSKG4xFDs8VBUYigq8VBU4qG4xFDo3Ntt9r7Y46Gw+KfHRcWGIo/v14hAdGQEMU7c0ZER9hYVwYGjJziUX8iiP5xHQv3AJEJfw2O1RqFUTRDfCq6eClMugVmTYPw0iKjCWJb8g3Y51uytMP69oCUJgPi60QzqaJdqLZV7vIgf9hxhY6ZNIIfyCykq8XCiyEOxp4Rij8f5ojcUl3ice+e5x0NJif0iL91WGTbxCNER9ss8KqL0y93rS955HBUpxEZH/bgtykkIURFCdJRNVgCFXkmosNi5LzHUjY7knhHJAUsSJ6OJQqmaos05MOpJmHM/LHwCzn/49I5TNkkkVzitW9DE14nm7A6NOdsP7ffGGEo8pUnkp8RSmgiinOTgRhOQWzRRKFWT9L/Fdm4vfsp2bnc9xfUi8g/C1LGQvQ0mvFfhFdjVmYjT7BTpdiShQ6+jUKomEYEx/4LEvnbywLRFlZ8TqgYkCVU+TRRK1TTRteFX79gO7alj4bUhsOYdKCqo+DV5OTBFk0RNpYlCqZooriXcsQIufg5KiuGT2+HZbvDln+0IKW95OTah5KTAhGmaJGogHR6rVE1nDOxcAsteg61zAIGuF9t5m5p0sh3XOak2SXQ43+1oVYDo8FilVMVEoN1Qezu0C1a8YS+i++ETQOzkghOmQ4fz3I5UuUQThVLqJw3bwAV/gWEPwYaZkLkaev8aWp/ldmTKRZoolFK/FFPXrsHd9wa3I1EhQDuzlVJK+aSJQimllE+aKJRSSvmkiUIppZRPmiiUUkr5pIlCKaWUT5oolFJK+aSJQimllE9hN9eTiGQDu9yOo5KaAAfcDuIUVLd4QWMOluoWc3WLFwIfcxtjTEJ5O8IuUVQnIrKyokm4QlF1ixc05mCpbjFXt3jB3Zi16UkppZRPmiiUUkr5pInCXZPdDuAUVbd4QWMOluoWc3WLF1yMWfsolFJK+aQ1CqWUUj5pogggEUkSka9F5AcR2SQid5dTZpiI5IrIWuf2qBuxlolpp4hscOL5xbqyYr0gIqkisl5E+rgRp1c8nb0+v7UickRE7ilTxvXPWUTeFJEsEdnota2RiMwXkRTnvmEFr73eKZMiIte7GO/TIrLF+Xf/SEQaVPBan39DQY75MRHJ9Pq3v6iC144Ska3O3/WDLsc8wyvenSKytoLXBudzNsboLUA3oAXQx3lcH9gGdCtTZhjwP7djLRPTTqCJj/0XAZ8DAgwElrkds1dskcA+7JjwkPqcgaFAH2Cj17angAedxw8C/yjndY2ANOe+ofO4oUvxXgBEOY//UV68lfkbCnLMjwH3V+LvZjvQHogB1pX9vxrMmMvs/xfwqJufs9YoAsgYs9cYs9p5fBTYDCS6G5VfjAOmGmsp0EBEWrgdlGM4sN0YE3IXXRpjFgMHy2weB0xxHk8BLi3npRcC840xB40xh4D5wKiABeooL15jzDxjTLHzdCnQKtBxnIoKPuPKGACkGmPSjDGFwHTsv03A+YpZRAS4GpgWjFgqookiSESkLXAmsKyc3WeLyDoR+VxEugc1sPIZYJ6IrBKRSeXsTwTSvZ5nEDoJcDwV/6cKtc8ZoJkxZq/zeB/QrJwyofp534StWZbnZH9DwXaH01z2ZgXNe6H6GQ8B9htjUirYH5TPWRNFEIhILPAhcI8x5kiZ3auxzSRnAC8CHwc7vnIMNsb0AUYDt4vIULcDqgwRiQHGAu+XszsUP+efMbYtoVoMQxSRh4Fi4N0KioTS39C/gQ5Ab2AvtimnupiA79pEUD5nTRQBJiLR2CTxrjFmVtn9xpgjxphjzuM5QLSINAlymGVjynTus4CPsNVyb5lAktfzVs42t40GVhtj9pfdEYqfs2N/abOdc59VTpmQ+rxF5AbgYuDXTnL7hUr8DQWNMWa/MabEGOMBXq8glpD6jAFEJAq4HJhRUZlgfc6aKALIaV/8D7DZGPNMBWWaO+UQkQHYf5Oc4EX5i3jqiUj90sfYzsuNZYrNBq5zRj8NBHK9mk/cVOGvr1D7nL3MBkpHMV0PfFJOmS+AC0SkodNscoGzLehEZBTwR2CsMSa/gjKV+RsKmjL9Z5dVEMsKIFlE2jk10/HYfxs3jQC2GGMyytsZ1M85GL36NfUGDMY2JawH1jq3i4BbgVudMncAm7CjLJYC57gcc3snlnVOXA87271jFuBl7CiRDUC/EPis62G/+OO9toXU54xNYnuBImwb+M1AY2ABkAJ8CTRyyvYD3vB67U1AqnO70cV4U7Ft+aV/z686ZVsCc3z9DbkY89vO3+l67Jd/i7IxO88vwo5M3O52zM72/5b+/XqVdeVz1iuzlVJK+aRNT0oppXzSRKGUUsonTRRKKaV80kShlFLKJ00USimlfNJEoZRSyidNFEoppXzSRKGUH4nIx84EbZtKJ2kTkZtFZJuILBeR10XkJWd7goh8KCIrnNsgd6NXqnx6wZ1SfiQijYwxB0WkDnZaiAuBb7HrDRwFvgLWGWPuEJH3gFeMMd+ISGvgC2NMV9eCV6oCUW4HoFSYuUtELnMeJwHXAouMMQcBROR9oJOzfwTQzZmCCiBORGKNM3mhUqFCE4VSfiIiw7Bf/mcbY/JFZCGwBaiolhABDDTGFAQnQqVOj/ZRKOU/8cAhJ0l0wS4TWw8415n5NQq4wqv8PODO0ici0juo0SpVSZoolPKfuUCUiGwGnsTOUpsJ/B1Yju2r2AnkOuXvAvo5K6/9gJ3tVqmQo53ZSgVYab+DU6P4CHjTGPOR23EpVVlao1Aq8B4TkbXYRWV2EILLsCrli9YolFJK+aQ1CqWUUj5polBKKeWTJgqllFI+aaJQSinlkyYKpZRSPmmiUEop5dP/B2ncmmLrQ3uLAAAAAElFTkSuQmCC\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From c50bececcf4bbc63174978245ca63a82fa0014d9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 251/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 6f6085a3c1f7c387a096454065b8e7d5a15597e0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 252/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- tests/test_fpca.py | 26 ++++ 4 files changed, 293 insertions(+), 69 deletions(-) create mode 100644 tests/test_fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py new file mode 100644 index 000000000..fff7be7d4 --- /dev/null +++ b/tests/test_fpca.py @@ -0,0 +1,26 @@ +import unittest + +import numpy as np +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather + + +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data + +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): + fpca = FPCABasis() + with self.assertRaises(AttributeError): + fpca.fit(None) + + + + +if __name__ == '__main__': + unittest.main() From 57210ddaf3d2c7a67d6afc22357f6c2352c1f508 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 253/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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9G2Z/plfrxkovGQMXfgf+9zJ490FY9BWvK5JuKPDlI2obW070Y3+/rIH3y1x/9pb2DuCDC6gzM+P5ZF4mOSmud0xmwghCB3uysOq9rg35vcehthgiomDKMphxvTub16CgwZG10I28Xf0jmLMShsd7XZF0QYEfxDo6LPuqjp4I9R1l9bxfVk+ZbwUmcP3ZJ6fGsHhiElNSY/rnAmpf1RTDjmdg+zPuAiwGxp0L598Nk69w7coy+C64B361CN7+qTvjF7+jwA8Sbe0d7K44ypaSWraV1rGltO5DUwyEhhjGJ49kfnYCk1NjTtySoweonb23aophx7NusM/Bje65tFlw4Xdh+jUQm+FtfeK6tU67BtY96HpAaQplv6PAH4KOTw62paSOraXutv1gHU2trklmZEQoU9Ni+eTcTKakxTAl1U0xEBnuR13qrIVDW2DXS1DwIhzc5J4/HvJTl0N8lqclSheWfB22PeUGtH3sbq+rkZMo8ANcR4dlT+VRtpbWsrWknq2lblrf4/3ah4eHMi09huvnjeGsjFimp8eRnTRy8Nvae6L1GOx9EwpegoKXob4UMJCR55oIpix3g33Ef42a7HpErfs1nHMnRMZ6XZF0osAPMJVHmtm8v5ZNB2p887bXcaS5DXC9ZKamxXJtXibT02M5KyOWcclR/hnu4M7iq3bDntdh9z9gzz+htdFdeB1/vmuTz7kYopK9rlR6Y/G/ulHM6x9yj8VvKPD9WEtbBzvK6tm034X7pv217K9uBFyb++TUaJbPSmNGRhxnZcQxPnkkYaF+3ivlaBXs/Sfsft0FfJ1v3r24sTDzRpi0FLIWQ5ifXDuQ3kubBRMuhHfuh/m36yK6H1Hg+wlrLaW1x9i0v9YX7jVsO1hPS5trdx8dE8msMXF8asEYZo2JZ1paLMMj/KjNvTtNdbB/HRSvdgFftgWw7k/97CWw6KvubD5hnNeVSn9a/HV4ZCls/qOmT/YjCnyPtLR1sLW0jg3F1eTvq2HTgVoqfKNTI8Pdiks3nZPFrMw4Zo6JIzU2QNZLPVIB+9e4QVDFa+DQVsC60a4Zc10zzfjzIXUmhOrjN2SNWQBps93F27xbNB7CT+h/3CCpa2xlw34X7vn7anivpJZm39l7VuIIFk9IYtaYOGaNiWfS6OgPFuXwZx0dUL0HSjd8EPKVBW5b2HDInAvn3QVjz4H0PA25DybGuBlIn74FilbBxEu8rkhQ4A8Ia910v+v3VZNfXEP+vmoKyo8AbpTqtPRYPr1gLHlZCcwZG+8/fd1Pp77MhfvBjb77Ta7JBtzsk2PPdu3wYxdC6gwIi/C2XvHWlGXwyrddW74C3y8o8PtBW3sH75c1sH5fNRuKa1i/r/rE5GHRw8KYPTaeK2ekkZeVwIyMuMBoez9W6wL9eLCXboCGMrctJAxSpsLUqyB9NqTPgeRcTY0rHxYa7trvX/sulG93nxnxlAL/DDS2tLGxuJZ391WzobiaTftrT/R7T48bztnjE8nLSiBvbDwTU6L9t1vkca3HXFt76QYo9Z29V+/+YHviBHeBNc0X7qOnQXiAXFMQb825Cd64z7XlL/uF19UEPQV+DzQ0tZJfXMO6PdW8u7eKLSV1tHVYQgzkjo5hxZwM5vgCPi3Oz4OwvQ0qdn7QLFO6EQ7vgA7Xl5/oNHfWPutGF/Bps2B4nLc1S+AakQBnXQtb/gwXf1+fJY8p8LtQ29jC+n01rNtTxbv7qtlWWkeHde3vZ2XE8rkl45iX7QK+Xxfr6G/WunVbOzfLlL3nBjeB6xqZNhsWfsWFfNpsLUYt/S/vs25tgi1/hvm3eV1NUFPg40avrt9bzbq91azdU8Wu8gashYiwEGZmxnHn+ROYPy6RWWPiGBHhx2/Z0SoozfedufvO3o9Vu21hke5C6uyVrlkmfbbr+67FQGSgpc1y3XA3POLa9PWZ84wfp9fAKa9vYu2eKt71hXzRYdeDJjI8hDlj4/nq9InMz05gRmacf00o1llrk6/dPd8tyl2a787mwS3MPWqKmyr4eLv7qMnuIpqIF/Juhr99CQ6sc330xRNBEfglNY2+9vdq1u2tYl+Va9KIGhZGXlY8V81OZ352ItPTY72d5/1U6stcX/f9a13AH9oKHb71YWPSXajn3ez6u6fN1HB28S/TroaX74b8RxT4HhpygW+tpbiqkXV7q1i3x53Bl9a6Zfhih4czNyuBTy0Yy7zsBKakxvjn3DPWugFNxWtg/ztQ/PYHZ+/hI11zzNlfcLNIpuep3V3837Aod/F20+9h6X9prnyPDLnAL6k5xnn/808AEkdGMC87gc8tzmb+uEQmpUQT4q9dJOsPugnFdv/DLbx9pNw9PzzBjVSddxuMORtGn6UpCSQwzVkJ+Q/D9r/A3Fu9riYoDbnkyIgfzn3XnMXsMXGMT44a+MWzz1TzEXfmfjzkK3e550eOcn3esxbCmHMgeZIucsnQMPosGDXVTaimwPfEkAt8YwzX5mV6XUbX6kph1wvutvct1wYfFunO4Gd/Gsad70YjKuBlKDIGZt4Ar9wNFbvcyYwMqiEX+H7FWijfBjtfgF1/d33gwY1cXXA7jL/ANdOER3pbp8hgOetaWHWPO8u/6LteVxN0FPgDoaLAreu57WmoKsIt0zfXLdM36XJInuhxgSIeiRoFORfBlj/BBfdo/qVBpsDvL3UlsPUpF/SHtgIGshbB2XdC7uXugy4irlmn4CW3tOWEC72uJqgo8PuirQUKXoSNv4Oi1wDrukkuvdctuK3ukiIfNXEpRMbB5scV+INMgX8mKgtdyL/3OBytcAOfzv0GzLhOS/WJnE7YMJi+AjY95tZTiIz1uqKgocDvqY4Ot3LP2gfc2qwhYe5MZfZKmHCB2iJFemPGdbD+N/D+825mVhkUCvzTaT7izuTX/tLNER+dBh/7Nsz6NESneF2dSGBKnwPxWe6alwJ/0Cjwu9NY7UL+3V+7PzvT58DVD7tl2zQJmUjfGOPm11n9E7fwfVSy1xUFBQX+yY4chnd+AesfhpYjkHsFLPwyZM7zujKRoWXa1fDWD2HHM27aZBlwCvzjjlTA6h+52fzam916rYv/FVKmeF2ZyNCUMhWSJ7vxKgr8QaHAb26Ad+6HNT93a7vOuA4WfQ2SJnhdmcjQN/1q+Mf33TiW2Ayvqxny/HBu4EHS1gLv/gZ+Ngv++V8w/mPwhXWw/AGFvchgmXqVu9/2F2/rCBLBeYZf8Aq89E035/zYRXD9E25ueREZXInj3aps256ChV/yupohL7jO8Kv3wh+vgz+uABMKNzwJNz2vsBfx0rSr3cSClUVeVzLk9UvgG2OWGmN2GWOKjDF3dbF9mDHmT77t64wxWf1x3B5rPQav/yfcPx/2vgkXfhfuWAMTL9ZUxCJem3YVYNzFWxlQfQ58Y0wocD9wKTAFuN4Yc3LXlluAGmvtBODHwA/6etwe2/MGPLAA3viBW9T7i/mw6CsQFjFoJYjIKcSkwdiFCvxB0B9n+POAImvtHmttC/AEsOykfZYBj/oePwVcYAZ6KaqmOvjbl+F3V4IJgZV/g2t+6z5cIuJfpi53q74d3ul1JUNafwR+OnCg09clvue63Mda2wbUAYknv5Ax5jZjTL4xJr+iouLMKyp4Ge5f4CY4O+eLcPvbbtlAEfFPuVe4+/ef87aOIc6vLtpaax+01uZZa/OSk89wqHVlIfzxk24GvltehYu/DxEj+rdQEelfMamQOR92KPAHUn8EfinQeRHZDN9zXe5jjAkDYoGqfjj2RyXlwA1/gn95AzLmDMghRGQATFkG5VuharfXlQxZ/RH464EcY0y2MSYCuA44+df0c8BK3+NrgH9Ya20/HLtrEy9xc26LSOCY/HF3r2adAdPnwPe1yd8JvAy8D/zZWrvdGPPvxpgrfbs9DCQaY4qArwEf6bopIkEubgykzVKzzgDql5G21toXgBdOeu6eTo+bgBX9cSwRGcKmLINXvwO1+90vAOlXfnXRVkSC3GRfo8D7f/O2jiFKgS8i/iNxPKRMU7POAFHgi4h/mbIMDqyF+jKvKxlyFPgi4l+ON+vsfN7bOoYgBb6I+JdRuZA0CXY863UlQ44CX0T8z5QrofhtOFrpdSVDigJfRPxP7hVgO6DgJa8rGVIU+CLif1JnQEwG7Py715UMKQp8EfE/xkDu5bD7H9By1OtqhgwFvoj4p9zLoK0Jdr/udSVDhgJfRPzT2IVumnM16/QbBb6I+KfQcJi4FApehPY2r6sZEhT4IuK/ci+HYzWw/x2vKxkSFPgi4r/GXwChw2DXC6ffV05LgS8i/mtYFIw7z02zMIBrJgULBb6I+Lfcy938+OXbvK4k4CnwRcS/TboUMOqt0w8U+CLi36JGQeY8BX4/UOCLiP/LvRwObXFNO3LGFPgi4v9yr3D3O9Vbpy8U+CLi/xLHQ3KuFkXpIwW+iASGSZdB8RporPa6koClwBeRwJB7Bdh2KHzF60oClgJfRAJD2iyITlWzTh8o8EUkMISEuGadoteg9ZjX1QQkBb6IBI7cy6C1Efa84XUlAUmBLyKBI2sJDItRs84ZUuCLSOAIi4Cci2DXi9DR7nU1AUeBLyKBJfdyaKyEA+96XUnAUeCLSGCZcBGEhMMuza3TWwp8EQkskTGQvQTe1xz5vaXAF5HAk3s51OyFip1eVxJQFPgiEngmXebu1VunVxT4IhJ4YlIhfY5mz+wlBb6IBKbcy+HgRqgr9bqSgKHAF5HAdHyO/F06y++pPgW+MSbBGLPKGFPou4/vYp+Zxph3jDHbjTFbjDGf7MsxRUQASJoIiRO09GEv9PUM/y7gNWttDvCa7+uTNQKfsdZOBZYCPzHGxPXxuCIS7IxxzTr73oJjtV5XExD6GvjLgEd9jx8Flp+8g7W2wFpb6Ht8EDgMJPfxuCIiMOly6GiDole9riQg9DXwU6y1Zb7Hh4CUU+1sjJkHRAC7u9l+mzEm3xiTX1FR0cfSRGTIy8iDkaPUPbOHwk63gzHmVWB0F5vu7vyFtdYaY7od9maMSQUeA1Zaazu62sda+yDwIEBeXp6G0InIqYWEwqRLYdvT0NYMYcO8rsivnTbwrbUXdrfNGFNujEm11pb5Av1wN/vFAH8H7rbWrj3jakVETpZ7BWx8FPa+6WbSlG71tUnnOWCl7/FK4NmTdzDGRAB/BX5nrX2qj8cTEfmw7CUQPlK9dXqgr4F/L3CRMaYQuND3NcaYPGPMQ759rgWWADcZYzb7bjP7eFwRESc8EnIudP3xO7psLRaf0zbpnIq1tgq4oIvn84FbfY9/D/y+L8cRETml3Ctgx7NQugEy53pdjd/SSFsRCXw5F0FImHrrnIYCX0QC3/B4yFqkaRZOQ4EvIkPDpMuhsgAqCryuxG8p8EVkaMj1zZGvpQ+7pcAXkaEhNgNSZ6p75iko8EVk6Mi9AkryoeGQ15X4JQW+iAwduZcBFna96HUlfkmBLyJDx6gpEJ+lZp1uKPBFZOgwxjXr7H0Dmhu8rsbvKPBFZGjJvRzaW6DwFa8r8TsKfBEZWjLnuznyd3xkLsegp8AXkaElJBSmXAkFr0DLUa+r8SsKfBEZeqYsg7ZjULjK60r8igJfRIaesQthRBLseMbrSvyKAl9Ehp6QUJj8cSh4GVoava7GbyjwRWRomrocWhuh6FWvK/EbCnwRGZrGLoIRiWrW6USBLyJDU2iYG4S16yVoPeZ1NX5BgS8iQ9fU5dB6FIpe87oSv6DAF5GhK2uxWw1LzTqAAl9EhrLQ8E7NOk1eV+M5Bb6IDG1TlkNLA+z+h9eVeE6BLyJD27hzITIOtv/F60o8p8AXkaEtNNxdvN3596CfW0eBLyJD3/QVbhDWzhe8rsRTCnwRGfrGnAMxGbD1Sa8r8ZQCX0SGvpAQmH417H4NjlZ5XY1nFPgiEhymr4CONtjxV68r8YwCX0SCQ8o0SJ4MW4K3WUeBLyLBwRg4awUcWAs1xV5X4wkFvogEj2nXuPttT3lbh0cU+CISPOLHQuYC16xjrdfVDDoFvogEl7NWQMX7cGir15UMOgW+iASXqVdBaARs/oPXlQw6Bb6IBJcRCW4GzS1/grZmr6sZVAp8EQk+sz4Fx2pgV/RU8l4AAAnnSURBVHBNtdCnwDfGJBhjVhljCn338afYN8YYU2KM+UVfjiki0mfjznNTLWx8zOtKBlVfz/DvAl6z1uYAr/m+7s73gDf7eDwRkb4LCYVZN7o58utKvK5m0PQ18JcBj/oePwos72onY8wcIAV4pY/HExHpHzNvACxsftzrSgZNXwM/xVpb5nt8CBfqH2KMCQF+CHz9dC9mjLnNGJNvjMmvqKjoY2kiIqcQnwXZS2Dz76Gjw+tqBsVpA98Y86oxZlsXt2Wd97PWWqCrkQyfB16w1p727yZr7YPW2jxrbV5ycnKPfwgRkTMy69NQsw/2BUdrc9jpdrDWXtjdNmNMuTEm1VpbZoxJBQ53sdvZwGJjzOeBKCDCGHPEWnuq9n4RkYE3+UoY/k1Y/5C7kDvE9bVJ5zlgpe/xSuDZk3ew1t5orR1jrc3CNev8TmEvIn4hPBJmf8YtfxgEF2/7Gvj3AhcZYwqBC31fY4zJM8Y81NfiREQGXN7Nbl6d/Ee8rsQp2QAVBQPy0sb66QRCeXl5Nj8/3+syRCQYPH49lKyHr26HsGHe1vLbpdB8BO5YfUbfbozZYK3N62qbRtqKiMy9FY5WwI7nvK2joRz2r4XJHx+Ql1fgi4iMOx8SJ8DaB7ydNvn95wALk68YkJdX4IuIhITA2V+Agxuh+G3v6tj2tFuGcdSUAXl5Bb6ICMCM62FEErz9U2+OX3sA9r8D069xyzEOAAW+iAhA+HCYfzsUvgLl2wf/+NuedvfTrh6wQyjwRUSOm3sLhI+ANT8f/GNvfQoy5kJC9oAdQoEvInLciASYvRK2Pgk1xYN33PLtUL71g0XWB4gCX0Sks3O+CCYU3rhv8I6Z/wiEDoPpKwb0MAp8EZHOYtNd0857f4TKwoE/XvMReO8JmLocRiYO6KEU+CIiJ1v0NQgbDq//58Afa9vT0NIAebcM+KEU+CIiJ4tKhgW3w/a/wKGtA3ccayH/YRg1FTLnDdxxfBT4IiJdOeeLEBkLr3x74Ebf7n0Dyt6DebcOWN/7zhT4IiJdGR4P530L9rwOO58fmGO8+T8QnQozbhiY1z+JAl9EpDtzb3XTHLz0LWhp7N/XLn4H9r0F53zJzcs/CBT4IiLdCQ2Dy/4b6vbDG/f23+ta615vRBLMuan/Xvc0FPgiIqeStcitirXm53Dg3f55zcJVsOefsPhrEDGif16zBxT4IiKnc/F/QEw6/PV2aDnat9dqa4aXv+WmY577uf6pr4cU+CIipxMZA8sfgOo98Oydfeu18897oaoQlv4AwiL6r8YeUOCLiPRE9hK44B7XN3/Nz87sNfavddMvz/wU5FzYv/X1QNigH1FEJFAt+qrrN7/q32DkKJh5fc+/t74M/vwZiB8LSwdhBG8XFPgiIj1lDHziV9BUC8/cAViY2YM+9Ecr4bHlbt6cTz/jBnR5QE06IiK9ET4crnvcNfE8c4cbidvW3P3+h3fCwxdDzT644U+QMjDLF/aEAl9EpLciRsCNT7oJz9b8DH55Dmx+3J3BH1d7AF79Lvx6CTQ3uDP77MXe1QwY6+UK7aeQl5dn8/PzvS5DROTUil5z3Swrdrp59GPToa0Fjhxy26evgIu+BzGpg1KOMWaDtTavq21qwxcR6YsJF8D4tVD8thtMVXsAQkIhORcmf3xAlyzsLQW+iEhfGeNG5GYt8rqSU1IbvohIkFDgi4gECQW+iEiQUOCLiAQJBb6ISJBQ4IuIBAkFvohIkFDgi4gECb+dWsEYUwEUe11HDyUBlV4X0QuBVi+o5sESaDUHWr0w8DWPtdYmd7XBbwM/kBhj8rubu8IfBVq9oJoHS6DVHGj1grc1q0lHRCRIKPBFRIKEAr9/POh1Ab0UaPWCah4sgVZzoNULHtasNnwRkSChM3wRkSChwBcRCRIK/B4wxmQaY143xuwwxmw3xny5i33OM8bUGWM2+273eFHrSTXtM8Zs9dXzkfUijfMzY0yRMWaLMWa2F3V2qmdSp/dvszGm3hjzlZP28fx9Nsb81hhz2BizrdNzCcaYVcaYQt99fDffu9K3T6ExZqWH9f63MWan79/9r8aYuG6+95SfoUGu+TvGmNJO//aXdfO9S40xu3yf67s8rvlPnerdZ4zZ3M33Ds77bK3V7TQ3IBWY7XscDRQAU07a5zzgea9rPammfUDSKbZfBrwIGGABsM7rmjvVFgocwg0i8av3GVgCzAa2dXruPuAu3+O7gB908X0JwB7ffbzvcbxH9V4MhPke/6CrenvyGRrkmr8DfL0Hn5vdwDggAnjv5P+rg1nzSdt/CNzj5fusM/wesNaWWWs3+h43AO8D6d5W1S+WAb+zzlogzhgzOCstn94FwG5rrd+NtrbWvglUn/T0MuBR3+NHgeVdfOslwCprbbW1tgZYBSwdsEJ9uqrXWvuKtbbN9+VaIGOg6+iNbt7jnpgHFFlr91hrW4AncP82A+5UNRtjDHAt8Phg1NIdBX4vGWOygFnAui42n22Mec8Y86IxZuqgFtY1C7xijNlgjLmti+3pwIFOX5fgP7/IrqP7/xz+9j4DpFhry3yPDwEpXezjr+/3zbi/9Lpyus/QYLvT1wz1226azfz1PV4MlFtrC7vZPijvswK/F4wxUcDTwFestfUnbd6Ia36YAfwceGaw6+vCImvtbOBS4AvGmCVeF9QTxpgI4ErgyS42++P7/CHW/Y0eEP2djTF3A23AH7rZxZ8+Q78ExgMzgTJcE0mguJ5Tn90PyvuswO8hY0w4Luz/YK39y8nbrbX11tojvscvAOHGmKRBLvPkmkp994eBv+L+3O2sFMjs9HWG7zmvXQpstNaWn7zBH99nn/LjzWG++8Nd7ONX77cx5ibgCuBG3y+pj+jBZ2jQWGvLrbXt1toO4Dfd1OJX7zGAMSYMuAr4U3f7DNb7rMDvAV/728PA+9baH3Wzz2jffhhj5uHe26rBq/Ij9Yw0xkQff4y7SLftpN2eAz7j662zAKjr1CzhpW7Phvztfe7kOeB4r5uVwLNd7PMycLExJt7XHHGx77lBZ4xZCnwDuNJa29jNPj35DA2ak64vfaKbWtYDOcaYbN9fitfh/m28dCGw01pb0tXGQX2fB+PqdaDfgEW4P9G3AJt9t8uA24HbffvcCWzH9QpYC5zjcc3jfLW856vrbt/znWs2wP24Xg1bgTw/eK9H4gI8ttNzfvU+434ZlQGtuDbiW4BE4DWgEHgVSPDtmwc81Ol7bwaKfLfPelhvEa6t+/jn+Ve+fdOAF071GfKw5sd8n9MtuBBPPblm39eX4XrS7fa6Zt/z/3v889tpX0/eZ02tICISJNSkIyISJBT4IiJBQoEvIhIkFPgiIkFCgS8iEiQU+CIiQUKBLyISJP4Py7qXM1peBkgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From ccf589e7f74349235f11c954647c42c9aab91406 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 254/624] Add docstring and references for fpca module --- docs/modules/exploratory.rst | 3 +- docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 5 files changed, 119 insertions(+), 30 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index 45f048bfa..edc2c8d73 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -10,4 +10,5 @@ and visualize functional data. exploratory/visualization exploratory/depth - exploratory/outliers \ No newline at end of file + exploratory/outliers + exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 98d4cfe86dcb3f0bcdeec56403b3dbfe553683fb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 255/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 1165a407069f8f06c4a493fb82bc99458db8d729 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 256/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 ++- examples/plot_fpca.py | 122 ++++++++++++++++++++++++++++++ skfda/exploratory/fpca/_fpca.py | 93 ++++++++++++++++++++--- 3 files changed, 214 insertions(+), 13 deletions(-) create mode 100644 examples/plot_fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py new file mode 100644 index 000000000..135b4bf2a --- /dev/null +++ b/examples/plot_fpca.py @@ -0,0 +1,122 @@ +""" +Functional Principal Component Analysis +======================================= + +Explores the two possible ways to do functional principal component analysis. +""" + +# Author: Yujian Hong +# License: MIT + +import numpy as np +import skfda +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.representation.basis import BSpline, Fourier +from skfda.datasets import fetch_growth +from matplotlib import pyplot + + +############################################################################## +# In this example we are going to use functional principal component analysis to +# explore datasets and obtain conclusions about said dataset using this +# technique. +# +# First we are going to fetch the Berkeley Growth Study data. This dataset +# correspond to the height of several boys and girls measured from birth to +# when they are 18 years old. The number and time of the measurements are the +# same for each individual. To better understand the data we plot it. +dataset = skfda.datasets.fetch_growth() +fd = dataset['data'] +y = dataset['target'] +fd.plot() +pyplot.show() + +############################################################################## +# FPCA can be done in two ways. The first way is to operate directly with the +# raw data. We call it discretized FPCA as the functional data in this case +# consists in finite values dispersed over points in a domain range. +# We initialize and setup the FPCADiscretized object and run the fit method to +# obtain the first two components. By default, if we do not specify the number +# of components, it's 3. Other parameters are weights and centering. For more +# information please visit the documentation. +fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized.fit(fd) +fpca_discretized.components.plot() +pyplot.show() + +############################################################################## +# In the second case, the data is first converted to use a basis representation +# and the FPCA is done with the basis representation of the original data. +# We obtain the same dataset again and transform the data to a basis +# representation. This is because the FPCA module modifies the original data. +# We also plot the data for better visual representation. +dataset = fetch_growth() +fd = dataset['data'] +basis = skfda.representation.basis.BSpline(n_basis=7) +basis_fd = fd.to_basis(basis) +basis_fd.plot() +pyplot.show() + +############################################################################## +# We initialize the FPCABasis object and run the fit function to obtain the +# first 2 principal components. By default the principal components are +# expressed in the same basis as the data. We can see that the obtained result +# is similar to the discretized case. +fpca = FPCABasis(n_components=2) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() + +############################################################################## +# To better illustrate the effects of the obtained two principal components, +# we add and subtract a multiple of the components to the mean function. +# As the module modifies the original data, we have to fetch the data again. +# And then we get the mean function and plot it. +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +mean_fd = basis_fd.mean() +mean_fd.plot() +pyplot.show() + +############################################################################## +# Now we add and subtract a multiple of the first principal component. We can +# then observe now that this principal component represents the variation in +# growth between the children. +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[0, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[0, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# The second component is more interesting. The most appropriate explanation is +# that it represents the differences between girls and boys. Girls tend to grow +# faster at an early age and boys tend to start puberty later, therefore, their +# growth is more significant later. Girls also stop growing early +mean_fd = basis_fd.mean() +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] + + 20 * fpca.components.coefficients[1, :]]) +mean_fd.coefficients = np.vstack([mean_fd.coefficients, + mean_fd.coefficients[0, :] - + 20 * fpca.components.coefficients[1, :]]) +mean_fd.plot() +pyplot.show() + +############################################################################## +# We can also specify another basis for the principal components as argument +# when creating the FPCABasis object. For example, if we use the Fourier basis +# for the obtained principal components we can see that the components are +# periodic. This example is only to illustrate the effect. In this dataset, as +# the functions are not periodic it does not make sense to use the Fourier basis +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) +fpca.fit(basis_fd) +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 9dfd9e1330087c0e3aa9930f11b01b42ff7459e9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 257/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From aef2d97b33efa30c5fec298f484a3cb5355c667d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 258/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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1rSJj9DBGqmfh96/BlPWep7LoTlN6yOs+1rrq/ax7uJ54KMXks0qZvmIUxv+xTowsp7j3uct4LNbGN45fxWRxLt+rtbIl38h5Xjs/Hz8KCbjuhR3sPRpDSCoUW8PMsgZwxAeJaiYirgxnZWm80JDkgFBBTDuxnrtH66Iy2U5lpJfsWBCPIc54zwDVrmGyzXEA/EkbA2En/rCFSNREImaCjICoaQRNTlpcpdR5K2lwV5AwWqhQelic2MscdT8l9hGKrVHMokJGFWmOZHE0mEdn3INVknGLAnZrDt6sXLJKShmSRP67WWGPVIlZUJhlqiev5BDbBpbQqZahTHahOK2M621DEm18tc1BTVhhR2wXlpwC+gaKMNuNmBf7+U333Vy5ezmiNAdHoo9FFxVTtnLhR72Ldf8mPeR1H0tyWmHH8y0cXt+NN9/GWavHkVvm+qfHhYIdfPWFyzickrjz2JeQcgr52mQLQzaJ71QV89nibF5r6+aLaxuQe9MYTSqzzCPkZfqwaylGTFDjCfJKu4NmcQIpwYEvPcLYaCNV0SZ8ahinwUSZKUqNrYmCrBAAsWEzkWEvvQkvrUKamJbEltLITZrxxgWM0STCOz5egsmEajJzwDeKN/MmsjV/PLIoMavvGJd3bmVK3gBqcYh8xzA2QWFAM7I7kUv/SBapuI2U+lZZStNwpDLIBhvbsifRYK8k2yxzUX4jgex2nm89h56yMSgldnyhASb0D7AyWsn8YZUthm783btwZZ9DOGomb4qZ+50/oLzBxbSeVWQkJ+M9Xcz73lVIVstHsZt1HwA95HUfO8PdEd54oA5/b4wJi4uZc1ElhpOs8tjavoFb1t+CdbCKm3tvomOUkzsmmMmyGLl/QgWTHFZufnkrrx1OIEZkCswRagwhShMdCLEwJvo4nBrDQec0kpKFkkQHM5L7OStyAGcAbAMKXmuE3Elh7LlpVMzEPHPZkjOaN3qO4WoPMXrAQG7UjGawIRgsSB4fxpx8LL4sbN5c7Fl52HPzcbjdCKIIIgiiwGA4zF8PtvN4V5yQKjAr3M6n9zxFeawfZ6WKozaO2xxhRBR51u3GmtA4I1nMkGE6/X1RhgPDJJUT5aGEaGHAmofXIDOxqJe9xTk8lVxFrDoHSU6zuP4A87QxLB80sdmVoPX4oxRY55ASRmN1m9g7/gX2xTfz+V2XEjVNIzvZzjlfmY9nQtVHvet174Me8rqPDU3TOPhmFzufb8HiMHLmtWMpHZ910sfuOvgAX97/C84+uowl6nmsH2fld1VmprtsPDBhFKFAgMvW7MffrmLQZKbK7VSFG3DHh5BSCVpso9icNZuowU2+2s7qwV2c3bKd1JABELBWeMibkcZKM2mjl/22hewKFROXJdKSiPYvXrfDoIk4NStOzYpHs5OlOsnWnBg1C8+S4c+kSAArDAZukOPYB9owpg/gyd6F3TuAXzXwW5+LbC3DlaVLcS37MeGYTOeh/exZv4muzjYccgw4cWkSZ1GUHbkzeH38RShWM5Maj7Ay4mZ5IIstWbCn92lKR1zYHTPJaFZCE1p5zPorbjw4HTFxOQY5wfxZItWfW4GgX6TktKaHvO5jIRnLsO7hetoPD1MxOYfF19T808yZv3ll83e5q+51rtx/HeNdNTx4ho3XCoxcluflJzUlPLZzL3fu8VPa0sLYeBNlsU6MahoN6POZWe+dS0CtJkvp54aW3Sw4vhkU0HIcxJZegst8gNHxN8ggsY3p7GQKombDo9lwqBacmgG3xY7d58XqdmJ12DA5LAhWA6LZACJkUEgqKZKZFPFEnGA4SCAUIhgK4g8FUNQTi96YDSYKPXl4bfls8FtZM5zAJYr8p9nOkgQICJiEI3iMf8QkthCTy9gkLsRgjzN33iXYZi5DMIgEYmlu+f2r+JsbqE00kRPvQwA0CVoLqqirnkxC8fKZwQjnJ0rZ5RN5kr1M378Tq+tSEL2QF+SR4p+yYNjC+MbPEDPlUW1pY9E912CwWT+6N4PuX6KHvO60198W4vX7jxELpZh76WgmLCo++dGjpvHwS5/l2foRzm1cTZXXxY9nOTnoNfDNigKuz7LxX396hGhDO5XBFsxqmoxoRHbZac0PsMuVS3x4OaIqcVXrZla17UcrqKCvsoROjxtJq+cC4XWyCNKoTqQpvhR7QMMRGoIiG6OWnYt38RxEi+nfer2yLDM0NERvby+9vb20t7czMjICQMLsY4dSQWdUYPGYbO5aMgZPTCaxrwGh4Qnc0nOIYpKwvIqIchmqAOYCF+ZRHgxlLu5v6eOXu7rI1UKcG9pITsqPrMZRkiIZyUBHTgWjhSxWmmbS4LNyb34356x7ErthEQbzWARjmldGP4LR0si1O1fSY55HdqqTc29fjKtKvzjJ6UgPed1pS9M0Dq/vZvuzzdg9Zs75bC15o/55cBVAVTL89KmVtB0ppXb4Yirc8P15PjqdEj91aZi3raNu9w5MmRQp0USHvQzZ6SJV1MRRTxvxvhWkY5OYGB3iy2E/cr6HJkuIASGEKCicI+xjhradpOygf08W0a40TePcZF+2ijkrP4/B/PZApKppjGRk+lIZ+lMZwrJCStVIqCoCYBVFLJKIxyBRYDZSYDbiNkjvWfYIhUK0trbS3NzM8cYmDiW8HJCLsRgFvnvOKC6ZOw5BEFB6W8j8+XosiQOkoi7ah1YQy5tKjrEKQTnRftJpZEM0xgEtiYfDZPXspbBW4kDCjb0jgiMeRRMkyuw1aPmTuGt6Lue/8ihZcSdm23IEwcDhkjdoKXyFb+4eR73yaSyZMGddnEfpivkf6HtA9+/TQ153WkolZNY/XE/rwSFGTcpmybVjsdhPXp7JpGJ864kVOHefhU+eRYk3xV3THeR3H+WcjqPEuzuQRYlW2ygaHaPptRQyzb2X7qytZEXH0TGwlIBg4ioBqg09dEgDyIKKz2hkakU+Z7T8DKvcTbDFxsE2F4dmFDJt9deYU3suGlAfS7IjGOVoJEF9LEFjLEVCVU+6re/GKYmUGAx4ZAFHSsEckokFk/ijaaIpmWhKJpaSUVQNEZVCMUS2EKVFySakWak2DFPhMWIrqCQvy830+DbmHb0DMRNjYI+bpqCL2FlzmbnoK2g9aRKtIYSUgopGvxDDHzpK0jXIprkyW8ILGHd0P7Xtx5DUNEaTm53jJuEd6aG8pwuL41OIkptedwO7qv7M9xsEjgW/TkawMKNihDNuu0qv059G9JDXnXYC/TFe/u8jhIYSzLm4kklnlrxraKQSAb7x8OVU7LsSUSzD62plu9hMWWcDkqogerNZXziJ49EyFMmIV0uy0nuQikw2gchYfiEouFFZKrRiMgcwIVMrtTFh+lLcuzbhTj0HaOxs9/L8xAqWrvo6UwqX8KY/zGvDYbYHo4TkE/XzbKOBsQ4LNXYL5VYzBWYj+WYjnrcu+WcWRTSgK5RgX3eQfb0hGoJxupMp4gYB1WlEcxjBeGIJBmNGJS8No2SR0YKEx2zEIL7dD7KqEY4m2NnYTUtYJF8MM9/YSq/q4aich0VLcJ/x18yUGujrsxHa6mbAJaLe+DkWXHMLg00BnnrqCFVxGI+EiEBaTdKaM8D9JVUcMGZYtuUA0wMNDCZPXJUt4vTgiAQRHGdiNk4kZgqwsfqPfGuoha7ubzFiKGG0oYUz79Xr9KcLPeR1p5X2w8O88cAxJKPIOZ+rpWjMu59lGY/28+37P0/FsStR0+1owhEyyQApi43q2fPZH0/xsHsG7qYIC5E4W5SZojoAkfuUEM9KIsUEmW9uxSvILGQTk7wpQsMzEeqeJac2xEjSyJ155cw+/5vgnMfTAyF2BKOoQJHZyCKfk1keB7M8DkrepRYfiKXZ3DTEpuND7Grz0xNMAGA2iNQWuRmT56Qq10FVnoOKbDsRg8CucIztwSib/RECsoJFFFjic3FFgY8lPtc/hD3As/u7ue2ZwzgMKovEBlxalKzCUixFY6nqfIxFAw8zlLHRuN2Br8/A8QIXR8+7hcqF83hmTwfHuyJcZohwdkYmT83DJFmIS7DTJzIQSLI8EWdP+gitsaM4Y2E0QDBkYbRfjCZZ2FbxBDcor6F0fYVmZTK56Q7Ov/NcbMX5H9A7Q/d+6SGvOy1oqsa+V9vZ9WIbOSVOzr1pAk7fu59wEw128MOffJPsjhKUdAOQYSC7iI7a2dx+5hzue3kdmjqeswdlZmLAhMCwOMLwwAF+Yi+lwZbPOKmf2WqIs3K7mRl6gLRWRueLGXInB/CUxllvtrNh4W2EPOfz0nCMmKIyympiZa6Xc3PcTHBY3/U/jJahKC8f7mP98UEOdQVRNfDZTcyuyGJqmZepZV7GFrgwGcSTPv9vZFVjZyjKK0MhXhgMMpyRyTUZWJXv4zPF2RSY3/7DcrAryA2P7CWSzHDzJDOJ1r3E43EqKys5t1wma/PtKEi82Sfh3mHDE4ONpWP4w7grEDwe/EmVWkM/n8qJkDnQTG7JNPKESrLSkEbDhECzJcOdRX3M2r+BLP+J1TEF02iM5mnUFTWzwn4/OUOr2R1ehDM1yHlfnET2tPH/+htC94HRQ153yqWTMuserqf1wBBjZuax+Oqak57cBCcGYxt2vMKaPzyEIREHRFxF4/jj1LlIuSX8JpOiad8QU+IurJrAEDK7XPvIdG3C02/hvjEXMyw4mUc/C405XFX6KLae1/A3Oxiqc+NcGqTQEuMvZQt4esLd7IwoWEWBC3O9XFngY4bb/q7B3h2Is/ZwHy8e6uVYbxhBgInFHhaNyWFxTS4TitxI4vuvVWdUjfX+MI/1jfD6cBhRgIvyvHy+JJdxjhOlkYFwks88tIeG/gh3XTiOErmHbdu2EY/HmVZs4tzAw0jJAMcReKU1myW7VVSDkddmXcb9nonIgoFqaZAlWRlM+19huKSEY1NXsaxP5cweDSsCaTSeLzHRYh6mdP2fMMhpEAQEqZCQr4Aziv7CuPQy1nUvR1KSnH2+m/JLlrzv16379+ghrzulwsMJXvrtYQL98fesv6uKQtPu7ex49lFGOrtBsOHS8sm99DLutti4vFfhor4EpqRITNBYp2V4w9qEhd+zcr+PusqZPGeeSkwzcV5K4cKxRcwb/g8s6XoGDrmpK66lJHc35YrMD2q/zu+yziPHZOD6omyuLcrGZzScdPtTssLrxwZ4bHcn21tOTHOcXOJhxaRCzp9QQL775P+NaJkM8vAwajyOlkqBICDabIhOJ5LX+/8duOxIpPhj9xCP9vmJKypnZ7n4r4oCxjmsRFMyN/15H1ubh/nGshpWzypi3759bN26FSkxzGetr+FOdpMwmPiRIYtxGzQmtmtQU839Uy/n2ZibSnEYhxxhZv82Aj4vTy+/BreQ5svbYUlMQkFDQqDbITIc2Ed7z2bSmopGGlVykpPfxWJfDus6riOFhTljw0z6sr5G/amgh7zulBloD/PSbw6hKhrn3FB70pUjM8kkRze+wb6Xnic0OACiDYNlDuWpMAUXrqS3Pcq0wImBz0P2NM9KIlvDUSyeV7iufj9W62SO55bzenoMqmbkkoSFSypD1LTeitUTpScwgfvGaNwabMKrwurxd9FcMIcvledzaZ4Xi3TyckrHSIy/7Ozgmf09+GNpijxWrphewsopRZT4bH9/nCxHifc2EN2xjdSxOjKN7SidwxCIv+uFoTSjgJptRC0yoY12QrUXoToXyWJDkuwYjR5MxiyMJh9xwcdTQTd/6leJKCqX5Hn52qh8CoxGvvLUIV481MvNiyr52jnVJJNJtmzZwoGdm1mlvUC51olsdPKE08a2HgvXrlNwJaD1zIv5knkaRcYQRRaF/OZtCC6Jpy+8DoeU5Jb9EucNi+yQ4jidDmqDKioq3dFGekMtdKgDIA8hShrV2TFGktcTFouY6O1k3l3Xnli+QfeR0UNed0q0HRri9T8ew+oyseI/JuHNt//D/al4jP2vrGH/y2tIRiNkl5YRGi7GYZ7GFEM3vuxqpKTCgEUgaOjmnlInjYNmpICfqTzIOT0eekqrCCtmXpHHIWomVmNlaecjjM5eizVLZmPufO619PNQ/wAG4PNTfsrZExZxbVEW5ncJon0dAe7f3Mprdf1IgsDZ461r7hgAACAASURBVPK4Ylouk/L9xBNNJBKdJBJdJNubYVMnpn1JjD0n2tKMGpkiDbkAVK8RzW0CgxFEE4IsQFqGVAYxKCP5FQx9CtLIiamYqhnSY0RSUwTik5Noln/8bEZx8LJ4Ba9oZ6EissrZwY35Gvdvy+KpAzFuXFjBbctqEAQBv9/P+tdfYVzDzxhHM0PGYgbNcb6VU845Lw2x8LBCtKiM26ouRvNZuHFBGQ3r1xON9/HUBZ/GKUW4Y5+Z2X6Jn0sh6qbnc86AygWdcUyKREKJ0pgO0xHYSyJVjyioOCxjSZkWUmUa5OyffArRYv7g31S6k9JDXveRO7yhm61PNpJT6uT8L0zC5np78DAVj3PglTXse+l5krEoFVNnUDp+Mm0vCZSb7eSZDCAKbM+SeKPAyLj29fypehKRVglruIlrh17E4KolYbMRj8NacQqCZuSWVJR5G++mYskQFneSe8rGsd5czaPtG5A0hReWPcKqSfNwGP55LEBVNV6vG+D+La0c7+2iNqeb88eGqckeRE41EY+3AxrIYN0r4dhmwdiSAQHE8YUYpoxFKqpG0Eqgy4yQMJH0tJDIaSSZ3UHC1owihk/aV0JEwNQGlmMi5qMihoCAatZITpKQZxZhKq3C5ivGmOMkrQ3QHQ/yp8gkNijTyNKGuFp7iMb6EjZ2z+fCmiZumQ9u9yRcron0dgdJP7GaMalDHBQnkGPy8/0JZ5DYcYBb3zRjDqf5a9US9o6bxE+vmcXgkUOs3b6Fp1Z8mlxhkDu2WZiYMHO7EKJ+UREJk8TPuobJ29dLvrUcRVPpTadojb3GQKgBDRHJPJFCYx4rf/ppTJ6Tn9im+2DpIa/7yGiqxvZnmzn4ZhflE7NZev14jOYToZpOxDnw6lr2rn2OZDRC5bSZzFq+isQ+P6lDSRyShEIKdWYJnxGjjJgFrt79Bg/UnkHyuMb4kfUsiw/jzy3GnIgxJDh4VZuMoBn4essuZjQ/Q+UlIgatm8+UzKAz6xIeO3YPFgEiVz9Pcemkf9peRVF47dB2NhxZh1M6TrWvnWzL4N/vt1pLcThqsAsVGNYNk3pmG8rgCKbKSpzLlmMomEamWyDTH0cVMiTH1BMt2k/YuBdFiwICdnsVbtdkHI4arNYSLJYiTKYcJMmGKJ442lXVBLISJ50cIrxzI9E1b6JsOY6QUkjWqESXKiij7LhSs8gtWEb+5PPYG09yW2MHDXGVmZYRsutaWddUyNllG1k15lkEASyWYpz28eRt20PeYCPrmY3FKLJj3mSeql/DV7d4GL9vhDpfGU/PXMZ3bzoXZbCX37+whqeXXU2p2sXdm53kyUb+gzCheQUMOozcaU6h/P73VGVNp8hYjRkDISXKcOw19vvbUTUVm1TKBV+9nqIzpnwk773/y/SQ130k5LTCmw/V0bJ/iAmLi5l3WRWiKJBOJt4O90iYijOmM3vZKiydRiI7exEUCKRTpD3HyPniZ7lgbxMpTeWa7et5aNo0MgfjXDrwPB53NqooYokG6bfl82pmAiIG7tj3DDNLMuTN6Iahw1ww9gYijjmsOXgrTknEvPpFhNyxf9/ORKKLEf9W6tvXk4ztxmaIAqAKHnJ8U/G4z8DtnoLTOR5JMxN48kmGf/NbFL8f26yZOM9ZhZIuJdXgP/G8qjDhyi2M8AayEsRozCI7ezE52Wfi9c7GYHC+r/5UQiECTzyJ/88PowyNoFS7CVwQIT0qiSHlJZtzKZx4HU+rbu5p60PUNGb0yWw/2M+Nc21cNamZcPgw4dBBUskexjVEyB9KczC3kCbjKKSa2dzd8TxnNVq58oUYaQWem7aU6799A8ZEjHuffIJnFl5MTaaVX2zNJiULfJ4I8pRs/Hk2fuQzErzvB2gGA9ayOczyj8UliahqjKZIHY2h/cSVEKPGTWbBZz5Ldom+7s2HRQ953YcuEU3z8m8P098WZu4lo5l0ZgmZVJKDr73EnhefJRkJM2rKNGYvvAxTm0jiyPCJM0OTCr3BPrKnHWH89d9j+eZj+NFYvW0LD06djHNvD5eHtpL05uAID5CSDIQsObyamkhGk7i3/lkWfv5iUv2/YNjv57LaOxAFF+uP/Ac+NYG4+hWUrDL8/q0Mj2wk4N9OItkJQCDppis2njGli1hYexZ2W+k/zHiJbt7MwN33kG5vxzp9Ou6V15PudpLpiyHajWgzRhjKeo6R8AYEwUhOztkUFlyGzzcXQTj59ND3Q02nCT7xJMO/+Q1KMIhx/mSCS1OEfIcB8CQWYqj8It9JONgZjFLaHGOwNcR3Voxj9dxRACSTvQRGtmN79W7cXS3UVznoLbCgak4OJVW6h0VWPO6jsK2PvRUTWPLrH2IyiNzxl0d5bvb5TIkf59fbC2hVZL5IHLnKSbLCzc8LHATu+wGxdJS6afksqltOjVEiz3CiPNedGKQ+8CaBdC81cxcw65IryCoq+cD6RneCHvK6D1VwMM7aXx0iGkxx9upxlI5zcfD1l9iz5hkSkTDlk6Yye+YlGFsg1RpCMEtEnCrbWtIYg43kL21n7rV3sXJjHY2azOe37eWBKTVM3bKL8cYRVFHCHT9CyDiKhNnL64lJRASJX1maWfTl66h/eiWHlVJuH30rxUKKN459BUe4k6Hz/5M+sQW/fyuqmkIQ7XREx7Klo5yAMpFr5izgwilFGP7H7BrZ72fg7nsIr12LadQoPKtuJD1ciDKcxJBtRZgfo8d4P4HgDgwGDyUl11FcdDUm08nXvf+gKJEII3+4H/9DDyHYbHhuXs1QQSuDwotoUgpnfDY7S7/Oz0ZUpIN+lIEEP181iYumFL/jxaXh8SvRWtaxw11NZ4ENn68fSUoja5Dud5O3NUqy1UvNPb/DWJjH1x59nLVnLGFhqI4f7yxmt5Dia1oapcRKZqyX21xWnH/5OcOhAQ7MdzL54IXkyXZmWDuxC5VIBjNDKT9N4c10x5oYO28hsy69Em9+4YfaX/+X6CGv+9D0tYR4+bcnjijP+Vw1/U1b2fPis8RDQUZNnMrMSSuRGhXkwQSS24x9biGH69o5dDCN238Ay4oOVl79S67Z1sDmTJJbdzfz50ofK3aux+w04gz6kXx7iEZmkLT6WJeYyKBo4HfzfYxdWMaGx69gi+ti1uQu4Uy7zO/3fBbbSDsHx7sIeI1YLEXYXYt4o3UMf9jpxGqy8B9Lqrh2ThnmkwzAhl99lf7vfg8lFsOz6jpEz0Iy3QkMuVbMi030GB9kYHANRqOP8rKbKCy8AoPB/k/tfJhSra303XEHib37sM2eRfa3vkZnx5P0y0+hihkS6au413UJbdsHMARS/P5T01g6Lu/tBtIxePgCtL5DNKplPCsuw+ELYyitw2XuIN94IhMMHSLZ+eeRO2k1t67ZzfrqGVzad5jbDo/iRUOEH8kaFFlJjvUwz5/ivD1/pX+ojV0LJaYevZisWBE15i1k95iwjZqLVZJIEqMusIO2yBGq5y9g1sVX4MnTl0X4d33oIS8IwgPAcmBQ07Tat27zAU8A5UA7cLmmaYH3akcP+Y+Xlv2DvPFAHXa3yKgJ/RxZv4Z4KEjFhOlMH7McsTGDGs1gLLDjXFCMpTaLDf+9nYa6DL6hrUQubOVzn3qQ/9zXyhORCDcf7me9M8mSxt1oJgPevnrU2mbi7bNIufLYHK+lXbLwq8snYslqZO26n7G94At0m/O5zryBL+/7Edn+NM1njMMw4Wo83iU8dsDIbza0kJJVrplVxq1nVuG1//P6M2oiwcDddxN86mkstbXYl9xEutOE6DDiOKsIf97LtHXcB2iUlHyG8rIb33et/YOgqSrBJ59k4N4fIxqNFNxzN4YzxtG0+4cMSWuRZRdPSXfz+m4RKSbzp+tnsKQi++0G4n54YBmEOglnDDxpuYrupA25QGaHbQ1ztVzm0YtUnALAZq3mjf4ynnZfyOWtAT7dXMz9xmEezpgwF9sI1bhxHR3hpva1RAONbF2SYWrDSooD4yg2HUKs349UcSmlTjceg4QsyRz376Y5coCqBXOZddEqXDm5p6g3P/4+ipBfAESBR94R8vcCfk3TfigIwm2AV9O0b7xXO3rIfzxomsahdV1sfboem72RVGQX8VCQqnGzOKN8KUJzBi2jYh7jxbmgCHOlB03VeP1nW2hpUcgaeJ32C5v5xnVP8aOGHn7RP8y1jSEGo8cZFerGmkgQj+zFOTFB5tg4kjmlHIxVcVDy8K3za4hanuBgdwtvum/CTZAv8nNWdrZS0N5H+uzbMc39Btubh/n2C0dpGYpx1tg8bj+vhoocx0lfT6qpie4vfYl0SyuuC68G20K0pIZjXhHMCHG89dtEo/XkZJ/NmDF3YLGcPmWGVFsbPV/5Cqm6erxXX03eN75OaKCBhsPfImY9xtbw1Tx4cA6CBr++fhrnl74j6EPd8KelkI6iJMNsKPgCW/uMiA6R19yvkU6W86mtMrOzDpBaYCOVGwGgUasmq6+WGc2L+IkWYW3KgaPEzkiNC9P+YS5vWos93cjGJTEmty6lqn8uPqmDVHAvCLOwufI5w53GorlQBZX2yBGOh/cxasF0Zl50Oc6s7Hd5tbp385GUawRBKAfWviPkjwOLNE3rEwShANioaVr1e7Whh/zpT1U1Nj9Wx6E3XgFlH3I6wtia+UzMXwidGRAFbJNycC4oxvjWyU+KrPLKjzbR0aWRNfAiB1Yc565Pr+HRbj/faOnlwvYwvs7tmLUkeb3d7PHVM77IjHykgETBaNpSpWwinwsnwricu9kuLuM1cQUT1CPcWzTM2OEEljd/BLNuZnDud7j7pXqeP9hLqc/G9y4Yz+Kadz9CjLz5Jj1f/waixYrj7JtRUyUYixx4LhpFT+bPtLf/CpMxmzHV3yE355yPqpv/JWo6zdBPf4r/4UewTp1K8X2/RPR66Nj7AO3B+9gbG8tv912HajFw+9WTuHHUO8oj/UdOHNEbrRAbomX693muPk00HuWA9wCtkon5W6v4wuEXESs8KF9fwOHEJvKkPjTVgDMwiZcGRvPXnhlYCj2ExrkpbAwzZfcz5AjHeX1BgDP651Lbfj42MYjGUaRQKTFHCZMyexh77sXEDw6jKSq98RYaI3sonDeRmRddhsP34Y5xfJKcqpAPaprmeet7AQj87ed3o4f86S0RSfDMDx9hoGU9aDEmjFnCWN8shAEFwSLhmFmAY04hkvvtMx3ltMLauzfS0y/g63+GTRfU8YtrX2FTIMnqI22c1T7A6LYdgEpZ4yH+MjnIEqMbQ52FWPFYQkYHa+LjqMlq5frJD/Jr4VbqxMlcG3iVu876NMaBo/DXy9FGn8Wfy+/hx683k5JVblpUyc2LKrEY330RtJHf/Y6hX96HqWos5gmfA9GJ+5xypDNU6hq+Qii0n/y8lVRXf/eUlmb+t0IvvUTfN7+F5PVS8tvfYBk7lnigm6M7v8yGiMx/H/gMSpaFy84bwz01xW+f8Xv8VXj8SrDnQHSQ6MV/4fnDQZqbm+myd7HHNkJl3Vy+f2gNhkgE8x3f5iZbjCnSLs5Wt4MpTDJjYWvvTHYmF9NWPYErBTPBR+/HIx7h1TnDTA9M4ozjqxAFBbO5A21IJOaspqrzOebeeQNywEV0WzdaUmU41UtTdB/Zc6uYsfJS7J53X4pad8IpD/m3fg5omvZPe0sQhBuAGwBKS0undnR0fCDbo/vgyJkM+19+me1PPY4mxxlbtJjxWdMRwiqS24xjXhH2GXknLmD9DumkzJrvb2BgWMA38Dhrlh/jd596la60keU765nf2kxlbx2OaBRX105+P9fBuZFsfC0RLFMdSL4wv6y7Do85xJLJT/K49aukRB8/avkVV634KggS/GkpSWcJn9LuZE9vmnmjs7nzwvHvWpoB0NJpem//JuG1a7FMWYSh+FJMRR58V9QQlHZwrO4rANRUf5/8/As+1L79oCWOHaP7C19ECQYp+tnPcC5ZjKaptO7+LQ/V7+XPDZchl9mZPquIB2tH4fnbomw7fguv/deJoM8kUFe/yrbmIOvWryNsCLPV3YqvcxE/b94CdXVw/fWsqJ2DPZ3igdY65NHbCFn3IokKrbEKttjP5dqqS9n0898TT+7kjenDzImMZlr9p8hoVhzWYdIjwyRtE6hofYEzLhxD9urPkjg0QmhjJ1ooQyQToDl2ANfsEqavvBib+z2PEf9P08s1uvdFkTMc3fAGO55+gkw4RpVnIWOzJyFlBIyFJwZTrROyEU6ywFcqnuH5721gOCjiHXyEx847xh+veglN8nHOhv3MOH6QomA/hd09DCm7+ev0Ipb6C5luP4x3bBRZEPnB7m8QydgwjX6OkeIv4NI0Hjr0VaafezuUzkb7w0Ji8QTnxr5LwpbPd1aMZ/nEgvdc3VGNxei+5VZi27ZhnXk5Uv6ZOOcV4TqnjPbuX9PW/iuczlom1P4Gq7X4Xds5ncnDw3Td9HmS9fUU3Pk9PJdcAkCo7xBfffp53uiZhlrrpmx0Fo9OqqDUagZNg5e+DHsfAIsbTE64YSMtA2Eef+px4qk4uz1NWEfm8otIO6lXXiW9ZAkXL7+KkkiKBw8aSc2I8+zgE5QUNFDoGCCBlazs82neqLC7ZS8bp4wwP5zH7OOfIazk4rDEUYP1xC3TKe18nRprI6U//gmm8lEkjo0QXNeK2p8iqcRpjR/GNiOXMy5aic3lPrUdfBo6VSH/Y2DkHQOvPk3Tvv5ebeghf3pQ5AzHNq5j53NPoAbT1HgXUW4fg0EQsVR7ccwvxlzpftcwTUTTPPedDQQjAp7hB/nTeXXcf/HT5LkqWLl2IxPq9+FKxhh39Biby+rpmpjH+RaNiuxOEGB4uISnBm5hf58RqWoTiVFXUkKKx3ddT/mc62Hel4j88QLMvbu4NHUHlZMXcMfycSedNfNOciBA1403kTx6FOv06zCOmofv8moMVUaO1X2JkZGNFORfQnX1nUjSu1/M5OPgnX/Mcv7zVrJuvBFBEIjFA1x233M0hrPQpmdhy3bz50mVTHbZQMnAIyuhe/eJRopnwLXPE4zEePDRBwkNhah3dGFKT+Rer4ngfb8iXjWG61bfwqSQyj0NBoKLDfxoywEGHBoLK3czLWsfJtKoyRz2NmV42ptiQcDBnObPMJipxGxSsIV3ErDMo7B3E9XtL1Bw+214Vq0CIN0Rxv9aM0pbHFnN0JlowDjVw+RLlmN16uvi/M1HMbvmMWARkA0MAN8BngeeBEqBDk5MofS/Vzt6yJ9aJ47c32TX809iihipzVtEnliMJgiYx2fhO7vs74Op7yYWTPHsdzcQjYE78Ad+vayJ353/ALX507ju8ecpbTyCLZVi0uFd7Luwm8pikQJzCjkpMdRVQpd/Ct05C3mpWcRQ1kSsZiFTjBke2XQp2dX/j73zjo6q2v74Z/pMyqT3HhIgIaGF3qUrPAtNqSKogILYfVh4YsOCYkFQFEFBpIj03kIvCZCEkkp6TyaZTKaXe39/xIfPZwF8/l5RPmvdxcrK5Mw5516+d5999tl7CNY7P+X8yifoVfUVr8kepdfYuQxsG/SrfQJwVFdT+sA07OUVaFIeRN2hF36TE3G615OROR2LpZTW8S8RFvbHKVAt2u1UvvAihu3b8XvoQQKefBKJREJds4Xb39uDQ7Tg6h6ESePHp0kxDPX3AmMtfNofXDYw66D7LLj9TRwOB19tWk1ZTik1ykbcPdrwYmJrap59DpNaw+MPP8kAsw8PVFmp7Kdm6ZFLnHDG4h4kMKBTNuPVqdjMudgdEk5aZbiVy2mXP5UyawoymYi/6Qg16gH41Z0m+crXaAf0J+T115D7tqSmdtSZqd+di+OKAYkoocpWiDTZjaR770Dt8cuuuT8Ltw5D3eJXcTpa3DJnt3yLl8WbpKB+eOGHXRDRuStJnJGM+3XEHVqKg2xekIrFClr9Ut4bXsT7t71L75ihPLPiS9zLiwlylhHnkY4zxYhKBmVNodgvyjDUhmH0j8cUHM3GYn/kgQaMHRMY5qVg2YG7cfMO5fzgdWzduIoF1rc443sXCQ+vQKtWXLdfjupqSiZPwVmrQ931ETwH9sJndGuMtstkZj2EINhpn/wpPj7dfo/p/FVMDhNFTUUUG4opMZRQZ67DYDdgsBtwuBzIpDKkEinucnf8NH74a/wJ9Qgl3jueGK8Y3BRu1/+Sf0AUBKpfeQX9uvX4TptG4DNPI5FIyCrXM2bZcSK1xZg7RVCiiOSDhCjGBPtC+TlYORw8AlvCLO/5FDrcB8D6g5u4dCwDi8xOWFQCs7r3pGzmLCwNDcyf/hh3O+Lpba/hagc31py6yglHLPipCOsZwto2zRSceR2n+goyKTQbwC2nL/klk5AgJcyaSrlqAB5N5+l86StUXlpCFy7Eo2/fa+NxNdup3ZuN7ZwOuahAZ69CaCun7fghaDz/+zfH/7+4JfK3+FmcdjsXD+/j3NYtBDpCSfDrhQZ37AopOU0OFMn+DJyaiExx/QIQjdUmNr96FIfNhbfxY14fVsYb3eZxe9v7eG3px/iSQYxvJpoQPU4BLhh8OJw3kW4FaXhjxxzZAZOHG+vqo5C5KTD1iOD+MB/eSJ2M1FDOisRVrD+Vx1blS7j82+I5cx/Ir5+v3FFTQ8mkKThr6tD0mIvPuAF4DopEp0vl4qU5KJW+dOzwBe7ucb/HlP4Eg93AyYqTpNekk1GbQb4+H0FsyR8vlUjxUfngpfJCq9SikClwCS4EUcDoMNJgbaDB+sPiV4KEGK8YugZ3pWtwV7oHd8dbff3NSFEUqXn1VRrXfoPv1KkEPvcsEomETefKeWpjJoMjT1HUJpHL0kRejwtjekQAnPsStj8G2nAw18O0vRDaEYCNp3eTvv8IckFOQvsk7u09kNKZs7Dk5PDRuKmMVfQizDePywEebMms5oQjFsFPRbt+4WxMieP0Nx9yWlhJ2xAL3nIRzBpq829HX9SXSFMmxYreqIwXSajcgG9lAz6TJxP49FNIVT/cb8HuonbfFUwnq1AJGoxOPfZWIq0nDETt9eez7G+J/C1+hNNu5+KhvWRu20WoEEO8dwoKlMjDPcgzu7hYaKDz8Ch63BmL5AbqldaXG9jyxkkEqw1f28e8MqSSp9tN5+424/hu2zME+F5CrTYhGOTsdkpIq4+jvORB7tHvIsxcjbNNLwxYWWeNQBCCsPYMZm7bUJ7LegUy1/Gq1wI21IRySPsK/nIL0hlHwCvsuv1y1NRSMnkKjqoa3Ho9jv/Dw3FPCaKqahNXsv+Kp2cCHdqvQKUK+D2m9RpNtiZ2F+3mYOlB0qvTcYpO3ORutA9oT6fATrTxbUOMNoZwz3CUsl/fR3AIDiqNleQ35pPfmE9WfRbna85jdpqRSWR0Ce7C0KihDI4ajK/6p1W3/o4oitS8/gaNa9bge//9BP71OSQSCS9tucTq0yXM6fAdhwJ7cE7alWejgngiJhjJjsfh3CrQ+IHSDR4+Au4tsetfHN9DxtE9+Ni9SOyYyJjBIyia+ziOEyfYOPgvjPT9C+oOuZwxyDhcZOaoPRrBV0XPQVGs7tiKI6s/5b36FQREmJmitiDxlCK45DSXdkWdF0yRfjhKSz4a+SZSTlagio8ndNEi1G1a/3hcgkjN4Ss0HSrG3aXFJliwhTuJvq8nboF/ntDLWyJ/CwDsVgsXD+4lb9dRwokjyrMdEokUTTs/FJ0C2bOlEF2Fif7jW9Ou7/VFFKCmsJGt75xBYjUT5FrKCwOreSS6Nz28VdTX70YqFRCK3bDlhPJCQgVKSyR1ZQ/T13SWTroMZJ0H0mBs5lu5ClNzB+yd/ZjXPYbZtTuQ7HySJcIYlkvHsiv8K8IrdsP92yG6z3X75WxooPjeCS0C3+9xgp68E3WcD+XlX5ObNx9fn94kJy/73fLOiKLI2eqzbMrbxMHSg9gFO9HaaG6LvI2BEQNJ9k9GJv19MlM6BAdXdFc4UnaE/SX7KTYUI5fKGRw5mHFtxtElqMvP7iuIokjNGwtpXL0a/zmzCXj0UWxOF6OXnaRUZ+Llriv5Wt2bE9J+zAj15+VYfyQrb4f63JbEZrH9YcJG+D6+/uVd2yi9tJ1IcxitWrfivnvGkL/gVSRbNnOqU096RoxDMqKeAxf1nGuQc9gcgctfxV+GteKjdlHsXbWEd5q/pMnLyadNtRh8onEFNSFT2HDVB1NTcDvW/ECuRmzg/gMmJEYzgU8/hc+kST8pLyiKItUnr1C/JxcfRwAu0YnJz0T4mC5oY//4uXFuifyfHLOhiQu7dlB/JJcoVSL+6jBEOXh0C8GzdxhNVhc7lmRiNTsZ/lASUUk3dtKwMqee7e+fQ241EK5axvrBVYz0UeMlNeByyamtiiF0q4EGz0QWDDiLhzWU6pKHaW0vZWjVftx7DqBaZ+Kgu4ky3QCcMR68OrIdY61XUa0ZwXFnIp+ELeSTpBx8DjwJA1+Efs9ct18uo4mSCZOxXS3AY8jTBP91NIpgd0pKP6egYCH+/oNIavcRMtm/Xp7OKTjZV7yPlZdXktOQg1apZUTsCO6Ju4cEv4TrN/AvIooieY15bL26la0FWzHYDcR5xzEtaRq3x9yOXCr/yeernn+Bps2bCXrxRXwnTaREZ2Lkh8eJCdDwTMJiltOd/dLhTAzw4Z1QAemn/UGthaYyGDQf+j51ra3JX36FVb+LJH0CQSFBTBw/kSvLV+D1+XKKohOIajMG+f3ubDuYR47Dj4OGIFyBaqaOaMOC+FC2fbGYd+xrENxgdUUZtYphFMiiCIg7jNKzFqdFS3NeB7bJCnjyfBiq01m49+lDyBuvowj8+ZPM1edzqN5+CR+zHzKJHJNbM/5D2+Db7cZWpv+L3BL5PymG+loyN+/AmtFAjFsSapk7aGV49Y/CPSUIqVpOeU4Duz+5iFwlY+SjHQiIvLHNq9KLNexakoFGWkhE2bzr2QAAIABJREFU8gqa4xvxkIFMFkZebhj6ohB6HzpJTrfuvJ+SipcjmLrCB9E6LYwq30Rg/06UVUO2VzNpukE4PRS8OakTrQ2NhG8YjlOEvX02MDVZjezzQRDZHSZ9B9exhkW7neL7H8KakY774LmEvjIFuY+aoqKPKCx6n8DAO2iX+B5S6fU3bH8NQRTYWbiTjzM+psJYQbQ2mgeSHmBE7AhUv8PL47dgdVrZU7yHLy9/SYG+gHCPcB5q/xB3trrzR2IvOp2UPzYX46FDhL7zDl5/Gcnui1XM+vo8U3uFcYf3a3zuTGSbZBTj/bx5V56JdMMU8IuDhkK4fwdE9275ToeLYR9/gkqxk+71KXh5eDFxwkSO7dxHwnvvYPIJQdp+GNpHO7Np6ymKlNEc0HnjCtbw9F2JPBYVyIbP3+JdcT3uSinflBfTFDyR7efvxD8wm4D43ahC8kGUkGNQ0qq2E9GfXEKmcSPk9dfwHDjwF+dDl19CyaYzeNZ7opF7YJfZcOseRMCgtsjc/7X7/9/GLZH/k1FXWkz2t/tRFEsI08QjkUiQRbvhO7AVqjjva9ZM9skqUtfk4B3sxsjZHfD0vbHY8MK0co5u+w6/2L24ReYgAGV2d6I1MzmaWoN3QyPdTp3h0PDBbGy1E60QTPPVSVidGsaVbyKitxfFdb4Y3WGvcwAWo4OXJneioVBPyslZ9JVdpOTuLcQnpsBnt7VkTJx5HDx/PVRSFATKZszFdOwAbv0fIvyd2ci0SoqLl3G1cBHBwXeT0PYtpP9k3d4sx8qP8f7598lrzCPBN4EZHWZwW8RtSCXX36D+dyCIAqllqSzPWs5l3WXivON4MuVJ+oT1uebGEWw2yh58CPOFC0Qs/RiPfv14edtlVp0sZumEZAKb/8pntli2SsYw3teL98o/RpL2GXh87/qYeRw8WvYyagxWBi/9GC/fLfSr7Yu71J2xY8ayPu0St7/zGkqZmvqUbgQ8Oobvthykwqsd+6o1OEPdeHN0MhNDfFn12QI+km0mRKrg6/JChI5P8sWB3mjsoNHk4xmXjlfsSWQqK2anlsgjnsi31eI76j6CnnsWqdsvRx0ZauvIXX8QeaFIgCocAQFJKzWBwxNQRfwxYu1vifyfAFEQKDyVRvXei/gY/dEq/XBJXWhS/PG9LQ75Pwi4KIqc3V5E+q5iwtv6MHxGMirN9YXP6Wwm6+QKqhs2otJWI7HLSbWIXLX6cY/6KS5lXiK0rIyEi1f4atxdpPmtRysNhvxRlDpCuLNmJwkdqsgzx6CWerM3YCANBQbGDm5Fbk49KVXr+JtiNbYhC1H1fgS2zoYLa2DyZmh123X7V/7U32jeuQFNr/FEfDQPmbuC0rKV5Oe/RlDQnbRLXPQvVWyqNFay8OxCUstSifCMYE6nOQyLHvZfI+7/jCiKHCg9wOJziylrLqNXaC9e6P4CkdpIAFxGY8vGdEkJUWu/RhIXz9hPTlFcb2LnnB7U5M7ic2ssWyRjmOilYVHaQ0gaCsFha7HkJ313zT+fVlTPxLWf4xX0HYN0g9BY1QwaPpxPyxqY+fYreFvtlPSIx/eBWezZe5jqwK7sKQVXhDufju3A7f5eLF0+j+XKXbQRlawqv4pi8Jss35kEDXZwNSOXOPGKPoOk/Tb83RxInUo0qS68r0YQ9dKHaJLa/ep8mA1NXNq8B9v5BsJVrVFIlTi9BHz7tcIjJRip+l97+f8nuSXyf2CsRhMFW45gy2wkQBqOVCLF5mnHt38sXt0ikSp/LGouh8DBr7LJT6shoXcI/Se0QfYzaQn+EZOpgPLyNVRUfIuIBXttKMFGH56W5iOXejNGnEZFSQVtrlwhsLyKRQ+Mp06+AjdpAL5Xh5Nhj6dnw2n6RJ0kXRVJqDmW1KShVJytJz7Gm/LyZjrIi1nLi0hbD4H71sLFjfDdQ9D3aRj00nXnoea9FTQsX4S643AiV76NTKOgvGItubkvERAwjKR2H/5mC94hOFh9ZTWfZH4CwKwOs5iUMAmF7H9jye9wOViXu46lGUtxCA5mdZjFlHZTUEgVOGpqKB53L0gkRK9fT4XMnTs+PEb7cC++eqA9GWn3s9LSls3SMTyqaOLFoxORuPtDYxHc9iL0/2GP5Mtjebx6bCMewRsZ3jwcdaOadl26ssSmYsHi1whsbKCkfzTSu2Zx8uQZasP7sPuqDaI9+ObeznT3cmPR8idYoz5MD4eCjyuuIh+1gnX7Y2jIb0JwWVG5HCARudxpMa3j9LRVmAEB1WUpYT5jiBr/ClL5r98Xh81KdmoqtQeyCXZF4q0MRJAKqBN90faMQBX7y6e5/1u5JfJ/QOovFlK5JxNNrRqNzAM7VmilImxkJ1QhP+9Xtxod7Poki6qCJnrcHUvnYVG/+DALgoO6+gNUlK+hUX8aRDlNxV1xZsbRva2VWcot2PFlhHkMBl0TXc6eRWqx88KMaSgsH6KQuhNXMogj5g60Ml3lHq/NHA7xpk1DCse6DqHkZCMahRSr2cmgWA2fmp9C7rLCrBNg1cMnfSE4ucX/K/t1cW5Yt5eaBU+iiOpAzHerkLkpvw+TfBY/vwG0T16GVPrroYq/RH5jPvOOzSO3MZfbIm5jXrd5hHiE/Ka2/tPUmGp48+ybHCg9QBufNizovYB2fu2wZmdTPHESyugoolev5tvsBp79NovnhrfloT6BpJ+ZyFfW9nwnHcPbxuNMOfcCBCVB7ZUf+ecBHll1gn1V+9GEbGKEYwTqCjUBsa341COUxR+9Q0h1GdWD4qjvN5HLl69QEzOIPTkGZK20bL8vhbbuKl5ePpPNmtPcbpXxVk0ZkvHrOXA6jJwTVYiCA7XDhCBTcK7VFxTHlDMvthsS3SFcKjuKJjWR8TMIb/vAdTOHiqJISeZ5crYfRl2lJMojEYVUBZ5StD0jcOsciNz7fyO9xS2R/4NgrTVQvvM8zjwjHqJXy6EZdRPefaMJuS3pZxOF/R19rZkdSzIxNtgYNDWB+C4/79+22qqprFhPReU67PZa1OpwJPqBZO3tiHdNBQNGNPOY5QsaxWBuaxiKYDLT5+hxmlRqnpr9CAG6d5BKJKRUDmCfoTPujmYmS9ZwIllK2/K+HOzcj/LLNqQNNuQSeG54Ag/Wv4nk4sYWwYjoDitvh7rcFsH3/vWiz4aDF6h4/EFkWj9itm5A4e9Nbd1eLl6cjY9PDzq0//w3RdEIosDqK6v54PwHeCo9md9zPoMiB910O/+NHCw9yBun36DB2sCcznOY2m4qpiNHKX/kUTz69yfsow+Zsz6LvZer2fxIb9oGiaSfvY/V1q58JxnFnsI36VB5AIl7YMtG+MzjoGk5lGV1uBj+zj6qZUdRBO/gbsXdyPPlyP0C+Do8gY+XfkRwaTb6IUlcaj+UmppaiiMGciC7AXUbb/aP70qYQsZTn93PAU0mU0zwTGM9TNlKWlYAZ7cXIYouVHYDLrmGoogv2R2VzVOdH6NPSQmVdeuxR7mQiipCwscSET75hg666crLyNi1HcP5SiJVbQnSRAEgj3DHo3MwmmR/ZB6/zVD4d3BL5P+HcRns1B3LxZBegZulJaZbL9RBrIqov3TDM+z6B3kqC/TsXnYRgDtmJRMS9+NTkqIo0th4ivKKr6mv348oCvj59Sc8bBJXD2hJT20ioPEyQyZoeKb6bSqdMXSv74naaGJA6hGq/HyYO/dpQqoXgmimf+0gDjcm0SiqmWL+mtK+ZlpVDmJrbEdKG1UocpoI8FCxalpX2tXugi0zYcA8GPBXOPoOHHoNRn0O7cf+6rhM54opm3E/uKzEbFyPKi4avT6dCxmT8fBIpHOn1chkN5cGAFos3heOv8CZ6jMMiBjAyz1fxk/zxypg0WRrYsGpBewv2U/34O683ud1FJsPUPPaa/jNmIFyxiPc/sExNAoZOx7rgww96afHsco+kCNif9IuPIhWLkfSXAWJd8GYL+D7VWFJvZFh7x1G5bsPISCV8b7jEbNEbAolW+JTWPrZF/gVnsM6oBOprbvgEkSyAvpwNLseryRfDt/bFS8JzFgxjjPqfJ5ocjLNbIZpe7icpyV1TS6IIkq7HqfcHWPQGr6Kz2RU/CieC5tK6aK5NARnY+0OolTAx6cXEeGT8fcfdN09GbvVQs6JI+TtP4pbgxtRnu3wUviDBFRx3rh1CETTzg/pDexh/Tu5JfL/YzjqzDSllWG4UIGyucW/aHDoMPmaCRqYSESPDjfsM8xLq+bgl9lo/TSMeLQ93oE/iJ7D0UR19WbKK9ZiNl9FofAhNGQsYWHjUasjOPVlOhdONxPUmMWwmVEsyP4rRY4kkhuT8dHp6Hf0GKVhfjzy+N8IrlqIVKhjRN1I0hpCuUwYI5t34T+0gqjmUXym8qdIHYDyRC1h3moOPNEft+Zi+LQfhHaC+7dBVSasGPKDaPwKlrx6yqY/hEtXQMTnX+DRqytGUz7nzo1DqfQjpfMGlMpfPgH6S5ytOsszR5/B4rTwXNfnGBU/6n/OP3ujiKLIloItLDy7EKVMyZt936TVJ/vQb9xI2AcfcDkuhQmfn+a+rhEsHNUei6WMs2dGs8IxHqPRm02ZTyAJ7QSV5+HuZdBxwrW2d2eUMmtdFiGhmzF6neXh6IexpdnQW22ktu7Mu6s3oc0/gaNHB3bGJeHl50+qqiNn83SEdArgwJgUFC4nU1bdzWVVOW/ozPxFVML0vRQWa9jz6UVEUURh0+NUeKLSrub95AxSglJ4r887uL7cQM1XS7ENUWMeJMMu6lCrwwgLm0hIyGhUyuuXGKwpLCDrwB4qzlwiVBFLtDYJN6knSEEV640m0Q91oh9y7/9MyOw/ckvk/8sRHQK24iaMl2swXqxBbmpxuzTYqmhSN6JNCSN+aN+byqMtCiJnthVybk8JofHe3D4jGbWHAlEUaGw8TWXVRurq9iAIdrTaToSHTSQw8A5kMhWiKHL00zNcyjAT1niBoU914r302eRbuhLTHENYWRk9T52mKMaXmXMW4lu/CLmjhPF1Y8nWqzgstKWjKZM7hmYQr32Ulyv15AZGojlagzsSDj3Vn0A3KawYDPqy75f7PrC8P9iM8MjJlp9/AXuFkbJHXsCeu4/gl1/D577RWG3VpKePQRSddEnZiEbz626en8yXKLLy8ko+OP8BUdoo3h/wPrHesTfVxv8qRU1FPHPkGfIa83gsaRaD3j6CLT+fmPXreP+qi2WpV/lkUgrDk4IxNF8iLX0Cy+1z6Ft+jtll30BAAuhLYeYx8Gt1rd2XN6WzKq2K1q3WU6XM5OmkpzGcMVFXV8fFyGTmbd2NZ85xHMkJbGvbjqi2iWwwRXGxsJH47iHsuqsTDoeJe7+6k1J5HUvqDfRV+8O0vVRVydmy+AKCS0Rub8apcCdQsZo3u18k0C2IJYOWEFrcTMWzz2GvLEPx5EAMHRrQN51BIpHj7z+Q0JBx+Pr2ve6GvN1qoeDsKa4cPYwxv5YwTRxR3u1wo8XnrwjzQJPgiyreB2W4JxLZv98ouCXy/2WIgoiz1ow1X4/xUjXOMhMSQYIguqizltOoqEPbKYz4QX3wCb75otF2q5MDK69QlFlPYu8Q+o1vg8NZTVXVd1RWfYvVWoZcriU46C5CQ8fi6flD6JkgiBz88Dh5OQ4i9ekMnT+IL1IfJMvQgyBrEG1yc2l/IYPiOC8em/kWiuZPUNouM71mEiXNdnbY2+HjaOTxvntITHidp9KzuBTeioAsPc1VJj6Z1JnhSSFw8FU4tgju/RoSRsKuZ+Dscpi85VfDJZ31FsrnfYbl2DK8xt5H6Kt/w+EwcP78fVisFaR0Xvuj8dwIZoeZF46/wIHSAwyNGsorvV/BXfH7pDv4X8HitPC3k39jd9Fu7tT2Ycq7F5G5uxO2dh3j1l6mQm9h3xP98PdQodMd5Xzmwyy3/pWXriyhtaMONS7wi4fp++D7qCOXIHL34v1crjPTof1GChxZvNbjNSpO6aktLqLavxUTDuzDPycdR2w0Ozp2omPf/rxX6EZBmYEu/cL59vb2NFkaGbv2TnSSJlbV1dPetw3cvx2dTsKmt87hsLmQOS245BqihTW81T8Hh0Tk7X5v08u7MzUL36Bp03eok5Pxfu0R6mWnqKr6DoejAZUyiJCQ0YSGjkWjibzuPBkbG8g5cYTsY6lYKhoJc4sj2rc9WnyRIEGikqFq5Y063htVnDdyf82/ZSV4S+T/wwh2F/ayZuzFBmzFTdhKmsDeMu8Gu45qSxEmdyM+HSOJ79WbwJhWv/nBMNRb2Lk0i8ZqM73HRBLcLpeqqo3oGo4DAj4+PQkNGUdAwNCfFMZwOQV2v32UklKB2KbTDFo4ho3bp5Ju6I7W7knXzCyic/MojvfgpakLMTrXorac5eHq8dQZ7eyyxNIscefZpG8J7/QG754/R1ZUHB0aneSn1TCqcziLxnaA0jMtqWw7ToC7PoaCA7Bm9LXc5b+Ey2CnauEumre/jLptW6LXfoUol5CZOZ1G/Vk6dliBr2/vX/z7n6PaVM2cQ3PIa8zjyZQnmZI45Q/rnrkeoiiy+spq3jv3Hv0bApn5eRXuvXpiffltRi49xYDWAXw6OQWJREJV1Saysp9ns34Oiy8tQO+fREjt+ZaUB4PmX2tTZ7Qx6K19OF1m2nXdSm7zFRb3X0zGaR1N2ZdwuAXT4+ReYnNzcAQGsqdHD/qNGsvTp01UVBsZOjiGzwYlUmWoZNzGe7ALFtbVVBET0QMmfouhSeDbN9OxNNuRCk4EqYLW9nUsvi2PCrGRp7s8zaSESTTv20/V/PmIdjtBzz2Hduzd6HSpVFZtQKc7Cgj4ePcgNHQcAQFDbmgvp6GygoK0UxScPYWusJQgTRSRfokEqaNQOFo2aaWeCpSRWlRRWpSRnijDPJHcQFbXm+WWyP8bcZkcOKpMOKqMOCpN2KuMOGvM8P00Gxw66ixl1NsqkYariOjSnlZduuMTcmMJwX6NitxG9izPQuWbQ5uBuZgdh3A6m1GpgluslZAxv2it2K1Odiw8QlWNhLaGY/Rb9ADb1kwhzdoDlUvKgNPn8Csrp6iNJ0vv/RtXFbvRGA/zQM09iM1uHDApyVa2YVrwFoiZzsGaCnLjW9FfJqHqdAOCKLJ7bl88JVb4pE9LublZJ1qqES3t2RKd8XAqKDQ/2z/B4qT2o9M0bXwJidxOzJbNKIICycn9GxUVa0hIeIvQkDE3NV9XdFeYc3AOJqeJRf0X0Sfs+onP/gycqTrDE6lPMOickwk7mvF/9FE2d7idN3blsPjeDtzTqaUsYlHRR+QULaWoaCiPlK3iYvRIkot3wtQdP0oid7aghvs+TyNc1UhwynYKDYUsG7SMradrkWemoZB7EXHhIJ1zinC6e3CoX1+GTH+Y+3dVoKs3M35Ea97sE89VXQETtt6LyuFgU20ZAa1HwthVmI0uNi06h6HW3PJcSaS0tW5mea/LXFHVMTp+NC90fwHqG6iaNw/TyVO49+5NyGuvoggJwWqtoqpq07VVrkzmRoD/EIKC78TXp88Nna9obqjnatoZ8tNOUZF9CTUehLrHEhGQiI88CLnt+zZkEhQh7ihDPVAEu7dcIe7/8kbuH17krQWN6LcXIvdRI/dVI/NVI/dRI/NRIfNQInWX/2p44c0g2Jy4DHZcBjuCwY5TZ8Gps+Kst+DUWRDMzmuftUusNFiqabBWUm+twOktEJqUQGRSByLatf/dypcJgkDm0VQKcjbiFZWGTNWITOZOQMAQgoPuwte3969GFVhNDra+0lKPtb35KClP3Mnuzc+RIe+JRLBxx6HTqBobKUz0YteoF0hVHsOteQf31g8jvLEVh40VHFH3pJ/mNFb/DhSipDwxkp4SB20bVKxLK2PDjJ50jfaFbXPg/Gp4YDdE9YRvp8GVbfDQQQjp8LP9Ex0ualdcxLDhHZzVmUSu+gL3bt0oK/+KvLwFREU+TFzcczc1Z6llqTx79Fm8Vd4sGbSE1j6tr/9HfyIK9YU8cmAWozZU0ueii4jPP+eBLMiraWbfE/0J9lIjiiKXrzxBcfU+wi94E2sp5mpACp0sxS0vcPUPz/eSPZksSi1nQEgDDVEbqTPXsXzoChYdryAi6xTuyPG6eJI+eaWIEhmnBg9i4GNPMuqbbJqbrMwe1Y5nukSTWXWBB/Y8QIDFxbd1ZXh2ngojF2OzONn6fgZ1JYZrUT5xlt3s6HiaVF89XYK6sHjAYryUWvTr11PzziIkUilBf30Or9GjkUgkiKKAXp9Gdc1Wamt343QaUCh8CQoaQXDQXWi1HW9oleewWanIvkxx1gVKLmZQX1qMSupGgHskkYHt8NeEona4IbH/8DcyLyUefcLw7Pvb6gr/4UW+Ni0f/YFCVIIamUUKzp9+RuomR+quQOqmQKKUIlHIkCikLSdCZZIWC0Bs8ZcjgugUEK1OBJsL0eZCsLkQjHZEu/CTth1yOybBgN5ci95Ujd5eR5OzHm14ECHxrQmJb0tEu/Zo/X+/vOWiKNLcfInqmr2UFW4HRTmiKMPPtx+hoXfj7z8ImeznreJ/xNhoY8srhzGYJKQ4jhPdwZ/U8r1c8eiKIDRyz66TCA47RQleZIx+kbWys3g0b2Covg9Davqx13KW7Yr+hMqr0Hg50Xm2obxtAG2tRubHtmbaqnQe7hfL83ckQM4uWDce+jwBg19uEfcNk39ycvJH4xREGtZm07R1A7aL6wl89ln8pj2ATneMjMxp+PsPpH3yMiQ3kVpga8FW5p+cT6JvIh8N+gh/zfUjLf6M1FvqeWLXLCYvvkyw0x3PL7/ljjXZdIvxZdUDXZFIJLhcVs6fn4C+poje5ys555WEv62BhDa9Wlxx3yOKIpOWHOBEhY25PQV2O5Zgc9l4f9AXPH2ijG4XT+LucuCZl8WA3GKkVjuZw4fR8+l5jPjiPBaTg/n3dWB6chjHSo4w+/Ac4o0CX+vKUPV9Bga+iMPmYueyLCqydfD98xBpOkZu4k5WhzoI9Qjh40FLiPWOxV5WRtXzL2BOS8O9b19CXn0FRfAPKYkFwYZOd4Tq6m3U6w4iCHbU6ggCA4YSEDgML22nG37mTPpGKvOyqczLoTI3m5rCfFxOJ2qZB/4e4YQGxOPrFoJHcjBRd/626mR/eJHPPXWMnR+8g/h9xR2lVIOXWwB+3uF4uPngptKilnmgkqqRi0qkohSJKEHikiARJCAAkh/mQaQlvlaQuHBKnDhFBw6XDYvDgMGko6m5FouzGYvLhNnZhFSlwD88Er+ISPzCIwluFU9QbBwK1e97Wk4UXej16dTV7aOubh9WWyWiKMVcF4+v13C6DZyEUnXjYYP6aiObXzuGzSrSjeNo67M5HquiWNsWwVHOmK1nMCllFLfzpWbsy7xvS8PTvIouxhRml41lm/0A2+iMXaGidUAektAhnAlSEGQ28F339kz8IgOVXMquuX1R23QtbhltCDx4COxG+LgbeIbAQ4eubdb9pI+7imjadgLzsbfw6N+P8I+XYDZfJS19NBpNBCmd199UTvjVV1bzdtrb9Azpyfu3vX/T5fT+bJgdZhauf4RRb53B2jqC/CeXMX97DgtHJTO+W4vrz2arJS3tHtyumulcVMiCuMeYVraeiFEfQJvbr7XVbLEz6M09GGwC748PZGHOPFQyFX/t8wlzzlUxMuskbjYj7qUFDM4rQqHTc3XEHbR+bj4jPzmN0+bk3SkpjG4dxPbcrTx/+kW66gU+ayxHNvwt6DETl0Ng34pLFF6oBSQgkRBkykQRt5KXI91RKkXeG/AuvcN6IwoCjWu/ofbdd5HI5QTNm4fXPXf/xFp3Opuprd1Lbd1uGhpOIIoOlMpAAgKGEBgwDG/vbjeV1dTpcFBbdJW6kqIfrtJiuoy8m15jJ/6m+/SHF3loKUJtqKtFX1ONvqaKppoqDHV1mA16zE1NmA16bCbTTbcrkUpRu3ug9vDA3ccXrX8gWv8APL//1zcsHE+/gP+3zTqbvZ4G3TF0DUdpaDiOw9GAVKpELe9O8ZnWWGo7Mmhy9xvOAf936ooa2fr2aQS7g66mvUhzTnJqWDeqNSFIjTmM2ZGJzlNDaVIgjomv83LtKTwcy4mxJfJO4XR2uo6w1+pHnls8fUPP0LH7Aywx61GZTXzTJoRNly18faaUb2f2JCXSB74ZD1cPwYwjEJgAmx6Ey5tbqg0FJ/1sH42nq2jceAnL6YVIFCIxm79D9IC0tFG4BDNdu2xGrb6x6CNRFFmSsYTlWcsZEjWEN/u+ed2qTL8HJr2N+gojDRUmGqpNmPU2zM12rEYHLtcP//dUGjkaTwUaTyVafw2+Ie74hbnjF+pxQ+UX/z9xCA5Wvj2FvqsyKLinM2tjZ5NV1sSex/sR4dvykmxuvkx6+jgSzlvxMJmY0v4dlhctxv/hfeD+w0opq7iW0Z+cIUBp56MZscw+8ggBbgGMab+Ylwp0TLpwGoWlAXVNGUMKinErq6R6xAi8n3mJ0Z+eQnCKfDG9GwOj/ViV+QXvZixmqM7BIkMVklGfQftxCC6B1K9zyT5RiVQiICBDaymhfeRCpkeEYFfpebbrM0xMmIhEIsFeWkrl889jST+HR//+BL/8NxQhP5+6wulspr7+MLV1e9HpjiAIFuRyT3x9+uDn1w9f376o1Tef9kIUBFxOJ3Llb3sm/xQifyM4HQ7sZhNOux2H3YbTbsdptyMKLqQyOVKZrOWSSlGoNag9PFFq/j0hUH/H5bJhMGSgazhGg+4ozcbLACgUfvj59sXPbyDFZ6M5t6sG/wgPbp+RjNb/+m6Zf6TkQiV7PrmIzGakc+lqBH0Zx0cOolGqxqP6HCNTC6n0cacqKQzFlDd5NvconopleAkxfJk3m+NiBqkNtaT69qN3cCaPjp3FwwUlmC0W3lBYiYxKZsLnZ3iwTwwvjkz8oV7osIXQ8xHI3gHDk3FpAAAgAElEQVTrJ8KA52HAz/vSLbkN1K+8hCNvDbacE0R99SWalE5kZDyAvimNzp3W4uXV6YbGK4gCb559k29yvmFU/Cjm95j/u1Vp+mfsFicll3SU5zZSntuIoc5y7XduWiUePio0WiUaDwUyeYt4i4Dd7MRitGM2ODDUWXA5W1alMoWUkFZehLX2ISrJD/8Ij/9I9I9LcLH34b8QdbyIg4/3Z3nVXSSFebP2wR5Iv09dXVu7l/z0GXRLN3LWM4lXY2fwrW0/HmNXXPOTA6zYn8GrBysYEOxi9n2hzDwwkzjvOGJC5/Nlo4XHMtKxNlehaKxjYFEZ3nn5mIcPx/TUi9z/eRoSYP3DPega5s27Z95hVc5XTKy18pxZh2TCeogfgiiKnNhUQOaBMpQSO3ZRidJhYJjvc9wfFUm9ZzV/iRnFgj4vopAqWqz6NWuoXfw+EomEgMfn4jNxIhLZLz8nLpeFhoZj1NcfRtdwFJutGgB399bXBN/bq/NvOnV9s9wS+f9inM5m9E3n0OvT0evTMBiyEEU7EokcL6/O+Pn2xdevH54eiZj0dvZ/cYXKfD0JvULod19r5MqbE6vLBwo4srEIN1M1HS4uxRbmy9GeXTAKDkLyTjLgXA1FgV7UJ0XiOXUhT54+jLv3MuQE8k3O4xSLNRyqOcp3AXcS513BqjmTGJ1VQrnJwoyqPOaMGcMdH51AIZOy67G+aIwlsKwPhKfA5K0tycc+7t6SG/6hwz/rprFXGqn7JAtX3VlMhz/F/7E5BDzyCAUFb1NS+ikJbd8iNPTGImkEUeD106+zIW8DU9tN5cmUJ393kXQ5BAoz6yhIr6Xkkg6XU0CpkRPW2puw1j4ERHriG+qO+gYLVQgugaY6Cw2VJqoKmijPbURXYQTAK1BDXEogbboH4xP8743ld5lMnLtzME69ni+euI0Dl27n1buSmdwz+tpnCos+xHbyLRLyjTwf9xgFblGsaaVF2WHcj9qa9vFeDpU5+WsfPxI62Jh7eC6dAzvTqH6MdKfAS/npVNZUIjMZ6FVaQWhmFsJtt3H1yRd5bHUmcpmE7bN60zbAgxeOPs/24h3MrTIxzW5E+sAOiOiGKIqc213MmW1FeMrNNDvdkAoOhmleY0G0ivO+NbTWduKLOz7CS9Vy0NBeXkH1KwswHT2GOimJkFcWoE5MvO7ciKKIyZSHruEoOt0R9Pp0RNGBRCLH0zMJb+8ueHt3w9urCwrFjR9qvFFuifx/CU6nCaMxm+bmSzQ3X8bQfAmTqQAQfngYvFLw9u6Kj0+PH2XRK8qs4+BX2bicIv3va03bnje3JBRFkdPfZHH+qA6fxhySL39O87h7OCQRsIvNtEk/Ruc8AzmhvjQntcJnysvMPXgUTcgnIHHn87y5KJ0SdlSuY1PACKQaKZsf78ecYiMX9EbuyUnjtYn38t6RclafLmmJpon0hlV3QM2VllOsXuHw3Qy49G2LwIe0/+kcNdmo+zgDl6EK454FaNq3J3LlF9Tp9nPx0qOEhU2gbZtXb2jM/yjwDyY/yGOdHvtdBd7YaOXS0QquHK/E0uzAzUtJXOdA4lICCYr1umbh/h6YDXaKMusoOFdLRW4jogjhbX1IHhBOdHv/3/W7fg1LTg6FY0ZzLkbk83v6oCu+m71PDCDcp8VaFUWBrMyHCT+yFa0eenf9is6mPJYOGY3U+4fIEbPNweCFO6mzSlk/vTOVsgzmHZtH77ABpIkPYHfC/PpDXC4wIHPYSK6sps3ps8h69ODM4/N5cVM2aqWcfbN7E+alYs7+RzledZJXKpu4UxSQPbS/xS0IZB0u59j6PHzVJhosLf3sJNvAqbjLrPQx4ikPYOXty2jrH/f9GESad++m+o2FuBob8b3/fgJmP/qrhUn+GafThL4p7SfGG4CbWwyenkl4eiah9UzG0zPxuhkzr8cfXuQbG09TUPAWbm4xaNxicHeLwe3769+xVPpn7PYGzOZCzOai769CTOarmM1F/D1gXqn0//4mt8fbuwteXp1+tq9Oh4uTm65yMbUc/wgPhj2YhHfQzY3J5RLY//YhrpZICa4+TbL1BOXTH+Do5UsgraNL6gliqm1cigjAmhBH4JSXmL3jBKqYT5Eg4YWSmXQzBrOj7hu2uben2C2KL6ZEssqlZW9dE4OvpPH8oL7olYGM/+w003rHMP8viXBqKeydB3cthU4TIXc3fHMf9H8Obnv+J/0U7C7qlmXiqGvGduE9XA11xGzZgs2jifT00bi7tyal89obShssiAJvnHmD9bnrmZ40nbmd5/5uAt9UZ+Hc7mJyTlcjiiLRyf4kDwgjvK3vv0VszQY7V05UcvloBcZGG9oADV3viKZ1tyCkv1Oo8K+hW7WK2jffYvlwKfta9aCz+0N89UD3a/PrcBjIPHY7HU9coVSeSK/uS5huPMtrIx/6UQHu7NIa7l52Gq1c4PDzd7CteBMLzy6kX/gd7BTG0cYsYbZjFeeztEhEiK6to8uRoyjbt2f33JdYtLsUdzcFh2f3ResmMm33A2TXX+HDSh29ZCrkMw6DT0s2ydwz1Rz6MhsflZFGkxJBqiBIvIy21Rqe9gOkIs92eo1JHYde65+rqYnad99Dv2EDitBQAuf9Fc/Bg3/Tc+RyWTEYMlsEv/kizc2Xrrl3ADSaKCIiphIRPuU33ZM/gcifobh4KWZzIVZb5Y9+p1D4olIFo1IFfX8Fo1T4IJdrkSu0KORa5HItUqkSiUSBRKpAKpEjkcgQRReC4EAUHQiCHUGw4XAacDqacDoNOJxNOOwN2Gw12GzVWG3V2Gw1uFzGa98vkSjQaKJwd4vBw7MdWs92eHomoVL9fBHif0RXYWT/yivoyo10GBhBz3ta3fQmnLWhme0v7qRWCCS6ZDedhoRxJjqSzKwsVBTRc086viYXGVGh0CaekMnzmPndaVRxy5FIzNxdN51ZtW05ZNjDPofIcb/ePD5ARUVMG1ZX6uiTn8m08AAGDB7KsPePIpVI2DO3H5rmYljWG2L6wYT137tpeoCbX8uhJ/mPhVoURRrW5WLJqkMipmLYspbwZUvR9O1CWvo9OJ3NdO26FbUq+OeG+ZO2Xj/z+u8u8MZGK2d3FJF7qhqJVEK7vqF0GBRx03sivxeCS6Awo55ze4qpLzPiFaCh219iiO8a9P/qtxcFgbIHH8KQfpanpooUS3vwep+/MabLD/mCjMZcKrYOpU2+no1+U5iTNJ15inLm9hn5o7bWHDzPi/ur6BUMax8fwbLMZSzNWEqPkFFsl9/N2CYY6nyFCxltEOVKAhoa6X/wEKroaNbNmc9nx3V4a1UcfrQPUrmFKbsmU6kv5YuKatqqfFHOPAjals35kss69iy/hDtGrCYXNpk7Kox0CfuQGSFOTAo93bRTWfqXOagVPxxOMp87R/XLC7Dl5+PeqydBzz+PKu766Yuvh91ej6H5Es2GSzQbswnwH0hIyOjf1NYfXuT/EZfLgsVSislc2CL61srvRbhFiB2Oht+pt39HikoV+P2LJBi1Khi1OhQ3t1jc3GJQq8NvuiKR4BI4v6+UtB1FqNzkDJySQHTyzcVyi4JAxfrtHNzdjEkTRNuybXRc+BBbz5+jpKQEf2cW3XZkI5eInI8MR52QSPikZ3h4w1lkrZYjk+iIt03lo6sduGy9xP6GdL4LvYs+sS46DOnBO8U1dK0sZERzLdOnT+f13Xl8eaqY9Q/3pFuUF6y8A+qy4ZEzLWGTm2dB1vqWcMnQjj/pb/Oxcpp2FqGKaaL+/WfxHjeO4Jfnk3VxFjpdKp06rcHHu+v1xy2KLEpfxFdXvmJa0jQe7/z4vyx4ToeLjP1lnNtTjCCItOsbRsqwKNz/C7IPQsuYizLrSdtZRH2ZkeBYLX3vbU1g1P9f/VJHTS1Fd91Fg7ecmeMacZn6cOD+xQRpf3jhVVdvRb12Gh4mKc9Hz+Or8IG8F65mQnzbH7X1yLJd7CoReaJ3AI+N7MrbaW+zJnsNib7jOeJxB3+zSwipm8vFi11wqd3xMDYz7MAh1D4+rJj5PN9k2QnydWP/o32wCDom7pyIpbmBNeXlhGsCUM46fK0+cG2JgR1LMhFtVlSWBppkASCKJHls4eOEfLIVFWhs3Vg6/HW6RP5gUIhOJ43r1lP30UcIRiM+EycQMHs2Mu1/R43Y/6jISySS4cAHgAz4XBTFX0xO8u/wyQuCDYfDgNNpuGaNOx0GBNGOKDgRRSeC6EAUXS0WvVSJ9O8WvlSJXP53698LhcILudzzX6ob+s80VJk4uOoKtSXNtOocSP8JrdHcZLEC8/kLXHn7C867DUaUyujsPEHU67NZt2kTTU16IpqP0WlXOWZ3uBAaiXe7ZKImPsG0tWlIY1Ygk5WjkE/j24vJNLka2FW+jg3R9+DuoWLaxN68VFRFZ6OOXlmnmTljBkVGKWM/OcXUXtG8fGc7OPUx7H0e7v4EOo6H/APw9Wjo13Jo5Z+xFjRSv+ISqjgN+q+fQ6KQE/vdd5TUrqSwaDGt4+cTEXH/DY3908xPWZKxhIkJE3mu63P/ssCXXNZx9JtcDPVWYjsF0Ht03H/Mcr8eoiCSc7qKU1sKsRjsJPYOodfoOFRu/z9lCg3791Mx5zEuD2/Hgk65hEtGsnvKwh99pujck0TuWIFJFc9D8bM47tORlcmtGBrwQ00Dm93B0De2UW5V8s20znSJD+GlEy+x7eo2ArVTydUOZKWXi6aMR8i50gOn1hel1cqIQ4dQS6R8NvUpNpa6ERXswe6ZvakyF3P/7vtRmWx8U1GEl3sI6kdSr4VyNtVZ2P5hBs0NFsIop8zVslfgLS2nOmE/qzwzEeyBjA5/nheHDkAp/2H17GxspO6DD9Cv34DM25uAx+bgPWYMEsV/thTkf0zkJS3qlwcMAcqBNGC8KIpXfu7zf/SN119DcAlkHCzj7LYiFCoZ/ca3/sXqTb+EraiIug8/JPf/2Dvv8KjK/It/pk8mk957hySkEAgBQg29VxVQiiALKDbUta5d13UtK1gREUWQ3pEqvUOAJBAS0nsvM8n0dn9/hEVZK6z+dpfd8zzzR57c5M5937ln3vt9z/eci1qudpqK0tRM/9gmxFNGsX7DBsQIRFduo/NBLQ0+kOMbhn9iVyLufoQZK88hDv0SibwQk/M8vs6Ow9MisKfyczaHplMpDuWJmd14ubaJJKz0OLqLyePHE5eQyKjFxzDbHOxb1B9VWxl80gciB8K0tWDRdzRByZw6rGilN65+bS0mGj64iFgtx163gbadOwj/ejXGUAMXs2bi7zeO+Ph3fhVZr8lfw5/P/JlxUeN4tc+r/1TAtklv5cTGQvJP1eHhr6Lf1E6ExN68P/2/AhajjXO7ysg+UInKRcaAe2KJSPp9unprn38BzcaNfDEnjV2+5xkTNI83hjx0/fcOh5WaDX0IzrtKg3wwM7rcxVXXGDZ2iyXV7Tt1UGFFHeM/Po1SKuLg08NRO0lZdHgRRyqPIHWdj0WVzurwWvL2vkBxYSpWL38kNhtjTp1G2dDAF3fOZ60xnM7Bbmyf15v81kvM3TcXX4OEVTWFODkH47TwMKg65tDYbmHnhzk0lrfRya2Oqy0+HSpPEfj4HuXtiEPoBSNepul8OH4OCUE3KmJMeXnUv/5nDJmZyMPC8Fn0KC7Dh//LzO1+juR/712aNKBIEIQSoWNreS0w/nc+538c6kq1rH8jk1Obiwnt4sm0F3veFMFbq6upee45isaM50KxC/mx0/FoK2b0SDna4emsWr0aFycZ3S6tJe6AlpIwMRf8wglK6UHMjEXM+OIc4sC1SOUF6F1n80FNIoFWOWcbdnA2MIISIpg5KoY36puJlUtIPb6XlMREunbtyoeHiilu1PP6xERUUhFse6CDyMe816GNPvRn0FbA2MU/IHiHxU7zV1cQHAKKsDratm/Da/48xHFB5F5ZhEoVRWzsa7/qxtlZspM/n/kzGSEZvJz+8j9F8OW5zax5+QxXz9TTfWQYU55L+48heAC5k5Q+k6O546nuKNVydn2Uw/4VuViMP+L38U/C75mnkYeGMnd3JZ66ZHZWf8qaK5uu/14sluEzbhs6tQJ3jrA8ZwUBxjpmZBdSoDddPy4m1J8XBgfRapVw39JDSEQS3h7wNqn+qTjal2G2ZPN4cSDJo+4lLCgLRW05dqmEbX3SaevShXu//oA/6M9QUKFh6udniPVI5N2B71LrZOHewE5YdJUYPswAYysATi5yJixKITTBi6saf+KCdEhsRnAINDRksPDCAnqautPi/Dl3bPgjL+3IRm/+bvyUcXGEfrWS4I8+QiSXUf3oIsrumoL+9OnffIz/WfzeK/k7gBGCIMy99vMMoKcgCA9+75h5wDyA0NDQ7uXl5b/b+/l3g0lv5fS2EnKPVePspqDflBgiu/767llbYyNNSz9Fs24dVokTeb0eppEAQppPM+DJYRxvqOfcuXNE+bkRvfNT3IvhbLKMelEwMd164TH+HuZ+eQlpwHZkrufQu9/N25IRpB3Xkd92lv3SfNarxtM/xYszgWrcJWJGnzvQEc02fz7lGgujlxxjdGIA701NgZMfwL7nYOJSSJ4K1efhsyHQ/V4Y87cb3rsgCLSuu4ohuxG3cf7UPjYLWUAAYWtXk5U7F632Ij1SN6NWd/7FcThceZhHDz1Kd7/ufDTkIxS3kOkKHSqkM9tKuLivAs9AZ4bcG49P6D8nbftXw25zcH53GZm7ynD1dmLY3C6/ea3ecOEi5dOnYx8xmruiC5E6l7A44z0yQr/LBdBeXYnLmofQ+oSiaZMyrvti5GovdnaPIUDxXTnykY+2s61Cwv29fHlqQg90Fh337buPgpYimnyeZJwskQV+y8jccI6GymBMYZ0REJGh0eC7Zy8Hu/Xn3eDRJEf78PXsNE7UHObxI48TYXJiZXU+Iucw1A8fAWXHytxhd3BkTQFXjtcQGWShoVSLTuaJXGTCgjNWv6usCV5Fu90Nt/bZvDamP4PjblyACXY72u07aFyyBFttLaqePfG+/35UPdP+31b2/8pyzS+S/Pfx31KucTgE8k/WcnpbMSadlaRBIaSNjUCu/HUbtJbKSpo//xztps0IdjuOMTM5q0vAaJXSxXiC5FfuZcvhw5SXl9M7UI7fyi+QNUnYPUiFrdWX+B79qE7ryTs725H670fmcRS963ieDZ3F4A1VaMz17NNvZZXfRNy9PNH1CMSOwNzKK2gLrzJ37lz8/Py5c+kpShp1fPvYALxMFR0WwpEZMG0NOGzwaQYYmmDhmes31d/Rfqwa7TcluAwNpW39G+hPnyZi00aqxd9QWrbkVzc8ZTVkMXffXGLcY/hs+Ge3HPbR1mRk3/Jc6kvb6NI/iL53RN90o9m/M2qKNOxfnouhzUL6pGiSBgX/pgRU/9ZbtCz/nJPznuId1XoUqjo+HbaUHv7fbZZrNo7F/fJRWn37UGVoYGK3TwhWq9maEo37NTWL2Wxm9BvbKDGp+GJWCv3jgmgxtTBr9yyq2utp9HuWP3kn0c3+JNkbWtDWeWCITMAhEpEmlRK+ajXZkV14NW4q8Z2C+Gp2Gkdq9vPU0afoZHZmRXUeDqdQXB4+jOha+pggCGR9W8nJzUX4+ooR11VQJwpGbW/EIHEHsZ0zYd+S5XMCXd1YhoWN5MWxXfBzvdGbymE2o1m7lubPlmNrbMSpWze871+Ac9++vzvZ/ytJvjfwkiAIw6/9/AyAIAhv/Njx/w0kX5HbzIlNRbTU6PGPdKP/1E6/erVoys+nedlntO3ejUgiwXX8BOq7jOb04TZkZi29fIrweHAa6zdvRq/XM1JVjWL1AawWCWtHq3Gv9CG5z1BWOgtk5kUj9TmOzHs3RvVg7oh5iOlrC3Gyitnf9AUbonpTaYvCe1g4DTYbL0sMFO7fw8iRI+nZsydfnSrj+W25vHNnMpNTAuDzEdBU0EHoLv5w/G/w7UswZRXEjb3xOoo0NH1+CWWcFxJFLnUvvIDfs8/CmEguZs0iwH8i8fFv/eJ4lGnLmLF7Bm4KN74a+RUeyp+ODPw5VFxpZt9nuQgOgYwZcUR3/2V5638iTDorB1bmUZbTRFSKD4PvjUem+G2+yBxmM6WTJ2Nv17Fo5INUeX+Ck0rPypFfXrdxFiw6zIs7IdhN4IjjnJMz93T9K93dnFmbHIXymsa/sKyKSUvPIJLI+fbJIfi6OlGnr2PGrhk0Gg20+P6JL2O7ICmfyZVNCoytzrRHJuAQiens6krCii+o8vDlha73EtYlkpWz0zhYtYdnjz1LF6sby6suYZMFoH70GOLv+eqUZjey7/MrKJQiQijnqsYfpU2Lq1MT9bZOGFUtHA3eSpFUgqRlEo8NTWJ6rzBk/9Cb4DCb0WzaRPOyz7DV1qJMSMBrzmxchg793TZo/5UkL6Vj43UwUE3HxuvdgiDk/tjxtzPJN1a2c3pLMRVXWnD1VtJ7YjRR3X65NCPYbOgOH6b16zXoT55E7OyM+9QpuEydwdF1hRQXWfFqzWPAKE8aU7qwfft2VEo54xp3Yd5VS5tczPKJaiLzvfHvMZBP7DW01A9F6nUOmd9mzKreJEU+zkPfXCHK4M2xxg18G6/g2+aBhI4Io8xhZ0mQK5fWfEVMTAxTp06lrs3E0HePkhLqzso5aYhOvg/7n4drBlE0F8PH6RA9BKauvuF6bK3XNlqdZXhM8qNs8kSUCQn4ffIm586PQyp1J63Hll9sYms2NjN913QMNgOrRq4ixPXmMl2hYwWXc7CKExsL8Qx0ZuSCRNx8bm9XSkEQyNpfyaktRXgGqRl1fyKuXr+NWsh46TJlU6diyxjKOPdUvDstxV2lYPWo1fiqOr44LUW7kK+aRn2QNz7VBjb73sWDcbMZ7ePGp13CkVy7H9bvP8nTB5qJ95az7bFhSMQiSrWlzPxmJlq7FKvP82xNDaQicxoFWwKxmVVoQzrjkEjxd3UldeMmTBY7r3SfhWtKMl/OTmN/5U6eP/E8XR0+LC29gFXqjfPDR5G6f2dy11jZzq6PcjAZbKSEa8m+LGAVK4hzOkutI5RWWyh1LqVkhh7maktvoly68MLYePrF/NBGXLBY0G7fTtOyZVjLK5D6+eExbRruU+5C6nFrC5Kfwr9aQjkKeI8OCeXngiC8/lPH3o4k31jRztmdpZTlNKFQSUkdFU7igOBfbGqyNTai2bSJ1nXrsdXWIvX3x2PaNDymTaVVC3veOYnWKCW69QR9/jSRk3V1nDx5klB3KcMufEHbOSmV3iI+n+BK0kVPaqOjueBso6FpNDLPHCT+a7E7JeMS/ARPZ5WQVuFNnuY0p+LPs7xmBgEZQZTJYXFMIHWb1mC321mwYAFOTk78YeV5jhc1su/RAYQ6qjrKNN8n9JXjoeYiLDzboZG/BsFqp+HjbGzNJnwWJlP37KMYLlwgYttmchufRduWRY/ULajVPx/gYbQZuW/vfRS2FrJ8+HKSfH5oj/BLsFsdHFlzlbyTtUQkezNkdvyvLpfdDijP7Xh6kUhFjJifSGC0+y//0a9A45IlNH30MYdnPsk7Fhse0cuIcAvnixFfXLd1Nm+6G/mlb6iPSsK/OIf3oz/g9aBEZgZ68Wan4GsBHgJPfLyZTRVKZvfw48XJHfx1pfkK9+6ajV7sjq/PC6zqJpBzYh5F26IRS1xo9AtHLFeglMnpfuYM3oVFvNf1Ttr6DuHLOWnsLt/CK6deoYcokPeLzmIXu6FceAi5T8T1a9Brzez6KIeGinZS05QUHy+hVRZImJBLiMsJzpimYLW5UeF+hUueWq60JDA0Pog/jY4jzOuH5ULBbkd39CitX61Cf/IkIrkc17Fj8LjzTpTJyb9JKee2b4YS7HZwOP7lWtXvo760jczdZdfJPXlwCEkZwT+rWXaYTLQfOIB2+3b0x0+A3Y5zejoed09DPXAgiCVc/OYqZ76pRGrR0905l8hn57Blzx7Ky8tJdW4g+fAudMVOXIiCdcNc6XnOi7NRMoyKOEq0/ZB75CIOWA3KWAy+T/BcYzODz8tpMddxNvILFjfejzQ5iEYvOS9FBeJ99ii5ubnce++9hIWFsetSLQ+svsCzo2KZ1zccPh8OzUUdTU8ufnBxdYfCZvS70OO+69cmCAKt6wswZDXgNTMe85Uj1D73HH5/+hOa9EZKy97/VRF+doedRYcXcbjyMO9lvMeg0EE3PTdmg5VdH1+iplBD6qhw0sZEIPp/8n75d0JrnZ5vPsqhvdnE4Hvj6NTjl7uJfwmCxULplKlYGxpYMPiPCH61aFw+oU9QHxZnLEYqloJRg21JAkaxAYkiEmVzJS/22s4ypYI/hvvzeETH+zAYDEz+61byTK4suyeFoYkdEZnn6s4xb+98jLIg+oa9wotheWSdfI2SndHI1F40eAXhpHbBZDLRub6BpIMH2RgzkKzhd/Pl3F58U7aJ18+8TndpCEsKToOgQjZvH07B3xmRWS12Dn6ZR9H5BiIT3ZGVXeZqWyAqm4YM3y+psfmSaZqIxKqiwa2SUzIpZQ5P5vSL4P6BUbg5/fh9bi4qomXVKrTbtiMYjcgjInCbOBG38eOQ+d2cZPr7+DmSl7z00ku3/I9/a3z66acvzZs376b/Tn/qFGVTpmIpKgKxCFlgICLp//+qzG53UHyhgcOr8jm7sxRju4XUkWEMvS+B0HgvpLIf1j8dJhO6Y8do/nQZtc89R9vObxDMZjymTiHgtdfwuncWishIDG1Wdv7lKHkX2/FqucKQwXKkU0ewat06NC1NjLUfJmzPRQzVCraki9jaT036WW9OddGhNI8jz5CMk2cBooBViJXRNHo/zoM2C4OOGRA5BHKDP+cL4120BgWjCVJxf4gPgzV1HDt2jIyMDJKTk9EarMz58hyRPs68OTkJ8ekPIGs1jPsAQtNA1whrp0JgCox6+wZ7WQsldZwAACAASURBVN2JGnRHq3AdGoYiTEzVAwtxSkpC/uAg8q8+R4D/JCIjH/3Z8RUEgb+c+ws7SnbwTNozjIsed9NzpGs1s33xRZoqdQyZHU/yoJD/2uBuJ7WcTmn+1BVrOyx5lRL8I/85h0SRRIJTSldav1rFQBcrn9pTGdo5miP1m9CYNfQL6odI5oTIIxxF1haqfSy46QTSyzOp6jSJz1o0+CmkJLuokMlkpIW4sPNiOTtzmxjfLRhXJxlB6iDi3Duxv2Q9pboc1J6z6RUkwio/TtNlBW5i0EoUBAQGUiY4qI+LZ9jp/XiUXuWvWm8eHTySMLcA1lbsINMvnhFNZZD5NcbA3ii9O8p+EomYqG4+SBUSLh+txe4ZQFqsnqpKG/mW/vhKDYx1/RM17jKMbbEktHsTJzFwvFjLB5kVIIKEIDek/1Cvl3p64jJwIB7TpyMPC8VSXo5202ZaVq4EAZzTbi0Z6uWXX6596aWXPv3RObkdVvKmvDxavviS9kOHcLS1IVKpUPfrh7p/P5x790YW+OuCJW4V2kYjV0/Xkney9rphVFJGMHHpAT9aArDW1aE/cZL2gwfRnziBYDIhVqtxGTYMt3HjUKX1uG7kJAgCBccrOfr1FWw2iNMdJ+2F6WQ2NnDo0CG8JAbGN23GdEyGxexgyRgJRQFK+l7wIzPBiH/TgxxCiZt3KVbf5UiVYdR7P8k9CjkTt+QTIg3hkvtKNrqFsd/WD1uiB5P9PHjB24nPli0jODiYGTNmIBaLeWZzDuszq9i2sA8J8jr4pB/EDO3YXBWJYON9cGVbR76nz3fSR1Oxhqbll1DGeuF5TyzVCxeiP32akI1fcKFuPlKp26+qw6/MXclbmW8xK34WT/R44qbnqbVOz/YlWZj1NkYuSCQk7j9H+/57wma1s//zK5RcbKTr0FDSJ0b90082TZ98QuN7i9k++RFWiMOYOvwSm4q/4onUJ5jVZRYIAo6vJyMUH6Q6KpjQwkoaHA/y6JhZHNYbWJ4QzshrXbHrdx/muSNthHsq+eaxIdc7UPfkfMMfLz6LVRHD4gFL8G98icLTmZQfCEQZGEKjqy8p3bqRnZ2NTCSiz+69mC1iVoxayLuPj+dc036eP/E8sYpA3r9yDmcHtI/4BL8+N/rHVOa3sO+zXOw2B/2HeZC3+Sw10kg87TUMDfwUsaSAj11m46hIxNPoj1Xq4ILETq2nlDkjY7ize/APyP77sJSXo922DaeUbqj73VqY/G1frvk7BKsV/dmztO/fT/uBA9gbmwCQhYXi3Ks3qtRUlAldkIeF3eCGdysw6ayUZDdy9XQdNYUaEEFIrAdJGSGEJXhdv0kEQcBaVYXxwgX0585hOHsOa0UFANKAAFwyMlAPHoRzjx6I/iEVRtdq5uBHp6mstOPSVk56khH/BXezbecOCouKSRAV0L8yk5bTUixKO8/dKcWoUNIj15OSGG/c6u9ln9SGX0A1es+lyOQB1Hk/zWBnVyZvOEQPeTeKFQc51qmIpbWzsHXzpp+nmhVxIXy5fDk6nY4FCxbg6urK6ZJmpn56uiOvdUQnWD4MWoq/K9MU7ofVd8CApyHjmevXYNOYaHj/ImKVDN+FXWnfv5uaPz6J71NPUdXtBC2tx0lN3YKL+kY/k3/E0aqjPHTwIQaFDOKdge/cdLNTfVkbO97PQiwWMfahrv/x+vffGg6HwLG1BVw+Wk3nXv4MmhH7TzlaClYrpXdNwdLQyKz+jxET7Y9f1Eb2le/j3YHvMjRsKGgqET5MpVltRyn1Q9XYRIXyM+4f1Ik8o4l1yVH0dFfjcDh45qP1rKtyYUqKL29O+U6WueHIOl4pex2HIo61I5ZgKrqf4hO1VJ/0Qh4SicbVm+EjRnD8+HHa29uJzb5EZEExKwbcy1OvzKVAd5InjzxJlCqQxZcv4uMwUdv9BcLG3/hU2d5iYs/SSzSUt9NtaDCy0hzO5zvhEEtJdjtHT6d3OOYbyifiJIKruhHekoQIETUSB/UeYgYPC2dyevgNFgm/Jf5rSP77EAQBc2EhhtOn0Z88heHcORzX4v/EajXK+HiUcXHIw8OQh4UhCw1DFuD/k0kwgiCgbTRSfqmZ0uxGago1CEJHiENs7wA6pXqjNGuxVlViqazEfLUAU34e5vyrOHQdrpRiNzdUqamoeqTi3LMnitjYHy0VCA6BS/uLOLWlFIfdQUzrMXo9PYl6D3e2bFyH0WhiuP0QgUUC7Reb0QbaeOxOBa4GJXHFrvhETya/IpqjShthoU00qT9AIfem1fcZYpQeTNqyjVHifjRJirjU4wteLXweQ3cfuriq2JISzeE9u8nMzOSee+4hJiYGk9XOqMXHsDoc7Ht0AE7nPuxQ00xeDol3gFkHH/UCmeoG6wLBaqfhkxxsTUZ8H+yKCD0lY8Yij4hA/OYICotfoVPM84SE3Puzc1msKWb6rumEuITcsIH3a1FXomXHkiyUahnjHul62ytobhWCIJC5q4yzO0qJTvVlyOx4JP8E0Rtzcym7awqN6YOZ6TWMv94Zy7b6FyhsLWTlyJXEesZet6O+GuVMTLkdi6UzFaFLmJ2goMlqY1u3aGKdnWhvb2f6O1vINnnx3l2JTOgWev08H29Zzodti5E4JbFzxF+ozJ1JyWGB+ixnJKHRGD39mDp1KsePH6e4uBi35hYyjhxhf0x/xix+mUYus+jQIkJVAbyTe4UwewvFofcRM+ftG+5Pm9XO0bUF5J2oJTDGnR49FZz+/DT18gjcHI0MDvwKF9FxPozqxlajlYTWPiS0DESqkeNAoEkBQfGejB0ZhX+Iy29aJrztSV4QhF8lRTQXF2O6fBnj5cuYLudiLihAMJu/O0gqRerpicTLC7GHF3qXQLRib5oc3jTZ3DHaO8jLRawjQFyDr6EIdUsxjtZWbM3NYPuu7VmkUqHs3BllXCyK2FickpJQdOr0i08QDRVtHP74NI2tUtw1BfROthH84CwOHdzNqYtX8KGZMWShOSxCXtdIUTcrfxqqJLTOmc5N/sS5LGSvRuCs0kZcJx0V0ndRyNywBT6PVOLOnfu2cqexB3aJmcI+L/Na0WtUdPEnwFnB7tRONBUXsn79etLT0xk2rMNb+519V3n/YBFf3ZdGP/fWDjXN98s0e56B0x/B7D0Q1vv6nLRuKMBwoWOjVRnnSfXDj6A7cgS/r98lq+lBPDx6k5y0/GfnTmPSMO2baRhtRtaOWYu/881tDtYWadjxQTYqFznjF6Xg4vnbhqvfjriwr5xTm4uJTPFh2H1drscU3goa3nmX5mXLWD7hMQ46h7PugS7cf3AmIpGINaPX4K3wQPhsELamXMpDXYkuaqbVupDq9Dnc425CIhKxo1sMQUo5eVcLmfnFBdpEzuxaNJAoHzUAgl3gtVVLWM9nqNSp7Bj6LHlZd1N+0JPGPBmERCH4hzBnzhyuXr3K3v37wWJlwLFjaBzOhC3+G2b/Zh459AjeCg/+UlRDorGCK87D6fTISmSKGz8z+adrObKmAIlExMBp0egOH+FsvgqbVEWMUz793N+lXGXntdAYsswNpCsy6K6dREu+gIuxg28FJwmhndwJj/UkIModzyDnf+oL9bYn+bJLTRz4Mg93XxXufk64+6lw8VTipJbj5CpD6SxHKhcjFouul1GsZjsWgwVjTSNtpbW0VTTS3qBHpxfQWpxoF1wQrrlLyq3teLSX4KEvw9tUjrPQjkguR+LujsTDA4mHB1IfH+QhwciCr70CA2+qJGRst3DiywtcvaRHZtURa71Aj+fuRqeWsmnNSur0IlJFuajanXDbk4NSbOHYMDMfJqroVO5Mf1EGsqZh7JVZyJbaSEowUiy8jVyiwj38FcqtLkw9sYc7G0LxUPhRmvYin2oe5rB/BC5qOXt6dMbDbOCTTz7B29ub2bNnI5VKya9rY8yS44xLDuTdOxO/1/R0tqNM8xPWBe0nqtHuKMF1SCiuQ8Jo272b6kWP4f3YIxQnb8VqbaVn2jfI5T9tnGV1WJm/fz7ZDdl8PuJzkn2Sb+pzUVPYyo4PclC7Kxj/aApqj38Pa+D/BGQfqOT4hkLCk7wZ8YeEWw4Td5hMlE6YiMVk5o60hxiRGsncwXJm7p5JrGcsy4cvR96Qh/BpBrX+StRWJS4tRuoMH1BzRy+mGprxV8jY3i0GD5mUdTv28cIJA35uKvY8PgiVvGPPy66z8Nja9zgo+wpv93Q29J9HzsVZVB6OoOmqgD04EmVYNPfddx9tbW0sW7seh7aVyIJCovOKcDz9Ai6DgnnwwIOIEPFmvY3erblcscUT9OAm3Pxv3NfT1BvYtzyXxop2EvoHkZyi4PjiA5QLYcgdRlJ9T5Eo/ZgdvoH8zc2ZVruRURGjSFfezdED7ZhqDITYJbg4OvhIIhXTfWQYPUZH/GAMfw1ue5K/mF3P3m1FBEikONqsGLSWWzq/SCzC2V2OZ4Aa7+BrrxA17n6q302BYbPaydlVQOaeCmx2MSFNp+gxIQafSSM4u+1jDhQakGMhWlxL1SkYWpoJ3g6+GmNjR4CK5BJ3xjg9QE2ZL3u8HRTYLKR2M5NnehO51JnOnV7jaLuKSZdPMjHPTGfXVKriPmafSx8+lvRA6ipne/dOJDkrWLFiBY2NjSxYsAAPDw/sDoHJH5+kosXAt48NwDP70w5vmr83PdmtP2pdYC7R0PjZJZSdPfGaEY9d00rJ6DHIgoMxvxxLdd1quiavwMur/0+OiyAIvHr6VTYUbODPff/M2KixP3nsj6H6ais7P8zGxVPJ+EUpOLv9j+BvFpePVHFkTUEH0c9PuOWVpiEzk/LpMygZMJaFHgNYPbcnOul5njjyBBOiJ/BK+iuI9j8PJ9/nUpyaLoUWbOIkGo0vUzSrEzOqakl2UbGuaxQKBP704WrW1HgyuosPH0z/zh/GUtnO7P2LyZGsI9xnAMtSx3M5+wGqjyTSVGjBEhSJV2wCs2bNQiQS8c7mbZjzc1G16ehz8iTmAaMIf3IqDxx+kBZTC6/aghheepAyoy+SqSsJSel9w3XZbQ5Obysha/93Xkf6s2c5saMKjVMIbo4m+gTswFu8gxVB0axUOHAAd8fezSD/aWw+38K+c9V4GiFJ5UTvXoFMHBNzS2N820sos5t0vHWpgn1GPUUeYqL7BpAxPILu/YIIT/QmqLMHIbGehMR7EtzZg5A4TyK7+hCT6kvnXgEkDgwmbUwE6ROjSBkaRuee/oTEeeIVpMZJLf9dCN5ud5D7bQm7l2RSWmDAvbWAftF1pL0yC5PuLOvWr+disxJ3oZW8JjdSj16ia10B4lgDr0yQcsxDSZ+yEPq0P0Jtswc7AwTKrVb6pFu5pPsLSpmaQYlvsV2jYHBpLsPPFdDVcyDNQfu5GqXiL8YhCO5yViZFku7hwsGDB7l8+TKTJk0iNLSj3rnyZBlrz1XyxqREUlTNsGEWxAyDwS90lGlOLoFL62HiJ9fzWm0aE02fXUbipsB7dgJimYTaF1/EdOUK6jfnUNKyhNCQ+wgOnv6z47Mmfw1Lc5YyJ2FOhxrjJlBXqmXH+9m4eDkx4bFu/yP4W4RvuCtOahnZByrRNhiIuAnzvO9DFhiIvaUFp282UxOTzMZKC38aloFEDKvyVuEsc6Zr9/kIOetx01opCRLj11SGIPfGNd+P5AFhfFrbTL7eyDhfT3p0DiMzM5MjtWJ81DKSQjq6RyVuCoZZotnbYqFCv4vMdgNT4mdiVa/Drg3HXNFAm9lKWUMTSUlJDExOIkemor6+loqIMJyvXkZYu4fZM17hlP4S600leIWPoHfjWazZWyjWuuIb1+36GIjFIkLjPfGPcKUws56cg1W4xoYz+P4eKAoyqWxSkG/qTa2+H8PtZUxvu4TG2ZP1zRfYV7WFnlFqXhgzDKWnC3ubtURGutM97NYUX7e9hFIQBPRmKwfyG9l0oZrjhY04BIj1d2FYF3+GxfvRJdD130IPbbPayT9aTua2AvQWOa7aEhI8a4heMIaGvM2U513khJCCSBCowp2eWh1p+zdiUTpQ9tLwUIoHzWIJw0u7EdwwFau/K2tlRnQWG2n9LJytfQ0nmRtTU9/l7So7qQ3lTPhmJ0MD7sbkXkJp70M80PAgBh8lb0QGMjvMl6KiIlatWkX37t0ZO7ZjxVyjMTL03SN0D/fky1ndEH0xuiPpaeHZDm+alpIOn/jvdbresNG6sCsyXxW64yeonDsX9/mzKEzdiEIRQI/UjYjFP028J2tO8sC3D9AvqB+LBy2+KSVNU5WOre9eQOEsY9IT/yP43wJ/r9HHpQeQMT32luSVdp2OkrHjMEvlTOx6P3MHd+aPwzvxxJEnOFBxgA8GfUA/owlW30FFpB+uGj1ubQ7qTB8iDY9hx3B/ni2q5p4AT97uHMKly7ksWJNDA25sXdj3Br/3uo1XucO2Ba15Ncl+fXguOoqq4qXUHOpFc1kbhsAIwlJSmTZtGlKplOXFlXy7dy+dG6pQa9tIPX+B6PlzeMn7CGfqzjIvcAjzTqzCZhO46DqF1IVvonRW33B9Jr2V4xsKuXq6Dq8gNYNnxeEiN3Hub9vJa/TGKnchQFxJD++NmBSnWOofyj6pDaVUyZTOU5gVPwtXuectq29u+5V8VsNF5h+4jwgfBY8N7Muc9E74uSqpajWyNaua1Wcq2Hi+iuJGHQaLHU9nOc6K/99mKZPeyvmtV9i3NJviy+0oNdXESrOR9nPD4Xwe2cUP+bbJh8t0RuKkJjyxG332rqTz6TNowyzYhrSxINYbi13K2CujCGibjKyXP0u1LUglYlL6mzhb/SrOcg8eTv+AV8otxGobGbN1FUMCpiJy0lPbZx2Pt/yRVm8Fc709ebxTIFqtllWrVuHh4cGUKVOQSCQIgsCidVlUtBj5YnYP3C59Dhe+gLFLILQXCAJsnAPt9XDPBlC4dGy0birEXNCK191xKCLccBgMVM5fgNTLi+YZ7RgtlaR0/RKF4oc+H39HeVs58/fPJ9gl+KZtgzX1Bra+dxGpTMyE/22y/mYIiHK/7vNjNtgI7eJ50wsmsVyOIjKC9tWriA105e0GF4Z38WdS7BCOVh1lc+FmBqXMxUPXhGtRFvnRSnwbjai869GWpZLspEQa7c6yqiYcAkyOjULVVs6JKit78xq4MzUU5bVmQ+cYT/qcVLPFw43a1h0UWZwYGpqKyGM/1pZo7NX1NOqN1Gm0xMfH093LHUtQGKtMIgJ0WirDQtCfOM2kMjWi7vGsrNtHaewQ+jbXE208Tea+b5FG9MbF67v9JKlcQmRXH3xCXSg637GqF2QK0uYNpEuiEsuFs1Qa/cgz9ae1rT9jTBruNmbRKlewqSWHr/O/RiVT0tX3h9GYvwY/t5K/LUi+oeQguSW72Vx/mtV5q2i21DA8thML+3dnRq8won3VtBltHMhrYFtWDcuOlbAzp5b8unbq28w4BAF3lexnGxZuBYIgUFus4eQX5zm8ppDqUhNuzVdxNWbS4FdKtPcherRv5oo5gD2iDMxyT6LSo5EU7aHrR2twatGh6mvnUpqBZwK8UOudGHPpPqJ8BtPW15t3s8oJ93YmsKeWrIpXcVF48/KAj3ms2ECAvo3x6z9hiP8k1DIXGtJX8IrtWYpcZQxTOLGkeyQOh4Ovv/6a9vZ2ZsyYgYtLh3b8m0u1fHiomKdHxpLh0w7rZ3as2Ie81FGmyVkPp96H4X/uSIAC9KdqaT9chcvgUNS9OvxqGt57D/2RI0j/NJRadtG508s/W4c3WA3M2z8Pk83E8uHL8Xb69WlG7S0mtv7tAoJdYMKiFNx9/yeT/C0R1Mkdi9lOzsEqHA6B4FsIUZGHhWGtrMTj251cCk/m23ob96RFMiC4P1uKtnCo4hBjB72B4uJq3G2elPia8K0uQRIRQdtFdwZEe9PkIWdZVRPecilTU7pQf+UUZ1qcyKvRMD6lw/dGJBHhHuNBtwMKtgb6UN+8k1KbKwMCY5F6H8HWFI29ppaGdj1NOgOxsbEkuTrj4+3NOwpPPHQmbGo5JWIx3fcWMjCsF5+ZD3M6NI6eEj8SzZlUn9hOudGbgE5dbvjC8/BTEZcegLHdwqXD1RScqcMzJoCU2f2JS1AiXDpHXZuKAmsfajTDSNe6M9OQhV1qIkHuTmTU8Fuan9u+XNN8/Fuy159B7bybfQHV7HB1wSASiHIJY3T0eEZFjiJIHYTN7uByTRunS5o5VdzMhYpW2k0dskeJWESUjzPhXs6EeakI9VQR6O6Ep7P8+ksllyL5h0dVQRCw2gV0ZhstejPNOgu11ToazlVgL9TiEFRIbCa8mi5ipBjfyHIGuF5CgZkz8gEcFbpjsgnIwuVcat/PnVvriKsCfawn0QmV/DlAzm61E6GN3oysfpTeY1L4urmFTReqGRLvR21IIeUVb+GuCuDdjKXMymtFYtBx19rFDHLLIFwZT33Kl3wa8ADb7GLirWIODE1EJBKxd+9eTp06xeTJk0lMTARAa7Ay+N0jBLgp2bKgF9KvxkJ9Liw83ZF6b2iBD1LBMxLm7AWxBHOJtmOjtZMHXjPjEYlFmK5cofTOu1CNHUjx8P14ew8hMeGDn1wBCoLAk0efZF/5Pj4e8jHpgem/ev5NOiub3jqPQWtmwmPd/tfo9DtBEAQOr8rnyola+k/tROLA4Jv+H7bWjk14vbs3kxPn8sK4BO7tE0FmXSZ/2PcH+gb3ZbFrd8Q7H6EsORG3igLcdRKa3VZgrnfBY0ESC5ob2d/cxrIu4fQUWXn4g82cMgfz1IjO3D8w+vq5zGVaDq3L5aG4iyhbPyXZJ4kH/EW015+m7lBfmiubMASEkzhgMGPHjkUsFnOkpZ05l0sJrW2kx+UzqCQW/OrqSLC280ZqIQZPFW+7dKP72c9pMSvJdJ5M7wUv4er9w6fT2mItR9ZcpblKR2i8J33visHD3xlzbT25n+/lap6FFnUkCA4CxKWk9FcTMW3aLc3Nba+uyf5oOyeyFAhiGQqHjiBxNq3ep9gXWMF55w5S6eoWTUbkaPqHDCTKPeq6011li5HcGi25NW3k17VR3mygosWA2eb40XOJRSCXipGKxVjsDiw2ByIB/O0ikgwmOlvEKKQdyTtu2mLc9TkE+10hNuAKSrEBh5MXJcGT2NnohUZjRqfSkelyioyLWiadFBArlQROiKdd2MVDvr6UKKR0L+3G3eFP0Hl4OI9uzeFihYZ5A6PYIz1GS/UH+LvG8MmQj7j7ciPNOj1TN3xEH2VnklR9aYnay87USSzRiPHR2ckc2RWFVEJeXh7r1q2jR48ejB49+vr1Pb0phw3nr1kXVK2F3U/C+I8g5Z6OA7Y+ADnrYP5R8OuCTWPusA5WSvF9sCtipRTBZqNsylSsdXU0vyzD7mShZ9o3yGQ/7XT4d8uCR7o9wtzEub967m0WO9sXZ9FQ3s64R7oSGPPbuCn+Dz8Oh93B7qWXKbvUxMh5iUSm/HTp7aeg3fkNNU88wf7B97DUK5X9jw0g0N2J1Xmr+cvZv7Aw+QEWXNiO0JDL+QQ1KRfrEAX2pr7ueZBJcFmQxNTCcnLajaxNjsK5opiH1+VQKXiy5g+96Bnpdf1c+nN17DxSwtOxOaibP6GTRxQP+IFDc4W6I/1pKKnBGBBBypDhjBw5ErFYTFabgXtyShCMVlIvXCFcV4BEJBBVVka5XwWbu2h4JHIcU4+tQGTWcbw1Fp+JL9MlY+gPFjEOu4NLR6o5u70Eq8VBfJ8AeoyJwNlNgWC1UrP9IJd351NhDyHGX8fAN2be0rzc9iQvOBxoz16kaOd5ykotNKmisEsUiAQ7ro5qrPLLZPtVkenbgFbZRJDMmd5eCXQL7k/3sEEEqANvmBxBEGhsN1OjNdGqt9Cst9CiN2O0OLDa7FgNNmi14NyoRVmnw2ZUIIikIDhw05bgb7pMtPMFAv2vIHex43ALpSyiJyeUoeQXmJG1yNBL9OR75JFuVDBqZy3y+lZcMtLxibjMaaGMJ719sAtSJmnmMGfSLEoEKw+vyUJvtvHMhHjeb9yEqf5zory68cng95l5uZa8Nh13bVtOmsOFdJdR6L0vc2pMGs9US3Fqs3FyUAIBrk60tLSwdOlSvLy8mDNnDtJrZm5/ty6Y3z+SZ3opOzzhw/p01N1FIig5AivHQd/HYMiLCFYHDUuzsTV0dLTKrpVImld8QcObbyI8kUpt5Cm6pazGw6PnT87fubpz/GHfHxgYMpC/Dfzbr673Cg6BvZ9dpvhiI8PnJty2YR//brBa7Gz7W4fJ27hHu960TbEgCFTOm4/+/HnmZjxOXFIMy2Z28NNzx59jZ8lOPkh9mv6bHsYU1ZMy4SKxRXps6W9QdyQJRaQb4ntimZBdRL3FyraUGHIPHOSV0yYkSmf2LhqI7/dSmzQ7S1hbUs/r0YV4Ni8hSOXDQj8BhbGK+iMDqCuqwBgQTvKgYYwePRqxWEyJwcyU7GKaTFb6luuQ5Z8hVKFFarMRWl3C7ohc/Hqn80JlOZ6VJylu9yTf+076z38SF88flhkNbRYyd5WRe7QasVRE8uAQUoaFoXC6lohVVY1DkOAUcmtOoLc9yX8fgiCgy7pE+b4LVBdoaDSqaVOHIYg7NmXEDjMioZZ2qYY2hQaNSotDacBLIcHb2ZlANx/85D64iD2QCc6YdQ50LWbatXb0OhFtRgVmvgtZUOlr8WwvxM+eS4jrJSTBjVQF+lLhHUGZixe5YhtljQ2ENYURog/BJrYhjhLTJyCCmK+OYDl1FkV0ND4TuiCpXsbb7m5scHfG2+DF693fo2f3JD46XMzfvi0gwtuZB8bF8mL+p4haNtDVvz/vZ7zNvZcrOavRMX7PanobrAzwGINNriF3SiDzq5wRt1nZ0i2GtFAPrFYry5cvR6PRMH/+fDyuhRf83brA5hDY+3BfnNZOhNpseOA0uAWB1dRB7VZFiAAAIABJREFU+oIDHjiFIFXSurEQw/l6vGbE4dSl44NtqaqmZOxYpF3DKZ+eTXj4A0RFPf6T81Wnr2PKzim4yl1ZM3oNarn6J4/9RxzfUEj2gUr63BFN1yGhv/wH/8NvBqPOwqa/nsekszLpj93xDLi52EVLVRUlY8bS1CmJ6RF38uE93RmdFIDJZmLm7plUtVexxnsAYSc/oqL/WJxz9uCpl2Lsu5OW3UbUA4LRZQQx9kIhDkFgS3IkX3+xli+qfIgNcGXjA32vb8QKdoGmL3NZatPzSWgFvs3v4iZTsNAPPKwa6o/2p/ZqCcaAcBIzhl4v3dSbrUzPKSG33cAYg5TMk1cZ7shHobShNBpRNV/ldJqB52L6EHf0fUxWEQeauxA07nG6DhuF+EcsUrSNBs5sK6EwswGFSkriwGCSBgXjpJb/4NibwW1P8ia9lbKcJqK7+/4gl9NhMtF+PpuGC8U0ljTR0mSlzeKEWe6GWeGOXfrLqTgihxWFWYPSrEFubkRMFVZFFRr3Oioj7BT5y2mWQKvDgkX4ztrAy+pFN303XFtdEUvFxCXHMSy+G/oVK9Bu2YpYpcJz5h3I9ZtotBfziJcvFU4ShqmG8/qE19GbYdH6bI4WNDK+ayDdewXw2vk3Uei+ZWDYaP7a91X+kFvBgeY2Rh3YSLq2mUG+oxFZJRRPUXBvgxc2nZU3Any5t0cYADt27OD8+fNMmzaNzp2/c4q8wbqgdSvseqLDQrjbjI4DDr4GR9+CGVshKgPdqRo024pxGRSC27Bw4LsVmuF8Jo0v2FEGx9C92xrE4h/31rbYLczeO5ui1iK+Hv01Ue5Rv3rO/96RmZQRTN+7Yv4t5LH/bdA2Gtn01nkkUhF3PJV603LV5uWf0/DWW3w5fD7f+iRw4LEBuKlkVOuqmbpzKt5KT1ZXVuFkt3AxwZ2kU5cRB/dC674Y/Zl6PKfFUh6lZuLFIlykElZG+fLe0g3s1YcyPrkjXP7vnwuHyUb9h1m86QcbfOoIbnkbiWDmfn8JQQ4dDScGUn2lCJNvMHEZw5gwYQJisRi93c6DVyrY3aRltMyJqyeqEWnqmCgtpE0iIDebQF+E37A47indg7K5gHytDznyDPre9ziBnX7ceK+hvI3MXWWUZjchlYvp0jeIrkNDUHvcmiLstif5KydqOPRVPgqVlNj0ABL6BeHu99PqCsFux9bUjK2uFkNFHcbmNqwGM2aDCa2mAZ1Vg0nQoRd0tCh0tKgNtKgFGp0dNKgciCUypFIFUrEUhUSBh9IDD4UHnkpPfFW+qLVqmq42UVVWhUKhoGfPnqR26oTxq69o/XoNAE5jJ2BXVxNs3syXLi585OWGk9iZv2T8lf6h/Tl0tYGnNuagMVp5cWw8V92trM16GYUpi6lxs3gqdREPXqlga6OWIUe3k6GtY6B/f2StvhRPEpij88ZksDJTcOLNMV0AyMnJYfPmzfTp04ehQ4deH4+rde2MXnKsw7pgmDt8lN4hlZy+qaNM05DX4VeTcAdMWoq5VEvjsksoY9zxmtXlum7677VW84xANH2b6Jm2Ayenn15hv3rqVdYXrOedAe8wLHzYr57v4gsN7Fl2mchkH4bPS0D8Xxj48e+ChvI2trx9Aa9gNRMeS/nRzISfgmCzUXrnXZgaGpmS/iije8Xwl8kdTXWnak6x4NsFDPHuyttnt2LpMZ1SzQ5iC7U4RrxDU1YPrNU6fB7oyhVnEXdmFeEtl/Kuu4Q3Vx0hyxbE0yNjWTDgu4WDtclI7YdZvJCgYJ9rM5Gt72CyNDLLR0GC3EDruRGUnr+M2cufmIzhTJo0CYlEgkMQeKOklvcrGkh3dsItr41DeQ1M8jHQuSWLWpkMqcWC0lbH8FQvuuR9jsUu4khdOELy3aRPmY6r94+XEptrdFzcW0HBuXoSBwTRb8rPp6L9FG57khcEgZoCDZeOVFOa1YjDIRDUyZ2YHn5EpfiiVP/+iVEGg4Hs7GzOnj1La2srarWatLQ0UkJDMaxZQ+uGjQgmE45eQ2nzdyFO9CVNKgNPePhR7CymX0BfXun3KiqJO69/k8fqMxV08lPz6uREFjeVcCH/ZWTWCp5Je4ZpsVP4Y145q+o19Duzj1HaGvoEdUFVFUvJCAv3iXzQ6S30a3Lw9T2pSCViGhoaWLZsGQEBAcyaNQvJtUdJh0Ng8icnKW828O2j/fDcdAfUZHWoadyCweGAFSOgqRAePIfN5tJhHayU4ruwK+JrNUW7RkPxqNE4fKRUP1RFfMI7BPhP+Mnx2lq0ledPPM/shNk81v2xXz3OjRXtbH7rfAepLEr5wZPb//D/j6LzDexddpmYHn4MnRN/U09VxkuXKJsylZLew1joM4Sv/9CT9KiO0t+Kyyt49/y7LFJGMCf/OLXjFiE/shgPvRhh1knqVzYjkorxe7Ar560WpmQXE6yQs0hXw+ID1ZQ7PFl+byqDYr9LXDIVa6hbcZlne6o55NRGsv5jqjWXmejlRIazBUPeZPKPnMHi5k34wGHcceedyK4lzq2tbeaPV6sIUkgZb5Sx4kAxaqWUF7oI1J/ZQ51CjSAS4enQM9itlDjdt9QZXTnaFENAxnR6TrgLpfrHy5FtTUYkMvEtN+/d9iT/fei1ZvJO1JB/ug5tgxGxWERIvCcRyd6ExHv+ZqHFADabjYKCArKzsyksLMThcBAaGkpaWhoRgGblStq+2YUgQHt0b+q9Qujutw4vp2o+dPbkK281Lor/Y+89w6sqs/f/z+k1vfdGQu8dKUqV3rsUQVCBEbuijmIfu44oXVERBOlVkN5LaCEJSQjpvef0uvf/RWgR1OjM/P7fcbivKy/gPPs5+zx7n3WevdZ938uTl7q8zIPRD3I+r4Zn1l8kt8rCrB6xDOgazmOXTmAueBc1Zj7p9SE9w3vyWlouy0pq6HThCBOsZbSN8MEjtRO53Z084hVArdlBXKaFnY90wVOtwGq1snz5cux2O48++iienp43P8O3J3N4dWsKn4xvzUjXHtj5NAz9rM5wDODsyrr/G7EEsfl4ypYl4Sq1EDi3NYqgW3nYopdepnbrVspfdODXbgjNm3/8q+uWWpnKlF1TaBvYliX9ltS1hGsAzLV2Nvyj7v4Yu6AjWs9/LY95D/8+JO7O4fTWLDoNjfnDJlsl77xD9XereW/wM+QExfLTkz1RK+pEec8deY6fc35mcbWVrtpQUpsH0vjAXghrj7vPRsqXJaOK9cL/4RacrDUzOekaMRoVI9LO8U2aEqtcz9a53YkPukWrtVwoo2R9Os/38uK40sb9zu9JLjnAfZ5KxvoISAumcn77fpx6bwK73s+khx5Cra5Lo5ytNTMrOYcal4un/PzYdzCHy4W1jGgTysPBFZz6cTVVHiHY1Wo0bgetyKCd7DxVtVLOmJoT3388bQcORaP/99J8/6eC/A2IokhFvomriaVkJpZhrLIB4B2kJeK630RgtCdeAZo/tvOwWsnMzCQ9PZ2rV69it9vR6/W0bNmSlgkJcPAUtRvWQ1YabrmKwuBuGCPDaRu4mVh1BjtVHnwUGEiF3MnA6IG82PlFFHjw0d4MvjmZQ6iXhg/HtuKaRsKrl3aiq/gCL4WGpf2+oJlvM15NzmR5hZnWKWd4TKwmPtSE99n+5LdyMTsygGqLA+9L1eyc0YUoPx2CILB27VquXbvGtGnTiIqKuvlZimut9Pv4CG0jvfl2RACSJd0homNd3l0iAUMxfNEJQtsiTtlC9aZMLIml+D3UFE2LWwwC86lT5E1/GOsgLdYxejp32oFcfvebuMZWw/gd4xEQWDdkHb7qholqXE43Wz6+QGWhiVHPtScg4h4X/v8SRFFk/6orpJ8uof8jzYnv0PB+pW6TmawhQ7CptIxsPZtZvRvzwoN1uWyL08LkXZMpNxayLieToPtfJKtgBQlppQgD38MqHUH1xqt49ArHa2AMh6oMTE3KprFWSYej+9laGYGflwfb5nXHR3drU2A4mEfZz7k83ceHc1IHYxT7OXDtW5po5MwMFPCsfpSTP+zGrdXh0bYbU2bMQH99F17ucPJYSi7Ha0xMDvYltMDK4oPX8NMrWTi0OfKqvaSu3YBKCKY0OARRKsXPUUVj+TWkxlpya31o1Hsk7QcNR+/rd9c1+aP4nwzyt0MURapLLOSnVpGXWknR1RpcjjoevEorxy9Mj1egBq8ADV4BWjQeCtQ6BSqtHFHiprCogLz8PPLz8yksKkAQBNQqDaGBUQRoQtFl5KM4fwR91hnkLhtmbTDFET1Qt1DRRLqBCFkWSTI1/wiK4LLKTiPvRizotICOwR3Zk1LCa9tSKDPamdolijl94nkzr5idGd+hr/2RWO9GLO6ziBBdCAsSk1llctMq9SxPezgJ8UnD98RIimOkPNrcjwqrE/npctZMaE/XuLqb58CBAxw5coTBgwfTsWPHemsy69tzHMssZ+/87kRuG1cneppzoi5NA3VK14w98PgJTFc11Gy5hscDEXgNiL45j2CzkTV8OA57BSULjLTv8gNeXu3ueh3cgpvH9z1OYmki3w78lhb+LRp8/W4EkAcfbUFc23tUyf+LcDsFtnxygfJ8IyOfbkdQjOfvH3QdxgMHKZgzh8S+41no2Ynt87rTLLTu+DxDHhN2TCDc6eTbggIsEz9B3DYPHxNI5yZSfciJ+XQJvpOaoG0VwN6KWmYkZ9NMJSdm3372WeLoEO3HdzM73/SGEUWRms2ZlJ0rYX5/X1JEF496pbIx+X38ZCIzA1zEM5+jq3bikspQNG/P1NmP4etbtylxCSLvZdfl6VvqNTzl48M/d6SRVmKkd5NAnn8wil3JX2DYsJ2EqnCq/cOp8PdHlErRCFb8nGWYjG5Co5vQecAgIlu0/pfIA3/5IJ+ceJafd+8kNDyCuBYtCQ4JxdvbG61Wi/Qunu6CW6Cq2EJZjoHSHANVRSaqKgyYLUYEmR2X3IJbbsalMOOWWUACiCB3eqBweKM1awksLcC/Ko2AiksoXGbcCg2Opl0Qu3VBLTuDf+kuguSVJMvVfB4UxwmlEU+lJ/PazmNswliyyq28s+sKh9LLaRriybujWuLwkDMvJYOaoi9RWU7TP2oAb973Bhq5hicPn2adqKbVlUQWxnqgEncTcGwyJcFq5rT3pdzmRDxZynv9mjKxU12x84bgqW3btgwbNqzeTbT7cjGPf3+elwY1YbZ8F+x9BUYsgTbXFXfpu2HtBOj9d+wRs+5aaAUo++RTKpcupeIJJ2ED5xMb87dfvU6fnf+MFZdXsLDrQkYnjP7Vcb/EDYOsP5MKuIf/t7AYHGx4LxG3S2DcSx3/UI654In5GA8d4pmBLyCPiGTT491uWo0cKTjC3P1zGWax85auGdkt44ncsRwhuAWK6YcoX5GCs8hUZ4wXrGNHWQ2PpuaQIBUJPnCSk7YoRrUN46Nxt4Kp6Bap/DaF8qxq5g/w44rLyfMh1Wy48CpmZw2TfV3093+eg0t/wmazIcY1Y8Lsx4mIiLh5znsqankqLQ+rW+CV2FCEbAOf7ruKSxCZ+0AjercSWHLxn1QdP8SDqXoCjL4UBwVTHhSAVVNHDpG6nSgEgTZt2jBw7Pg/te5/+SB/bMkHnM4uxqTWI/7CsVCr1aLV1nV+l8lkSKVSJBIJTqcTh8OBw+HAYrHguq2rE4BO44GXzhe92ht/iRK/yiqUeVeRZV5GkpsBooBEq0Pbqxe0b0OtKw/p1R3ESDLQyl2cVviyOjyWw5SjVWiZ0mwKU5pNwW5X8cm+DH44k4dOJWd+n3gmdYnk07wyvsxKxrfyn2DPY367+cxoMQOA2Tv2sV0fQKv0C3zaMQxjxVKCjz1Csa8Hczp5U+1w4T5ZxiMtw3l1aDMAysvLWb58OQEBAUyfPv1m8Qig1uqk38eHCfBQsXWcH/Ll999yk5RIwG6EL7qA2hPX+L2ULU6pK7TOaY1Ue2seW3o62aNHY+0oIsxrTru2a5D+Sn59f95+njz4JKPjR7Ow28IGX9ucpAp2Lk6iUbtA+j/S/B5V8r8AFQUmNr6fiH+4ByOebtvgzlLO0jKyBg/GFJ3A6LiJvDKkGY/0iL35+uKLi/ny0pe8XFHFuD7/ICfzU2KTM3H2X4i0xVxKF124WYiVahXsKKvhsdQcItwO/I8mcdkWwhN94nm63y0Gi2B3U74sieoKC88M8OWiw847MRp+uvwqyZWp9PV08Wij5zm+8hQ1JUXYQ6IZOmP2TRsQgFK7kyfT8jhYZaSvnycvBAew+OcMdl0uIdhTzdP9EogKL+HTcx9ztfgyvQu8GJ7tgyq9jHIfXyoC/Knx9yFGJzLkrUV/as3/8kHe+P3HlHy8HIdFgkmvx+jhgdFbh0GjxerhheDtjVSnQ6bRItWoQSZHqVCglCtQKORo5HL0Uik6UUTndKKvrERSUIgjPx9HVhbumpq6N1IoUDVritgkAYOfJxZTOpqKRGLVxXgrbdhEKTuDmrExwJvLljy0ci2Tmk5ievPpCC4NXx3P5uvjOdicbh7qEsUTfeLJcTl5Nj2fa2WH8K35Go1Mxvs936d7WHecTicTN2znWHAsrTMvs/T+cIpy3iXkxFwKtf7M7eKJ2S3gPFHKoEg/Fk1qh0wqwWazsXz5cmw2G7Nnz8bLy6veer20+TI/nMlj6+NdaLl7JNQW1DXk1l+XqO9+EU4vQZy2m7Id6nrWwTcgut3kTJyENTuF8oVSOvXehUZzdy+T7NpsJu6cSIxnDN8M/AalrGEF06piMxveS8Q7UMvIZ9uhuMek+a/B1cRS9q5IoUXPMHpNavz7B1xH9dq1lLz+BrsGz2a5tgl7n+xFpF/dfSeIAn/bP48TBUf5uspM/OQ12H8YhpfBhXRuIg6TP+VLk1DFeeM/ve6Jc09FLbOSc/C3mfE6lUW2zYcPxrRibIdbu3G3yUH50iQMZgfP9fflrNXGh42Dycj+go2ZW0hQuVnQYhZZmyvIT76EwzuAzuMm80DvPrd4+KLIV4UVvHmtCLVUymtxocTZ4N3daVzMr6FxkAdP9YtH55XJypSVnCs9R4DUi+muTnRLLkeWeAnPPt3we3XJn1rvv3yQzzfk8/2V1fS3e9Po1AlsF87iKLfhMMlxmOSIrj+++xO9vXD7+ODy9sTsqcestIGkFC+hhBC1kVCtAYVUwI2MiwHN+Dk8hj22PCpsVUR4RDCpySSGNxqO3aFk+dEsVp/MxexwM7BFMM8NaIy/j4Z3s4pZlV9IgGENouEArQJa8X7P9wnTh1FWWsrk3Qe5HNWEbrmp/LOPD1lXXif87LPkyUOZ19UDFyLO46W08dbx/SOdUStk9QqtU6dOJTo6ut7nOpNdxbilJ5nVI4aXtVvh8D9g3HfQbFjdgMLzsKIPYvsZVJtmYblYjt/UZmia1S8QVa3+ntK33qJ6movYGR8THHT3zk1mp5lJOydRbatm/dD1De7R6rC6+PEfidgtTsYu6HjPNvi/ECc2ZnLh5zwemNKEZveF/v4B1FmU5E5+CFtWNtPvf4b4hAi+nXGr+1OtvZaJ20ZjMxSx3rMDzladCFj/Ci6/aNSPncOUWEbNpkw87o/A68FoAA5UGph+ORtPixGPxFLKbRpWPdyJ7vG3yAOuWjvliy9hcbt5oZ8fx80W3ksIR2vazzun30ElcfO3Rt0JzezAuR1bcKu1RPYZxKgJk1Aqb21aMi02nk3L51StmW7eej5ICCc9q5r3f0ojp9JCQpCeOfc3IjyklG9TV3G44DCCKNAttBvTmk6hW3j3P7XW/7EgL5FIxgILgaZAJ1EUE297bQEwE3ADT4iiuOf35vuzQX5Pzh5eOvoSDsFBoCaQPpF96K6PpEN1KZqc47iuncddY0RwSHE7pNicGqyCEptTgsMFTmS45RJQgKgQkWsE1EoXapkLL4UNL6UdmaSuUCsixeIZzcXIlpzw9OCIKYccYx5yqZyeYT0ZGT+SHmE9SCky8u3JXLZdKsLpFhjaKpR5vRvRKFDP5tJq3rhWRKUph8jaJRitucxoMYN5beehkCq4ePYM81KyyYxqzPDKfF7uVMu1Kx8Sff7vZElCmNdVj0wqQTxZRpBUxsbHu91kDtxwlhw0aBCdOnWqt052V511gd0lsHe8J9pv+kPLMTDqukOpywHLHwBLJca2m6ndW4Fnvyg8+9QXNDlLSrg2aCDWKAvKt4bQvPlHd70uoijyzOFn2J+3n2X9ltE55Nf9a3553J5lyWRdqmD4/DaENfb5I7fDPfwfgSCI7Pj8IoVXaxj5TDuCY7x+/yDAlpFB9qjRVHTqxZTAgXw8rjWj2t16SkyvSmfKjvE0tZpZ3uszSjI/IzLxFPbuj6Hq+x7Vm6/WFWInN0Hbsu7p9EiVkalJ19CYjGgu1uBwKtnweFeaBN8qDjsrrJQvuYRdLuGVvr7sN5p5NjqY4V61PLl/JnmWWgYEhDDN61n2LV6CSxBQN2/HpMfm4ud3axMkiCJriqt441ohdkHk0fAAHg8P4HBqGV8eyiSj1ESEr4apXaLp0VTBwaIdbLq6ifGNx/8hc77b8Z8M8k0BAVgKPHsjyEskkmbAWqATEArsAxJEUXT/1nx/2tbA6aaotoY0w2n25u7lWOEx7G47comcVgGtaOHfgqYqP5o5XISbq1FW50F1DlirEK3VYK0GwQ1c76Su0CGqvZFofXB5hVHgGUi2WstlqZtL1hKSK1OxuW0opAo6BnekT2QfBkQPQHRr+Cm5hLVn87mUX4NWKWNk2zBmdI8hLkDP8Wojr18rIslgItb+M9aK9XgqPXin+zvcF3Yfoiiy4euVfKD0IS8sjjlSK5PCjpB/7TtiLr1JMkE83UmPTiFFcaYCwexk85z7iPCte5w9d+4c27dvp1OnTgwaNOiOdfpwTzqLDmbyzdTW9Dowqi73PucEaK4H0cMfwMG3cPRcTtnPIWia++E7qekdnYDy5jyG6dhhDG/402HIbuTyuws8ViWv4qNzH/F0+6d5uMXDDb6eNwqt3UY3om2/e540/82wmZysf/csgltk7IKGWx/cKOivHPkM+zSR7Hu6F/76W8fuytzKC8df4SGryNOTd1Gz+j78yoyIM/cgDe5I+bIknCVmAufUFWIBTlSbmHQxE4XFjOqiAZ1EwYbHut38/gA4ikyUL7uMWy/nw37+rK+qZWqoHwvjAnj78KNsKzhPhErJKy3eIHnpZgxlJQhB4QyePY/mLeqzxUrtTt68VsSG0mr8FXJejA1hfJAPB9PKWHYki8TcapQyKYNaBjO+YxitIzzQKv+cjuc/nq6RSCSHqB/kFwCIovju9X/vARaKonjyt+b5s0H+p+RiHlt9njYR3jzYIpj7m3hTI1zlVNEpzpacJb06HbvbXneuSAjQBhCqC8VL5YVeqUcnr7sJ3KIbt+im1l5Lla2KSmslxeZi3Nd/m+QSOU18m9A6sDWdgjvRJaQLdoecQxllbL9UzJGMclyCSFyAjildohjVPhxPtYKLBgsf5ZTwc6WBUEkZgTUrKaxNpW9kX17u8jL+Gn+qy0pZ8skHfN++L1U+AbwerKaT61MqS08Qd/kfHJP681IbLaFqBdoLVRQVm1j3aBdahdc5AGZlZbF69WpiY2OZOHHiTUXrDSQX1jL8i+OMahvGB57r4eSiOtuCRn3rBpSlwdIeCLEDKc58HLmXkoDH2yBV1Z/HsPdnCp94AsNIgSYvrsPL6+6dbE4Xn2b2z7PpE9mHj3p91OCCaUFaFds+u0hs20AGzLpXaP0roKLAyMb3zhEQ5cHwJxtWiL1BzXW6RMZ0nEvfNpF8NqFtvTHv7X+K1QX7eNezNfd1mYrm6/FI1F6o/nYFt1VK6ecXkChlBM1tc5MwkFhrZvz5dNx2J6pLNQRLlfz4WFcCPW6lA+15BipWJiPRK/hqcDCLSisZ6O/Fl82i2Je2iHfPL8MsSHgobihxiXqyT57ArdbRZMgoBo0cfdPV9QbOG8wszCziTK2ZBK2ap6ODGBroTWapiTWnc9l0vhCj3cX0btEsHNb8T63x/x9BfhFwShTF1df/vRLYLYriht+a588G+YJqC1svFrEnpYSkgloAYgN0dIn1o3OML20jPbFSREZ1BgXGAgpNhRSbizE6jBgdRiwuCwAyiQyZVIan0hMftQ++Kl/CPcKJ8Yoh2jOaeJ94LHYplwpqOJdTzdGr5SQV1iKKEOqlZmjrUIa2DqX5dX7vqVozn+WUcqjaiKfUxX0cJilvDRqFhpc716lcJRIJZ/bu4sedO9jUbyJutYbFTbR4FzyBzVhGoysfsU2q553mGlroNaguVHIlt4YV0zrSK6HuUbSiooIVK1bg4eHBzJkzb6rzbsDpFhi26DgVJjv7xqjwWjsUOjwMQz6pGyC44asHESszKZcvx2XWETi3DfJfqIPdRiNXB/bFoa5Bv2QusY2euOv1KDGXMG77OHzUPqwZvAadomEOhcYqGz++exa1TsGYFzugVP+/bdF4D/85ZJwt4eeVqbTuHUH3cfENOsZ88iR5D8/gWv8xzNN24evpHXmgyS2NhFNwMmttH1Iclazu/Ab6yv2E7f8eS6uBaEf9gD3XQPmyJNSN6lN/00xWRpxKwSSKqC7WEq9Qsu7RrnhpbjHHbgR6qU7B9pHhvFZQSgdPHV+1jMZmSOT1I3M5bXQSpQtgts80rq3aitvpQpXQnLFz5hMcXL/2JIoi28tr+SC7mKsWO420KuZHBTEy0AeHy82elBJi/fW0jvhz/RD+pSAvkUj2AXerlr0siuLW62MO8SeDvEQimQ3MBoiMjGyfm5vb0M91E4IoIgIyiYTCGit7U0o4erWCs9lVGO111EgvjYKmIR7EB3oQ5qMh1FtDkIcKvVqOXiVHrZDhFkTcgojdJVBldlBldlButJFdYSGn0szVMiP5VVagrpNU2whvesQH0DPBn9bh3ki1evusAAAgAElEQVSlEqxuge3lNawqrOC8wYK/Qs4gbTaXcxZTYMynf1R/FnRegL/GH2NVJRs+/gennSI7+4zDVylnSVwN9qynkLs9iUl+l6/VChbFq+jlo0d6vpLTVyv4YlI7Brasa7FnsVhYsWIFNpuNWbNm3bQOvh2f77/KRz9nsGx8E/ofGg5SOTx2DFTX0yynFsNPL2IMXEhtfgf8Z7RAHX/nPAWvPY9h/XbsbyTQZswmJJI72S52t53pu6eTbchm7eC1xHg1jNfudgps+ug81SVmxr7YAZ/gP2Zdew//93FkXQaXDxbw4OwWxLVrmKCt6MUF1O7YwbvDXyRTH8zep3uhv60/c0VNDuM3D0GJlLXj9mD/sS9BOQU4JqxE2WQMptPF1GzOvEPEl2uyMOT4JSrlKpRJNXRQq/luZmc0tzG4bg/0p8dG8VReMb4KOd+0jCFBZeX7kzP4Kj+TGreUweEDiDhgxpaZj1vnSadxU+jZf8AdOh1BFNlRXsunOSWkmm2EqBRMCfXjoRA/AlV/3mPrL5+uOV5tZN6VPIYHejMqyIeW+jqrApdbILXYwKX8GlKLjVwpNnCt3HSz5V9DoVHIiPbXERugo1WYF60jvGkR5nXzZhNFkXMGC1vKqtlQUk2Ny00jrYrh3hbyCr/lUP4Boj2jWdBpAd3CuiGKIie2beLk+tUcb9ODkx1601Kr5E3P7ZiLl+Gl7Ehw4lN86CPhh0glwwO8ES5U8HNKKe+PacW46/Qvp9PJ6tWrKSgoYNq0aURG3pm/ziitc5h8sHkwnysXQcoWmPkzhLevG1CdA192xalrT2nJ83gNjsOjR9gd85jPJ5I7eQrWB2Q0/3gvavXd2RILTyxk49WNfHr/p/SJ6tPgNT60Jp2UI4X3FK1/YbhdAps+PE9NiZmxCzr+plPsDbiqq8kaNBhHUAjDm0xnYpdo3hrRst6YS4lLmZ78OV20YXzQ/2Mky3qiEOQonkhFovWjetNVzGdK8J3cFG3LW4yaghoDw45dpEjrgSK1hj46HcumdLipigVw5BspX3kZqVZBycRGzMwvotrpZlGzSAb66bmc8S5fpX7PYZMCtUzLYEUP1NuuInGJaBOaM3rOfIKC79wjC6LIvkoDXxVUcKjaiEIi4bmYYJ6IargdxO34/yPINwfWcKvwuh+I/08VXi8YLHyaW8KBSiNOUaSRVkU/P0/6+HnSyUuH8he/pkabk+JaG2UGOya7C7Pdhc3lRi6VIJVIUMqlN/u6+utVBHqo7sgNW9wCp2pMHK4ysqO8hkK7E5VUQn8/L4b7CiTmfMuWzC0oZUpmtZzFtObTUMqUFKRfYceXn1JZVcnO/hPJCo9jtL+CCda/4zBdIsJ3FrJDfXkhDI75y5kV5o/pYgWbzxfy6pBmzOhetzMWBIENGzaQmppar0fr7XC5BUYvPkF+tZWf+1fht3s29H4Fej5XN0AU4bsRiLlnKTEvQtW+BT5j7vRmF51O0of0wlVbhf/atwmOubtaddPVTbx24jUeafkI89vNb/D1Sz9VzL5VV2g3IJKuIxv9/gH38F8LQ6WV9e+cRe+tZswL7RvkIlq7fTtFzz3PhREzeYmmrHmkM90a1e++tH7NYN505vFYo7GM89Djt/l1LNGt0E87iugSbhVi57apZ6yXV1bGuGMXyfEJRJZjYohcw6JJbVHI6gf6iq+TQSZBmNqUR8tKOWewMD8qiOeig6ms2M2hpBfZVCWSaoVQbQitsnwJvGBEVGpp0n8IA8dPqidIvB2ZFhvfFFbQw8eD/v4NYyD9Ev9Jds1I4HMgAKgBLoqiOOD6ay8DMwAX8KQoirt/b75/1bumyuliZ3kN28pqOFVjximKaGVS2nloaeeppZ2njgSdmgi1EsUf8CB3CAL5NgfJJiuXDFYuGM2cq7XgEEVUUgk9fDwYHuhNW42VTRnfsz59PW7RzfjG45nVchZ+Gj8MlRXsWPJPipPOUxIQxq7BUzFodDwbUEar8meRShU0CXqPwt1ePBEnI1sv5c34MFJPFrHhXAFP9U1gft+6XKYoiuzevZszZ84wYMAAunbtetfzXnbkGu/sSuPzoWEMPTwEglvC9B1wvUsW57+DbfOods/BGT6RgJktkNylKJb/6cuYlmyCBZ1pOm3VXd8ruSKZqbun0iGoA4v7LkYmbZhwqbrEzPp3EwmM9GD4k22QyhqmjryH/17kJleyY9Elmt4XQu8pTX93vCiK5M+ajeX8eV4a9jJlGm/2PNkT3W1pG9FYyqure7JFq+Sf939Ks7NvEZR8HsvAv6Pt/Cxug53Szy8gVcoInNf2pkU2QGZ2NnNOXCApNBZpmY1BLgWLJ9QP9M5SMxUrkxEcbvRTmvK608ia4iq6eutY3CwaL6GYy8lPcbosib2WQHIsBkLkgTROUhKZI0XmG0Svh2bQrtt9/xEywV9eDHU3mFxujlWbOFxt5JzBTKrJiuv6R5VLIFKtIlApx0chx0chQyGRIJFIkABmtxuDy02N002B3UGRzcmNtt5KiYSmejVdvfX08vGgs7eeQsM1VqWsYlfWLkREBscO5vHWjxPuEY7FUMuB71eRfvQAglvkUs/BHG7WGV+FlOfVawk2rMfbuzMJ+rc4sbOMp5upcKplLG0ezfb9WWy6UMiTfeN5su8tKfbRo0fZv38/Xbt2ZcCAAXf9/FnlJgZ+dpRe8f4sdbyEpCIdHj8G3tdTOoZixEWdcDiiqNZ9TMCctsh0d+40TJkXyRsxEVcbLc2/OYZMdifFq9Jayfgd45FJZKwbsg5vdcOKRy6Hmw3vncNca2f8y53Q+/w5L+17+O/DqS3XOPdTLr2nNqVpt5DfHX+jraSzVTuGhYzgoS7RvDmiPmXRduE7pp59iwKNnu8GfU3Adw+iNdngsWPI/Zthz6lrdqOK88Z/WnMkslvBNiUlhdeOJ3K8USswuehvlrJiXP1A76q2UbEyGVeNHb9JTdjhK+H59AK0MilfNouih7eG7JzPyc7+kjS3P3tMHuQYi/ERPWiUrqBRnh7P4Fj6TX+Exi1b//sWk//RIP9LWN0CKSYr1yx2sqx2sq12Kh0uqpx1fy4RREREEbQyKV5yGZ5yGWFqJVEaJZFqJc30Gpro1CilUixOC3tz97L56mbOl51HI9cwOn40U5pNIVQfirmmmgNrviHj2EFEtxtTaDSnhk7lokRJL52RyZYX8ZQYiIt7Hr/qB/n6SBbvNVESolbyTatYluxMY8vFIp7ul8ATfW6xES5evMiWLVto2bIlI0eOvLsBmyAyftlJ0kuM7OtymcBTb8LolXXCJwBRRFwzCa7uo4wv8Z07GEXAnflRQXCTOq4bkkwD4Zu/wivmzicGl+Bi9s+zSSpP4ruB39HU7/d3ZjdweE06yUcKGTy3FdEt/X//gHv4y0BwC2z750VKswyMebEDfmG/39v3RoP4Uw89zeum0DvTNqJI0ZrRjLen4+8dzeI2M/H/fgYO7wC0c1JBpsB0ppiaTZnouoTgPTyu3q767NmzLDlxhr3Nu+J0iXQ3wNrR9QO92+SgYlUKzkITXoNjKWrjy+zUXNLNNmaF+7MgNhSH8TxX0hZgMmdRoOrGAYPAxfIklIKc2DwNcYV6onya0nvywzRudXcK8h/FvSD/b4Ldbed08Wn25e5jT84eLC4L0Z7RjGg0gjEJY/BUelJ0NY2jP66l8PIFRFFE8A3EMmwS6/RBWN1uZip30NX2Nb4+XWnS+B0sZyW8nFfM1nAlPTx1fNE8ire3pLD1YhHP9k9gXu9bAT41NZUff/yR6OhoJk+efAcf9wZWHc9m4fZUPuytZ8zJEdByHIxaevN1MWkjkk0zqHE9jPrhhajj7r7zzlwxH+eHe1HPH0TM43dXtX5w9gO+Tf2Wd7q/w9C4u1sb3HXu692E2vaLpNvoe3n4/0WYa+2sf/ssSo2csS92QKn5bcqs6HKRM34CjpISnhq0AINcc0faBmMJJ5Z343FfHf2jB/Csu5ago+sxtR2Cfvj3ANTszsZ0uACvQTF49Kzvt3T48GE2nT7L7tb3Y1DIaGYQ2DmoNRrlrfcQHG6qfkjHllqJrnMwysExvJNTwsrCCmI1Kj5rGkk7vZyc3C/IzV2KXO6J4P8Qu8sL+Tl3H07BiZdJQWyhjnhHFH37TqBzv4F3bfzdUPzlg/zR03v4bv8iusT2YvTAh/Hy+vcY8QuiQGZNJokliZwpOcOJohNYXVb0Cj19IvswKn4UbQPbYq6u4tKBvSQd2oelvBRRKkUSEELIg8PZEhLPkRozTRUVzHC8QbTSQaO4Fwj0HULy1qvMV1pJ85IxPzyAJ6KCmL/2IvuulPL8g42Zc/+t4JeRkcEPP/xAaGgoU6ZMQaW6e2oju8LMwM+O0DnKk1XGx+p2Ko8dA/V1+baxFOHTjricgTgHbUHX5e6mYlW5hyge9RiScG+abDp+1xtwd/Zunj/yPBObTOSlzi81eF1ry62sf/sMPiE6Rj7bDtm9PPz/LAozqtn6yQXiGugyaktNJXvsOJz9BjFc04spXaJ4Y/gv+hJc+oEVB57jM19vnuvwDANPf4B/XgGO8ctQNR2PKIhUrU3DmlyB76T6jJsb9a5j585zqF0/srQq/E1u9j7QnFD9Lf2JKIgY9uZgPFSAqpE3fpOacNJu48m0fApsDmaFB/BcTDASWyZpaS9Ta7iAXt+M4OgnOVtTzeYrG0mqSQbAwywnosqDfrFDeGTS839qHf/yQf7DHxfyjWUjAFIBAm2exHrG0q5RF5qGtSRQG4i/xh9vlfcdreZcgguz00y5pZwSSwnF5mIyqzPJqM4gvTodo8MIQIguhO5h3ekT2YeOQR0xFBeTfvoEaadPUJOXDYBbrUUX3YiOQ0dz0i+UT/LKkIpOxour6cMeoiOmEx09Fwxy1m1N5fUwCSikLGoRTWe9llnfJHI2t4rXhzVnatfom+eYlZXFmjVrCAgIYOrUqWg0d5c+u9wC45aeJLPMxN64DQRnb4KHf6rr9gQgiji/GIm8/BjGVj/gObrvXedxOmtIefR+lKftRG1ci67JnY+U6VXpTNk9haa+TVnRfwUKWcM4vm6XwKYPzlFbbmXcSx3x9P/3tWO8h/9OnPsph1Nbsug1qTEtet5J3/0lSj/4gKqVX3HwsTd4v0Rbry8scD0dOYGnDOc5pNPyRffXabPhERRuCfK5F5B6hiM63ZQvv4yjyEzA7JaoIm952AiCwObNm0m6fJm0tv04rNeicoqsahPLA8H1n3rNiaVUb76KzFOJ3+SmOIK1vHmtiG+LKvFXynklNpQxQd5UlO8i89r72GyF+Pv3ISbmCUwSX/Zl/cy2CxvIdOfSQ2zL5zO/+VNr+JcP8gDFtUVsPLia01lHKJKWUqt1YFcJd4xTSBUopApkUhkOt+Om3cHt0Mg1JPgkEO8TT2v/1jTXJaCudlGQfoWc5CTKc67hNJsAcKs0yPyDaXJfTzr06MVxUcHbmQUUOtx0JJEp4jKaB3UnJuZvaLUxVCSX8/LlXLYGy2mpULK8fRxap8jUr85wrdzEx+PaMLT1LQ56Xl4e3333Hd7e3kyfPh2d7tdFQl8czOSDPel81qGK4cnzoP9b0O1WEw/79mWozj2Hye8JdHPfuMOTBup2MsnfT0D+VhIeD48k/IV37hhTa69l4s6J2Fw21g9dj7+m4fn0Yz9e5dL+fAY+2pLYtgENPu4e/roQBZEdiy5RmFHDmBc74B/+2/l5wWola9hwRKmMx+9/CrtMzk/z70zbmL7szKQgH2rVnixpOoKEra9jC4pBN+s8SKW4TQ7KvryEaHfXKbxvczp1u91s2rSJlJQUbO378o1cg6iS8niIP680CUd22xOHPc9A1fdpuE0OvIfEousSwiWjlZeuFnDeYKGDp5bXGoXRTi8nP/9rcvOW4HIZ8fPrRXT0XLy92mNxWrA5bPjqGtYO85f4ywd5t9uK1ZqHXl/nW221Wrl08jjnzx4kp/QKFsGAXeHEqnIjSEUEuQypWolKrkYj16BVaPFEh5dbi5dbi8okYDOasJuMWGuqEJzOm+8lKFS4NTr0oREkdOxC87btCAoOZm+lkY+yc0ixQDTZTBK/4YHACGJinkCvi0d0ujn8UybPScwUaCXMCfTjhWbhZJeZmbHqLNUWB0untKdH/K3Al5+fz+rVq9HpdDz88MN4ePx6X9PUIgPDvzhG/xgVi4omIGnUGyb+UNcEBLBfvoJiQx9cyjgUz+5Horq7p3vetZXUTv8AhdKXhF0Hkf4iLSSIAvP2z+Nk8Um+HvA1bQIbXjjKTqpg15dJtLw/nJ4TEn7/gHv4n4HF4GDd22dQaeSMXdARheq389Om48fJn/kIjonTGGFryYSOEbw7qlX9QZfWkbVjDpMiooj1a8I/RCuRiQcwdnsIj/5fAOAst1D25SVkegWBj9dviuN2u9m4cSOpqakEderLP4xy7IFqWmhUfNU6lkjNre+G2+yken06tvRqNK388RnRCDRy1pdU8da1YiqcLvr4evJ8bDDNNW4KClaTl/8VTmcVXl4diAifSkBAf6TSP6d6/csH+ZKSbaSkPoVe34yQ4JEEBQ1FpaoLlqIoUllZSWZqMjkpl6kuLsJSXYnLbELidiFxu0FwI0FE5Pqvs0yGKJMjyuSg0qDx8cUrKISw+MbEJDQmLCwMtVqNXRDYXJTH57lFXHNoCRKLGS3dxtiQEKIipqHV1jXNrik08vaxq3wfICFYlLKoTQzd/Dw5mFbG39ZeQKOUsXJah5tmYwA5OTmsWbMGvV7PtGnT7mj8cTvsLjfDFx2nwmhjr/41fEUDPHYUtHW7AmexCffSoShJhVlHkYbdPcAaDJdJ//s49HsgYtXX6Lt0uWPMoguLWJq0lFc6v8L4Jg1vVWassrHu7TN4+KoZ/Xx75Ip7DUDuoT4K0qrY+tlFmnQJps+0Zr87vuiFF6nduZMDT3/IBxkulk/tQL9mtylGRRF+mMS+wmM8FeDNmPhR/C3pe7xLy3FMXo26UR1RwJ5VQ/nKZJThHvjPbIH0NoGW2+3mxx9/JC0tjeb39efNHIGyaC0quYy/x4fycJj/zV29KIgYjxRg2JuDVK/Ed3Q86sa+mN1uviqo4Mu8Mqpdbh7w9WB2eAA9vGQUFa+nIP9brLY8wsIm06TxG39q7f7yQd7hqKK0dDvFJZsxGi8DUrw8W+Pn/wD+fvej1ze5w2fF6XRisVhu/gnCrdSOSqVCo9Gg0Wju6BMrCC6uVKayKj+XbQZfakUdoWI+EzXnGBuRQFjQEBSKuvye6BTYdegaf3caKdJImaTVs7B9DB4yKV8fz+Gtnak0CfZk5fQOhHjdyk1fu3aNtWvX4u3tzdSpU/H0/O2GyO/9lMbiQ9dYGXeEPkXL4eHdEFnn3e6qsWP6/G283Z/hfuBdZL3m3HUOp7OGcxsG4flmNZ7DBhP+jw/vGHMw7yBPHHyCEY1G8Ea3Nxos6hDcAls+vkBFgYlxLzVMzn4P/5s4vS2LxF059J3elMZdfps/f8PyQB4Zybwuj1NqcrDnqZ71LIkxlsAXnfk0MJiVMgsvt36UET+9hgQZ8rkXkXnUpUYtlyuoWnMFdWNf/KY0RXI7P97l4scffyQ9PZ2OPXrzxTU5F72lCAFq2nlo+ahJBE31t76/jgIjVeszcJVZ0HUMxmtwDFK1HKPLzcqCcr4qrKDM4SJBq2ZKqB8jAj2RGE+gVofezEb8Ufzlg3yWxc43hRX09fOkuaKEmvJdVFQevB7wQSbT4+nZCi/P1mh1jdBqY9BqopDLvX41UImigMtlwGLNxWLOItNQyJ4qFwdtEVwjDonoppPiKhP8XAyJ7IKHvj4NMCujkjcu5/KTr5QYl4QPW0RyX4gPVoebV7cm8+O5Avo3C+KT8W3q5RIzMjJYt24d/v7+TJkyBb3+t/OT53KrGLvkJGMjzbxXOgv6vg7dnwRAsDipXLwXP8NMCGuP9JHtcBdevSgKXDr/CNIXTqCy+tBo10/IfvHkkFObw8SdE4nyjOKbgd+gkjVcuHRD+NJvZjMSOjasM9Q9/G9CcAts/fQiZXlGxi34faO62m3bKHr+BSTzn2VYYSg94/1ZPrVD/e918kbcG2bwWNNOnHdUsajJSDrt+gBbYAS6Ry/dVIDfMDPTtg3EZ2xCvZqV2+1m8+bNJCcn07nrfeyu9mdzaTWSFj4IMgmPhAfwVFQQXorrflZOAcO+XIxHCpB5KPEaHIumlT8SiQS7ILC1rIYVBeUkGa3IJNDb15OZ4f7c7/vbG7pfw18+yG8rq2Feai4OUUQnk9LdR09HTx0tNE4inIk4TRcwGC5gMqVxu32ORCJHLve6vvOWUdc0RMDmMJHv1pAjRnGF5qTSglJJ3a4iQVHDQG+RCZFNiPG8c6dRW2bmo5PX+EbnBgk86uXN022jUMukZJWbmPP9edJKjPytdyOe6puA9LYb6cKFC2zbto3g4GCmTJmCVvvbO16Lw8Wgz47ictjY7ZqFR6OuMHEdSKUIdhcVKy7hVTofpSobydyTt9Suv0BOzpeUfvEJntvlhC/6HI++9Vk3BoeByTsnU2uvZd2QdYTof1+heAP5qVVs+/wizbqF8EADJOz3cA+majvr3jqDzkdV52/zG6m9G5YH1vPnObNwMX8/Uc67o1oysdMv7vVNs6lO2cT4+BaIcgVfaLxJOLsXU7th6Id9d3OYYX8ehp9z0XcPw2twTL0fC0EQ2LVrF4mJibRv34E8fWM+PJiJqqUvtf4qfBQynosJYUqIH/Lr32t7noGarddwFppQxXrhPSzuZhMTgCsmKxtKq9lYUs3McH/+9n/VoOzfhX+FXWN2uzlebWJfpYHDVUZybY6br4WoFESplUSq5eixoBJqUQhVuNxWbG4HNreTGreaSlFLuVtHvtsbJ3U3ll4q0NlTSU8/f/r7+xCjvfsO1lRr46uT2SzFSqVKylBRyd87xhDpUfcYtzOpmBc2JqGQSfhkfBvub3zLaVEURY4ePcqBAweIjY1l3Lhxd3jC3w0vbEhifWI+a72+oIu2EGYdBK0votNNxdcpKPNW4CX/BoZ/AW0fuuscVVXHubx7OgHvKvDsO4DwTz+p97pbcDP3wFxOF51mef/ldAi+6310V5hr676sGg8lY17scK8R9z00GDmXK9j5RRIte4XRc+JvpzAcBQVkDR2GtksXFrSbwoX8WnY90YNo/9ueAmy1sPg+UhQypnrJaRvYhrcLThGUX4Bt1CeoW80A6r6LtduzMJ0owmtgNB69Iuq9lyiK7Nu3j+PHj9O0aVPC2t7PUz9eplouEtI1lKtuJ/FaFc9EBzM00BuZRIIoiJjPlmDYk4NgdaFpHYBn3ygUt9GH3aKIQxDR/EnNyP9EkP8lKh0uLhgtJBkt5Fjt5Fkd5Nsc1LrcmNz1qZUSwF8pJ1ipIFiloJFWRXO9hqZ6DY216pu/yneDscrKitPZrJDYqFRJ6eiU8mrLSDqG1BVRa61OXt+ewqbzhbSL9GbRpHaEet92cd1udu3axblz52jVqhXDhg37VSXr7diZVMzcNeeZ45vI847F8MjPENwS0SVQufoK7ozTBKqeQ9J0CIxddZNlczts9hLOnBqCzwdOlOUa4nbuQO5fnw75UeJHrEpZxatdX2VswtjfPa8bEASRbZ9dpDSrlrELOuIbes8f/h7+GI5tuMqlffkNsp+u/Opryt5/H+2b7zI0RUNcoJ4fH+2K/PagmXMcVg1mS/N+/N2SxkPxI5lzcgkaqwsePYY8oK7YKwoiVevSsV4qx3t4HPqud9pqnzhxgr179xIWFka/oaNZsC2DE1mVtOsYQmmomiybg3itiiejghge6INcKkGwODEeKcB0vAjRLaBtG4S+exjKkH/9u/GXD/KCw427xo4isGEFPUEUsbiFOlthiQSZhD/kDCcKIplpFay6WswGtYtapZQuDinPNA6jR/Qtte3hjHJe2JBEucnOnPvj+Fvv+Hpe1VarlY0bN5KZmUn37t3p06dPg86joNrCwM+OEqeo4kfHXBSjl0KrsXU35w9p2JIKCPF7FqnMWad21d7JvRUEJ+cvTMK9NRXP9QKh77+H17Bh9cZszdzKK8df+cOKVoDEXdmc3pbNA1Oa0Oy+u3vP38M9/Bb+iHBOdLvJmTQJZ24eV99bztzdufWcW2/i59fg+Ke83WU8P5SeZEHCcMbuX4RL5416bgoSZV3AFV0Cld9fwXalCp9R8eg63VlLunLlChs3bkSv1zN+wkQ2XTHy6b4MPDVKRgyMY7/bTprZRoRaydRQPyaG+OGvlOM2OjAeysd8pgTRKaCK9UJ/XyjqJr71Cr5/BH/5IG+5VE7V2jQUwTo0rQPQtvK/o3XdvwpREKnNq2VXWilbLGaOedc5VvYRFDzeOJSuEbcCaZXZwfs/pfHD2XwaBer5aGzrO9p6lZWV8cMPP1BTU8PgwYNp3759g87D5RaYsOwUaYVV7JI+RWS3MfDgu4iCSPWmq1gSSwmM/QZl0QaYtg1iet51noyMNym6uIqgd3ToOnclYsmSej8wl8ov8fBPD9MusB2L+y1G8Qf4u0VXq9ny8QXiOwbR9+Fm9/q03sOfxh+xwLBfu0b2yFHoe/Xiw27T2ZZUzA+zu9Ip5rZNjssBK3rjNBQxp2UPEiuS+DCkDb2PbcIc0xr91MM3n3pFl0Dld6nYMqrxGZOArv2d+fLCwkLWrFmDy+Vi1KhRCJ4hPLP+EqnFBoa3CaVLt3DWV9ZwssaMUiJhWKA344J96eatR2pzYT5biulEEe5aO7ouIXX8+j+Bv3yQdxsdWJLKsV4qx5FXZ0Mg81OjjvNGFeuFIkyP3E9zV4Xnr0F0CTjLLJTn1nCwpJYDDhuHfKVY5BICXTBGr2dGq3DCdbep5ASRtWfy+HBvOkabi0e6x/BUvwTUv+ZIYUsAACAASURBVCgcpaWlsWnTJhQKBePHj79rR6dfwyc/Z/DZ/qt8olrGyGgXTN2CKJFTsyUT85kSfNpkokt7Eu57Evq9ftc5ioo3cCX1BUKXRSLNNBK7YzuKkFvF1BJzCRN3TkQj17Bm0JoGWwcDWI0O1r11BrlKxriXOt7r03oP/zJumNk1pKlMxfLllH/0MX7vf8C4dB02p8Cu+T3w1d0m/itLg2W9qI3qxkN6J7V2A5/JJLRNuYi5y2R0D355c6joFKj4NgV7Zg2+4xujbXNn2qimpoZ169ZRXFxMjx49uK9HL748nMXiQ5mo5TKe7JdA51ZBrCmpYn1JFSa3gL9CzuAALwb4e9HZQ4c0swaZj/pPp27+8kE+2WhhaUE5rT20tJTIickyI82qxZ5Vi2i/zqaRS1EEaJB5qZB5KJHqFXV+0td/tQWbC5fZQaHdySW7g0syN5e9ZCR7SxEkErwE6K/WMjYukPuCvOrJmkVR5GB6GR/syeBKsYEusb68MbwFCUH1Faoul4v9+/dz8uRJQkNDGT9+/G+KnH6JM9lVTFh2kuHKRD7x2QCPHEDU+lO98SqWc6V43qfCI3U8Eu8ImLkP5HeqWmtrL3Lu/ET8LkShXJZL8MLX8Jkw4ebrVpeV6T9NJ9eQy/eDvifOO67B5ycKIju/TKIgrZrRL7QnIOLXFbr3cA9/BIe+TyPlaBFD/9aayOa/bkAoulzkTJyEs6AAx4o1jFpzhR7x/qyY9gta5ellsPs5cnu/yKTCHQRo/PmsLIWoonLsIz5G1WbmzaGCw03lqhTs2bX4TmyCttWddhxOp5Ndu3Zx4cIFYmNjGT16NKUWkYXbU/n/2jvv8KiqrQ+/J1PSe0ghjYRAKAmhhCIgIAJSBKSIKNeGioh69WLB9tkFLKiIiF1QmlIEEaRKEemBACGkEdJ7L5PJtP39MYMkJAEJCSWc93nmyZnT5jc7c9bZZ+2119qTkE+IpwPPD2vPgA6e/FlUzvq8ErYXllJlEqglid4u9tzf2oMxntegkPfVpLFGfmtBKc/Fp5OvM9dulYDWlogaX2GFU5UJh0oDtuV60BowVhvR642UKSVK1BJFaokMewXpdhLVlt6+WkC4UkV/dyeG+LrS3dm+lmEHs3Hfd6aQeVvjOZpWQoCbHc/fEcroLj51XBQFBQWsXr2anJwcevbsybBhwxosB1YfJRodo+bvQVGZw0bbN3F8bAPCowPFqxPQHM3DcbAvTtnPIKUfhsf3QKu6s1qrq3M5dPguFKVK3N/UYtOhIwFLFiNZYudNwsQLu19gW+o2FgxewED/gZfzb+DYtjT2rUliwOT2hA+qP7uljExjMBeYOYKmTMc9r/bC3qXheRrahARSJkzEYcjtbJ/4X97cEMtrozry6K3B53eyzIYlcRsHx33C48fm0cerK3NObcKpUo94+A+UfudnfJt0Rgq+j0GXVobbpPp79ABHjx5l48aN2NraMnbsWEJCQtgWm8vcP+JILqgk3NeZmUPbMyi0FVUmwcGSCnYVl7OnqJy7vd2YEdC4+sYt3siD2eDm6PQcL6viZIWG1CodaVpzRE2J3kiVqW6yMivAVaXEXaUg0NaaYDtr2tpa08XRjk4ONnVqw55DZzCx4XgW3+09S2x2Gd5ONjx9ewiTIv1rFRg4pysqKootW7agVCoZO3YsHTp0uKzvZjIJHl1ymL8Sclilfouu/3kfEXw7RaviqYrOx2loIE6K5bBrToPhkiZTNVFHp1BZEUfgjxFUH40leN2vqNu0+WefT6I+4fuY73mux3M8FPbQZWnMOVvKrx8epU2EB8Onhcl+eJkmpyi7klVzDuMV5MSYZ7rVmmNyIQVffkX+p5/S+tNPeCHPg53xeaye3rf22JimCL4aAJIVv9w+k3eOzuMe//48f2AlVgobVDOikRzP++FN1UYKl5h79C53heDQu/75ItnZ2axdu5b8/HwiIyMZOnQoCqWKX49lMn9HIhnFVbT3cmBqvyDu6ub7jztXCNHo66bFG3mt3kiVzoirff1Jt8Bcp7XcYDb0VhIoJAkHhRVW/7JRhRDEZJax9lgGG45nUVCho52nA4/0r/2PqklhYSEbNmwgJSWFoKAgxo0bd8kUBfXxxa4kPtgcz1vKxTw4egii+2MUroxDe6oQp+FtcAo4Az/eBRGT4a5FdcIlhRCcjnuJ7OzVtEu5n8oPfsbrtddw+8+Uf/ZZnbCat/a/xaT2k3itz2uX9WPTVur55b3DIME9r/bE2q5xSZZkZC5F3P5sdiw5Ta/RQfQcFdTgfkKvJ+WeyehzcvBYtZbRP53Cygo2/vdWnGxq/D7TD8EPIyB0JHPadGJ53HL+59udB/etw+Dqi/Xjh0FtX+O8RgqXxaGNK6q36Mg59Ho9O3fuZN++fbi6ujJmzBiCgoLQGUz8Zukgns4uw9VOxdiuvozv7ku4b8Mz8C9FizfyW0/lMH1pFD0CXbmtgyeDO3jS3tPxonf6f4NWb+TQ2SJ2J+SzMz6P5PxK1Aorbu/oyeReAQxo51HvP0Wv17N//352796NUqlk2LBhdOvWrd5SfZdi/5lCpnyzn5FWB1jQtwpx+1wKfjyN7mwpzqODceyihC/7g60rTNtZ6wd5jrT0H0hMfJdA2wcxPrkemy5dCPj+u3/cNPsy9zFjxwz6tO7D54M/r5Nz/2IIIdj8dQwpxwsY90J3vIMaV21eRubfsn1xLAkHcxj7bDd8Q10b3E8bH8/ZiXfjNGwYOf99hUlfHWBoRy8W/ad77ev27/mw7XUMIz7gqbJjHMw+yOuu3twVtQ9dQFesH9wBihpFww0mcxz9yQIcbw/AaUhAg8Y5JSWF9evXU1xcTHh4OMOGDcPR0REhBAeSi1h6MJVtsbnoDCYe6R/E/9156cRs9dHijXxKQSVrj2awIy6PU1llADjaKInwcyHC35kgDwcC3Ozwc7XFyVaFnUrxzw3AYDSh0RvJK9OSUVxFRnEVsdllxGSWEpddjs5oQq20oneQGyPCfBgV7oNzAz1VIQQxMTFs376d0tJSOnbsyIgRIxrVewfIK9My8uPtOFVn81vHP7Ed8x0FS+LQ52lwu7s9dl3c4cexkHHEbOA966YNyM/fzomT02nlPgTneRq0p2IJ/m09Kl9zcYaE4gQe+OMBfB18WTJ8CQ7qS9farMnJXRnsWZlA3wkhdBv676OEZGQai05rYNWcI+i0Bia/1gtbx4af4PO/+IKCzxbgu+AzfrFpy7sbTzNreAeeGFQjoMBkghX3QPIuKh78jYei55Fens5sleD22Hh0YSNRT1he6wlZmMQ/AQ/2vb1xGRNSqzB4TfR6PXv37mXv3r0olUr69+9P7969UavNukur9Gw6mU17L0d6BDZ807oYLd7I1ySnVMuexHyi00uITishPrcco6nud7RRWWEwCgz1bHO0URLu60y4rzN92rrTJ8gd24tMyRdCkJCQwO7du8nKysLb25thw4YRHBzc4DGXwmA0cd/nWzmZrWG9/88EjV9IwU9nMVXocP9PJ2zau8LO2bD7fRj7BXSbUuccZeUxREVNxt4+hODYMeTP+RCf997FZcIEAPI0eUzZNAWTycSyUcvwtr+85GH56eWsfv8I/h3dGPVEl8sKUZWRuRIKMspZPTcK31AX7nwyosHfntDrOTvpHgx5eQStX8f/tqax6WQ2P07tTf92NWZ3Vxaan4iV1uTdv5opfz6B3qjjI20OkWdz0Pd7AtXQubXPbRKUbUmhfHcGNqGuuN3XEauL5MEvLCxky5YtJCQk4ODgwIABA+jevfu/muF+KVq8kT/3Hep7ZNIZTGSWVJFepCGjuIqKaj2V1Uaq9EaUVhI2KgU2Kiu8nGxo7WJLaxdbfJxs/pWrx2AwcPr0afbu3Utubi4uLi4MHDiQiIiIRrlmavLOL3/x3dEyPnZdy6jRr1G4Ogck8HgoDLW/I8RvhhWToet9cNcXdY7XarM4fGQCVpKSLh6fkjnpEez79MHvy0VIkkSlvpKHNz9MSlkKS4YvoaP75SUP02kN/DL7MAadiXte64mtQ8O9KRmZ5iBmTya7l8dzy/i2dB8W2OB+2oQEUibejX2/frh+Mp/xi/aRX17Nhqf74+daY5Z82gFYPApChpA0/B0e2PIQ7tbOzMuPoX12GcYRc1H0fqLO+SsOZlOyLgmVjz0eD3VG4XTxDK2pqans2LGDtLQ0HBwc6NWrF5GRkZdMSHgxWryRT0tLY926dURERNClSxdcXRv3yPNvKSws5OjRoxw7dgyNRoOHhwe33norYWFhKK6g4vo5Vu8+yvN/ZPOQ7V+8OGgSRVs0KN1t8HiwM0oPWyhIhG8Gg1swTN0Mqtqzew2GCqKO3kNVVQY9uq6gcNrbVKekELzhN1SenuiMOmbsmMGRnCN8NvgzBvjVPyu2IYQQbPs+lqQjudw1sxut2zVve8vI1IcQgi3fnOJsdD7jnu+Od3DD40GFixeTN/d9vN95m5LbRjJmwV4CPexYPb1v7aCJQ9/Apudh4CwOdxzK49sep5NLIB+c/RufomrEuEVYRdxX5/xVcUUULT+Nla0K94c6X3JSkxCC5ORk9u3bx5kzZ1AqlQwePJi+ffs2qi1uCiP/559/kpKSAoC/vz+hoaGEhITg5eV1xeF8JpOJnJwc4uPjOX36NHl5eUiSRGhoKJGRkQQHB19xz/0cR2Nimbw0kZ7KMywI74zmmB3W7V1xv68DVjZK0JbBt7ebw7+m7QKX2lnyTCY9J05Mo6j4byK6fIdYE0f+xx/T+qOPcL5zFEaTkVl/zWJLyhbe7fcuY0PGXrbG2L1Z7FwaR+8xQUSObDjCQUamuamuMvDLe4cwmQT3vNoLG/sGxstMJtKmPkLViRME/7qWPZU2PPrjESZ09+Oju7uctxFCwPqnIHopTF7OH9ZWvLjnRQZ4duCtuJ24lxlg4mKkznfV+QxdZgUFS04hqgy4TmyHXcS/i3nPzc3lwIEDhISE0Llz50a1Q4s38ucoKSnhxIkTnDp1itzcXADs7e3x8/PD29sbHx8fXFxccHJywtbWto7xF0Kg1WopLi6msLCQgoICMjIySE9PR6czpy4OCAigY8eOdO7cudEDqg2Rk57M6EUHsaWaxd5OqLNa4dCvNc4jg82DOiYT/PwfSNgMD6yHoFsv0G8iNvZ5cnLX06HDbNyKwkiZPBnHIUPw/eRjAGYfnM3K+JWNioUHKMysYNXcI/i0dWb0f7tecQSTjMyVkptSxtoPowgMc2fE9PAGO3X67GySx96FdXAwgUt/4tOdyczfkchLIzowfWCNgVi9Fn4YDgVJ8NifLM7dx7yoeYz0as9LsbtwKTchTV4OoSPqfIaxTEfhstPoUstwuNUX5+FBDQ7INiXNZuQlSfoQGA3ogDPAw0KIEsu2l4FHACPwXyHElkudrylTDZeVlXHmzBmSk5PJzs6moKCg1nalUolKpUKhUGBlZYVOp6O6upoL28PT05OAgAD8/f1p27btJSs1NRZtYQaTPt3IGb0H39lW428IxOWukNpJkXa9D7tmw/D3oc/0WscLIUhMfJf0jMW0DX6eAM8HOTt+AiatluB1v6JwcWHR8UV8Ef0FD3d+mJmRMy9b4z9RDVUG7nmtF3ZOsh9e5vogensaf69O4tZ72tHlNv8G9yv9fSNZzz9Pq2f+i/v06Ty94hgbT2azaEoPhofVCDwozYCvBoKtK+LR7XwS+z0/xPzAeK+2PBfzF44agTR5JbQfVuczhMFEycZkKvdnYx3sjNvkDiia+Vq5mJG/0mHdbcDLQgiDJEnvAy8DsyRJ6gRMBjoDrYHtkiS1FzXLMjUzTk5OdOvWjW7dugFQXV1Nfn4+paWllJWVUV5ejsFgwGAwYDKZUKvV2NjYYGNjg4uLC+7u7ri5uV1W6oHGYio6ywsLVnBSH84HUgltHDriPqVjrQoyxK43G/iIe6H343XOkZq6iPSMxfj7P0xg4HRyXn8dXWoqAYsXo3Bx4ee4n/ki+gvGth3L/3r877I1CiHYsyKB0jwNY57tJht4meuKiNv9yYwv5u81Sfi0daFVQP15k5zvHEXFzp3kL/wC+/638tHdEWSWVPHsz8dY5dKXcD+LX9/ZDyYtgR/HIq1+mP/d+zPlunJWJ6zGJqwPT8bsx3HlZKSJP0Cn2i5PSWmF69gQ1H6OlKxLInd+FK4T2mPbqeGcO82KEKJJXsA4YJll+WXMxv/cti3ALZc6R48ePcRNR+5pMfuNmSJw1u/ig1nLRcHy08KoNdTeJ/2wEO94CvHtUCF0VXVOkZGxQmzfESxiYmYKk8koSjdvEbGhHUTuR/OEEEKsS1wnwheHiye3Pyn0Rn2jZMb+nSk+f3yHOLghuVHHy8g0N1XlOrH4pb3ip9f2iWpNw79zQ0mJSBg4SCQNHyGMGo3IK9OKvnN2iJ7vbhNZJZraO0ctEeINJyHWPy0MBr14YdcLImxxmJi3eagonucuTG+6CHFseYOfpcutFDnzo0T6rD2iaG2CMFYbGtz3SgCOiAbsatOMFpqZCvxhWfYF0mtsy7Csq4MkSdMkSToiSdKR/Pz8JpRz/SMyjrJk4Xt8pR3MBIWOGfcMxm1yaO1Y2+JUc6ikozdMXg6q2mUBs3PWERf/Gu7uA+nYcS6G3DyyX38dm7AwWj39FJuSN/H6vtfp49OHeYPmXdZs1nMUZlWwZ0UCvqGuRI5sc4XfWkamebBxUDH0kc6UFWrZtSyujuv1HApnZ1rPnYMuJYWc996jlaM13z/UE43OyCOLj1BRbTi/c/cHoP9MOLoExYGFvHfrewzwG8DinByWdg6n2FkJ66abo3LqQeVph+eMrjgM8KPyYA55nx1De6akOb5+g1zSyEuStF2SpJh6XmNr7PMqYACWXa4AIcTXQohIIURkq1Z1U3i2VAyHN7H161d5q3oSA+1gzgsjsO92QSRQVQksnwRGHdy3Cuxrl+bLyd1AbOwLuLr0JjxsIZKwImvWSwi9Ht+PPmRb1i5e2fsKPbx6MH/wfKwVF4/frQ99tZEtX8egslUydGoneaBV5rqmdYgLvUYHkXgkj9N/Zze4n32fPrhPm0bp6jWUbvidUG9HFtzXjfjccqb9eIRqQw3P8uD/g87jYdvrqE5vZN7AeUR6R/JtbjErOnWmwN3GHHa5/S1zcMQFSEorXEYG4fFoOMIkKPjmJEWrEzBp9M3RBHW4pJEXQgwRQoTV81oPIEnSQ8CdwBRx/taZCdQc/fCzrLvpMWkNaL7/gOjf3uQZ3TQ6u1izaNYdqF0uKNyt15ojaQqT4J6ldVIH5+ZtIjb2OVxcIomI+AaFwpbC775Hc/Ag3q++wl6rZGbtmUWXVl34fPDn2CobVylrz4p4inM1DJ3aCXvny79JyMhcbXrcEYh/R1f2/JxAYWZFg/u1evopbLt3J+eNN9ClpHBbqCcfTuzCvjOFPLsy+vxMeSsrc+I//97w6+PYZJ9kweAFRLSK4JucElaGhpLpYw97P4Y1U0FfVe/n2YS44PVsdxwH+aE5mkvOvCgqD+cg6pl135RckbtGkqThwIvAGCGEpsam34DJkiRZS5IUBLQDDl3JZ10MIQTGCl1znb5JEAYT5X+loXl/Gskpy3jI8BI+bk788NRA7KwvcKEYDbDmEUj5y5w6+IISfnl5mzl16lmcnLoS0eVbFAo7NIcPkz9/Po7DhxMV6cxzu5+jo3tHvrj9C+xUjZtJd3pfNnEHcogc2Qb/DnXrxMrIXI9IVhJDHu6M2lbJlm9i0FfXH+8hKZX4zvsISaUic+ZzmHQ6xnf347VRHfkjJofX1sWcd/mobMzuUkcfWH439kWpfDHkC4uhL2V1UFuSgp0Rp9bBktFQUb/r2UqtwHl4EJ5PdUPpbkPxmkTyPjuKNqG4uZrjin3ynwOOwDZJkqIlSfoSQAhxCvgFiAU2A0+KZoys0Z4uInvuYYrXJ2Eo0TbXxzQKYTBReTiH3A93oNr6IFnGA9xvfBNnZ2eWTeuLh8MFvWMh4PdnIO53GD7XnD64BtnZa4k59V+cHLvQNeJ7lEp7DIWFZM58DpWfLycfvZXndj9PJ7dOLBqy6LITjp2jKKuSPSvj8W3vctGUrjIy1yN2TmqGPtyJ4lwNu1fEN+ifV/n44DNnNtrYWPI+/AiAR28NZsagtqw4lMaHW2oca+8BD6wDhTX8NA778jwWDVlEd8/ufJ9Txu/egcR0dkPknIBvboOMqAb1qVs70OqJCNzu64BJZ6Lg+xhK/jjb5O0ALWQylKGwirKd6WiO5oEEdt08cejbGnXr5olp/zeYNHoqDuVQ8XcWioqTeNh+QJpJxSTmIKnsWDX9FgLdL5j6LARs+z/YtwAGvAiDX621OT19CQmJb+Pq2pcu4V+iVNojjEbSH3sMzZEoznz4GC/nfE0Prx58fvvn2KsaVy9SpzWw+v0otBWXrsIjI3M9c2hDMoc3pjBoSiidb6039gOA3DlzKFryI36fL8BxyBCEELy6LoblB9N4enAIM4e2Pz9elnfanIPexhke3ozG1pmn/nyKqNwoHvB2ZYA2ne6JEorKEhg+B3o+WqfGQ02EwUTF/izUgU5YBzRuguVNM+PVUKKlfHcGlYdzwWBC7e+IfW9vbMM8zCkBmhlhElQnl6I5koMmphAMBly8t2Jf9hUptp35j+5lNEYrfnn8Ftp51RPHe26yU8/HYOSH56vGC0FKykKSz36Ch8cQwjp/hsIyiJq/cCEFCz4n9YmRvOCylX6+/fhk0CeN9sELIdj63SnOROUx+pmusptG5oZGmAS/LzxORnwx45/vgVeb+o2oSacj9d770KWnE7R2DWo/P0wmwSu/nmTl4fS6hj4zCpaMAWd/eGgjVdZ2PLvzWfZl7eNur1YMIJU+mT7YpJ80D9qOmgd2zXct3TRG/hwmjZ7Ko3lUHszGkF8FCgmb9q7Yhnlg0861SWefCYOJ6uRSqmIL0Z4uxFiqQ7JR4NDRgGPJbKyy95MYeC9TMsdjEBI/PdKLzq0vSKQkhLl03+73IeI+sx/+XN1Vk4HExHfJyPwJb++76NjhfawsYZCV+/eTNvURcvq157/9k7g9cAgfDPgAtaLx3+/4jnT2rkqkz13B9BjeptHnkZG5XtBW6Pl5tnlI8J5XemHjUP8ER116OmcnTETl60ub5cuwsrW9uKE/uweW3Q1ubeGB9ehtXXh176v8kfIHI1p5MVSdTKSmJy7R25HsW8HYzyFkSLN8x5vOyJ9DCIEurZyqkwVUnSzAWFoNgLKVLdZBzqh8HVB52aHyssfK9tI9faE3YijUos+vQp9RTnVqGbqMCjCYkFRWWLd3xS7MFVvtBqSdbwMQ0+cjHvjbHYWVxPJHe9ftwQsBO9+DPR+aa7OOXvCPgTcYKog59QyFhbsI8H+EkJCXkCTzNn1WFmcnTqTI2sBT92m4o+NY3ur7VqPi4M+RnVTCuo+PERDmzsjp4XJ+eJkWQ25KGWs/isKvvSujnopoMBS4Ys8e0h+fjvOY0fjMnYskSbUM/WO3BvHyiI7nj0/ebZ7H4uwPD/6GycGTuYfmsiJuBQPcvRlrm0yIzSCCok8i5cdD9wdhyJtN3qu/aY18TYQQ6LMqqT5TQnVyKdUppQjt+bFgSa1A4ajCykGNpLICyTxKb6o2YqoyYNLoMZXXiGtVSKh9HVAHOGEd4oJNWxeknCjYOBNyTkDwIKJ6zOWhVWk4WitZ9lgfgjzq8cHveNscetX9Abhz/j8GXqvN5viJx6isTKB9+zfx8z2f3tSk1ZJ8771UnE3kpfth9O1PMCNixhVl26wsreaX2YdRqhVMejlSrtMq0+I4l3++56g29BrdcEGfc9WkvF59Fbf7/wOAySR4a8MpluxPZVw3Xz6Y2AWVwhK3kvK3uUfv5AMPbkA4+vDliS/5IvoLurn6MskuEV/nnnQpDEBx8FuzL3/Im9Dt/n+u9ytFNvL1IITAWFKNPleDIVeDsawaY4UeU7kOYRRgskwJViuwslUi2SpRutqg9LBB6W6LyssO6Vwe6oIk2D0XTq4yh1jdMZvN4hae+Tkab2cblj3au3ZxAgCTETY+B1E/QI+HYNQn//zDS0qOcDLmaYxGDeFhC3B3H1BLd/LM/6LdvJ15E5WMuv9NJrSfcEVtYTKaWP9pNHkpZUyYFYmH37UbsJaRaS6EEPy55DRxB3O488kIAsPqzyUjTCYynnqaij17CFz8A3aRkf8cv3BnEh9tTWBA+1YsmtId+3Phz2kHYOlEcw/9/l/BvS2rE1bz3oH38LVz5QHnbPwdfYnweg67nfMhbT/4dDVPtAq5/aIDs/+Glm/kjQbQFJin/l9NCs+Ye+HRK0BpDb0fR/SfyXeHC3hv02m6+rvw7QORuF8YJqmvgjWPmsMk+8+E218HSUIIQXrGYpKS5mJj05ou4V/i4BBa69ATn72D6ovlrB1kza2vXX7Bj/rYtyaJY9vSGPJQR0L7+Fzx+WRkrlf0OiNr3o+iokTLpJd74uRRf4CCsbyclIl3Y6ysJGjNalRe57PBrjyUxiu/niTc15lvHojE08kykTEjCpbfbV6+7xfwi+Rg9kFm7pqJhImpHjqC1To6tH8X7zwt/PkulKaBfx+47WUIGthoY38xI9+UuWuuHQmb4eNOsHIKJG2vd2pxk2EyQcJWWDoBFnSHE6vMWSGfOU71oP/j1T9SeXfjaYZ39mbFY33qGvjKQvhpHMRthBEfwJA3QJIs/vf/kpj4Lh7ut9Ezcn0dA79p5WwUi5ZzsrM9k2evbhIDf+ZYHse2pRE2wFc28DItHpVawfDHwxBGweavYzDo65++o3B0xO/zBQiNhownZmDSnJ/rOblXAF/+pwcJuRWM/nwv0emWXDR+PeCRbWDtCIvvhPg/6O3Tm2Ujl+Fq48GCHBOHqr2JiZ1JnE0s8b6dpQAAHEpJREFUpqf2waiPoSQNfhwLW15plu/cMnryxalw+FuIXgaaQnAJMKf/DB0F/r3A6gpL8pmM5sex2HUQ+xtU5ICDN0RONbtaHL3ILq3iiaVHiU4v4fGBwcy6o0PdwZ2cGFh5L5TnwrgvIWw8YHbPxMa+QJU2g5C2LxAQ8Fgt/3qVoYqFv73GwLc3oXW1J/zX33FxufKnlsLMCtZ8EIWrjz3jn+uOQtUy7vkyMpciOTqfP748SYc+3gx+sGOD41nlu3aRMeNJHG67Db/P5iPVKO95OruMx348Ql55NbPHhTOxh595Q0WeOedU9nHzU3q/ZynVlfHSXy+xN3Mvt3oEcqf1aTycO9Op0zwc1P5w4mfwCjPfKBpBy3fXnMNQDac3QPRyc3iTSQ927ubHIb8e4NsD3NuZ/eYNDXiYTFCebZ7wkBtj9p2l7ofqUlDaQLuhEDbBfANRmkMV/04q4L8rjqHVG/nw7ghGhtfTIz69AdY+DjZOMHkZ+PbAZKom+exnpKZ+jY2NL507fYSLS+3/U1JxEm9u+h+PLEjCzWhD+9W/YhvYpvFtZEFboWfVXHMh7rtf7omDqzzhSebm4txEqX4TQ+g6JKDB/Yp+/Inc2bNxmzoVrxdfqL2tUseTy46yP7mQSZF+vDmmM3ZqJegqzWUET62FzuNg7EJMKlu+PfktC6MX4m/fivtdivFUaAgOnkmA/1QkqfGd0RZv5PX6EtLTlxAQ8ChKpSWCRVtqdt0kboOMw+ZEX+dQqMHBC9QOoLYz99RNBnPd1Mo88/I53EMgsB8ED4R2d4D1+UFJrd7Ih1vi+W7vWdq2suer+3sQ4nlBiKSh2pyd7sBC8I00G3hHb4qLDxKf8AaVlYm0bn0P7UJeQak8f24hBKsSVvHJvvd5dbmO4FyJNkuWYGcpgnIlmIwmNiw4TlZSCeNmXrwAsoxMS0WYzC6bs8fzufPpCAIaKOohhCD3nXcpXr4c77ffwnXSpFrb9UYT87cnsnBXEkEe9nw2uRthvs7m6Lm/PzVf/56dYOL34NmBg9kHeXHPi1TqK5nk3Zpu0ilcXbrTocNsHOzbNeq7tHgjn539K7Gnn8da7UVIyEt4eY2u+/ilKYLsaCg6CyWpUFlgvhHoNWClBCsV2LqCoxc4tYZWHcGzY4PxrDGZpcz8JZqE3Aru7xPIyyM7mO/gNSk8A6sfNj+29ZoGQ9+hWlSQlDSHnJx12Nj4Edr+TTw8bqt1WE5lDu8ceIe/0nfzzmZX2h8vxPfTT3G6o26pscbw188JnNiZweAHOtKxr+yHl7l50WkNrP0wioriaia+FImLZ/3J/ITBQPoTM6jcv5+Ar7/Cvm/fOvvsO1PA/36OpqhSx4xBIcy4rS3WSoW5s7l2mrl3f8dsiJxKflUBb+x7g78y/6KrWxDj7TMIC5hEu5CXGvU9WryRTy5N5o2/XmSMUwVuhkScnXvQtu0LuLr0bHKNZVo9H29N4Mf9Kbg7WPPBxC7cFnpBVXaTEQ5/B9vfNLt0xi7EEDKAtLTvSEv/HpNJR2DAo7Rp8yQKxfnRfZMwsTphNR9HfYzJZOT9E53x/u0gnrNm4f7wQ02iP/bvLHb+FEfEYH/6T2pcr0FGpiVRVlDFL3MOY+eoZuKsSNQNTIw0VlSQet8U9BkZBCxZgm14WJ19iip1vPnbKX47nkWIpwNzxofTs40blOfAuifgzJ8QOhJGfYxw9GZN4ho+PPwhkiTxaq9ZjA4Z16jv0OKja3Iqc0irzOfdlGx2MIDCihSOHp3MsWMPUFLacCa4y8FgNLHqSDqDP9rNkv0p3Nc7gO3/G1jXwOedhu/vgD9egIDeGB7bQqptJvv2D+ZsygLc3QbQu9cm2rZ9vpaBjy+KZ+qWqbxz4B3CPMJYUXQ33r8dxHXKFNweerBJvkP2mVJ2L4/Hv6MrfSe0vfQBMjI3AU4etgx/LIySvCq2/RDbYH53hYMD/t98g8LVlfRp06hOTq6zj5u9ms/u7cYPD/ekSmfk7i/38/SKY6TpnGDKGnNPPmkHLOyFdPhbJoaMY82YNYS6hmJopv52i+jJA5Tpylh4bCEr41firHbm/jY9CNHtxWQowsmpK35+9+PlOQIrq8sbYDSaBL+fyGL+jkSS8yuJ8Hfh3bFh5wv+nqOy0Jya4PC3YO2I7vbnSXEqIit7FUZjJW6u/Wnb9jmcnLrUOqywqpDPoz9nbeJaHNWOzOwxk0EHq8h9912cxoym9dy5SE0wK668SMuquUdQWSu4+6VIbOzlGa0yMjU5sTODv35OoPvwQG65q+FOkC41lZT7piCp1bRZvgyVT/0uz8pqA4t2neHbvckYTYIpvQN5fGAwPoYs88z45F3QujvcMRtTQG8kpEbPWm/x7hoqC+HoEoh8mLiqXN478B7R+dEEOgYwOaALQfpDaKtSUKnc8PIahafnKFyce/yTB6Y+Sqv0rInKYOmBVJILKgn1cuR/Q9tzR+cLSvRVl8Ohr2HvpwhdBZr2t5AYaE2h9gSSpMTLcxT+AVNxcqz9aFdaXcqSU0tYHrecakM1kztMZnrEdNiym6wXZ+EweDB+8z9FUl25MdZVGVj7URTlhVomvBiJW+vGpSCWkWnJCCHYtTye2L+yuO3+DnTq17rBfbWnT5N6/wMoPT0JXPoTSreGc9Hklmn5dHsCPx9Ox0qSGB3Rmkf6tSGsaCts/T9zSHboSHOqg1ahDZ7nYrR8Ix+9wlxMV2UPPR5E9J7On+VJfH7sc5JKkghxCWFC4C10lM5SVrwbk6katdoTN7d+uLr0wdW1FzY2/uiNgn1nCth4IpvfT2RTpTfSLcCFqf2CGBXuUzvuvTQT04HPIWoJVrpKir1aEe9npNLeCju7tvh434W39zhsbGrf5fM0eayMW8nyuOVU6isZFjiMJ7s9SbBzMGVbtpI5cyZ2kZH4f/0VVtZXHtZoNJrYuPAEmXHF3Pl0BP4d5dTBMjINUet6eSoC/04NXy+aI0dIe/Qx1P7+BCz+AaV7/dE550gv0vD932f5+XA6Gp2Rzq2dmBDuyt36DTgeWQg9p8LQtxulu8Ub+ZMZpaz9YzP3GtYRkrcVCRNS8G0Yw+9ms62a7+KXkViciLuNOxPb3UV/VzcUlYfJzj9KarE1Z0rbkFDSgbiiEDR6a+xURga1rebublZ08LRCCCNC6DFU5aFMOYTDmWM45mQiCchrpSbN1w58u+PmPoBWHkNwdAyr1dsXQnA07ygr41ayPXU7RmFkaOBQpkdMp52refCzdONGsl6chW14OP7ffovC4cp720IIdi2NI/bv7Ev2TGRkZMzUfPId/0IP3H0bzuVUeeAA6dOfQO3vR8DixZc09HDeS7D+eBbHLbNl+/nAPX2CGdO7Y6M0t3gjvzM+j7c3xHK2oBIfCrlPuYMJyr9pTT7Vkg1JdhFsdmrDNus8MqUzACiqQ6gs6oKhogPC4ISPo5ZOHplEeEQR6nIIpaRDMgnsNUacy/S4F+lxLdGhNIHOWkVpQAja8BHYtr4VJ6euqNW17/hCCBKKE/jj7B9sTtlMZkUmjmpHxoWM457QewhwOj/5onT9erJefgW77t3x+/LLJjHwAFGbUziwLpkeIwLpM1YeaJWR+beUF2lZ8/4RJCuJiS9FXrSIfeXBQ6RPn47KtzWBixej9PD415+TUlDJxpPZ7IzLY3REax7s26ZRelu8kT9HYUU1R9NKSMwrJzW/HLvcI3Qv30lX3VH8RTYAqUprfnbyZpu9FTlKc96KUKUrt9r70kXhRDhq3CvyofgsFCYjGc0Fwk3OvtBuKFYdxkDwoDqpEowmI6nlqZwqOMWB7AMcyDpAXlUeCklBH58+jAgawdDAoXWKapesWUP2a/+HXe/e+H+xECu7xhXdvpCEwzls+y6Wdj29GDq10xWlIZaRuRnJTytn7byjuHrZcdfMbqgvUl2u8tAh0h+fjsrHh4Bvv0HV+uo+Nd80Rv6ilKRBxhFzrvfcWERZBgmaHP5SGPnLVs1xa2uMFkPoaZLwV9jib+OOj0swDq5tsXf2x0Zpi96kR2fUUWWoIk+TR64ml6yKLBKLE9EazUXEXaxd6O3Tm1t8bmGQ/yDcbes+wgkhKPz2W/LnfYx9//74fb4AKxubJvmqGfHFbFgQjVcbJ8Y+003OSSMj00hSThaw6YsT+HdyZ+SMcBSKhq8lzZEjpD8xAytbW/y/+Qab0PZXTads5C+F0UCVvpK4smRO5J8gviiezIpM0svTya/Kb/AwG4UNXvZeeNt50861HR3cOtDBrQPtXNthdZHIHWE0kvvebIqXL8dp5Eh85s7BSt00JQnzUstY9/ExHNxsGP9c9wZLncnIyPw7YvdmsXNpnPmp+OFOF62Ypo1PIH3aNEwaDX4LP8e+V6+rolE28leAwWRAY9Cg0WuoMlShslKhVqixUdrgqHK8bDeISaMha9Ysyrdtx23qVDyff65J4uABinMqWfvRUVRqBeNf6CEnHZORaSKObkll/69nCB/kx633tLvoda/PyiLtsWno09LwmTsH51Gjml3fxYx84wuC3iQorZQ4qZ1wUtdf5f1y0KWnk/HkU1QnJeH1yiu4PXB/Eyg0U16k5bf50UgSjHmmq2zgZWSakG7DAqiq0BO9LQ0be+VFyweqWremzbKlpD/1FFnPPU91XBytnn22Vpriq4nsrL1KVPy1l7MT70afm4v/1183qYGvKtex4bNodFUGRj/dFRevphm8lZGRMSNJEn3Ht6VDXx8Ob0zh+J/pF91f4eJC4Pff43LvZAq/+Zb0x6djLCm5SmprIxv5Zkbo9eR98inp06ah8vIiaNUvOPTv12Tnr6rQsf7TaMoKtYx6sgutAhwvfZCMjMxlI0kSt00JJbhrK/b+kkjMnsyL769W4/PGG3i//RaVBw+SPG48mmvgjr4iIy9J0juSJJ2QJClakqStkiS1tqyXJEn6TJKkJMv27k0j98ZCl5JCyn1TKPzqK5zHj6PNiuWoAxouTnC5aCv0rP80mpI8DaOe6ELrdq5Ndm4ZGZm6WCmsGPZIZ9qEu7N7eTyn/rq4oQdwnTSJNsuXIalVpD7wIHnz5yP0+qug1syV9uQ/FEJ0EUJ0BX4HXresHwG0s7ymAYuu8HNuKITBQOHixSSPn4AuLQ3fTz+l9XvvYWXfdDljtBV61n16jJIcDSOfCL/o9GsZGZmmQ6GyYvi0cALD3dm17N8ZetvwcILWrMV57FgKF31JyuR7qYo5dRXUXqGRF0KU1XhrD5wL1RkL/CjMHABcJEm6KapTVJ08ydm7J5E3933sekYSvH4dTsPvaNLPqGXgZ4Q3WNFGRkameVCorBgxLZzAMLOhj92bdeljHOxpPWc2vvPno8/LJWXSJHLnzMFYUdmsWq/YJy9J0nuSJKUDUzjfk/cFao5MZFjWNQvCYEAbn9Bcp/9X6DIyyJo1i5RJ92AsNFdy8v/yS1TeV15wuyaVpdWs++ToPz142cDLyFwbFCorhj8eRkBnN3YujbvkYOw5nO4YRtuNG3G5ZxJFP/7EmRHDKf7lF4TBcOmDG8EljbwkSdslSYqp5zUWQAjxqhDCH1gGPHW5AiRJmiZJ0hFJko7k5zc88ehilG3axNmxY0l/fDqao8cadY7Gos/MJOftdzgzYiRlm7fg/shUgjdtxGn4HU2eSqA0v4q1H0ZRWqBl1IwuBHSWDbyMzLVEqVIwYno4QREe7P0lkUMbkvk3c48UTk74vPEGbVauQO3nT87rb5A7e3azaGyyyVCSJAUAm4QQYZIkfQXsEkKssGyLBwYJYUkg0wCNnQxlLC2laNkyin/8CWNJCXaRkbhMnozjkNubLFVATYQQaA4fpvinpZTv2AFWVrhMmIDHjCdQeXk1+ecBFGZW8Ntn0Rj1Ju58OgLvILn4tozM9YLJaGLn0jji9ucQfpsft97d7qIzY2sihKB8+3asg4Oxbtu4RILNNuNVkqR2QohEy/LTwEAhxERJkkZh7tWPBHoDnwkhLjm/90pnvJo0GkpWraJoyY/os7KwcnTEaeRIHIcMwa5XzyvKzy5MJrSxpynfupWyTZvQZ2SgcHHBZdIkXO+d3GB1mKYgK6mETV+cQKmyYvQzXXFv3XDqUxkZmWuDMAn+XpvE8e3ptOvpxeAHOqBUXZ0JUM1p5NcAoYAJSAWmCyEyJbOf4nNgOKABHhZCXNJ6N1VaA2EyoTl4kJK1v1K+bRtCq0WytcWuWzdsuoRj07kz6sBA1P7+WNna1jnepNNhyM1Fl5KKNu402phTaA4eNE9mUCiwv+UWnEaNwmnE8GZ5UqhJ/IFs/lwah5O7LaOfjsDJo65eGRmZ6wMhBEe3pHJgXTI+bZ0ZMT0cW8emyUt1MW7q3DUmrRbNoUNU7N6D5uhRqhMSwGj8Z7tka4uVvT1WajVCr8ek02EqLa11DpWvL3a9emHf9xbs+/W7aKmvpkKYBAc3JBP1Ryq+oa4MnxYm12WVkblBSDySy44lp7F3VnPnUxG4ejdvyc2b2shfiKmqiurERHTp6egzMjGWlGCqqEDoqpHUaiS1NUoPd5SeXqj8/bDp0AGF05XnrbkcdFoDf/4Yx5mjeXTs58PA+0IvmuJURkbm+iPnbCmbFp3EqDcx7NHOBDZjoIRs5G8girIq2fz1SUpyNfQZ15ZuQwPkgh8yMjcoZYVVbFp0ksLMCiJHtqHnqKDataKbCDkL5Q1CwqEcdi6NQ2WtYMyz3fALldMUyMjcyDi52zLxxR7sXpnAkY0p5JwpZdgjna+Kn/4csg/gOkCnNbDzp9Ns+z6WVgGO3PNqL9nAy8i0EJRqBbc/0JHb7u9A9plSfn73EGmnCq/e51+1T5Kpl6zEYnYsOU15oZbudwTSe0wQVrL/XUamxdGpX2taBTiy7ftYNiw4TucBvvQd3/aitWObAtnIXyN0VQYObTjL8Z3pOHnYMu657viEuFxrWTIyMs1IK39HJr0SycHfzhK9PY302EIG398R32Z8cm8RRl6vM3ImKo/2vb2bZVCjKRFCkBSVx9+rEqks0xE2wJdbxjX/3VxGRub6QKlS0G9CCEERHuxYHMu6T44R2sebvuNDsHNqel99i7AsiYdy2bk0jmPb0rhlXFsCw9yvy4iU3LNl7F+XRGZ8CR7+DgyfHi6nJ5CRuUlpHeLC5Nd7E7UphWPb0lBZKxh4b2iTf06LCKEUQnDmaD77152hLL8K31BXet3ZBp8Ql+vC2BdlVXJoQzJnjuVj66ii56ggOg/wve6fOmRkZK4ORdmV2DqqsHVoXE/+pomTNxpMnPorkyObUqgq1+Md7Ez34YG0CXP/18mCmgohBFmJJURvSyPlZCEqawXdhgUQcbu/7JqRkZFpUm4aI38Ovc5I3L5sjm1No7xIi4ObNR37tqZjXx8c3Zo314ymTEfi4VxO78+mMKMCW0cV4YP8CBvo2+i7tIyMjMzFuOmM/DmMRhPJx/I5/XcW6aeLQQKvNk4ERXgQ1KUVrj52TeLOKc3XkBpTRGpMAemnixEmgWegI536tya0tzdK9dXJRCcjI3NzctMa+ZqUFVQRfzCHs8cLyE8rB8DWUYVXkDPewU64+djj1MoWZw/bBo2yQW+koqia8kItBRkV5KWVkZdSRlmBFgBnT1uCu7aiQx8f3Fo3b0IiGRkZmXPIRv4CKoq1pMYUkn2mlJzkUkrzqmptV6qtUNsoUVkrEEJg0Jsw6k1Ua2qX53J0s8GzjSM+IS4Ehrnj4mnX7NplZGRkLkTOXXMBDq42dL7Vl863msvOaiv1lOZVUVqgoSy/Cq3GgF5rRK81IFlJKFVWKFQK7JzUOLpZ4+Bmg5uP/VXNPyEjIyPTGG5KI38hNvYqbIJUeAVd3ZTCMjIyMs2NnCRFRkZGpgUjG3kZGRmZFoxs5GVkZGRaMLKRl5GRkWnByEZeRkZGpgUjG3kZGRmZFoxs5GVkZGRaMLKRl5GRkWnBXFdpDSRJygdSG3m4B1DQhHKagxtBI8g6mxpZZ9NxI2iEq68zUAjRqr4N15WRvxIkSTrSUO6G64UbQSPIOpsaWWfTcSNohOtLp+yukZGRkWnByEZeRkZGpgXTkoz819dawL/gRtAIss6mRtbZdNwIGuE60tlifPIyMjIyMnVpST15GRkZGZkLkI28jIyMTAvmhjfykiQNlyQpXpKkJEmSXrrWemoiSVKKJEknJUmKliTpiGWdmyRJ2yRJSrT8db0Gur6XJClPkqSYGuvq1SWZ+czSvickSep+jXW+KUlSpqVNoyVJGllj28sWnfGSJN1xlTT6S5K0U5KkWEmSTkmS9Ixl/XXVnhfReb21p40kSYckSTpu0fmWZX2QJEkHLXp+liRJbVlvbXmfZNne5hrrXCxJ0tka7dnVsv6aXUcIIW7YF6AAzgDBgBo4DnS61rpq6EsBPC5Y9wHwkmX5JeD9a6BrANAdiLmULmAk8AcgAX2Ag9dY55vA8/Xs28ny/7cGgiy/C8VV0OgDdLcsOwIJFi3XVXteROf11p4S4GBZVgEHLe30CzDZsv5L4AnL8gzgS8vyZODnq9SeDelcDEysZ/9rdh3d6D35XkCSECJZCKEDVgJjr7GmSzEWWGJZXgLcdbUFCCH2AEUXrG5I11jgR2HmAOAiSZLPNdTZEGOBlUKIaiHEWSAJ8++jWRFCZAshjlqWy4HTgC/XWXteRGdDXKv2FEKICstbleUlgMHAasv6C9vzXDuvBm6XJEm6hjob4ppdRze6kfcF0mu8z+DiP9yrjQC2SpIUJUnSNMs6LyFEtmU5B/C6NtLq0JCu67GNn7I88n5fw911zXVaXAXdMPfqrtv2vEAnXGftKUmSQpKkaCAP2Ib5KaJECGGoR8s/Oi3bSwH3a6FTCHGuPd+ztOcnkiRZX6jTwlVrzxvdyF/v9BdCdAdGAE9KkjSg5kZhfo677mJYr1ddFhYBbYGuQDYw79rKMSNJkgOwBnhWCFFWc9v11J716Lzu2lMIYRRCdAX8MD89dLjGkurlQp2SJIUBL2PW2xNwA2ZdQ4nAjW/kMwH/Gu/9LOuuC4QQmZa/ecCvmH+wuece0yx/866dwlo0pOu6amMhRK7l4jIB33DehXDNdEqSpMJsOJcJIdZaVl937VmfzuuxPc8hhCgBdgK3YHZvKOvR8o9Oy3ZnoPAa6RxucYsJIUQ18APXQXve6Eb+MNDOMvKuxjzw8ts11gSAJEn2kiQ5nlsGhgExmPU9aNntQWD9tVFYh4Z0/QY8YIkO6AOU1nBDXHUu8GOOw9ymYNY52RJtEQS0Aw5dBT0S8B1wWgjxcY1N11V7NqTzOmzPVpIkuViWbYGhmMcPdgITLbtd2J7n2nki8Kflyela6IyrcWOXMI8b1GzPa3MdXa0R3uZ6YR61TsDst3v1WuupoSsYc3TCceDUOW2Y/YU7gERgO+B2DbStwPxorsfsG3ykIV2YowEWWtr3JBB5jXX+ZNFxAvOF41Nj/1ctOuOBEVdJY3/MrpgTQLTlNfJ6a8+L6Lze2rMLcMyiJwZ43bI+GPNNJglYBVhb1ttY3idZtgdfY51/WtozBljK+Qica3YdyWkNZGRkZFowN7q7RkZGRkbmIshGXkZGRqYFIxt5GRkZmRaMbORlZGRkWjCykZeRkZFpwchGXkZGRqYFIxt5GRkZmRbM/wNh8iyY1obflgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 6a3273bf724be7a58749b3ccfdd30ca23952d857 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 259/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From fd8282f1655e1da6dbfd6955bff40f3ed582616e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 260/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From a18a649e83af5e9800e2d4bb27b406f6a8d33086 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 261/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- skfda/representation/basis.py | 2 +- 5 files changed, 175 insertions(+), 97 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py index 7e2294ad9..619829ca4 100644 --- a/skfda/representation/basis.py +++ b/skfda/representation/basis.py @@ -366,7 +366,7 @@ def gram_matrix(self): return gram def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) + return self.to_basis().inner_product(other) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From 5d5f241f9ae0dfda804c331f1b7bf0c1022818c9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 262/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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GKf+y8kMoPQr9bnE6iUdpIfBFiWPgSBZs/8HpJEr5j/JyWP4WtBoAzbs5ncajtBD4og7nWwvWrJ3hdBKl/MfWBXBwe8CdDYAWAt8UEg4Jl8CGL6DkqNNplPIPy96Eek2hy4iT7+tntBD4qsQx1gUvW751OolSvi9nu/W71Od6CAlzOo3HaSHwVW3OtD696NxDSrku+W2QIEjyzzWJT0YLga8KCrbmQdk8F4rynE6jlO8qLoAV70OXi6FBS6fTOEILgS9LHA2lhbDpa6eTKOW7UmdC4SHoG3idxMdoIfBlcX0hqpWOHlLqVBkDS16Fpl0hfrDTaRyjhcCXBQVZzUNbF0BBjtNplPI9WxfA/vUw6M6AnrtLC4Gv6zYGykutoaRKqZpZ/ArUb/bbwk8BSguBr2veHaLb6+ghpWoqcx1s/Q76TbCuzQlgWgh8nYj1aWbHT5CX6XQapXzH4lcgtC4k3eh0EsdpIfAHXUeBKbcmolNKnVxeBqyZDj2vhrqNnU7jOC0E/qBpZ2iWqM1DSlXXsjetvjU/W3jmVLmlEIjIUBHZJCJpIvJQJc+Hi8g0+/mlIhJf4bm/2ts3iciF7sgTkBJHw+6lcGiX00mU8m7FR6wriTtfBNHtnE7jFVwuBCISDLwCDAMSgCtFJOG43W4CDhpj2gMvAP+yX5uAtdh9V2Ao8Kr9/VRNJY6yvqZ+6mwOpbxdymRrAZpBdzudxGu444ygH5BmjNlmjCkGpgIjj9tnJDDFvj8DGCIiYm+faowpMsZsB9Ls76dqqlE8xCZp85BSJ1JSCL+8BPFnQOvAWJi+OtxRCGKB3RUe77G3VbqPMaYUyAWiq/laAERkgogki0hyVlaWG2L7ocTRkLEGDmxxOolS3mnVB5CfAWf+xekkXsVnOouNMZOMMUnGmKSYmBin43inrpcBos1DSlWmrAR+fhHi+lmz96pfuaMQ7AVaVXgcZ2+rdB8RCQGigOxqvlZVV4MW1nwpqTOsOVSUUr9ZMw1yd1tnAwE8nURl3FEIlgMdRKSNiIRhdf7OPm6f2cB4+/4Y4DtjjLG3j7NHFbUBOgDL3JApcCWOggObITPV6SRKeY/yMvjpeWjRw1rqVf2Oy4XAbvO/E5gLbACmG2PWicgTInJszbe3gWgRSQPuAx6yX7sOmA6sB74B7jDGlLmaKaB1GQkSrJ3GSlW0eirkbNOzgSqI8cEmhKSkJJOcnOx0DO/1wWjrrOCeNfpDr1RpEbycBPWi4ZaFAf07ISIpxpik47f7TGexqoHEMdaFZXu0WCpFymTI3QVDHg3oInAiWgj8UefhEByuzUNKFeXDj89a1w20PcfpNF5LC4E/qhNldYit+8zqJFMqUC19DY5kwZDH9GzgBLQQ+KvE0daFMzsXOZ1EKWcU5MAvL0On4dCqr9NpvJoWAn/VcSiE1rOuKVAqEH3/DBTnwbn/53QSr6eFwF+F1bX6CtbPsq6oVCqQ7N8Ay9+CPjdAs+PnwFTH00LgzxJHW7Msbvve6SRKeY4xMPdhCK8P5zzidBqfoIXAn7U71+o41tFDKpBsnmutRXzWQ9a1A+qktBD4s5Bw6HIJbPjSmn5XKX9XXABfPwDRHaDfLU6n8RlaCPxd4hirw2zLt04nUar2/fhvOLQTLn4BgkOdTuMztBD4u/gzoF6MNg8p/5e5Dha9bC1I3+YMp9P4FC0E/i44BBIutdpNi/KcTqNU7Sgvgy/utfrELnjK6TQ+RwtBIEgcDaVHYdM3TidRqnYsfgX2LIMLn4a6jZ1O43O0EASCVv2hQaxeXKb8U+Z6+O5J6HwxdL/C6TQ+SQtBIAgKshasSVtgXXavlL8oLYbPJlhNQpf8V+cTOkVaCAJF4mgoL4GNXzqdRCn3WfgUZKy1ikC9Jk6n8VlaCAJFi57QuK2OHlL+Y9M38Mt/rWkkOl/kdBqfpoUgUIhYZwXbf4S8TKfTKOWagzvhs1uheXcY+ozTaXyeS4VARBqLyDwR2WJ/bVTFfuPtfbaIyHh7W10RmSMiG0VknYjo/2ZtSxwNptyaiE4pX1VyFD4Zb80pdPkUCK3jdCKfF+Li6x8CFhhjnhGRh+zHD1bcQUQaA48BSYABUkRkNlAEPGeMWSgiYcACERlmjPnaxUyqKk27QNOuVvNQ/wlOp/FLRaVlHMgvJiuviOz8Io6WlFFaZigtN4SFBFE/PJh6YSE0iQynZVQEEWHBTkf2LeXl8PltkL4Kxn1kNXcql7laCEYCZ9v3pwDfc1whAC4E5hljcgBEZB4w1BjzMbAQwBhTLCIrgDgX86iTSRxlDbU7tBsatnI6jU/LKywhZedBknccZFNmHlsy89iVU0C5qf73aFQ3lPgm9ejcvAGdm0fSuXkk3eMaaoGoyvf/tFbeO/8Ja5p15RauFoJmxph99v0MoFkl+8QCuys83mNv+5WINAQuAf5b1RuJyARgAkDr1q1diBzgjhWCdZ/C6fc4ncanGGPYmJHH3HUZLNiwn3XpuZQbCA4S2jSpR0LLBozo0ZKWDSNoUj+cJpHh1A0LJiRICAkKorisjPyiMvILS8nKLyT9UCF7Dx1l6/58vk7dx8fLdgEQEiQkxkbRr01j+rdpzMB20dQNc/VX1Q+s/MCaS6jXNTDobqfT+JWT/nSJyHygeSVP/W6ib2OMEZEafBb69fuHAB8DLxljtlW1nzFmEjAJICkpqcbvo2yN20LL3lbzkBaCatlzsIAZKXv4dMVeduUUIAJ9WjfirnM70K9NY3q1bujyH2pjDJmHi1i/L5fkHQdZviOHyb/sYNKP2wgLDqJfm8ac3SmGczo3pV1MfTf9y3zIus9h9l3WAvQXvaDXC7jZSX96jTHnVfWciGSKSAtjzD4RaQHsr2S3vfzWfARW88/3FR5PArYYY16sVmLlusTR8O0jcCANmrR3Oo1XMsbw/aYs3vllOz+nHQBgULtobj+7HUO6NCMmMtyt7yciNI+qQ/OoOpzb2TqxLiwpI2XnQb7ftJ/vN2Xx1JwNPDVnAx2a1mdYtxZc1K0FHZvVR/z9j+KW+TDzZojrC+M+hJAwpxP5HTHm1D9ci8izQHaFzuLGxpgHjtunMZAC9LY3rQD6GGNyROQpoAsw1hhTXt33TUpKMsnJyaecO+AdTof/JMA5D8NZD5x8/wBSXFrOrFV7efOnbWzOzKd5gzqM69eK0b3jaNW4rqPZ9hwsYMGG/Xyduo9l23MoN9A2ph4XdWvBsMQWdGkR6X9FYfNcmHYtxHSE8V9CREOnE/k0EUkxxiT9YbuLhSAamA60BnYCl9t/4JOAicaYm+39bgQetl/2D2PMuyISh9V3sBFrBBHA/4wxb53sfbUQuMG7w+HIAbhjqZ5mA2Xlhpkr9vDivM2k5xbSqVkkt57Vlkt6tCQ02Psut8nKK2Luugy+Tt3H4q3ZlBto37Q+I3u0ZETPlpwWXc/piK5b95l1JtAsEa79TCeTc4NaKQRO0ULgBsvfgjl/hom/QPNEp9M4xhjDvPWZPDt3E1v259M9Loo/nd+RszvG+Myn6+z8Ir5Zl8GsVeks227NJdWzVUNG9mzJRd1b0DTSB8fZL3vTWmmsVX+4apo1l5BymRYC9XtHDsBzHa0O4/MeczqNIzZn5vHorFSWbMuhbZN63H9hJ4YlNveZAlCZ9ENH+WJ1OrNWpbN+32GCBE5v34QRPVpyYWJzGtTx8lW7ystg7iOw9DXoOBTGvANhfnB24yW0EKg/en8UZKfBPasDqnkov6iUlxZs4Z2ft1MvPIT7L+zElX1bEeKFTUCu2JKZx2y7KOzKKSAsJIjzujRlRI9Yzu4UQ51QL7tWofCw1RS0ZS4MuN1aYCbIyzL6OC0E6o9WfgizboebF0DcH342/NJ3GzN5+NNUMg4XckVSKx4c1pnG9fx7FIoxhlW7DzFrVTpfrknnQH4xkXVCGJbYnJE9YxnQNprgIIc/COxbY00bcXAnDP839L3Z2Tx+SguB+qOjh+C5DtYv3dB/Op2mVuUeLeGJL9Yzc8UeOjWL5J+ju9G7daVTY/m10rJyFm3NZtaqdOauyyC/qJSYyHAu6d6SkT1b0j0uyrNNY8bAiinw1QNWZ/CYd+C0QZ57/wCjhUBV7uOrYG8K3Lfeb0/DF27az19nriUrv4jbzmrHXUPaEx7in//WmigsKeO7jfuZtWovCzdmUVxWTnx0XUb0jGVkz5a1f+Ha4XSYcz9smgNtz4ZRb0H9mNp9zwCnhUBVbu0MmHkTXD8H4gc7ncatCkvKeGrOej5YsouOzerz3NgedI/TceiVyT1awtzUDGat3suirdkYA91ioxjZsyUXd29J8yg3jjwqL7fOAuY9CmXF1vUsA+/02w8i3kQLgapc8RF4tj10v9xa5clPpO3P486PVrIxI48JZ7blzxd01LOAaso8XMgXq9OZvTqdNXtyEYEBbaIZ0yeOYd2auzadRvZW+OIe2PETxJ9h/cxFt3NfeHVCWghU1WbeYl3Bef9mn5/b3RjDJyl7eGzWOiLCgnn+8h6c06mp07F81rasfGavTuezlXvZmV1AvbBghndrwZg+cfSNb0xQdTuZy0phySuw8GkIDoMLnoTe4wNqtJo30EKgqrZ1Ibx/KYx+G7qNcTrNKTtSVMrDn61l1qp0BraN5sVxPWnWwLcLm7cwxpC88yAzkvcwZ+0+8otKad24LqN7xzEmKY7YhhFVvzhjLcy6E/atgk4XwUXPQYOWnguvfqWFQFWtvBz+2x2adLAu5fdBOw4cYcL7yaTtz+fe8zpyxzntnR8S6aeOFpcxd10GM1L28MvWAwhwXpdmXD8onoHton8bdVRSaE0b/ct/IaIRDH8WEi7VswAHVVUIdJJzBUFB0ONK+PFZyN0LUbEnf40XWbhpP/d8vJKgIOG9G/szuEMTpyP5tYiwYC7tFculvWLZc7CAj5ft4uNlu/l2fSYdmtbnukHxjGmym4iv74XsLdDjKrjwHzpXkBfzr0sp1anreSVgYPXHTiepNmMMryxM48bJy4lrVJcv7hysRcDD4hrV5S8XdmbRQ+fy3NgeNAouwnz5ZyI+uIjD+fkcuXw6XPaaFgEvp4VAWRq3hdNOh1UfWRf5eLmC4lJu/3AFz87dxCXdWzLztkGOTxMdyOqEBjOmwQamlf2Ja0PmMy9yFANy/8GA6fD8t5vIOVLsdER1AloI1G96XgU5W2H3UqeTnFBGbiFjX1/M3HUZPDK8C/8d11PX+HXS0UPw+R3w4RgkvD5y07ec/+d3mX7XeZzergkvf5fGmf9eyCsL0zhaXOZ0WlUJ7SxWvynKt2Yk7TYaRrzsdJpKrUvP5abJyeQVlvDyVb1+Xc1LOWTLPJh9N+RnwuB74awHIeT3q7dtysjj2bmbmL8hkxZRdbjv/I6M6h2nnfkOqKqzWM8I1G/C60PCSEj9zLrQzMt8tzGTsa8vRgQ+mThIi4CTCnNhlnUWQJ0ouHk+DHn0D0UAoFPzSN4an8TUCQNoGhnOX2asYdSrv5C6N9eB4KoyWgjU7/W6GorzYMOXTif5nXd/2c7NU5JpG1OPz+84nYSWDZyOFLh2LYXXBsOqj+GMP8OtP0Bs75O+bEDbaD6/43RevKInew8VMuJ/P/P4F+vIKyzxQGh1IloI1O+1HgQNT4NVHzidBIDycsPjX6zj8S/WM6RLM6bfOlAvEnNKeZk1xPjdYda1ADd9W+VZQFVEhEt7xbLgz2dxdf/TmLxoB+f95wcWbtpfi8HVybhUCESksYjME5Et9tdK5/UVkfH2PltEZHwlz88WkVRXsig3CQqCXtfA9h+teWEcVFRaxt1TV/LuLzu48fQ2vH5NH9fmuVGn7nA6vDcSvnsKul4GE39yaQ2LqIhQnrw0kc9uP52oiFBueHc5j3y2liNFpW4MrarL1TOCh4AFxpgOwAL78e+ISGPgMaA/0A94rGLBEJFRQL6LOZQ79boWJNiaIdIheYUl3Dh5OV+u2cdfh3Xm0UsStHPRKdt+gNcHw94VMPJVGP2W29YQ7tmqIbPvHMyEM9vy0bJdDH/pJ1J2HnTL91bV52ohGAkc+2sxBbi0kn0uBOYZY3KMMQeBecBQABGpD9wHPOViDuVODVpAp2Gw8gMoLfL42//COygAABmiSURBVGflFXHlm0tYsi2H58f24NazdHZKRxgDv7xkzUNVtwlM+N7qQ3LzFBF1QoN5eHgXpt4ygLJyw+VvLGbSj1vxxRGNvsrVQtDMGLPPvp8BVDaMIxbYXeHxHnsbwJPA80DByd5IRCaISLKIJGdlZbkQWVVL0o1QkA0bvvDo2+7MPsKY1xexdf8R3rouidF94jz6/spWlA8zboB5/wedL4ZbFkBMx1p9y/5to/nqnjO4IKEZT3+1kVveSyG3QDuSPeGkhUBE5otIaiW3kRX3M1b5rnYJF5GeQDtjTLVmOTPGTDLGJBljkmJidBWjWtf2HGgUD8nveuwtU/fmMvq1xeQeLeHDW/pzTmedPtoR2Vvh7fNh/Sw47+9w+XsQHumRt25QJ5RXr+7Noxcn8P2m/Vz08k86zNQDTloIjDHnGWMSK7nNAjJFpAWA/bWyrv+9QKsKj+PsbQOBJBHZAfwMdBSR71375yi3CQqCPtfDzp8ha1Otv13KzhyunLSEsGBhxsRBAbmesFfYuRjeGgJ5++CamTD4Tx6fLVREuHFwG6ZPHEh5uWHM64v4au2+k79QnTJXm4ZmA8dGAY0HZlWyz1zgAhFpZHcSXwDMNca8ZoxpaYyJBwYDm40xZ7uYR7lTz2sgKBRSJtfq2yzems21by8jun4YM24bRPumtbxWrqrcus+skUERjeGW76DduY7G6d26EbPuHExCiwbc/uEK/jt/i/Yb1BJXC8EzwPkisgU4z36MiCSJyFsAxpgcrL6A5fbtCXub8nb1Y6DLJbDqQyg5Witv8cPmLK5/dxmxDSOYfutAWp5ogRNVO4yBRS/DJ9dDy57WVcKN2zqdCoCYyHA+umUAo3rH8sL8zdz58Uqdr6gW6FxD6sS2/wRTLoZLX7MmpXOjb9dlcOdHK2nftD7v39SP6PrVvzBJuUl5GXzzECybZE0vctkbEOp9xdgYwxs/buNf32yke1xD3hmfpD8vp0DnGlKnJn4wNOlo/aFw44eGL1anc9uHK0ho2YCPbxmgv9ROKC6Aadda/7cD74Qxk72yCIDVbzDxrHa8cU0fNu47zJjXF7Mr+6SDDVU1aSFQJyYC/SdC+kq3TU/9SfJu7pm6kj6nNeKDm/sTVTfULd9X1UB+lnWmt+krGPZvawWxIO//c3BB1+Z8dEt/co4UM+q1RTqiyE28/39eOa/HOKjTEJa86vK3en/JTv4yYw2nt2/ClBv6UT9cp4zwuANp8PZ5kLkervgA+t/qdKIa6XNaY2beNpDwkCDGTVrCz1sOOB3J52khUCcXVs8aSrrhCzi065S/zVs/beP/Pk9lSOemvHldki4m44RdS6wiUJQP138JXS52OtEpad80kpm3DSKuUQQ3TF7GF6vTnY7k07QQqOrpdwsgVnvyKfjfd1t4as4GhndrzmvX9KFOqBYBj1s/C6aMsIaH3jzPpUnjvEHzqDpMu3UgvVo34u6pK5m67NQ/pAQ6LQSqeqLiIGEEpLxnfZqsJmMMz87dyHPfbmZUr1heGteLsBD9sfMoY2DxKzB9vDU89KZ5XjM81FVREaFMuaEfZ3WM4aFP1/LWT9ucjuST9DdSVd+A26Eo11rgvhqMMTz55QZeWbiVK/u15rmxPQgJ1h85jzo2PHTuw9Y1IdfNgnrRTqdyq4iwYCZdm8SwxOY8NWeDXnh2CvS3UlVfXF/rtvhlKDvxvPHl5YZHPk/lnV+2c/2geJ6+LJEgnUbas4oLYPp1sPR1GHAHjJ3itcNDXRUWEsTLV/ZidO84Xpi/mX9+vVGLQQ1oIVDVJwKD77M6jFNnVrlbaVk5989YzUdLd3Hb2e147JIExMPz1QS8/CyYcglsnAND/wVDn/aJ4aGuCAkO4tkx3blu4GlM+nEbj3yeSlm5FoPq0LF7qmY6DoWmCfDzf6Db2D/8cSkpK+feaauYs2Yf953fkbvOba9FwNP2b4SPxlrF4Ir3rSahABEUJDw+oiv1w0N49futHCkq5bmxPQjVJskT0qOjaiYoyDoryNpoXYxUQWFJGbd9sII5a/bxyPAu3D2kgxYBT9v2Pbx9AZQUwg1zAqoIHCMiPDC0M3+5sBOzVqVz+4crKCzR+YlORAuBqrmul1lrFfz0/K/TThwtLuOW95KZvyGTJ0d25ZYz/WNUik9Z+QF8MBoatLQWkont43QiR91xTnseH9GVeeszuXHycl0P+QS0EKiaCw6B0++F9BWw7XvyCksY/84yfk47wL9Hd+fagfFOJwws5eWw4EmYdQfEnwE3zYWGrZ1O5RXGD4rn+bE9WLo9h6vfWsqhgmKnI3klLQTq1PS8CiJbULrwGa55cwkrdh3kpXG9uLxvq5O/VrlP4WGYdg389Bz0Hg9Xf+K2heX9xeg+cbx6dW/Wpx9m3KQl7M8rdDqS19FCoE5NSDh5fe8mZM8Sovf/wuvX9OGSHi2dThVYsjZbq4lt/sYaGXTJfyFYJ/CrzIVdm/PO9X3ZlVPA2NcXsztHZy6tSAuBOiXph44yemkH9pgYXor5gvO66PrCHrXxK3jzXCjIgfGzYcBEjy8p6WsGd2jCBzf35+CRYsa+vpi0/XlOR/IaWghUje3MPsLY1xezL7+cksEPUD8n1ZqQTtW+shKY/zhMvRKi28GE7601I1S19G7diGm3DqS03HD5G0t0GmubFgJVI1sy8xj7+mIKikv5eMIA2px7o7VwzXdPWdMZqNpzcAe8O8y6hqP3dXDjN9BQ+2RqqkuLBsyYOJCI0GCunLSERWk6jbVLhUBEGovIPBHZYn9tVMV+4+19tojI+Arbw0RkkohsFpGNIjLalTyqdq3cdZDL31gMwLRbB5IYG2WNIDr3b3BgE6x4z+GEfiz1U3j9DMjaBGPegREv++10EZ4Q36QeM24bSIuGdRj/7jI+XbHH6UiOcvWM4CFggTGmA7DAfvw7ItIYeAzoD/QDHqtQMB4B9htjOgIJwA8u5lG1ZOHG/Vz15lIaRITyycSBdGwW+duTXUZA60HWWUGhnmq71ZED8MkNMOMGiOkEE3+CRP285A4toiL4ZOIg+sY35r7pq3lpQeBOVudqIRgJTLHvTwEurWSfC4F5xpgcY8xBYB4w1H7uRuCfAMaYcmOMnqN5oU+Sd3Pze8m0a1qPGRMHcVp0vd/vIAJD/wkF2fDjs86E9DfGWPM5vdLP6n85529ww9fWhXzKbaIiQpl8Qz9G9Y7lP/M28+DMNZSUlTsdy+NcLQTNjDH77PsZQLNK9okFdld4vAeIFZGG9uMnRWSFiHwiIpW9HgARmSAiySKSnJWV5WJsVR3GGF5ZmMZfZqxhYNtopk4YSExkFYvMt+wJva6GJa9D9lbPBvU3Odth6lUw40brwrBbf4Sz/qJDQ2tJWEgQz4/twd1DOjA9eQ/Xvr2U7Pwip2N51EkLgYjMF5HUSm4jK+5nrHOqmpxXhQBxwCJjTG9gMfBcVTsbYyYZY5KMMUkxMTE1eBt1KkrLyvn77HU8O3cTI3q05J3r+558feFzH4WQOvDVX36dekLVQPER6wrhV/rDth/gvMfhpvnQLMHpZH5PRLjv/I68cEUPVu46xIj//RJQI4pOWgiMMecZYxIruc0CMkWkBYD9dX8l32IvUHFoQ5y9LRsoAD61t38C9Hbh36Lc5HBhCTdOSWbK4p3cckYbXryiZ/VWFYtsBkMeha0LYO0ntR/UX5SVwsoP4eUk6wrhhJFwVzIMvtfqjFcec1mvOGZMHIQxhtGvLeKzlYHRiexq09Bs4NgooPHArEr2mQtcICKN7E7iC4C59hnEF8DZ9n5DgPUu5lEu2pl9hFGvLmJR2gH+Oaobj1yUULMFZfreZC1e881DcCS79oL6g/JyWDsDXu0Ps263CumNc2H0m9bEccoR3eKimH3XYHq2asifpq3mwRlrKCj27wnrxJVechGJBqYDrYGdwOXGmBwRSQImGmNutve7EXjYftk/jDHv2ttPA94HGgJZwA3GmJOuQJ2UlGSSk5NPObeq3NJt2Uz8IIVyA69d05tB7Zqc2jfKXA9vnGmNbhn1hntD+oPSYqsjeNFLsH+9tb7DOY9A54v06mAvUlJWzovzN/Pq91tpE12Pl67sZQ2Z9mEikmKMSfrDdl8cLqWFwL2MMbzzyw7++dUGWjeuy9vX96VNk3onf+GJfPcP+PHf1vKIXSsbTBaAjh6E5Hdh2STI2wcxXeDM+6HrKL9fPcyXLdp6gPumrSb7SBF/vqATNw9u47Nrb2shUJXKKyzhwZlr+GptBud1acbzl/cgKsINo1PKSqwFUnK2wm2LICrO9e/pi8rLYMfPsGYarPscSo5A27Nh0F3QboieAfiIg0eK+euna/lmXQaJsQ14ZlR3nzw70EKg/mB9+mHu+GgFu3IKeODCTkw4s617VxTL3mo1EbXoAeO/gKBg931vb5e5HtZMhTWfQF46hEVaZ0b9b4Xm3ZxOp06BMYavUzN4bPY6co4Uc/2geO4+twNRdX1nWK8WAvWrsnLDpB+38cK8zTSsG8r/rupNvzaNa+fNVn0Mn0+0lrc877HaeQ9vkZdhdf6umQoZayEoBNqfB90vh07DdUoIP5FbUMIz32xk6vJdREWEcve5HbhmwGnVG1nnMC0ECrBGBf15+mqSdx5kWGJz/nFZNxrXC6u9NzQGvrjbmodo1FvQfWztvZcTio/Ahi+tP/7bvgdTDi17Q49xVmd5vVPscFdeb336YZ7+agM/px2gVeMIbj2zHWP6xFEn1HvPfLUQBLiSsnKmLNrBf+ZtJjhIeGJkVy7tGeuZxeVLi+H9S2FPsjVNQpyPr6VbXgbbf4DV06zpH0qOQFRr65N/9ysgpqPTCZWHGGP4YXMWL87fwqrdh4iJDOf6QfFcntSq6qvwHaSFIIAt35HD/32eysaMPM7uFMPTl3WjZUMPN1McyYY3z4aSo3D9V775xzJjLayeajX/5GdAeJTV7t9jHLQaoCN/ApgxhsXbsnl14VZ+TjtASJBwfkIzLk9qxentm3hNs5EWggCUtj+fF+ZtZs7afcQ2jODRSxK4IKGZZ84CKnNgC7w7HCQIbvjKWljF2x1Ot66SXj0N9q+z2v07XGB98u84FELrOJ1QeZm0/XlMXbabmSv2cLCghMg6IZzbuSnnJzRjYNtoous7d6aghSCA7Mw+wisL05iRsoeI0GBuOqMtE89qS90wL5iuYP8GmHwRhERYSyx6YzEoyrOafFZPhe0/Asa6Wrr7FdaY/3rRTidUPqCotIxf0g7wTWoG89ZncrCgBIBOzSLp26YRiS2jSGjZgI7NIj3Wr6CFwM8ZY0jZeZA3f9rGt+szCQ0K4uoBrbnjnPY0cfATSKUy1sJ7I62O5CunQuv+TieyrnvY+p013n/jV1B61JryufsV1s0bC5byGaVl5azek8uSbdks2ZbNip0HOVJsregXHCS0ahRBXKO6tGpsfW0RVYfG9cKIrhdO4/phNK4bRkSY68VCCwFw+RuL2Zd7lKiIUKIiQmkYEUYD+37FW8O6v91vEBFKZHhIzebb8aD0Q0f5fNVePluxly3782lYN5Rr+p/GdQNPo2kDL262yN4KH46F3D0w7Bnoc4PnL64yBvausP74p86EggMQ0RgSR1l//OP66gVfqlaUlxt25RSwft9h1qcfZnv2EfbkFLDn4FGyjxRX+pqI0GAaRITw3Z/Ppt7JZgKuQlWFwAvaCjxnYNtodmYfIfdoCblHS8jIPUzu0VJyjxZTUlZ1QQwSaBARStPIcJo1qEPzBnVoHlXnD/ej64XVesEoKStn7d5cftiUxfebs1iz5xDGQJ/TGvH0Zd24tFdL72gCOpnodnDTPPj0FvjyT9bQy+HPQf2mtf/eOdusC73WTLOufA4Oh87DrT/+7YZASC0Op1UKCAoS4pvUI75JPYZ3a/G7544UlZJxuJCDR4rJOXYrKCYnv5jDhSVE1EIzUkCdEVTFGMPRkrJfC8ShgpJf7x+2vx4sKCbzcBGZhwvJyC3kQH4R5ccdutBgoWlkHZo1sApGs1+LxG+PoyJCiawTQnhI1f+Z5eWG/OJSDuQVsSungN0Hj5KWmceavbmsTz9MUWk5QQI9WzXknE5NGdGz5R9XDfMV5eXwy4uw8GkIqwvn/p+1MHuIm5uzDmyB9bOsW8YaQCB+sPXHP2EE1PG96QKUqiltGnKz0rJysvKLyMgt/LU4ZOYVkZlbSMZha1vm4SLyiyqfvjYsJIgGdUIIDQ5CsBbGMMaQV1RKflHpH9Z1qRsWTGJsFN1jo+jZuiGD2zehYV0/+uSatRnm3Ac7foIGcTDwdug+7tQ7ZksKYddi2LYQtsyzZvkEq7mnywir+SdQ5z9SAUsLgUPyi0qtopBbSGZeIYePlpJXWEJeUSl5haWUlJZjsJqrRaB+eAgN6oQQWSeUxvXCaB1dl1aN6tI0Mtxr+yncxhirw/aHf8PuJRAcBvFnQIfzIbYPNO0C4ZF/fF3xEauvIWOt9Wk/fRXsXgqlhRAUCq36Q5dLrFtUrOf/XUp5CS0EyrdkroNVH8HmuZC95bftoXWhbhPr4q2yUijOg8IKSwoGhVoFI34wtD0HThsE4fU9n18pL6SFQPmu3D2wbw0c2ARHDlg3U24t5h5a11rNq0GsVQBiOmtnr1JV0FFDyndFxdnt+cOdTqKUX3JpAgwRaSwi80Rki/21URX7jbf32SIi4ytsv1JE1orIGhH5RkR0qkallPIwV2dCeghYYIzpACywH/+OiDQGHgP6A/2Ax+yF7EOA/wLnGGO6A2uAO13Mo5RSqoZcLQQjgSn2/SlAZYvTXgjMM8bkGGMOAvOAoWCNmgTqiTULWgMg3cU8SimlasjVQtDMGLPPvp8BNKtkn1hgd4XHe4BYY0wJcBuwFqsAJABvV/VGIjJBRJJFJDkrK8vF2EoppY45aSEQkfkiklrJbWTF/Yw1/KjaQ5BEJBSrEPQCWmI1Df21qv2NMZOMMUnGmKSYmJjqvo1SSqmTOOmoIWPMeVU9JyKZItLCGLNPRFoA+yvZbS9wdoXHccD3QE/7+2+1v9d0KuljUEopVbtcbRqaDRwbBTQemFXJPnOBC+wO4kbABfa2vUCCiBz7eH8+sMHFPEoppWrI1esIngGmi8hNwE7gcgARSQImGmNuNsbkiMiTwHL7NU8YY3Ls/R4HfhSREvv117uYRymlVA355JXFIpKFVThqqglwwM1xaoPmdC9fyOkLGUFzupunc55mjPlDJ6tPFoJTJSLJlV1e7W00p3v5Qk5fyAia0928JaerfQRKKaV8nBYCpZQKcIFWCCY5HaCaNKd7+UJOX8gImtPdvCJnQPURKKWU+qNAOyNQSil1HC0ESikV4AKmEIjIUBHZJCJpIuI1U1mIyA57TYZVIpJsb6vWOg+1nOsdEdkvIqkVtlWaSywv2cd2jYj0djjn30Vkr31MV4nI8ArP/dXOuUlELvRgzlYislBE1ovIOhG5x97uNcf0BBm96niKSB0RWSYiq+2cj9vb24jIUjvPNBEJs7eH24/T7OfjHc45WUS2VziePe3tjv0eYYzx+xsQDGwF2gJhwGogwelcdrYdQJPjtv0beMi+/xDwLwdynQn0BlJPlgtr6bCvsaYVHwAsdTjn34H7K9k3wf6/Dwfa2D8TwR7K2QLobd+PBDbbebzmmJ4go1cdT/uY1LfvhwJL7WM0HRhnb38duM2+fzvwun1/HDDNQ//nVeWcDIypZH/Hfo8C5YygH5BmjNlmjCkGpmKtpeCtqrPOQ60yxvwI5By3uapcI4H3jGUJ0NCehNCpnFUZCUw1xhQZY7YDaVg/G7XOGLPPGLPCvp+HNa9WLF50TE+QsSqOHE/7mOTbD0PtmwHOBWbY248/lseO8QxgiIiIgzmr4tjvUaAUgkrXRHAoy/EM8K2IpIjIBHtbddZ5cEJVubzx+N5pn16/U6FpzSty2k0TvbA+IXrlMT0uI3jZ8RSRYBFZhTXj8Tyss5FDxpjSSrL8mtN+PheIdiKnMebY8fyHfTxfEJHw43PaPHY8A6UQeLPBxpjewDDgDhE5s+KTxjpn9Loxvt6ay/Ya0A5rqvN9wPPOxvmNiNQHZgL3GmMOV3zOW45pJRm97ngaY8qMMT2xprXvB3R2OFKljs8pIolY6650BvoCjYEHHYwIBE4h2Au0qvA4zt7mOGPMXvvrfuAzrB/qzGOnhFL1Og9OqCqXVx1fY0ym/QtYDrzJb80VjuYUazGmmcCHxphP7c1edUwry+itx9POdghYCAzEako5NqNyxSy/5rSfjwKyHco51G6CM8aYIuBdvOB4BkohWA50sEcVhGF1GM12OBMiUk9EIo/dx1qrIZXqrfPghKpyzQaus0c9DAByKzR3eNxx7aqXYR1TsHKOs0eRtAE6AMs8lEmwlmLdYIz5T4WnvOaYVpXR246niMSISEP7fgS/rWWyEBhj73b8sTx2jMcA39lnX07k3Fih8AtWP0bF4+nM75GneqWdvmH1yG/Gakt8xOk8dqa2WKMuVgPrjuXCar9cAGwB5gONHcj2MVYzQAlWW+VNVeXCGuXwin1s1wJJDud8386xBuuXq0WF/R+xc24Chnkw52CsZp81wCr7NtybjukJMnrV8QS6AyvtPKnAo/b2tliFKA34BAi3t9exH6fZz7d1OOd39vFMBT7gt5FFjv0e6RQTSikV4AKlaUgppVQVtBAopVSA00KglFIBTguBUkoFOC0ESikV4LQQKKVUgNNCoJRSAe7/AXRnkt0oG5BvAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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zrhvNwE7NvY4lEjRU7hJyvtm4l7vfSGFX3iF+eVovfnVWHxrGaLIvkYpU7hIyikp9PPbhWv751Ra6t2nCGzeewMhurbyOJRKUVO4SEpIz93PnvGQ25Rxk0vHduPec/rqeqciP0E+HBLWSMj/PfLaBGZ9vpH1cQ169YQwn9Yn3OpZI0FO5S9Bat7uAO+Ymk74rn4tHdOGhCwbSvFEDr2OJhASVuwQdn9/x/JebmP7xepo3jmHmz0cyblAHr2OJhBSVuwSVLXsOctcbKazYuo/xgzrwx58Opk2zhl7HEgk5KncJCs45Xl26lUfeX0uDaOOpy4czcXgnTfYlUk0qd/HcrrxDTJ2fypcb9nBK37Y8dvEQOrZo7HUskZCmchfPOOdYuGoHD72zhjKf4w8XDubqMQk6WhepBSp38cSeA8VMW7iaj9ZkkditFX+5bBjd2jT1OpZI2FC5S737MG030xaupqCojPvP6c//ndyTaE3NK1KrVO5Sb/IOlfLwO2t4c9UOBnVqzuuTh9OvQ5zXsUTCkspd6sWXG3KYOj+V7IJibjujN7ec0YfYGE3NK1JXVO5SpwpLyvjT+2t5ZelWerVtyps3ncCwri29jiUS9lTuUmdWbM3lrnkpbM0t5IaTenDP2f1o1EBT84rUB5W71LriMh9PLtrAzC820qllY2ZPHsvYnm28jiUSUVTuUqvSd+Zz57xk1u4u4IpRXXngvIE0a6inmUh9O+IrWmb2opllm1lahWVPmNlaM0s1s4Vm1rLCuvvNLMPM1pnZ2XUVXIKLz++Y8XkGE//2b/YeLOHFaxN59OKhKnYRjxzN6QovAeMrLVsEDHbODQXWA/cDmNlA4ApgUOAxM8xMg6xhbsueg1z29294/MN1jBvYgY9vP4Uz+rf3OpZIRDviYZVz7gsz615p2ccVPl0KXBK4PxGY45wrBjabWQYwGvimVtJKUHHO8dqybfzxX9/RINr4f1cM54JhmuxLJBjUxt/M1wNzA/c7U17239seWPYDZjYFmAKQkJBQCzGkPmXlFzF1fipL1udwcp94Hr9kqCb7EgkiNSp3M5sGlAGvHetjnXMzgZkAiYmJriY5pH69m7KTB95Ko7jMx+8nDuJnY7vpaF0kyFS73M3sWuA84Ezn3PflvAPoWmGzLoFlEgb2F5bwm7fX8G7KToZ3bcn0y4bRs20zr2OJSBWqVe5mNh6YCpzqnCussOod4HUzmw50AvoAy2ucUjy3ZH0OU+ensPdACXeP68uNp/YiJlrTB4gEqyOWu5nNBk4D4s1sO/AQ5WfHNAQWBf4cX+qcu9E5t8bM5gHplA/X3Oyc89VVeKl7hSVlPPL+d7y6dBt92zfjhUmjGNy5hdexROQI7L8jKt5JTEx0SUlJXseQSlZu28edc5PZmlvI5JN7cudP+mr6AJEgYmYrnHOJVa3TO0zkB8p8fp75LIO/Ls6gQ/NGmj5AJASp3OV/bNtbyO1zV7Fy234uGtGZhy8YRFyjBl7HEpFjpHIXoPwNSQtW7uCht9OIijKeufI4zh/WyetYIlJNKnchr7CUXy9czb9W72JMj9ZMv3w4nVvqDUkioUzlHuG+3riHu+alkFNQzNTx/fjFKb10PVORMKByj1AlZX7+8vE6Zn65iR5tmrLwlycypItOcRQJFyr3CJSRXcCv5iSzZmc+V41J4IFzB9AkVk8FkXCin+gI4pzj1WXb+OO/0mkSG8PMn49k3KAOXscSkTqgco8Q+wtLmDo/lY/Tszilb1v+fMlQ2jVv5HUsEakjKvcI8O2WXH41exU5B4p54NwBXH9iD6L0oqlIWFO5hzGf3zFjcQZPfrKerq2bsOCmExjapeWRHygiIU/lHqay8ou4Y24yX2/cy8ThnfjDhYP1TlORCKJyD0OL12Vz97wUCkt8PH7JUC4d2UUX0xCJMCr3MFJS5ufPH69j5heb6N8hjr9edRy928V5HUtEPKByDxOZuYXc8vpKUrbn8fOx3Zh27gBNzysSwVTuYeCT9CzunJeMA5772QjGD+7odSQR8ZjKPYSV+fz8ZdF6nv18I4M7N2fGVSNJaNPE61giEgRU7iEqu6CI22avYummXK4cncBD5w/UMIyI/IfKPQQt27SXW2evIr+olL9cOoyLR3bxOpKIBBmVewhxzjHzi008/tE6Elo34eUbRtO/Q3OvY4lIEFK5h4gDxWXcNS+Zj9ZkMWFIBx67eKjelCQih6VyDwFb9hxk8stJbNpzkAfOHcANJ/XQm5JE5Eep3IPckvU53Pr6SqKijJevH82JveO9jiQiIUDlHqS+H19/7MO19G0fx/PXJNK1tU5zFJGjo3IPQodKfNy7IJV3UnZy7pCOPHHpUF0pSUSOiRojyOzYf4jJs5L4bnc+95zdj1+e1kvj6yJyzFTuQSQlcz83zEqiuNTHC5MSOaN/e68jiUiIUrkHiQ9W7+KOecnEN2vI7Mlj6NNeszmKSPWp3D3mnOO5JeUvnI5IaMnMaxKJb9bQ61giEuJU7h4qKfPzm7fSmJuUyfnDOvHEJUM1P4yI1AqVu0fyCku56bUVfL1xL7ed0Zvbz+qri1aLSK1RuXtgV94hJr24nM17DjL9smFcNEITf4lI7Yo60gZm9qKZZZtZWoVlrc1skZltCHxsFVhuZva0mWWYWaqZjajL8KFoQ1YBF8/4mp37i5h1/WgVu4jUiSOWO/ASML7SsvuAT51zfYBPA58DnAP0CdymAM/WTszwsGJrLpc89w2lfsfcX4zlhF6aSkBE6sYRy9059wWQW2nxRGBW4P4s4MIKy1925ZYCLc1M13wDFqVncdXzy2jdNJY3bzqBQZ1aeB1JRMLY0Ry5V6W9c25X4P5u4Pt323QGMitstz2w7AfMbIqZJZlZUk5OTjVjhIY5y7fxi1eS6N8hjvk3Hq85YkSkzlW33P/DOecAV43HzXTOJTrnEtu2bVvTGEHrb4szuO/N1Zzcpy2vTx5LG53DLiL1oLpny2SZWUfn3K7AsEt2YPkOoGuF7boElkUc5xxPfLSOGZ9v5MLhnXji0mE0iK7x71IRkaNS3bZ5B5gUuD8JeLvC8msCZ82MBfIqDN9EDOccD7+bzozPN3Ll6ASmXzZcxS4i9eqIR+5mNhs4DYg3s+3AQ8CjwDwzuwHYClwW2Px9YAKQARQC19VB5qDm8zumLVzNnG8zuf7EHvzmvAGa1VFE6t0Ry905d+VhVp1ZxbYOuLmmoUJVqc/P3W+k8HbyTm49ozd3/qSvil1EPKF3qNaSkjI/t85eyUdrspg6vh+/PK2315FEJIKp3GtBqe+/xf7Q+QO57sQeXkcSkQincq+hUp+f22av4qM1Wfz2/IFcq2IXkSCgUzhqoMzn5/a5yXyQtpsHzh2gYheRoKFyryaf33HnvBT+lbqLX0/oz/+d3NPrSCIi/6Fyrwaf33H3Gym8k7KTqeP7MeWUXl5HEhH5Hyr3Y+Sc49dvrmbhqh3cPa6vzooRkaCkcj8Gzjn+9MFa5iZlcsvpvbnljD5eRxIRqZLK/Rg8u2QjM7/YxM/HduOucX29jiMiclgq96P0+rJtPP7hOiYO78TDFwzSO09FJKip3I/Ce6k7mfbWak7v15Y/XzpMF7IWkaCncj+CJetzuGNuMondWjHj6pGa3VFEQoKa6kekbt/Pja+soE+7OP4xaRSNY6O9jiQiclRU7oeRmVvI9S99S5tmsbx0/ShaNG7gdSQRkaOmuWWqsL+whGv/uZxSn2POlFG0i2vkdSQRkWOiI/dKist8THllBZm5h5j585H0bhfndSQRkWOmI/cK/H7H3W+ksnxzLk9feRxjerbxOpKISLXoyL2Cxz9ax7spO7l3fH8uGNbJ6zgiItWmcg+Yv2I7zy3ZyFVjErjxVM3wKCKhTeUOrNi6j1+/uZrje7bRu09FJCxEfLnv3H+IX7yygo4tGzHj6hF6k5KIhIWIfkG1sKSMyS8nUVTqY/bkMbRqGut1JBGRWhGx5e4PXHAjfVc+L04aRZ/2OuVRRMJHxI5BPPNZBu+v3s395/Tn9P7tvI4jIlKrIrLcP1ubxZOfrOei4zozWdc+FZEwFHHlvm1vIbfPSWZgx+Y8ctEQnRkjImEposq9qNTHja+uAOC5n42kUQPN8igi4SliXlB1zjFtYRrpu/L557WjSGjTxOtIIiJ1JmKO3F9fvo0FK7dz25l99AKqiIS9iCj35Mz9PPxOOqf2bcuvzuzjdRwRkToX9uWed6iUW15fSdu4hjx1+XCidf1TEYkAYT3m7pzjvgWp7M4rYt6Nx+sdqCISMWp05G5md5jZGjNLM7PZZtbIzHqY2TIzyzCzuWbmWaO+vnwbH6Tt5u6z+zEioZVXMURE6l21y93MOgO3AYnOucFANHAF8BjwpHOuN7APuKE2gh6rtbvz+d276ZzSty1T9EYlEYkwNR1zjwEam1kM0ATYBZwBzA+snwVcWMN/45gVlpRxy+uraN64AdMvG0aUxtlFJMJUu9ydczuAPwPbKC/1PGAFsN85VxbYbDvQuarHm9kUM0sys6ScnJzqxqjSw++kszHnAE9dPpz4Zg1r9WuLiISCmgzLtAImAj2ATkBTYPzRPt45N9M5l+icS2zbtm11Y/zAuyk7mZuUyc2n9ebE3vG19nVFREJJTYZlzgI2O+dynHOlwJvAiUDLwDANQBdgRw0zHrVdeYeYtnA1xyW05PazdD67iESumpT7NmCsmTWx8tm3zgTSgcXAJYFtJgFv1yzi0fl+fvYyv+PJy4YToysqiUgEq8mY+zLKXzhdCawOfK2ZwL3AnWaWAbQBXqiFnEc065stfJWxlwfOHUj3+Kb18U+KiAStGr2JyTn3EPBQpcWbgNE1+brHKiO7gEc/WMsZ/dtx5eiu9flPi4gEpZAfuygp83P73GSaNozh0Ys1P7uICITB9APPfLaBtB35PPezkbSLa+R1HBGRoBDSR+4rtu7jb4szuHRkF8YP7uB1HBGRoBHS5R4bHcWJveN58PyBXkcREQkqIT0sM6RLC165YYzXMUREgk5IH7mLiEjVVO4iImFI5S4iEoZU7iIiYUjlLiIShlTuIiJhSOUuIhKGVO4iImHInHNeZ8DMcoCtXuc4CvHAHq9DHCNlrh+hljnU8oIyV6Wbc67KS9kFRbmHCjNLcs4lep3jWChz/Qi1zKGWF5T5WGlYRkQkDKncRUTCkMr92Mz0OkA1KHP9CLXMoZYXlPmYaMxdRCQM6chdRCQMqdxFRMKQyr0SM+tqZovNLN3M1pjZr6rY5jQzyzOz5MDtQS+yVsq0xcxWB/IkVbHezOxpM8sws1QzG+FFzgp5+lXYf8lmlm9mt1faxvP9bGYvmlm2maVVWNbazBaZ2YbAx1aHeeykwDYbzGySh3mfMLO1ge/7QjNreZjH/uhzqJ4z/9bMdlT43k84zGPHm9m6wPP6Po8zz62Qd4uZJR/msfWzn51zulW4AR2BEYH7ccB6YGClbU4D3vM6a6VMW4D4H1k/AfgAMGAssMzrzBWyRQO7KX9DRlDtZ+AUYASQVmHZ48B9gfv3AY9V8bjWwKbAx1aB+608yjsOiAncf6yqvEfzHKrnzL8F7j6K581GoCcQC6RU/lmtz8yV1v8FeNDL/awj90qcc7uccysD9wuA74DO3qaqFROBl125pUBLM+vodaiAM4GNzrmge5eyc+4LILfS4onArMD9WcCFVTz0bGCRcy7XObcPWASMr7OgAVXldc597JwrC3y6FOhS1zmOxWH28dEYDWQ45zY550qAOZR/b+rcj2U2MwMuA2bXR5bDUbn/CDPrDhwHLKti9fFmlmJmH5jZoHoNVjUHfGxmK8xsShXrOwOZFT7fTvD80rqCw/8gBNt+BmjvnNsVuL8baF/FNsG6v6+n/C+4qhzpOVTfbgkMJb14mKGvYN3HJwNZzrkNh1lfL/tZ5X4YZtYMWADc7pzLr7R6JeVDCMOAZ4C36jtfFU5yzo0AzgFuNrNTvA50NMwsFrgAeKOK1cG4n/+HK/87OyTOJzazaUAZ8NphNgmm59CzQC9gOLCL8mGOUHElP37UXi/7WeVeBTNrQHmxv+ace7PyeudcvnPuQOD++0ADM4uv55iVM+0IfPq0mYoAAAG2SURBVMwGFlL+J2tFO4CuFT7vEljmtXOAlc65rMorgnE/B2R9P6QV+JhdxTZBtb/N7FrgPODqwC+kHziK51C9cc5lOed8zjk/8PxhsgTVPgYwsxjgImDu4bapr/2scq8kMF72AvCdc276YbbpENgOMxtN+X7cW38pf5CnqZnFfX+f8hfQ0ipt9g5wTeCsmbFAXoWhBS8d9ign2PZzBe8A35/9Mgl4u4ptPgLGmVmrwJDCuMCyemdm44GpwAXOucLDbHM0z6F6U+n1oJ8eJsu3QB8z6xH4C/AKyr83XjoLWOuc217Vynrdz/XxynIo3YCTKP8zOxVIDtwmADcCNwa2uQVYQ/mr80uBEzzO3DOQJSWQa1pgecXMBvyN8rMLVgOJQbCvm1Je1i0qLAuq/Uz5L55dQCnlY7o3AG2AT4ENwCdA68C2icA/Kjz2eiAjcLvOw7wZlI9Nf/98fi6wbSfg/R97DnmY+ZXA8zSV8sLuWDlz4PMJlJ/RttHrzIHlL33//K2wrSf7WdMPiIiEIQ3LiIiEIZW7iEgYUrmLiIQhlbuISBhSuYuIhCGVu4hIGFK5i4iEof8PxkPoyFe8qNYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mnphiXEvyvK2YnNdPrT1CfVEBx1eez+buXqr72mnqPEEgPApIOBQfZ/nbOevcBWxpuoCvHL4FOZ/hX09VMjr9T6gmF0gyxVUu3vbxRTg8llf3QBleU0bIG96SxvtibP3dSaaGMgx5OvFKGq7oPCqyYyzwuTFZvbSsLeaL3hy9GRWAUHiC9R1HUTUXB6llPJEHBBKCOm2YsxKnKIhOIGlxJEkGBEIIAg2zaSs8Rl22i015heJMElnLvqw+ulAQWAEFiSwSaSTp5d+vXNZDJhEkHfWSmvKgphzITifmgA9zUQilsBhTqBilZBZh2UlbX5ThoRimtEYBErMQFGBGemGlHF3oRHNhOoVKv1VDlocIykl+uXYTrSYLBeFxrtl/gMKhNpL5CCZJoik4SfHGt/FV0UzLdBvvmdCob/8Qg6aFIHQcboVLb1xCcJbRT/9mYYS84S1FTefZ/2g3J7YPkDXHaS3Yz6KJc5ByVpanuwmV1JMpsPLzdQXcEY/PtJt1jeWdxykdmuCIuYGR9Exr2iWSrEqdoDbRhTmVAMDq8JPPJdFyKktWzKFI3k7FdDfOmYUIULN+svm55MwLyFODJkKoWRNqdoyUmCRpCxOzTZO2TCLLCZzEcWsRArk4ASmHx6ShmGbKSqs2BqLFjAz7kfpVgskI8gvvk1WshEuqSc2eR2TeQg57K3jmdJScqtNAjlWWFCtcGewTEn5KsZlmuovSaITzIwxqk/xw7XI63QqzUlm+uqub2PgBhlOnAUGZX6NvRTW3s4t6Nc/1J5dwMv1edNmMySSx8WMLjYXO3iSMkDe8JQghOH1kgl33dpCKZmkP7cWmmamcWo4/M8Zyu47VW8Gh5UG+GhIMqzODoqHoJMtPnaA5Wcqo7gagQh/inNgRgtERJF1Dkn0o9iIkaYRsIsJFC0xUaMfx6FGEkMhkZ5OWNpAVZ5PTvUzl+jlt6eGYp4f9BT1MKHGcmhNnzolNt2HVrSiagkmfmXP/h5Z3XsqTk1T8xGnSxlmuTrBQTWBFMGWycdC9mBPaWiwjZnyDvRQMdVMx1Y+ia+QkE52BCjpqFrK3uJ5mUxA7KoscYS4oep7c8QSBTBke5xyKHDUUSjNdLhPmHE+VOdgRMnPeUJyr+rMcmNzKVLaDnJbH5rOwpzpMT2iKL3fJjI98jpi1FBCsfEcNizdWGfPp3+CMkDe86UUn0uy8u4P+U1Po7gm2Fj3F6oGN2LKFNEwdYXbVAtIOJz/dUMi9uTQ2WSKjCxacPoXck6FdK0ISOk3ZdpbFjmBPxkECu62InLQQk7WNIdpYHJriAi1MQImh6V4S2iUktQ1MqUm6RSu9tKH5JJzVi8lYSglP5QjHckRSeXQhIZDQX4h0s6SD0LFIOg5ZxWPKo6CBrr9s2xRyzKWd+dIp5ohhBLDX5mVL2WyU2nUsdDcQ6tBQ97UgHzlIYKgbgD53EQdmzWdz2VLGXX6WeyZZ636I6GEQaRj2LsXnb2KDYmVW3o2MzIRV4rhHsHwakvEBDqZasWT2E1EtqHY4Uh3mXMco81qupdNyLkgStfO9XPixxcgmY0D2jcoIecOblpbXOfpsP4ee7EWSBIOlj9ElVM7pvxRbLs3yaDP+2jW0VTj4wkI7vWoORQI5l2P+oeN0RAtB01iUPMXS+GFMah5dsVDq0ZkwXUS7ey+j7tNcPl3E5aYe3NYe8qKQiHolfdkS8vE+oq3NHKlqor22iX4cTKgm4sKK+CsHUGUJSn125oRczC6wU+VXqHHpSNk409PTjA72EhvuYJHezFJO4CBDjxzkDkcZT3ly2Lx2Vs5ayXmWRqqPRolu3om99TiyELQFKthccTb7ZzWytGSYZanHiLU4ycg2dgXOweP1cf2cCibHFRZOS1h1SMsCc07lQLQHp2MX8WgvwykPSVueROUU7xov4XntY+gmK/6AzJVfXD1zdSvDG44R8oY3peHOabbf2c70aIqSeTIPmr5J6dDbqYw0EYq0sMQhYQot4K6VPn7q1bDJMglNp3hqHPlIBFXVWBRtZkGyBZOWJ+/0YClSSHjG2enUmaN6eOfAIhY4T+NRHkFgoS+9ks0pJ+kxN3v9s2n3VRCTZy7gIaNTYBVUBmzMLfExp6yAkMdO90SSJ5tHODkcw2ExcXFTCW9rKqbc7yCpaoRTKiORDCPRNL1TKdpHY3RPJMnrM9+9mgIny2uCbGgIsaomSCoRY+TQE+SP3ElVphkvCQYpZqvlbJ5zSXTbeohZYzQVNrHRdTbLjudIPfQMtuF+0mYLz5YvY0vt2SytO0FpZxfqhJkuRzUHClawqcZEwfIltDdPc/FwjgVRHSEE0VyUdjnMfPFdnomXo0bNpOw5ltojjKRvJG0LYVV03vmV1bgCtlc6bIYzwAh5w5tKOqGy94Eu2vaN4g7aKD67n1t7f8+qrvfjVD3M63uKusbVTDtDfGmtj31SniKLmTE1T8WxLsRAgkWRE8xJdiFLOprPxUCxlfFQG3mpkEvHz2Z1fAkuZZyA8h0UeYRBqY6b4/MZzq6ix1GKkCRs5Ci3Zlhc7mXt/ArWLqrDYZvp5xZCsL19gh8820HzUJTygJ0PrKrm6qVluG3/+zIB2bxG60icAz1T7O8Os78nTCKbx2kxsXZeiCvPKuPcugLMk22kHvkMpuGDWMnRRQWbWUvYGWLEN8Ih+RBpc5qzChfzD/kmqp/pQ966HZOucbBoHtvrl9BQdRDpuE5GsrE1uAb8Qa57+3y+poIvkuPjHWnOnxBIQCSfwapsZUB6iu2TxXijZqzWND79XKKuNcjoXHLDIsrqjQHZNxIj5A1vCkIXtO4bYe+DXeTSGos2lDPs+A0PH4mxvO9SHLkoi3sfp3D5tTQH7PzrMhdhoeM1m5hKZGh89nnqp05RmR5AmBXsVSoHPXYG/BOsSC7g0olzmKXPQmgZnJlf4A88S9rk5t+zV/FQfh1CkglISWqUOOsbS9i0cj4V5eV/Muh4sDfMt55s5Uh/hPKAnRvWz+GyRaWY/4Y+62xe4/nuMJtPjbL55ChTSZUij5UrzirjfSsrKckNwcMfQQzOfD9aLIt5TF1BVrJjCVlodbZyVD+KYlK4xLeaTcftyA9sxZVOcCJYQ/OyCkK5fnKTEu3OOnYXrOKSOhuti+dwIJaiLKXx2+fTuNQcZlkhp6tYlPv5oasbRwd4Ugoe2UnGeQWyKciqS8pYfPErXujN8DoyQt7whjfWG2Pn3R2M98YoqfWy5p113Pv8x+k8tojZU2dRON3MwuQpHAvey31zHPyg0kzAYiapqtQePMji5n2E1EkyZjtlK0vZaj6GrhVyYXQl58YWY0ZBm+5FG91KqO4pXK40z+lL+Iz6YXJYqDVNcW4gx6aN5zF37lzM5j/tex6NZvjWU608cmyYIo+Nq9dVM7vaz1AkwlTfAOlIhGwsTlpVycgmUmaFsNtLMhBEd7twms0UWswUWRRKrAqzHVbmOm3MdtiwveQHQs3rbG0b575DA2xrH0eWJC5dVMqH19QwL98JD/wjTPegY6Kl5Eo2x+cQTyRw+9ykS9NsVjcT0SLMdVTz7lNllD58EG8mQWtBBaOLFdITkLR4eSy4AbfXyfJLFvDbWBpFE/zsQIK50wkS+SyF1iBCT3PCt50HM4eY12XCpJmQLU2YbauY01TIhuuXGzNv3gCMkDe8YaXjKs8/fJqWvSPY3RbO+Ydaqhc5+ebvrsPccgX+dIja7keYWxYkN2s931ri4imfRKNFwnp4L8sO7sSjxplWvOgN89HqDuDur+HCyDmU5grR8yny/c+THdiNqB2moDaBjzhfy7+PbWIZjQyzplRh47uuJRgM/tk6qnmdn+06zU+PDyBcEudmhijraWNOdwc1QwMEY5E/+7qXylmtjFdU0VdexamyKnbVzmWgsHjm8n9ArcPKcq+LFT4nK3wuyl7oFhoIp/jV7h7uOThAOqfx9gUlfGbDHGoGH4EnPwu5FMLmp23Zt9jdnWBoaAir1Yp3tpdd5l2cjJ2kxFzAJQdCLN7ZQzAdp7W8jJFCK2ldZo9/Bac8DVy8opAHAy6yms4XTqV520CaE9MHKXaeRanVhi6pPOnZw3D4efwDMgIrZvtqQsVNXPHldcaA7BlmhLzhDUfXdE7uHObAY93kMhpN55dx9turUbPDfOVXX6D09DU4cnnmN/+KyrWXM2qbzadXuhjSEmzqOEzRkd3Ys2mGrcWc9M9hzYoMZb121sSWoAiFTLoTvWUX6vARojUW2pqquULZjY7EN7TrMOXMhOzDvP+Gr1Lo//MLc41nc9zaMcLvu0Zp6D3BBYf2svzUMZyZNLosk62uwdrYiL+mGldFOSafD9ntRjKZEPk8ejpDfnKC/MQEuaFhMm0tZFrbEIkkAFqRn4kFtTQvXciO2fM5nguSEDP9+bNMcc6xDrHaPsJ8S5SM5uahllLuP+FGzcNlC6x8am0ps3Z9G7nl0ZkKz93EwMqvs+/AYVpaWrBYLMxqmMUeZQ/7JvfhF042bi3goiMDWLQ8nVUFDDoc9PuqeTywnrKQlbHllUxoGu/tVbm+Lc3Byc3ETPUslq0UeENk5TzPubZj6zrEZMqMZCrC6V7DO79+Fd5C46LhZ4oR8oY3lOHOaXbe3cHUUJKyeX7OvWYOgRInY0OHueWXD1A5cgGuVD8LW/6boqtu5pju4qtz09Sf3Mu89iPI+TzdjipOeBewqjDF2/KF1KUryEhZRtMdFBx+ADExzGDQw+6Vy4laHHxP+QVDFPJQ6kJi2dMEr1rHx1d/Bll6eT96Iq/x+ESE+0amOT4yzjt2bObSnc8Rikyh+f1416/Hd8F6HEuXYXL96dWVdD1PKtVNItFGKt1HOt1HOj1AOt2Pqo6DANM4WNtkrK0y1nYJOSuheSCz1ETP2mqOlSzksFbPSTGXPGY8xFjBXtaILfizkzzRfSE7BldjNWW5ou4x3l54mMbWCdzxLHGPndTaT5IJruPAgQFOnWpDURRqFtSwW9nNzrGdFCWcXLLZz/qOPrJWM+2hAAPFRTxYcBERZwHBdRV0y7BmPMf3jmY4Gd5BZ1aiWKqh0TKFP7CQuJxkIv8MxwaGyekJzJb5vP2TH2L28trX62NkeAkj5A1vCInpLHsf7KLz4BiugJXVV9ZRs7gQSZI43fIsv/1dK8WR+ZSO7qEhuhvf5V/modgQxxMHqO1pRTeZaCmoZ9C8gPUOM5cKF17NxahljF61h+IDmwkODxGz23hmwSIeKdnABabDfNP8K/r0Ep6ZquP+hkFu2PRVLq65+GV1O53K8OvBSe4ZDZNPpnjnM4/zD1ufwp1NYV2xkoL3vAv32rVIyh9nzuh6nkSyjWj0CPH4KRKJVpLJTnRdffE5FnMIm1yGVSrBZpqFRS7EYi3A6irE6i/GYvGQ3n2A+BNPkti+A5HL4Vy1Cv+170OsOodtkQRPTETZPBElKwT1DjNXFMg05cP8+NlpDg/KzClI8Mkle1jU/wTlPZNkbDIn57lJeJ1YbXVMT3vo65PIqRXMalzEw5lHOD51nKpBD+9/2kLjxChjHietswp5rmwtB53zKV4WoC9gZ34kz20H0vREj3As3onFfgH1Y1vR58+jXmsiLU1zOtxJS2QHQrIyd/klbLrhvciyceLU68kIecMZpeV0jm8d4OCTvQhNsPjCCs7aWIlimTnl/+jue3nqARVPqoi5nfdRW5hkqvHtPDe0CyUyQM5qp63hbDLxuVys2TgXKwDHva1M+fbi3Jqi8VQLmCR2lM/m4YaNdJgr+JT5QT5lvp/RTBGfLfAwWO7iR+f/iIWFC1+s297pBD/pH2NbOI4CXHNwP5fe/RsKU1HUFauZe9ON2BoaZrZDyxKNHmI6sp9o9Aix2HE0LQWAogRwyLOxpSpRxktQRoswTxUi66+8mqPsNGMOObFUuDEHdNIHNhO57x7y4+NY582j8PpP4jr/fKJ5jYfGI9wzEuZYPIXLJPOu4gCVEY1bN3cQz+T57EVzuK74NKb734+kppgs9tG/cCFxtQ9Nm1mXJ5t1kE5XYA42cPv4QTqSk6zbV8r79o5j1VS6Qz521a/gQc9alDoP0VofFUmNO/alGI23sX9qKzbH5YRSXfQVHWCh99U2uC4AACAASURBVDIa07Wk8lEOhA8wljyC01vFlV+4mYLyslf7o2T4C4yQN5wxfSen2HVvB9HxNFULClh9VR3eQvuLj29/+DaOPFuELSfRdOKXcFYprak08fQkUZeXzPL16GIOl/TkqcVEVE6y2b+H3Jweatv7mfWAii8aZSrk4Zd169hduBIZ+JJyJ+8xbWbIWsulJTlqgvP4yfk/odhZjBCC3dMJbukd5flokkKLmXdrKvVf/yZz+08xWVJF/be/RvDsJSSS7YTDuwiH9xCJHEDXs4CM21WP13MW9ngdSucstFYz5ATIEpYyF0qJE3OhA7Pfhuw0I9tfGJgUoGfyaPEcWiRLfjxFbjSJOpwATYBJwlrtQo8fJ/7k78kN9GNrbCR00004l58NwPF4il8MTPDI+DS6gAvdLtTmMHvbJzi7OsAP31FN6ePvhcGDIMmIdf9GavGlTEcP0j+whXj8EGbzzI9TmgB7ogl6JhXWPRRiTfcQEbuVffX13DHrEibLi1Hn+ynMCu7em2Q6O8D+gftQ7BsxO3xM6rcwWl/Px0Yvw68VMJyJcHTyYZL5KZZf/k5WXnkVssn0On/q/v4YIW943UXGUuy+v5O+5im8ITvnXj2Hyvkvn73y8C//k4FDc7BmhigYuJPRUi9pNUPCW8TxRet4r3s+c9vieDXoUiZ5tOBJBooneW9hHu9vxgkdjKHaLeydPYc7Kt/OsLmQWXKE72v/xUr7CU4VzOY9rixrKy/gm6u/iUNxcCSa5Cunh9kfTVJsUfhkRSHFjz5L6Oc/QNHyxK/9R+qvm89keAuTk1tR1QkAnM46Av5zCARW4zYtJLM/RvLgKHoyh+xWsDcWYG8MYqn0IFv+GGq6niOZ7CSR7CCZaCeV7kNVp1DVSfL5l6xTL5kxCw+mjAt52okyXYQlWYJ9Ik1uzxby46O4N26k6KbPoZSWAjCSVfnV4CS/GZoklddYlpDoOjSK1Szzo2sWsmbgVtj9HzNvULUarr4dHAHy+TwHDjxCW/v9+Hx9eDwTSJJOXIORQReLHtVwtWucnFXC3Qsu58isBvILA3jzgnv3pEiIaQ50/oq8fQGKfSV5963cU9fPtZPruHT6EsBMV6KLk+EncJeUsOmTn6KoZvZr+4H7O2eEvOF1o2byHHqyl+PPDWBSZJZtqmbB+WUvu9qQruncecsviHQWYYptQdV70WSJoLeGkw3raBTFrB/PI4RgjynCo8X30OI6ybsqL2Xd8Has/xnFNp1juKaY+ypX8Jx3BQKJS82HuTn/MCFbL0f9JXzQa+aDCz/CJxZ9gqFsnm+eHuah8QiFFjOfrirmUred567/N5r2b2aytAD7Z2eTcB5B0xKYTC6CwTUEg+cRCKzGZi1Gi2WJbx8kcWAENIGtPohrRQnW2T4k+Y9zxVOpHiYntxKe3kckcgBNm5lNI0kKdnslVksBiiWIYvbACwO/Qs+Ry02j5qbIZsfIZIZeLM8Ud+N7JoB15ziSyUzh9dcT+MAHkF5oIU+pef5rYJxfDk6SjWcpOBUjHslww/o6/rm8B/m+90M+Dc4QvPtumLUEgFgsxjPPPENb22HKyqI4i7pwKqexyqBFTXj3C9ItTh4t38SDleeROasQnwZ37UmRUTLsP/UzMrYgLusVRMu38ljoSUKai2/0XoePelShcTy8jd7EMZZcfBmrrno3itVYEuG1YIS84TUndEH7/lH2PXSaVExl3spiVlxWi9Nrfdnzctk8v/zCj8gMD6GrHUgI5i44m5C+goTsoi6hk1UkHpCzPO7azHToKZw5H19b/E8E7v4P7I9nSdkdHFvayIPelbSYKiiWotyi/BfLSoIoY9s47PLyyVCQL6z+GmsrN/Kj3jF+PjiBBHysPMQnKkIMdPYwcMOHKe8fIb5OEP+HHIotSGHhBRQWbCAQWIUsz9Rdz2rEt/UT3z0Muo7jrCI851dgfskaLtnsBKNjjzA29hjx+EkAHI5q/P6V+LzLcLnrcdirkOX/fckDgHw+SSp1mtj0SSb7dhNNHULEp/DcZ8Z+QkaeV0zpt7+He94fv9cTao4f943xm/5xlJYoYijFBfVF/OQiD/a7LofoIEgmeNt3YNk/wQsnMXV3d/PEE08wNTVFRW0RR6U7qfOPUW/VkWUwDUic7q3lJ44PMbqoBm8efv98irRNsP/Uj9HMJuzKVUwUD7On7BfEFME3T6+iKPseAmaZyWyYw5OPIHwyGz78SSqbFv0tHzXDn2GEvOE1NdYbY9c9HYz1xAhVeTj3mjqKq70ve46ua7Tu3MMzv/4NenYCWZeYZ/Vz1ttvJH0ygVmDAadM6zw3321rQyq4HbOjl9pUA99pvIjsN7+P0is4XVPNvgWL2SwtYFK4WC+a+YntFmwrPoQ4dBttiolPV87hexf8lHG5ips7BhjM5LiyyM/N1SHs6cMc3vFrin6wD2VaEHmvnYIrriBUuBGvdzGS9MeuFiEE6eZJok90o0VVHItDeC6owBz845hCLNbMwMBvGRt/AiFyuN3zKSq6hKLQJmy20ldtH+uazvTxwwy3PES6axvOx6aRsqC/dw4lH7qJQOCcF888PZ3K8OXOIbYeHUFpi1JV5OLed9cReuz9MHhgpsCma+DSH4My80OVz+fZuXMnu3btwuFw0K4M0BzYzrq8zIXWJKJER8/KHI4t4eHAlUyp1dy9P03YLXOi57cQm0CyX0Ta6WNP4w8YUlJ8prsW//QNNNhNWCSJnlQLxyafZe5553He+z6IzWlceerVYoS84TWRjGZ5/pFu2vaO4PBYWHl5LXOXF7+s6yKXzdCycysHHnmQ2MQoSG7mZTw0zT0P2VwBmmB/wMRTtTb0IjvbdzyDvfguZFnl3NQaPo0V9b8eIW8ys2P1Wlp85WzNz0UImS9KD/Ae+6NI679Edud3GdOzfKl+FTeuu5WfDOd5bCLCHIeNr1VKlMYfYGT0UUTfJIGfKIisCcd3bqT2wg+8LNj/QIupTD/YSaYtjFLixHfZbKyVnhcfj0aPcLr7B0xP78NkclJSciVls96L01nzmu5zoQtSxycYf+Jpkvt+gen0FOmFOrmP1lDV8ElCoY0vbs/OcJwbd7YzeXAcu9XEb69dwIrDX4Tme2cKK10C77oL3EUvlj88PMzDDz/M+Pg4ukPmcd9WZMLcdMBEWVWC1FIdyQJtYh5b1Yv5/L5Ghj0KrcnNmFoPk/MsxmpawdZFP6bXNsRH+6uxDn2CeRaJSrsNVeQ4Ft7ChHmI9f/4MeqWrXxN99ffi9c85CVJ+jVwMTAuhJj/wn0B4B6gCugFrhZCTL9SOUbIvzloeZ0TWwc5+GQPWk5n4fpylm6qetmp7dHxUY5ufoKT254hm0wim0OUu9awSFKw+SqQFJlxh8yn6i1kg1ZmJfKcOHkn1sLNyGqQd6bXcUXzYfTdHYzMLWT3gvPopJA9uWo8msYdwduZn9mCtvE7xHZ8C5GN8eOzLqV60bf4Rs8kWU3wTwXTbMj8gmRsP0hmhk/V0XDbALLNyZz//g3OeX9+ga1U8wSRh7rQVR3vxipcq0pf/OGKJ9o4ffr7TE1tQ1GCVFV+hNLSqzGb3a/Lvv8DXdWI7xhg8le/InviQXQfhP8pi7m+kprqGygquhhJksnqOl843MM9j3cg6fCxy+Zx0/TtSLtvmem6cRXNBH3pH7tQXtqql00yO53tjPqPcdkBH1c9P0X8HJi8yILLkWBML6a280L602s47e5G2fIQWqAcm34JWxt/x2nvKd4zWIVv4BP4tSz1Hi8BxUREn2D/yOOEzprD+dd9BKfP/7ruv7ea1yPk1wAJ4HcvCfnvAmEhxLclSboZ8Ash/uWVyjFC/o1voDXMzrs7iIylqGoKcs6VdfiKZk5nF0LQ13yMo08/RveRg0iSRNXs5bgjjVTbfNhMJiSrhvu8GrZ1T/LPVRIVVgXLUISh8V+geI9ijtVzY6SBZc89QX4yyeELzqLHU8sxrZTjWhmVeY176x+gaOBhMhd+g979P6Y6Nsbtyz/ErtKPs3kqxkLLFB/I30JIa8duryAmbeK2e238y5bf4vB5mPP7O7CUzfqTbdNVjcgjp0kdHkMpcxG4ei5KaGbb8vk4p7v/g8HB2zGbXVRWfITy8msxmc7sqfz5SJaJW58met/3EGqE1Af9RBYN43Y3UTf7Zvz+FQDsGJzmQ789SDadp+m8cu62bcG19ctgsgISXPELaHjHy8oeHh7mgQceYGpqitOWOMdLn6VhwMlN98Ux6zrPvmsx7hUT1EqnkVUXsakNNHvOwX7nrzH5AghxMftrtnKqeDfvGC2nrOd6lEyCIoeHepcNiyzRFT9GR+YQq699Pw1rzjcWO/v/9Lp010iSVAU8/pKQbwfWCiFGJEkqAbYLIV5xbVIj5N+4kpEse+7vpPPQON5CO6uvrnvxIs9qOsWpHc9xdPMTTA8PYvd4Ofvsy/BGZmGdzCNLEpmpdoKXzMd70VLuve8kny0RzLFYSLT1kMjfisnRj238HL7aa6Jy+zZGq4IcWLKClORkb7aCLopYZjbzu6YHsbfeTWTtTew6+XsumRzkgSUf4Qvea4hpgqvFHWySnqaocAOhoqv52V4v2zYf5JZ9P8ftdlBz5+1Yysv/ZPvyk2mm7mwlN5rEva4cz/oKJJOMEIKx8cfp7Pw6qjrFrFnvprbm0yiK7/U+BH+REILEni5GPv85tPF25IuWM3FlJ9ncKKHQJubU/TtWaxGj0TSX/Hwf49NpbEsLebDwKI3bbgbFAWoCNn4HVnz0ZWWrqsrTTz/NkSNHiEk6e0q24cik+dqdKsFojq1nn8e9157L5flHaLIcRtfsdCob0e/qRs8pZOzn0VE0wP7Kx1k/XkZ99ycxZ1KYhaDGV0iVzUyODEcmtkC1hQ0f/iTeUNFf2FLDX3KmQj4ihPC98H8JmP7D7f/xug8DHwaoqKhY0tfX96rUx/Dq0DWd5u1D7H+sGz0vWPK2ShZfWIFZMTHZ38uJ5zZzascW1HSaWTX1LKl/O64JJ9pEBlUXjMfGMJ3+NU23/QxreSW3P3CKfw3maTRbGD1xBM3+CyRTkuLeC/jasTbsPX0cXreQAX8NeSGxM1NJv1TIO4I+ftD0NKbnf8zwsuv47cAWPj86wJMV6/lg9RepED3caLmXleVrKSm5gsmUk4/feYSBjj5u238rbjNU3XE7lqqqP9nGdOsU4XvakWSJwDVzsc0NAKCqYdo7vsT4+JN43AuYO/ereDxNr/MR+L/ToikGPnYz6SPPosxehvj6QgYjv0SSLNTWfpayWe8mmta46rbn6RyLoy4K8m3/Kd637yYkiwsyEVj1z3DBV+B/LEvQ0tLCQw89RCaX45C/mQlHN1++D2r7M3Q2NPGJj3yGxfE+Pp97gGTRIVTsJLpKGNlnQfUvp8+psKP2Ls4Ol3J25ycw57KYUzFkfxUr/TLWvInJ7BDHottYcNXFLLpoE7JsnET1f3XGQ/6F29NCiFfseDNa8m8so91RdtzVzuRAgoqGAOe+cw5Or0zH83s4vuUpRjrakE1mFi25iDmBpdCjIlSdjMNMy0SG/MDzBLMPs+rOpzD7/Nz5SAs3eVUWSQq9R3cg+36D0K0sbF7NTft3MBzwc3TZQnJYyAkTW7O1jEh+PlhTzBcWHER6+l/onnM+v8+d5ObeCQ57G7hiwY+40nGKm2fXURRciSTJ7OyY4Ia7j2LKZPjl0duwjw1Teecd2OrrX7Z9My3gYaJPdKOUugi+tx6zf2a2yeTUdlpbbyaXi1BT/SkqKz/0Zwdo32iEEIx+4ydE7rgVU6ge/1duZMB3G9PTe/B4FtHY8H1ylPHO2/bRMZ4gvTjABy3H+Nqxf0OyByA5Dk1Xwzv+E8wvX5IhGo1y++23Mzk5yWnHCCcK9vPpZ2XOPppmtLKKD13/b1TGTNza3cuhuY8QCh5CaGbGj3lJRM+lQyvluTm3My9WzPltH8Os57BPDRIuaGKxeZyKQBlC1eiIHmIyOMEFH/0EwbI//avL8KeM7hrDXyWbzrPvwS5O7RrG6bNy7tV1uAMJmrc+Q+uubWRTSUIl1Syp34Q/WYA2nkFSZGwLCmgZS3LiZITygeew2ray/o7nMNns3PtkO5+2pZkvTPQefwIleA96toBLd83jsq7DHF62iIlgEZZsnLjZw5bcXCaFi88ureYTjV0kH7uOltpSxh1JVh9LE5W9vHPZf/KNpnmsC82skaLrgp9s7eKHz3Uwr8DBD5vvRD/4POX/dSuuc8992TYKTRB5/DTJfSPYG4P4r5mLbDEhhEZ393/Q23crLudcGhpuwe2u/3O76Q0tfNf9jH31S8ieMvwf/BLapjE6er6Ormepq/s8du8VvPu2/fSEk0hLCzg/9zz/2fJlZFcxxAahZi1ccydYXz7NUdM07rvvPtra2gibk+wv3sm7jmS5YFuWaEEhH/3Ul2iMW/heG9wa7CV41jMskvaST5uI9dZzaHgVW2ruZ1aqgItbP4JZ1/GPnGS4ZDVF0XZWNpWjxzxktRTHprdRtnExyy67EtOfuYiL4Y/OVMh/D5h6ycBrQAhx0yuVYYT8mdd7YpLtv28nFc3SeF4Ib2CQlh3PMtLVjslsZvHCTdR4FiAN5EETKGUunEuLsTQEeOa3J+lriVLT/SiZkkNc8sutmBULD27p5Hopwdy8xODJ+7AEH0OKV/Dxp10U2PK0NtYjmfO4xkeZ8FXybL6eKeHiGxfMZl3JEwx1/IiIz0xSt1Lc4mHhdBdfWPMbPnfORgotMycXTSdVbrz3GNvbJ/iHxbP4dNujxO+6i+KvfAX/NVe/bBv1rEb4961k2qdxrSnDu7EKSZbIqpOcOvUppqf3UVp6DXPqvoTJZP1zu+lNIb5tO0P//CkkWwD3xf+K+wO1dIW/Qnh6N8HgOgrLv8a1v2lnJJqheE0pVeHt3Nb6FSR/JXK4G8rPhvfcBzbvn5S9ZcsWduzaQ17OczC0j/P6J3jHI3nSDic33vBvrEj6uKFX4gumDIPLp/is6w6stJFL2ugaWMntyin8WS9XtHwYkw7FQwfoLV2PMzHESlMLrvnvQA/rjKX76LG0sPqj11FcW3cG9uKbw+sxu+YuYC1QAIwBXwIeBu4FKoA+ZqZQhl+pHCPkz5x0XGXXvZ10HBjF5ZvEG+xj4OQBctkMs0rnsahuA56oFz2aQ7KbcS4O4VhahKXURTad54kfH2akO87cznsYa+jg6h9tQTEpPL63j4+mw9RkBKMdv8Ua2IZnZDaf2CUxMreKuMdDwN2H1JJgrHgez2iNTOLi8+f2MMf1G3JaFCWjc3e2itrkYj7T9SueW/5F1m78f+ydd3hVVfa/39tLbkvvvZGQEHrooKAgKCoiiiIKiA3rqOPYRcWGo2AHsaOI0pWO9BpCSYeEJIT0enN7v+f3RxwcBixYvj/H4X2e++R5knP2XWefk3X2Xnvtz/obku8zMQpqO7nr88O0Wlw8PT6TcY1HafzHPwi65RbC/3FmQpff7qHtoxLc9RYMV6agyY0EwGQ6SlHRXXi8naSnP0tU5MT/83vwR2DLy6N25u2IlEGohj5I0HW9MEZs5ETly0ilBsLj5zHtcxsur4/BlyVhr1zDgtLZeCN6IG8uhohsuGklqIPOajs/P5+vv9mAUuSh1FBKuv0EE5Z4QCzlsTsfZqQ7mmsaxDyAnUOZGuamVaGrm4syyI7DEcxXRoEGUyDXld6G2CchoXEHFWGjkXrt9CxdSMzEGXjMkQhuP+Xmg8gHBDPw+skXpBHOwYXNUBf4UQRB4ER+C9sWH8BhKkQiLsdl60CjDqJ3xhgiJQnQ6gMRKJINBPQLR5UZgkjWtTBnN7tZ83o+HQ1WMko/4fjgBma8sAmZWMamgnpubW0h1uGjvWohMsM+MoszGdWmoT4+FhUWokKP0nHQQHt0Glsl6TS4g7k95yP6hRcT2CkQVG/ilvDLCVRezFdH7qUzeQwhN34OIhGCIPD5gVM8+00poVoF707pTZqtmZPXXY+qRw/iPvwA0b9N830WN20fFOFpdRB8Qwaq7l2Cac0t6yktfRC5PIwe2e/+V4ZnfgpbXh61t92OWB2Msv/9aIakIRvloajsHpzOeqRBj3PPqgiCA+TcfE0GZfsX8tLxVzHHDUNXtx9CUmDqatCEndV2SUkJC5d+S6DYQV1AHYGKQiZ+5ELl9vHsbQ8wwZdKrxY/M3FwMk7FzN4hdNv9JNqUMhQ6DxU2BVubQhl19G7EfhkZTd9SahiHX6Ykq3ABUbEa1KNm4an2YveaKfcdJmfGeGKzcs5xpf+7XHDyFzgnxiYj695eTnNlHoKvAYlYTk76SBJ12UjbROAHWUQA6l6hqHLCkBrODF3YTC5WzT2IucVKZslC8i81cs8TG5FL5Ow60cqUk3VE2710nnwXiTaPKw71QaONwiOTEactQS09RWtFMOo+Ur5uG8kxYwqzen/DNX2zkWz5lPCGCq7q9TDVqqHsODKTYLkC6Z27QKnH7vby+MpiVh6pZ0R6KK9P6onO56R64rUITieJK5YjDQk5bavX6KRtURE+s5vgqZkoUwMRBIFTpxZyovIV9Pre9Mh+D7n83HVe/9ux5eVRe/sdSAzhKHvdhzwxHP0NMRyvf4K2ti20MYUntw4gK0rHQ5OyyVv/PPdXLqA+5QqiT24BfTTc/A3ozpZqKC0t5fUlG4iSmOiUdyIKLuTa9zvR2d38c/rdTPFkEWhyMw0XxmA5vbsHc1PhCuze74jMNYLYzaFOLSG770NwBtGzdQklynHYAyLJqFtJRM1Ogm65H7c3HVGnQKO9GnuakwHTb0ShPrs61/8iF5z8BU7jdbupPprPoXWbqT92BAkQF9KLrISBqE1q8AhI9ArUPUNR9wpDFnHufyKr0cnKV/KwtVlJL3uXHeOtPPrgBhQSBUcbOplYUk2g04Pr5NsESssZXZGLW2fAYOwgtc9OHDYxUq2HgEgH7xdOJa+5D0+PkXPDkCEc/nQM2fXHubrPqxQpu7Ouag69GrYjmrERovtwosXCrM+PUN5i4YFRadx9UQoiEdTdcw/W7TuI//RT1L17nbbV02qnbVExfpePkGndUcTr8Pu9HC9/ioaGpYSFjSMzY+7vHn/3C35a7C3UWmppd7ZjdpkxuUx4/d7Tx6ikKvQKPTqFjjBVGDHaGAwKwx+yKci2bx+1t92OPLkb8u53IlapCJrSjSaWUFn1GiXmcbx+4BIuzQznsQlZHFj2AJOqv+BI5lR6nVgB2ki4Ze0ZMgj/4ujRo7y4bA8p0mZ8Eje+iFIu/6CBEJOdhVNmcounH1aXlemCD5dagiZJxzOWozTsX0PiaB/q8CpcfhG2ksswlV9M345FHPOPxRiUQTdZOZGb56NMTUM35TFsBQ7wwUlXCWHjM+l20Yj/+U1UF5z8/zh+n49TxQUc27OTiry9+J0eItTdSDT0IVIVjsgHYrUUVfcQ1L1CkSfoz9Cf+U/MbQ5WvZKHvcNKSvnbrLvawvN3b0QtU3Oiw8b4/HKkHg/y6jfIsrlJtHVD6vGQaC8lfEwxPrEYicyPw6bl05IbyOvsziNjujGxv47VX01gZFM71/d+jTpZBIuFPC7a+TCMegZh8P0sO1THU6tLUMslzLu+J0NTQwEwfvUVTU89Tdjf/07w9GmnbfW2OWhZWAg+gZAZWcijNPj9LopLHqC1dSPx8XeSnPQ3RKLfVq5OEAROmk9ytOUohW2FFLUWcdJ8EpfPdd5taWQakvRJZARnkBmcSU5oDkn6pN/FkZk3bKT+gQdQ5w5CljEDv9lH4FUpOBJLKS65j801I/m89BLuHZnKbSMSOfzZNIad+oa1vR5mbPHbiAzxcMu3EBByVtv7DxzgmdVFdFecRC2IEEXXM/zTUqJaTXw1cSo3+odQKWrnLkGKVyzGF6/hHncN8j3L0KapEOWWkaCw4rYG0370amKrt9BmHElTxACSo90kbn4Jf1srQdNuxxOYi3DChdvnpF5RRcaM0YQmJv7m/vlv5YKT/x9E8PtpKD/Gsb07KN+/B4/ZTqw+k2h1DuHyMCQiMeIAGaqsYFRZISiS9IgkP+/oOlvsrHolD1enhcSKt/h6golX79iIXqGnwebi8j1l2P1uEo4toFenAbVEQar4CPqUWuShDvw+MFXraGkbynf2TA55Y5k+OJGrcr18uPZWrjQFcHvWHCQKLZ/ESum/5FII74518mqeXFPGyiP1DEwKZt71PQnXdS3AuaqrqZ5wDepePYldtAjR9xt5vB1OWhcUIHj9hM7sgSwiAJ/PTmHRXXR07CI19QniYqf91OX+JD6/j4PNB9leu50dtTuos9YBoJVryQ7JJtWQSpwujhhtDGGqsNMjdrm4K/9cQMDhdWBymeh0dZ4e9ddaajnReYKy9jKsnq6yfaGqUAZEDmBA1ACGRg8lUPnrtV6MS7+i6emn0V0xHlnajbgrTWgGRyEe5qSg6DYWHLmU3fV9efuG3ozOCKZq0RUktOTzQe5L3Jb/FJLgZLh5zTkXY7/bvpOnN9WQrS4h3KtEFmUiZ+lhkutb+G7cJMbLRnFU1cgjPgV2txRvXAADOxoYWrkav0yg9jIj/VSnCJN7sTen4Si2oznSj+q4scTESelt/w7rsq+QJyYS8uBsOg7bkHVIsHg6sCbayZ42HmXA/14I54KT/x9BEARaTlZxfO9Oju3didDpJUabRmJoT7QePSJEOAFlRhChQ6N/dsT+nxibbKx6JQ+PyUJc1Zt8MsHI/OlrCQsIp8Pt4YodpRjdNoYWf0WmykpUUDmBoQ2IZAJ+r4iWoyF0HA/DpRlFgQ52eZK5okckg3uf4MN9zzPRm8OzyfeRrFbxaY8k4pdOhMajlE/YwO3ftlPTbuO+kWncfXEKku/tFjweTk6+AU9tLYlrViML7woleDudtL5XyCd6zwAAIABJREFUiN/lI3RmNvIoDV6vhaMFMzCZjpDR7QWioq79Vf180nSS1ZWrWVO5hhZ7CwqJgtzIXIZFD6NfZD8SdAmIf+PMALrCPafMpzjccpj9DfvZ37gfo8uIRCQhNzKX0QmjGRk3Er3i7BTHn6P1rbdpe+stQu+/H2n0KKx7GlCkGNBMDOJw2Sye2n4J9bZ4lt81lMxAP+3vjURia2HugHk8tf9elKGpXYuxqrPlHVav38QzOzvoocsj0aVDGeElae1hsiuqOXTJREYEXMqB4Drm2OV0OJQIUWo0DS1MbVuPyN3B4XFiIqhhrN6DWOqkvS6QoNWZlIfdREiohJEXyeic8zSexkaCpk9HMXwi7d9UoPAoafc0oBwWRtoVw0+/7P8XuODk/8IIgkBLdSXlB/Zw4sBepEYJUQGpxBkyUPm7RjQOqZg6qwdpsoF+M7qjVP+ywhX/Tnu9lVWv5OG3mImueYP3runkrRtXEqePx+b1cfXOYoJadzPavZaI0Brkcid+u4TW9kh0MiMV62KQyvS4A0ZSGWRkkyeDXvEGkjM3sKFqNRcrr+GL8Ku4KEDEgt5Z6PLegU1PsCdrNtOOpBMYIGP+9b0YkHTmwmjLvHm0v7eA6Dfmo7v0UgB8JhctCwrx2z2E3pqNPEaLx2PmyNGpWK3H6N79NcLDxp53Px9qPsTHJR+zo24HYpGYIdFDuDL5SobGDEUlVf18I78Rv+CnrKOMLTVb2FC9gTprHTKxjFHxo5iUNok+4X1+cUhHEAQaHnoY89q1RL8xH0lQD4wrTyANVhJ4UyK7Kx7loY3Dkcu0rLt/NMGeRhwLLqIJFc/kzuOtvbeijczsSq+UnXntfr+fT5au5NUCH1lBO8mwh6AOlRK59TB9S8qoHD6RnmFj2Bd9ivmtIupsWiRhKqRGC1fVrSXQVUfeWBFGVwd3+5LQJ+Xh84sQ7YqksuVhAgIUXHF/H5zvz6Pz669RpKYS+dJLGKs8OPe0IkdBO40EXpZK3PBeP9IDfy0uOPm/GILfT+OJ45Qf2EtdXiEah4ZwVSKRAUlIkXUVg0424ApUsmt/Iya7j6GTUskcEvWr4rqttRZWz81DsJiIrJvH/Akm3pr0JenBGZhtNby9dyFpns0YlO34fWJkpVLKK1IpCc1gaPB2anaHoNSE4ZFfSkPoKb71ZGDQKQlN/ZgqWznpwfezW53FzYpO5gwYjrS1FGHhCAoUfbmqYxYXpYfx6rU5BGvOXBh1lJRwctJ16MePJ+rFF4AuHfjWhYX4LG5CZmShiNPh9Vo4cvQWLJYSemS/Q0jIxed1/fsb9/PmkTcpbC0kUBHI5G6TmZg2kVB16Hn35e+FIAiUdpTyTeU3rDmxBovHQrI+mcndJnNlypUopT+fS+53uTg19Wacx48Tv3gxIlU07Z+VIZKKCLoplbXV/+SRTf3pEenmq7smIm3Ix/fxOPI16czp/QKLd05GlzKsq3as5MwdqV6vl1cXLWFRTQDZ4evI7oxCY1ARtL+QwUeP0jbgWpK7Xc7+uBreq3Zw3BKMLFBBslZKQv4qEp3l7Brppk2wc3vVREIytxMQVYLPKKOlcCqu9t6M//sgVNVHaHziSbxGI6F3z8Jw483ULDuA6JgXuUhBh6yFyIk9Cc1J/qNuxZ+CC07+L4Df76P+WCmVe/djKqhD7wsmQpWI5ns1RLFWhjI9CFVGELIkA0e21nLw22r0YWpGz8wiJObXVeFpq7Ow8uUDiCydhDfN55UJnbx+6QuEihpoal6H1dpV6s5kCsFfKCP1mw4WJl9NW0YIl8tX0FKgR2WIxyseTWdMOavtSdjFCjSJ7yLR+FCGPsJxQcczniPcdul0RH4vlreG4TbWM9bzMreOzmXGkETE/xFWEjweqiddh6+tjaS13yLR6fA7vLQuKMDb4SRkehaKBD1er42jBdMwmwvIznqL0NBLfvG1H+84zuuHXmdPwx4iAyKZkTWD8Snj/09G7eeDw+tgQ/UGlh5fSkl7CcHKYKZ2n8qktElo5D99371tbVRPmgReHwlffwUiLW0fl+Azuwm6LoUFVUuYvy+Vm3rV8+ykmYhKVsCy6XwReTmfdLuTpbuuw9BjAlzxxulygqftcjh4/J0vWdGupXvMcnq1JqNRBxB4tIxhhw7i7n0dkSMmcDCqlkWFjRyxRiJWSpkyII5Tq5bQrTOfzUOsWOQebi26A0VgO6G9F6PQmHA0ptBaOIlRU8cTGSWl+bnnMa9bh7JHD6JeeglCwqj8dCeqU3KkIhkWrYmoa3phyIj5I2/F/zcuOPn/UnxeD7WFRdTvLsBdaSZIHEGQPBKRSIQgFVAkGVCnB6NIDUQaqkIkEuGwutn8QQm1ZUbScsMZPjn9jGIe50N7vZWVL+4Dq5kI5ytsm9DJuKhoBHeXUqjZFkZbcwyNLaEM/+YAaqebOb2nYY4OZLL3c8yVStRBmSC5BE9yOcvbAznl06GM+ZDkhCjqddPpdLl5t2UJYybNxeGXsPujR7mkcQGz1f/g2imzyIzSndO2tvcW0DpvHjFvvYl21Cj8bh9tHxbjrrUQckt3lKmB+HwOjhbMoLPzIFlZ839xiMbitvDG4TdYenwpOoWOmdkzub7b9Sj+5BIHgiCQ35zP+4Xvs69xH1q5lmndpzElc8pPvpicx8s5OXkyyvR04j/9BL9LoP3TUty1FrSj43nwxA6+O2HgxdHlXDfib4i+exZ2v8Y/0h7iUMQwlu6dQtDA22Dkk2e1bTKZmPXGcva4VKTEf0G/pmzUUg2BZSe4OG8f0pzJhNx4A4X6et7fU8JuRwIg4qlrsqhavx510Wo2DujAHiBwW9HteFwRhGcsR5OyA4lcwFQ1hIzMv5E5pAfm9etpemY2fqeTsAcfJHDKjdhajFR9uhNtmxaZWIEtwEb4Fd3R58T8pdIuLzj5/yLsHSbqth/FUtKI1CjGIAtDIpIgIOAPEqHNjiQgIxR5rPasbJjWUxbWv1eEzexi+PXpZAyO/NUPcnuDhbXvfok6OB995HbEoV253RpNFmZjMvlHRBh9IQQYK5mwdg/NwWrm9LqDTl0gNzk/Q6jzoQrqg0IzEllmLSsqbRz1RqMIX8Pwfjls8w9G7TTyafnz5Nz0CUUWDa9+8Q0LbfdTFTSExLuWo5SdW/XRVVlJ9VVXoxk1kpjXX0fw+Wn/rAzn8Q6CJndD3SMUv99FQcFMOox76Z75GhER43/RdW+u2cxLB16i1dHK5G6TmdVrFjr5uV80f2ZK2kp4r/A9ttduJ0wVxl097+LKlCuRis/9wjetXUvDgw8RdPNUwh99FMHjo+OrchxFbYj6hnLDiRKMdjfvTahiYPYjiL6YhL96JxNy3sAcEMFXB24hZNTjkHv7WW03NjYy/Z1NHBf5iYtfzMCm/qgIIKiihpH7d6PoOZWwh6ZxjDoWbNrNFk83BJefpyZmEd5YzpElb7K5bxM2DdxRchtuRxyBmkr8yQsJT7Xg9yoI8F/PwDF/x9fWSeOTT2LbsRN1bi5RL8xBFh2NsaaOyiW70LcZUEk1OJVOgi9NxdA/DpH0v3+B9oKT/xPjs3kwHq2h43A1/kYXAT4tIpEYv+DDqXQij9cS0i8FdWow4p8YkR8/0MS2xcdQaWSMuS2b8MTzd0w+nxOjcR/1pzbQ1LgZqcqE4Icqtxitpi99Iu5m3YrddDodlIfFMGTfOi46WMaRVAPvpN9BiyyQG2xL0bZ0ogwcTHDUSPS9Oll+oIQtnjQUhgImXtaPT43hJHnb+SL/DsInLuC9UzHM33KcZYrnyJQ1Irvn4Dk33EDXekTNlJtwV1Z2hWmCgjEuK8d+uAXDVcloBkQhCD6Ki++jpXU9GRkv/yIdGovbwvP7n2dd9Tq6BXXj6YFPkxWSdd59+GfjcPNhXjv0GgWtBSTrk3ks9zH6R/Y/57FNz8/BuHgx0fNeRzdmDIJfwLypBsv2WuoTAphaV0e8tpp5VxrJiLsX0cIRuDxOhuS8hw4Py/NmYLh2EaSPOavt4tIybl18lA5lOyGxSxjRPBy5R0VYxUlGHNyHst8MouZM51h7DYvWb2WtvzuC1cvfrsjg6lAPi196ko3Z1Zg1ImaVzMTlSEQvacKpWUVkt3YU8afwu8PI6f0sISEjMS1fTsuLL4FIRPhjj6GfcDUikYiOujrKP9+KvkWPVhaEV+JF1TuU4ItSkAb992riXHDyfxIEn4Cn2YarxoSptB73KQtyV1fOtNfvwUwHokg5Qb0SiBiQgUT581kwPp+ffcsrKdhaS1SqgdEzs1Dr5D97HnRN7+32Kjo6dtHesRujcT9+vwO/R4G9IR1teQOPd7NySfhI+liHcaS4GInLxXcZ/bl3yVtkVDewZlAoGyJuodofznjLOhKMJ1EGjSA2/RIi+nv5cu0mVnu6IVVauH5ib95r8TFAaufjHdfi6TGTaTUjKa43Mzd2H9e2vglXvQc9J/+ozZ0rV9H46KNEznke/YQJmNZWY91dj25UHLpR8QiCQHn5bOrqPyMl5VHi42792X443HyYR3c9SrO9mdtzbmdm9swfHfH+NyIIAltPbeXV/Feps9YxNnEsD/V96KyFY8HtpuamqbgqKkhY9jWKpK6i5Na8RjpXnmBzoJjZHZ1clbyWO4dHkKa9CtEHl9IZlk3vlJfo5qzlq4IH0Uxb1SVs9h98u2UHj2xpRx5cjiR4GWM7xiKySYitqGLQ4UPIBk8j4fU7OFZdxftrN/Et2fg7Pcy8NJVZ3XV8+sIjLE8pwaISM6t4Oi5nClpJE0pZNTLhFPIRZSj0jRgMA0lPexJ5ZwCNjz6G/eBBtJdcQsSzs5EGdu0vaK+vpezLTchqxESqujaaiWIVBA9PRtkt6L9udH/Byf9/wmdx4z5lwV1rxlFlxFNvReTrCp84fXY6XA14dD60mRHEDOtF0Dnqjv4UdrObje8X01DRSY+LYxh0TQqSn9nQ5PF00tGxh46O3bR37MLlagRApUpAoxpI8TfBWGviyOxcyL1j6xiiGEBcTTI2lwtdcxOr+17CMx++SqDJxPuXR3BKeS2FrngGWg+Qa85HEXgJmQPGoutpZ/nS1azxpOIQaRh7VTeWWhyM00l5a+M42pWJjGx7EK1ayaujDIz47kqIHwg3LjtrAe90f5rNVI65DHlcHPFffI5lZz3mDScJGBiJYXwyIpGI6uq3qKp+nbi4W0lNefQn+0IQBD4q+Yj5h+cTFRDFS8NeIif0ryt85fQ6+aD4Az4o+gCFRMG9ve/luvTrzsjp9zQ1UX31BCTBQSR+/TViVVcs31HaTvsXx5gtcfCd28mj/f/JyJwrSbJGw4pbqc6ZwRDDVHItJXxe+QqqWzeCNuKM7xcEgfmfreSNUhmxCbuxyDczwXYNnnYPCeWV5BYUIIycRPprf6O8/CTvr1nHalkO/lY3k4bE89TgKBa/8iiLYw7ikEuZVXwzDlc6GnEbAQovkqo87EN1hGStQip3ERlxFQnx9+D4cgst8+YhDQwk8sUX0AwefNoma0c7hWvW4chvJU6ZgUqqQZAKqHPC0PSJOO+9JP+/uODk/2AEQcBvduNusOKpt+JusOGpt+AzuYGu/Gaju4l2ZwNWiQlNahhRfboTn9MLlfbXxXvb6618+3YBDouHi6Z0Iz034pzH+Xx2Ok2H6TQeoMO4B7O5EBCQSrUEBg4mOGgIQUFD8NhCWD57J267i562D3lwZDV9bAMJ7IxAazITbGxhY0ouD329CJfUzWvXRiB3X8Feexqp9kout65HaRjHgHFX0RhRws4VO9nnTqTaE0WfEbHsUfi5JTKQJzbfiL/jJKMdL5DbM5unL88kcMV1UHcQ7toPhh+vBNT03PMYlywhcdnX+N0hdHx5HFVOKEHXpSMSi6iv/5Jjxx8nIuIqMjPm/qRUgd1j58k9T7KpZhOXxl/K7EGzfzYT5a9CjbmGOfvnsK9xH7kRucwePJtozQ8DDOuePdTOuBXD5OuJfPrp0793nTRR/XExN7vMiAOsPNH/KXpkPEJsUSnkLWDfmPeZ4EjjIuNBPm77Evkt34D8zELnHo+HB95YyretBjKyVtPoOcj17ptxNppIOV5Bn6JSbOMvpuezs6msrGfhqm9YpeyJr9HFmF5RvDYulc9ff4IPQ3bgl8uZVXgjFlcWaqkRuVROUOVqTiZcTmDmMkLSDyIWiYiOnkyk+xJaH3kBd2UlQTffTOjfHkCs+GEh3e2wU7x1M6c2HybEG0GMOh2pWAZqCQFZoSgzg1EmG06rr/7ZuODkf0cErx9vmwNPsx1Po63LsTdY8Vs9XX9HwCm202ato91RT4enCVVCIHE9e5HYsw+hcQm/eSdeTXE7GxcVI1dIGHtXD8Lif3hReL0WOk2H6DTmYezMw2IpQhC8iEQSdNoeBAUPIzhoKFptNuLvQxKWDifLn96Oy+6lr2cJz/dvoJuxPwqvkvTycmQhOo54tNywYx014SJemxhCrG0Mezsz0bmM3GD7Cl3w5QyfNIGV9s9o3NZIkyuOve5UIjOCqI5T8XB8OEM3zaZ/0xIekjzC6GtmcElmOBz9AlbdCWNfhf4zf/SaHSUlnLx2EoGTJxM45R5aFxUhj9MSOiMbkVRMW9tWCgpvJzh4KD2yFyAW/3ioq85Sxz1b76HKVMV9ve9jWvdpv1umhd/np7PFQXu9FUuHE7vJjcvuwe8XEPwgU0pQqqWodQoM4WoM4Wp0Icr/80wPQRBYUbGCuflzEQSBh/o9xMTUiaftaH75FTo++oiYd95Be/FFp8/zNNvYtPAId9tMjIw/yeT018hMe5HItW+BsYaVE77hzno/l7duZ4FwCMm1n5xVL9ZsNnPja6spcmnJ7PU5Ha5axjun4q5vIaPkGD2OV9J2fRYDHp7PyZPNLFi+ipW6PvhOORiSGcbCa7NY+s4zvKfZiEymZFbhJDqcPZFKTCCoSK//mrKwaxECTqEcs5R4eS0SiYKYiJtQLbNg/mwZirQ0ol6dizIt7cx+8fs5VVJI8ebNOErbiValEBWQjAQpSEUoUwNRJBtQJBuQhav/NKP8C07+V+B3evG2O/G22rsceosdb7Mdb4cD/N8fJAKP2kunp4WGtnLarHV0ulvQR0cS2z2buKwc4rJ6olCrf/K7zofCbbXs/qqC4BgN4+7KQaFxYTLlY+w8QKcxD7OlGPAjEsnQ6bIxGHIJNPRHr++NVHr2SNVidLLiqW04HH76itfxcbKZEEcyKquJ4U2tNMSFYyyqZVBFEfszFCwcqybTNpbCljQsHglT7V8QG3EJva4dyZyKp9CV6giwJ7DOl4PIoMDcJ4iZhkDsG77mFdfz7A66huyZC9CrZGDvgDf7QEgaTFt/ljP4F4LfT83kG3DX1RH32XI6FlchDpARdmcOYrUMi6WUQ4evQ61Ook/vJUgkP97fJe0lzNoyC7ffzavDXmVQ9KDffE/a663UFLdTW9ZBY6UJn8d/+m9SuRhlgAyxRIRIJMLt8uGyefD7fvi/UwbIiEjWE5MeSEKPEPSh/3d5+A3WBp7a8xQHmg4wInYEzw16DoPSgN/t5uR11+NtaiJpzWqkoT/E770mF8/O38endhsP9dlGZsgaesY+SdBX/4Dw7rw/6iOerGplev0K5sRoEI04uyDciapqbliUR6fET0zWh4gRyDVdi6yhkeyiMjIra2i4JZJhsz7kxIkmPlj9DauC++GttDMwPZSPbujF0o/m8KZ4JRqJmrtLJtBs74NLbEXhVdGjczWl2kvxSvzk5b7PlCw1YtthpFI9Eb6LEJ7fB602wh56kMApU8458LKbOinduZXyPXsQmtxEq1OI0XVDSdfzJVZLkSfqkcdokUdrkEVrkASc/27y34MLTv4/EDx+fFY3Posbv8WDz+LCa3Th63Di/f4jOH6Qg0UMIoMMt9yN2dNGc8dJ6hpKMbla8Qs+gqJiiO3eg9ju2cRmZqPWn63n8Vvx+/zs+rqC43kFxPZuIb53CxbrEazWMkBAJJKj1+VgCOxPoCEXvb7XTzo7AGunk+VPbcNh95Oi2cWOIB9iv5qwhuNckTOUvJPHCdhbSEJ7M18P07F6gJ8s59U014ZzzB/Ota4V9E/sg+qiZOYUzya1LZX49mS2qwZQZ/MjDA6jt01E/cESNqoeQ2aIJuCu7SD7Poth9d1QsARu3wXhmT9qZ+eKlTQ+9hgRzzyPszYOwekl7K6eSINVuFzNHMyfAEC/vitRKM4ubPEvdtXt4sEdDxKoCOTdUe+SZEg639twGofVTdneRsoPNNNe3yUiFhwdQHR6IKFxWoKjNehDVMiUkrNG6YIg4LB4MLXY6Wi00VRtpvFEJ6YWx+l2ug2MJD03ApX2ly2i/xb8gp/Pyz7ntUOvEawM5pVhr9A7vHdXquqEa1D360fswgVnOEKnxcVVc3fQ6Pbw0oiPUSpL6K+6hYD1c2DI35idcCvv1rbyeNUC7hl6FaSNPut7v926mwc3tRMc1I4vegGJ2iRCG0YS3FRHj6IS0qvqqb1Tz8hpSzh2rJaP165nVWh/fBU2BqSG8PFNfVi29FVec31BIAHcW3oldfb+mCQ29F41KaYt1Cr74Jar2JD2AX37BjFW78Vk3IVEHIC+OBTZx/Xoeg4l8oU5yMJ+/NkxNtZzfO8uju3diaOpkzBlLDGB3QhVxSH3/HCPJAYF0jA1shAV0mAl0lA1kkAFEq0ckeLsZ+H34i/v5N31Vmz5TSAAfgHBL4AAgs+P4PLhd/oQXF78Lh9+u/dMB/4vJCKkgUrEehlumRu7z4zR2kRzezW1dSU4bRYApAoFEcmpRKZ2IzI1najUbgQYfr0i4E/h97uxWErpaMunvHAbgrwMmcrUZa5EjU7XE4O+L4bA/uh1vZBIfnkKmM3kYvmT32FzCGgD93NCLcEsM5NsquTGKc+y9Z15RO8vQOb38+74EPJT2sn0TkJSIWenqBvDvLuZ1D2M/CQLy09+zWDxYCIqIygN7kdePYh6BqOosyF0OtkS/Box9jJEt++AkO/rdNbsg4/GwOD74JJnf7wP7HYqR49BGhmJesSjeBqshM7sgSJeh89n59Dh67HbT9Kn99KfrOj0TeU3PLnnSdIC03h75Nu/WpKgs8XO0S21HN/XiNfjJzxRR3puBEk9Qwkw/LbNUqZWOycL26nIb6a52oxYIiIxJ5Teo+POCMn9UZS0l/DwjodpsDYwq+csZmTPwPTlUppmP0v4Y48SNHXqGcefaDAx7s099EHgzpEvIyjsDGrNRlq4Av+UFdztSGBFm4U3T7zGtROehuAzpQUEQeC5D1bw4QklAzOqKWYBo+Ovor00nviWOnoUlpB0qp7WBwK56LovKS6u5OONm1kTnov/mIV+yUF8dkt/lq+ax6vmT4j0a5hVNpaT9kGYAzzobDICjftxy2Kxq8PZF/85Dal1PN/vDjS2nbS0rEckSFHvEqHbpyX24RfQjhr1s/3U2dxE9dF8qo/kU1tciMgnIlARQUxYN8J08WjEBiQOMXjO9K0imRixVo44QIZYIUEklyCWixHJJSDpCgWpMn9d0Zq/vJN3lLTR8XUFIgldmRnirqkxElFXZyoliBVSRAoxPokfn8SDCxcuvw2zrZ1OUyNtrbV0tjRiN3WeblemVBEal0BofAKh8YlEpnYjJDYeseTcm3R+K253GybTEUymw3SaDmOxFOL3dy3eum0h6DS9iE8dgl7fm4CAtNMx9fPFbnax/IktGD0OPIEFWOQSTuhO0E3r4/4Jb7Lt7tuJLzhGuz6IuddFUmc4Rpb/WsJLXKyW9ieJau7rbeJDxVEqTSe4Oe5mHHsc1ASnsbkmAH+0GjrdDI8yMD9uD0F7ZsP4t6D3TV0G+DywYBi4LDDrAMh/XBq29c23aHv7bQKnvYDXGELQDV2bnQTBR2HRXbS1bSWnx0JCQi760TZWVqzk6b1P0y+iH29c/AYBsvOXorWZXOSvPUnp7gZEYhHpueH0GBlLcNQfs1jb3mClbE8jx/Y14rJ7icsMos/YBKJSfv9Z4r9jdVt5dv+zrK9ez8WxFzNnyByM9z6Mbd8+klavQp6QcMbxC3dU8sL6Yzwmd5E27DmUSj25BTZEdiPuO/Zy47EW9pmdLK59ixGT3wLFmf3lcrmYMncZB60GxgzOZ0/HMq7p/hAVeR5SW+rILiwiur4e+6MRDLt8MUeOlPHp9l18G94PodRM74RAFk/vz9JvX+N102ekunXcdmwUJxzDUcTJsZ1yEdBZilKkwKRLojZ8LeuSv2NG9gxuSR1Dfe0iGptWgd+P8oiYCNFIEu78J5JfKFfscTlprCin/ngJ9cdKaaw4htvRNSNTybRER6QTEhKHVh2MSqZFIVIi9csRC2JEXvC7fQguH/gFNIOi0I2K/1X37S/v5GsKj7Jrycddjl0kOj0l8no8eF1OPE4nHpcLl8MO/3m9IhGaoGAM4REYwqMwhEcQHBtPaFw8upCwP0yuVBB82GwnMJkOn3bqDsfJ702SodVmIaM7x3YYsLcmccnUocRmnq3ffb44LG6WPb6RZkkrNm0NMpGD7eGHSAkP5Pkhb3Bo2i3EVNVSmJDGK5PjcbONXp4rSChysFw2ELnUzUP98pjn2IdapmZ239kcWXOEUoWBTU0RCFIRgUoZz43O4IoII6KFIyD1Urhu8Q+pkXvmw+anYPKXkH7Zj9rqaW6hcswYFOl9kCVMRX9ZAtrhXdk3FSde5NSpRaSlPkVs7M0/2sZXx7/iuf3PMThqMPMumveLhLv+Hb9foGhbHfvXVOH3+MkcGkXfsQkE6P9vJA7cDi/FO+s5uuUUDouHxJwQBl2TgiHs91vn+U8EQWBx2WL+mf9PEvWJvJ71FK7Jd6BISSH+s08R/dsgx+cXmPjuXqoaLSxQN2HPfYlwcRKZuw8iSr0UyzWfcNWBo5x0uFlpWUEHcfdPAAAgAElEQVSPq+eelSLb2NTMVW/uwCgoGDj4Wwo78hne/RWaD5wivbmWHgUFBDfXIXoqnkGjPmH//iN8sT+fdRF9odhEr3gDn03P5ZP1L/GeaSm9bXpuqhjOccdIEnoHU1nYjqLzFHp3J+1BPRB0O1mQuZzs0GxeHvoyITIxdac+pq7mc/xSF/ImBXGptxPT+47zrhbm9/tor6ul7dTJrk9tDW21NVja2hAE/xnHikRiFGo1Co0GhSqArItG0WvMFb/qnv3lnXxdWTF5q5eBICD820cqlyNTKLs+SgXKAA1qfSABegNqvQG1wYA2KASp/I+PezqdjZjNBZjNBZjMBVgsRfh8dgBksiD0+t4Y9L3R6/ug1WZTfdTElo9LCdDLGXdXDkFRv70QgsPqZukT31KvOolHYSbKdZLPkwoI0QfzRs/XqZ5+G8FtnazrNZR3Jyagsi6hr3M4iSU+Noh606IK5daclXzuPsTAyEE8N+g51n69ju02L9uciYjbXYztFcXL47PQSf3w/kiwNnWlRv6rklDnKXg7F5Iugslf/KS9DY89jmnNNwRc9Azai7IxXJ2CSCSioXEZZWWPEBN9E+npz/zo+Z+Xfc5LeS8xPGY4/xzxz/PWnmmrs7L10zJaT1mI6x7M0EmpGML/OOf6U3jcPgq31pK/vga/z0/OxbH0G5eITPHHzCqhS33zoR0P4Rf8vOGcQMDLH5w7bNNiZewbuxgapOFvHKCp57tkGhOILMqHK9+mKfM6Lt97CI/LynptJVGDz5Y+2Lr/CHeuOkWIzoMu/UOcPicBcS8QdLiYpJY6eh4+gsrcgOapJHKHfcSWLTtZWVrOhoi+iIqM9Igx8MWtuby94WkWd65hhEnPhOrBlDpGkzE4gsqSdvxNTQSaK2gNG4BBms87A1bjE/t4PPdxrki+Ap/PTs2++dTVfYInzIPEpyAi5moioyeh0/b4TfF0v8+HtaMdc2sL5rYW7GYTLpsVp82Gy2bF5bCT2n8QWSN+Plx0Lv7yTv7PhtdrxWwpwmwqwGw+itlciMvdDIBIJEerzUCn64FO1xO9LgeVKuH0AyQIAoc31rB/VRURSXrG3pn9uyy+OW1uPnlyCU2aU4j9bga4i3g99Rg2lZZ3EmbjuPdxVHYn7w+fwIorEtG3vUGOI5uMMgOH3REcMvRmXMpK9soPMqvXPUzPms6HS1ayxWRilyYdWUkn04Ym8vS47xdQNz8Ne+bB5KVnbnNfMhmqtsOsvJ/MiXeWlVE94RpkKaPQX30bITd3RyQRYTIXcPjw9ej1feiZ8/GPhqyWlS9j9r7ZjIwbydxhc5FJfnnWgyAIFO+oZ8+yE8jVUoZOSiWlT9ifQtDK1uli/6pKju1vQhei5OKpGUSn/TFrQgC1llru23YflcYTLNicgKG4titsE39mWOHd7ZW8vOEYc/skkNb+KW2pXzOoXIvK2AZ37KZMFs4VeUUk2k+xqkcCAfH9zvquFz5ew8JjEi7JtFMgmUuSIY0Szd8YUnSI8LZ6eucfQuxpIezpTHr3f49Vq75hQ10zGyN6IynooE9cIJ/N6M+c9Q+zpnMLV7drGFU3mCL7WNJzw+lottNR0Uhw+yFaIoYT7Cti/WX7OGwp4fKky3k893E0cg3ezk6q3r6XNsk+nL0FBKlAQEAqEeFXEhY2BrX6z1dm8IKT/wPxei1YLKVYrKVYLMVYLCXYbCfoWgUGlSoeva4nOl0OOl0OWm0GYvG5R5Q+r5/ti49xbH8Tqf3CuXhqN6Q/ItJ1PlhMdhY9twiTpgOt2cHVCXaelW+gSKXiDflMNHMWgCDwyqhb2DEmkaCWF0lxRDH4eDdqjFbWRFxOTtgBXHH7eGnYXIIkaby2ZAM1Chv7ojNQ722hT4yBpbcN7JIErtkLH42FPjfDFfN/MOTYWvjyBrjkORh874/aKwgCNTfegrO4FP2N/yT8vkGIlVJcrlYO5l+FSCSlX9+VyOXnDl9tqN7A33f+naExQ5l30TxkP5Ez/584bR62fXaMqqOtxGcFM/LmjP+TDJfzpaHCyHefHsPc6iB7RAwDJyQjk/8xo3q7x84jux6hoHQbb30oQZeR3RW2+bdQptfn55p391JrdLDq4m40Vz+OK2gng444EUfkwLR1fNfUzE3HmrjMdIj3R1+D+D/KB7rdbia/soxDVj13jDHyec3LDEuYwCrvldxUkIfS3ES/vDzcMiOJT/Wne4+5LFnyJVtNDrYEZyMtMjIgMZiPbunL39feyXbzfm5tVpDb3J9Dtokk9gxB8AmcOtpIWPs+mkKHEuipouLSUla4txEVEMXc4XNPaxaZvvmWhpefxp7twnN1GDZpl/qqJiCd0LAxhIZeiiYg/U/x8r/g5H8HBMGP09mIzV6B1VLa5dgtJTicp04fI5eHodVmotPloNfloNP1QCb7ZaMsp9XD+gVFNFR00u/yRPqNS/hdHp7aI8Us/moNLoWbuCYr1988gJcOP8JKjZpXTw0h5ovt2BUyXrzkdg6MSCKw/TnCXXImVYyiqa6QpbET0QSYGDSwkEcGzOaLfa0s21mAIVngSHwakYUmXB1ONtw3jLhgNTjN8N5gEEngjt0/LLS5bV1hGoUWbt8JPzGyNm/cRv19d6HsdyOxbz6M1KDA73dz+MgULJYS+vZZ9qOZNDvrdnLf1vvICcvhvVHvnVcM3thkY+3bhVg6nAy8Opmci2P/NJtdzoXH5WP/6koKt9URFBnAmNuyCIz4Y+qb+vw+Xsx7kaavlzBrrZ+QJx4jdMpNZxxT3mzh8jd2Mzorgpeywjh64hYC7RVkVhhh5FMw9EHeLzrMk21i7rXu47HL7zgrPl9T18BVb+/FI1Fw3dhSvqpYzIDUv7HW0YN79+/F6W4jd/8BrEEmuj85mqSkf/DJJ5+wU6xkmzoVeZGRYakhvDMlh3vWziDfXMg/msV0b8tmj2U60emB6EKUlO2uJ860nzpdP9TeVjq6H+Sb2BKMrnYe7vswk7tNRiQS4amvp+Efj2I/eBDl+CGIZvSk3bqLTlM+ICCXhxIUOJigoEEEBg5CqYz8Q/r/57jg5M8Dn8+J01mH3V6NzXYCm/0ENtsJ7Paq0zF0AJUqDq2mO1ptJlptdzTa7ijkZ1ew/yV0Ntv59u0CLB1ORk7NIK3/uSUKzgfB72fve+/zXWMTgkhMz1Y3V8y+ic+WjuP1AAVzdkeRtLeWdp2S+WPuZW/POPSOF9G5rNxTNZmGii18kzCORkkY94zrIFR6Ca9tqcBisRCdLaIkKoF+Rh9FeU3MuTqLG3O/n76vuqsr9336Roj9N7XDrXNg5ytdm57if3wDks/hofLS8fjtVhKXr0GR0PWSPHb8SerrvyCr+3zCwy8/57n5TfncseUOkg3JfHDpB+clU1B/3Mj6BUWIJSIuu6MHkcnnXzf1/xenStvZ/GEpXo+fETek/6jExW9FEAQ+LPoAzd9fI71JTNy3qwmKOTMtct6WcuZtqeCT6f3p6TdypOo6Mk+0EdppR3TbdoTwLB7ZtZFPfRHMl1Vw3ZCz6+wu27yXv3/XTk64lJDuKzjcfJjQpBepcoZw984dGOmk/759WOPN9PnHjYSFT2PRokXsDo5mH1HISjq5uFsYr1+fyfQ1N3DCVsXLjT7iTelsM99NSJyO+O7B5K+tJsFRQJ08DYngQhzyLav6+Kl2HD5D6kLw+ej46CNa5r+B1GAg8sUXkfVLo719Ox3GPXR07MHj6QBAoYj8fpDXA50uB40m/RcP9H4L/9NOXhAE/H4Xfr8Dn8+Bx2PE7W7H7enA427H5W7F6aw//XG72844X6GIIECdQkBACuqAZAICUtFquiGVan8X+/7lXERiEWPvyCbyd0iRs1dVserNtynXa5G6tQx1wbBnb2HHJyN5VORh9hoFsdUO6iM0fHnlI6wPCUYrfROlq5rHamZSX7aZvNgs9kkGMKmvg8K6MI41Wegdq8Ma5aDQEMYEmYxtG2vpmxDIp9P7d806StfAVzfBsIfh4id+MMh4Et7qD5nj4ZpFP2q34BdoeGwR5lWvEXLfE4TeeWNXH9Uv4djxJ4iPu52UlLN3TwJUdVYxZf0UQlQhfDzmY4KUvzwT6fj+RrZ+egx9mIrL785BF/Lnqvz0S7AaXWz6oJjGEyZ6XBTD4IkpiH9GrO7Xsmnnx4Tf+TLHs3SM/HgdwaofcrtdXh+XzduF1y+w6YFhWCr3UFY9gwGHjMiCuiG6fTsexNyweQ37ZdEsixOTm9rnjPYFQeCet1bwbb2Su4frWG99BolYzqngp4lzyblq1zZaxSZy9+7D0tPEwPvvQy6/hEUffMCB1Bzy7XpkpZ2M7h7O89ckMXX19bTYmnivyUGII4WNpofRBqvJGh7N3uUniHIeo8Ufjl8iI16zmJfT4ukI2ESsLoZ/Dv8n6UHpQNc6Uf3DD+M+UUngTTcR9uDfECuVCIIfq/U4RuO+75MrCs+Y4ctkgahVCajVSahUscgVYSjkocjlocjlIUilGiSSgJ/UWvo5/vJOvrllPSUl9wESRCIxIlHXT7/fjd/v+slzRSI5SmUUKmUMSmUUSlUMKmUMKlU8AQHJv5szPxeluxvY8cVx9GEqxs3K+c3b2QWfj5MffsjqklI6DXpU1miGyzzkPjud8sWX84+2Bh5cLiLQ5KcqMYi8ybP5yCxGHboYheMgf6+bjrOskLJgEStVVxMfaOekUUNskIqHRnfjw5aTHJCqmaKCk0ftHG+ysOmBYUTqVWBthXdyQR8Lt245Mxzz5Y1QuQ3uyQdd1LltFwSMK8tpfeF2xDolKZvXIpJI6DQd4vDhGwkKHEhOziJEorPjzm2ONqasm4LT6+TzcZ+fIbb1cxTvqGPHknJiugUy5rYsFL+iyPmfBf//Y++8w6sosz/+uT03N733HkghgZCEAIKE3kILSBMLHbsrIoKi6KIooKjoooBIVQHpvUnoPYEQQnrvPbnJze3z+yOsiqCrWHZ/7n6fh4eHO8y8M+edOXPmvOd8vyYz527TTvuEOzBgWgcUyj+GMjl58cso1+9mzWR35jz9JW6q778ezufWMmH1BZ7qHcicgSEU3thAY/p8Im+pMXV/BcmAl2hQ1zDkzGWaJJYc7doRd5s7g5vmlhaGvLufMoOSZZNseePqc7R36UaSfDqPlunxyzpFNWrizp2j5cF6es56m6amQDZt3sylmF5cr5Yiy2gkIdKdl4e5MXHPWAzqZjZV1WNhDuZAwyvIlXJiE/w5vTUbR00+ar0CrcKBThZrme0Ujt7vGIJIw/y4eSQGJyISiTBrtVS9/z71GzYiDwrEc+lSLELvTh3q9XWo1TduZwPy0Gjy0Wjy0Ourf9Kmvr6zCAqcc1/z8W918iKRaBDwISAB1giC8M5P/d/7dfLNzZlUVu5FEMwImNr+FkyIxXIkYgvEEiUSsQKxRIlcZo9M5oBc7ohM5ohUav2nL5yYzQLnd+Zy7WgR3mEODJz+2x9GXXY2lxcv5pSrK0apBVaNofSwrCP27RnU7pzGoqvneXQfiAQx2SHu1D7+Dotv1CNrdxRL9UGmVibinw2p0lS2OY3FKEiRSKx5tm87xnf15vHLNzmrh4eMajopvfj7vnTeH9uRxM63NTO3PgqZB9soClxCvj+x3BOwcST0WQAPvviT568+U0rNJxvQpmzAc8VH2PTvj05fw6VLw5BILIiN2YVMdncKpdXYytTDU8muz+aLQV/8KqGPa8eKOPtNDn6RTgycHv67LHL/J+Dm6VJOfZX1h36ZmPV6biUMokZdybLnPPnH0M/xtv6+Wmr21uvsvlbKged60s7VmvSrr+B4fhXONSbMjyQhDYwkM+scQwqhPc3s7NsPxY96Ui6nZTJp0y1crGRMHVnLe1eXEuI7ldNCPO9fbaKu9SJ1+iZiz51DP6KB+Mc+JyvLwJ4DBzjfYxAZhXqkWU1M6OLDIw9KeOzAo1g1mdhWW4kgC2Vv3WuYzSK6jgzk4u48lM2lmDRamiy9iJVsZJlTELc8UkCZTUJAAgu6LsBS1lZC23zmLOXz5mFsaMDluWdxmDz5jv6Bn7SbWYdOV4NeX337Tw1GUwsmYwu2tlE4Oj54X/Pxb3PyorawKwvoD5QAl4EJgiCk3+v//yfk5P9o6LVGjq5NpyC1hohenvQYG/ybPqsFg4HqVas4feIEN8LDURgVqOojiLMppcvimehOvMPnX64j/oyYBisZ+WH+qGYt4W+H85BE3cCycSP9Gx5kQl4c37Zs4dvAfuQ0BZLQ0ZuFw8JRWkh5JDmT0806EmqLWRAfz6APz9It0JHPH4tpe0He3AnbHoe+r0PPF74/OZMBPu0BRi08efF7zpofofVmDTXrU9EkvY7c1x2/rVsAMykpj9LYlEJM9PZ7LrSaBTOzk2ZzvOg4y3svp69P319st+TDhZzfmUtglDP9p4Yj+X8mEvGvUJJZz6HPbiCViRn2bCccPX//ztyWCxcpevxx9vdUcrC/PasHrP6OD6iuRU/f95IIdLZi68xugJHrJxMJP3sGs+CP9InTSJ1U7D+2iqmSLkxSNrOsa4+7xli88QCf3RQYF2mPweMbvi36Fiuf16kzB7AuqZaLFldo0DQRfeE8PKKh99gtJCXd5MzlK5zuNZSirGbIVTOrVyA9Imt46thT+NRK+LqpGINNNAcaFtBYq6fL8ADSkkqgvhpFcxW1VkFEGreTGubMh+ZGFE7H8bLy5eN+HxBo17YOYayvp+L1haiPHEHZuTPuby1C4f/vKa/8OSf/R9/ZXYAcQRDyBEHQA18DI/7gMf9joa7TsmNZMoU3aug5rh0PTmj/mxx8a9pNMseOY3dyCjc6dMDWYId1bTRdbMrosngm5stf8u37X9DnjJh8NwXZMeH4zvmIuQfzkEcWo2zcRJA2glklwzjfdJCSKH8yG9vxt/6hfDg+CrmFhIev53BaraV/4S3eH9CLV3enIxbBopEd2hx8czXsnw0enaH7j8oiL6+B6gwYuPgnHby+WE3d15mY6y9gbqrB5YW/IRKJyMtbTn3DBdq3f/MnK2k+TvmYY0XHmBM751c5+LRTpZzfmUtwjAsDpv31HDyAV3t7Rs3ujADsfC+ZirzG330MVdc4bEeMYOh5A86VOqYcnkJeYx4ADio584eEcqWwnq1XihGLZYR2X01uO2csTLk0f7YAY20rQ+Mf57naI2xqtWJjbu5dY7w4vj+Rqia2pNYxxPlJvKy9EFd+hJYm3op1YKC+CzYqFclxXTFvsuDc4an06RNHWGAA3c8cxrm9LRIfKz49mcuNbHde7/46BY4GZln4YNV0kVEe7+AeYM2FnbkEdnZB4uKK2soTd00GqdLRBN3Qs8fZjLRyGsWNNYzePY4dmXsAkNrb4/nhB3i8+853usO1n69FMJl+d1v/FvzRd7cnUPyDf5fc/u2/DhX5jXzzzhXUNa0Mfbojkb297vtY5pYWKt9dQuq0aRwICqTM2wv3VndkdZFE2xYSt3gGhot7SHn+TXxzJZzsoKQ6ojMxc5bx7M5MJO0akLd8hkrwYWnBVK42HEeIb2J/yUBGdHTjqd5BqI0mJl7P41xDC32yUljUuzvHcpo4nV3D3MEheNjdTgEceLGNg2bkSpD8IOXUXA0nFkNg35+kLjDWaalZfxORwojuxj4su3ZF1a0bNTXfUlC4Eg/3sT+pz3q08Cirb6xmdPBoJoVO+sW2y75SycmvMvGLcKTv5LA/bHHyPwGOnlaMnhONhUrG7g9SKE6v+93HcJn7EhKVioXnPUAQmHZ4GoVNbfXkY6K9iPN34O0Dt6hW67BQuOHady3VDnKsjeuo/ewAxiYzLz04gt71l5lf2MCVevUdx5fJZLz3SA+sRHpe3nqLt7svpdXQTFjzai6qTGzxsmS4Q18slUqux3ZDs9JI8vmZJCYOx9vOhn7JJ1F0sEPhqeLdQxno6mOYGTmTa+56XhT7Y1FxiqGuywnp6sr148U4eVlh6+NMpTIIX306OYp+3PrWgzOuZxlouQCdxp3XL7zCrIPz0Jl0iEQibEeMIGDvHlQ9e1C1dCkFEyaiy8n53W19v/i33+EikWiGSCS6IhKJrlRX//SixP9npJ8tY+d7yUjlYhJfisY3/P6Y5gRBoOnoUXKHJnDj8GGODRyA0dEJ30Y/jI3BRDsU0PWd6bTs2UjWzLkIGjEb+ymRe8fQZ+5intyehca+CTEfIxLZsCprJuXqbCT9ktlcOAF3WwsWjYqk0Whi7LVcrjQ20/fWZZ7tFIbK0Y0396XT2ceOSf8sl7y5E9J3Qfy8O/PwAN++CYYWGPTOPeX8zK1GatbdRDCakShvYGqox+Vvz9PaWszN9NlYW4XTrt3Ce9ohuz6bV868QqRzJPPj5v/iNZWim7Uc+yId90BbBk7v8C+lEv8KsHFSkjgnGltnS/avTKU44/d19FIHB5yffw4h+QarpJMxCSamHJ5CcVMxIpGIt0ZFoDWYWXzwFgAOjj3Q9H0es9iMFa9Rvfo6KPxY6SXDQ1vB1Gu3qNQZ7hgj2M+bp2NtqdGJWX2gile7vkphfQpRhv18Gignvd7AuK5jkCsUpEXFUf1eFVkZ8xk3bhw2rS2MzktF18EOlZsl83fewFc8iuGBwznua+Bdkx+ynH3E268ibpg/ucnVSGQSXIIdKZSFEEgmZcpY9p6IZ1H5u2zo+jpKTT/OVu0jftNorpW3OXOZiwteK1bg8d4yDEVF5I9KpObTzxAMhrts9mfjj77LS4Ef9q573f7tOwiCsEoQhBhBEGKcne+P/vU/FSajmaQvMzmxMQPPYDseejn2vlkL9SUllMx6gpJnniW9XTBnHuyJk7s7HqXetGi8iHUrJm7RZOqWvkrRy29TZi1i+WgFgbIYhs9/m2d351Cqr0fssAoEI+9kTcJCL6K1xwn2VPWkptWeD8dHoxOLGHMtlzS1hn5pFxnp6kBcXBx/35dOi87Iu6Mj27paW2p+Ok1TlgLJGyFuFji3u+taBKOZ2s23MNa0Yj/Gn8ZvvkTVsyfyDiHcSHsKEIiI+Pie5FCNukaeO/EcKpmK5fHLkUt+WTdqTUkzh1alYe+uYuiTkUj/oO7Q/0RY2sgZ8XwnbJ2VHPgklZLf2dHbjR2LIiQE0T828FnPFehMOqYemUppcylBLlZM6+nPjuRSrha2jesTNo+yiE6o9CVITBuoXnMD65AJrGs+SJPJzPRrt9Cb7yTzmj4inljbFvZmNqPSdGJ08GhKKrbhbkxjQbSKpjO1PDpxGiK5nPSwrhS+fY2mxq8YPXo0koJcHlOXU9/BFmsnJS9svU4fp6eIc4/j6yD4Qu+FJGUdnS230H9KGFWFTbTU6fAKcSSX9gQr8qlTBLItdSrtd03nzIBR9Hd4GbWpikcOTuD1o1swm4W2qH7oUAL278Oqb1+qP/iA/DEPoUlO/l3t/WvxRzv5y0CwSCTyF4lEcmA8sOcPHvM/Ai2NOna9n8LNU6VEDfAh4emOWFj9+vI8s05Hzaefkjc0AfXVq1yfOoUUDw9C24dgdd2eJr0zXf2qiHnpIcqnjKZq7Q5SgkW8O07GA+poxr36Di/uzeVaaTVy/y+RGKuZWDycSH0wle3OkmI2cqY0lqd7B+HrYc3oazlkt2hJyLxCnMTM8OHDScqsZve1Mp7qHUSw6+2S0v2zb6dp/nFnmkYQ4MBLbYRkve6uaW8rlcxBl9OA/ehgWi8dwlRfj9OTT5CV/SZq9U3CQpehVPrcta/JbGLuqbmUt5SzPH45LpY/LfLwQ2ia9Oz/x3XkFhISnur4/7pM8n6htJYz4vkobJyV7P8kldLM+t/t2CKJBLdX5mMsK8dx+ylW919Ns6GZqYenUtlSyVO9g3CzseC13TcxmQVEIjHug7+h0c4Se/NXGNUF1KxNo338XJbnreCSxsxbOXfEgkgkEpY/Ho+NSMecbdd5OnI2IQ4hSKtXUiOqZnGYAuFINZOffAqzXM4t/27c/PtX2Nnn07t3b0TJF5mqMFIVYYfKVsHTm1N5LPA1AuwD+UeIkgNaV8QnFxMkPkTi7GjMZoGKvEY829uRrfMn0LYajcyJbcWv0LjyGd73MLK67yaUIld2lC2i1+ezSStre4lJHR3x+mA5nis+wtTYSOHEhyl75RWM9b+fzX8N/lAnLwiCEXgaOAzcArYKgnDzjxzzPwFlOQ1sffsyNSVqBkwLp3vir29MEQSBxv37yRs8hOoPPkQcH8/ZqVPIbGmhR9cHMCSJaTQ50L19PRHj4ygY0oumC7c42sPM8hESBpZ3YtyL7zDpqxSSsqqxCt2H1JhJePNAHlM/SKVdJpVeR9iU+Rgdve0Y39OPxJQcilp1TChOx6e+mnHjxmEQxLy6K41gFyueiL/d3fhdmuZlcPnRomjqVii5BP0WgsXdJY/qE8VorlZi3ccbZbgttWvXYtmtK41u+ZSVfY2vz0ycne/NxPfJtU84W3aW+XHz6eTS6RfZ0WgwcWBlKlq1gSFPRmJl/+dQBP8noi2ij8LaScm+T67/rouxlrGx2AwZQu2aNQS22rC6/2oadA3MOjYLIy28MjSUm2VNfH25rUlIrnBCNPwTJCYTlh6L0NdoqN5aw/DOQ5lSuoPPSms5UN1wxxhe7i7M7uFMvUHMK19d4f1e7yNGIKjpU444CWw3t6LM0PP4rFkY5XIynLpzZdEbREU5ExISgvTbg0xwtKAi0g4LSylPbbzF7Mgl2ChteSfMictaB9j3PM66M4ydH4uLnw2lmQ24+FmTo3bHx1GDWSRjZ/3fKVr3D7qlr+XUw18T6zCUBvlRxu5+jAX7zqLRt4kS2fTvT+D+fThOm0rj7j3kDRpM/bZtCD/6Svmj8YcnJQVBOCAIQjtBEAIFQXjrjx7v3wmzWeDy/nx2vZeMTC5hzNwYgmNcf/VxWq9fp3DCRMpmvx1Wx5MAACAASURBVIjY1haLjz5kn68PVXV1DB+YQPmORpoEW3pGNhMcLqNg1DAMtU0cHG5kTQ85g4vC8Rv0Mr0/O0BqsRabgJOIzRdRSQawKL8XzeIGGmLeZ1PWs5gEOa8ldmBcah4lWgPPttZgkZvJyJEjcXR05INjWZQ2tPLO6AgUUsntNM2L4BEF3Z+788R16jaeeI/O0HHiXdeluVZF05FCLDs5Y9Pfl4at2zDV1KB6PIGMzAXY2cUREPDCXfsBnC09y+obqxkVNIqH2t3dCn8vCILAiU0ZVOY30W9K2J+irvSfjn+mbixtFez75Dp15S2/27Fd5rwIYjFVS5YQ7hTOR70/orCpkCePP0mfUFu6Bjiw9HAm9S1tQjg2AYk0RvTGoSwfec9DGMpaqLkUxmuiXDqpM3kuvYCC1jubGR8Z/ADd7DQcztWQnm/m7z3+TrU6i6CWLSwLU5J6tghniR2PTJmM1sKCW5KuXHj/CRISeuHo6Ijb8X0MdLWmvKMdIqmYv20u4LWY99FLzSwI8yNfZ4Xw9aMoG1IY/nwnInp7UVWgxsbJgvwGB1xcxUgFPfu1C8nYkYzFlkdZ238eC7osQm5Zzo7K2fT5ZDUnMqsAEKtUuLz4IgE7d6AIDqZiwWsUjHmIlgsXfje7/yv89Vee/iS0NOjY82EKl/bmExzrythXYn91bbIuL4/SF2ZTMG48+tIS3N9ahG7R3/ny8mUEQWDMgBGkri2iWbAiPkaLe8Npip6fh1Rh5NRDWr4IV9K12Idrqom8fmo76hYnHFxTEBSHMFs8wNJLocglSuq6fUZSVSLXK5z429AQni8qo0Rr4G07CY0XThMXF0doaCgZFU2sPVvAhC7eRPvepgk48CLomu6upgE4/V4bf/zgJXeJcuvyG6nbloXc3wb7Me0QDAZqP/8cZXQU2YpVSKVWdAj/8J7UwZUtlcw7PY8guyDmxc37xfa8eaqUrIuVxA33JzDql6V2/hugslUw/NmOiCVi9n50DXWd9nc5rszdHacZ01EfOULL+fN0ce/CkgeXkFaTxuyTs3kloR1qrZFlRzK/28cuYRNalSXW1z5FOVqKvlhNi2Y6q3KWIjFomJ6Wj9b0feQrFotZ9ng8tiItL32TSoxzTyaHT6ax7jBK3XleiVRS8U0W3r7+jH3oITSWlqQ1RJHyxUzGjh2N2WQi9nISsa42NHSyR2Mw8do3dbzW5V0qaWBeaAS1egmGdSMR1+Xw4Lh29JscRqvagFQupqRehbWbNVbmRr4Vz+HyUQuE1X0Z6xzOjhFb8bR2osV+JTP3vMOTm69Q1dRmW0VwMD4bN+CxdCnGhnqKHp9M8cxZf0oVzl/GyQvGe+i2/kkoSK3h60WXqMxvos+jofSbHIbc4pd3sOoLCymbO5e8hGGok5JwfGIWgQcPcsPFhS3btuHi4sKo7gM5/VkuOkFOv26tWB9eTvWmg1gHyrg5qpWPfG0ILHfgrHYglZLj6NRROFjnoHfYhkHRgWfPuuFv2R51+ClyRSa2pD9ArwhX1tNKidbAqkAXCg/uxd3dnf79+yMIAgt2pWFjIeWlgbcrZ27tbUvV3CtNU5sL5z9pi+C97+QKN1RrqN2YjtTBAqdHwhBJxTTu2IGxshJNggUaTS7hYe+jUNy98G40G5l7ei5ak5b3er2HUvrLujcrC5o4vS0b3w6ORA/y+8Vz8d8CW2dLhj3dEV2rkb0rrqNt+X2qQBymTEHm5UXl228jGI308+3H691e52zZWTZmv8ukOG++vFREWmlbqkgkt0I8bAWWrUa0t2ZgO8qP1lwJDqpJrEh/gxvNWhb8KD/v6ebC7AecaDKImL3xHM92fpZo12gsaj8nR17KcpUBdVIJ7Tt2ZNjA/jRbW3M1x5/iY68zcuRIasrKeLg8B08nFYbOjlQ2aVmxX8xLMa9ySyjh1XZdMegNaD8dgNBUTvs4N8a+EouDuwoEqG6QIXJxxVFUyyXLaRy50A/jyj4EVNxi16itDPYfjMLlKCcb36Hv8gOsOZ2H3mhuW5gdlkDgwYO4vDgbzdWr5A0fQdmrr2IoLb2XOX8XSBYuXPiHHfzXYtWqVQtnzJjxq/fTJCdTOOkRRKK2N6boT1B6gjbu8ZNfZnJ+Zy62zpYMf64TPmEOv7ikT5eTQ9XSZZS/9jr6/AIcHn0Erw+WY9GjB3sOHuTChQt06NCB7u4RHNtUjNhsZGBUBea1i9GWqHEd1o6CsFJedLHGuV5FnSgOO8tCaqqGYWlRidFvLSaZG4NuhPKwOB6jcy35gR/xSdoCUCho7ORAud7Ipg5+5OzbSUtLC48++igqlYrtyaWsO1fAm8M7EOPnAK0NsHksOPjDyE/vitTZ9QQ0lbWpPf1Ax9PUrKd6zQ0wmXGeHonEVoFgMFD6txcQedtT2jsFP78n8PQcf08bfXLtE/bl7eON7m/Q1aPrL56XPR9cQ66QMuzZTn+oetL/Z6hsFbj625J6opjy7AbadXFDLPltFB8iqRSpqxv1m79E6uaGMjycUMdQFBIFm25tItRLQlGJDylFDTwU7Y1IJELiFIa27Cx2OalUhhix8x5I4xUlHVSXMRgqWSMOxE8pJ8zq+xd8RJAPl6+mcLJcRKizksnRCezJ3Y2lNpkz7g8SdF1NOz8HPMODkaobuVVXT3W2nnZOZVj7xpFy8QIPh7fjmFSOwl5BRVY9jQ0uJHb2YnfVUSrsoulTl4E2eTuymEko7awJ6eaOyWCmPLcRnQ5MljZ4KarIF0VSVBGMX+5cVBId/XotxMnShfM1e5HYJHM0Rcm+ZA3eDkr8HFWIpVIsO3fG7qGHEHQ6Grdvp27TZsQKOZadO9+X3d94443yhQsXrrrXtr9EJC+SSpF7elK5+B2y+/Sl6sMPMdbW/qFjFtyo4es3L5J5qZLowb489HJM25v+X0AQBJrPnqVo+gzyEobRdPAgDpMeJujYUVznzKFVJmP9+vWkpqbSp08fOog8OLK1ArlRw0CXE7SuXI5gMKKcM4l8+yxmO1lgqZUht/fEzWigtjIRqawJIXg9ZrGSoJpePKaJQCIXUxL6DocrXyFXLYJurlQYjGyODKDl6gVKSkoYPnw4Dg4ONGj0LD5wi2hfe8ZE327aOrYQWqpg+Iq70zTZRyHrUFs1jfX3RFWCwUztxluYGnU4PhqO1LHtIW3cswdDWRk1fUqxtYvG3//5e9rqXNk5VqeuZmTQSIYHDv9F8yKYBY6tS6elUcfAGR2wUP33VdL8Gni1t6ff42GU5zZyYlMGvwfNifWA/ig7d6b6oxWYmtty/lM6TOGxsMfYkbOVXjHpJBc1sDPl++jVYvgXCDIFNqfWYogowKq7J9UVU5lb9BVdW3OZk1lMZsv3aSWxWMySR+OxE7Uyb3sqCpEty3otQ6urwKNhDQvDFGTvyEQwmXlgxCi6+HlRb+/Akf3FhFk24OPjw9UD+/jQ254GOxlusa5cLqzj2o1YEoNGs0+SxUee/VDqymlc0R/BoEUiFdN9dBDDn+2EpbUcncZEidaFcJ8WapQhbKtcStWerYg2j2asV182DdmIi7Ul1gGfoVEmMWXdZR7/4jI5VW0NX1J7e9xemU/gkcPYjRmN3D/gN9v+XvhLRPIyV1fsEhOx6tEDQ2UFjdu+oW7DRnTZWUhsbZF5ev5uJGTqOi0nNmZwcU8e1o4WJDwVSfs4938ZARlraqj/6mvKF7xG/br1mDUanGZMx2PpUmz690dsaUlFRQXr16+noaGBMWPGwKVazpwzYqOtIL5lDS2nrmPhpeCrcfOxK13LYg8DDSIZUrEFwSXxZNc/SLNchzJ8A0ZTAyrZZF64pCbQOpKK8C9IV0byWWooyl7utIjhy8gAHKvKOHDgADExMfTo0cYd8vd96VwuqGPNo7G42Fi0KT0deBG6PQVRD//owvTw9QSwdLgd4bdFzYJZoG5rJrqsehzGh6Bs35bTF4xGSv/2AgZHHc2jJXSO2nhP4rFqTTUzj87E08rzV6k7pRwtIu1kKT3HtSOg01+r7+KPgqOHFWIxXP+2BLFUjEfwb6O7FolEKIKCqN+wAZFUgiouDpFIRFePruQ15HG07Bu8rfw5lipiYpwPcqkY5CqwsMXyxmGKNEk4D34Cc70SfamMIXVL+co7kaP1Gsa5OSC//RVpY22FvKWSw4UGCsuqmd6zG0qpktMF29DLLEm1CGBwhQGLADuCo6KpvHSGIpmSkgtpDOnTlfTSKpoK8hjXoxsbm5sJs1dx+XolAaoYPF3r2KdNQSnrTLfGK9SmJmEZ9wiIRNg6Kwnv6UFznZaa4maqG+W0byelpk5Ehr4f1sVncCz8CJf2wxgWNZPcxlyytQcJ92vlZq4b686UUq/R09HLDqVcgsTaGuv4+N/Ee/Nzkfxfwsn/EzI3N2yHDMFmyGAQgfrYcRq2bKVx7z6MdXVIbG2RODndl8M3GkwkHy7iyJo0Gio1xA71o9/jYVg7/LTykEmtRn30GNUffkTFwoW0nDmD3M8P52efxf2tRaji4hAr26LbjIwMNm/ejEwm4+GHJ5G7NoXUAis81dfpnPsxuiI19VHejAl5kcmtq9jiVcUtuQKpQc6Qkme4qg6iVGbEteM3tOqzMdg9yfTD14h3GILG4wYlgcm8kzKJ1hgnBIWULzsGECoysWnTJhwdHRk7diwSiYTrxQ28siuNyd39GRPjBUYdfDm27SEcux5+3Hx0/hNI2w6Jq+5ofGo6XEDLxQpsB/thFfe9Wk7T3r00bt9Bw1gtIf0+wta24112MwtmXkh6gWJ1MasGrPrF9fDVxWqOfn6TgE7OdB8d9B8hy/b/Be5BdjRWt5L6bZvK1G8Vjpe5uaHPy6Nhx05sR45EYmWFSCSil1cvLlVcoth4jPo6XwSDLQ8EtYntiNw7YcrYg21pIVk2Jfj0fpyWQids6s4S1XyWVfbxlOgMDHGy/W5uI4N9OH/lGifLIMpDxfCwHuQ05FBctZd8+3BEBTLi3O2Q2MgJ69aD3CN7KLdzovzcRQb26cOljExcDFpiO4SzRdtCjK2KpJQKeng8iMgym0NCPm6mMKIaL1B+8yrWsWNBJEIiFRMY5YKjp4q869XU1AjYOFuCRk2WuA/aEh3eeS+jtHRg8AMLUMosOVS0HRf3m3R2j2DnJQ2bLhRhMAl08LRte9H9Bvyck/9L8MnrjCbSShu/rwC5DbNOh/rIERq+2Y7m8mUwm5F5e2Pdrx+qbl1Rdo5GYvXzN7PZLJB1qYLL+/JpqtESGOVM9zFB2DjevQAomM3ocnLQXL5M87cnaLl0CQwGJM5O2I0YgW1iIoqAOz/JBEHg3LlzHD16FA8PDxKHDifprSQqzS50qPgGt9zjCIjYFD2AA4HD+MTua45ynG021ti0WvNw0QIOaUWkyo0ERh2gSnsatf1kxhzPZ4rFIFSWEvK7vsKn5cs45WSN3ErG150CibVWsn79esrLy5k5cyZOTk6YzAIjPzlLZZOW47N7YW0hgxNvw8l3YdIOCPoRCZi6AlZEg18PmLjlu5+bL5XTsCMHVRc37EZ972wFk4nswX3QGiuQ/2MiISGv39PmG9M3suTyEhZ0XcDY9mN/dn7+CaPexNbFV9BpDExYEHdfjWf/7TAaTOxefo3qYjWJL3b+zSWn+pIS8gYPwSYhAY/Fb3/3e522jkkHJlGhbqC54EmOP5OIt0MbhS8lV2BNXwq9lMiGfoybw0jqPj2IY90UlndawBK7B3mvvTcPe3xPDZJfVMrwlRdQKBScnj8YE62M3z+eMk0T1c5vsD7fht5ToxBJxbQ2q1kz72/UOnrhVV5Ku/g+fJuZydChCeyydePzkmp6lhu5fKOKZ/q7cbxxAWq9msVFEnpoUij2Gof3tDt9qaZJxzfvXkVdqwURqMSttJiUuLakMdj9XVQRcTByJTe0Vbx8+mWK1cWM8p9EWcGDHE2vxVEl58neQTwc54PFfdJd/+VFQ7ZdKWbON6nE+tnzZHwQ8e2d74rijLW1qI8fR33kKC0XL4LBABIJFuHhWISGYhHSHkW7dsg8PZE6OyMgIie5isv7Cmio1ODkbUX3xCC8Qx3a1KbUagzlFehzc9BmZaHLyKQ1JQVTY1vVgNzXF+v+/bDq2xdlx453iB5/d05GI/v37yclJYXw8HB6hsVy+IPLtAqWdMl+H2VlGRp7BUvin2dwQi96Va/ndN4aljra42XyJjH9Jc5g4KRUT2jHs5To99Jim0iXmwqmV9vT3qIDxdFLOW0zleV6D2RWMrZFBRFnZ8Xx48c5ffo0iYmJREZGArDxfAELdt9kxYQohnX0gKpb8GlP6JDYFqn/GDufgLRv4MkL4NjWKKXNqqdmXRqKIHucHgtH9IM0Vs3uzVTPXUTr0250evLQPQXNs+qzmLBvAt09uvNRn49+cTR+eksWqSdKGPZsR3zC7o8b6H+AVrWebYuvIAgCY+fH/mYR88olS6n74gv8d2y/Q1yjoLGAifsfpqlFTnfLN1j18Pc86sLup+HaJi7HuhPR+xhyozMtH76MyvgFE/rs55Kg4kB0uzsWYldsOcx7KUbGRTry7sSuZNdnM3H/RHRSP+SqOexWOOM5oC0dUlNcyMa3XqfRzQ/fslKs2rUno7WVx6dM4dU6HUerG+lRZOBSRjUvJTiypXQuSqmSpZl1ROgzKQh6Ar9Jd8piGPUmDnx64zsSOJFIQDALWOgbGaB6D2/fKhi5Eo3fAyy5vITt2dsJdQhlcvArbD6j5UxODZO6+rBoZMR92fkv7+Rb9Sa2XC5i9el8ShtaCXGz5on4QIZGuCO9R6epWaOh9do1Wi5eovXqVbSZmZjVbYshBqmKUs8elHr1QiezxcpYS3tdMi6tOaDTYdZqMdbUILS2fn9AiQS5nx/KTh2xjI7BMiYambf3zzoojUbDli1bKCwspFevXrhVyzl1pAGlpoKYjA8QaYxkRoRjnL2EhGgfTu9+CUnulzzv6kSgKJgBl58mx1bCdlMLYWGpFAtf0qrqjVtTV6afv0pfx5HU+xwlO8qSp2v6gqWU7VFBdHOwJjc3l40bNxIVFcWIEW3Mz9VqHX3eS6Kjlx0bp3ZBJAiwdiDU5cJTl0H1I8d5O+Ligeeh/xtttqtooWrldaT2CpxndUT8gzJSk1FPxqAumE06AvbsRWUddJdNdCYdE/ZPoLa1lh3Dd9whK/dzKLpZy94V14ns40XPsXdz5fwPvw7VRWq2L7mKe5Atw57p+JuYOk1NTeQOGIgiJASfL9be8UxcrbzKlEPT0Gu8WDtwNd0Cbi/at9QgfBRFg1JPXs8+dO68GWN5E3zWizqFmAEPbsBKKuVwTDuspG2Rr16vJ3HxN9xstWHbzK7E+DuxL28f807Po9V6CL1ax7CyVwhyrzZajuzLZ9m9eg3NHr54l5VhdnSkxdWVR6ZNZ2JGCZlqLZ2zNVzLr2PeSBWrc17Cz9qHpdcz8DKWUNhhPgEP3UnbYTa1cVXdOluOjZMF6lotglkABNobk+jtvgLJA7Og72scr7jAwnMLaTW28kzUMwTKB+Flb4Wf0/2lyX7Oyf8lcvIao5rkxh0sGTaEQGc7LhXU8eXFIrZeKaFZZ8TfSYWV4nuHI5LJkHt7o+rWFbvERGwfn0xDhwFkO/bipn0/6uxCcFBoiJDeoINwDSuhAYmVCqmTEzIvL1SxsVgPGIBt4iicnpiF68sv4/joo1j37YtFaCgSW9ufdfDV1dWsX7+e6upqhgwZRtnOAtIyLPApOUSHzA0gFqF/eQG9/v4mYstK1myaTETRYZ5zd8YNL/peegqNlzVf6tQEBeRRIlmPQRmNRD6WcUd3MMRzLCaLSoq6XeTZhofRW0hYFeJDX1c71Go1GzduxM7OjnHjxiG5rWazcM9N0soa+fzxWBxUijYu+KtfQMKHd9W9YzbDlkltPDVj14FUgalJR/WqGyARtZVKWt0ZAeZ++TzCvmysnhuPc2ziPe2y/OpyThSfYFmvZYQ63ptD/sfQthjY89E1rB0tGDitw1+aOvjPgspWgcpOwfXjxZiMZrxDf7le7o8hVigQKSxo+OorLCI6oPDz+26bh5UHbpaenKzczoncbCZ3TGgjv5NbIlJYo7xxgFpJBTpbBxy8umG0aI9N5meEtFrxuW07in+Qn5dIJIS7Kth9vYKkjEomPRBAmGMI9dp6Mst2ku7gi32agqgOrojEIhw9fRB0dVSlXKfG0xub6mpobKRcq+WFB7uzq6aBSnspQa0i9l5t4anuPTlcsp384Bi6l1XhXH6M/GY7HNp/71dFYhF+kU5IZGJyk6tx9rXGK9CKugoNNZIArjWPRJefhk3Gx4SF9GZY9NPkNebxZcaXlGivE+/XBXuL+xP9/svn5Pfm7mX+mfm4q9x5ucvL9PKM50RmNRsvFHIquxqxSES/UBdGdvKkd4gLMpGIurIWSrPqKcmopyy7AYPOhNJGTlC0C+E9PP4QJR2A3Nxctm7diiASY7QOx/ViHYLckc43P8S6vhhZeBC+Kz9HY6vg45SPyT69jbmacmZ4OyEWHEm49gKeMYG8nVeGg2Mh9bYrMSgCUDvO5uFd6xjvnoBziw1Z3f/BC8qXqRSJmG3vwJzOvpjNZjZs2EBJSQkzZszAxaVtQfN6cQMjPjnLzAcDmDckFBpL4ZM48O4Ck7bfTRWcsgl2PwWjVkHHcZh1JqpXpWKs1uA8syPyH9mutvYMJeOnITNaEXr0AiLp3Y1iF8ovMP3IdMa1H8erXV+9a/tP4fi6dLIuVTLm5Ricff44Pd7/Rpz8MpO0U6UMnN6BoOj77xgW9Hryhg0HqZSA3bvumv9nDr5DUtVmBrpNZ9nA24ymZhPCql4YGnO4EG1HdLf9qFSBGL54GmnBJpZE7WC5rQPL2nsz6Qf5+UVrd7EmS8bUOHcWjOqM3qTn8UOPk1abg8Z5IbtE7ekwqC21KAgCu957mdyMCpq9/HGrqMAMRD32GI6RUQxLzsYREcrLtVQ0tDJzaA2rM95luFc/5p3ehsiopaznRwQPvJvCI/tKJcfX3cLKQcHAaeFcWHuBonIx3BbrdpTm4+Vlwr33AG6qbrIkbTGJQYm8EHNvWo9/hb98uqY4o45jX6VSaM6lUlyCq6MT8f49cbCyp7ZRR3JBHXnFTUj1Ag6CGEeTCNHty7ZztcSrvT0BUc54trP7wyJBjc7IjiMnyb56BjVKTKXWhAo+2DQV0DHjM8RGI85PzcJ2xkx25e/h45SP8Sps5u/qUp7ytadOZMWoW3PokdCVF89lY5KVoHP9BIPUgQbXBfQ/fZRRZhdi9REUBe9ibvB4sg1S+rSI+XJ4WwVLUlISSUlJjBgxgqioKKBtYXn0p+cormvlxIu9sFZI4asJkH8SnjwP9n53Xoi2sW2x1d4fph5BEKB2YzrajDocHwtHGXJn1KfT15CyZiC2H2lwXfQ6DmPubnpq1DWSuCcRS6klW4dt/cVdrYU3a9m34jrRg33pOiLw10/K//CzMBnN7HwvmdqyFsbMjb5vmmwA9bFjlDz9DG5vvIH9uDsX040mEw98MRmN9BofxK+gr1+vtg1FF2HtAIp87ano0ImY6G2IDVrM70Vj0CqZ2HszV8QmDkS3I/x2fr6lpYVR7+4iR2/L3md6EO5pR0VLBaP3jKHBbIWL5QL2RUeg8mlbVDbodWycN5W6Zmh2C8SlqgqF3sCAha9TZGXH+Ot5dJTJqT9dTqvBRGKfm2zJ+ZxHfRN4JulzWvViagesJih+2F3XXJ7TwIGVNwAY/EQE4qZaDq9MoVnigMKkxihRYKLti1dpJyOyjycxA+6vVv7fKf/3p0BiUOMiqSFEFkqHhgdwTg/l5v4aTm/JJv1QERYZzXQyy4mwscTSVk6KpYm9lnrW2GnZ4yGQ5i6hTCHQavz92OEaNQbO5tTw/pFMxq08y4y3PiPn6mlqTNZEFNvSXhRASPbXRKV+jMLJAd9Nm7meEE7ivjG8ef5NulQ78VZ9BfO9bKmUKBhV+hwPzejHomsFNJsqMbqvwSi2pMlpDqHZWcQ2aoghjAb7HN5sP5xsoxSP/BZWDwwHID8/n5MnTxIZGUmnTt8zOO66VkpKUQNzB7Vvq6ZJ3wVZB6H3/LsdPMDJJW0kZUOWgEhE4748tLfqsBseeJeDFwQz6Tdno9yjQeLhgv2I0XcdThAE3jz/JnWtdbzz4Du/2MHrtUaSNmdg72ZJ7JB/j67mXx0SqZhBMyKQKSQcXpWGQXf/snZWffuijIqi5uOPMf9wPQuQSiS833sxJp0bL516ifzG/LYNPnHQcSLexU2YKlMoKPwUFNaIRr2PQlzAklObsEPMjLQCmo1t56ZSqXh1aAhyjDyz8SJGkxk3lRvLei1FYiyn1LCGRWeyEAxtz7pMrmDMvOUojE3Y1GRT7eKCxsKCC6+9Rie5mOUh3lzR6wiO90IEHDgdQYL/aDYU7mN9/HSsZQasDs0i9+zRu67ZPciO0XOjsbBqU+aq0yh5eMVwwtwb0IlViPVaOnCErqr1eEuuY83vr9wFf5GcvHXlEQJSZxCp3EuXQf74TOjKYYdt7LT8nJyAC8Qm+DNp0iC69PPngb6+DOrrR7tgB6ys5WRVNrP7WhnfXC3h05O5HLhRwcX8WtLLmihr1FLTrEOtNWIwC+iNZnQGM3qTmSatgcomLcX1Gm6UNHI2p4YDaeWsO1vAuwczWHokkx3JpaQVVBBjvImbUIcn1riUBiFv1RF3fQk2DQXYjR1LyauP8nrhP1ifvh57C3uelyYy/OYOlvkquKpUMl7zLDOmjeO5vWncqi7FJngtWqEVrfNL2DSLSTh/kDEBiRg0RuY8qCDZ6IwirYGvEzri42BJc3MzGzduxNramgkTJiC9/bncrDMyfcMVglyteWN4OCLtbeoCp+C2ztYfVwRVZ7XRF0RNgpgpqM+UQ16t8QAAIABJREFUoj5ehFUPT2z63s3/Xli0itpjX2N9RILriy+hvF3F80McLjjMp6mf8nTU0wzyH/SL5/zMtmxKMusZ+mQkNk6/7MXwP/x6yJVSnLytuH68mJZGPQEd76/BTCQSIffzpX7TJsQqFZbR0Xds93GwITnTlUJ9EmfKkhgeNAyFRAHeXRBdWYed3op0yQUcHeOx8OqJUJKCXd0ufOr7stFJSZFWz1Dntvy8j6c7xZnXOVMlQywY6Rrkgre1N1ZyKy4WbiPFSkz7Ym+Cg9rSPApLFR7t2pN++CAqpZoGW2+0IhG67dsZMm4MMomE9dX1jAh142Z6DU11gXQPga+L9mMfOZG4kvPobh6k2qYz9l5+d1yXhUpGuy6uVBepuX68GG2LkQdnPYCXq4nSa6UUiyPQNtsSJ1+Hb7AAAfH3Zd+/fE6+cP8Fjm8voZP1aTrZrEds6wI9/ka6bxc+TFvFubJzuKnceDz8cUYFjcJSZnnH/jXNOlJLGrhW3MiNkgbyalooqW/FZP51tpFJRPg5qgh1tyHU3QZPWQuZ5w6ja23FpdIeoyGEdrlb8Sw/h8TBnopnR/Ox8jy36m7haunKk52exDfTjNWJl9joL7Db2opJlrN4fsRMZm1OJimnEPfwdTQZKxE5z6FB7MfE/esZ32k49tkqnn+wgQsW3kjT6pkb5sUzfYMxm81s3ryZgoICpk+fjpvb97QD7x7KYGVSLjuf7E6Ujz3seQZSNsOME+D+oyYlQYBNiVByFZ65SmuhmNpN6ViEOeL4cCgi8Z15+8bGZK5eHY/bR47IGiwIPHIY8Y84hWpaaxi5eyQ+1j5sGLwB6T0YKO+Fsux6dr6X8r9qmj8RF/fkceVAAf0mh9E+zu1f7/ATKJ45C01yMkFHjyCxu7OztrhOQ79/rEPhvYruHl35pO8nSMQSuLASDr1Meicfmtw96RK7G3FjOcLHcWhNsbwX/AYfe0juyM/X1taS+P4hSs22HH4hnkBnKwRBYN6ZV9mftwfB5hmOdHwIj4DvFzqTj2zmxOdfYRdpR7k+EFlrK90rKui+ciUvFtfyVXkdT1vZsGF3JqHuKjzbb+V06Un+7j2S4SdXUKa1QZ+4gYC4+Luu22wWuLg7j+TDhbgF2DBoRgQW1jKufLiPa7ekmEQSOnrV8cBrE+7Lrn/5nHzhsRRObcmhSeKIla6CWLtvCbHehtjSFqInc8E/lpXZ20iuSsZOYcf4kPGMDh6Nm+qnb1a90UxpQys1zTrqWvTUt+jRm8yYzAIms4BCJsFaIUWlkOJsrcDD1gInK0VbdQBw5eI5Dhw6gkwvwrKhE0615XTM34iksY66bu35oI+WDHMpfjZ+TOkwhaH+Q7m4dRPOV95il5+JzbbWPOw5mZf6/I0Xtl5jV2o+PhEbqdMXonB5gVKLDgw9sZ1hnu3oWOTNvGgDSfbOWGU1ESVI+XpGNyRiEadPn+b48eMkJCQQE/P9PVBQ08KA5adI6OjO+2M7QcEZWDcUHngO+r95t0Fu7YMtD8Ogd9B7TaL6s1Skbiqcp0cg/pGUnsHQyKVLCUgzTdgsqcf11VdxmHQnHYIgCDx34jnOlp5l27BtBNj9slykyWDm60WXMJvMjF8Q9z/ysT8JZpOZXctTqC5uZtz8WOxcLf/1TveANjOL/JEjcZgyGdc5c+7avuRQBquvfYWF+w4eDXuUObFzwGSAld0xGdScjNDjG/A0gQEvwMmlcGIRlYY3eeKBeK5awoGY7/Pze4+dYvaxeoJdrNj7fB/EYhE6k47x+x4juzEHf8sF7Bo2FMkPKu8OfDafW9+mEjAgmPRCa0QGAz1z84hetozJdXrONqh5QWnLx7vS6RZki9xzLSlVySzxGkm/kx9QorGlZcinhPa+t3h9ztUqjm+4hVwhof/UcLza29NUVM2p5ccIiPUgbGKv+7LrX97JQ9tNmLYpiaunG9BI7bDWVxBhf4UIyw1IpSYI7MO1oB583pxDUukpxCIxPT17khicSA/PHr9YK/TnT8KMPu8MO3cc4pZGjlxrh321D1HF27ApSUWrkrKmH5xub6KzazQTQyfSz6cfIgEOfrycgIJPOe6r5zN7WyYEPszL3V/izX23WHc+G7+Ir6gxZKJ0fY5iRRSdbl4iobmOIaKOvOFjw2E3FQFVetTp9Rx4rifeDpYUFRXxxRdfEBYWxpgxY+4o65y2/grnc2s48WI8LkpgZXcQTPDEeZD/6AE2tMInXUCmwjjuKFWf3kQkE+PyZCckP2qWEQSBG2lPUVNzHN/PIzAVVhJ09ChiizvpH/5Zw/xC9AtM7jD5F5v4yoECLu7JI+GZjvctiP4/3B+a67VsWXQZKwcFo1+KRnqf3Zllc+fSdOgwgYcPIXO7M9Bq1hmJX5qE0m0PDbITvNXjrTZyuuxjsHk0FZFxpNsXEBuzE2tlEPyjG2atkbTmD5kU74CVpZwjt+vnTSYTz7y/mQO1jrw6uB3TegUDbbxIw3Y+RJMgYrLDu8wZ0uW78c1mE5tee4TqnEa6PDaYU+erMAkCcTfS6Dx7NmMVTpRo9TwlVvHBvgwGRdjRZPcJGXUZrPBOpHvSUgqb7aiPX0ZUwr3FbWpL2/SGG6o0RA/0JXaY/28Wlf/L18lDW42qayd/IgYFISvJorLCRJ6pEzcb+qM2hWHTkox/7laGVOQxzKkTFvaBnK5PZ0fOTjalbyKrPguTYMLewv6udM7PoqUGco5hPvMRyV+sZ1NqKxUmMZbNnnTIL6Bd/kZsKopJ6gAfT7CmQ8+RvPnAm0yLmEaQXRAmvZ6vX19ISPUGrvhoWeFgx6igUSx44P/Ye8/4qKrt//99pqT33ia9k0CoofeOCIgQQDpSRFFU4FqwUGyoCCqCIKDSQu/Se09CIKSQ3hPS+2T6zP9BFOQmXoF77+//vcr79eIB2efsM7PPmXX2Xnutz1rMt+eyWHshDZ/wvVRok5A6z6VK3AaHinsMzrjNc4Gd+czCnqNupvQxSLl7pZjPng8n0teexsZGfv75Z8zMzJgwYQJS6YM0/wvp5aw8lc4bA4LoHeQE5z+GtKMw5scWC29zeRWkHkb/7AbK98jRK7Q4zgxHYtfcF15UtI38gh/wUU1EvfkcjvPmYd7x4WevvLGcV868QpBdEB92+RCR8GgPeF2FghM/JOMb4UCHId6Pfo+e8h/ByFSCnas5CWcKUDdq8Qp3eKJ+jENCqd66FV1dLZZ9+z58DYkIa1MJe6+YEeJTyZGcvfRw74GjRyQU3cI8O54yN3sq6q7j5j4ewSEI4eZ6LJ1t8c7xZauz6L5/XiQS0cbLnhNx6ZzKkjOyrQfWplLMpeZ0cm3HgfSdxOlSiRR3w92xKfxWEEQEdupL8uUDFMSn0X/cSDIyi8h3c0V+9AhzjEXsd/EiQaRjqsyR6OvFdHXpg840iV0VsYS3nkZ48VmU6ZdIrbLCo1VEs5wZMysjQrq60lin5s65QgruVuERbPtv1R7+y/vkq+7JOb81lR7jAnGUNd0svU5P2q7L3DlXQIXgDIIIe00BPrY5BFicwY47aIDrdq6csXXiHAqq9E1Spp5mrkQ4hOFv44+PtQ9eJo7YCRIsNCrENflQnQsVaegK4sgoMCajqidZFkHU2BUj0otwKK3FqugE7bM0lNuISJjelYihk+ns2hmp+MGNLMsrYe/yD+hmdp5UzwaWOdgx0GsgK3quYMu1fD48nIh3q4NU6mPA8UVMxR0p00PU+f1MGTCI74os2eNpzARbCw7tSmdIuCtfj2uKnNmxYwdZWVnMmDEDNze3+9fU6PQMXnURnd7Aidd7YlxxF9b3gvCxMGpt88GtKYBvO2IIGEhF/UJU2bU4TA/DxL+5UmF9fQpxN0dja9sVu29FKJNT8D99CpHZg5emwWDg1bOvcu3eNXYP342P9aNFxhgMBo5+d4ei9Bpe+DASC9s/FoZ7yn+Xy3sySDhdwJDZ4fi2fbKN2JKPP6Z66zZ8Dx/C2O/h8Fed3sCwry9Rr6nGxPsbpCIJ0cOisWmogO8iUQT34qpDAr4+8/HxmQe7JmNIP0ml9WbWmtqzxs+Iz4M8mOTW9BLatv8XPryhpbWHNXte7nnf6G5L3sencR8gMenDxeFfYGn2YFVamnebHe+9g5G5QMSE+Zw6dQmDSIR7QSGdgJnPTUFi70Dfch1br+Qxs7cTcaqPKagv4GuP4XQ5/xWFcisy/F6l14vzEbeQGwJN8fTnt6VhMBjoNT7oifc7/vLumoK7VZzalIxSriWin4yOz/g85KutTMohcVcMOYViGo2awvwsNBU4mlTgZFaMm8ltbIUYMkwNxBsbE29iTIKJMVXih5ejpkoTwkrd8Kxwx17uj0QIRCs1osEyHZVZBajlWNbE0P9KJRIdiMaPIOD1d5CYPZyqrNfpubrnOnEHVjPIJZ47Ho0sd7Cjh3sPVvdZTXRsMe8dSMAr5DBVXEdrN4EIs96cE5ky/MJBJnTpyKEyS3Z4WDLdwYSrJ0pQa3Qcm98Ta1MpV69e5eTJkwwZMoTIyMiHrv3DpWyWH73LD5M70D/YATYOgOo8eCW2SS74n9k9FUPacWp9dtKQKMJ2TCDm7ZvXrdVq5cTGjUCnbaS16QqKXpiJ45tv4DBz5kPHHco6xLuX32VBhwVMaTXlke9x9u1yjq1LpOtof9oOaB7J85T/d+i0evauuEldpYLx70VibvP4xdG1VVVkDRiIedcueHzzTbP2yxkVTNx4g+l9RRwofZeOLh35rt93iE+9D9fWkNnvGfK1cXTqeBALnUXTRMSzB0X5b/BqKyk3bcT3/fMqlYo5X2zlXL0Ly0eEMrHLg4nF6yc+4nRJNL4WEzg4+uHSkklXt3Limx04+JpjHvE8SUnJCIKAZV0dXe4ksXLkBKo6dSY8V8mB+CJe7ufMNcXH5Nfl87XPGDqf+pQyhRkxluMZ9MYSjM1aliyoq1Rw9qe7BHdxJbiLa4vH/Bl/eSMPTent1/ZlknLlHpb2JvQcF4hXmP1DSyWDwUDZjWQyT6dQlKuiGlu0kgezTKlWjgkKJKiRCFp0BtAaBNSYohZZoBc/mD0aq6owmGRT5qRAIxgIcbKizcETGPLysejTB+e338LIs7kxKs6o5sT6I9QW7OZZ2V1uuDTysYMdvTx6sbL3SvbeLOHtfbfxCj5ElXADlW0UY5yGsVEh0DHxGuPtLUgwduYneydeMNUgLTZiZ2whO2Z2prOvPYWFhWzatInAwECioqIe+v4VDSr6fH6etl62/DStI8KN7+H4P2D0Rgh/vvmg5lyEn4aj9JpLRdpQLPvKsB7o3eL4J6csoKTkIO3abqXhnZ9QxMfjd+bMQyqfpfJSRh0ahb+NP5sHbW6KnHgE1EotO5bcwMhUwth3O/7b/sun/PtUl8jZ9VEsbgE2PDOvzRPJOpevWUPFN9/iHb0D09/lbvzG9B9jic2pYsHz1XwR/xEzw2fyashk+KY9ensfLgfUYGLqQYf2exBdWwOn3kfTdxOpZ5x5oYcFFhZGnOgQhKVETGpqKtN+vkW1yIozC/ribtPkatQb9Azd9RJFyquM8XmH93s+HN1yJnoRt/enENAjkHx8aWhoQNDr0SgUtIuNI9HTn5sTZ+BcZOBwQjEv9XEhRvUxubW5fB04mS7Hl1GllHJGM5jBiz7F2qn5BAma6i8g8MTy2H/5ZChoikftMymEUW+2RSwRcXTNHQ6tvk15fv39YwRBwLlzGN0Wj2XsD5OY+d1Axkx1olvrRsLsinA3qcQMOSK9DrVWgkErIDWArVCPt0kJrR2K6dNOznPTHHAdbUKRcyOWFmY8k5NL6683IBVEyNZ/j2ztd80MfEVhPYe/ucWu5WuoL9jOWL80rroq+NjBjt6y3qzsvZKDt0p5Z/9tZEFNBl5uE8UrvlH82KDDqyib/loFac5NBn6kso7+5h5ExxQyp5cfnX3tUSgU7NmzB0tLS0aMGNHsgfniRBoKjY73nwlFqC2AM0vBfwCENU9SQqeFY/9Ab+ZBRVo/zCIcsRrg1eLY37u3j5KS/fj4zMO0xIqGc+ewnTL5IQNvMBhYcm0JGp2GZd2WPbKBh6bN1oZqFb0mBD018P9HsHUxp9vz/uSnVJF4/snqk9pPnYrY3p6yL1e2WJHqnaHBNGp0ZGaF8VzAc2xI3MDZ8njouxhRQSytJUOor08kv2AjdJ4LjsFI4z/Eu7cLH8U1kqtQsyCtAIPBQHBwMFNCJGi1OhbuvHn/eiJBxK5nViKVBLIr53OOZ19/6DP0jfoUj/amZFxKJ8zZgFarxd7JCXdvb2I6R+KsqmPO26/iIE9hZFt31p4roYPR2/hY+zAv/SfODnkPOzMDg42PcfSD2RSmJLU4FoJI+K/VP/jLzOR/j06rJ+liEXFHc1HKNQR0dKbdIC8cPP59PZrU1FR++eUX6urqaFVfT/DxE5g4OuLw8lxsRo1qpstRllfHrZP5ZMTmolMex1iXxrjgfHZZKvjG1pp+nv34vOfnHLpdyoI98bgHHqRWFIPcJor3wqfxaWoOaDWMu30Zk8F9WC23YEhlPcv6tOOZNVfwtDNjz5yuSMUCu3btIi0tjWnTpiGTyR76HImFtTy75jLTu/nw3rCQpkIguVfg5etg04L748b3cGwRFdp30csG4TgjHKGFwgZyeTaxcSOwtAynXdstFL32BvKrV/E/ewax1QM98v0Z+3n/6vu81ektXgh5oVk/f0R1iZzopTEERjrTb0roI5/3lP8+BoOBo2vuUJhWzdi3Oz5RoZGqrdsoXb4c2frvsejZs1n7+weT2HYjn0OvdGJZ/Cvk1eWxY+g2vHdMxqCsJqlPNypqLtGp4xHMy4rhp2cw9FxEVdEovlPUsybAmBWBHkx2d6Curo6XVkZzRenO58+3ZkyHB7+R+JQsJsXPQaxvYO+z2wmweeDSUSkr2bL4BeqKxESMf4GL8en06NEDkUjEhQsXMFMqibx6lQYvf+K6jmFznp7ZvZ1J1q8isSKR94Im89y5b9A0yjlQGILvqPl0eGbUf9So/y1m8r9HLBHRpq+Mics6026QF7l3Kti5PIbD39ymILXqV/nPx6OmpoYdO3YQHR2NqKKCvqfP0ObKVdzefBO/48ewHTPmvoHX6fTkJJRzYGU8uz+JI/vWHQzqHdiLU5nSKos1dnq+sbVmmO8wPu/1OdGxxby5Ox73gAPUimJosIliSftZbE5MQS41ZtCtS5gOHcBquQX9SlWs7hbEon2JqDR6VkVFYCQRERMTw927d+nXr18zA28wGFhyOBk7MyNe7RfQVMkp4yT0XdyygZdXYDj7EUraobXug8Ok0BYNvE6nIil5HiKRCa1arUSdmU39yZPYTpr4kIEvkZewInYF7Z3bMz748ZI9Lu/ORGIkosuo5tLET/n/F0EQ6DMpGKmxmFObk9E9gSyI7dgxSGUyylZ+hUHf/PzX+gVgZiTmixPZfNX7K6QiKfPPv0HjwA8RagsJqXJDJDLj7t1/YPDuCmHPI1xZjW1/KTPqxHSt1vNeRhFJ9Y1YWVnx6tAInIV6PjyYSGndg5qx7UL9mGezGJ0g8MIvMylrLLvfZmxiz4g3l2JkqSFp/w7CAn25dOkSMpmMadOmYeLqyrm+fSlHy4h1b/NF6Wl2nMgmRFhAV7euLEn9kY19XkLiIGOMZyLlhz7h0JcfoWqUP9nAPyZ/mRDKlpBIxchC7GjV0x0jEwk5CeUkXywm9XoJqkYN5rbGf1roWalUcu7UKfbv20d1aRnhdxLompGJ99SpuH32aVP9SokEg95AeX49t07lc/anu9y9cg+dVoudcyKVuYcIcW5kiNsd3nW04pCZlCmhU1jceTEbL+Wx9GgCsuA91IriabCJ4sP2szkXF8cNaxf6xV/Eo0s3vlWb06NMy1ceRuypgB0xBXw0KoweAY4UFxezZ88e/P39GTJkSLMZwqGEYjZdyeWD4aF0cAJ2RIFjMDz79X1VvN+jP7IIofgm1aIPsZ/dA7F1yxtr6RnLqKw8S3jYN1hZhVP68SdoiopwX/nl/bKGBoOBhRcWUiwvZl3/ddiYPHr90NzECuKO5hI5wg/P0CeXu33Kfw8jEwk2TmbcOVuIXmd4bFliQSxGbGtLzY4dGHl7YxIU9FC7mZEEiUhgy/V8evnLGBzYgS13t5An6Blo6oH4zm5Mu7xFQdkuJBIrrFvNhriNCDWZmIx4kdbHCjnqKuVEXT1jXezwdnenJvMmN6pMSC+pZ0TbB/Wf2/vISLhrS5b2DMdzzzDCdygmkqZ9OHNLdyw9FGRcSUFXmY+JeyB3EpPo3r07Xbp0Qa1Wk9zYSGpgIF7piUxN+IXU9BIsPMbi4ytha+Yeqls9Sxe9hGD9LWrvFXL6dAJOPn5YOT65wudv/G1qvP4REiMxbgE2hPf2wM7VnIZqJXevlZB4rpD0mBJqyhTotHqkxhKkJmIEQUClUnH10GF2791LTlERHrl59C4sIGLiJNyXL8O0XXtqq7XkJ1eRcDqf8zvSSThTQHl+PbIQO0K7GVGWtY2StFhGdBAIEF/lVQ8PLksNLOiwgJfavMRXpzL48kwiHiE7qCWJetspLG4/g+rEW/xk4UKbjAQCZDI2mdjTqULLF/UN1HQIYf7O2wxq5cKiwcGoVCp+/vlnxGIxEydOxNj4YYPcqNYy6+ebeNmbsXxkOKJfFkJRHEzYBZbNw7UM+fEIx9+kQT8C82mvYuTasourrOw4mVmf4un5IjKPSaiycyhZuhS7KZMfin0+kHmAn1N+ZmGHhXT36P7I90yn1XNsXSLG5hL6Tw29n0n8lP972Lo0/aYSzxfiEWSLpf3jhbcaB/hTf/Yc8ouXsB03DuGfotrC3K05eLuIG9lVLOzXFROJMdvubsM8cCgRGRcxN5hT5+5N8b1dOMuikBo7QuwGxAGdMPdphd/lMrY6ichVqhnuZEOglzu342K4Ui7Fx8GcYNemVacgFtHXyp1DJbaUKU5ysfgqw32H3k+UdHBph8b4GjnXy7A1UlEjtiA/v4B27doRGBiIv78/twqLKLKzocDPjy6Zt2hzah8m+Y60bhXOpvJDJLgG08u+NX51V3CWVHLocCyNCg0ewa0QiZ88e/svHycvr6nm2p4ddHl+POY2jya6X1+lJCehnPzkKorSqtFq9GDQY0MhWnE6RTYCaiMpzqVlhCqlGIf1ReEciKJeQ12lkrpyxX1VPmNzCZ4hdniG2ePmZ0b8sd3EHzuErbUxUWEVFNYm8qqnD+XoWdJtCYO8hvL+wSR23EzFPXgbdfpc6uxn8U6bMXgUZjGnXoxTTTmdFXUc8AwmvFbBN0kKXF7uyoiNN1BodBx7rQfWplL27NlDSkoKU6dOxcur+cboylPpfH0mg12zu9DJcAd+HgHd34D+zeurGnQ6tJ/3QqQoQPXsOczatyw1oFAUEBM7HDMzX9q3i0YkMqL4H29Rd+IE/mdOI7FvykQtlZcy6uAoAu0C2TRo0yMnPQHcPp3PlT2ZDHu5Nd5PmHTzlP93qJVadi6PwaCHce91wsj00XSIfqPh0iUKZs7C+d13sZs0sVn7L4n3mLstnk+fCyeqo4w3L7zJ2fyzbLDvQcfYn1FNiuZa4VtYWraiXevNCOt6gE4Fc29QfbiANeVVfBtozGeBHkxxd+D0mTO8fboCpZEVZxb0wcnywYsp81gWI1XnEWq/pq1zO9b3X3t/Rq/TKTn68/NkHAfX8FDSNaZ07daNgQMHAqDX6/no9EXq4q5jrlZipdISdv0aHveKUXq7sMe/koIIF5aEDsXn7Ceo9Ebsz/ZGaRfGgFmv4BHc6onG/7/mkxcEYYwgCMmCIOgFQejwT21vC4KQKQhCmiAIg/6d6/wZhSl3KLuym03zZxFzcA9ajeZPz7G0MyGsmzP9+kgYHVlMhPgQZtqdZDtmkuNkhE2dgqBsMUaGYSSbjCAh34acO5XUliuwsDUmuIsrfScHM+69Tkz/vAf9p4eg16Sy/b153PzlIH27yJjqG88VZSYTPT1RG1uyefBmerkNYvqPsUTHJ+Eespl6QwF1Dq+xvF0UXRoqeL1CjYlaSeuaUg55BhOgqGZ1rA6PsRF8fC6DnEo5K8dGYGNmxM2bN0lOTqZPnz4tGviiGgXfX8hiWGtXOnmYwuH5YOcLvRa1MCKg2PYtUmUi6pAFf2jg9XoNScnzAQhrtRqRyAh1QQG1R45gGxV138AbDAaWXl+KRq9hadelj2XgFfVqYo/m4tnKDq+wp9IF/wsYmUjoP60VDdVKruzLfOzzzbt3x6xTJyrWrkXX0NxXPSTMhQ5etnxxMh25WseybsuQWcpYUJ9AqZULxmdWEOD3D2pqblBUuheGft6UtHhlNTbDfZmhkNCtSsf7GUUk1jfSq2dPhjlU0ajSsnh/4kPRPX4DfPi8vC0NdrO4VXqTNy+8iUbXZFPEYhP6R63BtWMd9xJT8DY2cPXqVZKSmqJmRCIRiwf0wmb0BC77h1NjY8XVXj3YPnIsiTYyoi7AO18UkPnWBmLqBqGpsuF5l2TaiG9SlHT7yQb/T3i8121zkoDngO9//0dBEEKBcUArwA04LQhCoMFgeHJB6n+BR9YljFLzUVtUkvvFCk5u3IhXx864hbdBbGKKQaVEr1Cil8vRlJSgKS5GWVREUV0dxc5OFLm7I3e0QWqwJtTBgS6DB+MWEPBI1zbo9WTEXOHq7u1UFubj4evJ5K46RDnb+Njdj2ipmtb2IXzV+yu0akvGrLtGZm06ziFbqNcrqHFcyMq2g+ihVzI0KReFtQNdsxI5E9IOmaac766a4hhpzwW1hh0xBbzU248ufvaUlJRw/PhxfH196d69ZTfIp8dSAXh7SDCc/xSqc2DKYZA2lyOQX07FJOsLtOZtMBk79w+/b1b2l9TV3SYs7FtMTZs2eCvWrUOQSLCbMf2zzsXIAAAgAElEQVT+cUeyj3Cx8CILOyzE0+rxkpduHMpGo9LR7fmA/1pY2VP+87j6WRPR35Nbp/Lxi3DE8zG0hQRBwGnBm+SOjaJq82Yc573SrH3xM6GMXHOFdeezWDAoiFV9VjH+6HgWePqyKekqbhVQatuFzMzPcIg8hkmrUXB5JUKbcThMCGHJultM6GzOrORcTnYIYspzg0nceIKTKSJ+SSxhWOumRCRBIqL/iGDm7New1lXBxcKfeP3866zsvRIjsRFmZl70n/weR2qXUJkQj3NYew4cOIC9vT2urq4IgsA7gTKkRlJWZ3vzXEMFnrkZ5JgIZAb54SYRYZEWi/2J2xSqtIAzVubV2Ir2wPOPHnn2yGP7n3DXCIJwHlhgMBjifv3/2wAGg+GTX/9/AvjQYDBc+1f9PKm7pvTsKe7+vB6T4kKMqpVIFFrEuqb3iVYiQSOV0mhmRoOFBXUODlQ7O1Fpbo5WJEIsCHi5udG6Y0dCQ0MxMno0oTKNUsndy+e5dfwwFQV52Lm5M7ibKy4528jU1LDIK4hMbS2TQyfzWrvXuJ3fwMvb41GKkzBy345GZEat4wLWRHSnt1TPc4dPc0cWSGR6AgmBrXESqvj+khpnc2uEKREMWXMZma0Ze1/qikGnYf369U2ZfHPmYGHR3G8em1vFmHXXeLWvP2+Eq2B9b4iYACO+bXasMqMa7c9zMBefglkXENxarhhfUXmehIQZuLtPIDhoGQDqwkKyBg/Bdvx4XN59p+k4RQUjDozAx9qHnwb/9Fgx8RWF9ez6KJbwPk9lhP8X0Wp07PooFrVSx/j3Oz22Hkvhq68hv3wZv1Mn768Kf8/86FscSyrh7ILeuNuYcjz3OAsvLGSCzpS3q2pRvHiA67fHYGsbSRvvJQjfdgS/vjBuG/K4Us6dzmJ2JzOecbZhXagX+w8e4uMbKnQm1px+szf2Fg/2tOqvFjEvp5jzlhexrP6R7u7dWdVnVZPOPZCetoJz644hL7HAENgGkY09s2bNwtz8QSjpD4XlLM4ooqu1OfMlBrYeOoetpgJToWlloKAGa3UDret0hLaPxHfanCcZ9n/prvl3Z/J/hDvw+6yCwl//9l8h3kTLDe9W4P3n/iyxWIyLiwtt3dzw8/PD19f3kQ27XqcjP/kOaVcvkXHjCqpGOY5e3jw/oT+eZYfQJEWzThbCBqkFlhIxa3uvpZtbNzZezuGTY6k4ut5EZLUHtUSGymUh2yLa0cFIYPbWaO4EdiAsL5XEgHDsxY2supmHo8ofuxdbM33fHVQaPavHNYVL7j/8C5WVlUyZMqVFA6/XG1h6OAUXKxPm9PCCLYPBzB4GLmt2rKZETt3WfTiKT0DHl/7QwKtUpaSkLMTCIpgA/3fv/73y+/UIgoD9izOAJjfN8uvLUWqVLO229LEMvMFg4PLuDIzNpHQc9rTa0/8iEqmYflND2bviJpd3Zzx2boPj/PnUnzlDxdp1uCx+t1n7wsHBHEsq4fPjqawa15bB3oO5U36HLSlbaG2oY1jcLvyC3iQjYzklznG49lwIZ5ZAxmnM2veja1YNL2XU8K1QQ1cbC8YMHMCNlA3sqTPj7X2JfD+p/f3Vo0UXN5anVzFZ35tCOzFXijYx78w8VvddjanEFP+AN6gZm0Dsz2VospORywLZtWsXkydPRvzrJuqLHo7YSsS8lprPMnNTvntlEkv33CEtK5eBngKu4hIqSsqJM5KQZabitX//FjTjTx2lgiCcFgQhqYV/I/4TH0AQhFmCIMQJghBXXl7+RH04ejmSHJjMFecrNAbIiQy2oK9xEr25Sn/zDNqa12Fdmod5ZiKWGQk4VBVjr6zHWClHUVONTtvch6/X66irKCPn9k1iD+1l/2dLWDNjPHs/eo+0a5fwbdeBKbOfZVJwJl63PiBWUcLo4Ai+k8jp7zWAvc/uJcy2E69sv8Xyo0n4BZ1BbrULtWlrpLIPOdSpE5FmUj7ctJkTgR3wKi0gR+aPtVTLJ1nH8CwNwHZkIOtT7nE1q5Ilz7bC19GCW7dukZCQQK9evfDxadkQ7okvJLGolreGBGN26wcovgVDPgPThzeldXUqKjbdwUb0HZg7I/R/p8X+DAYdyclvoNMpCGv1NeJf5R00RUXU7N+PzZgxSJ2b0rVP5J7gTP4ZXm77Mr7Wj1evMi+xkqK0GjoN9/nT0Nan/N/F2duKdgM9Sb1WQu6disc619jXB5vRo6neuRN1QUGzdncbU2b28OXA7WJuF9QA8Hr712nn1I4lTk6kx61DZt4Ta6u2pKcvQ9VhHNj7w7GFCDo1NiP9mV4vplu1jvcyikjTwgvP9KGtpJCTKaXsuVl4/1qCIOA6Ooiv0jQYG/dC7Dyb6/euM+fUHGpVtYhEEiLaryZkZCMSUxUWRVkUpKVy/Pjxhz7zaBc7fgr3JbNRRVRKDm+OCWN0rwi25lnwizyCkVNn0di5kYDwR3MRPy5/CXeNwWAgu7yWc6V7WH9nPQaDgXGBY5imMcYu9geoycdg7UmNc09S6525m1pMdfGDm4kgYGJugVgqRSQSo1Y2opI/vPlj6+aBLDSMAD8nZEI24oRtUJNPmo0rX8sCuNiQi4eFB4s7L6abezcupJezaE8ClYoq/Frtp1iVhMJiAD6yF9naJgAHscCqtWtYFdQF68YGFOaWmJuI+bBiFR1jZmDWxon0jo688MMNnm3jxldREZSXl7Nhwwbc3d2ZPHkyon8uz0eTHnefL87jYWvKvihXhLVdm0qKjd8Bv/Nv61U6ytffwbg8GhvRuiaZ4VajWhzf7JxvyMlZRUjIZ7i5PtC4uffhh9Tu3YffqZNIXVyoUlYx8sBI3C3c2TJ0yyNXeoIm0bboZTEYDDDu/U5P5Qv+x9Fp9Oz+NBZFg4bx70c+1ktbU1pG1qBBWPbvj/sXnzdr/01z3tvejN1zuiAIAuWN5Yw99DxmDeVEm7dG9OxybsQMx9GxP+EmI5uqmvV9D3ouQF3cQPqG20zpYoHeXMqx9gGc2LWTdWlG1ImtOT6/JzK7B5pWiruVXNyfyqzO5gRwi6qib5BZyljXfx2uFq7U1N7k2vnJZB7yQ683ocbVj8EjRjYTB0yqb2RyYg7VGh3fhXpiVq3hjZ23kau1vDsslImRnv8V7Zr/lpFvBWwHOtG08XoGCPizjdcnNfKHEop5Y+dtXuzhy9jO5mxI+o5fcn7BWGzM8wGjGSdxxDPpEORcAIMe7PzQeXaj2siHKp0llY1S5A0K9FoNep0OqYkpppaWWFhZ4GQtwVZUiXFFUpNgV/ldDECcVwe22tlztuYulkaWTA+bzsSQiajUYj47kcr2G/l4u1ZicP6JanUNtbbTGOL7DF8GyzAVYPP6dSz3jEBsAJ2RFAsTIxYrlxF5ZTrG5o6Ip7fimXXXsDCRcPiV7kgFPRs2bKCxsZE5c+ZgaWnZ4lj8VtLvwNyuRJyfBoVx8PINsH7gLTPoDVRuSUGdmomrxcsInh1h4r6HXgK/UV19g/hbE3FxfpbQ0C/uP4Sae/fIHDgIm9HP4fprrsWCCws4m3+WXc/swt/28TJUky8VcX5bGkPmhOMb8WTytU/5v0V5fj17Po3Dv4MTA6Y/Xmhg2cqvqFy/Hp99ezEJbe7y2RGTz9v7EvnuhXYMDW/aMI0vjWfG8an0kMtZNXA9eZJUsrNXEh7+HU5ntzQVHnklFmxkNFwrJvZMDjO6mRNiZcZGbwe+WrOBA8pWtPa0u19Z7Teq92ewN6+CxW1MGWRWSEbWx5hITFjbfy1BdkEUFPxIwo3PyD4ShF4woc7dj6hJkwn6p+SuMpWGqUk5xNc1Mt/LmUl2Nry19w6XMiqY1s2bD4b/50Mo/y0jLwjCKOAbwBGoAW4bDIZBv7a9C0wHtMB8g8Fw7M/6e1IjX9mg4tNjqey+WYi7jSkfDA/F372R9YnrOZFzAq1BS1e3rgxx7Uqf2mqsc69C3hVQN/z6RURgYgOmNiA2boqvVcuhofTBRaRmFHq04biDjKPqEjLrcrExtiEqKIrJrSZjIbFkV1wBK06kUdOooke7dO4ot6ETWVHj8BqLQ7sy08MRg0FP9PdrWOIWjsrEFEEQYWFqzAeiFYRf74l5VSgOcyOYeTyFmJwqDrzcjRBXK/bv309CQgKTJ0/G17dlN0h+ZSP9V17gmdaurAy6CwfmwNAvoNPDcr81h7JouFqMs886pGUnYe51sPdr1p9aXUlMzHBEYlM6dTyIRPLA/1+ydCnVu/fgf+I4Ujc3Tued5vXzrzOv7TxmtX68hDa1Usu2969j7WjKqAXtnkbU/IWIOZxN7NHcx9ae19XVkTlgIKbh4Xj+sKF5+6+a83K1ltNv9MJY0uQD35q4mc/iV/KaSsr06VeIjR+LWl1O5+BNSNf1hYABELUFg8FA1ba7HK2oZVEbU6Jc7BhTWcC6E/Fc1vjy9pBgZvd68JvQq3WUfXuLNfawwVPCdMdGYu6+T4OmgRU9V9DDvQd3U98iM+EIOUcD0EtMaPQKYurMWbi7P7wdqdDpeSejkB33quhsbc7aUC/O3L5HJx87Ap1bnrz9GX8LqeE6rY60wloW708irbSeTj52LBwUhLeTjj0ZeziQcYBieTESQUKYQxhtHdvQ2sgWT5USj/pKzBQ1oKwBnRqD2Jg6iZR8U0typFISDHJi63LIqcsBoI1jG0b6j+QZ32cwFhtzPr2clSfTSSyqpa23BEvZPm5VXEVj2gZT15dZExZOV1sL9Hod+9d+zRKnIKpsHJEYDJibGPOJ+Y/IEnQ4po/BZoQfPykb+fxEGh+PCmdCpCe3bt3i4MGD9OrViz59+vzhGMzeEseljArOzgnHZUs3cAiEacfhd26d+stF1B7JxqZ1IRbpc6DXP6BPc1+8waAn4c5Mqqqu0rHDXiwtH8ymNCUlZA0YiPXIkbguW0qNsoYRB0fgbObMtmHbkIoez58ecySH2CM5jF7UHhdf68c69yn/t9Hp9Oz5NA55jYrxH0RiavHoZTYrN22mbMUKPH/cjHnnzs3aL2WUM2ljDO8MDWZWzyaDbDAYWHRkIicrE/jecxStOo4jNm4ULi6jCC13gLPLYdJ+8OuLXqGldHU86zzEfO8u5gNfV/hlP3vu2ZCvs+LQK90JcX2gwaQplVP67W2WtzfngDV84GXMueQPSK1KZW7EXF4Mm0pCwnQK794l57g3OrEUfUBrXpz7Mra2zZM0d5dUsSitEDOxiK+CZQx0ePJn/y9v5M9W1jEnJZd3fN0Y52zLrtgCvjmbSXm9ii6+9kzv7kOfIEfSqu9yKu8UcaVxJFcmo9Vr7/chEUkwFTfFjzdoGjDwYFzMpea0c2pHJ5dODPAegLuFO2qtnlMppay/mEVCYS3uNqaM6trAweKV1KhrabAZxyDfsXwaJMNGKkGv13Hku1Ust/amwM0HI50Wc2NjVtoewjrtBrL4RZiGOZLZxYnxG64zrLUbX4+LoKysjA0bNiCTyZg0aVKLfniAq1kVTNhwgwUDA3ml6lO4ewhmXwKn4PvHKJIrqdyagkmIJfa1LyLoNU2z+Bbi5vPyfyAz8xMCAz9A5jH5obaS5R9RHR2N3/HjGHm489altziRc4LoZ6IJsgtq1te/Ql6rYuv71/FqZcfgWS1H9jzlf5vKogZ2fRyLb4Qjg2aGPfJ5epWKrMFDkDg44L1rZ4srvGmbY4jLreb8wgfhj41qORN29KBKp2LXs3tpqD1CXt5aIsI2YB/9JghieOkqSIxQ5ddRui6Bd7pbc9ZUz9fu1tzcvp0juja421tx8JVu91cJAPKbpZTvSWdhf1uuirVsCHXnctpXHM4+TB9ZHz6MXEha4jSq8uRkHnFFI4gxat2J6XNeajESLl2u5KWUXJIblLzh7cwin6dFQ1okq1HJW+mFXKpuoK2lGSuCPPA3Nmbr9Tw2XcnhXq0SmZ0pIyPcGd7GjUBnS5RaJVk1WRTUF1DYUEiDugGlTonBYMDCyAIrIytkljK8rb3xtPREIpI0FakuquVYUgm74wqpaFAhszNleg9nUtTbOJ57GJ3UDZxfZXlYV55zbnp7azUaDq/5ii+tPMj0CcVIq8HcSMq3zteQZG/CN+YzpFZWiKeGMnzdNUykIg7P647Rr354hULxL/3w98ulKbWceVaNya4o6P0O9P7H/WPUhfWUf38HibMZjiHHEV36FF7Y07R8/Sdqam8SHz8BB4e+hId999CPS1NaRtaAAVg9Oxy35cs5X3CeeWfn8VKbl5gb8cdJVH/E+W2p3L16j/EfRGLj9Bi1dZ/yP0XcsVxuHMxm4IutCOjQcuGMlqjZt59777yD+6pVWA1unjifUVrP4NWXeCHSk6UjHrxAcrJOM/7ia/ga2bBxzHFux49Gr1fS2W4h4uiJ0P9D6P46AHXnCyg5lcvMAbaUiA38o7GEK5eTOKMJZGYPH94d9vCeQNXudCoSSpk72J4snZZdbXxJv3eQz2M/x8nMiQ86voo27wOU5fakHrREbRAwa9eV6XNewtS0+YRKrdfzZW4p/e2t6Gj9+HLN8Dcw8tC0TNtfVsP7GUVUabSMdrFlgbcLbkZSTiSXEB1TwNWsCvQGkNmZ0sXXng7edgQ4WeDnZIGVibRZf1VyNQXVCpKLa7mZV831rEqKa5WIRQK9Ah15IVJGvTSej2M+RaGpo9FqKCODp7PY3xtbaVNkiVrRyL4vP2GtcyBp/uFItRrMpVLWyzLRZi/GL+FzJPX22M1tzYuHk4jNrWbfS11p5dbkh09MTGTy5Ml/GC4JsO1GHu/uT+K7sSEMPT8cjC1h9kWQNC2NtdVKytbcRpCIcBpnhnhLHwgZDs9vataXWl1JTOyziAQjOnY8iFRq9VB7yUcfU719O37Hj6FwsmLUwVHYmtgSPSz6ofq1j0LVPTnRy2II7+VOj6iniU9/ZfS6X0sGVigZ/0EkZlaP5rYx6HTkjByJQaPF9/AhBGnzZ+y9A0lsj8nnxPwe+Ds9mAid3j+J1+tuEyXrzyvtJ3LzZhQeHhMJupkO2eebNmGt3THoDVRsTiLnXj0zelpiLBHx3O1LXCu3JFFhw+ZpHekT9EApUq/WUbbmNmUqNbN6WlGl07Enwh+Umbx96W0K6guYEDCEdsqDSBrCSdyjR60zYNWxO1NfermZiOB/gr+Fkf+NWo2Wr/JK+bGoAq3BwBgXO2Z6OBJqYUpZvZLjSSVczqjgRk4VtYoH8fFGEhFWJlKMxAIqrR65WotS80Df2sHCiPZetvQPcaZ/iDNZ8kzeu76CwuqbaIx88POax0dh3YiwejAbbayrZe+nS9js3YaUgDZIdFqsJRI2+pUjz3gJr6x3MMkKwH5SCKvzK1h3IYsVo1sztqOM+Ph4Dh06RO/evendu/cff1+Fhj5fnMffyYKdHvsRYtfDjJMg6wSAXqmlbG0CuloVTnPCkR59HirS4OVYsHh4I8xg0HM7YTo1NTdo3343VpYPL6019+6RNXBQ0yz+o49YfHkxR7KPsH3YdkLtH7+gx9Hv7lCcXs3E5V0ey1f7lP9Nqorl7Pw4Bu8wBwbPDnvkDfb6s+conDsXlw8/xHZcVLP2ygYVvT8/T0cfOzZN7figobGKlZs7s9nCmI+6LSdIn0Bh4U909FuN1ZYZEDSkKXQY0NWrKV0dT6qDlBdDpMgkApFnDnGZCJSCEcde64Gz1QMRM01ZI2Xf3qLCw5wZYVLkOj372vrjZWzgs9jP2JexD09zJ4abFRAm6crtnSqUjY1Yd+rB1FdeQ9rCy+rf4W9l5H/jnkrN13llRN+rRKE30NnanNEutgx1sMHeSIJebyC3Uk5WuZzs8gaqGtXUKbRodHpMpCJMpWJcrU2R2ZkR5GyJzM4UrQGO3stmbcJaispPYhCZ4eg8jqUdptLT7mGd9MrCfPauWMbuVl1JDGyLSK/DUSzix8BaqtPm4FwehXV8fyz7yLjiZszcbfG8EOnJR6PCKS0tZcOGDXh6ejJx4sQ/9MMDLDuSwqYrORweY0PYoWFNkTRDm2KLDTo9FZuTUWXX4jA9DJPqPXD0TRjxHbRtrpGRk/MN2TmrCApahof7hOZj+v4H1Ozfj//xY1w3ZDP3zNymupvtXn3s+1OUXs2BlbfoPNKX9oO9H/v8p/xvEn8ij2v7sxgwI5TAjs2lrlvCYDCQ98JENAUF+J04jsisuVvv+wtZfHIslS0zOtEj4MHkRXvje2bf+oIEMwt+GryJmsz5iERSIhV9EF1YAZMPNuWR0CTvUbExibiuDrxspSJEpyL04nlO6sJpI7Nl+8zOD4VVNt4pp2p7KpWRTkxx0qAxwIG2/gSYm3Cp8BIf3fiIooYiOphpGe/Uk6xoFfLqSqwiIpny+sL/6Iz+b2nkf6Nao2X7vSq2FVeSrVAhEaCdlTldbCzoYGVGgLkJHsZGSP5Jr9xgMFCh0ZLdqOJ2fSMXS7O5lR+NqP4CYMDbZTjvdXyZSLvmD2p2fCyHvvmCw71GkuwVgkivx10q4seABkpTZ2Kn6IfDlXEY+9lQPcSTkWuvEuRiSfSszug1ajZs2IBarf5DXZrfyCpvYNBXFxnTzo1PSmeDqr4pJt7YEoPBQPXeDBrjSrEdE4h5gA6+7QTu7Zoe7H+aRVVVXeHW7Sk4Ow+nVejKZrMsdX4+WUOHYTt2LGZvvcZzB5/DXGrO7uG77+ttPyoGvYE9n8XRWKfmhSWdkRg9uY72U/630OsN7Pv8JjVljYx/PxLzPyhI8880xseTN+EFHOfPx2HO7GbtSo2OAV9dwNxIwtFXezwwxjotFd93J8pUjpGVO+u6vUFmykt4u8/A7/jOppDpOZfvuzZrj+dQf76QU8/JeFteQ5vqEjxTCzlV58pr/QJ4fcDDbsXaE7nUnyugargXL1CHAOyK8CPY3BSFVsGGOxvYnLQRAR0D7QNwP++AOv8exj6BTHtvKebm/35JUvibG/nfMBgMJDUoOFxWw+WaBhLqG9H9+tUlAlhLJFiImwTLGnV6arU6FDodUlUqJg3nMGmMQRAEImXDeKvdLPysmysr6vU6Yg/u5dyeHRwfPo1UZ08Egx5fIwk/BjZSmDIDK11bXC7PRWxphMm0Vjy38QZ1Si1H5nXHydKI6OhoMjMzmTp1Kp6e/1q98bfIgnNdE3G4tgzG74SgwQDUncun7kQeln1lWA/wgugXIOsszL3aJDf8O1SqUm7EDEcqtaVjh31IJM03f4oWLaL+5Cn8Tp7gg7RV/JLzC9uGbqOVw+Mnb2TElnJyYzL9poQQ3OXJogme8r9LdYmcnR/F4hlqx5A54Y/stimY+zKNMTFN4mUthCQevXOPl7c3ac6P6/S73072BW7vfJ5pbm509ejOXFdLSkv30dnmdcwOvgUDlkG3ptWoQaen/Ps7aEob2RIlY1VpJW3zM7CoMCWuQmDbjEi6+j+ob2DQG6j8OQVlejXVk4OYWFmKWm9gexs/2v7qui2oK2DF5blcKM/BWCSlTZ0v7nEN2Ji4M3nJJ9g7PfpG9B/xt6vx2hKCIBBuacY7fm780j6Q9O7hHGrrz1fBMubKnBjmaE17a3PCLU2JNKslUn+awMrF2JR9jIMmiUmhEzg5+hgb+ixv0cA3VFWy96P3OL1vF4fHziXVSQYGA6EmUrYEySm6OxNzUSBuN+chiARsJ4Xy5sEk8qsa+e6FdrhYm3Dp0iXS09MZNGjQnxr4M3dLOZdWzqudrXGI+QzCRt838I23y6g7kYdZhCNWA7yawinTjkKft5sZeL1eS1LyfHS6RsLDv23RwKsyM6k7fATbFyZwUZHI4ezDzGw984kMvE6j5/rBLOzdLQiMfLTl+lP+Wti6mNN5hC85CRWkx5T++Qm/4vT6fPSNjVSsXdti+9BwF9r/qjnfoHoQHo1vLyJ8B7Gouo6LhRe5qHLDSOpAouYIhoABcOEzqLsHNFWHshsXDAJMPVfJJFc7bnkGILdtwMPKiNd23qbsd7VhBZGA3bggJPYm2O/OZK+vJ5YSMWNuZ3K1uinZUmYl4+shB/my9QBCTJTEWWawv3cxJ32T+PCzycRcPP0Eo/jo/CWMfFZ1Ju+ffIszeWeoVFQ+0jnmEjGdbCwY72rPbDdjBpmk4yvfTWnGfK7emU1y/mY8zW1Z1m0ZZ8eeYVHHRbiYt2yUMmKu8tOieSSVlLFz4ptkWTqAINDTypRNfiXkJU/HROqJV/K76Oo02E9uxRcxuZxJLeOD4aF08rEjIyODc+fOER4eTqdOnf7lZ1dqdCw5nIK/ozlTCz8AqRkM/hQAVW4tVbvTMfKxwvb5QARlDfyyEFxaQ+eXm/WVnb2SmpoYgoOXY2HeskBS+dffIDIzQzRxNEuvLyXELoRZ4U9WpjHxQiF1FUq6jvZ7WtLvb0zrvjJc/ay5tDMdeY3qkc4xDgjAZswYqrfvQJWT06xdEAQWDwuhokHFuvNZDzcOXM64ejnDJA6svfMDdXYTaGhIpTAsDHQaOLn4/qESOxNsRwegLajn7RwtzztZE+8TjLVbAw1KDS9ti0f9u6LlIhMJ9pNDMejAYns6+0O8cTM2YvydLPaVVv/62UT0j1jJu23G875rI897hFDvKuV862JmZ7zBuB+GcyHv/OMP5CPw35Ia/n/K4ZNbOSg/yv57RwFwMXfBx8oHmaUMO1M7bIxt7mdhqnVqalQ1VCmryK/PJ68ujxJ5CQBSkZQIpwjGBI2hn2e/PzTqv1FfWcHZzevIjL1OfZvO/NxpMGpBAEFgrKM1C+1ukZHyFpYWYfikf4gyrxa7cUHsL61hw6UcpnTxYlIXb6qqqti7dy/Ozs4MHz78T5evGy5mk1/VyNYu95Deug6j1oOFE5oKBZU/pyCxNcFhUiiCRAS/vA/yiqaaruKHb3dFxVny8r/HzS0KV5eRLV5LkZxM/cmT2L88l49Tv6FeXc8PA3947HBJAKVcQzMLlxAAACAASURBVNwvuchC7fAMfVrx6e+MSCTQd3IIO5fHcG5bKsPmtn4kt43jvFeoO3KEsi++RLameW2Etp62jIhwY8OlbMZHeuJu82tcuq03Qtd5vH/5S9JbdebjO9EsC+hHRlU0zh0nYnR9I7SfCj49ADALd0QVWYP8QhGfTmtFraWSU54+dDYu5+btapYeSWb5yAfJe1JHMxwmh1K+MRGj6Az2Tw5hRmoec1PyyGpUssDbBUEQCAx4D0EQY12wiZFtn+Ue/dh4fDXZFgXsOriWXq/2/k8M70P8JYz8c52moF1TTll9OhWOWiStnKlV1ZJSlUKdqu6h7FUAAQFrY2tkljI6OHcgwDaAtk5tCbUPvV8Q4F+hVjQSd2Q/cUcOoNfruRc1i23WHogNenQiMa97OjFG2Ed66lfY2nTFO+9dGhPLsRrkzW1LEYs3JtEz0JH3nglFrVaza9cuAKKiov5U276wupE15zMZGmhB98R3IXAItB6LTq6hcnMSCOAwrRUiM2mTIFP8z9DtNXCLeKgfhaKQ5JQFWFiEEhjQvN7rb5SvXo3I2prYXs6cvrWe19u/ToDtk0mixh/PQ6XQ0vW55jo5T/n7YeNsRudRflzelUHqtRJCuv75/ozEwQH72bMpX7kS+fUbmHeObHbMosHBHP+d5vx9ur+O2e1trKqqZ5yFnrXFlcy2siDRNpt21jKEXxbCnEvw6wTG5hlfVLl11O1KZ8OrbRlzK5Hrzo607ahg6/V8wt2tier4wK1q7GuNXVQQVdtTMd2fxc6oQBZlFPFlbimZjSpWBskwl4gJ8H8HqcSK7JxVONpW8NPcnexav5nQyObf5T/BX2bjVaVScSB6O7kXTiGR12FqZU3n56II7tkbpViLTt8kgCkVSbE0snysYha/oaiv486ZE9w8sh9FfR2unbuzJaQbt4wtkeq06MUSPglwoV3NJ5SWHcHFeSTuRXNpOFuMxf/H3nmHR1V1ffuemt57TyAhJAFC6B1EEGmCdFARFEGKoGBviF3RR0FEFEEQFKRDpLcQeklIICRAICEhddIzk8n08/0xtBAIMcDzPS/OfV1cJLP32adkzjr7rL3Wb3X1o7itB0N/OoqngxXrp3TCwUrK+vXrSUlJYcyYMTRpcu+EoMkrE4i7UMQev8X4lZ+EKccRbDwpWnwWXZ4Kj5daYBXkCNXlsLAjWDvCxAMguxnjazRqSUgcgVp9hXZtN2NrG3zHfVUdO072uHHYTJ/IGOe1NHZuzLInlzXo2lWWVPPn7OOEtvGk17h/HlNv4dFEMAls+u40xVeVjJ7dHnsX63tuY9JqyejbD7GTEyHr1iKS1P4+fr3jPAvjLrNpamdaBtwS3nx2Hax/kf1dpzA952/6+rWhjzieFrKBeOz9Dfp8AR1vZm7rC6tQLEhCHuSIw/MRDNh1iBRbZ4KKtJQml7JmUgdiAmsuAisP5lCxNRP7zr449g9h4dUiPs/IJ8TGip+jgmjmYF6Qzc9fT9r5d7GzbUx09K9YW/s28Cr+CxZeNZp8Ll16h6dHP0X/me9iCo9GZTCyf9kv/Dr5BRJXrkKXXYSbtSvO1s7/yEiZjEayziSxc9F8fpkynkOrluMZ0hjb8dOZE9GdJJkdMkHA3sqKlRFONM1/mULFVho3eoPA0tdQ7cvDtrUXmi4+PL/0JFKxiCXPt8XRWkZ8fDwpKSn07NmzXgb+YHoR21MKmBZagl/BHnjyKwR7b0r/uoDuqhLXkU3NBh5gxztmFc3BC2sYeEEQuHDxQ5TKFKIiv7mrgRdMJhRz5yL18WZuUCoGwcBnXT5rkIEHOLHF7ENt/9Q/KyRi4dFGdM1tYxJg/4rz1GfSKbaywmPWTLRpaVRs2nzHPpN7NMbdXs5nW1NrjtlsKIR057ETf/BS+Bi2554iRdSMFMMujCGdIe4LUN5cDJZ52eH8VGO0l8rRHcpnTZcYIotyyfKwQhLlzITfE8gpU9fYt30XP+w7+6I6nIdyTzbTgrxY1zKUKqOJ/onpLM0pQhAEfHyGEh29hGpNLidODqKs7HjDLuK9rtdDGfW/jFJ5FkXRdo6fGICvbyXT3nmfiCHPoA6OQGPrwJl9u1g9+00WTRrL9gXfcnpHLHkX01CVlmAy3pS4v14NKicthcRtm4n97ksWTXqOdZ+9z4Uj8YR36kr0hOn81rQjc6w80UmkiMRiGtvbsDIkD/HFp1GrM2nR/CfcsgdQsTUTmyg3JH2DGffbScrVOpaNb0egmy2pqak3Flq7du16z3PUGUx8tOUcwc4yJmS/BWF9IHoUFdsyqU4pwalfCLbNr4V2XdgOyX+atTn8WtcYJzdvFfn56wgOnoqHR23dmutUbtuO5tw5Mkd2Ir74GDNbz/zHBbmvU5St5MKJAqIf98fB9d4zNQv/Lpw8bOj0dGOyU0tJO5xfr20c+/XDJjoaxfffYbqtwA+Ag7WMmb3DOXmljB0pBTcbRCLo/y3o1UzNzaSDTwd+z7nKVb2UtBAZgkEDuz+sMZZtGy9soj2o3H0Fu3IRC5o3olluBuU+NhSH2fP8spM1sudFIhFO/Rth19Yb5b6rVO7NppOLPXvahtPF2YF303MZmnSZDLUWN9cutG2zEZnMmYqK0w27gPfgkXHXKJVppJybgVqdQWDACzRq9CqlpVUcOHCAlOQkZGolrhgxlpegVSlvbigSIZXLEYxGjEYj3HI9HNw98AuPxC2sKeUiKavySjno2xi13ApviYgCE/Rzs2WK+DcqFGtwdIymWdT36I+LqNxxBZvm7tgODeX5Zac4fbWM38a1o0uYO/n5+SxduhRPT0/GjRtXrxTnX+Iv8/m28/zmG8tjVdtg6jFUZwXKYzOw7+SL08BG5oUrdSks7AB2HvDS/htJHgAVFYkkJI7B1bUT0S0WIxLdeVZu0unI6NsPg50Vz49Q0MKzJT/3/hmxqGFzgi3zTqPIVvLcJx3/cWFnC/8OBJPA5nmnUWQpGfVBOxzdagt53Y769GmyRo/BfcpkPKbXzro2GE30n3+Iar2R3TO71VCTZO/HcPBbSsesZuSZ/yAYq5numk+PqjY4Ju0wS3QHdbzR3aQxUDj/NJgEvKbHsHXfTpYUlHO8cRSiCj2dSk38ObYdcunNe0QwCZStu4g6UYFjn2AcHwtAEAT+zC9lzuVcdCaBKYGeTA3wxAoNEont/25lqAdFQ428xmgitaqaaDsR6emfk5u3Cmtrf8KbfISbWw+Ki4tJTEwkKSmJarUaKQLeDnbYy2XIRQJSkQipTI5UJkNmZ4/E1g69VEaJsorsq1c5Y2VPYlA4xfbOBEtFqEQSKg1GZnkqaFn8LkZjJUGBEwkOfgXVvnyUe7OxifbAYWgYU1efZk9aIfNHxTAw2helUsnixeYiCC+99NJdlSVvpbBSQ89v4ujoquLX8hdh0EKq5U9ekw12w+3ZCETXwxHXT4BzG80G3qfFjTG02iJOnHwKidiatm03IZPdXbu65LdlKL76it8nhnDQt5L1A9fjZdewhI3s1BJi5yfTZXgY0Y8HNGgMC/8OKourWf3JCbxCHHlqRst6GbzcmTNR7ttP421bkfnW9mlf15x//YkmTOt5S8CATg0/tge5LWeHLeL5XS/S1M6aiU5KuifrEdm4mdeybolI011VovgpGZsIVxxGhrJ06VJOiazY2SQGfbWBPhopy56OrnHcgkmgbM0F1ElFOPTwx7FPMCKRiEKtng8v5bJZUY6HXMrMYG/G+LhiVYeESV088j75LUXl9EtIZ8DpHM45z6RZy9WIxVYkn5nA6dPPIpdfpU+fPsycOZNnnn2W1h06orWx52K5ijMlShKLKzmRX8yR7HwOpKWzLyGJ3QlJxGphRYsu7Ipqj62bO0+4OZJtELAVafnKagHNCl7GzjaIdm230Cj4NSo2XkG5NxvbVp44Dg3jtbXJ7E4t5KOBUQyM9kWn07Fq1Sqqq6sZPXp0vQw8wBfb0tAbTXyg/BhCe6NzG0jp6vPI/B1wHRV+08CnboGza6HbmzUMvMmk42zKNAwGJc1b/FSngTdWVFC8aBFFLfz52+0qczrOabCBN5kEjmy4jKO7Nc26+d17Awv/ahzdbeg0NJSc82WcO5hXr208Zs4CQaDw69q1YAG6hnnQt5k3C/Zf4mrpLb5zuS30+xqKztP8Ujxvt3ubs0olsZUCV5oGQWEKnFpSYyx5gANOTwZTfa4EXWIxI0eOJLSskHFXzuJgLWWnk8CoXecwmW7G0IvEIlxGhGPX3htlXA5l69MRjAJeVjJ+jgpmW6swGttY8c7FHD5Iz/3nF60ePBIhlP3cnVCG+bE0p5ipadk4SKx4wm0R7ZzOUF00n7KE4Tg7t8PPbwyNG/chLMz8RDcYDJSWlqJSqdDpdBToDJwWJJzQweEqHTpBoIW9Dc+7O7GhsIRdJZX0lp5khHYebjaeNIr8Di+vAQg6E8XLU9FeLMOhZwC2PQN4bU0yW8/m837/CJ7vFIzRaGTt2rXk5+czcuRIfHzql85/5FIxm5LymO52kiBDGYauX1G8PBWxgxz35yMRX9d9qcyH2BngEw1dZ9YYI/3SF1RUnCIq6nsc7JveYS83KV74E8bKSr5uW8XQsKE8HvT4P/+DXOPiiQJKclQ88WIUEtkjMZ+w8JCJ6urL5UQFh9dfIjDSFUf3ut02cn8/3Ca+RPEPC6g6Mhy7Tp1q9flgQCRxF4r45O9Ufhl7y2Q3vC+E94O4Lxk+9QSpYUNZn74eL9cMZvhFYbXvM3Nxe/ubMsP2XfzQXi6n/O8MPINiGDJkCH/++Scfujgx3zaQA3IDj8WdY0OXCNzkZvMqEotwHhyK2F6Ocm82pio9rqOaIraS0MrJjo0xoRwsU+Fr/XBcmY+Eu+Y6JkHgYJmKTYoydhRVUGYwL6oGyDR4G9NxNWbjIq7GyS4IB7vG6KQ+KAU7rmj0nFNVo9CZU6F9rGT0dXegu52SzYWFbKpwxoVSJggL6WxvwN//Oby9ByMWy9AXqSlZmYahSI3L4DDkrT2ZtSaZLcl5N+pECoLAli1bOH36NAMGDKBNmzu+VdVCozfSb95BjOoydhpfQt7vOxQHIzCp9XhMjkbmcU2Nz2SClU9D9nFznK/7zdfS/PyNpKa9TmDAi4SF1S7zdyvay5fJGDSIIy2s2DTcl78G/IWtrGGFPAx6I398eAxbRznD3mpz823DgoV7oCzVsOrj43gGOjDo1Zh7fndMWi0ZAwYikslotGkjojvkmvwUd5mvdpznt3FteazpTaNNWZbZbRPWG/2wpby0+yWSFYm85SwwMikfUYsR5gi1WzCqdBTOO43YWoLnKzHEHTpAfHw8T/brzxyFjNM2ArZiMd9GBjLY07mG+0Z1JI/y2MvIvGxxey4SaT3WHurDI++TvxN6k8DpyipOVqpJqKgis1pLVnU1alPNL4wdKjxFFYRIigmVltBCdB53YyZbdc1YLwxDixV9pYeY4qmisU9/HB1v+gqrU4opXXsRkUSE6+imEOzI1D8S2XtewZtPhjOlRygA+/fv58CBA3Tr1o2ePXvW+xy+232ReXvTWWH1NV0iAigqex1drgqPCc2xCr7F5XJkAex6DwZ8D23G3/i4UplCQsIIHB1bEtPyd8Tiu7+4CYLA1QkvUZp4nBmTJCwavooIt4h6H+vtJO7K4uiGywx6LQb/8NpiUhYs1EXq4Tz2rzhPlxFhRPe891qOcv9+ciZPwfON13F78cVa7TqDiSfnxWM0Cex8tRvWslsWYeO/gX2fwJg1lAW2Y/TfI1Bq8lmEFc0vXYIXd9+oz3AdzaVyipecxTbGE6ehoaxatYqMjAxGj3mGjxMr2CMzIDjL6eXmyJxQXxrb3owq01wso2TVeQBcR4Zj09S1gVfpJv9KI38nBEFAaxLQm4yUqc6D+gIa9Xm0mgIMRhVVBh17DO1Zq21DicmOTnbVzG7kTgu30BpPY5PGQHlsBuqEQmQBDrg905QqKwkTlp/kVFYZHw9qxnMdggA4ceIE27Zto2XLlgwaNKjeq+eXi1T0/T6evlZn+d52CaVuy6i+aMR1VFNso28p9pF/Bn59HMKegJErb0gIa7UKTp56GhDRru0m5HL3O+/oGsq9e8mZOo3feomJfPkNxjUb94+u7a1oVHpWfHAUn1AnBkyNbvA4Fv69CILA1oVnyEkrY/i7bXDzvbck79WXJ1N14gSNt29D5lV7HenwpWKe+fU4r/VqwoxetyzCGnTwc7drUt3HuKQuZMzWkbiJqlmTW4WDYwC8FFdLFqRidxbKvdk4D2qMNMaVJUuWoFQqeX7ceD7Zl8uWikpE4c4IYnjGx42Zwd54WZldMoaSakpWpKIvUGPX0QfnfiGIZA2X3H7kF16NlTrKNqZjVOrq7CcSibCWiHGQyQh0aU6g3zCahL2PU+i37HL8gpc177KoujtNHD1ZE92Y9W07EO0edsMwC4JAdWoJhd8lok4sxKFHAJ6TWlCIwMifj5J0tZwfRsfcMPAJCQls27aN8PDwemnSXEcQBN7fmIK1SM/7xgWo3N6h+oIR5wGNahp4ndocTWPjCgPn3zDwRqOWM2cno9dXEN1i8T0NvEmrJeezT8hxF1HRvwNjo8bW2f9enNpxBb3GQMfBFvkCCw1DJBLR87kI5DYSdi9JxXhLlba74fXeu2AwoPjq6zu2dw51Z0ALHxbGXSK75JZFWKkcnpoPlbmwZw6hLqF80/07cg0SXvWxxVRwFo7+UGs8x8cDsW7qSnlsBqICLWPGjEEsFrPmr9V8ObAJz3q6Io7Lp4lWxMq8EtoeTeW189mkqaqRutngOTUG+y5+VB3Np3D+abRXKhp8verikTDy2isVVJ0qpGDuKSrjriLU4wuRq9Hxa04RQ09fov2xNOZlFdLS0ZZNMaFsjAmjm6tDzQLWBVUUL02h5PdURFZiPKe0xOnJYE5eLeepHw6RW1bN0nFtGdDCHMaVnJxMbGwsoaGhDB8+HMkdUq/vxobEXI5mlPAWy3H0fpKKC6E49AzAvvNtESq73jeX8nt6EdiZBb8EQeD8+XeorEwiKupbHBzu7XIp+PVnyCtkfX9nPuvxVYPj4cEcBnc2LoemnXxw83swBREs/DuxdZTT87kISnJVHNt8+Z795QEBuE2YQOW2bVQdu3P26Pv9I5GIRcyJPVczEzagHbSfBCd/hexjdAvoxoyWL3PCJOVbfx+E/V9AcXqNsURiEa4jw5G6WFHyRxqOEltGjx5NZWUla9f8xZwB4bzcIZiM/Tl0zzcwwsuFTYVlPHbyAk+cusAvBcVUPO6H+4vNEPQmtBkPx8g/Eu6a81XVrMpQ4H2xEu/LStxkEjxaeuHcyguNTIzSaCRHoyOzWkeqqpqTFVVka8yz/jBbK57ydGa0jxv+1rUXbLRZlSgP5KBJLUFkI8WxVyD2HXwQScSsOpHNh5tTCHCx5ZexbQj1NBu1lJQU1q9fT3BwMGPGjPlH9RzLqnQ8/u1+gnWXWOOwmMKyudi2C8b56ZouI1I2wLrx0OkVeOLTGx9fubKIyxlzadRoJiHBtaWFb0eXm8v5vn1IDDER9fNy2nq3vec2dbFryTkyk4p45uOO2Ls8+ILFFv59HFh1gZQDuTw1oyUBEXX7r00ajXkRViolZPMmxHcosbc4PoPPtqXx0zOt6Nv8lig3rcqcSCizhZcPIkjkvLVvAttzTvBRWSVDHcJh/Ha4TdpDX1CFYmESMh97PF5qTuqFNNatW0ejRo0YNWoUK47n8OnWVJp4OTB3TAxHtNWsLyzjjLIagABrOR0d7HjK04lenjXLiNaXR94nH6soZ1paFlrTvc/FSy6ljZMdbR3t6OXuSKhtzTR7QRAwlGioTimmOkmBvkCNyEaKfUcf7Dv7IbGTUanR8+GmFDYl5dGtiQc/jI7BycZsyJOTk9m0aRMBAQE8++yz91SVvJ031yWzISGbWPl7uOqmI43shOszETUjDIovwS89wDMCxm+7oZpXVLSbM2cn4+U1gKjI7+7pHhIEgRNjByNPusjZ715kbK/X/9Gx3k5RtpI1n5+kdd8gOgyyuGosPBj0OiNrPz+JrtrAqA/aY21f96RJdfgwV1+cgNvLk/B89dVa7QajiUE/Hkah1LJnZvcb9y5gVm79Y6g516Tne+iNesZt6c25ymIWFhTRqfsc6PByrTGv13u16+CDy+BQEhMT2bJlCxEREQwbNowjGaVM/SMRiVjEvFExdGviwWW1hrhSJYfKVByvUPGSvwevBTeskM4jb+QBjIJAvlbPlWotpXojlcVqlJnlSLNV2FQb8dIKBDvb4u5jj8TZComT1TXDKWCqNmIs12IoVqPNUmK65tuXBzpgG+OJbSsvxFbmp/eJzFJmrU0ir1zDjMfDmPpY6I16ksePH2f79u2EhIQwatSof1yo93p23suSLUwWydAFTsR9fDNEt8aY66vh115QmWcOl3TyB0CpOk9CwnDsbENp1WoVEsm9NWJS1y1B9P43xD/diJc+j70vN40gCGz+PomSXBXPfdIRuc0jkYJh4X+Eomwl6746RUgLd/pMbHbPCUze2+9Q8fffhKxfh3V4eK32szkVDPrxECPbBvLFkOY1GzdMhJT1MCkevKIory5g9KY+lGiNLFeUEfHSYXAJrjVm+bZMVPE5uAwNw66tN0ePHmXnzp1ER0czaNAgskqrmbTiFBcLVYzvHMxbTza9EeUjCAJ6QUD+EDJeHxkjfzcEownt5Qq0mRVor1Sgz1cjaAy1O4pFSFyssApwQB7shHUTF6S3iGmVq3V8uf08q09eJcDVhu9HtqR1kPnVURAEDhw4QFxcHE2bNmXo0KH/yEUDoNIa6PPtXqyUV9ko/RON+1d4TIpBbH2bsdw8DU6vgGfWQZhZYEyrLeTUqWEIgpG2bTdiZXXvDNWyohwu9HuSSnsxbWL34mrvcc9t6iLzTDHbFp6h68gwWjxmkS+w8OBJ3JnF0Y2X6Tm2KRGd6pblNZSVkdF/ADI/P4JXr7qjHPFnW1NZfDCTvyZ2oH2jW4rYVJXAj+3A0Rcm7AWpnPSCPby4ewZSvcBKSTC+Y7feCHS4jmAUKF6WgvZyBe4vNsO6sTMHDhxg//79REZGMmTIEAyCiC+3n2fZkSuEetrz6eBmdGh0/wV06jLyCILQ4H/AXOA8cAbYCDjf0vYOcAm4APSpz3itW7cWGorRaKp/X41e0CmqBF2BStAVVgn6co1gusv21TqD8OvBDCHm411Co3e2Cp9vTRWqtPob7Xq9XtiwYYMwe/ZsYcOGDYLBYGjQ8b+//rQQ/FascPz9HkLhN7sEg0pXu9PpPwRhtqMg7Jlzy/5VwvHjA4X9cc2Eisqz9dqX3qgXVr/4mJDStKmQFL++Qcd7Kwa9UVj54VHhj9lHBYPBeN/jWbBwJ4xGk7Dx2wRh0fQ4oTRfdc/+5X//LaSGNxVKli+/Y3uVVi90/nKv8Ng3+4Vq3W33beoW872295MbH8WlfCK0WxYpDP6liVBxdMGdj7FaL+R/e0rI+eiIoFNUCYIgCIcPHxZmz54trFy5UtDpzPd13AWF0PnLvULQW38LM1YlCrll6vpcgrsCnBLuYlfvN7pmN9BMEIQWwMVrhh2RSBQJjAKigCeBhaK7SR4+ABKyynjs2ziWH7mCWneHWfptiK2kyDxskXnZIfO0RXrDdXMTpUbPb4czefzbA3zydyqRPo7ETuvCO/0isL2WrqxSqVi+fDnJycn06NGDwYMH/6MomuscyyhhxYlcXpBsJ9RuBG4TuyOxu+1NoOAsbJ0FQV2ghzlz1VyE+xVUVedpFjUfR4dm9drf73++Q4tD+ZQ91YnorkP+8fHeTsqBXMoL1XQaGopE8kgEbFn4H0QsFtFrfBRSmZidi1Mw6Ix19nfs1w/77t1RfD8P3dWrtdpt5VI+f7o5GUVVLNx/qWZjxECIHgMHv4WrJwHoFvku00OacEUmY0byfDSFKbWP0VqK+7goRBIRxcvOYazS06lTJwYMGEB6ejorVqygqqqK7k082DOzO9N7hrLtbAE9vonj14MZDb84dXBfd6QgCLsEQbhuVY8B/td+HgSsFgRBKwhCJuYZfd3Vqe8TNzs5s7eco8Pne5m9OYUTmaWY6rEQeysGo4lD6cW8s+EsHT7fy5zYVLydrPljQntWTmhPpK/jjb45OTksXryY/Px8hg8fTo8ePRokE1qtM/LmysMEiQp42dqE0+QXkDjctlhbVQKrxoC1EwxbAhLpteIfsykpOUB4kzm4uz9Wr/1tPbeeRgv+RuVhT6cP5//j470djUrPya2ZBES6EtTMUrfVwsPF3sWKXuMjKcmt4uBfF+vsKxKJ8P5oNiKJhLy330Ew1n4odGviwdMxfvx04DLnCyprNvb9Ehz9YOMk0KkRicQM67CYF9ykJFjJmLV1LHq9utaYUldr3MZGYqzQUrIiFUFvok2bNgwbNozc3FwWL15MYWEh1jIJM58IZ9/r3Rnc0hd/l4ZJiNyLB+aTF4lEscBfgiCsFIlEC4BjgiCsvNa2BNguCMK6usa4X598QlYZSw9nsie1EK3BhLu9nDZBrrQKcqaRuz2+zja42skRicw6N8VKHQWVGi4UVJJ0tZxTWWWUq/XYyCT0bebN852CiQ6oGdJkMpk4cuQI+/btw8HBgZEjR+J7B4nT+vLRsjiWna9ipXw5HaYvQep+mzKlUQ8rnoarJ+CF7TeKgFwPlQwKmkxo4/pFxZwrOcf+V0bR87QB/9+X4dj2/mtKxv91kZS4HEZ+0K5eWYkWLDwIjm26TMKOLHqNjyS8fd0RKRVbtpD35lt4vj4LtwkTarWXVuno/Z8DeDtZs2lqZ2S3vo1mxsPygdBuIvQzK12Wlh5m+faxLDXY0tc2kC+GbrljxbTrETfWkW64PROBSCIiJyeH1atXo9PpGDhwIM2bsfwAUwAAIABJREFUN6+1XUOoyyd/zxAIkUi0B7jTVXxPEITN1/q8BxiAPxpwcBOBiQCBgQ2rPHSd1kEutA5yQaU1sDetkP3nFSRml7PjXME9t23sYUfvCC8ej/CiexMPbOS1/2jFxcXExsaSlZVFZGQkAwcOxMam4QJDhw5eZPl5Jc9KDtLhhc9rG3iAne/ClYPw9M83DHxBwRYuZ8zFy2sgjRvNrL3NHSiuLubnn19mcqIB27GjH4iBL82vIuVALlFd/SwG3sJ/lXYDQ8i7VE7cnxfwDHLAxdvurn0dBw5EuXcfinnzsevSBeumNZVYXe3kfD6kOZNWJLBg3yVe631LKc6QbtBhChxbaFatbNwTV9fOPNVhCqJ9P7CEbOz2zuDDXj/UepO3beGBSaWnfMtlytZfxGVYE/z9/Zk4cSJr165l/fr1pKen069fP6ytH17FtPueyYtEonHAJOBxQRDU1z57B0AQhC+u/b4T+EgQhKN1jdXQmbwgCFRVVWFvf2dDU6LSkl2qJr9CQ7laj4CACBHu9nK8nawJcrOrGSt7GzqdjqNHjxIfH49UKqVPnz7ExMQ0uIoLQHFaEQOX78VKVMWW4YE4trpDKb6E5RA7HTpOgz6fmbcr3s+Zsy/j5NSamJa/IRbfO0xTrVczdePzTJh7Dhd3f5puir1jksg/5e8FyeRfruDZjztgc7uLyYKFh4yqTMuaz09g4yBn2NttkN1hYnYdQ1kZGQOfQurqSvC6tYjvkL8y868kNifnsWlKZ5r73yIAqK+Gn7uDpgImHwY7d0wmA2dPPceek0dZZu/A+KZjeK3d23e0CZV7s6ncnYV9Z1+cBpgruBmNRuLj44mPj8fBwYF+/frRtGndMuB18dBCKEUi0ZPAf4DugiAU3fJ5FPAnZj+8L7AXCBMEoc6VkoYa+QsXLrB27VratWtH586dsbO7+1P9n2A0GklKSiIuLg6lUklkZCR9+/atd7GPu6G5UMqs5evZYfJibbtcWg2ZXLtT1lHza2JIVxizFiRSystPcTrpeexsG9Oq1R9Ipfc+DqPJyKv7Z9B23j7aZIgJ+esvbKKi7uv4AbLPlRD7QzKdhoYS0/v+3sAsWGgo2anm72HTjj70fK5pnRMvZVwcOS9PxvWFF/B6841a7RVqPU98fwBHaxmxr3SpqVRZcBYWPw7BXczhy2IxOl0pabseJzazjL8cHZjaciovR9dOlBIEgYq/M1AdzsOxVyCOvYJutF29epXY2FgUCgWdO3emd++7112ui4cpULYAcAB2i0SiJJFItAhAEIRzwBogFdgBTL2Xgb8fPDw8iIyM5MiRI8ybN4+dO3dSXFzc4PFUKhXx8fF8//33xMbG4uTkxLhx4xgxYsR9G/iqkwWsX7aRrSZfpnml0erp2l8Kii/B6tHgHAjDloJEilKZRvKZCVhb+9Cy5dJ6GXhBEPj65NfYbNxP24sC3m+88UAMvNFo4tC6Szh62NCih/+9N7Bg4SERGOlGm77BnD+Sz7n4uisrOfTogfOokZQuXYrqwIFa7U62Mr4a2oJ0hYrvdt+2qOvdHJ78Ai7vhSPzAJDLXQnp9jsjnIw8pVTxY9KP/JT8U61xrxf2tm3tReWebCp2XbmhmxMQEMCkSZPo3bs3ERENl/aui0cqGUqhUHDgwAHS0tIwmUwEBAQQFhZG48aN8fb2vmt4o9FoRKFQkJ2dzfnz57lyxfxHaNSoER06dCAsLOy+XDNgNrjKvdlc2XOEZ5HQSF7GurfHILW9zVirimBJL7OOxoTd4NoItfoKCYkjEYmktGm9Fmvr+i30/n7udzZs+ZrPVgo4du+B/48L7vs8AE7vzubI+kv0m9KCkBZ1K1xasPCwMZkEtv10hqvnShn0Wgy+YXfXfzFpNFwZNRpDQQEhGzcgu0OFtnc2nGX1yWz+eLE9nUJv+X4LglkvKnWLWU4ksAMAeXlrEa+fwg9iB7bY2zE5ejKToyfXutcEk0DZhnTUpwqx7+6P05PBD+R+hH9hxqtKpSIpKYmUlBQKCsyLrmKxGBcXF5ycnJDJZIjFYjQaDSqVirKyMgwGcySou7s7ERERNG/eHE9Pz7p2U28Eg4myTZeoOnWR1yWXOWsMYNuk5gSHhNXsqFPD8gFQmArj/gb/NlRX55J4ejRGYzWtW63Gzq5+mjCbL23m873vMX+FHFeZM402bkDi3DDxo1upKtfyx+xj+DZxtmjFW/ifQVttYN2Xp9Cq9Qx/py0OrndfyNRmZnJl6DCswsMJ+n05otuy09U6AwN/OIRSY2D7jK642d+yfqWpNGvPG3Xw8iGwNWe9p599G5+tP/OlsxebbWRMbDGRaS2n3dHQl2+5TNWxfOw7XfPRP4Cqaf86I38rKpWKzMxMFAoFxcXFKJVKDAYDBoMBGxsb7OzscHFxwdfXFz8/P1xd779Ky60YlTpK/khDd6WYVbb7+FHdibmPOzK8d9fbOhpgzVi4sA1G/QFN+6PR5JGQOAaDoYKYlr/j6Fi/cKvdWbt5Y/8sPt9sR0i6iqAVv2MbE/NAzmfXknNknC5i9Ox2OHk8nLheCxYaQllBFWu/PIWzpy1DXm+FtI6F2Mpt28idOeuu/vnUvEoGLzxMl1B3ljzfpqaxzkuCJb0hqLPZPy+RYjIZuBg/lNADcXwcGMZGsZYXm73IjFYzaht6QaBiayaqQ7nYRLnhMjL8Zq3mBnJfIZT/17G3t39gsaj/FG12JSUr0xCq9aT77mBhXjeGNzbWNvAmE2x5BS5shb5zbzPw5f/IwB/OPcyb8W8y46gLIWkKvOfMeWAGPvdCGeknC2nTP9hi4C38z+HibUfvF6LYtvAM+/84T69xkXd1hzj264f61ClKly7FOqIpTgMH1miP9HXkvX4RzN5yjqWHr/Bil5Cbjb4tof9/YMs02DMb+nyGWCwltMtKskq689HZdMThbViSsoQKXQXvt3+/Rhy9SCTCeUAjJM5WVGzNwLD4LO5jI2snQT4gHokcdKNKR/mWy5i095Y0+G8gmASUB3Mo+vkMIokIonYwK68VTex1fPx8/9s6C7D9TUj+Ex57D9pPRKPJJzHxGfT6smsGvkW99ptQmMCr+19lWLobHQ4ocHnmGVxGjngg52Q0moj/6yIObta07hN07w0sWPj/QEgLd9oNDOHi8UIStmfV2dfrnXewbduW/Pfep/rMmVrtYzsG0SvCiy+3p5GSe1tBj1bPmROkji6A5L8AkEod8O23hdxAF2ZfOMWLrtGsu7iOWQdmoTVqa43v0MUPt2cjMBRUofgxCV2uquEnXgePhJHXXq5AdTQPxQ8P70LVF0O5luIlZ6nYmol1uCtureJ4LdkZjdiOH196onaS1d6P4eRic/GPbm+g0eSReHoMOn0pMS2X19vAH8s/xuQ9k+lY5MyQjQpsO3bA6523H9h5nd2fQ2leFV2Gh9X5GmzBwv9v2vQLpkl7L45vyeDC8bsnQopkMvzmz0Pq6UnO1GnoCwtrtotEzB3WAnd7Kyb/kUC5+rbyon0+N2tJbXkFchMAsLHxx/Hp9ZS4WjEjMZY3g59ib/ZeJu2eRKXuNtkEwCbKHY9JLcAkUJ3S8IjAungkjLxttAceLzVH0BlRLExCGZ+DYPzvrjUIJgHViXwKv09Ed1WJy9Aw3Joe4au4LE4IEXw+LIZQr9siaQ7MhUP/gdbjoPcnVKkzOJUw/NoMfhlOTi3rte/4nHim7plKG6U7U/4oR+7vj/933yGSPhhvnKpMy4m/Mwlq5kZItCWaxsL/NiKRiJ7PRuDXxJl9K9LIvVh2175SFxf8F/6IqaqKnClTMaqqarS72MlZ+EwrCiu0vLLqNMZb9bAkMhixHOy94M+RUHYFACeX1oiG/UaVrZQx8T/xZdRLJBclM37HeAqqaj905P4OeM5ohWPvh/OG/EgYeQCrRs54zmiFdbgrFdsyUSz8783qdbkqihYlU77hEjIfW7ymt8JOspv1sZtZYuzHuI6BDG51i8a6IMC+z2D/p9BiJPT/D5XKFBISRyEIBlrFrMLJqX5+9L1Ze5mxfwZtDf7MWFGBxN6ewCW/PpBImuvEr76AYBToOvL+Q0ktWPhvIJGJeXJSc5zcbdi+6CxlBVV37WvdpAm+//kWzfnz5E5/BUFXc8YeE+jCx4OiOJhezDe7LtTc2M4dnl1n1phaOQzUpQC4+fZHPeQ/6KQCfXZ9woLWb5GrymX01tGcKartGpLYyR5IlM2deGSMPJgvlNtzEbiOaYqxQotiwWlK11zAUKZ5KPvTF6kp+TMNxQ+nMZRU4zK8CR4TWyDN/JPETd/zruElOjdy4f0BtyQgCQLs/gDiv4aYZ2HwT5RVnCLx9LNIJDa0brW6XsW3ATamb2TWgVm0k4Yyc6UKkdFE4JJfkd2HYNrtXD6tIDO5mLYDQiyLrRb+T2FtJ2PAtGjEEhGx85NRlt7dDjj06IHPp59SdeQoeW+/jWAy1Wgf1S6Q0e0C+SnuMtvP5tfc2CMcRq+C8mxYNcosgwB4Nn6eikGzMQl62v49k9+7fI2VxIrxO8azNWPrAz/fu/HIhlCa1Hoq466iOpIHAtjGeGLfyRf5fQppCYKANqMC1eE8NGkliGRi7Lv44dDVH7GNFI4tomD7lww0zsXGwY3N07rgYndt1dxkgh1vwYlfoM2L0O8b8hVbSEt7BxubQGJilmNtde8aj4IgsDB5IYuSF9HbuhWTl+RjKi4hcPkybB5gJJFWrefPOcexdZQz/O02iC1a8Rb+D1KUrWTTfxKxdbJiyOut6tRZKlmyBMXcb3AZMxqvDz6o8eaqNRgZ9csxLhQoWT+5ExE+jjU3PrcR1o6DJk/CiBUgNe8nL3E2nlvnYbSxR/XsVl5P+p5Thad4sdmLTIuZhlR8/27Vf3WcvKFci3J/NupEBYLehDzQAZtm7thEuSF1q5+CpGAU0OepqD5XjPpsMcYSDWJbKXbtfLDv7GsOfRIEiPuCyrj5jBB9Q47gzoYpnWly3Q9v0MHmqXB2DXSchtD7YzKuzOPKlR9xdm5Pi+YLkcnu7WLRG/V8dPQjtlzewjNOvRn6QzKmikoCFv/ywEIlrxP3x3lSD+Ux7O02eAY53nsDCxb+R8lLLyd2fhLO3rYMfi0GK9u7CxIWzp1L6ZKlZkP//vuIbqm7WlChYfCPhwHYOLUTPk632ZCTv5qL+zQdAMOXmf32QP6p9/HYvgCjlS28sIev0tex9uJa2ni14etuX+Nhe3/lN/8VRl5v1COT3P0PZ1LrqTpViPq0An2+2T8ndpQj93dA5mmL2EF2oxqTYBQwVekxlGkwKNTorioRdCYQg1VjZ2yjPbCN9kB0XcDIoIUt09Emr2Oc1X84qfJg2fh2dAm7tkipqYC/njVrUz/2PsbO00g9/yYKxTZ8fIbTNPxjxOJ7x8gWVhUy68AskouSedN5BB2+3YNJqyXw11+xaV6/qlD1JS+9nI3fJhLdK4Auw8LuvYEFC//jZJ0rYdvCM3gFOzJwektkVneOEhMEAcXcbyhduhTn4cPxnvNRDUOfll/J8EVH8XexYc3LHXG0vs3uHPsJdrwNUUNgyGKQmGfqhac/w3XrXASZNeLntrKtOpdPj32KjdSGL7t+SUffjg0+t0feyJ9WnObN+Df5sMOHdPXves/+hpJqNOdL0eWo0OUoMZRo4A5VpETWUqRu1sgDHbAKdsQq1KV2WT51KawZiynzEDNcFxKb78R3I6N5OuaacFf5VfhzBBRfhEE/Ut2kE2fPTkOpSiW08RsEBk6s12JmQmECs+JmoTaomWv9DN6fr0Ds4EDAzz9jHd7kntv/E/Q6I399egKTUWD0h+3vejNYsPB/jUsJCnb9moJPqDP9p7ZAbn1nV4kgCBR9P4+Sn3/GafBgfD75uIb8QfzFIl5YdpIOjdxYOq4tcultrszD881rb5GDYcgvIDVLIyjOfodT7MdITCJMw37lqnc0s+JmkVGRwaw2s3g+6vkGndcjn/FqLbHGXmbPlL1TGBI2hDfavIG9/O6+d6mbDfad/W78LpgETNUGTFV6EIFILEJsJ0N8ly/ADa6egLXjEVQKPgpcRmy6jLeebHrTwGfGm310Rj08s44iRyOpJ58CRES3+AV39573PDejyciyc8tYcHoBfva+/Fz+FKbPFyMLCyPg50XIvLzqcYX+GUc3XKZCUc2g12IsBt7CI0Voa09Mpkj2/JZG7PxkBrwSjZVN7ftcJBLh8eoMRFZyiuf/gEGhwG/e90iuqdB2a+LB50Oa8+a6M7z2VxLzRrVEeuuaVefpIBLBrvehuswsVWLlgGfz1yh18Ee+djK2f43H77HX+bPfH3xx8kvCnB/OG/MjMZMH0Bl1LExayG/nfsPT1pM5HefQya/TAz7Ca5iMcOQH2PcJgoMfH/vM57ekKl7qGsK7/SIQgbmSzK4PwC0U4/DfyKjcTPbVJTg4RNG82Y/Y2ATcay/kqfJ499C7JBQm0NfrMabsEFG9Yxf2PXvi+/VXSO5SJOV+uJpWypZ5SUT3DKDLCIubxsKjyeVEBbt+PYd7gD0Dp7fE+vY39FsoX7+e/NkfYRUSTMCiRcj8bk4Qfz2Ywadb03g6xo9vhkcjuT0MMmmVeS3Ou7lZ58be7HtXFp9Cv3owrsVKNI07YD18LVg3fN3rkXfXCJV56He/ibzPfzhTXcD7h98nsyKT3kG9eb3N6/jaP7iQQhTnYfMUyE1AaDqQz21msfhoHuM7B/PhgEhE1WXmak5psdB0AJW9ppN6eTZVVen4+z1HaOg7SCR1V2UyCSY2pG/g21PfIiAwx/k5Quf/jS47G48ZM3B7aUINH+GDQqvWs/qTE8isJIx4t60ls9XCI03mmWJ2/HIWVx87BkyLxs7p7vdl1dGj5EyfgUgux2/u19h1ujmB/HH/JebuvMCotgF8/nRzxLcb+os7Yc3zYOcBo1aCj1m9VaPJo3jTQHzPX8Jg74R02ErEwd0adC4Ps2jI/wRlZ+YhTYnFOC+KqMsHWdt/NdNjpnMw5yCDNg3ip+Sf0BjuM1ZeXQo734NFXaA0E9OQJXxi9w6Lj+bxfMcgs4HPiIOfOsGFHZh6fcjlNjGcOjsWg76SltG/ER7+0T0N/IXSC4zdPpY5R+cQ5dCEP3IGEPjGT+YF1qVLcZ808aEYeICDa9KpqtDx+LhIi4G38MgT0sKd/pNbUF6oZv3XCXUmTNl17Ejw6lVIXJzJfnECinnzEK7Jk099LJRXeoay+uRV3lh3Br2xZow9TfrA+K0gGGFJHzizFgBra198Rxwlp/dojHolJQmfP5TzfCRm8kZjNdmn38Pp0O+4lusxugQgeexD8oM78k3i9+zK2oWHjQcTmk9gWJNhyCX/QO2tqgQSlsKRBeYomZhn0Hb/kNe35xGbnMcLnUP4oJcvon2fwMlfEdybUN5zAqnlf6DR5OLtPZgmYR8ikznVuZsidRE/n/mZdRfX4Sh3ZLbQn6Df9qLPvorToEF4vf/eDX/gw+BSgoKdi1No2z+YdgMbPbT9WLDwv4Yiq5K/FyRjMgn0n9wCn9A6io6o1RR89hkV6zdg07o1Pp9+glVICIIgMH/vJb7bc5HHm3ry4zOtapYPBFApzDP67CPmRMgnvwQr8z1dlLMJB9dWWNs2rJTmI++uuU5Z6XEUcZPwu5SNvdqIyTkAccxYTvlF8kP6WhIViXjbeTM+ajyDQwdjK7tLBqfRAFmHIWUdnFkDBg2EPQGPz0bpHM7LKxM4fKmEt58MZ5JbEqKd70JVEdqWg0n1raZUeRI7uyaEN/kIF5f2dR5zhbaC5eeWszJtJXqjnhese9J/VznaQ0eQN2qE13vvYt+5c4OvSX2oKKpmzWcncPa2Y8gbrZBYkp4s/MuoKKrm7wXJKEs0PD4ugrA2dQc0VMTGUvDxJwhaLe5TJuP2wguI5HJWHMviw80ptAly4dexbXG6PR7fqIe4L82aVc5BMOhHCL7/+/vfYeQFAUQijEYN2Vk/o0qYh3+uEpdysw6F4BnBUZ+mLDQUkKzOxUFmx7CQ/gwPGUAAMlAVmCsy5SbAlUOgLgaZLTQbCh2ngmcEGUUqJq1IIKO4iiU99PTIWQTZRzB4NeFShC+5phRkMjeCgyfj7/csYvHdF3OuVFxhZdpKtlzeQrWhmmfFHXn6oAFj/FHE9va4T5mC67PPILpDVfkHidFgYsPcBMoV1Yx8ry2O7vVLELNg4VFDo9Kz7acz5F+uIOaJQDoMalRnlrdeoaDwiy9Qbt+BPLQxnjNnYf9YD7aezee1v5LwdbZh8dg2NxMibyXrKGycBOVZED0aen9yY1G2ITz6Rr7gLGyeBt1eh/D+IBaj0eSReWUBZZlr8FRo8FY7YldagkivIdlKzgpHB/bY2WIUiYjRaBioqqJ3VTXOjgEQ0B4iBkBob5CbZ/t7Ugt57a/TtBKn853PblzzDmC0dSI7xIMM1zJkcjeCAl/C3/9ZJJI7vyEodUr2ZO1ha8ZWjhccx8YkZVJZNJ1OKCEpFbGjI65jx+I69jkkjv+dDNNDa9NJ3nuVJyc1o3HMgyl3aMHC/1WMehMH16ZzLj4X/6YuPDEhChv7uidayv37KfzyS/RZ2djExODx6qukeTbm5T9OU60z8M3waPo2r11LFp0aDn4Lh+eBzAae+MSsSNsAHn0jnxkPsTOgNAM8I80z78jBYGVPdXUu2VcXk5+/EZNeibPghadVFM4iP5QiO7YqLxNbmU6GpggxYlp4tKCbfzfa+bQjwjUCg1HMN7EJlCVs5GWb3YQbL2GQybjib8VVXytsnSLx93sWb++nahl3QRDIrMjkSN4RjuYf5VjeMfQGLd2LPRiU7YH/yWyE8gpkgYG4jByB84gRD9XvfjsZSUVsX3SW5j386TbqwSZUWbDwf5nUw3nEr7qIraOcJyZE4d2o7jU1Qa+nfMNGin/8EYNCgXWzZoiHjeI1hRuJeVWMahvABwMisbO6Q+5Ncbo53DrqaYge2aDjffSNPJj96Oc2mp+MRWkgtzcb+vAnoVEPjFIpCsU2ChVbKS09iiDoEIkk2Nk1wc42jFyjFYnlJZwszSK90qwyJ0GEn1ZKjLaCYIMOT5ERwU2K3scPP59e+Hg8gZVtKAaTgUp9JUXqIhRqBVmVWZwvPc/50vNU6ipxqxDoUehKpzx7/M+XIiqrQGRtjX2PHjgPG4Zdp44PLWLmbpTkqVj/VQIu3rY8/XorpLcvElmw8C+n8EolO39JQVWupU2/YNr0DbqnSJ9Jo6Fi40ZKV6xEl5GBxNWNi1EdWCgKQRMWwadDWtCp8V1qMlxzOTeEf4eRv44gwNXjkLgC0raAthLEMvCKBO8W4BmB0dYFpaCgqjqTKnUG+qocRFUlyLV67KuMVKshQxBzxkpOopUtmdYyKupz7QUBRzU0LpHRttyVJgoJ3ldVyAvMRQskrq7YdeiAwxO9se/WDbHt/x/pXk2VnrVfnkKvNTLinTbYu9y9sr0FC/9mtNUG4ldf4OLxQrxCHOn9QmS9JLcFQaDqyBHK/1qDKi4OQaej1M6F4+5NELdtz/CJg/ELvIMLp4E88kbeUFqKLjMTmY8PUk/PmxWRjHqzwb+0B3IToeCMOcW4DspkXiRq/UgWQvFq2Ych/Z/CxlpOlb6K7LJMynMz0Sjy0RcWIioux7pCjVVhOVZ5pchyixBVVd8YS+bri3VUFDatW2HXsSNWYWH/9Rn77ZiMJv7+8Qy5F8oYPLMVPo3rfg21YMECpJ8s5MCqCxgNJtoNbER0T/96S28bVSpUe/dSvmMXFUePIdOoAVC6eeMa0wKXls2Rh4ZiHRHRYJmSR97IF8X+TfEbb5h/EYuRenoi9fBA4uCA2NHR/L+dndn4m3SITBowVAMCJr2BwnINl4sNZJfpwAiNnWQ0sRch16gxKZUYlUpMSiWmqjskS4jFyHx8kAcFIQ8ORh4chLxRY6yjIpG6uNzfBXnACILAwTXpnN2fQ49nwonq6nfvjSxYsACAslRD/OqLXDlTjJu/PT2eCcc75J9NkgSDgazDpzi8bieac+cILcvB89rE02X8eLzferNBx/bIG/kdh9L4Zcl2IqVqWsq1BBgqcaxWYqWtRlApMVVWYqqqQjAaEYxGuJapBmBChF4swSiRIpHLsba1QmptbX4wODggcXRAbH/tfwdHpB4eSD09bjxIpG5uiCT/N/zZiTuzOLrxskU+2IKFBiIIAplJxcT/dZGqCi2RnX1pNzCkTkmEu3G1VM3yI1fYeewi9oW59GwfxhsTnmjQcT3yRj6vvJodKQXEpxdx9HIJWsPNtGIbmQQHayl6o4kqrRGd0QSCgBgBX0crOjXx5PFIbx4L96wtF/oIceFYPnuWpRHW1ove4yMfWj1JCxb+Deg0Bk7EZnJ2fw5imZiYXgG07B14V+niOscymNibVkigmy1Rvg1znz40Iy8SiT4BBgEmQAGMEwQhT2QWSJ8H9APU1z5PvNd4D2LhVWswcllRRbpCSVaJmspqPSqtAZlEjJ2VFDc7OaGe9oR62uPvYvOvKEydlWIuluAT5szAadFIZI/uw8yChf8m5Qo1xzZlcDlRgY2jnNZ9gojs6ovsv6z99DCNvKMgCJXXfp4ORAqC8LJIJOoHvILZyLcH5gmCUHd+Pw+n/N+/naxzJWz/6SwuPrY8PbMV8jtoZ1uwYOH+KMio4Nimy+ReLMfGQUbLXoE06+7XoJl9Q3hoRUOuG/hr2AHXnxiDgN8F8xPkmEgkchaJRD6CIOTXGsTCQyMrpYRti/5fe/ceU2d9x3H8/YXSA+VSOEhPKZcWLAXb2guus9t0q1vmbclwSZMZ/9Asi2YXF82yRI3J5v4wcUu2JUuWuZl52UXr5mZsnG6zWqtLZqvdCqW1UCxQoFxKgVKQAuV898fzox4pF7Vwnuecfl/JCc/5PU/TT78r5DSuAAAJrUlEQVTlfDnP7/nxnDrChZnU3LvZGrwxC2R5+VJu+X41J5oG2P9yC/95/j32/6OVKz5byJXbij7SssuFctGvehF5GLgdOA1c54aLgLaYw9rdmDX5OJm8V3b+iiy+es/sH4pgjJkfK1bnsuJ7m+huGaR213EO7m6n9rU2Vq7PZ8O2YkquCMf9etic0zUisgtYPs2uB1X1hZjjHgDSVfVHIvIi8Iiq/tvtexW4T1UvmIsRkbuAuwBKS0uvam1t/cT/GOM59GYHe55uoKA0e85PvTHGLJzhgVHq3+jg0JsdjJwZJyscovLq5VRtLSQ3Mn/v7uOyukZESoGXVHW9iPwGeF1Vn3H7GoBtc03X2Jz8xdGosu/FZt55qYXSdfnccOe6uM0JGmNmNjEe5diBkxx5q5O2w32oelM8lVuXc3l1wZw3QZvLgs3Ji0iFqh51T2uAI257J3C3iOzAu/B6eiHn4/u7hql9tY2yjQUUV+ZdkqtHRkfOseuJw7TU9XLF5wr5wm2Vdl94YwIiNS2Fii0RKrZEGB4YpWFvF0fe6mLP0w28saORkqo8rtxWzKoNM9zX5iJc7Nu8R0SkEm8JZSvwLTf+Et7Kmia8JZTfuMi/Z1Z9ncM07Ovm0JsnSAulUroun7KNl7Fyff4lMVXR2z7EPx+rZ/DkCNd+vYIrtxVfEktDjUlEmbkhqm9YyebrS+ltG6Jpfw9N+7vp6xxekCafFL8MBXBufIL2I/001/bSXNfLyOAYKSnCijW5lK7Lp3RdmHBhZlI1v2hUOfDKcfbuPEYoM40b71zHiopg3UrBGDM3VSU6oaR+wl/ITPrfeJ1Ko0p3yyDNtSdpru2lv8u7IVBWXoiStWFK1+ZTXJWX0O/ye9vPsOfpRrqOnaZ8cwHbbqskI3thP0XKGBNMl1yTn+pM31naDvdx/NAp2o70MzZyDhGIlOVQsjaf4so8IqtyEmIuf2RojLf/3kL96+2EMtO4Zvtq1ly9PKnOUIwxH88l3+RjRSeidLec4fihUxw/3EdP6yCod2FkeflSitbkUrQmeE3/7NA4B3Ydp253O+NjE6y/toira8oT+mzEGDM/rMnP4uzwOJ1NA3Q0DNBxtJ/e9qELmn6kLIdlK3Pi3lBVld62IQ7uaadxXzcT41FWX7WMLV8pI7wiM65ZjDHBtWBLKJNBemYaZRsLKNvofVL62eFxThwd4ESj1/T3vdh8/mYNuZElLFuVTWTVUpatyiZcmDnv69CjUaW37QzNdb28t7+H/q73WbQ4hcqty9mwrZj8oqx5/fuMMcntkm/yU6VnplG+qYDyTV7THx05R0/rID0tg3Q3D9L+bj+Ne7vPH58VDhEuzCSvMJO8yBKywulk5YbIyguxOGPRjHPlGlXOvj/O8MAY/Z3DnOoYordjiM6m04yNnAOBoopcNlxXTMWWCKElNi1jjPn4rMnPIZSxiJKqMCVVYcCbQhnqH+Vk6xn6Oofp6xymv2uYjsYBJsajH/qzkiKkhVJJC6WyaHEKqt41gYlzyujQONGofujY3MgSVlcXUFSVR3FlmCU5tlrGGHNxrMl/TCJCdjid7HA65ZsLzo9Ho8pQ/1mGB8YY6j/LUP8oo++PMz46cf4hIqQuElIWpZCRmcaSpYtZkhMiN5JBXiQzUBd6jTHJwZr8PElJEXLyM8jJzwDsw7GNMcFgbx2NMSaJWZM3xpgkZk3eGGOSmDV5Y4xJYtbkjTEmiVmTN8aYJGZN3hhjkpg1eWOMSWKBuguliJzE+xjBT+IyoHce4yyERMgIlnO+Wc75kwgZIf45V6pqwXQ7AtXkL4aIvDPTrTaDIhEyguWcb5Zz/iRCRghWTpuuMcaYJGZN3hhjklgyNfnf+h3gI0iEjGA555vlnD+JkBEClDNp5uSNMcZcKJneyRtjjJnCmrwxxiSxhG/yInKjiDSISJOI3O93nlgi0iIiB0XkgIi848bCIvKKiBx1X/N8yPW4iPSISH3M2LS5xPNLV986Ean2OedDItLhanpARG6O2feAy9kgIjfEKWOJiOwWkcMickhE7nHjgarnLDmDVs90EdknIrUu54/deJmI7HV5nhWRxW485J43uf2rfM75pIg0x9Rzkxv37XWEqibsA0gF3gPKgcVALbDW71wx+VqAy6aM/RS4323fD/zEh1yfB6qB+rlyATcDLwMCbAX2+pzzIeAH0xy71v3/h4Ay932RGoeMhUC1284GGl2WQNVzlpxBq6cAWW47Ddjr6vRn4FY3/ijwbbf9HeBRt30r8Gyc6jlTzieB7dMc79vrKNHfyX8aaFLVY6o6BuwAanzONJca4Cm3/RRwS7wDqOobQN+U4Zly1QC/V89bQK6IFPqYcyY1wA5VHVXVZqAJ7/tjQalqp6r+122fAd4FighYPWfJORO/6qmqOuSeprmHAl8EnnPjU+s5WefngC+JiPiYcya+vY4SvckXAW0xz9uZ/Rs33hT4l4jsF5G73FhEVTvddhcQ8SfaBWbKFcQa3+1OeR+Pme7yPaebKtiM964usPWckhMCVk8RSRWRA0AP8AreWcSAqp6bJsv5nG7/aSDfj5yqOlnPh109fyEioak5nbjVM9GbfNBdo6rVwE3Ad0Xk87E71TuPC9wa1qDmcn4NXA5sAjqBn/kbxyMiWcBfgXtVdTB2X5DqOU3OwNVTVSdUdRNQjHf2UOVzpGlNzSki64EH8PJuAcLAfT5GBBK/yXcAJTHPi91YIKhqh/vaAzyP9w3bPXma5r72+JfwQ2bKFagaq2q3e3FFgcf4YArBt5wikobXOP+kqn9zw4Gr53Q5g1jPSao6AOwGPoM3vbFomiznc7r9S4FTPuW80U2LqaqOAk8QgHomepN/G6hwV94X41142elzJgBEJFNEsie3geuBerx8d7jD7gBe8CfhBWbKtRO43a0O2AqcjpmGiLsp85hfw6speDlvdastyoAKYF8c8gjwO+BdVf15zK5A1XOmnAGsZ4GI5LrtDODLeNcPdgPb3WFT6zlZ5+3Aa+7MyY+cR2J+sAvedYPYevrzOorXFd6FeuBdtW7Em7d70O88MbnK8VYn1AKHJrPhzRe+ChwFdgFhH7I9g3dqPo43N/jNmXLhrQb4lavvQeBTPuf8g8tRh/fCKYw5/kGXswG4KU4Zr8GbiqkDDrjHzUGr5yw5g1bPDcD/XJ564IduvBzvh0wT8Bcg5MbT3fMmt7/c55yvuXrWA3/kgxU4vr2O7LYGxhiTxBJ9usYYY8wsrMkbY0wSsyZvjDFJzJq8McYkMWvyxhiTxKzJG2NMErMmb4wxSez/ovW8NBpBJwkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 25ba10c0334690264b7a06d3c3c00037aac8dc04 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 263/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 5d9ad037dd1ea28955738eb6dbb2b0c13bafd973 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 264/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From 12a02c849430bc3371d96a2d754916c4bcbd4ce1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 265/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From 3b9c1451d1a6decdca8962ceacb5be75046777e8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 266/624] transfer files to new location and modify documentation --- docs/modules/exploratory.rst | 1 - docs/modules/preprocessing.rst | 13 +- docs/modules/preprocessing/dim_reduction.rst | 18 + .../dim_reduction}/fpca.rst | 10 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ----------------- skfda/preprocessing/dim_reduction/__init__.py | 1 + .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 437 +++++++++++++++++- tests/test_fpca.py | 6 +- 12 files changed, 456 insertions(+), 463 deletions(-) create mode 100644 docs/modules/preprocessing/dim_reduction.rst rename docs/modules/{exploratory => preprocessing/dim_reduction}/fpca.rst (75%) delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index edc2c8d73..832b93193 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,4 +11,3 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers - exploratory/fpca \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index 06f3eb6da..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -12,6 +12,7 @@ this category deal with this problem. preprocessing/smoothing preprocessing/registration + preprocessing/dim_reduction Smoothing --------- @@ -28,4 +29,14 @@ Sometimes, the functional data may be misaligned, or the phase variation should be ignored in the analysis. To align the data and eliminate the phase variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the -registration methods available in the library. \ No newline at end of file +registration methods available in the library. + +Dimension Reduction +------------------- + +The functional data may have too many samples so we cannot analyse +the data with clarity. To better understand the data, we need to use +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. +:doc:`Here ` you can learn more about the +dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst new file mode 100644 index 000000000..9da0452b7 --- /dev/null +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -0,0 +1,18 @@ +Dimension Reduction +=================== + +When dealing with data samples with high dimensionality, we often need to +reduce the dimensions so we can better observe the data. + +Projection +---------- +One way to reduce the dimension is through projection. For example, in +functional principal component analysis, we project the data samples +into a smaller sample of functions that preserve the maximum sample +variance. + +.. toctree:: + :maxdepth: 4 + :caption: Modules: + + dim_reduction/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst similarity index 75% rename from docs/modules/exploratory/fpca.rst rename to docs/modules/preprocessing/dim_reduction/fpca.rst index b80519747..7af947b89 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -9,9 +9,9 @@ of FPCA are orthogonal functions (usually a much smaller sample than the input data sample) that represent the most important modes of variation in the original data sample. -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. +For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, +where the process is applied to several datasets in both discretized and basis +forms. FPCA for functional data in a basis representation ---------------------------------------------------------------- @@ -19,7 +19,7 @@ FPCA for functional data in a basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis + skfda.preprocessing.dim_reduction.projection.FPCABasis FPCA for functional data in a discretized representation ---------------------------------------------------------------- @@ -27,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index e69de29bb..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -0,0 +1 @@ +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd4b4dadc..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import fpca +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index f966cce17..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,33 +1,426 @@ -"""Functional principal component analysis. -""" +"""Functional Principal Component Analysis Module.""" import numpy as np +import skfda +from abc import ABC, abstractmethod +from skfda.representation.basis import FDataBasis +from skfda.representation.grid import FDataGrid +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut -from ....exploratory.stats import mean +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" -def fpca(fdatagrid, n=2): - """Compute Functional Principal Components Analysis. +class FPCA(ABC, BaseEstimator, TransformerMixin): + """Defines the common structure shared between classes that do functional + principal component analysis - Performs Functional Principal Components Analysis to reduce - dimensionality and obtain the principal modes of variation for a - functional data object. + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + + def __init__(self, n_components=3, centering=True): + """FPCA constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + self.n_components = n_components + self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) + + @abstractmethod + def fit(self, X, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + pass + + def fit_transform(self, X, y=None, **fit_params): + """Computes the n_components first principal components and their scores + and returns them. + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + """Funcional principal component analysis for functional data represented + in basis form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + + """ + + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization_derivative_degree=2, + regularization_coefficients=None, + regularization_parameter=0): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + # basis that we want to use for the principal components + self.components_basis = components_basis + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients + + def fit(self, X: FDataBasis, y=None): + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + """ + + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # setup principal component basis if not given + if self.components_basis: + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range + g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. + j_matrix = X.basis.inner_product(self.components_basis) + else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object + self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix + + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 + + # Apply regularization / penalty if applicable + if self.regularization_parameter > 0: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix - It uses SVD numpy implementation to compute PCA. + # obtain triangulation using cholesky + l_matrix = np.linalg.cholesky(g_matrix) - Args: - fdatagrid (FDataGrid): functional data object. - n (int, optional): Number of principal components. Defaults to 2. + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - Returns: - tuple: (scores, principal directions, eigenvalues) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) + self.pca.fit(final_matrix) + + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) + + component_coefficients = np.transpose(component_coefficients) + + # the singular values obtained using SVD are the squares of eigenvalues + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case it is the inner product of our data with the components + return X.inner_product(self.components) + + +class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ - fdatagrid = fdatagrid - mean(fdatagrid) # centers the data - # singular value decomposition - u, s, v = np.linalg.svd(fdatagrid.data_matrix) - principal_directions = v.T # obtain the eigenvectors matrix - eigenvalues = (np.diag(s) ** 2) / (fdatagrid.n_samples - 1) - scores = u @ s # functional principal scores - - return scores, principal_directions, eigenvalues + + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ + super().__init__(n_components, centering) + self.weights = weights + + def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book, chapter 8. + + Args: + X (FDataGrid): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # get the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + + # if centering is True then subtract the mean function to each function + # in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # subtract from each row the mean coefficient matrix + fd_data -= np.squeeze(meanfd.data_matrix) + + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) + + weights_matrix = np.diag(self.weights) + + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 + + return self + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From d44de57523d5dc268db318d2d7bf3b161d0354d7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 267/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 6a0a115e74f331692f6638d693d59bf0e78ad6a6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 268/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From 6a218cd0027424aa1bcdaf1a32aace9e5e3d2b17 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 269/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 4ec9c7291ea722322b7197578e0d70e4287656d4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 270/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 36a36d76650284ceb886bd92c685f5159467bc81 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 271/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 7c9a558d131af0cd0374ea630c8e47937c593477 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 272/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From e22ee02de66959c76955e0f2ecef61ee4439264e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 273/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 422b28641bdb8223d004b886808411506d3bf621 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 274/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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JCb4lClWtAyYC04GlwIuqulhE7hCRC7zT7gHaAS+JyAIRmdLC5YwxpnUWPu9Wrys4y+9IYoavk5qo6tvA2032/S7g8ZiIB2WMiV8Vm9162Cfdam0TB8HeKWNM2/HVi6ANMPQKvyOJKZYojDFtgyoseB56joK8/n5HE1MsURhj2obNC9zkf8OsNHGwLFEYY9qGBc9DYqpN/ncILFEYY+JfXY2bsuOIsyE9x+9oYo4lCmNM/Fv5LuwqhWFX+h1JTLJEYYyJfwueg8zO0O90vyOJSZYojDHxraoUVkyHIZfZetiHyBKFMSa+ffUyNNTa2InDYInCGBPfFj7nphPverTfkcQsSxTGmPj1zTLY9KWVJg6TJQpjTPxa+DxIIgy+1O9IYpolCmNMfGqoh0WToeAMaNfZ72himiUKY0x8Wv0R7Nhs1U4hYInCGBOfFj4Padm2il0IWKIwxsSf3RWw9E04+mJISvU7mphnicIYE3+WvAZ1u2zKjhCxRGGMiT8LX4CO/aHHCL8jiQuWKIwx8aWsCNZ+6hqxRfyOJi5YojDGxJeFLwACQy/3O5K4YYnCGBNfvnoJ+p4MWT39jiRuWKIwxsSP4kIoWQkDL/A7krhiicIYEz+Wv+3ubexESFmiMMbEj+VToesQq3YKMUsUxpj4UFkM62dZaSIMfE0UIjJWRJaLyEoRua2Z46eIyHwRqRORS/yI0RgTIwrfAW2wRBEGviUKEUkEHgDOBgYBV4jIoCanrQOuA56LbHTGmJiz/G1o3w26DfM7krjjZ4liFLBSVVerag3wAjAu8ARVLVLVRUCDHwEaY2JE7W5Y+YErTdggu5DzM1H0ANYHbG/w9h00EZkgInNFZO62bdtCEpwxJoYUfQK1lXDEOX5HEpfiojFbVSep6khVHdmpUye/wzHGRNrytyE5E/qc7HckccnPRLERyA/Y7untM8aY1lN13WL7fweS0/yOJi61KlGIyDOt2XeQ5gAFItJXRFKAy4Eph3lNY0xbs3kh7NgEA6y3U7i0tkRxVOCG12PpsObvVdU6YCIwHVgKvKiqi0XkDhG5wHudY0VkA3Ap8LCILD6c1zTGxKHlUwGBAWf5HUncSgp2UER+CfwKSBeRisbdQA0w6XBfXFXfBt5usu93AY/n4KqkjDGmecvfhvzjIDPP70jiVtAShar+SVXbA/eoagfv1l5VO6rqLyMUozHGNK98I2xZZIPswixoiaKRqv5SRHoAvQOfo6ofhyswY4w5oBVT3b11iw2rViUKEbkb19i8BKj3ditgicIY45/lUyG3H+QV+B1JXGtVogAuAo5Q1epwBmOMMa1WvQPWfAyjJtho7DBrba+n1UByOAMxxpiDsuoDqK+x9okIOFCvp3/iqpiqgAUi8j6wp1Shqj8Ob3jGGNOC5VMhLRvyj/c7krh3oKqnud79PGwwnDEmWjTUw4rpUHAmJLa2Bt0cqqDvsKo+FalAjDGm1TbOg12lNsguQlrb6+krXBVUoHJcieOPqloS6sCMMaZFK6aDJEL/0/2OpE1obZltKq5bbOMCQpcDGcAW4Eng/JBHZowxLSmc7kZjp+f4HUmb0NpEMUZVhwdsfyUi81V1uIhcHY7AjDGmWRWbYMtXMOZ2vyNpM1rbPTZRREY1bojIsUCit1kX8qiMMaYlhe+4+wJrn4iU1pYobgQeF5F2uEkBK4AbRSQT+FO4gjPGmP2seAc69ITOA/2OpM1o7VxPc4DBIpLlbZcHHH4xHIEZY8x+6qph9Ucw9Hs2GjuCDjTg7mpV/beI3NpkPwCqem8YYzPGmH2t/dStjW3VThF1oBJFpnffPtyBGGPMAa14B5LSoO8pfkfSphxowN3D3v0fIhOOMcYEUTgd+pwMKRl+R9KmtHbN7AEi8r6IfO1tDxGR34Q3NGOMCVC8EkpX22hsH7S2e+wjwC+BWgBVXYQbdGeMMZFRON3dF5zhbxxtUGsTRYaqzm6yz8ZPGGMiZ8V0yDsCcvr4HUmb09pEUSwi/fDmexKRS4DNYYvKGGMCVe+AtZ/BgDP9jqRNau2Aux8Bk4AjRWQjsAa4KmxRGWNMoNUfQUOtdYv1SWsTxUbgCeBDIBc3Mns8cEeY4jLGmL1WTIfULOhlixT5obWJ4nVgOzAf2BS+cIwxpglVKHwX+p0GibYisx9amyh6qurYsEZijDHN2bwQdm6xbrE+am1j9mciMjjULy4iY0VkuYisFJHbmjmeKiKTveOzRKRPqGMwxkS5xtli+1u3WL8caK6nxpXtkoDrRWQ1UI2bQVZVdcihvrCIJAIPAGcAG4A5IjJFVZcEnHYDUKaq/UXkcuDPwPcO9TWNMTFoxXToPhzadfI7kjbrQFVP54XxtUcBK1V1NYCIvACMAwITxTjgdu/xy8D9IiKq2nRZVmNMPKosdutjj96vwsFE0IHmelobxtfuAawP2N4AHNfSOapaJyLlQEegOIxxGWOiReG7gEKBjZ/wU2vbKKKaiEwQkbkiMnfbtm2HdpH6Onj1h7BpQWiDM8YcusLpkNkZug3zO5I2zc9EsRHID9ju6e1r9hwRSQKygJKmF1LVSao6UlVHdup0iPWY29fC6g/h0dNh5t+gof7QrmOMCY36Olj5gStNJMTFd9qY5ee7PwcoEJG+IpKCm2RwSpNzpuAG9gFcAnwQtvaJjv3gh5/BwPPh/TvgyXOhrCgsL2WMaYX1s6C63KbtiAK+JQpVrQMmAtOBpcCLqrpYRO4QkQu80x4DOorISuBWILwtWhm5cMkTcNEk2LoY/nUSfPmsG/BjjImswumQkATfOs3vSNo8ibcORCNHjtS5c+ce/oW2r3NtFms/gc6D4IQfweBLISn18K9tjDmwB46HzDy47k2/I2kTRGSeqo5s7phV/LUkuxeMnwIX/gskAV7/Efz9aJjxFyjf4Hd0xsS37etg21IbjR0lWjuFR9uUkAjDroShV8CaGfD5A/Dhne7W+Si3gMqAs6DnKEi0t9KYkFnRuEiRJYpoYJ9urSEC3xrtbiWrYNlbblqBz++HT/8BR5wDVzzvb4zGxJPCd9wCRXkFfkdisKqng9exH5z4Y1dv+vPVMOJ6WD4VKvfrtWuMORQ1VbDmY1eaEPE7GoMlisOTlgXHXAMorPrA72iMiQ9Fn0DdbusWG0UsURyu7sdARt7eGS6NMYencDokZ0Dvk/yOxHgsURyuhATofzqseh8aGvyOxpjYpgor3oG+p0Jymt/RGI8lilDofwZUlcCmL/2OxJjYtm0ZlK+zaqcoY4kiFPqfDgisfNfvSIyJbcununvrFhtVLFGEQkYu9Bxp7RTGHK4V06DrEMjq4XckJoAlilDpfwZsnO8WWjHGHLzKYlg/G4442+9ITBOWKEKlYAzWTdaYw1D4DqAwYKzfkZgmLFGESrdjIKMjrHzP70iMiU3Lp0K7rrZIURSyRBEqCQnQ73RYad1kjTloddWuND7gLFukKArZbySU+o+BqmLYbMupGnNQ1n4KNTutfSJKWaIIUFpZc3gX2NNN9v3Du07tLiheCeu+cI+NiXfLp0FSmhtoZ6KOzR7rKa+qZcQf36VPx0xG9cnluG/lMqpvLj1zMlp/kcw86D7Mjac49WfNn1NfBzu3QPlGKF8PFRvd+haB21UBEwymZcPQy2HEddB54GH9jMZEJVVYMdXNzpxyEP9vJmIsUTQS+NXZA5m1poSpX29m8tz1APTITue4vo2JoyN9OmYgwWa07D8GZv4NFjwP1TugYkNAItgAOzaD1u/7nNQs1288qyf0GOHus3pCSiYsfhXmPAazHoL841zCGHSh/UOZ+PHNUrdQ0Um3+h2JaYEthdqMhgZl2ZYdzF5Twqw1pcxeU0qJVy2VnZHM4B5ZDOmZxZCe2QztmU3XrIA5aXZshX9927VVACSmQIceez/8Ax83bqd1CB5QZTEsfB7mPQklK11iGfo9GD4euh59WD+rMb6b+Td4/w64dRl06OZ3NG1WsKVQLVG0gqqyalsls9eUsmjDdhZtKGf51h3UN7j37twh3XjgyuF7n1BVCmVrICvfzSwbql4cqrD2M5cwlrwO9dXQY6QrZRz9XVcCMSbWPHoG1NfAD2b4HUmbZokiDHbX1rNkcwX/914h89eWsej2M4NXSYVaVSksfMEljeLlkNIehlwGI8ZDt6GRi8OYw7FzG/y1AEbf5m7GN8EShfV6OkRpyYkM75XDmEFd2FFdx+by3ZENICMXTvgv+NEs+P50GHgeLHgWHj4FJo12CaR6R2RjMuZg2WjsmGCJ4jAN6NwOgBVbffpQFoFex8NFD8H/LoOz/+IGL71xC/ztSHe/eZE/sRlzICumQvvuVgqOcpYoDtOALu0BHxNFoPQcOO4H8MPP4Ib3XO+ohZNdKeP9O6C+1u8IjdmrrhpWfehGY9va2FHNEsVhyslMoVP7VFZs3el3KHuJQP6xcOEDrpRxzNWuZ8ljZ0LpGr+jM8YpmmmjsWOEL4lCRHJF5F0RKfTuc1o4b5qIbBeRNyMd48EY0KVddJQompOeDePuh8uehpJVrnSx+DW/ozLGG42dDn1P8TsScwB+lShuA95X1QLgfW+7OfcA10QsqkM0oEt7CrfupKEhinuQDRoHN38MeQXw0nh481aojXADvDGNVN0iRf1Og+R0v6MxB+BXohgHPOU9fgq4sLmTVPV9IEq/qu81oEt7dtXWs3F7lM/LlNMHrp8G3/5vmPsYPDrGzSllTKRtXeymrLHeTjHBryk8uqjqZu/xFqCLT3GExIAurufT8i07yM+N8qk1klLgzD9Cn5Ph1ZtdVdT5/3BjMNqahnp47/ewegZ06O7dvJHzjY87dLdvvOGwwlsbe4CtjR0LwpYoROQ9oGszh34duKGqKiKHVWcjIhOACQC9evU6nEsdkoLGnk/f7GDMoBjJeQPOgps/gf/cAK/cBGtmwNn3tJ05pOqq3c+95HXo9W03F9f6WbCrbP9zMzp6icNLIFk9vCTSwz1u3x2S0/Z/nmnZ8mnQfTi0b+4jwkSbsCUKVR3T0jER2Soi3VR1s4h0A745zNeaBEwCNzL7cK51KDqkJdMtK40VW6K+lmxfWT1g/Jvw0Z9cr6gNc+HSJ+N/ltrqnTD5alj9IZx5J3x74t5jNVVQscnN4tt4K2+8Xw/rPofd2/e/ZmYn6PcdN/9W729bd89gdn4DG+fBab/yOxLTSn5VPU0BxgN3e/ev+xRHyAzo0j66usi2VmISnP5b6HMivDIBJp0G59zjutTG44ddVSk8ewlsWgDjHoRjrtr3eEoG5PV3t5bUVO5NJuUb3ePSVbDsLVg0GTofBcffDIMvtWqr5qyYjo3Gji1+JYq7gRdF5AZgLXAZgIiMBG5W1Ru97ZnAkUA7EdkA3KCq032KOagBXdrx+eoS6huUxIQY/IDt9x24+VN45UaYMhHWfAzn3Qup7f2OLHQqNsEzF7mxJN97Bo4899Cuk5Lpeo/lFey7v6YSvv4PzHoYpvw3vPt7GHk9HHujq7Iyzopprhqv62C/IzGt5EuvJ1UtUdXTVbVAVceoaqm3f25jkvC2T1bVTqqarqo9ozVJgGunqKlrYG1Jpd+hHLr2XeCa1+C038DXL8PDp8bP9B/FK+Gxs1wJ4Or/HHqSCCYlE4Zf69p+xr8BvU6AmffCPwbDyze4qr22rnb33rWx47HEGqdsZHaI7J3KIwarnwIlJLrV+ca/AbVVrgvt7Edcv/dYtXkhPH4W1FbCdW9A35PD+3oibhDZFc/Bj7+EUT9wk989ejo8crob8NjQEN4YolXRTPd3ZaOxY4olihAp8CYHLIzWEdoHq89J7ptx31Pg7Z+6QXq7mmnEjXZFn8KT57n1mL8/HbofE9nXz+0LY++CW5e4CRt3lbr3ctIpsOKd2E7Ah2L5VEjOdN2zTcywRBEimalJ9MhOp/CbGC9RBMrMgytfhDPucA21D5/ieqvEiuVT4d/fdV0wb5i+f5tCJKW2dxM2TpwLF02C3RXw3KXw+FiXzNoCVdeQ3e80604cYyxRhFBUz/l0qBIS4MRb4PqpoA3w+Nmw6CW/ozqwhS/AC1e5rr7XT3OD6KJBQqJbxnbiXDj3XigrgifPgWe+C5u+9Du68NrylccDQAsAABdqSURBVFtD3no7xRxLFCE0oEt7Vm+rpK4+Duuf80fBDz6GniNdz6gP/xS91SZf/Ate/YHr8jv+Dcjs6HdE+0tKgWNvgFsWwBn/zyWJSaNh8jVu8sZ4tGIaIDYaOwZZogihgi7tqalvYG1pld+hhEdGrusVNewqmHE3/OfG6JpYUBU+uBOm3QZHngdXvhT93XuT0+HEH8MtC2H0L936DI+dGZ/JYvlU6DEC2nX2OxJzkCxRhFDjnE+Fsd7zKZikFBj3AJz+e9eF9qnz3EhbvzU0uEb3j/8Cx1wDlz4VW/XgaR3cmtETPnJVfP++2K0nHS92bIFN8+EIq3aKRZYoQqhfpzjr+dQSETj5VrfGxZavXZfPrUv8i6euxlWHzXkUvv1juOCfbsR5LMrrD1dOhh2b4bnL3CC+eFD4jrsfYN1iY5ElihDKTE2iZ046K+Kp51Mwg8bB9W9DfY2rLil8L/Ix1FTBC1e4EdFj/gBn/r/YH8iVPwoueRw2L4CXroP6Or8jOnzLp0FWPnQ5yu9IzCGwRBFibhGjOC9RBOoxHG76AHL7uO6esyZF7rV3lcEzF7qRvuffByf9JHKvHW5Hngvn/NV9E3/rf6K340Br1O52EzAOGBv7SbyNskQRYgVd2rF6WyW18djzqSVZPVwX1AFjYerP4K2fhv9bcFkRPHGu6y10yRMwYnx4X88Px94AJ/8U5j8NM/7sdzSHbvlbbjR2OKZNMRFhiSLERvTKoaa+gU8Ki/0OJbJS28H3/g0nTIQ5j8Dz33ODykKpphIWvegaeu8bDtvXuQGBRzW7QGJ8+M5vYOiVbir4+U/7Hc2hmTUJcvpC31P9jsQcohht8Yteo4/oTF67FH703HxO+FZHTi7I46SCTvTrlInEe7E7IRHOutONgH7rf127xZWTIaf3oV+zvs5VWyx6EZa96b6ZZuW7QYDH3hA9A+nCRQQuuA92boU3fgLtusKAM/2OqvU2L4T1X8BZd7nBmyYmicZy3WczRo4cqXPn+jtL59cby3lhzjpmFhaztsSNqeielcZJBXmcXNCJE/vnkZuZ4muMYbf6I3jxWkhMgcufcw20raUKG+e7tR0WvwKV2yAt25UchnwP8o9vex861TvgyXOhuBCue9ONR4gFr090HQ1uXQrp2X5HY4IQkXmqOrLZY5YowmtdSRUzV27jk8JiPl1ZTMXuOkTg6O5ZXuLIY0TvHFKTEv0ONfS2rXBdPCs2wYUPwuBLgp9fsgq+eskliNLVkJjq+t0PvgwKzoCk1JCG19Cg3PX2Uj4u3Eav3Ax6d8ykd0d336djBt2z00lOjKKEtGMrPDbG9fS64R3o2M/viIKrKoV7B8LQK9y67CaqWaKIEnX1DXy1sZyZhcV8UljM/HVl1DUo6cmJHPetXE4u6MTJBXkUdG4XP9VUVaVu2dG1n7qRx6f+Yt+eLzu3uVLDohdh41xA3DTggy+DQRdAWlZYwlJV/vDGEp78rIhRfXOp2FVLUUklu2v3dkJITBB65qTTKzeDPk2SSH5uBmnJPiT34kJXpZeR6wbnRfPI80/vg3d/Cz/8zLrFxgBLFFFqZ3UdX6wqYWbhNmYWFrO62A2u6tIhlZP6d+KUAXmc2D+PvHah/SYdcXXVrn594XNw9CUw9m7XpfWrl9y91kOXwTDkMjj6YteLKszumb6MBz5cxU0n9+VX5wxERFBVtu2opqikiqKSStY13pdWsaa4kh279/bkEoGuHdLo3dElkV4d900m7VLD2PxX9Ak8db6rhrvoofC9zuFoqIf7jnHtSde/5Xc0phUsUcSIDWVVfFJYzMyVrppqe1UtAIO6dfAaxV01VUZKDPZBUIVP/g7v/2Hvvqx8Vx01+DLoMihioTz40Ur+Mm05V4zqxV0XHd2q0puqsr2qlrWlVawtqaSouIq1pZWsLXHbxTtr9jk/r12Kq8rap0rLJZPsjOTDLzF+dLfrCXXhQzDsisO7Vjgsn+Z6vl36VHz3SosjlihiUH2DsniTq6aaWbiNeWvLqK1XkhKEo3tkcVzfXEb1zWVkn1yy0pP9Drf1VrzjesEMGAs9Rka8Ufqpz4r4/ZTFjBvWnXsvGxay9c13VtextqQxcXjJxCuVbCrfd+LE9mlJAaWPxuost925fWrrkkhDPTx1gRtH8oMZ/q610ZxnLoJvlsFPFkFiDP19tmGWKOJAZXUdc4pKmVNUyuw1pSxcX05NfQMicGTXDhzXN5fj+uZybN/c2K+qCpOX523gpy8t5IxBXXjwquERa6jeXVvPhrIqior3VmUVeclkQ9ku6hv2/g+mJyfSu2OGaxfJ85JJrrvvnp2+b2Kr2AT/OtFV1d3wXvRMglhcCPePdGuvn/ozv6MxrWSJIg7trq1nwfrtzFpdyuyiEuatLdvTENuvUyaj+nbcU+ronp3uc7T+e/urzUx8bj4n9s/j0fEjo6aXWW19A5u276KopIp1JZV7EsjakirWllZRU7e3cT05UcjP2VsK6d0xgxHVsxny8QTqR95E4nl/9fEnCTD1FzDnMbf8q00pHjMsUbQBNXUNfL2pnNlrSpm1uoS5RWXsqHaNr/m56Yzqszdx9O6YET+9qlrhw2XfMOGZuQztmc3TN4yKmTaehgZlS8XugKqsKtaVeu0jJZVU1tQD8JukZ7gxaSq/TLmNdZ1P29Mzq1duJn3yXOkkYj9z9Q64d5CrWrz4kci8pgkJSxRtUH2DsnRzBbPXuKqq2UWllFa6BtcuHVIZ1bcjo/rkMKJ3Lkd0bR+yuvpo88XqEsY/PpuCLu147qbj6ZAWH/XlqkpJZQ1rSypZt207J3x4Je13beCW7H8yb3smZV5HiEad26fu1y7SNy+TPnkh7qE151E3Kv+G9yD/2NBd14SdJQqDqrLym53M8hLHrDUlbK2oBiAzJZFjeuUwvHcOI3rncEyv7Lj4QF2wfjtXPfIF3bLTmTzheDrGc9tN6Wp46BQ3XuG6tyiv0T3dewMb2YtKKvlmR/U+T81rl0rfPNcjq0+eSyB981wDe3rKQVTRqcKDx0NSmhvj0YZKrfHAEoXZj6qyvnQX89eVMW+tuy3bUkGDuv/vAZ3b70kcI3vnxFx11dLNFVw+6Quy0pN56eYT6NIhShp6w+mrl+E/3oyzp/+2xdOqaur2VGetKa5iTfFOioqrWFNSybYmSaRrhzT65GXsSRyNiaRXcwMOV8+Apy+AC/8Fw64Mx09owsgShWmVndV1LFy/fU/imL+ubM8gs46ZKXsSx4jeOQzukeXPyORWWL1tJ5c9/AVJCcJLN59Afm6G3yFFzusT4ct/w7WvwbdGH/TTd1bXUVTsuvYWFQckkpKqPVWX4L5MdM9K96qvXGnkgmU/p2PJXOp/soSUtDb0nseJqEsUIpILTAb6AEXAZapa1uScYcC/gA5APXCnqk4+0LUtUYROQ4OycttO5q0tY26RSxxrvNHjyYnCUd2z9iSOEb1zouJb+4ayKi576HOq6xqY/IMT6N+5nd8hRVZNJUw6DXZvh5s/CWmvo/JdtXuSyJpiL5GUVLFm207a7d7CzNRbeLj+fP7WcMWeqU/yczPIz8kgPzfdu88gJxQDDk3IRWOi+AtQqqp3i8htQI6q/qLJOQMAVdVCEekOzAMGqur2YNe2RBFeJTurmb/OlTrmry1j4YbtVHtdOHtkp7uqqj45DO+Vw5Fd25MUwUn1vqnYzWUPf05pZQ3PTzieo7qHZ56oqLd1MTzyHej9bbjqZTf9exipKrun/Z602f9k2nems2RXFmuKK1lfWsX6sl37lETAtYnl52bQs0kCyc9Np1tWOh3SkiyR+CAaE8VyYLSqbhaRbsBHqnrEAZ6zELhEVQuDnWeJIrJq6hpYsrliT+KYu7Z0TyN5Rkoiw/KzGdHbNZQPz88hKyM8jeRllTVcPukL1pdV8cwNxzGid05YXidmzHsK3vgxHHcznB3m1fFqd8PfB0GvE+DyZ/c7vLO6ziUNL3GsL61iQ1kV60t3sb6siiqvm2+j9OREunRIpXOHNLp2SKNrVhqd26fSNSuNLt6+zh1So2YsTLwIlij86lDeRVU3e4+3AF2CnSwio4AUYFULxycAEwB69eoVwjDNgaQkJTAsP5th+dnccFJfVJVN5bv3JI55a8t48KNVe0YfF3Rux4jeOQzLz2ZIz2wGdGl32KWOHbtrGf/EbNaUVPLkdcdakgC3NGzxCvj8fre63PE3h++1Fk2GqhIYNaHZw+1SkxjYrQMDu3XY75iqUlpZsyeBbK3YzZby3WzdUc3W8t0sWL+dLYt37zPwsFFORjJdOqR5t1RyM1PJzUwmNzOVjpkp5AbcMlISrZRyGMJWohCR94CuzRz6NfCUqmYHnFumqs3+dzeWOIDxqvrFgV7XShTRp7K6joUbtu9JHPPXbad8l+vnn5acwNHdsxjSM5uh+VkM7Zl9UD2sdtXUM/7x2cxfV8bD14zg9IFBv3O0LQ31bvGo5W+7xaOOODsMr9EADx7nusT+4OOwdIlVVcp31bKlYjdbK1wCcY/dbUvFbr6pqKasqoba+uY/z1KTEshKT6ZDejId0pLokJ5M+7S9jzukJdM+LYmMlETv5h6npySSGfA4IyUpbsccxWzVk4h0wCWJu1T15dZc2xJF9FNVikqqWLRhOwvXl7Nww3YWbyrfMwVJVnoyQ3pmMaSnSxxD87ObbSivrqvnpqfnMbNwG/ddfgznD+0e6R8l+tVUupXxti2H66dC92Ghvf7sR+Dtn8J3H4Uhl4b22gdJVdlRXUdZZQ0llTWU7qyhtKqG0kp3q9hVS8XuWip21bFjdy0Vu+v27GspwTQnNSlhTzJJTUogJSmB1KQEUpMS9zwOvE9KTCA5QUhMSCA5UUhKFJISEkhKEHcsUUhsfJwgJCQISQluX2KCkCh79+051mRforhz26Um0Scv85Dev2hMFPcAJQGN2bmq+vMm56QAU4E3VLXVy2NZoohNdfUNrNi60yUPL4Es37pjT5VVlw6prtTRM4uh+dkc0bU9v3ttMdMWb+HPFw/me8dalWOLdmyFR0+H+lq46f3QrTO+6CV45SboPwaueAESY2NqlKZUleq6Bip21VJVU09VTT27auuorK73tuvcvpp6Kmvq9txX1dRTU9dAdV2Dd7/vdk19A9W1DdQ1NFBbr9Q3KLX1DdQ16D4TQYbS0PxsXv/RiYf03GhMFB2BF4FewFpc99hSERkJ3KyqN4rI1cATwOKAp16nqguCXdsSRfzYXVvP4k0VLFy/nUUbtrNoQ/mexZ0a/e68QXz/pL4+RRhDti6Bx8+C7F6uZJG2f3vBQVn2Fky+xjVgX/0yJNvEkwdDValrUOrqlbqGBurqlVrvvt5LJPWqNDTonsTS4D2ncV+Dd07gvg5pyZzQr+MhxRR1iSKcLFHEt/JdtXy1oZwlm8sZ1M2tO25aadUH8O9LoN9pcMXkQy8BrPrQrYXedTBc+3p0L8dqWi1YooiileONObCs9GROKshjwin9LEkcrH7fgfPuhZXvwdSfubmZDta6L+CFK6FjgRujYUmiTYjNSkVjzKEZcR2UroFP/wG5/eDbE1v/3E0L4NlLoX03N0VIRm7YwjTRxRKFMW3N6b+HsjXwzm9g5xa3ZnnXwcG7tn6zzC1vmpblqptsQaI2xRKFMW1NQgJc9LB7/PmD8Nk/XVXS0d+Fo74LnY/c9/zS1fD0OLf29bWvQ3Z+5GM2vrLGbGPasspiWDoFvn4F1n4K2gCdB7mEcfR33UC6x8dCzQ647m3oMsjviE2YWK8nY8yB7dgKS16Hxa/Aus/dPkmE5AwYPwV6DPc3PhNW0TjXkzEm2rTvAsdNcLfyjS5pFK+AkddDt6F+R2d8ZInCGLO/rB5wwn/5HYWJEjaOwhhjTFCWKIwxxgRlicIYY0xQliiMMcYEZYnCGGNMUJYojDHGBGWJwhhjTFCWKIwxxgQVd1N4iMg23Kp5sSAPKPY7iIMQa/GCxRwpsRZzrMUL4Y+5t6p2au5A3CWKWCIic1uaWyUaxVq8YDFHSqzFHGvxgr8xW9WTMcaYoCxRGGOMCcoShb8m+R3AQYq1eMFijpRYiznW4gUfY7Y2CmOMMUFZicIYY0xQlijCSETyReRDEVkiIotF5JZmzhktIuUissC7/c6PWJvEVCQiX3nx7LdcoDj3ichKEVkkIr4ufSYiRwS8fwtEpEJEftLkHN/fZxF5XES+EZGvA/blisi7IlLo3ee08Nzx3jmFIjLex3jvEZFl3u/9VRHJbuG5Qf+GIhzz7SKyMeB3f04Lzx0rIsu9v+vbfI55ckC8RSKyoIXnRuZ9VlW7hekGdAOGe4/bAyuAQU3OGQ286XesTWIqAvKCHD8HmAoIcDwwy++YA2JLBLbg+oRH1fsMnAIMB74O2PcX4Dbv8W3An5t5Xi6w2rvP8R7n+BTvmUCS9/jPzcXbmr+hCMd8O/DTVvzdrAK+BaQAC5v+r0Yy5ibH/wb8zs/32UoUYaSqm1V1vvd4B7AU6OFvVCExDnhanS+AbBHp5ndQntOBVaoadYMuVfVjoLTJ7nHAU97jp4ALm3nqWcC7qlqqqmXAu8DYsAXqaS5eVX1HVeu8zS+AnuGO42C08B63xihgpaquVtUa4AXc7ybsgsUsIgJcBjwfiVhaYokiQkSkD3AMMKuZwyeIyEIRmSoiR0U0sOYp8I6IzBORCc0c7wGsD9jeQPQkwMtp+Z8q2t5ngC6qutl7vAXo0sw50fp+fx9XsmzOgf6GIm2iV132eAvVe9H6Hp8MbFXVwhaOR+R9tkQRASLSDvgP8BNVrWhyeD6ummQo8E/gtUjH14yTVHU4cDbwIxE5xe+AWkNEUoALgJeaORyN7/M+1NUlxEQ3RBH5NVAHPNvCKdH0N/QvoB8wDNiMq8qJFVcQvDQRkffZEkWYiUgyLkk8q6qvND2uqhWqutN7/DaQLCJ5EQ6zaUwbvftvgFdxxfJAG4H8gO2e3j6/nQ3MV9WtTQ9E4/vs2dpYbefdf9PMOVH1fovIdcB5wFVecttPK/6GIkZVt6pqvao2AI+0EEtUvccAIpIEfBeY3NI5kXqfLVGEkVe/+BiwVFXvbeGcrt55iMgo3O+kJHJR7hdPpoi0b3yMa7z8uslpU4Brvd5PxwPlAdUnfmrx21e0vc8BpgCNvZjGA683c8504EwRyfGqTc709kWciIwFfg5coKpVLZzTmr+hiGnSfnZRC7HMAQpEpK9XMr0c97vx0xhgmapuaO5gRN/nSLTqt9UbcBKuKmERsMC7nQPcDNzsnTMRWIzrZfEF8G2fY/6WF8tCL65fe/sDYxbgAVwvka+AkVHwXmfiPvizAvZF1fuMS2KbgVpcHfgNQEfgfaAQeA/I9c4dCTwa8NzvAyu92/U+xrsSV5ff+Pf8kHdud+DtYH9DPsb8jPd3ugj34d+tacze9jm4nomr/I7Z2/9k499vwLm+vM82MtsYY0xQVvVkjDEmKEsUxhhjgrJEYYwxJihLFMYYY4KyRGGMMSYoSxTGGGOCskRhjDEmKEsUxoSQiLzmTdC2uHGSNhG5QURWiMhsEXlERO739ncSkf+IyBzvdqK/0RvTPBtwZ0wIiUiuqpaKSDpuWoizgE9x6w3sAD4AFqrqRBF5DnhQVT8RkV7AdFUd6FvwxrQgye8AjIkzPxaRi7zH+cA1wAxVLQUQkZeAAd7xMcAgbwoqgA4i0k69yQuNiRaWKIwJEREZjfvwP0FVq0TkI2AZ0FIpIQE4XlV3RyZCYw6NtVEYEzpZQJmXJI7ELRObCZzqzfyaBFwccP47wH83bojIsIhGa0wrWaIwJnSmAUkishS4GzdL7UbgLmA2rq2iCCj3zv8xMNJbeW0JbrZbY6KONWYbE2aN7Q5eieJV4HFVfdXvuIxpLStRGBN+t4vIAtyiMmuIwmVYjQnGShTGGGOCshKFMcaYoCxRGGOMCcoShTHGmKAsURhjjAnKEoUxxpigLFEYY4wJ6v8DXRmeKE09EXUAAAAASUVORK5CYII=\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From 86b4cf4d0061d91c3b921a6f850aab5c8d0868de Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 275/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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cnQI7uvnR5WITodNwe6t53t71NmnUJI/1znyQEVHQpq9NHP0m2cQRjpZNgy/vs9cgnPxfbkfjG1mDIb6VTX6aKEKKJgrlH5Xl8M6v7PTXg6+Ds//Z8EnrIiJsM0ur7jDoKruvbL/TVOUkjyVTYcGTMObu8PkirbFtIcz4jV3TYey94dP0FhEJ3c6211McOQzRzdyOSDWQJgrle4eLYdrl8P0XcNafYNgtjf+yi0uDnNH2BnaZzXd/BR/ebkdSjbk7uGZPPVFFW22Hf1ImXPRiaI5w8qbHePh2qv1s5IxyOxrVQDo8VvlWyS54fqydtO7cJ+C03/rnF3GzJLjwRThlip0e4rVL7QihULb+Y/veVZbDpdNscgw3HU+3o93WznQ7EnUcNFEo38lfC8+OhMItdpROTWe1v0REwOi/wTn3wYaP4YWxNlGFmtI98Pov4JULIToOrngb0ru5HZV/RMXajvl1s/Q6mhCiiUL5Rt48eG6U/TV89fvQ5azAvfbg6+CSabB3IzwzAvasCtxrN0Z1tR3d9ehg28F75h1ww5ehM4fTieoxDg4W2L4YFRI0UajGWz0DXpwI8elw3WzI7B/4GHJGwTUfgqmCZ0fDxjmBj+F4FKyDF86BmTdB6z7wX9/AGb+zv7jDXZeREBljk6MKCZooVOMseAqmXwkZfeGaj+0FdW7J6AvXzYHU9vDyhT9eixFMKsvhs7vh8VMhfzVMeASufg9adnU7ssBplgQdz7CJQtfTDgmaKNSJqa6G2X+CWf9thzxeOSM4ppdIbmtrFp3PtL/WZ//Jxuq26mpY9Y5NEHPvgV7nwZTFMPCK8Bn+ejy6n2P7svJXux2JagBNFOr4VVbAOzfA1w/AoF/YieqC6Wrp2ETbZ5F7jY3xjV/AkUPuxFJVCcteg8dOhtevAgxc/iac/zQkpLsTUzDoNhYQnXo8ROh1FOr4HC6B6VfA5s/hZ/8Lp90WnL+II6PsdNypHWH2H+2KcJe8CvEtA/P6leWw9BWbqAq3QKtecMFz0PPc8Ljeo7ESW9ur69e+B8N/73Y06hg0UaiGK90NL18Ae1bDxMdgwGVuR+SdCJx6o+2zeGsyPHMWXPaGf/sDKsrsBWVfPwSlO6HtIDtXU84YO5xX/aj7OJvEC/Psv5EKWvrJVQ2zdQE8NRz2bbYXgwV7kvDUcyJc9Z69IO+ZEbDla9+/xoF8+PweeKCPvVo8rZO9HuK6OdB9rCaJunQ/x97rGhVBT2sUyjtjYOFT8NH/QHIWXPsRtOnjdlTHL3swXPcJvHIRvHQuTHwU+l50/OeproL9m2HPSnu9xm7nvnirPd51tJ2ypP0pvo0/HLXoDK162n6KcJuvK8xoolD1qzgIM260E/vljIHznoDmqW5HdeLSOsK1H8O0K+Ct6+0cUWf8rv4+lrL9PyaEmvv8NXYRIQCJtM1Y2YMh92roPh7ScwL254SF7ufYWXIP7guOUXOqTpooVN32brSd1vlrbKf1sFvDo/mkeSpc/padnfXzv9uO5nPute3knglhzyrbx1AjriW06W2vAm/dy95adtMZUBur+zi7cNX6WTDgcrejUfXQRKGOtmamnSI8IsoO5QzkdByBEBVja0dpHeHzu2HZq4Bz4VdENKR3t5PX1SSE1r0hoVVwju4KdRn9IDkb1r6viSKIuZooRGQM8CAQCTxjjLmn1vHTgQeAvsAkY8wbgY+yCamqhE//Yod0Zg6w01yntHM7Kv8QgeG32/6W7YtsW3nr3rYpKTLa7eiaDhHb/LTkBdvUGRPvdkSqDq4lChGJBB4FRgLbgUUiMsMY43mp5lbgauC2wEfYxBwogDevsesEDLoaxvyjaTSrdD/nx9E3yh3dz4EFT9j5uXpOcDsaVQc3G52HABuNMZuNMRXAa8BEzwLGmC3GmOVAEMzBEMa2LYInT7dDYCc+CuMfbBpJQgWHdkNt35FOEhi03EwUbYFtHo+3O/uOm4hMFpHFIrK4oKDAJ8E1CcbAwqfh+bPtlczXfqztxCrwIqMg52xY/yFUHXE7GlWHMBjGAsaYp4wxucaY3PT0Jjx/zvGoKIO3b4APbrMT6E2e68704EqBXaPicDFs+crtSFQd3EwUO4Bsj8dZzj7lb/s325Xolk+D4f9jJ9ALx2U3VejodCZENdfmpyDlZqJYBHQVkY4iEgNMAma4GE/TsG4WPDkcirfbeY+G/z48ro9QoS0mzg7DXvtBcEwLr37CtW8IY0wlMAX4CFgDTDfGrBKRP4vIBAARGSwi24ELgSdFJETWuAxC1VUw5y/w6iRI6wC/nAtdR7gdlVI/6jHeXuS48zu3I1G1uHodhTHmA+CDWvvu9NhehG2SUo1RugfevBa2fGk7q8fep6OaVPDpOspOi7J2JmQNcjsa5UHbHMJZdRWsfBOeGAbbF9upwSc+qklCBae4NGg/FNZ/5HYkqhZNFOGosgK+fQkeGQxvXGMX67n+09CaGlw1Td3OtsujFua5HYnyoIkinFQchPmPw0P9YcYUiE2w03Dc8BW07ul2dEodW84Ye7/hY3fjUD+hkwKGg0NFsOhpmyTK9kH7U2HCQ9D5LJ3IToWWFp2hRRd78d2Q692ORjk0UYSyA/kw/zFY+AxUlNrOwGG/1UVzVGjLGWMXyyo/YGvFynWaKEJR0Vb45mH49kWoLIde59oEkdHX7ciUaryc0TDvEdj8ub1iW7lOE0UoKVgPX90PK6YDAv0uhlNvgZZd3I5MKd9pdwrEJtnmJ00UQUETRSjY+R18+W+7oFBUMxh8PQydYtewVircREbbq7Q3fGyv0taZA1ynicIfFj4NW+dBZKz90EfFQmTMj/f1bdfed7jEztO/aQ7EJsNpt9pF6ONbuv0XKuVfOWNg1duwaym0Heh2NE2eJgpfW/u+nZE1qS1IhO1DqCq30ydXloOpOr7zxbWEs/4Eg6+FZsn+iVmpYNNlJCD24jtNFK7TROFr3/3HJombltt59murrjo6eVRV/Hj/w3a5Xca5/VA7YZpSTUl8C8geYvspzvyD29E0eZoofOlwiV3OcfC1dScJgIhI54tfv/yV8ipnNMz5M5TsgqQMt6Np0rSXyJfWf2hrAj3PdTsSpUKfXqUdNDRR+NKqdyAxE7IGux2JUqGvVU9IztZJAoOAJgpfKS+FjZ9Azwk6nE8pXxCxzU+bP4Mjh92OpknTbzRfWf+RNjsp5Ws5Y+BIma6l7TJNFL6y6m1IzIDsk9yORKnw0eE0iI6z/X/KNZoofOFwCWyYbWsT2uyklO9EN4NOw22N3Ri3o2my9FvNF2pGO/U6z+1IlAo/OaOheCvkr3E7kiZLE4UvrHrbXmSno52U8r2uo+29Nj+5RhNFYx0udkY7abOTUn6RlAEZ/XWYrIv0m62x1s2y025os5NS/pMzBrYvhIP73I6kSdJEcSLKS2HFGzDtcph5MyS3g6xct6NSKnzljAZTDRtnux1Jk6RzPR2PgvUw72FYPh0qD0NCGxh4BQy+TtemVsqfMvpDQmvbT9FvktvRNDmaKBpi63z4+kFY94FdOKjfJdD3YnvNhPZLKOV/ERF2TfjV79pZlyOj3Y6oSWnQt5yIvNSQfcdLRMaIyDoR2Sgit9dxPFZEpjnHF4hIh8a+5nHZ8S08OwqeG22TxRm/h1tWwfgHoP0pmiSUCqScMVBeYhcFUwHV0BpFL88HIhIJDGrMCzvneBQYCWwHFonIDGPMao9i1wKFxpguIjIJ+AdwcWNet0EqyuCzv8H8xyC+FYy9F/pfputCKOWmTsPt6o/rP4KOp7sdTZPi9SexiPxBREqBviJS4txKgXzg3Ua+9hBgozFmszGmAngNmFirzERgqrP9BnCWiJ87AzbPhcdPgXmPwMArYcpCGHK9Jgml3BabYKf00OspAs5rojDG3G2MSQT+ZYxJcm6JxpgWxpjGLjvVFtjm8Xi7s6/OMsaYSqAYaFH7RCIyWUQWi8jigoKCE4vmUBG8OwVenGCXML36fRj/oC4/qlQwyRkD+zbC3o1uR9KkNKiR3RjzBxFpKyJDReT0mpu/g2soY8xTxphcY0xuenr6iZ2kqsJeE3HqzfBf30CHYb4NUinVeDmj7P0GvfgukBrURyEi9wCTgNVAlbPbAF804rV3ANkej7OcfXWV2S4iUUAy4J8rbhJawU1LITbRL6dXSvlAagdI72Gbn075tdvRNBkN7cw+D+hmjCn34WsvArqKSEdsQpgEXFqrzAzgKmAecAHwqTF+nEJSk4RSwS9ntO1DPFysTcMB0tDxnZsBnw5cdvocpgAfAWuA6caYVSLyZxGZ4BR7FmghIhuB3wJHDaFVSjUxOWOguhI2fep2JE2G1xqFiDyMbWIqA5aKyBzgh1qFMebGxry4MeYD4INa++702D4MXNiY11BKhZmswdA81Q6T1TnWAuJYTU+Lnfsl2GYgpZRyV2QUdBkJGz6G6iqIiHQ7orDnNVEYY6Z6O66UUq7IGQ0rpsOOJZA9xO1owl5DRz2twDZBeSrG1jj+aozRuX+VUoHT5SyQSDukXROF3zW0M3sW8D5wmXObiU0Su4EX/BKZUkrVp3kqtDtFFzMKkIYOjx1hjBno8XiFiHxrjBkoIpf7IzCllPIqZzTM/iMUbYWUdm5HE9YaWqOIFJEf6nciMhio6UGq9HlUSil1LN3Otvdaq/C7hiaK64BnReR7EdmCvb7hehGJB+72V3BKKVWvFl0grZMmigBoUNOTMWYR0EdEkp3HxR6Hp/sjsECrqKzm5mnfkZ0WRzvn1j4tnoyUZkRH6roTSgUdEXvx3aJnoeIgxMS7HVHYOtYFd5cbY/4jIr+ttR8AY8y//RhbQBWWVbB2VymzV+/hSNWPA7wiI4T2LeLon5XCgHYpDGiXSrc2iZo8lAoGOaPtujGb50L3sW5HE7aOVaOoSdFhPwlS66RmfHrbcKqqDXtKDpO3r4xt+8vYur+MtbtL+WJDAW99Z+csbBYdQZ+2yQxol0r/7BSGdm5BSlyMy3+BUk1Qu6EQk2gnCdRE4TfHuuDuSef+/wITjvsiI4TMlOZkpjTnlM4/Ln1hjGF74SGWbiviu61FfLetkBe+3kJFVTV9s5KZMUWnJVcq4KJioMvPbD+FMbY5SvlcQy+4ywEeB1obY3qLSF9ggjHmr36NLoiICNlpcWSnxTG+XyYA5ZVV3PvROp756nuKyiq0VqGUG3LGwOp3YdcyyOzvdjRhqaEN7U8DfwCOABhjlmOnBW/SYqMiGdWrDcbAgu/3ux2OUk1Tl5GA6OgnP2pooogzxiystU+vnwD6ZiUTGxXB/M06i4lSrkhIh6xcXUvbjxqaKPaKSGec+Z5E5AJgl9+iCiGxUZEMap/Kgs1ao1DKNTmjYee3ULrH7UjCUkMTxa+BJ4HuIrIDuBm4wW9RhZiTO7Vgze4SisuOuB2KUk1Tzhh7v+Fjd+MIUw1NFDuA54G/Aa8Bs7FLlCrgpI5pGAMLt2itQilXtO4NSW21+clPGpoo3gXGYzuzdwIHgIP+CirU9MtO0X4KpdwkYpufNn0GleXHLq+OS0Nnj80yxozxayQhrFl0JAPapbDge00USrkmZwwsfg62fAldRrgdTVhpaI3iGxHp49dIQtzJnVqwamcJxYe0n0IpV3Q8HaKa6zBZP/CaKERkhYgsB4YB34rIOhFZ7rFfOU7q2AJjYJFeT6GUO6KbQ6czbD+Fqb0gp2qMYzU9jQtIFGFgQLsUYiIjWLhlPyN6tnY7HKWappzRNlEUrIVWPdyOJmwca66nvEAFEuqaRUfSPzuFBdqhrZR7uo629+s/1EThQzpXtg+d1CmNlTtLOFCuF60r5YrkttCmL6yZ6XYkYcWVRCEiaSIyW0Q2OPep9ZT7UESKROS9QMd4IoZ0TKOq2rAkr9DtUJRqunqfDzuWwP7NbkcSNtyqUdwOzDHGdAXmOI/r8i/gioBF1UiD2qcSFSHa/KSUm/pcYO9XvOFuHGHErUQxEZjqbE8Fzq2rkDFmDlAaqKAaKy4mij5ZyTqTrFJuSs6C9qfC8uk6+slH3EoUrY0xNZMK7gYaNUxIRCaLyGIRWVxQUND46BrhpI4tWL69iKKyClfjUKpJ63MB7NsAu3UUvy/4LdD+PjMAABnlSURBVFGIyCcisrKO20TPcsYYgzMr7YkyxjxljMk1xuSmp6c3Ku7GOndAJpXVhhtfW8rn6/I5UlXtajxKNUk9z4WIaFurUI3W0Ck8jpsxpt5r6EVkj4hkGGN2iUgGkO+vOAKte5skbhvVjSfmbuLq9QWkxcdwdu82TOiXyeAOaURE6FKNSvldXJqdxmPlmzDyzxAR6XZEIc1vieIYZmBnn73HuX/XpTj84tdnduG60zoyd10BM5fv4q1vd/Dygq20SWrGuL4ZjO+XSd+sZETX91XKf/pcAOtnQd430PE0t6MJaWJc6OwRkRbAdKAdkAdcZIzZLyK5wA3GmOuccl8C3YEEYB9wrTHG60Quubm5ZvHixX6N/3iVVVTyyZp8Zizdydz1+RypMrRvEcf4vplM6J9JTutEt0NUKvxUlMG/ukCf82HCw25HE/REZIkxJrfOY24kCn8KxkThqbjsCB+t2s3M5Tv5euNeqg10a53IhP6ZjOubQfsW8W6HqFT4eGuyvUr7tg0QFet2NEFNE0WQKigtZ9bKXcxYupPFzkV6/bJTGN83g3F9M2mT3MzlCJUKcRtmw8sXwKRXoPs5bkcT1DRRhIAdRYd4b9lOZi7fycodJYjAkA5pjO+Xydg+GaTFx7gdolKhp+oI3NcdOgyDi6Yeu3wTpokixGwqOMB7y3YxY9kONhUcJCpCGNa1JeP7ZjKqV2sSm0W7HaJSoeP92+C7l2zzU7Mkt6MJWpooQpQxhjW7SpmxbCczl+1kR9EhYqIiOLNbOmP7ZPCz7q00aSh1LNsWwrMj4dwnoP8lbkcTtDRRhAFjDN9uLWLmsp3MWrmLPSXlxERGcFrXlozp3YaRPVuTEqfNU0odxRh4sC+06AJXvO12NEHLW6Jw6zoKdZxEhEHtUxnUPpU7x/Xku22FzFqxm1krdzNnbT5REcIpnVswtk8Go3q2pkWCjvBQCgAR6HMhfHU/HMiHhFZuRxRytEYR4owxrNhRzAcrdjNr5S7y9pURIXbOqbF92jC6VxtaJenoKdXE5a+Fx06Cs/8JJ/3S7WiCkjY9NRE1fRqzVu5i1srdbMw/gAjktk9laOeW9MpMonfbZDKSm+lV4Y5DFVXsL6sgU9+T8Pf4MIhuBtd94nYkQUmbnpoIEaFnZhI9M5O4dVQ3NuwpZdbK3Xy4cjcPfbrhhxmXU+Oi6ZWZTK/MJHq1tfcdW8Q3uXmoFmzex02vLWV3yWHS4mPom5VM36wU+jn36YnafBdW+lwAn/zJLmiU1sntaEKK1iiaiLKKStbsKmXVzmJW7Shh1a5i1u8+QIUzu21cTCQ9MpJsrSMzmZ6ZSeS0TiQmKvxWy62qNjz62UYe+GQ97VvEc8XJ7Vm7u4Tl24tZv6eUaud/ibYpzemblUy/7BT6ZiXTp22yjjILZcXb4f5ecOb/whn/7XY0QUebnlSdKiqr2Zh/gJU7i1m9s4RVzv3BiioAoiOFrq0Sf2iy6pWZRI+MJOJjQ7ciml9ymJunLeWbTfs4t38mfz2vDwkef09ZRSUrd5SwfHsRS7cVsXx7MVv3lwG2T7RTy3j6ZafQL8smjx4ZSTSL1plJQ8bzY+FgAfx6of0HVT/QRKEarLrakLe/jFU7i1m548fkse+gXYhJBDq2iP+hycrekkPiyvEv1hfw2+lLOVBeyZ8n9ubCQVkN6pcoPFjB8h3FLNtW5CSQYvYeKAdsMu3eJumHGkfX1gl0Tk/QocrBavHz8N7N8MsvIKOf29EEFU0UqlGMMewpKf9J8li1s4QdRYd+KNM6KZbubZLonpFID+e+U8uEoGi6qqyq5t+z1/PY55vIaZ3Ao5cOpGsjZuw1xrCr+DDLtxexbLtNICu2F1NaXvlDmbT4GDqnx9M53SaOzq3sdlZqHJFNrC8oqJTth3tz4OQbYNRf3Y4mqGiiUH5RVFbhNFmVsGZ3CWt3lbIx/8d+j+hIoXN6Aj0ykujeJpHuGUn0aJNIemJswEYY7Sg6xI2vfseSvEIuGZLNneN60TzG901F1dWGbYVlbCo4wOaCg2wqOMCmfHtfUxsDiI2KoF92CoM7pJLbIY2B7VJJbq79HgH1yiTYtQxuWQUR7v+QCRaaKFTAHKmq5vu9B1mzq4S1u0tZ69zvKj78Q5ms1OZcOCibiwZnkZHc3G+xzF69h9teX0ZlVTV//3kfJvZv67fX8qaorIJNTvJYt7uUxXmFrNpRTGW1QcSuiliTOIZ0SNNZg/1t5ZvwxjVw6euQM8rtaIKGJgrluqKyCtbuLmXNrhI+XZvPlxv2EiEwvFsrJg3O5mfdWxEV6Ztfd+WVVdwzay3Pf72F3m2TeOSSgXRoGVzrfJRVVLJ0axGLthSyOG8/S/IKKXMGEbRNaU7X1gm0T4ujfYt4OrSMo11aPNlpzYmN0o7zRqusgIcHQlJbuOZD7dR2aKJQQWfb/jKmLdrG9MXbyC8tp1ViLBfmZnFxbjvatYg74fPm7TvIlFe+Y8WOYq4e2oE/jO0eEl+ulVXVrNlVyqIt+1mytZDvCw6ydX8ZBzz6PUQgM7k57VvYBJKd1pxWic1omRBDy4RYWiXGkhYf47OEG9YWPg0f3AZXv2+nIFeaKFTwqqyq5rN1Bby2cCufrcun2sCwLi2ZNCSbkT1bH9eX/IxlO/mft1YQIfCvC/sxulcbP0buf8YY9h2sIG9fGXn7Dv54v7+MvH1l7Pfo+6ghAqlxMbRMiCE9MZZ2aXF0Tk+ga+tEurZK0Kvyaxw5BA/0hda94Mp33I4mKGiiUCFhV/EhXl+8nWmLtrGj6BBp8TGcP7Atk4a0o3N6Qr3PO3ykiv+buZpXF25lYLsUHrpkAFmpJ14rCRVlFZXsLa2g4MBhCkor2HugnILScvYesLf80vKjEkp8TCRdWiXQpVUiXVsn0K11IrkdUpvmhYRfPwiz74TrPoWsQW5H4zpNFCqkVFUbvtq4l9cWbmX26j1UVhuGdEzjkiHZnN074ycXuG3YU8qUV75j3Z5SbjijM7eOyiFam15+Yt+BcjbmH2BD/gE2OrcN+aXsKbHXgkRGCP2ykhnWpSVDu7RkQLuUkGiua7TyUnigD7Q7BS551e1oXKeJQoWsgtJy3liynWmLtrJlXxnJzaM5b0BbLh6czfLtRdw1YzVxMZHcd1E/hnfT6aOPR/GhI6zaWcy8Tfv4auNelm0rotpA8+hIBndMY1iXFpzapSU92iSF7zxgn/8DPv873PA1tOntdjSu0kShQl51tWH+5n28umgbH63c/cO1Gid3SuPBSQNorVOpN1rJ4SMs2Lyfrzfu5euNe9mQfwCAFvExjO7dhgn9MhnSIS28ksahQri/D3QdCRc+73Y0rtJEocLK/oMVvL9iF0nNohjXN1OvdPaTPSWH+XrjXj5bV8Anq/dw6EgVGcnNGNc3g4n929IrMyk8OsY/uQu+egCmLIaWXdyOxjWaKJRSjVJWUckna/KZsXQHc9cXcKTK0KllPBP6ZzKhXyadvAw2CHoHCmxfRe/z4dxH3Y7GNZoolFI+U1RWwayVu5mxdCfzv9+HMTC4Qyo3nZXDqV1ahGYtY9bvYdEzcON3kNLO7Whc4S1RuDI8RETSRGS2iGxw7lPrKNNfROaJyCoRWS4iF7sRq1Lqp1LiYrhkSDtenXwy824/izvG9mB74SEuf3YBFz85n3mb9rkd4vEbeiMgdsisOopb4whvB+YYY7oCc5zHtZUBVxpjegFjgAdEJCWAMSqljqFNcjOuP70Tn902nP+b0Iu8/Qe55On5THpqHgs2h1DCSG4L/S+Fb1+C0t1uRxN03EoUE4GpzvZU4NzaBYwx640xG5ztnUA+kB6wCJVSDdYsOpKrhnZg7n+fyZ3jerKp4CAXPzWfy56Zz5K8/W6H1zDDbobqIzDvEbcjCTqu9FGISJExJsXZFqCw5nE95YdgE0ovY0x1HccnA5MB2rVrNygvL88/gSulGuRQRRUvL8jjibmb2HuggtNz0rllRFcGtDuqlTm4vHk9rH0fblkJcWluRxNQrnRmi8gnQF2T7dwBTPVMDCJSaIyp8xMkIhnA58BVxpj5x3pd7cxWKniUVVTy0rw8nvxiM/sPVnBmt3RuGZlD36wgbUXOXwOPnQyn/w5+dofb0QRU0I16EpF1wHBjzK6aRGCM6VZHuSRskvi7MeaNhpxbE4VSwedgeSVT523hqS82U1R2hBE9WnHziBx6t012O7SjTbscvv8Cbl4JzZLcjiZggm7UEzADuMrZvgp4t3YBEYkB3gZebGiSUEoFp/jYKH41vAtf/u5Mbh2Zw8Lv9zPu4a+44aUlrNtd6nZ4P3XarXC42A6XVYB7NYoWwHSgHZAHXGSM2S8iucANxpjrRORy4HlglcdTrzbGLPV2bq1RKBX8ig8d4dmvvue5r77nYEUl4/pmctNZXenSKkgu3PvP+bBzKdy8AmLCfyZiCMKmJ3/SRKFU6Cgqq+CpLzbzwjdbOHykign9MrlyaAcGZKe4e+He1vnw3GgY/gcYXtfo/fCjiUIpFdT2HijnybmbeHnBVsoqqujeJpHLTmrHxAFtSXJrrYw3roU1M+CGryD9qC7UsKOJQikVEg6UV/Lu0h28smArq3aW0Dw6kgn9Mrn0pHb0zUoObC3jQD48Mhha9bRLpkaE9zonmiiUUiHFGMPy7cW8smArM5bt5NCRKnplJnHpSe0Y06sNLRJiAxPId/+Bd38N4x6A3F8E5jVdoolCKRWySg4f4d3vdvDygq2sdUZIdWwZz6D2qeS2TyW3Qyqd0xP8U9swBqaOh13LYcpCSAztddi90UShlAp5xhhW7Cjm6437WJK3nyV5hRSWHQEgJS6aQe1SGdQhldz2aXRsGU9cTCTNoyMbv9DSvk3w2CnQbQxc9KIP/pLg5C1RRAU6GKWUOhEiQt+sFOeq7s4YY9hUcJBv8wpZnLefxXmFzFmbf9Tz4mIinVvUD9vxsVFH7YuLiSI+NpLmMVHEexxrHpNKRr/fkPXtvaybO43C7BFUGwP2P+ymce5tQvvh3lDHfsCjfLX56XNxyojYZWnjYqJoHhNB8+gomjvJr+Y+Jiow/SZao1BKhY39BytYklfI7uJDlFVUcbCiirLySsqOOPcVVc7+Sg4592Xldt+hI1X1njeKSt6LuYMkOcio8n9ygOC4tiIqQmgeE0mCk/j6ZqVw/8X9T+hcWqNQSjUJafExjOzZ+oSeW1VtOHSkijInedQkk5oEcnjvA3T76AJm9/uCLUPuQgQEW9P5cRtAiBBnv7NPEGq6UDwfR9Tx3JrH1QYOH6lyYqriUEUVh4/8mNQOVVT+sH2wvJKDFVW0SvRPJ78mCqWUAiIjhITYKBJioyCxjgJdR0DhZDIWPkXGsCshe3DAY3RLeA8MVkopXzrrj5CUCTNvhKojbkcTMJoolFKqoWIT4Zz7IH91k1o2VROFUkodj25nQ8+JMPefduhsE6CJQimljtfZ/4SoZvDOr6Cy3O1o/E4ThVJKHa/ENjDu37BtPrx5HVTXP7Q2HGiiUEqpE9HnAhh9t51h9r1baq6kC0s6PFYppU7UKb+Csr3w5X3QPAVG/B+4uY6Gn2iiUEqpxvjZH+FQoR0FFZsEp9/mdkQ+p4lCKaUaQwTG3gflB+DTv9hkcdJkt6PyKU0USinVWBERcO5jUHEQZv03xCZA/0vdjspntDNbKaV8ITIaLngOOp5hFzta/a7bEfmMJgqllPKV6GYw6RVom2vX3N74idsR+YQmCqWU8qXYBLjsdUjvDq9dDnnz3I6o0TRRKKWUrzVPgSvehuQs+M/PYd5jIX1RniYKpZTyh4R0uPo96DAMPvoDPDMCdq90O6oT4kqiEJE0EZktIhuc+9Q6yrQXkW9FZKmIrBKRG9yIVSmlTlhiG7h0Opz/LBRthafOgDl/gSOH3Y7suLhVo7gdmGOM6QrMcR7Xtgs4xRjTHzgJuF1EMgMYo1JKNZ6Ine5jyiLocxF8eS88cSps+crtyBrMrUQxEZjqbE8Fzq1dwBhTYYypmZYxFm0mU0qFsrg0OO9x23dRdQReOAdm3gSHityO7Jjc+vJtbYzZ5WzvBupc5FZEskVkObAN+IcxZmc95SaLyGIRWVxQUOCfiJVSyhc6/wx+NQ+G/ga+fREePclecxHEkwqK8VNwIvIJ0KaOQ3cAU40xKR5lC40xR/VTeBzPBN4Bxhtj9nh73dzcXLN48eITjFoppQJo53cw4zewewW07m2TR+/z7cV7ASYiS4wxuXUd81uNwhgzwhjTu47bu8AeEclwgssA8o9xrp3ASuA0f8WrlFIBlzkArv8MJjrDZ9/+JTzYD755GA6XuB3dD9xqepoBXOVsXwUcda27iGSJSHNnOxUYBqwLWIRKKRUIkdEw4DLbHHXZG5DWCT7+X7i/F3z8Ryips8U9oNxKFPcAI0VkAzDCeYyI5IrIM06ZHsACEVkGzAXuNcascCVapZTyNxHoOtJeezH5c7s97xF4oC+8/V+wZ7V7ofmrj8It2kehlAobhXkw/zHb6X2kDDqdCQMuh+7nQHRzn76Utz4KTRRKKRXsyvbD4udgyVQo3gqxydDnfOh/GbQd5JNV9TRRKKVUOKiuhi1fwtJX7JDaykPQsptd+6LvxZCUccKn1kShlFLh5nAJrH7HJo2t80AioOe5cOHzJ3Q6b4lCV7hTSqlQ1CwJBl5pb/s22YSBf374a6JQSqlQ16IznPVHv51e509SSinllSYKpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXoXdFB4iUgDkuR1HA7UE9rodxHEItXhBYw6UUIs51OIF/8fc3hiTXteBsEsUoUREFtc3t0owCrV4QWMOlFCLOdTiBXdj1qYnpZRSXmmiUEop5ZUmCnc95XYAxynU4gWNOVBCLeZQixdcjFn7KJRSSnmlNQqllFJeaaLwIxHJFpHPRGS1iKwSkZvqKDNcRIpFZKlzu9ONWGvFtEVEVjjxHLVcoFgPichGEVkuIgPdiNMjnm4e799SESkRkZtrlXH9fRaR50QkX0RWeuxLE5HZIrLBuU+t57lXOWU2iMhVLsb7LxFZ6/y7vy0iKfU81+tnKMAx3yUiOzz+7cfW89wxIrLO+Vzf7nLM0zzi3SIiS+t5bmDeZ2OM3vx0AzKAgc52IrAe6FmrzHDgPbdjrRXTFqCll+NjgVmAACcDC9yO2SO2SGA3dkx4UL3PwOnAQGClx75/Arc727cD/6jjeWnAZuc+1dlOdSneUUCUs/2PuuJtyGcowDHfBdzWgM/NJqATEAMsq/3/aiBjrnX8PuBON99nrVH4kTFmlzHmW2e7FFgDtHU3Kp+YCLxorPlAioic+KruvnUWsMkYE3QXXRpjvgD219o9EZjqbE8Fzq3jqaOB2caY/caYQmA2MMZvgTrqitcY87ExptJ5OB/I8nccx6Oe97ghhgAbjTGbjTEVwGvYfxu/8xaziAhwEfBqIGKpjyaKABGRDsAAYEEdh08RkWUiMktEegU0sLoZ4GMRWSIik+s43hbY5vF4O8GTACdR//9UwfY+A7Q2xuxytncDresoE6zv9zXYmmVdjvUZCrQpTnPZc/U07wXre3wasMcYs6Ge4wF5nzVRBICIJABvAjcbY0pqHf4W20zSD3gYeCfQ8dVhmDFmIHA28GsROd3tgBpCRGKACcDrdRwOxvf5J4xtSwiJYYgicgdQCbxcT5Fg+gw9DnQG+gO7sE05oeISvNcmAvI+a6LwMxGJxiaJl40xb9U+bowpMcYccLY/AKJFpGWAw6wd0w7nPh94G1st97QDyPZ4nOXsc9vZwLfGmD21DwTj++zYU9Ns59zn11EmqN5vEbkaGAdc5iS3ozTgMxQwxpg9xpgqY0w18HQ9sQTVewwgIlHAz4Fp9ZUJ1PusicKPnPbFZ4E1xph/11OmjVMOERmC/TfZF7goj4onXkQSa7axnZcraxWbAVzpjH46GSj2aD5xU72/voLtffYwA6gZxXQV8G4dZT4CRolIqtNsMsrZF3AiMgb4HTDBGFNWT5mGfIYCplb/2Xn1xLII6CoiHZ2a6STsv42bRgBrjTHb6zoY0Pc5EL36TfUGDMM2JSwHljq3scANwA1OmSnAKuwoi/nAUJdj7uTEssyJ6w5nv2fMAjyKHSWyAsgNgvc6HvvFn+yxL6jeZ2wS2wUcwbaBXwu0AOYAG4BPgDSnbC7wjMdzrwE2OrdfuBjvRmxbfs3n+QmnbCbwgbfPkIsxv+R8Tpdjv/wzasfsPB6LHZm4ye2Ynf0v1Hx+Pcq68j7rldlKKaW80qYnpZRSXmmiUEop5ZUmCqWUUl5polBKKeWVJgqllFJeaaJQSinllSYKpZRSXmmiUMqHROQdZ4K2VTWTtInItSKyXkQWisjTIvKIsz9dRN4UkUXO7VR3o1eqbnrBnVI+JCJpxpj9ItIcOy3EaOBr7HoDpcCnwDJjzBQReQV4zBjzlYi0Az4yxvRwLXil6hHldgBKhZkbReQ8ZzsbuAKYa4zZDyAirwM5zvERQE9nCiqAJBFJMM7khUoFC00USvmIiAzHfvmfYowpE5HPgbVAfbWECOBkY8zhwESo1InRPgqlfCcZKHSSRHfsMrHxwBnOzK9RwPke5T8GflPzQET6BzRapRpIE4VSvvMhECUia4B7sLPU7gD+DizE9lVsAYqd8jcCuc7Ka6uxs90qFXS0M1spP6vpd3BqFG8Dzxlj3nY7LqUaSmsUSvnfXSKyFLuozPcE4TKsSnmjNQqllFJeaY1CKaWUV5oolFJKeaWJQimllFeaKJRSSnmliUIppZRXmiiUUkp59f8rJFgbFDPVyAAAAABJRU5ErkJggg==\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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Ir0LdyVOhTFlzKKgiJaeAW6ugoaHpFuFoilSojWaznQQpLCwMrVEiKJAgKgIqILEf9r30ujwn5yII7oFA4ClRrpxicPAfyOfvoeCu4BP734Iyt/DZPz2PC3uTcP/n4Pb3o2p5Ble9kP/T9mpG3TTdc5L3phI8OxGnXdORkw7+6BxlqVBSUZirMnO6QG66DAoG0iY7+mZpLt+BLAvq9Z1MnPLRwhlUcjPm9X+EZcRJCI1mwMenVitT9uZZ9KuYVoR4JENYRtGlhqmFwBAIo4YsjLOUHWLSzDKSrjLTZFA3dJI1jfZCjLZqnIiKEtd70FqiWNEkMREn7kWxpPWw4+EKDxcPS5kY6GfmKxSObpO351gsTdG0YoAeLn7Kz8fjNmISQnwOuAGYV0ptXp53PvApIAx4wBuVUntEo5/NjwPPA6rAa5VS+x5vJ4JGTIHAby/PKzEy8i9MTH4BSZjvnLqB/SOX8ufn9/H8gWbE+BD+8X34dUFN76JECzFPx/gNtp9USqGcErI8hyzNoapZZG0JZRdQ9SVUvYjy6o1OwR5rO0JHmmE8M0QtHKcUSZCNpxlPtzLUsRqvrZ8mWcJwjjId3oNlxthR3cgzKxeQcRIoAyKXtdHy3LPriuBXaqEqhLgcKANffEhwvwX4qFLqh0KI5wFvV0pdufz6zTSC+8XAx5VSj3tJCoJ7IPDbRynF3PjNTOz/H/R8Ejt/IbVsPwNYWA8LKz5oS0xEwoxEUyRTIda1JehoiqJFDbRQ44GksHRy81VO7ptn9EgOz5O09idZv7OdHjFI9abvUy+ugMQGhNGopy55S8zrRWyxRDY/SLE0j2mF6Vu7lbbMJhaLUUZzVfKVeYziOPHcGB35CTqLM0Td2pk9lELgmCE8y8IxTVzDxNU1PE3g6AZ2KEzVNKgjkUJDKYHluUQdh4hjE3FsEvUaMdsm7D3YKlUCuWic0WQ7+zvXcU//JmbaTELGUXq1Ya6trqatfy2v/cPXntU5+JVaqCqlfi6EGPjF2TQeG0BjvPAHarBupHERUMBuIURaCNGplJo5qz0PBAJPG0op/MU69liR6tAMldPjGMUMnfw5ADkkkZYImbUZzPK9aKe+BGqKT/dezedXvZKX9/Xw2u4WOkIPb7Tj+5LhfVkO/myCuZEiZlhn/c4O1q9OY45MU715P3mRAf3FyNACC5WTnAyXKBmL2AvjuLUqRiRKePMzqCRvZHBecvvYEB17f8x52dNszY0RW+7F0RMaC/EIY21R7FAT1WicUiJONZkglMogEdRtB8cK4VlhpKajSR9D+uhPsKsWzfeJl8skl5ZILy2RyS+xOT/BjtkhXrf/ZmqhEGNtXRztXM2BzlYWmOe1T+mZajjbOve3AD8WQvxfQAMeGCeqG3ho0ubk8rxfCu5CiNcDrwfo6zu7TnMCgcC5Jasu9dNL1Afz2Kfy+IXlDA+jip2e4nhvns9PtBLqSvL3L7+AVcU91L7/50TyQ/yo+TI+su7/csO6bezqbiFh6A/bdr3icvTOKQ7fPkVlySbdEuZZV/fQhsI5vohzKIvtu/jZcdzqrdyfkIzFfGJ+GXs6z1KklfLqa5mN9TE7scCmPUe4cO52nrU4QnQ5mFdbWin2dTMYDzPf2koh04wyfjnsCaAufWpmiFK6naoVAqET8zTCFaDuUNezREN1Vvlp+u0OdDRmrAVmEwW8jE5LooWmaJpEKEGIEFJKPM+j6nkslIo4Q6exTg+Rmp5mYG6K9RMjvEgI7t9y/jk5d2cb3P8CeKtS6ptCiJcBnwWe/WQ2oJT6NPBpaFTLnOV+BAKBp5ibrVI7skD9WA5nsgQKRFhH79dYWnkL+cjt2E0DfOCe6xmbTvLW69by+m1xSj/8SzjxbWYi3Xz4/A+zZfsL+XZX85lWow/IzVQ4dNskJ++ZwXMla1cmWbcmhT5ZQu2do65c3OkDeFP78K0T3Nm3lkI4hFsoMCG7yHY8k+GOLprnJrj8vr28YPZLdBbmAagkEsz0dzHX1k62rZV6JILwHIRjo5TCcHUifgfUMuC4uPZJjvUa7N1yCQtN7RieZMOUy6Zxh9RCgfHMQcabjrAxkuYVS1cyUOqiKmyOagtMVSzE4haSy7euElgAihGDZEuYTEeMlo4o6c4ozRfESf9RFE2AV8oycs/PGP3+j6gfGqS/N31OzuPZBvfXAP/P8uuvA/+x/HoK6H3Iej3L8wKBwNOUUgp3pkLtyAK1I4t481UAzJ44iav6MFeFmXQ/w8TUFzDNZu5dfA3/9v1eNnam+N6bttA69g2cf/17ol6df1n5Z4Se+Vb+qa+b6ENSGZVSTBzPcfCnE4wfzREzNS5ekaDV8VG5GhRqKHuc6t6bkAuHMQdc7lqzkRl3A8NeC8Opdcyl29hSGuHa43eydeIEiUoZKQQLLS0cPH8rlQ0bqVlRajMT+LUc2uwoSdmMbqxBN1djRROk22JEEw4Ti7u5JWFwYONzqYWjrNY0LnEMDuydZtg5zlzLPhI9p7gufwlvW3g1aTeO16qRvnwlXVvbWGvp+J7EqXnUKy7VgkN5yaayZFPO1Sks1JgdXuLUfQ92ZWBoDq3mMG36IO3mKXamj2FcXkReePk5Oa9nG9yngSuA24GrgFPL878H/KUQ4is0HqgWgvr2QODpySvYVPfPU9033wjoAkIrUsQvXkl4cwtGKsTCwm0cPvkubHsGLfZi3nPH5Ywv6fyvq1bxyvUO1W+/iJbsPu5Jb+PwFe/jNVsuIfmQO3XpS07vnWffLeMsTpbpS5pctyJBKF+H+Sp6u4ln30fpJ/+FEFVCa2zuXL2OXWoj4+EBZEuCtbVp/nTiDlaNDZMqFpFCkG/vZuriq3C27qRQ8lk4cT/Osf2Ai6ancJIdFHuTtHa0093aiRUCpz7D+Imj3OZ3cODSZ+IaJmuKi6wbGcSan2QhNkoqNU6PDHH50oVsrT4HlOBEaI75zBjlkIK9R9D2aWiahq4JdE0nbBpELZOoaRLxS4TtcToZYm3oGKH2Cq6XoioGyBvbWLBXcKS4loPVxoUv2WyyLdbN5nNwfp9ItsyXgSuBFmAOeA9wkkbKowHUaaRC7l1OhfxX4DoaqZB/opR63DSYIFsmEPj1kI5P7cgC1X3z2ENLoMDqTxLd3kZkUzN6vJGr7bpLDA7+v8zOfYdodA178q/jo3eEWdUa40MvWoc8+M9sPfRpKnqUWy54O1c+6w20hx/M83Ztn2N3TXPw1gnsfJ31zSH6TQ2t5qElLEIrNKp3f4Pyrd9DWBpijc33W3ewL3weKpagiwIbpodZOTxMx3JHXrWWXmoD57G4ZivDtk+9MIxeHkKrF1BCw0014aZakJHYw0Zi0pREVooc6+hnqmcVUcdhRWGBpsISyrcRwsWSGhqP3ZWvQjWaNwmFEAqW/1ZKQ6kn1g2wruuYpoVlWOhYKMdgzZo1XP/Sq57MaTzjV82WefmjLPqlXvKXs2Te9OR2LxAInGvuXIXy7hmq++ZRto/eFCZxVR+x7W0YzQ8fwWg++2NOnnw3rrtEqvUNvPf2HRyaqvHKS/q4oXec1m9cQ191gl1919P1gg/xhy09Z95bLTocvn2Sw3dMEqp5nNcapjljITxJqD9BaECj+L3PMP+lW1ARi/ELWvlJx6XUIxma9To7CvOsOLyHFeNjWPU6dizJ2KaLOLWih8W4Cb6Hld2FmZ/H9FwwLIqtMU73gwrXuWL1Gra1bMOxHYrFIiODJ5kuFDHjaTaWC2w80Wh242gaFdOhbi6AkKz0VrFWmRixBUqts+ixcSJ6Dl130XUPTVcoK4MtYpSlTrFepuaUqUiHmpIYCAwEITTiaMQwiUgTIS08N4RvxynV05Rrzbh2glpVgl/FwyU3GXT5GwgEngTlSWrHFinfM4MzUgBdED2vldhFHVgDScQvjDPqOIucHPx75udvJh7fwLT2j7zxaw5Ry+f9L11N2+EPcsmBbzIZ7WH/jf/DM7Zdf+a9S/NVDt46wfF7ZsgoxaUtYZKmQChF7JJOQit18l/+DAsf/Q6F5ib2XHs5U8kuIoYiKSUrp0bYPHyatpkpfE1jqqeb4ZUrybZ1EJIxDMJ01OaoTx/Hd2yaUykGtdPcfb5Bu9vBhbGLSdhJ5u+f52b/5jP7VbVilNNd2EaEaiSJY0iEfRDLOciAFuVF+iqSeg4ndi8IBQi6ZJpQ7jxE0aCer2Nn6zgFF08pokIjKgRthoEIWeihMCISRYvF0cJxzHAcS0XQnAha3cKyNcKe8Yj/KrDNIot6iTt1+5eWPRWCYfYCgd8xsupS3j1D+Z5pZMlFbwoTv7iD6AXtZ6pdHqrRH8wPODn4D3heiY7uN/Kx3Rdxy7FFdq5t4Zr+Ia7b827a7UUObflTNt3wXqxQoyfDudEi+28ZZ3j/PF2WxuamEOGahxY3iV/WRXh9lPwXPsvUt77FSE8PR9avR1kmSkGpbrB+dIhtp46Q8AS1TAf5FVvQ2jYQ0zIYepRwKoSq5nEKJUxh4YUMZkWBGb3InFagttz5lq40WlWSVpmkRSZpUjFSKvq4VS1PBSV98B2U76B8F19JbKkoayaLZoyCDiVLYidNZFsUkTyFH76FNusgbUaZk/4m3njN987qs4Nh9gKB3wNerk551xSV+2dRjiS0NkP8JV2E12QQmnjE99h2lpOD7yabvYVk4jxqsXfw2q8XWarmedWz27hw+CO84M6bmUmuovhH/8W2FZeglGLsyCL7bxljanCJvpjBczsimDUPI2YSv26AyPo4c//1Re762E8Y6u4me911REWIuN9MsmCxpViiXRroLesQ/a9BaDpxoPUh++Yrn9J8lgltgWyyxqxaoqo1ugPQhaAn3UUk0UXOTDIsDfSJU0ybOkO9fRhI+jCZPjFPInEvW9NHWRl2scISfIk5JjCHFdaUjoxozGRshuIwGlEsJoBQhpbiOlpLq8hUOxBATMuR0OeIaAVCWgFLLGGJJUStjqjUURUPWfQhX0MrVYlLSUxoFFIryDadx3z7Nliq0LzqJE19UcxYLzX7JM/q3XZOfg9BcA8Efss5U2VKP5+kdjgLQhDd2kri8h7MjkfvJ1wpxezcdxkcfC9S1hhY8Td85dhlfObOcfq7E/zB1hFes+dNtLhLTF/4Zrqe8y58YXJy9wz7fzLO4lSFFWmL63uiGGUXI2GRfNFqwhsyDH/tqxz8j3upt6wls/klbJNJMk6C5APhJgQqY+M4ZVw9QkToSAHGyhSRC9Psu/fH7D24ByeWwI8nUYBULrPxLJVwhTXbXs7p5AY+ky/jSMmFY8foGD/Nd7dfSSme4nJNY/y+cVrMz/H8zUdItPjggX5cEDoUJ7q0ivK6OQ5ePMfPzDgHfYknJC2VbtZkt7Nltpf1TNNqnCCu/QwzmUf5FepKUDbiVPUoVc3ARqJJn7BRJxqrEZF1dCSaLtFNH00HW4AyS+jpoyT6voKMNi6yjmdRmFiBV7qWjLYd1j/1v4ugWiYQ+C1ljxcp/XSc+sk8IqQTu7iD+GXdGKnQY77PcRY4fuKdLCzcSiq5jVj7e/jrby9xZLrIFTvj/MH0v3Dj3E9YyKwj9ZJ/RzVv4diuaQ7+dIJy3mZ1W4T1YQ296GC0Rkhc1YvWGmboprsojRTIaC3EafT94qBYcEvEc4NYc6dxawtMx1Yi+i6mv1VHWSWMLTr1Ppv77z/E5LRHXTYa9UQjHtHwGLXmUci4RCJd1AghUUQ0Qad00RYXmAynWIo1EUGnubhEi3eSWLKKMECUwJqNEs4PIBItFHtOclpWmKlIYk6Znpqis5ogLuPoQmHrGhUjStGIU9JjFI0YJSNGXbOoahGqWoi6FsLVLWzNxNFMXM1AIh7IpQHEmb+lEHBmmUJTEs1TCKnQNYUmJFfWxnjrK951Vr+BoFomEPgd8tCgrkUNktcNEL+kEy38+P87Z7O3cPzEO/H9MqtXv4N9C9fwzs8cR0tZvGLHMG898k9kvBJLl/0N0R1vYe/P5zhyx93YVY+1KxKsbw0j5qvo8RDhZ3ThVh3mv30c09FJk8DQLE4Im93SJT23l6tPfodEvooXDzNxQSveM3Wspp/ihb7OkK+Ry/YxP7SC/L5OFHGS4UUGUvvoTDOVXugAACAASURBVExg6jlcPYRPGL+mo5wJwoZORNfxqxXyUlGKx+jQ51lrD2HqdURSgQIlQUqNQjxJpbuZckucvB+nWL+avMiwGMuQTWdYsDLkzdRjHjOhJBYOBi7mmeI85LWLhqSRGqkaoV2BriQhX2K5PqYvEb4ApaEQ+LqGRMMXGvF8MMxeIPB77RGD+s4utJD+uO/1vBKDg+9lZvZbJOKbWLnmg3zopw5f3XuEteeF+F+5j/P8I7ez1LKZ6rP/jUMHoxx/9/14nmTDpibWhXTU0BIipKO3RfAW61R2TePiMaXlOKUX+Jkuqaemef7cLl6+ewIrq9D6PNRzHSI9S6y1FwnPaiyMt3LEu4gTcgUuJmkKPIP72MIJ2uq5RsuZucf9SmcsmClORfs5ERvgZGSA4Wg/k+FOpsKt2JoFMRoFiHkVmpwCGbtAa2GeAWecZq9AT9imbcUa5twYJycXmc9VMZVioNlhZeo4XeIUKfKE9BqaVOiORbjcQbTURKhuoTugPBtNVgmTJ0KOkMqfGUBboWOLdgpWG6OJFg40dzKqRYgsKiz/kZ+H/KqC4B4IPM25cxUKPxqlfjz3pIM6QC5/D8ePvZ26PcvAwJvwIq/l5Z8/zKBtc/32Cd5z6oM0e0Vmtr2XY/lnM/jP80CeDTvaWGcJ/KOLjRAlQNk+paUSp5hiKjRLvmkIP7NIf3KUvzo5T+ten7jhEbrQJRT30ERjFKL6uMUBNnO/toUFmUTHI6EvYcYVdLRxZ/g6/su7gaLW6InRFxo+GlLoKMBQPqZycYVBTQtT0SOUjBhlPYqtP1gNFfWrrK5OsKk8yNWLd9PhLNDhzNNbm2VldZIWv/joB2roEeYtPdaRnX/YX57QKVhxpkJNjIUGGDa3MRxq52RsNcfjG1EiTdRxiDl14naNVKlMu1NBU4kndB6frCC4BwJPU17BpviTMap75xCWTvI5/cQv7X7CQd336wwN/xMTE58jEhnggu1f5ZZTrbzrpnsJr7T4/+wv8qqjNzEauYpbY29j6EcOhpFl8+WdrNUE7oEsvly+84zrTIRzHKodRGs7RLJ5mrXhadqW6jSPeDTVbQxTwVqwvQjZxHYWUms4WO9g0I8iKhUMKZmPpjnWNcDpth5MTSet6eD6KFcSNhTJeol1mPSF43iezZTvMuzWmQyHWIhH8fTGdzd9l97KHOeVB+muZumsZknVfaQXYtpMMBUKM6ZnmJJtaO5mhBFGaw9jqDK6rKDpAk/Xkb6LJR0i1AnjYCoPIUBD0ag4kctVLgJbW66cEQYejVLRIhS1OCUtjisNLN/D8l3Crkuk4hIpuGx1bC707kOohw+npymIO7DaX3xKfzcPCB6oBgJPM7LqUrxjkvJd06AU8Z1dJJ7Vix574i0Zi8XDHD3211Srp+npfhUdvX/Fe743xLdGslyyaoYPDn0Ao9jEXvMtTM03Y0UMtu3soN/zsY8sglSgCfyVYfb7R5mVt9DaNky3MU37gk3TvE/KbuSYu1WN/EKa3ZnL+e7Ol3Bnuod4cYHzJ0/Tl5vD13Sq8U6SRif9bprWuk2iXsX3a5RFjaKoURF1siEYbEoxnkozk2pmKda4o9V9n9byEq2lRmkpL5Gulh6WwS4UiOX/oNFVgAKU+PXGNw2BpXRMBBY+Bh4WNhZldDOPEcljxJbQkyXsWI1pTSPkp/mLF953Vp8XPFANBH4LKE9Svnua4m0TqLpH9Pw2ktf0YzSFn/A2pHQZHfsUo6P/imW1cP7W/2TWPo8bP7mP8bTkb1q/zfX7TnFf/a/J1lcQSZpcfkUbbSUbd99cY+hmQ+BsiXA/3wH9TrrD42zN1mgZ9EjYLlIJ8osxZsfDTFS7+NQVL+eH1z4TpQl2Tkzy0sHbMJ0iJiYr/C7CdclSZYlqJMekcBhVHujgG4K5ZBPTyWYmmtcwl2oCIGLXWT01xnPnBtlRPsoOcYykWcXwXfyswM5qlPMGLjpENFREg4iGjAhUWKAMAaaGMkCZAqULpKE3/jYEUhNIXaCWp2gCqTUuZlIAGkgNlJAoTSI1idAlLP+t6T5C9xC6RBNyua8ZiaZJfqHRL0pCyYdFXyPrC2ZcjWlHY9YRFJaiAFwrC0/ND+gXBME9EPgNU0pRP55j6QfD+It1QmszpK4bwOqKP6ntVCrDHDv2NoqlQ3S038iaNe/mq/sK/P3te1i9Isdnj/6Y2dwz+aH3UlIZk2dvbyaZreIfnMcFEFDf5nEs/GVC2l1sLi7RMeHQVHLwERxzViMnNCKHlpiNtPKf1/8B9297JpfkfN55YIxa6RRlUSOsWZi6QdmpM6hPgw6G55FazNFNntOdbRzoHuBESw+OJtFliW7nZ6xdGKXdGyElFpAJhZeE+xDsRqCEgRAGAtBEY4SgRmaKj4aHTmOeTqO6Q0ehiwde85DlCk2AsbwNXTSKponGVCg0odB1GqmKy+toy5/rKKhLgavAVuBIgS2hIgVl36DsC8pSUPQFOV+Q8wQOD0Z8SylWOS5XOg5rHZe1VZfwQudT8Cv6ZUFwDwR+g9y5CkvfH8Y+tYTRFqHlTzcTXpt5UttQSjI5+SVOD30QTYuwefO/EElew1u+fpibC0u8STtKx8/bOOa/kraM5Lmr2whNFFHHFvENgUJR3jzOeMvXSbgH2TZTo2PWwZSSrGznZv1yqoNVNuw7QC0c5TtXvwJn3eW8uruDl88Osnf8EFnNpzH6psAmB8YwWmyWYqzGfBimzBg5dGy/jiaPABBdgOjydygulxMANLpI0JXCoBGgWd66BKR48LX6xVvl3zBdKdJS0uz7bHQ9ur1G6XU92n1JxtCpJiMsNkeYD6cZQsPSLuJcjMUUBPdA4DdAVl2Kt45T3j2NsAxSz19J/JJOhP7k+kKp16c5dvxvyefvprn5Sjasfz8nsxZ/8cldrJQV3jli43jnkYznuXx1Gn2yCqfzaOkQXt2l0Lufmf7v0lw5zbYTNm1LNr7SGRPP4EeZaygeG+Z5u25FCY37tl+HWnkeL9qSYP/J3fx8qkbd8CjGF3Dio5Sjc0zrPjnJQxr0QESZZOo2m3yPXrdKj1unzXVJSklSShJSkpSKqJSElMJEYSzfcT8eBfiAL8BD4J2ZikeYB/7y1BYCR4ArNBzAFQJHCFzRGGdVLW/7wYsISAQhpYgoSUQqImq5SEnGl0SVwtZMFs0Qi2aIKSvOdMTiPkvjZkth6w6WsrEEhIUg4nlEhEab+ZgpOWctCO6BwK+RkorKnlmKt4wiax6xizpIXtP/iB16PeZ2lGJ29jsMnvoHlPJZv+59dHa+jM/fOcr3bhvlZfk6hh2lM5TjvP4kWq4NJqpY/Umc2RzZxM0sbL2Z9vwUl+xXJOwKrkpyf+QlfKj/Bnr37+Hl3/k8EdvmVP/FaJkMyTbJUf8gu8cKzKVyLEbnWDLLZ/apw/fZXPdZX6+zynUYcF36XQ9LCerKRJUUqqTh1zR8R8O3TTwL7HYodUC+SeCkBTLRqPOWmsD3Ie8LclJQ8KDgaxQ9yPsaFb9Rp20qMJVqFCBKI9CaCiwa8y0FBjq6L1BYKBFCaBYCrVFto8BAEsInho8SjYexSojGvixfGGxNI6vrVDSDiqZT1jTKQmNB1yki8JTE9cDxTFzPxPMspG3iSxMpQyg/gvIjIMON1zLCzkyQ5x4I/FZzpsrkv3Mad6KEtSJF+vkrn3S9OoDj5Dhx8l1ksz8ilbqAjRs+TM1t528/vJuWqSrXOhorQnNsaougOysQJZ3IBS042SVmva+xeOH36ZqrcemeKmFZpq76+XbmDbx79TVsObafN378H2lfWiSb7mNsRQuH19Y4vWKacaNA3qwAEFKKrY7DRfkaW2yH9bZD1WrmaGwNp+J9nKwJqqcmkfuG0CoKhCDU4qPW2tS2aBSbBHa7QC5/feVDqSSYkBpjrsaUL5h1NXJ+o2Y9KgRpXZI2JClL0qQrVvoxUk6GlN1MS7WTdGUl6XI/ISdzpo5dnEWvkB6KopKNqiKlKKIooygtT8uwPF0uQlFBUVme7z5OrNZRhJBYeIRkHY36k97HJyII7oHAOSbrHsVbxijfM40WM2n6w3VEzm/9pf7Un4iFhZ9x/MQ7cN0iq1e9na6OP+HHN41w7Pa7GfCgLzTO1rSLxsZGT4tXd+N7VSbHvshS9630jYdZf28BixxlVvHP7X/Jh1ddxZqJEf7+Y+9j4+Qw85k0X3neCo6tLzNhHaYiFJqC8+sOryhXubBms9o3yQqTg62b+GTX89jVdBGJQokb77iV6+65A18rUmpXcImDta5GsUNnJmWhjEbIWapbTDoGp3M+Iy5Muxr4Jr0qQbsBK8IVLkiXaDMkrZpOstKLWexGz3diV1qp1pqRvokpFRUtzYRrcRyQSKSo4VgWKmrgGwLHV9RdRb3uYbsS25M4KGoPFAE2irpQ2IJGcH6wm5hfoikIKQgrQUhBSAliSqPpF+aFz0wfPs/4hY2OhB+jYdWvIAjugcA5opSidniBpZuGkWWH2MWdpK7tR4s++ZF3PK/MqVPvY3rma8Tj69m04fOM7o3x/Y/eDXVJd2yWK+KLwHkISyf+zF7M/ggjuz9F0dpLTznGhr05TDHHEmv4YNdf8ImVV9OeW+D/fOJj7Bzcx53np/n6jRlOJ0rYokxcSi6v1LiyWmN7zSDRcRFTrXUO1gp81Hoht3dejmtYbD9xmLff/Dk2+GXGY2Xsl0hau8rozRqzYR2IsGRbnKroHHV9Tts6nhtmjd1Dvwjz7EiZ1vQ0rfECuiginSjVQjtLs2soLjYxWUjh+j74HppfRVMjCH8EVJSqSlElT0VLUBERKppFWTMpuZJSTVF9pBt3o5EXHwZCQEhASAiimkDXBJquIXSBMjSUqaFMHQyBLiQmHkJ4ILRGSqUAKQSOplE1QkjdACGW8+6B5amQCkP66L5PplSgY3aa9pkp2rLTxIyNv8Kv7NEFwT0QOAe8xRr57w5hD+Yxu2K0vHojVu/ZNTPP5/dw7PjfUK9P09P9RqoTL+Zb75/ELs/ipKq8IDpKSG4GvYvEZT3ELuti/K7/YemOA3TMxlnjjmNpw+S0Pj7U/Xf854rnEK9WeOtn/53V83fxoyuifP5Gg4rWCOjPK1d5TrlKW72VBXkxqU1rGbHuZehEme96L2LXuoswPY8r9u9h69Ap8lYY57wKqvMgfc1VfENjRpoMVkMcyClO1DXydgq/OnCmSLuDLDp3A4bw0IWPrnws38XyHEK+TcivE/VdwiKCLsL4WoSaFqFomBRNjYKmGnnpD2ECIU1gGDphS8cKa3gRAzei40Y06lEdGdYROvieg1XIEV1aIFPKkSwtEa1VCNs1wnaNkFPHcmx030WX8pFOzYOUQpcKw5cYCgwJIV8RcX0irsRyXUzXJVmtEHG9M2+rhGC2bQ5461n9Nh5LENwDgaeQ8iWlOyYp/mwcoWukblhJfGcXQn/yVTC+bzM88hHGxz9LyOwn4XyBez4nqRRGWEgontU5xsraAMiNxM8Pkbj+ArInfs7Uf36bpmyULnGAsH6Iot7CBzrfyCdXv5SQ7fC6r36GuP1zfnqlzpfCGiFZ5VnVGtdWa/T7fRypb+YAa1ndUyE5fwt7brf5+pZXsPeSLURrVbbvP0RxHnJNNs2XHeOC9mGUCXUP9tdCHKxrnK6ZRKoryZRX0+OmWGlViMeKRFsW0LU56iWLSj5KtRBGlTTitoPhGSg9jqOFKVhRsuFmxuMJHP3BMBXxbNqrc6yr5Giv5uioLNJeXaTJLtJULxGWLkoTjQGyl8sD3QmgJEpKUIpGy3zFA+1XlVju01E0Gjk9MJWi0dhJLZ8+XSo0KdGlQpcSbTmgW66P/iit/V1NUDcN6qbBbCpONp1morWVwb5exrr72Bzxn/Rv44kIgnsg8BRxJkvkv3EKd7ZCZEsL6RtWoj9O3+qPplQ6ytFjb6NcGkIrvJWhvVsp5+rIZhOrv8KfFk1EbRVW+xSZVz6P0uwoI5/+HyLZFP3mLmLGbVSJ8a+tr+TDa1+NknDjLZ9AWffwg0sFrtDYbNf5s4Uyl2gxFptexg+zFveisSoyxsZjP2Dq4Ar+6bp3cmjnBiKVKk2Hp9HyRS7o2c2lG3+OGbbxpeJA1eDeJYPJSpQtpU1syXWy0y4Q7zhG55rvE045KAecQ1Hqe+PUpuOomkXeypANZxhKd3EgvY5s9MH8/ky9SG95nk2LJ2izsyT9LFE5R4gyvi7xNYmng59SLGUgt9xgSSgIOxoRWydq64RtjZCrLefKC0DH0yWeIfE1tVwkUlNITSFUYzuNbSm0RgNUNNXI4HF1gauDa4Crg2doOIZBJRKmGLEoR2IUI0mK0WbysXbmMt2UY+0oPQahGH4oSsiRdOR9uhYddoxMoeuzT8nv7xcFwT0Q+BUp16d46zilOyfRYhbNr9pAZFPLWW1LSo/x8U8zNPQvVKavIH/i7ZQXoanbpLZKcUOhTLyQxjCPkrlhA3X9IqY+93OMfJxmczeJ8DcBn281Xc//XvsGirrFZXs/RS22h7s2aCR8+INSmas9n3a9G7nzI3zzzhMURsoknUU27j5EJZTkQ8//K/Zv2IxZs0mfmmVb6CQvWf0DUtYQQoPxmuCORYuRcoT1S1tYt9DKtoUFOrsHyay7j6RmY53S8L4RQR+L4lcMhjIrONyyksMrVjGeaD/TAKnZrtLqL9Frj+OkshSacjgxk5xIUCTMoOgA+kFYICykbuLpBr4u8DQN5RsIqSN8A6SGEhpSa9SJ+7qOp2v4mo6ra3iafqY7sDODaCzftT/Yd8Cjv27cwS/P+//Ze+8oya7q3v9zQ+Vc1TnnMD0zPd0TpQka5YASEiAy2PhhMM9pOYf3e35eDi9hP/kZ7IeNwAJJoIQQQtJIQmFy7p7UMz0dpnOsrq4cbjq/P6olBIKRRuAA9Gets0716apbt2+d/ta5e++ztyKD/GOicYTApQlqE2k2LWSoiMcpiV3CGU9CPoFlzCPMBcCgpLzhXc2Vt+NtE4dJkvQAcDuwIIRY+6bxXwc+R3EPwXeFEL+/Mv5HwKdWxn9DCLHn7U5iNXHYKj+rFEYTLD85hBHN4d5UTvC2xnflMAXIZi9x9tzvMX1eIn7hI2RjfiLVXkpbvdgvzNKYt2OTLqE2X8Le9mHieyeQ0jZU1/MEpIdwWXGOuzfwO+2/yZAnTO/gPxJ3nCFhk2nVNO5Lp2mRQui5dtbe/BEeO5FgfHocuaCx+cgxNEXlC+/9KKc7u7AXCnQvDnCb+igNgVHsToOCCYeyKodSKhXxRprmm4lcmsMRWaSsfpnyTBrnkIQyqGBmHPSVtnKksovTZa3MO4vVlWxCUC5kSpAoEzLVmozflFGNdxO0WEQgMGUwZd5YgQtlJS+MIrDk13PECKyVcSF/P2+MkC0MGUxZYLxxDOsNo41sFdf8qmWiYqFaArsJHt3AbRjYDYGig5Q3MQsCUxPouollpRDmMsJaRljfj4iRZYVwaRlVHV3UrOumqn0NgbLyd/W3Xy5x2DsR911AGnjwdXGXJOla4E+A9wghCpIklQkhFiRJWgM8AmwBqoCXgDYhxGWNSqvivsrPGlbBIPHcGJnDsyghB6F7WnG2XlnagNcRwmJy6iH69z3N4pk7yC/XEKpws/naGiZPTtE2ryNLURyOp1G6PkT6ghMyEobvEAH5AUKFWWaUcv5ry6/zbKiBtZP/wII6gSnBrmyOO6w8itlIYm47a3tLeCrVQHrsHAqCzvPnCc0tcP99v8SJrm48epZPzjxOu7qXYGkCxWYxXZB4OW1jMeFh/fw6qsYsjPwCJZUparUYgQsm6rjCcOla9tdu4ERpIxMOH5YkYRdQY8jUGDJ1hkJQksg5FLIOiYLdwpTyyEYSVUuimBlUPYMpTExhoFg6iqnjsTKoBR3ZMIppc1dMLKrdgc3hxO5wYLfbi7Z0s2hXF5bAMgWGAboBhiUjhAKSjISMJBUzzggUVuJZVsJbXo+DtECYCMxiEP5KX/y5AKKAWGmIPAjtLZ+roqj4/AHCleWUNbcRbmjG7vJgahpL05PMXxphbuQiG256D9vuue9dzZ2fKCukEGKvJEkNPzT8WeC/CyEKK895PWv9XcA3VsYvSZI0TFHoD72rM19llf+A5AZjxJ8cxkwW8G6vwn9zA7L9neVY/2Hy+RmOvvp5hg+0kFv8LL6IjR0fa8Ibz1N4fpQWYeBVniIXqiWf/mXECcgGT+Oq/Co1y8Po2Pi7mk/xd5WbaJp/gLA2z7Jq8b5Mhl12wZS5nsFLN6GUpDlQ2s6hE+fwOs9RvrRI44VBHr7tbl7r3oaMxQdmn2Sd+hoNNVNIEpzNybwSs1O+VE7veAdiKorDsUC9OkvJtIY21MSZiuvZ29zOwLoA8ZVLEDEl2iUVt9cBXguHPoc9NU48uYCRWcaTyBA0NCRhFG3bV3C93rwU1fPFln3zE143ofyoRevlfvdjkKRiaKQiKyiKjKwo2B0OHC4XTk8Ql9eD0x/EEYjg8AWxOZ0oioplWeTTKVJLiySji4yc6ufY89/F1PU3ziVUWU3d2m5K6xuv4Aq8c96tzb0N2ClJ0l9SLIr1u0KIY0A1cPhNz5taGXsLkiR9Gvg0QF1d3bs8jVVW+bfDyhvEnxkle3wetcxF6We6cdT739WxhBAMnX2aI09fIjn5Hhwek133tdIYcTL39DAibeBWjqFI86SleyCqkC45jVH/CA1Tl/Dm07zm3cIfN9yEN/U0ruiLZDD5VDrDJrfKgLWFb52/jlmHndOOENfPnaE92I8qG7SdPc8rG7fyt3d+hKzNyYalPm6RnqKrfABDwIGMyomESs9CI7eOVJGMTVJhnqEkJ2Hk13Cq4g6Oratj0A4JpeiEDNpVKvwKERaJpMbxRucITUcJaolipMrr2FWWXXliDp0qt5ugJThSuoXBUCOuTIb6sUu0xoaRC4ViGjKvl85tO1m/cze+SCl6PoeWy6Hlsmi5LIVslkImTSYRZ274ItHJcfLpVPGtXC4U1YZlWWi5bDFS5jJIsozd7cHhdCKrCpKsoqgqkiwjywqSLGEZJqahkzd0MtE45twiWj6HUSj8yGM6PB78kVJ8JaXUd/dSUluPt6qWyUCE/pzBY8ksN5f4aX5Xs+jyvFtxV4EwsA3YDDwqSVLTlRxACPEl4EtQNMu8y/NYZZV/E/LDyyw/PoSZKODbXYP/hnok9d1ZiZcX53j5kWeYO1+PrHbQc0uIDT1NpPeME9+TAHURj3ycjLgGw9xCpuIc8eqvUz+xRMX4DHNyhM813sms1Ucy8yB2y+QzhQxdPgcDmR18eWAL56QqlmwO7pw/zMZQjnQ4QMXsNKPVNfz5L/8Wi64ANfFLfMp6jK2RIxQseDGpcnHZwbWza7lj2I2xPEG5NEaV3sWlyCa+U13CWbvJslIsBO1yyzQSpz05QtnUKP7s4hur8KzqIuozGK/RaWzdRKC2i+8k+5gWBWpc9fiMCN9xNeOJJ2gfOsM1h18gmF7GkiQWymqYrm3G6N5EqKqeS7LMi0jYYwU8ig2PzYHXFcYtSShDA2QHjpA8fQJhGATqGlh79wfo2rGbSDD4xi5gIQR6IU8hm0HLZslnMmjZDIU3WrbYZzJo+RyWaSIsC8s0sSxzxdRjIatFwVdUW7HZVGxOFy6vD6fXi9Prw+Hx4g2FUQJhFhQbw9k8g5k8z2eK/fB0FmOqeL/R6LKzM3TlKSjeCe+oEtOKWeaZN9ncnwf+hxDilZWfRygK/a8ACCH+emV8D/BnQojLmmVWbe6r/EfF0kwSz10ic2gWtcRF6ANtOOre3Wq9kNXZ/9ReBg/oCCHT0Jti1x03YRyeJ314loJk4JaOY5ntQAitcoaZun+kLL5A/fgiMjr3l2zlJfcS00qeKt3gXpGlKehiKLqRkxPrOGi1ksPOx+afpV1fYqi5A7uhYSoyL2y7mYvuAKHsIndpT3Bj4EVyJryatjGz5GLXVA/qiMBKxvDam0k7N9AfrOCs3WRCLXoVXQ6L1vwM6xf7CWYmkQBNsrHoKCPlrWWx0s/5difpQDWoZZjSW53LvlScNUP9dA32E0lEsSSJ2cp6RmvbmaptQgmH8PhCGELCEAJdCAwhKFgWWdNCzqRYd/443QPHCKQTZJ1uzrd0c7ajl4WS7+dGlwGvKuNVlGJTZXwrfXFMxqt+v/cpMi5FRpUkFElClXjjMaagoJnkNROtYKDpFlrBJFMwSOsmGc0kbRT7uGYQKxjkDfP7oZSWoERVqbCpVNps1DhsVDvsuGWJ8gY/1VeY5vl1/jUqMT0FXAu8IklSG8UEzFHgaeBhSZL+hqJDtRU4+i7fY5VV/l0pjCdZfnQQYyn/E9nWTd3i1MsjHHtuBCNvJ9w8wjXvv4ZgvIz4P53DTOskHZOECk4stiHKE0w3/A3CdpY1pwVhbZGn3TV8Jexg2DZNhWHwG0aWxoiTkYWr+c6pLl4zOzAtk88sP8668XFOtW/iYngNNlPn7NU38bIriGpqvCf5JB/0PULebvFM3EZq0cPuqaupm0qzbAaoFG1cqm6l32EwYDfQJR2bYtGVn6Jn4QghLYpAYtZRzsWSzUxXNTG2phUz4CjatIWBy4jRGwhi6tMMLR5GlVSqpFaUGZ2Oi6epmxlFAmLOShwdnSRk8As395amuOPGrQSDbw0jFUIwc/EC/Xue4eLhA1imQfma9dTuvhHv2l6ulWUypkXaMEmbFinDJGNapEyTtGGRMgy0tIGWzpPMGCQzOuQspLyJXRM4dYFTt3DoAqcmcOgChyFQTbCZAuUyFh37SrsSeY6uNICO60rftbhfjrcVd0mSHgF2AyWSJE0B/xV4AHhAkqSzgAZ8QhRvAc5JkvQoMAAYwOfeLlJmlVX+oyF0i8RL46T3TqEEHJT8p3U4m4NXfhwhGDm5yP7Hz5FZj2pSaQAAIABJREFUFngqhthyk0xXy0dJPD1BbHiQuEMjKCUJFGrRvFHiPY+y5PgujaNB6mZinLLb+c3KWvqdEhEjz6fNLO2lNsYXtvBifxf79VZUK8fvpR9izbmLjAbbeW3rDUhYxDs38XSolKxqY0PmGL/q/iIOT5o9CRvJ+SC3TF3LQEIwlavAq7Qx6XXwjL3AtK2AJAlqjUU2Lh6nOjeOJdkZdddzONzLpcY2svUhHE6F0mSOiuwBUvoZwnqG317/IWS7yf/t/0sWrEra8ldRNrpI2+hT2A2dhM3P6eAmNrRUEWSUaMZJJJLlPbfdTlPTZgDyRp6UliKlpYilowz0H+T8qUMsx+aRHDZKb2qhtLWVlNfFKfMMxmgfZFWktP0Hmjttx52xUZFTIa8iiR/julUtcFgIu4VlF+C1EA6BKOYJxlTAVEGxSSgqSDZQVFBlgd0m4bbbsKkKsiphSDoFUUBHo2DlKYgCeStHQk+Q0OMktDhxPU5cWyahJRAIPlL1Ua5n3U8yZX8kqwWyV1nlTWjTaWKPDmLMZ/FsqSDwnkZkx5Xf4M5fSrLvsUHmR1M4ApPUbNnLtt2/hnSyhNSrk5gSFEjgNvwIeZbstmGmPF/Bl3XRcipPTIrxv8MRDrjtBE2Lu6Q868oFc4trGJtYz4l8I6ZZ4CPZA6y9cJZc3MWh7duIB8LkSmr4XlM7Uy4flflJfs12P3VcYl9GZWHez62TN3M8FyKcCJGz1XHapnHGbpBTFFxSnp7ls3QlTuEQgll3E32eRkZrWjBrPLTo03RM+fHks4z4v85w+QgOy8XHWj7B5qYePn/yfgaTftrnGukYvEg4EcVQ7Qy7WzjvaeOaihI83gNMZFJo7iXCDT7UQBmLuUUWsgssZBfIGtkfeU3thotgrqzY8uUEc2WEcuX48yUo4gc/o7yaIe1YJmtPkLWnyKkpsrYUWVuSnK34OG9Loyk5hCRwGS7chhuXWezdhhu7acdurbSVx4pQ3ijC/eOwsNBlHV3W0WQNQzGKu2oVC0uWwFIQhg0578StBdi1YSvvv/f6K55j8BPGuf9bsCruq/x7I0xB6pUJki9PIntshN7Xiqs9fMXHScXyHH5qhItH51GdaUrWPkHX9kbqpM+Q/M4kxlKetN3Aq6nIxIjVnSS+9nsU9FkaL5TjXBrkH0IBnva5cQnBHWj0VpnElxoYn+jhYqaGmCazPTPJzTOvopzPcr6njXOt6ynY3Zxs66a/pBK3kebj0pe5WtrL0YzC5IKf2ybu4WymAk/az7wa4IQtx0WHjCRBjT7PxuhRavLT5Fz19Lvb6Qs1oNX4CZWmuOniS3Se72IhEuBk5be4WD6Aatm5o+QePnj1e/nC6X+hfyhLx4SD1ktDqKZBPhSmz1vBQIlOiX8OyTVFXEpiSd+3cdhkG2XuMkpdpZS5y/AaDrIj0+SGEni1CioD6wi4WjATTvKJ7xsBJFkiWOYiWO4mWO7GH3HiDTvxRZz4wk7szrd+IWuaxvz8PIuLiyxGF4lGo0SjUeLLcX5YBx1OBy63C4fTgcPlwOF0oKgqwpQwDBNTFxi6QCsY6AUDraCjawZWHoQBkrRSi0oyEZKJpWhYslY0wL8Jt9NL74ZN3HDL7iuea7Aq7qusclmMpRyxbw6iTaRwbSgldGfzFe8y1fIGfS9M0PfiOMIyCLU9T9WGM3Q1/TfYFyR3Oopll0EzUChguPcwv3OClDhJKF1Bef8MTwRMvub3YUgSNwqTXZV5CulyLo1vYjZRxXjOg1mw80cLD+E9FSVR4mP/zh2k7QFGKhp4pWENll3lFuM73Ks+ysWszvkFH7eO38doqg5b1s+QTaVPzTLtsGEXGmtT5+mOn8IrFBZ9Xez1tDAf9JJv8tPgneDakZeoP93BYqiRU5XPcr78JIpQ2e24ld+48dN8bei7nDxwkc6ROCXxKLoqMV8l6KtKsRSOvXF9vLoHv+6j2q2wa+2NrKvZTb2/nogzgp43OPXSCc7tPUMypiCrFUiSEyia8YPlbkpqfZTUeAlVuAlVePCVOFEuU5JQ0zTm5uaYmZlhdnaWmZkZotHoGyIuyzKRSISSkhJCgTAuuxeb5EI2HaDZKaRMMvEc2aUUmUSBbFpgmG99P1XWcduzeOw53M4CbpeOxwPuoANP2I+7NIynug5nZT0WgnQ6TTweJ5FIEIvFiEajtLa20t3dfUXz7XVWxX2VVX4EQgiyfQvEvz0CEoTubsG9oeyKjmFZgguHZjny7VGySY1Q4xnCa75OY+vtVEQ/TvrFWYRuYgoLWQg8yvOc7RzErBlCsiwa+v0cFJP8v1CAmKKwyZJ4T3kau+FjeGwzsaUaZtNu+qwq/tvCwzSfvoQQgpPXdTMS6mDZ7WNPcw/xSIQW4wK/ovwjaFPsX3Rz7aUPs7zcTi7n4pxDol/NEbfZ8Jspepf76EgPYlPrGQ2s5zV3OR4lSaKzhMrgDNunD1BxPMKyv4O+6pcZLu1HEjKbC9fxOzd8ju/OnuDsS3tpH57EbphEAxoX6pKMVWbRrAC2XANb3OV4UjlcqQDVkUV2X7uNNZ2fRM8LpofiTJxbZOz0DOllQTG2ReDymtR2VVLZFKKk1kek2ovN8fZO7Gw2y8TEBBMTE4yPjzM7O4u1EtfudrkJB0rxOSM48CFyTnJJmURSJ5HUKJjWSp1VsVJOD2RZwyYlUeUsqpRBkbMocg5F1pHtErINJMVEyBI6KroFmikwTIFumugmaKjoKBioaLID3eZHV73oqg+f5GGNZmN9HqS2ELd/fP2VT2BWxX2VVd6ClTNYfmqY3KlF7A1+wve1o4acV3SM2eE4e795kehkmkBVgmDnFwhXm7QF/wrze0706TSoEhgCp3yI5cgzTPWCJaapXKhnbGKYL4bdjNtsNFsqd5ZkqHHC+fGNxKdbyGRVjuWquT1xgJvPHkHJCKY3hzjSdA1Zxc2h6nbON7TgIsuHpQfpNl7htWUbXUP3oi5tZSlv56TD4pRNo6AoVGhzbIqdpCE3i+Rcy9nAOo46HaxfukC8qwyrzaA3eprIUYOMfQ3H6w8wFj6LatjYEL+GT2z5JV5aPEBy32vUzaSxJMFYZYaphgIptZPJZAPubCO/VFmCpPUTjxfw+RbZ2OulqfKzzI8IJs/HmBtNICxAGJjGDC5Plo6rO9h421W4/a53dO11Xef80Ch950cYGJthNp6lIFQ0bAjJg2Y5yRsqBVMh/3ox7JUSeOZPuWSpKkvYFBmbImFXZVRZxqaATTKxY6KaBSqMAi0atBsqzcJHmOLfuSwXyLTBtk/e8K7ee1XcV1nlTRTGEsS+MYiZLOC/vh7ftbVI8jv/j88kChx6coTBI3O4AxKl6x/HWfE8teW/TOnwvWQPL4JSjI0W0iRhxxfZ1wW2yBxuI4DUn+WLAYM+p5NKU+bakMJmf4KRxVYWR9eh5dzEohIZHX594Fu4YhrZFokDvTuJSRWMhcp5pa2HgsvFDusV3i8epC+ZIzJ8PWWztzKVd3DCYXDabmBK0Jwbo3f5JOWGhuTaSL+vjX67zs0j+1BqbIzsrqRLm0Tvi4HRyYn6w0wHhnDoDtbPX0dPyw4GFl4icmqYUEomZzcZqytQWx1iNHU7+5dLsSPx0VoHpbYBZmZiOO0ZGktd+LmJmQuCfKa47d7pzpGND2AURqnrKmfTHXdTt7b7LSUHhRDEMhoTsSwTsSzj0QwD41FG5uJEsxppU0bnR6/o7YBHUfDaFPxOlaDLRlDJEDBm8GVGceWmcZPHJRk4w1W4SupxldTjKGtCDVRjsymosoyqSNgVGVWR3xBwVZGwyTI2VVoRcekHzt3KGxjRHEY0hz6bQZtOo02nEbligQ7JpeKolnF6pnCYB1BnvoO07Vdh1+9e4Swusiruq6xC0Wma/N44qVcmUUJOwh9sv6INSaZpcfrlKY599xKmYVHXO4695n/g8VbSyl+hfw+sFRHTpTylyj8xVn2K2TY3qpWkYjjMQ2aUp70eAhZs9Ea4LTLFcjbMxeGtmIkSbEspjuh1/Nro49RPRNHLBQM7W7kgdZNxeXm5qZvp8koqxRS/xD+RSZ+nMNpD68THGSnYOWYvxqcLIWjPDrFp+SRBoSI7t3DK18wpe45bh15lrT7Gybs7KS81uXRuAiVbz+nqfmKeWbwFF+1z1yCXumG2j+ZRHaeuEPUL4nV57inbwROLO3khXcAGvL/KTXt4jNGLk7j0IGHJhytdj92S8DgUwiEZkVvATESxK3b8oTJ8gQiqZMPSTUzNxDAFhmlhWkXThmFZCIrx1BoCDTCxMLGwXs/WqKg4HHacbhsenx1f0Ikv6MDmVJGsHNLSWaTF00gL/UhGAkk2kSrbkarXI9VtQKpdj+TyINnk4pcxFJPXCEGxtocASyA0E6tgIgpv6rM6ZkrDTGpYKQ0zpWMs5bDS+vcnjCJhq/Bgr/Ziq/Zir/Fhq/T84ELCssDUwHZld42vsyruq/zC82anqbu3jOBdzVcU4jg5EGPfoxdZnstS2Q7BjvsRjnPU+T9LoO96tJFUMS+4EAjHi0QcX2HvmgocvhhVC372zy3zz0EvBUlikxTglsoYbknQN7YVbboOtWAyG7PTtXCe3YOnEQ7B3LVeDgV2k7c8nKtu5mRjO5YicRdPsK7wNEPjlWwY+S0G806O20wu2Iql6takz9OT6MePD8W5mTOeevpcWW4eepXbJo4wcHML1o46Dg2eRaTLGCw/R86epizpoyy1iaQvQdXEBK1TbmQLxqpsmFUxPhC+j4em2vleroAT+Fi1j153lNxwgRI9hB8nHlnG+SPugkxhIBygBjzkZZmkYRIrGMzndZKG+Ub2GRWwC4EdA5uk4ZRMXJLAqzjxOd14XW4cdhuSKO5HEIaF0M3iY90C899WzySniuK3ofjsKCEntlIXasSFutK/2xQV7/j9V8V9lV9UhBBkT644TWUIvbcVd3fpO359cinHwceHGelbxF9ip27rXgzPl/E4O2iM/Sn6QfONlV7aNUaT9decazCI1kqUxwxmJ03uDziZtNnYUJC5usJJmzfKwEInseE1CMONa26JaNLGR86/gD1vkLpK5njbVmbSdSSCQfa2rmPBX8Ja0c8HzH9iaC5Nx9nfYyxXxhHV5KLdwm5qrEudozt5Go8cxubYxoCnimPuLFdPH+ETfc8RWxtm+r4NPD13FiPrYyI8giWbtMxHQGkipUzQccmkfs6NkCUu1jnI185xp/MzPDdeyYyusxGV29xOKnQLp/594SoIgelVcVV6SGuzDA8eZWl5GisSxOjZxbi7imOTSYYW0m8kZSy326jQJMI5iyBZ/K4oimMBS9JRFRuNDU2s37CW1tZWnM4fs7LNLMHAU8U2th9hgQh3ItrvQjTdggi2IHTxli+BH2iGiTBEMWmktJL6d6WXZAnJriA7FKSVJtsVZI8NxWdDsr27bKA/LVbFfZVfSKycwfK3hsidjmJvXHGaBt/Z7a+pW/S9OM6J58YBaNuRhrI/R5CiUf19nAfWYMaLObx1l4ab/4PqO8zR9irKCsvYJ2T+zqly2OWiQTPY5o6wrXKWhUwJFwY3I6VLcKXTLC2q3Db8CuVLSQotFoM7GjiV3opps9PX0MbZ2ma8pPiI+AokDuM/9UniiY0cly3O2Qwclk5vvJ+u9FkcSgiHYwfjrir2ewrUFy7y+y99HcUnGPzIBr7kGMbSXSx55lBNG+0zpcSCIaT8OGtH3VQtudBsMgMNgkxFhjsLv04y6qdayKxDwb2yeSdjmSwbEgnTQJTGab52DaUtEU69+Ax9L+1hTASJ1vQy661nZCU23aXKNDsclGYEkbRFhSnh8WnIpcss69PktSw2m42Ojg7Wrl1LU1MTNtuPCUc1dRh6Efofgot7wNIh0gpdd0PXe6FszZuqJ/18syruq/zCURhNEHt0xWl6Yz2+a96503R6cJlXHx4kPp+lodtNuOsBssb3CDuuoWr4s+jn88Un2iDpeYEW/YscbS1H9WQoHzN4RNh41O/FbQlu1RxsqssgbBLHRq9CmqpESCru2SiVE5NsHB/GCAtmb/ZwVL2GeKGUWCTAofY1LDuCXCteZFv+IRJn1iEvfJQTluCU3UAWJr3Lp1ifPoVd8eK07yTqqmOvS0fxzfAnLzxARSzO8HV1fH7dEmmbhKbm8eQD1MWqmCrJEYkusG40QCRpp+CUGarXafau46r0jZRodiIrtZGSdokUOjNxi5gukXPEqN0wzTV3vIdCAvY+/W2+d3aaUVcdk94mckLBJku0+9zUaBKRRY1yQ8btsVHW4kbzzDMdG2E5HkOWZVpaWli3bh3t7e3Fohs/jrkz0P8wnH4UslHwlML6+6D7g1C+9hdG0N/Mqriv8guDMK1iPdNXJ1HCTiIf7MBe63tHr82lNQ4+PsyFw3P4Ig46rhsho/x3JOy05P4c6WCkaNcFUhVzVCf+mFhZlrEaD81TCfZnVb4QCpCSZe5MFegtdeEvy3B6fg2Zc80YahBPIo5rIsHuC0eQFIvE9RJ99RsZinajOnWOtnVysbSRKjHFfeb/IzsSQ730h5wt2DnhMLCw2LB8lu50P25JxeHYSdrRzD6XQapilt898TXaT82xUOvm/usMhiqL/9818Q5sVpCp4Ai1cxnWD4cIZhTw2qGmhg1iG81aDSoSKQQXFYG31M3MXJxkXEFIJiIwRsfVUa669gOMDUR5+Jn9HIo7GXfXYkkKQYdCt9dDVcykbNnEjkRZvY+aNSGkYJLR6UGGhi4ihKC2tpbu7m7WrFmD2+2+zIcSh9PfhL6vFcVdtkH7rbDhw9ByAyjvrqThzwur4r7KLwTGUo7YNwbRJlO4N5YTvLPpHTlNhShuRDr4xAhazqBjlw1H3f8ilz9LhfwhwifuwFwsmmCsEGS5n1pe5lhzOQ3xZWaWBP8zHGTEbmdrLs8dloNAS4ZZrYyhUxuQckUbf3Bijq1nj+PLFshuMhnaVs+x6C4U02Ciuo4jLZ3oso07xBNULj6LdebXGcrUcdxhUECwfvkC3ZmTBMw8qnMnpquLQw7BUs04vxp9hLXPzmMAj+yUeG6ThMP0UhvfTcplMO89RsukxrqRMKGcjbpIO+FQJw1GIyoyi1i8ik4qYKer3Mfi6ShmXsFQssil51l/TZ616z7EE88P8HT/DINyBbpsI6QKtgYD1EQtAnEDVZGpXROmuacUX7XM+YtnOHXqFOl0Go/Hw4YNG+jp6aGk5G0KiM/0wbEvw9knQM9CZTds+Cisex+4rzwtxM8rq+K+ys81QgiyJxaIPz0CskTonhbc69+Z0zQ2m+G1hweZGYpT3uSm7qoXSOn/gktupGH6T7DOrHw52CWWSw7SHvtrLtYHsEs6ylyB+/0BXvG4qdYNPpPIUVankAjaODG0GcdwmIw/hG9piY6zF2ianUKrs5i71c0R7VpiiQCK386+NeuZ8FXTLga4Ifslsqd7mI7ewnHVICtDe3KUjYljRPRlZFcPivMq+hwK4+UTfDDwDbqeXCQ8o3GsReKBm2VUmvGbtzEdHCfDPjomDNaNRGgQ1bSW9FJpb8WGjRgWL6FzRDZZ2xBmrZCZPb2MMGU0+zL2yjNsus6F7LqJB56/wGuLNrKKCxc6G70u2nMuQks6qqpQ1xWmubeM2jVBLk2McOzYMcbGxpAkiba2Nnp6emhtbUVRLuOA1LJw7smiqM+cBNUF698Pm34Zqnp+CjPl549VcV/l5xYrqxd3mp6OYm8MrDhNHW/7OkMzOf7cGH0vTGBzKHReG0X3/wWWmaGh8Ac4DrcjCkUTTKF2meDyH2B5FpmLeKicSfE1l5evBfyoQvDp5SSbXDaWW+B0dA3icAVJfyWyaVJ3cYyN5/oQHkH8dsGZsl7OT3XQoETZ27KJ47VrsKNxl/k17IOjzE7+Fv0CkrKgNjfH9uh+SvUFFHs9qvtmLtnd9AcX2Nj4bTr3DrPlSI5lH/zLDTaWym4g797NpdAxbNmX6BiT2DpWR6d9Lc3BHjyyH00IXpV0nkYn77XxkXXVeC7Fmb2QRWCRd83jrT/Lpmva6Btv5JvHZhk2/UjColPV2WwPE5k1UZCoag3ScVUFzT1lFIwcJ06c4MSJE6RSKQKBAJs2bWLDhg34fG9jFosOwfEHig7SfAJK2mHzp4r2dNeVp1r+RWJV3Ff5uaQwGif2zYuYKQ3/TfX4dtW8I6fp5PkYrz48SHIxR1OvA3/H35M3jlIi30LZqY9hzq5sRAlLZJX/S11hD8OVQWqjSV6WnNwfDrKkKNyZSvPxjEayXWFYrWDqSCdyJkLK76dsbpHNxw/jyWbIXGNxaWMdZ6Y20VQYo6+0l1fX9LDgKGWr2E/n3COMD36OwWyQmCIoMVJcN/M9ysxZbHhRvHcSd5Sy35vBUf8cNTPH+fCeLME0vNrj5tDGX+FSxVpmvXvxLT9D5yWZG6fW0+nupcbThizJXBQGj0o6+9HY2RTm/Z3VLBwYZmlMwpIM8p4pwq1DtG/cxreOKjw7rpOT7ATMLNucTtqSHpwF8Je66NhWQfvWCnwRJxMTExw7doyBgQEsy6K5uZktW7bQ2tqKLF8mxtvU4cJ34fiX4dLeoi29846iqNdv/4V0jr4bVsV9lZ8r3uw0VSMuwve1vyOnaSGrc+DxYc4fnCVQ6qBp12Fyyj/gUKponP8viD5ncYeiXSZefojWxb9iusJFMFdgIi/xVyVhztvtrMtp/EFsGU+ZjZFaL8eHNlF20sZ0XQOufIENp89SNzpMoc1i9lYX51NbqFmaIe6t4smOa+gv6SIiFrkh+89MDaxjItrLoizwC4Mbp/dQqU9gMyVs7uvJu7s45DSYrNmHS/ken3opxcYRwUBDGd+66XMca62nIPYRWHqKDZds3Dm3nQ5vLwF7KXlh8oxk8BgaObvG+zdXc0tZmFPPnicz78aSNPK+CarXz+Op3sbDh+IcSzgRQLtIs81WQsmSjKoqtGwsY83OKiqbAxiGwZkzZzhy5Ajz8/M4HA56enrYtGnT29vSE1Nw4l/g5IOQnoNALWz8JPR+HLxXlrRtlVVxX+XnCCOaY+kbF9Cn0rg3lRO8oxn5HWQNHO1f5LVHBsmlNFqvymGr+QsslqgzfxvXwW5EthiPnatZIpj4IyTPIqYsYyUMPh8JssfjIawLfm95iassk5EOF4fy63C/5CUWqiPnctE6ucC6o/uRfDrLd1sM+dYTmshRZU/wQNU9vNqykZTsZbf1LMbwKOPj72dBCDwCrl7aR2f8DEjgUZrQArdzxiFxPHIOvfwp7jgV5X0HBP1t63n8pg/R11SNPXeQyMJjbJrw8f7o9bR4urHJDiatPF+XBa+Qp7RkgV+9rpuurMbRZxbQ4gFMuYAeGKdlm2DOaOPhk1HGTS92S2OzYrEhH8KdK67S1+6spuPqClxeO5lMhuPHj3P06FEymQzl5eVs2bKFdevWXT6E0bJg9GU49gBcfK646av1Rtj0qWIv//tuBPpZZlXcV/mZp+g0nS86TRWZ0D2tuNe9zSoRyCY19n7jIiMnFwhVylRueQjL8TIh224qz/wnzImiCcYMmljy5ymz9pP0OPAv5/lKwM9XA34sIfGJeIpPJRMs1Dk5VlLD9NEufDMq07U1BNJ5eo8coGR5kcxNFiPrqvGMutgoLvCU92YeW3stFzxt1ItRmmaeYmTwbhZ0By4L1urD7Bzbg26X8RhOzOD7mHSF2edbIFb3KJ2xUT6218uJjt08vetmFkJB/MnDhKPfYMtUhHuXbqDR3QXAISvNg4rCmDpPae0wf7irG+9EjLMv+TFTZZhKHlEyQdf2co6MwePDOsuyh6CZYYfDTXPMhUNINKwvYe2uamo7w0iyRDQa5fDhw/T392MYBi0tLVx99dU0Nja+JeHXD178GPR9vWhPX74E7hLo/VhxpR5q+MknxSqr4r7KzzZWVmf5W8PkzkRxNAUI3deOGri801QIwcUjc+x7bAg9b1K3eQBHzf04HeU0Lv4p4rgbLMAmkQp+l5b0F1kKuYgk8uxxuvjbSJgFRWZX0uBPEvM43ArnmgO8Nr+LNS/luNDSiKmotA8OsvbMGQrdJjM3OZBny9maO8dFtZUvtNzLvqrNgMTG+HeYONdINF2JA2gTaa4f/TqmIrAbAodjJ7FgL/tcBS7VPEHQeYLbz7fTX3cDBzZsxlQU6ub68SYfZstsOe+NX0+1sxnN0nnRSvJ1m8Ji4BjNNcP85toq8oMFJo9ug0wlplzAXjNHW285z/QvsGfJTUbxUC1y7LYFqIzKOJwqa3ZUse7aGvwRF0IIxsfHOXToEIODgyiKQnd3N9u2baOs7DLmEyFg6lgx4uXct8AsQN3VRVt65x2gvr2ze5V3zqq4r/IzS34kzvKjg5gp/R07TVOxPK8+NMjEuSWCVVki3X+LIzBLvfLbOF9bi5Uupl9NRUaoz/wxmaCOP2MwJKn8ZVmYszY7dXmVP4tNs94wGGl28YJtI5V7SonaHSyWlRFeirHt8CGc7hTL95hkRQUbo2Ogqvyf0AfZ07mDaVs1bdl+cgNLRJc6UAR0WhK9S98glIxiSRIBs5JU6d0cdaoMlR7CVvEi6zO7OFa5m+nSCjzZPOtmRnDoT9C+GOae5E2U2CrJmFmeMZPsKcsx5/0u11SPc2uJj9hgCcvnb0fOVGLJGoGWHCXVJo/1zXDQrKagOGlRdLabfkrjAl/YSfd1tazZXoXdpWJZFgMDAxw8eJCZmRncbjebN29m8+bNeL3eH3/RC2k481jRQTp3Buw+6L6vaHopX/NTnBGrvJlVcV/lZw5hWCRfGif12lTRafrBduw1l3eaCktwbt80B58cwRIGFd3P4a1/iorg3ZT2fxB9OAeA5sngt/4M1XMRuwFpHT5fGuJZlxuPYeNzS3E+nF1ioczB4eoaRs7tpOHkLAPfw2TcAAAgAElEQVSd7ciWxYa+fhqmR0nfbpCo9rJuNkaZkuIx+Vq+3v0eTvh7CWmLBAZPszDTDkKiS1eo5xCdY0fI21R8BQUr8F7O+Cu44J3B1r4Ph3c3JwNr0FUbnZcu0TuzRN59kJKUwj3Jm4nYyonry7wgZenrjKG7H2B7IEOzojA3sonk8M3YsuUI2aC0zcTmmODRgRgnXB3osp31TolNaReRtKCs3seGG+to7ilFVmQMw+D06dPs37+fWCxGJBLhqquuoru7+8fneAGYHyiaXU5/EwrJYhqAzZ+Cde8HxzvbGbzKu2dV3Ff5mUJfzBL75iD6VBrP5goCdzQh2y/vdIvPZ3n5a+eZHU7gr56iZMPfEy4rp2H5j9D3FVPBWoqF7PgKIdu3MRUZR87gy+EAX/EF0ZG4JS7zp4lLSA6ZgWY/z+Ru5qrH5jnf3EAiGKR6coqNJ04gejJkt0HDjEattMxZvYHPt3+IfTXbyRpOai/1szhehmWqrNEV6u0LtI49ihBg1028Sg9jZTs56TIwN8yyUNbOlC2AJ5vhhmOH2DKdoq8hRamR4b2pmwirZSS0JQ4qWWLbz4H6JGudGmYuyMz4VWRHr8aeLUdSBCUNOfKZkzw7a9EX6EaTHWz02tkYUwlkBfVrI/TeXE9lSwBJktA0jZMnT3Lw4EGSySSVlZXs3LmTjo6OHx/KmE8WNxudfBCmT4BiLybs2vQpqN2yGsb4b8hPJO6SJD0A3A4sCCHW/tDvfgf430CpECIqFb0r9wO3AVngk0KIk293gqvivgqsOE2PF52mkq3oNHWtvbzT1DIt+l+a5Ogzo0iyRsn6hyhpPU+z9w9RXqjFjBcQCHTnYarUz5N3mgTSOs/63fxtqIR5GbrSbv5ieYxGM89EjYunAtuI7AkgZwyGW1tw5vJsOn6cEsc02i0FShMWjVacqO7lb0rv4dU1uxlVmiibGCUzImEaDlp0mfVC4M4+TGk0hilLhPMBlivv5YDHw3y3jfHaIJqk0DY+zl2vPc+GqWUe2tZAvZLivenrCaulJLQo5+VZjN17cTqO45FgcamBpfmryI03485WI0kyoYo40ZkXOWSV0h/sISc76PW56FmAsAbNPaVsvKWB0rriajqXy3H06FEOHz5MLpejvr6enTt30tzc/KOdpELA5BE4+bWisOtZKO0sOkjXfxA8kX+NKbHK23A5cX8n1Qq+Cvw98OAPHbQWuAmYeNPwrUDrStsK/MNKv8oql8XM6MSfHCJ3bglHc4DwB9pR3sZpGp1K8fKDAyxOZPDVnKa892Ga6u8leOJ3KQwkMCmgq/NUKH9CzreMI1VgxLDzudpqTqsKpQU3/zO2xK35CZZCNp6pbuXEyHY2PTnCwNoK8lVOWoaG6Rw/Dbdk8Ms6TctJsobK/cr17N+6mwPuq3HOJvANjZDUXNQYMtsLKnn3XhpGTqArMm4N8N3Ea1XtnOh0s9Dqw2kY7Dp+hg+88E1qo3Hu372NaH2E38xsJqKUkbAWGdBewLjxBUKOKOmch/GJ7WQXWyBWhjfTgMe04fIusjT3LAf1Gk6V3EJa2FjvddGzIChPSrRtKaf35nrClR4AUqkUhw8f5tixY2iaRltbGzt27KCuru5HX+T0Ipx6pJi4K3oR7N5ifpfeT0D1xtVV+n9g3lbchRB7JUlq+BG/+lvg94Fvv2nsLuBBUbwdOCxJUlCSpEohxOxP42RX+fkkP7RM7LGLWBmdwG2NeHdUX9ZpauoWx54d5eSecRR7hqqrvkZjd5i65JfJPpKkYCQwJY2w+jfogaPYsxrprMwfVpfxnM2Jw7LzyajgN1MX0B0yx9ojPGzcwfavDlNRq3Fy8yZCsRjbD+3Ds2meyNoc9VqGvKbwjWwPL22+hn2l15BacOHpn0TP2gkJD9dlVPDO4F98irJ5DckSlOkNnKq/iT2tfhY7/dSlk3z0xRO8f88/48mn+WrvZgo7mvnP6S1U5GtImktcLHwD87Y9SCoMzteQXroLc8mPQ4sQzLdh5WzI8gLZ5Iuclcs53HAvy4bCGpeTnkVBTVqi8+pqem+qw19SLMScSCTYv38/J0+exLIsurq62LFjBxUVFW+9wJYJw9+Dvgdh8DmwDKjdCnd9AdbcDY7LOFZX+Q/DO68z9iYkSboLmBZCnPqhW7hqYPJNP0+tjL1F3CVJ+jTwaeDHrxpW+blGGBaJPWOk902jlrko+UQX9urLC8fcaIIXv3KC5CL46w/RvHOAtvLfQf+2SjYaRyBwKs+jBh/AWcjjTAv+sTzIg64geQQ7kl7+PH6RkDAYr3PxiP9aXC852Bqb5VzPRlTToPf4carDFym5OU2tlsHIy7yYaOGJrl2catzO+HINrsOL2FM5HLKT2zIqZYpOXHqYmuF5DFmiLGNnoeouvthcx9RaH1uji3z4OxNcf+Cr+DKz7K9r4cyOrdyX7KE+30yWJKPxb6Ld9ALTqo9zo904453ImopPLSNQaKIQt2Nay+jZ10jWlvJa9b2MpgSNqoObE9CYUejaVc2GG+rwhop3PfF4/A1RB9iwYQPbt28nEvkRZpT5gaJj9PSjkJopxqVv+yz0fAxK23/aH/8q/8pcsbhLkuQG/piiSeZdI4T4EvAlKNrcf5JjrfKzhz6fIfaNQfTZDJ5tlQRua7ys01QvmOx7/Djn96VR3TGart9Dz9V3Y9v/PrLPLCKhI0vjuPx/hlPEcGZMno14uN9bxqxs0pT18v8tz7BRG2cxYuexyi4Oje3kusfPcHFNJxcqvNRfGqMz3kf5xih1ZBAFOByv4asN25m7qpdj2R6cJ2LY40soisK1BRtdBYlL/r14R0/ilMCtWdhcW3msdzsj7V6uWVziQ08nWDf4JBXzx5nzBXn2o7dwTbyDW7NrKEhZxha+TX7jfg77ypgbuYZIpgo3Eg2VTchTIVJzHvJWFss4iH9DJS8q93Dg/2fvvuPjuu47739umd4HAwx6LwRAgiQAEqRYVChSvVqWZNlxrBQ7iTdOnuzmlSfJs3GSzWad9SZOHttx3GRbtixb1VSvJEVSbGIDC4jeOzCYXu/ce/YPKLIdO5YTSZZszfsvcHhJXpzD1/d18DttKklRRuGmlExbQmHtzgo6r6nB4flhqB86dIgzZ84A0NnZyfbt2/F6/81BXLG51SWM5x6ChfMgq9CwC677O2i+FtSfsfO04D3t51ot83pZ5ikhxFpJktYBL7M6YQpQCcwCm4G/Ag4IIR58/c8NAFe8WVmmMKH6/iGEIHl0jsgzY8gWBd8dTdhaf/Zk3EjvMAce6CcTs+NvepWemysojl9D+PExZE0AGRyOz6Cae3GlNc67LHzWG+SMCr6cnU+upLgrPUnKqnC2pohv5z/Ald+/QLS0lJnKStzRKOsHT1G9ZpxaRwxZgrORMu4LbCDZ0cYB43KM4SxyKIeswCbdwraIxKxnBvfCD7Cmsqi6QVGunMNNN3KxpogdSwnKF2WqZl+hdvwZJGFw+LZN1Gc20KqsRxcaM6GDxIoH2VfhRoq6ceSdmKwm1lY1Eh+AyIIPALN5iLorqnnZqODh0/OYJYmetEJXTqVjewVd19a+MVIPh8McOnSIs2fPIknSG6Hu8Xh+2KDZOFx6cnWUPvoKIKCie/VGo/bbwPHmO38L3hve6oTqjxFCnAfe2KImSdI40P36apkngP8iSdL3WJ1IjRbq7QX/So/nCD8ySGYgjLXFh++OZhTXvz8yTMbCvPTtF5k+H8DsjLPprjHWtn6cuW+PEg2NIiOwmh5G9jyCL5FiXqh8pryUp8wWVEPlQ8sK/zU+gKRAf52Tb9uvJfhslquiQ/Rv2ADA2r5ztPnO0tgZRpUNLsSC3OfqILejhldMVxMbMaMsxlFkQZPVyvXzEmlLjmnzI1SOzKLJMsVJlamyG3ihppFtIYO1l/L4oiM0jjyMK7bEhR2VyJ4dXJfbhqTITIRfZcmY5mijA1u2GWdIxhN00+YpYfZMlKlRJ0hWnN4QXbc0cSBTxx8cGCGbm2O9prItY6LzsnK6r6vF5V+9E/bfhnpXV9ePh7quwch+OPc96H8G8mnw1cHlfwIdd0JRwzve/wW/WG8a7pIkPQhcAQQkSZoGPi2E+Pq/8/gzrC6DHGZ1ZH/v2/SeBb/k0v0rhB8exMjqeG9pwLGl7N89l8QwNE7t38vpp1TyGR9VXUPsvH0Pk89GCD0/gAUJk3wO2f85ihJLpDMSXw76+IbFS0rWuSzu4K/CIxQbWWbKrTxX1MHwxbVsOzbAYGsrF6tdlM9M02kcp71xBptZpy8W4Jv2taQ2l3HCcTVzo36U+TSKnKbUbeGWOQVHXjDiPUTzyAnMsow1J7DbNvLs2m10xs1cNwO2zAJrZh/BN9nPRJ2Vge3X0CPvwa66mEqcZyIxzMU6O4oowZrXqawLUJ1TGX1thkFRg6yU4ynW2PnhNZxMC377uX7m47M05xV2ZKxctnk11D3FqxOlKysrHDp0iN7eXiRJoru7m23btq2GuhAwfWp1hH7h0dV7R21+2Pjh1bPSKzcVVrv8CitsYip4Rxk5negzYySPzWEqc+C/uwVT0PFTnxVCMD3+Ige/P0hkvA17UYid99Qzu2yj8vk5TIYZmWVU32cJZC4iJHje7+QL1hKmTHnq0g7+MjRPpxZmyW/mtYpy9kZu4vqHTzJXXcN8eRmuaJSNkZP0BPtwWPMMxv183dpCYk0Jl7xXMThegzqbQkJg95m5MWSmOmow5Z6kbHYvei6Hqhv4tSBHam+kLufFjozJSNAeexjfhVOEHDL9PV30KDfit5SypE3TlzrHUIkZJIm0I0VbIIhjNspk3xwm205ktQKnT+bye9qZtUn8zVN99M3HKTNkLk+p7OoqZ9P1dXiDq/eNrqyscPDgQXp7e5Fl+Y1Qd7vdsDL2eh39+xAaBsWyeu/o+rtX6+mFOvqvjMIO1YJ3RW4mwcr3+8kvpnHurMCzpxZJ/em7HiORMxx97lHGj2xG5K20XiGTXFNN7eOX8Kb9QAaL46t4eQFVF/T6rHzBUsJxG3g1K/9PKMlt6RnidpX+OhcPipvpeGgWs2piuLkJNZ+nbf4CV/qP4XVmGUn4+KqthlRDMeO+yzkz2Y4ymwYEis/EDs3KpimdmCWNyDyKe2mOrEmhKGVirHQ3NqUOFxZkcrTzOP4zB8mkFE5d1sx6yw1U2puJG1FOG+cZcmTRZI2MJ8YGcwnpCyMkwhpWz1UI6rA5VXpuacDc5OJvn+ln/+ASbiGxI61y47oyem6sf2OdeigU4uDBg5w7dw5FUejq6loNdTW/urno3EOrm42QoHb76gi97Wawen5quxf8ciuEe8EvlNAF8YNTxF6aRHaY8H+wGWuT76c+m05PcuHsF7j4fAXJ+XX4KnJwTQ1lB4/StFwPgNX8JE7zt7DmNaZdKl+3FvGYY7WufndY4g/jI+RVhbE6G0/bekgeKWdj/wSX2tvIWK3UzY9xjfUVSn0RRhI+vmyrJNNQxLx3O8enNsJsFgmB8Ki0OexcPaij6gaLlgPUjZ0gajHhyBqkXZtIuzrxGC4kdGrsL1N5cS9iRuHUxmpqXFfT7OokT57XlD4GTRHC6gqyI057ykukbxjDMOGvvJF0qhpFldm4u5qqrUE+/8oI33ttCpOAnozKHWtK2XZzA0WvLw39t6He3d3Nts3duOYOrwb60AtgaKu7RtfftXq2i6fyF9bnBe+OQrgX/MJoy2nCDw2Qm4xj6wjgvaURxfGTB09pWpjRsS9y/sAkS+duRZJMqDu9OEIH2DHeBriwKMdx2L6APR8halN41O7iKw4vKdngiriFvwiP4REG01UWjhXVcGB6N7c/eoyB9lZWiorwR5bZkz/AmsAUQ0kfX7EESTcUsezbybHJLsRcDgmB4VYpKXdwfZ9BMKozbx+jYeJxwrJAEgKrVMVy8VX4jGJAUGQ/R/3SfVhOG5xtKcVXvJ129zZMsoVe0yB9zDFpGsdlylI9q5BeWsHq8lLadCsrcwG0rMGay8pYf20ND/bO8MX9w2TyBhtyCnfVB9l1S+MbxwT8aPlFURQ2dXdzWbUJ19Dj0PcEZKPgLF3dNbr+7tWDuwp19PeNQrgXvOOEIUgenyP6zBioMr5bG7Cv/8lzv3U9y/T0txg4/zDTx+4gvdyEXKOSK3qVOyb8CL0VVRrCafsCTmOUjFnmoN3K5xwlTJsNWtJm/sfyLM35NDNlVvorvOyN38ieb5wjXFrKeH091kyaHYmjbC3qpT/r5cuWEnK1PmKenRyZ3owxpyEh0L0mHHUudg3maZvOkzAlKV7+PunMMhmziktzEA3swS7XAWA1LdLI/fgOT3Oh1A3l3Wz0XonL5GdYmeRSdoh+9SJ+WcE9kcHI5ylrbqO85VomL1mIh7JUt/vZclsDB+cjfOapSyxlNBo1mbvKA9x8ewvBOjfww4nSs2fPro7U2+vZZhnE1f8wxKZXjwFovXl1pUvdzsJtRu9ThXAveEflo1nCjwySHYpgafbhv6MJxf3j58IIYbCw8CTDw59jtred5b5bEapCqvoS90SGEdrNKNIyLvPXcErHyKkyAzYTn7cXcdSuEtBM/FkoxNXpCNN+J1MNJl7MbCfwFASjafpb12AoMusjF7jGe4iLhoOvWAOolX4Sru0cmt6CPpdHkgWGzwxNbraNa2wZ1BDoWJMvYlk+zYrTiiWvkndtw2TtAgSypFHr3k/Fwf0MOFSiFWvo9O6ixFbNohxiMHqKs/JZvLoZUyiL2WajdcdVlLdcQd+rSRbGYhRVONn2gUamTDp/8fB5hmNpgnmJOwI+PnxHK+WNq5uLwuHwGyN1SZLorjCxPf0CrqVTICnQuGu1jt5yPZjt70JvF7yXFMK94B0hhCB1donI3mEwBJ4b6nFsLv2JJY7h8DGGhv8XS5NRFk59gvRKkGxgidvUB7FkPgaoONUHcarPIGTBnM3MtywOHnI7MRsyH4+k+FhskUWnm5kmmXNSAwPn13P5oX761q0l5XBQFxvnevsB+kzwdYePohI/UedlHJjahj6nI8kgAmayLR7Wz2W48oKOKyPIG72UjT3DtNeMhIRsXYdi34UsGQhUSn3D1J15grFcmPmyKjq8O6l1tpOU0oyuHOFM/iQWzYSU0ymuqWPDnhsobdrEqWdnGT27hMNjpueWBpRaO59+6DxH56O4DIkbXW5+945Wql/fwBWJRDh48ODqOnUEXc5Ftsf24iYO5Z2vbzC6HZzFv/iOLnjPKoR7wdtOT2pEHh8ifSGEucaN/85m1CLbjz2TTA4zPPx3LC4eJDxwN0sXd6KrGltc91MnNpMTbTiUJ3GZvo9MlojNztMmmS96/SRluDmu80fhOTTVyXSzxKTTx77Z3dzwnTMMtrexUlREILXMdep++hxJ7vd6qfUEmLdezqtTm9AXVkNdD1rQWnxURxJcfQ4qVnRyzFM//D2mXVkyZhOqUonivAGTopAXVtyuOI2TzzG3eI6pYIAWz1ZavJuQJJmxyDF6EyfQNQ1JUWjZsp0Ne27AW9bAyafHuXh4FtUs07mnhorNJXzmsQs8ObqEImCX1cEf3dZO0/oAkiStHhNw8CBnzp5BEgadXGSHOILbG1gdoXfcCYGmd6mXC97rCuFe8LZK94UIPzaEkc7j2VODc8ePX32XzS0zNvqPzM49RDrUxvhrvw1RG1WOI2y3TJIy7sSuHMSt3o8qhYlbXZySNf7eV8S4WWFDCv77yhxB3cpQg5mVEpmDoZ1sfHCOcFEZ09XV2HNJrhCHGPAv8JDHy3pnMYPqVbw2uRGxnAcF8uV28g1uAuk4O/pk1k7l0UlSN/oIIXWKkMuOIjlRnDdgNznICheqCWrTx4mPPsdEsYca5zraAjtwSW4mk72cCx8hqcWw+3xs2H09HbuuxWxzc/alSc68MImuGbTvKGftniq+9PwQ95+bIScEm01W/vjGVrp6VjdvRaNRDj33OKf7x0AYdHKBHZYBPOuuWT0fvXDpRcHPoRDuBW8LPakReWKEdO8SpjIHvjtbMJf9cEOSrqeYnPw6E5NfJZdRGL3wKfShGmxKiKtdD6MoH8YiRnCr38QsT5Ayu5iUBP/ktXHYbqM8B3+6ssTmtMK5Sjep2ix98RbkF914QzDc3IyMwcb8SYaLR3jR7WWzvZjT8m7OjrdBRAcTaNVO9BoXZekoa8YlegYNVD1PxfRLGKljTATcIKkoth14bGXkZA+abqNEv4QYe4Qpv41iWy1twSsolctYzE3Su/wKK9lZylvb6Lr2Zhq6tyBJMv1H5zn+5CipaI76jcVsvqmOx16b5p+PjREWBmtkE//t6mZ2XVGDJEvEpvo49NxjnJ7JIoCNUj87Gp14u+6Axt2FDUYF/yGFcC94y1Lnl4jsHcFI53FfWYXriqo3NiQJoTM39xijo58jk11gau4jxE9sQcmZ2GB/kgZPK2RMeNVvYlXOkVXtLMluvuPM8qDbic2A341E+GBUcLo4iNYSYUnzM9y7jtajy/S3tZO1WKjNX2AseJFeh5/NjiIO69dwcbwRKa4jWSBX50GU2miPxrBHdLZeknFlwL98Bt/8kwwFneRUUMzt+Jz1CHMxyawXhz6Beeoh5twSTnMRTeU7aJZbiRlhLiwfZCY3zNrLd7PxmhsIVNcihGDifIgjj48QnktSWu9m622NHB8J8fcHh5k28pSh8Afb6rjzhmbkbJTY6Uc5fPQ1TiUCCCQ2OEPs7OnE230H2Lxv0voFBT9dIdwL/tP0eI7I3mHSF0KYKpz4P9iMqfSHo/VQ6CDDw58hkRxgObOZmcO3YlkppkQdYmtwGim9Dp/yHezKK+QlKyG1mhdtc3zJ6yEhS9wRT/A7Kxoj9joia5dAhTNjnazbu8Rg8zpiHg8OMcxkoJclq5/17mJeyuxheKIaKaWDXUKr92B2mdm1nGDFSLBuyEFJDByJaSrHH2I0IBG3CCSlmIC7BbOzklCiHFN+Bsv8Yyzbc6iKlarKHrqUzeSFxqXwMWZMY2y68TbaL78Ki331e14Yj3H0sWFmBiN4SmxsuaWBiUiKv395iD4jhxuZj3dW8zs3N6JO7CN+8mEOD0c4KdowkNlQZmbnNbfiq1377zV5QcHPrRDuBf9hQgjSvUtEnhjByOq4d9fg2lGJpKzWgePxSwwPf4aV8GGSVDB4+gM4RlpQ0NkcOIJX6cKffxan8gOQDEKmNs6q03zBb2HUbKInleGPQ2l00Ur/uhU87ggD881U/CDLTFELC8ESssok0/5ezBY/1Z5Kno/vYnYigJQ1wKWQq3cTlGQ+NJfmnGOJqjE3lWEz5myYmrG9zDujLDl0kKwUOWrxljQwG2lGaHOYl58gZk6CLOOtWst29QpshpWRRC+h4DKbbr2d6nXr31j5szKb5PiTo4yeWcLmMtF9XS0JSfCPLw7ymp7FLEl8qK2MP96ex9H/MLFzz/BqpoFTrENHYX1zNTuvvQ2/3/9udmvBr5hCuBf8h+ixLOHHh8lcWsFc7cJ3RzOmktU11ZnMHKOjn2Nu/jFyOBkY34Pn7HpSuTLqnBdoKi6lKNaLR/0OihQhZGpnwEjz7UCcw3YbNTmNP1pJ0JTsYv+aBBWl4ywmAijPOUika5ioqWbJOsa0p59q1Y/DX8/zK1cRnrQjaQLhU8lXu1ifhk9OZ3kyOIl3zElV2IOiZ6mcepm4eY55exoDHa+tjNLKOibDm8hnZlGiz5FWYuRlA7mmmqvkPQSNYubSoySacnTeeTOekuAbbRFdSvPaU2MMnJjHZFFYf1UVikPhS/tGOJhPo0twU42L/6/hFMWD3yWyPMdhaQtnaEcg09Gxjh07L//pNx8VFLxFhXAv+LkIQ5B8bZ7os+OIvIHnmhqc21bvM83nk0xMfpmJya+jGzrDS5spOdPCfKQHhxqjqzFFcHERr/otzPI4cbmGi/kgrxQN8T2PE5sh+HgkwfUrPTxfrlHSfJG8rhI7WoIxXMpAfR0T7knmHWN0mAJkfGt4aXEHqWkFSReIYjPmUgvXRBQ+MZvjW6UXsEzYqYiVAyoliycwpEnmrRE0I4nD7KGiqo7p6A60zAxSfB85KUJO1YnUutgtX8carZGYFkLrUGi962rM1h8u5UxGspx8Zpy+w7NIikT79nLMThMPHBpjn5EmIcN2X56/8nyfhvmnCeHhsONGelMlIMls3LiR7du34/P99DN1CgreDoVwL3hT2nyS8OPD5CZiWOo9eG9vwhSwIYTB3PxjDI/8H7TcEhPxtZScrWBxYRdpw8PG+iUqE1GKxcPYlJPkCHAqt5Fh/xm+5rcQlmVuj6e4d7mLC2YXmc4zeC0xli6VYjrm50xdC4P+CSLmRXosAaZ9HRya3Yw2YyAJMEotVHpkPrJk4roVnW8Vv4IybqUk2ULe7MYdGcBqjLNsWSCVW8asWCgrr2IxvZt8egojeRCdGGlznsl6iaukq9me2UzWSCN1Oqj9wBZk0w+37qcTOU4/P8n5A9MIXdDcE8RkVXjmxAwvyRmWFUGbJcFfyl9gszjHkmc9h6y7Ob9ooCgKnZ2dPzxPvaDgHVYI94J/l9B0YvumiL8yjWxV8NxQj72z5PUNNicZGPwfJBIXWElXY79UQnZiO3NaOw1FCept85Rn9uFQnsfAyvn8TiadA3y3KMVFi4UNmSx/sNiClFzL+Z7jVHkmiC55MO/zcqSkjQvFk+hqis32Yi66uzgx2YGY11aXd5dZ2GI2+N15C7UZwXedj2MZU/DlOkk5yjFn5nHpU6TVUVZSc8gSBIqDxI1r0FKz6OlXESJJ3KbR35Bhh9jB9cmrkJFRN3gpu20tsvWHd9Vkkhq9L0/Ru28KLatT1xHAZFY4dG6eA6Yck6pBpRzlz5RvcJ31EotNH+Rgeg0Xx+YwmUx0d3dz2WWX4XK53r3OLHjfKYR7wU+VGQoTfkw0dbQAACAASURBVHwYfSWDvbMEzw31KA4T6fQMwyOfYXHxGbI5D9JIKY7RNVxIXke5VdAYnKIieQaP8igSGSb0nVy0ZDjkG+Jpl4OSfJ7fWy5jfehG9rcfobbyLLmsGeUVN6+orZwtn8eGxHp3kGPWTVycaEJezoEC5lIzd+pw76IVXc+y13Q//kEDG1sJ+1uR83Ec0gwW6QIzsSWESOPyeslJu9AzIfKZYyAyrLhynG+IsSnfxZ3xG3DILtQ1bgI3t6C+fjUdrI7Uz740xfn902hZnYpmL7Iqc3pgmcNWjWGTgY8En1If5cMNGZbrbufgrIn+wSHMZjObN29m69atOBw//QKSgoJ3UiHcC36MnsgReWqU9Nkl1IAN722NWBu8q3X1iX9hfPJriJwgP1VG5bCX12Ifwav4aC5ZpEg/R8B4BFVeIGJs4KipkTHnfr7ltaFJEveE7dw6fy8ny85iW3MEu5pGnHHwcryZ06URSgwHdb5yDqk9jI2XI4dzYIJAkYk/SpnZEVOYIcqx3FeouZjFsO5kvrQHQR6reYFS/QQj0QT5fAiT1YakXoaRT6NlX0MSGvO+DBfqonSkm/lw/Hb8phLUCge+mxux1LjfaIN0PMeZFyc5/8oM+ZxOsMaNltMZmY9z1JajTzVwSmk+4TjIxzaXsRC8nMO9w4yNjWG1WtmyZQs9PT3YbLaf0dIFBe+sQrgXAKuXaCSPzxF9YQKh6biuqMJ9RRWoMD//OIPDn8XILqLPlNE0qnF65deQWEuzN4nDcp5g9hmsyjlyRjWnLLuZVZ/hviKYNJnYmZD4zfl7icgrzG58mTL3AtqUhX2TVZwK5KnTivCWVrM/v4WlMTdyXEOyQKvLxKejNso0g1PSKAvR+2k5myLp2clk1dUYsgK2EC35gwxGDTLZKSTFhGrqIG8IdK0XWehMFafor4nTEi3nnsQdVFhrkT0WvNfXYesIvLGkMRXLceaFCS4cnCGvGfjLHaQiWRZTOU45EpxWFCySxr2lY/zmrg7mCPLqkaPMzc3hdDrZunUrXV1dWK3WN2ntgoJ3XiHcC8iMRIg8MUJ+IYWl0Yv35gZMJXai0dNcGvxrkrFzSEt+GoZzjIeuJZa/lka7wOS5SDB9FKf8HAZ2pk230i+d4HF/mFftNmpzBr87fxNlsWqOrv8BLeUjpGMK+0aKOW+z05QvQ5TXcSC9meSYipzKo9jgapOF/xYzo5PhlHEIY+kF1pxNE/FtYajhZoRiJ2ePsV7fz2BUIZUeQkigqg3kJROG1o8kDMbKUoxUJGheLOL29J00OJuQzQquq6pwbatAMq3uok1Gs5x5YZKLB2fI5w3cRVbiKxkSQtDnmOWIujqqv6chw8ev3870zAxHjx4lHA5TVFTEtm3b6OjoQFXf9E75goJfmEK4v4/lIxmiT4+RPr+M4rPgvaEea3sRmhZiaPh/Mz//KErUTumIRHphI/O5D1NvtqK6JvFqp/DKjyCTICpdzSUpxSHvRR70OLEagl8PrWfL3A0caPwuLQ0jZITGvlEvw6KYFqOGUGU9r0Y60Sd0pIyO1Q4f0+3clZWZkZaYyP8A++R5Gs7nCPs7uNB6NygeMvYMHWI/kzGVWOoihmGgKGXkFTPkJjAkwXBlgplAmpZZH7v1u2nzNqEYAsfmUty7a1Ccq2e0RJfSnH1xkktHZjF0gcWukknmySkZhuzj7FdKyWLmjlYbH9+9kZmh8xw/fpxUKkVlZSXbtm2jpaUFWf7pd78WFLybCuH+PiQ0nfjBGeIHpgBwXVGFa2cFQhHMzDzA8OjnUBNx7GMufNOljGU+SZXZg9kSxyT3UiwewiyPkTHamZCrOec4whf9TlYUmRuiAW6Z/S2Ouh+hun0c7AlenrYxkaqmRW5krKKJ04ttMJlF0gy8DviDrJ2t+TyD6hjZ1CP4+qepGBUs+Ro40/FRVClAxqLRoh5hKS6Ipc6T1zRkyYOmmlG0JTTFYKAqQcSZo3W6mC7lDjqLmzBldSzNPrw31GEKrk5sLk8nOP38BMMnFwCQZDB0UEzz9NnmeEFpICdUbl5Xwq/3VLIwfI5Tp06haRpNTU1s27aNmpqanzibvqDgveQthbskSfcBNwKLQoi1r3/2WeAmIAeMAPcKISKv/96fAr8J6MCnhBDPv9kLFsL97SOEIHMxROTpUfRwFtu6AJ7r61B9ViKRk1wa+DTZaB/OSRflY1ZG039IUK3EKuto1gFK9aewK4fIiwARupg0H+NzAStnrRbWphU+MvtR+vPHKW0ex1Qe4qUlK/Mr9dRaW7lU0sqluVqUqRSSLqiyy/xxykqxFGHY2odr5WlKzobxLcCir5pjXffizJegqQaV1lNkUmlWUhfR0mkkrGiqippPkDHpDFTHyZoM2qdrabDcSE95PdaEhhq0r/400uxDCMHccJRTz44z2beCJIEQIKETsL7GSZvGXjaioXDLhnJuabIyN3CWwcFBANatW8e2bdsIBoNv0soFBe8NbzXcdwIJ4P4fCfc9wD4hRF6SpL8DEEL8iSRJbcCDwGagHHgJaBZC6D/r3yiE+9tDW0wReWKE7HBkNfRubsDa4CWbXWR4+O+Yn38cz4KZ4KCdufjH8Spt2GWJpHmOoL4Pt/ooAGmjm6g6yLd8Ot9zO/Ho8JHFy4ksxwmWD2FqXeblqIXQUhPFzo2cL2pjejqAOpMEQ7DWqvKppERWnWfOfpLqmaMUn0xgT0hMF9dwePO9+NPFSAj89j7Qwqyk+tFiMUAhryioeo6kNc9AVRxVl2idW0/QdhVb66pxrmSQnSbcu2twdJeCBOMXQpx4cpTlqcQb7eFSFimzHeCAu4KHMxvRhcQtG8q5ojjD1KXTLC0tYbfb6erqoru7u7DxqOCXzs8K9zedHRJCHJQkqfbffPbCj/zyGHDH61/fAnxPCJEFxiRJGmY16I/+J9674OdkZPLEXpokcWQWyazgvbkBR08ZQsozOXkfI2P/iC2SoOJSEYmlD5OUN1JukkmQxqSeoZ77MJnmyRqt5InwsqeXv/d5iSgy10cqcMwGkcxnKN0e4uWMhcRQJ3bfJkaq2zg1aUUZSmMiwXZV5SOpCDMsMek+RuPIBWpOZTFpEsOVa9h/za9RHvdTnDSwOcaxiWmW48NokQhgoMsKiqGTtGYYrkjgypjpnLkSl20L29aX41lOQSyH64pKXFdUIUwyl47NceLJMZKRLAASBrXmE5Q4j7O36Gr+JnQTRkbihvYAmxwrTPc/z+lLGcrKyrj11ltpb2/HZDK9ux1YUPAOeDum/n8D+P7rX1ewGvb/avr1zwreAcIQpE4tEH1+HCOp4dhUinvP6mRiNHqW/v4/JxvpwzMUgPF7MKStlJpkErqBJk9QrTyAXTlCXhSRMyoYtwzzP4v8nLEV0Zo2sW28ibLIPEVbjvO8bCY+1YVadBnnqlrJjIPSl8EsJ7lWkrhxeYzeQJSQ/zhrLoziOW9gSBL9tZ28uO0eqlecNIQMZNsCLscQ4eQosXAYgzyGBLKAsDPFZDBFMOFh8/QtWO3ruawrSNFyCrGYxL6xBPeeWnSzzKt7R7l0ZA4tu/pDoU2Js9b6BPbAPN9y3cMPZjcgQnBNs5tWplgeOsGoLNPW1kZPTw+VlZWFenrBr7S3FO6SJP05kAce+E/82Y8DHweorq5+K6/xvpSdjBF5YgRtOoG5xo333rWYK5zk83EGBv6W6elv45uyo/Tdi8XYgUdVSekGMZGg3PwcbvVBkHTyRjFpZZnP+/w85C7FpcPls5XUjGao7+zl2XaFxeVuJP/lnC9rRhvPo4QyWBWd23Nprpo5zaFGQSZ4kl2nZ3GOSGRMCuead/DM9g9QGbayfkpHWKI4nX0kIgOsxOJo0upNRBKw4Muw6MtQEy9ly+xNqLY2enpKKI1kMKbjmJu8eK6rI5zTeeJrF5gbiYIAEFSYL9Bt/z4rtS18SXyA5yYlzEmZXbVmqlJDaOPzpB0Odu7cSXd3N263+2c3bEHBr4j/dLhLkvQxVidad4kfFu5ngKofeazy9c9+ghDiK8BXYLXm/p99j/cbPZ4j+tw4qVMLyC4zvjubsW8sAWBx8XkGBv8K08ICjtMfRMnupkQ1k8JgSdMpt1zAZ/pnzNIMunAjE+MpV55/9JezosisjXhY029hY804z1wpcyCyCZ1d9BU3wFgGOZLEqea5OzLHFYMvcXBrAFHTywdeDWOdl4jaLby2dhdPb7+J4rjKlhEN3ZTC5riIvnyOeDJNVkkjpNVQnw6kiDk1mtON1CxuRbE00L21hKqUhj4eRSm14/31Ni5NxDn3T2dJRXMAWJUk7dan2eh9kVP19/L/Rv+Gw+NpnBaFa6sMisPnkWdTFJeX03PlbbS3txfWpxe87/xcSyFfr7k/9SMTqtcC/wBcLoRY+pHn2oHv8sMJ1ZeBpsKE6lsndIPEkTliL00g8gbO7RW4r6pCtqhkMrMMDP4lK7P7UE9ej3XlRsrMNnLCIKQJSswRAqb7cCivYGBGJkef6uFvA056bQrlGYW1g272mJd5oc3gYmoLGe81DOerkMeSyIk8LlOWj0xc4spzezm9u5qW3Cj+oynMMYkFn5Oxmut5duvVODSJjSM5JEnHZu3HPPMaiXyOlJoABAKYCqbImA3WaR1IqW5kUxUbuoPUGwb54Qiy24zUFeTUQJjJ/gjCEIAgaBllq/0+ysryvFD1+3xpupbemTg+m8JmT4KicB9WBdrb29m8eXOh9FLwK++trpZ5ELgCCAALwKeBPwUsQOj1x44JIX7n9ef/nNU6fB74QyHEs2/2goVw/9kyQ2EiT46QX0yvrue+qR5TsR0hdKam72dk5HOI3g1YJ+6iwrR6KuGilsdtkihRn8Vt+gYyGghBUrLyD94qHvNmsBjQOuXioysxjnVkeJkdpNw3MpkuRR1PIKV0/OYkdw+d4NoLzzC0q4SSXAjvEQ01IzFa5mem4nZe3LwFIUtsHchg0QQmyyjeiUOsqDpJJcq/hvpEaRIhSaxXetASG5DVIGs3BWmxyuTOLSGpCvEKJyfGosQjGgCqrNFieZktrgdQ1lzBXt/H+JdLZoYXEwQdMutMC5SmJ/A47XR3d9Pd3V04mbHgfaOwiemXVH4lQ+TpUTIXQyh+K94b67G2+pEkiVRqjIt9f8Jybx5b30epVvyYJAjlUyiqnRJ5HLfl/2BjAoEEQuYpaxf/VDzHgkmiNmThkxMZFptjfNOxjajzg8wnSjCNxSArKLWucNPIMW66sI/lnRZs6RyuYwZSTmKwupS5yrs5uH4tUYfM9r4s3pSBbJqhdGwfs7Y8aTmMBBiSYLIkhdlQWe/cSSK6Fkny0rqllHa/mdyJeQzNYNlu4uRcipyx+r27zStssn6bZl8vqQ0f43vKTXzjdJSZSJoKB7QYE1Qai1RVVtDT00NbW1uh9FLwvlMI918yIm8QPzRD7OVJJInVc1K2VyKZ5NXR+tQ3OXfkB6in76FOVOBQJOJ6hKThotSUx2b6Jn7lydf/MolRaTufDYR51RXDk5H5rXGdCv8Knw1exqLtbpYSQUxjccgJqm1z7Jk4xp6hE+Q3aahRgeMYCEPiUm0li6Uf4mjHGqYCsL1PozysI8khKsf2MeZNkxPLyGI11Gf9aZzYaQ/uIrLUjKHbaN4cZH2Ni9yrs4ikxrwhuJDIkzQABFW2PjbbvkVpucRcxyf5ZrST756aI57JU2PXaNTGqFbjrFu39o3SS0HB+1Uh3H+JZMejhB8fJr+QwtZehOemBlSvBYBkcpSTh/+O0KGN1KVbKTbJZEWUkKYQNDuxyKfxWz6DWaQAiOudfMNdw3cCJ8khce2c4B59iU/XbGbI/lHCsVJM46uhXmef5JqpY3QvD2JfE8I8K2F7TcJA4mJ9LaGSD3OyrY5LVRKbB3RaZjWQklRMHGAosIKhLaEaq6G+5M7iUz201OwhNFdHLqPSsDFAR4sP7fAMSkJjJW9wMW2wogtUOU+77Xk67Htxt6ynr+kTfG28mCd659ANQZM1QZMxSZ1boru7m66urkLppaCAQrj/UjBSGtFnx0m+No/iteC9pQFb6+qlykLoXOq9n96nVqgMdVFrlhHkiOrL2NQK7EQxWf+GYi4BkDWqOaZ8kH8qfYYha5LmuOAvVpb5auU6XvL8BvFI5Wqoa4Im1yjXTRymIbeIv34W65jAflwmL0tcrG8iEriH0y2VnGmS6BiBztEMkshTOnuMgcAUSmYekw4GgrAzR5GliObm61icqiKThOp2Pw1VLpTeRRzpPAld0J8zmMkaOM1xNlgeotV1BNPGW3kl+Gt87ZzG4eFlLDI0qUu0MEtrdQk9PT20trYWSi8FBT+iEO7vYUIIUmeXiD41ipHWVlfBXF2DbF691zM0P8q+B5/GMd7GGosJkyTQpX6SehMeRSVveogq5QEUDPJYmcn/Hl8uGucp30kcuuBPlqJMusr5QtknSa7UYhpPQF7Q6h3kxtlXCGoJfA0zOAd0HEcUDOB8QyPRwEc53xDkeJtE3azKjr4Uqi7wL59j2DeIKTWFOS8hEMRteUocQerXXsfCRDnJiE5JnYsSrxXnWIQgkBWCwZzBWNqgxDbOBstD1Aem0Lp/iyfM1/HV4wsMLiRwqQbNzNBqXqFz7Rp6enqoqCjsgyso+GkK4f4epceyhB8bJtO/grnKhfe2RszlTgByaY2Dj73A8nETHRYLbkVGYoiY7sapBBGmQbzKX+MmggGEjRt40dLNl0q/y4qS4QPRDF15M39W8/uEV9pQJpNIeUF70SWujxyiLBnF3rSM50IO1yEFDDjfUEu4+Nfpry3nyDoDT9TGNWfjODIy9tgoE+5ezPERLHkZgSBlyVPqqaR+4/XMjgaJL+dwFVmxmyRKozmqzRKGJDGc0RnJ6NTYX2OD9TFK65xENv4eD8TW8c0jkywlcgTULGukGda5s2zZvImuri6cTue720EFBe9xhXB/jxFCkDq9SOTJUdAN3NfU4rysHEmWEIbg4qsjnHx8kCbhoMYiA8sYzGCIdahyDt36WarFcQDipnLGU/+dfy7dy6vuPpqyeT4Ry/A3Vf+FsWgXymQKKS9YV3KRa/TD1C0sILWlcJ/N4t6voOThYl0VoeBHGaqu5kiHQU44ufFUhGBYRsmGmLGfwBK9iE1bDfWMWac0UE3zppuZGvQTmc9gtqlIOZ0Gk0S9VUECxrM6o1qWRstzdNifxd2xlcnWT3DfqIvvnZgkkzeoUGK0yXP01HjYsmW19KIoyrvaPwUFvyze0sFhBW+vHxut17rx3dGMKbB6D+fsUIQDD57BtQw7bTbMksAsHyWpr8ckrcfw7qM4+3ksQiOnKszmP8V+2c6XG/6enJTldyIpjnh/nd/07US5kEbVkrSX9HOb+1nK+8KIzVls0RzeLyjYUgp9NWUsl36U0ap6jnTkmXc4uf5MmObpGELPsWB9DVPqGN5lBYFEVtUpL6unbeftjJ5zcP5gClnJIgN1sqDRraIImM4ZTOpRms0P86HiE1g23cWZir185XSC5x+YRxLL1MohOmxL7OxooKfnbsrLy9/djiko+BVTGLn/AqXOLhL+wchPjNYT4SyHHx5g8ewyG5wCv2zGxCAaEtCE4VjEzF9Tqo8jgHl3PVORP+GLwYc57RxkQyZLmXw5Dyp3I4/nkHIGjUWj3FX9A4Ln4qhrE0jTGp7HVXwrMF7qZ6bqo4xVruFIh8ZQoIirLkboGtBRDFgxnUfEX8aZWd1+pCmCyqomOq7+IAOvmVmaXD1WVwLaK+xUZfKYdcGCZjBvzNBsfoCG0mnElt/lJdu1fPnVaU5PRbFIOk3yIps8Sa7cspHOzs5C6aWg4C0olGXeZUYmT2TvCKkzi5hr3Pg+uDpaN3SD86/McHLvCPWyQaNFQSKJVT5HSu9ByHksJd+nJPoIMoKURWFC+Tj7hYNvlDyOTI6rMkEetvxXtEkZKaNT6ZnlnqaHqJ1YRnZpZEQW+yMqVVOw6LExUXcXI9U9HO3QOFceYNNYjMvPZrFpKjFljFzySZzpPAJBXhZU1q1h7ZV3039MEJpJAmCyKHQ0uAkspbBqBuG8QVj002j5BmU1NtI9v88jyQ189dAoU5EsTilLm7LAlbVWdmzZVCi9FBS8TQrh/i7KTcUJPdiPHs7g3lWN68pqJEViYTzGK98dQMwm2OjSsQsLFvk1ckY1giA5/3lKs5/BrkcxgMmyciYXf58vBp6nzz7KuozCoPkPWZwuQ07mKXJE+OiaB2jNjaONW0k0xpAfNrH2AsRtKqN11zNUu4cjHYKz1X7ql1JcdyKOL2khJS+TSj+KM7Ua3nnZoLK+jfquuxk6mSO+kgHA6bfQ1ujFNRbBqRkkdIOEOEWj5ct413aysP6TfHOiiG8fHSeRMwhICdaZF7lxQxWXbemhrKzs3euIgoJfQYVwfxcIQxB/ZZrYixMobjP+u1uw1HrQsjrHfjBC34Fp1jmhWjGBtIBFmiFrdJKUIwRK76NoZR8AMbuZMfe17I/VcH/x01iEgUtcx6XQVSgrGjZLlo80f49NRWfIH/EQaQ+z8oqFbYcEiiExUruNgYYPcHi9hTO1bnzJLDcdX6Yy5CRLkrj2GI7EIhISedmgvLqdspY7GO3NvHFWelGFg8Y6N6a+EEWGIGMY5MSr1Dm+jq3rJvoafouv9OZ48twcuhDUyGG63XFu3tpOd3c3Dofj3eyKgoJfWYVw/wXTkxor3x8gOxjGti6A77ZGZLuJ2eEIL3/rEtZwhk6PgVlXsSin0PRmDOFksfgI69Kfx5RPISSJ4Ro/S3O/zed9x7loH6EsX8VA8rdh1oyi6lxXuZ+bG59GGjUTiQnOJ+GqZw1KIzATbKSv5aO83F3C6XoHiqFz08lpGia9yEInajyFLTaKBOiSIFDega/iJuaGM/zrf4lApYPKMgfWgTBBGTShIzhMte+7yJt/jVf8d/ClowucmIiiotOkLHNlpcR127tpa2srlF4KCt5hhdUyv0C56Tih71xCj+fw3tqIo6cUXTM48vAQffun2OBWKHeqCHkWi7FCTt9ETJrGV/oPdEVOAZC02Bkta+bo4g6+WvbIav07+yEGxjuQBKwv7uM32u7HJnJoz9t5OZiha7/MPSOCqNPLsc6P8OT2Dk41W8lLMrsvDtI0EMCT9xI3DmKKncaOQJcEnsAGbO49xFd0Mq8HuydoJ+g1456OUx7PYkgGsI/q0pfQtv4mD+l7+fKhcSYig9jJ0W1a5Ja1Aa7ctqdw1ktBwXtEIdzfRskT84T3DqM4zZT8znrMVS6WpuK88LWLmENpdvsUVF2gmE9h5NaQFcVMuF6gR3wFNZxFSBITQR8r0ev5fGqJk6V7MesNLM/ejZRwUeSe51PN36bKP4Xe6+DskkJmJcfHnpEQkkJf0w08sus6jrXZyKgql49epPpCgOp0CRn9POnEK5iFhiEJbJ4NKOZd5DXIrh5Fg9VloshtpjiUoSqTQ6gGZvkFiusvsdL1W3xu8W7uf3aCWHYQv5RklyPMXVsb2bJ5V+GGo4KC95hCuL8NhG4Q2TtC8sQ8liYv/rvXINtVzu2f5vijw6x1KFQ5VfLqNGYRQ8t1kRLjaMXfYXviGEKAJnsYK/dwenkX/3/wKClZIx2/nfh0N7I1ywdrf8Cepv3oKRPJR+w87dK586CgPAyzpRt5aPdHeKkrQNJiZtvMGYp7fbTGyxH5cdLJF5CNJAIwudqRld3IqoLTZyW6mEbXBQGfhfKMRk08i2QW2NTn8LWFGFt7L//70h3sfWSevDFGpRzhhmCWD16+gXXr1hUuly4oeI8qhPtbZKQ0Qg9cIjsSxXVFJe49tWTTefb9y3kiF0Jc6VWx6ALDcQJzcg0aQWbUF1hnvQ/b/23vzuOjqu/9j7++58w+k2Qm+0oWSAgQCIRNFhFkC4vgWitWsVq91qUu99rlWrWtvVZbf1q3ar1W61b3pVhp1eKCRUFZwh4gCRCyb5N19pnv74/Ex4NLQRE0k4Tv8/GYx5w553vgzTeHz5z5nm/OdPfOTmm2ZNItMnnYG8+H6WuIhLPx7D8fGUogO2kjPxqxGmdMG6EyB+uqAqQ3hbnxfUmP1cXLCy/j+YXj6bSaOK2pDNd2AwWtmTiCTfi8r6OF2hCAZsvHZFyE1WEhMdNBw4EOOpq9xNgMZMsIuZEwwgQ24z+JK5FsyF3BIxs6+OSFNnQiDNdbOKvQxlmzZ5Cdna2+4UhRBjhV3E9CsNlD69O7CLl9uL5TgL0khcYDnbzz2DYy/GFOjzEQ1tvQDfuJ9EwlJA/S7HicqeGPkEFBBBv1sXEc9I7m56lNtBh24WsvJVh/OgZnDVekvM6krF2E/Sa6X7HxmQxy7qc6loDks7Gl3HfReTS67Exw7yJ9k5v4xnyG+dwEfK8RCNYgkEhzFmbLclwpTtLynRza1Urt3nYsBsFIs0aeUaIhsFv+hfU0B2/HreDRdTVUflaNhSATTc18pySdeacvIyEhIdpdrijKcVLF/QT5KttpfW43QoOkK8dizomjfH09nz+/h0l2nVizhs+5FVtnCpHIBHrkh8Q7nqMk3ABATyQVv8XKy4ziz5k7CUsX3gNXIyOpjMh4g+uytxHjaCdQ7qRig4fcao3v1YeoT8zm9ot/wKbCXAo7q5jxWTmddRMY5zES9q4mEKxAAmFTPDbLOWQWZpM9NoG9nzVQ/kk9RgGFFo0RZtCEwGbbgj4jhZe083h87X5avZU4hYc5djcrZhQwfep8NZVRUQYhNRXyBHi2NtP28h4MCVYSLxuDFmfik9cqafu4hmKHAWEIEXSsw+yegYabJv1txhrfRJMRQNIqk+nWY/mv1FT2WA4S6J6Av3Y5RudeVqR8wMyM/YSDBuRbVqqbw0zfGSasG3l+0Xd5buF8hnkbObPiYzYfmsKs7hB2z0bCXEJuqwAAHyRJREFUgZ2AJGi0YrWcTcGkcYyemcbudfVUbmnGgCTPrFFgAV0YsMbsJjQzmz91ZvDchmo8IUma1sk0Vw8r5oynuLhYjacrygCnpkJ+g7o/raN9VSWm7FgSV44hEJa890AZiTVdlNgNBOOb0L21mN1nIPgMn+UtxrMFKQWhsJWAwc6n1kx+kdqDnwZ8dRcgfYUkZzzPTWl1JDub8R5y0f2Wn5Q6A7PavGzLH8+vLr8SzaJz7e7nWFc9lp6eySzq2kzYX0aYCCFdx2Sdz8QZcxi/YBiVG5t4+9HtaOEII8wahRaJLgxY4g7QOX0Ev2+YxJv/qCcsD5CtuZk3THL+mVMoKChA07Rod7OiKCdJFffjJKWk85/VdK2pxjIqnoQVhXR1BHj/gTIK/UHsJg3PsDLstWnI8Fgi8lUSrG9hk20gwOOLAUuY2xLH8F5MFSF/Fr5DF2KKOcTpeQ9yXkobBj2I//0kvBsjjKmWBIxh7lp5Df8qmcLlB1/nQGUcm32zmddeBr5VhAkSEQLsJUyefSETS/Oo2+vmzXs3E/KFyDNBoSOCQZgwOxuomzKCBw7k88HfW9GIMEJv4awCG2fPnaPmpyvKEKOK+3GQEUn7qkp61tdjm5iC69x8mg51su0P25ggJJpdx5P4Afbq6Wi04xNPkG3+B0JGAEFP0EmrI8xlGeNo1qvwt80k1DoHW+rLXJNSS2FCE97OGIKvxJJQoZHb0cGnRSXct+IK5nRv4YqP/8Jq7xwWt+1itPcZkD7AiN+WytQ5P2TqWUW01nXzxr2b8HT4yTFJRseFMQgbJqebfRNSuXefZMu7dZgIMc7QxDlF8Syas5iUlJRod6+iKN+CryzuQogngaVAk5SyqG9dPPASkAMcAL4jpXSL3vlxDwCLAQ9wmZRy87cTvX/IiMT92j48mxpxnJFJXGkO1WXNNP2lnFG6QGbo+IPrsB86A6PYiNT/Qa5hfe8wTNCANNpYHZ/BrxNDhCOt+Gq+h46FEcMf4JqkbmJs3XSVpyHe1MmrbcNrMvM/37+WnuwEbt7xFI90LWJWu+S8rhdBdoOw4zObGTv5cuZeOo9ut59Vv99Me6OXbFOYMXEhDMKB0dXDttHx3LU7yP4PqrELP1ONjZxfksHcM84jPj4+2l2rKMq36CsvqAohZgHdwDOHFfffAm1SyruFED8FXFLKnwghFgPX01vcpwIPSCmnflWIgXpBVUYk7lf34tncROy8YcTOy6ZiTTWhfxzArgvEREmofA+GnkLM2qvYDGuxa1UAeP0mNFOEazPPZIOpnLA/HW/NRZhd61iaUsaCpA4iYQP+v6cQ+7kkta2RfxVP4uWl53NFw8u81jyJSLeJae5PEJEO0OII4SVx5CwuuO4qwmHJ+0/voqGqk2xTiCJrCIOIxeAMsKUwjTt31lLXFcAlPBSbmrhgah4zZ0wnLi4uyr2qKMo35aQuqEop1wohco5YvRyY3bf8NPAh8JO+9c/I3neM9UIIpxAiTUpZf2LRo0dGJO5X9uLZclhhf3Uvhs8b0A0azOpErvNiCOVi0R/BaViPTjtSgi9io86u871hE+mW5QTapxBunUVM+gtck9RKvquN7tYEIi86ya5oRgL3X3Qlw+PdzNvxLg/657K45VOswXrQ4hBaMjLJwsqb7sMeF8e6l3dTWdZKhjHC4jg/RuFEd4bZNDyeX5XX0rR+P4mim4XWZi6YUci0aYvVdEZFOcWc6Jh7ymEFuwH4YuA2Azh0WLuavnWDqrj/n8I+P5uY2ZlUPLYVy4FOuk065qm1iLUuNBnArD+Iy/ApgiDhCEjNzpuJI7jLBTJyCF/9BWhhG3l5j3B1oo84Ww/uncPQV8dSuH8vO/IKeH/BYha1/pP7Dy5gcksP53lfB2FFMxbg0w5RetkPKZg4mY1v7WXXuu0k6ZLSWC9mzYUWa2ZjjpNf7qujddMBUkQni6xNnDdjDNOnn4XNZot2dyqKEgUnfUFVSimFEF97srwQ4irgKoBhw4adbIxvjJQS9+v7egv7gmzsk1M58LuNWNr9NDtMOAv3YvzXMIxiHxbDa8TqnyAE9IQ1DLqJ67MX8Im2FRmMwVt9NYbY7SzM+BdL4z1IqdHw3hiy17QQ113JK/PPYVRqPb66Zl5zj2JZ5996bxVgHksk1EzyxCTOvvxOtr1bzbM/W4tTCObGdGPTEsBmZmN2LL+oqqd9WzXpWgdLrU0snz6G6dOXqTN1RTnFnWhxb/xiuEUIkQY09a2vBbIOa5fZt+7fSCkfBx6H3jH3E8zxjZJS0rF6P56NjcScmYUl30XtvRsRvhDViTbSk3Zj2JiDRfsUq/Y2dkMZAJ0YabUmsCJ3Dt2BdUQ8uXjrLsCWsoor0/cx1tlFV3sCre8UMP2jTRxKTuOz+QvIDu3lmbpiprdtxBTxoplGIkQsIXsFK/7zdpqrdF64bR2WsM5sezcOPRFpNlGWE8Nt+xtw7+4iS3Mzy9rEktPGMGPGMvWdpIqiACde3FcBK4G7+57/etj664QQL9J7QbVjMI23d31YQ/fHtdinpWFIstL46Fa8oQg1qXZGGMvRy3Ow6n/Doa3BrO9DSvALE+8kTuKOhFj0wDoCbdMItk0nY9hTXJfSTJLDQ0NVPvY3DEyv3MTaiWcwLLuDjR6drBYrswMfI/RUNPscQoHNlJwznqzMK1jzx53IHiNTbV5chngiejy7hsdwW3UjjXu6GKa3c4alnoVTxzBz5nJiYmKi3X2KogwgxzNb5gV6L54mAo3AHcCbwMvAMOAgvVMh2/qmQj4MlNI7FfL7UsqvnAYzEGbLdG+op/2NCqzjk9CdZro/rKElJKlLtlEkq9DcadiNT2FnAya9hiAQxMqtuefzjr4bLdSKr/4cZDCeybnPcmlSFwY9QmXZZCb+ZTcRTWPH9Dl0W91sa3MyuqschBmj9QxkxIchcR/zL7iR7X8/REeTkWKblxRDDFKT7MuO4ZfNrVT3BMgydFKs1zC/pIDZs2er2S+KcgpTX7P3Fbw7W2l9bhfmfBfoAv/uNg4GIjQnWSmJHETrSiLG/CB2uQVda8OLwG1MZmXBpdR7V6GFNXoOrkQztbIs71UWJXbj8zmo/ngCc/66nsqsAgxj7bzszyG/bS+miB+DuRjNPJag9yMmLJmOvz6N6r2C0dYAOSYzUmhUpFj5TU8n+7r9ZBh7GCeqOWN0JnPnziUpKSlq/aUoysCg7i3zJQI1XbS9WI4x1U64w0+oycPOYISuOBNTgzUITwJ2y7045EZ0zUu3EGyKncg1OXMRnS+hh1x07b8SW9xWrsx9l7HxXbS2pCNeieeMHRsoL55GbUaE6hadIv82pDEFs30BMtyEIfYjik9bRvnHGjkGSWmcRBd2qmN07tcDfN7YTLrRy0LjQabmupg37zsD6uKzoigD1yld3ENuHy1P70RYdEIdfmRYslkKeow6MyPNaP4YHJa7iJFb0EQID4I/Zl7Moy4H1s4XkL5hdB68nKTk97glbx0JDg8H948l/4lGDMEmamaU8JFuJ6P2ICnCgG6bh2YcTtC7hvzJBbTtL6WtzMw8RxCzZqZRlzzlgr+1uEk0Bplr3M+EFBPz5y+loKBAfUGGoijH7ZQt7hFfiJY/70T6wkgp0WJMbPRF6PAFmR3Tg+Y3YbffSUx4O0JE6BQmrh11KxvYhrVrLeHOIjy1F5Cb9Ro/Hr4JTYO9G6cx/dlt1KcMp36kjaoOGBbcT8iSjcVcSiTchKa/RVbWdDr3ZDPZFiLWbKAjInjeBU+6u4jpkEw3HGB8rI95c89k/Pjx6i6NiqJ8badkcZcRSetfygk1eUCCMdPBpqCkrbqDuS4velDgsN9ObHgPAAdMiVxadActPa9hDlQRaJ2Jv3kBJbnP88O8rfgDdhr+Ucj097ZSN7qYj50W4lsaiNVsyNiF2LVCQt61uJIiBD1nk+kzkenQCUh4wyJ5yO9B65RMNNUxxtDEGTNOY+bMmZjN5ij3lKIog9UpWdw7/rEf/143AJbRCewxG6j/sIa58X70kIbN/jNiw1VI4MPYsVxTeBN62yMYwy14688i1DGFJXnPcu7wMtrdKegvOCk8UE3VlFFsDYRJ6GzA58gl1rAIIh5CvleJsY8hOTSO0bESDY3PRITfmgI0+0OMsbQzOrKfSUUjmTfvfFwuV3Q7SFGUQe+UK+49m5voXtv7e1X2aWk0JdvY/Uw5ZzqDGMNgtf8nrnANEeDR9GXcM+xc4pp/B5Eeeg5dRKSnkMtGPMXM3B3U144g808eAkJjQ3EOoe4uHJoVX8JsnJESQv4dGA3lJNuXM95uwa7rVIdCPGgPsd7vY5juZ6nYx5hUJ6Wll5CdnR3dzlEUZcg4pYq7f3877ld6h1piS3Pw5cax7t7NzIqNYJYhrI4biA81E0JwXcHNvO3Kx9n0a4iE8FavRHpzuGHU/1KUsZeDu4spevIgtcNGssMRwdbdid+eidU0n9iwjUDPamLNiYyNuYB0k4GeSJBHtQDPG3y4gNnGSkbb/Myfv4Di4mI1rq4oyjfqlCnuwSYPzU/sAAnOs0egjU7g3bs+Y6o1gl14sNuvwxnqwKOZOHfcb9llDONsuhspJYGDPyDiy+LmcX8kP+EA1etKKHyrmt2F+TSGurEGTbSljiXdP59wuImQ51XynUsocsSjIVgTCnKv0YdfSCZZmxklDzFt2kTmzJmD1WqNdtcoijIEnRLFPdjqpfGhLRCWxJ2Vh21KKm/dv5kxoRAugxu77UfEhrtpNsaxqORhWoP7iGv5M1IK5P4fEvKnc8P4x8mJqaf5rbGkb+1kfeEwZKCbsCWJrviJpHtHE/JvJ0bWMyltBfEGE/UhP3ebJZtEgBFWP8WhckZlJLN48RWkp6dHu1sURRnChnxxDzb20PSHrRCMYJ+ZTsyMDDa+VUFKbTdp5gZiLDdhjXiosGaxtOQhIp61ONpfRUQ0DFXX0R5M4ZriJ8m2NOP/SzaRTgtlmWEMAR/NyRkkRhaS4rET9rzHmNjRFDgmECbE85EAfzT4cegwW6+k0ORlwdJSNbVRUZR+MaSLu7+6k5Y/7UD6w5iHx+FckkdteQue9w8x0lJFnPlnGKWfT+OKuWjcb7F2voGlczWmsAlj5bU0hJO4auwzZOtuDE/Gc8CWQsjWgIaNA3lZjHQvhUgXMcGPmJoyF4fBzK6gl1+bIxyKhBlrbacoUsW0SeOZO3euure6oij9ZsgWd98+Ny3P7IQwaLEmEi4ehb/Tx97HN1FkLcdlupMIQV5LWcANBT8l1v0Upp6PiPXHYq2+lIpwCpeMeonsUCfm5+xscyVjDtfTE5NAR8JoRrknIwOVFJqDjHSVEsDH7yM+XjUGSTNHWBQuZ3SClWXLVqpbBiiK0u+GZHH37uq9EZgw6shwhITvjUKYNTb8ahXjLHtxme7Dj+TxrIv5Te6VxLU8gMm7iVRPCjH1S9kcymRx7ruM6O5AvGNnh8uEOdzKgcxYEiNzyOvIwh4sY7KrgDhjHLtDXdxm0miWYSZbmhjDIc6Yczqnn346BsOQ7GJFUQa4IVd5vDtbaP1LOXqchbDbR9yiHMxZMWz61W8p0twkmB6lS9P5Tc61PJl5HnFNv8Hk301uVw7xrdP5MJDPlNSNTG6twbPFSaetA6MMsq3QTnHrhViCJkZo+xiVOJEwPh7BywsGSarJz+JIOePS41m27D9ISUn56rCKoijfkiFV3D3bW2h7oRxjqo1Qiw9Tbhz2GWlU3H0J2f4EEkx/xm0wc2P+T3gn6Uycjb/EGKiksL2AtK7RrPaNJt9ZSWnDTpprEtC1OnwWG/uGxzK19kJssoeJNg+J5jEcCLfwE5OV+kiIEmMD4w2NLJw/l8mTJ6sLpoqiRN2QKe6ebc29t+7NjEEIQEDMkhSafjMFp3cCiaY/02x1sGLkr9geW0J8/c/RQ9UUtY8iz5fJG75xJFpbOa9hAzVtcdiDdRxKMhFwjWFa7emkai1McCSgC8lLdPCQbiLJ4GeJ3MOU/DTOOusanE5ntLtBURQFGCLF3VPWRNtLezDlxGLJd9H57kHMZ9rwPTEJc3ASLtOrHHLGsSz/XuotuaTX/TfBcA1FHYUUh5y86S9CQ3J+y3oa2w3Y/fVszRFkhUspbB7BGFMbubY0uiLN/NzsYFNAMNrUwlRjLUtKFzBx4kR1O15FUQaUQV/cezY34n5lL+bcOGIX5dD82DZID+JYt5BIZDh24/uUJ7tYmvcA3cY0Cmtuo5VaijpGMl2aeU8Op8mbyHm+dfhbOzGHfXxSaGRq+8WkBZ1MsvuIMSSzlVpuMcQiwn7mGiuYmetk+fKriY+Pj3YXKIqi/JtBXdw921t6C/twJ/EXj6Llie1ILUBK60qEdGDUy1mfFs8FOQ8TMiQw7eAdVOi1FHUUcKYOZYZ4ttUXMS28k8SGvfiFkc9HuZjT9B2yNQPjHTphqfG41c2zvhgytS5ON1axbMFspk6dqsbWFUUZsAZ1cTfnxGKfmoZzSS5d/6ohWNtNgvF3aMKPQfhZlZbM1bkPgx7Dkoo72GCpY2znCEpNPupiLby9bSG51DOh+mPcJhcVOWnMr59PkVky3GqhLVzHz2Li2O3RmWQ4xPwsjXPP/YH6/lJFUQa8QV3c9RgTrrNH4DnUQMc7lVi1z9CNGzBEwjyVnM6teQ9hEBYu2/Uz3ohtY1znCJYYu4hk9PDkpz/GJboorVpFgzkVd1oRpS1jmWSPkGC0sFvu5XpjGrrPT6mpggvmlDBr1ix0XY/2P1tRFOUrDeriDtBWs5eex9aikYY55veY/WEeTszkrvyHsGDgv3b8J390ehnTlcMyUysxeU388uOfISOCs6vfoNo6DN05nQXdaUx2gC4Er+mV3B9OJY0OlsQ3cckF56t7rSuKMqgM6uJ+cOtHWF95AiKXYU24gpgeL/clZPK7wj9gjwj+Z+f13OOEPE8a5xpbSchv5OFPr6YxkMDyhr9RY8nAaZvD6WEHxQ6N7lAn91i9rA0lMU6vY8W4OJYvu0rdlldRlEHnpIq7EOIm4AeABLYD3wfSgBeBBGATcImUMnCSOY+qqz1MRJyFNfkK4jvc3JeQwe9GPYZVwgM7ruGXTp0Uv5MLje0kDG/hr1vOYUvXaCa7N9JudJJpnM1c3UKBVafGX8fNVgstYQsLrVX8x1kzmDBhgpriqCjKoHTC0z2EEBnAj4BJUsoiQAe+C9wD3C+lHAG4gSu+iaBHYzaHkIk3kdrh5r6EdO4d9QQmqfH49uu5O07DEbKxwtxD0vAWPtl1Jn9rnkWmtwYTQQqMc1lusVJg1dni388lZjvdMshl6U38+prvUlJSogq7oiiD1snO5TMAViGEAbAB9cCZwKt9258Gzj7Jv+OYDpXfTZ67m/sTs3mg8GkMUuNPW27kntgQkYiJS8wB0rPbKKs4nZdq5mMJ+cnxNzJRn81ym4lUI7zpr+B6cwJJWic/n2rhlqtXqtkwiqIMeic8LCOlrBVC3AtUA17gXXqHYdqllKG+ZjVAxtH2F0JcBVwFnPAtcf+a+H1eisTyUfrPAclj62/h0ZQu2oWFyy2SYelutlTNYVXVFLqxM9m7lxn6DM60GTCJMPeFannTnEyRsYk7zythwvjiE8qhKIoy0JzMsIwLWA7kAumAHSg93v2llI9LKSdJKSed6JnyuLrtbE6+FZ+ucdf7t7EqsZ4Ko5ELzQby09x8vn8Bn1SMoJoMRniaWUIxC+xGNPzcEmlhldHJQlczT16/RBV2RVGGlJO5oDoP2C+lbAYQQrwOzACcQghD39l7JlB78jGPrjqYRYtZ5+bVv2Ff7h7W2Rws101MSHezsWoe2w+kUB4aThxeLo1kMcthoCfcww3CS7XBzNWFQW646CLMZvO3FVFRFCUqTqa4VwOnCSFs9A7LzAU2Ah8A59M7Y2Yl8NeTDXksDe0bueXZVzAW1fNanIOZmok56e1sqZrNtpoM6jri8dksXOu1MdtupDnUwQ/1CB4N7pmfzNlnnqYumiqKMiSd8LCMlHIDvRdON9M7DVIDHgd+AtwshKigdzrkn76BnEe1dEsrqTk9PJhiYYxm4Nz0drZVzWRjUz7WQx3st+dydtDMcpuJA8FWLjVAxODnue9P4Jy501RhVxRlyDqpee5SyjuAO45YXQVMOZk/97jl+LgzJ0CW0LksvZM9VdP4tHUcY3Z8xvOZFzEirHGjycKOQAvXm0xkGjp44cZFpCa6+iWeoihKtAzq2xp+pseRYTTwH2ldHKiYytr2yUzZ8gFrkhcghZFf6za2Blq51mSiyNzE6tvOV4VdUZRTwqC+/UD2pB5mJ3RSuWc6H3dPZPKmNexwTaLaksqPMFMbcPNjk4mZ5oM8efvV6Pqgfi9TFEU5boO6uIs9E9llKGYbCRRtX4vblsZncZMZi05KsIdbTEaW6GU8cPtP0VRhVxTlFDKoK54vSVIZiiWrfBMR3cKuhOWEBMwO+vm50cCKyBru/e8b0dRtehVFOcUM6uKe1TaZ2IN7MAXD6PEXU6ZLzghLHjboXB16jZt/dANmuz3aMRVFUfrdoC7uH9tqcHR3MsL1PV63CFIlrNEk1wdf5MLvXkF8proHu6Iop6ZBPeaeFT+DnMR81tl1mgigE+F7obeZPWMuOZNmRDueoihK1AzqM/cJvoPYbWZelgE0KZkd3MAZyRYmnHtZtKMpiqJE1aA+c++2BLiTHiRQ6NvHOVoZM659Uf3mqaIop7xBXdyf2R+mTgiSfY1cGv47k65/GIvDEe1YiqIoUTeoh2VKR5pw+Vs5V/uIkYuvI3VEQbQjKYqiDAiDurjPykvmfPERY9LzmbDorGjHURRFGTAGdXG3JWYwMiGdBdfcpMbZFUVRDjOox9xTcodz/q13RjuGoijKgDOoz9wVRVGUo1PFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQgSUspoZ0AI0QwcjHaO45AItEQ7xNekMvePwZZ5sOUFlflosqWUSUfbMCCK+2AhhNgopZwU7Rxfh8rcPwZb5sGWF1Tmr0sNyyiKogxBqrgriqIMQaq4fz2PRzvACVCZ+8dgyzzY8oLK/LWoMXdFUZQhSJ25K4qiDEGquCuKogxBqrgfQQiRJYT4QAixSwixUwhxw1HazBZCdAghyvoet0cj6xGZDgghtvfl2XiU7UII8aAQokIIsU0IURKNnIflGXlY/5UJITqFEDce0Sbq/SyEeFII0SSE2HHYunghxHtCiH19z65j7Luyr80+IcTKKOb9nRCivO/n/oYQwnmMfb/0GOrnzL8QQtQe9rNffIx9S4UQe/qO659GOfNLh+U9IIQoO8a+/dPPUkr1OOwBpAElfcsxwF5g9BFtZgN/i3bWIzIdABK/ZPti4O+AAE4DNkQ782HZdKCB3l/IGFD9DMwCSoAdh637LfDTvuWfAvccZb94oKrv2dW37IpS3gWAoW/5nqPlPZ5jqJ8z/wL4r+M4biqBPMAEbD3y/2p/Zj5i+/8Dbo9mP6sz9yNIKeullJv7lruA3UBGdFN9I5YDz8he6wGnECIt2qH6zAUqpZQD7reUpZRrgbYjVi8Hnu5bfho4+yi7LgTek1K2SSndwHtA6bcWtM/R8kop35VShvpergcyv+0cX8cx+vh4TAEqpJRVUsoA8CK9P5tv3ZdlFr1f6Pwd4IX+yHIsqrh/CSFEDjAB2HCUzdOEEFuFEH8XQozp12BHJ4F3hRCbhBBXHWV7BnDosNc1DJw3re9y7P8IA62fAVKklPV9yw1AylHaDNT+vpzeT3BH81XHUH+7rm8o6cljDH0N1D4+HWiUUu47xvZ+6WdV3I9BCOEAXgNulFJ2HrF5M71DCMXAQ8Cb/Z3vKGZKKUuARcC1QohZ0Q50PIQQJmAZ8MpRNg/Efv4/ZO/n7EExn1gIcSsQAp4/RpOBdAw9CgwHxgP19A5zDBYX8eVn7f3Sz6q4H4UQwkhvYX9eSvn6kdullJ1Syu6+5dWAUQiR2M8xj8xU2/fcBLxB70fWw9UCWYe9zuxbF22LgM1SysYjNwzEfu7T+MWQVt9z01HaDKj+FkJcBiwFLu57Q/o3x3EM9RspZaOUMiyljAD/e4wsA6qPAYQQBuBc4KVjtemvflbF/Qh942V/AnZLKe87RpvUvnYIIabQ24+t/Zfy3/LYhRAxXyzTewFtxxHNVgGX9s2aOQ3oOGxoIZqOeZYz0Pr5MKuAL2a/rAT+epQ27wALhBCuviGFBX3r+p0QohT4MbBMSuk5RpvjOYb6zRHXg845RpbPgXwhRG7fJ8Dv0vuziaZ5QLmUsuZoG/u1n/vjyvJgegAz6f2YvQ0o63ssBq4Gru5rcx2wk96r8+uB6VHOnNeXZWtfrlv71h+eWQCP0Du7YDswaQD0tZ3eYh132LoB1c/0vvHUA0F6x3SvABKANcA+4J9AfF/bScATh+17OVDR9/h+FPNW0Ds2/cXx/Fhf23Rg9ZcdQ1HM/GzfcbqN3oKddmTmvteL6Z3RVhntzH3r//zF8XtY26j0s7r9gKIoyhCkhmUURVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGoP8P/9diBtPqFfMAAAAASUVORK5CYII=\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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Ir0LdyVOhTFlzKKgiJaeAW6ugoaHpFuFoilSojWaznQQpLCwMrVEiKJAgKgIqILEf9r30ujwn5yII7oFA4ClRrpxicPAfyOfvoeCu4BP734Iyt/DZPz2PC3uTcP/n4Pb3o2p5Ble9kP/T9mpG3TTdc5L3phI8OxGnXdORkw7+6BxlqVBSUZirMnO6QG66DAoG0iY7+mZpLt+BLAvq9Z1MnPLRwhlUcjPm9X+EZcRJCI1mwMenVitT9uZZ9KuYVoR4JENYRtGlhqmFwBAIo4YsjLOUHWLSzDKSrjLTZFA3dJI1jfZCjLZqnIiKEtd70FqiWNEkMREn7kWxpPWw4+EKDxcPS5kY6GfmKxSObpO351gsTdG0YoAeLn7Kz8fjNmISQnwOuAGYV0ptXp53PvApIAx4wBuVUntEo5/NjwPPA6rAa5VS+x5vJ4JGTIHAby/PKzEy8i9MTH4BSZjvnLqB/SOX8ufn9/H8gWbE+BD+8X34dUFN76JECzFPx/gNtp9USqGcErI8hyzNoapZZG0JZRdQ9SVUvYjy6o1OwR5rO0JHmmE8M0QtHKcUSZCNpxlPtzLUsRqvrZ8mWcJwjjId3oNlxthR3cgzKxeQcRIoAyKXtdHy3LPriuBXaqEqhLgcKANffEhwvwX4qFLqh0KI5wFvV0pdufz6zTSC+8XAx5VSj3tJCoJ7IPDbRynF3PjNTOz/H/R8Ejt/IbVsPwNYWA8LKz5oS0xEwoxEUyRTIda1JehoiqJFDbRQ44GksHRy81VO7ptn9EgOz5O09idZv7OdHjFI9abvUy+ugMQGhNGopy55S8zrRWyxRDY/SLE0j2mF6Vu7lbbMJhaLUUZzVfKVeYziOPHcGB35CTqLM0Td2pk9lELgmCE8y8IxTVzDxNU1PE3g6AZ2KEzVNKgjkUJDKYHluUQdh4hjE3FsEvUaMdsm7D3YKlUCuWic0WQ7+zvXcU//JmbaTELGUXq1Ya6trqatfy2v/cPXntU5+JVaqCqlfi6EGPjF2TQeG0BjvPAHarBupHERUMBuIURaCNGplJo5qz0PBAJPG0op/MU69liR6tAMldPjGMUMnfw5ADkkkZYImbUZzPK9aKe+BGqKT/dezedXvZKX9/Xw2u4WOkIPb7Tj+5LhfVkO/myCuZEiZlhn/c4O1q9OY45MU715P3mRAf3FyNACC5WTnAyXKBmL2AvjuLUqRiRKePMzqCRvZHBecvvYEB17f8x52dNszY0RW+7F0RMaC/EIY21R7FAT1WicUiJONZkglMogEdRtB8cK4VlhpKajSR9D+uhPsKsWzfeJl8skl5ZILy2RyS+xOT/BjtkhXrf/ZmqhEGNtXRztXM2BzlYWmOe1T+mZajjbOve3AD8WQvxfQAMeGCeqG3ho0ubk8rxfCu5CiNcDrwfo6zu7TnMCgcC5Jasu9dNL1Afz2Kfy+IXlDA+jip2e4nhvns9PtBLqSvL3L7+AVcU91L7/50TyQ/yo+TI+su7/csO6bezqbiFh6A/bdr3icvTOKQ7fPkVlySbdEuZZV/fQhsI5vohzKIvtu/jZcdzqrdyfkIzFfGJ+GXs6z1KklfLqa5mN9TE7scCmPUe4cO52nrU4QnQ5mFdbWin2dTMYDzPf2koh04wyfjnsCaAufWpmiFK6naoVAqET8zTCFaDuUNezREN1Vvlp+u0OdDRmrAVmEwW8jE5LooWmaJpEKEGIEFJKPM+j6nkslIo4Q6exTg+Rmp5mYG6K9RMjvEgI7t9y/jk5d2cb3P8CeKtS6ptCiJcBnwWe/WQ2oJT6NPBpaFTLnOV+BAKBp5ibrVI7skD9WA5nsgQKRFhH79dYWnkL+cjt2E0DfOCe6xmbTvLW69by+m1xSj/8SzjxbWYi3Xz4/A+zZfsL+XZX85lWow/IzVQ4dNskJ++ZwXMla1cmWbcmhT5ZQu2do65c3OkDeFP78K0T3Nm3lkI4hFsoMCG7yHY8k+GOLprnJrj8vr28YPZLdBbmAagkEsz0dzHX1k62rZV6JILwHIRjo5TCcHUifgfUMuC4uPZJjvUa7N1yCQtN7RieZMOUy6Zxh9RCgfHMQcabjrAxkuYVS1cyUOqiKmyOagtMVSzE4haSy7euElgAihGDZEuYTEeMlo4o6c4ozRfESf9RFE2AV8oycs/PGP3+j6gfGqS/N31OzuPZBvfXAP/P8uuvA/+x/HoK6H3Iej3L8wKBwNOUUgp3pkLtyAK1I4t481UAzJ44iav6MFeFmXQ/w8TUFzDNZu5dfA3/9v1eNnam+N6bttA69g2cf/17ol6df1n5Z4Se+Vb+qa+b6ENSGZVSTBzPcfCnE4wfzREzNS5ekaDV8VG5GhRqKHuc6t6bkAuHMQdc7lqzkRl3A8NeC8Opdcyl29hSGuHa43eydeIEiUoZKQQLLS0cPH8rlQ0bqVlRajMT+LUc2uwoSdmMbqxBN1djRROk22JEEw4Ti7u5JWFwYONzqYWjrNY0LnEMDuydZtg5zlzLPhI9p7gufwlvW3g1aTeO16qRvnwlXVvbWGvp+J7EqXnUKy7VgkN5yaayZFPO1Sks1JgdXuLUfQ92ZWBoDq3mMG36IO3mKXamj2FcXkReePk5Oa9nG9yngSuA24GrgFPL878H/KUQ4is0HqgWgvr2QODpySvYVPfPU9033wjoAkIrUsQvXkl4cwtGKsTCwm0cPvkubHsGLfZi3nPH5Ywv6fyvq1bxyvUO1W+/iJbsPu5Jb+PwFe/jNVsuIfmQO3XpS07vnWffLeMsTpbpS5pctyJBKF+H+Sp6u4ln30fpJ/+FEFVCa2zuXL2OXWoj4+EBZEuCtbVp/nTiDlaNDZMqFpFCkG/vZuriq3C27qRQ8lk4cT/Osf2Ai6ancJIdFHuTtHa0093aiRUCpz7D+Imj3OZ3cODSZ+IaJmuKi6wbGcSan2QhNkoqNU6PDHH50oVsrT4HlOBEaI75zBjlkIK9R9D2aWiahq4JdE0nbBpELZOoaRLxS4TtcToZYm3oGKH2Cq6XoioGyBvbWLBXcKS4loPVxoUv2WyyLdbN5nNwfp9ItsyXgSuBFmAOeA9wkkbKowHUaaRC7l1OhfxX4DoaqZB/opR63DSYIFsmEPj1kI5P7cgC1X3z2ENLoMDqTxLd3kZkUzN6vJGr7bpLDA7+v8zOfYdodA178q/jo3eEWdUa40MvWoc8+M9sPfRpKnqUWy54O1c+6w20hx/M83Ztn2N3TXPw1gnsfJ31zSH6TQ2t5qElLEIrNKp3f4Pyrd9DWBpijc33W3ewL3weKpagiwIbpodZOTxMx3JHXrWWXmoD57G4ZivDtk+9MIxeHkKrF1BCw0014aZakJHYw0Zi0pREVooc6+hnqmcVUcdhRWGBpsISyrcRwsWSGhqP3ZWvQjWaNwmFEAqW/1ZKQ6kn1g2wruuYpoVlWOhYKMdgzZo1XP/Sq57MaTzjV82WefmjLPqlXvKXs2Te9OR2LxAInGvuXIXy7hmq++ZRto/eFCZxVR+x7W0YzQ8fwWg++2NOnnw3rrtEqvUNvPf2HRyaqvHKS/q4oXec1m9cQ191gl1919P1gg/xhy09Z95bLTocvn2Sw3dMEqp5nNcapjljITxJqD9BaECj+L3PMP+lW1ARi/ELWvlJx6XUIxma9To7CvOsOLyHFeNjWPU6dizJ2KaLOLWih8W4Cb6Hld2FmZ/H9FwwLIqtMU73gwrXuWL1Gra1bMOxHYrFIiODJ5kuFDHjaTaWC2w80Wh242gaFdOhbi6AkKz0VrFWmRixBUqts+ixcSJ6Dl130XUPTVcoK4MtYpSlTrFepuaUqUiHmpIYCAwEITTiaMQwiUgTIS08N4RvxynV05Rrzbh2glpVgl/FwyU3GXT5GwgEngTlSWrHFinfM4MzUgBdED2vldhFHVgDScQvjDPqOIucHPx75udvJh7fwLT2j7zxaw5Ry+f9L11N2+EPcsmBbzIZ7WH/jf/DM7Zdf+a9S/NVDt46wfF7ZsgoxaUtYZKmQChF7JJOQit18l/+DAsf/Q6F5ib2XHs5U8kuIoYiKSUrp0bYPHyatpkpfE1jqqeb4ZUrybZ1EJIxDMJ01OaoTx/Hd2yaUykGtdPcfb5Bu9vBhbGLSdhJ5u+f52b/5jP7VbVilNNd2EaEaiSJY0iEfRDLOciAFuVF+iqSeg4ndi8IBQi6ZJpQ7jxE0aCer2Nn6zgFF08pokIjKgRthoEIWeihMCISRYvF0cJxzHAcS0XQnAha3cKyNcKe8Yj/KrDNIot6iTt1+5eWPRWCYfYCgd8xsupS3j1D+Z5pZMlFbwoTv7iD6AXtZ6pdHqrRH8wPODn4D3heiY7uN/Kx3Rdxy7FFdq5t4Zr+Ia7b827a7UUObflTNt3wXqxQoyfDudEi+28ZZ3j/PF2WxuamEOGahxY3iV/WRXh9lPwXPsvUt77FSE8PR9avR1kmSkGpbrB+dIhtp46Q8AS1TAf5FVvQ2jYQ0zIYepRwKoSq5nEKJUxh4YUMZkWBGb3InFagttz5lq40WlWSVpmkRSZpUjFSKvq4VS1PBSV98B2U76B8F19JbKkoayaLZoyCDiVLYidNZFsUkTyFH76FNusgbUaZk/4m3njN987qs4Nh9gKB3wNerk551xSV+2dRjiS0NkP8JV2E12QQmnjE99h2lpOD7yabvYVk4jxqsXfw2q8XWarmedWz27hw+CO84M6bmUmuovhH/8W2FZeglGLsyCL7bxljanCJvpjBczsimDUPI2YSv26AyPo4c//1Re762E8Y6u4me911REWIuN9MsmCxpViiXRroLesQ/a9BaDpxoPUh++Yrn9J8lgltgWyyxqxaoqo1ugPQhaAn3UUk0UXOTDIsDfSJU0ybOkO9fRhI+jCZPjFPInEvW9NHWRl2scISfIk5JjCHFdaUjoxozGRshuIwGlEsJoBQhpbiOlpLq8hUOxBATMuR0OeIaAVCWgFLLGGJJUStjqjUURUPWfQhX0MrVYlLSUxoFFIryDadx3z7Nliq0LzqJE19UcxYLzX7JM/q3XZOfg9BcA8Efss5U2VKP5+kdjgLQhDd2kri8h7MjkfvJ1wpxezcdxkcfC9S1hhY8Td85dhlfObOcfq7E/zB1hFes+dNtLhLTF/4Zrqe8y58YXJy9wz7fzLO4lSFFWmL63uiGGUXI2GRfNFqwhsyDH/tqxz8j3upt6wls/klbJNJMk6C5APhJgQqY+M4ZVw9QkToSAHGyhSRC9Psu/fH7D24ByeWwI8nUYBULrPxLJVwhTXbXs7p5AY+ky/jSMmFY8foGD/Nd7dfSSme4nJNY/y+cVrMz/H8zUdItPjggX5cEDoUJ7q0ivK6OQ5ePMfPzDgHfYknJC2VbtZkt7Nltpf1TNNqnCCu/QwzmUf5FepKUDbiVPUoVc3ARqJJn7BRJxqrEZF1dCSaLtFNH00HW4AyS+jpoyT6voKMNi6yjmdRmFiBV7qWjLYd1j/1v4ugWiYQ+C1ljxcp/XSc+sk8IqQTu7iD+GXdGKnQY77PcRY4fuKdLCzcSiq5jVj7e/jrby9xZLrIFTvj/MH0v3Dj3E9YyKwj9ZJ/RzVv4diuaQ7+dIJy3mZ1W4T1YQ296GC0Rkhc1YvWGmboprsojRTIaC3EafT94qBYcEvEc4NYc6dxawtMx1Yi+i6mv1VHWSWMLTr1Ppv77z/E5LRHXTYa9UQjHtHwGLXmUci4RCJd1AghUUQ0Qad00RYXmAynWIo1EUGnubhEi3eSWLKKMECUwJqNEs4PIBItFHtOclpWmKlIYk6Znpqis5ogLuPoQmHrGhUjStGIU9JjFI0YJSNGXbOoahGqWoi6FsLVLWzNxNFMXM1AIh7IpQHEmb+lEHBmmUJTEs1TCKnQNYUmJFfWxnjrK951Vr+BoFomEPgd8tCgrkUNktcNEL+kEy38+P87Z7O3cPzEO/H9MqtXv4N9C9fwzs8cR0tZvGLHMG898k9kvBJLl/0N0R1vYe/P5zhyx93YVY+1KxKsbw0j5qvo8RDhZ3ThVh3mv30c09FJk8DQLE4Im93SJT23l6tPfodEvooXDzNxQSveM3Wspp/ihb7OkK+Ry/YxP7SC/L5OFHGS4UUGUvvoTDOVXugAACAASURBVExg6jlcPYRPGL+mo5wJwoZORNfxqxXyUlGKx+jQ51lrD2HqdURSgQIlQUqNQjxJpbuZckucvB+nWL+avMiwGMuQTWdYsDLkzdRjHjOhJBYOBi7mmeI85LWLhqSRGqkaoV2BriQhX2K5PqYvEb4ApaEQ+LqGRMMXGvF8MMxeIPB77RGD+s4utJD+uO/1vBKDg+9lZvZbJOKbWLnmg3zopw5f3XuEteeF+F+5j/P8I7ez1LKZ6rP/jUMHoxx/9/14nmTDpibWhXTU0BIipKO3RfAW61R2TePiMaXlOKUX+Jkuqaemef7cLl6+ewIrq9D6PNRzHSI9S6y1FwnPaiyMt3LEu4gTcgUuJmkKPIP72MIJ2uq5RsuZucf9SmcsmClORfs5ERvgZGSA4Wg/k+FOpsKt2JoFMRoFiHkVmpwCGbtAa2GeAWecZq9AT9imbcUa5twYJycXmc9VMZVioNlhZeo4XeIUKfKE9BqaVOiORbjcQbTURKhuoTugPBtNVgmTJ0KOkMqfGUBboWOLdgpWG6OJFg40dzKqRYgsKiz/kZ+H/KqC4B4IPM25cxUKPxqlfjz3pIM6QC5/D8ePvZ26PcvAwJvwIq/l5Z8/zKBtc/32Cd5z6oM0e0Vmtr2XY/lnM/jP80CeDTvaWGcJ/KOLjRAlQNk+paUSp5hiKjRLvmkIP7NIf3KUvzo5T+ten7jhEbrQJRT30ERjFKL6uMUBNnO/toUFmUTHI6EvYcYVdLRxZ/g6/su7gaLW6InRFxo+GlLoKMBQPqZycYVBTQtT0SOUjBhlPYqtP1gNFfWrrK5OsKk8yNWLd9PhLNDhzNNbm2VldZIWv/joB2roEeYtPdaRnX/YX57QKVhxpkJNjIUGGDa3MRxq52RsNcfjG1EiTdRxiDl14naNVKlMu1NBU4kndB6frCC4BwJPU17BpviTMap75xCWTvI5/cQv7X7CQd336wwN/xMTE58jEhnggu1f5ZZTrbzrpnsJr7T4/+wv8qqjNzEauYpbY29j6EcOhpFl8+WdrNUE7oEsvly+84zrTIRzHKodRGs7RLJ5mrXhadqW6jSPeDTVbQxTwVqwvQjZxHYWUms4WO9g0I8iKhUMKZmPpjnWNcDpth5MTSet6eD6KFcSNhTJeol1mPSF43iezZTvMuzWmQyHWIhH8fTGdzd9l97KHOeVB+muZumsZknVfaQXYtpMMBUKM6ZnmJJtaO5mhBFGaw9jqDK6rKDpAk/Xkb6LJR0i1AnjYCoPIUBD0ag4kctVLgJbW66cEQYejVLRIhS1OCUtjisNLN/D8l3Crkuk4hIpuGx1bC707kOohw+npymIO7DaX3xKfzcPCB6oBgJPM7LqUrxjkvJd06AU8Z1dJJ7Vix574i0Zi8XDHD3211Srp+npfhUdvX/Fe743xLdGslyyaoYPDn0Ao9jEXvMtTM03Y0UMtu3soN/zsY8sglSgCfyVYfb7R5mVt9DaNky3MU37gk3TvE/KbuSYu1WN/EKa3ZnL+e7Ol3Bnuod4cYHzJ0/Tl5vD13Sq8U6SRif9bprWuk2iXsX3a5RFjaKoURF1siEYbEoxnkozk2pmKda4o9V9n9byEq2lRmkpL5Gulh6WwS4UiOX/oNFVgAKU+PXGNw2BpXRMBBY+Bh4WNhZldDOPEcljxJbQkyXsWI1pTSPkp/mLF953Vp8XPFANBH4LKE9Svnua4m0TqLpH9Pw2ktf0YzSFn/A2pHQZHfsUo6P/imW1cP7W/2TWPo8bP7mP8bTkb1q/zfX7TnFf/a/J1lcQSZpcfkUbbSUbd99cY+hmQ+BsiXA/3wH9TrrD42zN1mgZ9EjYLlIJ8osxZsfDTFS7+NQVL+eH1z4TpQl2Tkzy0sHbMJ0iJiYr/C7CdclSZYlqJMekcBhVHujgG4K5ZBPTyWYmmtcwl2oCIGLXWT01xnPnBtlRPsoOcYykWcXwXfyswM5qlPMGLjpENFREg4iGjAhUWKAMAaaGMkCZAqULpKE3/jYEUhNIXaCWp2gCqTUuZlIAGkgNlJAoTSI1idAlLP+t6T5C9xC6RBNyua8ZiaZJfqHRL0pCyYdFXyPrC2ZcjWlHY9YRFJaiAFwrC0/ND+gXBME9EPgNU0pRP55j6QfD+It1QmszpK4bwOqKP6ntVCrDHDv2NoqlQ3S038iaNe/mq/sK/P3te1i9Isdnj/6Y2dwz+aH3UlIZk2dvbyaZreIfnMcFEFDf5nEs/GVC2l1sLi7RMeHQVHLwERxzViMnNCKHlpiNtPKf1/8B9297JpfkfN55YIxa6RRlUSOsWZi6QdmpM6hPgw6G55FazNFNntOdbRzoHuBESw+OJtFliW7nZ6xdGKXdGyElFpAJhZeE+xDsRqCEgRAGAtBEY4SgRmaKj4aHTmOeTqO6Q0ehiwde85DlCk2AsbwNXTSKponGVCg0odB1GqmKy+toy5/rKKhLgavAVuBIgS2hIgVl36DsC8pSUPQFOV+Q8wQOD0Z8SylWOS5XOg5rHZe1VZfwQudT8Cv6ZUFwDwR+g9y5CkvfH8Y+tYTRFqHlTzcTXpt5UttQSjI5+SVOD30QTYuwefO/EElew1u+fpibC0u8STtKx8/bOOa/kraM5Lmr2whNFFHHFvENgUJR3jzOeMvXSbgH2TZTo2PWwZSSrGznZv1yqoNVNuw7QC0c5TtXvwJn3eW8uruDl88Osnf8EFnNpzH6psAmB8YwWmyWYqzGfBimzBg5dGy/jiaPABBdgOjydygulxMANLpI0JXCoBGgWd66BKR48LX6xVvl3zBdKdJS0uz7bHQ9ur1G6XU92n1JxtCpJiMsNkeYD6cZQsPSLuJcjMUUBPdA4DdAVl2Kt45T3j2NsAxSz19J/JJOhP7k+kKp16c5dvxvyefvprn5Sjasfz8nsxZ/8cldrJQV3jli43jnkYznuXx1Gn2yCqfzaOkQXt2l0Lufmf7v0lw5zbYTNm1LNr7SGRPP4EeZaygeG+Z5u25FCY37tl+HWnkeL9qSYP/J3fx8qkbd8CjGF3Dio5Sjc0zrPjnJQxr0QESZZOo2m3yPXrdKj1unzXVJSklSShJSkpSKqJSElMJEYSzfcT8eBfiAL8BD4J2ZikeYB/7y1BYCR4ArNBzAFQJHCFzRGGdVLW/7wYsISAQhpYgoSUQqImq5SEnGl0SVwtZMFs0Qi2aIKSvOdMTiPkvjZkth6w6WsrEEhIUg4nlEhEab+ZgpOWctCO6BwK+RkorKnlmKt4wiax6xizpIXtP/iB16PeZ2lGJ29jsMnvoHlPJZv+59dHa+jM/fOcr3bhvlZfk6hh2lM5TjvP4kWq4NJqpY/Umc2RzZxM0sbL2Z9vwUl+xXJOwKrkpyf+QlfKj/Bnr37+Hl3/k8EdvmVP/FaJkMyTbJUf8gu8cKzKVyLEbnWDLLZ/apw/fZXPdZX6+zynUYcF36XQ9LCerKRJUUqqTh1zR8R8O3TTwL7HYodUC+SeCkBTLRqPOWmsD3Ie8LclJQ8KDgaxQ9yPsaFb9Rp20qMJVqFCBKI9CaCiwa8y0FBjq6L1BYKBFCaBYCrVFto8BAEsInho8SjYexSojGvixfGGxNI6vrVDSDiqZT1jTKQmNB1yki8JTE9cDxTFzPxPMspG3iSxMpQyg/gvIjIMON1zLCzkyQ5x4I/FZzpsrkv3Mad6KEtSJF+vkrn3S9OoDj5Dhx8l1ksz8ilbqAjRs+TM1t528/vJuWqSrXOhorQnNsaougOysQJZ3IBS042SVmva+xeOH36ZqrcemeKmFZpq76+XbmDbx79TVsObafN378H2lfWiSb7mNsRQuH19Y4vWKacaNA3qwAEFKKrY7DRfkaW2yH9bZD1WrmaGwNp+J9nKwJqqcmkfuG0CoKhCDU4qPW2tS2aBSbBHa7QC5/feVDqSSYkBpjrsaUL5h1NXJ+o2Y9KgRpXZI2JClL0qQrVvoxUk6GlN1MS7WTdGUl6XI/ISdzpo5dnEWvkB6KopKNqiKlKKIooygtT8uwPF0uQlFBUVme7z5OrNZRhJBYeIRkHY36k97HJyII7oHAOSbrHsVbxijfM40WM2n6w3VEzm/9pf7Un4iFhZ9x/MQ7cN0iq1e9na6OP+HHN41w7Pa7GfCgLzTO1rSLxsZGT4tXd+N7VSbHvshS9630jYdZf28BixxlVvHP7X/Jh1ddxZqJEf7+Y+9j4+Qw85k0X3neCo6tLzNhHaYiFJqC8+sOryhXubBms9o3yQqTg62b+GTX89jVdBGJQokb77iV6+65A18rUmpXcImDta5GsUNnJmWhjEbIWapbTDoGp3M+Iy5Muxr4Jr0qQbsBK8IVLkiXaDMkrZpOstKLWexGz3diV1qp1pqRvokpFRUtzYRrcRyQSKSo4VgWKmrgGwLHV9RdRb3uYbsS25M4KGoPFAE2irpQ2IJGcH6wm5hfoikIKQgrQUhBSAliSqPpF+aFz0wfPs/4hY2OhB+jYdWvIAjugcA5opSidniBpZuGkWWH2MWdpK7tR4s++ZF3PK/MqVPvY3rma8Tj69m04fOM7o3x/Y/eDXVJd2yWK+KLwHkISyf+zF7M/ggjuz9F0dpLTznGhr05TDHHEmv4YNdf8ImVV9OeW+D/fOJj7Bzcx53np/n6jRlOJ0rYokxcSi6v1LiyWmN7zSDRcRFTrXUO1gp81Hoht3dejmtYbD9xmLff/Dk2+GXGY2Xsl0hau8rozRqzYR2IsGRbnKroHHV9Tts6nhtmjd1Dvwjz7EiZ1vQ0rfECuiginSjVQjtLs2soLjYxWUjh+j74HppfRVMjCH8EVJSqSlElT0VLUBERKppFWTMpuZJSTVF9pBt3o5EXHwZCQEhASAiimkDXBJquIXSBMjSUqaFMHQyBLiQmHkJ4ILRGSqUAKQSOplE1QkjdACGW8+6B5amQCkP66L5PplSgY3aa9pkp2rLTxIyNv8Kv7NEFwT0QOAe8xRr57w5hD+Yxu2K0vHojVu/ZNTPP5/dw7PjfUK9P09P9RqoTL+Zb75/ELs/ipKq8IDpKSG4GvYvEZT3ELuti/K7/YemOA3TMxlnjjmNpw+S0Pj7U/Xf854rnEK9WeOtn/53V83fxoyuifP5Gg4rWCOjPK1d5TrlKW72VBXkxqU1rGbHuZehEme96L2LXuoswPY8r9u9h69Ap8lYY57wKqvMgfc1VfENjRpoMVkMcyClO1DXydgq/OnCmSLuDLDp3A4bw0IWPrnws38XyHEK+TcivE/VdwiKCLsL4WoSaFqFomBRNjYKmGnnpD2ECIU1gGDphS8cKa3gRAzei40Y06lEdGdYROvieg1XIEV1aIFPKkSwtEa1VCNs1wnaNkFPHcmx030WX8pFOzYOUQpcKw5cYCgwJIV8RcX0irsRyXUzXJVmtEHG9M2+rhGC2bQ5461n9Nh5LENwDgaeQ8iWlOyYp/mwcoWukblhJfGcXQn/yVTC+bzM88hHGxz9LyOwn4XyBez4nqRRGWEgontU5xsraAMiNxM8Pkbj+ArInfs7Uf36bpmyULnGAsH6Iot7CBzrfyCdXv5SQ7fC6r36GuP1zfnqlzpfCGiFZ5VnVGtdWa/T7fRypb+YAa1ndUyE5fwt7brf5+pZXsPeSLURrVbbvP0RxHnJNNs2XHeOC9mGUCXUP9tdCHKxrnK6ZRKoryZRX0+OmWGlViMeKRFsW0LU56iWLSj5KtRBGlTTitoPhGSg9jqOFKVhRsuFmxuMJHP3BMBXxbNqrc6yr5Giv5uioLNJeXaTJLtJULxGWLkoTjQGyl8sD3QmgJEpKUIpGy3zFA+1XlVju01E0Gjk9MJWi0dhJLZ8+XSo0KdGlQpcSbTmgW66P/iit/V1NUDcN6qbBbCpONp1morWVwb5exrr72Bzxn/Rv44kIgnsg8BRxJkvkv3EKd7ZCZEsL6RtWoj9O3+qPplQ6ytFjb6NcGkIrvJWhvVsp5+rIZhOrv8KfFk1EbRVW+xSZVz6P0uwoI5/+HyLZFP3mLmLGbVSJ8a+tr+TDa1+NknDjLZ9AWffwg0sFrtDYbNf5s4Uyl2gxFptexg+zFveisSoyxsZjP2Dq4Ar+6bp3cmjnBiKVKk2Hp9HyRS7o2c2lG3+OGbbxpeJA1eDeJYPJSpQtpU1syXWy0y4Q7zhG55rvE045KAecQ1Hqe+PUpuOomkXeypANZxhKd3EgvY5s9MH8/ky9SG95nk2LJ2izsyT9LFE5R4gyvi7xNYmng59SLGUgt9xgSSgIOxoRWydq64RtjZCrLefKC0DH0yWeIfE1tVwkUlNITSFUYzuNbSm0RgNUNNXI4HF1gauDa4Crg2doOIZBJRKmGLEoR2IUI0mK0WbysXbmMt2UY+0oPQahGH4oSsiRdOR9uhYddoxMoeuzT8nv7xcFwT0Q+BUp16d46zilOyfRYhbNr9pAZFPLWW1LSo/x8U8zNPQvVKavIH/i7ZQXoanbpLZKcUOhTLyQxjCPkrlhA3X9IqY+93OMfJxmczeJ8DcBn281Xc//XvsGirrFZXs/RS22h7s2aCR8+INSmas9n3a9G7nzI3zzzhMURsoknUU27j5EJZTkQ8//K/Zv2IxZs0mfmmVb6CQvWf0DUtYQQoPxmuCORYuRcoT1S1tYt9DKtoUFOrsHyay7j6RmY53S8L4RQR+L4lcMhjIrONyyksMrVjGeaD/TAKnZrtLqL9Frj+OkshSacjgxk5xIUCTMoOgA+kFYICykbuLpBr4u8DQN5RsIqSN8A6SGEhpSa9SJ+7qOp2v4mo6ra3iafqY7sDODaCzftT/Yd8Cjv27cwS/P+//Ze+8oya7q3v9zQ+Vc1TnnMD0zPd0TpQka5YASEiAy2PhhMM9pOYf3e35eDi9hP/kZ7IeNwAJJoIQQQtJIQmFy7p7UMz0dpnOsrq4cbjq/P6olBIKRRuAA9Gets0716apbt2+d/ta5e++ztyKD/GOicYTApQlqE2k2LWSoiMcpiV3CGU9CPoFlzCPMBcCgpLzhXc2Vt+NtE4dJkvQAcDuwIIRY+6bxXwc+R3EPwXeFEL+/Mv5HwKdWxn9DCLHn7U5iNXHYKj+rFEYTLD85hBHN4d5UTvC2xnflMAXIZi9x9tzvMX1eIn7hI2RjfiLVXkpbvdgvzNKYt2OTLqE2X8Le9mHieyeQ0jZU1/MEpIdwWXGOuzfwO+2/yZAnTO/gPxJ3nCFhk2nVNO5Lp2mRQui5dtbe/BEeO5FgfHocuaCx+cgxNEXlC+/9KKc7u7AXCnQvDnCb+igNgVHsToOCCYeyKodSKhXxRprmm4lcmsMRWaSsfpnyTBrnkIQyqGBmHPSVtnKksovTZa3MO4vVlWxCUC5kSpAoEzLVmozflFGNdxO0WEQgMGUwZd5YgQtlJS+MIrDk13PECKyVcSF/P2+MkC0MGUxZYLxxDOsNo41sFdf8qmWiYqFaArsJHt3AbRjYDYGig5Q3MQsCUxPouollpRDmMsJaRljfj4iRZYVwaRlVHV3UrOumqn0NgbLyd/W3Xy5x2DsR911AGnjwdXGXJOla4E+A9wghCpIklQkhFiRJWgM8AmwBqoCXgDYhxGWNSqvivsrPGlbBIPHcGJnDsyghB6F7WnG2XlnagNcRwmJy6iH69z3N4pk7yC/XEKpws/naGiZPTtE2ryNLURyOp1G6PkT6ghMyEobvEAH5AUKFWWaUcv5ry6/zbKiBtZP/wII6gSnBrmyOO6w8itlIYm47a3tLeCrVQHrsHAqCzvPnCc0tcP99v8SJrm48epZPzjxOu7qXYGkCxWYxXZB4OW1jMeFh/fw6qsYsjPwCJZUparUYgQsm6rjCcOla9tdu4ERpIxMOH5YkYRdQY8jUGDJ1hkJQksg5FLIOiYLdwpTyyEYSVUuimBlUPYMpTExhoFg6iqnjsTKoBR3ZMIppc1dMLKrdgc3hxO5wYLfbi7Z0s2hXF5bAMgWGAboBhiUjhAKSjISMJBUzzggUVuJZVsJbXo+DtECYCMxiEP5KX/y5AKKAWGmIPAjtLZ+roqj4/AHCleWUNbcRbmjG7vJgahpL05PMXxphbuQiG256D9vuue9dzZ2fKCukEGKvJEkNPzT8WeC/CyEKK895PWv9XcA3VsYvSZI0TFHoD72rM19llf+A5AZjxJ8cxkwW8G6vwn9zA7L9neVY/2Hy+RmOvvp5hg+0kFv8LL6IjR0fa8Ibz1N4fpQWYeBVniIXqiWf/mXECcgGT+Oq/Co1y8Po2Pi7mk/xd5WbaJp/gLA2z7Jq8b5Mhl12wZS5nsFLN6GUpDlQ2s6hE+fwOs9RvrRI44VBHr7tbl7r3oaMxQdmn2Sd+hoNNVNIEpzNybwSs1O+VE7veAdiKorDsUC9OkvJtIY21MSZiuvZ29zOwLoA8ZVLEDEl2iUVt9cBXguHPoc9NU48uYCRWcaTyBA0NCRhFG3bV3C93rwU1fPFln3zE143ofyoRevlfvdjkKRiaKQiKyiKjKwo2B0OHC4XTk8Ql9eD0x/EEYjg8AWxOZ0oioplWeTTKVJLiySji4yc6ufY89/F1PU3ziVUWU3d2m5K6xuv4Aq8c96tzb0N2ClJ0l9SLIr1u0KIY0A1cPhNz5taGXsLkiR9Gvg0QF1d3bs8jVVW+bfDyhvEnxkle3wetcxF6We6cdT739WxhBAMnX2aI09fIjn5Hhwek133tdIYcTL39DAibeBWjqFI86SleyCqkC45jVH/CA1Tl/Dm07zm3cIfN9yEN/U0ruiLZDD5VDrDJrfKgLWFb52/jlmHndOOENfPnaE92I8qG7SdPc8rG7fyt3d+hKzNyYalPm6RnqKrfABDwIGMyomESs9CI7eOVJGMTVJhnqEkJ2Hk13Cq4g6Oratj0A4JpeiEDNpVKvwKERaJpMbxRucITUcJaolipMrr2FWWXXliDp0qt5ugJThSuoXBUCOuTIb6sUu0xoaRC4ViGjKvl85tO1m/cze+SCl6PoeWy6Hlsmi5LIVslkImTSYRZ274ItHJcfLpVPGtXC4U1YZlWWi5bDFS5jJIsozd7cHhdCKrCpKsoqgqkiwjywqSLGEZJqahkzd0MtE45twiWj6HUSj8yGM6PB78kVJ8JaXUd/dSUluPt6qWyUCE/pzBY8ksN5f4aX5Xs+jyvFtxV4EwsA3YDDwqSVLTlRxACPEl4EtQNMu8y/NYZZV/E/LDyyw/PoSZKODbXYP/hnok9d1ZiZcX53j5kWeYO1+PrHbQc0uIDT1NpPeME9+TAHURj3ycjLgGw9xCpuIc8eqvUz+xRMX4DHNyhM813sms1Ucy8yB2y+QzhQxdPgcDmR18eWAL56QqlmwO7pw/zMZQjnQ4QMXsNKPVNfz5L/8Wi64ANfFLfMp6jK2RIxQseDGpcnHZwbWza7lj2I2xPEG5NEaV3sWlyCa+U13CWbvJslIsBO1yyzQSpz05QtnUKP7s4hur8KzqIuozGK/RaWzdRKC2i+8k+5gWBWpc9fiMCN9xNeOJJ2gfOsM1h18gmF7GkiQWymqYrm3G6N5EqKqeS7LMi0jYYwU8ig2PzYHXFcYtSShDA2QHjpA8fQJhGATqGlh79wfo2rGbSDD4xi5gIQR6IU8hm0HLZslnMmjZDIU3WrbYZzJo+RyWaSIsC8s0sSxzxdRjIatFwVdUW7HZVGxOFy6vD6fXi9Prw+Hx4g2FUQJhFhQbw9k8g5k8z2eK/fB0FmOqeL/R6LKzM3TlKSjeCe+oEtOKWeaZN9ncnwf+hxDilZWfRygK/a8ACCH+emV8D/BnQojLmmVWbe6r/EfF0kwSz10ic2gWtcRF6ANtOOre3Wq9kNXZ/9ReBg/oCCHT0Jti1x03YRyeJ314loJk4JaOY5ntQAitcoaZun+kLL5A/fgiMjr3l2zlJfcS00qeKt3gXpGlKehiKLqRkxPrOGi1ksPOx+afpV1fYqi5A7uhYSoyL2y7mYvuAKHsIndpT3Bj4EVyJryatjGz5GLXVA/qiMBKxvDam0k7N9AfrOCs3WRCLXoVXQ6L1vwM6xf7CWYmkQBNsrHoKCPlrWWx0s/5difpQDWoZZjSW53LvlScNUP9dA32E0lEsSSJ2cp6RmvbmaptQgmH8PhCGELCEAJdCAwhKFgWWdNCzqRYd/443QPHCKQTZJ1uzrd0c7ajl4WS7+dGlwGvKuNVlGJTZXwrfXFMxqt+v/cpMi5FRpUkFElClXjjMaagoJnkNROtYKDpFlrBJFMwSOsmGc0kbRT7uGYQKxjkDfP7oZSWoERVqbCpVNps1DhsVDvsuGWJ8gY/1VeY5vl1/jUqMT0FXAu8IklSG8UEzFHgaeBhSZL+hqJDtRU4+i7fY5VV/l0pjCdZfnQQYyn/E9nWTd3i1MsjHHtuBCNvJ9w8wjXvv4ZgvIz4P53DTOskHZOECk4stiHKE0w3/A3CdpY1pwVhbZGn3TV8Jexg2DZNhWHwG0aWxoiTkYWr+c6pLl4zOzAtk88sP8668XFOtW/iYngNNlPn7NU38bIriGpqvCf5JB/0PULebvFM3EZq0cPuqaupm0qzbAaoFG1cqm6l32EwYDfQJR2bYtGVn6Jn4QghLYpAYtZRzsWSzUxXNTG2phUz4CjatIWBy4jRGwhi6tMMLR5GlVSqpFaUGZ2Oi6epmxlFAmLOShwdnSRk8As395amuOPGrQSDbw0jFUIwc/EC/Xue4eLhA1imQfma9dTuvhHv2l6ulWUypkXaMEmbFinDJGNapEyTtGGRMgy0tIGWzpPMGCQzOuQspLyJXRM4dYFTt3DoAqcmcOgChyFQTbCZAuUyFh37SrsSeY6uNICO60rftbhfjrcVd0mSHgF2AyWSJE0B/xV4AHhAkqSzgAZ8QhRvAc5JkvQoMAAYwOfeLlJmlVX+oyF0i8RL46T3TqEEHJT8p3U4m4NXfhwhGDm5yP7Hz5FZj2pSaQAAIABJREFUFngqhthyk0xXy0dJPD1BbHiQuEMjKCUJFGrRvFHiPY+y5PgujaNB6mZinLLb+c3KWvqdEhEjz6fNLO2lNsYXtvBifxf79VZUK8fvpR9izbmLjAbbeW3rDUhYxDs38XSolKxqY0PmGL/q/iIOT5o9CRvJ+SC3TF3LQEIwlavAq7Qx6XXwjL3AtK2AJAlqjUU2Lh6nOjeOJdkZdddzONzLpcY2svUhHE6F0mSOiuwBUvoZwnqG317/IWS7yf/t/0sWrEra8ldRNrpI2+hT2A2dhM3P6eAmNrRUEWSUaMZJJJLlPbfdTlPTZgDyRp6UliKlpYilowz0H+T8qUMsx+aRHDZKb2qhtLWVlNfFKfMMxmgfZFWktP0Hmjttx52xUZFTIa8iiR/julUtcFgIu4VlF+C1EA6BKOYJxlTAVEGxSSgqSDZQVFBlgd0m4bbbsKkKsiphSDoFUUBHo2DlKYgCeStHQk+Q0OMktDhxPU5cWyahJRAIPlL1Ua5n3U8yZX8kqwWyV1nlTWjTaWKPDmLMZ/FsqSDwnkZkx5Xf4M5fSrLvsUHmR1M4ApPUbNnLtt2/hnSyhNSrk5gSFEjgNvwIeZbstmGmPF/Bl3XRcipPTIrxv8MRDrjtBE2Lu6Q868oFc4trGJtYz4l8I6ZZ4CPZA6y9cJZc3MWh7duIB8LkSmr4XlM7Uy4flflJfs12P3VcYl9GZWHez62TN3M8FyKcCJGz1XHapnHGbpBTFFxSnp7ls3QlTuEQgll3E32eRkZrWjBrPLTo03RM+fHks4z4v85w+QgOy8XHWj7B5qYePn/yfgaTftrnGukYvEg4EcVQ7Qy7WzjvaeOaihI83gNMZFJo7iXCDT7UQBmLuUUWsgssZBfIGtkfeU3thotgrqzY8uUEc2WEcuX48yUo4gc/o7yaIe1YJmtPkLWnyKkpsrYUWVuSnK34OG9Loyk5hCRwGS7chhuXWezdhhu7acdurbSVx4pQ3ijC/eOwsNBlHV3W0WQNQzGKu2oVC0uWwFIQhg0578StBdi1YSvvv/f6K55j8BPGuf9bsCruq/x7I0xB6pUJki9PIntshN7Xiqs9fMXHScXyHH5qhItH51GdaUrWPkHX9kbqpM+Q/M4kxlKetN3Aq6nIxIjVnSS+9nsU9FkaL5TjXBrkH0IBnva5cQnBHWj0VpnElxoYn+jhYqaGmCazPTPJzTOvopzPcr6njXOt6ynY3Zxs66a/pBK3kebj0pe5WtrL0YzC5IKf2ybu4WymAk/az7wa4IQtx0WHjCRBjT7PxuhRavLT5Fz19Lvb6Qs1oNX4CZWmuOniS3Se72IhEuBk5be4WD6Aatm5o+QePnj1e/nC6X+hfyhLx4SD1ktDqKZBPhSmz1vBQIlOiX8OyTVFXEpiSd+3cdhkG2XuMkpdpZS5y/AaDrIj0+SGEni1CioD6wi4WjATTvKJ7xsBJFkiWOYiWO4mWO7GH3HiDTvxRZz4wk7szrd+IWuaxvz8PIuLiyxGF4lGo0SjUeLLcX5YBx1OBy63C4fTgcPlwOF0oKgqwpQwDBNTFxi6QCsY6AUDraCjawZWHoQBkrRSi0oyEZKJpWhYslY0wL8Jt9NL74ZN3HDL7iuea7Aq7qusclmMpRyxbw6iTaRwbSgldGfzFe8y1fIGfS9M0PfiOMIyCLU9T9WGM3Q1/TfYFyR3Oopll0EzUChguPcwv3OClDhJKF1Bef8MTwRMvub3YUgSNwqTXZV5CulyLo1vYjZRxXjOg1mw80cLD+E9FSVR4mP/zh2k7QFGKhp4pWENll3lFuM73Ks+ysWszvkFH7eO38doqg5b1s+QTaVPzTLtsGEXGmtT5+mOn8IrFBZ9Xez1tDAf9JJv8tPgneDakZeoP93BYqiRU5XPcr78JIpQ2e24ld+48dN8bei7nDxwkc6ROCXxKLoqMV8l6KtKsRSOvXF9vLoHv+6j2q2wa+2NrKvZTb2/nogzgp43OPXSCc7tPUMypiCrFUiSEyia8YPlbkpqfZTUeAlVuAlVePCVOFEuU5JQ0zTm5uaYmZlhdnaWmZkZotHoGyIuyzKRSISSkhJCgTAuuxeb5EI2HaDZKaRMMvEc2aUUmUSBbFpgmG99P1XWcduzeOw53M4CbpeOxwPuoANP2I+7NIynug5nZT0WgnQ6TTweJ5FIEIvFiEajtLa20t3dfUXz7XVWxX2VVX4EQgiyfQvEvz0CEoTubsG9oeyKjmFZgguHZjny7VGySY1Q4xnCa75OY+vtVEQ/TvrFWYRuYgoLWQg8yvOc7RzErBlCsiwa+v0cFJP8v1CAmKKwyZJ4T3kau+FjeGwzsaUaZtNu+qwq/tvCwzSfvoQQgpPXdTMS6mDZ7WNPcw/xSIQW4wK/ovwjaFPsX3Rz7aUPs7zcTi7n4pxDol/NEbfZ8Jspepf76EgPYlPrGQ2s5zV3OR4lSaKzhMrgDNunD1BxPMKyv4O+6pcZLu1HEjKbC9fxOzd8ju/OnuDsS3tpH57EbphEAxoX6pKMVWbRrAC2XANb3OV4UjlcqQDVkUV2X7uNNZ2fRM8LpofiTJxbZOz0DOllQTG2ReDymtR2VVLZFKKk1kek2ovN8fZO7Gw2y8TEBBMTE4yPjzM7O4u1EtfudrkJB0rxOSM48CFyTnJJmURSJ5HUKJjWSp1VsVJOD2RZwyYlUeUsqpRBkbMocg5F1pHtErINJMVEyBI6KroFmikwTIFumugmaKjoKBioaLID3eZHV73oqg+f5GGNZmN9HqS2ELd/fP2VT2BWxX2VVd6ClTNYfmqY3KlF7A1+wve1o4acV3SM2eE4e795kehkmkBVgmDnFwhXm7QF/wrze0706TSoEhgCp3yI5cgzTPWCJaapXKhnbGKYL4bdjNtsNFsqd5ZkqHHC+fGNxKdbyGRVjuWquT1xgJvPHkHJCKY3hzjSdA1Zxc2h6nbON7TgIsuHpQfpNl7htWUbXUP3oi5tZSlv56TD4pRNo6AoVGhzbIqdpCE3i+Rcy9nAOo46HaxfukC8qwyrzaA3eprIUYOMfQ3H6w8wFj6LatjYEL+GT2z5JV5aPEBy32vUzaSxJMFYZYaphgIptZPJZAPubCO/VFmCpPUTjxfw+RbZ2OulqfKzzI8IJs/HmBtNICxAGJjGDC5Plo6rO9h421W4/a53dO11Xef80Ch950cYGJthNp6lIFQ0bAjJg2Y5yRsqBVMh/3ox7JUSeOZPuWSpKkvYFBmbImFXZVRZxqaATTKxY6KaBSqMAi0atBsqzcJHmOLfuSwXyLTBtk/e8K7ee1XcV1nlTRTGEsS+MYiZLOC/vh7ftbVI8jv/j88kChx6coTBI3O4AxKl6x/HWfE8teW/TOnwvWQPL4JSjI0W0iRhxxfZ1wW2yBxuI4DUn+WLAYM+p5NKU+bakMJmf4KRxVYWR9eh5dzEohIZHX594Fu4YhrZFokDvTuJSRWMhcp5pa2HgsvFDusV3i8epC+ZIzJ8PWWztzKVd3DCYXDabmBK0Jwbo3f5JOWGhuTaSL+vjX67zs0j+1BqbIzsrqRLm0Tvi4HRyYn6w0wHhnDoDtbPX0dPyw4GFl4icmqYUEomZzcZqytQWx1iNHU7+5dLsSPx0VoHpbYBZmZiOO0ZGktd+LmJmQuCfKa47d7pzpGND2AURqnrKmfTHXdTt7b7LSUHhRDEMhoTsSwTsSzj0QwD41FG5uJEsxppU0bnR6/o7YBHUfDaFPxOlaDLRlDJEDBm8GVGceWmcZPHJRk4w1W4SupxldTjKGtCDVRjsymosoyqSNgVGVWR3xBwVZGwyTI2VVoRcekHzt3KGxjRHEY0hz6bQZtOo02nEbligQ7JpeKolnF6pnCYB1BnvoO07Vdh1+9e4Swusiruq6xC0Wma/N44qVcmUUJOwh9sv6INSaZpcfrlKY599xKmYVHXO4695n/g8VbSyl+hfw+sFRHTpTylyj8xVn2K2TY3qpWkYjjMQ2aUp70eAhZs9Ea4LTLFcjbMxeGtmIkSbEspjuh1/Nro49RPRNHLBQM7W7kgdZNxeXm5qZvp8koqxRS/xD+RSZ+nMNpD68THGSnYOWYvxqcLIWjPDrFp+SRBoSI7t3DK18wpe45bh15lrT7Gybs7KS81uXRuAiVbz+nqfmKeWbwFF+1z1yCXumG2j+ZRHaeuEPUL4nV57inbwROLO3khXcAGvL/KTXt4jNGLk7j0IGHJhytdj92S8DgUwiEZkVvATESxK3b8oTJ8gQiqZMPSTUzNxDAFhmlhWkXThmFZCIrx1BoCDTCxMLGwXs/WqKg4HHacbhsenx1f0Ikv6MDmVJGsHNLSWaTF00gL/UhGAkk2kSrbkarXI9VtQKpdj+TyINnk4pcxFJPXCEGxtocASyA0E6tgIgpv6rM6ZkrDTGpYKQ0zpWMs5bDS+vcnjCJhq/Bgr/Ziq/Zir/Fhq/T84ELCssDUwHZld42vsyruq/zC82anqbu3jOBdzVcU4jg5EGPfoxdZnstS2Q7BjvsRjnPU+T9LoO96tJFUMS+4EAjHi0QcX2HvmgocvhhVC372zy3zz0EvBUlikxTglsoYbknQN7YVbboOtWAyG7PTtXCe3YOnEQ7B3LVeDgV2k7c8nKtu5mRjO5YicRdPsK7wNEPjlWwY+S0G806O20wu2Iql6takz9OT6MePD8W5mTOeevpcWW4eepXbJo4wcHML1o46Dg2eRaTLGCw/R86epizpoyy1iaQvQdXEBK1TbmQLxqpsmFUxPhC+j4em2vleroAT+Fi1j153lNxwgRI9hB8nHlnG+SPugkxhIBygBjzkZZmkYRIrGMzndZKG+Ub2GRWwC4EdA5uk4ZRMXJLAqzjxOd14XW4cdhuSKO5HEIaF0M3iY90C899WzySniuK3ofjsKCEntlIXasSFutK/2xQV7/j9V8V9lV9UhBBkT644TWUIvbcVd3fpO359cinHwceHGelbxF9ip27rXgzPl/E4O2iM/Sn6QfONlV7aNUaT9decazCI1kqUxwxmJ03uDziZtNnYUJC5usJJmzfKwEInseE1CMONa26JaNLGR86/gD1vkLpK5njbVmbSdSSCQfa2rmPBX8Ja0c8HzH9iaC5Nx9nfYyxXxhHV5KLdwm5qrEudozt5Go8cxubYxoCnimPuLFdPH+ETfc8RWxtm+r4NPD13FiPrYyI8giWbtMxHQGkipUzQccmkfs6NkCUu1jnI185xp/MzPDdeyYyusxGV29xOKnQLp/594SoIgelVcVV6SGuzDA8eZWl5GisSxOjZxbi7imOTSYYW0m8kZSy326jQJMI5iyBZ/K4oimMBS9JRFRuNDU2s37CW1tZWnM4fs7LNLMHAU8U2th9hgQh3ItrvQjTdggi2IHTxli+BH2iGiTBEMWmktJL6d6WXZAnJriA7FKSVJtsVZI8NxWdDsr27bKA/LVbFfZVfSKycwfK3hsidjmJvXHGaBt/Z7a+pW/S9OM6J58YBaNuRhrI/R5CiUf19nAfWYMaLObx1l4ab/4PqO8zR9irKCsvYJ2T+zqly2OWiQTPY5o6wrXKWhUwJFwY3I6VLcKXTLC2q3Db8CuVLSQotFoM7GjiV3opps9PX0MbZ2ma8pPiI+AokDuM/9UniiY0cly3O2Qwclk5vvJ+u9FkcSgiHYwfjrir2ewrUFy7y+y99HcUnGPzIBr7kGMbSXSx55lBNG+0zpcSCIaT8OGtH3VQtudBsMgMNgkxFhjsLv04y6qdayKxDwb2yeSdjmSwbEgnTQJTGab52DaUtEU69+Ax9L+1hTASJ1vQy661nZCU23aXKNDsclGYEkbRFhSnh8WnIpcss69PktSw2m42Ojg7Wrl1LU1MTNtuPCUc1dRh6Efofgot7wNIh0gpdd0PXe6FszZuqJ/18syruq/zCURhNEHt0xWl6Yz2+a96503R6cJlXHx4kPp+lodtNuOsBssb3CDuuoWr4s+jn88Un2iDpeYEW/YscbS1H9WQoHzN4RNh41O/FbQlu1RxsqssgbBLHRq9CmqpESCru2SiVE5NsHB/GCAtmb/ZwVL2GeKGUWCTAofY1LDuCXCteZFv+IRJn1iEvfJQTluCU3UAWJr3Lp1ifPoVd8eK07yTqqmOvS0fxzfAnLzxARSzO8HV1fH7dEmmbhKbm8eQD1MWqmCrJEYkusG40QCRpp+CUGarXafau46r0jZRodiIrtZGSdokUOjNxi5gukXPEqN0wzTV3vIdCAvY+/W2+d3aaUVcdk94mckLBJku0+9zUaBKRRY1yQ8btsVHW4kbzzDMdG2E5HkOWZVpaWli3bh3t7e3Fohs/jrkz0P8wnH4UslHwlML6+6D7g1C+9hdG0N/Mqriv8guDMK1iPdNXJ1HCTiIf7MBe63tHr82lNQ4+PsyFw3P4Ig46rhsho/x3JOy05P4c6WCkaNcFUhVzVCf+mFhZlrEaD81TCfZnVb4QCpCSZe5MFegtdeEvy3B6fg2Zc80YahBPIo5rIsHuC0eQFIvE9RJ99RsZinajOnWOtnVysbSRKjHFfeb/IzsSQ730h5wt2DnhMLCw2LB8lu50P25JxeHYSdrRzD6XQapilt898TXaT82xUOvm/usMhiqL/9818Q5sVpCp4Ai1cxnWD4cIZhTw2qGmhg1iG81aDSoSKQQXFYG31M3MXJxkXEFIJiIwRsfVUa669gOMDUR5+Jn9HIo7GXfXYkkKQYdCt9dDVcykbNnEjkRZvY+aNSGkYJLR6UGGhi4ihKC2tpbu7m7WrFmD2+2+zIcSh9PfhL6vFcVdtkH7rbDhw9ByAyjvrqThzwur4r7KLwTGUo7YNwbRJlO4N5YTvLPpHTlNhShuRDr4xAhazqBjlw1H3f8ilz9LhfwhwifuwFwsmmCsEGS5n1pe5lhzOQ3xZWaWBP8zHGTEbmdrLs8dloNAS4ZZrYyhUxuQckUbf3Bijq1nj+PLFshuMhnaVs+x6C4U02Ciuo4jLZ3oso07xBNULj6LdebXGcrUcdxhUECwfvkC3ZmTBMw8qnMnpquLQw7BUs04vxp9hLXPzmMAj+yUeG6ThMP0UhvfTcplMO89RsukxrqRMKGcjbpIO+FQJw1GIyoyi1i8ik4qYKer3Mfi6ShmXsFQssil51l/TZ616z7EE88P8HT/DINyBbpsI6QKtgYD1EQtAnEDVZGpXROmuacUX7XM+YtnOHXqFOl0Go/Hw4YNG+jp6aGk5G0KiM/0wbEvw9knQM9CZTds+Cisex+4rzwtxM8rq+K+ys81QgiyJxaIPz0CskTonhbc69+Z0zQ2m+G1hweZGYpT3uSm7qoXSOn/gktupGH6T7DOrHw52CWWSw7SHvtrLtYHsEs6ylyB+/0BXvG4qdYNPpPIUVankAjaODG0GcdwmIw/hG9piY6zF2ianUKrs5i71c0R7VpiiQCK386+NeuZ8FXTLga4Ifslsqd7mI7ewnHVICtDe3KUjYljRPRlZFcPivMq+hwK4+UTfDDwDbqeXCQ8o3GsReKBm2VUmvGbtzEdHCfDPjomDNaNRGgQ1bSW9FJpb8WGjRgWL6FzRDZZ2xBmrZCZPb2MMGU0+zL2yjNsus6F7LqJB56/wGuLNrKKCxc6G70u2nMuQks6qqpQ1xWmubeM2jVBLk2McOzYMcbGxpAkiba2Nnp6emhtbUVRLuOA1LJw7smiqM+cBNUF698Pm34Zqnp+CjPl549VcV/l5xYrqxd3mp6OYm8MrDhNHW/7OkMzOf7cGH0vTGBzKHReG0X3/wWWmaGh8Ac4DrcjCkUTTKF2meDyH2B5FpmLeKicSfE1l5evBfyoQvDp5SSbXDaWW+B0dA3icAVJfyWyaVJ3cYyN5/oQHkH8dsGZsl7OT3XQoETZ27KJ47VrsKNxl/k17IOjzE7+Fv0CkrKgNjfH9uh+SvUFFHs9qvtmLtnd9AcX2Nj4bTr3DrPlSI5lH/zLDTaWym4g797NpdAxbNmX6BiT2DpWR6d9Lc3BHjyyH00IXpV0nkYn77XxkXXVeC7Fmb2QRWCRd83jrT/Lpmva6Btv5JvHZhk2/UjColPV2WwPE5k1UZCoag3ScVUFzT1lFIwcJ06c4MSJE6RSKQKBAJs2bWLDhg34fG9jFosOwfEHig7SfAJK2mHzp4r2dNeVp1r+RWJV3Ff5uaQwGif2zYuYKQ3/TfX4dtW8I6fp5PkYrz48SHIxR1OvA3/H35M3jlIi30LZqY9hzq5sRAlLZJX/S11hD8OVQWqjSV6WnNwfDrKkKNyZSvPxjEayXWFYrWDqSCdyJkLK76dsbpHNxw/jyWbIXGNxaWMdZ6Y20VQYo6+0l1fX9LDgKGWr2E/n3COMD36OwWyQmCIoMVJcN/M9ysxZbHhRvHcSd5Sy35vBUf8cNTPH+fCeLME0vNrj5tDGX+FSxVpmvXvxLT9D5yWZG6fW0+nupcbThizJXBQGj0o6+9HY2RTm/Z3VLBwYZmlMwpIM8p4pwq1DtG/cxreOKjw7rpOT7ATMLNucTtqSHpwF8Je66NhWQfvWCnwRJxMTExw7doyBgQEsy6K5uZktW7bQ2tqKLF8mxtvU4cJ34fiX4dLeoi29846iqNdv/4V0jr4bVsV9lZ8r3uw0VSMuwve1vyOnaSGrc+DxYc4fnCVQ6qBp12Fyyj/gUKponP8viD5ncYeiXSZefojWxb9iusJFMFdgIi/xVyVhztvtrMtp/EFsGU+ZjZFaL8eHNlF20sZ0XQOufIENp89SNzpMoc1i9lYX51NbqFmaIe6t4smOa+gv6SIiFrkh+89MDaxjItrLoizwC4Mbp/dQqU9gMyVs7uvJu7s45DSYrNmHS/ken3opxcYRwUBDGd+66XMca62nIPYRWHqKDZds3Dm3nQ5vLwF7KXlh8oxk8BgaObvG+zdXc0tZmFPPnicz78aSNPK+CarXz+Op3sbDh+IcSzgRQLtIs81WQsmSjKoqtGwsY83OKiqbAxiGwZkzZzhy5Ajz8/M4HA56enrYtGnT29vSE1Nw4l/g5IOQnoNALWz8JPR+HLxXlrRtlVVxX+XnCCOaY+kbF9Cn0rg3lRO8oxn5HWQNHO1f5LVHBsmlNFqvymGr+QsslqgzfxvXwW5EthiPnatZIpj4IyTPIqYsYyUMPh8JssfjIawLfm95iassk5EOF4fy63C/5CUWqiPnctE6ucC6o/uRfDrLd1sM+dYTmshRZU/wQNU9vNqykZTsZbf1LMbwKOPj72dBCDwCrl7aR2f8DEjgUZrQArdzxiFxPHIOvfwp7jgV5X0HBP1t63n8pg/R11SNPXeQyMJjbJrw8f7o9bR4urHJDiatPF+XBa+Qp7RkgV+9rpuurMbRZxbQ4gFMuYAeGKdlm2DOaOPhk1HGTS92S2OzYrEhH8KdK67S1+6spuPqClxeO5lMhuPHj3P06FEymQzl5eVs2bKFdevWXT6E0bJg9GU49gBcfK646av1Rtj0qWIv//tuBPpZZlXcV/mZp+g0nS86TRWZ0D2tuNe9zSoRyCY19n7jIiMnFwhVylRueQjL8TIh224qz/wnzImiCcYMmljy5ymz9pP0OPAv5/lKwM9XA34sIfGJeIpPJRMs1Dk5VlLD9NEufDMq07U1BNJ5eo8coGR5kcxNFiPrqvGMutgoLvCU92YeW3stFzxt1ItRmmaeYmTwbhZ0By4L1urD7Bzbg26X8RhOzOD7mHSF2edbIFb3KJ2xUT6218uJjt08vetmFkJB/MnDhKPfYMtUhHuXbqDR3QXAISvNg4rCmDpPae0wf7irG+9EjLMv+TFTZZhKHlEyQdf2co6MwePDOsuyh6CZYYfDTXPMhUNINKwvYe2uamo7w0iyRDQa5fDhw/T392MYBi0tLVx99dU0Nja+JeHXD178GPR9vWhPX74E7hLo/VhxpR5q+MknxSqr4r7KzzZWVmf5W8PkzkRxNAUI3deOGri801QIwcUjc+x7bAg9b1K3eQBHzf04HeU0Lv4p4rgbLMAmkQp+l5b0F1kKuYgk8uxxuvjbSJgFRWZX0uBPEvM43ArnmgO8Nr+LNS/luNDSiKmotA8OsvbMGQrdJjM3OZBny9maO8dFtZUvtNzLvqrNgMTG+HeYONdINF2JA2gTaa4f/TqmIrAbAodjJ7FgL/tcBS7VPEHQeYLbz7fTX3cDBzZsxlQU6ub68SYfZstsOe+NX0+1sxnN0nnRSvJ1m8Ji4BjNNcP85toq8oMFJo9ug0wlplzAXjNHW285z/QvsGfJTUbxUC1y7LYFqIzKOJwqa3ZUse7aGvwRF0IIxsfHOXToEIODgyiKQnd3N9u2baOs7DLmEyFg6lgx4uXct8AsQN3VRVt65x2gvr2ze5V3zqq4r/IzS34kzvKjg5gp/R07TVOxPK8+NMjEuSWCVVki3X+LIzBLvfLbOF9bi5Uupl9NRUaoz/wxmaCOP2MwJKn8ZVmYszY7dXmVP4tNs94wGGl28YJtI5V7SonaHSyWlRFeirHt8CGc7hTL95hkRQUbo2Ogqvyf0AfZ07mDaVs1bdl+cgNLRJc6UAR0WhK9S98glIxiSRIBs5JU6d0cdaoMlR7CVvEi6zO7OFa5m+nSCjzZPOtmRnDoT9C+GOae5E2U2CrJmFmeMZPsKcsx5/0u11SPc2uJj9hgCcvnb0fOVGLJGoGWHCXVJo/1zXDQrKagOGlRdLabfkrjAl/YSfd1tazZXoXdpWJZFgMDAxw8eJCZmRncbjebN29m8+bNeL3eH3/RC2k481jRQTp3Buw+6L6vaHopX/NTnBGrvJlVcV/lZw5hWCRfGif12lTRafrBduw1l3eaCktwbt80B58cwRIGFd3P4a1/iorg3ZT2fxB9OAeA5sngt/4M1XMRuwFpHT5fGuJZlxuPYeNzS3E+nF1ioczB4eoaRs7tpOHkLAPfw2TcAAAgAElEQVSd7ciWxYa+fhqmR0nfbpCo9rJuNkaZkuIx+Vq+3v0eTvh7CWmLBAZPszDTDkKiS1eo5xCdY0fI21R8BQUr8F7O+Cu44J3B1r4Ph3c3JwNr0FUbnZcu0TuzRN59kJKUwj3Jm4nYyonry7wgZenrjKG7H2B7IEOzojA3sonk8M3YsuUI2aC0zcTmmODRgRgnXB3osp31TolNaReRtKCs3seGG+to7ilFVmQMw+D06dPs37+fWCxGJBLhqquuoru7+8fneAGYHyiaXU5/EwrJYhqAzZ+Cde8HxzvbGbzKu2dV3Ff5mUJfzBL75iD6VBrP5goCdzQh2y/vdIvPZ3n5a+eZHU7gr56iZMPfEy4rp2H5j9D3FVPBWoqF7PgKIdu3MRUZR87gy+EAX/EF0ZG4JS7zp4lLSA6ZgWY/z+Ru5qrH5jnf3EAiGKR6coqNJ04gejJkt0HDjEattMxZvYHPt3+IfTXbyRpOai/1szhehmWqrNEV6u0LtI49ihBg1028Sg9jZTs56TIwN8yyUNbOlC2AJ5vhhmOH2DKdoq8hRamR4b2pmwirZSS0JQ4qWWLbz4H6JGudGmYuyMz4VWRHr8aeLUdSBCUNOfKZkzw7a9EX6EaTHWz02tkYUwlkBfVrI/TeXE9lSwBJktA0jZMnT3Lw4EGSySSVlZXs3LmTjo6OHx/KmE8WNxudfBCmT4BiLybs2vQpqN2yGsb4b8hPJO6SJD0A3A4sCCHW/tDvfgf430CpECIqFb0r9wO3AVngk0KIk293gqvivgqsOE2PF52mkq3oNHWtvbzT1DIt+l+a5Ogzo0iyRsn6hyhpPU+z9w9RXqjFjBcQCHTnYarUz5N3mgTSOs/63fxtqIR5GbrSbv5ieYxGM89EjYunAtuI7AkgZwyGW1tw5vJsOn6cEsc02i0FShMWjVacqO7lb0rv4dU1uxlVmiibGCUzImEaDlp0mfVC4M4+TGk0hilLhPMBlivv5YDHw3y3jfHaIJqk0DY+zl2vPc+GqWUe2tZAvZLivenrCaulJLQo5+VZjN17cTqO45FgcamBpfmryI03485WI0kyoYo40ZkXOWSV0h/sISc76PW56FmAsAbNPaVsvKWB0rriajqXy3H06FEOHz5MLpejvr6enTt30tzc/KOdpELA5BE4+bWisOtZKO0sOkjXfxA8kX+NKbHK23A5cX8n1Qq+Cvw98OAPHbQWuAmYeNPwrUDrStsK/MNKv8oql8XM6MSfHCJ3bglHc4DwB9pR3sZpGp1K8fKDAyxOZPDVnKa892Ga6u8leOJ3KQwkMCmgq/NUKH9CzreMI1VgxLDzudpqTqsKpQU3/zO2xK35CZZCNp6pbuXEyHY2PTnCwNoK8lVOWoaG6Rw/Dbdk8Ms6TctJsobK/cr17N+6mwPuq3HOJvANjZDUXNQYMtsLKnn3XhpGTqArMm4N8N3Ea1XtnOh0s9Dqw2kY7Dp+hg+88E1qo3Hu372NaH2E38xsJqKUkbAWGdBewLjxBUKOKOmch/GJ7WQXWyBWhjfTgMe04fIusjT3LAf1Gk6V3EJa2FjvddGzIChPSrRtKaf35nrClR4AUqkUhw8f5tixY2iaRltbGzt27KCuru5HX+T0Ipx6pJi4K3oR7N5ifpfeT0D1xtVV+n9g3lbchRB7JUlq+BG/+lvg94Fvv2nsLuBBUbwdOCxJUlCSpEohxOxP42RX+fkkP7RM7LGLWBmdwG2NeHdUX9ZpauoWx54d5eSecRR7hqqrvkZjd5i65JfJPpKkYCQwJY2w+jfogaPYsxrprMwfVpfxnM2Jw7LzyajgN1MX0B0yx9ojPGzcwfavDlNRq3Fy8yZCsRjbD+3Ds2meyNoc9VqGvKbwjWwPL22+hn2l15BacOHpn0TP2gkJD9dlVPDO4F98irJ5DckSlOkNnKq/iT2tfhY7/dSlk3z0xRO8f88/48mn+WrvZgo7mvnP6S1U5GtImktcLHwD87Y9SCoMzteQXroLc8mPQ4sQzLdh5WzI8gLZ5Iuclcs53HAvy4bCGpeTnkVBTVqi8+pqem+qw19SLMScSCTYv38/J0+exLIsurq62LFjBxUVFW+9wJYJw9+Dvgdh8DmwDKjdCnd9AdbcDY7LOFZX+Q/DO68z9iYkSboLmBZCnPqhW7hqYPJNP0+tjL1F3CVJ+jTwaeDHrxpW+blGGBaJPWOk902jlrko+UQX9urLC8fcaIIXv3KC5CL46w/RvHOAtvLfQf+2SjYaRyBwKs+jBh/AWcjjTAv+sTzIg64geQQ7kl7+PH6RkDAYr3PxiP9aXC852Bqb5VzPRlTToPf4carDFym5OU2tlsHIy7yYaOGJrl2catzO+HINrsOL2FM5HLKT2zIqZYpOXHqYmuF5DFmiLGNnoeouvthcx9RaH1uji3z4OxNcf+Cr+DKz7K9r4cyOrdyX7KE+30yWJKPxb6Ld9ALTqo9zo904453ImopPLSNQaKIQt2Nay+jZ10jWlvJa9b2MpgSNqoObE9CYUejaVc2GG+rwhop3PfF4/A1RB9iwYQPbt28nEvkRZpT5gaJj9PSjkJopxqVv+yz0fAxK23/aH/8q/8pcsbhLkuQG/piiSeZdI4T4EvAlKNrcf5JjrfKzhz6fIfaNQfTZDJ5tlQRua7ys01QvmOx7/Djn96VR3TGart9Dz9V3Y9v/PrLPLCKhI0vjuPx/hlPEcGZMno14uN9bxqxs0pT18v8tz7BRG2cxYuexyi4Oje3kusfPcHFNJxcqvNRfGqMz3kf5xih1ZBAFOByv4asN25m7qpdj2R6cJ2LY40soisK1BRtdBYlL/r14R0/ilMCtWdhcW3msdzsj7V6uWVziQ08nWDf4JBXzx5nzBXn2o7dwTbyDW7NrKEhZxha+TX7jfg77ypgbuYZIpgo3Eg2VTchTIVJzHvJWFss4iH9DJS8q93Dg/2fvvuPjuu47739umd4HAwx6LwRAgiQAEqRYVChSvVqWZNlxrBQ7iTdOnuzmlSfJs3GSzWad9SZOHttx3GRbtixb1VSvJEVSbGIDC4jeOzCYXu/ce/YPKLIdO5YTSZZszfsvcHhJXpzD1/d18DttKklRRuGmlExbQmHtzgo6r6nB4flhqB86dIgzZ84A0NnZyfbt2/F6/81BXLG51SWM5x6ChfMgq9CwC677O2i+FtSfsfO04D3t51ot83pZ5ikhxFpJktYBL7M6YQpQCcwCm4G/Ag4IIR58/c8NAFe8WVmmMKH6/iGEIHl0jsgzY8gWBd8dTdhaf/Zk3EjvMAce6CcTs+NvepWemysojl9D+PExZE0AGRyOz6Cae3GlNc67LHzWG+SMCr6cnU+upLgrPUnKqnC2pohv5z/Ald+/QLS0lJnKStzRKOsHT1G9ZpxaRwxZgrORMu4LbCDZ0cYB43KM4SxyKIeswCbdwraIxKxnBvfCD7Cmsqi6QVGunMNNN3KxpogdSwnKF2WqZl+hdvwZJGFw+LZN1Gc20KqsRxcaM6GDxIoH2VfhRoq6ceSdmKwm1lY1Eh+AyIIPALN5iLorqnnZqODh0/OYJYmetEJXTqVjewVd19a+MVIPh8McOnSIs2fPIknSG6Hu8Xh+2KDZOFx6cnWUPvoKIKCie/VGo/bbwPHmO38L3hve6oTqjxFCnAfe2KImSdI40P36apkngP8iSdL3WJ1IjRbq7QX/So/nCD8ySGYgjLXFh++OZhTXvz8yTMbCvPTtF5k+H8DsjLPprjHWtn6cuW+PEg2NIiOwmh5G9jyCL5FiXqh8pryUp8wWVEPlQ8sK/zU+gKRAf52Tb9uvJfhslquiQ/Rv2ADA2r5ztPnO0tgZRpUNLsSC3OfqILejhldMVxMbMaMsxlFkQZPVyvXzEmlLjmnzI1SOzKLJMsVJlamyG3ihppFtIYO1l/L4oiM0jjyMK7bEhR2VyJ4dXJfbhqTITIRfZcmY5mijA1u2GWdIxhN00+YpYfZMlKlRJ0hWnN4QXbc0cSBTxx8cGCGbm2O9prItY6LzsnK6r6vF5V+9E/bfhnpXV9ePh7quwch+OPc96H8G8mnw1cHlfwIdd0JRwzve/wW/WG8a7pIkPQhcAQQkSZoGPi2E+Pq/8/gzrC6DHGZ1ZH/v2/SeBb/k0v0rhB8exMjqeG9pwLGl7N89l8QwNE7t38vpp1TyGR9VXUPsvH0Pk89GCD0/gAUJk3wO2f85ihJLpDMSXw76+IbFS0rWuSzu4K/CIxQbWWbKrTxX1MHwxbVsOzbAYGsrF6tdlM9M02kcp71xBptZpy8W4Jv2taQ2l3HCcTVzo36U+TSKnKbUbeGWOQVHXjDiPUTzyAnMsow1J7DbNvLs2m10xs1cNwO2zAJrZh/BN9nPRJ2Vge3X0CPvwa66mEqcZyIxzMU6O4oowZrXqawLUJ1TGX1thkFRg6yU4ynW2PnhNZxMC377uX7m47M05xV2ZKxctnk11D3FqxOlKysrHDp0iN7eXiRJoru7m23btq2GuhAwfWp1hH7h0dV7R21+2Pjh1bPSKzcVVrv8CitsYip4Rxk5negzYySPzWEqc+C/uwVT0PFTnxVCMD3+Ige/P0hkvA17UYid99Qzu2yj8vk5TIYZmWVU32cJZC4iJHje7+QL1hKmTHnq0g7+MjRPpxZmyW/mtYpy9kZu4vqHTzJXXcN8eRmuaJSNkZP0BPtwWPMMxv183dpCYk0Jl7xXMThegzqbQkJg95m5MWSmOmow5Z6kbHYvei6Hqhv4tSBHam+kLufFjozJSNAeexjfhVOEHDL9PV30KDfit5SypE3TlzrHUIkZJIm0I0VbIIhjNspk3xwm205ktQKnT+bye9qZtUn8zVN99M3HKTNkLk+p7OoqZ9P1dXiDq/eNrqyscPDgQXp7e5Fl+Y1Qd7vdsDL2eh39+xAaBsWyeu/o+rtX6+mFOvqvjMIO1YJ3RW4mwcr3+8kvpnHurMCzpxZJ/em7HiORMxx97lHGj2xG5K20XiGTXFNN7eOX8Kb9QAaL46t4eQFVF/T6rHzBUsJxG3g1K/9PKMlt6RnidpX+OhcPipvpeGgWs2piuLkJNZ+nbf4CV/qP4XVmGUn4+KqthlRDMeO+yzkz2Y4ymwYEis/EDs3KpimdmCWNyDyKe2mOrEmhKGVirHQ3NqUOFxZkcrTzOP4zB8mkFE5d1sx6yw1U2puJG1FOG+cZcmTRZI2MJ8YGcwnpCyMkwhpWz1UI6rA5VXpuacDc5OJvn+ln/+ASbiGxI61y47oyem6sf2OdeigU4uDBg5w7dw5FUejq6loNdTW/urno3EOrm42QoHb76gi97Wawen5quxf8ciuEe8EvlNAF8YNTxF6aRHaY8H+wGWuT76c+m05PcuHsF7j4fAXJ+XX4KnJwTQ1lB4/StFwPgNX8JE7zt7DmNaZdKl+3FvGYY7WufndY4g/jI+RVhbE6G0/bekgeKWdj/wSX2tvIWK3UzY9xjfUVSn0RRhI+vmyrJNNQxLx3O8enNsJsFgmB8Ki0OexcPaij6gaLlgPUjZ0gajHhyBqkXZtIuzrxGC4kdGrsL1N5cS9iRuHUxmpqXFfT7OokT57XlD4GTRHC6gqyI057ykukbxjDMOGvvJF0qhpFldm4u5qqrUE+/8oI33ttCpOAnozKHWtK2XZzA0WvLw39t6He3d3Nts3duOYOrwb60AtgaKu7RtfftXq2i6fyF9bnBe+OQrgX/MJoy2nCDw2Qm4xj6wjgvaURxfGTB09pWpjRsS9y/sAkS+duRZJMqDu9OEIH2DHeBriwKMdx2L6APR8halN41O7iKw4vKdngiriFvwiP4REG01UWjhXVcGB6N7c/eoyB9lZWiorwR5bZkz/AmsAUQ0kfX7EESTcUsezbybHJLsRcDgmB4VYpKXdwfZ9BMKozbx+jYeJxwrJAEgKrVMVy8VX4jGJAUGQ/R/3SfVhOG5xtKcVXvJ129zZMsoVe0yB9zDFpGsdlylI9q5BeWsHq8lLadCsrcwG0rMGay8pYf20ND/bO8MX9w2TyBhtyCnfVB9l1S+MbxwT8aPlFURQ2dXdzWbUJ19Dj0PcEZKPgLF3dNbr+7tWDuwp19PeNQrgXvOOEIUgenyP6zBioMr5bG7Cv/8lzv3U9y/T0txg4/zDTx+4gvdyEXKOSK3qVOyb8CL0VVRrCafsCTmOUjFnmoN3K5xwlTJsNWtJm/sfyLM35NDNlVvorvOyN38ieb5wjXFrKeH091kyaHYmjbC3qpT/r5cuWEnK1PmKenRyZ3owxpyEh0L0mHHUudg3maZvOkzAlKV7+PunMMhmziktzEA3swS7XAWA1LdLI/fgOT3Oh1A3l3Wz0XonL5GdYmeRSdoh+9SJ+WcE9kcHI5ylrbqO85VomL1mIh7JUt/vZclsDB+cjfOapSyxlNBo1mbvKA9x8ewvBOjfww4nSs2fPro7U2+vZZhnE1f8wxKZXjwFovXl1pUvdzsJtRu9ThXAveEflo1nCjwySHYpgafbhv6MJxf3j58IIYbCw8CTDw59jtred5b5bEapCqvoS90SGEdrNKNIyLvPXcErHyKkyAzYTn7cXcdSuEtBM/FkoxNXpCNN+J1MNJl7MbCfwFASjafpb12AoMusjF7jGe4iLhoOvWAOolX4Sru0cmt6CPpdHkgWGzwxNbraNa2wZ1BDoWJMvYlk+zYrTiiWvkndtw2TtAgSypFHr3k/Fwf0MOFSiFWvo9O6ixFbNohxiMHqKs/JZvLoZUyiL2WajdcdVlLdcQd+rSRbGYhRVONn2gUamTDp/8fB5hmNpgnmJOwI+PnxHK+WNq5uLwuHwGyN1SZLorjCxPf0CrqVTICnQuGu1jt5yPZjt70JvF7yXFMK94B0hhCB1donI3mEwBJ4b6nFsLv2JJY7h8DGGhv8XS5NRFk59gvRKkGxgidvUB7FkPgaoONUHcarPIGTBnM3MtywOHnI7MRsyH4+k+FhskUWnm5kmmXNSAwPn13P5oX761q0l5XBQFxvnevsB+kzwdYePohI/UedlHJjahj6nI8kgAmayLR7Wz2W48oKOKyPIG72UjT3DtNeMhIRsXYdi34UsGQhUSn3D1J15grFcmPmyKjq8O6l1tpOU0oyuHOFM/iQWzYSU0ymuqWPDnhsobdrEqWdnGT27hMNjpueWBpRaO59+6DxH56O4DIkbXW5+945Wql/fwBWJRDh48ODqOnUEXc5Ftsf24iYO5Z2vbzC6HZzFv/iOLnjPKoR7wdtOT2pEHh8ifSGEucaN/85m1CLbjz2TTA4zPPx3LC4eJDxwN0sXd6KrGltc91MnNpMTbTiUJ3GZvo9MlojNztMmmS96/SRluDmu80fhOTTVyXSzxKTTx77Z3dzwnTMMtrexUlREILXMdep++hxJ7vd6qfUEmLdezqtTm9AXVkNdD1rQWnxURxJcfQ4qVnRyzFM//D2mXVkyZhOqUonivAGTopAXVtyuOI2TzzG3eI6pYIAWz1ZavJuQJJmxyDF6EyfQNQ1JUWjZsp0Ne27AW9bAyafHuXh4FtUs07mnhorNJXzmsQs8ObqEImCX1cEf3dZO0/oAkiStHhNw8CBnzp5BEgadXGSHOILbG1gdoXfcCYGmd6mXC97rCuFe8LZK94UIPzaEkc7j2VODc8ePX32XzS0zNvqPzM49RDrUxvhrvw1RG1WOI2y3TJIy7sSuHMSt3o8qhYlbXZySNf7eV8S4WWFDCv77yhxB3cpQg5mVEpmDoZ1sfHCOcFEZ09XV2HNJrhCHGPAv8JDHy3pnMYPqVbw2uRGxnAcF8uV28g1uAuk4O/pk1k7l0UlSN/oIIXWKkMuOIjlRnDdgNznICheqCWrTx4mPPsdEsYca5zraAjtwSW4mk72cCx8hqcWw+3xs2H09HbuuxWxzc/alSc68MImuGbTvKGftniq+9PwQ95+bIScEm01W/vjGVrp6VjdvRaNRDj33OKf7x0AYdHKBHZYBPOuuWT0fvXDpRcHPoRDuBW8LPakReWKEdO8SpjIHvjtbMJf9cEOSrqeYnPw6E5NfJZdRGL3wKfShGmxKiKtdD6MoH8YiRnCr38QsT5Ayu5iUBP/ktXHYbqM8B3+6ssTmtMK5Sjep2ix98RbkF914QzDc3IyMwcb8SYaLR3jR7WWzvZjT8m7OjrdBRAcTaNVO9BoXZekoa8YlegYNVD1PxfRLGKljTATcIKkoth14bGXkZA+abqNEv4QYe4Qpv41iWy1twSsolctYzE3Su/wKK9lZylvb6Lr2Zhq6tyBJMv1H5zn+5CipaI76jcVsvqmOx16b5p+PjREWBmtkE//t6mZ2XVGDJEvEpvo49NxjnJ7JIoCNUj87Gp14u+6Axt2FDUYF/yGFcC94y1Lnl4jsHcFI53FfWYXriqo3NiQJoTM39xijo58jk11gau4jxE9sQcmZ2GB/kgZPK2RMeNVvYlXOkVXtLMluvuPM8qDbic2A341E+GBUcLo4iNYSYUnzM9y7jtajy/S3tZO1WKjNX2AseJFeh5/NjiIO69dwcbwRKa4jWSBX50GU2miPxrBHdLZeknFlwL98Bt/8kwwFneRUUMzt+Jz1CHMxyawXhz6Beeoh5twSTnMRTeU7aJZbiRlhLiwfZCY3zNrLd7PxmhsIVNcihGDifIgjj48QnktSWu9m622NHB8J8fcHh5k28pSh8Afb6rjzhmbkbJTY6Uc5fPQ1TiUCCCQ2OEPs7OnE230H2Lxv0voFBT9dIdwL/tP0eI7I3mHSF0KYKpz4P9iMqfSHo/VQ6CDDw58hkRxgObOZmcO3YlkppkQdYmtwGim9Dp/yHezKK+QlKyG1mhdtc3zJ6yEhS9wRT/A7Kxoj9joia5dAhTNjnazbu8Rg8zpiHg8OMcxkoJclq5/17mJeyuxheKIaKaWDXUKr92B2mdm1nGDFSLBuyEFJDByJaSrHH2I0IBG3CCSlmIC7BbOzklCiHFN+Bsv8Yyzbc6iKlarKHrqUzeSFxqXwMWZMY2y68TbaL78Ki331e14Yj3H0sWFmBiN4SmxsuaWBiUiKv395iD4jhxuZj3dW8zs3N6JO7CN+8mEOD0c4KdowkNlQZmbnNbfiq1377zV5QcHPrRDuBf9hQgjSvUtEnhjByOq4d9fg2lGJpKzWgePxSwwPf4aV8GGSVDB4+gM4RlpQ0NkcOIJX6cKffxan8gOQDEKmNs6q03zBb2HUbKInleGPQ2l00Ur/uhU87ggD881U/CDLTFELC8ESssok0/5ezBY/1Z5Kno/vYnYigJQ1wKWQq3cTlGQ+NJfmnGOJqjE3lWEz5myYmrG9zDujLDl0kKwUOWrxljQwG2lGaHOYl58gZk6CLOOtWst29QpshpWRRC+h4DKbbr2d6nXr31j5szKb5PiTo4yeWcLmMtF9XS0JSfCPLw7ymp7FLEl8qK2MP96ex9H/MLFzz/BqpoFTrENHYX1zNTuvvQ2/3/9udmvBr5hCuBf8h+ixLOHHh8lcWsFc7cJ3RzOmktU11ZnMHKOjn2Nu/jFyOBkY34Pn7HpSuTLqnBdoKi6lKNaLR/0OihQhZGpnwEjz7UCcw3YbNTmNP1pJ0JTsYv+aBBWl4ywmAijPOUika5ioqWbJOsa0p59q1Y/DX8/zK1cRnrQjaQLhU8lXu1ifhk9OZ3kyOIl3zElV2IOiZ6mcepm4eY55exoDHa+tjNLKOibDm8hnZlGiz5FWYuRlA7mmmqvkPQSNYubSoySacnTeeTOekuAbbRFdSvPaU2MMnJjHZFFYf1UVikPhS/tGOJhPo0twU42L/6/hFMWD3yWyPMdhaQtnaEcg09Gxjh07L//pNx8VFLxFhXAv+LkIQ5B8bZ7os+OIvIHnmhqc21bvM83nk0xMfpmJya+jGzrDS5spOdPCfKQHhxqjqzFFcHERr/otzPI4cbmGi/kgrxQN8T2PE5sh+HgkwfUrPTxfrlHSfJG8rhI7WoIxXMpAfR0T7knmHWN0mAJkfGt4aXEHqWkFSReIYjPmUgvXRBQ+MZvjW6UXsEzYqYiVAyoliycwpEnmrRE0I4nD7KGiqo7p6A60zAxSfB85KUJO1YnUutgtX8carZGYFkLrUGi962rM1h8u5UxGspx8Zpy+w7NIikT79nLMThMPHBpjn5EmIcN2X56/8nyfhvmnCeHhsONGelMlIMls3LiR7du34/P99DN1CgreDoVwL3hT2nyS8OPD5CZiWOo9eG9vwhSwIYTB3PxjDI/8H7TcEhPxtZScrWBxYRdpw8PG+iUqE1GKxcPYlJPkCHAqt5Fh/xm+5rcQlmVuj6e4d7mLC2YXmc4zeC0xli6VYjrm50xdC4P+CSLmRXosAaZ9HRya3Yw2YyAJMEotVHpkPrJk4roVnW8Vv4IybqUk2ULe7MYdGcBqjLNsWSCVW8asWCgrr2IxvZt8egojeRCdGGlznsl6iaukq9me2UzWSCN1Oqj9wBZk0w+37qcTOU4/P8n5A9MIXdDcE8RkVXjmxAwvyRmWFUGbJcFfyl9gszjHkmc9h6y7Ob9ooCgKnZ2dPzxPvaDgHVYI94J/l9B0YvumiL8yjWxV8NxQj72z5PUNNicZGPwfJBIXWElXY79UQnZiO3NaOw1FCept85Rn9uFQnsfAyvn8TiadA3y3KMVFi4UNmSx/sNiClFzL+Z7jVHkmiC55MO/zcqSkjQvFk+hqis32Yi66uzgx2YGY11aXd5dZ2GI2+N15C7UZwXedj2MZU/DlOkk5yjFn5nHpU6TVUVZSc8gSBIqDxI1r0FKz6OlXESJJ3KbR35Bhh9jB9cmrkJFRN3gpu20tsvWHd9Vkkhq9L0/Ru28KLatT1xHAZFY4dG6eA6Yck6pBpRzlz5RvcJ31EotNH+Rgeg0Xx+YwmUx0d3dz2WWX4XK53r3OLHjfKYR7wU+VGQoTfkw0dbQAACAASURBVHwYfSWDvbMEzw31KA4T6fQMwyOfYXHxGbI5D9JIKY7RNVxIXke5VdAYnKIieQaP8igSGSb0nVy0ZDjkG+Jpl4OSfJ7fWy5jfehG9rcfobbyLLmsGeUVN6+orZwtn8eGxHp3kGPWTVycaEJezoEC5lIzd+pw76IVXc+y13Q//kEDG1sJ+1uR83Ec0gwW6QIzsSWESOPyeslJu9AzIfKZYyAyrLhynG+IsSnfxZ3xG3DILtQ1bgI3t6C+fjUdrI7Uz740xfn902hZnYpmL7Iqc3pgmcNWjWGTgY8En1If5cMNGZbrbufgrIn+wSHMZjObN29m69atOBw//QKSgoJ3UiHcC36MnsgReWqU9Nkl1IAN722NWBu8q3X1iX9hfPJriJwgP1VG5bCX12Ifwav4aC5ZpEg/R8B4BFVeIGJs4KipkTHnfr7ltaFJEveE7dw6fy8ny85iW3MEu5pGnHHwcryZ06URSgwHdb5yDqk9jI2XI4dzYIJAkYk/SpnZEVOYIcqx3FeouZjFsO5kvrQHQR6reYFS/QQj0QT5fAiT1YakXoaRT6NlX0MSGvO+DBfqonSkm/lw/Hb8phLUCge+mxux1LjfaIN0PMeZFyc5/8oM+ZxOsMaNltMZmY9z1JajTzVwSmk+4TjIxzaXsRC8nMO9w4yNjWG1WtmyZQs9PT3YbLaf0dIFBe+sQrgXAKuXaCSPzxF9YQKh6biuqMJ9RRWoMD//OIPDn8XILqLPlNE0qnF65deQWEuzN4nDcp5g9hmsyjlyRjWnLLuZVZ/hviKYNJnYmZD4zfl7icgrzG58mTL3AtqUhX2TVZwK5KnTivCWVrM/v4WlMTdyXEOyQKvLxKejNso0g1PSKAvR+2k5myLp2clk1dUYsgK2EC35gwxGDTLZKSTFhGrqIG8IdK0XWehMFafor4nTEi3nnsQdVFhrkT0WvNfXYesIvLGkMRXLceaFCS4cnCGvGfjLHaQiWRZTOU45EpxWFCySxr2lY/zmrg7mCPLqkaPMzc3hdDrZunUrXV1dWK3WN2ntgoJ3XiHcC8iMRIg8MUJ+IYWl0Yv35gZMJXai0dNcGvxrkrFzSEt+GoZzjIeuJZa/lka7wOS5SDB9FKf8HAZ2pk230i+d4HF/mFftNmpzBr87fxNlsWqOrv8BLeUjpGMK+0aKOW+z05QvQ5TXcSC9meSYipzKo9jgapOF/xYzo5PhlHEIY+kF1pxNE/FtYajhZoRiJ2ePsV7fz2BUIZUeQkigqg3kJROG1o8kDMbKUoxUJGheLOL29J00OJuQzQquq6pwbatAMq3uok1Gs5x5YZKLB2fI5w3cRVbiKxkSQtDnmOWIujqqv6chw8ev3870zAxHjx4lHA5TVFTEtm3b6OjoQFXf9E75goJfmEK4v4/lIxmiT4+RPr+M4rPgvaEea3sRmhZiaPh/Mz//KErUTumIRHphI/O5D1NvtqK6JvFqp/DKjyCTICpdzSUpxSHvRR70OLEagl8PrWfL3A0caPwuLQ0jZITGvlEvw6KYFqOGUGU9r0Y60Sd0pIyO1Q4f0+3clZWZkZaYyP8A++R5Gs7nCPs7uNB6NygeMvYMHWI/kzGVWOoihmGgKGXkFTPkJjAkwXBlgplAmpZZH7v1u2nzNqEYAsfmUty7a1Ccq2e0RJfSnH1xkktHZjF0gcWukknmySkZhuzj7FdKyWLmjlYbH9+9kZmh8xw/fpxUKkVlZSXbtm2jpaUFWf7pd78WFLybCuH+PiQ0nfjBGeIHpgBwXVGFa2cFQhHMzDzA8OjnUBNx7GMufNOljGU+SZXZg9kSxyT3UiwewiyPkTHamZCrOec4whf9TlYUmRuiAW6Z/S2Ouh+hun0c7AlenrYxkaqmRW5krKKJ04ttMJlF0gy8DviDrJ2t+TyD6hjZ1CP4+qepGBUs+Ro40/FRVClAxqLRoh5hKS6Ipc6T1zRkyYOmmlG0JTTFYKAqQcSZo3W6mC7lDjqLmzBldSzNPrw31GEKrk5sLk8nOP38BMMnFwCQZDB0UEzz9NnmeEFpICdUbl5Xwq/3VLIwfI5Tp06haRpNTU1s27aNmpqanzibvqDgveQthbskSfcBNwKLQoi1r3/2WeAmIAeMAPcKISKv/96fAr8J6MCnhBDPv9kLFsL97SOEIHMxROTpUfRwFtu6AJ7r61B9ViKRk1wa+DTZaB/OSRflY1ZG039IUK3EKuto1gFK9aewK4fIiwARupg0H+NzAStnrRbWphU+MvtR+vPHKW0ex1Qe4qUlK/Mr9dRaW7lU0sqluVqUqRSSLqiyy/xxykqxFGHY2odr5WlKzobxLcCir5pjXffizJegqQaV1lNkUmlWUhfR0mkkrGiqippPkDHpDFTHyZoM2qdrabDcSE95PdaEhhq0r/400uxDCMHccJRTz44z2beCJIEQIKETsL7GSZvGXjaioXDLhnJuabIyN3CWwcFBANatW8e2bdsIBoNv0soFBe8NbzXcdwIJ4P4fCfc9wD4hRF6SpL8DEEL8iSRJbcCDwGagHHgJaBZC6D/r3yiE+9tDW0wReWKE7HBkNfRubsDa4CWbXWR4+O+Yn38cz4KZ4KCdufjH8Spt2GWJpHmOoL4Pt/ooAGmjm6g6yLd8Ot9zO/Ho8JHFy4ksxwmWD2FqXeblqIXQUhPFzo2cL2pjejqAOpMEQ7DWqvKppERWnWfOfpLqmaMUn0xgT0hMF9dwePO9+NPFSAj89j7Qwqyk+tFiMUAhryioeo6kNc9AVRxVl2idW0/QdhVb66pxrmSQnSbcu2twdJeCBOMXQpx4cpTlqcQb7eFSFimzHeCAu4KHMxvRhcQtG8q5ojjD1KXTLC0tYbfb6erqoru7u7DxqOCXzs8K9zedHRJCHJQkqfbffPbCj/zyGHDH61/fAnxPCJEFxiRJGmY16I/+J9674OdkZPLEXpokcWQWyazgvbkBR08ZQsozOXkfI2P/iC2SoOJSEYmlD5OUN1JukkmQxqSeoZ77MJnmyRqt5InwsqeXv/d5iSgy10cqcMwGkcxnKN0e4uWMhcRQJ3bfJkaq2zg1aUUZSmMiwXZV5SOpCDMsMek+RuPIBWpOZTFpEsOVa9h/za9RHvdTnDSwOcaxiWmW48NokQhgoMsKiqGTtGYYrkjgypjpnLkSl20L29aX41lOQSyH64pKXFdUIUwyl47NceLJMZKRLAASBrXmE5Q4j7O36Gr+JnQTRkbihvYAmxwrTPc/z+lLGcrKyrj11ltpb2/HZDK9ux1YUPAOeDum/n8D+P7rX1ewGvb/avr1zwreAcIQpE4tEH1+HCOp4dhUinvP6mRiNHqW/v4/JxvpwzMUgPF7MKStlJpkErqBJk9QrTyAXTlCXhSRMyoYtwzzP4v8nLEV0Zo2sW28ibLIPEVbjvO8bCY+1YVadBnnqlrJjIPSl8EsJ7lWkrhxeYzeQJSQ/zhrLoziOW9gSBL9tZ28uO0eqlecNIQMZNsCLscQ4eQosXAYgzyGBLKAsDPFZDBFMOFh8/QtWO3ruawrSNFyCrGYxL6xBPeeWnSzzKt7R7l0ZA4tu/pDoU2Js9b6BPbAPN9y3cMPZjcgQnBNs5tWplgeOsGoLNPW1kZPTw+VlZWFenrBr7S3FO6SJP05kAce+E/82Y8DHweorq5+K6/xvpSdjBF5YgRtOoG5xo333rWYK5zk83EGBv6W6elv45uyo/Tdi8XYgUdVSekGMZGg3PwcbvVBkHTyRjFpZZnP+/w85C7FpcPls5XUjGao7+zl2XaFxeVuJP/lnC9rRhvPo4QyWBWd23Nprpo5zaFGQSZ4kl2nZ3GOSGRMCuead/DM9g9QGbayfkpHWKI4nX0kIgOsxOJo0upNRBKw4Muw6MtQEy9ly+xNqLY2enpKKI1kMKbjmJu8eK6rI5zTeeJrF5gbiYIAEFSYL9Bt/z4rtS18SXyA5yYlzEmZXbVmqlJDaOPzpB0Odu7cSXd3N263+2c3bEHBr4j/dLhLkvQxVidad4kfFu5ngKofeazy9c9+ghDiK8BXYLXm/p99j/cbPZ4j+tw4qVMLyC4zvjubsW8sAWBx8XkGBv8K08ICjtMfRMnupkQ1k8JgSdMpt1zAZ/pnzNIMunAjE+MpV55/9JezosisjXhY029hY804z1wpcyCyCZ1d9BU3wFgGOZLEqea5OzLHFYMvcXBrAFHTywdeDWOdl4jaLby2dhdPb7+J4rjKlhEN3ZTC5riIvnyOeDJNVkkjpNVQnw6kiDk1mtON1CxuRbE00L21hKqUhj4eRSm14/31Ni5NxDn3T2dJRXMAWJUk7dan2eh9kVP19/L/Rv+Gw+NpnBaFa6sMisPnkWdTFJeX03PlbbS3txfWpxe87/xcSyFfr7k/9SMTqtcC/wBcLoRY+pHn2oHv8sMJ1ZeBpsKE6lsndIPEkTliL00g8gbO7RW4r6pCtqhkMrMMDP4lK7P7UE9ej3XlRsrMNnLCIKQJSswRAqb7cCivYGBGJkef6uFvA056bQrlGYW1g272mJd5oc3gYmoLGe81DOerkMeSyIk8LlOWj0xc4spzezm9u5qW3Cj+oynMMYkFn5Oxmut5duvVODSJjSM5JEnHZu3HPPMaiXyOlJoABAKYCqbImA3WaR1IqW5kUxUbuoPUGwb54Qiy24zUFeTUQJjJ/gjCEIAgaBllq/0+ysryvFD1+3xpupbemTg+m8JmT4KicB9WBdrb29m8eXOh9FLwK++trpZ5ELgCCAALwKeBPwUsQOj1x44JIX7n9ef/nNU6fB74QyHEs2/2goVw/9kyQ2EiT46QX0yvrue+qR5TsR0hdKam72dk5HOI3g1YJ+6iwrR6KuGilsdtkihRn8Vt+gYyGghBUrLyD94qHvNmsBjQOuXioysxjnVkeJkdpNw3MpkuRR1PIKV0/OYkdw+d4NoLzzC0q4SSXAjvEQ01IzFa5mem4nZe3LwFIUtsHchg0QQmyyjeiUOsqDpJJcq/hvpEaRIhSaxXetASG5DVIGs3BWmxyuTOLSGpCvEKJyfGosQjGgCqrNFieZktrgdQ1lzBXt/H+JdLZoYXEwQdMutMC5SmJ/A47XR3d9Pd3V04mbHgfaOwiemXVH4lQ+TpUTIXQyh+K94b67G2+pEkiVRqjIt9f8Jybx5b30epVvyYJAjlUyiqnRJ5HLfl/2BjAoEEQuYpaxf/VDzHgkmiNmThkxMZFptjfNOxjajzg8wnSjCNxSArKLWucNPIMW66sI/lnRZs6RyuYwZSTmKwupS5yrs5uH4tUYfM9r4s3pSBbJqhdGwfs7Y8aTmMBBiSYLIkhdlQWe/cSSK6Fkny0rqllHa/mdyJeQzNYNlu4uRcipyx+r27zStssn6bZl8vqQ0f43vKTXzjdJSZSJoKB7QYE1Qai1RVVtDT00NbW1uh9FLwvlMI918yIm8QPzRD7OVJJInVc1K2VyKZ5NXR+tQ3OXfkB6in76FOVOBQJOJ6hKThotSUx2b6Jn7lydf/MolRaTufDYR51RXDk5H5rXGdCv8Knw1exqLtbpYSQUxjccgJqm1z7Jk4xp6hE+Q3aahRgeMYCEPiUm0li6Uf4mjHGqYCsL1PozysI8khKsf2MeZNkxPLyGI11Gf9aZzYaQ/uIrLUjKHbaN4cZH2Ni9yrs4ikxrwhuJDIkzQABFW2PjbbvkVpucRcxyf5ZrST756aI57JU2PXaNTGqFbjrFu39o3SS0HB+1Uh3H+JZMejhB8fJr+QwtZehOemBlSvBYBkcpSTh/+O0KGN1KVbKTbJZEWUkKYQNDuxyKfxWz6DWaQAiOudfMNdw3cCJ8khce2c4B59iU/XbGbI/lHCsVJM46uhXmef5JqpY3QvD2JfE8I8K2F7TcJA4mJ9LaGSD3OyrY5LVRKbB3RaZjWQklRMHGAosIKhLaEaq6G+5M7iUz201OwhNFdHLqPSsDFAR4sP7fAMSkJjJW9wMW2wogtUOU+77Xk67Htxt6ynr+kTfG28mCd659ANQZM1QZMxSZ1boru7m66urkLppaCAQrj/UjBSGtFnx0m+No/iteC9pQFb6+qlykLoXOq9n96nVqgMdVFrlhHkiOrL2NQK7EQxWf+GYi4BkDWqOaZ8kH8qfYYha5LmuOAvVpb5auU6XvL8BvFI5Wqoa4Im1yjXTRymIbeIv34W65jAflwmL0tcrG8iEriH0y2VnGmS6BiBztEMkshTOnuMgcAUSmYekw4GgrAzR5GliObm61icqiKThOp2Pw1VLpTeRRzpPAld0J8zmMkaOM1xNlgeotV1BNPGW3kl+Gt87ZzG4eFlLDI0qUu0MEtrdQk9PT20trYWSi8FBT+iEO7vYUIIUmeXiD41ipHWVlfBXF2DbF691zM0P8q+B5/GMd7GGosJkyTQpX6SehMeRSVveogq5QEUDPJYmcn/Hl8uGucp30kcuuBPlqJMusr5QtknSa7UYhpPQF7Q6h3kxtlXCGoJfA0zOAd0HEcUDOB8QyPRwEc53xDkeJtE3azKjr4Uqi7wL59j2DeIKTWFOS8hEMRteUocQerXXsfCRDnJiE5JnYsSrxXnWIQgkBWCwZzBWNqgxDbOBstD1Aem0Lp/iyfM1/HV4wsMLiRwqQbNzNBqXqFz7Rp6enqoqCjsgyso+GkK4f4epceyhB8bJtO/grnKhfe2RszlTgByaY2Dj73A8nETHRYLbkVGYoiY7sapBBGmQbzKX+MmggGEjRt40dLNl0q/y4qS4QPRDF15M39W8/uEV9pQJpNIeUF70SWujxyiLBnF3rSM50IO1yEFDDjfUEu4+Nfpry3nyDoDT9TGNWfjODIy9tgoE+5ezPERLHkZgSBlyVPqqaR+4/XMjgaJL+dwFVmxmyRKozmqzRKGJDGc0RnJ6NTYX2OD9TFK65xENv4eD8TW8c0jkywlcgTULGukGda5s2zZvImuri6cTue720EFBe9xhXB/jxFCkDq9SOTJUdAN3NfU4rysHEmWEIbg4qsjnHx8kCbhoMYiA8sYzGCIdahyDt36WarFcQDipnLGU/+dfy7dy6vuPpqyeT4Ry/A3Vf+FsWgXymQKKS9YV3KRa/TD1C0sILWlcJ/N4t6voOThYl0VoeBHGaqu5kiHQU44ufFUhGBYRsmGmLGfwBK9iE1bDfWMWac0UE3zppuZGvQTmc9gtqlIOZ0Gk0S9VUECxrM6o1qWRstzdNifxd2xlcnWT3DfqIvvnZgkkzeoUGK0yXP01HjYsmW19KIoyrvaPwUFvyze0sFhBW+vHxut17rx3dGMKbB6D+fsUIQDD57BtQw7bTbMksAsHyWpr8ckrcfw7qM4+3ksQiOnKszmP8V+2c6XG/6enJTldyIpjnh/nd/07US5kEbVkrSX9HOb+1nK+8KIzVls0RzeLyjYUgp9NWUsl36U0ap6jnTkmXc4uf5MmObpGELPsWB9DVPqGN5lBYFEVtUpL6unbeftjJ5zcP5gClnJIgN1sqDRraIImM4ZTOpRms0P86HiE1g23cWZir185XSC5x+YRxLL1MohOmxL7OxooKfnbsrLy9/djiko+BVTGLn/AqXOLhL+wchPjNYT4SyHHx5g8ewyG5wCv2zGxCAaEtCE4VjEzF9Tqo8jgHl3PVORP+GLwYc57RxkQyZLmXw5Dyp3I4/nkHIGjUWj3FX9A4Ln4qhrE0jTGp7HVXwrMF7qZ6bqo4xVruFIh8ZQoIirLkboGtBRDFgxnUfEX8aZWd1+pCmCyqomOq7+IAOvmVmaXD1WVwLaK+xUZfKYdcGCZjBvzNBsfoCG0mnElt/lJdu1fPnVaU5PRbFIOk3yIps8Sa7cspHOzs5C6aWg4C0olGXeZUYmT2TvCKkzi5hr3Pg+uDpaN3SD86/McHLvCPWyQaNFQSKJVT5HSu9ByHksJd+nJPoIMoKURWFC+Tj7hYNvlDyOTI6rMkEetvxXtEkZKaNT6ZnlnqaHqJ1YRnZpZEQW+yMqVVOw6LExUXcXI9U9HO3QOFceYNNYjMvPZrFpKjFljFzySZzpPAJBXhZU1q1h7ZV3039MEJpJAmCyKHQ0uAkspbBqBuG8QVj002j5BmU1NtI9v88jyQ189dAoU5EsTilLm7LAlbVWdmzZVCi9FBS8TQrh/i7KTcUJPdiPHs7g3lWN68pqJEViYTzGK98dQMwm2OjSsQsLFvk1ckY1giA5/3lKs5/BrkcxgMmyciYXf58vBp6nzz7KuozCoPkPWZwuQ07mKXJE+OiaB2jNjaONW0k0xpAfNrH2AsRtKqN11zNUu4cjHYKz1X7ql1JcdyKOL2khJS+TSj+KM7Ua3nnZoLK+jfquuxk6mSO+kgHA6bfQ1ujFNRbBqRkkdIOEOEWj5ct413aysP6TfHOiiG8fHSeRMwhICdaZF7lxQxWXbemhrKzs3euIgoJfQYVwfxcIQxB/ZZrYixMobjP+u1uw1HrQsjrHfjBC34Fp1jmhWjGBtIBFmiFrdJKUIwRK76NoZR8AMbuZMfe17I/VcH/x01iEgUtcx6XQVSgrGjZLlo80f49NRWfIH/EQaQ+z8oqFbYcEiiExUruNgYYPcHi9hTO1bnzJLDcdX6Yy5CRLkrj2GI7EIhISedmgvLqdspY7GO3NvHFWelGFg8Y6N6a+EEWGIGMY5MSr1Dm+jq3rJvoafouv9OZ48twcuhDUyGG63XFu3tpOd3c3Dofj3eyKgoJfWYVw/wXTkxor3x8gOxjGti6A77ZGZLuJ2eEIL3/rEtZwhk6PgVlXsSin0PRmDOFksfgI69Kfx5RPISSJ4Ro/S3O/zed9x7loH6EsX8VA8rdh1oyi6lxXuZ+bG59GGjUTiQnOJ+GqZw1KIzATbKSv5aO83F3C6XoHiqFz08lpGia9yEInajyFLTaKBOiSIFDega/iJuaGM/zrf4lApYPKMgfWgTBBGTShIzhMte+7yJt/jVf8d/ClowucmIiiotOkLHNlpcR127tpa2srlF4KCt5hhdUyv0C56Tih71xCj+fw3tqIo6cUXTM48vAQffun2OBWKHeqCHkWi7FCTt9ETJrGV/oPdEVOAZC02Bkta+bo4g6+WvbIav07+yEGxjuQBKwv7uM32u7HJnJoz9t5OZiha7/MPSOCqNPLsc6P8OT2Dk41W8lLMrsvDtI0EMCT9xI3DmKKncaOQJcEnsAGbO49xFd0Mq8HuydoJ+g1456OUx7PYkgGsI/q0pfQtv4mD+l7+fKhcSYig9jJ0W1a5Ja1Aa7ctqdw1ktBwXtEIdzfRskT84T3DqM4zZT8znrMVS6WpuK88LWLmENpdvsUVF2gmE9h5NaQFcVMuF6gR3wFNZxFSBITQR8r0ev5fGqJk6V7MesNLM/ejZRwUeSe51PN36bKP4Xe6+DskkJmJcfHnpEQkkJf0w08sus6jrXZyKgql49epPpCgOp0CRn9POnEK5iFhiEJbJ4NKOZd5DXIrh5Fg9VloshtpjiUoSqTQ6gGZvkFiusvsdL1W3xu8W7uf3aCWHYQv5RklyPMXVsb2bJ5V+GGo4KC95hCuL8NhG4Q2TtC8sQ8liYv/rvXINtVzu2f5vijw6x1KFQ5VfLqNGYRQ8t1kRLjaMXfYXviGEKAJnsYK/dwenkX/3/wKClZIx2/nfh0N7I1ywdrf8Cepv3oKRPJR+w87dK586CgPAyzpRt5aPdHeKkrQNJiZtvMGYp7fbTGyxH5cdLJF5CNJAIwudqRld3IqoLTZyW6mEbXBQGfhfKMRk08i2QW2NTn8LWFGFt7L//70h3sfWSevDFGpRzhhmCWD16+gXXr1hUuly4oeI8qhPtbZKQ0Qg9cIjsSxXVFJe49tWTTefb9y3kiF0Jc6VWx6ALDcQJzcg0aQWbUF1hnvQ/b/23vzuOjqu/9j7++58w+k2Qm+0oWSAgQCIRNFhFkC4vgWitWsVq91qUu99rlWrWtvVZbf1q3ar1W61b3pVhp1eKCRUFZwh4gCRCyb5N19pnv74/Ex4NLQRE0k4Tv8/GYx5w553vgzTeHz5z5nm/OdPfOTmm2ZNItMnnYG8+H6WuIhLPx7D8fGUogO2kjPxqxGmdMG6EyB+uqAqQ3hbnxfUmP1cXLCy/j+YXj6bSaOK2pDNd2AwWtmTiCTfi8r6OF2hCAZsvHZFyE1WEhMdNBw4EOOpq9xNgMZMsIuZEwwgQ24z+JK5FsyF3BIxs6+OSFNnQiDNdbOKvQxlmzZ5Cdna2+4UhRBjhV3E9CsNlD69O7CLl9uL5TgL0khcYDnbzz2DYy/GFOjzEQ1tvQDfuJ9EwlJA/S7HicqeGPkEFBBBv1sXEc9I7m56lNtBh24WsvJVh/OgZnDVekvM6krF2E/Sa6X7HxmQxy7qc6loDks7Gl3HfReTS67Exw7yJ9k5v4xnyG+dwEfK8RCNYgkEhzFmbLclwpTtLynRza1Urt3nYsBsFIs0aeUaIhsFv+hfU0B2/HreDRdTVUflaNhSATTc18pySdeacvIyEhIdpdrijKcVLF/QT5KttpfW43QoOkK8dizomjfH09nz+/h0l2nVizhs+5FVtnCpHIBHrkh8Q7nqMk3ABATyQVv8XKy4ziz5k7CUsX3gNXIyOpjMh4g+uytxHjaCdQ7qRig4fcao3v1YeoT8zm9ot/wKbCXAo7q5jxWTmddRMY5zES9q4mEKxAAmFTPDbLOWQWZpM9NoG9nzVQ/kk9RgGFFo0RZtCEwGbbgj4jhZe083h87X5avZU4hYc5djcrZhQwfep8NZVRUQYhNRXyBHi2NtP28h4MCVYSLxuDFmfik9cqafu4hmKHAWEIEXSsw+yegYabJv1txhrfRJMRQNIqk+nWY/mv1FT2WA4S6J6Av3Y5RudeVqR8wMyM/YSDBuRbVqqbw0zfGSasG3l+0Xd5buF8hnkbObPiYzYfmsKs7hB2z0bCXEJuqwAAHyRJREFUgZ2AJGi0YrWcTcGkcYyemcbudfVUbmnGgCTPrFFgAV0YsMbsJjQzmz91ZvDchmo8IUma1sk0Vw8r5oynuLhYjacrygCnpkJ+g7o/raN9VSWm7FgSV44hEJa890AZiTVdlNgNBOOb0L21mN1nIPgMn+UtxrMFKQWhsJWAwc6n1kx+kdqDnwZ8dRcgfYUkZzzPTWl1JDub8R5y0f2Wn5Q6A7PavGzLH8+vLr8SzaJz7e7nWFc9lp6eySzq2kzYX0aYCCFdx2Sdz8QZcxi/YBiVG5t4+9HtaOEII8wahRaJLgxY4g7QOX0Ev2+YxJv/qCcsD5CtuZk3THL+mVMoKChA07Rod7OiKCdJFffjJKWk85/VdK2pxjIqnoQVhXR1BHj/gTIK/UHsJg3PsDLstWnI8Fgi8lUSrG9hk20gwOOLAUuY2xLH8F5MFSF/Fr5DF2KKOcTpeQ9yXkobBj2I//0kvBsjjKmWBIxh7lp5Df8qmcLlB1/nQGUcm32zmddeBr5VhAkSEQLsJUyefSETS/Oo2+vmzXs3E/KFyDNBoSOCQZgwOxuomzKCBw7k88HfW9GIMEJv4awCG2fPnaPmpyvKEKOK+3GQEUn7qkp61tdjm5iC69x8mg51su0P25ggJJpdx5P4Afbq6Wi04xNPkG3+B0JGAEFP0EmrI8xlGeNo1qvwt80k1DoHW+rLXJNSS2FCE97OGIKvxJJQoZHb0cGnRSXct+IK5nRv4YqP/8Jq7xwWt+1itPcZkD7AiN+WytQ5P2TqWUW01nXzxr2b8HT4yTFJRseFMQgbJqebfRNSuXefZMu7dZgIMc7QxDlF8Syas5iUlJRod6+iKN+CryzuQogngaVAk5SyqG9dPPASkAMcAL4jpXSL3vlxDwCLAQ9wmZRy87cTvX/IiMT92j48mxpxnJFJXGkO1WXNNP2lnFG6QGbo+IPrsB86A6PYiNT/Qa5hfe8wTNCANNpYHZ/BrxNDhCOt+Gq+h46FEcMf4JqkbmJs3XSVpyHe1MmrbcNrMvM/37+WnuwEbt7xFI90LWJWu+S8rhdBdoOw4zObGTv5cuZeOo9ut59Vv99Me6OXbFOYMXEhDMKB0dXDttHx3LU7yP4PqrELP1ONjZxfksHcM84jPj4+2l2rKMq36CsvqAohZgHdwDOHFfffAm1SyruFED8FXFLKnwghFgPX01vcpwIPSCmnflWIgXpBVUYk7lf34tncROy8YcTOy6ZiTTWhfxzArgvEREmofA+GnkLM2qvYDGuxa1UAeP0mNFOEazPPZIOpnLA/HW/NRZhd61iaUsaCpA4iYQP+v6cQ+7kkta2RfxVP4uWl53NFw8u81jyJSLeJae5PEJEO0OII4SVx5CwuuO4qwmHJ+0/voqGqk2xTiCJrCIOIxeAMsKUwjTt31lLXFcAlPBSbmrhgah4zZ0wnLi4uyr2qKMo35aQuqEop1wohco5YvRyY3bf8NPAh8JO+9c/I3neM9UIIpxAiTUpZf2LRo0dGJO5X9uLZclhhf3Uvhs8b0A0azOpErvNiCOVi0R/BaViPTjtSgi9io86u871hE+mW5QTapxBunUVM+gtck9RKvquN7tYEIi86ya5oRgL3X3Qlw+PdzNvxLg/657K45VOswXrQ4hBaMjLJwsqb7sMeF8e6l3dTWdZKhjHC4jg/RuFEd4bZNDyeX5XX0rR+P4mim4XWZi6YUci0aYvVdEZFOcWc6Jh7ymEFuwH4YuA2Azh0WLuavnWDqrj/n8I+P5uY2ZlUPLYVy4FOuk065qm1iLUuNBnArD+Iy/ApgiDhCEjNzpuJI7jLBTJyCF/9BWhhG3l5j3B1oo84Ww/uncPQV8dSuH8vO/IKeH/BYha1/pP7Dy5gcksP53lfB2FFMxbg0w5RetkPKZg4mY1v7WXXuu0k6ZLSWC9mzYUWa2ZjjpNf7qujddMBUkQni6xNnDdjDNOnn4XNZot2dyqKEgUnfUFVSimFEF97srwQ4irgKoBhw4adbIxvjJQS9+v7egv7gmzsk1M58LuNWNr9NDtMOAv3YvzXMIxiHxbDa8TqnyAE9IQ1DLqJ67MX8Im2FRmMwVt9NYbY7SzM+BdL4z1IqdHw3hiy17QQ113JK/PPYVRqPb66Zl5zj2JZ5996bxVgHksk1EzyxCTOvvxOtr1bzbM/W4tTCObGdGPTEsBmZmN2LL+oqqd9WzXpWgdLrU0snz6G6dOXqTN1RTnFnWhxb/xiuEUIkQY09a2vBbIOa5fZt+7fSCkfBx6H3jH3E8zxjZJS0rF6P56NjcScmYUl30XtvRsRvhDViTbSk3Zj2JiDRfsUq/Y2dkMZAJ0YabUmsCJ3Dt2BdUQ8uXjrLsCWsoor0/cx1tlFV3sCre8UMP2jTRxKTuOz+QvIDu3lmbpiprdtxBTxoplGIkQsIXsFK/7zdpqrdF64bR2WsM5sezcOPRFpNlGWE8Nt+xtw7+4iS3Mzy9rEktPGMGPGMvWdpIqiACde3FcBK4G7+57/etj664QQL9J7QbVjMI23d31YQ/fHtdinpWFIstL46Fa8oQg1qXZGGMvRy3Ow6n/Doa3BrO9DSvALE+8kTuKOhFj0wDoCbdMItk0nY9hTXJfSTJLDQ0NVPvY3DEyv3MTaiWcwLLuDjR6drBYrswMfI/RUNPscQoHNlJwznqzMK1jzx53IHiNTbV5chngiejy7hsdwW3UjjXu6GKa3c4alnoVTxzBz5nJiYmKi3X2KogwgxzNb5gV6L54mAo3AHcCbwMvAMOAgvVMh2/qmQj4MlNI7FfL7UsqvnAYzEGbLdG+op/2NCqzjk9CdZro/rKElJKlLtlEkq9DcadiNT2FnAya9hiAQxMqtuefzjr4bLdSKr/4cZDCeybnPcmlSFwY9QmXZZCb+ZTcRTWPH9Dl0W91sa3MyuqschBmj9QxkxIchcR/zL7iR7X8/REeTkWKblxRDDFKT7MuO4ZfNrVT3BMgydFKs1zC/pIDZs2er2S+KcgpTX7P3Fbw7W2l9bhfmfBfoAv/uNg4GIjQnWSmJHETrSiLG/CB2uQVda8OLwG1MZmXBpdR7V6GFNXoOrkQztbIs71UWJXbj8zmo/ngCc/66nsqsAgxj7bzszyG/bS+miB+DuRjNPJag9yMmLJmOvz6N6r2C0dYAOSYzUmhUpFj5TU8n+7r9ZBh7GCeqOWN0JnPnziUpKSlq/aUoysCg7i3zJQI1XbS9WI4x1U64w0+oycPOYISuOBNTgzUITwJ2y7045EZ0zUu3EGyKncg1OXMRnS+hh1x07b8SW9xWrsx9l7HxXbS2pCNeieeMHRsoL55GbUaE6hadIv82pDEFs30BMtyEIfYjik9bRvnHGjkGSWmcRBd2qmN07tcDfN7YTLrRy0LjQabmupg37zsD6uKzoigD1yld3ENuHy1P70RYdEIdfmRYslkKeow6MyPNaP4YHJa7iJFb0EQID4I/Zl7Moy4H1s4XkL5hdB68nKTk97glbx0JDg8H948l/4lGDMEmamaU8JFuJ6P2ICnCgG6bh2YcTtC7hvzJBbTtL6WtzMw8RxCzZqZRlzzlgr+1uEk0Bplr3M+EFBPz5y+loKBAfUGGoijH7ZQt7hFfiJY/70T6wkgp0WJMbPRF6PAFmR3Tg+Y3YbffSUx4O0JE6BQmrh11KxvYhrVrLeHOIjy1F5Cb9Ro/Hr4JTYO9G6cx/dlt1KcMp36kjaoOGBbcT8iSjcVcSiTchKa/RVbWdDr3ZDPZFiLWbKAjInjeBU+6u4jpkEw3HGB8rI95c89k/Pjx6i6NiqJ8badkcZcRSetfygk1eUCCMdPBpqCkrbqDuS4velDgsN9ObHgPAAdMiVxadActPa9hDlQRaJ2Jv3kBJbnP88O8rfgDdhr+Ucj097ZSN7qYj50W4lsaiNVsyNiF2LVCQt61uJIiBD1nk+kzkenQCUh4wyJ5yO9B65RMNNUxxtDEGTNOY+bMmZjN5ij3lKIog9UpWdw7/rEf/143AJbRCewxG6j/sIa58X70kIbN/jNiw1VI4MPYsVxTeBN62yMYwy14688i1DGFJXnPcu7wMtrdKegvOCk8UE3VlFFsDYRJ6GzA58gl1rAIIh5CvleJsY8hOTSO0bESDY3PRITfmgI0+0OMsbQzOrKfSUUjmTfvfFwuV3Q7SFGUQe+UK+49m5voXtv7e1X2aWk0JdvY/Uw5ZzqDGMNgtf8nrnANEeDR9GXcM+xc4pp/B5Eeeg5dRKSnkMtGPMXM3B3U144g808eAkJjQ3EOoe4uHJoVX8JsnJESQv4dGA3lJNuXM95uwa7rVIdCPGgPsd7vY5juZ6nYx5hUJ6Wll5CdnR3dzlEUZcg4pYq7f3877ld6h1piS3Pw5cax7t7NzIqNYJYhrI4biA81E0JwXcHNvO3Kx9n0a4iE8FavRHpzuGHU/1KUsZeDu4spevIgtcNGssMRwdbdid+eidU0n9iwjUDPamLNiYyNuYB0k4GeSJBHtQDPG3y4gNnGSkbb/Myfv4Di4mI1rq4oyjfqlCnuwSYPzU/sAAnOs0egjU7g3bs+Y6o1gl14sNuvwxnqwKOZOHfcb9llDONsuhspJYGDPyDiy+LmcX8kP+EA1etKKHyrmt2F+TSGurEGTbSljiXdP59wuImQ51XynUsocsSjIVgTCnKv0YdfSCZZmxklDzFt2kTmzJmD1WqNdtcoijIEnRLFPdjqpfGhLRCWxJ2Vh21KKm/dv5kxoRAugxu77UfEhrtpNsaxqORhWoP7iGv5M1IK5P4fEvKnc8P4x8mJqaf5rbGkb+1kfeEwZKCbsCWJrviJpHtHE/JvJ0bWMyltBfEGE/UhP3ebJZtEgBFWP8WhckZlJLN48RWkp6dHu1sURRnChnxxDzb20PSHrRCMYJ+ZTsyMDDa+VUFKbTdp5gZiLDdhjXiosGaxtOQhIp61ONpfRUQ0DFXX0R5M4ZriJ8m2NOP/SzaRTgtlmWEMAR/NyRkkRhaS4rET9rzHmNjRFDgmECbE85EAfzT4cegwW6+k0ORlwdJSNbVRUZR+MaSLu7+6k5Y/7UD6w5iHx+FckkdteQue9w8x0lJFnPlnGKWfT+OKuWjcb7F2voGlczWmsAlj5bU0hJO4auwzZOtuDE/Gc8CWQsjWgIaNA3lZjHQvhUgXMcGPmJoyF4fBzK6gl1+bIxyKhBlrbacoUsW0SeOZO3euure6oij9ZsgWd98+Ny3P7IQwaLEmEi4ehb/Tx97HN1FkLcdlupMIQV5LWcANBT8l1v0Upp6PiPXHYq2+lIpwCpeMeonsUCfm5+xscyVjDtfTE5NAR8JoRrknIwOVFJqDjHSVEsDH7yM+XjUGSTNHWBQuZ3SClWXLVqpbBiiK0u+GZHH37uq9EZgw6shwhITvjUKYNTb8ahXjLHtxme7Dj+TxrIv5Te6VxLU8gMm7iVRPCjH1S9kcymRx7ruM6O5AvGNnh8uEOdzKgcxYEiNzyOvIwh4sY7KrgDhjHLtDXdxm0miWYSZbmhjDIc6Yczqnn346BsOQ7GJFUQa4IVd5vDtbaP1LOXqchbDbR9yiHMxZMWz61W8p0twkmB6lS9P5Tc61PJl5HnFNv8Hk301uVw7xrdP5MJDPlNSNTG6twbPFSaetA6MMsq3QTnHrhViCJkZo+xiVOJEwPh7BywsGSarJz+JIOePS41m27D9ISUn56rCKoijfkiFV3D3bW2h7oRxjqo1Qiw9Tbhz2GWlU3H0J2f4EEkx/xm0wc2P+T3gn6Uycjb/EGKiksL2AtK7RrPaNJt9ZSWnDTpprEtC1OnwWG/uGxzK19kJssoeJNg+J5jEcCLfwE5OV+kiIEmMD4w2NLJw/l8mTJ6sLpoqiRN2QKe6ebc29t+7NjEEIQEDMkhSafjMFp3cCiaY/02x1sGLkr9geW0J8/c/RQ9UUtY8iz5fJG75xJFpbOa9hAzVtcdiDdRxKMhFwjWFa7emkai1McCSgC8lLdPCQbiLJ4GeJ3MOU/DTOOusanE5ntLtBURQFGCLF3VPWRNtLezDlxGLJd9H57kHMZ9rwPTEJc3ASLtOrHHLGsSz/XuotuaTX/TfBcA1FHYUUh5y86S9CQ3J+y3oa2w3Y/fVszRFkhUspbB7BGFMbubY0uiLN/NzsYFNAMNrUwlRjLUtKFzBx4kR1O15FUQaUQV/cezY34n5lL+bcOGIX5dD82DZID+JYt5BIZDh24/uUJ7tYmvcA3cY0Cmtuo5VaijpGMl2aeU8Op8mbyHm+dfhbOzGHfXxSaGRq+8WkBZ1MsvuIMSSzlVpuMcQiwn7mGiuYmetk+fKriY+Pj3YXKIqi/JtBXdw921t6C/twJ/EXj6Llie1ILUBK60qEdGDUy1mfFs8FOQ8TMiQw7eAdVOi1FHUUcKYOZYZ4ttUXMS28k8SGvfiFkc9HuZjT9B2yNQPjHTphqfG41c2zvhgytS5ON1axbMFspk6dqsbWFUUZsAZ1cTfnxGKfmoZzSS5d/6ohWNtNgvF3aMKPQfhZlZbM1bkPgx7Dkoo72GCpY2znCEpNPupiLby9bSG51DOh+mPcJhcVOWnMr59PkVky3GqhLVzHz2Li2O3RmWQ4xPwsjXPP/YH6/lJFUQa8QV3c9RgTrrNH4DnUQMc7lVi1z9CNGzBEwjyVnM6teQ9hEBYu2/Uz3ohtY1znCJYYu4hk9PDkpz/GJboorVpFgzkVd1oRpS1jmWSPkGC0sFvu5XpjGrrPT6mpggvmlDBr1ix0XY/2P1tRFOUrDeriDtBWs5eex9aikYY55veY/WEeTszkrvyHsGDgv3b8J390ehnTlcMyUysxeU388uOfISOCs6vfoNo6DN05nQXdaUx2gC4Er+mV3B9OJY0OlsQ3cckF56t7rSuKMqgM6uJ+cOtHWF95AiKXYU24gpgeL/clZPK7wj9gjwj+Z+f13OOEPE8a5xpbSchv5OFPr6YxkMDyhr9RY8nAaZvD6WEHxQ6N7lAn91i9rA0lMU6vY8W4OJYvu0rdlldRlEHnpIq7EOIm4AeABLYD3wfSgBeBBGATcImUMnCSOY+qqz1MRJyFNfkK4jvc3JeQwe9GPYZVwgM7ruGXTp0Uv5MLje0kDG/hr1vOYUvXaCa7N9JudJJpnM1c3UKBVafGX8fNVgstYQsLrVX8x1kzmDBhgpriqCjKoHTC0z2EEBnAj4BJUsoiQAe+C9wD3C+lHAG4gSu+iaBHYzaHkIk3kdrh5r6EdO4d9QQmqfH49uu5O07DEbKxwtxD0vAWPtl1Jn9rnkWmtwYTQQqMc1lusVJg1dni388lZjvdMshl6U38+prvUlJSogq7oiiD1snO5TMAViGEAbAB9cCZwKt9258Gzj7Jv+OYDpXfTZ67m/sTs3mg8GkMUuNPW27kntgQkYiJS8wB0rPbKKs4nZdq5mMJ+cnxNzJRn81ym4lUI7zpr+B6cwJJWic/n2rhlqtXqtkwiqIMeic8LCOlrBVC3AtUA17gXXqHYdqllKG+ZjVAxtH2F0JcBVwFnPAtcf+a+H1eisTyUfrPAclj62/h0ZQu2oWFyy2SYelutlTNYVXVFLqxM9m7lxn6DM60GTCJMPeFannTnEyRsYk7zythwvjiE8qhKIoy0JzMsIwLWA7kAumAHSg93v2llI9LKSdJKSed6JnyuLrtbE6+FZ+ucdf7t7EqsZ4Ko5ELzQby09x8vn8Bn1SMoJoMRniaWUIxC+xGNPzcEmlhldHJQlczT16/RBV2RVGGlJO5oDoP2C+lbAYQQrwOzACcQghD39l7JlB78jGPrjqYRYtZ5+bVv2Ff7h7W2Rws101MSHezsWoe2w+kUB4aThxeLo1kMcthoCfcww3CS7XBzNWFQW646CLMZvO3FVFRFCUqTqa4VwOnCSFs9A7LzAU2Ah8A59M7Y2Yl8NeTDXksDe0bueXZVzAW1fNanIOZmok56e1sqZrNtpoM6jri8dksXOu1MdtupDnUwQ/1CB4N7pmfzNlnnqYumiqKMiSd8LCMlHIDvRdON9M7DVIDHgd+AtwshKigdzrkn76BnEe1dEsrqTk9PJhiYYxm4Nz0drZVzWRjUz7WQx3st+dydtDMcpuJA8FWLjVAxODnue9P4Jy501RhVxRlyDqpee5SyjuAO45YXQVMOZk/97jl+LgzJ0CW0LksvZM9VdP4tHUcY3Z8xvOZFzEirHGjycKOQAvXm0xkGjp44cZFpCa6+iWeoihKtAzq2xp+pseRYTTwH2ldHKiYytr2yUzZ8gFrkhcghZFf6za2Blq51mSiyNzE6tvOV4VdUZRTwqC+/UD2pB5mJ3RSuWc6H3dPZPKmNexwTaLaksqPMFMbcPNjk4mZ5oM8efvV6Pqgfi9TFEU5boO6uIs9E9llKGYbCRRtX4vblsZncZMZi05KsIdbTEaW6GU8cPtP0VRhVxTlFDKoK54vSVIZiiWrfBMR3cKuhOWEBMwO+vm50cCKyBru/e8b0dRtehVFOcUM6uKe1TaZ2IN7MAXD6PEXU6ZLzghLHjboXB16jZt/dANmuz3aMRVFUfrdoC7uH9tqcHR3MsL1PV63CFIlrNEk1wdf5MLvXkF8proHu6Iop6ZBPeaeFT+DnMR81tl1mgigE+F7obeZPWMuOZNmRDueoihK1AzqM/cJvoPYbWZelgE0KZkd3MAZyRYmnHtZtKMpiqJE1aA+c++2BLiTHiRQ6NvHOVoZM659Uf3mqaIop7xBXdyf2R+mTgiSfY1cGv47k65/GIvDEe1YiqIoUTeoh2VKR5pw+Vs5V/uIkYuvI3VEQbQjKYqiDAiDurjPykvmfPERY9LzmbDorGjHURRFGTAGdXG3JWYwMiGdBdfcpMbZFUVRDjOox9xTcodz/q13RjuGoijKgDOoz9wVRVGUo1PFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQgSUspoZ0AI0QwcjHaO45AItEQ7xNekMvePwZZ5sOUFlflosqWUSUfbMCCK+2AhhNgopZwU7Rxfh8rcPwZb5sGWF1Tmr0sNyyiKogxBqrgriqIMQaq4fz2PRzvACVCZ+8dgyzzY8oLK/LWoMXdFUZQhSJ25K4qiDEGquCuKogxBqrgfQQiRJYT4QAixSwixUwhxw1HazBZCdAghyvoet0cj6xGZDgghtvfl2XiU7UII8aAQokIIsU0IURKNnIflGXlY/5UJITqFEDce0Sbq/SyEeFII0SSE2HHYunghxHtCiH19z65j7Luyr80+IcTKKOb9nRCivO/n/oYQwnmMfb/0GOrnzL8QQtQe9rNffIx9S4UQe/qO659GOfNLh+U9IIQoO8a+/dPPUkr1OOwBpAElfcsxwF5g9BFtZgN/i3bWIzIdABK/ZPti4O+AAE4DNkQ782HZdKCB3l/IGFD9DMwCSoAdh637LfDTvuWfAvccZb94oKrv2dW37IpS3gWAoW/5nqPlPZ5jqJ8z/wL4r+M4biqBPMAEbD3y/2p/Zj5i+/8Dbo9mP6sz9yNIKeullJv7lruA3UBGdFN9I5YDz8he6wGnECIt2qH6zAUqpZQD7reUpZRrgbYjVi8Hnu5bfho4+yi7LgTek1K2SSndwHtA6bcWtM/R8kop35VShvpergcyv+0cX8cx+vh4TAEqpJRVUsoA8CK9P5tv3ZdlFr1f6Pwd4IX+yHIsqrh/CSFEDjAB2HCUzdOEEFuFEH8XQozp12BHJ4F3hRCbhBBXHWV7BnDosNc1DJw3re9y7P8IA62fAVKklPV9yw1AylHaDNT+vpzeT3BH81XHUH+7rm8o6cljDH0N1D4+HWiUUu47xvZ+6WdV3I9BCOEAXgNulFJ2HrF5M71DCMXAQ8Cb/Z3vKGZKKUuARcC1QohZ0Q50PIQQJmAZ8MpRNg/Efv4/ZO/n7EExn1gIcSsQAp4/RpOBdAw9CgwHxgP19A5zDBYX8eVn7f3Sz6q4H4UQwkhvYX9eSvn6kdullJ1Syu6+5dWAUQiR2M8xj8xU2/fcBLxB70fWw9UCWYe9zuxbF22LgM1SysYjNwzEfu7T+MWQVt9z01HaDKj+FkJcBiwFLu57Q/o3x3EM9RspZaOUMiyljAD/e4wsA6qPAYQQBuBc4KVjtemvflbF/Qh942V/AnZLKe87RpvUvnYIIabQ24+t/Zfy3/LYhRAxXyzTewFtxxHNVgGX9s2aOQ3oOGxoIZqOeZYz0Pr5MKuAL2a/rAT+epQ27wALhBCuviGFBX3r+p0QohT4MbBMSuk5RpvjOYb6zRHXg845RpbPgXwhRG7fJ8Dv0vuziaZ5QLmUsuZoG/u1n/vjyvJgegAz6f2YvQ0o63ssBq4Gru5rcx2wk96r8+uB6VHOnNeXZWtfrlv71h+eWQCP0Du7YDswaQD0tZ3eYh132LoB1c/0vvHUA0F6x3SvABKANcA+4J9AfF/bScATh+17OVDR9/h+FPNW0Ds2/cXx/Fhf23Rg9ZcdQ1HM/GzfcbqN3oKddmTmvteL6Z3RVhntzH3r//zF8XtY26j0s7r9gKIoyhCkhmUURVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGoP8P/9diBtPqFfMAAAAASUVORK5CYII=\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From 53f5f255ef641b1003c726ffebcfcee615b0214d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 276/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From a84058e68c8dd9b85c71b18f1b9b307fa31ad9e6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 277/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yuqO1Uk2yuEBJ8P/23NW6SqWCoZvBrhPsHgex53mlQxNGd23GN+ei+elSLE2Nm7Kq+yoiMyKZd3aeWHFTB4gkL9RaxaXFTD85nZTcFNb1WkdWjh5Tdl7D3caYpUPdkPZOhsRAGP5F1R3TV9to6cLIH5XvbH4cCakRzB7kTLcWFsz7PYiA6DQ6N+7M1HZTORxzmC9vfKnuiIUnJJK8UGutCVjDhbsXWNB5Ac2MXRj7/WW0NFVsfr0dupc2Kksl+8wHp4HqDrVm0TeD13cpv/5xJJpF2Xw20hMbUz3Gb7/CnfQ83nR9k2cdnuXTq59yKv6UeuMVnohI8kKttCd8D9tDtvO6y+sMaT6Ej36+TtS9HD57tS229y/BER9wG64UHRMqM3OAl76D1HDYPRYTXQ2+HOVFflEJ476/TEFxKQs7L8TZzJmZp2aK8sS1mEjyQq0TkhrCIv9FdGzYkWle09h6KpI/g+8ye5Az3lYl8Ou7YN4Cnv+sfi2V/K+adYOBy5XyDieW42hlxPpX2hB0J4M5u2+gq6HLul7r0FJpMeX4FDERW0uJJC/UKpmFmUw9MZUGug1Y2WMl1+OyWHXwFoPcGzLauwn8Olo5Hu+lb0HbQN3h1nwdxiqliU+thJt+9HGxZnKfFuy+msDOS3E0NmzMyh4ricqIYsn5JWIithYSSV6oNWRZZt6ZedzNucvqHqtRlRoy6cerNDLV5eMXWyOdWgXRp+HZNWDlou5wawdJgsGfgI0X/DYeUm7xQe8WdGthwUK/YIISMujUqBMT2kxgb+Redt/ere6Ihf9IJHmh1vg2+FuOxx1nqtdUPCw9mL4rkOSsfD4b6YnxnbNw8mPweBXavqbuUGsXTR14+XtlD8HPo9AozmXdy20w09fm/R1XyMgrYmyrsXRu1JllF5YRmhaq7oiF/0AkeaFWuJJ0hXVX1tGvaT9ed3mdb85Fc/hmErMGueBhWqDsaLVoCc+uVneotZNxY3jhC0gJhf3TMTfUYeNrbUm4n8f0X66jklQs77YcUx1Tpp2YRnZhtrojFv4lkeSFGi81L5XpJ6djY2iDr7cvNxIyWLY/hL4u1rzj3RT2vCfG4Z+G5r2hxwy49gNc/YF2Tc2YNciZQzeT+OpMFOZ65qzssZKE7AQWnlsoxudrCZHkhRqtpLSEmadnklGYwSc9P4FSXSbuuIqloQ6rR7RGCvhKKaXbf7EYh38aesyEZt1h3zRIusnors0Y4GbNx3+GciM+g3bW7ZjkOYlDMYf4MfRHdUcr/AtPJclLkrRNkqRkSZKCHrpmJknSYUmSbj/4ucHTeJZQv2wJ3MKFxAvM7TgXJzMnFvoFk5Cex6evtsU0JwoOzQPHfkr5YOHJqTRg+JegYwS/jEIqzOHjF1pjYajDpJ1XySko5i23t+hm0401AWu4lXZL3REL/4+n9Sb/DfDotsJZwFFZllsARx/8XhD+tUt3L7ElcAvPOTzHsBbD2BeYyO4rCUzs5Ug7G0PYPUYZnnl+o1gP/zQZWcOLXykbpf6ciam+Np+81Ibo1BwW7b2JSlKxuMtijLSNmHlqJvnF+eqOWPgHTyXJy7J8Ckh75PLzwLcPfv0tMPRpPEuoH9Lz05l9eja2hrbM7TSXuxn5zPntBh52pkzs7QgnV0DidXhug5KUhKerWXfo+iFc3Q43/ejc3Jz3ejbnp4A49t9IxFzPnKVdlxKREcGagDXqjlb4B1U5Jm8ty3Lig1/fBR77lShJ0lhJkgIkSQpISUmpwnCE2kKWZRacW0Bqfiore6xET0Of6buuU1hcyrqX26CVcBHOrIW2r4PLYHWHW3f1nA2N28LeSZB5hyl9W+JhZ8qsXwO5k55HF5suvOH6Bjtv7eRE3Al1Ryv8jWqZeJWVafjHTsXLsrxVlmUvWZa9LC0tqyMcoYb76dZPHI87zhTPKbiZu/HNuWhO377HvMEuNDOWlE07JnYwcIW6Q63bNLSU8fniAvh9AloSbHilDSWlMlN+uqb87DkFpwZOLDi7gJRc8ZJWE1Vlkk+SJKkRwIOfk6vwWUIdcSvtFqsuraKrTVfecH2DsKQsVvwZSh9nK17t0ASOLYH7Uco4vI6RusOt+ywclfo2kSfgwiaamhuweKg7F6PS2HwyAm0NbVZ2X0lecR5zz8ylVC5Vd8TCI6oyyfsBox78ehSwpwqfJdQBecV5zDg1A2MdY5Z0WUJRicyUndcw0tFkxQutkeIuwvnPlZU0zbqpO9z6w3MUOD2rVPa8G8SwtjY859GYdUfCCL6TgYOpAzM6zMA/0Z/vb36v7miFRzytJZQ/Av6AkyRJ8ZIkjQZWAP0kSboN9H3we0H4WysvKYWwlnVdhrmeOWsP3+ZmYiYrXmiNpW4p7HlfGabp66PuUOsXSYIhn4JeA/htHFJJEYufd8NUX5tpPytzJS+2eJE+Tfqw7so6sayyhnlaq2tGyrLcSJZlLVmWbWVZ/kqW5VRZlvvIstxCluW+siw/uvpGEMocij7ErrBdvO3+Np0bd+Zq7H22norgJS9b+rlaw4nlkHobhqwXwzTqYGAOz62HpCA4vQZTfW1WDG9F6N0sNhy9jSRJ+HT2wUTbhDln5lBYUqjuiIUHxI5XQe2ScpLw9ffF3dydiW0nkl9UwvRdgVgb6zJvsCskXIZzn4Lnm8rWe0E9nAZB65fh9GpIDKSPizUvedny+Ylwrsbex1TXFF9vX8Luh7Hp+iZ1Rys8IJK8oFalcinzz86nqLSI5d2Wo6XSYt2R24QnZ7PihdYYa5bC7++DUSPov0Td4QoDV4C+Ofz+HhQXMn+wK41M9Jj2y3Xyi0roYdeDYY7D2Ba0jWvJ19QdrYBI8oKa/Rj6I/6J/nzk9RH2JvZci0tn66kIXvayo0dLSzi9BlJCYPA60DVRd7iCvpnyd5F0A06vwUhXi5UvtiYyJYdVB5Wx+BntZ2Ctb828s/PIK85Tc8CCSPKC2kSkR7D28lq623ZnRMsRyjDNL9exNtZl7mAXSAmD059AqxHQsr+6wxX+4vwMtHqpbNimi6MFb3ZuyrazUZyPTMVQ25AlXZYQkxnDusvr1B1tvSeSvKAWRSVFzD49G31NfXy9fZEkiQ1Hb3M7OZtlw1thrKMJf3yo1KYZsFzd4QqPGvQx6JmVDdvMGuRMEzN9Zv0aSH5RCR0adeA1l9fYEbqD84nn1R1tvSaSvKAWn1//nJC0EBZ6L8RCz4LrcelsPhnBiHa29HKygms7IOYM9FsEhmIndI2jbwbPPRi2ObMWfW1Nlg9rRXRqLmuPhAEw2XMy9sb2LDi7gKzCLDUHXH+JJC9UuytJV9gWtI1hjsPo06QPBcUlTN91HUsjHWU1TU6qUkLYrhO0fUPd4Qp/x/lZcBuuDNvcu423owUve9nx5ekoghIy0NPUY0nXJSTlJrE6QJzYpS4iyQvVKrswmzln5tDYoDEzO8wE4NOj4YQlZbN8eCtM9LSUBF+QqbwpqsQ/0Rpt4ArlbNi9U0CWmfOsC+YG2szYFUhRSSkelh685fYWu2/vxv+Ov7qjrZfEV5BQrT6+9DGJOYks77YcAy0DQhIz2XwyguGeNvR2toaoU3B9B3SZLE56qg2MrJUhtZgzcHU7JnpaLHrenZuJmXxxOhKACR4TsDe2x9ffl9yiXDUHXP+IJC9UmyMxR/g9/HdGu4+mjZVSzXDWr4GY6Gkx/1lXpdrhHx9CA3voPl3d4Qr/Vts3oUln5Tuw7BQGujdkkHtD1h25TWRKNrqauvh6+5KQncCGqxvUHW29I5K8UC3u5d1jkf8iXMxcmOAxAYBvz0VzPT6DBc+50sBAW6kRnxoOz36iDAEItYNKpZQ8KMyBg3MA8H3eDV1NFbN+vUFpqYyntSevOL3CjpAdXE2+quaA6xeR5IUqJ8syS84vIbsoW9nVqqFF/P1cVh+6RU8nS4Z4NIbUCGXjk/sL4NhH3SEL/5WlE3SbCjd+hvCjWBnpMu9ZVy5Gp7HjYiwAU9pNoaFBQxacXUBBSYGaA64/RJIXqtz+qP0cjT3KxLYTaW7aHFmWmf+7cub7kqHuSAB/zgINHRiwTK2xCk+g61Qwd1SG3ApzGeFlSxdHc1YcCCUpMx8DLQN8OvsQnRnNlutb1B1tvSGSvFClUnJTWHZhGa0tWzPKVTleYG9gIsdvpTCtvxO2DfQh7E+4fQh6zgKjhmqOWPifaekqJQ/SY+D0GiRJYunQVhSWlLL4j5sAeNt483zz59kWtI2Q1BA1B1w/iCQvVBlZlvH196WgpIAlXZagodIgPbeQRXuD8bA14S1veyjKgwMzwdIZOo5Td8jCk2rWTalUeW4DpEZgb2HAxF6O/BGYyKkw5XjA6e2n00C3AQvOLaCotEjNAdd9IskLVcYvwo+T8SeZ1HYSzUyaAbB0Xwj3c4tYPrw1GioJzm5Q3vwGrVTOFBVqv36LlKG3AzNBlhnXwwEHCwPm7wkiv6gEEx0T5nWcR2haKN8EfaPuaOs8keSFKnE35y4fX/wYTytPXnd9HYBz4ff45XI8Y7s74NrYGO5Hw5lPwG0YOPRQb8DC02PUEHrNgfDDELoPHU0NFg91JyY1l89PRADQp2kf+jXtx+brm4nNjFVzwHWbSPLCUyfLMj7nfCiWi1ncZTEqSUV+UQmzf7uBvbk+k/u0UDoenAuSCvovVW/AwtPXYSxYucKfs6Ewly6OFjzfpjGbT0QQmZINwKwOs9DW0Gbx+cXIsqzmgOsukeSFp2737d2cvXOWKZ5TaGLcBIANR28Tk5rLsmGt0NXSgNtHIPQPZdOTiY2aIxaeOg1NeGY1ZMQq+x+Auc+6oKOlYv6eIGRZxkrfismekzmfeJ59UfvUHHDdJZK88FTdyb7DqoBVdGjYgVecXwEgPDmLL05H8oKnLd6OFsrO1gMzlOV2nd9Xc8RClbHvopwFcHY9pEViZaTLjIHOnA1Pxe/6HQBGtBxBa4vWrLq0ioyCDDUHXDdVeZKXJClakqQbkiRdkyQpoKqfJ6hPqVzKgrMLkGWZRV0WoZJUyLLMvN+D0NfWZM4zzkpH/88gLUKpSa6po96gharVb7EyoX5gFgCvdmiCh60Ji/8IISOvCA2VBgs6LyCjIIO1l9eqOdi6qbre5HvJstxGlmWvanqeoAa/3PqFC3cvMM1rGjaGyhDMb1cTOB+ZxsyBzpgb6kBGApxaDc6DwbGvmiMWqpxxI2X/w+2DcOsAGiqJpcNakZZTwCeHlOMCncyceNP1TX69/SuXky6rOeC6RwzXCE9FQnYCay6voXOjzoxoOQKA9NxClu4LoW0TU15pb6d0POoLpSUwQEy21hsdxyv7IA7MhKJ83G1MeK1jU74/H0NIYiYA4z3G09igMYv8F1FUItbOP03VkeRl4JAkSZclSRpbDc8TqpksyyzyXwSAj7cPkiQBsPLgLdLzilg6tBUqlQTxARD4kzIO38BejREL1UpDS6k7nx4DFzYDMK1/S0z0tFjoF4wsy+hr6TO301wiMyL5OvhrNQdct1RHku8qy7InMAh4X5Kk7g83SpI0VpKkAEmSAlJSUqohHOFp2xOxh3N3zjHFcwqNDRsDcDX2Pj9ejOUtb3tlTbwsK/VpDK2VQlZC/dK8Fzg9owzVZSdjqq/NRwOcuBiVxt7ARAC623anf9P+bLm+Raydf4qqPMnLspzw4Odk4DegwyPtW2VZ9pJl2cvSUpzlWduk5Kaw8tJKPK08y1bTFJeUMve3IKyNdPmwX0ul441fIP4S9FkIOkZqjFhQm/5LoDgfji0G4JX2TXC3MWbZvhByCooBmNlhJtoa2iw5v0SsnX9KqjTJS5JkIEmS0V+/BvoDQVX5TKH6yLLM0gtLKSguwMfbB5Wk/HP6zj+Gm4mZLHjOFUMdTaXO+OGF0KgNeIxUc9SC2pg3V+oTXfkeEgPRUEn4DnHnbmY+G4+HA2Clb8UHbT/AP9GfwzGH1Rxw3VDVb/LWwBlJkq4DF4F9siz/WcXPFKrJ4ZjDHI09yntt3iurTZOUmc8nh8Po0dKSQe4PKkqe3QBZd5RxWXFma/3WfTromyk7YWWZdk0bMNzThi9PRxF1LweAl51exsXMhY8vfSyOC3wKqvQrTpblSFmWPR78cB/TPwEAACAASURBVJNlWSypqCPS89NZemEpLmYujHIbVXZ90R83KSopZdHzbsoEbEa8shnGbTg07azGiIUaQc8Ues1VzoQN2QvArEHOaGuqWLQ3GAANlQZzOs4hOTeZzYGb1RltnSBeq4T/ycpLK8ksyGRxl8VoqjQBOBWWwr7ARCb2cqSpuYHS8YgPIEM/X7XFKtQwnqOUujaH5kFxAVZGukzu04Ljt1I4GpIEQBurNgxzHMb3wd8TmR6p5oBrN5Hkhf/sVPwp9kbu5Z1W7+Bk5gRAflEJ8/cE4WBpwNgeDkrHuIvKhKv3B2DaRI0RCzWKhiYMXK4sqTz/OQCjvO1pbmnAoj9ukl9UAijHBepr6bPswjIxCfsERJIX/pPswmwW+S+iuUlzxrUuP+Tj8xMRxKTmsuR5d3Q0NaC0VFkyadQIukxRY8RCjeTQs3xJZVYS2poqfIa4EZOay1dnogAw0zVjsudkLty9wJ/RYirvfyWSvPCfrLuyjuTcZHy7+KKtoQ1A9L0cNp+I4Pk2jZUCZKAc6Jxw+cGSSUM1RizUWH8tqTy5AoBuLSzp72rN58fDSc7MB+CFFi/gau7KqkuryCnKUWe0tZZI8sK/dunuJX669ROvubyGh6VH2fVFf9xEW1PF3GdclAuFOcpYvE075Sg4QXgc8+bgNRoufwspYQDMecaFwpJSVj+oa6Oh0mBex3ncy7vHpmub1BltrSWSvPCv5BXn4XPOB1tDWz5o+0HZ9SM3kzgWmsyUvi2wMtZVLp5ZB1mJYsmk8P/rMQO0DR5M0IO9hQFvedvzy+V4ghKU0sOtLFsxvMVwtods5/b922oMtnYSX4HCv7L5+mZis2Lx8fZBX0sfUCZbff8IpoWVIaO87ZWOGQnKIc7uL4Jdh7//QEEAMLCArlPg1j6IOQfAxN4taKCvzeI/bpZNuE72nIyhtiFLLywVk7D/kUjywv/rVtotvg3+lqGOQ+nYqGPZ9a2nIolLy8N3iBtaGg/+KR1botSp6btQTdEKtU7HCWDUWFlSKcuY6GnxYb+WXIhK42CwsqSygW4DpnhO4XLSZXGK1H8kkrzwj0pKS1jkvwhjbWOmtZtWdj0uLZeNx8N5tnWj8snWxOtw/UfoNEEsmRT+PW196D1PmagP/g2Ake3taGltyLL9IRQUK0sqh7cYTiuLVqwJWEN2YbY6I65VRJIX/tHPYT8TeC+Q6e2nY6prWnZ9yb6bqCSpfLJVlpU3Mb0Gosqk8N95vAJWbsp5A8WFaGqomD/Yldi0XL49Fw2ASlIxt+NcUvNS2RK4Rb3x1iIiyQt/KyknifVX1tO5UWcGOwwuu34yLIWDwUl80MeRxqZ6ysXbhyHqlHIKkK6JmiIWai2VBvRbBPejIeArQFlS2dvZik+PhnMvuwAANws3hjoOZXvIdqIyotQYcO0hkrzwt1ZcXEFxaTHzO80vOwiksLgUX79gmlkYMLqrUpSMkmI4PB/MmkO7t9UYsVCrOfZRNkmdXAl56YCypDKvqIRPDoeVdZvkOQldDV1WXlqpnjhrGZHkhcc6HnucI7FHGO8xHjtju7LrX52JIvJeDgufc1V2tgJc2w4poUp9Gk1tNUUs1HqSpLzN592HM8qh3o5WhrzeqSk7L8YSelc5KtBCz4LxHuM5k3CGU/Gn1BlxrSCSvFBJTlEOSy8sxdHUsUKFycSMPD49dpv+rtb0dLJSLhZkw7Gl0KSzcji3IDyJRh7KBrrzmyA9DoApfVtgpKvFkj9CypZPvur8Ks1MmvHxxY8pLClUZ8Q1nkjyQiWfXf2M5NxkFnZeiJZKq+z6sv2hlJTKzB/sWt753AbISVa2qD8Y0hGEJ9J7nvLzCaXcgam+Nh/2bcGZ8HscC00GQEtDi5ntZxKbFcv2kO3qirRWEEleqCD4XjA7QnfwktNLtLFqU3b9XMQ99l6/w4SezbEzUzZDkXlHORDEbTjYeqkpYqHOMbWD9u/C9R2QopQ3eK1TUxwsDFh+IJTiklIAuth0oadtT7Zc30JKrjgf+u+IJC+UKS4txsffB3NdcyZ7Ti67XlRSio9fMHZmeozv0bz8huNLQS4RG5+Ep6/bVNAyKDsPVktDxYyBzoQnZ/NzQHxZt+ntp1NUWsS6K+vUFWmNJ5K8UOaHkB8ITQtlVodZGGmXH7b9nX8MYUnZLBjshq7Wg8nWu0Fw9QfoMBYa2KsnYKHuMrAA74nK6VEJlwEY4GaNV9MGfHI4rOzg7ybGTXjT9U38Ivy4nnJdnRHXWCLJCwAkZCew8dpGetj2oF/TfmXXk7PyWXc4jJ5OlvR1sSq/4fACZT1894/UEK1QL3R+H/TN4YhyqpgkScx51oV72QVsPVV+WtTY1mOx0rNixYUVlMql6oq2xhJJXkCWZZaeV47fndtxbtmaeIAVB0IpKC5l4XNu5dfDj0DEUaWCoF4DdYQs1Ac6RsrB31EnIeI4AJ5NGvBsq0ZsPRVZVnNeX0ufKe2mEJQaxJ7wPeqMuEaq8iQvSdJASZJuSZIULknSrKp+nvDfHYw5yOmE00xsM5FGho3KrgdEp7H7SgJjujejmcWDM1tLS+DQAmWIpv276glYqD+83gETOzi6SCmdAcwY6ERxaSlrj5RvkBrsMBgPSw/WXVlHVmGWuqKtkao0yUuSpAFsBAYBrsBISZJc//kuoTplFmby8cWPcTFz4VWXV8uul5TKLNgTTCMTXd7v5Vh+w7UdkBwMfX1AU6fa4xXqGU0d6Dkb7lxRxueBpuYGvN6pKT9diiMsSUnokiQxu+Ns7uffZ8t1UdfmYVX9Jt8BCJdlOVKW5UJgJ/B8FT9T+A/WXV5HWn4aPt4+aKo0y67vuBDDzcRM5j3rir72g+uFOcqKGtv24DpUTREL9Y7HK2DhpKy0KVEmXCf1boGBjibL94eUdXMzd2NYi2H8EPIDkRmRf/dp9U5VJ3kbIO6h38c/uCbUAFeTr/JL2C+85vIarubl32Cl5RSy+lAY3s3NeaZVw/Ib/DcqJz6JjU9CdVJpQJ/5cC8MAncC0MBAm4m9HDl+K4Vz4ffKuk5qOwldTaWujThcRKH2iVdJksZKkhQgSVJASorY0FBdikqK8D3nSyODRkxsM7FC25pDt8guKMZnyEOTrVlJyrF+LkOgSSc1RCzUa86DlTODjy+HImXCdZS3PTameizdH0JpqZLQzfXMmeAxgbMJZzkZf1KdEdcYVZ3kEwC7h35v++BaGVmWt8qy7CXLspelpWUVhyP8ZVvQNiIyIpjbcW7ZcX4AQQkZ7LgYy5udm9LSunytPCeWQUmBMhYvCNVNkqDPQsiMh4BtAOhqaTB9gBPBdzLZc708rYx0GYmDiQMrL60UdW2o+iR/CWghSVIzSZK0gVcAvyp+pvD/iM6IZmvgVvo37U8Pux5l12VZxscvGDN9bab0bVl+Q3IoXPlOWU1j3vwxnygI1cChh1KK+PRqKFAmXId4NMbdxpjVB8PIL1JOkNJSKXVt4rLi+P7m9+qLt4ao0iQvy3IxMBE4CIQAP8uyHFyVzxT+mSzLLD6/GB0NHWZ1qLiidc+1OwTE3GfGQCdM9MoLk3F4AWgbQfcZ1RytIDyizwLITYVznwGgUknMecaFhPQ8vj4bXdbN28abnnY92Rq4td7XtanyMXlZlvfLstxSluXmsiwvrernCf/ML8KPi3cvMqXdFCz1y4fHsguKWbY/hNa2Joxo99AIW+QJuH0Quk8DA/PqD1gQHmbTTpkX8t8IOakAeDe3oLezFZ8fDyctp3x4ZobXDFHXhhow8SpUn/v591kdsJo2lm14seWLFdo2Hg8nOasAnyFuqFQPJltLS5VzW02aQIdxaohYEB6j9zwoyoEzn5Rdmj3ImZzCYjYcvV12zc7YjlFuo+p9XRuR5OuR1QGryS7MZkHnBaik8r/6qHs5fHk6khc8bfFs8lCZgsCf4O4N5VtkLV01RCwIj2HpBB4j4eIXkKFMuLawNuLl9k3Yfj6G6Hs5ZV3HtBpT7+vaiCRfT5xPPI9fhB9vu79NiwYtKrQt/uMmOpoazBzkVH6xKA+OLYFGbcD9hWqOVhD+Hz1mglwKp1aVXfqwXwu0NVWsPBhadk3UtRFJvl7IL85nsf9imhg1YWzrsRXajoUmcSw0mcl9WmBl9NDb+vlNynK1/ktAJf6ZCDVMg6bg9TZc/R5SIwCwMtJlbHcH9t+4y+WY+2Vd/6prs/7K+npZ10Z89dYDWwO3EpsVy/zO89HVLE/kBcUlLNp7k+aWBozyti+/IeeecpByy4HQrFv1BywI/0a3j0BDG04sL7s0ppsDlkY6LNtffh7sX3Vt0vLT6mVdG5Hk67jw++F8HfQ1zzk8R6dGFXeqfnUmiujUXBY+54a25kP/FE6uhMJs6OtbzdEKwn9gZA0dx8ONXcohNoCBjiZT+7Xkcsx9DgbfLev6cF2bqIwodUWsFiLJ12Glcim+/r4YahvyUfuKh3vczcjns2Ph9HO1pnvLh3Yap0ZAwFfg+SZYOVdzxILwH3WZBLrGyvzRAyPa2dLCypCP/7xFUUn5ZOvDdW3qE5Hk67BdYbu4lnKNaV7TMNM1q9C2/EAIxaUy8599pPLzER/Q0IGec6ovUEH4X+k1gC6TIewAxF0EQFNDxexnnIm6l8OOC7FlXf+qa3Mm4Qyn4k+pK+JqJ5J8HZWSm8K6y+vo0LADzzevWN35UnQae67dYVx3B5qYl9etIfYChPgpb0dG1tUcsSD8jzqOBwPLCgeL9HKyopODGeuP3iYzv6is60iXkTQzaVav6tqIJF9Hrbi4goKSAuZ3ml/hOL+SUpmFe4JpbKLLez0fOgxElpWNT4bW0HniYz5REGoobQPlmMDo08oObZTJ1rnPuJKWU8jmExFlXf+qaxOTGcP2kO1qCrh6iSRfB52IO8GhmEOM8xiHvYl9hbYfL8ZyMzGTOc+6oKetUd4Q4gfxF6HXHNAxrN6ABeFJtXtL2Zn90Nt8K1sTnm/TmK/ORHEnPa+saxebLvS07cmW61vqRV0bkeTrmJyiHJZeWIqjqSNvu71doS09t5DVh27RycGMZ1uVn+VKcaEyFm/pDG1er96ABeFp0NSBnrOUYwJD/yi7/FF/J2QZ1hwKq9B9evvp9aaujUjydcxnVz8jKSeJhZ0XoqWhVaFtzaEwsvIfOQwE4PLXkBYJ/RaBhiaCUCu1fhksWiorbUqVssN2Zvq81cWe3VfjuXkns6xrE+MmvOn6Jn4RfgSmBKor4mohknwdEnQviB2hO3jJ6SXaWLWp0HbzTiY/XIjhjU5NcW5oXN6QnwEnVoB9N2jRv5ojFoSnSEMTes2FlFC48UvZ5fd7OmKsq8XyAyEVuo9pPQZLPUuWX1hep+vaiCRfRxSVFuFzzgcLXQsme06u0PbXYSCm+tp8+PBhIKDsbM1Lg/6LxbmtQu3nMgQaecDxZcowJGCir8UHvR05ffsep8LKx+ANtAz4sN2HBKUG4RdRd88yEkm+jth+czu37t9iTsc5GGkbVWjbG5jIxeg0pg9wwkT/oSGcjHilRk2rl6Bx22qOWBCqgEqlVE1Nj4Er35ZdfqNzU+zM9Fi2P4SS0vIDvv+qa7Pu8jqyC7PVEXGVE0m+DojLiuPza5/T2643fZr2qdCWU1DMsn0huNsY85KXXcUbjy1RViL0mV+N0QpCFWveB5p2USpUFuYCoKOpwYwBzoTezWL3lfiyrpIkMbuDUtdma+BWdUVcpUSSr+VkWWbJ+SVoqDSY3XF2pfbPT4RzNzMf3yFuaKgeGo5JDITrO6HjODBtUo0RC0IVkyToPR+yk+BieUGywa0b4WFnyppDYeQVlpRdd7NwY6jjUL4P+Z7ojGg1BFy1RJKv5fZF7ePcnXNM9pxMQ4OGFdqi7+Xwxakohre1oV3Th8oayDIcng96ptBtWjVHLAjVoGlnZSHBmXWQlw78tUHKhbuZ+Ww7W7FI2STPSehq6LLi4oqy6pV1hUjytVh6fjorL66ktWVrXmr5UoU2WZbx2RuMtqaKmYMeKTQWflTZGdh9hpLoBaEu6j0f8tPB/7OySx2amdHP1ZpNJyK4l11Qdt1Cz4KJbSdy9s5ZjsQeUUe0VabKkrwkST6SJCVIknTtwY9nqupZ9dWqgFVkFWaxsPNCNFQaFdoO3UzixK0UpvRtgbXxQ4eBlJYob/EN7KH9u9UbsCBUp0atwW04+H8O2eWramYNciavqKTCebAALzu9jLOZMysuriCnKOfRT6u1qvpNfq0sy20e/Nhfxc+qV84knCk7zq9lg4rLIvMKlcNAnBsa8dbDh4EAXNsByTehz0LQ1K6+gAVBHXrNheJ8OL2m7FJzS0NGdrBjx4VYIlPKV9RoqjSZ32k+KbkpbLq2SR3RVgkxXFMLZRdm4+vvi4OJA+M9xldq33g8nIT0PBY9746mxkN/xYU5cHwp2HiB27BqjFgQ1MTCEdq8qpyRkB5Xdnlyn5boaKr4+M/QCt1bW7bmhZYvsD1kO2H3wx79tFqpqpP8REmSAiVJ2iZJUoMqfla9sfbyWpJzk1ncZTHaGhXfxiNTstl6KpLhbW3o0KxiDXn8P4esROXcVrHxSagves5Sfj75cdklSyMdxvdozsHgJC5Fp1XoPrntZIy1jVlyfkmd2An7RElekqQjkiQFPebH88AmoDnQBkgE1vzNZ4yVJClAkqSAlJS6XxHuSV26e4mfw37mdZfXaW3ZukKbLMss9AtGR1PFrGcemWzNTFR2tzoPVlYeCEJ9YWKrzD9d2wH3ysfh3+3mgLVxxfNgAUx1Tfmw3YdcTb7KnvA96oj4qXqiJC/Lcl9Zlt0f82OPLMtJsiyXyLJcCnwBdPibz9gqy7KXLMtelpaWj+siPJBblMuCswuwM7JjYtvKNd//DLrL6dv3mNq/JVZGuhUbjy+BkkKlCJkg1Dddp4KmrjJc+YCetgbT+jlxNTad/TfuVuj+vOPztLVqyyeXPyE9P726o32qqnJ1zUO1bBkGBFXVs+qLz659Rnx2PL7evuhp6lVoyy0sZtEfymTrG52aVrwx8Tpc/UHZ+GTevBojFoQawtASOr8Hwb8pXw8PvNDOFueGRqw8GEphcfnQjEpSMbfjXLIKs2p9OeKqHJNfKUnSDUmSAoFewIdV+Kw671ryNbbf3M7LTi/TvmH7Su2fHgsnMSOfxUMfmWyVZTg4VzkLs/v0aoxYEGoY7w9A17TCod8aKolZg5yJSc3lO//oCt2dzJx4zeU1fr39K1eSrlRvrE9RlSV5WZbfkGW5lSzLrWVZHiLLcmJVPauuKygpYMG5BTQ0aMiH7Sr/vzI8OZsvT0fygqct7e0fmWy9tV85Fq3XHLHxSajfdE2g64dw+xDE+Jdd7tHSku4tLVl/9DapD22QAni/zfs0NmjMwnMLKSgpePQTawWxhLIW+PTKp0RlROHT2QcDLYMKbcpkaxC6WhrMenRna3Ghcm6rhRO0q3hKlCDUSx3GgmFDOOpbdkygJEksGOxCbmEJaw5XXDapr6XPgs4LiM6MZsv1LY/7xBpPJPka7tLdS3x38zteavkS3jbeldr33UjkbHgq0wc4YWmk88jNXyonPg1YKk58EgQAbX3oMR1i/ZXyHg84WhnxZuem7LwYW+EEKVDOhB3SfAhfB33NrbRb1R3xExNJvgbLLsxm/tn52BrZMs2rciGxjLwifPfexK2xMa91fGSyNTcNTq6A5r3BsW81RSwItUDbN8G0qfI2X1o+2TqlT0tM9LRY9EdwpSJl072mY6xjzMJzCykuLa7uiJ+ISPI12KqAVSTmJLKs6zL0tfQrtx8MJTW7gBXDW1csIwzKxo+CLOi/VGx8EoSHaWorc1R3AyGkfB28ib4WU/s7cT4yjT+DKi6pNNU1ZXaH2QSnBvNDyA/VHfETEUm+hjoRd4Ldt3fzjvs7lc5rBbgcc58fLsQyytueVrYmFRvv3VaGatq9Bdau1ROwINQmrUaApQscWwol5W/mI9vb4dzQiKX7Q8gvKqlwywD7AfS07clnVz8jLjPu0U+ssUSSr4HS8tNYeG4hTg2ceM/jvUrtRSWlzNl9g4bGukzr71T5Aw7NAy196DmnGqIVhFpIpQG950HqbQjcWXZZU0PFgsGuxN/P48vTkRVukSSJuZ3moqHSwNfft9aUPBBJvoaRZZnF/ovJKsxiWbdlaGloVerz5ekobiVl4TvEDUOdRyZUI45D2J/KYSCGYgexIPwt52ehsSecWAHF5csjvR0tGOBmzcbjEdzNyK9wS0ODhkzzmsaFuxfYGbrz0U+skUSSr2F2397NkdgjfND2g0olhAFiU3NZfzSMAW7W9HereBIUJcXKxifTptCxcnVKQRAeIknKod8ZcRDwdYWmuc+4UlIqs+JASKXbXmzxIt1surH28lqiMqIqtdc0IsnXIOH3w1lxcQWdGnVilNuoSu2yLDNvTxCaKhU+Q9wqf8DlryE5GPovBi3dyu2CIFTk0BPsu8Hp1VBQXlu+ibk+Y7o34/drdzgfmVrhFkmS8PX2RVdTlzmn51BUWlS9Mf9HIsnXEHnFeUw/NR19LX2Wd1uOSqr8V+N3/Q6nwlL4qH9LGplUrF1DTqqyXbtZD3AZUk1RC0ItJ0nKATo5KXBhc4Wmib1aYGOqx/zfgygqqTj+bqlvyfxO8wlKDeLLwC+rM+L/TCT5GmLlpZWEp4ezvNtyLPQsKrXfzylk8R838bA14Y3O9pU/4PgSZcnkoI/FkklB+C/s2oPTM3B2g7K/5AE9bQ18hrhxOzmbbWcqD8v0t+/PYIfBbAncQtC9mlt/UST5GuDP6D/ZFbaL0e6j8W5ceVcrgO/eYNJzi1j+uDXxideVMcUOY8HKpRoiFoQ6ptdcKMiEcxsqXO7nak1fFyvWHbnNnfS8SrfN7jgbS31LZp+eTV5x5faaQCR5NYvLisP3nC+tLVvzftv3H9vnyM0kfr92h/d7OeLa2LhioyzDgZmgb15+Ao4gCP9NQ3do9SKc3wxZSRWaFj7nhozMor03K91mrG3M0i5LicmMYfmF5dUV7X8ikrwa5RXn8eHxD5EkiZXdV6KlqrxcMiO3iDm/3cC5oRHv93Ks/CE3dil1OPouFFUmBeFJ9JwNpUXKJOxD7Mz0+aB3C/4Mvsvx0ORKt3Vo1IGxrcfyW/hv+EX4VVe0/5pI8moiyzI+53wIux/Gyu4rsTG0eWy/xftukppTyOoRHmhrPvLXVZANh+dD47bQ5vVqiFoQ6jDz5tD2DWXo8350haYx3RxobmnAQr/gSjthASZ4TMDL2osl55cQmR5ZqV2dRJJXk+0h29kftZ+JbSfS1abrY/scv5XMrsvxjO/hgLuNSeUOp9coB3MPWgkq8VcpCE+sxwxlN+zRxRUua2uqWPy8O7FpuXx+PLzSbRoqDT7u/jF6mnpMOzmN3KLc6or4/yUygxpcunuJNQFr6G3Xm3dbvfvYPpn5RczZfYMWVoZM6tOicofUCPD/DDxGgt1jj88VBOG/Mm6snCAVtAviLlZo8na0YGibxmw6GcGtu1mVbrXSt2J51+VEpEew9MLSSpUs1UUk+WoWnxXPRyc/ws7IjqVdlz52PTzAsn0hJGXms2qEBzqaGhUbZRn2fwQaOtDXp8pjFoR6pcsU5WCRP2dXKEUMsOA5N4x1tZix6zrFJZVr13jbeDPOYxx+EX7sCN1RXRH/I5Hkq1FmYSbvH32f4tJiNvTegKG24WP7HbmZxM5LcYzp7kAbu8dMpgb/BhHHoM98MGpYuV0QhP+djqFS7iAhAIJ+rdBkZqCNzxA3rsdnsO3s40saTPCYQE+7nqy6tIrzieerI+J/JJJ8NSkqKWLq8anEZsWyrtc6mpk0e2y/lKwCZv4aiGsjY6b2q1y7hvxM5Q2jkQe0f/xQjyAIT8hjpPI1dmQhFFYcXx/cuhH9XK1ZcyiMyJTsSreqJBXLuy6nmUkzpp2YpvayxCLJVwNZlll8fjEX7l7A19uX9g3b/22/Gbuuk11QzPpX2lQepgE4vhSyk2DwWmWCSBCEp0+lgoErIDNBmft6iCRJLBnqjramilm/3qC0tPLYu6G2IRt6bUCSJCYem0hGQUZ1RV7JEyV5SZJGSJIULElSqSRJXo+0zZYkKVySpFuSJA14sjBrt3VX1vFb+G+Maz2OIc3/vq7M9vMxHL+VwuxBzrSwNqrc4c41uLgV2o8Gm3ZVGLEgCDT1VupAnVkLmXcqNFkb6zL/WVcuRqfxw4WYx95uZ2zH2p5ricuKY9KxSeQX5z+2X1V70jf5IGA4cOrhi5IkuQKvAG7AQOBzSZLq5Wvnlze+ZFvQNl52epn32zx+RytAeHIWS/aF0KOlJaO87St3KC2BPz4EfQvoPb/qAhYEoVy/RVBaXGlJJcAIL1u6tbBgxYFQYlJzHnt7+4btWdZtGVeTrzLj1Ay1nA/7REleluUQWZYfd3z588BOWZYLZFmOAsKBerfOb2foTtZfWc8zzZ5hTsc5SH9TOKywuJQpP11DX1uDVS+2fny/y1/DnSswYJnY2SoI1cWsGXSaANd3QMLlCk2SJLHihdaoVBIf/nTtsattAAbaD2Rmh5kcjzuulqWVVTUmbwM8PNsQ/3/t3Xl8VNXdx/HPj6xsIQKBsksAZS9gFBAQcGcz7FstVRBkKW6PbWlRHrRSl1r70EIRFEXAsogLyCKI8oiyBwgQCEvYlwAJgbBln9M/7k2bJjMJJJnMZPJ7v155MblzZ+brMfObO+eee469LQ8RGSMiUSISlZCQ4KY4JW/RwUVM2zaNbvW68UbnN1wOlQT40+pYYs5e5a0BrakR4mQe+GsXYP3r1jTCrQa6MbVSKo8uL0OlmrDqZesbdQ51QsvzRt+W7Dp1hRlOLpLK9otmv2B0q9EsO7yMadumlejSgQUWeRFZLyIxTn4iiyOAMWaOMSbCGBMRFuYbtBy0mQAAEqhJREFUy9XN3TeXP237E93qdePdru86nZMm26q98czbfIKRnRryWO6VnrKtfhkyU6HXezqNsFIlLTgEHn3D+ia965M8d0e2qUPfNrX5+/dx7Dp12eXTTGw7kadbPs2SQ0v449Y/llih9y9oB2PMw4V43rNAvRy/17W3+TRjDDOiZzBn7xx6NOzBtM7T8i3wxxNv8LvP99KmXiiTejR1vtOB5RC7wlrYoLqTCcqUUu7XahDsmg/rX7NOxlb87zUfXu/bkh0nLvPikmhWPdcl79rLWN07L7Z7ET/x48N9H+IwDqZ0mIKfm0fJuau7ZgUwVESCRKQh0ATYXsBjSrX0rHRe2fQKc/bOoX+T/rzZ+c18C3xqRhbjP92Fv58w8xft8k4+BtYCBqtetsbr3v+cG9MrpfIlAj3fhfTrsH5qnrtDggN4b/DPOZV0kynLY1z2u4sIz7V9jjGtx/DFkS94YcMLbp/npqhDKPuJyBmgI7BKRNYCGGP2A0uBA8A3wARjTN6p23xEUmoSo9eNZsXRFYxvM56pHafm++lsjOF/l+8nNv4qfx3chjqh5Z3vuHYypCRB5EzwK/BLl1LKnWo0hQ7jYfeCPPPaALQPr8bEB5vwxa6zLI1yfQGUiDCx7UQmt5/MxrMbGbl2JAk33Xc+sqija740xtQ1xgQZY2oaYx7Lcd80Y0wjY8zdxpg1RY/qWkZWBtN3TffIBQc7L+xk0NeDiEmM4Z0H3mHcz8e5HEWTbf6WkyyJOs2vuzeme9MazneKW2+d0e/0AvyslRuSK6VuW9ffQUgdWPUSZOUdDvn8Q03o3Lg6ry7fz/5z+dejoU2HMr37dI4lH2PIyiFEX4x2S2SfuOI1OiGaeTHz6L+if4nNFZHhyGDWnlmMXDuSYL9gFvRcQI+GPQp83Ka4RF5feYCHm9VwPm0BWGu1fv0CVL/LmvpUKeUdgipZw5jP74MdeRfw9isnTB/ahqoVAhn/6S6SUzLyfbpu9bqxoMcCgv2DmX9gvlsii7dMhwkQERFhoqKiCvXY/Zf2M2njJE5cPcHwpsOZ0HYCIYEhBT+wEPYm7GXqlqkcuXyEXuG9eLXDq1QMqFjg405eusETMzZRMySIz8fdT+VgF332K1+0Fi4YuRbqty/m9EqpIjEGPh0EJzfDhK0QWj/PLjtPJjFk9la63hXGnBEReddlziU5LZlyUo7KgU6udL8FIrLTGBPh7D6fOJIHaFGtBUv7LGVY02EsOriIPl/2YdnhZWQ48v8kvR2nr51m8k+TeXL1kySnJTO9+3Te6vLWLRX4KzfTGTlvByLwwYgI1wX+8DqI+sia01oLvFLeRwR628OZv37BKvq53NOgKlP6NOe7gxd5+5uDBT5llaAqhS7wBfGZI/mcYi/F8ub2N9l9cTe1K9ZmRIsR9GvcjwoBFQr1fPsv7WfpoaWsiFuBXzk/hjUdxrOtn3U5VXBuqRlZPPnhNvaeSWb+qPvoEF7N+Y43LsGsjtbUBWM2gH9QofIqpUrA9g+sa1j6zoI2w53uMmV5DPO3nOTtAa0Ycm/eI/7ikt+RvE8WebBGsPxw5gfm7ptLdEI05f3L07VuVx5u8DDtarQjrILrC68cxkHspVg2ndvE+pPriU2KJdgvmMjGkYxuNZqaFWveco4sh2Hcwp18G3uBGcPa0at1LVeBYekIOLTGKvB6slUp7+ZwwLyecDEWJmyHynnrQmaWg6fn7WDL0UssGNWejo1cHOAVUZks8jlFX4xm5bGVrDuxjstp1hVptSrWokFIA2pUqEGgXyDGGJLTkjl/4zxHk4+SkpkCWN1AkY0j6RXe67b7+I0xvPJVDJ9uO8XUPs15qpPzOeQB2LMYvnzWWump84uF/C9VSpWoxCMwqxPc/TgMdn7iNDklgwGzNnPhaiqLx3SgRW0n6zUXUZkv8tkyHZnEJMawN2Ev+xL3ce76ORJSEkjPSgcgNCiUsAphNA5tTIvqLehYqyPVyhfuk9cYw2tfH2De5hOM79aI3z7u4opWsFaGf78L1GwBT63SeeKVKk1+fA++ew0GfgQtBzjd5eyVFAbN2kxapoPPxnYkPOzWunpvlRb5EpazwI/u0pA/9Gzmeux8Zjp89Ji1MPfYjXDHnSWaVSlVRFmZ9nv4CIzbAlWczsXIsYTrDHp/C0H+5fhs3P2uL4IshDIxusZbOByG11daBX5U5wIKPFiXSJ/bBZF/1wKvVGnk5w/951jF/quxeRb/zhYeVolPRt7HtbRMhs3Zyukk905nkE2LfDFKz3Tw0tJoPt5kzSr5Sq8CCvzB1bB1Jtw7GpoXy6SeSilPqNYIerwFxzfC1n+43K1lnSrMH3kfV26mM3j2Fo46WSO2uGmRLybXUjMY9ckOvoo+x28eu5tXexdQ4K+cgq/Gwc9aW9OYKqVKt7a/hKa9rf758/tc71b/DhaP6Uh6poMhs7cQffqKW2P5TJF3tphuSYm7eJ2+Mzex+egl3hnYmgndG+df4DPT4LOnrQUIBs2DACcLhSilShcR6PM3KH8HfP4MpDtfEhCgee0Qlo7tSHCAH0Nmb+HrPedc7ltUPlHk45NT6DH9RzbFJZb4a38Tc56+Mzdx5WYGC0e1Z3BEvfwfYIw1udHZKOg70/qap5TyDRWrQb/ZkHDI5dWw2RqFVWL5hE60rluFiYt2M339EbdE8okifz01kwyHgyfnbuOtNQfJcLHWYnG6lprBb5ftYezCnYSHVWTFxM63dqHD9g9g90J44DfaD6+UL2rUHbpPhn1LIWpuvrtWqxTEwmfaM6BdXcoHuqcc+8wQypvpmfxxZSyLtp+iWa0Q3ujbgnsaVC3mhNbwyHUHLvD61weIT05hXLdGPP/QXc4X/cjt+I8wPxKaPApD/wnlfOIzVimVm8MBi4bA0Q3WRIN178l39+w6XNA05a6UqXHya/efZ+qK/cQnpzLwnro8/1AT6lUt3Jw1ucWcTebNNbFsirtE4xqVeHtAq1v/IEk8AnMfgYph8Mx31rqRSinfdTMJZncFkwWjv4fKLtZwLgZlqsgD3EjLZMaGOD788RgOA5FtajOyU0Na1A657U/KzCwHP8Ul8uGPx/kpLpEq5QN46ZG7GN6+PgF+t3gkfu0CzH0YMlJg1LdQNZ/pDZRSviN+D3z0OITdDU+thsDiOeDMrcwV+WzxySnM2XiMRdtPkZrhoFFYRXq3rs39jarx83qhBAc4nz4g6UY6u05e5ofDCayJiSfxejo1KgcxsnNDhrevT4iraYKdSbtuTWKUeASeWgl18v/appTyMQdXw+Lh0Kw3DJrvlm7aMlvks125mc7qfef5KvosO04kYYw12ql2lfLUDAmiYpA/WQ7DjbRMzlxO4dINay6b4IByPNS0Jr1a1+KhZjUI8r/NOWUy02DRMDj2/zBsEdz1WIEPUUr5oC0zYe0foNPz8Mjrxf70+RX5MrE6dGiFQIa3r8/w9vW5cjOd7ceTOBB/lWMJN0i6kc7V1EwCyglVKgTSrFYId1avSNt6ofke7RcoM92aOvjod/DEDC3wSpVlHcZb81Ntmg4VqlnFvoQUqciLyCBgKtAMuM8YE2VvvxOIBQ7Zu241xowtymsVl9AKgTza4mc82sJ9J0HIyoDPnoLD30Cv96DdL933Wkop7ycCPf8MKZfh2ykQWAnuHVUiL13UI/kYoD8w28l9R40xbYr4/KVPZhosGwmHVkHPd0vsf6RSysuV87MmMsu4Cav+B/wCoN0I979sUR5sjIk1xhwqeM8yIjUZFg6Agyuhxztw32hPJ1JKeRO/ABj0CTR6EFZMhC2uJzMrLu68GqehiOwWkR9EpIurnURkjIhEiUhUQkKCG+O42dV4mNcLTm2B/h9A+2c9nUgp5Y0Cgq2BGM2egLW/hw1v5jv9QVEVWORFZL2IxDj5ye+a/HigvjGmLfAS8E8RcXr1jzFmjjEmwhgTERbmet3VAl06WvjHFtWprTCnK1w6BsOXQOvBnsuilPJ+/kEw8GNo8yT88BZ8MQYyUt3zUgXtYIx5+Haf1BiTBqTZt3eKyFHgLsA9yz6d3GwdRXecAA9OAf9At7xMHsbA9jnW0KjQ+jBiOdRoVjKvrZQq3fz8IXIGVL0Tvn/DmrVy2D+L/WXcMoRSRMKAJGNMloiEA02AY+54LQBqt4OIkbD571bBHzDX/VeVXo2H5ROsIZJNHrW6aMqHuvc1lVK+RcSarLBaY6hcyy0vUaQ+eRHpJyJngI7AKhFZa9/1ALBXRKKBZcBYY0xS0aLmIyAYev3FWi09MQ5m3Q8//dUayljcsjKtmST/0cH6QOn5LgxfqgVeKVV4LfpB/Q5ueWrfu+L1ymn4ZpI1wiWsqTXlZ9PeRb+U2OGAI+usVV8uHoA7u0Dv/4PqjYv2vEopVURl64rX0How9FM4tAbWToalv4Qaza0rzppH3v7sj6nJcGC5dVlywkEIbQCDF0CzPtZXLaWU8mK+dySfkyMLYr6AH9+1CrR/sNV/Ht4N6rW3+sFyL72XkWJNJnZmO8R9B3HrISsdaraE+5+Dlv2tsa5KKeUlytaRfE7l/KD1IGg1EM7uhD2LrW6c2BX2DmLNIxEcYn0gpN+AmzmWEAypA/c+Y/WX1b1Xj9yVUqWObxf5bCJQN8L66flna0x9fLR1xH79AqRdsz4QAspDSF2oFg51IqxhkVrYlVKlWNko8jmJWCdL9YSpUqoM0EVGlVLKh2mRV0opH6ZFXimlfJgWeaWU8mFa5JVSyodpkVdKKR+mRV4ppXyYFnmllPJhXjV3jYgkACcL+fDqQGKBe3leachZGjKC5ixumrP4lHTGBsYYp0vreVWRLwoRiXI1QY83KQ05S0NG0JzFTXMWH2/KqN01Sinlw7TIK6WUD/OlIj/H0wFuUWnIWRoyguYsbpqz+HhNRp/pk1dKKZWXLx3JK6WUykWLvFJK+bBSX+RF5HEROSQicSIyydN5chKREyKyT0SiRSTK3lZVRL4VkSP2v3d4INdHInJRRGJybHOaSyx/s9t3r4i083DOqSJy1m7TaBHpmeO+39s5D4nIYyWUsZ6IbBCRAyKyX0Set7d7VXvmk9Pb2jNYRLaLyB4752v29oYiss3Os0REAu3tQfbvcfb9d3o45zwROZ6jPdvY2z32PsIYU2p/AD/gKBAOBAJ7gOaezpUj3wmgeq5t7wCT7NuTgLc9kOsBoB0QU1AuoCewBhCgA7DNwzmnAi872be5/f8/CGho/134lUDGWkA7+3Zl4LCdxavaM5+c3taeAlSybwcA2+x2WgoMtbe/D4yzb48H3rdvDwWWlFB7uso5DxjoZH+PvY9K+5H8fUCcMeaYMSYdWAxEejhTQSKBT+zbnwB9SzqAMWYjkJRrs6tckcB8Y9kKhIpILQ/mdCUSWGyMSTPGHAfisP4+3MoYE2+M2WXfvgbEAnXwsvbMJ6crnmpPY4y5bv8aYP8Y4EFgmb09d3tmt/My4CER9y/MnE9OVzz2PirtRb4OcDrH72fI/w+3pBlgnYjsFJEx9raaxph4+/Z5oKZnouXhKpc3tvGv7a+8H+Xo7vJ4TruroC3WUZ3XtmeunOBl7SkifiISDVwEvsX6FnHFGJPpJMu/c9r3JwPVPJHTGJPdntPs9vyriATlzmkrsfYs7UXe23U2xrQDegATROSBnHca63uc141h9dZctllAI6ANEA/8xbNxLCJSCfgceMEYczXnfd7Unk5yel17GmOyjDFtgLpY3x6aejiSU7lzikhL4PdYee8FqgK/82BEoPQX+bNAvRy/17W3eQVjzFn734vAl1h/sBeyv6bZ/170XML/4iqXV7WxMeaC/eZyAB/wny4Ej+UUkQCswvmpMeYLe7PXtaeznN7YntmMMVeADUBHrO4NfydZ/p3Tvr8KcMlDOR+3u8WMMSYN+BgvaM/SXuR3AE3sM++BWCdeVng4EwAiUlFEKmffBh4FYrDy/cre7VfAcs8kzMNVrhXACHt0QAcgOUc3RInL1Y/ZD6tNwco51B5t0RBoAmwvgTwCzAVijTHv5bjLq9rTVU4vbM8wEQm1b5cHHsE6f7ABGGjvlrs9s9t5IPC9/c3JEzkP5vhgF6zzBjnb0zPvo5I6w+uuH6yz1oex+u0mezpPjlzhWKMT9gD7s7Nh9Rd+BxwB1gNVPZBtEdZX8wysvsFRrnJhjQaYabfvPiDCwzkX2Dn2Yr1xauXYf7Kd8xDQo4QydsbqitkLRNs/Pb2tPfPJ6W3t2RrYbeeJAabY28OxPmTigM+AIHt7sP17nH1/uIdzfm+3ZwywkP+MwPHY+0inNVBKKR9W2rtrlFJK5UOLvFJK+TAt8kop5cO0yCullA/TIq+UUj5Mi7xSSvkwLfJKKeXD/gUGlpBx9FpODgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bUs3Flve+P4PvzfudSIyM4bVVYOeqda6Mf+okOKWAUUVeKXhOrYKLv0LriU+dSRN4N5Fh//OlrKMla99q8PhS/xxEJEXw1s632Buyl0+8PmF6k+lYmBSsbfQ8Sniw8ZWNNHRpyPTj01lwasFjvXFsLUxZO6gBpe0tGbzuNNciErQDlg7aRuZpifDbENBlGugnUHKbKvJKwXLbF3aOhyododnHTzwtMiGNt9b6YGosWDeoIfZWT585ciPuBv3+7MfN+JssbbOUATUH5NvhmWext7Dny5e+5M3qb/K/y/9j5IGRJGc87JtTopg56wc3xMzEiAFrfLgTe787Zcnq8MpirZnZgTkGSq/kNlXklYIjLQF+HQLFnKHHk5tsJadnMuS7U0QnprPmOVoVBMQEMHDnQNJ16aztuJZWZVvpIXzeMjYyZlzDcYxrOI5DoYcYtGsQMakPG8WWdbRi3aAGJKZmMnCND3HJ97tT1nkd6vWHw4sgcI+B0iu5SRV5peD4YwzE3tTGjy0dcjwlK0sy8ic/Lt6OY1mfetR2fXprA7+7fgzeORhTI1PWdVxHjeI19JHcYN6s/iZLWy8lKDaIt3a+RXjSw5k1NUvbsWKAFzejk3nvB18ydFnagc6fQcmasGkYxN02UHIlt6girxQM5zfC+Z+g5Vhw837iaV/svcZflyOY9HIN2tZ4+nRHnzAfhu0ehqOlI+s7rS8QD1j/i5ZlW/JN22+4m3yXgX8OJCQ+5MEx74rFmftqLY4FRTNl6yVtnr2ppTZjKSNVjc8XAqrIK/lfzHXY/jGU84bmY5542p8Xwli69xq9PF0Z1NT9qR95JuIMI/aNoEyxMqzruI7SxUo/9fyCzsvFi9UdVpOcmczAnQO5du/ag2OveboyvFVFfvQJYc3RG9qLTlWgyxcQclyNzxdwqsgr+ZsuA34bqo2/v7oSjHOeIeMfFs/oX85Rr5w9s3p4PPWh6fnI8wzfOxxnK2dWtl9JCcsS+kqfr9QsXpN1HddhhBFD/xpKUGzQg2Nj2lelY00XZv9x+eEc+tq9oV4/OPy51sJZKZBUkVfyt/2ztRk1XZaCfc67NcUkpfP2+tPYWJjwbT/PpzYcuxx9mXd3v4ujhSOr2q8qMgX+bxXtK7Km4xqMhTFDdg0hOC4Y0LYT/Pz1OtQobcsHG84SEH5/4++O88HBHTa/AymxT/5gJd9SRV7Jv24cgSNfQP0BULN7jqdk6rIYseEMdxPS+La/FyVtnzyvPTgumHd2v4ONmQ2r26/OVy0K8pKbrRur2q9CIhm6a+iDMXorMxNWDdDWE7zzP19txo15Me1Bd/wd2PHkoTIl/1JFXsmf0hJgy3vaXeRT+tJ8vvsqx4KimdOjFnWfsm1fVEoU7+15DyNhxKr2qyhVrJQeQhccFewrsKr9KtKz0hny1xDuJN4BwMXOgq/7eXInNoWRP5/VNgZ39YJW4+DCL9oDcKVAUUVeyZ92TYTYW9p8eDPrHE/ZFxDBVweC6NOwLD09XZ/4UUkZSQzfM5yY1Bi+eukrytr+9026C5PKDpVZ2X4lSelJvLP7He6l3gPA082BKV1qsv9KJEv23n9A2+xjKNtI2wz8ntqHuSBRRV7Jf67thjPfQdMPoVzjHE8JvZfMqJ/PUaOULVO71HziR2XoMvj4wMdcvXeVRS0XUbPEk88tiqo5VmNpm6XcSbzDiL0jHqyM7deoHK/Vd2XJ3mvs9Y/QHni/ukLrG7T5XcjKMnBy5XmpIq/kL8kxWvtgp+pab5ocpGdm8f4GbSjh6371n9h0TErJtOPTOHbnGFO9p9Lctbk+kxdYXi5eLGi5gIvRFxlzcAwZWRkIIZjdw4OapW0Z+bMfN6KStKGzTvMh5Bic/MbQsZXnpIq8kr/s+ASSo+DVb8HEPMdT5uzw59ytWD7rVRu34jkP5QB8c/4bfg/6nRF1R9Cjcg99JS4UXir3EpMaT+Lw7cNMOzYNKSUWpsZ8088TYyPBu9/7kpqhg7p9oXIH2DsDogINHVt5DqrIK/nHpc1ad8mWY6FUnRxP+eN8GOuO3WBIs/J09Hjyw9PdN3fzld9XdK3YlWG1h+krcaHSq0ovhtcZzu9Bv7Ps7DJA63Gz+PW6BIQnMHP7ZRACuizR+vdvHa7tr6vka7lS5IUQa4QQd4UQFx95zVEIsVsIce3+rzk3G1EUgMRIbVVr6fpP7C4ZEp3M2N/OU6+cPeM6VXviRwXEBDDxyERqO9VmiveUAttN0hDerfMur1V+jZUXVrItaBsArauW5J0WFfjhZIi2faBtKei0QNs28MTXBk6sPEtu3cmvAzr+47VxwF4pZWVg7/3vFSVnO8dq0ya7f5XjqtZMXRYf/XwWIWBZn3pP3N0pKiWKD/Z9gK2ZLUtaL8HcOOchHyVnQggmNppIQ5eGTD02lTMRZwAY06Gq9pfrb+cJiU6G2q9DlU6wbyZEXXvGpyqGlCtFXkp5CIj5x8vdgO/u//47IOfVLIpy5U+4+Bu0/FTraZ6DpXuvcTYkltk9auHqkHPr4HRdOqP2jyI2NZalbZYWudWsucXU2JTPW31OmWJlGLl/JLcSbmFqbMTSN+ohBIz48QzpOqn1tjGxgC1q2CY/0+eYvLOU8u99xMKBHJcXCiGGCSFOCyFOR0ZG6jGOki+lxmnDNCVrQNOROZ7iExzD8v2B9PR0pWudJzcSm3NyDn6RfsxqNqvQtQzOa3bmdix/aTk6qWPE3hEkpCdQ1tGKBT1rcz40jgU7A8DGRWtLHOoDx780dGTlCfLkwauUUgLyCcdWSCm9pJReTk5OeRFHyU92T4XEcOi6PMfNuOOSMxj501nKOVoxreuT57hvvraZ3679xtBaQ+ng3kGfiYsMN1s3vmj9BSHxIXxy6BN0WTo6epRioLcbq44Es+dyBNTqBVU7w/45EBNs6MhKDvRZ5COEEKUA7v96V4/XUgqiG0fAdy00Hg6untkOSymZsOUCdxPSWPJGPYo9YY9W/2h/Zp+cTaNSjRhRd4S+UxcpDVwaMLHxRI7ePsqXftrd+vjO1alRypZPfzvP3cQ06LwQjExg+yhtsZSSr+izyP8ODLz/+4HAVj1eSyloMlLg9w+0BTatJ+R4yi++ofxxPozR7atS5wl9aeLS4hh1YBT25vYsaLEAY6Mnd6BU/pueVXo+mHGz5+YeLEyNWfJGXZLSMhn763mkbWloOxWu71e9bfKh3JpC+SNwHKgqhAgVQgwB5gHthBDXgLb3v1cUzYG52mYgXZbm2JsmOCqJab9foknF4rzTokKOH5Els5h4ZCIRyREsarUIRwtHfacusiY0mkDtErWZeGQiQbFBVHa2YXynauy/EskPJ0PAazC4NoBd4yEp2tBxlUfk1uyaPlLKUlJKUymlq5RytZQyWkr5kpSyspSyrZTyn7NvlKLqjh8cW65tGF2hZbbDuizJ6I1+mBob8XnvuhgZ5TzPffWF1RwMPcinDT6ljlPOi6eU3GFmbMaiVouwMLFg5P6RJKQnMMDbneaVSzDrj8sERadoi6RS4+CvSYaOqzxCrXhV8laWDrZ9BNYloP3MHE/59lAQZ0JimdGtJi52OfeHPxF2guV+y+lcvjNvVH1Dn4mV+1ysXVjUchGhCaFMODIBhGRhrzpYmBoz6mc/MkpU12ZIndsA1w8YOq5ynyrySt46tRrC/KDDHLDMvgjaPyyexbuv8nKtUk+cLhmVEsX4w+Nxt3VnqvdUtaI1D3m5eDGmwRgO3DrAyvMrcba1YG6PWpwPjWPZ3mvQ4hNwrAjbRmrPXRSDU0VeyTsJ4doKyQqtweO1bIfTM7P4eOM57CzNmNk9531as2QWk45MIiE9gc9afoaVac4LoxT96VutLy9XeJmvzn2FT5gPnWqVoqenK8v3B+J7J1lbJHUvGA4uMHRUBVXklby0awJkpsHLi7RGV/+wdO81/MPimfdqLRyts8+ZB1h/aT1H7xzl0wafUsWhir4TKzkQQjCl8RTK2ZRj7OGxRKVEMbVLDUrbWzLml/OkujaFOn3h2DLV8iAfUEVeyRuBe7XWBc0/huIVsx0+G3KPrw4E0svTlbY1ct579ULkBZacWUI7t3b0qtJL34mVp7AytWJRq0UkpCcw/vB4rMyMWPBabYKjkli46wq0mw6mVlrraDV33qBUkVf0LyNV2wTasWKOrQtS0nWM3niOUnaWTO6SczuChPQEPjn0CSWtSqpx+HyiikMVJjSawImwE6y4sIImlUrQr3E5Vh8NxjfaBNpM0ubOX1ZLZAxJFXlF/44s1ubEv7wITLPPlpm/M4DrUUl81rM2tham2Y5LKZl+fDrhSeHMbzEfO3O7vEitPIcelXrQpUIXvvb7mpNhJxnXqTql7Sz55JfzpNYdCC61tGG6tERDRy2yVJFX9CsqEI58rvU4qdg62+HjQdGsO3aDt5q406RSzl0jN13bxK4buxhRbwR1S9bVd2LlXxBCMKnxJNzt3Bl7aCypWbF81rM216OSWLQnCDovgvjbcOgzQ0ctslSRV/RHStgxGkwsof3sbIeT0zMZ+9t53ItbMbZjzpuABMUGMc9nHo1LNWawx2B9J1b+AytTKxa1XERSRhLjDo+jUQUH3mxUjlVHgvGVlaFuPzi+HCKvGDpqkaSKvKI/l7dqi2LaTAKb7A9TF+66SkhMMvNfq42lWfaeMxm6DO2hnqkVc5vPxUio/13zq8oOlZnQaAInw06y9tJaxnd+ZNim1WStdYV6CGsQ6k+Noh/pydrydmcPra/JP/jevMfaY8H0b+xGowrFc/yIb85/g3+MP1O9p6oNQAqA7pW608G9A1+e/ZIbCQEs+HvY5mgMvDQFgg9q+/gqeUoVeUU/jn4Bcbe0vUD/sZ1faoaOT389R2k7S8Y+Ya/Wc5HnWHVhFd0qdqNNuTZ5kVh5QUIIJjeeTAmrEow9NJZ6blb0vT9s41eyh/YQdvcUtRI2j6kir+S+ezfgyBfg0RPcm2Y7vGzfNYIik5jzaq0ce8SnZKYw6cgknK2cGdtwbB4EVnKLnbkdc5vNJTQxlLk+cxnXqRolbcwZt/kSme3maH/xH1tu6JhFiirySu7bNRGMjKHdjGyHLt6O45uD1+np6UrLKjnvBPaF7xfciL/BrKazsDGz0XdaJZd5uXgxxGMIWwK3cDx8HzO6eRAQnsCKW6WhehdttlV82LM/SMkVqsgruStwLwRshxZjwK7MY4cydFl88ut5HK3NmPxyzouejt85zoaADfSr3o+GpRrmRWJFD96r+x61S9Rm+vHp1HaTdKzpwpI91wj1mgBZmbB3uqEjFhmqyCu5JzMd/hwLDuXBO/s2fN8cCMI/LJ5Z3T2ws8q+6Ck+PZ7JRydT3q48H9X/KC8SK3piamTKvObz0GXpGH9kPFO6VsPM2Iix++ORjd+Hcz9CqK+hYxYJqsgrucfnW4i+Bh3ngYn5Y4euRSSwbF8gL9cuRYeaLjm+fd7JeUSlRDGn2RwsTHLuI68UHGVtyzKx8UR8I3z54+YGxnaqxtHAaLbYvAHWJWHnODWlMg+oIq/kjoRwODAfKreHqh0fO6TLknzy63mszY2Z3rVmjm/fc3MP265vY1jtYXiU8MiLxEoe6FKhC53cO/Gl35fUrhCPl5sD0/8KIaHZBAj10ZrWKXqlirySO/ZMA12adhf/D2uPBuN3K5ZpXWtSoph5tuNRKVHMOD6DGsVr8Hbtt/MgrJJXhBBM8p5ECcsSTDo2kWndqpCUlsnkG7XApbY2pTI92dAxCzW9F3khxA0hxAUhhJ8Q4rS+r6cYQKivNsbaeHi2NsK3YpJZ9NdVXqpWMsednqSUTD82naSMJOY2m4upUfaxeqVgszWzZWbTmQTHBfNH6GqGt6rElnMRnK05Vutrc2yZoSMWanl1J99aSllXSumVR9dT8oqUWpdBaydtRs1jhySTtlzESPDEnZ62BG7hQOgBPqr/ERXsK+RVaiWPeZf2pm+1vnzv/z0Nq8dQwcmaj45bo6vWVVs4lxBu6IiFlhquUV7M5S1w64TWn8b88Tnt286HcfBqJKPbV6W0vWW2t95OvM38U/PxcvaiX41+eZVYMZCRniNxt3Vn+okpTHjZnZCYZL6zGgS6DPeHbJoAACAASURBVDgw19DxCq28KPIS+EsI4SuEGPbPg0KIYUKI00KI05GRkXkQR8k1GamweyqUrAn1+j92KC45gxnbLlHb1Y6BTdyzvfXvvVoBZjWbpZqPFQGWJpbMbjabiOQIDkStomud0szzSSPOYwCcWa+6VOpJXvzJaialrA90At4XQrR49KCUcoWU0ktK6eXklPMKSCWf8vkWYm9Ch1naCtdHzNvpz73kDOb0qIWxUfZhmh/8f+B0xGnGNhhLmWJlsh1XCqfaTrUZWmsoW4O20rreXcyMjZgQ3RFpaq09vFdynd6LvJTy9v1f7wKbAbWMsTBIioJDC6FyB6j4eAMxn+AYfvS5xZBm5fEok30Xp6DYIL7w/YJWZVvRvVL3vEqs5BPv1n6X6o7V+eLcHN5rU5I/gjK4WnkIXNkBN48ZOl6ho9ciL4SwFkLY/P17oD1wUZ/XVPLI/jmQngTtZz72clqmjvGbzuPqYMnItpWzvS0jK4MJRyZgbWqt9motokyNTZnTbA6J6Ylc1a2jeikbhl1tRFYxF/hrsloglcv0fSfvDBwRQpwDfIA/pJQ79XxNRd/u+oPvWq1PvFPVxw59c+A6QZFJzOrugZVZ9g6TK8+v5HL0ZSZ7T1Y94ouwSg6V+KDeB+y7tY9O3re5mSDZUWIQ3D6tNv7OZXot8lLK61LKOve/akops+8BpxQ8f00GMxtoNe6xlwPvJvLl/kC61ilNq6ols73tYtRFVpxfQZcKXWjn1i6v0ir5VP8a/alfsj4/Bi6lu5cVH1+tSZpDFa15mS7D0PEKDTWlQfl3AvdA4G5tTrz1wzvxrCzJhM0XsDA1YvIr2TtMpmamMuHIBEpYlmBco3HZjitFj7GRMbOazkIndSTa/IyVhRmL5JsQcx181xk6XqGhirzy/HSZsGsSOLhDo3ceO/SL7y18gmOY0Lk6TjbZWxcsObOE4LhgZjadia2ZbR4FVvK7srZl+aj+R5wMP0pn71BWhFfirqMXHJgHaQmGjlcoqCKvPL9zGyDSH9pOf6zLZFRiGnN2BNCwvCO9vcpme5tPmA/f+39Pn2p98C7tnZeJlQKgT7U+eDp7ciByNbXcYExcT0iOUjtI5RJV5JXnk56szagp4wU1uj12aOb2y6Sk65jToxZG/5gTn5CewKSjk3C3dWeU56i8TKwUEEbCiJlNZpKRlYF92a0cTi6Lv0NrOP4lJEUbOl6Bp4q88nxOfg0JYdqWfo9Mezx0NZKtfncY3roilUoWy/a2BacWEJEcwexms7E0yd7aQFFAG7YZ6TkSv+gTNKkTzKiIzsj0RDi62NDRCjxV5JVnS47RNuau0vGxjblTM3RM2XqR8iWsea9VxWxv2xeyjy2BWxhaayi1nWrnZWKlAOpTrQ/1S9YnKGsDty3tOWL1EtJnJcTfMXS0Ak0VeeXZDi2E9ER4aepjL684dJ0b0cnM6FYTc5PH2xpEp0Qz/fh0qjtW593a7+ZlWqWAMhJGzGw6E11WJuWr/cm4ey8jdTo49JmhoxVoqsgrT3fvJpxaCXX6gvPDqZE3o5NYvj+QV2qXonnlx3sOSSmZeWImCekJzG42G1Nj1SNeeT7lbMvxUf2PCE4+jXCNZIvxS8gz6yEm2NDRCixV5JWn2z8bhBG0nvDgJSkl036/hJlxznPit13fxt6QvXxY70MqO2RvbaAoT9O3el/ql6xPut1m5qa2QoexNqVS+U9UkVeeLOw8nN8Ijd4Fu4edInddimD/lUhGtauCs+3jG26HJ4Uz9+Rc6pesT/8a/f/5iYryTEbCiBlNZyDRYVXlKGsz2yHP/6y101D+NVXklSfbMw0s7KDZw6mPSWmZzNh2iWouNgz0dnvs9CyZxaSjk8iSWcxqNgvjf7QfVpTn5Wbrxof1PyRGnuMba3dSjSy1f1Uq/5oq8krOrh+AoL1a+wJL+wcvL913jTtxqczu4YGJ8eP/+/wY8CMnw07yaYNPKWuTfVGUovwbb1Z/k/ol60OpvXye+RL4b4PbvoaOVeCoIq9kl5Wl7fhkVxYavP3g5asRCaw+HMzrXmXxdHN87C3X466z2HcxLVxb8GrlV/M6sVII/T1sI4wy2VwmjVhhQ9beWYaOVeCoIq9kd2kThPlp+7aaamPuf2/KXczChLGdqj12emZWJpOOTMLCxIJp3tNUj3gl17jZuvFhvQ9JswhgjHljjK7vgxtHDB2rQFFFXnmcLgP2zQJnD6jV68HLm8/exic4hnEdq+FobfbYW1ZfWM2FqAtMbjwZJyu1haOSu96s/iZ1nepy2uUWV4wdydw7S20s8i+oIq88zm8D3AuGNpMf7Nsal5zBnB3+1Ctnn60B2eXoy3xz7hs6l+9MB/cOhkisFHLGRsbMaDoDI+NMPnR0x/jWcbhx2NCxCgxV5JWHMtPg4AKtCVmVhwV74V9XiElKZ1Z3j8cakKXp0phweAKOFo5MaDQhp09UlFxR3q48H9QbwZ1iUfxk5UTqbnU3/7xUkVce8v0O4kO1sfj74+rnQ2P5/uRNBjZxp2bpxzflXnZmGUFxQcxoOgM78+wbditKbhpQYwDVHGqysIQtSeGnIPigoSMVCKrIK5r0ZDi8ENyaQYVWAOiytIetTsXM+bhdlcdOPxV+ivWX19O7Sm+almma/fMUJZcZGxkzr8VsMo0lU4o7E//nDHU3/xz0XuSFEB2FEFeEEIFCCLXvW351ahUkRjx2F7/BJ4TzoXFMeqUGNhYP+88kpicy6cgkytqUZbTXaEMlVoqgivYVebfOexwqZsqJpMvogg4YOlK+p9ciL4QwBr4EOgE1gD5CiOzNThTDSkuAI4uhUltw03ZuikxIY8HOAJpWKk6X2qUeO32ezzzCk8OZ03wOVqZWhkisFGFv1x5MGctKzChRnOAdU9Td/DPo+06+IRAopbwupUwHfgK6PeM9Sl478Q2kxEDriQ9emrvDn7SMLGZ083hs3vvekL1sDdrK0FpDqeNUxxBplSLOxMiEJW3nkWBkzArj2yQH7DZ0pHxN30W+DHDrke9D77/2gBBimBDitBDidGRkpJ7jKNmk3INjy6DaK1CmPgAnrkez6ext3mlZgYpOD3d7ikqJYvqx+z3i66ge8YrhVHWsyqvlB/BnMWu2756s7uafwuAPXqWUK6SUXlJKLycntZAmzx1bDmnxD1oJp2dmMXnLRco6WvJ+60oPTpNSMu3YNJIykpjbfC6mRqpHvGJYE5p/hEuWPV9ZJxB46ldDx8m39F3kbwOPrp5xvf+akh8kRcGJr8HjVXCuCcCao8Fcu5vItC41sTB92EVy07VNHAw9yCjPUVS0z77Vn6LkNVMjU6a1XkyssRFLfeeou/kn0HeRPwVUFkKUF0KYAW8Av+v5msrzOrIYMlOg1XgAbsemsGTPNdrXcOal6s4PTruVcIsFpxbQqFQj+lbva6i0ipJNU3cv2hrVYr9VJj/8Nd/QcfIlvRZ5KWUmMALYBfgDG6WUl/R5TeU5xd/Rpk3W6QMltN2bZmzT/tNM6fJwApQuS8fEIxMxFsbMajoLI2HwET5FecyUV7/FPT2LFaE/cC8l1tBx8h29/4mVUu6QUlaRUlaUUqqu//nF4UWQlQktPwVgX0AEuy5F8OFLlXF1eDgtcu2ltZy9e5bxjcbjYu1iqLSK8kS2xWwZ5NCdOGPJ2N/fN3ScfEfdlhVF925qLQzqDwAHd1IzdEz9/RKVShZjSLPyD04LiAngS78vaefWjlcqvGLAwIrydD26T6F3fCbHU8+zO/iAoePkK6rIF0WHFmibczcfA8BX+wO5FZPCzG4emJlo/0uk6dIYf3g8DuYOTGk8RfWIV/I1YWJO76rvUiE9g2mHxpOYnmjoSPmGKvJFTVQg+P0IDYaAXRmuRybyzcHr9KhXBu+KxR+ctuzMMgJjA5neZDr2FvZP+UBFyR8qvTScT+9lkSATmXFUPYT9myryRc3BeWBiDs1GIaVkytZLmJsaMb7zw92eHm0+1ty1uQHDKsq/YGJGzcYfMzAunj9DtnD8znFDJ8oXVJEvSiIuw4VfodE7UKwkf1wI40hgFJ90qEpJG22bv7i0OMYdHoebrZtqPqYUOPZNBjEwyQyXdMH4Q5NJykgydCSDU0W+KDkwB8xtoMmHJKRmMGPbZTzK2PJmIzfg4arWmNQY5rWYp5qPKQWPiTm2rcfwWVQY0akRLD692NCJDE4V+aLizlnw3wbe74OVI1/suUZkYhqzutfC+P5uT5uubWJPyB4+rPchNYvXNHBgRflvzBq8RRXhQLs4Y36++jOnwk8ZOpJBqSJfVOyfA5YO0Pg9Lt+JZ92xG/RtWI66ZbWHqsFxwcw/NZ9GpRoxsOZAA4dVlBdgYo5Fq9HMjr2BRYYNk49OITkj2dCpDEYV+aIg5CRc+wuafkSWmS2Tt17E3tKUTztoD1szdBmMOzwOM2MzZjedrVa1KgWekedAjC1L8uHddG4nhrL07FJDRzIY9ae5KNg/C6ydoOEwfvG9he/Ne4zrVA07K62T5DK/ZVyOvsz0JtNxtnZ+xocpSgFgaoFZy9H0T7+GU2w1Nvhv4EzEGUOnMghV5Au76wch+BA0H01Mhilz/wygobsjPT1dATgZdpJ1F9fRs0pPXir3koHDKkouqj8QnbUzU++FYCFKMOXYFFIyUwydKs+pIl+YSQn7Z4NNafAcxPw/A0hMzWRWD223p9jUWCYcnoCbrRufeH1i6LSKkrtMLTBuPoqWBFDyZj1uxt/ky7NfGjpVnlNFvjAL3AO3TkLLTzh9O5mfT99iSPPyVHG20aZLHp9GTFoMC1osUNMllcLJ8y2yrEsyNesEdhkt+J////C762foVHlKFfnCSkrYNxPs3cis3ZdJWy5S2s6CD9tobYV/vfYre0P2MrL+SKoXr27gsIqiJ6aWGDUbRQN5EedgV2xNtGGbNF2aoZPlGVXkC6uA7RB2DlqNY93JOwSEJzC1a02szU24EnOF+T7zaVK6Cf1r9Dd0UkXRL69BSOuSTLbcQXr4awTHBfOV31eGTpVnVJEvjLJ0sG82FK9MmFsXFu++SptqJWlfw5nkjGTGHByDjZkNc5rNUdMllcLP1BLR9CPqZp6nbGQK1azbsu7SOs5FnjN0sjyh/oQXRpc2Q6Q/tB7PzB1XycySTO+qrWCddWIWIQkhzG8+n+KWxZ/xQYpSSHgNBmsnZthv5/KFljhZOjPh8IQisUhKFfnCRpeprW4tWZMDJk3ZcSGcD9pUoqyjFVuDtrLt+jberf0uDUs1NHRSRck7ZlbQ9CNqpJzBQxeMe9YQbiXc4rPTnxk6md6pIl/YnP8JYoJIbzGeKb/7U8HJmrdbVCAoNog5J+fQyKURw2oPM3RKRcl7XoPBqgRzHXew168YL7v14derv3Lw1kFDJ9MrvRV5IcQ0IcRtIYTf/a/O+rqWcl9mOhyYD6XrsfxOFUJikpnVzYMs0hlzcAyWJpbMbT4XYyNjQydVlLxnZg1NP6RCvA8tLK4TeKUpVRyqMOXYFGJSYwydTm/0fSe/WEpZ9/7XDj1fSzm7HuJCCPMcwzcHr9OtbmmaVCrB3JNzCYoNYm7zuThZORk6paIYToOhYFWcOY47OBWcwCulxpCQnsC0Y9OQUho6nV6o4ZrCIiMFDi1ElvPmkzMlMDcxYuLL1dkWtI3NgZsZWmsoTUo3MXRKRTEsM2to8gFloo/RxfE26w6k8n7dD9h/az9bArcYOp1e6LvIjxBCnBdCrBFCOOR0ghBimBDitBDidGRkpJ7jFGKn10BCGMfd3uVIUDRjOlTlXsZNZhyfgaezJ8PrDjd0QkXJHxq8DZaOTLPbzs3oZDJjmtHQpSHzfOZxK+GWodPluhcq8kKIPUKIizl8dQO+BioCdYEwYFFOnyGlXCGl9JJSejk5qaGE/yQtEQ5/ToZbS0aeKIZHGVu61nfg4wMfY2Nmw8KWCzExMjF0SkXJH8yLQZMPKB52kMHu0Szfd53R9aZgJIyYcHgCmVmZhk6Yq16oyEsp20opPXL42iqljJBS6qSUWcBKQM3Z0xefbyE5ilWmfYlKTGNW95pMOTqJO4l3WNRqESUsSxg6oaLkLw3fBksHRpttJjlDxw9H45nQaAJ+kX6sPL/S0OlylT5n15R65NsewEV9XatIS4mFo0uJdW3D/Is2DGpanlP3fuNA6AHGNBhDvZL1DJ1QUfIfcxvwHoF1yD7G1kriR58QKlm14JUKr/DN+W84HX7a0AlzjT7H5BcIIS4IIc4DrYFRerxW0XV8OaTGMu5eV8rYW9K0VgzL/ZbTuXxn+lbra+h0ipJ/NRwGFva8lbkRW0tTpm+7zMRGE3Et5srYw2OJTY01dMJcobciL6XsL6WsJaWsLaXsKqUM09e1iqzESDj+FddKtGNndElGdSrB1OPjqWBXganeUxFCGDqhouRfFrbQZARmQX8xp5GOE9dj2O8fz2ctPyMmNYbJxyYXimmVagplQXZkMTIzhRHhnehUqzi/3ZpLZlYmX7T+QvWHV5Tn0fAdsLCnU/R3eJSxZfYf/rgVq8Joz9EcuHWADQEbDJ3whakiX1DF3UaeWsUBi7bcMSmDRalNXIy+yOxms3GzdTN0OkUpGCxswft9xNU/WdAEwuNTWbYvkDerv0lL15YsOr0I/2h/Q6d8IarIF1SHFpCVpWNy7Mu0aniJPbf+5IN6H9CmXBtDJ1OUgqXRO2BhR42rX9PL05XVR65zPSqJmU1n4mDuwKeHPiUpI8nQKf8zVeQLopjryLPf84t8iWLlUzgY+R2d3Dvxdq23DZ1MUQoeCztoPByu/MEEzwwsTI2Z9vsl7M3tmddCWyA1+WjBHZ9XRb4gOjCPDGnMYtGEaKt1VC9enelNp6sHrYryXzV6F8ztcPBZzOh2VTh8LYpdl8Jp4NKAj+p/xO6bu1l/eb2hU/4nqsgXNHf9kec38qWuDUYVt1HMzIqlrZdiaWJp6GSKUnBZ2kPj9yBgO/3c46nmYsPM7f6kpOt4q+ZbtC3XlsW+iwvk/HlV5AuYjD0zuYclP5ZLJl3GsqT1EpytnQ0dS1EKvsbvgrktJkc+Y0Y3D27HpvDl/kCEEMxoOgNXG1c+OfQJkckFq8eWKvIFye0zmFz9g0HFq5JiGsz0ptOp7VTb0KkUpXCwdNCGbfy30dDyDj3qlWHFoesE3k3ExsyGxa0Wk5SRxJiDY8jIyjB02ueminwBErN9CgvsnbhuG82H9T7klQqvGDqSohQujd8DMxs4tIAJnatjYWrEhM0XyMqSVHaozFTvqZy5e4b5PvMNnfS5qSJfQCRfPcjexDN872BJ94qvMrTWUENHUpTCx8pRG7a5vBWn5CAmdK6OT3AMv/hqLYhfrvAyA2sM5OcrP/NTwE8GDvt8VJEvCKRkx45PmVXckToODZnaZLKaSaMo+tJ4+IO7+d5eZWno7sicHQFEJaYBMMpzFC1cWzDPZx7H7xw3cNhnU0W+ANi2cyEL7BIphSMrOi1TveEVRZ+sHKHRMLi0BaOoAOa86kFyeiYzt18GwNjImPnN51PerjyjD47mRtwNw+Z9BlXk8zm/sAvMCfsOO51gZbcfVU8aRckL3iO0rQIPLqBSSRvea1WJrX53OHhVm1lTzKwYy9osw0SY8MG+D4hLizNw4CdTRT4fC4kP4b1dg7GSOqaVH0FZhzKGjqQoRYOVo9aK+NJmuBvA8FYVqVDCmklbLpCSrgPA1caVxa0XE5oYyscHPiZdl27g0DlTRT6fCk8Kp9/2QRhnJTMzwZGmbd4xdCRFKVq8R4CpFRz6DAtTY2b3qMWtmBS+2Hv1wSmezp7MaDIDn3AfJhyZQJbMMmDgnKkinw9Fp0QzZNfbJKVFsyI8ggbdFoJ60Kooecu6uLZN4MXfIOIy3hWL87pXWVYeus7ZkHsPTutSsQujPUez68Yu5vvMz3c9blSRz2eiUqIY8tcQQuNvsyQ8mnJl2mBa3tvQsRSlaGr6EZjbwt4ZAEx8pTouthaM/uUcqRm6B6e95fEWA2oMYEPABlZfXG2otDlSRT4fiUyOZPCuwYTEhfLa7VI0SU+iWOdZho6lKEWXlSM0GwlX/4Sbx7C1MGV+z9pcj0xi0V9XHjt1tNdoXq7wMkvOLGHjlY0GCpydKvL5RERSBIN3DSYsMRzb0O5MyDiBqNcfnKoYOpqiFG2N3gWbUrB7KkhJ88pOvNmoHKuOBHPqRsyD04yEETObzKSVaytmnpjJL1d/MWDoh16oyAshegkhLgkhsoQQXv84Nl4IESiEuCKE6PBiMQu30IRQBu0aRGRyJDaxw5mYfhgjE1NE6/GGjqYoipkVtBoPoT4QsB2A8Z2rU8bekk9+OUdyeuaDU02NTVnUahEtXFsw4/gMNl3bZKjUD7zonfxF4FXg0KMvCiFqAG8ANYGOwFdCCOMXvFah5B/tT78d/YhLi8PLYhxOt6NoxwmMmn8MNi6GjqcoCkDdN6FEFdgzHXSZFDM34bOedbgRncyCnY8P25gZm/F5q89pWqYp045NY/O1zQYKrXmhIi+l9JdSXsnhUDfgJyllmpQyGAgEGr7ItQqj43eOM2jXIMyMzRhW6XN2nDLmC4efwdZVm76lKEr+YGwCL02F6Gvg9z0A3hWL81YTd9Ydu8GBK3cfO93c2JwlrZfgXdqbKcem8P3l7w2RGtDfmHwZ4NYj34fefy0bIcQwIcRpIcTpyMiC1af5RWy/vp3he4dTulhpFjRZwaI/YhlVwodSyVeh3XTtn4iKouQf1V4G14awfy6kJwMwrlM1qrnYMOaXc9xNSH3sdHNjc5a2WUrbcm2Zf2o+S88sNcj0ymcWeSHEHiHExRy+uuVGACnlCimll5TSy8nJKTc+Ml/TZen4wvcLxh8eT12nuqxou5ppm29jKZMZnvUjlG0EHq8ZOqaiKP8kBLSbAYnhcHw5ABamxizrU4/EtExGbzxHVtbjRdzc2JyFLRfyWuXXWHlhJdOPT8/zXvTPLPJSyrZSSo8cvrY+5W23gbKPfO96/7UiLT49nhH7RrD64mp6VenFinYrWLTzFn63Yvmx2lGMkyOh41y18ElR8is3b6jeFY4shvg7AFR2tmHKKzU5fC2KFYevZ3uLsZExU72n8natt/nt2m+8t/s9YlNj8yyyvoZrfgfeEEKYCyHKA5UBHz1dq0C4FH2JPtv7cOLOCSY3nswU7yn86hvGjz63GN/YggqB30GdvlDG09BRFUV5mvYzIStTewh7X5+GZelcy4WFu648thr2b0IIPqz/IbOazuLM3TP03dGXoNigPIn7olMoewghQgFv4A8hxC4AKeUlYCNwGdgJvC+l1D35kwqvLJnFd5e+o9+OfqTp0ljdYTW9q/bmbMg9pm69RIsqTgxLXQNGpvDSFEPHVRTlWRzcwft9OP8ThGobewshmNujNs62FozYcJaYpJyblXWr1I01HdaQnJFM3z/6si1om97jivzUZ8HLy0uePl3wdkN/krDEMKafmM7R20dpXbY1M5rMwN7CnrsJqXRddhRTE8GfnVIo9ltfrcA3H23oyIqiPI+0BFjmCfblYMjuB0Os50Nj6fnNcRq4O/DdoIaYGOd8Hx2eFM7YQ2M5c/cMXSp0YWLjiVibWv/nOEIIXymlV07HCsWK1wxdBot9FxOdEm3oKIB2977BfwPdt3bnTMQZJjaayJLWS7C3sCclXcfb632JS8lgxRs1KbZvApSoCt4fGDq2oijPy9xGuzELPQUXHq5sre1qz6zuHhwNjOazXTnNLte4WLuwusNqhtcZzh/Bf9BrWy98I3z1ErVQFHm/SD/WX15Pl81d2OC/gcyszGe/SV9Z7vrRf0d/5vrMpW7Jumzutpk3qr2BEIKsLMmon/04HxrLkjfqUj1wFdy7AS8vAhMzg2VWFOU/qNMXStXR2h2kJT54ubdXWfo1Lse3h66z/fydJ77dxMiE9+q+x9oOa5FS8nPAz3qJWWiGa4Ljgplzcg4nwk5QzbEao+qPwru0d57thXoj7gZLzixhT8genCydGOk5ki4Vujx2/Tk7/Flx6DqTX6nBkGo6+NobavaAV1fkSUZFUXJZyElY0x6afADtHzYTTM/Mos/KE1y8HcdPwxpTr5zDUz8mOSOZjKwM7Mzt/lOMpw3XFJoiDyClZPfN3Sw8vZCwpDDqlazH27XepmmZphgJ/fyj5VL0JdZeXMvum7uxMLZgkMcgBtQYkG2bvnVHg5m27TIDvN2Y3qUG4vsecPssjDgFNs56yaYoSh74/UM4+z28cwhcPB68HJ2YRo+vjpGUlsnm4U0pV1x/CxyLTJH/W7ounc3XNrPywkoikiMoZ1OO3lV709G9I87WL15Q49Li2HVjF1sCt3Ah6gI2pjb0qtqL/jX6U8KyRLbzf/UNZcwv52hXw5mv36yPyeVN8NsQ6LxQ25RAUZSCKzkGlnuBY0UYvAuMHt5QXo9M5NWvj+Fobcam95pgb6WfYdkiV+T/lq5LZ/fN3fwU8BN+kX4A1CtZj+ZlmlOvZD1qOdXC3Nj8mZ+TmplKQEwAZ++e5fDtw5yJOINO6qjsUJkelXrQo1IPipkVy/G9Oy+GMfyHM3hXLM7qgQ2wSL8HXzbUpmEN2Q1Gqm+bohR4fj/ClnehyxLwfOuxQz7BMfRbdZJarnasH9wQa3OTXL98kS3yj7oee53dN3ez++ZurtzTnnobC2PKFCtDOdtyFLcoTjGzYpgZmZGmSyNVl0pEUgS3E28TmhBKptQe5lZ2qExL15a0dWtLDccaTx3z33/lLu+s98WjjC3/G9JI+4/762C4/Du8exhKVtfLz6ooSh6TEta9AhEXYcRpKPZ4i5YdF8IYseEMjcoXZ+2gBvy/vXuPjqo89zj+fcgdDJdIuBMuoiKCYsLNg6Cgp+KteEPoUWFZK/V4WbVqqxbr0nZVezw97amtVUEUHjvyswAADL9JREFUsR5EBUSUShFhSS0YRG7hVsItgCkxCUkwQEKS5/zx7pQxmRAkM7Mnk+ez1qzs2Xsn85s3mSd7v3vvdycnhHbjzop8HSXHSlhXsI5NhZvIO5xHXlkeJRUlfF35NRXVFSTFJ5Ecl0yn1p3odkY3erftzcCOAxnUcRDprU9tfJ0Pc/K5f846zumcyv/9YATtWifA1vdh7q0w5nG49CdhfpfGmIj6aju8MNKdTHHTjHqLF6zbz4NvbWDU2enMmJxFUnzoCr0V+QhbsG4/D7+9kQt7tOPVO4bRLiUBjh6C54dDm04wdTnEJfgd0xgTait+DSuegYlvwHnX1ls8d00ej8zbxNj+nfjTrZkh26KP+YuhosmsT3fz4FsbGNY7jdfvHO4KPMDin0B5IYz/oxV4Y2LVqIegyyB4/wEor39x5sShGfzqhoEs317A5JnZlB0L/4iUVuRDpKq6hicW5vDkoi1ccV5nXr1j6IkDLBvfclfFXfYodBvsb1BjTPjEJcD1L8LREvhL8C7ZW4f34veTLmLdvkNMfGk1/yw9FnS9ULEiHwLF5ZXcMWsNs1ft5a5RfXjxtqwTu2GH9sIHD7lx4i950N+gxpjw6zIQLnsEcubB5neDrvLdC7vx8pSh5BWVc90f/8bavcVB1wuFmCjyJUcqmfJKNlu+LIv4a2fvLubq36/ks13F/PrGQUy7ZgBxrbwzbmqqYcHd7sj7jdPdLcSMMbFv5I+h20Ww6EdQsi/oKpeek86Ce0fSOjGOSdNX82Z2XliixESRzys+wpb8Mq5//lOmf7Kz3t1ZwuHY8Wqe/XAb35uxmuSEVsy/59+YNCzjmyt98t+Q93e45jfuvHhjTMsQFw83zXQbevN+ANXBx9M6p3MqC+8dyYi+Z7KrsDwsUWLm7Jri8koem7+RJZsPktWrA09993wGdj+9cSAa82luIY+/m8PuwnImZPXgiesGkJpc52DqjqXwxgS4cBJc/4Ld7cmYlmjTO+7q9lEPnfR+EVXVNYjIiV6Ab6nFnEKpqsz/4gBPL95K8ZFKbsnqyX1j+9EzLTRjRmzaX8qzS7axckchGWmteebGQYzsV38YAw7tgZcuhXY94c6/2k25jWnJFt7nxra5fT6cNTYsL9Fiinyt0qPHeW7ZDmav2kONwrUXdGXyxb3JzGj/rUelPF5dw0dbDjJ71V5W7SqifesE7r2sH7df3Cv4Oa7Hj8LM77gDrj9cAWl9m/x+jDHNWOURmDEGyr+Cu5ZDh14hf4kWV+Rr5ZceZebK3czJzqO8spru7VO4amAXhvROIzOjPempSfWKflV1DbsLy8n5spTl275ixfYCyo5V0b19Cv8xPIPbL+5F27pdM7VqamD+XZDzDnxvLpw7LmTvxRjTjBXmwstj3d7995dAUvCxrk5Xiy3ytQ4fO87SLQdZtOFLPs0torK6BoA2iXF0bpdMirdFXnr0OAWHK6iscsvPbJPImP6dGHd+F8b079R4f9myX8LK38DYn8Poh0P+PowxzVjuMnjjZuh/DUyY/Y3RKpsqbEVeRCYATwLnAcNU9XNvfm9gK1B7/6vVqnp3Yz8vEsMaVFRVs/nLMtbnlbDv0BEKyiqoqKpGFdqlJJDeNon+XVI5t3Nbzu2SeuoHQr6YDe/dD5mT4brn7ECrMaa+Vc/Dkp/BiHvgyqdDVidOVuSbeuJ2DnAj8FKQZTtVNeou70yKjyMzowOZjdyp5VvZ/C4sesAdVLnmt1bgjTHBjbgHSvJg9Z+gTTqMCv8Fkk0q8qq6FYjYLfai0rbF7hSpHkPhltdtXBpjTMNE4Mpn3DhWy56ClPYw5PthfclwXoLZR0TWAWXA46q6Moyv5Y9ti+HtKe5mvre+HfKDKcaYGNSqlbt2pqIM3v8xVFXCiEZ7s09bo0VeRD4CugRZNE1VFzbwbflAhqoWiUgW8K6InK+q9cYdEJGpwFSAjIyMuotPXVUlxIfn1lpBrZ3lfkFdB8Nt8yC5beRe2xjTvMUnwsQ/u5sIffgIHD8Stq6bRg/vquoVqjowyKOhAo+qVqhqkTe9FtgJnNPAutNVdYiqDklPP7UbctRTuAP+kAnbPzy97/82ampg+dNuTIqzLocpiyAlhP37xpiWIT4JJsyCQRNc182SaeF5mXD8UBFJB4pVtVpE+gJnA7vC8VoAxCVC6zSYMxFG/9QN6RuOe6eWF8GCqZD7EQy+Da77X+uDN8acvrgEuGG6u5lQx6DbwU3WpCIvIjcAfwDSgQ9EZL2qXgmMBn4hIseBGuBuVQ3fWJoderkLDD54CD55FvatdqcxpvUJ3Wvs/BgW3g/lBXDt7yDrDjuLxhjTdK1awbinw/bjY+tiKFV3vvqSaVBTBWN+BsN/6HaLTlfpAfj4l7BhDpzZD2562Q0haowxUSKc58lHFxHImgL9rnBb9Ut/DtnT4ZIH4IKJkJR66j+raCdkz4DPXwGtcaPIjf4pJCSHL78xxoRYbG3JB1J1XSwrnoH9ayDxDOh/rfsH0HOYG0Mi8LLiqgoo2Ap7/w7bF8Oev7l+/UG3uD7+MAwqZIwxodBytuQDiUC/y91VqPvXwNrXXPHe+KZbHp/iDtbGJ0PFYThSBFrtlnUa4Ap75hRo29W/92CMMU0Uu0W+lojbcu85zN2lJX895G90p10eK4GqY64bp00n6DwAumXaVrsxJmbEfpEP1CoOume5hzHGtAAxcY9XY4wxwVmRN8aYGGZF3hhjYpgVeWOMiWFW5I0xJoZZkTfGmBhmRd4YY2KYFXljjIlhUTV2jYh8Bew9zW/vCBSGME64NIeczSEjWM5Qs5yhE+mMvVQ16F2XoqrIN4WIfN7QAD3RpDnkbA4ZwXKGmuUMnWjKaN01xhgTw6zIG2NMDIulIj/d7wCnqDnkbA4ZwXKGmuUMnajJGDN98sYYY+qLpS15Y4wxdViRN8aYGNbsi7yIjBOR7SKSKyKP+p0nkIjsEZFNIrJeRD735qWJyFIR2eF97eBDrldEpEBEcgLmBc0lznNe+24UkUyfcz4pIge8Nl0vIlcHLHvMy7ldRK6MUMaeIrJcRLaIyGYR+ZE3P6ra8yQ5o609k0UkW0Q2eDmf8ub3EZHPvDxzRSTRm5/kPc/1lvf2OecsEdkd0J6Dvfm+fY5Q1Wb7AOKAnUBfIBHYAAzwO1dAvj1AxzrzngUe9aYfBf7Lh1yjgUwgp7FcwNXAXwABRgCf+ZzzSeDhIOsO8H7/SUAf7+8iLgIZuwKZ3nQq8A8vS1S150lyRlt7CnCGN50AfOa101vAJG/+i8B/etP3AC9605OAuRFqz4ZyzgJuDrK+b5+j5r4lPwzIVdVdqloJvAmM9zlTY8YDr3nTrwHXRzqAqn4CFNeZ3VCu8cBsdVYD7UUkInc3byBnQ8YDb6pqharuBnJxfx9hpar5qvqFN30Y2Ap0J8ra8yQ5G+JXe6qqfu09TfAeCowF3vHm123P2nZ+B7hcRMTHnA3x7XPU3It8d2BfwPP9nPwPN9IU+KuIrBWRqd68zqqa703/E+jsT7R6GsoVjW18n7fL+0pAd5fvOb2ugotwW3VR2551ckKUtaeIxInIeqAAWIrbiyhR1aogWf6V01teCpzpR05VrW3PX3nt+TsRSaqb0xOx9mzuRT7aXaKqmcBVwL0iMjpwobr9uKg7hzVac3leAM4CBgP5wP/4G8cRkTOAecADqloWuCya2jNIzqhrT1WtVtXBQA/c3kN/nyMFVTeniAwEHsPlHQqkAY/4GBFo/kX+ANAz4HkPb15UUNUD3tcCYAHuD/Zg7W6a97XAv4Tf0FCuqGpjVT3ofbhqgBmc6ELwLaeIJOAK5xuqOt+bHXXtGSxnNLZnLVUtAZYDF+O6N+KDZPlXTm95O6DIp5zjvG4xVdUK4FWioD2be5FfA5ztHXlPxB14ec/nTACISBsRSa2dBr4D5ODyTfFWmwIs9CdhPQ3leg+Y7J0dMAIoDeiGiLg6/Zg34NoUXM5J3tkWfYCzgewI5BFgJrBVVX8bsCiq2rOhnFHYnuki0t6bTgH+HXf8YDlws7da3fasbeebgY+9PSc/cm4L+McuuOMGge3pz+coUkd4w/XAHbX+B67fbprfeQJy9cWdnbAB2FybDddfuAzYAXwEpPmQbQ5u1/w4rm/wzoZy4c4GeN5r303AEJ9zvu7l2Ij74HQNWH+al3M7cFWEMl6C64rZCKz3HldHW3ueJGe0tecFwDovTw7whDe/L+6fTC7wNpDkzU/2nud6y/v6nPNjrz1zgD9z4gwc3z5HNqyBMcbEsObeXWOMMeYkrMgbY0wMsyJvjDExzIq8McbEMCvyxhgTw6zIG2NMDLMib4wxMez/ASN+GbiV+kmbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 864d1820db148561d45fff7d21aead8ee568ce64 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 278/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 1f44320468fb7c2df7b0ac6034db5c99773d02f1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 279/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From e2b4a297ad0c9517b24ac4044ad6177cfb481df4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 280/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From d78e1dc739f1766790a3aa624398f9ee8e4896f9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 281/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 05c09d6ec9c9b9c354dbe43b982300d197716c3d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 282/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- tests/test_fpca.py | 78 ++--------- 4 files changed, 278 insertions(+), 136 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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RaMojaSFQVifx3h+h02hdZVS5T0AwDP6vtWbRyQMwuR8kLHI6lSqEFgL1Wydxx5udTqIqouaDYPwSa1jyxyOsPS68cLRiRaaFwNdpJ7EqD5GNYdxCaDXUGmb65R2Qe9bpVMoW4HQA5TDtJFblJSgMRrwHtdvAoqfg1BEY+RGEVnM6mc/TKwJft/FjiIjWTmJVPkTg4gdg2BRIXANTB+lsZA+ghcCXnT4KCT9Auxu0k1iVr3bXwS1fQfpRa+ObJN2OxElaCHzZ5plg8nUrSuWMhr1h7EJroML7V8LhjU4n8llaCHyVMdbmMzFdrTHfSjkh6iK4bR4EV4bp18DBNU4n8klaCHzV4Q2QvAM6jHI6ifJ1kY3gtu+svZI/GAoHVjudyOdoIfBVm2aAfzC0HuZ0EqWs5Shu+w4q14aPr4Mjm5xO5FO0EPii3LOw5XNoeRWEVnU6jVKWyrVh9GyrmejDayF5l9OJfIYWAl+0+3s4k6qdxMrzVK0HY+ZYHcgfDNWhpeVEC4Ev2vgJVK4Djfs7nUSpP6reBEZ/DdkZ8PH1kJXmdKIKTwuBr0lPtvYlbne9zh1QnqtWa7jhAzgeDzNHQ16O04kqNLcUAhEZLCK7RCRBRP6wH7GIBIvIZ/bzq0WkoX38UhFZJyJb7NtL3JFH/YltX4HJg/Y6Wkh5uMb94OrXYO8S+PY+XaiuDLlcCETEH5gEXA60AkaJSKvzThsLpBpjmgIvA8/bx1OAq40xbYExwIeu5lHF2PI51GoDNVs6nUSp4nW8Cfr8HTZ8CKvedDpNheWOK4KuQIIxZq8xJhv4FBhy3jlDgOn2/VnAABERY8wGY8y5jU23AaEiEuyGTKowJ/ZZ67u0HeF0EqVKrv9j0OIqWPAv2L/M6TQVkjsKQTRwsMDjRPtYoecYY3KBNKD6eecMB9YbYwpdm1ZExotInIjEJScnuyG2D9o6y7pto4VAeRERGPqWtZT157dC2iGnE1U4HtFZLCKtsZqL7izqHGPMZGNMrDEmNioqqvzCVRTGwObPoX5Pa4ieUt4kJAJGfgw5Z+DzMbqXgZu5oxAcAgr+zxJjHyv0HBEJAKoAx+3HMcBXwGhjzB435FGFSdoKKbu0WUh5r6iLYOibkLjW2s9AuY07CsFaoJmINBKRIGAkMOe8c+ZgdQYDjAAWG2OMiFQF5gKPGGOWuyGLKsrmmeAXYO0QpZS3ajUEutwBK9+wllBXbuFyIbDb/CcC84EdwExjzDYReUpErrFPmwpUF5EE4H7g3BDTiUBT4N8istH+qulqJnWe/HzY+gU0HWgt7KWUN7vsaYhqCV/dbc2LUS4T44Vjc2NjY01cXJzTMbzH/uXw/hUwfKo2DamKIWkbTO4PjfvCjTOtDmVVLBFZZ4yJPf+4R3QWqzK25XMIrAQXXe50EqXco1ZruOw/EL8A1k5xOo3X00JQ0eVmw/avocWV1ubhSlUUXe+AJpfAwsch9Ren03g1LQQV3Z5F1kqjba93OolS7iViLUEhfjDnXl2CwgVaCCq6LZ9DaCQ00ZVGVQVUtR5c9hTs+wnWve90Gq+lhaAiy86AnfOg9VDwD3Q6jVJlo/Nt0KiPtQTFyYPFn6/+QAtBRbZ7PuSegTbDnU6iVNkRgWvesFbV/e5hp9N4JS0EFdn2ryGsJtTv4XQSpcpWtQbQ7xHYNRd2fed0Gq+jhaCiys6A3Qug1TW6AY3yDd0nQFQLmPcQZGc6ncaraCGoqM41C7W+1ukkSpUP/0C48iVIOwBLX3A6jVfRQlBRabOQ8kUNe1m77y1/DZJ3O53Ga2ghqIi0WUj5skuftmbSz/+H00m8hhaCikibhZQvC4+Cvg9Zq5PGL3Q6jVfQQlARabOQ8nVdx1s7ms1/DPJynE7j8bQQVDTaLKQUBARZi9Kl7NIZxyWghaCi0WYhpSwXXWHNOP7xGWu9LVUkLQQVjTYLKWURgUHPQlYa/KzDSf+MFoKKRJuFlPq92m2s4aRr3oW087dSV+e4pRCIyGAR2SUiCSLySCHPB4vIZ/bzq0WkYYHn/mEf3yUig9yRx2dps5BSf9TvEcDAT885ncRjuVwIRMQfmARcDrQCRolIq/NOGwukGmOaAi8Dz9vf2wprs/vWwGDgTfvnqdLQZiGl/qhqfYi9HTZ8DCnxTqfxSO64IugKJBhj9hpjsoFPgSHnnTMEmG7fnwUMEBGxj39qjDlrjNkHJNg/T10obRZSqmgXPwgBIbD4P04n8UjuKATRQMFFwBPtY4WeY4zJBdKA6iX8XgBEZLyIxIlIXHJyshtiVzDaLKRU0cKjoMc91lXz4Q1Op/E4XtNZbIyZbIyJNcbERkVFOR3H82izkFJ/rudEa7e+RU85ncTjuKMQHALqFXgcYx8r9BwRCQCqAMdL+L2qONospFTxQqrAxffDnsWwf7nTaTyKOwrBWqCZiDQSkSCszt85550zBxhj3x8BLDbGGPv4SHtUUSOgGbDGDZl8izYLKVUyXcZZV84/Pe90Eo/iciGw2/wnAvOBHcBMY8w2EXlKRK6xT5sKVBeRBOB+4BH7e7cBM4HtwPfAPcaYPFcz+RxtFlKqZAJDoddfrc3uD6xyOo3HEOuDuXeJjY01cXFxTsfwDNkZ8L8m0PEmuPJFp9Mo5fmyM+HVdlCrDYz+2uk05UpE1hljYs8/7jWdxaoI2iyk1IUJqgQ974W9P8JBbYkGLQTeT5uFlLpwXcZBpeqwRGcbgxYC76ajhZQqnaAw66pgzyJI1GZmLQTeTJuFlCq9LndY8wp0BJEWAq+mzUJKlV5wOPSYAPEL4OhWp9M4SguBt9JmIaVc12UcBIXD8lecTuIoLQTeSpuFlHJdaDWIvQ22fgEn9jmdxjFaCLyVNgsp5R7dJ4D4w8o3nE7iGC0E3kibhZRyn4i60H4kbPgI0n1zZWMtBN5Im4WUcq9ef4Xcs7D6baeTOEILgTfSZiGl3KtGM2h5Nax9F7JOOZ2m3Gkh8DbaLKRU2ej9N8hKg3XvO52k3Gkh8DbaLKRU2YjuDI36wMpJVjORD9FC4G20WUipstPrb5B+1BpO6kO0EHgTbRZSqmw1uQRqtrKuCrxwif7S0kLgTbRZSKmyJWJtcp+0FfYucTpNudFC4E20WUipstf2Ouv3bOUkp5OUGy0E3kKbhZQqHwHB0PUOSFgIx3Y6naZcuFQIRCRSRBaKSLx9W62I88bY58SLyBj7WCURmSsiO0Vkm4joDhF/RpuFlCo/sWMhIARW+cZVgatXBI8Ai4wxzYBF9uPfEZFI4HGgG9AVeLxAwXjBGNMC6Aj0EpHLXcxTcW37SpuFlCovYdWh/SjY9JlPLDsR4OL3DwH62fenA0uAh887ZxCw0BhzAkBEFgKDjTEzgB8BjDHZIrIeiHExT8WUnQHxC60N6rVZyGNk5eRx+OQZDp08w+GTZ0jNzCHjbC7pZ3M5k50HgIjgJxDo70dEaCARIQFEhARSpVIgtSNCqFMlhBrhwfj5icN/GvUH3SfAumkQNxX6/eEzboXiaiGoZYw5Yt8/CtQq5Jxo4GCBx4n2sV+JSFXgauDVol5IRMYD4wHq16/vQmQvpM1Cjss4m8va/SfYcOAk24+cYseRUySmnvnDeSIQFhRApSB/RCDfgDGGszn5pGfnFjoiMdBfqBURQnTVUBpHhdMkKowmNcNpGhVOdNVQLRJOiWoOzQfDmnettYgCQ51OVGaKLQQi8gNQu5CnHiv4wBhjROSCB96KSAAwA3jNGLO3qPOMMZOByQCxsbG+M8AXtFnIIfFJp/lu61GW7DrG5sQ0cvMNItCoRhgd6lXl+th6xFQLJbpqKHWrhhIZFkRooH+R/3Hn5xvSs3M5dSaHk5k5HE3L4kjaGY6kZXEkLYuDJzKZv+0oJzKyf/2e4AA/LqpdmdZ1q9C6bgRtoqvQonZlQgL1yrBc9LgHpl8Nm2dC5zFOpykzxRYCY8zAop4TkSQRqWOMOSIidYBjhZx2iN+aj8Bq/llS4PFkIN4Y49tbBBVFm4XK1dG0LGbGHWT2xkPsSc4AoH29qtzRpzE9Glenc4NqhAWX7kLaz0+ICAkkIiSQmGrQJrpKoeedyMhmT3I6e46lk3AsnR1HTzFvyxFmrDkAgL+f0KxmOO1iqhDbIJLODavRuEYYInrl4HYNL4ba7ayhpB1vAb+KOdDS1aahOcAY4Dn7dnYh58wH/lugg/gy4B8AIvIfoAowzsUcFZc2C5U5YwzLElKYvuIXFu9MIt9A98aRjOnZkEGta1MrIqRc80SGBREZFkmXhpG/y5iYeoZth9PYdvgUWw6lsWB7EjPjEgGoVimQzg2q0blBJJ0bVKN9vSoEB+gHB5eJQI+J8NV4SPgBml/mdKIyIcaFadQiUh2YCdQHfgGuN8acEJFY4C5jzDj7vNuBR+1ve8YYM01EYrD6DnYC51Z4esMYM6W4142NjTVxcXGlzu1VPrsFDqyCB3bqFYGb5eUb5m87yltL9rDlUBo1woO4LrYeI7vUo0H1MKfjFSs/37A3JZ11v6QStz+VdQdS2WtfxYQE+tG1UXV6NalOr6Y1aFUnQvsaSis3G15tB1EXwejCPut6DxFZZ4yJ/cNxVwqBU3ymEGSdgheaWZekV77gdJoKwxjDkl3JPPvdDnYnpdOweiXu7teEoR2jvf5T9ImMbNbuP8HKPcdZnpBC/LF0wLpi6NGkOv2a16R/i5pEVQ52OKmXWfoSLHoS7l4BtVo7nabUiioErjYNqbK0ax7kZkHbEU4nqTC2HU7jv/N2sDzhOA2rV+K1UR25sm0d/CvIp+XIsCAGta7NoNbW+I6kU1ms2JPC8oTjLItPYd6Wo4hA+5iqDGxZkwEta9GidmXtXyhO51vh5/+DlW/C0Io3yUyvCDzZRyMgeSf8dXOF7aQqLxlnc3lp4W6mLd9HldBA/jqgGTd2a0BQgO+8r8YYth85xaIdx1i0I4lNiWkAxFQL5ap2dbm6fR1a1YnQolCUuQ/A+g/gvm0QXtPpNKWiTUPeJuM4vNjcGr526VNOp/Fqi3cm8c+vtnI4LYubutXnoUEtqFIp0OlYjjt2KovFO4/x3dajLEtIIS/f0DgqjKvb1WVIh7o0jgp3OqJnSUmANzpD34eh/6PFn++BtBB4m7VTYe79cOdSqNPO6TReKTM7l6e/3cGMNQdoXiucZ4e1pXODyOK/0QedyMjmu61H+HbTEVbtO44x0LVhJCO71uPyNnUIDfLuvhO3+WQkJK61rgoCy3c0mTtoIfA2066AjBS4Z7U1hE1dkM2JJ/nbpxvZdzyD8X0ac/+lzb2+I7i8JJ3K4sv1h/hs7QH2H8+kckgAQztEc1P3+rSoHeF0PGft+9maYHbN69BptNNpLpgWAm+Slggvt4b+j0Hfh5xO41WMMUxdto/nvttJVOVgXry+PT2b1HA6llcyxrB63wk+XXOAeVuPkp2bT++mNRh7cSP6NovyzeGoxsDbF0N+LkxY6XUf0nTUkDfZ+qV122a4szm8TMbZXB76YjNzNx9hcOvaPD+8nfYFuEBE6N64Ot0bV+eJzGw+WXOA6Sv2c9u0tTStGc643o0Y1inGpzrcf93B7Ou7YM9iaDrA6URuoVcEnuidPiB+MH6J00m8xt7kdO76aB0Jx9J5aHAL7uzTWEe/lIHs3HzmbTnClGV72XroFNFVQ7mnf1NGdPahgpCbDa+0gVpt4JYvnU5zQYq6IvCRvzkvkpIARzZBG507UFLL4lMYMmk5yafP8uHYbtzVt4kWgTISFODH0I7RfDOxN9Nv70pU5WAe/WoL/V9YwserfyEnL9/piGUvIMjawWzPIji2w+k0bqGFwNNsnQUItBnmdBKvMHPtQW6dtoboqqF8c29vejXV/oDyICL0bR7FVxN6Mv32rtSMCOaxr7Yy6JWfWbg9CW9sabggnW+3dzB70+kkbqGFwJMYA1tmQYNeEFHX6TQezRjDiwt28dAXm+nRpDqf39WDmGqVnI7lc84VhC/v7smU0VaLwx0fxDHq3VVssSesVUgFdzDLSHE6jcu0EHiSIxvheLwuKVGMs7l53PfZRl5fnMANsfV479YuVA7RTmEniQgDW9Vi/t/68PSQ1uxOSufqN5bx8KzNpKQ6mW4AABo/SURBVBbYX6FC6T4B8s5C3HtOJ3GZFgJPsnEG+AdD66FOJ/FYmdm5jJsex9cbD/PgZc15bnhbAv31n7GnCPT345YeDVny936M79OYWesTueTFJcyMO1jxmouimkOzy6wdzHLPFn++B9PfIE+Rm231D1x0OYRWK/58H3QqK4fRU9ewPCGF/w1vx8RLmmmnsIeKCAnk0StaMvcvvWkSFc5DszZzwzuriE867XQ09+o+ATKOWU26XkwLgadIWAiZx6HDjU4n8UjH089y47ur2JR4ktdHdeL6LvWcjqRKoEXtCGbe2YPnh7dl97HTXPnaMt5asoe8/ApyddC4H9Rsbe1g5sVXPFoIPMWmGRAWBU0ucTqJxzmalsUNk1cRn5TO5NGxXNmujtOR1AXw8xNu6FKfH+7vyyUtavL89zsZ8fYK9iSnOx3NdSLQYwIc2wb7fnI6TalpIfAEmSdg1/fQ9nrw107PghJTM7nunRUcTcti+u1d6X+Rdy7/q6BGeDBv3dyJV0d2YG9yBle8upSpy/aR7+1XB22vg7Ca1lWBl3KpEIhIpIgsFJF4+7bQxm0RGWOfEy8iYwp5fo6IbHUli1fb+gXk50D7kU4n8ShH0s4w6t1VpGXm8PG4bnRvXN3pSMpFIsKQDtEsvK8PFzerwdPfbue299eSku7Fna0BwdBlHMQvgOTdTqcpFVevCB4BFhljmgGL7Me/IyKRwONAN6Ar8HjBgiEiw4AKcI3ogk2fWtPVdbnpXx07lcWN767mZEYOH47tRvt6VZ2OpNyoZkQI746O5emhbVi59ziXv7qUZfFePB6/y1hrxN/qt5xOUiquFoIhwHT7/nSgsHGPg4CFxpgTxphUYCEwGEBEwoH7gf+4mMN7pcTDoTi9GiggJf0sN05ZTdKpLN6/vYsWgQpKRLilewPmTOxFldBAbnlvNc9/v9M7l6kIqwHtb7CGgGeecDrNBXO1ENQyxhyx7x8FahVyTjRwsMDjRPsYwNPAi0BmcS8kIuNFJE5E4pKTk12I7GE2zbAWmGt7vdNJPEJqRjY3T1lNYmom027tohvJ+IAWtSP4ZmJvRnapx1tL9nDDOys5mpbldKwL130C5J7xyglmxRYCEflBRLYW8jWk4HnGmi1S4l4fEekANDHGfFWS840xk40xscaY2KioqJK+jGfLy4WNn0DTgVC5sBrqW9LO5HDLe6vZm5LBlNFd6KZ9Aj4jNMifZ4e1440bO7Lr6Gmuen0pq/YedzrWhanZEpoMsCeYedds6mILgTFmoDGmTSFfs4EkEakDYN8eK+RHHAIKDvqOsY/1AGJFZD+wDGguIktc++N4mYSFcPoIdPpD/7nPOZ2Vw+j31rDr6GneuaUzvZvp4nG+6Kp2dZk9sRcRoYHcNGU1U5bu9a4ZyT3ugfSjsM27lqd2tWloDnDuf7ExwOxCzpkPXCYi1exO4suA+caYt4wxdY0xDYHewG5jTD8X83iXddMhvBY0H+R0EkdlnM3ltmlr2XYojUk3dtIhoj6uac3KzL6nFwNb1uQ/c3dw74wNZJzNdTpWyTS5BKJawso3vGqCmauF4DngUhGJBwbajxGRWBGZAmCMOYHVF7DW/nrKPubb0g5B/HzocJNPzx04k53H2OlrWX8glVdHduSy1rWdjqQ8QOWQQN6+uTMPD27BvC1HGDppOftTMpyOVbxzE8yOboH9y5xOU2K6Q5lTfvof/PgM/GUjRDZyOo0jsnLyuOODOJYlpPDKDR0Y0iG6+G9SPmd5QgoTP1lPvoG3bupET0/fcyIny9pzPLoz3DTT6TS/ozuUeZL8PFj/obVOiY8WgezcfCZ8vJ6l8Sk8P7ydFgFVpF5NazD7nt7UrBzMLe+t4cNVvzgd6c8FhkC3u6wr/qPeMU9WC4ET9vwIaQd8tpM4Jy+fiZ+sZ/HOYzxzbRuuj9UF5NSfq1+9El9O6Enf5lH86+ut/OvrrZ4936DrOAiqDMtedjpJiWghcMK6aVCpOrS40ukk5S43L5+/fbaRBduTeOLqVtzUrYHTkZSXqBwSyLujY7mzT2M+XPULY95bw8lMDx2mGVrNmm287Us4vsfpNMXSQlDeTh6EXfOg4y3WGiU+JC/f8PdZm5m7+QiPXtGCW3v5ZrOYKj1/P+EfV7TkhevaE7c/laGTlpNwzENXqOlxD/gHwfJXnE5SLC0E5W3tFOu2yzhnc5Sz/HzDP77czFcbDvHgZc0Z36eJ05GUFxvROYYZ47uRfjaXa99czk+7PXC1gfCa1ge+jTOsUYIeTAtBecrOhPXTocVVUNV32sWNMfx7zlZmxiXyl0uaMvGSZk5HUhVA5waRfH1PL2KqVeK2aWuYtnyf500+6/UXwFjzCjyYFoLytOVzOJNqjSjwEcYYnvxmOx+tOsCdfRtz36XNnY6kKpCYapWYdVcPBrSsxZPfbOfRrzysE7lqfWsdsXXvQ4bnrq6qhaC8GAOr37GWm27Q0+k05cIYw3/m7uD9FfsZ17sRjwxuoXsMK7cLCw7gnZs7c3e/JsxYc4DRU9eQmuFBnci974OcM7DidaeTFEkLQXnZv8zazq7bndbswwrOGMNz3+1k6rJ93NqzIY9d2VKLgCozfn7Cw4Nb8NL17Vn3SypD3/SgTuSo5tB2BKyZDOmFLcfmPC0E5WX129aQsrbXOZ2kzBljeGHBLt75eS83d6/P41e30iKgysWwTlYncobdifyzp3Qi930EcrNgmWeOINJCUB6Sd8POuRB7OwSGOp2mzL3yQzyTftzDqK71eOqaNloEVLk614kcXTWUW6et4X1P6ESu0RTaj4K4qXDqSPHnlzMtBOVhxavWnIFudzudpMy9viieVxfFc13nGJ4Z2hY/Py0CqvzFVKvEF3f35JIWtXjim+085gkzkfv8HfJzYemLzuYohBaCspZ2CDZ9Bp1GQ3gF2VCnEMYYXlywixcX7mZYp2ieG95Oi4ByVFhwAJNv6cxdfZvwyeoDzs9EjmxkrTa8fro1sdSDaCEoa6veBJMPPSY6naTMnBsd9PriBEZ2qcf/jWiPvxYB5QH8/IRHLm/Bi54yE7nP363bJc86l6EQWgjKUuYJiJtmjRioVjHX1MnPN/zz662/jg56dlhbLQLK4wy3ZyKfznK4E7lqPWvk4MZP4MhmZzIUQgtBWVrxGuRkWuOIK6DcvHwenLWJj1cfYEK/Jjo6SHm0zg0imT3R6kS+7f21vPuzQ9tgXvwghFaFBf/0mF3MtBCUldNJ1gSytiOsTa0rmKycPP7y6Qa+XG+tHfSQThZTXiCmWiVm3d2TgS1r8sy8HdzzyXrSy3sbzNCq1nDSfT9B/MLyfe0iuFQIRCRSRBaKSLx9W62I88bY58SLyJgCx4NEZLKI7BaRnSIy3JU8HmXZy5B7Fvr9w+kkbpeWaW00P2/LUf51VStdO0h5lfDgAN6+uTP/uLwF3289yjVvLCM+6XT5hoi9HSKbwMJ/QZ7z+zG7ekXwCLDIGNMMWGQ//h0RiQQeB7oBXYHHCxSMx4BjxpjmQCvgJxfzeIa0RGu8cIcboXrFWmXz8MkzXPfOCjYcSOW1UR0Z21uXklbeR0S4s28TPh7XnVNnchgyaTlzNh0uvwABQXDpk5C809qfxGGuFoIhwHT7/nRgaCHnDAIWGmNOGGNSgYXAYPu524FnAYwx+cYYz12V6UIsec667fuwszncbOfRUwx7cwVHTmYx/bauXNO+rtORlHJJjybVmfuXi2lVJ4K/zNjA47O3kpWTVz4v3uIqaNQHFj/t+NITrhaCWsaYc9PkjgK1CjknGig4aDYRiBaRqvbjp0VkvYh8LiKFfT8AIjJeROJEJC452UOmjRfm8AbY8BF0HV+hlppesusY1729EoNh5l09PH8DcaVKqFZECDPGd+f2Xo2YvvIXhk5azq6j5dBUJAJXvmQtSLfgn2X/en+i2EIgIj+IyNZCvoYUPM9Y3e8X0gUeAMQAK4wxnYCVwAtFnWyMmWyMiTXGxEZFeejELGPgu4chrAb0fcjpNG5hjGHyz3u4/f21xFSrxJcTetGyToTTsZRyq0B/P/59dSum3dqFlPSzXPPGMqav2F/2o4pqNINef4XNn8G+n8v2tf5EsYXAGDPQGNOmkK/ZQJKI1AGwbwu7vjkEFPxoHGMfOw5kAl/axz8HOrnwZ3Hels/h4GoY8G8IqeJ0Gpdl5eRx/8xN/HfeTi5vU4cv7u5BdNWKv1aS8l39W9Tku7/2oWeT6jw+Zxtjp8eRkn62bF/04gegWkP41l6u2gGuNg3NAc6NAhoDzC7knPnAZSJSze4kvgyYb19BfAP0s88bAGx3MY9zsk7Bwn9DnQ7Q4Wan07hsf0oGI95ewVcbDvHApc1548aOVAoKcDqWUmUuqnIw793ahSeubsWyhBQue/lnZm88VHZXB4GhcNUrcDwBFj1VNq9RDFcLwXPApSISDwy0HyMisSIyBcAYcwJ4Glhrfz1lHwN4GHhCRDYDtwAPuJjHOT88DqePwhUvgJ93T8+Ys+kwV72+jIMnzjBldCz3DmimcwSUTxERbu3ViG/v7U39yEr89dONjJ0ex+GTZfSJvUl/q19x1ZuONBGJ48uzlkJsbKyJi4tzOsZv9v0M06+21hMa9IzTaUrtTHYeT327nRlrDtCpflVeG9WRmGqVnI6llKPy8g3vr9jPC/N34e8nPDz4Im7s1sD9S6lkZ8LbvSEvG+5eXibNyyKyzhgTe/5x7/7o6gmy0mD2RIhsDP0fczpNqa3Zd4LLX/2ZGWsOcFffJnx2Zw8tAkoB/n7C2N6NWHBfHzrUq8q/Zm/j6teXsXrvcfe+UFAluPYdOHUYvp5QrstPaCFwhTEw515rAtnQt62/SC+TmZ3LE3O2ccPkleTmGz4Z141HLm9BoL/+01CqoHqRlfhwbFfeuLEjJzOzuWHyKu75ZD2JqZlufJEucNnTsPNba3WCcqK9f65YOwW2z4aBT0L9bk6nuSDGGBZuT+Lpuds5eOIMY3o04KHBLQgL1n8SShVFRLiqXV0GtKjF2z/t4e2f9rBwWxI3dqvPhP5NqFk5xPUX6T4BEuOsiWZ12kPTAa7/zGJoH0Fp7fkRPh4BjfvDjTO9qoM4Puk0T327naXxKTStGc5/hrahe+PqTsdSyuscPnmG1xfHMzMukUB/4daejRh3cSNqhAe79oOzM2DKQGtjq9vmQu22bslbVB+BFoLSSNoO7w2CKjFw+/deM2fgaFoWk35M4JM1BwgL8ue+S5tzc/cG2gyklIv2p2Twyg+7mb3pMIH+fgzvFMO4ixvRJCq89D/05EHr/5n8XLh9vrXDmYu0ELhL0nb44BoQfxj3g1csI5F0Kou3luzhkzUHyM83jOxaj/sGNqe6q59alFK/syc5nSlL9/HF+kRy8vIZ0KImo7rWp2/zKAJK84EreZdVDAJCYfTXEHWRS/m0EID1pkZEQ3Apq3TiOvjkOvAPgjHfWNPDPdiGA6m8v2I/czcfwQAjOsUw8ZKm1Iv0vk5tpbxJSvpZPlixn0/WHCQl/Sy1I0K4LjaGYZ1iaFQj7MJ+2NGt8OG1YPKsZuiYP/w/XmJaCPJy4I1YED8YNgViOpf8e42B9R/AvAehcm245WuPXV46LTOHeVuP8Nnag2w8eJLKwQFcF1uPW3s2pH51LQBKlaecvHwW7Uji07UH+Wl3MsZAyzoRXNm2Nle0rUPjkjYdHd8DHw2zJq1OjCt1S4QWAoD9y+DLO+H0Eeh+t7XGR6XIP/+elAT47iHYswiaXALDpxb/PeXsVFYOS3enMHvjIZbsSiY7L58mUWGM7tGQ4Z1jCNeRQEo57vDJM8zbcoR5W46w/sBJAOpHVqJ3sxr0aVaDHk1qUCU0sOgfkHkCds2DjqVfwkYLwTlnTsKCx6zNowPDoM0waH2tNUzr3H/wmSfglxXWObu/g6Bwa6exbneCn7/7/iCllJOXz44jp1iecJwlu46x7pdUcvMNUZWDuaZ9XYZ2iKZNdIQuC6GUhzp88gwLtyexND6FlXtSyMjOw0/gotoRdKhXhQ71qtK+XlWa1azs1hnMWgiAH7Yn4e8nRFcLJSbnFyqteQ12fAs5GdYJQZXB5P/2uFINq/p2nwCVi9wqoUydyc5jT3I6CcfS2XH0FBt+OcnmQyfJyskHrMvMfhdF0a95FLENI90/7V0pVaZy8vLZcOAkyxJS2HAglY0HT3I6y9q+MijAj8Y1wmhWqzLNaobTrGY4/VvUJCSwdB9ItRAA/V9Ywr6UjF8fVwkNpFEVoXfgLppJIrU4TkhgAHmV65BbqwN5dWMJCQ0lLCiASkH+VAryJyw4gOAAP5c+befnG9KzczmdlcvprBzSs3JJzczh6KksktKyOJKWRdKpLA6cyORgauavM80D/YXWdavQqX41OjWoSpeGkdSKcMMEFqWUx8jPN+w7nsHGAyfZlXSa+KTTxB9LJzH1DCKw46nBWgig9IUg6VQWialnOHTyDIdPnuFQqnWbkn6WE5nZpGbkkH62+I2k/QSCA/wJ8BcC/AR/Pz/7Vgjwl18/leflG3LzDDl5+eTlW7e5+YYzOXlFLiPi7yfUrBxM7SohRFcNpVnNyjSrZX0SaFA9jKAAHfOvlC/KzM7lwIlMWtQu/cZQRRUCn+pFrBURQq2IEDo3qFbkOWdz80jNyOF4xlkyzuaRkZ1L5tk8MrNzyczOs79yOZOdR54x1n/2+Ya8PPs23/rP3gCBdpEI9Be7aFgFo1KQP5VDAqkcEvDrbZXQQGpXCaFGeLA27yil/qBSUIBLReDP+FQhKIngAH9qV/GndhVtclFK+QZtZ1BKKR+nhUAppXycFgKllPJxLhUCEYkUkYUiEm/fFtoLKyJj7HPiRWRMgeOjRGSLiGwWke9FpIYreZRSSl04V68IHgEWGWOaAYvsx78jIpHA40A3oCvwuIhUE5EA4FWgvzGmHbAZmOhiHqWUUhfI1UIwBJhu358ODC3knEHAQmPMCWNMKrAQGAyI/RUm1uysCOCwi3mUUkpdIFcLQS1jzBH7/lGgsHUYooGDBR4nAtHGmBzgbmALVgFoBUwt6oVEZLyIxIlIXHJysouxlVJKnVNsIRCRH0RkayFfQwqeZ6wpyiWepiwigViFoCNQF6tp6B9FnW+MmWyMiTXGxEZFRZX0ZZRSShWj2AllxpiBRT0nIkkiUscYc0RE6gDHCjntENCvwOMYYAnQwf75e+yfNZNC+hgKs27duhQR+aUk556nBpBSiu8rb5rTvbwhpzdkBM3pbuWds0FhB12dWTwHGAM8Z9/OLuSc+cB/C4wougzrk38I0EpEoowxycClwI6SvKgxplSXBCISV9g6G55Gc7qXN+T0hoygOd3NU3K6WgieA2aKyFjgF+B6ABGJBe4yxowzxpwQkaeBtfb3PGWMOWGf9yTws4jk2N9/q4t5lFJKXSCXCoEx5jgwoJDjccC4Ao/fA94r5Ly3gbddyaCUUso1vjazeLLTAUpIc7qXN+T0hoygOd3NI3J65X4ESiml3MfXrgiUUkqdRwuBUkr5OJ8pBCIyWER2iUiCiJRovkJ5EJH99sJ7G0Ukzj5WosX8yjjXeyJyTES2FjhWaC6xvGa/t5tFpJPDOZ8QkUP2e7pRRK4o8Nw/7Jy7RGRQOeasJyI/ish2EdkmIn+1j3vMe/onGT3q/RSREBFZIyKb7JxP2scbichqO89nIhJkHw+2HyfYzzd0OOf7IrKvwPvZwT7u2O8RxpgK/wX4A3uAxkAQsAlo5XQuO9t+oMZ5x/4HPGLffwR43oFcfYBOwNbicgFXAN9hrR3VHVjtcM4ngAcLObeV/XcfDDSy/034l1POOkAn+35lYLedx2Pe0z/J6FHvp/2ehNv3A4HV9ns0ExhpH38buNu+PwF4274/EvisnP7Oi8r5PjCikPMd+z3ylSuCrkCCMWavMSYb+BRrwTxPVZLF/MqUMeZn4MR5h4vKNQT4wFhWAVXtmeZO5SzKEOBTY8xZY8w+IAHr30aZM8YcMcast++fxpo8GY0Hvad/krEojryf9nuSbj8MtL8McAkwyz5+/nt57j2eBQwQkTLfGPxPchbFsd8jXykEhS5851CW8xlggYisE5Hx9rGSLObnhKJyeeL7O9G+vH6vQNOaR+S0myY6Yn1C9Mj39LyM4GHvp4j4i8hGrGVtFmJdjZw0xuQWkuXXnPbzaUB1J3IaY869n8/Y7+fLIhJ8fk5bub2fvlIIPFlvY0wn4HLgHhHpU/BJY10zetwYX0/NZXsLaIK1ntUR4EVn4/xGRMKBL4C/GWNOFXzOU97TQjJ63PtpjMkzxnTAWrusK9DC4UiFOj+niLTBWmKnBdAFiAQedjAi4DuF4BBQr8DjGPuY44wxh+zbY8BXWP+ok85dEkrRi/k5oahcHvX+GmOS7F/AfOBdfmuucDSnWCvufgF8bIz50j7sUe9pYRk99f20s50EfgR6YDWlnFstoWCWX3Paz1cBjjuUc7DdBGeMMWeBaXjA++krhWAt0MweVRCE1WE0x+FMiEiYiFQ+dx9rQb6t/LaYHxS9mJ8Tiso1Bxhtj3roDqQVaO4od+e1q16L9Z6ClXOkPYqkEdAMWFNOmQRrv40dxpiXCjzlMe9pURk97f0UkSgRqWrfD+W3BSt/BEbYp53/Xp57j0cAi+2rLydy7ixQ+AWrH6Pg++nM71F59Uo7/YXVI78bqy3xMafz2JkaY4262ARsO5cLq/1yERAP/ABEOpBtBlYzQA5WW+XYonJhjXKYZL+3W4BYh3N+aOfYjPXLVafA+Y/ZOXcBl5djzt5YzT6bgY321xWe9J7+SUaPej+BdsAGO89W4N/28cZYhSgB+BwIto+H2I8T7OcbO5xzsf1+bgU+4reRRY79HukSE0op5eN8pWlIKaVUEbQQKKWUj9NCoJRSPk4LgVJK+TgtBEop5eO0ECillI/TQqCUUj7u/wEkTM2oT/b1jwAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..fff7be7d4 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,81 +1,25 @@ import unittest import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.datasets import fetch_weather +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data - def test_basis_fpca_fit_attributes(self): +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) - basis = Fourier(n_basis=1) - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataBasis(basis, [[0.9]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of elements - # of target basis - fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() - with self.assertRaises(AttributeError): - fpca.fit(None) - - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of attributes - # in the FDataGrid object - fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_basis_fpca_fit_result(self): - - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) - - # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) - fd_basis = fd_data.to_basis(basis) - - fpca = FPCABasis(n_components=n_components) - fpca.fit(fd_basis) - - # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] - results = np.array(results) - # compare results obtained using this library. There are slight - # variations due to the fact that we are in two different packages - for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): - results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) if __name__ == '__main__': From 1b41451fc27289023686e0f644008f21a3ed516a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 283/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From f3dae0714357dc7faddcd690a2502ec95b93aac3 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 284/624] Add docstring and references for fpca module --- docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 4 files changed, 117 insertions(+), 29 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From ab907daf61f8ff5eeb433eca53aab625eb67cde9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 285/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From e187d9b251390127d4482b7fc7073acfcb26eae8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 286/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- examples/plot_fpca.py | 28 +++++++--- skfda/exploratory/fpca/_fpca.py | 93 +++++++++++++++++++++++++++---- 3 files changed, 111 insertions(+), 22 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..135b4bf2a 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -27,6 +29,7 @@ fd = dataset['data'] y = dataset['target'] fd.plot() +pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -36,9 +39,10 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() +pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -51,6 +55,7 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() +pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -59,7 +64,8 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -71,6 +77,7 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() +pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -78,11 +85,12 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -92,11 +100,12 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -109,4 +118,5 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 25b69aac5084f9e722fc6e1ddd73e766dad91d0a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 287/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From f41e26412108ccdf487b0e9d36f25ef862ab05e9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 288/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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y8nKaJMVIOICgadOQej35v1TyQ/T6F3QaB2tehJzjf2u//ex2fjr6EzO6zWhUfoia8NJ48e7Id+kY1JFH1j3CnsyGnRlxNMbzmZQkJTn1Ca0mvHv2xH/MGHI+/5zynBxXy1FwBmXFsPRBCOkAI575x23Dnj2YcnPxH+XcoArFSDgA7x7d8e7dm9yFC5Fms+WiEJb9RbXW8odgva436nl+6/O0DWjLfX3uc6Fq++Pv4c8Hoz6gpW9L7vvrPo7mHnW1pGopWr8eAL8Rw12qozJhDz+EuaSECx9VvfpUaGJsXAD5p2HSe6D958HZwr/+Qnh44DfkcqfKUoyEgwiaPo2ykycp3rbtfxcDImHMXDi5CfZYynV/uP9DzhSd4YX4F/DSOG8J6SxCvEP4aPRHeGu8uXfNvWTqM10tqUqK1q1D26oVnh07ulrKRTzbtUM35RpyFy7CeOaMq+UoOJLsVNj6LvS+Cdpe9o/bUkoK/1qDb3w8Kl9fp0pTjISD8B87FrVOR/5PP/39Rr8ZlgRdf73AyfP7+ObQN1zd4WpiW8S6RqgTiPSL5L0r3iO/NJ/719yP3vjP0rCuxGwwULxtG34jRrhdwEDYffeBEGS9976rpSg4CiktZ6m03jD6hSqblCYnYzxzBv9RVzhZnGIkHIbKw4OASZMo/PMvTHl5/7shhOVwTEker655GC+1Fw/1e8h1Qp1E15CuLBi2gJTcFJ7c+CSmaqK8XEHxtu3I0lK32mqqQBsRQdC0aeT/9hulqamulqPgCJKXw7E1MOJp8Ku6pnzR+vUghEt8ZoqRcCCBU6cgjUbyl6/4+42WPdjYazKbjNncHTOZUO9Q1wh0MkOjhvLUgKdYn76eV3e96mo5Fylavx6Vry++/fvX3tgFhNx5ByofH7LeftvVUhTsjdEAq56G8G7Q/45qmxVt3oJXt24uibyzi5EQQowTQqQIIVKFELOruO8phFhivb9DCBFtvR4thDAIIfZavz6s1CdWCHHA2ucd4W77ADbg1aULXt26kffz37ecjGYjr5rPE1Nu5qZDay3LzWbCDV1uYEa3GSxMXsh3h79ztRyklBRv3ozPZYMQHh6ullMlmqAgQmbdRuGff6FPdO8oMYU6sv0Di7N6/Kugrro6pqmwEMPevfhe7lyHdQUNNhJCCDXwPjAe6AbcKITodkmzWUCulLID8Cbwf5XuHZNS9rF+3V3p+gfAHUBH65frjsE2AN3UKZQeOkzJoUMXr/2a+iunitJ5pP1UtGk74EDjOpncUB6Le4wRrUfw2q7X2JaxrfYODsR46hTGjAz8Bg92qY7aCJ45E03Llpx76SUl+V9TofgCbH7TEhofM6T6Ztu3g8mE3+Wu+Ru1x0piAJAqpTwupSwDFgOTL2kzGfjK+v2PwBU1rQyEEBFAgJRyu7Qk1/8auNoOWp2ObsIEhIcHeT9bkt6VlJfw4d4P6R3Wm+FD/gMRvWHNS2AscbFS56ESKl4Z8goxuhge3/A4aQU1pytxJEVbtwLgGx/vMg22oPLxocXsJyk9fJhcJV1H02DTAigrglHP19isePMWVL6+ePfpU2M7R2EPI9EKqPwuT7deq7KNlLIcyAcqNtdihBB7hBAbhBBDKrVPr2VMAIQQdwohdgshdmdlZTXsJ3EA6sBA/EeNIn/ZMsylpSxOXkymIZOH+j2EUKth1AuW5ebuz1wt1an4an15Z8Q7CCF4cN2DFBtdk36ieMtWtFFRaNu0ccn8dcF/7Fh8LhtE1tvvUH7hgqvlKDSE3JOw8xPoMw3Cu1bb7OJ26KBBCK3Wefoq4WrH9VmgjZSyL/AosFAIUaf6nFLKj6WUcVLKuDAnFompC7qpUzDn55P1xwo+Pfgpg1sNpn9Lq5O0/QhoPxI2vgYl+a4V6mRaB7RmwbAFnMg/wVObnsIszU6dXxqN6HfswDc+3u1CX6tCCEHLOXMwGwxkvvqaq+UoNIS1c0GlsUQ01YDx1CmMZ864bKsJ7GMkzgCtK72Osl6rso0QQgPogAtSylIp5QUAKWUCcAzoZG0fVcuYjQbfQYPQhIdzZMln5Jfm82DfB//eYNTzlgJFm99yhTyXMihiEI/HPc66tHV8sO8Dp85tOHAAc1ERvm7uj6iMZ7t2hNw+i/zffqNw3TpXy1GoD2f3W/yQl91rOWBbAxU1z30GDXKGsiqxh5HYBXQUQsQIITyAG4Cll7RZClQUY70WWCullEKIMKvjGyFEOywO6uNSyrNAgRBikNV3MQP4zQ5aXYJQq/G5cixBiccZEziIbiGX+PUjekPP6y2RDgUZrhHpQqZ1ncbVHa7mw30f8uepP502b/HmLaBS4TvI8SUg7UnYPffg2bkzZ597jvLcXFfLUagr6+eDpw7iH6y1qX7XLtRhoXhERzteVzU02EhYfQz3A6uBw8D3UsokIcSLQoiKpOefASFCiFQs20oVYbJDgf1CiL1YHNp3SykrspndC3wKpGJZYTSenNNVsLWHBrUZZp7vVHWDkc+ANMEG9zk/4CyEEMwZNIdeYb14ZvMzTsvxVLx1K149e6DW6Zwyn70QHh5Ezn8FU24e51+a62o5CnUhYw+krID4+8E7sMamUkr0u3bh27+/S7dD7eKTkFL+LqXsJKVsL6WcZ732nJRyqfX7EinldVLKDlLKAVLK49brP0kpu1vDX/tJKZdVGnO3lLKHdcz7rVFOjZJSUykfFK8ku6U3Aev3Vt0oKNqSsmPPt5B32qn63AEPtQdvDn8TH40Pj65/lMKyQofOZyosxLB/v9tHNVWHV9euhN13LwW//07eTz+7Wo6CrayfD16BMPDuWpsa09IoP38eHxcf8nS147pZ8Fvqb2SVZOM3cQKGxETK0tOrbnj5I5a0HZvecK5ANyHcJ5wFwxaQVpjGnC1zcORzgT4hAcxmfAe6bq+3oYTccQc+lw3i3AsvYEhKcrUchdpIT4AjqyD+AfCqPT5Hv2sXgGIkmjoms4kvk76kV2gvut9wFwAFy5dX3VgX1axXEwBxLeN4JPYR1pxewxdJXzhsHv3OXQitFu8+vR02h6MRGg2tXn8ddXAwZx54kHI3DAFXqMT6V8A7GAbeZVNz/c5dqIOC8Gjf3sHCakYxEg5mY/pG0grTuLn7zXhEReEdF0v+0mXVPyVf/qh1NfG6c4W6ETO6zWBM2zG8nfg2O87ucMgc+p078e7d26kVvhyBJjiYqHffpTwvj9N33Pm/aogK7kXaTkj9EwY/+I+SpNWh370bn7g4l4dnK0bCwXx7+Fta+rZkVBtLNSndVZMoO378b2k6/oauFfSbaVlN5J5yolL3QQjBi4NfpG1AW/698d+cKz5n1/FNhYWUHDqEz4ABdh3XVXj37EHU229TmppK2t33KIbCHVk/H3xCa0ziVxljRgbGM2dcvtUEipFwKCk5Kew8t5Mbu9yIRmVJ3hUwdgxotRQsXVZ9x8sfAaGCzc3TNwGWE9lvDX+LkvISHtvwGEaT0W5jV/gjmoqRAPAbcjmtXnsVw759nJoxk/LsbFdLUqggY48lFXj8/eBpW/36i/6IAYqRaNJ8d/g7vDXeTO049eI1dWAgfsOGkv/7iuoTtelaQZ+bYO9CKLTvU3Rjol1gO14a/BL7s/bbNbV4U/BHVEXA+PG0/uC/lJ04wYkpUy9+0Ci4mM1vWs5FxM2yuYt+925UAQF4dqomZN6JKEbCQVwwXGDF8RVMaj8Jneff4/B1E6/ClJWNfufO6geIfxDM5ZYDds2YMdFjmNltJotTFrPsWA2rrzrQVPwRVeE3ZAjRixai8vbm1MxbOPfyy38veqXgXLJT4dBSGHC7TRFNFej37MGnb1+EyvUf0a5X0ET54cgPlJnLuKnrTf+45zd8GCpfX/Kri3ICCGkP3a6G3Z83u5xOl/Jw7MPEtojlxW0vkpKT0qCxmpo/oiq8unYl+qefCLzuOnK//Y7UsePIXLCAslPN08flUra8BRpPGHiPzV1MBQWUpR5zm5Vu1VUuFBpEubmcH1J+YHDkYNrp2v3jvsrLC//Royn840/Mzz2HytOz6oEufxiSfoZdn8GQRx2s2n3RqDQsGLaA65Zdx+MbHmfxxMX4autXDL4p+iOqQu3nS8QLzxN0041kv/ceF774kguffoa2bRt8+sXiERODtkU4wscHlZc3QquxZCVWqy3/qtQIjRqVfwDayAi3eKJtdBRkwL7FEHsL+NmefNSw/wAA3r0VI9Fk2ZS+iUxDJk8Pqj7DY8DEieT/+itFGzcSMHp01Y0iekP7K2D7f2HQPZZC6c2UUO9QXh36Krf/cTsvbH2B/xv6f/UKDWyq/ojq8Orcmah338V4PpPC1aso3rqNos2bMP3yi81jCG9vvHv1wn/kCAImTEAT2jzK7TaYbe+DNFsc1nXAsG8vCIFXr14OElY3FCPhAH48+iNh3mEMjRpabRvfQQNRh4RQsHxF9UYCLJFOX02Evd9B/9sdoLbx0L9lf+7vcz/v7HmHuJZxXN/5+jqP0ZT9ETWhbRFO8IwZBM+YAYCpqBhTzgXMej1mvR5ZXg5mM7LcBGYTstyENJVjysmlNDUV/fZtnH9lPplvvEng9dcTes/daIKDXfxTuTH6HNj9BfSYakm5UwcM+/bh2aEDaj/bIqEcjWIk7My54nNsPrOZWT1moVVVXyREaDQEjB9P3vffYyoqqv4PIvpyaBUHW96BfrdUWwe3uTCr5ywSMhOYv3M+PUJ7/DOjbg2YioooOXSI0Ltrz5vT1FH7+aL2q9uWXenx41z47DNyFy2iYPlyIubNw3/kCAcpbOTs+hSMxZYt4zogzWYM+/YTMKaGB0cno2w02plfjv6CWZqZ0nFKrW11Eycgy8oo/POv6hsJYVlN5J2Cw5dmYG9+qISKVy5/hWCvYB5b/1idEgEa9uy1+CP6xzlQYdPFs107IufNI+bnn9BEtCT93nvJ/uQTV8tyP8qKLVGJncZBi+5163ryFOb8fJeVKq0KxUjYEZPZxE9HfyI+Mp4o/6ha23v17o02Kqr6XE4VdB4PQTEW34QCQV5BLBi2gHPF53huy3M2JwLUJyaAWo23m+z1Nla8OnUieuFCAiZMIOv1N8h6511XS3IvEr8BQ44lxU4dMey1ZIl2F6c1KEbCrmzJ2MJ5/Xmu7XStTe2FEARMnEDxtm01n5BVqS2O6/RdkKYckALoE96Hh2Mf5q/Tf/Hd4e9s6mNI3INXly6ofOsXGaXwP1ReXkS+9iq6qVPI/u9/yV20yNWS3IPyMtj6LrSJhzZ1L2Zl2LcPlb8/Hu3+GRXpKhQjYUd+PPIjIV4hDG893OY+uokTwWymYOWqmhv2mWY5tamsJi4yo9sMRrQeweu7X2d/1v4a20qjEcO+fXj36+ckdU0foVIR8eKL+A0fzrm585QT3gAHf4SC9HqHrBv27cO7Vy+3Cjl2HyWNnJySHDalb+Kq9lfV6LC+FM8OHfDs3Ln2LSdPP4idAYd+g7y0BqptGggheGnwS7TwbcHjGx4nv7T6Q4clycnIkhJ8+vV1osKmj1CriVywAG1UK848+SSmggJXS3IdZrOlTn2LntBhVN27FxdTeuSIW201gWIk7MbKEyspl+VMaj+p9saXEDBxAoZ9+yhLq+XDf8CdgIRdirOwAp2njgXDFpBlyOKZzc9gluYq2xkSEwGUlYQDUPv50uq11yg/n8m5F150tRzXkfI7ZKdYIprqcYan5PBhMJvx6tnDAeLqj2Ik7MSyY8voGtyVjkEd69xXd+WVABSs+L3mhoFtoOskSPgSSovqobJp0iO0B0/EPcGG9A18lfRVlW30CYloW7VC26KFk9U1D7x79SL0vnspWLGCoo0bXS3H+UhpydocFG1Jp1MPDAcPAuDdQzESTY5jecdIupDEVe2vqld/batWeMfGkr+8hmJEFVx2nyWX0z7FUViZG7vceLFQUeL5xL/dk1Ki35OorCIcTOjtt+PRti3nX34FWVbmajnO5eQmOJNgScxZz7NMJUmH0LRogSbM9hQezsAuRkIIMU4IkSKESBVCzK7ivqcQYon1/g4hRLT1+mghRIIQ4oD135GV+qy3jrnX+hVuD62OYNmxZaiFmvEx4+s9hm7iBMpSj1F65BDEg54AACAASURBVEjNDVsPsByu2/6BZQ9UAbD4J56Pf55Wfq14YsMT5JTkXLxnTE/HlJWNT6xiJByJ8PCgxdNPUXbyJDnf2hZx1mTY9Ab4hlsCTOpJycGDeHWv27kKZ9BgIyGEUAPvA+OBbsCNQohLj8HOAnKllB2AN4H/s17PBq6SUvYEZgLfXNJvmpSyj/Urs6FaHYFZmll+fDnxkfGEetc/p43/uHGg0dTuwAa47F7IOQZH/6j3fE0Rfw9/Xh/+OnmleTy96emL/gl9QgIA3n0VI+Fo/IYNw3fYULI/+KD5VMjL2APH11nel9r6pXsxFRVRdvIkXj2aoJEABgCpUsrjUsoyYDEw+ZI2k4GKzeIfgSuEEEJKuUdKmWG9ngR4CyGqSYnqnuw6t4vz+vP1clhXRhMUhO/gePJXrEDWtkLoOhkCWsHOjxo0Z1OkS3AXnhzwJFsytvB10teA5XyEyt8fz44dXKyueRD+0EOYCwvJ/fZbV0txDpvfqnNRoUspOXQIpMS7Ka4kgFZA5bCcdOu1KttIKcuBfCDkkjZTgUQpZWmla19Yt5rmiGpSfgoh7hRC7BZC7M7KymrIz1Evlh5bip/Wr05nI6pDN3Ei5RlnMezZU3NDtQZib4Vjay1FTRT+xnWdrmN029G8nfg2B7IOYNiTiHffPm4Ve96U8erWDb/hw7nw5VeYiopdLcexZKdawtL7z6pTUaFLKUmy1LxvkttN9kAI0R3LFtRdlS5Ps25DDbF+3VxVXynlx1LKOCllXJiTHT6lplLWnF7D6Laj8dI0PKuo/8iRCC+vmosRVRA7E1Ra2P1Zg+dtaggh+M9l/yHMJ4z/rHqU0qOp+ChOa6cSeu89mPPzyV240NVSHMvWty1FhQbZXlSoKkoOHkQTEeGWadjtYSTOAK0rvY6yXquyjRBCA+iAC9bXUcAvwAwp5bGKDlLKM9Z/C4GFWLa13IrN6ZspNhYzLmacXcZT+friP3IkhStXIY3Gmhv7hUO3ybDnO0tCMYW/ofPU8erQV9EdtdQIV/wRzsW7Vy984+PJ/eab2v+WGysFGbB3EfSdbnk/NoCSpCS8utue0diZ2MNI7AI6CiFihBAewA3ApelKl2JxTANcC6yVUkohRCCwApgtpdxS0VgIoRFChFq/1wITgYN20GpXVp1cRbBXMANa2s9+BUyciCkvj+KtW2tvPOAOKM2HAz/Ybf6mRJ/wPtxY1pdyFfzhc9zVcpodQTNupjwri8I//3S1FMdwsajQAw0axlRYSNnJk253PqKCBhsJq4/hfmA1cBj4XkqZJIR4UQhR4c39DAgRQqQCjwIVYbL3Ax2A5y4JdfUEVgsh9gN7saxE3OqYsd6oZ0P6Bka1GYVGZb8aD36XD0al05G/fEXtjVsPtKQA2Pmp5TCPwj/olGYmq7UfL+97neN5iqFwJn5DhqBt3Zqc75rglpM+x1J/vud1dS4qdCklhw4D7umPADv5JKSUv0spO0kp20sp51mvPSelXGr9vkRKeZ2UsoOUcoCU8rj1+lwppW+lMNc+UspMKWWxlDJWStlLStldSvmQlNJkD632YtOZTRjKDYyNHmvXcYWHBwFjxlC4Zg1mvb6WxgIG3A7nD0DaDrvqaAqYy8ooOXiQmCFX4qP14fGNj1NSXuJqWc0GoVYTdNNNGBISLCknmhI7PgSjvs5FhaqixHrSukkbiebI6pOrCfUOJbZFrN3HDpg4EanXU7huXe2Ne15nCb/b6VYLLbegJCkJWVpKyIDBzB08l6O5R1mwe4GrZTUrAqdcg/DyInfRYldLsR+lhbDjI+gyEcK7Nni4kqQkNJERblsOVjES9aDYWMzG9I2MbjsatUpt9/F94mLRtGhBgS1bTh6+0HeaJQyvyC3PG7oMQ6IllNinXz+GRA1hZreZLElZwl+naqgEqGBX1Dod/mNGU7ByJeaSJrKK2/0FlOTVq6hQVRiSDuLd3T39EaAYiXqxPm09paZSxkXbJ6rpUoRaTcCVV1K0eTOmvLzaO/S/HcxGSKg6uV1zRb8nEW3bNhfDCh/q9xDdQ7rz3NbnyCjKqKW3gr0IvOYazIWFFK1d62opDcdYAtveg5hhENXwXQRTQQHGU6fddqsJFCNRL1adXEW4Tzh9wh1XhzZg4gQwGin4w4bUGyHtof1ISPgCTOUO09SYkFJiSNyDT6XQV61ay2tDX8MszTy16SlMZrdyczVZfAYORBMRQd6vv7paSsPZtxCKzsOQx+wyXElyMoDbhr+CYiTqTGFZIVvObGFM2zGohON+fV7duuERE2PblhNA/zug4Iwlp70CZSdPYsrJwfuSIkOtA1rzzMBnSMxM5IukL1ykrnkhVCp0kydRvHkLxvONeEvUVG5JwdEqDmKG2mXI0goj0aWLXcZzBIqRqCPr0tZhNBvtdoCuOirqX+t37cJ47lztHTqNteRzSlA++KCSPyL2n1sCE9tNZGz0WN7f8z5JF5KcLa1Zops82VKm9/dG/BCT9DPknbKUJq1HUaGqKDmcjDo01O3Sg1dGMRJ15M9Tf9LStyW9Qns5fC7dhAkgJQW/r6y9sUoN/WZY8jnlnHC4NndHn5iAWqfDIybmH/eEEMwZNIdg72Bmb5yNodzgAoXNC8+YGDy7daVwVS213N0Vsxk2vwlhXaFT/UsCXEpJSjJenTvbbTxHoBiJOqA36tmWsY2RrUdSTb5Bu+IRHY1Xz562pQ8H6HszCBUkKg5sQ+IevPv2rTapn85Tx7zL53Gy4CSv737dyeqaJwFjx2HYtw9jRiMMGkheDpmHLKsIOyWKlGVllB1Nxaur+241gWIk6sSWjC2Umkq5os0VTptTN3ECJYcOUXrchtPCulbQaRzs+RbKm1llsEqU5+RQduIE3rUUGRoUMYgZ3WawJGUJG9ObYclNJxMwznLwtGB1I6uDYjbDhv+DkA7QY6rdhi09cQJpNOLZpeFnLRyJYiTqwNrTawn0DKRfC+cli/MfPx6EsN2BHXsrFGdBio3tmyAVqdZtyfz6YL8H6RjUkTlb5nDBcMHR0po1Hm3bNs4tp+TlcP4gDHvSsq1rJ/7ntFa2m5oERrORDekbGBY1zK65mmpDGx6Oz8CB5K9YXnv9a4AOV4CuNSR86XBt7oo+MRGh1eJlQ8I0T7Un84fMp7CskOe3PW/b71ih3lzccjp71tVSbMNBqwiwOK2Fpyce0dF2HdfeKEbCRnad20VhWaFTt5oq0E2cgPHU6Ys5XmpEpYZ+M+H4erhwrNbmTRFDQiJePXqg8rStyGGnoE483O9h1qet58ejPzpYXfPGf/RoANtSzrgDDlpFgOWMhGenTgiN8x4664NiJGxk7em1eGu8uSzyMqfP7T96NEKrrYMDezoIdbN0YJtLSylJSvrH+YjamN5tOgMjBvLartdIL0x3kDoFj5hotG3bULRuvaul1I4DVxFSSkqTk91+qwkUI2ETZmlm7em1XN7qcrtUoKsrap0O32FDyf/9d6TJhlPCARHQebylIFEzc2CXHDyINBqrPB9REyqh4sX4F1EJFf/Z+h/MspY64wr1QgiB//AR6Ldvx1zs5sWyHLiKKD9/HlNeHp5ufIiuAsVI2MCB7ANkGbIY2WakyzToJk7ElJWNfudO2zrE3Qr6bEhe5lhhboY+IREA7751W0kARPpF8kTcE+w8t5MlKUvsLU3Bit+IEUijkeJt21wtpXrMJlj/ikNWEcDF1OleXd07sgkUI2ETa06vQSM0DGk1xGUa/IYPR+XnR/6vv9nWod1ICGxryVjZjDAkJODRrh2aoKB69Z/ScQqDIwfzZsKbpBWk2VmdAoBPbD9U/v7u7ZfY/73lXMSIZ+y+igAoTUkBwLOTst3U6JFSsvb0Wvq37I/OU+cyHSovLwKuvJKC1asxFRXZ0EEFsTPh5CbITnW8QDdAms3o9+zBp5bzETUhhOD5+OfRCA3PbnlW2XZyAEKrxW/I5RRt2Ig0u+Hvt7wU1r0MEX2g29UOmaLkcDLaNm1Q+/k6ZHx7ohiJWjiWd4xTBadcEtV0KYFTpyBLSihYaUOaDoA+00GlaTb5nEpTUzEXFODdr2EpnFv6tuSJ/k+QmJnIwsNNsPSmG+A3YgSm7GzbIvacze7PIf80jPqP3U5XX0pJ8mG3TupXGcVI1MLaNEsO/BFtRrhYCXj16oVH+/bk//SzbR38W0CXCbB3oSUPfhPHkGjxRzRkJVHB1R2uZkirIbyd+DanCk41eDyFv+M3ZAio1e635VRSABtfs9SLaO8YH6SpqBjj6TQ8G0FkE9jJSAghxgkhUoQQqUKI2VXc9xRCLLHe3yGEiK507ynr9RQhxFhbx3QWa0+vpVdYL8J9wl0l4SJCCAKnTMGwd69taToAYm8BQw4cbvoObH1CIpqwMLStWzd4LCEE/7nsP2jVWuZsmaPUnrAz6sBAvHv3pnjzFldL+Tvb3gP9BcsqwkGUHjkCUuLl5uk4KmiwkRBCqIH3gfFAN+BGIcSlFTRmAblSyg7Am8D/Wft2A24AugPjgP8KIdQ2julwsvRZJF1IYkRr168iKtBNngRqNfk/27iaiBkOQdHNYsvJkJCAd2ys3ZIvtvBtwVMDnmJP5h6+PfytXcZU+B++g+MpOXiQ8txcV0uxUJQFW9+DbpOhlf1r11dQklwR2dR8tpsGAKlSyuNSyjJgMTD5kjaTgYqTXT8CVwjLO3kysFhKWSqlPAGkWsezZUyHs+nMJgCGRtmnwIg90ISG4jdsGHm//YYst6EKnUplWU2c2gJZKQ7X5yqMZ89izMiwKV9TXZjYbiLDWw/n3T3vcrrgtF3Hbu74xseDlOh37HC1FAvr5kJ5CYyc49BpSpNTUOl0aFq2dOg89sIeRqIVUDlWMN16rco2UspyIB8IqaGvLWMCIIS4UwixWwixOysrqwE/xj9Zn7aeCN8IOgZ2tOu4DSVw6hRMWdkUbdpkW4c+00GlbdL5nPRWf0RtmV/rihCCZwc+i1al5cVtLyq5neyId8+eqPz9Kd7iBltOZ/dbasQPuBNCHft+L0lOxqtLF6eUG7AHjd5xLaX8WEoZJ6WMC7NjdadSUynbz25naNRQt/vP9Bs6FHVIiO1bTn5h0HWi1YHdNAvsGBISUfn4OKSASwvfFjwS+wg7zu3g19QmUKfZTRAaDb6DBlK8Zatrja+UsGo2+ATD8CcdO5XJROmRI40iHUcF9jASZ4DKnsIo67Uq2wghNIAOuFBDX1vGdCi7zu3CUG5gWNQwZ05rE0KrRTdpEoXr1lOek2Nbp9hboSQPDtl4GK+RoU9MxLtPH4clS7u207X0C+/Hgt0LyDZkO2SO5ohvfDzGjAzKTp50nYhDv1q2Y0c+C971O4RpK2WnTiFLSty+hkRl7GEkdgEdhRAxQggPLI7opZe0WQrMtH5/LbBWWh4dlgI3WKOfYoCOwE4bx3QoG9I24K3xZkDEAGdOazOBU66B8nLyl9r4a4kZCsHtm+QJbFNhIaUpKXbfaqqMSqh4Pv55DOUG5u+c77B5mhu+8fEAFG/d6hoBRgP8MQda9LRkT3Yw/0vH0Tic1mAHI2H1MdwPrAYOA99LKZOEEC8KISZZm30GhAghUoFHgdnWvknA98AhYBVwn5TSVN2YDdVah5+JDekbGBQxCE+1bemmnY1nx4549epF3o8/2rZUF8LiwE7bDpmHHa7PmRj27gUp65zUr67E6GK4u/fdrD65mnWn3Sy+v5GibdMGbVQUxVtdlMdp0xuQnwbj5zsk/callCYng1aLZ7t2Dp/LXtjFJyGl/F1K2UlK2V5KOc967Tkp5VLr9yVSyuuklB2klAOklMcr9Z1n7ddZSrmypjGdxdG8o5wtPuuWW02VCfrX9ZSlHrt4iKxW+kwDtUeTW03odyeAWo13r14On+vW7rfSMagjc3fMpbCs0OHzNXWEEPjGx6Pfvh1pNDp38sxk2Pwm9PoXRF/ulClLklPwbN8e4eHhlPnsQaN3XDuCinrH7hT6WhUB48ej8vMjd7GNGUt9Q6DrJNi3GMr0jhXnRAwJCXh164bKx8fhc2nVWl647AWyDdm8nfi2w+drDvjGX4a5uJiSQ4ecN6nZDMseAk8/GPuy06ZtTOk4KlCMRBVsSNtA95DuhPnYL1rKEah8fNBNnkzhqlW2H0iKuxVK8yHpF8eKcxLmsjIMBw7Y/XxETfQM68m0rtNYkrKExPM2ruIUqsWnf38Aim1Ng28PEr+0bL2OmQe+oU6Zsjw7G1NWdqNJx1GBYiQuIbckl31Z+9x+q6mCwH9djzQayf/FxtDMtoMhtFOTOYFdkpSELC11qNO6Ku7vcz+t/Frx/LbnKTWVOnXupoYmJASP9u3R79zlnAkLz8Gfz0P0EOhzk3PmBEqs6cGVlUQjZ9OZTUgkQ1u791ZTBV6dOuHdrx95S5bYlna5woGdvgvOuWEGzjpyMamfE1cSAD5aH+YMmsOJ/BN8sv8Tp87dFPEZ0B9DQoJtWQQagpSw9EHLyeqr3ra8H5xEabK1hoQDzvI4EsVIXMKGtA2EeYfRNbjxxDEH3fAvyk6dsj29Qe8bQe3ZJFYT+t0JeLRtiybUOVsGlRncajAT203ks4OfkZrbPGp2OArfAQMw6/WO90skfAFHV8PoFyCkvWPnuoSSlGQ04eH1LojlKhQjUQmjycjWjK0MjRqKSjSeX43/2LGodTpyl3xvWwefYOh+jaX6Vpmb1xmuAWkyoU9IwGeA686yPNH/Cfy0fryw7QWlQFEDqPBL2Fyetz5cOAarn4F2w2HAXY6bpxpKk1ManT8CFCPxNxIzEykyFjUaf0QFKk9PdNdcQ+Fff1Fua/6quFuhtAAO/uRYcQ6kNCUFc0GBS41EsFcwT/R/gr1Ze/kh5QeX6WjsaEJD8Wjf3nHOa1M5/HwnqLUw+b8OKyZUHeayMkqPH8erc+PyR4BiJP7GhvQNeKg8GBgx0NVS6kzg9ddDeTl5P9sYtdR6IIR1bdRnJio+UHwG9HepjqvaXcWgiEG8mfgm54vPu1RLY8ZnQH8Mux3kl9gwH87sholvgq7KXKEOpez4cSgvV1YSjRkpJRvSNjAgYgA+WsfH29sbz3Yx+AwcaHFgm2wokCOEZTWRkQhn9zleoAPQ79yFtm0btC1auFSHEILnBj2HyWzilZ2vuFRLY8Zhfokjf1iqzfWZBj2m2ndsGylJTgYaX2QTKEbiIicLTnK68HSj22qqTNCNN2LMyKBo/XrbOvT6F2i8G+VqQppM6HfvxteFW02VaR3Qmnv63MOa02tYc2qNq+U0Si76JXbZMRQ29xT8fIclN9OE1+03bh0pTU5BeHri0batyzTUF8VIWGksp6xrwn/UFWhatiTnGxurqHkHQo8pcOAHKG1cKSbcwR9xKTd3u5nOQZ15ecfLSsqOemB3v0SZHr6/2RL2+q+vQettn3HrQUlKMp4dOzosS7EjUYyElQ3pG+gU1IlIv0hXS6k3QqMh6Kab0G/fTsmRI7Z1ir0VyorgwI+OFWdnLvoj+rvWH1EZrUrL8/HPk12ipOyoL3bzS5jN8MudlmJCUz6GYNcl1JNSWiKbOndymYaGoBgJIL80n8TziY16q6mCwOuuRXh6kvvtd7Z1iIqDFj0a3ZmJi/4INysB2SO0Bzd1uYklKUvYk7nH1XIaHT5xcZY8TskNLLW75gU4vAzGzoPO4+wjrp6UZ2Zhys1tlJFNoBgJALZmbMUkTY16q6kCTVAQuklXkb90Kaa8vNo7VJzAPrsPzjSOPETu5o+4lAf6PkCEbwQvbH2BMlOZq+U0KirSvRsSE+o/yK5PYctbEHcbDLrXTsrqT+kR60lrB0U2lZnKuHH5jaw57RhfmGIkALM00yesDz1De7pail0Imj4dWVJC3k82noHodT1ofRrNasId/RGV8dH68OygZzmWf4zPD37uajmNCm3LlmgjI9En1POBZd8SWPE4dBoH4191atqN6rgY2eSgdBy7z+3m4IWDaFVah4zf+LwoDmBCuwlMaDfB1TLshlfnzvgMGEDOd98RPHNm7c4yL50lNPDAT5asmF4BzhFaT9zRH3EpQ6OGMj56PB/v/5gxbcfgp47kfH4pOfoycovLKCotR0qJySwxS/D2UOPvpcHfS4vOW0tkoBdhfp5uV1/dGXjHxlrqS0hZt58/eQX8eo+lNsR1X1kOzrkBpckpaCIjUOt0Dhl/45mNeKo96d/SMe8HxUg0UYJuns6ZBx6kcN06AkaPrr1D3K2w5xs48D30v93xAhuAfsdOS0UzN/NHABjKTBzMyGdfWh6FGVdSXr6BSYsfpujkHdR14e6hUdEq0JvoEB+6RATQpaU/3SICiAn1RaNuupsAPrH9KFi2DGN6Oh6tW9feASyp73+6HSL7wo2LQOvlWJF1oCQl2WH+CCklG9M3MqDlALw1joneUoxEE8V/xAi0kZHkfvOtbUYish+07AW7v4S4WW6xTK8KaTSi37mTgIkTXS0FsLxJkzIK2HQ0m82pWew6kUuZyZLDKULnRZuW13PK+0umDstgbJvJhPh5EOjjgb+nBpVKoBIClQCD0URhSTkFBiN5eiMZ+QbScw2cyTVwLKuIzanZGE2WMrVeWhV9WgfSPzqYuOhg+rUJxN/LPZ6a7YG3NaOvPiHBNiOxdxH8dq8li8BNS8DT38EKbcdcWkrZiZP4jxrlkPFPFpwkrTCNGd1mOGR8UIxEk0VoNARNu4nM1xZQkpxc+0nPihPYyx+BMwmWqCc3xLB/P+biYnzj412mQUrJobMFLNt3luX7M0jPNQDQpaU/twyOZmBMMD2jdIT7eyHlSGb9kcS2C18xe9gUwnyCqxwzEIioYTeirNzMsawiDp8t4MCZfBJO5fLf9ccwmVNRCegWGcCQjmEM7RhGbNsgPDSNd6Xh2aEDqoAADAmJBF59dfUNpbQ4qP963pK074aF4OHrJJW2UZqaCiaTw05aO+N8l2IkmjCB115L1vv/5cLnn9Pq1Vdr79DzOvjjOUt0iJsaieItW0GlwneQ8/Nr5euN/JiYzsIdpziWVYxaJRjSMZQHr+jI8E5hhAf8c4ujImXH1KVTmb9zPq8Pr9+pXw+Niq4RAXSNCGBKvygAikrL2Xs6j10nc9h2/AKfbDzOB+uP4euh5rL2IQzpGMawTmFEh7rXB2dtCJUK77590NdUu728zPJAs/db6D4Frv7ArbaYKnB0DYlN6ZvoENjBoee7GmQkhBDBwBIgGjgJXC+l/EcdTSHETOBZ68u5UsqvhBA+wA9Ae8AELJNSzra2vwV4DThj7fOelPLThmhtjqh1OoKuu5ac7xYS/sgjaCMiau7g6Q99boSEL2H0S+DnfuVbi7dswatnD4c5AaviUEYBX249wdJ9GZQYzfRtE8i8a3owvkcEwb61F7SP1kVzV++7eHfPu6xPW8/w1sPtosvPU8PlHUO5vGMojwCFJUa2HbvAxqNZbDySzV+HMwFoF+bLqK4tGNklnLi2QY3Cn+HTL5asDRspz839Z/2Foiz44RY4tRmGzYbhs912e7QkJRnh7Y1HmzZ2H7uorIiEzARu7naz3ceuTENXErOBNVLK+UKI2dbXT1ZuYDUk/wHiAAkkCCGWAqXAAinlOiGEB7BGCDFeSrnS2nWJlPL+Bupr9gTPnEnOt9+R89XXtJj9ZO0d+t8BOz+21AAe+oTD9dUFU0EBhgMHCLnrTqfMl3Aqh/fWprIuJQtvrZpr+rZi2sC29GhVdwN1a/dbWXliJXO3z6V/y/74au3/dO/vpWVM95aM6W5x6J/MLmZ9SiZrkjP5YssJPt54nAAvDcM7h3NF13CGdwpH5+Oevgwfazlaw569+I8c8b8bJzZZHNSGXJjyKfS6zkUKbaM0OQXPTh0RarXdx95+djvl5nKGtnLs+a6GGonJwHDr918B67nESABjgT+llDkAQog/gXFSykXAOgApZZkQIhGIaqAehUvQRkYScOWV5H3/PaH33oM6oJbw1rBO0G4E7PocBj8CavfZkSzesQPMZvwGD3boPFtSs3l37VG2H88hyEfL42M6cfNl0ei86/+BqlVbUnbc/PvNvLvnXWYPmG1HxVUTHerLLaEx3DI4hqLScjYdyeKvw5msS8lk6b4M1CpBXNsgRnVtwRVdw2kX5udwTbbi1bMnQqvFkJhgMRKmcti0ADb8HwS3h+k/QcserpZZI1JKSlJSCBg71iHjb0zfiL/Wnz7hfRwyfgUN/QRoIaU8a/3+HFBVzuZWQFql1+nWaxcRQgQCVwGVE95MFUIMBY4Aj0gpK49Rue+dwJ0AbRywpGsKhMy6jYJly8hdvITQO++ovcPAu2DRDZC8HLrX4Dh0MsVbt6Ly8cG7d2+HjH/wTD7zVyazOTWbFgGePDuhKzcNbIOPh30MZe+w3tzQ5QYWHl7IuOhxDn9zV8bPU8P4nhGM7xmBySzZm5bHmsPnWZucybzfDzPv98PEhPpyRZdwrujagrjoILQu3JZSeXri1aOH5VBd5mH49V5LWvteN1iyuXq6j0GrjvLz5zHn5zskZ5NZmtl0ZhPxreLRqBz7IFfr6EKIv4CqAtKfqfxCSimFELKuAoQQGmAR8I6U8rj18jJgkZSyVAhxF5ZVysiq+kspPwY+BoiLi6vz/M0Bry5d8I2PJ+ebrwm+ZSYqj1r20TuOgcA2lm0ndzISW7biM2AAQmvfLZK0HD2v/5HCr3szCPTRMmdiN6YPaoOnxv5bBA/2fZANaRt4ZvMz/HDVDy6pXaJWCWLbBhHbNoh/j+tCWo6etcmWbamvt53i080nCPDSMKxzOKO6hjOsUxiBPrX7XuyNd5/e5Hz9Neb3hqDyC4Cpn0HPa52uo744sobE4ZzDZBuynZJKqFYjIaWsNsBXCHFeCBEhpTwrhIgAMqtodob/bUmBZUtpfaXXHwNHpZRvVZrzQqX7nwI2hAqYLQAAIABJREFUhOYo1ETwrNtIm3U7BcuWETi1lsIrKrXFN/HnHDh30C2W9WVpaRhPnyZ4+nS7jWkoM/Hf9al8tOE4QsA9w9tz97D2DdpWqg0/Dz/mXj6X21bfxpsJb/LMoGdq7+RgWgf7MDM+mpnx0RSVlrP5aBZrrNtSy6zbUrFtgxjdtQWjurUgxtHRUlLCod/wOfcdOSYzJf7D8Ln3I/ANdey8dsaRkU0b0zciEFze6nK7j30pDV2nLAVmAvOt//5WRZvVwMtCiIoQhTHAUwBCiLmADvjbEd8Kw2N9OQk43ECdzR7f+Hg8u3blwudfoLvmGkRtNX77Tod1L1tWE5PecY7IGihavwEAv2H2eXL689B5XliWRHqugav7RPLk+C5E6JxTb6B/y/5M7zqdbw9/y4g2I4iPdN2Zj0vx89QwrkcE43pEYDZL9qXnseZwJn8dPn9xW6p9mC+jurVgdNcW9G0ThFplx8iitJ2w+hlI34l3q86AEb1uHD6NzECAJbJJGxWF2s/+W2Ob0jfRM7QnwV5Vn7uxJw3ddJwPjBZCHAVGWV8jhIgTQnwKYHVYvwTssn69KKXMEUJEYdmy6gYkCiH2CiEqjMWDQogkIcQ+4EHglgbqbPYIIQi57TbKjh27+IFbIz7BlsiR/d+DPsfxAmuhaP16PGJiGlzZ6/QFPbO+3MUdX+/Gx0PNkjsH8dYNfZ1mICp4qN9DxOhimLNlDgVlBU6d21ZUKkHfNkE8PrYzqx4eyqZ/j+D5q7oRofPms00nuPbDbfSf9xeP/7CP1Unn0Jc1oAbEhWPw/+2dd1gU1/eH37vLsvQmggUbFuwFsffeoonRGGOiJjEx0Rg1zfRiYvJNLIn6syTRRI3plqiJxq6osWILiqIIKhYEBKTDwt7fH7MYVIrAwi4y7/Pss7N37tz5zMDumVvOOb+Pge/6QMIlGDwfm1f2Y1u3LmlHSxAR1oJknA0tlcivsWmxBMcG061G2aQ2EFI+OMP4AQEBMigoyNIyrBZpMHCh/wC0npWo/euvhQdPiwqGrztD3xnQ8eWyEZkH2ckpnO/QAffRo/GeVrxludlGyff7Ipi9NRQbjWBq7wY83am2RSdnT8We4qlNTzGgzgD+16V85cZOTDcQGBrD9jM32HU2msT0LGxtNHSu50nvRt70a+JNJSd94Q2lxkHgTMWBU2sLnSZDh0m3J6avv/8BiVu20ODggcJ7v1aEMS2N0NYBeE6YQOWXzbuSf+35tXy4/0NWD16Nn4d5jJAQ4qiUMk8PWutZ36hS6gidjkrPP0/URx+ReuBA4aEtqjSDmh3h8BIlLr/G/BO590PKgf1IgwGn7sV7cgqLTuKN1f9y/HICfRp788nDTaniannv3KaeTRnffDyLTy6mY7WODK472NKS7hsXOx2DW1RjcItqGLKNHLkYx/aQaLadiWLn2WjeX3+KzvU8GdKiGn2beN8bW8qQDoe/gT1zIDMJWo2GHu+A851rZOz9/UlYtYqMsDDsGpSfzG4ZYWFgNJbKyqbAyECqOFahgXvZ3A/VSFQwXB8dSuzixcQuWnx/8Y/ajVe8W89thoaWCaeevHs3GhcXHFq1KtJxWdlGvt0bztzt53G01TJvZEuGtKhmVeG3xzcfz6Hrh/jk4Cc09WxKHdc6lpZUZHRaDR3retKxrifvP9SIM9eT+PPfa/x58hqvrTqJ7R8aevp5MdS/Oj39PNGFrIUdn8Cty8pKuj4fg1ejPNu+7VR37Fi5MhKltbIpIzuDA9cPMKTukDL7Py4//TcVs6CxtaXSuHGkBgWReuRI4Qc0HAyuNeDAwtIXlwfSaCQ5cA9OnTsXaelreEwywxbvZ+bmUHo19GLrK914uGV1qzIQADYaG77o+gV6rZ43At8gIzvD0pJKhBCCxtVceLN/Q/ZO68GaCR0Z1bYmQZfiWfbTSs5/2hbWPk+mrQuM2QBPrsrXQADoatRAW9mz4DhOVkjGmbNoHB3R+ZjXP/jw9cOkZaWVaapl1UhUQNxGPIbW05PYxYsLr6y1gXYvwqV/lOiwZUz66dNkx8bi1KP7fdWXUvLL4csMmr+PS3GpLBjVisVPtaay832Mj1uIKo5VmNFpBqHxocw6MsvScsyGEMrS2Y866DhcZwm/2s7AW5PIq4YJ+EW+xehddgSei6GgeVEhBA7+rUkrbqY6C5EeEoK+UUOzz6MEXgnE3saetlXLLiujaiQqIBo7Oyo98wwp+w+QduJE4Qf4jwG9C+xfUPri7iJ51y4l6mvnwteDx6Vk8sLKo7y9Nhj/Wm5sntKVh5qXXnRMc9KtRjfGNB7Db6G/sT4sr5Xk5ZDUONg0DRZ3QHN5P/T6kEpvBfPGtA+Z2rsh524kMfb7wwyYt5c1R6+QmWXMsxkH/1YYrl7FEBVVxhdQPGR2Numhodg1bmzedqUk8EogHap2QK8tu4ce1UhUUNxHPo7WzY2Y++lN2LlA67EQsh4SLpe+uFwkbduGg7//vZFA72Lv+Rj6z93DrtBo3h3YiJXPtrOKyemiMLX1VNpVacf0A9P5N+ZfS8spPtkG5IFF3Fjgz5mTyznRbAihY1Zxq+040NlT1dWeKb3rs3daT2Y/1gIp4bVVJ+k6cxc/HLhIRlb2Hc3Z+7cGlHmJ8kDmxYvItDTsGpnXSJyLP0dUSlSZLX3NQTUSFRSNoyMeT48lJXAPaf/exw9SuxeVcMwHvy59cSYyLlwg43wYzv3751snK9vI53+fZfR3h3Gx17HupU4839UXjTkdvMoInUbH7G6z8XLwYuquqUSn5hXAwHq5nnydlXs+4Pnlrel0ZiG9vZ0ZUc2b0YlBDN/+PJ1/7Uzf1X354J8P2H9tP1qNZHhrHzZP7cLyZ9pQ08OBD9afpufsQH49fBmDKcOfXaOGCAcHUo8dt/AV3h/pIYrvr7l7EoFXFP+msgjFkRt1dVMFxv2p0cSt+IGYufOo+f13BVd29YEmQ+HYCug2DezdSl1f4pYtIATO+aRfjbqVzuRfjnP4YhxPtK3JBw81xt7WMst0zYWbnRvze87nqU1PMXXXVL7r912p5S42B9nGbAKvBPLjqe85EnMSgHoC+lfriF/tXnjaV8bOxo4UQwpXkq9wOvY0Wy9t5Y+wP/B19eXFFi/Sr3Y/uvspMaL2hcUye+s53lobzDd7wnl3YCN6NfLCvnlzUo+VD6e69JAQhK0tel/zrlQLjAykmWczPO3L1vtcNRIVGK2TI5XGjyf6iy9IOXio8GxvHSZB8CrFUHSaUur6krZsxd7fH5231z379p2PZcqvx0kzZDP38ZY80qp6Hi2UTxq4N+B/Xf7HK7te4bXdrzGv5zx0GuvK+yClZPvl7cw9OpfLSZepmmVkcnIKfRqNpHaPjwrMEpeRncGOSztYEryEaXumsfrcaj7q8BE1XGrQpX5lOtfzZMeZaP739xme+yGILvU9edevKXLl92Qnp6B1su5Me+lnzqD38zNrIMocL+uJLSearc37RR1uquC4PzESG29vYubOLXCVCQDVWkLtLsqQU1ZmqerKiIggIzQUl3597yjPNkrmbj/H6O8PUcnJlg2TOj1QBiKHXjV78V7799h7dS8f/PMBRpn3pK4lCI0LZdzWcby6+1VsE68x+0YMm7S1eX7UVmr3/bzQNKJ6rZ6BvgNZM2QNH3b4kJCbIQz7cxjbLm0DlBVNvRt7s3lqVz4c3JiTkQm8G6YBo5EEKw/RIaUkPSQEu0b5L+stDnuv7EUizZbVsCioRqKCo7Gzw/OliaSdOEHy7t2FH9B5KiRdg5O/lKquxL+VBIW5h5pikzN4etlh5m4/z9BW1Vn3UifqeTmXqg5LMsJvBJNaTuKv8L+YcXCGxQ1FXHoc0w9MZ8RfIzgfHcx7cUmsuhFPv75fYTP2LyVhVRHQCA3DGwznj4f/oL57fV7d/SqLTyy+/bCi02p4plMddr/RgyZ9OpGN4LvF6wg8F1Mal2cWDFevYUxMLJX5CG8Hb/zcSydXdkGoRkIFt6FD0dWqSczceUhjIT9EdXtB1Zaw7yslW1gpIKXk1vr1OLRtezsv94nIBB6av4/DEXF8MawZcx5rYbZkQNbM+Objebbps6w6t4r3/3mfLGPp3POCMGQbWHF6BQ+tfYh15/9glNGRvyLO87hXW2wmHlTyopfASbGKYxWW9VvGkLpDWHRyETOPzLyjV+vhaMvHI9tC3fo0iA5n7PeHefW3E8SnlG5vtjikh5wGwK6x+XoSGdkZ7L+2n24+3SziDKoaCRWETkfllyeTERp6+wk+/8pCyX0dHwGn15aKnvSTJzFcuozrww8DsOboFUZ8cwCdjeCPiZ14vE1Nq/OcLi2EEEz1n8pLLV9iw4UNTNszjbSstDI5t5SSwMhAHt3wKLODZtNC78maqJu8ee0yroMXwKjf7om1VFxstbbM6DTjdgj1zw9/fs/wp2eHtjSMv8yUbrXZcPIafb4KZNdZ61oBln7mDGi16M0YQiQoKkjxsi7jpa85qEZCBQCXgQPQ+/kRM3cexsxCntD8BkLlRrB3DhTW8ygGCevXI/R67Hv3ZsZfIby26iQBtdzZ8FJnGlcrJEf3A4gQghdbvMgbAW+w/dJ2xv49luvJ1ws/sARcSLjAhO0TmLRzEkgji/T1WBwciK93S5i4X8k3YmZDLYRgWptpjGk8hp/P/sx3p+5ccefQ2h+ZlsaLPpI/X+6Mp5OeZ5Yf4f11p0jLzM6n1bIlPSQEva8vGjvz+ejsjtyNndaOtlXKzss6N6qRUAFAaDR4TXsDQ2Qk8StXFlxZo4Gur0PMWSUPthkxZmaStOlv7Hr05LnVZ1i6L4KnO9ZmxbNtcXcs+xSa1sSYJmNY0GsBkUmRjNw4kn1X95n9HDGpMUw/MJ1HNzzKv7H/8qbfk6yNvEKX0EDo9SGMXq+kti0lhBC8FvAag3wHMe/YPP4K/+//y94/J9jfURpVdWHdS514rnMdVh68xOAF+zh19Vap6bpfMkLOmHWoySiN7IzcSafqnbCzsYxzqGokVG7j1KkTTt27E7toMVmxsQVXbjIUPHxh72wl3aSZSN65k+xbt5hl9OVg+E2+GNaMj4Y0sWjeB2uiq09Xfh70Mx52HkzYPoGP9n9EfHp8iduNS49j/rH5DPpjEOvC1jGq4RNsrDmCp7bNQZdtgGc2QZdXlQeEUkYjNHzS8RPaVGnDR/s/4mycElFV5+2Nrnp1Uk1xnOx0Wt57qDE/jmtHUrqBoYv+YcX+i4Wv0islsmJiyIqJMeuk9anYU0SnRtOrZi+ztVlU1G+eyh14TZuGMSODmHmFpCzVaKHzq3D9JIRtN9v5w5euINrRg8OeDfh1fHseb1N6T63llTqudfjtod94tumz/BH2B4PWDmJp8FJuZRT9Sfpc/DlmHJxB39V9WRK8hK4+XdnQ/0fejDiF+7YPwbcHvLgParYvhSvJH51Wx8yuM3G1deWVXa/cvjb71v6kHjt2hyHoXN+TzVO60rV+ZT7ccJpJvxwnOaPsJ/jTzyie1nozLn/dcXkHNsKmzL2sc6MaCZU70PvWwePJJ0lYvfr2P32+NH9cCSO++38l7k1IKVn28070p04Q1Lw76yd3oXWt0s/fW16x1drySutXWDtkLa28WzHv2Dz6rO7De/veY+flnSRlJuV5nMFo4FTsKZYGL+WxPx9j2IZhrDm/hoF1BrL+kfXMrvsENX4aCaF/KxkJn/hVSWVrATztPZnTfQ5RqVG8u+9dpJQ4+LcmOzYWQ2TkHXXdHW1ZMiaAaf39+Dv4OkP+bx9no8o2LWx6SAiA2XwkpJTsvLyTgCoBuOpdzdJmcXjw1xCqFBnPiRO4tX49Nz79jJorf8h/JZGNLXR7EzZMgtBNxU5KlJaZzbQ1/+Kz8meytTa88L+pOLlZbygKa6KuW10W9lpIaFwoP5/9mW0Xt7H+ghJFtppjNSo7VMbBxoGM7AziM+KJTIwkSypP2U0rNeXttm/Tv05/PPTuSs6Q7R+CczV4ZjPUaGPJSwOgpVdLXg94nc8Pf86qc6sY4q8knko9egzbmnf2MjUawcTu9fCv6c7LvxznkYX/MOORZgxvbd6cDvmRFnwK21q10Dqbx3cn/FY4FxMv8lSjp8zSXnEpUU9CCOEhhNgmhDhves8zVKcQYqypznkhxNhc5buFEKFCiBOml5epXC+E+E0IESaEOCSEqF0SnSpFQ+vqSuVXXyE1KIhba/8ouHKLJ6BSPdg5A4xFX2FyNSGN4V/vZ8fRcAZeP477wAE4ValcTOUVFz8PP6Z3nE7gyECW9l3KFP8ptPRqib2NPSmGFLQaLXVd6/J006eZ1XUWu0fs5peHfmFUo1F4GIFfnoCt70L9fvDiHqswEDmMajiKDlU7MDtoNje8bNG4uJBWQByn9r6V2Di5M61quPP6qpN8uP7U7WCBpUl6cDB2zZubrb3tl5Rh3B41e5itzeJQ0p7EW8AOKeXnQoi3TJ/fzF1BCOEBfAgEABI4KoTYIKXMmW17UkoZdFe744B4KWU9IcRI4Avg8RJqVSkCbsOHc2v9Bm7MnIlT927YVKqUd0WtjZKbePWzcGoNNB9x3+c4cjGOCT8eJcNgZJnHZWzSU/EYM8ZMV1Ax0Wl0tKvajnZVC4nDlcPlQ8rfLvkG9P/8v2i/VoQQgo87fcyj6x/l3f3v8VmrloVGhPVytmPluLZ8sfksS/ZGcDYqiUVP+lPJqXTyMBhu3CArOhr7Zk3N1uaOyztoXrk5Xg73xi4rS0o6J/EwsMK0vQJ4JI86/YBtUso4k2HYBuQf+/nedlcDvURF8Z6yEoRGQ9WPp2NMTeXG518UXLnxUPBuBrs+hWzDfbX/y+HLjFpyEGc7HWufa437xjU4dupk1i+ZSgEYjbBvLiwboCxCGLcF2k+wOgORQxXHKrzT/h1OxpzkrI8g88IFsuILXtVlo9Xw7qDGfPV4C05EJjBkwT+ltkw2PTgYALumzczS3rXka5yJO2PRVU05lNRIeEspc7x6ogDvPOpUB3LPMl0xleWwzDTU9H4uQ3D7GCllFnALyPNRVggxXggRJIQIiomx3pgu5RF93bp4jh9P4p9/krRrV/4VNRro9T7EX4TjBftYGLKNfLD+FG+vDaZDXU/WTexEpcAtZN+8ieeLL5j3AlTyJjkGfn5MmX9oOAhe2APVW1taVaEMqjOIbj7d+MHmMABpx+8vv8TQVj6sfrEjUkqGf72f9Seuml1bWvAp0GrN5iOx4/IOgPJhJIQQ24UQp/J4PZy7nlTWpBV1icuTUspmQBfTa3QRj0dK+a2UMkBKGVC5sjqWbW4qvTAefcOGXH/3vYJ9J+r3hZodYNdnkJ73qpK4lExGf3eIHw5cYnxXX5Y93QZnkcXNpUux9/fHPiCglK5C5TYRe+DrzhCxFwbNgRE/lEluEHMghOCddu8QUU1Lto0gtQgRYZv5uLLh5c4093Fjyq8n+GzTGbLMOE+RHhyMvkEDs3la77i8g3pu9ajlUsss7ZWEQo2ElLK3lLJpHq/1wA0hRFUA03tegVSuAjVyffYxlSGlzHlPAn4G2t59jBDCBnAFbhbnAlVKhsbWluqzZmJMSeHaO+/k76gkBPT7DFJilHAdd3HmeiJDFuzj2OUEvhzRgncGNkKrEdxctoysqCi8XplaYeIxWYTsLMWArxgCemd4fge0ec5qh5fyo5pTNca1nsB5b0nUgd1FOtbTSc9Pz7VjTIdafLsnnGeWHyEhteRBAqWUpJ06hX0z8ww13Uy7yfHo41bRi4CSDzdtAHJWK40F8srgvgXoK4RwN61+6gtsEULYCCE8AYQQOuAh4FQe7Q4HdkpLuVGqoK9fH69pb5CyZy/xK3/Mv2J1f2gxCg4ugriI28V/B1/n0UX7MWQb+f2FDjzqryxJNNy4wc0lS3Hu1w+HNtazmuaB49ZV+GEIBH4BLUfBC4FQxTw/aJZgdOPR3KjngSY0nOSkuCIdq9Nq+Pjhpnz+aDMOhccxZME/nLleMn8Kw6VLSnhwM82n7YzciVEa6V2rt1naKyklNRKfA32EEOeB3qbPCCEChBBLAaSUccAnwBHT62NTmR7FWPwLnEDpPSwxtfsdUEkIEQa8irJqSsWCuI8ahVOPHtyYOZOUw4fzr9jrA9DYwLb3MRolX247x4SfjuFXxZkNkzrTssZ/QxvRM2dBVhZeb7xeBldQQQnZoAwvXTsBQ7+FRxaBrXVndisMnUZHx37PYJMNqzd8Xqw2RratyS/j25ORlc2ji/az4eS1YutJM01a25tp+evmiM3UdqltkdwReVEiIyGlvCml7CWlrG8aloozlQdJKZ/LVe97KWU902uZqSxFStlaStlcStlESjlFSplt2pcupXzMVL+tlDK8JDpVSo4Qgmozv8C2Rg2uTplK5pV8Jv9cqirhOs78yZxvv2P+jvMM8/fh1/Ht8Xb5b7w2cds2EjdupNL48dj6lI2zU4UiLQHWjoffRysB+V7YAy0enFXkjXsMA+DSnr+5knSlWG20ruXOny93pml1Fyb/cpxPN4YUa54iLTgYYWeHvm7dYunITUxqDEeijtC/Tn+rGX5Vw3Ko3DdaZ2d8Fi5EZmUROW4cWfmsJoto8DQ3RGWGXJvL9EH1mf1Yc+x02tv7DdeuEfXBh+gbN8LzhfFlJb/icGEnLO4Iwauh+9vw3HbwrGdpVWbFxt0dbT1fGl+WfHn0y2K34+Vsx0/PtWdsh1os2RvB6O8OczM5o0htpAefwq5JE4RNyQNYbL20FYmkf+3CvATKDtVIqBQJvW8danzzNYboaC4/O+6eteq7zkYz5Jtj/E+Mw08TyVj+vOOJyJiaypWXJyMzM6k+ezbCtmKH/zYrafHw5xRYOVQZUnpuO3R/C7Q6SysrFVw6dKLRVcGuC1sJirrbH/f+sbXRMP3hpsx+rAXHLscz+P/2EXzl/vwpZGYm6SEh2Dc1z3zE5ojNNHBvQF23kvdKzIVqJFSKjIO/PzUWLSTz8mUuPj6SjPBwpJQs3BXGsyuOUMPdgddfngKNBkPgTIhTRguN6elEvvQS6WfOUG3WLPS+vha+kgcEKZVew4I2cGwldJhk8n3wt7SyUsWxXVu0mVm0jXNn5pGZZBcjLExuhrdW/CmEEAz7ej+rgiILPSb9zBlkRgb2rVqV6NygONCdiDlhVb0IUI2ESjFx7NCBWiuWY0xJIeLxkcx/ewGzNp9lcPNqrJnQER93BxgwEzQ6+OsVDNeucWn0GFIPHqLqZ5/i3NOy8WgeGOLC4cdHYc04JSLv+F3Q71PQPfgBEh3atAEhGJPhz5m4M2y4sKHEbTbzcWXDpE4E1HLnjdX/8saqk6Rm5h92PCc8iL1/yY3ElotbAFQjofLgYN+yJcYF33HB3pO+6xbx6+nlzPCOR4/pic6lGoaA14jZEET4wAFkXriAz4L/w+2RvKK3qBSJtHjY8i4sbAeRR2DALGV4qWoLSysrM7SurugbNaR6aDzNKzdn/vH5pBpSS9xuJSc9Pzzblpd71mP1sSsM/r99+S6TTTt2DJ2PDzqvksdX2nxxM00rNaWGS43CK5chqpFQKTarj17hkbXhfNR7KqkTXqXSrWiuTnyJ0NYBhPXtx/lu3Qmb/C2xp1xw8Eyhzor5OPeyDgehcktWJhz6Bua3UkJ7NxsBk45Au/FKDKYKhmO79qSfOMG05lOJTYtl2ellZmnXRqvhtb5+/DSuHYnpWTy88B9WHrgz652UktTjx83Si7iUeImQmyH0r2NdvQhQ80moFIN0QzYfrj/Nb0GRtPf1YP4TrfBytkNOfJqU/ftJDTqK4do1hI0N+vr1cGrbBP3Gx+Hwx9BkU4X8MSsxWZlw4kfY+yXcioQ63ZSkQFXNF5q6POLQri1xy5ZR70o2/Wr3Y/mp5QyvPxxvx7zCyBWdjvU8+XtKF15fdZL3159mV2gM/3u0Gd4udhgiI8mOjcXBv+RzP3+F/4VA0K92PzOoNi+qkVApEqFRSUz59Thno5KY1KMeU3vXx8aUf1rodDh164ZTt273HihnwR/jlR+5bm+UsepyTGYqnPxZidh6KxKqB8BDc6Fer3IXUqM0cAgIAK2WlEOHmPrsVHZe3snCEwv5uNPHZjuHp5Oe78e2Yfn+i8zccpY+Xwby0ZAm9Lyk5Nq2L6GRMEojf174k/ZV21PFsYo5JJsVdbhJ5b4wGiXf74tg8IJ9xCRlsOyZNrzez++2gSiU5iOg2WOw+zMI312qWh8Ibl2BbR/CV41h42vgXBWeWqPMO9TvrRoIE1onJ+yaNCH10GF8nH0Y1XAU68LWERoXatbzaDSCZzvXYdPkLtT3dubV30+y5bct4OyMvl7JfFCO3jjK1eSrDKk3xExqzYtqJFQK5UZiOmOXHebjv0LoUs+TzVO70sOviBN1QihPwJ4NYPU4JZ6Qyp0Ys+HCLvh9LMxtDvvnQ+0u8MzfMG4r1FONQ144tmtLWnAwxtRUnm/+PM62ziVysCsI38pO/P5CB94d2Ai38DMcdfRhUWA4GVnFX367Pmw9jjpHqwnodzeqkVDJFyklfxy/Qr+5ezhyMY5PhzZl6dgAKjsXM7uX3glGrISsdFg1Fgzp5hVcXrl5AXZ8ohiGlY9A+C7oMBGmnITHV0KtjqpxKACHtu3AYCD12HFc9a682OJF9l/bz76r+0rlfFqN4NnmHtRMvEG6XxNmbQllwNy97DobnX+U5HxINaSy9dJW+tXuh72NdS5bVo2ESp5ExqUydtkRXvntJHU8Hdk4uQtPtqtV8ngylRvAI4vhyhFY96KSIa2iISVEnYLAWfBtd/g/f9j3JXg1hOHL4LVzyqS0W01LKy0XOLT2B52OlAP7ARjpN5IazjWYEzSHLGP+Pg4lIfXECQCeeHYwy59pg1FKnll+hBHfHOBwxP1Hpt1+eTtpWWkMqWudQ01laoBUAAAWMElEQVSgTlyr3IUh28iK/ReZs/UcGgHThzThqfa10GrM+CTbeAj0+QS2va84gPX9xHxtWyvZBri0H0I3Ka+Ey0p59QDoPV2Zs3GpZlmN5RSNgwMO/v6k7N0Hb7yBTqtjqv9UXgt8jfVh6xnWYJjZz5l65AjodNg3b0Z3e3u2vuLJb0GR/N+O84z45gBdG1Tm5Z71CKjlXuCD1bqwdfg4+eDvZb3e8aqRUAGUoaVdodHM2HiG8JgUevhVZsbQZlR3K6UucMeXlR/K/fPB0RM6TSmd81iS9EQI264YhfNbIf0WaPVQtwd0eQ0aDABn8yzVrOg4de1C9KzZGKKi0FWpQp9afWhZuSULTixgQJ0BOOgczHq+1IOHcGjRAo298v2wtdEwun0thvv7sPLgRRbvvsBjXx+ghY8rz3auw4CmVbG1uXPgJjwhnCNRR5jiP8VqIr7mhWokVDhzPZHPNp1h7/lYfD0d+f7pAHr4eZXuP64QMOALSL0J2z5QJm27vFp65ysrbl2B0L8VwxCxF4wGcKgEDR8CvwFQt2e5z+dgjTh27gKzZpOybx9uw4cjhOD1Nq/z1KanWH56ORNbTjTbubITEkgPCcFz0kv37LO31TK+a11Gt6/NmmNX+H5fBFN+PcF0xxAeaVmd4a19aFzNBYDfz/2OjcaGofWGmk1baaAaiQrM6Wu3mL/jPFtO38DZzob3H2rM6Pa17nniKTU0Wnh0CQgN7JiuDMl0m1a+JmmlhKjg/4aRrp9Uyj3qQvsXwW8Q1GirOhCWMvoG9bHx9iZ5z17chg8HoEXlFoqD3enlDG8wHC+HkofOAEg5cgSkxLF9+3zr2Ntqeap9LUa1rUnguRhWHY1k5cGLfP9PBL6VHenRyJU/49bRu2YfKtlXMouu0kI1EhUMKSWHIuL4bl8E20IU4zC5V33GdaqDq4MFQkprbWDoN0o4692fQXwEDJ4HNsVcQVUWZGXCpX9MhuFvxckNoRiD3h8phqFyAwuLrFgIIXDs0pmkzVuQBgNCp/wvT/Gfwo7LO1hwfIHZHOxSDx5C2NvfV05rjUbQo6EXPRp6EZ+SyV//XmPL6Rv8GLwe2yopbN7vy82II7St40Gb2u40rOKCo966fpatS41KqXEr1cCf/15j5YFLhN5IwtVex9Te9XmmUx1c7S2cb0Bro6x4cq+jGIq4CHhsmXVN5KYl5Jpf2A4Zt8DGXplf6PYmNOgHTuZ5UlUpHk5dunJr9RrSTp5UPLGBGs41GNVwFCtDVvJkoyfx8yh5StCUQwdxCAgoci4Ud0dbRneozVPtazF8w2xupdcmoEFHjlyMY+fZaEDpRNf0cMDP25l6Xk74uDtQ3d2e6m7Ky9627HukqpEADoXfZM/5GJztdDjpbXC2s8HFToeznQ3Ot99tcLS1QWPOVT6lTFK6gcBzMWw4cY3doTFkZhtpXNWFmcOaM7hFNYv8w+WLEND9TeUJfN1EWNQBBs2BZsMtpynh8n/zCxf3gTELHDyh8WClt+DbHWzNOyGqUnwcO3YArZbkPXtvGwmA8c3Hsy5sHV8e/ZJv+nxTonNkxcSQGXYBt6HFn0c4GXOScwlnebfdu4xsqMTeik3O4NileM5GJREalcSZqER2nI0m23in34WdToO7gy1uDrZ4OOpwc7DFxU6Hi50N/ZpWwb+me4muLy9KZCSEEB7Ab0Bt4CIwQkoZn0e9scB7po8zpJQrhBDOwN5c1XyAH6WUU4UQTwOzgBy33AVSyqUl0VoQwVdv8XVg+D1/kLsRApz0igHJMSbOdja42Ouo5KinsrMeTydb07seL2c9Ho629x+6ooSkZmZx6moiRy7GsedcDEcvxZNllHg563mqfS0eblmN5j6uVr2SgiZDoUpzJT/zmnFwai30mQ6e9Uv/3MZsuHoMwrYphiFKSXCPZwMlkY/fQPAJUOcXrBStszP2rVqSvHcvXq++crs8x8Fu5pGZ/HP1HzpV71TscyTv+wdQ8qkUl+Wnl+Ni63KHb4Snk56+TarQt8l/sZuyjZIbielciU/jakIq12+lE5+SSXyqgYTUTOJSMrmWkEhSuoGk9CzqeDqWipEQRfUQvONgIWYCcVLKz4UQbwHuUso376rjAQQBAYAEjgKt7zYmQoijwCtSyj0mIxEgpZxUFD0BAQEyKKh4aQyllKQZsklKzyIp3UBiehbJ6Vm3P+cuv6Msw0BiWhY3kzNIybzXNV8I8HCwxdNJj6ezLR6Oeio52uJheuVsV3KyxcVeh51Oi52NFp1W3PFjLqUkyyhJSDUQl5LJzeQMbiSlExGbysXYFM7dSOJ8dPJtQ9e4qgvd/CrTrUFl2tT2MK+fQ1mQnaUsj907Bwxp4D9G+aE2d67mlFgI26EYhrAdkBanTKTXaKcYBb+BD1x+6AeZm0uXEj17DvV27kBX7b/hSkO2gYfXP4xeq2f14NVoi2nor0yZStrx49QL3F2sh62Lty4yZN0Qnmv2HJP9JxdLQ35IKYv9ACiEOCqlDMhrX0mHmx4Gupu2VwC7gTfvqtMP2CaljDOJ2Qb0B37JJbAB4MWdPYsyRQiBg60NDrY2eLvYFauN1MwsYpMyiUlOJyYpk5jkDGKSMojN9X4lPoG45EySMgr2BBUC7Gy0aAQYsiUGo5G87LkQ4ONuj6+nE30be9Oihhstarjh6WTFE7/3g9ZGWRLbajQEfgFHlykv3+7Q/HEljlFR5wCkhMRrEHkQLh2AywfgxmlAKsNIDfop7dbtCQ4epXBRKqWNc+/eRM+eQ9L27XiMGXO7/A4HuwvrebT+o0VuWxoMpPzzDy4D+hf7x/iHkB/QaXSMajSqWMcXRGmNEJTUSHhLKa+btqOAvDyDqgO5k8VeMZXlZiTwm7yzWzNMCNEVOIfSwyg84ayFcbC1oWYlG2pWKnycOiMrm/gUAzdTMohLUbqOiWkG0g1GMrKyb79nGxVHHZ1WoNNqcHPQ3e6FeDnr8XF3wE73AA9/OFWGQbOh6xtw/AcIWg7rJij7vJtBlabg1ViZ5LZ3A52jMneQnQHJMZB0HRKvQvRZiD6tZHQDpV6NNtDjHcUwVG0JGjVKTXnHtnZt9A0akLR12x1GArjtYDfv2Dx61+qNi61LkdpOPXoMY3Jy3qHw74Po1GjWh61ncN3BeNp7FqsNS1CokRBCbAfyCnL+bu4PUkophCju2NVIYHSuz38Cv0gpM4QQL6D0Unrmo288MB6gZs3yE+tGb6OliquWKq7F67VUOJy9FUPR+TW4Eax4MF/ar4QdP/lLwcfqXZUJ8UZDwLsJ+LRR5j206rqNBxHnPn2IXbSIrNhYbDz/+zEWQvB2u7d5YuMTzDs6j/c7vF+kdpMDAxE6XbHnI74L/o5smc24ZuOKdbylKPRbIqXsnd8+IcQNIURVKeV1IURVIDqPalf5b0gKlAnq3bnaaAHYSCmP5jrnzVz1lwIzC9D3LfAtKHMSBV6MSvlHo1HyOOfO5ZwWr/Qa0uLBkAIaHWhtlV6IUxV1BVIFw7lvH2IXLiRp507cR4y4Y1/jSo0Z1XAUP575kcF1B9PSq+V9t5scGIhDmzZoHIvuMR+VEsWqc6t4pN4j1HC2rhzWhVHS/vUGYKxpeyywPo86W4C+Qgh3IYQ70NdUlsMT5JqfADAZnByGAGdKqFPlQcbeXekp1GynzCfU6aJse/iqBqICom/QAF3NmiRt2Zrn/kmtJuHt4M3HBz/GYDTcV5sZ4eFkhofj1L17sTQt+XcJEsn45uOLdbwlKamR+BzoI4Q4D/Q2fUYIESCEWApgmrD+BDhien2cM4ltYgR3GQlgshDitBDiJDAZeLqEOlVUVCoIQghcBg4g5cABDNH3Dm446hx5u93bnI8/z9Lg+1tZn7hxEwiBc7+i56C+kHCBNefXMKz+MKo5WZGD6H1SIiMhpbwppewlpawvpeyd8+MvpQySUj6Xq973Usp6pteyu9rwlVKevavsbSllEyllCyllj7v3q6ioqBSE65CHwWgk8a+Nee7vVbMXA+sM5JuT33A69nSBbUkpSdy0CYc2bdB5F21FnZSSLw5/gYPOwaxBBssSdTmHiorKA4fetw52LZpza926fLPFvdPuHSrZV+LtfW+TnpV/lsSMs2fJjIjAZdCgIusIvBLIgesHmNhiIh525XNZtWokVFRUHkjcHnmEjHPnyDib90CEq96VGZ1mEHErgjlBc/JtJ3HjRrCxwblvnyKdPzkzmU8PfYqvqy+PN3y8SMdaE6qRUFFReSBxGTAAodORsGZtvnU6VOvAmMZj+DX0V/4K/+ue/TI7m1sbN+HYsQM27kULeTEraBbRqdF83OljdBoLB9EsAaqRUFFReSDRurnhPKA/t9auJTspKd96U1tPpbV3a6bvn87ZuDt7Hcl795J1/TpujxYtBWpgZCBrz6/l6SZP06Jyi8IPsGJUI6GiovLA4jFmLMbUVBLWrMm3jk6jY3a32bjoXXhp+0tcS752e1/Cr7+hreyJc688fXnz5OKti7y992383P14qeW92evKG6qRUFFReWCxb9oE+4DWxK/8EZl9bwDOHDztPfm699ekZafxwrYXiE2LJTMykuQ9e3AbNux2EqPCSMxMZPKuydhobJjXcx622qLlnLBGVCOhoqLyQOMxdiyGq1dJ2pq3c10O9d3rs6DnAqJSohj791guLZ6H0GpxH3V/wfiSM5OZsG0CkUmRzOk+h+pOd4eoK5+oRkJFReWBxrlnT2zr1SVm3nxkVsHRl/29/VnSdwkiNo609RtJ798JnVfhvhHXk6/z9OanCbkZwpxuc2hTpY255Fsc1UioqKg80AitFq9XXyXz4kUSVq8utH5Lr5bMCQ1AAK9V38tnhz4jPv2eXGoAZBuz+eP8Hwz7cxhXk6+ysNdCeta8//mL8oAaBlNFReWBx6lHDxzatCH6y69w6tmzwN5B2r//Yty4HY9nx9K7g+SXs7+wLmwdfWr1oU2VNng7eJOalUrIzRD+jvibyKRIWnm14pNOn1DLpVYZXlXZUKLMdNZGSTLTqaioPNhkREQQ8chQHNq3o8aiRQjtvXlYspNTuDh8OMbUVHw3bULr5MiFhAv8EPID2y5tIynzv6W0GqHB38ufJxs9Sc+aPdGI8jswU1BmOtVIqKioVBjifvqJG5/MwGPsWLzeevPOFMGZmVx97TWSduyk5rJlOLZre8ex2cZsLiddJj49Hr1WT02XmjjbOpf1JZQKpZm+VEVFRaXc4PHkk2RGXCRuxQqybt7Ea9ob6Ly8yIyMJOqj6aT88w/e77xzj4EA0Gq01HGtQx3XOhZQbjlUI6GiolKh8H7nbWw8KxEzbz6Jmzejq1oVw9WrCL2eKh9PvydRUUVHNRIqKioVCqHR4Pnii7j070/CH+swXLmC6+CHcHv8cXTe3paWZ3WoRkJFRaVCYlu7Nl6vTLW0DKun/E7Hq6ioqKiUOqqRUFFRUVHJF9VIqKioqKjki2okVFRUVFTypURGQgjhIYTYJoQ4b3rPM3WTEGKzECJBCPHXXeV1hBCHhBBhQojfhBC2pnK96XOYaX/tkuhUUVFRUSkeJe1JvAXskFLWB3aYPufFLGB0HuVfAF9JKesB8cA4U/k4IN5U/pWpnoqKiopKGVNSI/EwsMK0vQJ4JK9KUsodwB35A4XiD98TyAnLmPv43O2uBnqJ3P7zKioqKiplQkmNhLeU8rppOwooiidKJSBBSpkT4P0KkJOlozoQCWDaf8tU/x6EEOOFEEFCiKCYmJii6ldRUVFRKYBCnemEENuBKnnsejf3BymlFEKUebRAKeW3wLcAQogYIcSlYjblCcSaTVjpUR50lgeNoOo0N6pO81KWOvONcV6okZBS9s5vnxDihhCiqpTyuhCiKhBdBFE3ATchhI2pt+ADXDXtuwrUAK4IIWwAV1P9wrRWLsL570AIEZRfFERrojzoLA8aQdVpblSd5sVadJZ0uGkDMNa0PRZYf78HSiVG+S5geB7H5253OLBTPkgxzVVUVFTKCSU1Ep8DfYQQ54Heps8IIQKEEEtzKgkh9gKrUCagrwgh+pl2vQm8KoQIQ5lz+M5U/h1QyVT+KvmvmlJRUVFRKUVKFOBPSnkT6JVHeRDwXK7PXfI5Phy4J3C7lDIdeKwk2orBt2V8vuJSHnSWB42g6jQ3qk7zYhU6H6jMdCoqKioq5kUNy6GioqKiki+qkVBRUVFRyZcKbySEEP2FEKGmOFFWNUEuhLgohAgWQpwQQgSZyu4rXlYp6/peCBEthDiVqyxPXUJhvun+/iuE8Lewzo+EEFdN9/SEEGJgrn1vm3SG5lpcUdoaawghdgkhQoQQp4UQU0zlVnU/C9BpbffTTghxWAhx0qRzuqncquLEFaBzuRAiItf9bGkqt9j3CCllhX0BWuAC4AvYAieBxpbWlUvfRcDzrrKZwFum7beALyygqyvgD5wqTBcwEPgbEEB74JCFdX4EvJ5H3camv78eqGP6v9CWgcaqgL9p2xk4Z9JiVfezAJ3Wdj8F4GTa1gGHTPfpd2CkqfxrYIJpeyLwtWl7JPBbGd3P/HQuB4bnUd9i36OK3pNoC4RJKcOllJnAryhxo6yZ+4qXVZpIKfcAcXcV56frYeAHqXAQxYGyqgV15sfDwK9SygwpZQQQRh4r78yNlPK6lPKYaTsJOIMSlsaq7mcBOvPDUvdTSimTTR91ppfEyuLEFaAzPyz2ParoRuJ2jCgTueNHWQMS2CqEOCqEGG8qK0m8rNIkP13WeI8nmbrs3+carrO4TtNQRyuUp0qrvZ936QQru59CCK0Q4gRKBIhtKL2YEseJK22dUsqc+/mp6X5+JYTQ363TRJndz4puJKydzlJKf2AA8JIQomvunVLph1rdGmZr1WViMVAXaAlcB+ZYVo6CEMIJWANMlVIm5t5nTfczD51Wdz+llNlSypYooX7aAg0tLClP7tYphGgKvI2itw3ggeJwbFEqupHIiRGVQ+74URZHSnnV9B4N/IHyD38jp5spih4vqzTJT5dV3WMp5Q3Tl9MILOG/IRCL6RRC6FB+eH+SUq41FVvd/cxLpzXezxyklAkooX86YIoTl4eW2zpFEeLElZLO/qZhPSmlzACWYQX3s6IbiSNAfdPKB1uUiasNFtYEgBDCUQjhnLMN9AVOUYJ4WaVMfro2AGNMqzPaA7dyDaOUOXeN4w5Fuaeg6BxpWu1SB6gPHC4DPQIlDM0ZKeWXuXZZ1f3MT6cV3s/KQgg307Y90Adl/sSq4sTlo/NsrgcDgTJvkvt+WuZ7VFYz5Nb6Qlk1cA5l3PJdS+vJpcsXZXXISeB0jjaU8dIdwHlgO+BhAW2/oAwtGFDGRsflpwtlNcZC0/0NBgIsrHOlSce/KF+8qrnqv2vSGQoMKCONnVGGkv4FTpheA63tfhag09ruZ3PguEnPKeADU7kvipEKQ4kjpzeV25k+h5n2+1pY507T/TwF/Mh/K6As9j1Sw3KoqKioqORLRR9uUlFRUVEpANVIqKioqKjki2okVFRUVFTyRTUSKioqKir5ohoJFRUVFZV8UY2EioqKikq+qEZCRUVFRSVf/h8rdeoYhXY0awAAAABJRU5ErkJggg==\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From afa194373e3bc864b61a82ef5219c20575a85255 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 289/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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GKf+y8kMoPQr9bnE6iUdpIfBFiWPgSBZs/8HpJEr5j/JyWP4WtBoAzbs5ncajtBD4og7nWwvWrJ3hdBKl/MfWBXBwe8CdDYAWAt8UEg4Jl8CGL6DkqNNplPIPy96Eek2hy4iT7+tntBD4qsQx1gUvW751OolSvi9nu/W71Od6CAlzOo3HaSHwVW3OtD696NxDSrku+W2QIEjyzzWJT0YLga8KCrbmQdk8F4rynE6jlO8qLoAV70OXi6FBS6fTOEILgS9LHA2lhbDpa6eTKOW7UmdC4SHoG3idxMdoIfBlcX0hqpWOHlLqVBkDS16Fpl0hfrDTaRyjhcCXBQVZzUNbF0BBjtNplPI9WxfA/vUw6M6AnrtLC4Gv6zYGykutoaRKqZpZ/ArUb/bbwk8BSguBr2veHaLb6+ghpWoqcx1s/Q76TbCuzQlgWgh8nYj1aWbHT5CX6XQapXzH4lcgtC4k3eh0EsdpIfAHXUeBKbcmolNKnVxeBqyZDj2vhrqNnU7jOC0E/qBpZ2iWqM1DSlXXsjetvjU/W3jmVLmlEIjIUBHZJCJpIvJQJc+Hi8g0+/mlIhJf4bm/2ts3iciF7sgTkBJHw+6lcGiX00mU8m7FR6wriTtfBNHtnE7jFVwuBCISDLwCDAMSgCtFJOG43W4CDhpj2gMvAP+yX5uAtdh9V2Ao8Kr9/VRNJY6yvqZ+6mwOpbxdymRrAZpBdzudxGu444ygH5BmjNlmjCkGpgIjj9tnJDDFvj8DGCIiYm+faowpMsZsB9Ls76dqqlE8xCZp85BSJ1JSCL+8BPFnQOvAWJi+OtxRCGKB3RUe77G3VbqPMaYUyAWiq/laAERkgogki0hyVlaWG2L7ocTRkLEGDmxxOolS3mnVB5CfAWf+xekkXsVnOouNMZOMMUnGmKSYmBin43inrpcBos1DSlWmrAR+fhHi+lmz96pfuaMQ7AVaVXgcZ2+rdB8RCQGigOxqvlZVV4MW1nwpqTOsOVSUUr9ZMw1yd1tnAwE8nURl3FEIlgMdRKSNiIRhdf7OPm6f2cB4+/4Y4DtjjLG3j7NHFbUBOgDL3JApcCWOggObITPV6SRKeY/yMvjpeWjRw1rqVf2Oy4XAbvO/E5gLbACmG2PWicgTInJszbe3gWgRSQPuAx6yX7sOmA6sB74B7jDGlLmaKaB1GQkSrJ3GSlW0eirkbNOzgSqI8cEmhKSkJJOcnOx0DO/1wWjrrOCeNfpDr1RpEbycBPWi4ZaFAf07ISIpxpik47f7TGexqoHEMdaFZXu0WCpFymTI3QVDHg3oInAiWgj8UefhEByuzUNKFeXDj89a1w20PcfpNF5LC4E/qhNldYit+8zqJFMqUC19DY5kwZDH9GzgBLQQ+KvE0daFMzsXOZ1EKWcU5MAvL0On4dCqr9NpvJoWAn/VcSiE1rOuKVAqEH3/DBTnwbn/53QSr6eFwF+F1bX6CtbPsq6oVCqQ7N8Ay9+CPjdAs+PnwFTH00LgzxJHW7Msbvve6SRKeY4xMPdhCK8P5zzidBqfoIXAn7U71+o41tFDKpBsnmutRXzWQ9a1A+qktBD4s5Bw6HIJbPjSmn5XKX9XXABfPwDRHaDfLU6n8RlaCPxd4hirw2zLt04nUar2/fhvOLQTLn4BgkOdTuMztBD4u/gzoF6MNg8p/5e5Dha9bC1I3+YMp9P4FC0E/i44BBIutdpNi/KcTqNU7Sgvgy/utfrELnjK6TQ+RwtBIEgcDaVHYdM3TidRqnYsfgX2LIMLn4a6jZ1O43O0EASCVv2hQaxeXKb8U+Z6+O5J6HwxdL/C6TQ+SQtBIAgKshasSVtgXXavlL8oLYbPJlhNQpf8V+cTOkVaCAJF4mgoL4GNXzqdRCn3WfgUZKy1ikC9Jk6n8VlaCAJFi57QuK2OHlL+Y9M38Mt/rWkkOl/kdBqfpoUgUIhYZwXbf4S8TKfTKOWagzvhs1uheXcY+ozTaXyeS4VARBqLyDwR2WJ/bVTFfuPtfbaIyHh7W10RmSMiG0VknYjo/2ZtSxwNptyaiE4pX1VyFD4Zb80pdPkUCK3jdCKfF+Li6x8CFhhjnhGRh+zHD1bcQUQaA48BSYABUkRkNlAEPGeMWSgiYcACERlmjPnaxUyqKk27QNOuVvNQ/wlOp/FLRaVlHMgvJiuviOz8Io6WlFFaZigtN4SFBFE/PJh6YSE0iQynZVQEEWHBTkf2LeXl8PltkL4Kxn1kNXcql7laCEYCZ9v3pwDfc1whAC4E5hljcgBEZB4w1BjzMbAQwBhTLCIrgDgX86iTSRxlDbU7tBsatnI6jU/LKywhZedBknccZFNmHlsy89iVU0C5qf73aFQ3lPgm9ejcvAGdm0fSuXkk3eMaaoGoyvf/tFbeO/8Ja5p15RauFoJmxph99v0MoFkl+8QCuys83mNv+5WINAQuAf5b1RuJyARgAkDr1q1diBzgjhWCdZ/C6fc4ncanGGPYmJHH3HUZLNiwn3XpuZQbCA4S2jSpR0LLBozo0ZKWDSNoUj+cJpHh1A0LJiRICAkKorisjPyiMvILS8nKLyT9UCF7Dx1l6/58vk7dx8fLdgEQEiQkxkbRr01j+rdpzMB20dQNc/VX1Q+s/MCaS6jXNTDobqfT+JWT/nSJyHygeSVP/W6ib2OMEZEafBb69fuHAB8DLxljtlW1nzFmEjAJICkpqcbvo2yN20LL3lbzkBaCatlzsIAZKXv4dMVeduUUIAJ9WjfirnM70K9NY3q1bujyH2pjDJmHi1i/L5fkHQdZviOHyb/sYNKP2wgLDqJfm8ac3SmGczo3pV1MfTf9y3zIus9h9l3WAvQXvaDXC7jZSX96jTHnVfWciGSKSAtjzD4RaQHsr2S3vfzWfARW88/3FR5PArYYY16sVmLlusTR8O0jcCANmrR3Oo1XMsbw/aYs3vllOz+nHQBgULtobj+7HUO6NCMmMtyt7yciNI+qQ/OoOpzb2TqxLiwpI2XnQb7ftJ/vN2Xx1JwNPDVnAx2a1mdYtxZc1K0FHZvVR/z9j+KW+TDzZojrC+M+hJAwpxP5HTHm1D9ci8izQHaFzuLGxpgHjtunMZAC9LY3rQD6GGNyROQpoAsw1hhTXt33TUpKMsnJyaecO+AdTof/JMA5D8NZD5x8/wBSXFrOrFV7efOnbWzOzKd5gzqM69eK0b3jaNW4rqPZ9hwsYMGG/Xyduo9l23MoN9A2ph4XdWvBsMQWdGkR6X9FYfNcmHYtxHSE8V9CREOnE/k0EUkxxiT9YbuLhSAamA60BnYCl9t/4JOAicaYm+39bgQetl/2D2PMuyISh9V3sBFrBBHA/4wxb53sfbUQuMG7w+HIAbhjqZ5mA2Xlhpkr9vDivM2k5xbSqVkkt57Vlkt6tCQ02Psut8nKK2Luugy+Tt3H4q3ZlBto37Q+I3u0ZETPlpwWXc/piK5b95l1JtAsEa79TCeTc4NaKQRO0ULgBsvfgjl/hom/QPNEp9M4xhjDvPWZPDt3E1v259M9Loo/nd+RszvG+Myn6+z8Ir5Zl8GsVeks227NJdWzVUNG9mzJRd1b0DTSB8fZL3vTWmmsVX+4apo1l5BymRYC9XtHDsBzHa0O4/MeczqNIzZn5vHorFSWbMuhbZN63H9hJ4YlNveZAlCZ9ENH+WJ1OrNWpbN+32GCBE5v34QRPVpyYWJzGtTx8lW7ystg7iOw9DXoOBTGvANhfnB24yW0EKg/en8UZKfBPasDqnkov6iUlxZs4Z2ft1MvPIT7L+zElX1bEeKFTUCu2JKZx2y7KOzKKSAsJIjzujRlRI9Yzu4UQ51QL7tWofCw1RS0ZS4MuN1aYCbIyzL6OC0E6o9WfgizboebF0DcH342/NJ3GzN5+NNUMg4XckVSKx4c1pnG9fx7FIoxhlW7DzFrVTpfrknnQH4xkXVCGJbYnJE9YxnQNprgIIc/COxbY00bcXAnDP839L3Z2Tx+SguB+qOjh+C5DtYv3dB/Op2mVuUeLeGJL9Yzc8UeOjWL5J+ju9G7daVTY/m10rJyFm3NZtaqdOauyyC/qJSYyHAu6d6SkT1b0j0uyrNNY8bAiinw1QNWZ/CYd+C0QZ57/wCjhUBV7uOrYG8K3Lfeb0/DF27az19nriUrv4jbzmrHXUPaEx7in//WmigsKeO7jfuZtWovCzdmUVxWTnx0XUb0jGVkz5a1f+Ha4XSYcz9smgNtz4ZRb0H9mNp9zwCnhUBVbu0MmHkTXD8H4gc7ncatCkvKeGrOej5YsouOzerz3NgedI/TceiVyT1awtzUDGat3suirdkYA91ioxjZsyUXd29J8yg3jjwqL7fOAuY9CmXF1vUsA+/02w8i3kQLgapc8RF4tj10v9xa5clPpO3P486PVrIxI48JZ7blzxd01LOAaso8XMgXq9OZvTqdNXtyEYEBbaIZ0yeOYd2auzadRvZW+OIe2PETxJ9h/cxFt3NfeHVCWghU1WbeYl3Bef9mn5/b3RjDJyl7eGzWOiLCgnn+8h6c06mp07F81rasfGavTuezlXvZmV1AvbBghndrwZg+cfSNb0xQdTuZy0phySuw8GkIDoMLnoTe4wNqtJo30EKgqrZ1Ibx/KYx+G7qNcTrNKTtSVMrDn61l1qp0BraN5sVxPWnWwLcLm7cwxpC88yAzkvcwZ+0+8otKad24LqN7xzEmKY7YhhFVvzhjLcy6E/atgk4XwUXPQYOWnguvfqWFQFWtvBz+2x2adLAu5fdBOw4cYcL7yaTtz+fe8zpyxzntnR8S6aeOFpcxd10GM1L28MvWAwhwXpdmXD8onoHton8bdVRSaE0b/ct/IaIRDH8WEi7VswAHVVUIdJJzBUFB0ONK+PFZyN0LUbEnf40XWbhpP/d8vJKgIOG9G/szuEMTpyP5tYiwYC7tFculvWLZc7CAj5ft4uNlu/l2fSYdmtbnukHxjGmym4iv74XsLdDjKrjwHzpXkBfzr0sp1anreSVgYPXHTiepNmMMryxM48bJy4lrVJcv7hysRcDD4hrV5S8XdmbRQ+fy3NgeNAouwnz5ZyI+uIjD+fkcuXw6XPaaFgEvp4VAWRq3hdNOh1UfWRf5eLmC4lJu/3AFz87dxCXdWzLztkGOTxMdyOqEBjOmwQamlf2Ja0PmMy9yFANy/8GA6fD8t5vIOVLsdER1AloI1G96XgU5W2H3UqeTnFBGbiFjX1/M3HUZPDK8C/8d11PX+HXS0UPw+R3w4RgkvD5y07ec/+d3mX7XeZzergkvf5fGmf9eyCsL0zhaXOZ0WlUJ7SxWvynKt2Yk7TYaRrzsdJpKrUvP5abJyeQVlvDyVb1+Xc1LOWTLPJh9N+RnwuB74awHIeT3q7dtysjj2bmbmL8hkxZRdbjv/I6M6h2nnfkOqKqzWM8I1G/C60PCSEj9zLrQzMt8tzGTsa8vRgQ+mThIi4CTCnNhlnUWQJ0ouHk+DHn0D0UAoFPzSN4an8TUCQNoGhnOX2asYdSrv5C6N9eB4KoyWgjU7/W6GorzYMOXTif5nXd/2c7NU5JpG1OPz+84nYSWDZyOFLh2LYXXBsOqj+GMP8OtP0Bs75O+bEDbaD6/43RevKInew8VMuJ/P/P4F+vIKyzxQGh1IloI1O+1HgQNT4NVHzidBIDycsPjX6zj8S/WM6RLM6bfOlAvEnNKeZk1xPjdYda1ADd9W+VZQFVEhEt7xbLgz2dxdf/TmLxoB+f95wcWbtpfi8HVybhUCESksYjME5Et9tdK5/UVkfH2PltEZHwlz88WkVRXsig3CQqCXtfA9h+teWEcVFRaxt1TV/LuLzu48fQ2vH5NH9fmuVGn7nA6vDcSvnsKul4GE39yaQ2LqIhQnrw0kc9uP52oiFBueHc5j3y2liNFpW4MrarL1TOCh4AFxpgOwAL78e+ISGPgMaA/0A94rGLBEJFRQL6LOZQ79boWJNiaIdIheYUl3Dh5OV+u2cdfh3Xm0UsStHPRKdt+gNcHw94VMPJVGP2W29YQ7tmqIbPvHMyEM9vy0bJdDH/pJ1J2HnTL91bV52ohGAkc+2sxBbi0kn0uBOYZY3KMMQeBecBQABGpD9wHPOViDuVODVpAp2Gw8gMoLfL42//COygAABmiSURBVGflFXHlm0tYsi2H58f24NazdHZKRxgDv7xkzUNVtwlM+N7qQ3LzFBF1QoN5eHgXpt4ygLJyw+VvLGbSj1vxxRGNvsrVQtDMGLPPvp8BVDaMIxbYXeHxHnsbwJPA80DByd5IRCaISLKIJGdlZbkQWVVL0o1QkA0bvvDo2+7MPsKY1xexdf8R3rouidF94jz6/spWlA8zboB5/wedL4ZbFkBMx1p9y/5to/nqnjO4IKEZT3+1kVveSyG3QDuSPeGkhUBE5otIaiW3kRX3M1b5rnYJF5GeQDtjTLVmOTPGTDLGJBljkmJidBWjWtf2HGgUD8nveuwtU/fmMvq1xeQeLeHDW/pzTmedPtoR2Vvh7fNh/Sw47+9w+XsQHumRt25QJ5RXr+7Noxcn8P2m/Vz08k86zNQDTloIjDHnGWMSK7nNAjJFpAWA/bWyrv+9QKsKj+PsbQOBJBHZAfwMdBSR71375yi3CQqCPtfDzp8ha1Otv13KzhyunLSEsGBhxsRBAbmesFfYuRjeGgJ5++CamTD4Tx6fLVREuHFwG6ZPHEh5uWHM64v4au2+k79QnTJXm4ZmA8dGAY0HZlWyz1zgAhFpZHcSXwDMNca8ZoxpaYyJBwYDm40xZ7uYR7lTz2sgKBRSJtfq2yzems21by8jun4YM24bRPumtbxWrqrcus+skUERjeGW76DduY7G6d26EbPuHExCiwbc/uEK/jt/i/Yb1BJXC8EzwPkisgU4z36MiCSJyFsAxpgcrL6A5fbtCXub8nb1Y6DLJbDqQyg5Witv8cPmLK5/dxmxDSOYfutAWp5ogRNVO4yBRS/DJ9dDy57WVcKN2zqdCoCYyHA+umUAo3rH8sL8zdz58Uqdr6gW6FxD6sS2/wRTLoZLX7MmpXOjb9dlcOdHK2nftD7v39SP6PrVvzBJuUl5GXzzECybZE0vctkbEOp9xdgYwxs/buNf32yke1xD3hmfpD8vp0DnGlKnJn4wNOlo/aFw44eGL1anc9uHK0ho2YCPbxmgv9ROKC6Aadda/7cD74Qxk72yCIDVbzDxrHa8cU0fNu47zJjXF7Mr+6SDDVU1aSFQJyYC/SdC+kq3TU/9SfJu7pm6kj6nNeKDm/sTVTfULd9X1UB+lnWmt+krGPZvawWxIO//c3BB1+Z8dEt/co4UM+q1RTqiyE28/39eOa/HOKjTEJa86vK3en/JTv4yYw2nt2/ClBv6UT9cp4zwuANp8PZ5kLkervgA+t/qdKIa6XNaY2beNpDwkCDGTVrCz1sOOB3J52khUCcXVs8aSrrhCzi065S/zVs/beP/Pk9lSOemvHldki4m44RdS6wiUJQP138JXS52OtEpad80kpm3DSKuUQQ3TF7GF6vTnY7k07QQqOrpdwsgVnvyKfjfd1t4as4GhndrzmvX9KFOqBYBj1s/C6aMsIaH3jzPpUnjvEHzqDpMu3UgvVo34u6pK5m67NQ/pAQ6LQSqeqLiIGEEpLxnfZqsJmMMz87dyHPfbmZUr1heGteLsBD9sfMoY2DxKzB9vDU89KZ5XjM81FVREaFMuaEfZ3WM4aFP1/LWT9ucjuST9DdSVd+A26Eo11rgvhqMMTz55QZeWbiVK/u15rmxPQgJ1h85jzo2PHTuw9Y1IdfNgnrRTqdyq4iwYCZdm8SwxOY8NWeDXnh2CvS3UlVfXF/rtvhlKDvxvPHl5YZHPk/lnV+2c/2geJ6+LJEgnUbas4oLYPp1sPR1GHAHjJ3itcNDXRUWEsTLV/ZidO84Xpi/mX9+vVGLQQ1oIVDVJwKD77M6jFNnVrlbaVk5989YzUdLd3Hb2e147JIExMPz1QS8/CyYcglsnAND/wVDn/aJ4aGuCAkO4tkx3blu4GlM+nEbj3yeSlm5FoPq0LF7qmY6DoWmCfDzf6Db2D/8cSkpK+feaauYs2Yf953fkbvOba9FwNP2b4SPxlrF4Ir3rSahABEUJDw+oiv1w0N49futHCkq5bmxPQjVJskT0qOjaiYoyDoryNpoXYxUQWFJGbd9sII5a/bxyPAu3D2kgxYBT9v2Pbx9AZQUwg1zAqoIHCMiPDC0M3+5sBOzVqVz+4crKCzR+YlORAuBqrmul1lrFfz0/K/TThwtLuOW95KZvyGTJ0d25ZYz/WNUik9Z+QF8MBoatLQWkont43QiR91xTnseH9GVeeszuXHycl0P+QS0EKiaCw6B0++F9BWw7XvyCksY/84yfk47wL9Hd+fagfFOJwws5eWw4EmYdQfEnwE3zYWGrZ1O5RXGD4rn+bE9WLo9h6vfWsqhgmKnI3klLQTq1PS8CiJbULrwGa55cwkrdh3kpXG9uLxvq5O/VrlP4WGYdg389Bz0Hg9Xf+K2heX9xeg+cbx6dW/Wpx9m3KQl7M8rdDqS19FCoE5NSDh5fe8mZM8Sovf/wuvX9OGSHi2dThVYsjZbq4lt/sYaGXTJfyFYJ/CrzIVdm/PO9X3ZlVPA2NcXsztHZy6tSAuBOiXph44yemkH9pgYXor5gvO66PrCHrXxK3jzXCjIgfGzYcBEjy8p6WsGd2jCBzf35+CRYsa+vpi0/XlOR/IaWghUje3MPsLY1xezL7+cksEPUD8n1ZqQTtW+shKY/zhMvRKi28GE7601I1S19G7diGm3DqS03HD5G0t0GmubFgJVI1sy8xj7+mIKikv5eMIA2px7o7VwzXdPWdMZqNpzcAe8O8y6hqP3dXDjN9BQ+2RqqkuLBsyYOJCI0GCunLSERWk6jbVLhUBEGovIPBHZYn9tVMV+4+19tojI+Arbw0RkkohsFpGNIjLalTyqdq3cdZDL31gMwLRbB5IYG2WNIDr3b3BgE6x4z+GEfiz1U3j9DMjaBGPegREv++10EZ4Q36QeM24bSIuGdRj/7jI+XbHH6UiOcvWM4CFggTGmA7DAfvw7ItIYeAzoD/QDHqtQMB4B9htjOgIJwA8u5lG1ZOHG/Vz15lIaRITyycSBdGwW+duTXUZA60HWWUGhnmq71ZED8MkNMOMGiOkEE3+CRP285A4toiL4ZOIg+sY35r7pq3lpQeBOVudqIRgJTLHvTwEurWSfC4F5xpgcY8xBYB4w1H7uRuCfAMaYcmOMnqN5oU+Sd3Pze8m0a1qPGRMHcVp0vd/vIAJD/wkF2fDjs86E9DfGWPM5vdLP6n85529ww9fWhXzKbaIiQpl8Qz9G9Y7lP/M28+DMNZSUlTsdy+NcLQTNjDH77PsZQLNK9okFdld4vAeIFZGG9uMnRWSFiHwiIpW9HgARmSAiySKSnJWV5WJsVR3GGF5ZmMZfZqxhYNtopk4YSExkFYvMt+wJva6GJa9D9lbPBvU3Odth6lUw40brwrBbf4Sz/qJDQ2tJWEgQz4/twd1DOjA9eQ/Xvr2U7Pwip2N51EkLgYjMF5HUSm4jK+5nrHOqmpxXhQBxwCJjTG9gMfBcVTsbYyYZY5KMMUkxMTE1eBt1KkrLyvn77HU8O3cTI3q05J3r+558feFzH4WQOvDVX36dekLVQPER6wrhV/rDth/gvMfhpvnQLMHpZH5PRLjv/I68cEUPVu46xIj//RJQI4pOWgiMMecZYxIruc0CMkWkBYD9dX8l32IvUHFoQ5y9LRsoAD61t38C9Hbh36Lc5HBhCTdOSWbK4p3cckYbXryiZ/VWFYtsBkMeha0LYO0ntR/UX5SVwsoP4eUk6wrhhJFwVzIMvtfqjFcec1mvOGZMHIQxhtGvLeKzlYHRiexq09Bs4NgooPHArEr2mQtcICKN7E7iC4C59hnEF8DZ9n5DgPUu5lEu2pl9hFGvLmJR2gH+Oaobj1yUULMFZfreZC1e881DcCS79oL6g/JyWDsDXu0Ps263CumNc2H0m9bEccoR3eKimH3XYHq2asifpq3mwRlrKCj27wnrxJVechGJBqYDrYGdwOXGmBwRSQImGmNutve7EXjYftk/jDHv2ttPA94HGgJZwA3GmJOuQJ2UlGSSk5NPObeq3NJt2Uz8IIVyA69d05tB7Zqc2jfKXA9vnGmNbhn1hntD+oPSYqsjeNFLsH+9tb7DOY9A54v06mAvUlJWzovzN/Pq91tpE12Pl67sZQ2Z9mEikmKMSfrDdl8cLqWFwL2MMbzzyw7++dUGWjeuy9vX96VNk3onf+GJfPcP+PHf1vKIXSsbTBaAjh6E5Hdh2STI2wcxXeDM+6HrKL9fPcyXLdp6gPumrSb7SBF/vqATNw9u47Nrb2shUJXKKyzhwZlr+GptBud1acbzl/cgKsINo1PKSqwFUnK2wm2LICrO9e/pi8rLYMfPsGYarPscSo5A27Nh0F3QboieAfiIg0eK+euna/lmXQaJsQ14ZlR3nzw70EKg/mB9+mHu+GgFu3IKeODCTkw4s617VxTL3mo1EbXoAeO/gKBg931vb5e5HtZMhTWfQF46hEVaZ0b9b4Xm3ZxOp06BMYavUzN4bPY6co4Uc/2geO4+twNRdX1nWK8WAvWrsnLDpB+38cK8zTSsG8r/rupNvzaNa+fNVn0Mn0+0lrc877HaeQ9vkZdhdf6umQoZayEoBNqfB90vh07DdUoIP5FbUMIz32xk6vJdREWEcve5HbhmwGnVG1nnMC0ECrBGBf15+mqSdx5kWGJz/nFZNxrXC6u9NzQGvrjbmodo1FvQfWztvZcTio/Ahi+tP/7bvgdTDi17Q49xVmd5vVPscFdeb336YZ7+agM/px2gVeMIbj2zHWP6xFEn1HvPfLUQBLiSsnKmLNrBf+ZtJjhIeGJkVy7tGeuZxeVLi+H9S2FPsjVNQpyPr6VbXgbbf4DV06zpH0qOQFRr65N/9ysgpqPTCZWHGGP4YXMWL87fwqrdh4iJDOf6QfFcntSq6qvwHaSFIIAt35HD/32eysaMPM7uFMPTl3WjZUMPN1McyYY3z4aSo3D9V775xzJjLayeajX/5GdAeJTV7t9jHLQaoCN/ApgxhsXbsnl14VZ+TjtASJBwfkIzLk9qxentm3hNs5EWggCUtj+fF+ZtZs7afcQ2jODRSxK4IKGZZ84CKnNgC7w7HCQIbvjKWljF2x1Ot66SXj0N9q+z2v07XGB98u84FELrOJ1QeZm0/XlMXbabmSv2cLCghMg6IZzbuSnnJzRjYNtoous7d6aghSCA7Mw+wisL05iRsoeI0GBuOqMtE89qS90wL5iuYP8GmHwRhERYSyx6YzEoyrOafFZPhe0/Asa6Wrr7FdaY/3rRTidUPqCotIxf0g7wTWoG89ZncrCgBIBOzSLp26YRiS2jSGjZgI7NIj3Wr6CFwM8ZY0jZeZA3f9rGt+szCQ0K4uoBrbnjnPY0cfATSKUy1sJ7I62O5CunQuv+TieyrnvY+p013n/jV1B61JryufsV1s0bC5byGaVl5azek8uSbdks2ZbNip0HOVJsregXHCS0ahRBXKO6tGpsfW0RVYfG9cKIrhdO4/phNK4bRkSY68VCCwFw+RuL2Zd7lKiIUKIiQmkYEUYD+37FW8O6v91vEBFKZHhIzebb8aD0Q0f5fNVePluxly3782lYN5Rr+p/GdQNPo2kDL262yN4KH46F3D0w7Bnoc4PnL64yBvausP74p86EggMQ0RgSR1l//OP66gVfqlaUlxt25RSwft9h1qcfZnv2EfbkFLDn4FGyjxRX+pqI0GAaRITw3Z/Ppt7JZgKuQlWFwAvaCjxnYNtodmYfIfdoCblHS8jIPUzu0VJyjxZTUlZ1QQwSaBARStPIcJo1qEPzBnVoHlXnD/ej64XVesEoKStn7d5cftiUxfebs1iz5xDGQJ/TGvH0Zd24tFdL72gCOpnodnDTPPj0FvjyT9bQy+HPQf2mtf/eOdusC73WTLOufA4Oh87DrT/+7YZASC0Op1UKCAoS4pvUI75JPYZ3a/G7544UlZJxuJCDR4rJOXYrKCYnv5jDhSVE1EIzUkCdEVTFGMPRkrJfC8ShgpJf7x+2vx4sKCbzcBGZhwvJyC3kQH4R5ccdutBgoWlkHZo1sApGs1+LxG+PoyJCiawTQnhI1f+Z5eWG/OJSDuQVsSungN0Hj5KWmceavbmsTz9MUWk5QQI9WzXknE5NGdGz5R9XDfMV5eXwy4uw8GkIqwvn/p+1MHuIm5uzDmyB9bOsW8YaQCB+sPXHP2EE1PG96QKUqiltGnKz0rJysvKLyMgt/LU4ZOYVkZlbSMZha1vm4SLyiyqfvjYsJIgGdUIIDQ5CsBbGMMaQV1RKflHpH9Z1qRsWTGJsFN1jo+jZuiGD2zehYV0/+uSatRnm3Ac7foIGcTDwdug+7tQ7ZksKYddi2LYQtsyzZvkEq7mnywir+SdQ5z9SAUsLgUPyi0qtopBbSGZeIYePlpJXWEJeUSl5haWUlJZjsJqrRaB+eAgN6oQQWSeUxvXCaB1dl1aN6tI0Mtxr+yncxhirw/aHf8PuJRAcBvFnQIfzIbYPNO0C4ZF/fF3xEauvIWOt9Wk/fRXsXgqlhRAUCq36Q5dLrFtUrOf/XUp5CS0EyrdkroNVH8HmuZC95bftoXWhbhPr4q2yUijOg8IKSwoGhVoFI34wtD0HThsE4fU9n18pL6SFQPmu3D2wbw0c2ARHDlg3U24t5h5a11rNq0GsVQBiOmtnr1JV0FFDyndFxdnt+cOdTqKUX3JpAgwRaSwi80Rki/21URX7jbf32SIi4ytsv1JE1orIGhH5RkR0qkallPIwV2dCeghYYIzpACywH/+OiDQGHgP6A/2Ax+yF7EOA/wLnGGO6A2uAO13Mo5RSqoZcLQQjgSn2/SlAZYvTXgjMM8bkGGMOAvOAoWCNmgTqiTULWgMg3cU8SimlasjVQtDMGLPPvp8BNKtkn1hgd4XHe4BYY0wJcBuwFqsAJABvV/VGIjJBRJJFJDkrK8vF2EoppY45aSEQkfkiklrJbWTF/Yw1/KjaQ5BEJBSrEPQCWmI1Df21qv2NMZOMMUnGmKSYmJjqvo1SSqmTOOmoIWPMeVU9JyKZItLCGLNPRFoA+yvZbS9wdoXHccD3QE/7+2+1v9d0KuljUEopVbtcbRqaDRwbBTQemFXJPnOBC+wO4kbABfa2vUCCiBz7eH8+sMHFPEoppWrI1esIngGmi8hNwE7gcgARSQImGmNuNsbkiMiTwHL7NU8YY3Ls/R4HfhSREvv117uYRymlVA355JXFIpKFVThqqglwwM1xaoPmdC9fyOkLGUFzupunc55mjPlDJ6tPFoJTJSLJlV1e7W00p3v5Qk5fyAia0928JaerfQRKKaV8nBYCpZQKcIFWCCY5HaCaNKd7+UJOX8gImtPdvCJnQPURKKWU+qNAOyNQSil1HC0ESikV4AKmEIjIUBHZJCJpIuI1U1mIyA57TYZVIpJsb6vWOg+1nOsdEdkvIqkVtlWaSywv2cd2jYj0djjn30Vkr31MV4nI8ArP/dXOuUlELvRgzlYislBE1ovIOhG5x97uNcf0BBm96niKSB0RWSYiq+2cj9vb24jIUjvPNBEJs7eH24/T7OfjHc45WUS2VziePe3tjv0eYYzx+xsQDGwF2gJhwGogwelcdrYdQJPjtv0beMi+/xDwLwdynQn0BlJPlgtr6bCvsaYVHwAsdTjn34H7K9k3wf6/Dwfa2D8TwR7K2QLobd+PBDbbebzmmJ4go1cdT/uY1LfvhwJL7WM0HRhnb38duM2+fzvwun1/HDDNQ//nVeWcDIypZH/Hfo8C5YygH5BmjNlmjCkGpmKtpeCtqrPOQ60yxvwI5By3uapcI4H3jGUJ0NCehNCpnFUZCUw1xhQZY7YDaVg/G7XOGLPPGLPCvp+HNa9WLF50TE+QsSqOHE/7mOTbD0PtmwHOBWbY248/lseO8QxgiIiIgzmr4tjvUaAUgkrXRHAoy/EM8K2IpIjIBHtbddZ5cEJVubzx+N5pn16/U6FpzSty2k0TvbA+IXrlMT0uI3jZ8RSRYBFZhTXj8Tyss5FDxpjSSrL8mtN+PheIdiKnMebY8fyHfTxfEJHw43PaPHY8A6UQeLPBxpjewDDgDhE5s+KTxjpn9Loxvt6ay/Ya0A5rqvN9wPPOxvmNiNQHZgL3GmMOV3zOW45pJRm97ngaY8qMMT2xprXvB3R2OFKljs8pIolY6650BvoCjYEHHYwIBE4h2Au0qvA4zt7mOGPMXvvrfuAzrB/qzGOnhFL1Og9OqCqXVx1fY0ym/QtYDrzJb80VjuYUazGmmcCHxphP7c1edUwry+itx9POdghYCAzEako5NqNyxSy/5rSfjwKyHco51G6CM8aYIuBdvOB4BkohWA50sEcVhGF1GM12OBMiUk9EIo/dx1qrIZXqrfPghKpyzQaus0c9DAByKzR3eNxx7aqXYR1TsHKOs0eRtAE6AMs8lEmwlmLdYIz5T4WnvOaYVpXR246niMSISEP7fgS/rWWyEBhj73b8sTx2jMcA39lnX07k3Fih8AtWP0bF4+nM75GneqWdvmH1yG/Gakt8xOk8dqa2WKMuVgPrjuXCar9cAGwB5gONHcj2MVYzQAlWW+VNVeXCGuXwin1s1wJJDud8386xBuuXq0WF/R+xc24Chnkw52CsZp81wCr7NtybjukJMnrV8QS6AyvtPKnAo/b2tliFKA34BAi3t9exH6fZz7d1OOd39vFMBT7gt5FFjv0e6RQTSikV4AKlaUgppVQVtBAopVSA00KglFIBTguBUkoFOC0ESikV4LQQKKVUgNNCoJRSAe7/AXRnkt0oG5BvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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WAl8T1QRiO1vNQ0opz1j6BlSsHhDTSbiihcAXtbsRUjfD4U1OJ1HK9x1JtBaA6nyXNXgzAGkh8EVtroOgEJ1yQilPWPYmBIcFzHQSrmgh8EWVa0LTy63rBDojqVIXLisV1k2F9iOsmX4DlBYCX9VuGBw/oDOSKuWOFe9ZY3O6++96xKWhhcBXtRwIoZW0eUipC5VzHFa9b03oWLOZ02kcpYXAV/02I+nXUJDndBqlfM+qD6yJHPs84nQSx2kh8GVxwyDnGOxa4HQSpXxLXjYsG29da6vbwek0jtNC4MuaXgoVo3RMgVLna82nkH1EzwZsWgh8WXCo1ZV0+/eQe8LpNEr5hoI8awBZgx7QsIfTabyCFgJf124YFJyCbXPOva9SCjZ8bvW46/Ow00m8hhYCXxfbBSIbaO8hpUqjsAAWv2pNNd30cqfTeA0tBL4uKAjihsCuRZAVYGs5K3W+tnwN6buh98MBN9X02Wgh8Adxw8AUWj/kSinXiorg15chuhW0+ovTabyKFgJ/ENMaarXR3kNKnc3WWZC6BXo/Yp1Jq9/o0fAXcUMheSWk73E6iVLep6gQfnoRaraEttc7ncbreKQQiMgAEdkuIoki8piL58NF5Av7+RUi0qjYc4/b27eLSH9P5AlIcUOtr5u+cjaHUt5o80xI2wZ9H4OgYKfTeB23C4GIBAPjgYFAa2CEiLQusdudQIYxphnwKvBf+7WtgeFAG2AA8Lb9/dT5qtYAGnSHjdPBGKfTKOU9Cgvgp/9Yzaetr3U6jVfyxBlBFyDRGLPbGJMHfA6UXPRzMDDRvv8lcLmIiL39c2NMrjFmD5Bofz91IeKGWp96UnTBGqV+s+lLa1H6Sx/XawNn4ImjUg9IKvY42d7mch9jTAGQCdQo5WsBEJExIpIgIglpadpN0qXWpxesme50EqW8Q2GBdW2gdjvtKXQWPlMejTETjDHxxpj46Ohop+N4p8o17AVrvtIFa5QCWD8VMvbApU/ouIGz8EQhOADUL/Y41t7mch8RCQEigaOlfK06H3E3wPFkXbBGqfwc62ygbkdoMcDpNF7NE4VgFdBcRBqLSBjWxd/ZJfaZDYy07w8FFhpjjL19uN2rqDHQHFjpgUyBq9VV9oI12jykAtzK96wPRf2e0bOBc3C7ENht/uOAucBWYJoxZrOIPCsig+zdPgRqiEgi8BDwmP3azcA0YAvwA3CfMabQ3UwB7fSCNVt0wRoVwLLTrVHEzfpB4z5Op/F6IZ74JsaYOcCcEtueLHY/B7jhDK99AXjBEzmULe4G64xg1wJrSUulAs3iV6ylKK942ukkPsFnLhar89D0MmvBGm0eUoHoWBKsmAAXj4DabZ1O4xO0EPij0wvWbJujC9aowLPo39bXS59wNocP0ULgr+Ju0AVrVOA5vNHqMtr1bqhW/9z7K0ALgf+q31UXrFGBxRj4/h9QsTr0fsjpND5FC4G/0gVrVKDZPAP2LYHLn7SKgSo1LQT+LO4GXbBGBYa8k/Djk9ZUEh1vczqNz9FC4M9i2uiCNSowLH7NGjx21f/pNNMXQAuBv9MFa5S/y9gLS163zoAbdHM6jU/SQuDvdMEa5e/m/tOadbffs04n8VlaCPydLlij/Nm2ObDtW+jzCFSt63Qan6WFIBDogjXKH+WegDmPQK3W0ON+p9P4NC0EgUAXrFH+aOHzcPwgXPOGNZpeXTAtBIFAF6xR/iZ5Nax4DzrfBfU7O53G52khCBS6YI3yFwV58M0DUKWONXhMuU0LQaDQBWuUv/jlf9b1rqtfhgpVnU7jF7QQBApdsEb5g+TV8OsrcPFN1ocb5RFaCAJJ3A1wKsNasEYpX5N/Cr4eazUJDXzR6TR+RQtBINEFa5QvW/AcHNkBg9+CCpFOp/ErbhUCEYkSkXkistP+6nLKPxEZae+zU0RG2tsqich3IrJNRDaLiJb4sqYL1ihflbgAlo+HzqOh6aVOp/E77p4RPAYsMMY0BxbYj/9ARKKAp4CuQBfgqWIF4yVjTCugA9BTRHSB3bKmC9YoX3MiBWbebQ0cu/I5p9P4JXcLwWBgon1/InCti336A/OMMenGmAxgHjDAGJNtjFkEYIzJA9YAsW7mUefy24I12jykfEBRIcwYDblZMPRjCK3odCK/FOLm62OMMYfs+4eBGBf71AOSij1Otrf9RkSqAdcAr7uZR53L6QVrlrxhLVgTEe10Ir9QUFjE7iMn2XLwOEnp2Rw4dooDx06RejyXrNwCTuYVkJ1bSEFRESFBQQQHCSHBQmTFUKIqh1G9Uhg1I8KpH1WRhjUq0SCqEk2jI6hWKczpf5qzFr8Ke36GQW9CrVZOp/Fb5ywEIjIfqO3iqX8Wf2CMMSJy3rOaiUgIMBV4wxiz+yz7jQHGADRo0OB830YVF3eD9Qu25WvoMtrpND4pK7eAFbuPsjjxCGv2H2PboePkFvw+artmRBj1qll/1KtUCKVyeDCVw0MICRIKigwFhUXkFxqOn8onPTuP9JN5bD98gpQTOX+YG7BuZAVa142kdd2qtK1blfhGUURVDpDisPtnayH6tkOgw61Op/Fr5ywExpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4KdijycAO40xr50jxwR7X+Lj43UaTXecXrBm43QtBOfhcGYO3208xNxNh1mzP4OCIkN4SBDt61fjlm4NaVO3Km3qRtKwRiUqhF7Y4ig5+YUcOHaK/Uez2ZFygi2HjrPl4HEWbkuhyP6pb1Yrgs6NoujaOIqezWoSXSXcg/9KL3FsP3x5O9RoBte8DiJOJ/Jr7jYNzQZGAi/aX2e52Gcu8O9iF4ivBB4HEJHngUjgLjdzqPMVNxQWPGMt6lG9kdNpvNbJ3AJmrTvIrHUHWLk3HWPgojpVuat3E3o3r0mnhtUv+I++KxVCg2kaHUHT6AgubVXrt+05+YVsOpDJyr3prNqTzrcbDjJ15X4A2tarSt8WtejbMpr29asREuzjvcLzT8EXt0BhPgyfAuFVnE7k98S4MUe9iNQApgENgH3AMGNMuojEA2ONMXfZ+90BPGG/7AVjzMciEot17WAbkGs/95Yx5oNzvW98fLxJSEi44NwK6xPXa3Fw2b+sudzVHySmZjFp2V6+WnOArNwCmkZXZtDF9fjLxXVoGh3hdDwKiwxbDx3n5x1p/LQ9lTX7j1FYZIisGEq/1jFcFVebns1qEh7iY8s2GgMzx8KGz2HEF9BygNOJ/IqIrDbGxP9puzuFwClaCDzkowHWSON7l+upt231vgzeXLiTn7anERYcxFVxtbm1eyM6NqiGePExyszOZ3HiEeZvTWH+1hRO5BRQJTyEK1rHMLBtbfq0iPbomUuZ+eUlWPgc9H0C+v7D6TR+50yFwN2mIeXL4obCdw9bE3jVjnM6jaNW7U3njQU7+XXnEapXCuXhfi0Y0bUBNSN8o/09slIoV7erw9Xt6pBbUMjSxKPM2XiIH7ekMHPtAapWCOEvF9dlSMd6dGxQ3TuL2obpVhGIGwaXPOp0moCiZwSB7ORReLkFdB0L/V9wOo0jdqVl8e/vtrJgWyo1Kocxpk8TbunWkMrh/vEZKb+wiKW7jvL12gN8v+kQOflFNKpRies7xnJ9x3rEVq/kdETL3sUw6TqI7QK3zoAQ3yjAvkabhpRrn98M+5fDw9sCapWnjJN5vL5gJ5OX76NCaDD3XdqMUT0aUTHMB5pPLlBWbgHfbzzEV2uSWb47HRG4pEU0N3dtyGWtahEc5NBZQtp2+LAfRMTAnT9CRZcz1SgP0EKgXNv+A0y9EW78DC76i9Npypwxhumrk/n3nK0cP5XPiC4N+Fu/Fj7TBOQpyRnZTEtI5vOV+0k9kUvdyAqM6NKAGzvXp1bVCuUX5FgSfDwQCnLgrvnag62MaSFQrhUWwKutoV4nGDHV6TRlandaFk/M3Mjy3el0blSd56+No2XtwO6amF9YxIKtKUxevp/FiUcICRL6t6nNHb0a0alhVNm++YnDVhE4eRRGfQN1Li7b91N6sVidQXAIXDwClr5pTe5VxdUsIb6tsMjw3i+7eG3+TsJDgvjP9XHcGF+fIKeaQrxIaHAQA9rWYUDbOuw5cpIpK/YxLSGZ7zYeomODaozp04R+rWt7vtkoOx0+vdb6mbt1phYBh+kZgYIjO+GteOj3LPR80Ok0HpWUns1D09axam8GA9vW5plBbcq36cMHncwtYHpCEh8u2UNS+ika1qjEnb0aM7RTLJXCPPDZMScTJg6C1K1w83Rocon731OVijYNqbP7sD+cSof7VvrFmAJjDF+tOcDTszcjwDOD23Bdh3re2W3SSxUWGeZuPsyEX3azLunYb72qbu3e8MILQnY6TL4eDm+0Rg236O/Z0OqstBCos1szCWaPgzvnQf0uTqdxy/GcfB7/aiPfbTxEl8ZRvDLsYu/pJumDjDEk7Mv4bZxFjcphjO7ThFvPt5ttVhpMutZaZWzYJB017AAtBOrsck/ASy2tKaoHvel0mgu25eBx7v1sNUkZp3jkypaM6dPEuW6Rfmj1vgxeX7CTX3akEVU5jNG9m5Su2+3xQ/DpIKuX0Igp1rKpqtydqRD4+OxUymPCq1jLWG6aAXknnU5zQaYlJHHd20vIzivk8zHduKdvUy0CHtapYXU+vaMLM+7tQbvYSP77wzb6vrSIaauSKCw6w4fKY/ut3kHHD8ItX2kR8EJaCNTvOtwCeVmwxdUkst4rJ7+QR79cz6NfbqBTw+p890BvOjcq466PAa5jg+p8cnsXpo/tTt1qFXn0qw0MfP0XFm5L4Q+tDIc3wYdXWtefbpsFjXo6F1qdkRYC9bsG3SCqKaz51OkkpZZ6PIfhE5YzLSGZcZc2Y9KdXf1zfn4v1blRFDPu6cE7N3ckv9BwxycJ3PT+CrYfPmEtLPPxQEBg1ByI/VOLhPISWgjU70Sg0yjYvwxStjid5pw2Hchk8PglbD98gndv6cgj/VtqU5ADRISBcXX48W99eHZwG7YePs47b71I4aTrKaxSF+6aB7XbOh1TnYUWAvVH7W+G4DBY/bHTSc7quw2HGPruUgT48p7uDGhbx+lIAS80OIjbujVkae9NvBbyFqsKm3N5xhN8lQhFZ7p+oLyCFgL1R5VrQOtrYf3nXnnR2BjDq/N2cN+UNbSpG8mscb1oUzfS6VgKoCAXZo+j0s/PQJvriLhzNtWiavLw9PXcOGEZu9KynE6ozkALgfqz+Dsg9zhs+srpJH+QV1DEw9PX8/qCnQzpGMuU0Xo9wGtkpcGng2HtZLjkHzDkI9o2rMWMe3rwvyHt2JGSxcDXf+XtnxLJLyxyOq0qQQuB+rMG3aBWa0j4yOkkvzmRk8+dE1cxY80BHurXgpduaOd7yzD6q8Ob4P3L4OBaGPoRXPoEBFl/WoKChGGd6zPvoT5c3qoW//thO9eOX8KmA5kOh1bFaSFQfyZinRUcXAsH1jidhpTjOQx7bzlLdx3lf0Pb8cDlzXWqCG+x7Ture2hRPtz+PbQd4nK3WlUq8M4tnXjn5o6kHM9l8PglvPzjdj078BJuFQIRiRKReSKy0/7qckUJERlp77NTREa6eH62iGxyJ4vysHbDILSS42cFO1NOcP3bS9l/9CQfjerMsPj6juZRtqIi+Pl/1sJG0S1h9CKo1/GcLxsYV4f5D/VhcPu6vLkwkSHvLGW3XjtwnLtnBI8BC4wxzYEF9uM/EJEo4CmgK9AFeKp4wRCR6wH9SfA2FSKtNY03fQWnjjkSYdXedIa8s5S8wiK+uLs7l7SIdiSHKuFUBkwdDotesD4w3D4Hqpa+11a1SmG8Mqw979zckf3p2Vz9xmKmrNiPL0534y/cLQSDgYn2/YnAtS726Q/MM8akG2MygHnAAAARiQAeAp53M4cqC/F3QH42rC//BWt+3pHGrR+uoGaVcGbc04O29bRnkFc4vBEm9IVdC+Gql+C69yC04gV9q4FxdZj71z7EN6rOEzM3ctfEBI5k5Xo2ryoVdwtBjDHmkH3/MOBqVZN6QFKxx8n2NoDngJeB7HO9kYiMEZEEEUlIS0tzI7IqtbodoH5XWPEeFBWW29t+v/EQd01cRZOaEUy7uzv1o3TmUK+w/gv4oEJqS5cAABnYSURBVJ/VTfT2OdBltNtTlsdUrcDE27vw5F9a82viEa56/VdW7D7qocCqtM5ZCERkvohscnEbXHw/Y53XlfrcTkTaA02NMTNLs78xZoIxJt4YEx8drU0E5abbPZCxB3bMLZe3+3J1MvdNWUO72GpMHdMt4NYS9koFefDdIzBzjLWk6d2/eHSq8qAg4Y5ejZl1X08iwkMY8f5yxi9K1EFo5eichcAYc4Uxpq2L2ywgRUTqANhfU118iwNA8St8sfa27kC8iOwFFgMtROQn9/45yuNaXQNVY2H522X+VhOX7uWR6evp0bQmk+7sQmTF0DJ/T3UOxw/CJ1fBqvehx/3WxHERtcrkrS6qU5XZ9/fi6nZ1+b+527lj4irST+aVyXupP3K3aWg2cLoX0EjA1bSVc4ErRaS6fZH4SmCuMeYdY0xdY0wjoBewwxjT1808ytOCQ6DrGNj7q9U+XEbGL0rkqdmb6dc6hg9GxntmSUTlnj2/wnt9rCUlb5gIVz5v/TyUoYjwEN4Y3p7nrm3L0sSjXP3Gr6zel16m76ncLwQvAv1EZCdwhf0YEYkXkQ8AjDHpWNcCVtm3Z+1tyld0vM3qSrr8XY9/a2MML36/jf+bu53rOtTj7Zs7UiFUB4o5yhhY+qY1UrhidRi9ENq46gdSNkSEW7s1ZMa9PQgNDuLG95bz6bK92quoDOkKZap0vn0I1k6Cv22BCM9coykqMjw5exOTl+/nlm4NeHZQW4J09lBnnToGs+6Dbd/CRYPg2retRYscknkqn4e+WMeCbakMi4/luWvb6ohyN+gKZco9XcdCYZ7HBpgVFFrzBk1evp+xlzTlucFaBBx3cK3VFLTjB+j/Hxj2qaNFACCyYijv3xbP/Zc1Y1pCMje+t5zDmTmOZvJHWghU6US3gGb9YNUHkO/eL2JuQSH3fraGmWsP8Pf+LXlsYCudMsJJxsCqD+2pIgqsqSK63+t211BPCQoSHr6yJe/e0pGdKSe45q3Fet3Aw7QQqNLrMQ5OpsL6KRf8LbLzCrhrYgI/bknhmUFtuO/SZh4MqM5bbhbMGA3fPQSNL4G7f/Vo11BPGtC2DjPv60nlsGCGT1jOlBX7nY7kN7QQqNJrfAnU7QhLXofCgvN+eeapfG79cCVLEo/w0g0XM7JHI89nVKWXsgXev9SaRuSyf8FN06z1KLxYi5gqzLqvFz2a1uSJmRt5YuZG8gp04jp3aSFQpScCvR+CjL2w5evzeunRrFxGTFjOhuRjjL+pI0M7xZZNRlU666ZaU0efOmaNDejzyG9TR3u7yEqhfDSqM/f0bcqUFfu55YMVHNWpKdziG//zynu0vBpqtoRfX7HalkvhUOYphr23jN1HsvhgZGcGxumyko7JPwWzxsHXY63F5McuhsZ9nE513oKDhH8MaMXrw9uzPvkYg95awpaDx52O5bO0EKjzExQEvf4GqZtLNe3EvqMnGfrOMlKP5/LpHV11BlEnHUmED66wugH3fgRu/RqquJoezHcMbl+PL8f2oMgYhryzlDkbD537RepPtBCo8xc3FCIbwOKznxXsSDnBDe8uIzuvgCmju9GlcVQ5hlR/sHmmNWvo8YNw85dw+b/KfJRweYmLjWTWuJ60rluVez9bwyvzdug8RedJC4E6f8Gh0PMBSFoBu39yucu6pGMMe28ZANPu7k5crE4j7YiCXJjzd5g+Cmq1grG/QvN+TqfyuFpVKjBldFeGxcfyxoKd3PPZarJyz79DQ6DSQqAuTMfbrMnoFr3wp7OCpbuOcPP7y6laIZQvx/ageYyzg5ICVsZe+GgArJwA3e6DUXMg0n8v0oeHBPPfIe146prWzN+aypC3l7L/6DlnuFdoIVAXKiQcLvk7JK+CnT/+tnnelhRGfbyKetUr8uXY7jSooWsJOGLzTHi3NxzdBcMmwYB/Q0iY06nKnIhwe8/GTLy9C4eP5zBo/GKW7jridCyvp4VAXbj2N0P1RrDweTCGmWuTGTt5NRfVqcoXY7pTq2oFpxMGnvxT8M2DVlNQzRYw9hdoPcjpVOWuV/OazLqvJ9ER4dz64UqdtO4ctBCoCxccCpc8Boc38NOsD/nbF+vp2jiKz+7qSvXK/v/p0+ukbrPGBqz+BHr+Fe74wSrUAapRzcrMuLcHl7aM5slZm3Xw2VloIVBuMXE3kF6xEXXWvEq/VjX5aFRnIsL9ozeKzzAG1nxq9QrKSoVbvoJ+z1iFOsBVqRDKhFvjue/SpkxdmcTNHyzXdZFd0EKgLlhRkeH573fw/zIH0TIomXfitutaAuUt5zh8dRfMvh/qd4Z7lkCzK5xO5VWCgoS/92/FGyM6sPFAJoPfWsLmg5lOx/IqWgjUBcktKOSBz9fy4eI91Op6Iya2KyGLnofcE05HCxwH1ljTRm+eCZf9P3uAWG2nU3mtQRfX/cPgs283HHQ6ktfQQqDOW+apfEZ+tJJvNxzisYGteGpQG2TAfyArBRa/5nQ8/1dUCL/8H3zYDwrzYdR30OfvEKRnY+fStl4ks8f1ok3dSMZNWctLc7fr4DO0EKjzdCjzFMPeXcbqfRm8Prw9Yy9paq0lENsJ4oZZSxwe0+mBy0zGXvj4Kqun1kWD4J7F0LC706l8SnSVcKaM7sqN8fV5a1Eid0/WwWduFQIRiRKReSKy0/5a/Qz7jbT32SkiI4ttDxORCSKyQ0S2icgQd/KosrX98Amuf3spB4+d4pPbuzC4fb0/7nDFUyBBMP9pR/L5NWNg3RR4pxekboHr34ehH1lrCqvzFh4SzItD4nj6mtYs3JbK9W8vYd/Rk07Hcoy7ZwSPAQuMMc2BBfbjPxCRKOApoCvQBXiqWMH4J5BqjGkBtAZ+djOPKiO/7Ehj6LtLKTKGaWO707NZzT/vFBkLPe635rffu6T8Q/qr7HSYPhK+vgfqXGxdEG43zGtWEPNVIsKono359I4upBzP5S9vLA7Y6wbuFoLBwET7/kTgWhf79AfmGWPSjTEZwDxggP3cHcB/AIwxRcYYHQLoZYwxfLxkD6M+Xkm9ahWZcW9PLqpT9cwv6PU3qNYAvv2rNc+Ncs/OefBOD9g2B654BkbOto6v8piezWry7f29aBYTwbgpa3li5kZy8gudjlWu3C0EMcaY0/O+HgZczWlbD0gq9jgZqCci1ezHz4nIGhGZLiJnnBNXRMaISIKIJKSlpbkZW5VGXkERT8zcyDPfbOGKi2L46p4e1KtW8ewvCqsEV78CR3ZYK5mpC3MqA2beA58NhQrVYPQC6PVXvSBcRupHVWLa3d0Ze4m12M2145eQmBo4PeDOWQhEZL6IbHJxG1x8P2ON3z6fy+8hQCyw1BjTEVgGvHSmnY0xE4wx8caY+OhondO+rKWfzOOWD1cwdWUS4y5txru3dKJyaQeKNe8Hba6DX16y5sBX52f79zC+G2z4wuoNdPfPVpOQKlOhwUE8NrAVn9zembQTuVzz5hKmrNgfEFNTnLMQGGOuMMa0dXGbBaSISB0A+2uqi29xAKhf7HGsve0okA3MsLdPBzq68W9RHrIxOZNBby1mXdIxXh/enkf6tyQo6Dzbowe8CCEVYPY4q7ujOrfsdPhqNEwdDpVrwuiF1viAkHCnkwWUvi1rMefB3nRqWJ0nZm5k5MerOJyZ43SsMuVu09Bs4HQvoJHALBf7zAWuFJHq9kXiK4G59hnEN0Bfe7/LgS1u5lFuMMYwafk+hryzlKIiw7S7u/+5Z1BpVakNA1+E/ctg2VueDepvjLEGhY3vCptnWPM3jV4Edds7nSxgxVStwKd3dOG5wW1YtSedK1/9mZlrk/327EDc+YeJSA1gGtAA2AcMM8aki0g8MNYYc5e93x3AE/bLXjDGfGxvbwhMAqoBacDtxphzdkKPj483CQkJF5xb/dnJ3AKemLmRWesOckmLaF67sb37E8cZA1/cYk1TPeYniGnjiaj+JX0PzHkEEudD7XYweDzUaed0KlXM3iMneWT6ehL2ZXDFRTE8M7jNua+VeSkRWW2Mif/Tdl+scFoIPGvzwUwe/Hwdu9OyeKhfC+7t2+z8m4LO5OQReLsbRMTAXQsgVKemBqAgD5a+YY0QDgqxmoA6j/ab5SP9TWGR1Xvu5R93APDXK5pzR6/GhAb71phcLQTqTwqLDBN+2c0r87ZTrVIYr9/Ynh6uxge4a8dcmDIMOo2Ca7QnEXuXwLd/gyPbrdHBA/8LVes6nUqVQnJGNs98s4V5W1JoGVOF569rS+dGvrMW95kKgW+VM+UxSenZjJiwnP/+sI3LW8Uw9699yqYIALTob40vWP0JrJtaNu/hC44fhBlj4JOroOAU3DQNbpykRcCHxFavxPu3xfP+bfFk5RZww7vLuPez1ew94tujkvWMIMAUFhk+WbqXl3/cTpAIzwxqw/Ud61nzBZXpGxfApGshOQHumg+125bt+3mT/FPWHEyLX7V6UPUYB70fscZcKJ+VnVfAhF92M+GX3eQXFnFz14bcf1kzakR4by8vbRpSbDqQyeMzNrLxQCZ9W0bz/LVtia1ejn+MTqTAhEtAgq1iULVO+b23E073Bpr3FGTut5qBrnwuoFcN80epx3N4df5Ovli1nwqhwdzarSF39W5CdBXvKwhaCAJY+sk8Xp+/g0nL9xFVOZynB7Xm6rg6ZX8W4Mqh9fDRQKjRFG7/HsIjyj9Dedi/AuY/ZXWfjYmDAf+Bxr2dTqXKUGLqCd5amMjs9QcJCwliRJcG3Nmrcfl+2DoHLQQBKCe/kIlL9/LWokSy8wq5qUsDHunfksiKDi9huONHmHojNL0Mhk/xrwFTKVtg4XOwfY7VU6rv49DxNp0aIoDsTsvi7Z92MXPtAYwxXNm6NqN6NqJr4yhnPnwVo4UggOQVFDFjTTJvLUokOeMUl7WqxeMDW9E8porT0X63+hP45kFoeRXcMBFCfHyx+4x98NN/YP3nEF4Fej4I3e6BsMpOJ1MOOXDsFJOX72Pqyv0cy86nZUwVhnaKZXD7utSq6kw3ai0EASAnv5AvViXx7s+7OJSZw8WxkTw6oJXrKaO9wcr3rcFULa+GGz7xzWJwbL+1KtuaT61P/V3GWD2kKvlOl0JVtnLyC/l67QGmrkpifdIxggT6tIjmug71uPyiGCJKO4eXB2gh8GNJ6dl8tmI/0xKSSD+ZR+dG1bn/sub0bl7T8VPRc1oxAb7/OzTuA8MmQcVq536NNzi6Cxa/Yp0BINDhZujzKERe4JQcKiAkpmYxc20yM9cc4GBmDmHBQXRvWoN+rWPo1zqGmDI+U9BC4GdO5RWycFsqX65O4qcdaQSJ0O+iGEb1bES3JjWcjnd+1k2B2Q9YF5Bvnu7d8+0f3mSNCN44HYJCodNIqxkoMtbpZMqHFBUZVu1NZ96WFOZtTWHf0WwAWtWuQrcmNejetAbdGtcgspJnr+dpIcCaT6dSWLD3f0o+gxM5+SxJPMp3Gw+xYGsK2XmFxFQNZ3jnBozo0oDakT48fcOeX+DzWyAoCAa/Da2ucjrR74qKYOdcWDYe9v4KoZUg/g5rNbYqtZ1Op3ycMYadqVnM35rC0sSjJOxLJye/CBFoGVOF9vWr0S62GhfXj6RFTBW3prXQQgBc/cavHDx2iua1qtA8JoIWMVVoXiuCZjERREeEe12ByM4rYNOB4yzffZRfd6axdv8xCooMUZXDGNC2Nn9pV4eujWsQ7Kl5gZx2JBG+vB0Ob7Da2q942tmLraeOWWsCrHgX0ndD1XpWro636TUAVWZyCwpZn5TJsl1HWbM/g/XJxziWnQ9AhdAglj9+OdUqXdj1NC0EwOTl+9h8MJOdKVnsSDnB8ZyC356rEBpEbPVKxFavaN8qUa9aRWpVCadGRDjREeFUrRhSJsUiv7CIpPRs9hw5ye60k+xIOcGG5Ex2pp6gyFhL08bVi6RXs5r0al6TLo2iCPGxya5KrSAX5j8Ny9+GqrHQ/wVoPbj81uctKoI9P8PaybD1GyjMhdjOVg+giwZBsMNdb1XAMcawPz2bdUnHSEzN4uErW17w99JCUIIxhtQTuexMyWJXWhZJ6dkkZ5wi+Zj19XQFLi4sOIgaEWHUjAgnIjyEyuEhVKkQQuXwYCqHh1A5LISQYCFYhCARRKwFsnMLCsnJK+RUvnU7mVvIkaxc0k7kciQrl6Mn8yj+31CjchhxsZHW6WBsJB0bVHd/Smhfs385fPcIpGyE+t2g98PWymdlURCKiuDgGtg6GzbNtEYBV4iEuGHWReC6HTz/nko5QAvBeTqek8/BY6c4ciKPI1nWH+y0rFyOnMjj6MlcsnIKyMot4GReASdzC8nKLSCvoOiM308EKoQEUzEsmIqhwURXCf/tVjMinIZRlWgcXZkmNStf8Gmf3yksgDWfwK+vwvFkqNUa2t8MbYe4Pz1FdjrsWwK7f7IWhj9x0JoOuklfaH+T1aVVp8xWfkYLQTnILyyisMhQZAxFBoqMwRRBeGgQ4SFBXncNwmcU5lu9dFa8B4fWgQRBnfbQqBfU7wo1W1jz97gah1CYDycOQ2YSpGy2prg4tM7q/YOBkIrQ7HKr2afFlVCxenn/65QqN1oIlH84shM2fQW7f4bkVVB0uglPoEJVCI+0CkJBHuRnQ/ZRoNjPeMUoawWwBj2suX/qdfKvKS6UOgstBMr/5GVD6hZrcFf6LjiVATnHreIQHG79gY+Iseb7r1oPYlpDlTrld+FZKS9zpkKg6+Ip3xVWCWLjrZtS6oK51QdRRKJEZJ6I7LS/umxgFZGR9j47RWRkse0jRGSjiGwQkR9ExEsnxVFKKf/lbmf0x4AFxpjmwAL78R+ISBTwFNAV6AI8JSLVRSQEeB241BjTDtgAjHMzj1JKqfPkbiEYDEy0708ErnWxT39gnjEm3RiTAcwDBgBi3yqL1Z2mKnDQzTxKKaXOk7uFIMYYc8i+fxiIcbFPPSCp2ONkoJ4xJh+4B9iIVQBaAx+e6Y1EZIyIJIhIQlpampuxlVJKnXbOQiAi80Vkk4vb4OL7Gav7Uam7IIlIKFYh6ADUxWoaevxM+xtjJhhj4o0x8dHR0aV9G6WUUudwzl5DxpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4Cegvf39d9nfaxourjEopZQqW+42Dc0GTvcCGgnMcrHPXOBK+wJxdeBKe9sBoLWInP543w/Y6mYepZRS58ndcQQvAtNE5E5gHzAMQETigbHGmLuMMeki8hywyn7Ns8aYdHu/Z4BfRCTffv0oN/MopZQ6Tz45slhE0rAKx/mqCRzxcJyyoDk9yxdy+kJG0JyeVt45Gxpj/nSR1ScLwYUSkQRXw6u9jeb0LF/I6QsZQXN6mrfk9NPVTZRSSpWWFgKllApwgVYIJjgdoJQ0p2f5Qk5fyAia09O8ImdAXSNQSin1Z4F2RqCUUqoELQRKKRXgAqYQiMgAEdkuIoki4jVTWYjIXntNhnUikmBvK9U6D2Wc6yMRSRWRTcW2ucwlljfsY7tBRDo6nPNpETlgH9N1InJVsecet3NuF5H+5ZizvogsEpEtIrJZRB60t3vNMT1LRq86niJSQURWish6O+cz9vbGIrLCzvOFiITZ28Ptx4n2840czvmJiOwpdjzb29sd+z3CGOP3NyAY2AU0AcKA9UBrp3PZ2fYCNUts+x/wmH3/MeC/DuTqA3QENp0rF3AV8D3WtOLdgBUO53waeMTFvq3t//twoLH9MxFcTjnrAB3t+1WAHXYerzmmZ8noVcfTPiYR9v1QYIV9jKYBw+3t7wL32PfvBd617w8Hviin//Mz5fwEGOpif8d+jwLljKALkGiM2W2MyQM+x1pLwVuVZp2HMmWM+QVIL7H5TLkGA58ay3Kgmj0JoVM5z2Qw8LkxJtcYswdIxPrZKHPGmEPGmDX2/RNY82rVw4uO6Vkynokjx9M+Jln2w1D7ZoDLgC/t7SWP5elj/CVwuUjZL1x9lpxn4tjvUaAUApdrIjiUpSQD/Cgiq0VkjL2tNOs8OOFMubzx+I6zT68/Kta05hU57aaJDlifEL3ymJbICF52PEUkWETWYc14PA/rbOSYMabARZbfctrPZwI1nMhpjDl9PF+wj+erIhJeMqet3I5noBQCb9bLGNMRGAjcJyJ9ij9prHNGr+vj6625bO8ATbGmOj8EvOxsnN+JSATwFfBXY8zx4s95yzF1kdHrjqcxptAY0x5rWvsuQCuHI7lUMqeItMVad6UV0BmIAv7hYEQgcArBAaB+scex9jbHGWMO2F9TgZlYP9Qpp08J5czrPDjhTLm86vgaY1LsX8Ai4H1+b65wNKdYizF9BXxmjJlhb/aqY+oqo7ceTzvbMWAR0B2rKeX0jMrFs/yW034+EjjqUM4BdhOcMcbkAh/jBcczUArBKqC53asgDOuC0WyHMyEilUWkyun7WGs1bKJ06zw44Uy5ZgO32b0eugGZxZo7yl2JdtXrsI4pWDmH271IGgPNgZXllEmwlmLdaox5pdhTXnNMz5TR246niESLSDX7fkV+X8tkETDU3q3ksTx9jIcCC+2zLydybitW+AXrOkbx4+nM71F5XZV2+oZ1RX4HVlviP53OY2dqgtXrYj2w+XQurPbLBcBOYD4Q5UC2qVjNAPlYbZV3nikXVi+H8fax3QjEO5xzkp1jA9YvV51i+//TzrkdGFiOOXthNftsANbZt6u86ZieJaNXHU+gHbDWzrMJeNLe3gSrECUC04Fwe3sF+3Gi/XwTh3MutI/nJmAyv/cscuz3SKeYUEqpABcoTUNKKaXOQAuBUkoFOC0ESikV4LQQKKVUgNNCoJRSAU4LgVJKBTgtBEopFeD+P51Ronmlh4ZuAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BUr09XArkuKf8ICPZU9w41s/m+RuoBDc3wlClJB52ufo96xjc3d105bwOaIp8k1ccKSWTp5c5ct9l5saL+IIa667pYPO+bmItweftW1yYZ+78WWbPjzF3fozs5YtXXC0iiKqk8HkWycEI1733l0iHO3Fma1RHcswvVnAlpC2J9qzL1REwHxDkNMmSZTBnulcsWzyWAVvUWJM4Tx+TKOYyolojXC0RLVeIVQwyBY+2AkRMjVoohBEIYPl9mD4/pt9HPejH0nVcVcPVVFxNw1VVHE1FCoFccctI8Z0+rhLFa/jYheegug6q4xAwLUI1k3DNJF4xCdcMAoaBbtvPSHUlANk4zCcFuZigFPZjBRN4oW4CWg+mDDPjRJmxIyyIJFHho1VKNnpZNoUXGU6UCBNFFnvQjQwR+dw2D1OBmYCgpsKqqiToelwQWU4UnsLNnyUQDOBKsI1GA7SiakS625mLVTiqjGP4bX727BaE8zZMf6PRWxc267fH2fvRXSjaKzqaaJOXQVPkm7xiSE9y4ViWI/ddJjdVIZL0s/XmXtbt7XjGX+u5LouXLjA1OsL0mVPMnR+jXm6MCKnpfnQRxxY9aFo37a4kGF2mb8NGMkoP9lwNaVyJVbcETIYVZv0e85rJ+XSYS2Gdat2gPp2jWBDYaFzpQiroCGTZqjxO5+IEyeUKyVKd1rxN1I5QiUQpx6KUo1HKkRC1YAgzGMTRfS/4fV0cbMXCVlxcxcUTHoqqoGkaPs2HT/WhKzqa0NCEhipUhCuQrsRzPBzbwbEdLOv5jacAuqoS03VCroNWL6Pnl4jMzdM6s0i0VHnGz74Uhdl2P9nWCMVoGsvXw7y/n0k3xoSboiqCKNKl35hgiz7CroFjpHuq+Mtplsc2ky1voZoYpNUfoN+UdNWf+8C0hWRBrZMrj2NVJtHidUQmSs10Wbg4jm02hN/wuSwmTDodhfbqPsqBXQhFQ0iXgV7Jvk9dTyD6vRvUm7yyNEW+yQ+N63qcf3qBo/dNkJ+vEW8Nsv2WPtbsbgc8Fi6eZ2r0FNNnTjE7NopVb2SBTHZ00bl6HT47Sf2cQNd6SCqSmGIR0kMrUeUgfCpaa5Cq4/KkZ3FPp85YTEUaRWohPyVfkEDeJHE5SzXnYUsNQcPPrHoeu2tH2FF+ks7sEumSguKLk08mySeTLCfj1INhUK5Ymrp0QbWpaXUWtDx5f4WaVsdQDaLhKK3xVjoSHXTHu+mOdtMd6aJLDZGsl1GrWajnob4MtWUwS+BajQyWrgWeA6oOmh9UP2h+HF+MmpakpkSpihBVGaIiAxTLFQqFAsVikUKhgGFcCeNUFIWYXydg1fAtL5KYmqL3whzhemOfSlAw1xVjLhlnLjbMiegmzpGhih+/a7K6eo5N2gnW9Y6THiyjBmwWz6R4bOltHFi9j2gizqqqx9YFk5uXJRkbPK4MF+dKh4qdQwTyBHvj1BJRLi2Ocu7M08jlRo9kT/GIiDi2vhGh9yOUFjoSNm/6xE5SQ+2v7kXZ5BmaIt/kB0Z6kvNHFjh01yWK2Trp7gjbb+kl1WEzOXKMyyeOMj166hlLL93dS8+azfR2bCChtlI8kcVbdgitdPSRUlLS8oQH2kj1d6K3h1FTfk4cusT/qtS5r11HAm2VMtloBMfwaLucw5gxMV0NBY+AZ9Gfn2VH8ShbiqPE7SCFVAtL6TRLqSS2f8WSlJIIHnoAKqE6F0JZLniXKetlbNWmNdTKcGKYwcQgQ4khBhODDEZ78Ranmbo8xtT0NNPZPEsVk7zhUnADFIhQkwFsVFyUxluEUPELh4BwCCqNKS1KtFAkQ56MXKLbm2GVmCcuqs+qXQGxTkj0QWoA2tZjJNewpHWSrVhks1lyuRzZbJZ8Ps937tWATycg6/hys7RemGD1xUV0x8FVYL4lxOVMF0dbruLh2Abqqp+MmWVDeZQtvnFa+hdIDS4jYy775/q43/sgs12bkJpK24LBb12yeVMRJoKCcxFBd7FMnyEIKY2GVolEjzm4nWGOlo9xZPpxvFyOeOU7/Rw0FK0HRe8jGcxw08evpXP74Gtwpf5k0xT5Ji8bKSWXR5Y4eOdFlmYqJNs1etdVqRXHmTh5jFK2MS57sqOTwXW76E6vJeal8GYN7IXqM96TmicpG1XmvNOcWz3Oze/8RTb1Na5Fz3E5cOfT/GMd7uvwo0pJxrSY8fsJLFSJXMpRqTRcQP31BTbMX2BH/gRtwiafzpBraaGQjDfytUiJgkM6HMDJSMYicxwzjlPxKgC0BltZ37KeDekNbEhvYH16PVE9ydkLFzl7dpTRqQXO5FzOGQnyPDcDpIZHQndI+CER0gkH/Oi6D1X3oek+EALT9jBsF8N2qVouy1WTXMXC9Z57f6WCCqtiMBw22BjIsllcYI15Cn9+DKrZKztGO6B7J/RcDb17sNJrmc8uMTs7y+zsLHNzc+RyOaSUCAF+3UMrzJCemGTT2CJhw8LSBJcyGY5mtvNoywYWo2nWF0+zuTRCR8Ym2j9LYjBPNTHEl6x3ckhuxdN8tOUsfm/c4tqi5MmY4G/WB2hZzHLT6eMMGpKMv4NMoB1VaYRiOqrLuD7BlJxEK01QzxYp2yv5/JUYQV8H22/czbZ3vQN/KPyKXqdNGjRFvsnLYvrsMk/deZG58cv4fBP4/FPk5y4iPQ9fMMjqtXvoa9tEQmSQ8xZuwQRA+BTqistU3mTJ1bALY4zG7mX26gqffPN/4aruawBwLIvH7nqML1cD3NcdRpXQYjnMIohfzOHOWriuYHP+IlcvjLLKmkVEgiy0t7GcSoEQSOlgKSZhPYSvP8RI+BSniqdwpIMiFNYk17C9bTs72nawJbOF1lArVdPhyIU5nj4xwqHLeY4Xw5g0LNAQBmsCedamFPpbk3R3dtLTN0B3S5xkSH9+BInnQT2PrOeRVglpVZBWGcuoYTgSy1MwPUHRUshafiZqfi5XdS6VNBYqNjOFOtWVRGSqIuhOBtmQ0dkdW+Zq9Rxt5VOEF4+ilyYAkFoQ0bcHhm6G4ZshPYRpWUxPTzMxMcHExAQzMzM4K7l3VNVAy11ieHyWdReXUKQkG4myv20rBzs3IFIh1sw+QaudI97jkVw9S2DQx1PRX+afK5uootG3YPAH4w6bKh53xCV/tyVCvFhh14mn6L+0n6iaYMjfwupYBDW1GdvIoMrGG1tRmcaqL3GxUmW+egrDzQOCdFsv6296M0M7rybV2fVqX8o/MTRFvsn3RXaqxMOff5jZc8fAu4hrLwPQ27+Z4d6raFE7UBYlXm0lWVZUx98fR+kMM37iAsdH61hKgFjxNKMt32J0R5bfvPYP2Df4DoQQ1EpFDt5zP7dX49w90AJAd83hsuWSGM9iLEvWLU9w88IxushTzKSZb2/D1XWk9DBECUuHqBfFbrc5pD/NolhEEQob0xvZ1bGL7a3b2dq6lehKPvaJpSoPnbjIQ8fHOZhVsaSKgsc6dZJtqQIbezQGesO0tgfwvAoV22Te9liwoF6uopQKhMt54rUCLfVlkmaJhFUmaZfRpPfCFfkSeIDh06j7VIpqgFlauOB0cdhcy5PVLSzJJAKPTDBHR3iBntAsA2KWbm+Ztd4M3e4SmuNR9Fo4p1/NWPJGqm07aY2HaQlp+KwC9eV5FqcnmJmZRkqJVDwcc47uqRl2jswQqVuUfEEOtm1gZvVGgswTzZ3HF1JJrM4RWlvlYPsn+Jq1h6oLm+cN/ui8Q7zu8Q9xh9s3RIm7JntOnGLN2W+jSJuwE2adsUimx8/I4C78Ricb6oMEvYbrrOJaZM0lFmpjzFdHML0aqc5uBq+6msEdu+kYXo2ifO8+A01emKbIN3lRHMvi3MFDHLrrIZamRkDWCahh1g1fR09qHaFaGFm0AVBiPgKDCfyDCfyrYsiozrEvPsXxgyVsJUCiMMKp1vt4Yussn9jyi7x36yfRVZ3K8hIH7vg69xUi3L1hDWVdMFR2mCxbBMZz9M7N8eaFo/SrBZbbWsi1tCAVBderseTLIVWNhJUGn2Qkeoqp8BSt0Vau6byGvZ172d2xm5gvgmUtY5rznJuf55sncjwwJpmsNLr0dwbn2dJyhnUtY/QmJ8hrLczSxRydzNGJbYboKeVYW5lkU+Ucm8rnSTvFZ+qprviZCLSz6EuxpMfJ6zEKvjgVLYylBjDVIIYWxFT9gEBIDweBI0F1TOJ2hZhTIeGUabOW6DIW6DEX6DIX8T8rbUFZCzGlZjjiDPNgfRvHvGECoRrbWk9yVdsx+mJTPPulQnU8VEtQsqLMmK1cNrsoWVFKZgxPpkipISKOgq+aB8tAIjG9RVrnZrjq5AwtxRpFX4ixoW3I3jTL86NIKYl1WwQ2VXhy4N1807sB1xO8daLOb427LLgufxm1OLQmjj9Q5YZjp9gw+gSKV0UjQW/exaef4F/frFMNd/OmyhBvLfThd7airLw5FewqWeMSs7VRsvUp/LEIA9uvYuiqPfRt3oamN4c1fDm86iIvhPhH4FZgUUq5cWVdCvg3oB+4DLxHSpl/qXKaIv/a4Do2EyPHObv/Mc4dPIBrG6T8vQy27aI3sQqtvNJBP6DiH1hJazuUQMsEEULgeZLTdxzj6W/NUBdhEqUznE3fzb1bp3nXqrfwy9f8Z+L+OOXlHAfv+CoPTlS4f+cNTEd0hkoOywWTyMnLXD99nM3uNMW2NEuZhmVve0WmwgvgGLR5fYTdOMv+Zc4lxujtS7OvcyOb4u1EqGMY09SNKer1aRZLZQ7MbuPg3E4myj0IPFYnL7CzZYSONkkhsZoxBjnntDDtxUjaRa7LH2Fv8TjX5I8xYMwAYKFx0dfHZHANpfg6tPRqgq3DxFt7aY0FyUT9RPzay+oAJKWk4nos2w5zps2saTNjWMyYNlM1k1JhisjSOdZUL7G2epGN1XHWVS+hyoY7Z1rt5lFrLY+6m5hJbGbf+hA3DZdJqjOY2aNY+bNY5iKWLjF9Gu4LdC6uWGGKRhzDDCNMHcwghhGhUHOJXciz+/AU8ZpBMZrC3LSBy26RnFElmARta50H17yHR9lNQko+dqbGe6c87sLiczGXxVVRIhmLTSMH2X34GKpTRigtZIwYFf0YX7kuTy4muLVs8qu5Lpblb+BVkqQ0gSIErnSpqAWmCmeYKp3F1A2GrtrDmr3X0btxC6rWTKXwvXgtRP56oAJ84Vki/2fAspTyT4UQnwaSUsrffalymiL/6uG5LlOnRzj75GOcP/QkXt2mI7SazvBmOkId+FBBgK83RmBNEv9QAl9XFKFeETMpJRceGePA7WOUvCjRyiSF0Df4p10X2Jro5fdv/BsGkkOUl3IcuvN2Dhw+yZPXvZej7Ql6qi6xuSpdTzzO7voFlKSPxdbWhsXuFhmPT5KjyuqKTle4k1jQhPgc8UydtrCH7hbxvNpzvpOutzBe3sXDlzdzYKoNR6qs0y+xKTGD1zfA4fhGzisBXEWAlKwrjnPL3OPcUjjANuscAIYaoZC5Crf3GiKrryfWtwWhv/Zx3obrcbFucr5mcLZicDa/hDt7lNXLI+wqjrC3eJyIW8dB4ag3zMPeNha7b+GW6/Zy47o2VLsCZ+6BY1/Em3wCy69hDO7GGL4GI56kZsxSrExRN2Zx7TkUrgx0LiWYZphqzY8yL+g6UyIwL6ko3SypCS65dZSQQm1niHvXv4dzyhDDluS3j9XoK9j8BSb7gxJjIEpHf4Cd556g/+H96HYNoaRRAquYTO3n4fXz+JD8SrHI28tdHJJ/QC0XplUTZHRBbGWUL1M1mCqdZbo0RkkvMLRrN2v2XEfP+k0oL5SOuclr464RQvQD9zxL5MeAfVLKOSFEB/CIlHLNS5XRFPlXFiklc+fHGH38Yc49tR+lBr2x9XRHNpJQkihCIH0qofUpgmtT+IeTqC+S8nfh7DyP/v1TZM0YwXqWoLiLv9l9Al/Yz29d9dvcuva9GJUyB7/xFQ4/cB+ntt/KQxu3oEm4YXSWvqceoNNfYrGrHUfXUZwKxfQYtbY5Oj3BsOYQjroEAzWEuNKxKRDoJhweIBjsJxjsIRjoQapd3H1a5UtPXORSwSUqaqyPzbDc18lo6yDuihCIss32/AU+XH6UmyuPkDRmG606eO4AACAASURBVPXSuQOx5m0wfBO0b4bXqS9YSsm0aXOiVONoPk/x8lP0zuxnX+4QW2qNh9So18dj+l7iO97FLTdcTyrsg6ULcOxLcPzLUFmA9DDs/iXY8n7wR5BS4jhF6vVJarXLzM6PMDnzNNKZIRQso2n2lZOwQJ1XqOfDFAs6pXqYS8ND3DX4fpZEmtuyDr85UuesbfDfscmHFIyBKLvWtnDz3HGMO+6AShmhtlFKDXJ04H4upXMM1W3+6/ISweW9POn+EqV6gACSDs1jOKkSlD5wJK5wmK9dZroyRkHL0b97JxuufzNtg8PNdArP4kcl8gUpZWJlWQD57/z9XZ/7BPAJgN7e3h0TExOvyPn8JFNeyjH62EOcfuwh3KxBb3Qdq9KbCTmNkLeSJ1EH4vS+pY9Af/w5ece/m1rR4PG/fpDxGT+6U6Pbvp/PbXuI80mF9/TcyK9f+8eE8HP0m3dx6M7buZhZxcM3/AzzIT8fevII/ecO47SquF0e0eASvsgsaipHxm+jrRzW9RSMWgzTSdHdtZNVfXsJhwcJhVahqldSJOQqJp955AL//NRFqo4gEyxh9sRY7O0EVaDXXfo9hTf563yg+gjDs/egLp5uhFgOvAnWvxNW3wLRN24nnZLjcrBQ4dTMOfQz97Br4tvsqo8CcNQbZqTznbzpnZ+gt6MNHAtG74Sn/hZmj0EgDjs/Bnt+DcItzyvb8zxOnjnJPY/8C36rQCRQxR9eJKksEY8UkbErWmEbKlnRyoh/K3NykH0Xerj2UpK/lVXuQkWENcyBKLdtbufducuc+fLnsMpFhNbDVHeGJ4buw9Br/Fy+wieXypy+/FOcDL8PBw2EIOwU2LurlZaWNowzS3hlG4kkZ04zVTlLJVph6Ia9rLtuH9HU87/LTxo/cpFf+TsvpUy+VBlNS/4HxzYMzj99gNOPPEjl/AJdoWH6k5sIyYawF4Vgquagr06w+wNriSRf2iXhuR5Hv/QUR54o4AqdnuoRzgz+K18ccBn2pfjjG/+aDelNnHrkAZ78yhdYqpkc3vc+jvd28osnvs4gp6HLJBJbIhisPFNu2VFYKCXR8gpl0Y213I1QOrj5plvYvHkzivLc/CdV0+Fbp+f57OMXGZsr4QFai0p1IIUa1+hxBdck47y/r4Ud1ZNw+B/hzN3g2dC1Eza/Bzb8PxBpfcXr/PXAjGHx5OR5qof+hb2X7mC1O0Vd+ngsfB3q9Z/kTbtvbuS5nDrUEPvRu0APNsR+76+/6ANvfnGez9zxGQrZInE7hotL0Fpm7+Ipwtoc9R6VerdEdLioWiPKyJQBgqVe7EIH3yh1crQ4yILSjrc6yS9u7eLG88c49rV/xqxWcEL9HFlT5UzHYVptj/+Wy7J6OsojC7/AXPpqhOcgFY00Wa7/6FZauruojy5RO7mIm210vMsZM0zVxpA9GsP79jK0aw+6/yczpULTXfNjipSS+fFznHzwPuaePkOnPkB/bCMhJQoK6H0xJqsOJ84X8bcEuf59q+nbkP6e5V5+8gKPffEUZRklVblApvVu/mT9eYq6xsdXv4+P7/od5s6M8tD/+gsq1izm2gTVtXE2mieJRpdRlMY1ZdYDXLZUzkuLqWqKhamr2ZHN0ramGycXAgnXXHMN11xzDX5/Y6xSz5OcnCny8NlFHh1b5PhCCZzGyEVOVwi118/VoRo/s2Yj7+jIEJFWwy1x8DOQO9ewVrd+EHZ8FDKrX83qf91Rd1wePfIg5v5/4k2lh4iJOse1YU5u+gg7932I9fE4ZM/B438OI7eDosGOj8D1vw2RzAuWubC4wN/c8T+ZKyzSWW9FkxpCOlw/u0Dr/ifwdJXJ4SAj+1ZRXJekX0wwIC+hKPbKOQW4UOhn3BxiLrOND6y7ntaDT3Lk7m/gOg6LHd3sXz1KMZjj1nKN380tMTW5m6edj1APtSE8GylUhlNLXP/pWwnEw9iLNeojOSrH5/CyjcikZXOOWesivvVx1r/1JtoHh1+ran9d8KMS+f8BLD2r4TUlpfydlyqjKfLfH1a9xpn9jzD2wONE8hH6ouuJ6y1IAYGhBMEtGeYsj8e/cQGz5rD9rX3seGsfmv7SvudqvsbDf/4gE7kwAWOJjYlj3LHqdu5JBFgXaOOPb/wr4pVljt7zp5iBWcIdNfRgIwLEdVTKlRaqy0mWC0Huj88z6SviGe04izewe7nEm3fFKWWjLC8vs379em6++WaSySTFus3j57M8dHaRR8ey5AwLghqi5jTGSu0MsKt1hp9bleLmrTcR9gUaOWMOfRYOfQZqS9C5HXZ9vGG1699j8O+fAKazWR79+v9kz+xXGBBzzCtJ7hz4AMG9H+e2nh6S5Ul4/C/g+D836mvvp2DPr4I/8ryypJSMjo7ymfv/D3PuIv21NsJuGFVXuKpco/Ob96I6DpcyST73Mx/g4Kbd7Ktc5Jdnz1MOjVCNXyIRyaEIiScFOWWA3vhWKqfLjD18EVckOTbk43jHcdKO4M9yc6RNwYmZ9zPH2wGJFCo+u8yuHSqbf+W2Z/zxTq5ObSRL8fA0YqlxLS4aU+QDi7TesJY1+67HF/zxz33/WkTX/AuwD2gBFoD/DNwBfAXoBSZohFAuv1Q5TZF/aRYujnP6Ww9QP5mj2z9MOtAJgNYTJrK9neCmFgxX8ug/j3HpRI7Wvihv/vl1pLuef+M+GyklJ28/wsEHFnHQGJKnCW/4Jn8YnSYSEnygZzuDPijkn0bojY5QRiVAqdBGpZQhX2uHOR+zmsfxgaMshXIIM0Et+3b6l4J8Yo+Frg1z4sQJkskkt956K75kB/edmuf+MwscmcjjepJQ2o+eCFCYLIPtEc64fCB2kI9vXk3bzg80kn6VF2D/X8LRz4Ndg9VvhWt+A3r3QLMh7nnMF2rc9Y0vsu7SF7lOGSEvonyu990sbPsYHxgYZJs1Aw/+ccPFFW6FfZ+G7T/fqOvvwrIsHt//OP/w9DcoBBcYrLbRYragagqbbY++u+/GV6txtqebv/rgJ5no7uNT5xxuzNb5FgeYiFTQ4wWGEpcYSEwQUBo9pd16kMKEj2W7lbsiFabVIu8rGnyqkONzwUH0qU8g7SFUp46rBUmXz7H35gw9H/wpxLPi6Z2lOqWnZygdmkarqbjSZcGcwOtT6L91D+3DQ69Zvb/WNDtDvYGxLZOzjz/KzP0nSFbTdIQGUIQKKY3Yrm5CWzJoyQBSSs4emOeJr57HsT123baKrTf2oKgvneM7dzHHg3+9n5wZI1G9xI5rxjgQ+zrluGDYL/F9x/WS1ynPhVGm/EyrW6koKVxVJZ2vc8YXZXzVE8xFZlAdH7XsLWhLm/lg32XetufNPPDgIxiGwfqtVzEfWsV9Z7KcmmmkHl7THqV3fZrTdYPpkRxKxSEdrvB7wa/xszu2I/b+WsO6rObgib+CQ//QyPS45X0N67N17av+G3wHKSVexcZZNnCLJl7Vxq3YeNWVqe4gbQ/peEjbRdpeo4urABTRaOAWIHQFJaAhAhqKX0UEVNSIDzXuQ435UWM+1JgPEXx58fgvxcVshdvvuoPtl/+Rm9UjlJUQn+3+WZ5Y/zE+0N/PbdY4vgf+ECYPQMsaePufwcC+Fywrl8vxtTvv4PalkxiRcdZWOumudaOpKhsUhb677iZQLPLU+k383Xs+So+a5o/OWiykcty/OMpBOcC0HaI7Pc9A/zR7QucZ8I2gKI2wzqLpZ8R2yZd1Pj6zgG1G+XvlLWycfieqpzTqFMnA/ENsvamHlg++H63lSuOrlBJrpkL24bPYZ0vorg/bM1lS5gnv6mDoHdeir7gHf1xoivwbkPJyjjN3PED9xBJdviECagjX5xHZ0U7s6m70tiuJnqpFk4e+cJbJ00t0DMV584fWkWh76VdU1/F48u8eYXQ8TyQzQlffEWT7eTztii81ORZm7jzMlFKEawKna4iFaKPtPFEyqIoBnup7iMuJswgpEItXUyjewjZtiv/6/j2MHL/A2NgYarSFI3IVx3ONY2/rTXDjhjaM9iBfnsmRO55DXTSI63V+X/0i79rWh7jx9xuNpUYJnvhreOrvwanDpvfADb8D6Vcvs6F0POzFGvZ8FXu+hrNYw1mu4+bNhnB/F0pIQwnrKEENoSsIXW3MNQVWYvTxJFICUiItD89wkIaLZzh4hvucHPrfQQQ19EwQrSWIlgmitYTwdYZRU4EfWPwPXFjiC3fcw62FL/EO9RAFLc6f932If+//WT7c08lHakeJf/vTUJiADT8Dt/y3RqbM78LzPI4cOcK/3vcQDwdn8UePs640RG+1B1VVWSMEq+6+h1CpxP27ruXrb/lpfm0uwc6Ewp2FJzlf8jigrSNfAzflRyY11i6P8rPRx2gLnSHcWUP7ToNuXbIq5/BYeYDJqffQWVyL7lSwtQiR8hTrxv+F7ht3kPrwhwmseW47jPQk5dPzLDx4Gn1OQRM6FbeI3ePR99O7ifW2/UD1+HqjKfJvIGZPnWHqrsOEcyESvlY8PESvj5Y3rSawOvWczkkAF44t8siXxnAslz0/M8imG7pfMiRSSpdLx+/n5IGvobedJ5CcAsBzYcRQmTR0rnkoSuSk5FxnCqlqxFs7mUy0IaREr5q0hnfwZPgwJzOPYGgGqaUepnMfJODq/M71DsNt63ngW/fi2DaH7S7OeG1c1Z/m7RvbuXpthm+WK3x2apHShRL+8RKq6/Ap9Wt8omca/zv+DLp3NE7o2JfgoT+B6mLD177v9yDzkm33LxvpSZzFGuZECWuyjDVVxsnVVqxFGqGZmSBqKoiWCqAl/ajpIFrC3xD2kP6836RRzx6eZyOlhedZSOkihIai+FAUH0I8N+mZtD3csoVbMnFLFm7RxMnVG1O2jlu6kvpABDR8XWH0rgi+rij+VTHU2PdvmTqux5cPTvLNb9/Lr3tf4lrlFIvBDv5L30f5dsct/Hxnik9Mf5XWJ/4/ECrs+13Y/cugPX9glWKxyF1338PXz89xNnWKeGiMDeWN9Fa6EQhWex5Dd9+Dv17n3r1vorT5Nn7BDTHWU+TJkcNMBgd4oprBcjyUvghxKdDOXeLG5YfpTF2mvKtCS7zIgN9DFSAcwXyhHXviBmqzGxHlGK7io3/6fvov3Ut0zy5SH/kw4WuvRXxXpJZrOszcd4zK03PEnCRSepQDJRLX9tHxpo2Nh/IblKbIv85xbJuL3zpA+YkpWtxOVEXDCNSJ7eklfe3gC3ZQsuoOj//bOc4+NU9rX5SbPrqeZPsLp3F1nDJLS4+RzT3EwswDoFWQnoJe6aE11sq/XH6aO9QgV00L3n+PYKo9w1IgQiLZwmy6EykEgVKVicFr2FAs89WWz5MLZkmZYaxLb2VOXsWW+AK3Xb2VM4eeJlqfI+eFWExt4a07V3Prlg6EX+X/n1zki7NL1PMm6bESlWWD65WT/Enkq/S+5dcavmBFhYuPwrf+EyyMQM9uuOX/bQj/K4D0JPZcFXM8jzFewJosI1eyQSohDV9vDL0jjN4eRm8PobUEESsuLyk9TGsRoz6NYcxQN6YxatNY5UXsegHHLOIYBVy7gqfaSB2kBujwQuOGK0oAXU+g66mVeRKfr4VgoJtAsItgoIdAoBtdj+GZLk62hjVbwZ6pYM1UsOer4DTuXzUdwL8q3pgG4mjfI0QWYKli8if/fobsifv4w8BXWO1d4EJ6C7/S92ucja/hfUmdT43+JV1nvwKt6+Gdfwtd259fp1Jy/PhxvnzPwzzoaRit3ybqW2RPfQ+p5RSqojJcrTB0730IDx647m3sabuBjutbufPIw2TLBqOpnZycdfCCKht3trPdVRl/+Ntsmt/Ppe48Jzfm2eL3+DlZRMR17JWvZxS6qM0OUZ7fhTITY8PkHURmRvANDpL+6EeI/9RPIXzPfzgtnZ1g+u6jBBf9hNQoNiZywEfXbdvwd0Sft//rnabIv04xChUu3r4fzpnE1BSOtLC6JF23bSW86sU7eMyeL/DAP41SWTbY8bZ+dr6jH/W7fO+12mVyuYfILT1EofA0Ujp4Zojy3Gb0y2n2vuU2Zsf+B78tp5hXVX7uUY8NhQRnAin80Rbybd24ikJ0ucADm69mn93FmPVPHEoexu/prJocYqT6blxVZ3dPnctzCnvUcYLCIdizkXe94yaG2+PkbYe/m1zkH6ZzWK7LhkWHiycXSVDhD9X/w21b+xBv/e8QTkNxGu79XTh7D8R74eY/bljwP6Rf2q3aGGeWMM7lMS8U8KoN14jWFsK/Ko6vN4qvN4aWvuIGsawlStnjlEcPUL98FnNmAnc+h7rkoZRBqQqUGoiaQHw/t1DYj4iHEIkAxAKQCkBbELdF4GQ8rGQdWy9jmou4bvU5H9X1FJHwaiKRtUQiawhH1hAJr0HB13hgXSphXipiXS4+kyFUaw0RWJsksCaFvz/2zIPqhXhkbJHf//pJrq58mz8K/hthp8iBoXfzybYPUdDjfDhY5lNP/QcyhfNwzafghk/DC6R+yOfz3P61b3DnJZdz4QWC7d8k7MHbvbfhzrr4fTqdM7Nse/QxSqEIxW3vZMs79nDIXuLkyEnqiT7urXZRKtv4eiL85U9tJLCY58CXP0u9dIwHt+ephkx+sVDnQ2aRE5FrqLdV8MUnEKqLY4SpzG4hPJth1YlLeKfPorW3k/7Yx0i8+10owedHXRmVKuN3PopxbImM2oMqVKyETebmtUS2tr9kvb2eaIr864zS2Bwzdx0nmPM3fISiiH97it5br0INvnj2Pdf1OHT3JY5+a4JYS5CbP7qe9oFGlkUpJdXqeRaz95FdvI9KdQyAcGgYY2qIS8c34My3sXNVgXXXh/ja0/+RP0+HSNYlnzoeolCNkNe7qHX0Yvl8pBZzPDG0ibNrdvLzZw/z9fg/UdIrbCp2UDp3DaciO8n4lik6Ybboy6wX00RiCT7w3nfT1dVF2XH57HSWv/+/7J11lBxl9v4/bdM+3eOumfjE3d1DEiJAIFiQsDiLLB7cJTghIYQQiIcIcXef2GQyrj0uPe1aVb8/hg2bTWBZ1n9fnnPmzOlzqquq37fqqVv3fe5zy+twCiIj1VrqT9aSW+XgGsURXjJvIWzyay0VqKIAx79oSc2IAgx+rEXO9w9IIYNWL54LjXguNOIvtYHUYo2syQhD3dqMJiMMRWjIj2MnYCs/gfXoZjxnswgUW5BXeFE2/tXDRa9CHh2GMioSZVgUqvAYVGERyPWGFpWHUoFMqUSmUCAFAkg+H6LPj+T1ItjtCE1NBK1NCE1WgjU1CDbbZbtXJSSgbtcWVetUZGnhSK0N+Ax23K5inK48nM58RLFlcVImU2E0dsRk6o7J1B2zqTshqmiCdW68Bc1485rwldhAkJCpFWjahqHtFImmbTjykCultC5fkHe257Hm8AWe169nmrAFUR3Kmk4P8kfdUFRyBXd6TnPvyWcJM8e3RPVJva7YjyiKHDp0iO92nmC/EIcYtQul+Rhp8jRGBUbRWN6IWqUi5mIefU6dxBqZSETfYUg3j+OHrdtxef2UJgxgX6EbSSVnyMBkPh/ejqIjB9my+GMOpVsoTnTS1q3g84Yympz9OcbdKMLOo4m5gCHuLIoQH2JQhd7XBuNJH/KNZag0EYTfeithN85EYbwyUpdEkaLDx6nZco4oXzx6pYmgSsDYJx7ToBSUpv/uhdrfSf6/AFJQpOlwMU27i9F6tQTFAFZtA9Gj2xHfv9PfXEhzNHnZvjCbmmI77QfEMXBGa1RqBU5nDnV1W6mr34rbXQzIMJl6EB09FnWwF3veK6QxEEqsO5dBk5MInJzHe4klbDfo6WtVcl1BkPOu9gQjk3EbDEQ0NnFBH8eGUWPo2WAlqn4Jx41ZmANGul2I5hiTqFXHEK5uIiMynp5SAe6mGjp37syECROQq0JYXNnA+2U1NAUExkaE0rYhyOJdBWglN68oFjCxV1sY/UpL4VL1Wdj4UEvZfcZImPAuhKX+pjEWnH7cZ+pxn6kjYGmpslXG6NB2jEDbMRJVvP7SODuKsmjctQLX8WNIF+tQNLbcB5ICiNehTE9E06YDhva90LXqiCo+/qrk8I9AsNsJWCz4Kyz4S0rw5efhzc3DX1ra0pQEUCUloeveHW337uj69EKIluF05mG3n8Vmy8LuOIcotkgRdbo0wsMHERE+mLCwPsiCIfgKbXjzmvDkNCI6A8hC5GjaR6D7kfBlqssj1WPFjfxx5VlC7Xl8EbmSJHsWnqQBvN7paRa4DBhkIvdXruLu4kVo+94Dw565aq6+qqqKJSvWsrE+nGp1M9GpG3FhYaRpJO0a2lFtqUYuV9Dx+Ek6FObjT2xHxNzZ7C2zUVhYiC6lI8sawrE2etHG6Zl/fVd6amHXl5+x2bKNI5lWNJKCT2stpHjC2db0JB5tCl53AF3UcULj8tEnZqPS2QA52hoTIXvs6ApCiZwyi/Bbb0UZdvUCfEvOBfLX7sVQpydOmw4yULUyYh6aijrD/F/pmfM7yf8HITj91G/PxXOyHpUYgiPQhCPaSfr0AUS2Sv1V+yg+U8/uJRcRRYmhN7Ultm0dtXWbqa/bhsdbDsgJC+tDdNRYoqJGo1ZHk73uNIc21yCJIt2MBST4L2KVfuCZbkbKVUruqAuiz0uhSd8aZ3gkeqcTsVlgwahJuCJNjC85SI78W9xyD51rEzDmprI7eiSiXM7gtlHc3NHI0d1bCQaDTJgwgS5durCj0c6LhVUUeXwMDjNwb0wkX27O40BBA8PlZ3jDvJ7oqa9DxggIeGHPq3DkY9BFwrg3WtQcf+cNJPoFvDmNuE/X4S2wggiqeD26LtFoOkagimx5GxC8Xhp2LaN51wYCpwpR1LakNQSzDHn7WLRdu2LuPYrQbsNQaP6zpfGix4MvPx/36dN4TmXhzspCaGwEQJWSjGHQYAyDB6Hr3RtC5DicF7E1n6TJegir9Rii6EUmC8Fs7kFU5CiiosegVsXgK7HhOVePJ7sB0RVEplGi6xqFvmcMqgTDJfKyewO8sOECa7MsPBp5jPv8XyEXg9QO/BNPhk9kS6OLBNHJc7nvMlnZhGzalxB5pQbd5/OxfsNGlp9t5HQwlujE4wRMW9EqNMyJm0PT2Saam5sJCDBm5w4i7A5kY7tjveZWdu0/gE5voDh5IJtP1yMp5cwYkc7bg9uQf/QQK5e9y5b2pbi1Ag9ZPdzQbGOX7X5KvANRykWQ/Cg9tcjjRUJjdhKaWYhS1gSiDHUu6LK1xGXeQvQtc1CEhl51HhoqyjizdiPkekk1dEKj0CELV2EemoquWxSyv1Fc+O/E7yT/H0CgxkX9tjyCFx3IkVPrLUNoLaftdSMxRf06HxUhIHL4+0LO7bYQ09pFhzH52JxbcLtLkMmUhIX1Izp6HFGRIwkJabEr8LoD7HxtG2UNOsyuMrorTqLK3kHZBDfPpppQSTLuKpRRb+2LIyoOZTBISnUDC1IGkd+/IzrBQc/yReRosohwm+lz1kgRfTlp7kGiKcii2cOozDnFvn37iImJYcaMGdRr9MwtrGS/1UmGTs0LGQnom/08tCwLm8vD84rF3NhRh2zyR6ALb4ne186B+osti62jXgLtL9oaXTm+dW5cx6pxnapD8gZRmNToukWh6xZ9SV4qOB3UbVuCbetGxOPlyHwSolpC6mhG06cbESOmY+ow7Aq/nP82SJKEv7QU16HDOA/sx330GJLPh0ynwzh0KMZxYzEMGoRco0EQfNhsJ2ls2k9j4z5crgIATKbuPwYBY9GExOMrbsZ9qhZ3diMERVSxenQ9Y9B3j0aua0kZbj5fzdPfnydCaOC7uBXE1OyFhJ5kDXuLJ6wGsp0eejku8mLJfLoPugO6zbriIS1JEqdOnWLRDwfY508jqLbRqsMmKtwXGZ4wnPGK8Zw4cgp/IICh0caYPTuRa1XoHn+IH6qsNDc3E999MB/kCDitPmLSTayc2YMo0ceGhe+zWNxMZbSXiQE1cysLOeybzMWmWQjIUSAjNJiHQ5aGXPLR1GoZ3UaZMLqy8Qk1EARNgYpo82hSJj5DiOnqckp7Qx1ZGzfQfLSMDH1XzCHRoJFj7J+AoW/8pbTffxK/k/y/CZIo4c1tomlXEVKlj6AYoMKbi7p7OJ2mjUdrvHrEcDXY6t1sX3yIgHwPsZlnkFR5gAyzuTexMZOIjh6LSnW5qaclq4zt88/iRUt640GSctZiSPKzZZyfT8162ngDDM/LxKppQyAkhFYVFs6FtGFZlz4E25hIsJ1C0/wVLpmTdhWJZOaEsDthOMWqdKZ3C+fZ8Z3YtGE9BQUFdO3alf6jx/BORQNLqxoJVSp4LC2WWbERfL63kA93FZAqq+UT9ae0n3Bfi0eKKMCh92HvGy3R++SPW/qV/trxDYp4chpxHa3GV2wDhQxtZiT6XrGo01vcNCVBwLp/E/UrFyIcKkDmB8EA9InBOHoMcaPuJER3dZ+W/xWIXi/uEydw7NyFY/t2BKsVuU6HYfhwTNdOQd+v3yX5oMtVTH39VurqtuJwXgDAbO5NXNw0oqPGIQ+ocZ+tx3WyhoDFiUwlR9ctGkP/eFSxeiqbPTzwXRZZ5VbeaZvHtLqPkQXciKNeZnniVF4vrqQ+KDG9djvPKUuJmfAaaK8wm6W6uprFy9eyrj6CWlHPoB455HhXolPpeLzz4/jzJc6fOU1ArqDHmWza55xD0bsjeYPHkmWxkJzWih2a9hw+XYtCq+CZKR2Z3TmRs7u28t6BNzid3kQbSctHlkIUuh4sL78fpbclvaaVNyFzeXBrE/AH93NsXA4PdJtCeF0WdQ1bCOq8EACTvx3J3e8jMm4UcvmVa2Nuu41TG7+nYu9ZWmk6Ea/PQCaXoesajWFgAiHxv1xZ/q/E7yT/L4YUFHGfqcO6swSag7iDdko82YQOSKLrpGvQGn59LjcYdJGTtZqy4jVoBAh/TwAAIABJREFUo3KQySQMhg7Exk4iJnoCGs2VhSmSKHFs4QGyTvlQ+5rpeHExJlcZYdcn8UZEPrv0OkY0qImvGojbaCa8sZHI5gDvtJpIbXoYYoKc9Ool2IWDRHrD6HXOTEizka0po7FKEbwwqSOj0rSsWLECm83G2LHjKE1K58WiapqDQW5PiOTR1FiCXoGHlmVxuLiJqfIDvJx4DP2M+S1GYY1F8P0csJyAzGkw/p2WqP5XQPQGcR2rwXmoEsHuRxGmRt8nDn3PGBSGlijKW15M1ddv4dlyGHlTAFErIfaPxjxxMrHD70ClvpJ4/n+AFAziPn4c+5at2LdvR7TZUMXHY5o6FfPUa1HF/3S9uN1l1Nb9QHX1GjyeMhQKHdHR44mLm47Z1JNAtQvX0WpcWXUQFFG3MmHon4CijZm3t+ex4EAJg+MF5hsXoS3bA63H4Jr4IR/UC3xeUYsm6Oap2jXcMvw2FPFdrjhXr9fLytVr+e6il1whhh6t/ChjV5LTlM241HHMTrmDZRt2IGtqQOGHUbu2Eepx4L9hJhuDAgajEV3fsby1sxzBFaRXt1iWTuuKq66aD7/6ExtizqNVKPioro5uIWEcjn6drIN6ZJKEhESMJ5t6bWdCvBWsz/yKpK7teaT7w5iKz2A5/in2eAtiKChFPbGJU4mLn4bRmHlFDt7jsHNq0zryd+wnLSSTdHNXFJKCkDQTxiGJaNqG/dvz9r+T/L8Ioq+FfGz7ysElYPXVUug5Q/Sw9vSYMBmN4dc92SVJwmY7RVXVaqqrfwCZB8EbTWLyFJLTpmLQ/7yjnsfuZcuLW6l2hRJdl0W7gmVEThmNkHaOB4RCyhQappZ2RiANVSBA29JSjpuHsCIuDV9HMwp9GXE1n+Knia61bcg856NSH8+2yNGo1Vrm39wHvbua9evXo1ar6T1lGvMcAoebnfQI1fFW2yQ6GrScszQz5+vjWJ1uXlIsYkbvNGTj3myR2p1f3bK4Kle2LKx2mv6rxiXY7MN5uBLXsRokn4A63YRhcCKaNmEtUbsk0bzvB2q/+gjxeAUgEcxUo5s4lPgpj6Azpf6q4/z/AtHnw7FzJ7Y1a3EdOQKAYehQwm+9BV2fPpeI58/XW3X1GmrrNiEILgz6tiQm3UpszCTwKnGdqMF1pBrB5kMZqcU4NJGDIRKPrT2HHJFV3bNpc/btlsXzKZ9RlDCQJ8/lcMAjp5sjl7fiQ+jUc+qV5yiK7Nu3jwW7LnAsmEqsOYTxg/JYVfQlMboYXh/4OkfPNWM5dhilINCmxEKXk0dQtUlnf6eu1KpU9Bs9jhfO+7EUWjFGaFl+ay/ahWtYu/xD5tmX4tIKvOjwM9lhwz3yY37Yl0J9cYuNhjpYTFCKAZmckvDVbM3M4tqMa7mv631oLxRRtupFmqMK8XaRQCmh17cmLvZaYmOnoFZfns7xOB1kbVrH+a1bSVS2pUNUP0JEDapYPcahiWg7RV21UO5fgd9J/p8MwenHeagKx+FK8InUesop8p4hcURXuo//9eTu89VRXfM91dWrcLtLkAQNtrIemI2TGHztFFQhv9zbsvJ0GVs/OY1PrqN10VratpETc/9t5O+azf0GEaMnhn6VvQmEaEkpKUXh0/BNxrXkhkCwRxg6/xb0trVEBcwMLkxHX1pPTnJ79iiG0DZWz4Jb+lB07jh79+4lLiWF2n7DWVDTjE4h59lWcdwUF4FcJuP70xaeXH2WSKmJ+eoPyZz0MHS7CQIe2PoknFoMSX1h+pdgSvyb4xJs8mLfXY47qw6Q0HaKwjgogZDEljciweej5rt52JauRFbpRjBIMDqVmFseILLt+P9K9cO/G35LJc1rVtO8YiVCUxPqNm0Iv+VmQidORP4XC8uC4KamdiMWyxKczlyUSjMJ8deRkDALTUg8ngsNOPZWEKhyoTCpsfWM5NGcSnJq7Lw1SMH00heR1eVAv/uRRsxlbWU1cwsqaJJruStwkccHX4tBe2WRXk5ODp+v3sZObxqSQs0Tk3WsLHuDKlcVczrPoW3EZJZu3EJKnQV1QKLXkf0k1NVjGTCAwzExdO3Zk3361qzZU4JcJuOpSR24u1cKF84c4tH9j1FpcnKrW84fa0uRjXiBXMX17PsuDyEoIeDF5KrFqU9B5z/E5wPWI9MquafzPdzY7ka8e/ZR8/Hb2KNK8Y3Q4Y1xAHLCwwcQHzeDqKiRyOU/SSq9TienNq/n9OaNxClS6RI3HE1QiyJcg3FwIvoeMVeomP7Z+J3k/0kINnlx7LfgOlmDFBSpdBeQ7zxF2sje9Jo0/VeRuyj6aWjYQ3X1ahqb9iFJAnpNdyyne9CQ34VB0zvTcVDCL+5DkiSOvbmWrGIjar+NLnUbaP/ig+hDG9i+8S6eD4ukT00XwgJpGO12WpUUUhgxna8SY3Bo5AS7KjBZ56P059DH1olO2XL8DjvH2vXipKc7EzrF8tqUDuzYsons7GyM3XuzNjqVIo+f6TFhzM2IJypERVAQeWNLLgsPltBXnsMnkd8TceN8iO0EDQWw6jaozYaBj7RI7a7ibHjF+O6pwHWqFuSg7xWLcVAiyvAWUhJcTioXvYrjux+QW4MEUuRopg0g8fqn0JnSfvU8/l+C6PNh/2ETTUuW4MvLQxEVScTtswm7/jrk+p/IV5IkmptPUGH5moaGHQDExEwiNeUedLpW+PKt2PdU4C+149cpedMksK3axowukbxuWIny1EJI7g8zvqJZFcZrhzbzjSyF+GAz77dPZXBS+hXnVltby8JvV7G2IQarpOPpiemUSEvZULSBrlFdeaD3S7xysIRWeacweVzEVVTQ9/gJZNFR7OnYEW1mJmGDxvLk+jyEZj/9O8ewaEY3/K5mHvj2VrIMZQzwKHmvtgRdlxvxDH2LzQtyqSmygwxCXTnYdR0weMo53nUT+8NzSTOl8WTvJ+kX1QvrqlU0fPwJPkUjwk3pODs04wvWolKFExc3lYT4G9DpfrruPA47x9ev5szWH4hVp9M9aRRarw65QYVhYAKGvnHINf+apuS/k/w/iGCjB/vuCtxZtUhIlDovkGs7RtqQPvSdej2GsL+dW/Z4LFRWLaeqaiWBQCPqkBhi46birR3EwWUetAYVY+/uREzaLy/O2i/kseONXdTo2xHRmM2gYTri774NNv2RRcVrWaFuT/+aHsgIoXV+Pi5Jx/nYmWw0BghEapC1rsDcOB+V4GVm9WhU2XkE9UF2p40kx57BA8MzuLtfPCtWrKC0soq6IWP5QQohTq3ivXbJDAlviaZt7gD3fXuSg0VN3KbYyjMd6lFN/bxl0S17Lay/H5RqmPrF31xcDdp8OHaV4zpZCzLQ944ldGgSih8LUAI2K5b5c3Gv3IXcKRJopyJ09nUkjn8UpfJ37/hfA0mScB87RsP8+biPHEVhNhN+222EzboJxV8FJ15vFeUVX1FZuQxR9BIVNYbU1D8QaszEV2LDvrscb4GVpWqB+T43XRJNLOldjmnHoxBigBlfQepATpzbwcMWH0XaRG4xiTzfuQsG5eWyQ7fbzTfLVvJNkQqLaOaOgWl0bV/Ma8deRSaT8Uzfl1hYEYOUf5YulkJUfj+9sk6SVFFFTmYm5T17MGT6ddyzt5L6PCvmcA0rb+tNqwgtr618nFW+nST75CyqqyA6sS9c9w1nj7k4tKoQSYKwkAYcbh0godauYGGfKmxCPSOTR/J4r8eJIZTGL7+k6avFiGIQ9f3DcPby0ti8D0kKYjb3ISH+BqKixqBQtFyvjqYGjq5Zzvnd24nVp9ErbTxahw6ZWoGhfzyGgQk/20v5t+J3kv+NCDR4cOwux32mDkmSKHad5ULjYVL79qD/jJswx8b94vclSaCxcT+Wym9pbNwLyIiMHE5C/A2YzQM5tq6UMzsrSGhrZvQdmeh+QYol2O0Uv/0Zh0oTcOliaWM/zJAP/kCI3EZg6VRekQeoc/Ynxp+EydpMRu55ShNmsj06ifMECWTo0IRvQ+fYRJIvgdklo6ko3ouYLLLePJ1KZxSvT+3MkOQQvvvuO4pkKo53H0i5ALPiIpibEY/xxxu0osnN7YuOUtbg5FXlQq4b2h2GPw9IsOulFkvgpD4w/Ssw/fxbiegN4thnwXmwEkmU0PeOxTg06VJ1oeB1Uz7/OdxLtiB3SQS6agm/6zbih92LXP6viYj+EYiSiMPvwOq10uxrxhVw4Q168QreS/8BZMiQy+TIZXJkMhlapRa9Uo9epUen0mFQGYjQRmBQGf4lqSf36dM0fPYZrv0HkJtMRM6ZQ9hNNyL/K/tdv7+RCsvXWCxLCAYdREQMpVX6oxiNHfAWNWPfVsqu8iZexoteo+S7aWZa770Xmoph5Fzo/yCe2lze3L+O+ZFjSFAIzOvcjoFhlwsRgsEg69Zv4MusZi4KMYxsH82T10Tz9KHHudh0kVs73k6eOJ5j1dVMyj6JMuAipsZCv8Mn8JjNHOvbh2G33MKHdQp27ilFAbw9vQvTusSz+sBiXst/H70gsaixjta6WLhxJc1iImvfOYXHEUCrCiBzNOJWR9PW+z3zu9RRGFmMXC7jzk53MDtzNvIGG/Xz5mH7/nsUUZGEP343jk5OqqtX4vGWo1SaiYu7loT4G9DrW2oGrDVVHF75LbmH9hFjSqVP68lomzTIVAoM/eMwDEr8p5H97yT/dyJQ78axu6KF3GUSJZ5sztfuI75LBwZcfzPRqVe+ev4lfP4GqqtWUlm1HK+3kpCQKOLjryMh/gY0mnj8niDbv7xAWXYjnYYlMnB6xs/6vkuiiO37deR8/j3nk69Hhkj/jEY6PnELshNf4Nz+LE+GdibM2gelpKL9hYt4BJG6mHv4NkrAGhQRu6nQSV8S4sthdPNAxpVkcq56K1I3ie/8t+ENGvh0Vg+SlA6+XbGCEyntOBmXRpxaxbvtkhga/tPbxTlLM7MXHcXvcTJf/QH9rr2vxdvd0wxr7oTCHS2t98a9ddVKSABJEHEdr8G+sxzRFUDbNQrT6NRLaRkxGKRy6RvYv1iOvEkgmKkl8uH7iB1wOzLZf07THhAClNpLKXeUU+Ws+unPVUW9u55mXzOCJPzTjqdWqInURhKhiSBaF02iMZFEQyJJxiSSjEnEGeJQ/gMPO8/5bOrnzcN16BDK+DiiH3qI0IkTkSkuj7aDQQcWy1LKyhcQDNqIiZ5IevrDaLWpePOsnP6hgEcaGnHI4JPRqQyrfxNy1rX4Dk3+FAIejq9/lodN4ynWJXFrfATPZ8Sj/4vjSJLEvn37+HRnDieCyXSIC+WLW7uy6OIHrMxfSY+YHkRHPcTSZpGpFy8SWV+AIuinb9YZ4iosnOnSmcQ776QwoR2vrDkPtgA3DEjl1QkdOF16hPv3PIgg+fm40UZvCWQ3rkBM7MOWz89Req4RGSIRvnIa1KnEuU4RSN7O2wlh+PQXSTIk8+KAF+gV2wvPuXPUvPIq3nPn0HbtSvQzT+NNsFNZtZz6+h1IUoCwsP4kJd5MZOQIZDIFdaXFHFy+hJLTJ4mLak2/NlNQVcuRqeQY+sVjGJRwSSX2W/E7yf9KBOrcLZH72XokOVQI+Zwu3445NZ4hN99BYvvMn/2uJEnY7acpr1hMff32lsk29yUh8SaiIn/S3drq3Wz69Dy2WjeDbmhD5uCfj3Q957OpfuVl8ptiKEqfhMFTw+gbU4kbkAFr51BVdoS3VWMw+VphtlppdzaLwpTx1Op78F14kIAooe7rQmX/BKXg4KGqG0izhJDt2om3t4qvq2/HqNWx6LbeiE3lLNqyjT0d+1CtNXBjXDgvZiRcit4BdubU8sB3JwkXGvk6dD4ZN70HyX2gLheW3wjN5S3NJnrO/tkx8l5swra5hGCDh5A0E+YJaZcWVAFqtn5J49sfIa/0EUxTYX7wdhLHPvRvJ/cGTwPn68+T25RLQXMBRc1FlNvLCUo/+b5rlVoSDAnE6eOI1kUTrgnHrDYTpgkjTBOGQWVAo9SgVqjRKrWoFWpkyBARkX6U9QXFIJ6gB3fQjTvQ8mf322nyNtHoaaTB00CDp4Fady0WhwW/+JPlsEquIsOcQeuw1rQJa0ObsDa0D2+PWfP3yUVdhw9T9867eHNyULdtS/QTj2MYMOCK7QIBO+XlCyiv+ApJ8hMXN4O0tAdQq2IoPWLh7k0XKBYFnk2I5Jb2B1EcfBniu8IN34EuEvfmJ3jTpuOLhBlk6NR81jGVTOPlfQ/Onj3Lx2v3ssefTrxZx7d39eOMdRcvH30ZnVLHoLQ/sdAZS98aKz0v7iagUJJSV0WP/UdoiIzEdvMsoq6Zyu2rzxEod9Ix1czSm3vhCNRw+/pZNApWXmxwMcnnRDZ9EbSfyLk9FRxYWQCiRFSgggZVAjpfHUPDP+Sx8J7kxV4AVSPXpE/miV6PYQoJxbZuPXXvvovQ1IR5+nSiHnkY0SBSVbUaS+VSfL5qNJoEEhNuIj7+OlSqMCpyzrN3yULqSopIS+tO79TxUBZAppKj7xuPcfBvJ/vfSf5vINjkxb6zDPfpOlDIqFGVczR3HSFhegbNvIV2A4Zc4U39Z4higLq6LVRYFmO3n0WpNBIXO42EhBvR6y9vbFGZZ2XLF+dBgrFzOpHY9upVnkGrlfr33qdxzXouZt5GXXgX4tx5jHl+HPpgDqy/lxN+A+uFsSglHe0u5iJrrqMu5RFO6UPYp5MQ1GDqlodkXYIOE28V3423ppzSkH1Ye5hYVDCL9MhQFs/uRdnFc8w7c4FDrbugD1HxfrtkxkVdThTfHCll7oZsOspK+TJuPdE3fwnmZMjfDqtnt5iJXbcEUvpd/Tc1emjeUIQ3z4oySotpXBqa9uE/ldIXnaDixUeRH69HiJZjuGcqSdc/h0Lxr68mDIgBLjRcIKsui+yGbM43nKfGVQO0pFYSjYm0Mrciw5xBK3Mr0kLTSDAkYFKb/q1KHlESqXfXU+GooMJRQYmthHxrPvnWfOo99Ze2Sw1NpXNUZ7pEdaFLVBcyzBko5L9cgi+JIvYtW6if9wGBigqMo0YR89STl+ns/wyfr57Ssk+orFyOTKYkNfUPJCfdgcstY85nRznS5GS2XM0fO1nQlzyDLMQAM5dBfDc4+hkHjq3m/o5zsapMPNMqnrsSo5D/xTiWlJQw75v1bPWkY9RpWHpXX5TqOh7Z+wjljnJGp93Fcl9/0lxBpp5eR7OkQhPwMeDQUYxWK8UTJ9D58T8xc0cedafrMRtCWHprL5Ii4c51t5DnLeHeei/3uBuQjX8Het1BdVEzGz86S8ArYPDX4ZPpEGUKhio+pKR1BI/KTCgiDqNTGnm275NMbDUB0emk4ZNPaVq6FLlWS9RDDxE28wYkmURDwy4sliVYm48il6uJiZlEUuItGPTtuHhwLweWL8HZ2EBmt+F0jhmGkO9E3yeOsCm/rUXh7yT/MxAcfuy7y3EdrwEZWEMb2H9uOYJCoM+UGXSfMBlVyNXd5wIBK5WVy7FYvsHnr0WnSyMp8TZiY69FqbxSMnbhQCX7l+VjitYy/t7OmKOv7NwkiSLNa9ZQ9867uPxKsrvdj0MVSQdFDoNfvQHFwdcInvqa1Yoh5Aa7oHe56HLyBMVRqfjDZ7E9Rka234cQLic8YxOiYx8xYns+KphNUf1x6mL2UNmuHV9dmEi35DC+vLUn+48c5I1GD8VRCQww6fm4Ywpx6p+IVZIk3t+Rz4e7CxkpP8WHrU+jm7m4RR99YiFsfhxiMmHm8qvm36WAgH2vBce+CmRyOaEjkzEMiL9k4ep3NlL6/oMEV2YhySDkxr6kPvQBKu2vrw7+eyFKIhcbL3Ks5hjHa46TVZuFJ9ji7phkTCIzMpNOkZ3oFNmJtuFt0f4PLO42eZvIt+aT3ZDN2bqznK0/i9VnBcCkNtE7tjd94/rSL74fScakn92P6PfTtOgrGj7/HIDIe+4hfPbtyK/iye7xlFNQ+Cb19VvRaBJpnfEU5vBRPLXiLGvOVzMRFc+arEQrX0Lua4Apn7YUwl3cSOO6R/hjh2fYFtqNYeFGPmyfTFTIT/np6upqPli8io32ZGQhGr66vTcdEtQ8d+g5dpTtoFfsaHbLryPap+L+oi2U1DmRlCG0r6gg88hRKjp3JnPePB7ObeLEvnIUfpG3pnVmYpdIHt56P4cajzGlIcCLjmrkgx+HYc9gb/Ky4YMz2Oo8KANuNEEHTk0U3Xzf0aXTGR4MuZEjqt0otBW0Ce3BvBEvkxSahK+oiNpXX8N1+DCazEziXnoRTYcOADideVgql1Jd/T2i6MFk6kFS0u2EhQ4ma/MPHF+3CiEYoOeQKXS/ZhL6+IjfNP+/k/xfQXQHcOyvxHmoEkkQ8cT42H9+GTZnPZ2Gj6b/dTehN189yna6CqioWExNzfeIoo/wsIEkJd1GRMSQq6YUJFHiyPdFnN5RTnLHcEbfmYlae2Ue1VdURPXcuXhOnsLbfRQn9aMRJejfponMmzohW3sXDY31LGESdiJILyoisiyXwtRpeIy92JWiIL/JhZQiYg5fhOgtJNM/mjeKJnGucQ+O9lvIiR7J0uz+DG4TxSczu/DFrj18pgjFo9byZHoc96XEXBZRiaLESxuzWXyknBmKvbze1Yry2k9AroKdz8Phj6D1GJi+qKUP61/Bk9tE84YihCYv2s6RmCekX1LMSJKEZdO72N74CkWDiNQ/juTn38eQemWl5D8DroCLI1VH2FuxlwOVB2jytvSUTzel0yu2F71je9Mztifhml9XhfvfDkmSqHBUcKb+DMerj3Ok+gh17joAEgwJDE4czLCkYfSM7YnqKiX8gcpKat94E8eOHYSkpBA793n0/ftf9VhNTYcpKHgFpysPs7kPbVo/z4Kjcj7cVcBQtZoXfG4SzG+j8p6D4c/BoEeh/CjSshv4OnYiL6TehUGp5KP2yQyL+Onh3tjYyCeLl7GmIQ6PXMvnN/dgWNto5p+bzydnPiE1tD0XdXPQi2G8ZD1D9slTBEKjCPP76Ld9J4JGTcJ777FQH8viH/KQW/3cNSSdJ0a15rXDL7G65HsGNwp8YK9E0fUmZNd8iD8A2xZmU57dBGIQk7sCmyGNVPdBRqd9Qn6n+7izRsSh+wG5XOS6Vvfw9MA7kSHDvnkzta+/gdDURPgttxD1wP2XZKqBgJ3q6tVYLN/g8Zaj0SSRnHQbJt1Ijq75nuzdO+g8ahwj7/jDb5rv/yjJy2SyscAHgAJYKEnSGz+37b+a5EW/0FLEtM+C5Asipao4UriOivJskjt1ZegtdxKVnHrF9yRJwtp8lPKyL2hs2o9criY2dgpJibdiMPx8O7pgQGDX1xcpPFlH5uAEBl3f+ooFVtHno3H+FzQsWIBcp8M5+T6Ol8ag9jcz6powkuJyYeeLnFN2Zb2vD/KASPcTx6lTBbHH3UUwKY3lai+1di+qTk600meIgoMR9ut43NKfs8278fZay2HVzazJac+ETnG8NbUjj+7YywZ9FFFyicXd29HNdPnbR1AQeWLVadaeqeEOxWaeGWRGPvplEHwt9gQ566HXnTD2TVBc/tASnH6aNxThOdeAMkqLeXIrNBk/PTQdNecpfeFelHsbEOJCiH72MaJH3PwbZvSXYfPZ2Fm2kx1lOzhec5yAGMAYYmRgwkAGJw6mT2wfov7HfWx+LSRJosRewtGqoxypOsLR6qN4BS/GEOMlwh+cOPiKtxbngYPUvvIK/rIyzDOmE/3EE1e1XBbFIFVVKygqfg9BcJKcfBd7qybxyqYC+kcYeKlZJF45D51sL1LPO5GNf6ullmLpNHJlofyh10fkBpQ8khrDo6mxKP5sCe1w8MXX3/FdVRhWSc+713Xh2m6J7C7fzVMHnkKl0NJovBe5IoP35DWc3/gNTnMrlAoF3Y6fJKmiAvVDD3J87GSeWZeN3OJmaPtoPr6+K0sufsFn5z6nW5PEQlsFylajkN/wDaJCw5Hvizizoxy5AkIb82k2tyHcW8yk2BfQtu/O0uQHeC9/IYImByNteHvIqwxIbYdgt1P33ns0L1+BMi6O2OeexTh8+F/Mg0B9w07Ky7/EZjuFUmkkIX4mOvlwQsNa/So59tXwHyN5mUymAPKBUYAFOAHMlCQp52rb/6tI/pKaY1c5ojOAspWBbNshzp7YSmhUNENvuZOMXv2uyK9KkkB9/Q7KyuZjd5wjJCSSxMRbSIifSUjIL0+G1xVg82fnqC600W9qK7qNSr5i/67jx6mZ+wL+khKME6+hNGEEZ/NUmF3ljJ/TmrDyj/Dn72Cz/nrOuKKJrK+nVc5R8qJSUZpvRTsglXdLqnEJIubuFYiOLxDlBm6ouYHbGzqS7d6Lf9AytjkfYVNePDN7J/HHMa25cd9xzmtN9FUILOnfldC/0i57AwIPfnuC7bmN/FG5igfG90LW/z5wN8F317f4z4x+paWxx1/2KZUkPOcaaN5QiOgVCB2ejHFI4qXemaLop2T5U3jmbUbuBPX1/Un900coNL/cdPzvgcPvYE/FHraWbOVI1RGCUpBkYzLDk4czOHEwXaO7XjVy/b8GT9DDkaoj7KnYw96KvTT7mtEqtYxIHsH4tPH0i+93Sbkj+nw0fPwxjV8uQhkVRewLczEOG3bV/fr9TRQWvkF1zRq02mRyfc/xyjYPPRJMvBViILL8A4zKtYjp45HPXAQeK3w7A3djKU+NWMEKr4GhYUY+6ZBCxI8V3x6Ph8XfLufrYg21YihvTOvE9b2SKbQW8uCeB6l21RA03IZXP4hPDF4KV79Kg7Idos5AYlU1fQ4dgsGDqHv2Re7cWYB0sZlWMQa+ub03u6vW8OaJN2nbLONraznqxD4ob14NaiPZ+yzsX56PUq1AU1OAKzQJddDO+LA3iY214Zv0OY8XFLG7fgEg0N0wiw/G30eYXo076zQ1c+fiKyjAOGokMc88gyo29rKxstnOUF7xJXV1W5EeFkjWAAAgAElEQVTJ5KSnPUxq6v9YJC+TyfoBL0iSNObHz08BSJL0+tW2/2eT/CU1x5YSgvUeVKlGqvVl7N/xDaIo0GvSdHpPnoZKfbl/uCD4qKn5nrLyBXg8pWi1ySQn30Vc7LRLBQ+/BHuDh40fncXe6GHkrR1o3etyzwuhuZnad97BtnoNqsREop59noO77ZTWaYn35DLu/lZoDj5KrT3ACuV0mvxKOuTk4HfnUhc+BGPktYhD4nnzeAlBrZy4zsdwN69GCGnFPSVTmGpvxcXAHoLDvmVD/QtsyQtlzpB0xvWOY9apXBqVIdyhhZf7drvSfMkvcOdXRzhUYuMF1RJumzENOl8Htkr45lqwlrYUOHWccvlvsvuxrivEm9OIKtFA+Iw2lyx/Aazl+yl/9hFCjruRUg0kvvEeoV0H/Z0zenVIksTJ2pOsLVjLjrId+AQfcfo4xqaOZWzaWNqHt//d6uAXIIgCWXVZbCrexPay7Tj8DsI14YxJHcPU1lNpF94OAM/581Q//Qy+ggJCr7mGmKef+tnGG03WI+TmPovHU0qB527eOdSJ9nFGPu+djmHrh4SK8xHM3VDcvaZFsvntDCTLCb4b+y1PexOIVClZ0DGV7qY/pzsCLF22ggW5cqpEE69MyWRW3xRsPhuP7XuMo9VHUeivwRE6jYVRasrXP05lQzL+yDh0fj9Ddu5EFxaG+OEn3HDBiierHpNayeJbe1Hq28sLh+eS7JDzTWMZuogOhNzxA2jDKDlbz/aFF5Ar5SgaLARVekS5khHmr2lt2A5DnySn3fXcv/NZ6oVzyLytuLP9k9w7oDcKUaBx8WIaPvkUmVxO9BNPYL7+uivvOY+FCstiwsP6Exk5/GrD+TfxnyT56cBYSZLu/PHzzUAfSZLu/4tt7gbuBkhOTu5RVlb2Tzm2v9KJbVMxvmIbyigt3nYCu3cswlpdSauefRl6y52YYy5/sgaDDiyV31FR8RV+fz1GYyYpKXOIjhpDy0vJ30ZdmZ0fPjmHGBQZ/4dOxLe+/CZw7N5D9dznEZqsRMy+Hf2s2fzw2j4afKG0JZthM4LID77KKc0ANnu6ovL66HXsKMdSbWjEySS0GU9RBz0LjpUhhCtIbL0ep/0gAd0AHrk4mAnuJPJluwkOW8666jfZfFHBQyNaE9U2lKcKqlAF/LwcpeWmHlfmvt3+IHd8eZhjZTbeCvmS6TPvhPYTWxwkl0xpibpmLoO0n8hZkiTcp+to3lCMFBQxjU7BMCDhkjGTKPooXPsn/G9vRe6UoZ89keQHX2tpl/cPos5dx4aiDawtWEuFowKjysj49PFMTJ9Il6guvxP7b4Bf8HOw8iCbijext2IvftFPZkQm09tMZ1zaOLSSkobP59PwxRcoIyKIf/MN9H37XnVfguCjtOxTysrmc6GpOx+fvomUCAPfzOyBdu0ijLUvIigTkWatRZUQC8tmQsk+zo35lDvpRrUvwAsZ8cxOiEQmkxEMBvl2+UoW5EhYRDNzr+nA7QPSCIgBXj36KmsK1iBX98UZfieL46Oo2/sIhVl6fEmtADldT56iVU012jfe4gYpktoj1aj8IvOu70pIaDZ/2v8nol3wTV0ZJmMK6ru3gSGa2hI7mz49SzAgovTawO3Gqwmnn3k33XSfQfpQpGu/4OPcnSy88AGCJGByX8srI+5iWLsY/BYL1c89h/vIUXR9+xL3ysuEJP5tD6e/B//VJP+X+GdE8sFmH/ZtpbhP1yHXKwnpH8HhM2soOH6IsLh4ht16N2ndLh+LQMBKefkiKixLEAQn4WEDSEmZQ1hY/7+LKErPN7BtQTZaYwgT7+9CeNxPkazQ3EzNa69h37ARddu2xL/+Gl5TPOtf3odb1NAzopCemQcJFmxjlXYW+Z5wYmpqaJ19lHV9FbRqmkXmiLGsldxsya5BSICk+KU4nNn4TdN59HQ6432plGh24xu0irWV77A5R+DBkRk0JKhZVNNMfHMDH2bEMbBTxyvO3e0PMnvhIY6X23lPvZApNz/Y0sGp+hwsnQqSCLPWtMjgfoToDmBdV4jnXAMhKaGETW+NKuqn1Iut8TQlL9+DeqsdEgwkzfsMQ6erXoe/GpIkkVWXxdKcpeyp2IMgCfSM6cnU1lMZmTLyf0IJ878Cm8/GD8U/sDp/NYXNheiUOsanj2dW+1nEV3qpeuxx/KWlRNwxm6gHH0R2FQUOtChMLuQ8xsnyAB+evpfkCCPL7uqH/ugO1IfuRpRC8Q5cgn5IZ2RrZkPeZppHvMqDpvFsb7RzQ2w4b7ZNRC2XEwwGWbFqDfPPBygXw3hmfHvuGpyOJEksyl7EvKx5yFStcYU/zJLUdBynniR7ixN/Shv8Kh0JFgt9jxwldM4c7uk+nAsHq5A3+3l2QnvapVfx8J6HMbklvq4uI1IdgfqencjMSdjq3Wz88CxOqw+9XkK0lOM0JtHJcJ6Boa8h15lg2pdUR2Vw/44nybdnEXS0o6fhHl69ph9J4VqaV66i7q23kCSJ6Ef/SNjMmT8rzf578X8iXSP6gjj2WnAcqAQkDAPiKQle4OCaJYhBgb7TbqDHxGtR/kUE6fc3Ul7+JZbKpQiCm+iosaSkzCE0tNPfffzco9XsXpJLZKKBCfd1Rv8XjX8du/dQM3cuQauVyLvvJvKeOdQVNrBx3ikEQcaQNqW0U8+nyebkU/lNBAUlHbOz8blz2NrTSJ+6OfS7eTSvni3jdHkzQpsgiYYFOD0WvOF38OgxHRODbag0HsA1YDWrK95m8wUv945qzXEzHLS76VxVzPs9OtCxXbsrzr2F4A9wvNzJ+9qvmHzb4y1697IjLTl4tRFu/r7FF/5HeIuasa7MQ3AECB2dgnFwIjL5n6P3AMX7X8X9ynJUFhmaqYNJeW4ecu1vJ2C/4Gdb6Ta+yfmGi00XMalNTG09lWmtp5ESmvKb9/s7/jYkSeJs/VlW569ma+lWfIKPAQkDuCXtelIX78G2ahWajh2Jf+dt1GlXN4oTRT8lpZ+wNWsHH2TdTXK4mhX3DMdcdQbZ8umIggp7/EeYrh+KYucDkL0GcfDjvJt6B++W1dIzVMeizDSi1SoEQWD1mu/57IyHUjGcx8e05b5hLfrybaXbeOrA0wRlYbgjHmVJmx4EL77AyRUlBKNScYfGoHc6GbZ7DxE9e/LqzX9g28lGFLVebu+fyoTeXu7fdR86r8BXllKi5XrUf9iBPLI1HoefTZ+eo7bUTlyqAffpM9jMGaQoSxiX8RkKWxEMexpxwCN8c3EZ7596n2BQTbB2Bn/ofQ1zhqSjqK+l+vm5uA4eRNerF3GvvkJIcvI/PEf/SZJX0rLwOgKopGXh9UZJki5cbfvfSvLefCtNK/MQnQF0XaPwd5Cxc9nn1BYXkNK5GyPvuPcynxmfv4Hy8gVYLN8iil5ioieQmnofBkObXzjKz+PsrgoOriogsV0Y4+7pRMiPTnOCzUbta69hW78BdZs2xL/xOpoOHSg9VMC2rwtRBNyM7pxPkvN9zsvaszIwBE0wSL/DRzmSVsP51AjGuh6lzx2DuH/jeUoa3QgdncTKP8UTdOMJv58/HnQySepEXdgR7H3XsqLsTbbkOLltTGu2agKUe3wMLTzPS8MHkJFxZaFFC8Ef5Hi5g/f1XzN59tOQ0B2K9rS8PpsSWwje3KKtloIi9h1lOPZbUEZoCb+h7WUVqx5PObmf3UbIV1XItGriX3sN88gJv2lcoSWaXJ67nOV5y2nwNJBuSmdWh1lMTJ/4e9T+H4DVa2VV/iqW5S6jwdNAhjmDe209SP7kByS/n7gX5mKaPPlnv2+3n2PFvg9468hEEk1BVt07lghXGdKiyUi+AI3K1wmdMQZN3lw4vRQGP84PmffxwMUKwlQKvuqURhejDlEUWfP9Oj475aRYjOCJsW25d2jL9X2m7gz37XwAezCAN+KPfNNpBIrStziy+Ay+kGi88RlIgSADDxwkSalk8WPP8nWxhLLcxdjMWO4YIefBPfei9UssKi0iVhaCcs4OlLEdCPgFti/IpvR8I626R9K4+zDN5jZECRYmD96PumgVtJsIUz6jwFPLo3ufoMReiL+pHzHBabw4qRtD20RhW7uW2tffQAoGiX7kYcJmzbrCTuLvwX9aQjkemEeLhHKRJEmv/ty2v5XkA/VumtcVohsez4lD68javAFtaCjDbr2Ltv0HX0q5+Hx1lJV/8aO7np/YmEmkpt57RWXqr4Uk/T/2zjs8yjLrw/eUzGQmk2SSTHoPJCGFEEjoXRAQBaRJFREFARUsqBQFBBtWlF6kSO819N5DSyA9QALpvc5kJtPe74/4oay4u6Luurvc1zVXrkx73/eZ5DfPc55zfkfg8r5srh64S1BzV3qMiUDyo2907alTFH0wE3NFBZpXxqEZPx6RTEbq3kROxZWiNJTSO+oCmtp1bBD15bY1CMeaKtpeOM+qrnq0th485zCLxgOb8tKGa5TVmRCaFuFsWopJpEDnPJnJp+8xgBgqnK5S3X4fm+/OIS65mgG9GrNXXI9QX0/PlHjefLonwcG/bDxiMFkYveJsg8Ar19Bv7EzwbAa3j8HmEeDcCEbtAVVDmqGpTE/FpnRM+VrsWnng+EwQYtlPf5hFObvIn/M+ynNWpM0bEfjdaqSuj5aiWGGoYF3qOjalb0Jn0jXMHMNG0dbrl1lQj/nXY7QYOZh9kHWp68iozCDY5MLUg3LsU3JQDx2C+/TpDy2gArBYDGw9s4SZRwPwsq9h48tt8RaJEdb0QdDWUFY/G1n7rjia5yNKXAedp5LS8g1G3cyi3GRmfhM/nnV3wmq1snvPXhZeqSHL6sIHz4TzUoeGlURuTS4vHxpHgb4Eg8trbIzpj6JwKadXHKOuzhFraAv09UYi0tOJvH2HY29N5TOjBzYZ1cQGOPHm03KmnH0NpVnM8qxbeCFB8tJhZL5RWCxWTqxNI/NyMaFtPCg7cZ5yRSD2xlL6DyzB/tqH4NIIhm6k3smPb69/y7rUdUjMHtTkPMeTjZszs08EboZqCmfNQnf6DMrYWDw/+/SRY/X/E8VQd67Fc/z7pdSWlxLVvRcdh42+7+9ebyzj7t3FFBRsQhAseLj3IyBg4gNe0L8VwSpwZksmyafzCWvvSZcRTRCLRVjr6iie9zlVW7YgDwnB89NPUEQ0xMAvrz7HlXgjat1deoZuQ2W+yjyeR8ABv7vZRKRdYe5AMQ513kwI+xKbWC/GrrtKHQKiyEzs6laBzJtqpzd47WQiQ2lDtfoG1Z3i2Jk3m23Xy+jcM4ij1ONRr6P7jQu8/GxfQkN/mctvNFt5ZfU5Tt2p5hvFGp4d+35DvP3W0QaBdw2B5/eAXUMFXl1SGZXbMxFJRDgNDEYRobn/XhZLHZlnpmL85DCyXDGOY4bi+dYMRNLfbp5VWlfKmpQ1bMvchsFsoEdAD8Y2HUuo86/XIzzm34cgCMQXxbPsxjKuF17hxfNyep6rQxYRjt9332Hj/eveTPuvHuXNnTq8VCWsGO5MsGNL+KEfVBdTqp+N4BmLq/tyxGmbocs0Stu9zdjku1yq1vGmvzvvBnogCALbd+5i0fU67lmd+bh/JCNaN4TvyvXlvHRgHHe0tzGpX2Z7+xexLVvH0cXb0JWqkLVoR4XOgEdpKe3OnCV95Gje8G2NPLmaQBclM/rb8cGlySgtYpbeuYWXIIIX9qMMikWwCpzddoukk3kEt3RHdz2BIqMGubmWfiNkuFx/E8zGhky0Jr25kH+BGeffp0Jfian0KYTqjkzqFsJLHQKo27uX4k8+QT1wAO7Tpj3S5/BfL/LJp45xeMl8NL7+dB/7Gt6hYQCYTFXcy1lBbu5aBMGIh0d/AvwnolT+vhiuxWzl+JpUbl0toXkPP9r2b4RIJEKflEzBO+9gvHcP5xdfxPWNyYhlMgRB4NRXx0i9LcFdm0Y3vwXobMwsEg9GbrWhxbUE1HUZTB0sI6DUj5k9lnNXJWXS5gSsthJE4dexrd2AjTKSUvVExh4/x2ihHXXqTKo6x3Gg5APWxhcR3s2PBImFCF0lbRPOM2LQQMLCwn55/laBSesuEJdWxSeK9Qx/eQp4x0DGIdj6PLiFwfO7QemMYLZSfTAb7fkCZL72OI9oglT9U8pprTad9HVjUS4vRSyW4/3FVzg80f03j2mloZLlN5ezNWMrFsFC78DevNz0ZYLUf9/x8zF/Ha4UXWHZzWVYT1/ktf0CUhs5Pl9+iXOXbr/6miPJGUzYkEmQYxafPpVDtN8EpOuHIFQXUWaZi9EagnvgKqR3d0DXGRg7TmFqZh4bCysY6O7E1018kQoCm7duZ0mSmTyrmi8HN2NQTMOMWGvUMi5uIkk1CVgdhrH7iTeQV+zi0MKV1OTZ4di6I/nVddjV19Pl2DFqW7dndMdB2KRo0ShsmD3InrlX30QpSFh66xZeVgHT8J04NmmPIAhc2Z/Nlbi7BDbTICm5S3auBInVyFPPafC59z4UJkLnqdD5PSqN1cy8MJNTuadwojk5GX1o4ubOJwOa0lSqR6JWI1Y+Ws3If73IGw16kk8coVmPp5FIpZjNWnJz15CTuxKzWYu7+zMEBU7+XTP3/8dktHBoWTI5KeW07d+IFj39ESwWylesoHThIqQaDV6ffXo/rcxqFTgy9yB3Cm3xrb1Kd98vuGQbyglRF2RGC53PnqfOrYBpvW2JKAri6xE/cLyohvd3JyFRyxAHn0NWuxNb+1YUOIzj+WNHmCC0x2hfQFW33Zyo/IDF5/Px7OLNXalAp5oSwhMvMmjgQCIjf+maabUKTN0Sz9Yb5UyXb2fc2FfBJxbSD8DWUeAR2RCDVzhhrjJQsTEdY04tqnZeOPYOvF/YBDQ47n37AfZ7QBrqh/+i73/zcrPOVMcPqT+wJmUNerOefo36MbbpWHwdft1f5TF/bRJKEth07Bu6Lr2CXymUPd+D9u99hVTy8JXdvht5TNqUSKQmlXfbnSQmaCbKrRMRtKVUKD5HX+yFm88KZGX7odtMhA5v8d29Ej7NLqSdWsWqyABUItiweStLU6FYcGT+0Ob0bdZgrma0GJm47w3iq88iUj3Nvp6zkFYd5tCib6jKsse1VTuyauqRCgIdT51C6eLKiwPHY7htwV4sZvYgR+bdeBOlIGXprUw8zVb0Azaiad4wmblxIpdzW2/hHeqEu6KKpMs1CCIRnXo5EybdADc2QkgvGLAcQe7A+rT1fH3ta+ylzujzhlNW4cGoNv5M6RmKve2jpRb/14v8/2Ox1JOfv4G795ZgMlWg0XQnKOhN7FW/zCh5FIwGM/sX3qDoTjVdRjQhvIMXxrw8Ct59D/3169g/1QvP2bORODo2nI/ZStzMOHIr7PCvOkmvRt+xVNGTUnMTlNpaup46S07zSj7soCS6NJSl49azITGfTw6kY+MmRxpwCGntYVTqrtxTPc+QY3G8Zm2HSFFLZffdnDe8x1dnclF19KRSCs9WFeB24zIDBgwgKirqF+cvCAJzd19nVXwRr8v28/bLoxusgjOPNFgFe0bByJ2gUDdsZm9OR7AIOA0MRhn1U2zdajWSkfwhdZ9vQ3lVgqr3k3h/8vkDvUP/ESaLiW2Z21h2cxkVhgq6+XVjUvNJj2fu/0VcvXue7GnvEJlQSUK0PV4ff0znoO4P3VPZdDmHaTuTaOOVxLiozUT5vYtm3zyEugpq/RZSk+SIxmkhtvqj0PtLaDWWncWVvJGWg79CxvqoILykYn7YuJnl6VJKcWDJyBh6RjTUwlisFt7YO4NT1XFIFB2Je+ZLxNUnOLR0HhXpjri3iOW2HhAEYhNv4FtRycTnJ1NaZIeNRWDmAAe+TZ2CvUjO8ow0NCYLtc+swaNNQ1JBxqVCjv+QjquvirBQMZf23cUsURATY0PLmLsNvY6dgxqM/FwakVyWzJTTUyjSFRNuO4SLCREMbxXAx/1/e2Yf/A+IvNVqoqBwG3fvLqK+vghnp/YEBb2Fo2P0H3Zu9Xoz+xckUny3lifHhNM4xo2avXspmjMXRCI8Zn6AQ58+9/+ATfVm9s2Io1BrT2D5Pp5ssoaPVcNA74ZrcQEdL18mqXM1nzVTEVMTwYrx61lwKovvjt9C6iVH7r0LsfYMLpo+ZNgO5NmT+3jT1BKZDCq77+KaaApzTt9D2tYdwUbEC9UFcD2ePn36EBMT89BrmH8oifmnchhtc5RZLw1GFNAess/AhsHgGgqj9iLYOqI9k0f1obvYuCtxHhH2QO57fX0xyafHIf0yHVmuGM0bk9GMe+Wf3gwVBIFTuaf44uoX5NbmEuseyxsxb9DM9c8xJvtXIlgFzGYrZqMFs9GKxWTFahUQiUAkEiESixCJQCqTIFdI72/S/zdjtVq5PO9dHNfGkekFh1+JZmK3GURqfrnKXHr6Dp8dTOfJoDSGNFpCY80Q/E/uQ2SoRt9yDeUnJWhsPsVWuAj9l0OzIVyo1PJicjY2IhHrooKIsJWyet0mVt62pUqkYu1LrWnXqGH/SBAEpu76hAO1m7FRtORg30VQfYrDKz+i9KYT7k2bcdvcMJMOyc0jMjGRGSNf45beG6HOzLv9FCzPfA83G0eWpd7E0Wyhstf3+HToC0D2jVIOrUjG2dOOVl2cOLn8Oga5mnB/PV1GaGDLyIYLHbIeAjpQY6xh1vlZHMs5RjOXtsxqPYdgV49fjMs/w3+9yOcXbCE9fTqODs0JavQ2zk4P9zR/VAw6E/u+S6QsT0vPlyMJCFFSNGcO1Xv2ooiJwWvePGQ+P20w1etN7J4aR5lBRZPyzURGHGC+3TDkdfaEZKTTPPs2V7uX8nmwA62N0Swds5pPD2by/blsbHxlyN03IdJdwd9rOFclveh1No539RGoxPZUdt1HpnoSbx/PQmipwUluw/jaQkriz9O9e3c6dOjw0GvYdPEO0/akM0Byji9HdUYc2gtyLzdUsqr9YHQcgkxNxY5b6BNLUTTV4DQ45IHsmcqqK6Tvm4D9Qh0Soxyfr77B/omHe5g8jKyqLOZdmceFggsEOQbxduzbdPTu+JfPlhGsArpqIzXlemrL9FSXGdBWGtDXmjBojehrTehrjRgNv60rlFgqQq6QYmMrRWkvw04tx07d8FOlluPgqsDJXYlc+Z/vtVN5+CAF775HldzCZwNFtOg4kEnNJ+GieNBad96hdJacusPQqHye9JiHu7wZEfFpiEx6TE/voCzOgJNuKnJJCqIh66FJbzJ1BkbczKLMaGJlZCAdVHKWrvqB1blOGCV2bB3fjkjvhtW1IAjM2Po1+wxrkCuac+TZpZiqznD8hw8pvOKCR0RTblkbalw8a2pofeIkC54dyQVFM0zVRl7rLWLD3Zn42bqxJOUqtkaB8p4rCOzUkDZ6L6Wcg0uTULsp6NzPiyNfnUFr60agfRm93muJeMswqMiGPvOh+UgEQWBzxma+uPIFA4IH8H6b9x9pfP/rRd5qraei8iIuzp3/cMHQ1xrZ+10iFYU6nhrXFA9ZGflvvInx3j00r76KZsL4B/Jb9TUGtr13AK3VnmaVq5A2u8ku0dPI6+W0ib9CY3MdCZ2zmOvnRGtRNEtGrGHmnhQ2Xc5FFihH5rQKkT6JZoHjOWZpT4fLx3m/0gsXkSfVHY5QEjSBMQczMTZzxt9Ozpv6MlJPn6Bt27b06NHjodd/PKWAseuu0VGcxMrngrGJHgyFN2BNn4bsmRcPYrY6Uf5DKqYCbUNxUxffB94rL28D97bPwWmVBKmrG35LlmMb8s/VFdQaa1lyYwmb0jahkCqYGD2RIU2G/CWNwnTV9ZTnaSnP11Ger6W8QEtlUR0Wk/WB5ykdZCjsZSjsbRp+qmyQK6VIZRKkMjFSGwkSGzFiiaihC5SVH38KmI1W6vVmTAYz9XoLRr2Zupp6dFVGdFX1mOof/LJQ2NugdlOi9lCi8bHH1c8ejY8KG/mj51X/OzCkpZEzYQL1leV801dEepiKV5u/ypDQIffN0ARBYPquZDZdzuGdrkbC5TOwN6lokViOWGyDddh+KuLKcch9FRvJPRi5HVGjzpQaTQy7kUW6Ts+CMH96qGR8u2ItG4s9sVEo2TWxAwGahgp0wWLlvc3fctC8CjtlFEf6raC++jxH131AYbwG9/BwbgsNq1dHk4mOhw6xu8OT7PJ9krpyA2O617OzYC5N7HxZfPMiGCWUPrGYkO4DAMhNr+DAopvYu9jSY1RjDn14gGqFD96iXJ7+9Bls9r4EWSeh3SToPhvEEtLK0/C298ZB9mh9FP7rRf7PQldd39BEoFTPU+Ob4pB0lOJPPkXi6IjXF19g16b1A8+vKK5hx/vHMInsaFOzmPRWlSTp2mNrFNH11Fk8XR1Ji73ODG8XWthEsnTwWqbuSGFPYgHyYFtsVCsQG1LpHDqFbfoomidfYlaBDB8hmOrYE9THvsKg/enUhTsSoVLwrrWai4cOEh0dTb9+/R4q8An3Khi27BzBwj0291Fg1+7lhnZ9a3qDjRJePEh9lQPl69MQTFach4SiCP9pdmW1mrl1+xMqNq7DcYsU24hw/JY1+Jb8IwRBYF/WPr66+hWVhkoGBA9gUotJfxnPdovZSmluLcVZNRRlVVOUVY22sv7+43aOMly8VTh52aF2VWCvUeDgYou9iy1Smz9PYI0GM9rKeqpL6qgq1lNVUkdVcR2VRTr0tSagwfxT7a7Ezd8Bz8aOeAWrUbsr//KrInNZGbnjJ2BITeXoc0GsCLxLsFMw01pNo6VHy4bnWKy8su4aJzNKmD9Ig7P+TeTVFcTcrEWs1CC8cIiaEwUoE0cjkZTCqL2IA1tRY7bwQlIWl6p0fBTszUCVDV8uW8f2Kj9cHOzY9WoH3Bx+7CNcb+aNLYs5IaxErQznUL+V6GviObZuBgWXNLiGhpElUiCWSJFbLHQ4cpSERmF832IolSVGRj5Rw/6ieUTbB7Lg5jlIjmEAACAASURBVFkMehtKOn9HxFODAcjPrGT/opvYOcroPS6MwzP3UCHzxc14l95fPIfdpVkNTXdCe8OAFQ/tx/BbeCzyj4C2sp498xPQVhp46sVgxGu/pPbQIew6dMBr3me/ELmrNwq4+u0lBImCDnXfsKe9AkNpUxRGI92PnEId1oicsFNM8XQhQtGE5c+u5+2tyRxOKUYRpkBiuwyxIZ2+kVNZWRNG6O0kPsgup4k1htqwS0h6jOKZuFvUNFLR0l7JdKmew7t3ERoaynPPPYfkIdVyWSW1DFpwDJW5kh1dK3Ht8XZDH9bvezR40bx4EF2OPZXbM5Gq5biMCn/AOdJsriU5aRLG1eexPyLBrktnfL7++p9K88qpyWHOpTnEF8YT5RrF9NbTiXD5pWfOvxLBKlCWryU3rYK89EoKblXdn6HbO9viEeSAe6AjGl8VLt4qbO3+WisNQRDQVRkpza2lNKfhVny3Bn1NQ99Xhb0NXsFqvEOc8I90wUHz16wItup05L35JrozZ6kd3osPwlIoqCukf+P+vB37No5yR+qMZoYuv0RmcS0/jA6BijcR5d8kJlmHyLkxotFx6K7kID/xHGJxHdbhcUiDm2GwWBmfepdDZTW8HeDOKKWYeSs2s08XRKCrPdsmtMdR0fC5WmrqGbd7JfHCclzsQtjX93v0Vec5sXEWBRfdcAkO5a7EDhuZHMFspu2p05TaOTK/81iKygUGdy7lcMnXtHUMZX7icar1Cgrbf0nzvsMAKLxTzb4FiShUNjz9SjjHPtxLqdQHF91tun86HE3+Rjj0HrhFwPDNDdXlj8hjkf+NaCsN7Po6AX2NkSd7q7B8OQ1TQQFub76B85gxD5gKGc1WFu9JRh53C5HYlnbGz1nZ2gun0hAc9FqeOHQcm1axVAXuZ5KnhiC7IFb22ci729I5klqMMkKJWLYYsSGT4dHv8115I3zys5mekUGsuSN1PmnYDulHz8NZVHor6OxgxyxHEds3bsTX15eRI0di8xBHx9LaegZ8fQCd3sCOVpkE9p/Z4CK5qifUFiO8eIDaZHtqjuUgD3LEZWQY4p/FfvX6PG5cfxnZsmwUl8WohwzB44P3/2GBk8lqYm3KWpbeWIqN2IY3WrzB4NDBiP/Fjbj/H6PBTE5KBdk3SslNq7g/E3bytMM3zAmvxmo8ghyxU/9jC+m/IoIgUF2ip+BWFQW3qsi/VYm2omE14uShxC/SBf9IF7waq5FI/zobvYLJROHs2VTv2Inq2X7s7O/K6ox1OModmdZ6Gj39e1KmNTJwyQV09Wa2vtIcXfFMzJn7iE7RIvJsgWjUHurT7iDd3Q8BGyyD45BHhGC2CkzJyGVzUQWjvTWMl1v4bPVujhga0cLPifVj22D740rMWKTl+aPrSbEuxU0VxM6nV6GvPMmpLZ9QcNEdp6DG5MgcUCjtMOj1tLh2HVFdPV/1eJXsSgn9OuZyomwRTzpFMu/6IYrrVBS3/5yYfg1CX5xdw97vEpEpJPSZGMmJuXsoFvngVHOLzrOew1tyDba9CDI7GL6loeL8EXgs8r8BXVU9u76+jr7GSOeQYixLPkLqqsH7y69Qtmj+wHNvl9QyffUVumZUIhLb0szyBaub+eNVFYhHVTntj5/G1OkJrF6bmeClwUPpw+pnNjN9x22OpBZjF6kE6UKkxjuMj/2QTwu8cSov5p2UeDqZO2NyLMHhpS50PZFDmaucXg4qPvKy44fVq1Gr1YwZMwbbh6QtGkwWhsyPI6PczOYmF4ge9TmYDQ3VhIU3EIbvoPKaG3XXS1C2cMNpQPAD+e/V1QncuDIOh8U65KkCrm++icu4sf8wFJBUmsTsi7PJrMyku193praairud+999zZ+BQWsi60Yp2Yml5KZVYjFbsVXZ4BfhjG+YMz6hzqic/jNF/Z+hqriOe8nl3EspJz+zEqtZQKaQEtRMQ6MYN3zDnP8Sgi8IAmULF1G2aBF2nTqim/0qs69/Qmp5Kl18ujCjzQzq6lQMXHIBR4UN28e3obpkEbpr82maWosQ1AnxiJ2YM64i3vosZsEbc9+dKGMCGtKF7xSyOLeEge5OvCoxMG/9YU6bGtG7qQcLh7VA/KOhni6zgiFXdnHXvAgv+wC2Pb2a2rI4zu34mvzzHjj6B5GnUOOodqK6uprQjExcc/L4+ulJpNYo6NEunYuVaxiiiWX6lZ3kaNWUdfyc2H5DACjNqWXPtwnYyCX0fS2KU3N3USD4oK7KpNWU/gT7ljdkuDUbCt0+eKSxfCzy/yR1NUZ2f30dbaWBtuLzSA+uR9WlC16ffYpErb7/PEEQ2BCfw8JdNxldagKxHH++YXdoEJ46L4IL8mkef4XKLs/i4LKUcT4uOCrcWfXMZmbtyuFoajEOTZVYJAuQGrN4t83HzL6rQayvY9K1Qzwt6gBicJzQgm4XCilwlPK0yo6vgl35/vvvAXj55Zdx/DEf/+cIgsDrK48Sd6eepV6H6TnxKxBJYMsIuHUEa79VlF9uRH1WNQ7d/bDv9mDHqtLSo6RcmYTLYhukWRY8585FPXDA3x03o8XI4sTFrE5ZjUahYXrr6XTz+/Uqxz8Ds8nC3ZvlZMQXkZNcjtUqoHKWExTtSlC0K56NHH/RevF/AVO9hbz0CrISS8lKLMOoNyNXSgmMdiWkpTs+oU733UP/XVRu3UrR7A9RNG+O5+IFbM7by8KEhYhFYqa0nEKgrBsjVsbT1NuRDWNbU1G2j6oTkwnLrMIS+SySgWuwJh9AtGMkBktzzN1Woersj0gk4rt7xXySVUgfVzXjjJXM2xnPVbMv4zsHMfWpn6rByy/mMyjnCKWG7whQN2bjU99TVbSdi3sWk3fOA8eARuTZqnH38KC4uBjf/AJCbiax4OnXuVznSPuWl7ip3c0Et3ZMjN9MerWG2q7zaNl3EPCj0M9PQK6U0ndSFKfm7CLP7I26MpPIcU/RrI0KFE7wiNbDf0/kJbNnz36kN/0zWL58+exx48b9W46trzWyZ34CtWV6YvK3YHtxP65vTMZj5swHLHLLtfVM2pzAjpO3GFdhRhDLsbdZwLGgxnjoPWiemUlUShr5nYbjpV7Ia75OSGyd+b73BubsyedoajEuUSqMkm+RGbOZ22Een95xRCvAi1f2018ejdTogOOYJjxzs4IclZgeMgVLmvmyfv16tFotL7zwAhqN5qHXMX/PRdYl6XjP4ThDX/2wYRm4bzKk7MTSdR6lF8IwFepwGhSCfQfvBwQ+P38TafFTcF2oQJprxfurr3Ds2+fvjltGRQYTj0/kWM4x+gf3Z8ETC+53EvqzEQSBojvVXInLvm8YZTKYCe/oTaehIbTt3wj/SA0OLop/u5D9u5BIxTh52BEU7Up0N1/cgxywWgWyE0pJPV9I2sVC6uvMOLjY/ttSNRUREciDgqhYtx79+Qt0GPEOz4QPIK0ijY1pGyk332ZU826su1BMQZWega06I/FpTVFxHM6ZiRiNZUjbvQYKN2zurMZ0Owu9tgXyECfaOKlQScQszyul0t6RUW5ibt0r4HC2EVd7OVE+DZM3pa8DXe6o2Cl1p6zmIOcKLjE8ehpOXiKqdecpSzbiaq+i0GAiOCSEbJMJrYszA49uo6JJE87khhLpb+Fw1Vmcg5+iS/llilOukmfyxDs0HDtHOd6hTqScyScrsZxeU7tQde4KxbIAas7Ho5V74hPu8sgb5x9++GHh7Nmzlz/sscciT8Pyfs/8RKqLtTRLWYZTRSY+CxeiHjDggUE/nVnKqFWXKc2tYnyVGYtIjsl2KTd9GqOpd6FN4g1CikrIaDWKUNUXvO1nT5VcxbKea/k8roKjqcV4RDugFS1AZrzDvE5f8HWGgrtSW4ZcimOUgx+2lUHYDfXiuYJ6MqRW2ltsWN8hmC1btpCfn8+wYcPw+xX/6b2XUpl9rJCB8stMf208IgdPOPkxxC/BEvMGxVc6YtWb0IyOQPkzgzFBEMi+u4Csy5/ittAeaRn4LlqEfbdfb0VmtppZmbSSqWenIiDweafPGR05Gpnk4c6DfyRGvZnU8wWcWJfO9cM5VJXoCYp2pf3AxnQcGoJ/hAt2jvK/fKbJvxqxRITaXUlQtCvNuvni4q1CV1lP2sVCbp7Io+BWFRIbMWp35f1Qxr8KeXAwtuHhVG7cSO3x43g/PYB+TYfgJHdi562dJFQdomtQE3ZftqCQSegY1gxpUE8q7+3AMe0CdVITsnZvIJjNyPPWUZ+rQ1sYhCLcmZbO9jjZSFieV0atizuD5dVkFNWyN72GKB81gT+mVjoGO9PyqpQdai8qKw8QX3SF4c0/wMFDT3XNZcpT6nFWqcip1tIiJoas6mpKPT0ZcGQbpsAATha1IMSvmv01V2jk35UONVfIunGDEqs73qFhqNRyvEOcSD6TT/bNcnpN7Ur1hcsUSQMwXLtCLY74RfzjrLWH8fdE/n8+XGPQmdjzTQIV+TVEJS7Ey9sGn2/nP+CeZzBZ+PxQBqvOZ9PCQc4z2TWYBBllqpVUuDbCwWxPh0vx+BrNJEQ8T6xiFh8G2pCsULKo+zJWH5dwJLUYvxbOFAvfITek8FnHz/gh045TckeevnyYN53NqO90QtpTxUs2tlwz1RNVK3DomWbs27ePhIQE+vbtS4sWLR56HQl3Chiy8grR4izWvdIZuV8LuLYG9k3GEjyUolsvIJJJcB0TiY3HTxk0gmAhI2MWRTc24bbAEYlBgu/SJShjf72D072ae0w7O42ksiR6BfRiRusZqG3Vv/r8P4ryfC1Jp/LIuFyMud6CxldFZCdvglu63/fwf8xvp7bCQMalQtIuFFJTZsDOUUZkZx8iOnqhsP/zv7R/ji7+MnkTJiDRaPBbtQqZjzfZ1dnMODeDpLIk3MRtyErvwbLhHekR4YFem039ms44lFVT88w01DHvwa7xcHMzFaY3MXs/i2Z0BGKlDesKyng3I48Oajs6JVxgzW0lOrGK7RN+Kpay6s0cXZvIBO9r2FUspoVbcxZ3W0Ru1qdc3X2M4gQNtv6NKbdzomOnTpw7dw5brY4uR4+yo9MQNiuCCWm2kTLTHZbIGtE67QiHC4Jx7jOdln0awp4Ft6rYtyARB42Cvq9HcWrODu7qvQhVF9L9sxGPNG6PY/K/Qr3ezJ6vrlKeW0vTpKU06h6J+4zpiOU/bcrdLqnltY0JpBfV8lKUJz4nszEKcvLtV2F0CUFlVdD51GncbWyJb/w8rRWzWRxk5oSdks86fsGheHf23iigcYyGHGERcn0Cc9rN4Vy2Exts1LRLvsAH7ndwvf4s1igJrzdyIb5OT1CJkZMDY4i/cJ4TJ07QuXNnunZ9eHVpfoWWfl8fQmGpZc8wT5yjejU0/Vg/EItHB4rypiBxVKIZE4nU+aeNWoulnpTUN6hIOYL7QickZht8v1953xr5Yey9s5ePLn2ETCLj/dbv0yuw1x/3gTwEQRDIz6gk4WgOOSkVSGzEBMe6EdnJB7cA+8ez9T8Qq1UgJ7mcm6fyyE2tQCIVE9zSjejufrh4/7487t+C/sYNcsa9glihwP+Htcj8/O6vHJfeWAYWFcbCYWwfPZJwLweMtTmYVrRDrtVS3m8q7hFvwYZBCHcvUGr6CEHTCs1LkUjsZWwuLOfN9FxaOyhoceEUWwvcUNjZsee1jnipG8Ky5jI967YmMdvvGg7lS2nlEcuCJ77lVto73NibSGmSCzZ+jah1dOXJHj04duwYIq2ObkePcKDls6xRhxDQdA0GazmrzU6EZl9kT24YAYOn07xXQ/gzL72C/YtuonZT0vf1KM5+vJOQJ4IJfKrlI43ZY5F/CEaDmT3zLlJaYKBp+mqaThr0iw3GHdfyeH93MkqZhM+eCqFgWTx6QUWOw1rETsHYi+R0OngYFycN5/xGECufw76gWjY72PNu7LskpzVjy9VcmjTXcEe8HNu6y8xoPYPiEj8+tSgJz05hnud53M/1x+ouY0pbT85p63C/p+PU4JYU3rvN1q1biYqKon///g8VtDqjmQGf7yRfK2Jndy3B3V+E0gxY+SRWmTuF5R8j9XRF82IEEtVPszKzWcfNpFeoybyEx0JnxCYJfmtWY/uQ9oAAOpOOjy59xP6s/cS6x/Jpx0/xsHs0n41/BqvFyp3rpSQczaE0pxaFvQ1RXX2I7OSDreqvlb/+30hFoY6kU3mkXyrCXG8hIEpDTC9/PIJ+udn/Z2BITydn9IuI/l/ofRtcSVPKU3j75Dvka/OQ1fYk7oUP8XBQYqm6i2VZWwSLntJnp+MTOBZWdkfQVlCs/xLs/dC81BSpsy07iyt5Pe0e0Uo5TU4eZ1+VH4FuDuyc2AE7ecOK0HCnis+PZ/C911UcypfR3rsd8zt9SXLSWJL351Ge6ojYtxFGVy+eeeYZ4vbvx1ir5YljxzgV1YMVbqF4ha9ELhHYUG3GrSCVrXcjaPrCB0R2fRKAnNRy4hbfxMVLRb83on/Xnshjkf8bLCYre+acoLAEmuXvIOaz11BE/jR7rTOambknhe3X8mgT5MznTzfh9OxDaHGiwG49Iucg1BI5HXfvwdEngDMeI4iUfUZyQB7fOqt5IfwFtIVPsebCXZpGu5IuXY2t7hxTYqegNjRjYqUZz9IC5nscwutMN8QSFz7o5c3RWh0Ot2o4PCAGW1MNq1atwt3dnRdeeOGhufCCIPD6kt3E5UhZHZFEl+eng64cVj6BVVdLcc0XSIOCcRkVjvhn4QyzuZbEG2PQ3k7EY6ELYpPo7wp8SnkK755+lzxtHuObjWdc03FIxH9OxafVYuXWlWKuHLhLdYketbuS6O6+hLbx+FOrTB/zcAw6E0mn8rhxIpd6nRnvUCdievnj08TpT19FGdLTyXlhNCKl8gGh15l0vH1iJueLjqC0hLBr8CK87D2wFicjrOxCncxKWb9pBDg/i2hlN6wKD4oqPwWZCteXIrFxt2NPSSUTUu4Ro7DB/fBxjuuD6BHuzpKRsff3I2rO5fH23UKOOp7HvuJ7egX04qO2M0hIGEnafi0VmSqsfo2RePrRv39/du3ahbaqis4nT3G1cXsW+zXGqfFyfFXurM3NwbaigI1ZTWkzfhZN2nUC4G5SGQeXJuHmb0+fSdGPHHZ8nF3zMywmM/um7aWgSkEz/RnaLH4PeWDA/cczi2t5/vvLnL9TxqRuwczt2ZjD0/dSI9JQqtwMLoG4Sm3ptG079o3COOU2ggDxImr8MvhE40zvwN4oagaz4uxdYqPcSLbZgEJ3mlejX6WlvBNjcipR1Ncxz/koPtfCsNH689HT3hzS6pClV7OxRyQBDmLWrl2LjY0No0aNQvErDbBX7jvF98lW3nW9zOCx08Bqblimlt6iTDcLaUQLNM+HI/6Zx4nJVElC4ijqslLx/AcCLwgC69PW886Zd5BL5Sx4YgF9G/X9UwqbrFaBzMvFHFmZQur5QlROtnQZFkqnISG4BTj8T6Y//hWQyiR4hzgR2dkbhcqGuzfLSD6dT15GJY6uSuxd/nl76d98bI0Guw7tqd62neq4OOy7dUPi4IBMIuPpRj0oqlBws/owWzN2EO4SQoBXS0Se0ciub8VUcIlCHwecI19HfHkpdv5laGtaU3e1BHkjNRGejvgrZHxfWIlDY398s1M5WyxFEATa/uhaKfO1p1V6LZcED4qUCu4V7abCWMug6I8wKrejLbVSn1ODSWJDXnklQ4cO5XZ2Nunu7rROPE8ji5Qz0vZobU+T6tuU3lWlhNoVs+9oJmq/YJy9fFC7K3H2tOPG8VwMdWYCmj48a+4f8Ti75kfMNbUceGsTeUYPmtpl0v67yUgcGgyBBEFg27U8xq27ilWAFaNi6R+mZtfb26gSe1Ml24pF44e7jZwOm7egiIjhlHoIHuI1uPrG87a7K7EesQSLXuW749m0berOVeVulNpDvBgxhoEeAxh8JR2dQsVs6VmCb1uxL2zPZ73d2FuvR5pRzWctAniyiYb169dTXV3NqFGjcPkVj5gLiWm8dbCIXrYpzJ48EZHMDmHPBES3DlNRPwVJ9FM4D2nyQJFTfX0p1xNGUn/vDh4LnRGZ+FWB15l0TD07lXWp6+ji04Ul3ZcQ6Pj7m678LYIgkJVQyqFlyaSeK8BOLafL8FA6DA7G2cvuccz9L4JEKsYjyJGmXXywU8vIulFG0sk8irOrcfJQ/mkVw1JXV+zat6Nq23Zq4vbfF3qRSETXwOZk3QskreoKB3O3YLQYaRU+FJGtGlXSEWqqrlPs44yLzwDEV5dh18wRXVU4uktFyP0diPJ1wktuw+qSGjT+rjjk5HDojoHGbipC3Bv2e+xCnWh1qoQDLsGYZVZS83dhEUl5Jmoqetv1aAvkWAqrqbFAmVbHsKFDyczKIk3jQkzKZcJ0Es6pYigQn6AsuBtPFiYT6FDL7oNpuDcOR+3ugbOnHRpfFU3aemAje7TV6mORB+rv3ePIW2vJkYcT6VtDp7kjEP9Yoq+rNzN1RxLfnbhNq0Bn1r3cisZ2Fna+tYkKcSA66U4Mbl54yOS027QZebO2nLQbhJPNDqI8DjPR2w13R386O37Al4fu0j7cjXjHE9hVb6Nf4wFMChnHc3EnyPb051X9VVpVXUeTOZTPuzqxU2REequG0e7OTO4WzO7du7l9+zaDBg0iKOjhDTTyi8t4ftVlvEVlfD++B3IXP4QzXyCKX0q1aSRCizE4DQpBJPlJIA2GQhISR2AqLMD9OydERuFXBT6rKouxR8eSWJLIWzFvMbXVVGylf/yMrSirmiMrU0g8lovSUUbn4aF0fCzuf2nEEhFuAQ5EdvZGrrDh1rVibp7IoyJfi8bX/k/ZL5G6uqL6UehrDxzAvldPJD/2b+4aHMjZ6wEU68pIrNnPjdIbdGz9Brb6KpwyrlJkSqHMzxNXRTPE11dg90RL9KWe6C4VIvN3oLm/My4yKesr6vDwVCLJK2dfSiVdm7jh5mCLSCLGMdiJyMP5bPNtiqNEx+WcHahs3eke/ip1tmupueeAuKyGYoMJg8XKwAEDyMjOJt3ZiaiMRMJKJcS7hpBuOYUsYgAdcs/j4WBlz8FkvMMicdC44eRh98gCD39f5H/XGlgkEg0WiUQpIpHIKhKJYv/msWkikei2SCTKEIlEPX/PcX4vuosXOTFpGffsYwgLk9Bp+k+OjVmlWvovPs+uxHze7B7Cupda4yKqY8eU9ZRLQjBI91Ln5o6XTE67DRuxadaOE4qB2MsP08F5H296uSKydaS/12zmHcihbaiGKy5XUVaup5PPE0xr9hbjN2wlNSCMPlVpdBJ24JHyEl+2sme7zIw8u5Z2gg0zn4ng3LlzJCUl0bVrV8LDwx96LYZ6E+OXHsBkhWUD/FF5N0FIj0N08mN0lq5YY99osCkQ/1zgC7h2fRjG0hI8lrhCnQm/Vd8/VOAP3z3MsLhhVNdXs6LHCkZHjv7DBbeqpI5Dy5LY8fk1asr1dB3ZhCEzWtKoudv/bNHSfxo2MgnNe/gx6qN2tHw6gJzUCjbNief89lvU15n+8OPZhofjt3Illqoqcsa8hLmiouE8JGIWD2+Dg3Y4iuqhXC2+ytC4YaS2fgnBvz3htwzUZW4j1R+EwM6Ij72N6zNGJE62lK9JwXC7khe9Ncxp7EWSwgFlrAapUM9Lay5TUmMAQOpkS2z/JsxNMpCjGonGuRPzr8/ncGEa0THfEvTUHWRKCw5F2SRcvMD169d5+YUXcPH24UL7dvjV3ePV03WItNF8l3eYA+1exF+ayxPeeez6bDbFWbf/8PH6Ob830JkMDADO/PxOkUgUDgwFIoBewGKRSPRv2TWr3LyF8zPXkeXZnZAoe7q+3um+aB1OKaLvwvOUaY2sG9Oayd2DEevK2TZlJeXiCEziOGpdnfGV29J2/QakzdpxUjEAld05uii38L6PmkKZnNGN5/DJ3lKaBziR7HkLedkyolxj+Lz9x3y4dAXHI1rTrDKH5x0X4XXzNRaEq9jqBA75erxLTCwZ0YK7Wbc5fvw4kZGRdOrU6aHXIggCM5ZvIUnvwjetawmK7YFQkgFbx2K0NsbU8hPUzzb+G4Ev5Pr1EVhqKvFe4Y21pArfZUux/ZsvEbPVzJdXvmTK6SkEOwWz9Zmt961f/yjq9WbObb3Fptnx3EutoFWfQEbOaUt4B6/HMff/UGQKKa36BDFiThtC23iQeDyX9TMvkXw6D6vF+o/f4DegaBqJz5LFmPLyyH15LJbaWgBc7eUsGdmCquIWBBrfxSpYGXVkDLtbDUfs6EOLdAuVubvJiPJFcPBCsn8MriPckbrYUrYmFUNmJeN83ZgR5EmGWoO6mYIyrYGxa69gMDV4+9s2VtOnpR8TbptIs3sRP+dWzL04l6u1BiKaf0DgU5lIJGYci7I5fvAAt2/fZtwLo3D29eNSmzb41+cx9rAcsSGI94tPczV6MJHyDFq4FLPj01lUFOT/oWP1c37Xf5YgCGmCIGQ85KF+wGZBEOoFQcgGbgOtfs+xfvO5WSwUz/uchCUHuNVoEIGRTnR7JQaRWITFKvD5oXReWXeNRq527Hu9Ax2CNQg1RWya+h3lxGKRHKPKzZ4AhYLWP6xD0qw9p+wGoVLfoJ1kHUt95VyzlfFSk6l8uddEIzcVBY1LEErm4+8YxJInvmXV8uVsatoRT20l77p+h3vSCDa6ebLO2waPChPijGpWjIpBZNSxc+dO3N3d6du376/OnNfvO8yOfCcme6XT/dkXEeqqsK4chNUixRCzEMc+YQ+81lBfxPWEERi15XitCsCUnY/PggUo/6agqrq+mvHHxrM2dS3Dmwxndc/Vf6ixmCAIZMQXsXHWJW6czKVJO09GzmlDy6cD/+MaXzzm4dg5ynni+TCem9YSZ087Tm/KZMvHVyi4XfXHHqdVK3y++xZDZia5EyZg1esBaO7nxIf9Irh2y56OhUbT3wAAIABJREFUdh/R3L05M6/OY27TblitIlreVlBYdpCsVi0R9FVIDoxDMyYMG1cFZT+kYMio4HV/d94OcOeOmwduTUQk5tcwe2/K/WOrOngz0cGBJ4sErivHEeDclOlnp1MoCSa46WgCet5CZKnHoTCbXdu3UVxczPhRz2Pv60d861YEWgp5Ic4ZoV7NZH06ucHdaOeQhL9tMds/fp/a8rI/dKz+nz9r+uQN5P7s97wf7/uXYK2rI2/yZG7tiSc97Hl8QtX0fKUZYomYCp2R0asvs/jUHYa18mXLK23xVisQqvJYO2selZYuCJKzVLjKCFIqiV37A+Lo9pxSDcLBNZtIwypO+ZjZa2/H0MYvs/KQExp7GdZm9WgL5+Gm0LCm53JObNnGQr9oZFYLc13X4HwnnCPSGBaGyAmsh8orJXw1uBnBGgWbN28GYMiQIchkD68wTE5NZe4FA11s7zB53HgEwYpp6UjE9fnoI7/Bvm+bBwS+vr6EhISRGHVl+G1ogin5Nt5ffIGq44PtAbOqsxgeN5zrxdf5qP1HTGs9DRvJHxdXrSjQseebBI6tTkXlJGfQe7F0HdkEO8f/XhfI/2Vc/ex59q3m9HolEpPBwq4vr3NiXRoG7R8XwlF17oz35/PQX7tO3uTJCMYGP/1hrfwY2tKXVWfKGOo7lxcjX2Rr7lHGh8Wiryoittifu/qT5Me0g3vnkZyfg+blpti4KSn7IRV9egVTAjwY460h288H1yArm6/ksvlyDtDQp9d5QDBzikWE1NmQrnoVdzsfJp+YjNW5PwHhXfF/MgvBoMOuIItNGzZQW1vLpBdGIfP152rLljQWyhi+zxutwchEhRGtRyS93ZNwMBVwec+2P2yMfs4/FHmRSHRMJBIlP+TW7484AZFINE4kEl0ViURXS0tLf/f7mYpLuPf8KAou3yYlegIuvg48NT4KiY2YpLxq+iw4R3x2BfMGNuXTAVHY2kiwVGSx+JMP0en7gDieMleBRkolMavXIIpuzynVYJx9ynArW0GZbyXfOat5wqcncWcjEYtF+LZTkJ//MQ5SGWt7riD75AXmYU+typEPHPfiXFDKjaqhfBpuSxOxlILT+bzetTG9m3qwb98+SkpKGDhwIM7OD++YVFNTw6sbr+MiquXrl3shkiupX/EespqzGALewW7QgL8R+B+zaOqK8d8RTX18Ep5z5+DQ68GtkXP55xgZNxKtScuqnqvo1/gP+UgbPod6Cxd23GbLR5cpy9PSeXgoA9+LxT3g0dqbPeY/B5FIRKPmbgyb1ZrmPfzIuFjEhlmXSLtQyB9Vl+PQuzceH85Gd+Ys+e+9h2BpCKvM7htBmKcD72xLYmijCXzS4RMStDmMaNSE0ns3iTK1JUN+g4rgKIhfguTOLlxfboqNhx3l61Kpv1XFR8He9HdTkxfsi8pXxAe7k0nMbViRiOVSfIaF8dUNA1KzkirXd5BJbZl4fCIu/u/gHRaCX5cihOpKxDm32LBhA2azmXdfeB6ztx/XY2IIs1QybE8Qd6tzmOIXjFWpYXDjO3Tp//QfMjZ/yz8UeUEQuguCEPmQ256/87J8wPdnv/v8eN/D3n+5IAixgiDEurq6/raz/xsM6encHTKEyoIaktu+i8JJyTOvNUOmkLL1Si4Dl14AYPv4tgxp2WDyZShJ5fNvZkD1EBAnUOpWTyM7O1qsXgPNO3BaNRi3QD3inOV4+93jfXc3mrpEkXbzKWr0Zjp39yQh5yNsqWNVj6XUZ+Tzacptsv1CGSu/RkD1IQryZ/BBUwVN5DLyj+XSJdiVt54MIT4+nqSkJJ544gmCg4Mfek2C1cq0pVvIMzuwoLcrTt7B1G1YhW3hCupd+mL7wnsPCLzRWEZC4vMY9PkEHOmI4eQV3KdNRT1w4M/HnHWp63j1+Kt4qbzY/PRmot2if9fY/5y8jEo2z40n4WgOoW08GPFhGyI7ef/LTa8e8+/FRi6h3YDG/B975x0dVbn97+fMZCa9zaT33hsJndCrtIA0AcFypYmIShMRkC69K6CigoDSe+8ltEA6JCG9EdJ7nzm/P+IV/V6wgNzfvdx51pq1smbe857z7szsObPfvT976KwWmFjqcX7bfQ6uiqQkr+pvmd906FAspk2j4sRJHi1egiiK6MikbBzRjPpGNZN2RdLLqQ9be26lUkvO63Z2PIi5gKdef6Isc6g2t0E8/D6S8oSmIikLPYq236MhrYx13o50NjWgyNsawULK+G23KapsasYis9LHu5crn9+tJqvBEEvHT6lqqGLihQ9w8VqBjZ8+tq0qkRTnU5UUx969e5FIJMx+czSVVrbcad4cv/oKhh91JvzRLVYE90HaUI302uq/xS7/lxcVrjkMvCYIgrYgCM6AO3DrBZ0LgMpLl8gYMZI6iS5xHWaBlox+kwKRG8iYfTCO6ftiaOmk4Mik0F/kRSvzYpmzZSbGj95ElN6jwKICF0Mjmn37HQS05pLBEKw9oCrlK0Lt7vGhjSVGumZUZ40ivbCeEX1cOJaxGFlDNus6rcKkRMKavfsID+lMe3UW7euXUZW1iGneBjhpy6kOf4SFvjZrhgWRmZnBqVOn8PLyIjQ09Knr2r77R44V2zDNI5/m7XtReegCug9m0ajni3z8lt90qWpoKCcy6i1qarJwixpIzcELKMeMQfHGG4/HqBuYd30ey24vo7N9Z7a9sg1rA+u/5X9QX9PIxZ2JHFodiSAIDJzSjC6jvf/tIlca/rNQ2hrw6tRgOr/uRVFOJT8tvE3kmUzU6ue/q1f+420Ub71FyY4dFG/dCoCLuQGLX/XnTkYJK08nEWQRxK4+u7AyduJdCyVX7pzC2XIkd1xrUcm04MeRSKho0rcx1abwu3uosyr4xt+FIH1tKv3NyZOLvLfzLo0/bybrN7ck1MWM9xNruVStoJPPXNLL05l6dR7efpuwCinHwleNvCCXjDs3OXPmDNoyLT59czTF5tZEtGhOUHU9Q0/a8EP6cfZ1nwq9Pn9uezyJ502hHCgIQjbQBjgmCMIpAFEU44HdwD3gJDBRFEXV02d6PspPniRrwrsIzm7c6/QpNdUifSYGgKGM0d/cYvuNDMZ1cOH7t1ui0G9yOCUPo5m+bSrOGWNRyVIptCjG2cSE4G+/ReLVjEvGw7D11qYk9Tt6WUbyiY2CYi05VrUTiM5UMaGvJ99nrUNeG8us1rPxk7nx1bpVHOn8KnaNFbwjmYXq0TQ+crTBTKaFTUoVxaW1fDEyGKmqlj179qBQKBgwYACSpzQKiL1zjYVRenQxzGLs6LcoP52Azt3JINNFOnYPguxxJaxKVUN0zBiqqh7gnj2ays37MOrfD/OPPvxlTFVDFZPOT2Lfg32M8R/Dqk6r0JP9cb/WP0NGfBG75t/k3pUcgrrZM2x2S2zcTf+WuTX89yNIBHxCbRg+txUOvgrC9yVzYMVdSh9VP/fcFtOmYtT7FfKXr6DsyFEAwoJsGd7SgU2XUriQkI+NgQ3b++ykvUUwiw2k/HjnLAr714j0lCCWZ8OBcUj1tDB/JwCpoYzCb+PQyqtmV7AHTnIpDUEKrhVWsOzU4zwTkzBXRtfJ6F7QyLYSa0YFzSLiUQSLI7/Gz28tNm2TUThro5uXye1zp7l79y4mOtpMf3M0BUpLbrVsSfMSCQMuKph/fxu3C2Oe2xZP4nmzaw6IomgniqK2KIqWoij2/NVri0RRdBVF0VMUxRPPf6lPR695c4yGDCWh/XQKH9bSY4wf5foS+m+8yp3MElYPC2Rmb2+kP4cLHuXeYdLuSfgnTaBe+yFF5o9wNDUlZOu3SF28uWT2OrbexhRn7aSTwS22WMm4o61FkN5Yrt3TY2IPD74u3IG88iKjfP9Bf9ue7Fg8h73tByBoSZmhsxShohPTTVog0xLo0yDj5v0C5vb3wdfagN27d9PQ0MCwYcOe2L4PoLzoERP3paCUVLFy/KtUXc1FeuVjtCRZCK99i2DyeB9bra4nNvZdysru4FE1jorPf0CvTWtsFi78JZRTUF3AWyff4kbuDT5r8xnvB7//t8gT1Nc2cn7bfY6uj0amLeXVaSG0G+z+XIUdGl5e9I21eWW8P93e8qEkr4qfFt4i+lwW4nPc1QsSCdaff45eixbkfvIJVTduAjC3nw9eVoZ8tDuKh2U16Mv0WdNrK6PNW7FTqOTr2DtIXQeQ5KwDD07D9fVIjeSYjfFHoqNF4Tex6BfVcaClD0otAVWIKZsiMjge+xAAiVyKcqQ3s+/X41gHW8u8eCfwfU6mn+S71Jt4eM7CtlMMhha66OemcWzvbjIzM7HT12XcqNfJMzXnZutWhObq0DNczo8xV/4WG/9fXorkZKlSSYLzELISyug00osULRUDN16jrkHNT2NbM7DZ4y7oWTk3GXN4Au1ixlOnW0qJWTYOSiUtvv0Oqa0zly3fwMpLQXXJfnwbw4mwrGa3kQHNTQZy9rYdI9o4sLvhIpLi3XR06MVHgRM5sHwhB7xbk29mzRTdHzGurWGR8AYVcoGPlEq+P5/CoGA7RrR04OzZs2RnZxMWFoaFhcUT1yOqVMzYvJcctQkbBrkhS1HReGYT+tIL0PFjBLdOj8eKKuLvTaGo+DLu0veomrsDbRcX7NatQ/g5Uye1NJXXj79Oenk667usZ5DHoCee96+Sl1rGT4tuk3D9IcG9HBk2q+W/TaVQw38vgiDg2cqK4XNaYedlytU9Dzi4OpLyoppnnlMil2O3cQPaTo5kv/cetYlJTfH5kcFN8fmdkTSq1EglUqb1/prp+l6cq81ldVIKBd7deWQmRzz7GWTeRMtEB/Mx/qAloeDrWBTlDexv4YO2VEAdouCjA9GkFzbtK8jM9bAb4M6y21VU1zdyprEjQz2H8f2977lRrYe90xDsu0WhrSdDL+sBP23fRmlpKQGmRoQNe42Hxkqut2lDtxRjekfr//4in9U2L2TWfzP3wx+ScCOPFn2cuNBQzZjtEbhaGHD4vVCaOTwOGTzIvMKbJ8bRM+If1OhDmTIde6UZLbdtR8vcmqt2/8DMwwIp5zF6eBGpdR5LzJR4GbXg0o0WdPO2JMo0heqHX+BpFszK9gs5t3UTJwVd4jyDeU12E4/aE3xZv5RkPQkLzc1Zd/g+npaGLBzgR0JCAjdu3KBVq1b4/o5m+48/beNEuRPTfCvxlrtSdfAkJrItiC5dEDpO/2WcKIokJM4mP/84LoYTqPt0PxJDQ+y3bEZqaAhARF4Er594nTpVHd/2+pb2du2f295qlZrbx9LYv+IuokpkwJRg2gxwRSp7Kd5OGv5N6Jto0/vdALq+4U1BVgW7F90m+U7+M88nNTLCfvNmJHp6ZI0dS0NeHq4/x+cjMkpYc/bBL2NHDdjBikZj7lVksDQ9j+jAttRoC6h2D4eqIrSUupi/4w+iSOE3cTirJWzzd0bUkVLhb8q4HRG/FErpBZrj52/JpzE1RFRUU6sYRahtKItuLqLE8BUs7IJx6pmEFDUkx7Hzhx+oq6vjFWszAsJe5ZGRguvt2qJweDGNd16KT6VnSytCR3jwQ0Upy08l0i/Aht3j2mBl/DgUEpt+jjfPTqRvxAjq9cyoNE3CWqGg1Y4daOkbc81pLCau1phZxVMefYIgx3Q+srbGTNeWuKh++NmaouNXR0b6Esz1bfmm2zriz5zibGwsZ0P70UzykN71K9jbuJnr+jI+kxny45V0VCqRTa+HUFtVzqFDh7CxsaF79+5PXUtKTDjzY4wJNcxjdKuelPwYgZnOMjBUIgz66jeNflNSlpOb+xMO5v9AnH8JdW0t9ls2I7Nq0nk/mX6SsWfGotRRsqPPDnyVT/9i+bOUF9ZwYGUkt46k4d7coin27vbiu0JpeDkRBAGvNtYMm9USYws9Tn0Vx4Xt92moe7YtPJmNDfZbNqOurCRr3HjUVVWEBdkyJMSOLy4mcyutSQ4BLTk9Bu1kS1EVxZUPWZxbwrkAf4SqIhr2DAe1GpmFHmZv+aGubqTgmzjaGhqx2MYYlbGcWEs5848+LpQy6eNMb1HO8NxGvskpoaP3LJyNnZl6aQYG9tMwsTHGpXsxkppKyqNvcfDgQURR5F0XW3R79OGhkYLb5i+mP8NL4eQLquuZHZPOkdiHTOvpydrXgtD5lfb4rZTjvHPxA/pG9UaQuVNhGouZwpS2e/aiJdPmutsEDJxscAsqJP7ETvq4p/GBtQW1WjoUp72OQteYrl3NOZfwGTpSGdt7bqL4fjIndm3nUI8RmErqGaf6mIvSRRyRGzG+TEJOaR0x2WWsGBqInYk2e/bsQRRFhgwZgpbWkzWj6ysK+WB3LNqCiiV9u1DyQwJK3XVIyEcY8j3oP5Yhzcr6jozMzdhYDUd7XTp1qWnYrVuLjocHAD8l/MT0S9PxN/Pnh94/YGvw/LVoDyIe8ePCWxTnVtL9bR+6v+2Ltq6m7Z6G58fYXJdXpwUT3MuRe+EP2bPkNgVZFc80l46XF7Zr11KXnEzOtOmIajVz+/tir9Djw5+iKKv5uTDL1JGQ3uvZnpODdn0tC0tqOOzhgCz9JnUX5wAgtzNEOdqbxsIaCr+/xyhXZ97Sqkdtqcv3ZeUcjmrKDBdkUpTDvXg/oY6gWvgkuZgpbVYhk8r44PIsHDyXY+RYjFN7KbLyYlKunOPatWsIgsDnvi6Udu6Nwsf/b7Hl/+WlcPLR2aWkFVSxZVRzJnZ2+03e+NUHR5hwZQa97rVBT92WCtMYjI2NaH/8BFr1jdz0fBdte1uCu0u5tG0jgzxzWG4qJ1FLgnbpaOprzPhgkBdfxsxFS1XIpq5r0S1Tc3j1Ek51fpVKAyMmS5aTKB/CVrUH/QpUtHZQ8F14Om+3c6anrxVnz54lNzeXAQMGYGr6lIwTUWTVlm+IbbRjSQdzpIfyMNQ+jE7jNYRu88Ch1S9D8/NPkvRgIeZm3TE9pE/VlStYzZ6Nfps2iKLI17Ffs/DmQjrYdWBz980Yaz9fnFzVoObyj0mc/joepY0+wz5tiUfLF9cVSsP/JlKphDYDXAmbHER9TSN7l0YQezH7mQqoDELbYTlzJpXnz1OwejUG2lqsGRZEXnktcw7FPR7o3ReX4HfYnp6MjcyIBY0yDlorkF9ZT13KMQB03ExRDPOkPrOc4p0JLGrbnPaVRagcDZh8K4W0f8bnrfQx7+3M4huV6KhFPk2vY3nHteRX5/PprY24ey7F2CsOmwAjtAtyuXhgDykpKcglEnY1c2Ow1ZOLIZ+Xl8LJ9/S14vL0znT3+a3eyqXko7x/7RO6pnmhqOxLuWkUeoZ6dL4Wjiy/gEi/CUis7ekwzIxTXy6jq1MBlwxKOWKgi4WqL7m5ziwaGsj8+BXIauOZ2Wo2vnpu7FvyGbc9gkly8mak1jHqBUPW1/elZVEj0zxtmXHkHn62Rsx4xZP79+//Eof39vZ+6hrCj2xlc4EPr9mVERSli0x4gKHqG/DqC20m/jKupPQ28fc+xNgoCNt7HSj5fhumo0Zh+towRFFk9Z3VrL27lt7OvVndefVzSwSXF9Wwf8UdYi9mE9jVngFTgjEye3ITEw0a/g7svBQMm90SB28Fl39M4szWe88UvjEdOQKT4a9R9NXXlB44SDMHUz7o6s6hqFwORGY/Hth9PhaWAXyb9gBPI2c+0zXigIkh7HmTxvImSQO9AHNMwlypTSimdH8yWzu1wqW4gBp3I4YfifolPq/f2hoHVwWfRVaTUFXLnjIli0IXcTf/LhsfXMXZaRJmLW6hcFCgm5vO3m3fUVJS8kKltV8KJw9gqv/bgptzKUf54OpM2uXa4JD/OuWKGOR6MrrF30OWmEh80DjqzJzp8bYzJzYsxt2wAAxS+NxMibk0kOSkNiwY6MfS7J+g7Az9PV5nmGs/Dq9cRLKgxfnWPQmRpuKnusBa9Qc4VapYb6Tkk/BUVGqRDcODqa74c3H4kuQIProux1m7gnG1boi1FZjpr0QwsID+6+HnN0BVVTIxMePQ0bHDvX4C+QuXoB8aiuWM6ajUKuZdn8e38d8yzHMYS9ovQSZ5Pg2a9NhCdi+6TemjanqN8yN0iDtSjVqkhn8DugZyek8IoFWYC8kRj9i7NOIv59QLgoDVJ5+g16Y1D+fMofrOHd7t7EYLJ1NmH4wnq/jn+bS0YfC3mKjVfJVfTIhFMHNNTNkvl1O5sztqVZPksEFrG4y6OVB9Nx/VlXy+C/HAtKyMdHs9Jp2I/+WcpoPcaV8r4a08Ndtzi6jVa8XEoIkcST3C2UpdzC07YtPpDrpGekhT4tm17Xvqf9bfeRG8lJ/Y0ylHmXplJs1L9PHNGEupIh6pDvR8+BD5zZukNn+HElMvXhnvw7lvlqNTnU1zq/t8ZGODrpYZqff7M6GjG4cbIyl7uBUfi7bMbzWVC99tIS0lmUM9R2IiqWN440rWS5Ygq4eN+VJ2q+uIyChh0UC/38ThBw8e/NQ4vFhXySc/nKdINGKeqS2yknosXXchVGTCq1tAr+knXF3dI6Ki3kIikeFruoBHH85C7uiI7epVNAoiM67M+KXIaVarWc+VA69Wi9w4mMKxjTEYKnUY8kmT1rsGDf9OBIlA81ec6Pd+ENXl9execpuUyL+WfSPIZNitWYPc1pbs9yahys1l9bAgBOCDn6J+qWBF4QxhG9DPucsXWNLJvhOLlQr21FSTe2wAotg0zrCrA/ptrKm8nIN1lpSVZlrI6+o5Im/gu7tNmoxSfRmmwzwZF1tFUIOEqYlZ9HR/kz4ufdgQtYECo4EYmlrg0isLLUQq717n8KGDf5uuz//lpXPyJ1OOMv3qTAKrIDRhMoXKFJA30qu2Fu3TZ8huPpJso0D6vOvPrUNfUZx6j8FeWcw0V1AokfIo+TW6eThT71hFzIPFmOo58E3XlcRfOEvUmROc6P4aFfqGjBeXsV02m/xGbVbG11PW3JIvLqUwtLkdYUG2nD9/ntzcXMLCwp4qPAawZ9sGTtT68p5ZA855EsxbJyBN3Qvtp4JTk9xBY2MlUdHv0NBYhr/rOgo+XACA/Zdf0KAr4/0L73Mq/RRTQqbwfvD7z/XTr66mkeNfxnDnZAbe7awZNC0EE4u/pypWg4Znwd5bwdBPWmBqpc/JzXGE70v+S5IIUmNj7Dd9iahWkz1hPNYykYUD/biTUcLGCymPB/qEQYt30L7xJavs+9PHuTfrFCb8mH6fjDszgKY7dZN+ruj6m1F2PI2ORh68V5GFIIVZ2XkkFVQCoONqgmlHexaElyNRi4y/l8Enrebgq/Tl0/AF6Dt9grZpCV59pEhrq0g+c4ybN2/+rXb7Jy+Vkz+SfJgZV2cSVF1H34SPyFbkoZJX00tXF929+ygMCiPZqC2vjPMnPeokSdcvM7JFNV9rV3NdLkFVMABXIy9e6WzBzsiZyKVa7Oz5BeXp2Zzb+iXRQe1JcvBgiLCfW7K+RDdaMTuuBt+2dkw5Fo+ruQGf9fclNTWV8PBwQkJCntrhCSD71mHmp7jSUqeYgYUWGHeUoh37Gdi3go5NbypRVBEXP5mqqkT8fNZRMe876jMzsV23DpWNOZPOT+JazjXmtpnLm35vPpf9Sh9Vs29pBFnxxXR4zYPOr3uhpalc1fAfgKFCh1enBOPX0ZbIM5kc2xhNXU3jnz5e7uSE3do11KWm8XDmTPoH2jAgyIZ15x8Qm132eGCPhWDmiezQeywOnsow5358b2zEttt7ycncATT9wlAM9UBub0jJ7iTGNGtPz5R7qAy0GBCeQH1jU3zeqJsDjhYGzI6tJbqihpUZJazpvAZdLV0+ubEGO9c5yCwj8ehsi6ysiNLEuCdd+nPz0jj5gw8OMuvaLFrU1PJ65mQe6FXToF1GNwsL9Ldupdy3KzEm3en2lg91lYmE79lBvxAtYuoT2GJiiE5ta6RVrZk71I85Nz5Fq7GADV3WYKrS5/DKReSb2XKhZTcCJUnIBBVnG5vzdmo9A8xNmBWfTXlNAxtGNIPGeg4cOIBSqaRnz6d3PVSX5TD9cDKiIGVarTWGLc0xyJ4NggQGfQ3SpvDOg+QlFBVdxMPjM8Sf4qm8eBHLGTOQhPgz6dwkbj68yYJ2CxjsMfi57JcRX8SezyOoqWyg/+Qg/DvZafqsaviPQiqT0HG4J51GepJ9v4R9SyMozf/zcXr91q2xmDaVijNnKNryFfP6+2FuoM1Hux9vnCLTbfr81RQjOTKZWaELed2yHXv0DNh4di5FxU1KtoJMinK0D1JjOTW7U/mkVQieD1IpNtJi2KX7TWOkEhRDPelS0MjwMoHN2QXE1OiypvMacqtyWZFwHhvb0ei6n8WllQdegc3+dpvBS+LkT6WdYk74bFrX1DC+ZAx3GuXU6RYQam+PYv0GatxbEGE2gI4jvDA2q+L4xpU0c9dHt/4qM62s0RUdKMnsy9oRzZgSvR5JTRTjg6fR2jyII6uWUFZdw6GuQzCQ1NFWdYId4lC6laiZmC9ywFKLKw8KmdPPB09LQw4fPkxVVRWDBw9+agMQ1Gp+2LqB8EZPJiHi7GmFieFuhJwI6LcGTJpkkLNzdpKV9S329m9hnGRN4YYNGIeFoT1sIBPPTeT2o9ssCl30XDrwoigSeTqTYxuiMVToMOTj5th6aoTFNPzn4tvelv4fBFFT0cDezyPITij+08cq3ngDo759KVizBumdm3w+yJ8H+ZWsPpv0eJB1AHSdC4nHECK3Mb3nl4zStuOIVJdFp96hsrIpxCM1kGP2pi+IYHSumo9t9TDNLuK6pJHlcU3xeS0zXYz7uPDerXJ8BC0m38/E3MiHWa1mEZ4bzvFyPUxNWmASfAql64sJi74UTr55YTrDyyr4WDWCC/nm1Ojn0MzOHtv1G2i08+Cm1QhahbniEmTAoRULURhIaGN4myk2dtQJOhSkvMb8/s34Kv88lQV7aWHXm3f9RnD+283kJt13dpf5AAAgAElEQVTnXKeBFBuaMFDczveSiXirpMy9U0VhF1tWnE+mp68lI1o6cPfuXRISEujatSvW1k+X7007u5klj5rTVihlgLUzinZFCNdWQ7NR4PcqAMXF10hK+gylsjOOWiPInT4DHR8fjD6dzsTzE7nz6A6LQxfTz7XfM9tNpVJz4YcEwvcn49LMnEHTQzTpkRr+K7D1MGXwx83RN9Hm8Lpo4i5l//FBNMXUrRfMR9vDg5ypU2mrV8fwlg5suZzKnYxffVm0fhdcOsHJmQhFyUwbuJc36wTO1EuZeXoodfVFQJN2jXK0D40ltQRlWfJOeTKy4hpWPirkUkFTGEi/pRXG7qYsvFZOvVrNpPuZDHQfxDDPYXx3bxtZBmHItIzJe/R7LTqenZfCySsDRzHBZTqHk1ypMs7Aw8wCr61bEQ1MuG7/Ft6dnQnqbsuRVUuoKS1mmF8BKw2k3JeqKc0cwhstgilSFnI3eQUKAw++7DSf2POniDl7kiS/1sS4+NKFsxyXvoaBIGf5lXKMWloxPTwVYz0ZS14NoKioiJMnT+Li4kKbNm2eeq2qnCimXqxDBswwcMZ8hCOSYxObdvdfWQpAVVUKsXET0dNzxcd5ETnvT0aQSlGsXsrEqx8SlR/F0vZL6ePy7J1k6msaObYhmvvXHtK8txM9x/hp+q1q+K/C2FyXQdNCcPRVcGlXEld+SvpTG7ISXV3sNqwHIPu9Sczs7IitiS5TdkdTXf9znF8igQGbmtIr972DIJXxUd/t/KOskosV9Uw9OZBGVZOgmraTMYqhHqgyquivH0TvhNsINY2Mjk4lu6bu57RKD5xUAtOz1ISXVvJlZj4zWs4gxDKEhbdXYuC6FDfXGS/ETi+Fk69t0GLXaQNKTdKw0Tei+fFjqOsauOk6FtvmzrQf5s6Fb7eQfT+OEd2UXKxM4CcDHRqLO9DGKpQebc348tZMZFI5O3tuoDg1nXPfbKLa0o4zrbpjTy75gh0lojHLo2qxNdZli1BH4qMKVgwJxEhbwr59+9DS0vpdfXgaavlq23fcUbvzkZYuPu8EIr06G8pzYOAWkOvT0FBCdMw7CIKcAP8t5M/9nLqUVJTLFjHp3nyiC6JZ2mEpvZx7PbO9Kopr2b/iDjmJpXQe5UWr/i6a+LuG/0rkulq8MiGAwK72xFzI5tSWOBrr/7hwSm5vj+2K5dQlJVGxcD7LBwWQXlTNspOP9eIxsm6qU3kYBRcXI9gEMjl4MmNKy7hYUsKUk6+iUv9TpMwCo15O6CbUM9LOg4CYe9SJIoNuP6BWpUZqJMdkoBu94yrpqZaxNC2P+1UNrOy4EoWOgqlX51Fc++fDTn+Fl8LJX9u8l3yTdEykcjrFRKLKzCLKewyGPu70eMeX2LMniDl3kp5dvanJPsAcSysk9Y4oGwYwf7AvEy7ORNqQx4qOKzFV63Fk9RLQ1uZIhzAaZFJcSCRO9OLTEi188+u538acb8IzeLOtEx09zDl//jwPHz4kLCwMI6On9zBNPLKKVWWd6EwlQ0e2QlZ8AaJ2QOhHYN8CtbqemNh3qavLIzBgEzW7zlBx4iSmH0xiWs0PxBTEsLzjcno6PX1D948oyKxg79IIKopq6TspEJ92Ns88lwYN/wlIJAKhQ9wJHeJOanQBh9ZEUlP5x8VFBu3bYz55MuXHjuF57RhvtnXiu/B0wlMKHw/y7gfBb8DVNZB+DaHtJCYZejOmvIrzhdl8ev6NX/LbDTvaodfcEuf7eowwUWMel0uGqpH3YtMRRRE9f3P0g8yZdrkYhUTCu/cy0JWbsrbzWkrrStkYtfHF2OeFzPpvxvPVtpjp6dK7rBjV7QgS/d9A5exHn3cDeJgUz/nvNuMb5IVHyR6m29hTK8qpzRnOppEtGXtzA+qq24z0n0xXm5ac2LCSytISwpt3I1NpTWvxMpfozEhtPXrfLEHd0ZaPLyThZmHAx694kZaW9ku6pJeX11OvsTH9OlNvG6KPirldW6Jnr4Ij74NVwC/pkolJ8ygtvYW311JkySL5q1ah36M7nzlFEpEXweLQxXR3fHrl7B+REVfE/pV3kUgEXp0Wgr33i9HK0KDh/weBXe3pNcaPgqxK9i27Q1nBH+vTK8eNxaBbVx4tX8Fk6zqczfSZtieGitqGx4N6LQFTJzg4ARpqEAZ8yXvltYysV3E0J5rF16YgimJTWGaAG9pOxrTNdWJg7QO0U8o4WlLO1uwCAEzC3FDqyJmfUE9ydR3zknPwVnqzqdsmpjaf+kLs8lI4eTsnJwbLpKiOHSPbbxDFdi3p934gDXWlHF3zOQpra3ooI1ljICNOqqYyZxCfh3Xk67yL5OXtxMeqOzOavcWNAz+RHn2XXDd/bngH40ECt4T2tDTQ5f1zRcjsDVj8qJjiqnrWDAsCVQMHDx5EoVD8brok9VV8te0HYkUXZjpZ49jVEQ6/D3WVTVWtWnJycnaRm/sjjo7jMZOHkvPRFGR2dqzrqeLaw3DmtZ1Hb5fez2yjxJt5HPsiBhMLXQZ/3BylrcEzz6VBw38qrsEWhE0OoraqgX3LIniUVv674wVBwGbxYmRWVhROn8qKnk7kltX8Nmwj14cBX0JpJpyZDQoXJD0WMCMnh95SgR9TzrD+TtN+mqAlQfm6NwYG+vRRudMhIxpJfg2zk3O5UVqJRFcL0yEehKRW81a9jO9zizhdWEZzq+Z/WyvO/8tL4eTLjhyl+KuvKHLvTKp1V/pMDETfWIvDK5egamxgWKgOl0rj2W6gQ31xW17374toXsWZ+MUY6jnzbZeFZMZGE75nJ2pLO0617oG2UE0xFpjKdViW2Ii0TsVFXyNOxucxpYcnfrbGnDp1ivLycgYOHPj0dEkg+ceFrKnuRhedOoa80xohagcknYCuc8DCm9KyOyQmzUOp6ICL0wfkTp+BqqSEvaMdOVV4mZktZzLQfeAz2yf6XBZnv72HjbsJAz8KRt9Y+5nn0qDhPx1rNxMGTQtBpi3l4Oq7ZN37/Vi31MgI29WrURUWYrHhc95q7cj2GxmPtecBHNs0CQVGbIXkc9D8bQTXrizOeERbHZGv4newNWZL03wGcpSjfbCrN6WvnhEu8amIVY28FZNGbm09Om6mGLS1YczFYrxlMj5MyKKgvuEpV/f8vBROXq9tW4pDBhJrM5CeY/yxdDLi/LebeJT6gEGD21Aav51ZltaItbb4647kzc4OzL06HakA33dfh6q8mmPrliE3MuFsSBcKdI1QUkiZYMoX2qYYxBVTEWrNggvJtHJWMKa9C4mJiURGRtKuXTvs7e2fem0NMaf55L4SbWDRuO5NmjQnPwan9tD6XerqHhEbOxEdHWt8fddQ/NU3VF29ys3X/NnRGM6HIR8ywnvEM9lFFEVuHErh6p4HuDQzp+97Acg1+u8a/gcwtdLn1WkhGJvrcfSL6D/UvNH198NixgwqL11izMNw7Ex1+XhfzOMiKYAus8HMEw5PgtoyCNuAVKrN2lIDmuk2sjpyPbvu7wJAbmOAYpgnLcsceUX2CKPofMrqG3k7Lo1albppk1ahw4K71VSpVHxwP0ujXfN7JN2rIcqwGx1e98EpwIyYcyeJPX+a9n27Yx6/jmm2DlSqpeiWvsGGES0ZdWk+1KUyrfU8XA1sObpmKfW1tcS7BRJj746jmEoGzixxsMHpeBZa9gZ8lpGPAKwcGkhdbQ1HjhzB0tKSTp06PfW6xMoSftyzk1uiNzO7eGFtqQ8HJwICDPgCNQ3Exk5EpaoiwH8T9XcTKVi3nqzWTqywiWJ84Hje9nv7mWyiVotc2pnInRMZ+ITa0HOMH1oyTYqkhv8d9I21GfBRMywcDDm1JY774bm/O9505AgMe/WidP06VnhCamEV6849bhmITAcGfgkVeXByJhjZQO8V6Dy8z0rdIPx0Gll8azEHkw8CoOtrhrKHKz2r3QitTUQaU0xURQ0zH2QjyCQoBnvg9LCWKVUyzhWX821O4VOu7Pl4LicvCMJyQRASBEGIEQThgCAIJr96baYgCMmCICQKgvDs6SB/Ap92NvSdFIhve1seJidyfusmHP0DadF4io16UqKlKuoevcqm13qx4P4BSotO0spxGKPce3Fl53fkJt2n3M6Ny83aYiSUkSG4MNpaQa+rRajrVRxz0+dmWjGz+/pgZ6rHsWPHqK6uZuDAgU9XlxRF0jd/zNKGvrQ1E3ituxdEfAMZV6HXYjBxIDFpPmXlkXh7L0OnVknO1ClUWRoxq20Wb/i+ybuB7z6TPVSNak5/HU/8lVxCejnSaaQnEokmRVLD/x46+jL6T26GnbeC89sSiDqb+dSx/yyUktnaolw9n1FeRmy+nEpczq+0bWxDoP0UiN4JCcfBfwh498cs6iyf2rXAU1vF3GtzOJtxFgDDzvY4B3jQWzTGqygLrdRydj0sZufDYrSdjTFoa8OAy0UM1NPHVufpId/n4Xnv5M8AfqIoBgBJwEwAQRB8gNcAX6AX8IUgCC/sNlKQCDj6KqkuK+XwqiXomyoJa6VDxKMIthrpUV/SgjmdR5IuyeFS4iqMDHz4ov10HtwM586xg8jsnLkS1JZiLR1q0SXQQMbMGm1q7xVR1taa5VdT6OxpzpDmdsTFxREfH0+nTp2wsnp6d6SaQ7tYUGSHSpCz9O1OCKWZcGYuuHSGZqN+tdE6AQtlD3KmTaOhrJS5vSvo7TeIKc2nPFPuemODihObYkm5m0+7wW60HuCqyYHX8D+NTFtKnwkBuAabc21vMjcPpz41NCI1NMRuzWpUJSW8dek7FLpazNgX81iSGKDDNLDyhyOToboY+q5GkBvgcy+LyY7OOGiLTL88jdt5txEEAcVgd1pbBtBNWoRJShF6ZQ3MTMomtqIao55OyJQ6zLlUSnfDF5MM8VxOXhTF06Io/lMK7gZg9/PfYcCPoijWiaKYBiQDLZ/nXH+EWqXi6Npl1JaXM2hUH6pvrGGGlR2qOjNesRlHez8TPrs6HYlExg/dV1NTVMzJL9ega25JlL0XsZZOGInlaEtlbHF2pOZIKlI7A+ZmPEIulbDk1QAqKys5duwYtra2tGvX7qnXUpeYyamIA5xXBzOtlxf2prpNbwhBgP7rKCuPbNpoVXbE1eVDir76murrN9jSTcQ1pAuzW89+JsfcUK/i+BcxZMQX0WmkJ0HdHJ7HpBo0vDRIZRJ6vOOHdztrIo6nc21v8lMdvY6PD5afzKTuejirJfeIzy3nqytpjwdoyZuqYWtK4PjUpt7LryxDyLlL64Z2jLeUYaYF75+fREJxAoJMiuUoP3pp+RIqTUN1pxCZSuSduHQqBBHTIR6oSmopP53+Qtb+d8bk3wZO/Py3LZD1q9eyf37uXxAEYawgCBGCIEQUFBQ888mv7PqerPgYerz5Jqbhc5lnaU0BKixq32ZRWAgjz81BrM9mZptFOOhZcHTtMtSimmyFLeEBIeiLlZQLJnzh44r+iUzU9SqOuOoRkVHCZ/19sTTS5vDhwzQ0NDBw4ECk0if/MFGV1ZG/azbzGwfRzEKLN9q7Q+R2SL0A3edRr2dAbNwkdLSt8fVZTW1MHPnr1hHuI6G0WzDLOixDS/LXN0fraxs5uj6a7IQSuo72xrf98zfu1qDhZUIiEej8uhcBne2IPpfFld0PnuroTYYNw7B7d5Q/fsMoRTVrziaR+rNWPABWfk31LfH7IeEY+A8Gj17ILq+jreN0xior0UbF+DPjySrPQstEB88RrXhFVOKrzqXhdgHZtfVMTshE7miEyQA3DNq9mM/sHzp5QRDOCoIQ94RH2K/GzAIagR1/9QJEUdwiimJzURSbm5ub/9XDAXhwM5yII/sJ7N4b75IDHFKXcEZbQF3ck6+HD2R65A+UlpynrfMohrt25tpP28lLTqLexoWIkNaUCTKqBAMmOyhpm1NHbXwRxW0sWXktlW7elgxsZktUVBQPHjygW7dumJmZPXktjWrKt+5gdb01lYI+S0e2RVqRC6dmgVN7xJA3iL/3EQ0Nxfj7b0RSJyXtw8kUGoqcG+bG+m4bnqkna11NI0fWRfMwpYxub/vg1ebp4mgaNPwvIwgCoUPdCexmT+yFbC7vSkJ8gt7NP+PzWgolr5/5GkOxgZn7Y3/7pRD6AVj6wbEpTdk2fVeDVIbp5R8Idp3AGGUJ9apqxp0dR2FNITpupnTs2olukjIU5eUYpVVysrCcL7MKMGhljZbi+foxP40/dPKiKHYTRdHvCY9DPxvjTaAvMFJ8bIEc4Nd5hXY/P/dCsHb3JKhnX7oE6ZOZeJiFSjMaq1xY3GUSkdXJXEpah7FhIF+0+5D0qDvcPrwPPSd34u3diDGyQYJIGyMpUywtKT2cisRWn7np+ejKpSx+1Y/KykpOnTqFg4MDLVs+PepUejCeuKL97Fe3Z3wHZzwsDODoB6BqgP7rSMvYRHHxFTzc52Jo6EPy7BmIDx+xa5gFa/t9jZH86ZIIT6O2qoHDayLJTy+n5zu+eLR4+j6BBg0amhx4u0FuNOvhQNzlHC7uSnyio5eamGCzbBnq7CxWF1zgZlox++7+yo1JZRC2ASofwZk5Tdk2PRZA+hWci43xsWjHGGUFBdX5vHv2XSrrKzHu5EBf5zaEStOoSSrFqR4WpTYVSr0onje7phcwHegviuKv1fsPA68JgqAtCIIz4A7cep5z/R4GCiVd+3dFdepjpto4UquW0dvqI1p6mjDv2gwkEgN2dF9FXUUFJ75Yjb6ZBWkGZlz1DkBLrMdUClv8vag8kY66ppGDTrpEZpUyr78v5gbaHD16lMbGRsLCwp4qPlZ56yHqyDXMVYXhaCQwsZs3xPwED05D1zkU85C0tLVYWQ3AxmYYWXt3oD5xnmMd9fn4ne8x1/vrv2Lqqhs4vDaKwpxKeo33xzVY04dVg4Y/gyAItBnoSnAvR+5dyeXCjoQnOnr9Vi1Rjh2L2eWTjK5LZvHx+5RU/UoXx6YZtJ0Ed7+H1EtNOjfOHRDOzMXXfiruBgrGWMp5UJLE5AuTaVA34DayBa/o2eInzePh5VwspVqMi08nv+7FFEQ9b0x+A2AInBEEIUoQhE0AoijGA7uBe8BJYKIoin8sDfesNNbB3rf50tiQ+9IGLOpeZ1G/drx+fi5iw0NmtFmIg74ZxzespK66miKFNXdDQqhCjlrQ4it/TwwzKqm+84j8EDPW3Eynl68V/QNtiI+PJzExkc6dO6NUKp94+vrsCqoOn+QHQU2qaMOCwS3QqS2EEzPAvhW1QWHExX+Avr4bXp4LKE97QOGCxSTZS+nz2VYcjRz/8pLraxo5sj6aopxKXhnnj3PAk0NIGjRoeDKCINA6zIXmvZ24f+0h57fff6KjN39vIjqBAQy/sgN5UT6fn0j47YBOM0Hh0qRF1VAN/daBqEJ+aj7+vutw0ypijIMLt/JuMfvabAS5lK5v9aWLtBxTdS2SOwWUNapYkZ73Qtb5vNk1bqIo2ouiGPTzY/yvXlskiqKrKIqeoiie+L15npvoXdwpSeRrAx2oaM53Q8ewOO4A+YVnaOYwnJGuHbh1eB+ZsVHI3XxItXcmTtcKEQkfOylppa9HyYFkBKUOC/IK0ZdLWTjQj+rqao4fP46NjQ2tW7d+4qnV1Q0U/RBLuXQHGxvD6OdnRgcPczg+BRpqUPdbQ9y9j1Cra/H324BaJeHuxFGoUKP8fAG+VgF/ebn1tY0c3RBNQUYFPcf44eSvcfAaNDwLgiDQqr8LLfo6k3A9j0u7Ev9lM1aQybBdsQKJqGZ54j723M7gdvqvJA9kuk2SxCXpcGFxU2+ILrPhwWmMM5Jxd5uJpzqa0c6tOZ52nA1RG9CxMuTVHq/QViuVorwqupdL+Mzt/9PG638D5X4DmWTjhqpBwcIOs8lR57E/djk6ep581WEKuUn3ufbTdkxdPcnQ0uWSux+CqKadYSPvOTlQfi4TVXEtxzz0icwqY24/X8wMtDl58iS1tbWEhYU9MZtGFEWK9yShW7mTeQ3d0JbJmN0/sGm3/f4R6DSDlPLDlJVF4OW5CD09Vw7PHo1VWjlF7w0mNOSv69H8M00yL7WM7v/wxSXo2TarNWjQ8JgWfZwI7uVI/JVcru7516wbub09VnPnYJZ2nzGZl5l1IJaGX+fOO4VC87fhxheQHQGtxoFdSzg5AzvT3lhY9KZZw0X6OnZgS8wWDjw4gFOoN33tXfCWPuLMtUyi00teyNpeCie/7vphysUqeph9SHcfO967MA2ATV1WQG09x9YtR8/ElGyZPnEtmlEpamMkbWBTQCCNuVVUXsmmLEDJmjuZdPI0JyzIhsTERGJjY2nfvj2WlpZPPG/llRwaE6K5JKRzVe3P9N6+WGg3wPFpYOFDgYcfmZlfYWs7Aiur/uw9sATPw7Fkh7rTc8yCv7zOxgYVJ76MIedBKd3e8sEtRBOD16Dh7+CfoZuALnbEnM/mxsF/LZgy7t8foz59CIs5gToxgW+upv12km7zwNAaDr0HalXTpmxdJcLpT/H2WoKenj29tCJpbdWC+dfnE54bTvc3+tFFpwxjoY7w6BeTm/JSOPmxIUPoY7KWZf378fblFdTXJPFa4AyCFY6c+WojlcVF1Nm7ke/owB25DQiw2dcNM6kWJfsfIOhqsbSqSZJ00UB/6urqOHr0KBYWFrRv3/6J56xLL6PsZAoyw60saBxBoI0+I1o5wvlFUJ5LXc/Z3Ev8BENDX9zdPuV0whEUK7ZTY6JDp1Xb//IaVQ1qTm6OI+t+CV1GeePRUpNFo0HD34kgNDUf8W1vw91TGUQcT/+XMVZzZiNTKvksbg8bT8WTVfyrfBMdo6Y0yoL7EL4WzD2b0ixjfkIr4zZ+vmtRNRTztoWAs7EzUy5OIa06jSFDB9JXHofro3svZF0vhZO3MNRh6YBO/JB6mbisH7Ex78GngYO4f+UCSdevoAgIIU8l4YSzHwBjLCV0MlNSeS2HhpxKrgSYcDmliOk9PbE10eX06dNUVlYSFhb2RG0aVWU9xTsTMDC4yOoqP4oxYtGgZkgfRsKtzYjN3yKu7AdEsR4/37XEFt3n3qJPsCkGt+VrkBkZ/6X1qVVqTn8TT0ZcUyWrd1tNHrwGDS8CQRDoONwTz9ZW3DqSRuTp32rdSI2NsV60CGVhDiPjTvDZ4fjf3vF79ASfMLi8AopTm3RuFC5wbApGuu64uc2guuQin/p2QU9Lj4nnJqJrr8/AHr3oNPzZW3r+Hi+FkwdIryhg1c05SOTW7Ogyj7L8R5zb+iUKR2fSalTEtw2iStTGVVbFHK8AGotqKD+TQbW7MZ9HZRHsYMKoNk6kpqZy9+5d2rRpg63tv26EiGqR4p8SEasLyVSfYYeqG2+2dcbPSr9JukDfnAx3W0pLb+LpMY+iRi02fj2enrcb0Rs+GNPQjn9pXaIocmFHIqlRBYQOdddUsmrQ8IIRJAJdRnnhFmJB+P5k4i7/Noxi0D4Uk+Gv0f/BJfKvXuf0vUe/naDX5yCRwbGpoKUDfVZBcQpcXYW93ZsolZ0pzdrIsjZTKK8r571z7+HT0g8TExNeBC+Fk1er1Yw+NwNRVcGC0KUo5Lqc2LgKUYRiE0sq3Oy5JbFFJqjYGdwMLQFKDqWARGCdVj3V9SqWDgpArWrk6NGjmJqaPlVCuOJCFnUPSlE67GZu9RCUelp80N0Dbm2GvBiqOowjNXcLVpZh6Cu6MeX4BN44WIHgYIv99E/+8tqu708hIfwhzfs4Edjl6br1GjRo+PuQSCV0e9sHR38ll3Ylknznt3r0ltOmIbezY0b0bpbvv/Nb3XkjG+jyKaSca5I9cO0M/kPhyiqEwgf4eC9FLjOlPmc1S0MXkliSyIwrM35pCv63r+WFzPpvZm7kLkrKbtPWbRxh9kHcPryfnIR4TAJbUKISOWDXFKZZ7mqEo54eNXGF1CWVEBlowtH7j5jY2Q13S0OuXLlCcXExffv2fWKnp7rUUsrPZmDknsnhzGIi1W583McPo9o8OL8ItVsXouoPoKNti5v7HGZcmUGX/ekoKkUcl69Eoqv7l9Z191QGkWcy8etoS8u+zn+LrTRo0PDnkEol9Bzjh5WzMWe+jScr4XHapERPD5ulS1FUldDn6m42XUr57cEtx4B1UJPufE0p9FwEcj04+iFymQIf35VUV6dhUXWGGS1mcDHrIhuiNryQdbwUTn6CT39auU7kizZjeZSaTPjuH7D2DSS1rIq77ZpRjQ6d9Ct4zcEDdV0jZUdSqbPUZVHiQzwsDZjQyZWCggKuXr1KQEAArq6u/3IOVVUDxT8moqXQQqz8gqXqkQTbG/FqkA0cn4qISKKbEXUNBfj6rWF99NfUXrhMxxgVZmPHohsY+JfWdO9aLtcPpODe3IIOwzw0csEaNPx/QCaX0mdiACYWepz4Mpb8jMc9Y/WCm2H2zjv0yrhFxM7Dv92ElUih3xqoKoDzC8HAoin7JuMqRO1EYdoGJ6eJPMzbRxdTfd4NfJdeTpqY/FOx0TXk69DxiA31HF+/Al1DI3LlBtR42HBXsMVQqGFrcFsAys9koqqo51sLCXnltXw+KAAtCRw5cgS5XE6PHj3+ZX5RFCnZ9wBVVQNm3hdYmx9EkdqQ+QMCkCQegaSTlAf3Jrf6Kq4uH3H+USr7I77j/TNytL29MX/3rzX/SI0s4OIPCTj4KOj6pg+CpuGHBg3/39DRl9H//SB09GUc3RBN6aPHztz8vYlI3D14985uVuy5+dsDbZpBy7Fw+2vIvtMkeWDfCk5/ClVFODtNwti4OQmJs3nD4xU8FZ4v5PpfCif/Ty7v+I7/1959h0dZpQ0c/p3MZDLpyaRDCgmkEELviIBSDCCguxbWgouuLHbXCp9rA1FXUCzsilhW3cW1F1RCU4iAUjUkISQmQCghnfQ27Xx/zAAJhLKbDEnGc1/XXMycecszB/Iwed7znnP82ItEcgkAACAASURBVFG8+w+j2mTik262b8/v9umOh1aD8VgttT8WkJ/ox8rMQmaNiGJQpD9paWkcPnyYSZMm4eV15sT9ddsKacwqx2+slv27P+U9SzJ/GB5JUqALpDyKJTieX9x+wuB/CWVug1mwbQGPbvLDvcFCt+efR5xjke/THc2pYO3bmQT38CH5z33RaJ3qr0hRuiRPPzem3zcAgFWvpFFb0QSA0OmIWvICvqZ64j55ix9+PW269MseA+9Q+OY+kFa48mVoqob1j+PioiWpz1KE0LA36wGs1s45d02ncfCXXaSt/YbokWPILSknzV6mudKnhkuCIpFWSeWXeVj1Wp4rryDIy40Hr4intraWdevWERkZyYABA844rqmojspvD6CP88OjaDFPGW/E292VhyfFQ+rfoKaQfb30uLh64hf5EA+kPkjyQV/i9pQTeM896OPjLvgzlB2tZfXr6fgFe3Dl3f1xdVNrsipKZ+EXYvu5bKwz8fVraTTW2ZKyPj4ew5/ncPnRn/l02X8wmpvdCav3sY22KcqwDc4ISbRNaJa2EvK3otd3IyFhEdXVeziY/5pD4naKJF9fXcXa5a9g6B7B/iaJsVcIO0Q4PqKB5QNsKzjV7yrGeLiGlDhP9hbV8MS0RHz0rqxbtw6j0ci0adPOmGHSarRQ/kE2Lnot/v1zWJ1bw0+WBB68ojf+dQdg2+tU9RpAsbaAHrFP8+DWp9DVNjErpQl9YiIBt86+4M9QW9HIN8v2oNNrmXZPf/Seru3aR4qitF1wlA9T7uhLZUk9KcszsJhsCT30jrmYoqK59oeVvL8+o+VOiTMgdpLtRsmqAhjzCPhG2laVspgICZ5CdI97CAqc6JCYnSLJH87cQ1N9Pfo+g6htauLD7gMBeC+pO1qNBkudiao1B6mM8OS1fYWMjQtiat8w9u/fT3p6OqNHj6a1BUuqvj2AuaQew++607hxAYuss0kM8+aGoRGw+iGsru7sCTpCWNh1LN23nv2V+1mclgg1tYQ9uwhxlkW+T2dsMPPNsnSMjWauvLsfXv6OWTxAUZS2C08wMH5Wb47lVvLd+/uQUiJ0OmKXvIChqYaGV5dSXN14agchYMpikBZbPV7nAcnPQUkW7HgTgJiY+/Hx6euQeJ0iySeMGsPYex8l50gBaZeeKtOMDLStcVq1+iDWRgvL9GZMFisLZyRhNpv59ttvMRgMrU5dUJ9RRt32IrzGhqMveJO/V4yg0OLDghlJaPZ9AfmbORjji9Y7is1NEWw4vIGnmYHb+p8InHM7+oSEC4rdYrGyZkUGFYV1JM9JIjDcu137RlGU9hc3LJThM2LI3VnM9lUHAHDvm4TbjbOYcHA777/2ccsd/HvA6L/Yxs0f3AwJU6HXBNuslTWOmWL4BKdI8nV1dazflEpTbBjbCcdbNLB84GjANsdM/e5i0hJ9WJNbyr3jY4kM8GgxJt7VtWVpxFzZRMVnubiGe+E7yMjhrZ/wlvVKrh7YnSFhrrD2MRr8A8kPbKIm8Bb+vmc500Mm0Oedzeh69SRg7tzWwjyDlJJNK3M4sq+CcTfFE5nY+nz1iqJ0PoOTo0i8JIzdKYfI2noMgJiH7qc2uDtDP13OL9lHW+5wyX3gFwkpj4DVDJNfAEsTrHvcoXE6RZI/cOAAtSYTH9lH07yZ2A2tiwvSIqn8cj8mXx3PHy2jV7AXt18aQ3l5OVu3bqVv377ExMS0OJa0Sio+zgGrJOD6eMT6/+M58w1otK7Mm5wAqS9ATSGZUSb0oTeyYPebxPrHMvdHT8wlJXRbtAiXCxxNs2t1Ptk/FjJ0ag96j+rW7v2iKIrjCCEYc0M8kYkGNq3M4XBWOS56PT1feI6ghkrSnni25bw2ru5whb1Ms/MtCOhpS/wZH0P+VofF6RRJvm/fvuy9bAT16JnoVcu4YNtKS3U7CjEV1fFBuI6jlQ08c1USrhpBSkoKGo2m1THxtVuP0XSgCr9pMWjLN7Hj1yOkmAcxd2wvQpoOIbf9g6IwHxpD+7F0fyYSyRLv2dR+/BmGWbMu+Kan7J8K2fH1QRJGhDJU3c2qKF3SibtiDWGerFmRSXlBLYYRQzk+aQYj0jey/sO1LXdImAo9x9vKNLUlMPqBFhdhHcEpkvwXR3PZ0mjAgyaWDxwJ2O5QrVp3iKMRHvxzXyHXDA5nREwAOTk55OXlcdlll+Ht3bL+bSqqo2rtQfS9DXj098GaMp+F8nZCfdyYc2k0cvXDWDSC3B7ufFkfya8VubwwbCGW517DNTKSoPvuvaB4j+VWsPHf2YQn+DPupgR1N6uidGE6dy1X3t0PnZuGb5btobaiiZHP/pVyn0B0Lz1LfXWzRbqFgMl/A1MDbHj6tIuwKxwSn1Mk+exq2w0IL8cF42kf0VK9Lh9ro4mXZANeei3zJydgNBpJSUkhODiYYcOGtTiGNFs5/lEOLm5a/H8fi/hpGV8cjyDD1I1HkhNwz12FOJhKXpSOX7yvYN2Rzdw98G5iP92N6fBhwhYuvKC5aapKG0hZnolPoDvJc5LUzU6K4gS8/PVMvbs/TfVmVr+ejtTpcZv3OCE1paQ+/reWGwfGwsi7IO3fcGSn7dv94NkQEOuQ2JwiwzzSeyT/Topkendb2cNYUEvdjiK2xnqz42gVj1yRQICXG1u2bKGqqoopU6acsZxf9YZDmArr8P99LBpzEfU/LOMFbqFfuC9XJfphXTufGi8dP0cO4528zVwecTk3uYzi+Hvv4XfddXgOH9ZaaC0YG8x8+490pJRMvbMfbh5qLLyiOIugCG8m3taH0iM1fP/+PoZcPZGsfqMJX/c5x34+bez8mIdtq0itfsh2J+y0lyHuzPJxe2hTkhdCLBRCpAsh0oQQ64QQ3eztQgjxqhAiz/7+oPYJt3UaIZgQZABsI1YqV+2n0V3L0sJykrr7cP3QiBYXW3v06NFi/6b8KmpSj+I5LBT3xABY91feMCZTbPLg8SsTET++gktNETt7Gnij8DgR3hE8M3IBxU89jcZgIPihB88bo9UqWff2XiqL60mek4RfiIcjukJRlA4U3S+QkVf1JG9XCbtT8hn07FPUu+rJffQxpLXZnbBuXjDpGShMg5/fd2hMbf0mv1hK2U9KOQD4BnjC3j4ZiLU/5gCvt/E8F6w+rRTjoWo+jNBRXNPE09OTcBGc9WKrtdHM8Y9y0Pjr8Z0aAwc2Ubh3M29YpjG1bxhD/epg61IKgnS8relFvamBl8a9hOnTr2nMzCRk/jw0Pj7njeunz/M4lFnOmJlxhCcYHPXxFUXpYAMnRRI/PJTtqw5irXHl19/dSvCRXLJWnJbMk34PUZfAdwug/njrB2sHbUryUsrqZi89gRPjhWYA70ubbYCfEMLha9ZZm8xUrT5IYYie9/KKuWZwOIOj/MnOzj7rxdbKbw5gqWzCcH08LlorpDzKYpdbsQoN8yYnYF77MFZp5o2IPqRXHOavI/5KD6MPpS+/jOfo0fhMmXLeuPb9eIy0DUfoOy6cpDFqZSdFcWZCCMbdFE9ItA8b/pnFJTf+gb0hsRhffxVTcUnzDW13wjZWwqbnHBZPm2vyQohFQogjwI2c+ibfHTjSbLOj9rbW9p8jhNglhNhVWlra2iYXrPr7I1hqmnhFZ0Kv1fBosu1i65o1a1q92Nqwt4z6XcV4j4vALcoHdv2T9OImPm8YxK2jYwiv2YN2XwpfRhj4oqKcq3tdzYxeMyhe9CzSbCb0ySfOOzLmWG4lm1bmENHbn9HX9mrT51MUpWvQumqYPLcvek9XNr+zD/Odj+BiMrFn/lMtNwzpA0NuhZ1vQ8k+h8Ry3iQvhNgghMhs5TEDQEr5mJQyAlgJ3P3fBiClXCGlHCKlHNLa/DEXylRaT+2WAnb29GLLkQr+MjGOIO+zX2y11Bqp+DwX1+5e+IyPhIYK5MZneUZzF4FeOu4aF43p6z+T7+7KS7oAYv1jmT98PjUbN1Kzbh2Bd96JLuLcy/HVHG8k5Y0MfALdmfSnJFw0TnGdW1GUC+Dp68aUO/rRWGtCm69n/aApeP64kYqNqS03vOwxcPOGXe84JI7zZh0p5QQpZVIrj69O23Ql8Hv78wKgeQYMt7c5hJSSyq8PYNQIXiqrIC7Ei5tHRlFRUcHWrVtJSko642Jr5Vf7sTZaMFwXh9C6QOpi1tX1ZEdDN/4yMQ63jDcRZYd4uHsEZlx4ceyLuBklRQsX2qYumP3Hc8ZkNlpIWZ6B1Wxlyh191aySivIbFBTpzfg/JlJ8sJqwIddyyCuYQ48/ibW+2SpSHga4bZ1tSmIHaOvomuYDO2cA2fbnq4BZ9lE2I4AqKWVhW851Lo37jtP0awWfRuk5WtXIU9P74KpxYf369bi4uDBxYsspPOvTS2nIKMNnQhSuIZ5Qvh/z9rd4QTuHnkGeXNfXD75bwOLQALItZp4e9TTRvtGULvs75mOFhD399DkXApFSkvqfHEoP1zBhdiL+oZ6O+uiKonRyvQYHM2RKDyqzq9lx2T24lRVT8Opp67kGxduWDHSAttYPnreXbtKBScB99vbVwAEgD3gT+O/Wv/svuYZ6UjUokLcPlnBlvzBG9QwkPz+frKwsLrnkEnx9fU9ua6k1UvlVHq7hXniPCbc1rnucT+Rl7G/w4pHkBBrWzWGLxoX/uHtyffz1JEcn05idbRsTf+21eAwefM54MlMLyP6piKFTexDd/38vQSmK4hyGXhlNZJ8AAusCWBs3her336MxJ+einLuto2t+by/d9JNSTpNSFtjbpZTyLillTyllXynlrvYJt3Vag55Xm+pwEYLHpvbGarWSkpKCr68vo0aNarHtyTLNtXEIjYCDP1CfvYGl8g8MjvJnbMBhKrPW81hwIIkBiTwy9BGk1Urhk0+i8fUl+MEHzhnLsbxKtnycS4++AQydquakURQFXFwEE29NxNvghoiaSoV7EEeeeLrlBGaOOrfDz3ARpP5aytq9xdwzvhdhvu788ssvFBcXM3HiRHTNyipnlGmsFljzf7yjnUlJkyvzkuOp/fpW5gcGIl09WDJ2CTqNjsrPPqNxTzohjz6Cxs/vrHHUVTaxdkUm3oF6JsxWC3ArinKK3tOVyXP7osOFHwfeizF9D1VfnX5ps/05RZIP93fnuiHh3DY6msbGRr777jsiIyPp06fPyW1aLdOkraS8KJ/lxklMTAyhZ/X7fFBfTbqbG0+NWkCEdwSWykpKX3wJ98GD8Zk+/awxWMy2xT+MTRYmz+2rpixQFOUMgeHeXH5TAp5aP3Ym3kLh3xZjqa4+/45t4BRJvmeQFy9c0x83rYbU1FTq6+tJTk5uMYb9ZJnmGnuZpqkGvlvIMve51JsFD1wexp4fXuQtXx+uip5KcnQyAKWvvoqluprQx/96zjHxmz/OpehANeNn9Sagm5fDP7OiKF1T/PBQYi8Noy5oCIW6npS+8qpDz+cUSf6EsrIytm/fzsCBA+nW7dQiHKfKNJG4nhjpsmUph2vg39UDuX5oBJbMu3nGw5tInS/zR9ru6WrMyqLiw4/wv+GGcy7nl72tkL0/FDDoikh6DQ526GdUFKXrGz8zHhnoRlb8TRxelUpjVpbDzuVUSX7t2rVotVrGjx9/ss1WptmPa3cvvMfYh+5XHoEfl7HE60E0Ghdu6V/GPw5lUKHRsHjSCjxcPZBWK0ULFqLx8yPo3nvOes7yY7WkfpBD9zg/hk+POet2iqIoJ2g0Lsy8fyANWhfS+9zO4aefazmBWTtymiSfm5tLbm4uY8eOxcvrVLmk8usDWBvNp0bTAGxcRKYlilXHI5g9KpL12+5hs96d+3tMo3egrY5f9dUqGtLSCH7wwbNOQGZsNLN2RSauei0Tb+uj7mhVFOWCBQZ6EJwcTqNbAGnmgVR+/oVDzuMUWclisbB27VoMBgPDhw8/2d6wr5yGPaX4XBZxqkxTlAF7PuR59/vx93Clj/+7vGsSjLa6cvOYRbbjVVdTsmQJ7v3743v1Va2e88Qi3JXF9Uy6rQ+evm4O/5yKojiXG6fGkREoKA0awL4j51906H/hFEl+z549lJWVMWnSJLT2laGsTWYqv8xDG+KB97hmMyysf5ItmmFsqfDjjyN1/CP7G3wsVhZd9jLCxdYdpa8tw3L8OCGPP36y7XR7Nx8jd2cxw6bFEB7v7/DPqCiK89FqXLhqZgJ7dGYORDum3Kt1yFEvsn79+qHT6YiPjz/ZVr32EJZqI0E39LbNTQNwYBMy7zsWu79DN189udXzOIqG5V5xGHqMAaAxJ4eKlSvxu/463JP6tHY6Sg/XsPnjX4lMNDA4Ocrhn09RFOeVnBTGJ4MK0Ec4ZlSeUyR5rVZLUlLSyddNh6qp/ekYniPCbFMIA1itsP4JNugnsadSz7Ujf2RNZR2za+oYddXLgK0EU7zwGTQ+PgTff3+r52pqMLNmRQYe3jom3KpueFIUpW2EELzzx6EOO75TlGuak2YrFZ/novHR4Zvc49QbmZ9hPZbOi+JmIgKMbK36iPgmI3fH3QB+kQDUrFlD/a5dBN1/f6t3tkop+f79fdQeb2LSn5Jw9zr7JGWKoiidgdMl+ZrUo5iL6/G7qhcubvZfVMxN8P0CvvG+luxKF3y7r6DJCs9VN6Eb8zAA1sZGihcvxi0hAb9rr2n12JmpBRz4pZQRV/ckrKdvq9soiqJ0Jk6V5E0l9VR/fxj3foG49w449cbOtzFXHOVl09V0D9/NIdNR7j9eSewlD4O77Rv78XffxXyskJB58xCaM6f8LDtay9ZP84jqG8CACedeLERRFKWzcJokL62Sis9zEToNftN6nnqjoRJ+WMznhts5WF9GvffnDDYZuUH4wdA/AWAqLqFsxZt4T5yA54jhZxzbZLSw7q1M3Dy0jJ/V+7xL/imKonQWTpPk63YWYcyvxm9KNBrvZrXyrS/TVF/Ny7XjMER9iA4zzxeWohk3H7S2se2lS5eCyUTwww+3euwtn+RSUVzPhFsTcfdWdXhFUboOp0jylqomqlYfxK2nLx5DQk69UX0Mtr3OhyEPUqb7HqPrEebXNBLq3wv6zwSgISOTqi+/xHDLLHSRkWccO293CVmbjzFoUhQRCYaL9ZEURVHahVMkeeORGhAC/6tjW5ZSUl+gwaLh1epQ3ILWM8rFzIyyMrj8r+CisQ2ZfO45NAEBBMyde8Zxq8sb2LQym+AePgybrhYAURSl63GKcfLuSYGE9fLDRd/s45Tvh1/+xduhj9EgVuLjAs8WNyC6DYLe0wCoSUmh4eefCV24AI1XyxsRrBYrG97JwmqVTLqtDxo1L42iKF2Q02SuFgkeYNPz1AhvVjTloXEr4VGtIKC2AiY8CULYhkwuWWIbMvm7351xvJ2r8yncX8W4G+PxDXLMnBKKoiiO5jRJvoXivZDxCc+F3oLVZwsDXXXMOHIcosdCzDig2ZDJ+fPPGDJ5LLeS3avzSRgZStzQ0Isfv6IoSjtxziT//SJK3IL5WqTiavXkGVc/XBqqYPyTAJhKTgyZnIjn8GEtdjU2mNnwbhbege5cen1cR0SvKIrSbtolyQshHhRCSCFEoP21EEK8KoTIE0KkCyEGtcd5LsjRXZDzLfeFjgDXcq731hGRmw0JV0L4YADKXnsNaTIR/PBDZ+y++ZNcao83MnF2IrrTS0CKoihdTJuTvBAiApgEHG7WPBmItT/mAK+39TwX7LsFbPYJJ1Ok4dMQx50uBoSpHi5/HICm3FwqP/sc/z/MPGPI5P5fSsj+sZDBk3sQGqOmLVAUpetrj2/yS4FHANmsbQbwvrTZBvgJIcLa4VzndmAT9fk/MM9gwGry575QgXfWVug3E4Jta7SWLHkRF09PAu+4o8WudVVNbPp3DkGR3gyZ2sPhoSqKolwMbUryQogZQIGUcs9pb3UHjjR7fdTe1tox5gghdgkhdpWWlv7vwUgJ3y1kSXAU1aKS7vWDmG5xQ0grjJsHQN227dSmphIw53a0/v7NdpVs/Fc2JqOFCbMT1XBJRVGcxnmLzkKIDUBrQ0weA/4PW6nmfyalXAGsABgyZIg8z+Znl5PCrvIMPgkLwXh8FA8kWtBvXwuDbgb/KKTVSsnixWjDwjDcfHOLXfduPsahzHIuvT4WQ5hnWz6OoihKp3LeJC+lnNBauxCiLxAN7LHfZRoO/CyEGAYUAM2nagy3tzmG1Ur99wt4LDgUafInzhrFhPoDIARc+iAA1atTaNy7l25/ex4Xvf7krpXF9Wz9NJeI3v70HRvusBAVRVE6wv9cl5BSZkgpg6WUPaSUPbCVZAZJKYuAVcAs+yibEUCVlLKwfUJuReZnvGIu4phG0lBwDQ8NdkGb8QUM/iP4hmM1GilduhS33r3xmTbt5G5Wi5UN72ah0bpw+Sy1ypOiKM7HUWMEVwNTgDygHpjtoPMAsNM3gA98vZGVw+jnI7m0Ih9ctDD6AQAqVn6AqaCAyIULWizMvXvNIYoPVjPpT33w8ndzZIiKoigdot2SvP3b/InnErirvY59Pr4+4YRr49lXdCXzp9fgsu5jGD4XfMKwVFVRtnw5nqNH4zlq1Ml9Sg/XsOvbfGKHhhDbfOZKRVEUJ+IUd/sEaH05nH09Q8LKGVq4AzQ6GG1biLvsjRVYq6tb3PhkMVv57r196L1cGTNT3dWqKIrzcoqxgp/v2EmdyYPHR3VDZHwCw24Hr2BMBQVU/Otf+F51Ffr4+JPb70rJp7yglnE3JaD3dO3AyBVFURzLKb7J/+nyqQzueZT+u54CrTtcch8Apa8tAyEIuu/ek9uWHq7h55RDxA0PIbpfYAdFrCiKcnE4xTd5IQSDPGoh8zMY/mfwDKQpL4+qVavwv/FGXENtw/ybl2kuvU6VaRRFcX5OkeQB2PQ86Lxg1D0AlL7yCi7u7gTMuf3kJifLNDfGqzKNoii/Cc6R5IsyIetLGHEHeBhoSE+nZv0GDLfOPjl9QYsyTf+gDg5YURTl4nCOJN9wHMIGwMg7AShZuhSNwYDhlj8CqkyjKMpvl3Mk+egxMGcTuPtT9+OP1P+0jcC5f0bjZZuHZrcq0yiK8hvlHEkeQAiklJQsfRlttzD8Zs4EoPRIDbtTDhE3TJVpFEX57XGeJA/UrF9PY0YGQXfdjYtOh8Vi5fv39+Hm5aqW8lMU5TfJaZK8tFgofeVVdDEx+M6YDsCeDUcoO1LL2JlxqkyjKMpvktMk+aqvVmHcv5+g++5DaLVUltSz45uDRPcPJGagKtMoivLb5BRJ3mo0UrrsNfRJSXhPmoiUktQPctBoBGNmxmOf715RFOU3xymSfPWqVZiPFRL8wF8QQpD9UxFHsysY+bteagphRVF+05xi7hrfGTPQ+PvjOWoU9dVGtn6aS1gvX/qM7tbRoSmKonQop/gmL1xd8R4/HoAtH/+KyWhh3I0JaqUnRVF+85wiyZ+Qn1FG7q4ShkzuoRbkVhRFwYmSvLHRTOoHORi6eTLoiqiODkdRFKVTcJokv+2rA9RWNnHZTQlotE7zsRRFUdrEKbJh0YEqMjYdpe+4cEJjfDs6HEVRlE6jTUleCPGUEKJACJFmf0xp9t58IUSeECJHCHFF20M9RxwugojeBkbMiHHkaRRFUbqc9hhCuVRKuaR5gxAiEZgJ9AG6ARuEEHFSSks7nO8MIT18mH7vAEccWlEUpUtzVLlmBvChlLJJSnkQyAOGOehciqIoylm0R5K/WwiRLoR4Rwjhb2/rDhxpts1Re9sZhBBzhBC7hBC7SktL2yEcRVEU5YTzJnkhxAYhRGYrjxnA60BPYABQCLz43wYgpVwhpRwipRwSFKQmElMURWlP563JSyknXMiBhBBvAt/YXxYAEc3eDre3KYqiKBdRW0fXhDV7eTWQaX++CpgphHATQkQDscCOtpxLURRF+e+1dXTNC0KIAYAE8oE/A0gp9wohPgayADNwl6NG1iiKoihn16YkL6W8+RzvLQIWteX4iqIoSts4xR2viqIoSuuElLKjYzhJCFEKHPofdw8EytoxHEfoCjGCirO9qTjbT1eIES5+nFFSylaHJ3aqJN8WQohdUsohHR3HuXSFGEHF2d5UnO2nK8QInStOVa5RFEVxYirJK4qiODFnSvIrOjqAC9AVYgQVZ3tTcbafrhAjdKI4naYmryiKopzJmb7JK4qiKKdRSV5RFMWJdfkkL4RItq8+lSeEmNfR8TQnhMgXQmTYV83aZW8zCCHWCyFy7X/6n+84DojrHSFEiRAis1lbq3EJm1ft/ZsuhBjUwXF2itXImp0zQgixUQiRJYTYK4S4z97eqfrzHHF2tv7UCyF2CCH22ON82t4eLYTYbo/nIyGEzt7uZn+dZ3+/RwfH+a4Q4mCz/hxgb++wnyOklF32AWiA/UAMoAP2AIkdHVez+PKBwNPaXgDm2Z/PA/7WAXGNAQYBmeeLC5gCpAACGAFs7+A4nwIeamXbRPvfvxsQbf93obkIMYYBg+zPvYFf7bF0qv48R5ydrT8F4GV/7gpst/fTx8BMe/ty4A778zuB5fbnM4GPLlJ/ni3Od4FrWtm+w36Ouvo3+WFAnpTygJTSCHyIbVWqzmwG8J79+XvAVRc7ACnlD8Dx05rPFtcM4H1psw3wO2320Ysd59l0yGpkUspCKeXP9uc1wD5sC+R0qv48R5xn01H9KaWUtfaXrvaHBC4HPrW3n96fJ/r5U2C8EEJ0YJxn02E/R109yV/wClQdRALrhBC7hRBz7G0hUspC+/MiIKRjQjvD2eLqjH3cptXIHMVeKhiI7Vtdp+3P0+KETtafQgiNECINKAHWY/stolJKaW4llpNx2t+vAgI6Ik4p5Yn+XGTvz6VCCLfT47S7aP3Z1ZN8ZzdaSjkImAzcJYQY0/xNafs9rtONYe2scdm1eTUyRxBCeAGfAfdLKaubv9eZ+rOVODtdf0opLVLKAdgWGxoGJHRwSK06ACg5wwAAAdhJREFUPU4hRBIwH1u8QwED8GgHhgh0/STfqVegklIW2P8sAb7A9g+2+MSvafY/SzouwhbOFlen6mMpZbH9h8sKvMmpEkKHxSmEcMWWOFdKKT+3N3e6/mwtzs7YnydIKSuBjcBIbOWNE1OjN4/lZJz2932B8g6KM9leFpNSyibgn3SC/uzqSX4nEGu/8q7DduFlVQfHBIAQwlMI4X3iOTAJ28pZq4Bb7JvdAnzVMRGe4WxxrQJm2UcHjACqmpUhLjrRyVYjs9d/3wb2SSlfavZWp+rPs8XZCfszSAjhZ3/uDkzEdv1gI3CNfbPT+/NEP18DfG//zakj4sxu9h+7wHbdoHl/dszP0cW6wuuoB7ar1r9iq9s91tHxNIsrBtvohD3A3hOxYasXfgfkAhsAQwfE9h9sv5qbsNUGbztbXNhGA/zd3r8ZwJAOjvNf9jjSsf3ghDXb/jF7nDnA5IsU42hspZh0IM3+mNLZ+vMccXa2/uwH/GKPJxN4wt4eg+0/mTzgE8DN3q63v86zvx/TwXF+b+/PTODfnBqB02E/R2paA0VRFCfW1cs1iqIoyjmoJK8oiuLEVJJXFEVxYirJK4qiODGV5BVFUZyYSvKKoihOTCV5RVEUJ/b/5a6/q8cprRAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 918c1d4f907d594fa1d01b92de3fe2682dc4c593 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 290/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From 0ce6ad80002a20b3130eddb1249eb02107d72b0b Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 291/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- 4 files changed, 174 insertions(+), 96 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, From f7969b0b7b8c48daa1490308142513acb17ab2b1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 292/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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GKf+y8kMoPQr9bnE6iUdpIfBFiWPgSBZs/8HpJEr5j/JyWP4WtBoAzbs5ncajtBD4og7nWwvWrJ3hdBKl/MfWBXBwe8CdDYAWAt8UEg4Jl8CGL6DkqNNplPIPy96Eek2hy4iT7+tntBD4qsQx1gUvW751OolSvi9nu/W71Od6CAlzOo3HaSHwVW3OtD696NxDSrku+W2QIEjyzzWJT0YLga8KCrbmQdk8F4rynE6jlO8qLoAV70OXi6FBS6fTOEILgS9LHA2lhbDpa6eTKOW7UmdC4SHoG3idxMdoIfBlcX0hqpWOHlLqVBkDS16Fpl0hfrDTaRyjhcCXBQVZzUNbF0BBjtNplPI9WxfA/vUw6M6AnrtLC4Gv6zYGykutoaRKqZpZ/ArUb/bbwk8BSguBr2veHaLb6+ghpWoqcx1s/Q76TbCuzQlgWgh8nYj1aWbHT5CX6XQapXzH4lcgtC4k3eh0EsdpIfAHXUeBKbcmolNKnVxeBqyZDj2vhrqNnU7jOC0E/qBpZ2iWqM1DSlXXsjetvjU/W3jmVLmlEIjIUBHZJCJpIvJQJc+Hi8g0+/mlIhJf4bm/2ts3iciF7sgTkBJHw+6lcGiX00mU8m7FR6wriTtfBNHtnE7jFVwuBCISDLwCDAMSgCtFJOG43W4CDhpj2gMvAP+yX5uAtdh9V2Ao8Kr9/VRNJY6yvqZ+6mwOpbxdymRrAZpBdzudxGu444ygH5BmjNlmjCkGpgIjj9tnJDDFvj8DGCIiYm+faowpMsZsB9Ls76dqqlE8xCZp85BSJ1JSCL+8BPFnQOvAWJi+OtxRCGKB3RUe77G3VbqPMaYUyAWiq/laAERkgogki0hyVlaWG2L7ocTRkLEGDmxxOolS3mnVB5CfAWf+xekkXsVnOouNMZOMMUnGmKSYmBin43inrpcBos1DSlWmrAR+fhHi+lmz96pfuaMQ7AVaVXgcZ2+rdB8RCQGigOxqvlZVV4MW1nwpqTOsOVSUUr9ZMw1yd1tnAwE8nURl3FEIlgMdRKSNiIRhdf7OPm6f2cB4+/4Y4DtjjLG3j7NHFbUBOgDL3JApcCWOggObITPV6SRKeY/yMvjpeWjRw1rqVf2Oy4XAbvO/E5gLbACmG2PWicgTInJszbe3gWgRSQPuAx6yX7sOmA6sB74B7jDGlLmaKaB1GQkSrJ3GSlW0eirkbNOzgSqI8cEmhKSkJJOcnOx0DO/1wWjrrOCeNfpDr1RpEbycBPWi4ZaFAf07ISIpxpik47f7TGexqoHEMdaFZXu0WCpFymTI3QVDHg3oInAiWgj8UefhEByuzUNKFeXDj89a1w20PcfpNF5LC4E/qhNldYit+8zqJFMqUC19DY5kwZDH9GzgBLQQ+KvE0daFMzsXOZ1EKWcU5MAvL0On4dCqr9NpvJoWAn/VcSiE1rOuKVAqEH3/DBTnwbn/53QSr6eFwF+F1bX6CtbPsq6oVCqQ7N8Ay9+CPjdAs+PnwFTH00LgzxJHW7Msbvve6SRKeY4xMPdhCK8P5zzidBqfoIXAn7U71+o41tFDKpBsnmutRXzWQ9a1A+qktBD4s5Bw6HIJbPjSmn5XKX9XXABfPwDRHaDfLU6n8RlaCPxd4hirw2zLt04nUar2/fhvOLQTLn4BgkOdTuMztBD4u/gzoF6MNg8p/5e5Dha9bC1I3+YMp9P4FC0E/i44BBIutdpNi/KcTqNU7Sgvgy/utfrELnjK6TQ+RwtBIEgcDaVHYdM3TidRqnYsfgX2LIMLn4a6jZ1O43O0EASCVv2hQaxeXKb8U+Z6+O5J6HwxdL/C6TQ+SQtBIAgKshasSVtgXXavlL8oLYbPJlhNQpf8V+cTOkVaCAJF4mgoL4GNXzqdRCn3WfgUZKy1ikC9Jk6n8VlaCAJFi57QuK2OHlL+Y9M38Mt/rWkkOl/kdBqfpoUgUIhYZwXbf4S8TKfTKOWagzvhs1uheXcY+ozTaXyeS4VARBqLyDwR2WJ/bVTFfuPtfbaIyHh7W10RmSMiG0VknYjo/2ZtSxwNptyaiE4pX1VyFD4Zb80pdPkUCK3jdCKfF+Li6x8CFhhjnhGRh+zHD1bcQUQaA48BSYABUkRkNlAEPGeMWSgiYcACERlmjPnaxUyqKk27QNOuVvNQ/wlOp/FLRaVlHMgvJiuviOz8Io6WlFFaZigtN4SFBFE/PJh6YSE0iQynZVQEEWHBTkf2LeXl8PltkL4Kxn1kNXcql7laCEYCZ9v3pwDfc1whAC4E5hljcgBEZB4w1BjzMbAQwBhTLCIrgDgX86iTSRxlDbU7tBsatnI6jU/LKywhZedBknccZFNmHlsy89iVU0C5qf73aFQ3lPgm9ejcvAGdm0fSuXkk3eMaaoGoyvf/tFbeO/8Ja5p15RauFoJmxph99v0MoFkl+8QCuys83mNv+5WINAQuAf5b1RuJyARgAkDr1q1diBzgjhWCdZ/C6fc4ncanGGPYmJHH3HUZLNiwn3XpuZQbCA4S2jSpR0LLBozo0ZKWDSNoUj+cJpHh1A0LJiRICAkKorisjPyiMvILS8nKLyT9UCF7Dx1l6/58vk7dx8fLdgEQEiQkxkbRr01j+rdpzMB20dQNc/VX1Q+s/MCaS6jXNTDobqfT+JWT/nSJyHygeSVP/W6ib2OMEZEafBb69fuHAB8DLxljtlW1nzFmEjAJICkpqcbvo2yN20LL3lbzkBaCatlzsIAZKXv4dMVeduUUIAJ9WjfirnM70K9NY3q1bujyH2pjDJmHi1i/L5fkHQdZviOHyb/sYNKP2wgLDqJfm8ac3SmGczo3pV1MfTf9y3zIus9h9l3WAvQXvaDXC7jZSX96jTHnVfWciGSKSAtjzD4RaQHsr2S3vfzWfARW88/3FR5PArYYY16sVmLlusTR8O0jcCANmrR3Oo1XMsbw/aYs3vllOz+nHQBgULtobj+7HUO6NCMmMtyt7yciNI+qQ/OoOpzb2TqxLiwpI2XnQb7ftJ/vN2Xx1JwNPDVnAx2a1mdYtxZc1K0FHZvVR/z9j+KW+TDzZojrC+M+hJAwpxP5HTHm1D9ci8izQHaFzuLGxpgHjtunMZAC9LY3rQD6GGNyROQpoAsw1hhTXt33TUpKMsnJyaecO+AdTof/JMA5D8NZD5x8/wBSXFrOrFV7efOnbWzOzKd5gzqM69eK0b3jaNW4rqPZ9hwsYMGG/Xyduo9l23MoN9A2ph4XdWvBsMQWdGkR6X9FYfNcmHYtxHSE8V9CREOnE/k0EUkxxiT9YbuLhSAamA60BnYCl9t/4JOAicaYm+39bgQetl/2D2PMuyISh9V3sBFrBBHA/4wxb53sfbUQuMG7w+HIAbhjqZ5mA2Xlhpkr9vDivM2k5xbSqVkkt57Vlkt6tCQ02Psut8nKK2Luugy+Tt3H4q3ZlBto37Q+I3u0ZETPlpwWXc/piK5b95l1JtAsEa79TCeTc4NaKQRO0ULgBsvfgjl/hom/QPNEp9M4xhjDvPWZPDt3E1v259M9Loo/nd+RszvG+Myn6+z8Ir5Zl8GsVeks227NJdWzVUNG9mzJRd1b0DTSB8fZL3vTWmmsVX+4apo1l5BymRYC9XtHDsBzHa0O4/MeczqNIzZn5vHorFSWbMuhbZN63H9hJ4YlNveZAlCZ9ENH+WJ1OrNWpbN+32GCBE5v34QRPVpyYWJzGtTx8lW7ystg7iOw9DXoOBTGvANhfnB24yW0EKg/en8UZKfBPasDqnkov6iUlxZs4Z2ft1MvPIT7L+zElX1bEeKFTUCu2JKZx2y7KOzKKSAsJIjzujRlRI9Yzu4UQ51QL7tWofCw1RS0ZS4MuN1aYCbIyzL6OC0E6o9WfgizboebF0DcH342/NJ3GzN5+NNUMg4XckVSKx4c1pnG9fx7FIoxhlW7DzFrVTpfrknnQH4xkXVCGJbYnJE9YxnQNprgIIc/COxbY00bcXAnDP839L3Z2Tx+SguB+qOjh+C5DtYv3dB/Op2mVuUeLeGJL9Yzc8UeOjWL5J+ju9G7daVTY/m10rJyFm3NZtaqdOauyyC/qJSYyHAu6d6SkT1b0j0uyrNNY8bAiinw1QNWZ/CYd+C0QZ57/wCjhUBV7uOrYG8K3Lfeb0/DF27az19nriUrv4jbzmrHXUPaEx7in//WmigsKeO7jfuZtWovCzdmUVxWTnx0XUb0jGVkz5a1f+Ha4XSYcz9smgNtz4ZRb0H9mNp9zwCnhUBVbu0MmHkTXD8H4gc7ncatCkvKeGrOej5YsouOzerz3NgedI/TceiVyT1awtzUDGat3suirdkYA91ioxjZsyUXd29J8yg3jjwqL7fOAuY9CmXF1vUsA+/02w8i3kQLgapc8RF4tj10v9xa5clPpO3P486PVrIxI48JZ7blzxd01LOAaso8XMgXq9OZvTqdNXtyEYEBbaIZ0yeOYd2auzadRvZW+OIe2PETxJ9h/cxFt3NfeHVCWghU1WbeYl3Bef9mn5/b3RjDJyl7eGzWOiLCgnn+8h6c06mp07F81rasfGavTuezlXvZmV1AvbBghndrwZg+cfSNb0xQdTuZy0phySuw8GkIDoMLnoTe4wNqtJo30EKgqrZ1Ibx/KYx+G7qNcTrNKTtSVMrDn61l1qp0BraN5sVxPWnWwLcLm7cwxpC88yAzkvcwZ+0+8otKad24LqN7xzEmKY7YhhFVvzhjLcy6E/atgk4XwUXPQYOWnguvfqWFQFWtvBz+2x2adLAu5fdBOw4cYcL7yaTtz+fe8zpyxzntnR8S6aeOFpcxd10GM1L28MvWAwhwXpdmXD8onoHton8bdVRSaE0b/ct/IaIRDH8WEi7VswAHVVUIdJJzBUFB0ONK+PFZyN0LUbEnf40XWbhpP/d8vJKgIOG9G/szuEMTpyP5tYiwYC7tFculvWLZc7CAj5ft4uNlu/l2fSYdmtbnukHxjGmym4iv74XsLdDjKrjwHzpXkBfzr0sp1anreSVgYPXHTiepNmMMryxM48bJy4lrVJcv7hysRcDD4hrV5S8XdmbRQ+fy3NgeNAouwnz5ZyI+uIjD+fkcuXw6XPaaFgEvp4VAWRq3hdNOh1UfWRf5eLmC4lJu/3AFz87dxCXdWzLztkGOTxMdyOqEBjOmwQamlf2Ja0PmMy9yFANy/8GA6fD8t5vIOVLsdER1AloI1G96XgU5W2H3UqeTnFBGbiFjX1/M3HUZPDK8C/8d11PX+HXS0UPw+R3w4RgkvD5y07ec/+d3mX7XeZzergkvf5fGmf9eyCsL0zhaXOZ0WlUJ7SxWvynKt2Yk7TYaRrzsdJpKrUvP5abJyeQVlvDyVb1+Xc1LOWTLPJh9N+RnwuB74awHIeT3q7dtysjj2bmbmL8hkxZRdbjv/I6M6h2nnfkOqKqzWM8I1G/C60PCSEj9zLrQzMt8tzGTsa8vRgQ+mThIi4CTCnNhlnUWQJ0ouHk+DHn0D0UAoFPzSN4an8TUCQNoGhnOX2asYdSrv5C6N9eB4KoyWgjU7/W6GorzYMOXTif5nXd/2c7NU5JpG1OPz+84nYSWDZyOFLh2LYXXBsOqj+GMP8OtP0Bs75O+bEDbaD6/43RevKInew8VMuJ/P/P4F+vIKyzxQGh1IloI1O+1HgQNT4NVHzidBIDycsPjX6zj8S/WM6RLM6bfOlAvEnNKeZk1xPjdYda1ADd9W+VZQFVEhEt7xbLgz2dxdf/TmLxoB+f95wcWbtpfi8HVybhUCESksYjME5Et9tdK5/UVkfH2PltEZHwlz88WkVRXsig3CQqCXtfA9h+teWEcVFRaxt1TV/LuLzu48fQ2vH5NH9fmuVGn7nA6vDcSvnsKul4GE39yaQ2LqIhQnrw0kc9uP52oiFBueHc5j3y2liNFpW4MrarL1TOCh4AFxpgOwAL78e+ISGPgMaA/0A94rGLBEJFRQL6LOZQ79boWJNiaIdIheYUl3Dh5OV+u2cdfh3Xm0UsStHPRKdt+gNcHw94VMPJVGP2W29YQ7tmqIbPvHMyEM9vy0bJdDH/pJ1J2HnTL91bV52ohGAkc+2sxBbi0kn0uBOYZY3KMMQeBecBQABGpD9wHPOViDuVODVpAp2Gw8gMoLfL42//COygAABmiSURBVGflFXHlm0tYsi2H58f24NazdHZKRxgDv7xkzUNVtwlM+N7qQ3LzFBF1QoN5eHgXpt4ygLJyw+VvLGbSj1vxxRGNvsrVQtDMGLPPvp8BVDaMIxbYXeHxHnsbwJPA80DByd5IRCaISLKIJGdlZbkQWVVL0o1QkA0bvvDo2+7MPsKY1xexdf8R3rouidF94jz6/spWlA8zboB5/wedL4ZbFkBMx1p9y/5to/nqnjO4IKEZT3+1kVveSyG3QDuSPeGkhUBE5otIaiW3kRX3M1b5rnYJF5GeQDtjTLVmOTPGTDLGJBljkmJidBWjWtf2HGgUD8nveuwtU/fmMvq1xeQeLeHDW/pzTmedPtoR2Vvh7fNh/Sw47+9w+XsQHumRt25QJ5RXr+7Noxcn8P2m/Vz08k86zNQDTloIjDHnGWMSK7nNAjJFpAWA/bWyrv+9QKsKj+PsbQOBJBHZAfwMdBSR71375yi3CQqCPtfDzp8ha1Otv13KzhyunLSEsGBhxsRBAbmesFfYuRjeGgJ5++CamTD4Tx6fLVREuHFwG6ZPHEh5uWHM64v4au2+k79QnTJXm4ZmA8dGAY0HZlWyz1zgAhFpZHcSXwDMNca8ZoxpaYyJBwYDm40xZ7uYR7lTz2sgKBRSJtfq2yzems21by8jun4YM24bRPumtbxWrqrcus+skUERjeGW76DduY7G6d26EbPuHExCiwbc/uEK/jt/i/Yb1BJXC8EzwPkisgU4z36MiCSJyFsAxpgcrL6A5fbtCXub8nb1Y6DLJbDqQyg5Witv8cPmLK5/dxmxDSOYfutAWp5ogRNVO4yBRS/DJ9dDy57WVcKN2zqdCoCYyHA+umUAo3rH8sL8zdz58Uqdr6gW6FxD6sS2/wRTLoZLX7MmpXOjb9dlcOdHK2nftD7v39SP6PrVvzBJuUl5GXzzECybZE0vctkbEOp9xdgYwxs/buNf32yke1xD3hmfpD8vp0DnGlKnJn4wNOlo/aFw44eGL1anc9uHK0ho2YCPbxmgv9ROKC6Aadda/7cD74Qxk72yCIDVbzDxrHa8cU0fNu47zJjXF7Mr+6SDDVU1aSFQJyYC/SdC+kq3TU/9SfJu7pm6kj6nNeKDm/sTVTfULd9X1UB+lnWmt+krGPZvawWxIO//c3BB1+Z8dEt/co4UM+q1RTqiyE28/39eOa/HOKjTEJa86vK3en/JTv4yYw2nt2/ClBv6UT9cp4zwuANp8PZ5kLkervgA+t/qdKIa6XNaY2beNpDwkCDGTVrCz1sOOB3J52khUCcXVs8aSrrhCzi065S/zVs/beP/Pk9lSOemvHldki4m44RdS6wiUJQP138JXS52OtEpad80kpm3DSKuUQQ3TF7GF6vTnY7k07QQqOrpdwsgVnvyKfjfd1t4as4GhndrzmvX9KFOqBYBj1s/C6aMsIaH3jzPpUnjvEHzqDpMu3UgvVo34u6pK5m67NQ/pAQ6LQSqeqLiIGEEpLxnfZqsJmMMz87dyHPfbmZUr1heGteLsBD9sfMoY2DxKzB9vDU89KZ5XjM81FVREaFMuaEfZ3WM4aFP1/LWT9ucjuST9DdSVd+A26Eo11rgvhqMMTz55QZeWbiVK/u15rmxPQgJ1h85jzo2PHTuw9Y1IdfNgnrRTqdyq4iwYCZdm8SwxOY8NWeDXnh2CvS3UlVfXF/rtvhlKDvxvPHl5YZHPk/lnV+2c/2geJ6+LJEgnUbas4oLYPp1sPR1GHAHjJ3itcNDXRUWEsTLV/ZidO84Xpi/mX9+vVGLQQ1oIVDVJwKD77M6jFNnVrlbaVk5989YzUdLd3Hb2e147JIExMPz1QS8/CyYcglsnAND/wVDn/aJ4aGuCAkO4tkx3blu4GlM+nEbj3yeSlm5FoPq0LF7qmY6DoWmCfDzf6Db2D/8cSkpK+feaauYs2Yf953fkbvOba9FwNP2b4SPxlrF4Ir3rSahABEUJDw+oiv1w0N49futHCkq5bmxPQjVJskT0qOjaiYoyDoryNpoXYxUQWFJGbd9sII5a/bxyPAu3D2kgxYBT9v2Pbx9AZQUwg1zAqoIHCMiPDC0M3+5sBOzVqVz+4crKCzR+YlORAuBqrmul1lrFfz0/K/TThwtLuOW95KZvyGTJ0d25ZYz/WNUik9Z+QF8MBoatLQWkont43QiR91xTnseH9GVeeszuXHycl0P+QS0EKiaCw6B0++F9BWw7XvyCksY/84yfk47wL9Hd+fagfFOJwws5eWw4EmYdQfEnwE3zYWGrZ1O5RXGD4rn+bE9WLo9h6vfWsqhgmKnI3klLQTq1PS8CiJbULrwGa55cwkrdh3kpXG9uLxvq5O/VrlP4WGYdg389Bz0Hg9Xf+K2heX9xeg+cbx6dW/Wpx9m3KQl7M8rdDqS19FCoE5NSDh5fe8mZM8Sovf/wuvX9OGSHi2dThVYsjZbq4lt/sYaGXTJfyFYJ/CrzIVdm/PO9X3ZlVPA2NcXsztHZy6tSAuBOiXph44yemkH9pgYXor5gvO66PrCHrXxK3jzXCjIgfGzYcBEjy8p6WsGd2jCBzf35+CRYsa+vpi0/XlOR/IaWghUje3MPsLY1xezL7+cksEPUD8n1ZqQTtW+shKY/zhMvRKi28GE7601I1S19G7diGm3DqS03HD5G0t0GmubFgJVI1sy8xj7+mIKikv5eMIA2px7o7VwzXdPWdMZqNpzcAe8O8y6hqP3dXDjN9BQ+2RqqkuLBsyYOJCI0GCunLSERWk6jbVLhUBEGovIPBHZYn9tVMV+4+19tojI+Arbw0RkkohsFpGNIjLalTyqdq3cdZDL31gMwLRbB5IYG2WNIDr3b3BgE6x4z+GEfiz1U3j9DMjaBGPegREv++10EZ4Q36QeM24bSIuGdRj/7jI+XbHH6UiOcvWM4CFggTGmA7DAfvw7ItIYeAzoD/QDHqtQMB4B9htjOgIJwA8u5lG1ZOHG/Vz15lIaRITyycSBdGwW+duTXUZA60HWWUGhnmq71ZED8MkNMOMGiOkEE3+CRP285A4toiL4ZOIg+sY35r7pq3lpQeBOVudqIRgJTLHvTwEurWSfC4F5xpgcY8xBYB4w1H7uRuCfAMaYcmOMnqN5oU+Sd3Pze8m0a1qPGRMHcVp0vd/vIAJD/wkF2fDjs86E9DfGWPM5vdLP6n85529ww9fWhXzKbaIiQpl8Qz9G9Y7lP/M28+DMNZSUlTsdy+NcLQTNjDH77PsZQLNK9okFdld4vAeIFZGG9uMnRWSFiHwiIpW9HgARmSAiySKSnJWV5WJsVR3GGF5ZmMZfZqxhYNtopk4YSExkFYvMt+wJva6GJa9D9lbPBvU3Odth6lUw40brwrBbf4Sz/qJDQ2tJWEgQz4/twd1DOjA9eQ/Xvr2U7Pwip2N51EkLgYjMF5HUSm4jK+5nrHOqmpxXhQBxwCJjTG9gMfBcVTsbYyYZY5KMMUkxMTE1eBt1KkrLyvn77HU8O3cTI3q05J3r+558feFzH4WQOvDVX36dekLVQPER6wrhV/rDth/gvMfhpvnQLMHpZH5PRLjv/I68cEUPVu46xIj//RJQI4pOWgiMMecZYxIruc0CMkWkBYD9dX8l32IvUHFoQ5y9LRsoAD61t38C9Hbh36Lc5HBhCTdOSWbK4p3cckYbXryiZ/VWFYtsBkMeha0LYO0ntR/UX5SVwsoP4eUk6wrhhJFwVzIMvtfqjFcec1mvOGZMHIQxhtGvLeKzlYHRiexq09Bs4NgooPHArEr2mQtcICKN7E7iC4C59hnEF8DZ9n5DgPUu5lEu2pl9hFGvLmJR2gH+Oaobj1yUULMFZfreZC1e881DcCS79oL6g/JyWDsDXu0Ps263CumNc2H0m9bEccoR3eKimH3XYHq2asifpq3mwRlrKCj27wnrxJVechGJBqYDrYGdwOXGmBwRSQImGmNutve7EXjYftk/jDHv2ttPA94HGgJZwA3GmJOuQJ2UlGSSk5NPObeq3NJt2Uz8IIVyA69d05tB7Zqc2jfKXA9vnGmNbhn1hntD+oPSYqsjeNFLsH+9tb7DOY9A54v06mAvUlJWzovzN/Pq91tpE12Pl67sZQ2Z9mEikmKMSfrDdl8cLqWFwL2MMbzzyw7++dUGWjeuy9vX96VNk3onf+GJfPcP+PHf1vKIXSsbTBaAjh6E5Hdh2STI2wcxXeDM+6HrKL9fPcyXLdp6gPumrSb7SBF/vqATNw9u47Nrb2shUJXKKyzhwZlr+GptBud1acbzl/cgKsINo1PKSqwFUnK2wm2LICrO9e/pi8rLYMfPsGYarPscSo5A27Nh0F3QboieAfiIg0eK+euna/lmXQaJsQ14ZlR3nzw70EKg/mB9+mHu+GgFu3IKeODCTkw4s617VxTL3mo1EbXoAeO/gKBg931vb5e5HtZMhTWfQF46hEVaZ0b9b4Xm3ZxOp06BMYavUzN4bPY6co4Uc/2geO4+twNRdX1nWK8WAvWrsnLDpB+38cK8zTSsG8r/rupNvzaNa+fNVn0Mn0+0lrc877HaeQ9vkZdhdf6umQoZayEoBNqfB90vh07DdUoIP5FbUMIz32xk6vJdREWEcve5HbhmwGnVG1nnMC0ECrBGBf15+mqSdx5kWGJz/nFZNxrXC6u9NzQGvrjbmodo1FvQfWztvZcTio/Ahi+tP/7bvgdTDi17Q49xVmd5vVPscFdeb336YZ7+agM/px2gVeMIbj2zHWP6xFEn1HvPfLUQBLiSsnKmLNrBf+ZtJjhIeGJkVy7tGeuZxeVLi+H9S2FPsjVNQpyPr6VbXgbbf4DV06zpH0qOQFRr65N/9ysgpqPTCZWHGGP4YXMWL87fwqrdh4iJDOf6QfFcntSq6qvwHaSFIIAt35HD/32eysaMPM7uFMPTl3WjZUMPN1McyYY3z4aSo3D9V775xzJjLayeajX/5GdAeJTV7t9jHLQaoCN/ApgxhsXbsnl14VZ+TjtASJBwfkIzLk9qxentm3hNs5EWggCUtj+fF+ZtZs7afcQ2jODRSxK4IKGZZ84CKnNgC7w7HCQIbvjKWljF2x1Ot66SXj0N9q+z2v07XGB98u84FELrOJ1QeZm0/XlMXbabmSv2cLCghMg6IZzbuSnnJzRjYNtoous7d6aghSCA7Mw+wisL05iRsoeI0GBuOqMtE89qS90wL5iuYP8GmHwRhERYSyx6YzEoyrOafFZPhe0/Asa6Wrr7FdaY/3rRTidUPqCotIxf0g7wTWoG89ZncrCgBIBOzSLp26YRiS2jSGjZgI7NIj3Wr6CFwM8ZY0jZeZA3f9rGt+szCQ0K4uoBrbnjnPY0cfATSKUy1sJ7I62O5CunQuv+TieyrnvY+p013n/jV1B61JryufsV1s0bC5byGaVl5azek8uSbdks2ZbNip0HOVJsregXHCS0ahRBXKO6tGpsfW0RVYfG9cKIrhdO4/phNK4bRkSY68VCCwFw+RuL2Zd7lKiIUKIiQmkYEUYD+37FW8O6v91vEBFKZHhIzebb8aD0Q0f5fNVePluxly3782lYN5Rr+p/GdQNPo2kDL262yN4KH46F3D0w7Bnoc4PnL64yBvausP74p86EggMQ0RgSR1l//OP66gVfqlaUlxt25RSwft9h1qcfZnv2EfbkFLDn4FGyjxRX+pqI0GAaRITw3Z/Ppt7JZgKuQlWFwAvaCjxnYNtodmYfIfdoCblHS8jIPUzu0VJyjxZTUlZ1QQwSaBARStPIcJo1qEPzBnVoHlXnD/ej64XVesEoKStn7d5cftiUxfebs1iz5xDGQJ/TGvH0Zd24tFdL72gCOpnodnDTPPj0FvjyT9bQy+HPQf2mtf/eOdusC73WTLOufA4Oh87DrT/+7YZASC0Op1UKCAoS4pvUI75JPYZ3a/G7544UlZJxuJCDR4rJOXYrKCYnv5jDhSVE1EIzUkCdEVTFGMPRkrJfC8ShgpJf7x+2vx4sKCbzcBGZhwvJyC3kQH4R5ccdutBgoWlkHZo1sApGs1+LxG+PoyJCiawTQnhI1f+Z5eWG/OJSDuQVsSungN0Hj5KWmceavbmsTz9MUWk5QQI9WzXknE5NGdGz5R9XDfMV5eXwy4uw8GkIqwvn/p+1MHuIm5uzDmyB9bOsW8YaQCB+sPXHP2EE1PG96QKUqiltGnKz0rJysvKLyMgt/LU4ZOYVkZlbSMZha1vm4SLyiyqfvjYsJIgGdUIIDQ5CsBbGMMaQV1RKflHpH9Z1qRsWTGJsFN1jo+jZuiGD2zehYV0/+uSatRnm3Ac7foIGcTDwdug+7tQ7ZksKYddi2LYQtsyzZvkEq7mnywir+SdQ5z9SAUsLgUPyi0qtopBbSGZeIYePlpJXWEJeUSl5haWUlJZjsJqrRaB+eAgN6oQQWSeUxvXCaB1dl1aN6tI0Mtxr+yncxhirw/aHf8PuJRAcBvFnQIfzIbYPNO0C4ZF/fF3xEauvIWOt9Wk/fRXsXgqlhRAUCq36Q5dLrFtUrOf/XUp5CS0EyrdkroNVH8HmuZC95bftoXWhbhPr4q2yUijOg8IKSwoGhVoFI34wtD0HThsE4fU9n18pL6SFQPmu3D2wbw0c2ARHDlg3U24t5h5a11rNq0GsVQBiOmtnr1JV0FFDyndFxdnt+cOdTqKUX3JpAgwRaSwi80Rki/21URX7jbf32SIi4ytsv1JE1orIGhH5RkR0qkallPIwV2dCeghYYIzpACywH/+OiDQGHgP6A/2Ax+yF7EOA/wLnGGO6A2uAO13Mo5RSqoZcLQQjgSn2/SlAZYvTXgjMM8bkGGMOAvOAoWCNmgTqiTULWgMg3cU8SimlasjVQtDMGLPPvp8BNKtkn1hgd4XHe4BYY0wJcBuwFqsAJABvV/VGIjJBRJJFJDkrK8vF2EoppY45aSEQkfkiklrJbWTF/Yw1/KjaQ5BEJBSrEPQCWmI1Df21qv2NMZOMMUnGmKSYmJjqvo1SSqmTOOmoIWPMeVU9JyKZItLCGLNPRFoA+yvZbS9wdoXHccD3QE/7+2+1v9d0KuljUEopVbtcbRqaDRwbBTQemFXJPnOBC+wO4kbABfa2vUCCiBz7eH8+sMHFPEoppWrI1esIngGmi8hNwE7gcgARSQImGmNuNsbkiMiTwHL7NU8YY3Ls/R4HfhSREvv117uYRymlVA355JXFIpKFVThqqglwwM1xaoPmdC9fyOkLGUFzupunc55mjPlDJ6tPFoJTJSLJlV1e7W00p3v5Qk5fyAia0928JaerfQRKKaV8nBYCpZQKcIFWCCY5HaCaNKd7+UJOX8gImtPdvCJnQPURKKWU+qNAOyNQSil1HC0ESikV4AKmEIjIUBHZJCJpIuI1U1mIyA57TYZVIpJsb6vWOg+1nOsdEdkvIqkVtlWaSywv2cd2jYj0djjn30Vkr31MV4nI8ArP/dXOuUlELvRgzlYislBE1ovIOhG5x97uNcf0BBm96niKSB0RWSYiq+2cj9vb24jIUjvPNBEJs7eH24/T7OfjHc45WUS2VziePe3tjv0eYYzx+xsQDGwF2gJhwGogwelcdrYdQJPjtv0beMi+/xDwLwdynQn0BlJPlgtr6bCvsaYVHwAsdTjn34H7K9k3wf6/Dwfa2D8TwR7K2QLobd+PBDbbebzmmJ4go1cdT/uY1LfvhwJL7WM0HRhnb38duM2+fzvwun1/HDDNQ//nVeWcDIypZH/Hfo8C5YygH5BmjNlmjCkGpmKtpeCtqrPOQ60yxvwI5By3uapcI4H3jGUJ0NCehNCpnFUZCUw1xhQZY7YDaVg/G7XOGLPPGLPCvp+HNa9WLF50TE+QsSqOHE/7mOTbD0PtmwHOBWbY248/lseO8QxgiIiIgzmr4tjvUaAUgkrXRHAoy/EM8K2IpIjIBHtbddZ5cEJVubzx+N5pn16/U6FpzSty2k0TvbA+IXrlMT0uI3jZ8RSRYBFZhTXj8Tyss5FDxpjSSrL8mtN+PheIdiKnMebY8fyHfTxfEJHw43PaPHY8A6UQeLPBxpjewDDgDhE5s+KTxjpn9Loxvt6ay/Ya0A5rqvN9wPPOxvmNiNQHZgL3GmMOV3zOW45pJRm97ngaY8qMMT2xprXvB3R2OFKljs8pIolY6650BvoCjYEHHYwIBE4h2Au0qvA4zt7mOGPMXvvrfuAzrB/qzGOnhFL1Og9OqCqXVx1fY0ym/QtYDrzJb80VjuYUazGmmcCHxphP7c1edUwry+itx9POdghYCAzEako5NqNyxSy/5rSfjwKyHco51G6CM8aYIuBdvOB4BkohWA50sEcVhGF1GM12OBMiUk9EIo/dx1qrIZXqrfPghKpyzQaus0c9DAByKzR3eNxx7aqXYR1TsHKOs0eRtAE6AMs8lEmwlmLdYIz5T4WnvOaYVpXR246niMSISEP7fgS/rWWyEBhj73b8sTx2jMcA39lnX07k3Fih8AtWP0bF4+nM75GneqWdvmH1yG/Gakt8xOk8dqa2WKMuVgPrjuXCar9cAGwB5gONHcj2MVYzQAlWW+VNVeXCGuXwin1s1wJJDud8386xBuuXq0WF/R+xc24Chnkw52CsZp81wCr7NtybjukJMnrV8QS6AyvtPKnAo/b2tliFKA34BAi3t9exH6fZz7d1OOd39vFMBT7gt5FFjv0e6RQTSikV4AKlaUgppVQVtBAopVSA00KglFIBTguBUkoFOC0ESikV4LQQKKVUgNNCoJRSAe7/AXRnkt0oG5BvAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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WAl8T1QRiO1vNQ0opz1j6BlSsHhDTSbiihcAXtbsRUjfD4U1OJ1HK9x1JtBaA6nyXNXgzAGkh8EVtroOgEJ1yQilPWPYmBIcFzHQSrmgh8EWVa0LTy63rBDojqVIXLisV1k2F9iOsmX4DlBYCX9VuGBw/oDOSKuWOFe9ZY3O6++96xKWhhcBXtRwIoZW0eUipC5VzHFa9b03oWLOZ02kcpYXAV/02I+nXUJDndBqlfM+qD6yJHPs84nQSx2kh8GVxwyDnGOxa4HQSpXxLXjYsG29da6vbwek0jtNC4MuaXgoVo3RMgVLna82nkH1EzwZsWgh8WXCo1ZV0+/eQe8LpNEr5hoI8awBZgx7QsIfTabyCFgJf124YFJyCbXPOva9SCjZ8bvW46/Ow00m8hhYCXxfbBSIbaO8hpUqjsAAWv2pNNd30cqfTeA0tBL4uKAjihsCuRZAVYGs5K3W+tnwN6buh98MBN9X02Wgh8Adxw8AUWj/kSinXiorg15chuhW0+ovTabyKFgJ/ENMaarXR3kNKnc3WWZC6BXo/Yp1Jq9/o0fAXcUMheSWk73E6iVLep6gQfnoRaraEttc7ncbreKQQiMgAEdkuIoki8piL58NF5Av7+RUi0qjYc4/b27eLSH9P5AlIcUOtr5u+cjaHUt5o80xI2wZ9H4OgYKfTeB23C4GIBAPjgYFAa2CEiLQusdudQIYxphnwKvBf+7WtgeFAG2AA8Lb9/dT5qtYAGnSHjdPBGKfTKOU9Cgvgp/9Yzaetr3U6jVfyxBlBFyDRGLPbGJMHfA6UXPRzMDDRvv8lcLmIiL39c2NMrjFmD5Bofz91IeKGWp96UnTBGqV+s+lLa1H6Sx/XawNn4ImjUg9IKvY42d7mch9jTAGQCdQo5WsBEJExIpIgIglpadpN0qXWpxesme50EqW8Q2GBdW2gdjvtKXQWPlMejTETjDHxxpj46Ohop+N4p8o17AVrvtIFa5QCWD8VMvbApU/ouIGz8EQhOADUL/Y41t7mch8RCQEigaOlfK06H3E3wPFkXbBGqfwc62ygbkdoMcDpNF7NE4VgFdBcRBqLSBjWxd/ZJfaZDYy07w8FFhpjjL19uN2rqDHQHFjpgUyBq9VV9oI12jykAtzK96wPRf2e0bOBc3C7ENht/uOAucBWYJoxZrOIPCsig+zdPgRqiEgi8BDwmP3azcA0YAvwA3CfMabQ3UwB7fSCNVt0wRoVwLLTrVHEzfpB4z5Op/F6IZ74JsaYOcCcEtueLHY/B7jhDK99AXjBEzmULe4G64xg1wJrSUulAs3iV6ylKK942ukkPsFnLhar89D0MmvBGm0eUoHoWBKsmAAXj4DabZ1O4xO0EPij0wvWbJujC9aowLPo39bXS59wNocP0ULgr+Ju0AVrVOA5vNHqMtr1bqhW/9z7K0ALgf+q31UXrFGBxRj4/h9QsTr0fsjpND5FC4G/0gVrVKDZPAP2LYHLn7SKgSo1LQT+LO4GXbBGBYa8k/Djk9ZUEh1vczqNz9FC4M9i2uiCNSowLH7NGjx21f/pNNMXQAuBv9MFa5S/y9gLS163zoAbdHM6jU/SQuDvdMEa5e/m/tOadbffs04n8VlaCPydLlij/Nm2ObDtW+jzCFSt63Qan6WFIBDogjXKH+WegDmPQK3W0ON+p9P4NC0EgUAXrFH+aOHzcPwgXPOGNZpeXTAtBIFAF6xR/iZ5Nax4DzrfBfU7O53G52khCBS6YI3yFwV58M0DUKWONXhMuU0LQaDQBWuUv/jlf9b1rqtfhgpVnU7jF7QQBApdsEb5g+TV8OsrcPFN1ocb5RFaCAJJ3A1wKsNasEYpX5N/Cr4eazUJDXzR6TR+RQtBINEFa5QvW/AcHNkBg9+CCpFOp/ErbhUCEYkSkXkistP+6nLKPxEZae+zU0RG2tsqich3IrJNRDaLiJb4sqYL1ihflbgAlo+HzqOh6aVOp/E77p4RPAYsMMY0BxbYj/9ARKKAp4CuQBfgqWIF4yVjTCugA9BTRHSB3bKmC9YoX3MiBWbebQ0cu/I5p9P4JXcLwWBgon1/InCti336A/OMMenGmAxgHjDAGJNtjFkEYIzJA9YAsW7mUefy24I12jykfEBRIcwYDblZMPRjCK3odCK/FOLm62OMMYfs+4eBGBf71AOSij1Otrf9RkSqAdcAr7uZR53L6QVrlrxhLVgTEe10Ir9QUFjE7iMn2XLwOEnp2Rw4dooDx06RejyXrNwCTuYVkJ1bSEFRESFBQQQHCSHBQmTFUKIqh1G9Uhg1I8KpH1WRhjUq0SCqEk2jI6hWKczpf5qzFr8Ke36GQW9CrVZOp/Fb5ywEIjIfqO3iqX8Wf2CMMSJy3rOaiUgIMBV4wxiz+yz7jQHGADRo0OB830YVF3eD9Qu25WvoMtrpND4pK7eAFbuPsjjxCGv2H2PboePkFvw+artmRBj1qll/1KtUCKVyeDCVw0MICRIKigwFhUXkFxqOn8onPTuP9JN5bD98gpQTOX+YG7BuZAVa142kdd2qtK1blfhGUURVDpDisPtnayH6tkOgw61Op/Fr5ywExpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4KdijycAO40xr50jxwR7X+Lj43UaTXecXrBm43QtBOfhcGYO3208xNxNh1mzP4OCIkN4SBDt61fjlm4NaVO3Km3qRtKwRiUqhF7Y4ig5+YUcOHaK/Uez2ZFygi2HjrPl4HEWbkuhyP6pb1Yrgs6NoujaOIqezWoSXSXcg/9KL3FsP3x5O9RoBte8DiJOJ/Jr7jYNzQZGAi/aX2e52Gcu8O9iF4ivBB4HEJHngUjgLjdzqPMVNxQWPGMt6lG9kdNpvNbJ3AJmrTvIrHUHWLk3HWPgojpVuat3E3o3r0mnhtUv+I++KxVCg2kaHUHT6AgubVXrt+05+YVsOpDJyr3prNqTzrcbDjJ15X4A2tarSt8WtejbMpr29asREuzjvcLzT8EXt0BhPgyfAuFVnE7k98S4MUe9iNQApgENgH3AMGNMuojEA2ONMXfZ+90BPGG/7AVjzMciEot17WAbkGs/95Yx5oNzvW98fLxJSEi44NwK6xPXa3Fw2b+sudzVHySmZjFp2V6+WnOArNwCmkZXZtDF9fjLxXVoGh3hdDwKiwxbDx3n5x1p/LQ9lTX7j1FYZIisGEq/1jFcFVebns1qEh7iY8s2GgMzx8KGz2HEF9BygNOJ/IqIrDbGxP9puzuFwClaCDzkowHWSON7l+upt231vgzeXLiTn7anERYcxFVxtbm1eyM6NqiGePExyszOZ3HiEeZvTWH+1hRO5BRQJTyEK1rHMLBtbfq0iPbomUuZ+eUlWPgc9H0C+v7D6TR+50yFwN2mIeXL4obCdw9bE3jVjnM6jaNW7U3njQU7+XXnEapXCuXhfi0Y0bUBNSN8o/09slIoV7erw9Xt6pBbUMjSxKPM2XiIH7ekMHPtAapWCOEvF9dlSMd6dGxQ3TuL2obpVhGIGwaXPOp0moCiZwSB7ORReLkFdB0L/V9wOo0jdqVl8e/vtrJgWyo1Kocxpk8TbunWkMrh/vEZKb+wiKW7jvL12gN8v+kQOflFNKpRies7xnJ9x3rEVq/kdETL3sUw6TqI7QK3zoAQ3yjAvkabhpRrn98M+5fDw9sCapWnjJN5vL5gJ5OX76NCaDD3XdqMUT0aUTHMB5pPLlBWbgHfbzzEV2uSWb47HRG4pEU0N3dtyGWtahEc5NBZQtp2+LAfRMTAnT9CRZcz1SgP0EKgXNv+A0y9EW78DC76i9Npypwxhumrk/n3nK0cP5XPiC4N+Fu/Fj7TBOQpyRnZTEtI5vOV+0k9kUvdyAqM6NKAGzvXp1bVCuUX5FgSfDwQCnLgrvnag62MaSFQrhUWwKutoV4nGDHV6TRlandaFk/M3Mjy3el0blSd56+No2XtwO6amF9YxIKtKUxevp/FiUcICRL6t6nNHb0a0alhVNm++YnDVhE4eRRGfQN1Li7b91N6sVidQXAIXDwClr5pTe5VxdUsIb6tsMjw3i+7eG3+TsJDgvjP9XHcGF+fIKeaQrxIaHAQA9rWYUDbOuw5cpIpK/YxLSGZ7zYeomODaozp04R+rWt7vtkoOx0+vdb6mbt1phYBh+kZgYIjO+GteOj3LPR80Ok0HpWUns1D09axam8GA9vW5plBbcq36cMHncwtYHpCEh8u2UNS+ika1qjEnb0aM7RTLJXCPPDZMScTJg6C1K1w83Rocon731OVijYNqbP7sD+cSof7VvrFmAJjDF+tOcDTszcjwDOD23Bdh3re2W3SSxUWGeZuPsyEX3azLunYb72qbu3e8MILQnY6TL4eDm+0Rg236O/Z0OqstBCos1szCWaPgzvnQf0uTqdxy/GcfB7/aiPfbTxEl8ZRvDLsYu/pJumDjDEk7Mv4bZxFjcphjO7ThFvPt5ttVhpMutZaZWzYJB017AAtBOrsck/ASy2tKaoHvel0mgu25eBx7v1sNUkZp3jkypaM6dPEuW6Rfmj1vgxeX7CTX3akEVU5jNG9m5Su2+3xQ/DpIKuX0Igp1rKpqtydqRD4+OxUymPCq1jLWG6aAXknnU5zQaYlJHHd20vIzivk8zHduKdvUy0CHtapYXU+vaMLM+7tQbvYSP77wzb6vrSIaauSKCw6w4fKY/ut3kHHD8ItX2kR8EJaCNTvOtwCeVmwxdUkst4rJ7+QR79cz6NfbqBTw+p890BvOjcq466PAa5jg+p8cnsXpo/tTt1qFXn0qw0MfP0XFm5L4Q+tDIc3wYdXWtefbpsFjXo6F1qdkRYC9bsG3SCqKaz51OkkpZZ6PIfhE5YzLSGZcZc2Y9KdXf1zfn4v1blRFDPu6cE7N3ckv9BwxycJ3PT+CrYfPmEtLPPxQEBg1ByI/VOLhPISWgjU70Sg0yjYvwxStjid5pw2Hchk8PglbD98gndv6cgj/VtqU5ADRISBcXX48W99eHZwG7YePs47b71I4aTrKaxSF+6aB7XbOh1TnYUWAvVH7W+G4DBY/bHTSc7quw2HGPruUgT48p7uDGhbx+lIAS80OIjbujVkae9NvBbyFqsKm3N5xhN8lQhFZ7p+oLyCFgL1R5VrQOtrYf3nXnnR2BjDq/N2cN+UNbSpG8mscb1oUzfS6VgKoCAXZo+j0s/PQJvriLhzNtWiavLw9PXcOGEZu9KynE6ozkALgfqz+Dsg9zhs+srpJH+QV1DEw9PX8/qCnQzpGMuU0Xo9wGtkpcGng2HtZLjkHzDkI9o2rMWMe3rwvyHt2JGSxcDXf+XtnxLJLyxyOq0qQQuB+rMG3aBWa0j4yOkkvzmRk8+dE1cxY80BHurXgpduaOd7yzD6q8Ob4P3L4OBaGPoRXPoEBFl/WoKChGGd6zPvoT5c3qoW//thO9eOX8KmA5kOh1bFaSFQfyZinRUcXAsH1jidhpTjOQx7bzlLdx3lf0Pb8cDlzXWqCG+x7Ture2hRPtz+PbQd4nK3WlUq8M4tnXjn5o6kHM9l8PglvPzjdj078BJuFQIRiRKReSKy0/7qckUJERlp77NTREa6eH62iGxyJ4vysHbDILSS42cFO1NOcP3bS9l/9CQfjerMsPj6juZRtqIi+Pl/1sJG0S1h9CKo1/GcLxsYV4f5D/VhcPu6vLkwkSHvLGW3XjtwnLtnBI8BC4wxzYEF9uM/EJEo4CmgK9AFeKp4wRCR6wH9SfA2FSKtNY03fQWnjjkSYdXedIa8s5S8wiK+uLs7l7SIdiSHKuFUBkwdDotesD4w3D4Hqpa+11a1SmG8Mqw979zckf3p2Vz9xmKmrNiPL0534y/cLQSDgYn2/YnAtS726Q/MM8akG2MygHnAAAARiQAeAp53M4cqC/F3QH42rC//BWt+3pHGrR+uoGaVcGbc04O29bRnkFc4vBEm9IVdC+Gql+C69yC04gV9q4FxdZj71z7EN6rOEzM3ctfEBI5k5Xo2ryoVdwtBjDHmkH3/MOBqVZN6QFKxx8n2NoDngJeB7HO9kYiMEZEEEUlIS0tzI7IqtbodoH5XWPEeFBWW29t+v/EQd01cRZOaEUy7uzv1o3TmUK+w/gv4oEJqS5cAABnYSURBVJ/VTfT2OdBltNtTlsdUrcDE27vw5F9a82viEa56/VdW7D7qocCqtM5ZCERkvohscnEbXHw/Y53XlfrcTkTaA02NMTNLs78xZoIxJt4YEx8drU0E5abbPZCxB3bMLZe3+3J1MvdNWUO72GpMHdMt4NYS9koFefDdIzBzjLWk6d2/eHSq8qAg4Y5ejZl1X08iwkMY8f5yxi9K1EFo5eichcAYc4Uxpq2L2ywgRUTqANhfU118iwNA8St8sfa27kC8iOwFFgMtROQn9/45yuNaXQNVY2H522X+VhOX7uWR6evp0bQmk+7sQmTF0DJ/T3UOxw/CJ1fBqvehx/3WxHERtcrkrS6qU5XZ9/fi6nZ1+b+527lj4irST+aVyXupP3K3aWg2cLoX0EjA1bSVc4ErRaS6fZH4SmCuMeYdY0xdY0wjoBewwxjT1808ytOCQ6DrGNj7q9U+XEbGL0rkqdmb6dc6hg9GxntmSUTlnj2/wnt9rCUlb5gIVz5v/TyUoYjwEN4Y3p7nrm3L0sSjXP3Gr6zel16m76ncLwQvAv1EZCdwhf0YEYkXkQ8AjDHpWNcCVtm3Z+1tyld0vM3qSrr8XY9/a2MML36/jf+bu53rOtTj7Zs7UiFUB4o5yhhY+qY1UrhidRi9ENq46gdSNkSEW7s1ZMa9PQgNDuLG95bz6bK92quoDOkKZap0vn0I1k6Cv22BCM9coykqMjw5exOTl+/nlm4NeHZQW4J09lBnnToGs+6Dbd/CRYPg2retRYscknkqn4e+WMeCbakMi4/luWvb6ohyN+gKZco9XcdCYZ7HBpgVFFrzBk1evp+xlzTlucFaBBx3cK3VFLTjB+j/Hxj2qaNFACCyYijv3xbP/Zc1Y1pCMje+t5zDmTmOZvJHWghU6US3gGb9YNUHkO/eL2JuQSH3fraGmWsP8Pf+LXlsYCudMsJJxsCqD+2pIgqsqSK63+t211BPCQoSHr6yJe/e0pGdKSe45q3Fet3Aw7QQqNLrMQ5OpsL6KRf8LbLzCrhrYgI/bknhmUFtuO/SZh4MqM5bbhbMGA3fPQSNL4G7f/Vo11BPGtC2DjPv60nlsGCGT1jOlBX7nY7kN7QQqNJrfAnU7QhLXofCgvN+eeapfG79cCVLEo/w0g0XM7JHI89nVKWXsgXev9SaRuSyf8FN06z1KLxYi5gqzLqvFz2a1uSJmRt5YuZG8gp04jp3aSFQpScCvR+CjL2w5evzeunRrFxGTFjOhuRjjL+pI0M7xZZNRlU666ZaU0efOmaNDejzyG9TR3u7yEqhfDSqM/f0bcqUFfu55YMVHNWpKdziG//zynu0vBpqtoRfX7HalkvhUOYphr23jN1HsvhgZGcGxumyko7JPwWzxsHXY63F5McuhsZ9nE513oKDhH8MaMXrw9uzPvkYg95awpaDx52O5bO0EKjzExQEvf4GqZtLNe3EvqMnGfrOMlKP5/LpHV11BlEnHUmED66wugH3fgRu/RqquJoezHcMbl+PL8f2oMgYhryzlDkbD537RepPtBCo8xc3FCIbwOKznxXsSDnBDe8uIzuvgCmju9GlcVQ5hlR/sHmmNWvo8YNw85dw+b/KfJRweYmLjWTWuJ60rluVez9bwyvzdug8RedJC4E6f8Gh0PMBSFoBu39yucu6pGMMe28ZANPu7k5crE4j7YiCXJjzd5g+Cmq1grG/QvN+TqfyuFpVKjBldFeGxcfyxoKd3PPZarJyz79DQ6DSQqAuTMfbrMnoFr3wp7OCpbuOcPP7y6laIZQvx/ageYyzg5ICVsZe+GgArJwA3e6DUXMg0n8v0oeHBPPfIe146prWzN+aypC3l7L/6DlnuFdoIVAXKiQcLvk7JK+CnT/+tnnelhRGfbyKetUr8uXY7jSooWsJOGLzTHi3NxzdBcMmwYB/Q0iY06nKnIhwe8/GTLy9C4eP5zBo/GKW7jridCyvp4VAXbj2N0P1RrDweTCGmWuTGTt5NRfVqcoXY7pTq2oFpxMGnvxT8M2DVlNQzRYw9hdoPcjpVOWuV/OazLqvJ9ER4dz64UqdtO4ctBCoCxccCpc8Boc38NOsD/nbF+vp2jiKz+7qSvXK/v/p0+ukbrPGBqz+BHr+Fe74wSrUAapRzcrMuLcHl7aM5slZm3Xw2VloIVBuMXE3kF6xEXXWvEq/VjX5aFRnIsL9ozeKzzAG1nxq9QrKSoVbvoJ+z1iFOsBVqRDKhFvjue/SpkxdmcTNHyzXdZFd0EKgLlhRkeH573fw/zIH0TIomXfitutaAuUt5zh8dRfMvh/qd4Z7lkCzK5xO5VWCgoS/92/FGyM6sPFAJoPfWsLmg5lOx/IqWgjUBcktKOSBz9fy4eI91Op6Iya2KyGLnofcE05HCxwH1ljTRm+eCZf9P3uAWG2nU3mtQRfX/cPgs283HHQ6ktfQQqDOW+apfEZ+tJJvNxzisYGteGpQG2TAfyArBRa/5nQ8/1dUCL/8H3zYDwrzYdR30OfvEKRnY+fStl4ks8f1ok3dSMZNWctLc7fr4DO0EKjzdCjzFMPeXcbqfRm8Prw9Yy9paq0lENsJ4oZZSxwe0+mBy0zGXvj4Kqun1kWD4J7F0LC706l8SnSVcKaM7sqN8fV5a1Eid0/WwWduFQIRiRKReSKy0/5a/Qz7jbT32SkiI4ttDxORCSKyQ0S2icgQd/KosrX98Amuf3spB4+d4pPbuzC4fb0/7nDFUyBBMP9pR/L5NWNg3RR4pxekboHr34ehH1lrCqvzFh4SzItD4nj6mtYs3JbK9W8vYd/Rk07Hcoy7ZwSPAQuMMc2BBfbjPxCRKOApoCvQBXiqWMH4J5BqjGkBtAZ+djOPKiO/7Ehj6LtLKTKGaWO707NZzT/vFBkLPe635rffu6T8Q/qr7HSYPhK+vgfqXGxdEG43zGtWEPNVIsKono359I4upBzP5S9vLA7Y6wbuFoLBwET7/kTgWhf79AfmGWPSjTEZwDxggP3cHcB/AIwxRcYYHQLoZYwxfLxkD6M+Xkm9ahWZcW9PLqpT9cwv6PU3qNYAvv2rNc+Ncs/OefBOD9g2B654BkbOto6v8piezWry7f29aBYTwbgpa3li5kZy8gudjlWu3C0EMcaY0/O+HgZczWlbD0gq9jgZqCci1ezHz4nIGhGZLiJnnBNXRMaISIKIJKSlpbkZW5VGXkERT8zcyDPfbOGKi2L46p4e1KtW8ewvCqsEV78CR3ZYK5mpC3MqA2beA58NhQrVYPQC6PVXvSBcRupHVWLa3d0Ze4m12M2145eQmBo4PeDOWQhEZL6IbHJxG1x8P2ON3z6fy+8hQCyw1BjTEVgGvHSmnY0xE4wx8caY+OhondO+rKWfzOOWD1cwdWUS4y5txru3dKJyaQeKNe8Hba6DX16y5sBX52f79zC+G2z4wuoNdPfPVpOQKlOhwUE8NrAVn9zembQTuVzz5hKmrNgfEFNTnLMQGGOuMMa0dXGbBaSISB0A+2uqi29xAKhf7HGsve0okA3MsLdPBzq68W9RHrIxOZNBby1mXdIxXh/enkf6tyQo6Dzbowe8CCEVYPY4q7ujOrfsdPhqNEwdDpVrwuiF1viAkHCnkwWUvi1rMefB3nRqWJ0nZm5k5MerOJyZ43SsMuVu09Bs4HQvoJHALBf7zAWuFJHq9kXiK4G59hnEN0Bfe7/LgS1u5lFuMMYwafk+hryzlKIiw7S7u/+5Z1BpVakNA1+E/ctg2VueDepvjLEGhY3vCptnWPM3jV4Edds7nSxgxVStwKd3dOG5wW1YtSedK1/9mZlrk/327EDc+YeJSA1gGtAA2AcMM8aki0g8MNYYc5e93x3AE/bLXjDGfGxvbwhMAqoBacDtxphzdkKPj483CQkJF5xb/dnJ3AKemLmRWesOckmLaF67sb37E8cZA1/cYk1TPeYniGnjiaj+JX0PzHkEEudD7XYweDzUaed0KlXM3iMneWT6ehL2ZXDFRTE8M7jNua+VeSkRWW2Mif/Tdl+scFoIPGvzwUwe/Hwdu9OyeKhfC+7t2+z8m4LO5OQReLsbRMTAXQsgVKemBqAgD5a+YY0QDgqxmoA6j/ab5SP9TWGR1Xvu5R93APDXK5pzR6/GhAb71phcLQTqTwqLDBN+2c0r87ZTrVIYr9/Ynh6uxge4a8dcmDIMOo2Ca7QnEXuXwLd/gyPbrdHBA/8LVes6nUqVQnJGNs98s4V5W1JoGVOF569rS+dGvrMW95kKgW+VM+UxSenZjJiwnP/+sI3LW8Uw9699yqYIALTob40vWP0JrJtaNu/hC44fhBlj4JOroOAU3DQNbpykRcCHxFavxPu3xfP+bfFk5RZww7vLuPez1ew94tujkvWMIMAUFhk+WbqXl3/cTpAIzwxqw/Ud61nzBZXpGxfApGshOQHumg+125bt+3mT/FPWHEyLX7V6UPUYB70fscZcKJ+VnVfAhF92M+GX3eQXFnFz14bcf1kzakR4by8vbRpSbDqQyeMzNrLxQCZ9W0bz/LVtia1ejn+MTqTAhEtAgq1iULVO+b23E073Bpr3FGTut5qBrnwuoFcN80epx3N4df5Ovli1nwqhwdzarSF39W5CdBXvKwhaCAJY+sk8Xp+/g0nL9xFVOZynB7Xm6rg6ZX8W4Mqh9fDRQKjRFG7/HsIjyj9Dedi/AuY/ZXWfjYmDAf+Bxr2dTqXKUGLqCd5amMjs9QcJCwliRJcG3Nmrcfl+2DoHLQQBKCe/kIlL9/LWokSy8wq5qUsDHunfksiKDi9huONHmHojNL0Mhk/xrwFTKVtg4XOwfY7VU6rv49DxNp0aIoDsTsvi7Z92MXPtAYwxXNm6NqN6NqJr4yhnPnwVo4UggOQVFDFjTTJvLUokOeMUl7WqxeMDW9E8porT0X63+hP45kFoeRXcMBFCfHyx+4x98NN/YP3nEF4Fej4I3e6BsMpOJ1MOOXDsFJOX72Pqyv0cy86nZUwVhnaKZXD7utSq6kw3ai0EASAnv5AvViXx7s+7OJSZw8WxkTw6oJXrKaO9wcr3rcFULa+GGz7xzWJwbL+1KtuaT61P/V3GWD2kKvlOl0JVtnLyC/l67QGmrkpifdIxggT6tIjmug71uPyiGCJKO4eXB2gh8GNJ6dl8tmI/0xKSSD+ZR+dG1bn/sub0bl7T8VPRc1oxAb7/OzTuA8MmQcVq536NNzi6Cxa/Yp0BINDhZujzKERe4JQcKiAkpmYxc20yM9cc4GBmDmHBQXRvWoN+rWPo1zqGmDI+U9BC4GdO5RWycFsqX65O4qcdaQSJ0O+iGEb1bES3JjWcjnd+1k2B2Q9YF5Bvnu7d8+0f3mSNCN44HYJCodNIqxkoMtbpZMqHFBUZVu1NZ96WFOZtTWHf0WwAWtWuQrcmNejetAbdGtcgspJnr+dpIcCaT6dSWLD3f0o+gxM5+SxJPMp3Gw+xYGsK2XmFxFQNZ3jnBozo0oDakT48fcOeX+DzWyAoCAa/Da2ucjrR74qKYOdcWDYe9v4KoZUg/g5rNbYqtZ1Op3ycMYadqVnM35rC0sSjJOxLJye/CBFoGVOF9vWr0S62GhfXj6RFTBW3prXQQgBc/cavHDx2iua1qtA8JoIWMVVoXiuCZjERREeEe12ByM4rYNOB4yzffZRfd6axdv8xCooMUZXDGNC2Nn9pV4eujWsQ7Kl5gZx2JBG+vB0Ob7Da2q942tmLraeOWWsCrHgX0ndD1XpWro636TUAVWZyCwpZn5TJsl1HWbM/g/XJxziWnQ9AhdAglj9+OdUqXdj1NC0EwOTl+9h8MJOdKVnsSDnB8ZyC356rEBpEbPVKxFavaN8qUa9aRWpVCadGRDjREeFUrRhSJsUiv7CIpPRs9hw5ye60k+xIOcGG5Ex2pp6gyFhL08bVi6RXs5r0al6TLo2iCPGxya5KrSAX5j8Ny9+GqrHQ/wVoPbj81uctKoI9P8PaybD1GyjMhdjOVg+giwZBsMNdb1XAMcawPz2bdUnHSEzN4uErW17w99JCUIIxhtQTuexMyWJXWhZJ6dkkZ5wi+Zj19XQFLi4sOIgaEWHUjAgnIjyEyuEhVKkQQuXwYCqHh1A5LISQYCFYhCARRKwFsnMLCsnJK+RUvnU7mVvIkaxc0k7kciQrl6Mn8yj+31CjchhxsZHW6WBsJB0bVHd/Smhfs385fPcIpGyE+t2g98PWymdlURCKiuDgGtg6GzbNtEYBV4iEuGHWReC6HTz/nko5QAvBeTqek8/BY6c4ciKPI1nWH+y0rFyOnMjj6MlcsnIKyMot4GReASdzC8nKLSCvoOiM308EKoQEUzEsmIqhwURXCf/tVjMinIZRlWgcXZkmNStf8Gmf3yksgDWfwK+vwvFkqNUa2t8MbYe4Pz1FdjrsWwK7f7IWhj9x0JoOuklfaH+T1aVVp8xWfkYLQTnILyyisMhQZAxFBoqMwRRBeGgQ4SFBXncNwmcU5lu9dFa8B4fWgQRBnfbQqBfU7wo1W1jz97gah1CYDycOQ2YSpGy2prg4tM7q/YOBkIrQ7HKr2afFlVCxenn/65QqN1oIlH84shM2fQW7f4bkVVB0uglPoEJVCI+0CkJBHuRnQ/ZRoNjPeMUoawWwBj2suX/qdfKvKS6UOgstBMr/5GVD6hZrcFf6LjiVATnHreIQHG79gY+Iseb7r1oPYlpDlTrld+FZKS9zpkKg6+Ip3xVWCWLjrZtS6oK51QdRRKJEZJ6I7LS/umxgFZGR9j47RWRkse0jRGSjiGwQkR9ExEsnxVFKKf/lbmf0x4AFxpjmwAL78R+ISBTwFNAV6AI8JSLVRSQEeB241BjTDtgAjHMzj1JKqfPkbiEYDEy0708ErnWxT39gnjEm3RiTAcwDBgBi3yqL1Z2mKnDQzTxKKaXOk7uFIMYYc8i+fxiIcbFPPSCp2ONkoJ4xJh+4B9iIVQBaAx+e6Y1EZIyIJIhIQlpampuxlVJKnXbOQiAi80Vkk4vb4OL7Gav7Uam7IIlIKFYh6ADUxWoaevxM+xtjJhhj4o0x8dHR0aV9G6WUUudwzl5DxpgrzvSciKSISB1jzCERqQOkutjtANC32ONY4Cegvf39d9nfaxourjEopZQqW+42Dc0GTvcCGgnMcrHPXOBK+wJxdeBKe9sBoLWInP543w/Y6mYepZRS58ndcQQvAtNE5E5gHzAMQETigbHGmLuMMeki8hywyn7Ns8aYdHu/Z4BfRCTffv0oN/MopZQ6Tz45slhE0rAKx/mqCRzxcJyyoDk9yxdy+kJG0JyeVt45Gxpj/nSR1ScLwYUSkQRXw6u9jeb0LF/I6QsZQXN6mrfk9NPVTZRSSpWWFgKllApwgVYIJjgdoJQ0p2f5Qk5fyAia09O8ImdAXSNQSin1Z4F2RqCUUqoELQRKKRXgAqYQiMgAEdkuIoki4jVTWYjIXntNhnUikmBvK9U6D2Wc6yMRSRWRTcW2ucwlljfsY7tBRDo6nPNpETlgH9N1InJVsecet3NuF5H+5ZizvogsEpEtIrJZRB60t3vNMT1LRq86niJSQURWish6O+cz9vbGIrLCzvOFiITZ28Ptx4n2840czvmJiOwpdjzb29sd+z3CGOP3NyAY2AU0AcKA9UBrp3PZ2fYCNUts+x/wmH3/MeC/DuTqA3QENp0rF3AV8D3WtOLdgBUO53waeMTFvq3t//twoLH9MxFcTjnrAB3t+1WAHXYerzmmZ8noVcfTPiYR9v1QYIV9jKYBw+3t7wL32PfvBd617w8Hviin//Mz5fwEGOpif8d+jwLljKALkGiM2W2MyQM+x1pLwVuVZp2HMmWM+QVIL7H5TLkGA58ay3Kgmj0JoVM5z2Qw8LkxJtcYswdIxPrZKHPGmEPGmDX2/RNY82rVw4uO6Vkynokjx9M+Jln2w1D7ZoDLgC/t7SWP5elj/CVwuUjZL1x9lpxn4tjvUaAUApdrIjiUpSQD/Cgiq0VkjL2tNOs8OOFMubzx+I6zT68/Kta05hU57aaJDlifEL3ymJbICF52PEUkWETWYc14PA/rbOSYMabARZbfctrPZwI1nMhpjDl9PF+wj+erIhJeMqet3I5noBQCb9bLGNMRGAjcJyJ9ij9prHNGr+vj6625bO8ATbGmOj8EvOxsnN+JSATwFfBXY8zx4s95yzF1kdHrjqcxptAY0x5rWvsuQCuHI7lUMqeItMVad6UV0BmIAv7hYEQgcArBAaB+scex9jbHGWMO2F9TgZlYP9Qpp08J5czrPDjhTLm86vgaY1LsX8Ai4H1+b65wNKdYizF9BXxmjJlhb/aqY+oqo7ceTzvbMWAR0B2rKeX0jMrFs/yW034+EjjqUM4BdhOcMcbkAh/jBcczUArBKqC53asgDOuC0WyHMyEilUWkyun7WGs1bKJ06zw44Uy5ZgO32b0eugGZxZo7yl2JdtXrsI4pWDmH271IGgPNgZXllEmwlmLdaox5pdhTXnNMz5TR246niESLSDX7fkV+X8tkETDU3q3ksTx9jIcCC+2zLydybitW+AXrOkbx4+nM71F5XZV2+oZ1RX4HVlviP53OY2dqgtXrYj2w+XQurPbLBcBOYD4Q5UC2qVjNAPlYbZV3nikXVi+H8fax3QjEO5xzkp1jA9YvV51i+//TzrkdGFiOOXthNftsANbZt6u86ZieJaNXHU+gHbDWzrMJeNLe3gSrECUC04Fwe3sF+3Gi/XwTh3MutI/nJmAyv/cscuz3SKeYUEqpABcoTUNKKaXOQAuBUkoFOC0ESikV4LQQKKVUgNNCoJRSAU4LgVJKBTgtBEopFeD+P51Ronmlh4ZuAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From cd5a63aa7d2f4f5b26203da63efe306321b7c222 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 293/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 253e4d329b2d37cecda8c4661889f8a77ce0695c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 294/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From 24b78da532cb30a324b79fa38a3a9dd0ba4b44fd Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 295/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From 44efa3d932659d434bb4b27d95a2906b1ac80c4a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 296/624] transfer files to new location and modify documentation --- docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 12 files changed, 77 insertions(+), 550 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From c248096f213985be742c1e2a9cbed9e21d723a74 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 297/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 8224bb628caf3337443bcb8758f71d3e26131b84 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 298/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From f1cb8583abf713604678587ba5cdd8816d4ad853 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 299/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 5cbf5b276455295e9dab83f6a4229238c248be3e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 300/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From bf388b752eacc0e1cf660ab26d8a6e9e6651259d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 301/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 57bf40fea8950fea0151bb0c81983dc1f64f1b2a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 302/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From e340629c494606c6893b69afdb3c84cb8822207d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 303/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e32f505ea0c762447d29ee6a8443ce61084f2de1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 304/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Ir0LdyVOhTFlzKKgiJaeAW6ugoaHpFuFoilSojWaznQQpLCwMrVEiKJAgKgIqILEf9r30ujwn5yII7oFA4ClRrpxicPAfyOfvoeCu4BP734Iyt/DZPz2PC3uTcP/n4Pb3o2p5Ble9kP/T9mpG3TTdc5L3phI8OxGnXdORkw7+6BxlqVBSUZirMnO6QG66DAoG0iY7+mZpLt+BLAvq9Z1MnPLRwhlUcjPm9X+EZcRJCI1mwMenVitT9uZZ9KuYVoR4JENYRtGlhqmFwBAIo4YsjLOUHWLSzDKSrjLTZFA3dJI1jfZCjLZqnIiKEtd70FqiWNEkMREn7kWxpPWw4+EKDxcPS5kY6GfmKxSObpO351gsTdG0YoAeLn7Kz8fjNmISQnwOuAGYV0ptXp53PvApIAx4wBuVUntEo5/NjwPPA6rAa5VS+x5vJ4JGTIHAby/PKzEy8i9MTH4BSZjvnLqB/SOX8ufn9/H8gWbE+BD+8X34dUFN76JECzFPx/gNtp9USqGcErI8hyzNoapZZG0JZRdQ9SVUvYjy6o1OwR5rO0JHmmE8M0QtHKcUSZCNpxlPtzLUsRqvrZ8mWcJwjjId3oNlxthR3cgzKxeQcRIoAyKXtdHy3LPriuBXaqEqhLgcKANffEhwvwX4qFLqh0KI5wFvV0pdufz6zTSC+8XAx5VSj3tJCoJ7IPDbRynF3PjNTOz/H/R8Ejt/IbVsPwNYWA8LKz5oS0xEwoxEUyRTIda1JehoiqJFDbRQ44GksHRy81VO7ptn9EgOz5O09idZv7OdHjFI9abvUy+ugMQGhNGopy55S8zrRWyxRDY/SLE0j2mF6Vu7lbbMJhaLUUZzVfKVeYziOPHcGB35CTqLM0Td2pk9lELgmCE8y8IxTVzDxNU1PE3g6AZ2KEzVNKgjkUJDKYHluUQdh4hjE3FsEvUaMdsm7D3YKlUCuWic0WQ7+zvXcU//JmbaTELGUXq1Ya6trqatfy2v/cPXntU5+JVaqCqlfi6EGPjF2TQeG0BjvPAHarBupHERUMBuIURaCNGplJo5qz0PBAJPG0op/MU69liR6tAMldPjGMUMnfw5ADkkkZYImbUZzPK9aKe+BGqKT/dezedXvZKX9/Xw2u4WOkIPb7Tj+5LhfVkO/myCuZEiZlhn/c4O1q9OY45MU715P3mRAf3FyNACC5WTnAyXKBmL2AvjuLUqRiRKePMzqCRvZHBecvvYEB17f8x52dNszY0RW+7F0RMaC/EIY21R7FAT1WicUiJONZkglMogEdRtB8cK4VlhpKajSR9D+uhPsKsWzfeJl8skl5ZILy2RyS+xOT/BjtkhXrf/ZmqhEGNtXRztXM2BzlYWmOe1T+mZajjbOve3AD8WQvxfQAMeGCeqG3ho0ubk8rxfCu5CiNcDrwfo6zu7TnMCgcC5Jasu9dNL1Afz2Kfy+IXlDA+jip2e4nhvns9PtBLqSvL3L7+AVcU91L7/50TyQ/yo+TI+su7/csO6bezqbiFh6A/bdr3icvTOKQ7fPkVlySbdEuZZV/fQhsI5vohzKIvtu/jZcdzqrdyfkIzFfGJ+GXs6z1KklfLqa5mN9TE7scCmPUe4cO52nrU4QnQ5mFdbWin2dTMYDzPf2koh04wyfjnsCaAufWpmiFK6naoVAqET8zTCFaDuUNezREN1Vvlp+u0OdDRmrAVmEwW8jE5LooWmaJpEKEGIEFJKPM+j6nkslIo4Q6exTg+Rmp5mYG6K9RMjvEgI7t9y/jk5d2cb3P8CeKtS6ptCiJcBnwWe/WQ2oJT6NPBpaFTLnOV+BAKBp5ibrVI7skD9WA5nsgQKRFhH79dYWnkL+cjt2E0DfOCe6xmbTvLW69by+m1xSj/8SzjxbWYi3Xz4/A+zZfsL+XZX85lWow/IzVQ4dNskJ++ZwXMla1cmWbcmhT5ZQu2do65c3OkDeFP78K0T3Nm3lkI4hFsoMCG7yHY8k+GOLprnJrj8vr28YPZLdBbmAagkEsz0dzHX1k62rZV6JILwHIRjo5TCcHUifgfUMuC4uPZJjvUa7N1yCQtN7RieZMOUy6Zxh9RCgfHMQcabjrAxkuYVS1cyUOqiKmyOagtMVSzE4haSy7euElgAihGDZEuYTEeMlo4o6c4ozRfESf9RFE2AV8oycs/PGP3+j6gfGqS/N31OzuPZBvfXAP/P8uuvA/+x/HoK6H3Iej3L8wKBwNOUUgp3pkLtyAK1I4t481UAzJ44iav6MFeFmXQ/w8TUFzDNZu5dfA3/9v1eNnam+N6bttA69g2cf/17ol6df1n5Z4Se+Vb+qa+b6ENSGZVSTBzPcfCnE4wfzREzNS5ekaDV8VG5GhRqKHuc6t6bkAuHMQdc7lqzkRl3A8NeC8Opdcyl29hSGuHa43eydeIEiUoZKQQLLS0cPH8rlQ0bqVlRajMT+LUc2uwoSdmMbqxBN1djRROk22JEEw4Ti7u5JWFwYONzqYWjrNY0LnEMDuydZtg5zlzLPhI9p7gufwlvW3g1aTeO16qRvnwlXVvbWGvp+J7EqXnUKy7VgkN5yaayZFPO1Sks1JgdXuLUfQ92ZWBoDq3mMG36IO3mKXamj2FcXkReePk5Oa9nG9yngSuA24GrgFPL878H/KUQ4is0HqgWgvr2QODpySvYVPfPU9033wjoAkIrUsQvXkl4cwtGKsTCwm0cPvkubHsGLfZi3nPH5Ywv6fyvq1bxyvUO1W+/iJbsPu5Jb+PwFe/jNVsuIfmQO3XpS07vnWffLeMsTpbpS5pctyJBKF+H+Sp6u4ln30fpJ/+FEFVCa2zuXL2OXWoj4+EBZEuCtbVp/nTiDlaNDZMqFpFCkG/vZuriq3C27qRQ8lk4cT/Osf2Ai6ancJIdFHuTtHa0093aiRUCpz7D+Imj3OZ3cODSZ+IaJmuKi6wbGcSan2QhNkoqNU6PDHH50oVsrT4HlOBEaI75zBjlkIK9R9D2aWiahq4JdE0nbBpELZOoaRLxS4TtcToZYm3oGKH2Cq6XoioGyBvbWLBXcKS4loPVxoUv2WyyLdbN5nNwfp9ItsyXgSuBFmAOeA9wkkbKowHUaaRC7l1OhfxX4DoaqZB/opR63DSYIFsmEPj1kI5P7cgC1X3z2ENLoMDqTxLd3kZkUzN6vJGr7bpLDA7+v8zOfYdodA178q/jo3eEWdUa40MvWoc8+M9sPfRpKnqUWy54O1c+6w20hx/M83Ztn2N3TXPw1gnsfJ31zSH6TQ2t5qElLEIrNKp3f4Pyrd9DWBpijc33W3ewL3weKpagiwIbpodZOTxMx3JHXrWWXmoD57G4ZivDtk+9MIxeHkKrF1BCw0014aZakJHYw0Zi0pREVooc6+hnqmcVUcdhRWGBpsISyrcRwsWSGhqP3ZWvQjWaNwmFEAqW/1ZKQ6kn1g2wruuYpoVlWOhYKMdgzZo1XP/Sq57MaTzjV82WefmjLPqlXvKXs2Te9OR2LxAInGvuXIXy7hmq++ZRto/eFCZxVR+x7W0YzQ8fwWg++2NOnnw3rrtEqvUNvPf2HRyaqvHKS/q4oXec1m9cQ191gl1919P1gg/xhy09Z95bLTocvn2Sw3dMEqp5nNcapjljITxJqD9BaECj+L3PMP+lW1ARi/ELWvlJx6XUIxma9To7CvOsOLyHFeNjWPU6dizJ2KaLOLWih8W4Cb6Hld2FmZ/H9FwwLIqtMU73gwrXuWL1Gra1bMOxHYrFIiODJ5kuFDHjaTaWC2w80Wh242gaFdOhbi6AkKz0VrFWmRixBUqts+ixcSJ6Dl130XUPTVcoK4MtYpSlTrFepuaUqUiHmpIYCAwEITTiaMQwiUgTIS08N4RvxynV05Rrzbh2glpVgl/FwyU3GXT5GwgEngTlSWrHFinfM4MzUgBdED2vldhFHVgDScQvjDPqOIucHPx75udvJh7fwLT2j7zxaw5Ry+f9L11N2+EPcsmBbzIZ7WH/jf/DM7Zdf+a9S/NVDt46wfF7ZsgoxaUtYZKmQChF7JJOQit18l/+DAsf/Q6F5ib2XHs5U8kuIoYiKSUrp0bYPHyatpkpfE1jqqeb4ZUrybZ1EJIxDMJ01OaoTx/Hd2yaUykGtdPcfb5Bu9vBhbGLSdhJ5u+f52b/5jP7VbVilNNd2EaEaiSJY0iEfRDLOciAFuVF+iqSeg4ndi8IBQi6ZJpQ7jxE0aCer2Nn6zgFF08pokIjKgRthoEIWeihMCISRYvF0cJxzHAcS0XQnAha3cKyNcKe8Yj/KrDNIot6iTt1+5eWPRWCYfYCgd8xsupS3j1D+Z5pZMlFbwoTv7iD6AXtZ6pdHqrRH8wPODn4D3heiY7uN/Kx3Rdxy7FFdq5t4Zr+Ia7b827a7UUObflTNt3wXqxQoyfDudEi+28ZZ3j/PF2WxuamEOGahxY3iV/WRXh9lPwXPsvUt77FSE8PR9avR1kmSkGpbrB+dIhtp46Q8AS1TAf5FVvQ2jYQ0zIYepRwKoSq5nEKJUxh4YUMZkWBGb3InFagttz5lq40WlWSVpmkRSZpUjFSKvq4VS1PBSV98B2U76B8F19JbKkoayaLZoyCDiVLYidNZFsUkTyFH76FNusgbUaZk/4m3njN987qs4Nh9gKB3wNerk551xSV+2dRjiS0NkP8JV2E12QQmnjE99h2lpOD7yabvYVk4jxqsXfw2q8XWarmedWz27hw+CO84M6bmUmuovhH/8W2FZeglGLsyCL7bxljanCJvpjBczsimDUPI2YSv26AyPo4c//1Re762E8Y6u4me911REWIuN9MsmCxpViiXRroLesQ/a9BaDpxoPUh++Yrn9J8lgltgWyyxqxaoqo1ugPQhaAn3UUk0UXOTDIsDfSJU0ybOkO9fRhI+jCZPjFPInEvW9NHWRl2scISfIk5JjCHFdaUjoxozGRshuIwGlEsJoBQhpbiOlpLq8hUOxBATMuR0OeIaAVCWgFLLGGJJUStjqjUURUPWfQhX0MrVYlLSUxoFFIryDadx3z7Nliq0LzqJE19UcxYLzX7JM/q3XZOfg9BcA8Efss5U2VKP5+kdjgLQhDd2kri8h7MjkfvJ1wpxezcdxkcfC9S1hhY8Td85dhlfObOcfq7E/zB1hFes+dNtLhLTF/4Zrqe8y58YXJy9wz7fzLO4lSFFWmL63uiGGUXI2GRfNFqwhsyDH/tqxz8j3upt6wls/klbJNJMk6C5APhJgQqY+M4ZVw9QkToSAHGyhSRC9Psu/fH7D24ByeWwI8nUYBULrPxLJVwhTXbXs7p5AY+ky/jSMmFY8foGD/Nd7dfSSme4nJNY/y+cVrMz/H8zUdItPjggX5cEDoUJ7q0ivK6OQ5ePMfPzDgHfYknJC2VbtZkt7Nltpf1TNNqnCCu/QwzmUf5FepKUDbiVPUoVc3ARqJJn7BRJxqrEZF1dCSaLtFNH00HW4AyS+jpoyT6voKMNi6yjmdRmFiBV7qWjLYd1j/1v4ugWiYQ+C1ljxcp/XSc+sk8IqQTu7iD+GXdGKnQY77PcRY4fuKdLCzcSiq5jVj7e/jrby9xZLrIFTvj/MH0v3Dj3E9YyKwj9ZJ/RzVv4diuaQ7+dIJy3mZ1W4T1YQ296GC0Rkhc1YvWGmboprsojRTIaC3EafT94qBYcEvEc4NYc6dxawtMx1Yi+i6mv1VHWSWMLTr1Ppv77z/E5LRHXTYa9UQjHtHwGLXmUci4RCJd1AghUUQ0Qad00RYXmAynWIo1EUGnubhEi3eSWLKKMECUwJqNEs4PIBItFHtOclpWmKlIYk6Znpqis5ogLuPoQmHrGhUjStGIU9JjFI0YJSNGXbOoahGqWoi6FsLVLWzNxNFMXM1AIh7IpQHEmb+lEHBmmUJTEs1TCKnQNYUmJFfWxnjrK951Vr+BoFomEPgd8tCgrkUNktcNEL+kEy38+P87Z7O3cPzEO/H9MqtXv4N9C9fwzs8cR0tZvGLHMG898k9kvBJLl/0N0R1vYe/P5zhyx93YVY+1KxKsbw0j5qvo8RDhZ3ThVh3mv30c09FJk8DQLE4Im93SJT23l6tPfodEvooXDzNxQSveM3Wspp/ihb7OkK+Ry/YxP7SC/L5OFHGS4UUGUvvoTDOVXugAACAASURBVExg6jlcPYRPGL+mo5wJwoZORNfxqxXyUlGKx+jQ51lrD2HqdURSgQIlQUqNQjxJpbuZckucvB+nWL+avMiwGMuQTWdYsDLkzdRjHjOhJBYOBi7mmeI85LWLhqSRGqkaoV2BriQhX2K5PqYvEb4ApaEQ+LqGRMMXGvF8MMxeIPB77RGD+s4utJD+uO/1vBKDg+9lZvZbJOKbWLnmg3zopw5f3XuEteeF+F+5j/P8I7ez1LKZ6rP/jUMHoxx/9/14nmTDpibWhXTU0BIipKO3RfAW61R2TePiMaXlOKUX+Jkuqaemef7cLl6+ewIrq9D6PNRzHSI9S6y1FwnPaiyMt3LEu4gTcgUuJmkKPIP72MIJ2uq5RsuZucf9SmcsmClORfs5ERvgZGSA4Wg/k+FOpsKt2JoFMRoFiHkVmpwCGbtAa2GeAWecZq9AT9imbcUa5twYJycXmc9VMZVioNlhZeo4XeIUKfKE9BqaVOiORbjcQbTURKhuoTugPBtNVgmTJ0KOkMqfGUBboWOLdgpWG6OJFg40dzKqRYgsKiz/kZ+H/KqC4B4IPM25cxUKPxqlfjz3pIM6QC5/D8ePvZ26PcvAwJvwIq/l5Z8/zKBtc/32Cd5z6oM0e0Vmtr2XY/lnM/jP80CeDTvaWGcJ/KOLjRAlQNk+paUSp5hiKjRLvmkIP7NIf3KUvzo5T+ten7jhEbrQJRT30ERjFKL6uMUBNnO/toUFmUTHI6EvYcYVdLRxZ/g6/su7gaLW6InRFxo+GlLoKMBQPqZycYVBTQtT0SOUjBhlPYqtP1gNFfWrrK5OsKk8yNWLd9PhLNDhzNNbm2VldZIWv/joB2roEeYtPdaRnX/YX57QKVhxpkJNjIUGGDa3MRxq52RsNcfjG1EiTdRxiDl14naNVKlMu1NBU4kndB6frCC4BwJPU17BpviTMap75xCWTvI5/cQv7X7CQd336wwN/xMTE58jEhnggu1f5ZZTrbzrpnsJr7T4/+wv8qqjNzEauYpbY29j6EcOhpFl8+WdrNUE7oEsvly+84zrTIRzHKodRGs7RLJ5mrXhadqW6jSPeDTVbQxTwVqwvQjZxHYWUms4WO9g0I8iKhUMKZmPpjnWNcDpth5MTSet6eD6KFcSNhTJeol1mPSF43iezZTvMuzWmQyHWIhH8fTGdzd9l97KHOeVB+muZumsZknVfaQXYtpMMBUKM6ZnmJJtaO5mhBFGaw9jqDK6rKDpAk/Xkb6LJR0i1AnjYCoPIUBD0ag4kctVLgJbW66cEQYejVLRIhS1OCUtjisNLN/D8l3Crkuk4hIpuGx1bC707kOohw+npymIO7DaX3xKfzcPCB6oBgJPM7LqUrxjkvJd06AU8Z1dJJ7Vix574i0Zi8XDHD3211Srp+npfhUdvX/Fe743xLdGslyyaoYPDn0Ao9jEXvMtTM03Y0UMtu3soN/zsY8sglSgCfyVYfb7R5mVt9DaNky3MU37gk3TvE/KbuSYu1WN/EKa3ZnL+e7Ol3Bnuod4cYHzJ0/Tl5vD13Sq8U6SRif9bprWuk2iXsX3a5RFjaKoURF1siEYbEoxnkozk2pmKda4o9V9n9byEq2lRmkpL5Gulh6WwS4UiOX/oNFVgAKU+PXGNw2BpXRMBBY+Bh4WNhZldDOPEcljxJbQkyXsWI1pTSPkp/mLF953Vp8XPFANBH4LKE9Svnua4m0TqLpH9Pw2ktf0YzSFn/A2pHQZHfsUo6P/imW1cP7W/2TWPo8bP7mP8bTkb1q/zfX7TnFf/a/J1lcQSZpcfkUbbSUbd99cY+hmQ+BsiXA/3wH9TrrD42zN1mgZ9EjYLlIJ8osxZsfDTFS7+NQVL+eH1z4TpQl2Tkzy0sHbMJ0iJiYr/C7CdclSZYlqJMekcBhVHujgG4K5ZBPTyWYmmtcwl2oCIGLXWT01xnPnBtlRPsoOcYykWcXwXfyswM5qlPMGLjpENFREg4iGjAhUWKAMAaaGMkCZAqULpKE3/jYEUhNIXaCWp2gCqTUuZlIAGkgNlJAoTSI1idAlLP+t6T5C9xC6RBNyua8ZiaZJfqHRL0pCyYdFXyPrC2ZcjWlHY9YRFJaiAFwrC0/ND+gXBME9EPgNU0pRP55j6QfD+It1QmszpK4bwOqKP6ntVCrDHDv2NoqlQ3S038iaNe/mq/sK/P3te1i9Isdnj/6Y2dwz+aH3UlIZk2dvbyaZreIfnMcFEFDf5nEs/GVC2l1sLi7RMeHQVHLwERxzViMnNCKHlpiNtPKf1/8B9297JpfkfN55YIxa6RRlUSOsWZi6QdmpM6hPgw6G55FazNFNntOdbRzoHuBESw+OJtFliW7nZ6xdGKXdGyElFpAJhZeE+xDsRqCEgRAGAtBEY4SgRmaKj4aHTmOeTqO6Q0ehiwde85DlCk2AsbwNXTSKponGVCg0odB1GqmKy+toy5/rKKhLgavAVuBIgS2hIgVl36DsC8pSUPQFOV+Q8wQOD0Z8SylWOS5XOg5rHZe1VZfwQudT8Cv6ZUFwDwR+g9y5CkvfH8Y+tYTRFqHlTzcTXpt5UttQSjI5+SVOD30QTYuwefO/EElew1u+fpibC0u8STtKx8/bOOa/kraM5Lmr2whNFFHHFvENgUJR3jzOeMvXSbgH2TZTo2PWwZSSrGznZv1yqoNVNuw7QC0c5TtXvwJn3eW8uruDl88Osnf8EFnNpzH6psAmB8YwWmyWYqzGfBimzBg5dGy/jiaPABBdgOjydygulxMANLpI0JXCoBGgWd66BKR48LX6xVvl3zBdKdJS0uz7bHQ9ur1G6XU92n1JxtCpJiMsNkeYD6cZQsPSLuJcjMUUBPdA4DdAVl2Kt45T3j2NsAxSz19J/JJOhP7k+kKp16c5dvxvyefvprn5Sjasfz8nsxZ/8cldrJQV3jli43jnkYznuXx1Gn2yCqfzaOkQXt2l0Lufmf7v0lw5zbYTNm1LNr7SGRPP4EeZaygeG+Z5u25FCY37tl+HWnkeL9qSYP/J3fx8qkbd8CjGF3Dio5Sjc0zrPjnJQxr0QESZZOo2m3yPXrdKj1unzXVJSklSShJSkpSKqJSElMJEYSzfcT8eBfiAL8BD4J2ZikeYB/7y1BYCR4ArNBzAFQJHCFzRGGdVLW/7wYsISAQhpYgoSUQqImq5SEnGl0SVwtZMFs0Qi2aIKSvOdMTiPkvjZkth6w6WsrEEhIUg4nlEhEab+ZgpOWctCO6BwK+RkorKnlmKt4wiax6xizpIXtP/iB16PeZ2lGJ29jsMnvoHlPJZv+59dHa+jM/fOcr3bhvlZfk6hh2lM5TjvP4kWq4NJqpY/Umc2RzZxM0sbL2Z9vwUl+xXJOwKrkpyf+QlfKj/Bnr37+Hl3/k8EdvmVP/FaJkMyTbJUf8gu8cKzKVyLEbnWDLLZ/apw/fZXPdZX6+zynUYcF36XQ9LCerKRJUUqqTh1zR8R8O3TTwL7HYodUC+SeCkBTLRqPOWmsD3Ie8LclJQ8KDgaxQ9yPsaFb9Rp20qMJVqFCBKI9CaCiwa8y0FBjq6L1BYKBFCaBYCrVFto8BAEsInho8SjYexSojGvixfGGxNI6vrVDSDiqZT1jTKQmNB1yki8JTE9cDxTFzPxPMspG3iSxMpQyg/gvIjIMON1zLCzkyQ5x4I/FZzpsrkv3Mad6KEtSJF+vkrn3S9OoDj5Dhx8l1ksz8ilbqAjRs+TM1t528/vJuWqSrXOhorQnNsaougOysQJZ3IBS042SVmva+xeOH36ZqrcemeKmFZpq76+XbmDbx79TVsObafN378H2lfWiSb7mNsRQuH19Y4vWKacaNA3qwAEFKKrY7DRfkaW2yH9bZD1WrmaGwNp+J9nKwJqqcmkfuG0CoKhCDU4qPW2tS2aBSbBHa7QC5/feVDqSSYkBpjrsaUL5h1NXJ+o2Y9KgRpXZI2JClL0qQrVvoxUk6GlN1MS7WTdGUl6XI/ISdzpo5dnEWvkB6KopKNqiKlKKIooygtT8uwPF0uQlFBUVme7z5OrNZRhJBYeIRkHY36k97HJyII7oHAOSbrHsVbxijfM40WM2n6w3VEzm/9pf7Un4iFhZ9x/MQ7cN0iq1e9na6OP+HHN41w7Pa7GfCgLzTO1rSLxsZGT4tXd+N7VSbHvshS9630jYdZf28BixxlVvHP7X/Jh1ddxZqJEf7+Y+9j4+Qw85k0X3neCo6tLzNhHaYiFJqC8+sOryhXubBms9o3yQqTg62b+GTX89jVdBGJQokb77iV6+65A18rUmpXcImDta5GsUNnJmWhjEbIWapbTDoGp3M+Iy5Muxr4Jr0qQbsBK8IVLkiXaDMkrZpOstKLWexGz3diV1qp1pqRvokpFRUtzYRrcRyQSKSo4VgWKmrgGwLHV9RdRb3uYbsS25M4KGoPFAE2irpQ2IJGcH6wm5hfoikIKQgrQUhBSAliSqPpF+aFz0wfPs/4hY2OhB+jYdWvIAjugcA5opSidniBpZuGkWWH2MWdpK7tR4s++ZF3PK/MqVPvY3rma8Tj69m04fOM7o3x/Y/eDXVJd2yWK+KLwHkISyf+zF7M/ggjuz9F0dpLTznGhr05TDHHEmv4YNdf8ImVV9OeW+D/fOJj7Bzcx53np/n6jRlOJ0rYokxcSi6v1LiyWmN7zSDRcRFTrXUO1gp81Hoht3dejmtYbD9xmLff/Dk2+GXGY2Xsl0hau8rozRqzYR2IsGRbnKroHHV9Tts6nhtmjd1Dvwjz7EiZ1vQ0rfECuiginSjVQjtLs2soLjYxWUjh+j74HppfRVMjCH8EVJSqSlElT0VLUBERKppFWTMpuZJSTVF9pBt3o5EXHwZCQEhASAiimkDXBJquIXSBMjSUqaFMHQyBLiQmHkJ4ILRGSqUAKQSOplE1QkjdACGW8+6B5amQCkP66L5PplSgY3aa9pkp2rLTxIyNv8Kv7NEFwT0QOAe8xRr57w5hD+Yxu2K0vHojVu/ZNTPP5/dw7PjfUK9P09P9RqoTL+Zb75/ELs/ipKq8IDpKSG4GvYvEZT3ELuti/K7/YemOA3TMxlnjjmNpw+S0Pj7U/Xf854rnEK9WeOtn/53V83fxoyuifP5Gg4rWCOjPK1d5TrlKW72VBXkxqU1rGbHuZehEme96L2LXuoswPY8r9u9h69Ap8lYY57wKqvMgfc1VfENjRpoMVkMcyClO1DXydgq/OnCmSLuDLDp3A4bw0IWPrnws38XyHEK+TcivE/VdwiKCLsL4WoSaFqFomBRNjYKmGnnpD2ECIU1gGDphS8cKa3gRAzei40Y06lEdGdYROvieg1XIEV1aIFPKkSwtEa1VCNs1wnaNkFPHcmx030WX8pFOzYOUQpcKw5cYCgwJIV8RcX0irsRyXUzXJVmtEHG9M2+rhGC2bQ5461n9Nh5LENwDgaeQ8iWlOyYp/mwcoWukblhJfGcXQn/yVTC+bzM88hHGxz9LyOwn4XyBez4nqRRGWEgontU5xsraAMiNxM8Pkbj+ArInfs7Uf36bpmyULnGAsH6Iot7CBzrfyCdXv5SQ7fC6r36GuP1zfnqlzpfCGiFZ5VnVGtdWa/T7fRypb+YAa1ndUyE5fwt7brf5+pZXsPeSLURrVbbvP0RxHnJNNs2XHeOC9mGUCXUP9tdCHKxrnK6ZRKoryZRX0+OmWGlViMeKRFsW0LU56iWLSj5KtRBGlTTitoPhGSg9jqOFKVhRsuFmxuMJHP3BMBXxbNqrc6yr5Giv5uioLNJeXaTJLtJULxGWLkoTjQGyl8sD3QmgJEpKUIpGy3zFA+1XlVju01E0Gjk9MJWi0dhJLZ8+XSo0KdGlQpcSbTmgW66P/iit/V1NUDcN6qbBbCpONp1morWVwb5exrr72Bzxn/Rv44kIgnsg8BRxJkvkv3EKd7ZCZEsL6RtWoj9O3+qPplQ6ytFjb6NcGkIrvJWhvVsp5+rIZhOrv8KfFk1EbRVW+xSZVz6P0uwoI5/+HyLZFP3mLmLGbVSJ8a+tr+TDa1+NknDjLZ9AWffwg0sFrtDYbNf5s4Uyl2gxFptexg+zFveisSoyxsZjP2Dq4Ar+6bp3cmjnBiKVKk2Hp9HyRS7o2c2lG3+OGbbxpeJA1eDeJYPJSpQtpU1syXWy0y4Q7zhG55rvE045KAecQ1Hqe+PUpuOomkXeypANZxhKd3EgvY5s9MH8/ky9SG95nk2LJ2izsyT9LFE5R4gyvi7xNYmng59SLGUgt9xgSSgIOxoRWydq64RtjZCrLefKC0DH0yWeIfE1tVwkUlNITSFUYzuNbSm0RgNUNNXI4HF1gauDa4Crg2doOIZBJRKmGLEoR2IUI0mK0WbysXbmMt2UY+0oPQahGH4oSsiRdOR9uhYddoxMoeuzT8nv7xcFwT0Q+BUp16d46zilOyfRYhbNr9pAZFPLWW1LSo/x8U8zNPQvVKavIH/i7ZQXoanbpLZKcUOhTLyQxjCPkrlhA3X9IqY+93OMfJxmczeJ8DcBn281Xc//XvsGirrFZXs/RS22h7s2aCR8+INSmas9n3a9G7nzI3zzzhMURsoknUU27j5EJZTkQ8//K/Zv2IxZs0mfmmVb6CQvWf0DUtYQQoPxmuCORYuRcoT1S1tYt9DKtoUFOrsHyay7j6RmY53S8L4RQR+L4lcMhjIrONyyksMrVjGeaD/TAKnZrtLqL9Frj+OkshSacjgxk5xIUCTMoOgA+kFYICykbuLpBr4u8DQN5RsIqSN8A6SGEhpSa9SJ+7qOp2v4mo6ra3iafqY7sDODaCzftT/Yd8Cjv27cwS/P+//Ze+8oya7q3v9zQ+Vc1TnnMD0zPd0TpQka5YASEiAy2PhhMM9pOYf3e35eDi9hP/kZ7IeNwAJJoIQQQtJIQmFy7p7UMz0dpnOsrq4cbjq/P6olBIKRRuAA9Gets0716apbt2+d/ta5e++ztyKD/GOicYTApQlqE2k2LWSoiMcpiV3CGU9CPoFlzCPMBcCgpLzhXc2Vt+NtE4dJkvQAcDuwIIRY+6bxXwc+R3EPwXeFEL+/Mv5HwKdWxn9DCLHn7U5iNXHYKj+rFEYTLD85hBHN4d5UTvC2xnflMAXIZi9x9tzvMX1eIn7hI2RjfiLVXkpbvdgvzNKYt2OTLqE2X8Le9mHieyeQ0jZU1/MEpIdwWXGOuzfwO+2/yZAnTO/gPxJ3nCFhk2nVNO5Lp2mRQui5dtbe/BEeO5FgfHocuaCx+cgxNEXlC+/9KKc7u7AXCnQvDnCb+igNgVHsToOCCYeyKodSKhXxRprmm4lcmsMRWaSsfpnyTBrnkIQyqGBmHPSVtnKksovTZa3MO4vVlWxCUC5kSpAoEzLVmozflFGNdxO0WEQgMGUwZd5YgQtlJS+MIrDk13PECKyVcSF/P2+MkC0MGUxZYLxxDOsNo41sFdf8qmWiYqFaArsJHt3AbRjYDYGig5Q3MQsCUxPouollpRDmMsJaRljfj4iRZYVwaRlVHV3UrOumqn0NgbLyd/W3Xy5x2DsR911AGnjwdXGXJOla4E+A9wghCpIklQkhFiRJWgM8AmwBqoCXgDYhxGWNSqvivsrPGlbBIPHcGJnDsyghB6F7WnG2XlnagNcRwmJy6iH69z3N4pk7yC/XEKpws/naGiZPTtE2ryNLURyOp1G6PkT6ghMyEobvEAH5AUKFWWaUcv5ry6/zbKiBtZP/wII6gSnBrmyOO6w8itlIYm47a3tLeCrVQHrsHAqCzvPnCc0tcP99v8SJrm48epZPzjxOu7qXYGkCxWYxXZB4OW1jMeFh/fw6qsYsjPwCJZUparUYgQsm6rjCcOla9tdu4ERpIxMOH5YkYRdQY8jUGDJ1hkJQksg5FLIOiYLdwpTyyEYSVUuimBlUPYMpTExhoFg6iqnjsTKoBR3ZMIppc1dMLKrdgc3hxO5wYLfbi7Z0s2hXF5bAMgWGAboBhiUjhAKSjISMJBUzzggUVuJZVsJbXo+DtECYCMxiEP5KX/y5AKKAWGmIPAjtLZ+roqj4/AHCleWUNbcRbmjG7vJgahpL05PMXxphbuQiG256D9vuue9dzZ2fKCukEGKvJEkNPzT8WeC/CyEKK895PWv9XcA3VsYvSZI0TFHoD72rM19llf+A5AZjxJ8cxkwW8G6vwn9zA7L9neVY/2Hy+RmOvvp5hg+0kFv8LL6IjR0fa8Ibz1N4fpQWYeBVniIXqiWf/mXECcgGT+Oq/Co1y8Po2Pi7mk/xd5WbaJp/gLA2z7Jq8b5Mhl12wZS5nsFLN6GUpDlQ2s6hE+fwOs9RvrRI44VBHr7tbl7r3oaMxQdmn2Sd+hoNNVNIEpzNybwSs1O+VE7veAdiKorDsUC9OkvJtIY21MSZiuvZ29zOwLoA8ZVLEDEl2iUVt9cBXguHPoc9NU48uYCRWcaTyBA0NCRhFG3bV3C93rwU1fPFln3zE143ofyoRevlfvdjkKRiaKQiKyiKjKwo2B0OHC4XTk8Ql9eD0x/EEYjg8AWxOZ0oioplWeTTKVJLiySji4yc6ufY89/F1PU3ziVUWU3d2m5K6xuv4Aq8c96tzb0N2ClJ0l9SLIr1u0KIY0A1cPhNz5taGXsLkiR9Gvg0QF1d3bs8jVVW+bfDyhvEnxkle3wetcxF6We6cdT739WxhBAMnX2aI09fIjn5Hhwek133tdIYcTL39DAibeBWjqFI86SleyCqkC45jVH/CA1Tl/Dm07zm3cIfN9yEN/U0ruiLZDD5VDrDJrfKgLWFb52/jlmHndOOENfPnaE92I8qG7SdPc8rG7fyt3d+hKzNyYalPm6RnqKrfABDwIGMyomESs9CI7eOVJGMTVJhnqEkJ2Hk13Cq4g6Oratj0A4JpeiEDNpVKvwKERaJpMbxRucITUcJaolipMrr2FWWXXliDp0qt5ugJThSuoXBUCOuTIb6sUu0xoaRC4ViGjKvl85tO1m/cze+SCl6PoeWy6Hlsmi5LIVslkImTSYRZ274ItHJcfLpVPGtXC4U1YZlWWi5bDFS5jJIsozd7cHhdCKrCpKsoqgqkiwjywqSLGEZJqahkzd0MtE45twiWj6HUSj8yGM6PB78kVJ8JaXUd/dSUluPt6qWyUCE/pzBY8ksN5f4aX5Xs+jyvFtxV4EwsA3YDDwqSVLTlRxACPEl4EtQNMu8y/NYZZV/E/LDyyw/PoSZKODbXYP/hnok9d1ZiZcX53j5kWeYO1+PrHbQc0uIDT1NpPeME9+TAHURj3ycjLgGw9xCpuIc8eqvUz+xRMX4DHNyhM813sms1Ucy8yB2y+QzhQxdPgcDmR18eWAL56QqlmwO7pw/zMZQjnQ4QMXsNKPVNfz5L/8Wi64ANfFLfMp6jK2RIxQseDGpcnHZwbWza7lj2I2xPEG5NEaV3sWlyCa+U13CWbvJslIsBO1yyzQSpz05QtnUKP7s4hur8KzqIuozGK/RaWzdRKC2i+8k+5gWBWpc9fiMCN9xNeOJJ2gfOsM1h18gmF7GkiQWymqYrm3G6N5EqKqeS7LMi0jYYwU8ig2PzYHXFcYtSShDA2QHjpA8fQJhGATqGlh79wfo2rGbSDD4xi5gIQR6IU8hm0HLZslnMmjZDIU3WrbYZzJo+RyWaSIsC8s0sSxzxdRjIatFwVdUW7HZVGxOFy6vD6fXi9Prw+Hx4g2FUQJhFhQbw9k8g5k8z2eK/fB0FmOqeL/R6LKzM3TlKSjeCe+oEtOKWeaZN9ncnwf+hxDilZWfRygK/a8ACCH+emV8D/BnQojLmmVWbe6r/EfF0kwSz10ic2gWtcRF6ANtOOre3Wq9kNXZ/9ReBg/oCCHT0Jti1x03YRyeJ314loJk4JaOY5ntQAitcoaZun+kLL5A/fgiMjr3l2zlJfcS00qeKt3gXpGlKehiKLqRkxPrOGi1ksPOx+afpV1fYqi5A7uhYSoyL2y7mYvuAKHsIndpT3Bj4EVyJryatjGz5GLXVA/qiMBKxvDam0k7N9AfrOCs3WRCLXoVXQ6L1vwM6xf7CWYmkQBNsrHoKCPlrWWx0s/5difpQDWoZZjSW53LvlScNUP9dA32E0lEsSSJ2cp6RmvbmaptQgmH8PhCGELCEAJdCAwhKFgWWdNCzqRYd/443QPHCKQTZJ1uzrd0c7ajl4WS7+dGlwGvKuNVlGJTZXwrfXFMxqt+v/cpMi5FRpUkFElClXjjMaagoJnkNROtYKDpFlrBJFMwSOsmGc0kbRT7uGYQKxjkDfP7oZSWoERVqbCpVNps1DhsVDvsuGWJ8gY/1VeY5vl1/jUqMT0FXAu8IklSG8UEzFHgaeBhSZL+hqJDtRU4+i7fY5VV/l0pjCdZfnQQYyn/E9nWTd3i1MsjHHtuBCNvJ9w8wjXvv4ZgvIz4P53DTOskHZOECk4stiHKE0w3/A3CdpY1pwVhbZGn3TV8Jexg2DZNhWHwG0aWxoiTkYWr+c6pLl4zOzAtk88sP8668XFOtW/iYngNNlPn7NU38bIriGpqvCf5JB/0PULebvFM3EZq0cPuqaupm0qzbAaoFG1cqm6l32EwYDfQJR2bYtGVn6Jn4QghLYpAYtZRzsWSzUxXNTG2phUz4CjatIWBy4jRGwhi6tMMLR5GlVSqpFaUGZ2Oi6epmxlFAmLOShwdnSRk8As395amuOPGrQSDbw0jFUIwc/EC/Xue4eLhA1imQfma9dTuvhHv2l6ulWUypkXaMEmbFinDJGNapEyTtGGRMgy0tIGWzpPMGCQzOuQspLyJXRM4dYFTt3DoAqcmcOgChyFQTbCZAuUyFh37SrsSeY6uNICO60rftbhfjrcVd0mSHgF2AyWSJE0B/xV4AHhAkqSzgAZ8QhRvAc5JkvQoMAAYwOfeLlJmlVX+oyF0i8RL46T3TqEEHJT8p3U4m4NXfhwhGDm5yP7Hz5FZj2pSaQAAIABJREFUFngqhthyk0xXy0dJPD1BbHiQuEMjKCUJFGrRvFHiPY+y5PgujaNB6mZinLLb+c3KWvqdEhEjz6fNLO2lNsYXtvBifxf79VZUK8fvpR9izbmLjAbbeW3rDUhYxDs38XSolKxqY0PmGL/q/iIOT5o9CRvJ+SC3TF3LQEIwlavAq7Qx6XXwjL3AtK2AJAlqjUU2Lh6nOjeOJdkZdddzONzLpcY2svUhHE6F0mSOiuwBUvoZwnqG317/IWS7yf/t/0sWrEra8ldRNrpI2+hT2A2dhM3P6eAmNrRUEWSUaMZJJJLlPbfdTlPTZgDyRp6UliKlpYilowz0H+T8qUMsx+aRHDZKb2qhtLWVlNfFKfMMxmgfZFWktP0Hmjttx52xUZFTIa8iiR/julUtcFgIu4VlF+C1EA6BKOYJxlTAVEGxSSgqSDZQVFBlgd0m4bbbsKkKsiphSDoFUUBHo2DlKYgCeStHQk+Q0OMktDhxPU5cWyahJRAIPlL1Ua5n3U8yZX8kqwWyV1nlTWjTaWKPDmLMZ/FsqSDwnkZkx5Xf4M5fSrLvsUHmR1M4ApPUbNnLtt2/hnSyhNSrk5gSFEjgNvwIeZbstmGmPF/Bl3XRcipPTIrxv8MRDrjtBE2Lu6Q868oFc4trGJtYz4l8I6ZZ4CPZA6y9cJZc3MWh7duIB8LkSmr4XlM7Uy4flflJfs12P3VcYl9GZWHez62TN3M8FyKcCJGz1XHapnHGbpBTFFxSnp7ls3QlTuEQgll3E32eRkZrWjBrPLTo03RM+fHks4z4v85w+QgOy8XHWj7B5qYePn/yfgaTftrnGukYvEg4EcVQ7Qy7WzjvaeOaihI83gNMZFJo7iXCDT7UQBmLuUUWsgssZBfIGtkfeU3thotgrqzY8uUEc2WEcuX48yUo4gc/o7yaIe1YJmtPkLWnyKkpsrYUWVuSnK34OG9Loyk5hCRwGS7chhuXWezdhhu7acdurbSVx4pQ3ijC/eOwsNBlHV3W0WQNQzGKu2oVC0uWwFIQhg0578StBdi1YSvvv/f6K55j8BPGuf9bsCruq/x7I0xB6pUJki9PIntshN7Xiqs9fMXHScXyHH5qhItH51GdaUrWPkHX9kbqpM+Q/M4kxlKetN3Aq6nIxIjVnSS+9nsU9FkaL5TjXBrkH0IBnva5cQnBHWj0VpnElxoYn+jhYqaGmCazPTPJzTOvopzPcr6njXOt6ynY3Zxs66a/pBK3kebj0pe5WtrL0YzC5IKf2ybu4WymAk/az7wa4IQtx0WHjCRBjT7PxuhRavLT5Fz19Lvb6Qs1oNX4CZWmuOniS3Se72IhEuBk5be4WD6Aatm5o+QePnj1e/nC6X+hfyhLx4SD1ktDqKZBPhSmz1vBQIlOiX8OyTVFXEpiSd+3cdhkG2XuMkpdpZS5y/AaDrIj0+SGEni1CioD6wi4WjATTvKJ7xsBJFkiWOYiWO4mWO7GH3HiDTvxRZz4wk7szrd+IWuaxvz8PIuLiyxGF4lGo0SjUeLLcX5YBx1OBy63C4fTgcPlwOF0oKgqwpQwDBNTFxi6QCsY6AUDraCjawZWHoQBkrRSi0oyEZKJpWhYslY0wL8Jt9NL74ZN3HDL7iuea7Aq7qusclmMpRyxbw6iTaRwbSgldGfzFe8y1fIGfS9M0PfiOMIyCLU9T9WGM3Q1/TfYFyR3Oopll0EzUChguPcwv3OClDhJKF1Bef8MTwRMvub3YUgSNwqTXZV5CulyLo1vYjZRxXjOg1mw80cLD+E9FSVR4mP/zh2k7QFGKhp4pWENll3lFuM73Ks+ysWszvkFH7eO38doqg5b1s+QTaVPzTLtsGEXGmtT5+mOn8IrFBZ9Xez1tDAf9JJv8tPgneDakZeoP93BYqiRU5XPcr78JIpQ2e24ld+48dN8bei7nDxwkc6ROCXxKLoqMV8l6KtKsRSOvXF9vLoHv+6j2q2wa+2NrKvZTb2/nogzgp43OPXSCc7tPUMypiCrFUiSEyia8YPlbkpqfZTUeAlVuAlVePCVOFEuU5JQ0zTm5uaYmZlhdnaWmZkZotHoGyIuyzKRSISSkhJCgTAuuxeb5EI2HaDZKaRMMvEc2aUUmUSBbFpgmG99P1XWcduzeOw53M4CbpeOxwPuoANP2I+7NIynug5nZT0WgnQ6TTweJ5FIEIvFiEajtLa20t3dfUXz7XVWxX2VVX4EQgiyfQvEvz0CEoTubsG9oeyKjmFZgguHZjny7VGySY1Q4xnCa75OY+vtVEQ/TvrFWYRuYgoLWQg8yvOc7RzErBlCsiwa+v0cFJP8v1CAmKKwyZJ4T3kau+FjeGwzsaUaZtNu+qwq/tvCwzSfvoQQgpPXdTMS6mDZ7WNPcw/xSIQW4wK/ovwjaFPsX3Rz7aUPs7zcTi7n4pxDol/NEbfZ8Jspepf76EgPYlPrGQ2s5zV3OR4lSaKzhMrgDNunD1BxPMKyv4O+6pcZLu1HEjKbC9fxOzd8ju/OnuDsS3tpH57EbphEAxoX6pKMVWbRrAC2XANb3OV4UjlcqQDVkUV2X7uNNZ2fRM8LpofiTJxbZOz0DOllQTG2ReDymtR2VVLZFKKk1kek2ovN8fZO7Gw2y8TEBBMTE4yPjzM7O4u1EtfudrkJB0rxOSM48CFyTnJJmURSJ5HUKJjWSp1VsVJOD2RZwyYlUeUsqpRBkbMocg5F1pHtErINJMVEyBI6KroFmikwTIFumugmaKjoKBioaLID3eZHV73oqg+f5GGNZmN9HqS2ELd/fP2VT2BWxX2VVd6ClTNYfmqY3KlF7A1+wve1o4acV3SM2eE4e795kehkmkBVgmDnFwhXm7QF/wrze0706TSoEhgCp3yI5cgzTPWCJaapXKhnbGKYL4bdjNtsNFsqd5ZkqHHC+fGNxKdbyGRVjuWquT1xgJvPHkHJCKY3hzjSdA1Zxc2h6nbON7TgIsuHpQfpNl7htWUbXUP3oi5tZSlv56TD4pRNo6AoVGhzbIqdpCE3i+Rcy9nAOo46HaxfukC8qwyrzaA3eprIUYOMfQ3H6w8wFj6LatjYEL+GT2z5JV5aPEBy32vUzaSxJMFYZYaphgIptZPJZAPubCO/VFmCpPUTjxfw+RbZ2OulqfKzzI8IJs/HmBtNICxAGJjGDC5Plo6rO9h421W4/a53dO11Xef80Ch950cYGJthNp6lIFQ0bAjJg2Y5yRsqBVMh/3ox7JUSeOZPuWSpKkvYFBmbImFXZVRZxqaATTKxY6KaBSqMAi0atBsqzcJHmOLfuSwXyLTBtk/e8K7ee1XcV1nlTRTGEsS+MYiZLOC/vh7ftbVI8jv/j88kChx6coTBI3O4AxKl6x/HWfE8teW/TOnwvWQPL4JSjI0W0iRhxxfZ1wW2yBxuI4DUn+WLAYM+p5NKU+bakMJmf4KRxVYWR9eh5dzEohIZHX594Fu4YhrZFokDvTuJSRWMhcp5pa2HgsvFDusV3i8epC+ZIzJ8PWWztzKVd3DCYXDabmBK0Jwbo3f5JOWGhuTaSL+vjX67zs0j+1BqbIzsrqRLm0Tvi4HRyYn6w0wHhnDoDtbPX0dPyw4GFl4icmqYUEomZzcZqytQWx1iNHU7+5dLsSPx0VoHpbYBZmZiOO0ZGktd+LmJmQuCfKa47d7pzpGND2AURqnrKmfTHXdTt7b7LSUHhRDEMhoTsSwTsSzj0QwD41FG5uJEsxppU0bnR6/o7YBHUfDaFPxOlaDLRlDJEDBm8GVGceWmcZPHJRk4w1W4SupxldTjKGtCDVRjsymosoyqSNgVGVWR3xBwVZGwyTI2VVoRcekHzt3KGxjRHEY0hz6bQZtOo02nEbligQ7JpeKolnF6pnCYB1BnvoO07Vdh1+9e4Swusiruq6xC0Wma/N44qVcmUUJOwh9sv6INSaZpcfrlKY599xKmYVHXO4695n/g8VbSyl+hfw+sFRHTpTylyj8xVn2K2TY3qpWkYjjMQ2aUp70eAhZs9Ea4LTLFcjbMxeGtmIkSbEspjuh1/Nro49RPRNHLBQM7W7kgdZNxeXm5qZvp8koqxRS/xD+RSZ+nMNpD68THGSnYOWYvxqcLIWjPDrFp+SRBoSI7t3DK18wpe45bh15lrT7Gybs7KS81uXRuAiVbz+nqfmKeWbwFF+1z1yCXumG2j+ZRHaeuEPUL4nV57inbwROLO3khXcAGvL/KTXt4jNGLk7j0IGHJhytdj92S8DgUwiEZkVvATESxK3b8oTJ8gQiqZMPSTUzNxDAFhmlhWkXThmFZCIrx1BoCDTCxMLGwXs/WqKg4HHacbhsenx1f0Ikv6MDmVJGsHNLSWaTF00gL/UhGAkk2kSrbkarXI9VtQKpdj+TyINnk4pcxFJPXCEGxtocASyA0E6tgIgpv6rM6ZkrDTGpYKQ0zpWMs5bDS+vcnjCJhq/Bgr/Ziq/Zir/Fhq/T84ELCssDUwHZld42vsyruq/zC82anqbu3jOBdzVcU4jg5EGPfoxdZnstS2Q7BjvsRjnPU+T9LoO96tJFUMS+4EAjHi0QcX2HvmgocvhhVC372zy3zz0EvBUlikxTglsoYbknQN7YVbboOtWAyG7PTtXCe3YOnEQ7B3LVeDgV2k7c8nKtu5mRjO5YicRdPsK7wNEPjlWwY+S0G806O20wu2Iql6takz9OT6MePD8W5mTOeevpcWW4eepXbJo4wcHML1o46Dg2eRaTLGCw/R86epizpoyy1iaQvQdXEBK1TbmQLxqpsmFUxPhC+j4em2vleroAT+Fi1j153lNxwgRI9hB8nHlnG+SPugkxhIBygBjzkZZmkYRIrGMzndZKG+Ub2GRWwC4EdA5uk4ZRMXJLAqzjxOd14XW4cdhuSKO5HEIaF0M3iY90C899WzySniuK3ofjsKCEntlIXasSFutK/2xQV7/j9V8V9lV9UhBBkT644TWUIvbcVd3fpO359cinHwceHGelbxF9ip27rXgzPl/E4O2iM/Sn6QfONlV7aNUaT9decazCI1kqUxwxmJ03uDziZtNnYUJC5usJJmzfKwEInseE1CMONa26JaNLGR86/gD1vkLpK5njbVmbSdSSCQfa2rmPBX8Ja0c8HzH9iaC5Nx9nfYyxXxhHV5KLdwm5qrEudozt5Go8cxubYxoCnimPuLFdPH+ETfc8RWxtm+r4NPD13FiPrYyI8giWbtMxHQGkipUzQccmkfs6NkCUu1jnI185xp/MzPDdeyYyusxGV29xOKnQLp/594SoIgelVcVV6SGuzDA8eZWl5GisSxOjZxbi7imOTSYYW0m8kZSy326jQJMI5iyBZ/K4oimMBS9JRFRuNDU2s37CW1tZWnM4fs7LNLMHAU8U2th9hgQh3ItrvQjTdggi2IHTxli+BH2iGiTBEMWmktJL6d6WXZAnJriA7FKSVJtsVZI8NxWdDsr27bKA/LVbFfZVfSKycwfK3hsidjmJvXHGaBt/Z7a+pW/S9OM6J58YBaNuRhrI/R5CiUf19nAfWYMaLObx1l4ab/4PqO8zR9irKCsvYJ2T+zqly2OWiQTPY5o6wrXKWhUwJFwY3I6VLcKXTLC2q3Db8CuVLSQotFoM7GjiV3opps9PX0MbZ2ma8pPiI+AokDuM/9UniiY0cly3O2Qwclk5vvJ+u9FkcSgiHYwfjrir2ewrUFy7y+y99HcUnGPzIBr7kGMbSXSx55lBNG+0zpcSCIaT8OGtH3VQtudBsMgMNgkxFhjsLv04y6qdayKxDwb2yeSdjmSwbEgnTQJTGab52DaUtEU69+Ax9L+1hTASJ1vQy661nZCU23aXKNDsclGYEkbRFhSnh8WnIpcss69PktSw2m42Ojg7Wrl1LU1MTNtuPCUc1dRh6Efofgot7wNIh0gpdd0PXe6FszZuqJ/18syruq/zCURhNEHt0xWl6Yz2+a96503R6cJlXHx4kPp+lodtNuOsBssb3CDuuoWr4s+jn88Un2iDpeYEW/YscbS1H9WQoHzN4RNh41O/FbQlu1RxsqssgbBLHRq9CmqpESCru2SiVE5NsHB/GCAtmb/ZwVL2GeKGUWCTAofY1LDuCXCteZFv+IRJn1iEvfJQTluCU3UAWJr3Lp1ifPoVd8eK07yTqqmOvS0fxzfAnLzxARSzO8HV1fH7dEmmbhKbm8eQD1MWqmCrJEYkusG40QCRpp+CUGarXafau46r0jZRodiIrtZGSdokUOjNxi5gukXPEqN0wzTV3vIdCAvY+/W2+d3aaUVcdk94mckLBJku0+9zUaBKRRY1yQ8btsVHW4kbzzDMdG2E5HkOWZVpaWli3bh3t7e3Fohs/jrkz0P8wnH4UslHwlML6+6D7g1C+9hdG0N/Mqriv8guDMK1iPdNXJ1HCTiIf7MBe63tHr82lNQ4+PsyFw3P4Ig46rhsho/x3JOy05P4c6WCkaNcFUhVzVCf+mFhZlrEaD81TCfZnVb4QCpCSZe5MFegtdeEvy3B6fg2Zc80YahBPIo5rIsHuC0eQFIvE9RJ99RsZinajOnWOtnVysbSRKjHFfeb/IzsSQ730h5wt2DnhMLCw2LB8lu50P25JxeHYSdrRzD6XQapilt898TXaT82xUOvm/usMhiqL/9818Q5sVpCp4Ai1cxnWD4cIZhTw2qGmhg1iG81aDSoSKQQXFYG31M3MXJxkXEFIJiIwRsfVUa669gOMDUR5+Jn9HIo7GXfXYkkKQYdCt9dDVcykbNnEjkRZvY+aNSGkYJLR6UGGhi4ihKC2tpbu7m7WrFmD2+2+zIcSh9PfhL6vFcVdtkH7rbDhw9ByAyjvrqThzwur4r7KLwTGUo7YNwbRJlO4N5YTvLPpHTlNhShuRDr4xAhazqBjlw1H3f8ilz9LhfwhwifuwFwsmmCsEGS5n1pe5lhzOQ3xZWaWBP8zHGTEbmdrLs8dloNAS4ZZrYyhUxuQckUbf3Bijq1nj+PLFshuMhnaVs+x6C4U02Ciuo4jLZ3oso07xBNULj6LdebXGcrUcdxhUECwfvkC3ZmTBMw8qnMnpquLQw7BUs04vxp9hLXPzmMAj+yUeG6ThMP0UhvfTcplMO89RsukxrqRMKGcjbpIO+FQJw1GIyoyi1i8ik4qYKer3Mfi6ShmXsFQssil51l/TZ616z7EE88P8HT/DINyBbpsI6QKtgYD1EQtAnEDVZGpXROmuacUX7XM+YtnOHXqFOl0Go/Hw4YNG+jp6aGk5G0KiM/0wbEvw9knQM9CZTds+Cisex+4rzwtxM8rq+K+ys81QgiyJxaIPz0CskTonhbc69+Z0zQ2m+G1hweZGYpT3uSm7qoXSOn/gktupGH6T7DOrHw52CWWSw7SHvtrLtYHsEs6ylyB+/0BXvG4qdYNPpPIUVankAjaODG0GcdwmIw/hG9piY6zF2ianUKrs5i71c0R7VpiiQCK386+NeuZ8FXTLga4Ifslsqd7mI7ewnHVICtDe3KUjYljRPRlZFcPivMq+hwK4+UTfDDwDbqeXCQ8o3GsReKBm2VUmvGbtzEdHCfDPjomDNaNRGgQ1bSW9FJpb8WGjRgWL6FzRDZZ2xBmrZCZPb2MMGU0+zL2yjNsus6F7LqJB56/wGuLNrKKCxc6G70u2nMuQks6qqpQ1xWmubeM2jVBLk2McOzYMcbGxpAkiba2Nnp6emhtbUVRLuOA1LJw7smiqM+cBNUF698Pm34Zqnp+CjPl549VcV/l5xYrqxd3mp6OYm8MrDhNHW/7OkMzOf7cGH0vTGBzKHReG0X3/wWWmaGh8Ac4DrcjCkUTTKF2meDyH2B5FpmLeKicSfE1l5evBfyoQvDp5SSbXDaWW+B0dA3icAVJfyWyaVJ3cYyN5/oQHkH8dsGZsl7OT3XQoETZ27KJ47VrsKNxl/k17IOjzE7+Fv0CkrKgNjfH9uh+SvUFFHs9qvtmLtnd9AcX2Nj4bTr3DrPlSI5lH/zLDTaWym4g797NpdAxbNmX6BiT2DpWR6d9Lc3BHjyyH00IXpV0nkYn77XxkXXVeC7Fmb2QRWCRd83jrT/Lpmva6Btv5JvHZhk2/UjColPV2WwPE5k1UZCoag3ScVUFzT1lFIwcJ06c4MSJE6RSKQKBAJs2bWLDhg34fG9jFosOwfEHig7SfAJK2mHzp4r2dNeVp1r+RWJV3Ff5uaQwGif2zYuYKQ3/TfX4dtW8I6fp5PkYrz48SHIxR1OvA3/H35M3jlIi30LZqY9hzq5sRAlLZJX/S11hD8OVQWqjSV6WnNwfDrKkKNyZSvPxjEayXWFYrWDqSCdyJkLK76dsbpHNxw/jyWbIXGNxaWMdZ6Y20VQYo6+0l1fX9LDgKGWr2E/n3COMD36OwWyQmCIoMVJcN/M9ysxZbHhRvHcSd5Sy35vBUf8cNTPH+fCeLME0vNrj5tDGX+FSxVpmvXvxLT9D5yWZG6fW0+nupcbThizJXBQGj0o6+9HY2RTm/Z3VLBwYZmlMwpIM8p4pwq1DtG/cxreOKjw7rpOT7ATMLNucTtqSHpwF8Je66NhWQfvWCnwRJxMTExw7doyBgQEsy6K5uZktW7bQ2tqKLF8mxtvU4cJ34fiX4dLeoi29846iqNdv/4V0jr4bVsV9lZ8r3uw0VSMuwve1vyOnaSGrc+DxYc4fnCVQ6qBp12Fyyj/gUKponP8viD5ncYeiXSZefojWxb9iusJFMFdgIi/xVyVhztvtrMtp/EFsGU+ZjZFaL8eHNlF20sZ0XQOufIENp89SNzpMoc1i9lYX51NbqFmaIe6t4smOa+gv6SIiFrkh+89MDaxjItrLoizwC4Mbp/dQqU9gMyVs7uvJu7s45DSYrNmHS/ken3opxcYRwUBDGd+66XMca62nIPYRWHqKDZds3Dm3nQ5vLwF7KXlh8oxk8BgaObvG+zdXc0tZmFPPnicz78aSNPK+CarXz+Op3sbDh+IcSzgRQLtIs81WQsmSjKoqtGwsY83OKiqbAxiGwZkzZzhy5Ajz8/M4HA56enrYtGnT29vSE1Nw4l/g5IOQnoNALWz8JPR+HLxXlrRtlVVxX+XnCCOaY+kbF9Cn0rg3lRO8oxn5HWQNHO1f5LVHBsmlNFqvymGr+QsslqgzfxvXwW5EthiPnatZIpj4IyTPIqYsYyUMPh8JssfjIawLfm95iassk5EOF4fy63C/5CUWqiPnctE6ucC6o/uRfDrLd1sM+dYTmshRZU/wQNU9vNqykZTsZbf1LMbwKOPj72dBCDwCrl7aR2f8DEjgUZrQArdzxiFxPHIOvfwp7jgV5X0HBP1t63n8pg/R11SNPXeQyMJjbJrw8f7o9bR4urHJDiatPF+XBa+Qp7RkgV+9rpuurMbRZxbQ4gFMuYAeGKdlm2DOaOPhk1HGTS92S2OzYrEhH8KdK67S1+6spuPqClxeO5lMhuPHj3P06FEymQzl5eVs2bKFdevWXT6E0bJg9GU49gBcfK646av1Rtj0qWIv//tuBPpZZlXcV/mZp+g0nS86TRWZ0D2tuNe9zSoRyCY19n7jIiMnFwhVylRueQjL8TIh224qz/wnzImiCcYMmljy5ymz9pP0OPAv5/lKwM9XA34sIfGJeIpPJRMs1Dk5VlLD9NEufDMq07U1BNJ5eo8coGR5kcxNFiPrqvGMutgoLvCU92YeW3stFzxt1ItRmmaeYmTwbhZ0By4L1urD7Bzbg26X8RhOzOD7mHSF2edbIFb3KJ2xUT6218uJjt08vetmFkJB/MnDhKPfYMtUhHuXbqDR3QXAISvNg4rCmDpPae0wf7irG+9EjLMv+TFTZZhKHlEyQdf2co6MwePDOsuyh6CZYYfDTXPMhUNINKwvYe2uamo7w0iyRDQa5fDhw/T392MYBi0tLVx99dU0Nja+JeHXD178GPR9vWhPX74E7hLo/VhxpR5q+MknxSqr4r7KzzZWVmf5W8PkzkRxNAUI3deOGri801QIwcUjc+x7bAg9b1K3eQBHzf04HeU0Lv4p4rgbLMAmkQp+l5b0F1kKuYgk8uxxuvjbSJgFRWZX0uBPEvM43ArnmgO8Nr+LNS/luNDSiKmotA8OsvbMGQrdJjM3OZBny9maO8dFtZUvtNzLvqrNgMTG+HeYONdINF2JA2gTaa4f/TqmIrAbAodjJ7FgL/tcBS7VPEHQeYLbz7fTX3cDBzZsxlQU6ub68SYfZstsOe+NX0+1sxnN0nnRSvJ1m8Ji4BjNNcP85toq8oMFJo9ug0wlplzAXjNHW285z/QvsGfJTUbxUC1y7LYFqIzKOJwqa3ZUse7aGvwRF0IIxsfHOXToEIODgyiKQnd3N9u2baOs7DLmEyFg6lgx4uXct8AsQN3VRVt65x2gvr2ze5V3zqq4r/IzS34kzvKjg5gp/R07TVOxPK8+NMjEuSWCVVki3X+LIzBLvfLbOF9bi5Uupl9NRUaoz/wxmaCOP2MwJKn8ZVmYszY7dXmVP4tNs94wGGl28YJtI5V7SonaHSyWlRFeirHt8CGc7hTL95hkRQUbo2Ogqvyf0AfZ07mDaVs1bdl+cgNLRJc6UAR0WhK9S98glIxiSRIBs5JU6d0cdaoMlR7CVvEi6zO7OFa5m+nSCjzZPOtmRnDoT9C+GOae5E2U2CrJmFmeMZPsKcsx5/0u11SPc2uJj9hgCcvnb0fOVGLJGoGWHCXVJo/1zXDQrKagOGlRdLabfkrjAl/YSfd1tazZXoXdpWJZFgMDAxw8eJCZmRncbjebN29m8+bNeL3eH3/RC2k481jRQTp3Buw+6L6vaHopX/NTnBGrvJlVcV/lZw5hWCRfGif12lTRafrBduw1l3eaCktwbt80B58cwRIGFd3P4a1/iorg3ZT2fxB9OAeA5sngt/4M1XMRuwFpHT5fGuJZlxuPYeNzS3E+nF1ioczB4eoaRs7tpOHkLAPfw2TcAAAgAElEQVSd7ciWxYa+fhqmR0nfbpCo9rJuNkaZkuIx+Vq+3v0eTvh7CWmLBAZPszDTDkKiS1eo5xCdY0fI21R8BQUr8F7O+Cu44J3B1r4Ph3c3JwNr0FUbnZcu0TuzRN59kJKUwj3Jm4nYyonry7wgZenrjKG7H2B7IEOzojA3sonk8M3YsuUI2aC0zcTmmODRgRgnXB3osp31TolNaReRtKCs3seGG+to7ilFVmQMw+D06dPs37+fWCxGJBLhqquuoru7+8fneAGYHyiaXU5/EwrJYhqAzZ+Cde8HxzvbGbzKu2dV3Ff5mUJfzBL75iD6VBrP5goCdzQh2y/vdIvPZ3n5a+eZHU7gr56iZMPfEy4rp2H5j9D3FVPBWoqF7PgKIdu3MRUZR87gy+EAX/EF0ZG4JS7zp4lLSA6ZgWY/z+Ru5qrH5jnf3EAiGKR6coqNJ04gejJkt0HDjEattMxZvYHPt3+IfTXbyRpOai/1szhehmWqrNEV6u0LtI49ihBg1028Sg9jZTs56TIwN8yyUNbOlC2AJ5vhhmOH2DKdoq8hRamR4b2pmwirZSS0JQ4qWWLbz4H6JGudGmYuyMz4VWRHr8aeLUdSBCUNOfKZkzw7a9EX6EaTHWz02tkYUwlkBfVrI/TeXE9lSwBJktA0jZMnT3Lw4EGSySSVlZXs3LmTjo6OHx/KmE8WNxudfBCmT4BiLybs2vQpqN2yGsb4b8hPJO6SJD0A3A4sCCHW/tDvfgf430CpECIqFb0r9wO3AVngk0KIk293gqvivgqsOE2PF52mkq3oNHWtvbzT1DIt+l+a5Ogzo0iyRsn6hyhpPU+z9w9RXqjFjBcQCHTnYarUz5N3mgTSOs/63fxtqIR5GbrSbv5ieYxGM89EjYunAtuI7AkgZwyGW1tw5vJsOn6cEsc02i0FShMWjVacqO7lb0rv4dU1uxlVmiibGCUzImEaDlp0mfVC4M4+TGk0hilLhPMBlivv5YDHw3y3jfHaIJqk0DY+zl2vPc+GqWUe2tZAvZLivenrCaulJLQo5+VZjN17cTqO45FgcamBpfmryI03485WI0kyoYo40ZkXOWSV0h/sISc76PW56FmAsAbNPaVsvKWB0rriajqXy3H06FEOHz5MLpejvr6enTt30tzc/KOdpELA5BE4+bWisOtZKO0sOkjXfxA8kX+NKbHK23A5cX8n1Qq+Cvw98OAPHbQWuAmYeNPwrUDrStsK/MNKv8oql8XM6MSfHCJ3bglHc4DwB9pR3sZpGp1K8fKDAyxOZPDVnKa892Ga6u8leOJ3KQwkMCmgq/NUKH9CzreMI1VgxLDzudpqTqsKpQU3/zO2xK35CZZCNp6pbuXEyHY2PTnCwNoK8lVOWoaG6Rw/Dbdk8Ms6TctJsobK/cr17N+6mwPuq3HOJvANjZDUXNQYMtsLKnn3XhpGTqArMm4N8N3Ea1XtnOh0s9Dqw2kY7Dp+hg+88E1qo3Hu372NaH2E38xsJqKUkbAWGdBewLjxBUKOKOmch/GJ7WQXWyBWhjfTgMe04fIusjT3LAf1Gk6V3EJa2FjvddGzIChPSrRtKaf35nrClR4AUqkUhw8f5tixY2iaRltbGzt27KCuru5HX+T0Ipx6pJi4K3oR7N5ifpfeT0D1xtVV+n9g3lbchRB7JUlq+BG/+lvg94Fvv2nsLuBBUbwdOCxJUlCSpEohxOxP42RX+fkkP7RM7LGLWBmdwG2NeHdUX9ZpauoWx54d5eSecRR7hqqrvkZjd5i65JfJPpKkYCQwJY2w+jfogaPYsxrprMwfVpfxnM2Jw7LzyajgN1MX0B0yx9ojPGzcwfavDlNRq3Fy8yZCsRjbD+3Ds2meyNoc9VqGvKbwjWwPL22+hn2l15BacOHpn0TP2gkJD9dlVPDO4F98irJ5DckSlOkNnKq/iT2tfhY7/dSlk3z0xRO8f88/48mn+WrvZgo7mvnP6S1U5GtImktcLHwD87Y9SCoMzteQXroLc8mPQ4sQzLdh5WzI8gLZ5Iuclcs53HAvy4bCGpeTnkVBTVqi8+pqem+qw19SLMScSCTYv38/J0+exLIsurq62LFjBxUVFW+9wJYJw9+Dvgdh8DmwDKjdCnd9AdbcDY7LOFZX+Q/DO68z9iYkSboLmBZCnPqhW7hqYPJNP0+tjL1F3CVJ+jTwaeDHrxpW+blGGBaJPWOk902jlrko+UQX9urLC8fcaIIXv3KC5CL46w/RvHOAtvLfQf+2SjYaRyBwKs+jBh/AWcjjTAv+sTzIg64geQQ7kl7+PH6RkDAYr3PxiP9aXC852Bqb5VzPRlTToPf4carDFym5OU2tlsHIy7yYaOGJrl2catzO+HINrsOL2FM5HLKT2zIqZYpOXHqYmuF5DFmiLGNnoeouvthcx9RaH1uji3z4OxNcf+Cr+DKz7K9r4cyOrdyX7KE+30yWJKPxb6Ld9ALTqo9zo904453ImopPLSNQaKIQt2Nay+jZ10jWlvJa9b2MpgSNqoObE9CYUejaVc2GG+rwhop3PfF4/A1RB9iwYQPbt28nEvkRZpT5gaJj9PSjkJopxqVv+yz0fAxK23/aH/8q/8pcsbhLkuQG/piiSeZdI4T4EvAlKNrcf5JjrfKzhz6fIfaNQfTZDJ5tlQRua7ys01QvmOx7/Djn96VR3TGart9Dz9V3Y9v/PrLPLCKhI0vjuPx/hlPEcGZMno14uN9bxqxs0pT18v8tz7BRG2cxYuexyi4Oje3kusfPcHFNJxcqvNRfGqMz3kf5xih1ZBAFOByv4asN25m7qpdj2R6cJ2LY40soisK1BRtdBYlL/r14R0/ilMCtWdhcW3msdzsj7V6uWVziQ08nWDf4JBXzx5nzBXn2o7dwTbyDW7NrKEhZxha+TX7jfg77ypgbuYZIpgo3Eg2VTchTIVJzHvJWFss4iH9DJS8q93Dg/2fvvuPjuu47739umd4HAwx6LwRAgiQAEqRYVChSvVqWZNlxrBQ7iTdOnuzmlSfJs3GSzWad9SZOHttx3GRbtixb1VSvJEVSbGIDC4jeOzCYXu/ce/YPKLIdO5YTSZZszfsvcHhJXpzD1/d18DttKklRRuGmlExbQmHtzgo6r6nB4flhqB86dIgzZ84A0NnZyfbt2/F6/81BXLG51SWM5x6ChfMgq9CwC677O2i+FtSfsfO04D3t51ot83pZ5ikhxFpJktYBL7M6YQpQCcwCm4G/Ag4IIR58/c8NAFe8WVmmMKH6/iGEIHl0jsgzY8gWBd8dTdhaf/Zk3EjvMAce6CcTs+NvepWemysojl9D+PExZE0AGRyOz6Cae3GlNc67LHzWG+SMCr6cnU+upLgrPUnKqnC2pohv5z/Ald+/QLS0lJnKStzRKOsHT1G9ZpxaRwxZgrORMu4LbCDZ0cYB43KM4SxyKIeswCbdwraIxKxnBvfCD7Cmsqi6QVGunMNNN3KxpogdSwnKF2WqZl+hdvwZJGFw+LZN1Gc20KqsRxcaM6GDxIoH2VfhRoq6ceSdmKwm1lY1Eh+AyIIPALN5iLorqnnZqODh0/OYJYmetEJXTqVjewVd19a+MVIPh8McOnSIs2fPIknSG6Hu8Xh+2KDZOFx6cnWUPvoKIKCie/VGo/bbwPHmO38L3hve6oTqjxFCnAfe2KImSdI40P36apkngP8iSdL3WJ1IjRbq7QX/So/nCD8ySGYgjLXFh++OZhTXvz8yTMbCvPTtF5k+H8DsjLPprjHWtn6cuW+PEg2NIiOwmh5G9jyCL5FiXqh8pryUp8wWVEPlQ8sK/zU+gKRAf52Tb9uvJfhslquiQ/Rv2ADA2r5ztPnO0tgZRpUNLsSC3OfqILejhldMVxMbMaMsxlFkQZPVyvXzEmlLjmnzI1SOzKLJMsVJlamyG3ihppFtIYO1l/L4oiM0jjyMK7bEhR2VyJ4dXJfbhqTITIRfZcmY5mijA1u2GWdIxhN00+YpYfZMlKlRJ0hWnN4QXbc0cSBTxx8cGCGbm2O9prItY6LzsnK6r6vF5V+9E/bfhnpXV9ePh7quwch+OPc96H8G8mnw1cHlfwIdd0JRwzve/wW/WG8a7pIkPQhcAQQkSZoGPi2E+Pq/8/gzrC6DHGZ1ZH/v2/SeBb/k0v0rhB8exMjqeG9pwLGl7N89l8QwNE7t38vpp1TyGR9VXUPsvH0Pk89GCD0/gAUJk3wO2f85ihJLpDMSXw76+IbFS0rWuSzu4K/CIxQbWWbKrTxX1MHwxbVsOzbAYGsrF6tdlM9M02kcp71xBptZpy8W4Jv2taQ2l3HCcTVzo36U+TSKnKbUbeGWOQVHXjDiPUTzyAnMsow1J7DbNvLs2m10xs1cNwO2zAJrZh/BN9nPRJ2Vge3X0CPvwa66mEqcZyIxzMU6O4oowZrXqawLUJ1TGX1thkFRg6yU4ynW2PnhNZxMC377uX7m47M05xV2ZKxctnk11D3FqxOlKysrHDp0iN7eXiRJoru7m23btq2GuhAwfWp1hH7h0dV7R21+2Pjh1bPSKzcVVrv8CitsYip4Rxk5negzYySPzWEqc+C/uwVT0PFTnxVCMD3+Ige/P0hkvA17UYid99Qzu2yj8vk5TIYZmWVU32cJZC4iJHje7+QL1hKmTHnq0g7+MjRPpxZmyW/mtYpy9kZu4vqHTzJXXcN8eRmuaJSNkZP0BPtwWPMMxv183dpCYk0Jl7xXMThegzqbQkJg95m5MWSmOmow5Z6kbHYvei6Hqhv4tSBHam+kLufFjozJSNAeexjfhVOEHDL9PV30KDfit5SypE3TlzrHUIkZJIm0I0VbIIhjNspk3xwm205ktQKnT+bye9qZtUn8zVN99M3HKTNkLk+p7OoqZ9P1dXiDq/eNrqyscPDgQXp7e5Fl+Y1Qd7vdsDL2eh39+xAaBsWyeu/o+rtX6+mFOvqvjMIO1YJ3RW4mwcr3+8kvpnHurMCzpxZJ/em7HiORMxx97lHGj2xG5K20XiGTXFNN7eOX8Kb9QAaL46t4eQFVF/T6rHzBUsJxG3g1K/9PKMlt6RnidpX+OhcPipvpeGgWs2piuLkJNZ+nbf4CV/qP4XVmGUn4+KqthlRDMeO+yzkz2Y4ymwYEis/EDs3KpimdmCWNyDyKe2mOrEmhKGVirHQ3NqUOFxZkcrTzOP4zB8mkFE5d1sx6yw1U2puJG1FOG+cZcmTRZI2MJ8YGcwnpCyMkwhpWz1UI6rA5VXpuacDc5OJvn+ln/+ASbiGxI61y47oyem6sf2OdeigU4uDBg5w7dw5FUejq6loNdTW/urno3EOrm42QoHb76gi97Wawen5quxf8ciuEe8EvlNAF8YNTxF6aRHaY8H+wGWuT76c+m05PcuHsF7j4fAXJ+XX4KnJwTQ1lB4/StFwPgNX8JE7zt7DmNaZdKl+3FvGYY7WufndY4g/jI+RVhbE6G0/bekgeKWdj/wSX2tvIWK3UzY9xjfUVSn0RRhI+vmyrJNNQxLx3O8enNsJsFgmB8Ki0OexcPaij6gaLlgPUjZ0gajHhyBqkXZtIuzrxGC4kdGrsL1N5cS9iRuHUxmpqXFfT7OokT57XlD4GTRHC6gqyI057ykukbxjDMOGvvJF0qhpFldm4u5qqrUE+/8oI33ttCpOAnozKHWtK2XZzA0WvLw39t6He3d3Nts3duOYOrwb60AtgaKu7RtfftXq2i6fyF9bnBe+OQrgX/MJoy2nCDw2Qm4xj6wjgvaURxfGTB09pWpjRsS9y/sAkS+duRZJMqDu9OEIH2DHeBriwKMdx2L6APR8halN41O7iKw4vKdngiriFvwiP4REG01UWjhXVcGB6N7c/eoyB9lZWiorwR5bZkz/AmsAUQ0kfX7EESTcUsezbybHJLsRcDgmB4VYpKXdwfZ9BMKozbx+jYeJxwrJAEgKrVMVy8VX4jGJAUGQ/R/3SfVhOG5xtKcVXvJ129zZMsoVe0yB9zDFpGsdlylI9q5BeWsHq8lLadCsrcwG0rMGay8pYf20ND/bO8MX9w2TyBhtyCnfVB9l1S+MbxwT8aPlFURQ2dXdzWbUJ19Dj0PcEZKPgLF3dNbr+7tWDuwp19PeNQrgXvOOEIUgenyP6zBioMr5bG7Cv/8lzv3U9y/T0txg4/zDTx+4gvdyEXKOSK3qVOyb8CL0VVRrCafsCTmOUjFnmoN3K5xwlTJsNWtJm/sfyLM35NDNlVvorvOyN38ieb5wjXFrKeH091kyaHYmjbC3qpT/r5cuWEnK1PmKenRyZ3owxpyEh0L0mHHUudg3maZvOkzAlKV7+PunMMhmziktzEA3swS7XAWA1LdLI/fgOT3Oh1A3l3Wz0XonL5GdYmeRSdoh+9SJ+WcE9kcHI5ylrbqO85VomL1mIh7JUt/vZclsDB+cjfOapSyxlNBo1mbvKA9x8ewvBOjfww4nSs2fPro7U2+vZZhnE1f8wxKZXjwFovXl1pUvdzsJtRu9ThXAveEflo1nCjwySHYpgafbhv6MJxf3j58IIYbCw8CTDw59jtred5b5bEapCqvoS90SGEdrNKNIyLvPXcErHyKkyAzYTn7cXcdSuEtBM/FkoxNXpCNN+J1MNJl7MbCfwFASjafpb12AoMusjF7jGe4iLhoOvWAOolX4Sru0cmt6CPpdHkgWGzwxNbraNa2wZ1BDoWJMvYlk+zYrTiiWvkndtw2TtAgSypFHr3k/Fwf0MOFSiFWvo9O6ixFbNohxiMHqKs/JZvLoZUyiL2WajdcdVlLdcQd+rSRbGYhRVONn2gUamTDp/8fB5hmNpgnmJOwI+PnxHK+WNq5uLwuHwGyN1SZLorjCxPf0CrqVTICnQuGu1jt5yPZjt70JvF7yXFMK94B0hhCB1donI3mEwBJ4b6nFsLv2JJY7h8DGGhv8XS5NRFk59gvRKkGxgidvUB7FkPgaoONUHcarPIGTBnM3MtywOHnI7MRsyH4+k+FhskUWnm5kmmXNSAwPn13P5oX761q0l5XBQFxvnevsB+kzwdYePohI/UedlHJjahj6nI8kgAmayLR7Wz2W48oKOKyPIG72UjT3DtNeMhIRsXYdi34UsGQhUSn3D1J15grFcmPmyKjq8O6l1tpOU0oyuHOFM/iQWzYSU0ymuqWPDnhsobdrEqWdnGT27hMNjpueWBpRaO59+6DxH56O4DIkbXW5+945Wql/fwBWJRDh48ODqOnUEXc5Ftsf24iYO5Z2vbzC6HZzFv/iOLnjPKoR7wdtOT2pEHh8ifSGEucaN/85m1CLbjz2TTA4zPPx3LC4eJDxwN0sXd6KrGltc91MnNpMTbTiUJ3GZvo9MlojNztMmmS96/SRluDmu80fhOTTVyXSzxKTTx77Z3dzwnTMMtrexUlREILXMdep++hxJ7vd6qfUEmLdezqtTm9AXVkNdD1rQWnxURxJcfQ4qVnRyzFM//D2mXVkyZhOqUonivAGTopAXVtyuOI2TzzG3eI6pYIAWz1ZavJuQJJmxyDF6EyfQNQ1JUWjZsp0Ne27AW9bAyafHuXh4FtUs07mnhorNJXzmsQs8ObqEImCX1cEf3dZO0/oAkiStHhNw8CBnzp5BEgadXGSHOILbG1gdoXfcCYGmd6mXC97rCuFe8LZK94UIPzaEkc7j2VODc8ePX32XzS0zNvqPzM49RDrUxvhrvw1RG1WOI2y3TJIy7sSuHMSt3o8qhYlbXZySNf7eV8S4WWFDCv77yhxB3cpQg5mVEpmDoZ1sfHCOcFEZ09XV2HNJrhCHGPAv8JDHy3pnMYPqVbw2uRGxnAcF8uV28g1uAuk4O/pk1k7l0UlSN/oIIXWKkMuOIjlRnDdgNznICheqCWrTx4mPPsdEsYca5zraAjtwSW4mk72cCx8hqcWw+3xs2H09HbuuxWxzc/alSc68MImuGbTvKGftniq+9PwQ95+bIScEm01W/vjGVrp6VjdvRaNRDj33OKf7x0AYdHKBHZYBPOuuWT0fvXDpRcHPoRDuBW8LPakReWKEdO8SpjIHvjtbMJf9cEOSrqeYnPw6E5NfJZdRGL3wKfShGmxKiKtdD6MoH8YiRnCr38QsT5Ayu5iUBP/ktXHYbqM8B3+6ssTmtMK5Sjep2ix98RbkF914QzDc3IyMwcb8SYaLR3jR7WWzvZjT8m7OjrdBRAcTaNVO9BoXZekoa8YlegYNVD1PxfRLGKljTATcIKkoth14bGXkZA+abqNEv4QYe4Qpv41iWy1twSsolctYzE3Su/wKK9lZylvb6Lr2Zhq6tyBJMv1H5zn+5CipaI76jcVsvqmOx16b5p+PjREWBmtkE//t6mZ2XVGDJEvEpvo49NxjnJ7JIoCNUj87Gp14u+6Axt2FDUYF/yGFcC94y1Lnl4jsHcFI53FfWYXriqo3NiQJoTM39xijo58jk11gau4jxE9sQcmZ2GB/kgZPK2RMeNVvYlXOkVXtLMluvuPM8qDbic2A341E+GBUcLo4iNYSYUnzM9y7jtajy/S3tZO1WKjNX2AseJFeh5/NjiIO69dwcbwRKa4jWSBX50GU2miPxrBHdLZeknFlwL98Bt/8kwwFneRUUMzt+Jz1CHMxyawXhz6Beeoh5twSTnMRTeU7aJZbiRlhLiwfZCY3zNrLd7PxmhsIVNcihGDifIgjj48QnktSWu9m622NHB8J8fcHh5k28pSh8Afb6rjzhmbkbJTY6Uc5fPQ1TiUCCCQ2OEPs7OnE230H2Lxv0voFBT9dIdwL/tP0eI7I3mHSF0KYKpz4P9iMqfSHo/VQ6CDDw58hkRxgObOZmcO3YlkppkQdYmtwGim9Dp/yHezKK+QlKyG1mhdtc3zJ6yEhS9wRT/A7Kxoj9joia5dAhTNjnazbu8Rg8zpiHg8OMcxkoJclq5/17mJeyuxheKIaKaWDXUKr92B2mdm1nGDFSLBuyEFJDByJaSrHH2I0IBG3CCSlmIC7BbOzklCiHFN+Bsv8Yyzbc6iKlarKHrqUzeSFxqXwMWZMY2y68TbaL78Ki331e14Yj3H0sWFmBiN4SmxsuaWBiUiKv395iD4jhxuZj3dW8zs3N6JO7CN+8mEOD0c4KdowkNlQZmbnNbfiq1377zV5QcHPrRDuBf9hQgjSvUtEnhjByOq4d9fg2lGJpKzWgePxSwwPf4aV8GGSVDB4+gM4RlpQ0NkcOIJX6cKffxan8gOQDEKmNs6q03zBb2HUbKInleGPQ2l00Ur/uhU87ggD881U/CDLTFELC8ESssok0/5ezBY/1Z5Kno/vYnYigJQ1wKWQq3cTlGQ+NJfmnGOJqjE3lWEz5myYmrG9zDujLDl0kKwUOWrxljQwG2lGaHOYl58gZk6CLOOtWst29QpshpWRRC+h4DKbbr2d6nXr31j5szKb5PiTo4yeWcLmMtF9XS0JSfCPLw7ymp7FLEl8qK2MP96ex9H/MLFzz/BqpoFTrENHYX1zNTuvvQ2/3/9udmvBr5hCuBf8h+ixLOHHh8lcWsFc7cJ3RzOmktU11ZnMHKOjn2Nu/jFyOBkY34Pn7HpSuTLqnBdoKi6lKNaLR/0OihQhZGpnwEjz7UCcw3YbNTmNP1pJ0JTsYv+aBBWl4ywmAijPOUika5ioqWbJOsa0p59q1Y/DX8/zK1cRnrQjaQLhU8lXu1ifhk9OZ3kyOIl3zElV2IOiZ6mcepm4eY55exoDHa+tjNLKOibDm8hnZlGiz5FWYuRlA7mmmqvkPQSNYubSoySacnTeeTOekuAbbRFdSvPaU2MMnJjHZFFYf1UVikPhS/tGOJhPo0twU42L/6/hFMWD3yWyPMdhaQtnaEcg09Gxjh07L//pNx8VFLxFhXAv+LkIQ5B8bZ7os+OIvIHnmhqc21bvM83nk0xMfpmJya+jGzrDS5spOdPCfKQHhxqjqzFFcHERr/otzPI4cbmGi/kgrxQN8T2PE5sh+HgkwfUrPTxfrlHSfJG8rhI7WoIxXMpAfR0T7knmHWN0mAJkfGt4aXEHqWkFSReIYjPmUgvXRBQ+MZvjW6UXsEzYqYiVAyoliycwpEnmrRE0I4nD7KGiqo7p6A60zAxSfB85KUJO1YnUutgtX8carZGYFkLrUGi962rM1h8u5UxGspx8Zpy+w7NIikT79nLMThMPHBpjn5EmIcN2X56/8nyfhvmnCeHhsONGelMlIMls3LiR7du34/P99DN1CgreDoVwL3hT2nyS8OPD5CZiWOo9eG9vwhSwIYTB3PxjDI/8H7TcEhPxtZScrWBxYRdpw8PG+iUqE1GKxcPYlJPkCHAqt5Fh/xm+5rcQlmVuj6e4d7mLC2YXmc4zeC0xli6VYjrm50xdC4P+CSLmRXosAaZ9HRya3Yw2YyAJMEotVHpkPrJk4roVnW8Vv4IybqUk2ULe7MYdGcBqjLNsWSCVW8asWCgrr2IxvZt8egojeRCdGGlznsl6iaukq9me2UzWSCN1Oqj9wBZk0w+37qcTOU4/P8n5A9MIXdDcE8RkVXjmxAwvyRmWFUGbJcFfyl9gszjHkmc9h6y7Ob9ooCgKnZ2dPzxPvaDgHVYI94J/l9B0YvumiL8yjWxV8NxQj72z5PUNNicZGPwfJBIXWElXY79UQnZiO3NaOw1FCept85Rn9uFQnsfAyvn8TiadA3y3KMVFi4UNmSx/sNiClFzL+Z7jVHkmiC55MO/zcqSkjQvFk+hqis32Yi66uzgx2YGY11aXd5dZ2GI2+N15C7UZwXedj2MZU/DlOkk5yjFn5nHpU6TVUVZSc8gSBIqDxI1r0FKz6OlXESJJ3KbR35Bhh9jB9cmrkJFRN3gpu20tsvWHd9Vkkhq9L0/Ru28KLatT1xHAZFY4dG6eA6Yck6pBpRzlz5RvcJ31EotNH+Rgeg0Xx+YwmUx0d3dz2WWX4XK53r3OLHjfKYR7wU+VGQoTfkw0dbQAACAASURBVHwYfSWDvbMEzw31KA4T6fQMwyOfYXHxGbI5D9JIKY7RNVxIXke5VdAYnKIieQaP8igSGSb0nVy0ZDjkG+Jpl4OSfJ7fWy5jfehG9rcfobbyLLmsGeUVN6+orZwtn8eGxHp3kGPWTVycaEJezoEC5lIzd+pw76IVXc+y13Q//kEDG1sJ+1uR83Ec0gwW6QIzsSWESOPyeslJu9AzIfKZYyAyrLhynG+IsSnfxZ3xG3DILtQ1bgI3t6C+fjUdrI7Uz740xfn902hZnYpmL7Iqc3pgmcNWjWGTgY8En1If5cMNGZbrbufgrIn+wSHMZjObN29m69atOBw//QKSgoJ3UiHcC36MnsgReWqU9Nkl1IAN722NWBu8q3X1iX9hfPJriJwgP1VG5bCX12Ifwav4aC5ZpEg/R8B4BFVeIGJs4KipkTHnfr7ltaFJEveE7dw6fy8ny85iW3MEu5pGnHHwcryZ06URSgwHdb5yDqk9jI2XI4dzYIJAkYk/SpnZEVOYIcqx3FeouZjFsO5kvrQHQR6reYFS/QQj0QT5fAiT1YakXoaRT6NlX0MSGvO+DBfqonSkm/lw/Hb8phLUCge+mxux1LjfaIN0PMeZFyc5/8oM+ZxOsMaNltMZmY9z1JajTzVwSmk+4TjIxzaXsRC8nMO9w4yNjWG1WtmyZQs9PT3YbLaf0dIFBe+sQrgXAKuXaCSPzxF9YQKh6biuqMJ9RRWoMD//OIPDn8XILqLPlNE0qnF65deQWEuzN4nDcp5g9hmsyjlyRjWnLLuZVZ/hviKYNJnYmZD4zfl7icgrzG58mTL3AtqUhX2TVZwK5KnTivCWVrM/v4WlMTdyXEOyQKvLxKejNso0g1PSKAvR+2k5myLp2clk1dUYsgK2EC35gwxGDTLZKSTFhGrqIG8IdK0XWehMFafor4nTEi3nnsQdVFhrkT0WvNfXYesIvLGkMRXLceaFCS4cnCGvGfjLHaQiWRZTOU45EpxWFCySxr2lY/zmrg7mCPLqkaPMzc3hdDrZunUrXV1dWK3WN2ntgoJ3XiHcC8iMRIg8MUJ+IYWl0Yv35gZMJXai0dNcGvxrkrFzSEt+GoZzjIeuJZa/lka7wOS5SDB9FKf8HAZ2pk230i+d4HF/mFftNmpzBr87fxNlsWqOrv8BLeUjpGMK+0aKOW+z05QvQ5TXcSC9meSYipzKo9jgapOF/xYzo5PhlHEIY+kF1pxNE/FtYajhZoRiJ2ePsV7fz2BUIZUeQkigqg3kJROG1o8kDMbKUoxUJGheLOL29J00OJuQzQquq6pwbatAMq3uok1Gs5x5YZKLB2fI5w3cRVbiKxkSQtDnmOWIujqqv6chw8ev3870zAxHjx4lHA5TVFTEtm3b6OjoQFXf9E75goJfmEK4v4/lIxmiT4+RPr+M4rPgvaEea3sRmhZiaPh/Mz//KErUTumIRHphI/O5D1NvtqK6JvFqp/DKjyCTICpdzSUpxSHvRR70OLEagl8PrWfL3A0caPwuLQ0jZITGvlEvw6KYFqOGUGU9r0Y60Sd0pIyO1Q4f0+3clZWZkZaYyP8A++R5Gs7nCPs7uNB6NygeMvYMHWI/kzGVWOoihmGgKGXkFTPkJjAkwXBlgplAmpZZH7v1u2nzNqEYAsfmUty7a1Ccq2e0RJfSnH1xkktHZjF0gcWukknmySkZhuzj7FdKyWLmjlYbH9+9kZmh8xw/fpxUKkVlZSXbtm2jpaUFWf7pd78WFLybCuH+PiQ0nfjBGeIHpgBwXVGFa2cFQhHMzDzA8OjnUBNx7GMufNOljGU+SZXZg9kSxyT3UiwewiyPkTHamZCrOec4whf9TlYUmRuiAW6Z/S2Ouh+hun0c7AlenrYxkaqmRW5krKKJ04ttMJlF0gy8DviDrJ2t+TyD6hjZ1CP4+qepGBUs+Ro40/FRVClAxqLRoh5hKS6Ipc6T1zRkyYOmmlG0JTTFYKAqQcSZo3W6mC7lDjqLmzBldSzNPrw31GEKrk5sLk8nOP38BMMnFwCQZDB0UEzz9NnmeEFpICdUbl5Xwq/3VLIwfI5Tp06haRpNTU1s27aNmpqanzibvqDgveQthbskSfcBNwKLQoi1r3/2WeAmIAeMAPcKISKv/96fAr8J6MCnhBDPv9kLFsL97SOEIHMxROTpUfRwFtu6AJ7r61B9ViKRk1wa+DTZaB/OSRflY1ZG039IUK3EKuto1gFK9aewK4fIiwARupg0H+NzAStnrRbWphU+MvtR+vPHKW0ex1Qe4qUlK/Mr9dRaW7lU0sqluVqUqRSSLqiyy/xxykqxFGHY2odr5WlKzobxLcCir5pjXffizJegqQaV1lNkUmlWUhfR0mkkrGiqippPkDHpDFTHyZoM2qdrabDcSE95PdaEhhq0r/400uxDCMHccJRTz44z2beCJIEQIKETsL7GSZvGXjaioXDLhnJuabIyN3CWwcFBANatW8e2bdsIBoNv0soFBe8NbzXcdwIJ4P4fCfc9wD4hRF6SpL8DEEL8iSRJbcCDwGagHHgJaBZC6D/r3yiE+9tDW0wReWKE7HBkNfRubsDa4CWbXWR4+O+Yn38cz4KZ4KCdufjH8Spt2GWJpHmOoL4Pt/ooAGmjm6g6yLd8Ot9zO/Ho8JHFy4ksxwmWD2FqXeblqIXQUhPFzo2cL2pjejqAOpMEQ7DWqvKppERWnWfOfpLqmaMUn0xgT0hMF9dwePO9+NPFSAj89j7Qwqyk+tFiMUAhryioeo6kNc9AVRxVl2idW0/QdhVb66pxrmSQnSbcu2twdJeCBOMXQpx4cpTlqcQb7eFSFimzHeCAu4KHMxvRhcQtG8q5ojjD1KXTLC0tYbfb6erqoru7u7DxqOCXzs8K9zedHRJCHJQkqfbffPbCj/zyGHDH61/fAnxPCJEFxiRJGmY16I/+J9674OdkZPLEXpokcWQWyazgvbkBR08ZQsozOXkfI2P/iC2SoOJSEYmlD5OUN1JukkmQxqSeoZ77MJnmyRqt5InwsqeXv/d5iSgy10cqcMwGkcxnKN0e4uWMhcRQJ3bfJkaq2zg1aUUZSmMiwXZV5SOpCDMsMek+RuPIBWpOZTFpEsOVa9h/za9RHvdTnDSwOcaxiWmW48NokQhgoMsKiqGTtGYYrkjgypjpnLkSl20L29aX41lOQSyH64pKXFdUIUwyl47NceLJMZKRLAASBrXmE5Q4j7O36Gr+JnQTRkbihvYAmxwrTPc/z+lLGcrKyrj11ltpb2/HZDK9ux1YUPAOeDum/n8D+P7rX1ewGvb/avr1zwreAcIQpE4tEH1+HCOp4dhUinvP6mRiNHqW/v4/JxvpwzMUgPF7MKStlJpkErqBJk9QrTyAXTlCXhSRMyoYtwzzP4v8nLEV0Zo2sW28ibLIPEVbjvO8bCY+1YVadBnnqlrJjIPSl8EsJ7lWkrhxeYzeQJSQ/zhrLoziOW9gSBL9tZ28uO0eqlecNIQMZNsCLscQ4eQosXAYgzyGBLKAsDPFZDBFMOFh8/QtWO3ruawrSNFyCrGYxL6xBPeeWnSzzKt7R7l0ZA4tu/pDoU2Js9b6BPbAPN9y3cMPZjcgQnBNs5tWplgeOsGoLNPW1kZPTw+VlZWFenrBr7S3FO6SJP05kAce+E/82Y8DHweorq5+K6/xvpSdjBF5YgRtOoG5xo333rWYK5zk83EGBv6W6elv45uyo/Tdi8XYgUdVSekGMZGg3PwcbvVBkHTyRjFpZZnP+/w85C7FpcPls5XUjGao7+zl2XaFxeVuJP/lnC9rRhvPo4QyWBWd23Nprpo5zaFGQSZ4kl2nZ3GOSGRMCuead/DM9g9QGbayfkpHWKI4nX0kIgOsxOJo0upNRBKw4Muw6MtQEy9ly+xNqLY2enpKKI1kMKbjmJu8eK6rI5zTeeJrF5gbiYIAEFSYL9Bt/z4rtS18SXyA5yYlzEmZXbVmqlJDaOPzpB0Odu7cSXd3N263+2c3bEHBr4j/dLhLkvQxVidad4kfFu5ngKofeazy9c9+ghDiK8BXYLXm/p99j/cbPZ4j+tw4qVMLyC4zvjubsW8sAWBx8XkGBv8K08ICjtMfRMnupkQ1k8JgSdMpt1zAZ/pnzNIMunAjE+MpV55/9JezosisjXhY029hY804z1wpcyCyCZ1d9BU3wFgGOZLEqea5OzLHFYMvcXBrAFHTywdeDWOdl4jaLby2dhdPb7+J4rjKlhEN3ZTC5riIvnyOeDJNVkkjpNVQnw6kiDk1mtON1CxuRbE00L21hKqUhj4eRSm14/31Ni5NxDn3T2dJRXMAWJUk7dan2eh9kVP19/L/Rv+Gw+NpnBaFa6sMisPnkWdTFJeX03PlbbS3txfWpxe87/xcSyFfr7k/9SMTqtcC/wBcLoRY+pHn2oHv8sMJ1ZeBpsKE6lsndIPEkTliL00g8gbO7RW4r6pCtqhkMrMMDP4lK7P7UE9ej3XlRsrMNnLCIKQJSswRAqb7cCivYGBGJkef6uFvA056bQrlGYW1g272mJd5oc3gYmoLGe81DOerkMeSyIk8LlOWj0xc4spzezm9u5qW3Cj+oynMMYkFn5Oxmut5duvVODSJjSM5JEnHZu3HPPMaiXyOlJoABAKYCqbImA3WaR1IqW5kUxUbuoPUGwb54Qiy24zUFeTUQJjJ/gjCEIAgaBllq/0+ysryvFD1+3xpupbemTg+m8JmT4KicB9WBdrb29m8eXOh9FLwK++trpZ5ELgCCAALwKeBPwUsQOj1x44JIX7n9ef/nNU6fB74QyHEs2/2goVw/9kyQ2EiT46QX0yvrue+qR5TsR0hdKam72dk5HOI3g1YJ+6iwrR6KuGilsdtkihRn8Vt+gYyGghBUrLyD94qHvNmsBjQOuXioysxjnVkeJkdpNw3MpkuRR1PIKV0/OYkdw+d4NoLzzC0q4SSXAjvEQ01IzFa5mem4nZe3LwFIUtsHchg0QQmyyjeiUOsqDpJJcq/hvpEaRIhSaxXetASG5DVIGs3BWmxyuTOLSGpCvEKJyfGosQjGgCqrNFieZktrgdQ1lzBXt/H+JdLZoYXEwQdMutMC5SmJ/A47XR3d9Pd3V04mbHgfaOwiemXVH4lQ+TpUTIXQyh+K94b67G2+pEkiVRqjIt9f8Jybx5b30epVvyYJAjlUyiqnRJ5HLfl/2BjAoEEQuYpaxf/VDzHgkmiNmThkxMZFptjfNOxjajzg8wnSjCNxSArKLWucNPIMW66sI/lnRZs6RyuYwZSTmKwupS5yrs5uH4tUYfM9r4s3pSBbJqhdGwfs7Y8aTmMBBiSYLIkhdlQWe/cSSK6Fkny0rqllHa/mdyJeQzNYNlu4uRcipyx+r27zStssn6bZl8vqQ0f43vKTXzjdJSZSJoKB7QYE1Qai1RVVtDT00NbW1uh9FLwvlMI918yIm8QPzRD7OVJJInVc1K2VyKZ5NXR+tQ3OXfkB6in76FOVOBQJOJ6hKThotSUx2b6Jn7lydf/MolRaTufDYR51RXDk5H5rXGdCv8Knw1exqLtbpYSQUxjccgJqm1z7Jk4xp6hE+Q3aahRgeMYCEPiUm0li6Uf4mjHGqYCsL1PozysI8khKsf2MeZNkxPLyGI11Gf9aZzYaQ/uIrLUjKHbaN4cZH2Ni9yrs4ikxrwhuJDIkzQABFW2PjbbvkVpucRcxyf5ZrST756aI57JU2PXaNTGqFbjrFu39o3SS0HB+1Uh3H+JZMejhB8fJr+QwtZehOemBlSvBYBkcpSTh/+O0KGN1KVbKTbJZEWUkKYQNDuxyKfxWz6DWaQAiOudfMNdw3cCJ8khce2c4B59iU/XbGbI/lHCsVJM46uhXmef5JqpY3QvD2JfE8I8K2F7TcJA4mJ9LaGSD3OyrY5LVRKbB3RaZjWQklRMHGAosIKhLaEaq6G+5M7iUz201OwhNFdHLqPSsDFAR4sP7fAMSkJjJW9wMW2wogtUOU+77Xk67Htxt6ynr+kTfG28mCd659ANQZM1QZMxSZ1boru7m66urkLppaCAQrj/UjBSGtFnx0m+No/iteC9pQFb6+qlykLoXOq9n96nVqgMdVFrlhHkiOrL2NQK7EQxWf+GYi4BkDWqOaZ8kH8qfYYha5LmuOAvVpb5auU6XvL8BvFI5Wqoa4Im1yjXTRymIbeIv34W65jAflwmL0tcrG8iEriH0y2VnGmS6BiBztEMkshTOnuMgcAUSmYekw4GgrAzR5GliObm61icqiKThOp2Pw1VLpTeRRzpPAld0J8zmMkaOM1xNlgeotV1BNPGW3kl+Gt87ZzG4eFlLDI0qUu0MEtrdQk9PT20trYWSi8FBT+iEO7vYUIIUmeXiD41ipHWVlfBXF2DbF691zM0P8q+B5/GMd7GGosJkyTQpX6SehMeRSVveogq5QEUDPJYmcn/Hl8uGucp30kcuuBPlqJMusr5QtknSa7UYhpPQF7Q6h3kxtlXCGoJfA0zOAd0HEcUDOB8QyPRwEc53xDkeJtE3azKjr4Uqi7wL59j2DeIKTWFOS8hEMRteUocQerXXsfCRDnJiE5JnYsSrxXnWIQgkBWCwZzBWNqgxDbOBstD1Aem0Lp/iyfM1/HV4wsMLiRwqQbNzNBqXqFz7Rp6enqoqCjsgyso+GkK4f4epceyhB8bJtO/grnKhfe2RszlTgByaY2Dj73A8nETHRYLbkVGYoiY7sapBBGmQbzKX+MmggGEjRt40dLNl0q/y4qS4QPRDF15M39W8/uEV9pQJpNIeUF70SWujxyiLBnF3rSM50IO1yEFDDjfUEu4+Nfpry3nyDoDT9TGNWfjODIy9tgoE+5ezPERLHkZgSBlyVPqqaR+4/XMjgaJL+dwFVmxmyRKozmqzRKGJDGc0RnJ6NTYX2OD9TFK65xENv4eD8TW8c0jkywlcgTULGukGda5s2zZvImuri6cTue720EFBe9xhXB/jxFCkDq9SOTJUdAN3NfU4rysHEmWEIbg4qsjnHx8kCbhoMYiA8sYzGCIdahyDt36WarFcQDipnLGU/+dfy7dy6vuPpqyeT4Ry/A3Vf+FsWgXymQKKS9YV3KRa/TD1C0sILWlcJ/N4t6voOThYl0VoeBHGaqu5kiHQU44ufFUhGBYRsmGmLGfwBK9iE1bDfWMWac0UE3zppuZGvQTmc9gtqlIOZ0Gk0S9VUECxrM6o1qWRstzdNifxd2xlcnWT3DfqIvvnZgkkzeoUGK0yXP01HjYsmW19KIoyrvaPwUFvyze0sFhBW+vHxut17rx3dGMKbB6D+fsUIQDD57BtQw7bTbMksAsHyWpr8ckrcfw7qM4+3ksQiOnKszmP8V+2c6XG/6enJTldyIpjnh/nd/07US5kEbVkrSX9HOb+1nK+8KIzVls0RzeLyjYUgp9NWUsl36U0ap6jnTkmXc4uf5MmObpGELPsWB9DVPqGN5lBYFEVtUpL6unbeftjJ5zcP5gClnJIgN1sqDRraIImM4ZTOpRms0P86HiE1g23cWZir185XSC5x+YRxLL1MohOmxL7OxooKfnbsrLy9/djiko+BVTGLn/AqXOLhL+wchPjNYT4SyHHx5g8ewyG5wCv2zGxCAaEtCE4VjEzF9Tqo8jgHl3PVORP+GLwYc57RxkQyZLmXw5Dyp3I4/nkHIGjUWj3FX9A4Ln4qhrE0jTGp7HVXwrMF7qZ6bqo4xVruFIh8ZQoIirLkboGtBRDFgxnUfEX8aZWd1+pCmCyqomOq7+IAOvmVmaXD1WVwLaK+xUZfKYdcGCZjBvzNBsfoCG0mnElt/lJdu1fPnVaU5PRbFIOk3yIps8Sa7cspHOzs5C6aWg4C0olGXeZUYmT2TvCKkzi5hr3Pg+uDpaN3SD86/McHLvCPWyQaNFQSKJVT5HSu9ByHksJd+nJPoIMoKURWFC+Tj7hYNvlDyOTI6rMkEetvxXtEkZKaNT6ZnlnqaHqJ1YRnZpZEQW+yMqVVOw6LExUXcXI9U9HO3QOFceYNNYjMvPZrFpKjFljFzySZzpPAJBXhZU1q1h7ZV3039MEJpJAmCyKHQ0uAkspbBqBuG8QVj002j5BmU1NtI9v88jyQ189dAoU5EsTilLm7LAlbVWdmzZVCi9FBS8TQrh/i7KTcUJPdiPHs7g3lWN68pqJEViYTzGK98dQMwm2OjSsQsLFvk1ckY1giA5/3lKs5/BrkcxgMmyciYXf58vBp6nzz7KuozCoPkPWZwuQ07mKXJE+OiaB2jNjaONW0k0xpAfNrH2AsRtKqN11zNUu4cjHYKz1X7ql1JcdyKOL2khJS+TSj+KM7Ua3nnZoLK+jfquuxk6mSO+kgHA6bfQ1ujFNRbBqRkkdIOEOEWj5ct413aysP6TfHOiiG8fHSeRMwhICdaZF7lxQxWXbemhrKzs3euIgoJfQYVwfxcIQxB/ZZrYixMobjP+u1uw1HrQsjrHfjBC34Fp1jmhWjGBtIBFmiFrdJKUIwRK76NoZR8AMbuZMfe17I/VcH/x01iEgUtcx6XQVSgrGjZLlo80f49NRWfIH/EQaQ+z8oqFbYcEiiExUruNgYYPcHi9hTO1bnzJLDcdX6Yy5CRLkrj2GI7EIhISedmgvLqdspY7GO3NvHFWelGFg8Y6N6a+EEWGIGMY5MSr1Dm+jq3rJvoafouv9OZ48twcuhDUyGG63XFu3tpOd3c3Dofj3eyKgoJfWYVw/wXTkxor3x8gOxjGti6A77ZGZLuJ2eEIL3/rEtZwhk6PgVlXsSin0PRmDOFksfgI69Kfx5RPISSJ4Ro/S3O/zed9x7loH6EsX8VA8rdh1oyi6lxXuZ+bG59GGjUTiQnOJ+GqZw1KIzATbKSv5aO83F3C6XoHiqFz08lpGia9yEInajyFLTaKBOiSIFDega/iJuaGM/zrf4lApYPKMgfWgTBBGTShIzhMte+7yJt/jVf8d/ClowucmIiiotOkLHNlpcR127tpa2srlF4KCt5hhdUyv0C56Tih71xCj+fw3tqIo6cUXTM48vAQffun2OBWKHeqCHkWi7FCTt9ETJrGV/oPdEVOAZC02Bkta+bo4g6+WvbIav07+yEGxjuQBKwv7uM32u7HJnJoz9t5OZiha7/MPSOCqNPLsc6P8OT2Dk41W8lLMrsvDtI0EMCT9xI3DmKKncaOQJcEnsAGbO49xFd0Mq8HuydoJ+g1456OUx7PYkgGsI/q0pfQtv4mD+l7+fKhcSYig9jJ0W1a5Ja1Aa7ctqdw1ktBwXtEIdzfRskT84T3DqM4zZT8znrMVS6WpuK88LWLmENpdvsUVF2gmE9h5NaQFcVMuF6gR3wFNZxFSBITQR8r0ev5fGqJk6V7MesNLM/ejZRwUeSe51PN36bKP4Xe6+DskkJmJcfHnpEQkkJf0w08sus6jrXZyKgql49epPpCgOp0CRn9POnEK5iFhiEJbJ4NKOZd5DXIrh5Fg9VloshtpjiUoSqTQ6gGZvkFiusvsdL1W3xu8W7uf3aCWHYQv5RklyPMXVsb2bJ5V+GGo4KC95hCuL8NhG4Q2TtC8sQ8liYv/rvXINtVzu2f5vijw6x1KFQ5VfLqNGYRQ8t1kRLjaMXfYXviGEKAJnsYK/dwenkX/3/wKClZIx2/nfh0N7I1ywdrf8Cepv3oKRPJR+w87dK586CgPAyzpRt5aPdHeKkrQNJiZtvMGYp7fbTGyxH5cdLJF5CNJAIwudqRld3IqoLTZyW6mEbXBQGfhfKMRk08i2QW2NTn8LWFGFt7L//70h3sfWSevDFGpRzhhmCWD16+gXXr1hUuly4oeI8qhPtbZKQ0Qg9cIjsSxXVFJe49tWTTefb9y3kiF0Jc6VWx6ALDcQJzcg0aQWbUF1hnvQ/b/23vzuOjqu/9j7++58w+k2Qm+0oWSAgQCIRNFhFkC4vgWitWsVq91qUu99rlWrWtvVZbf1q3ar1W61b3pVhp1eKCRUFZwh4gCRCyb5N19pnv74/Ex4NLQRE0k4Tv8/GYx5w553vgzTeHz5z5nm/OdPfOTmm2ZNItMnnYG8+H6WuIhLPx7D8fGUogO2kjPxqxGmdMG6EyB+uqAqQ3hbnxfUmP1cXLCy/j+YXj6bSaOK2pDNd2AwWtmTiCTfi8r6OF2hCAZsvHZFyE1WEhMdNBw4EOOpq9xNgMZMsIuZEwwgQ24z+JK5FsyF3BIxs6+OSFNnQiDNdbOKvQxlmzZ5Cdna2+4UhRBjhV3E9CsNlD69O7CLl9uL5TgL0khcYDnbzz2DYy/GFOjzEQ1tvQDfuJ9EwlJA/S7HicqeGPkEFBBBv1sXEc9I7m56lNtBh24WsvJVh/OgZnDVekvM6krF2E/Sa6X7HxmQxy7qc6loDks7Gl3HfReTS67Exw7yJ9k5v4xnyG+dwEfK8RCNYgkEhzFmbLclwpTtLynRza1Urt3nYsBsFIs0aeUaIhsFv+hfU0B2/HreDRdTVUflaNhSATTc18pySdeacvIyEhIdpdrijKcVLF/QT5KttpfW43QoOkK8dizomjfH09nz+/h0l2nVizhs+5FVtnCpHIBHrkh8Q7nqMk3ABATyQVv8XKy4ziz5k7CUsX3gNXIyOpjMh4g+uytxHjaCdQ7qRig4fcao3v1YeoT8zm9ot/wKbCXAo7q5jxWTmddRMY5zES9q4mEKxAAmFTPDbLOWQWZpM9NoG9nzVQ/kk9RgGFFo0RZtCEwGbbgj4jhZe083h87X5avZU4hYc5djcrZhQwfep8NZVRUQYhNRXyBHi2NtP28h4MCVYSLxuDFmfik9cqafu4hmKHAWEIEXSsw+yegYabJv1txhrfRJMRQNIqk+nWY/mv1FT2WA4S6J6Av3Y5RudeVqR8wMyM/YSDBuRbVqqbw0zfGSasG3l+0Xd5buF8hnkbObPiYzYfmsKs7hB2z0bCXEJuqwAAHyRJREFUgZ2AJGi0YrWcTcGkcYyemcbudfVUbmnGgCTPrFFgAV0YsMbsJjQzmz91ZvDchmo8IUma1sk0Vw8r5oynuLhYjacrygCnpkJ+g7o/raN9VSWm7FgSV44hEJa890AZiTVdlNgNBOOb0L21mN1nIPgMn+UtxrMFKQWhsJWAwc6n1kx+kdqDnwZ8dRcgfYUkZzzPTWl1JDub8R5y0f2Wn5Q6A7PavGzLH8+vLr8SzaJz7e7nWFc9lp6eySzq2kzYX0aYCCFdx2Sdz8QZcxi/YBiVG5t4+9HtaOEII8wahRaJLgxY4g7QOX0Ev2+YxJv/qCcsD5CtuZk3THL+mVMoKChA07Rod7OiKCdJFffjJKWk85/VdK2pxjIqnoQVhXR1BHj/gTIK/UHsJg3PsDLstWnI8Fgi8lUSrG9hk20gwOOLAUuY2xLH8F5MFSF/Fr5DF2KKOcTpeQ9yXkobBj2I//0kvBsjjKmWBIxh7lp5Df8qmcLlB1/nQGUcm32zmddeBr5VhAkSEQLsJUyefSETS/Oo2+vmzXs3E/KFyDNBoSOCQZgwOxuomzKCBw7k88HfW9GIMEJv4awCG2fPnaPmpyvKEKOK+3GQEUn7qkp61tdjm5iC69x8mg51su0P25ggJJpdx5P4Afbq6Wi04xNPkG3+B0JGAEFP0EmrI8xlGeNo1qvwt80k1DoHW+rLXJNSS2FCE97OGIKvxJJQoZHb0cGnRSXct+IK5nRv4YqP/8Jq7xwWt+1itPcZkD7AiN+WytQ5P2TqWUW01nXzxr2b8HT4yTFJRseFMQgbJqebfRNSuXefZMu7dZgIMc7QxDlF8Syas5iUlJRod6+iKN+CryzuQogngaVAk5SyqG9dPPASkAMcAL4jpXSL3vlxDwCLAQ9wmZRy87cTvX/IiMT92j48mxpxnJFJXGkO1WXNNP2lnFG6QGbo+IPrsB86A6PYiNT/Qa5hfe8wTNCANNpYHZ/BrxNDhCOt+Gq+h46FEcMf4JqkbmJs3XSVpyHe1MmrbcNrMvM/37+WnuwEbt7xFI90LWJWu+S8rhdBdoOw4zObGTv5cuZeOo9ut59Vv99Me6OXbFOYMXEhDMKB0dXDttHx3LU7yP4PqrELP1ONjZxfksHcM84jPj4+2l2rKMq36CsvqAohZgHdwDOHFfffAm1SyruFED8FXFLKnwghFgPX01vcpwIPSCmnflWIgXpBVUYk7lf34tncROy8YcTOy6ZiTTWhfxzArgvEREmofA+GnkLM2qvYDGuxa1UAeP0mNFOEazPPZIOpnLA/HW/NRZhd61iaUsaCpA4iYQP+v6cQ+7kkta2RfxVP4uWl53NFw8u81jyJSLeJae5PEJEO0OII4SVx5CwuuO4qwmHJ+0/voqGqk2xTiCJrCIOIxeAMsKUwjTt31lLXFcAlPBSbmrhgah4zZ0wnLi4uyr2qKMo35aQuqEop1wohco5YvRyY3bf8NPAh8JO+9c/I3neM9UIIpxAiTUpZf2LRo0dGJO5X9uLZclhhf3Uvhs8b0A0azOpErvNiCOVi0R/BaViPTjtSgi9io86u871hE+mW5QTapxBunUVM+gtck9RKvquN7tYEIi86ya5oRgL3X3Qlw+PdzNvxLg/657K45VOswXrQ4hBaMjLJwsqb7sMeF8e6l3dTWdZKhjHC4jg/RuFEd4bZNDyeX5XX0rR+P4mim4XWZi6YUci0aYvVdEZFOcWc6Jh7ymEFuwH4YuA2Azh0WLuavnWDqrj/n8I+P5uY2ZlUPLYVy4FOuk065qm1iLUuNBnArD+Iy/ApgiDhCEjNzpuJI7jLBTJyCF/9BWhhG3l5j3B1oo84Ww/uncPQV8dSuH8vO/IKeH/BYha1/pP7Dy5gcksP53lfB2FFMxbg0w5RetkPKZg4mY1v7WXXuu0k6ZLSWC9mzYUWa2ZjjpNf7qujddMBUkQni6xNnDdjDNOnn4XNZot2dyqKEgUnfUFVSimFEF97srwQ4irgKoBhw4adbIxvjJQS9+v7egv7gmzsk1M58LuNWNr9NDtMOAv3YvzXMIxiHxbDa8TqnyAE9IQ1DLqJ67MX8Im2FRmMwVt9NYbY7SzM+BdL4z1IqdHw3hiy17QQ113JK/PPYVRqPb66Zl5zj2JZ5996bxVgHksk1EzyxCTOvvxOtr1bzbM/W4tTCObGdGPTEsBmZmN2LL+oqqd9WzXpWgdLrU0snz6G6dOXqTN1RTnFnWhxb/xiuEUIkQY09a2vBbIOa5fZt+7fSCkfBx6H3jH3E8zxjZJS0rF6P56NjcScmYUl30XtvRsRvhDViTbSk3Zj2JiDRfsUq/Y2dkMZAJ0YabUmsCJ3Dt2BdUQ8uXjrLsCWsoor0/cx1tlFV3sCre8UMP2jTRxKTuOz+QvIDu3lmbpiprdtxBTxoplGIkQsIXsFK/7zdpqrdF64bR2WsM5sezcOPRFpNlGWE8Nt+xtw7+4iS3Mzy9rEktPGMGPGMvWdpIqiACde3FcBK4G7+57/etj664QQL9J7QbVjMI23d31YQ/fHtdinpWFIstL46Fa8oQg1qXZGGMvRy3Ow6n/Doa3BrO9DSvALE+8kTuKOhFj0wDoCbdMItk0nY9hTXJfSTJLDQ0NVPvY3DEyv3MTaiWcwLLuDjR6drBYrswMfI/RUNPscQoHNlJwznqzMK1jzx53IHiNTbV5chngiejy7hsdwW3UjjXu6GKa3c4alnoVTxzBz5nJiYmKi3X2KogwgxzNb5gV6L54mAo3AHcCbwMvAMOAgvVMh2/qmQj4MlNI7FfL7UsqvnAYzEGbLdG+op/2NCqzjk9CdZro/rKElJKlLtlEkq9DcadiNT2FnAya9hiAQxMqtuefzjr4bLdSKr/4cZDCeybnPcmlSFwY9QmXZZCb+ZTcRTWPH9Dl0W91sa3MyuqschBmj9QxkxIchcR/zL7iR7X8/REeTkWKblxRDDFKT7MuO4ZfNrVT3BMgydFKs1zC/pIDZs2er2S+KcgpTX7P3Fbw7W2l9bhfmfBfoAv/uNg4GIjQnWSmJHETrSiLG/CB2uQVda8OLwG1MZmXBpdR7V6GFNXoOrkQztbIs71UWJXbj8zmo/ngCc/66nsqsAgxj7bzszyG/bS+miB+DuRjNPJag9yMmLJmOvz6N6r2C0dYAOSYzUmhUpFj5TU8n+7r9ZBh7GCeqOWN0JnPnziUpKSlq/aUoysCg7i3zJQI1XbS9WI4x1U64w0+oycPOYISuOBNTgzUITwJ2y7045EZ0zUu3EGyKncg1OXMRnS+hh1x07b8SW9xWrsx9l7HxXbS2pCNeieeMHRsoL55GbUaE6hadIv82pDEFs30BMtyEIfYjik9bRvnHGjkGSWmcRBd2qmN07tcDfN7YTLrRy0LjQabmupg37zsD6uKzoigD1yld3ENuHy1P70RYdEIdfmRYslkKeow6MyPNaP4YHJa7iJFb0EQID4I/Zl7Moy4H1s4XkL5hdB68nKTk97glbx0JDg8H948l/4lGDMEmamaU8JFuJ6P2ICnCgG6bh2YcTtC7hvzJBbTtL6WtzMw8RxCzZqZRlzzlgr+1uEk0Bplr3M+EFBPz5y+loKBAfUGGoijH7ZQt7hFfiJY/70T6wkgp0WJMbPRF6PAFmR3Tg+Y3YbffSUx4O0JE6BQmrh11KxvYhrVrLeHOIjy1F5Cb9Ro/Hr4JTYO9G6cx/dlt1KcMp36kjaoOGBbcT8iSjcVcSiTchKa/RVbWdDr3ZDPZFiLWbKAjInjeBU+6u4jpkEw3HGB8rI95c89k/Pjx6i6NiqJ8badkcZcRSetfygk1eUCCMdPBpqCkrbqDuS4velDgsN9ObHgPAAdMiVxadActPa9hDlQRaJ2Jv3kBJbnP88O8rfgDdhr+Ucj097ZSN7qYj50W4lsaiNVsyNiF2LVCQt61uJIiBD1nk+kzkenQCUh4wyJ5yO9B65RMNNUxxtDEGTNOY+bMmZjN5ij3lKIog9UpWdw7/rEf/143AJbRCewxG6j/sIa58X70kIbN/jNiw1VI4MPYsVxTeBN62yMYwy14688i1DGFJXnPcu7wMtrdKegvOCk8UE3VlFFsDYRJ6GzA58gl1rAIIh5CvleJsY8hOTSO0bESDY3PRITfmgI0+0OMsbQzOrKfSUUjmTfvfFwuV3Q7SFGUQe+UK+49m5voXtv7e1X2aWk0JdvY/Uw5ZzqDGMNgtf8nrnANEeDR9GXcM+xc4pp/B5Eeeg5dRKSnkMtGPMXM3B3U144g808eAkJjQ3EOoe4uHJoVX8JsnJESQv4dGA3lJNuXM95uwa7rVIdCPGgPsd7vY5juZ6nYx5hUJ6Wll5CdnR3dzlEUZcg4pYq7f3877ld6h1piS3Pw5cax7t7NzIqNYJYhrI4biA81E0JwXcHNvO3Kx9n0a4iE8FavRHpzuGHU/1KUsZeDu4spevIgtcNGssMRwdbdid+eidU0n9iwjUDPamLNiYyNuYB0k4GeSJBHtQDPG3y4gNnGSkbb/Myfv4Di4mI1rq4oyjfqlCnuwSYPzU/sAAnOs0egjU7g3bs+Y6o1gl14sNuvwxnqwKOZOHfcb9llDONsuhspJYGDPyDiy+LmcX8kP+EA1etKKHyrmt2F+TSGurEGTbSljiXdP59wuImQ51XynUsocsSjIVgTCnKv0YdfSCZZmxklDzFt2kTmzJmD1WqNdtcoijIEnRLFPdjqpfGhLRCWxJ2Vh21KKm/dv5kxoRAugxu77UfEhrtpNsaxqORhWoP7iGv5M1IK5P4fEvKnc8P4x8mJqaf5rbGkb+1kfeEwZKCbsCWJrviJpHtHE/JvJ0bWMyltBfEGE/UhP3ebJZtEgBFWP8WhckZlJLN48RWkp6dHu1sURRnChnxxDzb20PSHrRCMYJ+ZTsyMDDa+VUFKbTdp5gZiLDdhjXiosGaxtOQhIp61ONpfRUQ0DFXX0R5M4ZriJ8m2NOP/SzaRTgtlmWEMAR/NyRkkRhaS4rET9rzHmNjRFDgmECbE85EAfzT4cegwW6+k0ORlwdJSNbVRUZR+MaSLu7+6k5Y/7UD6w5iHx+FckkdteQue9w8x0lJFnPlnGKWfT+OKuWjcb7F2voGlczWmsAlj5bU0hJO4auwzZOtuDE/Gc8CWQsjWgIaNA3lZjHQvhUgXMcGPmJoyF4fBzK6gl1+bIxyKhBlrbacoUsW0SeOZO3euure6oij9ZsgWd98+Ny3P7IQwaLEmEi4ehb/Tx97HN1FkLcdlupMIQV5LWcANBT8l1v0Upp6PiPXHYq2+lIpwCpeMeonsUCfm5+xscyVjDtfTE5NAR8JoRrknIwOVFJqDjHSVEsDH7yM+XjUGSTNHWBQuZ3SClWXLVqpbBiiK0u+GZHH37uq9EZgw6shwhITvjUKYNTb8ahXjLHtxme7Dj+TxrIv5Te6VxLU8gMm7iVRPCjH1S9kcymRx7ruM6O5AvGNnh8uEOdzKgcxYEiNzyOvIwh4sY7KrgDhjHLtDXdxm0miWYSZbmhjDIc6Yczqnn346BsOQ7GJFUQa4IVd5vDtbaP1LOXqchbDbR9yiHMxZMWz61W8p0twkmB6lS9P5Tc61PJl5HnFNv8Hk301uVw7xrdP5MJDPlNSNTG6twbPFSaetA6MMsq3QTnHrhViCJkZo+xiVOJEwPh7BywsGSarJz+JIOePS41m27D9ISUn56rCKoijfkiFV3D3bW2h7oRxjqo1Qiw9Tbhz2GWlU3H0J2f4EEkx/xm0wc2P+T3gn6Uycjb/EGKiksL2AtK7RrPaNJt9ZSWnDTpprEtC1OnwWG/uGxzK19kJssoeJNg+J5jEcCLfwE5OV+kiIEmMD4w2NLJw/l8mTJ6sLpoqiRN2QKe6ebc29t+7NjEEIQEDMkhSafjMFp3cCiaY/02x1sGLkr9geW0J8/c/RQ9UUtY8iz5fJG75xJFpbOa9hAzVtcdiDdRxKMhFwjWFa7emkai1McCSgC8lLdPCQbiLJ4GeJ3MOU/DTOOusanE5ntLtBURQFGCLF3VPWRNtLezDlxGLJd9H57kHMZ9rwPTEJc3ASLtOrHHLGsSz/XuotuaTX/TfBcA1FHYUUh5y86S9CQ3J+y3oa2w3Y/fVszRFkhUspbB7BGFMbubY0uiLN/NzsYFNAMNrUwlRjLUtKFzBx4kR1O15FUQaUQV/cezY34n5lL+bcOGIX5dD82DZID+JYt5BIZDh24/uUJ7tYmvcA3cY0Cmtuo5VaijpGMl2aeU8Op8mbyHm+dfhbOzGHfXxSaGRq+8WkBZ1MsvuIMSSzlVpuMcQiwn7mGiuYmetk+fKriY+Pj3YXKIqi/JtBXdw921t6C/twJ/EXj6Llie1ILUBK60qEdGDUy1mfFs8FOQ8TMiQw7eAdVOi1FHUUcKYOZYZ4ttUXMS28k8SGvfiFkc9HuZjT9B2yNQPjHTphqfG41c2zvhgytS5ON1axbMFspk6dqsbWFUUZsAZ1cTfnxGKfmoZzSS5d/6ohWNtNgvF3aMKPQfhZlZbM1bkPgx7Dkoo72GCpY2znCEpNPupiLby9bSG51DOh+mPcJhcVOWnMr59PkVky3GqhLVzHz2Li2O3RmWQ4xPwsjXPP/YH6/lJFUQa8QV3c9RgTrrNH4DnUQMc7lVi1z9CNGzBEwjyVnM6teQ9hEBYu2/Uz3ohtY1znCJYYu4hk9PDkpz/GJboorVpFgzkVd1oRpS1jmWSPkGC0sFvu5XpjGrrPT6mpggvmlDBr1ix0XY/2P1tRFOUrDeriDtBWs5eex9aikYY55veY/WEeTszkrvyHsGDgv3b8J390ehnTlcMyUysxeU388uOfISOCs6vfoNo6DN05nQXdaUx2gC4Er+mV3B9OJY0OlsQ3cckF56t7rSuKMqgM6uJ+cOtHWF95AiKXYU24gpgeL/clZPK7wj9gjwj+Z+f13OOEPE8a5xpbSchv5OFPr6YxkMDyhr9RY8nAaZvD6WEHxQ6N7lAn91i9rA0lMU6vY8W4OJYvu0rdlldRlEHnpIq7EOIm4AeABLYD3wfSgBeBBGATcImUMnCSOY+qqz1MRJyFNfkK4jvc3JeQwe9GPYZVwgM7ruGXTp0Uv5MLje0kDG/hr1vOYUvXaCa7N9JudJJpnM1c3UKBVafGX8fNVgstYQsLrVX8x1kzmDBhgpriqCjKoHTC0z2EEBnAj4BJUsoiQAe+C9wD3C+lHAG4gSu+iaBHYzaHkIk3kdrh5r6EdO4d9QQmqfH49uu5O07DEbKxwtxD0vAWPtl1Jn9rnkWmtwYTQQqMc1lusVJg1dni388lZjvdMshl6U38+prvUlJSogq7oiiD1snO5TMAViGEAbAB9cCZwKt9258Gzj7Jv+OYDpXfTZ67m/sTs3mg8GkMUuNPW27kntgQkYiJS8wB0rPbKKs4nZdq5mMJ+cnxNzJRn81ym4lUI7zpr+B6cwJJWic/n2rhlqtXqtkwiqIMeic8LCOlrBVC3AtUA17gXXqHYdqllKG+ZjVAxtH2F0JcBVwFnPAtcf+a+H1eisTyUfrPAclj62/h0ZQu2oWFyy2SYelutlTNYVXVFLqxM9m7lxn6DM60GTCJMPeFannTnEyRsYk7zythwvjiE8qhKIoy0JzMsIwLWA7kAumAHSg93v2llI9LKSdJKSed6JnyuLrtbE6+FZ+ucdf7t7EqsZ4Ko5ELzQby09x8vn8Bn1SMoJoMRniaWUIxC+xGNPzcEmlhldHJQlczT16/RBV2RVGGlJO5oDoP2C+lbAYQQrwOzACcQghD39l7JlB78jGPrjqYRYtZ5+bVv2Ff7h7W2Rws101MSHezsWoe2w+kUB4aThxeLo1kMcthoCfcww3CS7XBzNWFQW646CLMZvO3FVFRFCUqTqa4VwOnCSFs9A7LzAU2Ah8A59M7Y2Yl8NeTDXksDe0bueXZVzAW1fNanIOZmok56e1sqZrNtpoM6jri8dksXOu1MdtupDnUwQ/1CB4N7pmfzNlnnqYumiqKMiSd8LCMlHIDvRdON9M7DVIDHgd+AtwshKigdzrkn76BnEe1dEsrqTk9PJhiYYxm4Nz0drZVzWRjUz7WQx3st+dydtDMcpuJA8FWLjVAxODnue9P4Jy501RhVxRlyDqpee5SyjuAO45YXQVMOZk/97jl+LgzJ0CW0LksvZM9VdP4tHUcY3Z8xvOZFzEirHGjycKOQAvXm0xkGjp44cZFpCa6+iWeoihKtAzq2xp+pseRYTTwH2ldHKiYytr2yUzZ8gFrkhcghZFf6za2Blq51mSiyNzE6tvOV4VdUZRTwqC+/UD2pB5mJ3RSuWc6H3dPZPKmNexwTaLaksqPMFMbcPNjk4mZ5oM8efvV6Pqgfi9TFEU5boO6uIs9E9llKGYbCRRtX4vblsZncZMZi05KsIdbTEaW6GU8cPtP0VRhVxTlFDKoK54vSVIZiiWrfBMR3cKuhOWEBMwO+vm50cCKyBru/e8b0dRtehVFOcUM6uKe1TaZ2IN7MAXD6PEXU6ZLzghLHjboXB16jZt/dANmuz3aMRVFUfrdoC7uH9tqcHR3MsL1PV63CFIlrNEk1wdf5MLvXkF8proHu6Iop6ZBPeaeFT+DnMR81tl1mgigE+F7obeZPWMuOZNmRDueoihK1AzqM/cJvoPYbWZelgE0KZkd3MAZyRYmnHtZtKMpiqJE1aA+c++2BLiTHiRQ6NvHOVoZM659Uf3mqaIop7xBXdyf2R+mTgiSfY1cGv47k65/GIvDEe1YiqIoUTeoh2VKR5pw+Vs5V/uIkYuvI3VEQbQjKYqiDAiDurjPykvmfPERY9LzmbDorGjHURRFGTAGdXG3JWYwMiGdBdfcpMbZFUVRDjOox9xTcodz/q13RjuGoijKgDOoz9wVRVGUo1PFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQgSUspoZ0AI0QwcjHaO45AItEQ7xNekMvePwZZ5sOUFlflosqWUSUfbMCCK+2AhhNgopZwU7Rxfh8rcPwZb5sGWF1Tmr0sNyyiKogxBqrgriqIMQaq4fz2PRzvACVCZ+8dgyzzY8oLK/LWoMXdFUZQhSJ25K4qiDEGquCuKogxBqrgfQQiRJYT4QAixSwixUwhxw1HazBZCdAghyvoet0cj6xGZDgghtvfl2XiU7UII8aAQokIIsU0IURKNnIflGXlY/5UJITqFEDce0Sbq/SyEeFII0SSE2HHYunghxHtCiH19z65j7Luyr80+IcTKKOb9nRCivO/n/oYQwnmMfb/0GOrnzL8QQtQe9rNffIx9S4UQe/qO659GOfNLh+U9IIQoO8a+/dPPUkr1OOwBpAElfcsxwF5g9BFtZgN/i3bWIzIdABK/ZPti4O+AAE4DNkQ782HZdKCB3l/IGFD9DMwCSoAdh637LfDTvuWfAvccZb94oKrv2dW37IpS3gWAoW/5nqPlPZ5jqJ8z/wL4r+M4biqBPMAEbD3y/2p/Zj5i+/8Dbo9mP6sz9yNIKeullJv7lruA3UBGdFN9I5YDz8he6wGnECIt2qH6zAUqpZQD7reUpZRrgbYjVi8Hnu5bfho4+yi7LgTek1K2SSndwHtA6bcWtM/R8kop35VShvpergcyv+0cX8cx+vh4TAEqpJRVUsoA8CK9P5tv3ZdlFr1f6Pwd4IX+yHIsqrh/CSFEDjAB2HCUzdOEEFuFEH8XQozp12BHJ4F3hRCbhBBXHWV7BnDosNc1DJw3re9y7P8IA62fAVKklPV9yw1AylHaDNT+vpzeT3BH81XHUH+7rm8o6cljDH0N1D4+HWiUUu47xvZ+6WdV3I9BCOEAXgNulFJ2HrF5M71DCMXAQ8Cb/Z3vKGZKKUuARcC1QohZ0Q50PIQQJmAZ8MpRNg/Efv4/ZO/n7EExn1gIcSsQAp4/RpOBdAw9CgwHxgP19A5zDBYX8eVn7f3Sz6q4H4UQwkhvYX9eSvn6kdullJ1Syu6+5dWAUQiR2M8xj8xU2/fcBLxB70fWw9UCWYe9zuxbF22LgM1SysYjNwzEfu7T+MWQVt9z01HaDKj+FkJcBiwFLu57Q/o3x3EM9RspZaOUMiyljAD/e4wsA6qPAYQQBuBc4KVjtemvflbF/Qh942V/AnZLKe87RpvUvnYIIabQ24+t/Zfy3/LYhRAxXyzTewFtxxHNVgGX9s2aOQ3oOGxoIZqOeZYz0Pr5MKuAL2a/rAT+epQ27wALhBCuviGFBX3r+p0QohT4MbBMSuk5RpvjOYb6zRHXg845RpbPgXwhRG7fJ8Dv0vuziaZ5QLmUsuZoG/u1n/vjyvJgegAz6f2YvQ0o63ssBq4Gru5rcx2wk96r8+uB6VHOnNeXZWtfrlv71h+eWQCP0Du7YDswaQD0tZ3eYh132LoB1c/0vvHUA0F6x3SvABKANcA+4J9AfF/bScATh+17OVDR9/h+FPNW0Ds2/cXx/Fhf23Rg9ZcdQ1HM/GzfcbqN3oKddmTmvteL6Z3RVhntzH3r//zF8XtY26j0s7r9gKIoyhCkhmUURVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGoP8P/9diBtPqFfMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From 5795a9811b725e2d7209a6da3400a24e6aae9c4a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 305/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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Ir0LdyVOhTFlzKKgiJaeAW6ugoaHpFuFoilSojWaznQQpLCwMrVEiKJAgKgIqILEf9r30ujwn5yII7oFA4ClRrpxicPAfyOfvoeCu4BP734Iyt/DZPz2PC3uTcP/n4Pb3o2p5Ble9kP/T9mpG3TTdc5L3phI8OxGnXdORkw7+6BxlqVBSUZirMnO6QG66DAoG0iY7+mZpLt+BLAvq9Z1MnPLRwhlUcjPm9X+EZcRJCI1mwMenVitT9uZZ9KuYVoR4JENYRtGlhqmFwBAIo4YsjLOUHWLSzDKSrjLTZFA3dJI1jfZCjLZqnIiKEtd70FqiWNEkMREn7kWxpPWw4+EKDxcPS5kY6GfmKxSObpO351gsTdG0YoAeLn7Kz8fjNmISQnwOuAGYV0ptXp53PvApIAx4wBuVUntEo5/NjwPPA6rAa5VS+x5vJ4JGTIHAby/PKzEy8i9MTH4BSZjvnLqB/SOX8ufn9/H8gWbE+BD+8X34dUFN76JECzFPx/gNtp9USqGcErI8hyzNoapZZG0JZRdQ9SVUvYjy6o1OwR5rO0JHmmE8M0QtHKcUSZCNpxlPtzLUsRqvrZ8mWcJwjjId3oNlxthR3cgzKxeQcRIoAyKXtdHy3LPriuBXaqEqhLgcKANffEhwvwX4qFLqh0KI5wFvV0pdufz6zTSC+8XAx5VSj3tJCoJ7IPDbRynF3PjNTOz/H/R8Ejt/IbVsPwNYWA8LKz5oS0xEwoxEUyRTIda1JehoiqJFDbRQ44GksHRy81VO7ptn9EgOz5O09idZv7OdHjFI9abvUy+ugMQGhNGopy55S8zrRWyxRDY/SLE0j2mF6Vu7lbbMJhaLUUZzVfKVeYziOPHcGB35CTqLM0Td2pk9lELgmCE8y8IxTVzDxNU1PE3g6AZ2KEzVNKgjkUJDKYHluUQdh4hjE3FsEvUaMdsm7D3YKlUCuWic0WQ7+zvXcU//JmbaTELGUXq1Ya6trqatfy2v/cPXntU5+JVaqCqlfi6EGPjF2TQeG0BjvPAHarBupHERUMBuIURaCNGplJo5qz0PBAJPG0op/MU69liR6tAMldPjGMUMnfw5ADkkkZYImbUZzPK9aKe+BGqKT/dezedXvZKX9/Xw2u4WOkIPb7Tj+5LhfVkO/myCuZEiZlhn/c4O1q9OY45MU715P3mRAf3FyNACC5WTnAyXKBmL2AvjuLUqRiRKePMzqCRvZHBecvvYEB17f8x52dNszY0RW+7F0RMaC/EIY21R7FAT1WicUiJONZkglMogEdRtB8cK4VlhpKajSR9D+uhPsKsWzfeJl8skl5ZILy2RyS+xOT/BjtkhXrf/ZmqhEGNtXRztXM2BzlYWmOe1T+mZajjbOve3AD8WQvxfQAMeGCeqG3ho0ubk8rxfCu5CiNcDrwfo6zu7TnMCgcC5Jasu9dNL1Afz2Kfy+IXlDA+jip2e4nhvns9PtBLqSvL3L7+AVcU91L7/50TyQ/yo+TI+su7/csO6bezqbiFh6A/bdr3icvTOKQ7fPkVlySbdEuZZV/fQhsI5vohzKIvtu/jZcdzqrdyfkIzFfGJ+GXs6z1KklfLqa5mN9TE7scCmPUe4cO52nrU4QnQ5mFdbWin2dTMYDzPf2koh04wyfjnsCaAufWpmiFK6naoVAqET8zTCFaDuUNezREN1Vvlp+u0OdDRmrAVmEwW8jE5LooWmaJpEKEGIEFJKPM+j6nkslIo4Q6exTg+Rmp5mYG6K9RMjvEgI7t9y/jk5d2cb3P8CeKtS6ptCiJcBnwWe/WQ2oJT6NPBpaFTLnOV+BAKBp5ibrVI7skD9WA5nsgQKRFhH79dYWnkL+cjt2E0DfOCe6xmbTvLW69by+m1xSj/8SzjxbWYi3Xz4/A+zZfsL+XZX85lWow/IzVQ4dNskJ++ZwXMla1cmWbcmhT5ZQu2do65c3OkDeFP78K0T3Nm3lkI4hFsoMCG7yHY8k+GOLprnJrj8vr28YPZLdBbmAagkEsz0dzHX1k62rZV6JILwHIRjo5TCcHUifgfUMuC4uPZJjvUa7N1yCQtN7RieZMOUy6Zxh9RCgfHMQcabjrAxkuYVS1cyUOqiKmyOagtMVSzE4haSy7euElgAihGDZEuYTEeMlo4o6c4ozRfESf9RFE2AV8oycs/PGP3+j6gfGqS/N31OzuPZBvfXAP/P8uuvA/+x/HoK6H3Iej3L8wKBwNOUUgp3pkLtyAK1I4t481UAzJ44iav6MFeFmXQ/w8TUFzDNZu5dfA3/9v1eNnam+N6bttA69g2cf/17ol6df1n5Z4Se+Vb+qa+b6ENSGZVSTBzPcfCnE4wfzREzNS5ekaDV8VG5GhRqKHuc6t6bkAuHMQdc7lqzkRl3A8NeC8Opdcyl29hSGuHa43eydeIEiUoZKQQLLS0cPH8rlQ0bqVlRajMT+LUc2uwoSdmMbqxBN1djRROk22JEEw4Ti7u5JWFwYONzqYWjrNY0LnEMDuydZtg5zlzLPhI9p7gufwlvW3g1aTeO16qRvnwlXVvbWGvp+J7EqXnUKy7VgkN5yaayZFPO1Sks1JgdXuLUfQ92ZWBoDq3mMG36IO3mKXamj2FcXkReePk5Oa9nG9yngSuA24GrgFPL878H/KUQ4is0HqgWgvr2QODpySvYVPfPU9033wjoAkIrUsQvXkl4cwtGKsTCwm0cPvkubHsGLfZi3nPH5Ywv6fyvq1bxyvUO1W+/iJbsPu5Jb+PwFe/jNVsuIfmQO3XpS07vnWffLeMsTpbpS5pctyJBKF+H+Sp6u4ln30fpJ/+FEFVCa2zuXL2OXWoj4+EBZEuCtbVp/nTiDlaNDZMqFpFCkG/vZuriq3C27qRQ8lk4cT/Osf2Ai6ancJIdFHuTtHa0093aiRUCpz7D+Imj3OZ3cODSZ+IaJmuKi6wbGcSan2QhNkoqNU6PDHH50oVsrT4HlOBEaI75zBjlkIK9R9D2aWiahq4JdE0nbBpELZOoaRLxS4TtcToZYm3oGKH2Cq6XoioGyBvbWLBXcKS4loPVxoUv2WyyLdbN5nNwfp9ItsyXgSuBFmAOeA9wkkbKowHUaaRC7l1OhfxX4DoaqZB/opR63DSYIFsmEPj1kI5P7cgC1X3z2ENLoMDqTxLd3kZkUzN6vJGr7bpLDA7+v8zOfYdodA178q/jo3eEWdUa40MvWoc8+M9sPfRpKnqUWy54O1c+6w20hx/M83Ztn2N3TXPw1gnsfJ31zSH6TQ2t5qElLEIrNKp3f4Pyrd9DWBpijc33W3ewL3weKpagiwIbpodZOTxMx3JHXrWWXmoD57G4ZivDtk+9MIxeHkKrF1BCw0014aZakJHYw0Zi0pREVooc6+hnqmcVUcdhRWGBpsISyrcRwsWSGhqP3ZWvQjWaNwmFEAqW/1ZKQ6kn1g2wruuYpoVlWOhYKMdgzZo1XP/Sq57MaTzjV82WefmjLPqlXvKXs2Te9OR2LxAInGvuXIXy7hmq++ZRto/eFCZxVR+x7W0YzQ8fwWg++2NOnnw3rrtEqvUNvPf2HRyaqvHKS/q4oXec1m9cQ191gl1919P1gg/xhy09Z95bLTocvn2Sw3dMEqp5nNcapjljITxJqD9BaECj+L3PMP+lW1ARi/ELWvlJx6XUIxma9To7CvOsOLyHFeNjWPU6dizJ2KaLOLWih8W4Cb6Hld2FmZ/H9FwwLIqtMU73gwrXuWL1Gra1bMOxHYrFIiODJ5kuFDHjaTaWC2w80Wh242gaFdOhbi6AkKz0VrFWmRixBUqts+ixcSJ6Dl130XUPTVcoK4MtYpSlTrFepuaUqUiHmpIYCAwEITTiaMQwiUgTIS08N4RvxynV05Rrzbh2glpVgl/FwyU3GXT5GwgEngTlSWrHFinfM4MzUgBdED2vldhFHVgDScQvjDPqOIucHPx75udvJh7fwLT2j7zxaw5Ry+f9L11N2+EPcsmBbzIZ7WH/jf/DM7Zdf+a9S/NVDt46wfF7ZsgoxaUtYZKmQChF7JJOQit18l/+DAsf/Q6F5ib2XHs5U8kuIoYiKSUrp0bYPHyatpkpfE1jqqeb4ZUrybZ1EJIxDMJ01OaoTx/Hd2yaUykGtdPcfb5Bu9vBhbGLSdhJ5u+f52b/5jP7VbVilNNd2EaEaiSJY0iEfRDLOciAFuVF+iqSeg4ndi8IBQi6ZJpQ7jxE0aCer2Nn6zgFF08pokIjKgRthoEIWeihMCISRYvF0cJxzHAcS0XQnAha3cKyNcKe8Yj/KrDNIot6iTt1+5eWPRWCYfYCgd8xsupS3j1D+Z5pZMlFbwoTv7iD6AXtZ6pdHqrRH8wPODn4D3heiY7uN/Kx3Rdxy7FFdq5t4Zr+Ia7b827a7UUObflTNt3wXqxQoyfDudEi+28ZZ3j/PF2WxuamEOGahxY3iV/WRXh9lPwXPsvUt77FSE8PR9avR1kmSkGpbrB+dIhtp46Q8AS1TAf5FVvQ2jYQ0zIYepRwKoSq5nEKJUxh4YUMZkWBGb3InFagttz5lq40WlWSVpmkRSZpUjFSKvq4VS1PBSV98B2U76B8F19JbKkoayaLZoyCDiVLYidNZFsUkTyFH76FNusgbUaZk/4m3njN987qs4Nh9gKB3wNerk551xSV+2dRjiS0NkP8JV2E12QQmnjE99h2lpOD7yabvYVk4jxqsXfw2q8XWarmedWz27hw+CO84M6bmUmuovhH/8W2FZeglGLsyCL7bxljanCJvpjBczsimDUPI2YSv26AyPo4c//1Re762E8Y6u4me911REWIuN9MsmCxpViiXRroLesQ/a9BaDpxoPUh++Yrn9J8lgltgWyyxqxaoqo1ugPQhaAn3UUk0UXOTDIsDfSJU0ybOkO9fRhI+jCZPjFPInEvW9NHWRl2scISfIk5JjCHFdaUjoxozGRshuIwGlEsJoBQhpbiOlpLq8hUOxBATMuR0OeIaAVCWgFLLGGJJUStjqjUURUPWfQhX0MrVYlLSUxoFFIryDadx3z7Nliq0LzqJE19UcxYLzX7JM/q3XZOfg9BcA8Efss5U2VKP5+kdjgLQhDd2kri8h7MjkfvJ1wpxezcdxkcfC9S1hhY8Td85dhlfObOcfq7E/zB1hFes+dNtLhLTF/4Zrqe8y58YXJy9wz7fzLO4lSFFWmL63uiGGUXI2GRfNFqwhsyDH/tqxz8j3upt6wls/klbJNJMk6C5APhJgQqY+M4ZVw9QkToSAHGyhSRC9Psu/fH7D24ByeWwI8nUYBULrPxLJVwhTXbXs7p5AY+ky/jSMmFY8foGD/Nd7dfSSme4nJNY/y+cVrMz/H8zUdItPjggX5cEDoUJ7q0ivK6OQ5ePMfPzDgHfYknJC2VbtZkt7Nltpf1TNNqnCCu/QwzmUf5FepKUDbiVPUoVc3ARqJJn7BRJxqrEZF1dCSaLtFNH00HW4AyS+jpoyT6voKMNi6yjmdRmFiBV7qWjLYd1j/1v4ugWiYQ+C1ljxcp/XSc+sk8IqQTu7iD+GXdGKnQY77PcRY4fuKdLCzcSiq5jVj7e/jrby9xZLrIFTvj/MH0v3Dj3E9YyKwj9ZJ/RzVv4diuaQ7+dIJy3mZ1W4T1YQ296GC0Rkhc1YvWGmboprsojRTIaC3EafT94qBYcEvEc4NYc6dxawtMx1Yi+i6mv1VHWSWMLTr1Ppv77z/E5LRHXTYa9UQjHtHwGLXmUci4RCJd1AghUUQ0Qad00RYXmAynWIo1EUGnubhEi3eSWLKKMECUwJqNEs4PIBItFHtOclpWmKlIYk6Znpqis5ogLuPoQmHrGhUjStGIU9JjFI0YJSNGXbOoahGqWoi6FsLVLWzNxNFMXM1AIh7IpQHEmb+lEHBmmUJTEs1TCKnQNYUmJFfWxnjrK951Vr+BoFomEPgd8tCgrkUNktcNEL+kEy38+P87Z7O3cPzEO/H9MqtXv4N9C9fwzs8cR0tZvGLHMG898k9kvBJLl/0N0R1vYe/P5zhyx93YVY+1KxKsbw0j5qvo8RDhZ3ThVh3mv30c09FJk8DQLE4Im93SJT23l6tPfodEvooXDzNxQSveM3Wspp/ihb7OkK+Ry/YxP7SC/L5OFHGS4UUGUvvoTDOVXugAACAASURBVExg6jlcPYRPGL+mo5wJwoZORNfxqxXyUlGKx+jQ51lrD2HqdURSgQIlQUqNQjxJpbuZckucvB+nWL+avMiwGMuQTWdYsDLkzdRjHjOhJBYOBi7mmeI85LWLhqSRGqkaoV2BriQhX2K5PqYvEb4ApaEQ+LqGRMMXGvF8MMxeIPB77RGD+s4utJD+uO/1vBKDg+9lZvZbJOKbWLnmg3zopw5f3XuEteeF+F+5j/P8I7ez1LKZ6rP/jUMHoxx/9/14nmTDpibWhXTU0BIipKO3RfAW61R2TePiMaXlOKUX+Jkuqaemef7cLl6+ewIrq9D6PNRzHSI9S6y1FwnPaiyMt3LEu4gTcgUuJmkKPIP72MIJ2uq5RsuZucf9SmcsmClORfs5ERvgZGSA4Wg/k+FOpsKt2JoFMRoFiHkVmpwCGbtAa2GeAWecZq9AT9imbcUa5twYJycXmc9VMZVioNlhZeo4XeIUKfKE9BqaVOiORbjcQbTURKhuoTugPBtNVgmTJ0KOkMqfGUBboWOLdgpWG6OJFg40dzKqRYgsKiz/kZ+H/KqC4B4IPM25cxUKPxqlfjz3pIM6QC5/D8ePvZ26PcvAwJvwIq/l5Z8/zKBtc/32Cd5z6oM0e0Vmtr2XY/lnM/jP80CeDTvaWGcJ/KOLjRAlQNk+paUSp5hiKjRLvmkIP7NIf3KUvzo5T+ten7jhEbrQJRT30ERjFKL6uMUBNnO/toUFmUTHI6EvYcYVdLRxZ/g6/su7gaLW6InRFxo+GlLoKMBQPqZycYVBTQtT0SOUjBhlPYqtP1gNFfWrrK5OsKk8yNWLd9PhLNDhzNNbm2VldZIWv/joB2roEeYtPdaRnX/YX57QKVhxpkJNjIUGGDa3MRxq52RsNcfjG1EiTdRxiDl14naNVKlMu1NBU4kndB6frCC4BwJPU17BpviTMap75xCWTvI5/cQv7X7CQd336wwN/xMTE58jEhnggu1f5ZZTrbzrpnsJr7T4/+wv8qqjNzEauYpbY29j6EcOhpFl8+WdrNUE7oEsvly+84zrTIRzHKodRGs7RLJ5mrXhadqW6jSPeDTVbQxTwVqwvQjZxHYWUms4WO9g0I8iKhUMKZmPpjnWNcDpth5MTSet6eD6KFcSNhTJeol1mPSF43iezZTvMuzWmQyHWIhH8fTGdzd9l97KHOeVB+muZumsZknVfaQXYtpMMBUKM6ZnmJJtaO5mhBFGaw9jqDK6rKDpAk/Xkb6LJR0i1AnjYCoPIUBD0ag4kctVLgJbW66cEQYejVLRIhS1OCUtjisNLN/D8l3Crkuk4hIpuGx1bC707kOohw+npymIO7DaX3xKfzcPCB6oBgJPM7LqUrxjkvJd06AU8Z1dJJ7Vix574i0Zi8XDHD3211Srp+npfhUdvX/Fe743xLdGslyyaoYPDn0Ao9jEXvMtTM03Y0UMtu3soN/zsY8sglSgCfyVYfb7R5mVt9DaNky3MU37gk3TvE/KbuSYu1WN/EKa3ZnL+e7Ol3Bnuod4cYHzJ0/Tl5vD13Sq8U6SRif9bprWuk2iXsX3a5RFjaKoURF1siEYbEoxnkozk2pmKda4o9V9n9byEq2lRmkpL5Gulh6WwS4UiOX/oNFVgAKU+PXGNw2BpXRMBBY+Bh4WNhZldDOPEcljxJbQkyXsWI1pTSPkp/mLF953Vp8XPFANBH4LKE9Svnua4m0TqLpH9Pw2ktf0YzSFn/A2pHQZHfsUo6P/imW1cP7W/2TWPo8bP7mP8bTkb1q/zfX7TnFf/a/J1lcQSZpcfkUbbSUbd99cY+hmQ+BsiXA/3wH9TrrD42zN1mgZ9EjYLlIJ8osxZsfDTFS7+NQVL+eH1z4TpQl2Tkzy0sHbMJ0iJiYr/C7CdclSZYlqJMekcBhVHujgG4K5ZBPTyWYmmtcwl2oCIGLXWT01xnPnBtlRPsoOcYykWcXwXfyswM5qlPMGLjpENFREg4iGjAhUWKAMAaaGMkCZAqULpKE3/jYEUhNIXaCWp2gCqTUuZlIAGkgNlJAoTSI1idAlLP+t6T5C9xC6RBNyua8ZiaZJfqHRL0pCyYdFXyPrC2ZcjWlHY9YRFJaiAFwrC0/ND+gXBME9EPgNU0pRP55j6QfD+It1QmszpK4bwOqKP6ntVCrDHDv2NoqlQ3S038iaNe/mq/sK/P3te1i9Isdnj/6Y2dwz+aH3UlIZk2dvbyaZreIfnMcFEFDf5nEs/GVC2l1sLi7RMeHQVHLwERxzViMnNCKHlpiNtPKf1/8B9297JpfkfN55YIxa6RRlUSOsWZi6QdmpM6hPgw6G55FazNFNntOdbRzoHuBESw+OJtFliW7nZ6xdGKXdGyElFpAJhZeE+xDsRqCEgRAGAtBEY4SgRmaKj4aHTmOeTqO6Q0ehiwde85DlCk2AsbwNXTSKponGVCg0odB1GqmKy+toy5/rKKhLgavAVuBIgS2hIgVl36DsC8pSUPQFOV+Q8wQOD0Z8SylWOS5XOg5rHZe1VZfwQudT8Cv6ZUFwDwR+g9y5CkvfH8Y+tYTRFqHlTzcTXpt5UttQSjI5+SVOD30QTYuwefO/EElew1u+fpibC0u8STtKx8/bOOa/kraM5Lmr2whNFFHHFvENgUJR3jzOeMvXSbgH2TZTo2PWwZSSrGznZv1yqoNVNuw7QC0c5TtXvwJn3eW8uruDl88Osnf8EFnNpzH6psAmB8YwWmyWYqzGfBimzBg5dGy/jiaPABBdgOjydygulxMANLpI0JXCoBGgWd66BKR48LX6xVvl3zBdKdJS0uz7bHQ9ur1G6XU92n1JxtCpJiMsNkeYD6cZQsPSLuJcjMUUBPdA4DdAVl2Kt45T3j2NsAxSz19J/JJOhP7k+kKp16c5dvxvyefvprn5Sjasfz8nsxZ/8cldrJQV3jli43jnkYznuXx1Gn2yCqfzaOkQXt2l0Lufmf7v0lw5zbYTNm1LNr7SGRPP4EeZaygeG+Z5u25FCY37tl+HWnkeL9qSYP/J3fx8qkbd8CjGF3Dio5Sjc0zrPjnJQxr0QESZZOo2m3yPXrdKj1unzXVJSklSShJSkpSKqJSElMJEYSzfcT8eBfiAL8BD4J2ZikeYB/7y1BYCR4ArNBzAFQJHCFzRGGdVLW/7wYsISAQhpYgoSUQqImq5SEnGl0SVwtZMFs0Qi2aIKSvOdMTiPkvjZkth6w6WsrEEhIUg4nlEhEab+ZgpOWctCO6BwK+RkorKnlmKt4wiax6xizpIXtP/iB16PeZ2lGJ29jsMnvoHlPJZv+59dHa+jM/fOcr3bhvlZfk6hh2lM5TjvP4kWq4NJqpY/Umc2RzZxM0sbL2Z9vwUl+xXJOwKrkpyf+QlfKj/Bnr37+Hl3/k8EdvmVP/FaJkMyTbJUf8gu8cKzKVyLEbnWDLLZ/apw/fZXPdZX6+zynUYcF36XQ9LCerKRJUUqqTh1zR8R8O3TTwL7HYodUC+SeCkBTLRqPOWmsD3Ie8LclJQ8KDgaxQ9yPsaFb9Rp20qMJVqFCBKI9CaCiwa8y0FBjq6L1BYKBFCaBYCrVFto8BAEsInho8SjYexSojGvixfGGxNI6vrVDSDiqZT1jTKQmNB1yki8JTE9cDxTFzPxPMspG3iSxMpQyg/gvIjIMON1zLCzkyQ5x4I/FZzpsrkv3Mad6KEtSJF+vkrn3S9OoDj5Dhx8l1ksz8ilbqAjRs+TM1t528/vJuWqSrXOhorQnNsaougOysQJZ3IBS042SVmva+xeOH36ZqrcemeKmFZpq76+XbmDbx79TVsObafN378H2lfWiSb7mNsRQuH19Y4vWKacaNA3qwAEFKKrY7DRfkaW2yH9bZD1WrmaGwNp+J9nKwJqqcmkfuG0CoKhCDU4qPW2tS2aBSbBHa7QC5/feVDqSSYkBpjrsaUL5h1NXJ+o2Y9KgRpXZI2JClL0qQrVvoxUk6GlN1MS7WTdGUl6XI/ISdzpo5dnEWvkB6KopKNqiKlKKIooygtT8uwPF0uQlFBUVme7z5OrNZRhJBYeIRkHY36k97HJyII7oHAOSbrHsVbxijfM40WM2n6w3VEzm/9pf7Un4iFhZ9x/MQ7cN0iq1e9na6OP+HHN41w7Pa7GfCgLzTO1rSLxsZGT4tXd+N7VSbHvshS9630jYdZf28BixxlVvHP7X/Jh1ddxZqJEf7+Y+9j4+Qw85k0X3neCo6tLzNhHaYiFJqC8+sOryhXubBms9o3yQqTg62b+GTX89jVdBGJQokb77iV6+65A18rUmpXcImDta5GsUNnJmWhjEbIWapbTDoGp3M+Iy5Muxr4Jr0qQbsBK8IVLkiXaDMkrZpOstKLWexGz3diV1qp1pqRvokpFRUtzYRrcRyQSKSo4VgWKmrgGwLHV9RdRb3uYbsS25M4KGoPFAE2irpQ2IJGcH6wm5hfoikIKQgrQUhBSAliSqPpF+aFz0wfPs/4hY2OhB+jYdWvIAjugcA5opSidniBpZuGkWWH2MWdpK7tR4s++ZF3PK/MqVPvY3rma8Tj69m04fOM7o3x/Y/eDXVJd2yWK+KLwHkISyf+zF7M/ggjuz9F0dpLTznGhr05TDHHEmv4YNdf8ImVV9OeW+D/fOJj7Bzcx53np/n6jRlOJ0rYokxcSi6v1LiyWmN7zSDRcRFTrXUO1gp81Hoht3dejmtYbD9xmLff/Dk2+GXGY2Xsl0hau8rozRqzYR2IsGRbnKroHHV9Tts6nhtmjd1Dvwjz7EiZ1vQ0rfECuiginSjVQjtLs2soLjYxWUjh+j74HppfRVMjCH8EVJSqSlElT0VLUBERKppFWTMpuZJSTVF9pBt3o5EXHwZCQEhASAiimkDXBJquIXSBMjSUqaFMHQyBLiQmHkJ4ILRGSqUAKQSOplE1QkjdACGW8+6B5amQCkP66L5PplSgY3aa9pkp2rLTxIyNv8Kv7NEFwT0QOAe8xRr57w5hD+Yxu2K0vHojVu/ZNTPP5/dw7PjfUK9P09P9RqoTL+Zb75/ELs/ipKq8IDpKSG4GvYvEZT3ELuti/K7/YemOA3TMxlnjjmNpw+S0Pj7U/Xf854rnEK9WeOtn/53V83fxoyuifP5Gg4rWCOjPK1d5TrlKW72VBXkxqU1rGbHuZehEme96L2LXuoswPY8r9u9h69Ap8lYY57wKqvMgfc1VfENjRpoMVkMcyClO1DXydgq/OnCmSLuDLDp3A4bw0IWPrnws38XyHEK+TcivE/VdwiKCLsL4WoSaFqFomBRNjYKmGnnpD2ECIU1gGDphS8cKa3gRAzei40Y06lEdGdYROvieg1XIEV1aIFPKkSwtEa1VCNs1wnaNkFPHcmx030WX8pFOzYOUQpcKw5cYCgwJIV8RcX0irsRyXUzXJVmtEHG9M2+rhGC2bQ5461n9Nh5LENwDgaeQ8iWlOyYp/mwcoWukblhJfGcXQn/yVTC+bzM88hHGxz9LyOwn4XyBez4nqRRGWEgontU5xsraAMiNxM8Pkbj+ArInfs7Uf36bpmyULnGAsH6Iot7CBzrfyCdXv5SQ7fC6r36GuP1zfnqlzpfCGiFZ5VnVGtdWa/T7fRypb+YAa1ndUyE5fwt7brf5+pZXsPeSLURrVbbvP0RxHnJNNs2XHeOC9mGUCXUP9tdCHKxrnK6ZRKoryZRX0+OmWGlViMeKRFsW0LU56iWLSj5KtRBGlTTitoPhGSg9jqOFKVhRsuFmxuMJHP3BMBXxbNqrc6yr5Giv5uioLNJeXaTJLtJULxGWLkoTjQGyl8sD3QmgJEpKUIpGy3zFA+1XlVju01E0Gjk9MJWi0dhJLZ8+XSo0KdGlQpcSbTmgW66P/iit/V1NUDcN6qbBbCpONp1morWVwb5exrr72Bzxn/Rv44kIgnsg8BRxJkvkv3EKd7ZCZEsL6RtWoj9O3+qPplQ6ytFjb6NcGkIrvJWhvVsp5+rIZhOrv8KfFk1EbRVW+xSZVz6P0uwoI5/+HyLZFP3mLmLGbVSJ8a+tr+TDa1+NknDjLZ9AWffwg0sFrtDYbNf5s4Uyl2gxFptexg+zFveisSoyxsZjP2Dq4Ar+6bp3cmjnBiKVKk2Hp9HyRS7o2c2lG3+OGbbxpeJA1eDeJYPJSpQtpU1syXWy0y4Q7zhG55rvE045KAecQ1Hqe+PUpuOomkXeypANZxhKd3EgvY5s9MH8/ky9SG95nk2LJ2izsyT9LFE5R4gyvi7xNYmng59SLGUgt9xgSSgIOxoRWydq64RtjZCrLefKC0DH0yWeIfE1tVwkUlNITSFUYzuNbSm0RgNUNNXI4HF1gauDa4Crg2doOIZBJRKmGLEoR2IUI0mK0WbysXbmMt2UY+0oPQahGH4oSsiRdOR9uhYddoxMoeuzT8nv7xcFwT0Q+BUp16d46zilOyfRYhbNr9pAZFPLWW1LSo/x8U8zNPQvVKavIH/i7ZQXoanbpLZKcUOhTLyQxjCPkrlhA3X9IqY+93OMfJxmczeJ8DcBn281Xc//XvsGirrFZXs/RS22h7s2aCR8+INSmas9n3a9G7nzI3zzzhMURsoknUU27j5EJZTkQ8//K/Zv2IxZs0mfmmVb6CQvWf0DUtYQQoPxmuCORYuRcoT1S1tYt9DKtoUFOrsHyay7j6RmY53S8L4RQR+L4lcMhjIrONyyksMrVjGeaD/TAKnZrtLqL9Frj+OkshSacjgxk5xIUCTMoOgA+kFYICykbuLpBr4u8DQN5RsIqSN8A6SGEhpSa9SJ+7qOp2v4mo6ra3iafqY7sDODaCzftT/Yd8Cjv27cwS/P+//Ze+8oya7q3v9zQ+Vc1TnnMD0zPd0TpQka5YASEiAy2PhhMM9pOYf3e35eDi9hP/kZ7IeNwAJJoIQQQtJIQmFy7p7UMz0dpnOsrq4cbjq/P6olBIKRRuAA9Gets0716apbt2+d/ta5e++ztyKD/GOicYTApQlqE2k2LWSoiMcpiV3CGU9CPoFlzCPMBcCgpLzhXc2Vt+NtE4dJkvQAcDuwIIRY+6bxXwc+R3EPwXeFEL+/Mv5HwKdWxn9DCLHn7U5iNXHYKj+rFEYTLD85hBHN4d5UTvC2xnflMAXIZi9x9tzvMX1eIn7hI2RjfiLVXkpbvdgvzNKYt2OTLqE2X8Le9mHieyeQ0jZU1/MEpIdwWXGOuzfwO+2/yZAnTO/gPxJ3nCFhk2nVNO5Lp2mRQui5dtbe/BEeO5FgfHocuaCx+cgxNEXlC+/9KKc7u7AXCnQvDnCb+igNgVHsToOCCYeyKodSKhXxRprmm4lcmsMRWaSsfpnyTBrnkIQyqGBmHPSVtnKksovTZa3MO4vVlWxCUC5kSpAoEzLVmozflFGNdxO0WEQgMGUwZd5YgQtlJS+MIrDk13PECKyVcSF/P2+MkC0MGUxZYLxxDOsNo41sFdf8qmWiYqFaArsJHt3AbRjYDYGig5Q3MQsCUxPouollpRDmMsJaRljfj4iRZYVwaRlVHV3UrOumqn0NgbLyd/W3Xy5x2DsR911AGnjwdXGXJOla4E+A9wghCpIklQkhFiRJWgM8AmwBqoCXgDYhxGWNSqvivsrPGlbBIPHcGJnDsyghB6F7WnG2XlnagNcRwmJy6iH69z3N4pk7yC/XEKpws/naGiZPTtE2ryNLURyOp1G6PkT6ghMyEobvEAH5AUKFWWaUcv5ry6/zbKiBtZP/wII6gSnBrmyOO6w8itlIYm47a3tLeCrVQHrsHAqCzvPnCc0tcP99v8SJrm48epZPzjxOu7qXYGkCxWYxXZB4OW1jMeFh/fw6qsYsjPwCJZUparUYgQsm6rjCcOla9tdu4ERpIxMOH5YkYRdQY8jUGDJ1hkJQksg5FLIOiYLdwpTyyEYSVUuimBlUPYMpTExhoFg6iqnjsTKoBR3ZMIppc1dMLKrdgc3hxO5wYLfbi7Z0s2hXF5bAMgWGAboBhiUjhAKSjISMJBUzzggUVuJZVsJbXo+DtECYCMxiEP5KX/y5AKKAWGmIPAjtLZ+roqj4/AHCleWUNbcRbmjG7vJgahpL05PMXxphbuQiG256D9vuue9dzZ2fKCukEGKvJEkNPzT8WeC/CyEKK895PWv9XcA3VsYvSZI0TFHoD72rM19llf+A5AZjxJ8cxkwW8G6vwn9zA7L9neVY/2Hy+RmOvvp5hg+0kFv8LL6IjR0fa8Ibz1N4fpQWYeBVniIXqiWf/mXECcgGT+Oq/Co1y8Po2Pi7mk/xd5WbaJp/gLA2z7Jq8b5Mhl12wZS5nsFLN6GUpDlQ2s6hE+fwOs9RvrRI44VBHr7tbl7r3oaMxQdmn2Sd+hoNNVNIEpzNybwSs1O+VE7veAdiKorDsUC9OkvJtIY21MSZiuvZ29zOwLoA8ZVLEDEl2iUVt9cBXguHPoc9NU48uYCRWcaTyBA0NCRhFG3bV3C93rwU1fPFln3zE143ofyoRevlfvdjkKRiaKQiKyiKjKwo2B0OHC4XTk8Ql9eD0x/EEYjg8AWxOZ0oioplWeTTKVJLiySji4yc6ufY89/F1PU3ziVUWU3d2m5K6xuv4Aq8c96tzb0N2ClJ0l9SLIr1u0KIY0A1cPhNz5taGXsLkiR9Gvg0QF1d3bs8jVVW+bfDyhvEnxkle3wetcxF6We6cdT739WxhBAMnX2aI09fIjn5Hhwek133tdIYcTL39DAibeBWjqFI86SleyCqkC45jVH/CA1Tl/Dm07zm3cIfN9yEN/U0ruiLZDD5VDrDJrfKgLWFb52/jlmHndOOENfPnaE92I8qG7SdPc8rG7fyt3d+hKzNyYalPm6RnqKrfABDwIGMyomESs9CI7eOVJGMTVJhnqEkJ2Hk13Cq4g6Oratj0A4JpeiEDNpVKvwKERaJpMbxRucITUcJaolipMrr2FWWXXliDp0qt5ugJThSuoXBUCOuTIb6sUu0xoaRC4ViGjKvl85tO1m/cze+SCl6PoeWy6Hlsmi5LIVslkImTSYRZ274ItHJcfLpVPGtXC4U1YZlWWi5bDFS5jJIsozd7cHhdCKrCpKsoqgqkiwjywqSLGEZJqahkzd0MtE45twiWj6HUSj8yGM6PB78kVJ8JaXUd/dSUluPt6qWyUCE/pzBY8ksN5f4aX5Xs+jyvFtxV4EwsA3YDDwqSVLTlRxACPEl4EtQNMu8y/NYZZV/E/LDyyw/PoSZKODbXYP/hnok9d1ZiZcX53j5kWeYO1+PrHbQc0uIDT1NpPeME9+TAHURj3ycjLgGw9xCpuIc8eqvUz+xRMX4DHNyhM813sms1Ucy8yB2y+QzhQxdPgcDmR18eWAL56QqlmwO7pw/zMZQjnQ4QMXsNKPVNfz5L/8Wi64ANfFLfMp6jK2RIxQseDGpcnHZwbWza7lj2I2xPEG5NEaV3sWlyCa+U13CWbvJslIsBO1yyzQSpz05QtnUKP7s4hur8KzqIuozGK/RaWzdRKC2i+8k+5gWBWpc9fiMCN9xNeOJJ2gfOsM1h18gmF7GkiQWymqYrm3G6N5EqKqeS7LMi0jYYwU8ig2PzYHXFcYtSShDA2QHjpA8fQJhGATqGlh79wfo2rGbSDD4xi5gIQR6IU8hm0HLZslnMmjZDIU3WrbYZzJo+RyWaSIsC8s0sSxzxdRjIatFwVdUW7HZVGxOFy6vD6fXi9Prw+Hx4g2FUQJhFhQbw9k8g5k8z2eK/fB0FmOqeL/R6LKzM3TlKSjeCe+oEtOKWeaZN9ncnwf+hxDilZWfRygK/a8ACCH+emV8D/BnQojLmmVWbe6r/EfF0kwSz10ic2gWtcRF6ANtOOre3Wq9kNXZ/9ReBg/oCCHT0Jti1x03YRyeJ314loJk4JaOY5ntQAitcoaZun+kLL5A/fgiMjr3l2zlJfcS00qeKt3gXpGlKehiKLqRkxPrOGi1ksPOx+afpV1fYqi5A7uhYSoyL2y7mYvuAKHsIndpT3Bj4EVyJryatjGz5GLXVA/qiMBKxvDam0k7N9AfrOCs3WRCLXoVXQ6L1vwM6xf7CWYmkQBNsrHoKCPlrWWx0s/5difpQDWoZZjSW53LvlScNUP9dA32E0lEsSSJ2cp6RmvbmaptQgmH8PhCGELCEAJdCAwhKFgWWdNCzqRYd/443QPHCKQTZJ1uzrd0c7ajl4WS7+dGlwGvKuNVlGJTZXwrfXFMxqt+v/cpMi5FRpUkFElClXjjMaagoJnkNROtYKDpFlrBJFMwSOsmGc0kbRT7uGYQKxjkDfP7oZSWoERVqbCpVNps1DhsVDvsuGWJ8gY/1VeY5vl1/jUqMT0FXAu8IklSG8UEzFHgaeBhSZL+hqJDtRU4+i7fY5VV/l0pjCdZfnQQYyn/E9nWTd3i1MsjHHtuBCNvJ9w8wjXvv4ZgvIz4P53DTOskHZOECk4stiHKE0w3/A3CdpY1pwVhbZGn3TV8Jexg2DZNhWHwG0aWxoiTkYWr+c6pLl4zOzAtk88sP8668XFOtW/iYngNNlPn7NU38bIriGpqvCf5JB/0PULebvFM3EZq0cPuqaupm0qzbAaoFG1cqm6l32EwYDfQJR2bYtGVn6Jn4QghLYpAYtZRzsWSzUxXNTG2phUz4CjatIWBy4jRGwhi6tMMLR5GlVSqpFaUGZ2Oi6epmxlFAmLOShwdnSRk8As395amuOPGrQSDbw0jFUIwc/EC/Xue4eLhA1imQfma9dTuvhHv2l6ulWUypkXaMEmbFinDJGNapEyTtGGRMgy0tIGWzpPMGCQzOuQspLyJXRM4dYFTt3DoAqcmcOgChyFQTbCZAuUyFh37SrsSeY6uNICO60rftbhfjrcVd0mSHgF2AyWSJE0B/xV4AHhAkqSzgAZ8QhRvAc5JkvQoMAAYwOfeLlJmlVX+oyF0i8RL46T3TqEEHJT8p3U4m4NXfhwhGDm5yP7Hz5FZj2pSaQAAIABJREFUFngqhthyk0xXy0dJPD1BbHiQuEMjKCUJFGrRvFHiPY+y5PgujaNB6mZinLLb+c3KWvqdEhEjz6fNLO2lNsYXtvBifxf79VZUK8fvpR9izbmLjAbbeW3rDUhYxDs38XSolKxqY0PmGL/q/iIOT5o9CRvJ+SC3TF3LQEIwlavAq7Qx6XXwjL3AtK2AJAlqjUU2Lh6nOjeOJdkZdddzONzLpcY2svUhHE6F0mSOiuwBUvoZwnqG317/IWS7yf/t/0sWrEra8ldRNrpI2+hT2A2dhM3P6eAmNrRUEWSUaMZJJJLlPbfdTlPTZgDyRp6UliKlpYilowz0H+T8qUMsx+aRHDZKb2qhtLWVlNfFKfMMxmgfZFWktP0Hmjttx52xUZFTIa8iiR/julUtcFgIu4VlF+C1EA6BKOYJxlTAVEGxSSgqSDZQVFBlgd0m4bbbsKkKsiphSDoFUUBHo2DlKYgCeStHQk+Q0OMktDhxPU5cWyahJRAIPlL1Ua5n3U8yZX8kqwWyV1nlTWjTaWKPDmLMZ/FsqSDwnkZkx5Xf4M5fSrLvsUHmR1M4ApPUbNnLtt2/hnSyhNSrk5gSFEjgNvwIeZbstmGmPF/Bl3XRcipPTIrxv8MRDrjtBE2Lu6Q868oFc4trGJtYz4l8I6ZZ4CPZA6y9cJZc3MWh7duIB8LkSmr4XlM7Uy4flflJfs12P3VcYl9GZWHez62TN3M8FyKcCJGz1XHapnHGbpBTFFxSnp7ls3QlTuEQgll3E32eRkZrWjBrPLTo03RM+fHks4z4v85w+QgOy8XHWj7B5qYePn/yfgaTftrnGukYvEg4EcVQ7Qy7WzjvaeOaihI83gNMZFJo7iXCDT7UQBmLuUUWsgssZBfIGtkfeU3thotgrqzY8uUEc2WEcuX48yUo4gc/o7yaIe1YJmtPkLWnyKkpsrYUWVuSnK34OG9Loyk5hCRwGS7chhuXWezdhhu7acdurbSVx4pQ3ijC/eOwsNBlHV3W0WQNQzGKu2oVC0uWwFIQhg0578StBdi1YSvvv/f6K55j8BPGuf9bsCruq/x7I0xB6pUJki9PIntshN7Xiqs9fMXHScXyHH5qhItH51GdaUrWPkHX9kbqpM+Q/M4kxlKetN3Aq6nIxIjVnSS+9nsU9FkaL5TjXBrkH0IBnva5cQnBHWj0VpnElxoYn+jhYqaGmCazPTPJzTOvopzPcr6njXOt6ynY3Zxs66a/pBK3kebj0pe5WtrL0YzC5IKf2ybu4WymAk/az7wa4IQtx0WHjCRBjT7PxuhRavLT5Fz19Lvb6Qs1oNX4CZWmuOniS3Se72IhEuBk5be4WD6Aatm5o+QePnj1e/nC6X+hfyhLx4SD1ktDqKZBPhSmz1vBQIlOiX8OyTVFXEpiSd+3cdhkG2XuMkpdpZS5y/AaDrIj0+SGEni1CioD6wi4WjATTvKJ7xsBJFkiWOYiWO4mWO7GH3HiDTvxRZz4wk7szrd+IWuaxvz8PIuLiyxGF4lGo0SjUeLLcX5YBx1OBy63C4fTgcPlwOF0oKgqwpQwDBNTFxi6QCsY6AUDraCjawZWHoQBkrRSi0oyEZKJpWhYslY0wL8Jt9NL74ZN3HDL7iuea7Aq7qusclmMpRyxbw6iTaRwbSgldGfzFe8y1fIGfS9M0PfiOMIyCLU9T9WGM3Q1/TfYFyR3Oopll0EzUChguPcwv3OClDhJKF1Bef8MTwRMvub3YUgSNwqTXZV5CulyLo1vYjZRxXjOg1mw80cLD+E9FSVR4mP/zh2k7QFGKhp4pWENll3lFuM73Ks+ysWszvkFH7eO38doqg5b1s+QTaVPzTLtsGEXGmtT5+mOn8IrFBZ9Xez1tDAf9JJv8tPgneDakZeoP93BYqiRU5XPcr78JIpQ2e24ld+48dN8bei7nDxwkc6ROCXxKLoqMV8l6KtKsRSOvXF9vLoHv+6j2q2wa+2NrKvZTb2/nogzgp43OPXSCc7tPUMypiCrFUiSEyia8YPlbkpqfZTUeAlVuAlVePCVOFEuU5JQ0zTm5uaYmZlhdnaWmZkZotHoGyIuyzKRSISSkhJCgTAuuxeb5EI2HaDZKaRMMvEc2aUUmUSBbFpgmG99P1XWcduzeOw53M4CbpeOxwPuoANP2I+7NIynug5nZT0WgnQ6TTweJ5FIEIvFiEajtLa20t3dfUXz7XVWxX2VVX4EQgiyfQvEvz0CEoTubsG9oeyKjmFZgguHZjny7VGySY1Q4xnCa75OY+vtVEQ/TvrFWYRuYgoLWQg8yvOc7RzErBlCsiwa+v0cFJP8v1CAmKKwyZJ4T3kau+FjeGwzsaUaZtNu+qwq/tvCwzSfvoQQgpPXdTMS6mDZ7WNPcw/xSIQW4wK/ovwjaFPsX3Rz7aUPs7zcTi7n4pxDol/NEbfZ8Jspepf76EgPYlPrGQ2s5zV3OR4lSaKzhMrgDNunD1BxPMKyv4O+6pcZLu1HEjKbC9fxOzd8ju/OnuDsS3tpH57EbphEAxoX6pKMVWbRrAC2XANb3OV4UjlcqQDVkUV2X7uNNZ2fRM8LpofiTJxbZOz0DOllQTG2ReDymtR2VVLZFKKk1kek2ovN8fZO7Gw2y8TEBBMTE4yPjzM7O4u1EtfudrkJB0rxOSM48CFyTnJJmURSJ5HUKJjWSp1VsVJOD2RZwyYlUeUsqpRBkbMocg5F1pHtErINJMVEyBI6KroFmikwTIFumugmaKjoKBioaLID3eZHV73oqg+f5GGNZmN9HqS2ELd/fP2VT2BWxX2VVd6ClTNYfmqY3KlF7A1+wve1o4acV3SM2eE4e795kehkmkBVgmDnFwhXm7QF/wrze0706TSoEhgCp3yI5cgzTPWCJaapXKhnbGKYL4bdjNtsNFsqd5ZkqHHC+fGNxKdbyGRVjuWquT1xgJvPHkHJCKY3hzjSdA1Zxc2h6nbON7TgIsuHpQfpNl7htWUbXUP3oi5tZSlv56TD4pRNo6AoVGhzbIqdpCE3i+Rcy9nAOo46HaxfukC8qwyrzaA3eprIUYOMfQ3H6w8wFj6LatjYEL+GT2z5JV5aPEBy32vUzaSxJMFYZYaphgIptZPJZAPubCO/VFmCpPUTjxfw+RbZ2OulqfKzzI8IJs/HmBtNICxAGJjGDC5Plo6rO9h421W4/a53dO11Xef80Ch950cYGJthNp6lIFQ0bAjJg2Y5yRsqBVMh/3ox7JUSeOZPuWSpKkvYFBmbImFXZVRZxqaATTKxY6KaBSqMAi0atBsqzcJHmOLfuSwXyLTBtk/e8K7ee1XcV1nlTRTGEsS+MYiZLOC/vh7ftbVI8jv/j88kChx6coTBI3O4AxKl6x/HWfE8teW/TOnwvWQPL4JSjI0W0iRhxxfZ1wW2yBxuI4DUn+WLAYM+p5NKU+bakMJmf4KRxVYWR9eh5dzEohIZHX594Fu4YhrZFokDvTuJSRWMhcp5pa2HgsvFDusV3i8epC+ZIzJ8PWWztzKVd3DCYXDabmBK0Jwbo3f5JOWGhuTaSL+vjX67zs0j+1BqbIzsrqRLm0Tvi4HRyYn6w0wHhnDoDtbPX0dPyw4GFl4icmqYUEomZzcZqytQWx1iNHU7+5dLsSPx0VoHpbYBZmZiOO0ZGktd+LmJmQuCfKa47d7pzpGND2AURqnrKmfTHXdTt7b7LSUHhRDEMhoTsSwTsSzj0QwD41FG5uJEsxppU0bnR6/o7YBHUfDaFPxOlaDLRlDJEDBm8GVGceWmcZPHJRk4w1W4SupxldTjKGtCDVRjsymosoyqSNgVGVWR3xBwVZGwyTI2VVoRcekHzt3KGxjRHEY0hz6bQZtOo02nEbligQ7JpeKolnF6pnCYB1BnvoO07Vdh1+9e4Swusiruq6xC0Wma/N44qVcmUUJOwh9sv6INSaZpcfrlKY599xKmYVHXO4695n/g8VbSyl+hfw+sFRHTpTylyj8xVn2K2TY3qpWkYjjMQ2aUp70eAhZs9Ea4LTLFcjbMxeGtmIkSbEspjuh1/Nro49RPRNHLBQM7W7kgdZNxeXm5qZvp8koqxRS/xD+RSZ+nMNpD68THGSnYOWYvxqcLIWjPDrFp+SRBoSI7t3DK18wpe45bh15lrT7Gybs7KS81uXRuAiVbz+nqfmKeWbwFF+1z1yCXumG2j+ZRHaeuEPUL4nV57inbwROLO3khXcAGvL/KTXt4jNGLk7j0IGHJhytdj92S8DgUwiEZkVvATESxK3b8oTJ8gQiqZMPSTUzNxDAFhmlhWkXThmFZCIrx1BoCDTCxMLGwXs/WqKg4HHacbhsenx1f0Ikv6MDmVJGsHNLSWaTF00gL/UhGAkk2kSrbkarXI9VtQKpdj+TyINnk4pcxFJPXCEGxtocASyA0E6tgIgpv6rM6ZkrDTGpYKQ0zpWMs5bDS+vcnjCJhq/Bgr/Ziq/Zir/Fhq/T84ELCssDUwHZld42vsyruq/zC82anqbu3jOBdzVcU4jg5EGPfoxdZnstS2Q7BjvsRjnPU+T9LoO96tJFUMS+4EAjHi0QcX2HvmgocvhhVC372zy3zz0EvBUlikxTglsoYbknQN7YVbboOtWAyG7PTtXCe3YOnEQ7B3LVeDgV2k7c8nKtu5mRjO5YicRdPsK7wNEPjlWwY+S0G806O20wu2Iql6takz9OT6MePD8W5mTOeevpcWW4eepXbJo4wcHML1o46Dg2eRaTLGCw/R86epizpoyy1iaQvQdXEBK1TbmQLxqpsmFUxPhC+j4em2vleroAT+Fi1j153lNxwgRI9hB8nHlnG+SPugkxhIBygBjzkZZmkYRIrGMzndZKG+Ub2GRWwC4EdA5uk4ZRMXJLAqzjxOd14XW4cdhuSKO5HEIaF0M3iY90C899WzySniuK3ofjsKCEntlIXasSFutK/2xQV7/j9V8V9lV9UhBBkT644TWUIvbcVd3fpO359cinHwceHGelbxF9ip27rXgzPl/E4O2iM/Sn6QfONlV7aNUaT9decazCI1kqUxwxmJ03uDziZtNnYUJC5usJJmzfKwEInseE1CMONa26JaNLGR86/gD1vkLpK5njbVmbSdSSCQfa2rmPBX8Ja0c8HzH9iaC5Nx9nfYyxXxhHV5KLdwm5qrEudozt5Go8cxubYxoCnimPuLFdPH+ETfc8RWxtm+r4NPD13FiPrYyI8giWbtMxHQGkipUzQccmkfs6NkCUu1jnI185xp/MzPDdeyYyusxGV29xOKnQLp/594SoIgelVcVV6SGuzDA8eZWl5GisSxOjZxbi7imOTSYYW0m8kZSy326jQJMI5iyBZ/K4oimMBS9JRFRuNDU2s37CW1tZWnM4fs7LNLMHAU8U2th9hgQh3ItrvQjTdggi2IHTxli+BH2iGiTBEMWmktJL6d6WXZAnJriA7FKSVJtsVZI8NxWdDsr27bKA/LVbFfZVfSKycwfK3hsidjmJvXHGaBt/Z7a+pW/S9OM6J58YBaNuRhrI/R5CiUf19nAfWYMaLObx1l4ab/4PqO8zR9irKCsvYJ2T+zqly2OWiQTPY5o6wrXKWhUwJFwY3I6VLcKXTLC2q3Db8CuVLSQotFoM7GjiV3opps9PX0MbZ2ma8pPiI+AokDuM/9UniiY0cly3O2Qwclk5vvJ+u9FkcSgiHYwfjrir2ewrUFy7y+y99HcUnGPzIBr7kGMbSXSx55lBNG+0zpcSCIaT8OGtH3VQtudBsMgMNgkxFhjsLv04y6qdayKxDwb2yeSdjmSwbEgnTQJTGab52DaUtEU69+Ax9L+1hTASJ1vQy661nZCU23aXKNDsclGYEkbRFhSnh8WnIpcss69PktSw2m42Ojg7Wrl1LU1MTNtuPCUc1dRh6Efofgot7wNIh0gpdd0PXe6FszZuqJ/18syruq/zCURhNEHt0xWl6Yz2+a96503R6cJlXHx4kPp+lodtNuOsBssb3CDuuoWr4s+jn88Un2iDpeYEW/YscbS1H9WQoHzN4RNh41O/FbQlu1RxsqssgbBLHRq9CmqpESCru2SiVE5NsHB/GCAtmb/ZwVL2GeKGUWCTAofY1LDuCXCteZFv+IRJn1iEvfJQTluCU3UAWJr3Lp1ifPoVd8eK07yTqqmOvS0fxzfAnLzxARSzO8HV1fH7dEmmbhKbm8eQD1MWqmCrJEYkusG40QCRpp+CUGarXafau46r0jZRodiIrtZGSdokUOjNxi5gukXPEqN0wzTV3vIdCAvY+/W2+d3aaUVcdk94mckLBJku0+9zUaBKRRY1yQ8btsVHW4kbzzDMdG2E5HkOWZVpaWli3bh3t7e3Fohs/jrkz0P8wnH4UslHwlML6+6D7g1C+9hdG0N/Mqriv8guDMK1iPdNXJ1HCTiIf7MBe63tHr82lNQ4+PsyFw3P4Ig46rhsho/x3JOy05P4c6WCkaNcFUhVzVCf+mFhZlrEaD81TCfZnVb4QCpCSZe5MFegtdeEvy3B6fg2Zc80YahBPIo5rIsHuC0eQFIvE9RJ99RsZinajOnWOtnVysbSRKjHFfeb/IzsSQ730h5wt2DnhMLCw2LB8lu50P25JxeHYSdrRzD6XQapilt898TXaT82xUOvm/usMhiqL/9818Q5sVpCp4Ai1cxnWD4cIZhTw2qGmhg1iG81aDSoSKQQXFYG31M3MXJxkXEFIJiIwRsfVUa669gOMDUR5+Jn9HIo7GXfXYkkKQYdCt9dDVcykbNnEjkRZvY+aNSGkYJLR6UGGhi4ihKC2tpbu7m7WrFmD2+2+zIcSh9PfhL6vFcVdtkH7rbDhw9ByAyjvrqThzwur4r7KLwTGUo7YNwbRJlO4N5YTvLPpHTlNhShuRDr4xAhazqBjlw1H3f8ilz9LhfwhwifuwFwsmmCsEGS5n1pe5lhzOQ3xZWaWBP8zHGTEbmdrLs8dloNAS4ZZrYyhUxuQckUbf3Bijq1nj+PLFshuMhnaVs+x6C4U02Ciuo4jLZ3oso07xBNULj6LdebXGcrUcdxhUECwfvkC3ZmTBMw8qnMnpquLQw7BUs04vxp9hLXPzmMAj+yUeG6ThMP0UhvfTcplMO89RsukxrqRMKGcjbpIO+FQJw1GIyoyi1i8ik4qYKer3Mfi6ShmXsFQssil51l/TZ616z7EE88P8HT/DINyBbpsI6QKtgYD1EQtAnEDVZGpXROmuacUX7XM+YtnOHXqFOl0Go/Hw4YNG+jp6aGk5G0KiM/0wbEvw9knQM9CZTds+Cisex+4rzwtxM8rq+K+ys81QgiyJxaIPz0CskTonhbc69+Z0zQ2m+G1hweZGYpT3uSm7qoXSOn/gktupGH6T7DOrHw52CWWSw7SHvtrLtYHsEs6ylyB+/0BXvG4qdYNPpPIUVankAjaODG0GcdwmIw/hG9piY6zF2ianUKrs5i71c0R7VpiiQCK386+NeuZ8FXTLga4Ifslsqd7mI7ewnHVICtDe3KUjYljRPRlZFcPivMq+hwK4+UTfDDwDbqeXCQ8o3GsReKBm2VUmvGbtzEdHCfDPjomDNaNRGgQ1bSW9FJpb8WGjRgWL6FzRDZZ2xBmrZCZPb2MMGU0+zL2yjNsus6F7LqJB56/wGuLNrKKCxc6G70u2nMuQks6qqpQ1xWmubeM2jVBLk2McOzYMcbGxpAkiba2Nnp6emhtbUVRLuOA1LJw7smiqM+cBNUF698Pm34Zqnp+CjPl549VcV/l5xYrqxd3mp6OYm8MrDhNHW/7OkMzOf7cGH0vTGBzKHReG0X3/wWWmaGh8Ac4DrcjCkUTTKF2meDyH2B5FpmLeKicSfE1l5evBfyoQvDp5SSbXDaWW+B0dA3icAVJfyWyaVJ3cYyN5/oQHkH8dsGZsl7OT3XQoETZ27KJ47VrsKNxl/k17IOjzE7+Fv0CkrKgNjfH9uh+SvUFFHs9qvtmLtnd9AcX2Nj4bTr3DrPlSI5lH/zLDTaWym4g797NpdAxbNmX6BiT2DpWR6d9Lc3BHjyyH00IXpV0nkYn77XxkXXVeC7Fmb2QRWCRd83jrT/Lpmva6Btv5JvHZhk2/UjColPV2WwPE5k1UZCoag3ScVUFzT1lFIwcJ06c4MSJE6RSKQKBAJs2bWLDhg34fG9jFosOwfEHig7SfAJK2mHzp4r2dNeVp1r+RWJV3Ff5uaQwGif2zYuYKQ3/TfX4dtW8I6fp5PkYrz48SHIxR1OvA3/H35M3jlIi30LZqY9hzq5sRAlLZJX/S11hD8OVQWqjSV6WnNwfDrKkKNyZSvPxjEayXWFYrWDqSCdyJkLK76dsbpHNxw/jyWbIXGNxaWMdZ6Y20VQYo6+0l1fX9LDgKGWr2E/n3COMD36OwWyQmCIoMVJcN/M9ysxZbHhRvHcSd5Sy35vBUf8cNTPH+fCeLME0vNrj5tDGX+FSxVpmvXvxLT9D5yWZG6fW0+nupcbThizJXBQGj0o6+9HY2RTm/Z3VLBwYZmlMwpIM8p4pwq1DtG/cxreOKjw7rpOT7ATMLNucTtqSHpwF8Je66NhWQfvWCnwRJxMTExw7doyBgQEsy6K5uZktW7bQ2tqKLF8mxtvU4cJ34fiX4dLeoi29846iqNdv/4V0jr4bVsV9lZ8r3uw0VSMuwve1vyOnaSGrc+DxYc4fnCVQ6qBp12Fyyj/gUKponP8viD5ncYeiXSZefojWxb9iusJFMFdgIi/xVyVhztvtrMtp/EFsGU+ZjZFaL8eHNlF20sZ0XQOufIENp89SNzpMoc1i9lYX51NbqFmaIe6t4smOa+gv6SIiFrkh+89MDaxjItrLoizwC4Mbp/dQqU9gMyVs7uvJu7s45DSYrNmHS/ken3opxcYRwUBDGd+66XMca62nIPYRWHqKDZds3Dm3nQ5vLwF7KXlh8oxk8BgaObvG+zdXc0tZmFPPnicz78aSNPK+CarXz+Op3sbDh+IcSzgRQLtIs81WQsmSjKoqtGwsY83OKiqbAxiGwZkzZzhy5Ajz8/M4HA56enrYtGnT29vSE1Nw4l/g5IOQnoNALWz8JPR+HLxXlrRtlVVxX+XnCCOaY+kbF9Cn0rg3lRO8oxn5HWQNHO1f5LVHBsmlNFqvymGr+QsslqgzfxvXwW5EthiPnatZIpj4IyTPIqYsYyUMPh8JssfjIawLfm95iassk5EOF4fy63C/5CUWqiPnctE6ucC6o/uRfDrLd1sM+dYTmshRZU/wQNU9vNqykZTsZbf1LMbwKOPj72dBCDwCrl7aR2f8DEjgUZrQArdzxiFxPHIOvfwp7jgV5X0HBP1t63n8pg/R11SNPXeQyMJjbJrw8f7o9bR4urHJDiatPF+XBa+Qp7RkgV+9rpuurMbRZxbQ4gFMuYAeGKdlm2DOaOPhk1HGTS92S2OzYrEhH8KdK67S1+6spuPqClxeO5lMhuPHj3P06FEymQzl5eVs2bKFdevWXT6E0bJg9GU49gBcfK646av1Rtj0qWIv//tuBPpZZlXcV/mZp+g0nS86TRWZ0D2tuNe9zSoRyCY19n7jIiMnFwhVylRueQjL8TIh224qz/wnzImiCcYMmljy5ymz9pP0OPAv5/lKwM9XA34sIfGJeIpPJRMs1Dk5VlLD9NEufDMq07U1BNJ5eo8coGR5kcxNFiPrqvGMutgoLvCU92YeW3stFzxt1ItRmmaeYmTwbhZ0By4L1urD7Bzbg26X8RhOzOD7mHSF2edbIFb3KJ2xUT6218uJjt08vetmFkJB/MnDhKPfYMtUhHuXbqDR3QXAISvNg4rCmDpPae0wf7irG+9EjLMv+TFTZZhKHlEyQdf2co6MwePDOsuyh6CZYYfDTXPMhUNINKwvYe2uamo7w0iyRDQa5fDhw/T392MYBi0tLVx99dU0Nja+JeHXD178GPR9vWhPX74E7hLo/VhxpR5q+MknxSqr4r7KzzZWVmf5W8PkzkRxNAUI3deOGri801QIwcUjc+x7bAg9b1K3eQBHzf04HeU0Lv4p4rgbLMAmkQp+l5b0F1kKuYgk8uxxuvjbSJgFRWZX0uBPEvM43ArnmgO8Nr+LNS/luNDSiKmotA8OsvbMGQrdJjM3OZBny9maO8dFtZUvtNzLvqrNgMTG+HeYONdINF2JA2gTaa4f/TqmIrAbAodjJ7FgL/tcBS7VPEHQeYLbz7fTX3cDBzZsxlQU6ub68SYfZstsOe+NX0+1sxnN0nnRSvJ1m8Ji4BjNNcP85toq8oMFJo9ug0wlplzAXjNHW285z/QvsGfJTUbxUC1y7LYFqIzKOJwqa3ZUse7aGvwRF0IIxsfHOXToEIODgyiKQnd3N9u2baOs7DLmEyFg6lgx4uXct8AsQN3VRVt65x2gvr2ze5V3zqq4r/IzS34kzvKjg5gp/R07TVOxPK8+NMjEuSWCVVki3X+LIzBLvfLbOF9bi5Uupl9NRUaoz/wxmaCOP2MwJKn8ZVmYszY7dXmVP4tNs94wGGl28YJtI5V7SonaHSyWlRFeirHt8CGc7hTL95hkRQUbo2Ogqvyf0AfZ07mDaVs1bdl+cgNLRJc6UAR0WhK9S98glIxiSRIBs5JU6d0cdaoMlR7CVvEi6zO7OFa5m+nSCjzZPOtmRnDoT9C+GOae5E2U2CrJmFmeMZPsKcsx5/0u11SPc2uJj9hgCcvnb0fOVGLJGoGWHCXVJo/1zXDQrKagOGlRdLabfkrjAl/YSfd1tazZXoXdpWJZFgMDAxw8eJCZmRncbjebN29m8+bNeL3eH3/RC2k481jRQTp3Buw+6L6vaHopX/NTnBGrvJlVcV/lZw5hWCRfGif12lTRafrBduw1l3eaCktwbt80B58cwRIGFd3P4a1/iorg3ZT2fxB9OAeA5sngt/4M1XMRuwFpHT5fGuJZlxuPYeNzS3E+nF1ioczB4eoaRs7tpOHkLAPfw2TcAAAgAElEQVSd7ciWxYa+fhqmR0nfbpCo9rJuNkaZkuIx+Vq+3v0eTvh7CWmLBAZPszDTDkKiS1eo5xCdY0fI21R8BQUr8F7O+Cu44J3B1r4Ph3c3JwNr0FUbnZcu0TuzRN59kJKUwj3Jm4nYyonry7wgZenrjKG7H2B7IEOzojA3sonk8M3YsuUI2aC0zcTmmODRgRgnXB3osp31TolNaReRtKCs3seGG+to7ilFVmQMw+D06dPs37+fWCxGJBLhqquuoru7+8fneAGYHyiaXU5/EwrJYhqAzZ+Cde8HxzvbGbzKu2dV3Ff5mUJfzBL75iD6VBrP5goCdzQh2y/vdIvPZ3n5a+eZHU7gr56iZMPfEy4rp2H5j9D3FVPBWoqF7PgKIdu3MRUZR87gy+EAX/EF0ZG4JS7zp4lLSA6ZgWY/z+Ru5qrH5jnf3EAiGKR6coqNJ04gejJkt0HDjEattMxZvYHPt3+IfTXbyRpOai/1szhehmWqrNEV6u0LtI49ihBg1028Sg9jZTs56TIwN8yyUNbOlC2AJ5vhhmOH2DKdoq8hRamR4b2pmwirZSS0JQ4qWWLbz4H6JGudGmYuyMz4VWRHr8aeLUdSBCUNOfKZkzw7a9EX6EaTHWz02tkYUwlkBfVrI/TeXE9lSwBJktA0jZMnT3Lw4EGSySSVlZXs3LmTjo6OHx/KmE8WNxudfBCmT4BiLybs2vQpqN2yGsb4b8hPJO6SJD0A3A4sCCHW/tDvfgf430CpECIqFb0r9wO3AVngk0KIk293gqvivgqsOE2PF52mkq3oNHWtvbzT1DIt+l+a5Ogzo0iyRsn6hyhpPU+z9w9RXqjFjBcQCHTnYarUz5N3mgTSOs/63fxtqIR5GbrSbv5ieYxGM89EjYunAtuI7AkgZwyGW1tw5vJsOn6cEsc02i0FShMWjVacqO7lb0rv4dU1uxlVmiibGCUzImEaDlp0mfVC4M4+TGk0hilLhPMBlivv5YDHw3y3jfHaIJqk0DY+zl2vPc+GqWUe2tZAvZLivenrCaulJLQo5+VZjN17cTqO45FgcamBpfmryI03485WI0kyoYo40ZkXOWSV0h/sISc76PW56FmAsAbNPaVsvKWB0rriajqXy3H06FEOHz5MLpejvr6enTt30tzc/KOdpELA5BE4+bWisOtZKO0sOkjXfxA8kX+NKbHK23A5cX8n1Qq+Cvw98OAPHbQWuAmYeNPwrUDrStsK/MNKv8oql8XM6MSfHCJ3bglHc4DwB9pR3sZpGp1K8fKDAyxOZPDVnKa892Ga6u8leOJ3KQwkMCmgq/NUKH9CzreMI1VgxLDzudpqTqsKpQU3/zO2xK35CZZCNp6pbuXEyHY2PTnCwNoK8lVOWoaG6Rw/Dbdk8Ms6TctJsobK/cr17N+6mwPuq3HOJvANjZDUXNQYMtsLKnn3XhpGTqArMm4N8N3Ea1XtnOh0s9Dqw2kY7Dp+hg+88E1qo3Hu372NaH2E38xsJqKUkbAWGdBewLjxBUKOKOmch/GJ7WQXWyBWhjfTgMe04fIusjT3LAf1Gk6V3EJa2FjvddGzIChPSrRtKaf35nrClR4AUqkUhw8f5tixY2iaRltbGzt27KCuru5HX+T0Ipx6pJi4K3oR7N5ifpfeT0D1xtVV+n9g3lbchRB7JUlq+BG/+lvg94Fvv2nsLuBBUbwdOCxJUlCSpEohxOxP42RX+fkkP7RM7LGLWBmdwG2NeHdUX9ZpauoWx54d5eSecRR7hqqrvkZjd5i65JfJPpKkYCQwJY2w+jfogaPYsxrprMwfVpfxnM2Jw7LzyajgN1MX0B0yx9ojPGzcwfavDlNRq3Fy8yZCsRjbD+3Ds2meyNoc9VqGvKbwjWwPL22+hn2l15BacOHpn0TP2gkJD9dlVPDO4F98irJ5DckSlOkNnKq/iT2tfhY7/dSlk3z0xRO8f88/48mn+WrvZgo7mvnP6S1U5GtImktcLHwD87Y9SCoMzteQXroLc8mPQ4sQzLdh5WzI8gLZ5Iuclcs53HAvy4bCGpeTnkVBTVqi8+pqem+qw19SLMScSCTYv38/J0+exLIsurq62LFjBxUVFW+9wJYJw9+Dvgdh8DmwDKjdCnd9AdbcDY7LOFZX+Q/DO68z9iYkSboLmBZCnPqhW7hqYPJNP0+tjL1F3CVJ+jTwaeDHrxpW+blGGBaJPWOk902jlrko+UQX9urLC8fcaIIXv3KC5CL46w/RvHOAtvLfQf+2SjYaRyBwKs+jBh/AWcjjTAv+sTzIg64geQQ7kl7+PH6RkDAYr3PxiP9aXC852Bqb5VzPRlTToPf4carDFym5OU2tlsHIy7yYaOGJrl2catzO+HINrsOL2FM5HLKT2zIqZYpOXHqYmuF5DFmiLGNnoeouvthcx9RaH1uji3z4OxNcf+Cr+DKz7K9r4cyOrdyX7KE+30yWJKPxb6Ld9ALTqo9zo904453ImopPLSNQaKIQt2Nay+jZ10jWlvJa9b2MpgSNqoObE9CYUejaVc2GG+rwhop3PfF4/A1RB9iwYQPbt28nEvkRZpT5gaJj9PSjkJopxqVv+yz0fAxK23/aH/8q/8pcsbhLkuQG/piiSeZdI4T4EvAlKNrcf5JjrfKzhz6fIfaNQfTZDJ5tlQRua7ys01QvmOx7/Djn96VR3TGart9Dz9V3Y9v/PrLPLCKhI0vjuPx/hlPEcGZMno14uN9bxqxs0pT18v8tz7BRG2cxYuexyi4Oje3kusfPcHFNJxcqvNRfGqMz3kf5xih1ZBAFOByv4asN25m7qpdj2R6cJ2LY40soisK1BRtdBYlL/r14R0/ilMCtWdhcW3msdzsj7V6uWVziQ08nWDf4JBXzx5nzBXn2o7dwTbyDW7NrKEhZxha+TX7jfg77ypgbuYZIpgo3Eg2VTchTIVJzHvJWFss4iH9DJS8q93Dg/2fvvuPjuu47739umd4HAwx6LwRAgiQAEqRYVChSvVqWZNlxrBQ7iTdOnuzmlSfJs3GSzWad9SZOHttx3GRbtixb1VSvJEVSbGIDC4jeOzCYXu/ce/YPKLIdO5YTSZZszfsvcHhJXpzD1/d18DttKklRRuGmlExbQmHtzgo6r6nB4flhqB86dIgzZ84A0NnZyfbt2/F6/81BXLG51SWM5x6ChfMgq9CwC677O2i+FtSfsfO04D3t51ot83pZ5ikhxFpJktYBL7M6YQpQCcwCm4G/Ag4IIR58/c8NAFe8WVmmMKH6/iGEIHl0jsgzY8gWBd8dTdhaf/Zk3EjvMAce6CcTs+NvepWemysojl9D+PExZE0AGRyOz6Cae3GlNc67LHzWG+SMCr6cnU+upLgrPUnKqnC2pohv5z/Ald+/QLS0lJnKStzRKOsHT1G9ZpxaRwxZgrORMu4LbCDZ0cYB43KM4SxyKIeswCbdwraIxKxnBvfCD7Cmsqi6QVGunMNNN3KxpogdSwnKF2WqZl+hdvwZJGFw+LZN1Gc20KqsRxcaM6GDxIoH2VfhRoq6ceSdmKwm1lY1Eh+AyIIPALN5iLorqnnZqODh0/OYJYmetEJXTqVjewVd19a+MVIPh8McOnSIs2fPIknSG6Hu8Xh+2KDZOFx6cnWUPvoKIKCie/VGo/bbwPHmO38L3hve6oTqjxFCnAfe2KImSdI40P36apkngP8iSdL3WJ1IjRbq7QX/So/nCD8ySGYgjLXFh++OZhTXvz8yTMbCvPTtF5k+H8DsjLPprjHWtn6cuW+PEg2NIiOwmh5G9jyCL5FiXqh8pryUp8wWVEPlQ8sK/zU+gKRAf52Tb9uvJfhslquiQ/Rv2ADA2r5ztPnO0tgZRpUNLsSC3OfqILejhldMVxMbMaMsxlFkQZPVyvXzEmlLjmnzI1SOzKLJMsVJlamyG3ihppFtIYO1l/L4oiM0jjyMK7bEhR2VyJ4dXJfbhqTITIRfZcmY5mijA1u2GWdIxhN00+YpYfZMlKlRJ0hWnN4QXbc0cSBTxx8cGCGbm2O9prItY6LzsnK6r6vF5V+9E/bfhnpXV9ePh7quwch+OPc96H8G8mnw1cHlfwIdd0JRwzve/wW/WG8a7pIkPQhcAQQkSZoGPi2E+Pq/8/gzrC6DHGZ1ZH/v2/SeBb/k0v0rhB8exMjqeG9pwLGl7N89l8QwNE7t38vpp1TyGR9VXUPsvH0Pk89GCD0/gAUJk3wO2f85ihJLpDMSXw76+IbFS0rWuSzu4K/CIxQbWWbKrTxX1MHwxbVsOzbAYGsrF6tdlM9M02kcp71xBptZpy8W4Jv2taQ2l3HCcTVzo36U+TSKnKbUbeGWOQVHXjDiPUTzyAnMsow1J7DbNvLs2m10xs1cNwO2zAJrZh/BN9nPRJ2Vge3X0CPvwa66mEqcZyIxzMU6O4oowZrXqawLUJ1TGX1thkFRg6yU4ynW2PnhNZxMC377uX7m47M05xV2ZKxctnk11D3FqxOlKysrHDp0iN7eXiRJoru7m23btq2GuhAwfWp1hH7h0dV7R21+2Pjh1bPSKzcVVrv8CitsYip4Rxk5negzYySPzWEqc+C/uwVT0PFTnxVCMD3+Ige/P0hkvA17UYid99Qzu2yj8vk5TIYZmWVU32cJZC4iJHje7+QL1hKmTHnq0g7+MjRPpxZmyW/mtYpy9kZu4vqHTzJXXcN8eRmuaJSNkZP0BPtwWPMMxv183dpCYk0Jl7xXMThegzqbQkJg95m5MWSmOmow5Z6kbHYvei6Hqhv4tSBHam+kLufFjozJSNAeexjfhVOEHDL9PV30KDfit5SypE3TlzrHUIkZJIm0I0VbIIhjNspk3xwm205ktQKnT+bye9qZtUn8zVN99M3HKTNkLk+p7OoqZ9P1dXiDq/eNrqyscPDgQXp7e5Fl+Y1Qd7vdsDL2eh39+xAaBsWyeu/o+rtX6+mFOvqvjMIO1YJ3RW4mwcr3+8kvpnHurMCzpxZJ/em7HiORMxx97lHGj2xG5K20XiGTXFNN7eOX8Kb9QAaL46t4eQFVF/T6rHzBUsJxG3g1K/9PKMlt6RnidpX+OhcPipvpeGgWs2piuLkJNZ+nbf4CV/qP4XVmGUn4+KqthlRDMeO+yzkz2Y4ymwYEis/EDs3KpimdmCWNyDyKe2mOrEmhKGVirHQ3NqUOFxZkcrTzOP4zB8mkFE5d1sx6yw1U2puJG1FOG+cZcmTRZI2MJ8YGcwnpCyMkwhpWz1UI6rA5VXpuacDc5OJvn+ln/+ASbiGxI61y47oyem6sf2OdeigU4uDBg5w7dw5FUejq6loNdTW/urno3EOrm42QoHb76gi97Wawen5quxf8ciuEe8EvlNAF8YNTxF6aRHaY8H+wGWuT76c+m05PcuHsF7j4fAXJ+XX4KnJwTQ1lB4/StFwPgNX8JE7zt7DmNaZdKl+3FvGYY7WufndY4g/jI+RVhbE6G0/bekgeKWdj/wSX2tvIWK3UzY9xjfUVSn0RRhI+vmyrJNNQxLx3O8enNsJsFgmB8Ki0OexcPaij6gaLlgPUjZ0gajHhyBqkXZtIuzrxGC4kdGrsL1N5cS9iRuHUxmpqXFfT7OokT57XlD4GTRHC6gqyI057ykukbxjDMOGvvJF0qhpFldm4u5qqrUE+/8oI33ttCpOAnozKHWtK2XZzA0WvLw39t6He3d3Nts3duOYOrwb60AtgaKu7RtfftXq2i6fyF9bnBe+OQrgX/MJoy2nCDw2Qm4xj6wjgvaURxfGTB09pWpjRsS9y/sAkS+duRZJMqDu9OEIH2DHeBriwKMdx2L6APR8halN41O7iKw4vKdngiriFvwiP4REG01UWjhXVcGB6N7c/eoyB9lZWiorwR5bZkz/AmsAUQ0kfX7EESTcUsezbybHJLsRcDgmB4VYpKXdwfZ9BMKozbx+jYeJxwrJAEgKrVMVy8VX4jGJAUGQ/R/3SfVhOG5xtKcVXvJ129zZMsoVe0yB9zDFpGsdlylI9q5BeWsHq8lLadCsrcwG0rMGay8pYf20ND/bO8MX9w2TyBhtyCnfVB9l1S+MbxwT8aPlFURQ2dXdzWbUJ19Dj0PcEZKPgLF3dNbr+7tWDuwp19PeNQrgXvOOEIUgenyP6zBioMr5bG7Cv/8lzv3U9y/T0txg4/zDTx+4gvdyEXKOSK3qVOyb8CL0VVRrCafsCTmOUjFnmoN3K5xwlTJsNWtJm/sfyLM35NDNlVvorvOyN38ieb5wjXFrKeH091kyaHYmjbC3qpT/r5cuWEnK1PmKenRyZ3owxpyEh0L0mHHUudg3maZvOkzAlKV7+PunMMhmziktzEA3swS7XAWA1LdLI/fgOT3Oh1A3l3Wz0XonL5GdYmeRSdoh+9SJ+WcE9kcHI5ylrbqO85VomL1mIh7JUt/vZclsDB+cjfOapSyxlNBo1mbvKA9x8ewvBOjfww4nSs2fPro7U2+vZZhnE1f8wxKZXjwFovXl1pUvdzsJtRu9ThXAveEflo1nCjwySHYpgafbhv6MJxf3j58IIYbCw8CTDw59jtred5b5bEapCqvoS90SGEdrNKNIyLvPXcErHyKkyAzYTn7cXcdSuEtBM/FkoxNXpCNN+J1MNJl7MbCfwFASjafpb12AoMusjF7jGe4iLhoOvWAOolX4Sru0cmt6CPpdHkgWGzwxNbraNa2wZ1BDoWJMvYlk+zYrTiiWvkndtw2TtAgSypFHr3k/Fwf0MOFSiFWvo9O6ixFbNohxiMHqKs/JZvLoZUyiL2WajdcdVlLdcQd+rSRbGYhRVONn2gUamTDp/8fB5hmNpgnmJOwI+PnxHK+WNq5uLwuHwGyN1SZLorjCxPf0CrqVTICnQuGu1jt5yPZjt70JvF7yXFMK94B0hhCB1donI3mEwBJ4b6nFsLv2JJY7h8DGGhv8XS5NRFk59gvRKkGxgidvUB7FkPgaoONUHcarPIGTBnM3MtywOHnI7MRsyH4+k+FhskUWnm5kmmXNSAwPn13P5oX761q0l5XBQFxvnevsB+kzwdYePohI/UedlHJjahj6nI8kgAmayLR7Wz2W48oKOKyPIG72UjT3DtNeMhIRsXYdi34UsGQhUSn3D1J15grFcmPmyKjq8O6l1tpOU0oyuHOFM/iQWzYSU0ymuqWPDnhsobdrEqWdnGT27hMNjpueWBpRaO59+6DxH56O4DIkbXW5+945Wql/fwBWJRDh48ODqOnUEXc5Ftsf24iYO5Z2vbzC6HZzFv/iOLnjPKoR7wdtOT2pEHh8ifSGEucaN/85m1CLbjz2TTA4zPPx3LC4eJDxwN0sXd6KrGltc91MnNpMTbTiUJ3GZvo9MlojNztMmmS96/SRluDmu80fhOTTVyXSzxKTTx77Z3dzwnTMMtrexUlREILXMdep++hxJ7vd6qfUEmLdezqtTm9AXVkNdD1rQWnxURxJcfQ4qVnRyzFM//D2mXVkyZhOqUonivAGTopAXVtyuOI2TzzG3eI6pYIAWz1ZavJuQJJmxyDF6EyfQNQ1JUWjZsp0Ne27AW9bAyafHuXh4FtUs07mnhorNJXzmsQs8ObqEImCX1cEf3dZO0/oAkiStHhNw8CBnzp5BEgadXGSHOILbG1gdoXfcCYGmd6mXC97rCuFe8LZK94UIPzaEkc7j2VODc8ePX32XzS0zNvqPzM49RDrUxvhrvw1RG1WOI2y3TJIy7sSuHMSt3o8qhYlbXZySNf7eV8S4WWFDCv77yhxB3cpQg5mVEpmDoZ1sfHCOcFEZ09XV2HNJrhCHGPAv8JDHy3pnMYPqVbw2uRGxnAcF8uV28g1uAuk4O/pk1k7l0UlSN/oIIXWKkMuOIjlRnDdgNznICheqCWrTx4mPPsdEsYca5zraAjtwSW4mk72cCx8hqcWw+3xs2H09HbuuxWxzc/alSc68MImuGbTvKGftniq+9PwQ95+bIScEm01W/vjGVrp6VjdvRaNRDj33OKf7x0AYdHKBHZYBPOuuWT0fvXDpRcHPoRDuBW8LPakReWKEdO8SpjIHvjtbMJf9cEOSrqeYnPw6E5NfJZdRGL3wKfShGmxKiKtdD6MoH8YiRnCr38QsT5Ayu5iUBP/ktXHYbqM8B3+6ssTmtMK5Sjep2ix98RbkF914QzDc3IyMwcb8SYaLR3jR7WWzvZjT8m7OjrdBRAcTaNVO9BoXZekoa8YlegYNVD1PxfRLGKljTATcIKkoth14bGXkZA+abqNEv4QYe4Qpv41iWy1twSsolctYzE3Su/wKK9lZylvb6Lr2Zhq6tyBJMv1H5zn+5CipaI76jcVsvqmOx16b5p+PjREWBmtkE//t6mZ2XVGDJEvEpvo49NxjnJ7JIoCNUj87Gp14u+6Axt2FDUYF/yGFcC94y1Lnl4jsHcFI53FfWYXriqo3NiQJoTM39xijo58jk11gau4jxE9sQcmZ2GB/kgZPK2RMeNVvYlXOkVXtLMluvuPM8qDbic2A341E+GBUcLo4iNYSYUnzM9y7jtajy/S3tZO1WKjNX2AseJFeh5/NjiIO69dwcbwRKa4jWSBX50GU2miPxrBHdLZeknFlwL98Bt/8kwwFneRUUMzt+Jz1CHMxyawXhz6Beeoh5twSTnMRTeU7aJZbiRlhLiwfZCY3zNrLd7PxmhsIVNcihGDifIgjj48QnktSWu9m622NHB8J8fcHh5k28pSh8Afb6rjzhmbkbJTY6Uc5fPQ1TiUCCCQ2OEPs7OnE230H2Lxv0voFBT9dIdwL/tP0eI7I3mHSF0KYKpz4P9iMqfSHo/VQ6CDDw58hkRxgObOZmcO3YlkppkQdYmtwGim9Dp/yHezKK+QlKyG1mhdtc3zJ6yEhS9wRT/A7Kxoj9joia5dAhTNjnazbu8Rg8zpiHg8OMcxkoJclq5/17mJeyuxheKIaKaWDXUKr92B2mdm1nGDFSLBuyEFJDByJaSrHH2I0IBG3CCSlmIC7BbOzklCiHFN+Bsv8Yyzbc6iKlarKHrqUzeSFxqXwMWZMY2y68TbaL78Ki331e14Yj3H0sWFmBiN4SmxsuaWBiUiKv395iD4jhxuZj3dW8zs3N6JO7CN+8mEOD0c4KdowkNlQZmbnNbfiq1377zV5QcHPrRDuBf9hQgjSvUtEnhjByOq4d9fg2lGJpKzWgePxSwwPf4aV8GGSVDB4+gM4RlpQ0NkcOIJX6cKffxan8gOQDEKmNs6q03zBb2HUbKInleGPQ2l00Ur/uhU87ggD881U/CDLTFELC8ESssok0/5ezBY/1Z5Kno/vYnYigJQ1wKWQq3cTlGQ+NJfmnGOJqjE3lWEz5myYmrG9zDujLDl0kKwUOWrxljQwG2lGaHOYl58gZk6CLOOtWst29QpshpWRRC+h4DKbbr2d6nXr31j5szKb5PiTo4yeWcLmMtF9XS0JSfCPLw7ymp7FLEl8qK2MP96ex9H/MLFzz/BqpoFTrENHYX1zNTuvvQ2/3/9udmvBr5hCuBf8h+ixLOHHh8lcWsFc7cJ3RzOmktU11ZnMHKOjn2Nu/jFyOBkY34Pn7HpSuTLqnBdoKi6lKNaLR/0OihQhZGpnwEjz7UCcw3YbNTmNP1pJ0JTsYv+aBBWl4ywmAijPOUika5ioqWbJOsa0p59q1Y/DX8/zK1cRnrQjaQLhU8lXu1ifhk9OZ3kyOIl3zElV2IOiZ6mcepm4eY55exoDHa+tjNLKOibDm8hnZlGiz5FWYuRlA7mmmqvkPQSNYubSoySacnTeeTOekuAbbRFdSvPaU2MMnJjHZFFYf1UVikPhS/tGOJhPo0twU42L/6/hFMWD3yWyPMdhaQtnaEcg09Gxjh07L//pNx8VFLxFhXAv+LkIQ5B8bZ7os+OIvIHnmhqc21bvM83nk0xMfpmJya+jGzrDS5spOdPCfKQHhxqjqzFFcHERr/otzPI4cbmGi/kgrxQN8T2PE5sh+HgkwfUrPTxfrlHSfJG8rhI7WoIxXMpAfR0T7knmHWN0mAJkfGt4aXEHqWkFSReIYjPmUgvXRBQ+MZvjW6UXsEzYqYiVAyoliycwpEnmrRE0I4nD7KGiqo7p6A60zAxSfB85KUJO1YnUutgtX8carZGYFkLrUGi962rM1h8u5UxGspx8Zpy+w7NIikT79nLMThMPHBpjn5EmIcN2X56/8nyfhvmnCeHhsONGelMlIMls3LiR7du34/P99DN1CgreDoVwL3hT2nyS8OPD5CZiWOo9eG9vwhSwIYTB3PxjDI/8H7TcEhPxtZScrWBxYRdpw8PG+iUqE1GKxcPYlJPkCHAqt5Fh/xm+5rcQlmVuj6e4d7mLC2YXmc4zeC0xli6VYjrm50xdC4P+CSLmRXosAaZ9HRya3Yw2YyAJMEotVHpkPrJk4roVnW8Vv4IybqUk2ULe7MYdGcBqjLNsWSCVW8asWCgrr2IxvZt8egojeRCdGGlznsl6iaukq9me2UzWSCN1Oqj9wBZk0w+37qcTOU4/P8n5A9MIXdDcE8RkVXjmxAwvyRmWFUGbJcFfyl9gszjHkmc9h6y7Ob9ooCgKnZ2dPzxPvaDgHVYI94J/l9B0YvumiL8yjWxV8NxQj72z5PUNNicZGPwfJBIXWElXY79UQnZiO3NaOw1FCept85Rn9uFQnsfAyvn8TiadA3y3KMVFi4UNmSx/sNiClFzL+Z7jVHkmiC55MO/zcqSkjQvFk+hqis32Yi66uzgx2YGY11aXd5dZ2GI2+N15C7UZwXedj2MZU/DlOkk5yjFn5nHpU6TVUVZSc8gSBIqDxI1r0FKz6OlXESJJ3KbR35Bhh9jB9cmrkJFRN3gpu20tsvWHd9Vkkhq9L0/Ru28KLatT1xHAZFY4dG6eA6Yck6pBpRzlz5RvcJ31EotNH+Rgeg0Xx+YwmUx0d3dz2WWX4XK53r3OLHjfKYR7wU+VGQoTfkw0dbQAACAASURBVHwYfSWDvbMEzw31KA4T6fQMwyOfYXHxGbI5D9JIKY7RNVxIXke5VdAYnKIieQaP8igSGSb0nVy0ZDjkG+Jpl4OSfJ7fWy5jfehG9rcfobbyLLmsGeUVN6+orZwtn8eGxHp3kGPWTVycaEJezoEC5lIzd+pw76IVXc+y13Q//kEDG1sJ+1uR83Ec0gwW6QIzsSWESOPyeslJu9AzIfKZYyAyrLhynG+IsSnfxZ3xG3DILtQ1bgI3t6C+fjUdrI7Uz740xfn902hZnYpmL7Iqc3pgmcNWjWGTgY8En1If5cMNGZbrbufgrIn+wSHMZjObN29m69atOBw//QKSgoJ3UiHcC36MnsgReWqU9Nkl1IAN722NWBu8q3X1iX9hfPJriJwgP1VG5bCX12Ifwav4aC5ZpEg/R8B4BFVeIGJs4KipkTHnfr7ltaFJEveE7dw6fy8ny85iW3MEu5pGnHHwcryZ06URSgwHdb5yDqk9jI2XI4dzYIJAkYk/SpnZEVOYIcqx3FeouZjFsO5kvrQHQR6reYFS/QQj0QT5fAiT1YakXoaRT6NlX0MSGvO+DBfqonSkm/lw/Hb8phLUCge+mxux1LjfaIN0PMeZFyc5/8oM+ZxOsMaNltMZmY9z1JajTzVwSmk+4TjIxzaXsRC8nMO9w4yNjWG1WtmyZQs9PT3YbLaf0dIFBe+sQrgXAKuXaCSPzxF9YQKh6biuqMJ9RRWoMD//OIPDn8XILqLPlNE0qnF65deQWEuzN4nDcp5g9hmsyjlyRjWnLLuZVZ/hviKYNJnYmZD4zfl7icgrzG58mTL3AtqUhX2TVZwK5KnTivCWVrM/v4WlMTdyXEOyQKvLxKejNso0g1PSKAvR+2k5myLp2clk1dUYsgK2EC35gwxGDTLZKSTFhGrqIG8IdK0XWehMFafor4nTEi3nnsQdVFhrkT0WvNfXYesIvLGkMRXLceaFCS4cnCGvGfjLHaQiWRZTOU45EpxWFCySxr2lY/zmrg7mCPLqkaPMzc3hdDrZunUrXV1dWK3WN2ntgoJ3XiHcC8iMRIg8MUJ+IYWl0Yv35gZMJXai0dNcGvxrkrFzSEt+GoZzjIeuJZa/lka7wOS5SDB9FKf8HAZ2pk230i+d4HF/mFftNmpzBr87fxNlsWqOrv8BLeUjpGMK+0aKOW+z05QvQ5TXcSC9meSYipzKo9jgapOF/xYzo5PhlHEIY+kF1pxNE/FtYajhZoRiJ2ePsV7fz2BUIZUeQkigqg3kJROG1o8kDMbKUoxUJGheLOL29J00OJuQzQquq6pwbatAMq3uok1Gs5x5YZKLB2fI5w3cRVbiKxkSQtDnmOWIujqqv6chw8ev3870zAxHjx4lHA5TVFTEtm3b6OjoQFXf9E75goJfmEK4v4/lIxmiT4+RPr+M4rPgvaEea3sRmhZiaPh/Mz//KErUTumIRHphI/O5D1NvtqK6JvFqp/DKjyCTICpdzSUpxSHvRR70OLEagl8PrWfL3A0caPwuLQ0jZITGvlEvw6KYFqOGUGU9r0Y60Sd0pIyO1Q4f0+3clZWZkZaYyP8A++R5Gs7nCPs7uNB6NygeMvYMHWI/kzGVWOoihmGgKGXkFTPkJjAkwXBlgplAmpZZH7v1u2nzNqEYAsfmUty7a1Ccq2e0RJfSnH1xkktHZjF0gcWukknmySkZhuzj7FdKyWLmjlYbH9+9kZmh8xw/fpxUKkVlZSXbtm2jpaUFWf7pd78WFLybCuH+PiQ0nfjBGeIHpgBwXVGFa2cFQhHMzDzA8OjnUBNx7GMufNOljGU+SZXZg9kSxyT3UiwewiyPkTHamZCrOec4whf9TlYUmRuiAW6Z/S2Ouh+hun0c7AlenrYxkaqmRW5krKKJ04ttMJlF0gy8DviDrJ2t+TyD6hjZ1CP4+qepGBUs+Ro40/FRVClAxqLRoh5hKS6Ipc6T1zRkyYOmmlG0JTTFYKAqQcSZo3W6mC7lDjqLmzBldSzNPrw31GEKrk5sLk8nOP38BMMnFwCQZDB0UEzz9NnmeEFpICdUbl5Xwq/3VLIwfI5Tp06haRpNTU1s27aNmpqanzibvqDgveQthbskSfcBNwKLQoi1r3/2WeAmIAeMAPcKISKv/96fAr8J6MCnhBDPv9kLFsL97SOEIHMxROTpUfRwFtu6AJ7r61B9ViKRk1wa+DTZaB/OSRflY1ZG039IUK3EKuto1gFK9aewK4fIiwARupg0H+NzAStnrRbWphU+MvtR+vPHKW0ex1Qe4qUlK/Mr9dRaW7lU0sqluVqUqRSSLqiyy/xxykqxFGHY2odr5WlKzobxLcCir5pjXffizJegqQaV1lNkUmlWUhfR0mkkrGiqippPkDHpDFTHyZoM2qdrabDcSE95PdaEhhq0r/400uxDCMHccJRTz44z2beCJIEQIKETsL7GSZvGXjaioXDLhnJuabIyN3CWwcFBANatW8e2bdsIBoNv0soFBe8NbzXcdwIJ4P4fCfc9wD4hRF6SpL8DEEL8iSRJbcCDwGagHHgJaBZC6D/r3yiE+9tDW0wReWKE7HBkNfRubsDa4CWbXWR4+O+Yn38cz4KZ4KCdufjH8Spt2GWJpHmOoL4Pt/ooAGmjm6g6yLd8Ot9zO/Ho8JHFy4ksxwmWD2FqXeblqIXQUhPFzo2cL2pjejqAOpMEQ7DWqvKppERWnWfOfpLqmaMUn0xgT0hMF9dwePO9+NPFSAj89j7Qwqyk+tFiMUAhryioeo6kNc9AVRxVl2idW0/QdhVb66pxrmSQnSbcu2twdJeCBOMXQpx4cpTlqcQb7eFSFimzHeCAu4KHMxvRhcQtG8q5ojjD1KXTLC0tYbfb6erqoru7u7DxqOCXzs8K9zedHRJCHJQkqfbffPbCj/zyGHDH61/fAnxPCJEFxiRJGmY16I/+J9674OdkZPLEXpokcWQWyazgvbkBR08ZQsozOXkfI2P/iC2SoOJSEYmlD5OUN1JukkmQxqSeoZ77MJnmyRqt5InwsqeXv/d5iSgy10cqcMwGkcxnKN0e4uWMhcRQJ3bfJkaq2zg1aUUZSmMiwXZV5SOpCDMsMek+RuPIBWpOZTFpEsOVa9h/za9RHvdTnDSwOcaxiWmW48NokQhgoMsKiqGTtGYYrkjgypjpnLkSl20L29aX41lOQSyH64pKXFdUIUwyl47NceLJMZKRLAASBrXmE5Q4j7O36Gr+JnQTRkbihvYAmxwrTPc/z+lLGcrKyrj11ltpb2/HZDK9ux1YUPAOeDum/n8D+P7rX1ewGvb/avr1zwreAcIQpE4tEH1+HCOp4dhUinvP6mRiNHqW/v4/JxvpwzMUgPF7MKStlJpkErqBJk9QrTyAXTlCXhSRMyoYtwzzP4v8nLEV0Zo2sW28ibLIPEVbjvO8bCY+1YVadBnnqlrJjIPSl8EsJ7lWkrhxeYzeQJSQ/zhrLoziOW9gSBL9tZ28uO0eqlecNIQMZNsCLscQ4eQosXAYgzyGBLKAsDPFZDBFMOFh8/QtWO3ruawrSNFyCrGYxL6xBPeeWnSzzKt7R7l0ZA4tu/pDoU2Js9b6BPbAPN9y3cMPZjcgQnBNs5tWplgeOsGoLNPW1kZPTw+VlZWFenrBr7S3FO6SJP05kAce+E/82Y8DHweorq5+K6/xvpSdjBF5YgRtOoG5xo333rWYK5zk83EGBv6W6elv45uyo/Tdi8XYgUdVSekGMZGg3PwcbvVBkHTyRjFpZZnP+/w85C7FpcPls5XUjGao7+zl2XaFxeVuJP/lnC9rRhvPo4QyWBWd23Nprpo5zaFGQSZ4kl2nZ3GOSGRMCuead/DM9g9QGbayfkpHWKI4nX0kIgOsxOJo0upNRBKw4Muw6MtQEy9ly+xNqLY2enpKKI1kMKbjmJu8eK6rI5zTeeJrF5gbiYIAEFSYL9Bt/z4rtS18SXyA5yYlzEmZXbVmqlJDaOPzpB0Odu7cSXd3N263+2c3bEHBr4j/dLhLkvQxVidad4kfFu5ngKofeazy9c9+ghDiK8BXYLXm/p99j/cbPZ4j+tw4qVMLyC4zvjubsW8sAWBx8XkGBv8K08ICjtMfRMnupkQ1k8JgSdMpt1zAZ/pnzNIMunAjE+MpV55/9JezosisjXhY029hY804z1wpcyCyCZ1d9BU3wFgGOZLEqea5OzLHFYMvcXBrAFHTywdeDWOdl4jaLby2dhdPb7+J4rjKlhEN3ZTC5riIvnyOeDJNVkkjpNVQnw6kiDk1mtON1CxuRbE00L21hKqUhj4eRSm14/31Ni5NxDn3T2dJRXMAWJUk7dan2eh9kVP19/L/Rv+Gw+NpnBaFa6sMisPnkWdTFJeX03PlbbS3txfWpxe87/xcSyFfr7k/9SMTqtcC/wBcLoRY+pHn2oHv8sMJ1ZeBpsKE6lsndIPEkTliL00g8gbO7RW4r6pCtqhkMrMMDP4lK7P7UE9ej3XlRsrMNnLCIKQJSswRAqb7cCivYGBGJkef6uFvA056bQrlGYW1g272mJd5oc3gYmoLGe81DOerkMeSyIk8LlOWj0xc4spzezm9u5qW3Cj+oynMMYkFn5Oxmut5duvVODSJjSM5JEnHZu3HPPMaiXyOlJoABAKYCqbImA3WaR1IqW5kUxUbuoPUGwb54Qiy24zUFeTUQJjJ/gjCEIAgaBllq/0+ysryvFD1+3xpupbemTg+m8JmT4KicB9WBdrb29m8eXOh9FLwK++trpZ5ELgCCAALwKeBPwUsQOj1x44JIX7n9ef/nNU6fB74QyHEs2/2goVw/9kyQ2EiT46QX0yvrue+qR5TsR0hdKam72dk5HOI3g1YJ+6iwrR6KuGilsdtkihRn8Vt+gYyGghBUrLyD94qHvNmsBjQOuXioysxjnVkeJkdpNw3MpkuRR1PIKV0/OYkdw+d4NoLzzC0q4SSXAjvEQ01IzFa5mem4nZe3LwFIUtsHchg0QQmyyjeiUOsqDpJJcq/hvpEaRIhSaxXetASG5DVIGs3BWmxyuTOLSGpCvEKJyfGosQjGgCqrNFieZktrgdQ1lzBXt/H+JdLZoYXEwQdMutMC5SmJ/A47XR3d9Pd3V04mbHgfaOwiemXVH4lQ+TpUTIXQyh+K94b67G2+pEkiVRqjIt9f8Jybx5b30epVvyYJAjlUyiqnRJ5HLfl/2BjAoEEQuYpaxf/VDzHgkmiNmThkxMZFptjfNOxjajzg8wnSjCNxSArKLWucNPIMW66sI/lnRZs6RyuYwZSTmKwupS5yrs5uH4tUYfM9r4s3pSBbJqhdGwfs7Y8aTmMBBiSYLIkhdlQWe/cSSK6Fkny0rqllHa/mdyJeQzNYNlu4uRcipyx+r27zStssn6bZl8vqQ0f43vKTXzjdJSZSJoKB7QYE1Qai1RVVtDT00NbW1uh9FLwvlMI918yIm8QPzRD7OVJJInVc1K2VyKZ5NXR+tQ3OXfkB6in76FOVOBQJOJ6hKThotSUx2b6Jn7lydf/MolRaTufDYR51RXDk5H5rXGdCv8Knw1exqLtbpYSQUxjccgJqm1z7Jk4xp6hE+Q3aahRgeMYCEPiUm0li6Uf4mjHGqYCsL1PozysI8khKsf2MeZNkxPLyGI11Gf9aZzYaQ/uIrLUjKHbaN4cZH2Ni9yrs4ikxrwhuJDIkzQABFW2PjbbvkVpucRcxyf5ZrST756aI57JU2PXaNTGqFbjrFu39o3SS0HB+1Uh3H+JZMejhB8fJr+QwtZehOemBlSvBYBkcpSTh/+O0KGN1KVbKTbJZEWUkKYQNDuxyKfxWz6DWaQAiOudfMNdw3cCJ8khce2c4B59iU/XbGbI/lHCsVJM46uhXmef5JqpY3QvD2JfE8I8K2F7TcJA4mJ9LaGSD3OyrY5LVRKbB3RaZjWQklRMHGAosIKhLaEaq6G+5M7iUz201OwhNFdHLqPSsDFAR4sP7fAMSkJjJW9wMW2wogtUOU+77Xk67Htxt6ynr+kTfG28mCd659ANQZM1QZMxSZ1boru7m66urkLppaCAQrj/UjBSGtFnx0m+No/iteC9pQFb6+qlykLoXOq9n96nVqgMdVFrlhHkiOrL2NQK7EQxWf+GYi4BkDWqOaZ8kH8qfYYha5LmuOAvVpb5auU6XvL8BvFI5Wqoa4Im1yjXTRymIbeIv34W65jAflwmL0tcrG8iEriH0y2VnGmS6BiBztEMkshTOnuMgcAUSmYekw4GgrAzR5GliObm61icqiKThOp2Pw1VLpTeRRzpPAld0J8zmMkaOM1xNlgeotV1BNPGW3kl+Gt87ZzG4eFlLDI0qUu0MEtrdQk9PT20trYWSi8FBT+iEO7vYUIIUmeXiD41ipHWVlfBXF2DbF691zM0P8q+B5/GMd7GGosJkyTQpX6SehMeRSVveogq5QEUDPJYmcn/Hl8uGucp30kcuuBPlqJMusr5QtknSa7UYhpPQF7Q6h3kxtlXCGoJfA0zOAd0HEcUDOB8QyPRwEc53xDkeJtE3azKjr4Uqi7wL59j2DeIKTWFOS8hEMRteUocQerXXsfCRDnJiE5JnYsSrxXnWIQgkBWCwZzBWNqgxDbOBstD1Aem0Lp/iyfM1/HV4wsMLiRwqQbNzNBqXqFz7Rp6enqoqCjsgyso+GkK4f4epceyhB8bJtO/grnKhfe2RszlTgByaY2Dj73A8nETHRYLbkVGYoiY7sapBBGmQbzKX+MmggGEjRt40dLNl0q/y4qS4QPRDF15M39W8/uEV9pQJpNIeUF70SWujxyiLBnF3rSM50IO1yEFDDjfUEu4+Nfpry3nyDoDT9TGNWfjODIy9tgoE+5ezPERLHkZgSBlyVPqqaR+4/XMjgaJL+dwFVmxmyRKozmqzRKGJDGc0RnJ6NTYX2OD9TFK65xENv4eD8TW8c0jkywlcgTULGukGda5s2zZvImuri6cTue720EFBe9xhXB/jxFCkDq9SOTJUdAN3NfU4rysHEmWEIbg4qsjnHx8kCbhoMYiA8sYzGCIdahyDt36WarFcQDipnLGU/+dfy7dy6vuPpqyeT4Ry/A3Vf+FsWgXymQKKS9YV3KRa/TD1C0sILWlcJ/N4t6voOThYl0VoeBHGaqu5kiHQU44ufFUhGBYRsmGmLGfwBK9iE1bDfWMWac0UE3zppuZGvQTmc9gtqlIOZ0Gk0S9VUECxrM6o1qWRstzdNifxd2xlcnWT3DfqIvvnZgkkzeoUGK0yXP01HjYsmW19KIoyrvaPwUFvyze0sFhBW+vHxut17rx3dGMKbB6D+fsUIQDD57BtQw7bTbMksAsHyWpr8ckrcfw7qM4+3ksQiOnKszmP8V+2c6XG/6enJTldyIpjnh/nd/07US5kEbVkrSX9HOb+1nK+8KIzVls0RzeLyjYUgp9NWUsl36U0ap6jnTkmXc4uf5MmObpGELPsWB9DVPqGN5lBYFEVtUpL6unbeftjJ5zcP5gClnJIgN1sqDRraIImM4ZTOpRms0P86HiE1g23cWZir185XSC5x+YRxLL1MohOmxL7OxooKfnbsrLy9/djiko+BVTGLn/AqXOLhL+wchPjNYT4SyHHx5g8ewyG5wCv2zGxCAaEtCE4VjEzF9Tqo8jgHl3PVORP+GLwYc57RxkQyZLmXw5Dyp3I4/nkHIGjUWj3FX9A4Ln4qhrE0jTGp7HVXwrMF7qZ6bqo4xVruFIh8ZQoIirLkboGtBRDFgxnUfEX8aZWd1+pCmCyqomOq7+IAOvmVmaXD1WVwLaK+xUZfKYdcGCZjBvzNBsfoCG0mnElt/lJdu1fPnVaU5PRbFIOk3yIps8Sa7cspHOzs5C6aWg4C0olGXeZUYmT2TvCKkzi5hr3Pg+uDpaN3SD86/McHLvCPWyQaNFQSKJVT5HSu9ByHksJd+nJPoIMoKURWFC+Tj7hYNvlDyOTI6rMkEetvxXtEkZKaNT6ZnlnqaHqJ1YRnZpZEQW+yMqVVOw6LExUXcXI9U9HO3QOFceYNNYjMvPZrFpKjFljFzySZzpPAJBXhZU1q1h7ZV3039MEJpJAmCyKHQ0uAkspbBqBuG8QVj002j5BmU1NtI9v88jyQ189dAoU5EsTilLm7LAlbVWdmzZVCi9FBS8TQrh/i7KTcUJPdiPHs7g3lWN68pqJEViYTzGK98dQMwm2OjSsQsLFvk1ckY1giA5/3lKs5/BrkcxgMmyciYXf58vBp6nzz7KuozCoPkPWZwuQ07mKXJE+OiaB2jNjaONW0k0xpAfNrH2AsRtKqN11zNUu4cjHYKz1X7ql1JcdyKOL2khJS+TSj+KM7Ua3nnZoLK+jfquuxk6mSO+kgHA6bfQ1ujFNRbBqRkkdIOEOEWj5ct413aysP6TfHOiiG8fHSeRMwhICdaZF7lxQxWXbemhrKzs3euIgoJfQYVwfxcIQxB/ZZrYixMobjP+u1uw1HrQsjrHfjBC34Fp1jmhWjGBtIBFmiFrdJKUIwRK76NoZR8AMbuZMfe17I/VcH/x01iEgUtcx6XQVSgrGjZLlo80f49NRWfIH/EQaQ+z8oqFbYcEiiExUruNgYYPcHi9hTO1bnzJLDcdX6Yy5CRLkrj2GI7EIhISedmgvLqdspY7GO3NvHFWelGFg8Y6N6a+EEWGIGMY5MSr1Dm+jq3rJvoafouv9OZ48twcuhDUyGG63XFu3tpOd3c3Dofj3eyKgoJfWYVw/wXTkxor3x8gOxjGti6A77ZGZLuJ2eEIL3/rEtZwhk6PgVlXsSin0PRmDOFksfgI69Kfx5RPISSJ4Ro/S3O/zed9x7loH6EsX8VA8rdh1oyi6lxXuZ+bG59GGjUTiQnOJ+GqZw1KIzATbKSv5aO83F3C6XoHiqFz08lpGia9yEInajyFLTaKBOiSIFDega/iJuaGM/zrf4lApYPKMgfWgTBBGTShIzhMte+7yJt/jVf8d/ClowucmIiiotOkLHNlpcR127tpa2srlF4KCt5hhdUyv0C56Tih71xCj+fw3tqIo6cUXTM48vAQffun2OBWKHeqCHkWi7FCTt9ETJrGV/oPdEVOAZC02Bkta+bo4g6+WvbIav07+yEGxjuQBKwv7uM32u7HJnJoz9t5OZiha7/MPSOCqNPLsc6P8OT2Dk41W8lLMrsvDtI0EMCT9xI3DmKKncaOQJcEnsAGbO49xFd0Mq8HuydoJ+g1456OUx7PYkgGsI/q0pfQtv4mD+l7+fKhcSYig9jJ0W1a5Ja1Aa7ctqdw1ktBwXtEIdzfRskT84T3DqM4zZT8znrMVS6WpuK88LWLmENpdvsUVF2gmE9h5NaQFcVMuF6gR3wFNZxFSBITQR8r0ev5fGqJk6V7MesNLM/ejZRwUeSe51PN36bKP4Xe6+DskkJmJcfHnpEQkkJf0w08sus6jrXZyKgql49epPpCgOp0CRn9POnEK5iFhiEJbJ4NKOZd5DXIrh5Fg9VloshtpjiUoSqTQ6gGZvkFiusvsdL1W3xu8W7uf3aCWHYQv5RklyPMXVsb2bJ5V+GGo4KC95hCuL8NhG4Q2TtC8sQ8liYv/rvXINtVzu2f5vijw6x1KFQ5VfLqNGYRQ8t1kRLjaMXfYXviGEKAJnsYK/dwenkX/3/wKClZIx2/nfh0N7I1ywdrf8Cepv3oKRPJR+w87dK586CgPAyzpRt5aPdHeKkrQNJiZtvMGYp7fbTGyxH5cdLJF5CNJAIwudqRld3IqoLTZyW6mEbXBQGfhfKMRk08i2QW2NTn8LWFGFt7L//70h3sfWSevDFGpRzhhmCWD16+gXXr1hUuly4oeI8qhPtbZKQ0Qg9cIjsSxXVFJe49tWTTefb9y3kiF0Jc6VWx6ALDcQJzcg0aQWbUF1hnvQ/b/23vzuOjqu/9j7++58w+k2Qm+0oWSAgQCIRNFhFkC4vgWitWsVq91qUu99rlWrWtvVZbf1q3ar1W61b3pVhp1eKCRUFZwh4gCRCyb5N19pnv74/Ex4NLQRE0k4Tv8/GYx5w553vgzTeHz5z5nm/OdPfOTmm2ZNItMnnYG8+H6WuIhLPx7D8fGUogO2kjPxqxGmdMG6EyB+uqAqQ3hbnxfUmP1cXLCy/j+YXj6bSaOK2pDNd2AwWtmTiCTfi8r6OF2hCAZsvHZFyE1WEhMdNBw4EOOpq9xNgMZMsIuZEwwgQ24z+JK5FsyF3BIxs6+OSFNnQiDNdbOKvQxlmzZ5Cdna2+4UhRBjhV3E9CsNlD69O7CLl9uL5TgL0khcYDnbzz2DYy/GFOjzEQ1tvQDfuJ9EwlJA/S7HicqeGPkEFBBBv1sXEc9I7m56lNtBh24WsvJVh/OgZnDVekvM6krF2E/Sa6X7HxmQxy7qc6loDks7Gl3HfReTS67Exw7yJ9k5v4xnyG+dwEfK8RCNYgkEhzFmbLclwpTtLynRza1Urt3nYsBsFIs0aeUaIhsFv+hfU0B2/HreDRdTVUflaNhSATTc18pySdeacvIyEhIdpdrijKcVLF/QT5KttpfW43QoOkK8dizomjfH09nz+/h0l2nVizhs+5FVtnCpHIBHrkh8Q7nqMk3ABATyQVv8XKy4ziz5k7CUsX3gNXIyOpjMh4g+uytxHjaCdQ7qRig4fcao3v1YeoT8zm9ot/wKbCXAo7q5jxWTmddRMY5zES9q4mEKxAAmFTPDbLOWQWZpM9NoG9nzVQ/kk9RgGFFo0RZtCEwGbbgj4jhZe083h87X5avZU4hYc5djcrZhQwfep8NZVRUQYhNRXyBHi2NtP28h4MCVYSLxuDFmfik9cqafu4hmKHAWEIEXSsw+yegYabJv1txhrfRJMRQNIqk+nWY/mv1FT2WA4S6J6Av3Y5RudeVqR8wMyM/YSDBuRbVqqbw0zfGSasG3l+0Xd5buF8hnkbObPiYzYfmsKs7hB2z0bCXEJuqwAAHyRJREFUgZ2AJGi0YrWcTcGkcYyemcbudfVUbmnGgCTPrFFgAV0YsMbsJjQzmz91ZvDchmo8IUma1sk0Vw8r5oynuLhYjacrygCnpkJ+g7o/raN9VSWm7FgSV44hEJa890AZiTVdlNgNBOOb0L21mN1nIPgMn+UtxrMFKQWhsJWAwc6n1kx+kdqDnwZ8dRcgfYUkZzzPTWl1JDub8R5y0f2Wn5Q6A7PavGzLH8+vLr8SzaJz7e7nWFc9lp6eySzq2kzYX0aYCCFdx2Sdz8QZcxi/YBiVG5t4+9HtaOEII8wahRaJLgxY4g7QOX0Ev2+YxJv/qCcsD5CtuZk3THL+mVMoKChA07Rod7OiKCdJFffjJKWk85/VdK2pxjIqnoQVhXR1BHj/gTIK/UHsJg3PsDLstWnI8Fgi8lUSrG9hk20gwOOLAUuY2xLH8F5MFSF/Fr5DF2KKOcTpeQ9yXkobBj2I//0kvBsjjKmWBIxh7lp5Df8qmcLlB1/nQGUcm32zmddeBr5VhAkSEQLsJUyefSETS/Oo2+vmzXs3E/KFyDNBoSOCQZgwOxuomzKCBw7k88HfW9GIMEJv4awCG2fPnaPmpyvKEKOK+3GQEUn7qkp61tdjm5iC69x8mg51su0P25ggJJpdx5P4Afbq6Wi04xNPkG3+B0JGAEFP0EmrI8xlGeNo1qvwt80k1DoHW+rLXJNSS2FCE97OGIKvxJJQoZHb0cGnRSXct+IK5nRv4YqP/8Jq7xwWt+1itPcZkD7AiN+WytQ5P2TqWUW01nXzxr2b8HT4yTFJRseFMQgbJqebfRNSuXefZMu7dZgIMc7QxDlF8Syas5iUlJRod6+iKN+CryzuQogngaVAk5SyqG9dPPASkAMcAL4jpXSL3vlxDwCLAQ9wmZRy87cTvX/IiMT92j48mxpxnJFJXGkO1WXNNP2lnFG6QGbo+IPrsB86A6PYiNT/Qa5hfe8wTNCANNpYHZ/BrxNDhCOt+Gq+h46FEcMf4JqkbmJs3XSVpyHe1MmrbcNrMvM/37+WnuwEbt7xFI90LWJWu+S8rhdBdoOw4zObGTv5cuZeOo9ut59Vv99Me6OXbFOYMXEhDMKB0dXDttHx3LU7yP4PqrELP1ONjZxfksHcM84jPj4+2l2rKMq36CsvqAohZgHdwDOHFfffAm1SyruFED8FXFLKnwghFgPX01vcpwIPSCmnflWIgXpBVUYk7lf34tncROy8YcTOy6ZiTTWhfxzArgvEREmofA+GnkLM2qvYDGuxa1UAeP0mNFOEazPPZIOpnLA/HW/NRZhd61iaUsaCpA4iYQP+v6cQ+7kkta2RfxVP4uWl53NFw8u81jyJSLeJae5PEJEO0OII4SVx5CwuuO4qwmHJ+0/voqGqk2xTiCJrCIOIxeAMsKUwjTt31lLXFcAlPBSbmrhgah4zZ0wnLi4uyr2qKMo35aQuqEop1wohco5YvRyY3bf8NPAh8JO+9c/I3neM9UIIpxAiTUpZf2LRo0dGJO5X9uLZclhhf3Uvhs8b0A0azOpErvNiCOVi0R/BaViPTjtSgi9io86u871hE+mW5QTapxBunUVM+gtck9RKvquN7tYEIi86ya5oRgL3X3Qlw+PdzNvxLg/657K45VOswXrQ4hBaMjLJwsqb7sMeF8e6l3dTWdZKhjHC4jg/RuFEd4bZNDyeX5XX0rR+P4mim4XWZi6YUci0aYvVdEZFOcWc6Jh7ymEFuwH4YuA2Azh0WLuavnWDqrj/n8I+P5uY2ZlUPLYVy4FOuk065qm1iLUuNBnArD+Iy/ApgiDhCEjNzpuJI7jLBTJyCF/9BWhhG3l5j3B1oo84Ww/uncPQV8dSuH8vO/IKeH/BYha1/pP7Dy5gcksP53lfB2FFMxbg0w5RetkPKZg4mY1v7WXXuu0k6ZLSWC9mzYUWa2ZjjpNf7qujddMBUkQni6xNnDdjDNOnn4XNZot2dyqKEgUnfUFVSimFEF97srwQ4irgKoBhw4adbIxvjJQS9+v7egv7gmzsk1M58LuNWNr9NDtMOAv3YvzXMIxiHxbDa8TqnyAE9IQ1DLqJ67MX8Im2FRmMwVt9NYbY7SzM+BdL4z1IqdHw3hiy17QQ113JK/PPYVRqPb66Zl5zj2JZ5996bxVgHksk1EzyxCTOvvxOtr1bzbM/W4tTCObGdGPTEsBmZmN2LL+oqqd9WzXpWgdLrU0snz6G6dOXqTN1RTnFnWhxb/xiuEUIkQY09a2vBbIOa5fZt+7fSCkfBx6H3jH3E8zxjZJS0rF6P56NjcScmYUl30XtvRsRvhDViTbSk3Zj2JiDRfsUq/Y2dkMZAJ0YabUmsCJ3Dt2BdUQ8uXjrLsCWsoor0/cx1tlFV3sCre8UMP2jTRxKTuOz+QvIDu3lmbpiprdtxBTxoplGIkQsIXsFK/7zdpqrdF64bR2WsM5sezcOPRFpNlGWE8Nt+xtw7+4iS3Mzy9rEktPGMGPGMvWdpIqiACde3FcBK4G7+57/etj664QQL9J7QbVjMI23d31YQ/fHtdinpWFIstL46Fa8oQg1qXZGGMvRy3Ow6n/Doa3BrO9DSvALE+8kTuKOhFj0wDoCbdMItk0nY9hTXJfSTJLDQ0NVPvY3DEyv3MTaiWcwLLuDjR6drBYrswMfI/RUNPscQoHNlJwznqzMK1jzx53IHiNTbV5chngiejy7hsdwW3UjjXu6GKa3c4alnoVTxzBz5nJiYmKi3X2KogwgxzNb5gV6L54mAo3AHcCbwMvAMOAgvVMh2/qmQj4MlNI7FfL7UsqvnAYzEGbLdG+op/2NCqzjk9CdZro/rKElJKlLtlEkq9DcadiNT2FnAya9hiAQxMqtuefzjr4bLdSKr/4cZDCeybnPcmlSFwY9QmXZZCb+ZTcRTWPH9Dl0W91sa3MyuqschBmj9QxkxIchcR/zL7iR7X8/REeTkWKblxRDDFKT7MuO4ZfNrVT3BMgydFKs1zC/pIDZs2er2S+KcgpTX7P3Fbw7W2l9bhfmfBfoAv/uNg4GIjQnWSmJHETrSiLG/CB2uQVda8OLwG1MZmXBpdR7V6GFNXoOrkQztbIs71UWJXbj8zmo/ngCc/66nsqsAgxj7bzszyG/bS+miB+DuRjNPJag9yMmLJmOvz6N6r2C0dYAOSYzUmhUpFj5TU8n+7r9ZBh7GCeqOWN0JnPnziUpKSlq/aUoysCg7i3zJQI1XbS9WI4x1U64w0+oycPOYISuOBNTgzUITwJ2y7045EZ0zUu3EGyKncg1OXMRnS+hh1x07b8SW9xWrsx9l7HxXbS2pCNeieeMHRsoL55GbUaE6hadIv82pDEFs30BMtyEIfYjik9bRvnHGjkGSWmcRBd2qmN07tcDfN7YTLrRy0LjQabmupg37zsD6uKzoigD1yld3ENuHy1P70RYdEIdfmRYslkKeow6MyPNaP4YHJa7iJFb0EQID4I/Zl7Moy4H1s4XkL5hdB68nKTk97glbx0JDg8H948l/4lGDMEmamaU8JFuJ6P2ICnCgG6bh2YcTtC7hvzJBbTtL6WtzMw8RxCzZqZRlzzlgr+1uEk0Bplr3M+EFBPz5y+loKBAfUGGoijH7ZQt7hFfiJY/70T6wkgp0WJMbPRF6PAFmR3Tg+Y3YbffSUx4O0JE6BQmrh11KxvYhrVrLeHOIjy1F5Cb9Ro/Hr4JTYO9G6cx/dlt1KcMp36kjaoOGBbcT8iSjcVcSiTchKa/RVbWdDr3ZDPZFiLWbKAjInjeBU+6u4jpkEw3HGB8rI95c89k/Pjx6i6NiqJ8badkcZcRSetfygk1eUCCMdPBpqCkrbqDuS4velDgsN9ObHgPAAdMiVxadActPa9hDlQRaJ2Jv3kBJbnP88O8rfgDdhr+Ucj097ZSN7qYj50W4lsaiNVsyNiF2LVCQt61uJIiBD1nk+kzkenQCUh4wyJ5yO9B65RMNNUxxtDEGTNOY+bMmZjN5ij3lKIog9UpWdw7/rEf/143AJbRCewxG6j/sIa58X70kIbN/jNiw1VI4MPYsVxTeBN62yMYwy14688i1DGFJXnPcu7wMtrdKegvOCk8UE3VlFFsDYRJ6GzA58gl1rAIIh5CvleJsY8hOTSO0bESDY3PRITfmgI0+0OMsbQzOrKfSUUjmTfvfFwuV3Q7SFGUQe+UK+49m5voXtv7e1X2aWk0JdvY/Uw5ZzqDGMNgtf8nrnANEeDR9GXcM+xc4pp/B5Eeeg5dRKSnkMtGPMXM3B3U144g808eAkJjQ3EOoe4uHJoVX8JsnJESQv4dGA3lJNuXM95uwa7rVIdCPGgPsd7vY5juZ6nYx5hUJ6Wll5CdnR3dzlEUZcg4pYq7f3877ld6h1piS3Pw5cax7t7NzIqNYJYhrI4biA81E0JwXcHNvO3Kx9n0a4iE8FavRHpzuGHU/1KUsZeDu4spevIgtcNGssMRwdbdid+eidU0n9iwjUDPamLNiYyNuYB0k4GeSJBHtQDPG3y4gNnGSkbb/Myfv4Di4mI1rq4oyjfqlCnuwSYPzU/sAAnOs0egjU7g3bs+Y6o1gl14sNuvwxnqwKOZOHfcb9llDONsuhspJYGDPyDiy+LmcX8kP+EA1etKKHyrmt2F+TSGurEGTbSljiXdP59wuImQ51XynUsocsSjIVgTCnKv0YdfSCZZmxklDzFt2kTmzJmD1WqNdtcoijIEnRLFPdjqpfGhLRCWxJ2Vh21KKm/dv5kxoRAugxu77UfEhrtpNsaxqORhWoP7iGv5M1IK5P4fEvKnc8P4x8mJqaf5rbGkb+1kfeEwZKCbsCWJrviJpHtHE/JvJ0bWMyltBfEGE/UhP3ebJZtEgBFWP8WhckZlJLN48RWkp6dHu1sURRnChnxxDzb20PSHrRCMYJ+ZTsyMDDa+VUFKbTdp5gZiLDdhjXiosGaxtOQhIp61ONpfRUQ0DFXX0R5M4ZriJ8m2NOP/SzaRTgtlmWEMAR/NyRkkRhaS4rET9rzHmNjRFDgmECbE85EAfzT4cegwW6+k0ORlwdJSNbVRUZR+MaSLu7+6k5Y/7UD6w5iHx+FckkdteQue9w8x0lJFnPlnGKWfT+OKuWjcb7F2voGlczWmsAlj5bU0hJO4auwzZOtuDE/Gc8CWQsjWgIaNA3lZjHQvhUgXMcGPmJoyF4fBzK6gl1+bIxyKhBlrbacoUsW0SeOZO3euure6oij9ZsgWd98+Ny3P7IQwaLEmEi4ehb/Tx97HN1FkLcdlupMIQV5LWcANBT8l1v0Upp6PiPXHYq2+lIpwCpeMeonsUCfm5+xscyVjDtfTE5NAR8JoRrknIwOVFJqDjHSVEsDH7yM+XjUGSTNHWBQuZ3SClWXLVqpbBiiK0u+GZHH37uq9EZgw6shwhITvjUKYNTb8ahXjLHtxme7Dj+TxrIv5Te6VxLU8gMm7iVRPCjH1S9kcymRx7ruM6O5AvGNnh8uEOdzKgcxYEiNzyOvIwh4sY7KrgDhjHLtDXdxm0miWYSZbmhjDIc6Yczqnn346BsOQ7GJFUQa4IVd5vDtbaP1LOXqchbDbR9yiHMxZMWz61W8p0twkmB6lS9P5Tc61PJl5HnFNv8Hk301uVw7xrdP5MJDPlNSNTG6twbPFSaetA6MMsq3QTnHrhViCJkZo+xiVOJEwPh7BywsGSarJz+JIOePS41m27D9ISUn56rCKoijfkiFV3D3bW2h7oRxjqo1Qiw9Tbhz2GWlU3H0J2f4EEkx/xm0wc2P+T3gn6Uycjb/EGKiksL2AtK7RrPaNJt9ZSWnDTpprEtC1OnwWG/uGxzK19kJssoeJNg+J5jEcCLfwE5OV+kiIEmMD4w2NLJw/l8mTJ6sLpoqiRN2QKe6ebc29t+7NjEEIQEDMkhSafjMFp3cCiaY/02x1sGLkr9geW0J8/c/RQ9UUtY8iz5fJG75xJFpbOa9hAzVtcdiDdRxKMhFwjWFa7emkai1McCSgC8lLdPCQbiLJ4GeJ3MOU/DTOOusanE5ntLtBURQFGCLF3VPWRNtLezDlxGLJd9H57kHMZ9rwPTEJc3ASLtOrHHLGsSz/XuotuaTX/TfBcA1FHYUUh5y86S9CQ3J+y3oa2w3Y/fVszRFkhUspbB7BGFMbubY0uiLN/NzsYFNAMNrUwlRjLUtKFzBx4kR1O15FUQaUQV/cezY34n5lL+bcOGIX5dD82DZID+JYt5BIZDh24/uUJ7tYmvcA3cY0Cmtuo5VaijpGMl2aeU8Op8mbyHm+dfhbOzGHfXxSaGRq+8WkBZ1MsvuIMSSzlVpuMcQiwn7mGiuYmetk+fKriY+Pj3YXKIqi/JtBXdw921t6C/twJ/EXj6Llie1ILUBK60qEdGDUy1mfFs8FOQ8TMiQw7eAdVOi1FHUUcKYOZYZ4ttUXMS28k8SGvfiFkc9HuZjT9B2yNQPjHTphqfG41c2zvhgytS5ON1axbMFspk6dqsbWFUUZsAZ1cTfnxGKfmoZzSS5d/6ohWNtNgvF3aMKPQfhZlZbM1bkPgx7Dkoo72GCpY2znCEpNPupiLby9bSG51DOh+mPcJhcVOWnMr59PkVky3GqhLVzHz2Li2O3RmWQ4xPwsjXPP/YH6/lJFUQa8QV3c9RgTrrNH4DnUQMc7lVi1z9CNGzBEwjyVnM6teQ9hEBYu2/Uz3ohtY1znCJYYu4hk9PDkpz/GJboorVpFgzkVd1oRpS1jmWSPkGC0sFvu5XpjGrrPT6mpggvmlDBr1ix0XY/2P1tRFOUrDeriDtBWs5eex9aikYY55veY/WEeTszkrvyHsGDgv3b8J390ehnTlcMyUysxeU388uOfISOCs6vfoNo6DN05nQXdaUx2gC4Er+mV3B9OJY0OlsQ3cckF56t7rSuKMqgM6uJ+cOtHWF95AiKXYU24gpgeL/clZPK7wj9gjwj+Z+f13OOEPE8a5xpbSchv5OFPr6YxkMDyhr9RY8nAaZvD6WEHxQ6N7lAn91i9rA0lMU6vY8W4OJYvu0rdlldRlEHnpIq7EOIm4AeABLYD3wfSgBeBBGATcImUMnCSOY+qqz1MRJyFNfkK4jvc3JeQwe9GPYZVwgM7ruGXTp0Uv5MLje0kDG/hr1vOYUvXaCa7N9JudJJpnM1c3UKBVafGX8fNVgstYQsLrVX8x1kzmDBhgpriqCjKoHTC0z2EEBnAj4BJUsoiQAe+C9wD3C+lHAG4gSu+iaBHYzaHkIk3kdrh5r6EdO4d9QQmqfH49uu5O07DEbKxwtxD0vAWPtl1Jn9rnkWmtwYTQQqMc1lusVJg1dni388lZjvdMshl6U38+prvUlJSogq7oiiD1snO5TMAViGEAbAB9cCZwKt9258Gzj7Jv+OYDpXfTZ67m/sTs3mg8GkMUuNPW27kntgQkYiJS8wB0rPbKKs4nZdq5mMJ+cnxNzJRn81ym4lUI7zpr+B6cwJJWic/n2rhlqtXqtkwiqIMeic8LCOlrBVC3AtUA17gXXqHYdqllKG+ZjVAxtH2F0JcBVwFnPAtcf+a+H1eisTyUfrPAclj62/h0ZQu2oWFyy2SYelutlTNYVXVFLqxM9m7lxn6DM60GTCJMPeFannTnEyRsYk7zythwvjiE8qhKIoy0JzMsIwLWA7kAumAHSg93v2llI9LKSdJKSed6JnyuLrtbE6+FZ+ucdf7t7EqsZ4Ko5ELzQby09x8vn8Bn1SMoJoMRniaWUIxC+xGNPzcEmlhldHJQlczT16/RBV2RVGGlJO5oDoP2C+lbAYQQrwOzACcQghD39l7JlB78jGPrjqYRYtZ5+bVv2Ff7h7W2Rws101MSHezsWoe2w+kUB4aThxeLo1kMcthoCfcww3CS7XBzNWFQW646CLMZvO3FVFRFCUqTqa4VwOnCSFs9A7LzAU2Ah8A59M7Y2Yl8NeTDXksDe0bueXZVzAW1fNanIOZmok56e1sqZrNtpoM6jri8dksXOu1MdtupDnUwQ/1CB4N7pmfzNlnnqYumiqKMiSd8LCMlHIDvRdON9M7DVIDHgd+AtwshKigdzrkn76BnEe1dEsrqTk9PJhiYYxm4Nz0drZVzWRjUz7WQx3st+dydtDMcpuJA8FWLjVAxODnue9P4Jy501RhVxRlyDqpee5SyjuAO45YXQVMOZk/97jl+LgzJ0CW0LksvZM9VdP4tHUcY3Z8xvOZFzEirHGjycKOQAvXm0xkGjp44cZFpCa6+iWeoihKtAzq2xp+pseRYTTwH2ldHKiYytr2yUzZ8gFrkhcghZFf6za2Blq51mSiyNzE6tvOV4VdUZRTwqC+/UD2pB5mJ3RSuWc6H3dPZPKmNexwTaLaksqPMFMbcPNjk4mZ5oM8efvV6Pqgfi9TFEU5boO6uIs9E9llKGYbCRRtX4vblsZncZMZi05KsIdbTEaW6GU8cPtP0VRhVxTlFDKoK54vSVIZiiWrfBMR3cKuhOWEBMwO+vm50cCKyBru/e8b0dRtehVFOcUM6uKe1TaZ2IN7MAXD6PEXU6ZLzghLHjboXB16jZt/dANmuz3aMRVFUfrdoC7uH9tqcHR3MsL1PV63CFIlrNEk1wdf5MLvXkF8proHu6Iop6ZBPeaeFT+DnMR81tl1mgigE+F7obeZPWMuOZNmRDueoihK1AzqM/cJvoPYbWZelgE0KZkd3MAZyRYmnHtZtKMpiqJE1aA+c++2BLiTHiRQ6NvHOVoZM659Uf3mqaIop7xBXdyf2R+mTgiSfY1cGv47k65/GIvDEe1YiqIoUTeoh2VKR5pw+Vs5V/uIkYuvI3VEQbQjKYqiDAiDurjPykvmfPERY9LzmbDorGjHURRFGTAGdXG3JWYwMiGdBdfcpMbZFUVRDjOox9xTcodz/q13RjuGoijKgDOoz9wVRVGUo1PFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQhSxV1RFGUIUsVdURRlCFLFXVEUZQgSUspoZ0AI0QwcjHaO45AItEQ7xNekMvePwZZ5sOUFlflosqWUSUfbMCCK+2AhhNgopZwU7Rxfh8rcPwZb5sGWF1Tmr0sNyyiKogxBqrgriqIMQaq4fz2PRzvACVCZ+8dgyzzY8oLK/LWoMXdFUZQhSJ25K4qiDEGquCuKogxBqrgfQQiRJYT4QAixSwixUwhxw1HazBZCdAghyvoet0cj6xGZDgghtvfl2XiU7UII8aAQokIIsU0IURKNnIflGXlY/5UJITqFEDce0Sbq/SyEeFII0SSE2HHYunghxHtCiH19z65j7Luyr80+IcTKKOb9nRCivO/n/oYQwnmMfb/0GOrnzL8QQtQe9rNffIx9S4UQe/qO659GOfNLh+U9IIQoO8a+/dPPUkr1OOwBpAElfcsxwF5g9BFtZgN/i3bWIzIdABK/ZPti4O+AAE4DNkQ782HZdKCB3l/IGFD9DMwCSoAdh637LfDTvuWfAvccZb94oKrv2dW37IpS3gWAoW/5nqPlPZ5jqJ8z/wL4r+M4biqBPMAEbD3y/2p/Zj5i+/8Dbo9mP6sz9yNIKeullJv7lruA3UBGdFN9I5YDz8he6wGnECIt2qH6zAUqpZQD7reUpZRrgbYjVi8Hnu5bfho4+yi7LgTek1K2SSndwHtA6bcWtM/R8kop35VShvpergcyv+0cX8cx+vh4TAEqpJRVUsoA8CK9P5tv3ZdlFr1f6Pwd4IX+yHIsqrh/CSFEDjAB2HCUzdOEEFuFEH8XQozp12BHJ4F3hRCbhBBXHWV7BnDosNc1DJw3re9y7P8IA62fAVKklPV9yw1AylHaDNT+vpzeT3BH81XHUH+7rm8o6cljDH0N1D4+HWiUUu47xvZ+6WdV3I9BCOEAXgNulFJ2HrF5M71DCMXAQ8Cb/Z3vKGZKKUuARcC1QohZ0Q50PIQQJmAZ8MpRNg/Efv4/ZO/n7EExn1gIcSsQAp4/RpOBdAw9CgwHxgP19A5zDBYX8eVn7f3Sz6q4H4UQwkhvYX9eSvn6kdullJ1Syu6+5dWAUQiR2M8xj8xU2/fcBLxB70fWw9UCWYe9zuxbF22LgM1SysYjNwzEfu7T+MWQVt9z01HaDKj+FkJcBiwFLu57Q/o3x3EM9RspZaOUMiyljAD/e4wsA6qPAYQQBuBc4KVjtemvflbF/Qh942V/AnZLKe87RpvUvnYIIabQ24+t/Zfy3/LYhRAxXyzTewFtxxHNVgGX9s2aOQ3oOGxoIZqOeZYz0Pr5MKuAL2a/rAT+epQ27wALhBCuviGFBX3r+p0QohT4MbBMSuk5RpvjOYb6zRHXg845RpbPgXwhRG7fJ8Dv0vuziaZ5QLmUsuZoG/u1n/vjyvJgegAz6f2YvQ0o63ssBq4Gru5rcx2wk96r8+uB6VHOnNeXZWtfrlv71h+eWQCP0Du7YDswaQD0tZ3eYh132LoB1c/0vvHUA0F6x3SvABKANcA+4J9AfF/bScATh+17OVDR9/h+FPNW0Ds2/cXx/Fhf23Rg9ZcdQ1HM/GzfcbqN3oKddmTmvteL6Z3RVhntzH3r//zF8XtY26j0s7r9gKIoyhCkhmUURVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGIFXcFUVRhiBV3BVFUYYgVdwVRVGGoP8P/9diBtPqFfMAAAAASUVORK5CYII=\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From ba29cee9f4cd3814ccf969fcaabeae1b5de8c7ae Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 306/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From a9b460b0e60ee523143c397035a4a1dfb302fba4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 307/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 2489a573c0d326dc228c303fe767611d8cdc3d88 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 308/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 41b1d81d9c3bd6a15552032a58c01d0d13781984 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 309/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From c28fcafa6150bbbb779dfb1855886488ef910a6e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 310/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From 5c4a51b1b3013ba222236b8a27f51ccec850438d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 311/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 78f17ea52b209b4cdaf7cae34fb75e8bf875699a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 312/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- tests/test_fpca.py | 78 ++--------- 4 files changed, 278 insertions(+), 136 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..fff7be7d4 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,81 +1,25 @@ import unittest import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.datasets import fetch_weather +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data - def test_basis_fpca_fit_attributes(self): +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) - basis = Fourier(n_basis=1) - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataBasis(basis, [[0.9]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of elements - # of target basis - fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() - with self.assertRaises(AttributeError): - fpca.fit(None) - - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of attributes - # in the FDataGrid object - fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_basis_fpca_fit_result(self): - - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) - - # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) - fd_basis = fd_data.to_basis(basis) - - fpca = FPCABasis(n_components=n_components) - fpca.fit(fd_basis) - - # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] - results = np.array(results) - # compare results obtained using this library. There are slight - # variations due to the fact that we are in two different packages - for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): - results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) if __name__ == '__main__': From dded624a59070220ff7143511053953b7f109063 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 313/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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JiikSxqaJsOj8qbSXTudrPid2v5s1O05QbpiMZcRWUq7+JW83buB7VXeyyp9Gmet23AMxxo+IcOS4gC3az6X3no8+XR0P58tEDXnVGUeWFf5y/2H6W32s+tlUHBlmhgNenv3de9Bjg8heZhzegkHrIv+/rsR2zc/4fn0Hz/W6mXt0D1cPJiNaCrhmuplZtihXOyXkqJaR5hTSkm243Ed49oUqBoIG9scLGFCsCIrCyEADyws9rLjqq9xcfTd9HdU8knCSO3gUARlPzMiJcCFC2UrK88egeGQSbpnEUBxCfkStCYlklISCIkUQDadmh4qF+zlpeIvG1ON4NU4qPbOZHJ8MCviHjvGiNZ0cUyrnCTq8hNk5/BdeNlYQT0pngnSMWCJKYVcL+njkgxpSCBks7Jq2kL7sEtZXQSw6SNGYv5B09TP8/p3v8afud1getlPWcQeJhJ7xk03s3e6lQnuCcx688bR9tqr/f2rIq844h7a2s++1ZuZdMZJRMzKp3nknr21NItNTSWnzM+R37Sd1go7UW+9HHHkOG/o9fLOunUV1NazuSqZIdHDJWXpC+ih3SjeTpnMB4Aon8XrjEnoGS3HJVoYVExokKoItXBZ5nSKHwNBwGE9Uj0FMsDSnHqMmwdHhbLBkUiq14DC7kWUjEWUKsmJHI/ShF06iEf0AyIqGoJLMkGJlUFEw6BXKZQ9G/B8e37A0lpdNZ9Pt3s+l7w0jxCWqZ1/IBvMUfiHaMAIvhas5GA2RSEpFNyaNfdnFzKtuYXzdXgKGKIrkJ9nnIqozMJA/hq+Fx9CSOMTSlRmIs77Ng9tv5tHOrRT7s1hY90NKKo3Eutx0u41ceIGO7AvOOR0frer/QA151Rmlv9XHq/dUUzQ+jfOuyGXHiyt4tLeMmW0rKG16mRGmPsLX/oAmcxrZIZksv8SBAR+OSJziACSAa0eLNGRb0B0cQueO4hBiBNEQVTSAgAaJPIZZFj/BcvEFNEqCw55sWgMp6E0yFcntnG3rJR7TcOxINrVJUzgpZ2DIHssCc4hJ+g0YhUMgRYgFRKJ+DUfHjeCwKUySr48JkThlsRgmZLyY6SCPFvLpJ5UiOpnHPgZJ4bf6ebQYO7npsI20A7WIGZnUzV5BRmIUWWhYpwTZG+0iyxJDLE5mR1EFAqAIApZAHwWtz1Dc62BEeydaScKmT8Ws9TDriq+QM+tC6gcPce+unyE2TWJy12JGT+vgxL4MkiNdrHj0SjQGdWKSLwM15FVnjFgkwQt3HUCWZFb9cDT7Xl3GnWEjFx37AXm+VsYVpHJ3RR6v5X3US2SMV2JZV4zSbh81ioaqiS3sSZ3M4kMtOBt6OG6Q6dc7ieosFGvc5IoBiocLOE//e8baj5KQBURBQRQgKmiIIWNTFAYHbNwlr+Qt6zSCWisa5dSECrIAs2M13FS3CXObm9ZxDn4134+kE5idGIUtMBrRKyGgIAIltJNtjOKYdQ1J2aVotVqoeYHcg7+miwz+zKW4dX6GXRIX7jvJSE8H/fZsjGfdQK4hgwYSbCJODwHMmgFyNH0Y4kFM3S0YFIH30yeyb84sJpyoZlF9E75QFwoSWr2esfMXMe3iVVT372PHukGMcRtj0wdo7C5lVkkf425Zfdo+a9X/nhryqjOCJMlsWXuMzjoXy79TSVPVan6Q8PD1Qz9htCaJNIuRH0+ysMep4Tqng2V5qazrGuCNIR8AgieKU+cms6uHsw+8hyX4t3PYKIKA1monJT2XzOFdnJt+nDpvGidM8/DNKqSu8zlmDgXI701ho+YsnjItQkBmIjEuI4UcqY3owefZZy7iyYrF2OMBRmsfo2mMxGL9IjQtEcIJLUYljKQzcmFGP0mdb3HUuZjM825mYlkB4sfnbj26Hl77Bv2Ckz+IF6OVrHgsQQoVKzPeqiKps4XgiIVYRs7HpvloYvCgIhMQQoSFAIOBXsL+Zp4tmUj1WWMp6B/mjiZIHmynK3qYVm8L5iQHi771PQI6E+/+oQtXUg1FnkISMYXL75iFOTfzc/l8Vf93asirvnSkhMxQV4BYOIHOoEGRFaq3ttN+zMXcVQX0d1zPL+LD/KL+FkZqsglrFW6em8whrczdZblcmZPKdpePK4+1kDfYy4Bbh8USYfGBDaT39RE2ONhnG0fCksyVo3QkCQkIBQj09xBsPczqvGri6Hj3nF+xzrUBW3ce8uBsahPJRAWRAkViqSSwSGsnxaBlc5LIb9xNjCnuIXXoCL0+DfXSxWgROVfXSKoYpFuyk6lx82Pd8yixAGuk1TydmI/CqWCfmO/gD6snkuMwfVQRb90Ke+4nnpTP2kgug7GJiIqGrqROxikwdn0DmT4PbRUjOZx5Dk6zDUWfQ4oiUopMGgaED8oPigpdFg2DWgktAhPdQYKxIAeCm/D4hphxyeV0R+0M7DYjWKtR/BPI1g1w4f2XIWrE03EaqP6X1JBXfam0H3ex/akThD7W9x1AoxMomDrE/sivCYSm8o2eFZgFIx2Sh1svKqU+FuPBigLOS03iye4h/rull5SQj7w9dRiG+pjuq0Kn09FmG8NG61QWOkzcNb8Sox5EfZhA3MvhJ37IwqQqEOCenOkILSOoU87lPTToFVgYjzE76kY0DxOw6xBTLHRKAjUDEWaWZzEhL5nhxiZODvTTE9WwLV5ORNFSnmrgoqklXD41n10N/dy+6Th9/gRXzSxkxaRcjvd4uWPTCYw6kZe+OZOiVMupg5YleP5yaH4HKi/Bc+x1XjYvoiuUT1gXpiplL3P2mrn0UDcxUctTIxdxuLKI9kguAOdrm7hsdAXhqio0cTtD2RPJTIjkh2R0CsSVEIKio9ZzDw0+DYU5NlqSpmHuHYtZcBFSnCQZI4y+YDQ5I5Jx5lr/9tuG6gtBDXnVl0ZnvZtNDxwlOcvCpMUFnAxUs6thKz3+AL16FyXxDK70LCQ/kYErIXOAHh6+aAwDCYnrctLojcV5a8iLP5FgfHcz4w7uxTDUR1LCT/YoB1WB8UQ1Y/i6aCBN1vzNe8vBQfSRN8jMeRG3exZb9f/FvVqFYUXmkr56Lux9l+FFCzjg8yKKImlpafgiEv0ePzadTMpgP9P2v0+Sz4dkMCBcfz1Jl13Bjc8f5WiXl+I0C7Ks0OYKUZZh5TeXjGVi/kdTLDT2+1n1yH5MOg0v3zCDrKQPWvQRH/zpPBhqhMnXwtHn6VAyeVGznEA8zo7UnYjDMne9L5N0YpB+u4Mji5awxTGbdleAJbo6vnXpPDavuZWgzsFD59yIMdXC8s4Y17ZEsUogIXFCfpH6jnZMOpmGnHwKfEvRyR89HQtgscD868aRV+H83M4J1f9MDXnVl0LIF2P9nVUYzVou+l4F92y7ioOdVmZ0LCYpkkWFUaTUIBJR4EgkzluFXt6YWExaNMTI4wdI7WrG4XNjCofQfTD2C8CAIZXkKWaaXdNZFMljGlrate04qt9C9nRQPdIEaSWUk8ZU8yPElWR+EL6NzRoD+WE3P4gfZN6ly9nmclFbW8uECRNYsGABTe44Kx/ex/g8Bw+XhOi75RY0SUk4r70W/7ZthA4eJO+RhzHNms3L1Z1sPd6PKMCi0VksH5+NViMSk2We7nGxoX8Yi0ZkqqDjT6+eIMNu4IVvzCDV+kHvlrAHXroaWt6DjEqI+PF63TxjvAZ3XMe2jPfwCgrPpX0L0yNPEmtpQTvjLL6ecz5uWeFbI4JMzGtj9+P78WodbJp+KT0V+aRGZH5zNMo4r4xXCrDXdYJ4aDthBQaTYpQ4bZTZvkJ3awyPJh1BTqAIIkuvG0HBlILTdKao/p4a8qovPEVWeP3Bo/Q0DnPhN9NYs/8KIu2LGT0wn+SEi8kmHWaLE3/vMfbEG3nk8ovpsFi56OQB8ne/iT4ew2+wMahNw6exEBP1BDUWeoxZuPUpLEHHf2FAi8yTpi2cX/8O6ZYgmmQ9Ju0gQ9YETknEJsssl26lOVHEkgk6fnvRAvQakQ0bNlBTU8OCBQuYNWsWdX0+Vrx0CK1F5Fedexj5wjNQPpK8dWuxZWYgRyK0rbqMxMAARRs2oMv42xEfJUXh5T4P97b10RmJMcZqIizLNIWijI+LtO7qpiDFzGNXTiHfaT61kSzD0efhvd+AtwNMyYQjcR5WVhJBw9bs9xA0UR4b+z0cJ7QM3HMvUnIK36+8HH+yjXWXVdDT8F3qNiVDRKK9cBSb5l2ErMCj1RIjAwpxJc5xbxPxgXdosHnRR7SYDRIT584hbdQlvPNMO/GYgk6J8JW752FOsZ6Gs0X199SQV33hVb/Zxv4NLUxbEOeP7u+T1riCAv8cigaqGFNUiaCz4ykc5o/lWWxAT6EGLn3vFTh+mDZLPrscMxk26dElHcJocCFLFuL+Qm5IjrAsOoWwS8sDopsDio8ndb9nrNhKRNFhFOIf7oNXMXND/CaUwjn8ZMlIxuY6kCSJjRs3UlNTw/yZMyk52cTxXfvxeodBgCzXACk+L9umzeb3l19LxGDEphHRiQJF/b38+rZbaK8Yzfu3/4Yyq4kMvY6mUIT1fW4irW1cfuwAs3IzmfSVy9DYbDzT6+LWxm6M7jAcdqFB4cqpWlZPH0Vuav6pHZXicPw12P17GKhDMjp5JzaWI2I5mzLfRBDjrC3+Kp7AWeju+BkGv5ffTroMd1EuD1wt0Nx4O29U3Ui0tRWbA55b9FVyI1HW74UeE+SFISFLBAMN/CWzBqmvA5NLwaqNMb7AwhHPN0jIOnItHi783YrTdMaoPk4NedUXWnuti81/PEpunosnnb+gpOWrFHhnMsJ7mNEV0xjSaNi0JItHfT5kFL5hFnA+vw5/dyd7U6aTsI9jrlaixODCJhswyHp0ioaUhAOjoqda9PJDJUZU0fKK8ivGGVv5k7ycvYzDrITIpQ+9KONKm8acSWM5Z3IFer0et9vNpk2baGlpoSI9j9ynnybF009zTh5+i51Ci4ms7AxsS5cyMGUajaEoTaEIw3GJmKIQlmRKNm1g4ePrePAr1/HKrPkfHvPXag+y+tE/IMZO3VzW5KSjW3M+HvEYtcP9/F75Lv2hVLLqm3ANORCQKXf2MLdM5NIpkynOGn/qclTjW7D7d9CxjyGS+YthMU8495DQSph7z0UKzeb26qdIa2vg2fKFNE+fwXfmPU4oJvDbwz+nvW+Yyc5Otk6axdeafVzSIfDbEVG+tu84eeZKTFoDEjLN+laGPMfoHGgg1RDHp1uNqMtj8VIbxUunnJ4TR/UhNeRVXwhBb5T22lPDB9idRuypJrpPeti5/iRm/QAvltzNqPZV5A9PZsTAbqyzFvJUho43cnREFYWlqXauGmzhyDOP4Y/F2Z12HpcbSpiHnpgQp0vXgykEiiISMyhEjIO8KfbySnAaOiHC2q61LCg+xhvMpcFXwJT555JTOZpQKERzczN1dXWEw2G0Wi0WiwWfz4dWq8VjLuDCZ/8IItx53XcZN2Uat4/Jx6jV/A9HDIos03nd1wkdPozx0UcYyLFje2QdPP8mVKYT/XYeweajOB6Ik8hQiPyimOTMs9DZprFmIIONLhklEEfTF0bsCyMGEyAojC/t5NezAuRln4/VMhKh6S3CG7+PKdhFq2Ekdzm8vG/WsaxoGd8e800Sd6/Fv2EDzUnZ1C44lynnPI7GPJOXW67mndphHGVROvJKeXFvmGcK9QwN72f63p0U6RZQrG1GzD2LlLiTPqGfZncV3cP1oMlGpy/lkp+sJLs8/7M+fVSfQg151Wl3Ym8vO59vIBGXP7HObu3mhcInmNy+mjR/MYVdb/LuquXskbrI8A4yUUhQLkcZrK8j6hli0JLOIed53KbJIAN4KWUztrpdzHnfh/jB+awAb5dM5YHKi3HG/XyzexMXVuxBQkPr4DQm3f3YJya0liSJtrY2Ghsb8YdCNCSl8ZpP4Na1d5E70Merd/2Ob8yfSb7pHz/qL0cllKiEaNUhyQG83kN4fTW4G9/CeFsTokdGNoMmIBCcLeFfpcVsKyTJPgnzUQvB257CsWIFWXfc/mGZXZEYB7xB+qJxZKBzwM2m/Z0EumKYk6PcNu4uMq1arJZyLKZSxIOHKGzaSUwwsN6Yw2NOLZJWYemI87mgLYvAbx/DGS8pUisAACAASURBVPLRk5mH/uJuYpURAnEzB+vG89Sob1EaEPnvQxEunWfjso0PkeERyI0UM7b1aRg3ikT6hTiEfDy4GfS2MBTtxhPtR7HZqJg1kynLLsBsT/p3nTaq/yU15FWn1cmqPt76Ux25I5OZdekIdAYN3sEwfleYgSN38GuhnvmNN2KPOChteIqNCyeSdWIPxtipERVFrY6QxkSvmMxJaymiZSRrBCM6Mc5vMtYyv0pCnzBj1CdISjHS3TZIna6Y9cXnU+LrZqlUQ3lOF4vYQV/EScbt9Qj6fz45xvvDAX7Q0ElzIMzt637HzOOHMd1/P8XnLgBAlhMMew8QDDQQjbiJdbmJDniRImEUMUHM1k3E1gbCR3/QRD9Y3tZgCqeTuuyrpJx3EaLPhn97J6FjQ5CQiZ54jVjDG2TefjfJK5f90/1TFIXvvlPPxu0tGEwi9y98H6fmMMFgI7IcxRpIMK7Wh6DArhF5uEkmFjcRjRkIoiNcLzD1rS4yQ8Nw3lSsN80nofi567U+Nk28hF/UhmnyR3l/upnlj96FXj8Fk64Cu7+bhMaCkGbEmRJjZLgAHToAolKUtsBRGoI1zL3+64yaNevfcOao/rfUkFedNgPtPl699xAZhXaWfXc8Gu1HT062v3Mr36mtZm7jN7HKOsoPPcCOKcWYB9rQFo7mwtUrCSdlsfLJGsIJmUiuluVD7XwvUIxb6+U+56OM6xlJyGz/sC+3Ikk0yznslvLII8Yc3Qlsgpfv8ygaQaLva1vIzj+LhCTzXFUHdT0+5pSlsWh0JoIg8FjXILc2dpOEwJUPrmX58Z1k/PSnpFx5BYqi0Ne/kaamu4nFBj48DkHSIqBH1OgRBT26eBqm3jISUpDhwm1kGVZROukWWjvup6vraTLTlpPT8y0CO3sRNCLmCWlos8z4+2vx3bMGxesh/WfrSLlkCoLunz9p+ptDbax77QQGrciG66dTkWklEukhFGoj0r2PjL+sIayFg+MKkfQJwP/X7u54I0bcB7OZ8WonxjlLMN12J48/9TgbnXkMp+Xx9O4Qd6RDdrqLilceI5JcSsI4lqg+RsAwTEjr41D+G3yzcQJZgSsoFIZxmJxEEiH2DLxK2bJFzFxx8Wd1Wqn+jhryqtMiEojz4q8PoKCw8idTMNk+aj0n3G1c/+QNjGv8LjZRouDQvRwrcRKXZPIrLqciqQiPJ8JQJM6AkiBm7GG8YqAkmkOtuYXG0s3EDucQ0ZmwD3sJD3eTMI9gZ8pE6kU7BaKbs7UtZA65uVTYQlraAK/mzsW2/Ckm2c38/MUa3qjtw6LXEIxJjMt3kDUziw0eH1PDERb9/gHmdR7GcfXVZP7oh4TDbdQ33IrHsxe7fTyZ8iqkzRb01jScl5RhKHb8zbEH/a1UHbgAk7+UnH3fQ5tswjgqhR7hafrMz2Drm0qR5mbs5xYzGNxCe8ejRCKdaIYg7S4DosWB/bxfknzRREyjnAhakVCojbb2dYSCzTgcUygsvJG7al386bU6DIrAc9dOZXLhR2PYUL8Z1q8GnRluaULWGvB4W/jL1ntIMtSSlDRAPKIl5S2FVwYv4LX8+ayMbeTP513HSB88fDBMq1EAQ4x4TwPdgcMY85NIysshqbSQxwc3cShYxU/fXYnbMIOpukNkZZ1NwhtmR98LlJ4/l7NWXfZ5nW7/0dSQV33uZFlh84NH6Trp4eKbJ5FRaP+b9U898RUGqleRJMdJqX+Q9jQLUlIuk3NWMSKspQOJPhSsSGRqYphlPV06F82WQSaXuajdcILGopE4XC58fh+9xokcMGYwZLRxSdMOLuncRXJ+LtnnVGLv/z2vps/nxpG/oLKlkYr2ZkwuP2NtZhYWprGnd5i9/jAaMUFZSxsTGmsxSnFSbryRtG9eRUfHY7R3PIQg6Ckt+SHJQ+fgeaEJfWESqVeNQjRq/+bYFEWi+tDlBIMnmTp5M7QZ8e/sIt4dQNCJeMdup8f+BKKo+6CuYtjt48jN+QoA3W88iOmBXgRBizZ/GkJyNsGKVjyVB8CkxWoYiS9Wg9VaxoTxz/GjWhevbDqJPqbw2BWTmFv+sT75z66Exq0w89tw7p0fvJ/Mn559Ak//EewluylN8iGEwJF6BbroJO7bto/NC1ZS1hvmhmMRMo068qIKsgA3j9Pj8zcyZ88blOVk8fLYJtxDHdyw+ypctjHYZT/T01LQxROngn7p2cy89HJ1svDPmBryqs+VFJd5+8k6mg4OMPcr5VTOzvmb9d11b/D42gPYfVHE4GHiWpHU4nMYp5uCNa7wa22UEyYPfUGF1NKHkEUvE/snke/LoLSpieyubt6dfw6+iJaaaDpt+kwSopZx2kF+UGlh2rRJ6HNzERIB/GvPIhCLsapiDb/bsBnL+/s+dd/9jlRi4yoouGoqkbQBevs2EI+7SE8/n7IRP0eqF3Cvr0dfaCf1mtGI+k/2sGlt+yMtLb9j1KjfkpW5/B+/j/84fX0bAUhPX4TdPuHDIJTlBO271jD80NMYjioI8qnligCajBHo8+cjL7TTnns3jqRJjB33J752uJN3t7agCya4b9V4LhiX/cEb9cPvKwABftgCxlM3RROJBOseX8dA3wCutKOsitWTGCuh0dhwnTSxS57C+hHXIA7H0B/zcFamnZt7FYyywsoZFiSDzGWvPoxZ4+elyU2M8IS5cfsoOjTTiTqKmZKSjElQ2N3/MsmTCph/7Q0YreqDU58VNeRVnzlJkhlo9dHTNMzxXT34XRFmXFzCxHNPPfoeDQWpffdt6vfsoLf5JAIgKJBlsjJ61g1YW0RcerjN5OVsRx/PdttI5DyO1jDAOZ0zmFHvo7KpATkQZuOSJbynq6BRycCciHCpcQ+Xi9sYKXaCRg+TroHsiYS234XG38vNI27ihrdPIlQdZnD11/nBkIO7rh6H1yxT7/fjkPuZoVQhx+oJxpqQ5VN91wVBS6pzHvn51+FwTCZUM3gq4PM/CHjDJwN+aGg7R2uuJyPjAipH/e5fasFGIj0M9LyB1O/G7i9GquvE9/om4t3diLZM5LNG0LPkHXIKVlM04jYuO9hI9bsdiJ4Yt19YyZUzCk8V9OaPYf86GHMpXPLYh+UHg0HuW3sfvogPXVsbl3YcwfftCmLWeiCOP2Zls3Y5O+WzERo15HWGeRwLLxLjDyONmJw6rnn1fvoyhnm7opMFw8nc2tBMsElLX3Ai5unfwaIVOebZSVusltHnLGTMOefhzM37P9eJ6h9TQ171mZEkmcPbOqjZ3knYf+rp0cxiO1OWFpE/yoksSdS8/SZ7X3qWsN+HJc1B2D+C1DDMTlMwjVlOvM3PXqeGJ3X9PLJiCo8d9rC+41dYDY18e2cB4452oo9Gcafo2TF2Jq8kz8Mlm1k2cJw7Sh7HbjXw4vjluBr+wmUDnSRLEgLQYcjgv4u+zuVDvaQ9+AaxK/LomdqNXhP5xHHo9elYreVYLWVYreVYrOVYzKVoNEYAggf6GNhchW/0DsK5dUSiPWg0JszmYuy20VhtowgFm2jveByrdQSTJr6ARmP6xPv8q5REAv+2bQz+8WFizSdR7EZ8CwOkrf4mOSNv4ua6DjZua0EzGGHllDB3LZuPTrDAmkKIh+Fb+yGt/MPy+vv7WfvIWrwaL/M3H8YZClPy2p/Z+8aP0TiasWREkBSRo8IEugZGcW7NKEbJmVxOgI7RSVj9Aa7Y8wR1Fb3U5rpJj2azuGMky4LbSdSkEBz3K7L0WuJKlO5gE/3hNpQcDWdf+zXSC4v/7fXzn0oNedVnIhKMs/mPNfS1eCkY46RiRhbZIxyYbHpkWaJ+z072vfwcw3295FWMZcaUi2l7rx+nYsGmERAEkagI95UZOKQN88BIM0pqCV/Z8GMqQ3v52RYL5iEfA+lJNNqtuIvSecV6AYOyjet6GvnRlFcQ/U3cWj6dOS37WRAKM2BxUm3P47mUFRyzjWCNo4Gcmx9F0sfx/DKXra15TCgcwYwRReh1KRgM6ZhMhYh+E9E2H0pcRrTo0CTp0SQZkEMJ/Ds66HGtZ7D8RdDIJDumY7YUI0lhgsFGAoG6D1v/6WmLGTnyTnQ6x/9Qe/8aRVHwrH+ToT8+hDR0EsmuELwhhUhJnB2xUTxRuxqlL8HEMc08fsG5JDfsgc3fB1sW3FgFxo/ukVTXVvOXl/9CSBziqufe5XDFZBY/tIZX7voG8WgIR7kPy+godq2fmGTAFshhOJLKjrAVd0YOrnoHk4/soy/TTc3YEB7ZR3IogxXuCNP3JNNW9D0KdJBj1UJCIKHEOOGtonD1dCrOnveZ1tN/is885AVB+BOwFBhQFGX0B8tSgBeAQqANWKkoiueflQFqyH+ZRAJxNt5/GHdvkPlXVVA25dTsQYos07B/N/teeg53TxdpBUXMmrUa0wkNkidKRFZIeDtIO3skzSYLPxGCtJlFvl6/h29cdz2Lnvhvxna9xHc3KXgcyVSPG0dCdJOUbWNjYBq1UhYrPcP8eu4OtA0v8Mro87C27uK8YIju2b/g/uwVPNc/TK5BzxrnbnQv/RbH0yL2e7/J2vg8Xq7uZv9P5pNsOdXTR5FkvFtaCeztOfUE1d9RUBgsfwFPwZs4U+ZQXn47JlPu3/yMLMcJhzvQ6ZLR61M+WchnyL+rG9fT24jUP4o85EH52RRSzllOzDKb5Y8fxT0QxTkRnlg4g3HPLoHBE5BeCVduAOtHN2if2fIMTVVNmD09LN26i9fPvpCLb15B7e6bCA1piEfzebuykjxbG/OjLejxETZ40GoSAPQHUgmezMVTHcY92sbBHBf9Qj+X9yuMrrqIztR52IJdXHzjVEInYsQavHQE60ldXUHZDLVP/b/q8wj5s4EA8NTHQn4N4FYU5TeCIPwYSFYU5UefVo4a8l8OkUCcDfcdZrg/xJJvjiG/0omiKDQd2Mfel55jqKMNZ24+Zy2+HEdnMtEGD3FrggN9MvQeYf7yDBwXr+SW5w/xXImRs48f5abJI7n9+HbS257hmh0pHJwyhaDto9Zml2Tn7Xg5E6MSzy88hqFqDY0VC9nmqubGATcPlX+LX2WuwigKnO+IsSL6W2T/AbJ+m4pBcJL28gam//d2lozJ4t5LxwEftIZfaSR0sB/L9CysM7MRjRqkQBzJG0XyxehLvEx79D5yc66grOxWBOGLNUPSX48huK+VaM0DSJ4BCl98EUNxEf5InHP/8A69wxLSVCePFARZvOFiELWQlAtXboTkwg/LueOJO5A7ZNJ6upmx931eHHMuaXNGMb5sJ4KwD1eikFukX5IS8PLyISOefC3X9HRSMaKVFdk7SBGaSSSyGdjjpL8+ztHZIkdsbdzT6qav5TcEzTmkBpq46IFLCR0aJvB2Fy2BGkbctJCs0rLTW5Ffcp/L5RpBEAqBTR8L+QZgrqIovYIgZAHvKYpS/ilFqCH/JTA8EOLNh2tPBfy3xpA/yslQRxtvP76W7vo6UrJyOatyBk43hD2FCBqZUGYDW06kYwnJzNa/xYh167jvmcOsydeQ3e/ngrad7EjrwNm3h6v25nNo8hQEWUZAYebufXgzSvhVwWJShDCbC54G//ucrCykWsniK4cOsM8xnp9Xfp154nvMZA8mxY9en0Zx/Cp8NzxA5i9vZUP+dG57vY6NN57FuLxTl1L8u7vxbmrBdk4eSecWfuJY/f7jHDh4EU7nPMaOWfeFC/i/UhIyQ0/VET7STGjXXRhKiyhc/xyCRkOfN8L5D7zBcEJDcFo2W/vvZWzXdgSd8VT/+Wu2QMqpa+ORRISfP/FzrN1WLAEvM/e8j83rw2Oyo7NbyCkppENvZF3hSFLtKdzSkckDWRFe6pbRTkxhSXwPZye/QLLRjRgbSc2GGFvGRfAYfLx03M2bw2tJ6Cykxjq5ZN3lDL/ZQmTfIMfD+zjrl1/DmqJORPJ/dbpCflhRFMcH/xcAz19f/9121wPXA+Tn509qb2//t+yP6t9HURT6mr3Uv9/Hyap+NBqB864bTWaxmX2vrqd602uk2QqYMeoiDINAQktIDPK+fR9VmkYK2ldgjWiZ1Pkk4194nHX7e7lbFyHF00t25wv02E+S5/HzrXeyODh1Jtqgn4TFTqqQy6zYfu6nkL1yJRuNv8RUrtCcEWGTvJiVNYeoDLVQddEDlDtiRCO9AFhtFaSlzqf3R78g8O67FG3fzjnrDpBpN/LyDTMBSLjC9N93CEOpA+eVoz7RC0aW4xw4eDGx2CDTp21Fp/tij8eiSAreN1rxrH+ZyMEnME2/Avv5KzBVpNCSFGXFw/vQGLWYxurZe+QqNIVnIfYeAWsGfO2tD6/RR6Uod268k0RtAoNsQIwMYvEMYRsOkD4coMwTRvD5kQWBYFoByXklPCgk02lKxrVgPH0Dca7I2clZzteRpSDBYT0HRAW9R2HZISf7Qj9B0lkwxr3MuKgYa1sEoTPCMXEvC2/7Pjr9Px4XSPXpTnvIf/DaoyhK8j/ZHFBb8l80sqzQsL+Xg2+04xsMo9WLlExIZ+oFhXTXV7F7/VMEBl3MH3sVKf400CnsMe5jp+UkDmEUzp5iDN4kjNFBxjY9Q/FDd/Pjrjjb4rtJdr+HpNQDkN6bzY/eCbN/+hzESAhNIomoXWaZdpjaaBe3Jq7hx7O0TM56HK+3ms3KEiw9du5ofpDYsnXoJ67+xL4nhoZonHcOyZddxoGlV/Pt5w/zyBWTOLfy1L2DoT8fJ9rqJfP7k9AkfTJY2toeornlHsaMXkt6+nmfbUX/G8VdYTqvu45YSz3WJXeBYkGXZWHf2OPcvM1OapqBZclv8dP2x5Dm/BjNznug4gK49M8fTvEH0OnuZPOOzQy2DJLwJz6cDFxCwmk14e32UNHQwOjWJoQPB4UT6M0bw56Zq9lYouW8/ENMD+3AqGtFowHCMr0ncpFqriJgPvXtQQOcbZMwCgKNlhpm33QtJnsSAY+L7hPHGepsx5KcQvmM2erAZ59CvVyj+v8mxWW2PlZL69Eh0gtsjJ6bQ1JqiPajB6jfuxN3dyfZBSOZnXExDEqYZ6Tz69qfkt05FwOjAAFHtIf0jl3kRhowr/sDl3W1MDz8MNp4J3IshfjwJGb5E1y5v5o9k+cgyxLFlhTaNRIT8u3kNT3BJdFbGZMd5Fuj70SjMbBRms+B4HTePnQD2pJz0Kxej6zAgTY3rUNByjJtTMhzMLRuHUMP/IHc1zexdEMHOo3ItpvORhQFou0+Btcdxb6oEPvcT/bZDoXaeb9qMU7nHMaOWff5V/6/KNraSsuyC7EvWULy6u8z/HoLcjzO+ooXePDIYopzTNwfuYUCyYN16tcQd66BC/8IE776j8uLRunrH+AXL7xCnFqyY0nY4jYCOj3vlU9ElgVSvB6mnDjGpe9sRtboCMz4Nj9MSqN7vIPLjr9GpGwrc1ODpGhljrZn46heSFiaTW7nuyjWdMryyrFodLT4awgLAYgrmDRWNKKOwUgH/UoHi79zM0XjJ33OtfnlcLpC/h7A9bEbrymKovzw08pQQ/6LIRGTeOPhY3QcdzPtwlxE6jmybQue/8feWYZJcaV7/FftMtLj7sLMoIO7BEhCgLgDMSLEhRB32XhCDAgkhAgxICQQILj7zDA+w7i7tXvV/TC7ZFkgdpPc3b38n6e/dFedOuetrn+959WmBhAEIlPSGDxyBn4FWkSrG98LE/n+ky8weTJRuszEGHOIUTbhH6xG0ycN4aqrmXF4HZbu99F7fFA1novaHsFURw7RbR1UJqQgeDwkaJSEjxnPvn17uTClnLml0xHkIk+OfAf/4DTebVGSq5rN5rz7SZYsyG4/QDsG7lyZw5GarhPzzwhU8cqqJ1H27ccb58xjW0krK+eOYHRyMADtHxbgbrES/tCwUzJWJUkiN+9GjMZjjBy5GY06/A+V7aHmQxxuPkyEPoKZSTPRKv74WHqAtrcW0vnBB8R9/hmq5H50fFyEUcziy9A9fFJ0DRNC21lmnk9Z1EQy5E6EpmNw2x4ITj7jmFaHmwkvrcUesIFoVQuZ7QNRS1oc0SLBgy5AssopyT7KnDVf42ez0TP6Xm71C8I51MCle17m4JAqHvV3YPARkZcEU3r8XrReJSPz3sQohiMNu5kIrS+yf/g+VAIyhRzR5sEodbCncTUTbruZjHFnwy7/FX9FdM2XwEQgGGgFnga+A74BYoFaekMou840Bpwl+X8HuBweNi7Op6GknsikWlpLDyO4IDQhiZTRo4lLGYhU4cC8rxG5rwpxQjSbP8vG7tXia9vNZU/PRZ8cf2I8URS5csNqyjteZWj3cKKMocj+2fwtSah7ughRyLhowaOs/OoVohPyeDP/WrqdBmZmfk6Wq4EWjwZzxHO8Uv0x1zaug1mrMcdM5LLFB6jvsvPkjAzGpQRzsLKTiiUfccn+r5k/7k5KgxN4akYGN4xJAMBZ1UP70gL8pyfi+y/lFgBaWtZRVHw/qSlPEhNzwx8mV0mSePnIy3xR+gUyQYYoiSQbknlj4hsk+v/xSUGizUbljBnI9T4kfLsG0QUdywupCXmZbR45HxXN4l7/rdzr+JiNmQ8xrXQZgiGu1z6vOHMZ5qyyeq5ZfozAkBwI3Eh6Zwbxlnh61F0cDcvGrXKTZI5m3idN+DpFjoy+i5fiIomJdRFe9gJV8SaeDhDx1ToIyQlmT9XL9NGvJr1kMx31ARQNvAurTzQpzmxGPnIJ6tQU7HntdK+twOmysqPhS8bMu570MRP+cJn9J+NsMtRZnBYejxenxYTH5UQQZPS0Wdi+Yi/GljwCZHYyDCMJ08Yj41+iSgRQ9wumVIT8/U1o7J10yz5j1n3XEDHwZPv4m1u283njM0ytG45W8iWxspLYujpEHxFxsIn8iiSUGh8ue/Y+DmQ/ho9vNW9mzaOqJ5H+SWsIS9AR5ZvMRs9Ezq/5nqfL34FRdyGd+wK3fpbNjtI2Pr1pOGP+rqV7zWYqp12AKyKakodfZXBcAEkhvTVTJEmi/YN8PF0OIhYMRVCerMVbLGVkZV+OXp/C0CHfIAi/3Pnp1+CfCX52+mzuH3I/R1qO8Pi+x/FKXpZMWUK/4H5/yLX+GeZt22i4625CFzxI0Ny5iA4PLZ8cojLiEfabElhWdCWf+bzFYE8+rw57ldkFr5OYNglmvI1LkGHxigQo5Kc4pV//Zifv5diYkmxHUb8MUeNPuDsZQZAj9BHY4d6Bqs3IU1+pCHDIeH/Y9aybPJhLj2+kXL+b7rBu7gtz4COTOHj0SiLqJ5GV9iIP51ZCdghF6TfSHtCPmPod9FUX4zNqFOq0Ydjy1DgtNnY3f8OoebPpM2rcHy6z/1ScJfmzOAmdjd2sf2s5pqZc3N6T89N0cl8yQ6YSrU1B0MvRDwlHGaoHQHJ6kOmUtNk97FpfjbXbQXTDTkqDfiBqrJLZNx8BQeBwVSd7yttRW1r5xPQ806oHoRY1DDhyjGy/FMKjbMRP2kflunjk/gqSzvVDpjyCVxRYmDeX0s6+vBV/kEvmvYBTFJmTX0Vg2ToWFz+HkDYdrvyUb3ObeeCbPJ6Yns7N43o1YUkUaXrwQUw/bib+66/R9j+ZOB3l3XR8VIjhoiR8RkWe9JvJlE9BwZ2IkothQ79Do4n4Q2QtSiKvHn2VlSUrmZMxhwVDF5wgzXpzPbdsuYVuRzfvTX6PYeF/bK9USZJovOcezDt3EbfiY3RDhyJ5RNq3HeG4awE7emL5rngGG3TPoJQ7mZn5HkaFL6JMgVHee88j3N3cY8/ihiHnIET32sNFUeTKV1aRZfTh0hQtUTsXIagEbGExeNR+yEwdHA3Lxa7u4bFVWsI7rfyYMpqN113O5HVLKQtT0ZDky4NpBxG9Wqo3PUCLYOfHjIW8V9JOSFEy+bIJNESfQ6ipmPTC5chddhRRKWhH34fXLbCv5VsiJw1g4NRpBIRHIsj+PcNb/yqcJfmzOIG6Y2WUL9tCok8aSllvk2aLzIlVdKFTqfD3aBFkAn4TY/AZH32SzdphdbNr5XEqc9rwk1tIObKYI2n1HB3j4uOpi1EkTWbxrkpe+bGUfnInXUmfMKk2Fa1HRUxZCUfGxjJFlYM6rgvB60ahEZHJRURRRm1HJF+3TqO8uT8LFF9x57z78EQOZl5xDV3lu1hV8BDyqMFw3Xd0OGVMeXM3icF6Vs8b3etMraig9W8vYT1wgNAH5xN0880nrVuSJNrez0W0uAl/cOjf67NX09zyHZ2duzGbC1Crwhg4cBm+vn1/k0ybLE18kP8BeW15BGuDGRg6kHFR4/BV+fLusXfZXred2emzeWjYQ6doxW22Nm7dciu15lqu7nM158Seg1KmpMvRRbutHQmJEF0IGYEZhOvDf3PBM6/ZTM0VV+I1Golb+TnqxN4XorOnm8Kc+/i6Rkl2+XC+Vz+HTK3ks4ELqHHLCLE1ESI5qJH5sleXylBTMS8MHYksbVrvmptbuPn9TRR7QvFXy0lXdBMl1ROqqMbuCkGOi2P6Cur9S7l3ewKD8qsBieohQ2m0ttKl0eNICWLChP0gqWg5NpNyj4uDUet4ub0DbP2oar6JFm8EOnsrE5IakFcVY88tQXfu4whyA/XWUjodzYiCB6VGg9JXR/DwJPpNO+//XSjmWZI/CwDqD+VhXlWDj8KAJ0pByMA4vEYnrgYznm4nMrUcTZ8AfMZGoQjQnHSuqcPO+nfzMLXbSLZlEXn4U3ZP8OXT4SZWq1KInv0d+8o7mP3RYRKCXIRo1pHRE0yEfyMxuiKUCVZkgoTbrcJjkWF2huJy+RIdO4KVxjyK2g20Vl3EFZqjvBpzCOcNG7m9uJbqmhw2F9yL2i8SbvoRdIHc8+UxfixsYcM9Y0kKUNP22ut0f/EFMp2O0PkPEHD1qY0qbPntdH1RSsDlqeiGhFJbu4TKqjcB8PfPJDh4MtFR16JQ+J5y7s8huzWbu3fcNzRSRwAAIABJREFUjUf0MCJ8BO32dkq7SvFKXgAUgoL7h9zPnIw5ZyToHkcPb+W8xdrytUinq63wd4TrwxkdOZrRkaMZGTESf/WvCyl01dZSM2s2gkJB/BcrUUb27mIkSaS8/EUW7uyktKYvX6tfI4RW8IsGp6n383fsM2SS79eXOy59GAy9TbuzsrJY9v1umnSJNHn0dFrdCAJckmwnoisPq1VOpW8n+YF7uKl+BkFZPaTVHUAHlE4aT1lzLT5+VuImN6ELObVonL0znvb8S7C1ZyB43QyNaiExTqJ94fvohl2DPHIYuE4+R5S81HmPk3zLJML7/P/Joj1L8mdB8fdbUe0TEZBhHRzIgGt+fSia0+ZmzavZWNqMZOS/j4+5ko/Ol3M0XeLtLisjbtyJUx/J9W+sJppikgIrCA5qwODfjEwu4TJqCC7Tke/NxGWup13qj6TScdVlc8jXZPPM7iWI9fPp4+vha+tcTFd/ya32eKra6jhQcAc+AnDzNjDEsPN4Gzd+fJT7pqRw7znJNNx9D5bt2zFcczUhd9+NIvDU2jGSV6T1zWxQyAi7dzCNTSs5XvY0YWEzSUl+FLU67HfJdF/jPu7beR8R+ggWTVlEjG9vOKbRaeRw82HsHjvDw4cT4fPrTD+d9k6Odx1HQsKgNhCiC0EmyGiyNFHUWcTRlqMcajqE2W1GJsgYHDqYq/pcxeTYySjlyp8d21FaSu2c61AEBRG38nMUQb3ZpZIkUVL6BMv2d7GlYjKzVXsZpa3FJOkocIVRLsVycWQ301sXk+PTB2fkUMZf9daJmPrs7Gw2btyIQqEkfdhYihwBLD9Qy8AoHy41ZFNRbsSocpIVvI+5nRfitaSSsmchIU4bzc+9xeNbyxip7aKvcz9yrR65Ph6z0oLLUEVmWAFejURP+SBacu8AZAQozfQdHoBi0RMoJTehDz+Oz8TJCDIZHqOT1s3FCBUuLN4eFJMDSJn2/8Nuf5bk/8thMxnpqKvFWNsMdhGlQYMm1IDW1w9zRzv1m3KIt6TiEj1UBvsw7eExv2nbv2nRMaryOsjMe5d6QwXrzhXpq3AwSwgga+py9nVLRBd+QEJwPn5+bQgCSJ0KhAIlBxSjGC42U0QfBFsTNrs/7qBwRqRNYcTFGUxffTnGylvRyoJY5/sS+UGpPJhwFzaPm8PljxPamgNzt0DEAGwuD1Pf3INWJWfDPWOxrf2WliefIuzRRwi8/vozzt+8vxHj+iqCbuiLLMHLgYMTCAgYxcABy353qYJd9bt4YNcDJBmSWDp1KQGan83z+8PgET0UdhSyr3EfG6o20GBpIMonitsH3s6MxBnIZWd2Fttycqi7aS7q5GTiv1h5opm5KHrIzbuRY1UtHM2/HUtkBBqNkiiDBrvby7c5jcwzHOFB21u8EncTNw0eT8jAS06M297ezqZNm6iqqsLf3x//vuN5aXcrA6MN3Jq4j92HbMgkiWNBOVxtmszoljgse16kLiiEp869FZtDjk6hZK6QhyN/H/bEKL5NzsZHUPConxNVoAlXUxI1B+9CYXXi0gQhVwiEWMoILd9GiL0KdXw8Mn8/1IlJyIdNxbTLiEJSYoox0/f26cjkf4wT/d8VZ0n+vwiSJNHd3EhjaTGNpcU0l5WgM+pJ8R9KqOanxB6bx4TZ3Y1KpiFAHYZJsnHEruTSp0bhF/zrY7OrctvZtKSApMrvaErJYtLTi0lQ+dNlt3Ndswx7YzZ3Sgvx9+2k06Wkpj2ccZ/1ILYqWDL+StJDbHiRESKaMFXXYYtPJ0Qfyx0LbuLJ/U/z1S4Dkq0Pt6e3ckgvsS9gCP5uic2ejcQfeRVmvgNDegn8ie8K+PxQHavmjWJIhJ6KKVNQxcYR9/lnZ3xpeTrttL6dgyrOj+Cb+lFR8RL1DSsYOWILOl38L67fK3qp6Kmg1daKXJAjIbGtdhtryteQEZTB0qlLf7XZ5I+GKInsbdjL+7nvU9JVQpJ/Endn3s05seecUR6mrVtpvPsegm65mdD5809873J1ceTwRXjNDtI9iwm98Ce+2Fnaxk2fHOGHoHeIt+XxUNojvHvRLcjUJ3d6qqysZMuWLbS2tqJLG8viPCdT08O41HcRPxYnYXC7KfU/jkwYxdNHHIg5H/P1gEtYnjkRwewBJCbrO+hXuAZ7uI7NfZvoVnZxhSyIMZENuMxhmIoGEZlbiim5L9ZwX0QcqDrjGNBSjsLYgaOsDMnlImj+I3TU++NnN9Cj6iRu3hh8I0P+lPvw74CzJP9fAK/HQ+HOreRsWo+1uQO90p8Y/zQSfAegFjVIegFNZhCKEC2udivuJgveHhdytQKzr56dRzoYe1UqAyZF//LFTlxT5NP525B1tmBTLeSmZQdRy9XYvCJXHisnsuMrLpevxCvK2N0aTL/tA+iXtRezUs/6acOYe6ELn5jpHF1TQMWBgzhT+yCoArj3/vsps5Vx5YpvcHeNJb6fH6VRvvi5LQzw+uE6cIBv5Y/SEXMe4Td9CYLAysO1PL62kFvHJ/LYBen0rF5N8xNPErviY/QjR552/qLLS/uyAjxtNsLuH4JXZ+bAgQmEhk6jb8brv7j+3LZcntj/BLWmk+spKQQFV6ddzd2Zd6NT6n61PP8siJLI1tqtvHfsPWpMNfQP7s89g+9hZMTp5dL85FP0rF5N/Fdfoh048MT3ZnMRR49cgdoURebI5egiYpAkCY/HzAsba9h1KIsdmgV8EX4+1uQLmDd51ilju91u1q9fT35+Prbo4XxTIXHFkDCGtj/GwfaRGCQZx/3LyY0dx7IlWwlqqab9rbd5rrqZsjYDgtmNn9zK7Kb14OikeqiSI8F1pKpFbvARUepsp12TvXE6Uy97A4XbRtOjj2HZsYPQZ56hxeiHT5UGr+TBGGIk7soRBMb993WmOkvy/+HoqqinbNl2Ar2haBU+J+qIAKgT/fEZE4kmPQhBdqr2VlPQwaYPCohND+SCOwb8JjNN4bZKdq+uJbjhfRLevIHhqTMBeOhoFmHGlxkkO0Zndxj1u4MZsbeNILuR7NBUqq/0YUJaHk7RQ/PRANrzgyAhELMmkSuuuIL4lHgmLX2ZtoaREKdHTNVxY8N33HnezYSGxuH4YAqOtkom2F9jbP8UZDKB9XlNTEgNYfkNw5DLBGpnz8HT1UXihh9Ouyav1U3Xl6U4K3sImpWOtl8w5RUvUVe3nFEjt6DTJfzs2nfW7eT+XfcTrg/njkF3EOcXhyRJiJJIkiHp/0x7/zl4RA/rKtexKHcRrbZWRkSM4NHhj5JkSDrpOK/FQtX0GcgNBhJWr0JQ/mTPb63bRNHx+0EmoNXH4nS24PVasHpCeGjP4ywJ/pbxPWuZNHQ5CzL6MiPx1EoloiiyefNmDh8+TFPQELY0yhgWp2aq9nW6auNxSOEcC8qn1X8aH778NsrkkRRdfwGbqhvZLqUjdjqR+cjJaMpjtL0Ql9RG1lAb9T5Gbq+5Cj8ZVPvvZWpDPYHVIdQPtSMO78DbdDHnzn4DyeWi/s67sB46RMLXX2H1amn9thA/uwGn10abTxMp159DcHzcn31L/jKcJfn/YFRuOoi0w4hSpkaKVhDQJwa5rwq5nwpVrB9y35+yE5sqeqjKacfc5QCht+57U3kPwTE+XHRfJhr9zzvn/hmiKPHpPRuRdbdTNngZL8zfjyg6+fHQYpz2FeglK9VVg4j5pp2EhlbygpPIH5ZM5uQ9jGx2Uuf7NIfXfYfkNRGZ2Z9Kj57ExEQmz7iYq1Z8Q01TCN4ILf3jWni1+FU4/z36DR4LRz+EDfNxX7iY11szWZXVgFeUuGJINAvO74NaIcfd2kbFxIkE33knIXfdeWLOkiThbrBgK+jAltOKaPcQcEkK+qFhuFyd7D8wgdCQ8+jb942fXXtRRxE3br6RJP8klp27DB/Vf1YDaqfXydelX7O0YCl2t517Bt/DnIw5P5UL4CezTeiCBQTNvemk81u37KWxcSWyDBFtQCQadSQ9xiyWHNBxpGY4R3wf5IgumSsGvsWzqbHcFBV8yotWkiR27drF7t27sYUNYEOLDpvTy8CQ4wyWavFY/DkUncXoY0lctX0bmlueZrVYjycghM9s8diarASH6HD02IkyVTHVtJ/c2EaaQ3y5pPB+EjT7WRvcQU7UToY1uLk4IAJVRj2+x4czfM7beNxKqi+6GJmPDwmrVyHT6TCWNNK2tgitSYvda0FxfiAJk0f8Jffkz8ZZkv83hdfjxuNyo9JqT3lIXE4HxYs3EtgchEOwEnx9PwLTY087jtvpZcdnJVRktaFQyvAN1iIIoFDKiOsfTOa5sShVv83xVLK9jB2rGpAbP6Tfw8MJ0Nipr1+NUt5DhzGM2vJhDNxWgtGqYMXgGRj6icwJfxfHAT8ammNwOV3IVWEMmnMxhytLKWtV0hQUQ2mTEhEZnkQ/LvY5xPVla1kV+Riv3TITLG3w7lCIGADXrz+pKuI/o+uzz2l98UUSN/yAOikJyStiPdqCeXcD3m4nyAQ0KQb8zotHFdlL0BUVr1Jbt5SRIzaj1yeddlzojXm/dsO1aBQaPr/gc4K1wb9Jbv9O6LB38PzB59lRv4MxUWN4aexLJxzEkiTRcOddWA8eJHH9elTRP5V4EJ0eWl7NQhmmI/iW/giCgCSJ7Dk6n7lrJ7Iodjvntn7Cswm3sTj2WqYF+/Nqn2hCVKcqEfv372fr1q1ExqfQGTyANbnNmCx2LtfnIOLgWMRhHlnuxcctoX38OVbn7mHs1PN4oV5BdUE7glciyFeFqcfM7M4faAytQaGeSEbraIb5f0AVk6gOc+Gx5TAsvQyZxkFgrYJhc3dizSmk7qa5GC6/jIjnnz8xp668Wjq/LEEpqpBN8Sf23P/8omdnSf4vgsfoxLKvEWdFD6LLizJMjybVgDY9CFEtYenuojWvDEteC+pOJSpRg0d00eVpweJjRJ0cQGBUNI52E4pcL+GKOEz6HpLvnYzK7/S2X6vRycZF+bTXmRk2I4FBU38doVus5XR37cfjtaJUBqDTxqHVxqPRhCOJAl89tgJVQBaevlsJ0YuIkoxaez96KoKxdIczICeH9X7D2JE2gsh4JzeXLcVYISBIAoExgzF1p+K60MKGohpajcOxoga5FSLUOOMiebh9BRMdZq6sv4zv75lEeoQfrJ0HBavh9gMQcuYY55prZyFaLCSu+x7R6aXz82Kc5T2o4vzQDwtHmxGITPcT4bhcXRw4OIHg4Mn067vwjOOaXCau33Q9rdZWPrvgs1PMHP+JkCSJVWWrePnIywRqAnl9wusMCh0EgLupicoZM9ENG0rMkiUnFA3J5aL1zU+w7Csg4KrpBM3pLbVs72nilk+XUtiaQY7hCQSvg8XB03g5aR4+SgUL02I5N/hUM1Z2djYbNmxALpeTOXQYee4IVh0oZYa6iFZNMxZZAw+vqMUeHEPrtCkUuG3ceMstLOty8+GhGhR1ViSHFx+5l4vrVmEJ6SFIuho/p4Epgc8TKjlocvWl3hKE+tLNdLUF01cRRb8rv6btzbfoXLaMqLfexG/atBNzsrZ0UffWPnT4opkRQdi4tL/gbvx5OEvyfwFs+e10rylHcnmx6W2YTR3oRT98FL0l9W0eM3JBgVreG9lilZuRDAIKUYnSqEAuynF4bVjcXRhUYchkMsjUEXPlsDPa0VtrTGxeWojd4uLcuX1JGPjL0QMej5my8hdobl592t8FQQWSEgkrAE7Rh07PFZTl65Fbe9Da7YRUZ/NB9BV0pScTL9Zz3r7vELweEuJMDL1qCRuWteFOMfKRqRajPZlonYeLp6Twheihy+PlncJnmGLwYXDptZzfL5KFV2dC7QH4eBqMfQCmPH3G+btbWqiYOImQ++4l6Nbb6Pi4EGdFDwGXpKAbFnZaWZVXvExd3YeMHPEjev3pqyza3DZu23obhZ2FLJmyhBER//ttfEuVkZqCDlQaBX1GhKM3/N9lYRZ3FvPArgdotbZyx6A7mJU+C51SR9enn9L6t5cIunkuwXfeiWXXLtreWoi7rg4EGUgSPtPnoU6ZgKvOzNGoTdxfP5rFgduZZvsIAuIpdcm5e/i7FIp6/pYazY1Rp+5+urq62LlzJ4WFhSgUCjyJYzlYWMlIZR15gXmEdvpyy/eF6B12RJkMp1aLYnAmTdfdyPOSD3U5Hchb7KQFyBmRtwJroESQ4jJ8nAZiYrYz0/0xgtfND+qZ6EftZ2ejL/MHPoIh/TJqZ8/Bcfw4scuXoxuceWJOxvoW6t8+gJ88EL/LEjAM/8+10Z8l+T8RkkekZ0MV1oPNuHxc7Kj4HJOzk9h+AwmJTUAj6tAZdajcahRqFdr4QIJHp6AK/Ekzl9wijrIubAUduLusKMP0+E+MQxF0aqijJEm0VJko3t9E2aEWdP4qps3rT2ic3ynH/iu6uvZTXPIwTmcrcbG3EB09G5UqGJerA5u9FrutFpu9ltKdhdjbQ/gyYDMvnvcZXy3/EZnCRWx1NWW++awKuhUxoQ/Ti9eTUFuONsBJwsRGpoz7hB++ktPU0MNS3yqMrgiGR7gJmDSADV0mwlRKllW+zuDWAzwQvpyNx03smD+RSF8FfDAenGa48zCo9Gdewyef0PrSyyT9uAlnrQLjpmoMlyTjM+L0CUcWazlHjswgPPxiMtJfOe0xbbY27tlxDyVdJbw+4XWmxk39RVn+HMxdDg58W0FFVhuCTEASJVRaBROuTT3R8Pz/AiaXiaf3P822um34KH0YETGC0eEjGfpZDs5v1584Tp2aSuiCB9EOzKRm9m24KvLxv/YV9EMzUCTJuGLtt7SZwjmkfwmFygnBfbDXHmDeyA/ZrIzjxZQo5kafXuHo6Ohg06ZNVFZWUuY3BE13BTGKHnaF78KtmkB6czDX5dYQ0tOK1FiAR6HgxynnsGXQaMpa/FD2uLgjMwDX2jfp0kvIDJOJM/Wh0beJ8wK+JbWuhKzx8WiCq9jdKuPZKw4jWd3UXjsLT2cnka++gu8555yYT3NRKW0f5hGoDsf/0mR8h0WepChIHhF3ixVXnRnR7kEeqEGTbDjJF/bvgLMk/y+QJAmvyYUgCMh8lKeNSvk1cLda6fqmDHejhVpvKUfq1pM0fCQTr78Zv+DQP3jWUFfUyYG1lXQ2WFCq5fQZEc6IixJ/0aHqdLZTVb2Qpqav0OkSyUh/DX//Qac9tnJnCT9+3QyOLymZdJyYiik4cdOnMI/PRlVRKl2Mr2cwsyq+ArsdXZwfqVMPkRw7D0fnbDZ/lsPa4C5qXIFoU7R0JwaCRyTGKrJQU8iYQ/dyoN9zXJuVzP1TUrl3SgrseQ12vABXrYT0GT+7lpqrrkZ0u4j98Ata3shCmx5I4Kz002rwougkJ2cWVlsVo0ZuRaU6uYeoKImsLlvNwpyFeEQPr4x7hUmxv65WeV1RJ8e21mHtcaL1VREW74chXEdrjYnjh1oAGHxuLJnnxmHtcbLj0xKaK40Mn5nA0Avif3MNmj8SuW25rK1Yy8GmgzRbm5EhcItjOBfa0wjoNwifSZMQ/p485Glvp/L8aejHjiX67V5T1+c73uWJLYm8oazgUvVLCKHpEDca96HF3Dr4bTb59OeNPjHMijx9z1ZJkti+fTu79h5gtzCAkZSgkdvZGbYDk34MbRFXc1W3lzt2VyLufA0hIJiqB+7k1R431VVawn3VrLo0no3vvIqxrY7m+H4k9EzGojYxWfcOxsb+eC/OR+7bQo8zlJmTVqLoUdN49z04iorwv/hiQh+cjyK4d8dxfM9erN/WE6qJQR6pRZcahNfhwVrZjtThRiadJmEuWIHf0Ch0A0JRBGpO/f0vxlmS/ztEmxvz7gasWS2IVg8AMp0CTVog+pERqGJ8f/HhkyQJd6MF69EWrEdb8EhuDrWsx6w3cs6N80gaMvxPmfuxLXUc+LYCvxAtQ86PI3lIKCqN4u9zErFYSujs2ofFUorXY+k9SRDweMwYjbmAl5joG0hMfAC5/PR/SlEU+eqONdicAp9nvshF3dMweRVEV5axdHwRRikTb/UlzKn/Ep1ahjs6iQnT8vGK7fRNWccnCzez0Veg3BSAO9kXMVxLqh0m6fXsPFbDF667MOLLdOcLjE8N46Prh6JoOQYfnQsZF8PlH/2sDNyNjVRMnkLIAw8g+I7DUdxJ+INDT2nf53J1YTYXUV3zDkZjDv36vkNY2PSTjintKuW5g89R0FHA8PDhPDHyCRL8fz6s8h/I21HPvm/K8QvWEBLri6XbSXu9GdEjIVfK6DM8jCEXxOP3Tzsxr0dk5+elHD/UQsaYCMZelfqbneF/NCRJotpYzZryNXxV+hXB2mAWTVl0ii+i7Y036fzoI5I2/4gqJgabrZHJb2xG69WwxtCNwfwsgiEGRtyOc+sz3ND/ZXb59uPd9FguDz+1zMQ/rr19+3bW7clmjyeFGdrjyEUHtfpaRHyw+wxG63ExqrKacZt/wJN+DqUTEnhRn465XGT60CjevjCD4wf3Urx3J4c6G4l2XoGoNHOD8Bjf258maMQadHGFyARQq8IIDBiP3w4d5qWrkWk0hNx7LwHXXI0gl1N++ADHP95Ggq4ffsog3JIbk6udLncrQpgSRYQWh9eGuboNTY+KKF0KQereGkBiAOj7hKGJ8kOmUyJo5MjUcmR6Ze/nL7jPZ0kecLfb6Pi4CG+3A1UfAz1CO3aTEa1Lj7ZHi+AGZYQe3ZAwdAOCkfv1EockSni7HThrTDirjTjKuxCNbkTJS5U5j1JrFgNnXsDQmZegVP85b/Taog52fP0V0QOaiO4rRyZXIBOUCDIldnsd3d2Hcbs7AdBoolEq/EHofZBkMhUG/yFERV1zSmy42WxGoVCg1faSUd6yzezLVgJfUxcuQ0sY4TV1fDS6BLtSh6XsLsZ2ZjE8wEbIwGEMyjTT1Pwqfj5PsXVTO9tCY6ms0yMECUgyBdPDgnnvmkxkMgHvrleR73qRpYnvoE2ZwNXDY1FaW2DZZJDJYd5e0P58aYDOj5bT9tprxH66lu5v2/GdFIP/efEAeDwW6uo+pKV1HXZ7b/KSUhlAaurThIfNPDGGy+tiSd4Slhcux1/tz4JhC5ieMP1Xa9Y1+R1sWJxPwoBgzru5H3Jlr5bncXuxGV3o/FQozvBQS5LE4e+ryP6xFq2fiug+Aai0CiRRQpIk/EO0JA8JxT/kr0+wKuoo4q4ddyEg8Om0T4n2/Slpzt3aSsXkKQRcew3hjz0GwBvfvcG7h9J4QS5wxfly1AduA5kCznkS+9ZnmZPxPAd8+/JqnxiujQhEdhr5SpLE2rVr+TqnhTxPJHMTLdg6KvG4PXjx4pXr0XrcaG02pm7fQ+uAKZQGmXgvdgbeJjevz8rk8v69ROty2HluxYuE5o1Hr6qlb9susv3ngn4Lx0d9z+XxQ/FasgEZacGP4XpjK7aDh1BnpBPx1FNoBw3C2NZK/rZNmNrbUev1xPYbQPygIag0J5tNbSYjNXk5NBzOQ6x2EKFMwKAORS4oTitbQSlDEaJFGeGDJsWAJjXgpMCAPwL/70neWW2k87NiEATaE9vYvflT3E4HgkyGJIooBBWpoUNJDRqO2t5L7jIfJYJKjtfkAo8IgAc3rbYaGq3luMMkUieM/dMbDFutzezZcj0q/0pAQKk0IEleRNGNJLlRq8Mw+A8lMHAsgYFjUat/3kzUWmOiubyZYxV7qG1tRJAkolvbSKltpjTyJlTeDrKTswjyhBJbWc3XQxw0GY5DxQ0oHUG8Mn4T/n5DcDocWKRvqKmeRGubgcNR/Shq8kEQHcQEt+E0JbLt/gn465RgaoZ3h0DSJLh6Ze9ETE3w2SVgbOytLhn+800zJEmiauZMZDo9+smP4Wm3E75gKDK1ArO5iLz823A6mwkKHE9AwCh8fNIwGIYhl//0gFb1VPHArgeoNFZyUdJFLBi24DclNXU2WljzajaGMB2XzB+MUv37NLSmih6Obamjs9GCx+XtfcEIYDO6QICkzBAGnxf3q/wsfyQquiu47sfrCNIEsXL6SvxUP12/8aGHsGzbTsrePcj0enpMpcx4Zw9OZxCrQhOIu1qP8Nml4DLDjLex/vgY16U8xn6//qTqNIw06IlSqwhTKxhl8CFO2/uceTweVq78gg+Py2khgLevycSjO8pT+59iVOQYfIWrUe3bSXhrK+fWC+z0M9IUYmBF8AxkDi/PzuzLoCh/3F4JBBcL1zzDyONXkKA6gL1ZQUvQcKyK/awa9ANz+p/HUHJw2Mrom7EQbY5A60sv42lrw2fyZHzGjkGm0yHabCCXox895qTQ0tPB6/HQdLyYusIC2osr6KlrQibKUMo0BIfGEhIai8E/HK2ow9viQLR5QC6gHxKG7/hoFL+hxMjP4f81ydvy2uj6pgx5gJpc7x6Kc3aSNHQEIy+9mtCERNwOBzV5xyjavY3q3Gx8lYFkJIwhQBeBIApY7F00tZTTYa9HMsjJGDeR9HGTCIz89eUB/gGrtYq6umUYTcdQq8OJib6e4OAz24BtthoOH5yNx9NFRNCDpGdee0ZTyy9BEiV2rSyhaH8zxoAC3CojgZ169NY6miP98SjlKFx6PMouENSklpRS0DeRH+LWE1ozgEr7tTwy+ADxmj0ofZro7g6ntHgiHo+KhtBMNrt9kLfaSO1zkLLS0SyeNZhp/SNAkuDLq6FqF9xxEAITob0MPr8U7D1wzZeQ8MuVAm05OdReO4ugOx/B1Zh4ovFHe/sWCoseQKk00L/fO/j7Dz7t+TvrdvLovkdRy9W8MOYFxkX/tuqENpOL1a9k4fWIXPHIUHwC/vhdm7nLQdGeRgp2N+Kye4hOCyBzaizRaQHI5H9NU4yslixu2XoLw8KGsWjKIhSyXu3UlnOM2muvJfy5Zwm48koAvtj2CI9tG8ckmcT7141CF26FD6eAXAWXLkNccwurfIdreOoXAAAgAElEQVSyOvVG8oUAjN5eZUkG3B4bymOJEcgFAYfDweKPPuXLRgNtop7pAyJITy3kg6LXmDfwDvIaUojM2kdGUTFpUWPZ2ryFytRh/KAdhczkPmn+CbGVRJjKGFNzKenaHXRW62kLG4ZMdGGWH+VYyj6uGGzGn24GDviAAPUwOpcupWftt3jbO04WhkJB6Pz5BN14w6+Wn8florniOA3FhdQXF9BcVorH3VsPOTAymn79JxGn64ursAck8JsUg+/EGATF/+7+/teTvCRJ4BFPaucmiRLmnfWYttaijPPlcM8GynMPMvG6mxl8wUWn3Z6bOtoo2LGVyqMH6W5pRhAEAiKiiOnbnz6jxxGelPq7HGaSJNHU9BXHy55DEGQEBozGYi3H4agnLOxC0vo8d0odc5O5kNzcm3BYnbgbnmD6zVf85uv+M3J+rOHgd1VoHDuoj1eQWGHFrRqFQ2VAFDy4fRqQB7UgNjaRUVyKcfhFPJ+6AoNJS3PjAwyPkTOlPgiFQobPmHrys0qQ+yhQD0vnvYZwZPk9jPXbzGCHCVvcBTxx0xW9yUyHl8KmBXDe32DUnVB7EL66pndrP2s1RJ7eAfyvaHr4YczbtuN3xUIEhYaQ+wZQ3/ARlVVv4Oc3gAH9P0CtPn1Ex6bqTTyy9xHSA9NZOGkh4frfFuFit7j4fmEuPa02Lpk/mLD4P1fDdtk9FO1tInd7HTajC5VWQUxaAGmjIojrH/SnO23Xlq/lqQNPcU3aNTw2otc8I0kS1RdehKBWk7B6FQA2WzVPf/UKq8pmMkOv5fVHxqNpL4Tl50NkJlz0Pqy7G2r3gVKPvc8MGtMvZ7EslZUtPdwWE8Kzyb2astls5tPPV7KlQUaJFIlHEkjuu4528QiLz/uCj7/cQVRnCzNzO6hJ1lHdUUPpgHFsTR6F3Auzo4NJkyl5cUMRiti3GF8zgtT2SQT7G/FUt6DCQodhAKJMCUIW/jM/JlwrMWTQJwQEjEDyenG0VNHY9g0d9j14PGY0FUo0i1uJfuRFDJdddlpZWazldHTsQKnwIzT0fJTKk02OHreblsoymo6XUFuQS31hPnKFgpEzriJZGoA9rwNFqJaAS1JQJ/x+i8B/Pck7Knro+qIE/cgI1EkGRJsby74mXLUmNAOC2FPzDdV5WUy5+Q4GTr3gD52zw9FMZ+duJMmDXp+Kv/9gZLKfbHNer43jx5+huWUNgYHjyMh4HbUqGFF0UVP7ATU176LRxNC///v4+vQmZHR07KSw6D4kr56KTXcy/bbpRCYbfvcc7RYXnyzYg6E9j7oMI/rOTi6y1hN+y4W0dWlxelQY6veS+/VhAo02qifPY2Gfb+lWdxNSeCk1mv48Fx+HMa8b+9RiuvNbMCqN7I48SkfQkygOS2RINaxXPIFC6NXWCEoBn1Co3Q+p58PVX0DlDvhqFvhHw+zVvVr9r4CrpobKGTPxmTgTe3Qy4sQGuqTd2O01hIZMIyPjtZPMMv+MbbXbeHD3gwwKHcSiyYt+U0Exl8ND4Z5G8rbV47R7uGBef2L7nj5i5M+A1y1SU9BBXVEntYWdWI0uYtIDmHpTX7R/cgjfG1lvsKJoBfMGzuOOgXcgCMKJTOP4NavR9u3tnlVZ9S4Ltx1nfdU0BoT4sOimYUTXb4Bvb4bht8G0V6DuEOR/DcXfg70LIjN5bOT7LO90s7RvPBeG9v633W43O3fuZOfBLArFKEpFLdrEt+gbksjEpOdo/upTYuobmOA/lB/aNiMYgqmJTuHggFHU6PwYooDZ+kCe3PQF2qgveKB0JG7jxbil3l2Xn7yNIGMBtdpxKFQd+E9/kUCNh8iIywGRtrYf8XotGAwjUKmC6WjfimCTCPhYTfqb61HF/pRxLoouyiteoqHhc6D3P69UBpKe9hIhIVPOKNee1hb2ffkJxw/uJSqtL+fOuB3n9ja8PU78psTiN+X3xer/15O8q8mCaVsdjpJO/tFYR+arwuecSLZsX0ptYS5Tb7mLAZPP+8PmKkkitbUfUF3zDqL4U3sapTKQkOApBAVNxOlspr7+E+yOehLi7yIh4e5TmkN3dx+hsOhePB4jYaHTcXtMdHRsQ6/vQ/mm29H7RHLpg/+7tOtDK3PJ3ttFuHctBVFBXO3YRR/1MSxNasz1Wiytarx2Oe2GQA5Nv50e3W7WBx8ipbgfOcJsrkmXEX1QjXa4haLm3fjKfLn9tju4o7yLI3sb0HRa+VL5Ik9zG3+bNZ6Bpt1Q8gPYuyFtBoy5B1oLYcUMCEqGOd+B/teRpeT1Ujf3Zux5x2hfoMEV3o4gKPD3yyQ2di7BwVPOqNmWdJYwZ9Mc0gLT+GDqB+iVZ46//2c4bW5yt9VTsKsBp63XbDLiokTC/xea1v8WXq9I0Z4mDqypQOunZMZdAwmK/PNq6nhFL88efJa1FWu5KOkiHhvxGGq7h/LxE/C/8EIinnsW6H0OCgvvZVNhAysKbkCp1vDetYMZV/kWHHofZr4NQ274+6BuKPoONs7HrdBz4bhvaPAI7B+Rjp/ip+fiH/Xp95e3s1NnRh25hufHvMjG3SYi6ys450gxwqh4jhQWkzhlOl0KDVtFJfvi0tB7PfSrdlEuf54QqZ31DS00DFnBwcO+WLqcgER/9yoqPBNx+akQx7xBUmgHarmG0JDziIm5/kT7R4uljPzcedhttYTtT6fvM+sRBAGns5XCwnvpMR4lOnoO8fF34XS2UFryGGZLEUmJ84mLu/1nd1zFe3ey7cNFKNVqZt71MD6NPmhSA9Ak/T5l7r+e5EXRi6WrE73KgLvViqCQ4dA7WP/WS7RVV3HuvHvoN/Hkt6vHY6Wubhlt7T/i9dowGIYRHX0d/n4Dz3CVnyBJIqWlj9PU/A0hIeeTlPgAcoUekzGPtvYf6ejYgdfbG8bo5zuApOSHCAwYdcbxnK4OKiteoaNzFzKZioiIy7E3XMTuL2qYcddA4vr9fu3R4/Ly8T2b0XeVU5vYTIS6m9nCWo4XpSMraMOuhoIEBbuGXEVx//E8lF/EM7GLCGiLobV9LhH+PcyxpKLUwFbDUpJ6kpg44zLuKzbTpgRVXjdPKz6hK2IcMy67gT7hp2mf57bDolEgens7PPmevhOT5BZxNVkQFL3RCIJMovmppzF++y09s0Q8mSEkZtxLWOz5v9imz+g0ctUPV+EW3Xwz4xuCtL9OhvUlXWxbUYzN5CJxUK8D9M82z/wWtNWa2LAoH49LZNq8/kT3+fOalYiSyPu577MsfxlxfnG8PP5lAl5fiXnzZpL37EHu0/vSFEUnRQcfpaB7P0ty76TZHszSWQOZlH1X7+5t7AMw9j7Q/P0l2VYKy88jL2gI5yc/xa3/ZLb5ByRJIjs7m2e/O0Zl9FoC/a08Ov4zDny4gpCuTs4vM3M4w4yxQ+L6197HNziEjSVl3N5swb/diqPqENroz3mi2ciFgh+qB3I4sr6OnC11AGS6l1NjGU13QBoVATsxj27h8fGPnRJG63b3kL39UqyqWoK8w/FLGkFD40q8XjvpaX8jPPzCE8d6vU5KSh+htXUdEeGX0qfP8z/rQ+tsqOP711/A2NbKhDk3k3n+jN9tivuvJ/myw/v5YeErpI4YQ2SfdLqbmyjctRUBgen3PnRK7LrT2U5u3k1YLMUEBo5DofCjq2svHo+JyMirSEl+9IwkIkleSkoepbllDfHxd5KYcP8pN8brdWK1Hkeh8EWr/e2JLx6Xl5VPH8InQM2lC4b87hsvSRIHH/+YY13xGMxfU54SwUTV99QWyRiebeOzSTJ29VfgMlxJQ8R0XsiuYFngy4iSFmXhVbSpo3l+YAnde0aQP/x7Qur8iElM5p3OWDrS/dAfaKePWM+66C9R3Lr9jAXF2PYM7Hurt+hYwvjTHmIv6qT723JEa68jTcKLM+9D3NXZWM9VYp/qR2bqSnz6/PJ2VpRE7tp+FwebD7Li/BUMDPnlFzdAw/Fufng3D78QLVNuSP/Lo1t+LUyddn54Lx9jm40RFyYyYFL0GcM2/wgcaT7Co3sfpcvRxUP6Sxj0+JeEPvzwSQ5JSZKoXLOEEs3HvJE/lyZrNJ/fkMnwwuch7wtQaCFuVK/pLnMONGbDJzOZP2oJ36jT2T8ijVjtqWUf9h3O4pZNh1AkfMCN/W7keMsgIo7uYXBWNkkuG7sDFPgkRXPtc++iUOhYllPAkz0ewo+2QdBC/IVWNjVUoRj3GKpzHqI6r52NSwpAglHCEhoaU6mPPAfBa0NwHkJHHo4BBsTJoxgTPZYBIQPwuG3kvn0Opn4dSCoJg/8w+qQ9j48+5ZT5SpJEdfU7VNe8g1oVRnj4xej1KShVAaiUgWi1cSiVP+0IHVYLm957g6qcowyediGTbrj1d92jnyN5+TPPPPO7Bv0zsHTp0mduvfW3L1Kl1SEIAmUH91Fx9BAd9bUkDhnORfMfJyLl5HrXXq+dnJxrsNlrGdB/CUmJ9xMWOo3oqNlIopuGxpW0tq7H1ycDrfbkCBpRdFNc8iAtrd+RkHAvSYn3nZaAZTIFanUYSmXA7yLo3G31VB1rZ8oNGb+pi9NJ6zSbaZz/IEfak1C5WmnqI+Ir1LNerOeaLS6OJWsYOP0abr1sEYusoQzuymKf7F00Oj263MnUqvtyTepqNMdG48ow0tJdRqAYyEZ3GvXpAfjX2XB3OPhA8RpR59575hDIlgJYezsMmgWj7jjtIfbjXXR+WowiVEfARclo+gZiXr8QV+lhvOcOovPiGvqlvo8hZcCvWvvS/KWsLl/NI8MfYUrcme2jJ02z2sgP7+bhG6zlkvmZGEL/75uBnAlqnZLU4WF0Nlkp3N1I3vZ6Go93015nxusW8Q/V/aHO2SjfKC5OvpgGSwMft61jTGcgyr3ZBFxzzYla9IIgYIgfhH5PAn2iV3K4J4Zvcns455K5hAy5pFcBaC2EvC/h+KZeJ7xcyYCst1gefTntbpH/Ye+8A6Oqtr79TM3MZCa9904aJEBo0kMHKQIiKoJiF0FBRfFawH5VqoqK9CJdeg8IhBZKgJBeCamTnsxkkpnMzPn+iBflEhQV731fv/f5jzN777PPHrLOnrXX+q3hrre7Kvx8vCjIqiBTX0dawyFe7fUEp/O1GBzt8UvLIqy8Cq3eRHbd9zj7O9EnpDfHUtMpUKqwXldjdrpAmVnF4JwDiNwicGzfCavFSlluPSXiODoHJuBfsJtaqwfN6i6Y5PfhnAXCj2uZLd5Ckb6Yfn4DcLbtivnZHfh7P0nY2EW3ZU//C5FIhKNjdxwdetDYmIW2Yj+VlQfRandTWrqZouJVNDUV4+AQh0SiQCqXE96zL2pHZ0LiuqGy/2Pumnnz5pXNnTt3WZtz+jvs5P+FYLXSpGtArrJFKms72SAj801KSzcTG7MSZ+e+t31eV3+J9PRXaWoqws93KoGB05FKNRgMhWRkzqGuLongoNcICHjuD8+zubGFpF35XE+twkYpxT/ahchenti7qijLq2fXwsv4RTkx/Pm7M2q3jZ+VRfGMGZQYnEmNegq1x0UK0HPc8ygvbakjqArS583nqsWTy4rz3KjfgbSlCFezIwZtPyrq4/CSlvNa1D60edPY5PIJfbR9aHaLYK2rPxKNDEViBSPt81jAQpiVDtI2xLesltaQuvoimHYeVLdnP1p0JrSLkpFo5Li+EINYLqF240bK572HyyszSG/3Hfb2nYiNWX5Xz36i6ATTj01neNBwPu718V0Zu+oSPTvmJ2OjkjL21c7/VSGx30tJdi15yZVoC+qpKWvEbLLi5GXLsOfa3/MXlSAILL+2nIN7FvPBOgsuL754i54/tBZrqdqQQrJ4FXOKeiAW2fDDtN4EuP7koss5Alsmg3s0TN4Nq4bwvrofS73GcqxLOyLUt29q6vRNdP9kB/LgBXT2bI+H+2yEfTtwbhEx5EwS0qIsrgS54/hcGR06z6OkIZYHC+uRnavB1+8gVZJjTC818LSxGlG35zD1nMO691IwmyxIZWKGB6/As2InmeYQMgyDKWvsj0ZXhJ1sCW/FW+np3ZMv47+k4u13qd+1G78VK7DtdneZ7RaLEaOxlJaWWkymGqprEikt3Yxc7kxMzIqbwRZ/lr/9Tv5fiEQiZArFHYv2lpfvJj9/Pv7+z+Pj/UibbRQKL7y8JtBibqC4eA03bqygXLuTgoIvaGmpIyL8A3x9J//hOVYV69j+2SVKc+vxDXdCJBaRfUFLyrFi8i9XcuXwDTROCoY91+EPJdvU79pF8bQXEUQiMsMewSito1RdQaltHrLaIsafsXCy5zjer7PjumwxetMhZIKa6aWj0Bc+QEGzA1LBzD/6LKAs/z5WOq+ht74nNiINazTtMPuoGVAnorC0ga/4J44dR0G7YW1P5tw3cGU9jPoCfNo+PK7dkUNLWSOuT0YjtbfBajRSPH06yqgoWp4JoarqCNFRi7CxaduP/y8EQeBAwQFeT3ydMMcwFvZbiFzy2xEoNWWN7F50BYlExAOvdELzP0CH5Pdg56zEP9qZqN7edBrsh5OXLbkXK8g4XUZAe5d7GoUjEono7N6ZOnsJJWlJOB66iN3AQUhdft7ViuUSbDt64q7rQHDdWfY3OrLvSib9gmpxtPND5BwCDn6Q9A3Ye0OPacT8+AbrvEaRbxIY6377GYNCLkNb2cjlIgmV4gQ6O9ly1DYO74rraL0D8bdacM+7TokqlCbHH4gJf4T0q7lkqxxpyHXnvggjOyXlpFg1xBWewD51M5aQERQVWBFJRJRau9NuZH/szEW4KgtApqO4pRv25VL62SaxVlKJyWJiwJiX0B05Qu3mzYhkcsQ2NojV6lsqa/07YrEUmcwRhcITW9sgXFz64+zcF23FXkpLt+B8FwmMd8N/dScvEomGAosBCbBcEIRP7tT2r5Q1MBgKOH9hNGp1OJ06fn9LmOOdaGhIQVuxn+bmEmxVwXh5T0Rh88dVBCsKG9i9+AoyGwnDn++Aq1+r37+xzkjaqVK0+fU4etnSeag/SvXv++O0mkxoP/qI6i1bsfbogT5uAGeyLTTZ5SJI6tnhe5xle22xyTUwcdBreMZtotpYQq3TZBZci0Xf0MIiUym1cidGaQ4zJC6BtXVDiHeMouxMEQn27cmNCWKUgz1nduXQz72JLyumwJNHwLeNXU3dDfiqOwT0hEe2tOmvbylvRLs4GU1fH+yHth541W7eQvm77+K7cgXXxHORSu3oEte2LHJ5YzkHCg6QWZNJalUqN3Q3iHWN5auBX92SrXknSnNq2f/NNcQSMWNmdsTJ8+6ib/6nU1dh4IfPk5HZSHjw9TgU6nubQi8IAh8deoMBb+5G6e5F+x/2IlbevgM3lerZv3U/r5XL8dGU8HqPXYT6DcbHexI26x9rzXqecRkuruCLtBQ+DHqWXR1D6OZwe9RQtd5I94+O4OS+CYPDVboETOFqVXtGp55HZZXQ68hems1GmuaAd0hXdPopPFneTPOFRibGeeHmfZS16esAK1MNVqZWGFhftRyfCBeK0mtwC9Aw7LkOqOxa/+6OfZdMxqU6wiu/4nS/ZLbaqfki/gt6KaIomT0bw9lzN+emiIzE4913bqmX+1s0NRWRnPwIFmsznTtt+tVCNnfDf+3gVdQaL5gNDAKKgQvAw4IgpLfV/q8y8haLkYuXxtPcXEq3rntQKLzu+T1+i6LMGg58cw2FrYwxMzv+YV97W7RotaTPeoWLNnJK/Px+itptxVnQstHnAvGCF48szmdXYE8uPOJBTssPtGhmYkcoP5yHZ7xbSC82EGnM5eURSyk0DuGjU4MZI09Db6PgQNc+BGqUjNFL+CIhh/3BO4nUn4OXU2434FYrbBjfGh897Vzrzq0NqtamY8yrw/P1LohVslbpguEjENvaYr9sFpevTCIy4lM8PW9PRDldcpqZx2fSZG7Cw9aDcKdw+vn0Y3TI6JtZmnfCYrZyYV8ByQcLsXNRMnJGzH9FL+avpDy/np0LLuMRZMfIGbFI/mRG5b9jspj4cNF4JnyXg3hoP8IXLm3TNSaYrezYdIXZqSU42eh5sfNS/Ox0xKgn4rj3Y7h/IXR6HMP68fRwn4a/gxu7uka3OdYbW5PZcqmIzhFbyCQFudMonBjCgIuJCLTQ5/Bh6rt6YzM6mejo73l7UzL7CUdZ0UzSnAHomop4fdMLpGlK6W2SMC5jODmmgfR9OJyTm7KRyMR4BtsjtZHQ0mymNKUUlb6EXr4f8VKwmmqNPdtGbcPD1gPT9es0Z2ZhzMulfvsPmKur8V+3FmWHu3exGgwFXEqeiEgkpXOnzbedAf4efs3I/9W50l2BXEEQ8gVBMAGbgNH3+iZWq5HS0i209cISBIHMrH+g16cTFfnZf9zAN+lNnNyczd4lV9E4KRj7aqd7auCbUlJIeupp9gb4Ux4QQNfu3eksccauNoqO2iqy3Q5jlluYWtkJq1XgaFQnCox7cDF0ps6xE0MqBUrlUFRShdrSyDC/00gkVh6Nn8Xn/TSoJSYud+uNwkbK0nA/NpwtJD7UgcjS7RA9tu2ImtMLIe8oDJp3RwNvKtLRnF6Npo/PTbEmY1YWpoICHB58kJLSjUil9ri5jbitb6m+lFdOvIKfxo/9Y/dzZPwRvoj/gnFh437TwNeWN7Ltnxe5dKCQdt09mPCPLn87Aw/gEWRP/8fCKcmu4+Tm7Db/Nv4Mcomc6S+s5GB/DRw8TvG6thVERVIxYyd1YsO4TlhaHPj4zOtczetGct1yjEo/hFNfAqAatZhXSrZw3mDhYJm2zbFeiA9HQIohux9DPYZiqtlNnjkBU2AfZGIFiX37oLxQhNXgiFa7hsFerng5GWmxWHlzTyr+LsGsfXIHgyojSJRbOO13HosZ9KWljH8jDv9oZ3Q1RqqK9OhrjYjNRurtgsnVPc3iilKajHrmJM7BYrUgDwjAbugQXKdNI2DrFqTOzpS89hpWk6nNubeFShVIbOwaLBYDl69Mxmis/P1fxF3wVxt5b6DoF/8u/unaTUQi0TMikeiiSCS6WFn5xx6yvHwXGZlzSEt7GYvFcPO6IFjIyf2I8vIdBAa+jItL/K+Mcm8xmyxcOnid9W+dJfV4MRE9PRn7aqd7qnlSv3s3p2a/zrGOsdg5OzNtxgy6SFVU5Lrj2GjFzncHx9UqpgQ/jG7LTk56xyB2uYpVZCXffwISq8DUAhE7qEKHLX1EOYRFp+Ps1I/mZnsyL50mtdN9FEskLI7w40JqBTWNJl7wzgfBAu3H3z6p/BOt2vBRY6HLU3ee+6HriG2lqHv9/NLVHT4MYjE2fTpRWXkYT48H2owzXnhpIVbBypL4JfhqfO96vSqLdPzweTKNdUaGPdeeAVMib8o1/x1p182DTkP9SU8s5fT2XATrvTX0LkoXhr23kqvBEur+uQDd5eQ7tu3WxZt9r/cnxMOORTmjSNMOIM9Hh6guD9PZveDgy8N9HiK8MZ8303OoN5lvG8PPWcXw9h7kWD1wzw5kZNBIbBt2sNrtOrFCLBaFktROcZDgT2XlETp08KZ30VVEgWoOXiljX2oZcqWKT6atpkdtEFsdtchtr3HteDEOihoGTY1i4ttdeXRedya+3Y1xHw3CxlhHkS6AJuUA3qis4aL2IouTF9/y0pQ6O+Mxbx4thTeoWbnqd62hRh1ObMxKTKZKCq4v+V1975b/jOrRryAIwjJBEOIEQYhzdf3t8nVt4en5IMHBs9FW7OPs2YHkFyyhtHQLyZcnUVS0Eh+fxwgMePEez/zOlOfXs/H985zbmY9XqAMT3+5Gv0fDsblH8qKC2UzZJ//k2MqVnO7WFS9vbyZNfpKy/VfYs7YYo8KBcOUyFrhqcJbZUb1yJ3KjkVOBvahwOU+zsjNGpRf9btQiQuCAwYRLSw1xEReRSpvx93+WXbt2keHux1m1Cy/4ujHI0Y5lJ/PpGuhEXPlmcAlrjZD4JQ2lsG1qq6TBqC/uGDffnFeHMbcOTT9fxDY/G9mGw4dRde5MpekYgtCCl/fE2/qW6cs4XHiYieET8VLf/a8yQ4OJvV9eRSoTM/a1zgTF/rH/a//b6D4qiPb9fbiaUMSuxVeoKW28p+NHukbj9PE8atQCmTOewdLUdMe2HvYKtrzQk/Y+9izPeYBsR3dMUjnWhC+wNLYgC4lnkV0NFWIlc88mtDnG8/1CMAlizmjFDJMMw1cTgLJmBd9GqenvHEe1iwtarRxLswT4ET+VgrHqKqx2Ml7ckMxnh7OwSm1Y/PR6/PWO7PJJpMliR/aSd+HyBqjOg+YGABw91LTvqMJg60FO4f2MMRjoWm/LqrRVzL84H7P15xeRuncvNIMGUvXtt5irqtqc+52wt+9Ip47fExry1u/qd7f81Ua+BPjlVsvnp2v3FJFIRID/s3TuvBmF0oeCgiVkZM7BYMgnIvwT2oXN/Y9V4sm7XMGOz5MRrAKjXo5lxLQYnLz+/IFeWVkZCQkJbF2zlmVvvsm66iquxsbi4eiPoiSS79+6xLGTVsxyW6L4kszALK7LZci1ckacb+aCWzg1XWuxiI00q/tiY7Hweo6RzS1a6qRqJnaqxj8gDQ/3MWRlWTiuN3IsOJq+jhreDPJkx+ViyhuamdbNCa6fguhxtxpxSwtsfbw1u/WhdWDTdsq9YBWo31+AxE6OuvvPJfuM+fmYcvNQDx5ESekm7O3j2kw22ZrdKo71ULuH7nrtBEEgYVUaRoOZEdNi/kfHwN9rRGIRvSeE0veRdlTe0LHx/SSOrEyjTmv47c53yeAO4yiaNhJ1ZSMHPnjmV11DSrmExRM70tRi5cfq1yj2kmAjnEe3/TgAsb2fZJrhEhutHhxJTbytf7S3Pb1CXMgWvDl58izzur2D2FLNRdkxKpscCVXakhsWhj4hhrLyLXTsGIEm+xqvjQzB7KHkq2O59PnsR84WGVg6dgWVtnnUKUo4V0wYGc4AACAASURBVH0/ph2z4ItO8IlvaznK4kt0eX4gtk3lVDTYUhT8Dt/VZOBc14416WuYcmAKubW5N+fmOmsWgslE1bI2g1x+FTu79kgkf03o7l9t5C8AoSKRKFAkEsmBicDuv+pmDvadieu8hT69k+nR/Ri9ep7By+vPqTf+Hoozazj8XRpuARoe+kcXfMPbropzNzQcPkzhlMfJGzqMfU8+xbfffMOZxESuX0vB1NyMu4c3XnTEnOmPJC+ToPxd9JIcZaTTqzg4X+UbR3sUFhnP7A/GxtzM1Qcmc11zGUHsilEZTXzqFeyttmxTiAl2KCbW4wvs7Tvh5PwS89NyORLVjQ4aFSuiAxAB35zIJ9rbjj6Go4DQauR/yZF3oCgJRi0B13ZtPRIAhuQKWkr02A0LvEU1VHf4MACWLhqamgrx9rp9F2+2mtmes50+Pn3wVv+6zvcvyU4qpyijll7jQ3Dx+ev0Xv6nIhKJiO7jzaT3u9NxkB/5VyrZ+F4SWUnl9+wejzz2CTe6+OK76yKf7JxJi6Xljm0DXWx5qIsv+zNVFPj0QhCBOHclxuv1IBLxyoCHiWou4qVSK9rSrNv6vxgfgt4i5qzOCUOugZ5evVHrDrIoFAZFjURtMJAtC6CxtgUvr0JEIhHtynL58uGOWLu4UIvA1NUXOZwr4cNu73E8eAuNZnu2mz+lIPBpSrwfxFRTgrDmfsS1uXSKFjAqHDl9LgDBzp9t9cnISkaSWZ3Pg3sf5Jur32AVrNgEBmI/ZjR1GzfRUlZ2z9b2z/KXGnlBEMzAi8AhIAPYIghC2l95TwCZzA6Vyv82MbC/En2tkcMr0rB3VzFyeuwfds0IgkDFgoWUzHgJs1ZLdftoLvj64KfX83DhDab4+jJ55hyk9d2wliuJTVlK1+ofCNYlIT/6A9V7pWgv29D9Gry0K5SootMkhPVia7gMmTELF0VXZGYzE8uNnBE1UWlR8FBMA3bB87nosojhV4pIDIqmj4Mt22JDUEslHEgto6CqkRf6BiNKXt0qI+vyi112zhE4t7RVdbAtP/1PmOuaqd+fj9xPgyrmVndJw+HDKGNjua5bjULhjbv77Wqh58vPU9Ncw5jgMXe9nsYmM6d/yMM90I6o3nf/Yvg7olTLuW9sCI99cB+ewfYkrEon89y9MUZikZj+n65BIpHhu+IwUw5OoVRfesf2L/QLAeCYbgYVLjbYSo/QsDcNQRCwUdrxTWwUTWIbpl+8gLWx+pa+3YOcebirL2kWD7afvMIT4VPAquO6OJF9RQ30DQ3BJJeTd64/2opNRES04+LFiwy0lbN5QBSi+9yQeqr4YF8GBjozudc4UjyPU1Pjy3cp7nyWJWXR9eGcrxnOha9WY9O9J641KTS0KLng/i3OkmYOiPejzn4YsaEDX135itdOvEaLtQXXF1qzuiu//PK2Z27OyqbktdnkjxpNyaxZ6E+dvidr/1v85T55QRD2C4IQJghCsCAIH/7V9/tvYLFYOfRdaqto1LPRyJV//DCvZsUKqpctw2HCBAL37OZiYCBOTk5M+vhjQlevQjX5WfauLECnbSAmeTE+gQpM168jFdVg7SBDH2bEtlLCc/utdM5K4Uefjix9eDJ2uqOIBBE5doPoXHyBSEkkO+ykqELt+d55JKMKApibr8XYYma2WsT3HUNRSyVYrAJfHM0lyNWWITZpUJUF3Z7/ecLNDbDnJXANh8Hv3/G5rM1mqjdkIlgEHCe0u6V4uqmoCGN6Btzni053jcDAGYjFt/90PXz9MCqpip7ePe96Pc/vyadJZ6LPxLA/XLD974bKTs7I6bF4t3Pg+PostNcb7sm4ck9PPGe8TOdcAfvz2UzYO4HE4ttdLgBeDkoGR3mwP62ZptiHkFhbsOiW0JzWatBDPYP4wEPCSU00Sw98Dc31t/T/x4hIvOxtONroS0V6IzGusdjpj/CFr5igTqMIy82h2saFlGO+hIW1yhifOHGCHg5qdncJQxLjjNxZwextV4nzGMvzz45DF3kdT0MsofoJ2Ioe4GLjo5zXDiZhQz5Vzu0RW80kn9BzOWg9TkITxxQfM7XaHpN2KIcLD/PmybeRennhOGkS9dt/oPFcayy9YLVSvXo118ePR3/iBFJPDxrPnqPoqae48cwzGPML7sn634n/+sHrvcBqNKI7duyeh4ndLWe351GeX0//x8Jx9Pjj/vfGs2epWLAQzdCheMybS1pmJhUVFfTv3x+5XE55fj3bPrmAvqKBmKtf4hnmRNOlZOwDDTiN80Lna+HNkTa88qySPd1fZeqgOSwY8wxyVyk2+kQU1iAEiT1vugRyViEmMcqOmiA1CrGYN7wceezSMd5pLGNmXIebNTn3XC0lS6tj5sBQJOeWgNoDoh74edKnFrQeuI7+qm1pA8B4vZ6KpVdpKdHjNCEM2b+FkOoOHwGg2CcBtToSD/fbd+ot1haO3jhKX9++KKR3F6FUXaLn2vESonp7/48VG/tvIZGJGfJ0NEo7GYdXpGFqvj2a5Y/gNPkxbEJDeOmkGl+pGy8cfYFFlxa16b4Z39mHWkMLGZrZNDjZ42Q5hPbENoSfKkg9EhXHSIWBTxyHkrx5BhhqbvZV20j5+rEuNIvkLDil5aHQCVjNWqrEqazJ1dJj+jRCMjKoNPqyc0cGjuYWLp09S25uLuG2SlbHBtHUwRGLRMzz6y8R4hDNGzOm8sz8ftz/RiQlIxJZ3vU1/H2eYpDX18QM8EMqtiKIJZw9Acuuf8Wq/C/xTnFnbXEl8or+HCzcx+vHPsF1+ovIg4Mpnj6Dqm+XceOJqVR88k9se/cm+NBB/L79ltATx3F743Waki+TP2oU2n9+ikWvvyffwb/ztzDy9bt2UfzCNK4/NJHGpPN/ejzBYqFux04q5s+nOSPjV9tmJZVz9VgRHeJ9CI37jdT7lhYMFy7QdO0agtV6y2emwkJKZr2CPCgQrw8/oLa8kf07jyCzqjm/tpZVs0+x/dNLCNpiulz4CFeNkaaLF3GO0OH0WD+OljmzM7CJComUXrlT2ezhSYWzG9Z2drRPW4dJYkTrNprh0ipMeS7M7qYGGzFfhviwv1MIrhdP4WBsYtiwYTcPqZtMFuYfySLC044R0otQcBJ6vgTSn7JxdVpI+rbVReNzex5Gi7aRqtVpVH6TgtXQgsvUaJRRLjc/N5qqKC3dinbXMlr8QHCT0T56SZvZyBfKL1BnrGOI/93VBBAEgZObsrFRSuk++u6Kk/z/hlItZ9ATUeiqmji1NeeejCmSyfB4912EMi2fXgxjXMhYVqSuYOK+iWTWZN7StneIC64aG3Zc0WLzwFqkFitK4V2yTn1IS0sDIpGIz+O64iGDF1wnoFsztrUm8E908HHgue4eFLTYkXFVg5vSDU9dAt86Cqj8YnDqFktIchJhhRmYDE1YJBK2rFlDUVERPRzU/LO9P/r2DhTWGHhp0xWMZgs2Khn+AR7MG/4mgS7+LPK0J9BymI5dDExdMohgMrGvy0VBE0a5PSUOPbhomsyzeYOJK5zMwaJNTN73FdJPFyP286dy4UKqU9JYFjeBgY4jeGpnDjlaHSK5HOfHHyf44AHsx4ymZvVqKj7//J58B//O30K7RtGuHTJvb/THj1O7bh1WQxO23bsjEv/+d5hgNlMycxbVy5bRlJxM3fbtKDvE3FIV5l+U5dZxYNk1PIMdGPhEJOJfcQc0Z2dT+Nhj1KxaTd3WreiOJCBxdEQeFERLSQk3nnoawWjEf+VK9IKaDZ8epkF+A0+tBNfyEhxvJBGRtoqAG0eQtRgQW/R4xNWgm/AMi4+bEPmd4HtHDVEVXahu6EWeRMAY48SIsmuUqXeiR0Oz46N8Kg/gWY2RBgSG6cTM6RFMVlYWJ0+eZODAgYSG/uxr/+xQFj9mVrJkgBK/Q0+CW3hrEQjxT2cdxz9uzWqdsPY28TH96RKq12dg0bdgN9APp4nhyH5KOrJYjOTmfkx6+ixqUg+h/sGMeFQE0ePXolS2nTy14toKChsKeafHO7+Z8ASQc0HL1aNF9JoQ+qeqav3d0TgrsJitpPxYjIu3Gsd7IO0g8/ICq0DduvX0cexMl2FPcLDoMBvSN2CymIhxjUEqliIWiyirb2JPShnPDO+PRGzFLuMkxuZLpBi2IZLa4GofQ0cHB76taqFUsGH4iZkQHA+2rWc63UM92HY6jXMlLTx6nzdJZftoUHenuUTgiQlDSc1IQVtVgXtTPp7lTZS7u5N6+iQKO3uGhIdSK4VLRiM3Mqo5lFpOSW0TSfk1XCqsI8rdh6NVCQSZWrDNycWx16P4xseSes2MSKFk3KtxiLGgLdQjNTbi3hREgK4j51Rr+PZyI2tUw9kbdB+HOg4ntG83Ovo7cjK7ivXnCglxUxPipkasUqGJj0fdty/qfn2RqP9YYMCvadf8LYy8SCxGERmJ48SHsNTXU7tuHabiIjTx8b/b0FfMn0/9tu24zZ6N1+ef0Xj8OA379uEwYQJi+c96MtWlevZ+eRWVnZzRL3f81aSalvJyCh+dBGYznh99iLp3HwwXL1K3aRO132+ketUqMJvxW/YtNmHt2P3hCSrJA4mesYpmPM03UF07jo2DGqdRfXH1S8etYyNZwz7jo90V3Oe1i4VeagIaPQjJfZYEpQWzry3xyiqCWccZatA5TmCmewwbiuvJUYuRXqxi/rAo3DVyNm/ejK2tLWPGjEH803ody9Qyd3caE70reSL18dZSfpO2g+onASmjDn54BsJHQNwTtzyv7nQJ9XvyUbRzwvWpaJRhToh+KkQtCFaupU6jvHwHnh7j8DwbjSk1h9AlW7Gxb1sXyGQx8e7Zd+nj04dhgXcQQ/sFjXVG9i1NwdlbTZ+H2/3Hwmf/t+IZ4kBhajWZSWW06+ZxTxLEVF27YqmtoXb9BhxPpfFQyHgMXo6sz9/CgYIDBNgH4Gfnh1wqZsvFYjr4OBDWdTiW+ibsc0/jVtFCgekklcZrdPS9H7FIzvIWV6Ia8wk9+wm0fxAUdojFIpxEjezNacRD7EKx6Dh+VjimiaafSMnAMSMQyyRU6osQ1WpRGG3QOThQdHQ/+TX1TOnakStyEcU2oG4wcyqriqSCas7m15CYDl7eWaSLLTxclgHdnsfGVol3O0cyz5aRdV5Lt1HB2MpbKCq24lZ/BbE0mPCqHlR57CMwvIxH+7RnzvD2jIoJJD7cg9Gx3pzNr2blqQK8HVVEerW6EWXubn/YwMOvG/m/hbvmX4gVCjzffRfXl1+iYfceyue997v89PpTp6lZsRKHiQ/hPPUJpI6OeL7/HubKSqq/+fpmu8oiHTsXXEYkFnH/tBgUtneOpBEsFkpffQ2rwYDf6lXYDR2Kw7ixBO3dg/fCBaj798d5ymSCdu5AGRtLZkImFfVWjKpqYuPi8HnzTZrT0rAJ9CZ4WihuopXYBjuTP24Pc3eXMN5xGwt9FXg029A7dRYHnSwIKimhfiK6VO7mmLQeq1hNsDgGWYaBM84S3Iub6KBR0snPgWvXrlFdXU18fDySn9Q7T2ZX8sL6S0RJS3mrajbEPAzPngSHX6Q8XN0ExoZbD2Fp9b/X78lHEemM82ORSP5NaC2/YDFVVUcJC3uX8ND3MRxIRN2nDzK3OyvxnSw+Sb2xnpHBI3/zO7RarCSsTsdisjLw8Yhf/XX1f7QikYoZNDUSS4uVo2sz7klmrEgkwuOdd/Bd9i1SZ2caPl/MQ68nsNb6OFKxlOcTnuf1k68T7aPATiHlSLoWRCIkY95DF7gEmUFJ3JUGVNeOkHziCaYUNBEul/NWxGwarSL44elWKWtgVO+OhNro2J9uZoDvYOqNiTgZm5hyvZhrjS3cN/5RHv94C70+n0K44QpWsQRBY0/5qQS+W7qUmWID0cFO5HR0YNCjkRx8awDX5g6mi78LFcVdyZULFKoECne3ZqS6+KgZM6sTchsJOxdcRunuSIiPiQr7WKKaduGicWRMxgzUeUoWX3uDETuHE7c+jsHbBvP5lbd470EnegQ78+rWq6w7e/1Pr/Vv8bfYyQuCcMtuTRUXh2BqoXbtWoA2tZ8FQSD7vJZzO/PIvVSBVd+Abs405D7e+CxefFM+VObhQUtRMXXbt2M/dizlpS3s/fIqMrmEMbM64eD+64k1VV9/Tf3OnXi+/x7q++4DwGg0YrZYsI2IQDNgALb33YfEzg5BEDj02QkMsmKaVAZGdfbC9PnL6NMr8Y3LQ24ugPteRDf8K1765ghPmr9jcYQAgpxhKW9zyU9NfqMJRUcH+ucdIzG4hfLGLKTSISwq7chsfzHBEiml57S8NqQdER5qtmzZgoODA0OHDkUQ4Ksfc3l9ewrBEi1rFAtxeHg59JwOsl8cdgoC7HwOHPwh/s2fL1usVK1JRyQV4/p0e8SyW/cQen0W6emz8PR4gOCgV6nfuZOGPXtxe302NoG3ll37JYuSF9HY0sicbnMQi+68L2n1w+eQd6mCvo+2wy/yP1d0+387SrUcha2MlGPFtBgt92zt5P7+OIwfh2bgQEz5+Qib9/DY2Hkog4LZlLWJM6WnCbe7j8Tsep7qFYhYLELePgZ90wCEknQ8avOpU5RTV24gOiuIDZ4yzEHx9E2e31ov2D0KsViMxFDNkcIWIhzdyWo+wsOOnqSb/VheVUdhTSPB9rYEu3ZF5WhP8dkUDK5OSMtLUKlUpObk0U9qxTcggB+qGviuuIq8ZhMzO/ux5ZQBG6czCAhE52Tj1P8ZRGIxSo2cdt09qC7Rk3KsGNdwT4SiXG6IYohvl4TZPRbbTF8GKO9nQJcehHmEYCuz5VTJKbZkb2RApAMaUSgrTxVhMFno4OOAQvbHQ77/9jv5M3nV9PzkGLM2X2HLhSJqGk24znwZ+7FjqfrqK2o3bbqlvWAVOLY2g4RV6dRVGKi80UDC5iJSfMbj+s8Ft8mmukx/EcFq5criHexecgWVnZwHXun0m5mTuuPHqfpqKXajRuIwpjVi5MKFC3z66ad8+umnHD9+/JZfGrk7z1IncqTBvho1etx2Tqb6VAm2Ea4oZ2yE2flY49/lzdVHmKZbzIYII7ViGQPTZqLpFkJSbSNCsB7v+v3sDaihtOYkGrM/m9MGsdRdjFEmwqPMiJNKxqgYLy5fvkxdXR3x8fHUGVqYsuo8nx/OZoR7LT+I38D1oSUQ2kZlpeuJUJUNXZ++5bL+VClmrQGHUcGI/00LXxAEsrLnIZFoCA19E3NlJRULFqLo0AF1v353XMPyxnISixMZETTiN33xV48WkXayhE5D/Ijs+Z9XGv3fTlRvL9r39+FKQhGXf6qFeq9QhIfjs3QpNqGhVL33Ic+GTuHL+C8pqC8gX7KI2mYdlwprARBJRNiPiEbxj50Ivj0JzzPS6LOTjuENjC428a3JlQzfgXDy85u7+Qf6d8VfUsf+ZBt6ePZkd8VG1jpLeKjUzK7aBvqez+Stszk49h1PmK4ek1iBMkyDuSiPXl27kJ+ViWzbOl7Ou8wEsYnjNQ08kVfEsA7BmOqjOahR4yEvIffssZvPZKOSMfz5DnQZEUDW+Qo8YwKRm3UcvRBG55gmeo4PofEG1KxxoEf5SD7q8TEHxx1kXOg4NmSupVz9AYM6NbDsZD7dPkpg6fHcNtfuz/K3MPJKuYT2PracyK5k9vYU+nz6I8sTC/CYNxd1376Uv/c+NRs23DSolxNukHm2nLjhAYyf5Ezv3C8ILthFpXMHdm+uprb8Vn0Pmbc35YNncL4mDHdvJWNf6/ybSpKN585R+sqrKMLD8Zw7F8Fq5dLOw+zbt4+AgAAiIiI4fvw4iYmtccSCIHBhTw5mSREWqYXh/MjOoj5YjSJODpuFOWgAglTB1+vWMb34VbaEG7lmY0N87hMMHRPP0vKD2IZ+ip38AyrFO5Hqj9K+MYJVeTNIa+/EIVcJU9ydOXdNyyPd/JBg5eTJk/j4+ODlF8jjqy+QVFDDJyNDWKJ/DVX0CAgZ0PbDXVgBCodbQinNtc00JBSiiHBCGXX7LlBbupvGS+fxzYynfvkmrk+ciNVgwOvDD37VZ/7N1W8AeCS87SIv/yL/SiWnt+cS3MmV7qP/nDb3/6+IRCJ6PxhKSGc3zvyQS9Y9SpT6F2KFAo+338JcXk7dtu309unNgn4LKG++jsprG4fTbs3AFckViMYuRYyEdvnNlHov41WRArVJ4PWgWQhVWZC5DwCVSsWDURqaLCL8LBMRIeLd0nd4bqCak1InHqywsry5kXkHM4geOhxVYyNmpQar1Yy5MIeZM2cyYMAAFC1GnH7cz/iU05jMFs7bg7EujkaRlZN2cioPfnHLxkwkFtF1ZBCxg/xIu9ZM+w4yBEHEjlU16K+kcv9Ye3zDNCTtymfj+0k0FFh4p8c7rByyErFIzLmmjxg14DSjOqkJcvlrsrH/Fu6acmMmW0rmMHNAe17s2ZuSumbWnC0kp6qRMS8+gjkjg9p169ElJFCRU0nieQleNlUEn1tK1eIlCIZGoua+SOCQTmQllZN2sgSVnRxHdxVVxXp+XJtJbqkSt8pL9HDNxWlQv1vuL5jNNOzZQ+2mTdR+/z3Vy1dQs2IFMh8ffL9bhsTOjkPTv+NETSEiQUJspYJOTaXobVVcyMzE2dmZkiMpZJfY0+iYgp2kjhFDBiNPqqTGaOFdTXuaL23A5vhchlav4TMvNcfUKvoWTuClCU/yZvpG6mzXYLXxpNFhHDEOYbyT056J2gdp8Lcwu50D7nIpwSXNpBbXs3hiRzJSLpOWlsbo0aP56GgRp3Or+PrRzow2H0SUvR8e+Bo0nrcvtk4Le1+CuKkQ9nM4Y82WLCw1zbg8HoX435LB9JeTKH1yBuoEMZaz2RiSkpD7B+CzaCGKyEigtVj0zOMzWXx5McnaZAQEzpaeZUXqCiZFTmJI4J1DJytv6Nj31VVcfDWMeL7DPddO//8JkUhEYAcXyvLrufZjMW7+dvdU60fm7U3jmbMYkpJwnPQo/vb+KKVKzlXvpKACnurS99aXvtIBkUiMKu0IZaoaHLpE4JDizEZHBYGCnsiq5JtZ1gHujuxLyuByqZgl48axK28H67LXk63IZmS0L7JaRzbIzcRL3RGf2k+xQwDu7lkUXSgluk884e07EBcXR3h4OKWZ6cgryznv7oVHlQYUF6mRCUyszqPedzh2LrdmbPuEO6LNryc7T8SgsCvU5teR3+RDVkoDsvRzeItK0EmduJaoRZp3gM7SHMZFPorZ1pVdBVu53nKISE8nOrp1/EPr+rd318jEMtyUbsw9O5f512by3lhv3hwezoHUcqZ8n4Ldoi/x/PADREolFzPkSE16wpK/QyQIuLzwAsGHD6GJj8c7zJEH53TBwV3FsbWZfDvjBFs/vkh+Zg3BQ33pGd1Iw+aNNKX+rMxgSE6mYNx4Sl9/g4b9B7BU1yB1d8Pt1VcI3LYVmYcHGV//QLrUBYvMgI/OFtXBddSuWkX0d8vxksvZuXMn546n0qTKwSiH/j5mUpTBtFy6iHN7C+dVL/Ky8VvM1kImuvpySKOix41RvPLA8+ytyyTfuh6TJIIaj3foYPVixvUsQuv6UCopYNeACIqaTcwL9GL7hWKGt/fESSkmMTGRgIAACk1qdl8tZUZ8KIMi3Fp36V6dWqUL2uLyWrCaW438TzSlVdOcUYPdQH+k/yalbMzLo2jqUwhWCw7vTyM4IYF2yZcI3Lb1ZoGFC+UXeC7hOQxmAwP8BpBRk8EbiW/w+cXP6enVk+kdp9/xu2/Sm9j/dQoKWxnDn2+PVP6fk7L4uyKRiRn+XHucvG05tDz1noqZATiMG4upsJDmlBQAJkdOJsi2M42q3ZwtzL+9Q7fnEFQuhJXKyC9exKT7nIistzDP90l0BafhJ9kDNzc3RocqaTDB1Uw79j6wl5c6vUR5YzlzTs9BLF6Ok0XgU5OVCGc3BJEIVWA0IpmZ4+u/unk7Dw8PJk+eTLvGOtrrqqlwtaGxujOXbEQY1Ubytnx4W0CHWCxi4BNRyJVSzjX0Z8ywJB6Qv0I7dRp1bpFkq7pgrdDiZkzjbEYEF/bkoFg1jFmXdrEn7m3Gho7F387/nq7zv/h7FPI2GxGubWOvRsOH5z/CVmrLssHLyLihZNbmq3g7Kvl0fAdcdVb2fZVC7wkhdIhvOx4boLjWwMuLz+DSYMDb350EvZ4qYws7HotG9PRjYLXi+tJLGM6fp37XLqSenrjPeQPNoEG3uR6sRiPfP72JYq8qZC7wqJ09tQvmkxL9DJ0kyRhyM9g99H4sPwXoRNsUc6aLCudNCYw9JVA9vo41rg6k28hpUFqxsSjplT+eaWOmIPjJGLtrAoLMQI3PRwRU63lNu5mYokk0CXr007sxrqCKiR5OhGlb+PhAJjteuI/GG2kkJCQw5fEneGJrPlKJmP0zeiMvvwzL41tlgju1UcfWaoHFMeAcDJN3tV5qNqNdcAmRUor7jI43QyUBrE1N5I4ejqm2DMnC+4nodXuyh86kY8zOMahkKtYPX4+9jT0Wq4XU6lQAOrh0uKM7RxAEDi5L5fq1KsbPjrtZTvH/uDfoaprZ8uEFbB3kjHs9Dtk9eoFadDpyevXGYdw4PN55G4ArZXlMOjieINs4dk/47vZOp5fAkbe52NEJ23aPUJH9GA9pmnmuaBNzw4Nvng9VV1dz//zD1Ik0JL4xEBeNAqtgZXXaahZeWkg3v+fYS0/2nUnhlPYiMl9vnKWHKT6n5MG57+AX8XOQxpUrV1h78DCbOg5AcSoLdcgnTG0wMam0noK4z+gybtJt0yzKrGH34iuEd3FlgPcmSF6LxSohRzaO0/l9aRZU2IoaaURD/15VRFa+D/U3IHIMjFgAtn/swPu/WRnqP4I1aS0tG6Yz8uoe1g1ajoDAlINT8PWsZN2TXTGZrUz4+iwbv0uhWSFitbaazw5lkphTSYvl1szTSt5IugAAIABJREFUohoDzy87yDxe458Ok5ljN49NT8eglEt4dlcuTku+RCSTUfaPf9Bw4ADOTz1J8L695IV34ZHvkuj/+XHe2nmNar0RgIKNh6jWONGs0NElOhj9mm+Q+zigcwjkvLg3UoOBHqdycajxY6w4kZOxAkeLf2R0qphCH4HnQ124bGuDj40/g+se5JEr7/D8yMn4xrgwedebIKtA5/ocPtU6xuxajldOP6RiOe5T43i3shFHqZRXfN359mQ+vUNdiHBTcvr0aUJCQkitl3G92sCrg8OQS8WQtR9EEgi/v+2FztwH9UW37OLr9uZj0ZlwGh92i4EHKF/6OZYb5RiecyK0+7ttDvnVla+obKrk494fY29jD4BELCHGNYYY15hf9ddnJZWTf7mSbqOC/s/A/wVonBQMnBpJdWkjJzdl37NxJRoN6vj+NBw4gGBpPTiN9QzGueV+CprOta13E/cE2NjTrtqV0tLNxPY0MabCwnLv8WRm/Nze2dmZl3t70WwRmLEmEUEQEIvEPBH1BD29epJe/j0yi4G1niEEaiuoaGwiZshspAoLB5e/Scq1aRQUfElN7Vnat29PsMaW6MYqrI7uiJrD2e3sgp1NE37nXyf1s4no03+8efgL4BvuRNzwADLPV3JJeBreuIHkzQLCZ8/nsUXDaefVSKNVhUiwcvy0C5dDt6LrMAfTpYNYDsy9Z2v8S/4WRv5GsR8byxdx8ZsSfDf8gzWDV2Ant+OpQ09hkKZwaGYf/hHpi50JMlwlJBXW8u2JfB5bcZ4eHx/jkwOZpJbUs+dqKaO+SOS1poV876qjl78vQ6zXqUmcxdJHO3OjxsA7qUaCDh4gcNcuQk8l4vbqqyQUNDBx2TkKqhoJcVOz6XwRAxacICFdS+apYppVRUglYiIPvkJLdSPOQVUM1XyMXu1NmUdPPCuuMEpYx5X+vfmx4jyLqjshqbOwL1bMeOMA3hd/R3zyLNoV9GXM1G4Exbnx6MalNMpP06QZjr3BlZFHttA5KhYf2zA0g93YqVFxVdfEB6He/JB0g5pGEzMHhXHmzBmampro378/Xx/PI8jVlsGRPyUhZR8Ev+63Za8CrWGTifPBKejmS0B3qgTDRS2avr7IfX82slZrCyWXV1O36nuau0qImrgeqfR2I5xRncHGzI1MaDeBaJfo2z7/NQwNJhI35+AZYk/swDv/Kvs//hz+Uc7EDQsg80zZPT2ItRs8GEttLU1Xr968Nj70EaxGFz5O+pQW679p3dhoIO4J1EU5aMxqcq6/z9tdAlFZLLyt6UtLYeHNpg8O6c1ADxNnik08uuQAJTV6RCIRL3Z8EZ2pgTiS2eOrJNgxAJHVSm6WgS5jxqErVlCSnkl+wUIuX57EtdRn6dWrK5E5KZg9lOgrelDZUs/G3s/ioJES3XgA9ZYxNH0YjDn/Z0XJLiMCCe3izrmd+RxemU5dRWshFblCysB3RzN6qAjX2lQEq8CZvaWsPdyV9WWLSUq++/qwv4e/hZEX+wUjOHuQFPwqRxPa4bb5H6wbsoZgh2BePv4yX15cgDi9Do8gO757szen34jn2twhfPtYZ2J9HfguMZ/7vzjF9I2XGabMIMnxBlcbFTzqMRKpjZoZdRcIl+XyyuAw9qWU8f2lUhTtwpDY2bHu7HUSPlnI7MLtrPDT8t3kOA6+3BsfRyVzvkngBq40qyrpQDrGUlusUiljfRbzsmICXj5bcY0zIxZZaZb4s/D6Lga490V8KIk6FajsRuCcPJJLKTpaIjR4PxpMmqiFB1Z8T5Z1JS3ydqjMPZlWno3aL5zQ5u60uFSi7xHCJwVl9HfS0Ekq58sfcxkW7UGgBk6fPk10dDS5jXLSyxp4rm9wa8JQbSFoU6HdHTJKc49C2RXoNRPEEgwpldTvy0cZ5Yzd4FZfotmso6DgC06f6UPZlx+BSETQ3BXY2t6uHdNiaWHu2bk42Dj8qs/9TiTtzsdstNB/Uvj/JTz9xXQZEYBniD3HN2bfFnn2R7Ht2RMkEvQnTt68NizKh+aK4RTpr7M1a+vtnbo9i0gkIbI+kPr6ZMSqk7yqFpHo2Jltuw+gO1WCYBUQi8V8/eJoBvsInC2z0ufT40z+9iRyiz8RThEY9ccwSCDFqzMB169zOSWFqH7j0Di7UpsSS5/eVwh2m0V1ViJiyQa8RALtNSYszWE4iWP4ouwoKU9uQzf5BKmOD2JsasK6ZgxmbeuvHbFYxKAnIokbHkD+5Uo2vHuOdW+dIWF1OmmJJah69GDMN5MZ2FkHCDjamnD2d8A2NvaerO2/8/fwydNaU/XEystkXmnAv/IowyY1YhzxIfMvLSD/qI644qEU9D/OyPsG0Nun9y1JNdqGZs4X1OCkkmGz62FSLg/HIg/AtTaNoDEaHhWt4Bm5D9Me2s/jqy9wJreKF/qHUF7fhOXINjSerb5KscXC8JhY4saPw2i2sOWVpZTKlDTZFvG4sImivY7k2XmRMOl1nugZQHy4GyKRiOtvz0H3w04+nOHO9AofHDZc4lRnW0xhS/hB1kxBw88l1aT2l1B47sQqdUQueYZ5GiXXkq8y1iYKdb09iqlKXtC5cVln4GhcO97eeIVLhbUkzOpL4qHd5Obm8uKLL/L8lgzyKxs5Obt/q6sm6Vs4MBumJ7f63H+JpQW+6QUtBnjxEs2FBqpWpiL31eD6ZDQimYT6hqukpDyDyVSFs7U7Ni9dweGhCXi+07abZv7F+a1+0n4LGejfRiz+r1B5Q8eWjy8QE+9Lrwdvrx71f9x79LXNbP7gArYONox/vfM9OeAufGwyFp2OoJ07bl4buvj/sXfe4VVVWRv/nduT3Nyb5Kb33kggBBKKQCihht6LohRR1LE37GBDR2XQsTcURRDpvddQAoRQQhLSK+nlpt1+vj/iABFnFB2/+T6H93nyz95n7XPvPjfr7L32Wu97mDr1uygdKtk2YdvVEN5VbFiIeGkj6QPjaRPrSUjcRcreA9RLtHx/RIpTgBbd7GgkP1IzHDmbxbKt6VxsVSNIZcxLqWJl7jJk3q8Q1ODD8+vfZVNEEL0TEvCxV7Dnw78RGRDNBamUdpUKz6Zygm4LZkehknWqeFT1NUR0XUlJczEJngkM9h9MeKmRrkcep1UdgstTp34yb0YKMqopy26gsqCJ9uaOHYrWzY7gODfaW8xkH7/C8LtjCO3xz6u+fwl/+pi80WDm8OrLDJzfnehwKHYbwpH1FuyPvceDoY+RWDkCa0gD6cJRHtj/AHftvIsS/bViDw+NijHdvIlvPkh65myM9tHoPOSUe97G5c0mpte6sMpQQkvdZd6f2Z1Bke68uy+Xw6ey0LqDT1sb9y9YgGtrG1svnOdsejpKmRSV3oLBvoJocjnhPAXXtiaK+tSg9PuEMttOKlorKNYX82pEDiYZPPc9yLaeplUFdrc9wSpFO3qrlTcmxfLN/O6MHXIMO++1WFShKCXzmFtRjNrOgR7GIDQNbjR13cdWVRhHG1tYHOrD2iOFHMmt5bmUaNrrKsjKyqJfv36UtXYUkM25LbDDwUNHPN41/EYHDx0vgJpsGPEGphozdV9fQuZqh+vsaAS5FL3+POnpM5FK7EnouRHPk11AIsX17hvTYZuMTVcd/NTwqTft4AFS1+WicpCTkBJ407a38NugdlaRPCeauooWDnyb/W+h9VYnDcCYnY258lp+/NQeftSVDEdv1PPJ+Z/JCOxzP4K5jS5tUZhM1VSUfcnr8kKuKJ1YNdwZY7Geuq8vXaVm6N89ijVPT+XprmYUNgPrDrggl8gJ5jQnXKUoo8cSXFjIiVOnKMwtROIdRppajUalIk7tQLWjFzknK3FubiDaV4HZ4MAA51dY2G0hde11LE1byl9qP2GLRz9c2i9Tdeibn8ybkq6D/Bi1sCtz3uzHrCW9SZoZgdbNjnP7S8k+fgVBIrD3y0yqi/89vP4/xZ/CyR/5LpesY1f4/rXT9F3YH395OZkOEznzw3n2L9uGVCph3t2j2TV5F0v6LiGvIY8Z22ZwuvK6XYPFxNrPTmNW+hIQks3EpWNIGOZNlXtP+hyPp10U+O7w8ziq5Hw6uydpzwxhoa4Uic3GhIkTcfP2ZuqA/nhUVbFp82Y2r/yGHG8jAjaCegRQmrMTG2DoHUOjsZG3Tr/FiHUjGL1hNOeEMgzP3w/VNUiMAntHeXLZK5qaZiNfzU1kdJwbH1xexIGKzbRrRmOxu4eUYweZPn0mpkOVxFoDaPDbS2lCL5bkVzDI2ZHy89W8fyCf6Ql+TIrzYMuWLeh0Ovr27cvnRwpxUEiZnvhjLNvQ1KHb+nOhmuZKOLgUwoZh9U2m7qtMJEoprnNikNjLMZnqOX/hPhQKHT17rsVBEkzT+vVoRoxA7nUtz76ytZIlx5cw+PvBrMhcweTwySzqteimn3VFbgPlOY30HBn4bxNGv4Vfh4AYHYmjg7h8soPh8/dCnZQE0ClkMy7OG4nZh0DlIFZlr6JYX9zZyDMGQgajOrcZd5chlJR+QXxYNyZX7uJzq4nGsYEYC5poOXZNlUoul3PHlHGMcm2kqlmOr7I7tfWp2BDZ6uZLcu8kfMrLSb9yBYO9Pa752fTs0ZXxTzxJf28b9WoXfFrr6V1xDpwVfHroCjMj5rNx/EZWJq2kR0MPtpldOIc74r5X/ukLUBAEnNztiRngw5gH45j7134MnReNb6QTVovIvhX/mtb8t+JP4eQHz45E56umvqKV9X9NJ/nZEbg2ZXHCPI+Keh1JTp/hcOw55JmbmKCL4/vRa9DZ6ViwZwE7z35P05YtpC1fRLMlGW3LaUY+9RAAPcdH4OEGuU5DeCDNlVVNmRhbawCQWdoobG0hsr4B1/iucORtdA1fMijjOMHVZaTn52GVWAnXtPJA8356XDIi7R7D0nEfsm7sOrZP2M7TiU/zdOLTbBm/BanEm7V3GVnwoIT+dy1n9alSRsZ783ZdHQkb7+N8zVmadPfSop1Ki0bHZ5Pu4/azV6h0DqYh+DL7Y0p4pCIIrSCh6fgVPjxYwIxEP16dEMuBAwdobGxkzJgx1LVZ2XyugqkJfmjtfnSSefs6ct8jbpTcY88LYDUijlhK/feXsbaY0M2ORubUIRCSm/caJlMtsTHvo1C40rRpI7bWVlxun3V1iFJ9KTO2zWBj3kbGho5l3dh1vNjnxV9FGfxTnNpWhJ1GQZf+t2gL/hPoOTKQoG6uHFufT1l2/S8b/AsoQkORe3vTcujQ1TadWklKVy/ycm5DLlHwzul3bjTs+xdoqSK0PQyrtYUSawYvVK9HYTPzmsqIMtIZ/a4irE3GqyZyuZy7xyXhIegpKw2l3lBDpFDMdm859gGDuPPFF3kwOZknXnwRn5goTqxfTUtDPUn3vEBQWz5lGne0eZcZ3UOHyWhl/OcnOJVVyO61e/Co98BX78c6yRTkknZKdq/4Vd9faS8nPMGTsQ92Z/jdXUh5oNvvms9/hj+Fk5dIJUx+sgcuXvbUV7SyZlkOZp9wEARAxOIYgpj+DaybB+/G4fvNNFZ6jiBJDMXx7hepeOJJHD/dTnDRVgbc3x9BELiUWsGaV9Jw8HbFJlOirUlB2iyw9fBLAKQdOIAgiiTERMKKUZj3LSHXVIPYXUXC/lRiz4p4VEWx1G0P8a2ueFVbcI/zgS9Hwcb78LPBrKhZzIqahVhlIGf/X9miUzFS240t50UkEoHtGpGT5XuQtp0mNmAeJofbQBDoX5LFglaoFaQsirNjaFgPXrc9RKveRMOBcuoaDCyfHsdrE2K5UlHOyZMn6dmzJ4GBgXx0KB+bKDKn73WEYNnbwF4HvgmdJ7b4OJxfA30fxFDlhPFyA9oRQSh8OzJlGhpOUlm5gQD/+Wg0sYiiSMO3q1DFxmLXreMHaxNtPHXkKUxWE2vHrOXFPi8S7hz+m57zlbxGyrIbiB/mf6vo6T8EQSKQPCcaJ3c7dn2aib62/ZeN/tlYgoB64EBajx3DZjBcbb9/UCht7Q5E2Y1jf+l+TlV2jnMTPAg8YrBLX4erbjBlFatwDenLE8Ur2F/fzKkkD0SrSPOhsk5mISEhxDsbqa+NQCFR4m09Q7ZGysWsauRePrj064dUpWLg7PlYzWaOrFqBRKKk7+zuOOnraXewJ7H4LOGJXjRUVLJu9bfUtpiorNcSU5yNzATfGkZhPvA2NquVm0FoDw80ul+neHaz+FM4eQCZQsr053vhEaShtcmEVKOhq+UEzk25HMxLYpt2Ow0T98PIN0EiRbNjEQ9+dh5Hi4SjvbtT6Z5AYMlunFpaKMio4cDKbGxWkcLztbjoZFS79eD+/dF8WXaQ9jY9GRcv4lNejpdxPeta8hkcFskkVTN3xbViloJzYzG5TsdJCUnhubYhIAikGtcy1lbK7Kr9nPp8IKaLO6nIrWTzK8+RFlWLDIG5A5ay+VwFMl8HtPY23PSr6aLrgkEzHESRmNxzvOEWzoKjzbx5qpi+V84jy29CdbGB0Y0SvpoWT+pTgxkX54PJZGLjxo2o1WqSk5MprG3lmxPFTEvwx1/3Y6m6xQiXd3Ws4iXXOU5RhF3PgMYH8bZHaNpeiMzNDnWfjhCMzWYm5/KLqFS+BAbeD0DbyZOYCgpwnnWNZ2Zn4U4u1F7g6cSnCXH6fZwy6btLsHOU/9cLcv+noVDJGLWwKzabyI6PL2A23ZxDux7qwYMRDQZajx+/2hbu4UhKrBdpGbG423mx5PgS2szXVd0KAvR5AGqyCLbGYDbXU+/pwtzSNURITbxUWYMk3o2WtCtY9cbrzARm9I9GIspxErtQVncUqWhjm7NA+6VrYuHOXj70SBnPpcP7qbicRUD4bKJDj4EIldnZPK5pJMX+MjaphB55Rfxlx0cEnC4gPOsc9SoP8gp9yV5zjZr8P40/jZOHjlXGuIe7o3W3w9RuIe6JGfQo+Irwqt1U5Day+iM9hwsH0TJtD63Rr2KoFlDF6GjWjiSrawz1PeK58tJijq1MR+erZvrzifRMCaK2TsRRZabW9Q4mb/dj5QePYBBFQqvK+PZyG983awh3iWZp/6Us7fUyLZow3KvPMHPiSF7pvZj2TZtp9zLzTIAL+lYF+aKS+To7Ptq3gLqPklCHHeGwWsWCsCkcuWzFYLbR6KVinCqdekMt02Me5nhTx0rnbpUC8UgTVyTNLNdKSD+vY7KykrS7+vDRrHgGRboj+7Eoafv27dTW1jJhwgQUCiUvbLqIUibhkaHXZaQUHARTM0SN7TyZmRugIh0GPUtruh5LbTvakUFXC55KS7+gtTWX8PAXkEo7yNoa169HotGgGdkR2xdFkRWZKwjWBpMSnPK7nm1TTRtFF2rpMsAHufLWKv4/DScPe4bOjaa2tIXT24p+8zj2iQlIHBxo2X+gU/uzKVFIBQUO+hkU64t5+cTLnWPdMZPA0Qv1hX04OISTbzmJTKXl1YbtlBhMfBNtDzaR5qPlncbt3SOOAJme6opIGgx1xMuL2eGjoPlU5xqAXhOnoXbRsffT95EIWnwGDqQraZjlco4fOYyn2cT4HduIPHeCwjvnsWzBMl7t3R2jtZqzwXGYln9Ca8lPzhP+Q/hTOXkAuVLK0LldaGsycexIK/4rviCg6gi9Dj6Fn+UyFw+WsvLZVC5/coAGN2/WBI5A75xPtWsbe8LC2Nc1FqfMDdw2MRSpTEL8MH/sHOVoAtxROChpdX+c+upg1M3NNAh9aGMpwwpeYNrHHsR+mY7DSzvJDpuBRBDx/H4bDavXYL5SxRe9ZHg2KXlG8yQvaZ+ka0sAnzppuTtGxuvejiRoQ5nd62m+PlEMWjkTQl04UriGGIdIPj5aCqJIpL4WbY4NmSjlIz85F6pkPJh4kHdmjcfdsfNW78yZM5w7d46kpCSCg4NZvi+XI7m1PJMS1fnarM2g1EBw0rU2UYRDb4JbFLbwyej3FqMM1qKK6iiSam8vp6DwPdxch+Lm2sFUaW1ppXnPXjQjRyJRdsTrs+qzyKrPYmbkzH/JA/9rcP5AGRKJQMyAW6v4/ysIjHUlopcnGftKaKr5bWEbiUKBQ//+NB880En32NvJjmdTojif5068ZhpbC7byetrrWP9RXSpTdOTNFx4iyG4ILW1ZmIIS6Xfpc8a5avigpp6qOB2tJyuxtV8TKVcoFPT2taO5KRKlxA6d6RSVSoHjtS1Y6q59B4XKjuT591NTUsTxtd/i5zsb++RCEqzZDNm7jyHrN+Dbtzcr3/6Aeb2TGTM0jLbGBNL8izAolWQHRVEwfSrm+uvOLUSx4+9/GX86Jw/gEaghYXQQeaerKdE7E7JjO15zZhJVtpneJ17Er2A30qpcDvXtC5gZ3y2BRYsWMXLkSOp0blzspsXW2CE6LFNIiRngQ3lOAyMf7kVQLzDYmwnJy+dMTDlefdrQ+unI9RzG1qpenPKchsLLE5cF96Dfvp2qV16hycfC4Qgpi/q9RvLsqSRPn8nKB7bxYfKHDI+YzCM9HuHDMatJL2mmsKYVi58DUc2HqWqrQnvCQIZXMAgCkUWFRNn82OvVxr5iE1Mj93HfqEeQSJSdvv/FixfZunUrISEhJCUl8enhApbvy2VSvC8zE6+rDjW2wKXNHaEa2XVjFB6Gmizo+xf0h8qxtVvQpgQjCEKH2EruYgDCw1+4atK8Zw9iezvaceOutu0o3IFMkDEiaMTvep4mg4WsY1cI7eGOg1b5ywa38L+G3uNDkEglnNiY/5vHcBwyGGtNLYaLFzu1T0/wY0J3Hw6ldWOw5xS+y/6OObvmdLCUiiLE3wkyO9wK8pHJnLjiZASjnsVcQiWR8LyfgMVopeUnlbqjE8JAVOAh6UpezREcBBs7fOS0pFZ0ui6kRyKxg4dxavN69OUSNJruqEbXsW/IIOo/eB//t99mSfJthDuoeL2mjkk9gyhr6E6xupjLEeEYzFayJ4yj+dRW2r6dg+UVbyyLXbn8THdWPX43G//6Cuk7tqCvrf7Nc/dr8Kdw8iZDO2mbfsBqufbGjh8RgFeolkPf5VDfJMH9kYcJ2b6NuON76JXiy6WYaIx2Uu6YewdxE1JQKpVorH4418QiAN9u384Py94g89A+ugzwQSqTkHOikmaHOhRmMzHurix57WMm3jmaya8kM/mpnsQND6bXuGCmvNQftwfuw3bXkxgjtbwxUUqYzJdjFb6EPbeD4X87TE5lM/18+rG472LmxsxFKVXy4eF8kEuYrGnm+/QPcGlRIgx9DFGQoDSbmC/T0CraWF6rJ8ylmpemPoZKdS3LxGQysWfPHn744Qf8/PyYNm0aXx0v5tXtWaTEerF0UmxnLpjzazok/BLmdZ7QtE/AXofFJ4WW1Ars4z1Q+HRwXZeXr6K2dh/BwQ93unfTxo3IA/yx695RtWcTbews2klfn743FrTcJLKPX8FssNJ1kN8vX3wL/6tQOyuJG+JH3pnq35znre7fH6RSmvfu69QuCAKvTYglylPL/uOJPBb3EgVNBdy5806mbZ3GxorDmLtOQXJxPb4uKRTKMrE5+eF57E1eD/Mm3WDkuwQtLanliOZr5wYJsRG4SNpprg6nydhIL2UR+7zk1J2pxNpi6vQZBs6ej9bDg01vv4ZGPhazuYwuMQJHU1Npbm7GXirhw+gAGswWLEGO0NyLXJdSzFKR9KE9EerqqHv4ISQXNpFTpybfFECoopBRmkM0leZyYMXHfPbAfLb87Q0aKju/ZP5d+FM4+dyTxziyagXfL3mG1sYOdRmJRCD5rmiUdjLW//UMaVsKaK43ILGzoz4zk7ywMLp160ZgcEfJvcVs5dTWItwDAhns6IDMaORSnZ5tn3/EpUNbCUv04MKpXLKysgi5nIv7+AlXQxA2q43izDryz1aTsbeEVS+e4MsnUzlSIKG2VzF5ajmhrjP57Gghw6I9qG9vZNq6J5i3cyHHKo4BkFfdwqHsGtSuJji6nAYHI/N6P8xOhRMSUSSRTNwrXPnGsQy9Wc2yWeOxt+8IXVitVk6fPs17771Hamoq8fHx3HHHHRwtaGDxlksM7+LB8ulxyK8nEBNFOPUZeHXrnFXTWNJRGBV/J017Owo1tD/SFjTpz3E592V0uoH4+10jKTOXl9N28iTaceOuvkQyqjOobK38VcLb/wqiTeT8gTI8gjR4BGl+11i38Meg+1B/VGo5xzf8ttW81MkJhz59aNq8+Sph2T9gp5Dy8R09kEokrD7gxpZxO3mhzwuYbWaeT32eyaZczklt+FeDDaiJ6QlXzjH+8gpGu2n5wMXGZay0nrm2WlYoFHRxlVJWHYqz0hlLwzZaJLDJQ9opvx5AYWfPxEWLkUgk7P/7Lgw13vj5X8RsNrFz504AotV23O6tY01jEynxgdRUDeaU7jRXFJ4cnTyGi9pIthUMZr/TKA74Tuds0ItIyvSMkxdx+9yFJIyZQGH6Kc7t3v6b5u+X8Kdw8l2ShjDqwSeoLsrnh1eeo725Y0WhcbVjyqIEfKNcOLWtiK+fOcb3Lx0hvUmPTSqlf//+V8c4v7+M5noDPUa4U1SayqDTB1GZLZiCozm0aR0+kSKN9lkoLFaia2rQDB0K/Cgc/eUlTm0txMnDgfAETwK7uhLUTUus+gt+cHLAVa5l+wlXksLdeG9GN4K6rMXqcJzTV85z39772F6wnTd25yAIIlPzt5MeWEOAgz9ligTaAZtEwpDqNtpscrYYXRnRxZMYH2cAqqqq+OCDD9i6dStarZY5c+YwduxYTDaB5zZcJMLDkXdndL96GHvtC38P1Zeg930/ppr+iFOfAQIG9ym0n69FPcAXqVaJyVTPhQv3o1S60yX6bYTrYuxNW7YAoB3bOVSjlCoZ5Dfodz3bogu1NFW3023wrVX8/1Uo7GT0HBlIWXYDpVm/LXfeacoULJWVtB49ekOfn4s9y6bFkV3ZzFepFUwJn8L6set5b/B7GBCZ6+1Hzf3fAAAgAElEQVTF/swfcHUZSI7iIrbo8Qj7l7A041k0opEl3W3U7S/AZri20x8c5YVNVJLgmEJWTRpdOcdHESrK0q50ug7A2dObqS++jtJBTc4GJ3L3l3NbXw2ZmZlcunQJgCeDvFBJJNT62iFp647MqStpbmkUSyScTkzgQnQM9TYtteVNbClsYlNrMhXbqql/6HEC9h1lzqvv0HvS9N80d7+EP4WTB4hM7Mv4J56nobKCzW+/djVP1V6jIOW+rtz+cm/6TAjBsfYShYEBOLaoKDzZjMlgoapQz6lthQTG6mhO/5wJHmlExVQyaN8+lBYDLb5hrNn+PRZ5K71SU3EdNRpBocBqtbH780vknq6mz4QQxvylGwOmh9NvahDN5d+j1eSQam9HhGYMzQaRh4aG8cLZ78huPMfUkMdpzXschSWEp488zYGSncTKLlNsl0WDg5GZkXP57EojzrY6FKKJ5LyubPVU0Gyy8sDgUABKSkr47LPPMBqNzJgxg3nz5hEQ0LHq/vhwARVNBl6bGINS9pNslLr8Dp4anx4QO/Vau6kN0r9GjEihcXcbUhcVmoG+iKKNzEuPYjLVERvzPnK501UTURRp2rAR+549Ufh27CwsNgu7i3eT5JuEg9zhdz3XjL2lqF2UhMS7/fLFt/AfQ5cB3qhdlBzfkI/NdvOHi46DBiLV6WhY8/3P9g+KcGd0Vy8+PJhPaX0bgiAw0G8ga0avoYtjAE85SqisVWC2NFDRJxkGP49rYy5vXXqZS/aOrPLch37zNcbLsX27IMGGviSWGF0M1aV/Q6hbzlz/txi0djBJa5L4+NzHVw96dT5+zHptGdFJg6nOcCVn/W48NPZs3bqVlpYWXBUy7vFzY3dLK8Pjfbl0MYl5gx7HK8WLkNEhTJg/gWGDUtA6m2kUL5MfFkrtwxPxiG+k9eRx6p9/AaXdv0+B63r8LicvCMIUQRAyBUGwCYLQ8yd9iwRByBMEIUcQhH+u3fZvQPOGleT26Yl3ax7D599LWdZFUtes7HSN1s2e+OEBaJzLMCmVeHnHkbalkC+fOMoPb57GTq2gz1h3ggo/oUyp5cDCN9FEeTFs23biJLXI6qsYkpuPe1UduZo+mI1Wdn+WSX56NbdNDqX7MH+qiwo4ueF7vn78ATSlO9igUyEXZOTnxxDjo+Wl6mo25qzArAjiK1tX+g+OoM54H0ZDDCrvNRic13AqqoHbvG5jY5aCNrkSqyCjn0WK3CCyvrWVviE6Yny0NDY2snr1ahwdHbnnnnuIiIi4GioxWqysOllMcpQ7PQJ+Qhtclw8rRnfkxE/8FCTX/QQurIX2BprbR2Gpbcd5QiiCXEpZ+TfU1x8hPPx5NJrYTsO1n83AVFyMduLEq21plWnUG+p/d6imqkhPRW4j3Qb7IfnpTuQW/k9BJpfSd0IoNSXNpO8suml7QaHAedo0Wvbvpz0z82eveTYlCoD39udebXNSOfFhyioirALPlR1Br4imqORjbP3+Ag+cYsS965iobOajgBFU5y5Dv68EURTRaR3xt7eQUWHko6EfMSZkNO5U0qQAma0L0Zpo/p7xd149+erVeynt7Rmx8BGGLJyAqdWG5eJxTE2NbN26FYB7/dxxkkkp91KiVsr4Yq+cKaF3E6gdwfMbm5mxq5a3a5PYLu9OtaqGvTVSzNNm4xnfQNuJkzT87fmbnrdfg9/7n3MRmAgcvr5REIRoYDrQBRgBfCAIwh+W3KzUWrC2Gql77RGijs1jZnwT2du/If9MWqfrRJuNzIYG7G02pj08jMlP9SSyrxc9RwUy5Zme6He8RKHayjQ/Hc+ceo27E4sRZTYi1+5jxLETuJ45jXHUHC5kGPj04UMUnK2h35QwnNxqWfHoQlY+9SBHV3+Nk6OcyIA6Njg60s9zJLlXwDHIkYyqE0gt1TzZfT7dtfbskJhojdTRFPo4nm1dabEzM9g1CaeiOI65eBItnkMvaBmQZ+GMvx0VzUZm9+lYqW/btg2LxcKMGTNwdOzM1b7zYiW1LSbu6BPYeaL0VzocvNUId27pTEYmiognPsaiCkOf44NmaACqMGfa28vIy3sTncsAfLxn3DD3TRs3ItjZ4Ths2NW2zfmbUcvV9Pftf8P1N4OMPSUo7GRE97tFYfD/AaE93QlL8CBtaxEFZ2tu2t5lzl1InZyoeWfZz/K/eGntmJHoz/r0ckrrrxVHOSgdeT/iLhytZj4pa6Sx/QqlZT8u8mRKnovvhSCR8lZoPIa9O6n55ALGEj29A7TUmBVUVRt4+baXOTJ1B3d0W06m/z1c4V5mRN3J2str2VqwtdPniBs4j8Q7fbGJ7ThVF5KdeZGioiI0MikP+LtztL2du0aFk1vdQp/X93PH52nUtZh4c3JXdj08gOSgwRyz+CDaRN4qs2F97K9oI6XIVQb+CPwuJy+KYpYoijk/0zUOWC2KolEUxUIgD0j8mev+LTgRHkJavIbqfDXznX1JVZZze8gFjn+8mIYr14ohas+kc8XFhS6enkgkEjyCNCTNiKDXmGBkQivK8o085u6B2arFVLKQdvtQHr5dhsTHhMYeLvq4op7UgyF3RRE70Jfxj3anpe4Q6157AREYuuAB7l3+dyaFFPC1WopNIsHWOAg7uZQjKisR1jSclE7cETYStVSGUhCwlwggkZAZ8QhSz9e52DiY1UHxOEpa8ZVbUYgwsNLMBokFT42K5CgPcnNzyc3NJSkpCTe3G8MY36WVEKizp3+o67VGixHW3I5oaMLQ72vqD8mo+vtZKpedoepv6dS//SVCTSb65hE4Jvnh+GMMvLDoPcBKZOSrN0obGgzot29HM2wYUnVHWKbJ2MSeoj2kBKeglP72dMeqQj15Z6qJTfJBobp5jptb+N+HIAgMnBmBm5+aHR9fYNenF6kpaf7V9lJHR1wX3ktrair6H895fop7koKRCAKfHO6sBeva827eamjjirGRH1p8yM9fRltbIQDeKgX3+7qy2X0w+RFZWKrbqPngHAP1CgA2HL9GDPZslC9LBTVnFDY2tCYToevGKyde4UpL5zTM7n2WEDy8BlNLM4415ezevRtRFLnbz40oBxVfmVtZfV9fnh4ZyfLpcex7LIlBXT1x19nx3ox4BoWN4rJEil2NHePOfMjc231Y3z/6V8/VzeCP2gP7ANfT1JX92HYDBEFYIAjCaUEQTtfU3PzbH8BJ6UTxhASkNoFe5xQsdnbgXQ9nxrufZsebz9CmbwLg7IH9iBIJPYffGD2qXL2I993sqZVJaSyeyqujxrJ56ofUa+U8M96T0OQSJN0DObTyc/wiVfSeEMC53V9wYt1qIgcNJmpuEp6mPTisTCajOoPVjo6kBI1j3wUrPkFaBEk7dY0nGBk0kkqzyM7aJqyiiJtcSnh5PqJEyhWlmmzvILo4NPOs+Czp1u7cVm3GHOfGkaJ6ZvbyB9HGzp070el09OrV64bvUddiJK2wnrFxPp3FNI69C+WnaXZ6htrNIobseiR2MmQ6O6QuKtTWH7DJnFHf/WBHZasg0NZWTGXlBny8Z3ZKl/wHmvfuw9bSgnbChKttWwu2YrKZmBI+5Tc9SwCbTeTI95ex1yiIH/HHiBvfwh8DhZ2MCY/Hk5ASSNHFOr5/7RT7vrqExfzrqA+cb78du549qFy8BFNJyQ39Xlo7xsV5s/ZMKY1t16U7qjTEd5nOo/V6zjQ1sL9ZxoWLD2CxdLxk7gv2xVNs5xXnBDzuD0QzIpAu1QL2WDh6ubbTPe4cEMIXZQJtJpEM1Z2YbTYWH1/caXehUnnTtc8CvBKroKGGmsxzFBUVoZRIeDfKnwazlUfKruAY5sRZNQw5c5nY1Eyijl5k1vkCHhodiZNPMqIo4bb2YcR79MDNwYs/Ar/o5AVB2CsIwsWf+Rv3S7a/BqIofiKKYk9RFHv+3Kr01yDIMYrIgA4h7QHpRmYHTeUbBzmpTnJ6SY+y+oXHqS0t4VJ9PW7t7XiEdOZQMdRXU1uxic1qB+Stg4h1i2VSvA9ejh6MC7yDHPtmTijtGBVrxtDczNdP/oUVjy4k68gBEiam8I39Ghakv8aIql284OHFfX4BeKm9CZJMo91spcBVRqL0ImabiXEh41hXWY8I2EkFpm/+kpTtX+Pa1oxW48jFXq48pX8WaeMD1IoCKSYZG6RmZBKB6Ql+pKWlUVdXx/Dhw5HJblzh7suqxibCsGiPa43GZsSjf8Oo7I++vBvalGC8nu2N27xYXGdH4zocFO2pSPrfhyLwmnBBadlXgJSAgHt+dt4bVq1C7uuLfWJHCqbZamblpZXEusYS4RLxm56lKIqc2JBPVaGevpNCb63i/x9CJpeSOCaYu17vS/yIALKPV7Lvq6xfxUEvSKX4vPEGSKWUP/IoNpPphmvm9Q/CYLbx7cmfvAQSF3BHUxPD7PzY0iByti6fc+cXYLW24yCV8qSXPWc00ezMPYlmoB+e98cRLbVyuUWGvqbx2meQCAwaF8U35034teuo10whtSKVjXkbO93Oz28OgX10aP2sKKtLObp3NwCxjvas7BpMk9nKYzmlfF5Wi59KweJQb54I9OS0vpVRZ3O5c1xXmh0DUNWAqXg08brBv2G2fxm/6ORFUUwWRTHmZ/42/QuzcuD6nDffH9v+EOy4UMmT687zlNAFm17P7OIAYl1jWezhgZOmnkBbFt8ueoxGlYowzY251iVf3seHbg7YSeypLevPwqSQq6GJ5/rfi0x05gVnH5SFO7n9/ll4hUWi8/Vn+v2zOdT0AZm2VpSCFLMgsEFsJMgplE+HfsZ3J2pwd7WnTS3D1nSAEG0IOr2Sb/M6fpyJx3ZiLczBPb4Py3t2odxs42+bsgk8+AarrWG4WGH46HDWppczKtYLO8HMwYMHCQsLIzz855kcd1+qxMfJji7e133Pc6sRTC006cfjMjUCx/4+CNLrVvkHXwe5AyTMv9pks5mpqtqCq+tglMobFWvazp6lPT0dl9mzEX48vP3i4heUt5SzsNvCm36GhlYzhedq2P7hBc7uKSFmgA/hiR6/bHgL/2ehtJfTZ3wIvccHk3e6mvz0X7dTl/v44L30dQyZmVQvfeOG/khPDf3DXPn6eBEmyzUqBHQhCOEjWFKYSaCjH982OlFYe5rz5+/BajUwNTyW8PYyXtM7YraJyD0dGJkQgBE5a75K7fQSkjkp6TKrC59mGOlpHIhJGcniE6900qCQSBSEhjyOb1IBMrmEK0f3UVfbsStIcnHkdJ9ojveKIqtfDKvjQrjHz53HgjzZ1TMcrUzK7ZlFjJg8FIlEoDwng48O/faq4X+FPypcsxmYLgiCUhCEICAMSPsFm9+MKT19+XRaFNKYruQ4+ZH7/hc8n/AS7aKNd/0jGORZhNbTG4nVSt6ZVL5Z9Ahpm34g7/RJDn70BmWmVI7b2aFoTSFYp+u0ClZKlUwPvZtKZRvbnLzQHX+B8dOGMnGwD9bUh1irEBEFgUEBQ3kq4SkUEgUudi5kl8nIr2mlzd+eHqpa8hoyCcpX8NmLiyiVyFEZ2oguyCR60izueuwpEnPbGVhlZoW7jsVJAiddZTwY7s3OnBqajRbu7BvIvn37sFgsDP+ZcBNAq9HC4dxahkZ7XIufiyLisY8x2cKQ9xyAfdxPHHb+gQ7+mv6PdBLwrm9IxWyux8vzxg2bKIrUffwJEq0Wp0kTMdvMfH7hc97PeJ+RQSNv6sC1rqKFHR9d4IvHj7D9wwtU5DbSZ0IIA6aH33AGcAv/P9F9WAA6HzUnNxdcVWz6JTgOHozLXXfRsGoVrSdO3NA/t18QVXoj2y78pEp06BIcTO0sM6gwi7C6NYTq+mNkZj6MVCLwrCSPfJkL35V1qFFNGxGLDBs76ptpO9OZXkDhrSZgYRzLSiX0N96LUaJj/p77+DDjY46UHSGjOoN6qS+OboGEDG1Hamhj/dLFmNo7DoVlEoEgeyXqn6Qwh9ir2Ng9DD+VgnuL6/CIiiZaUc+cxD8mXPO79sKCIEwA3gPcgG2CIGSIojhcFMVMQRC+By4BFuB+URR/Ox/pL+BsxjlSN2/i7Xvv5bzhdnTvv872j44ya+Qsvr70NZOkEioVdvjqm4mdM5+cY0c4smoFAAM983gy1gm1VE1ZeTBvjAsho7mNNworsYgijwZ68FifGazOWcnLGgPDa0pRfDGMVkHg7sBARKw80fMJZneZDYDZZuadM++QfTkaJ8dQKnUK+jTvodwm4JNjI23SfBAEuteUMfWJ5wkLC6M9u56mbQU86JtPgYcH26RepLhpme/ryogfjhDro8UVPVsyMujbty+urq4/Ow9HcmswWWwM7+J5rbE8HaExl1bpo2hTfiKo3VoHWx8G50Do01lMu6pyMzKZBp0uiZ9Cv3UrLQcP4vbYo+Qay3h+//Nk1WeR7J/Mkr5LfvVzqyrSs3HZWaRSgbih/gTGuuIRqEEqv5Uu+WeCRCIQP8KfPZ9fojizjsDYn//9/hRuDz9E8969VL7yCsEbNiDIrymBJYW5Eequ5vOjhYyP87m2IHALh0HPELz3RV5JnMUjNUfYp05keO0eysq+YlhoV3pdPM9bhV2Y5OOOWqWgu7uUc9V2lGzJIjzCGamj4up95K52eD8Qx5s7Cnij6lE2alfywbm/d/qc9jIlCfYtRPboh/5MIZ/cPxev0HAcnJzR+frTZWAy9prO1B4eSjnr40KZcS6f99WeTLZeJOdiOgGeNy+H+Uv4vdk1G0RR9BVFUSmKoocoisOv63tVFMUQURQjRFHc8fs/6j9HjrM7RomUtzdtw++OCRicdHjt2Yi7dTQ6Ox0rFAkYFCpi1S0kjJnI7a8v456PvmbugtEsj7JSLZfRYm1BHfw+MrdaJmXkkdNqoKjdyLRz+aQ3G5gcdC/t0gYe7DKGvBGvMj+mHxVYCdQEXnXwALdH346z3I8K6WqEUAWRMj2nK3YRUqVBmzCMVLUbgijyUUoyYWFh2NrMNHyfg82lFUP4G2yJUXGubxc+6xLIvqxq8qpbuKOXH5s3b0ar1ZKUdKPT/Qd2Z1bhZC8nIdD5apstfR2iKEWSMOGquDEAFhN8f0dHWuXEz0B+jZnSYmmlumY37u6jbiA/M1dUULnkZezi47kyJpHbt99OVVsVywYu452B76CS/Trhg9YmI9s/PI+dg5wZL/Si78RQvMOcbjn4PylC4t1ROyvJ2PvrZQMlKhUezyzClJdP47r1nfskAnNvC+JiuZ6ThT+psr3tIYibRXLat8x1imVHxQUyhVjy8t/A4BnIcyUrqLZJea+4Y+U+o28oJmSss1RTvyGXrOY28tuupTNKFFJcxoXx0oBEHqx9kAbvd3EJeIU3kt7njf5vMMhvCIdb5HzleYqz8SpcIyNp0zdRcvE8h7/9khWPLuRK3o1JiDqFjB+6h5Lg50NqSCwHHP+Ygr8/xX/UaF8PHGPisK8oYeqh05wdPZbuNbms+/4E98Q8iHOlJ3ZtbcRaNnTI2ekrUJftZ//5pZy1U+GicKM1/2EcFCpeOvY4zlIrexLC2Z8Qga9KwT2XirmzVwqSpmGk1h1gQs7H5LR1bBPnxMwBoKbZyKaMch5ZfYHy3FFI5A20295FUvASoigSa+jBeqUzVqmURCcHPHQdoRH9/lJsbWZKwt7AwycFD7dBeCjlWG0ib+7MJsTNAUlZOnV1dYwdOxal8ufTEs1WG/uyqxkS6XGNwkAUETM3YxS74XDbdQehoghbH4HiVBj3Pvh1VoSqrd2LzdaOp0fnUI1os1Hx9CKwWtG9upinjz2Dzk7HurHrSA5IvqnwSuoPeRjbLIy6rysOTreYJf/skEolxCR1sLk2Vrf9ssGPUA8ahCo2lrrPP0e0dKYbmBjvg7O9nM+PFnY2EgQY8y7ETOYvZ7fRy86br8pKKDVKyStaRoKLM1Maj7G8uIpD9c2MSwzDRW5hjaM9UzXtDDp9mdtOZvNQVgnW6+L0dpEuzB0cylvnlJTYAlh8xZUE36EsHfAG7ydMJ0hp5pxnDh96HkA/PZygp2aS+PyD2BwVrF+6+CqvVktLC8XFxVgsFjQyKV/GBvHIiCEsiI387RP8L/CncPKOMimPjkzG3kHNxKKLvB3XhzY7e+48sZrqTdVY7dxQNufR0mcKpC6Hd6JoXj+fpc6OCAi4tT6MiyKAIdGLEM3VDGA3bgo5WrmMz7oEUmeysLjwCvfH3Udb0b1M8n+Qwf6DsZfZ46/ow+wv0kh4dS8Prc7gYE4Nk7oPxOR2NwpTIY3WOvoXhGFwjeSCXwcdQZxxA+fO30P+6XdpTi2l0ecwKj83IiOuVdetOFZEfk0rwz0NXDx/joEDBxIS8s+VlY7l19HUbmZYl2vnCbbyi0iNpVg8hyG7nqL32HuQ8Q0kPQVdb0x1rKzahErpjZNTpyJm6ld8RVtaGh7PPsNmw0lKmkt4sc+LuNr9uu33P3Alr5HcU1V0H+qPq6/6pmxv4f8vInt7IQgdrKK/FoIgoFtwN+bSUvQ7d3XqU8ml3N47gL1ZVeRVt3Q2lMpg4ifIukzgzew0XORqvmpwpPDKdtq9Q1l6YTERKilzLhbyVlElbkm+1CT6UGAn8HSuiXs9dayprOeDks5xersoHcND3fkgrZUqo5k5FwoxWG30DruPhR4Cc50DkRgkfHzuYx49+Cj3nXmMj+POsLbbZV779lHOZ55n+fLlfPnll3zwwQc0NnZk9YxycyLI/o9Z7PwpnDyASqVidMooaKjjKWkb781eQGhjKVRcRGk0srVbGfdJ6ymY9R35g59mdmRPDAL0dU/hdJ6UO/sFsr7VGyeXZA4WrKagsaPYIsbRnscCPdlS04gu1JkIp66sO+TOvuJDOFh6MPnDdI4W1UGoBlU/T1xG+rNKY0En7c7cI2HMPB6Fl7onafEJSOnYAvaQl9HeXoz5kIAoNSL0kxAT8zlSqRJRFNmUUc7SHdl00VpovXyCnj17/sswDcCG9DI0KhkDI65t+SwHv0MUBeQDr3Pk5emw9yWIHgdJT98wjslUS339UTw8x3YiITNcvkzNsmWok4egmTCBlZdWEu8eTx/vPjf9rE5uLsBBqyB++K0c+P8mODgp8YvWkXOi8qb4bRyHDEEREEDDqlU39N3ZNxAHhYylO7JuNJRIYdwHuDgFsaxOT4PZyDcNagrIxMFmYI0ym15aB5YVV5Ejl+FdUQ9HqthUUId8Rwl9HOz4W3EVdabOOwjtiEC6WaS8Ui5yWt/G4zmlyGROeHlOoJtjHsk1ibwX8R5rx6zlb4P+xoPdH8TRScdm9WnuPjaPas9qRo8dTWtrKz/88AO268RS/gj8aZw8QHR0NF27dqXg5HH6+bnyxR13Ua/TUeIUywuj36JYX8y4Y08xvnAVRcY6pIKU8xf6Euqupt5bRaPFytI+j2Mns+OdM9dU4u/3d6ebox3PF5SzZHo3XL2ysYgGysq7YglxJHJUEHcODGZMmDvB9kpmaZVMPbIZk1mkyi+a0wNGcMkow0+oxlcpZUyPd+nmtApTTVdedrVn3No4Il88TLfFu0l4dR8Prc7AU2Gkm+EcQ4cmk5KS8i9DIa1GC7syq0jp6n2VjEwURYSCHZjlsSiiftwBWM2wcSGoPWDM8s68NT+iqmobomjF0+OaHKAoilS9+hoSBwe8lizhQt0FylvKmRg28Qb7X0JVoZ7yy43EDfW/JeP3X4iovl60NBgpy/71bJWCRILT1Cm0p6djzMvr1OeqVnL/oFD2ZlVz6PLPpGgq7GH4q8TUFPKsdzJZ7TbeqcvDYqfFo2gv33ULIbd/LJf7x7Kmlz/9hXxEpYnPqhs5s6uIVquNjwqrOg0pUcnQDPFn4IVmHlFr+aGqgeXFVfj53YUomggLL+XS2UtEOEcwxH8Id3e9m7WTNzIswx87A+yX7eeZgmcwxBvIrswm859w9fy78Kdy8gBjx44lISGBikuZuNhMHHWNZEeDltLyALZM2MIzvZ7h2V7P4qhwxIluVNRLeHBUBF9cqWOShzMRKOlu7s6hskPsyurYHsokAu9FBWAVYV5eCWbdKWwyH0y9e/PaqGi+7h6C1SZyuqmVg3VNfNVoZGnyTN656xlWJo3jpBFmiV9wRfBnkM4J0WyjZN1l/iJt53BtM7P7BPJwchhju3nTN0jDEE0lw6SZ3D5tMv369fvFWPe281doN1uZGH+tqNh8LgO5rQAxfPQ1+4xvoSYbUt4GO+efHauyahNqdRRq9bUYfsvBg7SdPInrAw8gc3FhR+EOFBIFQ/yH3PTzSd9djNL+Fh/NfysCu+pQ2svIPvbrQzYA2vHjQS6nce3aG/rm3BZIsJsDT/1wnqY2843G4SNAF8akogwWdl1AWpucxW5qbAUHQRRxlElRSCSEhYUxZ2A0A4WzLHFq4BGDFFmDkY8Lq6hvMXYa0iHRE6lOxZ2pDUxyd2ZpYSU7ml3w8BiLTneGpqYO6pF/4NDhwzhJwhh91J1HNbPxc/RjQ+UGdvnt4pFTj3C26uxNzcfN4E/h5C/VXWLB7gXoTXpkMhkpKSksWrSIZ59+mmGjBmDVKXl+UyYFlVJmRM7ATx1Ig7GBstIonh4ZyUZzOxIEnvR347vvvsO7whsHiwOvHXuN1rZWAMIdVKzvHoqHrZim1hx8PUayv3cU4z2cmZCRx+fltWS1GjDaRPrnnGFiZR59i7J4ytuRZbIXiFQ70WoTGKbT0Lgxj2V6PeWija/nJvLCmGgeTg7nicH+BFWnEi6tZf68uURHd+aysNlEVh4v4tkNFzia21F0YTBbef9gHtFeGnoGXHPc5iMdlK2KwT9yVFuMcPitDoGQiJ9nh2xtLUCvP9dpFQ9Q99HHyAP8cZ42FavNyq6iXQzwHYBacXPx9IbKVgoyaoi5xUfzXwuZXEp4ggcFGbUYWn/GIf8zO50OxyFDaNq4CZuxs8NVyaUsn9ad2s5wvo0AACAASURBVBYjizacv7GyVhCgx51QepKF3kOYHNCbjVIpL6pMWKoudbp00KBBJCYmkm/IQyI/y/jaNkxKCXdsPNcpxCRIJWiHBWCpbOVlo4o+Tg48kFXCRsX9GCXOdOmSxp49W2hr05OR8SX1DW/Sa8Q53ELUtGxOY3niX9k9eTczfWfSLDYzd9dcDhR2Vsb6d+FP4eTzL5zhRPlx5n0wnqLzHW9EuVyOVCrliSAvhg4JwqKSMuuzk8z/6hQPbf4OUZQyp/twXMKd2F7bxEMB7lReukhdXR3Tp0znL3F/oV5Wz7Jdy67ep4vajljrfhzljqxPmk+wvZJX8ivIazMiAgPO7EeHlaIuPXHOzeLBIC8mOxxDZ77EBdU4HCQSYg9XkZV+hZ2Ymd8/mF7BOgAsFgtr1qyhvb2dO+64A2/vG1e6b+zM5vlNmaw9Xcbtn5/kzi/SuOvLNIrr2nhmVNTVFbulwYCiZisWdVcE1x/j3hd+gKZSGPh0Z5GQ61BesQpBkOPpeY2LxpibS/u5czjPmIEgl3Oq6hS17bW/Sbf17J4SpDLJLRm//3JE3eaN1WIj50TlTdk5T52CtamJ5t17buiL9dXyxPAItl+o5JsTxTcad5sJUgVCxrc81/99xmsUbHRU82jqIozWay8NiUTCqP9h76zDo7q2/v85Y8lkIhP3ECEhBgkQIFAgaHCHoqVOhVuXW7/VW70VaEup0Ja2eKFIcbfgkhB3d88k4+f3xwBBQiCV970/3nyeJw9kzt77SOass8/aa33X2LHMmjULbGU4lB1GZjRxTjTww9G8q4ZUdndF7qVCtyufX8IDmO7uyOdFTTzEEl5VvcTKUF+mJ6xmTc1xVE71ODv74N43C4New6/vP4OTxJ7Hez/ClMw+qOtlHDuxvUPX41a5LYx8d7eexDZ1I82xkq+/fJHitNansyAIfBEVQNgIP/TeNpwtawCbNELVUcT178rzGUUMUNvyqK8bJ06cwNvbm6CgIOb0moOP1IeNtRspqbGES2bXZbO7YDczus1AJVeR16Ljp5JqRGBAbhJfzZnJVz27UWQwc7ZbNLGxMeTnL8XWLoa9NXIGVBgwnixnnYccK5mEBwYFXD7OnTt3UlxczKRJk9o08DmVTXxzKIfZfX258MYo/jk6lAvF9SQXN/D25EgGBrdGuLTs2YtcUoDQ967WAU59B66hENS2i8VkaqG09FfcXEdhZdW6eFu37leQyy8X6N6eux0bmQ1xPu0vBF+Lpk5H+vEywvp7YmOvuHmHTm5bXP3s8ApWc3ZXAQb9redI2sTGIvfxoW7duja3PzgokKHdXHlrSyoXiuuv3qhyhoA4SPsdqUTOwv6v8Ux9Lfuacnl096MYTFe/VYSGhrLwyccY6dWLoKpSRHdr3tuRRkF1a/inIBFwGB2AqVaH6VQ5n4d3YWdMCA/5uhPt6ImNTEUhXVkmPMzbtj9gClzC0DHbiRjnQFVeBV8umMU3C+9BnpvHyLOBDHf8X9Ku+f+BRpOIa0037KS2nI1oZOfSRVcV9VZKJazvG8LwwX6U9jKjE0oos+7B9HPZ+ForWBLuR3FJMVVVVURH9UDXrEEQBF7p/wo6iY6FOxaSVJnEC4dewE5hx/xwS/LTZzkliKKIg6aeJRNG4eTljVV2OuEluZxz8+VwyV5M9TryLzxBlWgmXitFuCec3ysbmNbbBxdbS8hUUlISJ06cIDY2loiIiDbPcXlCPlKJwFMjQ1DIJDwyJIhTr4wg8fV45sW2RqmIBjNC8kpEQYG0z8WompKzUHwaYu674Sy+pHQdRmMD3t5zW8cym6nfsgW7oUOROTpiMBnYlb+LYX7Dbjnp6RLn9xQimkSiR/p1qF8ntyf9JgagqdNxYlPOzRtfRJBIUE+fTvPx4+jzr5+tSyQC/7kzGieVgoUrztBy7QOk22iozYWqDNzdxzNGZcMb1TWcKDvBorOLrhtPJpMRd88Y7mt2wiyXYlDLeHH91e4gq2A1VkEO1O8t4P20IuYn5pLY2MyboeHsjBtH4rAhrIsOQiKRMO1cNhtqJMTP/pk7HghD3bUanxgbZr3zAZKQnlQZO15R61a4LYx8REQEns6eRDRHUmTfQGZjDkl7ro6pVcmkfB8ZwAKnIgCc1DE8H+DBAh9XBh1LZVBGBSecPdj3yTt8cd8slj42hcbT/2aGXRTZumzmbJ1Ddl027w16D2elM1V6I2vK60AQeM3LEXdXNxobG9mzZw8zTJZyYC/lWeN4+h2WOlnhIUi4c053VuZVYTCbeXCQRWKgsrKSTZs24evry8iLdWOvxWQW2ZJYwshwd9zsWo2rIAjXLco2n0jHxrwLc5cxrYurJ78DuQ1EtV1DUq+vJjd3EWqHPqjVrYlR2qQkTNXVlwuCHCk5QoO+ocMVn3TNBi4cKiaotxsOrsoO9e3k9sQr2JHIwd6c213Ivp/TqCnV3FI/hylTQCq1vGG2gZNKwcczo8ivbmbZkWuSpEIufm/TtyIIUlTdH2BqQxNj7T34IfkHzlWcu248QSZh+vgobA0m7DxFjmRXs+Fsq9aiIAg4jA/iGy8pn5RWEWwSOFurYcaxdMoPF2FuNjLQ0Y7tvUPor1bxZFohb+eU02fYBwy9914co45TWv8v7rlnCqNHd9wFeivcFkZepysiKvowXtUq5IKckmgFR9etwKC9utKKIAiU157Az86PTbFx+DXV8lxGEQZNI9bNTZyJjCVx/tMEDJZh0GtIXg/dypOZ1RLGXKe5bJq8iYHeAwH4+kI6JkHAT9vEnJ7dMRqNrFmzBpPJxPRxY3ndIZ9iXIkf6EayvZTXw3wxGkV+OpZPfLg7AS4qWlpaWLlyJQqFghkzZiCVth1SeCqvhqomPeO6X+HGydoDXw6AD4Nh85NQkYZoMiHZ/zKCoEMy9kVLu5Y6iz+++3Swdrhu7JaWAhITH8JobCKk2+tXPTSaDhwAiQTbgXcAsDV3K2ordYdj41OPlmLQmugV3xkX30krA2cG02OoD2kJpax84zjrPzpNTUn7xl7u7oZtXBx1GzYgGtpeuB0Q5MKIMDe+2p9NjeYKqWIHb/DoAekW37dtxP2IwN11F3Cysmfx2cVtjqdyVzHGSkWdqzMuMg1vbk6m+opom1x7Kd8FKhhbZeLjrdV8fFxDoWjildxSyj85jb5Ug1ou45ceQdzt5cwXBRXcn5yHi9e9REZ+TmNjMhmZ86iu3tuxC3iL3BZGvrk5F5MxmQG99xKgdybFoZwGTS3ndv5+dTtDMyfLTjLIZxDHtm3ixdxyrPRaWpS2aFT2OAkiu63VpIWpmPTSg3Tp3ovCA150JQ9JqgY7s6XMnkGnY1m5xef3n56hGAwG1q1bR2FhIRMnTkStsqbHljqWnG5klp0tX4V3YbK7I2tOFVLfYmDB4EBMJhNr166lrq6OmTNnYt+GBPIlDmVWIZUIDA656HfPPQi/zACzEfwHwvlV8GU/+CgMpWEPhtB/ILhZ6mFyfhUYWyDm/svjabWlZGS+TcKxeI4mDKWxKYXIiE+ws706rbpx/36UPXsiVaup19Wzt2Avo/xHIZfIuVXMZpGk/UV4dnXA1c/u5h06+T+DVCph0MwQ7n73DvpPDaKuvJkNH5+hobql3X7q6dMxVVXRuH//Dds8NyqURp2RFcevcesEx0PRSdDWW/z0Ht3xbpAwwt7EibITnChtWyx3WrgnepmULm4aGrVG3v69NfnqvZxSlFIJH0zsgcczvRmzoBf/8HFjs4+ck2oJ1T8mY9YZkUsE3gvx4Z1gb3ZWNTDmdCb5ikH07bMJa2tPtLqOhZXeKreFkXd2jiOmz3pUKmdGeebTYtZSFqnk+G9radG0pjsfLj6MzqTDq0DG4pPnabRVY1BYM0HQMyT9DDWigKdQyffCQqw9JjH5+dfwCgum8KALLopTbNmyBVEUWbL+V5qsbQiQmPFubuCbb74hLS2N0aNHExkeQdGyXVjV+TBgiBWfxnRlsrsjRpOZ7w7n0ruLI727OLFr1y5ycnIYP348fn7t+6kTcqrp7u2AnbUcdE2w4WFLfdYHdsOM7+HpFMQhL6MzhlKneA759DcsHUXRsuDqHQNe0QA0NaVz4uREiot/QWntTXDXl+kfuxs3t6tfFQ3lFehSUrEdYllg/S3rN3QmXYcrPuUnVdFQpe2MqOnkhtjYK+gV34Wpz/bGZDBzcFVGu+1tBw9C5uZG3eo1N2zTzcOOAUHOrDxRiOnK7Nquw0E0Qc4BAISgYdjXa4mVluMgt2ZF2vVZtQADne1xFiQ0ufkSJStjw9liDmRUcq6hmW1V9Tzi54aLlRy5qw0yJ2ueCvKki7WCD6NVaBt01G+1uI4EQeB+H1dWRgWhMZmYeDaL1woVhEWvwcd7Xgev3K1xWxh5URS5YPCkX9+NRDiG4Ck3c861CK2mkUUvP8+hQ4cwm83sLtiNnURF2bp9pPYZipdCjhkIzk0lTmbGVWZGbS5HKyh5LasEmVzO1OffwcHdlroTAmWnt/Lda//kW6kaRJEheaksX74cg8HA3Llz6RkZTMHXm5AVuqDvn4lLn9byfNsulFFU28KCwYGcPXuWY8eO0a9fP3r16tXuuWl0Rs4X1tE/yBJqScIX0FBsERazvjj7t3GiWTWfqsbnUIx/CEF28c+adwiqMqCPZRZvMmlJurAQQZDRt8/vREd/j5/ffW2W9ms6aLkJbOPiMItm1maspadbzw5XfErcV4StoxWB0R3Tt+nk/x5qdxtixvqTn1RNRX7DDdsJMhnqWTPRHD6MNv3GD4R5sV0ormthX9oV+jM+fUBhB1m7Lb+HTkAwG4k09aS3dRP7C/dT1VJ13VgyicAET0fSXJwJUpTjZSvhpQ1JvJFVjJNcygKfqxUklVIJbwd7k2UwsC7OBc3xMvSFrfVu45zsONg3lEd8XVlZWsPdF4po6YDUQ0e4LYz8itIaJp/NIqFRoG+fFQx3dqFa0YyhhyPyimL2bd3C1h1b2Z+3F498AcmAEZRY2yKXCEQoFTTn5xLerRvDZadJE8K5z8uJDRV17Kqqx8rGhpn/+gR7XyMU15NVUUmFiyd22ma8G2sZO3YwEycoaD6/ipQPjiLJd0LXL5XAifdePj6DycwnuzIIclURamdgy5YtBAQEEH9xQbM9TuXXYjSL9A90hpZai5EPHQ++rXXRTQ166rflouhij/JKre6T31oWXyMsce+Fhd/T3JxLRPhHqFSB1+7qKpr2H0Dm5YlVcDAbMjeQ35DP3LC57fa5luqSJorSaomM80YivS2+ap38zUQO9kZuJSVxX1G77ZzmzEGwsaH6u29v2GZkuDsutoqrFkqRyiEwDrL3Wt50fWLAKQj38hYGqR0wiSY2ZrVd9G6+tws6qUCBdyiD5IUUKEQS6jU8F+B5XWEQgJEuDoxxcWCRlZ4kLwV1m7OvisxRyaT8q6s3n4d3IaGuidez/p7iebfFnTfN3ZEgpRXPpheil6hYcMdylBKB1KALWKtssC/JYeuBr2gRdfSxiqBo+GSUEoFCrZ5wncWdE9TVln4t3yMiwUFuRTeVNS9kFNFkNGHn6MWU518ldE4We2fdBYLAK12dGD26BUPV22z52Yk5p3oxRS/hERctdb2mIwitf/RVJwrIqdLw3MhgNqz/FTs7u3YXWq8kIbsauVQgxt8REr4EXT0MefHydtEsUrM2HdFgxnFacOvCaVUWpG6GnneBXInZrKOgcBnOzkNwcrqj3X2a9Xo0CQnYDo7jaMlR3j/5PjHuMcR3uflD6UqS9hUhlUs6JQw6uWUUShmhsR5kniqnueH6+q6XkKrVOM6YQcPvW9Flt102Ty6VMCrCg71pFVeHUwYNsyQGVmVaQop7zESSn8BAn/kEKkysT1/RZj3acFslY5zsSfDzJ89Bjqy7E0KjAfty7XVtL/FxqC9eVnKe7aFkb3MzxWfKyG/RcaZBQ4nWcn5T3R35NtKf5wI8bjjOn+G2MPLWUgn/CfWlQKvnq4JKHG27MDloIud1JtTjinDt4k2GZwUqnYI5d73F5upG+jioMAPKvEx8fHxoatqEu1BDjJ2C3yrq+E+IDyU6A0+nF2I0izg7D+KI77sU4IQtGnyKp1OeuIvcoy/wcYsdkW52PD+qG1V6KdO/SuDNzSm06E0kFtXx3rY0y0y8JIm6ujqmTJmCjY3NLZ1bQnYV0b5qbEyNcPwri3qkR+Tl7fW/56DLrMNhQiBytyvG3P8uyKxhgKXiU3n57xgMNfj53nfTfdYePYjY3Mwi5REe3v0wnipPPhj8QYf04rUaA+nHygjp647StjP5qZNbJ3KID2ajSPrx9jNinRc8iESppOLDj27YZlx3T1oMJg5kXOGy6XoxITD7ooxA9GwQJLhnFzDA0YkCTQUp1W2Lhv071AdXJBzo1hNBMNOr2sQLvyayK6W8zfaOchk/9wjE3lrGk71tiGkop9+xVMaezqRXQgrzE3OoMRgZ56rGVXHrAQ0d4bYw8gCxaltGu9jzdVElDUYTD0Y/gZXUirXaOrJiT1KubiFEE8Gnx8+hMZlxVciRAYr8bCK7+1ObfB6/iieZrLAjo1mLUibllSAvNlXUMeFMJncl5rCsPgAQmaTMxMdlLk6pr/KxyUS4my0/PT6QR4d2Ze+zcdwV24VlR3Lp+dZOJn1xBLWNgufj3Dl+/Di9e/emS5dbCyVs0BpIKq63PCCOLQFdg0UD/tL2fYU0HSnB9g4vbK+sD1lwHC78Cv0eBls3RFGksOhHbGy64ug4oN19lmnK+G3ZS2jlUBTiyGv9X2PluJW42nSsak3qkVKMBjM9hvp0qF8nnTh5qnAPsCf9WGmbM+pLyJydcXn4IZr276dxT9u6L30DnHBSKfg96YoHhqM/OHe1hCEDqP2gx0yEU8uY4TsbKSJrk79oczxPKwU7QgJ49Gwms47t5JsJoYR7OfDQT6d4Y3Myp/JqOJBRyRf7sliw/BRjPjvEir3ZbIwMZImXB8+m63izGH7w9+ZZb1cO1DQy/Xg6TZobv7X8WW4LI2+oaKbqh2Se9HSl3mjiu6JKXG1cebX/v8jSwbrKBoKtJdzTM469ogI/KZRo9XgbtSglUtxSqvE5/jTWZyOIXZuPDFhfXstCPzcWhfnRaDKR3NTCQLUtIPBY1GxckqfyQ5OWesx8MDMaxcXFThuFjDcnRbL24f7M6uPHE8OD2fBof47t3Y5KpbphwlNbHMuuxixCf18ri5EPmwDulozYxgOFNOzIwyba9erardoGWP+g5Ys78CkAGhrO0th4AV+f+e3Oxut19SzY/gARyU1I7+jLT1NWMyNkBjbyW3vruMSlsEmvYDUuPp1hk510nNBYD6qLNVQVNrXbzmn+fKzCwyh95VWMlddLDcsuuWxSy9EarnTZDIe8w2C46GoZ/hrIrAg4tJIeNlbsLjyK0dS24XXxc2CGyRWFQUd2ahI/39+X2X39+OFoHtO/SuDuZSf4cEc6mRVNOKsU/JSQz51LEhjk7cjCQUGMS9cQuTSNWctyeP+UhhSDgVc3JNKSUv2Hr1d73BZGvqFOy4bGRrw25THS2Z5viirRmExMCJrAqvGreLvfszzmraZI/IVyB2d8slM4U9eIbXkJYz17oU33pLRrFp6v9MMzwoV+lUY2FFdjFkXu9HDicL8wTsSGk6/VE+ugwqvGQPm5CjZJjEyM9ibS+/okoz7+Trw+MYInR4SQfv4U5eXljBs3DmvrW5cDOJRZhY1CSu/sJaBrhLh/IppF6rblUr8tD2WUK453dkOQXDTc2nr4ZTrUF8HUby5H3xQW/ohMZoeHx+R29/fv4/9GmVaIg0aky4Q7b/0PcA15iVU01mg7Z/Gd/GG6xrgjkQmkHWs/dlxQKPD+4APMLS0U/uMfmFuuj7Ef290Djd50td581xGW/JGc/Zbf7T1h+vcIlRnMLayk3mTmyMoxsOdNqLi+IIl3zwA8zY6cPnkKlULKO1O6c+j5ofxwbx9WLYjl/Gvx7Ht2CD8/0I9VC2Ipa9Dy0E+nkXRzxP3p3qgnBOIwPpBJo0OYa2PLGi8pWTW3lvXbUW4LI7/LTuTVSGuOVjXykEFBjcHELyWWp2KEcwSTQu9mYL9NJFjfi1Q0MNBtLzpBQn+FkoRMORNpYGa2Gx8fycFxalfGNUKJycShqtYwro0VtRRq9Tzi50bDzjw2y4y0mMzcN6ALaWlpZGZmYjJdL7ZUVVXF/v37CQsLIywsrEPndTirilhPKYpTS6Hvg5jVYdSsSqPpQBGqWE+cZl5h4ItPw9dDLf/O+B78LOGbOl05FZXb8fScgUymuuG+tudtZ2vuVh6oDEOQy7G9SSWq9kjcV4itoxUBUZ1hk538MaxVcgJ6uJB5shyTqf3KSVZdu+L14QdoE5MoeuxxzM1X15CNDXTG0UbOtqQrHhiBQ0DpBImrWz8LHgEL9hMXMA6V2cy2xnzEI5/BVwPh3NXx88ooV8KM3tQ11JOWlgaAj6MNQ7q5ERvojL1ShlarxWw2E+PvxH9mRHM6v5YPtqcjc7TG9g5v7AZ6o4x04YWeXVBIJfzkeutrXh3htjDyk9wccZRJ+TVESeCOYgbYq1hSWInuirJaGlTsNEQRb6+hSWVZCAzKFvgPWqJ9YGKUF1/sy+bXxFKmDgrAUWfm8yRLGJfRLLKooIJuKmsGN4o0ptfwq9TAgEAnjm1fx6pVq/jll1/4/vvvqalprXhzKRNWoVAwduzYDp1TYU0zuVUaBlWtRnTwptnlYco/PUNLUhUOY/xRTwqyGHizGQ5/Ct/FWzTj52+0LM5epKh4BaJoajfRorChkLcS3qKXXTh+R3Kwi49HavvHaq9WFzdRnF5H9yE+nWGTnfwpQmM9aWk0UHDh5m4M+5Ej8Xz7LTRHj1Jw3/2YLtZOBUuUTXy4B7tTK1pdNjIFRE6F9K2WN+BLuIdjPWUJg3z7sltlTcG8jy1Z5Rv/YRH6uzSmqw3Bbv6opbbs2bMHwxUSCzk5OXz55Ze89957fPzxx6SkpDCuhyfz+3fhu8O5HLymgpWrQs6qqCDe6OrN38FtcRcqpRJmeTqx10GgTGfggUYppToDK0pbDe7ykmo0JjPPhvaj2f0ZlCYzOxrA1krKd/eN4uM7o+nj78hbW1LQe6i4v0XOIcHAipxy3s8tJV2j5Vl/dxq357PXWqRCZyRaWU1JSQmTJk1i8uTJVFVVsXTpUpKSkmhqamL9+vWUlZUxZcoU7Ow64JsWRfYe2A/AIPEc1cY3qFlfgsRahuvDUdjF+Vp867pGWDUHdv8LQsfBI4ctX8iLGAx1FBX9iIvLcGxs2l7srWqp4rG9jyEIAq8Vx2BubMRxXsfi4a8kcW+hJWzyjs6wyU7+HL4RTijt5KQcLrml9upp0/D+9BO0ycnk33UXhrLWxdaxPTxp0hkvF9sBoMcsMGrhwvrrxpoWvgCdKLAx6zvEGd+DjTNs+6cltv4iqmh3+rcEU11dzYYNGygpKWHDhg0sX74ck8nEsGHDsLe3Z+3ataSmpvLS2DCC3Wx5Zu35q7RvAPqpbVH+TZOi26Y8z93eLiwprGRHD3vmHyyn/zhnPsgpZZSzPVJB4MuCCoY62RFhq+RscTOB9WaOiSaeGhSCg40ldOmD6VGM+vQg721P451hIew6lMrTWF7xZns6MazMSE1+PavtTQTZKmnOPkTfvn3o2bMnAP7+/qxZs4Zff21VyIuPjyckJKTtg67MwJi5hy1ZOvRmgckeVSj09ZB3mPWldxMqqLBreQyTfQBOc/xQRrq0umda6iz+9+IzMPp96PfQdTLC+flLMRqbCAp8+vJnGbUZfJ34NRXNFUgMJsqLMtDKRD5zfwTDt4uxHTYMm4vn01E09TrSj5fTrb8H1rZ/TzhYJ/93kEoldB/iw4nNuZTnNuAecGN9p0vYx8cj/eYbihYuJG/OHPy+/RarwEAGBDnjoJSzNamUEeHulsY+MeAZDYc/gajZIG9dL+vj0RdnK3uO1lYyvXY3XnHPw9ZnoSABulgi1Gy6u+C93YnBXftyMOUEKSkpSCQSBg4cSFxcHHK5nNjYWJYvX866deu4//77WTS7J5M+P8LDP5/m27v74KD8+++T28LIi6KIQmeml70Ne6yM3HXSyKs1MmZYaZlwJhOJIKAzm3kr2Ju68xWkYiTcLCKVCMzq26qpEuCi4v6BASzZn809A/z53tqRdckVeA7wZpKXG9VfnOOMWkZGXSN3hYC0RcLgwYMv91er1dx///2kp6dTU1NDUFAQnp6ebR0ynPgGtj3P2/p5/GCy6MasyzSzxPY3Mmz6c17syhMKK+xmRKOMcG417gAmI6y9B0rOwZ0/WqJurqG+4TwFhT/g4THpcr3WjNoM5m2dh0IiZ855e+7YVoBCd8ml9RHygAA833rzD/8dkvYVYTKZ6TmiUzO+k7+GqGG+JB8sZvcPKUx6MhpbR2tEs3j1/XANqth+dPlpOQUPLiD/rvn4r16NwsebkeHu7EguQ2c0WQreCwKMeB1+mmwx4OM/BanFJEolUsYHTeHnlB85k/Y2Tn1+w3qPPZz9+bKRlzkrkXvbEllvR+Sjj1JeXo6Pjw+Ojq1lOBUKBbNnz2bp0qWsWbOGBQsW8PHMKJ5afY7JXxxhSk9vgt1sCXa3o6vbH3OR3gyhvTjU/2liYmLEU6dOdbjf74mlPLX6HHfE+7PNrGNrmRz3tHqKHong9aJyTIi80dWbXg1mDq1MZl5fJT65GrqZpfzyQOxVYzVqDQz5cD9BbrasvKcPVUsTMZRpEORSRJOZx11FijU6JnCK8NAQpk6d2vETzdkPyyeT3WUmw9MnMq+fH726OPLC+iTUVjJ0GgNWEoF9z8Shcm4jfHHnq3B0EUxYZKldeRGzWUdDQxLVNYcoLPwBuVxN3z6/IZc7Iooic36fQ6mmlB+bZ9H8JtwPHQAAIABJREFU/mfYDhmC7dChiDodEltb7EfFI1HdeHG2PVqa9Pz86jF8Qx0Z/VD3PzRGJ520RXFGLVsWn8doNCOVSTAZzDh5qRgyNxTPoOsj2y6hy84mb9Zs5B4e+K9excHCJu75/iSLZvdkYtQV7sQ9b8Kh/4DKDWzdQJCAnQfpUdOYfvodJqhhinc3YopcEFK2wHOZILfURWjYX0jD9jw8XuiDTH3jyLmioiKWLVtGUFAQs2fP5lhuDf/emkpyScNlD9CDgwJ4eVz4DcdoD0EQTouiGNPWtj/lBBIE4UNBENIEQUgUBGGDIAjqK7a9KAhCliAI6YIgjPoz+7kZfQIcievmyt59FlnR/d3tEfUmgg+WsbV3MDtjuhGjE6henkKKm2XRtaKwkfjw69OI7azlPDUyhBO5NezOqsL1wR7YDvBGGeZEyhhfzpQ2MD3MFqNeS48ePTp+sGaTxbfnFMhK54XIpQJPjAhhai8f1jzQDx8D+EukfH9Pn7YNfNI6i4Hv88BlAy+KInn5Szl4qA+nz8wkL+8L1A696N1rFXK5ZVZxoOgAF6ov8Jz3fFo+/QrbuDh8vvwCx5l34jT/LtRTp/xhAw9wYlMuBp2JvhPa18TppJOO4h3iyMxX+9JnXAA9hvgQM9Yfo97E5kXnqC6+cRy9VVAQ3p98gi4zk8pFixkc7Iq/sw0/XFtMZNirMPMXi9yBoz/Ye0N5Mt3WPUycfTB7m6wprT1LgVoD+kbIaK3FahNpiSBrSWp/cdjHx4cxY8aQmZnJli1b6OPnwJbHBpH4r3i2PDaQtyZFEB/x98ga/Fl3zS7gRVEUjYIgvA+8CPxTEIRwYBYQAXgBuwVBCBFF8dYLOnYANztrvprXm7uXnWB/vZ6NyibuH+pL495CBLkEuZsN9bvyEeQSMqIcsalvxNRiYuRF31xBQQFVVVUEBATg6OjIrD6+/Hg0j7d/T2HA44NQjw9EazDx4eLD+Dop8dLmU6JSERAQcJMja4Os3VCZhjhtGdt+ryIuxBVXO0sZQP+kWhbprXG+OxxliPP1fUvPW1b5/QbAqHcvf5ybu4jcvEW4uozE03MqanU/5PKrZzgrUlfgbuNO1JZ0mgQBjzdeR5D8NQs9RWk1JB8qJnKID05ef/xB0UknN0LtZkPf8a33W+Rgb1a/c4J9P6cx7fneN0zysx14B+pZM6lZvhz78eO5e4A/b2xO4VxhHdG+F+ekggBh4y0/l9Br4Jc7eSLjGNM8XNhr6oGNcAQfGwekF9ZfFv2TuSiRe6poSarEblD70TExMTHU19dz+PBhkpOT8ff3x8XFBX9/f+bFdu2QbEhH+FN3uSiKO0VRvFRM9RhwKftlErBKFEWdKIq5QBbQt60x/ipqa6p5Y1IEQmkz6S06Kga4Y3uHF5rjZdRtzkHmosTt0WjO6HVYNRqI8nHAw96KjRs3smzZMjZt2sTixYtJTExEJpXw7tTulNRZEhhSShp4avU5MiuaeHVMCDlZGXTv3v2WBMau48xyULmS6TyM4roWhoVaHjQtaTUWiYIBXijD2jDwmipYNRdsnCx+eJnljaS29hi5eYvx8JhC9+5LcHWNv87AVzZXcqz0GLPVw2ncshX1nTOQe/w1s4bq4ia2Lb2Ao6eKfhM7Z/Gd/M+gUlvRf0oQ5bkN5N1kFu327LNI7e2pXPQZ03v74Ggj5+0tKVfrzF+LQgUzvifYLOUBqSt7yrM4KYZS5qBHzNoFxtZsWGV3F/QFjRhrbixUBhYt+REjRnDPPfcQHh5OdXU1CQkJ/PLLL6xdu7bNPJu/gr8yZuc+YNvF/3sDhVdsK7r42XUIgrBAEIRTgiCcqmwjLflWOHfuHF988QXylhrGuliezqtLqlFPCMLzpX64P9Mbt4XR1Ktk5LToaCzVEB/uzoEDBzh79iwDBw7k0UcfxdfXl99++42SkhJi/J34YFoPTubVMHbRIbYnl/HKuDCc9eWYTCa6d/8DfufGckjfBtFzOFFgSbQaFOyCqUlP7boM5B42OIxp4+3AqIM186GpAmb+bPEbAqJoIj3jdZRKP7qFvHHDmcDugt2IiAw8UAOCgPO997bZrqNo6nRs+fw8MoWE8f+Iwkp5W6zjd/L/Cd36eWDvYs2prXntatxIbW1xuvdeNAcPIctI5ZVx4ZzKr+XZtee5UFxPSkkDx3OqqWi4xkjbukH/f/CPrFOMdIthRUkhXzu4gaEFc+6By81serqBAJpT7QuqXcLf359Jkybxj3/8g5deeonhw4eTkpLCzp07/9B1uBk3NfKCIOwWBOFCGz+TrmjzMmAEfunoAYii+LUoijGiKMa4unZMBOsSoaGhODg4sHnzZp4ZFIhQo2N1sSVGXmqvQO5qgyAInG6wpA1L6vREuQgcPHiQHj16MGLECNzc3Jg5cyYqlYpff/0Vg8HAtN4+7H46jg+m92DbE4N4YFAgiYmJODs74+X1B+LAUzZaqtJEz+N8YR3OKgXeDtY0/rwRlW45zlFnEEzX+BhNBkslqPwjMPlL8G4tMlJevgWNJpOgoGfbzWbdkbeDHtIuiJt34TBxAvIbRfx0AL3WyJYvzqNrNjJ+YRR2Trcu19BJJ38FEqmEXqO6UJHXQFFabbttHefOReLgQPWy75nW24enR4bw27lixi8+zNhFh5j59TH6vbuH1zclXz3Dj30EidKR9xuMTAuexnqdyIuuztSdWXS5iczRGqtgR5pPlSOaOhbIIpPJGDRoEOPHjyc2NvbmHf4AN516iaI4or3tgiDcA4wHhoutj9Ni4Mp6bz4XP/tbsLa2ZuTIkaxduxZtRR4hJgnpmLnQ0Eykfevi5al6DYIo4i+TkX7iACqVijFjxlzebmNjw+TJk/npp584evQocXFxdHFW0cXZYkBra2vJz89n6NChf8x/lrEdnIPBNYRzhQeI8nFAu/wTHErfRpCa4cAvkPA69L4HYu6zJGpsf8FS03XEG5Zi3Bcxmw3k5H6GrW0Ybq43rvJe2VzJmfIzvJ8ahajPwfn+Bzp+3NcgmkV2fpdMdbGGcQt7dNZu7eR/jdBYT05szuXsrgJ8w5xu2E5qq0I9eTI1K1ZgrK7m8eHBTIr2IqXE8kZtay1jZ3I5PxzNA+D1iRYhQKztIXou8uNL+dfYFDxVnnx+7nPq6tJYbNAgl1tsg21fD6p/TkWbWo0ysuNyHjExbQbG/CX82eia0cDzwERRFK8UjNgEzBIEwUoQhAAgGGi7Qu5fRFhYGB4eHuzbt49Hu3mCKPJZ6tXPlX3VDQj1BmI9rCgqKiQuLg6lUnlVm6CgIMLDwzl06BB1V6RGA5w6dQpBEIiKiur4AeqaLOX4QkbRqDWQVdnEgNoclHnvYHToBy8UwAN7oNtYi+Lk4l6wZAAUnoRJX8LAJ68arqxsAy0t+QQGPoUg3PjPuLtgN7bNZvx3JmMXH49V4B9YLL6Gc3sKyU+qZuCMYLpEtLF+0Ekn/0NI5RKihvtSmFJD5RXl9dpCfecMMBio/+03ALo4qxjT3ZMx3T0ZFOzKW5MjuWeAPz8czeN4zhV+/l7zwWxASFzJQ1EP8bRTd45YW/PtoccvN7EOc0bmoqRhd/5Vs3lzs4G6LTmUfXyKii/PoTlT3q5r6e/gz/rkPwfsgF2CIJwTBOErAFEUk4E1QAqwHVj4d0XWXEIikRAXF0dtbS2hQi3KBiO76xovX9AqvZELGi2SSi12NWmo1erLmarXcqks344dOy5/ptfrOX36NKGhoajV6jb7tUvOfjDpIWQ0idk1iCKMq/0GUaZCtuBnsHawZOBN+waeOGdJzJj0BTxxHnpeLTNgMmnJzV2MvX0ULs7D2t3tjrwd3HfSDnR6XJ94vN22t0JDVQvHfssmIMqF7kP+Hq2NTjrpCBGDvJBbSzm7s6DddlZBQSh796Zu/YYbtnlhTCju9lb8Z1dGqzF27QZ+/eHMTyCK3D3oA+KaW/i28DilTRbJBUEq4DDaH0NZM/XbchGLzqJf8TLVH62i6WgxMiclosFM7ZoMalalY9b/rebwKv5sdE1XURR9RVGMvvjz8BXb3hFFMUgUxW6iKG5rb5y/im7duuHk5MTxYwmMdLClRSFhba5lMXdzZR0i4KczYqouYPDgwchkbXur1Go1gwcPJjU1laysLABOnz6NVqulX79+bfa5KRnbwcoBoyqKI+vT8BdK8ZSeQjLwUQTba17v1H5oI0dT6eOORnK9/Ghe/pdodSUEBT3XrtuoTFNG9flT9D/egHraNKwC/3z0y8nfcxEEgcGzuv1tIV+ddNIRrGzkRAzyJut0BVVF7c/m7ceNRZ+dje7ifX0t1nIpj8QFcSK3hoTsK2bzUbOgOhNKzyNR+/Cc2QZRhM9O/OtyE2WkC6pYT/RHtsM3w1FkfI6L+UncZ5hxuScCt8d6Yj+qCy2JlVR+k4Sp8e8rFHIlt4VA2SUkEgkDBgygpKSE+9ykCHoT72aVYBZFvs2vQGg0ENJcgpOT001dLv3798fV1ZVff/2Vffv2sWfPHrp27XrLVZ2uwmyGjB2YPAdTsTSZZK2Ohcq9IJFDzPWRLgUF33E0YQiJSQ9z7PhIEhMfprnZksDR0JBIfv7XeHhMxsmxf7u73XVhI0/9ZkLq7ITrU0+22/ZWqCnVkH6sjMgh3tg6Wv3p8Trp5K+i9+guWKtk7Ps5HXM7oZF2I0aAINCwfccN28zq64eLrRXLjuS1fhg+yXK/Jq0FwC/sTqY3NLGt8Bj59fmXm6nH+eDi+AVmax9a4neD2gt5wgsgWqQY7If64TwvHGOZhvJFZ2k+W4F4EynlP8ttYeTNOhONh4sRjWaioqKwsbEh+9xJBpjllMrhjiMpZOv0qAo1eDbnMmTIkJvGuOvPVTOsMhSrFgkHDhzAxcWFyZMntz17FUUwmzA16Wk+X4E2vQbxiio0YvEZ0FRQnxWCxEZOlrWJcRywfHHsro5XLyvfTGbWv3FxGUZM77UEBDxJTe1Rjh0fxdlz93Dm7FysrNwJ7vpSu8dvbGjA/dWluNWD38efILtCT+OPcmJzLjKFlN6j/sCDrpNO/kasVXIG3hlMRV4D+39Ou6Ghl7u5oezdi8YdNzby1nIp03v7sC+9gvJLYZVKRwiOt2Scm00I3cawoL4eGSKfnnzrcl/h5DdImouQ3vkZygF9EIa+DBUpkLnrchtlhDOuj0QhtVdQszqd0ndPUPd7DoaK5msP5S/htjDyLUmV1G/JoWLJeSRakX79+pGZmcm74c6oK3Xk6vRICzUMqc/Bx82JyMjI9sdLqab210ycPVyY6TiMmYY7uHf8XGzb0lhvKIWlgxHf8aLxw9eoWZlO1ffJlLx1jOpVadTvyqf5l+8QRSli4EjMd4US1HIWG3PTVdEyAFptKenpr2FvH01kxGIcHHoRGPAY/fvvxdt7DjpdOS4uw+ndayUKxY0XPE1NTaTfPQfPohYKn5+JzV+wcl9Z0Ej2mQqihvuitOsszN3Jfx/BMe70GedP6tFSfv/iPJp6XZvt7EeNRpeZiS4n54Zjzezji8kssu50UeuHPWZAU5klgMKzJ85KV6bptewpPk56TTo018DBDy1Vp4KGWvpETsNg743hyKdXja/wssVtYTTO88NR+NnTdKQEzem2i4H/WW4LI6+K8cB5XhjGi7VeY3r2RqFQcPLwfg6PieItuT3/VLbgoy9g7NixSNpJ5ze3GKldn4ncU4XrA91xf7AHDtZ21G/JaXtVfNtziJUZGIx+qIUluE834XJvBDbRbugya2ncU4CV/jBm13443RNLYkUToyUnMMlUEDj08jCiaCY19QXMZgMR4R8hkbSuF1gpXOgW8jqx/bYRGfEp1tY3jtEX9XqKHnscMT2HpXfaETf3+T92Ua/h+OYcrGxkRI/sVJjs5L8TQRDoOyGQuDndKM6oY/XbJ8hLqrqunV28pc5ye7P5ABcV/QKcWHOqsPW+DxkNCjuLy0YiQYiazcKyGqwFkQ+PvQIHPrDUeBj5JiZTC+VNRTx7+EX6OsuJpYCXdy2kormi9XglAspwZ1zmh+P5Ut+byiL8UW4LIw+WRQ+nmd0wFDdhOlHN0KFDyczM5PyJI/RS1VGbcoTu3bvfVG+mYVc+Zo0Bx+khCDIJEhs59vFd0Oc10HJthZrqbEjdjEZ+J9XyDxFtPZGffg3rYAccpwbj+Uos3k+5IjMXIO07FUEikFhYzSjpaQiJv0q/uqj4Z2pqDxPc9UVsbP5YmKPBZCD3w3/TnJDAV2MEBs9+tsNFuNuiNLue/KRqesb7dWa1dvJfT+Rgb+58sQ82DlZsXZJEYVrNVdvl7u4oo6Np2LXrBiNYmNXXl/zqZo7lXOwvV1pkvVM2WQqA97kfe6TMNxg5XpnGngvL0YQMoGrbXM5+6s/MdfHsK9jFzK6TmKJpYVvJISZvnMy23OvjUKS2CqS2f88b8m1j5MFi6JVRrjTsK6R3UA+ioqI4dOgQmzdvxsfHh/Hjx7fbX1/USFNCCap+nii8W10zqhgPZG42NOzMQ7zS13duBaIgpaFuOA6TIqgY/A7nipswn14OWGYWQvKvgGCp3AS0ZB3GWWhAGtFaoq9Jk0lW1vs4Ow3G23tOh8/7XMU5pm+azqz3etLy02p2RQt4zpjDjJAZHR7rWkRRJGF9Fkp7BT2G+t68Qyed/Bfg5KVi6jO9cPSwYcfXF2i8RlfGLj4eXUoq+sLCG4wAYyI9sbOWsfbUFW16zABdA2TuALUfwsCneKiwlDCdnpddHNlee57tNZU85OyKlVHkPWM14+xreNlnFBvKqgmw8+P5g8+zcM9C9hXso0xT9rfHzd8WRt5UncXBNXeCvhn1+EAEmYSG7flMnjyZe++9l3nz5nHPPfdgZXXjiBDRaKZ2XQYSOwUOo/yv2iZIBexHdsFY2ULzudbXLTHld/RiJPLgIA4KRgZvsmGy/i3mbaxD21gHhhaLIFnwSLD3Qm80E1C5F4OggK6WV0ajsYmkpIVIpSrCwt7rcFhianUqD+x8AJ2mgZd22WF0UzP8g+W8HPvyXxLimHmynNLsemInBSK3+gOCbJ108r+EQilj7CM9MBnMHFqdcdW2yy6bnTeezVvLpUyM8mLrhVIatBdruAbEWXTnE9dYfh/yIvLxn/KZ73hcFHa8rnLkfWcnenn1Z5VDDCMKm2ks2EKRl5IuLU386DyYJ3o9QWJlIo/ve5yR60YS/2M0nyyLpTl9699yHW4LI78hdQULW1L54be5SO0U2A3xQZtSjT63gS5dutC1a9d2o2lEk0jt+kwMZc04Tu6KpA2XhDLCGbm3LQ078jDrjIi1+QjVqbSY+qEZ4s1Tq88T5mnP8/3tOGoM4ZPvvodd/7Is1AywJCElFdUyQjhBjccgsLLFoK0ldWE8qn8W0LX6bqys3Ns9T7NOhy47G1Fvia81mA28dPglHKwcWJTWB5uyOrp+8CkRfn9NirRea+Tor1m4dbEjrP+f17vppJP/aRxclfQZH0Du+Spyz7cKICp8fLAOD6fxJqJgd8b4ojWY2XLeUgYUiRSi51gKgFdlWWSKY+7Fc9AL/FpUxlKTEyvG/MzXI7/GcfRHCBIFERUuZDRtxODeDdmJpTwQdhe7pu/ih5iXealBR6hOzzKphv+kfP+3XIPbwshPHvAS8VaefNycyemMTdje4Y3UQUHd1pyr3StXYKzT0nS8lNoNmZR/cprmMxXYj+yCMrztqBVBIqCeFISpQU/1T6k0b7CkRstjR/HpyXxERL6c24tHJw1mqlct35f5U3x8HfScBwGDAMhPPISXUIMyegqNjakkfTQO2YFa5A1WNLz+bbuvjs1nzpI1bDg548aTNWIkDTt2siFzA1l1WbzVPArt6vU4zr8LVewfTNZqg9Pb89HU6xk0M6TdcmuddPLfTNQIXxw9VRxem4nxikxTu/h4Ws6fv6rg97X08HEgxN2WtaevuDf7LwSpFRz6qPWzrc9gpdcwYPxSurtFWd6i7dyh3wLs87NwMXuT5tkEtXmw6zWss/bSe+OzzDZasXj6Fn6I/5aHR3x63f7/Cm4LIy+TyHhr1Nd4mEx8cPIDkAvYx/tjKGqiJfFq+WKTxkD1ilTK3j9J3YYsms9XIrVX4DwvDPvh7UeOWPnZ4zg1GF1uPWLOEcxSO+r792dLYilz+3XBS23RwXlm/jSQKvjEd7GlRN9FlFlbMCCj0VXLyZNTUeyoQx4VTNftO8Fspvqbb9vcr6G8gqJHH0Viq8LjjTeQubhQ/MQTGF98l8dPueL08QqUvXrh/uyzf/JKtlJX0cy53QV0i/XAI/DGJdY66eS/HalUwuCZwTRUaTm7q1X64LLLZveeG/YVBIE7Y3w5W1BHaqlFzAxbN4uAYOJqSxGgE99YFGaHvMjJZjce+PEkYz47xNNrzpHgOQ8UtkRUOFPjZEWJjyMc/wpWzUZrJeVCbDhlhmR6efTF1a4zuqZdbBz9eczKnxRjPdtytmLT0w25jy11m7IxVrcAlsIc5Z+eoSW5Grshvrg/0xuvf/XHdUGPW1aOU/XxwPPFvqhcc5AE9Kdw7fPslD3NE6ywlPYDvNVK5sb6syFHoLDOEqurN5iIathHmnU30vLew7kmEmmliMus+5C7u+MwdQr1GzZgKK+4bp+Vn3yCubkZ3yVf4TjzTvxXr6JkdhxhmVoG7irFpm9ffD5fjKD461bnj6zLQiqV0H9K0F82Zied/G/hE+pE195unN6eT02pRSrEKjAQRdegdl02Zp2O8c05hGnK+ObQFXH1Q14AlxD4eRpsfRYxaDgfN49ixlcJJBbV425vxf70Smb/nMk6xURkGXuI8XqNwu6RnIu0J7mbLWf7+tKsLyE16UkKCr/72879tjDyOn0V+flfM7b73QTr9Sw9swhREHGa2Q2A8sXnqPjiHNU/JCOxkeG2MBqHUf6XdeZrNHq+PphNYlHdTfZkQSo0INRmYhLN9Cv9Gbm1DfanFlsKAl9kweBApILAVweyAUg8sQcvqih2NOLjczdeZYNBELAbOgQA5/vuQzSZqFn+41X70qamUr9xI453zbusINmCgTdDU/ny3X4EJxzF75uvkTndWGa1oxSm1JCXWEXMOH9UDp3yBZ3cHgycEYzCWsrWJYloNZaFVPv4UTSfOoWhtPS69sbaWvKmz6D2icf4eNdHCGtXUlJnmTBibQ/374RR/4aJn/OV1zss2pfHjN4+7HtmMF9EwIHZgbw1KYLPNCOpE1WUbfmG7tG/ETbhDN0mnaN/bS/67ksk7lgdmiNv0tSUcd0x/BXcFka+KWsNit9fodYBHqhvJLe5lH0F+5C72uD6SBTWwRbVSIcx/rg/1hOFV2t4pLm+mIeWHeLfW9OY8VUCmeXtCxwBUJAAgJh3lF2m3hwf+i66yDGIRz6DgmMAeDoomRHjw+qThWSWN1J15DsMohTvmAGEBL+K5thxrCMikF5UtFT4+WE/ehR1q1ZjarQcg0avIeft15DY2+Py0EOXd/9j8o/UaGt4rN/Tf4lcwZWIosixjdnYOVkT1Rky2clthEptxeiHutNYrWXNv09SklmLw9SpANSuWHFVW9FkouSZZ9Dn5+P10UfIhgzj3qTNfPftltZG1g7QfyHLdYN4f1cOk6O9eHdCKNWPPkzB/LspmjCBsen72fjMWI64zyWo7giLP36d4gvnkX032hJ51+dB8I4hLL2emvMf/i3nfVsYeUdFNzwrdGhOf0K8YwQ+ZgnfXfgOURSRu9rgPDcMt4XR2MX5IsgunrIowu43OPDRbE4Wa3nG5RhyCXy2J/PmO8xPAIkcmamZZYzBTvMYCfbHMdjYIG5caCnXBzw9MgQ7axlTPt3JAM0BUuyCCI9+H4xGtIlJ18kNON1/P2aNhtqVqzhUdIjn3h2KcPoCP8a28J/0r6horuBk2Um+Tvqa0f6j6eHa46++lOQlVlGR30jMOH+k8tvi69FJJ5fx6qpmyjO9QIQN/znL7s3VMHwytWvWYm5u1Y6pXLQYzdEEPP71Gg7jxxH40fuYbO3psukXdia3LtSuPFHAaxuTGRnuzoczoqhZsgTN0QTcnnsOuxHDqXjvfaxOH2Pcg29S79aHf+oWE7h5GtrmRrh7E4z7CMmcdYhqH3wNf49r9La4iyVBw2iU++OenoHBJ5x7a6pIqkriZNnJNttXtVSxfc8/STrxOWvt5uGsMPFQy/9r77zDo6rSP/45M0lmkknvnRBIICFAggEiCCJNQaT8wAW7KGtZUVF3RRYXy4ruWpFVsWEvgKAIAiJN6TUQILQECCG9kd4mmfP7Y4YQSAIICQnD+TzPPLlz7p2533kz951zz3nP+85lvP12ViZmUVh+gRSgqZuptXMiTXpi43WKrpEzCQl/lsRQLSI/GdPGdwDwcNTx5cQeTHZdirOoIHTENLRaHVXHjiGrq9Gfk0PHvksXDDf2J/fDOXwzZzL3rKjEGOCFZsxwvjv4HYN/GMwDKx8gyCmI6b2nN4vtziVh7UmcPPR0jmueQt8KRVvDN9SFO17oTe+RoaQdPsUfpkHkafzI++QTAIpXrSL/o49wvX0crmPHAuY6sb6TJtIz5zDvzlnK7DVJPLMggWk/7qN/uBf/uyMGUZBPweef4zzyNjwefAD/N99EFx5OxvTnqSmpwOWh5ZQMf593DU9wffGrbEuyJW3KU6RM+huFLk9Bv3+0yOe1CidfuPhn0r6twXjcluJTuxhVWoqHjYFP9zWMVtmRtYNRi0fyj/QV3Bngy3rH1Qzo5oHd2DmMqfgRY61kZaJ5FVqtqZHE/lWlkJGAqDzFkto+DI9ywt//dooNfXlVG8E9QT6kbH4bmZ+MlBK74te5T/6M0as9Tp3GAFCZeAAAfWRkg7f3eH4aJVojUxZV41ZlQ9isD3hlwGssHbOUSV0n8WSPJ/l62Ne46i+hcMkFKMwuJ/1wIV2BWsDoAAAZQ0lEQVT6+aPRWsVXQ6FoFFudltjhIdz1Uhyuvo7s6/43Ur7+hZOPPEr608+g79YNn+efP+s1nnfdiXB0ZOLJDby96ghLEzJ4uH8on90Xi95WS8FXXyNravB67DEANDod/m+8jqmoiMwZM5BaW5x63c39k2cwsDgN+yl/pXTLVkzl5WT/9y2yX/tPi3xWq7iSnYcOQd+lC+mb3bHZeQQ7B0/uFa5sydzCpvRNdcf9cGgef/1tEm7Gcr7MyGK4U0+wP8K2iqfZvOM1wlxLCNSk8e3+zxiycAgxX8fw1LqnKKmuN06fth0woUHyuzaK23rfRV5FHo+ufpR8Yw3JOice9vEg/4sbSdwwEuf1X2BfacJ22CzzwgmgMjERjYMDdiENU/Z+lP0jT0+UFE2dSIdflmLf1dzbD3IK4okeTzCp6yRcdC0T0nhgYwYajaCzWvikuEYwuOgY+WQ0Omc9h3s/TtmBwzjfcgvBn3yM5pwV8lonJ9zG/4UuyfFsn9SFPS8MYdrwCGy0GmpLSzk1bx5OQ4diV6/mhL5TJ7yeeorS1Ws49fU3SCmp+e5rJq35hGNuQbxx58uELFyI38xXcLvrz6c0uRiswslrDAaCPvoQrbsLBWtcKdY6clfqAUKcQ/jnxn+yPm09r2x5kZe3zSTMtppvUk8Q4GpDVb4e/xMjcawq5hFZykSDDVVh73Fcs4gQ5xDujLiT30/+ztO/P41JWhL7n9iMES250gXfEBccDcG8t/s9ymvKmXvzXOYMnUuWjQ2f2dkStXY9/tlVyH5/h9ABdXrzE3aQH+zCgiM/UFxdXNe+LnUdnyd+zs0x44mb+Cy2AVeuvF6t0cTBLZmEdPdUETWKawoHZzsG3htJca0jBVM+JuCN19G6NN6Rcr/LXIpT/vQDDnZnVsYXzp+PqaQEjwcfbPia++/DccAAsl99laQb+pHzxhs4DR5MxWuzWJNZzYKdaebKbRdInnipWIWTB7BxdydozsfUVmvIX1qKXXkR73aaiI2w4bE1jzH/yCJudKrlI4+BuNaY8B65jDIxjk81PzC/uJaHIu5B7xLIhBNljPyjG7fLDjzX6zmmx01na+ZWlhxdYj5R8hpqhQ2bTF0Y3bM7eRV5LDm6hLFhY2nv0p5o72huD7+db51dSBrwd7jnJ8Sgf9Xp/DxhLtWHj7DFMZtXtr3C0IVDeXPHmyw4vIBpG6cR4R7Bs72aJz3wn+FYQi6VpUYib2g6jbFCYa20i/Igsq8fe1alknWsqMnjbP39cRo6hMIfFmIqM8fbm6qrKfjiSxzi4uruvOsjNBoCZ7+L99SpGOLi8Jv5CgHvzmL8DeH0au/Of389RFGFscU+m9U4eQB9VHc8xkdRlWlHToIzoZn7WTjiW54MDmSaXzUz+r6J+6HNENgL/GMYkvYBwaYMXEZ/zOTeU5nt80+Gz7fj7s3xOLy2mNLCQ4wNG0sXjy7M2TOH6pIsyIhHL6vYrQmjf0Rv5h+ej9Fk5O6Iu+t0PBHzBAY7A2/VpEOHM4W2lx1bxvzV76A3wj1jXmD+iPn0D+zPNwe/4d9b/02wUzCzB85Gp73yPekDGzNwctcTFNF88fYKxdVE33FhGFx1rP3q4FnpDwDKiqqorqgBwP3eezEVF3Nq3nwAChcupCY3F49Jk5p8b2Fnh8fE+wl4601cx45FCIFGI5gxIpLCCiPvrb2IqL5LxCqcfKWxlkW70pBS4vXUe7iGlVFw2JH8xd+SlPgQoRxlUPQ7eBfZQMEx6P0w2Tt+4i+s4lD7+yB0AKaqKjJffhWdrwue3YvxzC7mxCxzbPrjMY+TUZbBoh3vUC3Nic5s2kdSi5EFhxdwY+CNhLiE1Olx1bvySLdH2JS+iTUnzEumd2Tt4F+b/sWgCvNxzl2jifSI5PX+r7Ny7EoW3raQeSPm4Wu48lEtRbnlpB06ReQNfmhUjhrFNYqdvQ0D74ngVFY5v36yn4KMMg5uzmDhf3fyxdRNfPrMBjYvSkbfrTuGG/uT9/77FHz9DbnvzMKhd28Mffv86XNGBbgwrkcgX2xOISWvrAU+lZU4+SUJGTzzQwLrDueAky/e9wzB3qua3LUCTUIKXbvOwcd7OGyeDY6+4H8drquf4aApGO2QGQAUL11KTWYmPs8+g2fnUvI8XdAszSInawV9/PvQw7sHn6atJks4k2Ly4aY+t7Ls2DKMBfk8/HkWh6+LJWfWrLrc0Hd0voMI9wie3/Q8s3bN4sm1TxLkFMR4eiL0enShoXX6fQw+dHLvhEa0zL9DSklJQWWD3slpEtdnIDSCzteroRrFtU1QpDs33tmJk4kFfP/yNtZ+dYiq8hriRofSqbcPu1elsm3JMfxefhmtuzvZM2eicXTEb+bMS07t/Y+bO2Gr1ZydNqEZsYoyP2NiAnhvbTJv/XaEAeHeaG95hYB9PUlZpsHlExvcRneHlI3m2ow3PQ/zJkBNFc+J5/nJ15x18tT389CFheFwy+2UHP0Az/Bc2Kwhc94MPJ8YxCNRD/DQ2sn87iSxL4lkTIcAxi2ZzLTlemxPJKHv0YP8Dz/Crl0IrmNGY6u1ZfbA2Tyx9gnm7p9LlEcUbw94m+pFU9F36oSwuTKmN1bX8uuH+0g9UIDeYMvgiZG0izqTabOqoobEDemERnvh6KYmXBWKqP4BBHZ2IzO5EFdvB3w7uCCEQEqJRqsh/rdU2kXFELrkZyr27TevXHc0XPL5vJ31fP1gb7r4OzfjpziDVfTkbbUapgwOIzGjmF8Ts8DZD9uRLxHUr4DaknKypk5B/joNdE6w/SMoOM4Mh+k4B0eh0Qiqjh6lMjER13HmsTKH6ybQOTCNKp0thlWnSE39mLi8dKIrqpjrZqAgMIYVKcvw3J5Mx6QyvJ+bSvDcT7Hv3p3cd9/FZMn37mvwZf6I+WycsJHvbv0OXwcfKg8cQN+lYXx8S7Fh/hFSDxYQOzwER3cdyz/cS/rhU3X7E9enU11ZS4+bVe1WheI0rt4ORPTxx6+ja10PXQhB33EdcfbQs+bLg9Ta6DD07nVZDv4017VzQ2/bMkV5rMLJA4yKDqCjtyNvrzpCrUlC7APob34Ar64llGzaRcmOI+Yiu06+lN+/moX5IcQEmRcUFS9bBhoNzsOHA2ATNQap0ZAb4kHNcVuMy/+D8benubPAlhKNhu8N63lp84vctV2HXfv2uI0fj9Bq8Zz8GDVZWWdVmxFC4KIz9wSMqamYysoaXQTVEuScKObgpkxiBgfTe2Qoo6bE4OJpz/I5e8k9WUJJQSXxK08QFOGGd7uW6UUoFNaEnd6GgfdGUJxXyfYlZw+vSClJPZDPjmXHSdmb1+Jl/S4Wq3HyWo3g6SHhJOeUsnh3unnh0fA3cH/uLXReNuQcDEDetxIe3kBCdQAmCTHBbkgpKfplGYa4OGy8vMxv5uRDvlcvQtungxR4bJBoqiuJMhp5PNcJb0cPxhWF45tegcekBxGWqlOGvn2x8fOjaMnPjWqs2J8INL7StSWI//UEOgcbYoeHmM9rsOW2J6Kx1duw6PVdzH9lO6ZaSf8Jna6IHoXCGggIdyOynz8Ja05yIjEfgJKCSpZ9sJelsxPYvvQ4yz7Yy29zEzHVmlpZ7WU6eSHEv4UQe4UQe4QQvwkh/C3tQggxWwiRbNnfo3nknp9buvgS6efMe+uSqbEYV8RMwHvm+xgLKji16SgIQXyqebgiOsiVit17MKam4nxOkW/H2Al0cM2m2D+AosJIjt+6mCCZTeeAW1lw2wLu2KXHxssL59tuO2MPjQaXkSMp27iJmtyzi5UAVOzZg7C3Rxce3oJWMFOYU87RPbl06R+AXb1yhk7uesZNjaVTb18CO7sx5pkeuPo4tLgehcKa6Du2I+7+jiyfs5flc/by/UvbSD9SSN9xHXno3RvpPSqU5J05rJ+f1Oo9+svtyb8hpewmpYwGfgFmWNqHAWGWx0PAnMs8z0Wh0QieGBTG8bwyftl7Jj+0oV8/HHr1Iu+DD6gtLWN36ilCPQ24GewoXLQQjYMDzjcPPeu97LuNpkbYUuxroiolnaSFXwAQOWACFfv2Ub5lK+7334/mnEIdLqNGgslE0S/LGuir2LMH+65dr8ik657VJ9FoBd1uCmywz9FNx013d+aWh7riFezU4loUCmvDTm/D6KdjCI/1IT+9lHZRHtzxr15EDw4258UZFkKPm4NJXJ9+VjUqAJNJcjwhl/iVJ86aH2spLsvbSCmL6z01AKd/skYBX0nzT9hWIYSrEMJPStkwM38zMzTSh86+TvxvbRK3dfdHqxEIIfD++zOk/GU8eZ9+yvbCzgyL8qO2tIziFb/iPHwYGsM5kyf2blRE3E6fyoUkJfjReddGTo7oQ5B/R9JeexyNiwuu48c3OL8uNBRdZATFK1bgMfH+unZTRQWVhw7h8cADLWwB88KNQ1sy6dzbV6UoUChaCL3BlkH3Nz30GjeqA8X5lWz58Si2dlq69A/gxL48ti05Rn76mZj4iD5+DLi7c4utUbnsMXkhxEwhxEngLs705AOA+lWp0yxtjb3+ISHETiHEztxGhjgulhqjOQZcoxE8PjCMo7llLN935jfFvls3nG+9lfzPPkNfkEOfjh4U/fgjsry8Lp3ouTjdPB2dow6/jqeoSrXBs9tfKdu8mZJVq3G/554mZ9Wdhw2jcu9eqtPS69oq9++Hmhrso6Mv+TNeLLuWp2CqlcQMbZgATaFQXBmERjDovgiCu7izft4RPpz8O8vn7MNYVcvQB7vw4Fv9uO6WdhzcnMkf3x1usWGdCzp5IcRqIcT+Rh6jAKSU06WUQcC3wOQ/K0BK+bGUMlZKGet1euLzT5KyL49v/rWVwmxz0v9hUb6EeTvyv7VJmExnDOf9zNOYJDwVP59eooi8OXNw6NWracfrEojNxKV43jkSrYszqc++zsnJj2PXvj0ekxomIjqN87BhAJSs/LWurTx+NwD2MS3r5Ityy0nckEFkXz811q5QtDI2tlpufaw7Qyd1ofugIIY8EMldL8UR1tMHvcGWuNEd6HFLOw5szGDv2rQW0XBBJy+lHCyljGrkcW4IybfA6S5xOlC/dlygpa1FcPM1UFtjYtkH5tqNGo1g8sCOHMkuZWW9Ki62/v78Mvg+ovOOUjh+LLK6Gt8XZpx/pZp/DNrx7xP85TfYR0Vh6HM9wZ9+gkavb/IldoGB6Lt3o2jxz3W/zqUb1qMLD2/2cn31qa6o4be5B9Daaeh5a8tktFMoFH8OjUYQFutD37EdCe/l26BWQ9zIUDpf74urb8t0yi43uias3tNRwCHL9hLgXkuUTRxQ1JLj8S5e9gx7uCvF+RX8+GY8hdnljOjmT6ingdlrk+scbUmlkc8NEax79N94TXmSkIU/oOtwcSW39J3CCZ77KUHvvXdRKYBdx46lKimJivh4jDk5VMTvxmnw4Mv6nI1RlFvBntWp/DY3kW9e2EpeagmD74/E4KrG4hWKqwHzsE4k7bp4XPjgS+Bywzz+I4ToBJiAE8AjlvblwHAgGSgHJl7meS6If5grIx+PZsVH+/juxa206+rJRB9P3tybyuqDOQyJ9GH5vkyqakzEjRmEZ3DL9agBXEaMIHfWu+S8/U5dXLzLyNsu8Ko/x4FNGfzx7WFMJomjmw7/Di5EDwnGN7RlioooFIqrD9HaMZz1iY2NlTt37rys9ygrqiJhzUmSd+ZQUlAJQKqzYOrzfRj38RZstRp+ndLvkpMJ/RkKF/1I5nRzLVaXsf+H/8yZzfbeGcmFLH4rnsDObgy4uzPOHvbN9t4KheLqQgixS0oZ2+g+a3Pyp5FSUpRTwa9LksnflccJm1oWGar59IGe3NTJu1nOcTGUrF6NMTsbt9tvR5wTU3+pVFfW8P1L29DYaBj/z55nLXZSKBTXHudz8lbrHYQQuPo4MOGv3VjseRhWpjPDz5cB4ZcWwXOptMQ4fPzKE5SeqmLss9cpB69QKM7LNeEhRo/pxDatLTuXp5C4IYOo/pdfOzX1QD6HNmeSl16Gi5c9MUOD8e/o2gxqz09JQSV7Vp8kvJePGntXKBQX5Jpw8gA9R7Qn50QJG+Yfwc3XgYDwsydeqytqSNqZTX56GToHGwI6uREQ7tpg7L70VCUbFyRxdHcu9k62+LR3ISelmJ/ejCeirx/9xodja3d5KUOllE3OGWxamIQA4kZfXFSQQqG4trlmnLxGIxj6YCSLXt/Fio/2cdvkaHzaO1NVbmTf7+nsWZ1KVXkNtjotNdW17FyegquPAxF9/AiKcEdKybHduSSsS0OaJL1HhRIzJBitjQZjdS07l6UQ/9sJso8Xc/OkKNz9zathpUmSsj+f/X+kkZ9ehsFVR5cb/OkU54vW5uwI1ozkQrb9fIysY0UYXHVEDw4m6saAuuXOqQfyORqfS++R7XFybzpOX6FQKE5jtROvTVGUW8HPs3ZTeqoKryBHTmWVY6yqJaSrB7G3tse7nRM1RhNH43NIXJ/RoHJ7x+u8iRvdARevhtEsqQfyWf35AYxVtUT1D0Bjo+HY7lwKs8txdNcRGO5Gblop+WmluPk60O8v4QRGuFFVXsPWxUdJ3JCBo5uOjrE+5KQUk5FUiHc7J/rf0Qkh4Jf3EtA52DL++Z7YtFCBAYVCcfVxTUbXnI/KMiO7V6WSk1KMs5c9Uf0CmszGWJRbQV5aCdIEPu2dL9iDLiuqYv33R0jZm4dJSgLCXIno60/HWG+0Wg1SSk7sy2fDgiMU51VicNVRWWbEVCvpdlMgvUeGYqvTIqUkaUc2G39IoqLECICDix2jpsTg7nf5lWgUCoX1oJx8K1BbY0JK2WSPu8ZYy6EtWWQdLULvaEtEHz88AhwbHFdZZiR5Vw7SJOkY6429Y/OEYSoUCutBOXmFQqGwYs7n5K2m/J9CoVAoGqKcvEKhUFgxyskrFAqFFaOcvEKhUFgxyskrFAqFFaOcvEKhUFgxyskrFAqFFaOcvEKhUFgxbWoxlBAiF3MZwUvBE8hrRjktwdWgEZTO5kbpbD6uBo1w5XW2k1I2WiyjTTn5y0EIsbOpFV9thatBIyidzY3S2XxcDRqhbelUwzUKhUJhxSgnr1AoFFaMNTn5j1tbwEVwNWgEpbO5UTqbj6tBI7QhnVYzJq9QKBSKhlhTT16hUCgU56CcvEKhUFgxV72TF0LcIoQ4LIRIFkI819p66iOESBFC7BNC7BFC7LS0uQshVgkhkix/3VpB12dCiBwhxP56bY3qEmZmW+y7VwjRo5V1viiESLfYdI8QYni9fdMsOg8LIW6+QhqDhBDrhBAHhBCJQognLe1typ7n0dnW7KkXQmwXQiRYdL5kaW8vhNhm0TNfCGFnaddZnidb9oe0ss4vhBDH69kz2tLeatcRUsqr9gFogaNAKGAHJACRra2rnr4UwPOctteB5yzbzwH/bQVd/YEewP4L6QKGAysAAcQB21pZ54vA3xs5NtLy/9cB7S3fC+0V0OgH9LBsOwFHLFralD3Po7Ot2VMAjpZtW2CbxU4LgAmW9g+BRy3bfwM+tGxPAOZfIXs2pfMLYFwjx7fadXS19+R7AclSymNSympgHjCqlTVdiFHAl5btL4HRV1qAlHI9UHBOc1O6RgFfSTNbAVchhF8r6myKUcA8KWWVlPI4kIz5+9GiSCkzpZTxlu0S4CAQQBuz53l0NkVr2VNKKUstT20tDwkMBBZa2s+152k7LwQGCSFEK+psila7jq52Jx8AnKz3PI3zf3GvNBL4TQixSwjxkKXNR0qZadnOAnxaR1oDmtLVFm082XLL+1m94a5W12kZKojB3Ktrs/Y8Rye0MXsKIbRCiD1ADrAK811EoZSyphEtdTot+4sAj9bQKaU8bc+ZFnu+I4TQnavTwhWz59Xu5Ns6N0gpewDDgMeEEP3r75Tm+7g2F8PaVnVZmAN0AKKBTOCt1pVjRgjhCCwCpkgpi+vva0v2bERnm7OnlLJWShkNBGK+e+jcypIa5VydQogoYBpmvT0Bd2BqK0oErn4nnw4E1XseaGlrE0gp0y1/c4CfMH9hs0/fpln+5rSewrNoSlebsrGUMttycZmATzgzhNBqOoUQtpgd57dSyh8tzW3Ono3pbIv2PI2UshBYB1yPeXjDphEtdTot+12A/FbSeYtlWExKKauAz2kD9rzanfwOIMwy826HeeJlSStrAkAIYRBCOJ3eBoYC+zHru89y2H3Az62jsAFN6VoC3GuJDogDiuoNQ1xxzhnHHIPZpmDWOcESbdEeCAO2XwE9ApgLHJRSvl1vV5uyZ1M626A9vYQQrpZte2AI5vmDdcA4y2Hn2vO0nccBay13Tq2h81C9H3aBed6gvj1b5zq6UjO8LfXAPGt9BPO43fTW1lNPVyjm6IQEIPG0NszjhWuAJGA14N4K2r7HfGtuxDw2+GBTujBHA7xvse8+ILaVdX5t0bEX84XjV+/46Radh4FhV0jjDZiHYvYCeyyP4W3NnufR2dbs2Q3YbdGzH5hhaQ/F/COTDPwA6CztesvzZMv+0FbWudZiz/3AN5yJwGm160ilNVAoFAor5mofrlEoFArFeVBOXqFQKKwY5eQVCoXCilFOXqFQKKwY5eQVCoXCilFOXqFQKKwY5eQVCoXCivl/R+iL1wXQfZQAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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\n", 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From c3af67c7dc5b2e49a89c10b6a1f8abdd9ffe8517 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 314/624] Add docstring and references for fpca module --- docs/modules/exploratory.rst | 1 + docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 2 +- skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 5 files changed, 118 insertions(+), 29 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index 832b93193..edc2c8d73 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,3 +11,4 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers + exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 0725d35afc3b32ac1379807cabccdcc0a579fd62 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 315/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From a8d42612c682e23195fe21744b20af983c1ec57f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 316/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- skfda/exploratory/fpca/_fpca.py | 93 +++++++++++++++++++++++++++---- 2 files changed, 92 insertions(+), 13 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 5dcb47c4f8764ea8a908251a016a6e9e294ee94a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 317/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2M1vr7pGm2/XJYpEWejOMg8yvj4O8cBxfOS9XcZCS9tQIRE7iteK9LM6P8lJ0D327deSfPh3EQfbqojhIyQxqBCLNcHdeiu5h8QtRXt9UxoAenflfl0zkS2crDlIyj/5Fi8Rwd/I/DOIg397aEAd57YwcunRUGphkJjUCEerjIHewOD/K+9sOMKJvV35wxRSuOktxkJL51Agkq9XWOX96Zxv3roiyYechRg/ozo+vOoPLFQcpWUSNQLJSdRgHuXRFlOI9hxk3qAc/mz+Vz52uOEjJPmoEklUqa2r5/dpSfr4qiIOcNLQXP//SdD6jOEjJYmoEkhWOi4Mc2Yfvfn4ycyYqDlJEjUAy2uHKIA5y2YtBHOQncvvywyvP4HzFQYoco0YgGak+DvKBF4vZV1HNJ/P6s/jaaZwzpn+ySxNJOWoEklH2V1Txy5c38cuXN3LgaA2zJwxk4ZxxnDWqb7JLE0lZagSSEfYequSBlzbyWEwc5J1zxnH6iN7JLk0k5akRSFrbdeAoy1YX8+vXgjjIz50+lDsUBynysagRSFratv8I960q4olGcZB55A3qkezSRNKOGoGklS17K1i6Msrv3ywBgjjI22YpDlIkHmoEkhaiuw6xdGWUZ97eRvt2xrUzcvjazLEM79M12aWJpD01AklpH+44wJL8KH9+dzudO7TjpvNyWXCB4iBFEkmNQFLSuyVBHOR/rwviIG+dOZabP6U4SJHWkJBGYGZzgZ8RhNc/4O6LmtzeGXgUOAvYC1zj7pvC274F3AzUAn/v7s8loiZJT2s372NJfiEr1u8+Fgf51U/m0qeb4iBFWkvcjcDM2gP3AhcDJcAbZrbc3dfFDLsZ2OfueWY2H/ghcI2ZTQLmA5OBYcDzZjbe3WvjrUvSy5rivSzOL+Tl6F76duvINz4zgRvOHaU4SJE2kIh3BDOAqLsXA5jZE8A8ILYRzAO+G15+ClhiwRe9zAOecPdKYKOZRcPHezUBdUmKc3deLNzDkvyGOMhvX3Ia152dozhIkTaUiP9tw4GtMddLgLNPNMbda8ysHOgfbl/T5L7Dm3sSM1sALADIyclJQNmSLM3FQX7385OYrzhIkaRIm5dd7r4MWAYQiUQ8yeXIKairc557P4iDXLc9iIP8tytO58qzhisOUiSJEtEISoGRMddHhNuaG1NiZh2A3gQnjVtyX0lz9XGQS/KjFO4K4iD//YtnMm/qMMVBiqSARDSCN4BxZjaa4CA+H7iuyZjlwI0Ec/9XAfnu7ma2HPiNmf2E4GTxOOD1BNQkKaC6to6n3ypl6coiNu45zPjBQRzkpWcMo73SwERSRtyNIJzzXwg8R7B89CF3f9/M7gYK3H058CDwWHgyuIygWRCO+y3BieUa4A6tGEp/lTW1PLW2hJ+vLKJkXxAHed/10/n0JMVBiqQic0+/6fZIJOIFBQXJLkOaOFpdyxOvb+H+1cXH4iD/fk6e4iBFUoSZrXX3SNPtaXOyWFLX4coafv3aZpat3sieQ5XMyO3Hj646g0/lKQ5SJB2oEcgpO3C0msdi4iA/lTeAhXMUBymSbtQI5GPbX1HFQy9v4uEwDnLOxEHcMTtPcZAiaUqNQFpsz6FKHnhxI4+9uonDVbV8ZnIQBzlluOIgRdKZGoGc1M5jcZCbqayp49IzhnHH7LFMHKI4SJFMoEYgJ1S6/wj3rSziyYIwDnLqMO6YncfYgYqDFMkkagRynM17D7N0RRG/f7MEM7jqrBHcNjOPnP7dkl2aiLQCNQI5JrrrEEtXRHnmb0Ec5HVnKw5SJBuoEQgf7jjA4vwof3l3O106tOcrYRzkIMVBimQFNYIs9m5JOffkF/LXdTvp0bkDt4VxkP0VBymSVdQIstDazftYnF/IyvW76dWlA//jwnF8RXGQIllLjSBLuDtristYnF/IK0V76de9E9/4zAS+fO4oeioOUiSrqRFkOHdndeEeluQX8samfcfiIL90Tg7dOunXLyJqBBnL3Xnhg10sXhHlb1v3M7R3F/7PZZO55hMjFQcpIo2oEWSYujrnv8I4yA+2H2Bkv6783y+czhemKw5SRJqnRpAhamrr+PO724/FQY5RHKSItJAaQZqrrq3jD2+VsnRFlE17Kxg/uAf3XDuNz50+VHGQItIiagRpqrKmlt8VBHGQpfuPMHmY4iBF5NSoEaSZI1W1PPHGFu5fVcyOA0eZOrIP37t8MrMnKA5SRE5NXI3AzPoBTwK5wCbganff12TMVODnQC+gFviBuz8Z3vYwMBMoD4ff5O5vx1NTpjpcWcOv1mzmFy8Ws+dQFTNG9+Pfv3gmn8zrrwYgInGJ9x3BXcAL7r7IzO4Kr3+zyZgK4MvuXmhmw4C1Zvacu+8Pb/+Guz8VZx0Z68DRah59ZRMPvrSRfRXVnD9uAAtn53G24iBFJEHibQTzgFnh5UeAlTRpBO6+IebyNjPbBQwE9iMntL+iiode2sgvX9nEwTAOcuGcPKbnKA5SRBIr3kYw2N23h5d3AIM/arCZzQA6AUUxm39gZv8KvADc5e6VJ7jvAmABQE5OTpxlp649hyr5xYvF/OrVzRyuqmXu5CEsnJOnOEgRaTUnbQRm9jwwpJmbvh17xd3dzPwjHmco8Bhwo7vXhZu/RdBAOgHLCN5N3N3c/d19WTiGSCRywudJVzvKgzjI37zeEAe5cHYeE4b0THZpIpLhTtoI3P2iE91mZjvNbKi7bw8P9LtOMK4X8Gfg2+6+Juax699NVJrZL4F/+ljVZ4CSfRXct6qI375RQq07l08dzu2zxyoOUkTaTLxTQ8uBG4FF4c9nmg4ws07AH4BHm54UjmkiBlwOvBdnPWlj057DLF0Z5T/fLA3jIEdy28yxioMUkTYXbyNYBPzWzG4GNgNXA5hZBLjV3W8Jt10A9Dezm8L71S8T/bWZDQQMeBu4Nc56Ul5010HuXVHEM2+X0qF9O74UxkEOUxykiCSJuaffdHskEvGCgoJkl/GxfLD9AEvyo/zlvSAO8vpzcvi78xUHKSJtx8zWunuk6XZ9sriVvVOyn8X5UcVBikjKUiNoJWs3l3HPC1FWbQjiIP/nReP4ynmj6d1NaWAiklrUCBLI3Xm1eC+LX4jyanEQB/nPcydwwzmKgxSR1KVGkADuzqoNu1mSH6Vg8z4G9uzMv3zuNK47W3GQIpL6dJSKg7vz/Ae7WJJfyN9KyhnWuwt3z5vM1RHFQYpI+lAjOAV1dc6z7+1gcX4hH+44eCwO8srpI+jUQWlgIpJe1Ag+hpraOv70znaWrIgS3XWIMQO78x9hHGQHxUGKSJpSI2iB6to6/vBmKUtXBnGQEwb3ZPG107hEcZAikgHUCD5C0zjIKcN7cd/1Z/HpSYMVBykiGUONoBlHqmp5/PUt3L+6iJ0HKpmW04fvXz6FWRMGKg1MRDKOGkGMQ2Ec5ANhHOTZo/vxk6unct5YxUGKSOZSIwDKj4RxkC9vZH8YB3nnnHHMGN0v2aWJiLS6rG4E+w5X8dDLG3n45U0crKzhwjAOcpriIEUki2RlI9h9sJIHXizmsTWbqaiq5bNThnDHbMVBikh2yqpGsKP8KPevLuLx17dQVR8HOSeP8YMVBykiKcwdyktg93oYfQF06JTQh8+qRnDn42/y5pb9XDFtOLfPGssYxUGKSCqpq4V9m4ID/u4Pg5971sPuDVB9OBhz+2swaGJCnzarGsF3Pj+Z3l07MrKf4iBFJIlqKmFvUXiQj/mzNwq1lQ3jeg6DgRNg+g3BzwEToE9OwsvJqkagcwAi0qaqKmDPhphX9uGfsmLw2nCQQd9RwUE+70IYODE86I+DLm1zzIqrEZhZP+BJIBfYBFzt7vuaGVcLvBte3eLul4XbRwNPAP2BtcAN7l4VT00iIm3uyP6GA/7uD8PLH8L+LQ1j2nWAfmODaZ3JlwcH/oEToH8edEruLEW87wjuAl5w90Vmdld4/ZvNjDvi7lOb2f5D4Kfu/oSZ3QfcDPw8zppERBLPHQ7vCV/ZfxjM29fP4x/a0TCufWcYMB5GzIBpMVM6/cYk/CRvosTbCOYBs8LLjwArab4RHMeCj+rOAa6Luf93USMQkWRyhwPbGr+yr5/SOVLWMK5Tj+AgP3ZO8LP+T59R0C698kjibQSD3X17eHkHMPgE47qYWQFQAyxy96cJpoP2u3tNOKYEGH6iJzKzBcACgJycxJ8sEZEsU1cL+zc3Pllbv0Kn6mDDuK59g3n7SZc1TOcMnAi9hkGGfPXMSRuBmT0PDGnmpm/HXnF3NzM/wcOMcvdSMxsD5JvZu0D5xynU3ZcBywAikciJnkdEpLHa6uDkbOwr+93rYW8h1BxtGNdjSHCQn3ptw8F+wAToPiBjDvgnctJG4O4Xneg2M9tpZkPdfbuZDQV2neAxSsOfxWa2EpgG/B7oY2YdwncFI4DSU9gHERGoPgJ7CmNe2Yfz+GVFUFfTMK5PTnCAHzMzZoXOeOjaJ3m1J1m8U0PLgRuBReHPZ5oOMLO+QIW7V5rZAOCTwI/CdxArgKsIVg41e38RkUaOHmh+hc6+zUA4WWDtod/o4EB/2qUNUzoDxkGn7kktPxXF2wgWAb81s5uBzcDVAGYWAW5191uA04D7zawOaEdwjmBdeP9vAk+Y2feBt4AH46xHRDLF4b3Nr9A5uK1hTPtO0H8cDJsOZ17bsEKn/1jo0Dl5tacZc0+/6fZIJOIFBQXJLkNE4uUOB3c0v0KnYk/DuI7dYeD4mJO14Rx+n1HQPqs+FxsXM1vr7pGm2/U3KCKtr64Oyrc0s0JnPVQeaBjXpXdwgJ94SXjQD+fwew2Hdu2SV3+GUyMQkcSprYayjcdP6ewphJojDeO6DwoO8GdcHXPCdgL0GJTxK3RSkRqBiHx81UeDL0hrNKWzIdhWV90wrvfI4CCfe37DlM6A8dBN6X+pRI1ARE6s8lDDh6wardDZBF4XjLF20Dc3eGU/YW7MCp3x0Flf9Z4O1AhEBCrKGr+yrz9pe6CkYUy7jsEXpA05A07/YswKnTzo2CV5tUvc1AhEsoU7HNrVJPAk/HM45rOgHboGK3RGndd4hU7fXGjfMWnlS+tRIxDJNHV1wSv5Yyt0YqZ0jsZ8s0vn3sEBf/ynG6/Q6T1SK3SyjBqBSLqqrQnm6ptboVMfawjQbUBwkJ9yZeMVOj2HaIWOAGoEIqmvPtbwuBU6hVAbk+PUa3hwgnb6l2NW6EyA7v2TV7ukBTUCkVRRdTg80DdZoVO2sUmsYW5wkB93UcOUzoBx0KVXMquXNKZGINLWjsUaNvla5PLmYg0nweQrwoP9+OCA37Fr8mqXjKRGINIa6mMNd394/JRObKxhhy7BwX3kjHBKZ3xw0O83Rit0pM2oEYjEwx0OlDY5WRv+PLKvYVynnsFBPu/C4JV9/UnbPjlpF2somUeNQKQl6mrDFTobjj/oVx1qGNe1XxhreHnjE7YZFGsomUeNQCRWTVVDrGHsQX/PBqitbBjXc2gYa/ilxh+66j4gebWLnCI1AslOVRXB8stjr+zDE7ZlxU1iDUcFB/mxsxqv0MniWEPJPGoEktmOxRp+2HhKZ/8WGscajgkO+Kd9vvEKHcUaShZQI5DMcHhv8yt0GsUadg4O7sPPCqd06lfojIUOnZJXu0iSqRFI+nCHg9ubX6FTsbdhXH2s4ZiZjVfo9M3VCh2RZsTVCMysH/AkkAtsAq52931NxswGfhqzaSIw392fNrOHgZlA/Tdh3eTub8dTk2SAujrYv7n5FTqNYg37hLGGn2v8HTqKNRT5WOJ9R3AX8IK7LzKzu8Lr34wd4O4rgKlwrHFEgf+OGfINd38qzjokHdXHGsaerN39IeyJNo417DE4jDW8pskKnYFakimSAPE2gnnArPDyI8BKmjSCJq4CnnX3ijifV9JJ9dFwhU6T0PK9RU1iDXOCKZ3RsVM646Fr3+TVLpIF4m0Eg919e3h5BzD4JOPnAz9psu0HZvavwAvAXe5eefzdwMwWAAsAcnJyTr1iaT2VB8PpnCZfi7x/c5NYw9FhrOFnY1boKNZQJFnM3T96gNnzwJBmbvo28Ii794kZu8/dm335ZmZDgXeAYe5eHbNtB9AJWAYUufvdJys6Eol4QWFHU5oAAAanSURBVEHByYZJa6koOz7wZPeG42MNB4xr/Mq+foWOYg1FksLM1rp7pOn2k74jcPeLPuJBd5rZUHffHh7Ud51oLHA18If6JhA+dv27iUoz+yXwTyerR9qIOxza2fwKncO7G8Z17BYc8HM/2XCy9lisoRaliaSDeP+nLgduBBaFP5/5iLHXAt+K3RDTRAy4HHgvznrk46qrg/Ktx38t8p71zcQaToDxcxtO1g4Yr1hDkQwQbyNYBPzWzG4GNhO86sfMIsCt7n5LeD0XGAmsanL/X5vZQMCAt4Fb46xHTqQ+1rDRCp31QQOojjl3331gGGt4VeMpnR6DtUJHJEOd9BxBKtI5go9QUwl7o8cHl++NNok1HNFwkI/90FW3fsmrXURa1SmfI5AUdSzWsMkKnX0bG1boHIs1nAjjLo750NV46NwzmdWLSApRI0h1R/Ydf7J294bjYw3758HgyTDlyoYPXfXPU6yhiJyUGkEqcA9W4jQ9Wbt7fbByp159rGHO2TDwyw0rdPqNVqyhiJwyNYK25A7lJY1P1tZP7Rzd3zCuU8/gFX3exY3n8RVrKCKtQI2gNdTHGsa+st/9IewpbBxr2K1/cJCffEXjFTo9h2qFjoi0GTWCeNRUQVnR8St09hQ2iTUcFhzkp13feIWOYg1FJAWoEbTEsVjD9Y3n8cuKwWvDQRZM3QycCGNnh9M5E4IG0KV3UssXEfkoagSxjpYfn2G7e/3xsYb9xwav6CfNa5jS6T8OOnVLavkiIqciOxvB4T3Nr9A5uL1hTH2s4YhI4ymdfmMUaygiGSW7GsGfvg7rnmkca9ipR3CQHzO78QodxRqKSJbIrkbQewRMvLTxCp1ew7VCR0SyWnY1gvP/MdkViIikHH1/sIhIllMjEBHJcmoEIiJZTo1ARCTLqRGIiGQ5NQIRkSynRiAikuXUCEREslxahteb2W5g8ynefQCwJ4HlpAPtc3bQPme+ePd3lLsPbLoxLRtBPMyswN0jya6jLWmfs4P2OfO11v5qakhEJMupEYiIZLlsbATLkl1AEmifs4P2OfO1yv5m3TkCERFpLBvfEYiISAw1AhGRLJexjcDM5prZejOLmtldzdze2cyeDG9/zcxy277KxGrBPv+Dma0zs3fM7AUzG5WMOhPpZPscM+5KM3MzS+ulhi3ZXzO7Ovw9v29mv2nrGhOtBf+uc8xshZm9Ff7bviQZdSaSmT1kZrvM7L0T3G5mdk/4d/KOmU2P6wndPeP+AO2BImAM0An4GzCpyZjbgfvCy/OBJ5Nddxvs82ygW3j5tmzY53BcT2A1sAaIJLvuVv4djwPeAvqG1wclu+422OdlwG3h5UnApmTXnYD9vgCYDrx3gtsvAZ4FDDgHeC2e58vUdwQzgKi7F7t7FfAEMK/JmHnAI+Hlp4ALzdI6vPik++zuK9y9Iry6BhjRxjUmWkt+zwDfA34IHG3L4lpBS/b374B73X0fgLvvauMaE60l++xAr/Byb2BbG9bXKtx9NVD2EUPmAY96YA3Qx8yGnurzZWojGA5sjbleEm5rdoy71wDlQP82qa51tGSfY91M8IoinZ10n8O3zCPd/c9tWVgracnveDww3sxeNrM1Zja3zaprHS3Z5+8C15tZCfAX4M62KS2pPu7/94+UXeH1AoCZXQ9EgJnJrqU1mVk74CfATUkupS11IJgemkXwjm+1mZ3u7vuTWlXruhZ42N3/w8zOBR4zsynuXpfswtJFpr4jKAVGxlwfEW5rdoyZdSB4S7m3TaprHS3ZZ8zsIuDbwGXuXtlGtbWWk+1zT2AKsNLMNhHMpS5P4xPGLfkdlwDL3b3a3TcCGwgaQ7pqyT7fDPwWwN1fBboQfDlbJmvR//eWytRG8AYwzsxGm1kngpPBy5uMWQ7cGF6+Csj38CxMmjrpPpvZNOB+giaQ7nPHcJJ9dvdydx/g7rnunktwXuQydy9ITrlxa8m/66cJ3g1gZgMIpoqK27LIBGvJPm8BLgQws9MIGsHuNq2y7S0HvhyuHjoHKHf37af6YBk5NeTuNWa2EHiOYNXBQ+7+vpndDRS4+3LgQYK3kFGCkzLzk1dx/Fq4zz8GegC/C8+Lb3H3y5JWdJxauM8Zo4X7+xzwaTNbB9QC33D3tH2n28J9/kfgF2b2dYITxzel+Ys6zOxxgoY+IDz38R2gI4C730dwLuQSIApUAF+J6/nS/O9LRETilKlTQyIi0kJqBCIiWU6NQEQky6kRiIhkOTUCEZEsp0YgIpLl1AhERLLc/wffK++zinbhSQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From 2b8024cb5286136f3fff59366032bfc1946bdbf2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 318/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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iVCHpk1A0K4KlCNHmRnTZMTpMmKwJTJIPs6ENgzAxZSSt1hBXlhFXzkXlbat+vE0GjQAyshgkFB8kHBnkpCBwwFFKjStKbb6LNTMX0JUWmTowxF27DzIYPEpGS5NrTjB3Xh3NS8/hgaM/ojid5kG/hY6+z3NaqQCgrM7BxZ+fjVHSFzn7KNFDXnfWOd3q5411nfhOR0k5hjjlOsm0kUVIikRN5wtMKk/hueNeom+EyPgSGJty+M9JJtbHYiAIOBNRlrUdZaYriz57DS+cGCetiJTahgjE3VhTUabGW2mUTyPGgghoNGSNMs0zSIkpjiaaScWd+NoyxLpdCM4aVE8licIKzM4KXIb/WY0yKaQYMI0yaB5jzBjAbwxgEIeozvSyKDlMTSaGooE37iDgrUXps2DwjiGIElJpJbYFS7DOnEfYXsi+o8NEx+KUA9UYkN7cpjmjynQrYVqlDILQT+ekYp6qmIxJhR/vD+I8fYiTgTdQtDQus0zRuXN4yLSdhBzkQV8IR+oe3uirQxMl3LkGrvn6In2I5UeIHvK6s8b4UJQ9z3XR1zyO5JDZl/sCnmAj5aEmsiPdNHY/Q/mnbwfDVBInfBhyLWxcnMu3UmFkDUzpFAu7mqmPRPAXzGZTRwhZUSl3DKAEjJRGBqiPd+CUIyAIiAaRSbZh5hX0kmdMkVBsjLXbCHlnEcibSrigGpO1CINgwA4IYhTRnUaocmIudWEpcDIuwMG+CCdP+2gdGcMfCyIaYmAMoZj81Bg7+Hi6myuiUWyaxk6LhR1yCaHOqcwaitM01IZRkdGKS8m77mP0zlnOvduH6fHGmCtEuNIZpDgcIydVQY55ogM1SYaRzAh/mFLO1iIrt3YnubEnxe6x9YzLg8iZBHa7geONKnvdvdwT8LPcdisvHZhD0pSF1ayy+r4l2FzmM/sL1/1N9JDXfeTFQin2v9hDy+4hjGaRQOnr7NP6WdZzDaY/197LkuSs/jLRPQE0RWN0SSG32xMMyxlEVWF6XxtN/b343NN5Y1RAzmSYKp0kyxemMtJPdiaEhoC7tJxMIo4z3sm5JZ0USnFG026ei13Bi9Iyeg0m4u/RbG0yCHisAmY1iT0Txq2GKBJDWEXlXY8xm804rCozU9uZmziIEYUX7Nn8PisLOT6f0sMuVnW1Mm28G0U0kFh0Ls2LLuV7HRkENcNSaz9L8rcR3g+5WgWm7HnUmwvIwkgGjTfyjPTYRG7sk+kM7qc53ouLTgIpA7JTYGfdKMsto3zGvpiNey/AayzFIChc85W55FXoQyw/7PSQ131kKRmVY1tPc3BjL0pGpXhGhseU71A6eAGTvQvIivXT2PYHyu+6EzVTT7onjFLr4tvTbWyMxkDTKPUOsaztGANCKcdSHizJAPPlAxSMj5ElhydmoNqyKa2ZiscJvfteY17ZGPOs3Yyobu7P3MBL6gJcmkiFlqYs6qVcylAyoxpnUx2+8XG8Xi8Doz7GQnFimomwZsav2ogxURM2ChpT8yTml1qYV2oj125CURTS6TTpdJpIJEIoFCIQCJD09bNY28tcjhHHzJPmJp53SVg8Hip80yl/tYvlXfuwZVL0V03h+foVbDKXUy36+HhtK4ae3YTaXYyYCxjOX8VlZgNTBA9ZGQMxAxhVCCVOs9O7m5JcgXDgFIG0jZGcJFrlMN/K8rC342ZaY1UIaJx/cw11i6vO8J2gey96yOs+kvpOjrPrmQ6Co3Eqp+YyXrKOxwf3cV77LThTBVT0baLe2I771m+SOBpHNQi8vLKA+5UoKU3Dmkqw8vheImEzXUoexcEupidPkh0PoAFeSwFkZZHnqmBWYyEt2zZjyTFyjeUl7MR5RLmYXemraMwEmHX6MBWnT5J16SrSF15AbyxGe3s7g4MTHbqiwcg4TnqTVhRrDitm1LJ68WSMBpHDfUH2do+z+dQog8EEogBLJ+Vx3ZwyVjbkYzb+dSenLMuMjY3hb9lB8YHvkpvqp5MKnmclbbYIkdwIjQVzqHg1Qu2OV8lNhjhVUMfDk1fR7y7mmqIhGi1PM7QzG1mR2JK7DMGVzWUNZZj8NlaMZbAqkNEynAjsZ8SSQ5P6e/YFs1FlA+MFEe4oDhAQ72ZPVykgMHupmwU3zvzgbwLd30QPed1HSsibYNfaDnqP+8jKtzL7Mg+/abuDkYFGFvVdiSWTpPHEw1ResBhD3nnIAzF6p+Xw5QqB7rSMoGlM7TxBUa+XUESlPNJNWWIAEQ2vKZdRZwkWl4MCg8aMnAY0/yAHlVxmSmv5mHEr7UoFu4IXMj3ViutgK+RWol6/mt6CQk61txEKhQAoKSlBdRXyUo9Kc9BAfVEWn1tRy6rGAowG8W2fS9M0Tg2HefnECOsODzAcSpJjk7hxfgWfWFhBvvMdNvhQFdT9D8Pm/yCjaLzIck4whaSYZDhnmFlTZlKzPU3++mdxJiLsL2zgsYYLKSoWua7xZYa2B4mPWjnmmsLx3JksnGxgW1kt5w2k+Vx7CqsKspqiOxkiy/467Ym9nB7NBwGmFI4yueHTvHqgHE00UlFh5KIvLsYgvf2z6c4sPeR1HwlyWuHwpj6OvNqPaBCYc3El1qo2vrjjq9R3X0+1fyaewCmaTj9H0b/8B8lOC1FJ4OEVuTyRjqMB7liIFdtfQfSHKY4NIGkZYpKdFtsk/Ln5TDZEqBYkJskVFBjsvKaY2Sd08QPpx1QJI7QGKilKegmesBAumcXIqvPpSKXwjY8jiiLV1dU0NTWRtufzg9f6ONQXoKHIxb+dV8f5jQV/82QiRdXY1enjyb19bG4ZRRJFrppZwh1Lq6nNd7z9gPEuePY2GD5KCDtrpYs5LZegoTHqHGXGzAZm7U4jrX0aczrJKxXz2DxlIbcs7iN9civeE7mMWQvZ6DmP2gKFrvlTGJdVHjwSZ7Evg6BNrBPuVQNgeYA/huwUjlqxWBIsnnkOe04tQhHNZDkUrvr6UuxZeofsh4ke8roPvb7mcbavaSMynmTSvAIWXl3LzpM/4sFjm1nZ/ilcyQJqutYzOS+CY8W/kuyMsm2aiwdKRcYVBVckwHm7NlE02IMtEyclmhhxV3NAqiedZeZ6Yzs1yWqq1QLcmoOdyPyXGmWu8SAPGn+FoKhocY3R5hy6q1bS29hIfzAIQEVFBVOnTqWhoYF0MsMfHt5A18GTVKcDzMvSKLYbSaVl4jYbCVcWsfx8AhXVRKtrUN1uTKJAltFIvslIvkmi2CxheUtNv8cX43e7ull7cIC0onLljBL+dWUdlR77X18kRYZtD8DOh0A0EMLFxtLbaRtIgwpel5cls+fQ+HIvhvXrSBhMrG1YiXV5CVOlJzm9vYCUaGeD+3yUnCzE5XX0aAI3d6f4bEcKX6Ifj6UUEZGUcJxHHa9i7EzhTEhUFbnwRq8kY8jFbFS44p4F5JW7PsA7RPde9JDXfWjFQil2re2g8+AYOYU2lt84mYIqBz9/8Sa29Bo4v+MmLBmNpuO/perC80Caw6Cq8P3F2ezNxKntOcXMloOUDPUgAP2WEsaLprJXLSVPUvhc1hhTAgW4tYlx67ulHh6LZWgzebhPeYQb7K+jKtAzUMSh7FV0ewpIyjLZ2dnMnDmTGTNm4ABCG16k7/kXMbaexKhOjJJJWW34s3OIGY0ogogjEScrGsGRiP/l8/UWlXCofipvTJvN0UmNqKKICFRZzUyyW5jqtDIvy85Ml41UUuHXO7p47I1eZEXjujmlfH5lHUVZ/5+VIjs2w7pPQToGaoZY0408b5xP+4kuBFXA7/Zz9ZQF2P77T7iaD9PnzOf1OYtYuGIbpzc7SYVNbMtdQkfWJPKXFNJusbBqSOb+E0maQ/swaiZqXdORRJEWywFelndR0pVEEkUk80IUaS4GEVbdPoWaOUUfzI2ie096yOs+dDRV4+SuIfY834Uiq8y+qIJZqypIJEf58nPXkOxfwuzBC3DFB5na9ijFt3yF1GkL6+tt/C47RP3JfTS1HsaaShA12jnpaGA0t55xwckCReQmS5yapAcBkX5phG32PXSdltltXY6dBGvF+6m2DTEm57A2cxleay6iKNLQ0MCsWbOoqqoiMziI75e/IrRxI6RSdBWUcmDqTA42TqGrpAKrJ5cZLjt1NjPlVjPlFhO5JiOOcAhzTzeZkydJ7t2LfPgwJJMoHg+B5StoXnE+B/NLaI8l6Yyn0ACDALOcdlZ5XMyxWNi09zRr9p9GFOHTy2q5c2k1VtNbOmj9PfDMzTByYuJxyRxil/+Wp3a+xumTp1FRSZenWG2bSuTB35AfGWdfeRPC6iipk0kiAw7a3NPY7FpA/hwXfZ5s5oxn+MXBBG2pU7SNvE69ZzV11mwMosBO2yF8o7uQfWmMxlxEyypEYyHzLyph9hWT9TVvzjA95HUfKuODUbY92cpId5iSyTksv2Ey2QU2RkaO8LkX76Sy+wYqA9MpGtnDFPkQWRffQ99Yil+VDGNr30vV6Q4UQWTIWcoh2zRGLIVMlaycmzFygSBg0yQiYojNrkO85tzFpONxuuSrOJzfwIr4CX7hegiLIc0r6lL2GmaTk5PD3LlzmT59Ona7nUwgwOjPfkbombVkBJE/zV/CS0tXMlJazSWF2azwuJif5aDQ/Lftmaomk0S3bSf00ovEtu9Ak2WkmZOQrp5NcFoORxJwNOnkQLqILmViMlMhXmamTjDeJnF8uJpca5gbp+xlScUIkuTAaHRiMbgpOrAde9dB0pIR0erBuPppxqQSHn3hUeKDceLGOPnTcsl9fpjZe7eQlEwcW16KyePFdzKXUE4Fa1wrMTdm4avIZVJE4dE9cQaFEQ50P4HVspRGRyMVdhOCYOCEcT9j/QcIpBIYTNMwWhczaZqHlXfN0TtkzyA95HUfChlZ4eDGXo682o/JamTRtbVMnl+IIAi0tW3g7te/z4L2O3HHS6jrWEd9fS6ZwhWsjx0h5D2CMxokandyqmIGXdESVIObSwUD52lmqjGgCDLd2ad4wraLI7aTXHTIQmlHJf899VpSRhNfltdxvetVJBSeES4hUbGYZYuWUVtbi/jmXq2nX9rI+HfuxxAJs3Hhufxh5RVoOLhvbjVXV+f97R2rSpJotJVwsJnoeBuxWDcJuRc5Oor9DQHbdgPGgIBcoJG41Iq6KA+jyUVALOSg0sAb6SoOpwtREakOj5JuieMLmmjMG+P2GdsptPWRSo2gKikq+xPU9MUJZBlpr3Zg8UzHUbgUX9jOq9u6UUJ2fC4feZZimp7YSZO/l97iAhLnhuk5WYTmzOUJ1wVEaguJ1edSmtR4cleUsBRnT9tvkKVSXNaLqDZ3UW1vQEPDm9zB3uFmMhiQbMspKmvk0i8txOowvZ+3kO5d6CGvO+NGekJsfayFwEic+gWFLPxY7V8CYc/+n/Kd/X9iReud2GUbTSceJv+8JRwd9dIfbkZUM4yU1bJ30hwC4RwafQauQWI+EkYERm2jHM3fwuPSUfyGKBcez+LKHQleKD+XtZNWUJ7xc7tlE9caN5PRDDySewFXXvstKooq/nJ+7V4/LV/9GpN2bqO9rIpffOwOWiJObqjO5z8vbcRmevd1XFQ1QzR6ilD4GJFwMyH/MeKpLhAmFkETMmZM8UJMsSJM8UKkVB5aRkHsGIDDxxACAcjOxbToPFznXomzoRJzVRYjssy6kQDPjARojyVwDicR20Mosspnzq3lM8uqEQmRTA6hnXgG12u/IC0JtNRa8eda0N7cnCSj5uIby8IfzqVLcsJWOzce245Fk/EuFjkeK0YR7WzKO5+2ijrkKW48isDjO6NoksLuU78gbjJhN1+HQWzGXWxhUXwuGcKcDJ6gLfAGgrGELPcKrvzqxbiL7O96rXTvDz3kdWdMJq2w/8Uejm7px55tZvlN9VQ05f7l9Ze2fInfHR9kRccnsKZiVHY9jK+xhkF/L4rByKlJMxiZu5z2uIVrjoS4SjVTjgE/Km2ecQ7m/ZJXGUHRYFGnh9XbkhgjCvctuIXWnEqaGOQa8z5u5gX8osSzC2/hrpX3YxQnQrs9luSRvYdZcv83qRge4I2rrudnjnmIssD3r5nKhVPe3rGoKEnC4aMEgwcIBg8SCh9BeXOJYYPswhKqwBypxN7MYRAAACAASURBVGFuJCtvGqbCLCLmI4TkA4QiR0gmB+DPu0OpYDks4txoQBoVSFepRK6wIOXVku1cQGHThTiLmtgfjvPbAS8vD/gxtoUQhxOU5dr44TXTWFD95vXs3wtrVkM6iqrIRBbfRLBuBsHQQQKBAyjKxPj+YMxFy3g+ZVsNzD3QTbzIyPGiPPyKg+bSpbxWNIvMjByyEfnt7igOYE/f74nLIQzmy0haFLrL1nN14nKmJCYRV6Ic9m1lMN6GyTqXy+6+ncppeofsB0kPed0ZMdwVYuvjLQRH4zQuLmbhNbWYrW/WiDWNP750By+esLGg73LMkTfQkvuISAKqZGPvtPmMzFpMnmJlycEgl0bAjsApFPa6w3RX/ZrD8T4MCMzrz+OKnRpV/SPsLWngwdk3khQlFkp93GR4hfPYRa9k5MQl3+PKGXcAMJKS+W73EK0793D/rx7EJIocuPXfuX/QQX2hi9/cPJsyt+3NU1UIR5rxj+/E799FKHwUTZMBAZu5Fpu/DqmrHGuoDkdFLfYZ+ZgmOfCFtzA8vA5/YDegIUlucrLnY3dMxm6rxmTKxWB0ICAipyPEXnyV6K9fgECc5DwzwSsjqNlgyVRQVHwVJXUfZ1TN4neDPh47OoB2MoCQULh4VjE/vGIqdrNxYjz9H66C8CCoGZi2Gi7/GZpBIhJtYf/hRwj5d+Fy+RAFjVjMgvughv2gyrDBQaulgMHS2azNX4Q8Ow+HKPLfe6MUyLA/spHw8Clk5zkYjY1sr3yQfGsp/z58OU6lBG/KxxHfRoKZJHMu+QRLb1p1Zm68f0J6yOs+UHJaYd+Gbo69dhpHjpkVNzVQ1uj+nx/QNB55bjX7j9fR0O+B2C4yQhKHNZej9efw0qxZfEqxM/VEiPl+BRmN15HZIHmxTN9GS2w3BkHjvIibc16xM6mjj6DLxeNzL2GzYwYOIc0njdu40fgyuZqXTrOF0McfZV71RcQVlV/2j/Hz/jFmHT/MN3/7Y6TiYh6+6HM8O6xx5Yxivnf1NAR1FL9/F+P+nfj9b5DJTIyZdzqnkJNzDk6mIxzwIB9LIpgNOM4pxj6/EJwyg4Nr6D/9e9LpMSyWEgoLryQ/70IcjnoE4b07J5VojPFf/xr/o4+C0YBw/gzG5w2RcHciaEYKPJdTWXsXCamSn3UP89j2HrSeCDaHxA+unc5lkwsgMgJPXA1jLRMbkNSshI//AUwTzSjj4+P8Ye2vkJR2HIWtFGaNYxQ1DONgOGGkvbeYVmsTL5ZfQHBOMTaDyH8dTFATVzlgPcL40VdQ8iZhzqxiR+0jdHra+NLQHBaHrscomOmJtHA88BqO4jqu+erdONzu9/zMur+fHvK6D8xQR5Ctj7cQ8iaYsrSEc66u+at1ybWMzM/WXM3Y3hpyxkdAi5JjMOOZfCU/nF7PnKDGbUMKOeEM46isJ80Gwzjmss3EbAcRUVlqErhwo4eioz4iDgeHZk5jS84cTihF1DLGb8zfp9CRxhwbp93mxHrLRiryp7F+LMi3u4YYSsnc3XGMy376INTUcs+c2ziVEPjWRQLzipoZH3+dWKwDALOpALd78ZtfizBqWYS39BPdPYhgNOBYVIxzSQlYBIaGnqa75yfIsp+cnIVUlN+J273ofw32d5Lu72f0ge8T3boVc309pkuuYdS8nVDRTjQxQ1Hh1VTX/BsxMY9v7u/mxde60RIKU6fl88hV08knPtF00793osCy+XDjM2DJAkBVVV7f+To7t+0kY4wQKTvIPDFFYaEXRMj0mGg9Xckzxhvomt2IySjw4LEk04IK+yu8eLc8gqmglEz6Qo6UbeNI6WssDIl8pv/z5BjqUDWFk8E36IweY8nNn2DGBRchivpGJO8XPeR177uMrLB3/UTt3ZVr4dyb6imt/+saXCoc5CcPXo/QYUNUU9hkM3Mr6+irupLTAlw0LGNX4KSk8qyc5HWimDzbMOftBkHlHFuKa1tdZD+XImaxc3jmDEY9BexMV9GvufmY9QTfU3/AiKecovEeupweCm7fTsiUx1faB3jdH2Gaw8r9w5047r2H5KQGfrxwBg2lbcwvakFT/QiCkezsuXhyz8XtXozdPukvI2riJ7wEX+xGDaexzy3EdUEFBocJv3837R33EYt1kJ09n7rar+ByTfu7r6mmaURe3czId+5DGfeTvfomhMqFDGWeIVi+BcFgpLz8NiorP0NPTOP2Z4/S2x5AcEp86uJJ3NOYj7TuNmh/GQQR8pvgEy+A3fOX9xgdHeWJPz5BxB+hzdVOQNH4mnYapXIYxQOZpIHD3tk8lX8z48ZCvtecYpE3w45pGsENP8KalU3UtJQBp5dtNU9SnpS5u/sKHOpFFEki0UyEo+ObkQtg1V2fpaCq5u++Lrq300Ne977y9kfY/PtTBIZj71h7T8aiHNn4Ars3PI0gg2gsp2Y0yNwLrqI/UUxJXCUjQEeZlYdG/bSmkhizDmPLfwXNGGWhu5hLxrqoeMZBOOrg6MwZjHs8JDIGtiUn4TXa+Q/XRm6V17K7qJb5Qy2MZRXivmMPj4zLPNQ7gigI3FtdxDU9xxj5zL8RLzYx9tkYJruMKDrweJaR5zmP3NxlSFLWX30+NS4TeKGTxHEfUrGd7CtrMZe7yGQidHR8l6HhZ7Bay6mrvReP5/x/+MQgJRxm7IcPEly7Fqm0FPddXyZyOslowdNEivZisZTRUH8/bvcifn/kNN9df5J0WsEzPY9fnFfHgh33wrE1IBjAXQ2fWA9ZJX8pX5ZlNr68kaOHj+I3+dmb3cZdvunMPLGB+DyF5EwVTRA4rMxhk3AJH2+p4cJhhY2zrWibf4qaTEDFYkYTVjbV/wYbCe7pWEg8egPTzAoOycxo4jRH/ZupOX8hC6+9EZPV9g+9Rv/s9JDXvS9UReXwK30ceKkXq1NixScaKH/LyJl4OMThP63nyKaXSCfiaKYyHKYFnBPpx1O/FC0t4jMJnKi1s8djZOOufgymASyFz2OwDjLZVcdqU4rK5zpItOZzbPo0fPn5oGRIRtxsMdeQkAz8XPopS209POrO5caBVuJZJYzeuJnP9UZojia4INfOv2W3IBx5CuM3jqLkanR82kHEsZAL51xLfu4CRPGdx3cn2/z4n+1Ajcm4zivHuawMwSAw7t9FS8s9pFJjVFTcSVXl5zEY3t9Fu2L79zPy9W+Q7usj56abMdVdga93N6PTHiNtHqGo6Fom1X0Nf1zilicP0dIXRCm0cvnyCh7s+ynWI4+CKIGjEG5ZD7l/Xas+deoUzz63jpSS4nDuYdxCOV985RDSWITR8w0kF2lYTCn6tEqEgfNZ0Tqfp2dmkX18DdHOVgpmLadrOJ9X6h8mafJzT+dsxsc/Qb3qpzI7D0kQ6IocpZtmltxyK7XzztFnyv6DvO8hLwjCI8ClwJimaVPefM4N/BGoBHqB6zRNC7xXOXrIf3QER+NsefQUoz1h6uYWsHT1JCz2iRmg8VCQAy8+x7FX/4ScTiF6DNiUa2iUCqkwCYhGM312kd9Um6hqyuON1jHajg9gyXsFKWcPFs3K3U03MulPfyC9VaB50jSGSktBVTH7I8SV+az3ZGES0jzKN6gqcXGfMcY3BrrRskp56qK1fHs4hUtU+D/WzdRHH0MIpsn7gYWUIvHdC2/i2kuuZfX8inf9fJqiEX61l8j2AYwFNtzXTcZU4kBVM/T0/Be9fb/EZqulsfEHZLmmf1CXHTUeZ+zBBwk8tQZTbQ25//I1IkfS+MrW4S9/Gau1lClNP8HhmMpPXu/gp1s6UK0GzDM9bEg/Qf2J34PBPNE2/4kXoKDpr8oPBoM8+dSTeMe89Dh6aM0e4L5eKyXrukhZRFpunERmup9SwwDJtJvy7gt5PuciSkOHCby+iZKmmfQkanmteB2jzh4+1z0deexWcqN9lGUXUWZ3oJDmhH8nao2Bc2+7k6z8wg/s+p2tPoiQXwpEgcffEvI/APyapj0gCMJXgBxN0+55r3L0kP/w0zSN5u2DvLGuE4MksuyGydTNmZiKHw34Ofjicxzb/DKKLFN/zmKiUT9V/lUUSRKaqiCUWflOjsCGYol7C/N4dGsnwcBhLEXrEI1hpqjT+Vb+Avw/f5TWgiZ6KysBEBMxnGPZBPLm8YxZId8Q4Q/a1zDU1fPldA+/HhzC4CjkU7N+xOtaIfPZyye1X+Ex28nLWkX8y2+g9Y3wwMV389XPX8GMsnff0k4Jpxlf00K6J4x9XiHZl9UgSCKplJfmk/9KMLiP4qLrmDTpPzEY3mEN+A9AdOdOhr/6NTLBILm33YUmnUMoeoSROb8lYwhQU3035eV3cKA3yGeeOsx4PE2qIYtfis9yRfsjYLSC0TwR9MV/vRmIoii88sor7Nu/n4gxxt78N7jCWM7KX53AHlYYaqzl17ecxzLHZuqFFgxpJ+2Ji7BY5zL++GM4PfloZcvZKL5Cl+cIq09PJWfgk7ji/dg0kYr8agokkbDs53hoB1WXLmDOpVdiMP5ty0To3u4Daa4RBKESeOktId8GLNc0bVgQhCJgm6Zpk9+rDD3kP9yigSRbH2/hdEuA8iY3K25uwJ5tJuL3cWDDOk5seQVFyTBl0UqmV60kuKcPe8ZFSlFQvQew3H4ZN/njdNkFvlOYx/c3HEG1rseUsw8p5eZmzuP8lk6Ojch01dWgIaAJAtaxUWzyMsZqy1kTDjFZGuVRvkFnwzy+Hm1m7VgAo2Diwhm/YMTs5lOGp7m6MJ/Cgkuw2qez9ZbPUn54J3+8/LN8/hufwuN492aVVHeQ8ada0VIK2VfVYp818Q8sEDxAc/NnyWSi1E++j6Kiqz+oy/6uMoEAI9/6NpFNm7DNm4fj4s8RbfYxNvcJws49uN1LmNL0X4RTVj7z5GH29fih0s4XrBv4Qs/v0CQbgmCEm9dB2by3lX/q1CmeXvssqpbhcN5BzG749HNDlJ1IEXfZue+OL+Kv0bgrvJZC1zEyqoWQ5SJCzwwQD8rULLuWp717OFa0lXOHm2jo/STOzAgW3zCUzmeGAywYGYp30Su1svD2myhtmHIGruRH35kK+aCmadlvfi8AgT8/fjd6yH84aZpGx4FRdjzdjpJRWfSxOpqWFBMZ97J//Tqat76CpmnMOOciGvIWorRE0VIK/ozCYNhPhfc5HPd/l9XdPgatAv/p8fDAxo0Y855GlPwUBpr49/FK0u3tNNdORpYkxIyMajCS7U1hVFcwNDWHpwa8LDR18Svjg2xonMpJrYN7eoOYknDZjF9gy7Hw/Ron9QWLEEWJsXCSR+79MVe89jjNq1Zz5Y+/jvQOOzb9WXT/MMEXujDmWsi9qQGpYGJc+fDwOlpav4bVWsrUKb/A4XjPusoHStM0Qs+/wMi3v43ocJB719eIt9kJV+9gtPJxLJZipk37FWZrHfe9dIrH9/ThLLBxbdbLfKP/1yRNWVi0DNy4FioXva388fFxfvHrh1HTCbodfbTkneK2To0Fz8eRVJVnVt/Kbxadxye7WrnK/hLhwv0ooplUXwXdW1Umzb+Ul6M+duQ/z7TxehZ23ooTH1n9zZwuv4AmKUa1y4WWga7wETKNIufcdBOOHH1s/f+LMx7ybz4OaJqW8w7H3QncCVBeXj67r6/vH3I+un+MZFRm+5o2Og+NUVjtYuWtjaCFObDhWZpf3wLA/HlXUWWZgtIbB4OAzzxE86gbNXCa+ezAcv/3+FjrMD4J7rFn8aNdv8GU9zJaxsXyzilcdjrMqfx8wllZOOVxIoILAYG8WBGkGhiak8UT7SNcbD7KPcWPsL/cSo4xTkNzhvxwjJumfp/ls1bxLxVlf+nIO9jr53s/28A3Xn4Iecp0Zj/9OIL4zgGvqRqhjd1Edw9hnpRD7g31iBYjmqbS3f0jevt+SU7OQqZO+fnbRt58WCTb2xn8wr+R7ukh5+bbUZRzSFjbGJrzS1QhTkPDDyjIv4in9/fz9fXNOBwmLi94nW/1/xyvtZC8dBBuWAM1K95Wdjqd5me//A2RgI+AMc6+wh3M0ASu/12C/GCKw3PP4T9uuIMLOzN8MTDE/qYN5OXsA1Vi9KgTMXYOg0X1rDH8nrJwORe034adIEW9e+gsuxSHHGJxmYQxnYuspmiN7Me9spbZl1+JZNJ3oPpb6M01uv9f+prH2fp4C8mYzLzLqiiulTn00nO0792N2WhlwYyrKVIqUAMyolPCMb+IPa3r6TjRSHaom/nOw9ju+y5XH+nDZ4RPI/Bw6/eRXM2o4QY+c9CO0WJiuKgIqxbBGhrB76zCoEFubAaSmEffrCyeODHIBdl7uX7GGhSTxnjGQG13EbNGjvKNad/imvPvZLrzz0sQaDz2Ri8PvXCEn23/CQUmjboNL2B8l1mXajKDf00rybYAjkXFZF1cjWAQUJQEp059iTHvyxQXr2bypG8iih/uNmM1FmP4W98ivOFFbPPmY5r6SVKxEKPLHiaqnaSi4tPUVN/N4f4gdzx+iIyqcWPFXu7pfYhW5ySq4gOYr/s9TL7w7WWrKo8/uYaOzi40QeFQ/j5k1zh3/VFjakcMb34h997xBaaHsvnagMizeadJT/sTsw17UNIiwfZiDCW38ND4YzhSWVzV8ilMmkLt0Cbaci9BEwTmBLZSMPcyCJmIyH7aMoepv3YFkxct1Ufh/C/OVMj/EBh/S8erW9O0L79XGXrIfzikkxneWNfJyZ1D5BTZaFqs0r7nT/SfOIrHWc7cuotxRbIho2Eqd+JYWIx1iodXf/c7Oo/U4PafYl5xN65vfpsr93QyZNS4ITbGC2MPIJj8eHoX8YnuGAMV5YioFOWeJNwsESquxyRKOL1zyMlzcqQmyLqTIstLd3Jj/bN0J0T2aJO5MFTO6q4neG7a3ay6/D9wGCdmUibSCvc+d5wXjg7xUMdzNLbuo+KxR7HNecd7n0wwhe/3zWS8CbKvqMExf2JRLVkOcPTY7YTDx6irvZeysts+MiGjaRqhdesY+fZ9GPPycFz876R9ZgLnPofP+DJ5eRfS1PgQpwMKt/x+PyOhJF+ddIBbuh9id85sClM+ai69DxqveMeyX960idf3HMYuynS6+mjJPcrH9hu5cEcCEZGfXncL1uzpfGXQzBpJZu1sH19yPI9b3I+SEtHkJfx0dJRQJsnqk3dhUuw0jT1Du+0CElYP9W1PUjetFvKXIcQERhO9DDh7mHfLxymq+/A0k33YfBCja9YAywEPMAr8J/AC8AxQDvQxMYTS/17l6CF/5g13hdjy6ClC3ghlk/yER/fg6+2hJn82TYWLMUdMCJKIdXoejnOKMZVMbDq99bdraDlUQJ73CDMbfOR+7Vtcs6OFboPK5f+XvbOOjupq1/hv3CfubpAQIrg7LdIWKNSFlhr1AlXqLlRvqVABihUt0kJxp0AIHich7jPRyUhGzrl/pKXtRQrf137Sy7PWWVkrs+ecffaZeebdrzxvQw47rR8hCnJGHe5NqFZBm9FAqKscY1g2FUfisYbHo5Hr8GoJJ7LHCZY3Gthd0Z3RYTt4wGcdz3sUSENvJLFJyzu5b1CUdAtx1396xgVT1mBl6qIjFNRZeNu3ntS5s/B/+GECHn7onPfpqrNinpeN4PDgd3sS6vgOT6KjvZbjx+/Ebi8jOfkjAgNG/WsW/k+G/eRJKh96GE9bG17XT8fdGkVb331UG+dhNKaRlvoFFqeBuxYcJquymY+SjjKu+D02+w3AIVUyftCtkHr9Oc+dkZHBtxt24SO106C0cjBoFylmgcnfQVCrjS19BlHRYzzTqo0slLTzSRc1g2RlTFZ+idq3EsGl5lBbOBsaLVyX/SCa9gBSWhdR5hlAs09noiu3EFu5Be+bn8ZlCUHiguK2k7QneOh72y0Y/QP+xav5n4/LxVCX8YfwuAQOrS/h6KZspNJcPO1ZKJ1ykoL7E6XtgtQpQe6nRtc3BF2PIKTaX10X+xZu4MR+DYH1h0npbiHimZeYuCuPXImbkVU7yRQWYGwKZuKpTrQF+aG12+nsd4BWl5OqkgQcYdGEBDQT5FOGNjCfebm3cLCmF3cZ1nOT4kfu8DcQnvgK7fU2Vp2cjju8D9o71oKsYw4/ZtXw9HcnkUokzB4VQfC0u1BERBD97RIkirNdLO2lLZi/yUWikOA/pSvK0I4fKputhGPH78DlaiEt9Ut8fPr86evs8rhocDTQ5mzDI3b0itUpdHipvNAr9H/qjsFVV0/lww/jyMrCOO5OBEk/HCl5VIZ/jFIZQHraXCSKKB759hjb8+uZm3yEEaffZ73/EI4aEnm2awryHref89y5ubnMWb4BA3YEqZuMoH3I5I3cs1xD18oWisKjyRo1hTvNwXyNg6/D5UgUEp5wbiPaZx36UBt2Qce6Bgkxhx7GyxpHF9cSGhviqA3uS5hQSsLeD1HHxKG7ZhquMiluwUWBJRPDoDB6TpiISntZt/4XXCb5y7ggzJUWNnyykcbKg0g9ZUToOpMU3A+D2xukoE70Q983BFW8NxLp70koY+leDu92EVh3mM69m0h66g0m78tnt9vOwPIV5El+pPvpVDq5o3Ar5ETVVxOSvp+ygkAsrgB8ergJCT6NUmVFEAJYXDyN3cUGHlesZIRuM/eHxaNNeI+GpkZ2HH8Anc4X6T3bQOuLw+XhtfW5LMkoJz3Cm49vSkfy3BNYDxwgZs1qVLGxZ92rPcdMw9IC5N4q/O/qity3I8+91ZLN8eNTAEhPn4/R8M+n8pntZjJrMzlhOsGpplOcbj5No+P8m1mtXEuUMYo47zhSA1LpFtiNBO8EZP+EsJfgcFDz4ou0fv8DuoFXIAmYiCuqjsou7yPiIjVlDnpjL6YtO86GrBoWJGUypORDfvAfwqKQa/gsXI5/nynnPHdJSQmfLvoOwePCIGmnwL+AUn0eN280MCjfgkOh4vjV9zDOkcjnOFikF/CEakkrKuRacSUh3SvQ+NmpcUqpz74KVdE1xPMDQjkUR44jwOAgJftLKCnAOP5mpNGj8ZS1Y3O3UmA/TNjYdFKvHIv8HD/k/99wmeQv45xorq9jx/w1lB3/CX+lgRivNCL1iUgFKfIADbqewWi7ByIznLvkP3PlEQ5tbyGoLpOQnlX0f/Z/mHaoiOVWCz3LvqJWyGBM/gBkOj+8mpqIri1GPew0tVU+GBPa8QprRiIRsdUnE5NwB3Pyo1mfVcuziiX0127jqcSrqQl8DLvdxv6cR/Gx1yG5Zzv4J1BU38bD3x4lv9bC1MGxPDGqM9a1a6h57nmCZj6D7x13nDXftoM1NK8rQhluwO/OZGQ/V+g2NWVw4uR9KORGunVbiFYb8w+vaUVrBZtKN7G5dDMFTQUAaOQaErwTiPeJJ0QXgr/GH4PSgEzSQd5trjZa2luobqumtLWUwqZCTHYTAH5qP4ZGDGVE5Aj6hvRFIbt0QhNFkYY5czD9z8douvdBHj0Zj5+V6j4fY3dV0CXpHfwDruGpVSdZfayKeZ0PMbzsI9YHDOHVmPv5ylhH2sBzE31NTQ2ffbOMWjuESVswGRo45PsTIzO1XHFCQmBTIwUjbqK3dgifyhwslbiRxhnRnK7hxrrNhAeeImxoKwq5hYbmQNqO3klwczXqwiLyo+5Ar5MwMLSI9gWfIdPr8XvweewmPzC5aHGaKRJO0Om6YST2H3Te7Kn/D7hM8pdxBi6Hg6LDBzm+eTOOsgaidElEGpJRSzVIlFI0qQHoegWjjDRc0HWQ+V0Wh7aaCKo9hLZ7PmNf/Ia3s8v5qL6eruVz0DeV08PUE1EmI7SikkBJBYpRlaARURrcOJ1qTNVJSGtuYsxdY3h19ynWHa/macVSBmk282KvmRxRjsBXLmNHyev4nN4Ct30HccNYdaSSF9Zmo1HKeP+GNIZ1DsRtMnF67FWoExOJXPDN777woihi2VlB65Yy1J198L01Camyg2DN5p1kZT+IRhNFetp81OpL72jk8rjYVLqJZfnLOGk+CUB6QDpDI4bSJ6QPib6JZzpRXQxEUaTaWs3RuqPsrtzN3sq92Nw2fNW+TIifwHUJ1xFhjLjkeTavWkXNSy+jiu+MMvV+BBXUD/+aFsdhEuKfJTz8Lp5bm83SQ+XM73SQYeUfsylgMA91eppPFEWMGXrnOc/b2NjIF98s5nijnM6yetrV7ewJ2EViBYw8Gkz3wnzq0ocQF3kjnysdfOsWkIXpUNva6Vu4nS7WHIJGyNFH5WGQeWirTkGal4j2eA6ng+9FolIxclIwkq/exn78OLr+/fG+/Ula9puRWERMjkoq1IWk3TqOqJT0S16XvwMuk/z/czgddoqPZnLqwD6acsoJUcYSqU9GLzciSkCT5Is2PRB1ou8Z8rsQDq3OI3NLDUG1GdhSM7j9lTUsrjDxZFEZSaWfkFIlw9cTiXdjI176KkI6lyIPb0YiAZvZh3JTKk01CXT2HsbY+7rzwsZcVh+rYrpiOSM1W5k5aC6H3KEM9NbzTcta9Lteg1Fv0pR6Ly+sy2b9yRr6xvryPzd1I8jY4W6pmjEDy9ZtxHy/DlXMr5a4KIq0burQoNF2C8TnugQkPxdEmUxbycp+BL2+M93Sv0GhOKuM44Joc7axJG8JywqWYbabifGK4dr4axkdPZoQ/Z/X/s7pcXKg+gCrC1ezu3I3HtHD4PDB3JtyL+mBl0Zqlp07qZo+A7l/ANpBM3C7VTSM+pZG5w4iI+8lLvZJnluby9JD5SztcpB+xR+zKWg4Uzo9zwvk88Cwm89pMVssFuYv+pYtVTK6ymtQyNxk+B9A42yib3YPJuzbjSU2hZCke/hSa2GRSw16OWlxfrj27WJow17wV+EaVU03lQmlzIOzIhHJbgu1nvtxaPwZdlsnAsv3YfrgQ0SPB/+HH0bVZRRNm4uRtkuoshbSEGii5+3XExh9tqvu74zLJP//ELbWFkpPHKXo4AHsBWaClNGENuPr6wAAIABJREFU6TuhkeoQRBGLSkbwiCh8+gQjVV+8lXnwu3yObK0mqPYgVcnbeOi1LWQ027nxWD4pRXPoWeWPRimQKD+EMrkJjc6K4JJgzvVBcHaj2BEJLgU9Y69g5G1pPLsum5VHKnlAsYqxhv083H8hBS4Vj0YG8jQFyBZPhC4T2JnyNk9/l0WTzcljIxJ4YGg8sp/jA2179lBx31T8H3mYgId+zaYRBZHmH05jPVCDrk8w3uPjz8QU6us3kZ3zGAZDV9LT5qNQGC96DexuO0vzlzIvex4t7S0MCBvA7Um30y+0H9J/oEHIpaDOWsd3hd+xNH8pze3N9AnuwwPpD9AjqMdFn8N27BgV901FZjSiu+Jp3C1Kmkf/QL1nDcHBE+jU6S2eWJXDuuPV/NBlJynFX7ElZBSTE2Zyq1DC28MmnLNy2OFwsHjpMlYVeoiXN+AvtZHnnUeLqphOJVdw7/rv8fiF4t3tQRb5NDNPCETwQL9eIYi5hSTmrEYjsVJ4tYYESTU9/MxIJB6EfB3mw5NpUqfTc1gg3Qb7Uff6G7Rt346qSxLBL76K06yjdWc5EjeUWfNoj/PQ89br8AoM+jOX/z8Wl0n+/wFEQaC2uJCSY0eoOZaLtB5CNNEEa2ORSxQIUqh3i9S4RKJGRZFyZdRZQdQLnl8UyfgunyPbagiuOUBW8g88+cp26l1yrvrpJN1zFtELO+EBeXgF1SCRgrvZSHOxjKojgUT17U1ekwrBIzK853j6XZ3EU6uP8d2RWqYo1jDCr4AHun+MFRmzkyK5StEKXwxB0AXyStDHLDhipnOQgQ9uTCM59NeqU8Fmo/jqa5Co1cSsXYNU2RE/ED0iTasLsR2pQz84DK8xMWfcT3V168nJnYHRmEZ62jzkcsNFr8GGkg18cPgDTHYTA8MG8ki3R+ji1+USntSfA5vLxspTK/km5xvMdjMjIkcwvcd0ooznV9b8LexZ2ZTffTdSvR7j+Odw1shpG7Gbatk3+PkNITHpYx5emsf2/Dq2Jf5IXMkS9kZcxfWxTzHEU828IVegO0fA0+Vyseq71SzKasNHYidBbqZOU0uJbxY+DeN4fNlyVDIV+j4PsSqgmbmqWBytIhFx3twUqad4+RxCrOVk9FfiVrZzi8IPn+iDSDwitmPpVFZOITbOyIiH+2Pbsf1MQxW/u+/G9+6ptOyuxHqwFokA5bZ8PMlyut8wAb2v3zlW4e+DyyT/N4QoijTXVlORm031yVwchY34EEiQJhq94mc1CZ0UdaI/hfV2jmc14h9lYOSULvgEX1rqmSiKHFyRy9GddQTX/MSe5O+Y+exGtOpA7tj2IyPrviY6oAS12orLpiDLnE6MyUFTeQut9VqSRw/nSKmAW3Rw1fDr6DEkkUdX7OGHY23cqPyelNA2nk18gmCVigUpMSSpJDBvFG7zaSbL3uFAizf3DY5lxhWdUMl/706qm/UujfPmEbV40ZmiJ9Et0Li8AHuWuUMDfkTkGYKvqV1Lbu6TeHv3JC31a+Tyi1uLoqYi3sh4g8N1h+nq15Unej1xSdbzXwW7287CnIXMzZ6LS3BxU+ebeCD9AYzKP96Z2HNyKL/rbqRaLd63vkJ7Edj6H6VC/wlGYyqJSV9w35IiMkvN7EtcTXDxdxyJGc+4iEdJ9TSyePAQ/FRnyw4IgsC6dd8z97AJJ3L6Kcqwy61kBx3D45rAzEXLCLC0oe1/Pz9GOVmojqTWpEBtUPLRpBQyvl2IvmA3GUke6oLc3Fh+PeGJW9BHHEewKTEVXIuyoQ9XPzcWhcdO3Tvv0PLdalQJ8YS89TbK6AQaNhfiOGxCIkgot+UjSdeSfv04tMb/TFmKfxaXSf5vAFEUaaqpoiI7i/rsQhwlzRg83virw/FWdhSHiHIRZawXusQAVPHemFra2b4gD0uDgx5joul5VTSyCwh0ne+6B5bncmxXHcE1+1ifsoLHHvwCf+rZlfU1wfICJBIRoUTNgdO92KkYyl3iRqz1DdhaVKRcNZrMfDdOWSvjx1xHWp8kpixdye6Teq7U7CQgRs3ciOsY6K3ny67R+CrkONc8jPLEIu52Pk6h9yDevyGNXtFnyxI48vMpmXQd3hOvJeS11zrm6/LQsCQfR34jXmNjMAwOPzO+umYVeXnP4OPTl7TUL5HJ/rg7kdPj5LPjn7EgZwFahZZpPaYxMX7iP5XW+FfAbDfzybFPWFO0Bl+1L0/3fppRUaP+MO/ekZtL+ZS7kGi1+D84C+sRO+3pBZQHvY9aE05C4lxu+6aUmqY29sYtwlj8I9mdbuCqoHuIFK0s69eXMN3Z6ygIAuvXb2BuRg3VghejdIVIPO3kB2ZTrh3PCwu/JbamGu3A+9iVqmOlU0F2cwASt8iMsZ2JbzpN1tI55IVbyI9zcG3+QwSoW4lNn4MnoAWX1ZfW3FFccefT+AQZaNuzh5rnX8Dd0ID/1Pvwv/9+BCeYNxbQfrQBBAmV9lPIuxtIvf5q1Dr9X/Uo/i24TPL/hRBFkcaqCiqysmnMKsFV2YZR9MVfFY5G3vEBFWQi8jAN+sQgVPHeKMMMP+uuCBzeUMqRjaUY/NSMnJJMSNylWzCiKLJ/aTbH95gIrt3N/iEruKJvMqr2AgTBhsOho7UkiLD1DcyKmEJlYAj3swFPbTVOu4zkK8dwJE9Cu9rM1aPHE50WwW1Lvyb/VAq9vPIQO3mzz7cn94b781JcGDIJnNg4l/RDj/OZexyNfWcy/YpO6FRnxwxEUaT89sm0FxURt2kjMm9vhHYPDQtyaC9pwXt8PPq+vwY/q6qWkV/wHL6+g0hN+RyZTPOH91/QWMDMfTMpbCpkQvwEZvSYgY/60oKz/2rkNuTyyoFXyG3IZVDYIJ7v+zyh+tALvseek0P55DuQhwTjP/09LNtMOBPKKY97F7lMS3DsV9z6TQ1q3GwJ/QJV6U4Kkm/nau8bMUoElvXuRoLxbJeXKIps3LiRL/dXku8OZILPKdR2GyV+JRwLGs1L874lqew02oFTOHFFPGvLq9gldEZsdHJlt1Be7OXNirde5rS2ioNd27jm1H0EWKJJjZiDLTYPeZCN9uYQ4qIfo1O36xBaW6l78y1a1q1D1bkzoW+/hTopCY/FienHPNqPNyETZdS2lyJLN9DlhitR/U3aEF4m+f8CCIKH+tPF1B0poK2wHrHBhZfEFy9lANKf86k9agFllLGD1KO9UARpz/KrN9fZ2Dovh/oyC4n9Qxh0fQJKzcUHVn+BKIrsW3KSU7mHCfFehyslF71KRCrVUVsfRU1VGOH7agkuL2Pm4McwqXy5U9iCoa4Yj0ckYdCVZBcYsesrGDn8CqTxEh5dtxhz2VUk+zdjSvKmVhPMO4nR3BzqR02LndmrtjCzfCoV8mjcd6wnNdL/vPNr3biRqukzCH75ZXxuuhHB4cY8PwdneSs+13c6owMPUFm5mIJTL+HnN5SUrp/9YZs+j+Bhfs58Pj3+Kd4qb17p/wqDwwdf8hr+u+AW3CzNX8rsY7MBeLLXk1yXcN0FrXprxiEq7r0XVVIigU++R9PactzBdVSkzkLAiSpoDnctaSTJX8YK3fvIqg5T3O0+xilHIsjUrOrZhS7eZyuJi6LI5s2b+XJfGcfdoUwKKkbX0kS9wcTOmOG8NH8JaYV5aAffRu2UESzbvZcfDN1xVzjoFOHF/EmJbP/kHbIajrG9RwsjT08mqimZJM0mrOpsFH3MKIwNyDydSO/1Gt7ePbHs2EHNSy/haWrG/4H78b/vPiQKBYLNRd3GHByHG1CISppcddBVTacbh6PS/neT/WWS/w+Ey9FO3ZECmrLLcFa1obQqMMr9kEk6CNktdSP6SNDG+mHoFIwq0ojM6/zkJIoiufuq2beyEJlcytBbE4nvEXjJ8xJFgZaWYxzbs4h2yU8odI0IAtQJRmJ0d7JzZwNOp8jI7Tuxye08OfJZLFYVEzlEdO1RRImbyF4DKT4Vj8U7n/Ru6ZSElfDlwQPYK28nNlBGSbI3BqmH+T1SSDEaWbC/lM+257KIF4hXmJE9uA+F7/kDiILdzumxVyHz8iLmu1WILhHzvGyclRZ8b0pEm/qrtkl5xXwKC1/H338kKV0/Riq9MMGb7Wae2vMUmbWZXBl1JS/0fQFv9QXbIPzHoqathhf3v8jBmoMMChvEK/1fIUB7ft0Xy7ZtVD76GLoBAwh85i0aFp/CpTNR1ed9XEIjLdrZTFtt5cauBt5qeRpJcwWVvR/mGnd32uU6VvZIIvkcOvCiKLJt2zY+213MMXcY10fXoK2tpE1lZWPngcxcvJyeucfRDr0e4ak7mb96LSt9U7EVteOrV7Hkju6Uf7+IncfXs7VXA33LriPZ1JdY1QGE5noccU70PTKQa1rw9xtOXNwTqF2B1L3xJq3r16NOSSHs3Vkof+4yJro8VG/Kwr6/DrWoxeax4IwUiL1hANqg/+yd2vlwmeT/zRA9Is5aC6Zjp7EU1IDZg1bQnyF0l+jEqW1HHqrHJzkcY2IoMh/VReuY2C1OdizKp/SkmfBEH0bc0QW9z8XrcHs8NpqaDmJu2I3JtBWnsw7BI0coCyLDVUu2VwjXyqZSkHcKqd3KuI1bKIhU8PyoV3BVQT9O0a92B0ichKR3o/p0P1r8TxIYGsDe4L0crWihvWIqgb4aylK9SXGV803/AeTWwBs/5lFitvJFwCpGWVbDjUsg6eoLztf0yaeYP/mEqEULUad0+5ng2/C9ORFtyq/Wf0XFN5wqfI2AgFF0Tf7ovM26f0FmbSZP7n4Sq8vKc32fY3zc+P8a9cnzQRAFluYv5cMjH6KWq3mh7wuMij6/6FrTihXUvvgSPrfcgt/9j2Oem41TMFM9+CPs7goOtX3AnJ8E3hrpx80n7wLBTUWfGYy3xuKQ61jRvTNdfc/egYmiyJYtW5i9p4JsTwg3JTcjL8lDkIr8mNSHGctX0+tkBrpR16N+7mHmL13Gcp9ONJwWUQrw6U3p+JYc5LvvP2dLLxMptVfSq2oUoYpsFDYL1jYRoV8zfkkbkClchARfS0zMNFy7s6l56SVEp5Pg557Fa9KkM89UFESqt52kZU8ZRrcPHtGDzcdG6NVpGJND/6ue/WWS/xdCFETcJhvOSguWU3XYSxqRtkqQ0eFycQnttElbwE+GLi6AgO4J6KMC/uEPVFlOA9sX5OG0uel3bRypw8IvKjXSZivB3LCLhobdNDdnIAhOpFINYlsKVSdTMR63sL3Ld+SFBzGm+SosFhvGulpG7drDzu46vh7xKk2FAjGYGF+zFqnMQWBqPM3lE2gNPI5H7mZT4CZsbj3WsgeRqVSYevgwtvUAT3VO541MBXsLzcQF6PiwWw2pe+6H3lNh7KwLzttVXc3psVehHzaU0DffxTQvG1dVG363JKLp+huCr1zEqVMvExBwJV2TP76gFrwoiszNnsvsY7OJNETywdAPSPBJuPiH8F+A4pZintv7HNkN2Vwbfy3P9H4GreLcLopfMpaCnn8er2uuwzQvm/ZmE7XDP6PVnc/i4vfYVyJn1SRvum29CfRBlPeZxrVmf2wKPSu6JZLid3bKoiiKrF+/gU8OmijwBHJrLwvOvCNoPBp2xafyyJof6Jp9CO3VE/F+4SkWfbuUZbpQyivVSFtdvHRNF4ZozHzz9Sts7F5FTENfhpRMxE9ejtRhQ2GupjayHz5J3xLQ+TgSiYTw8FsJ11yH6fl3sB08iOGKkQS/+ipyn99b7HXHT1H9/XG82nxRSlW0K9sx9ArFd1Ascu9/Tx/fS8Flkv+LIAoibrMdZ1UbrkoLjvIWXNVWJB3igrgEJ03OOqzSVhRhevxSowjrm4bWcPGFN+eDxy1wcF0xx7eW4xem44q7kvELO3/GgN1eRXNzBk3NGTQ1ZeBwVACg1cbh5zcEP98hZK1TknOojYjaPZxMXcLBoAR6m3qhcLQTXlREz+wc1gxQcbLX8xyq0aBpdzC5dhkaaSuB6cG0199NW0gOpsY6tgZvJcS/M5X5N2F2CFh7+/OQeTnJbm+eON0NnVLGtJGduL2rCsUXA8ArHO7Z1tFc+gKonD6dtp27iFn9Pc0/mnHVWPG7JQlN8q+k8osPvsNFM/uCFrzdbeeFn15gc+lmxkSP4aX+L6FT/D3VDV2Ci8+Pf87XWV8T6xXLe0PeI94n/qxxosdD5SOP0rZrFxFzPkfbqz/mhTk4yuupG/kVda5s3j32Fhanlq0TZfitvhFCu1Pe/T6urdVjVRj4rkfiOV03giCwes1aPjtioVjw5+5hrdQe20eAI4DMyAQe+n4rMfkZqCZcTchLr7Js+XKWCRpym/2R1Tu4d3AsUzrJmD/7Wb5PPk2ItSujTt2CVtKCQrDiU5dDScBoJNrTBF3zI16SfGQyLZERd6PfpaDhw8+R+/gQ+vZb6Pr3P2t+DaUVFK3cjbJKSoC6QzpCDJLhMzAWTRe/M3pH/2m4TPJ/AkSPgKvejqu6DVd1G85qK67qjl6mAB7cNDlqaWyvxUIT6igfgrp1JiotHe+gkD9169disrPl62zqyyx0HRLGgEnxyH8jRyAIbqy2IiytWTQ3Z9LUnIHDUQmAXO6Nj09vfHz64+83BI0mElEU2T33KDmHW4is20t9ylJ2e/Um1hJLQH09CYWFRFRUsmCEDFXsIyyWRCPUu7je9D3hlBHcw4jU8iTWiHxKThVzIPAAw3uMZkdGD07VteHq6cesuo/Q1VqZ3n4ft/aJZvoVnfDVKmDJ9VC6D+7fC/4Xtp5tmZmU3T4Zv6kPICoGdRD8rUlouvyG4Ku+paDgBfz9R5DS9ZMLEnydtY5Hdz5KXkMe03tM587kO/+p5+RyejBXtGGusGBpcNDW5KDd7sbjFhAFUKhlKNVy9N4qjAEafIK0BEQZUF5CxfGfgf3V+5m5dyY2l42ZfWZybfy1Z923YLVSetvtuCoriVm1EkVoBI0rCrBm12Aa/g35jkJey3iW1Ah/lg6oRbZqCiRdTVmniUyoM+CWq1nXO5XYc+Slezwelq9cxScnPdSJXkwd20Rexnai2qLID4rgwR/3EVB4EMWkUUS/8h6rVq1iqcXNEWc48gorV6WG8NLgIBZ+/DQr47MJcsVzVc7tyEQRpWglqCGbUmN/PFKB6gErGZemwNK0B7ncm1D11fBmBu78MnynTCFg+rQzBXS/haXBTPYPm2k7Uku4Mh6DwhdRIqKMMqLtGoCmi98ZBdP/BFwm+UuE4HDjqrH+SuY1bbjqbODpWCtRKmKVtFLXWoLZVkWz24Quyp+o1HSiUrsRHJeAVPbX5FEXHq5j1+J8JFIJw25PJCbN92dCz6bVkoXFkk1bWx6C0A6AQuGDt3dvfLz74OPTF50uAYnk9+Jdu746Qu7RVqLq9iCmbWGbshc+Th8Si4uJKy9HW1fHl6OlBIbdzDf+g3Dlt9HPcph+9v2EDZCicb9OVdhRqg5XUepXyt0T7uF/Nss5XNSAtJsvc2peJaKxklnhs3lmXA86B/+cbpc5FzbMgLHvQe97L3jfoiBQev0NuBsaMIx7C3eD+yyC/yVN0s9vGKkpn14wyJplyuKxnY9hdVl5Z/A7DI0YesnPQhRFGqraKDlhpjSrAVNZK798nWRyKTofFWqtHJlCikQiwdXuod3mwtrsxOMWAJBIwDdUR3iiLzGp/oTEeyG9xFqGfwRmu5ln9j5DRk0GY2PG8mK/F8/awTgrqyidNAl5cDDRy5YiUalp/uE0bQcrMQ9YyvrmFuZm385jI+KZrtsKW56DQU9wyqcrE0w+aKTwQ78ehOrPTq90u93MX7yUzwrUtMv1TB5dwrHM3SQ1J1HtE8C9W4+hL/oJ6a2jiH/2fVavXs2yRhsH5bEoClvpHevLJ9d2YulnM1kUmkGQEMHErMm43AZU0lZ8moppVMbiUHlxNHo5N183EH/7QcwNO5DLjPgUxyL7OAdNdBfCPnj/d3pHv4XL2U7+3t0UbtqLrlVPuL4TXooOt6DcX4MqzgtVrDeqOC9k+gvHfP5KXCb5c0D0CHhanLjNdlwmG26THXe9DZfJhmBxnRkn0chwap00tddSUZtHXXMJba4mvEPDiE7tRlRqOhFdUlD+xfm2LqeHvSvyKc46QlCnOqJ7NuNw5f+O0GUyPQZDMkZDVww/H1pt9O9I/XdrIIjs/OoweccsRNXvRZ5+nL1iElJRZGSzDZ9jx5CYzMy+RoI0cigF3rdRnmUmuL2O6xpXEz28DaPqXTbrvkN1VIXL6GLGfTN4ckMlu4/WIOlk5J3a97nGuZ8jo9cyoE+/Xy3GhtMwZyBE9oXbVnew3QXQsmED1Y8/gW7kA0i9uuN3WxKapF8Jvrp6BXn5M/HzG0pqymcXJPitZVuZuXcm/hp/Zg+ffcn+d6fdTUFGLbk/VWOuaAMJBEUbiUjyJTDaSECEAZ238ry7AlEQaWtup7HGSl1JK7Wnm6kqbEZwi6i0cuK6B9JlYCiBURdWAv1n4RE8zM2ey6fHPyXSEMlHwz4izjvud2Pa9u6l4r6peI0fT8hbb/78vyqaN56moft3zKr25mBtbxbf1Yv+ea/D0YUw8StOtsuY1BxEsOhgzcB++J+jwUd7ezsff7WQb6r80Wi1DB+0j7ys43Rr6IFF58VtO06gLclAvGcUnae/z7p161hZ18QeQyKKnGa6hnox/9Y0Vs1/ka+9dhBIELeevIVWZxhyiQVNay0SUUGLMY5C7+/xH+fH/Uljqar4ErN5GzI06LZL0O2SE/bUy3iNP7vl4S8QRZGqvByyd22lMjOLIHkk4T6J+MlDkAod3y+5vwZFqA5lmB5FqB5FiA6pTvEvCeD+7UleaHfjaXEiugXwiIgeAdEtINjdCLZfDheeViee5nY8zQ48rU74za1L1DLkAVpEowSrp5n6ljJOlxyhruY0ADpvHyJT0onsmkZk17S/vAWZILiwWouwWLIx1R6juvwIcl0ZUlnHD9ClEvr/hSiI7Pgik/wTbUTW78GeXEmuLByLvJE7w7vA14sRmpp4f6Kc6ph4erumsa6qAY/Dwy11y0gdWYbK60U+cXxO0qkkvBReTHtoGjP2lrF1dxmeIDX3tW3gBc9cXGM/RNH7rl8v7nHDvFHQUAQPHgDjhYt1BKeT4jFjERwyNEOew39yVzSJv/p7q6tXkZf/DH6+g0hJmXPBPPil+Ut5K+MtUgNS+Xj4x/iqz93g+1xwWF2c2FFB1s5K2m1uAiINJPUPIa57IFrjP2fFOR1uKvIaKT5uovioCbdLwC9cT+qwcDr3CUYm/+us+8zaTJ7Y/QQOt4PXBrzGldFX/u510+xPMH/6KcGvvILPjTcAYM9toGFZHlWxG3i0PAGX6MWWx4bhv+Z2qMyEKT9yoKqUmy0RdBKaWDVkKEbV2e4Ni8XC258vZGVTBDGBOiK6LKOipJye9f0R5Wqu3XsCXckxhIdH0+XB91m/fj0ry2vZGZCC4kQT8QE6Fk/pyZolr/KlaiNBEn/uyp5Ivb0LHokdtbUJH0cd9b7dMCv3c2LoEd4ZNotguZOS0k8wmbYgccrQ7hEJUYwl4qk3keouHJNpt9koOLCXnF3bqDmVj48qmOigVMJ8O6H1GKDNc2asRCVD7q9B7qtG5qVCqpMj1SmQ6RRI1HIkcunPh6Tj///gbuBvT/K2EyYal+ZfcIxEIUVqUCL3ViH7+RDUIhZnI+bWCqpK8qguzKOtqaNzj0KtIaJLV6JS0olMSccvPPIv+0X+LaG3WrLPcrl4XGpclmhCwrsTGt3rkgn9/0IURLZ/foiCLCuhjbupiW2mXu1DiaGIGb0mIXvqHTyWNl67Wc/pSA3PVj3Nu2on9XUyxpo2ceXQvbR43c5HTYsZZB6En9WP2yffyUMnG8k9UI2ok/NolIXHS+5H0nk03LDo95b67lmw8w24bj50nfiH8zV/PR/Te7PQDJxG0JPXo+n8KzHX1HxHbt7T+PoOJDXli/MSvCiKzD42m6+yvmJoxFBmDZ6FRv7HVa8AHo9A1s5KMjeU4rS7iU0PoPvoKIKi//kA+rnQbndTmFlH9p4qGirb0Puo6HZlFF0GhPwu9vJnos5ax4zdMzhpOsmU5Ck82v3RMxr4osdDxdT7sR06RPSqlag7dQLAWd1Gw4IcMvQ7eLy6C/0i6lg4eRzSuaPAaYP7drIt9wB32mLo56llyfDRKOVnxx/q6+t56YuVbLRGMSrZSJ3hAywNdnrUDkYtwJh9JzCWZyPMGEPy3e/y448/sqqkkm0h6SiONRLprWHJ3b1Yv+pNvpCtJwQvHsq5klLbANwyFwq7nZCWE1QHDMIt5rO0zyKm9X+MGzvfSFtbPmWlc6ir3wCCiK7AQPyQN/BPueqi1q3VbKIo8wCFh/ZTlZeLKAoYDP4kxPcmxD8Bo8oPhVOBp9GBx+JEdArnPZd+SDjeY/6xhjV/e5J3NztwlrV26ITLpUhkEpBJEKQCTtGOw22nrdlMS30dLfW1NNdWYyorOUPoAMaAIEI7JRLWuQuhnZPwj4xC+hfok4iigM1WTGvrSVotJ2ltzfo/LhcdBkMyel1XqrL9OH3QSGBYZ664K+Wfthahg+C3fZbBqWwbPpYdlEW5cShlHAo4yuNJkwh+ag5uu4Pn74ykNLCOlyufZF6oP/n5duJtp3mg5zwKDN1ZYjnCVdKrUJ9WE9tzIG+06XHkNiGTSFh0UxcGbLu244v+wE+g/Y21XH0cvh4BydfCpK//cL4uUxOnr7gSqTGSiDlf/s4HX1Ozhty8J/H16U9q6pfIZOcOhLkFN68eeJU1RWuYlDCJ5/s+f9FNPKoLm9m1JJ+mWhuRyb70nxh/wSymPxOiKFKe28iRH0upOd2C3ldFv2vjSOgZ9JcYHE6Pk1mZs1hesJw+wX2YNWTWmZ2Ou6GB4nHjkfv5Eb1yBdKfhck8rU7MC3OYbclgcUsET/Q/xIMfg5NWAAAgAElEQVR9JiGddxX4RMFdW1ixfzWPil253lPKx8PHnVOPvrS0lMfnbuGIK4wHR/qw3vwMKpc/qZW9Mba7GHbgCL4VpxCfHU/yLW+wdu1a1lWZ2B6WjvJYI0FaJUvv7cPmDbP4TFhDnMebx/J7kt12DahAYnPhb9qDOXAoMk8NC3rOoV9SP17u/zIGpQG7vYrizLeos25CVIkY2uOI6fk0/v5DkUgujgdsLc2UnjhKefZJyrNPYGno6O4lk8vxj4whMDoG74BQvIyBGPS+aFRGFHJVB894ROT+mjP9hi8Vf3uSLzqcwdYvZyMKAqIoIooC7vZ2PG73WWOVGg1eQSEEREQREBWDf1QMgVExaL3+/KpGURRxOKp+JvOOw2LJweNpA0Am02LQJ2MwpmA0pJyx0NuanGz6Iov6MgvdR0fRZ1ws0kuQBT7vfASRrZ8c5FSuDaVnB9WhcpSyZn4IOcRUwyB6vbMVp8vFs3f0pjIwkwmtd2LV9WVXbikOFMzs9CH5vko2O6w8EvcIZVvLaNSHsDwiBUVhKwqbmw0PDSDxyMtweD7c8T3E/EYOwOOCL4eB1QQPHQTNhasLBaeH8rufw565jpBZc/Ee92vKW23t9+TkzvhZbOyr82rROD1Ontj9BDsrdnJ/2v08mPbgRRGk2+kh4/tijm+vwOinZtANnYhK8fu3FMiIokhVQRM/fVeEuaKNoBgjg27s9JftJNYWreW1A6/hq/Hlw6Ef0tW/o9/tL7r9vndMJmjmzDPjBacH04oCbivIphqYPWYHQ/yuQbb0Vki7CcZ/xvvbFvOuIpXHJcU8OfTcu7fDhw/zxOo8ygRfZk6S82neU/iq+5FWEo+vzUb/jEwCK4qRvH4DXcY9z6pVq1hvamFHRBrqI40EaJSsnNqX1Zve4mvnOro7vXi0MIaDlikodTKcbW505p9w+PZCJrSxpusXEKXk/SHvk+SXBICjvpT8xVNpii5C8AaVMpiwsJsIDb0BleritelFUaS5roba04XUl5ymvqSI+rJSHJbWs8ZKpFJUWh09xo6n76SbLuFJ/eYcf3eSrysu4uT2TUgkUiRSKRKpBLlCiVpvQK03oDEYMPgF4BUYhFr/1wWz2p1mLD+T+S9WusvVsVuQSJQY9IkYjKkYjSkYDanodHFnWQkV+Y1s+ToHj1tg5J1diE3/c3z/giCy7eMDFJxqxaXaR6uXnFB5CV+EHuc6WzzjvizEgcgbk4ZRGL2TMGEwdzbeyreVBzipSuQOvyVI409yRBLLyz3f4IfFm6kTpKzrOhBpSRvyGjtfTu7JFeo8WDge+j0Mo974/SR2vws7X4ebvoXEC2+HRZdA3Se7afrqMXT9hxL59cdnXqs3bSY7+xG8vHqSnjb3vATvcDuYtmsaP1X9xMzeM7kl6ZaLWqvmOhsbv8iisdpK18Fh9JsY9y9PczwXBEGk4GANB9cWY7c4SR0RQZ9xsSj+AhdObkMu03dOx2Q38ULfF7g24VoAal9/g6bFi4n46kv0gwadGS8KIifWnuKGQ6dI8s/hxaE5dGvthHTPe3D1h4jd7mDa1hUsVyXxobqcm/uNO+d11/ywgVf323DKddxzTSXz8majMd5I/9M6fCyt9D6UQVBVOeqPJ9Np4AyWLVvGphY7O8PTUB9pIFinYsXUfizc8iLf2jcxzObDw6UGtrdOQ6lT42hzIWvOQqqNQpRKORq3gMPhpTzd+2mu73Q9EokEURBomD+X8u0fYB8uxRHrQCKR4e8/gpDgSfj5Df7D6unzod1mo9VUR0t9HdbmRtptNpx2G+02K5Fd00jofXbu/sXgb0/y/w643VYslhxaLSd+ttJP4HBU/fyqFL0u4WdCT8Vo6Ipen3jBD4YoihzbUs7BtafxDtYxZmrXS9Z9Px8EQWTzOzvIr27FZjiGIId+/qW8rD/CgGofJi9txiqXsvCK/uxMPYZcGsBXhTNYZP2J9bJ00pVZjOyzgFqvSfQy3sv6dZtQqWxs6D4UatsRcpqYPrITjw0Khs/6g1wJ9+8DxW/Itz4P5gyCLuPgunkXnK/oFmhYkkfTkg9wV2cSv3kjirAwABoadnPi5FQMhq50S/8Gufzc21uby8YjOx4hszaTl/u/zMSEP/b9AxQfM7FtQS4ymZSRd3UhKvk/r9lEu93NgTWnydlThdFfzfDJSYR1+vM1V5odzTy550kO1hxkcpfJzOgxA4nT1ZHK2txE3A8/IPs/omSfLT3BrBOVTElewtUJAt2L2pGU7IO7NuEKTOHWbevZr4xkiX8LQ9JGnHVNj8fDx3OX8HmxF1EBelJ7bGJr2WbafWdwTYEFg6WB7oczCaytwOeLB4lJu5uFCxey0yVhR3BXNEcaCDOoWTG1Hx9tnMGG9r1c3+bNlErYaHkRqVJFu82NpL0ClajGqfCiKXg5S+MPMyZmDC/1+7UYzn7iBFUzHsfuqYFH02kKOIXL1Yhc7kVg4BiCg8bh7d3rH46N/Zm4TPL/JATBjdV6itbWXwm9zVoIdARRNOrIDjI3pmE0pmIwdLkorfJf4LS72b4wj+JjJuK6BzJ8cuKfZjl63B42PL2KAhxY9cV4yeVM6OJgRuMqooo13LfOSatKwZ5+qczv14hUNPNm5TOU2ur4xqbDrVQwbcCbSMKeZNfxzlQU5ZPk28imboPQOsCxv47+cf58c2cvpBumw9EFcNdmiOj9mwX0wNwroKkUHjoEuvOrS4oekcalebT9dALbrtfwvfNOgp5+CoCmpoMcP3EXWm0c3bstOW/LPovTwkPbH+KE6QSvD3ida+Ku+cN1EkWRo5vLOLi2mMAoA6OnpmD4Dyp2OReqTjWxc1E+LWY7PcdE0+uq6D89x94tuHk3812+zf+WAWEDeHfwuyhOV1Jy/Q14XXUVoe+8/bvxgiByw+x95Nc18erAl4jRRZN+tBCJKMLUPbRKFIzfs48qmZEfE/TER5/do9ZqtfLM7G/5oTmUG3v6UaR8l3JLNbW+L3BnVgkym4numYfxaaoiYsFMAsOuZu7cuWRovdnu2xnt0QYivTQsu7cPL2+4j32u4zzUomVSnZMfbLNwC0o8bg9yWlG0tmDVhqHRruF/uu0j0hDJe0Peo7NvZwA8ra3UvPAils2b0Q7qj/rZCZhtuzCZt+Dx2FAo/PD3G4q//3B8fQdcdJexPxuXSf4S4HK10tbWkX/e8TefNuspBMEBdBQXdVjnaRi90jAaUlEqLz4N7/+iscbKpi+yaK63039iHGkjIv40d5KjvJLvX1pHUZAHp7qRzmFhTEhTM/Pwy0gK1dy7UaBJp6ZgYDLvpBlQyvYxyvYgI2si+KL+OMe8u/Fg+hxcxptZ8JMPvnInqX7VbE7uTYBCifKACcEj8uNjg/Ct/QkWTYD+j8CVr/9+Ivtnw5bnYdJcSLnuvPMVBZHGFQXYj5twlczHWZxN/NYtyLy9O5Qxj09GrQ6je7clKJXntrBb2lu4f+v95Dfm887gd85KBzwXBEFk77JTZO+pIqFXECMmJyFT/Puts4uBq93D3uWnyNtfQ3CskSvuSsbof3FZQ5eCladW8ubBN4kwRjB7+Gw089fS8PkcIr6Yg37IkN+NPW1qY/RHe+ivbWVy3+cJtSeSeOwQkugBcOsqyhtqGH2sGF+PhQ190vDyDT/rejU1Ndzz6UZy3EG8NimIz4seRaIIosT7aaYfOIzVY6bH4cNobTUkLXgbjb4nc+fOJTMkhj2acHRHG4n10/Ltvb2YtvY2TroLebNBysAWFz84P8HaJgGJBLXcgay+jFZDAkGeDXwy/DBtHisze89kYsLEDveNKNK8bBl1b72N1MtI2KxZqHunYTbvwGTeRkPDHtzuFiQSBV5e3fH27oW3dy+8jOnn3Wn+2bhM8r+BKIp4PG20t9djt5dhs5dhs5Vit5VisxXjaK8+M1ah8EWvT8SgT8JgTMHLmIZa/eeRcNGRenYszEOulDLqnq6Edf5zttyiINCwdDk/bqqkNNqNKHFwxdAh9IvX8fnam6jI1zJ5h0C9QUPdiG68FtkJdIvxlo/li5wxfFu1giXBY+gfkgGKAH6qiGdiegiiu4gVIXFEa1TEF9r5qcDE8ql96RGsgM/6gVzdIU/wWzdNw2n4vD/EDe/wxZ+vQEgUaV5ThPVQLepOdkyzHiNg2mP4338/FksOR4/dikLhQ4/uy1Gpzi2h3OZs494t91LQVMAHQz+4qCpWt9PDlrk5lJww0+3KSPpNiLuk3rf/KSjMrGPXknwkMgmj7ulKRNI/bnicD5m1mczYNQNBFHiv31sEPfIuHksbsT98j8zwewv2o22n+GhbIa/HVhAS/y7x1RFEFR2DYc/DkCc5UJzF9SXtDLKdYvEV1yBTn20BHziUySNrirHJ9Lx8i5TXMp9C4zuGNs1NPLJ3P3USM90PH0EhraPP/E9od0Ywb/58DndOJ0P0Q3O0gfQIb766I5XbVk2g1lXP1+Z2OrvkbPB8jrnWjVwhQyFzozHn0aDrQqh9N0tHZ5PnPMXVsVfzQt8Xzgi5OQoKqJo+A2dJCX5330XAo48iUSoRBDctrcdoMO+gsWk/FksuICCRyNBq49DrEzsOXSc0mkjU6rDzZoL9o/jbk3yrJZvq6hU/+8akSCQyRNGNx2PH47Hi8dhxuy04nfW0t5sQBPvv3i+XG9BootFqY9DrOqM3dBC7Uhn4lwRpBY/AwbXFHNtaTlCMkdH3dUXv8+c89PbTp6l46VW26rpQF+xA5Ra59fabiAzWsW3BCA6ekDNpv0iNlw7rNYN4z7s3DYb3EBWxrMh5iBN1m/jQK5F2rYqUgGwahFG8Or4rW0pz+UzQkqCUcZ1Hx/ubCnhubBL3Do6FjU9Dxhdw95bfu2lEscO6rzra4aYxhpx33i0bS7DsrsQwLILmpa/QfuoU8Vu3YKOao0dvQSpV0aP7cjSasHO+3+ay8cC2BzhpOsmHwz68OIJ3efjx8ywq8hoZdEMC/8veeUdHVW7v/zN9Jr33npBCSCEkEDoI0jvSQUUE5YoIighWULGgoig2EBGQ3nvvHQIJhPTee08m08/vj3DFiHoR8d7f9XuftViszDln3nP2JHv2u/eznx3e2/OPmvv/K9RVqDn4dSI1JU10GR3wUHeF/0RBQwGzT84mpy6HxTaTCVywBpvRo3F95+1W52kNRgYuP4feYGJF8HUqLb6mfZI5tjVFiJ46DJ4dWR9/lpdrrfhH4xXeHDwdfkGtFASBlRt38kmiFD8nC3p2jWNj6o9oneYQoI9g9I0rFFBBVNx1xHYV9PzyB0pLJWzYuJHrMb24XidHcbOa3sFOvDPag/E7R2HQqNleWYODwoHDoq8pzGhCppSAyYiLOokCaSjO6niOdEnmiuoaPtY+LOu57CchN5NaTdkHH1K7dSvK0FDcPv7oHkkEg6GRurp4auviaGxIprExtVXwCCCT2aNUuCCVWSGVWiOTWuHg0BtHx3+98/w1/EedvEgkGgAsByTAd4IgfPBb5z6ok6+oOEZK6qsIggkwIggCIpEYicTszj8VEokFCrkjcoXTT/+rVJ6YqXyQyez+bdQ4db2Oo6tvU5RWS7se7nQb0+ahpAZMOh1VK1dRtGY9x3uOoNFKg70apr78HBbWZqR934ejZ+rpGy+QZ2+JaPRA1ts8wg3JO4jEGl4reRWfojJWaLO4aNOZXh6X6Nl+GlNivVmRms0HZQ20MWr4KDCIyd9epnewEyundEBUfANW9YGYp2Hwx61vKnE77Jj2L7VpGs4UUncoB/NOLshcKimY+hTOCxegGtubuOvjAIEOUZswM/v1RhGNQcOsE7O4VnaNpT2W/q5e+j9h0Bs59E0i+cnVPDIlmJAuv991+98CncbAibUt9Z2gTi70nhL80LtlG3WNLDy3kNOFp3knvg1Bh1Pw3rgBs6ioVuddzq5i/MrLPNvTjxGqXZTqVtH5mha5yh7RP86D0pqFZw+zxujCF6LbjOk1+Z61tFot85ZvYF+1M890d+em8AFZdXkUOb3NEwWWBJUnkKsppf3164gDa+n33nauX8/i0LFjxPUYyK0CPbLkWka1d2faI3IePzAJ2waB3bXFqOxDOCFdTsaNauQqCUaDiTaSdFKb/bHVZJHgf5lz/lkYaOb1Tq8zPOCu7EH9sWOUvv4Gpl/Rqf816PV1NDVloNEU0awpRKMpQqstx2Cox2CoR6+vw8N9Ir6+zz/QZ/Ifc/KiFn5gOvAoUAhcAyYIgpD8a+f//5CT/ytRmlPHkZW3aW7U02tiEMGdfzuy/SNQx8VR8uZblNQ2cqZ3P/RSPQH1YiYsmYdEqaR64wQO7LhFdAqkuVhhPmYMZ9z6sa3qI2Sym0QK81iU7Mqmkk2s8xhKG7tivp8+BRcbK5bnFPN+bjn+dZVs69WRyatvoNYaOTynOzYKMazqBY0VMOsqKH+mONhcCyti7koI/0ZjWdO1Ump2ZKCKcMR2bCD5EyeiLyvDY9+PxN+ejF5fT4eoTVhYBP7q9XqjnhdOvcD5ovMs6bbkvoqsRr2JQ98mkne7it5Tgmnb9e/h4P8JwSQQdyiXq/ty8Ai2ZeAzYQ80AvL3YDQZWR6/nI03vuer76XYOXjiv2sXol90tM7ffpMdN4rYN6sr4vzPaChdTXRCPULAMMST1qI3CYw/cZA4iRP7XJoID+19z1qlpaVMXnGMbIMtX0/1ZdH1pxHL3ciyW8jyOA1aZQrZ1UVEXr+OooeOvnN2cODAca7dSuTyI8PISquHjHqe7uZL17AyXjj1AkFVEjY15CAJGsx58ZvcOlWEXCVBrzMSZltIYrkz5roK1E6H2RCqoY40RgaMZGGnhT91SuvLyih+ZUGLTn2/fri+vfgettG/C7/n5P/q6lJHIFMQhGxBEHTAZuC3VYD+phAEgdtni9j1yQ1EYhGjX+7wUBy8sbaWkjffIm/yFFJtHTjx6CMYxdCpRsrED15BYmaGdu88LmxIIDoFrvtYYTFxMgURo9mcsx25LAGx+WMsSfXleuURTvlGIZEIfDN1NC42VnySU8r7ueUElBWwOtSbL0/lkV3RxCdjI7Axk8OVr6E0sWXQh/IXkrIn3wF1JQz59DcdvDqxkpqdGSgCbbEbE0jT2TM037yJ3TNTSUydiVZbTmTEqt908AaTgfln53Ou6BxvdH7j/lg0JoHja5PJu11Fr0lBfzsHDyASi4gZ7Msjj4dQlF7LrmU3aKrTPtQ1JGIJL3Z4kfk93mTlI0b0GZkUfP/tPectHBiCtUrGon3JBHZ8AzPPCWT7qBBn7kF/eBUyiZiVXXviYGxkWr6OmrLMe97DxcWF1wYEoELHom0FvNbxLeqb0vFs2sniKAtCa0Lwc3YjoUMHtGekXNgyncGDB+Lr5krs+cM4BFoj9bHku/M5pOd68XLMy6Q6GHhR4YUobT/d7DbTeaQ/umYjMrmEW5UeRPo2oJHaIKscyWsJAvLaXuzK3M2kA5PIqcsBQObsjNf3q3Ga9xINJ0+SPWIkTVeuPlQ7Pwz81U7eHSj42c+Fd177CSKRaIZIJIoTiURxFRUVf/Ht/Pth0Bk5uT6VMxvT8AiyZeyrMTh6/TmalWA0UrN5C1kDBlKxexdxEyZxrY0vMp0lfRvEDPjkFcQKBYb9b3Lp8wP45Yg51dYSl8eegG5jWHLiKEr7feiUEaxM7EFxUxoXPUxk63x5qY8bnvbOLM0p4aPcUgJL85lvIaJQb8WGK/lM7+5L1wAHqMmDU+9B4EAI+UVjS+H1Fhnhjs+A270UOQBNRg3Vm1ORe1lhPzkExFCx/HNknp7kBRyhsTGVsHYrsLaO+tXrBUHg7Utvczz/OK/EtDSy3A8u7swkM66cziP9Ce3+6/n9vwtCurgy+Llwasub2fHhdWrL1A99jbFBY3n82RUkBEioWvElGWlXWh23NZczr18QV3OqOZBYSkj7JdS3G0iNtQzJlddQn4vD3tySVW29KJXb8fyVS5i0jfes06trLGN9tJQ0GjkT58C4oHFoqvehMySxKFxFb30Unvb2JER1oGZzLTfPvc64ceOwl8sYcOsCkhBrVO7mvH8oFTtjX8a1GccpN4HPxW6ILiwjyuUyfZ4IQa81IlNKiM+zI6KdGBCR3/AUX+cUoch/jOyaEsbuG8+hnENAS7eq/dNP47N5M2KlkvwnnqB0yXuY1A/f1g+K/zhPTBCElYIgRAuCEO3o+NcqO/67UV/ZzM6Pb5B6sYToQT4Mfi4C5Z+cLKOOjyd3zFhKFy2iqW1bTk15giyTAfN6dwYJ0PmT+YikUgzbXiL+g83Yl4rZ28mcwEFP4jpwErO2nEfpvQmTxJonyibgoNZyQ3WF40IsYa4mnurRkQ9zSlmWW0Z4dQkjyrLp0LkHr+y4RYirFfP6B7UUVA/OA0Qw6KPWjBmjAfbPAUsX6P3qrz6DrqCBqvXJyBxVODzRFrFcQsORI2hTU9GMtKam4TIhwe/j4HDv1v2f+CL+C3Zl7mJmxEwmt703l/truHmigITjBYT18qB9P68/Yvb/WniH2jPyxfYY9EZ2fXKD6uKmh75GD8+eRCz5HIlJ4OKr07la0jqaHRfjSaibFe8dTEFjgPD2K8iLisYk0cPJZ2g4k0+URwBv22k5bhnG8hMb71lDJBIxd+JgwhWVbEsoI8ZyMr7WvjjWfkecpZoNMgPDgwfhYmFBYvto8j5LpLRwI+PHj0dRXcmEkgya2tpg7qBk7tYE+rnOINalE6u9ZBwR7BH2zCLYo5BBM8MRjAJSuZjrqUraRtugMNQTp36GVWUp+BQ8hrrRifln57Po4ttojS07JFW7UHx37sB2yhRq1q8ne8RI1NeuPXRbPwj+aidfBPycsuBx57W/PfKSqtj6/jXqKpoZ9I/wP60/Y6iooHjBQvImTERfWUnlK/PZ7+tDXb0G28oghjiJiHh/LiKjFu0Xo0n5cB/SOgnrH1ER3X0q7cZMZfyqs4jddyAS1eIsfZrHi624pTnNGVcPtEYVn4zryQe5pXyWV0Z3QxOdE68wcvhw3tqfRqPWwPLxkSikEkjZCxlH4ZHXwOYXjJRrq6D0Fgz4AJT3NisZKpup/OE2Ygs5Dk+FITaTIRgMVCz/HLysKAu8QYD/K7i6jv5NW2xK3cSqxFWMbjOamREz78t+2fEVnN+egV97R7qNbfNfNaT5z8LJ24oRL7bsiHZ/eoOqonsj5T+LthGPYDntSTom6fli1dPsy9r30zGJWMTiYaGU1Gn4+nQWEokZbTv/SE6QJ2ZCJtpT71N7MIcnIrowmiKWKqM5E7fvnjWsrKxYPDoKG1EzC7cl81bse2j0tQQ0refLQAWJcWVMGjcNG6mU5PBobr61A7ksi8GDB0PqbaaLGqkKs0GqkvLsjzeZE/4O7pYevOVlQ7pRhnHDWHy8NAyf2x6JTIxEKib+FvjHemGjLeGiYToLC1OYJX0UbWVPdmRsY9SuCRTUtyQrxGZmuLz2Kt7r14EgkDfl8f8vovq/2slfA9qIRCJfkUgkB8YDe//iNf+jEEwCV/dls3/FTSxslIxZGI1v+G93eP4rGBubqPhiBZn9B1B34AAWTz9NwsxnOZGXh1RtjmNJIIPbiQlZ8DSiqizUi7qR9V0STSYJnw9T0CvySUJGT6bvJ/vRWVxEoriN1mos39x0p6A5jZyYTK6WRfNsL382NzbyRX45wy1ktL1wjC6dO5NQK+N4Sjkv9w8i0NkS9M1w5HVwCm1Jx/wc9cVw8l0IeBTa3lt6MTbqqFhzGwRweKodkjuqmnV79qLLzaV6QBVe3tPw8vptJs7R3KO8f+V9enn24vXY1+/LWVcVNXLsh2ScvK14dGrbhyL29t8GO1dzRr4UhVgiZveyeCoKGh76Gt7/mIPE04MZJ6W8fnYh65PX/3Qs2seOEZFufHs2m/wqNXK5A+6D91Blb4ateBNNF09TuyOTpZ37EqgvZ2a1FYUlGfes0T48jCdCpFRrTGw40cjs9rOprb2EjfY8b4QpqdmXz1NzX8JcEEhtE8OpV98hJMSByMhIjGePM81eSU2ELWqjkec3pLGky6eglDPLxY3G5gYM60bj4iFl9MsdUFnJEItF3ErQ4tolBGdtFnGiiQRezONAoC+WtdPJqy9k6K7RbEs5+NM9msXE4Ldn992ofugwGk6deuj2vl/8pU5eEAQDMAs4AqQAWwVBSPor1/xPQtOoZ/+XN7l2IJegTi6MfqUDNk4PNjFK0Oup3rCBrP79qfzySyx69MBszffslElJSU/Hot4Tx2J3hvYzJ2DmOEjaRf2rfcjbrabMQsz742UM9Z1EWVhnBi3biVpcgczxEFpVB5bebIfEoKWy0z425YzA10FJrY8VXxWUM8XZhsBzR3B0cKBdTFcW7U2mg7ctU7veoS9eWgF1+TDwA5D8grFx7M0WpclfpnBoUSqsXJuMsU6H/ZOhyO50ZZp0Osq++BidlwnrfoMJCFjwm477Wuk1FpxbQIRjBEt7LL0vuWBNk56D3yQiV0gY9GzYX6bH/t8AG2czRr7UHqlczJ5P46ksfLgRvVihwGX+fBxKm5lVEMzSa0v5Iv4L/sngWzAwBKlYxJKDLeQ6M3M/pCPXYBKDld0imm4U0Lwlk+/aBaETy5gRn4ROd2/BeObYAUSqatiZWEWgYhAdXToir15PtryCz8z1mK7X8PTLLyM3Gkh3jObIuzMZMKAXzs7OWBzfx1APa+rCbcmvUfP+nmqWdPuAMjMd/7DxRVSZin7LVGydVDz2SjQOni0dq8nxTVjGtMeHDFLlg0nZ3sxJnwLGOi9Fr3Hg7auvMHHHQho0LZ3xP0X1P65HpFJSOPMfFDw3C33Rvz+R8Zfn5AVBOCgIQqAgCP6CICz511f8d6Ist54t712lMK2GXpOC6PNEyAOpAwpGI3X7D5A1ZAhl77yLws8P7y2bKRg3liASKHcAACAASURBVLUHD6Jt0mBd0Q7HCgXDJrnjMaovwsFXqHrnOYrOmpHnAosmSuhnPoLVghnfHoij3mSOme9mTBI7+pc9SozGlhLvM+yrC6Si2Yag3j58V1TJNHcHuqQn0NjQwPDhw1m0PxWN3sjSx8KRiEUtkfq5ZRA8pLWEMEDeJUjcBl1fADvfXzyTQPWmVPSFDdhPCEbhdTeNU7puGabSGkwT/Gnbdulvij3l1ecx59QcPCw9WNFnxX0N/DAZTRxdnURjtYaBz4ZhbvPbE6P+r8Da0YyRL0UhlUvY+3nCQy/GWvbti1lMDD0PlzDOfQgrb61kyZUlmAQTLtZKnusdwJGkMi5mVbbcj8cAGrpPxbK+Flnox2hSqrE92sQyWzU3VD58cHbXPWsolUoWj47GQqTlpU3XeaPT2ygkUvwbVrHRW8zRG4WotDKemjUbidFIijGMs98/z5gxY0AQCL96ikgvGwxhdlzNrWbvJWteiHqBW3Ya3lT6Ics6gv7IW5hbKxj5UhQBHVo6rDNv1WMKiaateQ4Fqi7s3eHF/LJV7BvyHa48SmLjfrqvf4yNcQk/fbGZRUfjt2sXTi/Po+niRbIGD6Hy25WYtA+X7fR7+I8XXv/bIQgCt88UsvPj64hooUeGdnf/wzlfwWCgdvdusgcPoXjePMRyBR7ffI3jt9+wJzmZI0eO4Ki0R5UfjmNtLSNmt8Mp2g3Tqv6UfLGZ8gRrsgJMvDZBSlh9d1ZoyjHmQanOAxu/nUADVvIpvFToTqUyhzT3yxzP70loDw921zfwlLsDU0yN3ExIoFu3btyoEnMsuYyX+gXi73hHf+P4YjAZ7tWmMRnh0Mtg5Q7d5txjn9p9WWhSqrEZ5o/qZ6qOTdUZVK9ahz5QRtuJ639zLmudto7nTjyHRCThyz5fYq2w/tXzfomr+3MoSK6m54QgXPzu75r/C7ByUDHshUgEk8De5Qk01mge2nuLRCKcFryCsaaG6fH2TG03lS1pW1hwdgF6o55p3Xxxt1Hx3sEUTKYWR2jXYxkN7n44Zl9E6HsZTVoNnW+6MEWfzleSYE7cPnfPOhHtQpjURkSpWmDNiVLe7PwmtY1peDTtZ3GokuxdGdi7ujFxwgQEEcTnupJzdQUjR46ksriYKeU52HpZYhZiw56EYupKujHYbzB73QysxR3Zlc/RX9+IVC6h39OhxAz2ASA/pY5ql3BiPEupVAaz7cyjOGyaytGRL/NM0CJMsjLeuzWDIatXklhY12ITmQz7adPwP7Afi+7dqPj0U7IGDqRuzx4E029PinpY+J+T/xPQNOo59E0iZzal/0SPdPL+Y4McTBoNNVu2kjVwECULFiJSKnFfvhzfPbup8fPjm2++ISMjA3+ZD8bsYFya8hn5dh+sFekYPu1J/qYi6nLMSInW8+pjMtyLgzhrXUBkZRSpeh+cPC9hlCahtR7Px3FWGEU6KmKWsyH9GRRtbLihEhjrYsur7rbs27cPZ2dnQjvE8tbeJNp72TCtm1/LjRbGwa3NEPuPeyJ1bqxr4cv3ewfkreWRG84U0nS5BIueHlh0vstJNxgaSPvscSR1Au7z30Wh+HXBMb1Rz4unX6S4sZjPen+Gp+X9SQ8UpFRz/XAeIV1cadvt78eF/7OwczVn2OxItGo9e5cn0Nyge2jvrQoNxXr4cGrWrWOW81jmdpjLodxDzDk9B5HYwLz+gdwuqmfvzTut/iIR5mN2g0iCWdJHSIdr0WbUMDvfj5DmQp4vESitr75nndnj+hEsr2HdtRLcJJ0Y5j8MXfUuGshgsbWehnMFeEVGMrRrVwxSKadPNqAwpREbG0v61cu8ZSFC7W2OnY8Vn5/MpIPZDNrZt2OFvxkXjLaI9s3CkHsZkUhEx6F+9JsWilgiojSrgUyDP92jNDTJndmeNI2Kj8czyyeQ3SO24WzmSr5sBaO3vMbMDVfJrmhJi8nc3PD44gu8fvgBqa0dxa8sIGf0YzSeO89f2ZT6t3Dy+tJSSt9+B11e3r9tzaL0Gja/e5W821V0fSyAIc9FoLS4f3qkvrSU8k+WkdmzF6VvvYXEygqPr77Ed9dOLB7ty/kLF1izZg1isZgAdRvqC7zw0aUwYukgzBLeR7N6JjmHrdHUKkjr28xbj6pwKnWl1LWSHrkTiDO54+JYiNpiPxpVJ2bGWeIjdqI6YiOnagaRqXKmxteCQQ7WLAvy4tDBgzQ3NzNy5EjeP5yOWmvko3+maQQBDi8AcyfoMa/1gzTXwIm3wbsrhLbWbFcnlFN/OBdVhCPW/X1+et1k0pN45VkUB2qQx7bDvtuvD5AQBIF3r7zL1dKrLO6ymCjnX+fM/xLqeh3H1iRj62xG93G/3kj1P4CjlyWDn4ugvkrD/hU30WuN//qi+33vuXNAKqX8k094qt1TvBH7BmcLzzLn1BwGtHOgnbsVHx1JQ6NvWVNs4w193sS+Rktd0RwsRjkjymrmoyItzSIZz125jPEXjtDc3JxFw8NQomf2j5eZH70ANwtX3OpWccrJwJbEYgyVzYQNGUp3Fwc0ShU7vz9Nh0hfXFxcSDu0j/d8nChuY4Gjizmv7UxjaptFWCqseDPIi2KDDMPaUZhq8gFoE+PM6PkdUFnKqClpIi7HlkceVWEUK9hV/BI5H87Frzydg2O2MsL/MRQOZzjXuJh+K3axcGcipXUtOybz2E74bNuK28cfY6qvp2D6dPImTKTpcuseg4eFv4WTb46Pp3bbNrIGDKRg1izUcXF/2Tej0WDi8p4sdn8aj1QuZvT8DkT29bov5ULBaKTxwgUK584ls09fqlavxqxjDF7r1uKzfRuWjzxCU1MTGzZs4MSJEwT6B2KX6UlNjSNhihQGvRGNbOtQavfsJfekK0aZBXkDa3gjxhL7Wgu0ViYGpb7IKZENVlZqGp3XYZS60L4oktH6tjQ4J5FjU8Dmqt7o29nQ09aSr0O9SU1OIikpiV69epHRIGFPQjHP9vInwOlO01biNii8Bn3fAsUvGrlOfwCaWhj4Yatiqza3jupt6ch9rbEbE9jKPukZ76LfcQ1xkwi3eYt+017rktexM2Mn08Om31c3K9zpaF2ThK7ZQP/p7ZAp/u8WWu8Hbm1sGDC9HRX5DRxdnfRTCuXPQubsjP20aTQcOow6Pp6xQWNZ1HkR54vOM/f0HOb186Ootpm1F3N/ukYS+zwGl7b4phVRICzG5rEAXLKdWFR0lQtSN5Zfv3DPOp2iwhnpbSS3zsSG8/m81+09mrTleDdsYmmQgrR9GQiCQI/ZLxKhrqFJZcGGT79myJCBGAwGtKeOMNvHmYIQKyws5byyJY8FUe9TKzQxPzgSo0FDw5f9EO40aDl5WzHxrVicvC1R1+k4fUFE79EeyI3NHGqaT/wnq1Cc+4x3urzBRz0+wtKyBgu/5ezK2EnPj06y5EAy5fUaRGIx1kMG43/oIC6LFqEvK6P55s2HYvtf4m+hQgktPPLqjRup3bgJY10dirYh2IwYidXQIUhtH46Eb0V+AyfWJlNV1ERwZxe6jwv8l8M9BEFAl5VF3b791O3Zg6G0FLG1NTajRmE7aRJyj7tdl9nZ2ezcuRONRkPX8E5k71ejE+R08SomvFs5wqmPKb3pTF2aQI2PD8aON5ntZ4e5Ro61xp9HsqezRWWkSWFEEvwdRkMRCtlMVl+1xNbCmpwu83mvfBmJHraEW5qxq0MAJrWar776Cjs7OyZNeYJBX7T8IR2e0wOlTAK6JvgiGiycYPqp1kqBZcnwTTfo8CQMWXb3s6jWUP5lAmKlBKfnIhGb3d3hFBdvJTVuIa5vmWPZrRceX9wd6/dzXCy6yMwTM+nj1YePe36M+D6n71w/nMvl3dn0mhT0t+9ofZhIPF3I2c3phPXyoPu4h9NHYFKryezXH4WPD17r1yESidiVsYu3Lr5FrGss2qLHuZHXxNmXe2NrfmdqWlkSwjfdKHGSoRu4CIeykdTsSmFJVBp7HTqxI9SVzs6tJUFqa2sZ8tFhKgQLzr7Shy1Zq1iVuIpmh9lENLVnrZ8HFtEuGA16dkydTIpvEA4iNbHDJrBv3z569urNWntPjuVXY3OtCmdLBTMG1rDk2pv0M3nzce45Ki3CcXjpNKI7Eh2CSeD0hlSSL5QgEovoMdiR29uuUaX0Jlh7gF69c5E8topSQcdr51/jaulVHMUdyE0bhBRLxkZ78EwPfzztWth3gk6HYDIhVj6YGu3vaddIFi1a9EBv+ldg5cqVi2bMmPFA14rNzTGPjcV28iRkri5ok5Kp27mT6rVr0SQlg8mEzNnpgYxo1Ju4uj+bE2tTQASPTgslqr/3b6r7CUYjmlu3qNmwgbK336Hyq69pjo9HFdUep7lzcH17MZY9eyKxasnfG41GTp8+zd69e7GysqKbRxRJh/WIjHr6dawi2Ox7dJf2kH/JF3WenmPBsfjHXOAFb1skeglh6v7Epo9jn41AmciEdfgR9NobaGyms/B8A6HKIErCvuGwxbMctnTDVSrlQMcgLCUSdu7cSWVlJZMnT+b7q6UcSSpjxcQo/P5ZbD37MaQfhjE/gO3PukQFoUVhsrm2RSf+joa8SWOg4rtETM1GHGeEIbW5a++6ungSbz+P4wkPxMn1eHz2KVL7e3PxhQ2FzDg2Aw9LD77s8yVyyf3N0yzLrefY98kEdHAidoT//6mGpz8LZx8r9BoDt04WIldJH0qhWiSTIZIrqN28GVVkBHJvb0LsQ3C1cGV98nocHcrIzvVDbxTTM/BOt7uFExh0WKWcI8t0Havovphb+dL+UiEnXIzsqW5mrKcbqp9NwFIqlVgbazicrSGjqIK3B4/iXNE5NHVnSHbohk1yM1HBjkiUcrx69KL6h9XkO3nRXJiOV1A74q5d45mOUVxCTK2FhLqMOhrqnegXZsuuqrMY5CE8UnudotQkrGJamvREIhG+EY5Y2CrIvVVJbloT3l0CMC+6RZY0lqJ0Gf6587AO7M3QyBlYyCw4U7IXB9dbtHcJ5uANPWsu5pJX1YSfgzn21mb3iLv9ESxevLhk0aJFK3/1c/g7RPIldc2suZDLhI5e+DrcLfxp0tKp272buv37MFZUgkSCWfv2mHXpjFlUFKrwcMRmv89jz0uq4vzWDGrL1ATHutB1TJt7pAlMGg3ajAzUcddRX72KOi4OU0MDSKWYd+yIRd8+WPbti8zp3mEX9fX17Nixg7y8PCIjIrHKkJKSbYaVppiBPTKwL/iS2kJbiq6Y0YSMNVEDmO27hlke1tQjYVDtMzhmhHDcTcQtdTN+MZmUN36H2mooI64pecHUk0aXOK53qmd+43AURrjSqx3OSjkJCQns3r2b/v374xQQxsDPzjEwzIXl49u33FxtfouSZNAgGLOm9Y0n74WtU1rJCAtGgap1SWgyanB4qh3KgLs7KK22nGvXRiBukGC3sAHLfv1wX7r0Hns0G5qZcnAKxU3FbB68GS+r+5MfMOiMbH3vGnqtkfFvdERh9ufkI/4vQjAJHFl1m6yECgY+E/ZQhsgLOh1ZAwchtrbCd/t2RHd2gnuz9vL6+dexl7SjMHU8J+b2xcv+zt+ivhlhRQzNxkpudPQiJvYgmpONJN9cybjoEfQyh7WdYlp9iRuNRqYs3cLFOmt+eCIKL7dmxu0bh0gZRJXVXPbWmRE6JgSAvBtXuLTkQ1LDwmnjbEuFVkAQBAY/+RQjkvJRljRTfb2CMR3cUNt+x4XiC7xTrmJoYzL5Qc/jNaE1u6w8r55dn9zAoDNh46TCSZdLRo0DFtoShrp8gO3IOdBxOmk16Sw4t4DM2kyG+IxCWjeUbdcq0OhN9Ax05B+9/Onk92Azhf/2Q0P23Sxm7pYEDCaBrgH2TOrkzaNtnZHd+bYXTCY0t27RcPo0jafPoE1La4lEJRLkvj4o/PxRBPgjc3NDYm+P1MGRRr2cK6dryUtvxNpORmxvG9ydBQzV1RhKy9CXlaLLzUWblo4uNxfuUKHk3t6YdYzBrGMnLHp0R2L92xFReno6u3fvRq/X0693X/K3FFCqtcNTm0K/dtuRViaQfisQUVY9t+19SRk1ngkNC5ntbkWZVMb48pcxy3EjPkjJibJawmM0ZDe+i14ZjF9Zdz4qcsdCIePGI1uZa3gBnc7IljA/enjaUVdXx1dffYWLiwuPP/44U76/RmJRHSdf6oWj5R0q47apkHYQZsW1li/Qa+DLGJBbwjNnf2qKqt2XReOFYmxGBmDR6e6W2mTScSN+Eg0NKQScGkDj9sP4HzyA3Nu7lT0EQWDh+YUczD7Iij4r6OHxCy7+7+DC9gwSjhcwdHYEXm3//xu+/d8Cg87IrmXx1JQ0MfqVDti7/fnxdXV79lD8ygLcP12G1cCBP72+K2MXb158E1NjO/raz2P5+A53L0o9AJsnkuFvRVN4f8LDVlK3K5mtld/xVuCTvOvnxNPerVlTmTl5jFwZh5lKyblXB7IzcytLrixBazOFEN0jbA73QxXUMjHr8vqvyNl7irTQUII83UkvLCYkJAT3vgMZdysL30ItBbcrmdvPi1P1b1DRXMGK7BrCtMUUd/kYrwFPt1q7oVrD9g/iUNe3sJTcHQ2UlOiRYOQR1acExDrAsBVozWxYfmM5Pyb/iJOZE3PbLyQ7z4u1l/J4orM3z/dp80A2/ts7eYDyeg1b4wrYdLWAotpmHCwUjI32YER795Z2/J/BWF9Pc0IC6vh4tOkZ6DIz0RUUgMmERmFDrvdASlw6IxYM+OQewrPwFGLB0HpBsRiZuzuKoECUgYEoAoNQtY9E5uz8L+/VaDRy4sQJLl68iLOzMz0Do7myqYBmsSURolPEOn9NZbkdBZetkGv1XOszFs9JUfgefIaXPS3JlysYU/AStmVeFEZb8WNqCTHtzUjSL0aECLH8OZZcLCRK2YGbHTYx32kGVc1GnhKZ8V7/EARBYP369RQUFDBz5kzO5KmZu+Um745ox+TYO4437xKsGQA95rdo1PwcFz6HY2/AlN3g3yIi1ni5hNrdmVh0dcNmqH+r01PT3qCoaCMhDoupn7QU6xHDcX3nnXvs8mPyj3x47UNmRc7imYhn7jn+WyjOaJHTbdfdnZ4Tg+77uv/h19FYo2Xb+9eQKiSMWRD9p0X1BKORnBEjEPQG/Pbva5WW+Odnrq/twK6xywlxvRMUCQJsGIMp7xwXOpjh2+4d3F0nUbdyHbMcmzhr14lDHdsSatG6Ke6Ddfv4JlnMs13ceGVoJP848Q8ul1yl3PltXilyYdakSMRyCYIgsOfVZ2jKqScjOBgfNzdyi4sZNmwYN529WJhWQGiWmqysWt55zJWVmS9gI7Ng5e3bWOk11AzZgEds6ylO6node5bHU13UBCKQy0TQVI9Oak64YRdd2xxGPHw5hAzhZsVNFl1cRGZtJgN9B/Ji1MtYyW0xkz9YyuZv7+Tz6vN478p7vNjhRQJsAjmbXsGGK3mcTC3HJECwiyXDIt0YGu72U6Hjl6jMq+XW0SzS4utAEAjwMhDq3YxKrEUkkyJWqRCZmSGxtkbm6orUweGBcmg1NTXs2LGDwsJCOnTogEOOhITbEqQGNd0U3xJge4WL8cE45dRS5+KF7XuLOVp/kAHnvuNNLwtSFEqGpD9HgDEUdQ8Hlp7LIrqtDbdlHyPV59Nov4AZR+OYYjaIPPcEXozsSmGzGO/0Jk7N6IJSJuHatWscOHCAIUOGEBwWSe+PT+Nmo2LXzC4tui4mE6zqDY3l8Hxca+57UxV83r5lzN/k7UCLbHDlmtso29hi/0RoKyZNaekekpJfxMtrOhZrm6jbvQf/I4eRubWOwhLKE3jy8JP08OjBZ70/u+9Cq05jYMu7LaqH417v+C8L4f/D/aEkq47dy27gHmTLkOfCEUv+HBGv4cQJCp+bhcs7b2M7prUs9KdxK/g+6Vtc6MvRx5fdTcNUZSF8FUuNmws3/Y10jNmLSupL8efTGBw2GUsze450DcdccpdBpVarGfL+bvINVpx4qRfm5hpG7RmFGmuq7N5kn8mOdgNaghCtuontTz+GUWxPdkAAttbWNKrVzJgxg4+qNawrqCQ4qYGSiiYWjVWyNOFFYmyC+ez6MZp0SoxP7McluLWUtk5j4Miq2+QnVWNlr6S+SoNYMGASSXHUpjDU9T1UHUdA/3fRyy34LvE7ViauxEJmwWuxrzHAZ8AD2fdvX3hNrEhkU9omNqRsoFpTxfDgzoyN9mdiJ288bFVkVzSy/XoRay7kcjSpjJI6DQqZBHuljNyblZzbms6l3TnUVOgJjnWh/zNhtO0XjGVYMKp27VC2bYuiTRsUPj7IXF2RWFj8lFv8I0hOTmbjxo00NTUxuE9/KneXkVlmg11jKiMc3qS2QUvxeUesq5qwevwJihcMZ9mVd3gm4TgfeJtxU6ni0bSniHXsgry/G28dTSXc35Yky7XINbeod3iOvpczmGndnQaJlrmdfCjQq5DEVbJqeBi+DhZUV1ezZcsWfH19GTBgAMuOpXM2vZJvp3TA1eZOVJSwAeK+b2HMuLVv/RAnFkP+JRi/AcwdMVRrqFh9G6mdEoep7RDL7v7BNTVlcStxBlZWEbQxf4HSN97Cdvx4rAcPbvWWtZpaph+bjrXcmm8e/Qal9P6L4+e3ZVCQWsOgmeHYOD+YTtD/cC8s7ZSY2yi4eaIAg96EV9s/Nxhc7utL0/nzNJ48he2E8a0CpFjXGM5lFZKtO0xpnYbePp1bDpjZIdI3o7p9mDp7G0rUV3D3fAyLNl1pe/o1Vjn2prxRxwCXu7UfmUyGm0LLgbR6bueV80TXdnhbebM/czMikZE4nS+jrS2RWimQyuQ4R8WQv30D5gYxZSolEpGIvPx8XujdjSsNzaSag32lnoupJv7Roz3bc3fQ2OZR+pXGU37tEELwMMys706DkkjFtIl2orlBR2FqDe5BNpjZKGms0aKWOHCraRCKgrM4pS5BYuNBTPjj9PXqS3x5PH42foTahz6QfX+v8Pq3cPLeVt6MbjMatUHNtvRtbM/YjkqqooNrO6K87BgX48XoKA+crZRUljWRfqOcnHPFZB/KJ+d6BQ1qPT5dXRk6ox1BnVwfetFOr9dz5MgRjh49ioWNPfY6NwqPq2kQrIlo3EQH5w3kpQYgT6zHMjgIli7kQ4dLHDm3no8KC1nuLeeCmYremZN5rMMIRLH2zNl2kyB3azJcj6FoPEqj9RiC882ZJ3NDWe/IrB6QhS2SG1WM83VkWjc/TCYTmzZtQq1WM3nyZEoajby4NYFRUR483tmn5Wa1DbB5Eji1bZEL/jlDpTIT9vwDoh6HqCktomOrb2NqNuI0IwyJ1V1ZAqOxmfiEJzCZdLSPXEvVRyvQ5ebisfwzxOZ3dwaCIDD/7HxSa1L55tFv8LD0uG+7FmfWcnZTOhGPeBLa4390yYcNRy9LNA06bp0sxNpRhYPHg+fnRSIRMk9PajZsQGJljVn79q2O9ffrwfdX4klRH8DJzIm29m1bDnrEwM3NODRKybAqxCTocfAaiIdOjyHvBKvlQQQoZARb3v2C9/Vw4WZ8POfLJYS6mNO3TQTl6nLSSvaSbxOKNFVMbKgLIpEIC1sH9E4qGg8dRSaRU2dhQWN9PQgCMzpGsrOyDp29HF1eI0WldgwMt2ZL4WHsfPrRo+oqWeePoIocicL8rm1EYhHeYfZIFRKSz5dgYaug16QgGgoqqG+SkGfqxM2aR9AlHUOUcQSP4FhGRTxFW/u2D8wI+9uza4pTy7i24xYWHu7oVGrOlZ8mtzEXO4UdsXZdcZd4UVfRTHVxE80NegDESgm1tlKumbTEa5oRRGAml9DO3ZpgF0sCnS0JcrHE09YMR0tFS+fnH0Cj1kBWeSO3s4vIvHIMQV1Lrt6B6DwJIosgzJuL6aH6CpHYGe2FHEQSCYqZU/k+uJQ9OfsIK7Pkncp8PvOWcdLcjB75Y5g76FlKrERM+yEOHydzCv1TkFStQK/qgtw0lFcyk4lVd2ROrMB1S1v8CzSo8xo4/mJPbM3lXLhwgWPHjjFy5EgiIiKY9sM1ruRUc3JeT5ws70TPxxfB+U/h6ZPg0aH1Q22eBNmnYXY8grkjNVvSUN+swP7J0J8KWv9ESspCiku2EhnxPRbVbmQPG479tKdwmte6Y3Zd0jo+ivuIBR0XMClk0n3b16g3sWXJVfQ6IxPe7PS/NM1fBKPRxN7PEijPq+exBdF/uhCbN3Uq2vQMAo4dvYfZ9u3ZdD5LXIjcIpMVfVbQ3aN7y4GkXbDtSUo79CTJPIXoDluwtmqP5quBjPR4kkyLIE50bouX2d0gIye/gOFfX8VMpeTsqwMxCBoe2/cYpc3NVDm+y36VB2Hd7xb+d3/2EoZ91ygPDKXCyQlBEHj8ySdRO7ow9EYm3k1GCs6X0K2NLUr3H7haepUvZCF0SzvA1eZI2r26p1VE/09kXi/n+JpkzKzkDHw2DHFzHUc+vUgNDi11B5EIMXrsbTSE9g8ltHfAA9n1b5+TL9i7mSvHamjEBbXBCkG465BNmDDItFg5KvHxdsXRyxL3QFvs3Mx/+tYsrm0mLq+GuNxqbhfVkV7WSKP2bqFVKhbhbKXE0VKBpVKKuVyKmUKCVCxCbxTQG03ojSZqmvRUNmqpaNTSoNHjL64iVpaHCRG2tUpsGwLQyywIatxPpHcB1efKMVRUIe/fh4MD7VlbeQCTycST9Z0YXrCfZd5yTpqb0a96Aq+Ne4EMjYYn1lzF3c6M8pAqhPJ3kci8qbWZy7TzB5lg3pk3Ahw57WjGaImKAwczWT4+kuGR7pSVlbFy5UratGnDuHHjOJNewZNrrrFwYDDP9LxTKK3Ohi87tcgTjPrFvM7cC/DDIHjkdejxMg0Xiqjbl43Vo95Y9WlNcywp2UVyyjx8vGfi7z+Pwtkv0HThAv7Hj7VqTEusSOTxw4/T06Mn9O6okgAAIABJREFUn/b69A9FMdcO5HB1Xw6DnwvHJ+zB9fr/h3+NpjotW969itJcxmMLov/UF6r6+nXyJk3Gaf587J+a2uqYRm+k58eHMTp/iVhRyQ8DfmiJ6AUB1g5FKLvNlU5uCEoLOsbsQ1KRQd4PY+jT4QfaSM3Z1zMU6c+CsffW7GFlmpQ5Pb2YMzCMmxU3efzQE+iVsXjKp7O3ZygK25bgRq/TsubliTiklFEY2J4aO1uUKhXPv/ACJxp1TE/KJbYeEi4VMTHWkdu8S42mhlVNVgQXnOecrgcdX9+C4lco2eV59Rz6JpHmRj29JwUR2MmF21/v5eJ1MQaJEntpGUpJBQHBIto9N+ee6+8Hf/ucPFJbSi9n0M/lW7oqV9C+TT7th4XTflxP6tvnsNXsaw6rNpNseRVXPxvaeYa0arCxVMoIcrGkd7AT42K8mNnLn7ExnnQPcCDax45gF0usVTL0RoEmnYGKBi25VU3kVKopa9BQ3aijXqNHKZfgbW9OtKclnSTZuGtycVYp8My2RSAcM10Ffaz2YFtRSt25NAQfT45Pj2Sh+wVuNKUy0HsATxaE0qNgI8u8lZw0N2OM5CnenvoS6fVqnvj+Kk7WKurCNRjL30MhsaTUaSFDLp3gab+2fGTnw1EXJXPdHNi1O52uAQ68MiAYo9HIxo0bMRqNTJ48GcRSpq+Pw8ZMzidjI+/uUvbMgto8mLCxtXyByQTbngREMPo7tHlqqrekoQy2w2Z4QCvn3NiUwa1bz2Bt3YGQkA/RJKdQ/v772M+YgWXPnj+dV6+rZ8axGZhLzfmq71d/KA9fU9rE0e+T8I9yInqgzx//ffkf/hDkSimOXpYknCigsUaDX6TjA6cVZG5uNN+4QcOx4y25ednd1KhUIsZcrmT/FVvsnZM4lHuAR70fxUphBc6hiC5/g71dN7JFCQiCATuvkVjX5eKZdYDVjt3RV2vo4XY3iIgMcOfApURO5zUzrqMX/rYeCIKJ64U7KbJwx5RlRpdgJ0QiERKJFJ+IGM5dOolfaR5NZvY0SSTk5eYyvnsXTAJs1zTR1dKcw3HlTGj3KGlNJzlpJqGXyYa2mjjOXUjFrdNApLLW6V5zGwWBHV0oy6nj5olCtM0GoqZ0JjBYQdXFBMpEXph0ctp09MM2yOeB7Pq3z8mnb7/ArQJbbtd1Ry2OxEN2AeXt1ciSt9LG2puxsQsIcAonrSaNHRk72Jq2lRpNDY5mjtir7uVUi0QirJQyfB3MifCwoWuAA/3buTAqyoPxMV5M6ezNU918ebq7H0919eWJLj5M6ezDmGhPwm0N5Fzcj7q6HPdqMFV2xIgFMaZDhGgT0Jy7gRYjx0f78FpMFrfEJYxqM4olMYtR7LpCZMUPfOxtxklzFdPdZjF/5CySSxuYsvoK1mZydFFimsveRSUSKHF6jejkNBaEubFKHchOTznzvJ25cSqfqkYdPzzVESuVjNOnT5OUlMSoUaNwc3Pj+/M57L1ZzLKxEXf1aXLOthRVe70Cgb+o8Cduh6vfwqCPMFqEUrE6EYmlHIenWhdajUYtNxOexCToiGq/DpnMipI33sBYW4v7sk8QK1q204IgsPDcQpIqk/iq71d4W7Xmy/8eBJPA4ZW30aoNDHku4n/aNP8mWDmoEIng1slCLGyVf2oYvczDg5ofNyCxtmmVmwcIdrVkT3wVcl0IGtUFThecZojfEBQ2XlCbj+zWbkyhw8ir2IaDfS+U/oMJPr+YAqk3P0gd6CiR423dEk0rFAqs9DUcztGQU1LFsChvIp0iuVh8kcbqU5yxjaVPowoXl5ZnMbO0xdxRztWEVNrqSmgSWVKh09FYX8+TsdEkN2o4KdISI5Kz51o1L3Ttz5nSPcQ5efJoox5/zQ1OnM3EM7YvUnnrLm2ZQkJgR2f0WiO3ThZSlFaDfzdfwsfGYFWZRn6eHklDDT6PhD+QTX/Pyf8tBMrCZwxkzNOeuFBCUmNb1qW9xUXjUjQqfzj2JtJPQxlwYRUb3IexvveXdHLtxIaUDYzaO4rx+8ezLmkdBQ0Ff+oeTNV5nN30Gd+v/o7myjpsKkLRa7oSrLlMP912zM8foTYzjW2DrHh8agN729QzPeIZjjx2hOf9pnP9w7eJVv/I294WnDRXMbfdS8x+9BkSCmqZuOoyZgopRKtoKP8ApaCm3O5FPMqbmOtvwZZyTzZ7y5nuaotTmZbL2dW8NjgENxsVhYWFnDt3joiICEJCQqhp0vH5yQx6BTnySPAdTr/JCIcXgrUXdJ7V+sH0zS3O3yUcIXQMVRtSEHRG7KeEIP7Ftj0r+yMam9Jo2/YjFApn1DfiaTpzFvtp05BY3nUKuzJ3cTz/OLOjZhPu+Md+qVMulVCcUUuX0QGYWd2f3MH/8HAQPdAHz7Z2nN2c/qfGB5p16IB5l85UrV59z/xTmUTM3L6BZBaZM87zDfLr83n57MsYTcaWVKFYil92HQqFE8kp8zEpzRH1eZP30xfj3dzEc6n5VDbflU0e3qcLHSzqOZbZwLWcSmRiGe93fx+Z2Ihl9Upm5xehVet/Oj+s+xiCegWSpFMQo6zFtrqG6zducOXsWVaEeBFooSLJX4W/syUf721mdthiUmrTea1dDCZzW3qIDnD43edpbqi/57nFEjHdxrSh37RQqooa2fLuNTLjygmeOpCJS/vQZcHIB7bp7+FvEckDmLs7EjK0PY7GIirSSsnW+JBYGE6jshe2IUEoy88hurkBl4Qt9NeLGePVF2f7YFLUJezN2c+GlA0cyztGUWMRzYZmrOXWmMl+g5JnMkFVJmQchxtrqTvwDmuPZpNYK0bR7IBFdQSBDQkE1O3CPuEsddXFbOsmZsUwKdadOjMn5iVei32NWLdYypLTOPXBLHranWeehx03zBS82fktJoVNJC63mse/v4q1mQxlJ1sqyj9EoS9EbfUs0v/H3lmGV3GubftcGnd3I06CRnCCu7u7tdCWlhptd0uVQlsohdICpbhb8eIaIAkQgbi7e7KybL4foUAIexfo3t+7375cx5EfmWdm1swza91zP7dcl9aBxco84lSm/ORoyygDOa852TN/+21C3S34cJAfarWa7du3I5VKGT9+PDKZjG/PJHIzvYz1k9pjafggURX1K9zZBkNWg21A03sN/6FRuHvkRiouq1HcL8N8nDe67k2TTKWlV0hK+hhHxyk4O00DIO+999HW1+OwcsXDZXl2VTaLLiyinXU7Puzw4XMt++urlZxYF4ONqzFd/o+Jcf83QCQS4exnTuLNAtLvFuPT0e6f8jf9Gf6VN+9pY8TpuALuZ0t5PawtO+K3o9Ao6OjWF7RqRJG/YNx2MZkVx0AQMPebizzxGMEl1/nFphf3M8oZ4d4YhhGLxXhb6HAkpoAbKcVM7uSBma4p5rrmXM7cT7GuAXW5lnRr8YhyxKNNTxIiD5KaV0WPQC+Kc0qILSzEXCJmYtsAdhaWIbbWQ5pfT1SKlPldWrM3dQ9lvn3pUZSMozqe0xdScA3ujuwpXFkWDoZ4trchP6WC6PM5VJfU49rGHrn+iyuX/e2ra6rLFIQfSqXDcA+MzHURBIHM4zeIOppMgcgRBC3WokJ8fDV4tchCJ+8C5N0GoZGKINvYlotmVlyUarmjrUVF43Z7qRFuMmNcxXo4IMFUUYNJTQn61YUI6gYaSqxIq+tFhqUFWhEYVzhjXxCBadE1XAoaqNSHC6H6VA3qRBeffnR17IqhvLE6QRAELmzZRemldXR2TeQVe2vSdWQs7/o1fVz7EJ5ayswtEVgZ6SAKNqGwYDkyRQJyvUkUmHZjbnIE+u6GrNBtRW+1hs092zB9cwS3M8s5/UZXHM30OXnyJDdv3mTy5Ml4eHiQW1FP2IqLDGltz8rRrRonr74C1rQFS2+YfqJpyWRtSWPjk0snaj1XUX4gGcNujpj2byoaolSWcfPWAGQyU4LaH0Yi0aX2xg2ypk3H5v33MJ8yBQC1Vs20U9NIq0zj4JCD2BrYPtdzvrAtnoTwAsZ+EIy5vcGfH/AS/xHkJZdz+Ns7eHewo+cU3xc+T9aMGSgSk55aaXP6XgFzt0WxYlQgSeqt7E7czeedP2eIU09Y0w6M7bnfsS0FRb8R1P4IRuWV8Etf1vmsYJlNMB8bmDAv+NH3dMkPe9mXY8CygV5M6eKJIAgsurCIS9lXqLD+kAMeXQnyesTVU16UxrZ3XkWiAx3aDyb8dizF1tZ0DwxE0r0X42LSCEZO4vksPKwMCesQxeZ7G5nlNphXr2yhuk7D6fo+9H9vJcZWzTmroFGeMuJEBlEnMjAw1aHHZF+cXrAf4W+feM1NLCfqZAaxl3ORSMVYuxpj7uOM3+BWuNkp0aanUFBtQFqZFXfjbcjI96LSZDBa9wEYtAzFTN+QVg1KhlaUMq24gM611biqVEhV9eSrqghXl3O1oZS0fA3l6c7UZnanpGQU8SZtKTHVIlVJsUmPwy/hCJ7pach0dSma1Bu7L79gyNil9PYagKeZ58Nkb0VRJTs/+BSDtO20cU1hjoMd+bq6rOnxA92du3MpqZhZWyOwM9VDCDKlqGAFMsV9bHRGk2LdnyH3wvEMseBLrT+h1Sq29m7Dwdu5/HItg38M9qezpxXp6ekcP36c4OBgQkJCAFh29D6JhdWsn9wOY90HyaHznzbG48ftAKMnjO6ZjyD7FsruP1N6oBAdd1PMRzXlhhcEgXv336CmJonWrX9FV9cWQRDIW/I2APZfffWw8eXnmJ85mnaUTzt+ShubJ5qs/gSF6VVc2p1Eq55OeIc838vhJf69MLLQQ6PREnshB3M7gxd+4cqcnCjfvv2p3ryHlQHnE4q4mFTM2hHjiC2JZlfCLkKdumJr6QcRGzDzmkquOobKytvY+7yOqCyNdknriTAdwy5U9JTpYmPS2ODX1t2GwzcSuZhWxcQObujKJHS068jx9OOoq25wrj6QiW72D/mu9AzMMLKTknAxBoVOIfYeIQiJicTW12OSmU6HkGC2lFXQ1dmMiJhCdDVehHrK2Jl2GHmbSXTIjcKRdI6cjMPevx0Gps3pzkViEY7eZjj5mZN1rxQDEx3sPF6M/fNv78kDVJXUc2VPEhmxpVg4GNBxRAuc/MwfLuk1KjVpv90g7UYWuWW61Msaww0irRo9VQUGknr0ZFpkcpBKQKvWoNFoUTSIqVfLqBUZodRpPEYpL6bGJAmtWIOPsp6WFy4irqhCx8sLi9mzMO7f/6mUB4paFVf3XCf27EY6W9xG166IV+3tUcn1WdfrR1pbt+bwnVze2heNu7UhDW2NKMpfgUwRRyu9kZyxGkpI4m3GdnHg7QIrWlZq2NPRlzo9CX2+vUxLBxN2zApBqWzgxx9/RCKRMG/ePORyOQkFVfRffYXZXdx5f8AD76skGdaFQusJMGRN04stToJ1oWhbTaEwfgKIwHphGyRP8Jjk5u4mIXEpni2W4uw8A4CaS5fInjsP208+wWzsGADiSuKYdGISfV37srzr8ud6toJWYP/ySGrKG5j4SShyvZc18f/T0Gi0HFp5m4rCOsZ+EIyR+YvxoGfNmIkiMfGp3vyFxCKmb47g8+EtGdjKhPHHx6PQKNg9YCc228dAfTn5oz7kfvL7eHt/iqNBZ1jTnmLvsYSZzMBYC2fCWmLwwKHZcOA0n0eoGNvamuXjggGIKY5hysmp1OkGMMryPb7o0XRlcmrz69w7lUKbocEkZ8mRJyWR6eyMG5A+YiLrS2sZ3iDj5MUM+re0xthpPycyTvBOizFMvPADFQoZB/Pa0PO1T3Bt9c+VzdQqDWKJuJFW5AXwrzz5v0XiFRqz/wMWBNJ/XgBKhYaja6I5suoOBemNYroSmRTPkZ3pu2ICMzaNYOLbvnTrJMLbphJTQw0NIn0K1RZk1NuSWOtEqsKJLJUTFSILxHr62FlqCPCuwzgwk0rzeIyFBnqf+Z3AI8cwCQ7FeesW3I4cxmTw4GYGXlGr4taxNDa9/gPxZ75mqMMNqpxKmeFgj56RHdsH7qC1dWs2XE7j9T13aeVsiqKdAUX5K5AqYullPJpz5oNwz01lYkdv3iuwwKNG4Bd7a4ysDXhrXzQaQWD5yEDEYhHHjx+nqqqK4cOHI3+Q5V9xKhFDHSkLuj9GHnZ6KUj1oMeHzSf07D8QZPqUFQxHU6vCYrJfMwNfV5dOUvJnmJt1xulBHF7QailavRqZkxOmIxoTSXWqOt678h5W+lYsDV365Cf9KeLD8ynKrKbjyBYvDfx/CSQSMb1n+KHVCJzdfP+FFaUsX30FTWkp5bv3NBvr7mVFOxcz1pxLQVdixJoea6hT1fHm5SWoei+DymxsM/IwM+tAaurXNOjpQeh8rO5vY7VBHek68N6lpIfnmzywG346Fey7W0RSQWNiNNAqkCVBb6FTf4fdVQe5llXa5Bp6T1mBRQsJd47epH0HL3KdnWlRVka6IGCxfQOTq4s4rKNieDdXTsYVoSwYSw+nnixP2cvBnm9iqicwxvE25799l5hzp//pPEhlkhc28H+Gv40n/zg0ai33ruQSeSKD+moVjj5mtO7tjPNjnv3zQBAEoq9e5fT58yi0Wnzi4wnMzsFi1CjMxo9rRrb1ByqL64g+l8O9q8koKn7HVBTDcI90jpho+cbchACrQL4P+x4zHXO+OBHPxqvphPnZkOimpSL3S6QNaYyymsAmcVcM62t5y1aHT+vlWNVJ+bVShM+ENvxyLYNPj93nyxEBjA92Jjo6mkOHDtG9e3e6d+8OwK30Msb8FM7b/bxZ0P1BR13KWdg+Enovg06vNb3w9CuwZRD1jgspTemL2WgvDNo1ZdfUatVE3R5DXV0moSEn0NFpHK86eZLcNxZjv/wrTIYOBeCzG5+xN3EvG/tsJNgu+LnmXlGrYsc/bmBmq8/wN9u+TLb+lyEhPJ9zW+IJGer+wj0LWTNmokhIoMXZM828+espJUzYeJOPBvkxo7MbpzJOseTSEib5TuKd5CjIvkHt7N+4GTMea+u+tPT4BFa3Bht/PrRdzgYdFevMLBnRupEu48L1COb+loefrSGHXuuBSCRqjM+feZ2L+ReRWSzlct8RGMoeOROVpSlse/dVBK2UwPELuXA1nBBzc24XFiLWahHLDfipW39G1euy90oGE0LsKTP8iWt51/jQZyqjL/yAQqFkd6o3Tt1GETZtDhLpv5c65f+EJ/84JFIxgWFOTPq0A6HD3CnLr+XYmmh2f3qLu2ezqK1seKbzaBsayDp8mA3vvcfhc+fQKylhcH4+/SZPwfvCeazfXNzMwCsVahJu5HP4uzts+zCc6DNnaajYgq/BTUZ7xbHcVo+V5ib0cunNpj6bMJCa8tqeu2y8ms7QIEduu9RRmf0ROqpMXnF5hR2qYCQaDTP1tHylkGLcIObHxHq8RwaSVFjD8lMJ9PK1YVyQE2VlZRw/fhwnJye6dGlsCRcEga9OxmNjrMP0jg8SURoVnHofzNwgZN4TN62F35ei1bOnNKU7Bh3smhl4gKysjVRVRePjveyhgRdUKopWrULH0xPjQYMAiCiIYE/iHib7TX5uAw9w62g6DbUquoz1emng/wvhHWpLi/bWRBxNpzC9edngs8Dy1VfQlJVRvmdvs7GOLSzp4G7Buoup1CnV9HPtxyTfSWyP384p/96gqMLg9kFcXedTWHiU0toY6P4eZFzhQ6csWtYKvFNcTGZpo0Zrt9B2dDOv5m6BghMxuUBj1dAX3T/DSmqDouIH3g2PanINJhYt6DV/Eqp6LRnnt+Lu7kZUVRUjhw5FTyJFIShZum0dCcXxjOngzM6bedgr59PVoSufJmxhW9gr6BoaMskznrJru9jzyXvUlJU2u9f/FP6WnvyT0Ki1JEcWEnM+h+KsakRiEU6+ZrgGWOLkZ46Jld5DAyIoldSGh1Ny4iQR2dkkuLshAjoYG9Np3Dh0nZsrFdWUN5AZV0JmXCnZCeWoGzQYmKoQVBepyr3LQK9SdOUpvOHoSqJIzYLWC5gTOIfiaiVztkYSnVPJpO7u7JdmIitcjh71vO/1Fp+k61FsasW06nyOWFqjbhCz6ZaKdlMCwd6AYWuvU1yt4NTrXTHTk7J582aKi4uZP38+pqaN+YM/qhS+GhHAuOAH137zJzj5dqNsn09TRkiid8OhuZRpl6C2G4zV7ABET5TJ1dQkcitiGFZWvQho+SiWX757NwUff4Ljj+swCgujXl3PqN9GISBwYMgB9KRNub//DCU51ez9PIKWXR3oOv4lT/x/KxrqVOz+7BYSiZixHwS/UINa5vTpNCSnNMbm9Zp+TyIzyhi1Ppx3+/swr5sHKq2KmadnklCWwG4dH9wTTqN99QY3E2YjaDWEtP8NyfpuIJaSPPAE/VKy8VKL+a1PADKJmNT0DEb+HIFU14Cr7/dt1DIGEkoSGHd8Igq5M993WU8fx6bOze87XyH2SCZ+vdoTW66PkZERU6ZM4bfNm0ksKcGiuATdWgV5oaPYnKFhSgdHaoy3cCbrDK94T2Ru1EEoSeFsoTdJSjf6zHkVz+COLz7xj+FvX13zZxCLRVg6GuHfxYEWba2R6UrIS64g6VYhsRdySLqaQcW5i1Rt30LJl8u4E32X87Y25NtY421nx8TZc2jRJQy1VJ/y/DoK0ipJjykm5lw21w6mEHEsnYzYUtRKDW6tzbG0SyX33k5MlSlM9MskSaeQuY7OVMl1+bb7t4z0GklMTiUTNt4gv1LB/CG+/KoIR6dwOSZSCcsDP+bzmDoy7VwZVpjOZQdHqlTw000NrTu7YNDKmhWnEzkVV8Ca8W1o6WDCpUuXiI2NZdiwYbg8UFxSa7TM3x6FuYGcL0cENMb86spgzyRwCoGeHzUtmVTVI+yagFptR5V0AVazAxHrPSF1qFURHTMbrbaB1q02IpE0Lq+1dXXkLFqErq8v1osXIxKJWHN7DZdyLvFd9+9wNXF9rmcmCI1SdCqllv7zApDKX3a2/rdCKpNg5WRE9LlslAoNLi2fX5lL7uBA+Y4dSM3N0GvdlKPd3lSPu9kVHIvJZ2KIM3oyGR3tO3I45TAXJSqGlOShU1+FQdtFZOf8ikiig5nLCIj4GQsnN6y03myRNKDKraGriwXmZqaUZyVwpVCKWtVAZ69GY26pb4md1pZL+fv5vTCdsZ590H+Mq97FtyfpiXvIuJlPcFgocSnZCILA0PHjMTM0JC43l2oDfVpdPcbQmkwOZmuwsRuAr5OGHSn7KfYZQGdBD09VBIb6Opw8dovKkmKcWwYikf218M3fvuNVEATqKiueaV9zewNCB7kwarwJQ/1T6F6wkbbHXsPiyAqyClM5HtaTqKD2aLHCtLQ1pdHebH//Dj+/dolf37nG/uWRnN4Qx43DaRRnV2PrbkLHkS0Y+0EQnUfJyIhaQ+zp7fRpUcFQl9usNpMx39YaG1NXdg/cTVfHruyPymH0T+HIJGKmjfRlbeFudAtX4mHsxPp237LiShZJzt50z00m2s2DYqXAmkglLW2NMOrqyI20Un6+nMb4YGd6+tqQmZnJ5cuXadWqFQEBj5qZ9kflkFpcy5K+Pkj/EH24+CU0VDWnEQaE62sRVedR3jATi0n+TaiD/0Bm5k9UV8fh4/0ZcvmjH3PZ1m1oikuwfrPRwN8ruceW+1sY6TnyhcI06XdLyE+pJGSI+19WJnqJ/zwcvMxo1cOJ2Is5ZCeUPffx+kFB6AcHU7JxI1qFotn44t5eVNSp+OVqBgA2Bjas6LqCzNo8Pm7RBuHuDsw1FlhbDyQzcz31Lq3AuSNc+ILxwVYMrRKxVlXD5fQSAOaP6Im7tJyN17LILnvUdTu07VAGGoyFuitMPLuaxyMdUqkug19dgZ6FkrsH99La14vw8HBSU1NpHRTEK6+/jpmzM3fatSXe3oSl93bSbeXbuO/VY579OPan/cardjbUtJuGnzSOGW3zybp2kl/ffIWUiBvPPWfPir8UrhGJRKOBjwFfIFgQhMjHxt4DZgIaYJEgCP88tfwALxquSY26ybFVX9Nu4DCChoxswgSnbWhAmZ5OQ2oqDUnJ1EdHUx8Tg/CgnVrs7UVOSAgxEgmV9fWYm1oQ4BmEuZ4DGpUWlVKDCJDKJch0JBia62JkroORuS46+jIEQSArNppr+7aTn5SAv5OYHjapZCoLece5BcnaWsb7jGdxu8VotVI+OnKP/VE5hLib4xRiwZHkVejWXqG7U0/e9VrI4oOnuBTQidZ56Sjd3UlRNPBdbCkdqkyxea0dVWIY8P0VdKRiji/qglirYv369YjFYubNm4fOA36YeqWGsJUXsTPV5eD8jo3hqKJ4+LETtJ8OA79pOok1RQjftUKhDEQzYDOGHZonk6ur44mIHI61dX9a+n/3cLu6vJzU3n3QDwrC6cd1qLQqxh0bR4WigkPDDmEsN36u56lRa9n1yU3EUjHjPgj6y6pEL/H/B2qlhj2fR6BWahj3UQg6z1kJVXvzFllTp2KzdCnmkyc1G5+zNZLw1FKuvBOGqX5j1djG2I2svr2apRV1jLMJRTFsJeE3emNh0YVAs1mwsQd0fZuqNovpdT0ehUzEhS7+WOjJOXDyHO9cqiXE1ZQd8x7pCasbVAzdPYdMohgR+DnL2gxuch2J0Rs5sfIAxtZm1LuGomhQMn/+fAwMDBAEgf03I4k4dxZ9VQNWZRW0uXUTs4oK6pxtuGBbQpWXLTNa9cYl5nu0YgkXK9pwN0ND0NBRdJ0w7fknnn8drvmr9WhxwAigCS+tSCTyA8YB/oA9cFYkEnkJgqD5i5/3VBiVlNCzII/Sb74jctUajC0s0RNEaMvK0FQ85uFLJOh6e2MybBhV3l4kAPdSU1EqlTg4ODCga1c8PT0RP4Pqk0atJjH8CrdP/EZeUjy2VgbM7KLGoCScbUYurNV3wlCuw9pOX9PVsSvJhdUs2HGTlOIa5oe8F01dAAAgAElEQVR5EGVawPF7r6OrymZ2wFwmO4xk8Y7dXGoThk9BBoK7B0kKJZ+lJhGS74LFbD9EBjLe3BpJaY2Sgws6oi+XsHv3Pqqrq5kxY8ZDAw/w6/UMCqoUrB7XutHACwKcfKeRXTKseRmj+tDHSNQNNHi9iUmoXbNxrVbJ/fglyGSmeHt91GSsdMNGtLW1WL3RSJO6OW4zSeVJfB/2/XMbeIC4y7lUFtcz8N8gO/cS//8glUvoOc2Xg19HcXVf8nN3w+oHB6HXvh2lGzZgOmb0Q0K7P7C4jxf9V19hw5U0lvT1AWBGyxlEFkaygnDapJ7Gu/g13FxfITVtJaX2E7DwHw7hazEOns1ae1uGlxWy6Hoy23v4MbhnFw5G7uBahoSz9wvo5dfYZCfVkbE16DP6RM7hYOwygi3tGeT0SFvBK3AmOUNvcHdfAY7mGSRpTDly5Ajjx49HJBIxOjQIfSdX1p85TytZCsX9+qKpVtMiK4t+d8qQ3MqlfvuvxEtMkRuAhzwOF30TZOXlf/EJPB1/6RckCEK8IAiJTxkaCuwWBKFBEIR0IAV4/jX7M0I//xb6NUU46akwkEBVQT65FSVUe7hiMG0KDt9+g92+fYj37SVh9ix2GxmyKz6e2ORk/Pz8mDFjBrNmzcLb2/tPDXxZXi7h+3exceFMjq1ajrYqn8lhBkywOU9WXSzjvAL5Tk+gs2NXDg45SGf7Lvx6LZ3BP1ylrFbJ1xNac0R0nvuJb2JENT/2+pFpzmP44NetnGwThmtRDgYu7sQq1LxddJFeKS6Y9HdDx92EjVfTOJ9QxNKBvrR0MCE8PJzExET69OmDo+MjRaWKOiXrLqbQw8eaEPcHIZX4o5B+qdHA6zdtnVYlRSNJ3UW93hBMxvV8ahVLRsY6amri8fH+DJnsUfeeKj+f8u3bMRk6FF0vL9Iq01gfvZ6+rn0Jcw577mepqFURcTwdRx+zF4rtvsT/LGzdTGjbz4WE6/mkx5Q817EikQirV15BXVRExYEDzcZ9bI0ZFGjP5msZlNQ0VsiJRWI+7/Q5xrpmLLG1oe7Mhzg7TUdPz4Wk5GVou78DagVcXkFQG3verJVzTqxi0/085HI5iwe1xURUz9IDd1GoHvmgFn4O/CB/H0SGvH9pEYnl6U2us9uw73DupCQnJgkvQ4GkpCSuX7/+cJ+BDlYsHNSP3cF9yPAKRM/ahHR/d3aMHMvlRa9yeEwgh4MlpPo7I5g7Iqmpwij7yvNO97PN67+jukYkEl0E3vojXCMSiX4AbgiCsP3B/5uAk4Ig7H/KsXOAOQDOzs7tMjMzn/vzi4uKiDu/A/20s8iUFSgsAkipMScnvxytTI7Y0BilqDGBIpFI8PDwwMfHBz8/P3SfQiD0ODRqFQWpKWTHRZN06zrFGWkA+AW408G5BpPsUxQLStZ5tOGgshArfSveD3mfns49ySqtY8n+aG6ml9HNy4qwzqZ8HbMCSe0NPC3asb7H18irNCzbsIFdHQdhV16Ek50dN5UCC2v2MfV6X/T8LTGf6Mud7ArGrA+nl68NP05qS3Z2Nps3b8bHx4cxY8Y0McxfnIhnw5U0Tr7WBR9b40YmybXBIDeEuVdA8mgBp23QoFwxALk6Bu2cSKT2zWX0qqpiiYwaiY3NEPz9VjYZy1u6lKrfjuJx6iQSezumnpxKelU6h4cexlLv+cU8rh1I4e7ZLMa8H4SV04vT2b7E/xw0ai37voykrlrJ+I+C0TN8drZQQRDInDgJVV4eHr+fRvwEZW9qcQ29v73EjE5ufDDI7+H2G/k3mPP7bIZX1/BJn/WUmMuJjplFixbv4hJ9D+5sh1cjUOs4MPZ0LBEmYk6198LXWJ8P1mxjR54FC7u78Wa/R+fUVCvZsPUka4yXYygz4NiQnVjpP+K3qayK5dA3r1CaYIx5UEeyalVMnjwZd3f3h/tcKK1ielw6rjoyFilrOXPpJubqMmSiRn6sSlklagM1oXZedGrVFVefVs893/AX6+RFItFZkUgU95S/oS90NU9AEISfBUFoLwhCeysrqz8/4Cm4nhTOpYRKTiqD+I3e/F5qS1qDHI25FfoWZki1GuTFuehlJmKacQ9JcgwVMRHEnjnB/SsXSIm8SWrUTVIibhB7/nduHtrL6fWr2bn0TX6YMY7dHy3h2t7tyORShgwJZtFAXfqrtyLN/I21boEMcnXliLqESX6TODL0CN0cwth8LZ1+qy9zP6+KL0e0RNcvhRU3pyGpjWRKywXsG7AJSVkDn65fx54OA7CsLsfd1pabSoEZqm1Mud0DqYU+ZqO8qKxXsXDnHWxNdFk+KpC6ujr279+PqakpQ4cObWLg8yrq+fV6BiPaODYaeIDrP0BFFvRf3sTAC4JA9bad6KpvoGm96KkGXqtt4H78EuQyS7w8m3bGNqSkUHnoMGYTJiBzcGB3wm7uFt/lnaB3XsjAV5XUE3MhG59Q25cG/n8xJFIxvab70VCr4vKupD8/4DGIRCIsFyxAXVBA5cFDzcY9rAwZ3saRbTcyKax6lKANtQtlVsuZHDQy5MSFD7A074qlZU/S09fQ0GEGiKVw4QukBnJWeTtjpBKYEZlKtUbLgpE9cRWXsf5yepMkrMRIzpTOHQhVv0atspzxJ2dRWv+ovt3EOICeM1/B0L6W8tvhmEvF7N+/n4rHQsRhFsZsD3QnR6nmM6k+0+dNodC1L78rvagx98LV2h2DWgMS44s5cPfsc83Vs+JPY/KCIPR6gfPmAk6P/e/4YNt/BN6+3hyqPERsYSweRh5M9xpDWEEcOre3IGqoAAsXlMH9yNUEkFokUJSVRdz5M6gammfx/4C+iSmWTs607daFFhYNWKnTkKYeguQyig0s2BbQi32KbGpUufR37c/CtgtxMnLiRlopH/92m4SCarp5WTGxhxkf3VlGbdUtTA28+CnsS1paeFGUkcaXm39hb5dhmNVW4WNuwhUVjNdsZ05sF8RqXSwm+SLIJbz2622KqhXsm9cRIx0JO3ceora2lpkzZzZbiXx3pvFHtbiPV+OGyhy4+i34DgG3rk32rbmcjV7Wd2j17ZENfOOp85CWvoba2mRatdqETNaUPKlo1SrE+vpYzJtLXk0eq2+vppN9Jwa5D3reRwhA+OFUxCIRIUM8/nznl/ivhqWjIcGD3bhxOA33NoV4tm/eUPfPYNCpI3qtWlHy80+YjhiO6Alv/rWenhy5m8vaCyksG9ry4fYFbV4lIv13lmkzCYj4Cc+Apdy81Y+Uwi34h86Dq6ug0yKc/AP4LrmE6TIFC6JS2RrsydRAQz6/q+HDg3f5ddaj2nW9QCs+i23JWN3XKKhdxeRTM9jW75eHYkNOzmPpMCWKy+vvoE2+i8bFj7179zJ9+nRkD8oiO5sZcaStJxOj05gYn8EvQ325H2vJ16cTsTK04aOhg7lbvZMQh5C/MuX/FP+pcI0/sJPGOLw9cA7w/LPE64tW1yhUGk7G5qNvdp/Vd1aRXZ2Nm4kbU70n0K9Bg0HMPsi4ClpVI1eLjT+CbQAaA1sUIgPUyBEAkaBBR6hHrqlCUpEOBXGNuqcIaOVG3PQI5bCBHmfK76ERNPR26c2Mlo0q6ylF1Xx7JokTsQU4mOrxTn93Tlcf4lLadkCgl+csvgmZi0QsIT85ka+2bmV/t+GY1lbhbWzIdeSMFPbzeqIrepnemE/0QT/Aiq9PJbDuYipfDA9gQogzV65c4dy5cwwcOJCgoKAm85BYUE3/1ZebLmX3z4CE4/DKLTB7pMCkSKmgbvMqzGXfIYzYiChwdLN5raqKISJyJHZ2I/Hz/arJWN3tO2ROmIDVa4uwmDeP+efmc7vwNoeHHsbe8Ok0D/8KBWmVHPg6ivYDXAkZ4v7nB7zEfz20Gi0HV96moqiO8R+FYGDy7HzpNVeukD17DrbLPsFszJhm4+8fimVfZDYX3uqOo9mjarq8qhxGHRyAi0bL1kk3yMr9mYyMtbT324jJr1Mb+0Mm7kOrULN6512Wu0lZ4mzNHGsjZq7cza0GezZPCyLM5xE9sKZGSfTa20zyT0VS9i1uxo780nfTw9WqRtPA1fNjuLtDiVRmSom1G62CgputsnMUSiZEp5FWr+DjFg6010p5+0AMSYU1DGttz0eD/TE3eDEhnP+YkLdIJBoOrAGsgArgriAIfR+MLQVmAGrgdUEQTv7Z+V7UyO++lcW7B2PxtTPm3f5e1Eqj+CXuFxLLE9GV6NLDuQdhtiEE1zdgnh8D+TFQGAeKf1JbLxKDqQvVNr7cNrHmslTDxYp4iuqKMJIbMdBtIFP8puBk7ER6SS3fn0vmyN1cdGUSZnV2w845hZV3vkWpLMLApCPfd36XYMtGSoHUqFus2n+Agz1HY1ZbhaexIeHIGSU6xLxsML3XC+Nezhj3cuFEbD4LdtxmfLATX44IJCUlhR07duDv78/IkSObJUhnbYngZloZl98Ow8xADpnXYXN/6PYOhL3/cD91uYLiNeFYC7MR2zoimn0enkg4a7UN3IoYilpdTWjIKaTSR+ETQRDIHDe+MW56+hTH88/x/tX3eS/4PSb4Tnju5ycIAgdX3KaypJ5Jy0L/klD0S/x3obyglj2fR+DsZ07/eQHPTE0hCAIZY8aiKSvD49TJJlqw0BiW7L7iIiPaOvDVyKbqYmdvruKNhE1MM2vF6wM3EH6jNzKZKcENPRCd+wSmnQDXTtQnlfHqrVSOO8jYGuCG7F40r58qwsDImPNLej7shAWoiy3h1MkkXm+ZiVnpNzgZ2vJjrx9xMmoMWNTX53LxxAgSD1si1Tej1MqFHn37PaQX+QOVKjWvxmdxprSK4damfNHCgc2X01l3IYVxwU58NuwJ0Z5nxH/MyP+78aJGXqsVOBabz9enEsgpryfU3Zy5Xd0xNcvjWNoxTmWcokrZyKvhbuKOh6kH7ibumMuMMNJqkGk1KDUq6rRKCjR15CrKSKxIIr2yMaOuL9Wno33HhxUjMpGcKyklbLmewYXEInSkYqaEuhDoVcCq2LUUViehkTkzzG8Rn7bqg+TBFzv6zEnWnb/EsZ6jmhj4MeIjTCvNxfrWZPQCLTEf70NSYQ3D113Dx9aIXXNCqamsYMOGDZiYmDBz5syH7JJ/ICKjjNHrw1nS15tXwlo0Svr93A3qyuHVCJA3ejuCSkPR+hh0S3/BhK0w/SS4NG+tTk37loyMtbQK3IilZdMqmaoTJ8hd/CZ2n3+GZkB3hh4ZipuxG1v6b0Esev6CrdTbRZz6OY7uE73x79I8L/AS/7tx50wW1w+k0Gu633NpAVRfvEjOvPnYff4ZpiNHNhv/+Ld7bLuRybnF3XC1bMpp/9mWTuyhip+6fYuHvoq4uIX4eHyIw/6vwNQZZpwGkYj8Q0mMF1eTayLlWFsPfvpxBwfLHXk1zIO3HpRp/oHSXQn8WF3JeudMbEtXYSCVsbbnWlpaNoaMysqucfXUAlKPOyE1NKfU2pnR48bj7+/f5DxaQWBNZhHL0/Npoa/LOj9nJDVqbIx1//s8+X83XtjICwL3aurx0tNh+40sNlxOo6BKgZeNIaPbOTEo0JpiVRo38m8QWxxLWmUaOTU5aB8oQz0OqViKnYEdHiYeBFgFEGgVSFvrtsjEMhILqzkWnc+xmDwySuuwNNRhfLATfh4FbLj/M8llMWgkltjbjeeH4An4GD1Sgbq2Zzs/J6Tye9ehWFeV4mFiTDhyxkqOMr4mEscbS5BZGWA1N5AKlYbh665Rp9RwbGFnTHVEbNy4kZqaGubMmYOZWVMBAkEQGLU+nOyyOi4tCUNPLoHIX+DYGzBqM7Qc8XC/8r1JKO4mYmcwD1GLsEaxkCdQXX2PiMjh2NoMxc9vRdO5bmggbcBAxIaGuB08wNtX3+Vc1jn2D96Pu+nzh1k0ai07P7mJVNbIe/Kfolt9if85aLUCh1beprygtjFsY/psYRtBEMgYNRpNVRUeJ4438+aLqhV0/foC/Vva8d3YplQIiuybjD81hXJdY/aPPElmwiJqa5PoKJ+B9OR7MG4X+AxA26DmzrrbTPKXYWmsyzeGWt7bGUmGYMnxP6rTHkBTq6Lguyje8tfhqkE+7hWrqFdV8GmnT+nr2heA7JxtRJ5dQfppF8TG5lTbuTFt5kycnJx4EpfKqlkYn0mZSs1iV1sWOtsg+w/wyf8tjPye/DJeS8hitK0ZH7jbYyaR8Ft0HttuZBKdXYFYBG2dzejiaUWouzm+9sboyaFaWU1VQxVqrRodqQ56Uj3MdMyQiCVotALZZXXE5lYSnlZKeGop6SW1iEXQwcOC4W3tkBnFsOn+FtIrEtFIzJCYD+fDNhMZbWv1cFmqVNRzcu13bFdJuRLSB8fSApwtzLmOnAnSE4yoP41b1BeItFJsXm2NSl/KpI03icmtZPecUFo7mrB3714SExOblWf9gd/vFTBnW9TDuD01xfBD+0a91qlHH9IXVF/NpfJYGtauW5AXHYIFN8GyRZNzabVKIiJHoFSWEhpyqlmytXTTJopWrMT5l03ccmxg0YVFLGyzkDmBL8Y5FH0um6v7khm0sBUu/i/r4v+uqCisY89nt3DwMWPggsBnDttUnz9PzoJXsPvyS0yHD2s2/uXJeH6+nMbvr3fF06ZpRVbSnrGMr79HsG0IX3V6k8iIoTjaj8P79+MgkcP8ayCWoEit4NT++7wapE9Hc0M6R15jXaopXvbmHHqlM5LHDK8isYzMrfeYE2ZCnriCwNr1JJXFMtlvMm+0ewOZWEZC4j+Iu3yErPNOCPrGaDz8mTVvHubmzaX9ylRqliblcKiogmkOlnzl5dhsn2fB397I16g1rM4s5KfsYmRiEfOdrJnlaImpTEpqcQ1H7uRyKamYmNxK/rhdexNdbE10sTTUwUCnMQas1gqU1TZQUq0ks6wWharR0zfUkRLkakYPXxs6eelxNf8Um+9vo6SuALXUHsF0ILN8hzHf2QED6aM4XkVBPodWfs5+Z3+iAjrgUZiFha09twQpk2S/M1ixF8+YVQgVIqzmBCK1N2TR7jsci8ln7YS2DAy049KlS1y4cIG+ffvSoUOHZveu1mjpt/oKWq3A7290beSoOTQfYvc1fomtGtkbFakVlGyKxcCtHNO8qYiC5zSWVD6BtPTvSU9fTWDAT1hZNS2sUpeXk9qnL3pt22C2ZiXDDg/DVNeU3YN2IxM/P7+MolbF9g/DsXY1Zsii1n9+wEv8r8YfL/SeU33x6dC8q/ppEASB9JEj0dbW4nH8eDNBnrJaJV2Wn6e7tzVrJz6hvFSayu4t3fncwpS3g94mWJJKTu4OOpq8jt7R92HYj42qaEDFb6nszChiWYAeo0z1qDz4O1dU7g957B9HxbE0UiLzmB5mjI4MBoiOcihpN4FWgXzW6TNcjByJjp5J+u17ZJxzQCvXQ+Lfjhlz5mJi8nR5v6NFFQQa6eGi92Ji3n97PnlDqYSlHvZcCvahm5kRKzMKaB9+n09T85AYyljcx5sjr3bm9ge92TwtiHf6+RDiboGeXEJmaR2RmWVEZZYTl1tJvVKDi4U+E0Nc+HpkIL+92ok7H/Zi4QApscr1jDzenxWRK8jXGKO0Xsy44E1c672At9ydmxj4jOjbbP7obX4J7EZUQAf8c9MwdnDmliBlpuwoA5Xb8Ez+Dm0JWEz0Re5oxMrfEzkWk8+7/X0YGGjH/fv3uXDhAoGBgYSGhj713g/cziGlqIa3+3k3GviMaxC9EzoufGjg1eUKynbGIzXXxVTyEyJdU+j+brNzVdckkJGxFhubIc0MPEDJD2vR1tVhs2QJ30Z9S4mihGUdl72QgQeIPJlBQ72ajiNa/PnOL/G/HoFhjti1MOHK3mRqyv95+fLjEIlEWC1YgCozi6oTJ5qNmxvImdnZjeOx+dzLq2w6aOHBWM9RhNXV813UtyhNByCVGhPPNQS71nDhC1A3ds4a93NleIOUGXka9lfUY9opAAdxJStOJzSpnQcw6eeKk7k+30TVUaIUiJKP5tPOy8mozGD00dFsi9+Jj9932Pmb49GvCIlKgTougi0//0RNTc1T73OwtekLG/g/w9/Ck38S92rqWZVRyPHiCrRAR1NDBlmZ0NPC+LkmMrsqmwOpxzmSeoyS2kwEkR4Kg46YWfRhbosgxtmaYyhtSoGrUau5vm8HZ8/+zpHB0yk0tiA04z5VXgHEq7S8Kt9LB+Vv+GT9iCaeh6pLu25l8d7BWMYHO/PF8Jbk5OSwZcsWbG1tmTp16sOa28ehUGnovuIitia6HFrQEZFGBT91AVVdYyhGrv8w0aouqcemfw7SU7MbycmCZjU5l1arIjJqJApFAR1CTzehLgBoSEsnbcgQTEeNJGfeQGacnsF0/+ksbr/42R/MY6gsrmfnxzfwDrGlx3NynLzE/15UFtex+9Nb2HuaMujVVs8UthG0WtKHj0BQKnE/dhSRpOlvrrJeRZfl5wl2M2fj1KZlxVQXUv5DG0Y52KJv5MB3bUeQmfoZbU3mYnb0c+j7JXRYAEBDeiWFP8fwUZgZp2VqwhKiicyyILSFFVtmBDe5VlVxHUXf3+GSryFv2Ql0MzdipYchX938jIs5F2lh2oJFgdOR5X5FZbaElJPWqDRadAODmbHwNfSfUMD6q/hPEpT9V8LfUI8NLV3JUyjZV1DOnoIy3k/OheRcnHTlBBjq4Wuoi72OHAuZFH2JGC0CSq1AYkUmUfmXSCi4QG1dMgAqHS8kVnPo49qPsfZ2BJsYIH7Kl7OyqIDjq1cQVVXL0TELUYgl9EuNIdmvLVkqDUvkW2itPINvyU+o47UY93XBoJ0Nx2LyeP9QLN29rfh0qD/l5eXs2rULIyMjxo8f/1QDD49IyFb9QUJ2Yy0UJ8D4PY0GXhAoP5iCKrcGiwnuSM/NBZuW0G56s3NlZW2guvoeAS3XNTPwAEUrVyLW0cFo/mw+vjYHJyMn5ree/8LPKPxQKmKJ6GVN/P8xmFjp02F4C67sSSL+ej5+nf68p0IkFmO5YAG5r71G1YmTmAxu2mxnoidjTld3Vv6exJ2scto4P/b9NbLBLHg+X0asYZadmq056Qw09OW+4igd3bogurIS2kwCXWN03Eww7mjPBxfzKBpoyWWfQPzq7nM5WcTB27mMbPcoXi6z0sd0iAddDySzzNqGD8qq+Vgi4cew1VzKucA3kd+w6PJS2lm1JNT8Pv6jdUg6ZkvdnXA2raxn2htLMDL6/9PV/bf05J+EIAik1ys5X1bFzYpa7tfUk1bfgACgVSBrSEauiEFeH41UnQ+AWMcdR8tudHbqxQD7FgQY6j3VsEOjp3H3zAku79zCHd/2nAnujX5DPf2Ls7ni7k+lRsNbkrX4qW/gW/kTqnANhp3sMRnkzsWkYuZsjaS1kylbZ4SARsmmTZuoqalh1qxZWFo+nR6gok5J168v0M7FjM3TgxtpC9aGgEePhxUz1VdyqDye3lh3L9sLFz6DqcfArWntbk1NErcihjZTevoDf1DAWr3xBlvb1bD53mY29dn0Qjzx8KjxKWigK8GDXxr5/2sQtAJHVt2hKKua8R+FYGT+r/mjGo/Rkj50GIJWi/tvR5p58zUNarp+fQF/e2O2zXyic1RRCatbs9rWkY1U8En7eZgUfouP4UgcTvwEXd+GHo3MrFqlhqLv71AhaJnbxYi02joso3JR1+pw6o2uOJg+Uq0SBIHyfUnU3S7i4CgnvqiuYIKdOSu9nVBrVexO2M2We1soqi/CQSbQwcAc82stUGYWIXJwZer7/8DC8sWoXJ7E3z7x+qxQqBWkVaaRWpFKdHEsUUV3SKtIRitokIrl+Fq2pZNDZ/q7hOFu0lzm72kozc3m95/WkJGazLUh04i0dsG5tICeYhX7LV3QE2t5U7sMN1E2PtVrUV5uwCDYFtPhLbiVXsaUX27haWPIztmh6EtFbN++naysLKZMmYKrq+s//dwvT8Tz85U0Tizqgq+tEewa38gy+cotMHWiPqGM0i330POzwHywMaK1QeDZG8ZsbXIerVZNVNRo6hU5hIacaiIEAiBoNKSPGo2mogLl9pVMODedEZ4j+EeHfzz3/EPjD+PA11FUlymYtKzDC0nFvcT/flSV1LPr01vYuRszeFHrZwrb/CES7/DtNxgPGNBsfMPlND4/Ec+eOaGP2Ff/QPhaVKffZ1pgV9IVpXzm3RJR9VW65LdEmnYVFt0Bo0bqhYbMKorXR1MbYs1ESyVFdfXII8oIsjJlx6yQJmW+gkpD0bpo1OUNbB3tyOqiUkbbmvGdtzNSsQilRsmxtGPsuLeJpMosxICTyhLzNBU29RZMnvEBgQFPhJheAH97I3+36C6bYjdhKDfEUGaIRCxBpVGhFtRUNlRSXFdMcX0xBbUFCI3+O3pSPQIsA2ht3Zo21m1oa90Wfdmzx8nqa6q5cWA3d08fp8zOmVP9J5ItlhOckUB7Dzd+FnRx09HwWsNi7OTgU7eG+rOV6Le1xmyUF3dzKpi86RY2xjrsndsBM30ZBw8eJC4ujuHDh9Oq1T9no8urqKf7yosMCrTj2zGtIXY/HJgJfT6DjgtRFdRStC4aqaUuVvNaIT46F+7/1tgU9Ri1ATQqPaWkfk1L/9XY2DTnnCnftYuCT5Zh880KZms2U6Go4PCwwxjJX2ypmRJVxOkNcYRN9nmmpfpL/H0RdzmXSzsTn7kJTtBqSRsyBJFIhNuRI4ie6NKuV2rotuICrpYG7JkT2vTFoVLAD+3JNjBjtKGKFiauTNOPxUmnHd5nj0PbqTDo24e7V5xIo+ZyLnVTvRmSn01dgxoiyvmwuxezuzZdfarLFBSuuYPERIedg+34OquQgVYm/Ojngvyxa4zK3MfO2x+T0KBDVoPq4XYzwRhPOx+GeAxhaIsX433828fkyyqLSMqOAwM5NeoaBEFAJpEhFUkxkhthpW9FkEkQDpBlT2cAACAASURBVIYOeJh60MK0Bc7Gzi9UFaJU1BNz5iQ3D+2lvr6erOFTOWDphlzVwKj0uxi278C6CgXBepXMrluEtaEzXlVfU3u2BL1AS8xGehGZVc70zRGYG8jZPisEcwM5J06cIC4ujp49e/5LAw+w6mwSCI2SaNSWNIpyO7SD0AVoapSUbLmHSEeCxVR/xAURjeWUXZc0M/C1tamkpa/Cyqov1tYDm32Ouryc4lWr0Q8JYb9jLkl3G4VAXtTAa1Rawg+lYOFg8MwldC/x94V/F3tSbxdxbX8KTr7mGFv+a6F3kViM5bz55L31FtW/n8G4X98m43pyCa+EteAfv93jWkopnT0fC3XKdCHsfZwOz+fDHot5N30/N52DECmu4OLfD92oXyF0wcO+EZPeLijiyzA6nMGeGZ4Mi05GFWzJV1dS6OJl2aRJSmqui8U4b0p+vceU6+Xod7Pn49Q8psams8Hf9WFxRjuX0bgZ2RMdMxuV1JYKyWhOnD1CmbScrNr75OsHwn+g0Oxv4cmf2rGFuN/2IZHJCRo8gqDBw9HRN/jzA58DdVWV3Dl1jLunj6GoqcYgqDP7g/oQpxXjXpzLZJmaiy6+XKqsY6BuMmPql2Jv1RfHnEXUXipAr5UV5mO8uJFRzswtEdga67Jzdii2JrqcP3+ey5cv07FjR/r06fMvryO5sJq+qy4zvZMbHw7yg/0z4f4RmHsZwcKH4g2xKHNrsJ4biNxeDzaENb4IFkaC/NGcCIKGqKix1NalExp6Gh1589h//iefULF3H7Kt3zPm/lv0dO7Jim4rmu33rLh7Notr+1MYvKgVzn4vG59eAqpK69m97BbWrsYMfa01oj/p+BQ0GtIGD0EkleJ2+FAzb75BrSFsxUWsjR9UnD3uzWs1sL4zqBUsbdOPY+kneMPBkACZLm2vxCPy7NUknNmQVUXxj9EYtLflhpeK+XlVKJHhmV7P6ZkdmnDbwKNmQ8PODhxra8I7Sdl46euyJcAN58eq+sorIoiJmYtIJMXf70dObD1PQcQ1rPxaMe2DT15oHv/2dfLdR47FZeAoGnT1uXlwN+vnTuHMzz9QmJbCX3mJadRq0u9EcnTVcn6eP5UbB3Zh49uSytc+4fO2ffh/7J13eFTV9r/fMzWT3htJIBAIoUMggEgT6QhKB0ERFBti12u7ei3YsAsWpCgCUqQIIr1JbwkBAiE9pPc2febs3x+DtIRigO/1x533eXj0OXvPPiUza9asvdZnnbHYGZyZxOstGvNjSFP2Vhl4QrOa8cZ/0azRdMLSn0a/swC3uGB8x0SzO72UhxYcpIG3jl8edRj4ffv2sWvXLtq3b0/fvn2veU0fbjiNm0bl0KdJ/gNOrIAeLyACYyhfmYIlqwrf0c3QhHvAoR+gIBEGzLjEwANkZf9AZVU80c3+XaeBN506RcXSZXiPG8t/CufjqnblX3G1c+uvF5PeyuH1mUS09HUaeCfn8fTT0W1kFLnJ5Zz889pq5JJSif/jj2E+c4bqrVtrjWtVSqb3aUrC2Qq2nS66dFChhD7/hrJ0XlVHEOYexsISBXmWTKpa9XA4SzkXnExthCcePcLQHyqgtyaYZ6vy0AgbyZE6pv9xsta53buF4tY1hJrdudybZWZxmybkmi0MPJLC7vLq8/N8vDvRMXYZSqWOxOMPMmBCe3pNf5kBkx/9G0/u+rktPHkhBAZDGnl5gt+WLMKSk4m2uhwh2/EMCKJJxzjCY1oT1KQpHn7+V9zksVkslGRnUpCeSvaJBLIS47EYjbh4eBJzZ0/KO9zJeyVG8iQVjUvymealRm7emjcyCnGTzDxlf5cYVR4xzT5CuTMUw9Ei3O9sgNfgSNYl5vP8smM0CXTn5ylx+LlriY+PZ82aNcTExDBq1Khrth7cn17K2O/389KAaJ7oEuDIptH5wtQdVO0upGpD5nkFS6ry4etOEB4HE349L20AjqKnQ4fuI8C/D61afVXreQghyJowEUt6OvFfTuHdpM+ZcecM7mlyD/Vl97IUErefZczrcfg1cK/3Ok5uP4QQrP3qGPlplYx9PQ6vgKuHbYTNRvrgIUguLkSuWlnLm7faZe7+dCc6tZLfp3e/RJYAIRzKrGXpnLh/ERM3T6W9pzsPeVRy55EapIAYmLTu/OdFWGUKv4pHmGy4PtyM9xct5JfmXTCpVDzu5cWbcZf2PhCyoPSnJEzJZfhNiCE30p1JxzNIM5h5umEQzzcKPq9PYzYXcyzxEaqrj9Oo4RM0bvwMklS/RITbfuM1P38VSadeolHDRwkNfZT9+w+zd9dOKC/BGxuW4kJkm2OjQ6Nzxd3HF1dvbxQKJSAwG4zoy0vRV1QgzomWufv507hdRxq1iyXVzYePc8tIUrviYdIzwVrFpK5xvF9UzeqiCtqocphqfZMmPq1oHvkBNcuKMadX4tm3IR53hTN3dwbv/n6KuEa+zHmgI16uahISEli9ejWNGzdm/PjxqFRX3x4RQnDvrD0UVZvZ/kIvXP54FuIXwsNbMBSHU/ZL8nkFS0mSYPkkOL0entgHfhfeiLJsPqdNU0LnuD/QaGrraVSuXUfeiy+ie/15xkhzaB/Unm/6fHPdeiOXU1FkYMl/DtC8SzC9JzoLn5zUprrMxC9vH8A/3IN7n21/zbBN5brfyXvhBUI/+hCvoUNrjf+emM+Ti4/ywfDWjI27LFMu+wDM6wd3vcE8X18+O/IZY3ytPEwzgo/shPHLodmFsKnlbDVF3yTg2j6Is82MLFy7nl/b3Y3RVcUzwQH8q8WlejOy2U7JD8ex5NXgN7EFclMvXk/JZUl+Ge08XPk4OozWHo4kD7vdRPKZt8jPX05Y2ANEN6tf1tptb+RttmrOpLxLfv4K3N1jaBHzIUKEs3//fo4ePYrJYMBbqyLIzRWdJMBqxlRdjZBlkCQ0Li64+/rh7utHQMNIPIJDKdcb2ZaVw3IjJPsEorVZGCaMvBLbirNKLU8kZZFvtjBKWs49Yi1NmzxDiNs4ShckYSsz4TO8Kbr2gbz7+ynm7clgcOsQPhndFhe18hIDP3bs2FqywXWxLjGPaYvj+WhkG0Z7JcOiEXDHdExNnqdk3gk0ER4ETG6NpFZA6lb4ebijaXfPly5ZJzVtJllZ39C2zRz8/e+qdR5Zrydt4CBUAQF8NNWPIyXx9W4E8hcbvj9O1skyJrzd5W81jnDyv0XSnjy2LzxN9zFNadO7tmrjxQhZJmPkSOTKKhr/sb5WL9i/lFmzywxsf6EX7trLnKgl4yBzN/JTR3l076scLTzE8/41jE3WoNB4w2N/OsI756jcmEn19rP4PtiC345t5nBKNstjemL20zIuyIcPmoejvegXhWy0UfzDcayFevwntcQlyoc1ReW8diaXMquNKWH+PNsoGF+147ry8pbj7d0JV9dG9Xp2t31MXqXyoEXMh7Rp/R0WSzEHDw0jN/d9evbqwLPPPss9w4bhFxFJSpWBhHI9CTU2CnxCMEbGQEx77FGtKHb341SViRU79/Dy2k3cn5zHuy6BZHn5MU4ncbhba96/605mlRoZFp+C1ZzPv8UrTPIp4o4uGwg0D6d49jHsNVYCprRCtPLjsZ+PMG9PBpO7RfLVuPb1NvAWm8zHG5OJDvJgRLQO1jwBgS2wtniG0p+SUPnp8J/YwmHgrSZY/wL4RUG3py9Zp6LyCFlZ3xEaMrpOAw9Q8s032IqKOD2pO7sL9vJc7HM3ZODzUytIO1pMh34RTgPv5KrE3BFCREs/9q1Mo6LIcNW5kkJB4HPPY83NpeKXpbXHJYnXB8dQXG3mu51ptRfo8yZYalDs+ogZd87AVe3BTxWuJDf2g6KTkHjpmp59IlAFuVKxMpWBd/UnQCMYn3sKdUY1SwrLuedICtlG8/n5Cp0K/8mtUPvrKP0xCVNqOcMCfdjduTkTQ/34IaeEuH1JfJCeT5nVRmjoqHob+GtxW3jyOSYLKwvL6efvSWO1haysWZzN+QmFQk1IyCgiwiej04VhNBrJyckhPz+fwsJC9Ho9BoMBixDkefmR4ulPorsPeklJsErBoxGBTGgQgIdKyebiYl48nUGhTcXdYiOT3Q7RJuppfLy7U7Mzh6rNWagCXfGbEEOOJJj602HSS/S8PjiGh7o5VOzqY+ABFuzJ4K21Scyf1JHeCc9ByiZsYzdQvNyGAAKfaIfqL43u7e/Dzg/ggTXQuNf5NWw2PQcPDUEImc5x6y7p9PQXpuQzZIwYgXZwPya03Ucz32bM6z+vXo1A4ELhU02ZifudhU9OroOacjNL3j6AXwM37nuuw1XDNkIIsh+ajDk5mSabN6F0r73XM31JPJuSCtj2fC9CvS+L9a97Fo78CE/sY5e5kCe3PkkPdyufF7igNlvhqSOO1MtzWPJqKJqVgEu0L4WxguXLlyMiuzKvVAXt/HBXK/kwOpyhgd7nX2OvsVA85zi2EiO+Y6JxbeOocD1VY+SzrEJ+K6pAq5C4J8CbyWH+dPCsX1bgbe/J76+oYUZ6Pr0OJtP9SC7f2CeQ0mg1+V4PcDh3G1v3D2Z/wlTOlq5HG+KLe7tYND3vJrd7P3bc0Y8vWnXnpwbNifcKoG+QH4vaNOZwt9ZMDfPkVMFuhu1Zx8QTuSisRbyvm8/HLVvRK24F3pqulP2URNWmLHRtAwh8sh27y2oY+vVuSmrMLJwcd97AHzhwoF4Gvtpk5cttqdzRxI9ehs1weh1y91cpWeeI/fk/1OqCgS867Wja3WrkJQYeIDXtQ4zGs7SI+ahOAy9kmYJ//xulhwez76jBKlt5+463623gAVIPF1GYUUXnYY2dBt7JdeHuo6X7mKbkp1aSuD3nqnMlSSLw+eewl5dTNm9+nXNeGhCNLGDmxuTag71fc2SdbXqdHmE9GN98HLtq1CwJlqEqBw5+d8l0Tag7Xv0bYUoqpWGNLx06dEDK2MeEUDek3QXobIKpJzN5+EQGxRbHHqDSXeNIZw73oGzJaar35CKEIMZdx/ctG7EjLppxIX5sLKlkc0lV/R7aNbgtPHmAfLOFzSVVbCqt4miVnjLrVXuGA6BEpqnWSjs3me7uJjroalBY8zCacsmsyGCxoSVbxd1oJAsTPZKZ1rg1AT6xSJKE4XgxFatSkS0y3oMj0XQK4sutqczakUpMsCffTYwl3NchErZ9+3Z27dpF8+bNGTFixBUFx+pi5sZkvt6eytoHImm9ui8iuC3F5nex5Bjwf6gVLlHnvAbZDnP7ORqPP3kQ3C9oYhQXbybx+GNEhE+hadNX6zxP+S9LKXjrLYqeH8s0zQpe7PgiD7R84Lqv83JsVjuL3zyA1k3FqFc6OTs+ObluhBCs/+Y4Z0+VMfb1OLyDrl6JnvP0M9T8+SdRmzaiqkPr6cMNp/lmRxq/TetGmzDvSwf3fAmb34AJKzFH3snY3+4jvyabpSYtDUuK4Kmjl3yWhCwomXcCS1YVPo+1Yv7qn9HrDaQH9+SPU8UMHNqM9WYD7ioFrzQOYVywHyqFhLDaKV2SjCmpFNfYIHzujXKEV8+ht9uxyQIvdf3qU2/7jdfLEUKQZ7ZyRm+i2GqjxGJDFgKrOR/ZmIK7+RQ6UzwBtlNosF7y2hL8+V0xhu2iBzIKRvjaeD26FYEu53bDq8xU/J6B8Vgx6jB3fEdHU6CC6b/EE59dweiOYfxnaCt0GiWyLLN+/XoOHz5M+/btGTJkCErl9Xu0+ZVGes/cQb+YQL40/AtRfIoyvwUYM7T4jm2Oa9uLxI32fgWbXocRc6H1yPOHTaZ8Dhwcgk7XgI6xy1EoasfFbSUlpA0ajDI6ikmDMmno1YifBvyEUlF/7/voxiz2rUpj2DPtCGteO4PHiZOroa80s+Q/B/AJduW+F2Kv6iSYMzJIH3IPPmPHEvzG67XGq01Wen28gyaB7rXlDmxmRyqyygUe2016dTaj1t5HE6WVJamFKNvdD0O/vGQ9e5WZws+PovRxgREhzJn3A6HhDdlsjuJARhmvjGrDWkwcrNTT3M2F1xqHcLefJwio2pJF9bazqBu44zeuOaprVPleL7e9rMHlSJJEAxcNDVwuD4kEARc6ENntRszmAqw2PburJZYWy2wtt6NAYkyIL081DDyvPy9sMjV7cqnaehZhl/G8OwL3XmGsPVHA66tOAPDVuPbc09axSWmz2Vi1ahUnT56kW7du3H333X87BfHDP04jC3jR9Xc4c5CaoHcwpmvxGdH0UgNfmgbb3oXoQdDqQsNjIeycTHoeISy0avlFnQYeoPCDDxFGI4sHu2OwGnnnjnduyMAbqiwc/iOTRm38nQbeSb1w89LSfUwztsxP4tiWs7Tvd2XBQG1kJN4jR1K+dCk+99+PtvGlnZw8XNQ83y+aV1cdZ21iPkPbXpRIoNJC37dh2UQ4+iONO03hhQ7TmXH4c2Y1Cuepoz8hdXoYQtqcf4nSU4vPiGaULkzCPcHIwIEDWbt2LRO6BmOTffhwxXG+GteeR1oG8G5aHhOPZ9DS3YVpEUEMvjsCTQMPypYnU/jFUbwGNMKta+g1U0ZvhNsiJi+EQLZcOzxzMTZZcKDKzoe5Gu46ITHljJXDNfBoeCD7usQws3k4DXVahF2gP1JI4WdHqPwjE20TL4Kfi0XfKZCpi47y9C8JNA1yZ/3T3c8b+JqaGn788UdOnjxJ37596du379828EeyylmdkMfUFjLhCZ9g8hlJZVZ7vIY0xq3TRR3vZRnWTHO8WQd/eknRU2bmbCoqDhDd7D+4ukbWcRao2b2HqnXrqBzTh+XmvTzR7ol6NeS+mIPrMrBbZO4Y3uTak504uQLN4oKIbOvPgd/SKS/QX3VuwLQnUWi1FH1Yu6UlwJhO4bQJ8+LddUnUmG2XDsbcAw3vdHSKMlUytsVk7giIYq6wcdzDCza8ApdFPHQt/XDrHEzNrhxauEcSGxvLoX17eCHOjdZhXkxbEo8xu5o/Ozfn8+bhWGTB40lZtNt7khkaIzlTW6CM9KRibTrF3yViyanmVnFbhGsKz5Ry+rcUItsGEdq1ASr3Sz14qyw4a7KQbjRzqsbIoUo9hyr1lNvsaCSJ7j4ejAr2YWCA1/lcV9low3C0kOq9edhLTahD3PAc0AhtUx+WHMrmg/Wnscoyz/eN5qFujRyt94CioiIWL15MTU0N9913Hy1btvzb9yPLgvtm7yG/Qs825VO4KLwpKH8fz75N8exzmUdz4DuHQNmw2dD+/vOHKyoOc+ToOIKD7qFFi0/q/JKRDQbSh92LLMHjD5jw8wpm0eBF9W7nB1CaV8PSdw7SqlcYPcY0q/c6TpyA41fhkv8cwDNAx4gXO6BQXtkvLZ07j6KPPyb8++9w79Gj1njC2Qrum72HKd0ieX1Ii0sH8xLg+17Q9Uno/x4VpjKG/doHjc3K6swc3Eb9CC0uVYiULXaKZiUgV1vwe7Iti9b8Ql5eHmMmPMjbW/P4M6WEF/tH80SvJghgW1k1S/PL2FhSiUUIfFRK7kBNdEo1zYqtxMWGEnp3o3o9p9s+Jr8quYDH8woA0NoFHpKERq0ElYIqm50au3zJ/ChXLR093ejj50lvX4/zKnGyxY45pRzjiVKMJ0oQVhl1uAeevcJxaeFLwtkK3lmXxNHsCu5o4sf7w1vT0O9CylNKSgorVqxArVYzbtw4GjS4tnxqXfx6JIfnlx/jk8A/GF6zkgLj5+i6d8JrYKNLjXXRafi+JzTqDvcvP+/FW60VHDx4D5JCRVyn3+rMpgEoeG8G5QsXsubZjixzPckvQ36hmc+NGea1Xx2jMKOSCW93xcW9/l8WTpz8RcqhQjbNPUnX+5rQoX/DK84TFgvp9wwFSXI0Fqkjg+2VlYksO5zD+undiQ6+7HPx23SI/xke2w1BLfgzYwVP7nqL3hY7n+tVSE8euiSlEs61Afw6AXWQK673N2HOvB8QQvDApId4Z1MmaxLyGNYulPeHt8ZV44iOV1ht7CirZktpFXsrasg7Jzv8kLsH73eq36/f297I55stHKzUk1uiJyuzgqpyEzYhQCnhrVPj5aklwt2Fxl6uNPPU4aNSgiywV1uwV1iwFuixnK3GklsDNhnJRYVra3/cOgejCfMgt8LIzI3JrIrPJcBDy0v9oxkZG3be4MqyzK5du9ixYwfBwcGMGzfuil3Zr4XebKP3zB2EUsRKyxOUW19G1X0sngMa1d4wmtMHqvPh8b3nGx4IIXPs2BTKyvfTMXYpnp5t6jyP4fBhsiY+QPngzjza+jAvdHyBB1s+WK9r/ovsk6Ws/eoY3UZG0e7u62u64sTJtRBCsPH7E2QcL2H0q53wC72y9lH1jh3kPPY4gS+/jN9Dk2qNl+kt3PXJDpoFedTehDWUwVcdILAFTPodJImPdz7AT5nxTC+r4JHYp6HHC7XWNCQWU7b4NO7dQjF1cmPevHl4eXnx4KRJzN+fx6dbztAs0INZ93cgKrD2tRdbrByvNhKiVRPjXr+N2NveyF+OsMqYksswni7Dkl2N7RrVc6gkNKHuaCI8cWnuizbSE0mpIKfcwOwdaSw/fBZJknikeySP94q6pERar9ezcuVK0tLSaNOmDUOGDLnuHPi6+HjjaWZtT2OV5g2i5LbIPd7Es2/D2uGWja/Bvq8d/VyjB5w/nJ7+BRmZXxId/Q5hDcbXeQ7ZaCT93nuxWS1MnagnKrQVP/T74YZy4mW7zNL3DmGz2Bn/ZheU6ttiu8fJPwRDlYUlbx/A08+FES/FXjVskz11Ksaj8TTZ8EedKZWLD2Tz6qrjfDamLfe1v1R3hiMLYO3TcN930HYsNpueqWt6cLjazNelVfSYshu8azswFWvTqNmTh+/45hR61PDzzz8THBzMAw88wMHsKqYviUdvsfPs3c14pHvk+fDuzeJ/zshfjmyyYSs3Y68yI4znNl0kCaWHGqWXFqWXFkl14aEfz6nkx32ZrElwSJ+O7hjOE72jLunvCJCdnc2KFSvQ6/UMHDiQ2NjYeot4AZwtM9Dnk+0MFruZoTyAtccPePatYxM0bTssvBc6PQyDPzl/uKRkO8cSHyYkeAQxMR9e8VoKP/iQsgULWPxEc7b4F7By6EpC3G+sicfJP3PZsSiZAVNb0aRD4A2t5cRJXfzVVazzsMZ0HNjoivPM6RmkDx2K19ChhM54r9a4XRYM/2YvZ8sMbH62B37uF2WdyTLM7QsVWTDtMOi8yS3azMObnqbSKvGLphkR41dekuAAjuy74u8TsRboCXi8HWkV2SxbtoyIiAjGjx9PpVnwxpoTbDxZSIsQT14Z1Jw7o66siPt3uWUVr5IkfSxJ0mlJkhIlSVolSZL3RWOvSJKUKklSsiRJ/a+2zq1G4aJCE+KGLtoX13aBjn9tA9A29kblp0NSKagwWFh6KJv7Zu/hnq93s/54PuPiItj5Ym/eu6/1JQbeZrOxZcsW5s+fj0KhYMqUKXTs2PGG/mBCCP694hAqu4nnlDuw9vi6bgOvL4XVj4N/NPR95/xhgyGLk0nP4eHekujot694LYaj8ZT9+CMF/dqy2iuVVzu/esMG3mK0ceC3dEKivGjc/uY0Jnbi5HKiYgOJ6hjIoXUZFGZeuTpU2zgSv0kPUrlyJfqDB2uNKxUSH41oQ7XJytvrki4dVCgcjpOh1JFtAzQI7MtLLXoiFPC0IQn9iRW11pRUCvwmxCC5qCj98STNwpswfPhwsrOzWbhwIR5qwXcTOzL7/g5UGq1MnHuQcXP2syO5CFm+tY72DXnykiT1A7YJIWySJH0IIIR4WZKkFsASIA4IBbYAzYQQV81zrHd2TZWJ3SkldGniV8vbvhJCCFKKajiQXsrmU0XsTS3BJgsa+7sxsWtDRsSG4elSe+OwoKCAVatWUVhYSIcOHejfvz9a7Y0Lb63fn8ITq8/wqnIZ9981Fbc+3WpPku2waCRk7oaHt57P3bXZajhyZDQmcwFxndag09Wt4CcbjWQMH4HZWMOUCVXcEdWHmT1n3rA3sffXVOI3ZzPyXx0JauR57Rc4cVJPTHorS987iEIhMea1ODS6ukt9ZKPxQgepNatR1PEZ/XzLGT7fksLcBzvSJybo0sHfX4DDcx2fswYdsForWLCtL1/nG+lpEXw2cQ9KnXetNS051RR/l4g61J2AR1qTnHqG5cuX4+/vz/jx4/Hy8sJss/PLwbN8tS2VkhozDbx1jOoYxj1tQ2kSUL9eC/8n4RpJku4DRgoh7pck6RUAIcT758Y2Am8JIfZdbY36Gvllh8/y0opEAMJ9dbQM8aKhnyshXi64qJVoVAr0FjuVBgt5lSZSi2o4U1hNhcGxqx3h68qg1iEMbh1CqwaedRo9i8XCrl272Lt3LzqdjmHDhtGs2c1JEazIL6f/lxvxpYzlvYJw7z+i7ol/iY/d8wXETgIcBU/HEh+lrGwXbdvOw8/3ziuep+DtdyhfvJhvJwdzMlLJintW4O1S+436dygv0PPLOweJ7hzMXQ84teKd3HryUytY9Wk8UR0C6Dul5RWdlJrdezj78MP4P/E4AdOn1xq32GTu+Wo3lUYrm57rcalTZ6yA2V3ON+VBpaGkZDtztj7CYpOOCbpIXh79W53n/Wsj1rV9ID6jm5GWlsayZctQq9WMHTuW8HCHE2a22dmcVMjiA9nsSy9lao/GvDKwfp+h/6uK18nAX/qcDYD9F43lnDtW18VNBaYCRETULyNjZIcwWoV6cSCjlAPpZaQUVbMtuQiLTa4110unJirQnQEtg+nQ0IcukX6E++qu+EYRQnD69Gk2bNhAZWUlbdu2pV+/fri53ZwespazlXz63XcUiZZ8HWu6soFP2Qw7P4R29zs6y/91OGUGpaXbiY5+56oGvnrHDsoXL+b43ZHsDM5jfs/5N2zghRDsXp6CSq2gy73Owicn/zeERHkTd08kB9akExbjS4tudUthu9/ZDc+h91Ay5wc8Bw5EVpb8VgAAIABJREFU27TpJeMalYKPRrbhvtl7eH/9Kd4fflEmms7bUVz4yzjY/Rn0ehl//97c23Y0hj1L+dmYQdj+D7m/y8u1zuvaJgBbsZGqzVkovTREDYji4YcfZsmSJSxYsIB+/frRqVMntColQ9qEMqRNKEVVppv6jC7mmp68JElbgOA6hl4TQqw5N+c1oCMwXAghJEn6GtgvhPj53Phc4A8hRO1g1kXczI1XWRaUGyyYbTIWm4yrVomXTo1Wdf3l+hkZGWzbto2zZ88SGBjI4MGDadjwynm6fxdDYjEnls1grO0uxoaUMePpK6QwlmfCdz3BKxymbAKNQ0cnJ+dnks+8SXj4ZJo1fe2K57GVlJA+7F5qPFRMGVXCtLhnmdJ6yg1ff0ZiCetnJ3LnqKa07XP1Jg9OnNxMZFmw9ssECtIqGfVqJ3xD6na6bGVlpA8ajLphBI0WLUKqowPbjPWn+H5XOj9NjqNHs8v2lFZMhqTf4NFdENQCm62Gg7v7sfh0MX+6uPB5r0/o3ahfrTWFEFSsTkV/oACvwZF4dA/DYDCwcuVKUlNTady4McOGDat3qvXl3NJwjSRJk4BHgT5CCMO5Y/+n4ZqbiRCCtLQ09uzZQ0ZGBh4eHvTo0YMOHTr8LXGxa52jems21m1fMllEkKMMZ9urQ/ByrSP10lTpUJeszodHtp9v5VdSsp3E44/i59eLNq2/uWJvSCEEZx97DP2+/bw8SUGDNl2Y1WfWDaVLgkNlcsl/DqBUKRjzRhzKm5wS5sTJtdBXmln67kFcPTWMfLkjKk3dn4HK338n7/kX8J/+FAFPPFFr3GS1M+Sr3VQZrWx4pge+bhd9DvUlMCsOfBrBlM2gUFJefoDkLSP5olxHmosrPwz8iTYBtetRhCwoW3Ia4/ESfEY1wy02CCEER44cYePGjQB069aNO+6444bSruHWZtcMAF4Chv5l4M/xGzBWkiStJEmRQFOg9jb3TcJkMpGamoos1w7PXC/V1dXs27ePWbNm8fPPP1NUVET//v2ZPn06nTp1umkGXjZYKV14Cvv2b/lVkc8xEcW/R3Sq28DbbQ5PojQVRi88b+ArKg5z/MQ03N2b07LFZ1dt/lu+ZAn6nbtY1c8DfZgv79353g0beIBjW89SVWKi++hmTgPv5L+Cm5eWuye1oDRPz87FyVzJYfUaPBjPIUMomTUb4/HjtcZd1Eq+GNuOcoOFl39NvHQdN38Y+BHkHnH0agB8fDrj3+FpXpOr8LeYeHzTVJLLauvVSwoJ3zHRaKO8KV9xBkNCEZIk0bFjRx5//HGaNm3Kjh07+PLLL9mzZw8m060J2dxodk0qoAVKzx3aL4R47NzYazji9DbgGSHEH9dar76efHx8PGvWrMHDw4OWLVsSFRVFRETEVb8dbTYbBQUFZGRkkJaWRmZmJgChoaF07tyZli1bXrO59t/Fcraa0sWn0FavpUyxgkHWD+nVPJjvHuhU957A+pccjQvu+RJiHaGc6uokjsaPR6MJILbDL2g0flc8n/HESbLGjSOjqQev3qtn3oD5tA9sf8P3UVNuYtGb+4lo4cfAx1rf8HpOnNwIB9emc+j3THqOa0arnmF1zrFXVpI+7F4ULi5ErlqJQlc7C2/OrnTeW3+K94e3ZtzFzb+FgF+nQNIaR7i0QSyybCPh4Gi8d/3J1IBgrDoffhz4E428GtVaVzbbKf3xJOaMSnxGNMOt44VMnrNnz7J161YyMzPp2LEjQ4YMqdczuO2LoaxWKykpKSQkJJCWlobd7sjU9PHxwcvLC51Oh0ajwWKxYDabqaiooLy8/Pw3dmBgIDExMbRq1YqAgJuf5y2EQL83j4r1Gbi77sDd8gkjFZ+SSQM2PdeDQA+X2i/aNws2vgpdp0F/R0GHwZDB4SNjUCg0dIxdhovLlXuv2isryRgxkipjOdMmmHi2z5uMajbqptzPprknSY8vZvxbnfG8SXrYTpzUFyELfp+dyNlTZdz3fAeCG9cd59bv30/2pIfwHjuGkLfeqjUuy4KJ8w5wNKuCtU/deakEgbECvunmUHt9dBdo3TGZ8kje0AfPk0U8GN4QrasfPw74sc6eyLLFTunCJMwpFXjfF4V750trU/Ly8tDpdPj4+NTrGdz2Rv5iLBYLWVlZ5ObmUlRURHV1NSaTCYvFgkajQaPR4OXlhb+/P0FBQTRq1OimZcrUha3CTPmvZzCnVODVYC/upe/zned0PijqzBdj2zGsXR1JR/GLHM26Y4bCqAWgUKLXp3E0fgJC2IjtsBQ3tyvLAQshyHlyGtW7dvL6eGjfewxvdH3jptxPzuky1nyeQMdBjeg89MYkiZ04uVmY9FaWv38Iu1Vm1Kudrtg0vvCjjymbN4/Qjz/G657aXnNBpYlBX/6Jr5uGNU92w+0iCRMyd8OCIdDhgfONREpKtqNfPQ5TkY2HIiLx0vkzt//cOg29sMqU/pyEKbkcjz4ReN4d8X9S8XrbGfl/CkIIDEcKqVibDrLAP2Y72jMfc7rBSIZmjaR3dADfTqhDBuHUOkcDg8ieMH4pqLTU1JwhPmEiAO3bLcTd/er5+aVz51L08UwW9tOQO7AdP/T7AbXyxhUh7VaZX949iCwLxr0Rd8WNLidO/huU5NTw64eHCWzkydBn2tW5VySsVrImPYQpKYnIZUtrpVUC7EktYeLcAwxqHcJX49pf+hnd/Cbs+RxGzodWwwFIS/0Y/zUfkGnX8nhYBG4aD37o9wMNPWtn4gmbTPmqVAxHCnFtF4DPyGaXSKrUl9u+kfc/DWuJkZL5JylfkYI6xI2QTuvRnvkYY8woplVNxEun5r37Wtc28KlbYcVD0CAWxvwMKi3V1ac4Gn8/oKBD+8XXNPD6gwcp+vRTjsRoONazAZ/1/uymGHiAIxuzqCg00HNcM6eBd/KPwz/MnV4TmpOXUsHuZSl1bsRKajUNPv0UhZsbOdOfxl5TU2tOtyh/XugfzbrEfObvybx0sPdrEN7F0ain6BQAjZs8T263gbSwGPi+woTZZmLShkmklqfWPr9Kgc/Ipnj2b4ghoZiib49hK7t1OfJwGxl52Wi79qRbfQ1mO5UbMij87AiWrCq8B4cTEDIHxdGvoeNk3lY9RVqJns9Gt8Pf/bKfk8kbYMlY8G8G45eB1p2ysj0cOToWhUJDbIfFuLldveDIkpPD2enTKfRR8NMwD77p+y2+Ljen/V5FoYEjGzJp2jGQiBZX3ux14uS/SXTnYNr1jeDEzlwSt+fUOUcdFEiDTz7Bkp1N7nPPIWy1bcfjPZvQt0UQM9afYn966YUBlcYRQtW4wdIJYKpCkhQ06/gtaW2jaVWUwRxlMBISkzZOIr4ovtbakiTh2TsCv4kx2EqMFH4Zj/FEyc16BLW4LYy8MbmM/A8OUrMvD3GLxX7qQthkavblUTDzMNU7cnBtG0Dwk01wT38KKX4h9HiR3yNeZMmhHB7r2YQ7m14mf5r0m+MNE9gCHlwLrr7kF6wm4dgUXFxC6Ri74ort+/7CXqMn+7HHMJhrmDlaw8zBs4nwvDma7kIIdixORqVW0m1U7Z+3Tpz8k+h6XxMi2/qzZ3kKmcfrNp5uneMIfvPf6Hf9SeGM92t5/ZIk8cnotjT0c+XRhUdIK77I4/cMcRj6sgyHWKAQqFTuNLx7BVmRvkSf3MRcv854a715eOPDbMjYUOc16Fr6EzS9Ayp/F0p/PkXlpsyb9AQu5bYw8io/HZpwDyrWpFE0OwFz9pUV6m4mwmqn5kA+BTMPU7EmDZWfCwGPt8W3t4TylwGQvR/u/Yasts/yr5XHaR/hzXN9Lwu3JCyB5ZMgtD08+BtC5016xlckJT2Pt1cssR2W4uJydZVIYbdz9oXnMKen8dm9Cp6/d2adxRn15czBQnKTy+l6b+Mrbmg5cfJPQaGQ6Du5Jf7hHmz64SQlV+if6jN6NL6TJ1O+eDFlC36sNe7pomb+pDhUConJCw5RprdcGGzUDfq9A6fXnVer1OnC8b53JUUBrjT6czYLGo+mlX8rXtz1Ij8c/6HO8JHK14XAx9ri0Tscl+hb0/T+ttl4FUJgPFZMxbp05BorLs198bw7Ak1Y3a3vbgRbhRn9/nz0B/ORDTbU4R549WuINsobKXULrJgCSjWMXUxNUCzDZ++hqNrM2ml3Eu7r+tcFw44PHIJjkT1h7CKsCkHSqRcoKdlKcPC9xDSfgUJxbaOaO+Ndqn5axPx+Kvo8N5P+jW6esrNJb2XxW/vx9Ncx4sXYW9pV3omTm4m+wszyDw4jSTDipVjcfWqnKgtZJvfZ56jeuJGQd9/Be+TIWnOOZJUzbs5+WjfwYtHDnXFRn9uPEgJ+ewriF8LQr6GDIzmitHAL6p/H4maQMd+/nLdyNvJHxh/0bdiXt+94G3dN/ZQmr8b/VHaNbLZRszef6l05CKMNTbgHbp2D0bXyR+FS/+Ime40F44lSDMeKsWRWAuDSwg+PbqFoIr2QZDtsf9chZhTUCsYtQfYM5/FFR9hyqoifJsfRLepcmMZmdvSTTPwF2k2AIZ9RY8og8fjjmEy5NG36GmENJl5XelX+nG+p+OQL/uioIPo/HzK4Sf2KKa7ElgVJpBwsZNSrHfG/BV+YTpzcSkpyqlk18yhu3lrue6EDOvfaBZKyxULOk9PQ797tSK0cMrjWnHWJeUxbHM/dMUF8M6ED6r8yd+xWWDwaMnY59tKi+gBQnLkYt6XT0FglmPQHP1ck8fnRzwn3COezXp8R5RN1U+/zf8rI/4VssqE/XIj+YD62IiMoJLSRnmib+qBp4I46xA2Fm7pOQyrsMrYS47nerzWY0yqw5usBUAXocG0bgGuHIFS+5zyDylxHRVz2PodC5MAPQa3jiy0pfLblDG8MacGUO8/F1CtzHOGZnENw1+uIO5/jbM4CUtNmolZ70brV13h71/m3qkXer0uofO1t9sUo8P/oPYY2vfdmPLrzZCaW8PvsRGdOvJP/r8k9U87ar47hF+rGsGfbo6nD2ZNNJs5OfRTDkSOEvPcu3vfW/iz9uDeTN387yZA2IXwxtj3Kv37Vmqpg/kAoS4cJK6FhVwAKz3yP168voxQqpIc2EC/JvLjzRQw2A8/FPseY6DHOPPmbgRACS3Y1plOlGE+VYSu8ILEjqRUoPDQozqUDCllG1luR9RfttisltA090TbxxqWFH+pg1wt/GCHg2C/wx8sg7DDkc2jjqCpdeyyPp5bEM6JDGDNHtXG8JnUL/PqI49t/2NeYmsSRdOolysv34e/fh5jmM9BoavekrIusjauofvZVksMV+M76jN5RtZXwbgST3sovbx/AxV3NqFc6obwJubxOnPy3yEgs4Y9vjxPa1Jsh09qgUtdOAZb1es5Om4Zh336CXn0F3wceqDXnu51pvP/HaUbGhvHRiDYo/jL0NUUwfxBUF8ADqyHMYW9Lkufg8etLKIUCcf9yKgJb88aeN9iTt4euIV15u9vbBLvVJfL79/ifNvKXY9dbsebXYM03YK8yI1dbkC0OYTNJAQp3DUp3NUo/HeogV9SBrnUXK1QXwNpn4MwfjrzZe2efFxDbk1rCpPkHaR/uw09T4nCRbLDjfdj9OQS2QB41l7PGPWRkfAFA06avExoy+rq/1Y//vhDx8gzy/JUEzv2WDk2urCNfX7b+mETygUJGvhxLYENntycn//+TvD+fLQtOEdnWn/6PtKrTcZEtFvKef4HqzZvxnTKZwOeeQ7pMnPCvjlIjY8P4YHjrC025q/IcHr2hHO5fBhFdAChN/RnXFdPRWAWW4V/i0mICy88sZ+bhmagkFdM7TGdUs1EoFfWvPbmakUcI8Y/5FxsbK+pDiaFEfHzwY1FqLK3X6/8WNosQe2cJMSNMiHcChdj7tRB22/nh4zkVouW/N4h+n+4UFQaLELnxQszqIsSbnkKsflKUFe4U+/cPFFu2NhbxCVOEwZD9t06/YemHIqFlc7G5Z2uRnHH4Zt+dEEKIzOMl4utHt4p9q1NvyfpOnPy3SNx+Vnz96FaxbtYxYbPY65wjW60i7623RFJ0c5H18CPCVlFx6bgsi883nxENX14nHv7xkDBaLnz+RXm2EF+0F+KdICGSN5w/XJm7RdR8HCDktzxF9eZnhJBlkV2ZLaZsnCJaLWglRq8dLY4XH6/3fQGHxRXs6m3xG/xA/gEWnlrIoJWD+PbYtxishmu/6O8iBKRtg2+7w8ZXIDwOHtsDXZ+Ec9/AJ/MqmTD3AF46NT9NaInXnhnwQx8wlmMY/ikJjUwcPfEQVlslrVvPpm2bOVfsx3o5equeWd89QvDb86kMdKPDkt9o1ij2pt+mSW9l+8+n8Q11o9Ogq+fmO3Hy/xute4XRc3w0mYklrP82EZuldttpSaUi5M03Cf7Pf9Dv30/GqNEYExIujEsST9/dlP8MbcnmpEIenHeQKpOjlSje4TB5IwREw5JxcMSRmukZ2gflI7spD/TDffc8qhd0pYHalTl95/BRj48oMhSxM2fnLbnn2yZck16RzpfxX7I1eyt+Ln5MbDGR0dGj8dDchIyQzD2w/T3I2gPeETDgA4geBBeFV07kOgy8q0rBmt6FBOx9F6rzsLYcREoTL/IrtqBSedCw4WOEhz2AUnn96o0JRQks/+opxv5agjHMjzaLV+LiH3jj93UZQgg2fn+CjMQSRrzkDNM4uX1J2pPH9p9PExbtw6An2qC+gkyH4Wg8eS+8gLWwEP9HH8X/8ceQ1BdkQtYk5PL8smM08nfj+4mxNP6rEbe5GpY94HAMO05x2AyVBrvNROna4QQc24PFRYt94Hu4tn2Eaks1aoUaF1UdirTXwe0fkxcCZBso1SQUJTArYRb78/fjrnZnVPQoxkSPoYF7nS1mr4zd5oi3H/gOMv8E9yDo/rwje0Z96R/icGYZUxYcooc6iZl+69AWHMYW0IT05qGclU6hVLoT1uB+GjZ8FLX6+tt9VZor+fLolxgX/sLEbTL2djHEzPkRpcetSWVM2pPH9oWn6XpfEzr0v3ltDp04+Sdyel8+W386RXCkJ4OeaFNneiWAvbqawnffpXLNb2iimhD8+hu4del8fnxvWglPLjqKTRZ8Na49vaLPOWCyHba+7RA0C4uD4d+BryNLrez4LLQb3sJNb6E6ojm6wXNQBdW/gPG2N/LG08tQrn4Ke9tRuHR7FckzlJOlJ1lwYgGbsjYhhKBraFdGNB1B7/DeVxfsKkmBEyvh6E9QlQOeYdDlMce38bneqhfzR2IeS5Yv4jn1atrJJ7C5epLR0INsfxMabRDh4ZNoEDoOtfr6vWKr3cqvKb/y/ZHZDF1fSv8jMrq+fYiY+QkK7a2pOK0oNLD0vYMERXox7Ol2zqInJ/8TpMUXsXluEh5+LgyZ1havgCv/wq7etp3CGTOw5uTg0a8fAU9NO69iebbMwCM/HSa5sJqn+zRlWu+oCxuyJ1fBb0+DbHVUyXacApKE1VRExbr78Uk6iFJATadReAz6oV73cdsb+YqkOchb38S3VI8sSVjD26COGY0iqi8FLu6sSvuNlakrKdAX4KX1ond4b/o27EuX4M5oTJWO1l6ZuyFtOxSddCwa2RPipkKzAaCsI6/WYmTHr98Qcmo+MYpszBotmeFq8kJc8PbvTmjoaAL8+1xXxepfmGwm1qWvY07iHAyFufx7nY6wjBp8J00i8MUXau3y3yzsNplfPzpCVamRsa93xt3HKV3g5H+H/NQKfp+diEIpMfiJtgRFXtkhk00mSufOpWzuPGSjEc+BA/CdNAldmzYYLDZeXXmc1Ql5tI/w5vMx7Wjod65XRWUu/DbNEb4Ji4MB759Ps6zK24px8zMom92Df9cZ9bqH297IA9jtJopOz8Z+aDY+hWW4GR0bKkKpBv+myK4B7FUJfheV7LRXUoOMuyzoajTSxWiiq1kmPDQWmg+BFkPBs46uS0JgydyC4cC3qJL34i4MZCn8qWpsxdSkDf7BAwgJGX7Vjk11kV6ZzprUNfya8iuV5koGVjTkwaUlKI0WQt59B6/BtSvwbiZ7fk0lYXM2Ax9tTeP2N78zlhMn/3TKC/Ss/eoYhkoLve6PpnnXq+tF2crLKZs3n/JFi5ANBlxatcJn3Dg8+vfn97RKXl91HJsseHlAcyZ0aegonBICEhY5Qjg1hdByONz5LIS0OadrI5Dq2X/59jfyFr2jkjQgGiHslJXtpjhlPiJjO656M25GgYusQy2rUEoqLC5eHNK5sVWrZo+9kiKbo5q1gXsD2gS0oYVvc5p7hdJI54GwFGLP3IYm/QAeeZnoDBZMqNlkj6UqMoIefe4gILAfOt31x/xtso2TpSfZn7efrdlbOVV2CoWkoG9wTybuUaFZtgF1RDhhX36FS/TV9eNvlLT4IjZ8d4JWPRrQc3z0LT2XEyf/ZIw1FjbOOUFucgVteodxx8ioazapt9fUULlmDeVLlmBJTUPSaHDr3h17997MKPZiU66ZFiGevD2sJR0bnRMgM1c7amYOfAuWGmhyF3R6GJr2c2he1YPb38if+BVWTAa/ptB8sOOhNYjFrlJSXr6f0tKdVFbFU1OTjBDWi16oQKFwodiuIdkEyQYrWSaZCtkRj1YIQZjNRqTVRoTVhlb4kFjTlmyXLrw29G56RDVGcZVvXqtspdRYSqGhkKyqLFLKUzhTfobE4kRqrDVISLTyb8WgyEH0LgnE9NGXWFLT8B47hqAXX0RxC9sSgiMOv+z9Q/gEuTL8hViU6tsio9aJk3oj22X2/prGsW1nCW7sRd8pLfD0u3YmnBACY3wCVRv+oHrjJmyFhQCYG0Wx3TWCvZ6NiOjWiUeHxtIs6FzihLECDs9zJHfUFECnR2DwzHpd9+1v5KsL4dRvDtnPzN2OTBtJAQHNHVWoPpHg5o+s1mGRTFgtZVhNhdjN5SiMVSj1FagM1WirK1DrqyhWKjih1XLcy580Nx+SZAUF9ipQXNpcQCkpcVW5olVpcVG6oFKosMpWzHYzZpuZGmsNggvPV6PQ0MS7CS39W9IlpAtxwXG4lxoo+uRTqtavR92gAcFvvYl79+43+iividVsZ8WHhzFUWhj9Wic8fOuXuuXEye1IyqFCti86jSRJ9J7QnKjY609ZFrKM6fhx9Pv2od+3H8PRo2B1OJdFOm+qGjWlcbdONIxri7ZpU1QB/khpWxzp2UEt63W9t72RNyYkUPzNN2gjG6MJD0brZkSjzENVdRrKM6A8E+yWK7xaAvdA8AgBvyjHQw5qSbFnDAuPm1i0P4tSvYU+MQFM7umNTldDsbGYEmMJpcZSjDYjRpsRs92MVbaiVWrRKDVoFBq8tF4EuAYQ5BpEmHsYEZ4RqBSOTVxrXh4l331PxcqVSJKE3yOP4PfIwyhcbr2xFUKweV4SKYcLueepts5OT06c1EFlsZFNc09SlFlF8y7BdBvVFBe3vx9OkU0mjImJlB09RvLOgyhSThNSc6GZieTujkvTpniPHIn3iOH1utarGfn6a+/+g8jNLaE49Sxe+w4gWcznjyu9vdE0boYmsj/ahg3QhAWjDfVFHRKMpHUFlRZ0PufjYBUGC1tPFfH73nz+TDmBTRb0aR7Ioz2b0KnRjQv6C1lGv38/5YsWU71tGygUeI8cgf/UqahDrr7RczM5vD6TlEOFdLm3sdPAO3FyBbwCdAx/sQOH1mUQvzGbrJOl9BgbTZMOAX9LPVLh4oJbXBxucXGEP/YIlQYrq/88xZ6th1BlpdOkpog2ZSV4ny2h0y24j9vCk1+XmMezSxOw2eyEmCq5U6untb2chjVFeJXkoc7LRiovv/ACjQYahGMKCafEL4RM1wAO2Dz40+yKWaUl1MuFwW1CGN+5IZH+NxYXl00mjAkJVG/bdj5Wp/T2xnvkCHzGj0cd+vcycW6UlEOFbJp7kuguwfR5MOamSZ06cXI7U5JTzbafTlOcXU1kW3/uHNUUT//rr1qvCyEEhzLLWXssjz9OFPBQt0Y82bt+OvO3fbgGwGixczirjH1ppRzKLCO5oJoq04UYurvFQFhNERHVRYRVFxFe4/hviL4U5UVxczkgCPemTdBEhKMOCkIVFIw6OAhVcDBKLy8Ubm5IWu0lxlEIgVxVha2sDFtxMZa0NMypaZiST2NKPI6wWM7vunsOGIBH37v/T8Iyl1OQXsnqT+MJbOTBsKfbOzdanTj5G8h2mWPbcji4Nh0hQ9s+YcQOaIRGd+MBEbsssNhkdFeQV7gW/xNG/nKEEBRWmcmtMFJcbaJMb8Umy9jsAp1GiZdOjZ+bhkZearxKC7BkZGLJSMecno4lLR1rXh72i73/i1EqUeh0IMsIu93R7d1+qdCRws0NbVQUuthYXOM64dqxE0r3W5stczXK8vWsmnkUjU7JyH91vGIJtxMnTq5OTbmZA2vSOL2/AJ2HmrghkcR0C/2v9lz4nzTyNwPZZMJWVIS1oABbYSH2qipkvQFZr0c2GpAUSlAqkFRqlD7eqPz8UPr6om3cGFVw8D8mFFJVamTlx0eRZcHwFzrgHVhbnsGJEyd/j6KsKnYvTyE/tRJ3Hy2xAxoSc0fof+UX8i0z8pIkvQMMA2SgCJgkhMiTHNbtC2AQYDh3/Oi11vunGfnbAUOVhZUzj2CqsXLvcx3wD7v5TYSdOPlfRQjB2VNlHFqXSUF6JW7eWtrdHU5Mt1C0NyGMc73cSiPvKYSoOvf/04EWQojHJEkaBDyFw8h3Br4QQnS+ylKA08jfbAxVFtZ8Hk9VsZGhz7QnpMn1K2A6ceLk+hFCkHO6nMPrM8lLqUCtVdK8awhteofhHXTrfznfshTKvwz8Odzg/A7mMOCncx1L9kuS5C1JUogQIv9Gzufk+qkpN7Hm8wRqyk0MerKN08A7cXILkSTp/7V357FxlGccx7+Pj6wdn+s7ju2NTVySOAnG5W6KCgUCSDRFpRV/QQ8J0RbaoznPAAAJ3klEQVRB/6gECKnQP5BopR6q1BYVlaugAqUtRC20HEkL5QghYCdO4sSOD2LjM7G9DrZje/ftH/MaNo7XBHzM7Pj5SCvPvrPR/vxk9/HOO7MzlK/Po3x9Hv0fjNCw4yj7X+9i3386qajJY/0lpVRuLnBlKmfe2xMicj9wEzAMXGaHVwNHYx7WacdOa/IicgtwC0BFRcV84yggPDDG879+n7ETk1x3ey2la3PdjqTUslFYkcUV397Axdefxf7Xujj4Zjf/fqiRQEYKZ19QwrpLVlFYvjjXhJjNp07XiMgrwGyXE7/HGPN8zOPuBtKMMfeKyD+AB4wx/7PrXgXuNMbMORej0zXz19cR5p+/20tkMsp1t9dSvEav7qSUm6JRQ+fB4xx8s5vWhn6iU4b81RlUn1/M2i8Wz3kO+zM1r+kaY8wVZ/g8TwIvAPcCXUDsxUvL7NiiGD8xyYfNQ5TX5MW9jNdycOS9Pl555ADp2Sv42h215JfqTlal3JaUJFTU5FNRk8/4iUkO7+6leXcPbz/XytvPtVIUyrINv4jM4MJ/f2Ze0zUiUm2MabZ3twFNdnk7cJuIPIWz43V4Mefj2/b2s+PxJlJSkyjfkEdVbSFrNhWQlvn5TtuZaEzUsOdfHeza3kpJVTbX3LqZldl6HLxSXpOWmcrmy8rYfFkZ4YExWvb00bKnjzeebeHE4Em2fLN6wZ9zvnPyD4jI2TiHUHYAt9rxF3COrGnBOYTyO/N8njl94cISMvPSaKsfoK2hn7aGASRJKK3Ooaq2kMpzCn17lsXR8ASvPnqADw4cp/r8Yi6/aR0pqct3a0apRJFdkE7d1hB1W0MM9Y4u2k5Z330ZyhhDX8cIbfX9tNb3M9gzCjg7QyrPKWDNpgIKyjM980Wl+WjfN8DOJ5o4+dEUW75VTc2XS33xeymlPptl/Y3XwZ6PaGsYoLW+n972MBhYmbOC0MZ81mwsoGx9kBVpiXUyzrGRCV5/ppnm3b3klWZw5Xdr9EtOSi1jvj/V8FyCJRkESzKo2xpiNDxBR+MxOhoHOLKnj4NvdJOUIqyuziW0sYDQpnxPf+U/Mhll785O3n2xnamJCBdcV0nd1pCr58xQSnmb7z/JxxOJROluGaZj3wAdjcc+ntbJLV5JqCafsvVBSqtzPfEpf2oyQtNbPbz/UgfhgXEqavL50jfWklfq3gnPlFLesayna87UcP8YHY0DdOw7RtfhISJTUZKShKI12ZStC1K2LkhJZc6SfmNt5Pg4TW910/jfLkbDExSFsrhwm17oQyl1Km3yn9HUZISeI8N0Ng3SeWiQvvYwxkByahJFFVkUVWZTUplDcWU2mcHAgu7sHDk+TluDc5RQ56FBMFCxIY9zr6pg9dlB3bGqlDqNNvl5Ojk2xYeHB+k6PERvW5j+D0aITEUBSM9KJW9VBrklGQRLVhIsWUlGboCM7ACBjJS4TTkyGWXk+DjhY2MMdo/S2x6mt22Y8MA44EwbrT2viPUXr5r3FWiUUv62rHe8LoRAegqV5zjH2wNEpqIc6zpBT2uY/qMjDHZ/RPPuXibGpk75d0nJQiAjleQUITnZmeaZnIgweTLC5PipFxnJDAYorsxm01fKCG3MJ1ii8+1KqfnTJv85JKckURTKpij0yXlhjDGMhicY6h1lNDzB6PAEo+EJxkcniU5FiUw5W0ypgWRSA8kEVqaQlZ9Gdn46OUXpZOQE3Pp1lFI+pk1+gYgIGTkBbdZKKU/RA6yVUsrHtMkrpZSPaZNXSikf0yavlFI+pk1eKaV8TJu8Ukr5mDZ5pZTyMW3ySinlY546d42I9ONcRvDzKAAGFjDOYkiEjKA5F5rmXDiJkBGWPmfIGFM42wpPNfn5EJF3452gxysSISNozoWmORdOImQEb+XU6RqllPIxbfJKKeVjfmryf3A7wBlIhIygORea5lw4iZARPJTTN3PySimlTuenT/JKKaVm0CavlFI+lvBNXkSuFpFDItIiIne5nSeWiLSLyD4RqReRd+1Ynoi8LCLN9mfQhVwPi0ifiDTGjM2aSxy/sfXdKyJ1Lue8T0S6bE3rReTamHV325yHRGTrEmUsF5GdInJARPaLyB123FP1nCOn1+qZJiLviEiDzflTO14pIrtsnqdFZIUdD9j7LXb9GpdzPioibTH1rLXjrr2PMMYk7A1IBo4AVcAKoAHY4HaumHztQMGMsZ8Dd9nlu4CfuZDrUqAOaPy0XMC1wIuAABcBu1zOeR/w41keu8H+/weASvu6SF6CjKuAOrucBRy2WTxVzzlyeq2eAmTa5VRgl63TM8CNdvxB4Pt2+QfAg3b5RuDpJapnvJyPAjfM8njX3keJ/kn+AqDFGNNqjJkAngK2uZzp02wDHrPLjwFfX+oAxpjXgOMzhuPl2gY8bhxvA7kissrFnPFsA54yxpw0xrQBLTivj0VljOk2xrxnl0eAg8BqPFbPOXLG41Y9jTHmhL2bam8GuBx41o7PrOd0nZ8Fvioi4mLOeFx7HyV6k18NHI2538ncL9ylZoCXRGSPiNxix4qNMd12uQcodifaaeLl8mKNb7ObvA/HTHe5ntNOFZyL86nOs/WckRM8Vk8RSRaReqAPeBlnK2LIGDM1S5aPc9r1w0C+GzmNMdP1vN/W81ciMn3RZ9fqmehN3uu2GGPqgGuAH4rIpbErjbMd57ljWL2ay/o9cBZQC3QDv3A3jkNEMoG/Aj8yxoRj13mpnrPk9Fw9jTERY0wtUIaz9bDO5UizmplTRDYCd+PkPR/IA+50MSKQ+E2+CyiPuV9mxzzBGNNlf/YBf8d5wfZOb6bZn33uJTxFvFyeqrExpte+uaLAQ3wyheBaThFJxWmcTxpj/maHPVfP2XJ6sZ7TjDFDwE7gYpzpjZRZsnyc067PAY65lPNqOy1mjDEngUfwQD0TvcnvBqrtnvcVODtetrucCQARyRCRrOll4CqgESffzfZhNwPPu5PwNPFybQduskcHXAQMx0xDLLkZ85jX49QUnJw32qMtKoFq4J0lyCPAH4GDxphfxqzyVD3j5fRgPQtFJNcupwNX4uw/2AncYB82s57Tdb4B2GG3nNzI2RTzh11w9hvE1tOd99FS7eFdrBvOXuvDOPN297idJyZXFc7RCQ3A/ulsOPOFrwLNwCtAngvZ/oyzaT6JMzf4vXi5cI4G+K2t7z7gPJdz/snm2IvzxlkV8/h7bM5DwDVLlHELzlTMXqDe3q71Wj3nyOm1em4G3rd5GoGf2PEqnD8yLcBfgIAdT7P3W+z6Kpdz7rD1bASe4JMjcFx7H+lpDZRSyscSfbpGKaXUHLTJK6WUj2mTV0opH9Mmr5RSPqZNXimlfEybvFJK+Zg2eaWU8rH/A39HeFztaOEpAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 908ff89ea397e8572b0dd40af818632144f92c9f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 319/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From ac2285305675eeacd3d6bc332e90e9a25f5569a7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 320/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From c7d62619cec7a8f60098f105fcf3baabb0a75014 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 321/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- 4 files changed, 174 insertions(+), 96 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, From 8a1d4813012a0ddd28dd76c0fbbfe8ce0aaa0b9d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 322/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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T2PpV2PHtKBAkcrD2ZXDWC2DZ86LO359SPQyZ9QJmPJ8Zz2fOCygEAYWGR3xqO20TG+ia3ELf7DbaKuNk67PYRA+zCZBoprKVZDzezoTbzmRsEROd65mIdVBwu/Ckm4B2bM3hBGmSdZtUwSeW97DnPKQR7c9ppu6V/U/hL3tyJigYhnFG2jg0x199ayte+cf89rnfoTs5RDq9ihXL30Nn5zXHngsIPNh3ZzMQfBNqeYi3RoHg7FdGgcCJLXiMoh+wu1xjV6XGaN1rFvrHCv8j8+WgWcBryNryPq6Y28CVc4/y4vxGWr0yvsaZsTvYm17HtpZLqLR10nA78N02QjuH2lksMtiejVMPsWohWvVxJzw6Cg2ytWBerkKgiGULLR0JWrsytK5L0toVpWxXkmxnAsc9PXcumaBgGMYZRVX55N37+eK93+BVq77D0uxeEolBViz/AD09L0OkWRhO74V7/gW2fQ2qsxDPRp3DZ78yahpyjjUnzXo+u5qF/65yjV3lOrsrNUbq3nHHbrEt2l2HDrEZrIRcWAzpn5iibWIKa66B1D2CwMHX9RzmUv5bE4T6kxTOdaCO7VokUg7xtEs85dC2qIV0a4xUa4xUNt6cj6bxlINoiAYB6vkQ+M35MjqZJ0ilsHOnvv/DBAXDMM4YfhDyt996AAof4p0XPYAb62b5svfS3/crx+4imtwJd/0jbPkS2DFYd30UCFY8H5w4qsqeSp0HJqa5P1/iwXyZ/dXG0WMkLYuV6ThX5DKsTsVZUoaWoSrM1ClMVJkbnaGUP34cI88SsvEq8dYUTmsbbq4bJ5PFjVs4MRs3bkfTmIVzdP7Icgs37pBIOzgxG/V9/MlJvNFRvNGD+KOjeDtH8SbGCSanmJueZmp6Gq2e7KE3CAVSr389y9797lN+DkxQMAzjjFCu+7zvq5/kvOzHyPUXWbL4bSxb9tvYdiLaYHwr3PUPsPVrUefw5b8DV7ydeqqTTcUq9x+e48FCmQfzZWa8qDmm3bW5pDXN6/o6WJdJsiqdoD/mMD1UYu+jk+x7dJSh8QoAMbtBzjlMvxwilzlMrqVObukiWtetJ7bySsgtfsLfoKoEs7N4o6P4h8bwRseojxxmbmiYxsgIwfg4zMwg4fFBx08kaGQy1FMpCpkk+fZl1C1oaIivSiBKAIQoIYqi9JZmWXZKz0DEBAXDMJ52ozMTfPH2P+Sq7h/jyTIuufDTZLPrmys3wV3vh+3fhFgLPPsdlC9+K1+vOHxp9ywPF0aph9GAdSuSca7raOWSXJpLWtOsSMYREcJQGdubZ98PDvLDDROUZuqIwEDbGOe1foslsfvI5JLI8udEQ00s/TVoW3JcHlWVsFjEGx3DHxvFGx3DGxvFGx2lNnwYb3SUcHIS8Y5vkgosi0oqFaVMhkp3N5VUikY2S6M1i59KQKMGxVmC2WnCWhWoQ3MMPieZxE2lSaXSxDMtxDMZEi1Zlq2/4LScCxMUDMN4Wm3c8x327fkz1uQKkLmR6y764+g5g8OPRDWDnd+BeCv63Hey8ewb+exsyNc2jlMOQs5KxXnzQCeXtqa5qDVNV+z4B9Xmxitsun2YPY9MUC00sB1hcKDGJS23saz6RRIJhfNfBxd9B7pWRw+EAWG1Su3RR6lt3059927qu/dQ270bzeeP238oQi2ZpJJKRoX+iuVUUino6MDp6yM5OEjLwADZ1la6XAc7CKjNTDJz6CCzh4YpHB4nGGrgWnFSySy5RavJtnaRTudwrRiWOkggiKfR1AepCVIS7FwKrjj158MEBcMwnhaeV+DHj7yboPxtKn4/S1d+hAtWXA5eFf73nfDAxyCRo/zc/8f/DN7Ap2d8dm2bImlZXN+d43V97Vzcml5wdNKxfXke/d4h9m2YxLKFZesyrEhtYcn4R4hVh6LB557/Z3DeawnVpbZjB7Xvfp7qli2UN28mOHAAmk08fjxGPtvKXEcHxWVLqaRSBG1txPr7yfQO0JFsJ+PbtNYVuxYiZZ+w1CCshFhbwdpUxArL2OJgi0NSYnRaa4G10E6UjgiAmWYCQg2JqgwCCNEvFUSExrYFRlM9BUxQMAzjZ256+kc8sukP0WCGByZfxo0v/CsG21uj8Ye+8haY2snQeb/O3y99M1/Ph3jDJZ6VTfGPqwe5vjtHi/PYO37CUDmwaYpHbzvE2L488ZTDhVflODf4FOk9/wVhQLjsWirtv0dtNkHlq9uovvdN+CMTWE4CcdP42S5quQvwn30tmmrDSbWSiKXpFZdFoYUVNewjITAJMnmSUVTns5vpSbDmDdan6NGwAFDOeAt95Sk7bUFBRP4TeBkwoarnNJedD3yU6DkOH3ibqj4gUaj/EPASoAK8SVUfOV15Mwzj6aGqHDjwb+zd90FGyz08PPdXvPdVN5CNWfCjf4Lb/5Z6qpP3XPoRPpU4l/Yy/NpAJ6/tb2dNeuEnj/1GwI77xtj0vYPUpmu05+I8/9md9NfuJ9ywkUJlMbPWvxBqCh61ETsOtguyhOQ5L4Fzjt9fdv6HEKg1+xM0ICAgCH1CQrAFy7axHBtbHCy1UC9krjrBaHUv47WDNIIqMStJX3I5A+mVtMTa8R2PmlWnalcpW2WqeNiBTTJM0OblaNH00cNXrCoTzizjsWlmrBnyOscENYZDYZnVxbm86JSfo9NZU/g08BHgM/OWvR/4S1W9VURe0vx8FfBiYGUzXQr8e3NqGMbPCd8vsW37O5mc/C73j11IIfaH/OPrL8ItHILP/yYM3cc9A9fya0t+l1Smgw8s7eWXe9uIN0ceVVXCkoc3XsGfqlAfLTO3aw5/pkYa5bkikHWjZp8teYqsAdZAM5YcueZWVQIJCe0AtZRAPBpBhWJ5hnJtllpQpuIXaWiDXEcPba29tCa7SNGCW03gVpQApQxUQqUcCnMJn92FreydPUBVQe0YVlsf1YQym6wzGxuj5B7GJyAVpuj1Oumpd9Bb6yCtaQTwtEGBKebYT50q+CFuzSdVqZAr52mv5Omq5FlZL5Krlbl/1brTcp5OW1BQ1btEZOmJizkWiFuBI687uh74jKoqcJ+I5ESkT1VHT1f+DMP42alU9rNp829RKu/llp2voK//Tbzv5WcjG/+L8DvvpKbCH615F7cPvIh3LO3lV7NZrKka3n1jVMbLUSCYqBBW/KP7DBQCVRzXil5v2fA5UvSrV8MrjzKlMwylA7yeNIm4QFiiOjfB9PAB6uXonQi27bJ08Dx6+1bS4p6LU/CwJiep5yeo759mrHqIqfocsUYJD/CABoKKhSIEIqgIHWLRhhCKhYoQiIVv2YQiqIAKgILMkvT3kvZrUfLqJAKPuB+lmO9jqZ74J4w6tRMJqskk1bY07Un/MducCj/rPoXfB74rIv9IdPaO9J0PAEPzthtuLjNBwTCe4aambmfrtnfQ8IUPPPRbrF58NX9xTR/V/3oj7q5H2Zx8Obd1vZpfqXXw7q2K3rGXmfKxAk8SNm5PmuQ5nVQs4cAj48RKPj0xi5QI2qgSzOwnmD2INA4x1lLnvq4e7J4sbRJSHDpA7XARANtx6O5bzNKBi/CKNtWpWfzZSSojm5mt3ElveZpscHxbfclNUohnqDgxIMAVj7gEuIES90OsZieDLYJDsztYFQlDJFQsVayj0xAJQwLHwXNdvJiL57o0kimqR+Zdl7pr03AdKuk4XksKq7ONeFcXHW3ddOW66cq2sn5g4LScr591UPgt4B2q+mUReRXwSeAFP80OROQtwFsAFi9+4odJDMN4eqiGHNj/7wxt+yxu7XJ+sPE6fj3Tw5XTFUb/9k40vAmw6W7AG/JgpSvYHUmctR24PWncnhRuTwppcSntyXPga3tITVVZYglhXAmGf0x11+3Y4T5SiwMe7V/FJmspLZqAqUOEo1Fb/Yp4Dreew5mZIVkYoeXhncfls+G4lFpaqeZaGB5YTS0eoxxzqLgupZYktWSMwLZRLCwswAJLottXT/bWtnlCwBcLz7KoWj4Nu0bdnqPm5Kk6ZQJHyKY66csNsqZvJZcsPo91XauI2cfGa1JVAj/Eb4TRdHacmDy2NnEqiC5QTTllO4+aj741r6M5D+RUVZudy3lVzYrIx4A7VPULze12Alc9UfPRRRddpA899NBpy79hGD8Z9UP8qSreRNTM0xgrUBo6gFVMY4XHnh1QJ8QJ9hM4k2xZ+izOPmctPf0tOB0JrNTxzxgEhQblR8eZ+dFhnJJHoEpZatibv0i4716SfQG6osr97ipGKjnKdaUepuj1LRbPTbN0bD9u4BOIUExlKKUz1DIpKpkUpXSGciZNOZ2mHo9j+T4S+CgavcRGFdGwOR+iAlaoSBAgYuFkO6hnu5hKJBlOxMnH4/i2Q8qDRTWLfi9GW91j2NnEvelbybvR8w05v4tebzHd9UV01wfpqi+ipdEGGt09dWSqoRIdXgm8EN87/glogGetHuLyd9z4pM6XiDysqhcttO5nXVMYAZ4H3AE8H9jdXP4N4HdE5L+JOpjzpj/BMM48GireeAVvtIQ/cSwI+DNVmFdu+ekZaqlDBKv6+cTuLNoa46YlP+Tsbf/Mo53rCW74DC9atPAgDf5sjeIPhyg/NAYKBT9kTstkHvgYyand2OcMos+rcUtwIY+wmvZCg3X5UZ41toeeYnSDfyHTwr4Vyxnt72Oyq5tMSzutrTlshcbUFNWxIeqlSZzZEVzfw7Y7wMohkkWslqMJSeLXHsbzN3Ng8XkcXHkZ+/u7mclGRWe65tMzO8F5hTyvH29jcdXhwcxWbm39MT/OzNLl93NJ+Vr6wiX06xLSdhbbtrFjFlZCEDt65kAssEQQS5CgjlQmkco4UhpDyiNIrIpaHsSThK3dhK09dKw7+7Sc49NWUxCRLxDdWdQJjAN/AewkuvXUAWpEt6Q+3Kw1fAR4EdEtqW9W1SesApiagmGcXkGhTuNQkcZQkfqhIt7hItoc1x9LcDoTuN0pnO4UbneKenqELYffRmjVyPa9nzd+PmBRJuD9LR/j3NE7uG3xKzjvNf9GTyr92GPl6xRuH6L8wBihKgdqASNejb4tn6NraiPW857L3fEid/jLKTntvPDAw1y2ZwOJRoPAspjs6mK8f5B6z0rSnctw4zkKlRizh/YTNvYTeAdBS828t2LFewkzHRQyMWpuHRyPVDxB2k2RsGMExRnmDu9m44pz2Xj2pZSTaZzApyc/Qaa8G7uxgSsKWV49fR1tQZZHkzv5UXwrfiBkvEyzqWlhjuNg2zaWZUU1ktCLhgAPfVRDQoQQmwCb8CT7WbpmLW96zauf1Hl9vJrCaW0+Ot1MUDCMUydsBHgjpaNBoHGoSJCvRyttwe1LExtsIbY4S6w/jdOZROxjBdbU9B1s2fJ2XLeNjsF/5Q03jzEYn+SD8n4WlQ5x28V/wrUv+kNc+/hCLig2KN4xROn+UTRQhjxlZ7lB18HbGBi/i+0v/GW+ZyWohMJgMMNluzawcs8eYp7H9OBy/MELSXWeS8ruYLxqccBrUAwnUH8LWt6NBnUQG02k8VpyNFpa0dgJb2kD4vE4qVQKx3E4VCxyf3s/u1acjSIsnpsiPnE/TrABy8rzrPJaLi9eQCyIUUzXCHsT+DGo1BtUalXqtRpevUHoNaImqJNQILSPJAFbogfdLMAKwfFRpwF2Hew6YtUQqWJTZ8Zeyr++9p+f1Lk+k5qPDMM4wzRGSpTuGaGyYRL8qBZgt8WJLWkhNjhAbHELsf4M4p78yndk5BZ27Hw3mfQaepZ9hNd+YjfrU9v4QOUfUISHrv8sL7ngpcd9Jyh7FO8apnzPCOqHjFkWm/MNUqW9ZIe/xw8uv4Kq/St0SYnVtSlW7djJyr17sYOAxtLzqC+7hmouw7CWmbUn8WOHCLzDWIVRnHIRBfxMjjA7gNWaodgaMMQoFWuCXDrH+q71nNN5DkkrSaPRYK5YZPvoGEP5IjhxLpwe5cqxQzihP+854nOPzm20DoEFoWfhjTqENth2iGtFKZkOSaKkREkppFVIqU0iSBAPEySCFAk/jR2ksepJLD+BPE7t4gglRK2ArQOn5/leU1MwjF9AGoRUt05TumeExoEC4lqkzu8msbad2GALdsvCbyp7zH5U2b//w9PkjHAAACAASURBVOw/8GHa259D39IP8NqPb+Sq2K28a/YTDGWWYL3uCyzpX3PsO35I8a5hincOo/WAuUyMh0bKhFTIDH2dR9YM4qYUWxSKVc7bupFVhw4hCjOLz+bg6nM51GZRl+i2Vcf3SVfzMDmK6wmZdCfdQRLbDxg/q4tpF7wgRFWwLRcvDKiHjx0iwsLFsWI4auOogyXg4uGqkCRDt58lrRI1+dg2qdAlHcSJBTGsn2Aci1B86naVajNVrCoVu0rDaeDFAtQVxHVoYFH2bQpVm3zZAS9BPEjQG0vSY8fIiUPMV9Krk5z7miuf8LgLMTUFwzCAqKmm/MAY5ftHCQoN7PYErS9ZRvqinsfc/fNEwtBj584/Z2T0Fvp6/w99S/6S137iQV4fv5mbZr7KpoGrOev1N5NKtR79Tv1Qgdkv78Yfr1DrTHJ/vky+UCTVuJtDbT5cOIClUJzzuHbz3SwdmwCFg0uXsm3dWkotLXTRwoXdy+hx0gSHprHLFrYdp7QoZFrKTFkFdkiRglUF9aEBcVwyJEiTIEWChJ0gRgLRGBYxXBIkfB/L86jELZRZVOskHYd2ieNIiSA9DRKilo9aHhKzCBMJvGSammsxXp9mT2E/u7wZJuwGVatO2a6C7dGd62Rxx1mscDtZpClyXkiPFeCoz56JgAPDDsOjSebmMiQCh4xatFsBfYDrJaLmL0JGxWPUqeC0zLJUAuDJBYXHY4KCYfwCaIyWKd01TGXTJARKfGWO3CvPIrG6HbF+gkHdTuD7ZbZsfTvT03eydOlv0zvwdt7w6Xu5iQ9xw8wP2LTuRs694YOIFV1Bh42AwncPULpnhDBus8Wy2LNvAjuzh6n0JOrazIYZJqsxLh3awHO2bSZeb3B47cXUll9Gj9vPL9ktpCQB1YDgUMiwNc1ua5Kp1jIlakfzFlgemgzp7x+g96yLOJTrY5uv7CrX2Fup4x1pHVEFEZYf2MGVD/yALd39xM+5n8XxA5yfjrHEbVDDYyS0Sftr6Fp+NZmWVcT9dhp5YcveB9l14CGGtm1GyzVSdaW/pjzXs8n6NklPiHshlh+iwTjCFjSmaBzUhVoMNKZ0x6ArBhoDzSrqNOfdKOEq6iq2rcSsEEFo2Amc4VbgN576P8cJTPORYfwcCwp18rcdpPLwOOLapC7sJnN5P2536knvs96YYuPGX6dY3Mbq1X9JV8+rufFzP+am2b/mBfn72XHpH7HmRe86+m6C2q5ZZr+ym2CuzjDCI4UCtY4DFKxxFOFQmGPGy3KFX+B5Y9N0+0loX4rTMnBslFABqyPOrvoetuZ3Mx0PCG0LcWHCGWM8MUVV5lg7eAVdZ7+KXY00d8+WyPvRG9iWJGKsSifIOjYP58scqDXoL07z/LtvoTc+ROf6MbqyeRLNypJTTeGMJnCmHZyajQZV/KAC+FGh3izQ9UhhHoMwpmhcjq7DUawwer7BDqJ5uzlPCH4Yo6IZymQok6ZEhhItlCQTzUuGorRQtFoo2Bnm3BbmnCwlJ7pz66bDX+Gv3/BXT+ocmuYjw/gFEzYCSkfa7UMlc+UA2ecP/tRNRCcqlXaxcdNv0GhMsX79R2lru5qb/udO/u/0u7iwuJ2D17yfNc/5zSgPFY/Zb+6j+ugEZeChYp253jHysX10hjmyjfXktIX/g0UH8ag0WgSBX8OKxREVYitzlM62+dFDP+Tg6Cih42KloWdxH7c2bmVvYorB0iq6+q/G7+xjszdHy/D3GXAqvCNRZ0mmSpddptGYZWxmAhqzvJQaMakjGeVkg4z6yQr+8gosP7ZMQ6gHNkU/i1fLEHgZfD9Lw09SJ0HNi9PwYlTsBGU7QdlOUbGTlO1kc1mSshvNV+zHD8pu6NHqF8n6ZbJBifZwjqXVYVrCAlnNkw1KnBXLPKVzeTImKBjGzxENlcojE+RvO0BYaJA8p4PWFy/D6Vh42OmfxvT0nWze8rvYdpILn/UFMplzeeuXv88fD/8RK6rDTL/i4yw5/wZUlfKjk8x8bTc0QnbXfGbaS7TG86ysZukOn4eDRYhS0BLMDlEf20mtNkfyvHXE3Evxsxb71lV4dMd3KN5aBQ3JpgLOOb+NRvtuts/cystclw6rSlZ+BPwoGqnuCB8oWdhBgnwhxJ31WFaq48ZDrGSIYyluI8QuK1ITYhJn0ulg3B5kwm1jIpFiIpZj0m1j2m1jym1nMtbOTCwHMeAkZbqlAVm/TItfJhNUSAdVUmGNLm82mj+aaqSDKjm/SM4v0OqXaPMK5PwCOa9EKqzyeI16IQ7j2Zc/5XO6EBMUDOPnRG3PHPlv78MbLeMOttDxujXEl7Y+8RefgKoyPPwZdu3+azKZNZy3/mPEYr287evf5s/2/AHtfp76626he+XzqUxVGfn0VhJTVWpBSDWuLEvC6koOyLGfBl+nitQ3kzr8COfvPkS6OkN1eR9d172dfMlnU/eDbJ3J4z0itKQnWHHWPrq6DuK6DQDcsk1/oovZsINy6gJ6W5awzEqSrHu4lTLs3024cwtMD+GmpnHSIbYoM06WfeEge1nEvuQg+1oGGOrq5XCih8lYx2N+dzKo0dWYodObY1F9nHPKe2jxyySDGo76KIIvNp44CIqjAYICFqFlETZHSg3EJhTwsWjYNlWnlQnJNV+ZI9FAeQq2hsRDxQ1D3BB8calZcUp2jJLjkLeUGdtjTho0/Bov7B3gw0/57D6WCQqG8QznTVbIf2c/te0z2Lk47a9ZTXJ915PqQD5RGHrs2v1eDh/+PJ2dL+DsdR9ArRRv/8aX+Ottf4gNOG/+NhVnDfd/dBNd++aICyBCyrYI/Rr7rRnul4Avhq10h4e5auh/WTNrs+rgNjRlEfxqgrm1MTYUP8XBxhq8iRRtbYcZ6NpBX98g1czF7Jq+gLFJj3S9zqLZ/VxZnGKgYwa7uAWrMnU0v2UrwYHkAFu6z2LzyuvYl17E4UQvI7Fuis6x5hZbfQZrYwzWxrhq5kGSQQ0UGtjU1KIRxMGPEwY26oXEfMV1cmhygKnQothQao0AC0i6kHZqxKQYXd2rABaoDWojoSChhavWvHWKAqpBc4ylADQkICTQkJqGiIZY2qA1rNKmAUvCEFuPjSVSP+ydjpuPTFAwjGcqDZXyvSPM3XoAsYXsi5bScuXA4z5k9tPwvAJbtrydmdm7WbL4LaxY8UeUAuXPvv5p3rflTylabRQv+zyHPu/TM/kQ/XY0dk856bPJ38ewTHNYPe6jl47WKV4vX2CFM8rinXVSB3yq60OmXwujs2dxePsa6l6cFrfI2tgjLG9M0j6Up2f3DuL6neOGUi5bSQ4levmstZr7lryCA4lFjMc6yLsZanbiuN/QUZultzrNhXPbyVbLpGt10tU66ZqPhg4BQqghR263SREQvSjZA0oL/l2SwGPrFSdQjmv+OfZ2ZeZNFVsFW8FWnTdVbDTqlD4y2EWsjhurYrt1rHgdK1ZH5zYDNz1RTn5qJigYxjNQUKgz88Vd1HfPkVjdRtsNq37iB85+EpXKQTZu+g2q1UOsXfP39PffwJ5KjU9+8yO8e8snubfxG0h4DQP/W2StLeBY+GnhzsSjzMU20ZoboyueZ2lrnpe4RZK1gPQei/Z7lGS2Qf5VWeZivezeeC4TdNLLBNfwY5Z5h5iIdTBmdbMjtoqHMs9mTtMUtYURu4+9LQMMZ1oZzaSpO1HxlfDqtFbLLJ6boLVaIlcp0Vot0Vot44bR3Ueu2sTUIYaNG6ZxFJxQcULFDZRYoLihh0gZyy1DvIIkykgySmQqkKliOQ1EFLUVkRAEVELEBmkOZS1PvYKGr1APoRYKdYWKCvUQ6irUQqipMJg7NcH/RCYoGMYzTGXzJHNf3YN6IblXnEX60l7kVJRETbOzD7B5y9tQVS44/2ba2i7ltvFZNnzhk1y1P8YO699ZFbNwbEEdoeGOcXD1j5iNP8Byd4yOQoN0MYCpGMm9IR1eCfvItfhaOCj93CrXMum3k7QCVrk2fZzHZONqxjybsldlqlpkeyZgZ1uGkVwnI7lOam40XlG2WmL1xAHW5vexPr+TxbVJ4g1BqhZScGDGxqn4uLUybq2CXS0hQY2g1cfvU+p9Sqkbqp02fptFPRlQdkM8G7zobtFoTKIw6q8OVJpveYtWSAgSNIc0EtDoNTvRd5q9Csc+Q6hR3cNXoaFRge+F0Ygi9WahX1ehqkKtOe89bjdz5GX1J36K+skwQcEwniHCms/cN/ZSeWQCd1GG9levxu168s8bLGRk5Evs2PlukslBzlv/ccJ6Lx/63CbaHxzmufbFDCQELPCXzDCRuo1i5+3kSkXOmvFpn4a0VwWgaiXYm+xiZ7KLwQPDtA4V2dl9EbtWXse4P46DxdnuAEGlwFS5wIHEJA0ZpR532NO9iJ09q5hobQegIz/LZTs3cNnsBq4N7+Es9yD10Gam2MpBTXM43qDQAXNpl6qtNOIhNRsqIlRFqSJUgao61EKoquBps9D1AO/0FK4nslWJq5JoTuOhktKAtlBJhyFpbU5DJa3NaRgety4VKhmNpuVVl5+WfJqgYBjPAPV9eWZu2UlQqNNyzWKyzx88boTSpyoIauza9ZeMjN5CW+5K2ty/4YefnqG4bT/Pjlv0JFsIrAbFZXdTb7mFjlmfVcM+6X3TCIpHgs3p9Xx98GLubLsQbfRz/Y/v5QXf+yyKxdzl69g0eD6zwSgJHOris9U/CDGIV6pUPJfNy5bz6NJzaDgx+ku7+aWxz7M22IQm8kz3wY5ei3tDi3wwQD4QyiHN+sf8ZrOoI9cOlBREA9KhZAjpQclISEaVbBCSCUKSYUgyUFJBSMoPSQUhsSBqVrIAW0HQI+9bQxRsNDqKNgczJbp7KLrbCAIRfAt8y8K3BM+y8GwL37aoWVGqWhZ1ERoWNOzme5/FwpcogJUsju4rFCGwhNCyCCxBLQu1hM5cO+8+Zf8Bx5igYBhnMA2VwvcOUrxjCLs9QddbzyO+OHtKj1Gp7Gfzlt8hP3sAp/DnbL9jJfGZPZyVtOjMOIRWgVL/12mrb2DR4SFsKqhaVFjFnS3XcvOiS/h+17lYnkXHoVmuu/V7XLt1hK7pDUys6GXHujVMxdohCAAlq3toje1jMtvg3q6z2BHvoxyWcf0f0DfxKTTIU1XlXuBegHLUbNQShnT4Ib1ewDlBSEcY0qEB7VZIOz4tVkibH9LW8Mn4Ia4XFdYLCYCybVG2hZJlUbAs8pbFpGVHy8SiKkJVLCqWRPNWc9mRz2Idna+JnJrOBEDVQv0EhAk0SKJBGvUzaJAh9NNokEH9DCs67ehVZaeYCQqGcYYKSg1m/nsn9T1zpC7qIffyFVjxU9vUMTb+TTbc/y/M7n4uxYN/QI8Kz2oJack4+O4YYe4WOqo7WDx1CFWHcng5P0g/i/9cfBEP9g4SitA5PAGbCqzbvYEbDzxI3G2w+Zx2DvZeTSlepe5MILGNBIkZZpyACWzqRwrQ+kaob6QnCOkJfHr9gF7fpycIjs37AT1BQHKBIXkCAd8SGpZQt4S8bXHAdplyLSZTNuOWxYxlM2vb5C2h5NhUbQvPgZglxERxsLBDhxgOsdAhHqRJ+m3Ea1kS9SzpMEu3JsmECdJ+nGTo4qgdvbFZLSwE++i8ha3H5gmFEhYFLOZUmFNhFmFOoQiUm6kElFBKKNXHOV8OSgZIo5wTSzzOlk+eCQqGcQaqHyow8/ntBGWPthtWkr6o95Tu32vUuPe2/2DvAw6ViT+hL25xSS5GvBFQie0iHv8SvfXNOMUCvvYwpzdyc/vl/NvKReRTLWTDgFf8+A4Su8ZYPD5MvHWU7asT3LKuykRyhryz+7jjpcOQRZ7PyorPxeoSlyxdwMr8NMumysQDwcsIjS7w2iFwhEAtan6c3djcbQvjIkxiMxlajKvFRGhRxUJCi0wYkJWQdELodmz6bYu2eJV2J2TQUtKWkq53kCgPECv2Ey/0Eyv3Eyv3YvvHvwXOQyk6UHItCq5QcIVSTCg4MGJFBXgxDCh5IdWGT63RwPN8Gn5II1DqqtQRagh1sdDjahBRb7WrSkIhDsRDIa7QpcLi0CahQkKjZXEVkgopFdKh4HLs9tYZe+6U/k8cYYKCYZxBVJXyfaPMfWsfdjZG92+dT2zg1I1xUyk02HjHNjbfeRCvfA5dLR7PXZYhPlvDkrvIJr/OQLgdakLNvZDKwOv56vRSPrjKYaylhcGZSX7re1+je+dt7FxssfV8h7vaPOp2dBXf4/tcUauzrKG0q0O/naCe7uMO91zu6bucXXaWF274Ic/fdhuJgSL1VcKei9LR28YCpTBtsdW3eQiLg55N9HobBb/59K+dY43XwTW1NIuSRTpTI4S5PDhB8w8ouJVu4uV+YrP9SKWfYrGb2UoHY6FNjYA6Hh4+DVvxrRn8+DQBShgEeH5A3RPqdYsq9tFUFpuK2JQti5JY1B/zYKAgakcv0wmFNhUyoZBuFubRFOKWYNsW6lrUHaHhCg1HqLty3OfivM91hwW3e/ZI4ZT9X8xngoJhnCHCRsDcV/dQeXSCxOo22l+9+ikPYHfEzGiZR757kN0PjhIGQlvPJJcsayVxOAWlQ7QlPkyajfhhjgIXoy95F49M9fPeyhjbl2bpnhrlVXf9E+Ptu/n0eRbBBSAacJZX4xXlOmcHDXo1zVj+PEZii1iSnWOXHeNfuq9jT/tZdMzN8Kpbv8V1M9/He2UD7zKPUqiEpZADYzZ3hjG2iY2ngoNNPHA5x89ysd3CCgeclI+THIX4YeAwAHY9S7w4CMPPwi91USl1UihlKfpQDXzqgaK+jRXG8NWjIhZliVGWJGVbKQmULaUsSsmKUkOAE/7klkIKISFCXCxabCFmRbfkJmiQ0AapsEY6KJNolEg2SiTrJVK1EqlamVStTKJRI9GoEW80iDUaCDQ7jh1Cy46GxbBsQhFUoo5ktW1UjtQtFF8CQgnxCQgI8VeugV99xSn5/5jPBAXDOAP4U1WmP7cNb7xC9toltFw9eEqGqZg+XOKh7xxgzyMT2E5AbumPODvbQfvwhTAS4KT/h27vS6ABFVnHaMJl9uUf4u82DvPj/gpt9RlecM/fcah7mNvPhSVewJsKRc4JPAaTIUHSpVDIssO7mo21JXT4Y9Rma/zlkhs42L+I/skxbvrm57hiwKfjFdspOWW0GvLAiMXXgmSz+Qf6S2leXFzBua19DGQmCDu34ycPRD8idLBLPej08v/P3ntHyXXcd76furHj9HRPjsAMMAE550ASjCAkkpJJUVS0bEuW43v2Or63tt9Kttf2s62jXdnelUxliSIVSTGTIkEEIkcCGAwGwOQ809O5+96+99b+0UOKkhglUtq1+nNOnepbfW/3Pd3V9a2u+gXsdIzMXIREvJpcLkjRDZAWQTJqgKxikJkf5LMKZDRJRvfIKBLnpSUcb76U9oVVTUGbz1NQIYr4ZYFwMUOllaQ6P0dtNk51bo5QPkswnydYyBPIW/gtm4D9k9nbXo4HFHQVS1MoaiULJFtRyOulBSDN9dCckuey6c2H2ZYSIeV8PKTSRrni/fD45T3igGL/zP3jlSiLQpkyv2DyPbPE7+9FKILqjyzH1xn9mV9zeijN8UcHuHp6Gt0U1C8/QmPoEk1D70OZ9eHFeqnJfg6/00NWWYDhznJAWcHXa67niYk8FYFhtp77JFfCCc42Sa7J5bk9m6c5CmMNYXyn/aTPm3hNYfbXbyPn+mjq7+e+nXu4sKiTutlprn/yMYoxg+U3T2HqB4kX4dkZlSfzfvSiDyO5nkh2CXqxDlfPc0jLczAJXqIDr38XxaJOsQhBR6PS1fFRSntZVARzimQuKEkqEvnSSFkapFXPw+fa+IoWFcU8zXaWqJWkypqjLjdDbT5OU2aWmmyKgOO96mcIJReGgq5i6Rq2ZmDpBjPhMHZMp6DrWLqOZWgUdANL07ANg4KuUzB0iqqGVObNU1VwVYGjlMqLj21Np6jrFDWDoqZjawaOquNoOo5q4KrztWLgqgaqJ9BdMF2X2kLxbUixUxaFMmV+YUgpyewbJfl4P3pjiKr3L0GL/WwWJZP9KY4/2s/AC7MYfpVFW8YIBO6jof8u/MPbETEXQ36B6uz38YRKQrQgs9P8U/j3+dzGnbQmHmJ5/98zYRSZCrp8IJ3jGp+FXRfk5NwGpp7OYg1XMFbTgq/dIhOuJZDKIfJZPvm+36aoaOhn4zgzcVo6L3FdywE8JE8mNZ5JadRkK9Hje1ALi9H0PNJI4ZoTCAlaQcHJg1vQ8YsAhjBJKYIxXWHY+OFM33AdqgtJ2nKztKQnWZQYoz47S6yQImalCTjWj3wmeR2yfsj4IOuDbEAwFNG5rEYpGBVYRgU5fyXZQIS5ihjxSJR4JMp0tIpMsAJPff1hUngS3QXDkaXgeY5E9V6c5Zee11wHvWhhFG0EAilUECpCKJiomKgIBIZTxLAcDM/B8FwMz0XzXHQvW6qlgypdmkJzP1NfeTXeNlEQQnweeAcwJaVc/rL23wN+h5Kp8CNSyj+Zb/9z4Nfn239fSvnE23VvZcr8opGOx9x3L5M7MYl/ZTXROztRjJ/e3HRyIMXRh64ydCGOGdRYeZOBEv4Ukd6VRC7+KYpfxas7Su3cVzCUK0wai6gqXOVSsoa/WP7/oIaPsGj0N5lVBY3S5oP5PF0RGDTa+OrYBvqOtzAWqEep8rg1c4imygJTkQZCM3M8uPIa+jraCSUyrDn/Ahtq9rJ251F0xeVwVuVIXOO6uTD/lnovxaYeshu+jNCLFOI+pgciXBjtoLewhFmzhWk9hOUvDUvRQoqumSGaM9M0ZmZoykxTn5tF0WySIZVkABJBl0STzfGQJBUoDfoZn0nOV0vBX49j1qFRDUoltl5JxoxgGRVI1fcjA7jhSML5ArFClmYrz5JJi/DICEGniOl4GA5ojkR4Kq6j43oC13XxHBfPKyK9IlLmkTIPXn7+cQG8XKlNWrzoavfaKIAKQgE0ECWD11JbqRZCA6HR3PD2JNl529JxCiF2UrLe+vKLoiCEuA74f4E9UkpLCFErpZwSQiwF7gM2Ao3A00CnlNJ9rfcop+Ms838ibsZm9qs92AOpknfyDa0/deyi1Eyeww9epe/YJP6wzspddfia7qNweoCay3ejFoOoiyXmyBeIeg9SVAL0qU10pXv5rPtBHllbicg/yKAmWWrbvMfIo+pNDMe72Du+jlPuQjwUWlMTfGjgMZbMDXN4yzYy4RApXef7a68j7QuycfgSW8cO0LbsAFWhFL0FhSsTHjdOK9SldjLWNIixeAChSM5cWsJjAzcy6jVRUE3kfMrN1tQEy2b76UwMEzAzDDfmmavwyBkWmUCArD+KbcZQqcDnVhBwgwQ8P4bnQ3N94PlxpQ/F00oDfpGXBn3zxfDX3ut/zlJ64GXwvDlw40gvgfSSSJlGelmkV+DFfYkfRwgFwzAxTQPT1DGN+aIp6JooFaUUTM9DRQq1tMGMgieU+ThKAk+KUhsC15Ol+EmexHU9nKKNa9ss2bmL9Xtu/6n6zS8kHaeUcp8QYuGPNf8W8HdSSmv+nKn59tuBb8y39wshLlMSiENv1/2VKfOLoDiRZeZL53HTRWL3dBNYVfNTvY6VK3Li8UHOPjOCELD+1oW0rhtg+PR/xnx4D5WpXegtAQryIlUD9+JXT3AhtJrK7BCBkQT/V+dHiFc8x1CxSAMOv4+FYXTywsgaTuTaGc5XsHL6Mr8/+QBr6KV6OsVodQvP3HAjUlMZaOviiaZuQnaeXzt5go66h2jceJ68B2fHBLf1J8ikmulbCOq650jnozx64i5Ozy4jpUURwmNJcpAlcyMszKUJ+gJM18aYaW9lVF1HRcFPdU6nIfXGxNIVkqIq8YSNJAdYqBQwVQef6qL7XYTiID0LZAEpbaRrIT0bp5jDyqVxrCye+8ozes0wMHw+NCOAqlWgqBqKqiIUFaEoKIoy79EsQUo8z0NKScHzyBc9pO2iKApiviiq/tJ1P6xL/xSFEKVJwou1Uqo1IdAQmPNp3wLh8E/Vd16Pn/eeQiewQwjxN0AB+CMp5TGgCTj8svNG5tvKlPkPQ75nlvh9vQhTpfY3V2K0vPkftet6nN83yrGHByjkinRvqmfN7ijjo59i9rtBGkZ+FyWoYK6roHD2BI3i71HVKR4J7mBX/BAP5jby7NoZjhhP4ZeSD3oFKsRSpkaWMmFVUzE5wccu3M+i6VGsBoGe8xAphTPbV3OpqZuiKXi4exuTldWsGM9wz8xThJffT8wocjmlsKkvyaq5AOfamvG6HM73rOVA30b6fAsBaCvE2ZUZpEWtQDcWo1YvLX02QAhQs4KUHzLBIonKNFJkULw8mufh5NPomTn8+TiGnUEr5lBkkZfP2n88GpQ1X94oQlFRNRVVN1A1DUXT0HQd5WWD/4tCIBTxigP7y9uEKJ0jEPNC4SE9D891S8fzxSm+KFgl81OkRL6svHj8cvLp9JvsPW+Mn7coaEAM2AxsAB4QQrS/9iU/ihDiY8DHAFpbW9/yGyxT5q1GSklm/yjJx0obytUfWooaMd/0a/SfmeH571wmOZWnqSvK5juayMsH6H/qHNW9t6E4IQJrashOTSNPPUeD8dcUVMGg28TasZP817aFPBkewhKCW1yLsLeK/EQHacfEn05x95HvUTUbx64Ct0biG4d8U5gTt9/CqK1yKtbGye4VKELwO72DxGr+lgXLZ0gWBd6lAnePepwqNvF4tpvnT+7gfLgFV1GoUl225VWWFDWiXhPFICRCRSxfElUk0J05pDWLk5pFn5vFjBfRFLe06aoEkUUX1XVQXmGp2xMKqDpS0wiG5jADBrGaVYQqG/EFQ6USrsAMBjF8fmZHhhl84RRD587gOQ7RxmaWXXM9S7ZfS0X1a/9rk1LO5znwsDz5Ul2cz6Lmzd+fN3/uUXnXeQAAIABJREFUS+GzZenYBVwp58v84/nrXmxzpHxZKZ3jSInjlWpXQnH+/NZI8FXv9Wfh5y0KI8B3ZEnyjgohPKCakjdKy8vOa+ZFD5UfQ0r5WeCzUNpTeHtvt0yZn40f2VBeUU30rje/oTw7mmHfNy4x1pcgWh/g1t9aghp9goGzn6DqhdupS74ftVnHbIySOT5BUH2IqHEvGcVHuJjnggFfXlZBv26z2XGoKK7BnlpC0ZMYjs3640doHxigGAK72cMYUbCDQcY/8mGeszNYjscTi7cw3lRFd9JmS+ozLGt/nrAqSU+4dB0M0BvfzN8bG3m6egHTQY2QB2ttjQWAFQ2RCKv0einc2avYczOYaQfd8qjL5dDtIVQvR9h6cTgSlKL8CKCAAISmU9PcSlN1AGfK5Nuilqh/hoLhp6lrkJpYD9H6u4nW3UneU8l5HvH5QTs/MUr++b24pw8jMmm8QIjCuu2kVm6ir76Z/RKs0RTWcPKlgb4wX9vzg39hvn5tA9afDeFJzKLEX5SYtsRXlPhtiWl7Lz322aXab3scX1XNde9e+pbfx89bFL4HXAc8K4TopBTzdgZ4CPi6EOKfKW00dwBHf873VqbMW8pPbChf3/qmHNLsgsPRh/s5+8wIpl9j5z0dxBYfZajvrwjt30LTyB+gBBSCOxpJn53APTpOqOKvidnHcBGkCvDJ5noeD0K9q7A110FufBuNIo3Ao3VwkI1HjoIqKXR4GP0K3rjOkeW7eW7VEppzw4yGqjnSvZ540ODG0V7WRz7B8uYC+USY6f3Xk05s5clQmEPNDnOqJOp5LDNt/ME5grk5mJslOBAnaicJOxmUl63XSwR50yMXFCT9JnNhgVkMsGBaoSKVIBMMM7V0LfHFy5mIxkjnbOLCj6WBp76CsE4Ck0PzLy5ZMHKFdS88z6KhSziKypWF3fTuWMPEgi50XcdUBGamUKoVBZ8iCKoqUV1gKgKforz03IvHxsuOTRe0gotqlfYMpCXBcpG2C7aHtDxk0cOzXSjK0jlFD2l7eC/Wdul5d77ttRAKmAEdM6DhC/roro684b70Zng7TVLvA64FqoUQI8BfAZ8HPi+EOAfYwIfn/zWcF0I8AFyglOzod17P8qhMmf+dscezzH7lAm7KIvbeLgKra9/wtVJKLp+Y4uA3+8imbJZua2Dx9iFGJz6G9YMGGi//IWoxgH9tLU7GJrN/FNvfT6zqL4lm5/CA+4J1fK5VJakoXOuoXBr5NWopoCppAtksO5/bR0UqRWqRRmi2iK9P4UR9F59ZeQeLKjIsdoc419jJ4cVLiNkud038K3tq96GgMHX6Lqb7dnGRDM9XQUIrEibLpkIPa6ZOo7s/9PTNKz5EIEg8FuVC0xpygQg6EeZCBoMNPmyjFiFh2aXTbDy9n1hyltnKah6/9t3El62lRnUIx8cwxy3GXWiUYwQ9i2homoZoP02xjTRV7yCkmwRVBdN1SB07yMgPHiU9OowvUsnSX7mHlTfsJhqNoryKlZeUErvgkpzNkk7myKQL5NI5smmLQqZIIetgZR3srEsx52HloPDaDs0lFAmaB/qLtYfUXKTmgd+FsATdQ+jztU8iTA9hSITpYusFbDVPQctRIIflWRTcApZjIepvZjm/8ob71RvlbTNJ/XlQNkkt878bUkqyz4+ReKwfxa9R9cGlbyr/QWIyx75v9DLcM0d1S4gVN0+Rcv8Fd6xIQ+9HMRKNGAsq0BoCZI5N4lIk2/Il2ucewm+59Ksmf1MT5ajfoMstUpHbijq1liZlBqRk6fkLLD93jlxUQ40U8Q0IxiuifGrF3YxW13GD3osXMDnSuZm+qkqWzw7zYfVTNEYGyYx1M/NsOwfVao5GusmoIWqtKTYkTtBqjaEqteTManp8lWTrBMWaBvoWtZDRQj+Sa0BxHQLeHEp2gKWXzrCmZ5xQ3mKiupGhzdfzuzfdwPbxp3EPfJUro1v4O7mOq8oUO4x+DM1hafcztC1aRFPbH2MpYRJWgvGZYc6fPMCVnlPknTxmVYTY4nYCddUUPItCwcZNKngZFZHRUXImet6PmQ/is8IErAi6+8r7PJaao6BlKejZ+TpDfv7Y0rJYWh5bLVBUCxQVi6JqzR9beMpPzm2FFBgYaGgoUkF4AiEFeKBKtRR2WyqoUsXwTPyeH5/nw3BNDNfA8Ax0V6O6rp4/+43/9OY7Ka9tkloWhTJl3iLcjM3cNy9R6J3D1x0jemcHash4/QuBou1y8vFBTj45iKYpdF+bQKn5NMV0nPr+XyM0uBolbBBYU0vy7DRqwmam+iw0/yvLL43geXBvRZTPR4MowPWKwZnhD7NVcynmU/gKBXY+t49QKkFukULFFQ9L0/li124eW7iJa7SrNOkpBqub2N+1HlsRvHP2Se6ovhfP9jP3XCtHJxrYW7WTuBGjypul0TfJYquS9niMlC/MMyHJxQ4/bksIVZnffBUKzdPTdIwZ1CQLjFV/m7nQIDX9GVZeiWIWBSMNCzi57jret2Udvz72PQpHvsbR/Fq+bjRzWJ8iqk8R0JI4Rhbhz2ApPtLFAu4rLCaYxQDRfB2V+TqqC43E8g1E8rUEC5U/cp5E4vktvKANwSIi5KKEXdSwxAgqGEEVI6jgCxqYhoGhGhhKqTZVE13V0dCQtsSxHOyCjZW3sAoWVt6ikC+USq5ALpsjnytg2zbFYhHXc95855KgoKMKHU0x0FWD7q6l3HrntW/+tSiLQpkybzuF3jjxb17CKzhU3tpOcEvDG3JIk1IycHaG/Q/0kZ4t0LQ8Q6T7X/DEFeqmP0Blzy4oKgTW1ZJNWIi+BGkzSbrrs4S943RfznLCNPlkdYyrhs4mz0KzduJNrKRVJHCkR+PICOuOHiNTLYimLfQ0PNm6ni8u3cOiSo+l9jmKgSDH29fyQkMDDekkvyf+mQXBc2TP1XDuZCP7gtu4GO4mqOfwt6dpyXays8dG8SSHQy69S/MsD02y9OmzTNVW8/Vrb2Nd73k294wRLa6np2YvlxYfpJhIsuNsHZUZhcHGZg6t2UBDM2yZfZyJmXNc1EKM6VYp7yUgpILpGgQVSV2kgobKZcT8NfhsjfSpKZyrkgq3mcrgIlQnSjH3w/FM0xUq6wNE64PEGoJEav2Eoj5CUZNAxEB9jXSmruuSSqWYm5sjkUi8VCcSCbLZLLlcjkKh8KrXq0JDwUA4GjgqwtMQUkVIFVXR8QdMfAETw9QxfTo+v4HpN/AFTPxBA3/Qhz9kEomFCIWDmKZZ8oV4iyiLQpkybxOy6JF8vJ/MwTG0ugBV93Sj178xU8HEVI799/cxdH6WULVFzeovYsaOU+PcRtW5dyOnwGyP4DYEKBweB8/Bqf42QyueoOtKisC0zT/FojwYDlLnOlzv93N24D2sM4MUZidwVZWVZ85SMTFCMGwRGXHoj9Tz6VV3odQ2sUacwZQ2AzVNHOxcR9rQ2Z08xN2Vn4a4Qv+Beo6k13OweiuOotLcPE6xopvdZyVVaY/+gIVY+iy3LU8gPjNC9MQAn7nrQ/S0L+R9jz1JKrqJ6coixxZ8lzljmk0XGlg8opPzqRxcZTBW1c+LXgSKhAq7hpRVT96qZ5lRQWcuTcA2WbVqjG2b/oDsdBOD58a5cmKAXFpHiNK/MM1QqGoKEWsIEq0PEm0IEGsIEo75XndjP5/PMzU1xeTkJFNTU8zMzDA3N0cqlfoRvwAhBKFgGL8ZQsdEOhqepeBkBXZGIFwNPB1LahSFjhoxUSt01LCOGtQQfg1MFalLik6efCFPoVDAdRw8t1Sk55RCZ7gO0nNf8mVwPbfk2+B54LkgXZAeu7srec89v/ZT9duyKJQp8zZQnMwSv6+X4kSW0NZGIrsXIvTXNzd9+VKRUBxqlj1MZNFj1IRupO7y+3HOuagRA3NjPWNHR6hMeijaMaaWfplEdZLVpxM8oQb479EIGUVhj1ZAddcRH1hPl8wy57oYts3KEyfQZIKGqRyup/Hl7ps51LaFXZEJzOwAmXCUI+0r6a1vor4wx2+r/0C7uMTkqSrOne/i6dobmVarqK+cIruggWsvB+keLZLSbfxLHmH1qnO8YL2HpX/1DaoSc3zurlvYeKqXplmPp7dcy7GWZ5gKD9E2XsmGCxUELMHF1jQnuuYIGGFuSE7SaXdzNXcHD+dqmJAa62o8NmtHyc8ECCsGC2oWY8XriY9lAZDSRXrTVDWaLNu5ipYlDVTWB1BeZ/B3XZfZ2VkmJydfKlNTUySTyZfOMQ2TinAUvxZEun6sgkk2p5JIKWSsUi7mnJDkhcQ2FCxDkFchLz2yrkvefWNjqYpLAAsTGw0XFQ8hJKqUmIAf8CPwCQVTKOhCQRcCVSioopT6U0VhVYfBnR985xt6zx+nLAplyryFSCnJHhkn8XA/ik8lemcn/u7YG7qu//QM+7/ZSyZuE1l4gpoV99HQvJnGmV/H2mchHY/glgb6Jmdo6LNQRByj4rNcWNOLbhdReyz+/2glvabBcrfILdVw/OoeFhWbUYeuMltVRc3UFHWXe1iQmSYUdzlSt4R71+xhTcigSvYgpaSntZMj7cspqgrvdL7PHfp95K74GDjWxD59F2fDXZi6hbEIOtKNbO/Jl7xqFx5h2bpvcyJwJ89ebOW//Ot/Q5FFnlwT5pZjSb52yxIOLRomZ6bw51U29kRpmwgyF5Y8u6GVQKWPf+5/nNbA7Xwnfj33FhxGpceGkMoOo5/iRATNqkT1StFidZ+KP5gjMX4axx6ma0sX2+56L5Hautf8rPP5PJcHhrl4dYjLwxMMTc6SdURpJo+GVP24+LBdnYKjUnAVLASWkFiilPv51QgqDjE1S4w0VTJOpZekUqSJiCyVZOdrm4ivCr9Rjc+swVBjaIRRCSGdIF7RxHM0pKsgHYF0QBbf3FgcvqaZyO62N3XNi5RFoUyZtwg3YzP37T4KPXHMziixuzpRw6+/mVyyKrrIcE8CMzJO3Zqv0rKkiVbzd7Efc3Emc5gdlfRFLWpPzOJzffi1h+nveoZk4xzmiM2DGZPHQwHqHJdfqSjg0xvpuXwra6+OMGaapCIRGgcHaB48z4KJDBndz2dW/goDzYu5xejHzU8zW1XH/o41jEVr6HT6+Kj634mNzjJ4rI5LiaX8oGEXaWFS1ZTCrGjj1rM5ollIRsdYuvl/ciK8hvu4iw2nTvFnX/gs8ZDkaoPHoSUxjnVk8BQPJKwYrGFVbxAhBc+v3caFpYv5p5Fvs4ZbuG+kmWfyFhFHoQOFescDZz7dmW7R0l3FwqUN5JOXOPX4V8jOzdCxcSvb7/kQscZmLMdlOJ7j6nSW/pksI3N5JpNZJufSzKQtkgWHvKvg/kTQix+iSfAJQUhRqNRVKnVJlWoTU/JUiSRRb5YKO07YihPBoUI4hPAICBXNV41n1CDVSqQaxiOM5wXwHAPPVvEsgbRf2edACWgoYQM1pKP4NYShloquvFQrhoowFIQ+364ppfhH6nwcJEWAKlBDBmrFGzNk+HHKolCmzFtA4dIc8W/24uUcIrvbCG1tfN0166LlcuzRK5x5ehgUi+pl36N9vcWiBb8PhyrJHBpDrTAZWaZgnuwlVmhFE1cZa3yUXMdJsopLz2X4mt+PBHa7km0LCpwc28jCK13Q28uVjsUonkdNfx/rLvcQSLs827qGf1+xh/VBi6biJVxF4cTilZxu7cDE4v3ii2yJH2L8cCXDY43srbqZgWAdoVAOb2E9N13x6Bh3SRsFGtZ+ldFmhW8oH8AqFnn3U5/lAw9fZrBB4QvXK1xsBgQonsK6uRV0nTbQrCkGG9t4etsNbPN6+Hh8Hff3w6jl0WmrVHulAVv4Z8gLC9eXZssNK9l6zRbGLp5n71fu5fLwJKJ1GZXrd5ElzNRMnvhslkzKxkS8tMwSQhLGJYQkAIRQCEi1VFAJ6ip+teRwpslSdjNctxSkn58uOi0wP6ArKD6tNNgH9FLt11BC8wN/2EB9UQRCOuI1Nrd/npRFoUyZnwFZdEk+NkDm+TG02gCxe7oxGl57M1l6kt6jYzz/nQvkUyoVCw6xePtlupd/HP90J3Pf6cNNWKSW+Jmd2E/b3DJAMB05jFj+CDP+KcZHPL7q+hnTNK7LWuyoVQhUKJy9cAOrHrvE1UXtzNTWEkin6HzhBJ1DUyRDAf5xxfu40tjKbtFHwJ5luH4BB7tXMOuPskXu5735byAOeAz0V3M2sIZDtetBBRYG2ZQ22dxn4SIRbQfR1xznPuX95OwC2ux3uPvxy+w6I/nqLpUn1wBCYDg+tsQ30HqpGpE+jWWYPLvlZqxqwW8MLuDMmIduC1odBRVBuGqOaOws+VwMYYVpiNWyqKWdYqrA8NUxLAs0NYBfqIQQBN/gwC2lLOU41hQUU0Xza2gBDaE4CHcOxZpG5MYR+UnARggHEYpBtAkRa0FUNiLCVWCaCFUgtNIsXRilWbxizs/qTbXU/hakS/1FURaFMmV+SuzRDPH7L+JM5QltayRySxtCf+3Z3tCFafY9cIrkhIZZOUT79uOs2vpeKs2NJB/pJ3dyCqdS47J+hKUzNXiylbQ5iLb2KGP+h4mnXB6a9XHcZ9Bh29yZN6hfmmUk04jyjUr8ls6FZcsAiExNsv34Efy5Ik91rOXfOt/FYl+adU4Pts/k+PKlnK1eQo2c5MPFL9JyZpLJEzqDRiuP119PVg0jalXaAhXs7skRsBQSVQPUbf4ej/pvIV60mZv5JssvJ/jwM5KjHfDdrQq2LqgoVLFtaBv1iVqyuZMEcrNcXLQKu205twzUoOUVaqVCUBH4VAhqLrqn/8TnJYEsHikpSQvIITGDOuEKH4am4NpFkslZZotT5JUsRVxEMUhFoJ7G1lZqFkSpbqsg2hxE1eY3+l0HLn4fDv0LjBwrtelBaF4PrZtLpWk9+N64Y+F/JMqiUKbMm0R6kvRzI6SeHkQN6kTv6sTX8dq5k6eHUzx3/2EmL2togRlaNxxj/fW7qaq+lsK5WRIPXcHNFRmouMqi9Dieu42imkKuSZAw/5ZJcjw3rvKo4aPC8/hoIkdVdRR/c4oLvUtZ9O0s/Z3dTNfWYlgWqy4co713lGTMz98s+zB91a1cr/TS4ozSv6SZZ1s2kxEhbnaeZPOlK6jPDTGuRTgQ20JvuAvhh1BTJXdeyVA9pzNr5Amve5CeBUsYL8QZmHyIyozDB56VZHzwre0KGb+gMdHGpqFbqXM1MkqczlSSYLCVYEUbTUUf2stm9paQeP4kBX2MOccgbgniCsxU1nPKMjiTzJMDKospVoY1Nje30ZDXiA+lSFkz5AOj2GYchCRkRlm8oJu161bTtKgGVXsFcS6k4NRX4PD/gOQQRNtg/UegbSfUrYA3kFrzl4GyKJQp8yZw4gXiD/RiD6RKqTLvWIwS+MkZ7oukZvPs++ZBBk8LFCNH46qjbLxlO/WNN+GlbOa+d5lCT5w5X5qYux+K1+JhYHc7qLX/ylV5iuPjgm+pAYpC8L5Umq12BYVleVIEST3cQGTY5IWVK5BCUJ2dYPtzz6PlPX6wYjX/rfVuqvUsd3oHcBZInly0jfP6Cha6/bzrUi/Fc0OEJgc4U7mMA9WbKSomojnEO5N5uoZMMsIjsfAMoe0Oh2fOMZA4jeJ57DoFLbOShzYrzFYIumc6uWPiHlplCJ+hUufqKKI0M89JScKVZB3JjCYwV9hUL/x3hsanGR1dzUSqijG1nlGtkYGUhwBanRzteZsOESXizOemFh5qfYK0PkzWTuIz/axZs5rVa1ZTV/caFkeJITjyP+HEl8BOQ+tW2PI70LUblJ8+zel/VMqiUKbMG0BKSe7kFImHrgBQecdiAqtrXtUzOZ+xef57B+g95ICU1C49zsZ3rKRlwR6QguyxCRKPXsW1HYS+D91uw2UBdp2Df9UBruS+QM+MxzdcP1OaxvXZHL8TzzDc2IBckKJ/uJ3Wr1v0dqxktqYGw82y9eJ+6l5Ikmow+aulv0FveAF3i/2sbOjh+c7lfN8opWe8ZaiPurMO0bHvMmA2cDC2lRmzGq/SYKPP45o+gXRV+iJxGq69wFPJJ5ix0oBkwQRcc1Hy1ApBPGqwe2o7N8VvppUwqhDkpEUyP8aYyHBCqydgVRB2FNSwxopdcWz/5xgY0BgcW8GlXD3DSj0jdmmjvFmFjoyg2zYJSUGwUqGps4aKBo3J3FUuXnmBXC5HbW0tmzdvZsWKFej6qwsyk+dh3z/ChQdLx8veBVt+G5rWvYU94z8eZVEoU+Z1cLNFEt/tI39uFqMtQuw9nWhR3yue6xRdjjx6gBeeyeJaBtFFZ9n0zoW0dd6GomgULieIP3wFbyKHq10l4M5gyY24fpfgNRmuZv6YwUSOB/I+LhoG3ZbDn83OEpMRBlZAxjBJPtGAN1XH1UWLEJ7DIqeH1Y9fQDiCR1Zu4n80vYv1ykXe3/AkFzvq+abvbkZEK11z49xwUGNWPUTlcA97q7czEFyI9Ck01BjcfTWLkQ1xRbfJLTvJycAD5OdjCNXYHntecNjXaBINL2H3zBY25FdjoDKreZxnlOzQM8w4cc5XbqZdrKTONdBCsGTHBEnlKwwO1XJqcjV9Tg2jshJXCupNjc6sS0fOIOp6+AIzLL+mjdU3riOenOHw4cOcO3cOz/Po7Oxk8+bNtLW1vXaYkOQoPPu3cPprYIZh3a/Cpt+ESPPb0Dv+41EWhTJlXoOSqeklvFyRyE0LCe1oelXLksGLF/jBF3vJJyKEG/tYv6eK7jW3oyg6xZk88e/3UexNIsUsAXGWnLcFFAP/1hBj2p8zmurl4TmdfT4/NY7H78Sz3J5NcLy2hVxXluGxZnxPxbjUuhxXVahWB9hw+hQVFxwmmir5i6W/ih4U/HHdVxhfHORbwTs5JdYTsZLcdFxQmZjDSj3ARXU55yqWIQQoCyr44NQUVePVpPQCR6NTXG39Ap4eR0Gy2nO484TNgWgHraFtXJtcT8QLk1Mkz9QoXC5cpOrcY2iOzXB4GfW+9UScGJ5psXjjCAkeYXCslTPJLi649SSlj6ipsgKNBTOSOldBOhPUthbZ9p5tNHd30N/fz969exkcHMQwDNasWcPGjRupqqp6nS8rCQc+BYf/DaRXEoLtfwiB13ceLPNDyqJQpswr4Nkuycf6yR4aR6sLELu7C6Mx9Irn2laaZx/4Dpefb0Tzp1j7zjzrrrkLRTHxckXmnugjd3QaIW18yhlyXicKUYzOEIm2rzMef4DnphW+6wuiS7gnJfmtxChpEeLiUj/pCo34wQWM5laQDwYJiQnWjR6nbn8G11D58oprOdi0ij+r/Qp2h8PDoT08yS2o0mHz+RxbeiUXYy+Qm5jgWOUGbMXAqFK4Xsuy7EoFUgoO+/OcaXkYWXmYSlXyTs/izp5mnrWaaKi8llX5boq4HI2qfK/VJDN1mo0nfkDAypIOtlNZtwE1UY+nFKlaeoGCfoqByTbOWy30evXkPI1FIZOVcY/2nIrwsiD76NpUx+Z330I4Vs3IyAjPPPMMV69eJRwOs3XrVtasWYPP98r/yl7CseH4vfDcP0A+Divvhl3/GSrLKXl/GsqiUKbMj2GPpInf34sznSe0vYnIzQtf0dRUSsmVi0+w/2sT5GZaqeua4OaPXE+4sg7pStLP9ZD4wRiKq6OLyxRkEypBtNYQ1pIzjCc+walJl68aQTKKwo05nT+ZHSHquByvayLfmWVqpo746aVM+VrwiSzLU0doe2oK4QgudLbz70tW8WtVh6nomOHZ8E6+xXvJEaBtbITbj4XJGgXOufu4IlaQ1CM021NUtVdx82ABma5mKjTLQ6FRis3fpDsQ5+N6BaunruPkeZtobCsNTh0zWpYHWn18a0EFy84dYuPJZzGcAkqokZp16+jv9xNLN6PWX6AYucBgookLTj2XZR22J1hd6WPpuE2jZSCLowTCg6y5eQ3Lr7sew+dncnKSZ555ht7eXgKBADt27GD9+vWvvV9Q+gLg/HfgB5+AuQFovxZu/AQ0rHo7usUvDWVRKFNmHulJ0nuHST09hBrSib6nE9/iVzY1zWb72ffg1+k/uBZFlWz5lSpWXbMJgPyxM8w8PIiwoihMYRNBw0RdFIG144yM/BGD43N8QQsxpOussnT+PJFhWW6S3kAtU8scEmqAmQuLGUytwkCyyD3Bkif70ZMwurCeJ5cvp7uqjwWdQxyLruIb8oNMKI1EUgO885if1lmVZ6uG6M9rJPQqGnJTtFek2aBWok82I/0JnvDnGGx4jD11Z/mNphuoiu+i54lLxMIrCEo/F/1jfG2hn+djVew49Cxreg9T0MAfrKLl1u3sGx2gtXcHiuJSiJ5nUEh6ZANXnCqEgI0hyZIxhyqnAulO0Lg4w8bbt9PUtQQhBLOzs+zdu5cXXngB0zTZunUrmzdvxjRfOaHNj9C/H576Sxg7CXXLS2Kw+Pq3sjv80lIWhTJlgOJUjrlv92EPpvCvqiF6+6JXNDV1XYu+3ns5+p0C6ZHV1LTlueWj11IRC+IMX2Xia/sgsQhBBgcfCiqiK0Zgi01//x+hDfbwbzLIwYCflqLKH9ghbph6gbgS5HK3SSKmM35lESNTq8A1aVEvsOLZHvxjLhO1Ec6vXIuvbpDO9gHO1rdxHx/mitKBYU2y7tIUuy40ciZc5KgyRUpUEbPivGP0ecLtXQQznUhPZbx6gEfNGT6+4hzv3fynXDicJH10lk6ntGZ/sOIcDzZIRouNbDpykGUTZ5gL+TB1k447buK4uo+Ko9egzC3GNmeYCfdz3tfEmUwUnwYbRJyuSahUmlCULIvXa2y7axuBcMkZLJlMsm/fPk6dOoWiKGzevJmtW7cSCARe/4ua6oGn/z+49DhUNJeWiVa+p2xa+hZSFoUyv9R4BYfU00Nknh9DGCrROxa9as7k2fgBTuz7HP373oFbiLLxtgaqehCWAAAgAElEQVTW3bQU8nOMffV+6G9HYlIKd6bgdEep2RXi8qW/IDrwNGcTgr+LxbCFwsfVdj50eT8IuNhSycxCGJ1YwPjQKvJWJQ3KAKuPniR8pUgqrLNvXTutNYLWhb1cbK3jAfX9nBLr0Z0EdROHeNfJtSSKCk+HU8xJHxXFFDeP7qdDTSKr3olj16JU9/GI6XLjpiidK29i776TdAyb3DgNtmLzaOV+9ocmic+sZOelqzRkzpExNfyqRseNmxhuOIh7YTHW5d1IKchFrjK3sIpHx4KkbY+1+atsSusEjS5UzWXldbVsun3FS45kxWKRgwcPcuDAATzPY/369ezYsYNwOPz6X1Rq7IcWRUYYdvxhaSNZ979VXaHMPGVRKPNLifQkuROTJB8fwMsVCW6op+KmBa+YItOyJunt/Rt69sHM+dsIRRV2/+Z6ahsNxh/5EvJoEM9bgMRDIsgtitB6az19vf9AaOA+wuMF/jYa5QfBAMuUGH99ZYDFJBiqqGCoW2XMqmf0ykqS6QZiTLH+xBGil/MUggpPLvUT6YixMTbM+cW1PGi8i4PsRPUs/IlH2d7bQeNYI88EbcYVScjJsHXmKDeMnSTZfiM2m9ECM4w29nC2agdmVzUX5wp8cNDj3cM2Qno8HNvLI+GDiLGd7Bot4EufxVYkYVfStKmRZHcv2ZFWMr17MAp1eP40VZt83Ndv8UJSpb4wxQ25NA3qUhRFYdX1Lazb3Ybp/6GHcF9fH48++ihzc3MsW7aMG264gWj0tb3AgZIX8sFPl0JSSBc2fBR2/lHZouhtpCwKZX7psAZTJB66QnE0g7GggsrbFmE0/aRlkec5jIx+hd5zn2fk0PvJTXWyeH01193Tzdzpb+E+MYBnb0cCAsFsc4COO9q4fOVfMPs/R9toiv26j/9SHSOjanx0XPDR/ACWrnOl00d/qIrRK8uZml1MyE2w8cxhai6lKIQFpxojXNiR5IN4XOis46HQO3iOXSA9/KmnaRvJsWTkOnoci8u6h8/NsSFxipsHDiGrqshEP4wUBkrbIR7WajnZso5ghcFvXZjjjgkFTQqejhzmq9WPUTO+jJ0jQexUDx6Smkye2qU68TUW09OLyY6vIJzqRJEatR0Wz6VHeDxbj8DjejfFKrsJz1Ho3lTPxtvaCcd+aC2UTCZ5/PHH6enpoaqqij179tDe3v76X5Jjw4kvwnN/B7lZWHFXaakouvAt6wdlXpmyKJT5pcFNWSQfGyB3agq1wiByaxv+Va/slZxMnqa39y8Zv6Qwcfxj4PnZ+d5uairPkf/uQyjpd+LhQyAYrzXpvrODsav3ol7+DK2TCQou/NeqGA+HgizOqXxyZoxlrsVIo4+zDTWMjXcyPrEE3bLYcP4ITRdnKFQKBmoiZJcnWF1hc2FxCw9X3cQz3IiHIJDcS8vAC4TcD1Mcs7mou+jYrEucYsvUCRamEyQWvYeMWIFWe5F9FXn21m6iW4nzW6en2UArqubjkO8F7m38FrGJEJuH6ymmxlClpGk2RUWDx+jmBsaT7ThWmHB2EWa2HsOfZzi3n8cC3UybNSw3HW4sRNCSHi1LY2x99yKqm3+4DOS6LocPH2bv3r1IKdm5cydbt25F014nvpDnQc+DJYui+FVYuANu+iQ0rnmru0OZV+EXIgpCiM8D7wCmpJTLf+y5/wT8I1AjpZwRpV/sp4FbgRzwq1LKk6/3HmVRKPMi0vFIHxgl/cwQ0pWEdzYTvrYFxfzJzcliMcGVK//I8PC3iJ9/PzMXt1HVHGLrHgXruX8hNLkbVzYAMBbRWHR3N6nhr6Cc+2eaZ+dQPdgX9PGJqhqmFcH7E3n+IDFNIahysGkBvbMrSMQXoBVs1l04RmvvOFYVzDaa1DWnqYvlea6tm0cbruVpcQsuKqHUQTp79jJR9etEBnT63SICj1WpU6ybO03XzDRKfTfjwbtRgnNcqB7lQMVCrp+6wJ3nZ2hs2YXii/CCOsTnGr6OOZ1h7WAtSj6PT3q0TiSgJsTVNW3MOQ1IJJW+avxjnbi2Sr5wnENBwcnwMiK6wh41TMOETU1LmK3vXkzLkh9dyhkYGOCRRx5henqarq4ubrnlltdfKspMlYLVnfgSJAahdum8RdEN8Frey2Xecn5RorATyABffrkoCCFagH8HuoF186JwK/B7lERhE/BpKeWm13uPsiiUkVJS6ImTeOQq7mwB39IqKve0oVX95OaklC6jY/dz9eo/k42bTJ/4U9JTIZZsriBmf47G/hXYciUgmPYLat7ThTbxDTjzj9QlEkgB/QGDzwciPBTy0+RI/n5miuWWxeHqhezNbKeYq0Yr2Kw+f4r2vkGsJklugUd3TQo9IHiicTlPtG3lKeUWbEwimaN0n3mUeOUdZPKtpGYtPCSd+V62zByhITNHk1AYr/0Ill7JRM0AR7UQHzj/LOuvDuJfeQ9adSejpLg3dj/F+ABLhiOojqTSsamLF5hb0MjggjYcYeLpBZqCFWjDDRSyC/DcOWaqRnncv4jJPGz1B1g34VET87P59nY61tf9iHd3Mpnkqaee4ty5c0QiEXbv3k13d/erf0GeB/3PwYkvwMVHwHNK/wzW/WopTlHZougXwmuJwtsWR1ZKuU8IsfAVnvoU8CfAgy9ru52SeEjgsBCiUgjRIKUcf7vur8z/+RSnciQevop1aQ6t1k/1ry9/1fDWicRxLl36BOnMeYrT72Po+V2oqkLXsqOsuJTB8t6HjUJah+Bt7SxM3g8P30M0m6KoCi5WBPmWbvCdcAgQvC+X4Q+m5hg2q/k75VaKUzE0y2b1uZN0XL6CaCui3pxjRSRLQfHx7ar1HOhcx9P6TeRFkKr0KdpPP4tPX0JPzUcpDCg40mJxMc7WiUeIFtMsSGawam9lILSBdMU4R5UUdx07xfsGj6AvuQ3jug9hC8l3jR9wNbmPzhN+hAhTa6XxFwOMLe7k+IpqPDwI5VjsqiQvprG1dThqFUZDhiONdTx9xUedo/HetMLiosqGOxezfGcT6suc+YrFIs8//zz79+8HYOfOnWzfvh3DeJV0kJnpkhXRiS/CXD/4o7Dp4yUxqO54aztCmbeUn2twcSHE7cColPLMj63xNgHDLzsemW/7CVEQQnwM+BhAa2vZxf2XkR8xMdUVIu9oJ7Sl4RVTHRasCa5c/gcmJh9EU1rJ9/4bg2c0ItEE1ypHYPRGLAxsRaJcU0uL9y3E03cTtLLkDZWjVdU8LDweCgUBwS3FHP/3xBwBqfMleQfjhQWotsOKC6fp7Osj2J6j7qYUwf/F3nkG2FVe5/rZ5fQzp8zMmV41mqJp0hR11BBFEsU04wbGhdiOy03s3CQ3yb1xEjs3PXbiktgxYIzpBgwCI4RACKHey2h67/X0vvf+7o+RHXxFbGOEA/g8v6S99+xZM+fMeff6vrXe5dWYSLn4F8fVdDY2sde2haiURV60g+Jzg9isViaLtrEw5MEICqpSOu0Lz5IXn8AdS5AjljJZcTMhV5CTRoSVI1N89fQ9SCWrMW37O6wmO+fo4tjCsziCOmWqhTw9gWYrZGhpC7qqElFD5DojFPsFM8d6CdjXoFi2YHbIhNb6+PbZFKmBEFckTayJmmi7qozWa8uwvK53QwhBZ2cnu3fvJhAIUF9fz9VXX/3GS0WpKHQ/D+d+BH17wEhD+XrY8mew7AYw/RIriwzvCH4lUZAk6QEhxJ2/7NgvuYcd+FPgmjcX4s8jhPgu8F1YXD56K/fK8O5CCEHsxAzBXYMY0TSO9gJc175xialhJBkZ/T5DQ99E1zWsiT9l4EAtkfkka10XyBdLEfp1GAhEvUqR6xmkY/dg0ZKEHCp73GW8SJwX7FZkJK7T43x+coEcHZ4RV9Bj1KMZKg1dHdT2duErD5KzPUrCJnMiVMJP5Gam2us54FlPWHfimxzA0jeBEILJdDN6UIFpKE3DynAHJYF9yIagJKwSzP8kHXkyXbpG42ycPz77TRSzndT2vybXkoufGV6efZxwZArFlsJuMhPLqWHA68UQaead09SZPRT1+gnPzhL1LsNb+jniERNqs4cnkxE6jw+zxFDYGjWzdnUxq26oxPn/ucJOT0/z/PPPMzQ0RF5eHnfddReVlZU//4tOJ6D/Jeh4Crp+AukoZBXBms9Ay53gq3073xIZ3gZ+1Uyh4fX/kSRJAd6sYXkVUAn8NEsoAU5KkrQKGAdKX3dtycVjGTIAkJ6K4v9xH6mhEOayLDwfa8Bc8sYNUXNze+np/Qrx+DCm1IeYPrmdhZEkNbY5aj0WYDkCEHkhCvKfRel/DMXQmPOYecZZy2tanL1WgRkr1+spPj81iy8t2E07Z1PLiZuzKB8eoqXzFMVlc1ivS9CTyGX3QhUnCyuZXtXMcXMziRkVa2cAazhEGBsgkBwqeZLCigVBSXwGR+hphIiTHU1jtlxBR1MNw2k3jQtRPtN1L9bEDMkrP0aJuQrD0Di5sIe+4Emms+M4cl2I7BamFYWkMUfYM0BzykfO2XlSqUlya1rJKbuLqQGDlGqis83Cc/2TOJG4MWrmmlofa29eSs7/V6obi8XYu3cvx48fx2q1smPHDtra2lCUi+v/rxeC7l2LQ21sXmi6bbHzuGwdyO+MAfUZ3jy/UBQkSfoTFp/ubZIkhX56GEhx8Wn9V0UIcQ74WRupJElDQPvFjeZngM9LkvQIixvNwcx+QgYAI6kTemmYyGvjyFYV763V2Nvy39DaOhYbpKf3q8zPv4KUWkmo638z2QVV1iBr3SBLixYPsm2E3LynMU3vRoQF0z4Lz9tqOKgZHDHFsCkGt2g6n52ewZfW2S838GC0jbAjB0/Uz4Zju6nKG2b+KjMvTVUwNJhNV3k1/Q0tjOm5GB0GkpFARWCS0lSVjDLiaaR5xsqa/iRqOkY6uhOhTWBJaeQl8umq2cyUUUhN0GDN4HN4AqdIX3UVXrkdi+FhONzBsdDLnC+YhuIcikQLQk8zZu3D67KwdNJG+NACcXOUuvWbsXs30HkwhObXibZ7eGh0lmB/iNaEwk352Vz5iWqKa35+CUjXdU6cOMHevXtJJBK0t7ezZcuWRWuKdAJ6d10qBI03Q/1Ni+MulV9ibpfhXcGvVH0kSdLfCCH+5E3dWJIeBjYDucA08GUhxD2vOz/Ef4qCBHwT2MZiSerHhRC/tKwoU3303kUIQfz8PMFn+9GDqcVu5G0VKI5LP3g0LcLQ0LcZGb0XPeEjPvwHjJ9xUW7WabCmUaQsDARmuYfs3Kcwh15DUyTGC608Z61gf9LMedWP0zC4Ia1x99wseSmdU6Zy9k2vJugpRNU0WvpO0+o8Q2+2m5cmmxh0ltHrrWJaLkI3Fp+iTU4dlxoiHTTTVnOO83kbqe120N6fQNZ0jPhLEDuHIUNBRGW2dC3jcjNlwkLR5GHy514ivcFDrnwTHqOSheQkr8Ve4KWSCyTcbhoCTcjpMD3eEVooIrsnTsIfxOXLZ8W115FdspojT4/in4phqXWx04hxZjZMoSZxizWLm2+upar15/s2NE3j7Nmz7N+/H7/fT0VFBdu3byc/Nxv698K5xxf3Cn4qBMtuyAjBu5zLUpIqSVIxUM7rsgshxKuXJcJfk4wovDfR5uMEnukn0e3HVOjAc9NSLOWuS64TQjA9/Qy9fX9LPBoiNfb7jJ+qokgWNNpSmCQnaUnDQTdZjsewaSdIKzKjJVaetZbyctROv2ket65zczLBncEgeQmNAUsuuybXEbQVk7RZWTrWx2b5NcZdNh6Z38Br2WuZNV1Mei0SWq4NX/YCy8R5zndXk7M0QSCngqZuC639SRRDIMVPYgq8QsQq4UoapLNaiNquwC5bcIUGKZ15imSbHxe3U0ILSSPGq+k9PFy6j1iWmRXzy4hJIfy2eTYEK5G75zA0jfLmFlq2XY+vvJGDTw7Sf3IGa46FzjITTw3OYjJgKzY+vaOahg3FKK/bjE+lUpw8eZKDBw8SCoUoLCxk08aN1NoDSOd/tJgVxObB6oH6GzNC8B7iLYuCJEl/C3wQuADoFw8LIcSNly3KX4OMKLy3EGmD8L5RQq+MIskyrmvKca4tQlIuXSoKhy/Q3fMXBBZOE5/4INNnNuFLyzTaUlhkOwk5ThbdZJkexS6dI6XKDJda2WkqYU/MyqgpgEcz+FAizK2ROPnxFNMWB89PryaULmLB5yM7OM81yVdI2BPsmapnV8Fm+tQaLCaNdJmTeKGLctswG6Mvc7B7BbMl5ShON6u6dVoGkshCoMYHcE0/zVSWwGQIXKZyYs4bEYoFZ3iEkrlnCTfMYNOvptqyFlVSOSIO8e2Kp0mZFKoDpUxZFijRzDSP5ZAen8dktdGwaSsrrr0Oh6eAs3vHOLV7GCEg1ezm/uFp5jWdZl3l99YvYcO2SszW/1wpjkajHD16lKNHjxKPxyktLWXj8kqW+l9F6ngCAiOg2haH3jffDlVbQf0vSk8zvCu5HKLQDTQLIZKXO7i3QkYU3jskev0Enu5Hm4tja87Fc90SFPelnvvpdID+gX9mbOxh4lPrmT/3ITxRMw02DZtsJaaEsUld5MmPYpG7SJoUhkqs/NhUyp6YiSlTEG8aPhb1syORoiCeJGQx82JwOeGxAoaXLMGkpdkUO0SOeZh9E5W8VLKSU9Ja0kJFVNhJVnuplTrZqu3i/Hg1h7xXYldMrO9Ms2IwiYTAEp2gYOxRRjwaSVUhR3YTc74fobpxhkcomn6W4LIoeryOJtcmXOYcOuVuvlb2MCE5gS+eTUCeZ120grxeDS0Sw1tUQsu111G/cSvppMKZl0bo2D9BOqmTVefmyUiI05EYuYbE79aX8JHblmF1/udT/cLCAocOHeLUqVNomkZtVTnrvfOUjf4Yps+BpEDVlkUPorrrFmcfZ3hPcjma1wYAE/COEoUM7370YJLAcwPEz86h5ljJ/UQj1ppLa+Bf340cmvIS7Pg77PNe1toMHA6VmBoiYjrBEv1xzHIfCZOZzjIHT8llvBhXmCOEx1D5w9kI2xNhfFqauFnmJbmWyGu59DY2kaiysjzeQZt6jBeSpRz0bOV05VX401kYbhNavZs250mu1Z9jKFzKD5x3Y8qxcG1nmuahCBICe3SGiv4HGXXH6PXZsWPD6riRqLkcZ3iE/KkfEliSpDc/ixZxK6W5tUzLs/xj0bfpsQxjTdvwJhXWzeWg9EkIEaasdSUt226gvHE5wdkEB54YpvvwFEJAflM2uyNh9kxOIQHvL8zhT+9cjvd1Hd0TExMcOHCACxcuIEkSy4vtrNMP4ev/OiCgZCVs//vFDmPnG1uKZ/jt4RdmCpIkfQMQLDaSLQde4nXCIIT4H293gL+ITKbw7kXogsjBCUIvDiMMA9fmUrI2lb7hSMxA8AQ9PX/J/NQUwQt3Yx6rZpkVshSVlDLDvOM8yxNPYpaHSKhO+solnlLK2RMzWDBF8CbNfDoQ5PrYAm50UqrEWVsh0RddXKhoYbqggPz0DFdJL/Oc5qUjq5pesZWhaA5CkRE1DjYUH2RD+lWOp9p5yXkt5qiJjR1pmodTcFEManoeZN7qZyDPgyzJqLb1YGnHGRkjd3InCyVJZpNQ7qhnRe5VyLLKQ76fsMd5HN3QqIu4aRz1ok0HsDqcNF55DSuu2YE7r4CZ4RAnXxim/9QsiiKzZFU++8JhnhiZJQGsczn58w82UVe16FEkhKC/v58DBw4wODiIRZVpd06zOrgTlwhCbg003Q5Nt0L2r+BomuE9xa+9fCRJ0l2/6MZCiPvfYmxviYwovDtJDocIPNVHeiqKpcaL931Vb+hVlEzO0tf/d4yP7CLQ/X7U/g3UWmTcioKhTDPsPsbK6E6s0jhJOYe+CpknlGJejCcJmKJkJ2x8LhDgxugMVlmQMMsMeF2EXnExqtfSWb8MFZ02ZR+HFIVJtY4J8xo6wvkYcZALVLbUvEa91s0e9RpOW1txJmKs6ZJY2ZdEMgT22Cz1vfeT1Ga4UJpL3GTCbKlCsl6FPbGAZ/IZ5nMThAwJi2xlWek11MrL6LD1c5/3WWaUCTb7q3D1xdHjCXxlFazYdgPLrtiEarYw3hPg5K4hRjv9mK0KyzYUcTwS4/sXxglIgmVWC//npkbWrSgAFstKOzo6OPDafqZnZnEqadYax2gTp7B6CqDhFmi8BQqaMyZ0v8VkrLMzvCPQo2lCu4aIHptCcZvx3FCFtSHnEltrw0gzNvYD+vu/yXzPaqSuW6hVLHhUGZQZhrNfZXlkF04xRZJi+ksdPG7NYnciSkiNkZuw84WAnxti05gkCDhNjBTamDvlhZNeTrSuJOJ0YLecZFxNo+n1TOfUczpegj6hI1lhfc0xyq0zPOO8mVk1l+z4OA0DXtZ2JzFrYI3P0dR3L6bQOBfKfUw77CiKG9l2DXZd4Jh+lvmsOElJAgVSFaXskHbg0p085X6Fk+YjtE/mo4/Oo6gq1avXs/zq7RTXNYCAwTNznHhhmJmhEDaXmeVXltATT/DNI0OMo1OkqPzx1bW8b3MFsFhJdOrYYQ4e2E8wliYXP+s5SlNWGLXxfYtiUNyaEYIMwOXZaD7H4jLS6wkCx4GvCiHm33KUvwYZUXh38LMJaM8PYiR0nFcU49pa9oa21gsLB+jq/itm+pwEz99JVcpLhUUGaZpA/m7KQi/hMuZIGlUM5pXwiCfBC8kgETVGfsLB7/nn2BGfRZJgJNvFbJnE7JgX3xNwprKN/op85u29pGRwpeoZLS/jtFGF0Z1ASuvUFQyQVxjjpZyr0JGpiJ4lf6KCdZ0CZ1JgTszR1Pt9shYGGSry0pPrRUgqinUtFrxY51/Eb4tjSBIpq8T56hRrTeu5yb+FCWWWA9a9yAOTaLEE3sJimrdeS/2mrdhdbnTNoOfoNKd2D+OfiuHKtdJydRnjqTRf29tLl5HGLcl8dm0ld19Xg6LIRCd7OfrSMxwd8BM3VEoZ5wprH9VNK5GbboGSVZnu4gyXcDlE4e9ZLEV96OKhDwJ2YAq4Qghxw2WK9U2REYV3PqmJCIEf95EaCWOucOG9aSmmAscl18Xj4/T2/V9GujuZP3cnjvkKWhxgliRU1yPY9Bfw6AskjWWMOVfwgG+cXcY0UTVOSdzG7wdmuTqxQBqVUwWlaGUBQgkH+Q/rjCWrOdFSS7dnCLNhIUeqpbeqlDNqHVJnFGUuidcWwLHUTF9RNY6URm3oZYxQDes6PeREDNTUAk3dP8Az38t8QRYX8nOIoCCbqjArRZiCRwhbUkjAbA4cq5nDafPyx2OfoDRdQI9+jrOju0GBpavWsfyqbZTUNyFJEumkzoXXJji9Z4SIP0lOiZOWq0u5MBfhuwcG6TTSWJC4s6mYP7i1HtvCeRZO/4RD5wc5FStAQ6XWNMn6Gh9lK7dB2dqMJXWGX8jlEIWTQojWNzomSdI5IUTTZYr1TZERhXcuRlIj9OIIkYPjyDYV9/Yl2NvyLlkq0vUEwyP/Qe+FR5k9ewOxkZWscEgUqSom+SRJ54OUpbpJGVWMmbbzw/wenlOGialxKuNmvhSYZlMiTFA42VW6AlfxACZVw7ZbhSMejq9u4VR+FAmBz1LGmaU1nDU3YR4OoPZFkDCQKx2Eq3IojqZYFvwxk1opK3sbKJ3XkbUQDV0PkTN3jpmSbAZ8bgKGDLILVS1BifWQVDWQBP2laY4tnSZtFnx8/GZuimwhpcc5Mvschg8ar7yWZRs2Y3MulnrGQik69o9z9uUxEtE0RdUemq8s5sjAAt87NsIAGjYkbqv18cXWebJHdjHReYQDsQouUI0ELC+ysm7zNfhq3vDvO0OGN+RylKQqkiStEkIcvXjDlcBPH0W0yxBjhvcIQgji5+YIPDuAEU7hWFWA+9oKZLvpkuvm5vbQ2fGPjJ9uwt/7ZYpVhQ1uAxUNw/E1fPoBSJoZ4w6+XxjgGctu4kqCurjCF2dnWZuIM46Pv6q4lQrfSUpsXdBlJvsxM11LG9i3w4smJ8lXyumoLeMFayuWYIScY/1EYzb0XBvJeg+t0SRlg/fTqZjJHt7B2kkDSQ9T2/ck+dOHGS0vprOsiogOiCwUkw+Sg2jGBeJWjVM1UbpKA1h0C6snlvPx8A7y1WLGE71El6W46jO/R/6SpUiShBCCsW4/HfvHGTg1i6ELKppzadpcxAunp/jYI6cYlXSyJJnPVMp8zvVjnH3P0D+cy05pDYPiWiyqzLrWFay+YjMu16Wd3hkyvBV+1UxhJXAvsDhhBELA3UAHcJ0Q4rG3M8j/ikym8M4iPRcn8HQfyd4ApqKL9hRll35oRaMDdHV9hcETMvMXbkJJOliTncKjO7AoO3FaHsNm+Inqm9iZXc43so4TMkVYERP8XnCGtniKHqWUry65mxrvCdbZDqAHVHIeg0CsiBdX1hI0gVvJp3NZEcftbSiRJL6+YeZnXAiLglHr5kothT32BGfN0zRN3k3DqIJipKkc2kX+9KsMlJUy6RSkDANJzkFW85ASPWiyTtiR4mCDn8mcBMWBbGrGfWzwV9GWfTWyLJNqlqi4ZQ1m62JVVTSQpOvwJJ0HJgnOxrHYVerWFFLZ6uNH+4d4qHOSadnAK0t8Im+Mu+P/iJKc57yplaPqaqbiKllZTtasWUtbWxtWa2Y2QYZfn8tWfSRJkhtACBG8TLG9JTKi8M5ApHVCr4wRfmUUSZVxX1OOY82l9hSaFmFw8FtcOHKcmbO3kArls9QH9WkNVZrHbv86br2LtFFGt2U7f+ce4nRWJ2Upg7+Yn6U1LjhtWcIfVn2Jclc375cexqykcb4koR5w8srGFQw7nDilHHrq8zhqa0NMJckanSceNiMksBTYeZ9ZZ157kvP2DlonPkvDuBdZSBRPvIZv9hX6C4tYsETQhUBSS1CVfIidJa2kidjSHGxcYNajsWw8n6VjVnIjKqsLr6fIXIVcZCXvjibUbCuGbjWgvUcAACAASURBVDB8fp4LByYZPj+PMARF1R7q1xeieMzcs7uXn4wtEJQFebLg045DfDT1bywoPk54ruNM2EsyrePz+Vi3bh1NTU2o6m90LlaG9yhvpU/hDiHEDyVJ+tIbnRdC/PNlivHXIiMK//0kuhfwP9OPPp/AtsKHZ8cSFNfP++QsGtft5OyR+xk/sZXYTB0Oj0yraYHstAe75R480gsgFPzcyAPOPB7KfoGUkuR3AkE+7o9x2rmMP6j8I8yuMJ8Pf51s7zzmHpmsxxTONjRyuqAUq/DSW+flqLwCYzyNOhVF6DKGQ6XUY+WWZIqTyk663K+xof9jLJ2rQpJs+GZPkxV8jRGvm4g8C4BsqkFRilCiR0gocWIWjSP1fqZyUjQO5VM7rGJOQ1P9VpaJdqSUhOuacrI2lhCcjdN5cJKuQ5PEQinsLjN1awupasvjzIVZ7j80xJFEnLQE1XKST8lPskPZTadrC6fkZsaDaRRFob6+nvb2dsrKyi7Zi8mQ4a3wVvYUflomkjFByfBzaIEkwWf7iZ+fR/XZyL27EevSS+0pwuFOzp38R/oPLiE0/DmwCTxlUTaGrFj0QTy2f8MsZokZazhj3sHfuPYy6D5MYyLJX04u4MHBzXXfYNBXwB+P/S0V2X3IsoT7HoUxsYRntzSgChfDlV5OJhvQuzTkSAhFFkj5DraoZjbPRfmx9jiPuA+y/dw1bIz8LzRzLs7QIKbkYabMBqM5KSR5DtXchCyXo4T2EVd7CFt1TtQFGPclqB/K5YqzJuyqk+Yrr6bOvgrtdAg1z4b7lmpGpuO89LVTTPQGkGSJ8sYc6tYVggQ/fmWIPzvQy4CqowjYII/zeeU+8uxpTrqu5euBL5AO6/h8HrZta6O5uXlxjkGGDL9hMs1rGd4UQjeIHJggtGcYYYBraylZG0qQ1J+vhRfCoL/v3zn+kwEWeq7GQGG0XOHO6AzZSTMu6zdxcpy0UcisfCf/YtZ4ueBJhKTzBX+ADwfD/EfWR/nb5Xdw1+h9bCh4GcWsY39ZJtRRyv6WFsDFqaI8OqOV6DM6kgCTW8fr9XBHXMU2F+SZ/KdISme47ngrnnQbEdcSTIlZUvpxUtoswphDMUmYrC3o1KKEXiYhTZNWDU4vDTBSEKduyEPNqI1sTxmrb34f1ZUrCe0cRpuOoTTl0otE9/EZUnENl89G/fpC8ivddJ6Z4UcnxjhiJAkoAjcat0v7+bDpWSZzr+Bkuoq5cBKz2UxjYyOtra0UFxdnsoIMbzuXoyS1Bvg3IF8I0ShJUjNwoxDiq5c31DdHRhR+sySHgvif6kObjmGty8ZzYxVq9qUbnqnUPEf2/TVdL7aSChXRXSrRYp/hqhkPDuU5PMqjSBgExU28ar6G+7zfoM/ppz2e4C/nFojHW/hM7ecwciN8KfZPOLPDmHoh/mIZh+taCSk5HHMVMhrOQyRAMglcBUmaTAV8cFbnXDTEwZJH8fiH2X60BFVZwWxeO2hhwhxDjQ4gGSFMdgW7o514sh4p8ipJvR9dEZxbEmSwKEbdsJPqUReFpa1s/Mit5NvLiOwbI9kXwLAqdMkyvRMxFJNMVYuPpa15hP0J9h0YY898kAtmnbQEDdIMdys/os6Z4JxzI10LEoZhUFJSQmtrKw0NDVgslzrCZsjwdnE5RGEf8IfAd4QQLRePnRdCNF7WSN8kGVH4zaBHUgSfHyJ2YhrFY1m0p6jPfsMn2oWFY+x9/EdMndlKymJwvtHMn01M4oyF8Ji/iVkaI260MWX5LPcoj/NCfgcygi/NB7hmvpR/ld/Hs8tr+FTiOywp7keOgL4zjyPZq+mxVHLWnI8/uriaaco2KC0IsS1UTstkiufkecaKH6amO8jmk1aSjmZGSzZjSDJzli4cc0eQ9QD2bAduZxvz/jpE4gha6gyGJOgqC9NRGaZuJItlI7kUlF/BNZ/6EO6oidAro6THIuhmmYGkQU8wTVaBnYYNRdiyzPScnGFP5zQnVI1Rk4EJg+ulw9xu2kfC18LpeCHBWAq73c7y5ctpaWkhLy/jSJrhv4fLIQrHhBArJUk69TpROC2EWHGZY31TZETh7UUYguixKYK7hhBJnayNxWRdWYZsvrRbVgiDrnP3cejxNPHZGoaLklyf28+S0WI86g9wKi+jCR8B6dMcVkd4KPdFztlMrI/F+b1ZDxNDW/jX2graHYdYV3UETDryK1kcjqzhsLONCyKftK4iLDK2Yp327Ene37+UlD/Nc45RLDmP03TEYOW5CIHsFQxW7EBTbYy7F3BPv4Yp3o/VaSXPt4q5uWpS6Qvo8aMYksZwfozjy/zkL1hp762g0LuRqz9xMzkpmfC+UbSZOGmzTFdEYyimU1Tnpaolj8BMlFNHJzmaSnDGqhOSBAVSkLuUn7DKFeSCdRX9C4szqaqqqmhtbaW2tjZTQZThv53L0bw2J0lSFRf9jyRJug2YvEzxZXgHkhq/aE8xGsZc6cZ7UxWm/EvtKQBSqQVe+vG3GNrfgi5U4vVTfC4YxzGWwGv+IjIhQvothO1udlq+zX94HViFzP+aEbR03c6DSoxQZZLPVX8bxZOAXhNHeldz3LqOs/YSNE1B91nxlETZahng9tNVnBgo5oncoyzJ3cWOVxXq+mZZyG3iVOvvoqtuxj0atsBRcocOISuQnbWUhLaSialR9PgDCFLMZSd4rXEBScDG88soN65m/Ue2UGkzEX1uFH8gSdwk0xHVmIpAVVsebbk2Ri4s8PijnZywanSZNTSbxDrpPB+zvExN5RL2xtrZOR3ApTrYtKmFlpYWPB7Pb/gVzJDh1+NXzRSWAN8F1gF+YBD4iBBi+O0N7xeTyRQuP8IQhPYME947iuww4b5uCfYVvv9y83N64ji77n+NyHAzCc8C27M7cPub8Kj34VBeJWVUkLQ0M8Ju/tLnodNiZlMkzQeGtnF60M/x8lJua96NvXiOdETm0KlGzostnNMrSekKeo6F3KURrpe7ue54Fc8aClMFh2ifOUTVqzIl0zPMees42/hhUHKYc0mk00PkTj+Prsdwyh6EaRMJaRojcQJBmohTY1/jLIGsNCsHl1I//T6a17fTXGAneXwaI5omKEt0htKErCrlTTloKZ3Bc3MM6xonstL0ILCR4DblVT5aPEPWsi28PG6lp28Ah8PBpk2baG1tzWQFGd6RXI7lIwtwG1ABZLPY0SyEEH91GeN802RE4fKiR9MsPNpNssePvTUPz/VLLrGn+ClCCF555QF6dtrR4m4Kis6zRlOxpON4Td9EJkJMbEaVXuE7Xif3eVxk6YKPTC8ldSaXV/MqubXmFQqX9jMvyxzsqGAgfC0dWi3JtIrhMeNbGuIW01lWn6jgR1hw5++h9UIvFYdTuKIh5ry1nFz+AVTyCdskFixhysaeIpmexWyYUC1rScox9ORpQCPlFOxbNsO4L8GymRLaB26juriJVVUu6JxHpAzmBHRGNAyfjZwiJ3NjYQIzMUatBifsEfoMM9mE+ITjIHeuKkKv3s7eU/2cO3cOq9XK+vXrWb16NWZzZqZxhncul0MUdgEB4CSLbqkACCH+6XIF+euQEYXLR2o8wvwPL6CHUnjeV4VjZcF/mR2Eogs8ed+DRM8vw2RbYJtrH7K2Ga/8g59lB7pw023r4su+bAbMJtaHrJSeWc4hex3XFx6hpeYYPQ6VV0dymJ7dQW9yBfGUiuFUyasOcbv1KM2n83gcN0tce2k5OkHJuQAmLcVEXjOnlt+INV1I0gQTbsHSsSeJx4aRkDCrDaQUGSPVAegs5GgcrJllzpsiP+Zhbe+t1EmtrKlyYxkPI3TBpGbQE9exlbsx2RTGu/3omoE/O8p+QvQYXoqkeT5dNsHtW9eSzlvOvv37OXXqFIqisHr1atavX4/NdumwoAwZ3mlcDlF405VGkiTdC1wPzPz0ayVJ+gfgBiAF9AMfF0IELp77E+CTLIrO/xBCvPDLvkdGFC4P0ePT+H/ch+JQybmjHnPpf92ruOfUIfofHUALFFLtPkiT2YJJFz/LDqL6ZiT1IN/yWnnQ7cSjSTT31dIZ38S17tNcW/Ui+3Is7Pc7CU9czXB8HbGECcOukF8V5MNZ+6g6n80+w8JScYRlRyfJ640hJJmhknbOLd+KPVqCIcGIT6V25AnS4V6SiowqF2MoTvR0D2AwXBTnRLWfhEWnLrKEyolNNESX01LixBlMIgSMJA0GdYGr0k0ilmZuNIKiGkQ9Q+zWDLoppsQU4vMrVG7Zdi0L4RgnTpzg1KlTGIZBW1sbGzduJCsr09+Z4d3D5RCF7wLfEEKcexPfdCMQAX7wOlG4BnhZCKFJkvR3AEKIP5YkqR54GFgFFAF7gBohhP7Gd18kIwpvDaEZBHb2Ez0yhaXKTfaH6lCcb7zsEUimeeCRR1GO+DBJca7K2oVZ2YxH/iEOZT8powIhzJyxj/AXPi+jJhM107lMTt3KVlsPHyx8mmcKLeyNm9FnNjIVuZpozIywyORVBfmI9wWKezz0JXWW+k9TfmyOrClBSnXQW7GGC8vbyAqUoxow5JOpGd2JefYsCw4LMk50kwvSExiSoKcsTEdFCG/cSpuxkoLRKynFwzKvBVtSRwOGEjpjqoKzyMHCRJR4JI3THiVqPs7TUiHdopRye5LPbVrCjpW1dHde4MSJE0xMTKAoCo2NjWzevBmv99Iu7gwZ3um8Fe+jn05cU4FqYABIsuiUKoQQzb/kG1cAz75RliFJ0s3AbUKIj1zMEhBC/M3Fcy8AfyGEOPSL7p8RhV8fLZhk4YedpEbDODeV4L6m4hIDu5/ybM8wQw/uQ5ouocJykpYsM2Yjjtf8TWQRJWnUkFZ6+FqOi8ddWbiSZrL7VuJVSvms+wF2lhq8rJtRA+34wzcSCVsQJpn8JQE+kvcM2f1ZBEMadSNn8J2IYo5KhJwF9JddydnlNeTO+7ClYCQHyid+QuHQYQZ9HoSkkDJbMKViaIpBd1mYidwEFUEPa53Xokw1UyLMVNpVVEMQBfpjOn6nCbPLzOxwGCEEpa4e5uQzPCK10CNKWOKW+NzVjbT64OzpU3R0dJBKpfD5fLS1ZSwoMrz7eSslqde/DfH8lE8Aj178dzFw+HXnxi4euwRJkj4FfAqgrKzsbQzvvUuiP8DCQ12ItEH2R5Zhb8p9w+tmkmm+9exeCvcmUHUfW1x7cJhacUvfx2Hejy68CBSOOQf5P7nFLChQOZGHOreFu50vcqJiD5+VrSihZcQDHyQctIIikVcV5M7CJ8gas2AcMljaewjX+TSSLjGeX81I3XWcqS+lcM5O6aRg2mXgnX6GbScPcL60iIE8DymTjDmtgxGhuzJGQtaoixZyk7QNI1ZBSUylwLJovTFrCHojGnGnCTnLTHg+gSUUptHxIqPmSb4ub2NAu4GlOTb+/ooKCrUJzhx+hvvn5jCZTDQ0NNDW1kZJSUnGgiLDe55fKApvV8mpJEl/xuJwngff7NcKIb7LYnks7e3t717jpv8GhBBEXh0nuGsQNddGzp31mPIufeIVQvDQ0AzTD/6EvLFy8tRZVmXbsIgcvOoXUaQwhjARUYL87+wyXskyyEpYaTpfzwZ7msrK7/APHjczyULk+d9lft4NEuRVBPloySNkTShYX9ao7ujEPmyQMMPZJbXM53+Is0vzKJ6XqB0VLDgMbMHn2XHoRY7UlXOyshBNMVB1CYM0Y6UCNazRHq6kvO5q1NFiSqYknCYJXZUZTBn0hdNoZoW0JiCQwmcbZ7nrGTrtKn/OLQwl7NT6nPzZMicOfy9du1/jgmFQWlrKjTfemLGgyPBbx2+8iFqSpI+xmIFsFf+5djUOlL7uspKLxzJcJoykhv/xHuLn57E15eK9rRrZcunLPxhL8K2nj1J3cBpruoSNrm7cSile6R4cppcRQkJC8Li9nH/KFcQVjaUjXtqCS7g5by/fKHXyDS0HNfQxQlM1kBJ4i6J8svIHlAwGUZ4zUXFqElMA5j2wd3UFSc8n6S0poNivs3xIJ2w1UGOvUDK2i+E8H/66UgxJRxYShgThPAXbrEGzXk7V8m2YR3IoGBOoskTcpnImmGIkkMa4+DPlWv1U23aRrxxkV9YWPp++g9GoSk2enc/WpJDGjzF+OILD4WDNmjW0tLTg8/l+sy9QhgzvEH6joiBJ0jbgj4BNQojY6049AzwkSdI/s7jRXA0c/U3G9l4mPRNj/oELaHNx3DsqcW641IlTMwTfOTJM6unjVAayKbakWebRsEtxcsyfQpaiAAxL9fyRz06ncxpXzMyOnkpuzxriSP0hPmnKwp+6ltTkVkRYYHZrfGD5k1zVewT9sSzyz4SRU9BXLvHi+hzM1o8z6aujwK+xsj9FzCyYM+8nqL1IwGrH58jHEbv43KDIKFYL5kiCUnsFdRt3YB214xlZLFebt6h0+ZP4A2kAPF6DuqxDLI09QEBV+b7rUzzuv4FIAOpyzXzAPY11/hjxsER1dTUtLS3U1NSgKJmB9xl+u3nbREGSpIeBzUCuJEljwJeBPwEswIsXP5QOCyE+I4TokCTpMeACi8tKn/tllUcZfjVi5+bwP96DZJLJvbsJa9WldgsnRv3sfPg4vgGFarOVBk8AE1nkmr6CRelAAhJ6Dfc42rg37xhpKcLywSzuiMt4K0/z17lZnNfaSc99AGNaQTILrmw8wEenn8D+kEpWBxhqiJMNMj9aYcXL7UScG8gPGqzrSpBUDTpzXmXW9DI1Iw6Ko16Mi5olSwqSBLJmUFbSQE3OZmwTKpYRiAlBjyHRF0mTFmlUs8yymiBt+jdxRY5xWLqC/5n9l+yZcSGnoCXboCTehycSJDs7m9arr6K5uTkz5zhDhteRmafwHkXoguALQ0ReHcNUmkXOHctQ3T+/Nh6Op/nuY2exHZmnXJWptyUxSVk4lCfwqA8gSTqayKHT+CR/XnCYPucQ7qiJTwxauNYzyjfKs3mWIiKRuxCjbiRDUF/ey2f4AfmvJMk6Y6A5BPtaFR5eIZGTvhrZchM5IYUVgykMyeB8/qsk0vuoG7ZjSykISUYSxs9iNFltrFiznaLEMszTGjIwoxkMJAymtcX3rtOtsKrqLHVz/0AqGWWn68Pcq13DBb+M0yzRaPVTmhzCY4aGhgZaWloy08wy/FZzOQzxMryL0CMpFh7uItkfxLG6AM8NVT83BEcYgmf3DjHybA9NhoXqLBmTZEGmD5/5nzHJ0whk/Om7+GFWLvflP4GGxpWDJr6oL7C/1s1H7OUMpT6KMVKOHNMp8M3yCd9DNO8eI+ucgZwWHFkl8e/rVFSpGZf6cbJSDladSyAbGt05ryFHDlLTZUYxslh8K2o/E4TcsgqWr9uBfdiHbSiBTprBpMFgyiAhS+iaIMcnsaZwN+Uz/87chJt/yf4CD6QbmJ8V5Ft11puHqJTmqMgrpqVlGw0NDZmB9xky/BIyovAeIzkcYuGhTvRoGu9tNTja83/ufF/3PHt+eIbasMx1FguqpCDTg9v0DHZ5P5IECWMJncaX+ErxY/Q69uKLKHx5Io4918SXi/J41bgFfawVeT6NwxHlA/XPcMXhTnIfi2AJCgaWwjc2q0znFONWPkN2uIB15xLY0gnGHEewBk5S2ZtAwgzILO4KaEiyTM3aDdS1XUXykE7W4RiCOANJg2FZJq0oJAyD3OwUq1w/oiL6GBcWGvmf3q/zzHQu6QmoMIdpM41TZdNYsWI5LS3vz8wtyJDhTZARhfcIQheEXh4hvHcExW0h73dXYC52/ux8aD7OrvvPUjyaYIdZRbIKrNIBZLkfr7oTSUoiUJhPfZ6HXQr35v8zBhofHNW5gygP1Pl42LKO8Nx2pNE0qpLg6qpX2TDVRfW9o7imUviz4eu3ypxY6iXLfDcufRnbTsZwxmOElHNI4dPk+ucAGYQEkgHoWBwO2q+/heLKtcw+P4n6TBgzMJISjNtV4pJCNJAkxxNjS8H9lBkv8pJ0A3+cdR9H5yyYooIqeZp6ywxtNaW0tm6nuro641CaIcOvQeav5j2ANhdn4dFuUqNh7C15eN5XhWxdfGlTCY1jj3RhPjfHOpOCYTFwyLtRpDPYlQHM8uJYjLio5YLxWb5S+jj99hHKI/B/5/x0FJTxydwyekMfgV4VOZWiuaiTrcoZap8dp7RvCk0RPLxZZudKJzbLXUSzV7P6XIIVAxFSeifp6AksxiygIgkJIRkgCbw+Dxvv+gIS5UztHEQ7MoQPGEsL5nKsBOI6obkE2c4gG7K/h089yeOe3+F7obuYnJdwSina1VFW5WqsbVvO8uW3ZTaNM2R4i2RE4V2MEILY8WkCO/tBlsn+UB325Yv19bpm0P90P4kjE5TLMmmTAebnKeRRNPKwyd0IJAxUFtKf4Ycuhe/nfR2BzqfGE7RbnXyrsZE96TtId+Uhh9Pku6e5ofA1Cs6kWHHqPI5gkv2NEg9sthF3f4BA/haUgMSnXwhjj/lJR19A6OMITItmKZIGkqDMJ7P5C3/D1KiL4R+NUCF6KZclpnRBoDiL6UCSwGgUr22Oaz33ke3o49/cv88Pp3+X+JREnhRmq3WO7c3FtLduo7y8PLNpnCHDZSIjCu9S9Gga/5O9JDrmsSxx4729FtVjQWgG47uGiB4YxyFAknX8nl3Uxr+PEC5UKYRKEIAEVXQYn+YrJT9mwD5KSQz+fCHFgSWlfNpyHf6RFShTCRyWMNuL9+OblVl+aJjKnjEGCuGeG80Ml95IMG87MdXGTYcjLBtNoqVOkIodBrSLYpBGlgzqPAusv+PznJtcxbHvTFKt+qmVJeaQmK50MzwVZaE7gNc8zTXuH5KTO8O/ub/AD4aySUahQvazpVjnurWNNDbelNk0zpDhbSAjCu9CEj1+Fh7vwYilF5vRrihGkiUCJ6ZZeLIXsy7QjDQDBXtZEn+CpfEpDKwo8hRCqAgJ5rWP80CWk/vzvo0h6Xx8QqfM5+X/NK2hJ3gdUkcC1YhxRe4JqlMx8s+HWXn4BCk1ybd3qBxbvpW5vFuIWF0sH0hw9elZLIkwqdgu0CYvigHIkkGLZ5yVbVUcTv09ex/XqbNMUW6WCckS4dpseocWmDs1h0ed5Cr3I/iWSHzP/inu61aJzwsqFT8fbs3mxk3XkJ+f/0t/PxkyZPj1yYjCuwiR1gk+P0Tk4ARqnp3cjzdgLnJiJDSG77+AOhgkrhv05u8ny/sMK0dGMAOSpINYLElNyoWcMz7FV0qeZ9A2RnFC8Pm4nacbG/hXbkfvNqHMxSmxT7JR7cO+INNy+jRlQ6P8pF3mhY3rGM//EEFnLkVzKe54eY7ssI6WPEEqdgSEvuihKwkaXVNsKpnhqPhDXjjXSJ0VXHaVmEkm2JBDR+cks4emcCmTXOX5EXnLi7jP/Ad873ScWNqgQl7g+iqFu27alrGdyJDhN0Smee1dQmoiwsKj3WjTMZzrinBvr0AyKYQ755l9qAtTSmdAChBt/Ro1vXGqUl2AwGDR8E6W4sxr7+f77hwe8D0PGNy5YCdVWsIPsm4lPFuOuduPYuhcaT9BQVqiYGaCNYeO01WU5PHtjfSV3Mmsp5T8YJKtp5IUzi9gTWok4s8ip+fg4nup3BliS7uP0xMb8AcaqbWa8aoyKYuC3ujl9JlhZubMuJQp2r0/oXBdPfca13Lv0VkiKZ1yeYGN2RHuuOFKampqMvsFGTJcZt7ykJ13Kr8NoiAMQeS1cYIvDCHbVbJvq8Fam41I64w/0o3omCOqC/rz9+FwvczKsT7sUhQQpEUlZnmApJTHaeN3+GrRSwxZJ6hKq2xTC3io4Fp69XVYOxYQCzpV1hHa5BlyIiHaj5+C+CSPbavgtaZPMe+txBeJccVZndrxOYQwIxKnMWKHkYQASZDtsXHlB+7i6JE8giNRmm0KRWYZ3apAvYNjp4eYXsgiS5mmvfAAhVtWcX+knXsOjRJOaFQoflot09y8ZRVr167NlJRmyPA2kRGFdylaMIn/sW6S/UGs9Tl4b61GcZiIDwWZvK8Dc1JnyIgQbPgOzYOjlIi+xeYzvQ5VnkOR5vAb2/meq5QHc3cjSwa3xQo4X76afZbrkEfTWHoDqEJjra2bJYkIlQPnaTnTx4ur3Ty447OM5TaSHY+y/rxO82CEmBLCnpRJRnci6X4AbFkuNt/1RToPyUwNBCk1STQ5VFRZQm0wc+JCL8PzhTjlOdqrLlC8bSP3T5Zwz2uDhBIaS8xhGhhmy4pqrrrqqkxZaYYMbzMZUXgXEjs7i//JPjAMPDdUYW/PB0Mw9VQf2vEpEgb0uk/gcz5Jy8IFFAwEJhL6GmzqfjTJyQnu5qsFBxi2TtKoucjPaeLprNtIxD14O4aJ+a0ssY7RJmbwhXrY8so5UmqKe2+9kedWfxBXMsamCxEa+1Q0YxpkG0r0PHriCCBQVJW26z/J9Egh04MhbBK0uk3kAkq+mZ5AL+enC7DIMdrqxqh43/9r777D46ru/I+/z/SmUbd6c5Fsucu2XOSCC65g0wwYEkwLSzaEhGwK2fwSkoUkkA0h2ZANLfRAHFqIAQMGAzZ23HAvsq3euzTS9HLP7w8Nfrxem7ZYI0fn9Tzz6OreM56Pjq7m63PunXsX8EyFnse2VNHrD1No91MUqmRcVjzLly8nJyfn07pFUZQvgbr20XlE84fpebUS7942jDlxJF1VhDHFSqDZTeOfDmByR2iMBPDlP0VZ+xacXS6EkAS0EUhM2Awf0COn8JBjPM+nvIQZHQsMY9icdSXv6UaQXFOLvsKNBsyzHqXQW0l++T5KjvWwrziDn19zJ12JKSw6Xk/JQTvGcC9+GcQS0RH0vERE6x8dFJRcTCA4iQMfeIBeRjmNjDEJhIBOYzvbjtnRiWRKCusovHoZz5eP4fonq3H5QoxLlBRoR8gxw6IVi5g0aRI6ne4T+0VRlIGhisIgEqhx0bXuGJGeVNFRsQAAIABJREFUAHELc3EuyAEhaHujCv/mBqQGhy3HKIp7hPz244Rl/1VPPZEFWPQ7EXjZK67hx5kV1Jo3MUZLpztzBetMF2Dr6yDn8EHaXckUmJsp81chunax9L0GdELHHy6/mhcXriSvy8Nlb/eR2SsJufYRceRgDFQS9O9AAvFpEzDaltFcHQI8JFj1lGXYMHT5Cei9bGsP06c5GJNdx6Rr57O+ZQz/+thBur0hStKM5OtPkBDopXRWKfPmzcNqtca0zxVF+Z9UURgEpCbp+6CB3rdr0CdaSL11IuY8J6FOHw2P7cPYHaY1HEaXuo5F3nVofSak0IF04tPGYDNsIqBL4jnT1TyYtgmT0DHcOY8P49citAjjKrdSU5WFGzsX6I+S79pGXs0xZpRrVGZn8/9u+S4uZwordnuZXVtLA22Y27sJxhei9b2K1HoQ5mQc8VcRDFoIBkPo9YI545KIb+xD6/FyyOehssfC8JR6Vlw9jX2GCVzx8lEq2z1MyrCxwlGLwdXIiBEjWLr0WnWKqaIMUqooxJjmDdH11+P4y7uwTkwl8bKRCJOe7s219G6oBQ3KaaAk7pckeOvxaSnY9e14IzMxijpshs006Kfy84R0PnS+TYoujbr026kx5DK6ZxvhowYqevMZbmplSdsOWq27WP6hH5tX8NTyK3h6xaWMrw1x+469GHVHOO4dTpouHZeth7D7L2h6HWbHSgzGkWhSAJIJk5IZ6QsTqe2lPexnj0dPkqONy68eRU/Bar71+lG2nNhNdryJr+S50bfsIikpkaVr1qhTTBVlkFMHmmMo2Oim889HibgCJKwYjn1mBlpfiIYndqFv1mgPRwib1zPF8DhdkSwS9B3oAHd4MQ7jG0gRYbNhNXenldNp6MHinEdtwg2kBE8wofYwO2qmYBZhFniOMMz/JgVuF1M/itCUks5dt9xOjzOHG/bvY5H3JZ4LXEZ2XyqaVkPYtwVkAL89hyTLKoxWGwFPmMwRTqYXOIl81EJY09jnlQT0TcxYmoJt5lweeLeCdbvqsJv0zEnsJbn7KA6bhbKyMqZPn47RaIx1lyuKgjr7aFDy7Gqh+9UK9HYjSdeOwZzrpPdAA13rKiCsozLUwzjLz3Dae+nxpJNhOkBAG01Yy8Ru2ESfMYGHzSt4JuVDzDo77am3EzTlU9a5gbryfFq8aRSJJi6rfJ3WggoWf2AhvsfD3+Yu4dFVa1heWcEPWn/PO74ldHlnYQw1Efa+g9RcuOwQ71hJRtIEulu8OFMslF2QhXlXPdIlqQ9qVAc7mDJHkLtyBU/sqOe/36vEFwozLcFHgecoiXYzs2bNYtq0aZjN5k/vEEVRBowqCoOIDEXofrUS7+5WzCMTSLq6CJ3FQN1zm9Ef0eOKSLojmylNeIQaXRm5wY8wi3bc4RUYdPuw6us5ap3I3XFJHLRXYrSOpTn5W6T6jlNUXcuuxik4dR6uaPiQFOMmUvpyKT5UR6/dwS+v/zqhuBTuq/lPXD1j2OO7BH2oh5D3bWSkFb8xQmNWGrPjb6G3KYDQ65i2JJfMzk5Ch734NTjid5M3oYexa1bxZoWLezeU09jjo8gRoDh4nMw4PWVlZUydOhWTyRTr7lYU5QzUKamDRLjTR+ezRwk1e4hbkINzUR6hDjfVv3kfi9dKbcBPhvk3jByXSOXx+Yw2/J0IqbhCV+EwvAiGCC/aLuY3SSfw6mvxJN1IyDqVmU1vUllRxO7AZEpDx7nyyAu0541k7IFcUruq2TyplD+tWsN36p9neLWJbe7vEdHCRDzriYRr0JAczfeQkbiSGX0z6K7zUzAxhcmjjPg2VRHWTNQFQhhzGlh6/UUcceu46ql97KnrIc0cYrGxkkKLpGzhPKZMmaKKgaKcx9RIYYD4jnTS9ddjIARJVxVhKUqk+8Ma+l6vI6wJ6kMVTClYR2fGFSQfeIw4fTme8Bw8YR/DLLvpsDj4jXUB6xP2IYwZdKbcQaqvkcyKHo60jyHN2MVXjr6DOcFCYnsyo6o2ENHBg6uvY5jTz4q6ZsrdiwlqOlyBLZj9BwFoSfKztxguD34TeSKOuCQLs+bZCW9txBGMwx3RcMU1Mu7G2fQ4UvnVhnJe3d+EXR9hoqhjckKAuXNmU1JSoo4ZKMp5Qk0fxZCMSHo31tD3fgPGLAfJ145BZzNQ/8Q+9LU+OkIRhO55xi0bzontYQoDDwLQFVyFSb5OnKWXHYmF3GNNoNbcgt+xmJBzCeNqt1NZU0woYmBe8DCLjm+hO30Vo46/R3brQfYWFrN+0Qquqq2l1TOdkNTRFtpDnGcHOiIEDYJNJc0kx01gzrGr0PlMlMw0Yq1pJs6dhg7oNnZScE0R+hGF/PG9Ch7dUoWmaRTrmpmV6GbB3DImT56sioGinGdiUhSEEI8DFwFtUspx0XVJwDogH6gBrpRSdov+cxR/BywHvMD1Uso9n/Yag70oRPqCdD1fTqDKhb00nYSLR+Cv76PlyYPoAxo1oW6KbPfgmHkj4X+8QbxhM4HIaBo8GRQ4txAywJPxc3gorpaw3owr+VZSPQEcFRFqXXkUWBr56r6N9GTmMaw9ldEnXsAQCfLssmsYoTkR7uFEpA5fcDsB/0Esmg8JnMgJsmNsGwtcVzC8fCbDCwIMCzdh6R5BgsGAR+cl9ZIs4qeO5a+76vjVhiN0+zWG6zq5ILmX5fNmMGnSJHXBOkU5T8WqKMwF3MDTpxSFXwFdUsp7hRB3AolSyh8IIZYD36S/KEwHfielnP5przGYi0KgxkXnc+Vo3jCJl4zEVjKMtteqCGxrxK9Bp7aVkuTH6Ui8i5Sm32ESFbQFltJjqaGII1TGObjXNpXttiqC5mIiCVeSX1FBdeNozPogS3x7KDn+ER25V1JQuZmiml0cHl7MidGXkuAdBkgS/Bs5FmomLtx/p7Wg0cqGGTWE7RYWHL2O8Vo8Oc5GIt1jyTNZ0ISGdXYiqcvH88GxVn76t/3UuiKkCjdL09xcubCU4uJi9Hp9bDtXUZT/k5hNHwkh8oHXTikKx4ALpJTNQogM4H0pZZEQ4uHo8vOnt/ukf38wFgUpJe6tTbjeqEafaO6fLrIbqX/kAMZOP83hAMmGB8hJd9PYfTuZ8h6Moo7d3gsYlbwfp7+dDcPy+YXNTq/w4E5YTYo7CSqttHuHMd5xnGt3vMOJ7PEM82Yy4fBz2H1uXp97GSmMJyzjyeU1jrvq0bQQ/b9dQV1WHO9POEiWdxSrji1mrLODHtdEiizx2HQC3UgraWsmsKmqjfteP0hFj4adAEvSffzLsqnqQ2eK8k9kMJ19lHbKG30L8PG9FbOA+lPaNUTX/a+iIIS4BbgFIDc399wl/QK0QJjuF0/gO9iBpTiZpNWF9B7toufF44iIRoWsZYr5x8jkr9DaMo0s0/9DL5o4Fspisv1dXGEd9w2bynP2diJ6C8SvJeeEl7q2PBLN3VwfeYP8XeWcKFhLfsOHTD3yKtUZ2RwuW0xiYCY62YrN/RRHw6Ah0AMRQyrbSzuoSDjIzKbpXNOdRnM4BYt3DJNtOog3kLKmmI2d3dz/23eo6ZPYCXBxZoRvrphK4YiCWHeroigDKGaTwlJKKYT43MMUKeUjwCPQP1L40oN9QaFWD53PHiXc4SN+WT72WZk0/LkcXXkXnoiG2/YiM7VtdBseQLZBmul76EU7EskoUy0H4+38LKGYCl0rAes0kjwT8exyUh/MpCxpN5dv3szu3Gk05VzF3F2PktzTyfZpJQxLGYnfOwOD7y26AxVIRHR0oMeTVsQbkzcT1kJ859h8jK4Z+A2ZzIrTodfrcF6Yx9tGH799ZgsNHoFDBLgiT/CtlTPIycqMcY8qihILA10UWoUQGadMH7VF1zcCp15MPzu67rzg3ddG90snEGY9KTePB6uB6l/sxOwLU4eXzPi7yfRNpz3yAIbwAdLMdyPwoyF40x5Htz2H+516QloHOvsKEityaenJI8vexHXhDcTvqGPb6K+T37qNhbseoCU5hY4lBdhCc6no7iPkfwQhQ/h0FmyaH68pF/f0RDbY11PaWsLshgWYZA5T4sCOHnNRIu9laPzm/X20+HU4RYCvjDLxzZVzSUtNiXV3KooSQwNdFP4OrAXujX599ZT1twkh/kL/gWbXpx1PGAykJnG9VoV7WxOmfCdJa4ro3NmK/906hCY54TzKxMjr+D3/hh83iYa7sel3ArBTJPBuYjyHkqZyQNuLFA4SAivpPD4Rn9SxLGMTy97dzgcFZTQXXciSDx8mt62Z2glZDM+DjZ1L8Pu2gNZDr8GJIxzGKMFUuIydee/g6evmhv3/hj2Qw1inRrowoHea2JWn455jVbQf05OgC3LzWDvfWHkhifHqbmeKopzDoiCEeB64AEgRQjQAd9FfDP4qhLgJqAWujDZ/g/4zjyroPyX1hnOV68vUu7EW97YmHGWZOOZnU/vYYcwtHlyaxJvzIuNa8glplxFv+CN2/Q6khLDU8au4YWiJs/iL04jO9w9sxkz0NRfS2DuGEfFVXBLchH5bJ+tLvkNByw5WbrwXt8OOZZFGWBvJa00SGX6bPr0Tvd5BfLgXR8o4rDNz2di0k+Kjq0jx5DIqLswYpxkR0jiRoedHbS00HTSQrA9x22Q7X794PnabLdbdqCjKIKI+vPYFefa20b3uGPbSdMSYRDqfLccY1miyayTZXyehIx+n4SUs+o/QpAXQ8KDxx/RSXs+4lFbv3zGGakmK5NNa+VXC0sTyrHeZ//5O3hk5H799PFe88wT5LY14RtuJz/fx9675EKghIqz4DMnYQw0YTE7i5k7mYGsr6W0ziQsmkmTzMjNjGIbOAO02+JnfxT5NMMzg57opqdy8bDoWi7pInaIMVeoTzV+yQG0v7Y8ewJTrJJBiRe5oISAlngIPmU3bcfIeFv1+QlocAW0OFt0mjlsTuXfE13nf7iC+82EMBNHa5+BqX0qBs4bVjjfw7+zlH5O/y4TKLVz63hv4HRZSSnp4SZuD7O1BABFTEfpQPcg+jIUjqBOJZHWUYtIsGBwNLCoag7k6QACNhzUfLxImy+znhhlZXHfhVPWBM0VRBtUpqee9cLefzmeOoHea8fgiGHe20KOTJKd8QG7zG5j1R4hgo9q3FoduFEbrvdyb+xUezb4ci+sV4jvexCwtdFffCsFMLh25nnm1B9kQLKOrZBI3/+135LY1oxXq2JOVTU/PcPRaJ3rjaNDZILAHHHZ64qcwrH0OBUiy4uqZlF2IvnMEotLPmwT5AwFSbD5+fcEILpk9Qd0DWVGUz0QVhc9BhjU6nz2KDEbQdAJDZx8h8x4KxTOY+yrQhI16WUan+9ukmPexKXszvxz+JK0GE/nNv8Ct1aJ5htNRfwPZtnZumvxrkrZEeCb7NiYe38Xt6+4h6DBxfGYa5YF0DF1uDIYUDLZphHybkOFegs4i4nWLGRfWGBHfTpo+E6GNwteq8aYWYj1B9E4v9y8pZn7JaPWBM0VRPhdVFD4H1xvVhBrdYBAEe4MIw0YKdP9FWCYRIpWt7rWki9k0ZPyNH46azl7nYkZ2fYC571ncBPC3riDcPYvlue9x0fA3aX93JOvSruBfXn6E7PYWWgud7IkfhfT2YNQZ0NtXEpEeQu5XwJBMTuKNZNqt5Bj0GLChGZ3stWg81+dhrxZijN3N95dPYG5JsSoGiqJ8IaoofEbeg+24tzUB4BGCrmAP0+P+RFDLRhq6eK/zP0hy2vj12P28lnYDqf52lp14iI9MW4lE7PjqbicuYuFfp/6ePHsjO/Yuw+SK8OP199KeEM+W0mL6AgEI+TFY5yNMown73mKYTpKXdj2Z1mTMwoBGkI4UG+t1EZ5t64FQmPGWbh5cOIYFs5ar6xIpivJ/oorCZxCo6qHruXIAvBk2Wmv6mGx/CYGHsF7wQfcv2D2hl8fzxqGJkdxQ9wzl/kPstjQRdhfia7yaElsdN826HxE08taBNVz02jsM625nz9gcWgwWCITRm6disE4nWecnQ1dPdvzFWPQWJH4iHGVr/Eiet1rY19KFVYSYaGzjq7PyWTJ/CRaLJca9pCjKPwNVFD6BlBLPrhZ6XqkACUxPp21LE6PMnTj0rxLSGXna/kMeLc2k0TKZ5e0fMKfhBR5M9BIwhwm0LsfSO4E1wzazYNwGfH3xVL4zha+9+xxHszI4OGEEESnRGUeSEl9GvimZDIOGRe8gLOMx6PYgOMTjciHvOouodflx9nmYYWhi1YQ0ll64msTExFh3k6Io/0RUUTgLzRui++UT+A51AhB3UQEHX6sm36Qj0fhLEBEeTv4OPx87m0J3JX/e/0teNPVwf5KGjNjwN1zPWE3HhbkvUzxyH91d6ST+t2By30dsHD+KsE5Dp0tHi5/MbOsYskwGwlqYllAtuYbXsBuPc7/vX9hoXUNnKEyqr5e5xnpm59pYtnQlOTk5n/ITKIqifH6qKJyBv6Kb7r8eJ+IOAmArTaehqpd8wCDWY9afoFw/m3uLl3JBxxauq/4lP0xJxafXiHjysbRfxDzZybThm8nNOUFXYxbZv+3hUFYmvakJCJ2DsGMCkYQUVslROPWC/T27yLE/xag4F/f5buN1eSMeA+Sb/EymirFJgsWLL6S4WB1EVhTl3FFF4RQyrOF6qwb3lkb0yRZ0ViM6uxFjcRKOJw7Tp/WRH/844bCF2ydcS0qwC1PXI3w/NQOhCxLomMfkcA4jZQPFhVtIT6/HfTiNyKuSbSNyiegM6K0T2Te8hzldRcwXyeiExkeuVylOfJ1H5VpeDUwgqBeMTxbkesrJ0vuZu2QuM2bMUB88UxTlnFPvMlHhTh+dzx4l1OzBPiMDpMSzs4WEy0fR/uxh/Bo4nXdhDYdYlzyTg85iJtf9O3tMRpACXculXGH0Ywz0MG7sZpKSW3BtT6d2bzyRZD3NjlH0ZYdodVZzQ+11lFhNeCJemnwPU5WczHfDvyYidMzKMpPjPYrF08nEiRNZuHAhTqe6WJ2iKANDFQX6RwgdTx0h0hckeW0xOpuR9of2Y5uWjmvDMWQIGrRdjNW10SfN/HDMvzOq/S/Ua63IiJUxnjKm6Prw+mDShPeJi+ui/sM8Og/baLVnciIrnsa8DymtW8G36y9jlM1Aq78Zv+73/CHuSvZEhjMzzUqJtRZPay2ZmZksW3aTOm6gKMqAU0UB8B3qINzmJfmrxVhGJdL6+73o4oxofR7CbWH2eMKk5D9GutvFj4ffhtlfRbd3A0gzw10LmOQL4jaGmTHhQ8wWD5WbCuioTufdnCLcIzci9UGuOfA95pqSSbPoqPXsp9n+Bj+VdxASZtbkhTA174KInVWrVjFx4kR1WQpFUWJCFQWgb2sThlQrljFJ9L1fT7jVi3VOBr4tzRzzB9GSn2KOu4ltzgk8nrGYxOY7EAj89Wsp1LrR65opm7IdvS5M5YYRfOhdyJ4pFRhS1jG+aR4X1q9iht2MXSep8bzKegesk98g1yGYyRGsbV5mlM1i7ty56vMGiqLE1JAvCoG6XkL1fSSsGkG400fvpjosY5Nx7TiO1DTadfu5Sqyn3ZDAtePuIb71x+i0EJ66mxjjNzJcv5+RZQeJhPUc2TCXJ81zCZU8QaKMcPHeH1MQSqbUYUTKIFWhZ/gP+zyqZBpT7T2MCVcwunAkS5YsISVF3fFMUZTYG/JFwb21CWHRY500jM6njyAMOqrch8gMZnBQ7uKK+P8kKPRcMum/MHfejz7Sjb15FQFPPktsz1FYto+A18ab79/MhkQNR8ZjzK9fxNjOGeQZ9Ix36PGEu9hh3MDP9Zdj1sNi3THGJxhZsmQNhYWFse4CRVGUk4Z0UYi4AvgOduCYlYn/UAfBahfHC3ooqrbRZKgkKeU+jG7JbUU/pMe3AWOwmtyOKRx2zWSZdStjZu+hryeRh3feQdWww0yK+Ji99weYpZ7xVgMFZj2dwRP8wdTO26wkW+divrWBpfPLmD59ujrFVFGUQWdIvyu5dzSDlFjHp9DxxCFabEHGNbzBsVwLLeEXWN3l5/GMVew3+jH0bSWnN5uOuvkkO3pYNfslelqTeXDvd/DGHWRtwxSc4QRMhCm1m0g16qkN/4PbTXl0k0qpoY41UzJYtOjrxMXFxfpHVxRFOaMhWxRkSMOzowXLmGR6NjcQCHpx5n+PLakh8o63sdoX4M/py3g6dTay5xGSg/GM3TaSv+ancNv4R3HVJvLC3q9isntY3bQADYlTSKY7TFh18BYfcbehGKfwc2N2JzdcspKsrKxY/9iKoiifaMgWBe/+djRPCJluxb/5GC2ld9IQ6GHOoV6SNMn3R97BVud4UloewKPXWLvBxH1j5zEm4Rjpne3s2D2NupRcrnFZkTJMutHIVJsggp+fiW42MYpiczd3rRzLtMnqFFNFUc4PQ7IoSClxb21EJJlwv3eQtkl/pMvVySXVvbQbbVwx7l66jInMrPk1b8W5uHFTHG/Pno+/08xMyx4at6SwMWs2c3xmDDLMSIuFsRYdHbKHb+gEHdi4dbyBO65YjdlsjvWPqyiK8pkNyaIQrO4l1OxB6I7gGn4E6T3ARbVudtiGccv4P2CVQb528Of8Nt3N3HIrhoXD2H50IqOSa7Bv7eGDpDJyNCfjQhqTbGZyzXp24+JOoSfNFuLlr5QybnhmrH9MRVGUzy0mcxpCiDuEEIeFEIeEEM8LISxCiAIhxA4hRIUQYp0QwnSuXt9X9R5GcQBXch8O3Tpm1Lp525HGtZOfwCg17tv5Mx5OCZDbaWTeFB0bWuag00lmHthKi3kYHst4LvXpKHOYyTUbeAY3dwCrJyfx7o9WqoKgKMp5a8CLghAiC7gdmCqlHAfogauB+4AHpJQjgW7gpnOVYac9n3abxGn/DSPrfbwYl8jXJj2OPezn6b3f4/5hdoQW4arhUBdK50jXaMZ5j2IN+SmPv5DrA2bmxZlxGgQ/wcMLpjDP3Tydu6+aiUGvjh0oinL+itU7mAGwCiEMgA1oBhYAL0a3PwVccq5evFTqSXPeS3aLh8fiE7hj3EOAkacP/5AnrQVU2zu5OlUj2RLg0WPXY9YHmdW0jWpnGTfINObEmQjoItwifPSk6dj4/cXMHJl6ruIqiqIMmAEvClLKRuDXQB39xcAFfAT0SCnD0WYNwBnP3xRC3CKE2C2E2N3e3v6FMnQ1/ZrU7j7uTx7G3YV3ETKl84fyeygPJ7MhuZqFdo3RZsl95f9OwG9kUdM7uE05XGyeSqnDSDVB1ooAU8c7eeFbF5LsUAeTFUX55zDgB5qFEInAKqAA6AFeAJZ+1udLKR8BHgGYOnWq/CIZXKU/4ZbeJt5JnE7AVsL3Kp4gt7uStdkGRpgizDcaeab6FmrbE8kMNJLna6Uw5WYm2vTslH5+JHx8d2EuN1046Yu8vKIoyqAVi7OPFgHVUsp2ACHEy0AZkCCEMERHC9lA47kK8ORHf+R9RzbehCtY2biFVa1/4cbMDBzGEKsNNtbXXMcWfTGGiJsF7R9QkHApJXYbewlzj+jjwSvHsbBEXbNIUZR/PrE4plAHzBBC2ET/zYYXAkeA94Arom3WAq+eqwAeXzN9yV9nfHcLlzTfx9VZaYRMYa7SO/hH1bW8mTMVfYOHCa5DFFsmMC0+m3Khcb9o46E1o1VBUBTln1YsjinsoP+A8h7gYDTDI8APgO8IISqAZOBP5ypDQmg5mZ4uFjR+mx8MSyTNHOFWi5X6yotZP3EKCXuasET8rAyGmZk8nVoheVhXwz0X51I6sfhcxVIURYm5mHx4TUp5F3DXaaurgNKBeH1v86tM8RzlOaeR6dYIy81mDu1dyN+mzaRw+2FOhHO51NfMopR5VBLhL/pjXD8ji9mzZg5EPEVRlJgZkifVTwuF2GWTXBEX5lK7kYo9c3ll4gKm7NpGa18CGZEg33IUcoAQ6417mV1kY/ny5fTPdimKovzzGpJFIbPcw/cTIpTadJzYUcabBbOYtXUDsilIr9HJD/QJfEiY13X7GJFuYvXq1ej1+ljHVhRFOeeGZFGoL7KRaBFUbZ/NHvtw5r/3MmnNLexJnM4cDDSg8bJ2mFEpkmuuuQar1RrryIqiKANiSF4Qr7MgDd+WXOq9kolH30ea0zmUeTkRoZEuBesiNVyeFeH6G24mISEh1nEVRVEGzJAsCs7KeLobaknzewimzGSUdQYP6v0USXg70sHaLDdfu+Vr2O32WEdVFEUZUENy+qgWDwGDgfaiK7nYNpun9CFMUlIlA6xObeUb/3qrKgiKogxJQ3KkMCwvnxrHeL7RauNDXYiDIgJSconhOHf+23cxGIZktyiKogzNojA7ModVbUF8ugi/wgPomeHey3/87FZVEBRFGdKG5PRRKBDET4jbw80EhJ6cQAPfXjQGZ8qwWEdTFEWJqSFZFEZc7uGbcQ1UGZyYIgG+JTYy7eJzdvsGRVGU88aQnCvZ1JlHXV8ABPwk/CjTbvgFeoMx1rEURVFibkiOFP763A7COiNjA8cYk5FG3oTJsY6kKIoyKAzJkcJ1pVm0bqrjAfNDZNy4JdZxFEVRBo0hOVIotTXyvvP7WIoWEZeSHus4iqIog8aQLAoJaRm47SPIWvPLWEdRFEUZVIbk9JFjwlIcEz7zbaEVRVGGjCE5UlAURVHOTBUFRVEU5SRVFBRFUZSTVFFQFEVRTlJFQVEURTlJFQVFURTlJFUUFEVRlJNUUVAURVFOElLKWGf4woQQ7UBtrHN8BilAR6xDfE4q88A43zKfb3lBZT6TPCll6pk2nNdF4XwhhNgtpZwa6xyfh8o8MM63zOdbXlCZPy81faQoiqKcpIqCoiiKcpIqCgPjkVgH+AJU5oFxvmU+3/KCyvy5qGMKiqIoyklqpKAoiqKcpIrCl0QIkSOEeE8IcUQIcVgI8a0ztLlACOESQuyLPn4Si6ynZaoRQhyM5tl9hu2Eo4yuAAAFpklEQVRCCPFfQogKIcQBIURJLHKekqfolP7bJ4ToFUJ8+7Q2Me9nIcTjQog2IcShU9YlCSE2CiFORL8mnuW5a6NtTggh1sYw738KIcqjv/dXhBAJZ3nuJ+5DA5z5p0KIxlN+98vP8tylQohj0f36zhhnXndK3hohxL6zPHdg+llKqR5fwgPIAEqiy3HAcaD4tDYXAK/FOutpmWqAlE/YvhzYAAhgBrAj1plPyaYHWug/53pQ9TMwFygBDp2y7lfAndHlO4H7zvC8JKAq+jUxupwYo7yLAUN0+b4z5f0s+9AAZ/4p8N3PsN9UAsMBE7D/9L/Vgcx82vb7gZ/Esp/VSOFLIqVsllLuiS73AUeBrNim+lKsAp6W/bYDCUKIjFiHiloIVEopB90HGKWUm4Gu01avAp6KLj8FXHKGpy4BNkopu6SU3cBG4JzfJvBMeaWUb0spw9FvtwPZ5zrH53GWPv4sSoEKKWWVlDII/IX+380590mZhRACuBJ4fiCynI0qCueAECIfmAzsOMPmmUKI/UKIDUKIsQMa7Mwk8LYQ4iMhxC1n2J4F1J/yfQODp9hdzdn/gAZbPwOkSSmbo8stQNoZ2gzW/r6R/hHjmXzaPjTQbotOeT1+lim6wdrHc4BWKeWJs2wfkH5WReFLJoRwAC8B35ZS9p62eQ/9Ux0Tgd8DfxvofGcwW0pZAiwDviGEmBvrQJ+FEMIErAReOMPmwdjP/4Psnw84L079E0L8CAgDfz5Lk8G0D/0RGAFMAprpn445X6zhk0cJA9LPqih8iYQQRvoLwp+llC+fvl1K2SuldEeX3wCMQoiUAY55eqbG6Nc24BX6h9anagRyTvk+O7ou1pYBe6SUradvGIz9HNX68dRb9GvbGdoMqv4WQlwPXARcGy1k/8tn2IcGjJSyVUoZkVJqwKNnyTKo+hhACGEALgPWna3NQPWzKgpfkuh84J+Ao1LK35ylTXq0HUKIUvr7v3PgUv6vPHYhRNzHy/QfWDx0WrO/A9dFz0KaAbhOmQKJpbP+r2qw9fMp/g58fDbRWuDVM7R5C1gshEiMTn0sjq4bcEKIpcD3gZVSSu9Z2nyWfWjAnHa869KzZNkFjBJCFERHnFfT/7uJpUVAuZSy4UwbB7SfB+KI+1B4ALPpnw44AOyLPpYDtwK3RtvcBhym/2yH7cCsGGceHs2yP5rrR9H1p2YWwB/oP1vjIDB1EPS1nf43+fhT1g2qfqa/YDUDIfrnrG8CkoF3gRPAO0BStO1U4LFTnnsjUBF93BDDvBX0z71/vD8/FG2bCbzxSftQDDM/E91PD9D/Rp9xeubo98vpP0OwMtaZo+uf/Hj/PaVtTPpZfaJZURRFOUlNHymKoignqaKgKIqinKSKgqIoinKSKgqKoijKSaooKIqiKCepoqAoiqKcpIqCoiiKcpIqCoryBQkh/ha9ONnhjy9QJoS4SQhxXAixUwjxqBDiwej6VCHES0KIXdFHWWzTK8qZqQ+vKcoXJIRIklJ2CSGs9F86YQmwlf7r5fcBm4D9UsrbhBDPAf8tpfxQCJELvCWlHBOz8IpyFoZYB1CU89jtQohLo8s5wFeBD6SUXQBCiBeAwuj2RUBx9JJMAE4hhENGL9ynKIOFKgqK8gUIIS6g/41+ppTSK4R4HygHzva/fx0wQ0rpH5iEivLFqGMKivLFxAPd0YIwmv5bldqBedErnBqAy09p/zbwzY+/EUJMGtC0ivIZqaKgKF/Mm4BBCHEUuJf+q7E2Ar8AdtJ/bKEGcEXb3w5Mjd4R7Aj9V3VVlEFHHWhWlC/Rx8cJoiOFV4DHpZSvxDqXonxWaqSgKF+unwoh9tF/A5RqBuGtQBXlk6iRgqIoinKSGikoiqIoJ6mioCiKopykioKiKIpykioKiqIoykmqKCiKoignqaKgKIqinPT/AWSMyCaGw0mAAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3ni4FWgU5RqVUOPN4YMk/IaErdB7jdjQhy81EkQikez3PcLZV5Gbg8/J2iMgkEVkpIiuzs7P9GKJSKqxt/Qyyt8CQ3+ucTj5Ui09GRCYC/YByJ4U3xkw2xvQzxvRLSEgIbnBKqerJGFj8NDRqD90vczuakObmdRSZgPe8uK2cbT8jIiOAh4FzjTEnghSbUircpX4Je9fB2BchUi8p88XNGsUKIFlE2olIDDAemO1dQETOBF4DxhpjslyIUSkVjkprE3GtoNd4t6MJea4lCmNMMXAH8AWwGZhpjNkkIo+LyFin2NNALPC+iKwVkdkVHE4ppSpv5zeQvgwG3wNRMW5HE/JcrW8ZY+YAc8pse9Tr8YigB6WUCn+Ln4Z6TeHMiW5HUi1Ui85spZTym8zVsGMRnHMnRNdxO5pqQROFUqpmWT0VoupA3xvcjqTa0EShlKo5ik/AplnQ9WKoHed2NNWGJgqlVM2R+iUU5EKvX7kdSbWiiUIpVXNs+ADqNLKLE6lK00ShlKoZThyDrZ9D90shMtrtaKoVTRRKqZph6xwoPg49r3I7kmpHE4VSqmbY8AHEJdpV7NQp0UShlAp/+Qdh+wLocbnOEnsa9BNTSoW/Hz4GT7E2O50mTRRKqfC34UNonAzNe7kdSbWkiUIpFd5yM2HXt9DzSl3q9DRpolBKhbdNswADPa50O5JqSxOFUiq8bfgAWvSGJh3djqTa0kShlApfB1Jh71rtxK4iTRRKqfC18QNA7LBYddo0USilwpMxttmpzSCIa+l2NNWaJgqlVHjatx5yUuxoJ1UlmiiUUuFpw/sQEQXdxrkdSbWniUIpFX48Htg4CzoMh7qN3I6m2tNEoZQKP+lL4UimjnbyE00USqnws+F9uy5259FuRxIWNFEopcJLSRFs+tgmiVqxbkcTFjRRKKXCS9pCOH5Qm538yNVEISKjRGSriKSKyIPl7B8qIqtFpFhEdIybUurkNrwPteOh43C3IwkbriUKEYkEXgZGA92ACSLSrUyx3cANwHvBjU4pVS0V5sOWz6DrWIiq5XY0YSPKxXMPAFKNMWkAIjIdGAf8UFrAGLPT2edxI0ClVDWTMg8Kj2mzk5+52fSUCKR7Pc9wtp0yEZkkIitFZGV2drZfglNKVUPb5kKdRtB2sNuRhJWw6Mw2xkw2xvQzxvRLSEhwOxyllBs8HkiZDx1HQESk29GEFTcTRSaQ5PW8lbNNKaVO3Z41kH8Aki9wO5Kw42aiWAEki0g7EYkBxgOzXYxHKVWdpcwDREc7BYBricIYUwzcAXwBbAZmGmM2icjjIjIWQET6i0gGcBXwmohscitepVSIS5kHrfrr3E4B4OaoJ4wxc4A5ZbY96vV4BbZJSimlKnYsC/ashvMecTuSsBQWndlKqRoudYG9Tx7pbhxhShOFUqr6S5kHsc2geS+3IwlLlUoUIvJ2ZbYppVTQlRTD9gXQcSRE6G/fQKjsp9rd+4kz/UZf/4ejlFKnKGMFFORqs1MA+UwUIvKQiBwFeonIEed2FMgCPglKhEop5UvKF3bJ0w7nuR1J2PKZKIwxTxhj6gNPG2PinFt9Y0xjY8xDQYpRKaUqljIfWp9tZ4xVAVGp4bHGmIdEJBFo4/0aY8ziQAWmlFInlZsJ+zfCyMfdjiSsVSpRiMiT2CunfwBKnM0G0EShlHJP6nx7r9N2BFRlL7i7DOhsjDkRyGCUUuqUpMyH+CRI6OJ2JGGtsqOe0oDoQAailFKnpPiEXfY0eSSIuB1NWPNZoxCRF7FNTPnAWhFZAPxYqzDG3BXY8JRSqgK7v7eLFGmzU8CdrOlppXO/Cp3ZVSkVSlLmQ2QtaDfU7UjCns9EYYyZEqxAlFLqlGz7wq5kF1PP7UjCXmVHPW3ANkF5y8XWOP5qjMnxd2BKKVWhg2mQkwL9b3E7khqhsqOePscOi33PeT4eqAvsA/4LXOL3yJRSqiIpX9p7nbYjKCqbKEYYY/p4Pd8gIquNMX1EZGIgAlNKqQqlzINGHaBxB7cjqREqOzw2UkQGlD4Rkf5A6erlxX6PSimlKlKYDzuX6GinIKpsjeIW4E0RiQUEOALcIiL1gCcCFZxSSv3Czm+guECbnYKosnM9rQB6iki88zzXa/fMQASmlFLlSpkH0XWhzSC3I6kxTnbB3URjzDsicl+Z7QAYY54JYGxKKfVzxthpxdudC9G13Y6mxjhZH0XpAOX6FdyUUip4DqTA4d3QSfsngulkF9y95tz/OTjhKKWUDylf2PuO2j8RTJVdM7uTiCwQkY3O814i8khgQ1NKqTJS5kHTbtAgye1IapTKDo99HXgIKAIwxqzHXnSnlFLBUXAEdn2vo51cUNlEUdcYs7zMtipfPyEio0Rkq4ikisiD5eyvJSIznP3LRKRtVc/pU7Eut6FUyNqxCDxFev2ECyqbKA6ISAec+Z5E5Epgb1VOLCKRwMvAaKAbMEFEupUpdjNwyBjTEXgW+EdVzunT8cPwXC+Y+xAcywrYaZRSpyllHtSKg6Sz3I6kxqlsorgdeA3oIiKZwD3ArVU89wAg1RiTZowpBKYD48qUGQeUzmD7ATBcJEArlJQUQccRsOw1eP4MmP8o5B8MyKmUUqfIGDuteIfzIFLXUAu2yiaKTOAt4G/YL/T5wPVVPHcikO71PMPZVm4ZY0wxdsbaxmUPJCKTRGSliKzMzs4+vWhiE+DSl+H25dBlDHz7AjzXE776m61tKKXcs38jHN2rzU4uqWyi+AQ7Q2wRsAc4BuQFKqhTZYyZbIzpZ4zpl5CQULWDNekIV7wBv/seOg6HxU/ZJqlFT2kNQym3pMyz9zos1hWVneuplTFmlJ/PnQl4j3Fr5Wwrr0yGiEQB8UBw1r5o2hWungp718PCJ+Drv8GSZ+CM8TDwNkjoHJQwlFLAtnnQojfUb+Z2JDVSZWsU34lITz+fewWQLCLtRCQGO9y27HKrs/mpietK4CtjTNkFlAKrRS+YMA1u+w56Xglr34OXB8Crg2Hx05C9LajhKFXj5B+EjOXa7OQin4lCRDaIyHpgMLDaGcq63mv7aXP6HO4AvgA2AzONMZtE5HERGesU+w/QWERSgfuAXwyhDZpm3WHcS3DvJrjgrxBVB776K7zcHz64ybWwlAp7278C49FE4SLx9QNdRNr4erExZpffI6qifv36mZUrVwbnZLmZ8M2zsOJ1uPFzaHNOcM6rVE0y67e2j+IPqRARefLy6rSIyCpjTL/y9vmsURhjdvm6BSbcaiQ+EUY+DvWawsIn3Y5GqfDj8UDqfDt0XZOEayrbR6EqElMXBt1trxrd9b3b0SgVXvasgfwcbXZymSYKf+h3E9RLgEVaq1DKr1LmAWKHqivXaKJwGGN4fXEaB/MKT/3FMXVh0D2QtlBrFUr5U8o8aNUf6jZyO5IaTROFI+1AHv+ct5Xr3lxG7vGiUz9Aaa1i8VP+D06pmuhYFuxZrYsUhQBNFI4OCbG8em1ftu47yo1vLSfvxClOjhtTF8650w7lS18RmCCVqklSv7T32j/hOk0UXs7r3JQXJ5zJuoxcbpmykoKiklM7QL+boU4jWBS4SW6VqjFS5kFsc2jey+1IajxNFGWM6tGCf17Vi6U7crjtnVUUFnsq/+JasTDoLjuc7+3L4cs/w7oZsGctFOYHLmilwk1JMaR+BckjIEATRqvKq+xcTzXKZWe24nihhz99tIGrXvueMT2b06d1Q3okxlM7+iRjuc+5CwpyYevnzkIrpU1YAg3bQEIXO09UQhd7a9LJJhil1E8ylsOJXG12ChGaKCpwzVmtqRMTwfNfpvD3OVsAiI4UurWM58ykBvRp05A+rRuQ2KAOP1siIyISRjxmbyVFcDANsrdA9lbI2mzvt38FJV6jq+JbO8nDSSBNu9oEUjsuiO9YqRCSMg8ioqD9MLcjUZxkCo/qKBBTeGQdLWDt7sOs3n2Y1bsPsT7jMAVFtkkqoX4tbhrUjtuGdaj8AUuK4dBOJ4F43Q6kQHHBT+Xik+CMCXDWrVDvF8twKBW+/j0I6jSEG/7ndiQ1hq8pPDRRnIaiEg9b9x1lze5DzF63hzW7D/Ptg+fTLK521Q7sKYHDu36qfaQvg21zIbou9LkezrkD4lv5500oFapyM+HZbnZ6nEF3ux1NjeErUWjT02mIjoygR2I8PRLjGZKcwLB/LmT68nTuHpFctQNHREKj9vbWebTdlrUFvn0Olk+GFW/Y9TAG3WMXWFIqHKXOt/faPxEydNRTFbVtUo8hyU2Ytnw3xSWnMEKqspp2gctehbvXQr8bYcP78FI/mHk97F3n//Mp5bZt85x+uy5uR6Icmij8YOLANuw7UsCCLVmBO0mD1nDR03DPBhh8r+0Qf20ovHMF7PoucOdVKpiKT9ipcJJH6rDYEKKJwg+Gd2lKi/javLM0CDOvxzaFEf8P7t0Iwx+112i8NRr+c6H9JRZmfU6qhtn1HRTlabNTiNFE4QdRkRFMGNCaJSkH2HkgLzgnrR0PQ35vaxijn4YjmfDeVfDqENjwge0YV6q6SZkPkbWg3RC3I1FeNFH4yfj+SURFCNOW7w7uiWPqwlmT4K41cOm/oeQEfHiz7cdYNcVW5ZWqLlK+gLaDIKae25EoL5oo/KRpXG1GdmvGzJXpnCh24dd8ZDT0vgZ+twyufhtqxcGnd8HzZ8CyyXalMKVC2YEUyEmFzhe5HYkqQxOFH11zVmsO5Rcxd+M+94KIiIBuY2HSQrj2I2jcET7/A7x3NeQfdC8upU5m6xx732mUu3GoX9BE4UeDOjShTeO6vLs0yM1P5RGBDufD9Z/CmGfsvFOvDoGMwF6MqNRp2/o5NO8JDZLcjkSVoYnCjyIihIlntWH5zoPM2+RircKbCPS/GW6eZ2sbb42GlW/q6CgVWvIO2JkItNkpJGmi8LPrzmlDtxZx/O7d1byxJI2QmSKl5ZkwaRG0Gwr/uxc+uQOKjrsdlVJWyjwwnp9mJFAhRROFn9WKimTapIEM79qUv362mVvfWXV6S6sGQt1GcM1MGPpHWPsOvHkhHArCtR9KnczWOVC/JbTo7XYkqhyuJAoRaSQi80UkxblvWEG5uSJyWESq1RSS8XWieXViXx4Z05UFm7O45MVv2JiZ63ZYVkQknP8wTJgOB3fC5HMhdYHbUamarKjALlLUebRejR2i3KpRPAgsMMYkAwuc5+V5Grg2aFH5kYhwy5D2zPjtQIpKPFz+7+94d9mu0GmK6jwaJn0N9VvYaUAW/1OH0Cp37Fxir8bW/omQ5VaiGAdMcR5PAS4tr5AxZgFwNFhBBULfNo347K4hDGzfmIc/2si9M9aSd6L45C8MhsYd4JYvoccV8NVfYMZEuzqfUsG05TOIidWrsUOYW4mimTFmr/N4H9DMpTiColG9GP57Q39+P7ITs9ftYdzL35KyP0TyX0w9uOINGPWkvSp28nmw/we3o1I1hcdj11zpcD5E1XI7GlWBgCUKEflSRDaWcxvnXc7YtpgqtceIyCQRWSkiK7Ozs6sUd6BERAh3Dk/mnZvP4nB+IWNf+paP1mS4HZYlAgNvs9dcnDgKbwyHjR+6HZWqCfauhaN7tdkpxAUsURhjRhhjepRz+wTYLyItAJz7Ks3PbYyZbIzpZ4zpl5CQ4I/wA+acjk2Yc9cQeraK594Z63ho1gYKikJkAr8258BvF9uLnj64Ceb+ya77rVSgbP0cJEJniw1xbjU9zQaudx5fD3ziUhyuaBpXm/duOYvbhnVg2vLdXP7Kd+zKCdKssycT1wKu/x8MmARLX4apl8KxAK6zEeqMgR2L7eqCO5boNCj+tvVzSBqoa8KHOFfWzBaRxsBMoDWwC7jaGHNQRPoBtxpjbnHKLQG6ALFADnCzMeYLX8cOxprZ/vTVlv3cO2MdHo/h6at6MapHC7dD+sm6GfDp3VCngZ1oMKm/2xEFV0EufPZ7u6qgt9jm0KwbNO0Gzbrb+4QuEF3FNdNrmsO74bmeMPIvMOgut6Op8Xytme1Kogik6pYoADIO5XP7e2tYl36Ymwa148HRXYiJCpFrIfdtsKOhcjNh9JPQ7+aaMdY9fbmdrj03E859AM74lZ3ZdP8PkPUD7N8E2VvttO4AEgmdLv5b8ncAABqMSURBVLR9PW2H1IzPqKqWTbYTVt652o7AU67SRFENFBZ7+Puczfz3u52c2boBL1/Th5YN6rgdlnX8EMyaZKdZ6D0RLnnOTmsejjwlsOQZWPgExCfC5W9A67PKL1tSDAfTIGsTZK6Cte9Bfg607AND74dOo+38Wqp8Uy+1C27dscLtSBSaKKqVz9bv5YEP1xMdKTzzq96c17mp2yFZHg8sehIW/QM6DIerp0KtWLej8q/D6TYh7v4OelwJFz9jVxKsrKLjsG4afPMcHN5lm6QG3wfdL4PIqMDFXR0V5MJTHeDs38HIx92ORqGJotpJyz7G795dzZZ9RxnQthFjerVgdI/mNI0LgTbw1VNtv0WL3vDr96FeE7cj8o9NH9uFnjwlMOZf0OtXp998VFJshxd/8wxkb4GG7WDwvXDGBIiK8W/c1dXGD+3Iupu+gNYD3Y5GoYmiWiooKuGNJWl8um4vW/cftbOFtwmRpLFlDnxwI8QlwrWzoGFb92KpqsI8mPugTYCJfe3Fh43a++fYHg9s/cxOj7J3rf28Bt0NfW/UhPHhLbD9K7g/xc4/plyniaKaS806ymfr9zFnQwgljd1L4b1f2atpJ35or72oTnK2w6q3YM27tg9m8L1w3p8C0/diDGxfAIv/ZZu1Errafp6a+ku6pAie7gBdLoZLX3E7GuXQRBFGKkoaF/VszuieLWgWzKSRtQXeudxezT3+XbvWRSgrLrS/8Fe+ZVf8i4iCLmNg4O0Vd1j729a5MOd+yE2HvjfAiMegTrmTJ4evHYthyiXwq3eg6yVuR6McmijCVEgkjdxMmywOpsHlk23Hbag5tBNWTYE170BeFsS3hr7Xw5nXQn0Xphk7ccyOqlr6CtRtAqOesBMz1pQhtXMfghX/gQd22LnGVEjQRFEDuJo08g/CtAl2KcuLnoYBvwncuSqrpNhOcrjyTbvehgh0GgX9brIT0IVCu/jedXZgwJ41diTZxc9U7/6eyjAGnj/DXqD465luR6O8aKKoYcpLGp2b1adHYjy9WsXTIzGebi3iqB3txy/LouN2FMvWOTDkfjj/EXd+Iedm2o7p1VPh6B67alqf66DPtRDfKvjxnIynxE4PsuBx+3jYA3D2HeF7nUrWZnhlIFz8HPS70e1olBdNFDVYatZRPt+wj1W7D7EhI5ecvEIAIiOE5Kax9EyMp2ereHomxtO1qsmjpBg+uw9WT4EzJ8LFzwfn+gFPia01rHrLTlltDHQcYb+Iki+sHtcw5GbC53+ELf+zV3ZPnBWeI6MW/9OufXLfFjuvmAoZmigUAMYY9uYWsCEzlw0ZuWzIzGVj5s+TR6dm9emZGOckkAZ0aV7/1JKHMbb9fdE/bFPPlW9BTN3AvKGj+2HN27b/IXc31Gtqaw59roeGbfx2mm37j7JpTy5dmseR3DSWqMgAXm29eirMvtPWgi55Ifz6LV4fDsZjV1dUIcVXoqgGP7WUv4gILRvUoWWDOlzYvTlgk8ee3AI2ZNiksT4zly83ZzFzpV0rIypCSG5Wn16J8fRoFU+vxHg6+0oeInaYaWxT+Ox+mDoOrpkBdRtV/Q0UHbdzMO1YbJfPzFgJpgTanQsX/MWuaeDnX+GzVmdw//vr8Di/p2pHR9C1hZNIndpYxwQ/Jo8+19nO9yX/sld2D7zNP8cNBUf3Q+ZKOO8RtyNRp0hrFOoXjDFkHj7Oxkxb61jvJJFD+XZtiiin5lHa39EzMZ4uLepTK6pM8vhhtr2wqmFbe61Fg6RTC6S40H6x7Fhik0PGcigptBPwJfaB9sPs1c4BmlDug1UZ/OGDdZzdvjEPjOrCzpw8NmTYZLopM5e8QruOSGny6JVoP49OzerToWkssbVO83eYxwMzr7X9Pde8D8kj/PiuXLRqir36/dZvoXkPt6NRZWjTk6qy0uRR2mRVejvslTw6No2lW8s4ure0neXdWsYRv38ZTLvGDoOc+KGdnrsiJcX2CuYdi+0tfRkU5QMCLXrZ6zTanWsvVKtVP6Dvd+bKdB74cD2DOjTh9ev6USfm50nQ4zHscBJH6WfhnTwAWsbXpkPTWDo6t+Sm9UluGkvDepWo9Zw4Bm+OsnNG3fIlJHT291sMvvfG21l371kffk1qYUAThQoIYwwZh47/2Nfxw94jbNpzhOyjJ34s06phHUY2PsDvsx6ilikk99KpNO42DBGxv5z3b3ASwxLY9R0UOmuJN+0O7YbYjt22g4J6UdqMFbt5cNYGBne0SaKyfTQej2FnTh4pWcdILXM77rWKYfuEepzVrhED2jViQLvGJFY0S/DhdHj9PIiJhd985Z/mO7cU5sNT7exFhqP/4XY0qhyaKFRQZR0t4Ic9R/hh7xF7v+cIhTk7mRL9JIlygPejLubs+IO0z1tHxInD9kWNOzo1hqHQZjDEurOk7bTlu3lo1gaGdkpg8rV9/TKE2OMx7Mk9TmrWMTbvPcrKnQdZvvMgRwuKAUhsUMcrcTSiXZN6NpGC7ZP57xhIOguu/aj6DpvdMgemT4DrPrFNhirkaKJQrss7UUzKzp0kzrmRhNz17PA0Z4XpzKGmA+k08CIG9+lFdCBHE1XCu8t28fBHGxnWOYFXJ/onSVSkxGPYuu8oy3fksHznQZbvOMiBY3b0WZPYWnRuHku7JvVo3ySWs4/Np+vSP+DpeyMRFz9bPZttPrnD9ln9cXv1TXYhbtbqDEo8hiv7tvrph8Yp0EShQocxUJBL+vEY3l+ZzsyVGew7UkCT2Biu6NuK8f1b065J8Kd1eHvpLv7v442c36Up/57Y55cd8wFmjCHtQB7Ldxxk5c5DpGYfIy372I+1jgeipnFb1Ke8VHsSGxPH0y6hHu0a16N5fG2ax9emWVxt4mpHndYXRMB5SuBfnW1t8co33Y4mLHk8hqFPf02bxnV595bTm2xSh8eq0CECdRqQVAfuu6Azd4/oxKJtWUxfns4bS3bw2qI0zmrXiAkDWjOqR/OA/qovNfX7nTz6ySZGdG3Ky78OfpIAO3S5Q0IsHRJimTCgNWCTR05eIWnZeaRldWfb94e57fAbPLgnkdc3d6LY8/MfeXWiI2kWV4tmcTZ5NI+rTdsm9ejcvD6dmtU//VFYVbXrW8jLtsOXVUB8u/0AGYeO88dRXQJyfK1RqJCRdaSA91dlMGNFOrsP5hNfJ5rLzkxkwoDWdG7un1FOJR5DxqF8tmcfIy07j017jvDRmkxGdmvGy9f0CZ21ystz4ij850LIzaD4pvnsiUpi35EC9h0pIOtIAfty7eP9R0rvT1BY7Pnx5UmN6tC5WRxdmtenc/P6dGlen3ZN6gX2AkKA6b+2AxXu+wGiQ2R53zBz+7ur+W77AZb+afhp/9DRpidVrXg8hqVpOUxbkc4XG/dRWOLhzNYNmNC/NRef0YK6MSf/ZZx7vIg0Jxls97rflZNPYclPX54N6kZzQbdm/PXSnqGdJEod3g2Tz7OjwH67yOfsqx6PHdK8Zd9Rtu47wpZ9R9my7yg7DuRR4tRGakdHcFa7xgxJbsLQTgkkN431b/PVwR3wwpkw5Pcw/P/8d1z1o5xjJxj4xAKuO7st/3exj+HnJ6GJQlVbB/MKmbU6g+kr0knNOkZsrSguOaMlEwYk0b1l/M9qB9uzj7E9O4+07DwOHPtpiG5UhNC6cV3aN4mlQ0I9OiTE0j6hHu0TYmlUmWsaQk3aInvFe78b4eJnT/nlBUUlbM8+xtZ9R1mfkcuSlGy2Z+cB0CyuFoM7JjC0UxMGdWxCk9haVYt17kOwfDLcs1HndgqQ1xen8bc5m5l/71CSm51+zVsThar2jDGs2nWI6SvS+d/6PRQUeRCxfeOlGtaN/lkSKH3culFd10dU+d28R+C7F2HCdOg8usqHyzx8nG9SslmccoBvUw/8eCFl95ZxDElO4LzOCfRv24iIiFOobRQcgWe6QedRdolZ5XfGGIY/s4iGdWP48LZzqnQsTRQqrBwpKGLO+r1kHj5OUsO6dGhqh5FW6orncFF8At4YDkf2wu++t3Nr+UmJx7AxM5dvUg+weFs2q3YdothjaBZXizE9W3LJGS3ondTg5E1US1+FuQ/ALV9Bq75+i0/9ZPmOg1z92vc8fWUvrup3ilPklBFyiUJEGgEzgLbATuBqY8yhMmV6A/8G4oAS4G/GmBknO7YmClVjZG2ByefaYafXzAzY9RXHThTz9ZYsPl23h4Vbsyks8ZDUqA6X9GrJJWe0pEvz+r9MGp4SeLGvTWA3zwtIXArum7mW+Zv2s+zh4ZXqu/PFV6Jwqz7+ILDAGJMMLHCel5UPXGeM6Q6MAp4TkQZBjFGp0Na0C4z8C6TMs4sfBUhpv9Dk6/qx4pERPH1lL9o1ieW1xWmMfn4JI59dzKuLtnPsRPFPL9r2BRzaEV6z34aY3ONFzNmwl7G9W1Y5SZyMW9dRjAOGOY+nAAuBB7wLGGO2eT3eIyJZQAJwODghKlUNDPiNXfJ13iO2ZhHgyQPj60RzVb8kruqXRM6xE3y+cR+z1+7hyc+38O+F27lpUDtuGNSW+GX/hrhW0OWSgMZTk32yNpOCIs+P190Ekls1imbGmL3O432AzxXuRWQAEANsD3RgSlUrIjDuFTtM9sNb7NTsQdI4thYTB7Zh5q1n88ntg+jfthHPfrmNm558C3YsJr/3TdVjdcFqyBjDtOXpdG8ZR4/E+ICfL2CJQkS+FJGN5dzGeZcztpOkwo4SEWkBvA3caIzxVFBmkoisFJGV2dnZfn0fSoW8+s1g7Euwbz18/VdXQjgjqQFvXN+Pz+4azO/jvyLf1GL4wjY8MWfzz2YTVv6xITOXzXuPMD4ItQkIYNOTMabC1VZEZL+ItDDG7HUSQVYF5eKAz4CHjTFLfZxrMjAZbGd21SJXqhrqcpGdwvvbF6DjSDtFuwu6xxdB3lcc7n41AzwdeH1JGlO+38mEAa357dAONI+v7Upc4Wba8nRqR0cwrnfLoJzPraan2cD1zuPrgU/KFhCRGOAjYKox5oMgxqZU9XTh36FRe/joVjh+6OTlA2HlW1Byggbn3cnz48/ky/vO5eJeLZn6/S6GPvU1j3y8gYxD+e7EFibyThQze20mY3q2JK52cGbidStRPAmMFJEUYITzHBHpJyKlwzeuBoYCN4jIWufW251wlaoGYurBFa/DsX3w2e9/fjViMBQXworXoeOIHzvV2yfE8s+rzmDh/cO4om8rZqxIZ9jTC3ngg/XsyskLbnxh4rP1e8krLGHCgKpdN3Eq9II7pcLN4qfhq7/CZZPhjF8F77zrZsBHk+yStx3Lb3nec/g4ry3azrQV6ZR4DOPOaMltwzpUaeqJmubyV77lSEEx8+8d6td5uULxOgqlVKAMvg+SBtpaxYGU4JzTGFj2b2jSCToMr7BYywZ1+PO4Hnzzx/O4aVBbPt+4j5HPLmbS1JWsTdeR7yezbf9RVu8+zPj+SUFde0QThVLhJiLSzq0UVQumTYCC3MCfM30Z7FkDZ91aqSvEm8bV5uEx3fj2wfO56/yOLE3L4dKXv2X85O/5aE0G+YXFJz1GTTRt+W5iIiO4vE+roJ5XE4VS4ahBElw9BQ6mwazfgqfckeX+s/QVqN0Azhh/Si9rVC+G+y7ozHcPDedPF3Uh49Bx7p2xjv5//ZI/vL+OpWk5eDzh1Tx+ugqKSvhoTSYXdG8W9FmP9WoYpcJV28Ew6gn4/I+w6B9w3kOBOc+hXbD5UzjnTp/rY/gSWyuKSUM7cMvg9izfeZAPV2UwZ8Ne3l+VQVKjOlx+Ziuu6NOK1o3r+jn46uOLTfs4nF/E+P7BuXbCmyYKpcLZgEmwZy0sehJa9IIuY/x/ju9fAomEAb+t8qEiIoSB7RszsH1j/jyuO3M37uPD1Rm88FUKzy9IYUDbRgzrkkCPlvH0TIyvUTMGT1+eTlKjOpzToXHQz62JQqlwJmIXN8rebJugfrPAv/NBHcuC1VPt6Kr4RP8dF6gbE8XlfVpxeZ9WZB4+zsdrMvloTSZPzd36Y5nEBnXomRhPj0Q7lUXPxHgaV3WxpRC0PfsY36fl8IcLO5/amiB+osNjlaoJcjNg8jCoHQ+/+creV1X+QXj7UsjaDLd9B02Sq37MSsjNL2Ljnlw2ZOay0bntzPnpIr6W8bVpHl+bqMgIoiOFyIgIoiOEyAghOjKCyAghKlKIihBbJsIpEynOvghnn1MmIoKoSKG4xFDs8VBUYigq8VBU4qG4xFDo3Ntt9r7Y46Gw+KfHRcWGIo/v14hAdGQEMU7c0ZER9hYVwYGjJziUX8iiP5xHQv3AJEJfw2O1RqFUTRDfCq6eClMugVmTYPw0iKjCWJb8g3Y51uytMP69oCUJgPi60QzqaJdqLZV7vIgf9hxhY6ZNIIfyCykq8XCiyEOxp4Rij8f5ojcUl3ice+e5x0NJif0iL91WGTbxCNER9ss8KqL0y93rS955HBUpxEZH/bgtykkIURFCdJRNVgCFXkmosNi5LzHUjY7knhHJAUsSJ6OJQqmaos05MOpJmHM/LHwCzn/49I5TNkkkVzitW9DE14nm7A6NOdsP7ffGGEo8pUnkp8RSmgiinOTgRhOQWzRRKFWT9L/Fdm4vfsp2bnc9xfUi8g/C1LGQvQ0mvFfhFdjVmYjT7BTpdiShQ6+jUKomEYEx/4LEvnbywLRFlZ8TqgYkCVU+TRRK1TTRteFX79gO7alj4bUhsOYdKCqo+DV5OTBFk0RNpYlCqZooriXcsQIufg5KiuGT2+HZbvDln+0IKW95OTah5KTAhGmaJGogHR6rVE1nDOxcAsteg61zAIGuF9t5m5p0sh3XOak2SXQ43+1oVYDo8FilVMVEoN1Qezu0C1a8YS+i++ETQOzkghOmQ4fz3I5UuUQThVLqJw3bwAV/gWEPwYaZkLkaev8aWp/ldmTKRZoolFK/FFPXrsHd9wa3I1EhQDuzlVJK+aSJQimllE+aKJRSSvmkiUIppZRPmiiUUkr5pIlCKaWUT5oolFJK+aSJQimllE9hN9eTiGQDu9yOo5KaAAfcDuIUVLd4QWMOluoWc3WLFwIfcxtjTEJ5O8IuUVQnIrKyokm4QlF1ixc05mCpbjFXt3jB3Zi16UkppZRPmiiUUkr5pInCXZPdDuAUVbd4QWMOluoWc3WLF1yMWfsolFJK+aQ1CqWUUj5pogggEUkSka9F5AcR2SQid5dTZpiI5IrIWuf2qBuxlolpp4hscOL5xbqyYr0gIqkisl5E+rgRp1c8nb0+v7UickRE7ilTxvXPWUTeFJEsEdnota2RiMwXkRTnvmEFr73eKZMiIte7GO/TIrLF+Xf/SEQaVPBan39DQY75MRHJ9Pq3v6iC144Ska3O3/WDLsc8wyvenSKytoLXBudzNsboLUA3oAXQx3lcH9gGdCtTZhjwP7djLRPTTqCJj/0XAZ8DAgwElrkds1dskcA+7JjwkPqcgaFAH2Cj17angAedxw8C/yjndY2ANOe+ofO4oUvxXgBEOY//UV68lfkbCnLMjwH3V+LvZjvQHogB1pX9vxrMmMvs/xfwqJufs9YoAsgYs9cYs9p5fBTYDCS6G5VfjAOmGmsp0EBEWrgdlGM4sN0YE3IXXRpjFgMHy2weB0xxHk8BLi3npRcC840xB40xh4D5wKiABeooL15jzDxjTLHzdCnQKtBxnIoKPuPKGACkGmPSjDGFwHTsv03A+YpZRAS4GpgWjFgqookiSESkLXAmsKyc3WeLyDoR+VxEugc1sPIZYJ6IrBKRSeXsTwTSvZ5nEDoJcDwV/6cKtc8ZoJkxZq/zeB/QrJwyofp534StWZbnZH9DwXaH01z2ZgXNe6H6GQ8B9htjUirYH5TPWRNFEIhILPAhcI8x5kiZ3auxzSRnAC8CHwc7vnIMNsb0AUYDt4vIULcDqgwRiQHGAu+XszsUP+efMbYtoVoMQxSRh4Fi4N0KioTS39C/gQ5Ab2AvtimnupiA79pEUD5nTRQBJiLR2CTxrjFmVtn9xpgjxphjzuM5QLSINAlymGVjynTus4CPsNVyb5lAktfzVs42t40GVhtj9pfdEYqfs2N/abOdc59VTpmQ+rxF5AbgYuDXTnL7hUr8DQWNMWa/MabEGOMBXq8glpD6jAFEJAq4HJhRUZlgfc6aKALIaV/8D7DZGPNMBWWaO+UQkQHYf5Oc4EX5i3jqiUj90sfYzsuNZYrNBq5zRj8NBHK9mk/cVOGvr1D7nL3MBkpHMV0PfFJOmS+AC0SkodNscoGzLehEZBTwR2CsMSa/gjKV+RsKmjL9Z5dVEMsKIFlE2jk10/HYfxs3jQC2GGMyytsZ1M85GL36NfUGDMY2JawH1jq3i4BbgVudMncAm7CjLJYC57gcc3snlnVOXA87271jFuBl7CiRDUC/EPis62G/+OO9toXU54xNYnuBImwb+M1AY2ABkAJ8CTRyyvYD3vB67U1AqnO70cV4U7Ft+aV/z686ZVsCc3z9DbkY89vO3+l67Jd/i7IxO88vwo5M3O52zM72/5b+/XqVdeVz1iuzlVJK+aRNT0oppXzSRKGUUsonTRRKKaV80kShlFLKJ00USimlfNJEoZRSyidNFEoppXzSRKGUH4nIx84EbZtKJ2kTkZtFZJuILBeR10XkJWd7goh8KCIrnNsgd6NXqnx6wZ1SfiQijYwxB0WkDnZaiAuBb7HrDRwFvgLWGWPuEJH3gFeMMd+ISGvgC2NMV9eCV6oCUW4HoFSYuUtELnMeJwHXAouMMQcBROR9oJOzfwTQzZmCCiBORGKNM3mhUqFCE4VSfiIiw7Bf/mcbY/JFZCGwBaiolhABDDTGFAQnQqVOj/ZRKOU/8cAhJ0l0wS4TWw8415n5NQq4wqv8PODO0ici0juo0SpVSZoolPKfuUCUiGwGnsTOUpsJ/B1Yju2r2AnkOuXvAvo5K6/9gJ3tVqmQo53ZSgVYab+DU6P4CHjTGPOR23EpVVlao1Aq8B4TkbXYRWV2EILLsCrli9YolFJK+aQ1CqWUUj5polBKKeWTJgqllFI+aaJQSinlkyYKpZRSPmmiUEop5dP/B2ncmmLrQ3uLAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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zrhvNwE7NvY4lEjRU7hJyvtm4l7vfSGFX3iF+eVovfnVWHxrGaLIvkYpU7hIyikp9PPbhWv751Ra6t2nCGzeewMhurbyOJRKUVO4SEpIz93PnvGQ25Rxk0vHduPec/rqeqciP0E+HBLWSMj/PfLaBGZ9vpH1cQ169YQwn9Yn3OpZI0FO5S9Bat7uAO+Ymk74rn4tHdOGhCwbSvFEDr2OJhASVuwQdn9/x/JebmP7xepo3jmHmz0cyblAHr2OJhBSVuwSVLXsOctcbKazYuo/xgzrwx58Opk2zhl7HEgk5KncJCs45Xl26lUfeX0uDaOOpy4czcXgnTfYlUk0qd/HcrrxDTJ2fypcb9nBK37Y8dvEQOrZo7HUskZCmchfPOOdYuGoHD72zhjKf4w8XDubqMQk6WhepBSp38cSeA8VMW7iaj9ZkkditFX+5bBjd2jT1OpZI2FC5S737MG030xaupqCojPvP6c//ndyTaE3NK1KrVO5Sb/IOlfLwO2t4c9UOBnVqzuuTh9OvQ5zXsUTCkspd6sWXG3KYOj+V7IJibjujN7ec0YfYGE3NK1JXVO5SpwpLyvjT+2t5ZelWerVtyps3ncCwri29jiUS9lTuUmdWbM3lrnkpbM0t5IaTenDP2f1o1EBT84rUB5W71LriMh9PLtrAzC820qllY2ZPHsvYnm28jiUSUVTuUqvSd+Zz57xk1u4u4IpRXXngvIE0a6inmUh9O+IrWmb2opllm1lahWVPmNlaM0s1s4Vm1rLCuvvNLMPM1pnZ2XUVXIKLz++Y8XkGE//2b/YeLOHFaxN59OKhKnYRjxzN6QovAeMrLVsEDHbODQXWA/cDmNlA4ApgUOAxM8xMg6xhbsueg1z29294/MN1jBvYgY9vP4Uz+rf3OpZIRDviYZVz7gsz615p2ccVPl0KXBK4PxGY45wrBjabWQYwGvimVtJKUHHO8dqybfzxX9/RINr4f1cM54JhmuxLJBjUxt/M1wNzA/c7U17239seWPYDZjYFmAKQkJBQCzGkPmXlFzF1fipL1udwcp94Hr9kqCb7EgkiNSp3M5sGlAGvHetjnXMzgZkAiYmJriY5pH69m7KTB95Ko7jMx+8nDuJnY7vpaF0kyFS73M3sWuA84Ezn3PflvAPoWmGzLoFlEgb2F5bwm7fX8G7KToZ3bcn0y4bRs20zr2OJSBWqVe5mNh6YCpzqnCussOod4HUzmw50AvoAy2ucUjy3ZH0OU+ensPdACXeP68uNp/YiJlrTB4gEqyOWu5nNBk4D4s1sO/AQ5WfHNAQWBf4cX+qcu9E5t8bM5gHplA/X3Oyc89VVeKl7hSVlPPL+d7y6dBt92zfjhUmjGNy5hdexROQI7L8jKt5JTEx0SUlJXseQSlZu28edc5PZmlvI5JN7cudP+mr6AJEgYmYrnHOJVa3TO0zkB8p8fp75LIO/Ls6gQ/NGmj5AJASp3OV/bNtbyO1zV7Fy234uGtGZhy8YRFyjBl7HEpFjpHIXoPwNSQtW7uCht9OIijKeufI4zh/WyetYIlJNKnchr7CUXy9czb9W72JMj9ZMv3w4nVvqDUkioUzlHuG+3riHu+alkFNQzNTx/fjFKb10PVORMKByj1AlZX7+8vE6Zn65iR5tmrLwlycypItOcRQJFyr3CJSRXcCv5iSzZmc+V41J4IFzB9AkVk8FkXCin+gI4pzj1WXb+OO/0mkSG8PMn49k3KAOXscSkTqgco8Q+wtLmDo/lY/Tszilb1v+fMlQ2jVv5HUsEakjKvcI8O2WXH41exU5B4p54NwBXH9iD6L0oqlIWFO5hzGf3zFjcQZPfrKerq2bsOCmExjapeWRHygiIU/lHqay8ou4Y24yX2/cy8ThnfjDhYP1TlORCKJyD0OL12Vz97wUCkt8PH7JUC4d2UUX0xCJMCr3MFJS5ufPH69j5heb6N8hjr9edRy928V5HUtEPKByDxOZuYXc8vpKUrbn8fOx3Zh27gBNzysSwVTuYeCT9CzunJeMA5772QjGD+7odSQR8ZjKPYSV+fz8ZdF6nv18I4M7N2fGVSNJaNPE61giEgRU7iEqu6CI22avYummXK4cncBD5w/UMIyI/IfKPQQt27SXW2evIr+olL9cOoyLR3bxOpKIBBmVewhxzjHzi008/tE6Elo34eUbRtO/Q3OvY4lIEFK5h4gDxWXcNS+Zj9ZkMWFIBx67eKjelCQih6VyDwFb9hxk8stJbNpzkAfOHcANJ/XQm5JE5Eep3IPckvU53Pr6SqKijJevH82JveO9jiQiIUDlHqS+H19/7MO19G0fx/PXJNK1tU5zFJGjo3IPQodKfNy7IJV3UnZy7pCOPHHpUF0pSUSOiRojyOzYf4jJs5L4bnc+95zdj1+e1kvj6yJyzFTuQSQlcz83zEqiuNTHC5MSOaN/e68jiUiIUrkHiQ9W7+KOecnEN2vI7Mlj6NNeszmKSPWp3D3mnOO5JeUvnI5IaMnMaxKJb9bQ61giEuJU7h4qKfPzm7fSmJuUyfnDOvHEJUM1P4yI1AqVu0fyCku56bUVfL1xL7ed0Zvbz+qri1aLSK1RuXtgV94hJr24nM17DjL9smFcNEITf4lI7Yo60gZm9qKZZZtZWoVlrc1skZltCHxsFVhuZva0mWWYWaqZjajL8KFoQ1YBF8/4mp37i5h1/WgVu4jUiSOWO/ASML7SsvuAT51zfYBPA58DnAP0CdymAM/WTszwsGJrLpc89w2lfsfcX4zlhF6aSkBE6sYRy9059wWQW2nxRGBW4P4s4MIKy1925ZYCLc1M13wDFqVncdXzy2jdNJY3bzqBQZ1aeB1JRMLY0Ry5V6W9c25X4P5u4Pt323QGMitstz2w7AfMbIqZJZlZUk5OTjVjhIY5y7fxi1eS6N8hjvk3Hq85YkSkzlW33P/DOecAV43HzXTOJTrnEtu2bVvTGEHrb4szuO/N1Zzcpy2vTx5LG53DLiL1oLpny2SZWUfn3K7AsEt2YPkOoGuF7boElkUc5xxPfLSOGZ9v5MLhnXji0mE0iK7x71IRkaNS3bZ5B5gUuD8JeLvC8msCZ82MBfIqDN9EDOccD7+bzozPN3Ll6ASmXzZcxS4i9eqIR+5mNhs4DYg3s+3AQ8CjwDwzuwHYClwW2Px9YAKQARQC19VB5qDm8zumLVzNnG8zuf7EHvzmvAGa1VFE6t0Ry905d+VhVp1ZxbYOuLmmoUJVqc/P3W+k8HbyTm49ozd3/qSvil1EPKF3qNaSkjI/t85eyUdrspg6vh+/PK2315FEJIKp3GtBqe+/xf7Q+QO57sQeXkcSkQincq+hUp+f22av4qM1Wfz2/IFcq2IXkSCgUzhqoMzn5/a5yXyQtpsHzh2gYheRoKFyryaf33HnvBT+lbqLX0/oz/+d3NPrSCIi/6Fyrwaf33H3Gym8k7KTqeP7MeWUXl5HEhH5Hyr3Y+Sc49dvrmbhqh3cPa6vzooRkaCkcj8Gzjn+9MFa5iZlcsvpvbnljD5eRxIRqZLK/Rg8u2QjM7/YxM/HduOucX29jiMiclgq96P0+rJtPP7hOiYO78TDFwzSO09FJKip3I/Ce6k7mfbWak7v15Y/XzpMF7IWkaCncj+CJetzuGNuMondWjHj6pGa3VFEQoKa6kekbt/Pja+soE+7OP4xaRSNY6O9jiQiclRU7oeRmVvI9S99S5tmsbx0/ShaNG7gdSQRkaOmuWWqsL+whGv/uZxSn2POlFG0i2vkdSQRkWOiI/dKist8THllBZm5h5j585H0bhfndSQRkWOmI/cK/H7H3W+ksnxzLk9feRxjerbxOpKISLXoyL2Cxz9ax7spO7l3fH8uGNbJ6zgiItWmcg+Yv2I7zy3ZyFVjErjxVM3wKCKhTeUOrNi6j1+/uZrje7bRu09FJCxEfLnv3H+IX7yygo4tGzHj6hF6k5KIhIWIfkG1sKSMyS8nUVTqY/bkMbRqGut1JBGRWhGx5e4PXHAjfVc+L04aRZ/2OuVRRMJHxI5BPPNZBu+v3s395/Tn9P7tvI4jIlKrIrLcP1ubxZOfrOei4zozWdc+FZEwFHHlvm1vIbfPSWZgx+Y8ctEQnRkjImEposq9qNTHja+uAOC5n42kUQPN8igi4SliXlB1zjFtYRrpu/L557WjSGjTxOtIIiJ1JmKO3F9fvo0FK7dz25l99AKqiIS9iCj35Mz9PPxOOqf2bcuvzuzjdRwRkToX9uWed6iUW15fSdu4hjx1+XCidf1TEYkAYT3m7pzjvgWp7M4rYt6Nx+sdqCISMWp05G5md5jZGjNLM7PZZtbIzHqY2TIzyzCzuWbmWaO+vnwbH6Tt5u6z+zEioZVXMURE6l21y93MOgO3AYnOucFANHAF8BjwpHOuN7APuKE2gh6rtbvz+d276ZzSty1T9EYlEYkwNR1zjwEam1kM0ATYBZwBzA+snwVcWMN/45gVlpRxy+uraN64AdMvG0aUxtlFJMJUu9ydczuAPwPbKC/1PGAFsN85VxbYbDvQuarHm9kUM0sys6ScnJzqxqjSw++kszHnAE9dPpz4Zg1r9WuLiISCmgzLtAImAj2ATkBTYPzRPt45N9M5l+icS2zbtm11Y/zAuyk7mZuUyc2n9ebE3vG19nVFREJJTYZlzgI2O+dynHOlwJvAiUDLwDANQBdgRw0zHrVdeYeYtnA1xyW05PazdD67iESumpT7NmCsmTWx8tm3zgTSgcXAJYFtJgFv1yzi0fl+fvYyv+PJy4YToysqiUgEq8mY+zLKXzhdCawOfK2ZwL3AnWaWAbQBXqiFnEc065stfJWxlwfOHUj3+Kb18U+KiAStGr2JyTn3EPBQpcWbgNE1+brHKiO7gEc/WMsZ/dtx5eiu9flPi4gEpZAfuygp83P73GSaNozh0Ys1P7uICITB9APPfLaBtB35PPezkbSLa+R1HBGRoBDSR+4rtu7jb4szuHRkF8YP7uB1HBGRoBHS5R4bHcWJveN58PyBXkcREQkqIT0sM6RLC165YYzXMUREgk5IH7mLiEjVVO4iImFI5S4iEoZU7iIiYUjlLiIShlTuIiJhSOUuIhKGVO4iImHInHNeZ8DMcoCtXuc4CvHAHq9DHCNlrh+hljnU8oIyV6Wbc67KS9kFRbmHCjNLcs4lep3jWChz/Qi1zKGWF5T5WGlYRkQkDKncRUTCkMr92Mz0OkA1KHP9CLXMoZYXlPmYaMxdRCQM6chdRCQMqdxFRMKQyr0SM+tqZovNLN3M1pjZr6rY5jQzyzOz5MDtQS+yVsq0xcxWB/IkVbHezOxpM8sws1QzG+FFzgp5+lXYf8lmlm9mt1faxvP9bGYvmlm2maVVWNbazBaZ2YbAx1aHeeykwDYbzGySh3mfMLO1ge/7QjNreZjH/uhzqJ4z/9bMdlT43k84zGPHm9m6wPP6Po8zz62Qd4uZJR/msfWzn51zulW4AR2BEYH7ccB6YGClbU4D3vM6a6VMW4D4H1k/AfgAMGAssMzrzBWyRQO7KX9DRlDtZ+AUYASQVmHZ48B9gfv3AY9V8bjWwKbAx1aB+608yjsOiAncf6yqvEfzHKrnzL8F7j6K581GoCcQC6RU/lmtz8yV1v8FeNDL/awj90qcc7uccysD9wuA74DO3qaqFROBl125pUBLM+vodaiAM4GNzrmge5eyc+4LILfS4onArMD9WcCFVTz0bGCRcy7XObcPWASMr7OgAVXldc597JwrC3y6FOhS1zmOxWH28dEYDWQ45zY550qAOZR/b+rcj2U2MwMuA2bXR5bDUbn/CDPrDhwHLKti9fFmlmJmH5jZoHoNVjUHfGxmK8xsShXrOwOZFT7fTvD80rqCw/8gBNt+BmjvnNsVuL8baF/FNsG6v6+n/C+4qhzpOVTfbgkMJb14mKGvYN3HJwNZzrkNh1lfL/tZ5X4YZtYMWADc7pzLr7R6JeVDCMOAZ4C36jtfFU5yzo0AzgFuNrNTvA50NMwsFrgAeKOK1cG4n/+HK/87OyTOJzazaUAZ8NphNgmm59CzQC9gOLCL8mGOUHElP37UXi/7WeVeBTNrQHmxv+ace7PyeudcvnPuQOD++0ADM4uv55iVM+0IfPq0mYoAAAG2SURBVMwGFlL+J2tFO4CuFT7vEljmtXOAlc65rMorgnE/B2R9P6QV+JhdxTZBtb/N7FrgPODqwC+kHziK51C9cc5lOed8zjk/8PxhsgTVPgYwsxjgImDu4bapr/2scq8kMF72AvCdc276YbbpENgOMxtN+X7cW38pf5CnqZnFfX+f8hfQ0ipt9g5wTeCsmbFAXoWhBS8d9ign2PZzBe8A35/9Mgl4u4ptPgLGmVmrwJDCuMCyemdm44GpwAXOucLDbHM0z6F6U+n1oJ8eJsu3QB8z6xH4C/AKyr83XjoLWOuc217Vynrdz/XxynIo3YCTKP8zOxVIDtwmADcCNwa2uQVYQ/mr80uBEzzO3DOQJSWQa1pgecXMBvyN8rMLVgOJQbCvm1Je1i0qLAuq/Uz5L55dQCnlY7o3AG2AT4ENwCdA68C2icA/Kjz2eiAjcLvOw7wZlI9Nf/98fi6wbSfg/R97DnmY+ZXA8zSV8sLuWDlz4PMJlJ/RttHrzIHlL33//K2wrSf7WdMPiIiEIQ3LiIiEIZW7iEgYUrmLiIQhlbuISBhSuYuIhCGVu4hIGFK5i4iEof8PxkPoyFe8qNYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 7badfe464c297c7a384a3d77c72f1ee7acaa01a1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 323/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 74adbf46c396820468ad118a44d9cd69d0a70452 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 324/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 20 +++++++++++--------- 2 files changed, 26 insertions(+), 15 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -36,9 +38,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -59,7 +61,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -78,10 +80,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -92,10 +94,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -109,4 +111,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() From 92ca982c8ef6597209e2c300612dcaa4acad9e44 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 325/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From 47ae278213d614a7a1b3300016f1a1ac11c3bd5a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 326/624] transfer files to new location and modify documentation --- docs/modules/exploratory.rst | 1 - docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 13 files changed, 77 insertions(+), 551 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory.rst b/docs/modules/exploratory.rst index edc2c8d73..832b93193 100644 --- a/docs/modules/exploratory.rst +++ b/docs/modules/exploratory.rst @@ -11,4 +11,3 @@ and visualize functional data. exploratory/visualization exploratory/depth exploratory/outliers - exploratory/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From c1324717faf12c653de46bbb249ecd9ef8503dfe Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 327/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 60a1124bdca9a74f79949f4041133969db61a5e9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 328/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From ca7fdb9ab183849ea6f48a8164c9b8058c7ff086 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 329/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From d296b1d7150b8fff4447318c2d45941534bb9fa7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 330/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From d2f76da41f812cebdbd59fbb773b32e5a280bdca Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 331/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 241b568eafd5ff7ceb34272fa91884786e5531cc Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 332/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From d87bc4265a872857d9fb83d290c0e94f29dff7c7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 333/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4253cdcd456df2eda9a164d49058be5e1821d10d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 334/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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X478bFeWam11iZpuvErV3gha3QcfB0KC7dFesIuXpCvkj0BMIUEolA6OAmcBMpVQsUAwMthzFxyml5gH7AAPwrPSUEaJ6MZk082OSGb/iAGdyi7m9XV1evakZ9XzdLryPNvH13q+ZunMqjX0aM6nnJBp6N/z3RumHYdsM2Pm9ecyWwOZw04fQ9j5w86vkdyXOp8yZbF1RUVF6x44d1i5DCJu363gmo5bGsft4Jh3q+zDq1pa0Dbv4VZxZRVm8vfFt1ievZ0DDAYzqOgo3R8svAq3h8DrY+iUcXG4ezjbydnP3xbAu0g+9kimlorXWUWWtkytUhagF0nKKGL/iAL9EJxPo6cyke9pye7vQS05ht+/sPl5e+zKn808zvMtw7m12r7l9vaQA9vxsDvXUfeAWAD1ehajH5ORoNSHhLoQNKzaYmLP5CJP/iqfQYOSp6xvx/A1N8CjHrEcL4xcydstY/Fz9mN1vNm0C20BBJmz/GrZMg/wzUKc1DPzC3ONFLjCqViTchbBR6w+l8e6vcSSm5dGzWSAjb4mkUaDHJffLL8ln7NaxLE1cSteQrozrMQ7fkmL4cyRsnwnFORDRB659wdw/XZpeqiUJdyFszKmsQt77LY5le08R7u/GzEeiuKF5OS4uAvaf3c9r61/jeM5xnm77NE+F9cH+r/dg51wwlZjb07u/BCFtKvldiKsl4S6EjTAYTczefJRJfxzEYNK82rcpT/RohLOD/SX31Vozd/9cJkVPwtfFl6+vGUOnuOWw5B3zSdJ290O3YeaJLUSNIOEuhA3YdTyTtxfuZd/JbHo2C+S921pR3//CXRtLyyjMYOSmkaxNXkvP4M6MybfH58dHzLMSdRkK3Z6Xk6Q1kIS7EDVYVkEJE1YeYO7WYwR5Ol90yICybD+1nTfXv0lGUTpvukZw/7bFKGUPnZ+E7i+CZ51Kfgeiski4C1EDaa1ZsusE7/++n/S8IoZ0a8hLfZrg6VK+OUBLjCV8vutzZsbOpL6dC1OTT9LCkGzuytj9JTlStwES7kLUMIlpuYxYHMvfiWdpW8+bWUM60Sq0/NPYxWfE89a61zmYlcCdufm8nnEKt/YPmcd8kREZbYaEuxA1RJHByLS1iXyxJhFnRzvG3N6K+zvXx/4SFyKdY9Imvts7kym7puJhKGHKmbP0anIHPPAW+Miw27ZGwl2IGiD6aAZvLthDfGout7aty4hbWhDkWf6Lhk5mH+edP4ayLe8YPfPyGe3VFv/BYyC4ZSVWLaxJwl2Iaiy3yMDElQeZvfkIIV4ul9VnHUCbTPz29wd8kPAzJm3iPZMPt/efhmrYvfKKFtWChLsQ1dSag6m8syiWE1kFPHxNA17r17xcwwaccyZxFe+vf4NVdkV0MCre7/A6Ye0elitKawkJdyGqmbO5Rbz32z6W7DpBRJAH84d2pWOD8g+ZqzOP8+uK5xmXf4hCOzteCu7B4N6fYO/oXIlVi+pGwl2IakJrzeJdKbz36z5yiwy80LsJz/RqXK4rTAEozuPUug95L3EeG1ydaecaxHs3TqVhYKvKLVxUSxLuQlQDyRn5DF8Uy7pDabSv78O4O9vQNNizfDubTOjdP7Fw0xgmutthcHXljVaPM6j9M9jblfMXg7A5Eu5CWJHRpJn99xEm/nEQgNG3RvJQ1/Byd2/k2BZSVrzGaNNJtni60smnGe/2+oQwr7BL7ytsmoS7EFZy8FQObyzYw67jmfRsFsj7t7e66FR3/5JxFOOfI/kxeRVT/HxQ9t6M6PQadzW7Bzslc5QKCXchqlyRwcjnaxKZtjYBD2cHPr23HQPb1S3feDAlhfD3FOK2TOY9P0/2+ftybcg1jOz2LnU96lZ+8aLGkHAXogpFH03njQV7SUjN5Y72obxzcwv8PcrZiyX+T/KWvcpUlckPdfzwc/ZlwjVvc1ODm8o9UJioPSTchagCuUUGxq84wHdbjlLX25Vvh3SiV7Og8u2ceQxWvMWqY6v4IDCQNDtv7ml2D8M6DMPLyatyCxc1loS7EJVszYFUhi/ay8nsQgZ3Dee1m5rhXp6LkQxF8PdnnNw0iQ98PVgbHEhTnyZM6jaKtoFtK79wUaNJuAtRSdLzinnv1zgW7zpBkyAP5g/tRscGvuXbOeEvSpa9xg+GND6vGwj2jrzc7lkejHwQR7vyDesrajcJdyEqmNaapbtP8O6v+8gpLLm8i5Eyj8PKt9l6eCUfBgWTaO/LdaHXMvya4YR6yHC8ovwk3IWoQCcyC3hncSyrD6TSNsyH8Xe2oVmdclyMZCiGzVM5teljJnq7sTIkmFCPUKZ0eoOeYT3lhKm4bBLuQlQAk0kzd9sxxi0/gNGkeefmFgy5tmH5LkZKXEPxsleZY0hlRog/JjtHnmnzOENaDsHFofzD+gpRmoS7EFcpKS2XNxfsZduRdK6N8OfDO9qUb3LqrBT4Yzjrk5YzLjCIY/Y+9K5/A691ek2aYMRVu2S4K6VmArcAqVrrVuetewWYCARqrc8o89+Ok4EBQD7wiNY6puLLFsL6SowmvtqQxKd/xePiYMf4u9pwd8d6l25CMRTD1mkc3zCB8T5urK0TRLhnfaZ3eZtrQ6+tmuKFzSvPkfssYCowp/RCpVQY0Bc4Vmpxf6CJ5dYFmGa5F8KmxKZk8fr8Pew7mU3/VnV497aWBHmVowklaR35y17lG8NpZoX4YW/vzEvtnuahFg/haC+9YETFuWS4a63XK6XCy1j1CfA6sKTUsoHAHK21BrYopXyUUiFa65MVUawQ1lZYYuTTv+L5akMSfu5OTH+wA/1ahVx6x+wTmFYOZ8nRlXzm70+anTcDGvbn5Y4vE+xe/pmVhCivK2pzV0oNBFK01rvP+xM0FDhe6udkyzIJd1HjbU06y5sL93L4TB73RNVj+IBIvN0ucbRtLIGt09n+90QmeLuyP9CfNv6t+KTLm3IhkqhUlx3uSik34G3MTTJXTCn1JPAkQP36MvO6qL6yCkoYt+IAP2w9RpifK98/1oXuTQIuvePhDRxb/jKTSGdVoBd1XAIY1+k1+jfsL10bRaW7kiP3xkBD4NxRez0gRinVGUgBSg8kXc+y7D+01jOAGQBRUVH6CuoQolJprfltz0ne/XUf6XlFPN69IS/3bYqb0yX+2+ScInvlW8w4sZq5Xl442vvwfJsnebjlYOnaKKrMZYe71nov8L8Rj5RSR4AoS2+ZpcBzSqmfMJ9IzZL2dlETHU/P553F5pmRWod6M2tIJ1qFel98J6MBw9bpzN/+CV94upDp7cXtjW7l+Y4vEegWWDWFC2FRnq6QPwI9gQClVDIwSmv9zQU2X4a5G2QC5q6QQyqoTiGqRInRxDcbD/PpX4ewV4pRt0bycHlmRjr6NxtXvMhEuywSfdzo5N+K17qOpIV/i6opXIjzlKe3zKBLrA8v9VgDz159WUJUvZ3HMnhr4V4OnMqhT2Qw797Wkro+rhffKec0+1e8zKSzW9ni6kqYcx0+7TqSG+rfIO3qwqrkClVR62UXljBx5UG+23KUYE8XvnyoIze1rHPxnYwGUv6exGex3/C7mxPe7t681vZp7mv5ME72TlVTuBAXIeEuai2tNctjTzF6aRxpuUUM7hrOqzc1w+MSY61nJvzBV2vf4keHIuzcnHms8R082vkVmThDVCsS7qJWSs7IZ9SSOFYdSKVlXS++ejiKtmE+F92nMPMoPyx/hq8LjpDroBgY2JFnr/+IOh7luIhJiCom4S5qlRKjiW83HeaTP+MBeOfmFjzSLRwHe7sL7mMsKeTXVa8xNWU1px3suM69Li/2mkjTILkISVRfEu6i1ticeJaRS2KJT83lxhZBjL6tJfV8Lzx6o9aajTFf8snuL4i317R0cOXDa96hU7Pbq7BqIa6MhLuweanZhYxdtp8lu05Qz9eVrx+O4sbIi4/nEnd0HZ+sf4utphzqARMiBtG36xvY2ZVjNiUhqgEJd2GzDEYTszcf5ZM/D1FsMDHshgie6RWBi+OFAzo58zBTVr3E8txEfI0m3gyI4p6+n+HoIidLRc0i4S5s0vYj6YxYHMuBUzlc3zSQd29rSXiA+wW3zyjMYMaGkfyUshYHbeIJhyCG9JuCZ3DrKqxaiIoj4S5sSlpOER8tP8CCmGTqersw/cGO3NQy+IIXFBUYCpi78wu+2TeHfG3kjmLF011HEdz6niquXIiKJeEubILRpJm79SgTVh6ksMTIMz0b89wNERcc5MtoMrL00Hym7phEqjGfngVFvNj4Lhr3HAmOMriXqPkk3EWNF3MsgxGLY4k7kc21Ef68e1srIoI8ytxWa82G5PV88vd7JBSm0rqwiHGeLYi6bQr4NqjiyoWoPBLuosZKzytm/IoD/LT9OMFezky9vz03tw65YBPM3rS9TNo8hh0Z+6lfUsLHJi/69JmKanR9FVcuROWTcBc1jtGk+Xn7ccavPEBuoYEnezRiWO8mFxw24Hj2cSZvH8/K5LX4GY28nWfgrmtew7HjoyBdG4WNknAXNcqe5ExGLI5ld3IWXRr6Meb2VjQN9ixz2/TCdL7c9QXzDs7D0WTiqexcHmlyNx69hoPrxYcaEKKmk3AXNUJmfjETVh7kh23H8Hd35tN72zGwXd0ym2AKDAV8FzeHmXu/otBQxB05OTzj257AB8ZDQBMrVC9E1ZNwF9WayaSZH53MRysOkJlfzCPdwnmpT1O8XP47MbXBZGBxwmK+iJlCWlEGN+Tl8wI+NOr3FTTpY4XqhbAeCXdRbcWdyGLE4lhijmXSsYEvYwZ2IbLuf68U1Vqz9vhaPt3xMUk5R2lbWMTE3BI6XPs6dHoc7P/7i0AIWyfhLqqdrIISPvnzEHM2H8HXzYkJd7Xhzg71sCtjqrvdabuZtP1jYtJ2Em4w8ml6Bje0GITqNRzc/au+eCGqCQl3UW1orVm0M4UPlh3gbF4RD3ZpwKt9m+Ht9t8j7yNZR5gcM5m/jv2FvwlGpKdzR0AHHB/+CIJbWqF6IaoXCXdRLRw4lc3IxXFsO5JO2zAfvn2kE63ref9nuzMFZ5i+ezrzD/2Ck9Y8k5HJYOWH280zoNkAkHlLhQAk3IWV5RSWMPmveL79+wieLg58+H+tuTcq7D9NMPkl+cyKm8WsuFmUGAq5KyeXobnFBHR/Fa55GhycrfQOhKieJNyFVWit+XXPSd7/bR9puUXc1ymM129qjq/7vyeXLjGVsPDQQqbtnsbZwrP0KTTwQloaDVrfCzeMBM+Lj8suRG0l4S6qXEJqDiOXxPF34llahXrx5UMdaV/f91/baK1ZdWwVk2MmcyT7CB2M9kw+fYq2wR3h0R+gbnsrVS9EzSDhLqpMXpGBKavj+WbDYdyc7Blzeyvu71wf+/OaYGJOxzApehK703bTWLnw2ak0rncKQN06HVr+n7SrC1EOEu6i0mmtWRF7ijG/7eNEViF3dazHm/2bE+Dx73bypMwkPo35lDXH1xBk58K7Z7O4reAMDt1fhm7Pg6Orld6BEDWPhLuoVIfP5DFqaRzrD6XRvI4nUwa1Jyrc71/bpOan8sWuL1iUsAhX5cCwPAMPpsXj2upuuHE0eIdapXYhajIJd1EpCkuMfLEmgenrknBysGPkLZE83LUBDvZ2/9vmXz1gjMUMMjjzZHI8fnXawaOzIayzFd+BEDWbhLuocH/tO83oX+NIzihgYLu6DB/QgiCvf2Y3MpqMLElcwtSdU0krSKOfgz/DjiUS5hIAt30Bbe4FO7uLvIIQ4lIuGe5KqZnALUCq1rqVZdkE4FagGEgEhmitMy3r3gIeA4zAMK31ykqqXVQzx9PzeffXOP7an0pEkAc/PNGFbo0D/rXN3yf+5uMdH3Mo4xBtnQP5JDWTtoWnodsL0P1lcC57BiUhxOUpz5H7LGAqMKfUsj+Bt7TWBqXUOOAt4A2lVCRwH9ASqAv8pZRqqrU2VmzZojopMhiZsS6JqWsSsLdTvNW/OUOubYiTwz9H3wkZCUyMnsimlE2EOvkyMUfT93A0KnIg9HkPfMOt9waEsEGXDHet9XqlVPh5y/4o9eMW4C7L44HAT1rrIuCwUioB6AxsrpBqRbWz7lAao5bEcuRsPgNa1+GdmyOp6/NPr5YzBWf4fKrhwmoAABcYSURBVNfnLIxfiLu9C6+avBl0cDdOwa1h8DRoeJ0VqxfCdlVEm/ujwM+Wx6GYw/6cZMuy/1BKPQk8CVC/fv0KKENUpROZBYz5bR/LY0/RMMCd2Y925vqmgf9bX2AoYE7cHGbGzqTYWMT9TiE8dWgbPi4+cMun0OFhmeJOiEp0VeGulBoOGIC5l7uv1noGMAMgKipKX00douoUG0zM3HSYKaviMZo0r/RpypPXN8LZwRzUJm3it6TfmBIzhdP5p+ntHs5LSXtoUHgcOg+F61+XKe6EqAJXHO5KqUcwn2jtrbU+F84pQFipzepZlgkbsO1wOsMX7SU+NZcbWwQz6tZIwvzc/rd++6ntTNg+gf3p+2npXo+Pcu2JOrwemvSFmz6QKe6EqEJXFO5KqX7A68D1Wuv8UquWAj8opSZhPqHaBNh21VUKq8rIK+aj5Qf4ecdxQn1c+frhKG6M/GfArmPZx5iwYwJrj68lxCWAj1Qw/WP/xs6/CTwwX6a4E8IKytMV8kegJxCglEoGRmHuHeMM/GmZoHiL1nqo1jpOKTUP2Ie5ueZZ6SlTc2mtWRiTwthl+8kqKOGpHo144cYmuDmZvza5xbnM2DOD7/Z/h5OdIy+4N+XBfWtwcfSAmz6Ezk/IFHdCWIn6p0XFeqKiovSOHTusXYYoJTEtl3cWxbI56Szt6/vwwR2taRFinr/03EVIk2Mmk1GYwUDvFgyL305gfgZ0fAR6DQf3gIu/gBDiqimlorXWUWWtkytUxb8UlhiZtjaRaWsTcXa0433LyI3nJs+IPh3NuG3j2J++n3ZeDfkio4CWh1dA+HXQ70Oo09rK70AIARLuopS/E84wfHEsh8/kcVvburxzSwuCPM3DBpzIPcGk6EmsPLKSYJcAxjvUp9/udSif+nDPHGhxmwzFK0Q1IuEuOJNbxNjf97NoZwr1/dyY82hnelj6rOeX5DMzdiaz4mahgKc9IxmybzWuyhFuGAFdnwNHl4u/gBCiykm412Imk2bejuN8uPwA+cUGnusVwXM3RODiaI/Wmt8P/84n0Z+Qmp9Kf7/WvJy4mzqZK6D1PdDnXfCqa+23IIS4AAn3WioxLZe3Fu5l2+F0Oof7MfaOVjQJ9gRg39l9fLD1A3an7SbSqxETsKND9O8Q3BqGfAUNulm5eiHEpUi41zIlRhMz1icxeVU8Lg52jLuzNXd3DMPOTpFVlMVnOz9j3sF5+Dr78J5XOwbuXYadkzsMmAgdh4C9fGWEqAnkf2otsic5kzcW7GX/yWwGtK7D6NtaEuTpgkmbmH9oAZNjJpNdnM39AVE8s38DXrl7zGPA9B4pXRuFqGEk3GuBgmIjn/x1iK83JBHg4cz0BzvSr1UdAGLPxDJ2y1hiz8bSwacpb+c50mz7AgjtCIN+Mt8LIWocCXcbtynhDG8t3Mux9HwGdQ7jzf4t8HZ1JKMwg8kxk1kYvxB/F18+8GjFLbtWoFz9YODn0PZ+mQ1JiBpMwt1GZeWXMHbZPubtSCbc340fn7iGro39MZqMzDs4jyk7p5BbnMtD/h14et86PAr2QOcnoedbMmqjEDZAwt0GrYg9yYglcaTnFTP0+sa8eGMTXBzt2ZO2h/e3vM/+9P108m3B2zknidixCBpcC/3HQ51W1i5dCFFBJNxtSEZeMSOWxPLbnpO0rOvFt490olWoN9nF2UzYPJlfDv1CoKs/4z3b0m/n7yj3QPi/r6H1XXJ1qRA2RsLdRvwRd4q3F8WSVVDMq32b8tT1jXGwUyxLWsb47ePJKMrgwaAuPLtvHe65u6DT43DDO+Dibe3ShRCVQMK9hsvML+bdX/exaGcKkSFefPdYZ1qEeHEs+xjvb3mfzSc308o7gmmFzrTYOg/qtodBP5vvhRA2S8K9Blu1/zRvLdxLel4xL/RuwnM3RKAxMH33dL7a8xWOdo687dORe/b8jr2Dq/lCpKhHZe5SIWoBCfcaKKughDG/7WN+dDLN63gy09K2vv3UdsZsGcPhrMP09W/LG0l7CIpfBG3uhT5jwDP40k8uhLAJEu41zObEs7wybxenc4p4rlcEz/eOoNCYy4hNI1icsJhQtzp84dSI63b8Cv5N4OGl0Oh6a5cthKhiEu41RLHBxMd/HmTG+iQa+ruz4OlutAvz4c+jfzJ2y1gyizJ5LKAzT+39E9eSQvPJ0m7DwMHZ2qULIaxAwr0GSEjN4YWfdhF3IptBnesz4pYW5BszeXnty/x59E9aeDVier4jzbfPN8+IdOtk8G9s7bKFEFYk4V6Naa35fstRxi7bj5uTAzMe6kifyGCWJi5l/PbxFBoKecGvA4N3LcfRwQVu+wzaPyR91oUQEu7VVVpOEW8s2MPqA6n0aBrIxLvaYLTL4OlVT7MpZRPtvZvw7qkTNExcDJEDzVeYetaxdtlCiGpCwr0a2hCfxks/7yK70MCoWyN56Jr6LIifz6ToSWg0b3m25r7dy7HzCIZ750KLW6xdshCimpFwr0YMRhOTV8UzdU0CTYI8mPv4NXh75vHMqqfZfHIzXX1bMurYIULjfzdPnNHnXbnCVAhRJgn3auJ0diHDftzJ1sPp3BNVj9G3tmRNykrGrhmLwVjCCO923L3zN5RXKDy0GBr3snbJQohqTMK9Glh/yNwMk19s5OO729K7pQcjNr/BH0f/oK13BB+cSKZ+wlLzrEh9x4KLl7VLFkJUcxLuVnR+M8zPD3TgRPFO7lg6isyiTF7wbs0ju1fg4BEMDyyAJjdau2QhRA0h4W4lZ3KLeO6HGLYkpXNvVBhvDGjEZ7s/Zv6h+UR4hDEtr4Dmib+bZ0Tq96FMoCGEuCyXDHel1EzgFiBVa93KsswP+BkIB44A92itM5RSCpgMDADygUe01jGVU3rNtft4JkO/jyY9r5iP725Ly4a5DF45iKPZRxni157ndq3AycUL7vsRmg+wdrlCiBqoPJNkzgL6nbfsTWCV1roJsMryM0B/oInl9iQwrWLKtB3zth/n7i83Y6cU84d2pdBtHff/fj95xTl8ZRfKy9FLcGp0PTy9WYJdCHHFLnnkrrVer5QKP2/xQKCn5fFsYC3whmX5HK21BrYopXyUUiFa65MVVXBNVWww8d5vcXy/5RjdIwJ4//8a8vGuEaw9vpbrfSMZcyga34Js6D8BOj8hV5kKIa7Klba5B5cK7FPAubFkQ4HjpbZLtiz7T7grpZ7EfHRP/fr1r7CMmiE1u5Cn58YQfTSDodc3pmfbbB5bNYiMwgzecG/BAzErUEEt4aGlEBxp7XKFEDagPM0yF2U5StdXsN8MrXWU1joqMDDwasuotvYkZ3LLZxvZfzKbKYPa4BWymif/fBw35cDcPEcejF2J6vI0PLFagl0IUWGu9Mj99LnmFqVUCJBqWZ4ChJXarp5lWa20bO9JXp63C393Z2Y9HsnXB8aw+eRmbvNvy/C9a3Czc4L7f4Gmfa1dqhDCxlzpkftSYLDl8WBgSanlDyuza4Cs2tjerrVm6up4npkbQ8u63nw4yIu3tz5K9Olo3vVoydgdv+IW1BKGbpBgF0JUivJ0hfwR88nTAKVUMjAK+AiYp5R6DDgK3GPZfBnmbpAJmLtCDqmEmqu1IoORNxfsZdHOFAa2DaFjm/28sH4iwS7+fFfgQmTicuj6HNw4GuwdrV2uEMJGlae3zKALrOpdxrYaePZqi6qpzuYW8dR30ew4msGw3vU57fw9E3Yso4dPcz44sBVvE3DfD9D8ZmuXKoSwcXKFagU5djafh2du5WRWIe/dWYdFJ0aSdDKJYd5teGznb9iFtIO7Z4FfQ2uXKoSoBSTcK8De5CyGzNqGwaR55y5HvjzwAmjNNPv6dNv1G7R7EG7+GBxdrF2qEKKWkHC/SusPpfH099F4uznycO/jfLznU8Ld6/LZqdOEndkkFyUJIaxCwv0qLIxJ5vX5e4gIdqVduzV8tW8R1/tG8tG+zXjYOcDDS6DhddYuUwhRC0m4X6Hp6xL5aPkBOjd2win0W34/spPHfdvxXMyv2NdpDffNBR/bvvJWCFF9SbhfJq0141ceZNraRHq1NpLiPIH09HTGuUcyIGYpRN4Ot08DJzdrlyqEqMUk3C+DyaR599c4Zm8+Sp8OOcQapuBidGKWMYBWsSvguleg1ztgd9WjOgghxFWRcC8no0nzxoI9zI9Opk+nZHbkT6eBe12+OJVG3TN7YeDn0P5Ba5cphBCAhHu5FBtMvPTzLn7fe4LrO+9mS85PdPZtwSeHYvAyFMNDi6BhD2uXKYQQ/yPhfgmFJUaemRvD6gMn6dJpDTE5f3FrQEfe3bkCR89gGLwMAptau0whhPgXCfeLKCwx8sScHWxMPEHbqCXsy43mqeBreXbrPFSd1vDAfPCw3eGKhRA1l4T7BRSWGHnqu2g2Jh2jebtfOJJ/kFGB13HXlrnQqCfc+z04e1q7TCGEKJOEexmKDEae/j6a9UmJNGr9A6eLUpjo3ZE+2+ZCyzvgji/BwdnaZQohxAVJuJ+n2GDi2bkxrE06QL3IOeQYs/ncpRlddy6ATk9A/3FgZ2/tMoUQ4qIk3EspNph49ocYViftJrjZHLAz8Y0plNZxy6Dn23D96zJGjBCiRpBwtzAYTQz7cSerknbgFzELdyd3virxpFH8aug3Dq4Zau0ShRCi3CTcMV95+ubCvfyRtBXfxrMJdPPlmzwH6iatgVs+gahHrV2iEEJcllof7lpr3v99P4v2bcCn4WxC3AP5OttEnSMb5apTIUSNVevD/bPVCcze+Ree4XOo51GHrzOLCDq+A/5vBrS559JPIIQQ1VCtDvdZmw4zedPveDT4jgZedfnmbC4BJ/bAXTPNXR6FEKKGqrXDFy7emcKY1YtxbzCbxt5hfJtRREDKbvM8pxLsQogarlaG+8b4M7z+20Lcw74jwrsh32SV4Hd8O9z5FbS41drlCSHEVat14b7/ZDZDf1mIS9gsGniF8nWuxvfwJhj4BbS609rlCSFEhahV4X4yq4CHv1+ICvmKEI8gZha54pew2tzdsd0ga5cnhBAVptaEe3ZhCQ/MXkKh/zQC3LyZpQMIPLgS+o+HqCHWLk8IISpUrQj3YoOJR79fRqr7ZLxdXJnj1pyQuKXQexR0ecra5QkhRIWz+XDXWvPygrXsZwLuzvbMCepOWPR3cM2z0P0la5cnhBCVwubD/bO1u1mdNRZnpxJmhw+k0cbPoM290Pd9GQRMCGGzrirclVIvKaXilFKxSqkflVIuSqmGSqmtSqkEpdTPSimniir2ci2PO8b0A+/g4JzO9Gb303zVBxDRxzysgJ3N/14TQtRiV5xwSqlQYBgQpbVuBdgD9wHjgE+01hFABvBYRRR6ufadyOC1da9h73qMD5sNofMfYyG0I9wzG+wdrVGSEEJUmas9fHUAXJVSDoAbcBK4AZhvWT8buP0qX+Oync0t4qElb6Dc9zGsySPcvPZj8G0A988DJ/eqLkcIIarcFYe71joFmAgcwxzqWUA0kKm1Nlg2SwZCy9pfKfWkUmqHUmpHWlralZbxH8UGE3f+PIpit83cXu8entgxB+wc4IFfwM2vwl5HCCGqs6tplvEFBgINgbqAO9CvvPtrrWdoraO01lGBgYFXWsZ/DFkwmbMOvxPl24f3jmyAnJMw6CfwDa+w1xBCiOruapplbgQOa63TtNYlwELgWsDH0kwDUA9Iucoay+2jtUvYXTCLuo7t+aooA3V8K9wxHcI6VVUJQghRLVxNuB8DrlFKuSmlFNAb2AesAe6ybDMYWHJ1JZbP8oM7+T5pDK66LvMDwnGIW2i+SElGeBRC1EJX0+a+FfOJ0xhgr+W5ZgBvAC8rpRIAf+CbCqjzohLTT/DGpheww5kfm9yC59+Tof1DcpGSEKLWuqrJOrTWo4BR5y1OAjpfzfNejvziAu5fOhSTymV8kxeJWPMqhF9nHgxMLlISQtRSNfpKHpM2ce+iF8jjCPcFD2XA1g/BIxjulr7sQojarUZPs/fhhtkcKdxMc8d7GH5yARRkwGN/gLu/tUsTQgirqtHhfl+L/2Nvch7fuR1ExW8xz30a0sbaZQkhhNXV6GaZxoHe/NTEFcddc8wnT2UmJSGEAGp4uHP0b1j+unkwsBtGWLsaIYSoNmp2uDt7QsMecOfXYGdv7WqEEKLaqNFt7tRpDQ8tsnYVQghR7dTsI3chhBBlknAXQggbJOEuhBA2SMJdCCFskIS7EELYIAl3IYSwQRLuQghhgyTchRDCBimttbVrQCmVBhy1dh3lEACcsXYRl0lqrho1reaaVi9IzWVpoLUucxLqahHuNYVSaofWOsradVwOqblq1LSaa1q9IDVfLmmWEUIIGyThLoQQNkjC/fLMsHYBV0Bqrho1reaaVi9IzZdF2tyFEMIGyZG7EELYIAl3IYSwQRLu51FKhSml1iil9iml4pRSL5SxTU+lVJZSapflNtIatZ5X0xGl1F5LPTvKWK+UUlOUUglKqT1KqQ7WqLNUPc1KfX67lFLZSqkXz9vG6p+zUmqmUipVKRVbapmfUupPpVS85d73AvsOtmwTr5QabMV6JyilDlj+3RcppXwusO9Fv0NVXPNopVRKqX/7ARfYt59S6qDle/2mlWv+uVS9R5RSuy6wb9V8zlpruZW6ASFAB8tjT+AQEHneNj2B36xd63k1HQECLrJ+ALAcUMA1wFZr11yqNnvgFOYLMqrV5wz0ADoAsaWWjQfetDx+ExhXxn5+QJLl3tfy2NdK9fYFHCyPx5VVb3m+Q1Vc82jg1XJ8bxKBRoATsPv8/6tVWfN56z8GRlrzc5Yj9/NorU9qrWMsj3OA/UCodauqEAOBOdpsC+CjlAqxdlEWvYFErXW1u0pZa70eSD9v8UBgtuXxbOD2Mna9CfhTa52utc4A/gT6VVqhFmXVq7X+Q2ttsPy4BahX2XVcjgt8xuXRGUjQWidprYuBnzD/21S6i9WslFLAPcCPVVHLhUi4X4RSKhxoD2wtY3VXpdRupdRypVTLKi2sbBr4QykVrZR6soz1ocDxUj8nU31+ad3Hhf8jVLfPGSBYa33S8vgUEFzGNtX1834U819wZbnUd6iqPWdpSpp5gaav6voZXwec1lrHX2B9lXzOEu4XoJTyABYAL2qts89bHYO5CaEt8BmwuKrrK0N3rXUHoD/wrFKqh7ULKg+llBNwG/BLGaur4+f8L9r8d3aN6E+slBoOGIC5F9ikOn2HpgGNgXbASczNHDXFIC5+1F4ln7OEexmUUo6Yg32u1nrh+eu11tla61zL42WAo1IqoIrLPL+mFMt9KrAI85+spaUAYaV+rmdZZm39gRit9enzV1THz9ni9LkmLct9ahnbVKvPWyn1CHAL8IDlF9J/lOM7VGW01qe11kattQn46gK1VKvPGEAp5QD8H/Dzhbapqs9Zwv08lvayb4D9WutJF9imjmU7lFKdMX+OZ6uuyv/U466U8jz3GPMJtNjzNlsKPGzpNXMNkFWqacGaLniUU90+51KWAud6vwwGlpSxzUqgr1LK19Kk0NeyrMoppfoBrwO3aa3zL7BNeb5DVea880F3XKCW7UATpVRDy1+A92H+t7GmG4EDWuvkslZW6edcFWeWa9IN6I75z+w9wC7LbQAwFBhq2eY5IA7z2fktQDcr19zIUstuS13DLctL16yAzzH3LtgLRFWDz9odc1h7l1pWrT5nzL94TgIlmNt0HwP8gVVAPPAX4GfZNgr4utS+jwIJltsQK9abgLlt+tz3ebpl27rAsot9h6xY83eW7+kezIEdcn7Nlp8HYO7Rlmjtmi3LZ537/pba1iqfsww/IIQQNkiaZYQQwgZJuAshhA2ScBdCCBsk4S6EEDZIwl0IIWyQhLsQQtggCXchhLBB/w/aBm8p4DUkbQAAAABJRU5ErkJggg==\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From dd33974b49cf2622cf4510fe07a300183c2864db Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 335/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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T2PpV2PHtKBAkcrD2ZXDWC2DZ86LO359SPQyZ9QJmPJ8Zz2fOCygEAYWGR3xqO20TG+ia3ELf7DbaKuNk67PYRA+zCZBoprKVZDzezoTbzmRsEROd65mIdVBwu/Ckm4B2bM3hBGmSdZtUwSeW97DnPKQR7c9ppu6V/U/hL3tyJigYhnFG2jg0x199ayte+cf89rnfoTs5RDq9ihXL30Nn5zXHngsIPNh3ZzMQfBNqeYi3RoHg7FdGgcCJLXiMoh+wu1xjV6XGaN1rFvrHCv8j8+WgWcBryNryPq6Y28CVc4/y4vxGWr0yvsaZsTvYm17HtpZLqLR10nA78N02QjuH2lksMtiejVMPsWohWvVxJzw6Cg2ytWBerkKgiGULLR0JWrsytK5L0toVpWxXkmxnAsc9PXcumaBgGMYZRVX55N37+eK93+BVq77D0uxeEolBViz/AD09L0OkWRhO74V7/gW2fQ2qsxDPRp3DZ78yahpyjjUnzXo+u5qF/65yjV3lOrsrNUbq3nHHbrEt2l2HDrEZrIRcWAzpn5iibWIKa66B1D2CwMHX9RzmUv5bE4T6kxTOdaCO7VokUg7xtEs85dC2qIV0a4xUa4xUNt6cj6bxlINoiAYB6vkQ+M35MjqZJ0ilsHOnvv/DBAXDMM4YfhDyt996AAof4p0XPYAb62b5svfS3/crx+4imtwJd/0jbPkS2DFYd30UCFY8H5w4qsqeSp0HJqa5P1/iwXyZ/dXG0WMkLYuV6ThX5DKsTsVZUoaWoSrM1ClMVJkbnaGUP34cI88SsvEq8dYUTmsbbq4bJ5PFjVs4MRs3bkfTmIVzdP7Icgs37pBIOzgxG/V9/MlJvNFRvNGD+KOjeDtH8SbGCSanmJueZmp6Gq2e7KE3CAVSr389y9797lN+DkxQMAzjjFCu+7zvq5/kvOzHyPUXWbL4bSxb9tvYdiLaYHwr3PUPsPVrUefw5b8DV7ydeqqTTcUq9x+e48FCmQfzZWa8qDmm3bW5pDXN6/o6WJdJsiqdoD/mMD1UYu+jk+x7dJSh8QoAMbtBzjlMvxwilzlMrqVObukiWtetJ7bySsgtfsLfoKoEs7N4o6P4h8bwRseojxxmbmiYxsgIwfg4zMwg4fFBx08kaGQy1FMpCpkk+fZl1C1oaIivSiBKAIQoIYqi9JZmWXZKz0DEBAXDMJ52ozMTfPH2P+Sq7h/jyTIuufDTZLPrmys3wV3vh+3fhFgLPPsdlC9+K1+vOHxp9ywPF0aph9GAdSuSca7raOWSXJpLWtOsSMYREcJQGdubZ98PDvLDDROUZuqIwEDbGOe1foslsfvI5JLI8udEQ00s/TVoW3JcHlWVsFjEGx3DHxvFGx3DGxvFGx2lNnwYb3SUcHIS8Y5vkgosi0oqFaVMhkp3N5VUikY2S6M1i59KQKMGxVmC2WnCWhWoQ3MMPieZxE2lSaXSxDMtxDMZEi1Zlq2/4LScCxMUDMN4Wm3c8x327fkz1uQKkLmR6y764+g5g8OPRDWDnd+BeCv63Hey8ewb+exsyNc2jlMOQs5KxXnzQCeXtqa5qDVNV+z4B9Xmxitsun2YPY9MUC00sB1hcKDGJS23saz6RRIJhfNfBxd9B7pWRw+EAWG1Su3RR6lt3059927qu/dQ270bzeeP238oQi2ZpJJKRoX+iuVUUino6MDp6yM5OEjLwADZ1la6XAc7CKjNTDJz6CCzh4YpHB4nGGrgWnFSySy5RavJtnaRTudwrRiWOkggiKfR1AepCVIS7FwKrjj158MEBcMwnhaeV+DHj7yboPxtKn4/S1d+hAtWXA5eFf73nfDAxyCRo/zc/8f/DN7Ap2d8dm2bImlZXN+d43V97Vzcml5wdNKxfXke/d4h9m2YxLKFZesyrEhtYcn4R4hVh6LB557/Z3DeawnVpbZjB7Xvfp7qli2UN28mOHAAmk08fjxGPtvKXEcHxWVLqaRSBG1txPr7yfQO0JFsJ+PbtNYVuxYiZZ+w1CCshFhbwdpUxArL2OJgi0NSYnRaa4G10E6UjgiAmWYCQg2JqgwCCNEvFUSExrYFRlM9BUxQMAzjZ256+kc8sukP0WCGByZfxo0v/CsG21uj8Ye+8haY2snQeb/O3y99M1/Ph3jDJZ6VTfGPqwe5vjtHi/PYO37CUDmwaYpHbzvE2L488ZTDhVflODf4FOk9/wVhQLjsWirtv0dtNkHlq9uovvdN+CMTWE4CcdP42S5quQvwn30tmmrDSbWSiKXpFZdFoYUVNewjITAJMnmSUVTns5vpSbDmDdan6NGwAFDOeAt95Sk7bUFBRP4TeBkwoarnNJedD3yU6DkOH3ibqj4gUaj/EPASoAK8SVUfOV15Mwzj6aGqHDjwb+zd90FGyz08PPdXvPdVN5CNWfCjf4Lb/5Z6qpP3XPoRPpU4l/Yy/NpAJ6/tb2dNeuEnj/1GwI77xtj0vYPUpmu05+I8/9md9NfuJ9ywkUJlMbPWvxBqCh61ETsOtguyhOQ5L4Fzjt9fdv6HEKg1+xM0ICAgCH1CQrAFy7axHBtbHCy1UC9krjrBaHUv47WDNIIqMStJX3I5A+mVtMTa8R2PmlWnalcpW2WqeNiBTTJM0OblaNH00cNXrCoTzizjsWlmrBnyOscENYZDYZnVxbm86JSfo9NZU/g08BHgM/OWvR/4S1W9VURe0vx8FfBiYGUzXQr8e3NqGMbPCd8vsW37O5mc/C73j11IIfaH/OPrL8ItHILP/yYM3cc9A9fya0t+l1Smgw8s7eWXe9uIN0ceVVXCkoc3XsGfqlAfLTO3aw5/pkYa5bkikHWjZp8teYqsAdZAM5YcueZWVQIJCe0AtZRAPBpBhWJ5hnJtllpQpuIXaWiDXEcPba29tCa7SNGCW03gVpQApQxUQqUcCnMJn92FreydPUBVQe0YVlsf1YQym6wzGxuj5B7GJyAVpuj1Oumpd9Bb6yCtaQTwtEGBKebYT50q+CFuzSdVqZAr52mv5Omq5FlZL5Krlbl/1brTcp5OW1BQ1btEZOmJizkWiFuBI687uh74jKoqcJ+I5ESkT1VHT1f+DMP42alU9rNp829RKu/llp2voK//Tbzv5WcjG/+L8DvvpKbCH615F7cPvIh3LO3lV7NZrKka3n1jVMbLUSCYqBBW/KP7DBQCVRzXil5v2fA5UvSrV8MrjzKlMwylA7yeNIm4QFiiOjfB9PAB6uXonQi27bJ08Dx6+1bS4p6LU/CwJiep5yeo759mrHqIqfocsUYJD/CABoKKhSIEIqgIHWLRhhCKhYoQiIVv2YQiqIAKgILMkvT3kvZrUfLqJAKPuB+lmO9jqZ74J4w6tRMJqskk1bY07Un/MducCj/rPoXfB74rIv9IdPaO9J0PAEPzthtuLjNBwTCe4aambmfrtnfQ8IUPPPRbrF58NX9xTR/V/3oj7q5H2Zx8Obd1vZpfqXXw7q2K3rGXmfKxAk8SNm5PmuQ5nVQs4cAj48RKPj0xi5QI2qgSzOwnmD2INA4x1lLnvq4e7J4sbRJSHDpA7XARANtx6O5bzNKBi/CKNtWpWfzZSSojm5mt3ElveZpscHxbfclNUohnqDgxIMAVj7gEuIES90OsZieDLYJDsztYFQlDJFQsVayj0xAJQwLHwXNdvJiL57o0kimqR+Zdl7pr03AdKuk4XksKq7ONeFcXHW3ddOW66cq2sn5g4LScr591UPgt4B2q+mUReRXwSeAFP80OROQtwFsAFi9+4odJDMN4eqiGHNj/7wxt+yxu7XJ+sPE6fj3Tw5XTFUb/9k40vAmw6W7AG/JgpSvYHUmctR24PWncnhRuTwppcSntyXPga3tITVVZYglhXAmGf0x11+3Y4T5SiwMe7V/FJmspLZqAqUOEo1Fb/Yp4Dreew5mZIVkYoeXhncfls+G4lFpaqeZaGB5YTS0eoxxzqLgupZYktWSMwLZRLCwswAJLottXT/bWtnlCwBcLz7KoWj4Nu0bdnqPm5Kk6ZQJHyKY66csNsqZvJZcsPo91XauI2cfGa1JVAj/Eb4TRdHacmDy2NnEqiC5QTTllO4+aj741r6M5D+RUVZudy3lVzYrIx4A7VPULze12Alc9UfPRRRddpA899NBpy79hGD8Z9UP8qSreRNTM0xgrUBo6gFVMY4XHnh1QJ8QJ9hM4k2xZ+izOPmctPf0tOB0JrNTxzxgEhQblR8eZ+dFhnJJHoEpZatibv0i4716SfQG6osr97ipGKjnKdaUepuj1LRbPTbN0bD9u4BOIUExlKKUz1DIpKpkUpXSGciZNOZ2mHo9j+T4S+CgavcRGFdGwOR+iAlaoSBAgYuFkO6hnu5hKJBlOxMnH4/i2Q8qDRTWLfi9GW91j2NnEvelbybvR8w05v4tebzHd9UV01wfpqi+ipdEGGt09dWSqoRIdXgm8EN87/glogGetHuLyd9z4pM6XiDysqhcttO5nXVMYAZ4H3AE8H9jdXP4N4HdE5L+JOpjzpj/BMM48GireeAVvtIQ/cSwI+DNVmFdu+ekZaqlDBKv6+cTuLNoa46YlP+Tsbf/Mo53rCW74DC9atPAgDf5sjeIPhyg/NAYKBT9kTstkHvgYyand2OcMos+rcUtwIY+wmvZCg3X5UZ41toeeYnSDfyHTwr4Vyxnt72Oyq5tMSzutrTlshcbUFNWxIeqlSZzZEVzfw7Y7wMohkkWslqMJSeLXHsbzN3Ng8XkcXHkZ+/u7mclGRWe65tMzO8F5hTyvH29jcdXhwcxWbm39MT/OzNLl93NJ+Vr6wiX06xLSdhbbtrFjFlZCEDt65kAssEQQS5CgjlQmkco4UhpDyiNIrIpaHsSThK3dhK09dKw7+7Sc49NWUxCRLxDdWdQJjAN/AewkuvXUAWpEt6Q+3Kw1fAR4EdEtqW9W1SesApiagmGcXkGhTuNQkcZQkfqhIt7hItoc1x9LcDoTuN0pnO4UbneKenqELYffRmjVyPa9nzd+PmBRJuD9LR/j3NE7uG3xKzjvNf9GTyr92GPl6xRuH6L8wBihKgdqASNejb4tn6NraiPW857L3fEid/jLKTntvPDAw1y2ZwOJRoPAspjs6mK8f5B6z0rSnctw4zkKlRizh/YTNvYTeAdBS828t2LFewkzHRQyMWpuHRyPVDxB2k2RsGMExRnmDu9m44pz2Xj2pZSTaZzApyc/Qaa8G7uxgSsKWV49fR1tQZZHkzv5UXwrfiBkvEyzqWlhjuNg2zaWZUU1ktCLhgAPfVRDQoQQmwCb8CT7WbpmLW96zauf1Hl9vJrCaW0+Ot1MUDCMUydsBHgjpaNBoHGoSJCvRyttwe1LExtsIbY4S6w/jdOZROxjBdbU9B1s2fJ2XLeNjsF/5Q03jzEYn+SD8n4WlQ5x28V/wrUv+kNc+/hCLig2KN4xROn+UTRQhjxlZ7lB18HbGBi/i+0v/GW+ZyWohMJgMMNluzawcs8eYp7H9OBy/MELSXWeS8ruYLxqccBrUAwnUH8LWt6NBnUQG02k8VpyNFpa0dgJb2kD4vE4qVQKx3E4VCxyf3s/u1acjSIsnpsiPnE/TrABy8rzrPJaLi9eQCyIUUzXCHsT+DGo1BtUalXqtRpevUHoNaImqJNQILSPJAFbogfdLMAKwfFRpwF2Hew6YtUQqWJTZ8Zeyr++9p+f1Lk+k5qPDMM4wzRGSpTuGaGyYRL8qBZgt8WJLWkhNjhAbHELsf4M4p78yndk5BZ27Hw3mfQaepZ9hNd+YjfrU9v4QOUfUISHrv8sL7ngpcd9Jyh7FO8apnzPCOqHjFkWm/MNUqW9ZIe/xw8uv4Kq/St0SYnVtSlW7djJyr17sYOAxtLzqC+7hmouw7CWmbUn8WOHCLzDWIVRnHIRBfxMjjA7gNWaodgaMMQoFWuCXDrH+q71nNN5DkkrSaPRYK5YZPvoGEP5IjhxLpwe5cqxQzihP+854nOPzm20DoEFoWfhjTqENth2iGtFKZkOSaKkREkppFVIqU0iSBAPEySCFAk/jR2ksepJLD+BPE7t4gglRK2ArQOn5/leU1MwjF9AGoRUt05TumeExoEC4lqkzu8msbad2GALdsvCbyp7zH5U2b//w9PkjHAAACAASURBVOw/8GHa259D39IP8NqPb+Sq2K28a/YTDGWWYL3uCyzpX3PsO35I8a5hincOo/WAuUyMh0bKhFTIDH2dR9YM4qYUWxSKVc7bupFVhw4hCjOLz+bg6nM51GZRl+i2Vcf3SVfzMDmK6wmZdCfdQRLbDxg/q4tpF7wgRFWwLRcvDKiHjx0iwsLFsWI4auOogyXg4uGqkCRDt58lrRI1+dg2qdAlHcSJBTGsn2Aci1B86naVajNVrCoVu0rDaeDFAtQVxHVoYFH2bQpVm3zZAS9BPEjQG0vSY8fIiUPMV9Krk5z7miuf8LgLMTUFwzCAqKmm/MAY5ftHCQoN7PYErS9ZRvqinsfc/fNEwtBj584/Z2T0Fvp6/w99S/6S137iQV4fv5mbZr7KpoGrOev1N5NKtR79Tv1Qgdkv78Yfr1DrTHJ/vky+UCTVuJtDbT5cOIClUJzzuHbz3SwdmwCFg0uXsm3dWkotLXTRwoXdy+hx0gSHprHLFrYdp7QoZFrKTFkFdkiRglUF9aEBcVwyJEiTIEWChJ0gRgLRGBYxXBIkfB/L86jELZRZVOskHYd2ieNIiSA9DRKilo9aHhKzCBMJvGSammsxXp9mT2E/u7wZJuwGVatO2a6C7dGd62Rxx1mscDtZpClyXkiPFeCoz56JgAPDDsOjSebmMiQCh4xatFsBfYDrJaLmL0JGxWPUqeC0zLJUAuDJBYXHY4KCYfwCaIyWKd01TGXTJARKfGWO3CvPIrG6HbF+gkHdTuD7ZbZsfTvT03eydOlv0zvwdt7w6Xu5iQ9xw8wP2LTuRs694YOIFV1Bh42AwncPULpnhDBus8Wy2LNvAjuzh6n0JOrazIYZJqsxLh3awHO2bSZeb3B47cXUll9Gj9vPL9ktpCQB1YDgUMiwNc1ua5Kp1jIlakfzFlgemgzp7x+g96yLOJTrY5uv7CrX2Fup4x1pHVEFEZYf2MGVD/yALd39xM+5n8XxA5yfjrHEbVDDYyS0Sftr6Fp+NZmWVcT9dhp5YcveB9l14CGGtm1GyzVSdaW/pjzXs8n6NklPiHshlh+iwTjCFjSmaBzUhVoMNKZ0x6ArBhoDzSrqNOfdKOEq6iq2rcSsEEFo2Amc4VbgN576P8cJTPORYfwcCwp18rcdpPLwOOLapC7sJnN5P2536knvs96YYuPGX6dY3Mbq1X9JV8+rufFzP+am2b/mBfn72XHpH7HmRe86+m6C2q5ZZr+ym2CuzjDCI4UCtY4DFKxxFOFQmGPGy3KFX+B5Y9N0+0loX4rTMnBslFABqyPOrvoetuZ3Mx0PCG0LcWHCGWM8MUVV5lg7eAVdZ7+KXY00d8+WyPvRG9iWJGKsSifIOjYP58scqDXoL07z/LtvoTc+ROf6MbqyeRLNypJTTeGMJnCmHZyajQZV/KAC+FGh3izQ9UhhHoMwpmhcjq7DUawwer7BDqJ5uzlPCH4Yo6IZymQok6ZEhhItlCQTzUuGorRQtFoo2Bnm3BbmnCwlJ7pz66bDX+Gv3/BXT+ocmuYjw/gFEzYCSkfa7UMlc+UA2ecP/tRNRCcqlXaxcdNv0GhMsX79R2lru5qb/udO/u/0u7iwuJ2D17yfNc/5zSgPFY/Zb+6j+ugEZeChYp253jHysX10hjmyjfXktIX/g0UH8ag0WgSBX8OKxREVYitzlM62+dFDP+Tg6Cih42KloWdxH7c2bmVvYorB0iq6+q/G7+xjszdHy/D3GXAqvCNRZ0mmSpddptGYZWxmAhqzvJQaMakjGeVkg4z6yQr+8gosP7ZMQ6gHNkU/i1fLEHgZfD9Lw09SJ0HNi9PwYlTsBGU7QdlOUbGTlO1kc1mSshvNV+zHD8pu6NHqF8n6ZbJBifZwjqXVYVrCAlnNkw1KnBXLPKVzeTImKBjGzxENlcojE+RvO0BYaJA8p4PWFy/D6Vh42OmfxvT0nWze8rvYdpILn/UFMplzeeuXv88fD/8RK6rDTL/i4yw5/wZUlfKjk8x8bTc0QnbXfGbaS7TG86ysZukOn4eDRYhS0BLMDlEf20mtNkfyvHXE3Evxsxb71lV4dMd3KN5aBQ3JpgLOOb+NRvtuts/cystclw6rSlZ+BPwoGqnuCB8oWdhBgnwhxJ31WFaq48ZDrGSIYyluI8QuK1ITYhJn0ulg3B5kwm1jIpFiIpZj0m1j2m1jym1nMtbOTCwHMeAkZbqlAVm/TItfJhNUSAdVUmGNLm82mj+aaqSDKjm/SM4v0OqXaPMK5PwCOa9EKqzyeI16IQ7j2Zc/5XO6EBMUDOPnRG3PHPlv78MbLeMOttDxujXEl7Y+8RefgKoyPPwZdu3+azKZNZy3/mPEYr287evf5s/2/AHtfp76626he+XzqUxVGfn0VhJTVWpBSDWuLEvC6koOyLGfBl+nitQ3kzr8COfvPkS6OkN1eR9d172dfMlnU/eDbJ3J4z0itKQnWHHWPrq6DuK6DQDcsk1/oovZsINy6gJ6W5awzEqSrHu4lTLs3024cwtMD+GmpnHSIbYoM06WfeEge1nEvuQg+1oGGOrq5XCih8lYx2N+dzKo0dWYodObY1F9nHPKe2jxyySDGo76KIIvNp44CIqjAYICFqFlETZHSg3EJhTwsWjYNlWnlQnJNV+ZI9FAeQq2hsRDxQ1D3BB8calZcUp2jJLjkLeUGdtjTho0/Bov7B3gw0/57D6WCQqG8QznTVbIf2c/te0z2Lk47a9ZTXJ915PqQD5RGHrs2v1eDh/+PJ2dL+DsdR9ArRRv/8aX+Ottf4gNOG/+NhVnDfd/dBNd++aICyBCyrYI/Rr7rRnul4Avhq10h4e5auh/WTNrs+rgNjRlEfxqgrm1MTYUP8XBxhq8iRRtbYcZ6NpBX98g1czF7Jq+gLFJj3S9zqLZ/VxZnGKgYwa7uAWrMnU0v2UrwYHkAFu6z2LzyuvYl17E4UQvI7Fuis6x5hZbfQZrYwzWxrhq5kGSQQ0UGtjU1KIRxMGPEwY26oXEfMV1cmhygKnQothQao0AC0i6kHZqxKQYXd2rABaoDWojoSChhavWvHWKAqpBc4ylADQkICTQkJqGiIZY2qA1rNKmAUvCEFuPjSVSP+ydjpuPTFAwjGcqDZXyvSPM3XoAsYXsi5bScuXA4z5k9tPwvAJbtrydmdm7WbL4LaxY8UeUAuXPvv5p3rflTylabRQv+zyHPu/TM/kQ/XY0dk856bPJ38ewTHNYPe6jl47WKV4vX2CFM8rinXVSB3yq60OmXwujs2dxePsa6l6cFrfI2tgjLG9M0j6Up2f3DuL6neOGUi5bSQ4levmstZr7lryCA4lFjMc6yLsZanbiuN/QUZultzrNhXPbyVbLpGt10tU66ZqPhg4BQqghR263SREQvSjZA0oL/l2SwGPrFSdQjmv+OfZ2ZeZNFVsFW8FWnTdVbDTqlD4y2EWsjhurYrt1rHgdK1ZH5zYDNz1RTn5qJigYxjNQUKgz88Vd1HfPkVjdRtsNq37iB85+EpXKQTZu+g2q1UOsXfP39PffwJ5KjU9+8yO8e8snubfxG0h4DQP/W2StLeBY+GnhzsSjzMU20ZoboyueZ2lrnpe4RZK1gPQei/Z7lGS2Qf5VWeZivezeeC4TdNLLBNfwY5Z5h5iIdTBmdbMjtoqHMs9mTtMUtYURu4+9LQMMZ1oZzaSpO1HxlfDqtFbLLJ6boLVaIlcp0Vot0Vot44bR3Ueu2sTUIYaNG6ZxFJxQcULFDZRYoLihh0gZyy1DvIIkykgySmQqkKliOQ1EFLUVkRAEVELEBmkOZS1PvYKGr1APoRYKdYWKCvUQ6irUQqipMJg7NcH/RCYoGMYzTGXzJHNf3YN6IblXnEX60l7kVJRETbOzD7B5y9tQVS44/2ba2i7ltvFZNnzhk1y1P8YO699ZFbNwbEEdoeGOcXD1j5iNP8Byd4yOQoN0MYCpGMm9IR1eCfvItfhaOCj93CrXMum3k7QCVrk2fZzHZONqxjybsldlqlpkeyZgZ1uGkVwnI7lOam40XlG2WmL1xAHW5vexPr+TxbVJ4g1BqhZScGDGxqn4uLUybq2CXS0hQY2g1cfvU+p9Sqkbqp02fptFPRlQdkM8G7zobtFoTKIw6q8OVJpveYtWSAgSNIc0EtDoNTvRd5q9Csc+Q6hR3cNXoaFRge+F0Ygi9WahX1ehqkKtOe89bjdz5GX1J36K+skwQcEwniHCms/cN/ZSeWQCd1GG9levxu168s8bLGRk5Evs2PlukslBzlv/ccJ6Lx/63CbaHxzmufbFDCQELPCXzDCRuo1i5+3kSkXOmvFpn4a0VwWgaiXYm+xiZ7KLwQPDtA4V2dl9EbtWXse4P46DxdnuAEGlwFS5wIHEJA0ZpR532NO9iJ09q5hobQegIz/LZTs3cNnsBq4N7+Es9yD10Gam2MpBTXM43qDQAXNpl6qtNOIhNRsqIlRFqSJUgao61EKoquBps9D1AO/0FK4nslWJq5JoTuOhktKAtlBJhyFpbU5DJa3NaRgety4VKhmNpuVVl5+WfJqgYBjPAPV9eWZu2UlQqNNyzWKyzx88boTSpyoIauza9ZeMjN5CW+5K2ty/4YefnqG4bT/Pjlv0JFsIrAbFZXdTb7mFjlmfVcM+6X3TCIpHgs3p9Xx98GLubLsQbfRz/Y/v5QXf+yyKxdzl69g0eD6zwSgJHOris9U/CDGIV6pUPJfNy5bz6NJzaDgx+ku7+aWxz7M22IQm8kz3wY5ei3tDi3wwQD4QyiHN+sf8ZrOoI9cOlBREA9KhZAjpQclISEaVbBCSCUKSYUgyUFJBSMoPSQUhsSBqVrIAW0HQI+9bQxRsNDqKNgczJbp7KLrbCAIRfAt8y8K3BM+y8GwL37aoWVGqWhZ1ERoWNOzme5/FwpcogJUsju4rFCGwhNCyCCxBLQu1hM5cO+8+Zf8Bx5igYBhnMA2VwvcOUrxjCLs9QddbzyO+OHtKj1Gp7Gfzlt8hP3sAp/DnbL9jJfGZPZyVtOjMOIRWgVL/12mrb2DR4SFsKqhaVFjFnS3XcvOiS/h+17lYnkXHoVmuu/V7XLt1hK7pDUys6GXHujVMxdohCAAlq3toje1jMtvg3q6z2BHvoxyWcf0f0DfxKTTIU1XlXuBegHLUbNQShnT4Ib1ewDlBSEcY0qEB7VZIOz4tVkibH9LW8Mn4Ia4XFdYLCYCybVG2hZJlUbAs8pbFpGVHy8SiKkJVLCqWRPNWc9mRz2Idna+JnJrOBEDVQv0EhAk0SKJBGvUzaJAh9NNokEH9DCs67ehVZaeYCQqGcYYKSg1m/nsn9T1zpC7qIffyFVjxU9vUMTb+TTbc/y/M7n4uxYN/QI8Kz2oJack4+O4YYe4WOqo7WDx1CFWHcng5P0g/i/9cfBEP9g4SitA5PAGbCqzbvYEbDzxI3G2w+Zx2DvZeTSlepe5MILGNBIkZZpyACWzqRwrQ+kaob6QnCOkJfHr9gF7fpycIjs37AT1BQHKBIXkCAd8SGpZQt4S8bXHAdplyLSZTNuOWxYxlM2vb5C2h5NhUbQvPgZglxERxsLBDhxgOsdAhHqRJ+m3Ea1kS9SzpMEu3JsmECdJ+nGTo4qgdvbFZLSwE++i8ha3H5gmFEhYFLOZUmFNhFmFOoQiUm6kElFBKKNXHOV8OSgZIo5wTSzzOlk+eCQqGcQaqHyow8/ntBGWPthtWkr6o95Tu32vUuPe2/2DvAw6ViT+hL25xSS5GvBFQie0iHv8SvfXNOMUCvvYwpzdyc/vl/NvKReRTLWTDgFf8+A4Su8ZYPD5MvHWU7asT3LKuykRyhryz+7jjpcOQRZ7PyorPxeoSlyxdwMr8NMumysQDwcsIjS7w2iFwhEAtan6c3djcbQvjIkxiMxlajKvFRGhRxUJCi0wYkJWQdELodmz6bYu2eJV2J2TQUtKWkq53kCgPECv2Ey/0Eyv3Eyv3YvvHvwXOQyk6UHItCq5QcIVSTCg4MGJFBXgxDCh5IdWGT63RwPN8Gn5II1DqqtQRagh1sdDjahBRb7WrSkIhDsRDIa7QpcLi0CahQkKjZXEVkgopFdKh4HLs9tYZe+6U/k8cYYKCYZxBVJXyfaPMfWsfdjZG92+dT2zg1I1xUyk02HjHNjbfeRCvfA5dLR7PXZYhPlvDkrvIJr/OQLgdakLNvZDKwOv56vRSPrjKYaylhcGZSX7re1+je+dt7FxssfV8h7vaPOp2dBXf4/tcUauzrKG0q0O/naCe7uMO91zu6bucXXaWF274Ic/fdhuJgSL1VcKei9LR28YCpTBtsdW3eQiLg55N9HobBb/59K+dY43XwTW1NIuSRTpTI4S5PDhB8w8ouJVu4uV+YrP9SKWfYrGb2UoHY6FNjYA6Hh4+DVvxrRn8+DQBShgEeH5A3RPqdYsq9tFUFpuK2JQti5JY1B/zYKAgakcv0wmFNhUyoZBuFubRFOKWYNsW6lrUHaHhCg1HqLty3OfivM91hwW3e/ZI4ZT9X8xngoJhnCHCRsDcV/dQeXSCxOo22l+9+ikPYHfEzGiZR757kN0PjhIGQlvPJJcsayVxOAWlQ7QlPkyajfhhjgIXoy95F49M9fPeyhjbl2bpnhrlVXf9E+Ptu/n0eRbBBSAacJZX4xXlOmcHDXo1zVj+PEZii1iSnWOXHeNfuq9jT/tZdMzN8Kpbv8V1M9/He2UD7zKPUqiEpZADYzZ3hjG2iY2ngoNNPHA5x89ysd3CCgeclI+THIX4YeAwAHY9S7w4CMPPwi91USl1UihlKfpQDXzqgaK+jRXG8NWjIhZliVGWJGVbKQmULaUsSsmKUkOAE/7klkIKISFCXCxabCFmRbfkJmiQ0AapsEY6KJNolEg2SiTrJVK1EqlamVStTKJRI9GoEW80iDUaCDQ7jh1Cy46GxbBsQhFUoo5ktW1UjtQtFF8CQgnxCQgI8VeugV99xSn5/5jPBAXDOAP4U1WmP7cNb7xC9toltFw9eEqGqZg+XOKh7xxgzyMT2E5AbumPODvbQfvwhTAS4KT/h27vS6ABFVnHaMJl9uUf4u82DvPj/gpt9RlecM/fcah7mNvPhSVewJsKRc4JPAaTIUHSpVDIssO7mo21JXT4Y9Rma/zlkhs42L+I/skxbvrm57hiwKfjFdspOWW0GvLAiMXXgmSz+Qf6S2leXFzBua19DGQmCDu34ycPRD8idLBLPej08v/P3ntHyXXcd76furHj9HRPjsAMMAE550ASjCAkkpJJUVS0bEuW43v2Or63tt9Kttf2s62jXdnelUxliSIVSTGTIkEEIkcCGAwGwOQ809O5+96+99b+0UOKkhglUtq1+nNOnepbfW/3Pd3V9a2u+gXsdIzMXIREvJpcLkjRDZAWQTJqgKxikJkf5LMKZDRJRvfIKBLnpSUcb76U9oVVTUGbz1NQIYr4ZYFwMUOllaQ6P0dtNk51bo5QPkswnydYyBPIW/gtm4D9k9nbXo4HFHQVS1MoaiULJFtRyOulBSDN9dCckuey6c2H2ZYSIeV8PKTSRrni/fD45T3igGL/zP3jlSiLQpkyv2DyPbPE7+9FKILqjyzH1xn9mV9zeijN8UcHuHp6Gt0U1C8/QmPoEk1D70OZ9eHFeqnJfg6/00NWWYDhznJAWcHXa67niYk8FYFhtp77JFfCCc42Sa7J5bk9m6c5CmMNYXyn/aTPm3hNYfbXbyPn+mjq7+e+nXu4sKiTutlprn/yMYoxg+U3T2HqB4kX4dkZlSfzfvSiDyO5nkh2CXqxDlfPc0jLczAJXqIDr38XxaJOsQhBR6PS1fFRSntZVARzimQuKEkqEvnSSFkapFXPw+fa+IoWFcU8zXaWqJWkypqjLjdDbT5OU2aWmmyKgOO96mcIJReGgq5i6Rq2ZmDpBjPhMHZMp6DrWLqOZWgUdANL07ANg4KuUzB0iqqGVObNU1VwVYGjlMqLj21Np6jrFDWDoqZjawaOquNoOo5q4KrztWLgqgaqJ9BdMF2X2kLxbUixUxaFMmV+YUgpyewbJfl4P3pjiKr3L0GL/WwWJZP9KY4/2s/AC7MYfpVFW8YIBO6jof8u/MPbETEXQ36B6uz38YRKQrQgs9P8U/j3+dzGnbQmHmJ5/98zYRSZCrp8IJ3jGp+FXRfk5NwGpp7OYg1XMFbTgq/dIhOuJZDKIfJZPvm+36aoaOhn4zgzcVo6L3FdywE8JE8mNZ5JadRkK9Hje1ALi9H0PNJI4ZoTCAlaQcHJg1vQ8YsAhjBJKYIxXWHY+OFM33AdqgtJ2nKztKQnWZQYoz47S6yQImalCTjWj3wmeR2yfsj4IOuDbEAwFNG5rEYpGBVYRgU5fyXZQIS5ihjxSJR4JMp0tIpMsAJPff1hUngS3QXDkaXgeY5E9V6c5Zee11wHvWhhFG0EAilUECpCKJiomKgIBIZTxLAcDM/B8FwMz0XzXHQvW6qlgypdmkJzP1NfeTXeNlEQQnweeAcwJaVc/rL23wN+h5Kp8CNSyj+Zb/9z4Nfn239fSvnE23VvZcr8opGOx9x3L5M7MYl/ZTXROztRjJ/e3HRyIMXRh64ydCGOGdRYeZOBEv4Ukd6VRC7+KYpfxas7Su3cVzCUK0wai6gqXOVSsoa/WP7/oIaPsGj0N5lVBY3S5oP5PF0RGDTa+OrYBvqOtzAWqEep8rg1c4imygJTkQZCM3M8uPIa+jraCSUyrDn/Ahtq9rJ251F0xeVwVuVIXOO6uTD/lnovxaYeshu+jNCLFOI+pgciXBjtoLewhFmzhWk9hOUvDUvRQoqumSGaM9M0ZmZoykxTn5tF0WySIZVkABJBl0STzfGQJBUoDfoZn0nOV0vBX49j1qFRDUoltl5JxoxgGRVI1fcjA7jhSML5ArFClmYrz5JJi/DICEGniOl4GA5ojkR4Kq6j43oC13XxHBfPKyK9IlLmkTIPXn7+cQG8XKlNWrzoavfaKIAKQgE0ECWD11JbqRZCA6HR3PD2JNl529JxCiF2UrLe+vKLoiCEuA74f4E9UkpLCFErpZwSQiwF7gM2Ao3A00CnlNJ9rfcop+Ms838ibsZm9qs92AOpknfyDa0/deyi1Eyeww9epe/YJP6wzspddfia7qNweoCay3ejFoOoiyXmyBeIeg9SVAL0qU10pXv5rPtBHllbicg/yKAmWWrbvMfIo+pNDMe72Du+jlPuQjwUWlMTfGjgMZbMDXN4yzYy4RApXef7a68j7QuycfgSW8cO0LbsAFWhFL0FhSsTHjdOK9SldjLWNIixeAChSM5cWsJjAzcy6jVRUE3kfMrN1tQEy2b76UwMEzAzDDfmmavwyBkWmUCArD+KbcZQqcDnVhBwgwQ8P4bnQ3N94PlxpQ/F00oDfpGXBn3zxfDX3ut/zlJ64GXwvDlw40gvgfSSSJlGelmkV+DFfYkfRwgFwzAxTQPT1DGN+aIp6JooFaUUTM9DRQq1tMGMgieU+ThKAk+KUhsC15Ol+EmexHU9nKKNa9ss2bmL9Xtu/6n6zS8kHaeUcp8QYuGPNf8W8HdSSmv+nKn59tuBb8y39wshLlMSiENv1/2VKfOLoDiRZeZL53HTRWL3dBNYVfNTvY6VK3Li8UHOPjOCELD+1oW0rhtg+PR/xnx4D5WpXegtAQryIlUD9+JXT3AhtJrK7BCBkQT/V+dHiFc8x1CxSAMOv4+FYXTywsgaTuTaGc5XsHL6Mr8/+QBr6KV6OsVodQvP3HAjUlMZaOviiaZuQnaeXzt5go66h2jceJ68B2fHBLf1J8ikmulbCOq650jnozx64i5Ozy4jpUURwmNJcpAlcyMszKUJ+gJM18aYaW9lVF1HRcFPdU6nIfXGxNIVkqIq8YSNJAdYqBQwVQef6qL7XYTiID0LZAEpbaRrIT0bp5jDyqVxrCye+8ozes0wMHw+NCOAqlWgqBqKqiIUFaEoKIoy79EsQUo8z0NKScHzyBc9pO2iKApiviiq/tJ1P6xL/xSFEKVJwou1Uqo1IdAQmPNp3wLh8E/Vd16Pn/eeQiewQwjxN0AB+CMp5TGgCTj8svNG5tvKlPkPQ75nlvh9vQhTpfY3V2K0vPkftet6nN83yrGHByjkinRvqmfN7ijjo59i9rtBGkZ+FyWoYK6roHD2BI3i71HVKR4J7mBX/BAP5jby7NoZjhhP4ZeSD3oFKsRSpkaWMmFVUzE5wccu3M+i6VGsBoGe8xAphTPbV3OpqZuiKXi4exuTldWsGM9wz8xThJffT8wocjmlsKkvyaq5AOfamvG6HM73rOVA30b6fAsBaCvE2ZUZpEWtQDcWo1YvLX02QAhQs4KUHzLBIonKNFJkULw8mufh5NPomTn8+TiGnUEr5lBkkZfP2n88GpQ1X94oQlFRNRVVN1A1DUXT0HQd5WWD/4tCIBTxigP7y9uEKJ0jEPNC4SE9D891S8fzxSm+KFgl81OkRL6svHj8cvLp9JvsPW+Mn7coaEAM2AxsAB4QQrS/9iU/ihDiY8DHAFpbW9/yGyxT5q1GSklm/yjJx0obytUfWooaMd/0a/SfmeH571wmOZWnqSvK5juayMsH6H/qHNW9t6E4IQJrashOTSNPPUeD8dcUVMGg28TasZP817aFPBkewhKCW1yLsLeK/EQHacfEn05x95HvUTUbx64Ct0biG4d8U5gTt9/CqK1yKtbGye4VKELwO72DxGr+lgXLZ0gWBd6lAnePepwqNvF4tpvnT+7gfLgFV1GoUl225VWWFDWiXhPFICRCRSxfElUk0J05pDWLk5pFn5vFjBfRFLe06aoEkUUX1XVQXmGp2xMKqDpS0wiG5jADBrGaVYQqG/EFQ6USrsAMBjF8fmZHhhl84RRD587gOQ7RxmaWXXM9S7ZfS0X1a/9rk1LO5znwsDz5Ul2cz6Lmzd+fN3/uUXnXeQAAIABJREFUS+GzZenYBVwp58v84/nrXmxzpHxZKZ3jSInjlWpXQnH+/NZI8FXv9Wfh5y0KI8B3ZEnyjgohPKCakjdKy8vOa+ZFD5UfQ0r5WeCzUNpTeHtvt0yZn40f2VBeUU30rje/oTw7mmHfNy4x1pcgWh/g1t9aghp9goGzn6DqhdupS74ftVnHbIySOT5BUH2IqHEvGcVHuJjnggFfXlZBv26z2XGoKK7BnlpC0ZMYjs3640doHxigGAK72cMYUbCDQcY/8mGeszNYjscTi7cw3lRFd9JmS+ozLGt/nrAqSU+4dB0M0BvfzN8bG3m6egHTQY2QB2ttjQWAFQ2RCKv0einc2avYczOYaQfd8qjL5dDtIVQvR9h6cTgSlKL8CKCAAISmU9PcSlN1AGfK5Nuilqh/hoLhp6lrkJpYD9H6u4nW3UneU8l5HvH5QTs/MUr++b24pw8jMmm8QIjCuu2kVm6ir76Z/RKs0RTWcPKlgb4wX9vzg39hvn5tA9afDeFJzKLEX5SYtsRXlPhtiWl7Lz322aXab3scX1XNde9e+pbfx89bFL4HXAc8K4TopBTzdgZ4CPi6EOKfKW00dwBHf873VqbMW8pPbChf3/qmHNLsgsPRh/s5+8wIpl9j5z0dxBYfZajvrwjt30LTyB+gBBSCOxpJn53APTpOqOKvidnHcBGkCvDJ5noeD0K9q7A110FufBuNIo3Ao3VwkI1HjoIqKXR4GP0K3rjOkeW7eW7VEppzw4yGqjnSvZ540ODG0V7WRz7B8uYC+USY6f3Xk05s5clQmEPNDnOqJOp5LDNt/ME5grk5mJslOBAnaicJOxmUl63XSwR50yMXFCT9JnNhgVkMsGBaoSKVIBMMM7V0LfHFy5mIxkjnbOLCj6WBp76CsE4Ck0PzLy5ZMHKFdS88z6KhSziKypWF3fTuWMPEgi50XcdUBGamUKoVBZ8iCKoqUV1gKgKforz03IvHxsuOTRe0gotqlfYMpCXBcpG2C7aHtDxk0cOzXSjK0jlFD2l7eC/Wdul5d77ttRAKmAEdM6DhC/roro684b70Zng7TVLvA64FqoUQI8BfAZ8HPi+EOAfYwIfn/zWcF0I8AFyglOzod17P8qhMmf+dscezzH7lAm7KIvbeLgKra9/wtVJKLp+Y4uA3+8imbJZua2Dx9iFGJz6G9YMGGi//IWoxgH9tLU7GJrN/FNvfT6zqL4lm5/CA+4J1fK5VJakoXOuoXBr5NWopoCppAtksO5/bR0UqRWqRRmi2iK9P4UR9F59ZeQeLKjIsdoc419jJ4cVLiNkud038K3tq96GgMHX6Lqb7dnGRDM9XQUIrEibLpkIPa6ZOo7s/9PTNKz5EIEg8FuVC0xpygQg6EeZCBoMNPmyjFiFh2aXTbDy9n1hyltnKah6/9t3El62lRnUIx8cwxy3GXWiUYwQ9i2homoZoP02xjTRV7yCkmwRVBdN1SB07yMgPHiU9OowvUsnSX7mHlTfsJhqNoryKlZeUErvgkpzNkk7myKQL5NI5smmLQqZIIetgZR3srEsx52HloPDaDs0lFAmaB/qLtYfUXKTmgd+FsATdQ+jztU8iTA9hSITpYusFbDVPQctRIIflWRTcApZjIepvZjm/8ob71RvlbTNJ/XlQNkkt878bUkqyz4+ReKwfxa9R9cGlbyr/QWIyx75v9DLcM0d1S4gVN0+Rcv8Fd6xIQ+9HMRKNGAsq0BoCZI5N4lIk2/Il2ucewm+59Ksmf1MT5ajfoMstUpHbijq1liZlBqRk6fkLLD93jlxUQ40U8Q0IxiuifGrF3YxW13GD3osXMDnSuZm+qkqWzw7zYfVTNEYGyYx1M/NsOwfVao5GusmoIWqtKTYkTtBqjaEqteTManp8lWTrBMWaBvoWtZDRQj+Sa0BxHQLeHEp2gKWXzrCmZ5xQ3mKiupGhzdfzuzfdwPbxp3EPfJUro1v4O7mOq8oUO4x+DM1hafcztC1aRFPbH2MpYRJWgvGZYc6fPMCVnlPknTxmVYTY4nYCddUUPItCwcZNKngZFZHRUXImet6PmQ/is8IErAi6+8r7PJaao6BlKejZ+TpDfv7Y0rJYWh5bLVBUCxQVi6JqzR9beMpPzm2FFBgYaGgoUkF4AiEFeKBKtRR2WyqoUsXwTPyeH5/nw3BNDNfA8Ax0V6O6rp4/+43/9OY7Ka9tkloWhTJl3iLcjM3cNy9R6J3D1x0jemcHash4/QuBou1y8vFBTj45iKYpdF+bQKn5NMV0nPr+XyM0uBolbBBYU0vy7DRqwmam+iw0/yvLL43geXBvRZTPR4MowPWKwZnhD7NVcynmU/gKBXY+t49QKkFukULFFQ9L0/li124eW7iJa7SrNOkpBqub2N+1HlsRvHP2Se6ovhfP9jP3XCtHJxrYW7WTuBGjypul0TfJYquS9niMlC/MMyHJxQ4/bksIVZnffBUKzdPTdIwZ1CQLjFV/m7nQIDX9GVZeiWIWBSMNCzi57jret2Udvz72PQpHvsbR/Fq+bjRzWJ8iqk8R0JI4Rhbhz2ApPtLFAu4rLCaYxQDRfB2V+TqqC43E8g1E8rUEC5U/cp5E4vktvKANwSIi5KKEXdSwxAgqGEEVI6jgCxqYhoGhGhhKqTZVE13V0dCQtsSxHOyCjZW3sAoWVt6ikC+USq5ALpsjnytg2zbFYhHXc95855KgoKMKHU0x0FWD7q6l3HrntW/+tSiLQpkybzuF3jjxb17CKzhU3tpOcEvDG3JIk1IycHaG/Q/0kZ4t0LQ8Q6T7X/DEFeqmP0Blzy4oKgTW1ZJNWIi+BGkzSbrrs4S943RfznLCNPlkdYyrhs4mz0KzduJNrKRVJHCkR+PICOuOHiNTLYimLfQ0PNm6ni8u3cOiSo+l9jmKgSDH29fyQkMDDekkvyf+mQXBc2TP1XDuZCP7gtu4GO4mqOfwt6dpyXays8dG8SSHQy69S/MsD02y9OmzTNVW8/Vrb2Nd73k294wRLa6np2YvlxYfpJhIsuNsHZUZhcHGZg6t2UBDM2yZfZyJmXNc1EKM6VYp7yUgpILpGgQVSV2kgobKZcT8NfhsjfSpKZyrkgq3mcrgIlQnSjH3w/FM0xUq6wNE64PEGoJEav2Eoj5CUZNAxEB9jXSmruuSSqWYm5sjkUi8VCcSCbLZLLlcjkKh8KrXq0JDwUA4GjgqwtMQUkVIFVXR8QdMfAETw9QxfTo+v4HpN/AFTPxBA3/Qhz9kEomFCIWDmKZZ8oV4iyiLQpkybxOy6JF8vJ/MwTG0ugBV93Sj178xU8HEVI799/cxdH6WULVFzeovYsaOU+PcRtW5dyOnwGyP4DYEKBweB8/Bqf42QyueoOtKisC0zT/FojwYDlLnOlzv93N24D2sM4MUZidwVZWVZ85SMTFCMGwRGXHoj9Tz6VV3odQ2sUacwZQ2AzVNHOxcR9rQ2Z08xN2Vn4a4Qv+Beo6k13OweiuOotLcPE6xopvdZyVVaY/+gIVY+iy3LU8gPjNC9MQAn7nrQ/S0L+R9jz1JKrqJ6coixxZ8lzljmk0XGlg8opPzqRxcZTBW1c+LXgSKhAq7hpRVT96qZ5lRQWcuTcA2WbVqjG2b/oDsdBOD58a5cmKAXFpHiNK/MM1QqGoKEWsIEq0PEm0IEGsIEo75XndjP5/PMzU1xeTkJFNTU8zMzDA3N0cqlfoRvwAhBKFgGL8ZQsdEOhqepeBkBXZGIFwNPB1LahSFjhoxUSt01LCOGtQQfg1MFalLik6efCFPoVDAdRw8t1Sk55RCZ7gO0nNf8mVwPbfk2+B54LkgXZAeu7srec89v/ZT9duyKJQp8zZQnMwSv6+X4kSW0NZGIrsXIvTXNzd9+VKRUBxqlj1MZNFj1IRupO7y+3HOuagRA3NjPWNHR6hMeijaMaaWfplEdZLVpxM8oQb479EIGUVhj1ZAddcRH1hPl8wy57oYts3KEyfQZIKGqRyup/Hl7ps51LaFXZEJzOwAmXCUI+0r6a1vor4wx2+r/0C7uMTkqSrOne/i6dobmVarqK+cIruggWsvB+keLZLSbfxLHmH1qnO8YL2HpX/1DaoSc3zurlvYeKqXplmPp7dcy7GWZ5gKD9E2XsmGCxUELMHF1jQnuuYIGGFuSE7SaXdzNXcHD+dqmJAa62o8NmtHyc8ECCsGC2oWY8XriY9lAZDSRXrTVDWaLNu5ipYlDVTWB1BeZ/B3XZfZ2VkmJydfKlNTUySTyZfOMQ2TinAUvxZEun6sgkk2p5JIKWSsUi7mnJDkhcQ2FCxDkFchLz2yrkvefWNjqYpLAAsTGw0XFQ8hJKqUmIAf8CPwCQVTKOhCQRcCVSioopT6U0VhVYfBnR985xt6zx+nLAplyryFSCnJHhkn8XA/ik8lemcn/u7YG7qu//QM+7/ZSyZuE1l4gpoV99HQvJnGmV/H2mchHY/glgb6Jmdo6LNQRByj4rNcWNOLbhdReyz+/2glvabBcrfILdVw/OoeFhWbUYeuMltVRc3UFHWXe1iQmSYUdzlSt4R71+xhTcigSvYgpaSntZMj7cspqgrvdL7PHfp95K74GDjWxD59F2fDXZi6hbEIOtKNbO/Jl7xqFx5h2bpvcyJwJ89ebOW//Ot/Q5FFnlwT5pZjSb52yxIOLRomZ6bw51U29kRpmwgyF5Y8u6GVQKWPf+5/nNbA7Xwnfj33FhxGpceGkMoOo5/iRATNqkT1StFidZ+KP5gjMX4axx6ma0sX2+56L5Hautf8rPP5PJcHhrl4dYjLwxMMTc6SdURpJo+GVP24+LBdnYKjUnAVLASWkFiilPv51QgqDjE1S4w0VTJOpZekUqSJiCyVZOdrm4ivCr9Rjc+swVBjaIRRCSGdIF7RxHM0pKsgHYF0QBbf3FgcvqaZyO62N3XNi5RFoUyZtwg3YzP37T4KPXHMziixuzpRw6+/mVyyKrrIcE8CMzJO3Zqv0rKkiVbzd7Efc3Emc5gdlfRFLWpPzOJzffi1h+nveoZk4xzmiM2DGZPHQwHqHJdfqSjg0xvpuXwra6+OMGaapCIRGgcHaB48z4KJDBndz2dW/goDzYu5xejHzU8zW1XH/o41jEVr6HT6+Kj634mNzjJ4rI5LiaX8oGEXaWFS1ZTCrGjj1rM5ollIRsdYuvl/ciK8hvu4iw2nTvFnX/gs8ZDkaoPHoSUxjnVk8BQPJKwYrGFVbxAhBc+v3caFpYv5p5Fvs4ZbuG+kmWfyFhFHoQOFescDZz7dmW7R0l3FwqUN5JOXOPX4V8jOzdCxcSvb7/kQscZmLMdlOJ7j6nSW/pksI3N5JpNZJufSzKQtkgWHvKvg/kTQix+iSfAJQUhRqNRVKnVJlWoTU/JUiSRRb5YKO07YihPBoUI4hPAICBXNV41n1CDVSqQaxiOM5wXwHAPPVvEsgbRf2edACWgoYQM1pKP4NYShloquvFQrhoowFIQ+364ppfhH6nwcJEWAKlBDBmrFGzNk+HHKolCmzFtA4dIc8W/24uUcIrvbCG1tfN0166LlcuzRK5x5ehgUi+pl36N9vcWiBb8PhyrJHBpDrTAZWaZgnuwlVmhFE1cZa3yUXMdJsopLz2X4mt+PBHa7km0LCpwc28jCK13Q28uVjsUonkdNfx/rLvcQSLs827qGf1+xh/VBi6biJVxF4cTilZxu7cDE4v3ii2yJH2L8cCXDY43srbqZgWAdoVAOb2E9N13x6Bh3SRsFGtZ+ldFmhW8oH8AqFnn3U5/lAw9fZrBB4QvXK1xsBgQonsK6uRV0nTbQrCkGG9t4etsNbPN6+Hh8Hff3w6jl0WmrVHulAVv4Z8gLC9eXZssNK9l6zRbGLp5n71fu5fLwJKJ1GZXrd5ElzNRMnvhslkzKxkS8tMwSQhLGJYQkAIRQCEi1VFAJ6ip+teRwpslSdjNctxSkn58uOi0wP6ArKD6tNNgH9FLt11BC8wN/2EB9UQRCOuI1Nrd/npRFoUyZnwFZdEk+NkDm+TG02gCxe7oxGl57M1l6kt6jYzz/nQvkUyoVCw6xePtlupd/HP90J3Pf6cNNWKSW+Jmd2E/b3DJAMB05jFj+CDP+KcZHPL7q+hnTNK7LWuyoVQhUKJy9cAOrHrvE1UXtzNTWEkin6HzhBJ1DUyRDAf5xxfu40tjKbtFHwJ5luH4BB7tXMOuPskXu5735byAOeAz0V3M2sIZDtetBBRYG2ZQ22dxn4SIRbQfR1xznPuX95OwC2ux3uPvxy+w6I/nqLpUn1wBCYDg+tsQ30HqpGpE+jWWYPLvlZqxqwW8MLuDMmIduC1odBRVBuGqOaOws+VwMYYVpiNWyqKWdYqrA8NUxLAs0NYBfqIQQBN/gwC2lLOU41hQUU0Xza2gBDaE4CHcOxZpG5MYR+UnARggHEYpBtAkRa0FUNiLCVWCaCFUgtNIsXRilWbxizs/qTbXU/hakS/1FURaFMmV+SuzRDPH7L+JM5QltayRySxtCf+3Z3tCFafY9cIrkhIZZOUT79uOs2vpeKs2NJB/pJ3dyCqdS47J+hKUzNXiylbQ5iLb2KGP+h4mnXB6a9XHcZ9Bh29yZN6hfmmUk04jyjUr8ls6FZcsAiExNsv34Efy5Ik91rOXfOt/FYl+adU4Pts/k+PKlnK1eQo2c5MPFL9JyZpLJEzqDRiuP119PVg0jalXaAhXs7skRsBQSVQPUbf4ej/pvIV60mZv5JssvJ/jwM5KjHfDdrQq2LqgoVLFtaBv1iVqyuZMEcrNcXLQKu205twzUoOUVaqVCUBH4VAhqLrqn/8TnJYEsHikpSQvIITGDOuEKH4am4NpFkslZZotT5JUsRVxEMUhFoJ7G1lZqFkSpbqsg2hxE1eY3+l0HLn4fDv0LjBwrtelBaF4PrZtLpWk9+N64Y+F/JMqiUKbMm0R6kvRzI6SeHkQN6kTv6sTX8dq5k6eHUzx3/2EmL2togRlaNxxj/fW7qaq+lsK5WRIPXcHNFRmouMqi9Dieu42imkKuSZAw/5ZJcjw3rvKo4aPC8/hoIkdVdRR/c4oLvUtZ9O0s/Z3dTNfWYlgWqy4co713lGTMz98s+zB91a1cr/TS4ozSv6SZZ1s2kxEhbnaeZPOlK6jPDTGuRTgQ20JvuAvhh1BTJXdeyVA9pzNr5Amve5CeBUsYL8QZmHyIyozDB56VZHzwre0KGb+gMdHGpqFbqXM1MkqczlSSYLCVYEUbTUUf2stm9paQeP4kBX2MOccgbgniCsxU1nPKMjiTzJMDKospVoY1Nje30ZDXiA+lSFkz5AOj2GYchCRkRlm8oJu161bTtKgGVXsFcS6k4NRX4PD/gOQQRNtg/UegbSfUrYA3kFrzl4GyKJQp8yZw4gXiD/RiD6RKqTLvWIwS+MkZ7oukZvPs++ZBBk8LFCNH46qjbLxlO/WNN+GlbOa+d5lCT5w5X5qYux+K1+JhYHc7qLX/ylV5iuPjgm+pAYpC8L5Umq12BYVleVIEST3cQGTY5IWVK5BCUJ2dYPtzz6PlPX6wYjX/rfVuqvUsd3oHcBZInly0jfP6Cha6/bzrUi/Fc0OEJgc4U7mMA9WbKSomojnEO5N5uoZMMsIjsfAMoe0Oh2fOMZA4jeJ57DoFLbOShzYrzFYIumc6uWPiHlplCJ+hUufqKKI0M89JScKVZB3JjCYwV9hUL/x3hsanGR1dzUSqijG1nlGtkYGUhwBanRzteZsOESXizOemFh5qfYK0PkzWTuIz/axZs5rVa1ZTV/caFkeJITjyP+HEl8BOQ+tW2PI70LUblJ8+zel/VMqiUKbMG0BKSe7kFImHrgBQecdiAqtrXtUzOZ+xef57B+g95ICU1C49zsZ3rKRlwR6QguyxCRKPXsW1HYS+D91uw2UBdp2Df9UBruS+QM+MxzdcP1OaxvXZHL8TzzDc2IBckKJ/uJ3Wr1v0dqxktqYGw82y9eJ+6l5Ikmow+aulv0FveAF3i/2sbOjh+c7lfN8opWe8ZaiPurMO0bHvMmA2cDC2lRmzGq/SYKPP45o+gXRV+iJxGq69wFPJJ5ix0oBkwQRcc1Hy1ApBPGqwe2o7N8VvppUwqhDkpEUyP8aYyHBCqydgVRB2FNSwxopdcWz/5xgY0BgcW8GlXD3DSj0jdmmjvFmFjoyg2zYJSUGwUqGps4aKBo3J3FUuXnmBXC5HbW0tmzdvZsWKFej6qwsyk+dh3z/ChQdLx8veBVt+G5rWvYU94z8eZVEoU+Z1cLNFEt/tI39uFqMtQuw9nWhR3yue6xRdjjx6gBeeyeJaBtFFZ9n0zoW0dd6GomgULieIP3wFbyKHq10l4M5gyY24fpfgNRmuZv6YwUSOB/I+LhoG3ZbDn83OEpMRBlZAxjBJPtGAN1XH1UWLEJ7DIqeH1Y9fQDiCR1Zu4n80vYv1ykXe3/AkFzvq+abvbkZEK11z49xwUGNWPUTlcA97q7czEFyI9Ck01BjcfTWLkQ1xRbfJLTvJycAD5OdjCNXYHntecNjXaBINL2H3zBY25FdjoDKreZxnlOzQM8w4cc5XbqZdrKTONdBCsGTHBEnlKwwO1XJqcjV9Tg2jshJXCupNjc6sS0fOIOp6+AIzLL+mjdU3riOenOHw4cOcO3cOz/Po7Oxk8+bNtLW1vXaYkOQoPPu3cPprYIZh3a/Cpt+ESPPb0Dv+41EWhTJlXoOSqeklvFyRyE0LCe1oelXLksGLF/jBF3vJJyKEG/tYv6eK7jW3oyg6xZk88e/3UexNIsUsAXGWnLcFFAP/1hBj2p8zmurl4TmdfT4/NY7H78Sz3J5NcLy2hVxXluGxZnxPxbjUuhxXVahWB9hw+hQVFxwmmir5i6W/ih4U/HHdVxhfHORbwTs5JdYTsZLcdFxQmZjDSj3ARXU55yqWIQQoCyr44NQUVePVpPQCR6NTXG39Ap4eR0Gy2nO484TNgWgHraFtXJtcT8QLk1Mkz9QoXC5cpOrcY2iOzXB4GfW+9UScGJ5psXjjCAkeYXCslTPJLi649SSlj6ipsgKNBTOSOldBOhPUthbZ9p5tNHd30N/fz969exkcHMQwDNasWcPGjRupqqp6nS8rCQc+BYf/DaRXEoLtfwiB13ceLPNDyqJQpswr4Nkuycf6yR4aR6sLELu7C6Mx9Irn2laaZx/4Dpefb0Tzp1j7zjzrrrkLRTHxckXmnugjd3QaIW18yhlyXicKUYzOEIm2rzMef4DnphW+6wuiS7gnJfmtxChpEeLiUj/pCo34wQWM5laQDwYJiQnWjR6nbn8G11D58oprOdi0ij+r/Qp2h8PDoT08yS2o0mHz+RxbeiUXYy+Qm5jgWOUGbMXAqFK4Xsuy7EoFUgoO+/OcaXkYWXmYSlXyTs/izp5mnrWaaKi8llX5boq4HI2qfK/VJDN1mo0nfkDAypIOtlNZtwE1UY+nFKlaeoGCfoqByTbOWy30evXkPI1FIZOVcY/2nIrwsiD76NpUx+Z330I4Vs3IyAjPPPMMV69eJRwOs3XrVtasWYPP98r/yl7CseH4vfDcP0A+Divvhl3/GSrLKXl/GsqiUKbMj2GPpInf34sznSe0vYnIzQtf0dRUSsmVi0+w/2sT5GZaqeua4OaPXE+4sg7pStLP9ZD4wRiKq6OLyxRkEypBtNYQ1pIzjCc+walJl68aQTKKwo05nT+ZHSHquByvayLfmWVqpo746aVM+VrwiSzLU0doe2oK4QgudLbz70tW8WtVh6nomOHZ8E6+xXvJEaBtbITbj4XJGgXOufu4IlaQ1CM021NUtVdx82ABma5mKjTLQ6FRis3fpDsQ5+N6BaunruPkeZtobCsNTh0zWpYHWn18a0EFy84dYuPJZzGcAkqokZp16+jv9xNLN6PWX6AYucBgookLTj2XZR22J1hd6WPpuE2jZSCLowTCg6y5eQ3Lr7sew+dncnKSZ555ht7eXgKBADt27GD9+vWvvV9Q+gLg/HfgB5+AuQFovxZu/AQ0rHo7usUvDWVRKFNmHulJ0nuHST09hBrSib6nE9/iVzY1zWb72ffg1+k/uBZFlWz5lSpWXbMJgPyxM8w8PIiwoihMYRNBw0RdFIG144yM/BGD43N8QQsxpOussnT+PJFhWW6S3kAtU8scEmqAmQuLGUytwkCyyD3Bkif70ZMwurCeJ5cvp7uqjwWdQxyLruIb8oNMKI1EUgO885if1lmVZ6uG6M9rJPQqGnJTtFek2aBWok82I/0JnvDnGGx4jD11Z/mNphuoiu+i54lLxMIrCEo/F/1jfG2hn+djVew49Cxreg9T0MAfrKLl1u3sGx2gtXcHiuJSiJ5nUEh6ZANXnCqEgI0hyZIxhyqnAulO0Lg4w8bbt9PUtQQhBLOzs+zdu5cXXngB0zTZunUrmzdvxjRfOaHNj9C/H576Sxg7CXXLS2Kw+Pq3sjv80lIWhTJlgOJUjrlv92EPpvCvqiF6+6JXNDV1XYu+3ns5+p0C6ZHV1LTlueWj11IRC+IMX2Xia/sgsQhBBgcfCiqiK0Zgi01//x+hDfbwbzLIwYCflqLKH9ghbph6gbgS5HK3SSKmM35lESNTq8A1aVEvsOLZHvxjLhO1Ec6vXIuvbpDO9gHO1rdxHx/mitKBYU2y7tIUuy40ciZc5KgyRUpUEbPivGP0ecLtXQQznUhPZbx6gEfNGT6+4hzv3fynXDicJH10lk6ntGZ/sOIcDzZIRouNbDpykGUTZ5gL+TB1k447buK4uo+Ko9egzC3GNmeYCfdz3tfEmUwUnwYbRJyuSahUmlCULIvXa2y7axuBcMkZLJlMsm/fPk6dOoWiKGzevJmtW7cSCARe/4ua6oGn/z+49DhUNJeWiVa+p2xa+hZSFoUyv9R4BYfU00Nknh9DGCrROxa9as7k2fgBTuz7HP373oFbiLLxtgaqehCWAAAgAElEQVTW3bQU8nOMffV+6G9HYlIKd6bgdEep2RXi8qW/IDrwNGcTgr+LxbCFwsfVdj50eT8IuNhSycxCGJ1YwPjQKvJWJQ3KAKuPniR8pUgqrLNvXTutNYLWhb1cbK3jAfX9nBLr0Z0EdROHeNfJtSSKCk+HU8xJHxXFFDeP7qdDTSKr3olj16JU9/GI6XLjpiidK29i776TdAyb3DgNtmLzaOV+9ocmic+sZOelqzRkzpExNfyqRseNmxhuOIh7YTHW5d1IKchFrjK3sIpHx4KkbY+1+atsSusEjS5UzWXldbVsun3FS45kxWKRgwcPcuDAATzPY/369ezYsYNwOPz6X1Rq7IcWRUYYdvxhaSNZ979VXaHMPGVRKPNLifQkuROTJB8fwMsVCW6op+KmBa+YItOyJunt/Rt69sHM+dsIRRV2/+Z6ahsNxh/5EvJoEM9bgMRDIsgtitB6az19vf9AaOA+wuMF/jYa5QfBAMuUGH99ZYDFJBiqqGCoW2XMqmf0ykqS6QZiTLH+xBGil/MUggpPLvUT6YixMTbM+cW1PGi8i4PsRPUs/IlH2d7bQeNYI88EbcYVScjJsHXmKDeMnSTZfiM2m9ECM4w29nC2agdmVzUX5wp8cNDj3cM2Qno8HNvLI+GDiLGd7Bot4EufxVYkYVfStKmRZHcv2ZFWMr17MAp1eP40VZt83Ndv8UJSpb4wxQ25NA3qUhRFYdX1Lazb3Ybp/6GHcF9fH48++ihzc3MsW7aMG264gWj0tb3AgZIX8sFPl0JSSBc2fBR2/lHZouhtpCwKZX7psAZTJB66QnE0g7GggsrbFmE0/aRlkec5jIx+hd5zn2fk0PvJTXWyeH01193Tzdzpb+E+MYBnb0cCAsFsc4COO9q4fOVfMPs/R9toiv26j/9SHSOjanx0XPDR/ACWrnOl00d/qIrRK8uZml1MyE2w8cxhai6lKIQFpxojXNiR5IN4XOis46HQO3iOXSA9/KmnaRvJsWTkOnoci8u6h8/NsSFxipsHDiGrqshEP4wUBkrbIR7WajnZso5ghcFvXZjjjgkFTQqejhzmq9WPUTO+jJ0jQexUDx6Smkye2qU68TUW09OLyY6vIJzqRJEatR0Wz6VHeDxbj8DjejfFKrsJz1Ho3lTPxtvaCcd+aC2UTCZ5/PHH6enpoaqqij179tDe3v76X5Jjw4kvwnN/B7lZWHFXaakouvAt6wdlXpmyKJT5pcFNWSQfGyB3agq1wiByaxv+Va/slZxMnqa39y8Zv6Qwcfxj4PnZ+d5uairPkf/uQyjpd+LhQyAYrzXpvrODsav3ol7+DK2TCQou/NeqGA+HgizOqXxyZoxlrsVIo4+zDTWMjXcyPrEE3bLYcP4ITRdnKFQKBmoiZJcnWF1hc2FxCw9X3cQz3IiHIJDcS8vAC4TcD1Mcs7mou+jYrEucYsvUCRamEyQWvYeMWIFWe5F9FXn21m6iW4nzW6en2UArqubjkO8F7m38FrGJEJuH6ymmxlClpGk2RUWDx+jmBsaT7ThWmHB2EWa2HsOfZzi3n8cC3UybNSw3HW4sRNCSHi1LY2x99yKqm3+4DOS6LocPH2bv3r1IKdm5cydbt25F014nvpDnQc+DJYui+FVYuANu+iQ0rnmru0OZV+EXIgpCiM8D7wCmpJTLf+y5/wT8I1AjpZwRpV/sp4FbgRzwq1LKk6/3HmVRKPMi0vFIHxgl/cwQ0pWEdzYTvrYFxfzJzcliMcGVK//I8PC3iJ9/PzMXt1HVHGLrHgXruX8hNLkbVzYAMBbRWHR3N6nhr6Cc+2eaZ+dQPdgX9PGJqhqmFcH7E3n+IDFNIahysGkBvbMrSMQXoBVs1l04RmvvOFYVzDaa1DWnqYvlea6tm0cbruVpcQsuKqHUQTp79jJR9etEBnT63SICj1WpU6ybO03XzDRKfTfjwbtRgnNcqB7lQMVCrp+6wJ3nZ2hs2YXii/CCOsTnGr6OOZ1h7WAtSj6PT3q0TiSgJsTVNW3MOQ1IJJW+avxjnbi2Sr5wnENBwcnwMiK6wh41TMOETU1LmK3vXkzLkh9dyhkYGOCRRx5henqarq4ubrnlltdfKspMlYLVnfgSJAahdum8RdEN8Frey2Xecn5RorATyABffrkoCCFagH8HuoF186JwK/B7lERhE/BpKeWm13uPsiiUkVJS6ImTeOQq7mwB39IqKve0oVX95OaklC6jY/dz9eo/k42bTJ/4U9JTIZZsriBmf47G/hXYciUgmPYLat7ThTbxDTjzj9QlEkgB/QGDzwciPBTy0+RI/n5miuWWxeHqhezNbKeYq0Yr2Kw+f4r2vkGsJklugUd3TQo9IHiicTlPtG3lKeUWbEwimaN0n3mUeOUdZPKtpGYtPCSd+V62zByhITNHk1AYr/0Ill7JRM0AR7UQHzj/LOuvDuJfeQ9adSejpLg3dj/F+ABLhiOojqTSsamLF5hb0MjggjYcYeLpBZqCFWjDDRSyC/DcOWaqRnncv4jJPGz1B1g34VET87P59nY61tf9iHd3Mpnkqaee4ty5c0QiEXbv3k13d/erf0GeB/3PwYkvwMVHwHNK/wzW/WopTlHZougXwmuJwtsWR1ZKuU8IsfAVnvoU8CfAgy9ru52SeEjgsBCiUgjRIKUcf7vur8z/+RSnciQevop1aQ6t1k/1ry9/1fDWicRxLl36BOnMeYrT72Po+V2oqkLXsqOsuJTB8t6HjUJah+Bt7SxM3g8P30M0m6KoCi5WBPmWbvCdcAgQvC+X4Q+m5hg2q/k75VaKUzE0y2b1uZN0XL6CaCui3pxjRSRLQfHx7ar1HOhcx9P6TeRFkKr0KdpPP4tPX0JPzUcpDCg40mJxMc7WiUeIFtMsSGawam9lILSBdMU4R5UUdx07xfsGj6AvuQ3jug9hC8l3jR9wNbmPzhN+hAhTa6XxFwOMLe7k+IpqPDwI5VjsqiQvprG1dThqFUZDhiONdTx9xUedo/HetMLiosqGOxezfGcT6suc+YrFIs8//zz79+8HYOfOnWzfvh3DeJV0kJnpkhXRiS/CXD/4o7Dp4yUxqO54aztCmbeUn2twcSHE7cColPLMj63xNgHDLzsemW/7CVEQQnwM+BhAa2vZxf2XkR8xMdUVIu9oJ7Sl4RVTHRasCa5c/gcmJh9EU1rJ9/4bg2c0ItEE1ypHYPRGLAxsRaJcU0uL9y3E03cTtLLkDZWjVdU8LDweCgUBwS3FHP/3xBwBqfMleQfjhQWotsOKC6fp7Osj2J6j7qYUwf/F3nkG2FVe5/rZ5fQzp8zMmV41mqJp0hR11BBFEsU04wbGhdiOy03s3CQ3yb1xEjs3PXbiktgxYIzpBgwCI4RACKHey2h67/X0vvf+7o+RHXxFbGOEA/g8v6S99+xZM+fMeff6vrXe5dWYSLn4F8fVdDY2sde2haiURV60g+Jzg9isViaLtrEw5MEICqpSOu0Lz5IXn8AdS5AjljJZcTMhV5CTRoSVI1N89fQ9SCWrMW37O6wmO+fo4tjCsziCOmWqhTw9gWYrZGhpC7qqElFD5DojFPsFM8d6CdjXoFi2YHbIhNb6+PbZFKmBEFckTayJmmi7qozWa8uwvK53QwhBZ2cnu3fvJhAIUF9fz9VXX/3GS0WpKHQ/D+d+BH17wEhD+XrY8mew7AYw/RIriwzvCH4lUZAk6QEhxJ2/7NgvuYcd+FPgmjcX4s8jhPgu8F1YXD56K/fK8O5CCEHsxAzBXYMY0TSO9gJc175xialhJBkZ/T5DQ99E1zWsiT9l4EAtkfkka10XyBdLEfp1GAhEvUqR6xmkY/dg0ZKEHCp73GW8SJwX7FZkJK7T43x+coEcHZ4RV9Bj1KMZKg1dHdT2duErD5KzPUrCJnMiVMJP5Gam2us54FlPWHfimxzA0jeBEILJdDN6UIFpKE3DynAHJYF9yIagJKwSzP8kHXkyXbpG42ycPz77TRSzndT2vybXkoufGV6efZxwZArFlsJuMhPLqWHA68UQaead09SZPRT1+gnPzhL1LsNb+jniERNqs4cnkxE6jw+zxFDYGjWzdnUxq26oxPn/ucJOT0/z/PPPMzQ0RF5eHnfddReVlZU//4tOJ6D/Jeh4Crp+AukoZBXBms9Ay53gq3073xIZ3gZ+1Uyh4fX/kSRJAd6sYXkVUAn8NEsoAU5KkrQKGAdKX3dtycVjGTIAkJ6K4v9xH6mhEOayLDwfa8Bc8sYNUXNze+np/Qrx+DCm1IeYPrmdhZEkNbY5aj0WYDkCEHkhCvKfRel/DMXQmPOYecZZy2tanL1WgRkr1+spPj81iy8t2E07Z1PLiZuzKB8eoqXzFMVlc1ivS9CTyGX3QhUnCyuZXtXMcXMziRkVa2cAazhEGBsgkBwqeZLCigVBSXwGR+hphIiTHU1jtlxBR1MNw2k3jQtRPtN1L9bEDMkrP0aJuQrD0Di5sIe+4Emms+M4cl2I7BamFYWkMUfYM0BzykfO2XlSqUlya1rJKbuLqQGDlGqis83Cc/2TOJG4MWrmmlofa29eSs7/V6obi8XYu3cvx48fx2q1smPHDtra2lCUi+v/rxeC7l2LQ21sXmi6bbHzuGwdyO+MAfUZ3jy/UBQkSfoTFp/ubZIkhX56GEhx8Wn9V0UIcQ74WRupJElDQPvFjeZngM9LkvQIixvNwcx+QgYAI6kTemmYyGvjyFYV763V2Nvy39DaOhYbpKf3q8zPv4KUWkmo638z2QVV1iBr3SBLixYPsm2E3LynMU3vRoQF0z4Lz9tqOKgZHDHFsCkGt2g6n52ewZfW2S838GC0jbAjB0/Uz4Zju6nKG2b+KjMvTVUwNJhNV3k1/Q0tjOm5GB0GkpFARWCS0lSVjDLiaaR5xsqa/iRqOkY6uhOhTWBJaeQl8umq2cyUUUhN0GDN4HN4AqdIX3UVXrkdi+FhONzBsdDLnC+YhuIcikQLQk8zZu3D67KwdNJG+NACcXOUuvWbsXs30HkwhObXibZ7eGh0lmB/iNaEwk352Vz5iWqKa35+CUjXdU6cOMHevXtJJBK0t7ezZcuWRWuKdAJ6d10qBI03Q/1Ni+MulV9ibpfhXcGvVH0kSdLfCCH+5E3dWJIeBjYDucA08GUhxD2vOz/Ef4qCBHwT2MZiSerHhRC/tKwoU3303kUIQfz8PMFn+9GDqcVu5G0VKI5LP3g0LcLQ0LcZGb0XPeEjPvwHjJ9xUW7WabCmUaQsDARmuYfs3Kcwh15DUyTGC608Z61gf9LMedWP0zC4Ia1x99wseSmdU6Zy9k2vJugpRNU0WvpO0+o8Q2+2m5cmmxh0ltHrrWJaLkI3Fp+iTU4dlxoiHTTTVnOO83kbqe120N6fQNZ0jPhLEDuHIUNBRGW2dC3jcjNlwkLR5GHy514ivcFDrnwTHqOSheQkr8Ve4KWSCyTcbhoCTcjpMD3eEVooIrsnTsIfxOXLZ8W115FdspojT4/in4phqXWx04hxZjZMoSZxizWLm2+upar15/s2NE3j7Nmz7N+/H7/fT0VFBdu3byc/Nxv698K5xxf3Cn4qBMtuyAjBu5zLUpIqSVIxUM7rsgshxKuXJcJfk4wovDfR5uMEnukn0e3HVOjAc9NSLOWuS64TQjA9/Qy9fX9LPBoiNfb7jJ+qokgWNNpSmCQnaUnDQTdZjsewaSdIKzKjJVaetZbyctROv2ket65zczLBncEgeQmNAUsuuybXEbQVk7RZWTrWx2b5NcZdNh6Z38Br2WuZNV1Mei0SWq4NX/YCy8R5zndXk7M0QSCngqZuC639SRRDIMVPYgq8QsQq4UoapLNaiNquwC5bcIUGKZ15imSbHxe3U0ILSSPGq+k9PFy6j1iWmRXzy4hJIfy2eTYEK5G75zA0jfLmFlq2XY+vvJGDTw7Sf3IGa46FzjITTw3OYjJgKzY+vaOahg3FKK/bjE+lUpw8eZKDBw8SCoUoLCxk08aN1NoDSOd/tJgVxObB6oH6GzNC8B7iLYuCJEl/C3wQuADoFw8LIcSNly3KX4OMKLy3EGmD8L5RQq+MIskyrmvKca4tQlIuXSoKhy/Q3fMXBBZOE5/4INNnNuFLyzTaUlhkOwk5ThbdZJkexS6dI6XKDJda2WkqYU/MyqgpgEcz+FAizK2ROPnxFNMWB89PryaULmLB5yM7OM81yVdI2BPsmapnV8Fm+tQaLCaNdJmTeKGLctswG6Mvc7B7BbMl5ShON6u6dVoGkshCoMYHcE0/zVSWwGQIXKZyYs4bEYoFZ3iEkrlnCTfMYNOvptqyFlVSOSIO8e2Kp0mZFKoDpUxZFijRzDSP5ZAen8dktdGwaSsrrr0Oh6eAs3vHOLV7GCEg1ezm/uFp5jWdZl3l99YvYcO2SszW/1wpjkajHD16lKNHjxKPxyktLWXj8kqW+l9F6ngCAiOg2haH3jffDlVbQf0vSk8zvCu5HKLQDTQLIZKXO7i3QkYU3jskev0Enu5Hm4tja87Fc90SFPelnvvpdID+gX9mbOxh4lPrmT/3ITxRMw02DZtsJaaEsUld5MmPYpG7SJoUhkqs/NhUyp6YiSlTEG8aPhb1syORoiCeJGQx82JwOeGxAoaXLMGkpdkUO0SOeZh9E5W8VLKSU9Ja0kJFVNhJVnuplTrZqu3i/Hg1h7xXYldMrO9Ms2IwiYTAEp2gYOxRRjwaSVUhR3YTc74fobpxhkcomn6W4LIoeryOJtcmXOYcOuVuvlb2MCE5gS+eTUCeZ120grxeDS0Sw1tUQsu111G/cSvppMKZl0bo2D9BOqmTVefmyUiI05EYuYbE79aX8JHblmF1/udT/cLCAocOHeLUqVNomkZtVTnrvfOUjf4Yps+BpEDVlkUPorrrFmcfZ3hPcjma1wYAE/COEoUM7370YJLAcwPEz86h5ljJ/UQj1ppLa+Bf340cmvIS7Pg77PNe1toMHA6VmBoiYjrBEv1xzHIfCZOZzjIHT8llvBhXmCOEx1D5w9kI2xNhfFqauFnmJbmWyGu59DY2kaiysjzeQZt6jBeSpRz0bOV05VX401kYbhNavZs250mu1Z9jKFzKD5x3Y8qxcG1nmuahCBICe3SGiv4HGXXH6PXZsWPD6riRqLkcZ3iE/KkfEliSpDc/ixZxK6W5tUzLs/xj0bfpsQxjTdvwJhXWzeWg9EkIEaasdSUt226gvHE5wdkEB54YpvvwFEJAflM2uyNh9kxOIQHvL8zhT+9cjvd1Hd0TExMcOHCACxcuIEkSy4vtrNMP4ev/OiCgZCVs//vFDmPnG1uKZ/jt4RdmCpIkfQMQLDaSLQde4nXCIIT4H293gL+ITKbw7kXogsjBCUIvDiMMA9fmUrI2lb7hSMxA8AQ9PX/J/NQUwQt3Yx6rZpkVshSVlDLDvOM8yxNPYpaHSKhO+solnlLK2RMzWDBF8CbNfDoQ5PrYAm50UqrEWVsh0RddXKhoYbqggPz0DFdJL/Oc5qUjq5pesZWhaA5CkRE1DjYUH2RD+lWOp9p5yXkt5qiJjR1pmodTcFEManoeZN7qZyDPgyzJqLb1YGnHGRkjd3InCyVJZpNQ7qhnRe5VyLLKQ76fsMd5HN3QqIu4aRz1ok0HsDqcNF55DSuu2YE7r4CZ4RAnXxim/9QsiiKzZFU++8JhnhiZJQGsczn58w82UVe16FEkhKC/v58DBw4wODiIRZVpd06zOrgTlwhCbg003Q5Nt0L2r+BomuE9xa+9fCRJ0l2/6MZCiPvfYmxviYwovDtJDocIPNVHeiqKpcaL931Vb+hVlEzO0tf/d4yP7CLQ/X7U/g3UWmTcioKhTDPsPsbK6E6s0jhJOYe+CpknlGJejCcJmKJkJ2x8LhDgxugMVlmQMMsMeF2EXnExqtfSWb8MFZ02ZR+HFIVJtY4J8xo6wvkYcZALVLbUvEa91s0e9RpOW1txJmKs6ZJY2ZdEMgT22Cz1vfeT1Ga4UJpL3GTCbKlCsl6FPbGAZ/IZ5nMThAwJi2xlWek11MrL6LD1c5/3WWaUCTb7q3D1xdHjCXxlFazYdgPLrtiEarYw3hPg5K4hRjv9mK0KyzYUcTwS4/sXxglIgmVWC//npkbWrSgAFstKOzo6OPDafqZnZnEqadYax2gTp7B6CqDhFmi8BQqaMyZ0v8VkrLMzvCPQo2lCu4aIHptCcZvx3FCFtSHnEltrw0gzNvYD+vu/yXzPaqSuW6hVLHhUGZQZhrNfZXlkF04xRZJi+ksdPG7NYnciSkiNkZuw84WAnxti05gkCDhNjBTamDvlhZNeTrSuJOJ0YLecZFxNo+n1TOfUczpegj6hI1lhfc0xyq0zPOO8mVk1l+z4OA0DXtZ2JzFrYI3P0dR3L6bQOBfKfUw77CiKG9l2DXZd4Jh+lvmsOElJAgVSFaXskHbg0p085X6Fk+YjtE/mo4/Oo6gq1avXs/zq7RTXNYCAwTNznHhhmJmhEDaXmeVXltATT/DNI0OMo1OkqPzx1bW8b3MFsFhJdOrYYQ4e2E8wliYXP+s5SlNWGLXxfYtiUNyaEYIMwOXZaD7H4jLS6wkCx4GvCiHm33KUvwYZUXh38LMJaM8PYiR0nFcU49pa9oa21gsLB+jq/itm+pwEz99JVcpLhUUGaZpA/m7KQi/hMuZIGlUM5pXwiCfBC8kgETVGfsLB7/nn2BGfRZJgJNvFbJnE7JgX3xNwprKN/op85u29pGRwpeoZLS/jtFGF0Z1ASuvUFQyQVxjjpZyr0JGpiJ4lf6KCdZ0CZ1JgTszR1Pt9shYGGSry0pPrRUgqinUtFrxY51/Eb4tjSBIpq8T56hRrTeu5yb+FCWWWA9a9yAOTaLEE3sJimrdeS/2mrdhdbnTNoOfoNKd2D+OfiuHKtdJydRnjqTRf29tLl5HGLcl8dm0ld19Xg6LIRCd7OfrSMxwd8BM3VEoZ5wprH9VNK5GbboGSVZnu4gyXcDlE4e9ZLEV96OKhDwJ2YAq4Qghxw2WK9U2REYV3PqmJCIEf95EaCWOucOG9aSmmAscl18Xj4/T2/V9GujuZP3cnjvkKWhxgliRU1yPY9Bfw6AskjWWMOVfwgG+cXcY0UTVOSdzG7wdmuTqxQBqVUwWlaGUBQgkH+Q/rjCWrOdFSS7dnCLNhIUeqpbeqlDNqHVJnFGUuidcWwLHUTF9RNY6URm3oZYxQDes6PeREDNTUAk3dP8Az38t8QRYX8nOIoCCbqjArRZiCRwhbUkjAbA4cq5nDafPyx2OfoDRdQI9+jrOju0GBpavWsfyqbZTUNyFJEumkzoXXJji9Z4SIP0lOiZOWq0u5MBfhuwcG6TTSWJC4s6mYP7i1HtvCeRZO/4RD5wc5FStAQ6XWNMn6Gh9lK7dB2dqMJXWGX8jlEIWTQojWNzomSdI5IUTTZYr1TZERhXcuRlIj9OIIkYPjyDYV9/Yl2NvyLlkq0vUEwyP/Qe+FR5k9ewOxkZWscEgUqSom+SRJ54OUpbpJGVWMmbbzw/wenlOGialxKuNmvhSYZlMiTFA42VW6AlfxACZVw7ZbhSMejq9u4VR+FAmBz1LGmaU1nDU3YR4OoPZFkDCQKx2Eq3IojqZYFvwxk1opK3sbKJ3XkbUQDV0PkTN3jpmSbAZ8bgKGDLILVS1BifWQVDWQBP2laY4tnSZtFnx8/GZuimwhpcc5Mvschg8ar7yWZRs2Y3MulnrGQik69o9z9uUxEtE0RdUemq8s5sjAAt87NsIAGjYkbqv18cXWebJHdjHReYQDsQouUI0ELC+ysm7zNfhq3vDvO0OGN+RylKQqkiStEkIcvXjDlcBPH0W0yxBjhvcIQgji5+YIPDuAEU7hWFWA+9oKZLvpkuvm5vbQ2fGPjJ9uwt/7ZYpVhQ1uAxUNw/E1fPoBSJoZ4w6+XxjgGctu4kqCurjCF2dnWZuIM46Pv6q4lQrfSUpsXdBlJvsxM11LG9i3w4smJ8lXyumoLeMFayuWYIScY/1EYzb0XBvJeg+t0SRlg/fTqZjJHt7B2kkDSQ9T2/ck+dOHGS0vprOsiogOiCwUkw+Sg2jGBeJWjVM1UbpKA1h0C6snlvPx8A7y1WLGE71El6W46jO/R/6SpUiShBCCsW4/HfvHGTg1i6ELKppzadpcxAunp/jYI6cYlXSyJJnPVMp8zvVjnH3P0D+cy05pDYPiWiyqzLrWFay+YjMu16Wd3hkyvBV+1UxhJXAvsDhhBELA3UAHcJ0Q4rG3M8j/ikym8M4iPRcn8HQfyd4ApqKL9hRll35oRaMDdHV9hcETMvMXbkJJOliTncKjO7AoO3FaHsNm+Inqm9iZXc43so4TMkVYERP8XnCGtniKHqWUry65mxrvCdbZDqAHVHIeg0CsiBdX1hI0gVvJp3NZEcftbSiRJL6+YeZnXAiLglHr5kothT32BGfN0zRN3k3DqIJipKkc2kX+9KsMlJUy6RSkDANJzkFW85ASPWiyTtiR4mCDn8mcBMWBbGrGfWzwV9GWfTWyLJNqlqi4ZQ1m62JVVTSQpOvwJJ0HJgnOxrHYVerWFFLZ6uNH+4d4qHOSadnAK0t8Im+Mu+P/iJKc57yplaPqaqbiKllZTtasWUtbWxtWa2Y2QYZfn8tWfSRJkhtACBG8TLG9JTKi8M5ApHVCr4wRfmUUSZVxX1OOY82l9hSaFmFw8FtcOHKcmbO3kArls9QH9WkNVZrHbv86br2LtFFGt2U7f+ce4nRWJ2Upg7+Yn6U1LjhtWcIfVn2Jclc375cexqykcb4koR5w8srGFQw7nDilHHrq8zhqa0NMJckanSceNiMksBTYeZ9ZZ157kvP2DlonPkvDuBdZSBRPvIZv9hX6C4tYsETQhUBSS1CVfIidJa2kidjSHGxcYNajsWw8n6VjVnIjKqsLr6fIXIVcZCXvjibUbCuGbjWgvUcAACAASURBVDB8fp4LByYZPj+PMARF1R7q1xeieMzcs7uXn4wtEJQFebLg045DfDT1bywoPk54ruNM2EsyrePz+Vi3bh1NTU2o6m90LlaG9yhvpU/hDiHEDyVJ+tIbnRdC/PNlivHXIiMK//0kuhfwP9OPPp/AtsKHZ8cSFNfP++QsGtft5OyR+xk/sZXYTB0Oj0yraYHstAe75R480gsgFPzcyAPOPB7KfoGUkuR3AkE+7o9x2rmMP6j8I8yuMJ8Pf51s7zzmHpmsxxTONjRyuqAUq/DSW+flqLwCYzyNOhVF6DKGQ6XUY+WWZIqTyk663K+xof9jLJ2rQpJs+GZPkxV8jRGvm4g8C4BsqkFRilCiR0gocWIWjSP1fqZyUjQO5VM7rGJOQ1P9VpaJdqSUhOuacrI2lhCcjdN5cJKuQ5PEQinsLjN1awupasvjzIVZ7j80xJFEnLQE1XKST8lPskPZTadrC6fkZsaDaRRFob6+nvb2dsrKyi7Zi8mQ4a3wVvYUflomkjFByfBzaIEkwWf7iZ+fR/XZyL27EevSS+0pwuFOzp38R/oPLiE0/DmwCTxlUTaGrFj0QTy2f8MsZokZazhj3sHfuPYy6D5MYyLJX04u4MHBzXXfYNBXwB+P/S0V2X3IsoT7HoUxsYRntzSgChfDlV5OJhvQuzTkSAhFFkj5DraoZjbPRfmx9jiPuA+y/dw1bIz8LzRzLs7QIKbkYabMBqM5KSR5DtXchCyXo4T2EVd7CFt1TtQFGPclqB/K5YqzJuyqk+Yrr6bOvgrtdAg1z4b7lmpGpuO89LVTTPQGkGSJ8sYc6tYVggQ/fmWIPzvQy4CqowjYII/zeeU+8uxpTrqu5euBL5AO6/h8HrZta6O5uXlxjkGGDL9hMs1rGd4UQjeIHJggtGcYYYBraylZG0qQ1J+vhRfCoL/v3zn+kwEWeq7GQGG0XOHO6AzZSTMu6zdxcpy0UcisfCf/YtZ4ueBJhKTzBX+ADwfD/EfWR/nb5Xdw1+h9bCh4GcWsY39ZJtRRyv6WFsDFqaI8OqOV6DM6kgCTW8fr9XBHXMU2F+SZ/KdISme47ngrnnQbEdcSTIlZUvpxUtoswphDMUmYrC3o1KKEXiYhTZNWDU4vDTBSEKduyEPNqI1sTxmrb34f1ZUrCe0cRpuOoTTl0otE9/EZUnENl89G/fpC8ivddJ6Z4UcnxjhiJAkoAjcat0v7+bDpWSZzr+Bkuoq5cBKz2UxjYyOtra0UFxdnsoIMbzuXoyS1Bvg3IF8I0ShJUjNwoxDiq5c31DdHRhR+sySHgvif6kObjmGty8ZzYxVq9qUbnqnUPEf2/TVdL7aSChXRXSrRYp/hqhkPDuU5PMqjSBgExU28ar6G+7zfoM/ppz2e4C/nFojHW/hM7ecwciN8KfZPOLPDmHoh/mIZh+taCSk5HHMVMhrOQyRAMglcBUmaTAV8cFbnXDTEwZJH8fiH2X60BFVZwWxeO2hhwhxDjQ4gGSFMdgW7o514sh4p8ipJvR9dEZxbEmSwKEbdsJPqUReFpa1s/Mit5NvLiOwbI9kXwLAqdMkyvRMxFJNMVYuPpa15hP0J9h0YY898kAtmnbQEDdIMdys/os6Z4JxzI10LEoZhUFJSQmtrKw0NDVgslzrCZsjwdnE5RGEf8IfAd4QQLRePnRdCNF7WSN8kGVH4zaBHUgSfHyJ2YhrFY1m0p6jPfsMn2oWFY+x9/EdMndlKymJwvtHMn01M4oyF8Ji/iVkaI260MWX5LPcoj/NCfgcygi/NB7hmvpR/ld/Hs8tr+FTiOywp7keOgL4zjyPZq+mxVHLWnI8/uriaaco2KC0IsS1UTstkiufkecaKH6amO8jmk1aSjmZGSzZjSDJzli4cc0eQ9QD2bAduZxvz/jpE4gha6gyGJOgqC9NRGaZuJItlI7kUlF/BNZ/6EO6oidAro6THIuhmmYGkQU8wTVaBnYYNRdiyzPScnGFP5zQnVI1Rk4EJg+ulw9xu2kfC18LpeCHBWAq73c7y5ctpaWkhLy/jSJrhv4fLIQrHhBArJUk69TpROC2EWHGZY31TZETh7UUYguixKYK7hhBJnayNxWRdWYZsvrRbVgiDrnP3cejxNPHZGoaLklyf28+S0WI86g9wKi+jCR8B6dMcVkd4KPdFztlMrI/F+b1ZDxNDW/jX2graHYdYV3UETDryK1kcjqzhsLONCyKftK4iLDK2Yp327Ene37+UlD/Nc45RLDmP03TEYOW5CIHsFQxW7EBTbYy7F3BPv4Yp3o/VaSXPt4q5uWpS6Qvo8aMYksZwfozjy/zkL1hp762g0LuRqz9xMzkpmfC+UbSZOGmzTFdEYyimU1Tnpaolj8BMlFNHJzmaSnDGqhOSBAVSkLuUn7DKFeSCdRX9C4szqaqqqmhtbaW2tjZTQZThv53L0bw2J0lSFRf9jyRJug2YvEzxZXgHkhq/aE8xGsZc6cZ7UxWm/EvtKQBSqQVe+vG3GNrfgi5U4vVTfC4YxzGWwGv+IjIhQvothO1udlq+zX94HViFzP+aEbR03c6DSoxQZZLPVX8bxZOAXhNHeldz3LqOs/YSNE1B91nxlETZahng9tNVnBgo5oncoyzJ3cWOVxXq+mZZyG3iVOvvoqtuxj0atsBRcocOISuQnbWUhLaSialR9PgDCFLMZSd4rXEBScDG88soN65m/Ue2UGkzEX1uFH8gSdwk0xHVmIpAVVsebbk2Ri4s8PijnZywanSZNTSbxDrpPB+zvExN5RL2xtrZOR3ApTrYtKmFlpYWPB7Pb/gVzJDh1+NXzRSWAN8F1gF+YBD4iBBi+O0N7xeTyRQuP8IQhPYME947iuww4b5uCfYVvv9y83N64ji77n+NyHAzCc8C27M7cPub8Kj34VBeJWVUkLQ0M8Ju/tLnodNiZlMkzQeGtnF60M/x8lJua96NvXiOdETm0KlGzostnNMrSekKeo6F3KURrpe7ue54Fc8aClMFh2ifOUTVqzIl0zPMees42/hhUHKYc0mk00PkTj+Prsdwyh6EaRMJaRojcQJBmohTY1/jLIGsNCsHl1I//T6a17fTXGAneXwaI5omKEt0htKErCrlTTloKZ3Bc3MM6xonstL0ILCR4DblVT5aPEPWsi28PG6lp28Ah8PBpk2baG1tzWQFGd6RXI7lIwtwG1ABZLPY0SyEEH91GeN802RE4fKiR9MsPNpNssePvTUPz/VLLrGn+ClCCF555QF6dtrR4m4Kis6zRlOxpON4Td9EJkJMbEaVXuE7Xif3eVxk6YKPTC8ldSaXV/MqubXmFQqX9jMvyxzsqGAgfC0dWi3JtIrhMeNbGuIW01lWn6jgR1hw5++h9UIvFYdTuKIh5ry1nFz+AVTyCdskFixhysaeIpmexWyYUC1rScox9ORpQCPlFOxbNsO4L8GymRLaB26juriJVVUu6JxHpAzmBHRGNAyfjZwiJ3NjYQIzMUatBifsEfoMM9mE+ITjIHeuKkKv3s7eU/2cO3cOq9XK+vXrWb16NWZzZqZxhncul0MUdgEB4CSLbqkACCH+6XIF+euQEYXLR2o8wvwPL6CHUnjeV4VjZcF/mR2Eogs8ed+DRM8vw2RbYJtrH7K2Ga/8g59lB7pw023r4su+bAbMJtaHrJSeWc4hex3XFx6hpeYYPQ6VV0dymJ7dQW9yBfGUiuFUyasOcbv1KM2n83gcN0tce2k5OkHJuQAmLcVEXjOnlt+INV1I0gQTbsHSsSeJx4aRkDCrDaQUGSPVAegs5GgcrJllzpsiP+Zhbe+t1EmtrKlyYxkPI3TBpGbQE9exlbsx2RTGu/3omoE/O8p+QvQYXoqkeT5dNsHtW9eSzlvOvv37OXXqFIqisHr1atavX4/NdumwoAwZ3mlcDlF405VGkiTdC1wPzPz0ayVJ+gfgBiAF9AMfF0IELp77E+CTLIrO/xBCvPDLvkdGFC4P0ePT+H/ch+JQybmjHnPpf92ruOfUIfofHUALFFLtPkiT2YJJFz/LDqL6ZiT1IN/yWnnQ7cSjSTT31dIZ38S17tNcW/Ui+3Is7Pc7CU9czXB8HbGECcOukF8V5MNZ+6g6n80+w8JScYRlRyfJ640hJJmhknbOLd+KPVqCIcGIT6V25AnS4V6SiowqF2MoTvR0D2AwXBTnRLWfhEWnLrKEyolNNESX01LixBlMIgSMJA0GdYGr0k0ilmZuNIKiGkQ9Q+zWDLoppsQU4vMrVG7Zdi0L4RgnTpzg1KlTGIZBW1sbGzduJCsr09+Z4d3D5RCF7wLfEEKcexPfdCMQAX7wOlG4BnhZCKFJkvR3AEKIP5YkqR54GFgFFAF7gBohhP7Gd18kIwpvDaEZBHb2Ez0yhaXKTfaH6lCcb7zsEUimeeCRR1GO+DBJca7K2oVZ2YxH/iEOZT8powIhzJyxj/AXPi+jJhM107lMTt3KVlsPHyx8mmcKLeyNm9FnNjIVuZpozIywyORVBfmI9wWKezz0JXWW+k9TfmyOrClBSnXQW7GGC8vbyAqUoxow5JOpGd2JefYsCw4LMk50kwvSExiSoKcsTEdFCG/cSpuxkoLRKynFwzKvBVtSRwOGEjpjqoKzyMHCRJR4JI3THiVqPs7TUiHdopRye5LPbVrCjpW1dHde4MSJE0xMTKAoCo2NjWzevBmv99Iu7gwZ3um8Fe+jn05cU4FqYABIsuiUKoQQzb/kG1cAz75RliFJ0s3AbUKIj1zMEhBC/M3Fcy8AfyGEOPSL7p8RhV8fLZhk4YedpEbDODeV4L6m4hIDu5/ybM8wQw/uQ5ouocJykpYsM2Yjjtf8TWQRJWnUkFZ6+FqOi8ddWbiSZrL7VuJVSvms+wF2lhq8rJtRA+34wzcSCVsQJpn8JQE+kvcM2f1ZBEMadSNn8J2IYo5KhJwF9JddydnlNeTO+7ClYCQHyid+QuHQYQZ9HoSkkDJbMKViaIpBd1mYidwEFUEPa53Xokw1UyLMVNpVVEMQBfpjOn6nCbPLzOxwGCEEpa4e5uQzPCK10CNKWOKW+NzVjbT64OzpU3R0dJBKpfD5fLS1ZSwoMrz7eSslqde/DfH8lE8Aj178dzFw+HXnxi4euwRJkj4FfAqgrKzsbQzvvUuiP8DCQ12ItEH2R5Zhb8p9w+tmkmm+9exeCvcmUHUfW1x7cJhacUvfx2Hejy68CBSOOQf5P7nFLChQOZGHOreFu50vcqJiD5+VrSihZcQDHyQctIIikVcV5M7CJ8gas2AcMljaewjX+TSSLjGeX81I3XWcqS+lcM5O6aRg2mXgnX6GbScPcL60iIE8DymTjDmtgxGhuzJGQtaoixZyk7QNI1ZBSUylwLJovTFrCHojGnGnCTnLTHg+gSUUptHxIqPmSb4ub2NAu4GlOTb+/ooKCrUJzhx+hvvn5jCZTDQ0NNDW1kZJSUnGgiLDe55fKApvV8mpJEl/xuJwngff7NcKIb7LYnks7e3t717jpv8GhBBEXh0nuGsQNddGzp31mPIufeIVQvDQ0AzTD/6EvLFy8tRZVmXbsIgcvOoXUaQwhjARUYL87+wyXskyyEpYaTpfzwZ7msrK7/APHjczyULk+d9lft4NEuRVBPloySNkTShYX9ao7ujEPmyQMMPZJbXM53+Is0vzKJ6XqB0VLDgMbMHn2XHoRY7UlXOyshBNMVB1CYM0Y6UCNazRHq6kvO5q1NFiSqYknCYJXZUZTBn0hdNoZoW0JiCQwmcbZ7nrGTrtKn/OLQwl7NT6nPzZMicOfy9du1/jgmFQWlrKjTfemLGgyPBbx2+8iFqSpI+xmIFsFf+5djUOlL7uspKLxzJcJoykhv/xHuLn57E15eK9rRrZcunLPxhL8K2nj1J3cBpruoSNrm7cSile6R4cppcRQkJC8Li9nH/KFcQVjaUjXtqCS7g5by/fKHXyDS0HNfQxQlM1kBJ4i6J8svIHlAwGUZ4zUXFqElMA5j2wd3UFSc8n6S0poNivs3xIJ2w1UGOvUDK2i+E8H/66UgxJRxYShgThPAXbrEGzXk7V8m2YR3IoGBOoskTcpnImmGIkkMa4+DPlWv1U23aRrxxkV9YWPp++g9GoSk2enc/WpJDGjzF+OILD4WDNmjW0tLTg8/l+sy9QhgzvEH6joiBJ0jbgj4BNQojY6049AzwkSdI/s7jRXA0c/U3G9l4mPRNj/oELaHNx3DsqcW641IlTMwTfOTJM6unjVAayKbakWebRsEtxcsyfQpaiAAxL9fyRz06ncxpXzMyOnkpuzxriSP0hPmnKwp+6ltTkVkRYYHZrfGD5k1zVewT9sSzyz4SRU9BXLvHi+hzM1o8z6aujwK+xsj9FzCyYM+8nqL1IwGrH58jHEbv43KDIKFYL5kiCUnsFdRt3YB214xlZLFebt6h0+ZP4A2kAPF6DuqxDLI09QEBV+b7rUzzuv4FIAOpyzXzAPY11/hjxsER1dTUtLS3U1NSgKJmB9xl+u3nbREGSpIeBzUCuJEljwJeBPwEswIsXP5QOCyE+I4TokCTpMeACi8tKn/tllUcZfjVi5+bwP96DZJLJvbsJa9WldgsnRv3sfPg4vgGFarOVBk8AE1nkmr6CRelAAhJ6Dfc42rg37xhpKcLywSzuiMt4K0/z17lZnNfaSc99AGNaQTILrmw8wEenn8D+kEpWBxhqiJMNMj9aYcXL7UScG8gPGqzrSpBUDTpzXmXW9DI1Iw6Ko16Mi5olSwqSBLJmUFbSQE3OZmwTKpYRiAlBjyHRF0mTFmlUs8yymiBt+jdxRY5xWLqC/5n9l+yZcSGnoCXboCTehycSJDs7m9arr6K5uTkz5zhDhteRmafwHkXoguALQ0ReHcNUmkXOHctQ3T+/Nh6Op/nuY2exHZmnXJWptyUxSVk4lCfwqA8gSTqayKHT+CR/XnCYPucQ7qiJTwxauNYzyjfKs3mWIiKRuxCjbiRDUF/ey2f4AfmvJMk6Y6A5BPtaFR5eIZGTvhrZchM5IYUVgykMyeB8/qsk0vuoG7ZjSykISUYSxs9iNFltrFiznaLEMszTGjIwoxkMJAymtcX3rtOtsKrqLHVz/0AqGWWn68Pcq13DBb+M0yzRaPVTmhzCY4aGhgZaWloy08wy/FZzOQzxMryL0CMpFh7uItkfxLG6AM8NVT83BEcYgmf3DjHybA9NhoXqLBmTZEGmD5/5nzHJ0whk/Om7+GFWLvflP4GGxpWDJr6oL7C/1s1H7OUMpT6KMVKOHNMp8M3yCd9DNO8eI+ucgZwWHFkl8e/rVFSpGZf6cbJSDladSyAbGt05ryFHDlLTZUYxslh8K2o/E4TcsgqWr9uBfdiHbSiBTprBpMFgyiAhS+iaIMcnsaZwN+Uz/87chJt/yf4CD6QbmJ8V5Ft11puHqJTmqMgrpqVlGw0NDZmB9xky/BIyovAeIzkcYuGhTvRoGu9tNTja83/ufF/3PHt+eIbasMx1FguqpCDTg9v0DHZ5P5IECWMJncaX+ErxY/Q69uKLKHx5Io4918SXi/J41bgFfawVeT6NwxHlA/XPcMXhTnIfi2AJCgaWwjc2q0znFONWPkN2uIB15xLY0gnGHEewBk5S2ZtAwgzILO4KaEiyTM3aDdS1XUXykE7W4RiCOANJg2FZJq0oJAyD3OwUq1w/oiL6GBcWGvmf3q/zzHQu6QmoMIdpM41TZdNYsWI5LS3vz8wtyJDhTZARhfcIQheEXh4hvHcExW0h73dXYC52/ux8aD7OrvvPUjyaYIdZRbIKrNIBZLkfr7oTSUoiUJhPfZ6HXQr35v8zBhofHNW5gygP1Pl42LKO8Nx2pNE0qpLg6qpX2TDVRfW9o7imUviz4eu3ypxY6iXLfDcufRnbTsZwxmOElHNI4dPk+ucAGYQEkgHoWBwO2q+/heLKtcw+P4n6TBgzMJISjNtV4pJCNJAkxxNjS8H9lBkv8pJ0A3+cdR9H5yyYooIqeZp6ywxtNaW0tm6nuro641CaIcOvQeav5j2ANhdn4dFuUqNh7C15eN5XhWxdfGlTCY1jj3RhPjfHOpOCYTFwyLtRpDPYlQHM8uJYjLio5YLxWb5S+jj99hHKI/B/5/x0FJTxydwyekMfgV4VOZWiuaiTrcoZap8dp7RvCk0RPLxZZudKJzbLXUSzV7P6XIIVAxFSeifp6AksxiygIgkJIRkgCbw+Dxvv+gIS5UztHEQ7MoQPGEsL5nKsBOI6obkE2c4gG7K/h089yeOe3+F7obuYnJdwSina1VFW5WqsbVvO8uW3ZTaNM2R4i2RE4V2MEILY8WkCO/tBlsn+UB325Yv19bpm0P90P4kjE5TLMmmTAebnKeRRNPKwyd0IJAxUFtKf4Ycuhe/nfR2BzqfGE7RbnXyrsZE96TtId+Uhh9Pku6e5ofA1Cs6kWHHqPI5gkv2NEg9sthF3f4BA/haUgMSnXwhjj/lJR19A6OMITItmKZIGkqDMJ7P5C3/D1KiL4R+NUCF6KZclpnRBoDiL6UCSwGgUr22Oaz33ke3o49/cv88Pp3+X+JREnhRmq3WO7c3FtLduo7y8PLNpnCHDZSIjCu9S9Gga/5O9JDrmsSxx4729FtVjQWgG47uGiB4YxyFAknX8nl3Uxr+PEC5UKYRKEIAEVXQYn+YrJT9mwD5KSQz+fCHFgSWlfNpyHf6RFShTCRyWMNuL9+OblVl+aJjKnjEGCuGeG80Ml95IMG87MdXGTYcjLBtNoqVOkIodBrSLYpBGlgzqPAusv+PznJtcxbHvTFKt+qmVJeaQmK50MzwVZaE7gNc8zTXuH5KTO8O/ub/AD4aySUahQvazpVjnurWNNDbelNk0zpDhbSAjCu9CEj1+Fh7vwYilF5vRrihGkiUCJ6ZZeLIXsy7QjDQDBXtZEn+CpfEpDKwo8hRCqAgJ5rWP80CWk/vzvo0h6Xx8QqfM5+X/NK2hJ3gdUkcC1YhxRe4JqlMx8s+HWXn4BCk1ybd3qBxbvpW5vFuIWF0sH0hw9elZLIkwqdgu0CYvigHIkkGLZ5yVbVUcTv09ex/XqbNMUW6WCckS4dpseocWmDs1h0ed5Cr3I/iWSHzP/inu61aJzwsqFT8fbs3mxk3XkJ+f/0t/PxkyZPj1yYjCuwiR1gk+P0Tk4ARqnp3cjzdgLnJiJDSG77+AOhgkrhv05u8ny/sMK0dGMAOSpINYLElNyoWcMz7FV0qeZ9A2RnFC8Pm4nacbG/hXbkfvNqHMxSmxT7JR7cO+INNy+jRlQ6P8pF3mhY3rGM//EEFnLkVzKe54eY7ssI6WPEEqdgSEvuihKwkaXVNsKpnhqPhDXjjXSJ0VXHaVmEkm2JBDR+cks4emcCmTXOX5EXnLi7jP/Ad873ScWNqgQl7g+iqFu27alrGdyJDhN0Smee1dQmoiwsKj3WjTMZzrinBvr0AyKYQ755l9qAtTSmdAChBt/Ro1vXGqUl2AwGDR8E6W4sxr7+f77hwe8D0PGNy5YCdVWsIPsm4lPFuOuduPYuhcaT9BQVqiYGaCNYeO01WU5PHtjfSV3Mmsp5T8YJKtp5IUzi9gTWok4s8ip+fg4nup3BliS7uP0xMb8AcaqbWa8aoyKYuC3ujl9JlhZubMuJQp2r0/oXBdPfca13Lv0VkiKZ1yeYGN2RHuuOFKampqMvsFGTJcZt7ykJ13Kr8NoiAMQeS1cYIvDCHbVbJvq8Fam41I64w/0o3omCOqC/rz9+FwvczKsT7sUhQQpEUlZnmApJTHaeN3+GrRSwxZJ6hKq2xTC3io4Fp69XVYOxYQCzpV1hHa5BlyIiHaj5+C+CSPbavgtaZPMe+txBeJccVZndrxOYQwIxKnMWKHkYQASZDtsXHlB+7i6JE8giNRmm0KRWYZ3apAvYNjp4eYXsgiS5mmvfAAhVtWcX+knXsOjRJOaFQoflot09y8ZRVr167NlJRmyPA2kRGFdylaMIn/sW6S/UGs9Tl4b61GcZiIDwWZvK8Dc1JnyIgQbPgOzYOjlIi+xeYzvQ5VnkOR5vAb2/meq5QHc3cjSwa3xQo4X76afZbrkEfTWHoDqEJjra2bJYkIlQPnaTnTx4ur3Ty447OM5TaSHY+y/rxO82CEmBLCnpRJRnci6X4AbFkuNt/1RToPyUwNBCk1STQ5VFRZQm0wc+JCL8PzhTjlOdqrLlC8bSP3T5Zwz2uDhBIaS8xhGhhmy4pqrrrqqkxZaYYMbzMZUXgXEjs7i//JPjAMPDdUYW/PB0Mw9VQf2vEpEgb0uk/gcz5Jy8IFFAwEJhL6GmzqfjTJyQnu5qsFBxi2TtKoucjPaeLprNtIxD14O4aJ+a0ssY7RJmbwhXrY8so5UmqKe2+9kedWfxBXMsamCxEa+1Q0YxpkG0r0PHriCCBQVJW26z/J9Egh04MhbBK0uk3kAkq+mZ5AL+enC7DIMdrqxqh43/9r777D46ru/I+/z/SmUbd6c5Fsucu2XOSCC65g0wwYEkwLSzaEhGwK2fwSkoUkkA0h2ZANLfRAHFqIAQMGAzZ23HAvsq3euzTS9HLP7w8Nfrxem7ZYI0fn9Tzz6OreM56Pjq7m63PunXsX8EyFnse2VNHrD1No91MUqmRcVjzLly8nJyfn07pFUZQvgbr20XlE84fpebUS7942jDlxJF1VhDHFSqDZTeOfDmByR2iMBPDlP0VZ+xacXS6EkAS0EUhM2Awf0COn8JBjPM+nvIQZHQsMY9icdSXv6UaQXFOLvsKNBsyzHqXQW0l++T5KjvWwrziDn19zJ12JKSw6Xk/JQTvGcC9+GcQS0RH0vERE6x8dFJRcTCA4iQMfeIBeRjmNjDEJhIBOYzvbjtnRiWRKCusovHoZz5eP4fonq3H5QoxLlBRoR8gxw6IVi5g0aRI6ne4T+0VRlIGhisIgEqhx0bXuGJGeVNFRsQAAIABJREFUAHELc3EuyAEhaHujCv/mBqQGhy3HKIp7hPz244Rl/1VPPZEFWPQ7EXjZK67hx5kV1Jo3MUZLpztzBetMF2Dr6yDn8EHaXckUmJsp81chunax9L0GdELHHy6/mhcXriSvy8Nlb/eR2SsJufYRceRgDFQS9O9AAvFpEzDaltFcHQI8JFj1lGXYMHT5Cei9bGsP06c5GJNdx6Rr57O+ZQz/+thBur0hStKM5OtPkBDopXRWKfPmzcNqtca0zxVF+Z9UURgEpCbp+6CB3rdr0CdaSL11IuY8J6FOHw2P7cPYHaY1HEaXuo5F3nVofSak0IF04tPGYDNsIqBL4jnT1TyYtgmT0DHcOY8P49citAjjKrdSU5WFGzsX6I+S79pGXs0xZpRrVGZn8/9u+S4uZwordnuZXVtLA22Y27sJxhei9b2K1HoQ5mQc8VcRDFoIBkPo9YI545KIb+xD6/FyyOehssfC8JR6Vlw9jX2GCVzx8lEq2z1MyrCxwlGLwdXIiBEjWLr0WnWKqaIMUqooxJjmDdH11+P4y7uwTkwl8bKRCJOe7s219G6oBQ3KaaAk7pckeOvxaSnY9e14IzMxijpshs006Kfy84R0PnS+TYoujbr026kx5DK6ZxvhowYqevMZbmplSdsOWq27WP6hH5tX8NTyK3h6xaWMrw1x+469GHVHOO4dTpouHZeth7D7L2h6HWbHSgzGkWhSAJIJk5IZ6QsTqe2lPexnj0dPkqONy68eRU/Bar71+lG2nNhNdryJr+S50bfsIikpkaVr1qhTTBVlkFMHmmMo2Oim889HibgCJKwYjn1mBlpfiIYndqFv1mgPRwib1zPF8DhdkSwS9B3oAHd4MQ7jG0gRYbNhNXenldNp6MHinEdtwg2kBE8wofYwO2qmYBZhFniOMMz/JgVuF1M/itCUks5dt9xOjzOHG/bvY5H3JZ4LXEZ2XyqaVkPYtwVkAL89hyTLKoxWGwFPmMwRTqYXOIl81EJY09jnlQT0TcxYmoJt5lweeLeCdbvqsJv0zEnsJbn7KA6bhbKyMqZPn47RaIx1lyuKgjr7aFDy7Gqh+9UK9HYjSdeOwZzrpPdAA13rKiCsozLUwzjLz3Dae+nxpJNhOkBAG01Yy8Ru2ESfMYGHzSt4JuVDzDo77am3EzTlU9a5gbryfFq8aRSJJi6rfJ3WggoWf2AhvsfD3+Yu4dFVa1heWcEPWn/PO74ldHlnYQw1Efa+g9RcuOwQ71hJRtIEulu8OFMslF2QhXlXPdIlqQ9qVAc7mDJHkLtyBU/sqOe/36vEFwozLcFHgecoiXYzs2bNYtq0aZjN5k/vEEVRBowqCoOIDEXofrUS7+5WzCMTSLq6CJ3FQN1zm9Ef0eOKSLojmylNeIQaXRm5wY8wi3bc4RUYdPuw6us5ap3I3XFJHLRXYrSOpTn5W6T6jlNUXcuuxik4dR6uaPiQFOMmUvpyKT5UR6/dwS+v/zqhuBTuq/lPXD1j2OO7BH2oh5D3bWSkFb8xQmNWGrPjb6G3KYDQ65i2JJfMzk5Ch734NTjid5M3oYexa1bxZoWLezeU09jjo8gRoDh4nMw4PWVlZUydOhWTyRTr7lYU5QzUKamDRLjTR+ezRwk1e4hbkINzUR6hDjfVv3kfi9dKbcBPhvk3jByXSOXx+Yw2/J0IqbhCV+EwvAiGCC/aLuY3SSfw6mvxJN1IyDqVmU1vUllRxO7AZEpDx7nyyAu0541k7IFcUruq2TyplD+tWsN36p9neLWJbe7vEdHCRDzriYRr0JAczfeQkbiSGX0z6K7zUzAxhcmjjPg2VRHWTNQFQhhzGlh6/UUcceu46ql97KnrIc0cYrGxkkKLpGzhPKZMmaKKgaKcx9RIYYD4jnTS9ddjIARJVxVhKUqk+8Ma+l6vI6wJ6kMVTClYR2fGFSQfeIw4fTme8Bw8YR/DLLvpsDj4jXUB6xP2IYwZdKbcQaqvkcyKHo60jyHN2MVXjr6DOcFCYnsyo6o2ENHBg6uvY5jTz4q6ZsrdiwlqOlyBLZj9BwFoSfKztxguD34TeSKOuCQLs+bZCW9txBGMwx3RcMU1Mu7G2fQ4UvnVhnJe3d+EXR9hoqhjckKAuXNmU1JSoo4ZKMp5Qk0fxZCMSHo31tD3fgPGLAfJ145BZzNQ/8Q+9LU+OkIRhO55xi0bzontYQoDDwLQFVyFSb5OnKWXHYmF3GNNoNbcgt+xmJBzCeNqt1NZU0woYmBe8DCLjm+hO30Vo46/R3brQfYWFrN+0Qquqq2l1TOdkNTRFtpDnGcHOiIEDYJNJc0kx01gzrGr0PlMlMw0Yq1pJs6dhg7oNnZScE0R+hGF/PG9Ch7dUoWmaRTrmpmV6GbB3DImT56sioGinGdiUhSEEI8DFwFtUspx0XVJwDogH6gBrpRSdov+cxR/BywHvMD1Uso9n/Yag70oRPqCdD1fTqDKhb00nYSLR+Cv76PlyYPoAxo1oW6KbPfgmHkj4X+8QbxhM4HIaBo8GRQ4txAywJPxc3gorpaw3owr+VZSPQEcFRFqXXkUWBr56r6N9GTmMaw9ldEnXsAQCfLssmsYoTkR7uFEpA5fcDsB/0Esmg8JnMgJsmNsGwtcVzC8fCbDCwIMCzdh6R5BgsGAR+cl9ZIs4qeO5a+76vjVhiN0+zWG6zq5ILmX5fNmMGnSJHXBOkU5T8WqKMwF3MDTpxSFXwFdUsp7hRB3AolSyh8IIZYD36S/KEwHfielnP5przGYi0KgxkXnc+Vo3jCJl4zEVjKMtteqCGxrxK9Bp7aVkuTH6Ui8i5Sm32ESFbQFltJjqaGII1TGObjXNpXttiqC5mIiCVeSX1FBdeNozPogS3x7KDn+ER25V1JQuZmiml0cHl7MidGXkuAdBkgS/Bs5FmomLtx/p7Wg0cqGGTWE7RYWHL2O8Vo8Oc5GIt1jyTNZ0ISGdXYiqcvH88GxVn76t/3UuiKkCjdL09xcubCU4uJi9Hp9bDtXUZT/k5hNHwkh8oHXTikKx4ALpJTNQogM4H0pZZEQ4uHo8vOnt/ukf38wFgUpJe6tTbjeqEafaO6fLrIbqX/kAMZOP83hAMmGB8hJd9PYfTuZ8h6Moo7d3gsYlbwfp7+dDcPy+YXNTq/w4E5YTYo7CSqttHuHMd5xnGt3vMOJ7PEM82Yy4fBz2H1uXp97GSmMJyzjyeU1jrvq0bQQ/b9dQV1WHO9POEiWdxSrji1mrLODHtdEiizx2HQC3UgraWsmsKmqjfteP0hFj4adAEvSffzLsqnqQ2eK8k9kMJ19lHbKG30L8PG9FbOA+lPaNUTX/a+iIIS4BbgFIDc399wl/QK0QJjuF0/gO9iBpTiZpNWF9B7toufF44iIRoWsZYr5x8jkr9DaMo0s0/9DL5o4Fspisv1dXGEd9w2bynP2diJ6C8SvJeeEl7q2PBLN3VwfeYP8XeWcKFhLfsOHTD3yKtUZ2RwuW0xiYCY62YrN/RRHw6Ah0AMRQyrbSzuoSDjIzKbpXNOdRnM4BYt3DJNtOog3kLKmmI2d3dz/23eo6ZPYCXBxZoRvrphK4YiCWHeroigDKGaTwlJKKYT43MMUKeUjwCPQP1L40oN9QaFWD53PHiXc4SN+WT72WZk0/LkcXXkXnoiG2/YiM7VtdBseQLZBmul76EU7EskoUy0H4+38LKGYCl0rAes0kjwT8exyUh/MpCxpN5dv3szu3Gk05VzF3F2PktzTyfZpJQxLGYnfOwOD7y26AxVIRHR0oMeTVsQbkzcT1kJ859h8jK4Z+A2ZzIrTodfrcF6Yx9tGH799ZgsNHoFDBLgiT/CtlTPIycqMcY8qihILA10UWoUQGadMH7VF1zcCp15MPzu67rzg3ddG90snEGY9KTePB6uB6l/sxOwLU4eXzPi7yfRNpz3yAIbwAdLMdyPwoyF40x5Htz2H+516QloHOvsKEityaenJI8vexHXhDcTvqGPb6K+T37qNhbseoCU5hY4lBdhCc6no7iPkfwQhQ/h0FmyaH68pF/f0RDbY11PaWsLshgWYZA5T4sCOHnNRIu9laPzm/X20+HU4RYCvjDLxzZVzSUtNiXV3KooSQwNdFP4OrAXujX599ZT1twkh/kL/gWbXpx1PGAykJnG9VoV7WxOmfCdJa4ro3NmK/906hCY54TzKxMjr+D3/hh83iYa7sel3ArBTJPBuYjyHkqZyQNuLFA4SAivpPD4Rn9SxLGMTy97dzgcFZTQXXciSDx8mt62Z2glZDM+DjZ1L8Pu2gNZDr8GJIxzGKMFUuIydee/g6evmhv3/hj2Qw1inRrowoHea2JWn455jVbQf05OgC3LzWDvfWHkhifHqbmeKopzDoiCEeB64AEgRQjQAd9FfDP4qhLgJqAWujDZ/g/4zjyroPyX1hnOV68vUu7EW97YmHGWZOOZnU/vYYcwtHlyaxJvzIuNa8glplxFv+CN2/Q6khLDU8au4YWiJs/iL04jO9w9sxkz0NRfS2DuGEfFVXBLchH5bJ+tLvkNByw5WbrwXt8OOZZFGWBvJa00SGX6bPr0Tvd5BfLgXR8o4rDNz2di0k+Kjq0jx5DIqLswYpxkR0jiRoedHbS00HTSQrA9x22Q7X794PnabLdbdqCjKIKI+vPYFefa20b3uGPbSdMSYRDqfLccY1miyayTZXyehIx+n4SUs+o/QpAXQ8KDxx/RSXs+4lFbv3zGGakmK5NNa+VXC0sTyrHeZ//5O3hk5H799PFe88wT5LY14RtuJz/fx9675EKghIqz4DMnYQw0YTE7i5k7mYGsr6W0ziQsmkmTzMjNjGIbOAO02+JnfxT5NMMzg57opqdy8bDoWi7pInaIMVeoTzV+yQG0v7Y8ewJTrJJBiRe5oISAlngIPmU3bcfIeFv1+QlocAW0OFt0mjlsTuXfE13nf7iC+82EMBNHa5+BqX0qBs4bVjjfw7+zlH5O/y4TKLVz63hv4HRZSSnp4SZuD7O1BABFTEfpQPcg+jIUjqBOJZHWUYtIsGBwNLCoag7k6QACNhzUfLxImy+znhhlZXHfhVPWBM0VRBtUpqee9cLefzmeOoHea8fgiGHe20KOTJKd8QG7zG5j1R4hgo9q3FoduFEbrvdyb+xUezb4ci+sV4jvexCwtdFffCsFMLh25nnm1B9kQLKOrZBI3/+135LY1oxXq2JOVTU/PcPRaJ3rjaNDZILAHHHZ64qcwrH0OBUiy4uqZlF2IvnMEotLPmwT5AwFSbD5+fcEILpk9Qd0DWVGUz0QVhc9BhjU6nz2KDEbQdAJDZx8h8x4KxTOY+yrQhI16WUan+9ukmPexKXszvxz+JK0GE/nNv8Ct1aJ5htNRfwPZtnZumvxrkrZEeCb7NiYe38Xt6+4h6DBxfGYa5YF0DF1uDIYUDLZphHybkOFegs4i4nWLGRfWGBHfTpo+E6GNwteq8aYWYj1B9E4v9y8pZn7JaPWBM0VRPhdVFD4H1xvVhBrdYBAEe4MIw0YKdP9FWCYRIpWt7rWki9k0ZPyNH46azl7nYkZ2fYC571ncBPC3riDcPYvlue9x0fA3aX93JOvSruBfXn6E7PYWWgud7IkfhfT2YNQZ0NtXEpEeQu5XwJBMTuKNZNqt5Bj0GLChGZ3stWg81+dhrxZijN3N95dPYG5JsSoGiqJ8IaoofEbeg+24tzUB4BGCrmAP0+P+RFDLRhq6eK/zP0hy2vj12P28lnYDqf52lp14iI9MW4lE7PjqbicuYuFfp/6ePHsjO/Yuw+SK8OP199KeEM+W0mL6AgEI+TFY5yNMown73mKYTpKXdj2Z1mTMwoBGkI4UG+t1EZ5t64FQmPGWbh5cOIYFs5ar6xIpivJ/oorCZxCo6qHruXIAvBk2Wmv6mGx/CYGHsF7wQfcv2D2hl8fzxqGJkdxQ9wzl/kPstjQRdhfia7yaElsdN826HxE08taBNVz02jsM625nz9gcWgwWCITRm6disE4nWecnQ1dPdvzFWPQWJH4iHGVr/Eiet1rY19KFVYSYaGzjq7PyWTJ/CRaLJca9pCjKPwNVFD6BlBLPrhZ6XqkACUxPp21LE6PMnTj0rxLSGXna/kMeLc2k0TKZ5e0fMKfhBR5M9BIwhwm0LsfSO4E1wzazYNwGfH3xVL4zha+9+xxHszI4OGEEESnRGUeSEl9GvimZDIOGRe8gLOMx6PYgOMTjciHvOouodflx9nmYYWhi1YQ0ll64msTExFh3k6Io/0RUUTgLzRui++UT+A51AhB3UQEHX6sm36Qj0fhLEBEeTv4OPx87m0J3JX/e/0teNPVwf5KGjNjwN1zPWE3HhbkvUzxyH91d6ST+t2By30dsHD+KsE5Dp0tHi5/MbOsYskwGwlqYllAtuYbXsBuPc7/vX9hoXUNnKEyqr5e5xnpm59pYtnQlOTk5n/ITKIqifH6qKJyBv6Kb7r8eJ+IOAmArTaehqpd8wCDWY9afoFw/m3uLl3JBxxauq/4lP0xJxafXiHjysbRfxDzZybThm8nNOUFXYxbZv+3hUFYmvakJCJ2DsGMCkYQUVslROPWC/T27yLE/xag4F/f5buN1eSMeA+Sb/EymirFJgsWLL6S4WB1EVhTl3FFF4RQyrOF6qwb3lkb0yRZ0ViM6uxFjcRKOJw7Tp/WRH/844bCF2ydcS0qwC1PXI3w/NQOhCxLomMfkcA4jZQPFhVtIT6/HfTiNyKuSbSNyiegM6K0T2Te8hzldRcwXyeiExkeuVylOfJ1H5VpeDUwgqBeMTxbkesrJ0vuZu2QuM2bMUB88UxTlnFPvMlHhTh+dzx4l1OzBPiMDpMSzs4WEy0fR/uxh/Bo4nXdhDYdYlzyTg85iJtf9O3tMRpACXculXGH0Ywz0MG7sZpKSW3BtT6d2bzyRZD3NjlH0ZYdodVZzQ+11lFhNeCJemnwPU5WczHfDvyYidMzKMpPjPYrF08nEiRNZuHAhTqe6WJ2iKANDFQX6RwgdTx0h0hckeW0xOpuR9of2Y5uWjmvDMWQIGrRdjNW10SfN/HDMvzOq/S/Ua63IiJUxnjKm6Prw+mDShPeJi+ui/sM8Og/baLVnciIrnsa8DymtW8G36y9jlM1Aq78Zv+73/CHuSvZEhjMzzUqJtRZPay2ZmZksW3aTOm6gKMqAU0UB8B3qINzmJfmrxVhGJdL6+73o4oxofR7CbWH2eMKk5D9GutvFj4ffhtlfRbd3A0gzw10LmOQL4jaGmTHhQ8wWD5WbCuioTufdnCLcIzci9UGuOfA95pqSSbPoqPXsp9n+Bj+VdxASZtbkhTA174KInVWrVjFx4kR1WQpFUWJCFQWgb2sThlQrljFJ9L1fT7jVi3VOBr4tzRzzB9GSn2KOu4ltzgk8nrGYxOY7EAj89Wsp1LrR65opm7IdvS5M5YYRfOhdyJ4pFRhS1jG+aR4X1q9iht2MXSep8bzKegesk98g1yGYyRGsbV5mlM1i7ty56vMGiqLE1JAvCoG6XkL1fSSsGkG400fvpjosY5Nx7TiO1DTadfu5Sqyn3ZDAtePuIb71x+i0EJ66mxjjNzJcv5+RZQeJhPUc2TCXJ81zCZU8QaKMcPHeH1MQSqbUYUTKIFWhZ/gP+zyqZBpT7T2MCVcwunAkS5YsISVF3fFMUZTYG/JFwb21CWHRY500jM6njyAMOqrch8gMZnBQ7uKK+P8kKPRcMum/MHfejz7Sjb15FQFPPktsz1FYto+A18ab79/MhkQNR8ZjzK9fxNjOGeQZ9Ix36PGEu9hh3MDP9Zdj1sNi3THGJxhZsmQNhYWFse4CRVGUk4Z0UYi4AvgOduCYlYn/UAfBahfHC3ooqrbRZKgkKeU+jG7JbUU/pMe3AWOwmtyOKRx2zWSZdStjZu+hryeRh3feQdWww0yK+Ji99weYpZ7xVgMFZj2dwRP8wdTO26wkW+divrWBpfPLmD59ujrFVFGUQWdIvyu5dzSDlFjHp9DxxCFabEHGNbzBsVwLLeEXWN3l5/GMVew3+jH0bSWnN5uOuvkkO3pYNfslelqTeXDvd/DGHWRtwxSc4QRMhCm1m0g16qkN/4PbTXl0k0qpoY41UzJYtOjrxMXFxfpHVxRFOaMhWxRkSMOzowXLmGR6NjcQCHpx5n+PLakh8o63sdoX4M/py3g6dTay5xGSg/GM3TaSv+ancNv4R3HVJvLC3q9isntY3bQADYlTSKY7TFh18BYfcbehGKfwc2N2JzdcspKsrKxY/9iKoiifaMgWBe/+djRPCJluxb/5GC2ld9IQ6GHOoV6SNMn3R97BVud4UloewKPXWLvBxH1j5zEm4Rjpne3s2D2NupRcrnFZkTJMutHIVJsggp+fiW42MYpiczd3rRzLtMnqFFNFUc4PQ7IoSClxb21EJJlwv3eQtkl/pMvVySXVvbQbbVwx7l66jInMrPk1b8W5uHFTHG/Pno+/08xMyx4at6SwMWs2c3xmDDLMSIuFsRYdHbKHb+gEHdi4dbyBO65YjdlsjvWPqyiK8pkNyaIQrO4l1OxB6I7gGn4E6T3ARbVudtiGccv4P2CVQb528Of8Nt3N3HIrhoXD2H50IqOSa7Bv7eGDpDJyNCfjQhqTbGZyzXp24+JOoSfNFuLlr5QybnhmrH9MRVGUzy0mcxpCiDuEEIeFEIeEEM8LISxCiAIhxA4hRIUQYp0QwnSuXt9X9R5GcQBXch8O3Tpm1Lp525HGtZOfwCg17tv5Mx5OCZDbaWTeFB0bWuag00lmHthKi3kYHst4LvXpKHOYyTUbeAY3dwCrJyfx7o9WqoKgKMp5a8CLghAiC7gdmCqlHAfogauB+4AHpJQjgW7gpnOVYac9n3abxGn/DSPrfbwYl8jXJj2OPezn6b3f4/5hdoQW4arhUBdK50jXaMZ5j2IN+SmPv5DrA2bmxZlxGgQ/wcMLpjDP3Tydu6+aiUGvjh0oinL+itU7mAGwCiEMgA1oBhYAL0a3PwVccq5evFTqSXPeS3aLh8fiE7hj3EOAkacP/5AnrQVU2zu5OlUj2RLg0WPXY9YHmdW0jWpnGTfINObEmQjoItwifPSk6dj4/cXMHJl6ruIqiqIMmAEvClLKRuDXQB39xcAFfAT0SCnD0WYNwBnP3xRC3CKE2C2E2N3e3v6FMnQ1/ZrU7j7uTx7G3YV3ETKl84fyeygPJ7MhuZqFdo3RZsl95f9OwG9kUdM7uE05XGyeSqnDSDVB1ooAU8c7eeFbF5LsUAeTFUX55zDgB5qFEInAKqAA6AFeAJZ+1udLKR8BHgGYOnWq/CIZXKU/4ZbeJt5JnE7AVsL3Kp4gt7uStdkGRpgizDcaeab6FmrbE8kMNJLna6Uw5WYm2vTslH5+JHx8d2EuN1046Yu8vKIoyqAVi7OPFgHVUsp2ACHEy0AZkCCEMERHC9lA47kK8ORHf+R9RzbehCtY2biFVa1/4cbMDBzGEKsNNtbXXMcWfTGGiJsF7R9QkHApJXYbewlzj+jjwSvHsbBEXbNIUZR/PrE4plAHzBBC2ET/zYYXAkeA94Arom3WAq+eqwAeXzN9yV9nfHcLlzTfx9VZaYRMYa7SO/hH1bW8mTMVfYOHCa5DFFsmMC0+m3Khcb9o46E1o1VBUBTln1YsjinsoP+A8h7gYDTDI8APgO8IISqAZOBP5ypDQmg5mZ4uFjR+mx8MSyTNHOFWi5X6yotZP3EKCXuasET8rAyGmZk8nVoheVhXwz0X51I6sfhcxVIURYm5mHx4TUp5F3DXaaurgNKBeH1v86tM8RzlOaeR6dYIy81mDu1dyN+mzaRw+2FOhHO51NfMopR5VBLhL/pjXD8ji9mzZg5EPEVRlJgZkifVTwuF2GWTXBEX5lK7kYo9c3ll4gKm7NpGa18CGZEg33IUcoAQ6417mV1kY/ny5fTPdimKovzzGpJFIbPcw/cTIpTadJzYUcabBbOYtXUDsilIr9HJD/QJfEiY13X7GJFuYvXq1ej1+ljHVhRFOeeGZFGoL7KRaBFUbZ/NHvtw5r/3MmnNLexJnM4cDDSg8bJ2mFEpkmuuuQar1RrryIqiKANiSF4Qr7MgDd+WXOq9kolH30ea0zmUeTkRoZEuBesiNVyeFeH6G24mISEh1nEVRVEGzJAsCs7KeLobaknzewimzGSUdQYP6v0USXg70sHaLDdfu+Vr2O32WEdVFEUZUENy+qgWDwGDgfaiK7nYNpun9CFMUlIlA6xObeUb/3qrKgiKogxJQ3KkMCwvnxrHeL7RauNDXYiDIgJSconhOHf+23cxGIZktyiKogzNojA7ModVbUF8ugi/wgPomeHey3/87FZVEBRFGdKG5PRRKBDET4jbw80EhJ6cQAPfXjQGZ8qwWEdTFEWJqSFZFEZc7uGbcQ1UGZyYIgG+JTYy7eJzdvsGRVGU88aQnCvZ1JlHXV8ABPwk/CjTbvgFeoMx1rEURVFibkiOFP763A7COiNjA8cYk5FG3oTJsY6kKIoyKAzJkcJ1pVm0bqrjAfNDZNy4JdZxFEVRBo0hOVIotTXyvvP7WIoWEZeSHus4iqIog8aQLAoJaRm47SPIWvPLWEdRFEUZVIbk9JFjwlIcEz7zbaEVRVGGjCE5UlAURVHOTBUFRVEU5SRVFBRFUZSTVFFQFEVRTlJFQVEURTlJFQVFURTlJFUUFEVRlJNUUVAURVFOElLKWGf4woQQ7UBtrHN8BilAR6xDfE4q88A43zKfb3lBZT6TPCll6pk2nNdF4XwhhNgtpZwa6xyfh8o8MM63zOdbXlCZPy81faQoiqKcpIqCoiiKcpIqCgPjkVgH+AJU5oFxvmU+3/KCyvy5qGMKiqIoyklqpKAoiqKcpIrCl0QIkSOEeE8IcUQIcVgI8a0ztLlACOESQuyLPn4Si6ynZaoRQhyM5tl9hu2Eo4yuAAAFpklEQVRCCPFfQogKIcQBIURJLHKekqfolP7bJ4ToFUJ8+7Q2Me9nIcTjQog2IcShU9YlCSE2CiFORL8mnuW5a6NtTggh1sYw738KIcqjv/dXhBAJZ3nuJ+5DA5z5p0KIxlN+98vP8tylQohj0f36zhhnXndK3hohxL6zPHdg+llKqR5fwgPIAEqiy3HAcaD4tDYXAK/FOutpmWqAlE/YvhzYAAhgBrAj1plPyaYHWug/53pQ9TMwFygBDp2y7lfAndHlO4H7zvC8JKAq+jUxupwYo7yLAUN0+b4z5f0s+9AAZ/4p8N3PsN9UAsMBE7D/9L/Vgcx82vb7gZ/Esp/VSOFLIqVsllLuiS73AUeBrNim+lKsAp6W/bYDCUKIjFiHiloIVEopB90HGKWUm4Gu01avAp6KLj8FXHKGpy4BNkopu6SU3cBG4JzfJvBMeaWUb0spw9FvtwPZ5zrH53GWPv4sSoEKKWWVlDII/IX+380590mZhRACuBJ4fiCynI0qCueAECIfmAzsOMPmmUKI/UKIDUKIsQMa7Mwk8LYQ4iMhxC1n2J4F1J/yfQODp9hdzdn/gAZbPwOkSSmbo8stQNoZ2gzW/r6R/hHjmXzaPjTQbotOeT1+lim6wdrHc4BWKeWJs2wfkH5WReFLJoRwAC8B35ZS9p62eQ/9Ux0Tgd8DfxvofGcwW0pZAiwDviGEmBvrQJ+FEMIErAReOMPmwdjP/4Psnw84L079E0L8CAgDfz5Lk8G0D/0RGAFMAprpn445X6zhk0cJA9LPqih8iYQQRvoLwp+llC+fvl1K2SuldEeX3wCMQoiUAY55eqbG6Nc24BX6h9anagRyTvk+O7ou1pYBe6SUradvGIz9HNX68dRb9GvbGdoMqv4WQlwPXARcGy1k/8tn2IcGjJSyVUoZkVJqwKNnyTKo+hhACGEALgPWna3NQPWzKgpfkuh84J+Ao1LK35ylTXq0HUKIUvr7v3PgUv6vPHYhRNzHy/QfWDx0WrO/A9dFz0KaAbhOmQKJpbP+r2qw9fMp/g58fDbRWuDVM7R5C1gshEiMTn0sjq4bcEKIpcD3gZVSSu9Z2nyWfWjAnHa869KzZNkFjBJCFERHnFfT/7uJpUVAuZSy4UwbB7SfB+KI+1B4ALPpnw44AOyLPpYDtwK3RtvcBhym/2yH7cCsGGceHs2yP5rrR9H1p2YWwB/oP1vjIDB1EPS1nf43+fhT1g2qfqa/YDUDIfrnrG8CkoF3gRPAO0BStO1U4LFTnnsjUBF93BDDvBX0z71/vD8/FG2bCbzxSftQDDM/E91PD9D/Rp9xeubo98vpP0OwMtaZo+uf/Hj/PaVtTPpZfaJZURRFOUlNHymKoignqaKgKIqinKSKgqIoinKSKgqKoijKSaooKIqiKCepoqAoiqKcpIqCoiiKcpIqCoryBQkh/ha9ONnhjy9QJoS4SQhxXAixUwjxqBDiwej6VCHES0KIXdFHWWzTK8qZqQ+vKcoXJIRIklJ2CSGs9F86YQmwlf7r5fcBm4D9UsrbhBDPAf8tpfxQCJELvCWlHBOz8IpyFoZYB1CU89jtQohLo8s5wFeBD6SUXQBCiBeAwuj2RUBx9JJMAE4hhENGL9ynKIOFKgqK8gUIIS6g/41+ppTSK4R4HygHzva/fx0wQ0rpH5iEivLFqGMKivLFxAPd0YIwmv5bldqBedErnBqAy09p/zbwzY+/EUJMGtC0ivIZqaKgKF/Mm4BBCHEUuJf+q7E2Ar8AdtJ/bKEGcEXb3w5Mjd4R7Aj9V3VVlEFHHWhWlC/Rx8cJoiOFV4DHpZSvxDqXonxWaqSgKF+unwoh9tF/A5RqBuGtQBXlk6iRgqIoinKSGikoiqIoJ6mioCiKopykioKiKIpykioKiqIoykmqKCiKoignqaKgKIqinPT/AWSMyCaGw0mAAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From 96fbf41475d0788167ad82fcf29150e78e5768cb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 336/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 78336400a3d22c7e6cec60fe367f3e56985aaa42 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 337/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 59dcb13efd71b0e1f3b9716389db491c1f02e584 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 338/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 0d292eb86d6ef95975b1335f23cbf76823031c2a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 339/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From dae1488801ef74f102b8456e72fd9269200c97f0 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 340/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From 52e1671165902b6f3c96d6f00f45142a3bc5ffed Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 341/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From a658b97376191ace810b61360aa0482c3c81e249 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 15:42:43 +0100 Subject: [PATCH 342/624] Creating tests --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 124 ++++++++++------- skfda/exploratory/fpca/test.ipynb | 211 ++++++++++++++++++++++++++--- tests/test_fpca.py | 78 ++--------- 4 files changed, 278 insertions(+), 136 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..279fe2df9 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..dd89acac1 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,19 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the parameter is + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,7 +118,8 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # if the principal components are in the same basis, this is + # essentially the gram matrix g_matrix = self.components_basis.gram_matrix() j_matrix = X.basis.inner_product(self.components_basis) else: @@ -104,6 +127,10 @@ def fit(self, X: FDataBasis, y=None): g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +139,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +194,15 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +212,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +228,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +258,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..355646e58 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -604,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -636,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -671,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -982,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1512,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=65)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1461,18 +1529,81 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", + " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", + " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", + " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", + " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", + " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", + " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", + " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", + " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", + " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", + " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", + " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", + " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", + " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", + " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", + " 1.89644488e-03 -1.32629634e-03 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1484,7 +1615,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1623,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..fff7be7d4 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,81 +1,25 @@ import unittest import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.datasets import fetch_weather +from skfda import FDataGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.datasets import fetch_growth, fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data - def test_basis_fpca_fit_attributes(self): +class MyTestCase(unittest.TestCase): + def test_basis_fpca_fit(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) - basis = Fourier(n_basis=1) - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataBasis(basis, [[0.9]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of elements - # of target basis - fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() - with self.assertRaises(AttributeError): - fpca.fit(None) - - # check that if n_components is bigger than the number of samples then - # an exception should be thrown - fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - # check that n_components must be smaller than the number of attributes - # in the FDataGrid object - fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) - with self.assertRaises(AttributeError): - fpca.fit(fd) - - def test_basis_fpca_fit_result(self): - - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) - - # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) - fd_basis = fd_data.to_basis(basis) - - fpca = FPCABasis(n_components=n_components) - fpca.fit(fd_basis) - - # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] - results = np.array(results) - # compare results obtained using this library. There are slight - # variations due to the fact that we are in two different packages - for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): - results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) if __name__ == '__main__': From 7674a1d0e88a6d7313925ea0de0d09b9c2cb1579 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 343/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 37 +++++- skfda/exploratory/fpca/test.ipynb | 182 +++++++++++++----------------- tests/test_fpca.py | 72 +++++++++++- 3 files changed, 183 insertions(+), 108 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index dd89acac1..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -103,7 +103,20 @@ def __init__(self, n_components=3, components_basis=None, centering=True): def fit(self, X: FDataBasis, y=None): - # check that the parameter is + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + # if centering is True then subtract the mean function to each function # in FDataBasis @@ -118,11 +131,16 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is - # essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix @@ -195,6 +213,19 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 355646e58..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -672,7 +672,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -704,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -739,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -1029,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1491,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1512,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=65)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1521,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1529,81 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=65, period=364),\n", - " coefficients=[[-9.22677129e-01 -1.42900235e-01 -3.54441680e-01 -8.99100789e-03\n", - " 2.38177480e-02 2.91055669e-02 1.51239405e-03 1.05039844e-02\n", - " 8.86703696e-03 -5.07589361e-03 3.44455543e-03 -6.07066551e-03\n", - " 1.27266086e-02 2.23223946e-03 2.75127218e-03 6.80121065e-04\n", - " 3.81907926e-03 -5.51048461e-03 5.40824796e-03 -4.47923946e-04\n", - " 4.75544016e-03 -7.21569573e-03 1.27220633e-03 -3.59498588e-04\n", - " 8.57397485e-04 5.05814791e-03 -1.07227648e-03 -1.35472431e-03\n", - " 1.81734331e-03 -4.98578252e-03 -6.02512977e-03 -2.92664587e-03\n", - " -4.83062694e-03 -6.27285447e-03 5.36789078e-03 -3.25611256e-03\n", - " 4.44537626e-03 -6.97065173e-04 3.90309524e-03 5.75241884e-03\n", - " 4.16203793e-03 9.23870576e-03 -1.37371258e-03 6.23092892e-03\n", - " 1.44162123e-04 4.65299173e-03 -3.57950237e-03 -1.11467087e-03\n", - " -1.33883051e-04 -5.40677312e-04 2.75579888e-03 1.35665579e-03\n", - " 1.61255963e-03 3.05731826e-03 2.00403515e-04 2.20007152e-04\n", - " 1.89644488e-03 -1.32629634e-03 2.83890870e-03 8.04480341e-04\n", - " 1.68008717e-03 -3.45227402e-03 3.18845499e-03 -4.21780016e-03\n", - " 2.79603874e-04]\n", - " [-3.31326075e-01 -3.72604512e-02 8.89188681e-01 1.74093955e-01\n", - " 2.40573067e-01 3.78152852e-02 3.78490310e-02 -2.44353848e-02\n", - " 1.17261218e-02 -9.15011649e-03 -1.62164628e-02 2.21935431e-02\n", - " -2.05912314e-02 7.74093882e-03 -9.17304917e-03 -2.19288999e-02\n", - " 1.40836428e-02 1.57507271e-02 1.65500932e-02 1.26034046e-02\n", - " -1.52405577e-02 2.06307473e-03 3.86618647e-04 2.04002336e-02\n", - " 3.20342430e-03 1.29153501e-02 -1.27958246e-03 4.14305666e-03\n", - " -3.36952779e-03 1.42394297e-02 -5.48427792e-03 -1.24025141e-03\n", - " -8.27798205e-03 6.42033933e-03 -6.89395077e-03 1.17291847e-02\n", - " -1.34718838e-02 -5.86453561e-03 -4.45038381e-03 -9.27714845e-03\n", - " -1.23517510e-02 -2.16268891e-02 -7.75201307e-03 -2.02842293e-02\n", - " -6.47646807e-04 -1.57788062e-02 1.22167974e-05 -6.18681651e-03\n", - " 3.69259759e-03 5.16111927e-03 -2.43303381e-03 -2.93466954e-03\n", - " 7.21503469e-03 3.28077604e-04 2.51518816e-03 -1.10025128e-03\n", - " -2.93749331e-03 3.82232285e-03 5.68453112e-03 9.78150611e-03\n", - " 6.02701827e-03 -9.23368287e-03 -7.37570742e-03 -4.85626459e-03\n", - " -8.58497495e-03]\n", - " [-1.30613000e-01 8.65288515e-01 -3.28224995e-03 2.56659276e-01\n", - " -2.13435509e-01 1.71603314e-01 2.21569182e-02 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P4vzgZdBZYMpFcgrqlAug+GMQgip7FRnmjKiByExLZkSLICACNLobu1gEKQb5jb9zCmnnPQfdceusW5mWOI1nDz/ba6vA6XPycsHLvFb4Gm6/u1dzRgPu48fRZmZ2eEOPhPX880EIWj/5tN/3sn0kp4ha1l3Q5Zxh9mzcJ04gvN5+X19h7FL/p2fR5eURf801g3aP8ScEfh/s/bOcFZTSdTdpuCdBiw0kCXKWs9/WtgM41FDGdfw4Z66/gTP/tx2XcQlogzt6888Hey1UH6WytTKiWyhEpjmTWmdtl4drk7uJgAhEtwhcUSyCXgiBSlJx68xbKW4u5khdz37pgAhw90d384tdv+CRHY/w9fe/jsfv6XHeaMBVWIi+XcXRaOhnzkSTkYFtc889JKLR+tGH6PLz0efndTlnmDkT4fXiLo6+01xhfOIuKsJ19CiJN9+MpFb3PKGfjD8h2PUMNJXCim9GPK22hlxDwdo8E8/hqORlQZJcKyj08Gz+17/DcxqO69ouMPl8+e/ij6lx1nTZUdyeTEsm0HUvQTRXj0krl5nobBGEykvo1fqo92rPBbkXoFFp2HRmU49jN53ZxM7Knfxo+Y94bNVj7KvZx+P7YlOQbzgJuN14Tp9GP717txDI8aX4q6+m9bPP8JSX9/le3spK7Dt3Yb3wwojnDTNlMXIVdBOLUhhzCE83L1SOBnj3AWz/cztIYF05Z1DXMn6EIOCHLb+BzT+GqV+A6ZdGHNa5S5nIWUaFRsNcTRzx+njKbfKDoPWTTzDPm0T8JAe2PcUIXzClMy4TUqYjij4Kp4BGI1oKaehBH2luijElomuoN9ZAiDhdHIvSFrGralePY/9+/O9MipvETdNv4qrJV/HF6V/kxWMvcqDmQK/vNxJxnyyCQADD9J4tAoDEm9eDJNH48is9jg14PG0/D0D9s8+BECR+8aaI43WTJiHp9bgLlAZC4wX7rl0cX7SY2scjvFS5muGFy2D3n3CWt6Kz+tC+fAH8/Rb497dhEBI9xo8QIMk7iaddAtf/SXb7RCAcLLbJ3coaE7JxqVRk2pvIMGVQ5ajC39KC58wZTGkezJMtBOwOXO1/ifPX0lK+C1/A120AN9RsprNF0F0WUIoxJVyZNESto5ZUU3TLIxKL0hdR2FjYbVXSitYK9tXs45op16CS5B+VexbfQ7Ihmcf3Pz6qM49cx2QXX+htvCe0GRnEfeFiml57LWoROl9dHeXf+S6FCxZSuGgxpV+7g8qf/pTGV14hcf0X0WZlRZwnaTTop0/HdVwRgvFC/dPPgM9H3R+ewt/c3PHkuw9A/UnEl17H2RKHcdWlcM43oWQbnPpIFooYM36EQKWCW16H9S+DIXrkXWU2gUqFP+gaqnTVAjChoZQMcwZV9ircwfo0hkARphVy+qlzX7s884nnUC/kXP32JSI6E2oi09kiqHXK94zkVopkEdQ4undBRWJx+mICItDtm/2nZXJw9KLci8LHTFoTd827i91Vu9lZNXrrFzn37UedlIR2Ytdd39FI/e53ET4f1Y8+2uWct7qaM7fcQuunn5J0220k3rweb3U1Ta++hvWii0i7775ur22YMQNXQcGoFleF3iG8Xhy7dmFcLNc861BrqnSnXBZ/5XfwWWbhr6vDsGg5XPwo/PAM3HMMjAkxX9P4EQIAnbnHIZIkobJYwhZBKKsns/40GfpEWQhOBCtWmm1oF1+BNjsbR/t6MbkraQgGdrqzCPRqPUmGpC41gOqcdRg1RszarutNMiRR52oTAiEEtc7aLg1pemJeyjw0koZ9NdE7cG2t2EpuXG6XEhk3TruRZEMyfz321z7dcyTh2LcP0+JFUTO6IqHLzSX17m9j2/wBdU89FT7uKS+n5Mu34a+rZ+KfXyD9gR+S/uCDTN7wDjMOHST78d+hMpm6uTIYZs0k0NKCt6ys359JYXTgLipCeL0krl+PKi4O5/7gy1jADxvvg7gsWHNfeDe7cc7sQV+TZtDvMApRWyzhBvbhTWFeL+keNy2eFuwlBaj0GjRmIH8tpsWf07plC0II+cFizaA+LhPw9+i7n2Ce0NU15KgjxZgS8SGVYkyh2d2M1+9Fq9Zi89pw+919tghMWhOzkmextzryjll/wM/+mv1cmtc1lqJT67hu6nU8d+S5cIrsaMJTUoK3tJSkW2/t89ykr30N98mT1P7ucdzFpzFMn0b9n19EeL1MfO5ZjMGdyCEkrTbKlTpinD8fkMtQ6NpZKQGPB9v7m9Hl5mKcO7gBQ4WhwXVMTgowzJmNYebM8L/Z+wJUHYIbngedGdeRo6DR9CqzbaCML4ugl6iscr0hkDN4NJKaeLWB1Eb5bc1RegatVSBNXAaGeIxLFuNvaMBz+kz4GvUpkwBI0iV2e6+Qu6k9tc7aqA/2cO/iYApprSPoRupjjABgQdoCjtYdxevvmr9+ovEErd5WFqUvijj3+mnXI4TgHyf/0ef7Djetn3wCyBvF+oqkUjHhZz8j+a67sG3eTM2vfo02I4NJf32piwj0Bf3UqUgmU9vbIbK1d/a+H3D2vvs4s3499h07+n19hZGDu7gYSatFl5uLYdYs3IWFiMYK+OBhuQDmbLnsjevwYfRTp6IyGHq44sBRhCACqnYWQYOrgSRDMlL+eSSdlSv/ectL0epbYerFAJgWyQ/L9nXrG+IyUAlBoq370s8Z5gwq7ZUdfMN1zrqo5SI6N7GvcdQAkeMJPTE3ZS6egIcTTSe6nAu5jBanLY44N8uSxfIJy3n39Lujyq8thKDpX/9GP3Mmupycfl1DUqtJu/cepu3YztTPt5H3xuttHe/6iaTRYFqwAPuutrhL68efYHv/fZK+8hW06enU/OrXo+p7rRABVzPe0jNoMzPlXeWzZiE8HtwvfAN8Trj8NyBJCCFwHj2Kcc7QWIGKEERAbbGEg8UNrgY54DvviyTaauXUrcoatOYAzJPTAXV5ebKv70Db21y9wUJCIIC6dHu395pgnoDD58DmbcveqXVGzwIKxRxCAeNQYLmvMQKAualy3aVIbSz3Vu9lgnlCuG9CJC7KvYiSlhJONp3s872HC8fOXbgLCki48YYBX0tlMKCJ0Megv5hXr8ZTdApvRQXC66Xmf/8XXV4eaffeQ9Kdd+A6ciRcH0lhlFH0ITx7IfxiIt6976H1noa/34LBIVt5roO74ZKfQ4pcANFbVkaguRmDIgTDh8pqDQeLZYsgCaZfRoI5HbMLVG4/2vyZEC9XFZVUKozz5+M80GYR1Ac8JAlVj/0MQplDIfeQw+vA7rX3aBGEhCBkEXRXcC4ameZMkgxJHK7rKARCCPbX7I/qFgqxbuI6JCQ+KPmg23Fvn3qbta+u5aFtD+EfYMe3gSD8fqp//nO0mZkkXHddzxOGGOs6eTNi81tv0fj663hOnybtBz9A0mqJu/RS0Gho2ag0ux91fPYr+Ot14KiH83+E1xuPNisTqo+iO/oEkkbgSrgQlt4ZnhJqXzpUcSFFCCIQbk5DOyHQ6Ei67DekBlN4tWtu7zDHuHAB7qJTctXS4LxkQyKc+gQi+OBDZJg67iUIPeCjuXo6l5moddRi1VoxabvPSomEJEnMS5nXRQhKbaXUOetYlNa9EKQYU1iUvojNJdFLL1S2VvKTz3+Cw+vgzaI3eevUW31eZ6xoev0N3IWFpN1//5D4XfuKLjcX8+rV1D7xe6offQzTihXhOIYmMRHTwoW0bts6vItU6Bu7n4WPHoG5N8F/fE5g6bfx25xoz10P3z2A9GAJhrkLcXXqQOs8fARJpxuwy7G3xEQIJEm6RJKkQkmSiiRJeiDCeb0kSa8Gz++UJGlS8PgkSZKckiQdCP75YyzWM1DCzWmEaBMCwDTlIlIcclqoJm9mhzmmBQtACJwH5XLC9c56kuNzwd0s9z6OQnh3cTBNtbs9BCBn7MTp4sKWQHdupN4wN3Uup5tP0+JpCR8LZRItyVjS4/x1OesoaioK77juzMsFLxMQAf59zb+ZkTSDvxz7S7/XOhACDge1v/sdpiVLsH7h4mFZQ2/I+MlPMMyZg2nRIjL/55cdMsfMK8/BXXAcX2NjN1dQGDFUHoJ3fyhXMrjmKdAaw5sRtROCmXbGBAwzZ+EuKEAEAuGpriNH0M+c0euss4EyYCGQJEkNPAlcCswCbpYkaVanYXcAjUKIKcBvgV+2O3dKCLEg+OcbA11PLFBZreDz4WhtxOlzkmiQM38kSSLLbQRAk9rRJ2+YNw9UqnCcoN5VT1LKTNBZ4dBrUe+VakpFp9JR3io/SENCkGKK7urJtGSG01prHDUDE4IUOU7QvgDd3uq9JBmSyIvrWiCtM+flnAfAp+VdK3P6A342nN7AeTnnkWnJ5Jop11DUVMTp5qEvrtb0xj/wNzaSeu89fdo7MNTosrPIe+1Vcv/6Etq0jj9jphUrQAgcO0fvRr5xg98Hb30bjIlw7R9BLWfq++vkV39NatvvrGHWTAIOB95SuaOh8PtxHT2Kcc7cIVtuLCyCZUCREKJYCOEB/g5c3WnM1cCLwa/fAC6QRvBvY6gUdXO9/LBt308g3SkXdtOkdnxQqy0W9FOn4jxwAKfPidPnJNmcDnOvh6P/hJbIDWhUkopsazalLfIPQTgdtJssoGxLdvgNvNpRTZqx74HiEHNS5iAhdQgY763ey6K03m22mhg3kbz4PD4p+6TLuX01+6hz1nFJ3iUAXDBRLsH8UelH/V5vf2l6800M8+ZhWrhwyO8dK4xz56Iym7F/3n0CgsII4MgbUHkQLvkFmNoSCny18u+3OqXt+WGYJb83uwoKAPCcOUPA4RiyQDHERgiygPbbIcuDxyKOEUL4gGYgtNMqT5Kk/ZIkfSpJ0uoYrGfAhOoNtTbK7pc4XVu9+mS7CqdJjUrftdKnccECnAcP0uSQawEl6BPg3O/KvQ823Au+yLX8c6w5lLXK38IyWxlmrVmeG4UsSxYVrRU4vA6q7FVddv72BavOSn58Pvtr5EB3lb2KitaKHgPF7Tkv+zz2VO/pUrfo/TPvY1AbWJO1BpBTZWcnz+ajsqEVAs+ZM7gLCoi//LIhvW+skTQaTMuXY9+uCMGIJuCXA8Tpc2DO9R1OhYSgvUWgnzIFtFpcx2QhCO8oHsINhMMdLK4EJgohFgL3AK9IkhSxS4gkSV+XJGmPJEl7aoPfzMFCFbQI7E2yEFh11vC5hNYALZbIdcFNy5YSaG2l+YDsY4/TxUFSvlwnpHAD/HIS/GoaPLFEbm8Z9AnmWHMot5UjhKDUVspE68Ru38azrFm4/e6wLz8vvmcXTneck3kOu6t24/Q5w2/2KzNX9nr+2py1+AI+tp3dFj7mC/jYXLKZ1dmrOwSy101cx6HaQ2HLZyho3Savy7JuXQ8jRz7m5cvwlpXhPXt2uJeiEI3j70D9SVhzX5filr66OtBqUSe0vehJOh36KVPCO4ydBw+iMpnQ5Q3s97ovxEIIKoD2O3Oyg8cijpEkSQPEA/VCCLcQoh5ACLEXOAVELBAvhHhGCLFECLEkNbX/PvHeEOpJ4GySM3PidW2uIWuLjyZL5Ie0eeVKkCTc2+Q3trBLacV/wJf/JXdEm3YJWDPg/f+C9+S4el58Hk6fk4rWCkpbSnt8w8+NywXgg9IPwvMHwprsNXgCHj4t/5RNZzYxKW4S+fH5vZ4/P3U+8fr4Du6hXZW7qHfVc3ne5R3Grs1eC8h1jDpT1lLGmyffpNkd2+qKjh070WZl9XsD2UjCtFxun2rf2XMJcYVhYt9Lcr2gmVd1OeWrqUWT0rV8jGH2LJxHjiD8fhw7d2FcsnhQG9F0JhZCsBuYKklSniRJOmA90DlH8C0glG95A/CREEJIkpQaDDYjSVI+MBUojsGaBkTINeRqll087S0CU4uHelMg4jxNYiKGeXNhpxwwbu9SYvL58oaRqx6H29+G5d+AXU/D6c+YmSRnIB2uO8zZ1rNMtHYvBHNT5iIh8c+T/0QjaXoc3xPLMpaRbcnmB5/+gD3Ve7h+6vV9CqhqVBoumHgBH5d+jNPnBOCd4new6qyszu7o7ZuWOI10UzqflXdsBl9lr+KWjbfw0OcP8a0PvxWzHbRCCLnA3JKeM6BGA/pp01AnJEwynF8AACAASURBVCgB45FKSyWc+hDm39ylHzqAr74eTXLX+mOWlSsJNDdj27QJT3Ex5uUrhmK1YQYsBEGf/7eBTUAB8JoQ4qgkSQ9LkhSSxOeAZEmSipBdQKEU0zXAIUmSDiAHkb8hhOhYbH8YUFvkqp+eliag7YEuhMDQ5KDO7MMX8EWca1m9Bm1hCRaHiNq0HkmCC38KCRNh4/1MjZuEWlLz+onX8Qs/M5NnRp4XxKqzMiVR3oG4fMJyDJqB5cSrVWp+uOyH6FQ6ZiXP4qbpkRuodMeV+Vfi8Dl49/S72L12Pij9gItzL0an1nUYJ0kSq7NXs71ye4caR0/sfwKHz8H66es5WHuwV01zeoOvshJ/fb0s0GMASaXCtGwZ9p07lXITI5FDr4IIwIIvRTztb2xEndS1/ph51SrQaqm4516QJKwXXxRh9uARkxiBEGKjEGKaEGKyEOKx4LGHhBBvBb92CSFuFEJMEUIsE0IUB4//QwgxO5g6ukgI8XYs1jNQQl3KvC2yi8KiCza0b2lB5QvQaJFo9bRGnGtZsxpJCBYWi44WQWe0Rrj4MagtwFD4LrOSZ7G7ajcAS9OX9rjGO+fciV6t57qpsdkhe17OeXx444e8fNnL/dqctjh9MTOTZvLMoWd4ZMcjOH1ObpgWuYzDmqw12L328MO+uLmYd4rfYf309dy75F6sOivvFL8zoM8TwhneoTk2hADk/QS+yko8xcNuPCu0Rwg48ArkrIDkyRGH+Bsb0SR2FQJ1XBzJt98GQNyVVwy5G3O4g8UjEpVZtgj8NhtmrRmNSs4BDkX8m8xE7exlmDMHd4KJpSeJ2E+gAzOugJTp8PnjXDvlWkDuE5Bg6LnxxGX5l7Hrll1cPCl2m6MSDAnhz9pXJEnigWUPUG2vZkPxBq6dci1zUiJnPazMWkmcLo5/F8l9n5868BR6tZ6vzf0aBo2BFRNWsKNyR0zeeN3HC0GlQj+t597EowXLeecBYPtw6NNwFbqhYh/UFUa1BgB8TU2oEyJXJE79/vfJ++c/yPzFLwZrhVFR+hFEQFKrUZnNBOytHd7qQ0LQaIEWb0vkuSoV5QszWbC1COHxIEVIMw2jUsHKb8Nbd3OtJpn4tb9mVdaqXq8z1D5ypLAofRGvXfka5bZy1mSviTpOr9Zz1eSr+NvxvzHt8DTeO/Med829K7yDe1nGMjaXbKbcVk5O3MDejNzFxWizs0dkSYn+os3IwDB3Li3vvkvK1+8a7uUohDjwMmiMMPuaiKcDLhfC4UAdwSIA+bkT2lMw1IysJ8kIQmW1IrU6OgSK24RA6rbXb9HcJAxeepfvPfcmMCah2ftnLp50cb/cMiOJqYlTOX/i+agjBMrac9e8u4jXx/O7fb9jUtwkvjbna+Fz81LnAXC04eiA1+M5dQp9fu8zoEYL8ddcjbugIJxzrjDMeF3yJrKZV0ZthetvkmOO0YRgOFGEIArq+HjUrc6IFkF3riGAwjwdLoOK1g8/7PlGWoNsSh7fAK01A173aCHJkMSrV7zKz1b9jJcufSkchwGYnDAZtaTmREPXPgl9Qfh8eM6cQTd5DArBVVehio+n9vEneuVCE4EAze9soHnDBiXIPBgUbpCbynfjFvIHa0RFChYPN4oQREEdH4+21d3JIqgDvR6nvnshaPTbKJmVjO2jjzsUkorK4q/Iu4/3j94ewP0hw5zBlZOv7BIT0av1TIqbxInGgQmBt7wc4fWiz48cuBvNqK1WUr/1TexbtlD72/9DeKNXuAWo/vkvOHvffZy99z7qnvj9EK1yHLH7OUjIhbzoLtGQEEQKFg83ihBEQR0fj87h7WIRqFNTQOreNdTiaaF6QTb++vpw/ZBuSZkKuatg34vh3cbjnWlJ0yhsLBzQNdzBrBr9GLQIABJvvZX4G66n/plnKL7mWlq3RK5y69i3j8aXXiJh/RexXnIJ9c8+i7cmNtan7aOPqf/znxEeT0yuNyqpPgol2+R+At24RH0NQYtAEYLRgzohHqPD39EiqKtDm5qKSlJ1KNvcmWZ3M61z5d2+jh293Piz+CvQeKbHRjbjhemJ06myVw1ol3EovVI3BmMEEOyf/MgjZP/hSYTPS9ldd1H16GMdXD/C66XqJz9FM2EC6T/4AWnf+y7C46Hl7YFnavvq6ii/+25qfvFLGv4yPOXFRwQ7/gAaAyy8tdthYdeQIgSjBynOitkpiOsULNampmHRWqJaBAERwOaxoc+YgG7yZOw7e9lwfOYVPZasHk9MS5TTPQfiHnKfKkadmoI6rpv9HKMcSZKwrltH/ttvk3jbl2n861+pbicGDS++iPvkSTL+60eozGZ0kyZhmD+P5g0bBnzv5n+/BX4/6uRkGl9/fcDXG5VUHpL3Diz+Socqo5HwNzaCJI3In0dFCKLgtRjQ+SGetiweX20tmtRUrDprVCGweWwI5M1k5uXLcezZ2zuzWWuEWVdBwVvgdcbqY4xapidNBwYoBMWnxmR8IBIqnY70Bx8k6atfpfHll6l+5FFaP/uM2if/gOWCC7BecEF4bNxFF+E+VoC3qmpA97Tv2IEuP5+Ub3wDb0np+CqE526Fsl3w6q1gToO1P+xxir+pEXVcHJJm5GXtK0IQBY9ZLo2Q4JZ9fgGXi4DNhiY1hThdXFQhCLmM4vXxmFYsRzgc4d2tPTLvJnC3wIn3upwSPt+4yvZINaZi0Vr63cRGCIHnVPGYjQ9EQpIk0u7/AUm3307jK69Q9vX/hyY5mYyHHuowzrxGDmi2fvZZpMv0CuH349i7F/OK5RgXyT0eHPv29zBrlBMIyOWlfzUdfp4Fz10Ebhusf6VHawDA19g4It1CoAhBVJwmWbXj3PK3yNeus1B3FkGLWxaCOF0cpkVyTX/ngYO9u+mk1WDJgEMdzWznwYOcXLWa2v/7XZ8/x2hFkiTy4/P7LQS+mloCra3oxolFEEKSJNIffIDcV14h839+Sd6b/0Sb3rFxkX7qVDSZE2j9tP9C4C0vRzgcGGbPwTB9OpLRiPNQL3/ORyuf/FzuPzxhnlwr7Lo/wXf2QfbiXk33NzYpQjDacJjkb43FLmfxtG8oYdVZowaLmz1ycDNeH48mJQVtZibOw4d6d1OVGubeACffB2dbX9q6p/6Iv6mJ+qefDgvSeCAvPq/fQuApPgWM3YyhnjAtWkj8VVeFS6q3R5IkLGvXYt++nUA/s33cp4Lf3ymTkTQa9Hl5eIqHvgXpkFFbCFt+LVcV/dJrsOr7sgVv7P2DXS4417PlMBwoQhAFm1n+1phscn52WAhSUrq3CDxtFgHIvYxdB3spBACzroGAF07KvQYCTif27dsxLlgAgGPP3r5/mFFKXnwetc7ablN1o+E+FcoYGl8WQW+xrFmDcDhw7N7dr/nuIlkIdJMnh/92B8V3TLLtcdDo5UKR/eyy629sRJ3Ycx2x4UARgig0m+X/bH2zHLjtrUUQcg2FSlAb583De/Zs79/ksxbLwadCOavDdeQIwu0m+c47kAwGnPvHuB+2HaHmOMXNfa+y6Sk+hcpqRZM2uE2MRivmFSuQ9HpaP/20X/PdRSfRZGSgtsg7wvX5efjOVhJwOGK5zJGBu1UuHzHvi2Du2kugNwgholYeHQkoQhCFRq0Xnwq0TXYgGCNQqVAnJWHVWXH6nBF7EoQtAr1sERiDdfCdh3pZE0algumXyBaBz4PruLypyjB3HobZs3Ee6oN1McrJT5CFoD/uIXfRKfSTJ/epwc54QmU0Ylq+rN9C4Ck6JffaDaLLk/+vPGfOxGJ5I4vij8Hngjn9L/kesDsQXm/UyqPDjSIEUbD5Wmk2A41yoShfbS3q5CQktRqrNtjcPkJPgmZ3M3q1Hr1arjpqmDUL1OrexwkApl8OHhuc2YKr8DjqhAQ0aanoJ0/Gc3oM+2E7kWXJQqvS9ssicJ86NSZrDMUSy9q1eEtK+/zwFoEA7uJi9JPb3G6hWEzIJTemOL5RLiQ38Zx+X8LfKPfbUoLFo4wWTwutFg2+erlvsbeiAm1mJtDWujKS77rF09Khx7HKZEI/dWrf4gT5a0FrgsKNuE+eRD99OpIkoZs0CX9TE77Gxp6vMQbQqDTkxuX22SLwNTbir69HP3lKz4PHMZa1cv/o9laBfecuSv/f/6P+2Wejpit7z55FuFzoprQJgTY3F1QqPKfHmBAE/HI699QvgFrb78u07SpWYgSjCpvHhtOqw18r+/a9FWfRZWUB7YTA21UImt3NYbdQCOPcuXJj6t7WEdIaYfI6KHwXb2kZuly5Wb1u0iR5LSUl/flIo5L+ZA55xniNoVihy85GN3kytk8+AcB54ABld96JfctWan71a1re6tx6XMZ98iRAB6FV6XRoc7LHnkVQthOcDTD90gFdZiQXnANFCKLS4mnBkWTEW1WF8PvxVlaizcoGerYIOreoNM6fR6ClBc+ZPjzAp19KoP4s/sZGtNnyfUNC4B6Lftgo5MXnUWYrw+OPnubo9Xt58+Sb3P/Z/dy56U6efuvHAOzQVxAQShG/7oi75BIc23fQ8NeXKb/7O2jS05m6bSv6mTOpe/qZiFaBp13qaHv0efljz3VZuBFUWphy4YAu4xvBdYZAEYKotLhbcKTH4W9okHOmvV60nS2CCEIQySIwzJMbrfRpw83Ui/HY5U1tumz5vtrMCQD4BlgaYDSRH59PQAQoaYksos3uZr787pd56POH2Fu1F0/AQ1q5Hade4p7jP+O7H30Xt989xKsePSR99atos7OpfvRRhM9H9pNPoklMJOnWW/AUF+M6dqzLHHfRKTRpaV1q5uhyJ+IpLR1bO+AL34W81WAYWH0gf+PIbUoDihBExea14cmQN3/Yg+V9tTn9swj0kyejMplw9SXjx5KGVy8XXgtZBCqDAXViIt7K8SMEUxJk98PJxpNdzgkheHDLg5xoPMGv1/6aD278gL9c+hdWNqeRvGAp9y97gE/LP+XRHY8O9bJHDWqLmbw3Xifz178i719vYpgu/8xZ1q0DtRrb5s1d5rhPnepiDYAcJxAuF74YlbgedmpPQH0RTL9swJfyNzSARoMqwga/kYAiBFFocbfgz5S35re8J9f+CfUT7ckiSNB3DAhJajWGefN6X2oiiFcXFILEtn67mgkZeKsq+3Sd0Ux+Qj5alZbjDce7nPug9AO2VGzhnsX3cPGki5EkiYDHg6uwEOPcudw661bunHsn/yr6F5tLuj7QFGTUCQnEX3452vT08DFNYiKmhQuxf9axLLoQIpiR1TUQH4pl9ckFOpIp3Cj/PcD4AICvoR5NUtKITWdWhCACQghsHhuqnEzQaHAdPow2Kysc6DFrzEDXYLHH78Hpc4Y3k7XHOG8erhMnCLhcvV6Hx5+EShNAXdvW00CbMQHfOLIItCotUxKmUNDQscGPEILntz/B3VusXLzNgfD7AXDu2wdeL8Zgnaf/WPAfzEqexWM7Huu2h4RCV8yrzsV17Bi+hobwMd/ZswiHo0PqaIiwEJSOISGYMB/iswd8KX99A+rk/m1GGwoUIYiA0+fEJ3yY4pIwBv37xoULw+fVKjUWraXLPoJw5VFdBCFYMB98vog+12h4G11o41RIhe+Gj2kzMgZcPni0MTtlNkfrjuIP+MPHtlRs4fzXi1i9tZH6X/+W2t/L7RdbP9sCWi3m5csBWUgeOuchGt2NPLHviWFZ/2jFfO4qAOzbPg8fC9cYmtrVItBOmICk1Y6NrLaWs3KZ6emXx+Ryvvp6NIoQjC7a1wtK/fa3MC5cSNp993YYE6nMRJNLDghFswigD5VIAW95mRwgPvk+OOS3Ms2EDAItLQTs9t5/oFHOorRF2Lw2ipqKwsde2/Y0KwsECbffRvz111H/1B/l5uxvvYV55TmozObw2NnJs/ni9C/yauGrHK07OhwfYVRimDUTdUIC9q1tLTDbUke7WgSSWo02JwfPWBCCI/8AhFwEMgb46+vRJI/MgnOgCEFE2guBeeVKJv3tFbQZGR3GRCo8177yaGc0KSlos7J6XSJCCIGnvALt9IVyEbrDbwCyawgYV1bBonTZzbO7Si6Qdqj2ENKuA6gDkHjddWT8+McYZs3i7H334a+rI+XOO7tc4+6Fd5NsTObhHQ/jDXTf6F1BRlKrMa9cSevn28KZQK6jR9FmZqJOiLwxSpebOzZiBIdfh8xFkDzwooVCCHwNDaiTFItgVBF6wLfvV9yZSO0qQ/11IwkByPsJnAcO9Cq9zt/QgHA60U1fABnzYP9LIATaCbIgjafMoSxLFlMSpoQDvi8ceYGFpRpUSUnop01DZTCQ88zTpHzrW2T/4UlMS5d2uYZVZ+WBZQ9wrP4YD255kCp7VcRaUQodMZ97Lv7aOtwn5E5xzqNHMcyeFXW8bmIwhbS3mydHItXHoPIgzL0xJpcL2B0IlwtNiiIEo4pwcxl99NzhOF0crd6OMYKehMC0dCm+qqpe+VC95eVAMHV0ydeg6hCc3IwmI7SXYPxkDgFclncZ+2r28cT+J/ig9AMWVBswL10azsLQpKSQeve3sa5bF/UaX5j0Be5ZfA/vn3mfi964iIUvLeSC1y/gzZNvDtXHGHWYV50LgH3rNnyNjXhLSjHMnhN1vG5yPsLtHt1tK7f+Ri7xMu+LMbmcv0EuU6NYBKOMUDZQnDa6EERyDXUXLAYwr1wJQOu2bT2uwVMWEoIsWHALJE+Bd76HVt0MkjSuLAKA9TPWk2ZM45lDzzBDm4OxpiWcztsXvjrnq/zzqn/yn8v/k28t+BZZliwe+vwhPi79eBBWPfrRpqejnzoF+7at2LfKP7fmldGLr+mnTAXaYgmjjsL3ZLfQ8m/0u+R0Z0L1ypQYwSijNxZBpGBxs7sZtaTGrDVHnKOdOBFtdnaHLIxohCwCXVYWaHRww/PgsSM9fQ4agx/vR0/DhnvB2/t01NGMVWfl5ctf5rFVj/HkpPsBMMyc0a9rTUmcws0zbuYb87/Bsxc/y9TEqfx6768VV1EUzOeuwrF7D/UvPI86MRHDnOgWQWijWagMxaihthDeexBe+zJkzIW198fs0v6gECjpo6OM0AM+2gMdwKKzYPfaO9SyaXI3Ea+Pj7ppRJIkzOeei2PnToS3+4Clt6IcdVJSW/bLhPnwze1w8aNoUpPweYyw+1l474E+frrRS4Y5g6smX4WmuAIA/fT+CUF7dGod35j3DUpaSthasbXnCeOQxC/dLJeePlZA4pdvRVJFf2yo4+LQZGSE+2iMeFzN8MbX4MllsOsZuUPgbW/JhR9jhK8+mPGnCMHoosXTglVrRaPSRB0Tp4sjIAI4vG0dmZrdzV3KS3TGsnoVAbu9xxaBntIydBMndrppJqy8G+30JfhU6XDOt2HvC1A9vlIiPadPo7JYYtZ97PyJ55OgT2BD8YaYXG+soZs4kdy/vEj6fz5I8h139DjeMGc2rsO9bMQ0nPh98Mp6OPZvWH0v3HMcrv8TmGLrwmmLESiuoVFFpMJxnYlUZqLZ07W8RGfMq1ahMploeffdbsd5SkvR5U6MeE6Tlo63qlr+4dWaYfsfur3WWMNzuhhdXl7MtutrVVoumHgB2yq2ddi0ptCGafFikm67DZVe3+NY45y5eEpK8Dc3D8HKBsBn/wuln8PVf4ALHgLL4LQ19dXVo4qLQ6XTDcr1Y4EiBBFodjdHzfwJEXrzD+0dADm20NM8lcGAZd06bO9vjuoeCrhc+Cor0Xa2CIJoMtIJ2GwEhB7mXAvH/gWeMdgrNgru02fQ5U2K6TWXT1iOzWvrUspCoe8YFywAwLFnzzCvpBsaTsOWX8mZQfNjkx0UjVCdoZGMIgQRaPb07OIJPfCb3E1t83ohIABxl12Gv7kZ++eRg8bhQPHE3IjnQ5vbvNU1MPcm8LTCqQ97vO9YICSSod4MsWJphrz3YEfljphedzxiWrQQldlM6yf964c8JHz6S1Bp4ML/HvRb+WvrUI/gPQSgCEFEevNmn6iXC9B1EIJeCAiAZdW5qBMSaPrXvyKe95SWAnTrGgLwVVdB7krQx8HJ8VFdMyySOZG/N/0lxZjClIQp7KrcFdPrjkcknQ7L2jW0bNqEv7UVf1MTzsOHe0yQGDLqiuDQq7D0ToibMOi381ZWop2QOej3GQiKEESgc9/hSCQY5FhAqL6QN+DF7rX3yiKQdDrirryS1g8+xN/U1OW8pyQoBFFcQ9oMWQi8VdVyH9X882QhGEsNQaLgKSsDQJcz8IqQnVk+YTn7a/bj9Y+QB9YoJulrdxBoaeH0dddzcu15nLnxJkq+fBsB9whoErTtt6DWwbnfG/RbCb8fb01NlxI1I42YCIEkSZdIklQoSVKRJEld8hklSdJLkvRq8PxOSZImtTv3YPB4oSRJX4jFegaCEKJXLp6QUIQsgp52FXcm4bprEV4vze90zVTxlJagio+PWs9Fkx6yCKrlA1MvAttZqOl9ZdPRije00S4nJ+bXXpS2CJffpcQJYoBxzmwm/Pzncq+Da68h9d57cB44QMMLfx7ehTWXw8FXYdFtgxYcbo+vrl7ubpg5+JbHQBiwEEiSpAaeBC4FZgE3S5LUecvnHUCjEGIK8Fvgl8G5s4D1wGzgEuAPwesNG3avHb/w9+ji0aq1mLXmsBDUO+UUsWRD73yBhpkz0c+aSfM//9nlnLekNKo1AMFOZfHxeKuDu4unXCT/ffL9Xt17NOMpL0MymQYlFW9+6nwADtb2rYGQQmQSrr2GvNdeZcJPf0rKXXdhXrOaxpdfRviGcePe578HBKy8e0hu56uUS21oJoxxIQCWAUVCiGIhhAf4O3B1pzFXAy8Gv34DuECSc/+uBv4uhHALIU4DRcHrDRvhMhG9eLNP0Ce0CYErKATG3geFEq69DtexY7iOd+y+5S4u7jEYqsnIwFcVtAjiJkDabDg19sskeMvK0WVnD0qnp3RzOhnmDEUIBomEG2/EV1uLffswBeTt9bDvRbmYXEJsY0zR8FTImx/HQ4wgCyhr9+/y4LGIY4QQPqAZSO7lXAAkSfq6JEl7JEnaU1tbG4NlRybk4ulpHwEEhcDVP4sAIO6Ky5G0WpraWQW+xkZ8VVUYZnS/a1aTkd6xFPXk86F0+5hPI/WWlw2KWyjEgtQFihAMEpbVq5F0ug79DYaUnX8ErxNWfX/IbukpPg2SFDXxY6QwaoLFQohnhBBLhBBLUlMHz7cX7inQQ7AYIMmQRINL3j4eEoIUY0qv76VJTMSybh0tb7+D8HgAwh3Muiv1C3INIm/wbQOAyevA74GSngvajVaEEHiCFsFgMT91PlX2Kqrs46uo31CgMhgwLVmC/fNh+Bl122DX0zDjckidPnS3LT6FNjsblcHQ8+BhJBZCUAG0f0XLDh6LOEaSJA0QD9T3cu6Q0heLIM2URo2jBpBdQ3q1vtv6RJFIuP46/I2N2D75BABXsHFNTxaBNiubQEsL/pZg4bvclaAxwKmP+nT/0YS/rg7hcg2qRaDECQYX87nn4j5ZhDeU6DBU7Hleriu0+p4hva3nVDH6/PwhvWd/iIUQ7AamSpKUJ0mSDjn4+1anMW8Btwe/vgH4SMjdWd4C1gezivKAqcCwJnL3VEq6PWmmNBpcDXgDXuqcdSQbkvvsuzafey6atDSa/yG7h1q3bsMwe3bUjKEQ2uBbcSivHq1RFoMxLAQdSnMPEjOSZqBX6xUhGCTa9zcYMrwu2P4k5K2FrMVDdtuA04n79Gn006YO2T37y4CFIOjz/zawCSgAXhNCHJUk6WFJkq4KDnsOSJYkqQi4B3ggOPco8BpwDHgP+JYQYliLvfQlDTTVlIpAUO+sp8ZRQ6qp7y4rSa0m/tprad2yhdYtW3EeOIB59aoe54Uehp6QEIDsHqo9Ds3DalQNGp5SuaGPLjfyjutYoFVrmZ08WxGCQUI/bRrq5GTsO4cwYHzwFWgN1uYaQpz794PXG7FjHkCVvYrXCl8Lu5eHk5jECIQQG4UQ04QQk4UQjwWPPSSEeCv4tUsIcaMQYooQYpkQorjd3MeC86YLIbqvxDYEtLhb0Kv1GDQ9+/TSjGkA1DhqqGitINvaP9910lduR2WxUHbXXUgqFYk39twiL5Re2qFReCiNtHBjv9Yx0vGWloJKJfdoGETmp86noL4At38EbH4aY0iShGnZUhy7dveqZeuACfhh2+OyJZC3ZtBvJzweHPv24bfZZHevWo1xUVcrxOv38vXNX+eRHY/wzQ++Oey9MEZNsHio6G2ZCJBdQwBnW89Saa8k29I/IdAkJpLzxz9iXrmSzF/+Am0vHnRqqxVNaiqeU8VtB9NmQOrMcKP7sYanpBRtZibSIFdxnJ86H2/AS0G9srFsMDAvWya3bC0r63nwQCn+GBpPyyXbByHluD0iEKDsP75JyZdu4eTa82j8y0vEXXYZakvXuOHHZR9zuvk05+Wcx9H6o3xYOry1whQh6ESLu6XXQpBllR/YOyp3EBCBflsEIBfqmvj8c8Rddlmv5+imTMbduRPU/PVQtgOqRkE9+D7iKe1+o12smJ+mBIwHE9MyeauQY9cQhAP3/QVMyXK20CBj/3w79m3bSLh5PZY1a7BceAFpP7gv4thPyz8lThfHb9b+hmxLNq8Wvjro6+sORQg60eBqINGQ2Kuxcbo4Jpgn8EHpBwBkWQbXZdEZ/eQpuE+dQgTauqSx+Ha5R8GHD4+q2kPOw0eo/vnPcR6M/vD1lJaiHYJ87BRjClmWLEUIBgldfn4wTjDIQtBaC8c3wvybQdNzH4WBYtu8GclkIv2BB8j+v9+S8/vfo01Lizh2x9kdnJt5Llq1lqsmX8Weqj3DmrKsCEEnGlwNfdodPC1xWrhX8YykgbdO7AuGWbMQDkfH/rDGRFj3X3K5ib+th00/gvf+E45vGLHC4D17lpLbbqPhxb9Q+tWvdYx7BPE3NRFobo5amjvWzE+dz8Ga0iXKhgAAIABJREFUg0Pjxx5nSJKEecUK7J9/3vElJtYc+jsEvLDwy4N3j3bYt23Dcu7KHpv31DpqqXHWMC91HgCX5V+GQPDe6feGYpkRUYSgE/XO+j7tDl6QJjfhmBg3Mdy1bKgwLZGDUI69ewG5Mmf9c8/jyb4Szv8vKNsl50/veR7+/iW52f0IfLDVPfUUCEHuy39FCEHdH57qMqan0tyxZlHaImqcNZxpOTMk9xtvWM5bi7++HteRI4NzAyFkt1DOcjl2Nsj4m5vxlpdjmDevx7GhooazkuVNo7lxucxJnsPG08OX5KEIQTvcfjc2r61PFsHts27nq3O+yn8u/89BXFlktDk5aDIyaP34EzwlJZy58SZq/vd/KfnSLQSW3w0/PA0/qoQHy+Vg2Z7n5DrsI4iAw0HLho3EXX4ZpsWLSbjhBpo3bOhSnrun0tyxZm3OWgA+Kh27+zKGE8vq1aDV0rJhkB5+ZTuh7oRcZXQIcBXID3fDrO4rAgAUNhQCMD2pbYfz5fmXU9BQQHFTcbRpg4oiBO1ocMr5vEmG3le21Kq13LP4HlZMWDFYy4qKJEkkXHcdrZ9+SvE11wKQ8dOf4Kutpbl90xu1Bi56GLKWyLEDr2vI1xoN+46dBBwO4q+8EoD4q68Gnw/bBx90GOc5XQwq1aDuKm5PhjmDuSlzeaf4HcU9NAioExKwnreW5rffJuAYhPpYe18EnRVmXRP7a0fAXSg/3A0zZ/Y4tqSlhDRjWocqBJfkXYJKUrHhdNey9EOBIgTtCFcQ7YNraLhJuu3LmJYsQZuRQc4fnyJx/Xp0eXnYPuz0JqtSw4U/gZYK2VU0QrDv2I6k12NctAiQayxpc3Jo2dhxS4mr8AS6vLxeNU+PFTdMu4GipiI+Pxu5pajCwEj66lfxNzRQ+8TvYyu2rmY4+ibMvR70lthdtxs8JXIPkd70Ji6zlZET1/GFJsWYwvKM5Wws3jgsLx6KELQjtMOvL66h4UadkEDuX19i8rsbw03DzatW4di9u2s3qLw18p+tvx0xVoFj+w5MixehCu4NkCSJuEsvxb5zJ76Gth2X7sJCDNOnDenaLs+/nGxLNj/b+TNqHYNX8Xa8Ylq0iIQbb6ThhRc4dfEXqPzv/8bf3DzwCx9+A3zOIXMLgey61PXSWi2zlTHR2tXFeeXkKylvLeejsqF3RypC0I5wKelRJASRMC1binC5cBdE2BC1+l6w18DBvw39wjrhq6vDffIkphXndDged9ml4Pdje19utONvacFbXo5+2tBVjQTQq/X8bPXPqHHUcMWbV/DjbT/mTPOZHufZXF4qm52UNThocXkV11I3ZPz0J0x47FH006fR9PoblH/vewP7fgUCsOsZSJ8LmYtit9Ae6O0eF4fXQa2zlhxrV9G4NO9S8uPz+dmOn1FQXzCkPzeaIbvTKGA0uoYiYZw9GwDnsWNhKyFM3lrIXAifPy6/MamGryGcY/duAMwrlnc4rp8+HV1+Pi0bNpK4fn04K8q4cOGQr3Fh2kJevfJVXjjyApvObGLTmU08ecGTLM2Q68eUNTh4/1g1B8uaOF7VwtkmF63ujuUCTDo1uclm8lPNTE4xk59qIT9V/tuiH9+/gpJaTcL115Nw/fU0vPIK1Q8/gn3rVjmY3B9ObpLrbV37zKDvJA4hvF68Z88Sd3nPm0HLW+XaYJ1dQwAalYb/WfM/3PH+Hdz0zk3E6+O5cOKF3L/0fkxaU8zX3eHeg3r1UUa9sx6z1tyrOkMjGc2ECagTE3EdPdr1pCTJTbtfvx0K3obZ/5+98w6Polob+O/sbnrvpHcSILQkEAhI79JFQVFBRb32cq/t+ontWq71iih2xYKIIFKk9yIdQockENJI7z3Z3fn+mCQQsukVmN/z5GH3zDkz7w67857znre0z2aaIUrPnAUjo1opt4UQWN86gcyFn1GRlkbxocMIIyPMejfsmtcW+Nn48eagN3mi7xPM2zSP53Y+x3Mh3/L9rjQOx+cA4GZjSnc3ayL9HXG1McXGzAiVEOSWlHM5t5T4rCJOJeex/mQK+qsmeu62ZkT42jOhpytDujphrLl5F+m2M2aQ+ckC8laurF8RSJJs/kk+DD63yFHDQsirgV0fgI0nhExvN7m16emg0zUqNUxivpxWw9CKAGRPolVTVrEtcRtR6VGsjF1Jfnk+Hw37qFVlvhZFEVxFVklWkzyGOitCCEy7BVMWE2u4Q7dJ4BAI296EoPHtEnVpiLKYGEx8fAzmDrKeMIHMTxeSs3Qp+evXY96/f4cX93Ayc2K6x/N8ePoRnlm/ECftJF4YF8zEXq542jduxlam1ZGQVcyFjCIuZBRyLrWArefS+eNYMo6WJszs58GMME98HMwRQlCh05OaV8rl3BLKtHqCXa1wtrq+Jyp1oTI2xmrMGPLXr0fSahEaA48nSYK1T8ORH0BlJFcd8x4Ew16E2K2ycpjyGaiN2k3uqkqBRl0arkucUCC7QdelCEA2Td/e9XZu73o73tbefHrsU46mHSXUpe1MXYoiuIrs0uzr3ixUhbGPL3mrVyNJUu0aCSo1jP8v/Dwd9n4CQ5/vEBnLYmIw693b4DETX1+sxo4la9EXALi89GK7ySVJEsXlOvJLKygp15FRUMaxxFz+PJbMudQC7Hx7YuqyjzUzXsferHF5qaow0agJdLEi0OVK8GG5Vs+e2AyWHEhg0Y4LfLb9AtamGow1arKLymqsIABuCXTkoSF+DA5wbJPazR1G9EbMy3aSW1hI6eG9mA0YWrvPqRWyEhj0FIyYL9cg3vEOLJbdj+kzW/5rRypSKhWBa5cG+yYWJGJrYtvofGb3dL+Hn8/8zM9nf1YUQXuRVZqFt3X7pDBoa4x9fdEXFqLLzERjqLRnwEjoMV3+EbmEQHDjk921BrrCIiqSk7G9fUadfbrMfwWhVmPs74fV6NFtKk9xuZY/jiaz5vhljiflUlpRO/VBT3cbPry9NwGe3ty9/k5WX/iDuSFzW3xtY42KEcEujAh2ISmnmO3nMzifmo9OL+FkaYKbrRnudmZoVCoOxGWx9GAi93x7kD6etjx4ix+DAx2xMWvcDFinl7iUVUS5Vk+QixUqVTsoknPrYP3zYGoLUz8HVwMmvtit8OssLGxk80rh109i1nsnmF1VoKkkBza8KKeUHvmqPKHp9wD0mgnRG8DCUd4Da2flqE1NAUDTiBVBcmFyk7IUm2nMGO87nuXRyykoL2iz7AWKIriKzJJMQp3bz9OgLTH28wWgLC7OsCIAmPwp5MbD73Nh5k/QdWy7yVd+QTZbmQTWXb1J4+CA+0cftqkckiSx/lQqb6w5Q2p+KcFdrLizvxeuNqZYmRphZqTG1tyI4C7WdLGpMsl4EOocym/nf+Oe7vegvmbDPTE/kTf3v0mZrowX+r9QnUqgMXjYmXPPgLonIwP9HXhkmD8rjiTz2fZYHltyFAAzIzXGGhVqlUCtElgYq/FysMDb3hw7C2MyCko5m1LA+dQCSirk2k9+ThZ8cHtvQr0al2SxWWTGwPL7wNYbijJgyR3wyN9gfpUJtqwAVj8BjkFo5m2hZM1gVuWU4bBiFtPuWgeqyn2TzfOhOBvu/qOmk4OJJfSse0LR1lSkpKKysjKYbvpaUotS8bNpWunKsT5jWXJuCftT9jPau20mRIoiqKREW0JuWS5dLBpe3l0PmPjKiqD8YhwWlWl/a3eyhNnL4adpci6i276BHtPaRb6ymBhZhHoUQVtzIaOQ19ecYVd0Bt1drfnfrD5E+No3ytxyZ/CdPLfrOfZe3ssQjysFT4orinlo80PkluVipDLisa2P8eeUPxtV8a6xmGjU3BXhxR3hHhyJz+FoQi5ZhWVo9RJavR6dHvJLK0jIKiYqIYf8Ui125kYEVSq5bq5WSMCCrTHc/sU+3pnWkzv6tVHE9tY3ZFv+nDVQkALfjIJVj8OsX67M3Lf9B/IvwwOLiS6+zBnHcrzS4fXyBMbu+i/mw16CS3vk3EGRTxpeUXQg2vQ0NC6Gs4xeS1pxGpFukU06f0+nnlgYWbD/sqII2pyqFLA3iiLQdOmCMDWlPC6u/o7m9jBnNfxyB/zxENj7gathu31rUhYTgzA1ra693J4k55bw3Z44ftx3CVONmvkTu3PvQG806sZ77Iz0GomTmRO/nvu1hiL49NinJBUm8f3Y7zEzMmPW2lksPr2YJ0OfbPXPoVGriPBzIMKv/n0tnV5CbcAENC6kC48vOcbzK06QV1LBg0Nauch6TrzsmXbLs2DlIv+Neg02vSzb9sPmQtxuecO3/0Pg2Y8NRxeQ7yIIvQglkopdhxYwrrwIon4FOx95U7iToc3MQuPYcJnagvICiiqKcDF3adL5jVRG9HPpx/6UtivvefP6ql1DSpFs53O1aNjOdz0gVCqMfX0pu9SAIgAwtYFZS+QU1mufkd3w2piymBhMAgIQqrb9Cl7OLWHj6VQWbovhn8uOM+GT3Qx6dxvf741jah93tv1rGPcP9m2SEgA5x9SMrjPYm7yXi3lyorCo9Ch+OfsLM4NmEt4lnB4OPRjhOYLl0cup0FU0+zNIksTKmJW8f+h90orSmjzekBIAsDY14pt7w7m1lytvrTvL+xvPtW4Q09HF8qw//P4rbQMeBb9hsOEl+PtTWPmwPPkY9RqSJLEpfhOm/gEIvYRfgTn7nX3kfma28urVuGHzS3ujzcpC49Cwk0lLJpsD3AaQUJBAcmHb1CNXVgSVVP0nuVm6dbAkrYeJrw8lJxuZ5tfCQa5jsPoJuLgNAka1qWylMTFYDm5m0FAjOJqQw3sbzrH/4pU0FV2sTfFzsuBfY7oyta87HnYtC9KZGTSTxacX8/6h9/lg6Ae8svcVulh04ZmwZ6r73Nb1NrYlbmNX0i5Geo9s1nX+ivuL+X/PB2Bfyj6W3roUY3XrlOs01qhYMKsv1qZGfLb9Ail5pTw7umuL7w3acjj6E3QdBzZXVn352kJ+DIokrCSVgZv+Dyyc4c5fwdiCc1lnic+PJ7j3A0A0g/T+bLbKg+cuyJOUDgx+rA9tZiYaR8cG+6UVy0rcxaJpKwKgOqnlgZQDTA9s/RgJRRFUklKUgkqocDJveIl3vWDs40v+ho3oy8urc/nUS6+Zsk33wFdtqgi0OTnoMjLbbH/g8x2xvL/xPC5Wpjw3NohIfwe6ulhh0cpRvA5mDjwZ+iTvHnyXW5begk7S8cWoL2pklYx0i8TJzImVsSubpQj0kp7Pjn1GD4cePNjrQZ7e/jR/xv7JHUF3tNrnUKsEb08LwcHCmEU7L/DH0WTszI2wtzDG3sKY3h62TO3rToi74X2OgtIKLmQU4WVvjr1F5ffs3Bo5lcnVqwHg5T0vsyNxB5jCTzO/o4//eDCWlc7GSxtRCzUD+00nnS/pWmzFdwWnKDQyxbKTKgF9URFScTFqxyasCMybviLws/HDzsSOI2lHFEXQUjbHb8ZcY84g90G1jiXkJ+Bq4YqRqv0CUdoaY18f0OupSEzExN+/4QEaE/mHu/M9yLoADo0Y0wzacqP4f1ui+d+WGCb1duOd6T3bPIXDXcF3Yaw25lDKIaYETGGgW828SRqVhkn+k1h8ejGZJZk4mjU8c7yaQ6mHSCpM4vG+jzPCcwQ9HXvy/anvmR44HY2qeZ8tuzQbSyPLGqsKIQT/GhvErP6ebDiVSlxmEbnFFaTll/Lj/ni+2RNHf1975gz0YXCAIxV6PX9fyGLt8cvsiM6gXKtHJeD2ME/mT+qOxaFvZU8h/yvKLzonmh2JO7ivx32si1vHexdX8EvwdASy+WvjpY1EuEbg4OxFlr09XbIl8ITY3NjqAlCdDW2WnJamMXsEacVpqIQKR/OmfQdA/v/p49yHqPSoJo9tDDeVIlh0fBGOpo4GFUF8frzBjIDXM8Y+PgCUX7rUOEUA8gbezvfg5O9ttjFXFlvlOhrQaueUJImPN0ezYFssM8I8+O9tveq0jbcmQojqKNC6mOw/me9OfceGuA3c3f3uJp1/S/wWTNWmjPQaiRCCB0Ie4OkdT7Pp0iYm+DU99mPtxbW8vOdl/Gz8WDpxKSbqmlHlHnbmzLul5qZxXkkFyw4l8sPfl6rdVatwsTZhdoQX3l0KWHdxA8uOlpMVF8U3RXth1OtXXD8rP4tKqJgbMhdPa0/e2PcG+1L2EekWyZmsMyQVJvFgrwcBMPb2xiq1AHrLhVw6rSLIrFIEjVsROJo6NnuyGeocyo7EHeSU5jS6rnpjuak2i8Ocw4jKiEKrr5kUTJIkEvITbphgsiqMveXPU37pUuMHWbuB10A4/WfDfZtJWUwMKisrNC5Nt5UaQpIk3tt4ngXbYpnVz5P32kkJNBZ/W3+62Xdj7cW1TRonSRK7knYxwG1Adf6r4V7D8bfx5+uTX6OXrmzqb7y0kUkrJ/HgpgfJLMk0eL5SbSnvHnwXvaQnNjeWFdErGiWHjZkRDw7xY9fzw/llXgQvjQ/mvRHWnPBdyH7dnTyX/hRfRT/J2dIVjBy8m1mFP1GEGZe8a5owdiTuoI9TH+xN7ZniPwVnc2e+OvEVICsojdAw0kteQRh7eyOSUrAytuJ8zvkm3bf2RJsl3+vGbBanFaW1yCtxWuA0ds/a3epKAG42RdAljBJtCeeyz9Vozy7NpqCi4IZTBGpra9QODpQ15EJ6LT2mQsZZyGibH2BZTAwmgYGtkh5Br5f4z19nWbTjArMjvHh7Ws/2iZZtIhP9JnI663S1h1FjSChI4HLRZQa7Da5uUwkVD/Z6kNjcWLYmbAVkJfD8rudRCRVR6VH8c8c/DXr/bI7fTF5ZHt+O+ZYA2wC2JGyp1ac+1CrBoABHHo5w5I5zT2GdcxoRNpfNpSkUaEsI1thwJGMrYerDfC+mMWPxec5czgegqKKI8znn6e8qx7QYq425P+R+jqQdYVvCNlbFrmK09+jqeAtjH2+06en0MPfv1IpAlykrAnUjNotTi1ObtVFchY2JTavGo1zNzaUInOVi70fSjtRoj8mVbdZ+tq3sR90JMPb1adqKAKDbZEC0yapAkiTKYmJbZX8gv7SCJ5Ye49s9ccyN9OE/U0M6pRIAOde8SqhYe6Hxq4IDKQcAiHCtmaZ7nM84fG18effAuyyKWsSLu16kj1Mffr31V17o/wJH04+yPXF7rfOti1uHu6U7/br0Y7jncI6mHaWgvKBpH0SSYNVjkHMJ7lwKE95nR/BwXIUJb8afQ4uebQEDGf+PdzFSq7jjy30sOZBAVPoJ9JKePk5XTDzTA6fjbO7MU9ufokRXwv09r2wsV5k1+5R3ISYnpsbqpzOhzcwCIRqsTCZJEmlFaU2OIWgvbipF4GTuhJeVVy1FcC5LXiF0s2+43uj1hrGPD+WX4ps2yNoVvAbAmdZXBNr0DPR5eS1SBFmFZfx6MIFxH+9i/ckUXhofzKuTunfqBGxO5k4MchvEipgVlGhLGjXmYOpBnM2da61U1So17w95H62k5fPjnxPmEsZnIz/D3MicqQFTcbVw5eezP9cYk1eWx/6U/YzxHoMQgnCXcHSSjlOZjXQvrmL/53KQ2KjXwDsSSZI4mnmScN8xBP3jMA7GNhzy6IW/iw0rHomkl4cN/155kqdWrgQEzsZXqsyZacz4Zsw3zAqaxSfDPyHY/ko68qra1AHFVpRoS0guaBv/+ZaizcxEbWdnOFPqVRRUFFCsLe60Aas31WYxQJhLGFsStqDVa6u9Ls5mn8XF3KVNbG8djYmPD3mZK9AVFKC2akLCqu5TYcMLsnnIqfUqgzXVYyivpIJTyXmcSMrjRFIuJ5LySM6VH6S9PGz4bHYofdsyV04rMq/nPOZsmMPv53/n3h71l1HUS3oOpR5ikNsggwouyD6I9dPXk1qcio+1Dyohz+k0Kg2zgmfx8ZGPOZ99niB7+f9uW8I2tHotY33kfFIhTiEAnMw8WcvTqU7i98n5foInQuQTgGy+yi7Npq9LX4S9D+FuAzicdhhJknCzNeOXeRFsPJ3Ga4d+pLjUhdEfHiTc245Jvd0Y37MLvja+vDzg5epL5JVUcCmzCA97eebslq8Ga7iYd9FgMZeORpuV2ej9AWheDEF7cNMpgkHug1gZu5JTmaeqPRGOZxwnxDGkgyVrG4wrcw6VxcZi3pQKX90ny5keT/8Jw15oNXmuKALDHkN6vcSumAy2nUtnT2wmFzOKqo952ZvT18uWuZE+hHrbEepl26lXAdcS6hJKRJcIvj31LRP8JtTrShqTE0N2aXa1Td0Q5kbmBhOY3RZ4G4uiFrHk3BJej3wdgPVx6/Gw9KhOgGdtbI2PtU/jVwQJB+SEcbbecgbRyvt+Plu231edN9wlnI2XNpJUkISntSdCCEZ3d+L1EwlM9BmNV2BX1hxP4dXVp3l9zWkifB3wsDMjNb+U6LQC0vKv1NleaWKOOqkAussupEM9DaSl7mB0mVltHkPQHtx0imCg20A0QsO2xG30ce5DYkEiyYXJzOkxp6NFaxNMguQZYVl0TNMUQbX30B+trgjUjo4GbarHE3N5dlkUFzKKMDNSE+Fnz/S+7vTysKWXhw225q0TTduRPNfvOWavm82Lu17ki9Ff1BkLsPfyXoAmJygDeVNxkv8kVl9YzdOhTyMhcTD1IPeF3FdDcQbZBzWsCDJjYed/4dRysPOV81KZXtmwjMmNQSVU+NvI7slVJTwPpx2unsHH5sZSWFHILV79mOQfyOMjAolOK2Dt8cusP5XKpawiHC1NGBTgSFcXK3wczDmbUkDqDjsyDsRg2dOeC7kXmnwf2gNtZmajSqimFnfuXGY3nSKwNrYm0j2SdRfX8VTfp9iZuBO4EsJ9o2Hk5obKwoKy883wvAiZDuv+BWlnwKXxqZTrQ/YYqrkakCSJn/bH8+baMzhbmfLJrD6MC+mCiaZzRpO2hCD7IF6OeJn5f8/n1b9f5c1Bb1abda5mb/Jeutp1xdm8cVktr2V2t9n8Hv07y6OXo5f06CQdE3xrxh0E2gay8dJGiiqKakRDA6DXyWUfd74LGlPZFDTo6Zrpo5FXLl5WXtXurX42ftib2nM47TDTAuVMtlV7cmEuYdXjurpY8eyYIJ4dY9jsOC7ElYsrumJ5KprcPHt2xJ2iqL+21aPDW4IkSY3OM5RWVBlM1sSAwvbiptosrmJawDTSitNYfWE1y6KX0dOxJ742vh0tVpsgVCpMgoIoPXu2RntZTAyX//0yeatW1T24+xRQaSDql1aRRdLrKYut6TFUWKblyaVRzF91msEBjqx9YjBT+rjfkEqgimmB03i0z6OsvrCadw68U8vVs6iiiKPpRw0GPjYWf1t/bnG/hQXHFrAwaiHDPIYRaFdzX6arnbxxG5MTU/sEfz0LO94mpfskcv6xC0a/UUsJVI29+rxCCEKdQ2s4ZBxLP4aLuUuTEzpaeHvhVJRN3y5B5OuSmLRwF9FpTfRyakP0RcVIJSVonBrhOlqUiqOZY7Ojwduam1IRjPAaQQ+HHsz/ez5xeXE8EPJAR4vUppj16UPpqVPoy2T7qy4/n/i595H3xx9cfuFFCnfvMTzQ0lkuDH7sZ6honKdLfVQkJyOVlFQrgvOpBUxeuIe/TlzmubFBfDunH3YW17/5pzH8o9c/mNN9DkvPL+Xz45/XOHYg5QBavbZG/EBzeGPQGwx2H8wg90G8GvlqreNVD/Aq9+lqon6FIz+wOXQGY4ujuGP7owbdTIsrikksSCTQtqaCCXMJI7kwmZTCFNmrqLLeblP3c4zc3ZFKSpjhGYRQVZBXnsmUhXv581jn8CDSVQaTqRuzIihuWTBZW3NTKgKVULFw5ELu6X4Pbw56s9lZIa8XzMPDkCoqKD15EoDMzz5Dl52N969LMPLwIHPRoroH95sHpblw6o8Wy1G1UazxD+D7vXFM+WwP+SVafp4XwWPDAzptDEBbIITgn+H/ZIr/FL44/gWb4zdXH1t7cS12Jnb0dW7Cno4BHM0cWTRqEV+M+sKgScLN0g0LIwuis6OvNBZnw4YXkbwj+bgiBQmJ1KJUlpxdUmv8xbyLSEi1VhpVcQ97Lu8hqTCJ9JL0ZlX+q6pV4VdsCcBrtznQ08OGp3+L4s21Z9DqOja2oDrPkEPjVgSdNYYAblJFAPKP5Pl+zzM1YGpHi9LmmIeFgZERBVu2UhYbS/YvS7C9/XbM+/bF7q67KDl6tO6gM59bwLkH7P4QWpBTH6D4vPzAuX19Cq+vOcNAPwfWPTmYSP/OaTdta4QQzB84n15OvXh5z8tE50STVJDE9sTt3Op3K0bqtk2AqBIqAmwDaq4Idn0AZfnED32WxMJEXhnwCqHOoayLW1fLhFVlUrpWEQTYBuBu6c72hO1sS9gGNG8PzshDrl/cJVeeIGSWJ/DLvAjmRvrw7Z44Hlh8mKIybX2naFO0GZXpJRowDUmSpKwIFDoetY0NVsOGkrtsGYmPPIrK3Bynp58CwGq0nG66cOdOw4OFgJHzIfuCXCqwGej18mbw+lV7SDOzQ2VpxZf3hPHd3H44W5s2fIIbGGO1MR8P+xhLI0se3fIoT2x7Ao3QMLfH3Ha5fle7rsTkxMgP+awLcPAr6DObA+VyHYcI1wjG+ozlYt5FEgsSa4yNzonGTGOGp1VN/34hBBN8J7A7eTcfHP6Ano498bHxabJsxu6yItCk5+Bo5siF3AsYqVW8NrkH70zvye6YDO765gDZReXN+/AtpLF5hvLL8ynRlty4KwIhhL0QYrMQIqbyX4ORPUKIOZV9YoQQc65q3yGEOC+EiKr8a56LhEKDOD7+OBKynd71jder3TeNPT0x9vevWxGAXNTeexBsfxtKcpp03bySCmZ9vZ9X/jyFf14SNr168NeTgxnbo8t1FQPQljibO7NgxALMNGZklWTx7i3vtlvgUaBdIPnl+XLRlM3zQW0MI/6PA6kH6GLRBS8rr+rcp6RJAAAgAElEQVRN632X99UYG50TTaBtoEGvp7u7342jmSMqoeLxvo83SzaVhQVqOzsqkpLwt/Wvkafpzv5efHF3GOdS8pmx6G8Ss4ubdY2WoMvMBJUKdQPpJVpSkKa9aOmK4EVgqyRJgcDWyvc1EELYA68CEUB/4NVrFMZsSZL6VP6lt1AehTowDQoiYMtmArZuwXrcuBrHLIcNpejQYXSFRYYHCwHj3oWSbLnQeCMpKtNy3/cHiUrI5cMJ/jjmpOER0VdRAAYIcQxhzbQ17JrV/EpmzaFqozfm9O9wbi0M+Rd6S2cOpR4ioksEQgi8rLxws3Dj78t/V4+TJInzOedrmYWqsDe1569pf7F66upmxUJUYeThQUVSEoG2gcTkxFChv2KeHNOjCz/PiyCzsIzbFv3N6ct5zb5Oc9BmZsnpJdT1e7h19mAyaLkimAIsrny9GDBkcB8LbJYkKVuSpBxgMzDOQD+FNkZjb4+Ra20XPsuhQ6GigqJ9fxsYVYlrL7nA+KFv4fKxBq9VWqHjoZ8OczwpjwV39mW8eSEApt1bJx5BoYno9XJ08JHFcrR4vlyju+pBHn3kK3AIgIGPcT77PLlludWbvkIIBroN5GDqweoU7unF6eSV5VWnsDCEuZF5izP6Gnm4U56cRG+n3pTqSmu5uvbzsWf5I5GohGDywr08tfQYq6KSySwsq+OMrUejYwgqVwSdeY+gpU6tLpIkpVS+TgUMrX3cgauNi0mVbVV8L4TQASuA/0h1VM8WQjwEPATg5XVjFZDpaMz79EGYmVG8bz/Wo0fX3XH4v2Xvoc3zYc6aOrtV6PQ8vuQYe2Oz+OiO3owL6UL2j5sAMOvRo7XFV7iaskJIPwNFGXIMiF4LiQfk/7e8q3+GAnyHYOMZgYseYqQSmP4DaEwMZj0d6DaQFTErqlOzROfIG/9VsQhthbGHBwVbttLTQU4BE5UeVZ3OooquLlaseWIwn++IZfmRJFZFXUYIWUm8OD6Y0DbKRaXNzGhUreLUotROHUwGjVAEQogtgCFV9vLVbyRJkoQQBh/i9TBbkqRkIYQVsiK4BzC4IylJ0lfAVwDh4eFNvY5CPQhjY8zDwynav7/+jqY2MOgp2PQyJB4Cz341Dmf98ANZX33NgZChbLEfxJtTejA9VHYBLD19Go2TExqnG6cmdKfj2M+w4d9Qdo2JRKUB36FyxlCPflCcBdEb4cRvELeTrp6+RDsHgLsc+bs/dT++Nr41opojukQgEOxL2Ucf5z7VNQLqMg21Fkbu7lBRgVORGjcLN/Zd3sdd3e6q1c/JyoRXJ/Xg/27tzqnkPHZGZ7DkQALTP/+b1yf3YE6kT6vLpsvMqi7+VB9pRWmdOpgMGqEIJEmqs4q5ECJNCOEqSVKKEMIVMGTjTwaGXfXeA9hRee7kyn8LhBBLkPcQmueaotAiLAYMIP3996lIS8OovsphYXNh9wew52O484pvednFONLfex+9BOG7VvLuk6HMGuhTfbzk9GnFLNSWHPpWjgb2HQIDHgWrLrI5SNKDczCYXJV51s4b3ENh+EugqyAwaiH7zvxIRaV78NG0o0zxn1Lj9LamtnR36M7+y/t5pPcjnM48jbulO9bG1m36sYzc5YmENjmZoZ5DWRmzkuKKYsyNzKnQVVCsLa5RrEWtEvT2tKW3py33D/blmd+ieHX1aTRqweyI1is8JUkS2szMRtUqTi1K7dRmIWj5HsFqoMoLaA5gKF/BRmCMEMKucpN4DLBRCKERQjgCCCGMgIlAE5OjK7QWFgNlP+/ihlYFJpYQdh9Er6+2M0uSxPFvl6CTYO7olyizc2TAnpXVfufajAzKYy9gFh5W35kVmkvyUVj/AgSOhbtXQtB4cOsLHmHyqs2knvTjaiO6OXRDq9dyJvsMJzJPUKItMej3H+kWyfGM4+SX53Mw9WB1grm2pCqWoDwpiQm+EyjVlbLk3BLWXVzHuBXjGLx0MAuOLjA41tJEw+ezQxkR7Mz8VafZFZ3RanLpi4qQysoatUeQVJiEh6VHq127LWipIngXGC2EiAFGVb5HCBEuhPgGQJKkbOBN4FDl3xuVbSbICuEEEIW8cvi6hfIoNBOT4GDUtrYU7WtAEQD0mS3PNE8u42RSHnd+vZ+cjZs47xbEu4+MxvvJRyk5epTifbK7YZXJyWJg871HFOqgNA+W3yevAKZ9Aeqmmx/6d5FTXe+/vJ/tCdvRqDT0c639kB/oNhCdpOOr41+RX55fq3JaW2BUGUtQkZxMH+c+DPEYwidHP+GF3S/gYObAMM9hfH3ya/anGP7eGqlVLLizL4HOljz2y1FiWilXkTZdNn5onOtfEVToKkgpSqkVa9HZaJHRSpKkLKCWr5skSYeBeVe9/w747po+RYAyRewkCJUK84gIivbvR5Kk+l08HQPQe/Qna/f3TFrjj6+qDK/CdBwevgfnbi7o/W8jc9EXZH7xJRaRkeSvW4/ayRHTbsF1n1PBMCeXw8GvQVcOfWdD33tBU5mPSa+HPx6G3ES4f4PBpHCNwd7Unh4OPVhzcQ0l2hIGug40aPLp49QHRzNHFp9ZjI2JDSM8R7TkkzUKlbExGmdnKpLk/EIfDv2Q5dHLsTW1ZbzPeLSSlokrJ/LNiW/qjF62NNHwzZxwpn72N/cvPsSfjw7CwdKkRXJp02RPII1z/bEBl4suo5f0nV4RKJHFCtVYREaiTU2lLNpANsqrqNDp+bkkEqfSOJ7vVcbSgXJ0sFV/eRapMjbGYd4DFB88SNr771O4axe2U6Y06G+tcA1b34QVD8izfkkPf/0TvhoKSYfldB8bX5JNdOPeBc+6C9g0htndZhOfH096cTp3d7/bYB8jtRFvDnqTEIcQ5g+Yj7mReYuu2ViMfXwoj4sDwFRjyt3d72ai30TUKjUmahPu6HoHB1IPkJifWOc5POzM+freMNLy5ZiDZYcT2XQ6lSUHEvho03l+2neJrCa4nFZUKgIjl/pjYKuisT2sOrdpqPNuYyu0O1ajRpL6+uvkr1+HaZBht0C9XuKFFSfYltyNu800POp4jPQTtggjI0y7Xan5bHfnneStXkP2t9+htrPDfu7cdvoUNwgHv5Y35UPvhYn/A6GC8+vlDeFvRoLaBHRlEPEI9H+wxZe71e9WynVyqob6AsAGuw9msHvLsqI2FZPAQPL+/LPOleqtfrey4NgCtiVuq7fAVF8vO36ZF8Hzy0/w/PIT1e1CgCTBexvO89HMPozu3nAEsDat0jRUn2MFkFSQBNDpVwSKIlCoRuPggMWAAeSvW4/TU0/V+tFJksTb687yx9Fk/jk6DFXaKDj1B6WnB2ASGIgwvpJCWhgZ4fXDDxRu34ZZ39BG+VsrVHJqBax7DoImwK0fg6pyJRU8AXwGw/FfITsO/EdA4OjqspEtQSVU3Nb1thafpy0wCQxAX1SENiUFIze3WsfdLN0Isgtie+L2BisN9vOxZ+uzQ4lJL6Rcq8fB0hgXa1Ni0wt5bvlxHv7pMO9O78Ud/ep/cGvT0lBZW6MyM6u3X2JBIqZqU5zMOrfbtGIaUqiB9YTxVCQkVKesvppFOy/wzZ445kb68PiIAAiZgZSXTOnpk5j2qO0aqra0wGbSJIw93Gsdu+nRaeHSXji9Ujb1lBfJbYe+gRXz5DKht31be/PX1BoiHobx70LXMa2iBDo7VfUrqtKYG2KY5zCOpR8jtzS3wfOpVIKgLlb09LDBzdYMdeX7pQ8NYFCAIy/8cYJVUfXXPKhIT2vQLASyIvCw8uj0aVUURaBQA6sxY1BZWJC9uGY4x6IdF3hvw3mm9HFj/sTu8hc7aDzaCnN0+YWYdK071YDCNVw+Bl/eAj9MgN/nyqaedzzgbTd5H8BvGMz+HYzbxwbf2TEJkEub1qcIhnsORy/p2ZW8q9nXMTfW8NU94fTzsefZZcfZeDq1zr7atPQGN4rhiiLo7CimIYUaqK2tsZ01k+zvf6Dw7nkkmNjx++FENp1JY3JvNz64vfeVAjImlpRZDwTOYuLn05FiXz9Eb4Lf7pY9fKZ/A87dIDceUk/KqwKvAdB1PKiUOVoVahsbNC4ulMXE1tmnm0M3HM0c2Z20m8n+k5t9LTNjNd/N7cfsbw7wyM9HeHx4AP8Y5o+5cc1HpTYtrUbJVUPoJT1JBUkMdBvYbHnaC0URKFRz+nIe3++9xOl8P/6LYPNL/+XTPjMwM1Lz/LggHh7ij/qaKmLlqq7AWUw0dc+eGkVJruwJY9m5bakt4swqWP4AuHSXA78sKoORuoTIJUEV6sQkMJDSmOg6j6uEilvcb2FLwha0em2L0jlYmmhYMi+CV/48xYJtsSzeF8/UPm7MCPOkp4cNkk4nRxU3YBpKKkiiVFdaq5RnZ0SZdiig1el5668zTPx0DxtPp+LXzZe0QWMYn3iYP2/358DLI3l0WEAtJQBQlq9BbSKhjv29+QLs+xw+CIQPAuDPx0Db9pkj2xVJkj/jsjlyxO+9q68oAYVGYdotmLKYWPQlddfOvsXjFgrKCziecbzF17Mw0fDRzD6seCSSwYGO/HookUkL9/DwT4dJvZQMen39qVigOjFfW+djag2UFcFNTl5xBY//epTdMZncFeHFC+OCsTEzomJYF2LHbsFl7W9Yh9UufF5F2YWLmHh1QcRskG3fbk2ss3tmtewP33Uc2PvB/s+hLB9u/+GKt8z1Sm4CxG6F40shcb/sBXTbt4rtvxmYhYXB199QcvwEFgMMRzQPcB2ARmjYlbSLMJfWiVUN87YjzNuOvJIKft4fz8JtsTxz7Div0nAwWUxODAKBv61/q8jSligrgpuYc6n5TFq4h/0Xs/jvbT15e1pPbMzkOrlGbm7YTJxI3qrV6IsMF6yRJImy2FiM+wwCC2d5Nl+cLZt4cuLl0oeGs4rLlBXCun+Ba2+Y+TOMewfGvg1nV8P65+sf255Ikmy6KmtEeoKM83Ka7oX94X89Ye3TkJ8MkxbAzF8UJdBMzENDQQiKDtSdAsXK2Ir+rv3ZeGljrfrK9VFy8iRJTz1N+iefoC8zvBq1MTPiseEB/PFoJE4lcnbXvXn1ewLF5MbgZe2FmaZuF9PY9EL+js2kpFzXaHnbgptqRfDRpvN42Jtze1jnd+e6Fq1OT2GZFhszoxbLXlBawc/7E1iwNQZLUw1LHxpAmHft9AS2t88gb+VK8jdsxPa26bVlSk9HX1CASXB3GPAl/HIHfNBVjoKVKr/Y7uEw41uw86ktyN+fQmGa/ICsKtQ+8DEoSIW/F4ClCwx9vkWftdmUF8PJZXIQ16W9UF6pBCycZK8ev+Fypk9bT1n5xWyCw9/Juf9VGvlY2BwIGAWOXW8KN8+2RG1tjXlYGAWbN+P81FN19pvoN5F/7/k3URlR9HVueHVacfkyCXPmIgHSxo2UxcTg8emndf7Gurla81xPC4q2w7N7Mvmr9Bi3h3mglyRS80rJKCijaxcrRgY7E5MTU+f+gFan55VVp/j1oBx53MXalLemhTCym0utfr8eSmTl0SQKy7RE+DrwzzFdsTU3NnTaZnPTKIIKnZ4Dcdks2BbLupMpfDKzLzbmRh0tVoNIksTP++P5YFM0eSUVBLlY8eyYrozt0XBa25S8EvZfzCI+q5i0/DLS80tJLyjjfGoB5To9o7o589a0nrjUUUDerG9fjH19yV2xwqAiqEpFYRIQCP794eGdcrCTxhRsveSH6Y634bvx8MAm+aFZRV6y/LDvPrVWXQNGvQ6F6bD9Lci/LNdAsPdt/E1rCboK2Ty1539yaU47X+g5Q67epddC2mm4uB1OVu6JqIygqnyiQwCMfhN6zwJLpfx2a2M1fhxpb/6HkhMnMOvVy2CfkV4jMdOYsfbC2kYpgoxPFyLpdPj99RcFmzaR/t575K1ahe1UQ8UWZYzSUlDZ2vLA2J58szuONccv1+rT09OceMt4xvuON3iOt9ad5deDiTw0xI9wbzs+2hzNA4sPMzfSh5cmBGOiUROVmMsrf57iZHIePd1tcLc1Y/OZNF6Z2Prp3EVTllCdhfDwcOnw4cNNHqfXS/y0P57//HUGTztzvpkTjp+TZRtIWJMTSbks2BrDxcwigrtY8fjwQLq7NS6P+9e7LvLWurMMDnBkUIAjy48kciGjiFt7uvLm1BDsLWrODArLtKyKSmbJgQROX84H5Mmog4UxTlamOFuZ0NXFkgk9XenbiMpNmV9+RcbHHxOwfVutMpdZ335L+vsf0HX/PtS2toZPkHoSvr9VfjDevwEsKiOMV8yT9wceP2h4taCrgE3/JwdY6bXgEAj95snlMtvKtbIoE5bcAclHIGA0DH4GvCNrz+YlSa4CFrdbXtGYWoPnANn1U5n5txm6wiJiR47ENCgIr++/qzN31Uu7X2JbwjY23rYRW9M6vpeALi+PmCFDsZk2FdfXXkPS64mffTflFy/it2E9GjvDv4+E++9HV1iE77LfyCuu4ExKPsYagbOVKU5WJmw4lcqLf63ByPMz3op8n8mBNSvzbjuXxv0/yA/91ybLFfvKtDr+u/483+2Nw93WDEcrE44n5uJoacJrk7tza09XhBBodXo06uZ//4UQRyRJCq/VfjMpgioOxmXzj5+PoNXp+Xx2GIMDG5f+oEyrQ6NSGfSeqYuf98fz6urT2JkbE+5tx4G4LArLtCyaHcaoBnKanLmcz+SFexjd3YXP7gpFpZK/CF/uusj/tkRjY2bEQ0P8CPO2J7e4nK3n0ll1LJmich3dXK2Z3tedQQGOBLpYYtTML09ZXBwXx0/A5eWXsb+nZjKy5Oeep/jQIQJ3bK//JPF/w0/TZJ/5OWsgfh8suR2GPAcj/q/+sbkJcO4vWWkk/A09b4dpX7W+MshLgh+nyuUcpy6CkNorIIWOJ3f5clL+7xUsBg3C7d13DFa8i82JZfrq6dwXch/PhD1T57nyVq/m8vMv4LPst+oVRun5aOKmTsX+vvtwef45g+NiR43GrHdv3D/8oM5zv7J9IX8mfEkP7Yf8NHdU9e8vvaCUCZ/swdHSmFWPD8JEU1OZ7Tifzs/748krqWBEsAt3D/DCyrT1LBeKIriGxOxi5i0+TGxGIfMndufegd4G7YKSJLH9fDqfboslKjEXU42aO8I9eGF8cK0gk2vZejaNeT8eZlhXJ/5XaYrKKSpn7vcHOZtSwA/39SMywLAS0ur0TPv8b1LyStjy7NBaNsGzKfm8uuo0By9lV7eZaFRM7OXGXRFehHrZtto+yIWJE9E4OOK9+Ica7RcnT8HI1RXPL79o+CTnN8DSu8DaTZ5FOwXB/Zsav3kqSbDrA9j+Hxj/npxmoTHotJB+Wt5vsKrDnJYZIyuBsny46zd5FaDQKZEkidzflpH2zjuoTE3x+Gwh5uG1nmu8sOsFtiduZ83UNbhYGJ5wpbwyn/wNG+i6f1+N1cXll/5N/l9/4b9hfa3cRvqiIs6H98PxicdxevTROuV8ctuTHE87T/zxJ5kd4cV/poag1UvM/uYAJ5JyWfXYYIK61FMwqI2oSxHctF5DnvbmrHg0kuFBTry6+jSTF+7lj6NJ5JXI9l6tTs/umAxmfbWf+384THZROU8MD+DWXq78tD+ee789SH5pRZ3nP5uSz5O/HiPEzYbPZodW70fYWRjz4wMReDuY89iSoyTlFBsc/+2eOE4m5/HGlBCDG0PdXK1Z9o+BbP/XMH64rx/LHh7Isfmj+fCO3oR527XqZrjVqFEUHz6MNienuk1fXk7ZxYuYBDUytUTQOLhjsWwG6j0L7lnVNA8aIWDIv+TN161vQklOw2PSz8Fn/eDLIfBhMKx+AoqyavZJPgLfjZUzec5dqyiBTo4QArtZM/Fd+Qdqe3sSH3mU8sTa6acf7/s4Or2Oj458VOe5io8cwSy0by0Tk9MTjwOQseDTWmPKYmNBkjCt53svSRLH0o9xi2c/Hh7qxy8HErjvh0PM/HIfB+OyeWd6zw5RAvVx0yoCkCMIv7wnnPdn9KKgtIJnlx2nzxub6PfWFnq9vol7vj1IXGYRb0zpwZZnh/LsmCA+uL03C+8K5XhSLvd/f8ig21dGQRnzFh/G0lTD1/eG11o52JgZ8eU9YWh1Eg//dITSiprniE0v5KPN0Yzt4cL4kPo3hX0dLRgW5Ex/X/sGVyjNxWrkKNDpKNy+o7qt/OJF0GoxqSNdtUG6TZIftpM/bV5AlRAw+g3Zg2f/ovr7luTALzNkF9Wpi2DAIxC1BD7rD0d/kt1b9y+SN7KNLOC+DbIbq8J1gYmfH55ffw16PWlvvV3ruKeVJ3ND5rIubh1R6VG1jmuzsii/eNHgasLIzQ27u+8mb9UqSs/XjGYujZbfm3St+3sflx9HblkuoS6hvDA2mBfHB3MqOY+0/DI+vL030/p2wtxDkiRdd39hYWFSa6PT6aWDcVnSJ1uipRdXHJdeXXVKWnM8WSqt0Brs/9eJy5LPi2ul+78/KFVoddXtxWVaaepne6Tg/1svnUzKrfeaW86kSt4vrJWe+e2YpNfrJUmSpNIKrTThk11Sn9c3Sml5Ja33AVuAXq+XoocNlxIeebS6LWflSulMULBUGhvb/gItnS1Jb3tKUkk993f1U5L0mp0kJR6+0pZ6SpK+HCZJr1pf+ftxmiQVZra9zAptQsYXX0pngoKlknPnah0rKi+ShiwdIj28+eFax/I2bpTOBAVLRUeOGjyvNidHOtevv5TwUM2xl1+ZL50L7yfpdTqD4yRJkpaeXSqF/BAixeXGNe3DtAPAYcnAM/WmXhFcjUol6Odjz5MjA3lnei9em9yDib3cam3mVDGhpytvTAlh67l0nlt+gtIKHbnF5cz57iBRibl8PLM3Ie429V5zZDcXnh4VyB9Hk3nrr7Ncyizi2d+Oc/pyPu/N6I1zHW6d7Y0QAquRIynauxd9sWzKKomKQmVhgbG3d/sLdMs/oSxPntkbIjMGji6WPYw8roowdekBD26Duetg0ifwwGa4e4WS7uE6xm7mHQgTE3J+WVLrmLmRObO7zWZv8l4u5V2qcaz48GGEiQlmIT0Mnldta4vjQw9SuHMnRQcPXhl34ADm4eGIepwVtiZsxdvaG2/rDvhtNBNFEbSAewZ488/RXVl5LJmIt7cS+e42jiXmsGBWX8aFuDZ8AuDJEYHMjvDimz1xDPtgB+tOpfDvCcGNqpLUnliNGoVUVkbh7j0AFB86jFlYKELTAaEobn3BexAc+FLeDL6Wnf8FjZmsMK5FCPAZBGFz5fKOirvndY3a1hbrSRPJW7MGXV5erePTA6ejERr+jP2zRnvJ4SOY9e5do5jStdjdfTeaLl1I//BDJEmiPCmJ8vh4zPvXXRY0tzSXg6kHGeU16roKWlUUQQt5YmQgvz44gLE9XLgt1INVjw1mUu/aVZTqQqUSvDWtJysfjeQ/U0PY8NQQHhrS+XKTmIeHoba1pWDLFiqSkym/cAGLen4Qbc6ARyEvAc6tqdmedkYu+N7/wRs7k6lCNXYzZyKVlFCwZWutY45mjoR1CWNb4rbqNl1hIaXnzhncH7galakpTk88QenxE+T+/ju5S5eCSoX1uLF1jtmeuB2dpGO09+jmf6AO4KaJLG5LBvo7MNC/ZeaFvl52jQrw6iiERoPl8OEUbNmC2k4O0rEaN66BUW1I0HjZA2nfZ3J0ctXsa9t/wMRKjkZWuCkwDQnByM2Ngk2bDEbAD/cczrsH3+VS3iV8bHwoOXYM9HrMw2smpqvQV6DT6zDVXDHJ2kydQv5ff5E6X068aD1xYq3AyqvZkrAFNws3uju0fvRvW6KsCBQajf09d6MvKiLnx5+wHD4cY48O9H5QqSHySUg6JOcDAjlw7fxfcrt57dxJCjcmQgisxo6l8O+/0RXUTgw4wnMEIM/WQTZrotFg1qdPdZ+zWWcZsWwEk/+cTHpx+pVzq9V4LPwUh388jP0D9+P6+mt1ylFYXsi+y/sY5X19mYVAUQQKTcC0e3c8F32O/dy5uL79VkeLA6H3glMwrH1Gjjz+42E5x9GARzpaMoV2xmrMaKiooHB77Sh3V0tXutl3Y0fiDkCOHzDt3h2V+ZU4lncOvkNuWS4pRSl8cbxmgKTK3Bznp5/G5bnnUFlY1CnDzqSdVOgrrjuzECiKQKGJWA4disuLL9SZh6VdURvBjO/kpG/L7oHSPLmOgUnb549S6FyY9e6NxsWF/E2bDB6PdIvkRMYJCguzKT1xosb+QFJBEsfSj/Fs2LNM9p/M+rj1lGpLmyzDlvgtOJs508vJcEK8zoyiCBSub1x6wKMH5FTWTxwB99YpSKJwfSFUKqxGj6Zo9x50hbXrZwxwG4BW0nJi5x9IFRU19ge2JsibzKO8RzHWZyyFFYVEZdQOQquP4opi9iTvYYTXCFTi+nusXn8SKyhci6UTdJuoeAnd5FiPHSO7OO/cUetYX+e+mKhNSN0new+Zh4ZWH9sSv4Vg+2A8rTwJdwlHIzTsv1x3ARxD7L28l1Jd6XVpFgJFESgoKNwgmIWGonZypGBjbfOQidqEMJcw1MfPYxIYWJ02Pb04naiMKEZ5jQLkILTujt05ln6sSdfeHL8ZOxM7Ql1CG+7cCVEUgYKCwg2BUKuxHj2Gwl27qiPgrybSsT+e8cXQ50o08bYEeYUwyntUdVt3++6cyz6HXtI36rplujJ2Ju5khNcINKrr0yNfUQQKCgo3DFZjxyKVllK4c2etY/0zrTErh4tBVzJ/bknYgq+Nb40C890dulOsLSY+P75R19x3eR/F2uIayuR6Q1EECgoKNwzm4WGoHRzI37Cx1jH7qEto1bDZMRWQ00EcTj1cbRaqoioY7EzWmUZdc3P8ZqyMrIjoEtFC6TsORREoKCjcMAi1Gutx4yjcvh1tdnaNY0W795Dd1YVtmfsoqihiw6UN6CRdrZm8n60fxipjzmadbfB6FfoKdiTuYJjnMLNPemwAAAmHSURBVIzUnb8Gel0oikBBQeGGwu7OWUjl5eQu+726rSIlhbLoaByGjqJUV8qv537l13O/0s2+G93su9UYb6QyIsg+iLPZDSuCQymHyC/Pv269hapQFIGCgsINhUlAABaRA8lZuhSpvByA3D/+AKDr9HuJdIvkk6OfcDHvIg/3fthgOohu9t04m3UWqYFSvpsTNmOuMSfS/fqubKcoAgUFhRsO+/sfQJuaStZ336PLzyfnlyVYDBqEsZcX7w15j7k95vJG5BuM9BppcHyQfRAFFQWkFKXUeQ2dXse2hG0M8RiCidqkrT5Ku3B9+jopKCgo1IPl4EFYjR9HxiefkLN0KbrcXJyefQYAGxMb/hluoFbFVQTZyzWJz2Wfw83ScFr5o+lHyS7Nvq69hapQVgQKCgo3JG5vv43tzDvQuDjj/sn/MOthuBqZIQJtAxEIzuecr7PPlvgtmKhNuMX9ltYQt0Np0YpACGEP/Ab4AJeAOyRJyjHQbwMwANgjSdLEq9p9gaWAA3AEuEeSpPKWyKSgoKAAoDIzw/W115o11tzIHC9rL6Kzow0e10t6tsRvYZDbIMyNzA32uZ5o6YrgRWCrJEmBwNbK94Z4H7jHQPt/gY8lSQoAcoAHWiiPgoKCQqvQ1a5rnSuCExknSC9JvyHMQtByRTAFWFz5ejEw1VAnSZK2AjUqRgh5q34EsLyh8QoKCgrtTbB9MIkFiRRV1M5muiV+CxqVhqGeQztAstanpYrARZKkqm31VKApFdcdgFxJkqqqjycB7nV1FkI8JIQ4LIQ4nJGR0TxpFRQUFBpJVXzBtRHGkiSxJWELA1wHYG1s3RGitToNKgIhxBYhxCkDf1Ou7ifJDrf1O922AEmSvpIkKVySpHAnJyXdsIKCQtsS4hgCyGagqzmbfZbkwmTGeI/pCLHahAY3iyVJqtMIJoRIE0K4SpKUIoRwBdLr6muALMBWCKGpXBV4AMlNGK+goKDQZtiZ2uFp5VlLEay7uA6NSsNwz+EdJFnr01LT0GpgTuXrOcCqxg6sXEFsB2Y0Z7yCgoJCWxPmEsaR9CPo9DpADiJbF7eOW9xvwdbUtoOlaz1aqgjeBUYLIWKAUZXvEUKECyG+qeokhNgN/A6MFEIkCSHGVh56AXhWCBGLvGfwbQvlUVBQUGg1IlwjyCvL41z2OQAOpB4goySDSf6TOliy1qVFcQSSJGUBtWK0JUk6DMy76r3BiAtJki4C/Vsig4KCgkJbEekWiVqo2RS/iR6OPVh2fhk2JjYM8RjS0aK1KkpksYKCgkId2JvaM8h9EKsvrGbf5X1sS9jGzKCZ131uoWtRFIGCgoJCPTzU6yGySrJ4aPNDOJs7M7fH3I4WqdVRks4pKCgo1ENvp958MvwTDqQe4K7gu7Aytmp40HWGoggUFBQUGmC413CGe9047qLXopiGFBQUFG5yFEWgoKCgcJOjKAIFBQWFmxxFESgoKCjc5CiKQEFBQeEmR1EECgoKCjc5iiJQUFBQuMlRFIGCgoLCTY6Qs0FfXwghMoD4Zgx1BDJbWZy2QJGzdbke5LweZARFztamveX0liSpVmWv61IRNBchxGFJksI7Wo6GUORsXa4HOa8HGUGRs7XpLHIqpiEFBQWFmxxFESgoKCjc5NxsiuCrjhagkShyti7Xg5zXg4ygyNnadAo5b6o9AgUFBQWF2txsKwIFBQUFhWtQFIGCgoLCTc5NowiEEOOEEOeFELFCiBc7Wp4qhBCXhBAnhRBRQojDlW32QojNQoiYyn/tOkCu74QQ6UKIU1e1GZRLyCyovLcnhBChHSzna0KI5Mp7GiWEmHDVsZcq5TwvhBjbjnJ6CiG2CyHOCCFOCyGeqmzvNPe0Hhk71f0UQpgKIQ4KIY5Xyvl6ZbuvEOJApTy/CSGMK9tNKt/HVh736WA5fxBCxF11P/tUtnfY7whJkm74P0ANXAD8AGPgONC9o+WqlO0S4HhN23vAi5WvXwT+2wFyDQFCgVMNyQVMANYDAhgAHOhgOV8D/mWgb/fK/3sTwLfyO6FuJzldgdDK11ZAdKU8neae1iNjp7qflffEsvK1EXCg8h4tA2ZVtn8BPFL5+lHgi8rXs4Df2un/vC45fwBmGOjfYb+jm2VF0B+IlSTpoiRJ5cBSYEoHy1QfU4DFla8XA1PbWwBJknYB2dc01yXXFOBHSWY/YCuEcO1AOetiCrBUkqQySZLigFjk70abI0lSiiRJRytfFwBnAXc60T2tR8a66JD7WXlPCivfGlX+ScAIYHll+7X3suoeLwdGCiFEB8pZFx32O7pZFIE7kHjV+yTq/4K3JxKwSQhxRAjxUGWbiyRJKZWvUwGXjhGtFnXJ1Rnv7+OVy+vvrjKtdQo5K00TfZFniJ3ynl4jI3Sy+ymEUAshooB0YDPyaiRXkiStAVmq5aw8ngc4dISckiRV3c+3Ku/nx0IIk2vlrKTd7ufNogg6M4MlSQoFxgOPCSGGXH1QkteMnc7Ht7PKVckiwB/oA6QAH3asOFcQQlgCK4CnJUnKv/pYZ7mnBmTsdPdTkiSdJEl9AA/kVUhwB4tkkGvlFEKEAC8hy9sP+P92zpg1iigKo+dCoglBIgsWgilcSGtlkYBtRO2EFFbZIj/CIpCfkM4qBAsVCyGS1DHpk0JNVtRk2xQJCFoGwZfi3jVjsmO5b+B9B4aZeTPF4WPfXva+x7aA5xkVgXIKwTEwVbm/E2PZSSkdx/kUeI9/qE/6PwnjfJrP8B/qvBqVb0rpJCbgH2CVi3ZFVk8zG8W/YN+klNZjuFGZDnJsap7h9hPYAWbxVsrIAJe/nvF8EviRyfNRtOBSSukMeEkD8iylEOwB07Gr4Bq+YLSZ2QkzmzCzG/1r4CHQxd068VoH2MhjeIU6r01gIXY9zAC/Ku2OoXOpr/oUzxTc81nsIrkLTAO7Q3IyYA34mlJaqTxqTKZ1jk3L08xumdnNuB4H5vD1jB1gPl67nGU/43lgO3595fD8Vin8hq9jVPPMM4+GtSqd+8BX5A/xXuJSbp9wauO7Lj4DX/peeP/yA3AEbAGtDG5v8TbAb7xXuVjnhe9yeBHZHgD3M3u+Co99fHLdrry/FJ7fgcdD9HyAt332gU9xPGlSpv9xbFSewD3gY/h0geUYb+OFqAe8A67H+Fjc9+J5O7PnduTZBV5zsbMo2zzSX0wIIUThlNIaEkIIUYMKgRBCFI4KgRBCFI4KgRBCFI4KgRBCFI4KgRBCFI4KgRBCFM45V3rVHXpaflcAAAAASUVORK5CYII=\n", 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index fff7be7d4..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,9 +1,10 @@ import unittest import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier from skfda.exploratory.fpca import FPCABasis, FPCADiscretized -from skfda.datasets import fetch_growth, fetch_weather +from skfda.datasets import fetch_weather def fetch_weather_temp_only(): @@ -14,12 +15,77 @@ def fetch_weather_temp_only(): return fd_data class MyTestCase(unittest.TestCase): - def test_basis_fpca_fit(self): + + def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() with self.assertRaises(AttributeError): fpca.fit(None) + basis = Fourier(n_basis=1) + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataBasis(basis, [[0.9]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of elements + # of target basis + fd = FDataBasis(basis, [[0.9], [0.7], [0.5]]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_discretized_fpca_fit_attributes(self): + fpca = FPCADiscretized() + with self.assertRaises(AttributeError): + fpca.fit(None) + + # check that if n_components is bigger than the number of samples then + # an exception should be thrown + fd = FDataGrid([[0.5], [0.1]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + # check that n_components must be smaller than the number of attributes + # in the FDataGrid object + fd = FDataGrid([[0.9], [0.7], [0.5]], sample_points=[0]) + with self.assertRaises(AttributeError): + fpca.fit(fd) + + def test_basis_fpca_fit_result(self): + + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 + + # initialize basis data + basis = Fourier(n_basis=n_basis) + fd_basis = fd_data.to_basis(basis) + + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = np.array(results) + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + results[i, :] *= -1 + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From 75bc3e5e3f5b2acd0e4d6f5506792b113a0f35b5 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 344/624] Update docstring --- docs/modules/exploratory/fpca.rst | 13 +++ skfda/exploratory/fpca/fpca.py | 127 +++++++++++++++++++++++------- 2 files changed, 112 insertions(+), 28 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..0a8687cf7 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 5660ac674..715541df7 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + """Computes the n_components first principal components score and + returns them. - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,62 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +266,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 20a5aaf6c1d7000f3011e4f4527e787ac0d5a434 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 345/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- examples/plot_fpca.py | 28 +++++++--- skfda/exploratory/fpca/fpca.py | 93 +++++++++++++++++++++++++++---- 3 files changed, 111 insertions(+), 22 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..135b4bf2a 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -27,6 +29,7 @@ fd = dataset['data'] y = dataset['target'] fd.plot() +pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -36,9 +39,10 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() +pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -51,6 +55,7 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() +pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -59,7 +64,8 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -71,6 +77,7 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() +pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -78,11 +85,12 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -92,11 +100,12 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -109,4 +118,5 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 00edb58a2976e285ea5fdecbbd43d7dcab840179 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 346/624] add doctest --- skfda/exploratory/fpca/fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From c80099b0e0c85302cc73cc87b66b9e657f511cb5 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 347/624] regularized PCA support --- skfda/exploratory/fpca/fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 79895f89a3799d4226823c25d839bd596dc61758 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 348/624] Finilized Module testing --- skfda/exploratory/fpca/fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 43686feab32f65df1505108c2649d1cbb54a0a7f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 349/624] FPCA parameter finding --- skfda/exploratory/fpca/fpca.py | 98 +++++++++++++++++++++++++++------- 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From d2990af0ac695d0344eecd5e6d389ac1c039172c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 350/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/fpca.py | 117 ++++------------ skfda/exploratory/fpca/test.ipynb | 23 +++- 4 files changed, 174 insertions(+), 96 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 279fe2df9..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from .fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, From d41e4f99ffbb141c2bdd51bc7e412bd99ce93c70 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 351/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 446f4ca1ffa2ddfd4d99619b68c0c1f68bf062c2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 352/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From ef219378cbf088412bfafaf0f1ee52ded3c94e79 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 353/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From fa9ff1b48eec97801c5aa9b554a6659f1dcfb844 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 354/624] Adjust doctest --- skfda/exploratory/fpca/fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From 0c20554ba33ffa20782b39f1ff8ef8b8ff4c1486 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 355/624] transfer files to new location and modify documentation --- docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 11 files changed, 77 insertions(+), 549 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From 5f853b93cccce9ddadf5d938c41354a548f54b71 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 356/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 9e3b67db80d67d9209f83c5003a5fd45fda7e353 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 357/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From ed73298af101de7fab52ca134bade08fb0ac0f08 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 358/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 16a3fc412240dcbd7516973c3dc407408c373bb1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 359/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 6267f7b52c05983e30996983ec35f5fd973f7a08 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 360/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From c874f2b7f2ddeae152b1de90cb745369fad514bd Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 361/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From 3c017ed802b52337d668613aa5520b9e5f36c056 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 362/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 25cae391e1cce965b5539cb3c7745418f27baa03 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 363/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From d721294915b1c5e6653ac0e69773f427438bcc8f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 364/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From ce92269e8671156e017f5e309e4569db2d594cc7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 365/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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EyJvWRu+XTZONZoHaHeHBZUZ/8CLaGXaR6b8cY39EAgFernz44C3c2dwHmyumokvNTuXVP15la+RWhjQcwqvtX8XepoJdUWprD27exq0cKe53hYbArUqpqUAGME5rvRuoBezItV6kZdlVlFIjgBEAtWvXLmYZQggA4kJh/ctw6jeoWh/u/9rozljEI8nTcalMW3+UXw+fp0ZlJ6YPbs69bXyxs726C2NkciQv/PYC4Ynh/K/9/3ig8QMltTeiBBQ33O0AT6AD0Bb4XilVtygb0FovBBaC0VummHUIcXPLSoM/34dtc43ufn2mQ9snizweS2JaNnN/O8kX209jb2vDSz0b8uStdXF2yHtkxj0xexi7dSw5OocFdyygY82Oea4nrKe44R4J/KiNfpS7lFJmwAuIAnKfRfG1LBNClCSt4djP8MsESIyAFg9AzylQqXqRNpOVY+arHWeY+9tJEtOzuT/Ij7E9G1Ktcv7DC6w4sYK3d76Nr5sv83rMo07lOte7N+IGKG64rwK6A1uUUg0BByAOWAN8o5SahXFCtQGwqyQKFUJYJEbBz2ONy++9m8CwdeDfuUib0Fqz6Wgs76w7SnhcKl3qezGxXxOa1qyc73MyTZlM2zmNFSdX0KlmJ2beNpPKDvmvL6yrMF0hlwHdAC+lVCQwGVgCLFFKhQBZwFDLUfxhpdT3wBEgB3hOesoIUUK0Ni5Z3/i60Wuj19vQfmSRm2DC41J586fDbD1+gXrernw2rC3dGv23B8yVolOiGbN1DIcvHuap5k/xXKvnsLWRyTTKMrlCVYjyID4M1owyxoDxvxUGzAXPIp3mIi0rh4+2hPLpH+E42Nnw4h0NGNrJH/s8TpbmtiN6B+N/H0+2OZu3u7xNj9o9rmdPRAmSK1SFKK/MJmO4gM1TjKsi+8+BNsOK1AtGa8264Bje/vkI0YkZDG5di1f7NqZapfzb1S8/b0nIEubun0tA5QDmdJ+Dv7v/9e2PKDUS7kKUVfFhsHKkMSZKg17Qfza4+xZpE6GxyUxec5i/Qy/S1KcyHz54C0H+ngU+LyUrhdf+fo1NEZvo7d+bKZ2m4GLvUtw9EVYg4S5EWaM17PvC6AljYwd3fwIt7i/S0XpqZg4fbD7Jkr/CcXGw5a2BgTzUvg62NgVv48jFI7z8+8tEpUQxLmgcjzV97Jrt8aJsknAXoixJuQA/jYLj64y29bs/LvLR+obDMbyx5jDRSRncH+THy70bUdWtgAkyMJphlh1bxnt73qOKUxUW915Mm+pFG1xMlB0S7kKUFcd/gTXPQ0YS9H4H2j8DNoWf3CI6MZ3Jqw+z4ch5GteoxLyHW9O6dpVCPTcxM5HJ2yazOWIzXX278nbnt6niVLjnirJJwl0Ia8tKhV8nGt0cqzeDx9YUaeRGk1nzxfbTvPfrcUxa82rfxjzRJaDAXjCXHbpwiPF/jOd86nlphqlAJNyFsKZz++GHJ4yTp51Gwe2TwK7gJpTLQqISmfBjMMFRidzW0Ju3BzXDz7NwJz7N2syXR75kzt45VHOpxtK+S2nh3aK4eyLKGAl3IaxBa6OL44ZJxmw8w9aCf5dCPz01M4dZG0/w2d/hVHVzZN5DxqiNhT3ijk2LZdJfk9gevZ07at/BG53eKF/D9IoCSbgLUdrS4mH183D8Z2jYFwbNN2b7KaSNR84zeXUI0UkZPNy+Ni/3boy7c+GvUt14ZiNvbn+TLFMWr3d8nXsb3CvNMBWQhLsQpensLvjhcUiOgd7TjDlMC3u0nZzB5NWHWR8SQ+Malfjwoda0qVP4k56p2alM2zmN1adWE1g1kOm3TpeLkiowCXchSoPZDNs+gM1vGdOwPbEBarUu1FO11qzYF8Vba4+Qnm1ifJ9GPHVr3UKfMAU4EHuACX9O4FzqOUa0GMHIliMr3qQa4j8k3IW40VIuwMqn4dRmCLwb7voAnArXvh15KY2JK0P448QF2vpXYfo9Lajn7Vbol842Z/PJwU/4NPhTfFx9+LzP59xS7Zbi7okoRyTchbiRwv+EFU9C+iVj+IA2wwvVDGM2a77aeYZ31x9DA1MGBvJI+zpXTXN3LScunWDSX5M4Gn+UAfUGMKHdBNwcCv+HQZRvEu5C3AhmE/wxE35/FzzrwSMroEazQj017EIKr6w4xO7Tl7i1gRfTBjfHt0rhx3XJMeewJGQJCw4uoLJDZWZ1m0XPOj2LuyeinJJwF6KkJUXDj08Zw/O2fAj6zQTHgo+Yc0xmPv0znNmbTuBkZ8PMe1twbxvfIvVkOXnpJJP+nsSRi0fo49+Hie0nypWmNykJdyFK0slNsHIEZKfDoAXQ6qFCPe3IuSTGrzhISFQSfQJrMGVQYIFD8uZ25dH6+7e9Ty//XsXdC1EBSLgLURJM2fDb2/D3HKgWCPd9Dt4NC3xaZo6Jeb+FsmDrKTxcHFjwcGv6Nvcp0kvnPlrv7d+bie0n4ulU+H7zomKScBfieiVEGEMIRO6CoMeNQb/snQt82r6IS4z/4RChsSkMbl2L1/s3xcPFodAvm2XKYnHIYj499CmVHCrJ0br4Dwl3Ia7H0bWw+lljOIF7P4Nmgwt8SlpWDu/9eoLPtoXjU9mJz4a3pXujakV62X3n9/Hm9jcJSwyjj38fJrSfIEfr4j8k3IUojpxMY6LqnR+DTyu477NCzWm6LTSOV38MJiI+jUc71OGVvo1xcyz8f8OkrCTm7J3D8hPLqelak496fERX367XsyeigpJwF6KoLp6CH4ZD9EHo8Czc8UaBIzkmZWQzbd0xlu2KIMDLle9GdKB93aqFfkmtNRvPbGTarmnEZ8TzWNPHeK7VczL1nciXhLsQRXFoOax9EWzt4YFl0LhfgU/ZciyWiSuDOZ+UwdNd6zKmZ0Oc7G0L/ZIxqTFM3TGVrZFbaeLZhHk95hFYNfB69kLcBCTchSiMrFRYNx4OfAW1O8I9iwqc/i4hLYspa4/w474oGlZ34+NHOtPSz6PQL5ljzuHbY9/y4f4P0WjGBY3j4SYPY2cj/21FweRTIkRBzh+G5cMh7gR0fRluexVsr/1f55eQGCatCiEhLYtRPRrwXPd6ONoV/mh9f+x+pu6YyvFLx+lcszOTOkzCt1LR5lIVNzcJdyHyozXs/Qx+mWAM9PXYaqh72zWfEpeSyeTVh/k5OJrAmpVZ+nhbAmsWfhKMi+kXmb13NqtPraa6S3VmdZvFHbXvkPHWRZFJuAuRl/QE+Gk0HFkF9XrA3Z+Am3e+q2utWXPwHG+sOUxqpomXezdiRNfCD8trMpv4/sT3fLjvQ9JN6TzR7AlGtBghJ0xFsUm4C3GlyD1Gb5ikc3DHm8bcpjb5h3RMYgaTVgWz6Wgsrfw8mHlvCxpUr1TolzsQe4B3dr7D0fijtPdpz8T2E6nrXnC3SiGuRcJdiMvMJmP4gC3vQKWaMPwX8Gub7+paa5bvieStn4+QlWNm0p1NGN45ANtCDssbnxHPnL1zWBm6kmou1Zh520x61+ktTTCiRBQY7kqpJUB/IFZr3eyKx14C3gO8tdZxyvhUfgD0A9KAYVrrfSVfthAlLCECVo6EM38bE2r0nw3O+Y+mGHkpjQk/BvPnyTjaBXjy7j0tCPByLdRL5ZhzWH5iOfP2zyMtO43hgcMZ2XKkNMGIElWYI/fPgXnAF7kXKqX8gF5ARK7FfYEGllt7YIHlpxBlV/APsHYsaDMM+hhaPpDvhBpms+brnWeYbplE462BgTxchEk0tp/bzozdMwhNCKVdjXZMbD+Reh71SnBnhDAUGO5a6z+UUv55PDQbGA+szrVsIPCF1loDO5RSHkopH611dEkUK0SJykiEn8dB8Pfg1x4GL4Qq/vmufjoulfErDrErPJ5bG3jxzt3N8fMs3NF2RFIEM/fMZOvZrdRyq8XsbrPpUbuHNMGIG6ZYbe5KqYFAlNb64BUfzlrA2Vy/R1qWXRXuSqkRwAiA2rVrF6cMIYrvzDb48WlIioJuE+HWl/Ltu55jMrPor3DmbDqBva0NM+5pwX1BhZtEIyUrhYWHFvLl0S9xsHFgdOvRPNr0URxtrz1cgRDXq8jhrpRyASZiNMkUm9Z6IbAQICgoSF/PtoQoNFM2bJ0Of80Cj9rw+K/XPGkaHJnIKysOcSQ6iZ5Nq/PWwGbUcC94Eg2T2cSq0FXM3T+X+Ix4BtYbyOjWo/F2yb87pRAlqThH7vWAAODyUbsvsE8p1Q6IAvxyretrWSaE9V08ZUxWfW4ftHoE+k4Hx7y7LKZl5TB74wkW/xWOl5sjHz/Smj7NCjeJxp6YPczYPYOj8Udp5d2K+T3mE+glY8GI0lXkcNdaBwP/DD6tlDoNBFl6y6wBnldKfYtxIjVR2tuF1WkNez+HXyeCrQPctxQCB+W7+h8nLjBxZTCRl9J5qH1tXunTGHdn+wJfJiolivf3vM/GMxup7lKdGV1n0Me/j7SrC6soTFfIZUA3wEspFQlM1lovzmf1dRjdIEMxukIOL6E6hSiepGhY8wKEboSArkZvGPdaea56MSWTt38+ysr9UdTzduX7pzvSLqDgCTCSs5JZFLyIr458hY2y4dmWzzKs2TCc7QqejUmIG6UwvWUeLOBx/1z3NfDc9ZclxHXS2ujiuG6cMbFG35nQ9sk8rzTVWrNyfxRvrT1CSmZOoQf6yjZl8/2J7/n44MckZCbQv25/RrceTQ3XGjdqr4QoNLlCVVQ8qXGwdgwcXQO+bY2jda/6ea4acTGN/60yLkZqXduD6fe0oGEBQwdordkUsYk5e+cQkRxBuxrteCnoJZpWbXoj9kaIYpFwFxXLsXXw0yhj4K8ek6HzaLC5+gg8x2Rmyd/hzNp4Ajsbm0JfjHTwwkHe2/0eBy4coJ57PT7q8RG31rpV2tVFmSPhLiqGjERjaN4DX0P15vDoKqjRLM9VD5xN4H8rgzl8zujeOGVgID7u124fP5t0ljn75rDhzAaqOlVlcsfJDKo/SCbOEGWWfDJF+Re2FVY9B8nn4NZxcNsrYOdw1WqJadnM+PUY3+yKoFolRxY83Jo+zWpc86g7ISOBTw59wrfHv8Xexp5nWj7DsMBhMg6MKPMk3EX5lZUGmybDroVQtQE8sRF8g65a7fIJ03fWHeVSWjaPdw5gTM+GuDnm//HPNGXyzdFv+PTQp6TmpHJ3/bt5ttWzVHOplu9zhChLJNxF+XR2lzGKY/wpaP8M9HgdHK4+mj55PplJq0LYGR5P69oefPF4c5rWrJzvZs3azPrw9czdN5dzqefoUqsLY9uMpUGVBjdyb4QocRLuonzJSoMtU2H7R+DuB0N/MvqvXyEtK4e5m0NZ9GcYbk52TB/cnCFBftc8Ybo7Zjfv7XmPIxeP0NizMW92fpMOPh1u5N4IccNIuIvy48w2WP0cxIdB0OPQc0qewwdsPHKeN9YcJiohnfva+PJq38ZUdct/oK6whDBm753N1sitVHepztQuU+lftz82qnBT5AlRFkm4i7IvKxU2vWm0rXvUhsfW5DlRdeSlNN5Yc4RNR8/TqHollo/sSFv//K8wjUuPY8GBBaw4uQInOydGtx7NI00ewcmu4IHBhCjrJNxF2Rb+B6x+HhLOQLunjbZ1R7f/rJKVY2bRX2HM3XwSG6WY2K8xwzsH5Ds5dXpOOl8c/oIlIUvIMmUxpNEQRrYciadTwUMNCFFeSLiLsikzGTa+DnuWgGddGL4e6nS6arUdYRd5bVUIJ2NT6B1Yncl3BVLTI+8+6yaziTWn1jBv/zxi02PpUbsHL7Z+EX93/xu8M0KUPgl3UfaEboafRkNiJHR8Hrr/76qeMHEpmbyz7ig/7ovCt4ozS4YFcXvj6vlu8u+ov3l/7/ucvHSSFl4tmHnbTFpXb32j90QIq5FwF2VHRiL8+j/Y/yV4NYQnNoBfu/+sYjZrvtkVwYxfjpGebeL57vV5rnt9nB3yHuTrePxxZu2dxbZz2/B182XmbTPpXae3DBcgKjwJd1E2nNhgHK2nxEDnF6HbBLD/74nNkKhE/rcqhINnE+hYtypvDWpG/WpueW4uJjWGefvnsebUGio5VOLloJd5oPEDONhefeWqEBWRhLuwrvRLxpgwB5eBdxN44Cuo1eY/qyRlZDNrwwm+2H6BsnwAABwpSURBVH4aT1dH5tzfioGtauZ59J2ancri4MV8eeRLTNrE0MChPNn8Sdwd3Utph4QoGyTchfUc+9kYmjc1Drq+bNzs/u2PrrXmp0PRvL32CBdSMnm0Qx1e6tUoz1mRss3Z/HjiR+YfnE98Rjx9A/oy6pZR+FbyLc09EqLMkHAXpS/1IqwfDyE/GCM4PrwcfFr+Z5WwCym8vvowf4XG0byWO4uGBtHC1+OqTWmt2Xp2K7P2zuJ00mnaVG/DRz0+oplX3iNCCnGzkHAXpevIavj5JaM5ptsE6DL2PyM4ZmSbmL8llI9/D8PR3hhn/aH2dbDNY9iAkLgQ3tvzHnvP78W/sj9zu8+lm183OVkqBBLuorSkXIB1Lxnh7tMyz/HWtx6PZfKaw5y5mMagVjWZeGcTqlW6+mrRqJQoPtj3AevD1+Pp5Mmk9pMY3HAw9jYFT2ItxM1Cwl3cWFpDyApY9zJkpcDtrxmzI9n+G8QxiRlMWXuYdcEx1PV25Zsn29OpvtdVm0rMTGRR8CK+Pvo1tsqWp5o/xePNHsfNIe8eM0LczCTcxY2THANrx8Lxn40eMAM/gmpN/nk4x2Tm822nmb3xBDlmzcu9G/HkrQFXTUydbcrm2+Pf8vHBj0nOSmZg/YE81+o5mYhaiGuQcBclT2s48A38OgFyMqHnW9DhWbD99+O290w8/1sZwrGYZG5vXI03BwTi5+lyxWY0v575lQ/2fkBkSiSdanZibJuxNPJsVNp7JES5I+EuSlZipHExUugmqN0RBswDr/r/PHwpNYt3fznGt7vP4uPuxMePtKF3YPWrToLuj93Pe3ve49CFQzSo0oCP7/iYzrU6l/beCFFuSbiLkqE17P0cNrwG2gR9Z0Dbp8DGGJnRbNb8sDeSaeuPkpyRw9Nd6zKqRwNcr5jq7kzSGebsncOmiE1Uc67GlE5TGFBvALY2eQ8vIITIm4S7uH7x4fDTKGN43oCucNdc8Az45+HjMclMWhXM7tOXCKpThal3N6dRjf9OshGfEc/HBz9m+fHlONg68Hyr53m06aMyEbUQxSThLorPbIbdn8KmN0DZQv850GYYWJpY0rJy+GDzSRb/GU4lJztm3NOCe9v4/mequ4ycDL46+hWLgxeTnpPOPQ3u4ZlWz+DlfHVvGSFE4Um4i+KJC4U1z0PEdqh/B9z1Abj/e6l/7qnuhgT58mrfJni6/nuxktaa9eHrmbNvDtGp0XTz7caYNmOo61HXGnsjRIUj4S6KxmyC7fNgyzvGODCDFkDLB/85Wi/MVHcHLxxkxu4ZHLpwiCaeTZjaZSpta7S1xt4IUWEVGO5KqSVAfyBWa93MsmwmcBeQBZwChmutEyyPTQCeAEzAKK31rzeodlHaYo8aE1RH7YVGd0L/WVDJ6GuebTKz+K9wPth0EoAJfRvzeJf/TnUXnRLN7H2zWR++Hi9nLzlZKsQNVJgj98+BecAXuZZtBCZorXOUUu8CE4BXlFJNgQeAQKAmsEkp1VBrbSrZskWpMmXDX3Pg93fBsRLcsxia3fPP0fqu8HgmrQrmxPkUejatzhsDAqmVa6q7tOw0FgUv4osjxkdoRIsRPNHsCTlZKsQNVGC4a63/UEr5X7FsQ65fdwD3Wu4PBL7VWmcC4UqpUKAdsL1EqhWlL/oQrH4WYoIhcLDRxdHNG4D41CymrTvK8r2R1PJw5tPHgujZ9N+p7i7PWTp3/1zi0uPoF9CPF1u/iI+bj7X2RoibRkm0uT8OfGe5Xwsj7C+LtCy7ilJqBDACoHbt2iVQhihROZnwx3vw1yxw9oT7v4ImdwFGn/Xle88ybf0xUjJyGHlbPUb1qI+Lw78fp90xu5mxewbH4o/R0rslH3T/gBbeLay1N0LcdK4r3JVS/wNygK+L+lyt9UJgIUBQUJC+njpECYs+CCufgdjD0OIB6DMNXIyTosdikpi0MoQ9Zy7Rzt+Tt+9uRsPq//ZZj0iKYNbeWWyO2IyPqw8zus6gj38fGYZXiFJW7HBXSg3DONHaQ2t9OZyjAL9cq/lalonywJQNf74Pf8wEl6rw4HfQqA9g6bO+6SSL/gqnspMdM+81+qxfDu2krCQWHlzI18e+xsHGgVG3jOLRpo/iZHf1kL1CiBuvWOGulOoDjAdu01qn5XpoDfCNUmoWxgnVBsCu665S3HjnD8PKkRBzCJoPgb7v/nO0vvV4LJNWhRB5KZ37g/x4tW9jqlj6rJvMJlacXMG8/fNIyEzg7gZ388ItL8hFSEJYWWG6Qi4DugFeSqlIYDJG7xhHYKPlyG2H1nqk1vqwUup74AhGc81z0lOmjDPlwN9zYOt0cHL/T9v6heRM3lp7hDUHz1HP25Xvn+5Iu4B/+6zvPb+X6bumcyz+GEHVg3il3Ss09mxsrT0RQuSi/m1RsZ6goCC9Z88ea5dx87lw3DhaP7cPmg6CO98HVy+01izfE8nUdUdJzzLxbPd6PNOt3j/jrMekxjBr7yzWh6/Hx9WHcUHj6Fmnp7SrC1HKlFJ7tdZBeT0mV6jejC5fZfrbVHBwhXs/g2aDAWNi6okrg9kRFk87f0/eGdyM+tWME6aZpkyWHl7KouBFmLWZZ1o+w/Bmw3G2c77WqwkhrEDC/WYTFwqrnoHIXdC4P/SfDW7VyMox88nvp/hwSyiOdjZMG9yc+4P8sLFRaK3ZcnYLM3bPIColip51evJS0EvUcsuzl6sQogyQcL9ZmM2w6xPY9CbYOcDgT6H5faAUe8/E8+qKYE7GptC/hQ+v39X0n4mpwxLCeHf3u2w7t436HvX5tNendPDpYOWdEUIURML9ZpBw1jhaP/0nNOhljLde2YekjGxm/HKMr3ZEUMvDmSXDgri9sXGFaXJWMgsOLmDZ0WU42znzartXGdJoCPY29gW8mBCiLJBwr8i0huDl8PM4Y3akAR/CLY+CUmw4HMOkVSHEpWTyRJcAxvZsiKujHWZtZnXoaubsm8OljEsMbjCYUa1H4enkWfDrCSHKDAn3iiotHn4eC4dXgl97uPsT8AzgYkomk9ccZu2haJr4VGbR0CBa+HoAcDz+OFN3TmV/7H5aerdk/h3zCawaaOUdEUIUh4R7RXRqC6x6FlJj4fbXoMsYtLJhzYEo3lhzmNRME+N6NeTp2+phb2tDSlYK8w/O55uj31DZoTJTOk1hYP2B2Cibgl9LCFEmSbhXJNnpxgnTnQvAqxE8uAxqtiImMYNJq4LZdDSWVn4ezLy3BQ2qV/pnNqSZu2cSlx7HvQ3vZXTr0bg7ult7T4QQ10nCvaKIPgg/joALx6Dd09DzTbSdE9/timDquqNkm8xMurMJwzsHYGujCE8MZ+rOqeyM3kkTzyZ80P0Dmns3t/ZeCCFKiIR7eWc2w7a58NvbxmBfj6yA+ndwNj6NCT/u4q/QONoHePLuPS3w93IlPSedTw98ymeHP8PZ1pmJ7ScypOEQmQ1JiApGwr08Sz4PK5+GsC3QZADc9QFmpyp8ue007/5yDAW8PagZD7WrjY2NYkvEFqbvms651HMMqDeAMW3GyABfQlRQEu7lVegmY1yYzGToPwfaDCMyIZ2Xv9rJ9rCLdG3ozbTBzanl4UxMagzv7HyHLWe3UN+jPp/1/oygGnkORyGEqCAk3MubnCz4bQps+xCqNYWhP6G9G7N8TyRT1h5Ba830wc25v60fZm3m66NfM3ffXMzazJg2Y3i06aNyIZIQNwEJ9/IkPgx+eMIYxTHoCeg9ldh0xYSle9h8LJb2AZ68d19L/DxdOB5/nDe3v0lwXDCda3ZmUodJ+FbytfYeCCFKiYR7eRH8A/z0ItjYwJAvoekA1h46x6RVIaRnmXi9f1OGdfIny5zJnL1zWHp4KZUdKzP91un0C+gnw/EKcZORcC/rcjLhlwmwZzH4dYB7FnHJvjqvfbOPtYeiaennwfv3taR+NTe2n9vOWzve4mzyWQbVH8RLbV7Cw8nD2nsghLACCfeyLCECvh9qNMN0GgU9JvNXWAJjv/+DS2lZjOvVkJG31SM5O5GJf07kp7CfqFO5Dot7LaadTztrVy+EsCIJ97Lq5Cb48UljYo37vyKrwZ28/+txPvkjjHreriwZ1pZmtdzZcHoDU3dOJSkziREtRjCixQgcbR2tXb0Qwsok3Msaswl+nwG/vwvVA2HIF4SZqzN6wTaCoxJ5uH1tJt3ZlDRTAi9tfYkNZzbQtGpTPu31KQ2rNLR29UKIMkLCvSxJvWgcrZ/6DVo+hL7zPZYfjGfymr9wtLfhk0fb0KtpdX498yvv7HiHlOwURrcezbDAYdjZyD+lEOJfkghlRUwwLHsIUs7DXR+Q2PghJiwPZl1wDB3rVmX2/a2wc0jhpd9fYuOZjTSr2oy3Or9F/Sr1rV25EKIMknAvC46sNq42dXKHx9ezN6cuL8z9k9jkTF7p05inbg1gw5lfmLZrGmnZaYxpM4bHmj4mR+tCiHxJOliT2Qxbp8EfM8C3LXrIlyw+mM709dup6eHMimc6Uccbxv85jo1nNtLCqwVvdX6Luh51rV25EKKMk3C3lsxk42j92Fpo9QiJPd5l/Kpj/Hr4PL2aVmfmfS0Jid/F4DWvcSnzEi+2fpFhgcNk9EYhRKFIuFtDfDgsexDiTkCf6YT4PsizC3ZzLiGdSXc24aEONZizbybLji2jvkd95t8xn8aeja1dtRCiHJFwL20RO+HbB8FsQj+ygm/i6vLmx9up6urAd093wNkthgd+foDwxHAeafIIL7Z5UfqtCyGKTMK9NIX8aDTFuNciY8h3TPg9jZX7Q+ja0Jv3hzRndfjXfPT7R3g6e7Kw50I61uxo7YqFEOVUgeGulFoC9AditdbNLMs8ge8Af+A0MERrfUkZo1N9APQD0oBhWut9N6b0ckRr+HsObHoD/DoQ3W8xT34fzpHoJMb2bMj97d2Z8PcL7IzZSW//3rzW4TWZx1QIcV0KM73950CfK5a9CmzWWjcANlt+B+gLNLDcRgALSqbMcsyUDT+NNoK92T3s7PoZ/RcdJeJiGouHBtGmcSxDfr6PQ3GHmNJpCjO7zpRgF0JctwLDXWv9BxB/xeKBwFLL/aXAoFzLv9CGHYCHUsqnpIotdzKT4ZshsG8pustLfFFzEg9/dgB3F3t+eLY9h1KXMXLTSDydPFl25zLubnC3DM0rhCgRxW1zr661jrbcjwGqW+7XAs7mWi/Ssiyam01qHHx9L0QfIvvOOfzvTGu+33SUO5pUY3z/6kzZ+TwHLxzk3ob38krbV3Cyc7J2xUKICuS6T6hqrbVSShf1eUqpERhNN9SuXft6yyhbEiLgy7shMZLEgZ8zbFtV9kdE8sLt9WnVKIrhG17EpE3M7DqTPgFXtngJIcT1K0ybe17OX25usfyMtSyPAvxyredrWXYVrfVCrXWQ1jrI29u7mGWUQbFHYXFvSLlARP9vuPNXN45GJ/HRQy2xrbqeF7eOxreSL8v7L5dgF0LcMMUN9zXAUMv9ocDqXMsfU4YOQGKu5puK7+xuWNIHtIm9Pb7mzpU5ZOaYWTy8Cati3mRxyGLua3gfX/b9Er/KfgVvTwghiqkwXSGXAd0AL6VUJDAZmA58r5R6AjgDDLGsvg6jG2QoRlfI4Teg5rIpdBN89yi4VWdNy/mMWZVAg2pujB/oypt7R3Ax/SJTOk3h7gZ3W7tSIcRNoMBw11o/mM9DPfJYVwPPXW9R5c7x9fD9Y2ivhsyt+S6zf7nEbQ296dX+NOP+mo63szdf9PuCwKqB1q5UCHGTkCtUr9eRNfDDcMw1WjDe+Q1+2J7Iw+1rYVdtFdP3rKCjT0fe7fouVZyqWLtSIcRNRML9eoSsgBVPYarZmhHmCWw+nMKY3j7sy5jNvtB9PNn8SZ5v9byM5CiEKHUS7sV18FtY9QxZtdrzUOoYDpzP5JUB7qyOnkhcehwzus6gb0Bfa1cphLhJSbgXx74vYc0LpPt2ZlD880Qka14ckMPnp17C1d6Vz3p/RnPv5tauUghxE5NwL6r9X8Ga50n2vY0+0U+Tqm14tHcYC48voLFnY+bePpcarjWsXaUQ4iYn4V4UwT/A6udJqtmF7mefwtHZnm5tt7Ds1Fp61unJ1C5TcbZztnaVQggh4V5oR9bAjyNIrN6O7pEjqOxuR50m37M5cidPt3iaZ1s9i40q7jVhQghRsiTcC+PEBvjhcRKrtqB71EiqeEEl/085FBfOW53fYlD9QQVvQwghSpGEe0FObYHvHiGxckO6Rz+Hl08OpmrziU5NZl6PeXSu1dnaFQohxFUk3K8lcg98+xBJrrW5PXY03n6pJLkvxAlHPu/zOU2qNrF2hUIIkScJ9/xcOA5f30uqfVXuuDAG77oXiHVagq+LLwvuWEAtt1rWrlAIIfIl4Z6XxCj4cjAZZlvuTBqLZ71ooh2+pKVXSz68/UOZBk8IUeZJ944rpcXDV4PJSbvEfSljsa0XSZT9Ujr6dOSTnp9IsAshygU5cs8tKw2WPYD54imGZo0nyT+CePt19KrTi+m3Tsfe1t7aFQohRKFIuF9mNsEPj6PP7uKFnNGcrnOWJIetDG4wmNc7vC6DfwkhyhUJ98t+mQAn1vOmaTh76pwjzWEHjzV9jHFB41BKWbs6IYQoEgl3gB0fw65PWGzuxzq/RNId9vBsq2cZ2WKkBLsQolyScD/+C/rXCWwmiAU1Hch03MPo1qN5svmT1q5MCCGK7eYO9+iDmH8YzhH8GV+tBtnO+3ix9Ys80fwJa1cmhBDX5eYN98QozF8PISbHhaFeDch2PcTYNmMZ3uzmmdNbCFFx3Zz93LPSMC17kJTURAZXbU6W21FeavOSBLsQosK4+cJda8xrXsAcc4gBnm1IrXSKcUHjGNZsmLUrE0KIEnPThbve9iEq5AeGeLbhYuUIxrQZw9DAodYuSwghStTNFe6nfsO8cTLPVmlMqHssI1qM4PFmj1u7KiGEKHE3zwnV+HCyvh3KNPda/OWRxkONH+L5Vs9buyohhLghbo5wz0wh7Yv7Wepszw+eigH1BvFKu1fkAiUhRIVV8ZtltCZl+TP8ZIpiflVXuvvewZROb8h8p0KICq3CJ1z635+w49xG3q7qSVC1TrzfbYYMAiaEqPCuK9yVUmOUUoeVUiFKqWVKKSelVIBSaqdSKlQp9Z1SyqGkii0q09m9hPz5Ji97e1PPPZD5PefIsL1CiJtCscNdKVULGAUEaa2bAbbAA8C7wGytdX3gEmCda/nTLxG87GFGVa+Ku6MPn/f9GGc7Z6uUIoQQpe16m2XsAGellB3gAkQDtwM/WB5fCgy6ztcoOq0JXvooL3sqzLZufHXXEjycPEq9DCGEsJZih7vWOgp4D4jACPVEYC+QoLXOsawWCeQ5k7RSaoRSao9Sas+FCxeKW0aejq+dyhs2J7lo58infRfjW8m3RLcvhBBl3fU0y1QBBgIBQE3AFehT2OdrrRdqrYO01kHe3t7FLeMqMYe3MCNqKaEODky79QNaVAsssW0LIUR5cT3NMncA4VrrC1rrbOBHoDPgYWmmAfAFoq6zxkLLTI5nxtbn2OXsxPNNxtG73m2l9dJCCFGmXE+4RwAdlFIuyrgaqAdwBNgC3GtZZyiw+vpKLLyZ39zLRjdb+rv35Kn2Ml6MEOLmdT1t7jsxTpzuA4It21oIvAKMVUqFAlWBxSVQZ4EWrhjPd04XaGOqwTsD3y+NlxRCiDLruoYf0FpPBiZfsTgMaHc92y2qTQfW8mnSOhpk2/LhoytlWAEhxE2v3I8tczr+LFP2TsQDM+/cvphKzm7WLkkIIayuXA8/kJadxshV95NpY2Kcz1Aa129v7ZKEEKJMKNfhPnfdDM7ZJDEqy5/e/V6xdjlCCFFmlOtwH93qLuamVOfBx76xdilCCFGmlOs2d+c6bej2/GZrlyGEEGVOuT5yF0IIkTcJdyGEqIAk3IUQogKScBdCiApIwl0IISogCXchhKiAJNyFEKICknAXQogKSGmtrV0DSqkLwBlr11EIXkCctYsoIqm5dJS3mstbvSA156WO1jrPqezKRLiXF0qpPVrrIGvXURRSc+kobzWXt3pBai4qaZYRQogKSMJdCCEqIAn3ollo7QKKQWouHeWt5vJWL0jNRSJt7kIIUQHJkbsQQlRAEu5CCFEBSbhfQSnlp5TaopQ6opQ6rJQancc63ZRSiUqpA5bb69ao9YqaTiulgi317MnjcaWUmquUClVKHVJKtbZGnbnqaZTr/TuglEpSSr14xTpWf5+VUkuUUrFKqZBcyzyVUhuVUictP6vk89yhlnVOKqWGWrHemUqpY5Z/95VKKY98nnvNz1Ap1/yGUioq1799v3ye20cpddzyuX7VyjV/l6ve00qpA/k8t3TeZ6213HLdAB+gteV+JeAE0PSKdboBa61d6xU1nQa8rvF4P2A9oIAOwE5r15yrNlsgBuOCjDL1PgNdgdZASK5lM4BXLfdfBd7N43meQJjlZxXL/SpWqrcXYGe5/25e9RbmM1TKNb8BjCvE5+YUUBdwAA5e+X+1NGu+4vH3gdet+T7LkfsVtNbRWut9lvvJwFGglnWrKhEDgS+0YQfgoZTysXZRFj2AU1rrMneVstb6DyD+isUDgaWW+0uBQXk8tTewUWsdr7W+BGwE+tywQi3yqldrvUFrnWP5dQfge6PrKIp83uPCaAeEaq3DtNZZwLcY/zY33LVqVkopYAiwrDRqyY+E+zUopfyBW4CdeTzcUSl1UCm1XikVWKqF5U0DG5RSe5VSI/J4vBZwNtfvkZSdP1oPkP9/hLL2PgNU11pHW+7HANXzWKesvt+PY3yDy0tBn6HS9rylKWlJPk1fZfU9vhU4r7U+mc/jpfI+S7jnQynlBqwAXtRaJ13x8D6MJoSWwIfAqtKuLw9dtNatgb7Ac0qprtYuqDCUUg7AAGB5Hg+Xxff5P7TxPbtc9CdWSv0PyAG+zmeVsvQZWgDUA1oB0RjNHOXFg1z7qL1U3mcJ9zwopewxgv1rrfWPVz6utU7SWqdY7q8D7JVSXqVc5pU1RVl+xgIrMb6y5hYF+OX63deyzNr6Avu01uevfKAsvs8W5y83aVl+xuaxTpl6v5VSw4D+wMOWP0hXKcRnqNRorc9rrU1aazPwaT61lKn3GEApZQcMBr7Lb53Sep8l3K9gaS9bDBzVWs/KZ50alvVQSrXDeB8vll6VV9XjqpSqdPk+xgm0kCtWWwM8Zuk10wFIzNW0YE35HuWUtfc5lzXA5d4vQ4HVeazzK9BLKVXF0qTQy7Ks1Cml+gDjgQFa67R81inMZ6jUXHE+6O58atkNNFBKBVi+AT6A8W9jTXcAx7TWkXk9WKrvc2mcWS5PN6ALxtfsQ8ABy60fMBIYaVnneeAwxtn5HUAnK9dc11LLQUtd/7Msz12zAj7C6F0QDASVgffaFSOs3XMtK1PvM8YfnmggG6NN9wmgKrAZOAlsAjwt6wYBi3I993Eg1HIbbsV6QzHapi9/nj+2rFsTWHetz5AVa/7S8jk9hBHYPlfWbPm9H0aPtlPWrtmy/PPLn99c61rlfZbhB4QQogKSZhkhhKiAJNyFEKICknAXQogKSMJdCCEqIAl3IYSogCTchRCiApJwF0KICuj/hkQuW6a35OIAAAAASUVORK5CYII=\n", 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\n", 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" ] From 6e499f993bab588410baddaf8efb42a8fbaf3544 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 366/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bUs3Flve+P4PvzfudSIyM4bVVYOeqda6Mf+okOKWAUUVeKXhOrYKLv0LriU+dSRN4N5Fh//OlrKMla99q8PhS/xxEJEXw1s632Buyl0+8PmF6k+lYmBSsbfQ8Sniw8ZWNNHRpyPTj01lwasFjvXFsLUxZO6gBpe0tGbzuNNciErQDlg7aRuZpifDbENBlGugnUHKbKvJKwXLbF3aOhyododnHTzwtMiGNt9b6YGosWDeoIfZWT585ciPuBv3+7MfN+JssbbOUATUH5NvhmWext7Dny5e+5M3qb/K/y/9j5IGRJGc87JtTopg56wc3xMzEiAFrfLgTe787Zcnq8MpirZnZgTkGSq/kNlXklYIjLQF+HQLFnKHHk5tsJadnMuS7U0QnprPmOVoVBMQEMHDnQNJ16aztuJZWZVvpIXzeMjYyZlzDcYxrOI5DoYcYtGsQMakPG8WWdbRi3aAGJKZmMnCND3HJ97tT1nkd6vWHw4sgcI+B0iu5SRV5peD4YwzE3tTGjy0dcjwlK0sy8ic/Lt6OY1mfetR2fXprA7+7fgzeORhTI1PWdVxHjeI19JHcYN6s/iZLWy8lKDaIt3a+RXjSw5k1NUvbsWKAFzejk3nvB18ydFnagc6fQcmasGkYxN02UHIlt6girxQM5zfC+Z+g5Vhw837iaV/svcZflyOY9HIN2tZ4+nRHnzAfhu0ehqOlI+s7rS8QD1j/i5ZlW/JN22+4m3yXgX8OJCQ+5MEx74rFmftqLY4FRTNl6yVtnr2ppTZjKSNVjc8XAqrIK/lfzHXY/jGU84bmY5542p8Xwli69xq9PF0Z1NT9qR95JuIMI/aNoEyxMqzruI7SxUo/9fyCzsvFi9UdVpOcmczAnQO5du/ag2OveboyvFVFfvQJYc3RG9qLTlWgyxcQclyNzxdwqsgr+ZsuA34bqo2/v7oSjHOeIeMfFs/oX85Rr5w9s3p4PPWh6fnI8wzfOxxnK2dWtl9JCcsS+kqfr9QsXpN1HddhhBFD/xpKUGzQg2Nj2lelY00XZv9x+eEc+tq9oV4/OPy51sJZKZBUkVfyt/2ztRk1XZaCfc67NcUkpfP2+tPYWJjwbT/PpzYcuxx9mXd3v4ujhSOr2q8qMgX+bxXtK7Km4xqMhTFDdg0hOC4Y0LYT/Pz1OtQobcsHG84SEH5/4++O88HBHTa/AymxT/5gJd9SRV7Jv24cgSNfQP0BULN7jqdk6rIYseEMdxPS+La/FyVtnzyvPTgumHd2v4ONmQ2r26/OVy0K8pKbrRur2q9CIhm6a+iDMXorMxNWDdDWE7zzP19txo15Me1Bd/wd2PHkoTIl/1JFXsmf0hJgy3vaXeRT+tJ8vvsqx4KimdOjFnWfsm1fVEoU7+15DyNhxKr2qyhVrJQeQhccFewrsKr9KtKz0hny1xDuJN4BwMXOgq/7eXInNoWRP5/VNgZ39YJW4+DCL9oDcKVAUUVeyZ92TYTYW9p8eDPrHE/ZFxDBVweC6NOwLD09XZ/4UUkZSQzfM5yY1Bi+eukrytr+9026C5PKDpVZ2X4lSelJvLP7He6l3gPA082BKV1qsv9KJEv23n9A2+xjKNtI2wz8ntqHuSBRRV7Jf67thjPfQdMPoVzjHE8JvZfMqJ/PUaOULVO71HziR2XoMvj4wMdcvXeVRS0XUbPEk88tiqo5VmNpm6XcSbzDiL0jHqyM7deoHK/Vd2XJ3mvs9Y/QHni/ukLrG7T5XcjKMnBy5XmpIq/kL8kxWvtgp+pab5ocpGdm8f4GbSjh6371n9h0TErJtOPTOHbnGFO9p9Lctbk+kxdYXi5eLGi5gIvRFxlzcAwZWRkIIZjdw4OapW0Z+bMfN6KStKGzTvMh5Bic/MbQsZXnpIq8kr/s+ASSo+DVb8HEPMdT5uzw59ytWD7rVRu34jkP5QB8c/4bfg/6nRF1R9Cjcg99JS4UXir3EpMaT+Lw7cNMOzYNKSUWpsZ8088TYyPBu9/7kpqhg7p9oXIH2DsDogINHVt5DqrIK/nHpc1ad8mWY6FUnRxP+eN8GOuO3WBIs/J09Hjyw9PdN3fzld9XdK3YlWG1h+krcaHSq0ovhtcZzu9Bv7Ps7DJA63Gz+PW6BIQnMHP7ZRACuizR+vdvHa7tr6vka7lS5IUQa4QQd4UQFx95zVEIsVsIce3+rzk3G1EUgMRIbVVr6fpP7C4ZEp3M2N/OU6+cPeM6VXviRwXEBDDxyERqO9VmiveUAttN0hDerfMur1V+jZUXVrItaBsArauW5J0WFfjhZIi2faBtKei0QNs28MTXBk6sPEtu3cmvAzr+47VxwF4pZWVg7/3vFSVnO8dq0ya7f5XjqtZMXRYf/XwWIWBZn3pP3N0pKiWKD/Z9gK2ZLUtaL8HcOOchHyVnQggmNppIQ5eGTD02lTMRZwAY06Gq9pfrb+cJiU6G2q9DlU6wbyZEXXvGpyqGlCtFXkp5CIj5x8vdgO/u//47IOfVLIpy5U+4+Bu0/FTraZ6DpXuvcTYkltk9auHqkHPr4HRdOqP2jyI2NZalbZYWudWsucXU2JTPW31OmWJlGLl/JLcSbmFqbMTSN+ohBIz48QzpOqn1tjGxgC1q2CY/0+eYvLOU8u99xMKBHJcXCiGGCSFOCyFOR0ZG6jGOki+lxmnDNCVrQNOROZ7iExzD8v2B9PR0pWudJzcSm3NyDn6RfsxqNqvQtQzOa3bmdix/aTk6qWPE3hEkpCdQ1tGKBT1rcz40jgU7A8DGRWtLHOoDx780dGTlCfLkwauUUgLyCcdWSCm9pJReTk5OeRFHyU92T4XEcOi6PMfNuOOSMxj501nKOVoxreuT57hvvraZ3679xtBaQ+ng3kGfiYsMN1s3vmj9BSHxIXxy6BN0WTo6epRioLcbq44Es+dyBNTqBVU7w/45EBNs6MhKDvRZ5COEEKUA7v96V4/XUgqiG0fAdy00Hg6untkOSymZsOUCdxPSWPJGPYo9YY9W/2h/Zp+cTaNSjRhRd4S+UxcpDVwaMLHxRI7ePsqXftrd+vjO1alRypZPfzvP3cQ06LwQjExg+yhtsZSSr+izyP8ODLz/+4HAVj1eSyloMlLg9w+0BTatJ+R4yi++ofxxPozR7atS5wl9aeLS4hh1YBT25vYsaLEAY6Mnd6BU/pueVXo+mHGz5+YeLEyNWfJGXZLSMhn763mkbWloOxWu71e9bfKh3JpC+SNwHKgqhAgVQgwB5gHthBDXgLb3v1cUzYG52mYgXZbm2JsmOCqJab9foknF4rzTokKOH5Els5h4ZCIRyREsarUIRwtHfacusiY0mkDtErWZeGQiQbFBVHa2YXynauy/EskPJ0PAazC4NoBd4yEp2tBxlUfk1uyaPlLKUlJKUymlq5RytZQyWkr5kpSyspSyrZTyn7NvlKLqjh8cW65tGF2hZbbDuizJ6I1+mBob8XnvuhgZ5TzPffWF1RwMPcinDT6ljlPOi6eU3GFmbMaiVouwMLFg5P6RJKQnMMDbneaVSzDrj8sERadoi6RS4+CvSYaOqzxCrXhV8laWDrZ9BNYloP3MHE/59lAQZ0JimdGtJi52OfeHPxF2guV+y+lcvjNvVH1Dn4mV+1ysXVjUchGhCaFMODIBhGRhrzpYmBoz6mc/MkpU12ZIndsA1w8YOq5ynyrySt46tRrC/KDDHLDMvgjaPyyexbuv8nKtUk+cLhmVEsX4w+Nxt3VnqvdUtaI1D3m5eDGmwRgO3DrAyvMrcba1YG6PWpwPjWPZ3mvQ4hNwrAjbRmrPXRSDU0VeyTsJ4doKyQqtweO1bIfTM7P4eOM57CzNmNk9531as2QWk45MIiE9gc9afoaVac4LoxT96VutLy9XeJmvzn2FT5gPnWqVoqenK8v3B+J7J1lbJHUvGA4uMHRUBVXklby0awJkpsHLi7RGV/+wdO81/MPimfdqLRyts8+ZB1h/aT1H7xzl0wafUsWhir4TKzkQQjCl8RTK2ZRj7OGxRKVEMbVLDUrbWzLml/OkujaFOn3h2DLV8iAfUEVeyRuBe7XWBc0/huIVsx0+G3KPrw4E0svTlbY1ct579ULkBZacWUI7t3b0qtJL34mVp7AytWJRq0UkpCcw/vB4rMyMWPBabYKjkli46wq0mw6mVlrraDV33qBUkVf0LyNV2wTasWKOrQtS0nWM3niOUnaWTO6SczuChPQEPjn0CSWtSqpx+HyiikMVJjSawImwE6y4sIImlUrQr3E5Vh8NxjfaBNpM0ubOX1ZLZAxJFXlF/44s1ubEv7wITLPPlpm/M4DrUUl81rM2tham2Y5LKZl+fDrhSeHMbzEfO3O7vEitPIcelXrQpUIXvvb7mpNhJxnXqTql7Sz55JfzpNYdCC61tGG6tERDRy2yVJFX9CsqEI58rvU4qdg62+HjQdGsO3aDt5q406RSzl0jN13bxK4buxhRbwR1S9bVd2LlXxBCMKnxJNzt3Bl7aCypWbF81rM216OSWLQnCDovgvjbcOgzQ0ctslSRV/RHStgxGkwsof3sbIeT0zMZ+9t53ItbMbZjzpuABMUGMc9nHo1LNWawx2B9J1b+AytTKxa1XERSRhLjDo+jUQUH3mxUjlVHgvGVlaFuPzi+HCKvGDpqkaSKvKI/l7dqi2LaTAKb7A9TF+66SkhMMvNfq42lWfaeMxm6DO2hnqkVc5vPxUio/13zq8oOlZnQaAInw06y9tJaxnd+ZNim1WStdYV6CGsQ6k+Noh/pydrydmcPra/JP/jevMfaY8H0b+xGowrFc/yIb85/g3+MP1O9p6oNQAqA7pW608G9A1+e/ZIbCQEs+HvY5mgMvDQFgg9q+/gqeUoVeUU/jn4Bcbe0vUD/sZ1faoaOT389R2k7S8Y+Ya/Wc5HnWHVhFd0qdqNNuTZ5kVh5QUIIJjeeTAmrEow9NJZ6blb0vT9s41eyh/YQdvcUtRI2j6kir+S+ezfgyBfg0RPcm2Y7vGzfNYIik5jzaq0ce8SnZKYw6cgknK2cGdtwbB4EVnKLnbkdc5vNJTQxlLk+cxnXqRolbcwZt/kSme3maH/xH1tu6JhFiirySu7bNRGMjKHdjGyHLt6O45uD1+np6UrLKjnvBPaF7xfciL/BrKazsDGz0XdaJZd5uXgxxGMIWwK3cDx8HzO6eRAQnsCKW6WhehdttlV82LM/SMkVqsgruStwLwRshxZjwK7MY4cydFl88ut5HK3NmPxyzouejt85zoaADfSr3o+GpRrmRWJFD96r+x61S9Rm+vHp1HaTdKzpwpI91wj1mgBZmbB3uqEjFhmqyCu5JzMd/hwLDuXBO/s2fN8cCMI/LJ5Z3T2ws8q+6Ck+PZ7JRydT3q48H9X/KC8SK3piamTKvObz0GXpGH9kPFO6VsPM2Iix++ORjd+Hcz9CqK+hYxYJqsgrucfnW4i+Bh3ngYn5Y4euRSSwbF8gL9cuRYeaLjm+fd7JeUSlRDGn2RwsTHLuI68UHGVtyzKx8UR8I3z54+YGxnaqxtHAaLbYvAHWJWHnODWlMg+oIq/kjoRwODAfKreHqh0fO6TLknzy63mszY2Z3rVmjm/fc3MP265vY1jtYXiU8MiLxEoe6FKhC53cO/Gl35fUrhCPl5sD0/8KIaHZBAj10ZrWKXqlirySO/ZMA12adhf/D2uPBuN3K5ZpXWtSoph5tuNRKVHMOD6DGsVr8Hbtt/MgrJJXhBBM8p5ECcsSTDo2kWndqpCUlsnkG7XApbY2pTI92dAxCzW9F3khxA0hxAUhhJ8Q4rS+r6cYQKivNsbaeHi2NsK3YpJZ9NdVXqpWMsednqSUTD82naSMJOY2m4upUfaxeqVgszWzZWbTmQTHBfNH6GqGt6rElnMRnK05Vutrc2yZoSMWanl1J99aSllXSumVR9dT8oqUWpdBaydtRs1jhySTtlzESPDEnZ62BG7hQOgBPqr/ERXsK+RVaiWPeZf2pm+1vnzv/z0Nq8dQwcmaj45bo6vWVVs4lxBu6IiFlhquUV7M5S1w64TWn8b88Tnt286HcfBqJKPbV6W0vWW2t95OvM38U/PxcvaiX41+eZVYMZCRniNxt3Vn+okpTHjZnZCYZL6zGgS6DPeHbJoAACAASURBVDgw19DxCq28KPIS+EsI4SuEGPbPg0KIYUKI00KI05GRkXkQR8k1GamweyqUrAn1+j92KC45gxnbLlHb1Y6BTdyzvfXvvVoBZjWbpZqPFQGWJpbMbjabiOQIDkStomud0szzSSPOYwCcWa+6VOpJXvzJaialrA90At4XQrR49KCUcoWU0ktK6eXklPMKSCWf8vkWYm9Ch1naCtdHzNvpz73kDOb0qIWxUfZhmh/8f+B0xGnGNhhLmWJlsh1XCqfaTrUZWmsoW4O20rreXcyMjZgQ3RFpaq09vFdynd6LvJTy9v1f7wKbAbWMsTBIioJDC6FyB6j4eAMxn+AYfvS5xZBm5fEok30Xp6DYIL7w/YJWZVvRvVL3vEqs5BPv1n6X6o7V+eLcHN5rU5I/gjK4WnkIXNkBN48ZOl6ho9ciL4SwFkLY/P17oD1wUZ/XVPLI/jmQngTtZz72clqmjvGbzuPqYMnItpWzvS0jK4MJRyZgbWqt9motokyNTZnTbA6J6Ylc1a2jeikbhl1tRFYxF/hrsloglcv0fSfvDBwRQpwDfIA/pJQ79XxNRd/u+oPvWq1PvFPVxw59c+A6QZFJzOrugZVZ9g6TK8+v5HL0ZSZ7T1Y94ouwSg6V+KDeB+y7tY9O3re5mSDZUWIQ3D6tNv7OZXot8lLK61LKOve/akops+8BpxQ8f00GMxtoNe6xlwPvJvLl/kC61ilNq6ols73tYtRFVpxfQZcKXWjn1i6v0ir5VP8a/alfsj4/Bi6lu5cVH1+tSZpDFa15mS7D0PEKDTWlQfl3AvdA4G5tTrz1wzvxrCzJhM0XsDA1YvIr2TtMpmamMuHIBEpYlmBco3HZjitFj7GRMbOazkIndSTa/IyVhRmL5JsQcx181xk6XqGhirzy/HSZsGsSOLhDo3ceO/SL7y18gmOY0Lk6TjbZWxcsObOE4LhgZjadia2ZbR4FVvK7srZl+aj+R5wMP0pn71BWhFfirqMXHJgHaQmGjlcoqCKvPL9zGyDSH9pOf6zLZFRiGnN2BNCwvCO9vcpme5tPmA/f+39Pn2p98C7tnZeJlQKgT7U+eDp7ciByNbXcYExcT0iOUjtI5RJV5JXnk56szagp4wU1uj12aOb2y6Sk65jToxZG/5gTn5CewKSjk3C3dWeU56i8TKwUEEbCiJlNZpKRlYF92a0cTi6Lv0NrOP4lJEUbOl6Bp4q88nxOfg0JYdqWfo9Mezx0NZKtfncY3roilUoWy/a2BacWEJEcwexms7E0yd7aQFFAG7YZ6TkSv+gTNKkTzKiIzsj0RDi62NDRCjxV5JVnS47RNuau0vGxjblTM3RM2XqR8iWsea9VxWxv2xeyjy2BWxhaayi1nWrnZWKlAOpTrQ/1S9YnKGsDty3tOWL1EtJnJcTfMXS0Ak0VeeXZDi2E9ER4aepjL684dJ0b0cnM6FYTc5PH2xpEp0Qz/fh0qjtW593a7+ZlWqWAMhJGzGw6E11WJuWr/cm4ey8jdTo49JmhoxVoqsgrT3fvJpxaCXX6gvPDqZE3o5NYvj+QV2qXonnlx3sOSSmZeWImCekJzG42G1Nj1SNeeT7lbMvxUf2PCE4+jXCNZIvxS8gz6yEm2NDRCixV5JWn2z8bhBG0nvDgJSkl036/hJlxznPit13fxt6QvXxY70MqO2RvbaAoT9O3el/ql6xPut1m5qa2QoexNqVS+U9UkVeeLOw8nN8Ijd4Fu4edInddimD/lUhGtauCs+3jG26HJ4Uz9+Rc6pesT/8a/f/5iYryTEbCiBlNZyDRYVXlKGsz2yHP/6y101D+NVXklSfbMw0s7KDZw6mPSWmZzNh2iWouNgz0dnvs9CyZxaSjk8iSWcxqNgvjf7QfVpTn5Wbrxof1PyRGnuMba3dSjSy1f1Uq/5oq8krOrh+AoL1a+wJL+wcvL913jTtxqczu4YGJ8eP/+/wY8CMnw07yaYNPKWuTfVGUovwbb1Z/k/ol60OpvXye+RL4b4PbvoaOVeCoIq9kl5Wl7fhkVxYavP3g5asRCaw+HMzrXmXxdHN87C3X466z2HcxLVxb8GrlV/M6sVII/T1sI4wy2VwmjVhhQ9beWYaOVeCoIq9kd2kThPlp+7aaamPuf2/KXczChLGdqj12emZWJpOOTMLCxIJp3tNUj3gl17jZuvFhvQ9JswhgjHljjK7vgxtHDB2rQFFFXnmcLgP2zQJnD6jV68HLm8/exic4hnEdq+FobfbYW1ZfWM2FqAtMbjwZJyu1haOSu96s/iZ1nepy2uUWV4wdydw7S20s8i+oIq88zm8D3AuGNpMf7Nsal5zBnB3+1Ctnn60B2eXoy3xz7hs6l+9MB/cOhkisFHLGRsbMaDoDI+NMPnR0x/jWcbhx2NCxCgxV5JWHMtPg4AKtCVmVhwV74V9XiElKZ1Z3j8cakKXp0phweAKOFo5MaDQhp09UlFxR3q48H9QbwZ1iUfxk5UTqbnU3/7xUkVce8v0O4kO1sfj74+rnQ2P5/uRNBjZxp2bpxzflXnZmGUFxQcxoOgM78+wbditKbhpQYwDVHGqysIQtSeGnIPigoSMVCKrIK5r0ZDi8ENyaQYVWAOiytIetTsXM+bhdlcdOPxV+ivWX19O7Sm+almma/fMUJZcZGxkzr8VsMo0lU4o7E//nDHU3/xz0XuSFEB2FEFeEEIFCCLXvW351ahUkRjx2F7/BJ4TzoXFMeqUGNhYP+88kpicy6cgkytqUZbTXaEMlVoqgivYVebfOexwqZsqJpMvogg4YOlK+p9ciL4QwBr4EOgE1gD5CiOzNThTDSkuAI4uhUltw03ZuikxIY8HOAJpWKk6X2qUeO32ezzzCk8OZ03wOVqZWhkisFGFv1x5MGctKzChRnOAdU9Td/DPo+06+IRAopbwupUwHfgK6PeM9Sl478Q2kxEDriQ9emrvDn7SMLGZ083hs3vvekL1sDdrK0FpDqeNUxxBplSLOxMiEJW3nkWBkzArj2yQH7DZ0pHxN30W+DHDrke9D77/2gBBimBDitBDidGRkpJ7jKNmk3INjy6DaK1CmPgAnrkez6ext3mlZgYpOD3d7ikqJYvqx+z3i66ge8YrhVHWsyqvlB/BnMWu2756s7uafwuAPXqWUK6SUXlJKLycntZAmzx1bDmnxD1oJp2dmMXnLRco6WvJ+60oPTpNSMu3YNJIykpjbfC6mRqpHvGJYE5p/hEuWPV9ZJxB46ldDx8m39F3kbwOPrp5xvf+akh8kRcGJr8HjVXCuCcCao8Fcu5vItC41sTB92EVy07VNHAw9yCjPUVS0z77Vn6LkNVMjU6a1XkyssRFLfeeou/kn0HeRPwVUFkKUF0KYAW8Av+v5msrzOrIYMlOg1XgAbsemsGTPNdrXcOal6s4PTruVcIsFpxbQqFQj+lbva6i0ipJNU3cv2hrVYr9VJj/8Nd/QcfIlvRZ5KWUmMALYBfgDG6WUl/R5TeU5xd/Rpk3W6QMltN2bZmzT/tNM6fJwApQuS8fEIxMxFsbMajoLI2HwET5FecyUV7/FPT2LFaE/cC8l1tBx8h29/4mVUu6QUlaRUlaUUqqu//nF4UWQlQktPwVgX0AEuy5F8OFLlXF1eDgtcu2ltZy9e5bxjcbjYu1iqLSK8kS2xWwZ5NCdOGPJ2N/fN3ScfEfdlhVF925qLQzqDwAHd1IzdEz9/RKVShZjSLPyD04LiAngS78vaefWjlcqvGLAwIrydD26T6F3fCbHU8+zO/iAoePkK6rIF0WHFmibczcfA8BX+wO5FZPCzG4emJlo/0uk6dIYf3g8DuYOTGk8RfWIV/I1YWJO76rvUiE9g2mHxpOYnmjoSPmGKvJFTVQg+P0IDYaAXRmuRybyzcHr9KhXBu+KxR+ctuzMMgJjA5neZDr2FvZP+UBFyR8qvTScT+9lkSATmXFUPYT9myryRc3BeWBiDs1GIaVkytZLmJsaMb7zw92eHm0+1ty1uQHDKsq/YGJGzcYfMzAunj9DtnD8znFDJ8oXVJEvSiIuw4VfodE7UKwkf1wI40hgFJ90qEpJG22bv7i0OMYdHoebrZtqPqYUOPZNBjEwyQyXdMH4Q5NJykgydCSDU0W+KDkwB8xtoMmHJKRmMGPbZTzK2PJmIzfg4arWmNQY5rWYp5qPKQWPiTm2rcfwWVQY0akRLD692NCJDE4V+aLizlnw3wbe74OVI1/suUZkYhqzutfC+P5uT5uubWJPyB4+rPchNYvXNHBgRflvzBq8RRXhQLs4Y36++jOnwk8ZOpJBqSJfVOyfA5YO0Pg9Lt+JZ92xG/RtWI66ZbWHqsFxwcw/NZ9GpRoxsOZAA4dVlBdgYo5Fq9HMjr2BRYYNk49OITkj2dCpDEYV+aIg5CRc+wuafkSWmS2Tt17E3tKUTztoD1szdBmMOzwOM2MzZjedrVa1KgWekedAjC1L8uHddG4nhrL07FJDRzIY9ae5KNg/C6ydoOEwfvG9he/Ne4zrVA07K62T5DK/ZVyOvsz0JtNxtnZ+xocpSgFgaoFZy9H0T7+GU2w1Nvhv4EzEGUOnMghV5Au76wch+BA0H01Mhilz/wygobsjPT1dATgZdpJ1F9fRs0pPXir3koHDKkouqj8QnbUzU++FYCFKMOXYFFIyUwydKs+pIl+YSQn7Z4NNafAcxPw/A0hMzWRWD223p9jUWCYcnoCbrRufeH1i6LSKkrtMLTBuPoqWBFDyZj1uxt/ky7NfGjpVnlNFvjAL3AO3TkLLTzh9O5mfT99iSPPyVHG20aZLHp9GTFoMC1osUNMllcLJ8y2yrEsyNesEdhkt+J////C762foVHlKFfnCSkrYNxPs3cis3ZdJWy5S2s6CD9tobYV/vfYre0P2MrL+SKoXr27gsIqiJ6aWGDUbRQN5EedgV2xNtGGbNF2aoZPlGVXkC6uA7RB2DlqNY93JOwSEJzC1a02szU24EnOF+T7zaVK6Cf1r9Dd0UkXRL69BSOuSTLbcQXr4awTHBfOV31eGTpVnVJEvjLJ0sG82FK9MmFsXFu++SptqJWlfw5nkjGTGHByDjZkNc5rNUdMllcLP1BLR9CPqZp6nbGQK1azbsu7SOs5FnjN0sjyh/oQXRpc2Q6Q/tB7PzB1XycySTO+qrWCddWIWIQkhzG8+n+KWxZ/xQYpSSHgNBmsnZthv5/KFljhZOjPh8IQisUhKFfnCRpeprW4tWZMDJk3ZcSGcD9pUoqyjFVuDtrLt+jberf0uDUs1NHRSRck7ZlbQ9CNqpJzBQxeMe9YQbiXc4rPTnxk6md6pIl/YnP8JYoJIbzGeKb/7U8HJmrdbVCAoNog5J+fQyKURw2oPM3RKRcl7XoPBqgRzHXew168YL7v14derv3Lw1kFDJ9MrvRV5IcQ0IcRtIYTf/a/O+rqWcl9mOhyYD6XrsfxOFUJikpnVzYMs0hlzcAyWJpbMbT4XYyNjQydVlLxnZg1NP6RCvA8tLK4TeKUpVRyqMOXYFGJSYwydTm/0fSe/WEpZ9/7XDj1fSzm7HuJCCPMcwzcHr9OtbmmaVCrB3JNzCYoNYm7zuThZORk6paIYToOhYFWcOY47OBWcwCulxpCQnsC0Y9OQUho6nV6o4ZrCIiMFDi1ElvPmkzMlMDcxYuLL1dkWtI3NgZsZWmsoTUo3MXRKRTEsM2to8gFloo/RxfE26w6k8n7dD9h/az9bArcYOp1e6LvIjxBCnBdCrBFCOOR0ghBimBDitBDidGRkpJ7jFGKn10BCGMfd3uVIUDRjOlTlXsZNZhyfgaezJ8PrDjd0QkXJHxq8DZaOTLPbzs3oZDJjmtHQpSHzfOZxK+GWodPluhcq8kKIPUKIizl8dQO+BioCdYEwYFFOnyGlXCGl9JJSejk5qaGE/yQtEQ5/ToZbS0aeKIZHGVu61nfg4wMfY2Nmw8KWCzExMjF0SkXJH8yLQZMPKB52kMHu0Szfd53R9aZgJIyYcHgCmVmZhk6Yq16oyEsp20opPXL42iqljJBS6qSUWcBKQM3Z0xefbyE5ilWmfYlKTGNW95pMOTqJO4l3WNRqESUsSxg6oaLkLw3fBksHRpttJjlDxw9H45nQaAJ+kX6sPL/S0OlylT5n15R65NsewEV9XatIS4mFo0uJdW3D/Is2DGpanlP3fuNA6AHGNBhDvZL1DJ1QUfIfcxvwHoF1yD7G1kriR58QKlm14JUKr/DN+W84HX7a0AlzjT7H5BcIIS4IIc4DrYFRerxW0XV8OaTGMu5eV8rYW9K0VgzL/ZbTuXxn+lbra+h0ipJ/NRwGFva8lbkRW0tTpm+7zMRGE3Et5srYw2OJTY01dMJcobciL6XsL6WsJaWsLaXsKqUM09e1iqzESDj+FddKtGNndElGdSrB1OPjqWBXganeUxFCGDqhouRfFrbQZARmQX8xp5GOE9dj2O8fz2ctPyMmNYbJxyYXimmVagplQXZkMTIzhRHhnehUqzi/3ZpLZlYmX7T+QvWHV5Tn0fAdsLCnU/R3eJSxZfYf/rgVq8Joz9EcuHWADQEbDJ3whakiX1DF3UaeWsUBi7bcMSmDRalNXIy+yOxms3GzdTN0OkUpGCxswft9xNU/WdAEwuNTWbYvkDerv0lL15YsOr0I/2h/Q6d8IarIF1SHFpCVpWNy7Mu0aniJPbf+5IN6H9CmXBtDJ1OUgqXRO2BhR42rX9PL05XVR65zPSqJmU1n4mDuwKeHPiUpI8nQKf8zVeQLopjryLPf84t8iWLlUzgY+R2d3Dvxdq23DZ1MUQoeCztoPByu/MEEzwwsTI2Z9vsl7M3tmddCWyA1+WjBHZ9XRb4gOjCPDGnMYtGEaKt1VC9enelNp6sHrYryXzV6F8ztcPBZzOh2VTh8LYpdl8Jp4NKAj+p/xO6bu1l/eb2hU/4nqsgXNHf9kec38qWuDUYVt1HMzIqlrZdiaWJp6GSKUnBZ2kPj9yBgO/3c46nmYsPM7f6kpOt4q+ZbtC3XlsW+iwvk/HlV5AuYjD0zuYclP5ZLJl3GsqT1EpytnQ0dS1EKvsbvgrktJkc+Y0Y3D27HpvDl/kCEEMxoOgNXG1c+OfQJkckFq8eWKvIFye0zmFz9g0HFq5JiGsz0ptOp7VTb0KkUpXCwdNCGbfy30dDyDj3qlWHFoesE3k3ExsyGxa0Wk5SRxJiDY8jIyjB02ueminwBErN9CgvsnbhuG82H9T7klQqvGDqSohQujd8DMxs4tIAJnatjYWrEhM0XyMqSVHaozFTvqZy5e4b5PvMNnfS5qSJfQCRfPcjexDN872BJ94qvMrTWUENHUpTCx8pRG7a5vBWn5CAmdK6OT3AMv/hqLYhfrvAyA2sM5OcrP/NTwE8GDvt8VJEvCKRkx45PmVXckToODZnaZLKaSaMo+tJ4+IO7+d5eZWno7sicHQFEJaYBMMpzFC1cWzDPZx7H7xw3cNhnU0W+ANi2cyEL7BIphSMrOi1TveEVRZ+sHKHRMLi0BaOoAOa86kFyeiYzt18GwNjImPnN51PerjyjD47mRtwNw+Z9BlXk8zm/sAvMCfsOO51gZbcfVU8aRckL3iO0rQIPLqBSSRvea1WJrX53OHhVm1lTzKwYy9osw0SY8MG+D4hLizNw4CdTRT4fC4kP4b1dg7GSOqaVH0FZhzKGjqQoRYOVo9aK+NJmuBvA8FYVqVDCmklbLpCSrgPA1caVxa0XE5oYyscHPiZdl27g0DlTRT6fCk8Kp9/2QRhnJTMzwZGmbd4xdCRFKVq8R4CpFRz6DAtTY2b3qMWtmBS+2Hv1wSmezp7MaDIDn3AfJhyZQJbMMmDgnKkinw9Fp0QzZNfbJKVFsyI8ggbdFoJ60Kooecu6uLZN4MXfIOIy3hWL87pXWVYeus7ZkHsPTutSsQujPUez68Yu5vvMz3c9blSRz2eiUqIY8tcQQuNvsyQ8mnJl2mBa3tvQsRSlaGr6EZjbwt4ZAEx8pTouthaM/uUcqRm6B6e95fEWA2oMYEPABlZfXG2otDlSRT4fiUyOZPCuwYTEhfLa7VI0SU+iWOdZho6lKEWXlSM0GwlX/4Sbx7C1MGV+z9pcj0xi0V9XHjt1tNdoXq7wMkvOLGHjlY0GCpydKvL5RERSBIN3DSYsMRzb0O5MyDiBqNcfnKoYOpqiFG2N3gWbUrB7KkhJ88pOvNmoHKuOBHPqRsyD04yEETObzKSVaytmnpjJL1d/MWDoh16oyAshegkhLgkhsoQQXv84Nl4IESiEuCKE6PBiMQu30IRQBu0aRGRyJDaxw5mYfhgjE1NE6/GGjqYoipkVtBoPoT4QsB2A8Z2rU8bekk9+OUdyeuaDU02NTVnUahEtXFsw4/gMNl3bZKjUD7zonfxF4FXg0KMvCiFqAG8ANYGOwFdCCOMXvFah5B/tT78d/YhLi8PLYhxOt6NoxwmMmn8MNi6GjqcoCkDdN6FEFdgzHXSZFDM34bOedbgRncyCnY8P25gZm/F5q89pWqYp045NY/O1zQYKrXmhIi+l9JdSXsnhUDfgJyllmpQyGAgEGr7ItQqj43eOM2jXIMyMzRhW6XN2nDLmC4efwdZVm76lKEr+YGwCL02F6Gvg9z0A3hWL81YTd9Ydu8GBK3cfO93c2JwlrZfgXdqbKcem8P3l7w2RGtDfmHwZ4NYj34fefy0bIcQwIcRpIcTpyMiC1af5RWy/vp3he4dTulhpFjRZwaI/YhlVwodSyVeh3XTtn4iKouQf1V4G14awfy6kJwMwrlM1qrnYMOaXc9xNSH3sdHNjc5a2WUrbcm2Zf2o+S88sNcj0ymcWeSHEHiHExRy+uuVGACnlCimll5TSy8nJKTc+Ml/TZen4wvcLxh8eT12nuqxou5ppm29jKZMZnvUjlG0EHq8ZOqaiKP8kBLSbAYnhcHw5ABamxizrU4/EtExGbzxHVtbjRdzc2JyFLRfyWuXXWHlhJdOPT8/zXvTPLPJSyrZSSo8cvrY+5W23gbKPfO96/7UiLT49nhH7RrD64mp6VenFinYrWLTzFn63Yvmx2lGMkyOh41y18ElR8is3b6jeFY4shvg7AFR2tmHKKzU5fC2KFYevZ3uLsZExU72n8natt/nt2m+8t/s9YlNj8yyyvoZrfgfeEEKYCyHKA5UBHz1dq0C4FH2JPtv7cOLOCSY3nswU7yn86hvGjz63GN/YggqB30GdvlDG09BRFUV5mvYzIStTewh7X5+GZelcy4WFu648thr2b0IIPqz/IbOazuLM3TP03dGXoNigPIn7olMoewghQgFv4A8hxC4AKeUlYCNwGdgJvC+l1D35kwqvLJnFd5e+o9+OfqTp0ljdYTW9q/bmbMg9pm69RIsqTgxLXQNGpvDSFEPHVRTlWRzcwft9OP8ThGobewshmNujNs62FozYcJaYpJyblXWr1I01HdaQnJFM3z/6si1om97jivzUZ8HLy0uePl3wdkN/krDEMKafmM7R20dpXbY1M5rMwN7CnrsJqXRddhRTE8GfnVIo9ltfrcA3H23oyIqiPI+0BFjmCfblYMjuB0Os50Nj6fnNcRq4O/DdoIaYGOd8Hx2eFM7YQ2M5c/cMXSp0YWLjiVibWv/nOEIIXymlV07HCsWK1wxdBot9FxOdEm3oKIB2977BfwPdt3bnTMQZJjaayJLWS7C3sCclXcfb632JS8lgxRs1KbZvApSoCt4fGDq2oijPy9xGuzELPQUXHq5sre1qz6zuHhwNjOazXTnNLte4WLuwusNqhtcZzh/Bf9BrWy98I3z1ErVQFHm/SD/WX15Pl81d2OC/gcyszGe/SV9Z7vrRf0d/5vrMpW7Jumzutpk3qr2BEIKsLMmon/04HxrLkjfqUj1wFdy7AS8vAhMzg2VWFOU/qNMXStXR2h2kJT54ubdXWfo1Lse3h66z/fydJ77dxMiE9+q+x9oOa5FS8nPAz3qJWWiGa4Ljgplzcg4nwk5QzbEao+qPwru0d57thXoj7gZLzixhT8genCydGOk5ki4Vujx2/Tk7/Flx6DqTX6nBkGo6+NobavaAV1fkSUZFUXJZyElY0x6afADtHzYTTM/Mos/KE1y8HcdPwxpTr5zDUz8mOSOZjKwM7Mzt/lOMpw3XFJoiDyClZPfN3Sw8vZCwpDDqlazH27XepmmZphgJ/fyj5VL0JdZeXMvum7uxMLZgkMcgBtQYkG2bvnVHg5m27TIDvN2Y3qUG4vsecPssjDgFNs56yaYoSh74/UM4+z28cwhcPB68HJ2YRo+vjpGUlsnm4U0pV1x/CxyLTJH/W7ounc3XNrPywkoikiMoZ1OO3lV709G9I87WL15Q49Li2HVjF1sCt3Ah6gI2pjb0qtqL/jX6U8KyRLbzf/UNZcwv52hXw5mv36yPyeVN8NsQ6LxQ25RAUZSCKzkGlnuBY0UYvAuMHt5QXo9M5NWvj+Fobcam95pgb6WfYdkiV+T/lq5LZ/fN3fwU8BN+kX4A1CtZj+ZlmlOvZD1qOdXC3Nj8mZ+TmplKQEwAZ++e5fDtw5yJOINO6qjsUJkelXrQo1IPipkVy/G9Oy+GMfyHM3hXLM7qgQ2wSL8HXzbUpmEN2Q1Gqm+bohR4fj/ClnehyxLwfOuxQz7BMfRbdZJarnasH9wQa3OTXL98kS3yj7oee53dN3ez++ZurtzTnnobC2PKFCtDOdtyFLcoTjGzYpgZmZGmSyNVl0pEUgS3E28TmhBKptQe5lZ2qExL15a0dWtLDccaTx3z33/lLu+s98WjjC3/G9JI+4/762C4/Du8exhKVtfLz6ooSh6TEta9AhEXYcRpKPZ4i5YdF8IYseEMjcoXZ+2gBvy/vXuPjqo89zj+fcgdDJdIuBMuoiKCYsLNg6Cgp+KteEPoUWFZK/V4WbVqqxbr0nZVezw97amtVUEUHjvyswAADL9JREFUsR5EBUSUShFhSS0YRG7hVsItgCkxCUkwQEKS5/zx7pQxmRAkM7Mnk+ez1qzs2Xsn85s3mSd7v3vvdycnhHbjzop8HSXHSlhXsI5NhZvIO5xHXlkeJRUlfF35NRXVFSTFJ5Ecl0yn1p3odkY3erftzcCOAxnUcRDprU9tfJ0Pc/K5f846zumcyv/9YATtWifA1vdh7q0w5nG49CdhfpfGmIj6aju8MNKdTHHTjHqLF6zbz4NvbWDU2enMmJxFUnzoCr0V+QhbsG4/D7+9kQt7tOPVO4bRLiUBjh6C54dDm04wdTnEJfgd0xgTait+DSuegYlvwHnX1ls8d00ej8zbxNj+nfjTrZkh26KP+YuhosmsT3fz4FsbGNY7jdfvHO4KPMDin0B5IYz/oxV4Y2LVqIegyyB4/wEor39x5sShGfzqhoEs317A5JnZlB0L/4iUVuRDpKq6hicW5vDkoi1ccV5nXr1j6IkDLBvfclfFXfYodBvsb1BjTPjEJcD1L8LREvhL8C7ZW4f34veTLmLdvkNMfGk1/yw9FnS9ULEiHwLF5ZXcMWsNs1ft5a5RfXjxtqwTu2GH9sIHD7lx4i950N+gxpjw6zIQLnsEcubB5neDrvLdC7vx8pSh5BWVc90f/8bavcVB1wuFmCjyJUcqmfJKNlu+LIv4a2fvLubq36/ks13F/PrGQUy7ZgBxrbwzbmqqYcHd7sj7jdPdLcSMMbFv5I+h20Ww6EdQsi/oKpeek86Ce0fSOjGOSdNX82Z2XliixESRzys+wpb8Mq5//lOmf7Kz3t1ZwuHY8Wqe/XAb35uxmuSEVsy/59+YNCzjmyt98t+Q93e45jfuvHhjTMsQFw83zXQbevN+ANXBx9M6p3MqC+8dyYi+Z7KrsDwsUWLm7Jri8koem7+RJZsPktWrA09993wGdj+9cSAa82luIY+/m8PuwnImZPXgiesGkJpc52DqjqXwxgS4cBJc/4Ld7cmYlmjTO+7q9lEPnfR+EVXVNYjIiV6Ab6nFnEKpqsz/4gBPL95K8ZFKbsnqyX1j+9EzLTRjRmzaX8qzS7axckchGWmteebGQYzsV38YAw7tgZcuhXY94c6/2k25jWnJFt7nxra5fT6cNTYsL9Fiinyt0qPHeW7ZDmav2kONwrUXdGXyxb3JzGj/rUelPF5dw0dbDjJ71V5W7SqifesE7r2sH7df3Cv4Oa7Hj8LM77gDrj9cAWl9m/x+jDHNWOURmDEGyr+Cu5ZDh14hf4kWV+Rr5ZceZebK3czJzqO8spru7VO4amAXhvROIzOjPempSfWKflV1DbsLy8n5spTl275ixfYCyo5V0b19Cv8xPIPbL+5F27pdM7VqamD+XZDzDnxvLpw7LmTvxRjTjBXmwstj3d7995dAUvCxrk5Xiy3ytQ4fO87SLQdZtOFLPs0torK6BoA2iXF0bpdMirdFXnr0OAWHK6iscsvPbJPImP6dGHd+F8b079R4f9myX8LK38DYn8Poh0P+PowxzVjuMnjjZuh/DUyY/Y3RKpsqbEVeRCYATwLnAcNU9XNvfm9gK1B7/6vVqnp3Yz8vEsMaVFRVs/nLMtbnlbDv0BEKyiqoqKpGFdqlJJDeNon+XVI5t3Nbzu2SeuoHQr6YDe/dD5mT4brn7ECrMaa+Vc/Dkp/BiHvgyqdDVidOVuSbeuJ2DnAj8FKQZTtVNeou70yKjyMzowOZjdyp5VvZ/C4sesAdVLnmt1bgjTHBjbgHSvJg9Z+gTTqMCv8Fkk0q8qq6FYjYLfai0rbF7hSpHkPhltdtXBpjTMNE4Mpn3DhWy56ClPYw5PthfclwXoLZR0TWAWXA46q6Moyv5Y9ti+HtKe5mvre+HfKDKcaYGNSqlbt2pqIM3v8xVFXCiEZ7s09bo0VeRD4CugRZNE1VFzbwbflAhqoWiUgW8K6InK+q9cYdEJGpwFSAjIyMuotPXVUlxIfn1lpBrZ3lfkFdB8Nt8yC5beRe2xjTvMUnwsQ/u5sIffgIHD8Stq6bRg/vquoVqjowyKOhAo+qVqhqkTe9FtgJnNPAutNVdYiqDklPP7UbctRTuAP+kAnbPzy97/82ampg+dNuTIqzLocpiyAlhP37xpiWIT4JJsyCQRNc182SaeF5mXD8UBFJB4pVtVpE+gJnA7vC8VoAxCVC6zSYMxFG/9QN6RuOe6eWF8GCqZD7EQy+Da77X+uDN8acvrgEuGG6u5lQx6DbwU3WpCIvIjcAfwDSgQ9EZL2qXgmMBn4hIseBGuBuVQ3fWJoderkLDD54CD55FvatdqcxpvUJ3Wvs/BgW3g/lBXDt7yDrDjuLxhjTdK1awbinw/bjY+tiKFV3vvqSaVBTBWN+BsN/6HaLTlfpAfj4l7BhDpzZD2562Q0haowxUSKc58lHFxHImgL9rnBb9Ut/DtnT4ZIH4IKJkJR66j+raCdkz4DPXwGtcaPIjf4pJCSHL78xxoRYbG3JB1J1XSwrnoH9ayDxDOh/rfsH0HOYG0Mi8LLiqgoo2Ap7/w7bF8Oev7l+/UG3uD7+MAwqZIwxodBytuQDiUC/y91VqPvXwNrXXPHe+KZbHp/iDtbGJ0PFYThSBFrtlnUa4Ap75hRo29W/92CMMU0Uu0W+lojbcu85zN2lJX895G90p10eK4GqY64bp00n6DwAumXaVrsxJmbEfpEP1CoOume5hzHGtAAxcY9XY4wxwVmRN8aYGGZF3hhjYpgVeWOMiWFW5I0xJoZZkTfGmBhmRd4YY2KYFXljjIlhUTV2jYh8Bew9zW/vCBSGME64NIeczSEjWM5Qs5yhE+mMvVQ16F2XoqrIN4WIfN7QAD3RpDnkbA4ZwXKGmuUMnWjKaN01xhgTw6zIG2NMDIulIj/d7wCnqDnkbA4ZwXKGmuUMnajJGDN98sYYY+qLpS15Y4wxdViRN8aYGNbsi7yIjBOR7SKSKyKP+p0nkIjsEZFNIrJeRD735qWJyFIR2eF97eBDrldEpEBEcgLmBc0lznNe+24UkUyfcz4pIge8Nl0vIlcHLHvMy7ldRK6MUMaeIrJcRLaIyGYR+ZE3P6ra8yQ5o609k0UkW0Q2eDmf8ub3EZHPvDxzRSTRm5/kPc/1lvf2OecsEdkd0J6Dvfm+fY5Q1Wb7AOKAnUBfIBHYAAzwO1dAvj1AxzrzngUe9aYfBf7Lh1yjgUwgp7FcwNXAXwABRgCf+ZzzSeDhIOsO8H7/SUAf7+8iLgIZuwKZ3nQq8A8vS1S150lyRlt7CnCGN50AfOa101vAJG/+i8B/etP3AC9605OAuRFqz4ZyzgJuDrK+b5+j5r4lPwzIVdVdqloJvAmM9zlTY8YDr3nTrwHXRzqAqn4CFNeZ3VCu8cBsdVYD7UUkInc3byBnQ8YDb6pqharuBnJxfx9hpar5qvqFN30Y2Ap0J8ra8yQ5G+JXe6qqfu09TfAeCowF3vHm123P2nZ+B7hcRMTHnA3x7XPU3It8d2BfwPP9nPwPN9IU+KuIrBWRqd68zqqa703/E+jsT7R6GsoVjW18n7fL+0pAd5fvOb2ugotwW3VR2551ckKUtaeIxInIeqAAWIrbiyhR1aogWf6V01teCpzpR05VrW3PX3nt+TsRSaqb0xOx9mzuRT7aXaKqmcBVwL0iMjpwobr9uKg7hzVac3leAM4CBgP5wP/4G8cRkTOAecADqloWuCya2jNIzqhrT1WtVtXBQA/c3kN/nyMFVTeniAwEHsPlHQqkAY/4GBFo/kX+ANAz4HkPb15UUNUD3tcCYAHuD/Zg7W6a97XAv4Tf0FCuqGpjVT3ofbhqgBmc6ELwLaeIJOAK5xuqOt+bHXXtGSxnNLZnLVUtAZYDF+O6N+KDZPlXTm95O6DIp5zjvG4xVdUK4FWioD2be5FfA5ztHXlPxB14ec/nTACISBsRSa2dBr4D5ODyTfFWmwIs9CdhPQ3leg+Y7J0dMAIoDeiGiLg6/Zg34NoUXM5J3tkWfYCzgewI5BFgJrBVVX8bsCiq2rOhnFHYnuki0t6bTgH+HXf8YDlws7da3fasbeebgY+9PSc/cm4L+McuuOMGge3pz+coUkd4w/XAHbX+B67fbprfeQJy9cWdnbAB2FybDddfuAzYAXwEpPmQbQ5u1/w4rm/wzoZy4c4GeN5r303AEJ9zvu7l2Ij74HQNWH+al3M7cFWEMl6C64rZCKz3HldHW3ueJGe0tecFwDovTw7whDe/L+6fTC7wNpDkzU/2nud6y/v6nPNjrz1zgD9z4gwc3z5HNqyBMcbEsObeXWOMMeYkrMgbY0wMsyJvjDExzIq8McbEMCvyxhgTw6zIG2NMDLMib4wxMez/ASN+GbiV+kmbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 076e8044f047c1bff1f0ee3ece601d66595eeebb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 367/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From 421da68f896c903bd6070e60437e22cd4780ba60 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 368/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From 946c82a33472424d3524e3379a2771b3264a5b51 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 369/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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Pgbs3wJKH5AMmhNXGXgn2Tij6wOpKhrxBB75Syg94ElgGjANuVEqN67XZ14F6rXUW8BjwyGD3O2D2Ltj4G/jtHCjfDisehdtXyegbITzFiFwITzLdOsKlnNGlkwcUa61LAZRSLwNXAwd6bHM18EPHz68DTyillNZaO2H//asvg7fuNnPe5KyAFb+AyOEu3aUQ4gLZbGYK8d2vmOHRstqbyzijS2cEcKzH7+WOx/rcRmvdCTQCcb1fSCl1l1IqXymVX11dffEVaW2mKn5qLlTthZW/gxtekrAXwlONucJc0V66zupKhjSPGpaptX5aa52rtc5NSEi4uBc5Uwev3Axv3wPJk+GejTDlRpnFUghPlnaJWRHuoHTruJIzunQqgJ4LsqY4Hutrm3KllD8QBdQ6Yd//TGuo3GNmspx1r/m6KITwbP6BkL0YCt4zy4P6+dYAQndxRhpuA0YrpdKVUoHADUDvyTHeAW51/Pwl4COX9d+HxcF922DONyXshfAmY64w05oc3WR1JUPWoBPR0Sd/H7AaOAi8qrXer5T6sVLqKsdmzwBxSqli4LvAPw3ddCpZUlAI75O1CPyCZLSOCznle5PW+j3gvV6PPdjj51bgOmfsSwgxRAWFQ8ZCKFgFSx+W824uIH0eQgjPkbMUGsqg+pDVlQxJEvhCCM+RvdTcF75vbR1DlAS+EMJzRA43w6kLJPBdQQJfCOFZspdC+VazzKhwKgl8IYRnyV4K2i6TqbmABL4QwrMkTzGTqUk/vtNJ4AshPIvNZq66LV4Lne1WVzOkSOALITxP9jJob4ayDVZXMqRI4AshPE/GQvAPhsLVVlcypEjgCyE8T2AopC+AwlVmQkThFBL4QgjPlL0E6o9AdYHVlQwZEvhCCM/UfdXtKmvrGEIk8IUQnilqBCRNkn58JxqSgX+mvdPqEoQQzpCzDI5tMSvZiUEbcoHf3NrBlB+t4eon1vPI+4dYX1RDa0eX1WUJIS5G9hK56taJhtw6Yp1dmrsXZLCxpJY/fFrKU+tKCPSzMW1UNHMz45mTFceklGgC/IbcsU6IoSd5KoQnmqtuJ99gdTVeT7lqpcHBys3N1fn5+YN6jVNtnWw7UsfG4ho2FNdyoLIJgLBAP/LSY5mbFc+czHjGJEVgs8liC0J4pLfvgwPvwA9KwC/A6mo8nlJqu9Y6t6/nhlwLv6fwIH8uzRnGpTnDAKg73c7m0lo2ltSwsbiWjwsOAhAbFsjsjDjmZMUxJzOetLhQlKy2I4RnyF4CO1+Eo5sh/RKrq/FqQzrwe4sNC2T5xGSWT0wGoLKxhY3FtWxwHADe3VsJwPCoYOZkxTMn0xwAkqJkjVwhLJOxEPwCoWi1BP4gDekunQuhteZwzWk2lNSyqaSGTSW11J/pACAjIYy5mfHMzYpjVkYc0aGBbqtLCAG8sBKaKuC+bVZX4vF8tkvnQiilyEgIJyMhnFtmjcJu1xyobGJTifkG8MaOcl7cXIZSMH54JHMy45mdEUduWgwRwdKvKIRLZS+B9++HulKIzbC6Gq8lLfwB6uiys/tYAxuKzTmAnUcbaO+yY1MwYUQUM9NjmZURR25aLFEhcgAQwqnqSuHxqbD0EZh1t9XVeLRztfAl8C9SS3sXO4/Ws/lwHZtLa9nlOACc/QYwMz2Omemx5KXHSheQEM7wm1yIHgm3vGV1JR5NunRcICTQz5zYzYoHoLWji51HG9hyuJYtpXX8eXMZz6w/jFIwJimSWRmx3QeBmDA5AAhxwbKXwNanoe0UBIVbXY1XksB3kuAAP2ZnxjE7Mw6Ats4udh9rZHNpLVsO1/LXrUd5bsMRAMYkRXR3AeWlxxIXHmRh5UJ4iewlsOkJKF0HY6+wuhqvJIHvIkH+5uKuvPRYYDTtnXb2lDewxdEF9Gp+Oc9vKgPMKKDcUTHkpsUyIy1WrgMQoi+psyEo0lx1K4F/USTw3STQ30ZuWiy5abHce2kWHV129lY0sqW0ju1ldXxw4ASv5pcDEBcWSG5aDLmjYslNi2H88CgC/WUqCOHj/AIg8zIoWgN2u1n7VlwQCXyLBPjZmJYaw7TUGCATu11TWnOKbUfqyT9ST35ZHav3nwAgyN/GlJHRzEiLZXqa+RsZCSR8UvZSOPA3qNoNw6daXY3XkcD3EDabImtYBFnDIrgxLxWAk82tbD9Sbw4CZXU89UkJXR9rlIKcxAimjzLhPyU1mvS4MJkPSAx9o78AKCj8QAL/IsiwTC9ypr2TXUcbyC+rZ9uROnYebeBUm5n7PyokgMkjo5kyMpqpqdFMSYmW0UBiaPrjIrB3wV0fW12JR5JhmUNEaKD/54aCdtk1xSdPsetYPbuONbDzaANPfFSE3XEMT4sLZWpqTPdBYExSpJwLEN5v9BL4+H/g1EkIH2Z1NV5FWvhDzOm2TvaUN7LzWD27jjaw81gD1c1tgDlxPGF4JJNSopk4IoqJKVFkJoTjJ11BwptU7oHfXwJXPwlTb7a6Go8jV9r6MK01xxtb2XW0gV3H6tl5tIH9x5tocawCFhLgx7jhkUwcEcX44ZFMTIkiKyEcf1kgRngqreHRcZCSC19+0epqPI506fgwpRQjokMYER3CiklmWuguu6ak+hR7yxvZd7yRfRWNvJp/jDPt5iAQHGBjbLI5CEwYHsWEEVGMTgyXVcKEZ1AKshfD3jegsx385VzVQEkLXwDmIHC45hR7KxrZW97EvopG9h9v5LTjIBDgpxg9LIKxyZGMTY5gXHIkY5Mj5cSwsMah9+DlG+Grb5v58kU3aeGL8/LrMSz0GsdoN7tdc7j2NPsqGjlwvIkDlU18UljNGzvKu/8uKTKYsclnDwTmlh4fJucFhGtlLAC/IChcLYF/ASTwRb9sNkVmQjiZCeFcPWVE9+PVzW0crGzqcWvm06IauhzDg4IDbOQkRTKux4FgTFKErBsgnCcwzKx+Vbgalv7U6mq8hgS+uGAJEUEkRCQwPzuh+7G2zi6KTpzqPgAcrGxi1b4q/rr1WPc2I2NDGJv02TeBccmRjIwNkXmDxMXJXgrvfQ9qiiE+y+pqvIIEvnCKIH8/JowwJ3jP0lpT1dTafRA4cNx8I1hz8ARnTx1FBPkzpleXUE5iBCGBfhb9lwivMXqxuS9aLYE/QIMKfKVULPAKkAYcAa7XWtf32mYK8BQQCXQBD2mtXxnMfoV3UEqRHBVCclQIl41J7H78THsnBVXN3d8EDlY28eaOCk61mdlDbQrS48M+901gbHIkiZFB8m1AfCZmFCSMNbNnzr7X6mq8wmBb+PcDa7XWDyul7nf8/m+9tjkDfFVrXaSUGg5sV0qt1lo3DHLfwkuFBvozNTWGqakx3Y/Z7Zpj9Wc4WNnEAceBYNexBv6+p7J7m5jQgM8dAMYmR5I1LFyuHvZl2Yth05PQ2gTBkVZX4/EGNSxTKVUALNRaVyqlkoF1Wuuc8/zNbuBLWuuic20nwzIFQFNrB4cqmzlwvNF8I6hqoqCqmbZOO2CGi2YmhH/uIDA2OUIWlfEVZRvhuWVw3fMwfqXV1XgEVw7LTNRan22CVQGJ59pYKZUHBAIl/Tx/F3AXQGpq6iBLE0NBZHBAj4VkjM4uO0dqT3d/EzhY2cT64hre3FnRvU1SZDATU6KY5JhCYuKIKDkIDEUpeRAcDUUfSOAPwHkDXyn1IZDUx1MP9PxFa62VUv1+XXB8A3gRuFVrbe9rG63108DTYFr456tN+CZ/P1v3NQNXTR7e/Xjtqbbu8wL7jzeyp6KRNQdOdD8/IjqESSlRjgOBmU8oKlSGino1P3/IWmQCXxZFOa/zBr7WelF/zymlTiilknt06ZzsZ7tI4F3gAa315ouuVohziAsPYt7oIOaNju9+rKm1g/0VTeytaGBPeSN7KxpZta+q+/lRcaFMHBHFpJQopqXGMGFEFMEBMkLIq2QvgX2vw/GdkDLd6mo82mC7dN4BbgUedty/3XsDpVQg8Bbwgtb69UHuT4gLEhkc8LnF5QEazrSzr6KJPRUN7C1vZOfRz04OB/gpxg834T9tVDTTR8WQHBViVfliILIWgbKZ0ToS+Oc02JO2ccCrQCpQhhmWWaeUygXu1lrfoZS6GXgO2N/jT2/TWu8612vLSVvhTtXNbew8Ws/2o/XsLGtgd3lD94nh5KhgpqXGMDXVHAAmjIiSieQ8zTNLoLMF/uVTqyuxnEyPLMQFau+0c7CyiR1H69lxtIEdZfVUNLQAZkrp6aNimJkey8yMOCaPjCLIX7qBLPWPR2Htj+C7hyAy2epqLCWBL4QTnGhqZXtZPVtKa9lyuI5DVc2AWWR+amo0M9PjmJkRy7TUGDkP4G4n9sNTc+DKx2H6rVZXYykJfCFcoP50O1uP1LGltI4th2s5UNmE1hDoZ2PKyGjmZsUzb3Qck1OiZUEZV9MafjURkifDDS9ZXY2lZHpkIVwgJiyQJeOTWDLejFpubOkg/0gdWw7Xsamkll+tLeSxDyE8yJ9ZGXHMy4pj3uh4MhPCZYoIZ1PKzK2z+2XobAN/ueaiLxL4QjhJVEgAl49N5PKx5vrDhjPtbCypZX1xDRuKa/jwoLkmICkyuLv1PzcrnmERwVaWPXRkL4X8Z+DIesi63OpqPJIEvhAuEh0ayPKJySyfaE4iHqs7w/riGtYX1/DRoRPdC8nkJEZwyeh4FuQkkJceKyeAL1b6JeAfYubIl8Dvk/ThC2EBu11zwDElxPqiGrYeqaO9005IgB+zM+NYmJPAguwERsWFWV2qd/nLl+HkQfjWbtPN44OkD18ID2Ozqe71A+5ekMmZ9k42l9bySUE16wqr+eiQuWg9PT6MBdkJLMhJYFZ6nKwTcD6jF5sLsGoKIeGc8zj6JAl8ITxAaKA/l41J7F434EjNadYVnOSTwmpe3naUP208QpC/jZkZcSx0HAAy4sPk5G9v2UvMJC6FqyXw+yBdOkJ4uNaOLrYermNdQTWfFJ6kpPo0YJaMXJCdwILsYczJjCMsSNpvADw118ygefu7VldiCenSEcKLBQf4MT/77BrC4zhWd4ZPCqtZV1DNmzsq+PPmowT62chLj2VhTgILcxJ8e+hn9hJY/ytoaYCQaKur8SjSwhfCi7V1drH9SD3rCqv5+NBJik6eAsxU0Cb8fbD1f3QLPLsYvvQsTPii1dW4nVxpK4SPqGhoYV3BSdYVVLOxuIbT7V0E+tmYkR7DwuxhLMxJIGvYEG/927vg51nmBO61v7e6GreTwBfCB7V32sk/Use6wmrWFZyk8MRnrf8FOQkszE5gblb80Gz9v3kXFK2B7xeDzbdGNkngCyGoaGgxwz4LTrLB0foP8FPMSIvt7v4ZPVRa//vegNe/Bl9fAyPzrK7GrSTwhRCf095pJ7+sznEAqKbghJn5c0R0CPOzzYnfuVnxhHtr67+lAX6WAfO+DZc/aHU1biWBL4Q4p+MNLazro/WfO+qz1n92ope1/p9bAa2N8I31VlfiVhL4QogB66/1PzwqmAU5Ztz/vNFe0Prf8GtY8yB8Zz9EpVhdjdtI4AshLtrxhhbHuP+TbCiu5VRbJ/42RW5aDAtzzMifnMQIz2v9VxfAk3lwxWOQ+zWrq3EbCXwhhFO0d9rZXlbfPfTzbOs/ISKIOZlxzM2MZ05WHCkxoRZXilkU5deTIWEMfOVVq6txG7nSVgjhFIH+NmZnxjE7M45/Xz6W4w0t/KOomg3FtWworuHtXccBGBUXypzMeOZlxTM7M47YsED3F6sUjFkB2/4Ibc0QFOH+GjyMtPCFEE6htabgRDMbimvZWFzDlsN1nGrrBGBcciRzs+KYkxVPXlqs+8b+l22C55b61FW30qUjhHC7ji47e8ob2Vhcw4aSGnaUNdDeZcfPppgwPJIZabHMSI9lRlqs674B2Lvgl2Ng1By4/nnX7MPDSOALISzX0t7FtiNmwfdth+vZVd5Ae6cdgNHDwpmRHkue4yAwIjrEeTv+v2/DnlfhByUQ4MTX9VDShy+EsFxIYM9ZP820z3srGtl6uI6th+t4Z9dx/rLlKGCGgE4eGW1uKdFMTIm6+GGg466C7c9ByUemT9+HSeALISwRHOBnunXSYrn3Uuiyaw5WNrH1cB07jzWw+1gDq/ZVAW7b9R0AAA0NSURBVOb86+hh4UxKMQeBiSOiyE4MJzRwABGWdgkER8HB//OawO+ya/xszh/mKoEvhPAIfj2WfTyr7nQ7u8tN+O8pb+TjQyd5fbtZ/F0pGBUbypikSHKSIhibHEFOUiSpsaGfD0u/AMhZDgXvQVeH+d2DdNk1JdWn2FPeyJ7yBrYdqScxMog/3e78OYAk8IUQHis2LJBLc4Zxac4wwIwEKq9vYf/xJgqqmjlU1cShqmZWH6ji7OnIQD8bI2NDSI8PIy0ujPSEMKZFL2Bs619pL15HYM4XLPlv0VpT1dRKafVpSqtPUVJ9mgPHm9h3vJEz7V0AhAX6MSU1mjmZcS6pQQJfCOE1lFKMjA1lZGwoSyckdT9+pr2TohOnOFTVRGnNaY7UnOZIzRn+UVRDW6edIILZERTE3158ikeDICkqmOSoYBIjg4kPDyI6NIDo0ACiQgKICgkkKsSfIH8/gvxt5j7ARqCfDaWg067psuvu+/ZOO82tHTS3dtLkuK873c7JplaqmlqpamrjRGMr5fVnOO0IdjDhnp0UwfW5I5k4IorJI6NIjw93SVfOWRL4QgivFxro332Stye7XVPZ1MqRmtPUrV3Iypp8DoxNoLKpg4qGVraX1VN/psMlNdkUxIcHkRQVTGpcKLMz48hMCCMzIZyMhHASI4PcPh2FBL4QYsiy2RQjokPMMM+W6+GN1Tw0/YwZl+/QZdc0tXTQ0NJBY0sHDWfaaWzpoK3TTnunvce9aZ372xQ2m8LfpvCz2Qj0U0QEBxAR7N99Hx0aQEJ4EP5+Nqv+0/skgS+E8A3ZS8A/GPa/9bnA97MpYsICibFi+gc386zDjxBCuEpQhFnndv9b0NVpdTWWkMAXQviOCV+E09VQ5luLopwlgS+E8B3ZSyAw3Kx564Mk8IUQviMgxFxte+Ad6Gy3uhq3k8AXQviWCV+E1gYzt46PkcAXQviWjEshONonu3Uk8IUQvsU/EMZdbebWaT9jdTVuJYEvhPA9E74I7aegaLXVlbjVoAJfKRWrlFqjlCpy3MecY9tIpVS5UuqJwexTCCEGLW0eRCTD7petrsStBtvCvx9Yq7UeDax1/N6fnwCfDnJ/QggxeDY/mHwDFK2B5hNWV+M2gw38q4GzC0U+D6zsayOl1HQgEfhgkPsTQgjnmHwT6C7Y+6rVlbjNYAM/UWtd6fi5ChPqn6OUsgG/BL43yH0JIYTzJGRDygzY9Rfw0LW9ne28ga+U+lApta+P29U9t9NmNfS+3rV7gPe01uUD2NddSql8pVR+dXX1gP8jhBDioky5CU4egMrdVlfiFuedLVNrvai/55RSJ5RSyVrrSqVUMnCyj81mA5cope4BwoFApdQprfU/9fdrrZ8GngbIzc31jUOuEMI646+FVfebVv7wKVZX43KD7dJ5B7jV8fOtwNu9N9Baf0Vrnaq1TsN067zQV9gLIYTbhUTD2CtMP35nm9XVuNxgA/9h4AtKqSJgkeN3lFK5Sqk/DrY4IYRwuSk3QUs9FL5vdSUup7SHnqzIzc3V+fn5VpchhBjq7F3wq4mQkAO3vGV1NfDeD8xFYSt/e1F/rpTarrXO7es5udJWCOHbbH4w/XYzmVptibW12O2w/03obHXJy0vgCyHEtK+CzR/yn7W2jortZoGW7GUueXkJfCGEiEiEsVfCzj9DR4t1dRx8B2wBMLrfwZGDIoEvhBAAM+4w8+Tve9Oa/WsNB96GjIUQ0u+0ZIMigS+EEACj5kLCGNj2B2uuvK3cDQ1lZupmF5HAF0IIAKUg7044vhPKNrp//wfeBuVnlmB0EQl8IYQ4a8pXIDQeNj7u3v1qDQf+BunzITTWZbuRwBdCiLMCQiDvLnMR1slD7ttv5S6oK3Vpdw5I4AshxOfl3QkBobDxN+7b586XwD8Yxl/j0t1I4AshRE+hsTD1ZtjzCjQdd/3+OlrMXD5jrzRz+7iQBL4QQvQ2+17Qdlj/K9fv69C70NpoDjIuJoEvhBC9xaSZAN7+HDQcc+2+dr4I0amQNt+1+0ECXwgh+jb/++b+H79w3T6qC6B0HUy9BWyuj2MJfCGE6Ev0SJh+m5luwVWTqm38jTlZm/s117x+LxL4QgjRn0u+B/4hsPo/nP/azVXmxPCUr0BYvPNfvw8S+EII0Z+IRFjwfTMuv+hD5772lt9DV4c5QewmEvhCCHEuM78BsZnw/v3Q4aR56k/XwNY/wLirIC7TOa85ABL4QghxLv6BsPznUFsEnzzsnNf89BfQcRoufcA5rzdAEvhCCHE+WZebRVI2/BrKB7n0anUhbPuj6btPyHFOfQMkgS+EEAOx+CGIGA5v3gktDRf3GnY7/P3bEBgGl/+3c+sbAAl8IYQYiOBI+NIz5kKsN+4wi59fqC2/g7INsPgnEJ7g/BrPQwJfCCEGKnUWLP8ZFK+BD/7rwhZKKc+HNQ9CzgpzoZUF/C3ZqxBCeKvcr5mpkzc/aU7oXv7fZvGUc6kuhJeug8jhcPUT59/eRSTwhRDiQi19GLraYf1j0HAUrngMgqP63vboZnj5JrD5wS1vuXSBk/ORwBdCiAtls5mQj06Fj34CZZtg/vdg4pc+C/66w7D5t2ZETkwa3PSaW8fc90VpKxbrHYDc3Fydnz/I4U9CCOFq5dth1fehYjsoG0SmQFcbnDphfp9+m+n2cfFc92cppbZrrXP7ek5a+EIIMRgp0+GOtVC+DYo/NF08yg8Sx8G4lRA1wuoKu0ngCyHEYCkFI/PMzYPJsEwhhPAREvhCCOEjJPCFEMJHSOALIYSPkMAXQggfIYEvhBA+QgJfCCF8hAS+EEL4CI+dWkEpVQ2UWV3HAMUDNVYXcQG8rV6Qmt3F22r2tnrB9TWP0lr3Odm+xwa+N1FK5fc3d4Un8rZ6QWp2F2+r2dvqBWtrli4dIYTwERL4QgjhIyTwneNpqwu4QN5WL0jN7uJtNXtbvWBhzdKHL4QQPkJa+EII4SMk8IUQwkdI4A+AUmqkUupjpdQBpdR+pdS3+thmoVKqUSm1y3F70Ipae9V0RCm111HPP60XqYzHlVLFSqk9SqlpVtTZo56cHu/fLqVUk1Lq2722sfx9Vko9q5Q6qZTa1+OxWKXUGqVUkeM+pp+/vdWxTZFS6lYL6/25UuqQ4//7W0qpPtffO99nyM01/1ApVdHj//3yfv52qVKqwPG5vt/iml/pUe8RpdSufv7WPe+z1lpu57kBycA0x88RQCEwrtc2C4G/W11rr5qOAPHneH45sApQwCxgi9U196jND6jCXETiUe8zMB+YBuzr8djPgPsdP98PPNLH38UCpY77GMfPMRbVuxjwd/z8SF/1DuQz5Oaafwh8bwCfmxIgAwgEdvf+t+rOmns9/0vgQSvfZ2nhD4DWulJrvcPxczNwEPCchSov3tXAC9rYDEQrpZKtLsrhcqBEa+1xV1trrT8F6no9fDXwvOPn54GVffzpEmCN1rpOa10PrAGWuqxQh77q1Vp/oLXudPy6GUhxdR0Xop/3eCDygGKtdanWuh14GfP/xuXOVbNSSgHXA391Ry39kcC/QEqpNGAqsKWPp2crpXYrpVYppca7tbC+aeADpdR2pdRdfTw/AjjW4/dyPOdAdgP9/+PwtPcZIFFrXen4uQpI7GMbT32/v4b5pteX832G3O0+RzfUs/10m3nqe3wJcEJrXdTP8255nyXwL4BSKhx4A/i21rqp19M7MN0Pk4HfAH9zd319mKe1ngYsA+5VSs23uqCBUEoFAlcBr/XxtCe+z5+jzXd0rxjvrJR6AOgEXupnE0/6DD0FZAJTgEpMF4m3uJFzt+7d8j5L4A+QUioAE/Yvaa3f7P281rpJa33K8fN7QIBSKt7NZfauqcJxfxJ4C/N1t6cKYGSP31Mcj1ltGbBDa32i9xOe+D47nDjbHea4P9nHNh71fiulbgOuAL7iOEj9kwF8htxGa31Ca92ltbYDf+inFo96jwGUUv7AtcAr/W3jrvdZAn8AHP1vzwAHtdaP9rNNkmM7lFJ5mPe21n1V/lM9YUqpiLM/Y07S7eu12TvAVx2jdWYBjT26JazUb2vI097nHt4Bzo66uRV4u49tVgOLlVIxju6IxY7H3E4ptRT4AXCV1vpMP9sM5DPkNr3OL13TTy3bgNFKqXTHN8UbMP9vrLQIOKS1Lu/rSbe+z+44e+3tN2Ae5iv6HmCX47YcuBu427HNfcB+zKiAzcAci2vOcNSy21HXA47He9asgCcxoxr2Arke8F6HYQI8qsdjHvU+Yw5GlUAHpo/460AcsBYoAj4EYh3b5gJ/7PG3XwOKHbfbLay3GNPXffbz/DvHtsOB9871GbKw5hcdn9M9mBBP7l2z4/flmJF0JVbX7Hj8T2c/vz22teR9lqkVhBDCR0iXjhBC+AgJfCGE8BES+EII4SMk8IUQwkdI4AshhI+QwBdCCB8hgS+EED7i/wO9gnhaWPSDlQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From 33bfad09befbe499286a57fdb7ae9bc1aa74130a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 370/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From cfdcd0c895700f7f63847833619fcad95a0021e9 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 371/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 155 ++++++++++++++------ skfda/exploratory/fpca/test.ipynb | 235 ++++++++++++++++++++++++------ tests/test_fpca.py | 50 ++++--- 3 files changed, 328 insertions(+), 112 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,32 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,14 +131,24 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +157,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +212,28 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +243,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +259,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +289,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -605,6 +673,31 @@ { "cell_type": "code", "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -636,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -671,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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9G2Z/plfrxkovGQMXfgf+9zJ490FY9BWvK5JuKPDlI2obW070Y3+/rIH3y1x/9pb2DuCDC6gzM+P5ZF4mOSmud0xmwghCB3uysOq9rg35vcehthgiomDKMphxvTub16CgwZG10I28Xf0jmLMShsd7XZF0QYEfxDo6LPuqjp4I9R1l9bxfVk+ZbwUmcP3ZJ6fGsHhiElNSY/rnAmpf1RTDjmdg+zPuAiwGxp0L598Nk69w7coy+C64B361CN7+qTvjF7+jwA8Sbe0d7K44ypaSWraV1rGltO5DUwyEhhjGJ49kfnYCk1NjTtySoweonb23aophx7NusM/Bje65tFlw4Xdh+jUQm+FtfeK6tU67BtY96HpAaQplv6PAH4KOTw62paSOraXutv1gHU2trklmZEQoU9Ni+eTcTKakxTAl1U0xEBnuR13qrIVDW2DXS1DwIhzc5J4/HvJTl0N8lqclSheWfB22PeUGtH3sbq+rkZMo8ANcR4dlT+VRtpbWsrWknq2lblrf4/3ah4eHMi09huvnjeGsjFimp8eRnTRy8Nvae6L1GOx9EwpegoKXob4UMJCR55oIpix3g33Ef42a7HpErfs1nHMnRMZ6XZF0osAPMJVHmtm8v5ZNB2p887bXcaS5DXC9ZKamxXJtXibT02M5KyOWcclR/hnu4M7iq3bDntdh9z9gzz+htdFdeB1/vmuTz7kYopK9rlR6Y/G/ulHM6x9yj8VvKPD9WEtbBzvK6tm034X7pv217K9uBFyb++TUaJbPSmNGRhxnZcQxPnkkYaF+3ivlaBXs/Sfsft0FfJ1v3r24sTDzRpi0FLIWQ5ifXDuQ3kubBRMuhHfuh/m36yK6H1Hg+wlrLaW1x9i0v9YX7jVsO1hPS5trdx8dE8msMXF8asEYZo2JZ1paLMMj/KjNvTtNdbB/HRSvdgFftgWw7k/97CWw6KvubD5hnNeVSn9a/HV4ZCls/qOmT/YjCnyPtLR1sLW0jg3F1eTvq2HTgVoqfKNTI8Pdiks3nZPFrMw4Zo6JIzU2QNZLPVIB+9e4QVDFa+DQVsC60a4Zc10zzfjzIXUmhOrjN2SNWQBps93F27xbNB7CT+h/3CCpa2xlw34X7vn7anivpJZm39l7VuIIFk9IYtaYOGaNiWfS6OgPFuXwZx0dUL0HSjd8EPKVBW5b2HDInAvn3QVjz4H0PA25DybGuBlIn74FilbBxEu8rkhQ4A8Ia910v+v3VZNfXEP+vmoKyo8AbpTqtPRYPr1gLHlZCcwZG+8/fd1Pp77MhfvBjb77Ta7JBtzsk2PPdu3wYxdC6gwIi/C2XvHWlGXwyrddW74C3y8o8PtBW3sH75c1sH5fNRuKa1i/r/rE5GHRw8KYPTaeK2ekkZeVwIyMuMBoez9W6wL9eLCXboCGMrctJAxSpsLUqyB9NqTPgeRcTY0rHxYa7trvX/sulG93nxnxlAL/DDS2tLGxuJZ391WzobiaTftrT/R7T48bztnjE8nLSiBvbDwTU6L9t1vkca3HXFt76QYo9Z29V+/+YHviBHeBNc0X7qOnQXiAXFMQb825Cd64z7XlL/uF19UEPQV+DzQ0tZJfXMO6PdW8u7eKLSV1tHVYQgzkjo5hxZwM5vgCPi3Oz4OwvQ0qdn7QLFO6EQ7vgA7Xl5/oNHfWPutGF/Bps2B4nLc1S+AakQBnXQtb/gwXf1+fJY8p8LtQ29jC+n01rNtTxbv7qtlWWkeHde3vZ2XE8rkl45iX7QK+Xxfr6G/WunVbOzfLlL3nBjeB6xqZNhsWfsWFfNpsLUYt/S/vs25tgi1/hvm3eV1NUFPg40avrt9bzbq91azdU8Wu8gashYiwEGZmxnHn+ROYPy6RWWPiGBHhx2/Z0SoozfedufvO3o9Vu21hke5C6uyVrlkmfbbr+67FQGSgpc1y3XA3POLa9PWZ84wfp9fAKa9vYu2eKt71hXzRYdeDJjI8hDlj4/nq9InMz05gRmacf00o1llrk6/dPd8tyl2a787mwS3MPWqKmyr4eLv7qMnuIpqIF/Juhr99CQ6sc330xRNBEfglNY2+9vdq1u2tYl+Va9KIGhZGXlY8V81OZ352ItPTY72d5/1U6stcX/f9a13AH9oKHb71YWPSXajn3ez6u6fN1HB28S/TroaX74b8RxT4HhpygW+tpbiqkXV7q1i3x53Bl9a6Zfhih4czNyuBTy0Yy7zsBKakxvjn3DPWugFNxWtg/ztQ/PYHZ+/hI11zzNlfcLNIpuep3V3837Aod/F20+9h6X9prnyPDLnAL6k5xnn/808AEkdGMC87gc8tzmb+uEQmpUQT4q9dJOsPugnFdv/DLbx9pNw9PzzBjVSddxuMORtGn6UpCSQwzVkJ+Q/D9r/A3Fu9riYoDbnkyIgfzn3XnMXsMXGMT44a+MWzz1TzEXfmfjzkK3e550eOcn3esxbCmHMgeZIucsnQMPosGDXVTaimwPfEkAt8YwzX5mV6XUbX6kph1wvutvct1wYfFunO4Gd/Gsad70YjKuBlKDIGZt4Ar9wNFbvcyYwMqiEX+H7FWijfBjtfgF1/d33gwY1cXXA7jL/ANdOER3pbp8hgOetaWHWPO8u/6LteVxN0FPgDoaLAreu57WmoKsIt0zfXLdM36XJInuhxgSIeiRoFORfBlj/BBfdo/qVBpsDvL3UlsPUpF/SHtgIGshbB2XdC7uXugy4irlmn4CW3tOWEC72uJqgo8PuirQUKXoSNv4Oi1wDrukkuvdctuK3ukiIfNXEpRMbB5scV+INMgX8mKgtdyL/3OBytcAOfzv0GzLhOS/WJnE7YMJi+AjY95tZTiIz1uqKgocDvqY4Ot3LP2gfc2qwhYe5MZfZKmHCB2iJFemPGdbD+N/D+825mVhkUCvzTaT7izuTX/tLNER+dBh/7Nsz6NESneF2dSGBKnwPxWe6alwJ/0Cjwu9NY7UL+3V+7PzvT58DVD7tl2zQJmUjfGOPm11n9E7fwfVSy1xUFBQX+yY4chnd+AesfhpYjkHsFLPwyZM7zujKRoWXa1fDWD2HHM27aZBlwCvzjjlTA6h+52fzam916rYv/FVKmeF2ZyNCUMhWSJ7vxKgr8QaHAb26Ad+6HNT93a7vOuA4WfQ2SJnhdmcjQN/1q+Mf33TiW2Ayvqxny/HBu4EHS1gLv/gZ+Ngv++V8w/mPwhXWw/AGFvchgmXqVu9/2F2/rCBLBeYZf8Aq89E035/zYRXD9E25ueREZXInj3aps256ChV/yupohL7jO8Kv3wh+vgz+uABMKNzwJNz2vsBfx0rSr3cSClUVeVzLk9UvgG2OWGmN2GWOKjDF3dbF9mDHmT77t64wxWf1x3B5rPQav/yfcPx/2vgkXfhfuWAMTL9ZUxCJem3YVYNzFWxlQfQ58Y0wocD9wKTAFuN4Yc3LXlluAGmvtBODHwA/6etwe2/MGPLAA3viBW9T7i/mw6CsQFjFoJYjIKcSkwdiFCvxB0B9n+POAImvtHmttC/AEsOykfZYBj/oePwVcYAZ6KaqmOvjbl+F3V4IJgZV/g2t+6z5cIuJfpi53q74d3ul1JUNafwR+OnCg09clvue63Mda2wbUAYknv5Ax5jZjTL4xJr+iouLMKyp4Ge5f4CY4O+eLcPvbbtlAEfFPuVe4+/ef87aOIc6vLtpaax+01uZZa/OSk89wqHVlIfzxk24GvltehYu/DxEj+rdQEelfMamQOR92KPAHUn8EfinQeRHZDN9zXe5jjAkDYoGqfjj2RyXlwA1/gn95AzLmDMghRGQATFkG5VuharfXlQxZ/RH464EcY0y2MSYCuA44+df0c8BK3+NrgH9Ya20/HLtrEy9xc26LSOCY/HF3r2adAdPnwPe1yd8JvAy8D/zZWrvdGPPvxpgrfbs9DCQaY4qArwEf6bopIkEubgykzVKzzgDql5G21toXgBdOeu6eTo+bgBX9cSwRGcKmLINXvwO1+90vAOlXfnXRVkSC3GRfo8D7f/O2jiFKgS8i/iNxPKRMU7POAFHgi4h/mbIMDqyF+jKvKxlyFPgi4l+ON+vsfN7bOoYgBb6I+JdRuZA0CXY863UlQ44CX0T8z5QrofhtOFrpdSVDigJfRPxP7hVgO6DgJa8rGVIU+CLif1JnQEwG7Py715UMKQp8EfE/xkDu5bD7H9By1OtqhgwFvoj4p9zLoK0Jdr/udSVDhgJfRPzT2IVumnM16/QbBb6I+KfQcJi4FApehPY2r6sZEhT4IuK/ci+HYzWw/x2vKxkSFPgi4r/GXwChw2DXC6ffV05LgS8i/mtYFIw7z02zMIBrJgULBb6I+Lfcy938+OXbvK4k4CnwRcS/TboUMOqt0w8U+CLi36JGQeY8BX4/UOCLiP/LvRwObXFNO3LGFPgi4v9yr3D3O9Vbpy8U+CLi/xLHQ3KuFkXpIwW+iASGSZdB8RporPa6koClwBeRwJB7Bdh2KHzF60oClgJfRAJD2iyITlWzTh8o8EUkMISEuGadoteg9ZjX1QQkBb6IBI7cy6C1Efa84XUlAUmBLyKBI2sJDItRs84ZUuCLSOAIi4Cci2DXi9DR7nU1AUeBLyKBJfdyaKyEA+96XUnAUeCLSGCZcBGEhMMuza3TWwp8EQkskTGQvQTe1xz5vaXAF5HAk3s51OyFip1eVxJQFPgiEngmXebu1VunVxT4IhJ4YlIhfY5mz+wlBb6IBKbcy+HgRqgr9bqSgKHAF5HAdHyO/F06y++pPgW+MSbBGLPKGFPou4/vYp+Zxph3jDHbjTFbjDGf7MsxRUQASJoIiRO09GEv9PUM/y7gNWttDvCa7+uTNQKfsdZOBZYCPzHGxPXxuCIS7IxxzTr73oJjtV5XExD6GvjLgEd9jx8Flp+8g7W2wFpb6Ht8EDgMJPfxuCIiMOly6GiDole9riQg9DXwU6y1Zb7Hh4CUU+1sjJkHRAC7u9l+mzEm3xiTX1FR0cfSRGTIy8iDkaPUPbOHwk63gzHmVWB0F5vu7vyFtdYaY7od9maMSQUeA1Zaazu62sda+yDwIEBeXp6G0InIqYWEwqRLYdvT0NYMYcO8rsivnTbwrbUXdrfNGFNujEm11pb5Av1wN/vFAH8H7rbWrj3jakVETpZ7BWx8FPa+6WbSlG71tUnnOWCl7/FK4NmTdzDGRAB/BX5nrX2qj8cTEfmw7CUQPlK9dXqgr4F/L3CRMaYQuND3NcaYPGPMQ759rgWWADcZYzb7bjP7eFwRESc8EnIudP3xO7psLRaf0zbpnIq1tgq4oIvn84FbfY9/D/y+L8cRETml3Ctgx7NQugEy53pdjd/SSFsRCXw5F0FImHrrnIYCX0QC3/B4yFqkaRZOQ4EvIkPDpMuhsgAqCryuxG8p8EVkaMj1zZGvpQ+7pcAXkaEhNgNSZ6p75iko8EVk6Mi9AkryoeGQ15X4JQW+iAwduZcBFna96HUlfkmBLyJDx6gpEJ+lZp1uKPBFZOgwxjXr7H0Dmhu8rsbvKPBFZGjJvRzaW6DwFa8r8TsKfBEZWjLnuznyd3xkLsegp8AXkaElJBSmXAkFr0DLUa+r8SsKfBEZeqYsg7ZjULjK60r8igJfRIaesQthRBLseMbrSvyKAl9Ehp6QUJj8cSh4GVoava7GbyjwRWRomrocWhuh6FWvK/EbCnwRGZrGLoIRiWrW6USBLyJDU2iYG4S16yVoPeZ1NX5BgS8iQ9fU5dB6FIpe87oSv6DAF5GhK2uxWw1LzTqAAl9EhrLQ8E7NOk1eV+M5Bb6IDG1TlkNLA+z+h9eVeE6BLyJD27hzITIOtv/F60o8p8AXkaEtNNxdvN3596CfW0eBLyJD3/QVbhDWzhe8rsRTCnwRGfrGnAMxGbD1Sa8r8ZQCX0SGvpAQmH417H4NjlZ5XY1nFPgiEhymr4CONtjxV68r8YwCX0SCQ8o0SJ4MW4K3WUeBLyLBwRg4awUcWAs1xV5X4wkFvogEj2nXuPttT3lbh0cU+CISPOLHQuYC16xjrdfVDDoFvogEl7NWQMX7cGir15UMOgW+iASXqVdBaARs/oPXlQw6Bb6IBJcRCW4GzS1/grZmr6sZVAp8EQk+sz4Fx2pgV/RU8l4AAAnnSURBVHBNtdCnwDfGJBhjVhljCn338afYN8YYU2KM+UVfjiki0mfjznNTLWx8zOtKBlVfz/DvAl6z1uYAr/m+7s73gDf7eDwRkb4LCYVZN7o58utKvK5m0PQ18JcBj/oePwos72onY8wcIAV4pY/HExHpHzNvACxsftzrSgZNXwM/xVpb5nt8CBfqH2KMCQF+CHz9dC9mjLnNGJNvjMmvqKjoY2kiIqcQnwXZS2Dz76Gjw+tqBsVpA98Y86oxZlsXt2Wd97PWWqCrkQyfB16w1p727yZr7YPW2jxrbV5ycnKPfwgRkTMy69NQsw/2BUdrc9jpdrDWXtjdNmNMuTEm1VpbZoxJBQ53sdvZwGJjzOeBKCDCGHPEWnuq9n4RkYE3+UoY/k1Y/5C7kDvE9bVJ5zlgpe/xSuDZk3ew1t5orR1jrc3CNev8TmEvIn4hPBJmf8YtfxgEF2/7Gvj3AhcZYwqBC31fY4zJM8Y81NfiREQGXN7Nbl6d/Ee8rsQp2QAVBQPy0sb66QRCeXl5Nj8/3+syRCQYPH49lKyHr26HsGHe1vLbpdB8BO5YfUbfbozZYK3N62qbRtqKiMy9FY5WwI7nvK2joRz2r4XJHx+Ql1fgi4iMOx8SJ8DaB7ydNvn95wALk68YkJdX4IuIhITA2V+Agxuh+G3v6tj2tFuGcdSUAXl5Bb6ICMCM62FEErz9U2+OX3sA9r8D069xyzEOAAW+iAhA+HCYfzsUvgLl2wf/+NuedvfTrh6wQyjwRUSOm3sLhI+ANT8f/GNvfQoy5kJC9oAdQoEvInLciASYvRK2Pgk1xYN33PLtUL71g0XWB4gCX0Sks3O+CCYU3rhv8I6Z/wiEDoPpKwb0MAp8EZHOYtNd0857f4TKwoE/XvMReO8JmLocRiYO6KEU+CIiJ1v0NQgbDq//58Afa9vT0NIAebcM+KEU+CIiJ4tKhgW3w/a/wKGtA3ccayH/YRg1FTLnDdxxfBT4IiJdOeeLEBkLr3x74Ebf7n0Dyt6DebcOWN/7zhT4IiJdGR4P530L9rwOO58fmGO8+T8QnQozbhiY1z+JAl9EpDtzb3XTHLz0LWhp7N/XLn4H9r0F53zJzcs/CBT4IiLdCQ2Dy/4b6vbDG/f23+ta615vRBLMuan/Xvc0FPgiIqeStcitirXm53Dg3f55zcJVsOefsPhrEDGif16zBxT4IiKnc/F/QEw6/PV2aDnat9dqa4aXv+WmY577uf6pr4cU+CIipxMZA8sfgOo98Oydfeu18897oaoQlv4AwiL6r8YeUOCLiPRE9hK44B7XN3/Nz87sNfavddMvz/wU5FzYv/X1QNigH1FEJFAt+qrrN7/q32DkKJh5fc+/t74M/vwZiB8LSwdhBG8XFPgiIj1lDHziV9BUC8/cAViY2YM+9Ecr4bHlbt6cTz/jBnR5QE06IiK9ET4crnvcNfE8c4cbidvW3P3+h3fCwxdDzT644U+QMjDLF/aEAl9EpLciRsCNT7oJz9b8DH55Dmx+3J3BH1d7AF79Lvx6CTQ3uDP77MXe1QwY6+UK7aeQl5dn8/PzvS5DROTUil5z3Swrdrp59GPToa0Fjhxy26evgIu+BzGpg1KOMWaDtTavq21qwxcR6YsJF8D4tVD8thtMVXsAQkIhORcmf3xAlyzsLQW+iEhfGeNG5GYt8rqSU1IbvohIkFDgi4gECQW+iEiQUOCLiAQJBb6ISJBQ4IuIBAkFvohIkFDgi4gECb+dWsEYUwEUe11HDyUBlV4X0QuBVi+o5sESaDUHWr0w8DWPtdYmd7XBbwM/kBhj8rubu8IfBVq9oJoHS6DVHGj1grc1q0lHRCRIKPBFRIKEAr9/POh1Ab0UaPWCah4sgVZzoNULHtasNnwRkSChM3wRkSChwBcRCRIK/B4wxmQaY143xuwwxmw3xny5i33OM8bUGWM2+273eFHrSTXtM8Zs9dXzkfUijfMzY0yRMWaLMWa2F3V2qmdSp/dvszGm3hjzlZP28fx9Nsb81hhz2BizrdNzCcaYVcaYQt99fDffu9K3T6ExZqWH9f63MWan79/9r8aYuG6+95SfoUGu+TvGmNJO//aXdfO9S40xu3yf67s8rvlPnerdZ4zZ3M33Ds77bK3V7TQ3IBWY7XscDRQAU07a5zzgea9rPammfUDSKbZfBrwIGGABsM7rmjvVFgocwg0i8av3GVgCzAa2dXruPuAu3+O7gB908X0JwB7ffbzvcbxH9V4MhPke/6CrenvyGRrkmr8DfL0Hn5vdwDggAnjv5P+rg1nzSdt/CNzj5fusM/wesNaWWWs3+h43AO8D6d5W1S+WAb+zzlogzhgzOCstn94FwG5rrd+NtrbWvglUn/T0MuBR3+NHgeVdfOslwCprbbW1tgZYBSwdsEJ9uqrXWvuKtbbN9+VaIGOg6+iNbt7jnpgHFFlr91hrW4AncP82A+5UNRtjDHAt8Phg1NIdBX4vGWOygFnAui42n22Mec8Y86IxZuqgFtY1C7xijNlgjLmti+3pwIFOX5fgP7/IrqP7/xz+9j4DpFhry3yPDwEpXezjr+/3zbi/9Lpyus/QYLvT1wz1226azfz1PV4MlFtrC7vZPijvswK/F4wxUcDTwFestfUnbd6Ia36YAfwceGaw6+vCImvtbOBS4AvGmCVeF9QTxpgI4ErgyS42++P7/CHW/Y0eEP2djTF3A23AH7rZxZ8+Q78ExgMzgTJcE0mguJ5Tn90PyvuswO8hY0w4Luz/YK39y8nbrbX11tojvscvAOHGmKRBLvPkmkp994eBv+L+3O2sFMjs9HWG7zmvXQpstNaWn7zBH99nn/LjzWG++8Nd7ONX77cx5ibgCuBG3y+pj+jBZ2jQWGvLrbXt1toO4Dfd1OJX7zGAMSYMuAr4U3f7DNb7rMDvAV/728PA+9baH3Wzz2jffhhj5uHe26rBq/Ij9Yw0xkQff4y7SLftpN2eAz7j662zAKjr1CzhpW7Phvztfe7kOeB4r5uVwLNd7PMycLExJt7XHHGx77lBZ4xZCnwDuNJa29jNPj35DA2ak64vfaKbWtYDOcaYbN9fitfh/m28dCGw01pb0tXGQX2fB+PqdaDfgEW4P9G3AJt9t8uA24HbffvcCWzH9QpYC5zjcc3jfLW856vrbt/znWs2wP24Xg1bgTw/eK9H4gI8ttNzfvU+434ZlQGtuDbiW4BE4DWgEHgVSPDtmwc81Ol7bwaKfLfPelhvEa6t+/jn+Ve+fdOAF071GfKw5sd8n9MtuBBPPblm39eX4XrS7fa6Zt/z/3v889tpX0/eZ02tICISJNSkIyISJBT4IiJBQoEvIhIkFPgiIkFCgS8iEiQU+CIiQUKBLyISJP4Py7qXM1peBkgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -961,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -982,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1461,18 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]\n", + " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", + " 0.02103448 0.00270691 0.04696796]\n", + " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", + " 0.20046433 -0.16454415 0.16810248]])\n", + "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1484,7 +1593,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1601,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,11 +3,18 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.datasets import fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data + +class MyTestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() @@ -28,7 +35,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() + fpca = FPCADiscretized() with self.assertRaises(AttributeError): fpca.fit(None) @@ -46,36 +53,39 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) + basis = Fourier(n_basis=n_basis) fd_basis = fd_data.to_basis(basis) - fpca = FPCABasis(n_components=n_components) + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] results = np.array(results) # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From 6c12eed3e4b61c4fa288e607b1f98e127eca334e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:23:54 +0100 Subject: [PATCH 372/624] Add docstring and references for fpca module --- docs/modules/exploratory/fpca.rst | 13 ++ skfda/exploratory/__init__.py | 1 + skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/{fpca.py => _fpca.py} | 130 +++++++++++++++---- 4 files changed, 117 insertions(+), 28 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst rename skfda/exploratory/fpca/{fpca.py => _fpca.py} (72%) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..ed18458d4 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..2310a2def 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,3 +2,4 @@ from . import outliers from . import stats from . import visualization +from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..2669dae95 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/_fpca.py similarity index 72% rename from skfda/exploratory/fpca/fpca.py rename to skfda/exploratory/fpca/_fpca.py index 5660ac674..f7bbe3ca3 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. + """Computes the n_components first principal components score and + returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,65 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline + smoothing as an augmented least squares problem. In *Functional + Data Analysis* (p. 141). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +269,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From a65673d0b8b16dccac7abcaead8781362df7d324 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 373/624] Update docstring --- docs/modules/exploratory/fpca.rst | 2 +- skfda/exploratory/fpca/_fpca.py | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index ed18458d4..0a8687cf7 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -10,4 +10,4 @@ Functional Principal Component Analysis for basis representation .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index f7bbe3ca3..715541df7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -102,7 +102,7 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): """Defines the common structure shared between classes that do functional - principal component analysis + principal component analysis Attributes: n_components (int): number of principal components to obtain from @@ -153,12 +153,9 @@ def fit(self, X: FDataBasis, y=None): References: .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* + expansion of the functions. In *Functional Data Analysis* (pp. 161-164). Springer. - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). HSpline - smoothing as an augmented least squares problem. In *Functional - Data Analysis* (p. 141). Springer. """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From efe0448611ec8a5e71cf67ccbd30c41a90f25053 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 374/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- examples/plot_fpca.py | 28 +++++++--- skfda/exploratory/fpca/_fpca.py | 93 +++++++++++++++++++++++++++---- 3 files changed, 111 insertions(+), 22 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..135b4bf2a 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -27,6 +29,7 @@ fd = dataset['data'] y = dataset['target'] fd.plot() +pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -36,9 +39,10 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() +pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -51,6 +55,7 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() +pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -59,7 +64,8 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -71,6 +77,7 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() +pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -78,11 +85,12 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -92,11 +100,12 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() +pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -109,4 +118,5 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() +pyplot.show() diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 3e9a43a219d9e2da3df0f1176fd79e61b5858afb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 375/624] add doctest --- skfda/exploratory/fpca/_fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From ede5a44b1f44bc95fef64a3f2ea0d831eb884496 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 376/624] regularized PCA support --- skfda/exploratory/fpca/_fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 253746d3798cd55f8cbc62fe0288e796738e3b80 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 377/624] Finilized Module testing --- skfda/exploratory/fpca/_fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 458e4c268c6557a6c4fe0e9690053c6c9a25c319 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 378/624] FPCA parameter finding --- skfda/exploratory/fpca/_fpca.py | 98 +++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From 799c69037861a22fbe37e12db0bf614f95aeef0c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 14 Mar 2020 17:37:48 +0100 Subject: [PATCH 379/624] Rename regularization parameter search module --- skfda/exploratory/fpca/__init__.py | 4 +- skfda/exploratory/fpca/_fpca.py | 117 ++++------------ .../fpca/_regularization_param_search.py | 126 ++++++++++++++++++ skfda/exploratory/fpca/test.ipynb | 23 +++- 4 files changed, 174 insertions(+), 96 deletions(-) create mode 100644 skfda/exploratory/fpca/_regularization_param_search.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 2669dae95..6f30cdf85 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1 +1,3 @@ -from ._fpca import FPCABasis, FPCADiscretized \ No newline at end of file +from ._fpca import FPCABasis, FPCADiscretized +from ._regularization_param_search import RegularizationParameterSearch, \ + FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 0f594060d..07dd0a1c9 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -251,18 +250,28 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # using np.linalg.solve + # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) + + #component_coefficients = np.linalg.solve(np.transpose(l_matrix), + # np.transpose(self.pca.components_)) + + #component_coefficients = np.transpose(component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ - @ l_matrix_inv) + @ l_matrix_inv) - final_matrix = np.transpose(final_matrix) @ final_matrix """ + final_matrix = np.transpose(final_matrix) @ final_matrix + if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -313,10 +322,11 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - +""" def find_regularization_parameter(self, fd, grid, derivative_degree=2): fd -= fd.mean() # establish the basis for the coefficients + # TODO check differences between normal inner and regularized if not self.components_basis: self.components_basis = fd.basis.copy() @@ -339,12 +349,12 @@ def find_regularization_parameter(self, fd, grid, derivative_degree=2): param_grid=param_grid, cv=LeaveOneOut(), refit=True, - n_jobs=35, + n_jobs=12, verbose=True) _ = search_param.fit(fd) return search_param - +""" class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -437,7 +447,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -519,83 +528,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py new file mode 100644 index 000000000..9248eb2f5 --- /dev/null +++ b/skfda/exploratory/fpca/_regularization_param_search.py @@ -0,0 +1,126 @@ +import numpy as np +from skfda.representation.grid import FDataGrid +from sklearn.model_selection import GridSearchCV, LeaveOneOut + + +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree). \ + inner_product(second.derivative(derivative_degree)) + + +class FPCARegularizationCVScorer: + r""" This calculates the regularization score which is basically the norm + of the orthogonal component to the projection of the data onto the + components + Args: + estimator (Estimator): Linear smoothing estimator. + X (FDataGrid): Functional data to smooth. + y (FDataGrid): Functional data target. Should be the same as X. + + Returns: + float: Cross validation score, with negative sign, as it is a + penalization. + + """ + + def __call__(self, estimator, X, y=None): + projection_coefficients = inner_product_regularized(X, + estimator.components, + estimator.regularization_derivative_degree, + estimator.regularization_parameter)[ + 0] + + for i in range(len(projection_coefficients)): + estimator.components.coefficients[i] *= projection_coefficients[i] + data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) + + result = 0 + + for i in range(estimator.components.n_samples): + data_copy.coefficients -= estimator.components.coefficients[i] + result += data_copy.inner_product(data_copy) + #result += inner_product_regularized(data_copy, data_copy, + # estimator.regularization_derivative_degree, + # estimator.regularization_parameter) + + return -result + + +class RegularizationParameterSearch(GridSearchCV): + """Chooses the best smoothing parameter and performs smoothing. + + + Args: + estimator (smoother estimator): scikit-learn compatible smoother. + param_values (iterable): iterable containing the values to test + for *smoothing_parameter*. + scoring (scoring method): scoring method used to measure the + performance of the smoothing. If ``None`` (the default) the + ``score`` method of the estimator is used. + n_jobs (int or None, optional (default=None)): + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See + :term:`scikit-learn Glossary ` for more details. + + pre_dispatch (int, or string, optional): + Controls the number of jobs that get dispatched during parallel + execution. Reducing this number can be useful to avoid an + explosion of memory consumption when more jobs get dispatched + than CPUs can process. This parameter can be: + + - None, in which case all the jobs are immediately + created and spawned. Use this for lightweight and + fast-running jobs, to avoid delays due to on-demand + spawning of the jobs + + - An int, giving the exact number of total jobs that are + spawned + + - A string, giving an expression as a function of n_jobs, + as in '2*n_jobs' + verbose (integer): + Controls the verbosity: the higher, the more messages. + + error_score ('raise' or numeric): + Value to assign to the score if an error occurs in estimator + fitting. If set to 'raise', the error is raised. If a numeric + value is given, FitFailedWarning is raised. This parameter does + not affect the refit step, which will always raise the error. + Default is np.nan. + """ + + def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, + verbose=0): + super().__init__(estimator=estimator, scoring=scoring, + param_grid={'regularization_parameter': param_values}, + n_jobs=n_jobs, + refit=True, cv=LeaveOneOut(), + verbose=verbose) + self.components_basis = estimator.components_basis + + def fit(self, X, y=None, groups=None, **fit_params): + + X -= X.mean() + + if not self.components_basis: + self.components_basis = X.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > X.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + self.estimator.n_components = max_components + + return super().fit(X, y, groups=groups, **fit_params) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 8b01e51e1..5319cef7b 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -88,6 +88,27 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataGrid' object has no attribute 'norm'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" + ] + } + ], + "source": [ + "fd_data.norm()" + ] + }, { "cell_type": "code", "execution_count": 14, From cfabe90c11f1511ccafb8926cc1f90ca534bff13 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:26:48 +0100 Subject: [PATCH 380/624] preparing the branch for review --- .../fpca/_regularization_param_search.py | 126 - skfda/exploratory/fpca/test.ipynb | 3080 ----------------- 2 files changed, 3206 deletions(-) delete mode 100644 skfda/exploratory/fpca/_regularization_param_search.py delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/_regularization_param_search.py b/skfda/exploratory/fpca/_regularization_param_search.py deleted file mode 100644 index 9248eb2f5..000000000 --- a/skfda/exploratory/fpca/_regularization_param_search.py +++ /dev/null @@ -1,126 +0,0 @@ -import numpy as np -from skfda.representation.grid import FDataGrid -from sklearn.model_selection import GridSearchCV, LeaveOneOut - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree). \ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationCVScorer: - r""" This calculates the regularization score which is basically the norm - of the orthogonal component to the projection of the data onto the - components - Args: - estimator (Estimator): Linear smoothing estimator. - X (FDataGrid): Functional data to smooth. - y (FDataGrid): Functional data target. Should be the same as X. - - Returns: - float: Cross validation score, with negative sign, as it is a - penalization. - - """ - - def __call__(self, estimator, X, y=None): - projection_coefficients = inner_product_regularized(X, - estimator.components, - estimator.regularization_derivative_degree, - estimator.regularization_parameter)[ - 0] - - for i in range(len(projection_coefficients)): - estimator.components.coefficients[i] *= projection_coefficients[i] - data_copy = X.copy(coefficients=np.copy(np.squeeze(X.coefficients))) - - result = 0 - - for i in range(estimator.components.n_samples): - data_copy.coefficients -= estimator.components.coefficients[i] - result += data_copy.inner_product(data_copy) - #result += inner_product_regularized(data_copy, data_copy, - # estimator.regularization_derivative_degree, - # estimator.regularization_parameter) - - return -result - - -class RegularizationParameterSearch(GridSearchCV): - """Chooses the best smoothing parameter and performs smoothing. - - - Args: - estimator (smoother estimator): scikit-learn compatible smoother. - param_values (iterable): iterable containing the values to test - for *smoothing_parameter*. - scoring (scoring method): scoring method used to measure the - performance of the smoothing. If ``None`` (the default) the - ``score`` method of the estimator is used. - n_jobs (int or None, optional (default=None)): - Number of jobs to run in parallel. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` - context. ``-1`` means using all processors. See - :term:`scikit-learn Glossary ` for more details. - - pre_dispatch (int, or string, optional): - Controls the number of jobs that get dispatched during parallel - execution. Reducing this number can be useful to avoid an - explosion of memory consumption when more jobs get dispatched - than CPUs can process. This parameter can be: - - - None, in which case all the jobs are immediately - created and spawned. Use this for lightweight and - fast-running jobs, to avoid delays due to on-demand - spawning of the jobs - - - An int, giving the exact number of total jobs that are - spawned - - - A string, giving an expression as a function of n_jobs, - as in '2*n_jobs' - verbose (integer): - Controls the verbosity: the higher, the more messages. - - error_score ('raise' or numeric): - Value to assign to the score if an error occurs in estimator - fitting. If set to 'raise', the error is raised. If a numeric - value is given, FitFailedWarning is raised. This parameter does - not affect the refit step, which will always raise the error. - Default is np.nan. - """ - - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'regularization_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=LeaveOneOut(), - verbose=verbose) - self.components_basis = estimator.components_basis - - def fit(self, X, y=None, groups=None, **fit_params): - - X -= X.mean() - - if not self.components_basis: - self.components_basis = X.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > X.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - self.estimator.n_components = max_components - - return super().fit(X, y, groups=groups, **fit_params) - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 5319cef7b..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3080 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataGrid' object has no attribute 'norm'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataGrid' object has no attribute 'norm'" - ] - } - ], - "source": [ - "fd_data.norm()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 2175e92074a44dcda10afed00f319e478b03fd28 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 381/624] polish code --- skfda/exploratory/fpca/__init__.py | 2 - skfda/exploratory/fpca/_fpca.py | 121 ++++------------------------- 2 files changed, 13 insertions(+), 110 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index 6f30cdf85..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -1,3 +1 @@ from ._fpca import FPCABasis, FPCADiscretized -from ._regularization_param_search import RegularizationParameterSearch, \ - FPCARegularizationCVScorer diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 07dd0a1c9..022bcbb4a 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -244,14 +244,11 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - - # using np.linalg.solve - # l_inv_j_t_v2 = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ @@ -259,49 +256,17 @@ def fit(self, X: FDataBasis, y=None): self.pca.fit(final_matrix) - #component_coefficients = np.linalg.solve(np.transpose(l_matrix), - # np.transpose(self.pca.components_)) + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - #component_coefficients = np.transpose(component_coefficients) + component_coefficients = np.transpose(component_coefficients) + # the singular values obtained using SVD are the squares of eigenvalues self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - """ - final_matrix = np.transpose(final_matrix) @ final_matrix - - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + coefficients=component_coefficients) return self @@ -322,39 +287,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) -""" - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - # TODO check differences between normal inner and regularized - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=12, - verbose=True) - - _ = search_param.fit(fd) - return search_param -""" + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -418,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -474,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): From 9da2cdff50269922732545d7e40b057d23f8f694 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 382/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 8 -------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 135b4bf2a..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -29,7 +29,6 @@ fd = dataset['data'] y = dataset['target'] fd.plot() -pyplot.show() ############################################################################## # FPCA can be done in two ways. The first way is to operate directly with the @@ -42,7 +41,6 @@ fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components.plot() -pyplot.show() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -55,7 +53,6 @@ basis = skfda.representation.basis.BSpline(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() -pyplot.show() ############################################################################## # We initialize the FPCABasis object and run the fit function to obtain the @@ -65,7 +62,6 @@ fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -77,7 +73,6 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) mean_fd = basis_fd.mean() mean_fd.plot() -pyplot.show() ############################################################################## # Now we add and subtract a multiple of the first principal component. We can @@ -90,7 +85,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # The second component is more interesting. The most appropriate explanation is @@ -105,7 +99,6 @@ mean_fd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() -pyplot.show() ############################################################################## # We can also specify another basis for the principal components as argument @@ -119,4 +112,3 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components.plot() -pyplot.show() From e8a2f02decc44bcdc428f92a3b12ce70293d178e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 383/624] Adjust doctest --- skfda/exploratory/fpca/_fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/_fpca.py +++ b/skfda/exploratory/fpca/_fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From ef77e07cd947368477529bcb178e285281cb8e6d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 384/624] transfer files to new location and modify documentation --- docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/__init__.py | 1 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/_fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 12 files changed, 77 insertions(+), 550 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/_fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 2310a2def..7d58f75c6 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -2,4 +2,3 @@ from . import outliers from . import stats from . import visualization -from . import fpca diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/_fpca.py b/skfda/exploratory/fpca/_fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/_fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From 2a2c4bab7e4ffcf9b562f9ec33fa9f5cc81ec76a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 385/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From fe026277d5b707bfc2d5f0dcdf7a596706a3a606 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 386/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From f874f7142d9a716b403cae9b1c67f570a0897ff1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 387/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 9be4fadf2db52c791f3b9d9908565eb1df6e3b1f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 388/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 15fb4817a079312380ba9c579057f8c39a093ac2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 389/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From 9b2904155246cc88a70ca9d401f13b6fd1c87c27 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 30 Nov 2019 23:11:40 +0100 Subject: [PATCH 390/624] Functional principal component analysis for a FDataBasis Object --- skfda/exploratory/fpca/__init__.py | 0 skfda/exploratory/fpca/fpca.py | 113 +++++++++++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 skfda/exploratory/fpca/__init__.py create mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py new file mode 100644 index 000000000..711ce82a0 --- /dev/null +++ b/skfda/exploratory/fpca/fpca.py @@ -0,0 +1,113 @@ +import numpy as np +import skfda +from skfda.representation.basis import FDataBasis +from skfda.datasets._real_datasets import fetch_growth +from matplotlib import pyplot + +class FPCA: + def __init__(self, n_components, components_basis=None, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + self.centering = centering + self.components = None + self.component_values = None + + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object + + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.coefficients -= meanfd.coefficients + + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape + + # setup principal component basis if not given + if not self.components_basis: + self.components_basis = X.basis.copy() + + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) + + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) + + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + + basis = skfda.representation.basis.BSpline(n_basis=7) + basisfd = fd.to_basis(basis) + # print(basisfd.basis.gram_matrix()) + # print(basis.gram_matrix()) + + basisfd.plot() + pyplot.show() + + meanfd = basisfd.mean() + + fpca = FPCA(2) + fpca.fit(basisfd) + + # fpca.components.plot() + # pyplot.show() + + meanfd.plot() + pyplot.show() + + meanfd.coefficients = np.vstack([meanfd.coefficients, + meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + + meanfd.plot() + pyplot.show() + + # print(fpca.transform(basisfd)) + + + + + + From 3fdd07a2c9a2e51fa12b9f19471e28ed1fc43a97 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 1 Dec 2019 21:58:18 +0100 Subject: [PATCH 391/624] Functional principal component analysis for a FDataGrid Object (partial) --- skfda/exploratory/fpca/fpca.py | 113 +++- skfda/exploratory/fpca/test.ipynb | 930 ++++++++++++++++++++++++++++++ 2 files changed, 1021 insertions(+), 22 deletions(-) create mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 711ce82a0..765dbd248 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -4,7 +4,7 @@ from skfda.datasets._real_datasets import fetch_growth from matplotlib import pyplot -class FPCA: +class FPCABasis: def __init__(self, n_components, components_basis=None, centering=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components @@ -74,38 +74,107 @@ def fit_transform(self, X, y=None): pass -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] +class FPCADiscretized: + def __init__(self, n_components, centering=True): + self.n_components = n_components + # component_basis is the basis that we want to use for the principal components + self.centering = centering + self.components = None + self.component_values = None - basis = skfda.representation.basis.BSpline(n_basis=7) - basisfd = fd.to_basis(basis) - # print(basisfd.basis.gram_matrix()) - # print(basis.gram_matrix()) + def fit(self, X, y=None): + # for now lets consider that X is a FDataBasis Object - basisfd.plot() - pyplot.show() + # if centering is True then substract the mean function to each function in FDataBasis + if self.centering: + meanfd = X.mean() + # consider moving these lines to FDataBasis as a centering function + # substract from each row the mean coefficient matrix + X.data_matrix -= meanfd.coefficients - meanfd = basisfd.mean() + # for reference, X.coefficients is the C matrix + n_samples, n_basis = X.coefficients.shape - fpca = FPCA(2) - fpca.fit(basisfd) - # fpca.components.plot() - # pyplot.show() + # if the principal components are in the same basis, this is essentially the gram matrix + j_matrix = X.basis.inner_product(self.components_basis) - meanfd.plot() - pyplot.show() + g_matrix = self.components_basis.gram_matrix() + l_matrix = np.linalg.cholesky(g_matrix) + l_matrix_inv = np.linalg.inv(l_matrix) - meanfd.coefficients = np.vstack([meanfd.coefficients, - meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # The following matrix is needed: L^(-1)*J^T + l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) - meanfd.plot() - pyplot.show() + # the final matrix (L-1Jt)-1CtC(L-1Jt)t + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] + + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] + + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) + + self.component_values = eigenvalues + + return self + + def transform(self, X, y=None): + total = sum(self.component_values) + self.component_values /= total + return self.component_values[:self.n_components] + + def fit_transform(self, X, y=None): + pass + + + +if __name__ == '__main__': + dataset = fetch_growth() + fd = dataset['data'] + y = dataset['target'] + # + # basis = skfda.representation.basis.BSpline(n_basis=7) + # basisfd = fd.to_basis(basis) + # # print(basisfd.basis.gram_matrix()) + # # print(basis.gram_matrix()) + # + # basisfd.plot() + # pyplot.show() + # + # meanfd = basisfd.mean() + # + # fpca = FPCABasis(2) + # fpca.fit(basisfd) + # + # # fpca.components.plot() + # # pyplot.show() + # + # meanfd.plot() + # pyplot.show() + # + # meanfd.coefficients = np.vstack([meanfd.coefficients, + # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) + # + # meanfd.plot() + # pyplot.show() # print(fpca.transform(basisfd)) + print(fd.data_matrix) + diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb new file mode 100644 index 000000000..ec5a3d962 --- /dev/null +++ b/skfda/exploratory/fpca/test.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.representation.basis import FDataBasis\n", + "from skfda.datasets._real_datasets import fetch_growth\n", + "from matplotlib import pyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 81.3]\n", + " [ 84.2]\n", + " [ 86.4]\n", + " ...\n", + " [193.8]\n", + " [194.3]\n", + " [195.1]]\n", + "\n", + " [[ 76.2]\n", + " [ 80.4]\n", + " [ 83.2]\n", + " ...\n", + " [176.1]\n", + " [177.4]\n", + " [178.7]]\n", + "\n", + " [[ 76.8]\n", + " [ 79.8]\n", + " [ 82.6]\n", + " ...\n", + " [170.9]\n", + " [171.2]\n", + " [171.5]]\n", + "\n", + " ...\n", + "\n", + " [[ 68.6]\n", + " [ 73.6]\n", + " [ 78.6]\n", + " ...\n", + " [166. ]\n", + " [166.3]\n", + " [166.8]]\n", + "\n", + " [[ 79.9]\n", + " [ 82.6]\n", + " [ 84.8]\n", + " ...\n", + " [168.3]\n", + " [168.4]\n", + " [168.6]]\n", + "\n", + " [[ 76.1]\n", + " [ 78.4]\n", + " [ 82.3]\n", + " ...\n", + " [168.6]\n", + " [168.9]\n", + " [169.2]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(fd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + } + ], + "source": [ + "print(n_points_discretization)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd.sample_points[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "weights_matrix = np.diag(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "observe that we obtain the same by decomposing using eig directly" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", + " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", + " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", + " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", + " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 -1.92028262e-01\n", + " -1.95624282e-01 -1.98937513e-01 -2.01862032e-01 -2.04288111e-01\n", + " -2.06225610e-01 -2.07614907e-01 -2.08673474e-01 -2.09402232e-01\n", + " -2.09908501e-01 -2.10248402e-01 -2.10603645e-01]\n", + " 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3.22102688e-01, -6.07418041e-02,\n", + " -1.34843838e-01, -6.80577804e-02, -1.33751802e-01,\n", + " 6.28476061e-02, -5.92645965e-02, -3.46044300e-02,\n", + " -4.94697622e-02, 2.59731624e-02, 3.29663205e-02,\n", + " 2.31111564e-02, -1.28514082e-02, -5.13394329e-02,\n", + " -5.29541835e-02, 9.66802769e-02, -3.94827344e-02,\n", + " -4.41277598e-01, 4.72247516e-02, 2.78319985e-01,\n", + " -2.94597056e-01, 1.54945070e-01, -2.33344166e-02,\n", + " 1.14712213e-01, 4.47979837e-03, 9.15337573e-02,\n", + " -6.07273657e-01, 1.69089289e-02, 2.54918562e-02,\n", + " 2.91317775e-02],\n", + " [-2.10248402e-01, 3.33915971e-01, -8.18578911e-02,\n", + " -1.68901480e-01, -7.63761295e-02, -1.71913570e-01,\n", + " 9.78621427e-02, -7.97597727e-02, -2.24051792e-02,\n", + " -1.28667947e-01, 3.70288753e-03, 9.92342171e-02,\n", + " 1.33161134e-02, -7.89427049e-02, -1.21326967e-01,\n", + " 6.82549448e-02, 2.85788347e-02, 2.17876169e-01,\n", + " -1.93634602e-01, -1.71525496e-01, 9.13072016e-02,\n", + " -1.03160419e-01, 3.71545311e-02, -6.00672107e-02,\n", + " -1.25837609e-02, -8.69977728e-02, -1.10142037e-01,\n", + " 5.65088436e-01, 2.20007770e-01, -2.14197856e-01,\n", + " -3.63864313e-01],\n", + " [-2.10603645e-01, 3.43759951e-01, -9.95118482e-02,\n", + " -1.92224035e-01, -7.93701407e-02, -1.78829680e-01,\n", + " 1.02710801e-01, -9.88999112e-02, -3.31951831e-02,\n", + " -1.59432362e-01, -9.20089451e-03, 1.61902054e-01,\n", + " 1.36542967e-02, -1.18052285e-01, -1.14843063e-01,\n", + " 2.70403055e-01, -1.23008061e-01, 2.81180388e-01,\n", + " 5.11270590e-01, -4.86321572e-02, -2.50758086e-01,\n", + " 1.84034295e-01, 3.21367617e-05, 3.44785565e-02,\n", + " -2.74494564e-02, 5.76685921e-02, 6.92704420e-02,\n", + " -2.13873128e-01, -1.36127667e-01, 1.32581482e-01,\n", + " 1.79287867e-01]]))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:scikit-fda] *", + "language": "python", + "name": "conda-env-scikit-fda-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 372b0fe214666e1e160077487f666b0fbeb88a37 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 18:54:42 +0100 Subject: [PATCH 392/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 98 +-- skfda/exploratory/fpca/test.ipynb | 1310 +++++++++++++---------------- 2 files changed, 606 insertions(+), 802 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 765dbd248..a915a84f4 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -75,12 +75,14 @@ def fit_transform(self, X, y=None): class FPCADiscretized: - def __init__(self, n_components, centering=True): + def __init__(self, n_components, weights=None, centering=True, svd=True): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.centering = centering self.components = None self.component_values = None + self.weights = weights + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -92,42 +94,48 @@ def fit(self, X, y=None): # substract from each row the mean coefficient matrix X.data_matrix -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape + # establish weights for each point of discretization + if not self.weights: + # sample_points is a list with one array in the 1D case + self.weights = np.diff(X.sample_points[0]) + self.weights = np.append(self.weights, [self.weights[-1]]) + weights_matrix = np.diag(self.weights) - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) - g_matrix = self.components_basis.gram_matrix() - l_matrix = np.linalg.cholesky(g_matrix) - l_matrix_inv = np.linalg.inv(l_matrix) + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # k_estimated is not used for the moment + # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + if self.svd: + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.component_values = s**2 + else: + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + # the eigenvectors are the principal components + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + principal_components_t = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self @@ -141,42 +149,6 @@ def fit_transform(self, X, y=None): -if __name__ == '__main__': - dataset = fetch_growth() - fd = dataset['data'] - y = dataset['target'] - # - # basis = skfda.representation.basis.BSpline(n_basis=7) - # basisfd = fd.to_basis(basis) - # # print(basisfd.basis.gram_matrix()) - # # print(basis.gram_matrix()) - # - # basisfd.plot() - # pyplot.show() - # - # meanfd = basisfd.mean() - # - # fpca = FPCABasis(2) - # fpca.fit(basisfd) - # - # # fpca.components.plot() - # # pyplot.show() - # - # meanfd.plot() - # pyplot.show() - # - # meanfd.coefficients = np.vstack([meanfd.coefficients, - # meanfd.coefficients[0, :] + 10 * fpca.components.coefficients[0, :]]) - # - # meanfd.plot() - # pyplot.show() - - # print(fpca.transform(basisfd)) - - print(fd.data_matrix) - - - diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index ec5a3d962..3ae7a0153 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", + "from fpca import FPCABasis\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" @@ -15,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -24,878 +25,709 @@ "y = dataset['target']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from here onwards is the implementation that should be inside the fit function" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = np.squeeze(fd.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples, n_points_discretization = fd_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "what weight vectors should we use?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data set: [[[ 81.3]\n", - " [ 84.2]\n", - " [ 86.4]\n", - " ...\n", - " [193.8]\n", - " [194.3]\n", - " [195.1]]\n", - "\n", - " [[ 76.2]\n", - " [ 80.4]\n", - " [ 83.2]\n", - " ...\n", - " [176.1]\n", - " [177.4]\n", - " [178.7]]\n", - "\n", - " [[ 76.8]\n", - " [ 79.8]\n", - " [ 82.6]\n", - " ...\n", - " [170.9]\n", - " [171.2]\n", - " [171.5]]\n", - "\n", - " ...\n", - "\n", - " [[ 68.6]\n", - " [ 73.6]\n", - " [ 78.6]\n", - " ...\n", - " [166. ]\n", - " [166.3]\n", - " [166.8]]\n", - "\n", - " [[ 79.9]\n", - " [ 82.6]\n", - " [ 84.8]\n", - " ...\n", - " [168.3]\n", - " [168.4]\n", - " [168.6]]\n", - "\n", - " [[ 76.1]\n", - " [ 78.4]\n", - " [ 82.3]\n", - " ...\n", - " [168.6]\n", - " [168.9]\n", - " [169.2]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" + " 16.5 , 17. , 17.5 , 18. ])]\n" ] } ], "source": [ - "print(fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "from here onwards is the implementation that should be inside the fit function" + "print(fd.sample_points)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "fd_data = np.squeeze(fd.data_matrix)" + "weights = np.diff(fd.sample_points[0])\n", + "weights = np.append(weights, [weights[-1]])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "weights_matrix = np.diag(weights)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fd.sample_points" + "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "31\n" + "(31,)\n" ] } ], "source": [ - "print(n_points_discretization)" + "print(s.shape)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])" + "array([[-6.46348074e-02, -6.80259397e-02, -7.09800076e-02,\n", + " -7.36136232e-02, -1.52001225e-01, -1.66509506e-01,\n", + " -1.79517115e-01, -1.91597131e-01, -2.03391330e-01,\n", + " -2.14297296e-01, -1.58737520e-01, -1.62341098e-01,\n", + " -1.65953620e-01, -1.69411393e-01, -1.72901084e-01,\n", + " -1.76607524e-01, -1.80405503e-01, -1.84322127e-01,\n", + " -1.88237453e-01, -1.92028262e-01, -1.95624282e-01,\n", + " -1.98937513e-01, 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-7.40946806e-02, -4.33483790e-02,\n", + " 2.25504501e-01, -3.45155737e-01, 4.09687748e-01,\n", + " -3.80929637e-01, 2.73897261e-01, -1.84614293e-01,\n", + " 2.11193536e-01, -2.58802223e-01, 1.54908597e-01,\n", + " 1.28755371e-01, -3.73250939e-01, 2.87520840e-01,\n", + " 8.05199424e-03, -1.14712213e-01, 1.25837608e-02,\n", + " 2.74494565e-02]])" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fd.sample_points[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "principal_components = np.transpose(vh)\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" + "components = fd.copy(data_matrix=vh[:2, :])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" + "components.plot()" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "observe that we obtain the same by decomposing using eig directly" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "observe that we obtain the same by decomposing using eig directly" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']\n", + "\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)\n", + "# print(basisfd.basis.gram_matrix())\n", + "# print(basis.gram_matrix())\n", + "\n", + "basisfd.plot()\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-6.46348074e-02 -6.80259397e-02 -7.09800076e-02 -7.36136232e-02\n", - " -1.52001225e-01 -1.66509506e-01 -1.79517115e-01 -1.91597131e-01\n", - " -2.03391330e-01 -2.14297296e-01 -1.58737520e-01 -1.62341098e-01\n", - " -1.65953620e-01 -1.69411393e-01 -1.72901084e-01 -1.76607524e-01\n", - " -1.80405503e-01 -1.84322127e-01 -1.88237453e-01 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(vh)" + "\n", + "meanfd = basisfd.mean()\n", + "#\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "#\n", + "# # fpca.components.plot()\n", + "# # pyplot.show()\n", + "#\n", + "meanfd.plot()\n", + "pyplot.show()\n", + "#" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.34718386e+05 1.02805310e+02 2.71985229e+01 9.39226467e+00\n", - " 3.67840534e+00 1.65819915e+00 1.38068476e+00 1.19223015e+00\n", - " 6.59966620e-01 5.06723349e-01 3.01234518e-01 2.57601625e-01\n", - " 1.97639361e-01 1.47572675e-01 1.01509765e-01 8.28738857e-02\n", - " 5.81587402e-02 3.86702709e-02 2.66249248e-02 2.18573322e-02\n", - " 1.58645660e-02 1.10728476e-02 9.07623198e-03 6.87504706e-03\n", - " 4.38147552e-03 3.70917729e-03 3.18338768e-03 2.42622590e-03\n", - " 1.96628521e-03 1.53257970e-03 9.04160622e-04]\n" - ] + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 32, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "np.linalg.eig(np.transpose(final_matrix) @ final_matrix)" + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()" ] }, { @@ -922,7 +754,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.5" } }, "nbformat": 4, From 7acf5dffbdf7296ca11bf2c7812ae0fafe5a5444 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 3 Dec 2019 23:45:01 +0100 Subject: [PATCH 393/624] Continuing the implementation of discretized fpca --- skfda/exploratory/fpca/fpca.py | 26 +- skfda/exploratory/fpca/test.ipynb | 657 ++++++------------------------ 2 files changed, 137 insertions(+), 546 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a915a84f4..3b6e3fc51 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -85,14 +85,19 @@ def __init__(self, n_components, weights=None, centering=True, svd=True): self.svd = svd def fit(self, X, y=None): - # for now lets consider that X is a FDataBasis Object + # data matrix initialization + fd_data = np.squeeze(X.data_matrix) + + # obtain the number of samples and the number of points of descretization + n_samples, n_points_discretization = fd_data.shape + # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function # substract from each row the mean coefficient matrix - X.data_matrix -= meanfd.coefficients + fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization if not self.weights: @@ -102,12 +107,6 @@ def fit(self, X, y=None): weights_matrix = np.diag(self.weights) - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # obtain the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - # k_estimated is not used for the moment # k_estimated = fd_data @ np.transpose(fd_data) / n_samples @@ -117,12 +116,12 @@ def fit(self, X, y=None): # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(coefficients=vh[:self.n_components, :]) + self.components = X.copy(data_matrix=vh[:self.n_components, :]) self.component_values = s**2 else: # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) # sort the eigenvalues and eigenvectors from highest to lowest # the eigenvectors are the principal components @@ -133,8 +132,8 @@ def fit(self, X, y=None): # we only want the first ones, determined by n_components principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(coefficients=np.transpose(principal_components_t)) - + # prepare the computed principal components + self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues return self @@ -145,7 +144,8 @@ def transform(self, X, y=None): return self.component_values[:self.n_components] def fit_transform(self, X, y=None): - pass + self.fit(X, y) + return self.transform(X, y) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 3ae7a0153..5fd2e81b0 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,532 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis\n", + "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth\n", "from matplotlib import pyplot" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "from here onwards is the implementation that should be inside the fit function" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = np.squeeze(fd.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "n_samples, n_points_discretization = fd_data.shape" + "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "k_estimated = fd_data @ np.transpose(fd_data)/n_samples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "what weight vectors should we use?" + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "y = dataset['target']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(fd.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "weights = np.diff(fd.sample_points[0])\n", - "weights = np.append(weights, [weights[-1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "weights_matrix = np.diag(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True)" + "fd.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 43, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(31,)\n" - ] - } - ], "source": [ - "print(s.shape)" + "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n"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" ] }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "principal_components = np.transpose(vh)\n" + "discretizedFPCA = FPCADiscretized(2)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" ] }, { - "cell_type": "code", - "execution_count": 45, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "components = fd.copy(data_matrix=vh[:2, :])" + "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -539,65 +113,51 @@ } ], "source": [ - "fd.plot()" + "discretizedFPCA = FPCADiscretized(2, svd=False)\n", + "discretizedFPCA.fit(fd)\n", + "discretizedFPCA.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scores (percentage) the first n components has over all the components" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "array([0.80414823, 0.13861057])" ] }, - "execution_count": 46, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "components.plot()" + "discretizedFPCA.transform(fd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "observe that we obtain the same by decomposing using eig directly" + "Now we study the dataset using its basis representation" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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\n", @@ -618,15 +178,14 @@ "\n", "basis = skfda.representation.basis.BSpline(n_basis=7)\n", "basisfd = fd.to_basis(basis)\n", - "# print(basisfd.basis.gram_matrix())\n", - "# print(basis.gram_matrix())\n", "\n", - "basisfd.plot()\n" + "basisfd.plot()\n", + "pyplot.show()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,39 +202,28 @@ } ], "source": [ - "\n", + "# obtain the mean function of the dataset for representation purposes\n", "meanfd = basisfd.mean()\n", - "#\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "#\n", - "# # fpca.components.plot()\n", - "# # pyplot.show()\n", - "#\n", + "\n", "meanfd.plot()\n", - "pyplot.show()\n", - "#" + "pyplot.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -687,28 +235,70 @@ } ], "source": [ - "fpca.components.plot()" + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "fpca.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = fetch_growth()\n", + "fd = dataset['data']\n", + "basis = skfda.representation.basis.BSpline(n_basis=7)\n", + "basisfd = fd.to_basis(basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -720,14 +310,15 @@ } ], "source": [ - "\n", + "meanfd = basisfd.mean()\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", + " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", "\n", "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", + " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", "\n", - "meanfd.plot()" + "meanfd.plot()\n", + "pyplot.show()" ] }, { From c0a21c8882694491d758dee42a03f1a80592fa3a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 00:26:36 +0100 Subject: [PATCH 394/624] Polishing work on fpca with FDataBasis --- skfda/exploratory/fpca/fpca.py | 63 ++++++++++++++---------- skfda/exploratory/fpca/test.ipynb | 79 +++++++++++++++++++++++++++---- 2 files changed, 110 insertions(+), 32 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3b6e3fc51..91f54c468 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,13 +5,14 @@ from matplotlib import pyplot class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True): + def __init__(self, n_components, components_basis=None, centering=True, svd=False): self.n_components = n_components # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis self.centering = centering self.components = None self.component_values = None + self.svd = svd def fit(self, X, y=None): # for now lets consider that X is a FDataBasis Object @@ -27,41 +28,55 @@ def fit(self, X, y=None): n_samples, n_basis = X.coefficients.shape # setup principal component basis if not given - if not self.components_basis: + if self.components_basis: + # if the principal components are in the same basis, this is essentially the gram matrix + g_matrix = self.components_basis.gram_matrix() + j_matrix = X.basis.inner_product(self.components_basis) + else: self.components_basis = X.basis.copy() + g_matrix = self.components_basis.gram_matrix() + j_matrix = g_matrix - # if the principal components are in the same basis, this is essentially the gram matrix - j_matrix = X.basis.inner_product(self.components_basis) - - g_matrix = self.components_basis.gram_matrix() l_matrix = np.linalg.cholesky(g_matrix) + + # L^{-1} l_matrix_inv = np.linalg.inv(l_matrix) - # The following matrix is needed: L^(-1)*J^T - l_inv_j_t = np.matmul(l_matrix_inv, np.transpose(j_matrix)) + # The following matrix is needed: L^{-1}*J^T + l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # the final matrix (L-1Jt)-1CtC(L-1Jt)t - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t))/n_samples + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + if self.svd: + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # vh contains the eigenvectors transposed + # s contains the singular values, which are square roots of eigenvalues + u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) + principal_components = vh @ l_matrix_inv + self.components = X.copy(basis=self.components_basis, + coefficients=principal_components[:self.n_components, :]) + self.component_values = s ** 2 + else: + final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) + @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) + # perform eigenvalue and eigenvector analysis on this matrix + # eigenvectors is a numpy array, such that its columns are eigenvectors + eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # sort the eigenvalues and eigenvectors from highest to lowest + idx = eigenvalues.argsort()[::-1] + eigenvalues = eigenvalues[idx] + eigenvectors = eigenvectors[:, idx] - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] + # we only want the first ones, determined by n_components + principal_components_t = principal_components_t[:, :self.n_components] - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) + self.components = X.copy(basis=self.components_basis, + coefficients=np.transpose(principal_components_t)) - self.component_values = eigenvalues + self.component_values = eigenvalues return self diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 5fd2e81b0..9d127e51f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -156,7 +156,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -186,7 +188,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -218,9 +222,66 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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uuOud9v3X/ht+Mh9e/xF0tXldnUhs2PGUu9lKzTkhEbuB3y97Klx3H9zyivtI+eKd8KM58MoPXN9gEQmerU9A3hwYp7FzQkGB32/CAvj4k/CZv8LkZfDK9+HHZ8JL33EfO0UksJqqoOYtnd2HkAJ/sImL4MaH3cXdqRfBa3fDj2bDn26Fg5u9rk4kemx/0j3P+aC3dcSQBK8LCFv5c+H6+93YPGt/CRsfgs0Pw+TlsPRWmHE5xOvtEzllW/8IE89yw6FISOgM/71kT3UXd7+yAy79LjRXw6Mfc2f9L/4HNO71ukKRyHNkF9RthTnXeV1JTFHgj9SYDDdGzxc2wg1/gAnzYfWP4acL4P5/dBefejq9rlIkMmx5FEwczL7a60piitokTlZ8Asx8v3u0HoBND8GG38Mfb4akVDjjA+6sZcoFavIRGYqvDzY/AtMugdR8r6uJKUqk05E2Ac77Oiz/KlS+Clsed3Nybn4YkrNh1tUw9zqYtMSN4ikisO9VaK11TaQSUgr8QIiLc2f0Uy6AD9wNu1+AbU/Apj9A2W8gOQdmXAYzr4ApF0JSsrf1inhp0x/cHe0lV3hdScxR4AdawijXrHPGB6DrKOx+zo3OufNp2PQgJIyBqRe6u3unXuwGdhOJFZ2t7v/C/BshcbTX1cQcBX4wjRrrbiqZcy30dkPVatj1LOxa5R4AOSXuADD1Iph8jvsZkWi14yno7YB5H/G6kpgUlYOnra9qYvaENEYnhmm7ubVQt91Nw7jnZXcg6O2EuESYtNg1DU1e5iaDSBzjdbUigXPf5dB+BG5bp7Hvg+REg6dF3Rn+4bZOrv3FGyTFxzF/UgaLi7NYMiWLRZMzSU4Kk1/XGMif4x7LPu+6c+5f48J/z1/h5e8D1h0ACha68C9cBoVLNJqnRK7GvVD9hptzWmHviag7w+/s6WN1RT1r9zWydm8D2w600uezJMQZ5hSks2RKFkuLs1lUlEna6MQgVB4AHU1Qvdad+Ve/CQc2uvk+MW6gqYml7lFQCjkz3EVjkXD30nfcaLRf2qZrV0F0ojP8qAv8wY529bK+qom1ext4a18jm2ua6emzxBmYNSGNJcXZLC7OYnFRFpkpSQGoPAi62928vNVvQtUb7gDQ1eqWjUpzA7/1HwAmlrqhn0XCSV8P3D3LNVN+5BGvq4lqMR34g3V097Gxusl9AtjXwMbqZrp6fQDMzE91TUD+g0Bu6qiA7z8gfD5o2O0OArVl7rluO9g+tzyj0P3HKlgEExbC+Hm6GCze2v4UPH4TfOQxmPE+r6uJagr8E+jq7WPz/hbe2tfA2n2NlFU20dHjgnNqbgqLi7NZOiWLxcVZjE8P4wuo3cfcaJ61ZVC7HmrWQ0u1W2biIHemux4wYaE7EOTNhvgwbdKS6PPAVW4gwi9u1k2IQabAPwk9fT621bYcvwZQVtlEW1cvAIVZySwpzmLJlGyWFGcxMXMMJpwvPh09Agc2QO0G//N6ONbglsWPgvFnvnMAKFgIWVN1PUACr2EP3LMQLvwmnP8Nr6uJegr809Dns+w82Mqave4TwLrKRpqP9QAwIX00i4qyOKsok9LJWZTkpxIfF8YHAGvdaJ+16wccCDZBT7tbPirdDQpXsPCd5qC0CepRIafn+W/Dm/fCl7dD2nivq4l6CvwA8vksbx9uY+3eRt6qbKSsspG61i4AUkclsGByJqWTMyktymT+pIzw6Qo6HF+fG6q2/xNA7QZ3PcDnDmqMzfcfAPzNQRMWQHKWtzVL5OjpcBdrJy+DGx7yupqYoMAPImstNU0drK9qYl1lI+urmthV14a1EB9nmDMhjUWT3aeARUWZjEuNgNvJezqhbpsL//5PA/Vvv7M8a8o7nwAKFrmmId0gJkMp+y088yX45CooOsframKCAj/EWjp62FDdRFmluwi8af87PYEmZydTOjmL0qJMzirKZGru2PC+DtCvs8U1/wxsDmqtdctMvGsKKjzbDQ9RuFSfAsQ1Id67xI0v9dlX1TQYIgp8j3X3+th+oIWyyibKqtxBoKG9G4CM5EQWFWaycHImCyZlcOakDMaOCvNmoH5th/yfAsqgeo3rHtrnmrcYNxsmn/3OXcJqu409FS/Cg9fCNb+CeTd4XU3MUOCHGWstlQ3HXBNQZRPrqhrZe8RdODUGSvJSmT8pgwWFGSwozGRa7ljiwvlicL+eTnf2X7Xa3SC2/y3oPuqWZU2BonPdQHHF5+sTQCx48Fo4tNXdWZsQpjc1RiEFfgRoOdbDpppmNlU3s3F/Exurm2npcBdOU0clcOakdBZMymRBYQbzJ2WQPTZMbwobqK8XDm1x4V+1Gipf998hbNzF36kXuvkBJi12H/slehwuh58vgQu/Bed/3etqYooCPwJZa9lX385G/wFg0/5mdh5so8/n/r0Ks5LdJwB/M9Cs8WE8Omi/vl53DaB/lNCade7u4MRk1/Y//VIouczdKSyR7clb3Lj3X9oGKdleVxNTFPhRoqO7j621LWysbjp+IOjvEhofZ5g+bixnTkxnbkE6cydmMDM/NbwPAp2t7qx/78tQ8RI07nGvj5vtgn/G5a4XkG4GiyyNe+GeUlj6z/C+73ldTcxR4Eexgy0dbKlpYWtNC1tr3aPRf0E4Ic4wIy+VMyemM6cgnTMnplOSn8qohDA9CNRXwNvPwq6/uIHibB+k5Loz/5nvdzOEaZak8LfiC25e5y9u0cV6DyjwY4i1ltrmDrbVtrgDgf8g0H93cGK8oSQ/lbkFGcyakMas8WnMzE8lJdx6BnU0ubP+Xc9CxQuuW2hSqpsXeNbVMO1itfuHo5Za+Mk8WPgJN7+zhJwCP8b13xy21X8QcAeDZlo73RhBxsDkrGTOGO8OAGeMT+OMCWlMSB8dHvcI9PXAvr+5ERd3Pg2dzW5Y6JLLYfY17sxfvUDCw7P/Cm/9L3xhI2RO9rqamKTAl7/T/0lg58E2dh5sZefBVnYcbKWq4djxddLHJHLG+FR3APAfDKbnjfW2Seh4+P8Jdj7jwn9MFsy9Dubd6Hr/hMNBKhY1V8M9i+DMD8NVP/O6mpilwJcRO9rVy65Drew42MaOA+5AsOtQ2/Eho+PjDEXZyZTkpzIj751HUXYyCfEhvrja1+N6+2x+GMpXupu+cme6m3zO/LAb+E1C56l/ga1PwBc2QPpEr6uJWQp8OS19PktVQzs7/OH/dl0bb9cdpbKhnf4/n6T4OKaOG0tJ3lhm5KdS4j8QFGSMCc1NYx3N7qx/88Owf62bA2DKhVD6KdfbJz7MrlFEm8M74RfLYOm/qGeOxxT4EhQd3X3sOXL0+EFgV10bbx9q40BL5/F1kpPimZ6X6g4EeamU+A8Guamjgnd9oGEPbH4ENj3kxvtJnQCLbnIXEnXWHxwPfwQqX3MTnOguak8p8CWkWjt72F3Xxq5DR/2fBtyj/mj38XUykhP9zUFjKclLpSQ/jZK8VNKTAzgLV18v7H4O1v0G9rzkBnkruRzO+gxMuUBt/YFSuRp+d4Xuqg0TQQt8Y0wW8ChQBFQC11trmwatMx/4BZAG9AHfs9Y++l7bVuBHn/qjXS78D7Wxq+7o8a/7ZxQDyE8bTUl+KjP91whK8lOZNm7s6d9A1rjXDdW78UHoaIRxs+Ds29zFXnXvPHV9vfCr89yQGZ97C5KSva4o5gUz8H8INFpr/8sYczuQaa3910HrzACstXa3MWYCsB44w1rbfKJtK/Bjg7WWAy2dvH2ojXJ/01D5oTb2HD5Kd58bUjrOQFFOyvGDwMx894mgMCv55GcY6+mE7U/CGz+Dw9vdBC9LboFFn1JTxKlY+2t49utw/QMw6yqvqxGCG/i7gAustQeNMeOBV6y1Je/xM5uB66y1u0+0ngI/tvX0+ahqaHcHAf/BYFddG9WNx45fKB6dGMf0cQMPAu4xbiTXB6yFPX+FN3/mnhOTYcHHYdnnIWNS8H/BaNDeAPcsgPHz4RN/VhNZmAhm4DdbazP8Xxugqf/7YdZfDNwPzLbW+k60bQW+DOVYdy+7646yq66NXYf8j7o2jrR1HV8nIzmRmfmpzJ6QzuwJacyekM7U3JThu43WbXdzrm55zH0//yNw7lcgsyj4v1Ake+pzsOURuHU1jJvpdTXid1qBb4x5EcgfYtE3gfsHBrwxpslamznMdsYDrwA3WWvXDLPOLcAtAIWFhYuqqqpOWJtIv8b2bv8BoJVddW3sONhG+cHW4zONjUqIY2Z+KrOOHwTczWTvujbQUgOv/xg23A/W5/rzn/tVN5a/vNvuF+Gha937c/EdXlcjA3jepGOMScOF/fettU+MZNs6w5fT1dvnY299O9sPtLC9tpXtB1rZfqDl+JAScQam5o49/ilgrn+k0ZSuw7D6J7D+d+7mrnk3wAX/pqaefp2t8POlkDQWbn1NF73DTDAD/y6gYcBF2yxr7TcGrZMEPAs8ba398Ui3rcCXYOgfV6g//Puf+4eZjjMwIy+VeRMzWDquh/MOP0TWzgcxAIv/yZ3RxvrF3ae/CBsegJtfgIlD5op4KJiBnw08BhQCVbhumY3GmFLgVmvtZ4wxHwN+C2wf8KOftNZuOtG2FfgSSkfautha28ym/S1s3t/M5prm4yOMTkls4ttj/8z5HS/Sl5DMsbM+R9oFX8CMGutx1R7Y+Qw8+lF3cfvS73pdjQxBN16JnCRrLVUNx9i0v5lN/gNA14HtfNk8wj/Er6eeDP6S90/0zr2R0uIczhifdvJdRCNNczX8cjlkFsPNz6spJ0wp8EUCoLvXR/mhVmq3vMzMLT+kuHMHW31F/EfPJyhPmsOCwgzOKsrirKIs5k/KYExSmE40cyr6euC3l8ORXfDZv+lCdhhT4IsEmrWw9Ql6n7+DhKMH2JJxMXf5PsLrR8ZgrZttbE5BOouLs1g6xR0EUkcHcNiIUFv5NVj3v3Ddb2HOB72uRk5AgS8SLN3trkfP6p8A0HnW51hb8AnW1nRSVukmn+/u8xEfZ5hbkM7ZU7M5e0o2pUWZJCdFyAiea38Fz37DDUWhkTDDngJfJNia98OL/w7b/uhG57zkTpj7ITr7LOurmnhzTwNv7m1g8/5men2WxHjDvIkZLJuazdKp2SwszAzPCed3vwB/uB5mXAYffhDiwrBGeRcFvkioVL0Jf7kdDm6CgkXwvu9D4dLji9u7eikbcADYWtOMz0JSQhwLCzNYNjWHc6blMG9ieugnlBms6k148FrIngKf+gvEYq+kCKTAFwkln88NOfDSd6DtoJt0/ZI7Iav471Zt6+xhXWXj8QPA9gOtWAupoxM4e0o2y6fnsHxaDsU5KaGdX3j/Ovj9NZCaB59c5Z4lIijwRbzQ3Q5v3OPa9329sORWOO9rMDp92B9pau/mjT0NvF5xhNd211PT1AFAQcYYlk/LYfl09wkgKyWIk7ZXr4GHrnc3mH1qlSaNiTAKfBEvtR6Av34XNv3Bhej5/wqLPvme/dittVQ3HuO13fW8vrueN/bUHx8WYvaENJZPz+HcabmUFgWw/X/7U/DkLW5O2ptWaG7aCKTAFwkHBzbB899yUwGmTXRn+ws+BvEj667Z2+dja20Lr++u57WKejZWN9HTZxmVEMfi4iyWT3Nn/7PGp538PMI+H6z+sWuGmrQYbngYUrJP4ZcUrynwRcKFtbD3Zfjr96C2DDImw/nfgLnXQ8LJNdO0d/Wydl/D8U8Auw8fBSA7JYll03I4198ENCFjzIk3dPQIPHUrVLwIs6+Bq38Bie/xMxK2FPgi4cZa2P08vPw9OLgZUsfDks+6mbfGDDulxAnVtXby+u56Xq9wj/45Aqbkprj2/2k5nD01+50bwKx13Uj/8m/Q2QKXfR9Kb9ZEJhFOgS8SrqyFipfgzXtg7yuQmALzb3Szb42fd8rha61lV13b8QPA2r2NdPT0ER9nmD8pg2vyG7iy7mekHVrj9nPVzyF/TmB/N/GEAl8kEhzcAmt+DtuehL4uyJsL8z4MM99/2mPXdPX2saGykeoNzzF9930s7FlPs03hp9zI/uIPcc70PJZPz2Vqboi7f0rAKfBFIklHk2tq2fggHNjoXhs3C6b/A0xa6i6qpuSMbFvdx9y1gt0vuANJaw2k5NdvIYgAAAkhSURBVNK56LO8ln4lL1d3s7qinqqGYwCMTx/NOdNyONff/TNnrEbEjDQKfJFI1VQJ5augfCXsXws+N0Y/6YXuDtjMIhiTBaNS3bAHvV3Q1eqGMm7cB4d3up+JS4CpF8Pc6+CMf/y7i7L7+7t/VhxhdUUDLR1uPzPzUzl3eg7Lp+eyuCgrukYAjVIKfJFo0NPhunbuXwN1O6BxrzsgdDa7G7v6xY+CjELInAz5c6FwmftUMMKLwX0+y7baFnfxd3c966ua6O7zkRQfR2lR5vFPALMnpEf/HAARSIEvEs2sdQcD2wcJYyA+sKNwHuvu5a19jayuqOe13fWUH2oDICM5kSXFWSydks3SKdmU5KWefP9/CbgTBX6EjM8qIsMyBpKSg7b55KQELigZxwUl4wA3HeRqf9fPtfsaeG57HaADQCTQGb6InJaapmOs3dvImr0NrNnXwP5GN/6PDgDe0Bm+iATNxMxkJi5K5tpFbtyd/gPA2n0NrNnb+K5PAKWTM1k4OZNFhZnMm5QRnnMARDEFvogE1OADQG1zB2v3NrBmbwPrq5p4cedhwE0DObsgnUWFmSya7B756aO9LD3qqUlHREKqsb2bjdVNrK9yj801zXT2+AA3DPTCyZnMm5jOnIJ0Zk9Ii+y5gD2gJh0RCRtZKUlcfEYeF5/hJlXp6fOx40CrOwBUN1FW2cjTmw8A7np0cU4KcwvSmVugg8Dp0hm+iISd+qNdbK1tYVtNC1tqW9hW28LBls7jywsyxlCSn8r0vLHMGJdKSX4q08aNjehrAvVHu9ha08KWmhZGJ8bx2fOnntJ2dIYvIhElZ+woLiwZx4X+rqDguoNuq21h+4EW3q47ytv+weG6+1xzkDFQmJVMcU4Kk7OSmZSVzOTsFAqzkinMSg6bu4Q7e/rYV9/OniNH2XO4nZ0HW9la20Jts+vdZAycOz33lAP/RHSGLyIRq7fPR2XDMXbXtbGrro3ddUepbGinuuEYbV2971o3Z+wo8tJGMS51FHlpoxmXOopc/3P6mERSRyeQNjqRtNGJjB2dcNJ3EXf29NHa0UOL/9HY3k1daycHWzo51OKea5qPUdPUQX/s9h+k5hakM29iBnP91y7Gjjr1c3HdaSsiMcVaS/OxHqoaj1HdeIzqhnb2N3ZwuK2TutYuDrd10dDexYniLyUpnsSEOBLi4kiMNyTEGxLj4sC46w49vZaePh/dfT66en109/qG3E5CnCEvbTT56aOZkDGGqbkpTM0dy9TcsUzJTQl4M5SadEQkphhjyExJIjMlifmThh5DqLfPR0N7N0faumjt6KG1s5fWzh7aOntp8z939/ro9Vl6+9xzT58PC4yKjyMxPo7EBENifBxJ8XGkjUkkbUwi6f5HxphExqePJnvsqLAZc0iBLyIxKSE+jry00eSlxU7f/zivCxARkdBQ4IuIxAgFvohIjFDgi4jECAW+iEiMUOCLiMQIBb6ISIxQ4IuIxIiwHVrBGHMEqPK6jhHKAeq9LuIkRFq9oJpDJdJqjrR6Ifg1T7bW5g61IGwDP5IYY8qGG7siHEVavaCaQyXSao60esHbmtWkIyISIxT4IiIxQoEfGL/2uoCTFGn1gmoOlUirOdLqBQ9rVhu+iEiM0Bm+iEiMUOCLiMQIBf4IGGMmGWNeNsbsMMZsN8Z8cYh1LjDGtBhjNvkfd3hR66CaKo0xW/31/N18kcb5qTGmwhizxRiz0Is6B9RTMuD922SMaTXGfGnQOp6/z8aY+4wxh40x2wa8lmWMecEYs9v/nDnMz97kX2e3MeYmD+u9yxhT7v93/5MxZshpod7rbyjENd9pjKkd8G9/xTA/e5kxZpf/7/p2j2t+dEC9lcaYTcP8bGjeZ2utHu/xAMYDC/1fpwJvA7MGrXMB8IzXtQ6qqRLIOcHyK4BnAQMsBdZ6XfOA2uKBQ7ibSMLqfQbOAxYC2wa89kPgdv/XtwM/GOLnsoC9/udM/9eZHtV7KZDg//oHQ9U7kr+hENd8J/C1Efzd7AGmAEnA5sH/V0NZ86Dl/wPc4eX7rDP8EbDWHrTWbvB/3QbsBAq8rSogrgIesM4aIMMYM97rovwuBvZYa8Pubmtr7atA46CXrwLu9399P3D1ED/6PuAFa22jtbYJeAG4LGiF+g1Vr7X2eWttr//bNcDEYNdxMoZ5j0diMVBhrd1rre0GHsH92wTdiWo2xhjgeuDhUNQyHAX+STLGFAELgLVDLD7bGLPZGPOsMWZ2SAsbmgWeN8asN8bcMsTyAmD/gO9rCJ8D2Q0M/58j3N5ngDxr7UH/14eAvCHWCdf3+9O4T3pDea+/oVC7zd8Mdd8wzWbh+h6fC9RZa3cPszwk77MC/yQYY8YCfwS+ZK1tHbR4A675YR5wD/BUqOsbwnJr7ULgcuBzxpjzvC5oJIwxScCVwONDLA7H9/ldrPuMHhH9nY0x3wR6gYeGWSWc/oZ+AUwF5gMHcU0kkeJGTnx2H5L3WYE/QsaYRFzYP2StfXLwcmttq7X2qP/rVUCiMSYnxGUOrqnW/3wY+BPu4+5AtcCkAd9P9L/mtcuBDdbausELwvF99qvrbw7zPx8eYp2wer+NMZ8EPgB81H+Q+jsj+BsKGWttnbW2z1rrA/53mFrC6j0GMMYkAB8EHh1unVC9zwr8EfC3v/0G2GmtvXuYdfL962GMWYx7bxtCV+Xf1ZNijEnt/xp3kW7boNVWAJ/w99ZZCrQMaJbw0rBnQ+H2Pg+wAujvdXMT8Och1nkOuNQYk+lvjrjU/1rIGWMuA74BXGmtPTbMOiP5GwqZQdeXrhmmlnXAdGNMsf+T4g24fxsvXQKUW2trhloY0vc5FFevI/0BLMd9RN8CbPI/rgBuBW71r3MbsB3XK2ANsMzjmqf4a9nsr+ub/tcH1myAe3G9GrYCpWHwXqfgAjx9wGth9T7jDkYHgR5cG/HNQDbwErAbeBHI8q9bCvzfgJ/9NFDhf3zKw3orcG3d/X/Pv/SvOwFYdaK/IQ9r/r3/73QLLsTHD67Z//0VuJ50e7yu2f/67/r/fges68n7rKEVRERihJp0RERihAJfRCRGKPBFRGKEAl9EJEYo8EVEYoQCX0QkRijwRURixP8HnonzEr8PWK0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", + " -0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n", + "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", + " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" + ] + }, { "data": { "image/png": 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\n", @@ -235,9 +296,11 @@ } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(2, svd=True)\n", "fpca.fit(basisfd)\n", "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", "pyplot.show()" ] }, @@ -251,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -293,12 +356,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From dbb8de6b32ab10d0afdef70385842d2976970749 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 11:23:21 +0100 Subject: [PATCH 395/624] Illustrate fpca using the weather dataset --- skfda/exploratory/fpca/test.ipynb | 266 +++++++++++++++++++++++++++++- 1 file changed, 259 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 9d127e51f..7f12efa5a 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -10,7 +10,7 @@ "import skfda\n", "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", - "from skfda.datasets._real_datasets import fetch_growth\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot" ] }, @@ -81,9 +81,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -113,9 +113,9 @@ } ], "source": [ - "discretizedFPCA = FPCADiscretized(2, svd=False)\n", - "discretizedFPCA.fit(fd)\n", - "discretizedFPCA.components.plot()\n", + "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", "pyplot.show()" ] }, @@ -384,6 +384,258 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Canadian Weather Study " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd_data)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "\n", + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", + " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", + " 0.00182958]\n", + " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", + " -0.03755887]])\n", + "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", + " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2, svd=True)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fetch the dataset again as the module modified the original data and centers the original data.\n", + "The mean function is distorted after such transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "\n", + "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basisfd = fd_data.to_basis(basis)\n", + "basisfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "meanfd = basisfd.mean()\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", + " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", + "\n", + "meanfd.plot()\n", + "pyplot.show()" + ] + }, { "cell_type": "code", "execution_count": null, From 8b7af633c3c7b4d993a863c6c241b487188ec792 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 4 Dec 2019 12:32:35 +0100 Subject: [PATCH 396/624] Add score calculation to both cases --- skfda/exploratory/fpca/fpca.py | 108 ++++++++----- skfda/exploratory/fpca/test.ipynb | 254 ++++++++++++++++++++++++++---- 2 files changed, 295 insertions(+), 67 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 91f54c468..3ef0a6bed 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,20 +1,76 @@ import numpy as np -import skfda +from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis -from skfda.datasets._real_datasets import fetch_growth -from matplotlib import pyplot - -class FPCABasis: - def __init__(self, n_components, components_basis=None, centering=True, svd=False): +from skfda.representation.grid import FDataGrid + + +class FPCA(ABC): + """Defines the common structure shared between classes that do functional principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis + components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or + discretized form + component_values (array_like): this contains the values (eigenvalues) associated with the principal components + + """ + + def __init__(self, n_components, centering=True, svd=True): + """ FPCA constructor + Args: + n_components (int): number of principal components to obtain from functional principal component analysis + centering (bool): if True then calculate the mean of the functional data object and center the data first. + Defaults to True + svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. + Defaults to True as svd is usually more efficient + """ self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.components_basis = components_basis self.centering = centering + self.svd = svd self.components = None self.component_values = None - self.svd = svd + @abstractmethod def fit(self, X, y=None): + """Computes the n_components first principal components and saves them inside the FPCA object. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + self (object) + """ + pass + + @abstractmethod + def transform(self, X, y=None): + """Computes the n_components first principal components score and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the n_components first principal components + """ + pass + + def fit_transform(self, X, y=None): + self.fit(X, y) + return self.transform(X, y) + + +class FPCABasis(FPCA): + + def __init__(self, n_components, components_basis=None, centering=True, svd=False): + super().__init__(n_components, centering, svd) + # component_basis is the basis that we want to use for the principal components + self.components_basis = components_basis + + def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object # if centering is True then substract the mean function to each function in FDataBasis @@ -81,32 +137,22 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - pass + return X.inner_product(self.components) -class FPCADiscretized: +class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): - self.n_components = n_components - # component_basis is the basis that we want to use for the principal components - self.centering = centering - self.components = None - self.component_values = None + super().__init__(n_components, centering, svd) self.weights = weights - self.svd = svd - def fit(self, X, y=None): + # noinspection PyPep8Naming + def fit(self, X: FDataGrid, y=None): # data matrix initialization fd_data = np.squeeze(X.data_matrix) # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() @@ -154,16 +200,4 @@ def fit(self, X, y=None): return self def transform(self, X, y=None): - total = sum(self.component_values) - self.component_values /= total - return self.component_values[:self.n_components] - - def fit_transform(self, X, y=None): - self.fit(X, y) - return self.transform(X, y) - - - - - - + return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 7f12efa5a..23f346793 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -119,31 +119,114 @@ "pyplot.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scores (percentage) the first n components has over all the components" - ] - }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.80414823, 0.13861057])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-75.06492745 -18.81698461]\n", + " [ 7.70436341 -12.11485069]\n", + " [ 24.47538324 -18.13755002]\n", + " [-15.367826 -20.3545263 ]\n", + " [ 22.32476789 -21.43967377]\n", + " [ 11.3526218 -13.83722948]\n", + " [ 20.78504212 -10.76894299]\n", + " [-36.78156763 -15.05766582]\n", + " [ 24.99726134 -15.5485961 ]\n", + " [-64.18622578 -5.57517994]\n", + " [ -7.01009228 -15.99263688]\n", + " [-43.94630602 -19.63899585]\n", + " [-16.84962351 -18.68150298]\n", + " [-43.59246404 -11.59787162]\n", + " [-31.41065606 -1.74400999]\n", + " [-37.67756375 -9.86898467]\n", + " [-26.15642442 -16.01612041]\n", + " [-29.11750669 1.64357407]\n", + " [ 5.7848759 -13.75136658]\n", + " [ -7.69094576 -12.24387901]\n", + " [ 18.04647861 -15.07855459]\n", + " [ 11.38538415 -16.44893378]\n", + " [ 1.79736625 -21.01997069]\n", + " [ 21.8837638 -14.19505422]\n", + " [ 10.0679221 -16.70849496]\n", + " [-12.08542595 -19.03299269]\n", + " [-14.58043956 -7.12673321]\n", + " [ 30.96410081 -13.67811249]\n", + " [-82.16841432 -10.8543497 ]\n", + " [ -6.60105555 -18.50819791]\n", + " [-30.61688089 -9.61945651]\n", + " [-70.6346625 -13.37809638]\n", + " [ 3.39724291 -12.03714337]\n", + " [ 7.29146094 -18.47417338]\n", + " [-63.68983611 0.61881631]\n", + " [-19.038978 -14.54366589]\n", + " [-49.94687751 -2.00805936]\n", + " [-38.4910343 0.85264844]\n", + " [ -0.46199028 -13.94673804]\n", + " [ 29.14759403 19.24921532]\n", + " [ 12.66292722 7.28723507]\n", + " [ 2.88146913 31.33856479]\n", + " [ 0.96046324 11.14405287]\n", + " [ 2.33528813 2.85743582]\n", + " [ 22.97842748 3.07068558]\n", + " [ 47.85599752 -7.88504397]\n", + " [-77.41273341 26.84433824]\n", + " [ 9.83038736 15.62844429]\n", + " [-28.10539072 16.62027042]\n", + " [ 23.10737425 -2.58412035]\n", + " [ 24.64686729 7.28993856]\n", + " [ 79.48726026 -5.06374655]\n", + " [ 3.49991077 1.13696842]\n", + " [-11.50012511 14.67896129]\n", + " [ 65.61238703 0.28573546]\n", + " [ 19.55961294 23.2824619 ]\n", + " [-25.53676008 24.31600802]\n", + " [ 7.92625642 15.99657737]\n", + " [ -5.3287426 10.30006812]\n", + " [-16.28874938 13.63992392]\n", + " [ 15.48947605 14.95447197]\n", + " [ 23.8345424 11.43828747]\n", + " [ 47.12536308 9.63930875]\n", + " [-31.00351971 -7.64067499]\n", + " [ 57.27010227 -1.45463478]\n", + " [ 7.37165816 14.85134273]\n", + " [ 8.97902308 8.18674235]\n", + " [ 74.15697042 -8.80166673]\n", + " [ 11.79943483 0.66898816]\n", + " [ 15.47712465 8.04981375]\n", + " [ 4.82966659 25.32869823]\n", + " [ -7.45534653 0.26213447]\n", + " [ 19.28260923 10.84078437]\n", + " [ -3.41788644 11.79202817]\n", + " [ 19.68112623 2.78305787]\n", + " [ 36.70407022 -4.13740127]\n", + " [-36.63972309 15.82470035]\n", + " [-11.29544575 11.60419497]\n", + " [-10.86010351 17.23517667]\n", + " [ 22.37710711 11.71658518]\n", + " [ 69.93817798 0.1837038 ]\n", + " [-23.52029349 16.63785003]\n", + " [ 3.88508686 8.8950907 ]\n", + " [ 19.51822288 8.81957995]\n", + " [ 24.94175847 12.63592148]\n", + " [ 29.4438398 10.62909784]\n", + " [ 60.8940826 13.91957234]\n", + " [-16.65019271 -6.96853033]\n", + " [ 2.44106998 5.34263614]\n", + " [ -7.7688224 -0.1303435 ]\n", + " [ 13.21116977 8.22090495]\n", + " [-14.40137836 23.47471441]\n", + " [-13.04900338 20.49414594]]\n" + ] } ], "source": [ - "discretizedFPCA.transform(fd)" + "scores = fpca_discretized.transform(fd)\n", + "print(scores)" ] }, { @@ -222,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -265,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,6 +387,117 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-5.30720261e+01 -1.20900812e+01]\n", + " [ 5.93932831e+00 -8.13503289e+00]\n", + " [ 1.87359068e+01 -1.29753453e+01]\n", + " [-1.02271668e+01 -1.41114219e+01]\n", + " [ 1.78816044e+01 -1.61153507e+01]\n", + " [ 8.76982056e+00 -9.64548625e+00]\n", + " [ 1.51595101e+01 -7.48338120e+00]\n", + " [-2.57711354e+01 -1.02616428e+01]\n", + " [ 1.88410831e+01 -1.11580232e+01]\n", + " [-4.64293496e+01 -2.83317044e+00]\n", + " [-4.31966291e+00 -1.10533867e+01]\n", + " [-3.03723709e+01 -1.34939115e+01]\n", + " [-1.10945917e+01 -1.28105622e+01]\n", + " [-3.09084367e+01 -7.52073071e+00]\n", + " [-2.34011972e+01 -2.11592349e-01]\n", + " [-2.70364964e+01 -6.22251055e+00]\n", + " [-1.77541148e+01 -1.10945725e+01]\n", + " [-2.08566166e+01 1.20259305e+00]\n", + " [ 4.67719637e+00 -9.63524550e+00]\n", + " [-4.76931190e+00 -8.60596519e+00]\n", + " [ 1.37391612e+01 -1.05089784e+01]\n", + " [ 9.29873449e+00 -1.17272101e+01]\n", + " [ 2.45160232e+00 -1.48677580e+01]\n", + " [ 1.67240989e+01 -1.02844853e+01]\n", + " [ 8.27541495e+00 -1.17247480e+01]\n", + " [-7.15374915e+00 -1.35331741e+01]\n", + " [-1.03861652e+01 -4.22348685e+00]\n", + " [ 2.29727946e+01 -9.98599278e+00]\n", + " [-5.91216298e+01 -6.47616247e+00]\n", + " [-3.79316511e+00 -1.29552993e+01]\n", + " [-2.15071076e+01 -6.53451179e+00]\n", + " [-5.05931008e+01 -8.25681987e+00]\n", + " [ 2.76682714e+00 -8.21125146e+00]\n", + " [ 6.51234884e+00 -1.33064581e+01]\n", + " [-4.64214751e+01 1.34282277e+00]\n", + " [-1.32994206e+01 -9.85739697e+00]\n", + " [-3.61853591e+01 -4.17366544e-01]\n", + " [-2.79000508e+01 1.27619929e+00]\n", + " [ 3.83941545e-01 -9.91228209e+00]\n", + " [ 2.00328282e+01 1.31744063e+01]\n", + " [ 8.97265235e+00 4.81618743e+00]\n", + " [ 4.77386711e-02 2.24502470e+01]\n", + " [-2.42567821e-01 8.20945744e+00]\n", + " [ 1.64451593e+00 2.11944738e+00]\n", + " [ 1.70071238e+01 1.39105233e+00]\n", + " [ 3.46799479e+01 -6.01866094e+00]\n", + " [-5.75717897e+01 1.99259734e+01]\n", + " [ 6.35085561e+00 1.06703144e+01]\n", + " [-2.14964326e+01 1.20955265e+01]\n", + " [ 1.61427333e+01 -1.65416616e+00]\n", + " [ 1.71124191e+01 5.00985495e+00]\n", + " [ 5.74126659e+01 -4.35566312e+00]\n", + " [ 2.19564887e+00 1.09803659e+00]\n", + " [-8.42094191e+00 9.75168394e+00]\n", + " [ 4.74057420e+01 -4.83674882e-01]\n", + " [ 1.31250340e+01 1.57485342e+01]\n", + " [-2.01007068e+01 1.76386736e+01]\n", + " [ 5.36884962e+00 1.04679341e+01]\n", + " [-4.38076453e+00 7.20057846e+00]\n", + " [-1.22134463e+01 9.36910810e+00]\n", + " [ 1.11712346e+01 9.66522848e+00]\n", + " [ 1.69187409e+01 7.32866993e+00]\n", + " [ 3.37743990e+01 5.94571482e+00]\n", + " [-2.16792927e+01 -5.24099847e+00]\n", + " [ 4.18716782e+01 -1.95360874e+00]\n", + " [ 4.11001507e+00 1.06495733e+01]\n", + " [ 5.63261389e+00 5.64013776e+00]\n", + " [ 5.44902822e+01 -7.34128258e+00]\n", + " [ 8.39573458e+00 3.04649987e-01]\n", + " [ 1.05275067e+01 5.77760594e+00]\n", + " [ 1.95982094e+00 1.77073399e+01]\n", + " [-5.87053977e+00 6.47053060e-01]\n", + " [ 1.33985204e+01 7.19578032e+00]\n", + " [-3.04394208e+00 8.36580889e+00]\n", + " [ 1.41550390e+01 1.77507578e+00]\n", + " [ 2.67208452e+01 -3.29012926e+00]\n", + " [-2.73473262e+01 1.16262275e+01]\n", + " [-8.74844272e+00 8.17414960e+00]\n", + " [-8.43776443e+00 1.21123959e+01]\n", + " [ 1.58369881e+01 7.66443252e+00]\n", + " [ 5.10908299e+01 -1.14474834e+00]\n", + " [-1.80355733e+01 1.18449590e+01]\n", + " [ 2.14815859e+00 6.45250519e+00]\n", + " [ 1.37622783e+01 5.66582802e+00]\n", + " [ 1.78128961e+01 8.11180533e+00]\n", + " [ 2.13905012e+01 6.42618922e+00]\n", + " [ 4.40377056e+01 8.51163491e+00]\n", + " [-1.16537118e+01 -4.69794014e+00]\n", + " [ 1.39292265e+00 4.02622781e+00]\n", + " [-5.58202988e+00 9.06925997e-02]\n", + " [ 8.56960505e+00 6.05912637e+00]\n", + " [-1.19302857e+01 1.69879571e+01]\n", + " [-1.06671866e+01 1.47062675e+01]]\n" + ] + } + ], + "source": [ + "print(fpca.transform(basisfd))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -356,12 +550,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -400,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -438,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -472,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -502,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -551,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -578,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -608,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { From ea68b4ef2ee9d463b3d6f42793af63b4211ebac4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 15:52:05 +0100 Subject: [PATCH 397/624] Adding several comments --- skfda/exploratory/fpca/fpca.py | 20 +++++++++++++++++--- skfda/exploratory/fpca/test.ipynb | 31 +++++++++++++++++-------------- 2 files changed, 34 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 3ef0a6bed..a007762a5 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -54,11 +54,20 @@ def transform(self, X, y=None): y (None, not used): only present for convention of a fit function Returns: - (array_like): the scores of the n_components first principal components + (array_like): the scores of the data with reference to the principal components """ pass def fit_transform(self, X, y=None): + """Computes the n_components first principal components and their scores and returns them. + + Args: + X (FDataGrid or FDataBasis): the functional data object to be analysed + y (None, not used): only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the principal components + """ self.fit(X, y) return self.transform(X, y) @@ -101,6 +110,9 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check + # TODO make the final matrix symmetric + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis if self.svd: final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -137,6 +149,7 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + # in this case it is the inner product of our data with the components return X.inner_product(self.components) @@ -153,11 +166,11 @@ def fit(self, X: FDataGrid, y=None): # obtain the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then substract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix fd_data -= np.squeeze(meanfd.data_matrix) # establish weights for each point of discretization @@ -200,4 +213,5 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + # in this case its the coefficient matrix multiplied by the principal components as column vectors return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 23f346793..4e8663e4d 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -11,7 +11,8 @@ "from fpca import FPCABasis, FPCADiscretized\n", "from skfda.representation.basis import FDataBasis\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot" + "from matplotlib import pyplot\n", + "from sklearn.decomposition import PCA" ] }, { @@ -122,7 +123,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -305,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -320,13 +323,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -348,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -508,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -550,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -594,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -632,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, From 96e0bf7cf931ea1acb05d29e8f9aa9a51c697e9e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Jan 2020 20:09:41 +0100 Subject: [PATCH 398/624] Use PCA implemented in scikit learn --- skfda/exploratory/fpca/fpca.py | 29 +- skfda/exploratory/fpca/test.ipynb | 431 +++++++++++++++++++++++++++++- 2 files changed, 440 insertions(+), 20 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index a007762a5..aa51e2f96 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid +from sklearn.decomposition import PCA class FPCA(ABC): @@ -78,6 +79,7 @@ def __init__(self, n_components, components_basis=None, centering=True, svd=Fals super().__init__(n_components, centering, svd) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis + self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): # for now lets consider that X is a FDataBasis Object @@ -110,12 +112,17 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO switch to multivariate PCA of sklearn (maybe only for discretized case) and check # TODO make the final matrix symmetric # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + + self.pca.fit(final_matrix) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=self.pca.components_ @ l_matrix_inv) + """ if self.svd: - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) @@ -124,8 +131,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=principal_components[:self.n_components, :]) self.component_values = s ** 2 else: - final_matrix = (l_inv_j_t @ np.transpose(X.coefficients) - @ X.coefficients @ np.transpose(l_inv_j_t)) / n_samples + final_matrix = np.transpose(final_matrix) @ final_matrix # perform eigenvalue and eigenvector analysis on this matrix # eigenvectors is a numpy array, such that its columns are eigenvectors @@ -145,6 +151,7 @@ def fit(self, X: FDataBasis, y=None): coefficients=np.transpose(principal_components_t)) self.component_values = eigenvalues + """ return self @@ -157,6 +164,7 @@ class FPCADiscretized(FPCA): def __init__(self, n_components, weights=None, centering=True, svd=True): super().__init__(n_components, centering, svd) self.weights = weights + self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): @@ -176,8 +184,11 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - self.weights = np.diff(X.sample_points[0]) - self.weights = np.append(self.weights, [self.weights[-1]]) + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: + # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + differences = np.diff(X.sample_points[0]) + self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -185,7 +196,11 @@ def fit(self, X: FDataGrid, y=None): # k_estimated = fd_data @ np.transpose(fd_data) / n_samples final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_**2 + """ if self.svd: # vh contains the eigenvectors transposed # s contains the singular values, which are square roots of eigenvalues @@ -209,7 +224,7 @@ def fit(self, X: FDataGrid, y=None): # prepare the computed principal components self.components = X.copy(data_matrix=np.transpose(principal_components_t)) self.component_values = eigenvalues - + """ return self def transform(self, X, y=None): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 4e8663e4d..e5e4669c8 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -56,6 +56,292 @@ "pyplot.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trapezoidal rule implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", + " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = np.diff(fd.sample_points[0])\n", + "differences" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0]/2], weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", + " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", + "31\n" + ] + }, + { + "data": { + "text/plain": [ + "31" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(weights)\n", + "print(len(weights))\n", + "len(fd.sample_points[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=3)\n", + "X = fd" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = np.squeeze(X.data_matrix)\n", + "\n", + "# obtain the number of samples and the number of points of descretization\n", + "n_samples, n_points_discretization = fd_data.shape\n", + "\n", + "# establish weights for each point of discretization\n", + "\n", + "differences = np.diff(X.sample_points[0])\n", + "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", + "weights = np.concatenate(([differences[0] / 2], weights))\n", + "\n", + "weights_matrix = np.diag(weights)\n", + "\n", + "# k_estimated is not used for the moment\n", + "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", + "\n", + "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", + "pca.fit(final_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.80909337 0.13558824 0.03007623]\n", + "[556.70338211 93.29260943 20.69419605]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n" + ] + } + ], + "source": [ + "print(X.copy(data_matrix=pca.components_))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", + " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", + " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", + " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", + " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", + " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", + " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", + " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" + ] + } + ], + "source": [ + "print(fpca_discretized.component_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -65,12 +351,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -79,13 +365,90 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ 0.0301562 ]\n", + " [ 0.04427131]\n", + " [ 0.04728343]\n", + " [ 0.05024498]\n", + " [ 0.08350374]\n", + " [ 0.12469084]\n", + " [ 0.1428609 ]\n", + " [ 0.15392606]\n", + " [ 0.16414784]\n", + " [ 0.185423 ]\n", + " [ 0.17731185]\n", + " [ 0.15056585]\n", + " [ 0.1562045 ]\n", + " [ 0.16035723]\n", + " [ 0.16710323]\n", + " [ 0.17146745]\n", + " [ 0.17403676]\n", + " [ 0.17857486]\n", + " [ 0.18564754]\n", + " [ 0.19469669]\n", + " [ 0.2076448 ]\n", + " [ 0.22112651]\n", + " [ 0.23137277]\n", + " [ 0.2370328 ]\n", + " [ 0.23762522]\n", + " [ 0.23844513]\n", + " [ 0.23774772]\n", + " [ 0.23691089]\n", + " [ 0.23653888]\n", + " [ 0.23718893]\n", + " [ 0.16855265]]\n", + "\n", + " [[-0.00444331]\n", + " [ 0.00268314]\n", + " [ 0.00915844]\n", + " [ 0.01355168]\n", + " [ 0.04096133]\n", + " [ 0.04974792]\n", + " [ 0.07535919]\n", + " [ 0.11740248]\n", + " [ 0.16609379]\n", + " [ 0.15244813]\n", + " [ 0.13069387]\n", + " [ 0.11127231]\n", + " [ 0.11601948]\n", + " [ 0.12865819]\n", + " [ 0.14523707]\n", + " [ 0.17744913]\n", + " [ 0.21594727]\n", + " [ 0.24988589]\n", + " [ 0.26144481]\n", + " [ 0.23456892]\n", + " [ 0.17285918]\n", + " [ 0.08524828]\n", + " [-0.00841461]\n", + " [-0.10122569]\n", + " [-0.17851914]\n", + " [-0.23488654]\n", + " [-0.27708391]\n", + " [-0.30554775]\n", + " [-0.32274581]\n", + " [-0.33517072]\n", + " [-0.24414735]]]\n", + "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", + " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", + " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", + " 16.5 , 17. , 17.5 , 18. ])]\n", + "time range: [[ 1. 18.]]\n", + "[556.70338211 93.29260943]\n" + ] } ], "source": [ "fpca_discretized = FPCADiscretized(2)\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", - "pyplot.show()" + "pyplot.show()\n", + "print(fpca_discretized.components)\n", + "print(fpca_discretized.component_values)" ] }, { @@ -97,12 +460,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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\n", 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" ] @@ -241,9 +604,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -273,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -308,7 +671,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[557.67384688 92.00703848]\n", + "FDataBasis(\n", + " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", + " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", + " 0.33056519]\n", + " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", + " -0.42255908]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca = FPCABasis(2)\n", + "fpca.fit(basisfd)\n", + "print(fpca.component_values)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": false }, @@ -323,13 +728,13 @@ " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", + " 0.42255908]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -351,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { From c5ec1adaef70730f070e587eb2626bc064a23e3c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Jan 2020 12:10:02 +0100 Subject: [PATCH 399/624] Comply with scikit pipeline --- skfda/exploratory/fpca/fpca.py | 24 +- skfda/exploratory/fpca/test.ipynb | 439 +++++++++++++++++++++++++++--- 2 files changed, 407 insertions(+), 56 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index aa51e2f96..6c0a43063 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -3,9 +3,10 @@ from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid from sklearn.decomposition import PCA +from sklearn.base import BaseEstimator, ClassifierMixin -class FPCA(ABC): +class FPCA(ABC, BaseEstimator, ClassifierMixin): """Defines the common structure shared between classes that do functional principal component analysis Attributes: @@ -18,7 +19,7 @@ class FPCA(ABC): """ - def __init__(self, n_components, centering=True, svd=True): + def __init__(self, n_components=3, centering=True): """ FPCA constructor Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -29,7 +30,6 @@ def __init__(self, n_components, centering=True, svd=True): """ self.n_components = n_components self.centering = centering - self.svd = svd self.components = None self.component_values = None @@ -75,14 +75,14 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - def __init__(self, n_components, components_basis=None, centering=True, svd=False): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, components_basis=None, centering=True): + super().__init__(n_components, centering) # component_basis is the basis that we want to use for the principal components self.components_basis = components_basis - self.pca = PCA(n_components=n_components) def fit(self, X: FDataBasis, y=None): - # for now lets consider that X is a FDataBasis Object + # initialize pca + self.pca = PCA(n_components=self.n_components) # if centering is True then substract the mean function to each function in FDataBasis if self.centering: @@ -112,7 +112,7 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric + # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) @@ -161,13 +161,15 @@ def transform(self, X, y=None): class FPCADiscretized(FPCA): - def __init__(self, n_components, weights=None, centering=True, svd=True): - super().__init__(n_components, centering, svd) + def __init__(self, n_components=3, weights=None, centering=True): + super().__init__(n_components, centering) self.weights = weights - self.pca = PCA(n_components=n_components) # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + # initialize pca module + self.pca = PCA(n_components=self.n_components) + # data matrix initialization fd_data = np.squeeze(X.data_matrix) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e5e4669c8..f29c79572 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -443,7 +443,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()\n", @@ -477,7 +477,7 @@ } ], "source": [ - "fpca_discretized = FPCADiscretized(2, svd=False)\n", + "fpca_discretized = FPCADiscretized()\n", "fpca_discretized.fit(fd)\n", "fpca_discretized.components.plot()\n", "pyplot.show()" @@ -754,47 +754,6 @@ "pyplot.show()" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n", - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2, svd=True)\n", - "fpca.fit(basisfd)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -1002,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1016,7 +975,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1038,6 +1004,389 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.6]\n", + " [-3.1]\n", + " [-3.4]\n", + " [-4.4]\n", + " [-2.9]\n", + " [-4.5]\n", + " [-5.5]\n", + " [-3.1]\n", + " [-4. ]\n", + " [-5. ]\n", + " [-4.8]\n", + " [-5.2]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-4.4]\n", + " [-4.6]\n", + " [-5.9]\n", + " [-5. ]\n", + " [-4.9]\n", + " [-5.2]\n", + " [-5.3]\n", + " [-5.9]\n", + " [-5.7]\n", + " [-5. ]\n", + " [-4.5]\n", + " [-4.5]\n", + " [-3.3]\n", + " [-4.1]\n", + " [-4.7]\n", + " [-5.5]\n", + " [-5.4]\n", + " [-5.5]\n", + " [-5.6]\n", + " [-5. ]\n", + " [-5.8]\n", + " [-5.9]\n", + " [-5.4]\n", + " [-6.1]\n", + " [-5.6]\n", + " [-4.6]\n", + " [-5.1]\n", + " [-4.8]\n", + " [-5.1]\n", + " [-6. ]\n", + " [-4.6]\n", + " [-5.3]\n", + " [-4.6]\n", + " [-6. ]\n", + " [-7. ]\n", + " [-6.5]\n", + " [-5.1]\n", + " [-5.2]\n", + " [-5.2]\n", + " [-4.4]\n", + " [-6.2]\n", + " [-5.8]\n", + " [-4.5]\n", + " [-3.9]\n", + " [-4.3]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.5]\n", + " [-3.6]\n", + " [-3.5]\n", + " [-4.1]\n", + " [-4.1]\n", + " [-3. ]\n", + " [-3.5]\n", + " [-4.8]\n", + " [-3.9]\n", + " [-3.4]\n", + " [-4.2]\n", + " [-4. ]\n", + " [-3.6]\n", + " [-2.2]\n", + " [-1.5]\n", + " [-1.8]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.4]\n", + " [-2.1]\n", + " [-2.1]\n", + " [-1.3]\n", + " [-1. ]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.4]\n", + " [-0.2]\n", + " [-0.5]\n", + " [-0.3]\n", + " [-0.8]\n", + " [-0.4]\n", + " [ 0.1]\n", + " [ 1.1]\n", + " [ 0.9]\n", + " [ 1.2]\n", + " [ 0.5]\n", + " [ 1. ]\n", + " [ 1.1]\n", + " [ 0.7]\n", + " [ 0.2]\n", + " [ 0. ]\n", + " [ 0.7]\n", + " [ 1.1]\n", + " [ 1. ]\n", + " [ 1.4]\n", + " [ 1.6]\n", + " [ 1.2]\n", + " [ 2.3]\n", + " [ 2.6]\n", + " [ 2.3]\n", + " [ 2.1]\n", + " [ 1.7]\n", + " [ 2.5]\n", + " [ 3.5]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2.8]\n", + " [ 3.7]\n", + " [ 4.8]\n", + " [ 4.7]\n", + " [ 4.6]\n", + " [ 4.5]\n", + " [ 5. ]\n", + " [ 3.6]\n", + " [ 2.8]\n", + " [ 4.2]\n", + " [ 4.6]\n", + " [ 5.6]\n", + " [ 5.4]\n", + " [ 5.6]\n", + " [ 6.3]\n", + " [ 6.4]\n", + " [ 5.8]\n", + " [ 6.8]\n", + " [ 6.3]\n", + " [ 6.6]\n", + " [ 6.6]\n", + " [ 6.8]\n", + " [ 6.1]\n", + " [ 6. ]\n", + " [ 6.2]\n", + " [ 5.7]\n", + " [ 6.1]\n", + " [ 7.1]\n", + " [ 7.2]\n", + " [ 7.4]\n", + " [ 8.4]\n", + " [ 8.7]\n", + " [ 8.3]\n", + " [ 8.8]\n", + " [ 9.5]\n", + " [ 9.2]\n", + " [ 8.3]\n", + " [ 8.6]\n", + " [ 8.6]\n", + " [ 9.8]\n", + " [ 9. ]\n", + " [ 8.7]\n", + " [ 8.8]\n", + " [ 9.1]\n", + " [ 9.8]\n", + " [10.1]\n", + " [10.6]\n", + " [12.1]\n", + " [11.9]\n", + " [11.2]\n", + " [13. ]\n", + " [13.4]\n", + " [13.1]\n", + " [11.6]\n", + " [11.9]\n", + " [11.6]\n", + " [12.6]\n", + " [11.3]\n", + " [12.5]\n", + " [12.9]\n", + " [13.3]\n", + " [14. ]\n", + " [13.3]\n", + " [12.8]\n", + " [13.5]\n", + " [13.7]\n", + " [13.8]\n", + " [13.8]\n", + " [14. ]\n", + " [14.7]\n", + " [14.8]\n", + " [15. ]\n", + " [15.6]\n", + " [15.6]\n", + " [14.9]\n", + " [15.4]\n", + " [15.6]\n", + " [15.8]\n", + " [15.7]\n", + " [15.2]\n", + " [16. ]\n", + " [15.9]\n", + " [15.8]\n", + " [14.9]\n", + " [15.6]\n", + " [15.1]\n", + " [15.3]\n", + " [16.8]\n", + " [16.2]\n", + " [16. ]\n", + " [16.8]\n", + " [17.1]\n", + " [16.7]\n", + " [16.3]\n", + " [16.9]\n", + " [16.3]\n", + " [16.5]\n", + " [16.5]\n", + " [16.5]\n", + " [16.6]\n", + " [16.4]\n", + " [16. ]\n", + " [16. ]\n", + " [16.4]\n", + " [16.2]\n", + " [15.9]\n", + " [15.8]\n", + " [15.8]\n", + " [15.9]\n", + " [15.2]\n", + " [15.4]\n", + " [14.9]\n", + " [14.3]\n", + " [14.7]\n", + " [14.5]\n", + " [14. ]\n", + " [13.1]\n", + " [13.3]\n", + " [13.8]\n", + " [13.5]\n", + " [14.5]\n", + " [14.4]\n", + " [14.2]\n", + " [13.9]\n", + " [13. ]\n", + " [12.7]\n", + " [12.2]\n", + " [11.8]\n", + " [11.3]\n", + " [12.7]\n", + " [13.2]\n", + " [12.5]\n", + " [12.7]\n", + " [13. ]\n", + " [12.5]\n", + " [12.5]\n", + " [11.6]\n", + " [11.6]\n", + " [11.5]\n", + " [11.5]\n", + " [11.3]\n", + " [11.4]\n", + " [11.6]\n", + " [11. ]\n", + " [11.2]\n", + " [11.1]\n", + " [11.3]\n", + " [11.4]\n", + " [10.8]\n", + " [11.4]\n", + " [10.9]\n", + " [10.4]\n", + " [ 9.6]\n", + " [ 9. ]\n", + " [ 8.6]\n", + " [ 9. ]\n", + " [10. ]\n", + " [ 9.6]\n", + " [ 8.7]\n", + " [ 8.6]\n", + " [ 9.3]\n", + " [ 9.2]\n", + " [ 8.1]\n", + " [ 7.9]\n", + " [ 7.2]\n", + " [ 7.2]\n", + " [ 7.8]\n", + " [ 7. ]\n", + " [ 7.1]\n", + " [ 7.6]\n", + " [ 6.3]\n", + " [ 6.3]\n", + " [ 6.9]\n", + " [ 6.1]\n", + " [ 5.9]\n", + " [ 5.7]\n", + " [ 5.1]\n", + " [ 5.8]\n", + " [ 6. ]\n", + " [ 6.7]\n", + " [ 6. ]\n", + " [ 4.9]\n", + " [ 4.6]\n", + " [ 4.8]\n", + " [ 3.6]\n", + " [ 4.1]\n", + " [ 5.1]\n", + " [ 4.5]\n", + " [ 5.5]\n", + " [ 5.9]\n", + " [ 4.5]\n", + " [ 4.4]\n", + " [ 3.7]\n", + " [ 3.7]\n", + " [ 3.5]\n", + " [ 3.2]\n", + " [ 3.9]\n", + " [ 3.6]\n", + " [ 3.6]\n", + " [ 3.4]\n", + " [ 2.7]\n", + " [ 2. ]\n", + " [ 3. ]\n", + " [ 2.6]\n", + " [ 1.3]\n", + " [ 1.2]\n", + " [ 1.9]\n", + " [ 1.3]\n", + " [ 1.4]\n", + " [ 1.9]\n", + " [ 1.4]\n", + " [ 1.3]\n", + " [ 0.6]\n", + " [ 2.2]\n", + " [ 1.2]\n", + " [ 0.2]\n", + " [-0.6]\n", + " [-0.8]\n", + " [-0.3]\n", + " [-0.1]\n", + " [-0.1]\n", + " [ 0.3]\n", + " [-1.2]\n", + " [-1.9]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.8]\n", + " [-1.7]\n", + " [-2.5]\n", + " [-2.2]\n", + " [-2.2]\n", + " [-1.8]\n", + " [-1.5]\n", + " [-1.9]\n", + " [-2.8]\n", + " [-3.3]\n", + " [-2.2]\n", + " [-1.9]\n", + " [-2.2]\n", + " [-1.7]\n", + " [-2.3]\n", + " [-2.9]\n", + " [-4. ]\n", + " [-3.2]\n", + " [-2.8]\n", + " [-4.2]]\n" + ] + } + ], + "source": [ + "print(fd_data.data_matrix[0,:])" + ] + }, { "cell_type": "code", "execution_count": 18, From 46d2bd64331761cd2007ff58e74400b5a101b9c8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 21:36:04 +0100 Subject: [PATCH 400/624] Unit test complete --- skfda/exploratory/fpca/fpca.py | 155 ++++++++++++++------ skfda/exploratory/fpca/test.ipynb | 235 ++++++++++++++++++++++++------ tests/test_fpca.py | 50 ++++--- 3 files changed, 328 insertions(+), 112 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6c0a43063..5660ac674 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -2,44 +2,56 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.decomposition import PCA class FPCA(ABC, BaseEstimator, ClassifierMixin): - """Defines the common structure shared between classes that do functional principal component analysis + # TODO doctring + # TODO doctext + # TODO directory examples create test + """ + Defines the common structure shared between classes that do functional + principal component analysis Attributes: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis - components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or - discretized form - component_values (array_like): this contains the values (eigenvalues) associated with the principal components + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional data + object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components """ def __init__(self, n_components=3, centering=True): - """ FPCA constructor + """ + FPCA constructor Args: - n_components (int): number of principal components to obtain from functional principal component analysis - centering (bool): if True then calculate the mean of the functional data object and center the data first. - Defaults to True - svd (bool): if True then we use svd to obtain the principal components. Otherwise we use eigenanalysis. - Defaults to True as svd is usually more efficient + n_components (int): number of principal components to obtain from + functional principal component analysis + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True """ self.n_components = n_components self.centering = centering self.components = None self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): - """Computes the n_components first principal components and saves them inside the FPCA object. + """ + Computes the n_components first principal components and saves them + inside the FPCA object. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: self (object) @@ -48,26 +60,35 @@ def fit(self, X, y=None): @abstractmethod def transform(self, X, y=None): - """Computes the n_components first principal components score and returns them. + """ + Computes the n_components first principal components score and returns + them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ pass def fit_transform(self, X, y=None): - """Computes the n_components first principal components and their scores and returns them. - + """ + Computes the n_components first principal components and their scores + and returns them. Args: - X (FDataGrid or FDataBasis): the functional data object to be analysed - y (None, not used): only present for convention of a fit function + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function Returns: - (array_like): the scores of the data with reference to the principal components + (array_like): the scores of the data with reference to the + principal components """ self.fit(X, y) return self.transform(X, y) @@ -77,18 +98,32 @@ class FPCABasis(FPCA): def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) - # component_basis is the basis that we want to use for the principal components + # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - # initialize pca - self.pca = PCA(n_components=self.n_components) - # if centering is True then substract the mean function to each function in FDataBasis + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the basis + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + if self.n_components > n_basis: + raise AttributeError("The number of components should be " + "smaller than the number of attributes of " + "target principal components' basis.") + + + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function - # substract from each row the mean coefficient matrix + # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients # for reference, X.coefficients is the C matrix @@ -96,14 +131,24 @@ def fit(self, X: FDataBasis, y=None): # setup principal component basis if not given if self.components_basis: - # if the principal components are in the same basis, this is essentially the gram matrix + # First fix domain range if not already done + self.components_basis.domain_range = X.basis.domain_range g_matrix = self.components_basis.gram_matrix() + # the matrix that are in charge of changing the computed principal + # components to target matrix is essentially the inner product + # of both basis. j_matrix = X.basis.inner_product(self.components_basis) else: + # if no other basis is specified we use the same basis as the passed + # FDataBasis Object self.components_basis = X.basis.copy() g_matrix = self.components_basis.gram_matrix() j_matrix = g_matrix + # make g matrix symmetric, referring to Ramsay's implementation + g_matrix = (g_matrix + np.transpose(g_matrix))/2 + + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) # L^{-1} @@ -112,15 +157,15 @@ def fit(self, X: FDataBasis, y=None): # The following matrix is needed: L^{-1}*J^T l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) - # TODO make the final matrix symmetric, not necessary as the final matrix is not a square matrix? - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for eigen analysis - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) self.pca.fit(final_matrix) self.component_values = self.pca.singular_values_ ** 2 self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ @ l_matrix_inv) + coefficients=self.pca.components_ + @ l_matrix_inv) """ if self.svd: # vh contains the eigenvectors transposed @@ -167,16 +212,28 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): - # initialize pca module - self.pca = PCA(n_components=self.n_components) + + # check that the number of components is smaller than the sample size + if self.n_components > X.n_samples: + raise AttributeError("The sample size must be bigger than the " + "number of components") + + # check that we do not exceed limits for n_components as it should + # be smaller than the number of attributes of the funcional data object + if self.n_components > X.data_matrix.shape[1]: + raise AttributeError("The number of components should be " + "smaller than the number of discretization " + "points of the functional data object.") + # data matrix initialization fd_data = np.squeeze(X.data_matrix) - # obtain the number of samples and the number of points of descretization + # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape - # if centering is True then subtract the mean function to each function in FDataBasis + # if centering is True then subtract the mean function to each function + # in FDataBasis if self.centering: meanfd = X.mean() # consider moving these lines to FDataBasis as a centering function @@ -186,10 +243,12 @@ def fit(self, X: FDataGrid, y=None): # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight vector is as follows: - # [\deltax_1/2, \deltax_1/2 + \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] + # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight + # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, + # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))] + self.weights = [sum(differences[i:i + 2]) / 2 for i in + range(len(differences))] self.weights = np.concatenate(([differences[0] / 2], self.weights)) weights_matrix = np.diag(self.weights) @@ -200,7 +259,7 @@ def fit(self, X: FDataGrid, y=None): final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_**2 + self.component_values = self.pca.singular_values_ ** 2 """ if self.svd: @@ -230,5 +289,7 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): - # in this case its the coefficient matrix multiplied by the principal components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose(np.squeeze(self.components.data_matrix)) + # in this case its the coefficient matrix multiplied by the principal + # components as column vectors + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index f29c79572..e15192651 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -15,6 +15,40 @@ "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(basis.gram_matrix())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -351,12 +385,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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5K0lpkwLqRFGFZRdkM231NKJSo1g0ahGt6rUyOySbOX7uOLP3zmZD9AZ83Xy5p+M9jG85HicHJ7NDq7mO/QRfTTK+2Cd+Bg4VqFDISoEvboKze+H696HTzbaL8yrlFxZxPCGDI7FpHD6bxpG4NFIy8yksKvrzC7+weAIo+ltCKCyq2PegCDg5/JlAnCyCo4MDjhYjkTheJsE4Wo9zdhSUgvxCRX5hEfmFRRQUKvKszwuLFFN6NWFKryY2+q2V9Bl0oqh2krKTeHHbi6w/vZ7Zg2czqPEgs0Oyi30J+3hr91vsSdhDsGcwM7rOYHiT4aYPIKxxYvfDJ6PAr6XR1uBsgxJcbgZ8PRlOboI71xvtGHaWlpPPoTNpHI5Nu5gYjidkkFdo9KhzdnSgdYAnDTxdcHRwwGIRHB0Ei8OFnw5/fW25zHbrzwtf6hdKDIVFF77IjdJGfqGRhP58bnyx5xcZzwsK1cXnF5JA8WMLitTF7QJGMnF0wNmaQC48jzmXzYnEDNY+MpAm9T3s8rvViaKaiEqNYn30etafXs+BxAMoFI+GPcrUdjV7vSalFJtjNvP2nrc5fv44beu3ZWbXmfRu2Nvs0GqG1DPw8RAQC9y1DjwDbHfu3HR4uyM0CoNbvrXdeYvJyitgzeF4lu8/y6bwRPKt1UC+dZxpE+hF24ZetA00Hk19PXC01LybjPi0HPq9sp5/9grhmevaXvkN5aATRRVVpIo4mHSQ9afXsyF6A5GpRm+gtvXbck3jaxgSPKRWrSJXWFTIisgVvLfvPWIzY+kV2IuZ3WbSrn7VHS9S5eVmGCOrU6KMhuiA9ra/xq9vwrrnbFqqyCsoYnN4Ij/uP8vaw/Fk5xcS4OXKdZ0C6dvCl7YNvWjg6WqTa1UXM77ay8ZjCWx/agjuzrZfnFQniiokvzCfHXE7LiaHxOxELGIhLCCMwY0Hc03jawisE2h2mKbKLczlm6PfMO+PeZzPPc+IkBHM6DKDJl72q5+tkYoKjaqhiF9g8mIItdNqhLnp8FZ7COkHN39R7tMUFSm2nUxm2b6z/HQwjtTsfOq6OzGqQyDjOjWke0g9HGpAo3F57YpKYcKHW/nfDR2Y3DPY5ucvLVGYtmZ2bZKel85vZ35jw+kN/HrmVzLyM3BzdKNfUD+uaXwNAxoNwNvF2+wwqwwXiwu3truVG0JvYOGhhXx2+DPWnlrLjaE3ck+ne/Bz9zM7xOrh56chfDWMft1+SQLAxRN63AWbX4fEcKMd5Cpk5RWwdHcMn2yJ4mRSJu7OFoa39Wdc5yD6hfriVAOrksqjW5O6tAn0YtHWKCb1aFyp3eR1icJOErIS2Bi9kfWn17M9bjsFRQXUc63HNY2vYXDwYHoG9sTF4mJ2mNVCUnYSc/fPZUn4EpwsTkxpM4Vp7afh5exldmhV1x9LYOkd0PMeGPWK/a+XkQhvt4cOE2Dce2V6S3xaDgt/j+LL7adJzc6nU2Mfbu8bwvC2Abg5W+wccPX01Y7TPPndH3x7T2+6h9Sz6bl11VMle2HrCywOXwxAsGcwg4MHMzh4MB19O2Jx0P8Byut02mnm7J3DT1E/4e3izePdH6/0xZqqhcRwY3K/gPYwbSVYKqnL8cr/g92fwswD4NXwsodFxKfz0eZIfth3hsIixYh2AdzZvyldg+tWu8GklS0rr4Ce/1vHwJZ+zJnc1abn1lVPlSg6LZrF4YsZ3XQ00ztOp5l3M/3HbyPBXsG8OvBVbmt/Gy/veJmnfnuKnMIcbmp5k9mhVR15WfDtVHB0gQkLKi9JAPR+AHZ9Atve/9tgPqUUO06m8NHmSNYdTcDVyYFJPYK5o19Tu3X3rIncnR2ZGNaYT3+PIiEthwZeldOgb2rln4iMFJFjInJcRJ4oYf8jInJYRA6IyDoRqfKtmWtPG0uBPtztYZr7NNdJwg7a1G/DxyM+pn9Qf17c9qJefrW4Vf8yJu0bPw+8gyr32vWaQrvxsGshZBvTsxQWKVb9Ecv17//OPz7axt7o88wcGsrvTwzh+XHtdZIohym9mlBQpPhqR3SlXdO0RCEiFuA9YBTQFpgkIpd2EN4LhCmlOgJLgFcrN8qrF34unAbuDQjwsGFfde1vnByceH3g63Tw7cBjmx9jZ9xOs0My397PYd/nMOBRCB1qTgx9H4K8dPK3f8xnW6MY/MZG7vtiD+ez8njh+vZseXwwM4e2pJ6Hsznx1QBNfT0Y0NKPL3ecIr+wcqbuN7NE0QM4rpSKVErlAV8D44ofoJTaoJTKsr7cBjSq5BivWsS5iFo19sFM7k7uvDfkPYI9g3lw/YMcTTlqdkjmiT8EKx+FkP4w6EnTwkjxak2UTy9SN77Liz/uxcfdmQ9u6cr6/xvEP3s10Y3UNjK1dxPi03L55VB8pVzPzEQRBBQvO8VYt13OHcBPJe0QkekisktEdiUmJtowxKuTX5RPZGokLeteXfdArfy8Xbz5cNiH1HGuwz1r7iE6rfKK41VGbjosngquXnDjfDChw8Sp5Ez+88NB+ry8jicThuDLeVYPjOaH+/owqkNgjZg0ryoZ1KoBjeq6sWhrVKVcr1p0UBaRKUAY8FpJ+5VSHymlwpRSYX5+5vWxP5V6ivyifEJ9dImiMgV4BDB36FwKVAF3r72bpOwks0OqPEoZK9OlnDCShGflLY17LjOPb3dFc/vCnVzz+ka+3nmasZ0a8vyD90DDrjQN/wTRqxrahcVBmNKrCdtPpnAszv6LgZmZKM4AjYu9bmTd9hciMhR4GhirlMqtpNjKJeJ8BIAuUZigmU8z3h/yPknZSdy39j4y8jLMDqly7PoEDi6Fa56Gpv3tfrmE9Bw+23aKKR9vJ2zWWv615ADH4tK5e2Bzfnt8MK9O6ERogBf0mwnnTsLhH+0eU201Mawxzo4OlVKqMLN77E4gVESaYiSIm4HJxQ8QkS7AXGCkUiqh8kO8OhHnIrCIhabeTc0OpVbq6NeRNwe9yYx1M3how0O8P/T9mj2o8ew+Y22JFkOh3yN2uYRSiqjkLNYdiefnQ3HsOnUOpaCZrwd3D2jGyPYBdAjy/nvvvtbXQv0WsOVtaHfD5VfO08qtnoczYzs15Pu9Z3h8VGu8XO3XFdq0RKGUKhCRB4CfAQvwiVLqkIg8D+xSSi3DqGqqA3xr/UM8rZQaa1bMVxJxLoIQrxCcLbpHh70opTh0No0f953hbGoOQT5uhNT3IMTXnaa+HvQJ7MsL/V7gyV+f5InNT/D6wNdr5iDH7PPGeAkPP7jho4qtLXGJmHNZbD2RbDwik4lNzQGgTaAXM4e0ZFSHAEIb1Cm967eDBfo8CMsfhMiN0Pwam8Wn/enW3k1YsjuG73bHMK2v/W5QTR1wp5RaBay6ZNszxZ6b1MevfCLOR9DBt4PZYdRI8Wk5/LD3DEv3xBAen4GzxYGGPq6sORR/cS0CAFcnB0Lqe9G03iTWnv6K21c8yb3tHqOZXx38PF1qxrgWpeDH+yE1xlhbwqN+hU6XkJbD1kgjMfx+IpnTKUZHw/oezvRqXp/ezerTP9T36sc8dLoZNvzPKFXoRGEXHRv50KmxD4u2nWJqnxC7/X3rkdk2kpmfyZmMM4wPHW92KDVGdl4hvxyOY+meM/wWkUiRgi7BPrx4fXuu69gQb3cnCosUZ89nE5WcSVRyFlFJmUQlZXIysQ8FDrHs4Sdu/T6PvKRheDhbaGItfRilEA+a+nrQNtALD5dq9F/h93fh6AoYPguCe5b5bQWFRZxOySI8PoPjCelEJGRw8EwqJxIzAfBydaRns/rc1jeEPs19ael/hVLDlTi6QK97Ye1/jZXwGnYp/7m0y5rauwmPLN7PluPJ9Au1z1LJ1eh/R9UWcc5oyNY9nipGKcXOqHMs3R3Dqj9iSc8tIMjHjfsGtWB81yCa+dX5y/EWB6FxPXca13On/yW/+vyC/jy++T+sYTlj2oXiUzCIqKRMjsSm88uheAqsy18GeLny2R09CPX3rKyPWX6HfoA1z0CbsdD7/r/tzswtICE9l4S0HOLTc4lKyiQ8Pp3jCRlEJmb+pfQV5ONGqwBPJoY1pk9zY40Hm3djDbsNfn0DfnsbJn5q23NrAIzuEMiLK4+waGuUThRV3cUeT/V0j6fyOJWcyXd7zvDd3hiiU7Jxd7Ywqn0gN3YLolfT+uVah8DJ0cKrg57n4Y3prIuey6sDm/PfkJGAsc7ymXPZHI1L5z8/HuSmuVv59LYedGrsY+uPZjMFJ3/D8t100ny7srzh00StPEK8NSkkpueSkJ5LRm7B397XuJ4boQ08GdjKj9AGnoQ2qEOLBnUqpxTl6g1ht8PvsyElEuo1s/81axlXJwv/6N6YuZtOcOZ8NkE+bja/hk4UNhKeEo6HkwcNPS4/a6b2V2k5+aw6EMvSPTHsjDqHCPRt7svDQ1sysn2ATVbxcnRw5LUBr3H3mrt58tcn8Xb2pnfD3jhZHAjxNaqf2gR6MmX+dibP28a8qWH0aW6fu7KyUEpxKjmLk0mZnEzK5JS1So3Eo8zOeoIkVY8bY6ZzPuYErk4O+Hu54u/pSpuGXgz0dKGBpysNPF3w93KlgZcLjeq62WU1tKvS827Y8o4x9fnAx8yNpYa6pWcwczed4Ittp3hsZGubn19PM24jU3+aikKxaNSiSr92daGUInXnYjKO/MLvmUF8eTaAAwWNCfHz5MaujbihSxAN7XA3BJCam8q01dM4m3GWT0Z8Qjvfvy6vGp+Wwz/nbycqOYt3J3VhRLvKmasrO6+Q/THn2X3qHHtOnWP36XOcz8q/uL+OiyNd62XzdvpjuEg+G/t/SYPgVjSp745fnWrUOD9/BORlwr2/mR1JjXXXol0kpOXww/19y/V3odejsDOlFH2/6svoZqP5d69/V+q1q6qCwiJOJmVyODaNw2fTOHL2PIPOzOV29T1ZygV3McZOFjp54tCkF9KkNzTpazR4Otpn7ENCVgL/XPVPcgpzWDRq0d+WVj2flce0BTs5EHOeVyd0YkI3204tVlikiE7J4uDZ1IuJ4dDZtIttJc39POjWpC5dg+sS6l+HkPoe1HPMQRaMMQavTVsJDTvbNKZKs/U9+PkpmLEH6jc3O5oaKSUzD283p3K3M+n1KOwsLjOO9Pz0WtuQnZlbwNE4IyFcSAxH49LJLTAaTr0s+XzgMZe+6neOBd1I2uCX6OidjcvZnVhObYHTW2HdGuNkjq4QFAZNekOTPtCoB7jUKeXqZdfAvQFzh83l1p9u5e41d7No1CIauDe4uN/H3Zkv7uzJ3Z/t5tFv95OWnc/t/a6+b3pmbgEnkzI5npDBiUTrI8GoSrrQmOzq5EDnxj7cPbAZ3ZrUpUvjutS9dEbVgjz48lZIPAKTv6m+SQKMxvefn4Ijy6Dfw2ZHUyPZc0ZeXaKwgc0xm7l/3f0sGrWILg1qbhdApRSJ6bkcsiaDC4khKjmTC39GPu5OtA30Mh4NvejgnU3ztXfhELsPRsyCXveVPEo3M9lIGKe3wqktEHsAVCGIBQI7GqWN4N7Go4LjBg4lHeL2n28nyDOIhSMX/m1J1dyCQh76ah+rD8Xx4OAWPDysZYlF+aIiRcy5bA7Hpl78XRyJTefM+eyLx1gchCb13GnmV4fmDTxo7leH1gGetAn0Kn0taKXg+3vgwNcw7n3ockuFPnOV8JF1LMX0DebGoZVIlyjsLPxcOAAtfFqYHIntFBYpTiZlcKhYKeFIbBpJGXkXjwmu507bQC9u6BJEm0Av2jX0ItDb9c8v1dgD8NXNxijiSV9Bq1GXv6BHfWhzrfEAY0bU6B3WxLEVdsyDrXOMfX6tjdJGk77GOZ2vbiBYO992vH3N29y37j5mrJvB3GFzcXX8c6UwF0cLcyZ34anv/2D2+uOcz87niVGtiUzM/Eup6UhsGunWXkYWB6G5nwdhIXWZ7B9Mcz8jKQTXd8fFsRwjw9c9bySJa/5dM5IEQNuxsPZZOH8afILNjka7CrpEYQOPbXqMA0kHWH3j6kq9rq1k5RVwJDb94hfg4dg0jsWlkZNvVJM4WYSW/p4XSwntGnrTOtCz9Llljq6CpXeCm49RbRJQwRHrBbnGoK1TW4zEEb0dctPAuQ60ux46T4HgXlc1p94jSVAAACAASURBVNDqk6t5bPNjDGo8iDcHvYmjw1/vm5RS/G/VEeb9evIv2z2cLbSx/i4u/E5a+nvi6mSjqUJ2fmysP91tGlz7ds2ZJyn5BLzbFUb8r8QxIJq5dInCzsLPhVebxYoS0nP+cld8ODaNk0l/Vh15uTrSrqE3t/RscvFLsLlfHZwdyziXkFLGnf8v/zHq1Cd9DZ426EHk6GIkguBe0B8oKjSSxb4vjEFoez+Hes2h82ToNKlMy4CObDqSlJwUXtrxEq/ufJWnej71l/0iwlOj29A9pB4Hz6bRyt+Tdg29CK7nXq5xHWVy8DtjOdOWI2H0GzUnSYDRiO3fAQ4v04mimtGJooLyCvOISoticPBgs0P5mwsT6K05HM/e6PMcPptGUsafM7U3qutG20AvxnZqeDEpBPm4lb/LZWG+cSe851NoOw6u/xCc3W30aS7hYLFWP/WBka8Y01nv+wLWvwAbZkHzwdD5Fmg1GpwuvwD95DaTOZNxhkWHF9GqbitubHnjX/aLCMPbBTDc3t1lz0XBz08bU3MEhcGET8BSA/97th1rzP+UHmebGwitUtTAv8TKFZkaSaEqrDIjsvMLi9h5MoVfDsfzy6E4zqbm4CDQ0t+TgS39rFVHXrQJ8MLb3YbTEmefg8W3wsnN0P9RY30EG85oWiqXOkY9fpdbjNG/+76EfV/BktvA1Qc63GTsC+xc4h36w90e5vj547y4/UWa+TSr3A4JeVnw21vGgDQHCwx5Bno/YLcuwqZrM9ZI5EeWQ4+7zI5GKyPdRlFBy04s4+nfnubH63+kmbc50xNk5RWwOTyRXw7Fs+5oAqnZ+bg4OtA/1I/h7fwZ0roB9evY8Ysn+QR8+Q/jrnjsu9B5kv2uVVZFhXByE+z9wvhSKsyFBu2MhNHxH+Dx19HXqbmpTF45mYz8DL659hsCPOx8t6sUHP4Bfv43pMVA+wkw7PkyVZlVe3N6QJ0GMG2F2ZFoxeg2Cjs6knwEF4sLwZ6V24sjOSOXdUcS+OVwHL9GJJFbUIS3mxND2jRgeNsABrT0rZypG6K2wDe3AAJTlxlVQVWBg8Wofmo+2Oh1dXCpUTX181PGpHotRxpVU6HDwOKEt4s37w5+l8mrJvPg+gf5dNSnuDnaZ5Q48Yfhp8cg6lejzv7GeVXn91YZ2o41JgrMTPpbwtaqJl2iKKfTaaeZs3cOP0X9RM/Annw8/GP7XzM5i18Ox/HLoXh2nUqhSBkzgA5r68/wdv70CKmHY2l9821t35ew7EGo19To2VQdJnxLOGI0fB/4BjITwaMBdL/TWLrT0YVN0ZuYsX4GI5uO5JX+r9h2ioz0ePjtTaOrr6sXDP43dLvNSGq1SewBmNsfrpsN3aaaHY1mpafwsKHk7GQ+3P8hS8KX4GRxYkqbKUxtNxVvF2+bX+tCY/Qvh+L45XA8R62LqLcO8DQaWNv6066hV+XP91NUZDQa//YmNB0IExcZ3WCrk8J8iFgDexZB+E9GtdQNH0BgJz7+42Pe2fMOM7vO5I4Od1T8WglHjJ5gBxZDUYGRHAb/G9zrVfzc1ZFSMLuz0Uvtn9+ZHY1mpauebCAzP5NFhxax8NBCcgtzmdByAnd3vBs/dz+7XG9TeCL//uEPolOycRAIC6nHv8e0YXjbAILr26knUVnkZcH30416/263wejXwGK/tXrtxuIErUcbj2OrjSU75w2GAf/ijn6PcCzlGO/seYfQuqEMaDTg6s+vlLEE6NY5cHwtOLpB11uNkem1fa4jEaNX3Nb3jE4QbnXNjki7Al2iuIL8wnyWRCzhw/0fkpKTwrAmw3iwy4OEeIfY7BrF5eQX8urqY3yy5SShDepw14Bm9m+MLqu0WGOkdez+0qfjqI6yUox2gz++hcBOZF/3DlN3v0R0ejRfjP6CZj5lrFYryDPaQ7bOgfiDRtVWz+kQdkftLUGUJGY3fDzY6EJdFTo/VGFFqogFBxcQnxXP1HZTCapjnw4PuuqpnOIy47jzlzs5lXaKMP8wHu72MB39Otrk3CUJj0/nwa/2cjQunam9m/Dk6Da2G+1bUcfXwfd3GyWKCfNLn46jOju8DFY8DLlpxPabwc0J6/B09uTzUZ/j41pK9VpqjNHDavcCSI8FvzbGoLKOE2tuV9eKUAream/M4zXpK7OjqbIKigp4ZsszLI9cDoCboxsPdX2ISa0n4SC2bY/UiaKcnvj1CdaeWsubg96kf1B/u7UFKKX4bNspZq08gqerI69N6MQ1rRtc+Y2VoTAf1r8IW942vvxuWgAN2pgdlX1lJhnJ4sgy9jTqyJ0uGXg6e9E/qD9967Wnt8UTn/NnjFldE49B4lHISjbe23yIUdJqMaTmlLbs5acnYNcn8NgJcKkGy9BWsrzCPB7b/BjrTq/jwS4PMqbZGJ7f9jxbzmyhs19nnuvzXNlLumWgE0U55BXm0fervoxrMc6ua0wkZeTy2JIDrD+awKBWfrw2oRN+nlXkDvTcKVh6B8TsNOYdGvGS/UZaVzVKGVVIqx7lAHks8gtkq+SS5iCIUrTPzaNPvqKvWxAdfNvj2KAttByh2x+uxqmtsGAk3DgfOkwwO5oqJbsgm5kbZvL72d95oscT3NLGmBhSKcWKyBW8svMVsvKzuLfTvUxrPw0nh4q3E5aWKCqxL+XfichIETkmIsdF5IkS9g8QkT0iUiAilfqXtD9xPzmFOfRt2Ndu19h4LIGRb//Kb8eT+O91bVkwrXvVSRKHfzS6MCYegwkL4Lp3ak+SAKM00GEC3Ledjm1v4nUJYHP9IXzeZAL3NhmNQ8MuzPNy51bHZAZk7uHhnAh25CWbHXX10rgn1PE31qjQLkrPS+eeNfewLXYbz/d5/mKSAGNKmeuaX8cP435gUONBzN47m8krJ3Mk+YhdYzKt15OIWID3gGFADLBTRJYppQ4XO+w0MA14tLLj23p2KxaxEBZQYoKtkJz8Ql7+6SgLf4+ilb8nn9/Zg9YBXld+Y2XIzzEGpe2aDw27GnMO1bv6xXtqDE9/I0kCFqCT9XEvxmju7bHb2XJ2C5tjNrP29FoGNRrEv7r/i2AvPY32FTk4QOtrYf9XRttXbboRuYxzOee4Z+09hKeE8+qAVxkRMqLE43zdfHlz0JusPbWWWdtnMWnlJKa1m8a9ne/FxWL7m00zu8f2AI4rpSIBRORrYBxwMVEopaKs+4oqO7jtcdtp59sOT2fb1p0ei0vnoa+NButpfUJ4YlTrqtNgnRhuzI8UfxD6zIDBz4Cj/VbNqu68XbwZHjKc4SHDySnI4YsjXzDvj3lc/+P13N7+du7ocIf9RnfXFG3HGTclx9caI7ZrscSsRO765S5iMmJ4Z/A7ZeqWPbTJULoHdOf1Xa8z/+B8tsVu48sxX9q8odvMRBEERBd7HQP0NCmWv0jPS+dg0kHu7HCnzc6plGLR1lPMWnUEL1dHFtzWnWtaVZEGa6WMUdarHgUnN7hliTG1hVZmro6u3NHhDsY2H8sbu99g7oG5rIhcwePdH2dQ40GVPyiyumjSF9zqGdVPtThRnMk4w12/3EVydjIfDP2A7gHdy/xebxdvXuj7AqOajiIlJ8XmSQJqyIA7EZkOTAcIDq54kX9n3E6KVBG9AntV+FwAiem5PLZkPxuOJXJNKz9eu6kTvlVhXAQYK8mteAT+WAwh/WH8PPAKNDuqasvP3Y+X+7/MjaE38r/t/+PBDQ8yoNEAnuj+BI29GpsdXtVjcYTWY4w1RQpya2VX4ui0aG77+TayCrKYN3xeubvg92lov/nCzGzMPgMU/5/TyLrtqimlPlJKhSmlwvz8Kj5SelvsNlwtrnTy61Thc204msCodzbz+4lknh/Xjk+mda86SeLsPpg7AA4uMaYFv/VHnSRspHtAdxZft5hHwx5lV9wurv/xet7f9z45BTlmh1b1tB0HeelwovatpZ2am8p96+4jtzCXBSMW2HWcVkWYmSh2AqEi0lREnIGbgSrR/WF77Ha6+XfD2VL++vmc/EKeXXaI2xbuxLeOC8tn9OPW3iFVowpCKdj2IcwfZjReT10BAx+rfZPT2ZmTgxNT201l+Q3LGdJkCB/s/4Drf7yezTGbzQ6tamk6EFy8a13vp/yifB7d9CgxGTG8fc3btKrXyuyQLsu0RKGUKgAeAH4GjgCLlVKHROR5ERkLICLdRSQGuAmYKyKH7B1XfGY8kamRFap2OhaXzrg5W1j4exS3923KD/f3paV/FRlQlJUCX02C1Y8bU3DfuwVC7NcFWIMG7g14dcCrzB8+HxeLC/evu59ntjxDel662aFVDY7Oxkj/oyuNAZ61gFKKl7e/zLbYbfy393/p5t/N7JBKZWobhVJqFbDqkm3PFHu+E6NKqtJsj9sOQK+G5UsU8Wk5TJq3DQcRFt7WnUFVpcEa4NTvsPROyEiAkS9Dz3v06OFK1COwB0uuW8IH+z9g/sH5bI3dyvN9nqd3w95mh2a+tmPhwNfGCokthpgdjd19efRLFocv5vb2t3N9i+vNDueKTB1wVxVtO7uNui51aVm3fEubzll/nIycAr6e3qvqJInCAtjwEiwcAxZnuHMN9LpXJwkTOFmceLDrg3w26jNcLa5MXzOdF7e9SFZ+ltmhmav5YHDyqBXVT7/G/MqrO19lcOPBPNT1IbPDKROdKIpRSrE9djs9AnuUq4tZanY+S/fEMLZzQ1o0qGOHCMsh/hAsGAWbXoYOE+HuzdCwEteE1krU0a8j3173Lf9s+08WH1vMhOUT2BO/x+ywzOPkZkyBcnSlsYxtDRVxLoJ/bf4XLeu25KX+L9mlK6s9VI8oK8nJ1JMkZCeUu33i213RZOUVMq1PiG0Du1pKGQOYPrsBPuhjTFp343wYP9dYWU2rElwdXXms+2PMHzGfIlXEtNXTeGPXG+QW5podmjnajjVWHTy91exI7CI5O5kZ62fg7ujOu4Pfxd2p+oxE14mimK2xxh9oeRJFYZFi4e9R9AipR/sg2692Vyb5ObD7U3i/F3x+o7E285Bn4KH9etK1Kqx7QHeWjl3KhJYTWHhoIbf/fHvtrIpqMQwcXY15xmqY3MJcZm6YSVJ2ErMHzybAI8DskK6KThTFbIvdRlCdIBp5Xn37+doj8cScy2Za3xDbB3YlGYlGG8Rb7YyV2ixOcMNcmPkH9P8/vWBONeDh5MEzvZ/htYGvcTDpII9uepT8otrRA+gilzrGNO1HVxml4hpCKcWzvz/LvsR9zOo3i/a+7c0O6arViJHZtlBQVMCuuF2XnYTrSj757SRBPm4Mb+tv48hKkXDEWE7ywGIozIWWI43FckL664bqampkyEjSctN4YdsLPPv7s7zY98WqMfamsrQeDcdWQtwBCKz4gNeq4OM/PmZF5Aoe6PxAub9fzKYThVVSdhIBHgHl6hZ78Ewq20+m8PToNjha7FxIUwpOrDcSxIl1xlrMXW4xFsvxDbXvtbVKMbHVRJKzk3l///v4ufkxs9tMs0OqPC1HgjgYpYoakCg2x2xm9t7ZjGk2hukdp5sdTrnpRGEV4BHA9+O+pzwLOX2y5STuzhYmdrfjXD75OcZ6zlvfM1ZWq+MPg/8N3W4Hj/r2u65mins63UNidiLzD87Hz93vL2sS1GgevsY6FUdXwjVPmh1NhRQWFfLaztdo7t2c5/o8V61LhjpRXOJq/zET0nNYvv8sk3sE4+1W8VWm/iYzCXbOh53zjB4h/u3h+g+g/Y21cgK12kJEeLrn06TkpPDKjleo71qfkU1Hmh1W5Wg1Gtb8x1hhsW4Ts6Mpt1UnVxGVFsWbg960yxoRlUkninIoKlKcTM7kQMx5lu07S0GRYlpfGyzuoxSkx0FSOCRHQMxuYznOwlwIHW60PzQdqNsfagmLg4VXBrzC9F+m8+RvT+Lj6mOzGY2rtNZjjERx7CfodY/Z0ZRLQVEBcw/MpVXdVgwJrv4jzXWiuAKlFLGpORyIOc++6FQOxJznjzOppOcUAODmZGH6gGY09fUo+0nzcyDlBCRFGI/kCCM5JB03ZtG8wLkOdJ4Eve4Hv/KNFNeqNxeLC+8OeZdpq6cxc8NMFoxYQJv6bcwOy77qNwffVkajdjVNFCsjV3Iq7RRvX/N2tRlUVxopT518VRYWFqZ27dplk3Mt2hrF+xtOEJdmTA3tZBFaB3jRsZE3nRr50LGxNy386pTcgK2UUVWUFP5nErhQUjh3Cij2e/dqZDRE+4aCb0uo38L46dVQlx40ABKyEpiyagp5hXl8NvozGnvW8LUt1j4LW2bDYyfAra7Z0VyV/KJ8xn4/Fk9nT7659ptq0zYhIruVUiWu/axLFCVQSvHW2ghmr4ugd7P63DuoOR0bedMm0OvKy5bmZcLymRD+M+Sm/rnd0Q18WxjrUHe8+c/EUL8FOF9FaUSrlRq4N+DDYR8y9aep3L3mbr4c/SU+rj5mh2U/rcbAb29BxBroONHsaK7K8hPLicmIYc7gOdUmSVyJThSXUErx2s/HeH/jCSaGNeKl8R2xOFzFP/b6F43eSV2mGA3PF0oJXkHGYvKaVk7NvJsxZ8gcpq2exks7XuKVAa+YHZL9BHUzevYdXVmtEkV+YT4fHfiI9vXbl2nN6+pCf3MVo5TipZ+O8v7GE0zuGczLV5skzkfDzo+NcQ3j5hj1qy2GgE9jnSQ0m+jk14npHaez6uQqNkZvNDsc+3FwMMZUHF9rLJFaTfxw4gfOZJzhvs731ZjSBOhEcZFSiudXHOajzZHc2rsJs65vj8PVJAmATdY7vIFP2D5ATbO6s8OdhNYNZdb2WTV7TqjWYyAvA07+anYkZZJXmMdHBz6io19H+gX1Mzscm9KJwioyKZOvdpzm9r5NeW5su6u/G0gMh31fQNgdRglC0+zEycGJZ3o9Q1xmHB/u/9DscOyn6UBjjYpjK82OpEy+j/ieuMw47u98f40qTYBOFBc196vDqgf7859r25TvH3nDi+DkDgMetX1wmnaJzg06c2PojSw6vIjwc+Fmh2MfTq7QYrAxnqKoyOxoSpVbmMtHf3xE1wZd6R1Y81YsLFOiEJHPyrKtumvmV6d8SeLMHmNq5N4PGFMQaFolmNl1Jl7OXryw9QWKVNX+Ii23VmMgPRZi95odSamWhC8hISuhxrVNXFDWEkW74i9ExAJU7dXAK9O658G9vjFyWtMqiY+rD/8X9n/sS9zH9xHfmx2OfbQcAWIxJgmsonIKcpj/x3zC/MPoEdDD7HDsotREISJPikg60FFE0qyPdCABqHmri5RHxFqI3GCs+6BXj9Mq2djmYwnzD+PN3W+SkpNidji2514PgnvDsaqbKJaELyExO7HGlibgColCKfWSUsoTeE0p5WV9eCql6iulqvfUjuWlFJzdZywU9GF/+OJG8A42GrE1rZKJCP/p9R+yCrJ4Y9cbZodjH61HQ8JhSDlpdiR/k1uYyycHP6F7QHe6B3Q3Oxy7KVPVk1LqSREJEpE+IjLgwqOiFxeRkSJyTESOi8jf+pSKiIuIfGPdv11EQip6zXLLTTdmcf2gL3w00OgK6+wBw56HO342Gt40zQTNfJpxW7vbWHZiGTvjdpodju21Gm38rIKliguliXs73Wt2KHZVppHZIvIycDNwGCi0blbA5vJe2NrO8R4wDIgBdorIMqXU4WKH3QGcU0q1EJGbgVeAf5T3muWSHgdb58CuhcaEfQEd4Nq3oM1Y3XCtVRl3dbyLVSdX8cK2F1h63VKcLHaY8t4s9ZpCg7ZGO0UVagfMLczlkz8+oWuDroT5lzhFUo1R1ik8bgBaKaVsOUSyB3BcKRUJICJfA+MwktEF44Bnrc+XAHNERFRlzGR4/jRseQf2fAZF+dBuPPS8BxqF6Yn6tCrHzdGNp3o+xf3r7mfhoYXc1fEus0OyrVaj4bc3ISulyqwB/33E9yRkJzCr/6wa2zZxQVl7PUUCtr5FCQKii72OsW4r8RilVAGQCth3ObekCPjhPpjdBXZ/Cp1uhhm7YcJ8aNxdJwmtyhrQaADDmgxj7oG5RKdHX/kN1Unr0aCKjMk2q4jF4YtpX789PQN6mh2K3ZVaohCRdzGqmLKAfSKyDrhYqlBKPWjf8MpGRKYD0wGCg4PLd5L0OFj9BBz6ARxdoftd0GcGeF+auzSt6nq8++NsObOFWdtn8cGQD2rOnW5gF/AMNEZpd55kdjQcSzlGxLkInu75dM35HZfiSlVPFxZ22A0ss/G1zwDF57poZN1W0jExIuIIeAPJl55IKfUR8BEY61GUKxrnOsbAuX4PQ6/7oI5fuU6jaWby9/BnRpcZvLLzFTbFbGJQ40Fmh2QbDg7QahTs/8ZY+MvkziMrI1fiKI6MCBlhahyVpdREoZT61I7X3gmEikhTjIRwMzD5kmOWAVOBrcAEYL3d2idc6sCDe8HhCutNaFoVd3Prm1l0eBGfH/685iQKMEZp7/oETm4yBuKZpLCokJUnV9IvqB91XavXokrlVdYpPP4QkQOXPH4VkbdEpFxtBtY2hweAn4EjwGKl1CEReV5ExloPmw/UF5HjwCOAfadl1UlCqwEcHRyZ2Goi2+O2c+L8CbPDsZ2m/cHZ01ijwkS74neRkJXAmOZjTI2jMpW1MfsnYCVwi/WxHKNaKg5YWN6LK6VWKaVaKqWaK6VmWbc9o5RaZn2eo5S6SSnVQinV40IPKU3TSjc+dDxODk58ffRrs0OxHUcXY32X8NWmThK4InIFHk4eDGo0yLQYKltZE8VQpdSTSqk/rI+ngYFKqVeAEPuFp2laedRzrcfIkJEsO7GMjLwMs8OxndZjICMezuw25fI5BTmsObWGYU2G4epYewbZljVRWETk4mxXItIduFBPU2DzqDRNq7BJrSeRVZDF8sjlZodiO6HDjEkCTVqjYmPMRjLzM7m22bWmXN8sZU0UdwLzReSkiERhtB3cJSIewEv2Ck7TtPLr4NeBdvXb8fXRr6mMMaqVwq0uhPQ1bTbZlSdW0sC9QY0fiX2pss71tFMp1QHoDHRSSnVUSu1QSmUqpRbbN0RN08prUutJRKZGsiNuh9mh2E6rMZB0DJIrt6H+XM45fjvzG2OajsFSyzq+XGma8SnWn4+IyCMYcy/dUey1pmlV2MimI/Fx8alZjdqtrZMEVnLvp5+jfqZAFTCmWe3p7XTBlUoUHtafnpd5aJpWhblYXLgh9AbWR68nLjPO7HBswycY/DtU+myyKyJXEFo3lFb1WlXqdauCK61HMdf687mSHpUToqZpFTGx5USUUiyNWGp2KLbTejREb4fMpEq5XHRaNPsT99e6RuwLyjrgrqWIrBORg9bXHUXk3/YNTdM0W2jk2Yi+QX1ZGr6U/KJ8s8OxjVYXJglcXSmXW3FyBYIwuunoSrleVVPWXk/zgCeBfACl1AGMKTc0TasG/tHqHyRmJ7IxeqPZodhGYCfwalQpvZ+UUqyMXEn3gO4EeATY/XpVUVkThbtS6tJuE3r8hKZVE/2D+hPoEcg3R78xOxTbEDEmCTyxHvKy7Hqpg0kHOZV2qtZWO0HZE0WSiDTHmHIcEZkAxNotKk3TbMriYGFCywlsj9vOydSqt/Z0ubQeDQXZELnRrpdZEbkCZwdnhjYZatfrVGVlTRT3A3OB1iJyBpgJ3GO3qDRNs7nxoeNxFEcWH6shQ5+a9AMXL7uO0s4vymd11GoGNR6Ep3Pt7ehZ1kRxBlgAzAK+BtZgTP+taVo14evmy5AmQ/jxxI9kF2SbHU7FOTobU3ocWw1FhXa5xNazW0nJSanV1U5Q9kTxI3AdRmP2WSADyLRXUJqm2cc/Wv2D9Lx0Vp+snN5CdtdqNGQlQcxOu5x+ReQKvF286RfUzy7nry6utMLdBY2UUiPtGommaXYX5h9Gc+/mLD62mBtCbzA7nIoLHQYOTsYo7eBeNj11Zn4mG05vYFyLcThZnGx67uqmrCWK30Wkg10j0TTN7kSEm1rdxMHkgxxKPmR2OBXn6g0h/ewySnvd6XXkFObU+monuPJcT3+IyAGgH7BHRI5ZV7e7sF3TtGpmbPOxuDm61ZxG7dZjIPk4JIbb9LQrTqwgqE4Qnfw62fS81dGVShTXYrRNjAJaAMOtry9s1zStmvF09mR009GsilxFWl6a2eFUXKtRxk8b9n5Kyk5ie9x2rm12LSJis/NWV1ea6+lUaY/KClLTNNua2GoiOYU5LDu+zOxQKs67kTFS24ajtDdFb6JIFTGsyTCbnbM6K2sbhaZpNUjb+m3p4NuBxeGLa8aiRq3GGD2fMhJscrqNMRsJ9AikZd2WNjlfdacThabVUhNbTeRk6kl2xtmna2mlaj0aUHDspwqfKqcgh21ntzGw0UBd7WSlE4Wm1VIjQ0bi5ezFN8dqwPxP/u3BO9gmvZ92xO0gpzCHQY0HVTyuGsKURCEi9URkjYhEWH/Wvcxxq0XkvIisqOwYNa2mc3V0ZWzzsayPXk9KTorZ4VSMiFGqiNwIeRUbC7wpehNujm50D+hum9hqALNKFE8A65RSocA66+uSvAb8s9Ki0rRaZnzoeAqKClhxogbci7UaDQU5xoyy5aSUYlPMJvo07IOzxdmGwVVvZiWKccCn1uefAteXdJBSah2QXllBaVptE1o3lI6+Hfku4rvq36jdpI8xAK8CvZ+OphwlPiuegY0G2jCw6s+sROGvlLowTXkc4G9SHJpW640PHc+J1BPsT9xvdigVY3GC0BEQ/hMUlm+5nI0xGxGEAY0G2Di46s1uiUJE1orIwRIe44ofp4zbmArdyojIdBHZJSK7EhMTKxS3ptU2I5uOxM3Rje8ivjM7lIprOxayz0HkhnK9fXP0Zjr4daC+W30bB1a92S1RKKWGKqXal/D4EYgXkUAA688KdX5WSn2klApTSoX5+fnZInxNqzU8nDwY1XQUq6NWk5GXYXY4FRM6HNzqwr4vr/qtiVmJHEw+7odhIAAAFyJJREFUqKudSmBW1dMy/lzPYirGNOaapplkfOh4sguyWR1Vzacfd3SBDjcZs8lmn7+qt26O2QygE0UJzEoULwPDRCQCGGp9jYiEicjHFw4SkV+Bb4EhIhIjIiNMiVbTariOvh1p4dOC7yO+NzuUius0CQpz4dDVVaXp0diXZ0qiUEolK6WGKKVCrVVUKdbtu5RSdxY7rr9Syk8p5aaUaqSU+tmMeDWtphMRxoeO50DSAcLP2XYW1krXsAv4tYZ9X5X5LTkFOWyP3a5HY1+GHpmtaRoA1za7FkcHx+pfqhAxShUxOyDpeJnesiNuB9kF2QxsrKudSqIThaZpANR1rcuQ4CEsj1xObmGu2eFUTMd/gDjA/rKVKvRo7NLpRKFp2kXjQ8eTmpvK+tPlH91cJXgFQvPBsP9rKCoq9dDio7FdLC6VFGD1ohOFpmkX9QrsRUOPhiyNWGp2KBXXaRKkxUDU5lIPO3bumB6NfQU6UWiadpGDOHBD6A1sj91OdHq02eFUTOsx4OJ9xUbt/2/v3sOjqO89jr+/uRDCnRAIICEEiAFEgxhB5CJIoggKAl4QT8Vajw9eavv0qI+ttrV3LWrPadUq7VGxR9SqgEjhlATCReViSAG5BBIuIUAIl3BJCCG33/ljJhrC7iYkOzu7nu/refbZ2Z3fznyYLPlmfjPzm1WF1tXYo3uNDlCw0KOFQil1gdv7306YhLEof5HbUVomMhoGT4Wdi+G89yHjVheu5srYK4mNjg1guNCihUIpdYHubbszsudIFuUvorq2eWMmBY2UmVBVDjs83/L166ux9Wwnn7RQKKUuMj1pOkfLj/LF4S/cjtIy8cMgpp/XIT3WHloL6NXYjdFCoZS6yJj4McS0juHj3SF+ULvumoqCz+Dk/otmrypcRfe23fVq7EZooVBKXSQyLJIp/aaw+uBqjp877naclkmZAQhsufCWr+drzrO+SO+N3RRaKJRSHk1NmkqNqeGT/BAfs7NTPCSOti6+q3dzpg1FGzhXfU7vjd0EWiiUUh4ldkxkaLehLMxfGPp3v0uZCSf3wYH1X7+15uAavRq7ibRQKKW8mn75dArOFLCpeJPbUVpm4G0Q2RY2vwt8czX2iB4j9GrsJtBCoZTyKj0hnXaR7UL/7ndR7WDQFNi+CCrL2XVyF0fOHtFupybSQqGU8io6IppJfSexvGA5ZyrPuB2nZYbMhMpSyP0HqwpXAejV2E2khUIp5dO0pGmcrznP0r1L3Y7SMgkjoWNv2DKfrMIsroq9Sq/GbiItFEopnwZ1GcSAmAGh3/0UFgYpMzh0YC07TuxgfMJ4txOFDC0USqlGTUuaxs6Snew4scPtKC2TMoMV0a0BSOud5nKY0KGFQinVqImJE4kKjwr9vYou/ciMiePyGujdrpfbaUKGFgqlVKM6RnUkPSGdpXuXcq76nNtxmu1Y+TE2SyVpZ05B3nK344QMLRRKqSaZljSN0qpSMgsy3Y7SbCsPrMQA6WGdIOs3F1yprbzTQqGUapLUuFR6t+8d0ne/yziQQZ8Ofeg3+mk4shV2fup2pJCghUIp1SQiwrSkaWwq3sT+0/vdjnPJTlWcIvtINmkJachVd0OXJMj6LdTWuB0t6LlSKEQkRkQyRCTPfu7soc0QEVknIttFZKuI3O1GVqXUN6b0n0K4hLMgP/QOamcVZlFjakhLSIPwCBj3Yzi2E7aF3r8l0Nzao3gaWGGMSQJW2K8bKgfuM8ZcAUwA/lNEOgUwo1KqgdjoWMb0GsPi/MVU1Va5HeeSZB7IpGfbngyKGWS9MWgqdLsCVv0OakL8Tn4Oc6tQTAHm2dPzgNsbNjDG7DbG5NnTh4GjQNeAJVRKeTQ9aTonKk6w5uAat6M0WVllGesOr2N8wvhv7j0RFgY3PgMle2Dr++4GDHJuFYo4Y0yRPX0EiPPVWESGAa2APV7mPyQi2SKSfezYMf8mVUpdYORlI+kW3S2krqlYc3ANVbVVpCekXzgjeSL0vBpWvQDVle6ECwGOFQoRyRSRbR4eU+q3M9ZA917PURORHsDfgO8aY2o9tTHGzDXGpBpjUrt21Z0OpZwUERbBlP5T+OzQZxw5e8TtOE2SeSCT2OhYUrqmXDhDBMY9C6cPwL/ecSdcCHCsUBhj0owxgz08PgGK7QJQVwiOelqGiHQA/gE8Y4xZ76mNUirwpiZNpdbUhsTd785Vn+OzQ58xvvd4wsTDr7z+4yH+OljzIlSF7sWETnKr62kxMMuengVc9G0TkVbAQuAdY8xHAcymlGpEfPt4hvcYzsL8hdR63tEPGl8c+oJz1eess508EYEbn4XSIsh+M7DhQoRbheJ5IF1E8oA0+zUikioif7Xb3AWMAe4Xkc32Y4g7cZVSDU1Pms6hskNsPLLR7Sg+ZRzIoGNUR66Ju8Z7o8TRkHgDrH0ZzpcFLlyIcKVQGGNOGGPGG2OS7C6qEvv9bGPMg/b0/xhjIo0xQ+o9NruRVyl1sRt730iHVh1YsDt4D2pX1lSyqnAV43uPJzIs0nfjG5+F8uOwcW5gwoUQvTJbKdUsUeFR3NbvNjIPZHKq4pTbcTxad3gdZ6vOXny2kyfxwyDpZvj8v6DitPPhQogWCqVUs03tP5Wq2iqW7F3idhSPlhcsp32r9gzvPrxpHxj3E6g4BeteczZYiNFCoZRqtuSYZK6MvZKP8z7GBNlIrFU1VWQVZjEufhyR4Y10O9XpOQQG3gbrXoXyEmcDhhAtFEqpFpmWNI38U/l8dfwrt6NcYH3RekorS7kp4aZL++DYn0BlmdUFpQAtFEqpFrol8RaiI6KD7krtjIIM2kW2Y0TPEZf2wbhBcOUdsOENKC12JlyI0UKhlGqRtpFtmdBnAsv2LaOsMjhOLa2qrWJl4UrGxo+lVXirS1/A2B9DbTUse1JvboQWCqWUH9yVfBfl1eV8sic4rtT+8siXnD5/umlnO3nSpZ81DPmOT2BbiNyo6cxhqD7vyKK1UCilWmxw7GCuir2K93PfD4ortTMKMmgT0Ybre17f/IVc/wPodS384z/gTFHj7d326Q/hL+MdWbQWCqWUX9wz8B72n9nPusPrXM1RXVvNygMruaHXDbSOaN38BYVHwO2vW3+lL/5+cHdBnS+FvVmQOMaRxWuhUEr5xc0JN9OldRfm5853Ncem4k2UVJSQ3qeZ3U71xfaHtOcgPwNygnh02bwMqKmEAZMcWbwWCqWUX0SGR3Jn8p2sPbiWwjOFruXIKMggOiKaUZeN8s8Chz0EfUbDP38CJ/f7Z5n+lrsE2sRC7+scWbwWCqWU39x5+Z2ESzjv7XrPlfXX1NaQWZDJqMtGER0R7Z+FhoXB7a8BAosehVr3j8FcoPo87F4OybdAWLgjq9BCoZTym25tupGekM6ivEWUV5UHfP05R3M4UXHi0i+ya0yn3jDhd1DwGWx43b/Lbql9a6Cy1Lqi3CFaKJRSfjVz4ExKq0oDPv6TMYa5W+fSoVUHxvRy4KDu1f9mDRq44hdwbLf/l99cOz+FVu2sYdIdooVCKeVXKV1TGBgzkPdy3wvo+E+rD65mfdF6HhnyCG0i2/h/BSIw+Y8QGQ2LZkNNtf/Xcalqa2DXUkhKh8gWnOHVCC0USim/EhHuGXAP+afy+fLIlwFZZ1VNFS9mv0hix0TuSr7LuRW17w6TXoJDm+DzPzi3nqYq3Ahnj8GAWx1djRYKpZTf3ZJ4C52iOgXsVNn5ufMpOFPAk6lPNn6DopYaPB2umAarXoCirc6uqzG5SyC8FST5+ZhMA1oolFJ+1zqiNdOTppNVmMXhssOOrqukooQ3trzBqMtGMbrXaEfX9bVJL0GbGPjoASg7Fph1NmSMdXwi8QZo3cHRVWmhUEo54u7kuwH4YNcHjq7n1X+9Snl1OU+mPunoei7QJgbueAtOH4R3JsPZ44Fbd53i7XCqAAY62+0EWiiUUg7p0a4HN8bfyMd5H1NRXeHIOnaV7OKjvI+YMWAGfTv1dWQdXvUZCTPfh5K9MG8ynD0R2PXnLgEEkic6viotFEopx8wcOJPT50+zbN8yvy/bGMOc7Dm0b9Weh1Me9vvym6TvWLjnfSjZA/NuC2w31M4lED8c2nVzfFVaKJRSjkmNS6V/p/7Mz53v91Nlswqz2FC0gUdSHqFjVEe/LvuS9BtnF4u9MO9WKDvq/DpP7ofirwLS7QQuFQoRiRGRDBHJs587e2iTICI5IrJZRLaLyGw3siqlmk9EmDlwJrkluX69V0VlTSUvZr9I3459uTP5Tr8tt9n6jYN7/w6nDsDbk6D0iLPr22lfzOjwabF13NqjeBpYYYxJAlbYrxsqAkYYY4YAw4GnRaRnADMqpfxgcr/JXNv9Wn76+U95Z7t/RmCdv3M+haWFPHXtU86fDttUiWPg3o/g9CGrWJxx8Gyv3CUQNxhiEp1bRz1uFYopwDx7eh5we8MGxphKY0zd7Zqi0G4ypUJSVHgUf077M+kJ6czJnsPLm15uUTfUiXMneGPrG4zpNYaRl430Y1I/6DMSvrPA2qN4e5JVNPyt7CgcWB+wvQlw75dvnDGm7pZRR4A4T41EJF5EtgKFwAvGGI8lWkQeEpFsEck+dsylc5qVUl5FhUcxZ8wc7k6+m7e2vcWznz9LVW1Vs5b1yuZXqKiu4InUJ/yc0k96XwffWWgd2H57Ipzy85Dru5YCJmDHJ8DBQiEimSKyzcNjSv12xvrTwuOfF8aYQmPMVUB/YJaIeCwoxpi5xphUY0xq165d/f5vUUq1XHhYOM8Mf4ZHhzzK4j2LeXzl45c8wuyukl0syFvAjAEzSOwYmG6XZokfBvctgvKT8ObN1jEFfx3M37kEOiVYXU8B4lihMMakGWMGe3h8AhSLSA8A+9nnaQL2nsQ2IECXXSqlnCAizE6Zzc9H/JwvDn/Bg8sf5GTFySZ9tqa2huc3Pk+HVh2YnRIC57b0SoX7P4WoDvDBvfDuHXA8v2XLrDgD+1ZbQ4qL+CdnE7jV9bQYmGVPzwIuOh1CRHqJSLQ93RkYBewKWEKllGPuuPwOXh77MrtP7ua+Zfd5Heaj+Gwxy/Yt49frf830xdPJLs7mR9f8yN3TYS9FjxSYvRZu/p01gN9r10HGz+F8WfOWl7fcvuVp4LqdACSQwwB/vVKRLsDfgd5AAXCXMaZERFKB2caYB0UkHXgJq1tKgFeMMXMbW3ZqaqrJzs52ML1Syl9yinN4bOVjtA5vzWtprxEVHkVOcQ45R3PYVLyJQ2XWweDoiGiGdB1CWkKas6PDOqnsKGQ+B5vfhfY94aZfWQMMXsqewYf3w7618MRuv9/NTkQ2GWNSPc5zo1A4SQuFUqEl72QeszNnc7T8mx7omNYxXN3taoZ2G8o1cdeQHJNMRFiEiyn9qHAjLH0CirZAwiiY+HuIu6Lxz1VVwJx+MHgaTP6T32P5KhTfki2vlApVSZ2T+ODWD1iQt4AurbtwddzVJHZIRALYBx9Q8cPg37MgZx6s+CW8PhqufRBGPg4de3n/3L7VUFkGA5y75ak3ukehlFJuKS+Blb+CTW8DYp3yOvxh6xTbhoVy8fdh20J4ag9ERPk9iq89Cr2ITSml3NImBm79A/xgC4x4FPaugrcmwNwbYPN8qLavOa6tgVz7lqcOFInGaKFQSim3deptHdz+0U6rcFSfh0UPwx+ugJW/ga8+hPLjAb3Irj49RqGUUsGiVVtIfQCu+a61d7HhDVgzBzDQLg6SJ7kSSwuFUkoFGxFrRNp+4+DEHsh5BxKuh8jWrsTRQqGUUsGsSz9I/4WrEfQYhVJKKZ+0UCillPJJC4VSSimftFAopZTySQuFUkopn7RQKKWU8kkLhVJKKZ+0UCillPLpWzd6rIgcw7oZUiiIBY67HeIShFpe0MyBEmqZQy0vOJ85wRjT1dOMb12hCCUiku1tWN9gFGp5QTMHSqhlDrW84G5m7XpSSinlkxYKpZRSPmmhcNdctwNcolDLC5o5UEItc6jlBRcz6zEKpZRSPukehVJKKZ+0UDhIROJFJEtEdojIdhH5gYc2Y0XktIhsth8/cyNrg0z7ReQrO0+2h/kiIn8UkXwR2SoiQ93IWS9Pcr3tt1lEzojIDxu0cX07i8ibInJURLbVey9GRDJEJM9+7uzls7PsNnkiMsvFvHNEJNf+uS8UkU5ePuvzOxTgzM+JyKF6P/uJXj47QUR22d/rp13O/EG9vPtFZLOXzwZmOxtj9OHQA+gBDLWn2wO7gUEN2owFlridtUGm/UCsj/kTgWWAANcBG9zOXC9bOHAE65zwoNrOwBhgKLCt3nu/B562p58GXvDwuRhgr/3c2Z7u7FLem4AIe/oFT3mb8h0KcObngCea8L3ZA/QFWgFbGv5fDWTmBvNfAn7m5nbWPQoHGWOKjDE59nQpsBO4zN1UfjEFeMdY1gOdRKSH26Fs44E9xpigu+jSGLMGKGnw9hRgnj09D7jdw0dvBjKMMSXGmJNABjDBsaA2T3mNMcuNMdX2y/VAL6dzXAov27gphgH5xpi9xphK4H2sn43jfGUWEQHuAt4LRBZvtFAEiIj0Aa4GNniYPUJEtojIMhG5IqDBPDPAchHZJCIPeZh/GVBY7/VBgqcAzsD7f6pg284AccaYInv6CBDnoU2wbu8HsPYsPWnsOxRoj9ndZW966d4L1m08Gig2xuR5mR+Q7ayFIgBEpB3wMfBDY8yZBrNzsLpJUoA/AYsCnc+DUcaYocAtwKMiMsbtQE0hIq2AycCHHmYH43a+gLH6EkLiNEQReQaoBt710iSYvkN/BvoBQ4AirK6cUHEPvvcmArKdtVA4TEQisYrEu8aYBQ3nG2POGGPK7OmlQKSIxAY4ZsNMh+zno8BCrN3y+g4B8fVe97Lfc9stQI4xprjhjGDczrbium47+/mohzZBtb1F5H7gVuBeu7hdpAnfoYAxxhQbY2qMMbXAX7xkCaptDCAiEcA04ANvbQK1nbVQOMjuX/xvYKcx5mUvbbrb7RCRYVg/kxOBS3lRnrYi0r5uGuvg5bYGzRYD99lnP10HnK7XfeImr399Bdt2rmcxUHcW0yzgEw9t/gncJCKd7W6Tm+z3Ak5EJgBPAZONMeVe2jTlOxQwDY6fTfWS5UsgSUQS7T3TGVg/GzelAbnGmIOeZgZ0OwfiqP7/1wcwCqsrYSuw2X5MBGYDs+02jwHbsc6yWA9c73LmvnaWLXauZ+z362cW4FWss0S+AlKDYFu3xfrF37Hee0G1nbGKWBFQhdUH/j2gC7ACyAMygRi7bSrw13qffQDItx/fdTFvPlZfft33+XW7bU9gqa/vkIuZ/2Z/T7di/fLv0TCz/Xoi1pmJe9zObL//dt33t15bV7azXpmtlFLKJ+16Ukop5ZMWCqWUUj5poVBKKeWTFgqllFI+aaFQSinlkxYKpZRSPmmhUEop5ZMWCqX8SEQW2QO0ba8bpE1Eviciu0Vko4j8RUResd/vKiIfi8iX9mOku+mV8kwvuFPKj0QkxhhTIiLRWMNC3Ax8jnW/gVJgJbDFGPOYiMwHXjPGfCYivYF/GmMGuhZeKS8i3A6g1LfM4yIy1Z6OB74DrDbGlACIyIfA5fb8NGCQPQQVQAcRaWfswQuVChZaKJTyExEZi/XLf4QxplxEVgG5gLe9hDDgOmNMRWASKtU8eoxCKf/pCJy0i8QArNvEtgVusEd+jQCm12u/HPh+3QsRGRLQtEo1kRYKpfznf4EIEdkJPI81Su0h4LfARqxjFfuB03b7x4FU+85rO7BGu1Uq6OjBbKUcVnfcwd6jWAi8aYxZ6HYupZpK9yiUct5zIrIZ66Yy+wjC27Aq5YvuUSillPJJ9yiUUkr5pIVCKaWUT1oolFJK+aSFQimllE9aKJRSSvmkhUIppZRP/wefUD2sZn3vkgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -432,13 +468,45 @@ " [-0.30554775]\n", " [-0.32274581]\n", " [-0.33517072]\n", - " [-0.24414735]]]\n", + " [-0.24414735]]\n", + "\n", + " [[ 0.06304934]\n", + " [ 0.11742428]\n", + " [ 0.12543357]\n", + " [ 0.13288682]\n", + " [ 0.2144686 ]\n", + " [ 0.23211155]\n", + " [ 0.30066495]\n", + " [ 0.29069737]\n", + " [ 0.24459677]\n", + " [ 0.21382428]\n", + " [ 0.15093644]\n", + " [ 0.11564532]\n", + " [ 0.10764388]\n", + " [ 0.09065738]\n", + " [ 0.07140734]\n", + " [ 0.03953841]\n", + " [-0.0070869 ]\n", + " [-0.07615571]\n", + " [-0.15031009]\n", + " [-0.2248465 ]\n", + " [-0.29268468]\n", + " [-0.31869482]\n", + " [-0.31185246]\n", + " [-0.26157233]\n", + " [-0.17380919]\n", + " [-0.07718238]\n", + " [ 0.00287185]\n", + " [ 0.05987486]\n", + " [ 0.0942701 ]\n", + " [ 0.12153617]\n", + " [ 0.10283463]]]\n", "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", " 16.5 , 17. , 17.5 , 18. ])]\n", "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943]\n" + "[556.70338211 93.29260943 20.69419605]\n" ] } ], @@ -605,6 +673,31 @@ { "cell_type": "code", "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", + "fpca = FPCABasis()\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "scrolled": false }, @@ -636,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -671,39 +764,52 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "The sample size should be bigger than the number of components", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" + ] + } + ], + "source": [ + "fpca = FPCABasis()\n", + "basis = skfda.representation.basis.Fourier(n_basis=1)\n", + "fd = FDataBasis(basis, [[0.9], [0.7]])\n", + "\n", + "fpca.fit(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[557.67384688 92.00703848]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[ 0.08496812 0.11289386 0.16694664 0.21276737 0.31757592 0.35642335\n", - " 0.33056519]\n", - " [-0.00738993 0.06897138 0.10686955 0.18635685 0.47864279 -0.78178633\n", - " -0.42255908]])\n" + "ename": "AttributeError", + "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", + "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "fpca = FPCABasis(2)\n", + "fpca = FPCABasis(9)\n", "fpca.fit(basisfd)\n", "print(fpca.component_values)\n", "fpca.components.plot()\n", @@ -961,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -982,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1423,14 +1529,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1444,7 +1550,7 @@ "source": [ "fd_data = fetch_weather_temp_only()\n", "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", + "basis = skfda.representation.basis.Fourier(n_basis=8)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1453,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1461,18 +1567,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=7, period=364),\n", - " coefficients=[[-0.92331715 -0.14308529 -0.35425022 -0.0089843 0.02421851 0.0291243\n", - " 0.00182958]\n", - " [ 0.33133158 0.03526095 -0.89315001 -0.17531623 -0.24006175 -0.03851005\n", - " -0.03755887]])\n", - "[1.50817792e+04 1.43809210e+03 3.13967267e+02 8.07288671e+01\n", - " 1.43851817e+01 9.74183648e+00 3.80956311e+00]\n" + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]\n", + " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", + " 0.02103448 0.00270691 0.04696796]\n", + " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", + " 0.20046433 -0.16454415 0.16810248]])\n", + "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1484,7 +1593,7 @@ } ], "source": [ - "fpca = FPCABasis(2, svd=True)\n", + "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", "print(fpca.components)\n", @@ -1492,6 +1601,42 @@ "pyplot.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.04618614415675301" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(1.363 - 1.429 )/1.429 \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ramsay implementation without penalization\n", + "\n", + "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", + "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", + "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", + "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", + "\n", + "values 15164.718872 1446.091968 314.361310 85.508572" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..1ec27cf89 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,11 +3,18 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.datasets import fetch_weather -class FPCATestCase(unittest.TestCase): +def fetch_weather_temp_only(): + weather_dataset = fetch_weather() + fd_data = weather_dataset['data'] + fd_data.data_matrix = fd_data.data_matrix[:, :, :1] + fd_data.axes_labels = fd_data.axes_labels[:-1] + return fd_data + +class MyTestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() @@ -28,7 +35,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() + fpca = FPCADiscretized() with self.assertRaises(AttributeError): fpca.fit(None) @@ -46,36 +53,39 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 9 - n_components = 3 - - fd_data = fetch_weather()['data'].coordinates[0] - fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), - np.arange(0.5, 365, 1)) + # initialize weather data with only the temperature. Humidity not needed + fd_data = fetch_weather_temp_only() + n_basis = 8 + n_components = 4 # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) + basis = Fourier(n_basis=n_basis) fd_basis = fd_data.to_basis(basis) - fpca = FPCABasis(n_components=n_components) + # pass functional principal component analysis to weather data + fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.0100063], - [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] + results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.010006303], + [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718], + [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, + 0.24922635, 0.213305250, -0.180158701, 0.154863926]] results = np.array(results) # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - np.testing.assert_allclose(fpca.components_.coefficients, results, - atol=1e-7) + for j in range(n_basis): + self.assertAlmostEqual(fpca.components.coefficients[i][j], + results[i][j], + delta=0.03) if __name__ == '__main__': From 60e7bd0d38216c5b0115935695373fcd109e67eb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 1 Feb 2020 23:36:30 +0100 Subject: [PATCH 401/624] Update docstring --- docs/modules/exploratory/fpca.rst | 13 +++ skfda/exploratory/fpca/fpca.py | 127 +++++++++++++++++++++++------- 2 files changed, 112 insertions(+), 28 deletions(-) create mode 100644 docs/modules/exploratory/fpca.rst diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst new file mode 100644 index 000000000..0a8687cf7 --- /dev/null +++ b/docs/modules/exploratory/fpca.rst @@ -0,0 +1,13 @@ +Functional Principal Component Analysis +======================================= + +This module provides tools to analyse the data using functional principal +component analysis. + +Functional Principal Component Analysis for basis representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCABasis \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 5660ac674..715541df7 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,3 +1,5 @@ +"""Functional Principal Component Analysis Module.""" + import numpy as np from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis @@ -6,29 +8,35 @@ from sklearn.decomposition import PCA +__author__ = "Yujian Hong" +__email__ = "yujian.hong@estudiante.uam.es" + + class FPCA(ABC, BaseEstimator, ClassifierMixin): # TODO doctring - # TODO doctext + # TODO doctest # TODO directory examples create test - """ - Defines the common structure shared between classes that do functional + """Defines the common structure shared between classes that do functional principal component analysis Attributes: n_components (int): number of principal components to obtain from - functional principal component analysis + functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first components (FDataGrid or FDataBasis): this contains the principal components either in a basis form or discretized form component_values (array_like): this contains the values (eigenvalues) associated with the principal components - + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ def __init__(self, n_components=3, centering=True): - """ - FPCA constructor + """FPCA constructor + Args: n_components (int): number of principal components to obtain from functional principal component analysis @@ -43,36 +51,34 @@ def __init__(self, n_components=3, centering=True): @abstractmethod def fit(self, X, y=None): - """ - Computes the n_components first principal components and saves them + """Computes the n_components first principal components and saves them inside the FPCA object. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function - Returns: - self (object) + Returns: + self (object) """ pass @abstractmethod def transform(self, X, y=None): - """ - Computes the n_components first principal components score and returns - them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function + """Computes the n_components first principal components score and + returns them. - Returns: - (array_like): the scores of the data with reference to the - principal components + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components """ pass @@ -95,14 +101,62 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): + """Defines the common structure shared between classes that do functional + principal component analysis + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ def __init__(self, n_components=3, components_basis=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + components_basis (skfda.representation.Basis): the basis in which we + want the principal components. Defaults to None. If so, the + basis contained in the passed FDataBasis object for the fit + function will be used. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + Returns: + self (object) + + References: + .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function + expansion of the functions. In *Functional Data Analysis* + (pp. 161-164). Springer. + + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -212,6 +266,23 @@ def __init__(self, n_components=3, weights=None, centering=True): # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object. + + Args: + X (FDataBasis): + the functional data object to be analysed in basis + representation + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + + References: + .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing + the functions. In *Functional Data Analysis* (p. 161). Springer. + """ # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: From 1269e9f5e474dff69febf2986258677ccad33190 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 2 Feb 2020 23:16:54 +0100 Subject: [PATCH 402/624] Create example of FPCA --- docs/modules/exploratory/fpca.rst | 12 +++- skfda/exploratory/fpca/fpca.py | 93 +++++++++++++++++++++++++++---- 2 files changed, 92 insertions(+), 13 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 0a8687cf7..2ba724481 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -4,10 +4,18 @@ Functional Principal Component Analysis This module provides tools to analyse the data using functional principal component analysis. -Functional Principal Component Analysis for basis representation +FPCA for functional data in basis representation ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.exploratory.fpca.FPCABasis \ No newline at end of file + skfda.exploratory.fpca.FPCABasis + +FPCA for functional data in discretized representation +---------------------------------------------------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 715541df7..ed4702653 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -13,7 +13,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctring # TODO doctest # TODO directory examples create test """Defines the common structure shared between classes that do functional @@ -101,8 +100,8 @@ def fit_transform(self, X, y=None): class FPCABasis(FPCA): - """Defines the common structure shared between classes that do functional - principal component analysis + """Funcional principal component analysis for functional data represented + in basis form. Attributes: n_components (int): number of principal components to obtain from @@ -111,13 +110,21 @@ class FPCABasis(FPCA): object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either - in a basis form or discretized form + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) - associated with the principal components + associated with the principal components. pca (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + Construct an artificial FDataBasis object and run FPCA with this object + + """ def __init__(self, n_components=3, components_basis=None, centering=True): @@ -138,8 +145,10 @@ def __init__(self, n_components=3, components_basis=None, centering=True): self.components_basis = components_basis def fit(self, X: FDataBasis, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. + """Computes the first n_components principal components and saves them. + The eigenvalues associated with these principal components are also + saved. For more details about how it is implemented please view the + referenced book. Args: X (FDataBasis): @@ -157,6 +166,7 @@ def fit(self, X: FDataBasis, y=None): (pp. 161-164). Springer. """ + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -171,7 +181,6 @@ def fit(self, X: FDataBasis, y=None): "smaller than the number of attributes of " "target principal components' basis.") - # if centering is True then subtract the mean function to each function # in FDataBasis if self.centering: @@ -255,22 +264,70 @@ def fit(self, X: FDataBasis, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case it is the inner product of our data with the components return X.inner_product(self.components) class FPCADiscretized(FPCA): + """Funcional principal component analysis for functional data represented + in discretized form. + + Attributes: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + """ + def __init__(self, n_components=3, weights=None, centering=True): + """FPCABasis constructor + + Args: + n_components (int): number of principal components to obtain from + functional principal component analysis + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + centering (bool): if True then calculate the mean of the functional + data object and center the data first. Defaults to True + """ super().__init__(n_components, centering) self.weights = weights - # noinspection PyPep8Naming def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them - inside the FPCA object. + inside the FPCA object.The eigenvalues associated with these principal + components are also saved. For more details about how it is implemented + please view the referenced book. Args: - X (FDataBasis): + X (FDataGrid): the functional data object to be analysed in basis representation y (None, not used): @@ -360,6 +417,20 @@ def fit(self, X: FDataGrid, y=None): return self def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + # in this case its the coefficient matrix multiplied by the principal # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( From 8a8bdddd8001f998955e69e5e2fb5483e528fbb7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 3 Feb 2020 11:56:01 +0100 Subject: [PATCH 403/624] add doctest --- skfda/exploratory/fpca/fpca.py | 37 +++- skfda/exploratory/fpca/test.ipynb | 299 ++++++++++++++++++------------ 2 files changed, 210 insertions(+), 126 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index ed4702653..66e7a5a4e 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import numpy as np +import skfda from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -13,8 +14,6 @@ class FPCA(ABC, BaseEstimator, ClassifierMixin): - # TODO doctest - # TODO directory examples create test """Defines the common structure shared between classes that do functional principal component analysis @@ -122,8 +121,18 @@ class FPCABasis(FPCA): sklearn to continue. Examples: - Construct an artificial FDataBasis object and run FPCA with this object - + Construct an artificial FDataBasis object and run FPCA with this object. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) + >>> basis_fd = fd.to_basis(basis) + >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = fpca_basis.fit(basis_fd) + >>> fpca_basis.components.coefficients + array([[ 1. , -3. ], + [-1.73205081, 1.73205081]]) """ @@ -303,6 +312,26 @@ class FPCADiscretized(FPCA): In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. + + Examples: + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCADiscretized object, fit the artificial data and obtain the scores. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_discretized.components.data_matrix + array([[[-0.4472136 ], + [ 0.89442719]], + + [[-0.89442719], + [-0.4472136 ]]]) + >>> fpca_discretized.transform(fd) + array([[-1.11803399e+00, 5.55111512e-17], + [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index e15192651..2e1d9573f 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -2,19 +2,148 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import skfda\n", - "from fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation.basis import FDataBasis\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[1.],\n", + " [0.]],\n", + " \n", + " [[0.],\n", + " [2.]]]),\n", + " sample_points=[array([0, 1])],\n", + " domain_range=array([[0, 1]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "fd" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2M1vr7pGm2/XJYpEWejOMg8yvj4O8cBxfOS9XcZCS9tQIRE7iteK9LM6P8lJ0D327deSfPh3EQfbqojhIyQxqBCLNcHdeiu5h8QtRXt9UxoAenflfl0zkS2crDlIyj/5Fi8Rwd/I/DOIg397aEAd57YwcunRUGphkJjUCEerjIHewOD/K+9sOMKJvV35wxRSuOktxkJL51Agkq9XWOX96Zxv3roiyYechRg/ozo+vOoPLFQcpWUSNQLJSdRgHuXRFlOI9hxk3qAc/mz+Vz52uOEjJPmoEklUqa2r5/dpSfr4qiIOcNLQXP//SdD6jOEjJYmoEkhWOi4Mc2Yfvfn4ycyYqDlJEjUAy2uHKIA5y2YtBHOQncvvywyvP4HzFQYoco0YgGak+DvKBF4vZV1HNJ/P6s/jaaZwzpn+ySxNJOWoEklH2V1Txy5c38cuXN3LgaA2zJwxk4ZxxnDWqb7JLE0lZagSSEfYequSBlzbyWEwc5J1zxnH6iN7JLk0k5akRSFrbdeAoy1YX8+vXgjjIz50+lDsUBynysagRSFratv8I960q4olGcZB55A3qkezSRNKOGoGklS17K1i6Msrv3ywBgjjI22YpDlIkHmoEkhaiuw6xdGWUZ97eRvt2xrUzcvjazLEM79M12aWJpD01AklpH+44wJL8KH9+dzudO7TjpvNyWXCB4iBFEkmNQFLSuyVBHOR/rwviIG+dOZabP6U4SJHWkJBGYGZzgZ8RhNc/4O6LmtzeGXgUOAvYC1zj7pvC274F3AzUAn/v7s8loiZJT2s372NJfiEr1u8+Fgf51U/m0qeb4iBFWkvcjcDM2gP3AhcDJcAbZrbc3dfFDLsZ2OfueWY2H/ghcI2ZTQLmA5OBYcDzZjbe3WvjrUvSy5rivSzOL+Tl6F76duvINz4zgRvOHaU4SJE2kIh3BDOAqLsXA5jZE8A8ILYRzAO+G15+ClhiwRe9zAOecPdKYKOZRcPHezUBdUmKc3deLNzDkvyGOMhvX3Ia152dozhIkTaUiP9tw4GtMddLgLNPNMbda8ysHOgfbl/T5L7Dm3sSM1sALADIyclJQNmSLM3FQX7385OYrzhIkaRIm5dd7r4MWAYQiUQ8yeXIKairc557P4iDXLc9iIP8tytO58qzhisOUiSJEtEISoGRMddHhNuaG1NiZh2A3gQnjVtyX0lz9XGQS/KjFO4K4iD//YtnMm/qMMVBiqSARDSCN4BxZjaa4CA+H7iuyZjlwI0Ec/9XAfnu7ma2HPiNmf2E4GTxOOD1BNQkKaC6to6n3ypl6coiNu45zPjBQRzkpWcMo73SwERSRtyNIJzzXwg8R7B89CF3f9/M7gYK3H058CDwWHgyuIygWRCO+y3BieUa4A6tGEp/lTW1PLW2hJ+vLKJkXxAHed/10/n0JMVBiqQic0+/6fZIJOIFBQXJLkOaOFpdyxOvb+H+1cXH4iD/fk6e4iBFUoSZrXX3SNPtaXOyWFLX4coafv3aZpat3sieQ5XMyO3Hj646g0/lKQ5SJB2oEcgpO3C0msdi4iA/lTeAhXMUBymSbtQI5GPbX1HFQy9v4uEwDnLOxEHcMTtPcZAiaUqNQFpsz6FKHnhxI4+9uonDVbV8ZnIQBzlluOIgRdKZGoGc1M5jcZCbqayp49IzhnHH7LFMHKI4SJFMoEYgJ1S6/wj3rSziyYIwDnLqMO6YncfYgYqDFMkkagRynM17D7N0RRG/f7MEM7jqrBHcNjOPnP7dkl2aiLQCNQI5JrrrEEtXRHnmb0Ec5HVnKw5SJBuoEQgf7jjA4vwof3l3O106tOcrYRzkIMVBimQFNYIs9m5JOffkF/LXdTvp0bkDt4VxkP0VBymSVdQIstDazftYnF/IyvW76dWlA//jwnF8RXGQIllLjSBLuDtristYnF/IK0V76de9E9/4zAS+fO4oeioOUiSrqRFkOHdndeEeluQX8samfcfiIL90Tg7dOunXLyJqBBnL3Xnhg10sXhHlb1v3M7R3F/7PZZO55hMjFQcpIo2oEWSYujrnv8I4yA+2H2Bkv6783y+czhemKw5SRJqnRpAhamrr+PO724/FQY5RHKSItJAaQZqrrq3jD2+VsnRFlE17Kxg/uAf3XDuNz50+VHGQItIiagRpqrKmlt8VBHGQpfuPMHmY4iBF5NSoEaSZI1W1PPHGFu5fVcyOA0eZOrIP37t8MrMnKA5SRE5NXI3AzPoBTwK5wCbganff12TMVODnQC+gFviBuz8Z3vYwMBMoD4ff5O5vx1NTpjpcWcOv1mzmFy8Ws+dQFTNG9+Pfv3gmn8zrrwYgInGJ9x3BXcAL7r7IzO4Kr3+zyZgK4MvuXmhmw4C1Zvacu+8Pb/+Guz8VZx0Z68DRah59ZRMPvrSRfRXVnD9uAAtn53G24iBFJEHibQTzgFnh5UeAlTRpBO6+IebyNjPbBQwE9iMntL+iiode2sgvX9nEwTAOcuGcPKbnKA5SRBIr3kYw2N23h5d3AIM/arCZzQA6AUUxm39gZv8KvADc5e6VJ7jvAmABQE5OTpxlp649hyr5xYvF/OrVzRyuqmXu5CEsnJOnOEgRaTUnbQRm9jwwpJmbvh17xd3dzPwjHmco8Bhwo7vXhZu/RdBAOgHLCN5N3N3c/d19WTiGSCRywudJVzvKgzjI37zeEAe5cHYeE4b0THZpIpLhTtoI3P2iE91mZjvNbKi7bw8P9LtOMK4X8Gfg2+6+Juax699NVJrZL4F/+ljVZ4CSfRXct6qI375RQq07l08dzu2zxyoOUkTaTLxTQ8uBG4FF4c9nmg4ws07AH4BHm54UjmkiBlwOvBdnPWlj057DLF0Z5T/fLA3jIEdy28yxioMUkTYXbyNYBPzWzG4GNgNXA5hZBLjV3W8Jt10A9Dezm8L71S8T/bWZDQQMeBu4Nc56Ul5010HuXVHEM2+X0qF9O74UxkEOUxykiCSJuaffdHskEvGCgoJkl/GxfLD9AEvyo/zlvSAO8vpzcvi78xUHKSJtx8zWunuk6XZ9sriVvVOyn8X5UcVBikjKUiNoJWs3l3HPC1FWbQjiIP/nReP4ynmj6d1NaWAiklrUCBLI3Xm1eC+LX4jyanEQB/nPcydwwzmKgxSR1KVGkADuzqoNu1mSH6Vg8z4G9uzMv3zuNK47W3GQIpL6dJSKg7vz/Ae7WJJfyN9KyhnWuwt3z5vM1RHFQYpI+lAjOAV1dc6z7+1gcX4hH+44eCwO8srpI+jUQWlgIpJe1Ag+hpraOv70znaWrIgS3XWIMQO78x9hHGQHxUGKSJpSI2iB6to6/vBmKUtXBnGQEwb3ZPG107hEcZAikgHUCD5C0zjIKcN7cd/1Z/HpSYMVBykiGUONoBlHqmp5/PUt3L+6iJ0HKpmW04fvXz6FWRMGKg1MRDKOGkGMQ2Ec5ANhHOTZo/vxk6unct5YxUGKSOZSIwDKj4RxkC9vZH8YB3nnnHHMGN0v2aWJiLS6rG4E+w5X8dDLG3n45U0crKzhwjAOcpriIEUki2RlI9h9sJIHXizmsTWbqaiq5bNThnDHbMVBikh2yqpGsKP8KPevLuLx17dQVR8HOSeP8YMVBykiKcwdyktg93oYfQF06JTQh8+qRnDn42/y5pb9XDFtOLfPGssYxUGKSCqpq4V9m4ID/u4Pg5971sPuDVB9OBhz+2swaGJCnzarGsF3Pj+Z3l07MrKf4iBFJIlqKmFvUXiQj/mzNwq1lQ3jeg6DgRNg+g3BzwEToE9OwsvJqkagcwAi0qaqKmDPhphX9uGfsmLw2nCQQd9RwUE+70IYODE86I+DLm1zzIqrEZhZP+BJIBfYBFzt7vuaGVcLvBte3eLul4XbRwNPAP2BtcAN7l4VT00iIm3uyP6GA/7uD8PLH8L+LQ1j2nWAfmODaZ3JlwcH/oEToH8edEruLEW87wjuAl5w90Vmdld4/ZvNjDvi7lOb2f5D4Kfu/oSZ3QfcDPw8zppERBLPHQ7vCV/ZfxjM29fP4x/a0TCufWcYMB5GzIBpMVM6/cYk/CRvosTbCOYBs8LLjwArab4RHMeCj+rOAa6Luf93USMQkWRyhwPbGr+yr5/SOVLWMK5Tj+AgP3ZO8LP+T59R0C698kjibQSD3X17eHkHMPgE47qYWQFQAyxy96cJpoP2u3tNOKYEGH6iJzKzBcACgJycxJ8sEZEsU1cL+zc3Pllbv0Kn6mDDuK59g3n7SZc1TOcMnAi9hkGGfPXMSRuBmT0PDGnmpm/HXnF3NzM/wcOMcvdSMxsD5JvZu0D5xynU3ZcBywAikciJnkdEpLHa6uDkbOwr+93rYW8h1BxtGNdjSHCQn3ptw8F+wAToPiBjDvgnctJG4O4Xneg2M9tpZkPdfbuZDQV2neAxSsOfxWa2EpgG/B7oY2YdwncFI4DSU9gHERGoPgJ7CmNe2Yfz+GVFUFfTMK5PTnCAHzMzZoXOeOjaJ3m1J1m8U0PLgRuBReHPZ5oOMLO+QIW7V5rZAOCTwI/CdxArgKsIVg41e38RkUaOHmh+hc6+zUA4WWDtod/o4EB/2qUNUzoDxkGn7kktPxXF2wgWAb81s5uBzcDVAGYWAW5191uA04D7zawOaEdwjmBdeP9vAk+Y2feBt4AH46xHRDLF4b3Nr9A5uK1hTPtO0H8cDJsOZ17bsEKn/1jo0Dl5tacZc0+/6fZIJOIFBQXJLkNE4uUOB3c0v0KnYk/DuI7dYeD4mJO14Rx+n1HQPqs+FxsXM1vr7pGm2/U3KCKtr64Oyrc0s0JnPVQeaBjXpXdwgJ94SXjQD+fwew2Hdu2SV3+GUyMQkcSprYayjcdP6ewphJojDeO6DwoO8GdcHXPCdgL0GJTxK3RSkRqBiHx81UeDL0hrNKWzIdhWV90wrvfI4CCfe37DlM6A8dBN6X+pRI1ARE6s8lDDh6wardDZBF4XjLF20Dc3eGU/YW7MCp3x0Flf9Z4O1AhEBCrKGr+yrz9pe6CkYUy7jsEXpA05A07/YswKnTzo2CV5tUvc1AhEsoU7HNrVJPAk/HM45rOgHboGK3RGndd4hU7fXGjfMWnlS+tRIxDJNHV1wSv5Yyt0YqZ0jsZ8s0vn3sEBf/ynG6/Q6T1SK3SyjBqBSLqqrQnm6ptboVMfawjQbUBwkJ9yZeMVOj2HaIWOAGoEIqmvPtbwuBU6hVAbk+PUa3hwgnb6l2NW6EyA7v2TV7ukBTUCkVRRdTg80DdZoVO2sUmsYW5wkB93UcOUzoBx0KVXMquXNKZGINLWjsUaNvla5PLmYg0nweQrwoP9+OCA37Fr8mqXjKRGINIa6mMNd394/JRObKxhhy7BwX3kjHBKZ3xw0O83Rit0pM2oEYjEwx0OlDY5WRv+PLKvYVynnsFBPu/C4JV9/UnbPjlpF2somUeNQKQl6mrDFTobjj/oVx1qGNe1XxhreHnjE7YZFGsomUeNQCRWTVVDrGHsQX/PBqitbBjXc2gYa/ilxh+66j4gebWLnCI1AslOVRXB8stjr+zDE7ZlxU1iDUcFB/mxsxqv0MniWEPJPGoEktmOxRp+2HhKZ/8WGscajgkO+Kd9vvEKHcUaShZQI5DMcHhv8yt0GsUadg4O7sPPCqd06lfojIUOnZJXu0iSqRFI+nCHg9ubX6FTsbdhXH2s4ZiZjVfo9M3VCh2RZsTVCMysH/AkkAtsAq52931NxswGfhqzaSIw392fNrOHgZlA/Tdh3eTub8dTk2SAujrYv7n5FTqNYg37hLGGn2v8HTqKNRT5WOJ9R3AX8IK7LzKzu8Lr34wd4O4rgKlwrHFEgf+OGfINd38qzjokHdXHGsaerN39IeyJNo417DE4jDW8pskKnYFakimSAPE2gnnArPDyI8BKmjSCJq4CnnX3ijifV9JJ9dFwhU6T0PK9RU1iDXOCKZ3RsVM646Fr3+TVLpIF4m0Eg919e3h5BzD4JOPnAz9psu0HZvavwAvAXe5eefzdwMwWAAsAcnJyTr1iaT2VB8PpnCZfi7x/c5NYw9FhrOFnY1boKNZQJFnM3T96gNnzwJBmbvo28Ii794kZu8/dm335ZmZDgXeAYe5eHbNtB9AJWAYUufvdJys6Eol4QWFHU5oAAAanSURBVEHByYZJa6koOz7wZPeG42MNB4xr/Mq+foWOYg1FksLM1rp7pOn2k74jcPeLPuJBd5rZUHffHh7Ud51oLHA18If6JhA+dv27iUoz+yXwTyerR9qIOxza2fwKncO7G8Z17BYc8HM/2XCy9lisoRaliaSDeP+nLgduBBaFP5/5iLHXAt+K3RDTRAy4HHgvznrk46qrg/Ktx38t8p71zcQaToDxcxtO1g4Yr1hDkQwQbyNYBPzWzG4GNhO86sfMIsCt7n5LeD0XGAmsanL/X5vZQMCAt4Fb46xHTqQ+1rDRCp31QQOojjl3331gGGt4VeMpnR6DtUJHJEOd9BxBKtI5go9QUwl7o8cHl++NNok1HNFwkI/90FW3fsmrXURa1SmfI5AUdSzWsMkKnX0bG1boHIs1nAjjLo750NV46NwzmdWLSApRI0h1R/Ydf7J294bjYw3758HgyTDlyoYPXfXPU6yhiJyUGkEqcA9W4jQ9Wbt7fbByp159rGHO2TDwyw0rdPqNVqyhiJwyNYK25A7lJY1P1tZP7Rzd3zCuU8/gFX3exY3n8RVrKCKtQI2gNdTHGsa+st/9IewpbBxr2K1/cJCffEXjFTo9h2qFjoi0GTWCeNRUQVnR8St09hQ2iTUcFhzkp13feIWOYg1FJAWoEbTEsVjD9Y3n8cuKwWvDQRZM3QycCGNnh9M5E4IG0KV3UssXEfkoagSxjpYfn2G7e/3xsYb9xwav6CfNa5jS6T8OOnVLavkiIqciOxvB4T3Nr9A5uL1hTH2s4YhI4ymdfmMUaygiGSW7GsGfvg7rnmkca9ipR3CQHzO78QodxRqKSJbIrkbQewRMvLTxCp1ew7VCR0SyWnY1gvP/MdkViIikHH1/sIhIllMjEBHJcmoEIiJZTo1ARCTLqRGIiGQ5NQIRkSynRiAikuXUCEREslxahteb2W5g8ynefQCwJ4HlpAPtc3bQPme+ePd3lLsPbLoxLRtBPMyswN0jya6jLWmfs4P2OfO11v5qakhEJMupEYiIZLlsbATLkl1AEmifs4P2OfO1yv5m3TkCERFpLBvfEYiISAw1AhGRLJexjcDM5prZejOLmtldzdze2cyeDG9/zcxy277KxGrBPv+Dma0zs3fM7AUzG5WMOhPpZPscM+5KM3MzS+ulhi3ZXzO7Ovw9v29mv2nrGhOtBf+uc8xshZm9Ff7bviQZdSaSmT1kZrvM7L0T3G5mdk/4d/KOmU2P6wndPeP+AO2BImAM0An4GzCpyZjbgfvCy/OBJ5Nddxvs82ygW3j5tmzY53BcT2A1sAaIJLvuVv4djwPeAvqG1wclu+422OdlwG3h5UnApmTXnYD9vgCYDrx3gtsvAZ4FDDgHeC2e58vUdwQzgKi7F7t7FfAEMK/JmHnAI+Hlp4ALzdI6vPik++zuK9y9Iry6BhjRxjUmWkt+zwDfA34IHG3L4lpBS/b374B73X0fgLvvauMaE60l++xAr/Byb2BbG9bXKtx9NVD2EUPmAY96YA3Qx8yGnurzZWojGA5sjbleEm5rdoy71wDlQP82qa51tGSfY91M8IoinZ10n8O3zCPd/c9tWVgracnveDww3sxeNrM1Zja3zaprHS3Z5+8C15tZCfAX4M62KS2pPu7/94+UXeH1AoCZXQ9EgJnJrqU1mVk74CfATUkupS11IJgemkXwjm+1mZ3u7vuTWlXruhZ42N3/w8zOBR4zsynuXpfswtJFpr4jKAVGxlwfEW5rdoyZdSB4S7m3TaprHS3ZZ8zsIuDbwGXuXtlGtbWWk+1zT2AKsNLMNhHMpS5P4xPGLfkdlwDL3b3a3TcCGwgaQ7pqyT7fDPwWwN1fBboQfDlbJmvR//eWytRG8AYwzsxGm1kngpPBy5uMWQ7cGF6+Csj38CxMmjrpPpvZNOB+giaQ7nPHcJJ9dvdydx/g7rnunktwXuQydy9ITrlxa8m/66cJ3g1gZgMIpoqK27LIBGvJPm8BLgQws9MIGsHuNq2y7S0HvhyuHjoHKHf37af6YBk5NeTuNWa2EHiOYNXBQ+7+vpndDRS4+3LgQYK3kFGCkzLzk1dx/Fq4zz8GegC/C8+Lb3H3y5JWdJxauM8Zo4X7+xzwaTNbB9QC33D3tH2n28J9/kfgF2b2dYITxzel+Ys6zOxxgoY+IDz38R2gI4C730dwLuQSIApUAF+J6/nS/O9LRETilKlTQyIi0kJqBCIiWU6NQEQky6kRiIhkOTUCEZEsp0YgIpLl1AhERLLc/wffK++zinbhSQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fpca_discretized = FPCADiscretized(2)\n", + "fpca_discretized.fit(fd)\n", + "fpca_discretized.components.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.11803399e+00, 5.55111512e-17],\n", + " [ 1.11803399e+00, -5.55111512e-17]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.transform(fd)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 0.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_discretized.weights" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean = fd.mean()\n", + "np.squeeze(mean.data_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 2, @@ -229,122 +358,6 @@ "print(pca.singular_values_**2)" ] }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n" - ] - } - ], - "source": [ - "print(X.copy(data_matrix=pca.components_))" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -371,10 +384,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FDataGrid' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" + ] + } + ], + "source": [ + "FDataGrid\n" + ] }, { "cell_type": "markdown", @@ -695,6 +722,34 @@ "fpca.fit(fd)" ] }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26726124, -0.80178373],\n", + " [ 1.38873015, -0.9258201 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", + "sample_points = [0, 1]\n", + "fd = FDataGrid(data_matrix, sample_points)\n", + "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis_fd = fd.to_basis(basis)\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, { "cell_type": "code", "execution_count": 3, From 09c6a81d2e5bc07660268b4a16e167148fd9d67f Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 9 Feb 2020 18:12:37 +0100 Subject: [PATCH 404/624] regularized PCA support --- skfda/exploratory/fpca/fpca.py | 32 +- skfda/exploratory/fpca/test.ipynb | 978 ++++++++++++++++++------------ tests/test_fpca.py | 24 +- 3 files changed, 621 insertions(+), 413 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 66e7a5a4e..6ea504432 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA @@ -13,7 +13,7 @@ __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, ClassifierMixin): +class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis @@ -136,7 +136,14 @@ class FPCABasis(FPCA): """ - def __init__(self, n_components=3, components_basis=None, centering=True): + def __init__(self, + n_components=3, + components_basis=None, + centering=True, + regularization=False, + derivative_degree=2, + coefficients=None, + regularization_parameter=0): """FPCABasis constructor Args: @@ -152,6 +159,13 @@ def __init__(self, n_components=3, components_basis=None, centering=True): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis + self.regularization = regularization + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.regularization_derivative_degree = derivative_degree + self.regularization_coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -220,6 +234,16 @@ def fit(self, X: FDataBasis, y=None): # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix))/2 + # Apply regularization / penalty if applicable + if self.regularization: + # obtain regularization matrix + regularization_matrix = self.components_basis.penalty( + self.regularization_derivative_degree, + self.regularization_coefficients) + # apply regularization + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix + # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -238,6 +262,8 @@ def fit(self, X: FDataBasis, y=None): self.components = X.copy(basis=self.components_basis, coefficients=self.pca.components_ @ l_matrix_inv) + + final_matrix = np.transpose(final_matrix) @ final_matrix """ if self.svd: # vh contains the eigenvectors transposed diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 2e1d9573f..34d59c1cc 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -12,9 +12,181 @@ "from skfda.representation import FDataBasis, FDataGrid\n", "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", "from sklearn.decomposition import PCA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with Ramsay version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.11070697, -0.37248058, 0.84605883],\n", + " [ 0.53124646, -0.74164593, -0.26637188],\n", + " [-0.83995307, -0.41997654, -0.27998436]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(3, regularization=True,\n", + " derivative_degree=2,\n", + " regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", + " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", + " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.transform(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", + " coefficients=[[1. 0. 0.]\n", + " [0. 2. 0.]\n", + " [0. 0. 3.]])\n" + ] + } + ], + "source": [ + "print(basis_fd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# test penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FDataBasis' object has no attribute 'penalty'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 22, @@ -724,17 +896,17 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.26726124, -0.80178373],\n", - " [ 1.38873015, -0.9258201 ]])" + "array([[ 1. , -3. ],\n", + " [-1.73205081, 1.73205081]])" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +915,7 @@ "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", "sample_points = [0, 1]\n", "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,2), n_basis=2)\n", + "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", "basis_fd = fd.to_basis(basis)\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", @@ -1122,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1136,14 +1308,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fd_data = fetch_weather_temp_only()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n", + "time range: [[ 1 365]]\n" + ] + } + ], + "source": [ + "print(fd_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "can't set attribute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" + ] + } + ], + "source": [ + "fd_data.domain_range = [[0.5, 364.5]]" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1167,7 +1457,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "scrolled": true }, @@ -1176,376 +1491,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-3.6]\n", - " [-3.1]\n", - " [-3.4]\n", - " [-4.4]\n", - " [-2.9]\n", - " [-4.5]\n", - " [-5.5]\n", - " [-3.1]\n", - " [-4. ]\n", - " [-5. ]\n", - " [-4.8]\n", - " [-5.2]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-4.4]\n", - " [-4.6]\n", - " [-5.9]\n", - " [-5. ]\n", - " [-4.9]\n", - " [-5.2]\n", - " [-5.3]\n", - " [-5.9]\n", - " [-5.7]\n", - " [-5. ]\n", - " [-4.5]\n", - " [-4.5]\n", - " [-3.3]\n", - " [-4.1]\n", - " [-4.7]\n", - " [-5.5]\n", - " [-5.4]\n", - " [-5.5]\n", - " [-5.6]\n", - " [-5. ]\n", - " [-5.8]\n", - " [-5.9]\n", - " [-5.4]\n", - " [-6.1]\n", - " [-5.6]\n", - " [-4.6]\n", - " [-5.1]\n", - " [-4.8]\n", - " [-5.1]\n", - " [-6. ]\n", - " [-4.6]\n", - " [-5.3]\n", - " [-4.6]\n", - " [-6. ]\n", - " [-7. ]\n", - " [-6.5]\n", - " [-5.1]\n", - " [-5.2]\n", - " [-5.2]\n", - " [-4.4]\n", - " [-6.2]\n", - " [-5.8]\n", - " [-4.5]\n", - " [-3.9]\n", - " [-4.3]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.5]\n", - " [-3.6]\n", - " [-3.5]\n", - " [-4.1]\n", - " [-4.1]\n", - " [-3. ]\n", - " [-3.5]\n", - " [-4.8]\n", - " [-3.9]\n", - " [-3.4]\n", - " [-4.2]\n", - " [-4. ]\n", - " [-3.6]\n", - " [-2.2]\n", - " [-1.5]\n", - " [-1.8]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.4]\n", - " [-2.1]\n", - " [-2.1]\n", - " [-1.3]\n", - " [-1. ]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.4]\n", - " [-0.2]\n", - " [-0.5]\n", - " [-0.3]\n", - " [-0.8]\n", - " [-0.4]\n", - " [ 0.1]\n", - " [ 1.1]\n", - " [ 0.9]\n", - " [ 1.2]\n", - " [ 0.5]\n", - " [ 1. ]\n", - " [ 1.1]\n", - " [ 0.7]\n", - " [ 0.2]\n", - " [ 0. ]\n", - " [ 0.7]\n", - " [ 1.1]\n", - " [ 1. ]\n", - " [ 1.4]\n", - " [ 1.6]\n", - " [ 1.2]\n", - " [ 2.3]\n", - " [ 2.6]\n", - " [ 2.3]\n", - " [ 2.1]\n", - " [ 1.7]\n", - " [ 2.5]\n", - " [ 3.5]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2.8]\n", - " [ 3.7]\n", - " [ 4.8]\n", - " [ 4.7]\n", - " [ 4.6]\n", - " [ 4.5]\n", - " [ 5. ]\n", - " [ 3.6]\n", - " [ 2.8]\n", - " [ 4.2]\n", - " [ 4.6]\n", - " [ 5.6]\n", - " [ 5.4]\n", - " [ 5.6]\n", - " [ 6.3]\n", - " [ 6.4]\n", - " [ 5.8]\n", - " [ 6.8]\n", - " [ 6.3]\n", - " [ 6.6]\n", - " [ 6.6]\n", - " [ 6.8]\n", - " [ 6.1]\n", - " [ 6. ]\n", - " [ 6.2]\n", - " [ 5.7]\n", - " [ 6.1]\n", - " [ 7.1]\n", - " [ 7.2]\n", - " [ 7.4]\n", - " [ 8.4]\n", - " [ 8.7]\n", - " [ 8.3]\n", - " [ 8.8]\n", - " [ 9.5]\n", - " [ 9.2]\n", - " [ 8.3]\n", - " [ 8.6]\n", - " [ 8.6]\n", - " [ 9.8]\n", - " [ 9. ]\n", - " [ 8.7]\n", - " [ 8.8]\n", - " [ 9.1]\n", - " [ 9.8]\n", - " [10.1]\n", - " [10.6]\n", - " [12.1]\n", - " [11.9]\n", - " [11.2]\n", - " [13. ]\n", - " [13.4]\n", - " [13.1]\n", - " [11.6]\n", - " [11.9]\n", - " [11.6]\n", - " [12.6]\n", - " [11.3]\n", - " [12.5]\n", - " [12.9]\n", - " [13.3]\n", - " [14. ]\n", - " [13.3]\n", - " [12.8]\n", - " [13.5]\n", - " [13.7]\n", - " [13.8]\n", - " [13.8]\n", - " [14. ]\n", - " [14.7]\n", - " [14.8]\n", - " [15. ]\n", - " [15.6]\n", - " [15.6]\n", - " [14.9]\n", - " [15.4]\n", - " [15.6]\n", - " [15.8]\n", - " [15.7]\n", - " [15.2]\n", - " [16. ]\n", - " [15.9]\n", - " [15.8]\n", - " [14.9]\n", - " [15.6]\n", - " [15.1]\n", - " [15.3]\n", - " [16.8]\n", - " [16.2]\n", - " [16. ]\n", - " [16.8]\n", - " [17.1]\n", - " [16.7]\n", - " [16.3]\n", - " [16.9]\n", - " [16.3]\n", - " [16.5]\n", - " [16.5]\n", - " [16.5]\n", - " [16.6]\n", - " [16.4]\n", - " [16. ]\n", - " [16. ]\n", - " [16.4]\n", - " [16.2]\n", - " [15.9]\n", - " [15.8]\n", - " [15.8]\n", - " [15.9]\n", - " [15.2]\n", - " [15.4]\n", - " [14.9]\n", - " [14.3]\n", - " [14.7]\n", - " [14.5]\n", - " [14. ]\n", - " [13.1]\n", - " [13.3]\n", - " [13.8]\n", - " [13.5]\n", - " [14.5]\n", - " [14.4]\n", - " [14.2]\n", - " [13.9]\n", - " [13. ]\n", - " [12.7]\n", - " [12.2]\n", - " [11.8]\n", - " [11.3]\n", - " [12.7]\n", - " [13.2]\n", - " [12.5]\n", - " [12.7]\n", - " [13. ]\n", - " [12.5]\n", - " [12.5]\n", - " [11.6]\n", - " [11.6]\n", - " [11.5]\n", - " [11.5]\n", - " [11.3]\n", - " [11.4]\n", - " [11.6]\n", - " [11. ]\n", - " [11.2]\n", - " [11.1]\n", - " [11.3]\n", - " [11.4]\n", - " [10.8]\n", - " [11.4]\n", - " [10.9]\n", - " [10.4]\n", - " [ 9.6]\n", - " [ 9. ]\n", - " [ 8.6]\n", - " [ 9. ]\n", - " [10. ]\n", - " [ 9.6]\n", - " [ 8.7]\n", - " [ 8.6]\n", - " [ 9.3]\n", - " [ 9.2]\n", - " [ 8.1]\n", - " [ 7.9]\n", - " [ 7.2]\n", - " [ 7.2]\n", - " [ 7.8]\n", - " [ 7. ]\n", - " [ 7.1]\n", - " [ 7.6]\n", - " [ 6.3]\n", - " [ 6.3]\n", - " [ 6.9]\n", - " [ 6.1]\n", - " [ 5.9]\n", - " [ 5.7]\n", - " [ 5.1]\n", - " [ 5.8]\n", - " [ 6. ]\n", - " [ 6.7]\n", - " [ 6. ]\n", - " [ 4.9]\n", - " [ 4.6]\n", - " [ 4.8]\n", - " [ 3.6]\n", - " [ 4.1]\n", - " [ 5.1]\n", - " [ 4.5]\n", - " [ 5.5]\n", - " [ 5.9]\n", - " [ 4.5]\n", - " [ 4.4]\n", - " [ 3.7]\n", - " [ 3.7]\n", - " [ 3.5]\n", - " [ 3.2]\n", - " [ 3.9]\n", - " [ 3.6]\n", - " [ 3.6]\n", - " [ 3.4]\n", - " [ 2.7]\n", - " [ 2. ]\n", - " [ 3. ]\n", - " [ 2.6]\n", - " [ 1.3]\n", - " [ 1.2]\n", - " [ 1.9]\n", - " [ 1.3]\n", - " [ 1.4]\n", - " [ 1.9]\n", - " [ 1.4]\n", - " [ 1.3]\n", - " [ 0.6]\n", - " [ 2.2]\n", - " [ 1.2]\n", - " [ 0.2]\n", - " [-0.6]\n", - " [-0.8]\n", - " [-0.3]\n", - " [-0.1]\n", - " [-0.1]\n", - " [ 0.3]\n", - " [-1.2]\n", - " [-1.9]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.8]\n", - " [-1.7]\n", - " [-2.5]\n", - " [-2.2]\n", - " [-2.2]\n", - " [-1.8]\n", - " [-1.5]\n", - " [-1.9]\n", - " [-2.8]\n", - " [-3.3]\n", - " [-2.2]\n", - " [-1.9]\n", - " [-2.2]\n", - " [-1.7]\n", - " [-2.3]\n", - " [-2.9]\n", - " [-4. ]\n", - " [-3.2]\n", - " [-2.8]\n", - " [-4.2]]\n" + "Data set: [[[ -3.6]\n", + " [ -3.1]\n", + " [ -3.4]\n", + " ...\n", + " [ -3.2]\n", + " [ -2.8]\n", + " [ -4.2]]\n", + "\n", + " [[ -4.4]\n", + " [ -4.2]\n", + " [ -5.3]\n", + " ...\n", + " [ -3.6]\n", + " [ -4.9]\n", + " [ -5.7]]\n", + "\n", + " [[ -3.8]\n", + " [ -3.5]\n", + " [ -4.6]\n", + " ...\n", + " [ -3.4]\n", + " [ -3.3]\n", + " [ -4.8]]\n", + "\n", + " ...\n", + "\n", + " [[-23.3]\n", + " [-24. ]\n", + " [-24.4]\n", + " ...\n", + " [-23.5]\n", + " [-23.9]\n", + " [-24.5]]\n", + "\n", + " [[-26.3]\n", + " [-27.1]\n", + " [-27.8]\n", + " ...\n", + " [-25.7]\n", + " [-24. ]\n", + " [-24.8]]\n", + "\n", + " [[-30.7]\n", + " [-30.6]\n", + " [-31.4]\n", + " ...\n", + " [-29. ]\n", + " [-29.4]\n", + " [-30.5]]]\n", + "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", + " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", + " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", + " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", + " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", + " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", + " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", + " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", + " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", + " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", + " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", + " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", + " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", + " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", + " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", + " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", + " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", + " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", + " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", + " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", + " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", + " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", + " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", + " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", + " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", + " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", + " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", + " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", + " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", + " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", + " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", + " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", + " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", + " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", + " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", + " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", + " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", + " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", + " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", + " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", + " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", + " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", + " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", + " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", + " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", + " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", + " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", + " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", + " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", + " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", + " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", + " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", + " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", + " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", + " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", + " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", + " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", + " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", + " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", + " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", + " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", + "time range: [[ 1 365]]\n" ] } ], "source": [ - "print(fd_data.data_matrix[0,:])" + "print(fd_data)" ] }, { @@ -1577,21 +1638,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", + " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", + " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", + " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", + " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", + " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", + " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", + " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", + " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", + " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", + " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", + " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", + " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", + " 365])]\n" + ] + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "print(fd_data.sample_points)" + ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 3)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(0,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1604,8 +1724,8 @@ ], "source": [ "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=8)\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", "fd_basis = fd_data.to_basis(basis)\n", "\n", "fd_basis.plot()\n", @@ -1614,7 +1734,77 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", + " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", + " [ 117.91048476 -78.29623089 -147.99771918]\n", + " [ 105.64601919 -87.48751862 -135.23786638]\n", + " [ 130.41525077 -68.03400727 -117.56196272]\n", + " [ 100.44054184 -86.56110769 -157.01740098]\n", + " [ 101.11363823 -73.29578447 -179.87563595]\n", + " [ -95.66841575 -101.81332746 -218.82950503]\n", + " [ 59.96125842 -80.13360204 -209.51804361]\n", + " [ 43.6817805 -79.47391326 -211.60839615]\n", + " [ 78.63054053 -76.70039418 -198.32081877]\n", + " [ 79.32089798 -70.62376518 -186.38162541]\n", + " [ 117.7284124 -74.49860223 -195.51372983]\n", + " [ 111.67543758 -72.96278011 -199.5791436 ]\n", + " [ 139.29219563 -71.22916468 -169.13804592]\n", + " [ 140.18018698 -70.14769133 -168.99937059]\n", + " [ 47.74788751 -74.91102958 -200.75128544]\n", + " [ 48.12299843 -76.44333055 -242.23286231]\n", + " [ -1.92277569 -81.08021473 -247.06920225]\n", + " [-134.27412634 -122.6017788 -236.3687109 ]\n", + " [ 53.27128059 -66.12896207 -228.82111637]\n", + " [ 13.96281174 -67.97763734 -242.037578 ]\n", + " [ -63.97320093 -89.60462599 -272.57192012]\n", + " [ 43.84140492 -52.68768517 -199.30406145]\n", + " [ 76.70948389 -48.51619334 -167.07086902]\n", + " [ 167.54308753 -37.09503437 -163.97149634]\n", + " [ 190.36695728 -32.15075301 -91.84336183]\n", + " [ 183.93137869 -30.4104988 -82.15417362]\n", + " [ 73.79549727 -37.36315001 -161.21790136]\n", + " [ 133.89364065 -33.95458738 -74.24172996]\n", + " [ -15.44356138 -48.61881308 -207.5718941 ]\n", + " [ -90.25342609 -55.29068221 -295.12780726]\n", + " [ -94.7351896 -100.41993164 -284.34377575]\n", + " [-183.34401079 -125.4783037 -208.44723865]\n", + " [-175.18346554 -103.92929252 -283.31282874]\n", + " [-314.24776026 -115.66685935 -230.93921551]])\n" + ] + } + ], + "source": [ + "print(fd_basis)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "365\n" + ] + } + ], + "source": [ + "print(fd_data.dim_domain)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1622,21 +1812,21 @@ "output_type": "stream", "text": [ "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]\n", - " [-0.13762736 0.91079734 -0.01523155 0.26094593 -0.22364715 0.17466634\n", - " 0.02103448 0.00270691 0.04696796]\n", - " [ 0.1248126 0.00782831 -0.26652392 0.43910996 0.74478444 0.26511308\n", - " 0.20046433 -0.16454415 0.16810248]])\n", + " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", + " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", + " 0.00253204 0.01019684 0.0094896 ]\n", + " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", + " 0.03855143 -0.02475171 0.01049033]\n", + " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", + " 0.02554942 0.00108415 0.0470334 ]\n", + " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", + " 0.20726074 -0.17024835 0.16232288]])\n", "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1ec27cf89..d78220bfa 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,28 +53,21 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - # initialize weather data with only the temperature. Humidity not needed - fd_data = fetch_weather_temp_only() - n_basis = 8 - n_components = 4 + n_basis = 3 + n_components = 2 # initialize basis data basis = Fourier(n_basis=n_basis) - fd_basis = fd_data.to_basis(basis) - + fd_basis = FDataBasis(basis, + [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], + [0.0, 0.0, 3.0]]) # pass functional principal component analysis to weather data fpca = FPCABasis(n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.9231551, 0.13649663, 0.35694509, 0.0092012, -0.0244525, - -0.02923873, -0.003566887, -0.009654571, -0.010006303], - [-0.3315211, -0.05086430, 0.89218521, 0.1669182, 0.2453900, - 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.91250892, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718], - [0.1247078, 0.01579953, -0.26498643, 0.4118705, 0.7617679, - 0.24922635, 0.213305250, -0.180158701, 0.154863926]] + results = [[-0.1010156, -0.4040594, 0.9091380], + [-0.5050764, 0.8081226, 0.3030441]] results = np.array(results) # compare results obtained using this library. There are slight @@ -84,8 +77,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], - delta=0.03) + results[i][j], delta=0.00001) if __name__ == '__main__': From 1ebddfd71ef7e4205d27695d7e9f8370cd037241 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 18 Feb 2020 20:21:13 +0100 Subject: [PATCH 405/624] Finilized Module testing --- skfda/exploratory/fpca/fpca.py | 53 +- skfda/exploratory/fpca/test.ipynb | 1130 ++++++++++++++++++++++++++++- tests/test_fpca.py | 28 +- 3 files changed, 1157 insertions(+), 54 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 6ea504432..0ddde3aee 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -80,7 +80,7 @@ def transform(self, X, y=None): """ pass - def fit_transform(self, X, y=None): + def fit_transform(self, X, y=None, **fit_params): """ Computes the n_components first principal components and their scores and returns them. @@ -165,8 +165,6 @@ def __init__(self, self.regularization_derivative_degree = derivative_degree self.regularization_coefficients = coefficients - - def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -490,3 +488,52 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) + + +class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): + """ + + """ + + def __init__(self, derivative_degree=2, coefficients=None): + self.derivative_degree = derivative_degree + self.coefficients = coefficients + + def fit(self, X: FDataBasis, y=None): + """Compute cross validation scores for regularized fpca + + Args: + X (FDataBasis): + The data whose points are used to compute the matrix. + y : Ignored + Returns: + self (object) + + """ + return self + + def transform(self, X: FDataGrid, y=None): + """ + Args: + X (FDataGrid): + The data to penalize. + y : Ignored + Returns: + FDataGrid: Functional data smoothed. + + """ + return self + + def score(self, X, y): + """Returns the generalized cross validation (GCV) score. + + Args: + X (FDataGrid): + The data to smooth. + y (FDataGrid): + The target data. Typically the same as ``X``. + Returns: + float: Generalized cross validation score. + + """ + return 1 diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb index 34d59c1cc..8b01e51e1 100644 --- a/skfda/exploratory/fpca/test.ipynb +++ b/skfda/exploratory/fpca/test.ipynb @@ -1,21 +1,940 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skfda\n", + "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", + "from skfda.representation import FDataBasis, FDataGrid\n", + "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", + "from matplotlib import pyplot\n", + "from skfda.representation.basis import Fourier, BSpline\n", + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_weather_temp_only():\n", + " weather_dataset = fetch_weather()\n", + " fd_data = weather_dataset['data']\n", + " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", + " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", + " return fd_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finding lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", + " 0.0017787 0.0105183 0.00913199]\n", + " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", + " 0.03756656 -0.02437487 0.01133841]])\n", + "[15086.27662761 1438.98606096]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000002e+00, -1.65502423e-08],\n", + " [-1.65502423e-08, 1.00000023e+00]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", + " + fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 1.38777878e-16],\n", + " [1.38777878e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca.components.inner_product(fpca.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDataBasis(\n", + " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", + " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", + " 0.00103464 0.00321583 0.00279164]\n", + " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", + " 0.02173688 -0.00739345 0.00334435]])\n", + "[15058.25775083 1410.7365378 ]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9)\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", + "fpca.fit(fd_basis)\n", + "fpca.components.plot()\n", + "print(fpca.components)\n", + "print(fpca.component_values)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.59561036e-08, -2.03098938e-08],\n", + " [-2.03098938e-08, 1.76404890e-07]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived=fpca.components.derivative(2)\n", + "derived.inner_product(derived)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99840439, 0.00203099],\n", + " [0.00203099, 0.98235951]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod = fpca.components.inner_product(fpca.components)\n", + "in_prod" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -9.84455573e-17],\n", + " [-9.84455573e-17, 9.99999997e-01]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_prod + derived.inner_product(derived) * 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.13318664, 0.00793026],\n", + " [0.00793026, 0.09678453]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "derived = fpca_basis.components.derivative(2)\n", + "derived.inner_product(derived)*0.0001" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataBasis(\n", + " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", + " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", + " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", + " 8.27705008e-01]\n", + " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", + " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", + " -1.14509808e+00]\n", + " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", + " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", + " 4.89740559e-01]\n", + " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", + " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", + " -1.48240258e+00]\n", + " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", + " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", + " -8.83919777e-01]\n", + " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", + " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", + " -2.54635122e+00]\n", + " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", + " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", + " -3.23786555e+00]\n", + " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", + " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", + " -3.47823175e+00]\n", + " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", + " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", + " -2.58755972e+00]\n", + " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", + " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", + " -3.66504512e+00]\n", + " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", + " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", + " -3.48198336e+00]\n", + " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", + " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", + " -3.82921672e+00]\n", + " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", + " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", + " -4.04282785e+00]\n", + " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", + " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", + " -1.81811953e+00]\n", + " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", + " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", + " -1.35385457e+00]\n", + " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", + " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", + " -3.28301380e+00]\n", + " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", + " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", + " -3.22319903e+00]\n", + " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", + " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", + " -3.84870184e+00]\n", + " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", + " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", + " -3.09680658e+00]\n", + " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", + " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", + " -3.56248062e+00]\n", + " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", + " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", + " -2.84105467e+00]\n", + " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", + " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", + " -3.73710564e+00]\n", + " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", + " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", + " -2.97497323e-01]\n", + " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", + " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", + " -1.07426684e+00]\n", + " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", + " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", + " -4.44797345e+00]\n", + " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", + " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", + " -3.49884105e+00]\n", + " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", + " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", + " -1.66074555e+00]\n", + " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", + " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", + " -3.86170049e+00]\n", + " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", + " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", + " -1.69638452e+00]\n", + " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", + " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", + " 1.66035500e+00]\n", + " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", + " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", + " 8.13746375e+00]\n", + " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", + " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", + " 2.53460133e-01]\n", + " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", + " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", + " -2.57478775e+00]\n", + " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", + " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", + " 8.39050660e+00]\n", + " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", + " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", + " 1.44190615e+00]],\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " keepdims=False)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", + "fd_basis = fd_data.to_basis(basis)\n", + "fd_basis" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0.00127419, 0.07401235],\n", + " [0.05234239, 0.002548 , 0.07397945],\n", + " [0.05234239, 0.00382106, 0.07392463]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", + "np.transpose(basis.evaluate(range(1, 4)))" + ] + }, { "cell_type": "code", "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", + " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", + " 6.80630538e-01]\n", + " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", + " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", + " -1.24141020e+00]\n", + " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", + " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", + " 3.91548980e-01]\n", + " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", + " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", + " -1.54310715e+00]\n", + " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", + " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", + " -9.97171826e-01]\n", + " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", + " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", + " -2.70796419e+00]\n", + " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", + " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", + " -3.52164922e+00]\n", + " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", + " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", + " -3.68029169e+00]\n", + " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", + " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", + " -2.78348167e+00]\n", + " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", + " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", + " -3.82569943e+00]\n", + " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", + " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", + " -3.67156904e+00]\n", + " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", + " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", + " -3.97983037e+00]\n", + " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", + " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", + " -4.19762933e+00]\n", + " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", + " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", + " -1.84302764e+00]\n", + " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", + " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", + " -1.36876895e+00]\n", + " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", + " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", + " -3.55110682e+00]\n", + " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", + " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", + " -3.55277222e+00]\n", + " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", + " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", + " -4.31493906e+00]\n", + " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", + " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", + " -3.54749651e+00]\n", + " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", + " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", + " -3.90180193e+00]\n", + " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", + " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", + " -3.32251108e+00]\n", + " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", + " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", + " -4.27270050e+00]\n", + " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", + " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", + " -7.14485425e-01]\n", + " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", + " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", + " -1.39465809e+00]\n", + " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", + " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", + " -4.54131572e+00]\n", + " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", + " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", + " -3.53185140e+00]\n", + " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", + " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", + " -1.69971678e+00]\n", + " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", + " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", + " -4.06491676e+00]\n", + " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", + " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", + " -1.71820184e+00]\n", + " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", + " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", + " 1.42431949e+00]\n", + " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", + " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", + " 7.86876570e+00]\n", + " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", + " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", + " -2.45843153e-01]\n", + " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", + " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", + " -2.85985275e+00]\n", + " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", + " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", + " 8.26014480e+00]\n", + " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", + " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", + " 1.43394056e+00]]\n" + ] + } + ], + "source": [ + "print(fd_basis.coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Monomial(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fd_basis.plot()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", + " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis.evaluate(list(range(10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", + " 0. , 0.07402332, 0. , 0.07402332],\n", + " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", + " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", + " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", + " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", + " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", + " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", + "np.transpose(fourier_basis.evaluate(range(4)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" + "## Test convert to basis" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "fd_data = fetch_weather_temp_only()\n", + "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FDataGrid(\n", + " array([[[ -3.6],\n", + " [ -3.1],\n", + " [ -3.4],\n", + " ...,\n", + " [ -3.2],\n", + " [ -2.8],\n", + " [ -4.2]],\n", + " \n", + " [[ -4.4],\n", + " [ -4.2],\n", + " [ -5.3],\n", + " ...,\n", + " [ -3.6],\n", + " [ -4.9],\n", + " [ -5.7]],\n", + " \n", + " [[ -3.8],\n", + " [ -3.5],\n", + " [ -4.6],\n", + " ...,\n", + " [ -3.4],\n", + " [ -3.3],\n", + " [ -4.8]],\n", + " \n", + " ...,\n", + " \n", + " [[-23.3],\n", + " [-24. ],\n", + " [-24.4],\n", + " ...,\n", + " [-23.5],\n", + " [-23.9],\n", + " [-24.5]],\n", + " \n", + " [[-26.3],\n", + " [-27.1],\n", + " [-27.8],\n", + " ...,\n", + " [-25.7],\n", + " [-24. ],\n", + " [-24.8]],\n", + " \n", + " [[-30.7],\n", + " [-30.6],\n", + " [-31.4],\n", + " ...,\n", + " [-29. ],\n", + " [-29.4],\n", + " [-30.5]]]),\n", + " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", + " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", + " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", + " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", + " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", + " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", + " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", + " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", + " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", + " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", + " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", + " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", + " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", + " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", + " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", + " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", + " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", + " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", + " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", + " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", + " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", + " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", + " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", + " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", + " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", + " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", + " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", + " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", + " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", + " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", + " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", + " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", + " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", + " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", + " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", + " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", + " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", + " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", + " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", + " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", + " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", + " domain_range=array([[ 0.5, 364.5]]),\n", + " dataset_label=None,\n", + " axes_labels=None,\n", + " extrapolation=None,\n", + " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", + " keepdims=False)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -25,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -35,7 +954,7 @@ " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -45,23 +964,56 @@ " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" + "fpca_basis.components.coefficients\n", + "# np.linalg.norm(fpca_basis.components.coefficients[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.86681336, -0.00793026],\n", + " [-0.00793026, 0.90321547]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", + " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", + "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.11070697, -0.37248058, 0.84605883],\n", - " [ 0.53124646, -0.74164593, -0.26637188],\n", - " [-0.83995307, -0.41997654, -0.27998436]])" + "array([[-0.10101525, -0.40406102, 0.90913729],\n", + " [ 0.50507627, -0.80812204, -0.30304576]])" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -69,27 +1021,25 @@ "source": [ "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(3, regularization=True,\n", - " derivative_degree=2,\n", - " regularization_parameter=0.0001)\n", + "fpca_basis = FPCABasis(2)\n", "fpca_basis = fpca_basis.fit(basis_fd)\n", "fpca_basis.components.coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-6.71543091e-01, 1.11496681e+00, 1.66533454e-16],\n", - " [-1.30579728e+00, -8.99571523e-01, -1.11022302e-16],\n", - " [ 1.97734037e+00, -2.15395284e-01, -3.05311332e-16]])" + "array([[-0.70710678, 1.1785113 ],\n", + " [-1.41421356, -0.94280904],\n", + " [ 2.12132034, -0.23570226]])" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -98,12 +1048,122 @@ "fpca_basis.transform(basis_fd)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BSpline test with Ramsays version" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00000000e+00, -4.30211422e-16],\n", + " [-4.30211422e-16, 1.00000000e+00]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "fpca_basis = FPCABasis(2)\n", + "fpca_basis = fpca_basis.fit(basis_fd)\n", + "fpca_basis.components.coefficients\n", + "fpca_basis.components.inner_product(fpca_basis.components)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.09991746, 0.02828496])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fpca_basis.component_values" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", + " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", + "meanfd = X.mean()\n", + "# consider moving these lines to FDataBasis as a centering function\n", + "# subtract from each row the mean coefficient matrix\n", + "X.coefficients -= meanfd.coefficients\n", + "n_samples, n_basis = X.coefficients.shape\n", + "components_basis = X.basis.copy()\n", + "g_matrix = components_basis.gram_matrix()\n", + "j_matrix = g_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "components_basis.penalty(derivative_degree=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", + " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", + " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", + " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j_matrix" + ] }, { "cell_type": "code", @@ -1292,20 +2352,6 @@ "## Canadian Weather Study " ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -1838,6 +2884,10 @@ } ], "source": [ + "fd_data = fetch_weather_temp_only()\n", + "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", + "basis = skfda.representation.basis.Fourier(n_basis=3)\n", + "fd_basis = fd_data.to_basis(basis)\n", "fpca = FPCABasis(4)\n", "fpca.fit(fd_basis)\n", "fpca.components.plot()\n", diff --git a/tests/test_fpca.py b/tests/test_fpca.py index d78220bfa..4d8f18ddc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -53,21 +53,27 @@ def test_discretized_fpca_fit_attributes(self): def test_basis_fpca_fit_result(self): - n_basis = 3 - n_components = 2 + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather_temp_only() + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=n_basis) - fd_basis = FDataBasis(basis, - [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], - [0.0, 0.0, 3.0]]) - # pass functional principal component analysis to weather data - fpca = FPCABasis(n_components) + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[-0.1010156, -0.4040594, 0.9091380], - [-0.5050764, 0.8081226, 0.3030441]] + results = [[0.9231551, 0.1364966, 0.3569451, 0.0092012, -0.0244525, + -0.02923873, -0.003566887, -0.009654571, -0.0100063], + [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, + 0.03548997, 0.037938051, -0.025777507, 0.008416904], + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight @@ -77,7 +83,7 @@ def test_basis_fpca_fit_result(self): results[i, :] *= -1 for j in range(n_basis): self.assertAlmostEqual(fpca.components.coefficients[i][j], - results[i][j], delta=0.00001) + results[i][j], delta=0.0000001) if __name__ == '__main__': From 0d2dfddd301f0bfb92433a419aaa73f8661f0105 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 20 Feb 2020 23:49:34 +0100 Subject: [PATCH 406/624] FPCA parameter finding --- skfda/exploratory/fpca/fpca.py | 98 +++++++++++++++++++++++++++------- 1 file changed, 80 insertions(+), 18 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 0ddde3aee..0f594060d 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" @@ -140,7 +141,6 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization=False, derivative_degree=2, coefficients=None, regularization_parameter=0): @@ -159,7 +159,6 @@ def __init__(self, super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis - self.regularization = regularization # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter self.regularization_derivative_degree = derivative_degree @@ -188,6 +187,12 @@ def fit(self, X: FDataBasis, y=None): """ + # the maximum number of components is established by the target basis + # if the target basis is available. + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis + n_samples = X.n_samples + # check that the number of components is smaller than the sample size if self.n_components > X.n_samples: raise AttributeError("The sample size must be bigger than the " @@ -195,8 +200,6 @@ def fit(self, X: FDataBasis, y=None): # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis if self.n_components > n_basis: raise AttributeError("The number of components should be " "smaller than the number of attributes of " @@ -210,9 +213,6 @@ def fit(self, X: FDataBasis, y=None): # subtract from each row the mean coefficient matrix X.coefficients -= meanfd.coefficients - # for reference, X.coefficients is the C matrix - n_samples, n_basis = X.coefficients.shape - # setup principal component basis if not given if self.components_basis: # First fix domain range if not already done @@ -233,7 +233,7 @@ def fit(self, X: FDataBasis, y=None): g_matrix = (g_matrix + np.transpose(g_matrix))/2 # Apply regularization / penalty if applicable - if self.regularization: + if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( self.regularization_derivative_degree, @@ -314,6 +314,37 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) + def find_regularization_parameter(self, fd, grid, derivative_degree=2): + fd -= fd.mean() + # establish the basis for the coefficients + if not self.components_basis: + self.components_basis = fd.basis.copy() + + # the maximum number of components only depends on the target basis + max_components = self.components_basis.n_basis + + # and it cannot be bigger than the number of samples-1, as we are using + # leave one out cross validation + if max_components > fd.n_samples: + raise AttributeError("The target basis must have less n_basis" + "than the number of samples - 1") + + estimator = FPCARegularizationParameterFinder( + max_components=max_components, + derivative_degree=derivative_degree) + + param_grid = {'regularization_parameter': grid} + + search_param = GridSearchCV(estimator, + param_grid=param_grid, + cv=LeaveOneOut(), + refit=True, + n_jobs=35, + verbose=True) + + _ = search_param.fit(fd) + return search_param + class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -490,14 +521,29 @@ def transform(self, X, y=None): np.squeeze(self.components.data_matrix)) +def inner_product_regularized(first, + second, + derivative_degree, + regularization_parameter): + return first.inner_product(second) + \ + regularization_parameter * \ + first.derivative(derivative_degree).\ + inner_product(second.derivative(derivative_degree)) + + class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): """ """ - def __init__(self, derivative_degree=2, coefficients=None): + def __init__(self, + max_components, + derivative_degree=2, + regularization_parameter=1): + self.max_components = max_components self.derivative_degree = derivative_degree - self.coefficients = coefficients + self.regularization_parameter = regularization_parameter + self.components = None def fit(self, X: FDataBasis, y=None): """Compute cross validation scores for regularized fpca @@ -510,30 +556,46 @@ def fit(self, X: FDataBasis, y=None): self (object) """ + # get the components using the proper regularization + fpca = FPCABasis(n_components=self.max_components, + regularization_parameter=self.regularization_parameter, + derivative_degree=self.derivative_degree) + fpca.fit(X, y) + self.components = fpca.components + return self def transform(self, X: FDataGrid, y=None): - """ + """ Transform function for convention + Not called by GridSearchCV as it only fits the data and then calls score Args: X (FDataGrid): The data to penalize. y : Ignored Returns: - FDataGrid: Functional data smoothed. + self """ return self - def score(self, X, y): - """Returns the generalized cross validation (GCV) score. + def score(self, X, y=None): + """Returns the generalized cross validation (GCV) score for the sample + Args: - X (FDataGrid): + X (FDataBasis): The data to smooth. - y (FDataGrid): - The target data. Typically the same as ``X``. + y (None): + convention usage. Returns: float: Generalized cross validation score. """ - return 1 + results = inner_product_regularized(X, + self.components, + self.derivative_degree, + self.regularization_parameter)[0] + results **= 2 + for i in range(len(results)): + results[i] *= len(results) - i + return sum(results) From 012bd6b2ff7605853afad4b2453c5d6637b07ac2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 19:46:01 +0100 Subject: [PATCH 407/624] polish code --- skfda/exploratory/fpca/__init__.py | 1 + skfda/exploratory/fpca/fpca.py | 208 +++-------------------------- 2 files changed, 22 insertions(+), 187 deletions(-) diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py index e69de29bb..c5d0eb7e5 100644 --- a/skfda/exploratory/fpca/__init__.py +++ b/skfda/exploratory/fpca/__init__.py @@ -0,0 +1 @@ +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 0f594060d..022bcbb4a 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -9,7 +9,6 @@ from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV, LeaveOneOut - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -33,7 +32,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -141,8 +140,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - derivative_degree=2, - coefficients=None, + regularization_derivative_degree=2, + regularization_coefficients=None, regularization_parameter=0): """FPCABasis constructor @@ -161,8 +160,8 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = derivative_degree - self.regularization_coefficients = coefficients + self.regularization_derivative_degree = regularization_derivative_degree + self.regularization_coefficients = regularization_coefficients def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,7 +229,7 @@ def fit(self, X: FDataBasis, y=None): j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix))/2 + g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable if self.regularization_parameter > 0: @@ -245,54 +244,29 @@ def fit(self, X: FDataBasis, y=None): # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) - # L^{-1} - l_matrix_inv = np.linalg.inv(l_matrix) - + # we need L^{-1} for a multiplication, there are two possible ways: + # using solve to get the multiplication result directly or just invert + # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = l_matrix_inv @ np.transpose(j_matrix) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + np.sqrt(n_samples) self.pca.fit(final_matrix) - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=self.pca.components_ - @ l_matrix_inv) - - final_matrix = np.transpose(final_matrix) @ final_matrix - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - principal_components = vh @ l_matrix_inv - self.components = X.copy(basis=self.components_basis, - coefficients=principal_components[:self.n_components, :]) - self.component_values = s ** 2 - else: - final_matrix = np.transpose(final_matrix) @ final_matrix - - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(final_matrix) - # sort the eigenvalues and eigenvectors from highest to lowest - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] + # we choose solve to obtain the component coefficients for the + # same reason: it is faster and more efficient + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) - principal_components_t = np.transpose(l_matrix_inv) @ eigenvectors + component_coefficients = np.transpose(component_coefficients) - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - self.components = X.copy(basis=self.components_basis, - coefficients=np.transpose(principal_components_t)) - - self.component_values = eigenvalues - """ + # the singular values obtained using SVD are the squares of eigenvalues + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -314,37 +288,6 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components) - def find_regularization_parameter(self, fd, grid, derivative_degree=2): - fd -= fd.mean() - # establish the basis for the coefficients - if not self.components_basis: - self.components_basis = fd.basis.copy() - - # the maximum number of components only depends on the target basis - max_components = self.components_basis.n_basis - - # and it cannot be bigger than the number of samples-1, as we are using - # leave one out cross validation - if max_components > fd.n_samples: - raise AttributeError("The target basis must have less n_basis" - "than the number of samples - 1") - - estimator = FPCARegularizationParameterFinder( - max_components=max_components, - derivative_degree=derivative_degree) - - param_grid = {'regularization_parameter': grid} - - search_param = GridSearchCV(estimator, - param_grid=param_grid, - cv=LeaveOneOut(), - refit=True, - n_jobs=35, - verbose=True) - - _ = search_param.fit(fd) - return search_param - class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented @@ -408,7 +351,7 @@ def fit(self, X: FDataGrid, y=None): """Computes the n_components first principal components and saves them inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented - please view the referenced book. + please view the referenced book, chapter 8. Args: X (FDataGrid): @@ -437,7 +380,6 @@ def fit(self, X: FDataGrid, y=None): "smaller than the number of discretization " "points of the functional data object.") - # data matrix initialization fd_data = np.squeeze(X.data_matrix) @@ -465,39 +407,11 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # k_estimated is not used for the moment - # k_estimated = fd_data @ np.transpose(fd_data) / n_samples - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) self.pca.fit(final_matrix) self.components = X.copy(data_matrix=self.pca.components_) self.component_values = self.pca.singular_values_ ** 2 - """ - if self.svd: - # vh contains the eigenvectors transposed - # s contains the singular values, which are square roots of eigenvalues - u, s, vh = np.linalg.svd(final_matrix, full_matrices=True, compute_uv=True) - self.components = X.copy(data_matrix=vh[:self.n_components, :]) - self.component_values = s**2 - else: - # perform eigenvalue and eigenvector analysis on this matrix - # eigenvectors is a numpy array, such that its columns are eigenvectors - eigenvalues, eigenvectors = np.linalg.eig(np.transpose(final_matrix) @ final_matrix) - - # sort the eigenvalues and eigenvectors from highest to lowest - # the eigenvectors are the principal components - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - principal_components_t = eigenvectors[:, idx] - - # we only want the first ones, determined by n_components - principal_components_t = principal_components_t[:, :self.n_components] - - # prepare the computed principal components - self.components = X.copy(data_matrix=np.transpose(principal_components_t)) - self.component_values = eigenvalues - """ return self def transform(self, X, y=None): @@ -519,83 +433,3 @@ def transform(self, X, y=None): # components as column vectors return np.squeeze(X.data_matrix) @ np.transpose( np.squeeze(self.components.data_matrix)) - - -def inner_product_regularized(first, - second, - derivative_degree, - regularization_parameter): - return first.inner_product(second) + \ - regularization_parameter * \ - first.derivative(derivative_degree).\ - inner_product(second.derivative(derivative_degree)) - - -class FPCARegularizationParameterFinder(BaseEstimator, TransformerMixin): - """ - - """ - - def __init__(self, - max_components, - derivative_degree=2, - regularization_parameter=1): - self.max_components = max_components - self.derivative_degree = derivative_degree - self.regularization_parameter = regularization_parameter - self.components = None - - def fit(self, X: FDataBasis, y=None): - """Compute cross validation scores for regularized fpca - - Args: - X (FDataBasis): - The data whose points are used to compute the matrix. - y : Ignored - Returns: - self (object) - - """ - # get the components using the proper regularization - fpca = FPCABasis(n_components=self.max_components, - regularization_parameter=self.regularization_parameter, - derivative_degree=self.derivative_degree) - fpca.fit(X, y) - self.components = fpca.components - - return self - - def transform(self, X: FDataGrid, y=None): - """ Transform function for convention - Not called by GridSearchCV as it only fits the data and then calls score - Args: - X (FDataGrid): - The data to penalize. - y : Ignored - Returns: - self - - """ - return self - - def score(self, X, y=None): - """Returns the generalized cross validation (GCV) score for the sample - - - Args: - X (FDataBasis): - The data to smooth. - y (None): - convention usage. - Returns: - float: Generalized cross validation score. - - """ - results = inner_product_regularized(X, - self.components, - self.derivative_degree, - self.regularization_parameter)[0] - results **= 2 - for i in range(len(results)): - results[i] *= len(results) - i - return sum(results) From 402a5b76f0f55b303bb58f4a6c760ed54fd195b7 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 20:13:34 +0100 Subject: [PATCH 408/624] improve documentation --- docs/modules/exploratory/fpca.rst | 21 +++++++++++++++------ examples/plot_fpca.py | 20 +++++++++++--------- 2 files changed, 26 insertions(+), 15 deletions(-) diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst index 2ba724481..b80519747 100644 --- a/docs/modules/exploratory/fpca.rst +++ b/docs/modules/exploratory/fpca.rst @@ -1,10 +1,19 @@ -Functional Principal Component Analysis -======================================= +Functional Principal Component Analysis (FPCA) +============================================== -This module provides tools to analyse the data using functional principal -component analysis. +This module provides tools to analyse functional data using FPCA. FPCA is +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. -FPCA for functional data in basis representation +For a detailed example please view `FPCA example +<../../auto_examples/plot_fpca.html>`_, where the process is applied to several +datasets in both discretized and basis forms. + +FPCA for functional data in a basis representation ---------------------------------------------------------------- .. autosummary:: @@ -12,7 +21,7 @@ FPCA for functional data in basis representation skfda.exploratory.fpca.FPCABasis -FPCA for functional data in discretized representation +FPCA for functional data in a discretized representation ---------------------------------------------------------------- .. autosummary:: diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..32635c4ab 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,9 +10,11 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth +from matplotlib import pyplot + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -36,9 +38,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCADiscretized(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components_.plot() +fpca_discretized.components.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -59,7 +61,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -78,10 +80,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) + 20 * fpca.components.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -92,10 +94,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) + 20 * fpca.components.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -109,4 +111,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components_.plot() +fpca.components.plot() From 4ceda0c7b5915f68f1a432a30350c5ab08d084f1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 19 Mar 2020 23:05:56 +0100 Subject: [PATCH 409/624] Adjust doctest --- skfda/exploratory/fpca/fpca.py | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py index 022bcbb4a..a99c8b0d7 100644 --- a/skfda/exploratory/fpca/fpca.py +++ b/skfda/exploratory/fpca/fpca.py @@ -115,13 +115,15 @@ class FPCABasis(FPCA): the passed FDataBasis object. component_values (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. + The resulting principal components are not compared because there are + several equivalent possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] @@ -130,9 +132,6 @@ class FPCABasis(FPCA): >>> basis_fd = fd.to_basis(basis) >>> fpca_basis = FPCABasis(2) >>> fpca_basis = fpca_basis.fit(basis_fd) - >>> fpca_basis.components.coefficients - array([[ 1. , -3. ], - [-1.73205081, 1.73205081]]) """ @@ -315,21 +314,14 @@ class FPCADiscretized(FPCA): In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the FPCADiscretized object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) >>> fpca_discretized = FPCADiscretized(2) >>> fpca_discretized = fpca_discretized.fit(fd) - >>> fpca_discretized.components.data_matrix - array([[[-0.4472136 ], - [ 0.89442719]], - - [[-0.89442719], - [-0.4472136 ]]]) - >>> fpca_discretized.transform(fd) - array([[-1.11803399e+00, 5.55111512e-17], - [ 1.11803399e+00, -5.55111512e-17]]) """ def __init__(self, n_components=3, weights=None, centering=True): From f1983cb7e4e173dfb1e885dfa4c944541e4f6892 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 20 Mar 2020 22:47:15 +0100 Subject: [PATCH 410/624] transfer files to new location and modify documentation --- docs/modules/exploratory/fpca.rst | 30 -- docs/modules/preprocessing.rst | 10 +- docs/modules/preprocessing/dim_reduction.rst | 4 +- .../preprocessing/dim_reduction/fpca.rst | 16 +- examples/plot_fpca.py | 2 - skfda/exploratory/fpca/__init__.py | 1 - skfda/exploratory/fpca/fpca.py | 427 ------------------ skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 126 +++--- tests/test_fpca.py | 6 +- 11 files changed, 77 insertions(+), 549 deletions(-) delete mode 100644 docs/modules/exploratory/fpca.rst delete mode 100644 skfda/exploratory/fpca/__init__.py delete mode 100644 skfda/exploratory/fpca/fpca.py diff --git a/docs/modules/exploratory/fpca.rst b/docs/modules/exploratory/fpca.rst deleted file mode 100644 index b80519747..000000000 --- a/docs/modules/exploratory/fpca.rst +++ /dev/null @@ -1,30 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. - -For a detailed example please view `FPCA example -<../../auto_examples/plot_fpca.html>`_, where the process is applied to several -datasets in both discretized and basis forms. - -FPCA for functional data in a basis representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.fpca.FPCADiscretized \ No newline at end of file diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..c40695328 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimensionality Reduction ------------------------- +Dimension Reduction +------------------- -The functional data may have too many features so we cannot analyse +The functional data may have too many samples so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimensionality reduction* methods that can reduce the number of features -while still preserving the most relevant information. +*dimension reduction* methods that can extract the most significant +features while reducing the complexity of the data. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index ded6b831f..9da0452b7 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimensionality Reduction -======================== +Dimension Reduction +=================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..7af947b89 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,14 +2,12 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. +a common tool used to reduce dimensionality while preserving the maximum +quantity of variance in the data. FPCA be applied to a functional data object +in either a basis representation or a discretized representation. The output +of FPCA are orthogonal functions (usually a much smaller sample than the input +data sample) that represent the most important modes of variation in the +original data sample. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis @@ -29,4 +27,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 32635c4ab..bee98828d 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,8 +13,6 @@ from skfda.exploratory.fpca import FPCABasis, FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth -from matplotlib import pyplot - ############################################################################## # In this example we are going to use functional principal component analysis to diff --git a/skfda/exploratory/fpca/__init__.py b/skfda/exploratory/fpca/__init__.py deleted file mode 100644 index c5d0eb7e5..000000000 --- a/skfda/exploratory/fpca/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/exploratory/fpca/fpca.py b/skfda/exploratory/fpca/fpca.py deleted file mode 100644 index a99c8b0d7..000000000 --- a/skfda/exploratory/fpca/fpca.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Functional Principal Component Analysis Module.""" - -import numpy as np -import skfda -from abc import ABC, abstractmethod -from skfda.representation.basis import FDataBasis -from skfda.representation.grid import FDataGrid -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut - -__author__ = "Yujian Hong" -__email__ = "yujian.hong@estudiante.uam.es" - - -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - """ - - def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - self.n_components = n_components - self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """ - Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented - in basis form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - Construct an artificial FDataBasis object and run FPCA with this object. - The resulting principal components are not compared because there are - several equivalent possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) - >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) - >>> fpca_basis = fpca_basis.fit(basis_fd) - - """ - - def __init__(self, - n_components=3, - components_basis=None, - centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - # basis that we want to use for the principal components - self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients - - def fit(self, X: FDataBasis, y=None): - """Computes the first n_components principal components and saves them. - The eigenvalues associated with these principal components are also - saved. For more details about how it is implemented please view the - referenced book. - - Args: - X (FDataBasis): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-2] Ramsay, J., Silverman, B. W. (2005). Basis function - expansion of the functions. In *Functional Data Analysis* - (pp. 161-164). Springer. - - """ - - # the maximum number of components is established by the target basis - # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis - n_samples = X.n_samples - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the basis - if self.n_components > n_basis: - raise AttributeError("The number of components should be " - "smaller than the number of attributes of " - "target principal components' basis.") - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients - - # setup principal component basis if not given - if self.components_basis: - # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() - # the matrix that are in charge of changing the computed principal - # components to target matrix is essentially the inner product - # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) - else: - # if no other basis is specified we use the same basis as the passed - # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() - j_matrix = g_matrix - - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - - # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) - # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix - - # obtain triangulation using cholesky - l_matrix = np.linalg.cholesky(g_matrix) - - # we need L^{-1} for a multiplication, there are two possible ways: - # using solve to get the multiplication result directly or just invert - # the matrix. We choose solve because it is faster and more stable. - # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) - - # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) - - self.pca.fit(final_matrix) - - # we choose solve to obtain the component coefficients for the - # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) - - component_coefficients = np.transpose(component_coefficients) - - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case it is the inner product of our data with the components - return X.inner_product(self.components) - - -class FPCADiscretized(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either - in a basis form. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) - """ - - def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ - super().__init__(n_components, centering) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal - components are also saved. For more details about how it is implemented - please view the referenced book, chapter 8. - - Args: - X (FDataGrid): - the functional data object to be analysed in basis - representation - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - - References: - .. [RS05-8-4-1] Ramsay, J., Silverman, B. W. (2005). Discretizing - the functions. In *Functional Data Analysis* (p. 161). Springer. - """ - - # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: - raise AttributeError("The sample size must be bigger than the " - "number of components") - - # check that we do not exceed limits for n_components as it should - # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: - raise AttributeError("The number of components should be " - "smaller than the number of discretization " - "points of the functional data object.") - - # data matrix initialization - fd_data = np.squeeze(X.data_matrix) - - # get the number of samples and the number of points of descretization - n_samples, n_points_discretization = fd_data.shape - - # if centering is True then subtract the mean function to each function - # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) - - # establish weights for each point of discretization - if not self.weights: - # sample_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the weight - # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, - # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] - differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) - - weights_matrix = np.diag(self.weights) - - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 - - return self - - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - - # in this case its the coefficient matrix multiplied by the principal - # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 641ba946c..03763dc90 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection +from . import projection \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..c5d0eb7e5 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCABasis, FPCADiscretized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..8ee9d1370 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular +from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -22,9 +22,17 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first + components (FDataGrid or FDataBasis): this contains the principal + components either in a basis form or discretized form + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -35,6 +43,9 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering + self.components = None + self.component_values = None + self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -87,29 +98,26 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented + """Funcional principal component analysis for functional data represented in basis form. Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. + components (FDataBasis): this contains the principal components either + in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -144,11 +152,6 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -183,8 +186,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = (self.components_basis.n_basis if self.components_basis - else X.basis.n_basis) + n_basis = self.components_basis.n_basis if self.components_basis \ + else X.basis.n_basis n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -233,8 +236,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + g_matrix = g_matrix + self.regularization_parameter \ + * regularization_matrix # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -243,27 +246,25 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / - np.sqrt(n_samples)) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ + np.sqrt(n_samples) - # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + self.pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = np.linalg.solve(np.transpose(l_matrix), + np.transpose(self.pca.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 - self.components_ = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values = self.pca.singular_values_ ** 2 + self.components = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,32 +284,30 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return X.inner_product(self.components) -class FPCAGrid(FPCA): +class FPCADiscretized(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + components (FDataBasis): this contains the principal components either + in a basis form. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. + component_values (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca (sklearn.decomposition.PCA): object for principal component analysis. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. Examples: In this example we apply discretized functional PCA with some simple @@ -320,8 +319,8 @@ class FPCAGrid(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) + >>> fpca_discretized = FPCADiscretized(2) + >>> fpca_discretized = fpca_discretized.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -340,19 +339,11 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. - - The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them + inside the FPCA object.The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. - In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix - defines the numerical integration). By default the weight matrix is - obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis @@ -407,13 +398,10 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + self.pca.fit(final_matrix) + self.components = X.copy(data_matrix=self.pca.components_) + self.component_values = self.pca.singular_values_ ** 2 return self @@ -434,5 +422,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components.data_matrix)) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 4d8f18ddc..9d7340102 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,7 +3,8 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.datasets import fetch_weather @@ -14,7 +15,8 @@ def fetch_weather_temp_only(): fd_data.axes_labels = fd_data.axes_labels[:-1] return fd_data -class MyTestCase(unittest.TestCase): + +class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): fpca = FPCABasis() From 53c3eac870c073e27edf51bd36f7754e780aec26 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:31:33 +0100 Subject: [PATCH 411/624] fix plot imports --- examples/plot_fpca.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index bee98828d..fee579149 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,8 @@ import numpy as np import skfda -from skfda.exploratory.fpca import FPCABasis, FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ + FPCADiscretized from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth From 4e6ecee91a81133e4fb024c95ac151c0c4eb93ca Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 22 Mar 2020 11:36:39 +0100 Subject: [PATCH 412/624] remove unused import --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8ee9d1370..1d78ead0e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,7 +7,6 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from sklearn.model_selection import GridSearchCV, LeaveOneOut __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" From 7462abce4e26e2ac8bff70dffe5d9f75b862a393 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 22:59:00 +0100 Subject: [PATCH 413/624] fix newline and conform to scikit learn --- skfda/preprocessing/dim_reduction/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 70 +++++++++++-------- tests/test_fpca.py | 4 +- 3 files changed, 42 insertions(+), 34 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 03763dc90..641ba946c 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -1 +1 @@ -from . import projection \ No newline at end of file +from . import projection diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1d78ead0e..5bab71980 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -21,17 +21,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first - components (FDataGrid or FDataBasis): this contains the principal - components either in a basis form or discretized form - component_values (array_like): this contains the values (eigenvalues) - associated with the principal components - pca (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. """ - def __init__(self, n_components=3, centering=True): + def __init__(self, n_components=3, centering=True): """FPCA constructor Args: @@ -42,9 +34,6 @@ def __init__(self, n_components=3, centering=True): """ self.n_components = n_components self.centering = centering - self.components = None - self.component_values = None - self.pca = PCA(n_components=self.n_components) @abstractmethod def fit(self, X, y=None): @@ -106,14 +95,14 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for PCA. + pca_ (sklearn.decomposition.PCA): object for PCA. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -151,6 +140,11 @@ def __init__(self, function will be used. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True + regularization_parameter (float): this parameter sets the degree of + regularization that is desired. Defaults to 0 (no + regularization). When this value is large, the resulting + principal components tends to be 0. + """ super().__init__(n_components, centering) # basis that we want to use for the principal components @@ -251,19 +245,21 @@ def fit(self, X: FDataBasis, y=None): final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ np.sqrt(n_samples) - self.pca.fit(final_matrix) + # initialize the pca module provided by scikit-learn + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca.components_)) + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values = self.pca.singular_values_ ** 2 - self.components = X.copy(basis=self.components_basis, - coefficients=component_coefficients) + self.component_values_ = self.pca_.singular_values_ ** 2 + self.components_ = X.copy(basis=self.components_basis, + coefficients=component_coefficients) return self @@ -283,7 +279,7 @@ def transform(self, X, y=None): """ # in this case it is the inner product of our data with the components - return X.inner_product(self.components) + return X.inner_product(self.components_) class FPCADiscretized(FPCA): @@ -298,12 +294,12 @@ class FPCADiscretized(FPCA): passed FDataBasis object is modified. components (FDataBasis): this contains the principal components either in a basis form. - components_basis (Basis): the basis in which we want the principal + components_basis_ (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values (array_like): this contains the values (eigenvalues) + component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. - pca (sklearn.decomposition.PCA): object for principal component analysis. + pca_ (sklearn.decomposition.PCA): object for principal component analysis. In both cases (discretized FPCA and basis FPCA) the problem can be reduced to a regular PCA problem and use the framework provided by sklearn to continue. @@ -338,11 +334,20 @@ def __init__(self, n_components=3, weights=None, centering=True): self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object.The eigenvalues associated with these principal + """Computes the n_components first principal components and saves them. + + The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. + In summary, we are performing standard multivariate PCA over + :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\\mathbf{X}` is the data + matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + defines the numerical integration). By default the weight matrix is + obtained using the trapezoidal rule. + + Args: X (FDataGrid): the functional data object to be analysed in basis @@ -397,10 +402,13 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca.fit(final_matrix) - self.components = X.copy(data_matrix=self.pca.components_) - self.component_values = self.pca.singular_values_ ** 2 + + self.pca_ = PCA(n_components=self.n_components) + self.pca_.fit(final_matrix) + self.components_ = X.copy(data_matrix=self.pca_.components_) + self.component_values_ = self.pca_.singular_values_ ** 2 return self @@ -421,5 +429,5 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components.data_matrix)) + return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( + np.squeeze(self.components_.data_matrix))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9d7340102..b1fa402f2 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -81,10 +81,10 @@ def test_basis_fpca_fit_result(self): # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 for j in range(n_basis): - self.assertAlmostEqual(fpca.components.coefficients[i][j], + self.assertAlmostEqual(fpca.components_.coefficients[i][j], results[i][j], delta=0.0000001) From 1620813925f3a9a05a252587ef096fc36fe66743 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 24 Mar 2020 23:19:08 +0100 Subject: [PATCH 414/624] fix documentation --- docs/modules/preprocessing.rst | 10 +++++----- docs/modules/preprocessing/dim_reduction.rst | 4 ++-- docs/modules/preprocessing/dim_reduction/fpca.rst | 14 ++++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index c40695328..ae14a2938 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -31,12 +31,12 @@ variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. -Dimension Reduction -------------------- +Dimensionality Reduction +------------------------ -The functional data may have too many samples so we cannot analyse +The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use -*dimension reduction* methods that can extract the most significant -features while reducing the complexity of the data. +*dimensionality reduction* methods that can reduce the number of features +while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 9da0452b7..ded6b831f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -1,5 +1,5 @@ -Dimension Reduction -=================== +Dimensionality Reduction +======================== When dealing with data samples with high dimensionality, we often need to reduce the dimensions so we can better observe the data. diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 7af947b89..86bd559b3 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -2,12 +2,14 @@ Functional Principal Component Analysis (FPCA) ============================================== This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality while preserving the maximum -quantity of variance in the data. FPCA be applied to a functional data object -in either a basis representation or a discretized representation. The output -of FPCA are orthogonal functions (usually a much smaller sample than the input -data sample) that represent the most important modes of variation in the -original data sample. +a common tool used to reduce dimensionality. It can be applied to a functional +data object in either a basis representation or a discretized representation. +The output of FPCA are the projections of the original sample functions into the +directions (principal components) in which most of the variance is conserved. +In multivariate PCA those directions are vectors. However, in FPCA we seek +functions that maximizes the sample variance operator, and then project our data +samples into those principal components. The number of principal components are +at most the number of original features. For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis From 4832697f707edafcfd4f870bb8a76844144daf5e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 28 Mar 2020 22:26:05 +0100 Subject: [PATCH 415/624] address issues in comments, np.testing, docstring and change FPCADiscretized to FPCAGrid --- .../preprocessing/dim_reduction/fpca.rst | 2 +- examples/plot_fpca.py | 19 +++-- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 69 ++++++++++--------- tests/test_fpca.py | 20 ++---- 5 files changed, 53 insertions(+), 59 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 86bd559b3..5b1b8eb3e 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -29,4 +29,4 @@ FPCA for functional data in a discretized representation .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCADiscretized \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index fee579149..7ac15a417 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,8 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.representation.basis import BSpline, Fourier from skfda.datasets import fetch_growth @@ -37,9 +36,9 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCADiscretized(n_components=2) +fpca_discretized = FPCAGrid(n_components=2) fpca_discretized.fit(fd) -fpca_discretized.components.plot() +fpca_discretized.components_.plot() ############################################################################## # In the second case, the data is first converted to use a basis representation @@ -60,7 +59,7 @@ # is similar to the discretized case. fpca = FPCABasis(n_components=2) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() ############################################################################## # To better illustrate the effects of the obtained two principal components, @@ -79,10 +78,10 @@ # growth between the children. mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[0, :]]) + 20 * fpca.components_.coefficients[0, :]]) mean_fd.plot() ############################################################################## @@ -93,10 +92,10 @@ mean_fd = basis_fd.mean() mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] + - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.coefficients = np.vstack([mean_fd.coefficients, mean_fd.coefficients[0, :] - - 20 * fpca.components.coefficients[1, :]]) + 20 * fpca.components_.coefficients[1, :]]) mean_fd.plot() ############################################################################## @@ -110,4 +109,4 @@ basis_fd = fd.to_basis(BSpline(n_basis=7)) fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) -fpca.components.plot() +fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index c5d0eb7e5..fd2b66bf4 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCADiscretized +from ._fpca import FPCABasis, FPCAGrid diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5bab71980..5f82bb9f4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -7,6 +7,7 @@ from skfda.representation.grid import FDataGrid from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from scipy.linalg import solve_triangular __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -86,26 +87,29 @@ def fit_transform(self, X, y=None, **fit_params): class FPCABasis(FPCA): - """Funcional principal component analysis for functional data represented + """Functional principal component analysis for functional data represented in basis form. Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + pca_ (sklearn.decomposition.PCA): object for PCA. + In both cases (discretized FPCA and basis FPCA) the problem can be + reduced to a regular PCA problem and use the framework provided by + sklearn to continue. + + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_ (FDataBasis): this contains the principal components either - in a basis form. components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -143,7 +147,7 @@ def __init__(self, regularization_parameter (float): this parameter sets the degree of regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting - principal components tends to be 0. + principal components tends to be constant. """ super().__init__(n_components, centering) @@ -179,8 +183,8 @@ def fit(self, X: FDataBasis, y=None): # the maximum number of components is established by the target basis # if the target basis is available. - n_basis = self.components_basis.n_basis if self.components_basis \ - else X.basis.n_basis + n_basis = (self.components_basis.n_basis if self.components_basis + else X.basis.n_basis) n_samples = X.n_samples # check that the number of components is smaller than the sample size @@ -229,8 +233,8 @@ def fit(self, X: FDataBasis, y=None): self.regularization_derivative_degree, self.regularization_coefficients) # apply regularization - g_matrix = g_matrix + self.regularization_parameter \ - * regularization_matrix + g_matrix = (g_matrix + self.regularization_parameter * + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -239,11 +243,11 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = np.linalg.solve(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = X.coefficients @ np.transpose(l_inv_j_t) / \ - np.sqrt(n_samples) + final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / + np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn self.pca_ = PCA(n_components=self.n_components) @@ -251,8 +255,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient - component_coefficients = np.linalg.solve(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + component_coefficients = solve_triangular(np.transpose(l_matrix), + np.transpose(self.pca_.components_)) component_coefficients = np.transpose(component_coefficients) @@ -282,21 +286,13 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -class FPCADiscretized(FPCA): +class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. Attributes: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - components (FDataBasis): this contains the principal components either + components_ (FDataBasis): this contains the principal components either in a basis form. - components_basis_ (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. pca_ (sklearn.decomposition.PCA): object for principal component analysis. @@ -304,6 +300,16 @@ class FPCADiscretized(FPCA): reduced to a regular PCA problem and use the framework provided by sklearn to continue. + Parameters: + n_components (int): number of principal components to obtain from + functional principal component analysis. Defaults to 3. + centering (bool): if True then calculate the mean of the functional data + object and center the data first. Defaults to True. If True the + passed FDataBasis object is modified. + weights (numpy.array): the weights vector used for discrete + integration. If none then the trapezoidal rule is used for + computing the weights. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -314,8 +320,8 @@ class FPCADiscretized(FPCA): >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> sample_points = [0, 1] >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_discretized = FPCADiscretized(2) - >>> fpca_discretized = fpca_discretized.fit(fd) + >>> fpca_grid = FPCAGrid(2) + >>> fpca_grid = fpca_grid.fit(fd) """ def __init__(self, n_components=3, weights=None, centering=True): @@ -347,7 +353,6 @@ def fit(self, X: FDataGrid, y=None): defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. - Args: X (FDataGrid): the functional data object to be analysed in basis diff --git a/tests/test_fpca.py b/tests/test_fpca.py index b1fa402f2..a71602c28 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -3,19 +3,10 @@ import numpy as np from skfda import FDataGrid, FDataBasis from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, \ - FPCADiscretized +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid from skfda.datasets import fetch_weather -def fetch_weather_temp_only(): - weather_dataset = fetch_weather() - fd_data = weather_dataset['data'] - fd_data.data_matrix = fd_data.data_matrix[:, :, :1] - fd_data.axes_labels = fd_data.axes_labels[:-1] - return fd_data - - class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): @@ -37,7 +28,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCADiscretized() + fpca = FPCAGrid() with self.assertRaises(AttributeError): fpca.fit(None) @@ -58,7 +49,7 @@ def test_basis_fpca_fit_result(self): n_basis = 9 n_components = 3 - fd_data = fetch_weather_temp_only() + fd_data = fetch_weather()['data'].coordinates[0] fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) @@ -83,9 +74,8 @@ def test_basis_fpca_fit_result(self): for i in range(n_components): if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): results[i, :] *= -1 - for j in range(n_basis): - self.assertAlmostEqual(fpca.components_.coefficients[i][j], - results[i][j], delta=0.0000001) + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) if __name__ == '__main__': From b541d0e282ffe01daa42dbd2024c02871b390373 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 3 Apr 2020 23:02:55 +0200 Subject: [PATCH 416/624] adapt to use linear differential operator --- .../dim_reduction/projection/_fpca.py | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 5f82bb9f4..756b8d918 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -109,7 +109,11 @@ class FPCABasis(FPCA): components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - + regularization_lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. If you input an integer + then the derivative of that degree will be used to regularize + the principal components. Examples: Construct an artificial FDataBasis object and run FPCA with this object. @@ -130,9 +134,8 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization_derivative_degree=2, - regularization_coefficients=None, - regularization_parameter=0): + regularization_parameter=0, + regularization_lfd=2): """FPCABasis constructor Args: @@ -148,6 +151,9 @@ def __init__(self, regularization that is desired. Defaults to 0 (no regularization). When this value is large, the resulting principal components tends to be constant. + regularization_lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. """ super().__init__(n_components, centering) @@ -155,8 +161,7 @@ def __init__(self, self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_derivative_degree = regularization_derivative_degree - self.regularization_coefficients = regularization_coefficients + self.regularization_lfd = regularization_lfd def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -230,8 +235,8 @@ def fit(self, X: FDataBasis, y=None): if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( - self.regularization_derivative_degree, - self.regularization_coefficients) + self.regularization_lfd + ) # apply regularization g_matrix = (g_matrix + self.regularization_parameter * regularization_matrix) From 6db9ae1b7df1e72a2dcb90a6b7d410e613e8b698 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 4 Apr 2020 16:51:48 +0200 Subject: [PATCH 417/624] add monomial basis example for the plot example --- examples/plot_fpca.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 7ac15a417..513c94bf4 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -11,7 +11,7 @@ import numpy as np import skfda from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.representation.basis import BSpline, Fourier +from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.datasets import fetch_growth ############################################################################## @@ -110,3 +110,16 @@ fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components_.plot() + +############################################################################## +# We can observe that if we switch to the Monomial basis, we also lose the +# key features of the first principal components because it distorts the +# principal components, adding extra maximums and minimums. Therefore, in this +# case the best option is to use the BSpline basis as the basis for the +# principal components +dataset = fetch_growth() +fd = dataset['data'] +basis_fd = fd.to_basis(BSpline(n_basis=7)) +fpca = FPCABasis(n_components=2, components_basis=Monomial(n_basis=4)) +fpca.fit(basis_fd) +fpca.components_.plot() From 3def6c2d54ae76e2e32caf9cd3b30d5b13b5eb1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Sun, 5 Apr 2020 20:36:05 +0200 Subject: [PATCH 418/624] Fix covariances formulas. --- skfda/misc/covariances.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index f433a38a3..e2a1115c9 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -211,7 +211,7 @@ def to_sklearn(self): class Gaussian(Covariance): """Gaussian covariance function.""" - _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(\frac{||x - y||^2}{2l^2}" + _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||^2}{2l^2}" r"\right)") _parameters = [("variance", r"\sigma^2"), @@ -238,7 +238,7 @@ def to_sklearn(self): class Exponential(Covariance): """Exponential covariance function.""" - _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(\frac{||x - y||}{l}" + _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||}{l}" r"\right)") _parameters = [("variance", r"\sigma^2"), From db85110b7073b479c6f1ebf6cbdf62b4015a4e86 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 6 Apr 2020 00:27:35 +0200 Subject: [PATCH 419/624] Relative import of linear regression. --- skfda/ml/regression/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index 03dd84e82..ac29834dc 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,5 +1,4 @@ -from skfda.ml.regression.linear import MultivariateLinearRegression - from ..._neighbors import KNeighborsRegressor, RadiusNeighborsRegressor +from .linear import MultivariateLinearRegression From 81dc7ab2aa493acc1cb48b166d0f596ee0e18a9b Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 7 Apr 2020 15:26:02 +0200 Subject: [PATCH 420/624] correct minor mistake in solve_triangular --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 756b8d918..b0b5be378 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -248,7 +248,8 @@ def fit(self, X: FDataBasis, y=None): # using solve to get the multiplication result directly or just invert # the matrix. We choose solve because it is faster and more stable. # The following matrix is needed: L^{-1}*J^T - l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix)) + l_inv_j_t = solve_triangular(l_matrix, np.transpose(j_matrix), + lower=True) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = (X.coefficients @ np.transpose(l_inv_j_t) / @@ -261,7 +262,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_)) + np.transpose(self.pca_.components_), + lower=False) component_coefficients = np.transpose(component_coefficients) From 0b006317cf279679acee80ff844cb0587cfdd3a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 9 Apr 2020 18:59:20 +0200 Subject: [PATCH 421/624] Trying to fix numpy array print in doctest --- skfda/inference/anova/anova_oneway.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index fa96eac57..1752d3907 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -257,6 +257,7 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): >>> from skfda.inference.anova import oneway_anova >>> from skfda.datasets import fetch_gait >>> from numpy.random import RandomState + >>> from numpy import printoptions >>> np.set_printoptions(precision=6) >>> fd = fetch_gait()["data"].coordinates[1] @@ -268,8 +269,9 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) - >>> dist - array([ 163.357652, 208.594951, 229.767803]) + >>> with printoptions(precision=4): + ... print(dist) + [163.3577 208.595 229.7678] References: [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An From de9ac3012f1f3b0cd530b479003a3c73e947e511 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 9 Apr 2020 19:20:15 +0200 Subject: [PATCH 422/624] Trying again to fix numpy array print in doctest --- skfda/inference/anova/anova_oneway.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 1752d3907..af632d528 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -59,7 +59,7 @@ def v_sample_stat(fd, weights, p=2): Finally the value of the statistic is calculated: - >>> v_sample_stat(fd, weights) + >>> stat = v_sample_stat(fd, weights) 0.01649448843348894 References: @@ -271,7 +271,7 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): ... return_dist=True) >>> with printoptions(precision=4): ... print(dist) - [163.3577 208.595 229.7678] + [ 163.3577 208.595 229.7678] References: [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An From 49def564b1d43ecaee77527566c38124ba0eb0b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 9 Apr 2020 19:28:46 +0200 Subject: [PATCH 423/624] Fixing bug with set_printoptions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index af632d528..3b5f32298 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -258,7 +258,6 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): >>> from skfda.datasets import fetch_gait >>> from numpy.random import RandomState >>> from numpy import printoptions - >>> np.set_printoptions(precision=6) >>> fd = fetch_gait()["data"].coordinates[1] >>> fd1, fd2, fd3 = fd[:13], fd[13:26], fd[26:] From fd541e3ea7549b52d84f5bb7664fc12a6eb55050 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Thu, 9 Apr 2020 19:33:43 +0200 Subject: [PATCH 424/624] Fixing print in sample stat MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 3b5f32298..c828763c8 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -59,7 +59,7 @@ def v_sample_stat(fd, weights, p=2): Finally the value of the statistic is calculated: - >>> stat = v_sample_stat(fd, weights) + >>> v_sample_stat(fd, weights) 0.01649448843348894 References: From 044c65dd59a9acf9b01d2618cb27dfbf5246662e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 10 Apr 2020 15:28:01 +0200 Subject: [PATCH 425/624] Update skfda/inference/anova/anova_oneway.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Carlos Ramos Carreño --- skfda/inference/anova/anova_oneway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index c828763c8..03ab8558a 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -279,7 +279,7 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): """ if len(args) < 2: - raise ValueError("At least two samples must be passed as parameter.") + raise ValueError("At least two groups must be passed as parameter.") if not all(isinstance(fd, FData) for fd in args): raise ValueError("Argument type must inherit FData.") if n_reps < 1: From dfa88514df15dcc1cc8542fbd6e6ff132e262e6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 10 Apr 2020 20:31:49 +0200 Subject: [PATCH 426/624] Including .DS_Store in .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 741aff5c6..53f5bcc28 100644 --- a/.gitignore +++ b/.gitignore @@ -107,3 +107,6 @@ ENV/ .idea/ pip-wheel-metadata/ + +# macOS DS_Store +.DS_Store From 08c3dec9ef3253c20031584d51586d9245e3b564 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 10 Apr 2020 20:34:09 +0200 Subject: [PATCH 427/624] Concatenate function for a list of FData objects. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway_synthetic.py | 11 ++---- skfda/inference/anova/anova_oneway.py | 4 +- skfda/representation/_fdatabasis.py | 10 +++++ skfda/representation/_functional_data.py | 20 ++++++++++ skfda/representation/grid.py | 48 ++++++++++++++++++++++++ tests/test_grid.py | 41 ++++++++++++++++++++ 6 files changed, 124 insertions(+), 10 deletions(-) diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 4c5583c61..47961d139 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -89,11 +89,10 @@ # In the plot below we can see the simulated trajectories for each mean, # and the averages for each group. -fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) +fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -fd.plot(group=groups, legend=True, alpha=0.6) -fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() +FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() ################################################################################ # In the following, the same process will be followed incrementing sigma @@ -122,8 +121,7 @@ fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -fd.plot(group=groups, legend=True, alpha=0.6) -fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() +FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() ################################################################################ # Plot for :math:`\sigma = 10`: @@ -146,8 +144,7 @@ fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -fd.plot(group=groups, legend=True, alpha=0.6) -fd1.mean().concatenate(fd2.mean().concatenate(fd3.mean()).concatenate()).plot() +FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() ################################################################################ # **References:** diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 03ab8558a..5444ac558 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -295,9 +295,7 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): raise ValueError("All FDataGrid passed must have the same sample " "points.") - fd_means = fd_groups[0].mean() - for fd in fd_groups[1:]: - fd_means = fd_means.concatenate(fd.mean()) + fd_means = FDataGrid.concatenate_samples([fd.mean() for fd in fd_groups]) vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/_fdatabasis.py index 172ac9d4b..ca0f0bc79 100644 --- a/skfda/representation/_fdatabasis.py +++ b/skfda/representation/_fdatabasis.py @@ -791,6 +791,16 @@ def concatenate(self, *others, as_coordinates=False): return self.copy(coefficients=np.concatenate(data, axis=0)) + @staticmethod + def concatenate_samples(objects, as_coordinates=False): + if len(objects) < 1: + raise ValueError("At least one FDataBasis object must be provided " + "to concatenate.") + if not isinstance(objects[0], FDataBasis): + raise ValueError("Items in list must be instances of FDataBasis.") + return objects[0].concatenate(*objects[1:], + as_coordinates=as_coordinates) + def compose(self, fd, *, eval_points=None, **kwargs): """Composition of functions. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 5a1a0294c..ae1a44ec5 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -788,6 +788,26 @@ def concatenate(self, *others, as_coordinates=False): """ pass + @staticmethod + @abstractmethod + def concatenate_samples(objects, as_coordinates=False): + """Join samples from a list of similar FData objects. + + Joins samples of FData objects if they have the same dimensions and + sampling points. + + Args: + objects (list of :obj:`FData`): Objects to be concatenated. + as_coordinates (boolean, optional): If False concatenates as + new samples, else, concatenates each value as new components + of the image. Defaults to false. + + Returns: + :obj:`FData`: FData object with the samples from the original + objects. + """ + pass + @abstractmethod def compose(self, fd, *, eval_points=None, **kwargs): """Composition of functions. diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 1f1c9b006..56aba71a6 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -787,6 +787,54 @@ def concatenate(self, *others, as_coordinates=False): else: return self.copy(data_matrix=np.concatenate(data, axis=0)) + @staticmethod + def concatenate_samples(objects, as_coordinates=False): + """Join samples from a list of similar FDataGrid objects. + + Joins samples of FDataGrid objects if they have the same + dimensions and sampling points. + + Args: + objects (list of :obj:`FDataGrid`): Objects to be concatenated. + as_coordinates (boolean, optional): If False concatenates as + new samples, else, concatenates each value as new components + of the image. Defaults to false. + + Returns: + :obj:`FDataGrid`: FDataGrid object with the samples from the + original objects. + + Raises: + ValueError: In case the provided list of FDataGrid objects is empty. + + Examples: + >>> fd = FDataGrid([1,2,4,5,8], range(5)) + >>> fd_2 = FDataGrid([3,4,7,9,2], range(5)) + >>> FDataGrid.concatenate_samples([fd, fd_2]) + FDataGrid( + array([[[1], + [2], + [4], + [5], + [8]], + + [[3], + [4], + [7], + [9], + [2]]]), + sample_points=[array([0, 1, 2, 3, 4])], + domain_range=array([[0, 4]]), + ...) + """ + if len(objects) < 1: + raise ValueError("At least one FDataGrid object must be provided " + "to concatenate.") + if not isinstance(objects[0], FDataGrid): + raise ValueError("Items in list must be instances of FDataGrid.") + return objects[0].concatenate(*objects[1:], + as_coordinates=as_coordinates) + def scatter(self, *args, **kwargs): """Scatter plot of the FDatGrid object. diff --git a/tests/test_grid.py b/tests/test_grid.py index 2026cefa2..94011596a 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -99,6 +99,47 @@ def test_concatenate_coordinates(self): fd = fd1.concatenate(fd2, as_coordinates=True) np.testing.assert_equal(None, fd.axes_labels) + def test_concatenate_samples(self): + fd1 = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) + fd2 = FDataGrid([[3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) + + fd1.axes_labels = ["x", "y"] + fd = FDataGrid.concatenate_samples([fd1, fd2]) + + np.testing.assert_equal(fd.n_samples, 4) + np.testing.assert_equal(fd.dim_codomain, 1) + np.testing.assert_equal(fd.dim_domain, 1) + np.testing.assert_array_equal(fd.data_matrix[..., 0], + [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], + [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) + np.testing.assert_array_equal(fd1.axes_labels, fd.axes_labels) + + def test_concatenate_samples_coordinates(self): + fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) + fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) + + fd1.axes_labels = ["x", "y"] + fd2.axes_labels = ["w", "t"] + fd = FDataGrid.concatenate_samples([fd1, fd2], as_coordinates=True) + + np.testing.assert_equal(fd.n_samples, 2) + np.testing.assert_equal(fd.dim_codomain, 2) + np.testing.assert_equal(fd.dim_domain, 1) + + np.testing.assert_array_equal(fd.data_matrix, + [[[1, 3], [2, 4], [3, 5], [4, 6]], + [[2, 4], [3, 5], [4, 6], [5, 7]]]) + + # Testing labels + np.testing.assert_array_equal(["x", "y", "t"], fd.axes_labels) + fd1.axes_labels = ["x", "y"] + fd2.axes_labels = None + fd = fd1.concatenate(fd2, as_coordinates=True) + np.testing.assert_array_equal(["x", "y", None], fd.axes_labels) + fd1.axes_labels = None + fd = fd1.concatenate(fd2, as_coordinates=True) + np.testing.assert_equal(None, fd.axes_labels) + def test_coordinates(self): fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) fd1.axes_labels = ["x", "y"] From d33dff272a6e438b41491ec39825bf72523a9b0c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Fri, 10 Apr 2020 21:13:36 +0200 Subject: [PATCH 428/624] Including missing docstring for FDataBasis method. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/representation/_fdatabasis.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/_fdatabasis.py index ca0f0bc79..41042d5af 100644 --- a/skfda/representation/_fdatabasis.py +++ b/skfda/representation/_fdatabasis.py @@ -793,6 +793,29 @@ def concatenate(self, *others, as_coordinates=False): @staticmethod def concatenate_samples(objects, as_coordinates=False): + """Join samples from a list of similar FDataBasis objects. + + Joins samples of FDataBasis objects if they have the same + dimensions and sampling points. + + Args: + objects (list of :obj:`FDataBasis`): Objects to be concatenated. + as_coordinates (boolean, optional): If False concatenates as + new samples, else, concatenates each value as new components + of the image. Defaults to false. + + Returns: + :obj:`FDataGrid`: FDataGrid object with the samples from the + original objects. + + Raises: + ValueError: In case the provided list of FDataBasis objects is + empty. + + Todo: + By the moment, only unidimensional objects are supported in basis + representation. + """ if len(objects) < 1: raise ValueError("At least one FDataBasis object must be provided " "to concatenate.") From 86fa2b1f49946213286c43bfa2bd69f0cbec1db4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 13 Apr 2020 00:17:09 +0200 Subject: [PATCH 429/624] Add tests for FDatagrid compatibility with ufuncs. --- skfda/representation/grid.py | 4 +-- tests/test_fdatagrid_numpy.py | 47 +++++++++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 2 deletions(-) create mode 100644 tests/test_fdatagrid_numpy.py diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 1f1c9b006..9218a7e8c 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -1127,8 +1127,8 @@ def __getitem__(self, key): def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): for i in inputs: - if isinstance(i, FDataGrid) and not np.all(i.sample_points == - self.sample_points): + if isinstance(i, FDataGrid) and not np.array_equal( + i.sample_points, self.sample_points): return NotImplemented new_inputs = [i.data_matrix if isinstance(i, FDataGrid) diff --git a/tests/test_fdatagrid_numpy.py b/tests/test_fdatagrid_numpy.py new file mode 100644 index 000000000..b1a3b13cb --- /dev/null +++ b/tests/test_fdatagrid_numpy.py @@ -0,0 +1,47 @@ +from skfda import FDataGrid +import unittest +import numpy as np + + +class TestFDataGridNumpy(unittest.TestCase): + + def test_monary_ufunc(self): + data_matrix = np.arange(15).reshape(3, 5) + + fd = FDataGrid(data_matrix) + + fd_sqrt = np.sqrt(fd) + + fd_sqrt_build = FDataGrid(np.sqrt(data_matrix)) + + self.assertEqual(fd_sqrt, fd_sqrt_build) + + def test_binary_ufunc(self): + data_matrix = np.arange(15).reshape(3, 5) + data_matrix2 = 2 * np.arange(15).reshape(3, 5) + + fd = FDataGrid(data_matrix) + fd2 = FDataGrid(data_matrix2) + + fd_mul = np.multiply(fd, fd2) + + fd_mul_build = FDataGrid(data_matrix * data_matrix2) + + self.assertEqual(fd_mul, fd_mul_build) + + def test_out_ufunc(self): + data_matrix = np.arange(15.).reshape(3, 5) + data_matrix_copy = np.copy(data_matrix) + + fd = FDataGrid(data_matrix) + + np.sqrt(fd, out=fd) + + fd_sqrt_build = FDataGrid(np.sqrt(data_matrix_copy)) + + self.assertEqual(fd, fd_sqrt_build) + + +if __name__ == '__main__': + print() + unittest.main() From 0ddb3ecba9428710abb9b1fdbf7f068a663d10da Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Apr 2020 02:12:25 +0200 Subject: [PATCH 430/624] Split basis in several files. --- skfda/_utils/__init__.py | 3 +- skfda/_utils/_utils.py | 11 +- skfda/misc/_lfd.py | 10 +- skfda/representation/basis.py | 1451 ----------------- skfda/representation/basis/__init__.py | 7 + skfda/representation/basis/_basis.py | 399 +++++ skfda/representation/basis/_bspline.py | 453 +++++ .../basis/_coefficients_transformer.py | 44 + skfda/representation/basis/_constant.py | 64 + .../representation/{ => basis}/_fdatabasis.py | 14 +- skfda/representation/basis/_fourier.py | 302 ++++ skfda/representation/basis/_monomial.py | 212 +++ 12 files changed, 1504 insertions(+), 1466 deletions(-) delete mode 100644 skfda/representation/basis.py create mode 100644 skfda/representation/basis/__init__.py create mode 100644 skfda/representation/basis/_basis.py create mode 100644 skfda/representation/basis/_bspline.py create mode 100644 skfda/representation/basis/_coefficients_transformer.py create mode 100644 skfda/representation/basis/_constant.py rename skfda/representation/{ => basis}/_fdatabasis.py (99%) create mode 100644 skfda/representation/basis/_fourier.py create mode 100644 skfda/representation/basis/_monomial.py diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 8e2219e47..329b72770 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -2,4 +2,5 @@ from ._utils import (_list_of_arrays, _coordinate_list, _check_estimator, parameter_aliases, - _to_grid, check_is_univariate) + _to_grid, check_is_univariate, + _same_domain) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index c6beb356f..c6c46f8cc 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -6,6 +6,7 @@ import numpy as np + def check_is_univariate(fd): """Checks if an FData is univariate and raises an error @@ -21,12 +22,13 @@ def check_is_univariate(fd): if fd.dim_domain != 1 or fd.dim_codomain != 1: raise ValueError(f"The functional data must be univariate, i.e., " + f"with dim_domain=1 " + - (f"" if fd.dim_domain==1 + (f"" if fd.dim_domain == 1 else f"(currently is {fd.dim_domain}) ") + f"and dim_codomain=1 " + - (f"" if fd.dim_codomain==1 else + (f"" if fd.dim_codomain == 1 else f"(currently is {fd.dim_codomain})")) + def _to_grid(X, y, eval_points=None): """Transform a pair of FDatas in grids to perform calculations.""" @@ -112,6 +114,11 @@ def _coordinate_list(axes): return np.vstack(list(map(np.ravel, np.meshgrid(*axes, indexing='ij')))).T +def _same_domain(fd, fd2): + """Check if the domain range of two objects is the same.""" + return np.array_equal(fd.domain_range, fd2.domain_range) + + def parameter_aliases(**alias_assignments): """Allows using aliases for parameters""" def decorator(f): diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index 23cfb2746..c47772838 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -4,6 +4,8 @@ import numpy as np +from .._utils import _same_domain + __author__ = "Pablo Pérez Manso" __email__ = "92manso@gmail.com" @@ -115,8 +117,7 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, Otherwise, defaults to (0,1). """ - from ..representation.basis import (FDataBasis, Constant, - _same_domain) + from ..representation.basis import FDataBasis, Constant num_args = sum( [a is not None for a in [order_or_weights, order, weights]]) @@ -156,9 +157,8 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, .reshape(-1, 1)).to_list()) elif all(isinstance(n, FDataBasis) for n in weights): - if all([_same_domain(weights[0].domain_range, - x.domain_range) and x.n_samples == 1 for x - in weights]): + if all([_same_domain(weights[0], x) + and x.n_samples == 1 for x in weights]): self.weights = weights real_domain_range = weights[0].domain_range diff --git a/skfda/representation/basis.py b/skfda/representation/basis.py deleted file mode 100644 index 7e2294ad9..000000000 --- a/skfda/representation/basis.py +++ /dev/null @@ -1,1451 +0,0 @@ -"""Module for functional data manipulation in a basis system. - -Defines functional data object in a basis function system representation and -the corresponding basis classes. - -""" -from abc import ABC, abstractmethod -import copy -import scipy.signal - -from numpy import polyder, polyint, polymul, polyval -import scipy.integrate -from scipy.interpolate import BSpline as SciBSpline -from scipy.interpolate import PPoly -import scipy.interpolate -from scipy.special import binom -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.validation import check_is_fitted - -import numpy as np - -from .._utils import _list_of_arrays -from ._fdatabasis import FDataBasis, FDataBasisDType - - -__author__ = "Miguel Carbajo Berrocal" -__email__ = "miguel.carbajo@estudiante.uam.es" - -# aux functions - - -def _polypow(p, n=2): - if n > 2: - return polymul(p, _polypow(p, n - 1)) - if n == 2: - return polymul(p, p) - elif n == 1: - return p - elif n == 0: - return [1] - else: - raise ValueError("n must be greater than 0.") - - -def _check_domain(domain_range): - for domain in domain_range: - if len(domain) != 2 or domain[0] >= domain[1]: - raise ValueError(f"The interval {domain} is not well-defined.") - - -def _same_domain(one_domain_range, other_domain_range): - return np.array_equal(one_domain_range, other_domain_range) - - -class Basis(ABC): - """Defines the structure of a basis function system. - - Attributes: - domain_range (tuple): a tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis (int): number of functions in the basis. - - """ - - def __init__(self, domain_range=None, n_basis=1): - """Basis constructor. - - Args: - domain_range (tuple or list of tuples, optional): Definition of the - interval where the basis defines a space. Defaults to (0,1). - n_basis: Number of functions that form the basis. Defaults to 1. - """ - - if domain_range is not None: - # TODO: Allow multiple dimensions - domain_range = _list_of_arrays(domain_range) - - # Some checks - _check_domain(domain_range) - - if n_basis < 1: - raise ValueError("The number of basis has to be strictly " - "possitive.") - - self._domain_range = domain_range - self.n_basis = n_basis - self._drop_index_lst = [] - - super().__init__() - - @property - def domain_range(self): - if self._domain_range is None: - return [np.array([0, 1])] - else: - return self._domain_range - - @domain_range.setter - def domain_range(self, value): - self._domain_range = value - - @abstractmethod - def _evaluate(self, eval_points, derivative=0): - """Subclasses must override this to provide basis evaluation.""" - pass - - @abstractmethod - def _derivative(self, coefs, order=1): - pass - - def evaluate(self, eval_points, derivative=0): - """Evaluate Basis objects and its derivatives. - - Evaluates the basis function system or its derivatives at a list of - given values. - - Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (numpy.darray): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - """ - if derivative < 0: - raise ValueError("derivative only takes non-negative values.") - - eval_points = np.atleast_1d(eval_points) - if np.any(np.isnan(eval_points)): - raise ValueError("The list of points where the function is " - "evaluated can not contain nan values.") - - return self._evaluate(eval_points, derivative) - - def __call__(self, *args, **kwargs): - return self.evaluate(*args, **kwargs) - - def plot(self, chart=None, *, derivative=0, **kwargs): - """Plot the basis object or its derivatives. - - Args: - chart (figure object, axe or list of axes, optional): figure over - with the graphs are plotted or axis over where the graphs are - plotted. - derivative (int or tuple, optional): Order of derivative to be - plotted. Defaults 0. - **kwargs: keyword arguments to be passed to the - fdata.plot function. - - Returns: - fig (figure): figure object in which the graphs are plotted. - - """ - self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) - - def _numerical_penalty(self, lfd): - """Return a penalty matrix using a numerical approach. - - See :func:`~basis.Basis.penalty`. - - Args: - lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. - """ - from skfda.misc import LinearDifferentialOperator - - if not isinstance(lfd, LinearDifferentialOperator): - lfd = LinearDifferentialOperator(lfd) - - indices = np.triu_indices(self.n_basis) - - def cross_product(x): - """Multiply the two lfds""" - res = lfd(self)([x])[:, 0] - - return res[indices[0]] * res[indices[1]] - - # Range of first dimension - domain_range = self.domain_range[0] - - penalty_matrix = np.empty((self.n_basis, self.n_basis)) - - # Obtain the integrals for the upper matrix - triang_vec = scipy.integrate.quad_vec( - cross_product, domain_range[0], domain_range[1])[0] - - # Set upper matrix - penalty_matrix[indices] = triang_vec - - # Set lower matrix - penalty_matrix[(indices[1], indices[0])] = triang_vec - - return penalty_matrix - - def _penalty(self, lfd): - """ - Subclasses may override this for computing analytically - the penalty matrix in the cases when that is possible. - - Returning NotImplemented will use numerical computation - of the penalty matrix. - """ - return NotImplemented - - def penalty(self, lfd): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2]_: - - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds - - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. - - Returns: - numpy.array: Penalty matrix. - - References: - .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the - roughness penalty. In *Functional Data Analysis* (pp. 106-107). - Springer. - - """ - from skfda.misc import LinearDifferentialOperator - - if not isinstance(lfd, LinearDifferentialOperator): - lfd = LinearDifferentialOperator(lfd) - - matrix = self._penalty(lfd) - - if matrix is NotImplemented: - return self._numerical_penalty(lfd) - else: - return matrix - - @abstractmethod - def basis_of_product(self, other): - pass - - @abstractmethod - def rbasis_of_product(self, other): - pass - - @staticmethod - def default_basis_of_product(one, other): - """Default multiplication for a pair of basis""" - if not _same_domain(one.domain_range, other.domain_range): - raise ValueError("Ranges are not equal.") - - norder = min(8, one.n_basis + other.n_basis) - n_basis = max(one.n_basis + other.n_basis, norder + 1) - return BSpline(one.domain_range, n_basis, norder) - - def rescale(self, domain_range=None): - r"""Return a copy of the basis with a new domain range, with the - corresponding values rescaled to the new bounds. - - Args: - domain_range (tuple, optional): Definition of the interval - where the basis defines a space. Defaults uses the same as - the original basis. - """ - - if domain_range is None: - domain_range = self.domain_range - - return type(self)(domain_range, self.n_basis) - - def same_domain(self, other): - r"""Returns if two basis are defined on the same domain range. - - Args: - other (Basis): Basis to check the domain range definition - """ - return _same_domain(self.domain_range, other.domain_range) - - def copy(self): - """Basis copy""" - return copy.deepcopy(self) - - def to_basis(self): - return FDataBasis(self.copy(), np.identity(self.n_basis)) - - def _list_to_R(self, knots): - retstring = "c(" - for i in range(0, len(knots)): - retstring = retstring + str(knots[i]) + ", " - return retstring[0:len(retstring) - 2] + ")" - - def _to_R(self): - raise NotImplementedError - - def _inner_matrix(self, other=None): - r"""Return the Inner Product Matrix of a pair of basis. - - The Inner Product Matrix is defined as - - .. math:: - IP_{ij} = \langle\phi_i, \theta_j\rangle - - where :math:`\phi_i` is the ith element of the basi and - :math:`\theta_j` is the jth element of the second basis. - This matrix helps on the calculation of the inner product - between objects on two basis and for the change of basis. - - Args: - other (:class:`Basis`): Basis to compute the inner product - matrix. If not basis is given, it computes the matrix with - itself returning the Gram Matrix - - Returns: - numpy.array: Inner Product Matrix of two basis - - """ - if other is None or self == other: - return self.gram_matrix() - - first = self.to_basis() - second = other.to_basis() - - inner = np.zeros((self.n_basis, other.n_basis)) - - for i in range(self.n_basis): - for j in range(other.n_basis): - inner[i, j] = first[i].inner_product(second[j], None, None) - - return inner - - def gram_matrix(self): - r"""Return the Gram Matrix of a basis - - The Gram Matrix is defined as - - .. math:: - G_{ij} = \langle\phi_i, \phi_j\rangle - - where :math:`\phi_i` is the ith element of the basis. This is a - symmetric matrix and positive-semidefinite. - - Returns: - numpy.array: Gram Matrix of the basis. - - """ - fbasis = self.to_basis() - - gram = np.zeros((self.n_basis, self.n_basis)) - - for i in range(fbasis.n_basis): - for j in range(i, fbasis.n_basis): - gram[i, j] = fbasis[i].inner_product(fbasis[j], None, None) - gram[j, i] = gram[i, j] - - return gram - - def inner_product(self, other): - return np.transpose(other.inner_product(self.to_basis())) - - def _add_same_basis(self, coefs1, coefs2): - return self.copy(), coefs1 + coefs2 - - def _add_constant(self, coefs, constant): - coefs = coefs.copy() - constant = np.array(constant) - coefs[:, 0] = coefs[:, 0] + constant - - return self.copy(), coefs - - def _sub_same_basis(self, coefs1, coefs2): - return self.copy(), coefs1 - coefs2 - - def _sub_constant(self, coefs, other): - coefs = coefs.copy() - other = np.array(other) - coefs[:, 0] = coefs[:, 0] - other - - return self.copy(), coefs - - def _mul_constant(self, coefs, other): - coefs = coefs.copy() - other = np.atleast_2d(other).reshape(-1, 1) - coefs = coefs * other - - return self.copy(), coefs - - def __repr__(self): - """Representation of a Basis object.""" - return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " - f"n_basis={self.n_basis})") - - def __eq__(self, other): - """Equality of Basis""" - return (type(self) == type(other) - and _same_domain(self.domain_range, other.domain_range) - and self.n_basis == other.n_basis) - - -class Constant(Basis): - """Constant basis. - - Basis for constant functions - - Attributes: - domain_range (tuple): a tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - - Examples: - Defines a contant base over the interval :math:`[0, 5]` consisting - on the constant function 1 on :math:`[0, 5]`. - - >>> bs_cons = Constant((0,5)) - - """ - - def __init__(self, domain_range=None): - """Constant basis constructor. - - Args: - domain_range (tuple): Tuple defining the domain over which the - function is defined. - - """ - super().__init__(domain_range, 1) - - def _evaluate(self, eval_points, derivative=0): - return (np.ones((1, len(eval_points))) if derivative == 0 - else np.zeros((1, len(eval_points)))) - - def _derivative(self, coefs, order=1): - return (self.copy(), coefs.copy() if order == 0 - else self.copy(), np.zeros(coefs.shape)) - - def _penalty(self, lfd): - coefs = lfd.constant_weights() - if coefs is None: - return NotImplemented - - return np.array([[coefs[0] ** 2 * - (self.domain_range[0][1] - - self.domain_range[0][0])]]) - - def basis_of_product(self, other): - """Multiplication of a Constant Basis with other Basis""" - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Ranges are not equal.") - - return other.copy() - - def rbasis_of_product(self, other): - """Multiplication of a Constant Basis with other Basis""" - return other.copy() - - def _to_R(self): - drange = self.domain_range[0] - return "create.constant.basis(rangeval = c(" + str(drange[0]) + "," +\ - str(drange[1]) + "))" - - -class Monomial(Basis): - """Monomial basis. - - Basis formed by powers of the argument :math:`t`: - - .. math:: - 1, t, t^2, t^3... - - Attributes: - domain_range (tuple): a tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis (int): number of functions in the basis. - - Examples: - Defines a monomial base over the interval :math:`[0, 5]` consisting - on the first 3 powers of :math:`t`: :math:`1, t, t^2`. - - >>> bs_mon = Monomial((0,5), n_basis=3) - - And evaluates all the functions in the basis in a list of descrete - values. - - >>> bs_mon.evaluate([0, 1, 2]) - array([[1, 1, 1], - [0, 1, 2], - [0, 1, 4]]) - - And also evaluates its derivatives - - >>> bs_mon.evaluate([0, 1, 2], derivative=1) - array([[0, 0, 0], - [1, 1, 1], - [0, 2, 4]]) - >>> bs_mon.evaluate([0, 1, 2], derivative=2) - array([[0, 0, 0], - [0, 0, 0], - [2, 2, 2]]) - - """ - - def _coef_mat(self, derivative): - """ - Obtain the matrix of coefficients. - - Each column of coef_mat contains the numbers that must be multiplied - together in order to obtain the coefficient of each basis function - Thus, column i will contain i, i - 1, ..., i - derivative + 1. - """ - - seq = np.arange(self.n_basis) - coef_mat = np.linspace(seq, seq - derivative + 1, - derivative, dtype=int) - - return seq, coef_mat - - def _coefs_exps_derivatives(self, derivative): - """ - Return coefficients and exponents of the derivatives. - - This function is used for computing the basis functions and evaluate. - - When the exponent would be negative (the coefficient in that case - is zero) returns 0 as the exponent (to prevent division by zero). - """ - seq, coef_mat = self._coef_mat(derivative) - coefs = np.prod(coef_mat, axis=0) - - exps = np.maximum(seq - derivative, 0) - - return coefs, exps - - def _evaluate(self, eval_points, derivative=0): - - coefs, exps = self._coefs_exps_derivatives(derivative) - - raised = np.power.outer(eval_points, exps) - - return (coefs * raised).T - - def _derivative(self, coefs, order=1): - return (Monomial(self.domain_range, self.n_basis - order), - np.array([np.polyder(x[::-1], order)[::-1] - for x in coefs])) - - def _evaluate_constant_lfd(self, weights): - """ - Evaluate constant weights of a linear differential operator - over the basis functions. - """ - - max_derivative = len(weights) - 1 - - _, coef_mat = self._coef_mat(max_derivative) - - # Compute coefficients for each derivative - coefs = np.cumprod(coef_mat, axis=0) - - # Add derivative 0 row - coefs = np.concatenate((np.ones((1, self.n_basis)), coefs)) - - # Now each row correspond to each basis and each column to - # each derivative - coefs_t = coefs.T - - # Multiply by the weights - weighted_coefs = coefs_t * weights - assert len(weighted_coefs) == self.n_basis - - # Now each row has the right weight, but the polynomials are in a - # decreasing order and with different exponents - - # Resize the coefs so that there are as many rows as the number of - # basis - # The matrix is now triangular - # refcheck is False to prevent exceptions while debugging - weighted_coefs = np.copy(weighted_coefs.T) - weighted_coefs.resize(self.n_basis, - self.n_basis, refcheck=False) - weighted_coefs = weighted_coefs.T - - # Shift the coefficients so that they correspond to the right - # exponent - indexes = np.tril_indices(self.n_basis) - polynomials = np.zeros_like(weighted_coefs) - polynomials[indexes[0], indexes[1] - - indexes[0] - 1] = weighted_coefs[indexes] - - # At this point, each row of the matrix correspond to a polynomial - # that is the result of applying the linear differential operator - # to each element of the basis - - return polynomials - - def _penalty(self, lfd): - - weights = lfd.constant_weights() - if weights is None: - return NotImplemented - - polynomials = self._evaluate_constant_lfd(weights) - - # Expand the polinomials with 0, so that the multiplication fits - # inside. It will need the double of the degree - length_with_padding = polynomials.shape[1] * 2 - 1 - - # Multiplication of polynomials is a convolution. - # The convolution can be performed in parallel applying a Fourier - # transform and then doing a normal multiplication in that - # space, coverting back with the inverse Fourier transform - fft = np.fft.rfft(polynomials, length_with_padding) - - # We compute only the upper matrix, as the penalty matrix is - # symmetrical - indices = np.triu_indices(self.n_basis) - fft_mul = fft[indices[0]] * fft[indices[1]] - - integrand = np.fft.irfft(fft_mul, length_with_padding) - - integration_domain = self.domain_range[0] - - # To integrate, divide by the position and increase the exponent - # in the evaluation - denom = np.arange(integrand.shape[1], 0, -1) - integrand /= denom - - # Add column of zeros at the right to increase exponent - integrand = np.pad(integrand, - pad_width=((0, 0), - (0, 1)), - mode='constant') - - # Now, apply Barrow's rule - # polyval applies Horner method over the first dimension, - # so we need to transpose - x_right = np.polyval(integrand.T, integration_domain[1]) - x_left = np.polyval(integrand.T, integration_domain[0]) - - integral = x_right - x_left - - penalty_matrix = np.empty((self.n_basis, self.n_basis)) - - # Set upper matrix - penalty_matrix[indices] = integral - - # Set lower matrix - penalty_matrix[(indices[1], indices[0])] = integral - - return penalty_matrix - - def basis_of_product(self, other): - """Multiplication of a Monomial Basis with other Basis""" - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Ranges are not equal.") - - if isinstance(other, Monomial): - return Monomial(self.domain_range, self.n_basis + other.n_basis) - - return other.rbasis_of_product(self) - - def rbasis_of_product(self, other): - """Multiplication of a Monomial Basis with other Basis""" - return Basis.default_basis_of_product(self, other) - - def _to_R(self): - drange = self.domain_range[0] - return "create.monomial.basis(rangeval = c(" + str(drange[0]) + "," +\ - str(drange[1]) + "), nbasis = " + str(self.n_basis) + ")" - - -class BSpline(Basis): - r"""BSpline basis. - - BSpline basis elements are defined recursively as: - - .. math:: - B_{i, 1}(x) = 1 \quad \text{if } t_i \le x < t_{i+1}, - \quad 0 \text{ otherwise} - - .. math:: - B_{i, k}(x) = \frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x) - + \frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x) - - Where k indicates the order of the spline. - - Implementation details: In order to allow a discontinuous behaviour at - the boundaries of the domain it is necessary to placing m knots at the - boundaries [RS05]_. This is automatically done so that the user only has to - specify a single knot at the boundaries. - - Attributes: - domain_range (tuple): A tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis (int): Number of functions in the basis. - order (int): Order of the splines. One greather than their degree. - knots (list): List of knots of the spline functions. - - Examples: - Constructs specifying number of basis and order. - - >>> bss = BSpline(n_basis=8, order=4) - - If no order is specified defaults to 4 because cubic splines are - the most used. So the previous example is the same as: - - >>> bss = BSpline(n_basis=8) - - It is also possible to create a BSpline basis specifying the knots. - - >>> bss = BSpline(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) - - Once we create a basis we can evaluate each of its functions at a - set of points. - - >>> bss = BSpline(n_basis=3, order=3) - >>> bss.evaluate([0, 0.5, 1]) - array([[ 1. , 0.25, 0. ], - [ 0. , 0.5 , 0. ], - [ 0. , 0.25, 1. ]]) - - And evaluates first derivative - - >>> bss.evaluate([0, 0.5, 1], derivative=1) - array([[-2., -1., 0.], - [ 2., 0., -2.], - [ 0., 1., 2.]]) - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - - """ - - def __init__(self, domain_range=None, n_basis=None, order=4, knots=None): - """Bspline basis constructor. - - Args: - domain_range (tuple, optional): Definition of the interval where - the basis defines a space. Defaults to (0,1) if knots are not - specified. If knots are specified defaults to the first and - last element of the knots. - n_basis (int, optional): Number of splines that form the basis. - order (int, optional): Order of the splines. One greater that - their degree. Defaults to 4 which mean cubic splines. - knots (array_like): List of knots of the splines. If domain_range - is specified the first and last elements of the knots have to - match with it. - - """ - - if domain_range is not None: - domain_range = _list_of_arrays(domain_range) - - if len(domain_range) != 1: - raise ValueError("Domain range should be unidimensional.") - - domain_range = domain_range[0] - - # Knots default to equally space points in the domain_range - if knots is None: - if n_basis is None: - raise ValueError("Must provide either a list of knots or the" - "number of basis.") - else: - knots = list(knots) - knots.sort() - if domain_range is None: - domain_range = (knots[0], knots[-1]) - else: - if domain_range[0] != knots[0] or domain_range[1] != knots[-1]: - raise ValueError("The ends of the knots must be the same " - "as the domain_range.") - - # n_basis default to number of knots + order of the splines - 2 - if n_basis is None: - n_basis = len(knots) + order - 2 - - if (n_basis - order + 2) < 2: - raise ValueError(f"The number of basis ({n_basis}) minus the " - f"order of the bspline ({order}) should be " - f"greater than 3.") - - self.order = order - self.knots = None if knots is None else list(knots) - super().__init__(domain_range, n_basis) - - # Checks - if self.n_basis != self.order + len(self.knots) - 2: - raise ValueError(f"The number of basis ({self.n_basis}) has to " - f"equal the order ({self.order}) plus the " - f"number of knots ({len(self.knots)}) minus 2.") - - @property - def knots(self): - if self._knots is None: - return list(np.linspace(*self.domain_range[0], - self.n_basis - self.order + 2)) - else: - return self._knots - - @knots.setter - def knots(self, value): - self._knots = value - - def _evaluation_knots(self): - """ - Get the knots adding m knots to the boundary in order to allow a - discontinuous behaviour at the boundaries of the domain [RS05]_. - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - """ - return np.array([self.knots[0]] * (self.order - 1) + self.knots + - [self.knots[-1]] * (self.order - 1)) - - def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - - It uses the scipy implementation of BSplines to compute the values - for each element of the basis. - - Args: - eval_points (array_like): List of points where the basis system is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - Implementation details: In order to allow a discontinuous behaviour at - the boundaries of the domain it is necessary to placing m knots at the - boundaries [RS05]_. This is automatically done so that the user only - has to specify a single knot at the boundaries. - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - - """ - if derivative > (self.order - 1): - return np.zeros((self.n_basis, len(eval_points))) - - # Places m knots at the boundaries - knots = self._evaluation_knots() - - # c is used the select which spline the function splev below computes - c = np.zeros(len(knots)) - - # Initialise empty matrix - mat = np.empty((self.n_basis, len(eval_points))) - - # For each basis computes its value for each evaluation point - for i in range(self.n_basis): - # write a 1 in c in the position of the spline calculated in each - # iteration - c[i] = 1 - # compute the spline - mat[i] = scipy.interpolate.splev(eval_points, (knots, c, - self.order - 1), - der=derivative) - c[i] = 0 - - return mat - - def _derivative(self, coefs, order=1): - deriv_splines = [self._to_scipy_BSpline(coefs[i]).derivative(order) - for i in range(coefs.shape[0])] - - deriv_coefs = [BSpline._from_scipy_BSpline(spline)[1] - for spline in deriv_splines] - - deriv_basis = BSpline._from_scipy_BSpline(deriv_splines[0])[0] - - return deriv_basis, np.array(deriv_coefs)[:, 0:deriv_basis.n_basis] - - def _penalty(self, lfd): - - coefs = lfd.constant_weights() - if coefs is None: - return NotImplemented - - nonzero = np.flatnonzero(coefs) - - # All derivatives above the order of the spline are effectively - # zero - nonzero = nonzero[nonzero < self.order] - - if len(nonzero) == 0: - return np.zeros((self.n_basis, self.n_basis)) - - # We will only deal with one nonzero coefficient right now - if len(nonzero) != 1: - return NotImplemented - - derivative_degree = nonzero[0] - - if derivative_degree == self.order - 1: - # The derivative of the bsplines are constant in the intervals - # defined between knots - knots = np.array(self.knots) - mid_inter = (knots[1:] + knots[:-1]) / 2 - constants = self.evaluate(mid_inter, - derivative=derivative_degree).T - knots_intervals = np.diff(self.knots) - # Integration of product of constants - return constants.T @ np.diag(knots_intervals) @ constants - - # We only deal with the case without zero length intervals - # for now - if np.any(np.diff(self.knots) == 0): - return NotImplemented - - # Compute exactly using the piecewise polynomial - # representation of splines - - # Places m knots at the boundaries - knots = self._evaluation_knots() - - # c is used the select which spline the function - # PPoly.from_spline below computes - c = np.zeros(len(knots)) - - # Initialise empty list to store the piecewise polynomials - ppoly_lst = [] - - no_0_intervals = np.where(np.diff(knots) > 0)[0] - - # For each basis gets its piecewise polynomial representation - for i in range(self.n_basis): - - # Write a 1 in c in the position of the spline - # transformed in each iteration - c[i] = 1 - - # Gets the piecewise polynomial representation and gets - # only the positions for no zero length intervals - # This polynomial are defined relatively to the knots - # meaning that the column i corresponds to the ith knot. - # Let the ith knot be a - # Then f(x) = pp(x - a) - pp = PPoly.from_spline((knots, c, self.order - 1)) - pp_coefs = pp.c[:, no_0_intervals] - - # We have the coefficients for each interval in coordinates - # (x - a), so we will need to subtract a when computing the - # definite integral - ppoly_lst.append(pp_coefs) - c[i] = 0 - - # Now for each pair of basis computes the inner product after - # applying the linear differential operator - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - for interval in range(len(no_0_intervals)): - for i in range(self.n_basis): - poly_i = np.trim_zeros(ppoly_lst[i][:, - interval], 'f') - if len(poly_i) <= derivative_degree: - # if the order of the polynomial is lesser or - # equal to the derivative the result of the - # integral will be 0 - continue - # indefinite integral - integral = polyint(_polypow(polyder( - poly_i, derivative_degree), 2)) - # definite integral - penalty_matrix[i, i] += np.diff(polyval( - integral, self.knots[interval: interval + 2] - self.knots[interval]))[0] - - for j in range(i + 1, self.n_basis): - poly_j = np.trim_zeros(ppoly_lst[j][:, - interval], 'f') - if len(poly_j) <= derivative_degree: - # if the order of the polynomial is lesser - # or equal to the derivative the result of - # the integral will be 0 - continue - # indefinite integral - integral = polyint( - polymul(polyder(poly_i, derivative_degree), - polyder(poly_j, derivative_degree))) - # definite integral - penalty_matrix[i, j] += np.diff(polyval( - integral, self.knots[interval: interval + 2] - self.knots[interval]) - )[0] - penalty_matrix[j, i] = penalty_matrix[i, j] - return penalty_matrix - - def rescale(self, domain_range=None): - r"""Return a copy of the basis with a new domain range, with the - corresponding values rescaled to the new bounds. - The knots of the BSpline will be rescaled in the new interval. - - Args: - domain_range (tuple, optional): Definition of the interval - where the basis defines a space. Defaults uses the same as - the original basis. - """ - - knots = np.array(self.knots, dtype=np.dtype('float')) - - if domain_range is not None: # Rescales the knots - knots -= knots[0] - knots *= ((domain_range[1] - domain_range[0] - ) / (self.knots[-1] - self.knots[0])) - knots += domain_range[0] - - # Fix possible round error - knots[0] = domain_range[0] - knots[-1] = domain_range[1] - - else: - # TODO: Allow multiple dimensions - domain_range = self.domain_range[0] - - return BSpline(domain_range, self.n_basis, self.order, knots) - - def __repr__(self): - """Representation of a BSpline basis.""" - return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " - f"n_basis={self.n_basis}, order={self.order}, " - f"knots={self.knots})") - - def __eq__(self, other): - """Equality of Basis""" - return (super().__eq__(other) - and self.order == other.order - and self.knots == other.knots) - - def basis_of_product(self, other): - """Multiplication of two Bspline Basis""" - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Ranges are not equal.") - - if isinstance(other, Constant): - return other.rbasis_of_product(self) - - if isinstance(other, BSpline): - uniqueknots = np.union1d(self.inknots, other.inknots) - - multunique = np.zeros(len(uniqueknots), dtype=np.int32) - for i in range(len(uniqueknots)): - mult1 = np.count_nonzero(self.inknots == uniqueknots[i]) - mult2 = np.count_nonzero(other.inknots == uniqueknots[i]) - multunique[i] = max(mult1, mult2) - - m2 = 0 - allknots = np.zeros(np.sum(multunique)) - for i in range(len(uniqueknots)): - m1 = m2 - m2 = m2 + multunique[i] - allknots[m1:m2] = uniqueknots[i] - - norder1 = self.n_basis - len(self.inknots) - norder2 = other.n_basis - len(other.inknots) - norder = min(norder1 + norder2 - 1, 20) - - allbreaks = ([self.domain_range[0][0]] + - np.ndarray.tolist(allknots) + - [self.domain_range[0][1]]) - n_basis = len(allbreaks) + norder - 2 - return BSpline(self.domain_range, n_basis, norder, allbreaks) - else: - norder = min(self.n_basis - len(self.inknots) + 2, 8) - n_basis = max(self.n_basis + other.n_basis, norder + 1) - return BSpline(self.domain_range, n_basis, norder) - - def rbasis_of_product(self, other): - """Multiplication of a Bspline Basis with other basis""" - - norder = min(self.n_basis - len(self.inknots) + 2, 8) - n_basis = max(self.n_basis + other.n_basis, norder + 1) - return BSpline(self.domain_range, n_basis, norder) - - def _to_R(self): - drange = self.domain_range[0] - return ("create.bspline.basis(rangeval = c(" + str(drange[0]) + "," + - str(drange[1]) + "), nbasis = " + str(self.n_basis) + - ", norder = " + str(self.order) + ", breaks = " + - self._list_to_R(self.knots) + ")") - - def _to_scipy_BSpline(self, coefs): - - knots = np.concatenate(( - np.repeat(self.knots[0], self.order - 1), - self.knots, - np.repeat(self.knots[-1], self.order - 1))) - - return SciBSpline(knots, coefs, self.order - 1) - - @staticmethod - def _from_scipy_BSpline(bspline): - order = bspline.k - knots = bspline.t[order: -order] - coefs = bspline.c - domain_range = [knots[0], knots[-1]] - - return BSpline(domain_range, order=order + 1, knots=knots), coefs - - @property - def inknots(self): - """Return number of basis.""" - return self.knots[1:len(self.knots) - 1] - - -class Fourier(Basis): - r"""Fourier basis. - - Defines a functional basis for representing functions on a fourier - series expansion of period :math:`T`. The number of basis is always odd. - If instantiated with an even number of basis, they will be incremented - automatically by one. - - .. math:: - \phi_0(t) = \frac{1}{\sqrt{2}} - - .. math:: - \phi_{2n -1}(t) = sin\left(\frac{2 \pi n}{T} t\right) - - .. math:: - \phi_{2n}(t) = cos\left(\frac{2 \pi n}{T} t\right) - - Actually this basis functions are not orthogonal but not orthonormal. To - achieve this they are divided by its norm: :math:`\sqrt{\frac{T}{2}}`. - - Attributes: - domain_range (tuple): A tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis (int): Number of functions in the basis. - period (int or float): Period (:math:`T`). - - Examples: - Constructs specifying number of basis, definition interval and period. - - >>> fb = Fourier((0, np.pi), n_basis=3, period=1) - >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi]).round(2) - array([[ 1. , 1. , 1. , 1. ], - [ 0. , -1.38, -0.61, 1.1 ], - [ 1.41, 0.31, -1.28, 0.89]]) - - And evaluate second derivative - - >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi], - ... derivative = 2).round(2) - array([[ 0. , 0. , 0. , 0. ], - [ -0. , 54.46, 24.02, -43.37], - [-55.83, -12.32, 50.4 , -35.16]]) - - - - """ - - def __init__(self, domain_range=None, n_basis=3, period=None): - """Construct a Fourier object. - - It forces the object to have an odd number of basis. If n_basis is - even, it is incremented by one. - - Args: - domain_range (tuple): Tuple defining the domain over which the - function is defined. - n_basis (int): Number of basis functions. - period (int or float): Period of the trigonometric functions that - define the basis. - - """ - - if domain_range is not None: - domain_range = _list_of_arrays(domain_range) - - if len(domain_range) != 1: - raise ValueError("Domain range should be unidimensional.") - - domain_range = domain_range[0] - - self.period = period - # If number of basis is even, add 1 - n_basis += 1 - n_basis % 2 - super().__init__(domain_range, n_basis) - - @property - def period(self): - if self._period is None: - return self.domain_range[0][1] - self.domain_range[0][0] - else: - return self._period - - @period.setter - def period(self, value): - self._period = value - - def _functions_pairs_coefs_derivatives(self, derivative=0): - """ - Compute functions to use, amplitudes and phase of a derivative. - """ - functions = [np.sin, np.cos] - signs = [1, 1, -1, -1] - omega = 2 * np.pi / self.period - - deriv_functions = (functions[derivative % len(functions)], - functions[(derivative + 1) % len(functions)]) - - deriv_signs = (signs[derivative % len(signs)], - signs[(derivative + 1) % len(signs)]) - - seq = 1 + np.arange((self.n_basis - 1) // 2) - seq_pairs = np.array([seq, seq]).T - power_pairs = (omega * seq_pairs)**derivative - amplitude_coefs_pairs = deriv_signs * power_pairs - phase_coef_pairs = omega * seq_pairs - - return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs - - def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - - Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - """ - (functions, - amplitude_coefs, - phase_coefs) = self._functions_pairs_coefs_derivatives(derivative) - - normalization_denominator = np.sqrt(self.period / 2) - - # Multiply the phase coefficients elementwise - res = np.einsum('ij,k->ijk', phase_coefs, eval_points) - - # Apply odd and even functions - for i in [0, 1]: - functions[i](res[:, i, :], out=res[:, i, :]) - - # Multiply the amplitude and ravel the result - res *= amplitude_coefs[..., np.newaxis] - res = res.reshape(-1, len(eval_points)) - res /= normalization_denominator - - # Add constant basis - if derivative == 0: - constant_basis = np.full( - shape=(1, len(eval_points)), - fill_value=1 / (np.sqrt(2) * normalization_denominator)) - else: - constant_basis = np.zeros(shape=(1, len(eval_points))) - - res = np.concatenate((constant_basis, res)) - - return res - - def _penalty_orthonormal(self, weights): - """ - Return the penalty when the basis is orthonormal. - """ - - signs = np.array([1, 1, -1, -1]) - signs_expanded = np.tile(signs, len(weights) // 4 + 1) - - signs_odd = signs_expanded[:len(weights)] - signs_even = signs_expanded[1:len(weights) + 1] - - phases = (np.arange(1, (self.n_basis - 1) // 2 + 1) * - 2 * np.pi / self.period) - - # Compute increasing powers - coefs_no_sign = np.vander(phases, len(weights), increasing=True) - - coefs_no_sign *= weights - - coefs_odd = signs_odd * coefs_no_sign - coefs_even = signs_even * coefs_no_sign - - # After applying the linear differential operator to a sinusoidal - # element of the basis e, the result can be expressed as - # A e + B e*, where e* is the other basis element in the pair - # with the same phase - - odd_sin_coefs = np.sum(coefs_odd[:, ::2], axis=1) - odd_cos_coefs = np.sum(coefs_odd[:, 1::2], axis=1) - - even_cos_coefs = np.sum(coefs_even[:, ::2], axis=1) - even_sin_coefs = np.sum(coefs_even[:, 1::2], axis=1) - - # The diagonal is the inner product of A e + B e* - # with itself. As the basis is orthonormal, the cross products e e* - # are 0, and the products e e and e* e* are one. - # Thus, the diagonal is A^2 + B^2 - # All elements outside the main diagonal are 0 - main_diag_odd = odd_sin_coefs**2 + odd_cos_coefs**2 - main_diag_even = even_sin_coefs**2 + even_cos_coefs**2 - - # The main diagonal should intercalate both diagonals - main_diag = np.array((main_diag_odd, main_diag_even)).T.ravel() - - penalty_matrix = np.diag(main_diag) - - # Add row and column for the constant - penalty_matrix = np.pad(penalty_matrix, pad_width=((1, 0), (1, 0)), - mode='constant') - - penalty_matrix[0, 0] = weights[0]**2 - - return penalty_matrix - - def _penalty(self, lfd): - - weights = lfd.constant_weights() - if weights is None: - return NotImplemented - - # If the period and domain range are not the same, the basis functions - # are not orthogonal - if self.period != (self.domain_range[0][1] - self.domain_range[0][0]): - return NotImplemented - - return self._penalty_orthonormal(weights) - - def _derivative(self, coefs, order=1): - - omega = 2 * np.pi / self.period - deriv_factor = (np.arange(1, (self.n_basis + 1) / 2) * omega) ** order - - deriv_coefs = np.zeros(coefs.shape) - - cos_sign, sin_sign = ((-1) ** int((order + 1) / 2), - (-1) ** int(order / 2)) - - if order % 2 == 0: - deriv_coefs[:, 1::2] = sin_sign * coefs[:, 1::2] * deriv_factor - deriv_coefs[:, 2::2] = cos_sign * coefs[:, 2::2] * deriv_factor - else: - deriv_coefs[:, 2::2] = sin_sign * coefs[:, 1::2] * deriv_factor - deriv_coefs[:, 1::2] = cos_sign * coefs[:, 2::2] * deriv_factor - - # normalise - return self.copy(), deriv_coefs - - def basis_of_product(self, other): - """Multiplication of two Fourier Basis""" - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Ranges are not equal.") - - if isinstance(other, Fourier) and self.period == other.period: - return Fourier(self.domain_range, self.n_basis + other.n_basis - 1, - self.period) - else: - return other.rbasis_of_product(self) - - def rbasis_of_product(self, other): - """Multiplication of a Fourier Basis with other Basis""" - return Basis.default_basis_of_product(other, self) - - def rescale(self, domain_range=None, *, rescale_period=False): - r"""Return a copy of the basis with a new domain range, with the - corresponding values rescaled to the new bounds. - - Args: - domain_range (tuple, optional): Definition of the interval - where the basis defines a space. Defaults uses the same as - the original basis. - rescale_period (bool, optional): If true the period will be - rescaled using the ratio between the lengths of the new - and old interval. Defaults to False. - """ - - rescale_basis = super().rescale(domain_range) - - if rescale_period is False: - rescale_basis.period = self.period - else: - domain_rescaled = rescale_basis.domain_range[0] - domain = self.domain_range[0] - - rescale_basis.period = (self.period * - (domain_rescaled[1] - domain_rescaled[0]) / - (domain[1] - domain[0])) - - return rescale_basis - - def _to_R(self): - drange = self.domain_range[0] - return ("create.fourier.basis(rangeval = c(" + str(drange[0]) + "," + - str(drange[1]) + "), nbasis = " + str(self.n_basis) + - ", period = " + str(self.period) + ")") - - def __repr__(self): - """Representation of a Fourier basis.""" - return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " - f"n_basis={self.n_basis}, period={self.period})") - - def __eq__(self, other): - """Equality of Basis""" - return super().__eq__(other) and self.period == other.period - - -class CoefficientsTransformer(BaseEstimator, TransformerMixin): - """ - Transformer returning the coefficients of FDataBasis objects as a matrix. - - Attributes: - shape_ (tuple): original shape of coefficients per sample. - - Examples: - >>> from skfda.representation.basis import (FDataBasis, Monomial, - ... CoefficientsTransformer) - >>> - >>> basis = Monomial(n_basis=4) - >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] - >>> fd = FDataBasis(basis, coefficients) - >>> - >>> transformer = CoefficientsTransformer() - >>> transformer.fit_transform(fd) - array([[ 0.5, 1. , 2. , 0.5], - [ 1.5, 1. , 4. , 0.5]]) - - """ - - def fit(self, X: FDataBasis, y=None): - - self.shape_ = X.coefficients.shape[1:] - - return self - - def transform(self, X, y=None): - - check_is_fitted(self, 'shape_') - - assert X.coefficients.shape[1:] == self.shape_ - - coefficients = X.coefficients.copy() - coefficients = coefficients.reshape((X.n_samples, -1)) - - return coefficients diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py new file mode 100644 index 000000000..3d65c4839 --- /dev/null +++ b/skfda/representation/basis/__init__.py @@ -0,0 +1,7 @@ +from ._basis import Basis +from ._bspline import BSpline +from ._coefficients_transformer import CoefficientsTransformer +from ._constant import Constant +from ._fdatabasis import FDataBasis, FDataBasisDType +from ._fourier import Fourier +from ._monomial import Monomial diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py new file mode 100644 index 000000000..e4d1c248e --- /dev/null +++ b/skfda/representation/basis/_basis.py @@ -0,0 +1,399 @@ +"""Module for functional data manipulation in a basis system. + +Defines functional data object in a basis function system representation and +the corresponding basis classes. + +""" +from abc import ABC, abstractmethod +import copy + +import scipy.integrate + +import numpy as np + +from ..._utils import _list_of_arrays + + +__author__ = "Miguel Carbajo Berrocal" +__email__ = "miguel.carbajo@estudiante.uam.es" + +# aux functions + + +def _check_domain(domain_range): + for domain in domain_range: + if len(domain) != 2 or domain[0] >= domain[1]: + raise ValueError(f"The interval {domain} is not well-defined.") + + +def _same_domain(one_domain_range, other_domain_range): + return np.array_equal(one_domain_range, other_domain_range) + + +class Basis(ABC): + """Defines the structure of a basis function system. + + Attributes: + domain_range (tuple): a tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis (int): number of functions in the basis. + + """ + + def __init__(self, domain_range=None, n_basis=1): + """Basis constructor. + + Args: + domain_range (tuple or list of tuples, optional): Definition of the + interval where the basis defines a space. Defaults to (0,1). + n_basis: Number of functions that form the basis. Defaults to 1. + """ + + if domain_range is not None: + # TODO: Allow multiple dimensions + domain_range = _list_of_arrays(domain_range) + + # Some checks + _check_domain(domain_range) + + if n_basis < 1: + raise ValueError("The number of basis has to be strictly " + "possitive.") + + self._domain_range = domain_range + self.n_basis = n_basis + self._drop_index_lst = [] + + super().__init__() + + @property + def domain_range(self): + if self._domain_range is None: + return [np.array([0, 1])] + else: + return self._domain_range + + @domain_range.setter + def domain_range(self, value): + self._domain_range = value + + @abstractmethod + def _evaluate(self, eval_points, derivative=0): + """Subclasses must override this to provide basis evaluation.""" + pass + + @abstractmethod + def _derivative(self, coefs, order=1): + pass + + def evaluate(self, eval_points, derivative=0): + """Evaluate Basis objects and its derivatives. + + Evaluates the basis function system or its derivatives at a list of + given values. + + Args: + eval_points (array_like): List of points where the basis is + evaluated. + derivative (int, optional): Order of the derivative. Defaults to 0. + + Returns: + (numpy.darray): Matrix whose rows are the values of the each + basis function or its derivatives at the values specified in + eval_points. + + """ + if derivative < 0: + raise ValueError("derivative only takes non-negative values.") + + eval_points = np.atleast_1d(eval_points) + if np.any(np.isnan(eval_points)): + raise ValueError("The list of points where the function is " + "evaluated can not contain nan values.") + + return self._evaluate(eval_points, derivative) + + def __call__(self, *args, **kwargs): + return self.evaluate(*args, **kwargs) + + def plot(self, chart=None, *, derivative=0, **kwargs): + """Plot the basis object or its derivatives. + + Args: + chart (figure object, axe or list of axes, optional): figure over + with the graphs are plotted or axis over where the graphs are + plotted. + derivative (int or tuple, optional): Order of derivative to be + plotted. Defaults 0. + **kwargs: keyword arguments to be passed to the + fdata.plot function. + + Returns: + fig (figure): figure object in which the graphs are plotted. + + """ + self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) + + def _internal_representation(self): + """ + Returns an internal representation of the basis. + + This representation may have several operations available that return + objects of the same kind, and can be used to build operators in an + analytical, but generic, way. + + """ + return NotImplemented + + def _numerical_penalty(self, lfd): + """Return a penalty matrix using a numerical approach. + + See :func:`~basis.Basis.penalty`. + + Args: + lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. + """ + from skfda.misc import LinearDifferentialOperator + + if not isinstance(lfd, LinearDifferentialOperator): + lfd = LinearDifferentialOperator(lfd) + + indices = np.triu_indices(self.n_basis) + + def cross_product(x): + """Multiply the two lfds""" + res = lfd(self)([x])[:, 0] + + return res[indices[0]] * res[indices[1]] + + # Range of first dimension + domain_range = self.domain_range[0] + + penalty_matrix = np.empty((self.n_basis, self.n_basis)) + + # Obtain the integrals for the upper matrix + triang_vec = scipy.integrate.quad_vec( + cross_product, domain_range[0], domain_range[1])[0] + + # Set upper matrix + penalty_matrix[indices] = triang_vec + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = triang_vec + + return penalty_matrix + + def _penalty(self, lfd): + """ + Subclasses may override this for computing analytically + the penalty matrix in the cases when that is possible. + + Returning NotImplemented will use numerical computation + of the penalty matrix. + """ + return NotImplemented + + def penalty(self, lfd): + r"""Return a penalty matrix given a differential operator. + + The differential operator can be either a derivative of a certain + degree or a more complex operator. + + The penalty matrix is defined as [RS05-5-6-2]_: + + .. math:: + R_{ij} = \int L\phi_i(s) L\phi_j(s) ds + + where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis + functions and :math:`L` is a differential operator. + + Args: + lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. + + Returns: + numpy.array: Penalty matrix. + + References: + .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the + roughness penalty. In *Functional Data Analysis* (pp. 106-107). + Springer. + + """ + from skfda.misc import LinearDifferentialOperator + + if not isinstance(lfd, LinearDifferentialOperator): + lfd = LinearDifferentialOperator(lfd) + + matrix = self._penalty(lfd) + + if matrix is NotImplemented: + return self._numerical_penalty(lfd) + else: + return matrix + + @abstractmethod + def basis_of_product(self, other): + pass + + @abstractmethod + def rbasis_of_product(self, other): + pass + + @staticmethod + def default_basis_of_product(one, other): + """Default multiplication for a pair of basis""" + from ._bspline import BSpline + + if not _same_domain(one.domain_range, other.domain_range): + raise ValueError("Ranges are not equal.") + + norder = min(8, one.n_basis + other.n_basis) + n_basis = max(one.n_basis + other.n_basis, norder + 1) + return BSpline(one.domain_range, n_basis, norder) + + def rescale(self, domain_range=None): + r"""Return a copy of the basis with a new domain range, with the + corresponding values rescaled to the new bounds. + + Args: + domain_range (tuple, optional): Definition of the interval + where the basis defines a space. Defaults uses the same as + the original basis. + """ + + if domain_range is None: + domain_range = self.domain_range + + return type(self)(domain_range, self.n_basis) + + def same_domain(self, other): + r"""Returns if two basis are defined on the same domain range. + + Args: + other (Basis): Basis to check the domain range definition + """ + return _same_domain(self.domain_range, other.domain_range) + + def copy(self): + """Basis copy""" + return copy.deepcopy(self) + + def to_basis(self): + from . import FDataBasis + return FDataBasis(self.copy(), np.identity(self.n_basis)) + + def _list_to_R(self, knots): + retstring = "c(" + for i in range(0, len(knots)): + retstring = retstring + str(knots[i]) + ", " + return retstring[0:len(retstring) - 2] + ")" + + def _to_R(self): + raise NotImplementedError + + def _inner_matrix(self, other=None): + r"""Return the Inner Product Matrix of a pair of basis. + + The Inner Product Matrix is defined as + + .. math:: + IP_{ij} = \langle\phi_i, \theta_j\rangle + + where :math:`\phi_i` is the ith element of the basi and + :math:`\theta_j` is the jth element of the second basis. + This matrix helps on the calculation of the inner product + between objects on two basis and for the change of basis. + + Args: + other (:class:`Basis`): Basis to compute the inner product + matrix. If not basis is given, it computes the matrix with + itself returning the Gram Matrix + + Returns: + numpy.array: Inner Product Matrix of two basis + + """ + if other is None or self == other: + return self.gram_matrix() + + first = self.to_basis() + second = other.to_basis() + + inner = np.zeros((self.n_basis, other.n_basis)) + + for i in range(self.n_basis): + for j in range(other.n_basis): + inner[i, j] = first[i].inner_product(second[j], None, None) + + return inner + + def gram_matrix(self): + r"""Return the Gram Matrix of a basis + + The Gram Matrix is defined as + + .. math:: + G_{ij} = \langle\phi_i, \phi_j\rangle + + where :math:`\phi_i` is the ith element of the basis. This is a + symmetric matrix and positive-semidefinite. + + Returns: + numpy.array: Gram Matrix of the basis. + + """ + fbasis = self.to_basis() + + gram = np.zeros((self.n_basis, self.n_basis)) + + for i in range(fbasis.n_basis): + for j in range(i, fbasis.n_basis): + gram[i, j] = fbasis[i].inner_product(fbasis[j], None, None) + gram[j, i] = gram[i, j] + + return gram + + def inner_product(self, other): + return np.transpose(other.inner_product(self.to_basis())) + + def _add_same_basis(self, coefs1, coefs2): + return self.copy(), coefs1 + coefs2 + + def _add_constant(self, coefs, constant): + coefs = coefs.copy() + constant = np.array(constant) + coefs[:, 0] = coefs[:, 0] + constant + + return self.copy(), coefs + + def _sub_same_basis(self, coefs1, coefs2): + return self.copy(), coefs1 - coefs2 + + def _sub_constant(self, coefs, other): + coefs = coefs.copy() + other = np.array(other) + coefs[:, 0] = coefs[:, 0] - other + + return self.copy(), coefs + + def _mul_constant(self, coefs, other): + coefs = coefs.copy() + other = np.atleast_2d(other).reshape(-1, 1) + coefs = coefs * other + + return self.copy(), coefs + + def __repr__(self): + """Representation of a Basis object.""" + return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " + f"n_basis={self.n_basis})") + + def __eq__(self, other): + """Equality of Basis""" + return (type(self) == type(other) + and _same_domain(self.domain_range, other.domain_range) + and self.n_basis == other.n_basis) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py new file mode 100644 index 000000000..adeed082d --- /dev/null +++ b/skfda/representation/basis/_bspline.py @@ -0,0 +1,453 @@ +from numpy import polyder, polyint, polymul, polyval +from scipy.interpolate import BSpline as SciBSpline +from scipy.interpolate import PPoly +import scipy.interpolate + +import numpy as np + +from ..._utils import _list_of_arrays +from ..._utils import _same_domain +from ._basis import Basis + + +class BSpline(Basis): + r"""BSpline basis. + + BSpline basis elements are defined recursively as: + + .. math:: + B_{i, 1}(x) = 1 \quad \text{if } t_i \le x < t_{i+1}, + \quad 0 \text{ otherwise} + + .. math:: + B_{i, k}(x) = \frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x) + + \frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x) + + Where k indicates the order of the spline. + + Implementation details: In order to allow a discontinuous behaviour at + the boundaries of the domain it is necessary to placing m knots at the + boundaries [RS05]_. This is automatically done so that the user only has to + specify a single knot at the boundaries. + + Attributes: + domain_range (tuple): A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis (int): Number of functions in the basis. + order (int): Order of the splines. One greather than their degree. + knots (list): List of knots of the spline functions. + + Examples: + Constructs specifying number of basis and order. + + >>> bss = BSpline(n_basis=8, order=4) + + If no order is specified defaults to 4 because cubic splines are + the most used. So the previous example is the same as: + + >>> bss = BSpline(n_basis=8) + + It is also possible to create a BSpline basis specifying the knots. + + >>> bss = BSpline(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) + + Once we create a basis we can evaluate each of its functions at a + set of points. + + >>> bss = BSpline(n_basis=3, order=3) + >>> bss.evaluate([0, 0.5, 1]) + array([[ 1. , 0.25, 0. ], + [ 0. , 0.5 , 0. ], + [ 0. , 0.25, 1. ]]) + + And evaluates first derivative + + >>> bss.evaluate([0, 0.5, 1], derivative=1) + array([[-2., -1., 0.], + [ 2., 0., -2.], + [ 0., 1., 2.]]) + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + + """ + + def __init__(self, domain_range=None, n_basis=None, order=4, knots=None): + """Bspline basis constructor. + + Args: + domain_range (tuple, optional): Definition of the interval where + the basis defines a space. Defaults to (0,1) if knots are not + specified. If knots are specified defaults to the first and + last element of the knots. + n_basis (int, optional): Number of splines that form the basis. + order (int, optional): Order of the splines. One greater that + their degree. Defaults to 4 which mean cubic splines. + knots (array_like): List of knots of the splines. If domain_range + is specified the first and last elements of the knots have to + match with it. + + """ + + if domain_range is not None: + domain_range = _list_of_arrays(domain_range) + + if len(domain_range) != 1: + raise ValueError("Domain range should be unidimensional.") + + domain_range = domain_range[0] + + # Knots default to equally space points in the domain_range + if knots is None: + if n_basis is None: + raise ValueError("Must provide either a list of knots or the" + "number of basis.") + else: + knots = list(knots) + knots.sort() + if domain_range is None: + domain_range = (knots[0], knots[-1]) + else: + if domain_range[0] != knots[0] or domain_range[1] != knots[-1]: + raise ValueError("The ends of the knots must be the same " + "as the domain_range.") + + # n_basis default to number of knots + order of the splines - 2 + if n_basis is None: + n_basis = len(knots) + order - 2 + + if (n_basis - order + 2) < 2: + raise ValueError(f"The number of basis ({n_basis}) minus the " + f"order of the bspline ({order}) should be " + f"greater than 3.") + + self.order = order + self.knots = None if knots is None else list(knots) + super().__init__(domain_range, n_basis) + + # Checks + if self.n_basis != self.order + len(self.knots) - 2: + raise ValueError(f"The number of basis ({self.n_basis}) has to " + f"equal the order ({self.order}) plus the " + f"number of knots ({len(self.knots)}) minus 2.") + + @property + def knots(self): + if self._knots is None: + return list(np.linspace(*self.domain_range[0], + self.n_basis - self.order + 2)) + else: + return self._knots + + @knots.setter + def knots(self, value): + self._knots = value + + def _evaluation_knots(self): + """ + Get the knots adding m knots to the boundary in order to allow a + discontinuous behaviour at the boundaries of the domain [RS05]_. + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + """ + return np.array([self.knots[0]] * (self.order - 1) + self.knots + + [self.knots[-1]] * (self.order - 1)) + + def _evaluate(self, eval_points, derivative=0): + """Compute the basis or its derivatives given a list of values. + + It uses the scipy implementation of BSplines to compute the values + for each element of the basis. + + Args: + eval_points (array_like): List of points where the basis system is + evaluated. + derivative (int, optional): Order of the derivative. Defaults to 0. + + Returns: + (:obj:`numpy.darray`): Matrix whose rows are the values of the each + basis function or its derivatives at the values specified in + eval_points. + + Implementation details: In order to allow a discontinuous behaviour at + the boundaries of the domain it is necessary to placing m knots at the + boundaries [RS05]_. This is automatically done so that the user only + has to specify a single knot at the boundaries. + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + + """ + if derivative > (self.order - 1): + return np.zeros((self.n_basis, len(eval_points))) + + # Places m knots at the boundaries + knots = self._evaluation_knots() + + # c is used the select which spline the function splev below computes + c = np.zeros(len(knots)) + + # Initialise empty matrix + mat = np.empty((self.n_basis, len(eval_points))) + + # For each basis computes its value for each evaluation point + for i in range(self.n_basis): + # write a 1 in c in the position of the spline calculated in each + # iteration + c[i] = 1 + # compute the spline + mat[i] = scipy.interpolate.splev(eval_points, (knots, c, + self.order - 1), + der=derivative) + c[i] = 0 + + return mat + + def _derivative(self, coefs, order=1): + deriv_splines = [self._to_scipy_BSpline(coefs[i]).derivative(order) + for i in range(coefs.shape[0])] + + deriv_coefs = [BSpline._from_scipy_BSpline(spline)[1] + for spline in deriv_splines] + + deriv_basis = BSpline._from_scipy_BSpline(deriv_splines[0])[0] + + return deriv_basis, np.array(deriv_coefs)[:, 0:deriv_basis.n_basis] + + def _penalty(self, lfd): + + coefs = lfd.constant_weights() + if coefs is None: + return NotImplemented + + nonzero = np.flatnonzero(coefs) + + # All derivatives above the order of the spline are effectively + # zero + nonzero = nonzero[nonzero < self.order] + + if len(nonzero) == 0: + return np.zeros((self.n_basis, self.n_basis)) + + # We will only deal with one nonzero coefficient right now + if len(nonzero) != 1: + return NotImplemented + + derivative_degree = nonzero[0] + + if derivative_degree == self.order - 1: + # The derivative of the bsplines are constant in the intervals + # defined between knots + knots = np.array(self.knots) + mid_inter = (knots[1:] + knots[:-1]) / 2 + constants = self.evaluate(mid_inter, + derivative=derivative_degree).T + knots_intervals = np.diff(self.knots) + # Integration of product of constants + return constants.T @ np.diag(knots_intervals) @ constants + + # We only deal with the case without zero length intervals + # for now + if np.any(np.diff(self.knots) == 0): + return NotImplemented + + # Compute exactly using the piecewise polynomial + # representation of splines + + # Places m knots at the boundaries + knots = self._evaluation_knots() + + # c is used the select which spline the function + # PPoly.from_spline below computes + c = np.zeros(len(knots)) + + # Initialise empty list to store the piecewise polynomials + ppoly_lst = [] + + no_0_intervals = np.where(np.diff(knots) > 0)[0] + + # For each basis gets its piecewise polynomial representation + for i in range(self.n_basis): + + # Write a 1 in c in the position of the spline + # transformed in each iteration + c[i] = 1 + + # Gets the piecewise polynomial representation and gets + # only the positions for no zero length intervals + # This polynomial are defined relatively to the knots + # meaning that the column i corresponds to the ith knot. + # Let the ith knot be a + # Then f(x) = pp(x - a) + pp = PPoly.from_spline((knots, c, self.order - 1)) + pp_coefs = pp.c[:, no_0_intervals] + + # We have the coefficients for each interval in coordinates + # (x - a), so we will need to subtract a when computing the + # definite integral + ppoly_lst.append(pp_coefs) + c[i] = 0 + + # Now for each pair of basis computes the inner product after + # applying the linear differential operator + penalty_matrix = np.zeros((self.n_basis, self.n_basis)) + for interval in range(len(no_0_intervals)): + for i in range(self.n_basis): + poly_i = np.trim_zeros(ppoly_lst[i][:, + interval], 'f') + if len(poly_i) <= derivative_degree: + # if the order of the polynomial is lesser or + # equal to the derivative the result of the + # integral will be 0 + continue + # indefinite integral + derivative = polyder(poly_i, derivative_degree) + square = polymul(derivative, derivative) + integral = polyint(square) + + # definite integral + penalty_matrix[i, i] += np.diff(polyval( + integral, self.knots[interval: interval + 2] + - self.knots[interval]))[0] + + for j in range(i + 1, self.n_basis): + poly_j = np.trim_zeros(ppoly_lst[j][:, + interval], 'f') + if len(poly_j) <= derivative_degree: + # if the order of the polynomial is lesser + # or equal to the derivative the result of + # the integral will be 0 + continue + # indefinite integral + integral = polyint( + polymul(polyder(poly_i, derivative_degree), + polyder(poly_j, derivative_degree))) + # definite integral + penalty_matrix[i, j] += np.diff(polyval( + integral, self.knots[interval: interval + 2] + - self.knots[interval]) + )[0] + penalty_matrix[j, i] = penalty_matrix[i, j] + return penalty_matrix + + def rescale(self, domain_range=None): + r"""Return a copy of the basis with a new domain range, with the + corresponding values rescaled to the new bounds. + The knots of the BSpline will be rescaled in the new interval. + + Args: + domain_range (tuple, optional): Definition of the interval + where the basis defines a space. Defaults uses the same as + the original basis. + """ + + knots = np.array(self.knots, dtype=np.dtype('float')) + + if domain_range is not None: # Rescales the knots + knots -= knots[0] + knots *= ((domain_range[1] - domain_range[0] + ) / (self.knots[-1] - self.knots[0])) + knots += domain_range[0] + + # Fix possible round error + knots[0] = domain_range[0] + knots[-1] = domain_range[1] + + else: + # TODO: Allow multiple dimensions + domain_range = self.domain_range[0] + + return BSpline(domain_range, self.n_basis, self.order, knots) + + def __repr__(self): + """Representation of a BSpline basis.""" + return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " + f"n_basis={self.n_basis}, order={self.order}, " + f"knots={self.knots})") + + def __eq__(self, other): + """Equality of Basis""" + return (super().__eq__(other) + and self.order == other.order + and self.knots == other.knots) + + def basis_of_product(self, other): + from ._constant import Constant + + """Multiplication of two Bspline Basis""" + if not _same_domain(self, other): + raise ValueError("Ranges are not equal.") + + if isinstance(other, Constant): + return other.rbasis_of_product(self) + + if isinstance(other, BSpline): + uniqueknots = np.union1d(self.inknots, other.inknots) + + multunique = np.zeros(len(uniqueknots), dtype=np.int32) + for i in range(len(uniqueknots)): + mult1 = np.count_nonzero(self.inknots == uniqueknots[i]) + mult2 = np.count_nonzero(other.inknots == uniqueknots[i]) + multunique[i] = max(mult1, mult2) + + m2 = 0 + allknots = np.zeros(np.sum(multunique)) + for i in range(len(uniqueknots)): + m1 = m2 + m2 = m2 + multunique[i] + allknots[m1:m2] = uniqueknots[i] + + norder1 = self.n_basis - len(self.inknots) + norder2 = other.n_basis - len(other.inknots) + norder = min(norder1 + norder2 - 1, 20) + + allbreaks = ([self.domain_range[0][0]] + + np.ndarray.tolist(allknots) + + [self.domain_range[0][1]]) + n_basis = len(allbreaks) + norder - 2 + return BSpline(self.domain_range, n_basis, norder, allbreaks) + else: + norder = min(self.n_basis - len(self.inknots) + 2, 8) + n_basis = max(self.n_basis + other.n_basis, norder + 1) + return BSpline(self.domain_range, n_basis, norder) + + def rbasis_of_product(self, other): + """Multiplication of a Bspline Basis with other basis""" + + norder = min(self.n_basis - len(self.inknots) + 2, 8) + n_basis = max(self.n_basis + other.n_basis, norder + 1) + return BSpline(self.domain_range, n_basis, norder) + + def _to_R(self): + drange = self.domain_range[0] + return ("create.bspline.basis(rangeval = c(" + str(drange[0]) + "," + + str(drange[1]) + "), nbasis = " + str(self.n_basis) + + ", norder = " + str(self.order) + ", breaks = " + + self._list_to_R(self.knots) + ")") + + def _to_scipy_BSpline(self, coefs): + + knots = np.concatenate(( + np.repeat(self.knots[0], self.order - 1), + self.knots, + np.repeat(self.knots[-1], self.order - 1))) + + return SciBSpline(knots, coefs, self.order - 1) + + @staticmethod + def _from_scipy_BSpline(bspline): + order = bspline.k + knots = bspline.t[order: -order] + coefs = bspline.c + domain_range = [knots[0], knots[-1]] + + return BSpline(domain_range, order=order + 1, knots=knots), coefs + + @property + def inknots(self): + """Return number of basis.""" + return self.knots[1:len(self.knots) - 1] diff --git a/skfda/representation/basis/_coefficients_transformer.py b/skfda/representation/basis/_coefficients_transformer.py new file mode 100644 index 000000000..073c2eb63 --- /dev/null +++ b/skfda/representation/basis/_coefficients_transformer.py @@ -0,0 +1,44 @@ +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.utils.validation import check_is_fitted + +from ._fdatabasis import FDataBasis + + +class CoefficientsTransformer(BaseEstimator, TransformerMixin): + """ + Transformer returning the coefficients of FDataBasis objects as a matrix. + + Attributes: + shape_ (tuple): original shape of coefficients per sample. + + Examples: + >>> from skfda.representation.basis import (FDataBasis, Monomial, + ... CoefficientsTransformer) + >>> + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> fd = FDataBasis(basis, coefficients) + >>> + >>> transformer = CoefficientsTransformer() + >>> transformer.fit_transform(fd) + array([[ 0.5, 1. , 2. , 0.5], + [ 1.5, 1. , 4. , 0.5]]) + + """ + + def fit(self, X: FDataBasis, y=None): + + self.shape_ = X.coefficients.shape[1:] + + return self + + def transform(self, X, y=None): + + check_is_fitted(self) + + assert X.coefficients.shape[1:] == self.shape_ + + coefficients = X.coefficients.copy() + coefficients = coefficients.reshape((X.n_samples, -1)) + + return coefficients diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py new file mode 100644 index 000000000..9d2c119e9 --- /dev/null +++ b/skfda/representation/basis/_constant.py @@ -0,0 +1,64 @@ +import numpy as np +from ..._utils import _same_domain +from ._basis import Basis + + +class Constant(Basis): + """Constant basis. + + Basis for constant functions + + Attributes: + domain_range (tuple): a tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + + Examples: + Defines a contant base over the interval :math:`[0, 5]` consisting + on the constant function 1 on :math:`[0, 5]`. + + >>> bs_cons = Constant((0,5)) + + """ + + def __init__(self, domain_range=None): + """Constant basis constructor. + + Args: + domain_range (tuple): Tuple defining the domain over which the + function is defined. + + """ + super().__init__(domain_range, 1) + + def _evaluate(self, eval_points, derivative=0): + return (np.ones((1, len(eval_points))) if derivative == 0 + else np.zeros((1, len(eval_points)))) + + def _derivative(self, coefs, order=1): + return (self.copy(), coefs.copy() if order == 0 + else self.copy(), np.zeros(coefs.shape)) + + def _penalty(self, lfd): + coefs = lfd.constant_weights() + if coefs is None: + return NotImplemented + + return np.array([[coefs[0] ** 2 * + (self.domain_range[0][1] - + self.domain_range[0][0])]]) + + def basis_of_product(self, other): + """Multiplication of a Constant Basis with other Basis""" + if not _same_domain(self, other): + raise ValueError("Ranges are not equal.") + + return other.copy() + + def rbasis_of_product(self, other): + """Multiplication of a Constant Basis with other Basis""" + return other.copy() + + def _to_R(self): + drange = self.domain_range[0] + return "create.constant.basis(rangeval = c(" + str(drange[0]) + "," +\ + str(drange[1]) + "))" diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py similarity index 99% rename from skfda/representation/_fdatabasis.py rename to skfda/representation/basis/_fdatabasis.py index 172ac9d4b..8387016ae 100644 --- a/skfda/representation/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -5,9 +5,9 @@ import numpy as np -from . import grid -from .._utils import constants -from ._functional_data import FData +from .. import grid +from ..._utils import constants +from .._functional_data import FData def _same_domain(one_domain_range, other_domain_range): @@ -168,8 +168,8 @@ def from_data(cls, data_matrix, sample_points, basis, Data Analysis* (pp. 86-87). Springer. """ - from ..preprocessing.smoothing import BasisSmoother - from .grid import FDataGrid + from ...preprocessing.smoothing import BasisSmoother + from ..grid import FDataGrid # n is the samples # m is the observations @@ -668,8 +668,8 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, numpy.array: Inner Product matrix. """ - from ..misc import LinearDifferentialOperator - from .basis import Basis + from ...misc import LinearDifferentialOperator + from ..basis import Basis if not _same_domain(self.domain_range, other.domain_range): raise ValueError("Both Objects should have the same domain_range") diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py new file mode 100644 index 000000000..07656948b --- /dev/null +++ b/skfda/representation/basis/_fourier.py @@ -0,0 +1,302 @@ +import numpy as np + +from ..._utils import _list_of_arrays +from ..._utils import _same_domain +from ._basis import Basis + + +class Fourier(Basis): + r"""Fourier basis. + + Defines a functional basis for representing functions on a fourier + series expansion of period :math:`T`. The number of basis is always odd. + If instantiated with an even number of basis, they will be incremented + automatically by one. + + .. math:: + \phi_0(t) = \frac{1}{\sqrt{2}} + + .. math:: + \phi_{2n -1}(t) = sin\left(\frac{2 \pi n}{T} t\right) + + .. math:: + \phi_{2n}(t) = cos\left(\frac{2 \pi n}{T} t\right) + + Actually this basis functions are not orthogonal but not orthonormal. To + achieve this they are divided by its norm: :math:`\sqrt{\frac{T}{2}}`. + + Attributes: + domain_range (tuple): A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis (int): Number of functions in the basis. + period (int or float): Period (:math:`T`). + + Examples: + Constructs specifying number of basis, definition interval and period. + + >>> fb = Fourier((0, np.pi), n_basis=3, period=1) + >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + array([[ 1. , 1. , 1. , 1. ], + [ 0. , -1.38, -0.61, 1.1 ], + [ 1.41, 0.31, -1.28, 0.89]]) + + And evaluate second derivative + + >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi], + ... derivative = 2).round(2) + array([[ 0. , 0. , 0. , 0. ], + [ -0. , 54.46, 24.02, -43.37], + [-55.83, -12.32, 50.4 , -35.16]]) + + + + """ + + def __init__(self, domain_range=None, n_basis=3, period=None): + """Construct a Fourier object. + + It forces the object to have an odd number of basis. If n_basis is + even, it is incremented by one. + + Args: + domain_range (tuple): Tuple defining the domain over which the + function is defined. + n_basis (int): Number of basis functions. + period (int or float): Period of the trigonometric functions that + define the basis. + + """ + + if domain_range is not None: + domain_range = _list_of_arrays(domain_range) + + if len(domain_range) != 1: + raise ValueError("Domain range should be unidimensional.") + + domain_range = domain_range[0] + + self.period = period + # If number of basis is even, add 1 + n_basis += 1 - n_basis % 2 + super().__init__(domain_range, n_basis) + + @property + def period(self): + if self._period is None: + return self.domain_range[0][1] - self.domain_range[0][0] + else: + return self._period + + @period.setter + def period(self, value): + self._period = value + + def _functions_pairs_coefs_derivatives(self, derivative=0): + """ + Compute functions to use, amplitudes and phase of a derivative. + """ + functions = [np.sin, np.cos] + signs = [1, 1, -1, -1] + omega = 2 * np.pi / self.period + + deriv_functions = (functions[derivative % len(functions)], + functions[(derivative + 1) % len(functions)]) + + deriv_signs = (signs[derivative % len(signs)], + signs[(derivative + 1) % len(signs)]) + + seq = 1 + np.arange((self.n_basis - 1) // 2) + seq_pairs = np.array([seq, seq]).T + power_pairs = (omega * seq_pairs)**derivative + amplitude_coefs_pairs = deriv_signs * power_pairs + phase_coef_pairs = omega * seq_pairs + + return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs + + def _evaluate(self, eval_points, derivative=0): + """Compute the basis or its derivatives given a list of values. + + Args: + eval_points (array_like): List of points where the basis is + evaluated. + derivative (int, optional): Order of the derivative. Defaults to 0. + + Returns: + (:obj:`numpy.darray`): Matrix whose rows are the values of the each + basis function or its derivatives at the values specified in + eval_points. + + """ + (functions, + amplitude_coefs, + phase_coefs) = self._functions_pairs_coefs_derivatives(derivative) + + normalization_denominator = np.sqrt(self.period / 2) + + # Multiply the phase coefficients elementwise + res = np.einsum('ij,k->ijk', phase_coefs, eval_points) + + # Apply odd and even functions + for i in [0, 1]: + functions[i](res[:, i, :], out=res[:, i, :]) + + # Multiply the amplitude and ravel the result + res *= amplitude_coefs[..., np.newaxis] + res = res.reshape(-1, len(eval_points)) + res /= normalization_denominator + + # Add constant basis + if derivative == 0: + constant_basis = np.full( + shape=(1, len(eval_points)), + fill_value=1 / (np.sqrt(2) * normalization_denominator)) + else: + constant_basis = np.zeros(shape=(1, len(eval_points))) + + res = np.concatenate((constant_basis, res)) + + return res + + def _penalty_orthonormal(self, weights): + """ + Return the penalty when the basis is orthonormal. + """ + + signs = np.array([1, 1, -1, -1]) + signs_expanded = np.tile(signs, len(weights) // 4 + 1) + + signs_odd = signs_expanded[:len(weights)] + signs_even = signs_expanded[1:len(weights) + 1] + + phases = (np.arange(1, (self.n_basis - 1) // 2 + 1) * + 2 * np.pi / self.period) + + # Compute increasing powers + coefs_no_sign = np.vander(phases, len(weights), increasing=True) + + coefs_no_sign *= weights + + coefs_odd = signs_odd * coefs_no_sign + coefs_even = signs_even * coefs_no_sign + + # After applying the linear differential operator to a sinusoidal + # element of the basis e, the result can be expressed as + # A e + B e*, where e* is the other basis element in the pair + # with the same phase + + odd_sin_coefs = np.sum(coefs_odd[:, ::2], axis=1) + odd_cos_coefs = np.sum(coefs_odd[:, 1::2], axis=1) + + even_cos_coefs = np.sum(coefs_even[:, ::2], axis=1) + even_sin_coefs = np.sum(coefs_even[:, 1::2], axis=1) + + # The diagonal is the inner product of A e + B e* + # with itself. As the basis is orthonormal, the cross products e e* + # are 0, and the products e e and e* e* are one. + # Thus, the diagonal is A^2 + B^2 + # All elements outside the main diagonal are 0 + main_diag_odd = odd_sin_coefs**2 + odd_cos_coefs**2 + main_diag_even = even_sin_coefs**2 + even_cos_coefs**2 + + # The main diagonal should intercalate both diagonals + main_diag = np.array((main_diag_odd, main_diag_even)).T.ravel() + + penalty_matrix = np.diag(main_diag) + + # Add row and column for the constant + penalty_matrix = np.pad(penalty_matrix, pad_width=((1, 0), (1, 0)), + mode='constant') + + penalty_matrix[0, 0] = weights[0]**2 + + return penalty_matrix + + def _penalty(self, lfd): + + weights = lfd.constant_weights() + if weights is None: + return NotImplemented + + # If the period and domain range are not the same, the basis functions + # are not orthogonal + if self.period != (self.domain_range[0][1] - self.domain_range[0][0]): + return NotImplemented + + return self._penalty_orthonormal(weights) + + def _derivative(self, coefs, order=1): + + omega = 2 * np.pi / self.period + deriv_factor = (np.arange(1, (self.n_basis + 1) / 2) * omega) ** order + + deriv_coefs = np.zeros(coefs.shape) + + cos_sign, sin_sign = ((-1) ** int((order + 1) / 2), + (-1) ** int(order / 2)) + + if order % 2 == 0: + deriv_coefs[:, 1::2] = sin_sign * coefs[:, 1::2] * deriv_factor + deriv_coefs[:, 2::2] = cos_sign * coefs[:, 2::2] * deriv_factor + else: + deriv_coefs[:, 2::2] = sin_sign * coefs[:, 1::2] * deriv_factor + deriv_coefs[:, 1::2] = cos_sign * coefs[:, 2::2] * deriv_factor + + # normalise + return self.copy(), deriv_coefs + + def basis_of_product(self, other): + """Multiplication of two Fourier Basis""" + if not _same_domain(self, other): + raise ValueError("Ranges are not equal.") + + if isinstance(other, Fourier) and self.period == other.period: + return Fourier(self.domain_range, self.n_basis + other.n_basis - 1, + self.period) + else: + return other.rbasis_of_product(self) + + def rbasis_of_product(self, other): + """Multiplication of a Fourier Basis with other Basis""" + return Basis.default_basis_of_product(other, self) + + def rescale(self, domain_range=None, *, rescale_period=False): + r"""Return a copy of the basis with a new domain range, with the + corresponding values rescaled to the new bounds. + + Args: + domain_range (tuple, optional): Definition of the interval + where the basis defines a space. Defaults uses the same as + the original basis. + rescale_period (bool, optional): If true the period will be + rescaled using the ratio between the lengths of the new + and old interval. Defaults to False. + """ + + rescale_basis = super().rescale(domain_range) + + if rescale_period is False: + rescale_basis.period = self.period + else: + domain_rescaled = rescale_basis.domain_range[0] + domain = self.domain_range[0] + + rescale_basis.period = (self.period * + (domain_rescaled[1] - domain_rescaled[0]) / + (domain[1] - domain[0])) + + return rescale_basis + + def _to_R(self): + drange = self.domain_range[0] + return ("create.fourier.basis(rangeval = c(" + str(drange[0]) + "," + + str(drange[1]) + "), nbasis = " + str(self.n_basis) + + ", period = " + str(self.period) + ")") + + def __repr__(self): + """Representation of a Fourier basis.""" + return (f"{self.__class__.__name__}(domain_range={self.domain_range}, " + f"n_basis={self.n_basis}, period={self.period})") + + def __eq__(self, other): + """Equality of Basis""" + return super().__eq__(other) and self.period == other.period diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py new file mode 100644 index 000000000..acf79affe --- /dev/null +++ b/skfda/representation/basis/_monomial.py @@ -0,0 +1,212 @@ +import numpy as np +from ..._utils import _same_domain +from ._basis import Basis + + +class Monomial(Basis): + """Monomial basis. + + Basis formed by powers of the argument :math:`t`: + + .. math:: + 1, t, t^2, t^3... + + Attributes: + domain_range (tuple): a tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis (int): number of functions in the basis. + + Examples: + Defines a monomial base over the interval :math:`[0, 5]` consisting + on the first 3 powers of :math:`t`: :math:`1, t, t^2`. + + >>> bs_mon = Monomial((0,5), n_basis=3) + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> bs_mon.evaluate([0, 1, 2]) + array([[1, 1, 1], + [0, 1, 2], + [0, 1, 4]]) + + And also evaluates its derivatives + + >>> bs_mon.evaluate([0, 1, 2], derivative=1) + array([[0, 0, 0], + [1, 1, 1], + [0, 2, 4]]) + >>> bs_mon.evaluate([0, 1, 2], derivative=2) + array([[0, 0, 0], + [0, 0, 0], + [2, 2, 2]]) + + """ + + def _coef_mat(self, derivative): + """ + Obtain the matrix of coefficients. + + Each column of coef_mat contains the numbers that must be multiplied + together in order to obtain the coefficient of each basis function + Thus, column i will contain i, i - 1, ..., i - derivative + 1. + """ + + seq = np.arange(self.n_basis) + coef_mat = np.linspace(seq, seq - derivative + 1, + derivative, dtype=int) + + return seq, coef_mat + + def _coefs_exps_derivatives(self, derivative): + """ + Return coefficients and exponents of the derivatives. + + This function is used for computing the basis functions and evaluate. + + When the exponent would be negative (the coefficient in that case + is zero) returns 0 as the exponent (to prevent division by zero). + """ + seq, coef_mat = self._coef_mat(derivative) + coefs = np.prod(coef_mat, axis=0) + + exps = np.maximum(seq - derivative, 0) + + return coefs, exps + + def _evaluate(self, eval_points, derivative=0): + + coefs, exps = self._coefs_exps_derivatives(derivative) + + raised = np.power.outer(eval_points, exps) + + return (coefs * raised).T + + def _derivative(self, coefs, order=1): + return (Monomial(self.domain_range, self.n_basis - order), + np.array([np.polyder(x[::-1], order)[::-1] + for x in coefs])) + + def _evaluate_constant_lfd(self, weights): + """ + Evaluate constant weights of a linear differential operator + over the basis functions. + """ + + max_derivative = len(weights) - 1 + + _, coef_mat = self._coef_mat(max_derivative) + + # Compute coefficients for each derivative + coefs = np.cumprod(coef_mat, axis=0) + + # Add derivative 0 row + coefs = np.concatenate((np.ones((1, self.n_basis)), coefs)) + + # Now each row correspond to each basis and each column to + # each derivative + coefs_t = coefs.T + + # Multiply by the weights + weighted_coefs = coefs_t * weights + assert len(weighted_coefs) == self.n_basis + + # Now each row has the right weight, but the polynomials are in a + # decreasing order and with different exponents + + # Resize the coefs so that there are as many rows as the number of + # basis + # The matrix is now triangular + # refcheck is False to prevent exceptions while debugging + weighted_coefs = np.copy(weighted_coefs.T) + weighted_coefs.resize(self.n_basis, + self.n_basis, refcheck=False) + weighted_coefs = weighted_coefs.T + + # Shift the coefficients so that they correspond to the right + # exponent + indexes = np.tril_indices(self.n_basis) + polynomials = np.zeros_like(weighted_coefs) + polynomials[indexes[0], indexes[1] - + indexes[0] - 1] = weighted_coefs[indexes] + + # At this point, each row of the matrix correspond to a polynomial + # that is the result of applying the linear differential operator + # to each element of the basis + + return polynomials + + def _penalty(self, lfd): + + weights = lfd.constant_weights() + if weights is None: + return NotImplemented + + polynomials = self._evaluate_constant_lfd(weights) + + # Expand the polinomials with 0, so that the multiplication fits + # inside. It will need the double of the degree + length_with_padding = polynomials.shape[1] * 2 - 1 + + # Multiplication of polynomials is a convolution. + # The convolution can be performed in parallel applying a Fourier + # transform and then doing a normal multiplication in that + # space, coverting back with the inverse Fourier transform + fft = np.fft.rfft(polynomials, length_with_padding) + + # We compute only the upper matrix, as the penalty matrix is + # symmetrical + indices = np.triu_indices(self.n_basis) + fft_mul = fft[indices[0]] * fft[indices[1]] + + integrand = np.fft.irfft(fft_mul, length_with_padding) + + integration_domain = self.domain_range[0] + + # To integrate, divide by the position and increase the exponent + # in the evaluation + denom = np.arange(integrand.shape[1], 0, -1) + integrand /= denom + + # Add column of zeros at the right to increase exponent + integrand = np.pad(integrand, + pad_width=((0, 0), + (0, 1)), + mode='constant') + + # Now, apply Barrow's rule + # polyval applies Horner method over the first dimension, + # so we need to transpose + x_right = np.polyval(integrand.T, integration_domain[1]) + x_left = np.polyval(integrand.T, integration_domain[0]) + + integral = x_right - x_left + + penalty_matrix = np.empty((self.n_basis, self.n_basis)) + + # Set upper matrix + penalty_matrix[indices] = integral + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = integral + + return penalty_matrix + + def basis_of_product(self, other): + """Multiplication of a Monomial Basis with other Basis""" + if not _same_domain(self, other): + raise ValueError("Ranges are not equal.") + + if isinstance(other, Monomial): + return Monomial(self.domain_range, self.n_basis + other.n_basis) + + return other.rbasis_of_product(self) + + def rbasis_of_product(self, other): + """Multiplication of a Monomial Basis with other Basis""" + return Basis.default_basis_of_product(self, other) + + def _to_R(self): + drange = self.domain_range[0] + return "create.monomial.basis(rangeval = c(" + str(drange[0]) + "," +\ + str(drange[1]) + "), nbasis = " + str(self.n_basis) + ")" From f6881bc0f54c2e115a71c802ef18e96b4209ae44 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Apr 2020 21:08:02 +0200 Subject: [PATCH 431/624] Add linear differential operator regularization. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 39 +- skfda/misc/regularization/__init__.py | 1 + .../_linear_diff_op_regularization.py | 419 ++++++++++++++++++ skfda/representation/basis/_basis.py | 26 +- skfda/representation/basis/_constant.py | 5 + skfda/representation/basis/_monomial.py | 19 +- 7 files changed, 473 insertions(+), 38 deletions(-) create mode 100644 skfda/misc/regularization/__init__.py create mode 100644 skfda/misc/regularization/_linear_diff_op_regularization.py diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 329b72770..e549c4fd9 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -3,4 +3,4 @@ from ._utils import (_list_of_arrays, _coordinate_list, _check_estimator, parameter_aliases, _to_grid, check_is_univariate, - _same_domain) + _same_domain, singledispatchmethod) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index c6c46f8cc..7cbff0e10 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -1,7 +1,6 @@ """Module with generic methods""" import functools - import types import numpy as np @@ -186,3 +185,41 @@ def _check_estimator(estimator): instance = estimator() check_get_params_invariance(name, instance) check_set_params(name, instance) + + +singledispatchmethod = getattr(functools, 'singledispatchmethod', None) +if singledispatchmethod is None: + # For Python versions prior to 3.8 + + class singledispatchmethod: + """Single-dispatch generic method descriptor. + Supports wrapping existing descriptors and handles non-descriptor + callables as instance methods. + """ + + def __init__(self, func): + if not callable(func) and not hasattr(func, "__get__"): + raise TypeError(f"{func!r} is not callable or a descriptor") + + self.dispatcher = functools.singledispatch(func) + self.func = func + + def register(self, cls, method=None): + """generic_method.register(cls, func) -> func + Registers a new implementation for the given *cls* on a *generic_method*. + """ + return self.dispatcher.register(cls, func=method) + + def __get__(self, obj, cls=None): + def _method(*args, **kwargs): + method = self.dispatcher.dispatch(args[0].__class__) + return method.__get__(obj, cls)(*args, **kwargs) + + _method.__isabstractmethod__ = self.__isabstractmethod__ + _method.register = self.register + functools.update_wrapper(_method, self.func) + return _method + + @property + def __isabstractmethod__(self): + return getattr(self.func, '__isabstractmethod__', False) diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py new file mode 100644 index 000000000..c74708d5b --- /dev/null +++ b/skfda/misc/regularization/__init__.py @@ -0,0 +1 @@ +from ._linear_diff_op_regularization import LinearDifferentialOperatorRegularization diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py new file mode 100644 index 000000000..9c33edb2e --- /dev/null +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -0,0 +1,419 @@ +import functools + +from numpy import polyder, polyint, polymul, polyval +import scipy.integrate +from scipy.interpolate import PPoly + +import numpy as np + +from ..._utils import singledispatchmethod +from ...representation.basis import Constant, Monomial, Fourier, BSpline +from .._lfd import LinearDifferentialOperator + + +class LinearDifferentialOperatorRegularization(): + """ + Regularization using the integral of the square of a linear differential + operator. + + Args: + lfd (LinearDifferentialOperator, list or int): Linear + differential operator. If it is not a LinearDifferentialOperator + object, it will be converted to one. + + """ + + def __init__(self, linear_diff_op=2): + if not isinstance(linear_diff_op, LinearDifferentialOperator): + self.linear_diff_op = LinearDifferentialOperator(linear_diff_op) + + def penalty_matrix_numerical(self, basis): + """Return a penalty matrix using a numerical approach. + + Args: + basis (Basis): basis to compute the penalty for. + + """ + indices = np.triu_indices(basis.n_basis) + + def cross_product(x): + """Multiply the two lfds""" + res = self.linear_diff_op(basis)([x])[:, 0] + + return res[indices[0]] * res[indices[1]] + + # Range of first dimension + domain_range = basis.domain_range[0] + + penalty_matrix = np.empty((basis.n_basis, basis.n_basis)) + + # Obtain the integrals for the upper matrix + triang_vec = scipy.integrate.quad_vec( + cross_product, domain_range[0], domain_range[1])[0] + + # Set upper matrix + penalty_matrix[indices] = triang_vec + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = triang_vec + + return penalty_matrix + + @singledispatchmethod + def penalty_matrix_optimized(self, basis): + """ + Return a penalty matrix given a basis. + + This method is a singledispatch method that provides an + efficient analytical implementation of the computation of the + penalty matrix if possible. + """ + return NotImplemented + + def penalty_matrix(self, basis): + r"""Return a penalty matrix given a basis. + + The penalty matrix is defined as [RS05-5-6-2]_: + + .. math:: + R_{ij} = \int L\phi_i(s) L\phi_j(s) ds + + where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis + functions and :math:`L` is a differential operator. + + Args: + basis (Basis): basis to compute the penalty for. + + Returns: + numpy.array: Penalty matrix. + + References: + .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the + roughness penalty. In *Functional Data Analysis* (pp. 106-107). + Springer. + + """ + matrix = self.penalty_matrix_optimized(basis) + + if matrix is NotImplemented: + return self.penalty_matrix_numerical(basis) + else: + return matrix + + +@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +def constant_penalty_matrix_optimized( + regularization: LinearDifferentialOperatorRegularization, + basis: Constant): + + coefs = regularization.linear_diff_op.constant_weights() + if coefs is None: + return NotImplemented + + return np.array([[coefs[0] ** 2 * + (basis.domain_range[0][1] - + basis.domain_range[0][0])]]) + + +def _monomial_evaluate_constant_linear_diff_op(basis, weights): + """ + Evaluate constant weights of a linear differential operator + over the basis functions. + """ + + max_derivative = len(weights) - 1 + + seq = np.arange(basis.n_basis) + coef_mat = np.linspace(seq, seq - max_derivative + 1, + max_derivative, dtype=int) + + # Compute coefficients for each derivative + coefs = np.cumprod(coef_mat, axis=0) + + # Add derivative 0 row + coefs = np.concatenate((np.ones((1, basis.n_basis)), coefs)) + + # Now each row correspond to each basis and each column to + # each derivative + coefs_t = coefs.T + + # Multiply by the weights + weighted_coefs = coefs_t * weights + assert len(weighted_coefs) == basis.n_basis + + # Now each row has the right weight, but the polynomials are in a + # decreasing order and with different exponents + + # Resize the coefs so that there are as many rows as the number of + # basis + # The matrix is now triangular + # refcheck is False to prevent exceptions while debugging + weighted_coefs = np.copy(weighted_coefs.T) + weighted_coefs.resize(basis.n_basis, + basis.n_basis, refcheck=False) + weighted_coefs = weighted_coefs.T + + # Shift the coefficients so that they correspond to the right + # exponent + indexes = np.tril_indices(basis.n_basis) + polynomials = np.zeros_like(weighted_coefs) + polynomials[indexes[0], indexes[1] - + indexes[0] - 1] = weighted_coefs[indexes] + + # At this point, each row of the matrix correspond to a polynomial + # that is the result of applying the linear differential operator + # to each element of the basis + + return polynomials + + +@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +def monomial_penalty_matrix_optimized( + regularization: LinearDifferentialOperatorRegularization, + basis: Monomial): + + weights = regularization.linear_diff_op.constant_weights() + if weights is None: + return NotImplemented + + polynomials = _monomial_evaluate_constant_linear_diff_op(basis, weights) + + # Expand the polinomials with 0, so that the multiplication fits + # inside. It will need the double of the degree + length_with_padding = polynomials.shape[1] * 2 - 1 + + # Multiplication of polynomials is a convolution. + # The convolution can be performed in parallel applying a Fourier + # transform and then doing a normal multiplication in that + # space, coverting back with the inverse Fourier transform + fft = np.fft.rfft(polynomials, length_with_padding) + + # We compute only the upper matrix, as the penalty matrix is + # symmetrical + indices = np.triu_indices(basis.n_basis) + fft_mul = fft[indices[0]] * fft[indices[1]] + + integrand = np.fft.irfft(fft_mul, length_with_padding) + + integration_domain = basis.domain_range[0] + + # To integrate, divide by the position and increase the exponent + # in the evaluation + denom = np.arange(integrand.shape[1], 0, -1) + integrand /= denom + + # Add column of zeros at the right to increase exponent + integrand = np.pad(integrand, + pad_width=((0, 0), + (0, 1)), + mode='constant') + + # Now, apply Barrow's rule + # polyval applies Horner method over the first dimension, + # so we need to transpose + x_right = np.polyval(integrand.T, integration_domain[1]) + x_left = np.polyval(integrand.T, integration_domain[0]) + + integral = x_right - x_left + + penalty_matrix = np.empty((basis.n_basis, basis.n_basis)) + + # Set upper matrix + penalty_matrix[indices] = integral + + # Set lower matrix + penalty_matrix[(indices[1], indices[0])] = integral + + return penalty_matrix + + +def _fourier_penalty_matrix_optimized_orthonormal(basis, weights): + """ + Return the penalty when the basis is orthonormal. + """ + + signs = np.array([1, 1, -1, -1]) + signs_expanded = np.tile(signs, len(weights) // 4 + 1) + + signs_odd = signs_expanded[:len(weights)] + signs_even = signs_expanded[1:len(weights) + 1] + + phases = (np.arange(1, (basis.n_basis - 1) // 2 + 1) * + 2 * np.pi / basis.period) + + # Compute increasing powers + coefs_no_sign = np.vander(phases, len(weights), increasing=True) + + coefs_no_sign *= weights + + coefs_odd = signs_odd * coefs_no_sign + coefs_even = signs_even * coefs_no_sign + + # After applying the linear differential operator to a sinusoidal + # element of the basis e, the result can be expressed as + # A e + B e*, where e* is the other basis element in the pair + # with the same phase + + odd_sin_coefs = np.sum(coefs_odd[:, ::2], axis=1) + odd_cos_coefs = np.sum(coefs_odd[:, 1::2], axis=1) + + even_cos_coefs = np.sum(coefs_even[:, ::2], axis=1) + even_sin_coefs = np.sum(coefs_even[:, 1::2], axis=1) + + # The diagonal is the inner product of A e + B e* + # with itself. As the basis is orthonormal, the cross products e e* + # are 0, and the products e e and e* e* are one. + # Thus, the diagonal is A^2 + B^2 + # All elements outside the main diagonal are 0 + main_diag_odd = odd_sin_coefs**2 + odd_cos_coefs**2 + main_diag_even = even_sin_coefs**2 + even_cos_coefs**2 + + # The main diagonal should intercalate both diagonals + main_diag = np.array((main_diag_odd, main_diag_even)).T.ravel() + + penalty_matrix = np.diag(main_diag) + + # Add row and column for the constant + penalty_matrix = np.pad(penalty_matrix, pad_width=((1, 0), (1, 0)), + mode='constant') + + penalty_matrix[0, 0] = weights[0]**2 + + return penalty_matrix + + +@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +def fourier_penalty_matrix_optimized( + regularization: LinearDifferentialOperatorRegularization, + basis: Fourier): + + weights = regularization.linear_diff_op.constant_weights() + if weights is None: + return NotImplemented + + # If the period and domain range are not the same, the basis functions + # are not orthogonal + if basis.period != (basis.domain_range[0][1] - basis.domain_range[0][0]): + return NotImplemented + + return _fourier_penalty_matrix_optimized_orthonormal(basis, weights) + + +@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +def bspline_penalty_matrix_optimized( + regularization: LinearDifferentialOperatorRegularization, + basis: BSpline): + + coefs = regularization.linear_diff_op.constant_weights() + if coefs is None: + return NotImplemented + + nonzero = np.flatnonzero(coefs) + + # All derivatives above the order of the spline are effectively + # zero + nonzero = nonzero[nonzero < basis.order] + + if len(nonzero) == 0: + return np.zeros((basis.n_basis, basis.n_basis)) + + # We will only deal with one nonzero coefficient right now + if len(nonzero) != 1: + return NotImplemented + + derivative_degree = nonzero[0] + + if derivative_degree == basis.order - 1: + # The derivative of the bsplines are constant in the intervals + # defined between knots + knots = np.array(basis.knots) + mid_inter = (knots[1:] + knots[:-1]) / 2 + constants = basis.evaluate(mid_inter, + derivative=derivative_degree).T + knots_intervals = np.diff(basis.knots) + # Integration of product of constants + return constants.T @ np.diag(knots_intervals) @ constants + + # We only deal with the case without zero length intervals + # for now + if np.any(np.diff(basis.knots) == 0): + return NotImplemented + + # Compute exactly using the piecewise polynomial + # representation of splines + + # Places m knots at the boundaries + knots = basis._evaluation_knots() + + # c is used the select which spline the function + # PPoly.from_spline below computes + c = np.zeros(len(knots)) + + # Initialise empty list to store the piecewise polynomials + ppoly_lst = [] + + no_0_intervals = np.where(np.diff(knots) > 0)[0] + + # For each basis gets its piecewise polynomial representation + for i in range(basis.n_basis): + + # Write a 1 in c in the position of the spline + # transformed in each iteration + c[i] = 1 + + # Gets the piecewise polynomial representation and gets + # only the positions for no zero length intervals + # This polynomial are defined relatively to the knots + # meaning that the column i corresponds to the ith knot. + # Let the ith knot be a + # Then f(x) = pp(x - a) + pp = PPoly.from_spline((knots, c, basis.order - 1)) + pp_coefs = pp.c[:, no_0_intervals] + + # We have the coefficients for each interval in coordinates + # (x - a), so we will need to subtract a when computing the + # definite integral + ppoly_lst.append(pp_coefs) + c[i] = 0 + + # Now for each pair of basis computes the inner product after + # applying the linear differential operator + penalty_matrix = np.zeros((basis.n_basis, basis.n_basis)) + for interval in range(len(no_0_intervals)): + for i in range(basis.n_basis): + poly_i = np.trim_zeros(ppoly_lst[i][:, + interval], 'f') + if len(poly_i) <= derivative_degree: + # if the order of the polynomial is lesser or + # equal to the derivative the result of the + # integral will be 0 + continue + # indefinite integral + derivative = polyder(poly_i, derivative_degree) + square = polymul(derivative, derivative) + integral = polyint(square) + + # definite integral + penalty_matrix[i, i] += np.diff(polyval( + integral, basis.knots[interval: interval + 2] + - basis.knots[interval]))[0] + + for j in range(i + 1, basis.n_basis): + poly_j = np.trim_zeros(ppoly_lst[j][:, + interval], 'f') + if len(poly_j) <= derivative_degree: + # if the order of the polynomial is lesser + # or equal to the derivative the result of + # the integral will be 0 + continue + # indefinite integral + integral = polyint( + polymul(polyder(poly_i, derivative_degree), + polyder(poly_j, derivative_degree))) + # definite integral + penalty_matrix[i, j] += np.diff(polyval( + integral, basis.knots[interval: interval + 2] + - basis.knots[interval]) + )[0] + penalty_matrix[j, i] = penalty_matrix[i, j] + return penalty_matrix diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index e4d1c248e..40bb67462 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -11,7 +11,7 @@ import numpy as np -from ..._utils import _list_of_arrays +from ..._utils import _list_of_arrays, _same_domain __author__ = "Miguel Carbajo Berrocal" @@ -26,10 +26,6 @@ def _check_domain(domain_range): raise ValueError(f"The interval {domain} is not well-defined.") -def _same_domain(one_domain_range, other_domain_range): - return np.array_equal(one_domain_range, other_domain_range) - - class Basis(ABC): """Defines the structure of a basis function system. @@ -134,17 +130,6 @@ def plot(self, chart=None, *, derivative=0, **kwargs): """ self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) - def _internal_representation(self): - """ - Returns an internal representation of the basis. - - This representation may have several operations available that return - objects of the same kind, and can be used to build operators in an - analytical, but generic, way. - - """ - return NotImplemented - def _numerical_penalty(self, lfd): """Return a penalty matrix using a numerical approach. @@ -185,10 +170,11 @@ def cross_product(x): return penalty_matrix - def _penalty(self, lfd): + def _linear_diff_op_inner_product(self, lfd): """ Subclasses may override this for computing analytically - the penalty matrix in the cases when that is possible. + the penalty matrix associated with a linear differential operator + inner product in the cases when that is possible. Returning NotImplemented will use numerical computation of the penalty matrix. @@ -248,7 +234,7 @@ def default_basis_of_product(one, other): """Default multiplication for a pair of basis""" from ._bspline import BSpline - if not _same_domain(one.domain_range, other.domain_range): + if not _same_domain(one, other): raise ValueError("Ranges are not equal.") norder = min(8, one.n_basis + other.n_basis) @@ -276,7 +262,7 @@ def same_domain(self, other): Args: other (Basis): Basis to check the domain range definition """ - return _same_domain(self.domain_range, other.domain_range) + return _same_domain(self, other) def copy(self): """Basis copy""" diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 9d2c119e9..922ca2cb0 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -38,11 +38,16 @@ def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) + def _internal_representation(self): + return NumberRepresentation.from_basis(self) + def _penalty(self, lfd): coefs = lfd.constant_weights() if coefs is None: return NotImplemented + internal_repr = self._internal_representation() + return np.array([[coefs[0] ** 2 * (self.domain_range[0][1] - self.domain_range[0][0])]]) diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index acf79affe..9890d72db 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -43,21 +43,6 @@ class Monomial(Basis): """ - def _coef_mat(self, derivative): - """ - Obtain the matrix of coefficients. - - Each column of coef_mat contains the numbers that must be multiplied - together in order to obtain the coefficient of each basis function - Thus, column i will contain i, i - 1, ..., i - derivative + 1. - """ - - seq = np.arange(self.n_basis) - coef_mat = np.linspace(seq, seq - derivative + 1, - derivative, dtype=int) - - return seq, coef_mat - def _coefs_exps_derivatives(self, derivative): """ Return coefficients and exponents of the derivatives. @@ -67,7 +52,9 @@ def _coefs_exps_derivatives(self, derivative): When the exponent would be negative (the coefficient in that case is zero) returns 0 as the exponent (to prevent division by zero). """ - seq, coef_mat = self._coef_mat(derivative) + seq = np.arange(self.n_basis) + coef_mat = np.linspace(seq, seq - derivative + 1, + derivative, dtype=int) coefs = np.prod(coef_mat, axis=0) exps = np.maximum(seq - derivative, 0) From 48ddf304dd2fee50f7e60573894116781fc6d80a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Apr 2020 22:22:41 +0200 Subject: [PATCH 432/624] deleted test notebook --- skfda/exploratory/fpca/test.ipynb | 3059 ----------------------------- 1 file changed, 3059 deletions(-) delete mode 100644 skfda/exploratory/fpca/test.ipynb diff --git a/skfda/exploratory/fpca/test.ipynb b/skfda/exploratory/fpca/test.ipynb deleted file mode 100644 index 8b01e51e1..000000000 --- a/skfda/exploratory/fpca/test.ipynb +++ /dev/null @@ -1,3059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import skfda\n", - "from skfda.exploratory.fpca import FPCABasis, FPCADiscretized\n", - "from skfda.representation import FDataBasis, FDataGrid\n", - "from skfda.datasets._real_datasets import fetch_growth, fetch_weather\n", - "from matplotlib import pyplot\n", - "from skfda.representation.basis import Fourier, BSpline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def fetch_weather_temp_only():\n", - " weather_dataset = fetch_weather()\n", - " fd_data = weather_dataset['data']\n", - " fd_data.data_matrix = fd_data.data_matrix[:, :, :1]\n", - " fd_data.axes_labels = fd_data.axes_labels[:-1]\n", - " return fd_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finding lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92321326 -0.14305151 -0.35426565 -0.00898117 0.02415526 0.02912168\n", - " 0.0017787 0.0105183 0.00913199]\n", - " [-0.33139612 -0.03518506 0.89267801 0.17537891 0.24018427 0.03852789\n", - " 0.03756656 -0.02437487 0.01133841]])\n", - "[15086.27662761 1438.98606096]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000002e+00, -1.65502423e-08],\n", - " [-1.65502423e-08, 1.00000023e+00]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.derivative(2).inner_product(fpca.components.derivative(2)) \\\n", - " + fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.00000000e+00, 1.38777878e-16],\n", - " [1.38777878e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca.components.inner_product(fpca.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=9, period=364),\n", - " coefficients=[[-0.92413848 -0.14193772 -0.35129594 -0.00785487 0.02119231 0.01694925\n", - " 0.00103464 0.00321583 0.00279164]\n", - " [-0.33303402 -0.03547108 0.89500958 0.15396134 0.21074998 0.02212515\n", - " 0.02173688 -0.00739345 0.00334435]])\n", - "[15058.25775083 1410.7365378 ]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(2, regularization=True, regularization_parameter=100000)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.59561036e-08, -2.03098938e-08],\n", - " [-2.03098938e-08, 1.76404890e-07]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived=fpca.components.derivative(2)\n", - "derived.inner_product(derived)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.99840439, 0.00203099],\n", - " [0.00203099, 0.98235951]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod = fpca.components.inner_product(fpca.components)\n", - "in_prod" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -9.84455573e-17],\n", - " [-9.84455573e-17, 9.99999997e-01]])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "in_prod + derived.inner_product(derived) * 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO, analisis de los productos internos, donde se usa uno de puede usar el otro" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.13318664, 0.00793026],\n", - " [0.00793026, 0.09678453]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "derived = fpca_basis.components.derivative(2)\n", - "derived.inner_product(derived)*0.0001" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataBasis(\n", - " basis=Fourier(domain_range=[array([ 0, 365])], n_basis=9, period=365),\n", - " coefficients=[[ 8.95997071e+01 -7.56653047e+01 -1.14531869e+02 5.60410553e+00\n", - " 4.13831672e+00 -8.81388351e+00 -1.28702668e+00 3.22313889e+00\n", - " 8.27705008e-01]\n", - " [ 1.17492968e+02 -7.70327394e+01 -1.49082796e+02 -1.14875790e+00\n", - " -1.07468747e+00 -7.91124972e+00 -2.74298661e+00 9.71720938e-01\n", - " -1.14509808e+00]\n", - " [ 1.05260551e+02 -8.63332550e+01 -1.36356388e+02 6.04906258e-01\n", - " 4.43809965e+00 -1.05423840e+01 -9.23182460e-01 1.52557219e+00\n", - " 4.89740559e-01]\n", - " [ 1.30133656e+02 -6.70355028e+01 -1.18479289e+02 -2.59667770e+00\n", - " -3.87697018e+00 -5.89304221e+00 -5.60514578e-01 5.70029306e-01\n", - " -1.48240258e+00]\n", - " [ 9.99635007e+01 -8.52358795e+01 -1.58197694e+02 -4.34606119e+00\n", - " -3.87220304e-01 -9.62818845e+00 -3.32913142e+00 1.23294045e+00\n", - " -8.83919777e-01]\n", - " [ 1.00549736e+02 -7.17801965e+01 -1.81015491e+02 -7.39885098e+00\n", - " -6.50588963e+00 -9.10036419e+00 -5.67562430e+00 1.58058671e+00\n", - " -2.54635122e+00]\n", - " [-9.66554615e+01 -9.99618149e+01 -2.20328659e+02 -9.48461265e+00\n", - " -7.74471767e+00 -8.21298036e+00 -9.39213882e+00 5.22694508e+00\n", - " -3.23786555e+00]\n", - " [ 5.92254168e+01 -7.84023521e+01 -2.10815160e+02 -1.76066402e+01\n", - " -1.46533565e+01 -9.52292860e+00 -8.56695109e+00 2.17923028e+00\n", - " -3.47823175e+00]\n", - " [ 4.29155274e+01 -7.77212819e+01 -2.12903658e+02 -1.70440515e+01\n", - " -1.43090648e+01 -1.03854103e+01 -7.41809992e+00 2.09848175e+00\n", - " -2.58755972e+00]\n", - " [ 7.79639933e+01 -7.50441651e+01 -1.99544247e+02 -1.33145220e+01\n", - " -8.78594650e+00 -6.74641858e+00 -4.84079135e+00 1.65819960e+00\n", - " -3.66504512e+00]\n", - " [ 7.87020210e+01 -6.90788972e+01 -1.87522605e+02 -1.52903724e+01\n", - " -1.05172941e+01 -7.04729876e+00 -3.95480050e+00 2.84356867e+00\n", - " -3.48198336e+00]\n", - " [ 1.17126571e+02 -7.28701653e+01 -1.96711739e+02 -1.38157965e+01\n", - " -9.80785781e+00 -7.47626097e+00 -3.56941745e+00 1.93089223e+00\n", - " -3.82921672e+00]\n", - " [ 1.11049619e+02 -7.12961542e+01 -2.00775455e+02 -1.35397898e+01\n", - " -1.01824395e+01 -6.94532809e+00 -3.64630675e+00 1.90859913e+00\n", - " -4.04282785e+00]\n", - " [ 1.38822493e+02 -6.98070887e+01 -1.70221432e+02 -6.74710279e+00\n", - " -3.32536240e+00 -7.06603384e+00 -3.99267367e-01 -7.38202282e-01\n", - " -1.81811953e+00]\n", - " [ 1.39712313e+02 -6.87310697e+01 -1.70074637e+02 -8.83772681e+00\n", - " -4.45321305e+00 -5.66448775e+00 -2.25264627e-01 -1.25517908e+00\n", - " -1.35385457e+00]\n", - " [ 4.70296394e+01 -7.32225967e+01 -2.01980827e+02 -8.89612035e+00\n", - " -1.72137075e+01 -9.58686725e+00 -5.12841209e+00 3.66458527e+00\n", - " -3.28301380e+00]\n", - " [ 4.72442433e+01 -7.44058899e+01 -2.43599289e+02 -1.42471764e+01\n", - " -2.36604701e+01 -4.23862386e+00 -4.63016214e+00 4.69728412e+00\n", - " -3.22319903e+00]\n", - " [-2.88930005e+00 -7.89821975e+01 -2.48489713e+02 -1.03929224e+01\n", - " -2.27856025e+01 -2.22545926e+00 -8.59694423e+00 7.16579192e+00\n", - " -3.84870184e+00]\n", - " [-1.35383598e+02 -1.20565942e+02 -2.38095634e+02 -3.91410333e+00\n", - " -1.02701379e+01 -1.07324597e+00 -4.30182840e+00 8.77966816e+00\n", - " -3.09680658e+00]\n", - " [ 5.24523113e+01 -6.41833465e+01 -2.30056452e+02 -7.51303082e+00\n", - " -2.13295275e+01 -3.08427990e+00 -3.22773474e+00 5.24827574e+00\n", - " -3.56248062e+00]\n", - " [ 1.30384899e+01 -6.59269437e+01 -2.43332823e+02 -1.26868473e+01\n", - " -2.56570108e+01 -4.45738962e-01 -4.06851748e+00 8.69736687e+00\n", - " -2.84105467e+00]\n", - " [-6.51244044e+01 -8.73126093e+01 -2.74128065e+02 -1.71332977e+01\n", - " -2.02354828e+01 -4.66641098e-01 -6.73544687e+00 8.34268385e+00\n", - " -3.73710564e+00]\n", - " [ 4.31248970e+01 -5.09797645e+01 -2.00337050e+02 -5.74564500e+00\n", - " -1.99243975e+01 3.69004430e+00 -2.97182899e-01 7.95765582e+00\n", - " -2.97497323e-01]\n", - " [ 7.61634150e+01 -4.70525906e+01 -1.67969170e+02 4.89155923e+00\n", - " -1.22572757e+01 2.01904825e+00 -2.89979400e+00 5.93871335e+00\n", - " -1.07426684e+00]\n", - " [ 1.67134493e+02 -3.56542789e+01 -1.64768746e+02 1.16046125e+01\n", - " -1.42872334e+01 -6.14542385e+00 -4.68348094e+00 -2.20105099e-01\n", - " -4.44797345e+00]\n", - " [ 1.90269830e+02 -3.13128163e+01 -9.23771058e+01 1.27012912e+01\n", - " -2.08134750e+00 -1.77059404e-01 -6.88114672e-01 1.71993443e-01\n", - " -3.49884105e+00]\n", - " [ 1.83863121e+02 -2.96563297e+01 -8.26438161e+01 1.18733494e+01\n", - " -1.24087034e+00 1.07081626e+00 -6.31222939e-02 3.51685485e-01\n", - " -1.66074555e+00]\n", - " [ 7.32688807e+01 -3.59603458e+01 -1.62018614e+02 6.02997696e+00\n", - " -1.81691429e+01 -1.96537177e+00 -6.55706183e+00 2.53041088e+00\n", - " -3.86170049e+00]\n", - " [ 1.33787155e+02 -3.32778024e+01 -7.47483362e+01 1.05204495e+01\n", - " -4.45317745e+00 1.53550369e+00 -1.51877016e+00 -9.61774607e-02\n", - " -1.69638452e+00]\n", - " [-1.62732498e+01 -4.68314258e+01 -2.08596543e+02 3.89029838e+00\n", - " -2.06021149e+01 6.03636479e-01 -5.86235956e+00 1.64773130e+00\n", - " 1.66035500e+00]\n", - " [-9.15259071e+01 -5.27824471e+01 -2.96450992e+02 -6.25789174e+00\n", - " -2.73940543e+01 5.71293380e-01 1.95862226e+00 1.70156896e+00\n", - " 8.13746375e+00]\n", - " [-9.59750104e+01 -9.79833386e+01 -2.85998666e+02 -8.76487317e+00\n", - " -7.02828969e+00 5.69548629e+00 -4.28222889e+00 7.87967705e+00\n", - " 2.53460133e-01]\n", - " [-1.84412716e+02 -1.23690319e+02 -2.10089669e+02 -9.05327476e+00\n", - " 6.89788781e+00 4.29782080e+00 -7.22167038e-01 6.25245888e+00\n", - " -2.57478775e+00]\n", - " [-1.76529952e+02 -1.01420944e+02 -2.84930634e+02 1.15521966e+01\n", - " 2.34304847e+01 1.72152225e+01 4.06231081e+00 -6.82922460e-01\n", - " 8.39050660e+00]\n", - " [-3.15582751e+02 -1.13614200e+02 -2.32503551e+02 1.26509970e+01\n", - " 3.37666761e+01 9.81570243e+00 3.74850021e+00 -4.51727495e-02\n", - " 1.44190615e+00]],\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " keepdims=False)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0,365])\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fd_basis" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0.00127419, 0.07401235],\n", - " [0.05234239, 0.002548 , 0.07397945],\n", - " [0.05234239, 0.00382106, 0.07392463]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3, domain_range=[0,365])\n", - "np.transpose(basis.evaluate(range(1, 4)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8.99091291e+01 -7.66543475e+01 -1.13583421e+02 5.44231094e+00\n", - " 3.83515561e+00 -8.99363959e+00 -1.11826010e+00 3.07572675e+00\n", - " 6.80630538e-01]\n", - " [ 1.17931874e+02 -7.82957088e+01 -1.47967475e+02 -1.40972969e+00\n", - " -1.27977838e+00 -8.16916942e+00 -2.61402567e+00 7.08222777e-01\n", - " -1.24141020e+00]\n", - " [ 1.05632931e+02 -8.74878381e+01 -1.35256374e+02 4.21625041e-01\n", - " 4.18065075e+00 -1.07611638e+01 -7.20116154e-01 1.29607751e+00\n", - " 3.91548980e-01]\n", - " [ 1.30439990e+02 -6.80334034e+01 -1.17526982e+02 -2.87963231e+00\n", - " -4.01337903e+00 -6.07850424e+00 -4.78848992e-01 3.29481412e-01\n", - " -1.54310715e+00]\n", - " [ 1.00460999e+02 -8.65606083e+01 -1.56988474e+02 -4.61115777e+00\n", - " -5.51072768e-01 -9.93526704e+00 -3.15969917e+00 9.49508717e-01\n", - " -9.97171826e-01]\n", - " [ 1.01173394e+02 -7.32943258e+01 -1.79791141e+02 -7.73015377e+00\n", - " -6.60778450e+00 -9.47478355e+00 -5.53686046e+00 1.23002295e+00\n", - " -2.70796419e+00]\n", - " [-9.55872354e+01 -1.01811346e+02 -2.18714716e+02 -9.95819769e+00\n", - " -7.83046219e+00 -8.79053897e+00 -9.27284491e+00 4.80115252e+00\n", - " -3.52164922e+00]\n", - " [ 6.00679601e+01 -8.01309974e+01 -2.09367167e+02 -1.80932734e+01\n", - " -1.45711910e+01 -1.00493454e+01 -8.44360445e+00 1.75428292e+00\n", - " -3.68029169e+00]\n", - " [ 4.37794929e+01 -7.94715281e+01 -2.11470231e+02 -1.75233810e+01\n", - " -1.42591524e+01 -1.08863679e+01 -7.28731864e+00 1.68470981e+00\n", - " -2.78348167e+00]\n", - " [ 7.87004512e+01 -7.66986876e+01 -1.98221965e+02 -1.37077895e+01\n", - " -8.81182353e+00 -7.13822378e+00 -4.77155105e+00 1.28327264e+00\n", - " -3.82569943e+00]\n", - " [ 7.93932590e+01 -7.06219988e+01 -1.86279307e+02 -1.56892780e+01\n", - " -1.04921656e+01 -7.42159261e+00 -3.88024371e+00 2.48127613e+00\n", - " -3.67156904e+00]\n", - " [ 1.17798001e+02 -7.44969036e+01 -1.95415331e+02 -1.42136663e+01\n", - " -9.82743312e+00 -7.83401068e+00 -3.48239641e+00 1.55017050e+00\n", - " -3.97983037e+00]\n", - " [ 1.11747569e+02 -7.29610194e+01 -1.99477149e+02 -1.39441205e+01\n", - " -1.02115144e+01 -7.30367564e+00 -3.57616419e+00 1.52273594e+00\n", - " -4.19762933e+00]\n", - " [ 1.39316561e+02 -7.12285699e+01 -1.69103594e+02 -7.01448162e+00\n", - " -3.48438443e+00 -7.26054453e+00 -3.14952582e-01 -1.00752314e+00\n", - " -1.84302764e+00]\n", - " [ 1.40206596e+02 -7.01470467e+01 -1.68962028e+02 -9.13057055e+00\n", - " -4.57799867e+00 -5.86745297e+00 -1.89726857e-01 -1.51265552e+00\n", - " -1.36876895e+00]\n", - " [ 4.78498925e+01 -7.49085396e+01 -2.00607050e+02 -9.41208378e+00\n", - " -1.72983817e+01 -9.96333341e+00 -5.03485543e+00 3.30864127e+00\n", - " -3.55110682e+00]\n", - " [ 4.82479471e+01 -7.64402805e+01 -2.42056185e+02 -1.49136883e+01\n", - " -2.37146519e+01 -4.64758263e+00 -4.73305156e+00 4.37243175e+00\n", - " -3.55277222e+00]\n", - " [-1.78425396e+00 -8.10768334e+01 -2.46873332e+02 -1.10764984e+01\n", - " -2.28773816e+01 -2.73323146e+00 -8.74049075e+00 6.86249329e+00\n", - " -4.31493906e+00]\n", - " [-1.34204217e+02 -1.22600072e+02 -2.36269859e+02 -4.55175639e+00\n", - " -1.05340415e+01 -1.53058997e+00 -4.42982713e+00 8.48072636e+00\n", - " -3.54749651e+00]\n", - " [ 5.33823633e+01 -6.61262505e+01 -2.28664045e+02 -8.10514422e+00\n", - " -2.14955004e+01 -3.38320888e+00 -3.34539488e+00 4.98792170e+00\n", - " -3.90180193e+00]\n", - " [ 1.40909211e+01 -6.79745102e+01 -2.41856431e+02 -1.33874582e+01\n", - " -2.57425132e+01 -8.34490326e-01 -4.28871685e+00 8.47350073e+00\n", - " -3.32251108e+00]\n", - " [-6.38514776e+01 -8.96016547e+01 -2.72399803e+02 -1.78038768e+01\n", - " -2.02887963e+01 -9.69980940e-01 -6.95177976e+00 8.09125038e+00\n", - " -4.27270050e+00]\n", - " [ 4.39220502e+01 -5.26857166e+01 -1.99190029e+02 -6.30586886e+00\n", - " -2.01249904e+01 3.50374967e+00 -6.15733447e-01 7.95566994e+00\n", - " -7.14485425e-01]\n", - " [ 7.67726352e+01 -4.85146518e+01 -1.66981573e+02 4.49241512e+00\n", - " -1.25720162e+01 1.85973944e+00 -3.09720790e+00 5.93280473e+00\n", - " -1.39465809e+00]\n", - " [ 1.67634664e+02 -3.70927990e+01 -1.63842007e+02 1.12774988e+01\n", - " -1.46630857e+01 -6.23875717e+00 -4.62473594e+00 -4.02778745e-01\n", - " -4.54131572e+00]\n", - " [ 1.90390951e+02 -3.21501673e+01 -9.18094341e+01 1.25522321e+01\n", - " -2.42724157e+00 -1.69466371e-01 -7.07282821e-01 6.41204212e-02\n", - " -3.53185140e+00]\n", - " [ 1.83942627e+02 -3.04102242e+01 -8.21382683e+01 1.17354233e+01\n", - " -1.57723785e+00 1.08897578e+00 -1.30579687e-01 3.17111025e-01\n", - " -1.69971678e+00]\n", - " [ 7.39065583e+01 -3.73604390e+01 -1.61060861e+02 5.61262738e+00\n", - " -1.84168919e+01 -2.14884949e+00 -6.61869612e+00 2.42369905e+00\n", - " -4.06491676e+00]\n", - " [ 1.33922934e+02 -3.39538723e+01 -7.42003097e+01 1.03237162e+01\n", - " -4.72515513e+00 1.52205009e+00 -1.59541942e+00 -1.03384875e-01\n", - " -1.71820184e+00]\n", - " [-1.53458792e+01 -4.86164286e+01 -2.07433771e+02 3.40086607e+00\n", - " -2.09406843e+01 4.49080616e-01 -6.11572247e+00 1.80965372e+00\n", - " 1.42431949e+00]\n", - " [-9.01820488e+01 -5.52889399e+01 -2.95026880e+02 -6.89468388e+00\n", - " -2.78222133e+01 5.23794149e-01 1.50640935e+00 2.01626621e+00\n", - " 7.86876570e+00]\n", - " [-9.46899349e+01 -1.00418827e+02 -2.84279785e+02 -9.29074932e+00\n", - " -7.33746725e+00 5.28775101e+00 -4.66574532e+00 7.83939424e+00\n", - " -2.45843153e-01]\n", - " [-1.83356373e+02 -1.25478605e+02 -2.08464718e+02 -9.44438464e+00\n", - " 6.68643682e+00 3.89309402e+00 -9.08761471e-01 5.95155168e+00\n", - " -2.85985275e+00]\n", - " [-1.75319935e+02 -1.03932624e+02 -2.83505797e+02 1.14930532e+01\n", - " 2.25420553e+01 1.72358295e+01 3.37805655e+00 -2.38897419e-01\n", - " 8.26014480e+00]\n", - " [-3.14397261e+02 -1.15670509e+02 -2.31150611e+02 1.27607042e+01\n", - " 3.29877908e+01 9.78873221e+00 3.45314540e+00 3.60913293e-02\n", - " 1.43394056e+00]]\n" - ] - } - ], - "source": [ - "print(fd_basis.coefficients)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Monomial(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", - " [ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", - " [ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis.evaluate(list(range(10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.05234239, 0. , 0.07402332, 0. , 0.07402332,\n", - " 0. , 0.07402332, 0. , 0.07402332],\n", - " [0.05234239, 0.00127419, 0.07401235, 0.002548 , 0.07397945,\n", - " 0.00382106, 0.07392463, 0.00509298, 0.07384791],\n", - " [0.05234239, 0.002548 , 0.07397945, 0.00509298, 0.07384791,\n", - " 0.00763193, 0.07362884, 0.01016183, 0.0733225 ],\n", - " [0.05234239, 0.00382106, 0.07392463, 0.00763193, 0.07362884,\n", - " 0.01142245, 0.07313672, 0.01518252, 0.07244959]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fourier_basis = skfda.representation.basis.Fourier(n_basis=9, domain_range=[0, 365])\n", - "np.transpose(fourier_basis.evaluate(range(4)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test convert to basis" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[ -3.6],\n", - " [ -3.1],\n", - " [ -3.4],\n", - " ...,\n", - " [ -3.2],\n", - " [ -2.8],\n", - " [ -4.2]],\n", - " \n", - " [[ -4.4],\n", - " [ -4.2],\n", - " [ -5.3],\n", - " ...,\n", - " [ -3.6],\n", - " [ -4.9],\n", - " [ -5.7]],\n", - " \n", - " [[ -3.8],\n", - " [ -3.5],\n", - " [ -4.6],\n", - " ...,\n", - " [ -3.4],\n", - " [ -3.3],\n", - " [ -4.8]],\n", - " \n", - " ...,\n", - " \n", - " [[-23.3],\n", - " [-24. ],\n", - " [-24.4],\n", - " ...,\n", - " [-23.5],\n", - " [-23.9],\n", - " [-24.5]],\n", - " \n", - " [[-26.3],\n", - " [-27.1],\n", - " [-27.8],\n", - " ...,\n", - " [-25.7],\n", - " [-24. ],\n", - " [-24.8]],\n", - " \n", - " [[-30.7],\n", - " [-30.6],\n", - " [-31.4],\n", - " ...,\n", - " [-29. ],\n", - " [-29.4],\n", - " [-30.5]]]),\n", - " sample_points=[array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5,\n", - " 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5,\n", - " 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5,\n", - " 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,\n", - " 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5,\n", - " 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,\n", - " 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5,\n", - " 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5,\n", - " 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5,\n", - " 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5,\n", - " 90.5, 91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5,\n", - " 99.5, 100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5,\n", - " 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5,\n", - " 117.5, 118.5, 119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5,\n", - " 126.5, 127.5, 128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5,\n", - " 135.5, 136.5, 137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5,\n", - " 144.5, 145.5, 146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5,\n", - " 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5, 160.5, 161.5,\n", - " 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5, 170.5,\n", - " 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5,\n", - " 180.5, 181.5, 182.5, 183.5, 184.5, 185.5, 186.5, 187.5, 188.5,\n", - " 189.5, 190.5, 191.5, 192.5, 193.5, 194.5, 195.5, 196.5, 197.5,\n", - " 198.5, 199.5, 200.5, 201.5, 202.5, 203.5, 204.5, 205.5, 206.5,\n", - " 207.5, 208.5, 209.5, 210.5, 211.5, 212.5, 213.5, 214.5, 215.5,\n", - " 216.5, 217.5, 218.5, 219.5, 220.5, 221.5, 222.5, 223.5, 224.5,\n", - " 225.5, 226.5, 227.5, 228.5, 229.5, 230.5, 231.5, 232.5, 233.5,\n", - " 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5,\n", - " 243.5, 244.5, 245.5, 246.5, 247.5, 248.5, 249.5, 250.5, 251.5,\n", - " 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5, 259.5, 260.5,\n", - " 261.5, 262.5, 263.5, 264.5, 265.5, 266.5, 267.5, 268.5, 269.5,\n", - " 270.5, 271.5, 272.5, 273.5, 274.5, 275.5, 276.5, 277.5, 278.5,\n", - " 279.5, 280.5, 281.5, 282.5, 283.5, 284.5, 285.5, 286.5, 287.5,\n", - " 288.5, 289.5, 290.5, 291.5, 292.5, 293.5, 294.5, 295.5, 296.5,\n", - " 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5, 304.5, 305.5,\n", - " 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5, 314.5,\n", - " 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5,\n", - " 324.5, 325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5,\n", - " 333.5, 334.5, 335.5, 336.5, 337.5, 338.5, 339.5, 340.5, 341.5,\n", - " 342.5, 343.5, 344.5, 345.5, 346.5, 347.5, 348.5, 349.5, 350.5,\n", - " 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5,\n", - " 360.5, 361.5, 362.5, 363.5, 364.5])],\n", - " domain_range=array([[ 0.5, 364.5]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with Ramsay version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "# np.linalg.norm(fpca_basis.components.coefficients[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.86681336, -0.00793026],\n", - " [-0.00793026, 0.90321547]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2, regularization=True, regularization_parameter=0.0001)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.10101525, -0.40406102, 0.90913729],\n", - " [ 0.50507627, -0.80812204, -0.30304576]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n", - " [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.70710678, 1.1785113 ],\n", - " [-1.41421356, -0.94280904],\n", - " [ 2.12132034, -0.23570226]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.transform(basis_fd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BSpline test with Ramsays version" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.00000000e+00, -4.30211422e-16],\n", - " [-4.30211422e-16, 1.00000000e+00]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basis_fd=FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients\n", - "fpca_basis.components.inner_product(fpca_basis.components)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09991746, 0.02828496])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_basis.component_values" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "X = FDataBasis(skfda.representation.basis.BSpline(n_basis=4), \n", - " [[1.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 3.0, 0.0], [1.0, 0.0, 0.0, 1.0]])\n", - "meanfd = X.mean()\n", - "# consider moving these lines to FDataBasis as a centering function\n", - "# subtract from each row the mean coefficient matrix\n", - "X.coefficients -= meanfd.coefficients\n", - "n_samples, n_basis = X.coefficients.shape\n", - "components_basis = X.basis.copy()\n", - "g_matrix = components_basis.gram_matrix()\n", - "j_matrix = g_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "components_basis.penalty(derivative_degree=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.14285714, 0.07142857, 0.02857143, 0.00714286],\n", - " [0.07142857, 0.08571429, 0.06428571, 0.02857143],\n", - " [0.02857143, 0.06428571, 0.08571429, 0.07142857],\n", - " [0.00714286, 0.02857143, 0.07142857, 0.14285714]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "j_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[array([0, 1])], n_basis=3, period=1),\n", - " coefficients=[[1. 0. 0.]\n", - " [0. 2. 0.]\n", - " [0. 0. 3.]])\n" - ] - } - ], - "source": [ - "print(basis_fd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# test penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'FDataBasis' object has no attribute 'penalty'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m basis_fd=FDataBasis(skfda.representation.basis.Fourier(n_basis=3), \n\u001b[1;32m 2\u001b[0m [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbasis_fd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'FDataBasis' object has no attribute 'penalty'" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FDataGrid(\n", - " array([[[1.],\n", - " [0.]],\n", - " \n", - " [[0.],\n", - " [2.]]]),\n", - " sample_points=[array([0, 1])],\n", - " domain_range=array([[0, 1]]),\n", - " dataset_label=None,\n", - " axes_labels=None,\n", - " extrapolation=None,\n", - " interpolator=SplineInterpolator(interpolation_order=1, smoothness_parameter=0.0, monotone=False),\n", - " keepdims=False)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "fd" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.11803399e+00, 5.55111512e-17],\n", - " [ 1.11803399e+00, -5.55111512e-17]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.transform(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 0.5])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fpca_discretized.weights" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5, 1. ])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean = fd.mean()\n", - "np.squeeze(mean.data_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=8)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", - " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" - ] - } - ], - "source": [ - "print(basis.gram_matrix())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the Berkeley Growth Study data for the purpose of illustrating how functional principal component analysis works" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trapezoidal rule implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.25, 0.25, 0.25, 0.25, 1. , 1. , 1. , 1. , 1. , 1. , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ,\n", - " 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = np.diff(fd.sample_points[0])\n", - "differences" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "weights = [sum(differences[i:i+2])/2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0]/2], weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.125 0.25 0.25 0.25 0.625 1. 1. 1. 1. 1. 0.75 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5\n", - " 0.5 0.5 0.5 0.5 0.5 0.5 0.25 ]\n", - "31\n" - ] - }, - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(weights)\n", - "print(len(weights))\n", - "len(fd.sample_points[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=3)\n", - "X = fd" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fd_data = np.squeeze(X.data_matrix)\n", - "\n", - "# obtain the number of samples and the number of points of descretization\n", - "n_samples, n_points_discretization = fd_data.shape\n", - "\n", - "# establish weights for each point of discretization\n", - "\n", - "differences = np.diff(X.sample_points[0])\n", - "weights = [sum(differences[i:i + 2]) / 2 for i in range(len(differences))]\n", - "weights = np.concatenate(([differences[0] / 2], weights))\n", - "\n", - "weights_matrix = np.diag(weights)\n", - "\n", - "# k_estimated is not used for the moment\n", - "# k_estimated = fd_data @ np.transpose(fd_data) / n_samples\n", - "\n", - "final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples)\n", - "pca.fit(final_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.80909337 0.13558824 0.03007623]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca.singular_values_**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.56703382e+02 9.32926094e+01 2.06941960e+01 7.95971044e+00\n", - " 3.27921407e+00 1.63523090e+00 1.22838546e+00 9.73332991e-01\n", - " 6.08593043e-01 4.71369155e-01 2.76283031e-01 2.30928799e-01\n", - " 1.79929441e-01 1.44663882e-01 1.08128943e-01 7.56538588e-02\n", - " 5.77942488e-02 3.72920097e-02 2.25537373e-02 2.14987022e-02\n", - " 1.38201173e-02 1.04725970e-02 8.95085752e-03 6.64736303e-03\n", - " 4.35340335e-03 3.66370099e-03 3.06892355e-03 2.33855881e-03\n", - " 1.85705280e-03 1.44638559e-03 9.00478177e-04]\n" - ] - } - ], - "source": [ - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FDataGrid' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFDataGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'FDataGrid' is not defined" - ] - } - ], - "source": [ - "FDataGrid\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we do not transform the data to a certain basis. We analyse the functional principal components using the discretized data. Observe that there are abrupt changes in the principal components" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ 0.0301562 ]\n", - " [ 0.04427131]\n", - " [ 0.04728343]\n", - " [ 0.05024498]\n", - " [ 0.08350374]\n", - " [ 0.12469084]\n", - " [ 0.1428609 ]\n", - " [ 0.15392606]\n", - " [ 0.16414784]\n", - " [ 0.185423 ]\n", - " [ 0.17731185]\n", - " [ 0.15056585]\n", - " [ 0.1562045 ]\n", - " [ 0.16035723]\n", - " [ 0.16710323]\n", - " [ 0.17146745]\n", - " [ 0.17403676]\n", - " [ 0.17857486]\n", - " [ 0.18564754]\n", - " [ 0.19469669]\n", - " [ 0.2076448 ]\n", - " [ 0.22112651]\n", - " [ 0.23137277]\n", - " [ 0.2370328 ]\n", - " [ 0.23762522]\n", - " [ 0.23844513]\n", - " [ 0.23774772]\n", - " [ 0.23691089]\n", - " [ 0.23653888]\n", - " [ 0.23718893]\n", - " [ 0.16855265]]\n", - "\n", - " [[-0.00444331]\n", - " [ 0.00268314]\n", - " [ 0.00915844]\n", - " [ 0.01355168]\n", - " [ 0.04096133]\n", - " [ 0.04974792]\n", - " [ 0.07535919]\n", - " [ 0.11740248]\n", - " [ 0.16609379]\n", - " [ 0.15244813]\n", - " [ 0.13069387]\n", - " [ 0.11127231]\n", - " [ 0.11601948]\n", - " [ 0.12865819]\n", - " [ 0.14523707]\n", - " [ 0.17744913]\n", - " [ 0.21594727]\n", - " [ 0.24988589]\n", - " [ 0.26144481]\n", - " [ 0.23456892]\n", - " [ 0.17285918]\n", - " [ 0.08524828]\n", - " [-0.00841461]\n", - " [-0.10122569]\n", - " [-0.17851914]\n", - " [-0.23488654]\n", - " [-0.27708391]\n", - " [-0.30554775]\n", - " [-0.32274581]\n", - " [-0.33517072]\n", - " [-0.24414735]]\n", - "\n", - " [[ 0.06304934]\n", - " [ 0.11742428]\n", - " [ 0.12543357]\n", - " [ 0.13288682]\n", - " [ 0.2144686 ]\n", - " [ 0.23211155]\n", - " [ 0.30066495]\n", - " [ 0.29069737]\n", - " [ 0.24459677]\n", - " [ 0.21382428]\n", - " [ 0.15093644]\n", - " [ 0.11564532]\n", - " [ 0.10764388]\n", - " [ 0.09065738]\n", - " [ 0.07140734]\n", - " [ 0.03953841]\n", - " [-0.0070869 ]\n", - " [-0.07615571]\n", - " [-0.15031009]\n", - " [-0.2248465 ]\n", - " [-0.29268468]\n", - " [-0.31869482]\n", - " [-0.31185246]\n", - " [-0.26157233]\n", - " [-0.17380919]\n", - " [-0.07718238]\n", - " [ 0.00287185]\n", - " [ 0.05987486]\n", - " [ 0.0942701 ]\n", - " [ 0.12153617]\n", - " [ 0.10283463]]]\n", - "sample_points: [array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. ,\n", - " 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 ,\n", - " 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. ,\n", - " 16.5 , 17. , 17.5 , 18. ])]\n", - "time range: [[ 1. 18.]]\n", - "[556.70338211 93.29260943 20.69419605]\n" - ] - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()\n", - "print(fpca_discretized.components)\n", - "print(fpca_discretized.component_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can choose to use eigenvalue and eigenvector analysis rather than using singular value decomposition, which is the default behaviour. Please note that it is more efficient to use svd" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized()\n", - "fpca_discretized.fit(fd)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-75.06492745 -18.81698461]\n", - " [ 7.70436341 -12.11485069]\n", - " [ 24.47538324 -18.13755002]\n", - " [-15.367826 -20.3545263 ]\n", - " [ 22.32476789 -21.43967377]\n", - " [ 11.3526218 -13.83722948]\n", - " [ 20.78504212 -10.76894299]\n", - " [-36.78156763 -15.05766582]\n", - " [ 24.99726134 -15.5485961 ]\n", - " [-64.18622578 -5.57517994]\n", - " [ -7.01009228 -15.99263688]\n", - " [-43.94630602 -19.63899585]\n", - " [-16.84962351 -18.68150298]\n", - " [-43.59246404 -11.59787162]\n", - " [-31.41065606 -1.74400999]\n", - " [-37.67756375 -9.86898467]\n", - " [-26.15642442 -16.01612041]\n", - " [-29.11750669 1.64357407]\n", - " [ 5.7848759 -13.75136658]\n", - " [ -7.69094576 -12.24387901]\n", - " [ 18.04647861 -15.07855459]\n", - " [ 11.38538415 -16.44893378]\n", - " [ 1.79736625 -21.01997069]\n", - " [ 21.8837638 -14.19505422]\n", - " [ 10.0679221 -16.70849496]\n", - " [-12.08542595 -19.03299269]\n", - " [-14.58043956 -7.12673321]\n", - " [ 30.96410081 -13.67811249]\n", - " [-82.16841432 -10.8543497 ]\n", - " [ -6.60105555 -18.50819791]\n", - " [-30.61688089 -9.61945651]\n", - " [-70.6346625 -13.37809638]\n", - " [ 3.39724291 -12.03714337]\n", - " [ 7.29146094 -18.47417338]\n", - " [-63.68983611 0.61881631]\n", - " [-19.038978 -14.54366589]\n", - " [-49.94687751 -2.00805936]\n", - " [-38.4910343 0.85264844]\n", - " [ -0.46199028 -13.94673804]\n", - " [ 29.14759403 19.24921532]\n", - " [ 12.66292722 7.28723507]\n", - " [ 2.88146913 31.33856479]\n", - " [ 0.96046324 11.14405287]\n", - " [ 2.33528813 2.85743582]\n", - " [ 22.97842748 3.07068558]\n", - " [ 47.85599752 -7.88504397]\n", - " [-77.41273341 26.84433824]\n", - " [ 9.83038736 15.62844429]\n", - " [-28.10539072 16.62027042]\n", - " [ 23.10737425 -2.58412035]\n", - " [ 24.64686729 7.28993856]\n", - " [ 79.48726026 -5.06374655]\n", - " [ 3.49991077 1.13696842]\n", - " [-11.50012511 14.67896129]\n", - " [ 65.61238703 0.28573546]\n", - " [ 19.55961294 23.2824619 ]\n", - " [-25.53676008 24.31600802]\n", - " [ 7.92625642 15.99657737]\n", - " [ -5.3287426 10.30006812]\n", - " [-16.28874938 13.63992392]\n", - " [ 15.48947605 14.95447197]\n", - " [ 23.8345424 11.43828747]\n", - " [ 47.12536308 9.63930875]\n", - " [-31.00351971 -7.64067499]\n", - " [ 57.27010227 -1.45463478]\n", - " [ 7.37165816 14.85134273]\n", - " [ 8.97902308 8.18674235]\n", - " [ 74.15697042 -8.80166673]\n", - " [ 11.79943483 0.66898816]\n", - " [ 15.47712465 8.04981375]\n", - " [ 4.82966659 25.32869823]\n", - " [ -7.45534653 0.26213447]\n", - " [ 19.28260923 10.84078437]\n", - " [ -3.41788644 11.79202817]\n", - " [ 19.68112623 2.78305787]\n", - " [ 36.70407022 -4.13740127]\n", - " [-36.63972309 15.82470035]\n", - " [-11.29544575 11.60419497]\n", - " [-10.86010351 17.23517667]\n", - " [ 22.37710711 11.71658518]\n", - " [ 69.93817798 0.1837038 ]\n", - " [-23.52029349 16.63785003]\n", - " [ 3.88508686 8.8950907 ]\n", - " [ 19.51822288 8.81957995]\n", - " [ 24.94175847 12.63592148]\n", - " [ 29.4438398 10.62909784]\n", - " [ 60.8940826 13.91957234]\n", - " [-16.65019271 -6.96853033]\n", - " [ 2.44106998 5.34263614]\n", - " [ -7.7688224 -0.1303435 ]\n", - " [ 13.21116977 8.22090495]\n", - " [-14.40137836 23.47471441]\n", - " [-13.04900338 20.49414594]]\n" - ] - } - ], - "source": [ - "scores = fpca_discretized.transform(fd)\n", - "print(scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we study the dataset using its basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd = FDataBasis(basis, [[0.9, 0.4, 0.2]])\n", - "fpca = FPCABasis()\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -3. ],\n", - " [-1.73205081, 1.73205081]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]])\n", - "sample_points = [0, 1]\n", - "fd = FDataGrid(data_matrix, sample_points)\n", - "basis = skfda.representation.basis.Monomial((0,1), n_basis=2)\n", - "basis_fd = fd.to_basis(basis)\n", - "fpca_basis = FPCABasis(2)\n", - "fpca_basis = fpca_basis.fit(basis_fd)\n", - "fpca_basis.components.coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "y = dataset['target']\n", - "\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)\n", - "\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the mean function of the dataset for representation purposes\n", - "meanfd = basisfd.mean()\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain first two principal components, observe that those two are very similar to the principal components obtained in the discretized analysis, only smoother due to the basis representation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The sample size should be bigger than the number of components", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFDataBasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# check that the number of components is smaller than the sample size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m raise AttributeError(\"The sample size should be bigger than the \"\n\u001b[0m\u001b[1;32m 109\u001b[0m \"number of components\")\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: The sample size should be bigger than the number of components" - ] - } - ], - "source": [ - "fpca = FPCABasis()\n", - "basis = skfda.representation.basis.Fourier(n_basis=1)\n", - "fd = FDataBasis(basis, [[0.9], [0.7]])\n", - "\n", - "fpca.fit(fd)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "The number of components should be smaller than n_basis of target principalcomponents' basis.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfpca\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFPCABasis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasisfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponent_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/scikit-fda/skfda/exploratory/fpca/fpca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_components\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mn_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m raise AttributeError(\"The number of components should be \"\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\"smaller than n_basis of target principal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"components' basis.\")\n", - "\u001b[0;31mAttributeError\u001b[0m: The number of components should be smaller than n_basis of target principalcomponents' basis." - ] - } - ], - "source": [ - "fpca = FPCABasis(9)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5.57673847e+02 9.20070385e+01 2.01867145e+01 7.12109835e+00\n", - " 3.00574871e+00 1.33090387e+00 4.02432202e-01]\n", - "FDataBasis(\n", - " _basis=BSpline(domain_range=[[ 1. 18.]], n_basis=7, order=4, knots=[1.0, 5.25, 9.5, 13.75, 18.0]),\n", - " coefficients=[[-0.08496812 -0.11289386 -0.16694664 -0.21276737 -0.31757592 -0.35642335\n", - " -0.33056519]\n", - " [ 0.00738993 -0.06897138 -0.10686955 -0.18635685 -0.47864279 0.78178633\n", - " 0.42255908]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca = FPCABasis(2)\n", - "fpca.fit(basisfd)\n", - "print(fpca.component_values)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-5.30720261e+01 -1.20900812e+01]\n", - " [ 5.93932831e+00 -8.13503289e+00]\n", - " [ 1.87359068e+01 -1.29753453e+01]\n", - " [-1.02271668e+01 -1.41114219e+01]\n", - " [ 1.78816044e+01 -1.61153507e+01]\n", - " [ 8.76982056e+00 -9.64548625e+00]\n", - " [ 1.51595101e+01 -7.48338120e+00]\n", - " [-2.57711354e+01 -1.02616428e+01]\n", - " [ 1.88410831e+01 -1.11580232e+01]\n", - " [-4.64293496e+01 -2.83317044e+00]\n", - " [-4.31966291e+00 -1.10533867e+01]\n", - " [-3.03723709e+01 -1.34939115e+01]\n", - " [-1.10945917e+01 -1.28105622e+01]\n", - " [-3.09084367e+01 -7.52073071e+00]\n", - " [-2.34011972e+01 -2.11592349e-01]\n", - " [-2.70364964e+01 -6.22251055e+00]\n", - " [-1.77541148e+01 -1.10945725e+01]\n", - " [-2.08566166e+01 1.20259305e+00]\n", - " [ 4.67719637e+00 -9.63524550e+00]\n", - " [-4.76931190e+00 -8.60596519e+00]\n", - " [ 1.37391612e+01 -1.05089784e+01]\n", - " [ 9.29873449e+00 -1.17272101e+01]\n", - " [ 2.45160232e+00 -1.48677580e+01]\n", - " [ 1.67240989e+01 -1.02844853e+01]\n", - " [ 8.27541495e+00 -1.17247480e+01]\n", - " [-7.15374915e+00 -1.35331741e+01]\n", - " [-1.03861652e+01 -4.22348685e+00]\n", - " [ 2.29727946e+01 -9.98599278e+00]\n", - " [-5.91216298e+01 -6.47616247e+00]\n", - " [-3.79316511e+00 -1.29552993e+01]\n", - " [-2.15071076e+01 -6.53451179e+00]\n", - " [-5.05931008e+01 -8.25681987e+00]\n", - " [ 2.76682714e+00 -8.21125146e+00]\n", - " [ 6.51234884e+00 -1.33064581e+01]\n", - " [-4.64214751e+01 1.34282277e+00]\n", - " [-1.32994206e+01 -9.85739697e+00]\n", - " [-3.61853591e+01 -4.17366544e-01]\n", - " [-2.79000508e+01 1.27619929e+00]\n", - " [ 3.83941545e-01 -9.91228209e+00]\n", - " [ 2.00328282e+01 1.31744063e+01]\n", - " [ 8.97265235e+00 4.81618743e+00]\n", - " [ 4.77386711e-02 2.24502470e+01]\n", - " [-2.42567821e-01 8.20945744e+00]\n", - " [ 1.64451593e+00 2.11944738e+00]\n", - " [ 1.70071238e+01 1.39105233e+00]\n", - " [ 3.46799479e+01 -6.01866094e+00]\n", - " [-5.75717897e+01 1.99259734e+01]\n", - " [ 6.35085561e+00 1.06703144e+01]\n", - " [-2.14964326e+01 1.20955265e+01]\n", - " [ 1.61427333e+01 -1.65416616e+00]\n", - " [ 1.71124191e+01 5.00985495e+00]\n", - " [ 5.74126659e+01 -4.35566312e+00]\n", - " [ 2.19564887e+00 1.09803659e+00]\n", - " [-8.42094191e+00 9.75168394e+00]\n", - " [ 4.74057420e+01 -4.83674882e-01]\n", - " [ 1.31250340e+01 1.57485342e+01]\n", - " [-2.01007068e+01 1.76386736e+01]\n", - " [ 5.36884962e+00 1.04679341e+01]\n", - " [-4.38076453e+00 7.20057846e+00]\n", - " [-1.22134463e+01 9.36910810e+00]\n", - " [ 1.11712346e+01 9.66522848e+00]\n", - " [ 1.69187409e+01 7.32866993e+00]\n", - " [ 3.37743990e+01 5.94571482e+00]\n", - " [-2.16792927e+01 -5.24099847e+00]\n", - " [ 4.18716782e+01 -1.95360874e+00]\n", - " [ 4.11001507e+00 1.06495733e+01]\n", - " [ 5.63261389e+00 5.64013776e+00]\n", - " [ 5.44902822e+01 -7.34128258e+00]\n", - " [ 8.39573458e+00 3.04649987e-01]\n", - " [ 1.05275067e+01 5.77760594e+00]\n", - " [ 1.95982094e+00 1.77073399e+01]\n", - " [-5.87053977e+00 6.47053060e-01]\n", - " [ 1.33985204e+01 7.19578032e+00]\n", - " [-3.04394208e+00 8.36580889e+00]\n", - " [ 1.41550390e+01 1.77507578e+00]\n", - " [ 2.67208452e+01 -3.29012926e+00]\n", - " [-2.73473262e+01 1.16262275e+01]\n", - " [-8.74844272e+00 8.17414960e+00]\n", - " [-8.43776443e+00 1.21123959e+01]\n", - " [ 1.58369881e+01 7.66443252e+00]\n", - " [ 5.10908299e+01 -1.14474834e+00]\n", - " [-1.80355733e+01 1.18449590e+01]\n", - " [ 2.14815859e+00 6.45250519e+00]\n", - " [ 1.37622783e+01 5.66582802e+00]\n", - " [ 1.78128961e+01 8.11180533e+00]\n", - " [ 2.13905012e+01 6.42618922e+00]\n", - " [ 4.40377056e+01 8.51163491e+00]\n", - " [-1.16537118e+01 -4.69794014e+00]\n", - " [ 1.39292265e+00 4.02622781e+00]\n", - " [-5.58202988e+00 9.06925997e-02]\n", - " [ 8.56960505e+00 6.05912637e+00]\n", - " [-1.19302857e+01 1.69879571e+01]\n", - " [-1.06671866e+01 1.47062675e+01]]\n" - ] - } - ], - "source": [ - "print(fpca.transform(basisfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_growth()\n", - "fd = dataset['data']\n", - "basis = skfda.representation.basis.BSpline(n_basis=7)\n", - "basisfd = fd.to_basis(basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 20 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Canadian Weather Study " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fd_data = fetch_weather_temp_only()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "can't set attribute", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfd_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m364.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: can't set attribute" - ] - } - ], - "source": [ - "fd_data.domain_range = [[0.5, 364.5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Bvj1ITk05L48dzBNFTnwHG3hpegZ+rZeFX3oIRHeSMSScNn0v3pVhhHnaueiFcynZ2s7at8sACBOSPumnx1hIpGcy49o/YPAfryJi1IhjcOSV/6V/1SB9LIPDFGA9sAcI9iffRqjdYTGQBtQQepW181+tSwUH5X+pqdLOkoeLAHDatrIkcwXHVZ5HUm86fXonFm+oeskgwKaF1GQNmWeNwKwVvP3IDswWLU+ODeeE3U7WDjVTF6cnwuPGYTQR1dMJeis9Ri26gBfcfgz4GLNnE2smnYKtpZPsvmb2pWXg1phJ8EhazBpGNBaxO2kUUmgw+QK49aEqqjEtXhq1QVqjjOQ5gmT0elieGvrQ7pX1PZhb9vDFxAz8Ni1J65Zh6DmdSQm7GfHnRexeVc+ODypwSWiL2svSgW9w2bYHAUjxljP/5V8dg6Ov/C/9KIPDD0kFB+V/xd/Xxlt3rsbRG4sr6XO8naOJ9MSxdsp9lAZayJNRTCw7jeO84zjol7R4goeWtWkF9oDklZOs6OPNXJQczZ1VzegdbqyFjXRNyyDYf1O3aAR52zbS6rRQO3UYAOEd3STsaKAzGE6Bdj9fHT+LnKpSBvQ0kdPTR5vRjK6tHq+hg88yrkLEh2Hd38B0bSXl1mhyynYQ0OnxJQ5le0oCSUST7tXxQVqogXnh8vs5zXMJ7V5IzDdRuV9ygkXHRkeAroAkcoqTZfUrmFIdanO48i+jMMREofx8/KvgcMwbpBXlx+zBd6/H1nspXWGVrI7ZxxlNJwFwsedBqpM/JucVKzkZY+mWQT7MEQyvEDSlGMip9NAdkOzOM+Gw6rirYQ/vOZIZtLsMc00P+8NzySmsoGxyqAF5+J5VFLXnsqBvCVurtHRExTN633o2BscB0OCN5TevP0iY2wlAMMKAzech4BH4NDoMwXYK9pbi05uIMbYxYNd63DojWiSGunKydhp47sKb2G40kGP3UR6pp2vgJeRVRZBslqws9ZOqE/QaBLmR3dT0WWneEMZI0wx8mh70QSsrn9jIqffMoaehg56aNlInDTo2J0U5KlTJQVEIdWFhb3URFmnAYAo9MzV1HeTp+5aR0jsEgCAONESQ0LKNloSxpEd0M1DEsmhCGC5PkKpEPX4hQEquK3JRFKVhXZaRWIedrnAL0zZ/RsGezQAUZw2lSI7ljOy1dEdqWLpjFickbiC9ZD9dejvRDgPtmkyKbGMY6dhDkqMeg/QgELhiUzDa2wki8FgiiOhoQ2o1iMDXpRavzkhl0ijWiCHkxDYwc8dn+HU6duWPpX3wSey1abmi0stJzX5+O8aMJyiJ9vupthoYX7uUJ0tn0DIinl1NlVS0VpNmD/XllGzpoaXbQEBr4tRLs8gYn3F0T5Tyg1LVSoryLZ2NfbidPpIH2vB5A6x6tZSKHa1odYIB+dHMujSf1/7+Jt796Wj9TkBDQGfC6qpH07UUU9xChkTHUR+p4YLJEd9Y9xhXOZMiEnjWF05QaJi8bTtG6Sd9fyGdWgvWMB01ljQKgzkAhOFkcKyJJPcK+lwDyajfSbinm6AUaAgigPLwbIIpA4jSSvoq7NiN4bgSIglIGFu/BqPXiScQidXpYmdiFjttI5gZXkWJPQxjlomg0JBRXUpqYxWNqdnsGjSW6gF59BmPHBjI5Gtl/QYzwgt6cymfz3awfOVmptZdDVICEoQGjfRx4ilWomO0RE4YjUanKiJ+alRwUJTDtNb08OEjO/D7gkxbmEvlzlYayroBCOJDgx6hkWiDAr+UHJdWQVlPPI32SGqSW9mXEc2fy7S8lBvBhlgNnSYdQ+o6qUi2YNC48Gl02Oq7aEtKQl/RTXhNJ0/rnyaIhqt81xNEg0SQIZp4Xv8Y7TKShwMXYiecuI6DjOsuYmnCbCSCeZ3L8E/SsqTrV9h623mVO/FtBUdbJLfMuIGuoIvri9+kzxzJ6PoKAA5GR/PltDs4gSBmjYvGYDhVohenfh+elk5S7AcRUtJhi2VP3hgK9u/CMWQ8y3KHktu9ni2JJ1HQ2MXf9oRu9tHD3uLisEZsu4cxvHkS+mg7gY5EdP4+/LpwAJI1Dcx77Gw0JuOxOanK/4kKDorSL+AP8smTu2ivd2AwaXF0efqnSFIaNpBT/i6bJtxOnCUJVxCSXaV47juHq4uqOb7By58P+gG4ZoyZLbE6kJJUaqkXoT6QhnZsY2/M2NAq3QFMa5u4Xvcei3QfAXCz53JK7Akkepq5NulLhuhCI7jd1zqXD4wzWdj8ES6zldfiz2RupuR3EytIijybLc9uYaR+A1H6V3C5YmlpuQFtwhD6OvYSPLASj6cUv7BQHelkpL8A89graSAABAlHixnBU9p9WGo28GXscYT7+5jetZFwv/PQ/g/MqCH/rMeY1RaB22gmr93HWzs8FLYtxzJWz77hel46sBgEzCq7lMyuXHR+LWG+bhymBMZoC5nw7C1H6UwqPwTVIK0ohNoVvnqtlMbybmacn0d8upXda+ppaCwnosGCwbGK6FF2Rrd8QkLib0MLWYdw4b569DrBoloPoKXEqmFLrA5NMMA5HS+xo30k5KaRdaCKzrpI4mNrGWZsodVupBYLF4iVVDmi0JsFg1v3Eu84wIT4JoboavmyKZtJcTVcbv2KyNpW5qTWkGgUXCfdmBp2Iha7aXVGkKNPIUK7GAC9SYMuMZ9wz3NYYy2446YSa1iHW9jZYDyRMS2nE7DXsbR5M/m+Groj8pibMZdL/Jl8Gusj09/MyK5C3o0/k5TUXnosBk776hMqqjOoePgprtZo6Y6MoT0slgrTccREDmb76sVcc/5rrKhaTqPfgTNhJ5rOkQw8oYfk+LEULi6lzJ7MOKcTTVjY958E5SdDlRyUX4zGim4+fGQHQ4+PZOL8QeikkfbqBurersOlhZ6uJ5h97995cnUZ+7t0TFnzITPiTuHpgXoya9sZqovk1kwnzfFxODVwTt2LLDlwKgAGi0T0+vEQqsMfpq3lRLmB3M4yyu0xTEqswRbp5sPSoZjjO/l19D7Ke2PZ0DOBuYmSON0y+rzhhOld+MjGIMrxemIwGDtwuvNwN3YTldlCRWcSOTFNtLtTiTXVH9o3F0Y0BNHKIM3+q9i3fz9p+8q+3vmUAryjLiRGZ2YJXkodm6nQWPCG2dDlGiHcxKylb+M3GWiNiEJ6IcVejzYQINmUjkunY+oELcZTr6JNdpGjj+b5e3YT3v+NhxYvAQzMOUVP+typR++kKv8VVa2kKMCXr5ZwcGcrJfoXiI1u5neJv6P3QAxjT4xACsHuva1sWziOy/dWY/ZLvBrJX3Z7sHa58PsFd0yNpEcfZFjZDhr1CTgOhtoOkBDj6wSDEYvWS4LU8YD+LjJFqP9Id0CHSRuqjvqwZjAnGKvQxQaZ23cXSbokojVm7tHdiFVfxwr/eHoCyYz4YiO74nTUneLkps5Qe0hpbyIfWhZwAy9gxMsOsqknnUnsoFCMJjdQjl4ESRFNnB+VzbDGSdjDJAQ6yWoKZ3i9l/QRlwPQSJDHcBMBmKnk46ljuKhoFfqAB31LDZ4eF5umHEd0ZycT9m1Dhw6Nxo/H72Xo2OMZMGE0H214GWv1JZTHbienvQARDJBo6kRoNEyanUDCSdOOyXlW/n2qWkn5xfB5AuiNX3doJ6WkrrQTs8VAXUknhB0gp+ty6HGw01tPqiYG2d/ZY2NXBG/Xt5PilrzYpmN2qp8HBxv422YfX6Xp6dFLFi19GH1zH16didVRk0jzt5Lqa8HiaKMjIpEwj4/xcdVkGpr4pH4wWdYubImClkrJoLgOTk8vBeAe3wVUGNOpBbwEcMsz+TuPsyJYwMrAGO5M6WHZuB6qw5uZ4PQy2h1kmWUuXgyskWNBBikSIzl9VAIxI29kdmoB7Q1VtBR+hG7f/ZzVlY3RXM/xchNfaKZRnJJMkbED2/JrMcQOImbslTwiQtU/3Qxh9HoH05nMPnopsrlIDhi4/aCF38wcy97BY7hw5cfYervRCtizbRV7tq0ib4iVB8bcjkvfS4QniuTuZJq8oX7OVr1YzDnTx6ExmY7uBaD8YFTJQfnZ6O108/ZdWxk4Oo5pC/PQG7XsW9/Amre+rl7xBXeg14wGYHJMH2sseh4YnwjAwhov76QbmF/v5XSblYsj3Oj9QSYfKKNN6yW7voK88p34rSm4+rqxBPqQgOj/tiFEcmHmTgyaAF84ktg/YDLDiw1s7ROIrCB3RrzOfjmQjc7fU2Cy0RPs4KbkF3EGkpnd7GHSWhc3TftdKH+6KlK07QwQDdhJIFVUEK0J8JL/RIo9yVhwcuVx+Vx53CDM/WNDB3wevPdn0RfUEyn60EsvdmHgwugCRneMJ/HgTqYXHqA3LApTziwMtixMtkzcQlKOh1GY2axpZWIwNFxpo8HP2kQzn6boye0JcHxVC2MdUOLfSnnDLmZklJA37zpuL2omZ98Jh46zkAHOPCVA/LyT/8dnXflvqGol5Rdh4wcV7FoZevtHZ5WcddM4lj62G61Ogy4YoKPDS3ygi26/QOjNOIdH8XKanl6DxGY10+DxAfDnPS4+zTVQ7u8jc88+Tty17NA2qixD8ccez55AO7/OqWbi1PnEfHQnbYUNfGGKYECYnXkDSqkOxnOw7WK2RE7geUJvRA0Ouhmma2Ocu4dx+vFs0ZSw19BGrwZKSCAYZeKPS15gRex43s+aTkCj5TTDPmJFD8nBWuqDkdRlnM2qyh7m6ArpiR3NuuZQ4X/uiGSeOGckGo1AFr6AWH4jaI0weRGse5g/J6bQ0XU8xqAROr/iimWeQ/u0JXUkkbRQHWtjUvo1pAo9nVo3KaOScWxvQ8vXJTGAHukiXOr5uPkVfN5O4q2SbkcAj+UMLP5EcqI7Ke1NY3RUJRPvv5KAL8DB4naEEGSPjuPwbvmVY0sFB+Vnz+vy89qtG0kbGkNNfCHuFan4w/swOiIZNz8dx+bNmHsHkKgDb8DBrkgdi6aGxpHKbu6lR9/HpG4jkZowzHVOFsf3MdheSl7JXsL1Lg5osxnhc3Jy/HwihY79ws72zM2c2ZVNpL2PWP2dyGAABLSSzBTv/RjQ4QTyels4BQOa6FY6tKFR10aXHiSneBtLLv4VHS4XNn8fc5wuzJ8tB2DrpFO5L+kEsuLC2d/c+419HZJs5c0rRvNpl4vmim7Kqrv4srSVBxcM45yxaaFSTNU6MEVCdCY8kEarOZJfhxcwvH04u9N2kFmhp9fg4KTtteSHPo+gyRbJxzPOZAAj6dPvJTHVTMEwC0XPfUZn4nSy9XrqUnM5rcGHOQh+gqxuWopdliC9Btx6P+FhVxEVF4OjrQ9D0MWwucPYvrz6UMFq5qX55PWX1JRj70c5TKii/JB2fVWH1x1gWNhH/KP9McriCjE6IgHYuXoHOZ503EFJl99PmN7KsoHRhPsCXLW8gaTarVzy3jNkrXqKyD2fUFu3nCvW/I3JO9cS4+lgVPoUrNFTmZl8KmG+PrwHV5MXjOCCgydj6sohUvcKQSJoZyKbgtOY772FAUKPCAaw+h3MjqiB+Eo6tD3MP20eQb2VnOJtAMwbN4q+pPGcsHYNps+WU52Rji8nh2ltBzh7bCqVbQ5OHZYEwKWTM0ixmfntcQO5pryFmw7U83jQwZmn5DA8NZIX1lchpQQhIGs6JI8MBYiIeOJddl6ZcAZhOBleO5pEQzYL8fHMPA1X/1bL2+OHk9Rt54zPP+BVbyetuliGv/oaJYWt9GrsHOgqpThW8tc0D2eP6OGlZAc6NMxKmsfgsJMZFjUVayCcFrEYe2sn/mAvbq2FbctCgcHg6cbgsbPv0z3H7BpR/jOq5KD8pMmgZNeXdWxaUoE5uYnVsffj6c5kRu11SCkxerpItsYwPEzHqh4fLefH8rJb0iWCjCndQXbJTtLbqzkYn02Yo5VEZ+gpvcyaR50hmXkDspjTHXtoe64tz+IXxfQNs7KnYwS57nomD9vICt1QNvbeyptCcHnp54y0rySxw0TJH66l+mA16enp5Obm8kFHgFPuvY1kezsAL849h4VWI+FvvcHGyZPouuI38O5i5qxcRtSa9Zz14mbqu4MUpEex+FcT0WgENS4PE7aUcm5SNMW9Thz+IDcZLLKFMXMAACAASURBVNz43m5iwg0YdRrmjkzh0nF+Ym05aN+/HEqXAtCiTearwBhms5YoelhhSuT2+Ai8IkDB7tHcsGILu4eOpa/TxZTGPXwydDq+bAPm6oMAFA2byKrJpxLd3sop7RFcWxM4dGw6/S2srHv10N8m0tEZxxLj66AlchSJ3iqa9emcd8twojLj/9eXhvJvUG8rKT9bRW+vZusGAElbu2Rk652M1mpJjdLxVK6emY1ahvdq2RYhKLJ08q43muza/Zy9finWPjsObRj22Jnow3OoStlIo38LzdiY6rVQ6YuluTuCW+il0VzJ9GYdWTEu5g3txCoaSY9qpDiYBcBH+mmsDXqZbA1wSsNm2qMHoB0TQ3NjM0NzcjgxPp6t2YNIeeE2Yvu6uWXRbfzqvdc5fc3nGFxOIufM4ZTbbmPMrmpOikviNL+fG9av4bLRT5Jgu4zmxLHM2FbGSbFWJBDd2831cXq2pSVz3c4yrjd6scaY6O72EGPT8/e1FSwtauLmKY9w2ohzEf3BISHQyHkGO3j7AJjpbmdb7dk0hZezYeQ2DjaFMWpX4aHja+nu4V1xEnMJBYfuVhMZJSW4Yq28mWdmdoeTPEeQRpMg2Z1ATnIKUUTg12RR3rKVPu+H6G0dCJlGsyETgC0vbWL2vfOP2jWi/N+okoPykyWl5G+LPqTJ2E2UZRWGpssI18AMi447hun4IiUMXVBy7243t4w0Y/S4yN+7g+O2fU6H0UpZipdO0zjedo0/tM495nJ2h5WzsONkVuDiAQKE4WWq2MUN+vfI1TTQKSOokKkUiDI0QrIxMIQLfLcywKrnrV9PY81fn6DS5z20zvnNrRjXrOaluQu5YPkHbJl4HHeedym3Fq3nxBefA+Cy+55Gk5xCpcvDc9LB4N9cidNo4vnLziR9dDerS8dgsERQlJhKRkMdzzz9F8J77IiJmQS21vL55Wfz/IhpdMtI0AoGte+icXckBCXnDvfzx96/YtCFQf58WHkHBAO0j7ia2B1PUMxgdpPH8vhSuvQOXmk5l66ly6mWJhJd3Vxw4h9Z4PkUf6KBgpNv5Z63N3JhwzusnDKH6oFjeXGLnUSPjlajIM3zzXPklN2sqHsNj3CTZhlNBzMQ0s8F903FGmM+KteJ8v1Ug7Tyk9bR6GDP6noyhscSl2bB5w5VZXjb6nnvmXo2pr1NQm8SuR1TSNe3c/+Jgyi3apkeZeHg3mK8egO6+j7m7v6QCLeDLksqHUMi2WNdx5UtZ3BCzwTWRhQSLsMp6At1z/05B7ifeFK0dl7SPcBAUUetTGBzcCib5UhsuiA2etALH1t92aRr7WiF5MILL+T+Jcvw+YL02iK55+mH0Xzr/5jhuOvZe8cCptrC4JNPWOzXsG7oKFZ19jIzxsrr+WnUXXElzi1bcBmMLPzLU3x8U2gUttYhwwmrqsQknPgzA5j2hpoNg2ZJ48N+ghoNH3MGS8RCcPnRl/egbXIRG9vFpRNaOHfEZQj3Hhyd2wg3phH96hWH8rU6bCKL4uuZLebwfslEBnoKefKzJWyPzyPW1U1SXwevnXQ6p57VSfXOPRSXDef9eZeSZe/inlIThb1OIsxtDN5VSEb0UIxxobEq3rMup6+yhPH6Kdi9HTR72tCFj+CsW89n4yvbOfEPMzBFqA77jgUVHJSfrKriNr56vRRPn/87pwcJENlTQ681i7CujdRkBfho9Hj+NHU8Uz123vhD6JsBCfToLKyLm8XdlgFk+b7+OMtT9ikl3Z/w+OkG7ik8ky5HJ7dkTUIrvNwa/iYLNZsocWRSZMgns64ZX1IuL9qmMH7MSC6fkslnn31GbGws27dvpwIdI7cVMW3XNpqjY0nsbMdlNPLWZb/jir8+AkDEac8Sc8FQwkaE6t09NT1oIw1019qJzLRRv/81mt2LCe6qJfpZLWUFqeRtD3WVIQ3gTQvSfb6fmAHzsZJE89IXsH6swTDrd2iu0BA/cCb1pHJHeQNru3rRVvWiP9CDBCKi3QxLKWV3aR5jE4p5KbGaQFoBzWvfJ925h/3WWJ6xprHZNR6vdRkXbNQxvCaBioEDGVNUxD7zALy/7mVqYiKvrjXSIlJZM/FkhNDyt/U93JHo5NxNnxBrOhmd1U9seC95njS6dHYS/KG2G3fAxcqGV3BjAk0UUyZOY+xvVTXTsaCCg/KT5PMEeOP2TRhMOmZdPoS6kk40WoHBrMPj9LF19SakPRGDt4eA8OPueZWE8Gya/a3MWfQ7ileuoGb3TupMKfjMKVwUOZUUEWpm66paismSTZ2/GnfVZlYkTCQuysvg9hqWWsewLSaXv656lIwZXcRYuikvtlE5+GROv/shzn6hkB213ei1gnV/OI6kyFD1iN1uZ/bGvTz6p+uIdDoQUrI3K5ff3XgneyYPhVfex15lpzt/G96Eemxpw7GZJxL4ewxS46czYwXOuH24Ig9g9g3Epa8g5jEdxopQ6aD5Pi/BcLCKAuLXXYbWH4Z1ZhoiykX9+adjyD8D6/yFxF0UGmbUEwxS6/LS4fZxzwd7aHU6aGv2feMY/3bkC4yO34MWC1kVLaQ1OtkxJJI32kdjCZqJSCwjR6vH5bJQUzqIUz79irK4gQyrqWD/lCH81TSZ7qgYao8fzq373Hi7PTgrXyRKexLS30KyMY+scDN6jYMvTZ+zM7GVe+oW0efrIigDaIWGLe0riRkxiaS8weQWJGFLTDqKV9kvm2qQVn6S9qypx9XrY/avhpGQYSUhwwqAc+UdeNp284XvdBL7qpgY42RjXzW2iBymJJzBPud+Pnn0PgAOxs6i2DKQM/RaUnw6Po/cRJerhnx7FftS/PRZrWxNuYjSQAJGfJycEE+hJ5cBvna2zpzJGPML1MpEvhhn45bbHqfJ7mJHbTenjUhmaXEjO2u7SRoWCg67/IJgZye2vl6ibryJQmeQ2zNyOU5rIs6o52C2g5qBjyGEDpM9nba2lTQFlqCdYUFoNfi1dvTOOJKbr8DpqcGVVknqvU/RfPPN+IaHk1J+CUZS0botdAz/CKduP7HbzsRsz0aEx+EMrqE6+T3Slp9FVF06lpNOIicjHX3bqzwwqYTM7Du5+7MG6ntdFKcYca1vZEvbOSycsoCurs20WztJsW8n52AXJ43Yj97wdSCxWttJSKiibTpkvH0AajQk76ijc040g+LKaPNkc/+QcJ7aHmDlyJN5LSedP2zoY2Iwkhqdi/dc9aRU2TB5D1Dc28gIazJeCSYBBTHHs3rbW7TstLBlsYsL7n+M6JTUo3/BKd+ggoPyo9TZ1Mf25dWkDYkmaaAN6XWy7rPfUzD5Fq6tfIfd2jgucg7A0v42b1sMZHn8FCSfC4DWPJC9lnxyNCZutYS6ysAHO8JKeSbxHeYePInCCaFGaOF2UieiCMOLEwMfe4eCgKzwHqK0rYjGAJp1bjpvDfW+WtXWR1Z3A/PyR6B//20Mt73Kfb+/Fp+I5SO9l5O2bkIKQczcOcyNjyeispA45/Ps3Sdp0Swj3JHP0GFP0f18LdZTM2iq/ZD2qI9wG2pIjD+D3IS78IV3sGXLCUQ2TEXnzUX76Km0NXxC3OZ8uuJW0pOzGbct9OVa49hnyFhzLzLOBu0HEAEtnjs+oM0haH/2BSy3/4Fq7WMId4Du7q2cnZbG0CFPss4RzmUV3eyoDmKNOZEBqReGjpPpQyzvX8KIii7K8sIJ9J5F/LBzGRCn5avVv8JqbaX3fD/1JyeR+3A743xBStwzGdlYzubMkdw+3Myfdmew3BjGNE0+BCHdb8aUmIa3eSc59WFszFhDQ+fZSCBVv5cx4aNYkHEdAHZvBx/88XHOvP16ogamHL0LTjmCCg7Kj4YMSjZ9WIkpXMfBnW1o9RqOuyA0iH1x8av8rmMz6W9dRbx9AVn6HoKBTmotLYS5opiSfAldJj17dEFOcGgZmprPyd4k+HpYZT6PWMWC4mH4bSYyDh6gPTMZnacHh9HAqd4+ND4HjZ5mArZwhmprOEP3OfbGUKlg+mtfMC9mJgsaqnh2zeNs793EvPoK4u0d5Dz2EItu+DODDlRw1prP0I3JoaLzCVLN56Nr/C3tgdBro+Hd+WTLewjPTMOV00PPsmrCGUXSvNMRQ3sIDx9MIGBnX/G1SCSJ3gvwHLRjtuQQ1DsJnHmA1tY3ABic+xARkXls2zaPzsxlaN0e9J8KBm78A32Oh/CNS0Zf1Ebb0kdI2KYFtHS/asNu30FL63JmDbicYVlRlFY5eGFnLTdOyg4dpCHzoeVGEtc/SkKHB01WOeSMBEBvuoVNG7aTnFJKRsYuWu4MMvDLz7j4lNMwddt57pM3+fjkC7AEYNFXnViDBm5OFVxXHyCjz8IXY0dij4wls/5xDsQk449eR2GvCVPXcHK1bbgHZmGu9jM+agIf3/wY5//9VvSxX39johxdqs1BOaYCviBlhc0kD7RRtbudTR/09+Ug4MTLhpAzNtTFxbuvXUbXiijcxkhksAehsYK3noQwG1PjTgPg/IlhhBd3cpPXQ44/Eq/w8YR5HSMb7BQPaWbYxmbqB00jraaGSfWFpE7u4HLvjZTIDB7zbcTijmVDzl76smz8ccd76PCzaUMeUfWhD+P2ZuVicThIb208lP+DSalkNdXz/pgTmLp/B1qNBvlHOz5raIQ1rTaC0aPewt5aDC+lEDUvl4gJyQTdfro/rkQToSfy5Ex6nfvYvftXeDzNACQlnUly9a9xbGiAZA9lg68CQKMxMGXyVvT6UBVbSektNDW9h75KEPewHuPg0XhKd9C1KIrksmG4Pl93KK9JD79LaeT1RHwqSZ91O01j0jn5qQNId4Dk2DDeuHgs2XH942HvXQLvXwZIuGQZZEzB7Xbz+cY1PL/tS6aJIMOyvsAY4zq0/qbdcbw5/AqG1KfSqothSLuPF4NOHq7vYoAuhjknRQNw3NYn2JtUBIDBK7ii8EGSnVtxxQ7D6A1nbGQ4pd1baDEf4ILHX/jBrjXlSKrNQfnRKvqsmm3LqgEQGkHWyDiGH5eKKUJPTEoEfq+X0o1raPi8EylbwR0aPwEh0QkDo+PPpVkPj3qd6IvdNLrt3Jr8KPeusGBzmjlN00dqQwMxZYn8cfw1+N0a/PGjiY0/nl63GScmbtCsZGr4EtaffycjvnyG7D0+NPi5Mu/PXPPhq4hZA9lVa2VwxW4AHjzvCq4uWknEgVp2LRxK4voaztz+FQC11xnQWZ0kd1+JcVQEcbHHU9/wFo2N7xCfchEJKWMA0Jh0mE4zsKv4cmxlBTj6DhwKDEOHPElCwhzc2i4ca+uhzkBk/jjsspDUlAsPBQaAvNw7AfDYGjAMsuMp3QGAK7uF7pws9GtA0//tQdd7q7Ho9Gg3VdPwyW9wjgvwREESd1juo6G8m3PeXMOzU95n9MjH0Q89A8w2eON0ePVUWPASpmFnMu+Ek6kMZnFw60vIAxOYkLuaiJVavFmSpHFt3MT9OFIjKCcXb6IRF7/nr7F+Xt6rJ2tNHS3RRvYNPpeUA/vQZyRwwegr6Vt7kHrLNPBAXvU/6MieSm7kWCrqdlC8bBlhUTbqyvaSP/U4Egfm/m8vSOUQVXJQjrrOxj66WvrIHhXP0qd3Ubsv9BZSTEoE864bhdEcembx+3y8f+/tNOzfh9BEYDLMYN4pIwhs6aTZrGNtrOTc1nCWBRu4X2MBJMa4FVyzbTUzd319XTdkpfBs1lx2RmSRFuxAow1gwMsBOYDLp8Rxk7UQ17r7MeAn3O+mzRDNzQOvw+eI5baH7qHzSh/uURLpMAAaRIQbJAhf6NVSU3Ay1iWtNOTpMQ6rJ+rgDGIrzsD2m3TMKQms31BAIOBE67ExasLL7C+/HYtlCEJoaGx891A+09KuwGYbR1xsqOtrKSU9X9QQ7PNhmZOKvbeQ6OgpCPHNXlL/yVVcTO2VV2GaPZOK6e8AkKJbQHbejRw89VQMWSNwl+7Ak+3BuD9U3ya1Esd7T3L6ljD0B3qwhDnIKYjinmlTGRJhhncugP1LwRwFC16CzGn0+uDyx58i39PL8etWUxOIJrOnHjHETMmJerJSqw/l6dPWObwTfwmrVjlYbqqgOXkTByxDSCtuJq2iBKdGT5wmFhlxDuGOBlz6B6hMiOf38i5a3HUkmtOwe9uo7dtPB02ceOt1xGdk/YBX4y+bepVV+dHweQM8v2gtAOfdOZ6lTxWTmB3JjPPz0Bu09Ha0seuLZYRF2tjz1eckOgeQHz8Zh0sgPe3YLAkEZRCNDHX73EKQ9tlWnvvgK7wprVg9y7jz7SClg/Jwm830RkRQmTiAJb6RDPJ1cIO5l7HZ6wjvKuORCTdQVvwX/trShhZYHjuVtxNPZVXMBK42tHHKZ+8Q/sFGfK//iuLGD4iNTCUrMY+IiDyQQQ4cuBtL00TyUu/GOj0d94Eu2l/bhy7aRIvlPdoHvs+AlEupa3iFuNYFtMV/cMTxMJszcLmqAZgxfS9a7X/31bAMBhEaDfvL/oTTWcXQIU9gMMTSdNdddP8jFDASHnyM5v3LEXVu+HID7nPjaZsVweV1t6IpdSDcAcLyI9l0ZiY2Qxw8ORx6Q6PacfyfYNqN2HvsPPbYYzTGNeKPimLwx1uYtbebty6bQ3rlNkwWD+nHNxJEy4v6qzm3eSdRcVuR2tAbUE3+ZFrfsVATSMMXs5ULNyRj0rbwm4vcADxT9jgJ3zFQUE1fCXHnDCF3ypT/6jgpIT/aaiUhxMvAHKBVSjm0Py0aeBfIAKqBs6WUXccqj8oPJ+APsmFx+aG/lz23m95ON/lTkvB7+yhb/AzrV27G1d/zRKJtIMOipoEPtEISHhaLKcfGAuwkmPRMq3TyisPJcbVF7I2XJFu2c0bJCCqyujmYFIEvMQa/P5ytvjQkMME1gK3GFxnVuJ6eqEy27r2fv3TZ6dFFcPHQB9gfnolDF86VSSbO1xfj3LIJmWdlX2ABd2zKZeV108hJsAChp3rxRibSHqRnby3mwXHYV1ShjTQSe9VQSgrPB6Cu4RU0ARO24pOxnJaHPWwDA9IuZ9euiwHISL+alpalpKSc+18HBgChCX0TMSjvnm+kx11zDb76BkyD8oieNxtT+hg63ynDHr0D9jSjnern5uRX2J11FSVr91BfAuMeLOTX8wZw1bnvE/H8ZDBYoGotTLuRSGsk8WnxiFoBbdCTV4Cm5EuKKjIZlb2Plu0m2lZ5iZrYzdX6p/El6DB2DKGvaSyFzfsZO2MLlnOgtmU8m1pcHDyniJ4wcJgF2Q3h1FW8Q13mBXic3YwMtNAblUqiMZL08Hxq3y2lvbGGCQsWotF+dylK+e8d05KDEGIa4ABePyw4PAR0SikfEELcAkRJKW/+V+tRJYefhpWv7OPA1haGTk8hfUgMn7+4F2/ffoymHfS2hxp5U20ZTE6ahWZGFo2fdRAjNXzZ40fnaEIfvZURf7ibMzaWIfZ04gsEMRtcWIyfkO0pIis4G50m1KAqRIBMWc5XgUms9WVzitfBtMYW9g97ipt0HYS7ggQJ9Vl/Xe4f2JOcw0NhX7GWTAY7XsVoD5B4q46oRVdxW9gUypp72XDzcYcGqnFXdtP+wh4iZ2fS82UN0heqpolemIdmcJCNGydj0CTgDbYQ5spjsHiGyJMyEdrQ8tuLzsFu386kieswm4/+K5syEKTr/XI6Xn0IX81mkha/SmT+CDQaI/XNW5j9iYPegx4C0UZiJiexqvBCoqQb4eyEW2pBZ6Cnp4ePP/2Y6Jhotm3eRv6+fWwxpGEYnMvQlUsZ01JJc6qN4qsH82LUQk4sE1y5cxusewPX8CBdvw599V7nSCQ5rJkAsKPZTMemODI6W4gNXkNX9KBDeTb7ejj9nGH0rWqkrq+MsBHxDLvoVDQGFSD+r3604zlIKdcBnd9Knvf/2DvrwLqqbI3/znW/N+7u0qZtUte0lJYaFOsAgxcbdIB5g9sw2AxanBYoTtGWQt0l9TZtGnfXm3uT63beHxfCdPAZ3sBj+uWPJEf2WWfvc/Y6e8m3gOVf/r0cOJlX/xtAc3kf1Xu7KJqbTOHsCDSGAS5+ZDxhmioi3BFodBpkWcOZGLII0a0nsNZCNFLq3QE8IkR37afWPZwvXjyIt7OUgGYv8tANRGjLuGVbGZfuiEEm0TH8SClq8wCCT0U92VT7wkikiwcjb2Rk8WOkJaehdorsjMinM1RLtyIUc7jA3ZJHsDp2UeT5lIzkK8ntuwGAttzJbK7s5qzC+CHF4Ld56F9RhTREiXZsNKaF6QhqGbpJcagLInA5g1QXiamXBX+PuBDTnNQhxQAwfNjzDMt/7hdRDACCVELooiwi//A7hIBI5zmXYt91GNEfQHcgkge1kYipGqRmF4kSKW8Yx8NAB/ic0H4YAIPBwIXnX8jcWXNJzEikPC8PQ4Yela8Dd2oYR+J1RLdaiHvTwlWffcjGaDn9PU0MaHXslY1H/5IaMSAhQddJ3WAati4T42KdDEzv5LVTBAIpj2IMVA/J7JTpCcg6MJySSII2i7DaEHbesxOf1/9dt3kS/wZ+jdFKUaIofmngpBOI+raDBEG4ErgSIDEx8T8k2kn8GIiiyME1TRgj1WQURWHpdrDtnSoM4SoyRql4+44/4rLZmHHp1eTLxhMaHkOAmUg8UmqxsC7uHq7VvUDXMTf+ik8Y01uGTyHSP+58Gt0foVd9AcE6PuS2jaZ83AIAlC4XPRHhTBqdj6b6Yz7159IhmrhD9QZHR6kxy0JobclBwh5uT7qVem0ikkCAv0XsZmzmaixuHf/zYRkPZeVj++h3KHPzuKzESkKomiunBJ2gYkDE/F4VfruXyGtGIFHJ0I6KQjvq68fU6WwBIDxsOtFRp6NQfDNWX6EIIzLyl6+vbJw/ExRLab9xMebX30MWkYl11Q5GGeN5JGkbtzOWytV12JKncBNvBU9q2gWxI0EqDxYWAhadsYgnnrgPfdcgXsFOS1IaXZlyQjc3o/J6OGXNDlr18awviEGWMYOKMCXTXttL3xfX8H6Ck0WfbiCx28Hhh1WcE+JijEbOEZmGOvcbzK07HY1ZQr+skE3vNTL3WiO+YgWeDX1EItBwsIOMccGMao/dgdfrRmsK+aW69DeDX6NyGIIoiqIgCN9q9xJF8WXgZQialf6jgp3E9+L4jnb2rgry/1fu7qC9xoIoQuFsePvOmwARn8dN9dtbGBNxGk6JA3VAQ6c3QIVdS0mSifCmO1HZ72R4ohqTJ5Lbfn8hu+OdhLWtJ78siVmVoPe2UFEUg//Lp1hwuxiIMeDrfYh3A2ewwj8VI16S8+rZK5/EK94r2d16EY7YIl6cMpPH67qo7ncwM+1qVColn++rY3u1mY33P8XYhgZ673iIjnIXyy4uQqeU4WmzYV5Rha/LQchZGSjigiYsv99FTe1fkUrUZGTcgdMVXDmoVHFIpb9+tlHjrIn0pI7DuX87nffehat0C5px45i18M/c1VaFxy+jvk7HFzHTmd2/lbbq7STsexlyFsC020ATilar5Yqbb+GPm+5GIzcxjzGU7oWSiV9O2v4+ogfMWE0mfBoVyKqwK+X09TSgkc8guSuoeKKWqzl0mZcslZ+0xADQBRkvY6qHAyVP0SOP5c0lzai9ZgxqH6NN6exf/hGpRX+gcuUW9PuUtDqrqVOXYYyMIiYji8xxkzBGfus35kl8D36NyqFLEIQYURQ7BEGIAbp/aYFO4ocR8AfwegIo1TIqSzoIidGQkBtK3YFuYjM1JGQPsv2tF4mITGRa/u+xdnaiMivo9Q6yy64izNGEqWMvgbSFnFN6N+36zxgpE2iYdQbXKiTYvJ8T1raSOHsUpzXqKKw7RntCKH6ZnJi6SgaMRXhMUq6SvoLE4uSKwAS0cjt3jnsMh9rL89zI5W2vEek1w6y3yTPokJdV0nq8izvbPDwzNYJhty/mBY9InKMP3fTpPOSNJUI/yNTMCES/SO/y44guH4ZTk9CO/roOcmfnp7S1vQNAePgpDA6WoVLF/79QDF/BsOBi+pc/iqt0CwCOPXuIedDIX7UiL0uaaXdF85KlmDls5jHNWC61dDJq30uw7yWY/zQUXkK0Npp3F3ydtBZpMvL03qfJsmRxeGQ+MoWRvLIy1o4JJWMwiz3jpMQ3NlKkCq60qjJHkFVxhOUH5LwTncIUfT/R7lD00ZXkpvhJP/AsNb4b8cvUOAklofIDfGOSCBWkvPeHG8lSj8eoTiZRk00i2Ryv3kVz+X46j1Ux/847fpF+/f+MX6NyWAVcDDzy5e+Vv6w4J/FjsOG1cmoPdFN8YTbtVfsxcZymTgF9YijNR6qp3jlITuIECjRT8VfY0KEDAY44BQz9u5gy3oBp4kVYHiuhLyyfU6Rn0Jwq437JPkJ6NjK9O4II52yUooqGPPClyhhUCyCK+AwXowyIyKVrkcqc3Bt6Lp4mJZcNf5+JBfexpE+N2yzjgo6PcMWPhtixuJ1etlT1ALC+vJPmQ++i7+tEJZFSa4zni6zT2VHTyx1zspFJJbhqLQQGPIRekINm2Ilmoq7uz1Eqo/H5bLR3vI/ZvIPY2N/9rP3b7ehmRdUKko3JqGVqRkaOJFQV+rO1H3LmGLw9dyMO1qIt0tJ5z104Sw8xKzeP4R3VrMjcxZsHzsWq0jCzt4Sbcu5iY/crdHRWkrj+bgSJDIb/DqRfTykTx0/EGmVl//YDiI1B38rq4T0odSnI9HZ6xVQKD62lOi240rrn92fxzBNlXLDFx+2LXByuv5wIiRWLx0JYcgcxC+tIef9FWqXFuCjAl6BgcPA4acYRpFGAKIroZyXRU1aLqk1OXshEANq76mirLCcuO/dn66//BvzSoazvAtOAcEEQWoF7CSqFFYIgXA40Aef+chKexHehpbwBhVpJVEoslm4HNftqQdCzefl+0uR9FIQsAqCtq5Y+aRPZI88hyxKB6A4QekE2czHfIgAAIABJREFU7hoLhzYexusU6Jiyidhrt7J8dyOtmm4KAsd5tGg8VbIv0Fk+YHjXZOKdwdoH8j4bCf1d1KenAyB1eSmc72dL42csbZrDy/7JBFolJBmbOSwfwOAI4XWzluiBXaR5nPQnnsrMR7fQawumDT93/ihufmMPg+vXsyl5HKF33cOasg72N/YzKT2ciyckA+As60WQS1BlhSCK/qFENFEMMDBwhJiYs3E6Guns/BSA6Kj5/1b/iqLIXbvuot3WzoK0BTy872Gcvq+pKpIMSayYtwKNXPNvXecryCM1xN42DmTjwe+n+9GHcR48iPHMsRj2jeeGmTOpkvtYt380p5t3s980jAeURRzJPoer+7cwb+W10FwCpz+HJxCgz+sjRqlgTuocTok/hXuff4ZKyS5cei1jNjRjNTnQRIyiNSmR+ZtXcThNjui8nzemB7jlY7jn004OjQ9hafEopJ4MXuv+M7dEu1AvOkazsxLfvivxS85gV8jrXOEfgScgcqzpKGPqmhhMGEXoqRFoZHK6lmwmWp3C6gcfYvr1fyBj7ISfpb/+G/CLKgdRFM/7jl0z/qOCnMSPQvPxPjRGBTKZhxX3B4vonHvv02x85mWifSLtXilSXOTFzEcar0aTFUHcZkhIGo6tw45PgD02H8V6JboRSqrXGhlU7CQneywtZgcPrC7HHzOejEio0bmZcfAoc3ZFc6wogszqKlLr6zEMDGBXqahPS0WQBMjOP4hN/h7rzbfgF2VIAm7UahWzJ8oorWwjofVhElOv5SHfcQDe7ogbUgwzc6OYOzyG56xtqPxeZBMmcdH4JM4fm0hFxwAF8SYkEoGAy4fzaA+qzBD8gp1d2ycTHX0G2Vn343K14fc70GmzUCmj6TNvJzz8FIzGkT+qTz+r+4w6Sx03Fd50wvaq/ipW1a0C4EDXAeJ18TxV/BSf1QVrQS8vX86m5k3MT/v3lNA/QpB/Gbwok6HMysJdXYMiKUjV4XrPxd9lEm6WncKCwG7+Uvfs0HnLxzwIISoqaw/w2NFa9g646PP6uFtr5toRY1EotPx+3jksfMOEpz2SikQpIViZpCyhIiMBUWnio8JOhvUZKEurZckCOzeu8hMZto+3JyfjVMQj6xvDEtleLlS7mKT30zXpVQbXPEazKpm3+u5mRPNs2sNHsnlDPZaQLqo+LmFKWgfObWvQnvIgo8KmsvaFp5ArlbStKSVl4mhipwz72frut4hfo1npJH5lcDsctNf08Pnz1QQ81cRoejg98XrM7nY++cv/MDvucjQyPdXWAzgDdmQSOeHn5CCP0iKPUNP3XhUaiUBgUjT9a1rZ/OIBdKKZgCSUxrhSbix8lsd3NxEQRaSxB+k0rWXS8QISvPlUTHCTnryXlJY2Wk8xkrFSSsdZatKS9yNXuAiNaGLT8Tk0DCRxvXI5l8b3Ybx8I0e6D3LmbhtZrh0UeRuJGewkkDaDZfVhLCiI4tKJyQyPNwHwQLYUdsC1V81HJpUgk8LIxGC0i7O8j743ygHQjIrE6WzE77fR1vYWKcnXYrMHQy11ukx0uly0uixCvzRn/BDabG3csTNoC58UN4mi6K/DzT+r+wyZRMboqNEc6TnCXePuIis0i6zQLAJigI9rP+aOnXdgUBiYmjD1hHZ3t+9md9turhlxDVq5FoCWwRZitbFIJT8uJ0CZlsrgps1I9Qpk4Wp8vcFVyxSDlmEDy1DjQoubN9R/45TSpzkr+252jboCk3mACSF6Gnq6+NuAjvM+/AOh5y8nJzWBSTIf5T47BsFFrT+cdqlAmNJISaaLYQPBe4+3x7MzdRuHUwfJqdrAs080s0cZyqazFuF0HOQhj5bRdinnR9uQFr3OqD1X8nrRVoa714EsH0tIMHu9T5NKyeFuco1q8FYRo84ncqCS7U8uZXrM+QS+sHC05jMGfL1kz5lOeEJSMLFREL6vW/6rcFI5nMT3wu/z8d6tt4ItgEEWg1HqI18zGaVUTawmnXkJf0AhUSCLVJNJ8AVXppuQRwUnJc2ISGoq+ynb0U7Bn88hKv0sOmImYiZoL78kqp8WTyiv7T6OLKaTSY7jBFzpJDnCCOvtJWbsIUJiu3BcCCbRQcdEGVp1F1q6ARHzQD4f9uYjlVu5VrKFgyn3kOb1keSTEu7qwyNIibG2gETOoeH30X+8hTnDoocmf4CYtlocERHIoyK/cf+2nW0gFZCf56VT8y5aV8bQvu7uNfT0rEcQFGi1mUilKsLDpv3ovn2vMkhnIREkXL/5ep4qforR0aMZcA/wRcMXTI6bzNPFTyMiIhG+TkmSCBKKE4pZVbeK6zZfx5joMbQOtpJiSiFWG8sH1R8EZffauG/CfbxV/haP7n+U87LP4/YxtwP84CSoSE3D/8GHmN9+G+eelYRdfRP64nFcUBaJov5qwg0Xsbs0lFt6rmC5+Bgfld7ArRm3cE3Le6Q422hVRbMxbDzrfU5+13oASXwRp6QbSKyuIjcrC22olgdLUkiTdqL3R9Ah7cBiTyZH7WdqSxEfTdjGg2/ZSW04RCqwr6aI4k4964qczFphwDpNQWThYZw1DZwtxqM4u5Q07200bf4zgs1IbPwmWnXj2d4/jNyDb5KYo2V8xHz8+BABd8BBaI2JUExsf+B5pt91I/0vVSAfayL+9MIfPYa/ZZzkVjqJ70X52k3INrrRyPRD25wBEcncVIxNA7jK+5An6YlcPAzzimoCdi8h52YhM30dqbPhlaM0ldQxcfcdBAQJWwtP4d3TLiDaYubd0e2csi2FlgEHp2rXkSQqiIuvQCW3Ey52IEb5qDmQiiHUSlRqH3arjs6ebKbN+TNfNL/J0sNd2Fou4L6pOiTNr/A/mbeiEASu6vyUOyufoGXin0jY9TcYeSG3+6/ik8OtbB9mQxMViW7qVAJ2O9WTp2CcO4eYv5xIORFw+Wi/vwTd9HgOSk8BIDXlj9Q3PHnCcVmZDxAff8FP6tcGawPnfX4eE2Mn8qfRf+KajddQa6k94Zhnpz/7jVXBV3B4HfS5+lh6bCkra1dSGFVIWW8ZDp+DhekLkQgSVtWt4rEpj3HLtlsIiMEM7lhtLDavjUcmP8Lk+MnfKZ+9pITmSy87YVvS22+hKSxkx86xGI1FHGxo5v7dV6PHwQrtErIDZXhQoBS/9o3UqBPJcDbDvKfoSZpHS0sLw4cPRyKRcPHDb1DitTF3RC8r9w4HUclc014iXBJ8gg+P382pDTKiStcj8wQp0Lsyi4iqPkBALdJ0LygNQa4mR28aCn0XbkscHlsUIWnb8XvUNG+9FY85jszmNcSHxaFS6tBeUEzV9naS3cFVlMM3QIu9iizjaPoD3Qx77KyfNJb/n3GSeO8kvhMBf4DerdXoosLo8jQiV6uJz8kHwNFrpfnvO1AFNHSoBJKKI1FGRWPzBEgaEYHo9TO4qx11TujQSuGEtj0O3lm2GGvpYkL6K1mZ9xxznHY2F1zGuqhg4trlPa3Yqg6Tn3aAcE0nBkMvok/AMahFG2Kjzx5K23sRBHwC6gQ/3doi/GFePjV+gcQfirPhBpIFG89GvcPM7AeY5W3id03vk9pfjkkuxXTdHsS1t3OPdR5vlvu4MEvH+Y9eDUDcU09i37MHy3vvk/TuO2hGnugncNdb6Hn5GPLzvZT1XgGAyTSWwcFjGI2FmM07iI5eSF7u33+wnw92HcTmsTE1YSp1ljrO+zzobvtg/gckGZIY9Axyx847qOirIDcsl5zQHK4Zcc2PGsOvzCHdjm76Xf1khWZR3V/NWauCk1yyIZlXZ73KxWsvpmUwGDYaq41l9cLVyKXyb28zEKDpoovw1NaR+MZyGs8+B+PChcTcfx+Hj1yC2bwDgFb3TDbURFLenseamJeR91fzincOIyU1tIoRXCZbS79Uz5rY05DP+gvzYiJRS4OroI9Wr+OOnS7cyAjRyDH5+mjw6EmUWEgzbCfOHYkoyDmnO4my+hIK67+uTeGTynj/zJvxJ+xhhFtFX+Vs+sc8yLiEIO25rysBpbGdQEBG9YaH8bv1SAIepuy4herTH6PDoiZaLqCx1JMfF6QB94s+JEjxTVRgTI0hJC/xN29m+tUS753EL4P9n31M09HD5BfPpHXDYbKdozDTg8XVwlHbdhY8eA+Hl31CnDkJjUzHLksN465bSOzwYAhn2JftCHIphmkJ32hfFEWay81s2r4M9/5iUIBNKCEqVspZ8zfw4NEqpro3UGRrItZXTejoNhQKFx6PEufOKB7gCk6rKkWW7yTeVI0/IEdhcGMWsxAC8IV2I8PCC7A2XE5NwMZTssd5LOIyFH4Pz1Q9hmyglYAuEvkZz4FSR0n+vbz5yl4uGJvITb6qYOKMINB2U7A0ZciFF35DMQB42mwADCoODW2zWPai0aSjVARNUFptxjfO+2d0O7q5ZO0lADww4QE+qf0ElVTF66e9TpIhCQC9Qs+S6UuCjLPCT2O1+WoCi9REEqkJypUZkskVw65gVd0qni5+mghNBB8t+Air20p1fzXXbrqWdU3rmJc6D4A6Sx0Huw5yTuY5CIKAIJGQ+OqriB4PUp0O3Yzp2DZvRrzvXiIjZmE27yAsbCrj0v8HPWfS1JPH2I6bUcklrLhmPFWdgyxZU4kgwqX+tZiR8mBNJ8+19bNpTDZSQWBMQS6n7nsXdcZ4cjSDbDrSSwMGmgMhtLnziI3+jCmdY1kR38el+vOw5E0gpGwv1c4OUjoruXIwkuL6BQRsCmK9blZiRe2Q4gxATU8IwwY6GJ7mJnL4B5hLpuCWpdE7Mxmb8Qip1jKIt9KmPoth+h58zTZkqS6cfYmodgs4djfTpj2MZJKevOL/zviYk8rhNw4xEMBlt6HS6TG3tbB/1Uc07TpIhqGQzreOEK9NxSfxUuHcS45uLONl8zn64Edk6kYwKOlnW+9KvCHzSMr78TH1e1bWc2htE1AECnB5PuSlWYf5dPYbrAqEknL8Gc6I7SIy3DZ0TsXhePJXB3gyeyGSMDlzMpooqXBhlamxpQ8bip+XhHvwuLMYqL+C4202PMPCWGx8hHptLJc3fo6qv4b/8V7BGefdxoSUoDL7+FAbBpWMu+fl0nfHm0gjwklctozmSy4lbPFiQi+9BF+fk4GNzajzw1HnBdWfp2UQqVGBxb4XnS4Xmy3omNbrc0hOvgabvZLo6NO/cf/lfeV4/B5GRI5gT8ce/rrnr8gkMsJUYdyz+x4A7h53N6nGb9Yl+KmK4ftww6gbuH7k9UPKQy1To5apidREkqBP4PYdt/PIvkcYGTGSXe278Aa89Dp7+cOIPwRlUShAoQBAM3o0g2vW4m1rJzbud2i06ei0mcjlRtJjJ3DXxAdpN1/N1BlnkBplYni8CbPNwv1rL0QSL+O6lndJczRzaf5D7GhtYFpCKnFxcSQbpQzWbaIJSNCYwAphGilmRypR1knUGmrJtsq4OP0JBmUN3BFyG1sVXm5aWkm52MrI+Aw+aHMilQiMrDqV3r5OPh9XTsBYy0EULLTDlKQ92I4XoY7ejq+oklgqIVhCnHjXEjZ+nkyxKoqB17ajufM+Gptb8bTbyKSQlk+qMGe04vW4CPj9xGRk/Wzj82vHSeXwG4a9p5+KJ75g0NrDoHqAnt4mUvTDOC3xChBFvH43SrkW42nJnDZ5Ou6mAXpeKCVNN4LBcJGttTr8sjkUTkxCIv1xk5bd4ubQ2kaiO/fREz4Mv0zDW5NKECUCqTGjeHHDw/wpoQlJiI/a8kzeNLZQ0BvDFcs6ORaVRl+cjgXh69ix3Y/gl+JVhYFUhtynISEzjk8cS7E13kIZNnyJGgKxGurRgNOHrylYq/lQIIPqdVXcKZOQGalnR00PUzIjUMmlOEtL0YwYgSozk4ydO4Yori2f1eOqNONpGUSVG1SE7sYBFClaBgePEx/3e0TRi91eQ3j4DDSaFMaMXnXCvYuiyIulL/J86fMA3Fp0K38/8HciNZG8MvMVko3JLPpsEekh6ZyZcebPNczfi28zi0gECS/PfJmPaz7G7DKzpWULcomccHU4L5S+gEwi49K8S08wOX21sup98QVsW7cRddttyOeNBiAj804sHWeTGP0ErdWv4+ydjEIRTlJgHRr5dbzvO48LNXuZ1beLMI+Zd46WMS0uGYlEwpQpU/j888/Jy8tj1qxZtDzxLqfmxPDqYStHzSNZqKrAJ/gY25nOoXArf417DUUgaMKs8LexqMtISVos8tpBSsLmkNe+lNABKb0mPzMOROBV+Amc1o42fy+66AocvWl4jueSatnBpxOkTInoQ2EIZ0X6cc44Fotkzw4mPRcM0+1bX0PCZmh5vgS3w45TsBH9ROZv3tT0FU76HH6D6KitQnCBZWUdukH9N/ZrRkaiKY4hoBJR6wyUbW+j+Xgfar0Cg8tHcqyGjze04nUH2S4vuH8cpqgfl2zVeLSHz58/Rljr4zSdo+Caue9wyLmX8O4ArT27kclfw+XSMdAbSdbjndx/bhg3f+xCKfEiO9eCJy+H2lIrPaVGQiZCc1cBloCWBN8EOPcA29e5OWYvwDU6ApPWQcn+33O9/3quyvAzQtOPruwt3p9Zwp8/KT9BrkfPGsZZaTpqJkwk8tZbCFu8eGiff8BNx0P7kOjlBAa9mE5PQ5FspPvpQ8jn+ylzX05e3lOYjIU0NS8lPe1P31p74YXSF3j+yPPkhOZQYa4AYGzMWJ6d/iwqWbBwjdPnRClV/qwrhH8X/oAf8cufm7fezNaWrUyOm8yTxU+i/JICRPT5qB43noAtuNqT6PVk7t0zpFzNa6rpqPuQztxlJ7S9t2MUS8su5trwI9wy+Dcqw0dxecofmasY5I6ZQSe+xWLBYDAgkUj4+OOPOXr0KN0BHV94cjg7TcLUkH3sPiagFAJUG2ooC6ni6ZecVKUUMTb5fF6ObeSIMwRvm5fwaAsjatbRd1oml8eexYOflDIv9x0yktsAaNl+AzE7qzgwegOZ4Q5MOSZcLRm87bHyZHsRlk9WY1q0COO8uSgzMihfsoUQ+9cZ8fILo7Ef7CRyVi66qDD+v+Pf8jkIghAJTARiASdQBhwQxS/DH07iF4Otu4/K1zYQOTaTxGmjAOhvbqPhqe0k6rLRoafT0ELOrBk4DnYhi9SgSNCjLfyahKzxWC/b36s+od2jNXK8bj9zrx2OzxP40YoBoOtoEwAlGZ1cP+oSOmxr0Lep6HLehFzhx2YLoXz7CFrteeT7lvHwW91IEFmXk43/YAwJ0lSsdVUIqWG0mFPYLsulwa0FqR3h80hEVxjyOCnZ8lam1x7m2KzVLNlxFabqqqAAKVM4Z3QyAUHK/gYzHx8OTgpzhsXgLNkJgLqgAAB3vRV5jBZXjQWAsAtysH7RgGVl3dD9uCOaoRUM+uGoVLFkZd7zrfd9uPswL5a+yNzUuTw86WHeqngLi9vC5fmXDykGCJp2fm34x9yHZ4qfYUXVCv6696+c+9m5vDzzZaK0UQgyGfpZp2L96GMM8+YxsHo13uZmFMnJAGhSwzFsm0SgqBeDYTgyo4qOzo+ZG2ZEo3yfF46cR7ThAs43v8/r9js5u+BJbvf7EaRSTCbT0PUnTJhAbW0tBUYDVZ1OPq5TETH1bD7y1DBS2k7OAIR7c6iJe5+CmjJePdVAboONCVXbKCo9zL7U0dw/4goCByUcrbHTJYtHUXE6ocqNqKUQaIhA7i0hiSnUyTeR0RmJLrEew57xCNlSFKmpWN5/H8v77yMNCSFj3VqqP9uBQReGrMSL841mFIKChmNbyX3kdKTS367x5TvvTBCEYuA2IBQ4TJAAT0WwvkKaIAgfAo+LojjwnxD0twzLmgYEAcRwKWZ/J8kjR+Gq6keZbMS6pwXbkQ7Cfp+D3KTG63bTU19PR201ikMBoknE/XkP/kk+pDIZTW/uJkGbRWPgOO5wHxOvvQSVVneCQvgKHpePzW9UEJ6gI3VEBPs+ayAkRkt/hx25SkrysG9STYuiH7/fhUwWXNr7fIM4HA1IJCp0ukx6GzqJyf8At8ZOa/8b+HuC5TpcqGitKcTeGs4eQYtdomR/VBYTOsrpMhrxKbwIQOvBNkS/HH+anka/hAa7lkCSlnTpUerr00AKc3u3crQ9jdyzrmJyYTxs6vxawBEXIJEInDcmkfPGJHLRhGR0Sil6lZzu0lKQSlHl5eHtddLz8lGQgCxcjUQrR5FoIOKqAtwNVnqXHkOZZqTXX4FMZkKt/m5a+IAY4ME9DxKjjeGusXchCAIX5l747z0UvxAEQWBR9iIiNZHcuu1WHtv/GI9PexyA6DvuwDB7NrKICAZWr8Z57NiQclCkGJGo5YR8uhAA7ZR48pJn0qtey7DQe7g6K5Q7j88lY9Ioxhy4hVRnK1XNx8hOGXHC9aOjo7npppuQSqUkbdvFfRvbeGFbIyCnN6BliyeNYuqoyylkVMMxNGWfIxrSSW8MsgCPqd/PJTGjeTUila4BNyBwSJpN8vadJEguQqE2UpFzJUqLjRXppcwfkDEl2c4Un8AO73bO+WI3Fdk5APj7+/EeOMjwC+Yi+kUa92xGjgJvwEOINJLGDftJmz3+PzEsvwi+T+3NAa4QRbH5n3cIgiAjWN5zJvDNwrgn8aPhrDRj29Y69L/Ha6H5ix3I3DI8Cg8KjwIJUP7w5/S4WkjS5WBURBATiEQmUeDCgUqqYd2jT1B49pmYrGEMmqxMuuPq77xm9b5O9q6qR66U4Rz0Muea4USlGMifEocowqqnD5M7KfYb59lsVRwvvxWbrZyEhMtQKsKpb3iGQCBY9zc393Ec6j2E5u6kAPB5zXT0ZCAJH6S7IhvFcQmRdfspLbyDcd4DyBRWAHwSP4nxIQwGxmFpX4NXKeGYM4sSXxIBjRRPppEqXyr+xGguaf6EhoZwLj39VM4sjIf+RnBZYcTvIXYE5J8Yoz4i4euvUmdpKaqsLCQaDa6qL8l+A+DrdmI8LQVBErQlq9JNxN4/AQSoP3IUg2HY99qZ1zeup7q/mkcmP4JOofu+4f5/g+LEYq4cfiXPHnmWhSsXMipyFNeNvI6+/BiW7H+SxVolnffdj6+3j7BLL0GikKLK9GL9ZBMBWy+Dq3cjDU1DNmcRjIH82M8JqZnIO93pjEZgguUQ0xsLmGyt4/ncJELlUmz+ABv6BpgeqscklVI4LIcp27bTEDaWFKEHS3cv673ZHPU5GC6D1QuSmbljB4J/PyarFWvBDIylm5gv9PL8jIlIm2wIPhFZo40uIYfynGXMrT0blV+D2x9Oel8ha00HGW8PIXLsbqpcShoq3yL5gxW4ysroee55ep5dgiIhHmVGBqoF0XRuPk7UGcPwLG/HfqAXfqAkR98HVfh6nURcnIdE8+1hw79WnPQ5/AQ4K/pABHXuz2NrFH0Bup46RCDgZ2fnJwgDAbL0oxEQEARJsLg74Ehxo2kI2n4dajsBk4DOGEqvv5W06ROwvlTNoNdMo+04w0Imo70gkZBhSd96zd5WGx88vJ+APzjuRXOSGT0vDonk66S1r+LmRVGkru5vWKz7iY46g+qaBxFFD4KgQBSDhZ7DwqYSHXUGzS1Lsdmqg9TdnTrE/REcj8/Cbg86dyV+L6euWcvdYy+ndkI2l659ib7wSBY276AzbQqyEZPp2Kanx/0G62Jm0SWGYwhT0Z+pQ+l9E6dpEcnuft4/eguVp67ilMLg1x0Hl8NnN8C1+yDiuyNJRL+f6jFjMZ6+gOh77sGyuh7bng6irhuBf8CDMt00pByczlb6LXswGkaxZ++ppKbcSErK9d/artvv5syVZ6KQKvhowUf/li/h2NZWKks6yJsch1QuYfdHtQwrjqfotOR/uc1/Bx6/h8XrF3O4O1j5LUYbw4BnALvXTkabyF+3REFLO9KwMBSJiTiPHAFRBIkEWXgCvu4mVGOuxLm4l8HAcV4qy2FX2yQqEh+me6CHBaNepFkWzFTXSCXIBBjwBbguMZK70mIRRZElS5bg9/uxWq2kpGVw7/Ggsg+RdzJXVo+pp4W5G/cD8NDiP3LJyveIVUbxyu23UWJup7iphrW2FMwuCRFxzyPqGhFEWHj0HjRuI4IgotOXEzP6U5Sh7Ticek6ZtgMkFjy7j9Nx6x2ITichF15I1B23Y9u8GWV6OhWv7cfgDMFUnEzvgVr00xOJnpAz1HdiQKTttf1QE+TxEkZriTtr1Hf2td/mQaKRDz2D/yn8Sz4HQRBuBqyiKC77p+2XA3pRFJ/6ecX8dcPb66RvedDJGXZpHuqsn06XHHD7cNdZUWWFErB7sK5vwtfr5IBrA53mWs68/T4kUinHt20iZ1wxXR8cRRqupOCKhfSuq0L0B4ibO2noKzae0YiiiL2wC91hGCafjE/vx5T/TROIx+WjqayP3R/XotTKOe+eMcgUUtyeOrZtH0F83IWkp/8ZQZAOtd/Z+QlNzS8BYLUeQiYzkJZ2N7Ex5zIwWAqA0TAKQRAwmYrYt+s6LD1uBtc5qEkbD3ZIamigKzqagiOlvJI1G5dazuJ1LyD3eYge04NsQj5VZZGI1cfxq4bxUcRpuH1GYnQ2GkbGIG11YLDoOdZ+A2pzPYcD6YzOTf/6xhq2gS4KwjO/s989LS0EHA4CdvuQv8HTMshA7jZqq/7A5En7hl5Kv9/B7pJgVrJOl41EoiIu7tv5IQc9gzxQ8gDNg828dMpLP1oxBAIiNrMLfagKr9vPnpX19LYO0tU4QMAn0t1UOXTs/s8a8PuCdTIKpif8RycPhVTBslnL6LJ30WHv4MbNNyIg8Mqpr3Dtxmv59PaJXLzei/PoUZyHD6OfPZuo229HajQgyOXUzjwView4uVOW0te3g1E9f2Vz6yT2SVSMc3ayb88irl9Ywgc9gzj8AcY5ailVxrOhrYW70mIRBIGsrCxKSkoAOH3+XNZWv0u9GI7GL6dfLscZaUQEyvLzCcjGBLw+AAAgAElEQVRslKWkElJWxtX33MMVfUFf2uUZs7k071Q67IWoDEFDiOC3IRcjQASXdSQNG0eijSklftJzbN86DZl6AJOpkGGb1tH39HP0v/km3rY2bJs3o506BeW8RUh3SrBvbUeNBvuqFhymSHyddvTFCTS8tgNFjUC3qwWjPAzXMTMxc4eDRMDTOIAiXje0kvC02ehechjdhFhMC9L+Y+P7Q/g+s9IFwLhv2f4mcAD4r1EOoihi+bQWpAJIYGBNA6qMkJ/0oor+AB3PHEDs8yJEyPGZXUj8UjqEepp6yzj33oeHYqhjM4NfIPHD8obOjzgt+1vbFQSB6HOG4x5txbK6nsjT008wgbidPra+XUntgaAZxRChZtYVuah1wfj1ltY1BAIemluW4XS1kJ+3BIlEFkxka3kVnS6HUSPfoanpRaKjT0enC8potYQjl8sxKN2gUCDpDdD58e+wDfoRZesQAgF2x0eiaN3D1E37KI/RcyA2j2m9O5AqlaiyleRFldB44I+IQi8OUcYqtZyA34AsU0NHahImn0iaHZK6ZagV9dzkvwFvzkKec7bCgAPCMqBmI+TMHypX+c9wVVbScMbCr/vLmILlszo8LYN0ZC0DP/SZtxIZMQuA5uZXh4612SpJTLj8hDKfVreVF0tfxOwyU2Opoc5Sx42jbmRC3A9TQfc0D1K2o43Wyn4GepxEpRhwO3xYe5zEpBnJGhvN2AWp9LbYsPW7MISp+eLFoxz4vBGAxmN9DJ8WT+rIiB+81s8FuUROvD6eeH08m8/djIiIWqZmbupc3qn9hNaZU7j51idIarajHjnyhGfPcNpszG+8iX9ggNDQSZyafhovHnHzpmMkY9mP4HfzdOtSFlfsIM/Vgsxr56WYM7g3/TqaHC6SNCqys7MpKSlh5MiRmEwmpifKyGo7TqM/hC5PBDlyP31hYZTn55FhGaA7KRL5QRsNYQZkmhTsSilxzhZ+L2nnYUsB8rB1mOx+VAPtoE7BohnA5DBgdFRj7Sigdee1hKWvR5C6sFj3s7fjBSbc/Ecs77+PbfNmAHwdnWTOmcr6/U9g7zSjjjYx3DMB85cEjfbmXuTV0OqvJfR3GTjX92JyhNF2Xwlf9Y5fGSBsXiaa4RH0rw3Spth2t6ObEofMpDphDHwWN6Lb960sBP+X+D7lIBNF0fvPG0VR9Aj/LYG+X8JxpAd3rYWDvevxBFyM9y7AuroeWZgKX58LebwOf78b57Fe9NMTUOeF4+t1IIvUBM0zfhHLunrEPi9Onw11jw4pUta0LmXQ18/8W27/t5NrlMlGoq47Mct33xd76Oh9CW9ARu7cMCJicsgZcT5SmRyzeRd9fdvo7FqF0VhERPgMausepbT0ciRSJSpVLDZbBVkZD9FV5yMt609DL77f7+e1114DYPKWWrQZGRwRxuCXhqN1fkx/nAl3oId25Se8OxneK4rD0fF7rq58B2NuJMnjJdgdlXi9Cg55ojiUGU9dnwFJuxPf2HD8RiWj9GoeyohHUG2hoOcZzIoYVg6MYfXYMHh1Jti6QB0KbmtQOXzX2O3bF+yfjAxUI4sZWGsFrASkLkACBOjqWk1kxCw6Oj+lofEZIiPn0N39BQBJSVchiiJ7O/fydvnb7GzfSUAMEK4Ox+w089S0pyhOLP7B8dm7qp4DXzQiV0oJj9eRlB9Gzb4u5EopC24oID7765Wo1vi1ie/yv08mEBA5trWV4zvaWfPSMUbMTGT8wjQk//Bx0lppZvt71cy6Ip+wuP8bv8c/Rl0tHraYjc0b2d66ncq+Sh6b+hiF/zQtGGbOxLzsVWzbtmGcP5+M4dcz5YNNrOueTGfEu0QPOpDseZ4CQwJkzYLkyZxqG+ReP2xoqGBx3kiecAjsnHEmzSYdcwMiKSkpdHd3c9bYHB7f0YlU7OFgUZAob0A+gB49e8aPoy0+fkgOa2MrI21y0mgDz18Yc3AZn4QmkCv62KWyc6VDh1WTicTvRnvUhnKzjJqcZ9DPuhWT/w02W/eivV7AuCyAYfQ07Hv3Ivj9jMlJQDZrAurRRWy59RkyVYXIJHL8lXYGvH2EnZ1JxriJ9Err6fqglAFnHzHqFHpcbRh94Vg+qqXzi2MonEp6nM2EKGPoWXqU6BuL8Flc+PpcBJw+LCtrwS9iOj0N+75OQs/NQhb+fx/19p0+B0EQjgGniKLY9U/bo4CNoij+asjQ/y98Dq5aC36rG2W6ia4nDmJxdbHbvpqY9Cwi6iOI0wRNGyIiAl+/FBKDAnV+OPbd7QSipRgyY3BW9OLvcdFir4LRGqLLInFkunFGuQlPSiF5+I/j/v9HiKKI19uPQvHt5i1L9wC7dixAFdKCIKgRvyRDk0q1xMWdR0fHx3i9ZuTyEEYUvIpeP4z9B05ncLAcCD4T0dFn0LjpPBqPDzDj/DSypwT9GIeWLWNVS5CjR+YxoBtIQ+7TIyLSr1uFXxdCla6MsohgeKm74nbi3f3MN39O9qIGunvTaKgvoiI0lpL+REStDGmvG32IA3eiluLQRF6alQ/2PgJLCpG4+vmb/zxeCZxOxdQ9SHc/DVP+BB1HIKYApt0O30FF3XbzzTiOHCF08XM4j/YiKKUo00xYwjfTpHoSnS4Hu72O0aM/4eDBc9DpchlRsAy7vRaJVIVel83SY0t5+tDTKCQKzss+j7mpc8kJy8Hr934nN9E/YqDXydv37CFlRATFF2ajVMuGxhB+mCH1K/j9AXZ9UMuxra0oNTJ83gAh0Royx0RTWdKBud2OIVzFObeNRqX7v3d+ttvaqe6v5rH9j9Fua+f12a8zIvLr6CMxEKB2WjHqggLilzwDwJYXD3FpYwcLtZt50r+UgESHIA0geIPEevypnklbdxOvkPLmjNNI2FY61N5fO9/jonm34kKJ1+vloadfQBQC6PGjt/azLC7AWTYTUqSo7Q7q0wuJaS8nurOT4abTeMZxhMPxE3H3exCAYaEisUY52sYq4q0pKAJaslWbKPcNoHDNwmDcS+icd7D5BUwykTjNWTiPhqF64nVCLrqQ/jfeBCD2b4/RVnqY8vYmopXheFzRVFv3sHj5W8jkwXHw+7yU79hC1Y7tjJp/OpaOTnyHrMTagu/UMfVezA1NTI0+96tvliHIY7R47S4YCOYd6afGYzwt5WcZw3+JeE8QhIuAG4BbgK/IZQqBvwHPiqK4/GeR7mfAz60cvD0Ouh4/CIA8XoenzcbnzS8y4bKLyBw7kaU3XI7EFXQaO32D6OWhiEBMVDoj5dO+0Z7F00NZ/06kqRrOvusv+Dxe5ErFj5Klt3cLPT3rSUq6Crk8BJnMgCAI1NY+SlPzy4wdswad7kR7e2e9lcMlz6OIWUp68lMkJM8mEHDR37+HtvZ36evbBggUDH+Z0NDJSCTBB9jnswEigYAXv9+GVIjllZuCx4bIB0gpjKVqfw+OwFr6Q4yo7XEMGHtAEAl3WTkcEUqCzYfO3MHbmT30t1/AtPCjbO0ew5ntnzJm9GGanZNwOEx4pDLeUY+B3uDiVIaHRVMGeHt7OMsuLmKGbTV8fjMiAjcoH2C1NYXCWDUf2i+FtOlw7o97/Opmn4Y8IQVJ2Hko04wY56SiiNOxf/9C/AEnebmPs//A2UMO9tFFn2AwDB86v8PWwdxP5jI+djz3T7ifcPU3w3u/Dz0tg+x4r5qeVhvn3zsWfajqh0/6AVTt7aR6XyemSA2dDQN0NwajyfOnxlG+q53YdBPzry9AIpX8ZAX0r2DQM8jClQtx+V0k6ZOYmzqX83POB6DzgQewfPIpmSW7kahUeNpsPPfuXp7udbE1/kmSe/fjj5uBNDQMjq2ACz7kvto2XlXk8V5BKmcea+HF7DgeP7KfJHsjb43IhPQgQ+6WLVt4emMVKomX/FGjWHagjRnGjcQ7YqnIHcW2iER+t3sdcf09ZElHst2yjw9CZ3JeupcPqwQkBQaizGa6PNtRhm/hhoOL0Qf0ZKs3U1uTT1f0GHL7lvPu6H2kZUso0rmw2pXE7Pdi/FCKIiYeUQzga+84oT+qY01IkTF3/RYE2XcbZ3wuD5337UUkgP6WdA5+tgrZQT+p+uH0G3oZ1FhRSNQ4DDaOr99EprGIbOMYpBFqYm751vn8J+NfckiLoviGIAg9wANAPsHPyePAPaIorvlZJPuF4TO7GNjQhGlhOvhFBIUEQSoJcvh/CW+rjS5lM6JWIHdyMTKFgov/9hzNZaV01lWTO2U6G5c+j8fhYFBlobb/EEZFJDW6UpydFlxOO6IOpl5+ORljJiAIAnKlArenF6v1IBHhpwyVnPxnuFztHCu7lkDATXvHCgCMxiLy856iqfllAOrq/07B8ODfbnc35v7dbHi7huiid/Ba80lMmYcgCEgkciIiZhIefgpW60GkUi16fc4J15PJ/tEcEUrL8R5AwGSpod+UQfeBTpy6VtzqMLQD8WQ0NBKX7GSfv4vOsGQSbD6iOzqoUu/E7pwNARVbu8cQq+hg/MQDDPgLcDhMqKOq2OO8DLo95Gq7eM13LxJge+kM3hHOZozBCituBUGCMPsRFFVZRFpbuDKyESoHYPRifgxErxdPSwu2TAd6XTcx549DqpUzOFjOwOBRMjPuQa/PIy/vCcrKrkOjST1BMQC8XfF2sGTn2Lt+smKoLOlg0/IKBInAjItzfhbFAJA1NpqssdFD//c0D+L3BYhONRKVbGDT8gr2fdbAqNlJfPrEYexWNwtvHvWTkhl/CvQKPc/NeI4lh5ewrXUbFeYKko3JTIidgK54Ov3vvIu9pAR9cTGKOB0XXj2JFx9ewx99i1jCn1BrEwk7PQ3qNsM7i/hD4jQ+jYnjyYpKQEth3UcU9ffxbsxcHm5rRC7t5JbkKIqLi3GoIrh2ZRN79lrQCTIOSaWUx27g4qwYtF1rkWkkONyhDPa3k+73kmGrJ2L9eorCCtkacwaDRgP/y955h0dVpv3/c6bXzKT3XggJEDpILyoqIjbABmJ3Lbv27q6964prRVfXiiKKgqiANCkBQiAJKaT3XmYyvZ/fH4NBpKr47u77/r7XlSsz5zztPOfMuZ/nLt9buV1OSNRuakIsJPXkYpS1Eh4TSacYoCL0cq5c3cVbPiPpYWo00dU4prWBDDKj56ObOoX+0m0YppxJ9UVnYp/sRz+jB91qAXddDaqso9sKAaRKOaaLVtJjW4uiOpzJl69hG5+wruADvObgYsXtsONzuzFGx1LauxWHz8JITsfb7RhQLf1Rgv+44X0HhcD/CkFwNHi7HDiKuvB2OfD1OJFHaQi7fDD2wk7qrCWUmbczZdZCtqxZxmkXX4bsIAmZPjyC3KkzyZ0aZGu8/KmXCPgDyORyqgvy6aqv5YK5j9BaUcbaN5cw7oL5DJ40baBfp7OJ3QXn4/P1k552Nykph2ISPJ4eqmueJjHhSrq6vkMUfYwc8TE9PRvx+e20tX3G9h2TAAgPn05Pz0aczlbU6njKyu/EZNpB/ETwufREGe454sH5ybPoZNC6pwGA1EAxW3UynNpmBBEUrlBGlG4lvmk3VME4uYBULMZsNBLW18dHtwio2mLQCXb6RC2zB60jLNZC9Q95eLTdrFCXYXEGkMpgluY5oq0W+tFykXsl0phQ9KX5IEjgtlKQynl20xgkKhO0J4IhCZJPLtOau7ke/H7s0W0IuduRaoMxEJ1d3yII0gHSvKjIs8jMeIDIg0bpn/Bm8Zu8X/4+ZySfQawu9qT6/AlOm4ety6uJSgnhjKty/rAXM0Bk0iGKlOzTYmmtMlH4fSMlmw5RoOxb18j0hYOP1cTvxqCwQbw681V6nD1ct+46btt0GyvmrCBh3FgkBgP9K79CPz1om4nQKbl2TDuv7UziVr2UC8s7mefPRDX1XvjubqIaN/C6uZcLhy8hQyUjcfOTnDboOpYBSwIp0NDBdz1mvhiewemjBsHXwaj8WIkFb38uvRHbeLXoVbRyLeGyUMZIInC5uwhHzfi+3SCKZDgauC1Bz9LWXjaKMqx9g9mWuIELzFmsMT0MgEwOPp+Eoty7GdcEvTVWzAEvxlHfw6RNMGQQjaZPaQn7EEqfhkcPzYd1LjRUvU521iuHzZPNVonL1Up4+DS6u9fTZf0amcyAy9VKZ9cXzLj6Brj6hoHygYAfp8WC1hiKy25j2Z13AtDzz1JEv4ggEwi/bDCKxCNpcn4vjul7JwjCYkEQtgmCsFUQhCsPHnv8WOX/G6EaFAqn6QjYPCAG3Ru73ywGn0hV/x4cPgvfr3kNgLwzzj5mOxKJdEC3mDnmNCbOvwK5UkXK8FHc8OYHjJh17kDZQMBLefk9gIhanUJd/RIslhLc7k66u9dxoPKvdHR8xZ7CBbS0fkxkxJmEho4nM/MBBmc/SWLiVQBER89hUNajgEBb2zKs1nJMph2Eai+ladNdRKk+ZsT035dMvae2CyHQxIZBYTh1zSQ1NjGvrJh5TcuDguGnefSKPH/F9YT329kzJByzUk2sWWCh0sn1BgPjYwtxuzS4FGbseiVK8RyEbh+S0AYut3fREjaeldM3UqocwVzTe5D/WtDIHBILu95C5upDotAi6W+CvAUgOfTYigER8WDMhrvJgmVzM35bcNXVWbQCAH+EBHPIdvx+B16vma6uNRiN45DLgz7zgiCQlHQNavUhI+aPLT/yWtFrTEucxgPjHjjpObP2ufC6/RSsacDr9jNz0eA/VDAcDZPmZZI9IZak3DDOv30EQ6fGU769ndIfW09c+XciQh3BG6e/gT/gZ9mBZUgUCkIXLMD6ww84Cgpw1wapSRZPTOe8tO/YZ3XzsM/O5W/ksy92Hqe7n2Nn8o1M6C8i21bLrP5CcNu4SGVlY+sSCvZezcQQNWU2F8s7+lAq5Aw+mPBnpNHDtJRkXJ1nIxU1XBLzIvHJQUp5n9+GXhrBtNpKZpQ3Em/pYmhUKIsjdPijVPj6h2NR9fLhqKcxq4LCZsHEbxk2IwGJP9i+ge2ks5yuinn4PFqKS6+mpfVDYmMuJj3tTlKM15LivYy3bSOR1kroEjbxE8tQR8cqduTPZNfucyguuY7y8ruorn4CnS6byZN2o1Il0N+/74j5lEikaI3BWBCVVkfK+FHU2orxm91ItTJEv4jzQN8fci+Pt3M4WxTFSQCCIHwBvA9kHKf8fx1KN61n3cevcNrFlzLmvIuwftuEY1cnZqEbeayWq+94grVvvkJ8ds7ADQIQxQCtrZ9g7t9DYsIiQkJGAIFjqod+jvKKuzH3F5Az+HnCw6eyu2AuewoXIAgQCARfakplDG53B36/h4TEKw+rn552JyH6oURGzkIqVRIRMYPmlg+xO2oRBCkB83wc3T0MHvfb2SPddfX0vvMOfV15WMNa0UiczOtbTXhyBKqcbPa/WIZNL+WBq4fw1pJSvhs/FUX8GJidwstZD+OzDicxtgtFy3BqM9bzWUMSYe0jkQl+NvXG0OfJQCZ6mEQBEQE/zRNvZ/GobMh4Ed6eERzEuBvBbYXdSyH7XDj3Zdj+Moy94bCx9i07EIxAvWEYfR9X4O/34CjpRD9exFSyFiUQ2XsDrZmvsXnLIR+KzMyHjnrtoiiy7MAyXtjzAskhybw09aWTMjo3H+hj/6YW6ot7UGpkeJw+cibHExb3P+t+CKDUyJm56NAuITbDgKnTwZZPKind0srpVw0mIuHUrzR/Qow2hnGx49jcvJm7x9xNyJULMX/+OY0LFwGQXVaK0TiWuRk3cdnowRQtz+bZjn4ueH0HkMA99QZ+lMDfat9gjLUUEkYj3b2UnIPtf7HpPGaNfZ8vO81cnxjFksvH8NGyz7h41unEJ6fzr6clmPsm8vIBM6eNDUXqtuBRSTCIalwqLb2aENK7LLz9lys4866b8Y5IwN86CrlpNzFGF8vzlpBgV3NtVTkjT88hMGkk1ZtrMSln0usHY281psqpRA79ls7yeCyva2m4pJ7EVWm4vBEw6Es6nRIi0h30m/eiUERSXnEPWm0GmRkPYrdXD6iJR+a+hEQiQ6vNwO4ICk6v14zVWobROHbAHmgy7aSs/C5CBo1my3e1xM8diVVtI+DyETvj2NQuvwfHEw7Kg6R7UkB5nHL/tRg8eTqtB8rJX7GM/BXLGDRmMqMyz6BgywfkXjSL0Nh4Lnn0WQCs1gqamt5BoQinq/t7XK5WQKCr6zsUikgkEgXD8/6JRnNsLwKzeQ+dnatJSbmZ2NggZfPo0Stoanwbr89MR8dXAGRlPkxd/cuEh00h1DjmsDakUhUxMcEsan5/gAjtnfT1nU939zrCwibTsUvAEKlGpf1t3iqi10vT4sV4u7qxTBuDU+Ml3lOBJ3EEFtd6bPnVKC0RvHNuIlXZD7Lgr58hercwp28zjyfvxC8JEOg5jblZTTQ1S6jSFTKhfSouv4uNGT56Qseg6XLh6/HwnGUjJbJkho44M9h53EgYdglEZUPSeNjxKrjMMOl20EXCrCcPG6vf4sG5vweAnn+V4e/3oB4STu/bj2J6vQS5QkQM0RI/aR6yOB8ebx9qVQL6kCFHzfXs9Xt5JP8RVtWuYlL8JO4fe/8JBUMgILJ3bSO7vq5DrpQybEYC1l4XHqePCRf+ZwQ0SaQSpl+RzYpn99DbamPVkiKmXDKI4g3N2EwuZi7OIWFQ6Ikb+hWYlTKLh7Y/xLiPxyGVSFl0/SimPxuME3BXVqLKycFgGEm/+SUyz4Q/dQ6jrDcbgyaEVVXDcMYOYropGPmMywwIgBi0N+37iMmta3krdi7uQICs5Hgeuec2JAd3lEsXjuIf3xfT1dNHUa2cKFk1/shh2LZ9w/Zp0wC44KvVlDqNVOx4jsu9Z7A95xw6Kq+godVPrvI9mtKqeM4Vxb3r72Xo5fmU5h+cHyloXZW0l59DhUlGVMccTBEgX7WcTk3QHX3KgWh+TOvgQq+PvTsvAb8IKimZ/psQtvcTP+9RdLpsECQDv2+tJh2TaQfl5XfT2bWGQMBNWNhkhue9h81Wzv7SW/B6TfikG1Fos9nw5VvY+noBEKUBhkw/45TePzi+cHgceDV4R/iJinL1KR/BvxEyuZxZf/oLyXkj2L1yOZUFW6lkKwjCYTaCQMBNyf4bcbmCHEiCICc+/grS026nsuoR7PYa7PYa9u69nOHD3xsIFPs5eno2UbL/RhSKKFKSD9kYVMoYsrKCOs6YmAuorX2e8PApREbOOu7KXxRFVr1cRFu1mRHn341g/JSszL9R/GkbcZnGY9Y7Eez5+fi6upDf/hiOqiBb6fC1XXjt9cz/cxyzawLMEUR2ZkUTYtmM6P0GgNXGj5H5FXjb5pJidtBTnUl96H7OzphJf3s/vpREGnWjCahluLtdRMkslOomoTz78QHaZwQBLnzrp0mHXW9B8iRIONJGIgZEej4oA4kAARFPgwVpmArteCNdT5QAIPEIKAcPI2RKIiHcc9zr9vg9PLLjEVbXreamvJu4Ie+Gk4p43rUqmNgoZVgEs67LRSY/8e7x34GQCDWX/W08VpOLb14tZu3bpciVUhDgx2WVXPrXcac0+vrctHP5svpLavtrSTOk8XbPDpbfJOWN1/049hSiyskhMeFK+vsLMYoTONMazcUzEyhpXMrqqmHsJYLThEoc469Al/8RTH8Itv09aIta8DHD1y7BGzOXMpuTkSHaAcEAcGZuDFPSjTzx8pt86gxnR2Id5zblsH3SpIEyrTFRxHm9mBtFZk5ejso8lH9OTyZ6Ux0NvsXk2t5ieWQjKrmGK767k7D4B+hrtWMMDdDnm4zRVEVAOBRfI2jmD3wOd+awLbOLK7+OIzDCiSQgQ7nSRGfLveDzISiUJF50uEZAp88hEPDQ1b2O2JgLQZDQ2voxPb0baWx8E4mgID3tbmrrnidtQhwV62tIzBmKXKVCpvxj1u7H81YqBOb/4tgnf8go/o0QBIHBE6eSPWEKa155nsodP5J3xjnoww95prS1f4HL1UJuzt8xGEahUsUN1B2SGwwUt9kq2bvvCnYXzGHIkH+gUadQVHQVKnU8qSm3Ul//CqLoIzfnBaTSo+ugw8MmER426ajnRFHE7w0gUwRfPo2lvbQdpJrevyaVq55fQ3+3E7u5/jcLB1dVFR1PPokkNJSifSacUZ3YhU509mASnaWvBI2bFfEK3MoilOYiFAE5Z3ZcwE7vATJaVawPHU+SciO99lBqk/KZ5LmMfezlS1MW/mQlU5wS9vY6uFi9h5l3fDCQ4e0wBPyw7BLob4IZR1f/uA704W2xEXpxJs79Pdh3FeGpWov3wOEuwoZZp5/wup0+J4u/X0x5bzm3DL+FG/JuOGEdCNoXin9oJn1kFLOuzf0f58X5tVDp5Kh0ci59eBz1Jd3Ephvpbray7p0yavd1k5QbRlu1mbgMIwr176OilkqkvH/2IXdjp8/JxGUTsYcLOAoLCVu0kOjo2RhDxyFz6WnfVIBQ6EAfZyNZ38o3tnQmitvxHDiYUCn3AmjcBtXr4KxnGKV8CYDP2/tIUimx+/0kqw+9JFUqFddecj4ln6ylXulnd6Ka4Z0B7Imga/XSnpDEiKYG8rtjUCjaUJdtQ54Xj2NYBEKRjbLOmzgtZQsf8C1ntuxg3Ew/hZu1WPvcOGURZLCOPoJqSr3/AFZpNlGde+iLSEIiTULphU/PnsVLcx7Ctm07zX+/FvAB4Covh4sOT/YUHXUuanUiet0QpFIlgYCP7u511Na+gN1eRXra3SQkLKS+YQlROQE6yhI46+bb0YdH/s97KwmCsBp4C1j7y0hpQRDSgMVAgyiK7x6l+n8dBEFg9p/vZsycC4lIOpy0rrX1E/T6XKKj5xzzRuh0gxg3dg0lJTdQWvpnQEQU/bg9nRQVB43IGRn3ExZ2cp42P0fZ1lYKv2/E2usiPF7L0GkJ/PhZFVqjkulXZPPNq8WUbmnF1OlAkAik/wZ6BTEQoOXWP+M390g9hkYAACAASURBVCO59E/UtjoRBT9W95FGsrJkHy7NREb0epnQMZ6v3OFMaj7AlvAxSEUfg5MsWPsayLVmsWNXKc3KGALDo0iSSpngaGc3UuaMH3J0wQBQtjL4EhCkkH3OUYvYCzqQhCjQjIhGFimn980bCfT34gYErZ6uG/sIrxhL2FWXHfOafQEfW5q3sLxqOeW95bw49UXOTDnzxHMliuz5toGK7UH/9okXZ/zHC4afQ6WTM3hCcIETEqkmNKae/JU17F4twdThIDJJz7z7Rp/Sa1LL1EyMn0hF/A5CCgsHyB2VighQgOHsVPpX1xFhu5ihhgZWtUzhSfUnhJksmMQ4FJokXEnJhNdthvKviB23mGuLV/AOF/NeWy8KQaBpWt5hfaakpPDUwnlcsv5tnJI6Vs24FWoXMlI1EmVsMuO3byXaH46kREZOupqlaVHcWt+FbKRAbKNIUfFktEkbWWbUc9uGm8mwjWCH7QoA9ofNJ8RcgyiXMjfhBQqrzyFdXE+BcD1WXQLziqezWR6MvVJlH9IkSAwGPM1HEF0jkcgwGkYd9j06+lyam99DIlETEzMXmUxLaOhE7PZirnpp8x+eke54++brgCnAAUEQCgRB+FYQhI2CINQRFBqFf6RgEAThLEEQKgVBqBEE4b4/qp9f9El0WgZS2SE9s9Vagc1WQWzsxSe8GUplFMOGvYlSGY0o+hk69HWG5/2L+LhLSU7+E4kJv57jv/lAH5s/rkRrUDDm3FQcFg+bP65Eo1cw/4ExJOWEET8olPyVtRzY0c6QqfGo9ScXYAfg8/loa2ujf9Mm9mlnUjzsZnbvbsOl6UAu1JDYYcEvCFxzxj1En2tmf2oS60cKeLQzeK7xaspcKbT7YL9+CM3qRGYmbsNtiSRAgANR6Xwy6Uw2jR+PRi7hxdQ43tlrZ7KsjCHTFxx7UAX/DBLpPdwDyiMNpwGHF1eVCc2wSASpgGPnBgL9vcQ99yzSsDDkV0/Gmy4SdfVNx71nz+x+hts230Z5bzl/GfmXkxIMALV7u9m9uh6ZQsKZ1+aesviFfwckEoHR56Rg6XFh6nCQmhdBd5OVqoLOE1f+lZidNpvCeBf+nh76/nV4EKN+YjyRNwwjbeifuWTMTdjRkC9eQQ96/uxdxEVLtrLPuwaLTob41a349Vnc3/cdQw6qej2iiDtwZP6xnPhQBG8UZl8HT0UG8350ajrxSyVsnTKZzkFDCflQR16qjrPTk7lVK8EUFoJvkI4ei8Ag/ems02qQ0Mg/4+t+1rIMizEDqzaVz53vkvXEY0R/Xk6pKgOHOoqkjjA6xEY8Pg+yiKAWQpGcjHb8eOw/bqXm9DPoevn49HRpqbeRlno7ecOWolIFXakjI07H5WrBZjtw3LqnAsdTK3UA9wD3CIKQAsQSzARXJYqi448clBB0+3mNYL6IFqBAEIRVoiiWH7/mr0N//z7q6l5GrUkie9DRvXQ7OlYiCHJioo/N3/NzKJXRjB+3FperFa026NwVHj75V49NDIggQMW2NtQhCubePgKZXEp8lpGNH1RwxtW5aEKCQmDOLXnUFHYiSAQyRkWdVPuOggLK9hSysa0Vp1SK3mLBm5GKwq3EExqKIHbQErmPmTtFGvWxtGmjOF/7Fp1nfoNc30OkPw1BdFGo9eO2qyk2DCNJ1cFZSZso6TmXA/EC29OGkuxzoa9zkxcdwpN7SlGKLp4d0g6Kn6nWemuh5LOg4dnnguZdwc+So69dnKW94BfRDA/ukGwbNyJPTiJkzhz0585m1+6z0UuGEJF75C6t1lzL1pataOQallcuZ8GgBdw39j5kkuOrUbxuP06rB7lSyo+fVmKM1nDJX8cdxm/034qM0dG4HT60RiWpwyJY/nQBu76uIyk3bICg8VTg9KTTeXdyFnuqKxn+wnNsS/Nw1qQrB1KRKlMNKFMNjPH50aws5knXWZRzBvEqO639Lg6YM1HlHuC03Q58Xz2PdtrtrP16IcuH38PthrOpc7gZrDucc0giEYhQJNDtL+W7A6vQyXX4IrQEukQ6YoMv3PLcHCK//BhFeDRXDb2YpwtqOKCWoAiRU1Kdiy/yGy6Ni6ZL2ku0ugPUtYSZJpI7OY6yH9twuwJ8/XIRWWOjiRf0eASQe2VIAkr+WfQlo+LT6PrwQc7InsPav/2JTMDb0kLvm2+hmzIFzcijU3nLZDpSU2857FhE5OlQ+RBd3d8fEcR6qnFSHMOiKDaIopgvimLRHy0YDmIsUCOKYp0Y5DX4FJh7qjsJiD76TNtobf2E3r5tA8f7+rbT2vYZfr+Lru51hIdPQS4/eW8OqVQ9IBhOFg6Lhw3vl9NZb8Hj8vH+AzvYt66J1mozCYNCBwyd8VmhLHxiAjFphkP9ySUMGh9L1tgYJNLj39KAx4M9P5/iW27l2/Y2VP39jLDbkWsMuJUWLKHluDQd5FJFkRaSu6XUGeN5aPZget1yVNpavKpspnd4qXY0Yrb5yHLVcLnrUx6Y9BxuR3Bc+wJjESUSXs3LpbrOzIr8Jg50WHlB9gZxOT/LnhUIBN1XtzwLT8bA8itB9EPW0bOoiKKIvbATWbgKebwO0ePBXlCAbuJEBEHAZNqOw1FLYtJVR901PLnrSV4sfJHHdz5OujGdO0bdcULBALDpwwo+fCifd+/ehtPqZei0hP8VggGCL9Ch0xJIGx6JIBGYcskgHBYPHz2UT9EPR6pAfitkEhmvn/02njuvxi8TML/4Mgu/XYjDe/grRSGTMk6jppwAIHDvhBUoJV6Ku0/DJzFgUYciMRcTyL0U6cyHGFb9OQBV7bXBfBK/QG50FoLcyjbzNqYlTmNGykTMCvPA+aakJKw7jBSs/zsd7S8zwdoDgoA2PQRTTygR6mF0yWTE9CmpDX+JVZmfEq7pob/Dxvi5qeQYdxKiMlO1uxNPMH0DoiAwzBzL62VPcs26a7i//FnO+XYB70YGnSW0EycijYyg64UXEf3+k55DpSKCsNCJdLR/id/vpL+/CIej4dfdiJPEf06W88MRDzT/7HvLwWMDEAThekEQ9giCsKe7u/s3dWI0jGba1DIUighaWoIkWl1d37OvaBEHDjxA4d4FuFzNhBqPxlx+avHjp5UcyO9g9+o6yn5sw252k7+yFke/5zcbmPfs2cOGDRvwH3z4AnY7ey+6gR//+jZbp01FIQhcdv75zH3+eTI1E4nozkPntSLDw+qYA8gcAjq7n8awEMzNpVxpcxJQduFR5XBWF3wqcxMQpIztKSAvqwy5xIfTkQiClEB0BKlKBSU1wQCd5HANTw3rYbq0CFJ+tpPqKj/oqngQ9VuCbKtH8VACcNea8TRa0E2MRxAE3DU1iA4HmtHB8p2d3yCThRAddbitYn/3fgo6CijoKGBRziKemfwMH53zERr5iQPUXHYv1Qcpz5UaGbOuG8LQqfEnqPXfi9h0AxfdM4rIJD35K2txHgwqPBWIUEdwzfS7SLj5L4ytEjnz3VJ2P3obfpvtsHJ3T00nVa3gzxO70cr2khjSREtXFsrebPoi7ciFZuzF5TD2etLdHSgCHorzP4SNR2oAJicHqe9FRFKdqWQJSeyO2kWLQcNUyTB8cjn+sCgcm8NoKn+fxzJ6uLlgHVd2FIBSSnf7XEZ50jmrPpP4kCm4JBJEyT46anrIFJYzXfUs8ydv44K7RnLxNVrkAQdOdSSLKg1c2S/hISGaUD90uVqpjRO46y8jiV+yhMibb8G5dy8HcofQ9tBDJy0kEhMX43K3sXnLMPYUXkRT8x+j3f+vzY4tiuJSYCkEifd+SxuCICCVqoiNnUdj41tYrRU0Nr6FRpNOWNhEWlo+AMBgOHYGp1MBj8tHQ0nQZ7ml0oS523nY+czRJ6cq+gmi10vLO+/wTWdQb5ycnExGRgZtqzezK34hVn0Nbk071113HTpBzvc3v02dP53kzl1UZH7JamMYLkmA8wuGA4W0ZDSyzvsY+sEGRKTIJUPZanXwgyKWQUIL8QkWQtMtpKTcTF2dhp5BUdgNcs6OMvLV93XkxoWw5s+TYcU1EJIQjHz+CfU/Bv//uQj0sbDzNUgYe9T8DKIoYlnXiNSgQDs2yC3kqgwmdFFmD8Zs3kN7xxfERM9FIjmkDmmztXHZt0HDtFqm5vph12NQGo5o/4j+AiLr3yuncX8PgkTg/NuHE5kcglzxn+mueioRmaRn8oIsPn18NxXb21GoZcRlGgmLPTVBfWFXXollzbdMrKiEiq10Bp4m7qlDcSw5U5PZNDWZ3t4fKSqGRH0bm8zpXFlyBXckOUlnCx2V96Eb9Q2qjOmMtFaRbxgOxY/DjIfBaQKJDFQhTE7Kgz0g8Rhp2d5Kg9CEM83FpuRuJjVMQt+5l8qcIQAI3x0gPOk7Fl/6BO+++y5pMRLqGrXcdef71I628JfP9mLQbKUowkSSVYF360cgB2XHduIuMsJrfyFOcQGNxjHQL2Fh+2ai+xqYLpVikUhYEmqgWFmPVKfFeNGF9H30IZ6aWvpXfIEsLJyoO24/Yq4CLheiy4XUGFwkRkRMZ8SIj+jt3YJGk0p01OxTck9+iZPaOQiCoBYE4fclHPh1aAUSf/Y94eCxPwTJSdchlxvZXXAuFmsJCQkLiYk+b+C8Xp97nNq/Hy0VJvy+ABMvziDgF7F0Oxk3N43z7xjBRfeMQvkrcs+6a2upnjqNsq++GjjW0NCA22ZjTcF+rCGVuDTtRMjjiYuLY81Tm2hwxxHXtYu61BV8EKEjwh3BwsJHya7VEQDqkhoQcWPSiljDb2DhXgcfiB5GRe3j9hkvkXVmGbEJ55IQfzNlZicrotMAOEOppqSlnwtHHqSlaNsXzPP8c3SWgi4GwlJBroLJd0LqkTYab4cd64YmPE1W9DOSEGTBR9ddWYmgVCLE6SnZfyNSqY7ExMWH1V1RtWLg87VDrz0pwQDQcsBEdUEnHpefceelEpcZ+n9CMPyE8HgdUcnB3cOWTypZ8cweXPYjUrz8JkiUSlJXfE7V0jtYPVbAvHIl3tYjf+KhoaeRnnY3UwzB7HweJLzRcgUiIPUU0fzoBsQ5r3Ha4NPYFzKYOWn3s6m+nLu+eh3Le0E7YZw+junqV+mvvYetuqms8+SRLc1BY9vIE7ku9qYOGeivJSQJa0U+MTE6xo0bR5IpuPh4c18rL66vAWSIvSPZZgjS0X9s/RN3i3ex2dIO7SWI3Qc4K/x5clRb6Dek81Hj3cxwv0CU30+G10uq14dFasPt9SDI5aR+9hnZJcUY58+nd+lSul58EfEXhvX2Bx6kavxptD3wIC1/uQ1fdzdhoaeRmXEf8XELfkGYeepwQuEgCMIcoAj4/uD34YIgrPpDRnMIBUCmIAipgiAogEuAP6xPudxAZuaDAGg0GcTHzUevH0pE+Azyhr0zEML+R6Gt2oxULmHo1ARCY7UgEDRuZYUeZls4EQIuF42LF+MxW+nMyEDhdhMFVGzcyPKbbqZd78Kl7gRExNZIuqu7iNz3JVO33cWY8EI+HCIwtRvSqxZB3RqGN25hc2Y8TqVAsvI2+uJfIsSZjN+mJSDAn6dJWCq9ma+0T5I06EVaWlqoC4sGYP3oLLaUdiKVCJyXFwf7V0BfLSSOO3zQpgYISzv+dTl9dC8twfJDE7IoDdpR0QPn3NXVKDMyaO/8Aq/XxKiRy45gVi3pKSE3PJdvLviG64ddf9LzWbyxGZVOzuJnJjLqrJSTrve/CXmnJyKVS4jNMOD1+Fn9j2J2ra7D2uf63W0LcjlnnHY5m0/TISKy9ZUHqTPXHVZGIpGTknIjC8+dzpdj03nz0hH0B7TY9ckYrG7shv14+6RcfpBDqcAwlEsbvXwUey6LYxZjNwfdjW+aMgKFVEZ9r5OegAZaxoHoJdq9ho2ZmcSphpEgglOjoWtnIt3dGxg3bhzJ7h4kCgnfbKynqc+BqJJi6z8Dj9CHW+pA6skmpXMiO8030f3qlZQ4Z1KpFMjTv4/e3oIqZDBz1IMoc51Nuflscv0G/ILI5M8mU9lXiUSrRVAoiHn4IYwLFtD79jv0vPrqwPUHnE4s69cD0P/ll1jXrsW+azfO/ftxV1f/7ntwPJzMzuERggZiM4AoikXAqck0cQyIougDbgHWAhXAclEUy051P36Lha4lSwh4PMTGnM/ECVsZO+YrJBIlEomMvLy3iYg4cZav34vWahMxqSFI5RIuvHMk1700hZDwX5/pyfL99+wPO4utU16gIyGRBK+X2H1F9CoU1KalonLEcmaDi9n6JKQeJRWLbyWqpxipDqxbyxhSKzBsr5FhZT8wu24r6xNH886ZIUilseyJHkWIIHLrj+vIj9cTHSKwy3gVO4QpfO7IZtrOcm5r7GFvUhaj9GqG6NR8X9rOhPRwIt3NsPLGYLTz2F+8nPvqITTluNdlL+wk4PBhPD+d6FuHD+waADyNjShSUmhr/wKDYSR6fc5hdUVRpKqvikFhg0gOSf5l08dE84E+Gkt7GX56Ilrj/0r2mJNC1pgYbvzHNC68axRZY6LparCwZ00Dq18pIhD4Tdrcw6CRa5gw4jy25QhEfLOLW96/iB1tO44oJzOqGHlhNnkpQceQhrAphJu8ZNieQrJyPrrvK/gyOYFcf1A9O9RaxQ7jCN7ZH6TgyI4JYdWtE9ly9zQS9FKa+qOYHDMJi2kdod1PURgbw5wJl6C3WKjUjqVw1+MEAuWoVEoSjUEVZ0Ajw5emJxBQsyB+EVsyPqVLX0+Lbi/6/rEs732Rbf03c4duMr0qG9E54QAo2nxsNl/PJtf12LcuYERfAk6/g+vXX4/dGwwwFeRyYh75GyFz5tDz9tv4rVYAbNu2gddL0nvvEvvM0wB4W5ppmDefujmHtBt/BE5GOHhFUez/xbHf/1ScAKIofiuKYpYoiumiKD554hq/HrYtW+h9403a7r0XAJUqDqn0t6ff8zQ1Yd+xY4B58mTQ3+2gp9lG0pDgg6TSyX91dKp91276V6+m9Z+f0BF7GhZ9I063i4ywMJKaDnmbhJg1ZE7LJnfWGCbsepjI3hLao8fSN1zAoRO554sAk/f2MLF5JwD/mDQPb0gTNnXwhTvz+2XUJI2lOM9I15gY3mvtYajbxgV7tyBaLewPSMl2Wlg6JJWqThsNvQ7OzI0JBrQFvHDBG0HV0U/wusDaFlQpHQPuOjP939QhDVehGx+HcNBrSxRFqiuextvWSiBKjsNRS2zsxUfU73H2YHKbyArNOuLcsSAGRH5cVoUxWsOwGYknrvB/BJPnZzF9YTYzFg3G1OGgqaz3lLR7x6g7GPf4q8iVam5cL+GvPz6ExWM5atmYEBWxBhWf+4KqR2nAg6xvB/6SF0j+VyWrpkxiW2gz66dPYbqtnPdchoGER9kxISSHa5maFUFnQMc84yKuyr0KqbuK7eHN0OxhpsGAKJFQUTKZ0rK7iYzUM7u7kCu8O4lIglRvFyLweUkec8bdzm2PnM+MG4bSpW0cGON55bewoesZahpVKAIOJH4PEr8HpbObXsNgbv4Obtw2A59J4NXCd1hX0YTPH0AQBBQXXgxeHx8u+RQA2w8/IDUY0IwejWHuXCR6PaaPDxFVBA6yF/wROBnhUCYIwmWAVBCETEEQ/gEcKdr/C2GYM4fw667F+v1avG1tv6st29Zt1J4zm6arr6FhwSX4LUd/uH+JA/nBwJzjxSfYd++m9Y47aH/4YZr/dBOi55D3iLe9nabFi2m7+x66+pX4pA6cmla01jCy4uPRuFxckDsUrS2JhJYyVKp+2m64EqXEQm9oNip1DR3o2TQ8uDpamTOLbrWB986+COc4LwJuvMpsZuWvZIq6mD3pmQA4ZRJMPj/JNWUkuGzM3bWexdu+4X51gPjyT3nqmxJ0Shln5cZA43YwJoPxF+yRnaXB/z9TK4miiGNfF5YNTdj3dtK9dD/AEcnVzebdtBa/AwGRLtlWpFLdER5KAGW9wQ1ndtixk678Es0VfZg7HYw+J+X/lI3hRFDp5ORMjCNrbDS6UCU7v67D7z0y8OzXQiPXMDxnBjH33kdmjYMR+d3MWzWPG9bfwNO7nqbd1s6Gxg0MfX8ova5e5uTF8XG9nrb5n1AwdhK9Rjk66beIHjcql0DG8Dmgj2G2QUKHzEBt+8HFWkcp2HuYPz6dABK+LuzgmqHXICDB4S0lv89G1pz5jCosxCFGUF6STndTMw6lF5leyuLi70hx9eBP02N2+XhiVQdVndDfn0PzmL30atqojQoGp9kkkBOxgwV35jK48iMCUgVeRQgelZE9qXei8E3jsqK/8t3eb3lg+zxevn8Nn325jvMqH8aikmLdspmC2i6sm7fQO2wst31RyswXt9CrDcX3M+9Mx76i3z3/x8LJCIdbgVzADXwC9AO3/WEj+h+GccECEEW6l7yCp6HhN7fT969/IQ0JIfSKKwjYbJhXfHFEGVEUaSztZcMHFXQ3W+luslK8oZn0EZHHVCOJXi9Ni67E8u13mD9fgW3TJkwrDhlYLd9+C6JI7BOP45pwHl5FOwigcmbR/dnXIIro8veisaUQ11VAy2Nv4DM7+MfpOt6b3EFnkkhGWh0fTJTy8ux0lmadzl9ueArx2kXEtn0LgFeVzXXxs5mc8iQHQmRIGw+5Hcb1tHPxxYdW7CNC7TR+9ThbaszcODWNyJoVcOCbo8ctFC8DmWog7SOA5Ycm+j6rxLK+EdPyKmSRarRjYgg5uIL3es1U1zzD3n2XIesKCjS30cbQIf9AJjsymrq4uxiZICMnPOeIc0eDz+Nn6/Jq9GEq0kf+ehqS/wuQyiRMuSSL3hYb3y/dj8NyalxdjfPnocobxmVlRuweK32uPr4u+5ylj17Ei5uCmXTKe8u5ZEwiIvB6VRLDkt6nX5iJ0utDJd+Apym4KKusegRDIMgTuqu6ALxOeHMiPJ/OsCg5qSHwdZOMV7+vJzc8D61zJ0/kKKgo8xGZnkpyQwOdPem4JcHfpcLtximTk9zVSmyihP4pUajVMhYs3cldK0pobL+Qh1+8nKqIIbyftoplw5YwUv4aIZlJjF1yJxFCF3K1IhjDI0jwHHSKmFZ7GTNqrkBt0dKzTobT46Q8OcAw0wG2fLmRQH8/b0qtfF1SS12PnWqCxmd5chJIpVi++xbR5zsl8/9LHFc4HIxUfkwUxQdFURxz8O8hURR/vzXqPwSKhATUI0fS//XX1J47B2dZGZ7GRnwm00m34bdYsOfnY5w/j5iHHkSVk4P1hx+OKFe6pZVvXi3mwI52lj9ZwPKnCpArpUy46NgBc87i4sO+KzMz6Hzscfo++AC/1Ur/mjWohg7FePHF9AcMiJIeQvV6FB4f3pZgqIhz3bforM1obW1oY92UX3U6ac2x5DSGMT2+nJV6LQgS6gyXAgJTYlehKp+PS7obVSAbNQa6On0sb3MQkApITG7ezU3hPp8JmURCamoql156KWPGjCG0t5CvAhMRELkwzgRr7oDUqXDm40H3wrKV4LFDayHs+xhyLwR10EXPtrMN64YmNCOjiLgqF3VeJJHXDSX0okwUCXpEUaSs/A6amt4GIMYe9GAZc9EPhIdPOer8lXSXkBWWhVp2curCXavrMXc6mLEo+z+WYfU/Aal5kUy9NIvG0l6WPbrr1BioBYGwRYtQtfTwted6Pp/zOf+smsT81SYWfhakZm+0NJIWqWPh+GQ+2tnEXpWIasbl2NVSlCFvUdJ2NQ5HAy0tH6EO7MTgs1LYbQnuXn/qp6WA968aQ7Lcytv5rTQ1DAVvB1bJAe7Teci55wky+3pIrK8j29CCoXEX0W0tODUa4sRy5pXlc3akAcfgQ84itZ0OqrtsVHfZ6XWl4JHAHjWITbvQjBjBgjcu4epnJzB1xz2E24M7GZ21hUh7IsnmXOySoHpoket5urKjiLI5iFi3Er9EQsWIApKGvMMLlyRQERrcfUu0WhQpKfR/8SXdJ6Dh+K04rnAQRdEPHJ0m9H8Rou+/H8PcueDz0fnU09TOOovm64/OzNlU3sv2FdUDekwIuo8SCKDOCxJ/6WbMwLlvH76+QxmaAgGRXavrSMgO5bJHxjFkajzj5qax4KGxhEQc+8Vl37EDJBKiH3qImMcexXhwld751NM0Lb4Kd3kFIbPPIeAPYLJI8CjdJKakkOovR+6x0RE9BqnPxei9z4EAT5wr56mITegNJoTIPhbGxbA8RI/WnkaNOxRfuo5Bmr185/AT3avGnHgbdqnATWM0bMrWQUDkjTOzGWTtRSgvISUlBXdAICYxjdmzZ0NjPl/5JzFeUk7cp2eAygAXvQMyJWx5Dj5fDE/FBaOifc6g6yoQcPno/74RZYaR0IuzUA0KI/zSbKQhh4zBJtMOenu3kJnxIGNGr0RRL0ORkY7cePTczr6Aj/09+8mLzDvq+V+io66f4h+ayJ0ST0J22EnV+b+MIVMTmHf/GHy+APkrT97OdjyEnHMOuhkz6H7p77irq5GuDTIXDK8XiTKJ1JqD/dx3djbxRjUvra8mPGs8TQlq9HYvMlsR+TtnktxsJ7ldRbqngUqZHv+Ojw510lVBUmwUr10yjCnyWlpbs9DJjAz2bWG/UcoPrVZi//EqZlcfpkobAYcUqcwHgoC31YvcXU7kN8vRRilxTYvBPSEKUQIL3tqJ2xcgVjsUfGrWajQ4dn040K1Erab/kc8xGTIYlClg0x/KPBiFm4hY0FY7EEZMIyDAuOYSymK1eJQ6/IKVT5oepWXqGTgUasIWLSL0ssuCeTHOP/+UzP0vcTJqpX2CIKwSBGGhIAgX/vT3h4zm3wCX1093XBqxzzyNbuZMnIWFweP79+OuO9ytzlVTw+pXiin6oZmuRuvAcU99AwDK1KBhVTd9Gogitk2bBsr0tthw230MnhBLaIyWqZcOYvTZKQP8SD+HY88eet5+m4Ddjn37DlRDh6CbPw/N3LkY588n+sEHQmrCmQAAIABJREFUMVxwAa6yoD7dcN55tNf24xP8eBUQo3AwxPImAIGD2ekkYoBApIf8cCVuiQRrbgvGlD4aFEE33Y6OC/HGa/BlGHjOeR5uoCL3IazSQ7r+AqMEpdWHsq2cDz/8EJPJRNbgIZz/2nbGPPkDe+p7Ke50US/GcoHkIB3JdRtBd9Ce0nIweYsgBU0EXPopRAR3Ta4DfYguHyGnJx2TDbS+/h8oFdHEx1+ONpCEc+9e1MOHH7UsQI25BqfPybDIYccs8xN8Hj8b3q9AG6r8j0nS89+AyCQ9uZPiqC3swmZy/+72BEEg5uGHED0e2h/+K6LTSfQjfwNgXlsCX1R/wXf136GUSbhyQjKFjSa6HTrCp7+BX6FhSKmElCYXGfUOMqtbGOItpVKbjKRuJevGP0pVWF4wKh/Izs5miMGLUgStfQy1pp1E2Oq53m3mvGYbOTPPxmZ2IIkWMUYHI+RFv5pASTchPW1c01LBORqRoVjwDTJgcgTVa/VGBR7rEDZpNPgqvsBjPbRIDI3WEPCJdHUcUsVpbS3YA2EIxXuReK0YSwexa1DwN7BnkI/skLE8N+U5qk3V7A99gHnn30LhoNOY3RTNvoeWoMz4YxJ0noxwUAG9wAxgzsG/c49b478Eoiiy+L3dTHl+E+9tb6Aq/vA4v+7dhQOf/f39lM6/duB78Q9NrH+vjK/+vhdXXQPI5cgTDq4EMgfhjUqg5eFH6F+zBoDWqqCa6nhUGAGPh64lS2hcfBXdL75E5zPP4Ny/H+2ECSxfvpx33nmHgFxO2MIriHnkb3ivWoz88cdwuSWse30v0kDQuK3f8hpeswwQGZ3yLbqEIHdN/oygbjLequRfxhD+HqkgxB/AUX8jhtAExEFBnb3CtR+/NBK/IriF/XOVmyujw5D6RIbZRNoOGu9TUlJY266gttuOxx/gw21VrHSOQCkJcNb5C+GGH8FwcE48dmjdG9wp/LUX7qmFQYfycjvLepGEKFAkhRw2J31926mrXsKewgX0dxeQGHoFvsZWGhcuIuBwEHb55cecz31dQbrxk9k57P5JnbRwMArVfy1xwL8Fw6YnIIoi+ze3nJL25LGxqIYOxVlUhCwyEuMFF6BIS2NCV/C3c8+P9/D07qeZkhm0Ce2o7SUq7jwkl61AFgiQ3mAjIA0+y9P7d2GTafk48mYWKacxZegrdPcG1a1SqZSZ06cRLrFjahpOqCqUaNObEHDT6/XRETmNlbc+yQvnP06rYj5Snw9NUnAxpe5pg442cnZvZmzRdmSJh2hYAtFqfNahuKQCezUSml6/gp7mRrB2kppoQRDAZJGR2LSOkfte4uzcfISAn+6okYS6mvD2RPPurHDevuJC1o9yc/X6duLue56HMy8jgB+ptoZrP9qMSfMBd3+18TAtxqnECX8Foihe9Yf0/B+AHbXdFDnfRBkj5bFvILVfxeuAKWsouppy9v64j4RL5gHQv2o1Fn0KADEp2gGuHYCNYjwxg89jsCw4nQ+u2E9czt0keNqQ3nc/ipQU6otcGCOU6EKPTe9s+uhjet94E93pM/G2tWH+fAUWfRJNNSI1phoA3n1yGdc8eDkShYIvnU4oK2N4gQmHM43YznV0xWbi3OxHZpMiiQhgSHHhnjydPTX95EfVkegSyatdjHn4x3iwYZFKUKcsxRZ2KxIxj1xfAW2uMiTKCdx2wMXL2SrSI/RU19mQbW/jnFmDaMtvY8SIEfRH5vHeqjIuH5dEQIRlu5uAWZyXLiFk9LzDL66zLGiMix8NgoAYEPG225HHahEkAp5GC6o0w2G7BqezmaL8RUQ9KkceKhLbF4LD+QZ1gdeQGo0kvLIE1eDBdNo70Sv0R/AkbW/dToIugQRdAseCz+Onek8n+9Y3kTMpjsTB/1+d9GsREqEmfVQUReubUOvlDJuecEICyBMh8ra/0PXsc4TfcD0SpRL18OF4vvySldf/nU8le1h2YBlbW7YSGjmdHTVxzB+diJAyEdf0b3CuXYc7MIxo4/2M6W9AQoC7cg7lLfvBb+TSQAAkEkaNGsWwQjMb6uw8PvQ+7i24l1uLvuST3AXcb/SCB9Ksdt4aksR1m/bjjo7FOKEf0w43zq52/Bo9MmB8UyVbR6Yi6fcQ6TXT781EipZVIXKebd3JW/fdwq2ZP6IGRp29D6VGxr61CmRx5xN9+3gu3biVtSu76fEGY3Hm7X8QWUCBss9LrW8GdQofl6y5nVr7o2xN3EZd1HfIjXtB6qTVfDEJoSfmCPu1OJkI6fcEQXj3l3+nfCT/BuzpW4vcuBdFaAGCopv6kFjeGDqXH86/CYdMSebGlXS/9hoAjt27seqTkPpc5LSuIrx3P9kHPiS0vxqTEEFF2Aw+eWQndcXdNBR0okZGryIJd1Q65X+6n7YaM8Y9X9L77nsD/ZuWLaPt3nvxdnXR9/HH2PPzkcfFkfjqqxhmB/lSygdfSYk8+MKSeXW0Bep54dnnefTRRwfaqfXaUNlb8cb4EMQA2oO+z9ZIH80yKUtLnRT2CTQoJcg9EXyrSOSaEdfgBaS9lyIJJKI0v43S/AHtba8hiG5urR9JXY6BSLmMx4qbeGdbPeEaBer2fbjdbtRRSTyxpoLpgyJ55Lxcrp6YMjCeayZnHjnZ7QcN67FBFY/5qxq6/rGPvo8r8Fs9+C0e5L9Iev//2Dvv6Diqs43/ZntfaSXtqvdqWbIs25J7BwM2GGNKgukQEgKh904K+QIkpJCEUBIIHTtgbDDFFffem3rvZbWr7WXm+2MUywYCjoG04+ecPZJm986M7szOvfd93+d5uro+wLhOgdItoGlSoLbGY51/Hpa5c8lashjz7Nksrl7M7CWzOetvZ9HsbiYYDbK2eS17u/eyrWMbk1Mm/0NPh/52L2/+dDtr/iqXH1ae9+VM7dP4x5ixqJDUIhubltTy6etVX3t/pkmTyF723rHvgaFC9lqO3vwQ942+k8cmPoZRbSQa/xobG4dzgIapJRguuZL4H45HOWYecc4BLpI+QidK/LpDQYIQ5lNLKbiGdT1HZScRRomnxUKRcQp/s33ClTWylEdJbyv03YXC91NiBpz0izpanGVEYuMwR/txOBzMnj2bosYqEjReIrkWZjcfRbTqEX3FbDUpQRml1DJ8vPxxGspmp1M4MZnGGj99bR48ycUMCjFE1UaQRAQgKkRI9Z2FqNQQVRtY4f0FlkAiYztHE2uSV8UqYw1HOo4TrvwGcTLr5/eP+10HLAC+HingPwQ/HHMJtoGdPNGxBrX5AKHQdJblTMHQ6CUjwUZFm4/e3z1DNH80uzqSaE2tJHawDja+wyhAmRBP0p5tDMTkcqTyVpydPlb++QDjQioGFCIWScHhCbfj6vKiCQ2S0r6Jnt+uwTJvLmq7nZ4//IFoTy+u94aVQcxz5gAQc+GFDKxZj18XT1B3AFXYhLWvCFfcXvzCsDBfJAges5+YQBMhh57YgJ+oPsrLE1WosyA9ZOVe86fsV9l4T2WEQCYuQcWnrZtQ63LoskxGYUonxv8L9N51mCQrl7fMID3Bzcs6iPNBYyjKc5ePwY6LpYvlqo/VrQII8IuFpaiVCvIcZv6Qt4v4ro2MKvj4853dsVdWW7WkEO7y4t0uh8D8h/pQD4m5aVJO1IgZePUtLMtVaPNySX7ql2jS01Doh5P3g6FBfrXzV2Rbs+nx93DDqhuw6Wzs7Rmu/b4w//PEOAB3r583frINJCg/K4OUvJgvzP+cxslBo1cx76ZSNr5dw4F1rYyalY4t+ZsR6QOZkxTp7KLn17/Gu249F8y5gInJEzlzyRwGNZ/yUU02M3NK0Cq1GEYNlSBLc2DTr7mu5w88bDwX/2EXE3J07DYX4XrzYwwLL0NtN1CcngA0cP+6AaLKycTkb2UgawOf5N/Ew40P0qn0ggQ613DuQIqzou6v5pprvks0qmHt2rXM278ZVaybGWW7WFr1AN7OUiTDdl6yxXOV2ESDWoVHUMC2lcSefzWjZqVxeGM7i3++E1GUiLEb0LlcuP1qHJH1vJ11gDk11xLWyKFWZ0AOq8V5Ulm0/cdsKf4J+61BdrbVccaI5G+sr/+Or1w5SJL0t+NeryH7Sn+xnvJ/GdT73uTyzS9RGAyhTViNueh+FLpWorHv8acLO7hx7vkQa2PdcztpjZc1gRRhmXEcc/HF5G/YQOKDD1B448VcfauWhbZ7iATBIikwjInjiCqCszeMQq2kovEvZD52L1IwiPPV15CiUUT3IIJGg6DXIxjkZaFx0kQAgnoj6pseIqB3EdEMovfaGF3/Kmd4BqnYUsOM1WtIb2yi/FA1kiJMsW0rrYp0Yjo6WZ+n5MNxCppNSsIaiafjrYhxMjehLSAbhOzrqcKtTCWaYyEcX0xYLS9nz+tfgBhMJzx+GrW+IP5eP4WJZs4sTiQakPdROnosyw90ct6oZOwWnTxCBdycE/yIinTzFyqq0rxV1lUSBLy7ukEp4LhjDChgYE0tg8k7UR83OESjQdTvdiKMSiLjtdfQFeSfMDAAvF//Pp6wh8enPM6vp/+aHn8P+3v3c2PZjdw59k4eqHyAAtsX60XW7OwCCebeWMqE83NIL447pXvoNIYhCAJj52ai0irZ8HY17TVOPM5vpupdUCqJu/YaBK0W/245F5hoTGRqykw0ceu5e8vV3LL2lhMbpVYg6q0kdQYIpjZBRCJPMtOsT6azbze3b63FHYkyJkOW5IhIAlLESrZmNsv7PmTRqrnUK5p5fPLjKAQlA7ow4zdvIc7lIqKw4u9XsfgXV7Fjx0yuuiqfB+68g7njm4lGm8iydBD15WIRR/OixUiLVsF5qclcmpJIpFvWRDJYNJx/x2hGTEmmdEYq828dzZRrx6D3ddOrmM3MptsIayxMTfgAbWgADUEsunpUkhZBMHPOgaFnkqabbwOnknnLA/45Den/VJRcDE2bmdD8IUe18qxRE7celekogwqBHkeUT0rOQk0RSd3bWVpQTjMZfG/OpZx7803sbRlg1KJL5bDFh/eQqKlmuuX3rFHnUms8yFrj9RQaEpi7IJ/MkvcA2bXM+eabmOeciRQM0vmDO3lKyGNmkYPzR9rRxZt5dWsTDy49yD0aN+6YanRigLmHfo3UEobmvViAlsxK0g/Xk+Bzc6CsiHXWCoI+iO/uZfsI+eF8UKthr04uBa3SyP9fOJiKwxbGxyCikIzR68ZrtDCtewzbYjqY6ipk4awUcEqoBYGeaifnl8sENJfLJfv+Zo7Gt2UvC0anyOYqL82D1u1yn5Z99/P9PNgFfbVQfgUgezJoMyyoEwyok000W5/GnbKJRO9EDC3xDLz9NuJUBwov6GdPQmmxfH6fwIr6FeTG5FIcJ6vmrrxwJaFoiATDl5PXomGRg5+2kZRjJbPki8tgT+PUoDdpGD8/hw1vVdN61IlGp2ThPWO/EalvQa1GV1yMf9/+Y9uuH3U1n7bJnKJNbZtoGWwhzTwkeaJUwfgbiV/7OC0D7yEoryRpUz+U6/lzZiqLzSITuwf4bnIc5xQnsOJQDwpEOuqmcP50LcFIkBvKbiDLmsVzB16kzeZm8sE2RIWC7eMryS1NpW5/K5q4MAbjW6SmXkwo0IqqAybYdlMTczYdjZMxZu/hNvvwPSk6G+XvjSAQl2xi6iXD0i5qrRJV2EtO/buEVUbMcW6EMWPJGujiaLiAypiDbOiUw5/R0Fgm1EnYkr8d/7WTyTkMCoLg/vsLWA7c862czb8YPr+CzeIdXFirZ9IhWQZAZTqMoJDLzArTfSw3liEplDguncF6KUxjTBI/sYxlwcv7OP/3m7h7yX56GnvY+asjLKtK49ECN8/lr2B9sJmKnAbe0gSOzUqX7GrFNf8SRLebxoVyuOOxKpFDHYP8bk0tF724kxte282DS2VZib6ebhAkpgU+Itoapss+nCx1WnJRGFLQRAI4Aj4CPkASSW5rJ0bQo2j6LpHjZvDb9DrUURVhnY2OTDkqGOPUcOnS53lo6TIe6pzNO1VPUh0jH2NCjJEHHQmIg2F0ng5WrFiBy+XCbDazu8WFVqWgMssGtavlgSF5NIz/IVTe8PmOXvkQCArInY0YiBBu96DJkglEhrEO3MmyGouzbxtNl19O/8t/pe+WJwCwlk//wmu3vnU9e3v2siB3wbFtVq31KwcG70CQlx/YjMcZPK2b9C2hdEYq591cxpRL8hAl2L6s/qsbnST0JSMJHDlyTNa6NKGUKdbbifTLhMi1zWtP+Lxi4s0ETGbsm5bQMeFHGLJlxdO/OWRBzVUNMrnuqUvKWXtLJTNjnbQPKpgZdxNPTHuCLKtcnp4Xk82mYjmvYRviL8UZC7GlJuM9nIrHVU173TY0b/Rg/4mGM1auIVRmozCzFClgp0EzrOycEfqQ8E9TOfDak5/7/zR6FYrUZBQ5Vs6+v4ik5FY+3ZVJs9OMKuxhb8OYY58d1GcwqvtCYpd9MxpXn8XJhJXMkiRZjnvlS5L0eW2I/0Js+cs29qxsYW3fPZx9+AKuF4oQFMNUdJOpj9sny6P697b3orZ9ytQxNQTCInU9XnISjCze1crrT21iW86tiJ6L0DR1MiOhHJUk4bDspW3Az5xfr+e3q2u4c/E+5q4awHiJPLv2TZhGozWZ2UUOrpqQQb83xLqqHrIUfUxUNaDWRhFEgeZ2H0jwwNQKnOMs1BYuwK/Loib7YkRBgatnFT2GHvJ7ahCiIWq05+Dyj0AxlKTTBuQHvuS1k5jwIFb/H4kqYylrdDLGWMp8/QwCui7E4gaWl5lJUKtYUpaLokcOCXQc3ML27dtpa2vDarVyoNXFiGQLKqUCOocSzVe+D2f9XJ6tHY/+Btj/Nky8GRzFhJrcIIE2S14NqEZFQJDP07X5E6L9TqJWCaVHALMWy8jPczCf3/88t629jRxrDt8p/M4/dc0/faOKsD/CzCsKT8tjfItIG2GjdEYapdNTqN/X+41JbGgyM5GCQSLdw6GUqcmz8HedTaY5l9XNq09soNYTWfgHBCRK9zdgtWwhThHAo5JXMhu8Htz7ezBoVGQlxXP59JHoCHPDX7fR2DPIo8sO8fq2ZhINSXTFulk8/xqyvvcIqmiUju4etMm5hMIOxJdULL73MXobctg1ppy2zkLy+5vYnalD8MlOkgrk1XunUoU66kHc/gJd9bUQHJTVA4YQG6+iX5mNqnYxjcFxqNQKShZNxhTsYVAvOxBqA8P5j7ET/008B0EQVp/Mtv9GlIwAQYrisWTQmjqD4pc03Lj2Bkpa8vgOFg72HSTsHkDQKNDF9qN3fER7dPmx9itvm8Z9Zxei8ssPt7aUKUzufJKHi35JRSDMruAB7BYlNd0efrWy+li77/aksiR/BjfbZ2HQKLm7woRu3zuMEDs4T9rENE09+ape/DFu9CEFOYcV7MkRcOet4r5ZUYK2AbyGJCIaC902PQXNHmInxpJRu4dOG2xX5IGkRl13DecdKMLZfT6SJKARAvh0IUTBQMC0kHnuBMbq5RvXk7yVhilZbCZCpMZFNCrx1s4WsqxKDIJs8NLb24s1JpZD7S5KUoakA5xNMqFN+w8MR/a/Jf+slBnnwQY3KIRjfIbBQZmQFBs7kcjmakS1RPfDYQw/nE/2W++i0A4zpHd27uTi5Rfz2z2/ZWLKRF6Y8wIa5cknkXtaBmnY18uYczIpmpj8D6uYTuObQ35FIpIoceDTb4gDkSZzb0LHqQ2nx8n5uuKYiezp3kOf/8SZtCnrPKJnP442JJGjnkaBuBMAR7AXt1rJprX1SBF5JTJlXBnfLzcREJVM/+V6XtrcyP3vHmDVfh+SFGLx9EqU6gziIhG6QyGqB310JCfT489FbYhhe2UltXl5VOcXMveA7HLoCk0jZvA2PM0yJ+fNvlzWBxLIM/dxZPlL8EQ2PFMBITk8ZE2zMxBJQmzYRGukjJwxdsaenYnNPlwGH1abiO0/isHXRXpW1zfSt5/FPxwcBEHQCYJgA+IFQYgVBME29MrkM37O/63oNeQgDTGINWE3R/KvIaorZFLrTdi33s0Fh25lb/VhzHYlUyq2IyHRFexGUHqIN2kgEubybC2C0ojV+TG9lsWICg07n3qXRbEldEshfjJxK7U/O5vFP5jA29+fwIQ0PWfrq0i0h1gY2sytPWv5+J13CIgiEzSN3Nx+4qIss7cdo09gZbkSvUpPvxDkYPLBY+8fSA2Q4VQx7UgsCq9Er0VACstVDb3hPD7UjCOsKWbA+zNa827Hmfh/9KX9iVlCITnmUl4sHGBjwU4CeVX8pS8NQlF8tS5+8dFRars9lOj6sNvt2Gzy6sOaXog3FKUsbYjMN9AMsV/ik1D9MaSOA4tcTRFscKFOMaEYUjsd9BxGEJTkZN+GtlYgnK1g/IzVZNz8f2izT5Ty/tWuX9Hn7+P2Mbfz9PSnidf/c/mCqi2dKFQCI6f+T9y+/xWISzGRN9bOzg8aqd319ROnmvQhAcaW4cEm3SYPDnblWCQk1rWs+1w7/YjLQKEi1Z/APGkpl1paeLfpVyilKOvNTsIdcvm3QqHghgUz0CnkCd+Z6qNUqpqo75IfzE5FP85mNw6TiV7TcB7FGRdHJDYGs8vFj66+AovLhTYa4eFwHwqzhpZWB1G/fO7vlXq4sUjPXrOSmIal9EYNHPJakYYmUrFp8UTR0hEqIhAxHjP8isuKPXY8UaHGGZNHTlI1CunrM9O/CF+2cvg+sAsoHPr599d7wDNf0u6/BkUTkyiaKPsZm9NODDGEJDN2dxbxzgw2RD9hTes6pvrkEtK7z9Oy/EeT6XvhBZrOOAOLuxG3ph/zPBvJlkFa/AmMTLgRqySw/uhbqIgyLtNGRZaNBQl9RMx62pOTcVuttDv09IVDpDU3E1WpWKedJJ/PQCEmdw7ZDTvwGSRuN8WzZcxjnG8bx4pcDU7Vc3ycdT/9ZtD5IgR3vIp2UIHTYOXvl1XreJ9wwV/RZzyHOmUXUU0GUU0KC3atY0Z9N7/P1PPHjDRuzZzBb2KfYo3Li7IrgBCVeHFjAzaDGutALcXFxVx++eWcffbZtIXkiqFxmUP5D2fj56W4Qa5gWv8ktO+GvDPkPm33yGS3vJhjdelebx16fTomRR7qNiVJs67FYMg8YVeuoItf7vwlB3oPcNmIy7h65NWoFP9cLUU0KlK9o5Osknh0xm/X2e80TsTMK4tIyrGy6i+H6aj9ejX56qQkUCoJNQ37JyRb9Vj1ajq7bSQbk1ndvJpez2cemDoLJJejat3HSLORRcLrZI+9hCmufaxKBO/eZgZWNBDu9KJTK3n3+jFcYT3KqEQdP7/mLGxR+X5TRPq5p1BNeuLwxMXR3k5ffDxeYyzWASf93V04/L24dbEED+1ngiFMZcchEA2EnH8PMSlZY9BTFNPJs3yXxczjoyeXEentJW6oau+QJBNJHUkSeLpJnCjbmWoIyhWBCiW14kwaDJ8hnH5D+IeDgyRJv5EkKQu4U5KkbEmSsoZeoyRJ+p8YHARBYNw8+SJ73FEKMrvIc8m0jiLjOjK1O1BpIhxKXs9FfRU82h5BJUmEw3s57NzI+hV/AiCj+WOcul5KtNMZMX8MIY2FPS+u5yzVCNZrBKI1nwAg1ayisaaKjMZGbiktZdFAK8agl3hnH+fNqETn99Oamo4gKlEP9tEXXYKu30MkMYw2eoD9717B5fuXYYi289a4QzQkeqn0yA/pdQ4f+hDUq0oJViYQHq9CHbsFAEV0ABhWGFWLUT7oNrA8YXjbWqc88Bl9Uf581Vjy7Ca+X6ZDKUhk5BYQVuoZO66C5fs7SInRkxqrh31vgbMBEr7AK2H/W7Dmp/LvxQsIdXjp/q1M3NEVxrJ125nU1P4cn68egyEb/+7dsrx4hZxj2Ny2mes+vg5f2McPV/+QVw6/wtmZZ3NR/ql9EVoO9+MfDFMwPvGU2p/GqUOlVnL2DSWYbFo+fO4gIf+pS0wLajW6kcV41q49NsFQKATGZsSys9nJlJTpbG7fwtjH3+f1bc0nNk6vhPbd2CzjcLn2Ei5dwEVpKbToE1lWV4dnfSvuVfKgU5SZxP233ci1115LdnY249PkuL4y1MXmBBW/NmVSfPAgFVu3ERdjJqJWE9Jo0HsG6aqrIcEIokLJoLOXB5+8jUe3/YV0dwfBzvPIGbgXrbOEdXozn4j5RJBXJbUJKay9/mq6G7ahUArUDIxCqVJg3Hkvgy/NJa08jbgUEyGGQ63+wfAJOm/fJE4mIf07QRBGCoJwsSAIV/z99a2czb8BplgtGr2KoC9CS7cdrUZOwqqkEHNjH+f62It4MeEx4qoXsaL7CQqCId7e/wEPrLwDjS/KgNVK7EAtXq2T+tfUrHm9BoPgpdpQwdi/JnDe5uks3fky7Pwz7//tR/jCEexd3ZhmzSJvTB9S0tu8W7IKV/0jpDUPLZUFHc9PfZYdWbUo/QJv5+q4KCWJK5ITWZiaxMJ++UG+5aJNbM+RyWSFNfJsuFWbg9Wmw6bchiBIDOgfx237KVH1sIm6Ia2YTqUOyapB3+7GrlSSLMm3QrFagdR2kJW3TyPG34FTl8z03+2i4vHVXP/XnexqcnLTzFwEgA2/BHsxTLjxc/0qtckDQThmAlJMDs4l1US0Tnxn7iAY24bPV09z8wt4vdUY9Jn4du4EtRr9KJlB/UHDB2zr3MYjmx9hf89+7q24lyemPYFJc2pm6lXbOtEZ1af5DP8m6E0azrimGL87xO6Pm766wZcgZsEFBGtq8Kxdd2zbhJw46nu8/OUTM1EpgspYxStb5eNIkiQPJOkTIBoiScxEkkIcOHADZycYyfE180BpGkfH2PAf7SfUMkig2olOp0Onkx/ck/LzESMGVH75O/ppvJ4S5UgqF13Dwp8/gTA0UMWFQ3TUVpOSnQlAoqebqCCwdXwlMxNauFS7lzJnDWcOZHPNMiNH/JNesi8fAAAgAElEQVSIEqL44EH64uKwtray5sXfE5cqh8qSso0s8u5mojmA54PFJKo+n1+IcXzz0hlwcgnpR4DfDb1mAE8A36556b8QgiCQlCvH9Hw+gVZtBUhRutWT6Cu6i6ZQOfuXy7pGHiGesa4Ynv+th8feyGYgpZSPzz6LxsxUvrO2F1NfHXEpJnySEVGppS57AQbVhQhvl7D2oQ95NyoTsiwRN6rYWOr6a3jFaqFbrWJJ149IbfZStP8wl5Vn89L2EeQO8dAbHCcmTtUKFS+c+QKfrHqalclyQnbS3ij9Zhu7skcwm3ehfzHKcCpSjB37QDcaRRIzO8M4/CIvJSZyaIacA4h0hnGvaKbvkxY0m7pQNRxgw4YNtLe309rayt7w8Ex79dFuBAHmlyVDTxX0VsG4a0B7nOzF6h/DiruQajYSiI6mq/MBOv5vO+E2D+Fzqmjh9+zYceLtYzDm4N2+HX1x8TGi264umej0UeNHAMxMn3nK1zjoC9Owt5e8cQ6Uqq+n+XMapw5HpoW8cQ72rm75Wv4P1gsWoC0ooP3uuwlUyYUeiyozyEkwEvVnIEbMqKz7qO4aZDAQ5PqV13P9yusJp8hloIa+XuJsU3EObKWq42ne2XcLMUKYZzNUEJXo/v1eev98kEjfsBLBhMI0xKADVbgNo1JBWCGwdfwcdIVTUCqVXOwPcO6yZcTGxNJVX0NK+Sz0Ph8ewcSGqVNoysjA4najEaIAqBCoLj8Ln9GIvauTxI5OEARcljhiXIPkjJK/17nJ7dSqlRQ1S7Te+Qj2V++XORLAhAU5GKwaXN3/Jp4DcCEwC+gcEuEbBVi/vMl/D6KRMEk5ZjQ6OcQyoEhHExqky2XkzbUT+cD5ED3qbEyDsjbKmY1yGCq9vYpOh/yA3Vkxju3jKxlR/xYX3juWKx6XWc75lQ5suhY6kidxOOMGCpwTMXi9hH0ioaZq1oqyNffN2ssxixOICkHy6w4x+MxrGFbv50fL5QoKySwiRowQ1ZPmVbHMqqTr+XdYXrcYpTYKM2bRWjKeR8svR3Ic4tNG2SkueSCdiFpLR2I6Hp2Oig43NzS3Yg5LMCRwJ3jlJX5llg2FJ0KiQnbS+vTTT+lx+2nyKrnzzHx+f2k5ALkJJgwaFbTLKwMypwx3pqtNXk1sfw6Fu5qAUAGA6AljPTebgPHv+jISWm0i4ys/IT/vIeK1UwgcOIhx4gQA6gbqaPO0sTBvIQ6DgwlJE7AbTp13Wbe7h2hEPB1S+g/A+POzQYI3fryNroaTs9L9LBQaDWnP/hFBqaTn6acB0GuUfHLbNOaVphBxlaE1VyEKbl7ev5StHVvZ2rGVD7u3Q1wuwpFllI38Ezk5d9Pv24tdJXBtYD8bvX78c9JRGOX8gmdrx7FjptmMCCE7Kuq40biGBKWP99M1BBvl/yHnhzdgscZgnD2LoNeLS6GjZP8B3GYrPQkJaD1uZq5ajdbjw1o1rD5UtmcPs9ZuQT/oIkqUHnsCuZ5+rAlB5t9aRt6Q7ejEI/KzQIhGMLsOY7BqKJ+TwRU/nUjFud+OJtjJDA5+SZJEICIIggXoBv4n2EOSKPLynTdSu/U5rnpiMjEOedZq8siiW9rIIPnVb2L0tFHok/MG/gZ5EPHp9XhNJmL64smuq6M1LQ2/Ts4TmW067BlmOutcTLxsEhp1Gz5tG36TD3tXN72JC1nxwBI+1eopNiSTuTsZQYywobADXVBE1SoLlykk8BgkFGo13rq7GKy9C93ARFrUapo9KzlslRjnFvnogpv5Xs6F1MSmYTQcxioaWbhzDFGL/LBN6+sn3x0lKG4nL/82Ho4+f6wPll1dyarbp/Ly1RXcN9FKnMKPzWajqqqKhugQIS4njrmlSbx8TQV/vEweJOg6KFt82o7zPjgyrBElSjoovRjHbeVY52ZhmpCM11tNXNw0RhQ9xZjytzAac0hLu4rgroMgihgnycn4Vw6/glqh5ubym1l10SqeO/O5U7q+YlRk98dNrH31KLGJBuwZn7cRPY1/LSxxes64ZgTRiMiaV44QCpxa/kGdlETs5ZfjWbfuWHJaqRD4+QUlvHbJLYhEMeX/jOeP/By9So9NZ2Nj20ao+L7sQrj1D9gTZOvaoNXGuR3yCvXTXAPJD01AmxtDoHqYeyAIAnFRWXrmLwdfxNH/BzbFKunu9lDnCxAyJpDzwccUXHQJlgQ7q157kWxnD3M+/IjK4MdoWmvoN+o4//3lnLVnAyP3HyCmt5+wNR4BqIvLoF/rpC05ntiBIP1tLaQW2uhslFkDDm88a2ZPozUlhQxrC+PPNdLT3IhS/e2thE9mzzsFQYgBnkeuVtoNbPnWzuhfiMMb1uLsaKf1yEEQw2SMlEsjY51HKTryMpVbHqV0komzXPdTsKAURTSMvqWaAUs2zZlyElYVTqBc7EYRFanKSGbHn35PyO+jaFYsTucATXUi33u4AntCI8pIhNIjR2iI76bNNJ5Z687lTO8UmnoN2NV9FE6WWdNKSeTvtu3tcQLqqJ5KeywOUywHhqwx12b78CkV5CeMY2t9HwUOM6ZyG0rpEI5WAVN3L3XJ2SBJPFsV4OW9PtyWOl45egMPfToXXZOHyVYjZWkx5NrN6DVK7NEejEYjo0aNwiep2RFJI8GspTRVLludlp9Art0MrbtgyzNgH3Ei6a1xI8RmErqmjs7g82hy01E7jJinpBIVffh8DZhNI0hKWoBen0rg8GEGlizBs2YtCqMRfWkpNc4a3ql5h4vyL8Km+3ry2XtXtxxzKMsb5zjNa/gPQU65nTnXjaS/3cvzt64/5RVEzEUXgkLBwLvvHttm1qkZm1LALeW3IETiEYliVpuZkDyBre1biYy7Rl7t7ngRgy4VozGPTlUrOa1rKFSLvN8pcyS0uTFEunxEB4fJeyXWckId5zEmfixdrn0IkS7WSSEmbTvKDSsP0/dmFWqtjnNuupOB7k6cl1yEPSsJt0GeUPaY5fyFJAiQP4IV0SI+QJ71J44dSzcaPMZYolEVg4cPQzhAfe8h1CGJ9pyx9MQncqBkJFapnxW/e4i/3nXTt+blAF/tIS0AP5ckaUCSpGeBM4Ar/1c8HvIqJpBTPApVNEpb9ZFjJWToDCR1bSc07QIcP/kViW/sJjrhChI7t6COeGnIOJvQ/DloVDoWPToP0z2vYRiQaMrIoOqDD/j196/knRWvImU2cWBtK+0uB92BICltbaTdfheN87twKbfgtc0l6fUegpoYCmbkUpw4XIlzJCWe2swinpmnQAibuW56Ho+cW0w4asQRhGq9nICWCsayp3mAgHKAkVVvE5WCqMRktpRPw2O04Aj6sHk0vBdq47X6BaxrLiIlRs89mUksKZeltYPBIAcPHuTIkSOkpaVRUlKCKjEPEQW/uaQM9We1+bf8Tv55fCJaFOXBIXMyofYwIrEnGPf09HyMJEWIi5t+bFv7Aw/S8eBDuJYuxVBRgaBWs7FtIxIS3yv93te6tqIocWBtK/ZMC5MuzGXUaamM/yhkjYpn3NxMALYsrT2lfagdDoyTJ+F6dylSNHrCe9eVXMdo1QMAjE0cyxkZZ+AMOtnUtomO0oUsFvsIbPglJUe8hJUgiFHm1bzENrefR2vb+OtQ3ULguNLbKfl2ggMTqVDKCgcm/05eSm7B1nYb6y27CNY4ae5v5kXnEsyVhew+uIfMV98mFJZVFnpNcuK4v2wU2tvv4EhOHuuTSnl2zEWEb7mBpMRSBEFBX1wc6n3boWUbRzQKsvvjGLRYECJ9uGNiGOjsPHZO7VVHTqnvTgZf5SEtASuO+7tRkqT9X9LkvwrRXRsoWvoBE2vaaNy1HXOcnATqyp1Nd/6Z9ObL9fmYEli7fj9ecwuDxiQ85ihHGhsoGVVMbKIRV4+fcmkDmnCI7qKRhLOKEIHBYB8ag4IDGxvwqlRo/WqW70ninvGP0TW/F4EI9dnzUUTDqJJTGOgZXsaKhQW0pEBnjIJwJIZVQpRnBuT3FcbhB92qtkOkuOs4b9NzJPTKUtW7Rl/AljFyAneyaw9qSctHmgBxuj623KZl070z+d7kDJ5//nlWrFjBO++8w5IlSwiFQmRnZ2Oz2TBnlqBWCozJHCbeAASOdCAd+oBI/uVQcpwcdtdBCAxA5hRCzW4UJjXKWLnkbtfuSzl85C4MhiysVjkpKEWjhOqGfYdjLlwIyO5t6eb0f5rg9ln0NA3K+kkzUimbnY5Gf9rd7T8JgiBQcW42ledl0VY1cMoJ6pgLFhLp6sK7+fPBjIr0TDx1d3Br2X1MTZ2KTWdjae1SHuxez4/j43hq/x8wNu4no1OiO8HItW3vkBnu4dmWHn7a20dTnAJh6zMylweYOyYHFVG214qkmdNwRBvoj7yPMtqL1reFEPDzDT9lcfViPkmtIeD10HRgLwOtHowOHwGNitpLNewqL2SMxYAYp0MUFHxUNo3LajogpRwRkcZMB+FuPx//5I9UiVpSB7MQRJE6rfz9dkpwfuohxsc30bh/z6legq/EyYSVdguCMO5bO4N/J/rrEH1BTMEwXa+/yso/3ockRUktS8M16ypqj/rYt6aFPavXcLBlC7X5aWycUkl7mvyQrqiQE67O9i5G2g6RE9jLoMGIhEBGXS2RSARzikRri5yA0gbV+N0hoo16fnPm05jiTCiUAqJSzZq/HkXa9QdSJvWTNq2PWY6tKGLlWGpXOIuX+5zsDgeR9FE6Qj1oFFomJU/iqHsHKRFZ2MxpDoMk4DHmISrlpexUfwdvEuSoP4HpaZtIiJNlfpubm2lra2P79u1UVQ2bs+QO+dHuaR5gRJIFrWqYCwEQ3PQpAiEC4dEn9mWDLBUQMo2mLfgKQkYIQRAIh90MDGwDYFTpC8dCO6HmZqRQiNjLL8dx/32YZ81CkiT29eyjzP6PfaFPFi1H+kCA9BGnnd3+k5E71gFA/Z6eU2pvmjkDZUwMA+98Xu6tNNWKFEqgriuCSlAxL3seq5pXsb1Lls94y2Lm9yOmowkEaExVYRm3iNU7rmJJXiwq4IWCIPqu33Dk+fO5cNcRJI2KbH2AnZ1hSuNHIYZqUQflCY4q1MKWWDcbB+RBqiXUgVqvY+uSNwn5/NgKB1BoovRJJsJCD2JvN5kFsYRKYnEXyCvst6Uw3QqR+qw8NkybypaMQmLax2BVpJHS2ka9zYlSDOMzm8k29TMhvoXWIwdOqd9OBiczOFQCWwRBqBMEYb8gCAcEQfhaqwdBEC4SBOGQIAiiIAhjP/PefYIg1AqCUCUIwpyvc5yvgn7quWRc0oGoEShQt5OpXcekC7RMW1RA4cQklCoF217bzZYlizG73WQ0NuKO0RIXlii2nMF7jx9lw1vVRJs282CylcdHtjJ2x1bOXracUQcOARAxuhhwylUPqmArgrTvmMVo0OdFq11N3pgw0XAzMYpGajMF6i6/B5vYgpjag0KSaBbHgyAgiG5UBRtAEcCquoMdussIGMYhWesx5xWhK89GpbCBYlhvyNeTxDPIbNGKxF2oVHJStqam5oS+uOuuu7j11ltBa2JzbS97mweozP48J0A5KAvt+b3HVUiIUdj1F8KqfJrf2EV3yhs0pMgEOLdbntmMLvvrCczn4NDxreedh+0KmTbT5G6iP9DPaPtnBp5TQN2eHuwZFvTm0wY+/8mIsRuITTTQfOjUlEUVGg2W887Fs2o1EafzhPdKU+Rc2eqjXZT9eCXLNw7LvNwx5g4AnvXXU6VWYx2M0BiXgDriZfILpdzV8Dwfmm28njiXX2VcyUZ3kMPeABVJajwRBXGqAtzBPhSii9iIDWW0hw8S5EHnkoJL8EV8WEvy6aitAkFgziXLSc4rwt1kwqTzsWbNGs62xyAmG5DMau7NSgSNkgYxE+WQk4I6HAJTEQJQeOgQLq0BZcSJM9bG0YiWJrWKMYNvE9j60in13Vf27Ul8Zg6QA8wEzgXmDf38OjgIXACsP36jIAgjgO8AxcBZwB8EQVB+vvk3g4/6DzIpKxUhVcDVp+GBAjUhZzWdNUdISI1y9ZOTsUqd9MbHk9LWxvit25i5cifG1gK6q4NY4nXsX9vKgHsjW/R6RCUcTWkizuJFaTdj6+2ly1lNVNGH2e3GG3XhH1hN475mBvsDePs242zby4FVvyPsWcLB2FauTE/gsrqXuCQrn1ctZhIDOkSthWsSgiR33IEh+BEqZTaHHYW0RUwMxt+I29pNfn4+TZFOAroM8iQ5Dvk96Rk+GpBDUL+YGyDJfD+NjY0A1NbWkp2dzW233cZdd92F0WjEaLZwwR82cekL2whFRSYcPzjseQ1euxitZxUR0UGoSzucDNvxAvTV4vYtJGwccnijDlEM43LtARRYLKNO6PvwkHCaJitz+BDd8kBSlvD1Vg7OTi+9LR7yxzm+1n5O41+D9OI42qoHCPrCp9Q+ZuFCpHAY5xtvnLDdalCTbjPwl02NuPxhmruM5Kjnc1nRZSwqWsS9FfciIPCOLZ7AoMjFR17lZZsso/Oj5teY6tzJHQV3s9wuy3t39Q0wOcGEApGujuHQ7qW9cvj5oPIjbFIMczLkOa2YJ4dG0wtLMIixTLzoCsJ+FaaBIPX19Uwe7GG81cgP0hL4fpqdOzMTqTHKfucuTR/pHXtRRiKM27GLNpuDkGckvWoXLquVxzQJXJVsJ9PUR/e+DafUb1+Fk2FINyGXrs4c+t13Mu2+Yp9HJEn6IqPZ+cCbkiQFJUlqAGqBiq9zrC9DVkw2EUFgY3IYuwuiYYHe3a+y9hc38edbv49KLZFdqUdUKkmKl2vku8Z/H78hkdTCWObeKD/wumPksE54YAwvT1IRma8hM7ednPp6BoM+fEYf8b29OI1ytULI18C+VS2I0RPdVg9b5JDLA5UP0KQS8CgUNPfPQ2ddzs7DjxAJB0kPjKY/8Y4T3Naak5OxlxTT6e8gqMlmWpN8g1eaJ9AiqpnjMJOMnTVr9vPSSy+xfv16enp6yM3NxWq1YjTKAmIrDnTQ2CcTai4em8qk3KG4f8c+eO+HUPMx6mg1IakQKRhlYGktuNth9Y+JOqbgFyeinjZcPeF278Pl2o3JVIhKdSKzOdTaitJqRWka3r6zaydWrZXsmK9Xt129owsEyB37v+FJ9b+O/AoH0YjIh386wLrXqxjo+udIXbqCAsxz5tD37J8It7Wd8N6IJDlkYzNqOHtkIp2NM7h73N2olWoWFS1ietp0Vhn0LMZISIzyqSOJUPpoFN99g2eSQH9cMVDjym3YdvhJUbhYuSdAmbWMuenn8J0rvw8ISJIHRzST7QE5T7fLpuHMH9xMadY5vPPcK6TklJA4UkX3YRexWjX1e3aztDyPR3NT0CsV3JmViCnezDK1kk8TN7E8s5mZHy6lOyGFN++8HyFUSqfRQ1ijIWlAR79Syaz0FN60fzv5tJNlSN8D3De0SQ28+q2cjaz22nLc3618iwqwQjgRDTo2J8qVP8l9ICVUcXnWHvTKME0H9gIyS9Iy70z2lBcR1MuVFePn52C26SisdNBodmOMKJhh/w6IGhbFxKKO7SO7uQGbqw8EgfieHpLGVaIzmVEILexb00Jxwk6mj+pAozeQpHNTr1Pi8FkYFzePlRet4oWxb4LlEDrtGnr8nUw4FMf4rRGCCjNIEroBN0gSg5YRLDpwOwBlnixsPvmBm76plC4kMhMtbNu2DYfDQWxsLGvWrEGn01FcXHysLzpcfp74qIqseCP1j5/DExeOQvN3NvH6J0FrhYJz5H5LLURfGo93RyfihmeRwn66m68ABALqNhQKPaCgr389LvderNbyz/V9uLUNdWrqsb8lSWJrx1YqEytRCKc+95AkiZodXaTkx2K0ar+6wWn822HPsJBf4aC9eoBD69vYuLiGfatb+PBPB/AOnJziqONe2X+s78UXT9ieEitzl6bnJzA1P4FOd4C6Hu+x9+fnzqebCO8ZDKiROBR0sj7bjZg3C/uE69g9YQRL9vSiEiM4NT6SxBjGKp24IuDZOZlHxj9KTGICDoO8ShVI44+tfkSFhSOuekpmnMlVwjP8NPUZ6pvrGH3uZJSaCMrGnbS31uL3+RCPq7SqyLTR7xmJUq3mSIbA1ZfNY+99D1GUlkLQmEWnWS77HdtoZmSjyLlroVD8ElXkr4GT+RYuQJbL8AJIktQOfCWbSBCEVYIgHPyC1/yvd8rH9n+9IAg7BUHY2dNzasms9n4/hp5y2mPlWXh2H2zRy7P7RN0g3Q31eN2yqFVnSwMd0RBBz1EWPTYeR5YFPD3MGrWbRi0Yg1YmZhayKOsRfEonvyiYgq1IpGLTdrLr6vCGfdgzskgvKUMUW5AkiYdHBbgqJcQP77qc83IOUa3R0Osv4OY39vDuzn60NgtKUxUq1TzuqJlJXquJ2vQ82V5QgtnbV4AUJKQrJiJ58dt+yI9rkhiIUSNIEp5OH1HAblfj8XioqKjgpptu4rvf/S7XXnstVusw0f2D/R20Dfh58sJSFEPsaXz9sOm3cGQ54azL6Ou7jJCYSSRlPtazs4Ao7HuTsK6CqJBMzPk5BMMdWMwjsVhKaG5+kWjUS4x1DJ9FuLX1hMGhwd1At6+b8cnjT+la/l0/p6d5EFe3n/yK0yGl/yaccU0xP3hmOpXzs2k62MfGxTXU7+lh14eNJ9VenZSEafp0BteuO6H2f2F5KpNy47h/bhGj0+UcxME217H3Z6bN5ALHBKb6/Fyn9RGWBLYPeujrkyPesXoNZeNHEB8eoN8gYhhl56JIKpmKPvZGUqhtkcOoD094CAQtOxLL6I1EQZNCj6eR1kAIl0KOECyuXUZe0XXknhklOBAm1bCFxT99kPd++TMA3CE3Z+UlIEkaio23YPItwB+dTrnFQIXVSNAaj1stV3WZIhYefkNk7g6JrPpvxqf7sziZwSE0VNIqAQiCcFJmsJIkzZYkaeQXvN77kmZtnMi+Th3a9kX7f06SpLGSJI1NSDg1Ry9LoINZniQGXbcRERSkdJhZZjZxVaKdtPgom956hUG3fCO5avYwLq4FT38PVrue5oP7aP7ZJKRlN9GoVuMJppISq+fuqediClfyjtTA2/fcTP7sKNMN6wmnQIHiIAVZMYT9LsrPhAF9MRLwq1U3MSk9iYBCYHCwnEPtbh5Zdoi39u5CECSSrCPZiI7aypk0puYiIJHX3cUPfCMx9niJaPMImufxg/YCtLYW3HFarGGJziEqnSok/w8ZGRkolUoKCgr4bJ/V9XiwGTWMzTyuumfFXbDyIaSEIrr3TcbfaqE79AxCfBaqWB2W7GYUoS4GXVOxnJGBaXwyoVAvGk08ttiJiKJ808bEDkcGowMDtN58C6GmJmotPsJROc68rUOuaBqf9M8PDtGwyDtP7uLZH63j3V/uRq1Vkl122uXtvw0KpYLyORlM+24+Ey/IJbUwlqZDfSdN9DJOnEiko4NQQwNiKIQUDjMi2cJr140n3qQlN8GEVqXgUPvw4CAIAo9Nf4rfd/VypeUscq1ZrBtU43LvG97v+CTsUoB+oxpTZRLpUjy3pGYjIbB5nzzRm5o6lREjX8dnyCfZJ1IUTSUabGVpZzcK0QPA0u6/4YnCWZesQGtWEWgV6Kmrpn7Xdp5f9xsmvTGJbJ0byaKmtjmF7pbx6DVRRmmVTLWZwarBFzGjiARxWyysLxZYPFmg/7zrvtkLMYSTGRzeFgThT0CMIAjfA1Yhs6W/DSwDviMIglYQhCwgD9j+LR2LAyb5QWiLQrspHlu3HI7Zpdch2OQHW293F+pIhOmKj5hqbySWfrzOfj587kl+liayKMmBT6GgP5TNH7v72TTg4akZjxHx5vLcoWe5vkhLbU6QRVl7sR99nryDj6FA5PfBWlyO+wlrC/hrjAVxKIeQm6jix+cXIwjw9l7Z1KcYBUtnXcy7o2fSkJaHhECF00SiPoNFtUdRSgL5+oXMb1bjtAfo0wnEhiQakZer/q4GrFYrcXH/WJG0tttDbsJxeYHBTjj0DpRcRHjuB0jS8GJR0MoxTpPqQ0TJiF+sxDRe9sUIhfrQaOKx288GwGjMQ6cd1jRqv+9+Bteu5aOJOu5O28zPtsmzpq3tW0kxpQybw58kolGRda8fpbPeTUZxHHljHZx/++jTng3/pVAoBEZOS2X0melkjYrH3Rs4aQ6Eaaqs8zW4chUNF1xA89XXnPC+SqmgMNHMvhbXiQ11FrAXYezv4sqR19ARVrC1Y5g3ISgEHCqJTkGPNttKyk8mMuOCMgQkduzuoPuZvYj+CGfYY9FIcEVjiMz+eBSSn79UfYwghfFaFxKQ/Pxm929QKtVkji7C2yGT4hQqNa8cfAWAxr69GOL1tHZ6iEZFrtz+F9becjWmaJjRCUrEUBwuzSDNifE8XbaI14qnsL/1X2/2A4AkSU8BS4C/AfnAw5Ik/e7rHFQQhAWCILQCE4APBEH4eOhYh4C3gcPAR8CNkiRF//Gevh7mZ6bgNJgZp25h56RKsnvdGJVyqKVX66agYjxBUUQTDhMwB3g0zoZR62ffqg9psR5ms1HPAZ0c1xaDCWwI+LnpSBNjMpOYYL0WKaqlSnRziyOBd9NGIiaPRgh7KS23ssYhJ7NFpZz0/WHxNSAaaFbs5v8ig2RmWLFJMs/B/PZbx845pJXDXrluuVvG+608cjDAbq2S28s0vF/Vw0G/C1tIosGmId6koauplpKSkn8oHyFJErXdHnLsxy0KmzaBJNLffgbdz8pWnqZJstCgQqOAjv0oGj8kknc5jtvGo9CrEMUQkYgLtSYOs7mYSZM2Mbrs5WO79B88hGftWsJXLeDPU8PorXEsrV1K62ArOzp3/NOrBnevnw+e2cfRLZ2MnZvJOTeUMvOKIuwZlq9ufBr/8fj7dexr837FJ2Wok5PRl5XR8/TThGrr8O3cSail5YTPTC+ws6Opn+a+zyS9MyZBwwbOsXXlGXYAACAASURBVI7AqtKwrO0IR47cx6HDd1BX9xTpGgVNWjuSrx9BrSQm0USsIcqasWkcHvAwuLmN61ITaJxcwnXTcylPkFnRXucKQMBvmk2OMI336t6jz99HzlmzUKijKGxqPI40fFp5Bb142TOM0coluXHhfnThAGIkwqN/vJuWqqsR1AG6BBtuSzw+aSzBrvO4ZNy3w/4/2czfAWADcunp12ZdSJL0riRJqZIkaSVJckiSNOe4934mSVKOJEkFkiR9+HWP9WWIUauQKiYRFQTCMQaUeiVjXA+iQKBGiDJv8EnQKtGEvFyW7OBvFhM+a4DdK5ZxJDmA8rjlrsb+CeX6NrpCEVb0unjx0rn8eeYy9F55Bv2wys0obR8/S0rlA9N2JIX8kLeZZTGvMY5zifjSEdT1pGg1VFkUWIVWNGEF/bbUE867sHovZX1BwkiMVCRzTkeEu+o62RGv5d3RU+nQGZhmt1CrgXSzAkmSKCz8AkOeIbT0+3H6wscqOwCkpi3/z955h8dRnW3/d2Z7l1a9y+qyLLngjo07mOYE001CDZ28aZAXUoD0QkghJCG0QAgkxiQxLdgU2xjcu2TZlqze+0rb68z3xwivHdtghzi85NN9XXtJu3PO7NlpzzlPuW8UYcLfGffdOy4sIPnmSozFZnj5TrAko7/0XnRpqlEJR1TRc71eXaEYDekYDPH+I6tXIwwGtsxKRBISjy9RCfX+d+P/4ol4Tsk4dDUM8/Iv9/DKr/bwp/u30lk/zILPlzHjDDFTjuGTgzNTva4GO7yn3Cfp1luOed/97fuRfXHjctX0HCQh+POOfxICmvtVQEG/+1kuLriAuqBES+eL9PSspqX1dySLHrxaC4P9LUe6iNJE/E4zX6gS/HmP6hatj4SpHOhkTbZ6v+lC9aTE8rEJK1H9IqJylPkvzueK97+Cb3krjhkRBpMlQnrVBdxh9zB5w/PIFQ4Kk+MrnF1SNQoyWnMDQ1EnJiXK1dJOrq0IkWqPa0v/O3Eq2UpfQHXtLEel794qhLjxw3t9ejC7tJg/zVRtU096Onf9+itMqzFTm5CJUnE5fp0ZOz5GRiuOw7YIMXmIBpOEXtGhxNRsCK2pDZ3rBTINOv7cPYgCTM/P5JaqL+BvjfsE/2KUWGPPOPI+EJzOA9Mf4gtPNUIwD43o5Q5xgAU5BxGGEcwBDbunzEY/qhM7eXAHy9b9nb9IggV4iES0BByNzJn8JOeGRui3JSBcIf6ysYW6Xg8pkg+TyURmZuZJj8GOFvWhPm2cE0VRcL/dSqx2M6FoEfbF4zBPTsVW1IN45U6MuQbE1t9CTzUsexRMcXqNSHiUtEx/PPWFoih43nkHy9lns3lkD+XOckqdpSzJW0L1QDWJhkRmZc760HMVi8i8/ptqOg65aD/oomphNp/73izGn33y3zaGTy/0Ri32ZCOt+wfY82Ybg50fbSRsCxaQ+4enKXxzLbbzzsO/dSsDj8e94BkOE4vKUnlxRzuh6FFOCXumKgZU+3emps9BBtInvEBq5WrWBXOQ/GqAunUwHgJ1O1Xj5bNZ+N2wRDAU5m89LgKywuaYjEVW3dZX+M5lqdHM9rRcgs7ryE8oI8WUwkqPhs26biLZalZ/haeAYWMQD8M4jB6sgV4Ui42oRqHH7iLNr7pnGw0jdKYV4TNZkRqr6eo6NiX+34VTWTncA0xWFOV6RVGuA85CTW39r8A8p42A3siwyUp9njr7vPktH1tlLw+EZLxWK2jirJEeYwQpqx9ZCAIiRtg1ndDAfAD6fW3ckOnkPZeXLx5sYzgS5Q2T4IsXfZYvTvofRMyBXklC1sYfngMxiZC7nJDPx9QR9eF6f+39VDf9ku5kD4o1n/qc8RRSj1BiXBH6E1ZdAk8LlS2yHRlP4RZKih9Av78L41ud6LcPMOSJICuQHe0iJycHSTr+VCuKwpf/soevrdpHgllHSaqNUNMI7rdbkXxNxIyF2Bbm4ryyFIf3u7D3edj0K9j1DBTMh9Klx+wvHB4AQKc/PrYRrD1AtLsbw4K5VPdXMyNDpfH41sxvccekO3hk4SM4DB8uE9JR5yIciDLrkkKu+vZ05lxWjM15ZmZNY/i/gUmLc+lpcrP5bw389ae7CHjDH9nHMmsW+txc0h98AIDgoWPJ6a6ZmcegL8za2n9SVStaBO5Oxv9VJZTcte4Bvvz6NbzSP8h2jxr/WzkC32vsoi8UwaPVIIZUPeehVCfXvV3DBpea3eiNyXwr/F2+03YHl6cv4f7CDD7fHMZjXUxx8c/4+fyfk6Azsi0aplZqJ1MnMy9JnQC2pPvJ7G0ndaAbW34B3iQNsqSQ680BBNGiMD1pydTOmE9Iq+PlLds+ziE+KU7FOAwCR4uUekY/+9QjJsewihDFZgMus43+1DR+Nv1qRlLSyRyE7gP7kTUaNiaEMAotekXgNgv6U/xoZEAoFDsLCPcvJWS8BU/YTWmghi/npfHXXhfn7qxnk9vHBsLcMvFmEqSbCOFCq1dXDmI4jGzT8fSONjKtA7w1//bjxuixTydPaWRYk8dZhh5yMzpYeE2c8G5n2R+pWv4wDcPZvD1gJ9sY5SrDHubnmbj/wjJiIz0nXTWsO9TH6r3qrOPBiyuQJEGguh8N/UgigPHsWQhJwFAzDI8uw7c/DiPtMPHq4/YXCqkpxQb98ZlCnrffAkmifryDqBI9YhwcBge3T7z9lPiUWmsG0Bo0VC3MjjPojuG/GhPmZXH5fVM5/7ZKIqHYadF7axMTsV9wAeGGxmM+n1uUTHaiib/u6mBHyxDD/lGDM+1m+OzvSA8HKQyH+VWkk0Fk8tFRH5QRcoA/ksNv2vr4er0ay9B0+UFWiFQ6edesodoTQBNSVyTvpCRwduJMHEvySUqz8qX6EAt7IqzrclGSVEVLsmq8BmOCchMUpwZICSRzoNCDJfwSO0r2oc/T4U1SJ13OWCJaTSKSGMDZ2Uqab4TVk+Zimn72xz3MJ8SpGIcGYJsQ4sHRgritQL0Q4qtCiK+ekVH9h7CpaxOLVi3iRutuzivMwxbxo880sHnO2ZT0VvI5g+pu6kjwoImVYg0Z6dXKbHZoiUng1OXw4jW3krAkCyfZSIrEY289xkKvejLbgupF1x2K0B30MyR+A7KWvMTFwOiFpZM47A4wVJIJQk/QPBN7xMqchlJSXQ56ExagI0a3bOeyXJVz6EBLvFx+u2cGO1pD7GlR7fU9sxMxiSjXlQnmpKkXaXr6iRXQ1tb2YDdqqfv+Uj47Wa01jPT6MWWo+9LkV6oNWzerf9MrwT+IWn685Lj9hUIqh9QHcYZofz+ulS8S7ujA/drrWGbOYKu/Fp2k+5f4k7qbRkgfZ0erO2OMKmP4PwYhBKl5drJLVfflwGnEHwD0RYVEurqQ/fEAtCQJFpSm8m59P5c/toWFD79LrzsIejNMWoFY8iDfGRhiiqLnx+kLeKC7g6AcZfbA8wAUmgysGVCN1DzfISbtq2VGc0d8zM3qXHqbJ0DaXZPRJhgQQmA/L5+FvVF6NDDp/f1EtFkoqho75427CCE6uDgBhKSh2dHDgCPEu5r3cdvDCAXOGpeMw6cl29fMuMFuZjQfYM2iGVyfc2aYAE7FODQCqxmtcwBeBppRC+E+1dJa2dZsMiwZ/KHm91RkZSAUhTSjWhGdKBXQdbgVFIWAPcZg9zykiJ2DOh19Gg26qJ5fzP0D3VENPRLkDI+QEkihy9xF04FaflySzdfy07gnP53WYJhp776JIESg51L6AwUgKxCJgaIgpWnpS83E7nHhSb6TKY1ziUrjGcj5NkLoaBAl5EcFU59pJl//ZboDcZ6i7Z3ZXP3EVrY09GEgSkl2KlarFZfLxauvvorFYiE3NxeAmBwPoCuKwqaGQWYXJh9hXlUUBaW3GUtkldooVQ2W07xRjS2UXTR64KaC5QSuo1APOl0SkqTyLrXdeis9DzxA4+IlRDo6sH/2s2zs3Mik1EmYtKbTOleRUIzBDi/pBf81CrVjOA3oTWr8YaDd89GNj4KxpAQUheChQ8d8fn6lOmG6cmoO3lCUa57cxpDvgxXEF5j4+Td49sp3WLrop0zWJ5OgCMyB9/nH3hv4dbmaHeTUafjlrVfx1JUL+HJfFKlPfXZoOtTVRFs0QjASj2vYF+Rw/fWTuagzgluWQQj8afeRn345Rbk30WWYgzbVxnWhUh5Le4A59mnUyr30WRXsPi2lBSFsYTPeSB+Whmq8Pd1k688cFf2ppLJ+58NeZ2xk/wEUJBRwfcX1uEIuOEq2wBAIoph11BUV4AtpmKz7NRF3LkHZSr9OC0JgclUhrBb+52AboJDiGWaSbRI+nY/dLbu5OtnGPeMyWJHp5LrMJLQhlX9J0hfSOeSHUIz0vAQciiBY4ESRJG6Mqhd+R1IGr53zWXqdaShC4mrjTqZvHODHPi+W7bNo8WRiEIKXbpvFhZWqi+qdw8MkCD/Jyck4HA6qq6vxeDx85jOfwWw2s76uj8nffZNNDeqqps8TonM4wIyCeNGb7I2QJH8NnXc7aAxgdqpV0gdWQ/nFkKTSeVN8LFluMNjFvupbcA1txmhUbzrf5s2EDhwk6ZZbsM6fT+KKFRyekkrzSDMXF5w+b+OBTV0oCmSVJJx23zH8dyBtnIPuhpHTUj8zTVZXqIHdu4/5fHZhMru+tZifXFbFjy6ppKHPy5u1cREdsqeCKQF0JjTzvs6KYRd7RYzfGz3ohzfwUI6HTTPKSUlMJCsrixk3LSKpphfD+m5ETCHRF0G26Wjo8xKKxmgfUlcu2gQjP3Tr+GsTrJ5cxOTUiRw2f4a5+/zcG/4KPxDf5WBePuPnncvS8kUEFWgxhEl1Gelt2o4jYiKojxGTg+AZZnh4mDOFU8lWmiqE+LsQYvcoZXf1x6Xs/r+EqWkqY/jarX8mJ0edEcw1OxnX1IQUjtCaUM6r+1S//IgxHgzzRSs5f28Du91+QFCXkcdN56sxg15tL/X19QBkGPT8pDSHSxOGsOgTuH7hDGSLamC+NCmdkaPOQJESwRIJsbdyFookMat2KwCzrIJXIkHeJ0qkz8+BQIhSu5Gp+U4eXTEZ46iObJbGQ2JiIgUFamBdCEFhYSGyrPCdV2pxB6N87zW1ZuFQj2qIyo9KX430uNEK1XgQGy2sObAaokHVH5s7E9IqYcLyY45hbe1XGRh4h0CwDYNBNVYjL7+MZLeTfNed5Dz2O9Lv/zZ/aViFXW/n/HHnn9Y58gwF2fZyE7kVTrJKEz+6wxj+K5FdlojfHWao69TqHgC0SUno8/PxbT++ljbJqtYoLZ+SRaJZx65W13FtACiYz+3Dbr5uyGWz2cTVG+7jp5vuYFvH20eamFNs5CSYEGEZB1Ecvn5km45tdZ2c89P1zP3p+iOrCEOBg7xGL9PNJjKNZoajqrEzCkGZMsyrxmW0DbRR45KP7D9tyMBQ+wCJIbWtxxwl2XCYB9c/yMbWY8it/204FbfS88AfgEtRqbo/eH3qEfL7CB3uRh+W2Fb3LtMKcrnnnns465ZrES1uXvOUMTi3Al2pg8j4BIQubqVHNOnMT4x71Wozx/HqiBGbzsZB50Huq76PoeDQke1NrlqmpFRy7+Q8cjNsTEm1sXLDe6Ao6MNBinSCgN9PJl0E9GoGToFeDSQXjMRTX1uK7OwnxvgMM+vWrSMUClEkq+l1VQkRtFotEyeqbqcLLrgAjUbDlqZBWgb9FKRYqOv1MBKIHJkllaXHf4PcuDd+cD6IKdStgYQ8SK8kZDSwa0YuQYs53kcO4fbE5wpGQway34/n7Xewn3cukl7VU+jz97GubR2XFF2CUXvqGUahQJR1fzyIIivMu7p0TAf6/2PklKur3Ka9p8elZl24EN/mLURPwsEmhGBKbiJ72k8yC0/IA4OdFcLJXH+ASr16/b/b8e4xzQrS1FVtkjRMSdgPRg2PbW+l161OtJoHVKOmz3eArBBu96A7imTS5o/xza06QsLIS73N/D5cynDqN5mQcR4TlFwC/UYyZdXARLI99A6F2ODbwF/fOTM8qKdiHPoVRXlFUZRmRVFaP3idkdH8h7F17Ru8/NPvkuwz4kqIsv1vf8FoMGDKSGfn3d+jtyiLdjmKJ9+KI1+HJMX9nRFTKjvdx85g9roGKE8qJ6AJ0Bpt5bd7f8sz+59hV+8uGocbqUyuRCMEekmQZdBRn5ZNdk8rRcN96PQGYnIdRm38Am1KzEQjK5hfj2fmfL6hkxjg661l48aNbNu2jSnaDpbrqylMU1cBycnJ3HvvvUybNg1/OMoDr9SSajPwrQvLURSY9v23eX5bG1likARzXAxH7lb9ssqKVXDFsxANQ8t7UHIeCEF390sMD2+jrT3OfOnx1CLL8fL91LQL8axfj+L3Y78oPodYVb+KqBLlytIrT+scrX28ho5DLmYtL8SefHpxijH8d8HmNJJVmsChLd0M9/kJB6Kn1C/hsstAUWi/866TuqRK0220DPiIxuTjNwoBaRPQuDv5bW8/D0mwKHcRGzs2HuEGA/jiknLGa3qYoO3h8rJCALqt8YnQ4T41mK4fzbTzvNtBtDF+v/cbBOO8MjkhNxsCWmJIRIxlRFNvIzd/AvVSJQftpQC8Mc7L6un9SDLMjhad0nE4XZyKcXhACPGkEOJqIcTyD15nZDT/YeSOL4eiHNIshQzZQgwMdrPhj2rBzCPLKtl3xznsmV3BfflJLNetByA8OIMpu6pIMvvxjl5IV7/+LHkdDexp72d2MJVZ5lmkBFNYWbeSh3c9zPVrrkdBYXqGSkDX5w+yo64et8nKxNrtFKWm0B4IodE00U0W4w1BhKKwJVlLWijGkHLsBbu0Ih2HX82OWL9+PZIAuxRiwoQJR9oYjepF+cDLtTT2e/nFlZOYMS6JFJsBo07ibu1KntH9GEJxgycGDqMgIQrmgd6i6jhE/JA/BwBFUW/GaNQ7+j5GW/szCKFn4sSnKCz8OokJ0/Cu34AmKQnzNNVlt617G8/sf4bFuYvJsZ96qX9XwzDtB13MuqSQqgVnhiJgDJ8uTDgnG/dAkOfv38qfHtiKeyDwkX0MBeNI++Y3CFZXE9iz94Rt8pMsRGWFzuGT7C8xDzw9RAxG7F1tTB3qxh12890t8bBrabqdR26YR3GKmQUVJRiCEeQUI0WafgQKDb3qvSZZVd6vUL2L8ChvVJYsiEmCP0y1U+rScJB0okJLntLJ1mEfL5bP4o/n3sybE64kplFdq7nGGNcZL+DKW75+ysfvdHAqxuEGYBKqMtsHLqWLzsho/sMwJzQzadGbVJh6iAmFvul97F37Ooe3q6mbCTotFo0GV/dTvFyvLt0SuitwigQ8aXHft8EmKI0dwOVIZuBvh7ja7mBa5Nj0sjJ7GZOSJ9F1uI4RBXqcaZQ37iff7cIiy3hlhWgwgwGRyvkOJxUjqkFIs3UQvWE8AM/eOJ03v3IOP7xwHFoRnwFVVFQwc+ZMqqqqjvnO9w73s2pXB19cWMzZRclYdIJtU99l723Z3KV9mWKpE5reheeWozyxCMl7CNmQBdpRHYTDa9W/ubMBCATUWge/v4lo1Meu3VfT1/c6WZlXkpw0n/y8W1FkGd+WLVhmzUKMFt59e9O3EULwtalfO63zc2hLN1qDhsr52R/deAz/X6Bgcgr5VckUTU0l7I+y8xQpvR3LliEMBtyvv37C7fnJarXzB66f42DLAE8Prqp52Eb8XF2zhjn+AG80vYo/4icSUVNbi4qK+OIXv4jVaqUqEkVONaHP0mMTQfa0qPG8o12jUZ2EIyyz8h03C1wy/0iWMMbipJG3epuJKApv6WwUNR8gq7sFRaj35zx7hGsWzT+l3/+v4FTyoKYpilJ6xkbwCSIhYQaJSecxRVnLul4jB1IDZGu1vPSHp/ji+CqsVit1+7fzct3fmZMyg4sm383K957hz9fcDIDT1c9QYgqt+XmMz9nNm8CI1UHThm0sXtRFSduVjNT20a8VSKZknux7klDrYVjyOdIlOKejgVhSGt7mRiisYjB8OQDlUQs9w8PsT9DwucwMttYPoRHgiA5TklbMzp2jWrVXXsnAwADTp0/HYDhe2Ob16m5sBi13LRhddtavRdryCOw4ilR35TUACMAkIOocjTX4h2DLb9T0Vata1OYPtACqK6ml9beMjOyitOS7ZGZecWR3ka5uYoODmKdNU/tE/HT7urlr0l1k2079IR8Nx2jc1UfR5BR0hrG6hjGokCTBhXeok6D1zx+ibmsPsy4pxGT9cK1wjdWKdf583GvWkHbfvQjtsY++/GQ1jlDTMcLmxkHmlaTElRBBpddQYkSL5/OetANjKMbnD3l432zi7zvvwujZyHppEffO+PaR6/z+KXmsONDB3rJyxrnr2duhZloJIUhcXkyo1c1AoaCgw4tRhtmKlvWxGAMWdeInKTEWbZ7IhRMidJXaWfDWSiRZ5rEVS9AOP0ehQcbna/h3HdrjcCorh82j2s7/dTAYUnhc+hL3iV9wllVDZ0TiTwub6TH3sW2bWpL+wl8eJirJVHQnMz+9GIslTh8syVEMwQD708pJRp0V2GdNY6TFgbvVwvDafSgd3SS3dDG9qJTuxjpWFaiVwHM1MbKzayks3E5yv1p09rNy1RWUe3CEWxpCPJyo44r8abxe00WaGGH1i2oRTl1dHXa7nbKyMubOnXtCw3Cox80/aro5pzRFVXSrWwNr7lU3Ro9dOivLfousV1c6onSh+mHrZtWlNEulElAUBZ+vEb0+FVkO0tr6GMnJi8nOvgZJis90ov19AOgy1SB6u0etJM1znJ5aVUvNIOFgjJKZJy7gG8MYqhZkE4vIPH//Vl57dB9rHt9PS83ASdvbL7qQ2OAgvq3H002kWA1kJZj4xdv1PL6xiWue3MbLe4+SkrGp17MlZiJskHDbtFRo9UiKwt7OTTw9qOfdzs2sb19/pMu07Ex2LTwLnSIgy4knrPCHdw+xuWEAy/R0nJeX0B4Mk+dUDdMMp5ocsjVhlOlZaOg3Ch7cH+Q5exINdz5AzaRp2EzllBT/iBRLFi7XFmT51GIvp4tTMQ4zgb1CiLrRNNaa/5ZU1sFwlNUDATpELn7bVSyxRYhoZToSh2hoaCAYCNAVUrN6/Pua0EWjNBWrdvLSuh0MJaSQMzhAvyGVAaHSQRjHz0ORFVrezkbSyTgX6tFotdDXxe6SQg4XqnGBHI+LgoLdJJHIHSNncUNjiBK5i+/UDePcO4glBlfmFvKPmm7ahwKUaNRMiyeeeILDhw8zadKkD83c+eVbh9FIgrvPLQXfIKz8HAy3wuXPHGkjL/8TffyKoYNT8EbV9FLNhHPVjW1b1FqHLFXFLRTqJhodISf7uiP901IvPO57owPqzRmwqRd4q1vNXciznZ5x6KxzoTdqyCoZS10dw4mRlGml/OwMQv4orh4f3Q3DvP6bavrbTlwoZz3nHCSrFfdrrx23TQjBxRMzkRX42pISytJtPPlec7zBKFmmXaSQmXkVqWkXYrvkWYoiEfa7JdrC6uq23dMOG3+mvgC7VsNMtHidThSt4DtvNrHiyW0c6HITkRW6QxHGZdrJfHAWZ83NwzSqwpjlH2VpNUkI4Ol+F69EJNbOXIZTH2JIk4vBkMbw8Daamh7+dx3SY3AqbqWlH93k04m1g3FK3A7rZ7lNfpr6QQOD9iBDvXVseectPOYImphg5cyr+Mv71XRVqEHlfnsSskbLin47f7HF+JtlCXotuG2JOPR6ouEwjiI3mcUN9MnLOfTeNnbd+C0KXHU0JZYiu5ppMeXwvdoVXEaIsoYIG7q7sSXZUUjj+wS5vNPFOwf7SLXqyIuoOdidowLq06dPP/4HjSIQjrGhvo8rpuYwLtkCm54COQK3b4a0CnC1QNiPp6uCcLATagYIcBmGK76AIXnUBdW6CbKnHYk/eL1qJlNC4jRKS79HS8tvSUqaf9x3x0aNwyWbbuRqbmdz52a0Qkue/fSMQ1+rm5Q8W1yydAxjOAEWXFNG5bxsknOseF0h/viNzXQdHiYl93jyBslgwLpwAd4NG4h5PGhsx7b54sIiqrIdnD8hHZNew/dfP0jboJ/cJDPY1LRy4emhfNoPjvQZv8nM6tFEjQStRNNIExzcoW48524ArihO587GDliUiRSOIh328LmaJn6gy0cGckx6JKOWmKzwAYnBtc1hflRhpCdFD+4gb4WC5Jr1DEejDGvKGQnFSMv6LLFYgPSMM5MfdCoV0q2o0p0LR//3n0q/TwOuSnfyjynFXJeZxJ5AFF8gkXxLFJctjF2uY8fG9bgtUbQ46cwqoEvSoQuraZsbMwrQxqLM6YN5HSG6NSbS9XraQxFmLr8KgE55Bq/qv8Qvyq5i4/QlxDRasg1qlpHJV8M/Di8D4CXC/Jgg3b50nveacaOwlgiPbmziQLebfIeGoxcJd911F1bryYnnGvu9BCMyswqSQJZh1x9UOuK0CrXBnK+gnHMfvt1H5X1rJfSTRrmUQh41UylvNooSo7HpF+yrvhkhNFgtpWRnrWDO2e+j0x0vqhMZGEAW4DHDY/seo3qgmmVFyzDrzMe1PRmikRgDHd4x0Z4xfCSEJEjJtSGEwOY0Yrbr6f8Qig3rnDnEhoepnzadoT89f8w2i0HLBZUZCCGYU6zGG3a2jtYqWVNBaMDdfUyfGwvUmN10eyolhhjNw03galVfYbUqenlOEvdnp3F+3wgmJUK0IpEuPdywvwWAXKMaL7nrhd0k1rlZ4rDymQEZSVHocujwJxupkWJcleHkxqwUukIRvDEZg/NSXnL8lg2+MyOJeyoV0g+gUnTfN/qRDjgzVRf/YUhCMMVhocpmxheT8RpnMc4eQZagsz8IvZ0M2yJE9Zk43YNcv/IRitrqMI/OZi+ORbhV8fHsoHoxGjSC9mCY6Z+5jMl33MM5V1zLP6Kqu2nXRJU5qKUfKgAAIABJREFU0WLyIRSFixov44CriNRR4q0oMLcAaoeg8WyVuG5v+zANfV6cwo/BYGD+/PnMmTPnQ+U+AQZHOWJSbAbVPTTUBGddD4ASlRl4tpbuH29HdodJvLwEfZ6d1FsrEbufgV+fBY/NBUWGvFl0d/+dlpZHATAas9FqP5wNta+jHrcJylNU99nUtKmnnaXUWTeMHFPILB6jyhjD6SE5x/ah/EuWs89GjMbohletItzRgRI93mdfnGrDZtDGq6YlDVjTwHOscRiXOY1Vnd3cnz4FpyZCf2CACAqgwICq0yCE4I7iDO5NTuDife8zuXcQTVN8jDlGPf5wlDf29zDc7KZxbSvDaSbSggrdFomWNHW8lSYjc23mIyR3j+xr57muQZoDn5BMKHAJsAzwASiK0sWnnHDvn1FiUQPBhoLLyB1VZBpKCBHEjcccJWgqI080k+LqI2x3UGDQsnzXBm6L6uhGQfijEJEJesK0B8MMRGN8XZ/MlUMyfkXLldbDCGBeopWgkkhCROZ5wgQUDVcTz7L4wnzVVbQlEjlmfJZAL7m5ucyfP5/Fixd/ZJXwkE+9WJwWPVSvBJ1F5UYCfNt7CB4cQvZGMI5PwjwlldTbJ6LvWw2vfRkGG8Cl+lqV3Bm0tT+J0ZCJ1VpOSfG3j/uu4eAwETlCIBpAURT6Ww/htgp+v+T3/GbRb3jqvKew609vBdC4uw+tQUN22Vi8YQynh5QcK0PdfqKRE6sLa5OSKNm+jZQvf5lQXR2Ni5fQefc9x7XTSIKqHAc1nUfpTdszwP1PwjpJhZSFIzhDAodGQUFhYFQYjL5jyf5yJheSEPAx5eAmEgfiiS17DvXz1gFVW0KnEXSNBHk07CPLL9MuKXSWqvdPVp2b8R1BtLKCLqbwZFCtN8o2nhm99FMxDmFFLStUAIQQlo9o/6lCzBdhXFR92D4xlMoW2704dGZCFS7609SHtNdaRZG1ngnX1eFPsJKIQqE4TE+vymkiAOGJ0OcPMxiJ8lBzDx1Bte/8RBuvB0pRgNZAmLBSjjGk8DQh5qBluS0+Ey9JU//fUKdm/CwqclCic2H1dlBcXPzRP2akA9q3MzgqiJJkUKB2NXLhBYR7VaMX7vIiWXVk/3guydeOjxuag6+AIxfuVWsZ5IJ5rHuvCp/vMAUFX2HG9NdITl5wzNdFYhHmrpzL9Wuu5+w/n81ta27BVtdJoCADh8HBOdnnIIlT90DKssKeN9s4uKWbspnpY9TcYzhtJOfYUGTlQ/mXJIMB27nnos/PB0nCs2YNke7u49oVp9po7PPGq6rtmcetHLBlgiUFU2cDCRq1Xa9WC5IO+o8VGTI7rVybeB5WScPFDjfjPTKpe1387M167n+5loJEHd+ZoYaBe4RCntlAp1HQYtdilBWS6oeJ7RugIKCQKzRUDqsG8H3XqXNNnQ5O5c59UQjxeyBBCHEz8Dbw5BkZzSeAoT8fIvjQLvK0Wmq9QV4ITiUtYSKdaIjM6EcvBFFdLjm0ojXKuDUGrLEIpWXv098fTyGVvBEiJvVh9seuQaY7VBu6weXBG5P5QnYyLcEwB2NOQsEwaaYAd6Yp7BvXzRMrJvDmV84hzWbEpNPQPRLETJicjreZrWlAp4vzJZ0UIS/8ogKeWsKgJ4BOI7B3boTQCIPVE+j7zV5kf4ToQABtyj/RULi7VVru8cvA6IAvVeNachcAQmhJSztxzeOhIXVmVN1fTSwaJunVzVgCCs6F5/4rp4JNqw6z+W8NZJcmMmPZmCb0GE4fKbnqBOtkGUsfwFAwjsI1b5D3x2cBCB0+fFybolQrvnCM7pHRWb4t87iYA/0HIXcW2sZ3ybOoyRw99jRILjlu5QCQVJpObjSZRHcj71w8mVsn5dDhCjASiDApVkf97s0syZXY3+WmNhimPxbjzb5hCtEQaXYTrHdRbjDg0QtGdIJiJL5ZmHHc9/w7cCoB6Z8BLwF/BUqB+xVFeeSMjOY/jLd7h7kwPcKQXvDnbi21Z0/ArJGQjEX0RBRq/DESjU4QEmUWB7LIw6exovW0YjL6cLnVDJwfLn6f9AjI2vjhlACzRmKJrOPHewPMHc36GdJLeLwxpmYmsHZkK7vr9zFQu5nCZDOSJChIUY1KuuRGkiQWL17MPffcc8JahmPQuunIvwPd7SRIEkrDJhShJSSrgeZg4zDR/gC6lKOCw4FhtRBOjsG0Ua3rxDy8ETUFde6crUjSiQuM9vbHqQi+WVfO59fJtOdbmL78to8++P+E/jYP1es7qFyQzbIvTcJoOTNL5TH8d8OeZEJn1DDQfmqiQIbRFXlolEX5aBSnqoZm5wdxh4QcCI2oBaIAnh743Wx11S1HqVDyAeixOSG17LiVA4A+20aa7MDv9zMwMMCsQjV+WJlhwT6qwe7vUpXrmurV72kOR+gbCPDLWIABOcbEDAd90RhtFolr6oMknJkyh1MKSP9EUZS3FEW5R1GUuxVFeUsI8ZMzM5z/LMxDIdosEntTdRgPubBLGorMBvy6QhTALUs4nIswayQumv57IiZ19u50d2Dqn0hvzIBGxFhYuZCl2Spj5M0RPXflprJ1xMeXctP46W4/i3uj5A4fdQY9EUJtNUfe1tXV8b3vfY9nn32WW+aOAyBPM8yVV17JnDlzjjcMARe4u/nb7g4W//xdDvd6oCv+oB7saMMRUYgd2kxUW4I21YEwaPC+14nsi6grh/q1sO4HsOY+6NoDn/kNOMcd2YfXW4dBn4ZOl4isyDx34DkaXMdWY+7r3weATW9jYl0YXWkJi1/fhtF6+oI8+9a1ozNqmLGsYIx5dQz/MoQkSM62MtBxaqJAGocDbXo6gZr9x22ryk5gXLKFu1/cx7pDvXHxqz6V9p49zx21IwMZLg9GWaZbr1VVE4fbwHssE6wu3UKqrN4fDQ0NSEOt/P2OWXxztmqIbrjhBsYZ1Swn4Y5LBAz3+PgrYW6T/BTqdJyXbOdKu42lrSHcb50ZHtRTcSsdrwcJp0fI/38UE3R6jLLCc9lauvxhXH+tpygq0UWc5M1nqKRYlhh6ppaoVc3AqWqfQtrB6+nWgdOg4B7JoyrJChGZFk+Qg94g2UYdX8xLRfaqsYekg3GueOGOkCipVcrnnht3wTQ3N5MSaOOZS3PJlVzYbCeJ+79wFaGHJ3DPqn009Hl5emO9mnqaXIIy5QYagxqyRSNa3wFC4RIMBQlYZqQTbvOAAHNVCqy6ATb+FPa9ALP/ByYdqwnt9dZhtaqsKfsH9vPTHT/lklcuYTg4TMzrZfill+ip3ckjq+y8Gr4Nua4Bx6JFSJrTjxMoskJrzSAFk1IwmM6cstUY/v9ASo6NgQ4vsnxiBtZ/hm3xYjzr1hFuPfYha9Jr+NvtsylIsfDAK7VEkkeNw6ob4NmLYd33442dBRgbNpEejdElIvhSVFeP8nAxbIo7WjROIwlaKwLB2rVrWb16NSZ/H8ODA2i1WnJycphcXsj59k6+MS+L5d4+Zht1rLp0Ms/MK6VHlrn7z3t5anw+vzqrkJTlxVjPyfp4B+wkOKlxEELcLoSoAUqPFvkRQjQD/xUV0vaiRGwhhRojPK4N49/dR2b1EN3RuBulVc5gXLufYJ2LQIv6sEwKK+jCdnrsWrRhHy+88AKZCSY0vQHeckq8M+RmWUoi0S4fyqjYeHN1P7qdA+h2DCD5oiSZNMybN49p06axdOlSrrrqKgoLC9m0aROGqBchOLFxGG6D9q1slccTU8AmQry16xB074WMidSWfolWJZ3zNG8jCBGKlKDPtmHPqydhUjuJlxajkXsg4oOsqXDJ47BIFTqX5QiKoiDLEXy+BiyjxmFD+4YjX//DbT+k75e/pPtb3+abj/SS3jBE349+BLKMdcHCf+k89Ld7CPoiR/j6xzCGj4P0QgfRsExn/UnEe/4Jzms/j6TX03jeUlo/fy3h1lYURaH3pw/RO2cm96R6aR8KsGfICDoz+PrUGB3AhaPVyXoLwj9EWixGf2iEBt+bPGu3sU+vg7cfPBKrEJLAkG5DIW64tm3bRn9/PykpKUiSRHp6OmnhLiz9+0ndtZmlNZuYkZXAvPOLeHTFZDzBKPs6VKpvy9R0tAmnro9yOviwadoLwBvAj4B7j/rcoyjK0Im7fLqgKArKoRGYmMCa8RZ+UlpA5duqnOfE9Pk0Du6iWTZQ5FEDUr0tGqiA16QW0qc6aK+WSBPq0s8iRdE0eYhlqzGDC1/rpK+nkZe1UdYoYabFNGgG1RTTMmuItLRUFixQs39mzpwJQCwWY9WqVTQ0NCCEwGI5KjGs9wAER6DlfQD+opyLTQpxk/Qqv4xehntkCIt9PK/scaNBZqlGrdIMy2XYcyxIv7sSK8CUF2HfHkDAZU9BYj6xWJD21mdpavo5yckLKRj3FRQljNVaBsD69vVMS5/GjPQZPLH913zuRXA7tRCJklxcic7lxTx9OqbKOGX46aBmfQdanURuxZhxGMPHx7iqZAwWLW8+UYuQoOisNNLG2SmYnIJOf/zKVp+bS/6LK3G//jqu51+g9drrsJ17Lq7nVLdRQeNeYCL7u9xMv+5VUBQQAu/WpwmXrcD57kNqXRCQFo3SFgmzp+5dHk5KxCnBu41t+Hb8HMuihwDQpZkR/QIFhRkzZrBt2zaEEEco97OzVeK+hgbVjdvV1UVXVxdZWVlMy1EnjFsaBzkr78zeLyc1DoqijAAjwNUna/NpR687hLfPT57HTmuGmZ02iUpJi1aBssL/5cYpFlbsb2V8mg2DQaYmpga5Xh1OQBPJwB3qpkg7WoDidyEFYqzYNsJkvZ4MvyA2KYWH9qrBpVqiZOm0vP/dc/nFrx7BZjt+KZiZqZboNzY24nA4kKSjFnYrPwdD6r4aMi5mTfNk7tKsplyoqac/iH6OmzaYeV3Tw3R8JAp1rLIhHZ3+KDKy1berF3fhQkjMx+9vZdv2C5DlIJJkor//TSIRNbfbZi2nw9NBw3AD90y9h2srriW0fiO68G4ePU9ieEIeay598WOdg+E+P3Xbe6lamP2RzJpjGMOpQKvXcO6NFexa08pQl4+aDR3UbICimlSKp6aRlGXBkXJsxb6hsJCU//kfbOedR/NnPovrueewX3ghkY4OOFhLSsV0arvcMGfqkT4TdvYi7VpPY3k5wtUCoK4cNBq2+CTQgUtWGDDpiR18Bv0530ans6NLt7Bs11Ri0+1kJGexjW0oikJ+fj7AEbligCVLlrBu3TpqampIS0vjT08/joVx1HePcKbxiTh4hRAPoepChIFG4AZFUYZHt90H3ATEgP9RFGXtmRrHgdEDfPm4FH42NMSrTf3cn2ym3B9hpztAYkQNjE7ISUQTcXM4BIRjCAVe3tuNQGF8kgZGIOgZxqTTYLboWezX4PxcKbuiERg1DjJQbJBwuVz4PG7s9vLjxpOQkIDBYCAUCh25UI7Aq9Y+ULSEJ7gZo8bNDdo3iChaDIRZGVvASoBYlJuSE8AL0WkPkD5nKnS/o/Y9+0uw6Vfq/4vuB2BgcB2yHKS87Eekpy9n374bGXJtwmBIx2Ip4a2G1WpXTQkjL7/Mpb25uC2H+NpNj5Js+/iMqTUbOpAkweQluR97X2MYwwfIrUgityIJRVEY7PRyYFM3Nes7aNip3kfn31pJweTjaSeMpaVkPvQQvi1bSH/wAfoffhjXyhepWGyltiv+QP5AFEhWoMtQQJZnM6RXkeprIioEm3VGsi2pdPj62OrQs3jIj8ezH6dzNvpsKymKHbaBsq2TpfMX0eHtZdIklbFZkiSWL1+Oy+Vi5syZdHR0UF1djdPpxOPxYJdC1Lb3s6NliH3tw3xh7plJ+/6kOJLeAiYoilIF1DNKzTFKDX4VUIFK+PdbIcQZq4RKt5u4fnYeS3PUStxt/R60qWaqBiLscfvZ5/KSFJJJz7RhPiuNToPALKsGY1yyhStt9cwsTsdgMOByuUh3GHElGcj43+kY8h1HNGkrRnWa/eEWHnnkEWKxGHb78VXDQogjxW4FBUed8OAIhD2w+DuEr3qRNxqCnF9ixSm8pEnDPKx7jByN/0jzC5eeBfe2ob3wq2gcBugYJQKbezcs+zV8YR1kqhfi4OC7mEz5ZGZegSRpKSv7ERqNldSUpQghqB6oJj1qIXbT3XT97724/74a64yZTMuexThHPLvpX0X7gSGyShKwOD4iVXcMY/gXIIQgOdvG3CuK+fwPZnH5fVOxOg3sWtNyUslQx8UXkfnDHyDp9ejy8lCCQc6yqTKfwdHK6x3Ncc96g8iFaAgkLUWauCv41srbAagxmzCGZfzd7wGgz7NjPy8f86QUhEFD8WAyl112GZqjkjmqqqqYN28eGo2GWbNm4ff7+cc//kFycjKpZkGXJ8rlj23h+68fPLl63cfEJ2IcFEV5U/lAcxK2Ah+owHwG+IuiKCFFUZqBBuDk9KMfE+Mz7VSEDvL2c0+jU6AjFsVfksAkj0JIUXglFKDQK6NLM9Ph0OI1SWQYdez+9hJeumkSxoibpKQknE4nfX19pNkN9LnjZfHv1vdTlm7juZtnclGZnTyhprUlJiYyfvyJJTIuueQSrrnmGiorK+MfDqqrD5KK2Nk6hDsYZelZcf2l86WDvHHR2Txx9WR+u2w8aRNS1GI2gJ4a2PEUlF4ARjtMuRayVRpul2sbQ0PvkZ7+mSP7MpmymDXrHQoLVenBmv4arq5xEBscRONUfZzG8uNXPaeLXWtaWP/cQVw9frLHAtFjOMMQQmBPMpGaZ+espfn0tXqo3977kf30War7t0J4ickKdT1qimx1xwhGnYTTomdreJTJuGs3lQlxJoPF9hJyrNn0Jp/NCzYru/e8CNGwOpYFOTivKsM8MYVgvQvlRNrVo8hOy2Lx4sWkpaVx0UUXMS7ZQiAWf3S/tq/rpH0/Dv4vsKveiBr4BsgC2o/a1jH62XEQQtwihNgphNjZ399/oiYfiWg0Sm1tLcMuF+OUKHKinqfrephxVvwrS6IStX1elv76fRSrjtnJNpwWPYcO1AKqLGBubi6tra0YlRA97iChaIw/b29jR8sQFY4oYc8QF6X7cOhkvvGNb3DXXXedcOUAoNFoKC4uPjbecMQ4FNLUr5bKl5mMRGR1nBJeTBVOlrj+yAXvLYy7oLqr4anzVD3oc79/9NegKDHq67+D0ZhFXu7Nx2wz6JPRaAz4I346euuZ+l4v1sWLyPvTn9AXFuJYdvG/dLw/QHfjCFtXN3FgUzcpuTbKZ52ZCs8xjOFEKJ+dQWKGhbefOUB344f77nWjweH8qNpuT5uaAXWge4SydDvlGTY2uxxgUic4hqRisoxJZESjWD09VCZXsdvdxI+SnXwzFqb3z5ces39jSSJKKKammZ8AoVY3XQ9sZnxnCrfffjv5+fnMGx9XVJyda6Yg5cPJMP9VnDHjIIR4Wwix/wSvzxzV5puohKTPn3xPJ4aiKI8rijJVUZSpKSn/GmVte3vcDuW7+1Hsen5/uJuvxtxHPp+i0/PgGweITXSCJFiSkYiiKOzevZucnBxSUlJYsmQJCQkJxDyDtA8FuPR3m7nvbzWUpFqRWrbw2GOPMTQ0RFJSEnq9/pjl46kg2nIQBUFMn0WHS6XGcHQNoRW9KJIJIWJoND7Y8SQEhuCdUdHz9T8ArR5uXgdJhUf25/XWsW59CV5fHUVF96LRmE74vQeHDjKnJobOFyL51ltVyoHXX0Ofd3raDP+MvW+3YTBruenhuVx+31SM1rFq6DH856DRSiy/ewp6g4aaDR0f2lY3miRiG+6nIMXCWwd7URSFA11uxmfaKU+3c7DXizwqioUlhb9fuJKXuwfgpRuZUb0aV2j4yP529O6BWLwg1lCUAJIgeJK02+BB1X3l39NHzKNmRi47O06lU+HazIKSD2dp/ldxxoyDoiiLFUWZcILXywBCiOuBi4BrlLjzrxOOqkBT3U2dnCFoNBpKSkqYOnUqjiaVXjcyKYmd0XhlYrHewGazQiDJQK5RT6Veor29ncHBQc46S70gtFot5eXlhEfUFcz+Tje/WTGF31+Sh0WoRXCtra0kJPwTBXXdGnjtqzDczocheqCamJKCf7+HdpefrAQTomkHQkRhwugs3t2p8isBVL8IQ81w+E2Yct0RFavOrpU0NPyEjs4XALDbqkhNObmW077+fZxTI6MtKcJ0tJvrY2Cwy0vT3n4q5mZhtOjGqqHH8InAaNFRPD2d5n39J2VwBZBMJjRJSUQ7O7moMoNNDYPc8fxu3MEoU3ITmZKXSDgqsz7rVrVD6VJM1jRMBQsh7GHW8LFejQN6zZGsQwDJqEWfa4sbh54a2P83iATgmYuI1W1F6NTHdOCgSq+h0Uisum0Wv744m0gkcswk99+JT8StJIRYCnwdWKYoiv+oTa8AVwkhDEKIcUAxsP1MjSM3N5cVK1Ywb948MsMBxofj7IYTXVHuqw3yjhRBTjfxuZRE/ppt4/Ff/Jynn34agLKysiPtCwoK0CqqIRifYWNOb5jOvXGZwVAoRGLiP1FQr/8+7HwKdjxx0jEqsoIUaCUqZxI8OEjHkJ8cpxnRuxMAUa4KBrHmXlUbev59EAvD6jvU3Ov8OQCEw4McOvQNWtsep7PzT6SlXcy0aX9HfAhr6ub9b1DcDYkXnJh473Qx1O3j9UerMZp1TFyU89EdxjCGM4j8yiSiYZmu+uEPbafLziLS2ckdC4pYWJbKG/t7SDTruKgqg8m56oTvprVh3rmijsPaEm7+405GznkQLnyYjIJFXB6I4tA7KDbaqDXokXtreGT3I9z2tspBZixNJNLpJdbZBI/NgZduIPrm76DlPSyDP8c6JwuN00hg/+CRMVUJLfOzspEkicbGxhMN+2Pjk+IqeBQwAG+Nzhy3Kopym6IotUKIF4EDqO6mOxVFOblZ/zfBZrNRVVVFaOe7JOcuZr8JGpx6Ljksc43XB2Yddxaks+GvLx6T4WA0xisTc3JySNf40MTgbosdzzttdJib0Ol0REb1GY4xDsER6FXjFjSrWQyKrBCoHUCXbjlCjhcbCqClk4B2AYGmYZp0fpboDGjDe4lZxqFJGQ0Ot24iWDiDvfq3mZCagbVts/p5tpqXPTCw7pjfnJF+cmnBgd8/zkFNL5rDKoeM5ezZp3lEj8VgpxejVcd7K+sJBaJceGcVZvtYTcMYPllklyai1Um07B8kt+Lkrhl9VhaB/bUYdRoeXTGZV/Z2MSUvEaNOQ4bDxHeWVfDAK7X87t0mClOsvHWgl5/YcvnhJV8AUyL3v3Qj9533ON9tfpa1/i3UHVzJU4E6ZEXhsOsweSUZuNe2Envvj3zgcJa2/xgEKIoZQ4EDJabg3dSJHFRdUv2/U3nNll21jIyMMxOz+0SMg6IoRR+y7QfAD062/UyhsrKSHTt2kCkH2dkZw5+kp/2WctrWHcDgD9GybTPt7e1MmDCB/fv3H1OoAqqhKM5I4Fv0kN6oZYu2g4NyOyVFJWi1WlpbW4/VZGjfoc7sc2aoqaZhP573BnC/1YrQS6TeOQldmoVIeycm4UNbMoHmfTKecIzycBSD4SBy1sWqfCGgSFo2ZTagl1PpMY9QBMj2TCSTapCGR3ai0yUysepxurpW4XSefcLjEPP66P/FL0gGrksyINn1GE+SWXUq6Gt1s+pHO4+8P/uyIjKLxhTexvDJQ6vXkFWW+P/aO+/wuIqrD7+zXbuSVlr13qzqXuSGG7gALkDAOKa3QAjwJV9CjwOEAPmSkEpIIIQSuukYbAi4G2zj3mQVq1m997Laer8/7lqykWUItpCJ5n0ePb479+7ot2PtPXfmnDmHskONKMtSB1zi1MfE0r52HYrHg9mgY/nkE/fkXDc9kbKmbl7dUUZ9h7optjfUNXUBaI3o81czNnEm71fs4KftOej1WpwINpRv4JbRt6Dx1yMKP8LhzQS9H0bPXgAUoceYEIjQaejcUklPYQtee5/PYmRUKvqIwSmxczZEK50V6N6ow6wz4dfThKfejlkjeL6iEYfVgK2xmc2bN+N0OklJSeGWW25h+fLl/fqIj4+nqq6a/cYyDuvUdcCk6ASWLVvGXXfdhc3WF7LpqjnEKs90vKOXgeJFaS6la1ctWqsRxaPQtbMWAG+ZmvZXP3osORbBSHGUC3VvoxFd6CZcoIanLn2B6u8/CEKQPeldLBlXA9AWoTrq7fYKGhvXY7VOwGqdQGbm/zHQ9pHjUxcHNzkwj5+A+AbJ9I6Ru7Uv/73FamDU7MFJEiaRfBNGTAynvbGHXatLB7xGHxMDLhfOo0dRvCcPOR0XH4TD7aW8uRujTkNZUzcerwLGABgxF/I+ZGzENACqdDou19gJN5opai1CaATmuAZ07gJchrEYJvXN1HXGdjQGLYb4QISfjp7DTbhrfSvxAroPNp5Mzhlh2BsHb7cLT5cLb1MPMY5gDM0lCI9CmlfLmqY20AqmWUz4+6vhYjExMURHR5+Y98hHfHw8bsXDEapJjkkk25VCZqgaJfTlp5KnD7j5iesO3s9Vp4SuvMN4Wh1YL0zElBaM/ZB6U1Xq1CIkmsg0Cv1dvGF4hFjdSghNh2P+hlGX0uwqxmiMwmSKJmrK7yifdi6HottwuVrYt/86FMVLYuLtXzkePXmqMVq3RL2J+8/9Zsn0jlFV0EL8yBAW3T6G7z8wWVZ3k5xVpE+JJGNqJLvWHGXHB2peNWePm9a6PleoMT0NgJJFiylZvATF03+le2pS34PfTTOScHq8VB/bnDZiHrRXkYKeebZgftjRxq01LYRrXRS1FoHXi7X0al6xBvCzxEIqYkb29qUV6gxEaAXmMaF05zThKG9HH+OPMdlK55ZKug98s1D+r2JYG4fuAw1U/3on9kOq9Y3yBCMcHaSG+iEq+pzTC6JDuOOOO7jiiisIDw8/aV+KVyGmu2+5ZPbMmYz1JKLrOPmTRk6TaiwePg6NAAAgAElEQVSK81UfhuvwYYRJh9/IUAJ7niDccQ1KRzOiuQgFLQQnENK1CX9hp2f8n+Hm9WrRc0BRvLS0fEFQULbauRD4Z9+NS+lk796rsNvLGDP671gDv6KaHFC6YRUtFgi54QZS1q0l6LLLvvI9A9Hd7qS1rpuY9CASR4fK3EmSsw4hBOddm0nmOVHs/ugouZ9Xs+ZvB3n1oS8o9d10/caM6b3eWVKC8+jRfv2EB5p45OKRLBodxZx09R5R0ui7h8RMAEBTc4D/HXsN59k8WLs8JDg7ONpWSlfuGwjglcAAtnsa+EhpZ92yZ/h9QhJOdyPrSv/Nx6UfY8mOBLcXV2Un+jA/rAuT0UVa8HY6++k5Ewxr42BMCgQUWt9Xsx9GedWbe0YwFBY0kdnSjWF7PZPTEzGZTKSnpw/YV8fGCrreLWWOcSzTJk0hPi0ZtAJ3q6P/xW4HxQ51E9xWdHg1FpTaIvynRiG6qzHUrEQrmvFu+AM6+z48lkxqO9wsdb5PqyUZ00XXoxgsvc7xxqaNuFxNhIb01XgODp6G0RhJZ1cBIbZZBAdPPUHCq3mvct9nfcl2u13dvLP5KQzbD5I/IZTLM5ZhiI09rSWlhgp1Y09E4sk3/EkkZwNCI5h9ZTox6cFsfCWf6kI1emn/OnVpWGi1hN97L/5z5gDQk5t70n6umZbI366aQFKouqpQ2uALLQ/PAq0BKncRH3cjKd/bjTc0lfGN3bgVDwXbH8QF1OnU79qW8vXcv/d3vKjxcEd4KD/dcjf3bLmHhqInKDW/h4KCPsqCIcafiPObsEwYoO7LaTKsjYPXoiFveoM62NEW/DFhMpiI03fh9ii07GvC0tlDdMiJN7fugw246vumnV6nh47NFZhGhjD7nks4f/GFCI1AG2TEcxLjoNTnU66o/oD9ePhjz3J0xkYCZsVA4VoAXN54tPuexKjJwxs9i/o9q8jUVNA8/jZc7na2vzCJA9eMw9FZy+HDP0OvDyYkZE7v7xBCQ0jIbAAiI7934u9XFH6z8zesKVnDwYaDHGo4xE1PzSf0nidwG7UsuvtJ9JrT35jWUKYah9C4wfnjlUjOFFqthgtvHc30S0ew8EejyV6cRHVRK12+72/IDdcT+9cnEAYD9oOHTtlXqL+BAKOO0mMzB51RdUzv+Rei+ShGvyg0M+8mya5GMbZ2dVKh1+ERggCPl0PN+fR41NnADr++iMgLylZyW8Ja3gpdy0PuP9JVnwOvX4FY/9AgjMgwNw6rc17iZw0P0nxFJ2E/HKMm6TIF4apX1x5bXRqiLSf6CrwOD82v5VP3xz29be4GO4rTqybS0vZdrwsy4mlRcy3Zcxppei0Pr9NDW0UuTgzcaIWJaPmHdw5Ocw8asx5KNqH4R9Hs+llvP5rsZYQfeJJKJZTQaVdRV7ca86oujLudlD1xLx5PJ1mZj6PXn2jERqTcR2rqLwgPP7FwX0lbSe/x/Z/dz1+euYl7nm0hTBdE+ouvEZH21ctPX4eaolasYX6yupvkO4HRT8f4BfEkjQ1jxMRwUGDPJ2W9M3Sh12OZNZO2VavwtLcP2I8QgqQwS9+yEsCCR1Qj8dr31XrtmYuJ0arh6o1eDVv1EQDc2Nn30DnLTzUQD9lGMyVwBFafr+OFsPdZW7mO59bfDRodzLr7jI7DMYa1cThPMaFTFD7duQKNQYsuxI9g/HF1tmBCteqpESc+9Tor+nKgdHymbr13N6mOJ13IiWkotEEm3K0OFK9C08oC7AcbqfvzXqpzVadvVlIsVyeG4kJLfo/vSb1iB97EmXQZ0ql1PEmD8Vl0JieR7YdYqb+UQIuZ+vqPQFGNkOelnQSsNWKz9d+LoNcHEh93AxqNHkdhIe5m1bmV06jWy70y40qUkjJ+/FoHfnHxpL77Pubj1ldPh/YmO+V5zaRmR5yR/iSSbxNblIXgSDOHNlZSsr/P4Rt22214u7qo/vnPB4xcAjVrc+nxxsGWDIv/DE2FkLsKDBbCZ69AqyhUa/Rs0oeDomFZWDZhHg8BnaOJ1t/LK7V1LOhu4VnLKD4vryLbrj5sCgQvOiqoHLkYAqMHZQyGtXGwjruK2YEj+EDroLt8G/poC4l2G6mpqeh8ZfwWjOjbz+Cq7aJjg1pcx5AYSNuaUhylbQMaB12wEW+HUw1LdXuxZEeiuLxUF5Whwcvs9t9yod/vAMizB0NLGW3tbSzIX8T1mi6cSgLGaVNxFW3Ai6Ap+WI8HjutrXsx1pronubFPsaL/0cCny3rR/0f/kheRiYlSy6ieP4CPB0dlFYf5tZ/w+UPbOLxdyzoDCZGvPwa+ogzdyPP84WwZp4jk+pJvpssul2dQdcf7ZslmLKyCL/7LjrXraf1nXcGfO+IMH+qWu10Ovr2JJCxGCzhkL8GAN3km4nUmqnSayjyc6M4YjEnz2dDeRW6ynns3OcmWa9B01AMNQdQosbxw852JrscPBQ7B63Xy590PQMoOH2GtXFAUbg+7fu0arU8s/cJjClBRHQEcPmsi/g5Fm5EwwKz2XepQuNLuThK2rBkRxJ64ygQ0HOkhZ4jrWgCDGiMJzpvtUEmUKD1/SIMCYEEXTqCgBnRNAk7CzU7CKteg6n0E6YaSzmkJEHBxzzrXkRxp55yu5PaixIJmB1Ly5HtFHmjuXBSOjmHf4Jod0K7g4AxM3Gca0Y4vHR/8UW/j+duaaH5xRd7X3u7uuhYuw7Tx1s5b58bV3k5gWPGE/2LB9DZzlzabI/HS+7n1SSMCiEw5ORJ/SSSsx1rmB8hMRaaqrtOaLdddx2mkSNpfvHFAWtCZEUHoiiQX3Pc8pNGAynnQslGdWkJmBQ6hn/7W2j2a8PZmcIej7o/+MPa+3lq12PY7cH4tTRA9X5cofHEBMA/auqZV7OfX33iZtkDuyhvKxuUzz+8jcP+1xj39q18z6nh+fZccoLVtfiOzRXMwo8b8cfum1I6KzrwNPdgnBbG9dq7eLnwFfwD1qD57GGcpW1YTrJ8og3uK2ATtDgZIQSmJIVGTFym3YJi8AetgVvMm9njTaM1dx0veM5nelIQOo3gyfxavF4Fv/p95GvTSAs6QGPjeiId6t6DmGk/YMpNOxAGA107TkxB5ayooGbFL1CcTpJWrSJt9250UVF0rFtH8vZyuoNMJK1aRdw/nibosoFTafwndDT3sOPDEvK21tDd7mTUTLnhTfLdxhbtT1Nl5wltQgiCr7wSZ1Ex3Tt3nfR9WdGq/2//5t2UXXMt3Xv3qScyFkF3ExR8BIrCtbo0MhxOgnXh+Nvn8WKRBbvXSO3uINpKzXhyHWg9XnLtVm4tmURhYBQ6j0Jg4QESDmgJtMO6T/4xKJ99WBuHznD15nW310y028NfC59EG+JLcKUVBMyJw1HSRk9xK85SNZ/7rowiStpKWHV4NVPbJ7NbFBMwSYs1/1IoXHdC/7qgPuNg8EXs6EUtPYqOmZpDiMm3wMhLmeXYhJUunioOpRMzD148mvsuzGDLkQbWbn6XAG87nVYjh3N+iFZrIbRb3c9gTE1FYzBgTE3FkZ/X+7vcDQ0ULVlM54YNGDMyMKWnofW34D9rFp0bNhBX66ZxyVRMvs09ZwJFUVjz94PsXnOUza8VYA40ED9qcFIJSyTfFpHJgXS2ODh6sJH3/rCXVx7YTnluE4GLFqK1Wml59cRqA927dnFk2nQ6L1nIzdXbiPj77+jetYvGJ/8KQJFtFt6gRLUm/JOTsJXt4LmqNn43dSXfGzeCj3IbuKfqJrwu9dYs2tRlqQjRwqamZB4+eh92RY+jrS/IY3rx4AR8DGvj4PDT0eGvR9tWzDVtbRxszKEiSU2da0wJIuC8OLTBRto/OYqrthut1cCbpW8DUNRVioLCTm8GARH7oLmYLZ/+jMONh3v71/pKX2ptfeFotJaTKqrRCS9lwZ3Yp16D0Oq5Q/ce//AsYYSxhYzIQC7OasGi72LTpq0AKDY1ncbYMc/gKi5Ha7OhC1FvvqasTHpy83qnuLUHdiB6nOTEC7bfOJGXDr/E3DfncjShT0fQzDlndCxb67ppquwkLlPN5ZQ+NRKNRqbjlny3SRkfDgLW/P0gTdWdOB0eNr6Sj6IzYF16GR3r1+Oq6UsR07ZmDZ6WFhCCS3e+y4i2Krp0Jrq2baeysp55f97Gdc57UEbMh6YiQivXUaJEExNs4fvZqn/T1tCBotVSExBGqTeTdsWPENFBielqbul6g62WGfS0qAEswaldRFsLB+WzD2vjEGKbgXfEufh1O7jAF0K2J+YIAefFEXxpKhqDFv+p0TjLO+g+1EhdZDt76vaQYk1F0Th4znI/9+rfYNWeIzznns/t/grL1yzH5VW9w0KnIfTm0YTfdlxoaEsps7QH2SeSKGp7l23519MY5GKkRl03nBGqOrebm9aQFZJPhreSLsWILaKOyZPXEBw8FUdhIcbjkviZsrLwtLZSkrcdRVGoOKKG2a5cGsZvWt7g8d2PU2+v5zmNmqm1zQxJE/s2zJ0JKvJUozrnqgy+d+cEpiwZnKLnEsm3iSXIyLlXZ5AxNZLL78tm7nWZdDY7yN9eg+3KK2kPTODAn97qvb5r23b858xhxCefYL30UgyTsnl+opplYOd6den3s+YgVo38C/xgA4WtE9lQMZaIQBNpEQH87coJzPPU4JeVRUdUPD3NTvaf9wpeX3TilboNWM0BOFr1CK2XiPFtOMbG9hd+BhjWxgFAN2o5AggyBjBSmNjWsgPrgsTeJSHzBF+6DLeXvVa1INC0IDWxXZOfuhb5Zn0MvzbM7u3zl9t+2XtsSglCe3zaiNocgkQXaw0TSR3xc0JsM+ky64gUzVwTs4bKhM109dRRV/cBo2xdTNccpkDEEB46jgD/DBSvF2dhEca0viUhc7a6zPSXZ2/mk7JPaC8rxK2Bhy9+gkhLJGnBafxwzA/ZIUp565Z0HvofG2Hmb1Y9byBqi1vxDzYSGOpHdGoQWv2w/9OS/JeQdU40c6/PwhrmR3yWjfDEQPZ8XIYmIordY+9kR/soOg8X4G5pwVVejjl7EkKvJ/rXj5HyykukzFfvDRvWbCMlzEJ6RAB/31SEEjMB979ruHD7F7h2q76LCxIthJUXYs7OxhQXR3B7I5+1R3Kx8xG6lr2NGy0Taz+ks96KMcKM0IA9esAk16fFsP8Gm2IuwKXT4NYJZrS3cbDxIG2ONrZUbmFt6Uc02tdBrLpL8oClgBitBfva9eg8Wg4Z1Zv+uWIfWrOa1XF+/Dw+Kv0Ih8e3M/rtG+HIJ32/0FcFqtUcTXz8TYwb9wI1aT/hiF7PuqBt7Gg5wOebbyehoJJ7K18nTVNFpTGa9PRfAWDftw9vdzfG1BFc9/F1PLj1QQwpKXT4azl/j5e8T97EXVlNS5COURFj+eSyT3hj8RtcnnY5AG+FFBMbnXHGK7DVl3UQLtNkSP7LEUIweXESHc09fPF+X5Gdkrc34vAlrfxyivubL5tCl9FCemctj14ymltmJXOkrpPNh/pKlFb86EfY9++nbfVqFJeLwAvOJ3Z0Gkavm/fX7qfOPxNL1nwO2c6npciMs8WD7ccPwl1FxCf9cFA+67A2Du2ffkrRjNm0GUPROLqZ2dGCV/Ey/+353L7+dlZsXcHBQ/9D4/Q3CL99HMUdhWS1N5DjTSHZIThkMuE2WfiB7iO0hgYsLgOLY2bj9ro52HCQ1flv4sx5B15bpv5CRUFpVPM4OQNVZ7jb6+bWoldZGhtFpy+8rTVnOwmVdkxO1Qk+bfFP8fdPw1FaStlVvnTcSaHsrd/Le0XvkduUy6YsL8l1cMGfthORV0d3lBUAjdCg0+iIsEQwPnw8AImBiWd0HHu6XLQ12AlPkGkyJP/9xI+0EZMW1Jt7CeBohejNaGw8rkIkQKCfgbCxI1ls6WRaSghLxkYTEWjkV3//GICdi29AZ7NRfvMt1D36GH7jxmEaPZrE8Wp21rjOeuZnqdGQ5ef8hvVV4yApBesl3wP/M7sCcDzD2jgYU1LwdnVhb4xC73Qy2uFktn8iLq+LqVGTsXvcHO7R8vuSPZRaqqnpriHK5SZXSWCCs5Mjej3tWQsQAgL8ajB2B5G2MReTQ+Gnm37K/Tse4Y3A426YTcXgslOvWHH7K1z6waWsK1/XT1eeb0ZC4kxa7z3KA7Vv81reazQ88QTCYiby2afZHlDXe/1vd/2W96dpaE5QncGWbi/dU0b263fFlBVk2DK4IGngutH/KZ0tDnatUWdNcZlnbq+ERHK2ciyTa3CkmVGzYkg2V1GpTaZ1fz766Gh0Xy4HDBgz0nEUFqJ4PBh0Gh5aMpIF/qp/8Xs3XkTUY4+hj4wg5Ac/IO6fzyCEwJii+u3mme3clLuGkosvYWRdEelNFdSfoRQ3p2JYJ70xpqRgSErCUwKMBJdO8Hi3C+eyTRSUv8IXNTt5rtEIuLj+39fiVLz4eY0YcDHV0cFKYaI0dQHWfe8hdPVM3x9Jx7aXuTvTj0cuUZ/6t/mZuKa9A7Y8DhseRUGQ740n17MO2/4Ctq++E8NkQWrkSEaV76RMoyXXYKB5/m3YJt7Pk/ufYGv1VnIKP2fcp7B6opdW5RP8WvwINAQSYAhgX/0+AoICCXjlad68/womH/FiWTCv3+dNt6Xz1pK3+rV/U9wuD68//AXOHg/xI22EJ8hlJcnwIDDUjysemgJAob2Akl1aqvaWkXLOxJNeb0rPQOnpwVlWhjE5mYWjo5gQ1Emrnx8BqSkIvZ7kDz884T3akBC0VisXVOzCsU71d/KzOzAAD7iSSX5hJ3+7cgIWowxlHRQC5s3DnluGxyHowoBzTx5djz2Od92/GBdgRiM0TLW46XSp0Uw9pkTGaYoZ43AwrthLTlUVOfE2ujUKc/PUAuBpxT2gKBiFlj0mo5qIY8OjAGiEwp9MY6lxHOHKjV6Wb/FyT34qKxe/wS1BkWQ6XBQa9GjGXItiDGBj+UZGBI1gQpGC8HjYMkrDhyUfUtBSQKI1kXnxqhGI9Y8l3ZbO2zM13HOTjhGJEwZ97GqL23D2eBg3P56Ft56ZnEwSyXcFIQRCCOKXnIPwummzJuM3KZv6snZyNlfy/N2fseUNtbKi35jRgM9naLfT8MQTdGxYj9+4sQj9yTMgCyEwT87GUVAAej0p//4Y2/XXU7z8h5QHRrKpoIHpv9nAs5+VnPT9p8uwnjkABCyYT9M//0lrqT+N+f54ewTwDgEWhT+8/Rc8wXEU597BF4XqTmlXzBWMq9pCYKvg52u88Obz/OTnQVi6XcS0dOPy12Ds9BLcCZdHJvFMVxF1Wi0hFhv69gY2WGMoDNpKsNZKanMn4GBCvpp9UTf9TkI23olbCKodCnWuI9Tb67lj/B20Pb+CxgCwZGZBcx4HGg5wUcpF3DzmZrZWb+XGUTdi1Bp5Y/EbVHVWkRKUckbHye30oDOcmB6kMr8FoRFkL0yU0UmSYYspPASrsQd74gQqrBP5/Lia6Yc2VpI8LoyYtBFoQ0Lo2rEDEDT+/SkAgpd9/5R9+583l46167AuWYIhMZGI++5lkaKQ3eEgp6qNd/ZWEjJIRbSGvXFoiE7GkJhI/f6jvhYF44JwHJ82oP/744THNhLl6GCVq5W3UibTzURmaJ7H7okD1Ju6tdxFhFPdgFaVGkvivnJ+KXow2tW8KkfnPwBlbxPZ3sCG+FsRnf/k/+J/DI6HMGZm4sjLw93QgC1xKenTSmDHyxxsPIhAjSiaGD6B1iojR8eE8cyCfzJj5QwAEgITsBqtvHfxe72fJyski6yQE6MlTpf2Jjuv/2onE89PYNLCxN72ivwWIhIDMciU3JJhTti4FGpL2ijYo64enLN0BKNmxfDSim3s+6SM2PRgLNOn07lxE54mNTuy/9y52K67tl9fVUdaCAz1I8BmwnrREnQ29b3HEEIQEWgiItDE3MzBy3o8rB/33tlTyazHN+FZ3FcMJ2N5DQnBB9CYoftAIbSUQlA8yeNv4t4FT9HTVMV4TTEuT9/Gk0s6RrCgwQpCoTRe3ZxmbtCR0K7unNxrsNGW20q9fxQHutUqUylH1T0StquvAvpqN09Jv5sIcwS763ZzoOEAIaYQwmsd6Dt7mLX4R1iNVuYnzAdgcfLiQRubbe8U8fqvdlBT1Mr+dRW4HR52fFCCo1vd4Oewu2koayc2o7/zTSIZbljD/eho6qGhvIPpl45g3Lx4dAYt4+cnUJ7bzNa3CylKXUqVZSSdW7diXXoZcX97Eo3fiYkpXU4P7/9xH2//Vp19CK0W/9mzB1x6GkyG9SPfpET1xrZjzBxmjt2AbmIMiv1ZFA0ERHTTUWnC6xZo7C0QORr+Npnf+vYvtHYFoE+IRxsQyPQiBa05Abe1nlKtidkmP8wtZoIcRURlhPPUod/TENqFu+kCqizFLCj0o6f8Y/RxcQQsWEDNil/Qk5uL/6xZCCHIjsxmffl6PF4Pc+PnYj+wHwDzRNWP8NiMx3j0nEcx682DMi5NVZ3sW1cOCqx7MQ9Htwv/YCOdLQ7KD6s1Go4ebERRID5LRihJJEFhfTf5xDF9OcXGnBdLZUFzX9hrxjV4tEaSr7qq9xqXw4Pel9G5xleitLvNicftRasbuuf3YT1zSAixEGfzY3NpG4lvrCTkx7+g0WZA54WgpG68Lg1tVcHq7OGDO8BnGNp1NpyNdoyJSViXLKbn8GG6du3HGOphvKaIYqMN/SdNVLwXzJ357QS4dbwdGMAHQZ2kVefyg4866MnJIWDePLQBqpHpye1LnLc0bSl2tx2n18nStKX05OWh8fdHH6fmXvHT+Q2aYQAoPdAICpx7TQbtDXYcXW6mXZqCyV9PWU4TiqKQ+3k1gaEmIpOtg6ZDIvmuEBSh1o02WfQERfR9N7U6DYvvGMv1vz2HW5+cQ1RyABVjliES1F3NTdWdPPOTzexcrYaDH81p6n1vnS/Z51AxrI0DwNyMCD4rbOSv6wuZ9OsdFKXdhlunxTR+DKZRWTQWxeBd9iac/3/0/PgwI3ue47VJ7+KsrESfEI/1ssswZmUi/PwIGhXAudr9tJssvf3b96WyqryJC7qNeIMPMLNAzQ0fdPnlhP3vTwB1R+XxRcsnRkzk3ux7+fWMX5MdmY0jNw9TRgZC8+38d1UXtmCLtpA5PYrUSeFknRNFyvhwYtKCqTrSwtFDTVQXtjJ2bhxCJteTSAhPDGDhbWO4+tFp/bIPCCGwWI1odRrOWZaOwyH48K8HaCjvoHhPPQC7VpfS0+WiaHcdMWlBANQUD0PjIIR4RAhxUAixXwjxqRAi2tcuhBBPCCGKfOcHPR5z8ZgoHG4vf1h7hC6nh6cqF6K76yiaGz4i4ucrcDc00by5BKbdRo03mC78iNRrUbq7McQnoPX3J2nlStK2fo5lVCpRooXZ2oMAuI0asmrLCBNtXJd2A3qniSkFYFmyiKhHfoXGqOZvMmVl4aqsxNPW98dwddbVLElZAopCT2EhxszMwR4KQPUl1BS1EZMejBCCBT8YxbnXZKLVaYhODaKzxcGOVSVYrAZGzZL1GiQS8NWNHhP6lfXSIxIDWfCDkTRVd7Hqz/so2Nm3mXXL6wXYO1yMnRdPcKSZmiL1fuDscdPZcmLFtw0v5/Hi/VtpKO9gsBiqmcPjiqKMURRlHLAaeNDXfiGQ6vu5BXhqsIVMTAhmcqK6bn7R2Gi+KGmiR2sBnRHzhAlYpk+j7cPVANS0qjsao7obATDEq8s8wmBAYzZDuBolFDGhDVOWi/CMdjw9WqrDFjJq5vV8ELMCi0PBdtElJ2jwG63uESiaO4/2tWtPOOeqrkbx5VL6Njiyoxa3y0vG1Mh+5+JHquPUVNXJiEkRaLTDfuIpkfzHpEwIZ9FtY3B0u2lvsDNreRp+AXoKd9fjbzOSMCqEqBFB1BS3oXgVtrx+hBfv30bu59W4nR48bi8F22vpbHGQs6Vq0HQOybdbUZTjaudhAY7V2rsYeElR+QIIEkIMahFiIQSv3TyFL+6fy8XjonF5FPaVt/aJmzkLZ0kJrpoaqnzGIbRJjUIyJCSc2NmUWyFyDM0TriBpTAOWINVHETTlTjBYEOu2oLXZsEybesLbzFMmE3733Xg7O6n//e9RPJ7ec44iNReTcUQqg42iKORsqSIsPuCku52Dws1kTI3EEmRkwvkJJ+lBIpF8HWLSghh9biwRSYGkT4kkcUwoAKNmxaDRCKJGWHHa3TTXdFHnq2G98ZV8/vHjzTx9xya8XgWhERTvrUfxnrxU6ekyZI9+QojHhBAVwFX0zRxigIrjLqv0tQ0qOq2GSKuJSYk2DDoNb+3uk+A/ayYAbatXU92qTu1M5SVozOZeB3Ev1hi49TMi590BgDFQreLkKCpW6zdv2EjgBecjdCdOPYUQhNx0I1GPPYqrrBxnaWnvOWevcTizm9pORm1xG83VXadcLjr32kyueWQa5sDB2XgjkQwHhBDM+n4aS++dhMFPx8gZMYQnBJB1TjQAUSlqoEfVkVbaG+y9s/bjmbQwEUe3m5a67kHROGihrEKIdUD/tQlYoSjKKkVRVgArhBD3A3cAD/2H/d+CuvREfHz8N9Z5rHqaEAKrn54bz0niH1uKuXFGEqNirBhTUjBPnUrzC/+i44ZEQv2NuPIKMJ7KQRyaDoDO7EETGIg95xAaixmlp4fARYsG1GIaNQqAnvwCjCPUZSRHUTG68HC0gYOTt0hRFKoKWghPDOTwZ9UYTFpGTAof8HqNRoB0QkskZ5SIpEAuvz+793VgqB8GPx3Fe+vxehXSsiNIGBWCf7CJ2IxgOpp60GgFu1aXUlfahi3KcorevxmDZhwURemf+e3kvAp8hGocqoDjH8djfW0n6/8Z4BmASZMmfaN5VVlOE5tezScowsxFPxmHEILbzk3h4+3l/HVNPv+4RU2spfnxnXhuvp4lf7mL2ot/hiMvD+sllwzcsUYDC7pTV/gAAA4QSURBVB5FmEOxNG+na9s2PA2N6KKi8Bs/fsC3GZOSQK+n5dVXcdfWYLvpJhxFRYM6a8jbVsPGl/NBAAqMnh2DwTSst79IJEOOEILQWH+qffseQuMCCInx7z0fEuOP4lUwmnW01H7HZg6nQgiRqijKscKnFwP5vuMPgDuEECuBKUCboig1J+vjTHBsY1dni4OK3Gbismwc/OAoy+p1uOo7OXiwHnOkH/PfKiNi+o/5zdanuf2d3+AFzJMnn7rz6f8DgGWmlo61a+msrsF2042nDEcVBgOm1FTs+/Zh37cPjdWKo6SEoKWXncFPrc4WhBDUlrTx2RtH0Bm1mMw6OlsdjJQRSBLJWUFonGoc/G1GbNH9ZwZCI7jmselfGSH1TRkqn8NvhBA5QoiDwALgJ772j4ASoAj4J3DbYIoIifHn1r/NwWjRkf9FLQU7ajmwoYKYsSF4gS3vFLGztAVFAV1sLK9l9dVBsEyd8rV+h/WSSzBlZaGPicF25ZVfeX3kww/3Li81P/+CGqmUcuYilY7squWVB7ZTU9TK6r8dwGI1cs0j07j60Wlc+9j0E55OJBLJ0DH2vDjiR9rIXpg0YOXGwTIMAOLYmvt3mUmTJim7d+/+6gsHYOPLeeRurUGjEYQnBnDpXRO585HPSKlxU58dxHsVDex7cAHN7V2Ila+iCwsl6LKBn+Z7ulwYzbre/1Cv04nQaPo5ok9FzcMP0/r6SgCSV3/Y64M4HRSvwuuP7KSlRt2Ip9EKlj8wmeDIM79eKZFIzn6EEHsURZl0snMyUB0YNVtNouf1Ksy5KgOhESRNjaBLKHgOtTAmNgitRhAW5E/orT88pWHoaO7huTs/Y/2/+tJhaAwGhE5H3dF2Gis7v5amwPnze48NKWfG57D9/WJaaroYPScWa5gfc6/PlIZBIpGcFOl5BMLiA5ixLJXgSHPvssr45BD+ZCzlvB4D8davv9RyaJNaNLxgRy1TLk4mwGYCwOPx8s7v9qB4FZb9PJuw+ABa67pZ+0IuI2dG94awHcMyfToxf/ojGotlwCnlQORtq2bfp+XMuyGrd79C7udq26hZMcz8fipCpP1HfUokkuGFNA4+xp534p6FsXFW9hs9THJ4SSrpHw3Q3mTni/dLSJ8SSWxmMFrfbuH6snZ0eg1ul5fqIy2kT1X38DVXd/VuVsn5rAq8Cnnba1G8CvVH20keG4bJvy8tr6IoNEZNwmjW8Z94AVxOD5tfO4LH7eXIzjrCEwLxerxsfaeImPRgn2GQoagSieTUyGWlAQgw6Vl/9xwmzI2jqbSDT587TOFuNQ+K4lX4+OlDFO6qY/WTB1j5q510t6uFf9rq7SSPD8No1vWGoQE0lKk5UMITA8n9rJrcrTWMmhXD/BvVlBvVRa0n/P6CL2r5+OlDvP/HfVQXtpxSa1N1J83Vqh+hZF8DHrcXoRFU5KlFRRoqOnHa3YycES1TXkgkkq+FvFOcgsRQC7MvTCYuy0bxvno+ffYw1UWtFO2tp7Gik8lLkph4QQLtDXZ2fFCCy+mhs8VBUISZqBQr1UVtuBweKvKbqSlqxWjWMfWSZACiRliZtTyNlAnh6PQaKn03clCNz95PytAbtZgselb9eT9NVX2+isLddeRsqUJRFDa8lMfKX+1k5aM7aW+yc3BDBUERZrIXJdJc3YWzx01VgWpcon3ZHiUSieSrkMtKX4HJoueiH4/D2ePmX/dtZePL+Ti6XQRHWZh4YSIajcBhd5O7tZpU385ia7gfOr2Wo4eaePPXu2j1bW9PzY4gNj2YuddnkjhazaWi1WlIGhtK3he1tDf1kDQ2lIIvammp7WbeDVnEZdp4/Vc7eO+Pe/HzN9DeaMfrUZenKnKbKdnfQMKoEMpymtj2TjH1ZR3MWp6Gf7Ca8bW5uouqIy0ER5qxWI1DMIISieS7iJw5fE0MJh2zl6fhtLsJCPFj7rWZaioJYNTsGLxuhVV/3o/QCCKTrMRmqlXmWo/Le5IyPgwhBBlTozBZ+vwLkxYlYQk0+HZsF1BT3IY1zI8Rk8IxBxqYfUU6ji43Xa0OwhMCGDsvjoAQEyX7G4hICmTRbWMIifWneG89BpOW9KmRvY71hvKO3hTcEolE8nWRM4f/gPSpUb0O5uMJifYne1Eiez4pY+L5CQSGqiUDZy1Po7Gqk5nLUulucxIQYjppv7YoC1c/Mo0dH5awe81RLrtnIuEJAb3+gRETwwmNm4pGKwgMUfs2mHTs/ugos69IR2gECaNCaKrsJDIlCINJh96gRW/Skr+9BpfDQ0yaNA4SieTrI43DGWLykmSyFyWdUBlt9JzY3uNjBuNUZC9MJGNqJNaw/iVAg8JPbJt4fgJp2RG9JQkzp0dx+LMqshclAurW+pBoC7UlarrfGOlvkEgk/wHSOJxBTrdkpkarOalhOBlaveaEWrVB4WZ+8IdZJ1wTEuNPbUk7tmgLfgEyxbZEIvn6SJ/DfzHHai4kjw8bYiUSieS7hpw5/BczanYsToeHCQtk1TaJRPKfIY3DfzHmQAMzlg5+eVGJRPLfh1xWkkgkEkk/pHGQSCQSST+kcZBIJBJJP6RxkEgkEkk/pHGQSCQSST+kcZBIJBJJP6RxkEgkEkk/pHGQSCQSST+EoihDreG0EUI0AGXf8O2hQOMZlDMYfBc0gtR5ppE6zxzfBY3w7etMUBTlpPl1/iuMw+kghNitKMqkodZxKr4LGkHqPNNInWeO74JGOLt0ymUliUQikfRDGgeJRCKR9EMaB3hmqAV8Db4LGkHqPNNInWeO74JGOIt0Dnufg0QikUj6I2cOEolEIunHsDUOQogLhBAFQogiIcR9Q63neIQQR4UQh4QQ+4UQu31tNiHEWiFEoe/f4CHQ9bwQol4IkXNc20l1CZUnfON7UAgxYYh1/lIIUeUb0/1CiIXHnbvfp7NACHH+t6QxTgixUQiRK4Q4LIT4ia/9rBrPU+g828bTJITYKYQ44NP5sK89SQixw6fnDSGEwddu9L0u8p1PHGKd/xJClB43nuN87UP2PUJRlGH3A2iBYiAZMAAHgKyh1nWcvqNA6Jfafgfc5zu+D/jtEOiaBUwAcr5KF7AQ+BgQwFRgxxDr/CVw10muzfL9/xuBJN/fhfZb0BgFTPAdBwBHfFrOqvE8hc6zbTwF4O871gM7fOP0JrDc1/408CPf8W3A077j5cAb39J4DqTzX8DSk1w/ZN+j4TpzmAwUKYpSoiiKE1gJXDzEmr6Ki4EXfccvApd82wIURdkCNH+peSBdFwMvKSpfAEFCiKgh1DkQFwMrFUVxKIpSChSh/n0MKoqi1CiKstd33AHkATGcZeN5Cp0DMVTjqSiK0ul7qff9KMB5wNu+9i+P57FxfhuYK4QQQ6hzIIbsezRcjUMMUHHc60pO/Qf/baMAnwoh9gghbvG1RSiKUuM7rgUihkZaPwbSdTaO8R2+qfnzxy3LDblO35LGeNSnyLN2PL+kE86y8RRCaIUQ+4F6YC3qrKVVURT3SbT06vSdbwNChkKnoijHxvMx33j+SQhh/LJOH9/aeA5X43C2M0NRlAnAhcDtQohZx59U1PnmWRdmdrbq8vEUkAKMA2qAPwytHBUhhD/wDvC/iqK0H3/ubBrPk+g868ZTURSPoijjgFjU2UrGEEs6KV/WKYQYBdyPqjcbsAH3DqFEYPgahyog7rjXsb62swJFUap8/9YD76H+odcdm076/q0fOoUnMJCus2qMFUWp830pvcA/6VvqGDKdQgg96g33VUVR3vU1n3XjeTKdZ+N4HkNRlFZgIzANdRlGdxItvTp9561A0xDpvMC3fKcoiuIAXuAsGM/hahx2Aam+SAYDqkPqgyHWBIAQwiKECDh2DCwAclD1Xee77Dpg1dAo7MdAuj4ArvVFW0wF2o5bLvnW+dI67fdQxxRUnct90StJQCqw81vQI4DngDxFUf543KmzajwH0nkWjmeYECLId+wHzEf1j2wElvou+/J4HhvnpcAG30xtKHTmH/dAIFD9IseP59B8j74tz/fZ9oMaBXAEdV1yxVDrOU5XMmq0xwHg8DFtqOuh64FCYB1gGwJtr6MuIbhQ1z5vGkgXanTF33zjewiYNMQ6X/bpOIj6hYs67voVPp0FwIXfksYZqEtGB4H9vp+FZ9t4nkLn2TaeY4B9Pj05wIO+9mRU41QEvAUYfe0m3+si3/nkIda5wTeeOcAr9EU0Ddn3SO6QlkgkEkk/huuykkQikUhOgTQOEolEIumHNA4SiUQi6Yc0DhKJRCLphzQOEolEIumHNA4SyRnEl630rqHWIZGcLtI4SCQSiaQf0jhIJKeJEGKFEOKIEOJzIN3XdrMQYpcvb/87QgizECLAl7Nf77sm8PjXEsnZhDQOEslpIISYiJp+ZRzqzuFs36l3FUXJVhRlLGoah5sUNeX1JmCR75rlvutc365qieSrkcZBIjk9ZgLvKYrSrajZSo/l6BolhPhMCHEIuAoY6Wt/FrjBd3wDapI1ieSsQxoHiWRw+Bdwh6Ioo4GHUXP5oCjKViBRCDEHtUJazoA9SCRDiDQOEsnpsQW4RAjh58umu8TXHgDU+PwJV33pPS8BryFnDZKzGJl4TyI5TYQQK1DTP9cD5cBeoAu4B2hArZwWoCjK9b7rI4FS1EymrUOhWSL5KqRxkEi+ZYQQS4GLFUW5Zqi1SCQDofvqSyQSyZlCCPFX1PKvC4dai0RyKuTMQSKRSCT9kA5piUQikfRDGgeJRCKR9EMaB4lEIpH0QxoHiUQikfRDGgeJRCKR9EMaB4lEIpH04/8BiARKdGzxyh8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "fd_data.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set: [[[ -3.6]\n", - " [ -3.1]\n", - " [ -3.4]\n", - " ...\n", - " [ -3.2]\n", - " [ -2.8]\n", - " [ -4.2]]\n", - "\n", - " [[ -4.4]\n", - " [ -4.2]\n", - " [ -5.3]\n", - " ...\n", - " [ -3.6]\n", - " [ -4.9]\n", - " [ -5.7]]\n", - "\n", - " [[ -3.8]\n", - " [ -3.5]\n", - " [ -4.6]\n", - " ...\n", - " [ -3.4]\n", - " [ -3.3]\n", - " [ -4.8]]\n", - "\n", - " ...\n", - "\n", - " [[-23.3]\n", - " [-24. ]\n", - " [-24.4]\n", - " ...\n", - " [-23.5]\n", - " [-23.9]\n", - " [-24.5]]\n", - "\n", - " [[-26.3]\n", - " [-27.1]\n", - " [-27.8]\n", - " ...\n", - " [-25.7]\n", - " [-24. ]\n", - " [-24.8]]\n", - "\n", - " [[-30.7]\n", - " [-30.6]\n", - " [-31.4]\n", - " ...\n", - " [-29. ]\n", - " [-29.4]\n", - " [-30.5]]]\n", - "sample_points: [ 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6.\n", - " 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12.\n", - " 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5 18.\n", - " 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5 24.\n", - " 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5 30.\n", - " 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5 36.\n", - " 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5 42.\n", - " 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5 48.\n", - " 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5 54.\n", - " 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5 60.\n", - " 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5 66.\n", - " 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5 72.\n", - " 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5 78.\n", - " 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5 84.\n", - " 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5 90.\n", - " 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5 96.\n", - " 96.5 97. 97.5 98. 98.5 99. 99.5 100. 100.5 101. 101.5 102.\n", - " 102.5 103. 103.5 104. 104.5 105. 105.5 106. 106.5 107. 107.5 108.\n", - " 108.5 109. 109.5 110. 110.5 111. 111.5 112. 112.5 113. 113.5 114.\n", - " 114.5 115. 115.5 116. 116.5 117. 117.5 118. 118.5 119. 119.5 120.\n", - " 120.5 121. 121.5 122. 122.5 123. 123.5 124. 124.5 125. 125.5 126.\n", - " 126.5 127. 127.5 128. 128.5 129. 129.5 130. 130.5 131. 131.5 132.\n", - " 132.5 133. 133.5 134. 134.5 135. 135.5 136. 136.5 137. 137.5 138.\n", - " 138.5 139. 139.5 140. 140.5 141. 141.5 142. 142.5 143. 143.5 144.\n", - " 144.5 145. 145.5 146. 146.5 147. 147.5 148. 148.5 149. 149.5 150.\n", - " 150.5 151. 151.5 152. 152.5 153. 153.5 154. 154.5 155. 155.5 156.\n", - " 156.5 157. 157.5 158. 158.5 159. 159.5 160. 160.5 161. 161.5 162.\n", - " 162.5 163. 163.5 164. 164.5 165. 165.5 166. 166.5 167. 167.5 168.\n", - " 168.5 169. 169.5 170. 170.5 171. 171.5 172. 172.5 173. 173.5 174.\n", - " 174.5 175. 175.5 176. 176.5 177. 177.5 178. 178.5 179. 179.5 180.\n", - " 180.5 181. 181.5 182. 182.5 183. 183.5 184. 184.5 185. 185.5 186.\n", - " 186.5 187. 187.5 188. 188.5 189. 189.5 190. 190.5 191. 191.5 192.\n", - " 192.5 193. 193.5 194. 194.5 195. 195.5 196. 196.5 197. 197.5 198.\n", - " 198.5 199. 199.5 200. 200.5 201. 201.5 202. 202.5 203. 203.5 204.\n", - " 204.5 205. 205.5 206. 206.5 207. 207.5 208. 208.5 209. 209.5 210.\n", - " 210.5 211. 211.5 212. 212.5 213. 213.5 214. 214.5 215. 215.5 216.\n", - " 216.5 217. 217.5 218. 218.5 219. 219.5 220. 220.5 221. 221.5 222.\n", - " 222.5 223. 223.5 224. 224.5 225. 225.5 226. 226.5 227. 227.5 228.\n", - " 228.5 229. 229.5 230. 230.5 231. 231.5 232. 232.5 233. 233.5 234.\n", - " 234.5 235. 235.5 236. 236.5 237. 237.5 238. 238.5 239. 239.5 240.\n", - " 240.5 241. 241.5 242. 242.5 243. 243.5 244. 244.5 245. 245.5 246.\n", - " 246.5 247. 247.5 248. 248.5 249. 249.5 250. 250.5 251. 251.5 252.\n", - " 252.5 253. 253.5 254. 254.5 255. 255.5 256. 256.5 257. 257.5 258.\n", - " 258.5 259. 259.5 260. 260.5 261. 261.5 262. 262.5 263. 263.5 264.\n", - " 264.5 265. 265.5 266. 266.5 267. 267.5 268. 268.5 269. 269.5 270.\n", - " 270.5 271. 271.5 272. 272.5 273. 273.5 274. 274.5 275. 275.5 276.\n", - " 276.5 277. 277.5 278. 278.5 279. 279.5 280. 280.5 281. 281.5 282.\n", - " 282.5 283. 283.5 284. 284.5 285. 285.5 286. 286.5 287. 287.5 288.\n", - " 288.5 289. 289.5 290. 290.5 291. 291.5 292. 292.5 293. 293.5 294.\n", - " 294.5 295. 295.5 296. 296.5 297. 297.5 298. 298.5 299. 299.5 300.\n", - " 300.5 301. 301.5 302. 302.5 303. 303.5 304. 304.5 305. 305.5 306.\n", - " 306.5 307. 307.5 308. 308.5 309. 309.5 310. 310.5 311. 311.5 312.\n", - " 312.5 313. 313.5 314. 314.5 315. 315.5 316. 316.5 317. 317.5 318.\n", - " 318.5 319. 319.5 320. 320.5 321. 321.5 322. 322.5 323. 323.5 324.\n", - " 324.5 325. 325.5 326. 326.5 327. 327.5 328. 328.5 329. 329.5 330.\n", - " 330.5 331. 331.5 332. 332.5 333. 333.5 334. 334.5 335. 335.5 336.\n", - " 336.5 337. 337.5 338. 338.5 339. 339.5 340. 340.5 341. 341.5 342.\n", - " 342.5 343. 343.5 344. 344.5 345. 345.5 346. 346.5 347. 347.5 348.\n", - " 348.5 349. 349.5 350. 350.5 351. 351.5 352. 352.5 353. 353.5 354.\n", - " 354.5 355. 355.5 356. 356.5 357. 357.5 358. 358.5 359. 359.5 360.\n", - " 360.5 361. 361.5 362. 362.5 363. 363.5 364. 364.5]\n", - "time range: [[ 1 365]]\n" - ] - } - ], - "source": [ - "print(fd_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fpca_discretized = FPCADiscretized(2)\n", - "fpca_discretized.fit(fd_data)\n", - "fpca_discretized.components.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", - " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", - " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", - " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", - " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", - " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", - " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", - " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", - " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", - " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", - " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n", - " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n", - " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n", - " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n", - " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n", - " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n", - " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", - " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n", - " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n", - " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n", - " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n", - " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n", - " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n", - " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n", - " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n", - " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n", - " 365])]\n" - ] - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "print(fd_data.sample_points)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "range(0, 3)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(0,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "\n", - "fd_basis.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 1 365]], n_basis=3, period=364),\n", - " coefficients=[[ 89.92195965 -76.6540343 -113.56527848]\n", - " [ 117.91048476 -78.29623089 -147.99771918]\n", - " [ 105.64601919 -87.48751862 -135.23786638]\n", - " [ 130.41525077 -68.03400727 -117.56196272]\n", - " [ 100.44054184 -86.56110769 -157.01740098]\n", - " [ 101.11363823 -73.29578447 -179.87563595]\n", - " [ -95.66841575 -101.81332746 -218.82950503]\n", - " [ 59.96125842 -80.13360204 -209.51804361]\n", - " [ 43.6817805 -79.47391326 -211.60839615]\n", - " [ 78.63054053 -76.70039418 -198.32081877]\n", - " [ 79.32089798 -70.62376518 -186.38162541]\n", - " [ 117.7284124 -74.49860223 -195.51372983]\n", - " [ 111.67543758 -72.96278011 -199.5791436 ]\n", - " [ 139.29219563 -71.22916468 -169.13804592]\n", - " [ 140.18018698 -70.14769133 -168.99937059]\n", - " [ 47.74788751 -74.91102958 -200.75128544]\n", - " [ 48.12299843 -76.44333055 -242.23286231]\n", - " [ -1.92277569 -81.08021473 -247.06920225]\n", - " [-134.27412634 -122.6017788 -236.3687109 ]\n", - " [ 53.27128059 -66.12896207 -228.82111637]\n", - " [ 13.96281174 -67.97763734 -242.037578 ]\n", - " [ -63.97320093 -89.60462599 -272.57192012]\n", - " [ 43.84140492 -52.68768517 -199.30406145]\n", - " [ 76.70948389 -48.51619334 -167.07086902]\n", - " [ 167.54308753 -37.09503437 -163.97149634]\n", - " [ 190.36695728 -32.15075301 -91.84336183]\n", - " [ 183.93137869 -30.4104988 -82.15417362]\n", - " [ 73.79549727 -37.36315001 -161.21790136]\n", - " [ 133.89364065 -33.95458738 -74.24172996]\n", - " [ -15.44356138 -48.61881308 -207.5718941 ]\n", - " [ -90.25342609 -55.29068221 -295.12780726]\n", - " [ -94.7351896 -100.41993164 -284.34377575]\n", - " [-183.34401079 -125.4783037 -208.44723865]\n", - " [-175.18346554 -103.92929252 -283.31282874]\n", - " [-314.24776026 -115.66685935 -230.93921551]])\n" - ] - } - ], - "source": [ - "print(fd_basis)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "365\n" - ] - } - ], - "source": [ - "print(fd_data.dim_domain)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDataBasis(\n", - " _basis=Fourier(domain_range=[[ 0.5 364.5]], n_basis=9, period=364.0),\n", - " coefficients=[[-0.92321326 -0.13998864 -0.35548708 -0.00939677 0.02399664 0.02906587\n", - " 0.00253204 0.01019684 0.0094896 ]\n", - " [-0.33139612 -0.04288814 0.8923411 0.17120705 0.24317564 0.03754241\n", - " 0.03855143 -0.02475171 0.01049033]\n", - " [-0.13762736 0.91089487 -0.00737022 0.26476734 -0.21910974 0.17406323\n", - " 0.02554942 0.00108415 0.0470334 ]\n", - " [ 0.1248126 0.01012829 -0.26644643 0.42618909 0.75225281 0.25983432\n", - " 0.20726074 -0.17024835 0.16232288]])\n", - "[15086.27662761 1438.98606096 314.69304555 85.04287004]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "# fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1))\n", - "basis = skfda.representation.basis.Fourier(n_basis=3)\n", - "fd_basis = fd_data.to_basis(basis)\n", - "fpca = FPCABasis(4)\n", - "fpca.fit(fd_basis)\n", - "fpca.components.plot()\n", - "print(fpca.components)\n", - "print(fpca.component_values)\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.04618614415675301" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1.363 - 1.429 )/1.429 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ramsay implementation without penalization\n", - "\n", - "PC1 0.9231551 0.13649663 0.35694509 0.0092012 -0.0244525 -0.02923873 -0.003566887 -0.009654571 -0.010006303\n", - "PC2 -0.3315211 -0.05086430 0.89218521 0.1669182 0.2453900 0.03548997 0.037938051 -0.025777507 0.008416904\n", - "PC3 -0.1379108 0.91250892 0.00142045 0.2657423 -0.2146497 0.16833314 0.031509179 -0.006768189 0.047306718\n", - "PC4 0.1247078 0.01579953 -0.26498643 0.4118705 0.7617679 0.24922635 0.213305250 -0.180158701 0.154863926\n", - "\n", - "values 15164.718872 1446.091968 314.361310 85.508572" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the dataset again as the module modified the original data and centers the original data.\n", - "The mean function is distorted after such transformation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fd_data = fetch_weather_temp_only()\n", - "\n", - "basis = skfda.representation.basis.Fourier(n_basis=7)\n", - "basisfd = fd_data.to_basis(basis)\n", - "basisfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[0, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "meanfd = basisfd.mean()\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] + 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.coefficients = np.vstack([meanfd.coefficients,\n", - " meanfd.coefficients[0, :] - 30 * fpca.components.coefficients[1, :]])\n", - "\n", - "meanfd.plot()\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:scikit-fda] *", - "language": "python", - "name": "conda-env-scikit-fda-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 092f1a6c5aca2f67eac724334b3473b701feca76 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 19 Apr 2020 22:27:37 +0200 Subject: [PATCH 433/624] adress init comments --- .../dim_reduction/projection/_fpca.py | 41 +------------------ 1 file changed, 1 insertion(+), 40 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index b0b5be378..8adecd376 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -17,7 +17,7 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): """Defines the common structure shared between classes that do functional principal component analysis - Attributes: + Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data @@ -25,14 +25,6 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): """ def __init__(self, n_components=3, centering=True): - """FPCA constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ self.n_components = n_components self.centering = centering @@ -136,26 +128,6 @@ def __init__(self, centering=True, regularization_parameter=0, regularization_lfd=2): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - components_basis (skfda.representation.Basis): the basis in which we - want the principal components. Defaults to None. If so, the - basis contained in the passed FDataBasis object for the fit - function will be used. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - regularization_parameter (float): this parameter sets the degree of - regularization that is desired. Defaults to 0 (no - regularization). When this value is large, the resulting - principal components tends to be constant. - regularization_lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. - - """ super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis @@ -332,17 +304,6 @@ class FPCAGrid(FPCA): """ def __init__(self, n_components=3, weights=None, centering=True): - """FPCABasis constructor - - Args: - n_components (int): number of principal components to obtain from - functional principal component analysis - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. - centering (bool): if True then calculate the mean of the functional - data object and center the data first. Defaults to True - """ super().__init__(n_components, centering) self.weights = weights From 36fa67485b97037f527fb7e33c096dff90129746 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Apr 2020 00:03:49 +0200 Subject: [PATCH 434/624] Make test pass --- skfda/misc/__init__.py | 3 +- .../_linear_diff_op_regularization.py | 7 +- skfda/representation/basis/_basis.py | 95 +---------- skfda/representation/basis/_bspline.py | 116 ------------- skfda/representation/basis/_constant.py | 14 -- skfda/representation/basis/_fourier.py | 67 -------- skfda/representation/basis/_monomial.py | 105 ------------ tests/test_basis.py | 148 +---------------- tests/test_regularization.py | 157 ++++++++++++++++++ 9 files changed, 174 insertions(+), 538 deletions(-) create mode 100644 tests/test_regularization.py diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index f06f03ee0..4f805f977 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,3 +1,4 @@ -from ._math import log, log2, log10, exp, sqrt, cumsum, inner_product from . import covariances, kernels, metrics +from . import regularization from ._lfd import LinearDifferentialOperator +from ._math import log, log2, log10, exp, sqrt, cumsum, inner_product diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index 9c33edb2e..c4b74e067 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -24,8 +24,9 @@ class LinearDifferentialOperatorRegularization(): """ def __init__(self, linear_diff_op=2): - if not isinstance(linear_diff_op, LinearDifferentialOperator): - self.linear_diff_op = LinearDifferentialOperator(linear_diff_op) + self.linear_diff_op = linear_diff_op if ( + isinstance(linear_diff_op, LinearDifferentialOperator)) else ( + LinearDifferentialOperator(linear_diff_op)) def penalty_matrix_numerical(self, basis): """Return a penalty matrix using a numerical approach. @@ -224,6 +225,8 @@ def monomial_penalty_matrix_optimized( # Set lower matrix penalty_matrix[(indices[1], indices[0])] = integral + raise ValueError() + return penalty_matrix diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 40bb67462..4c8e75d0b 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -131,95 +131,18 @@ def plot(self, chart=None, *, derivative=0, **kwargs): self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) def _numerical_penalty(self, lfd): - """Return a penalty matrix using a numerical approach. + from ...misc.regularization import ( + LinearDifferentialOperatorRegularization) - See :func:`~basis.Basis.penalty`. - - Args: - lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. - """ - from skfda.misc import LinearDifferentialOperator - - if not isinstance(lfd, LinearDifferentialOperator): - lfd = LinearDifferentialOperator(lfd) - - indices = np.triu_indices(self.n_basis) - - def cross_product(x): - """Multiply the two lfds""" - res = lfd(self)([x])[:, 0] - - return res[indices[0]] * res[indices[1]] - - # Range of first dimension - domain_range = self.domain_range[0] - - penalty_matrix = np.empty((self.n_basis, self.n_basis)) - - # Obtain the integrals for the upper matrix - triang_vec = scipy.integrate.quad_vec( - cross_product, domain_range[0], domain_range[1])[0] - - # Set upper matrix - penalty_matrix[indices] = triang_vec - - # Set lower matrix - penalty_matrix[(indices[1], indices[0])] = triang_vec - - return penalty_matrix - - def _linear_diff_op_inner_product(self, lfd): - """ - Subclasses may override this for computing analytically - the penalty matrix associated with a linear differential operator - inner product in the cases when that is possible. - - Returning NotImplemented will use numerical computation - of the penalty matrix. - """ - return NotImplemented + return LinearDifferentialOperatorRegularization( + lfd).penalty_matrix_numerical(self) def penalty(self, lfd): - r"""Return a penalty matrix given a differential operator. - - The differential operator can be either a derivative of a certain - degree or a more complex operator. - - The penalty matrix is defined as [RS05-5-6-2]_: - - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds - - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. - - Args: - lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. + from ...misc.regularization import ( + LinearDifferentialOperatorRegularization) - Returns: - numpy.array: Penalty matrix. - - References: - .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the - roughness penalty. In *Functional Data Analysis* (pp. 106-107). - Springer. - - """ - from skfda.misc import LinearDifferentialOperator - - if not isinstance(lfd, LinearDifferentialOperator): - lfd = LinearDifferentialOperator(lfd) - - matrix = self._penalty(lfd) - - if matrix is NotImplemented: - return self._numerical_penalty(lfd) - else: - return matrix + return LinearDifferentialOperatorRegularization( + lfd).penalty_matrix(self) @abstractmethod def basis_of_product(self, other): @@ -381,5 +304,5 @@ def __repr__(self): def __eq__(self, other): """Equality of Basis""" return (type(self) == type(other) - and _same_domain(self.domain_range, other.domain_range) + and _same_domain(self, other) and self.n_basis == other.n_basis) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index adeed082d..7aea889c7 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -218,122 +218,6 @@ def _derivative(self, coefs, order=1): return deriv_basis, np.array(deriv_coefs)[:, 0:deriv_basis.n_basis] - def _penalty(self, lfd): - - coefs = lfd.constant_weights() - if coefs is None: - return NotImplemented - - nonzero = np.flatnonzero(coefs) - - # All derivatives above the order of the spline are effectively - # zero - nonzero = nonzero[nonzero < self.order] - - if len(nonzero) == 0: - return np.zeros((self.n_basis, self.n_basis)) - - # We will only deal with one nonzero coefficient right now - if len(nonzero) != 1: - return NotImplemented - - derivative_degree = nonzero[0] - - if derivative_degree == self.order - 1: - # The derivative of the bsplines are constant in the intervals - # defined between knots - knots = np.array(self.knots) - mid_inter = (knots[1:] + knots[:-1]) / 2 - constants = self.evaluate(mid_inter, - derivative=derivative_degree).T - knots_intervals = np.diff(self.knots) - # Integration of product of constants - return constants.T @ np.diag(knots_intervals) @ constants - - # We only deal with the case without zero length intervals - # for now - if np.any(np.diff(self.knots) == 0): - return NotImplemented - - # Compute exactly using the piecewise polynomial - # representation of splines - - # Places m knots at the boundaries - knots = self._evaluation_knots() - - # c is used the select which spline the function - # PPoly.from_spline below computes - c = np.zeros(len(knots)) - - # Initialise empty list to store the piecewise polynomials - ppoly_lst = [] - - no_0_intervals = np.where(np.diff(knots) > 0)[0] - - # For each basis gets its piecewise polynomial representation - for i in range(self.n_basis): - - # Write a 1 in c in the position of the spline - # transformed in each iteration - c[i] = 1 - - # Gets the piecewise polynomial representation and gets - # only the positions for no zero length intervals - # This polynomial are defined relatively to the knots - # meaning that the column i corresponds to the ith knot. - # Let the ith knot be a - # Then f(x) = pp(x - a) - pp = PPoly.from_spline((knots, c, self.order - 1)) - pp_coefs = pp.c[:, no_0_intervals] - - # We have the coefficients for each interval in coordinates - # (x - a), so we will need to subtract a when computing the - # definite integral - ppoly_lst.append(pp_coefs) - c[i] = 0 - - # Now for each pair of basis computes the inner product after - # applying the linear differential operator - penalty_matrix = np.zeros((self.n_basis, self.n_basis)) - for interval in range(len(no_0_intervals)): - for i in range(self.n_basis): - poly_i = np.trim_zeros(ppoly_lst[i][:, - interval], 'f') - if len(poly_i) <= derivative_degree: - # if the order of the polynomial is lesser or - # equal to the derivative the result of the - # integral will be 0 - continue - # indefinite integral - derivative = polyder(poly_i, derivative_degree) - square = polymul(derivative, derivative) - integral = polyint(square) - - # definite integral - penalty_matrix[i, i] += np.diff(polyval( - integral, self.knots[interval: interval + 2] - - self.knots[interval]))[0] - - for j in range(i + 1, self.n_basis): - poly_j = np.trim_zeros(ppoly_lst[j][:, - interval], 'f') - if len(poly_j) <= derivative_degree: - # if the order of the polynomial is lesser - # or equal to the derivative the result of - # the integral will be 0 - continue - # indefinite integral - integral = polyint( - polymul(polyder(poly_i, derivative_degree), - polyder(poly_j, derivative_degree))) - # definite integral - penalty_matrix[i, j] += np.diff(polyval( - integral, self.knots[interval: interval + 2] - - self.knots[interval]) - )[0] - penalty_matrix[j, i] = penalty_matrix[i, j] - return penalty_matrix - def rescale(self, domain_range=None): r"""Return a copy of the basis with a new domain range, with the corresponding values rescaled to the new bounds. diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 922ca2cb0..329f1f804 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -38,20 +38,6 @@ def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) - def _internal_representation(self): - return NumberRepresentation.from_basis(self) - - def _penalty(self, lfd): - coefs = lfd.constant_weights() - if coefs is None: - return NotImplemented - - internal_repr = self._internal_representation() - - return np.array([[coefs[0] ** 2 * - (self.domain_range[0][1] - - self.domain_range[0][0])]]) - def basis_of_product(self, other): """Multiplication of a Constant Basis with other Basis""" if not _same_domain(self, other): diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 07656948b..0369c0281 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -157,73 +157,6 @@ def _evaluate(self, eval_points, derivative=0): return res - def _penalty_orthonormal(self, weights): - """ - Return the penalty when the basis is orthonormal. - """ - - signs = np.array([1, 1, -1, -1]) - signs_expanded = np.tile(signs, len(weights) // 4 + 1) - - signs_odd = signs_expanded[:len(weights)] - signs_even = signs_expanded[1:len(weights) + 1] - - phases = (np.arange(1, (self.n_basis - 1) // 2 + 1) * - 2 * np.pi / self.period) - - # Compute increasing powers - coefs_no_sign = np.vander(phases, len(weights), increasing=True) - - coefs_no_sign *= weights - - coefs_odd = signs_odd * coefs_no_sign - coefs_even = signs_even * coefs_no_sign - - # After applying the linear differential operator to a sinusoidal - # element of the basis e, the result can be expressed as - # A e + B e*, where e* is the other basis element in the pair - # with the same phase - - odd_sin_coefs = np.sum(coefs_odd[:, ::2], axis=1) - odd_cos_coefs = np.sum(coefs_odd[:, 1::2], axis=1) - - even_cos_coefs = np.sum(coefs_even[:, ::2], axis=1) - even_sin_coefs = np.sum(coefs_even[:, 1::2], axis=1) - - # The diagonal is the inner product of A e + B e* - # with itself. As the basis is orthonormal, the cross products e e* - # are 0, and the products e e and e* e* are one. - # Thus, the diagonal is A^2 + B^2 - # All elements outside the main diagonal are 0 - main_diag_odd = odd_sin_coefs**2 + odd_cos_coefs**2 - main_diag_even = even_sin_coefs**2 + even_cos_coefs**2 - - # The main diagonal should intercalate both diagonals - main_diag = np.array((main_diag_odd, main_diag_even)).T.ravel() - - penalty_matrix = np.diag(main_diag) - - # Add row and column for the constant - penalty_matrix = np.pad(penalty_matrix, pad_width=((1, 0), (1, 0)), - mode='constant') - - penalty_matrix[0, 0] = weights[0]**2 - - return penalty_matrix - - def _penalty(self, lfd): - - weights = lfd.constant_weights() - if weights is None: - return NotImplemented - - # If the period and domain range are not the same, the basis functions - # are not orthogonal - if self.period != (self.domain_range[0][1] - self.domain_range[0][0]): - return NotImplemented - - return self._penalty_orthonormal(weights) - def _derivative(self, coefs, order=1): omega = 2 * np.pi / self.period diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index 9890d72db..cde90d506 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -74,111 +74,6 @@ def _derivative(self, coefs, order=1): np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])) - def _evaluate_constant_lfd(self, weights): - """ - Evaluate constant weights of a linear differential operator - over the basis functions. - """ - - max_derivative = len(weights) - 1 - - _, coef_mat = self._coef_mat(max_derivative) - - # Compute coefficients for each derivative - coefs = np.cumprod(coef_mat, axis=0) - - # Add derivative 0 row - coefs = np.concatenate((np.ones((1, self.n_basis)), coefs)) - - # Now each row correspond to each basis and each column to - # each derivative - coefs_t = coefs.T - - # Multiply by the weights - weighted_coefs = coefs_t * weights - assert len(weighted_coefs) == self.n_basis - - # Now each row has the right weight, but the polynomials are in a - # decreasing order and with different exponents - - # Resize the coefs so that there are as many rows as the number of - # basis - # The matrix is now triangular - # refcheck is False to prevent exceptions while debugging - weighted_coefs = np.copy(weighted_coefs.T) - weighted_coefs.resize(self.n_basis, - self.n_basis, refcheck=False) - weighted_coefs = weighted_coefs.T - - # Shift the coefficients so that they correspond to the right - # exponent - indexes = np.tril_indices(self.n_basis) - polynomials = np.zeros_like(weighted_coefs) - polynomials[indexes[0], indexes[1] - - indexes[0] - 1] = weighted_coefs[indexes] - - # At this point, each row of the matrix correspond to a polynomial - # that is the result of applying the linear differential operator - # to each element of the basis - - return polynomials - - def _penalty(self, lfd): - - weights = lfd.constant_weights() - if weights is None: - return NotImplemented - - polynomials = self._evaluate_constant_lfd(weights) - - # Expand the polinomials with 0, so that the multiplication fits - # inside. It will need the double of the degree - length_with_padding = polynomials.shape[1] * 2 - 1 - - # Multiplication of polynomials is a convolution. - # The convolution can be performed in parallel applying a Fourier - # transform and then doing a normal multiplication in that - # space, coverting back with the inverse Fourier transform - fft = np.fft.rfft(polynomials, length_with_padding) - - # We compute only the upper matrix, as the penalty matrix is - # symmetrical - indices = np.triu_indices(self.n_basis) - fft_mul = fft[indices[0]] * fft[indices[1]] - - integrand = np.fft.irfft(fft_mul, length_with_padding) - - integration_domain = self.domain_range[0] - - # To integrate, divide by the position and increase the exponent - # in the evaluation - denom = np.arange(integrand.shape[1], 0, -1) - integrand /= denom - - # Add column of zeros at the right to increase exponent - integrand = np.pad(integrand, - pad_width=((0, 0), - (0, 1)), - mode='constant') - - # Now, apply Barrow's rule - # polyval applies Horner method over the first dimension, - # so we need to transpose - x_right = np.polyval(integrand.T, integration_domain[1]) - x_left = np.polyval(integrand.T, integration_domain[0]) - - integral = x_right - x_left - - penalty_matrix = np.empty((self.n_basis, self.n_basis)) - - # Set upper matrix - penalty_matrix[indices] = integral - - # Set lower matrix - penalty_matrix[(indices[1], indices[0])] = integral - - return penalty_matrix - def basis_of_product(self, other): """Multiplication of a Monomial Basis with other Basis""" if not _same_domain(self, other): diff --git a/tests/test_basis.py b/tests/test_basis.py index 6c6eae607..ca6b29b37 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -9,24 +9,6 @@ class TestBasis(unittest.TestCase): # def setUp(self): could be defined for set up before any test - def _test_penalty(self, basis, lfd, atol=0, result=None): - - penalty = basis.penalty(lfd) - numerical_penalty = basis._numerical_penalty(lfd) - - np.testing.assert_allclose( - penalty, - numerical_penalty, - atol=atol - ) - - if result is not None: - np.testing.assert_allclose( - penalty, - result, - atol=atol - ) - def test_from_data_cholesky(self): t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) @@ -47,134 +29,6 @@ def test_from_data_qr(self): np.array([[1., 2.78, -3., -0.78, 1.]]) ) - def test_bspline_penalty_special_case(self): - basis = BSpline(n_basis=5) - - res = np.array([[1152., -2016., 1152., -288., 0.], - [-2016., 3600., -2304., 1008., -288.], - [1152., -2304., 2304., -2304., 1152.], - [-288., 1008., -2304., 3600., -2016.], - [0., -288., 1152., -2016., 1152.]]) - - np.testing.assert_allclose( - basis.penalty(basis.order - 1), - res - ) - - np.testing.assert_allclose( - basis._numerical_penalty(basis.order - 1), - res - ) - - def test_constant_penalty(self): - basis = Constant(domain_range=(0, 3)) - - res = np.array([[12]]) - - self._test_penalty(basis, lfd=[2, 3, 4], result=res) - - def test_monomial_lfd(self): - n_basis = 5 - - basis = Monomial(n_basis=n_basis) - - lfd = [3] - res = np.array([[0., 0., 0., 0., 3.], - [0., 0., 0., 3., 0.], - [0., 0., 3., 0., 0.], - [0., 3., 0., 0., 0.], - [3., 0., 0., 0., 0.]]) - - np.testing.assert_allclose( - basis._evaluate_constant_lfd(lfd), - res - ) - - lfd = [3, 2] - res = np.array([[0., 0., 0., 0., 3.], - [0., 0., 0., 3., 2.], - [0., 0., 3., 4., 0.], - [0., 3., 6., 0., 0.], - [3., 8., 0., 0., 0.]]) - - np.testing.assert_allclose( - basis._evaluate_constant_lfd(lfd), - res - ) - - lfd = [3, 0, 5] - res = np.array([[0., 0., 0., 0., 3.], - [0., 0., 0., 3., 0.], - [0., 0., 3., 0., 10.], - [0., 3., 0., 30., 0.], - [3., 0., 60., 0., 0.]]) - - np.testing.assert_allclose( - basis._evaluate_constant_lfd(lfd), - res - ) - - def test_monomial_penalty(self): - basis = Monomial(n_basis=5, domain_range=(0, 3)) - - # Theorethical result - res = np.array([[0., 0., 0., 0., 0.], - [0., 0., 0., 0., 0.], - [0., 0., 12., 54., 216.], - [0., 0., 54., 324., 1458.], - [0., 0., 216., 1458., 6998.4]]) - - self._test_penalty(basis, lfd=2, result=res) - - basis = Monomial(n_basis=8, domain_range=(1, 5)) - - self._test_penalty(basis, lfd=[1, 2, 3]) - self._test_penalty(basis, lfd=7) - self._test_penalty(basis, lfd=0) - self._test_penalty(basis, lfd=1) - self._test_penalty(basis, lfd=27) - - def test_fourier_penalty(self): - basis = Fourier(n_basis=5) - - res = np.array([[0., 0., 0., 0., 0.], - [0., 1558.55, 0., 0., 0.], - [0., 0., 1558.55, 0., 0.], - [0., 0., 0., 24936.73, 0.], - [0., 0., 0., 0., 24936.73]]) - - # Those comparisons require atol as there are zeros involved - self._test_penalty(basis, lfd=2, atol=0.01, result=res) - - basis = Fourier(n_basis=9, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3], atol=1e-7) - self._test_penalty(basis, lfd=[2, 3, 0.1, 1], atol=1e-7) - self._test_penalty(basis, lfd=0, atol=1e-7) - self._test_penalty(basis, lfd=1, atol=1e-7) - self._test_penalty(basis, lfd=3, atol=1e-7) - - def test_bspline_penalty(self): - basis = BSpline(n_basis=5) - - res = np.array([[96., -132., 24., 12., 0.], - [-132., 192., -48., -24., 12.], - [24., -48., 48., -48., 24.], - [12., -24., -48., 192., -132.], - [0., 12., 24., -132., 96.]]) - - self._test_penalty(basis, lfd=2, result=res) - - basis = BSpline(n_basis=9, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3]) - self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) - self._test_penalty(basis, lfd=0) - self._test_penalty(basis, lfd=1) - self._test_penalty(basis, lfd=3) - self._test_penalty(basis, lfd=4) - - basis = BSpline(n_basis=16, order=8) - self._test_penalty(basis, lfd=0, atol=1e-7) - def test_basis_product_generic(self): monomial = Monomial(n_basis=5) fourier = Fourier(n_basis=3) @@ -428,7 +282,7 @@ def test_fdatabasis__mul__(self): np.testing.assert_raises(NotImplementedError, monomial2.__mul__, monomial2) - def test_fdatabasis__mul__(self): + def test_fdatabasis__mul__2(self): monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) monomial2 = FDataBasis(Monomial(n_basis=3), [[1, 2, 3], [3, 4, 5]]) diff --git a/tests/test_regularization.py b/tests/test_regularization.py new file mode 100644 index 000000000..3b3d094e6 --- /dev/null +++ b/tests/test_regularization.py @@ -0,0 +1,157 @@ +from skfda.misc.regularization._linear_diff_op_regularization import ( + _monomial_evaluate_constant_linear_diff_op) +from skfda.representation.basis import Constant, Monomial, BSpline, Fourier +import unittest + +import numpy as np + + +class TestLinearDifferentialOperatorRegularization(unittest.TestCase): + + # def setUp(self): could be defined for set up before any test + + def _test_penalty(self, basis, lfd, atol=0, result=None): + + penalty = basis.penalty(lfd) + numerical_penalty = basis._numerical_penalty(lfd) + + np.testing.assert_allclose( + penalty, + numerical_penalty, + atol=atol + ) + + if result is not None: + np.testing.assert_allclose( + penalty, + result, + atol=atol + ) + + def test_constant_penalty(self): + basis = Constant(domain_range=(0, 3)) + + res = np.array([[12]]) + + self._test_penalty(basis, lfd=[2, 3, 4], result=res) + + def test_monomial_linear_diff_op(self): + n_basis = 5 + + basis = Monomial(n_basis=n_basis) + + lfd = [3] + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 0.], + [0., 0., 3., 0., 0.], + [0., 3., 0., 0., 0.], + [3., 0., 0., 0., 0.]]) + + np.testing.assert_allclose( + _monomial_evaluate_constant_linear_diff_op(basis, lfd), + res + ) + + lfd = [3, 2] + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 2.], + [0., 0., 3., 4., 0.], + [0., 3., 6., 0., 0.], + [3., 8., 0., 0., 0.]]) + + np.testing.assert_allclose( + _monomial_evaluate_constant_linear_diff_op(basis, lfd), + res + ) + + lfd = [3, 0, 5] + res = np.array([[0., 0., 0., 0., 3.], + [0., 0., 0., 3., 0.], + [0., 0., 3., 0., 10.], + [0., 3., 0., 30., 0.], + [3., 0., 60., 0., 0.]]) + + np.testing.assert_allclose( + _monomial_evaluate_constant_linear_diff_op(basis, lfd), + res + ) + + def test_monomial_penalty(self): + basis = Monomial(n_basis=5, domain_range=(0, 3)) + + # Theorethical result + res = np.array([[0., 0., 0., 0., 0.], + [0., 0., 0., 0., 0.], + [0., 0., 12., 54., 216.], + [0., 0., 54., 324., 1458.], + [0., 0., 216., 1458., 6998.4]]) + + self._test_penalty(basis, lfd=2, result=res) + + basis = Monomial(n_basis=8, domain_range=(1, 5)) + + self._test_penalty(basis, lfd=[1, 2, 3]) + self._test_penalty(basis, lfd=7) + self._test_penalty(basis, lfd=0) + self._test_penalty(basis, lfd=1) + self._test_penalty(basis, lfd=27) + + def test_fourier_penalty(self): + basis = Fourier(n_basis=5) + + res = np.array([[0., 0., 0., 0., 0.], + [0., 1558.55, 0., 0., 0.], + [0., 0., 1558.55, 0., 0.], + [0., 0., 0., 24936.73, 0.], + [0., 0., 0., 0., 24936.73]]) + + # Those comparisons require atol as there are zeros involved + self._test_penalty(basis, lfd=2, atol=0.01, result=res) + + basis = Fourier(n_basis=9, domain_range=(1, 5)) + self._test_penalty(basis, lfd=[1, 2, 3], atol=1e-7) + self._test_penalty(basis, lfd=[2, 3, 0.1, 1], atol=1e-7) + self._test_penalty(basis, lfd=0, atol=1e-7) + self._test_penalty(basis, lfd=1, atol=1e-7) + self._test_penalty(basis, lfd=3, atol=1e-7) + + def test_bspline_penalty(self): + basis = BSpline(n_basis=5) + + res = np.array([[96., -132., 24., 12., 0.], + [-132., 192., -48., -24., 12.], + [24., -48., 48., -48., 24.], + [12., -24., -48., 192., -132.], + [0., 12., 24., -132., 96.]]) + + self._test_penalty(basis, lfd=2, result=res) + + basis = BSpline(n_basis=9, domain_range=(1, 5)) + self._test_penalty(basis, lfd=[1, 2, 3]) + self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) + self._test_penalty(basis, lfd=0) + self._test_penalty(basis, lfd=1) + self._test_penalty(basis, lfd=3) + self._test_penalty(basis, lfd=4) + + basis = BSpline(n_basis=16, order=8) + self._test_penalty(basis, lfd=0, atol=1e-7) + + def test_bspline_penalty_special_case(self): + basis = BSpline(n_basis=5) + + res = np.array([[1152., -2016., 1152., -288., 0.], + [-2016., 3600., -2304., 1008., -288.], + [1152., -2304., 2304., -2304., 1152.], + [-288., 1008., -2304., 3600., -2016.], + [0., -288., 1152., -2016., 1152.]]) + + np.testing.assert_allclose( + basis.penalty(basis.order - 1), + res + ) + + np.testing.assert_allclose( + basis._numerical_penalty(basis.order - 1), + res + ) From 698438e316d313e3d57782bf79d2a6f592a44d2a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Apr 2020 01:42:21 +0200 Subject: [PATCH 435/624] Fixed staticdispatch. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 38 --------------- .../_linear_diff_op_regularization.py | 48 +++++++++---------- 3 files changed, 25 insertions(+), 63 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index e549c4fd9..329b72770 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -3,4 +3,4 @@ from ._utils import (_list_of_arrays, _coordinate_list, _check_estimator, parameter_aliases, _to_grid, check_is_univariate, - _same_domain, singledispatchmethod) + _same_domain) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 7cbff0e10..271b7d0bc 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -185,41 +185,3 @@ def _check_estimator(estimator): instance = estimator() check_get_params_invariance(name, instance) check_set_params(name, instance) - - -singledispatchmethod = getattr(functools, 'singledispatchmethod', None) -if singledispatchmethod is None: - # For Python versions prior to 3.8 - - class singledispatchmethod: - """Single-dispatch generic method descriptor. - Supports wrapping existing descriptors and handles non-descriptor - callables as instance methods. - """ - - def __init__(self, func): - if not callable(func) and not hasattr(func, "__get__"): - raise TypeError(f"{func!r} is not callable or a descriptor") - - self.dispatcher = functools.singledispatch(func) - self.func = func - - def register(self, cls, method=None): - """generic_method.register(cls, func) -> func - Registers a new implementation for the given *cls* on a *generic_method*. - """ - return self.dispatcher.register(cls, func=method) - - def __get__(self, obj, cls=None): - def _method(*args, **kwargs): - method = self.dispatcher.dispatch(args[0].__class__) - return method.__get__(obj, cls)(*args, **kwargs) - - _method.__isabstractmethod__ = self.__isabstractmethod__ - _method.register = self.register - functools.update_wrapper(_method, self.func) - return _method - - @property - def __isabstractmethod__(self): - return getattr(self.func, '__isabstractmethod__', False) diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index c4b74e067..01c2aa66f 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -1,4 +1,4 @@ -import functools +from functools import singledispatch from numpy import polyder, polyint, polymul, polyval import scipy.integrate @@ -6,11 +6,22 @@ import numpy as np -from ..._utils import singledispatchmethod from ...representation.basis import Constant, Monomial, Fourier, BSpline from .._lfd import LinearDifferentialOperator +@singledispatch +def penalty_matrix_optimized(basis, regularization): + """ + Return a penalty matrix given a basis. + + This method is a singledispatch method that provides an + efficient analytical implementation of the computation of the + penalty matrix if possible. + """ + return NotImplemented + + class LinearDifferentialOperatorRegularization(): """ Regularization using the integral of the square of a linear differential @@ -28,6 +39,8 @@ def __init__(self, linear_diff_op=2): isinstance(linear_diff_op, LinearDifferentialOperator)) else ( LinearDifferentialOperator(linear_diff_op)) + penalty_matrix_optimized = penalty_matrix_optimized + def penalty_matrix_numerical(self, basis): """Return a penalty matrix using a numerical approach. @@ -60,17 +73,6 @@ def cross_product(x): return penalty_matrix - @singledispatchmethod - def penalty_matrix_optimized(self, basis): - """ - Return a penalty matrix given a basis. - - This method is a singledispatch method that provides an - efficient analytical implementation of the computation of the - penalty matrix if possible. - """ - return NotImplemented - def penalty_matrix(self, basis): r"""Return a penalty matrix given a basis. @@ -94,7 +96,7 @@ def penalty_matrix(self, basis): Springer. """ - matrix = self.penalty_matrix_optimized(basis) + matrix = penalty_matrix_optimized(basis, self) if matrix is NotImplemented: return self.penalty_matrix_numerical(basis) @@ -104,8 +106,8 @@ def penalty_matrix(self, basis): @LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register def constant_penalty_matrix_optimized( - regularization: LinearDifferentialOperatorRegularization, - basis: Constant): + basis: Constant, + regularization: LinearDifferentialOperatorRegularization): coefs = regularization.linear_diff_op.constant_weights() if coefs is None: @@ -170,8 +172,8 @@ def _monomial_evaluate_constant_linear_diff_op(basis, weights): @LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register def monomial_penalty_matrix_optimized( - regularization: LinearDifferentialOperatorRegularization, - basis: Monomial): + basis: Monomial, + regularization: LinearDifferentialOperatorRegularization): weights = regularization.linear_diff_op.constant_weights() if weights is None: @@ -225,8 +227,6 @@ def monomial_penalty_matrix_optimized( # Set lower matrix penalty_matrix[(indices[1], indices[0])] = integral - raise ValueError() - return penalty_matrix @@ -287,8 +287,8 @@ def _fourier_penalty_matrix_optimized_orthonormal(basis, weights): @LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register def fourier_penalty_matrix_optimized( - regularization: LinearDifferentialOperatorRegularization, - basis: Fourier): + basis: Fourier, + regularization: LinearDifferentialOperatorRegularization): weights = regularization.linear_diff_op.constant_weights() if weights is None: @@ -304,8 +304,8 @@ def fourier_penalty_matrix_optimized( @LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register def bspline_penalty_matrix_optimized( - regularization: LinearDifferentialOperatorRegularization, - basis: BSpline): + basis: BSpline, + regularization: LinearDifferentialOperatorRegularization): coefs = regularization.linear_diff_op.constant_weights() if coefs is None: From 43bf064e4baa8d48b987968fecac0e0804493333 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 20 Apr 2020 21:58:08 +0200 Subject: [PATCH 436/624] Including white noise covariance, changes in concatenation, anova in basis and anova vectorization. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway.py | 15 ++- examples/plot_oneway_synthetic.py | 34 +++--- skfda/inference/anova/anova_oneway.py | 139 ++++++++++++----------- skfda/misc/covariances.py | 23 +++- skfda/misc/metrics.py | 62 ++++++---- skfda/representation/_fdatabasis.py | 33 ------ skfda/representation/_functional_data.py | 36 +++--- skfda/representation/grid.py | 47 -------- tests/test_basis.py | 14 +++ tests/test_covariances.py | 16 ++- tests/test_grid.py | 39 ++----- tests/test_oneway_anova.py | 33 ++++-- 12 files changed, 238 insertions(+), 253 deletions(-) diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 40ca6e47b..2160c0524 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -13,7 +13,8 @@ import skfda from skfda.inference.anova import oneway_anova -from skfda.representation import FDataGrid +from skfda.representation import FDataGrid, FDataBasis +from skfda.representation.basis import Fourier ################################################################################ # *One-way ANOVA* (analysis of variance) is a test that can be used to @@ -30,7 +31,7 @@ # of hips and knees from 39 different boys in a 20 point movement cycle. dataset = skfda.datasets.fetch_gait() fd_hip = dataset['data'].coordinates[0] -fd_knee = dataset['data'].coordinates[1] +fd_knee = dataset['data'].coordinates[1].to_basis(Fourier(n_basis=10)) ################################################################################ # Let's start with the first feature, the angle of the hip. The sample @@ -49,8 +50,7 @@ fd_hip.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_hip1.mean(), fd_hip2.mean(), fd_hip3.mean()] -fd_means = fd_hip.copy(data_matrix=[mean.data_matrix[0] for mean in means], - dataset_label='Hip angle (means)') +fd_means = FDataGrid.concatenate_samples(means) fig = fd_means.plot() ############################################################################### @@ -74,7 +74,7 @@ ################################################################################ # This was the simplest way to call this function. Let's see another example, -# this time using knee angles. +# this time using knee angles, this time with data in basis representation. fig = fd_knee.plot() ################################################################################ @@ -86,8 +86,7 @@ fd_knee.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_knee1.mean(), fd_knee2.mean(), fd_knee3.mean()] -fd_means = fd_knee.copy(data_matrix=[mean.data_matrix[0] for mean in means], - dataset_label='Knee angle (means)') +fd_means = FDataBasis.concatenate_samples(means) fig = fd_means.plot() ################################################################################ @@ -104,7 +103,7 @@ # sampling distribution of the statistic which is compared with the first # return to get the *p-value*. -v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_reps=1500, p=2, +v_n, p_val, dist = oneway_anova(fd_knee1, fd_knee2, fd_knee3, n_reps=1500, return_dist=True) print('Statistic: ', v_n) diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 47961d139..16ee7f5b5 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -14,6 +14,7 @@ import skfda from skfda.inference.anova import oneway_anova from skfda.representation import FDataGrid +from skfda.misc.covariances import WhiteNoise ################################################################################ # *One-way ANOVA* (analysis of variance) is a test that can be used to @@ -70,7 +71,7 @@ # p-value for the test should be near to zero. sigma = 0.01 -cov = np.identity(n_features) * sigma +cov = WhiteNoise(variance=sigma) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=start, @@ -90,9 +91,11 @@ # and the averages for each group. fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) -fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( +fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, + fd3]]) +fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() +fd_total.plot() ################################################################################ # In the following, the same process will be followed incrementing sigma @@ -102,9 +105,8 @@ ################################################################################ # Plot for :math:`\sigma = 1`: - -sigma = 1 -cov = np.identity(n_features) * sigma +sigma = 0.1 +cov = WhiteNoise(variance=sigma) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=t[0], @@ -118,16 +120,18 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) -fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( +fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) +fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, + fd3]]) +fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() +fd_total.plot() ################################################################################ # Plot for :math:`\sigma = 10`: -sigma = 10 -cov = np.identity(n_features) * sigma +sigma = 1 +cov = WhiteNoise(variance=sigma) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=t[0], @@ -141,10 +145,12 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = fd1.concatenate(fd2.concatenate(fd3.concatenate())) -fd.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( +fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) +fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, + fd3]]) +fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) -FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]).plot() +fd_total.plot() ################################################################################ # **References:** diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 5444ac558..3499f48a6 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -2,38 +2,35 @@ from sklearn.utils import check_random_state from skfda.misc.metrics import norm_lp -from skfda.representation import FData, FDataGrid, FDataBasis +from skfda.representation import FData, FDataGrid from skfda.datasets import make_gaussian_process -def v_sample_stat(fd, weights, p=2): +def v_sample_stat(fd, weights): r""" Calculates a statistic that measures the variability between groups of - samples in a :class:`skfda.representation.grid.FDataGrid` object. + samples in a :class:`skfda.representation.FData` object. The statistic defined as below is calculated between all the samples in a - :class:`skfda.representation.grid.FDataGrid` object with a given set of - weights, and the desired :math:`L_p` norm. + :class:`skfda.representation.FData` object with a given set of + weights. - Let :math:`\{f_i\}_{i=1}^k` be a set of samples in a FDataGrid object. + Let :math:`\{f_i\}_{i=1}^k` be a set of samples in a FData object. Let :math:`\{w_j\}_{j=1}^k` be a set of weights, where :math:`w_i` is related to the sample :math:`f_i` for :math:`i=1,\dots,k`. The statistic is defined as: .. math:: - V_n = \sum_{i 0 + if n_groups < 2: + raise ValueError("At least two groups must be passed in fd_grouped.") - # Creating list with all the sample points - list_sample = [fd.sample_points[0].tolist() for fd in fd_grouped] - # Checking that the all the entries in the list are the same - if not list_sample.count(list_sample[0]) == len(list_sample): - raise ValueError("All FDataGrid passed must have the same sample " - "points.") + for fd in fd_grouped[1:]: + if not np.array_equal(fd.domain_range, fd_grouped[0].domain_range): + raise ValueError("Domain range must match for every FData in " + "fd_grouped.") - sample_points = fd_grouped[0].sample_points - m = len(sample_points[0]) # Number of points in the grid start, stop = fd_grouped[0].domain_range[0] sizes = [fd.n_samples for fd in fd_grouped] # List with sizes of each group @@ -181,21 +168,26 @@ def _anova_bootstrap(fd_grouped, n_reps, p=2, random_state=None): # Estimating covariances for each group k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] + # Number of sample points for gaussian processes have to match the features + # of the covariances. + n_features = k_est[0].shape[0] + # Instance a random state object in case random_state is an int random_state = check_random_state(random_state) # Simulating n_reps observations for each of the n_groups gaussian processes - sim = [make_gaussian_process(n_reps, n_features=m, start=start, stop=stop, - cov=k_est[i], random_state=random_state) + sim = [make_gaussian_process(n_reps, n_features=n_features, start=start, + stop=stop, cov=k_est[i], + random_state=random_state) for i in range(n_groups)] v_samples = np.empty(n_reps) for i in range(n_reps): fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) - v_samples[i] = v_asymptotic_stat(fd, sizes, p=p) + v_samples[i] = v_asymptotic_stat(fd, sizes) return v_samples -def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): +def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): r""" Performs one-way functional ANOVA. @@ -233,13 +225,8 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): procedure. Defaults to 2000 (This value may change in future versions). - p (int, optional): p of the lp norm. Must be greater or equal - than 1. If p='inf' or p=np.inf it is used the L infinity metric. - Defaults to 2. - return_dist (bool, optional): Flag to indicate if the function should - return a - numpy.array with the sampling distribution simulated. + return a numpy.array with the sampling distribution simulated. random_state (optional): Random state. @@ -284,23 +271,37 @@ def oneway_anova(*args, n_reps=2000, p=2, return_dist=False, random_state=None): raise ValueError("Argument type must inherit FData.") if n_reps < 1: raise ValueError("Number of simulations must be positive.") - if any(isinstance(fd, FDataBasis) for fd in args): - raise NotImplementedError("Not implemented for FDataBasis objects.") fd_groups = args - # Creating list with all the sample points - list_sample = [fd.sample_points[0].tolist() for fd in fd_groups] - # Checking that the all the entries in the list are the same - if not list_sample.count(list_sample[0]) == len(list_sample): - raise ValueError("All FDataGrid passed must have the same sample " - "points.") - - fd_means = FDataGrid.concatenate_samples([fd.mean() for fd in fd_groups]) - - vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) + if not all([isinstance(fd, type(fd_groups[0])) for fd in fd_groups[1:]]): + raise TypeError('Found mixed FData types in arguments.') + + for fd in fd_groups[1:]: + if not np.array_equal(fd.domain_range, fd_groups[0].domain_range): + raise ValueError("Domain range must match for every FData passed.") + + if isinstance(fd_groups[0], FDataGrid): + # Creating list with all the sample points + list_sample = [fd.sample_points[0].tolist() for fd in fd_groups] + # Checking that the all the entries in the list are the same + if not list_sample.count(list_sample[0]) == len(list_sample): + raise ValueError("All FDataGrid passed must have the same sample " + "points.") + else: # If type is FDataBasis, check same basis + list_basis = [fd.basis for fd in fd_groups] + if not list_basis.count(list_basis[0]) == len(list_basis): + raise NotImplementedError("Not implemented for FDataBasis with " + "different basis.") + + # FDataGrid where each sample is the mean of each group + fd_means = FData.concatenate_samples([fd.mean() for fd in fd_groups]) + + # Base statistic + vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups]) + + # Computing sampling distribution + simulation = _anova_bootstrap(fd_groups, n_reps, random_state=random_state) - simulation = _anova_bootstrap(fd_groups, n_reps, p=p, - random_state=random_state) p_value = np.sum(simulation > vn) / len(simulation) if return_dist: diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index be8ff083c..0de6a0456 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -255,10 +255,31 @@ def __call__(self, x, y): y = _transform_to_2d(y) x_y = _squared_norms(x, y) - + print((self.variance * np.exp(-np.sqrt(x_y) / ( + self.length_scale))).shape) return self.variance * np.exp(-np.sqrt(x_y) / (self.length_scale)) def to_sklearn(self): """Convert it to a sklearn kernel, if there is one""" return (self.variance * sklearn_kern.Matern(length_scale=self.length_scale, nu=0.5)) + + +class WhiteNoise(Covariance): + """Gaussian covariance function.""" + + _latex_formula = (r"K(x,y)= \left\{ \begin{array}{lc} \sigma^2, & x = y " + r"\\ 0, & x \neq y\\ \end{array} \right.") + + _parameters = [("variance", r"\sigma^2")] + + def __init__(self, *, variance: float = 1.): + self.variance = variance + + def __call__(self, x, y): + x = _transform_to_2d(x) + return self.variance * np.eye(x.shape[0]) + + def to_sklearn(self): + """Convert it to a sklearn kernel, if there is one""" + return sklearn_kern.WhiteKernel(noise_level=self.variance) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 6aba2a115..02af5a8d5 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -6,7 +6,7 @@ from ..preprocessing.registration._warping import _normalize_scale from ..preprocessing.registration.elastic import SRSF from ..representation import FData -from ..representation import FDataGrid +from ..representation import FDataGrid, FDataBasis def _cast_to_grid(fdata1, fdata2, eval_points=None, _check=True, **kwargs): @@ -209,7 +209,7 @@ def pairwise(fdata1, fdata2): return pairwise -def norm_lp(fdatagrid, p=2, p2=2): +def norm_lp(fdata, p=2, p2=2): r"""Calculate the norm of all the samples in a FDataGrid object. For each sample sample f the Lp norm is defined as: @@ -244,7 +244,7 @@ def norm_lp(fdatagrid, p=2, p2=2): Args: - fdatagrid (FDataGrid): FDataGrid object. + fdata (FDataG): FData object. p (int, optional): p of the lp norm. Must be greater or equal than 1. If p='inf' or p=np.inf it is used the L infinity metric. Defaults to 2. @@ -279,30 +279,50 @@ def norm_lp(fdatagrid, p=2, p2=2): if not (p == 'inf' or np.isinf(p)) and p < 1: raise ValueError(f"p must be equal or greater than 1.") - if fdatagrid.dim_codomain > 1: - if p2 == 'inf': - p2 = np.inf - data_matrix = np.linalg.norm(fdatagrid.data_matrix, ord=p2, axis=-1, - keepdims=True) - else: - data_matrix = np.abs(fdatagrid.data_matrix) + if isinstance(fdata, FDataBasis): + if fdata.dim_codomain > 1 or p != 2: + raise ValueError - if p == 'inf' or np.isinf(p): + res = np.empty(fdata.n_samples) - if fdatagrid.dim_domain == 1: - res = np.max(data_matrix[..., 0], axis=1) - else: - res = np.array([np.max(sample) for sample in data_matrix]) + gram = fdata.basis.gram_matrix() - elif fdatagrid.dim_domain == 1: + for k, coefs in enumerate(fdata.coefficients): + l_triang = 0 + for i in range(fdata.n_basis): + for j in range(i): + l_triang += coefs[i] * coefs[j] * gram[i][j] - # Computes the norm, approximating the integral with Simpson's rule. - res = scipy.integrate.simps(data_matrix[..., 0] ** p, - x=fdatagrid.sample_points) ** (1 / p) + diag = np.dot(coefs ** 2, np.diag(gram)) + res[k] = 2 * l_triang + diag + + res = np.sqrt(res) else: - # Needed to perform surface integration - return NotImplemented + if fdata.dim_codomain > 1: + if p2 == 'inf': + p2 = np.inf + data_matrix = np.linalg.norm(fdata.data_matrix, ord=p2, axis=-1, + keepdims=True) + else: + data_matrix = np.abs(fdata.data_matrix) + + if p == 'inf' or np.isinf(p): + + if fdata.dim_domain == 1: + res = np.max(data_matrix[..., 0], axis=1) + else: + res = np.array([np.max(sample) for sample in data_matrix]) + + elif fdata.dim_domain == 1: + + # Computes the norm, approximating the integral with Simpson's rule. + res = scipy.integrate.simps(data_matrix[..., 0] ** p, + x=fdata.sample_points) ** (1 / p) + + else: + # Needed to perform surface integration + return NotImplemented if len(res) == 1: return res[0] diff --git a/skfda/representation/_fdatabasis.py b/skfda/representation/_fdatabasis.py index 41042d5af..172ac9d4b 100644 --- a/skfda/representation/_fdatabasis.py +++ b/skfda/representation/_fdatabasis.py @@ -791,39 +791,6 @@ def concatenate(self, *others, as_coordinates=False): return self.copy(coefficients=np.concatenate(data, axis=0)) - @staticmethod - def concatenate_samples(objects, as_coordinates=False): - """Join samples from a list of similar FDataBasis objects. - - Joins samples of FDataBasis objects if they have the same - dimensions and sampling points. - - Args: - objects (list of :obj:`FDataBasis`): Objects to be concatenated. - as_coordinates (boolean, optional): If False concatenates as - new samples, else, concatenates each value as new components - of the image. Defaults to false. - - Returns: - :obj:`FDataGrid`: FDataGrid object with the samples from the - original objects. - - Raises: - ValueError: In case the provided list of FDataBasis objects is - empty. - - Todo: - By the moment, only unidimensional objects are supported in basis - representation. - """ - if len(objects) < 1: - raise ValueError("At least one FDataBasis object must be provided " - "to concatenate.") - if not isinstance(objects[0], FDataBasis): - raise ValueError("Items in list must be instances of FDataBasis.") - return objects[0].concatenate(*objects[1:], - as_coordinates=as_coordinates) - def compose(self, fd, *, eval_points=None, **kwargs): """Composition of functions. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index ae1a44ec5..7124a9ad1 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -789,24 +789,32 @@ def concatenate(self, *others, as_coordinates=False): pass @staticmethod - @abstractmethod - def concatenate_samples(objects, as_coordinates=False): - """Join samples from a list of similar FData objects. - - Joins samples of FData objects if they have the same dimensions and - sampling points. + def concatenate_samples(objects): + """ + Join samples from an iterable of similar FDataBasis objects. + Joins samples of FDataBasis objects if they have the same + dimensions and sampling points. Args: - objects (list of :obj:`FData`): Objects to be concatenated. - as_coordinates (boolean, optional): If False concatenates as - new samples, else, concatenates each value as new components - of the image. Defaults to false. - + objects (list of :obj:`FDataBasis`): Objects to be concatenated. Returns: - :obj:`FData`: FData object with the samples from the original - objects. + :obj:`FDataGrid`: FDataGrid object with the samples from the + original objects. + Raises: + ValueError: In case the provided list of FDataBasis objects is + empty. + Todo: + By the moment, only unidimensional objects are supported in basis + representation. """ - pass + objects = iter(objects) + first = next(objects, None) + + if not first: + raise ValueError("At least one FData object must be provided " + "to concatenate.") + + return first.concatenate(*list(objects)) @abstractmethod def compose(self, fd, *, eval_points=None, **kwargs): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 56aba71a6..28184ec00 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -787,53 +787,6 @@ def concatenate(self, *others, as_coordinates=False): else: return self.copy(data_matrix=np.concatenate(data, axis=0)) - @staticmethod - def concatenate_samples(objects, as_coordinates=False): - """Join samples from a list of similar FDataGrid objects. - - Joins samples of FDataGrid objects if they have the same - dimensions and sampling points. - - Args: - objects (list of :obj:`FDataGrid`): Objects to be concatenated. - as_coordinates (boolean, optional): If False concatenates as - new samples, else, concatenates each value as new components - of the image. Defaults to false. - - Returns: - :obj:`FDataGrid`: FDataGrid object with the samples from the - original objects. - - Raises: - ValueError: In case the provided list of FDataGrid objects is empty. - - Examples: - >>> fd = FDataGrid([1,2,4,5,8], range(5)) - >>> fd_2 = FDataGrid([3,4,7,9,2], range(5)) - >>> FDataGrid.concatenate_samples([fd, fd_2]) - FDataGrid( - array([[[1], - [2], - [4], - [5], - [8]], - - [[3], - [4], - [7], - [9], - [2]]]), - sample_points=[array([0, 1, 2, 3, 4])], - domain_range=array([[0, 4]]), - ...) - """ - if len(objects) < 1: - raise ValueError("At least one FDataGrid object must be provided " - "to concatenate.") - if not isinstance(objects[0], FDataGrid): - raise ValueError("Items in list must be instances of FDataGrid.") - return objects[0].concatenate(*objects[1:], - as_coordinates=as_coordinates) def scatter(self, *args, **kwargs): """Scatter plot of the FDatGrid object. diff --git a/tests/test_basis.py b/tests/test_basis.py index 6c6eae607..926cec751 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,5 +1,6 @@ from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) +from skfda.representation.grid import FDataGrid import unittest import numpy as np @@ -578,6 +579,19 @@ def test_fdatabasis_derivative_bspline(self): [-120, -18, -60], [-48, 0, 48]]) + def test_concatenate_samples(self): + sample1 = np.arange(0, 10) + sample2 = np.arange(10, 20) + fd1 = FDataGrid([sample1]).to_basis(Fourier(n_basis=5)) + fd2 = FDataGrid([sample2]).to_basis(Fourier(n_basis=5)) + + fd = FDataBasis.concatenate_samples([fd1, fd2]) + + np.testing.assert_equal(fd.n_samples, 2) + np.testing.assert_equal(fd.dim_codomain, 1) + np.testing.assert_equal(fd.dim_domain, 1) + np.testing.assert_array_equal(fd.coefficients, np.concatenate( + [fd1.coefficients, fd2.coefficients])) if __name__ == '__main__': print() diff --git a/tests/test_covariances.py b/tests/test_covariances.py index 8eccd8066..878ed9d20 100644 --- a/tests/test_covariances.py +++ b/tests/test_covariances.py @@ -11,11 +11,14 @@ def setUp(self): self.x = np.linspace(-1, 1, 1000)[:, np.newaxis] - def _test_compare_sklearn(self, cov: skfda.misc.covariances.Covariance): + def _test_compare_sklearn(self, cov: skfda.misc.covariances.Covariance, + eval_y=True): cov_sklearn = cov.to_sklearn() - cov_matrix = cov(self.x, self.x) - cov_sklearn_matrix = cov_sklearn(self.x, self.x) + if eval_y: + cov_sklearn_matrix = cov_sklearn(self.x, self.x) + else: + cov_sklearn_matrix = cov_sklearn(self.x) np.testing.assert_array_almost_equal(cov_matrix, cov_sklearn_matrix) @@ -62,3 +65,10 @@ def test_exponential(self): cov = skfda.misc.covariances.Exponential( variance=variance, length_scale=length_scale) self._test_compare_sklearn(cov) + + def test_white_noise(self): + + for variance in [1, 2]: + with self.subTest(variance=variance): + cov = skfda.misc.covariances.WhiteNoise(variance=variance) + self._test_compare_sklearn(cov, eval_y=False) diff --git a/tests/test_grid.py b/tests/test_grid.py index 94011596a..2a8f549cb 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -100,46 +100,21 @@ def test_concatenate_coordinates(self): np.testing.assert_equal(None, fd.axes_labels) def test_concatenate_samples(self): - fd1 = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) - fd2 = FDataGrid([[3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) + sample1 = np.arange(0, 10) + sample2 = np.arange(10, 20) + fd1 = FDataGrid([sample1]) + fd2 = FDataGrid([sample2]) fd1.axes_labels = ["x", "y"] fd = FDataGrid.concatenate_samples([fd1, fd2]) - np.testing.assert_equal(fd.n_samples, 4) + np.testing.assert_equal(fd.n_samples, 2) np.testing.assert_equal(fd.dim_codomain, 1) np.testing.assert_equal(fd.dim_domain, 1) - np.testing.assert_array_equal(fd.data_matrix[..., 0], - [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], - [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) + np.testing.assert_array_equal(fd.data_matrix[..., 0], [sample1, + sample2]) np.testing.assert_array_equal(fd1.axes_labels, fd.axes_labels) - def test_concatenate_samples_coordinates(self): - fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) - fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) - - fd1.axes_labels = ["x", "y"] - fd2.axes_labels = ["w", "t"] - fd = FDataGrid.concatenate_samples([fd1, fd2], as_coordinates=True) - - np.testing.assert_equal(fd.n_samples, 2) - np.testing.assert_equal(fd.dim_codomain, 2) - np.testing.assert_equal(fd.dim_domain, 1) - - np.testing.assert_array_equal(fd.data_matrix, - [[[1, 3], [2, 4], [3, 5], [4, 6]], - [[2, 4], [3, 5], [4, 6], [5, 7]]]) - - # Testing labels - np.testing.assert_array_equal(["x", "y", "t"], fd.axes_labels) - fd1.axes_labels = ["x", "y"] - fd2.axes_labels = None - fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_array_equal(["x", "y", None], fd.axes_labels) - fd1.axes_labels = None - fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_equal(None, fd.axes_labels) - def test_coordinates(self): fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) fd1.axes_labels = ["x", "y"] diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index 4e34d3295..e7e30f4b9 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -2,6 +2,7 @@ import numpy as np from skfda.representation import FDataGrid +from skfda.representation.basis import Fourier from skfda.datasets import fetch_gait from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ v_sample_stat @@ -36,28 +37,38 @@ def test_v_stats(self): m3 = [3 for _ in range(n_features)] fd = FDataGrid([m1, m2, m3], sample_points=t) self.assertEqual(v_sample_stat(fd, weights), 7.0) - self.assertEqual(v_sample_stat(fd, weights, p=1), 5.0) + self.assertAlmostEqual(v_sample_stat(fd.to_basis(Fourier(n_basis=5)), + weights), 7.0) res = (1 - 2 * np.sqrt(1 / 2)) ** 2 + (1 - 3 * np.sqrt(1 / 3)) ** 2 \ + (2 - 3 * np.sqrt(2 / 3)) ** 2 self.assertAlmostEqual(v_asymptotic_stat(fd, weights), res) - res = abs(1 - 2 * np.sqrt(1 / 2)) + abs(1 - 3 * np.sqrt(1 / 3))\ - + abs(2 - 3 * np.sqrt(2 / 3)) - self.assertAlmostEqual(v_asymptotic_stat(fd, weights, p=1), res) + self.assertAlmostEqual(v_asymptotic_stat(fd.to_basis(Fourier( + n_basis=5)), weights), res) def test_asymptotic_behaviour(self): dataset = fetch_gait() fd = dataset['data'].coordinates[1] - fd1 = fd[0:13] - fd2 = fd[13:26] - fd3 = fd[26:39] + fd1 = fd[0:5] + fd2 = fd[5:10] + fd3 = fd[10:15] - n_little_sim = 50 + n_little_sim = 10 - sims = np.array([oneway_anova(fd1, fd2, fd3, n_reps=2000)[1] for _ in + sims = np.array([oneway_anova(fd1, fd2, fd3, n_reps=500)[1] for _ in range(n_little_sim)]) little_sim = np.mean(sims) - big_sim = oneway_anova(fd1, fd2, fd3, n_reps=50000)[1] - self.assertAlmostEqual(little_sim, big_sim, delta=0.01) + big_sim = oneway_anova(fd1, fd2, fd3, n_reps=2000)[1] + self.assertAlmostEqual(little_sim, big_sim, delta=0.05) + + fd = fd.to_basis(Fourier(n_basis=5)) + fd1 = fd[0:5] + fd2 = fd[5:10] + + sims = np.array([oneway_anova(fd1, fd2, n_reps=500)[1] for _ in + range(n_little_sim)]) + little_sim = np.mean(sims) + big_sim = oneway_anova(fd1, fd2, n_reps=2000)[1] + self.assertAlmostEqual(little_sim, big_sim, delta=0.05) if __name__ == '__main__': From 720012615a8f07486ce9b2913291430f7a2cf5a6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Apr 2020 22:22:01 +0200 Subject: [PATCH 437/624] add test for fpcagrid --- tests/test_fpca.py | 104 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 101 insertions(+), 3 deletions(-) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a71602c28..241060250 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -65,18 +65,116 @@ def test_basis_fpca_fit_result(self): -0.02923873, -0.003566887, -0.009654571, -0.0100063], [-0.3315211, -0.0508643, 0.89218521, 0.1669182, 0.2453900, 0.03548997, 0.037938051, -0.025777507, 0.008416904], - [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, - 0.16833314, 0.031509179, -0.006768189, 0.047306718]] + [-0.1379108, 0.9125089, 0.00142045, 0.2657423, -0.2146497, + 0.16833314, 0.031509179, -0.006768189, 0.047306718]] results = np.array(results) # compare results obtained using this library. There are slight # variations due to the fact that we are in two different packages for i in range(n_components): - if np.sign(fpca.components_.coefficients[i][0]) != np.sign(results[i][0]): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign( + results[i][0]): results[i, :] *= -1 np.testing.assert_allclose(fpca.components_.coefficients, results, atol=1e-7) + def test_grid_fpca_fit_result(self): + + n_components = 1 + + fd_data = fetch_weather()['data'].coordinates[0] + + fpca = FPCAGrid(n_components=n_components, weights=[1] * 365) + fpca.fit(fd_data) + + # results obtained using fda.usc for the first component + results = [ + [-0.06958281, -0.07015412, -0.07095115, -0.07185632, -0.07128256, + -0.07124209, -0.07364828, -0.07297663, -0.07235438, -0.07307498, + -0.07293423, -0.07449293, -0.07647909, -0.07796823, -0.07582476, + -0.07263243, -0.07241871, -0.0718136, -0.07015477, -0.07132331, + -0.0711527, -0.07435933, -0.07602666, -0.0769783, -0.07707199, + -0.07503802, -0.0770302, -0.07705581, -0.07633515, -0.07624817, + -0.07631568, -0.07619913, -0.07568, -0.07595155, -0.07506939, + -0.07181941, -0.06907624, -0.06735476, -0.06853985, -0.06902363, + -0.07098882, -0.07479412, -0.07425241, -0.07555835, -0.0765903, + -0.07651853, -0.07682536, -0.07458996, -0.07631711, -0.07726509, + -0.07641246, -0.0744066, -0.07501397, -0.07302722, -0.07045571, + -0.06912529, -0.06792186, -0.06830739, -0.06898433, -0.07000192, + -0.07014513, -0.06994886, -0.07115909, -0.073999, -0.07292669, + -0.07139879, -0.07226865, -0.07187915, -0.07122995, -0.06975022, + -0.06800613, -0.06900793, -0.07186378, -0.07114479, -0.07015252, + -0.06944782, -0.068291, -0.06905348, -0.06925773, -0.06834624, + -0.06837319, -0.06824067, -0.06644614, -0.06637313, -0.06626312, + -0.06470209, -0.0645058, -0.06477729, -0.06411049, -0.06158499, + -0.06305197, -0.06398006, -0.06277579, -0.06282124, -0.06317684, + -0.0614125, -0.05961922, -0.05875443, -0.05845781, -0.05828608, + -0.05666474, -0.05495706, -0.05446301, -0.05468254, -0.05478609, + -0.05440798, -0.05312339, -0.05102368, -0.05160285, -0.05077954, + -0.04979648, -0.04890853, -0.04745462, -0.04496763, -0.0448713, + -0.04599596, -0.04688998, -0.04488872, -0.04404507, -0.04420729, + -0.04368153, -0.04254381, -0.0411764, -0.04022811, -0.03999746, + -0.03963634, -0.03832502, -0.0383956, -0.04015374, -0.0387544, + -0.03777315, -0.03830728, -0.03768616, -0.03714081, -0.03781918, + -0.03739374, -0.03659894, -0.03563342, -0.03658407, -0.03686991, + -0.03543746, -0.03518799, -0.03361226, -0.0321534, -0.03050438, + -0.02958411, -0.02855023, -0.02913402, -0.02992464, -0.02899548, + -0.02891629, -0.02809554, -0.02702642, -0.02672194, -0.02678648, + -0.02698471, -0.02628085, -0.02674285, -0.02658515, -0.02604447, + -0.0245711, -0.02413174, -0.02342496, -0.022898, -0.02216152, + -0.02272283, -0.02199741, -0.02305362, -0.02371371, -0.02320865, + -0.02234777, -0.0225018, -0.02104359, -0.02203346, -0.02052545, + -0.01987457, -0.01947911, -0.01986949, -0.02012196, -0.01958515, + -0.01906753, -0.01857869, -0.01874101, -0.01827973, -0.017752, + -0.01702056, -0.01759611, -0.01888485, -0.01988159, -0.01951675, + -0.01872967, -0.01866667, -0.0183576, -0.01909758, -0.018599, + -0.01910036, -0.01930315, -0.01958856, -0.02129936, -0.0216614, + -0.0204397, -0.02002368, -0.02058828, -0.02149915, -0.02167326, + -0.02238569, -0.02211907, -0.02168336, -0.02124387, -0.02131655, + -0.02130508, -0.02181227, -0.02230632, -0.02223732, -0.0228216, + -0.02355137, -0.02275145, -0.02286893, -0.02437776, -0.02523897, + -0.0248354, -0.02319174, -0.02335831, -0.02405789, -0.02483273, + -0.02428119, -0.02395295, -0.02437185, -0.02476434, -0.02347973, + -0.02385957, -0.02451257, -0.02414586, -0.02439035, -0.02357782, + -0.02417295, -0.02504764, -0.02682569, -0.02807111, -0.02886335, + -0.02943406, -0.02956806, -0.02893096, -0.02903812, -0.02999862, + -0.029421, -0.03016203, -0.03118823, -0.03076205, -0.03005985, + -0.03079187, -0.03215188, -0.03271075, -0.03146124, -0.03040965, + -0.03008436, -0.03085897, -0.03015341, -0.03014661, -0.03110255, + -0.03271278, -0.03217399, -0.0331721, -0.03459221, -0.03572073, + -0.03560707, -0.03531492, -0.03687657, -0.03800143, -0.0373808, + -0.03729927, -0.03748666, -0.03754171, -0.03790408, -0.03963726, + -0.03992153, -0.03812243, -0.0373844, -0.0385394, -0.03849716, + -0.03826345, -0.03743958, -0.0380861, -0.03857622, -0.04099357, + -0.04102509, -0.04170207, -0.04283573, -0.04320618, -0.04269438, + -0.04467527, -0.04470603, -0.04496092, -0.04796417, -0.04796633, + -0.047863, -0.04883668, -0.0505939, -0.05112441, -0.04960962, + -0.05000041, -0.04962112, -0.05087008, -0.0521671, -0.05369792, + -0.05478139, -0.05559221, -0.05669698, -0.05654505, -0.05731113, + -0.05783543, -0.05766056, -0.05754354, -0.05724272, -0.05831026, + -0.05847512, -0.05804533, -0.05875046, -0.06021703, -0.06147975, + -0.06213918, -0.0645805, -0.06500849, -0.06361716, -0.06315227, + -0.06306436, -0.06425743, -0.06626847, -0.06615213, -0.06881004, + -0.06942296, -0.06889225, -0.06868663, -0.0678667, -0.06720133, + -0.06771172, -0.06885042, -0.06896979, -0.06961627, -0.07211988, + -0.07252956, -0.07265559, -0.07264195, -0.07306334, -0.07282035, + -0.07196505, -0.07210595, -0.07203942, -0.07105821, -0.06920599, + -0.06892264, -0.06699939, -0.06537829, -0.06543323, -0.06913186, + -0.07210039, -0.07219987, -0.07124228, -0.07065497, -0.06996833, + -0.0674457, -0.06800847, -0.06784175, -0.06592871, -0.06723401]] + + results = np.array(results) + + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components_.data_matrix[i][0]) != np.sign( + results[i][0]): + results[i, :] *= -1 + np.testing.assert_allclose(np.squeeze(fpca.components_.data_matrix), + np.squeeze(results), + rtol=1e-6) + if __name__ == '__main__': unittest.main() From d5db2aec8c6b55ecc9e5864b0aea3e9b5296c0e1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Apr 2020 22:27:26 +0200 Subject: [PATCH 438/624] Allow different kinds of regularization. --- skfda/misc/_lfd.py | 47 ------------ skfda/misc/regularization/__init__.py | 1 + .../_linear_diff_op_regularization.py | 3 +- skfda/misc/regularization/_regularization.py | 69 ++++++++++++++++++ skfda/ml/regression/linear.py | 6 +- skfda/preprocessing/smoothing/_basis.py | 12 +-- skfda/representation/basis/_basis.py | 14 ---- tests/test_regularization.py | 73 +++++++++++-------- 8 files changed, 122 insertions(+), 103 deletions(-) create mode 100644 skfda/misc/regularization/_regularization.py diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py index c47772838..30d269ec6 100644 --- a/skfda/misc/_lfd.py +++ b/skfda/misc/_lfd.py @@ -218,50 +218,3 @@ def applied_lfd(t): for i, w in enumerate(self.weights)) return applied_lfd - - -def _apply_lfd(X, basis, penalty): - """ - Apply the lfd to a single data type. - """ - penalty_method = getattr(basis, "penalty", None) - - if penalty_method: - return penalty_method(penalty) - else: - # Multivariate objects have no penalty - return np.zeros((X.shape[1], X.shape[1])) - - -def compute_lfd_matrix(X, basis, regularization_parameter, - penalty, penalty_matrix): - """ - Computes the regularization matrix for a linear differential operator. - - X can be a list of mixed data. - """ - from skfda.representation.basis import Basis - - # If there is no regularization, return 0 and rely on broadcasting - if regularization_parameter == 0: - return 0 - - # Compute penalty matrix if not provided - if penalty_matrix is None: - - # Convert the linear differential operator if necessary - if penalty is None: - penalty = LinearDifferentialOperator(order=2) - elif not isinstance(penalty, LinearDifferentialOperator): - penalty = LinearDifferentialOperator(penalty) - - if isinstance(basis, Basis): - penalty_matrix = _apply_lfd(X, basis, penalty) - else: - # If X and basis are lists - - penalty_blocks = [_apply_lfd(x, b, penalty) - for x, b in zip(X, basis)] - penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) - - return regularization_parameter * penalty_matrix diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py index c74708d5b..3f4fced2d 100644 --- a/skfda/misc/regularization/__init__.py +++ b/skfda/misc/regularization/__init__.py @@ -1 +1,2 @@ from ._linear_diff_op_regularization import LinearDifferentialOperatorRegularization +from ._regularization import Regularization, compute_penalty_matrix diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index 01c2aa66f..48b349b0f 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -8,6 +8,7 @@ from ...representation.basis import Constant, Monomial, Fourier, BSpline from .._lfd import LinearDifferentialOperator +from ._regularization import Regularization @singledispatch @@ -22,7 +23,7 @@ def penalty_matrix_optimized(basis, regularization): return NotImplemented -class LinearDifferentialOperatorRegularization(): +class LinearDifferentialOperatorRegularization(Regularization): """ Regularization using the integral of the square of a linear differential operator. diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py new file mode 100644 index 000000000..0afb11909 --- /dev/null +++ b/skfda/misc/regularization/_regularization.py @@ -0,0 +1,69 @@ +import abc + +import scipy.linalg + +import numpy as np + + +class Regularization(abc.ABC): + """ + Abstract base class for different kinds of regularization. + + """ + + @abc.abstractmethod + def penalty_matrix(self, basis): + r"""Return a penalty matrix given a basis. + + """ + pass + + +def _apply_regularization(X, basis, regularization: Regularization): + """ + Apply the lfd to a single data type. + """ + + if isinstance(X, np.ndarray): + # Multivariate objects have no penalty + return np.zeros((X.shape[1], X.shape[1])) + + else: + return regularization.penalty_matrix(basis) + + +def compute_penalty_matrix(X, basis, regularization_parameter, + regularization, penalty_matrix): + """ + Computes the regularization matrix for a linear differential operator. + + X can be a list of mixed data. + """ + from ...representation.basis import Basis + from ._linear_diff_op_regularization import ( + LinearDifferentialOperatorRegularization) + + # If there is no regularization, return 0 and rely on broadcasting + if regularization_parameter == 0: + return 0 + + # Compute penalty matrix if not provided + if penalty_matrix is None: + + # Convert the linear differential operator if necessary + if regularization is None: + regularization = LinearDifferentialOperatorRegularization(2) + elif not isinstance(regularization, Regularization): + regularization = LinearDifferentialOperatorRegularization( + regularization) + + if isinstance(basis, Basis): + penalty_matrix = _apply_regularization(X, basis, regularization) + else: + # If X and basis are lists + + penalty_blocks = [_apply_regularization(x, b, regularization) + for x, b in zip(X, basis)] + penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) + + return regularization_parameter * penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 59e2b0230..3026b043e 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -159,7 +159,7 @@ def _convert_coefs(self, x, basis, coefs): return coefs def fit(self, X, y=None, sample_weight=None): - from ...misc._lfd import compute_lfd_matrix + from ...misc.regularization import compute_penalty_matrix X, y, sample_weight, coef_basis = self._argcheck_X_y( X, y, sample_weight, self.coef_basis) @@ -181,10 +181,10 @@ def fit(self, X, y=None, sample_weight=None): inner_products = inner_products * np.sqrt(sample_weight) y = y * np.sqrt(sample_weight) - penalty_matrix = compute_lfd_matrix( + penalty_matrix = compute_penalty_matrix( X=X, basis=coef_basis, regularization_parameter=self.regularization_parameter, - penalty=self.penalty, + regularization=self.penalty, penalty_matrix=self.penalty_matrix) gram_inner_x_coef = inner_products.T @ inner_products + penalty_matrix diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 0097db79f..940dd9505 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -338,7 +338,7 @@ def _method_function(self): def _coef_matrix(self, input_points): """Get the matrix that gives the coefficients""" - from ...misc._lfd import compute_lfd_matrix + from ...misc.regularization import compute_penalty_matrix basis_values_input = self.basis.evaluate(input_points).T @@ -348,10 +348,10 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input - penalty_matrix = compute_lfd_matrix( + penalty_matrix = compute_penalty_matrix( X=None, basis=self.basis, regularization_parameter=self.smoothing_parameter, - penalty=self.penalty, + regularization=self.penalty, penalty_matrix=self.penalty_matrix) inv += penalty_matrix @@ -401,7 +401,7 @@ def fit_transform(self, X: FDataGrid, y=None): self (object) """ - from ...misc._lfd import compute_lfd_matrix + from ...misc.regularization import compute_penalty_matrix _check_r_to_r(X) @@ -410,10 +410,10 @@ def fit_transform(self, X: FDataGrid, y=None): if self.output_points is not None else self.input_points_) - penalty_matrix = compute_lfd_matrix( + penalty_matrix = compute_penalty_matrix( X=X, basis=self.basis, regularization_parameter=self.smoothing_parameter, - penalty=self.penalty, + regularization=self.penalty, penalty_matrix=self.penalty_matrix) # n is the samples diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 4c8e75d0b..0b89662b3 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -130,20 +130,6 @@ def plot(self, chart=None, *, derivative=0, **kwargs): """ self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) - def _numerical_penalty(self, lfd): - from ...misc.regularization import ( - LinearDifferentialOperatorRegularization) - - return LinearDifferentialOperatorRegularization( - lfd).penalty_matrix_numerical(self) - - def penalty(self, lfd): - from ...misc.regularization import ( - LinearDifferentialOperatorRegularization) - - return LinearDifferentialOperatorRegularization( - lfd).penalty_matrix(self) - @abstractmethod def basis_of_product(self, other): pass diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 3b3d094e6..baa883f82 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -1,3 +1,4 @@ +from skfda.misc.regularization import LinearDifferentialOperatorRegularization from skfda.misc.regularization._linear_diff_op_regularization import ( _monomial_evaluate_constant_linear_diff_op) from skfda.representation.basis import Constant, Monomial, BSpline, Fourier @@ -10,10 +11,13 @@ class TestLinearDifferentialOperatorRegularization(unittest.TestCase): # def setUp(self): could be defined for set up before any test - def _test_penalty(self, basis, lfd, atol=0, result=None): + def _test_penalty(self, basis, linear_diff_op, atol=0, result=None): - penalty = basis.penalty(lfd) - numerical_penalty = basis._numerical_penalty(lfd) + regularization = LinearDifferentialOperatorRegularization( + linear_diff_op) + + penalty = regularization.penalty_matrix(basis) + numerical_penalty = regularization.penalty_matrix_numerical(basis) np.testing.assert_allclose( penalty, @@ -33,14 +37,14 @@ def test_constant_penalty(self): res = np.array([[12]]) - self._test_penalty(basis, lfd=[2, 3, 4], result=res) + self._test_penalty(basis, linear_diff_op=[2, 3, 4], result=res) def test_monomial_linear_diff_op(self): n_basis = 5 basis = Monomial(n_basis=n_basis) - lfd = [3] + linear_diff_op = [3] res = np.array([[0., 0., 0., 0., 3.], [0., 0., 0., 3., 0.], [0., 0., 3., 0., 0.], @@ -48,11 +52,11 @@ def test_monomial_linear_diff_op(self): [3., 0., 0., 0., 0.]]) np.testing.assert_allclose( - _monomial_evaluate_constant_linear_diff_op(basis, lfd), + _monomial_evaluate_constant_linear_diff_op(basis, linear_diff_op), res ) - lfd = [3, 2] + linear_diff_op = [3, 2] res = np.array([[0., 0., 0., 0., 3.], [0., 0., 0., 3., 2.], [0., 0., 3., 4., 0.], @@ -60,11 +64,11 @@ def test_monomial_linear_diff_op(self): [3., 8., 0., 0., 0.]]) np.testing.assert_allclose( - _monomial_evaluate_constant_linear_diff_op(basis, lfd), + _monomial_evaluate_constant_linear_diff_op(basis, linear_diff_op), res ) - lfd = [3, 0, 5] + linear_diff_op = [3, 0, 5] res = np.array([[0., 0., 0., 0., 3.], [0., 0., 0., 3., 0.], [0., 0., 3., 0., 10.], @@ -72,7 +76,7 @@ def test_monomial_linear_diff_op(self): [3., 0., 60., 0., 0.]]) np.testing.assert_allclose( - _monomial_evaluate_constant_linear_diff_op(basis, lfd), + _monomial_evaluate_constant_linear_diff_op(basis, linear_diff_op), res ) @@ -86,15 +90,15 @@ def test_monomial_penalty(self): [0., 0., 54., 324., 1458.], [0., 0., 216., 1458., 6998.4]]) - self._test_penalty(basis, lfd=2, result=res) + self._test_penalty(basis, linear_diff_op=2, result=res) basis = Monomial(n_basis=8, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3]) - self._test_penalty(basis, lfd=7) - self._test_penalty(basis, lfd=0) - self._test_penalty(basis, lfd=1) - self._test_penalty(basis, lfd=27) + self._test_penalty(basis, linear_diff_op=[1, 2, 3]) + self._test_penalty(basis, linear_diff_op=7) + self._test_penalty(basis, linear_diff_op=0) + self._test_penalty(basis, linear_diff_op=1) + self._test_penalty(basis, linear_diff_op=27) def test_fourier_penalty(self): basis = Fourier(n_basis=5) @@ -106,14 +110,14 @@ def test_fourier_penalty(self): [0., 0., 0., 0., 24936.73]]) # Those comparisons require atol as there are zeros involved - self._test_penalty(basis, lfd=2, atol=0.01, result=res) + self._test_penalty(basis, linear_diff_op=2, atol=0.01, result=res) basis = Fourier(n_basis=9, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3], atol=1e-7) - self._test_penalty(basis, lfd=[2, 3, 0.1, 1], atol=1e-7) - self._test_penalty(basis, lfd=0, atol=1e-7) - self._test_penalty(basis, lfd=1, atol=1e-7) - self._test_penalty(basis, lfd=3, atol=1e-7) + self._test_penalty(basis, linear_diff_op=[1, 2, 3], atol=1e-7) + self._test_penalty(basis, linear_diff_op=[2, 3, 0.1, 1], atol=1e-7) + self._test_penalty(basis, linear_diff_op=0, atol=1e-7) + self._test_penalty(basis, linear_diff_op=1, atol=1e-7) + self._test_penalty(basis, linear_diff_op=3, atol=1e-7) def test_bspline_penalty(self): basis = BSpline(n_basis=5) @@ -124,18 +128,18 @@ def test_bspline_penalty(self): [12., -24., -48., 192., -132.], [0., 12., 24., -132., 96.]]) - self._test_penalty(basis, lfd=2, result=res) + self._test_penalty(basis, linear_diff_op=2, result=res) basis = BSpline(n_basis=9, domain_range=(1, 5)) - self._test_penalty(basis, lfd=[1, 2, 3]) - self._test_penalty(basis, lfd=[2, 3, 0.1, 1]) - self._test_penalty(basis, lfd=0) - self._test_penalty(basis, lfd=1) - self._test_penalty(basis, lfd=3) - self._test_penalty(basis, lfd=4) + self._test_penalty(basis, linear_diff_op=[1, 2, 3]) + self._test_penalty(basis, linear_diff_op=[2, 3, 0.1, 1]) + self._test_penalty(basis, linear_diff_op=0) + self._test_penalty(basis, linear_diff_op=1) + self._test_penalty(basis, linear_diff_op=3) + self._test_penalty(basis, linear_diff_op=4) basis = BSpline(n_basis=16, order=8) - self._test_penalty(basis, lfd=0, atol=1e-7) + self._test_penalty(basis, linear_diff_op=0, atol=1e-7) def test_bspline_penalty_special_case(self): basis = BSpline(n_basis=5) @@ -146,12 +150,17 @@ def test_bspline_penalty_special_case(self): [-288., 1008., -2304., 3600., -2016.], [0., -288., 1152., -2016., 1152.]]) + regularization = LinearDifferentialOperatorRegularization( + basis.order - 1) + penalty = regularization.penalty_matrix(basis) + numerical_penalty = regularization.penalty_matrix_numerical(basis) + np.testing.assert_allclose( - basis.penalty(basis.order - 1), + penalty, res ) np.testing.assert_allclose( - basis._numerical_penalty(basis.order - 1), + numerical_penalty, res ) From e16e61df3c4c2228bcaaa5014393a96eb8297beb Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Apr 2020 22:46:48 +0200 Subject: [PATCH 439/624] use reshape instead of squeeze --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 6 ++++-- tests/test_fpca.py | 8 +++++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 8adecd376..23b47ae1a 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -402,5 +402,7 @@ def transform(self, X, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return X.copy(data_matrix=np.squeeze(X.data_matrix) @ np.transpose( - np.squeeze(self.components_.data_matrix))) + return FDataGrid(data_matrix=X.data_matrix.reshape( + X.data_matrix.shape[:-1]) @ np.transpose( + self.components_.data_matrix.reshape( + self.components_.data_matrix.shape[:-1]))) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 241060250..97d2a0fe7 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -171,9 +171,11 @@ def test_grid_fpca_fit_result(self): if np.sign(fpca.components_.data_matrix[i][0]) != np.sign( results[i][0]): results[i, :] *= -1 - np.testing.assert_allclose(np.squeeze(fpca.components_.data_matrix), - np.squeeze(results), - rtol=1e-6) + np.testing.assert_allclose( + fpca.components_.data_matrix.reshape( + fpca.components_.data_matrix.shape[:-1]), + results, + rtol=1e-6) if __name__ == '__main__': From ec6f61012b5cee934b8eb764cc4452f9abe6b511 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 20 Apr 2020 22:57:10 +0200 Subject: [PATCH 440/624] address multiple comments --- .../dim_reduction/projection/_fpca.py | 54 +++++++++---------- 1 file changed, 26 insertions(+), 28 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 23b47ae1a..96eff4fb7 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -82,16 +82,6 @@ class FPCABasis(FPCA): """Functional principal component analysis for functional data represented in basis form. - Attributes: - components_ (FDataBasis): this contains the principal components in a - basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for PCA. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. @@ -107,6 +97,14 @@ class FPCABasis(FPCA): then the derivative of that degree will be used to regularize the principal components. + Attributes: + components_ (FDataBasis): this contains the principal components in a + basis representation. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + explained_variance_ratio_ (array_like): this contains the percentage of + variance explained by each principal component. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. The resulting principal components are not compared because there are @@ -228,19 +226,20 @@ def fit(self, X: FDataBasis, y=None): np.sqrt(n_samples)) # initialize the pca module provided by scikit-learn - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) + pca = PCA(n_components=self.n_components) + pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(self.pca_.components_), + np.transpose(pca.components_), lower=False) component_coefficients = np.transpose(component_coefficients) # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = self.pca_.singular_values_ ** 2 + self.component_values_ = pca.singular_values_ ** 2 + self.explained_variance_ratio_ = pca.explained_variance_ratio_ self.components_ = X.copy(basis=self.components_basis, coefficients=component_coefficients) @@ -269,16 +268,6 @@ class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. - Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. - pca_ (sklearn.decomposition.PCA): object for principal component analysis. - In both cases (discretized FPCA and basis FPCA) the problem can be - reduced to a regular PCA problem and use the framework provided by - sklearn to continue. - Parameters: n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. @@ -289,6 +278,14 @@ class FPCAGrid(FPCA): integration. If none then the trapezoidal rule is used for computing the weights. + Attributes: + components_ (FDataBasis): this contains the principal components either + in a basis form. + component_values_ (array_like): this contains the values (eigenvalues) + associated with the principal components. + explained_variance_ratio_ (array_like): this contains the percentage of + variance explained by each principal component. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -378,10 +375,11 @@ def fit(self, X: FDataGrid, y=None): # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) - self.pca_ = PCA(n_components=self.n_components) - self.pca_.fit(final_matrix) - self.components_ = X.copy(data_matrix=self.pca_.components_) - self.component_values_ = self.pca_.singular_values_ ** 2 + pca = PCA(n_components=self.n_components) + pca.fit(final_matrix) + self.components_ = X.copy(data_matrix=pca.components_) + self.component_values_ = pca.singular_values_ ** 2 + self.explained_variance_ratio_ = pca.explained_variance_ratio_ return self From c405cd9a26d4855ec854ba61b234c268050c4ad5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 20 Apr 2020 23:37:01 +0200 Subject: [PATCH 441/624] Fixing unexpected argument in docstring MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 3499f48a6..40f74ae38 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -250,8 +250,6 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): >>> fd1, fd2, fd3 = fd[:13], fd[13:26], fd[26:] >>> oneway_anova(fd1, fd2, fd3, random_state=RandomState(42)) (179.52499999999998, 0.602) - >>> oneway_anova(fd1, fd2, fd3, p=1, random_state=RandomState(42)) - (67.27499999999999, 0.0) >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) From 84d7f2395b3b40eb37ad2fc6babdc4d150503337 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 13:04:14 +0200 Subject: [PATCH 442/624] address more comments --- .../dim_reduction/projection/_fpca.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 96eff4fb7..76984a73e 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -91,11 +91,11 @@ class FPCABasis(FPCA): components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - regularization_lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. If you input an integer - then the derivative of that degree will be used to regularize - the principal components. + penalty (Union[int, Iterable[float],'LinearDifferentialOperator']): + Linear differential operator. If it is not a + LinearDifferentialOperator object, it will be converted to one. + If you input an integerthen the derivative of that degree will be + used to regularize the principal components. Attributes: components_ (FDataBasis): this contains the principal components in a @@ -125,13 +125,13 @@ def __init__(self, components_basis=None, centering=True, regularization_parameter=0, - regularization_lfd=2): + penalty=2): super().__init__(n_components, centering) # basis that we want to use for the principal components self.components_basis = components_basis # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.regularization_lfd = regularization_lfd + self.penalty = penalty def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -205,7 +205,7 @@ def fit(self, X: FDataBasis, y=None): if self.regularization_parameter > 0: # obtain regularization matrix regularization_matrix = self.components_basis.penalty( - self.regularization_lfd + self.penalty ) # apply regularization g_matrix = (g_matrix + self.regularization_parameter * From 7a9838f950ad148142bfda224f3525307f08f39e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 13:32:44 +0200 Subject: [PATCH 443/624] extract depth based median from boxplot --- skfda/exploratory/stats/_stats.py | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 55dfd7c2c..8a6c811d5 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -1,6 +1,6 @@ """Functional data descriptive statistics. """ - +from ..depth import modified_band_depth def mean(fdata, weights=None): """Compute the mean of all the samples in a FData object. @@ -67,3 +67,27 @@ def cov(fdatagrid): """ return fdatagrid.cov() + + +def depth_based_median(fdatagrid, depth_method=modified_band_depth): + """Compute the median based on a depth measure. + + The depth based median is basically the deepest curve given a certain + depth measure + + Args: + fdatagrid (FDataGrid): Object containing different samples of a + functional variable. + depth_method (:ref:`depth measure `, optional): + Method used to order the data. Defaults to :func:`modified + band depth `. + + Returns: + FDataGrid: object containing the computed depth_based median. + + """ + depth = depth_method(fdatagrid) + indices_descending_depth = (-depth).argsort(axis=0) + + # The median is the deepest curve + return fdatagrid[indices_descending_depth[0]].data_matrix[0, ...] From 41493a058e1ea7d553c0f26cf06187c3e3c2aa44 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 16:21:17 +0200 Subject: [PATCH 444/624] add reference in __init__ --- skfda/exploratory/stats/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 611d8fbbe..7b0c1681a 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1 +1 @@ -from ._stats import mean, var, gmean, cov +from ._stats import mean, var, gmean, cov, depth_based_median From eb7ce93fa041671ca786b998f5577882d4a3a1ab Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 16:35:11 +0200 Subject: [PATCH 445/624] trimmed means first implementation --- skfda/exploratory/stats/_stats.py | 35 +++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 8a6c811d5..9f725e1b1 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -91,3 +91,38 @@ def depth_based_median(fdatagrid, depth_method=modified_band_depth): # The median is the deepest curve return fdatagrid[indices_descending_depth[0]].data_matrix[0, ...] + + +def trimmed_means(fdatagrid, + trimmed_percentage, + depth_method=modified_band_depth): + """Compute the trimmed means based on a depth measure. + + The trimmed means consists in computing the mean function without a + percentage of least deep curves. That is, we first remove the least deep + curves and then we compute the mean as usual. + + Args: + fdatagrid (FDataGrid): Object containing different samples of a + functional variable. + trimmed_percentage (float): indicates the percentage of functions to + remove. It is not easy to determine as it varies from dataset to + dataset. + depth_method (:ref:`depth measure `, optional): + Method used to order the data. Defaults to :func:`modified + band depth `. + + Returns: + FDataGrid: object containing the computed trimmed mean. + + """ + n_samples_to_keep = (fdatagrid.n_samples - + fdatagrid.n_samples * trimmed_percentage) + + # compute the depth of each curve and store the indexes in descending order + depth = depth_method(fdatagrid) + indices_descending_depth = (-depth).argsort(axis=0) + + trimmed_curves = fdatagrid[indices_descending_depth[:n_samples_to_keep]] + + return trimmed_curves.mean() \ No newline at end of file From 36daab49f544349292e1f858d68eab7e7134169a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 16:36:28 +0200 Subject: [PATCH 446/624] fix small mistake --- skfda/exploratory/stats/_stats.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 8a6c811d5..2dd705b2f 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -90,4 +90,4 @@ def depth_based_median(fdatagrid, depth_method=modified_band_depth): indices_descending_depth = (-depth).argsort(axis=0) # The median is the deepest curve - return fdatagrid[indices_descending_depth[0]].data_matrix[0, ...] + return fdatagrid[indices_descending_depth[0]] From e4dfc5689a9cfe5f4345f8fb719bfd74f3bee72a Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 16:42:25 +0200 Subject: [PATCH 447/624] add to init file --- skfda/exploratory/stats/__init__.py | 2 +- skfda/exploratory/stats/_stats.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 7b0c1681a..c9c5fd629 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1 +1 @@ -from ._stats import mean, var, gmean, cov, depth_based_median +from ._stats import mean, var, gmean, cov, depth_based_median, trimmed_means diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 7979855ab..19408680c 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -125,4 +125,4 @@ def trimmed_means(fdatagrid, trimmed_curves = fdatagrid[indices_descending_depth[:n_samples_to_keep]] - return trimmed_curves.mean() \ No newline at end of file + return trimmed_curves.mean() From aa72e84a1661afd39d6b9ba3ffd50ce04f433a16 Mon Sep 17 00:00:00 2001 From: hzzhyj Date: Tue, 21 Apr 2020 19:42:31 +0200 Subject: [PATCH 448/624] Update skfda/exploratory/stats/_stats.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Carlos Ramos Carreño --- skfda/exploratory/stats/_stats.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 2dd705b2f..c55845c11 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -72,7 +72,7 @@ def cov(fdatagrid): def depth_based_median(fdatagrid, depth_method=modified_band_depth): """Compute the median based on a depth measure. - The depth based median is basically the deepest curve given a certain + The depth based median is the deepest curve given a certain depth measure Args: From 2e85ceca518a443a71e4542973caf1576743b653 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Tue, 21 Apr 2020 19:44:42 +0200 Subject: [PATCH 449/624] fix small mistake --- skfda/exploratory/stats/_stats.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 8f1695aa6..e8074855a 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -116,8 +116,8 @@ def trimmed_means(fdatagrid, FDataGrid: object containing the computed trimmed mean. """ - n_samples_to_keep = (fdatagrid.n_samples - - fdatagrid.n_samples * trimmed_percentage) + n_samples_to_keep = int((fdatagrid.n_samples - + fdatagrid.n_samples * trimmed_percentage)) # compute the depth of each curve and store the indexes in descending order depth = depth_method(fdatagrid) From b42174ad63dbd9098f62ef8d3379256689495c38 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 21 Apr 2020 21:08:18 +0200 Subject: [PATCH 450/624] Changes on pull request comments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 21 +++++++++++++++------ skfda/misc/covariances.py | 2 -- skfda/misc/metrics.py | 18 ++++++++++++------ skfda/representation/_functional_data.py | 2 +- skfda/representation/grid.py | 1 - 5 files changed, 28 insertions(+), 16 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 40f74ae38..18e2ac115 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -71,11 +71,17 @@ def v_sample_stat(fd, weights): raise ValueError("Number of weights must match number of samples.") n = fd.n_samples - coef, ops = [], [] + size = (n * n - n) // 2 + ops = np.empty(size, dtype='object') + coef = np.empty(size) + + index = 0 for j in range(n): for i in range(j): - ops.append(fd[i] - fd[j]) - coef.append(weights[i]) + coef[index] = weights[i] + ops[index] = fd[i] - fd[j] + index += 1 + return np.dot(coef, norm_lp(FData.concatenate_samples(ops)) ** 2) @@ -143,10 +149,13 @@ def v_asymptotic_stat(fd, weights): raise ValueError("Number of weights must match number of samples.") n = fd.n_samples - ops = [] + size = (n * n - n) // 2 + ops = np.empty(size, dtype='object') + index = 0 for j in range(n): for i in range(j): - ops.append(fd[i] - fd[j] * np.sqrt(weights[i] / weights[j])) + ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) + index += 1 return np.sum(norm_lp(FData.concatenate_samples(ops)) ** 2) @@ -291,7 +300,7 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): raise NotImplementedError("Not implemented for FDataBasis with " "different basis.") - # FDataGrid where each sample is the mean of each group + # FData where each sample is the mean of each group fd_means = FData.concatenate_samples([fd.mean() for fd in fd_groups]) # Base statistic diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index 0de6a0456..eb066bb3c 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -255,8 +255,6 @@ def __call__(self, x, y): y = _transform_to_2d(y) x_y = _squared_norms(x, y) - print((self.variance * np.exp(-np.sqrt(x_y) / ( - self.length_scale))).shape) return self.variance * np.exp(-np.sqrt(x_y) / (self.length_scale)) def to_sklearn(self): diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 02af5a8d5..5f3d8b5ec 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -244,7 +244,7 @@ def norm_lp(fdata, p=2, p2=2): Args: - fdata (FDataG): FData object. + fdata (FData): FData object. p (int, optional): p of the lp norm. Must be greater or equal than 1. If p='inf' or p=np.inf it is used the L infinity metric. Defaults to 2. @@ -281,22 +281,28 @@ def norm_lp(fdata, p=2, p2=2): if isinstance(fdata, FDataBasis): if fdata.dim_codomain > 1 or p != 2: - raise ValueError + raise NotImplementedError res = np.empty(fdata.n_samples) - gram = fdata.basis.gram_matrix() + # Gram matrix contains the inner product of the basis components taken + # by pairs. Let \phi_i be a basis element and \c_i its coefficient: + # = \sum_i\sum_j\c_i\c_j<\phi_i, \phi_j> + # To compute this value it is possible to sum the diagonal and the lower + # triangular matrix multiplied by two. + + gram = fdata.basis.gram_matrix() # Obtaining Gram Matrix for k, coefs in enumerate(fdata.coefficients): - l_triang = 0 + l_triang = 0 # Computing lower triangular matrix for i in range(fdata.n_basis): for j in range(i): l_triang += coefs[i] * coefs[j] * gram[i][j] - diag = np.dot(coefs ** 2, np.diag(gram)) + diag = np.dot(coefs ** 2, np.diag(gram)) # Computing diagonal res[k] = 2 * l_triang + diag - res = np.sqrt(res) + res = np.sqrt(res) # Norm is the square root of the inner product else: if fdata.dim_codomain > 1: diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 7124a9ad1..662533460 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -814,7 +814,7 @@ def concatenate_samples(objects): raise ValueError("At least one FData object must be provided " "to concatenate.") - return first.concatenate(*list(objects)) + return first.concatenate(*objects) @abstractmethod def compose(self, fd, *, eval_points=None, **kwargs): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 28184ec00..1f1c9b006 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -787,7 +787,6 @@ def concatenate(self, *others, as_coordinates=False): else: return self.copy(data_matrix=np.concatenate(data, axis=0)) - def scatter(self, *args, **kwargs): """Scatter plot of the FDatGrid object. From 4dbbdef32146d4934a3e51e13193f8adbd19a77e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 22 Apr 2020 02:54:37 +0200 Subject: [PATCH 451/624] Add CoefficientInfo --- skfda/_utils/_coefficients.py | 81 ++++++++++++++++++++ skfda/misc/regularization/_regularization.py | 17 ++-- skfda/ml/regression/linear.py | 65 ++++------------ skfda/preprocessing/smoothing/_basis.py | 7 +- tests/test_regression.py | 2 +- 5 files changed, 109 insertions(+), 63 deletions(-) create mode 100644 skfda/_utils/_coefficients.py diff --git a/skfda/_utils/_coefficients.py b/skfda/_utils/_coefficients.py new file mode 100644 index 000000000..2879a4992 --- /dev/null +++ b/skfda/_utils/_coefficients.py @@ -0,0 +1,81 @@ +from functools import singledispatch + +import numpy as np + +from ..representation.basis import Basis, FDataBasis + + +@singledispatch +def coefficient_info_from_covariate(X, y, **kwargs): + """ + Make a coefficient info object from a covariate. + + """ + return CoefficientInfo(type(X), shape=None) + + +class CoefficientInfo(): + """ + Information about an estimated coefficient. + + At the very least it should have a type and a shape, but it may have + additional information depending on its type. + + Parameters: + coef_type: Class of the coefficient. + shape: Shape of the coefficient. + + """ + + def __init__(self, coef_type, shape): + self.coef_type = coef_type + self.shape = shape + + def regression_matrix(self, X, y): + """ + Return the constant coefficients matrix for regression. + + Parameters: + X: covariate data for regression. + y: target data for regression. + + """ + return np.atleast_2d(X) + + def convert_from_constant_coefs(self, coefs): + """ + Return the coefficients object from the constant coefs. + + Parameters: + coefs: estimated constant coefficients. + + """ + return coefs + + +class CoefficientInfoFDataBasis(CoefficientInfo): + + def __init__(self, shape, basis): + super().__init__(coef_type=FDataBasis, shape=shape) + + self.basis = basis + + def regression_matrix(self, X, y): + xcoef = X.coefficients + inner_basis = X.basis.inner_product(self.basis) + return xcoef @ inner_basis + + def convert_from_constant_coefs(self, coefs): + return FDataBasis(self.basis, coefs.T) + + +@coefficient_info_from_covariate.register +def coefficient_info_from_covariate_fdatabasis(X: FDataBasis, y, **kwargs): + basis = kwargs['basis'] + if basis is None: + basis = X.basis + + if not isinstance(basis, Basis): + raise TypeError(f"basis must be a Basis object, not {type(basis)}") + + return CoefficientInfoFDataBasis(shape=None, basis=basis) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 0afb11909..afcfd1c17 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -3,6 +3,7 @@ import scipy.linalg import numpy as np +from ..._utils._coefficients import CoefficientInfo class Regularization(abc.ABC): @@ -19,7 +20,7 @@ def penalty_matrix(self, basis): pass -def _apply_regularization(X, basis, regularization: Regularization): +def _apply_regularization(X, coef_info, regularization: Regularization): """ Apply the lfd to a single data type. """ @@ -29,17 +30,16 @@ def _apply_regularization(X, basis, regularization: Regularization): return np.zeros((X.shape[1], X.shape[1])) else: - return regularization.penalty_matrix(basis) + return regularization.penalty_matrix(coef_info.basis) -def compute_penalty_matrix(X, basis, regularization_parameter, +def compute_penalty_matrix(X, coef_info, regularization_parameter, regularization, penalty_matrix): """ Computes the regularization matrix for a linear differential operator. X can be a list of mixed data. """ - from ...representation.basis import Basis from ._linear_diff_op_regularization import ( LinearDifferentialOperatorRegularization) @@ -57,13 +57,14 @@ def compute_penalty_matrix(X, basis, regularization_parameter, regularization = LinearDifferentialOperatorRegularization( regularization) - if isinstance(basis, Basis): - penalty_matrix = _apply_regularization(X, basis, regularization) + if isinstance(coef_info, CoefficientInfo): + penalty_matrix = _apply_regularization( + X, coef_info, regularization) else: # If X and basis are lists - penalty_blocks = [_apply_regularization(x, b, regularization) - for x, b in zip(X, basis)] + penalty_blocks = [_apply_regularization(x, c, regularization) + for x, c in zip(X, coef_info)] penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) return regularization_parameter * penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 3026b043e..797d9d071 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -6,6 +6,7 @@ from sklearn.utils.validation import check_is_fitted import numpy as np +from ..._utils._coefficients import coefficient_info_from_covariate class MultivariateLinearRegression(BaseEstimator, RegressorMixin): @@ -126,50 +127,19 @@ def __init__(self, *, coef_basis=None, fit_intercept=True, self.penalty = penalty self.penalty_matrix = penalty_matrix - def _inner_product_matrix(self, x, basis): - """ - Compute the inner product matrix of a variable. - - The variable can be multivariate or functional. - - """ - if isinstance(x, FDataBasis): - # Functional inner product - xcoef = x.coefficients - inner_basis = x.basis.inner_product(basis) - return xcoef @ inner_basis - else: - # Multivariate inner product - if basis is not None: - raise ValueError("Multivariate data coefficients " - "should not have a basis") - return np.atleast_2d(x) - - def _convert_coefs(self, x, basis, coefs): - """ - Convert to original form. - """ - if isinstance(x, FDataBasis): - # Functional coefs - return FDataBasis( - basis, - coefs.T) - else: - # Multivariate coefs - return coefs - def fit(self, X, y=None, sample_weight=None): from ...misc.regularization import compute_penalty_matrix - X, y, sample_weight, coef_basis = self._argcheck_X_y( + X, y, sample_weight, coef_info = self._argcheck_X_y( X, y, sample_weight, self.coef_basis) if self.fit_intercept: - X = [np.ones((len(y), 1))] + X - coef_basis = [None] + coef_basis + new_x = np.ones((len(y), 1)) + X = [new_x] + X + coef_info = [coefficient_info_from_covariate(new_x, y)] + coef_info - inner_products = [self._inner_product_matrix(x, basis) - for x, basis in zip(X, coef_basis)] + inner_products = [c.regression_matrix(x, y) + for x, c in zip(X, coef_info)] coef_lengths = np.array([i.shape[1] for i in inner_products]) coef_start = np.cumsum(coef_lengths) @@ -182,7 +152,7 @@ def fit(self, X, y=None, sample_weight=None): y = y * np.sqrt(sample_weight) penalty_matrix = compute_penalty_matrix( - X=X, basis=coef_basis, + X=X, coef_info=coef_info, regularization_parameter=self.regularization_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) @@ -194,8 +164,8 @@ def fit(self, X, y=None, sample_weight=None): basiscoef_list = np.split(basiscoefs, coef_start) # Express the coefficients in functional form - coefs = [self._convert_coefs(x, basis, bcoefs) - for x, basis, bcoefs in zip(X, coef_basis, basiscoef_list)] + coefs = [c.convert_from_constant_coefs(bcoefs) + for c, bcoefs in zip(coef_info, basiscoef_list)] if self.fit_intercept: self.intercept_ = coefs[0] @@ -241,15 +211,6 @@ def _argcheck_X(self, X): return X - def _get_coef_basis(self, x, basis): - if basis is None: - basis = getattr(x, 'basis', None) - return basis - else: - if not isinstance(basis, Basis): - raise ValueError("coef_basis should be a list of Basis.") - return basis - def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): """Do some checks to types and shapes""" @@ -275,8 +236,8 @@ def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): raise ValueError("The number of samples on independent and " "dependent variables should be the same") - coef_basis = [self._get_coef_basis(x, b) - for x, b in zip(X, coef_basis)] + coef_info = [coefficient_info_from_covariate(x, y, basis=b) + for x, b in zip(X, coef_basis)] if sample_weight is None: sample_weight = np.ones(len(y)) @@ -289,4 +250,4 @@ def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): raise ValueError( "The sample weights should be non negative values") - return X, y, sample_weight, coef_basis + return X, y, sample_weight, coef_info diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 940dd9505..5b5ba7b48 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -14,6 +14,7 @@ from ... import FDataBasis from ... import FDataGrid +from ..._utils._coefficients import CoefficientInfoFDataBasis from ._linear import _LinearSmoother, _check_r_to_r @@ -349,7 +350,8 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input penalty_matrix = compute_penalty_matrix( - X=None, basis=self.basis, + X=None, + coef_info=CoefficientInfoFDataBasis(None, self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) @@ -411,7 +413,8 @@ def fit_transform(self, X: FDataGrid, y=None): else self.input_points_) penalty_matrix = compute_penalty_matrix( - X=X, basis=self.basis, + X=X, + coef_info=CoefficientInfoFDataBasis(None, self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) diff --git a/tests/test_regression.py b/tests/test_regression.py index f336d0b98..87cea5738 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -240,7 +240,7 @@ def test_error_beta_not_basis(self): beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) scalar = MultivariateLinearRegression(coef_basis=[beta]) - with np.testing.assert_raises(ValueError): + with np.testing.assert_raises(TypeError): scalar.fit([x_fd], y) def test_error_weights_lenght(self): From 1ef7fe330d917020b36f8121f1a1c8d1c15038b8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 22 Apr 2020 16:05:39 +0200 Subject: [PATCH 452/624] Generalize penalty_matrix --- skfda/_utils/_coefficients.py | 23 +++---- .../_linear_diff_op_regularization.py | 62 +++++++++++++++---- skfda/misc/regularization/_regularization.py | 26 ++------ skfda/ml/regression/linear.py | 2 +- skfda/preprocessing/smoothing/_basis.py | 2 - tests/test_regularization.py | 10 +-- 6 files changed, 76 insertions(+), 49 deletions(-) diff --git a/skfda/_utils/_coefficients.py b/skfda/_utils/_coefficients.py index 2879a4992..7caba7613 100644 --- a/skfda/_utils/_coefficients.py +++ b/skfda/_utils/_coefficients.py @@ -5,15 +5,6 @@ from ..representation.basis import Basis, FDataBasis -@singledispatch -def coefficient_info_from_covariate(X, y, **kwargs): - """ - Make a coefficient info object from a covariate. - - """ - return CoefficientInfo(type(X), shape=None) - - class CoefficientInfo(): """ Information about an estimated coefficient. @@ -53,6 +44,15 @@ def convert_from_constant_coefs(self, coefs): return coefs +@singledispatch +def coefficient_info_from_covariate(X, y, **kwargs) -> CoefficientInfo: + """ + Make a coefficient info object from a covariate. + + """ + return CoefficientInfo(type(X), shape=X.shape) + + class CoefficientInfoFDataBasis(CoefficientInfo): def __init__(self, shape, basis): @@ -70,7 +70,8 @@ def convert_from_constant_coefs(self, coefs): @coefficient_info_from_covariate.register -def coefficient_info_from_covariate_fdatabasis(X: FDataBasis, y, **kwargs): +def coefficient_info_from_covariate_fdatabasis( + X: FDataBasis, y, **kwargs) -> CoefficientInfoFDataBasis: basis = kwargs['basis'] if basis is None: basis = X.basis @@ -78,4 +79,4 @@ def coefficient_info_from_covariate_fdatabasis(X: FDataBasis, y, **kwargs): if not isinstance(basis, Basis): raise TypeError(f"basis must be a Basis object, not {type(basis)}") - return CoefficientInfoFDataBasis(shape=None, basis=basis) + return CoefficientInfoFDataBasis(shape=(len(X),), basis=basis) diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index 48b349b0f..041f02547 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -1,4 +1,5 @@ from functools import singledispatch +from skfda._utils._coefficients import CoefficientInfoFDataBasis from numpy import polyder, polyint, polymul, polyval import scipy.integrate @@ -6,13 +7,15 @@ import numpy as np -from ...representation.basis import Constant, Monomial, Fourier, BSpline +from ..._utils._coefficients import CoefficientInfo +from ...representation.basis import Basis, Constant, Monomial, Fourier, BSpline from .._lfd import LinearDifferentialOperator from ._regularization import Regularization @singledispatch -def penalty_matrix_optimized(basis, regularization): +def penalty_matrix_basis_opt(basis: Basis, + regularization): """ Return a penalty matrix given a basis. @@ -23,6 +26,19 @@ def penalty_matrix_optimized(basis, regularization): return NotImplemented +@singledispatch +def penalty_matrix_coef_info(coef_info: CoefficientInfo, + regularization): + """ + Return a penalty matrix given the coefficient information. + + This method is a singledispatch method that provides an + implementation of the computation of the penalty matrix + for a particular coefficient type. + """ + return np.zeros((coef_info.shape[1], coef_info.shape[1])) + + class LinearDifferentialOperatorRegularization(Regularization): """ Regularization using the integral of the square of a linear differential @@ -40,9 +56,13 @@ def __init__(self, linear_diff_op=2): isinstance(linear_diff_op, LinearDifferentialOperator)) else ( LinearDifferentialOperator(linear_diff_op)) - penalty_matrix_optimized = penalty_matrix_optimized + penalty_matrix_basis_opt = penalty_matrix_basis_opt + penalty_matrix_coef_info = penalty_matrix_coef_info - def penalty_matrix_numerical(self, basis): + def penalty_matrix(self, coef_info): + return penalty_matrix_coef_info(coef_info, self) + + def penalty_matrix_basis_numerical(self, basis): """Return a penalty matrix using a numerical approach. Args: @@ -74,7 +94,7 @@ def cross_product(x): return penalty_matrix - def penalty_matrix(self, basis): + def penalty_matrix_basis(self, basis): r"""Return a penalty matrix given a basis. The penalty matrix is defined as [RS05-5-6-2]_: @@ -97,15 +117,35 @@ def penalty_matrix(self, basis): Springer. """ - matrix = penalty_matrix_optimized(basis, self) + matrix = penalty_matrix_basis_opt(basis, self) if matrix is NotImplemented: - return self.penalty_matrix_numerical(basis) + return self.penalty_matrix_basis_numerical(basis) else: return matrix +########################################### +# +# Implementations for each coefficient type +# +########################################### + + +@LinearDifferentialOperatorRegularization.penalty_matrix_coef_info.register +def penalty_matrix_coef_info_fdatabasis( + coef_info: CoefficientInfoFDataBasis, + regularization: LinearDifferentialOperatorRegularization): + return regularization.penalty_matrix_basis(coef_info.basis) + + +########################################### +# +# Optimized implementations for each basis. +# +########################################### + -@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register def constant_penalty_matrix_optimized( basis: Constant, regularization: LinearDifferentialOperatorRegularization): @@ -171,7 +211,7 @@ def _monomial_evaluate_constant_linear_diff_op(basis, weights): return polynomials -@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register def monomial_penalty_matrix_optimized( basis: Monomial, regularization: LinearDifferentialOperatorRegularization): @@ -286,7 +326,7 @@ def _fourier_penalty_matrix_optimized_orthonormal(basis, weights): return penalty_matrix -@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register def fourier_penalty_matrix_optimized( basis: Fourier, regularization: LinearDifferentialOperatorRegularization): @@ -303,7 +343,7 @@ def fourier_penalty_matrix_optimized( return _fourier_penalty_matrix_optimized_orthonormal(basis, weights) -@LinearDifferentialOperatorRegularization.penalty_matrix_optimized.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register def bspline_penalty_matrix_optimized( basis: BSpline, regularization: LinearDifferentialOperatorRegularization): diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index afcfd1c17..52ca71924 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -13,27 +13,14 @@ class Regularization(abc.ABC): """ @abc.abstractmethod - def penalty_matrix(self, basis): - r"""Return a penalty matrix given a basis. + def penalty_matrix(self, coef_info): + r"""Return a penalty matrix given the coefficient information. """ pass -def _apply_regularization(X, coef_info, regularization: Regularization): - """ - Apply the lfd to a single data type. - """ - - if isinstance(X, np.ndarray): - # Multivariate objects have no penalty - return np.zeros((X.shape[1], X.shape[1])) - - else: - return regularization.penalty_matrix(coef_info.basis) - - -def compute_penalty_matrix(X, coef_info, regularization_parameter, +def compute_penalty_matrix(coef_info, regularization_parameter, regularization, penalty_matrix): """ Computes the regularization matrix for a linear differential operator. @@ -58,13 +45,12 @@ def compute_penalty_matrix(X, coef_info, regularization_parameter, regularization) if isinstance(coef_info, CoefficientInfo): - penalty_matrix = _apply_regularization( - X, coef_info, regularization) + penalty_matrix = regularization.penalty_matrix(coef_info) else: # If X and basis are lists - penalty_blocks = [_apply_regularization(x, c, regularization) - for x, c in zip(X, coef_info)] + penalty_blocks = [regularization.penalty_matrix(c) + for c in coef_info] penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) return regularization_parameter * penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 797d9d071..bbbdba217 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -152,7 +152,7 @@ def fit(self, X, y=None, sample_weight=None): y = y * np.sqrt(sample_weight) penalty_matrix = compute_penalty_matrix( - X=X, coef_info=coef_info, + coef_info=coef_info, regularization_parameter=self.regularization_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 5b5ba7b48..806eb3b74 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -350,7 +350,6 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input penalty_matrix = compute_penalty_matrix( - X=None, coef_info=CoefficientInfoFDataBasis(None, self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, @@ -413,7 +412,6 @@ def fit_transform(self, X: FDataGrid, y=None): else self.input_points_) penalty_matrix = compute_penalty_matrix( - X=X, coef_info=CoefficientInfoFDataBasis(None, self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, diff --git a/tests/test_regularization.py b/tests/test_regularization.py index baa883f82..fe34b6581 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -16,8 +16,9 @@ def _test_penalty(self, basis, linear_diff_op, atol=0, result=None): regularization = LinearDifferentialOperatorRegularization( linear_diff_op) - penalty = regularization.penalty_matrix(basis) - numerical_penalty = regularization.penalty_matrix_numerical(basis) + penalty = regularization.penalty_matrix_basis(basis) + numerical_penalty = regularization.penalty_matrix_basis_numerical( + basis) np.testing.assert_allclose( penalty, @@ -152,8 +153,9 @@ def test_bspline_penalty_special_case(self): regularization = LinearDifferentialOperatorRegularization( basis.order - 1) - penalty = regularization.penalty_matrix(basis) - numerical_penalty = regularization.penalty_matrix_numerical(basis) + penalty = regularization.penalty_matrix_basis(basis) + numerical_penalty = regularization.penalty_matrix_basis_numerical( + basis) np.testing.assert_allclose( penalty, From 0284b67c1ee3da1358e39be425cf1b47d468af62 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 22 Apr 2020 18:07:35 +0200 Subject: [PATCH 453/624] Fixes Python 3.6 tests. --- skfda/_utils/_coefficients.py | 2 +- .../_linear_diff_op_regularization.py | 15 ++++++++++----- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/skfda/_utils/_coefficients.py b/skfda/_utils/_coefficients.py index 7caba7613..9e5f33451 100644 --- a/skfda/_utils/_coefficients.py +++ b/skfda/_utils/_coefficients.py @@ -69,7 +69,7 @@ def convert_from_constant_coefs(self, coefs): return FDataBasis(self.basis, coefs.T) -@coefficient_info_from_covariate.register +@coefficient_info_from_covariate.register(FDataBasis) def coefficient_info_from_covariate_fdatabasis( X: FDataBasis, y, **kwargs) -> CoefficientInfoFDataBasis: basis = kwargs['basis'] diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index 041f02547..391c6b8ce 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -131,7 +131,8 @@ def penalty_matrix_basis(self, basis): ########################################### -@LinearDifferentialOperatorRegularization.penalty_matrix_coef_info.register +@LinearDifferentialOperatorRegularization.penalty_matrix_coef_info.register( + CoefficientInfoFDataBasis) def penalty_matrix_coef_info_fdatabasis( coef_info: CoefficientInfoFDataBasis, regularization: LinearDifferentialOperatorRegularization): @@ -145,7 +146,8 @@ def penalty_matrix_coef_info_fdatabasis( ########################################### -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( + Constant) def constant_penalty_matrix_optimized( basis: Constant, regularization: LinearDifferentialOperatorRegularization): @@ -211,7 +213,8 @@ def _monomial_evaluate_constant_linear_diff_op(basis, weights): return polynomials -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( + Monomial) def monomial_penalty_matrix_optimized( basis: Monomial, regularization: LinearDifferentialOperatorRegularization): @@ -326,7 +329,8 @@ def _fourier_penalty_matrix_optimized_orthonormal(basis, weights): return penalty_matrix -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( + Fourier) def fourier_penalty_matrix_optimized( basis: Fourier, regularization: LinearDifferentialOperatorRegularization): @@ -343,7 +347,8 @@ def fourier_penalty_matrix_optimized( return _fourier_penalty_matrix_optimized_orthonormal(basis, weights) -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register +@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( + BSpline) def bspline_penalty_matrix_optimized( basis: BSpline, regularization: LinearDifferentialOperatorRegularization): From 6d839d46e9edd9148d177b38cd2bcb767bfb9dfa Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 24 Apr 2020 18:25:25 +0200 Subject: [PATCH 454/624] Add L2 regularization and endpoint difference regularization. --- skfda/_utils/_coefficients.py | 10 +-- skfda/misc/regularization/__init__.py | 2 + .../_endpoints_difference_regularization.py | 45 +++++++++++ .../misc/regularization/_l2_regularization.py | 13 +++ .../_linear_diff_op_regularization.py | 4 +- skfda/ml/regression/linear.py | 10 ++- skfda/preprocessing/smoothing/_basis.py | 4 +- tests/test_regularization.py | 81 ++++++++++++++++++- 8 files changed, 156 insertions(+), 13 deletions(-) create mode 100644 skfda/misc/regularization/_endpoints_difference_regularization.py create mode 100644 skfda/misc/regularization/_l2_regularization.py diff --git a/skfda/_utils/_coefficients.py b/skfda/_utils/_coefficients.py index 9e5f33451..e5eff3431 100644 --- a/skfda/_utils/_coefficients.py +++ b/skfda/_utils/_coefficients.py @@ -14,7 +14,7 @@ class CoefficientInfo(): Parameters: coef_type: Class of the coefficient. - shape: Shape of the coefficient. + shape: Shape of the constant coefficients form. """ @@ -50,13 +50,13 @@ def coefficient_info_from_covariate(X, y, **kwargs) -> CoefficientInfo: Make a coefficient info object from a covariate. """ - return CoefficientInfo(type(X), shape=X.shape) + return CoefficientInfo(type(X), shape=X.shape[1:]) class CoefficientInfoFDataBasis(CoefficientInfo): - def __init__(self, shape, basis): - super().__init__(coef_type=FDataBasis, shape=shape) + def __init__(self, basis): + super().__init__(coef_type=FDataBasis, shape=(basis.n_basis,)) self.basis = basis @@ -79,4 +79,4 @@ def coefficient_info_from_covariate_fdatabasis( if not isinstance(basis, Basis): raise TypeError(f"basis must be a Basis object, not {type(basis)}") - return CoefficientInfoFDataBasis(shape=(len(X),), basis=basis) + return CoefficientInfoFDataBasis(basis=basis) diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py index 3f4fced2d..2bc2b5f45 100644 --- a/skfda/misc/regularization/__init__.py +++ b/skfda/misc/regularization/__init__.py @@ -1,2 +1,4 @@ +from ._endpoints_difference_regularization import EndpointsDifferenceRegularization +from ._l2_regularization import L2Regularization from ._linear_diff_op_regularization import LinearDifferentialOperatorRegularization from ._regularization import Regularization, compute_penalty_matrix diff --git a/skfda/misc/regularization/_endpoints_difference_regularization.py b/skfda/misc/regularization/_endpoints_difference_regularization.py new file mode 100644 index 000000000..bf141cbdc --- /dev/null +++ b/skfda/misc/regularization/_endpoints_difference_regularization.py @@ -0,0 +1,45 @@ +from functools import singledispatch + +import numpy as np + +from ..._utils._coefficients import CoefficientInfo, CoefficientInfoFDataBasis +from ._regularization import Regularization + + +@singledispatch +def penalty_matrix_coef_info(coef_info: CoefficientInfo, + regularization): + """ + Return a penalty matrix given the coefficient information. + + This method is a singledispatch method that provides an + implementation of the computation of the penalty matrix + for a particular coefficient type. + """ + return np.zeros((coef_info.shape[0], coef_info.shape[0])) + + +class EndpointsDifferenceRegularization(Regularization): + """ + Regularization penalizing the difference of the functions + endpoints. + + """ + + penalty_matrix_coef_info = penalty_matrix_coef_info + + def penalty_matrix(self, coef_info): + return penalty_matrix_coef_info(coef_info, self) + + +@EndpointsDifferenceRegularization.penalty_matrix_coef_info.register( + CoefficientInfoFDataBasis) +def penalty_matrix_coef_info_fdatabasis( + coef_info: CoefficientInfoFDataBasis, + regularization: EndpointsDifferenceRegularization): + + evaluate_first = coef_info.basis(coef_info.basis.domain_range[0][0]) + evaluate_last = coef_info.basis(coef_info.basis.domain_range[0][1]) + evaluate_diff = evaluate_last - evaluate_first + + return evaluate_diff @ evaluate_diff.T diff --git a/skfda/misc/regularization/_l2_regularization.py b/skfda/misc/regularization/_l2_regularization.py new file mode 100644 index 000000000..ea7b268f6 --- /dev/null +++ b/skfda/misc/regularization/_l2_regularization.py @@ -0,0 +1,13 @@ +import numpy as np + +from ._regularization import Regularization + + +class L2Regularization(Regularization): + """ + Regularization using a sum of coefficient squares. + + """ + + def penalty_matrix(self, coef_info): + return np.identity(coef_info.shape[0]) diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/regularization/_linear_diff_op_regularization.py index 391c6b8ce..1bf64514a 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/regularization/_linear_diff_op_regularization.py @@ -1,9 +1,9 @@ from functools import singledispatch -from skfda._utils._coefficients import CoefficientInfoFDataBasis from numpy import polyder, polyint, polymul, polyval import scipy.integrate from scipy.interpolate import PPoly +from skfda._utils._coefficients import CoefficientInfoFDataBasis import numpy as np @@ -36,7 +36,7 @@ def penalty_matrix_coef_info(coef_info: CoefficientInfo, implementation of the computation of the penalty matrix for a particular coefficient type. """ - return np.zeros((coef_info.shape[1], coef_info.shape[1])) + return np.zeros((coef_info.shape[0], coef_info.shape[0])) class LinearDifferentialOperatorRegularization(Regularization): diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index bbbdba217..e7a81f8ac 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -1,11 +1,13 @@ +import warnings + from skfda.misc._math import inner_product from skfda.representation import FData from skfda.representation.basis import FDataBasis, Constant, Basis - from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted import numpy as np + from ..._utils._coefficients import coefficient_info_from_covariate @@ -157,6 +159,10 @@ def fit(self, X, y=None, sample_weight=None): regularization=self.penalty, penalty_matrix=self.penalty_matrix) + if self.fit_intercept and hasattr(penalty_matrix, "shape"): + # Intercept is not penalized + penalty_matrix[0, 0] = 0 + gram_inner_x_coef = inner_products.T @ inner_products + penalty_matrix inner_x_coef_y = inner_products.T @ y @@ -207,7 +213,7 @@ def _argcheck_X(self, X): X = [x if isinstance(x, FData) else np.asarray(x) for x in X] if all(not isinstance(i, FData) for i in X): - raise ValueError("All the covariates are scalar.") + warnings.warn("All the covariates are scalar.") return X diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 806eb3b74..fae7af5e9 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -350,7 +350,7 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input penalty_matrix = compute_penalty_matrix( - coef_info=CoefficientInfoFDataBasis(None, self.basis), + coef_info=CoefficientInfoFDataBasis(self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) @@ -412,7 +412,7 @@ def fit_transform(self, X: FDataGrid, y=None): else self.input_points_) penalty_matrix = compute_penalty_matrix( - coef_info=CoefficientInfoFDataBasis(None, self.basis), + coef_info=CoefficientInfoFDataBasis(self.basis), regularization_parameter=self.smoothing_parameter, regularization=self.penalty, penalty_matrix=self.penalty_matrix) diff --git a/tests/test_regularization.py b/tests/test_regularization.py index fe34b6581..6591a44b8 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -1,8 +1,17 @@ -from skfda.misc.regularization import LinearDifferentialOperatorRegularization +import unittest +import warnings + +import skfda +from skfda.misc.regularization import (LinearDifferentialOperatorRegularization, + EndpointsDifferenceRegularization, + L2Regularization) from skfda.misc.regularization._linear_diff_op_regularization import ( _monomial_evaluate_constant_linear_diff_op) +from skfda.ml.regression.linear import MultivariateLinearRegression from skfda.representation.basis import Constant, Monomial, BSpline, Fourier -import unittest +from sklearn.datasets import make_regression +from sklearn.linear_model import Ridge +from sklearn.model_selection._split import train_test_split import numpy as np @@ -166,3 +175,71 @@ def test_bspline_penalty_special_case(self): numerical_penalty, res ) + + +class TestEndpointsDifferenceRegularization(unittest.TestCase): + + def test_basis_conversion(self): + + data_matrix = np.linspace([0, 1, 2, 3], [1, 2, 3, 4], 100) + + fd = skfda.FDataGrid(data_matrix.T) + + smoother = skfda.preprocessing.smoothing.BasisSmoother( + basis=skfda.representation.basis.BSpline( + n_basis=10, domain_range=fd.domain_range), + penalty=EndpointsDifferenceRegularization(), + smoothing_parameter=10000) + + fd_basis = smoother.fit_transform(fd) + + np.testing.assert_allclose( + fd_basis(0), + fd_basis(1), + atol=0.001 + ) + + +class TestL2Regularization(unittest.TestCase): + + def test_multivariate(self): + + def ignore_scalar_warning(): + warnings.filterwarnings( + "ignore", category=UserWarning, + message="All the covariates are scalar.") + + X, y = make_regression(n_samples=20, n_features=10, + random_state=1, bias=3.5) + + X_train, X_test, y_train, _ = train_test_split( + X, y, random_state=2) + + for regularization_parameter in [0, 1, 10, 100]: + + with self.subTest( + regularization_parameter=regularization_parameter): + + sklearn_l2 = Ridge(alpha=regularization_parameter) + skfda_l2 = MultivariateLinearRegression( + penalty=L2Regularization(), + regularization_parameter=regularization_parameter) + + sklearn_l2.fit(X_train, y_train) + with warnings.catch_warnings(): + ignore_scalar_warning() + skfda_l2.fit(X_train, y_train) + + sklearn_y_pred = sklearn_l2.predict(X_test) + with warnings.catch_warnings(): + ignore_scalar_warning() + skfda_y_pred = skfda_l2.predict(X_test) + + np.testing.assert_allclose( + sklearn_l2.coef_, skfda_l2.coef_[0]) + + np.testing.assert_allclose( + sklearn_l2.intercept_, skfda_l2.intercept_) + + np.testing.assert_allclose( + sklearn_y_pred, skfda_y_pred) From 8ea16d0a2037df30af968d27fc6e33e4a56395b4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 24 Apr 2020 18:45:23 +0200 Subject: [PATCH 455/624] Fixes test. --- tests/test_regression.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_regression.py b/tests/test_regression.py index 87cea5738..5da9be571 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,7 +1,8 @@ +import unittest + from skfda.ml.regression import MultivariateLinearRegression from skfda.representation.basis import (FDataBasis, Constant, Monomial, Fourier, BSpline) -import unittest import numpy as np @@ -182,7 +183,7 @@ def test_error_X_not_FData(self): scalar = MultivariateLinearRegression(coef_basis=[Fourier(n_basis=5)]) - with np.testing.assert_raises(ValueError): + with np.testing.assert_warns(UserWarning): scalar.fit([x_fd], y) def test_error_y_is_FData(self): From d03de80d25a4828b254cd8e926cb13945724d9c6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 01:47:14 +0200 Subject: [PATCH 456/624] Rename penalty parameter to regularization. --- skfda/ml/regression/linear.py | 17 +++++++++-------- skfda/preprocessing/smoothing/_basis.py | 25 +++++++++++++------------ tests/test_regularization.py | 4 ++-- tests/test_smoothing.py | 14 +++++++------- 4 files changed, 31 insertions(+), 29 deletions(-) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index e7a81f8ac..510ce7cc6 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -9,6 +9,7 @@ import numpy as np from ..._utils._coefficients import coefficient_info_from_covariate +from ...misc.regularization import compute_penalty_matrix class MultivariateLinearRegression(BaseEstimator, RegressorMixin): @@ -43,16 +44,17 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): regularization_parameter (int or float, optional): Regularization parameter. Trying with several factors in a logarithm scale is suggested. If 0 no regularization is performed. Defaults to 0. - penalty (int, iterable or :class:`LinearDifferentialOperator`): If it + regularization (int, iterable or :class:`Regularization`): If it is + not a :class:`Regularization` object, linear differential + operator regularization is assumed. If it is an integer, it indicates the order of the derivative used in the computing of the penalty matrix. For instance 2 means that the differential operator is :math:`f''(x)`. If it is an iterable, it consists on coefficients representing the differential operator used in the computing of the penalty matrix. For instance the tuple (1, 0, - numpy.sin) means :math:`1 + sin(x)D^{2}`. It is possible to - supply directly the LinearDifferentialOperator object. - If not supplied this defaults to 2. Only used if penalty_matrix is + numpy.sin) means :math:`1 + sin(x)D^{2}`. If not supplied this + defaults to 2. Only used if penalty_matrix is ``None``. penalty_matrix (array_like, optional): Penalty matrix. If supplied the differential operator is not used and instead @@ -121,16 +123,15 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): def __init__(self, *, coef_basis=None, fit_intercept=True, regularization_parameter=0, - penalty=None, + regularization=None, penalty_matrix=None): self.coef_basis = coef_basis self.fit_intercept = fit_intercept self.regularization_parameter = regularization_parameter - self.penalty = penalty + self.regularization = regularization self.penalty_matrix = penalty_matrix def fit(self, X, y=None, sample_weight=None): - from ...misc.regularization import compute_penalty_matrix X, y, sample_weight, coef_info = self._argcheck_X_y( X, y, sample_weight, self.coef_basis) @@ -156,7 +157,7 @@ def fit(self, X, y=None, sample_weight=None): penalty_matrix = compute_penalty_matrix( coef_info=coef_info, regularization_parameter=self.regularization_parameter, - regularization=self.penalty, + regularization=self.regularization, penalty_matrix=self.penalty_matrix) if self.fit_intercept and hasattr(penalty_matrix, "shape"): diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index fae7af5e9..0ac64dc77 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -169,16 +169,17 @@ class BasisSmoother(_LinearSmoother): smoothing_parameter (int or float, optional): Smoothing parameter. Trying with several factors in a logarithm scale is suggested. If 0 no smoothing is performed. Defaults to 0. - penalty (int, iterable or :class:`LinearDifferentialOperator`): If it + regularization (int, iterable or :class:`Regularization`): If it is + not a :class:`Regularization` object, linear differential + operator regularization is assumed. If it is an integer, it indicates the order of the derivative used in the computing of the penalty matrix. For instance 2 means that the differential operator is :math:`f''(x)`. If it is an iterable, it consists on coefficients representing the differential operator used in the computing of the penalty matrix. For instance the tuple (1, 0, - numpy.sin) means :math:`1 + sin(x)D^{2}`. It is possible to - supply directly the LinearDifferentialOperator object. - If not supplied this defaults to 2. Only used if penalty_matrix is + numpy.sin) means :math:`1 + sin(x)D^{2}`. If not supplied this + defaults to 2. Only used if penalty_matrix is ``None``. penalty_matrix (array_like, optional): Penalty matrix. If supplied the differential operator is not used and instead @@ -257,7 +258,7 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='cholesky', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator( + ... regularization=LinearDifferentialOperator( ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) @@ -270,7 +271,7 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator( + ... regularization=LinearDifferentialOperator( ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) @@ -283,7 +284,7 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', ... smoothing_parameter=1, - ... penalty=LinearDifferentialOperator( + ... regularization=LinearDifferentialOperator( ... weights=[0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) @@ -313,8 +314,8 @@ def __init__(self, *, smoothing_parameter: float = 0, weights=None, - penalty: Union[int, Iterable[float], - 'LinearDifferentialOperator'] = None, + regularization: Union[int, Iterable[float], + 'LinearDifferentialOperator'] = None, penalty_matrix=None, output_points=None, method='cholesky', @@ -322,7 +323,7 @@ def __init__(self, self.basis = basis self.smoothing_parameter = smoothing_parameter self.weights = weights - self.penalty = penalty + self.regularization = regularization self.penalty_matrix = penalty_matrix self.output_points = output_points self.method = method @@ -352,7 +353,7 @@ def _coef_matrix(self, input_points): penalty_matrix = compute_penalty_matrix( coef_info=CoefficientInfoFDataBasis(self.basis), regularization_parameter=self.smoothing_parameter, - regularization=self.penalty, + regularization=self.regularization, penalty_matrix=self.penalty_matrix) inv += penalty_matrix @@ -414,7 +415,7 @@ def fit_transform(self, X: FDataGrid, y=None): penalty_matrix = compute_penalty_matrix( coef_info=CoefficientInfoFDataBasis(self.basis), regularization_parameter=self.smoothing_parameter, - regularization=self.penalty, + regularization=self.regularization, penalty_matrix=self.penalty_matrix) # n is the samples diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 6591a44b8..8766484b7 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -188,7 +188,7 @@ def test_basis_conversion(self): smoother = skfda.preprocessing.smoothing.BasisSmoother( basis=skfda.representation.basis.BSpline( n_basis=10, domain_range=fd.domain_range), - penalty=EndpointsDifferenceRegularization(), + regularization=EndpointsDifferenceRegularization(), smoothing_parameter=10000) fd_basis = smoother.fit_transform(fd) @@ -222,7 +222,7 @@ def ignore_scalar_warning(): sklearn_l2 = Ridge(alpha=regularization_parameter) skfda_l2 = MultivariateLinearRegression( - penalty=L2Regularization(), + regularization=L2Regularization(), regularization_parameter=regularization_parameter) sklearn_l2.fit(X_train, y_train) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index 097929afb..ff753ed37 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -1,15 +1,15 @@ import unittest +import skfda +from skfda._utils import _check_estimator +from skfda.representation.basis import BSpline, Monomial +from skfda.representation.grid import FDataGrid import sklearn import numpy as np -import skfda -from skfda._utils import _check_estimator import skfda.preprocessing.smoothing as smoothing import skfda.preprocessing.smoothing.kernel_smoothers as kernel_smoothers import skfda.preprocessing.smoothing.validation as validation -from skfda.representation.basis import BSpline, Monomial -from skfda.representation.grid import FDataGrid class TestSklearnEstimators(unittest.TestCase): @@ -79,7 +79,7 @@ def test_cholesky(self): fd = FDataGrid(data_matrix=x, sample_points=t) smoother = smoothing.BasisSmoother(basis=basis, smoothing_parameter=10, - penalty=2, method='cholesky', + regularization=2, method='cholesky', return_basis=True) fd_basis = smoother.fit_transform(fd) np.testing.assert_array_almost_equal( @@ -94,7 +94,7 @@ def test_qr(self): fd = FDataGrid(data_matrix=x, sample_points=t) smoother = smoothing.BasisSmoother(basis=basis, smoothing_parameter=10, - penalty=2, method='qr', + regularization=2, method='qr', return_basis=True) fd_basis = smoother.fit_transform(fd) np.testing.assert_array_almost_equal( @@ -111,7 +111,7 @@ def test_monomial_smoothing(self): fd = FDataGrid(data_matrix=x, sample_points=t) smoother = smoothing.BasisSmoother(basis=basis, smoothing_parameter=1, - penalty=2, + regularization=2, return_basis=True) fd_basis = smoother.fit_transform(fd) # These results where extracted from the R package fda From a53a06f220dc53bc6792ec08d4f4d047d7a0d0ff Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 03:15:18 +0200 Subject: [PATCH 457/624] Added support for different regularizations for different covariates. --- skfda/misc/regularization/_regularization.py | 52 +++++++++++++------- skfda/ml/regression/linear.py | 16 +++++- tests/test_regression.py | 43 +++++++++++++++- 3 files changed, 91 insertions(+), 20 deletions(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 52ca71924..7b6bec7bb 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -1,8 +1,9 @@ import abc +from collections.abc import Iterable +import itertools import scipy.linalg -import numpy as np from ..._utils._coefficients import CoefficientInfo @@ -20,16 +21,28 @@ def penalty_matrix(self, coef_info): pass +def _convert_regularization(regularization): + from ._linear_diff_op_regularization import ( + LinearDifferentialOperatorRegularization) + + # Convert to linear differential operator if necessary + if regularization is None: + regularization = LinearDifferentialOperatorRegularization(2) + elif not isinstance(regularization, Regularization): + regularization = LinearDifferentialOperatorRegularization( + regularization) + + return regularization + + def compute_penalty_matrix(coef_info, regularization_parameter, regularization, penalty_matrix): """ Computes the regularization matrix for a linear differential operator. X can be a list of mixed data. - """ - from ._linear_diff_op_regularization import ( - LinearDifferentialOperatorRegularization) + """ # If there is no regularization, return 0 and rely on broadcasting if regularization_parameter == 0: return 0 @@ -37,20 +50,25 @@ def compute_penalty_matrix(coef_info, regularization_parameter, # Compute penalty matrix if not provided if penalty_matrix is None: - # Convert the linear differential operator if necessary - if regularization is None: - regularization = LinearDifferentialOperatorRegularization(2) - elif not isinstance(regularization, Regularization): - regularization = LinearDifferentialOperatorRegularization( - regularization) + if isinstance(coef_info, Iterable): - if isinstance(coef_info, CoefficientInfo): - penalty_matrix = regularization.penalty_matrix(coef_info) - else: - # If X and basis are lists + if not isinstance(regularization, Iterable): + regularization = itertools.repeat(regularization) + + if not isinstance(regularization_parameter, Iterable): + regularization_parameter = itertools.repeat( + regularization_parameter) - penalty_blocks = [regularization.penalty_matrix(c) - for c in coef_info] + penalty_blocks = [ + a * _convert_regularization(r).penalty_matrix(c) + for c, r, a in zip(coef_info, regularization, + regularization_parameter)] penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) - return regularization_parameter * penalty_matrix + else: + + regularization = _convert_regularization(regularization) + penalty_matrix = regularization.penalty_matrix(coef_info) + penalty_matrix *= regularization_parameter + + return penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 510ce7cc6..7d84ba0d9 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -1,3 +1,5 @@ +from collections.abc import Iterable +import itertools import warnings from skfda.misc._math import inner_product @@ -136,11 +138,21 @@ def fit(self, X, y=None, sample_weight=None): X, y, sample_weight, coef_info = self._argcheck_X_y( X, y, sample_weight, self.coef_basis) + regularization = self.regularization + regularization_parameter = self.regularization_parameter + if self.fit_intercept: new_x = np.ones((len(y), 1)) X = [new_x] + X coef_info = [coefficient_info_from_covariate(new_x, y)] + coef_info + if isinstance(regularization, Iterable): + regularization = itertools.chain([None], regularization) + + if isinstance(regularization_parameter, Iterable): + regularization_parameter = itertools.chain( + [0], regularization_parameter) + inner_products = [c.regression_matrix(x, y) for x, c in zip(X, coef_info)] @@ -156,8 +168,8 @@ def fit(self, X, y=None, sample_weight=None): penalty_matrix = compute_penalty_matrix( coef_info=coef_info, - regularization_parameter=self.regularization_parameter, - regularization=self.regularization, + regularization_parameter=regularization_parameter, + regularization=regularization, penalty_matrix=self.penalty_matrix) if self.fit_intercept and hasattr(penalty_matrix, "shape"): diff --git a/tests/test_regression.py b/tests/test_regression.py index 5da9be571..fd0e0a197 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,7 +1,8 @@ import unittest +from skfda.misc.regularization import L2Regularization from skfda.ml.regression import MultivariateLinearRegression -from skfda.representation.basis import (FDataBasis, Constant, Monomial, +from skfda.representation.basis import (FDataBasis, Monomial, Fourier, BSpline) import numpy as np @@ -106,6 +107,46 @@ def test_regression_mixed(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_regression_mixed_regularization(self): + + multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5]]) + + X = [multivariate, + FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1]])] + + # y = 2 + sum([3, 1] * array) + int(3 * function) + intercept = 2 + coefs_multivariate = np.array([3, 1]) + y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) + y_sum = multivariate @ coefs_multivariate + y = 2 + y_sum + y_integral + + scalar = MultivariateLinearRegression( + regularization_parameter=1, + regularization=[L2Regularization(), 2]) + scalar.fit(X, y) + + np.testing.assert_allclose(scalar.intercept_, + intercept, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[0], + [2.536739, 1.072186], atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[1].coefficients, + [[2.125676, 2.450782, 5.808745e-4]], atol=0.01) + + y_pred = scalar.predict(X) + np.testing.assert_allclose( + y_pred, + [5.349035, 16.456464, 13.361185, 23.930295, + 32.650965, 23.961766, 16.29029], + atol=0.01) + def test_regression_regularization(self): x_basis = Monomial(n_basis=7) From 2f8f8ef11edb0649013f2efc3f250b6ac2659dbc Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 04:16:40 +0200 Subject: [PATCH 458/624] Minimum setuptools version for RTD. --- readthedocs-requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index be6a2f754..c505ad4e4 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -4,4 +4,5 @@ sphinx_rtd_theme sphinx-gallery pillow matplotlib -mpldatacursor \ No newline at end of file +mpldatacursor +setuptools>=41.2 \ No newline at end of file From a58e91c55f0e44a62840c36344cef51a65c107d3 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 04:20:55 +0200 Subject: [PATCH 459/624] Fix RTD requirements. --- readthedocs-requirements.txt | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index c505ad4e4..e8bf91baf 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -1,4 +1,9 @@ --r requirements.txt +matplotlib +numpy +scipy +setuptools +Cython +sklearn Sphinx sphinx_rtd_theme sphinx-gallery From aa76257803dc62f7d93f81dc6a4ffce575e03bd5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 04:22:49 +0200 Subject: [PATCH 460/624] Remove repeated line in RTD requirements. --- readthedocs-requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index e8bf91baf..7e07d788a 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -1,7 +1,6 @@ matplotlib numpy scipy -setuptools Cython sklearn Sphinx From 1642b2eb2bc0f61bacd0f7b26d34d7996fb6f7ab Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 16:51:04 +0200 Subject: [PATCH 461/624] Add documentation for regularization. --- docs/modules/misc/regularization.rst | 13 +++++++++++++ docs/modules/ml/regression.rst | 6 ++++-- skfda/ml/regression/linear.py | 2 -- 3 files changed, 17 insertions(+), 4 deletions(-) create mode 100644 docs/modules/misc/regularization.rst diff --git a/docs/modules/misc/regularization.rst b/docs/modules/misc/regularization.rst new file mode 100644 index 000000000..731e22ea6 --- /dev/null +++ b/docs/modules/misc/regularization.rst @@ -0,0 +1,13 @@ +Regularization +============== + +This module contains several regularization techniques that can be applied +in several situations, such as regression, PCA or basis smoothing. + +.. autosummary:: + :toctree: autosummary + + skfda.misc.regularization.Regularization + skfda.misc.regularization.LinearDifferentialOperatorRegularization + skfda.misc.regularization.L2Regularization + skfda.misc.regularization.EndpointsDifferenceRegularization diff --git a/docs/modules/ml/regression.rst b/docs/modules/ml/regression.rst index 72ba60f4b..700dbb7aa 100644 --- a/docs/modules/ml/regression.rst +++ b/docs/modules/ml/regression.rst @@ -8,12 +8,14 @@ Module with classes to perform regression of functional data. Linear regression ----------------- -Todo: Add documentation of linear regression models. +A linear regression model is one in which the response variable can be +expressed as a linear combination of the covariates (which could be +multivariate or functional). .. autosummary:: :toctree: autosummary - skfda.ml.regression.LinearScalarRegression + skfda.ml.regression.MultivariateLinearRegression Nearest Neighbors ----------------- diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 7d84ba0d9..4d384832a 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -2,9 +2,7 @@ import itertools import warnings -from skfda.misc._math import inner_product from skfda.representation import FData -from skfda.representation.basis import FDataBasis, Constant, Basis from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted From 6fa310d9a2842e4c3e34017ebf5fd5ceecae1413 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 23:42:49 +0200 Subject: [PATCH 462/624] Fix doctest example. --- skfda/ml/regression/linear.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 4d384832a..6c72b9dd1 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -70,7 +70,8 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): Examples: >>> from skfda.ml.regression import MultivariateLinearRegression - >>> from skfda.representation.basis import FDataBasis, Monomial + >>> from skfda.representation.basis import (FDataBasis, Monomial, + ... Constant) Multivariate linear regression can be used with functions expressed in a basis. Also, a functional basis for the weights can be specified: From a4859498edd8fd3915bff919f3f7b2621d9bc7b5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 25 Apr 2020 23:45:59 +0200 Subject: [PATCH 463/624] Set required numpy version. --- setup.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index 67e10792f..3fbfcf477 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,6 @@ import sys from Cython.Build import cythonize -from Cython.Distutils import build_ext from setuptools import setup, find_packages from setuptools.extension import Extension @@ -80,7 +79,7 @@ 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Software Development :: Libraries :: Python Modules', ], - install_requires=['numpy', + install_requires=['numpy>=1.16', 'scipy>=1.3.0', 'scikit-learn>=0.20', 'matplotlib', @@ -89,7 +88,6 @@ 'cython', 'mpldatacursor'], setup_requires=pytest_runner, - tests_require=['pytest', - 'numpy>=1.14'], + tests_require=['pytest'], test_suite='tests', zip_safe=False) From 3cf4c943d933230c33edc33fcfc5cda525cea883 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 26 Apr 2020 12:56:12 +0200 Subject: [PATCH 464/624] regularization for FPCAGrid and address comments --- .../dim_reduction/projection/_fpca.py | 96 ++++++++++++++++--- 1 file changed, 83 insertions(+), 13 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 76984a73e..43942a233 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -24,9 +24,16 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): object and center the data first """ - def __init__(self, n_components=3, centering=True): + def __init__(self, + n_components=3, + centering=True, + regularization_parameter=0, + penalty=2): self.n_components = n_components self.centering = centering + # lambda in the regularization / penalization process + self.regularization_parameter = regularization_parameter + self.penalty = penalty @abstractmethod def fit(self, X, y=None): @@ -91,6 +98,8 @@ class FPCABasis(FPCA): components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. + regularization_parameter (float): this parameter determines the amount + of smoothing applied. Defaults to 0 penalty (Union[int, Iterable[float],'LinearDifferentialOperator']): Linear differential operator. If it is not a LinearDifferentialOperator object, it will be converted to one. @@ -126,12 +135,10 @@ def __init__(self, centering=True, regularization_parameter=0, penalty=2): - super().__init__(n_components, centering) + super().__init__(n_components, centering, + regularization_parameter, penalty) # basis that we want to use for the principal components self.components_basis = components_basis - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter - self.penalty = penalty def fit(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. @@ -264,6 +271,46 @@ def transform(self, X, y=None): return X.inner_product(self.components_) +def _auxiliary_penalty_matrix(sample_points): + diff_values = np.diff(sample_points) + hh = -(1 / np.mean(1 / diff_values)) / diff_values + aux_diff_matrix = np.diag(hh) + + n_points = len(sample_points) + + aux_matrix_1 = np.zeros((n_points - 1, n_points)) + aux_matrix_1[:, :-1] = aux_diff_matrix + aux_matrix_2 = np.zeros((n_points - 1, n_points)) + aux_matrix_2[:, 1:] = -aux_diff_matrix + + diff_matrix = aux_matrix_1 + aux_matrix_2 + + return diff_matrix + + +def regularization_penalty_matrix(sample_points, penalty): + penalty = np.array(penalty) + n_points = len(sample_points) + penalty_matrix = np.zeros((n_points, n_points)) + if (np.sum(penalty) != 0): + # independent term + penalty_matrix = penalty_matrix + penalty[0] * np.diag( + np.ones(n_points)) + if len(penalty) > 1: + for i in range(1, len(penalty)): + aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) + aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) + if i > 1: + for k in range(2, i + 1): + aux_penalty_1 = (aux_penalty_2[:(n_points - k), + :(n_points - k + 1)] + @ aux_penalty_1) + penalty_matrix = (penalty_matrix + + penalty[i] * (np.transpose( + aux_penalty_1) @ aux_penalty_1)) + return penalty_matrix + + class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. @@ -277,6 +324,15 @@ class FPCAGrid(FPCA): weights (numpy.array): the weights vector used for discrete integration. If none then the trapezoidal rule is used for computing the weights. + regularization_parameter (float): this parameter determines the amount + of smoothing applied. Defaults to 0 + penalty (Union[int, Iterable[float]): the coefficients that will be + used to calculate the penalty matrix for regularization. + If you input an integer then the derivative of that degree will be + used to regularize the principal components. If you input a vector + then it is considered as a differential operator. For example, + [0,1,2] penalizes first derivative and two times the second + derivative. Attributes: components_ (FDataBasis): this contains the principal components either @@ -300,8 +356,14 @@ class FPCAGrid(FPCA): >>> fpca_grid = fpca_grid.fit(fd) """ - def __init__(self, n_components=3, weights=None, centering=True): - super().__init__(n_components, centering) + def __init__(self, + n_components=3, + weights=None, + centering=True, + regularization_parameter=0, + penalty=2): + super().__init__(n_components, centering, + regularization_parameter, penalty) self.weights = weights def fit(self, X: FDataGrid, y=None): @@ -346,7 +408,7 @@ def fit(self, X: FDataGrid, y=None): "points of the functional data object.") # data matrix initialization - fd_data = np.squeeze(X.data_matrix) + fd_data = X.data_matrix.reshape(X.data_matrix.shape[:-1]) # get the number of samples and the number of points of descretization n_samples, n_points_discretization = fd_data.shape @@ -355,9 +417,18 @@ def fit(self, X: FDataGrid, y=None): # in FDataBasis if self.centering: meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function + # consider moving these lines to FDataGrid as a centering function # subtract from each row the mean coefficient matrix - fd_data -= np.squeeze(meanfd.data_matrix) + fd_data -= meanfd.data_matrix.reshape(meanfd.data_matrix.shape[:-1]) + + if self.regularization_parameter > 0: + if isinstance(self.penalty, int): + self.penalty = np.append(np.zeros(self.penalty), 1) + penalty_matrix = regularization_penalty_matrix(X.sample_points[0], + self.penalty) + fd_data = fd_data @ np.linalg.inv( + np.diag(np.ones(n_points_discretization)) + + self.regularization_parameter * penalty_matrix) # establish weights for each point of discretization if not self.weights: @@ -366,9 +437,8 @@ def fit(self, X: FDataGrid, y=None): # vector is as follows: [\deltax_1/2, \deltax_1/2 + \deltax_2/2, # \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] differences = np.diff(X.sample_points[0]) - self.weights = [sum(differences[i:i + 2]) / 2 for i in - range(len(differences))] - self.weights = np.concatenate(([differences[0] / 2], self.weights)) + differences = np.concatenate(((0,), differences, (0,))) + self.weights = (differences[:-1] + differences[1:]) / 2 weights_matrix = np.diag(self.weights) From 98c35213f81be6c145fcc323d472b1fac9b41085 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 26 Apr 2020 15:31:21 +0200 Subject: [PATCH 465/624] correct mistake when computing the discretized fpca components --- .../dim_reduction/projection/_fpca.py | 28 ++++++++++++------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 43942a233..2af0b3f76 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -421,15 +421,6 @@ def fit(self, X: FDataGrid, y=None): # subtract from each row the mean coefficient matrix fd_data -= meanfd.data_matrix.reshape(meanfd.data_matrix.shape[:-1]) - if self.regularization_parameter > 0: - if isinstance(self.penalty, int): - self.penalty = np.append(np.zeros(self.penalty), 1) - penalty_matrix = regularization_penalty_matrix(X.sample_points[0], - self.penalty) - fd_data = fd_data @ np.linalg.inv( - np.diag(np.ones(n_points_discretization)) + - self.regularization_parameter * penalty_matrix) - # establish weights for each point of discretization if not self.weights: # sample_points is a list with one array in the 1D case @@ -442,12 +433,29 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) + if self.regularization_parameter > 0: + if isinstance(self.penalty, int): + self.penalty = np.append(np.zeros(self.penalty), 1) + penalty_matrix = regularization_penalty_matrix(X.sample_points[0], + self.penalty) + + # we need to invert aux matrix and multiply it to the data matrix + aux_matrix = (np.diag(np.ones(n_points_discretization)) + + self.regularization_parameter * penalty_matrix) + # we use solve for better stability, P=aux matrix, X=data_matrix + # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives (P^T)^-1*X^T + fd_data = np.transpose(np.linalg.solve(np.transpose(aux_matrix), + np.transpose(fd_data))) + + # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) pca = PCA(n_components=self.n_components) pca.fit(final_matrix) - self.components_ = X.copy(data_matrix=pca.components_) + self.components_ = X.copy(data_matrix=np.transpose( + np.linalg.solve(np.sqrt(weights_matrix), + np.transpose(pca.components_)))) self.component_values_ = pca.singular_values_ ** 2 self.explained_variance_ratio_ = pca.explained_variance_ratio_ From 78ddf6cb19e225f8e45790238c1386aacc227bfd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 26 Apr 2020 18:04:24 +0200 Subject: [PATCH 466/624] Make FDataGrid example more clear. --- skfda/representation/grid.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 9218a7e8c..1f59c54a0 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -52,19 +52,22 @@ class FDataGrid(FData): Examples: Representation of a functional data object with 2 samples - representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. + representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`, + with 3 discretization points. - >>> data_matrix = [[1, 2], [2, 3]] - >>> sample_points = [2, 4] + >>> data_matrix = [[1, 2, 3], [4, 5, 6]] + >>> sample_points = [2, 4, 5] >>> FDataGrid(data_matrix, sample_points) FDataGrid( array([[[1], - [2]], + [2], + [3]], - [[2], - [3]]]), - sample_points=[array([2, 4])], - domain_range=array([[2, 4]]), + [[4], + [5], + [6]]]), + sample_points=[array([2, 4, 5])], + domain_range=array([[2, 5]]), ...) The number of columns of data_matrix have to be the length of From debe03b777a8beb1af3b6db98de70def23374aec Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 27 Apr 2020 13:17:18 +0200 Subject: [PATCH 467/624] optimization for computation of the penalty matrix, plus docstring --- .../dim_reduction/projection/_fpca.py | 54 ++++++++++++++++--- 1 file changed, 47 insertions(+), 7 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 2af0b3f76..eb936d225 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -272,6 +272,22 @@ def transform(self, X, y=None): def _auxiliary_penalty_matrix(sample_points): + """ Computes the auxiliary matrix needed for the computation of the panalty + matrix. For more details please view the module fdata2pc of the library + fda.usc in R, and the referenced paper. + + Args: + sample_points: the points of discretization of the data matrix. + Returns: + (array_like): the auxiliary matrix used to compute the penalty matrix + + References. + [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized + partial least squares with applications to b-spline transformations + and functional data. Chemometrics and Intelligent Laboratory Systems, + 94:60–69, 11 2008. + + """ diff_values = np.diff(sample_points) hh = -(1 / np.mean(1 / diff_values)) / diff_values aux_diff_matrix = np.diag(hh) @@ -289,22 +305,44 @@ def _auxiliary_penalty_matrix(sample_points): def regularization_penalty_matrix(sample_points, penalty): + """ Computes the penalty matrix for regularization of the principal + components in a grid representation. For more details please view the module + fdata2pc of the library fda.usc in R, and the referenced paper. + + Args: + sample_points: the points of discretization of the data matrix. + penalty (array_like): coefficients representing the differential + operator used in the computation of the penalty matrix. For example, + the array (1, 0, 1) means :math:`1 + D^{2}` + Returns: + (array_like): the penalty matrix used to regularize the components + + References. + [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized + partial least squares with applications to b-spline transformations + and functional data. Chemometrics and Intelligent Laboratory Systems, + 94:60–69, 11 2008. + + """ penalty = np.array(penalty) n_points = len(sample_points) penalty_matrix = np.zeros((n_points, n_points)) - if (np.sum(penalty) != 0): + if np.sum(penalty) != 0: # independent term penalty_matrix = penalty_matrix + penalty[0] * np.diag( np.ones(n_points)) if len(penalty) > 1: + # for each term of the differential operator, we compute the penalty + # matrix of that order and then add it to the final penalty matrix + aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) + aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) for i in range(1, len(penalty)): - aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) - aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) if i > 1: - for k in range(2, i + 1): - aux_penalty_1 = (aux_penalty_2[:(n_points - k), - :(n_points - k + 1)] - @ aux_penalty_1) + aux_penalty_1 = (aux_penalty_2[:(n_points - i), + :(n_points - i + 1)] + @ aux_penalty_1) + # applying the differential operator, as in each step the + # derivative degree increases by 1. penalty_matrix = (penalty_matrix + penalty[i] * (np.transpose( aux_penalty_1) @ aux_penalty_1)) @@ -434,6 +472,8 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) if self.regularization_parameter > 0: + # if its an integer, we transform it to an array representing the + # linear differential operator of that order if isinstance(self.penalty, int): self.penalty = np.append(np.zeros(self.penalty), 1) penalty_matrix = regularization_penalty_matrix(X.sample_points[0], From ab2c50599f0c490c3a20aec87f7a3fe5af9c3a88 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 27 Apr 2020 13:23:44 +0200 Subject: [PATCH 468/624] correct small type mistake --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index eb936d225..84baadd64 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -272,7 +272,7 @@ def transform(self, X, y=None): def _auxiliary_penalty_matrix(sample_points): - """ Computes the auxiliary matrix needed for the computation of the panalty + """ Computes the auxiliary matrix needed for the computation of the penalty matrix. For more details please view the module fdata2pc of the library fda.usc in R, and the referenced paper. From 6371b1af48bfe463cee913032570d3bf1a2bb9a6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 29 Apr 2020 19:32:27 +0200 Subject: [PATCH 469/624] Regularization has to be passed explicitly. --- skfda/misc/regularization/_regularization.py | 25 +++++--------- skfda/ml/regression/linear.py | 2 ++ skfda/preprocessing/smoothing/_basis.py | 13 +++----- tests/test_regression.py | 14 +++++--- tests/test_smoothing.py | 34 +++++++++++++------- 5 files changed, 47 insertions(+), 41 deletions(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 7b6bec7bb..9b61dca2a 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -21,20 +21,6 @@ def penalty_matrix(self, coef_info): pass -def _convert_regularization(regularization): - from ._linear_diff_op_regularization import ( - LinearDifferentialOperatorRegularization) - - # Convert to linear differential operator if necessary - if regularization is None: - regularization = LinearDifferentialOperatorRegularization(2) - elif not isinstance(regularization, Regularization): - regularization = LinearDifferentialOperatorRegularization( - regularization) - - return regularization - - def compute_penalty_matrix(coef_info, regularization_parameter, regularization, penalty_matrix): """ @@ -50,24 +36,29 @@ def compute_penalty_matrix(coef_info, regularization_parameter, # Compute penalty matrix if not provided if penalty_matrix is None: + if regularization is None: + raise ValueError("The regularization parameter is " + f"{regularization_parameter} != 0 " + "and no regularization is specified") + if isinstance(coef_info, Iterable): if not isinstance(regularization, Iterable): - regularization = itertools.repeat(regularization) + regularization = (regularization,) if not isinstance(regularization_parameter, Iterable): regularization_parameter = itertools.repeat( regularization_parameter) penalty_blocks = [ - a * _convert_regularization(r).penalty_matrix(c) + 0 if r is None else + a * r.penalty_matrix(c) for c, r, a in zip(coef_info, regularization, regularization_parameter)] penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) else: - regularization = _convert_regularization(regularization) penalty_matrix = regularization.penalty_matrix(coef_info) penalty_matrix *= regularization_parameter diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 6c72b9dd1..8a1a364ab 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -147,6 +147,8 @@ def fit(self, X, y=None, sample_weight=None): if isinstance(regularization, Iterable): regularization = itertools.chain([None], regularization) + elif regularization is not None: + regularization = (None, regularization) if isinstance(regularization_parameter, Iterable): regularization_parameter = itertools.chain( diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 0ac64dc77..15d480425 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -252,27 +252,25 @@ class BasisSmoother(_LinearSmoother): penalize approximations that are not smooth enough using a linear differential operator: - >>> from skfda.misc import LinearDifferentialOperator + >>> from skfda.misc.regularization import ( + ... LinearDifferentialOperatorRegularization as LDiffReg) >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='cholesky', ... smoothing_parameter=1, - ... regularization=LinearDifferentialOperator( - ... weights=[0.1, 0.2]), + ... regularization=LDiffReg([0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) array([[ 2.04, 0.51, 0.55]]) - >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', ... smoothing_parameter=1, - ... regularization=LinearDifferentialOperator( - ... weights=[0.1, 0.2]), + ... regularization=LDiffReg([0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) @@ -284,8 +282,7 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', ... smoothing_parameter=1, - ... regularization=LinearDifferentialOperator( - ... weights=[0.1, 0.2]), + ... regularization=LDiffReg([0.1, 0.2]), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) diff --git a/tests/test_regression.py b/tests/test_regression.py index fd0e0a197..3d9da867c 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,6 +1,7 @@ import unittest from skfda.misc.regularization import L2Regularization +from skfda.misc.regularization import LinearDifferentialOperatorRegularization from skfda.ml.regression import MultivariateLinearRegression from skfda.representation.basis import (FDataBasis, Monomial, Fourier, BSpline) @@ -126,7 +127,8 @@ def test_regression_mixed_regularization(self): scalar = MultivariateLinearRegression( regularization_parameter=1, - regularization=[L2Regularization(), 2]) + regularization=[L2Regularization(), + LinearDifferentialOperatorRegularization(2)]) scalar.fit(X, y) np.testing.assert_allclose(scalar.intercept_, @@ -170,8 +172,10 @@ def test_regression_regularization(self): 0.023385, -0.001384] - scalar = MultivariateLinearRegression(coef_basis=[beta_basis], - regularization_parameter=1) + scalar = MultivariateLinearRegression( + coef_basis=[beta_basis], + regularization_parameter=1, + regularization=LinearDifferentialOperatorRegularization()) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients, atol=1e-3) @@ -205,7 +209,9 @@ def test_regression_regularization(self): beta_fd_reg = FDataBasis(x_basis, [2.812, 3.043, 0]) y_reg = [5.333, 3.419, 2.697, 11.366] - scalar_reg = MultivariateLinearRegression(regularization_parameter=1) + scalar_reg = MultivariateLinearRegression( + regularization_parameter=1, + regularization=LinearDifferentialOperatorRegularization()) scalar_reg.fit(x_fd, y) np.testing.assert_allclose(scalar_reg.coef_[0].coefficients, beta_fd_reg.coefficients, atol=0.001) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index ff753ed37..29ef1aab3 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -2,6 +2,8 @@ import skfda from skfda._utils import _check_estimator +from skfda.misc import LinearDifferentialOperator +from skfda.misc.regularization import LinearDifferentialOperatorRegularization from skfda.representation.basis import BSpline, Monomial from skfda.representation.grid import FDataGrid import sklearn @@ -77,10 +79,13 @@ def test_cholesky(self): x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = BSpline((0, 1), n_basis=5) fd = FDataGrid(data_matrix=x, sample_points=t) - smoother = smoothing.BasisSmoother(basis=basis, - smoothing_parameter=10, - regularization=2, method='cholesky', - return_basis=True) + smoother = smoothing.BasisSmoother( + basis=basis, + smoothing_parameter=10, + regularization=LinearDifferentialOperatorRegularization( + LinearDifferentialOperator(2)), + method='cholesky', + return_basis=True) fd_basis = smoother.fit_transform(fd) np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), @@ -92,10 +97,13 @@ def test_qr(self): x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = BSpline((0, 1), n_basis=5) fd = FDataGrid(data_matrix=x, sample_points=t) - smoother = smoothing.BasisSmoother(basis=basis, - smoothing_parameter=10, - regularization=2, method='qr', - return_basis=True) + smoother = smoothing.BasisSmoother( + basis=basis, + smoothing_parameter=10, + regularization=LinearDifferentialOperatorRegularization( + LinearDifferentialOperator(2)), + method='qr', + return_basis=True) fd_basis = smoother.fit_transform(fd) np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), @@ -109,10 +117,12 @@ def test_monomial_smoothing(self): x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = Monomial(n_basis=4) fd = FDataGrid(data_matrix=x, sample_points=t) - smoother = smoothing.BasisSmoother(basis=basis, - smoothing_parameter=1, - regularization=2, - return_basis=True) + smoother = smoothing.BasisSmoother( + basis=basis, + smoothing_parameter=1, + regularization=LinearDifferentialOperatorRegularization( + LinearDifferentialOperator(2)), + return_basis=True) fd_basis = smoother.fit_transform(fd) # These results where extracted from the R package fda np.testing.assert_array_almost_equal( From f9ec5cc2d5971036722d77d25147c288e85594a8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 29 Apr 2020 22:58:33 +0200 Subject: [PATCH 470/624] make weights callable --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 84baadd64..233295b25 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -359,9 +359,11 @@ class FPCAGrid(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array): the weights vector used for discrete - integration. If none then the trapezoidal rule is used for - computing the weights. + weights (numpy.array Union callable): the weights vector used for + discrete integration. If none then the trapezoidal rule is used for + computing the weights. If a callable object is passed, then the + weight vector will be obtained by evaluating the object at the + sample points of the passed FDataGrid object in the fit method. regularization_parameter (float): this parameter determines the amount of smoothing applied. Defaults to 0 penalty (Union[int, Iterable[float]): the coefficients that will be @@ -468,6 +470,8 @@ def fit(self, X: FDataGrid, y=None): differences = np.diff(X.sample_points[0]) differences = np.concatenate(((0,), differences, (0,))) self.weights = (differences[:-1] + differences[1:]) / 2 + elif callable(self.weights): + self.weights = self.weights(X.sample_points[0]) weights_matrix = np.diag(self.weights) From 5c336d9be6278b1dfde318540213f0313cd1ba2c Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Thu, 30 Apr 2020 15:50:04 +0200 Subject: [PATCH 471/624] small change --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 233295b25..b7e70bc78 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -472,6 +472,10 @@ def fit(self, X: FDataGrid, y=None): self.weights = (differences[:-1] + differences[1:]) / 2 elif callable(self.weights): self.weights = self.weights(X.sample_points[0]) + # if its a FDataGrid then we need to reduce the dimension to 1-D + # array + if isinstance(self.weights, FDataGrid): + self.weights = np.squeeze(self.weights.data_matrix) weights_matrix = np.diag(self.weights) From 765a59216f13e2797e0b87955cfaa85ab9d55787 Mon Sep 17 00:00:00 2001 From: hzzhyj Date: Fri, 1 May 2020 15:51:42 +0200 Subject: [PATCH 472/624] Update skfda/preprocessing/dim_reduction/projection/_fpca.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Carlos Ramos Carreño --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index b7e70bc78..aa23263c8 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -366,7 +366,7 @@ class FPCAGrid(FPCA): sample points of the passed FDataGrid object in the fit method. regularization_parameter (float): this parameter determines the amount of smoothing applied. Defaults to 0 - penalty (Union[int, Iterable[float]): the coefficients that will be + penalty (Union[int, Iterable[float]]): the coefficients that will be used to calculate the penalty matrix for regularization. If you input an integer then the derivative of that degree will be used to regularize the principal components. If you input a vector From 8665d2c701687f586b8b4930dfbbe06131c62d9d Mon Sep 17 00:00:00 2001 From: hzzhyj Date: Fri, 1 May 2020 15:51:59 +0200 Subject: [PATCH 473/624] Update skfda/preprocessing/dim_reduction/projection/_fpca.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Carlos Ramos Carreño --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index aa23263c8..0f8712929 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -359,7 +359,7 @@ class FPCAGrid(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - weights (numpy.array Union callable): the weights vector used for + weights (numpy.array or callable): the weights vector used for discrete integration. If none then the trapezoidal rule is used for computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the From e8bd6de4b0c764e4a21e2e164099e7942b054911 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 1 May 2020 15:57:11 +0200 Subject: [PATCH 474/624] add explained_variance --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 0f8712929..7bf262cb1 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -111,6 +111,8 @@ class FPCABasis(FPCA): basis representation. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. + explained_variance_ (array_like): The amount of variance explained by + each of the selected components. explained_variance_ratio_ (array_like): this contains the percentage of variance explained by each principal component. @@ -247,6 +249,7 @@ def fit(self, X: FDataBasis, y=None): # the singular values obtained using SVD are the squares of eigenvalues self.component_values_ = pca.singular_values_ ** 2 self.explained_variance_ratio_ = pca.explained_variance_ratio_ + self.explained_variance_ = pca.explained_variance_ self.components_ = X.copy(basis=self.components_basis, coefficients=component_coefficients) @@ -375,13 +378,16 @@ class FPCAGrid(FPCA): derivative. Attributes: - components_ (FDataBasis): this contains the principal components either - in a basis form. + components_ (FDataBasis): this contains the eigenvectors in a basis + form. component_values_ (array_like): this contains the values (eigenvalues) associated with the principal components. + explained_variance_ (array_like): The amount of variance explained by + each of the selected components. explained_variance_ratio_ (array_like): this contains the percentage of variance explained by each principal component. + Examples: In this example we apply discretized functional PCA with some simple data to illustrate the usage of this class. We initialize the @@ -506,6 +512,7 @@ def fit(self, X: FDataGrid, y=None): np.transpose(pca.components_)))) self.component_values_ = pca.singular_values_ ** 2 self.explained_variance_ratio_ = pca.explained_variance_ratio_ + self.explained_variance_ = pca.explained_variance_ return self From 02acce7b9359d62f647bc6d1942c204407627655 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 2 May 2020 21:47:11 +0200 Subject: [PATCH 475/624] remove redundant attribute --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 7bf262cb1..1e6ab80fe 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -109,8 +109,6 @@ class FPCABasis(FPCA): Attributes: components_ (FDataBasis): this contains the principal components in a basis representation. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. explained_variance_ (array_like): The amount of variance explained by each of the selected components. explained_variance_ratio_ (array_like): this contains the percentage of @@ -246,8 +244,6 @@ def fit(self, X: FDataBasis, y=None): component_coefficients = np.transpose(component_coefficients) - # the singular values obtained using SVD are the squares of eigenvalues - self.component_values_ = pca.singular_values_ ** 2 self.explained_variance_ratio_ = pca.explained_variance_ratio_ self.explained_variance_ = pca.explained_variance_ self.components_ = X.copy(basis=self.components_basis, @@ -380,8 +376,6 @@ class FPCAGrid(FPCA): Attributes: components_ (FDataBasis): this contains the eigenvectors in a basis form. - component_values_ (array_like): this contains the values (eigenvalues) - associated with the principal components. explained_variance_ (array_like): The amount of variance explained by each of the selected components. explained_variance_ratio_ (array_like): this contains the percentage of @@ -510,7 +504,6 @@ def fit(self, X: FDataGrid, y=None): self.components_ = X.copy(data_matrix=np.transpose( np.linalg.solve(np.sqrt(weights_matrix), np.transpose(pca.components_)))) - self.component_values_ = pca.singular_values_ ** 2 self.explained_variance_ratio_ = pca.explained_variance_ratio_ self.explained_variance_ = pca.explained_variance_ From 3a76586894f49ad1ecf85ac6686b3b1fd0ea351f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 2 May 2020 23:52:24 +0200 Subject: [PATCH 476/624] Improved regularization so that it accepts arbitrary linear operators. --- setup.py | 3 +- skfda/misc/__init__.py | 2 +- skfda/misc/_lfd.py | 220 ------------ skfda/misc/operators/__init__.py | 2 + .../_linear_differential_operator.py} | 324 +++++++++++------- skfda/misc/operators/_operators.py | 115 +++++++ skfda/misc/regularization/__init__.py | 6 +- .../_endpoints_difference_regularization.py | 45 --- .../misc/regularization/_l2_regularization.py | 13 - skfda/misc/regularization/_regularization.py | 66 ++-- .../regression}/_coefficients.py | 14 +- skfda/ml/regression/linear.py | 6 +- skfda/preprocessing/smoothing/_basis.py | 25 +- skfda/representation/basis/_fdatabasis.py | 2 +- ...y => test_linear_differential_operator.py} | 2 +- tests/test_regression.py | 18 +- tests/test_regularization.py | 34 +- tests/test_smoothing.py | 14 +- 18 files changed, 414 insertions(+), 497 deletions(-) delete mode 100644 skfda/misc/_lfd.py create mode 100644 skfda/misc/operators/__init__.py rename skfda/misc/{regularization/_linear_diff_op_regularization.py => operators/_linear_differential_operator.py} (55%) create mode 100644 skfda/misc/operators/_operators.py delete mode 100644 skfda/misc/regularization/_endpoints_difference_regularization.py delete mode 100644 skfda/misc/regularization/_l2_regularization.py rename skfda/{_utils => ml/regression}/_coefficients.py (86%) rename tests/{test_lfd.py => test_linear_differential_operator.py} (98%) diff --git a/setup.py b/setup.py index 3fbfcf477..0cae02189 100644 --- a/setup.py +++ b/setup.py @@ -86,7 +86,8 @@ 'scikit-datasets[cran]>=0.1.24', 'rdata', 'cython', - 'mpldatacursor'], + 'mpldatacursor', + 'multimethod>=1.2'], setup_requires=pytest_runner, tests_require=['pytest'], test_suite='tests', diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 4f805f977..099e3b17f 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,4 +1,4 @@ from . import covariances, kernels, metrics +from . import operators from . import regularization -from ._lfd import LinearDifferentialOperator from ._math import log, log2, log10, exp, sqrt, cumsum, inner_product diff --git a/skfda/misc/_lfd.py b/skfda/misc/_lfd.py deleted file mode 100644 index 30d269ec6..000000000 --- a/skfda/misc/_lfd.py +++ /dev/null @@ -1,220 +0,0 @@ -import numbers - -import scipy.linalg - -import numpy as np - -from .._utils import _same_domain - - -__author__ = "Pablo Pérez Manso" -__email__ = "92manso@gmail.com" - - -class LinearDifferentialOperator: - """Defines the structure of a linear differential operator function system - - .. math:: - Lx(t) = b_0(t) x(t) + b_1(t) x'(x) + - \\dots + b_{n-1}(t) d^{n-1}(x(t)) + b_n(t) d^n(x(t)) - - Attributes: - order (int): the order of the operator. It's the n coefficient in the - equation above. - - weights (list): A FDataBasis objects list of length order + 1 - - Examples: - - Create a linear differential operator that penalizes the second - derivative (acceleration) - - >>> from skfda.misc import LinearDifferentialOperator - >>> from skfda.representation.basis import (FDataBasis, - ... Monomial, Constant) - >>> - >>> LinearDifferentialOperator(2) - LinearDifferentialOperator( - weights=[ - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[0]], - ...), - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[0]], - ...), - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[1]], - ...)] - ) - - Create a linear differential operator that penalizes three times - the second derivative (acceleration) and twice the first (velocity). - - >>> LinearDifferentialOperator(weights=[0, 2, 3]) - LinearDifferentialOperator( - weights=[ - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[0]], - ...), - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[2]], - ...), - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[3]], - ...)] - ) - - Create a linear differential operator with non-constant weights. - - >>> constant = Constant() - >>> monomial = Monomial((0, 1), n_basis=3) - >>> fdlist = [FDataBasis(constant, [0]), - ... FDataBasis(constant, [0]), - ... FDataBasis(monomial, [1, 2, 3])] - >>> LinearDifferentialOperator(weights=fdlist) - LinearDifferentialOperator( - weights=[ - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[0]], - ...), - FDataBasis( - basis=Constant(domain_range=[array([0, 1])], n_basis=1), - coefficients=[[0]], - ...), - FDataBasis( - basis=Monomial(domain_range=[array([0, 1])], n_basis=3), - coefficients=[[1 2 3]], - ...)] - ) - - """ - - def __init__(self, order_or_weights=None, *, order=None, weights=None, - domain_range=None): - """Lfd Constructor. You have to provide one of the two first - parameters. It both are provided, it will raise an error. - If a positional argument is supplied it will be considered the - order if it is an integral type and the weights otherwise. - - Args: - order (int, optional): the order of the operator. It's the highest - derivative order of the operator - - weights (list, optional): A FDataBasis objects list of length - order + 1 items - - domain_range (tuple or list of tuples, optional): Definition - of the interval where the weight functions are - defined. If the functional weights are specified - and this is not, takes the domain range from them. - Otherwise, defaults to (0,1). - """ - - from ..representation.basis import FDataBasis, Constant - - num_args = sum( - [a is not None for a in [order_or_weights, order, weights]]) - - if num_args > 1: - raise ValueError("You have to provide the order or the weights, " - "not both") - - real_domain_range = (domain_range if domain_range is not None - else (0, 1)) - - if order_or_weights is not None: - if isinstance(order_or_weights, numbers.Integral): - order = order_or_weights - else: - weights = order_or_weights - - if order is None and weights is None: - self.weights = (FDataBasis(Constant(real_domain_range), 0),) - - elif weights is None: - if order < 0: - raise ValueError("Order should be an non-negative integer") - - self.weights = [ - FDataBasis(Constant(real_domain_range), - 0 if (i < order) else 1) - for i in range(order + 1)] - - else: - if len(weights) == 0: - raise ValueError("You have to provide one weight at least") - - if all(isinstance(n, numbers.Real) for n in weights): - self.weights = (FDataBasis(Constant(real_domain_range), - np.array(weights) - .reshape(-1, 1)).to_list()) - - elif all(isinstance(n, FDataBasis) for n in weights): - if all([_same_domain(weights[0], x) - and x.n_samples == 1 for x in weights]): - self.weights = weights - - real_domain_range = weights[0].domain_range - if (domain_range is not None - and real_domain_range != domain_range): - raise ValueError("The domain range provided for the " - "linear operator does not match the " - "domain range of the weights") - - else: - raise ValueError("FDataBasis objects in the list have " - "not the same domain_range") - - else: - raise ValueError("The elements of the list are neither " - "integers or FDataBasis objects") - - self.domain_range = real_domain_range - - def __repr__(self): - """Representation of Lfd object.""" - - bwtliststr = "" - for w in self.weights: - bwtliststr = bwtliststr + "\n" + repr(w) + "," - - return (f"{self.__class__.__name__}(" - f"\nweights=[{bwtliststr[:-1]}]" - f"\n)").replace('\n', '\n ') - - def __eq__(self, other): - """Equality of Lfd objects""" - return (self.weights == other.weights) - - def constant_weights(self): - """ - Return the scalar weights of the linear differential operator if they - are constant basis. - Otherwise, return None. - - This function is mostly useful for basis which want to override - the _penalty method in order to use an analytical expression - for constant weights. - """ - from ..representation.basis import Constant - - coefs = [w.coefficients[0, 0] if isinstance(w.basis, Constant) - else None - for w in self.weights] - - return np.array(coefs) if coefs.count(None) == 0 else None - - def __call__(self, f): - """Return the function that results of applying the operator.""" - def applied_lfd(t): - return sum(w(t) * f(t, derivative=i) - for i, w in enumerate(self.weights)) - - return applied_lfd diff --git a/skfda/misc/operators/__init__.py b/skfda/misc/operators/__init__.py new file mode 100644 index 000000000..98ba5cb8b --- /dev/null +++ b/skfda/misc/operators/__init__.py @@ -0,0 +1,2 @@ +from ._linear_differential_operator import LinearDifferentialOperator +from ._operators import Operator, gramian_matrix, gramian_matrix_optimization diff --git a/skfda/misc/regularization/_linear_diff_op_regularization.py b/skfda/misc/operators/_linear_differential_operator.py similarity index 55% rename from skfda/misc/regularization/_linear_diff_op_regularization.py rename to skfda/misc/operators/_linear_differential_operator.py index 1bf64514a..5cf0cfde4 100644 --- a/skfda/misc/regularization/_linear_diff_op_regularization.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -1,158 +1,237 @@ -from functools import singledispatch +import numbers from numpy import polyder, polyint, polymul, polyval -import scipy.integrate from scipy.interpolate import PPoly -from skfda._utils._coefficients import CoefficientInfoFDataBasis import numpy as np -from ..._utils._coefficients import CoefficientInfo -from ...representation.basis import Basis, Constant, Monomial, Fourier, BSpline -from .._lfd import LinearDifferentialOperator -from ._regularization import Regularization +from ..._utils import _same_domain +from ...representation.basis import Constant, Monomial, Fourier, BSpline +from ._operators import Operator, gramian_matrix_optimization + + +__author__ = "Pablo Pérez Manso" +__email__ = "92manso@gmail.com" + + +class LinearDifferentialOperator(Operator): + """Defines the structure of a linear differential operator function system + + .. math:: + Lx(t) = b_0(t) x(t) + b_1(t) x'(x) + + \\dots + b_{n-1}(t) d^{n-1}(x(t)) + b_n(t) d^n(x(t)) + + Attributes: + weights (list): A list of callables. + + Examples: + + Create a linear differential operator that penalizes the second + derivative (acceleration) + + >>> from skfda.misc.operators import LinearDifferentialOperator + >>> from skfda.representation.basis import (FDataBasis, + ... Monomial, Constant) + >>> + >>> LinearDifferentialOperator(2) + LinearDifferentialOperator( + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[1]], + ...)] + ) + + Create a linear differential operator that penalizes three times + the second derivative (acceleration) and twice the first (velocity). + + >>> LinearDifferentialOperator(weights=[0, 2, 3]) + LinearDifferentialOperator( + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[2]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[3]], + ...)] + ) + + Create a linear differential operator with non-constant weights. + + >>> constant = Constant() + >>> monomial = Monomial((0, 1), n_basis=3) + >>> fdlist = [FDataBasis(constant, [0]), + ... FDataBasis(constant, [0]), + ... FDataBasis(monomial, [1, 2, 3])] + >>> LinearDifferentialOperator(weights=fdlist) + LinearDifferentialOperator( + weights=[ + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Constant(domain_range=[array([0, 1])], n_basis=1), + coefficients=[[0]], + ...), + FDataBasis( + basis=Monomial(domain_range=[array([0, 1])], n_basis=3), + coefficients=[[1 2 3]], + ...)] + ) - -@singledispatch -def penalty_matrix_basis_opt(basis: Basis, - regularization): - """ - Return a penalty matrix given a basis. - - This method is a singledispatch method that provides an - efficient analytical implementation of the computation of the - penalty matrix if possible. """ - return NotImplemented + def __init__(self, order_or_weights=None, *, order=None, weights=None, + domain_range=None): + """Constructor. You have to provide either order or weights. + If both are provided, it will raise an error. + If a positional argument is supplied it will be considered the + order if it is an integral type and the weights otherwise. -@singledispatch -def penalty_matrix_coef_info(coef_info: CoefficientInfo, - regularization): - """ - Return a penalty matrix given the coefficient information. - - This method is a singledispatch method that provides an - implementation of the computation of the penalty matrix - for a particular coefficient type. - """ - return np.zeros((coef_info.shape[0], coef_info.shape[0])) - - -class LinearDifferentialOperatorRegularization(Regularization): - """ - Regularization using the integral of the square of a linear differential - operator. + Args: + order (int, optional): the order of the operator. It's the highest + derivative order of the operator - Args: - lfd (LinearDifferentialOperator, list or int): Linear - differential operator. If it is not a LinearDifferentialOperator - object, it will be converted to one. + weights (list, optional): A FDataBasis objects list of length + order + 1 items - """ + domain_range (tuple or list of tuples, optional): Definition + of the interval where the weight functions are + defined. If the functional weights are specified + and this is not, takes the domain range from them. + Otherwise, defaults to (0,1). + """ - def __init__(self, linear_diff_op=2): - self.linear_diff_op = linear_diff_op if ( - isinstance(linear_diff_op, LinearDifferentialOperator)) else ( - LinearDifferentialOperator(linear_diff_op)) + from ...representation.basis import FDataBasis - penalty_matrix_basis_opt = penalty_matrix_basis_opt - penalty_matrix_coef_info = penalty_matrix_coef_info + num_args = sum( + [a is not None for a in [order_or_weights, order, weights]]) - def penalty_matrix(self, coef_info): - return penalty_matrix_coef_info(coef_info, self) + if num_args > 1: + raise ValueError("You have to provide the order or the weights, " + "not both") - def penalty_matrix_basis_numerical(self, basis): - """Return a penalty matrix using a numerical approach. + real_domain_range = (domain_range if domain_range is not None + else (0, 1)) - Args: - basis (Basis): basis to compute the penalty for. + if order_or_weights is not None: + if isinstance(order_or_weights, numbers.Integral): + order = order_or_weights + else: + weights = order_or_weights - """ - indices = np.triu_indices(basis.n_basis) + if order is None and weights is None: + self.weights = (FDataBasis(Constant(real_domain_range), 0),) - def cross_product(x): - """Multiply the two lfds""" - res = self.linear_diff_op(basis)([x])[:, 0] + elif weights is None: + if order < 0: + raise ValueError("Order should be an non-negative integer") - return res[indices[0]] * res[indices[1]] + self.weights = [ + FDataBasis(Constant(real_domain_range), + 0 if (i < order) else 1) + for i in range(order + 1)] - # Range of first dimension - domain_range = basis.domain_range[0] + else: + if len(weights) == 0: + raise ValueError("You have to provide one weight at least") - penalty_matrix = np.empty((basis.n_basis, basis.n_basis)) + if all(isinstance(n, numbers.Real) for n in weights): + self.weights = (FDataBasis(Constant(real_domain_range), + np.array(weights) + .reshape(-1, 1)).to_list()) - # Obtain the integrals for the upper matrix - triang_vec = scipy.integrate.quad_vec( - cross_product, domain_range[0], domain_range[1])[0] + elif all(isinstance(n, FDataBasis) for n in weights): + if all([_same_domain(weights[0], x) + and x.n_samples == 1 for x in weights]): + self.weights = weights - # Set upper matrix - penalty_matrix[indices] = triang_vec + real_domain_range = weights[0].domain_range + if (domain_range is not None + and real_domain_range != domain_range): + raise ValueError("The domain range provided for the " + "linear operator does not match the " + "domain range of the weights") - # Set lower matrix - penalty_matrix[(indices[1], indices[0])] = triang_vec + else: + raise ValueError("FDataBasis objects in the list have " + "not the same domain_range") - return penalty_matrix + else: + raise ValueError("The elements of the list are neither " + "integers or FDataBasis objects") - def penalty_matrix_basis(self, basis): - r"""Return a penalty matrix given a basis. + self.domain_range = real_domain_range - The penalty matrix is defined as [RS05-5-6-2]_: + def __repr__(self): + """Representation of linear differential operator object.""" - .. math:: - R_{ij} = \int L\phi_i(s) L\phi_j(s) ds + bwtliststr = "" + for w in self.weights: + bwtliststr = bwtliststr + "\n" + repr(w) + "," - where :math:`\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis - functions and :math:`L` is a differential operator. + return (f"{self.__class__.__name__}(" + f"\nweights=[{bwtliststr[:-1]}]" + f"\n)").replace('\n', '\n ') - Args: - basis (Basis): basis to compute the penalty for. + def __eq__(self, other): + """Equality of linear differential operator objects""" + return (self.weights == other.weights) - Returns: - numpy.array: Penalty matrix. + def constant_weights(self): + """ + Return the scalar weights of the linear differential operator if they + are constant basis. + Otherwise, return None. - References: - .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the - roughness penalty. In *Functional Data Analysis* (pp. 106-107). - Springer. + This function is mostly useful for basis which want to override + the _penalty method in order to use an analytical expression + for constant weights. """ - matrix = penalty_matrix_basis_opt(basis, self) - - if matrix is NotImplemented: - return self.penalty_matrix_basis_numerical(basis) - else: - return matrix + coefs = [w.coefficients[0, 0] if isinstance(w.basis, Constant) + else None + for w in self.weights] -########################################### -# -# Implementations for each coefficient type -# -########################################### + return np.array(coefs) if coefs.count(None) == 0 else None + def __call__(self, f): + """Return the function that results of applying the operator.""" + def applied_linear_diff_op(t): + return sum(w(t) * f(t, derivative=i) + for i, w in enumerate(self.weights)) -@LinearDifferentialOperatorRegularization.penalty_matrix_coef_info.register( - CoefficientInfoFDataBasis) -def penalty_matrix_coef_info_fdatabasis( - coef_info: CoefficientInfoFDataBasis, - regularization: LinearDifferentialOperatorRegularization): - return regularization.penalty_matrix_basis(coef_info.basis) + return applied_linear_diff_op -########################################### +############################################################# # -# Optimized implementations for each basis. +# Optimized implementations of gramian matrix for each basis. # -########################################### +############################################################# -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( - Constant) +@gramian_matrix_optimization.register def constant_penalty_matrix_optimized( - basis: Constant, - regularization: LinearDifferentialOperatorRegularization): + linear_operator: LinearDifferentialOperator, + basis: Constant): - coefs = regularization.linear_diff_op.constant_weights() + coefs = linear_operator.constant_weights() if coefs is None: return NotImplemented @@ -213,13 +292,12 @@ def _monomial_evaluate_constant_linear_diff_op(basis, weights): return polynomials -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( - Monomial) +@gramian_matrix_optimization.register def monomial_penalty_matrix_optimized( - basis: Monomial, - regularization: LinearDifferentialOperatorRegularization): + linear_operator: LinearDifferentialOperator, + basis: Monomial): - weights = regularization.linear_diff_op.constant_weights() + weights = linear_operator.constant_weights() if weights is None: return NotImplemented @@ -329,13 +407,12 @@ def _fourier_penalty_matrix_optimized_orthonormal(basis, weights): return penalty_matrix -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( - Fourier) +@gramian_matrix_optimization.register def fourier_penalty_matrix_optimized( - basis: Fourier, - regularization: LinearDifferentialOperatorRegularization): + linear_operator: LinearDifferentialOperator, + basis: Fourier): - weights = regularization.linear_diff_op.constant_weights() + weights = linear_operator.constant_weights() if weights is None: return NotImplemented @@ -347,13 +424,12 @@ def fourier_penalty_matrix_optimized( return _fourier_penalty_matrix_optimized_orthonormal(basis, weights) -@LinearDifferentialOperatorRegularization.penalty_matrix_basis_opt.register( - BSpline) +@gramian_matrix_optimization.register def bspline_penalty_matrix_optimized( - basis: BSpline, - regularization: LinearDifferentialOperatorRegularization): + linear_operator: LinearDifferentialOperator, + basis: BSpline): - coefs = regularization.linear_diff_op.constant_weights() + coefs = linear_operator.constant_weights() if coefs is None: return NotImplemented diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py new file mode 100644 index 000000000..0f5669867 --- /dev/null +++ b/skfda/misc/operators/_operators.py @@ -0,0 +1,115 @@ +import abc + +import multimethod +import scipy.integrate + +import numpy as np + + +class Operator(abc.ABC): + """ + Abstract class for operators (functions whose domain are functions). + + """ + + @abc.abstractmethod + def __call__(self, vector): + pass + + +@multimethod.multidispatch +def gramian_matrix_optimization(linear_operator, basis): + r""" + Generic function that can be subclassed for different combinations of + operator and basis in order to provide a more efficient implementation + for the gramian matrix. + """ + return NotImplemented + + +def get_n_basis(basis): + n_basis = getattr(basis, "n_basis", None) + if n_basis is None: + n_basis = len(basis) + + return n_basis + + +def compute_triang_functional(evaluated_basis, + indices, + basis): + def cross_product(x): + """Multiply the two evaluations.""" + res = evaluated_basis([x])[:, 0] + + return res[indices[0]] * res[indices[1]] + + # Range of first dimension + domain_range = basis.domain_range[0] + + # Obtain the integrals for the upper matrix + return scipy.integrate.quad_vec( + cross_product, domain_range[0], domain_range[1])[0] + + +def compute_triang_multivariate(evaluated_basis, + indices, + basis): + + cross_product = evaluated_basis[indices[0]] * evaluated_basis[indices[1]] + + # Obtain the integrals for the upper matrix + return np.sum(cross_product, axis=-1) + + +def gramian_matrix_numerical(linear_operator, basis): + r""" + Return the gramian matrix given a basis, computed numerically. + + This method should work for every linear operator. + + """ + n_basis = get_n_basis(basis) + + indices = np.triu_indices(n_basis) + + evaluated_basis = linear_operator(basis) + compute_triang = (compute_triang_functional if callable( + evaluated_basis) else compute_triang_multivariate) + triang_vec = compute_triang(evaluated_basis, indices, basis) + + matrix = np.empty((n_basis, n_basis)) + + # Set upper matrix + matrix[indices] = triang_vec + + # Set lower matrix + matrix[(indices[1], indices[0])] = triang_vec + + return matrix + + +def gramian_matrix(linear_operator, basis): + r""" + Return the gramian matrix given a basis. + + The gramian operator of a linear operator :math:`\Gamma` is + + .. math:: + G = \Gamma*\Gamma + + This method evaluates that gramian operator in a given basis, + which is necessary for performing Tikhonov regularization, + among other things. + + It tries to use an optimized implementation if one is available, + falling back to a numerical computation otherwise. + + """ + + # Try to use a more efficient implementation + matrix = gramian_matrix_optimization(linear_operator, basis) + if matrix is not NotImplemented: + return matrix + + return gramian_matrix_numerical(linear_operator, basis) diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py index 2bc2b5f45..ec58432fd 100644 --- a/skfda/misc/regularization/__init__.py +++ b/skfda/misc/regularization/__init__.py @@ -1,4 +1,2 @@ -from ._endpoints_difference_regularization import EndpointsDifferenceRegularization -from ._l2_regularization import L2Regularization -from ._linear_diff_op_regularization import LinearDifferentialOperatorRegularization -from ._regularization import Regularization, compute_penalty_matrix +from ._regularization import (TikhonovRegularization, + compute_penalty_matrix) diff --git a/skfda/misc/regularization/_endpoints_difference_regularization.py b/skfda/misc/regularization/_endpoints_difference_regularization.py deleted file mode 100644 index bf141cbdc..000000000 --- a/skfda/misc/regularization/_endpoints_difference_regularization.py +++ /dev/null @@ -1,45 +0,0 @@ -from functools import singledispatch - -import numpy as np - -from ..._utils._coefficients import CoefficientInfo, CoefficientInfoFDataBasis -from ._regularization import Regularization - - -@singledispatch -def penalty_matrix_coef_info(coef_info: CoefficientInfo, - regularization): - """ - Return a penalty matrix given the coefficient information. - - This method is a singledispatch method that provides an - implementation of the computation of the penalty matrix - for a particular coefficient type. - """ - return np.zeros((coef_info.shape[0], coef_info.shape[0])) - - -class EndpointsDifferenceRegularization(Regularization): - """ - Regularization penalizing the difference of the functions - endpoints. - - """ - - penalty_matrix_coef_info = penalty_matrix_coef_info - - def penalty_matrix(self, coef_info): - return penalty_matrix_coef_info(coef_info, self) - - -@EndpointsDifferenceRegularization.penalty_matrix_coef_info.register( - CoefficientInfoFDataBasis) -def penalty_matrix_coef_info_fdatabasis( - coef_info: CoefficientInfoFDataBasis, - regularization: EndpointsDifferenceRegularization): - - evaluate_first = coef_info.basis(coef_info.basis.domain_range[0][0]) - evaluate_last = coef_info.basis(coef_info.basis.domain_range[0][1]) - evaluate_diff = evaluate_last - evaluate_first - - return evaluate_diff @ evaluate_diff.T diff --git a/skfda/misc/regularization/_l2_regularization.py b/skfda/misc/regularization/_l2_regularization.py deleted file mode 100644 index ea7b268f6..000000000 --- a/skfda/misc/regularization/_l2_regularization.py +++ /dev/null @@ -1,13 +0,0 @@ -import numpy as np - -from ._regularization import Regularization - - -class L2Regularization(Regularization): - """ - Regularization using a sum of coefficient squares. - - """ - - def penalty_matrix(self, coef_info): - return np.identity(coef_info.shape[0]) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 9b61dca2a..6386219cf 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -1,27 +1,44 @@ -import abc from collections.abc import Iterable import itertools +from skfda.misc.operators import gramian_matrix import scipy.linalg +from sklearn.base import BaseEstimator -from ..._utils._coefficients import CoefficientInfo +import numpy as np +from ..operators._operators import get_n_basis -class Regularization(abc.ABC): - """ - Abstract base class for different kinds of regularization. + +class TikhonovRegularization(BaseEstimator): + r""" + Implements Tikhonov regularization. + + The penalization term in this type of regularization is + + .. math:: + \| \Gamma x \|_2^2 + + where :math:`\Gamma``is the so called Tikhonov operator + (matrix for finite vectors). + + Parameters: + operator: linear operator used for regularization. """ - @abc.abstractmethod - def penalty_matrix(self, coef_info): - r"""Return a penalty matrix given the coefficient information. + def __init__(self, linear_operator): + self.linear_operator = linear_operator + + def penalty_matrix(self, basis): + r""" + Return a penalty matrix for ordinary least squares. """ - pass + return gramian_matrix(self.linear_operator, basis) -def compute_penalty_matrix(coef_info, regularization_parameter, +def compute_penalty_matrix(basis_iterable, regularization_parameter, regularization, penalty_matrix): """ Computes the regularization matrix for a linear differential operator. @@ -41,25 +58,18 @@ def compute_penalty_matrix(coef_info, regularization_parameter, f"{regularization_parameter} != 0 " "and no regularization is specified") - if isinstance(coef_info, Iterable): - - if not isinstance(regularization, Iterable): - regularization = (regularization,) - - if not isinstance(regularization_parameter, Iterable): - regularization_parameter = itertools.repeat( - regularization_parameter) - - penalty_blocks = [ - 0 if r is None else - a * r.penalty_matrix(c) - for c, r, a in zip(coef_info, regularization, - regularization_parameter)] - penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) + if not isinstance(regularization, Iterable): + regularization = (regularization,) - else: + if not isinstance(regularization_parameter, Iterable): + regularization_parameter = itertools.repeat( + regularization_parameter) - penalty_matrix = regularization.penalty_matrix(coef_info) - penalty_matrix *= regularization_parameter + penalty_blocks = [ + np.zeros((get_n_basis(b), get_n_basis(b))) if r is None else + a * r.penalty_matrix(b) + for b, r, a in zip(basis_iterable, regularization, + regularization_parameter)] + penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) return penalty_matrix diff --git a/skfda/_utils/_coefficients.py b/skfda/ml/regression/_coefficients.py similarity index 86% rename from skfda/_utils/_coefficients.py rename to skfda/ml/regression/_coefficients.py index e5eff3431..f5156252e 100644 --- a/skfda/_utils/_coefficients.py +++ b/skfda/ml/regression/_coefficients.py @@ -2,7 +2,7 @@ import numpy as np -from ..representation.basis import Basis, FDataBasis +from ...representation.basis import Basis, FDataBasis class CoefficientInfo(): @@ -18,9 +18,8 @@ class CoefficientInfo(): """ - def __init__(self, coef_type, shape): - self.coef_type = coef_type - self.shape = shape + def __init__(self, basis): + self.basis = basis def regression_matrix(self, X, y): """ @@ -50,16 +49,11 @@ def coefficient_info_from_covariate(X, y, **kwargs) -> CoefficientInfo: Make a coefficient info object from a covariate. """ - return CoefficientInfo(type(X), shape=X.shape[1:]) + return CoefficientInfo(basis=np.identity(X.shape[1], dtype=X.dtype)) class CoefficientInfoFDataBasis(CoefficientInfo): - def __init__(self, basis): - super().__init__(coef_type=FDataBasis, shape=(basis.n_basis,)) - - self.basis = basis - def regression_matrix(self, X, y): xcoef = X.coefficients inner_basis = X.basis.inner_product(self.basis) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 8a1a364ab..4271744b9 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -1,15 +1,15 @@ from collections.abc import Iterable import itertools +from skfda.representation import FData import warnings -from skfda.representation import FData from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted import numpy as np -from ..._utils._coefficients import coefficient_info_from_covariate from ...misc.regularization import compute_penalty_matrix +from ._coefficients import coefficient_info_from_covariate class MultivariateLinearRegression(BaseEstimator, RegressorMixin): @@ -168,7 +168,7 @@ def fit(self, X, y=None, sample_weight=None): y = y * np.sqrt(sample_weight) penalty_matrix = compute_penalty_matrix( - coef_info=coef_info, + basis_iterable=(c.basis for c in coef_info), regularization_parameter=regularization_parameter, regularization=regularization, penalty_matrix=self.penalty_matrix) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 15d480425..3af4d48c2 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -4,7 +4,6 @@ This module contains the class for the basis smoothing. """ -import collections from enum import Enum from typing import Union, Iterable @@ -14,7 +13,6 @@ from ... import FDataBasis from ... import FDataGrid -from ..._utils._coefficients import CoefficientInfoFDataBasis from ._linear import _LinearSmoother, _check_r_to_r @@ -249,17 +247,19 @@ class BasisSmoother(_LinearSmoother): [ 0.43, 0.14, -0.14, 0.14, 0.43]]) If the smoothing parameter is set to something else than zero, we can - penalize approximations that are not smooth enough using a linear - differential operator: + penalize approximations that are not smooth enough using some kind + of regularization: - >>> from skfda.misc.regularization import ( - ... LinearDifferentialOperatorRegularization as LDiffReg) + >>> from skfda.misc.regularization import TikhonovRegularization + >>> from skfda.misc.operators import LinearDifferentialOperator + >>> >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='cholesky', ... smoothing_parameter=1, - ... regularization=LDiffReg([0.1, 0.2]), + ... regularization=TikhonovRegularization( + ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) @@ -270,19 +270,20 @@ class BasisSmoother(_LinearSmoother): >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', ... smoothing_parameter=1, - ... regularization=LDiffReg([0.1, 0.2]), + ... regularization=TikhonovRegularization( + ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) array([[ 2.04, 0.51, 0.55]]) - >>> from skfda.misc import LinearDifferentialOperator >>> fd = skfda.FDataGrid(data_matrix=x, sample_points=t) >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', ... smoothing_parameter=1, - ... regularization=LDiffReg([0.1, 0.2]), + ... regularization=TikhonovRegularization( + ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) >>> fd_basis = smoother.fit_transform(fd) >>> fd_basis.coefficients.round(2) @@ -348,7 +349,7 @@ def _coef_matrix(self, input_points): inv = basis_values_input.T @ weight_matrix @ basis_values_input penalty_matrix = compute_penalty_matrix( - coef_info=CoefficientInfoFDataBasis(self.basis), + basis_iterable=(self.basis,), regularization_parameter=self.smoothing_parameter, regularization=self.regularization, penalty_matrix=self.penalty_matrix) @@ -410,7 +411,7 @@ def fit_transform(self, X: FDataGrid, y=None): else self.input_points_) penalty_matrix = compute_penalty_matrix( - coef_info=CoefficientInfoFDataBasis(self.basis), + basis_iterable=(self.basis,), regularization_parameter=self.smoothing_parameter, regularization=self.regularization, penalty_matrix=self.penalty_matrix) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 8387016ae..80c6ff7ee 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -668,7 +668,7 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, numpy.array: Inner Product matrix. """ - from ...misc import LinearDifferentialOperator + from ...misc.operators import LinearDifferentialOperator from ..basis import Basis if not _same_domain(self.domain_range, other.domain_range): diff --git a/tests/test_lfd.py b/tests/test_linear_differential_operator.py similarity index 98% rename from tests/test_lfd.py rename to tests/test_linear_differential_operator.py index 77de990cb..46fea7ba8 100644 --- a/tests/test_lfd.py +++ b/tests/test_linear_differential_operator.py @@ -1,4 +1,4 @@ -from skfda.misc import LinearDifferentialOperator +from skfda.misc.operators import LinearDifferentialOperator from skfda.representation.basis import FDataBasis, Constant, Monomial import unittest diff --git a/tests/test_regression.py b/tests/test_regression.py index 3d9da867c..6e5fbe189 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,10 +1,9 @@ -import unittest - -from skfda.misc.regularization import L2Regularization -from skfda.misc.regularization import LinearDifferentialOperatorRegularization +from skfda.misc.operators import LinearDifferentialOperator +from skfda.misc.regularization import TikhonovRegularization from skfda.ml.regression import MultivariateLinearRegression from skfda.representation.basis import (FDataBasis, Monomial, Fourier, BSpline) +import unittest import numpy as np @@ -127,8 +126,9 @@ def test_regression_mixed_regularization(self): scalar = MultivariateLinearRegression( regularization_parameter=1, - regularization=[L2Regularization(), - LinearDifferentialOperatorRegularization(2)]) + regularization=[TikhonovRegularization(lambda x: x), + TikhonovRegularization( + LinearDifferentialOperator(2))]) scalar.fit(X, y) np.testing.assert_allclose(scalar.intercept_, @@ -175,7 +175,8 @@ def test_regression_regularization(self): scalar = MultivariateLinearRegression( coef_basis=[beta_basis], regularization_parameter=1, - regularization=LinearDifferentialOperatorRegularization()) + regularization=TikhonovRegularization( + LinearDifferentialOperator(2))) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients, atol=1e-3) @@ -211,7 +212,8 @@ def test_regression_regularization(self): scalar_reg = MultivariateLinearRegression( regularization_parameter=1, - regularization=LinearDifferentialOperatorRegularization()) + regularization=TikhonovRegularization( + LinearDifferentialOperator(2))) scalar_reg.fit(x_fd, y) np.testing.assert_allclose(scalar_reg.coef_[0].coefficients, beta_fd_reg.coefficients, atol=0.001) diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 8766484b7..2951d4646 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -1,14 +1,14 @@ -import unittest -import warnings - import skfda -from skfda.misc.regularization import (LinearDifferentialOperatorRegularization, - EndpointsDifferenceRegularization, - L2Regularization) -from skfda.misc.regularization._linear_diff_op_regularization import ( +from skfda.misc.operators import LinearDifferentialOperator, gramian_matrix +from skfda.misc.operators._linear_differential_operator import ( _monomial_evaluate_constant_linear_diff_op) +from skfda.misc.operators._operators import gramian_matrix_numerical +from skfda.misc.regularization import TikhonovRegularization from skfda.ml.regression.linear import MultivariateLinearRegression from skfda.representation.basis import Constant, Monomial, BSpline, Fourier +import unittest +import warnings + from sklearn.datasets import make_regression from sklearn.linear_model import Ridge from sklearn.model_selection._split import train_test_split @@ -22,12 +22,10 @@ class TestLinearDifferentialOperatorRegularization(unittest.TestCase): def _test_penalty(self, basis, linear_diff_op, atol=0, result=None): - regularization = LinearDifferentialOperatorRegularization( - linear_diff_op) + operator = LinearDifferentialOperator(linear_diff_op) - penalty = regularization.penalty_matrix_basis(basis) - numerical_penalty = regularization.penalty_matrix_basis_numerical( - basis) + penalty = gramian_matrix(operator, basis) + numerical_penalty = gramian_matrix_numerical(operator, basis) np.testing.assert_allclose( penalty, @@ -160,11 +158,9 @@ def test_bspline_penalty_special_case(self): [-288., 1008., -2304., 3600., -2016.], [0., -288., 1152., -2016., 1152.]]) - regularization = LinearDifferentialOperatorRegularization( - basis.order - 1) - penalty = regularization.penalty_matrix_basis(basis) - numerical_penalty = regularization.penalty_matrix_basis_numerical( - basis) + operator = LinearDifferentialOperator(basis.order - 1) + penalty = gramian_matrix(operator, basis) + numerical_penalty = gramian_matrix_numerical(operator, basis) np.testing.assert_allclose( penalty, @@ -188,7 +184,7 @@ def test_basis_conversion(self): smoother = skfda.preprocessing.smoothing.BasisSmoother( basis=skfda.representation.basis.BSpline( n_basis=10, domain_range=fd.domain_range), - regularization=EndpointsDifferenceRegularization(), + regularization=TikhonovRegularization(lambda x: x(1) - x(0)), smoothing_parameter=10000) fd_basis = smoother.fit_transform(fd) @@ -222,7 +218,7 @@ def ignore_scalar_warning(): sklearn_l2 = Ridge(alpha=regularization_parameter) skfda_l2 = MultivariateLinearRegression( - regularization=L2Regularization(), + regularization=TikhonovRegularization(lambda x: x), regularization_parameter=regularization_parameter) sklearn_l2.fit(X_train, y_train) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index 29ef1aab3..50d861b82 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -1,11 +1,11 @@ -import unittest - import skfda from skfda._utils import _check_estimator -from skfda.misc import LinearDifferentialOperator -from skfda.misc.regularization import LinearDifferentialOperatorRegularization +from skfda.misc.operators import LinearDifferentialOperator +from skfda.misc.regularization import TikhonovRegularization from skfda.representation.basis import BSpline, Monomial from skfda.representation.grid import FDataGrid +import unittest + import sklearn import numpy as np @@ -82,7 +82,7 @@ def test_cholesky(self): smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=10, - regularization=LinearDifferentialOperatorRegularization( + regularization=TikhonovRegularization( LinearDifferentialOperator(2)), method='cholesky', return_basis=True) @@ -100,7 +100,7 @@ def test_qr(self): smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=10, - regularization=LinearDifferentialOperatorRegularization( + regularization=TikhonovRegularization( LinearDifferentialOperator(2)), method='qr', return_basis=True) @@ -120,7 +120,7 @@ def test_monomial_smoothing(self): smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=1, - regularization=LinearDifferentialOperatorRegularization( + regularization=TikhonovRegularization( LinearDifferentialOperator(2)), return_basis=True) fd_basis = smoother.fit_transform(fd) From 2e9d3d205e6383da428fde053455afce1c83f1d0 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 3 May 2020 01:41:26 +0200 Subject: [PATCH 477/624] Fixes tests. --- readthedocs-requirements.txt | 3 +- requirements.txt | 2 +- .../dim_reduction/projection/_fpca.py | 31 +++++++++++-------- skfda/representation/basis/_basis.py | 4 +-- 4 files changed, 23 insertions(+), 17 deletions(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 7e07d788a..291f6e430 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -9,4 +9,5 @@ sphinx-gallery pillow matplotlib mpldatacursor -setuptools>=41.2 \ No newline at end of file +setuptools>=41.2 +multimethod>=1.2 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 074553ffe..faec8816d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,4 @@ setuptools Cython sklearn mpldatacursor - +multimethod>=1.2 diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 1e6ab80fe..6c71f9652 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,13 +1,16 @@ """Functional Principal Component Analysis Module.""" -import numpy as np -import skfda from abc import ABC, abstractmethod +import skfda from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid + +from scipy.linalg import solve_triangular from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from scipy.linalg import solve_triangular + +import numpy as np + __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -239,7 +242,8 @@ def fit(self, X: FDataBasis, y=None): # we choose solve to obtain the component coefficients for the # same reason: it is faster and more efficient component_coefficients = solve_triangular(np.transpose(l_matrix), - np.transpose(pca.components_), + np.transpose( + pca.components_), lower=False) component_coefficients = np.transpose(component_coefficients) @@ -338,13 +342,13 @@ def regularization_penalty_matrix(sample_points, penalty): for i in range(1, len(penalty)): if i > 1: aux_penalty_1 = (aux_penalty_2[:(n_points - i), - :(n_points - i + 1)] + :(n_points - i + 1)] @ aux_penalty_1) # applying the differential operator, as in each step the # derivative degree increases by 1. penalty_matrix = (penalty_matrix + penalty[i] * (np.transpose( - aux_penalty_1) @ aux_penalty_1)) + aux_penalty_1) @ aux_penalty_1)) return penalty_matrix @@ -407,16 +411,16 @@ def __init__(self, self.weights = weights def fit(self, X: FDataGrid, y=None): - """Computes the n_components first principal components and saves them. + r"""Computes the n_components first principal components and saves them. The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the referenced book, chapter 8. In summary, we are performing standard multivariate PCA over - :math:`\\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\\mathbf{X}` is the data - matrix and :math:`\\mathbf{W}` is the weight matrix (this matrix + :math:`\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\mathbf{X}` is the data + matrix and :math:`\mathbf{W}` is the weight matrix (this matrix defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. @@ -459,7 +463,8 @@ def fit(self, X: FDataGrid, y=None): meanfd = X.mean() # consider moving these lines to FDataGrid as a centering function # subtract from each row the mean coefficient matrix - fd_data -= meanfd.data_matrix.reshape(meanfd.data_matrix.shape[:-1]) + fd_data -= meanfd.data_matrix.reshape( + meanfd.data_matrix.shape[:-1]) # establish weights for each point of discretization if not self.weights: @@ -491,11 +496,11 @@ def fit(self, X: FDataGrid, y=None): aux_matrix = (np.diag(np.ones(n_points_discretization)) + self.regularization_parameter * penalty_matrix) # we use solve for better stability, P=aux matrix, X=data_matrix - # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives (P^T)^-1*X^T + # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives + # (P^T)^-1*X^T fd_data = np.transpose(np.linalg.solve(np.transpose(aux_matrix), np.transpose(fd_data))) - # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index c3afaf5f3..a0969101d 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -252,8 +252,8 @@ def gram_matrix(self): return gram - def inner_product(self, other): - return self.to_basis().inner_product(other) + def inner_product(self, other): + return self.to_basis().inner_product(other) def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 From 6e092183534c6f3b3f1025b8b46d3aeadd0429f2 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 4 May 2020 17:33:42 +0200 Subject: [PATCH 478/624] tests for regularization --- tests/test_fpca.py | 141 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 97d2a0fe7..5050bbf75 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -57,6 +57,43 @@ def test_basis_fpca_fit_result(self): basis = Fourier(n_basis=9, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) + fpca = FPCABasis(n_components=n_components, + regularization_parameter=1e5) + fpca.fit(fd_basis) + + # results obtained using Ramsay's R package + results = [[0.92407552, 0.13544888, 0.35399023, 0.00805966, -0.02148108, + -0.01709549, -0.00208469, -0.00297439, -0.00308224], + [-0.33314436, -0.05116842, 0.89443418, 0.14673902, + 0.21559073, + 0.02046924, 0.02203431, -0.00787185, 0.00247492], + [-0.14241092, 0.92131899, 0.00514715, 0.23391411, + -0.19497613, + 0.09800817, 0.01754439, -0.00205874, 0.01438185]] + results = np.array(results) + + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components_.coefficients[i][0]) != np.sign( + results[i][0]): + results[i, :] *= -1 + np.testing.assert_allclose(fpca.components_.coefficients, results, + atol=1e-7) + + def test_basis_fpca_regularization_fit_result(self): + + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather()['data'].coordinates[0] + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) + + # initialize basis data + basis = Fourier(n_basis=9, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + fpca = FPCABasis(n_components=n_components) fpca.fit(fd_basis) @@ -78,6 +115,7 @@ def test_basis_fpca_fit_result(self): np.testing.assert_allclose(fpca.components_.coefficients, results, atol=1e-7) + def test_grid_fpca_fit_result(self): n_components = 1 @@ -177,6 +215,109 @@ def test_grid_fpca_fit_result(self): results, rtol=1e-6) + def test_grid_fpca_regularization_fit_result(self): + + n_components = 1 + + fd_data = fetch_weather()['data'].coordinates[0] + + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) + + fpca = FPCAGrid(n_components=n_components, weights=[1] * 365, + regularization_parameter=1) + fpca.fit(fd_data) + + # results obtained using fda.usc for the first component + results = [ + [-0.06961236, -0.07027042, -0.07090496, -0.07138247, -0.07162215, + -0.07202264, -0.07264893, -0.07279174, -0.07274672, -0.07300075, + -0.07365471, -0.07489002, -0.07617455, -0.07658708, -0.07551923, + -0.07375128, -0.0723776, -0.07138373, -0.07080555, -0.07111745, + -0.0721514, -0.07395427, -0.07558341, -0.07650959, -0.0766541, + -0.07641352, -0.07660864, -0.07669081, -0.0765396, -0.07640671, + -0.07634668, -0.07626304, -0.07603638, -0.07549114, -0.07410347, + -0.07181791, -0.06955356, -0.06824034, -0.06834077, -0.06944125, + -0.07133598, -0.07341109, -0.07471501, -0.07568844, -0.07631904, + -0.07647264, -0.07629453, -0.07598431, -0.07628157, -0.07654062, + -0.07616026, -0.07527189, -0.07426683, -0.07267961, -0.07079998, + -0.06927394, -0.068412, -0.06838534, -0.06888439, -0.0695309, + -0.07005508, -0.07066637, -0.07167196, -0.07266978, -0.07275299, + -0.07235183, -0.07207819, -0.07159814, -0.07077697, -0.06977026, + -0.0691952, -0.06965756, -0.07058327, -0.07075751, -0.07025415, + -0.06954233, -0.06899785, -0.06891026, -0.06887079, -0.06862183, + -0.06830082, -0.06777765, -0.06700202, -0.06639394, -0.06582435, + -0.06514987, -0.06467236, -0.06425272, -0.06359187, -0.062922, + -0.06300068, -0.06325494, -0.06316979, -0.06296254, -0.06246343, + -0.06136836, -0.0600936, -0.05910688, -0.05840872, -0.0576547, + -0.05655684, -0.05546518, -0.05484433, -0.05465746, -0.05449286, + -0.05397004, -0.05300742, -0.05196686, -0.05133129, -0.05064617, + -0.04973418, -0.04855687, -0.04714356, -0.04588103, -0.04547284, + -0.04571493, -0.04580704, -0.04523509, -0.04457293, -0.04405309, + -0.04338468, -0.04243512, -0.04137278, -0.04047946, -0.03984531, + -0.03931376, -0.0388847, -0.03888507, -0.03908662, -0.03877577, + -0.03830952, -0.03802713, -0.03773521, -0.03752388, -0.03743759, + -0.03714113, -0.03668387, -0.0363703, -0.03642288, -0.03633051, + -0.03574618, -0.03486536, -0.03357797, -0.03209969, -0.0306837, + -0.02963987, -0.029102, -0.0291513, -0.02932013, -0.02912619, + -0.02869407, -0.02801974, -0.02732363, -0.02690451, -0.02676622, + -0.0267323, -0.02664896, -0.02661708, -0.02637166, -0.02577496, + -0.02490428, -0.02410813, -0.02340367, -0.02283356, -0.02246305, + -0.0224229, -0.0225435, -0.02295603, -0.02324663, -0.02310005, + -0.02266893, -0.02221522, -0.02168056, -0.02129419, -0.02064909, + -0.02007801, -0.01979083, -0.01979541, -0.01978879, -0.01954269, + -0.0191623, -0.01879572, -0.01849678, -0.01810297, -0.01769666, + -0.01753802, -0.01794351, -0.01871307, -0.01930005, -0.01933, + -0.01901017, -0.01873486, -0.01861838, -0.01870777, -0.01879, + -0.01904219, -0.01945078, -0.0200607, -0.02076936, -0.02100213, + -0.02071439, -0.02052113, -0.02076313, -0.02128468, -0.02175631, + -0.02206387, -0.02201054, -0.02172142, -0.02143092, -0.02133647, + -0.02144956, -0.02176286, -0.02212579, -0.02243861, -0.02278316, + -0.02304113, -0.02313356, -0.02349275, -0.02417028, -0.0245954, + -0.0244062, -0.02388557, -0.02374682, -0.02401071, -0.02431126, + -0.02433125, -0.02427656, -0.02430442, -0.02424977, -0.02401619, + -0.02402294, -0.02415424, -0.02413262, -0.02404076, -0.02397651, + -0.0243893, -0.0253322, -0.02664395, -0.0278802, -0.02877936, + -0.02927182, -0.02937318, -0.02926277, -0.02931632, -0.02957945, + -0.02982133, -0.03023224, -0.03060406, -0.03066011, -0.03070932, + -0.03116429, -0.03179009, -0.03198094, -0.03149462, -0.03082037, + -0.03041594, -0.0303307, -0.03028465, -0.03052841, -0.0311837, + -0.03199307, -0.03262025, -0.03345083, -0.03442665, -0.03521313, + -0.0356433, -0.03606037, -0.03677406, -0.03735165, -0.03746578, + -0.03744154, -0.03752143, -0.03780898, -0.03837639, -0.03903232, + -0.03911629, -0.03857567, -0.03816592, -0.03819285, -0.03818405, + -0.03801684, -0.03788493, -0.03823232, -0.03906142, -0.04023251, + -0.04112434, -0.04188011, -0.04254759, -0.043, -0.04340181, + -0.04412687, -0.04484482, -0.04577669, -0.04700832, -0.04781373, + -0.04842662, -0.04923723, -0.05007637, -0.05037817, -0.05009794, + -0.04994083, -0.05012712, -0.05094001, -0.05216065, -0.05350458, + -0.05469781, -0.05566309, -0.05641011, -0.05688106, -0.05730818, + -0.05759156, -0.05763771, -0.05760073, -0.05766117, -0.05794587, + -0.05816696, -0.0584046, -0.05905105, -0.06014331, -0.06142231, + -0.06270788, -0.06388225, -0.06426245, -0.06386721, -0.0634656, + -0.06358049, -0.06442514, -0.06570047, -0.06694328, -0.0682621, + -0.06897846, -0.06896583, -0.06854621, -0.06797142, -0.06763755, + -0.06784024, -0.06844314, -0.06918567, -0.07021928, -0.07148473, + -0.07232504, -0.07272276, -0.07287021, -0.07289836, -0.07271531, + -0.07239956, -0.07214086, -0.07170078, -0.07081195, -0.06955202, + -0.06825156, -0.06690167, -0.06617102, -0.06683291, -0.06887539, + -0.07089424, -0.07174837, -0.07150888, -0.07070378, -0.06960066, + -0.06842496, -0.06777666, -0.06728403, -0.06681262, -0.06679066]] + + results = np.array(results) + + # compare results obtained using this library. There are slight + # variations due to the fact that we are in two different packages + for i in range(n_components): + if np.sign(fpca.components_.data_matrix[i][0]) != np.sign( + results[i][0]): + results[i, :] *= -1 + np.testing.assert_allclose( + fpca.components_.data_matrix.reshape( + fpca.components_.data_matrix.shape[:-1]), + results, + rtol=1e-6) + if __name__ == '__main__': unittest.main() From 20414bdcb99aa6abcffda3a4b1fde755b7b384a2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 4 May 2020 22:00:41 +0200 Subject: [PATCH 479/624] Including slow test label, numeric integral in norm_lp, and changing concatenate_samples to skfda namespace. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- conftest.py | 20 +++++++++ examples/plot_oneway.py | 4 +- examples/plot_oneway_synthetic.py | 23 +++++----- skfda/__init__.py | 1 + skfda/inference/anova/anova_oneway.py | 32 +++++++++----- skfda/misc/metrics.py | 24 ++-------- skfda/representation/_functional_data.py | 56 ++++++++++++------------ tests/test_basis.py | 3 +- tests/test_grid.py | 4 +- tests/test_oneway_anova.py | 2 + 10 files changed, 93 insertions(+), 76 deletions(-) diff --git a/conftest.py b/conftest.py index 889066c3a..3bf75b22f 100644 --- a/conftest.py +++ b/conftest.py @@ -10,3 +10,23 @@ collect_ignore = ['setup.py'] pytest.register_assert_rewrite("skfda") + + +def pytest_addoption(parser): + parser.addoption( + "--runslow", action="store_true", default=False, help="run slow tests" + ) + + +def pytest_configure(config): + config.addinivalue_line("markers", "slow: mark test as slow to run") + + +def pytest_collection_modifyitems(config, items): + if config.getoption("--runslow"): + # --runslow given in cli: do not skip slow tests + return + skip_slow = pytest.mark.skip(reason="need --runslow option to run") + for item in items: + if "slow" in item.keywords: + item.add_marker(skip_slow) diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 2160c0524..af80b5cb1 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -50,7 +50,7 @@ fd_hip.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_hip1.mean(), fd_hip2.mean(), fd_hip3.mean()] -fd_means = FDataGrid.concatenate_samples(means) +fd_means = skfda.concatenate_samples(means) fig = fd_means.plot() ############################################################################### @@ -86,7 +86,7 @@ fd_knee.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_knee1.mean(), fd_knee2.mean(), fd_knee3.mean()] -fd_means = FDataBasis.concatenate_samples(means) +fd_means = skfda.concatenate_samples(means) fig = fd_means.plot() ################################################################################ diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 16ee7f5b5..616360913 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -11,9 +11,12 @@ # sphinx_gallery_thumbnail_number = 2 +import numpy as np + import skfda -from skfda.inference.anova import oneway_anova from skfda.representation import FDataGrid +from skfda.inference.anova import oneway_anova +from skfda.datasets import make_gaussian_process from skfda.misc.covariances import WhiteNoise ################################################################################ @@ -33,12 +36,6 @@ # test is to illustrate the differences in the results of the ANOVA method # when the covariance function of the brownian processes changes. -import numpy as np - -from skfda.representation import FDataGrid -from skfda.inference.anova import oneway_anova -from skfda.datasets import make_gaussian_process - ################################################################################ # First, the means for the future processes are drawn. @@ -90,8 +87,8 @@ # In the plot below we can see the simulated trajectories for each mean, # and the averages for each group. -fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) -fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate_samples([fd1, fd2, fd3]) +fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) @@ -120,8 +117,8 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) -fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate_samples([fd1, fd2, fd3]) +fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) @@ -145,8 +142,8 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = FDataGrid.concatenate_samples([fd1, fd2, fd3]) -fd_total = FDataGrid.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate_samples([fd1, fd2, fd3]) +fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) diff --git a/skfda/__init__.py b/skfda/__init__.py index f1354bf93..8237a2c46 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -32,6 +32,7 @@ from .representation import FData from .representation import FDataBasis from .representation import FDataGrid +from .representation._functional_data import concatenate_samples from . import representation, datasets, preprocessing, exploratory, misc, ml diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 18e2ac115..553868dc0 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,12 +1,13 @@ import numpy as np from sklearn.utils import check_random_state +from skfda import concatenate_samples from skfda.misc.metrics import norm_lp from skfda.representation import FData, FDataGrid from skfda.datasets import make_gaussian_process -def v_sample_stat(fd, weights): +def v_sample_stat(fd, weights, p=2): r""" Calculates a statistic that measures the variability between groups of samples in a :class:`skfda.representation.FData` object. @@ -31,6 +32,9 @@ def v_sample_stat(fd, weights): weights (list of int): Weights related to each sample. Each weight is expected to appear in the same position as its corresponding sample in the FData object. + p (int, optional): p of the lp norm. Must be greater or equal + than 1. If p='inf' or p=np.inf it is used the L infinity metric. + Defaults to 2. Returns: The value of the statistic. @@ -82,10 +86,10 @@ def v_sample_stat(fd, weights): ops[index] = fd[i] - fd[j] index += 1 - return np.dot(coef, norm_lp(FData.concatenate_samples(ops)) ** 2) + return np.dot(coef, norm_lp(concatenate_samples(ops), p=p) ** p) -def v_asymptotic_stat(fd, weights): +def v_asymptotic_stat(fd, weights, p=2): r""" Calculates a statistic that measures the variability between groups of samples in a :class:`skfda.representation.FData` object. @@ -110,6 +114,9 @@ def v_asymptotic_stat(fd, weights): weights (list of int): Weights related to each sample. Each weight is expected to appear in the same position as its corresponding sample in the FData object. + p (int, optional): p of the lp norm. Must be greater or equal + than 1. If p='inf' or p=np.inf it is used the L infinity metric. + Defaults to 2. Returns: The value of the statistic. @@ -156,10 +163,10 @@ def v_asymptotic_stat(fd, weights): for i in range(j): ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) index += 1 - return np.sum(norm_lp(FData.concatenate_samples(ops)) ** 2) + return np.sum(norm_lp(concatenate_samples(ops), p=p) ** p) -def _anova_bootstrap(fd_grouped, n_reps, random_state=None): +def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=p): n_groups = len(fd_grouped) if n_groups < 2: @@ -192,11 +199,11 @@ def _anova_bootstrap(fd_grouped, n_reps, random_state=None): v_samples = np.empty(n_reps) for i in range(n_reps): fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) - v_samples[i] = v_asymptotic_stat(fd, sizes) + v_samples[i] = v_asymptotic_stat(fd, sizes, p=p) return v_samples -def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): +def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): r""" Performs one-way functional ANOVA. @@ -239,6 +246,10 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): random_state (optional): Random state. + p (int, optional): p of the lp norm. Must be greater or equal + than 1. If p='inf' or p=np.inf it is used the L infinity metric. + Defaults to 2. + Returns: Value of the sample statistic, p-value and sampling distribution of the simulated asymptotic statistic. @@ -301,13 +312,14 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None): "different basis.") # FData where each sample is the mean of each group - fd_means = FData.concatenate_samples([fd.mean() for fd in fd_groups]) + fd_means = concatenate_samples([fd.mean() for fd in fd_groups]) # Base statistic - vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups]) + vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) # Computing sampling distribution - simulation = _anova_bootstrap(fd_groups, n_reps, random_state=random_state) + simulation = _anova_bootstrap(fd_groups, n_reps, + random_state=random_state, p=p) p_value = np.sum(simulation > vn) / len(simulation) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 5f3d8b5ec..0759eaef2 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -283,26 +283,10 @@ def norm_lp(fdata, p=2, p2=2): if fdata.dim_codomain > 1 or p != 2: raise NotImplementedError - res = np.empty(fdata.n_samples) - - # Gram matrix contains the inner product of the basis components taken - # by pairs. Let \phi_i be a basis element and \c_i its coefficient: - # = \sum_i\sum_j\c_i\c_j<\phi_i, \phi_j> - # To compute this value it is possible to sum the diagonal and the lower - # triangular matrix multiplied by two. - - gram = fdata.basis.gram_matrix() # Obtaining Gram Matrix - - for k, coefs in enumerate(fdata.coefficients): - l_triang = 0 # Computing lower triangular matrix - for i in range(fdata.n_basis): - for j in range(i): - l_triang += coefs[i] * coefs[j] * gram[i][j] - - diag = np.dot(coefs ** 2, np.diag(gram)) # Computing diagonal - res[k] = 2 * l_triang + diag - - res = np.sqrt(res) # Norm is the square root of the inner product + start, end = fdata.domain_range[0] + integral = scipy.integrate.quad_vec( + lambda x: np.power(np.abs(fdata(x)), p), start, end) + res = np.sqrt(integral[0]).flatten() else: if fdata.dim_codomain > 1: diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 662533460..20124c1e5 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -788,34 +788,6 @@ def concatenate(self, *others, as_coordinates=False): """ pass - @staticmethod - def concatenate_samples(objects): - """ - Join samples from an iterable of similar FDataBasis objects. - - Joins samples of FDataBasis objects if they have the same - dimensions and sampling points. - Args: - objects (list of :obj:`FDataBasis`): Objects to be concatenated. - Returns: - :obj:`FDataGrid`: FDataGrid object with the samples from the - original objects. - Raises: - ValueError: In case the provided list of FDataBasis objects is - empty. - Todo: - By the moment, only unidimensional objects are supported in basis - representation. - """ - objects = iter(objects) - first = next(objects, None) - - if not first: - raise ValueError("At least one FData object must be provided " - "to concatenate.") - - return first.concatenate(*objects) - @abstractmethod def compose(self, fd, *, eval_points=None, **kwargs): """Composition of functions. @@ -1037,3 +1009,31 @@ def _concat_same_type( first, *others = to_concat return first.concatenate(*others) + + +def concatenate_samples(objects): + """ + Join samples from an iterable of similar FDataBasis objects. + + Joins samples of FDataBasis objects if they have the same + dimensions and sampling points. + Args: + objects (list of :obj:`FDataBasis`): Objects to be concatenated. + Returns: + :obj:`FDataGrid`: FDataGrid object with the samples from the + original objects. + Raises: + ValueError: In case the provided list of FDataBasis objects is + empty. + Todo: + By the moment, only unidimensional objects are supported in basis + representation. + """ + objects = iter(objects) + first = next(objects, None) + + if not first: + raise ValueError("At least one FData object must be provided " + "to concatenate.") + + return first.concatenate(*objects) diff --git a/tests/test_basis.py b/tests/test_basis.py index 926cec751..4c0c22e88 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,6 +1,7 @@ from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) from skfda.representation.grid import FDataGrid +from skfda import concatenate_samples import unittest import numpy as np @@ -585,7 +586,7 @@ def test_concatenate_samples(self): fd1 = FDataGrid([sample1]).to_basis(Fourier(n_basis=5)) fd2 = FDataGrid([sample2]).to_basis(Fourier(n_basis=5)) - fd = FDataBasis.concatenate_samples([fd1, fd2]) + fd = concatenate_samples([fd1, fd2]) np.testing.assert_equal(fd.n_samples, 2) np.testing.assert_equal(fd.dim_codomain, 1) diff --git a/tests/test_grid.py b/tests/test_grid.py index 2a8f549cb..b7069fdd0 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -3,7 +3,7 @@ import scipy.stats.mstats import numpy as np -from skfda import FDataGrid +from skfda import FDataGrid, concatenate_samples from skfda.exploratory import stats @@ -106,7 +106,7 @@ def test_concatenate_samples(self): fd2 = FDataGrid([sample2]) fd1.axes_labels = ["x", "y"] - fd = FDataGrid.concatenate_samples([fd1, fd2]) + fd = concatenate_samples([fd1, fd2]) np.testing.assert_equal(fd.n_samples, 2) np.testing.assert_equal(fd.dim_codomain, 1) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index e7e30f4b9..8924adc30 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -1,5 +1,6 @@ import unittest import numpy as np +import pytest from skfda.representation import FDataGrid from skfda.representation.basis import Fourier @@ -45,6 +46,7 @@ def test_v_stats(self): self.assertAlmostEqual(v_asymptotic_stat(fd.to_basis(Fourier( n_basis=5)), weights), res) + @pytest.mark.slow def test_asymptotic_behaviour(self): dataset = fetch_gait() fd = dataset['data'].coordinates[1] From 8b9c10ad850d739543c8ece6e39ed5d8ed58d8cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 4 May 2020 22:43:53 +0200 Subject: [PATCH 480/624] Fixing bug with p in anova bootstrap MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 553868dc0..2c724101f 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -166,7 +166,7 @@ def v_asymptotic_stat(fd, weights, p=2): return np.sum(norm_lp(concatenate_samples(ops), p=p) ** p) -def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=p): +def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): n_groups = len(fd_grouped) if n_groups < 2: From ef73f5a8371bbea38b2e904d64c00a3316973f4d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 4 May 2020 23:49:59 +0200 Subject: [PATCH 481/624] Changing concatenate_samples to concatenate and travis to pytest MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 3 ++- examples/plot_oneway.py | 4 ++-- examples/plot_oneway_synthetic.py | 12 ++++++------ skfda/__init__.py | 2 +- skfda/inference/anova/anova_oneway.py | 8 ++++---- skfda/representation/_functional_data.py | 15 +++++++++------ tests/test_basis.py | 6 +++--- tests/test_grid.py | 6 +++--- 8 files changed, 30 insertions(+), 26 deletions(-) diff --git a/.travis.yml b/.travis.yml index e48c60eda..ecd5d7098 100644 --- a/.travis.yml +++ b/.travis.yml @@ -34,13 +34,14 @@ install: # 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows # 'python3' is a 'command not found' error on Windows but 'py' works on Windows only +# python3 setup.py test || python setup.py test; script: - | if [[ $PEP8COVERAGE == true ]]; then flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - python3 setup.py test || python setup.py test; + pytest; fi diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index af80b5cb1..06d8b68b1 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -50,7 +50,7 @@ fd_hip.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_hip1.mean(), fd_hip2.mean(), fd_hip3.mean()] -fd_means = skfda.concatenate_samples(means) +fd_means = skfda.concatenate(means) fig = fd_means.plot() ############################################################################### @@ -86,7 +86,7 @@ fd_knee.plot(group=[0 if i < 13 else 1 if i < 26 else 39 for i in range(39)]) means = [fd_knee1.mean(), fd_knee2.mean(), fd_knee3.mean()] -fd_means = skfda.concatenate_samples(means) +fd_means = skfda.concatenate(means) fig = fd_means.plot() ################################################################################ diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index 616360913..dbd7fd41c 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -87,8 +87,8 @@ # In the plot below we can see the simulated trajectories for each mean, # and the averages for each group. -fd = skfda.concatenate_samples([fd1, fd2, fd3]) -fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate([fd1, fd2, fd3]) +fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) @@ -117,8 +117,8 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = skfda.concatenate_samples([fd1, fd2, fd3]) -fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate([fd1, fd2, fd3]) +fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) @@ -142,8 +142,8 @@ _, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) -fd = skfda.concatenate_samples([fd1, fd2, fd3]) -fd_total = skfda.concatenate_samples([fd.mean() for fd in [fd1, fd2, +fd = skfda.concatenate([fd1, fd2, fd3]) +fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( sigma, p_val) diff --git a/skfda/__init__.py b/skfda/__init__.py index 8237a2c46..c66f69d38 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -32,7 +32,7 @@ from .representation import FData from .representation import FDataBasis from .representation import FDataGrid -from .representation._functional_data import concatenate_samples +from .representation._functional_data import concatenate from . import representation, datasets, preprocessing, exploratory, misc, ml diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 2c724101f..e098b6a67 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -1,7 +1,7 @@ import numpy as np from sklearn.utils import check_random_state -from skfda import concatenate_samples +from skfda import concatenate from skfda.misc.metrics import norm_lp from skfda.representation import FData, FDataGrid from skfda.datasets import make_gaussian_process @@ -86,7 +86,7 @@ def v_sample_stat(fd, weights, p=2): ops[index] = fd[i] - fd[j] index += 1 - return np.dot(coef, norm_lp(concatenate_samples(ops), p=p) ** p) + return np.dot(coef, norm_lp(concatenate(ops), p=p) ** p) def v_asymptotic_stat(fd, weights, p=2): @@ -163,7 +163,7 @@ def v_asymptotic_stat(fd, weights, p=2): for i in range(j): ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) index += 1 - return np.sum(norm_lp(concatenate_samples(ops), p=p) ** p) + return np.sum(norm_lp(concatenate(ops), p=p) ** p) def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): @@ -312,7 +312,7 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): "different basis.") # FData where each sample is the mean of each group - fd_means = concatenate_samples([fd.mean() for fd in fd_groups]) + fd_means = concatenate([fd.mean() for fd in fd_groups]) # Base statistic vn = v_sample_stat(fd_means, [fd.n_samples for fd in fd_groups], p=p) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 20124c1e5..9ba2c101f 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1011,19 +1011,22 @@ def _concat_same_type( return first.concatenate(*others) -def concatenate_samples(objects): +def concatenate(objects, as_coordinates=False): """ - Join samples from an iterable of similar FDataBasis objects. + Join samples from an iterable of similar FData objects. - Joins samples of FDataBasis objects if they have the same + Joins samples of FData objects if they have the same dimensions and sampling points. Args: objects (list of :obj:`FDataBasis`): Objects to be concatenated. + as_coordinates (boolean, optional): If False concatenates as + new samples, else, concatenates the other functions as + new components of the image. Defaults to False. Returns: - :obj:`FDataGrid`: FDataGrid object with the samples from the + :obj:`FData`: FData object with the samples from the original objects. Raises: - ValueError: In case the provided list of FDataBasis objects is + ValueError: In case the provided list of FData objects is empty. Todo: By the moment, only unidimensional objects are supported in basis @@ -1036,4 +1039,4 @@ def concatenate_samples(objects): raise ValueError("At least one FData object must be provided " "to concatenate.") - return first.concatenate(*objects) + return first.concatenate(*objects, as_coordinates=as_coordinates) diff --git a/tests/test_basis.py b/tests/test_basis.py index 4c0c22e88..0fef3b9b7 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,7 +1,7 @@ from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) from skfda.representation.grid import FDataGrid -from skfda import concatenate_samples +from skfda import concatenate import unittest import numpy as np @@ -580,13 +580,13 @@ def test_fdatabasis_derivative_bspline(self): [-120, -18, -60], [-48, 0, 48]]) - def test_concatenate_samples(self): + def test_concatenate(self): sample1 = np.arange(0, 10) sample2 = np.arange(10, 20) fd1 = FDataGrid([sample1]).to_basis(Fourier(n_basis=5)) fd2 = FDataGrid([sample2]).to_basis(Fourier(n_basis=5)) - fd = concatenate_samples([fd1, fd2]) + fd = concatenate([fd1, fd2]) np.testing.assert_equal(fd.n_samples, 2) np.testing.assert_equal(fd.dim_codomain, 1) diff --git a/tests/test_grid.py b/tests/test_grid.py index b7069fdd0..4213b1451 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -3,7 +3,7 @@ import scipy.stats.mstats import numpy as np -from skfda import FDataGrid, concatenate_samples +from skfda import FDataGrid, concatenate from skfda.exploratory import stats @@ -99,14 +99,14 @@ def test_concatenate_coordinates(self): fd = fd1.concatenate(fd2, as_coordinates=True) np.testing.assert_equal(None, fd.axes_labels) - def test_concatenate_samples(self): + def test_concatenate(self): sample1 = np.arange(0, 10) sample2 = np.arange(10, 20) fd1 = FDataGrid([sample1]) fd2 = FDataGrid([sample2]) fd1.axes_labels = ["x", "y"] - fd = concatenate_samples([fd1, fd2]) + fd = concatenate([fd1, fd2]) np.testing.assert_equal(fd.n_samples, 2) np.testing.assert_equal(fd.dim_codomain, 1) From fb2ff2e29a16a7e93328a3e2e7089dbafc2c5a59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 4 May 2020 23:55:46 +0200 Subject: [PATCH 482/624] Trying to fix travis bug MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ecd5d7098..f8a8fa433 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - pytest; + pytest skfda; fi From f1106daea5fe3eb2b5c1e1ad7b6b2078d49ddce8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 4 May 2020 23:59:24 +0200 Subject: [PATCH 483/624] Trying to fix travis bug 2 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index f8a8fa433..24ff1c125 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - pytest skfda; + pytest fi From 5c2d8706d3b3e77a907315d77a899f8708c4547a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 5 May 2020 00:04:46 +0200 Subject: [PATCH 484/624] Trying to fix travis bug 3 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 24ff1c125..ecd5d7098 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - pytest + pytest; fi From a5b2c78be1daf0ffbef27bf37118876b2c618a87 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 5 May 2020 19:49:50 +0200 Subject: [PATCH 485/624] Trying to fix travis bug 4 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ecd5d7098..2aa632ef1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - pytest; + python3 setup.py test || python setup.py test; fi From 81b9b5c1936d252160336d0630dcfa44e41ae8ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 5 May 2020 19:57:41 +0200 Subject: [PATCH 486/624] Trying to fix travis bug 5 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 2aa632ef1..a597c1916 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - python3 setup.py test || python setup.py test; + pytest || pytest; fi From ea3c5fb19566ffef1bc64c93982f97d20ebd724a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 5 May 2020 20:02:32 +0200 Subject: [PATCH 487/624] Trying to fix travis bug 6 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index a597c1916..35de93aaf 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - pytest || pytest; + py.test; fi From de65b0f3275127a989a15270db525d30c177490a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 5 May 2020 20:06:03 +0200 Subject: [PATCH 488/624] Trying to fix travis bug 7 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 35de93aaf..32c8b263f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ script: flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - py.test; + py.test fi From 3aeb9a2f63a69bc75a74016fa22ea8dd2599761b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 6 May 2020 21:50:37 +0200 Subject: [PATCH 489/624] Travis configuration to previous version. Fixing misprint in ANOVA notebook. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 4 ++-- examples/plot_oneway_synthetic.py | 24 ++++++++++++------------ 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.travis.yml b/.travis.yml index 32c8b263f..1aea17c04 100644 --- a/.travis.yml +++ b/.travis.yml @@ -34,14 +34,14 @@ install: # 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows # 'python3' is a 'command not found' error on Windows but 'py' works on Windows only -# python3 setup.py test || python setup.py test; + script: - | if [[ $PEP8COVERAGE == true ]]; then flake8 --exit-zero skfda; coverage run --source=skfda/ setup.py test; else - py.test + python3 setup.py test || python setup.py test; fi diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index dbd7fd41c..a8844488f 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -67,8 +67,8 @@ # differences between the means of each group should be clear, and the # p-value for the test should be near to zero. -sigma = 0.01 -cov = WhiteNoise(variance=sigma) +sigma2 = 0.01 +cov = WhiteNoise(variance=sigma2) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=start, @@ -90,8 +90,8 @@ fd = skfda.concatenate([fd1, fd2, fd3]) fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) -fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( - sigma, p_val) +fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( + sigma2, p_val) fd_total.plot() ################################################################################ @@ -102,8 +102,8 @@ ################################################################################ # Plot for :math:`\sigma = 1`: -sigma = 0.1 -cov = WhiteNoise(variance=sigma) +sigma2 = 0.1 +cov = WhiteNoise(variance=sigma2) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=t[0], @@ -120,15 +120,15 @@ fd = skfda.concatenate([fd1, fd2, fd3]) fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) -fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( - sigma, p_val) +fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( + sigma2, p_val) fd_total.plot() ################################################################################ # Plot for :math:`\sigma = 10`: -sigma = 1 -cov = WhiteNoise(variance=sigma) +sigma2 = 1 +cov = WhiteNoise(variance=sigma2) fd1 = make_gaussian_process(n_samples, mean=m1, cov=cov, n_features=n_features, random_state=1, start=t[0], @@ -145,8 +145,8 @@ fd = skfda.concatenate([fd1, fd2, fd3]) fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, fd3]]) -fd_total.dataset_label = "Sample with $\sigma$ = {}, p-value = {:.3f}".format( - sigma, p_val) +fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( + sigma2, p_val) fd_total.plot() ################################################################################ From 40878a3369152d8cf8bcd6dad2200a3674871b9c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 6 May 2020 21:54:06 +0200 Subject: [PATCH 490/624] Travis configuration to previous version. Fixing misprint in ANOVA notebook. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- conftest.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/conftest.py b/conftest.py index 3bf75b22f..889066c3a 100644 --- a/conftest.py +++ b/conftest.py @@ -10,23 +10,3 @@ collect_ignore = ['setup.py'] pytest.register_assert_rewrite("skfda") - - -def pytest_addoption(parser): - parser.addoption( - "--runslow", action="store_true", default=False, help="run slow tests" - ) - - -def pytest_configure(config): - config.addinivalue_line("markers", "slow: mark test as slow to run") - - -def pytest_collection_modifyitems(config, items): - if config.getoption("--runslow"): - # --runslow given in cli: do not skip slow tests - return - skip_slow = pytest.mark.skip(reason="need --runslow option to run") - for item in items: - if "slow" in item.keywords: - item.add_marker(skip_slow) From 20a3e6596ec8d85a550e70b402e6c587625e1dd4 Mon Sep 17 00:00:00 2001 From: davidgarciafer Date: Wed, 6 May 2020 22:03:47 +0200 Subject: [PATCH 491/624] Fixing travis run of tests. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 1 - tests/test_oneway_anova.py | 1 - 2 files changed, 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 1aea17c04..e48c60eda 100644 --- a/.travis.yml +++ b/.travis.yml @@ -34,7 +34,6 @@ install: # 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows # 'python3' is a 'command not found' error on Windows but 'py' works on Windows only - script: - | if [[ $PEP8COVERAGE == true ]]; then diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index 8924adc30..c7534a1c6 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -46,7 +46,6 @@ def test_v_stats(self): self.assertAlmostEqual(v_asymptotic_stat(fd.to_basis(Fourier( n_basis=5)), weights), res) - @pytest.mark.slow def test_asymptotic_behaviour(self): dataset = fetch_gait() fd = dataset['data'].coordinates[1] From 44af9d6e8a69589125d0125f5657934aa2f2dba8 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Fri, 8 May 2020 21:19:46 +0200 Subject: [PATCH 492/624] adapt to scipy notation --- skfda/exploratory/stats/_stats.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index e8074855a..63987b422 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -93,9 +93,9 @@ def depth_based_median(fdatagrid, depth_method=modified_band_depth): return fdatagrid[indices_descending_depth[0]] -def trimmed_means(fdatagrid, - trimmed_percentage, - depth_method=modified_band_depth): +def trim_mean(fdatagrid, + proportiontocut, + depth_method=modified_band_depth): """Compute the trimmed means based on a depth measure. The trimmed means consists in computing the mean function without a @@ -105,7 +105,7 @@ def trimmed_means(fdatagrid, Args: fdatagrid (FDataGrid): Object containing different samples of a functional variable. - trimmed_percentage (float): indicates the percentage of functions to + proportiontocut (float): indicates the percentage of functions to remove. It is not easy to determine as it varies from dataset to dataset. depth_method (:ref:`depth measure `, optional): @@ -117,7 +117,7 @@ def trimmed_means(fdatagrid, """ n_samples_to_keep = int((fdatagrid.n_samples - - fdatagrid.n_samples * trimmed_percentage)) + fdatagrid.n_samples * proportiontocut)) # compute the depth of each curve and store the indexes in descending order depth = depth_method(fdatagrid) From 04765eb36c717d06a6a36e4b024cbcd465846d91 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 9 May 2020 18:02:57 +0200 Subject: [PATCH 493/624] fix init file imports --- skfda/exploratory/stats/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index c9c5fd629..d81e0ade3 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1 +1 @@ -from ._stats import mean, var, gmean, cov, depth_based_median, trimmed_means +from ._stats import mean, var, gmean, cov, depth_based_median, trim_mean From 7f8251c8bbb9e9660f983b5e120ae6f406b70fda Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 9 May 2020 18:36:25 +0200 Subject: [PATCH 494/624] make sure the rounding operation is the same --- skfda/exploratory/stats/_stats.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 63987b422..79a03ac9b 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -116,8 +116,8 @@ def trim_mean(fdatagrid, FDataGrid: object containing the computed trimmed mean. """ - n_samples_to_keep = int((fdatagrid.n_samples - - fdatagrid.n_samples * proportiontocut)) + n_samples_to_keep = (fdatagrid.n_samples - + int(fdatagrid.n_samples * proportiontocut)) # compute the depth of each curve and store the indexes in descending order depth = depth_method(fdatagrid) From 9d7381627f6b686b02dcf0a2e0b57f9b67fa4989 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 9 May 2020 18:39:34 +0200 Subject: [PATCH 495/624] doc --- skfda/exploratory/stats/_stats.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 79a03ac9b..5f0e3897e 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -102,6 +102,11 @@ def trim_mean(fdatagrid, percentage of least deep curves. That is, we first remove the least deep curves and then we compute the mean as usual. + Note that the difference with the trim_mean method of scipy is that there is + no axis argument. This is because of the nature of the data that we are + dealing with. The data are functions, therefore there is only one possible + axis. + Args: fdatagrid (FDataGrid): Object containing different samples of a functional variable. From 9cfe9d820a3fa744381eb84361fe19392fcf9b00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 10 May 2020 18:38:08 +0200 Subject: [PATCH 496/624] Changing Scikit-learn version in requirements.txt to avoid Travis problems temporarily. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 074553ffe..ce8f4bd46 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ numpy scipy setuptools Cython -sklearn +sklearn==0.21.3 mpldatacursor From 0b83a1382aed029ced8ebe07259e48205f4b6811 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 10 May 2020 18:47:11 +0200 Subject: [PATCH 497/624] Changing Scikit-learn version in requirements.txt to avoid Travis problems temporarily. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ce8f4bd46..78b52c100 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ numpy scipy setuptools Cython -sklearn==0.21.3 +sklearn==0.20 mpldatacursor From 41bc7df2a9e02f6a3ddf2f0ecde0778ee82c7cd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 10 May 2020 21:03:11 +0200 Subject: [PATCH 498/624] Changing Scikit-learn version in requirements.txt to avoid Travis problems temporarily. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index e48c60eda..01a0438b2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,6 +26,7 @@ matrix: - PEP8COVERAGE=true # coverage test are only install: - pip3 install --upgrade pip cython numpy || pip3 install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' + - pip3 install -r requirements.txt - | if [[ $PEP8COVERAGE == true ]]; then pip3 install flake8 || pip3 install --user flake8 From 24aa36002587f02ba2078e4668d363e4ec70d3a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 10 May 2020 21:06:45 +0200 Subject: [PATCH 499/624] Changing Scikit-learn version in requirements.txt to avoid Travis problems temporarily. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 78b52c100..056aaaaf4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ numpy scipy setuptools Cython -sklearn==0.20 +sklearn==0.20.1 mpldatacursor From acb961df396476606c539d2e8cf303aeebaa0e92 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 10 May 2020 21:09:08 +0200 Subject: [PATCH 500/624] Unable to find previous version of sklearn MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 056aaaaf4..074553ffe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ numpy scipy setuptools Cython -sklearn==0.20.1 +sklearn mpldatacursor From f341db0798c0cae72a6363ea243999dc5b2f90ba Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 11 May 2020 03:27:56 +0200 Subject: [PATCH 501/624] Pass FPCA tests. --- .../dim_reduction/projection/_fpca.py | 54 ++++++++++--------- tests/test_fpca.py | 15 +++--- 2 files changed, 38 insertions(+), 31 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 6c71f9652..dee26abd1 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -11,6 +11,8 @@ import numpy as np +from ....misc.regularization import compute_penalty_matrix + __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -31,12 +33,12 @@ def __init__(self, n_components=3, centering=True, regularization_parameter=0, - penalty=2): + regularization=None): self.n_components = n_components self.centering = centering # lambda in the regularization / penalization process self.regularization_parameter = regularization_parameter - self.penalty = penalty + self.regularization = regularization @abstractmethod def fit(self, X, y=None): @@ -103,7 +105,7 @@ class FPCABasis(FPCA): the passed FDataBasis object. regularization_parameter (float): this parameter determines the amount of smoothing applied. Defaults to 0 - penalty (Union[int, Iterable[float],'LinearDifferentialOperator']): + regularization (Union[int, Iterable[float],'LinearDifferentialOperator']): Linear differential operator. If it is not a LinearDifferentialOperator object, it will be converted to one. If you input an integerthen the derivative of that degree will be @@ -137,9 +139,9 @@ def __init__(self, components_basis=None, centering=True, regularization_parameter=0, - penalty=2): + regularization=None): super().__init__(n_components, centering, - regularization_parameter, penalty) + regularization_parameter, regularization) # basis that we want to use for the principal components self.components_basis = components_basis @@ -193,33 +195,34 @@ def fit(self, X: FDataBasis, y=None): X.coefficients -= meanfd.coefficients # setup principal component basis if not given - if self.components_basis: + components_basis = self.components_basis + if components_basis is not None: # First fix domain range if not already done - self.components_basis.domain_range = X.basis.domain_range - g_matrix = self.components_basis.gram_matrix() + components_basis.domain_range = X.basis.domain_range + g_matrix = components_basis.gram_matrix() # the matrix that are in charge of changing the computed principal # components to target matrix is essentially the inner product # of both basis. - j_matrix = X.basis.inner_product(self.components_basis) + j_matrix = X.basis.inner_product(components_basis) else: # if no other basis is specified we use the same basis as the passed # FDataBasis Object - self.components_basis = X.basis.copy() - g_matrix = self.components_basis.gram_matrix() + components_basis = X.basis.copy() + g_matrix = components_basis.gram_matrix() j_matrix = g_matrix # make g matrix symmetric, referring to Ramsay's implementation g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 # Apply regularization / penalty if applicable - if self.regularization_parameter > 0: - # obtain regularization matrix - regularization_matrix = self.components_basis.penalty( - self.penalty - ) - # apply regularization - g_matrix = (g_matrix + self.regularization_parameter * - regularization_matrix) + regularization_matrix = compute_penalty_matrix( + basis_iterable=(components_basis,), + regularization_parameter=self.regularization_parameter, + regularization=self.regularization, + penalty_matrix=None) + + # apply regularization + g_matrix = (g_matrix + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -250,7 +253,7 @@ def fit(self, X: FDataBasis, y=None): self.explained_variance_ratio_ = pca.explained_variance_ratio_ self.explained_variance_ = pca.explained_variance_ - self.components_ = X.copy(basis=self.components_basis, + self.components_ = X.copy(basis=components_basis, coefficients=component_coefficients) return self @@ -405,9 +408,9 @@ def __init__(self, weights=None, centering=True, regularization_parameter=0, - penalty=2): + regularization=2): super().__init__(n_components, centering, - regularization_parameter, penalty) + regularization_parameter, regularization) self.weights = weights def fit(self, X: FDataGrid, y=None): @@ -487,10 +490,11 @@ def fit(self, X: FDataGrid, y=None): if self.regularization_parameter > 0: # if its an integer, we transform it to an array representing the # linear differential operator of that order - if isinstance(self.penalty, int): - self.penalty = np.append(np.zeros(self.penalty), 1) + if isinstance(self.regularization, int): + self.regularization = np.append( + np.zeros(self.regularization), 1) penalty_matrix = regularization_penalty_matrix(X.sample_points[0], - self.penalty) + self.regularization) # we need to invert aux matrix and multiply it to the data matrix aux_matrix = (np.diag(np.ones(n_points_discretization)) + diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 5050bbf75..aeeb731c9 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -1,10 +1,12 @@ +from skfda import FDataGrid, FDataBasis +from skfda.datasets import fetch_weather +from skfda.misc.operators import LinearDifferentialOperator +from skfda.misc.regularization import TikhonovRegularization +from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.representation.basis import Fourier import unittest import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid -from skfda.datasets import fetch_weather class FPCATestCase(unittest.TestCase): @@ -58,7 +60,9 @@ def test_basis_fpca_fit_result(self): fd_basis = fd_data.to_basis(basis) fpca = FPCABasis(n_components=n_components, - regularization_parameter=1e5) + regularization_parameter=1e5, + regularization=TikhonovRegularization( + LinearDifferentialOperator(2))) fpca.fit(fd_basis) # results obtained using Ramsay's R package @@ -115,7 +119,6 @@ def test_basis_fpca_regularization_fit_result(self): np.testing.assert_allclose(fpca.components_.coefficients, results, atol=1e-7) - def test_grid_fpca_fit_result(self): n_components = 1 From 43411674919e86a3ae271680275eb5176101e9f6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 May 2020 03:05:21 +0200 Subject: [PATCH 502/624] FDatagrid linear differential operator penalty (provisional) --- .../_linear_differential_operator.py | 142 +++++++++++++++++- .../dim_reduction/projection/_fpca.py | 130 ++++------------ skfda/representation/grid.py | 4 +- skfda/representation/interpolation.py | 5 +- tests/test_fpca.py | 11 +- 5 files changed, 185 insertions(+), 107 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 5cf0cfde4..d7a4ab0eb 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -1,12 +1,15 @@ import numbers from numpy import polyder, polyint, polymul, polyval +import scipy.integrate from scipy.interpolate import PPoly import numpy as np from ..._utils import _same_domain +from ...representation import FDataGrid from ...representation.basis import Constant, Monomial, Fourier, BSpline +from ...representation.interpolation import SplineInterpolator from ._operators import Operator, gramian_matrix_optimization @@ -96,8 +99,10 @@ class LinearDifferentialOperator(Operator): """ - def __init__(self, order_or_weights=None, *, order=None, weights=None, - domain_range=None): + def __init__( + self, order_or_weights=None, *, order=None, weights=None, + domain_range=None, + derivative_function=None): """Constructor. You have to provide either order or weights. If both are provided, it will raise an error. If a positional argument is supplied it will be considered the @@ -115,6 +120,10 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, defined. If the functional weights are specified and this is not, takes the domain range from them. Otherwise, defaults to (0,1). + + derivative_function (callable): function used to evaluate the + derivatives. + """ from ...representation.basis import FDataBasis @@ -177,6 +186,9 @@ def __init__(self, order_or_weights=None, *, order=None, weights=None, "integers or FDataBasis objects") self.domain_range = real_domain_range + self.derivative_function = ( + LinearDifferentialOperator.evaluate_derivative + if derivative_function is None else derivative_function) def __repr__(self): """Representation of linear differential operator object.""" @@ -213,11 +225,19 @@ def constant_weights(self): def __call__(self, f): """Return the function that results of applying the operator.""" def applied_linear_diff_op(t): - return sum(w(t) * f(t, derivative=i) - for i, w in enumerate(self.weights)) + return sum(w(t) * self.derivative_function( + function=f, points=t, derivative=i) + for i, w in enumerate(self.weights)) return applied_linear_diff_op + @staticmethod + def evaluate_derivative(function, points, derivative): + """ + Default function for evaluating derivatives. + """ + return function(points, derivative=derivative) + ############################################################# # @@ -542,3 +562,117 @@ def bspline_penalty_matrix_optimized( )[0] penalty_matrix[j, i] = penalty_matrix[i, j] return penalty_matrix + + +def _auxiliary_penalty_matrix(sample_points): + """ Computes the auxiliary matrix needed for the computation of the penalty + matrix. For more details please view the module fdata2pc of the library + fda.usc in R, and the referenced paper. + + Args: + sample_points: the points of discretization of the data matrix. + Returns: + (array_like): the auxiliary matrix used to compute the penalty matrix + + References. + [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized + partial least squares with applications to b-spline transformations + and functional data. Chemometrics and Intelligent Laboratory Systems, + 94:60–69, 11 2008. + + """ + diff_values = np.diff(sample_points) + hh = -(1 / np.mean(1 / diff_values)) / diff_values + aux_diff_matrix = np.diag(hh) + + n_points = len(sample_points) + + aux_matrix_1 = np.zeros((n_points - 1, n_points)) + aux_matrix_1[:, :-1] = aux_diff_matrix + aux_matrix_2 = np.zeros((n_points - 1, n_points)) + aux_matrix_2[:, 1:] = -aux_diff_matrix + + diff_matrix = aux_matrix_1 + aux_matrix_2 + + return diff_matrix + + +def regularization_penalty_matrix(sample_points, penalty): + """ Computes the penalty matrix for regularization of the principal + components in a grid representation. For more details please view the module + fdata2pc of the library fda.usc in R, and the referenced paper. + + Args: + sample_points: the points of discretization of the data matrix. + penalty (array_like): coefficients representing the differential + operator used in the computation of the penalty matrix. For example, + the array (1, 0, 1) means :math:`1 + D^{2}` + Returns: + (array_like): the penalty matrix used to regularize the components + + References. + [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized + partial least squares with applications to b-spline transformations + and functional data. Chemometrics and Intelligent Laboratory Systems, + 94:60–69, 11 2008. + + """ + penalty = np.array(penalty) + n_points = len(sample_points) + penalty_matrix = np.zeros((n_points, n_points)) + if np.sum(penalty) != 0: + # independent term + penalty_matrix = penalty_matrix + penalty[0] * np.diag( + np.ones(n_points)) + if len(penalty) > 1: + # for each term of the differential operator, we compute the penalty + # matrix of that order and then add it to the final penalty matrix + aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) + aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) + for i in range(1, len(penalty)): + if i > 1: + aux_penalty_1 = (aux_penalty_2[:(n_points - i), + :(n_points - i + 1)] + @ aux_penalty_1) + # applying the differential operator, as in each step the + # derivative degree increases by 1. + penalty_matrix = (penalty_matrix + + penalty[i] * (np.transpose( + aux_penalty_1) @ aux_penalty_1)) + return penalty_matrix + + +@gramian_matrix_optimization.register +def fdatagrid_penalty_matrix_optimized( + linear_operator: LinearDifferentialOperator, + basis: FDataGrid): + + if (not isinstance(basis.interpolator, SplineInterpolator) + or basis.interpolator.interpolation_order != 1): + return NotImplemented + + coefs = linear_operator.constant_weights() + if coefs is None: + return NotImplemented + + return regularization_penalty_matrix(basis.sample_points[0], coefs) + + evaluated_basis = sum( + w(basis.sample_points[0]) * linear_operator.derivative_function( + function=basis, points=basis.sample_points[0], derivative=i) + for i, w in enumerate(linear_operator.weights)) + + indices = np.triu_indices(basis.n_samples) + product = evaluated_basis[indices[0]] * evaluated_basis[indices[1]] + + triang_vec = scipy.integrate.simps(product, x=basis.sample_points) + + matrix = np.empty((basis.n_samples, basis.n_samples)) + + # Set upper matrix + matrix[indices] = triang_vec + + # Set lower matrix + matrix[(indices[1], indices[0])] = triang_vec + + return matrix diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index dee26abd1..16a05254a 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -211,9 +211,6 @@ def fit(self, X: FDataBasis, y=None): g_matrix = components_basis.gram_matrix() j_matrix = g_matrix - # make g matrix symmetric, referring to Ramsay's implementation - g_matrix = (g_matrix + np.transpose(g_matrix)) / 2 - # Apply regularization / penalty if applicable regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), @@ -277,84 +274,6 @@ def transform(self, X, y=None): return X.inner_product(self.components_) -def _auxiliary_penalty_matrix(sample_points): - """ Computes the auxiliary matrix needed for the computation of the penalty - matrix. For more details please view the module fdata2pc of the library - fda.usc in R, and the referenced paper. - - Args: - sample_points: the points of discretization of the data matrix. - Returns: - (array_like): the auxiliary matrix used to compute the penalty matrix - - References. - [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized - partial least squares with applications to b-spline transformations - and functional data. Chemometrics and Intelligent Laboratory Systems, - 94:60–69, 11 2008. - - """ - diff_values = np.diff(sample_points) - hh = -(1 / np.mean(1 / diff_values)) / diff_values - aux_diff_matrix = np.diag(hh) - - n_points = len(sample_points) - - aux_matrix_1 = np.zeros((n_points - 1, n_points)) - aux_matrix_1[:, :-1] = aux_diff_matrix - aux_matrix_2 = np.zeros((n_points - 1, n_points)) - aux_matrix_2[:, 1:] = -aux_diff_matrix - - diff_matrix = aux_matrix_1 + aux_matrix_2 - - return diff_matrix - - -def regularization_penalty_matrix(sample_points, penalty): - """ Computes the penalty matrix for regularization of the principal - components in a grid representation. For more details please view the module - fdata2pc of the library fda.usc in R, and the referenced paper. - - Args: - sample_points: the points of discretization of the data matrix. - penalty (array_like): coefficients representing the differential - operator used in the computation of the penalty matrix. For example, - the array (1, 0, 1) means :math:`1 + D^{2}` - Returns: - (array_like): the penalty matrix used to regularize the components - - References. - [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized - partial least squares with applications to b-spline transformations - and functional data. Chemometrics and Intelligent Laboratory Systems, - 94:60–69, 11 2008. - - """ - penalty = np.array(penalty) - n_points = len(sample_points) - penalty_matrix = np.zeros((n_points, n_points)) - if np.sum(penalty) != 0: - # independent term - penalty_matrix = penalty_matrix + penalty[0] * np.diag( - np.ones(n_points)) - if len(penalty) > 1: - # for each term of the differential operator, we compute the penalty - # matrix of that order and then add it to the final penalty matrix - aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) - aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) - for i in range(1, len(penalty)): - if i > 1: - aux_penalty_1 = (aux_penalty_2[:(n_points - i), - :(n_points - i + 1)] - @ aux_penalty_1) - # applying the differential operator, as in each step the - # derivative degree increases by 1. - penalty_matrix = (penalty_matrix + - penalty[i] * (np.transpose( - aux_penalty_1) @ aux_penalty_1)) - return penalty_matrix - - class FPCAGrid(FPCA): """Funcional principal component analysis for functional data represented in discretized form. @@ -487,23 +406,38 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) - if self.regularization_parameter > 0: - # if its an integer, we transform it to an array representing the - # linear differential operator of that order - if isinstance(self.regularization, int): - self.regularization = np.append( - np.zeros(self.regularization), 1) - penalty_matrix = regularization_penalty_matrix(X.sample_points[0], - self.regularization) - - # we need to invert aux matrix and multiply it to the data matrix - aux_matrix = (np.diag(np.ones(n_points_discretization)) + - self.regularization_parameter * penalty_matrix) - # we use solve for better stability, P=aux matrix, X=data_matrix - # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives - # (P^T)^-1*X^T - fd_data = np.transpose(np.linalg.solve(np.transpose(aux_matrix), - np.transpose(fd_data))) +# if self.regularization_parameter > 0: +# # if its an integer, we transform it to an array representing the +# # linear differential operator of that order +# if isinstance(self.regularization, int): +# self.regularization = np.append( +# np.zeros(self.regularization), 1) +# penalty_matrix = regularization_penalty_matrix(X.sample_points[0], +# self.regularization) +# +# # we need to invert aux matrix and multiply it to the data matrix +# aux_matrix = (np.diag(np.ones(n_points_discretization)) + +# self.regularization_parameter * penalty_matrix) +# # we use solve for better stability, P=aux matrix, X=data_matrix +# # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives +# # (P^T)^-1*X^T +# fd_data = np.transpose(np.linalg.solve(np.transpose(aux_matrix), +# np.transpose(fd_data))) + + basis = FDataGrid( + data_matrix=np.identity(n_points_discretization), + sample_points=X.sample_points + ) + + regularization_matrix = compute_penalty_matrix( + basis_iterable=(basis,), + regularization_parameter=self.regularization_parameter, + regularization=self.regularization, + penalty_matrix=None) + + fd_data = np.transpose(np.linalg.solve( + np.transpose(basis.data_matrix[..., 0] + regularization_matrix), + np.transpose(fd_data))) # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 9218a7e8c..7fab00974 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -463,9 +463,9 @@ def derivative(self, order=1): "This method only works when the dimension " "of the domain of the FDatagrid object is " "one.") - if order < 1: + if order < 0: raise ValueError("The order of a derivative has to be greater " - "or equal than 1.") + "or equal than 0.") if self.dim_domain > 1 or self.dim_codomain > 1: raise NotImplementedError("Not implemented for 2 or more" " dimensional data.") diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 1bf6d9390..316664289 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -242,7 +242,10 @@ def _construct_spline_1_m(self, sample_points, data_matrix, def _spline_evaluator_1_m(spl, t, der): - return spl(t, der) + try: + return spl(t, der) + except ValueError: + return np.zeros_like(t) def _process_derivative_1_m(derivative): diff --git a/tests/test_fpca.py b/tests/test_fpca.py index aeeb731c9..9a390f3fc 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -227,8 +227,15 @@ def test_grid_fpca_regularization_fit_result(self): fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) - fpca = FPCAGrid(n_components=n_components, weights=[1] * 365, - regularization_parameter=1) + fpca = FPCAGrid( + n_components=n_components, weights=[1] * 365, + regularization_parameter=1, + regularization=TikhonovRegularization( + LinearDifferentialOperator( + 2, + derivative_function=( + lambda function, points, derivative: + function.derivative(order=derivative)(points))))) fpca.fit(fd_data) # results obtained using fda.usc for the first component From f08ad79e8590c800b5dace422ecf889ca18f6440 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 May 2020 19:21:13 +0200 Subject: [PATCH 503/624] Use second order central finite differences for computing the derivative in FDataGrid. --- readthedocs-requirements.txt | 3 +- requirements.txt | 1 + setup.py | 3 +- skfda/misc/metrics.py | 10 ++-- .../_linear_differential_operator.py | 1 + skfda/preprocessing/registration/elastic.py | 42 ++++++--------- .../representation/_evaluation_trasformer.py | 8 +-- skfda/representation/grid.py | 54 ++++++------------- tests/test_elastic.py | 47 ++++++++-------- tests/test_registration.py | 26 ++++----- 10 files changed, 86 insertions(+), 109 deletions(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 291f6e430..635d1b868 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -10,4 +10,5 @@ pillow matplotlib mpldatacursor setuptools>=41.2 -multimethod>=1.2 \ No newline at end of file +multimethod>=1.2 +findiff \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index faec8816d..29588726f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ Cython sklearn mpldatacursor multimethod>=1.2 +findiff diff --git a/setup.py b/setup.py index 0cae02189..b84b3ccff 100644 --- a/setup.py +++ b/setup.py @@ -87,7 +87,8 @@ 'rdata', 'cython', 'mpldatacursor', - 'multimethod>=1.2'], + 'multimethod>=1.2', + 'findiff'], setup_requires=pytest_runner, tests_require=['pytest'], test_suite='tests', diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 6aba2a115..e05a99d7e 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -483,13 +483,11 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - elastic_registration = ElasticRegistration(template=fdata2, penalty=lam, output_points=eval_points_normalized, **kwargs) - fdata1_reg = elastic_registration.fit_transform(fdata1) srsf = SRSF(initial_value=0) @@ -570,7 +568,6 @@ def phase_distance(fdata1, fdata2, *, lam=0., eval_points=None, _check=True, elastic_registration.fit_transform(fdata1) - derivative_warping = elastic_registration.warping_(eval_points_normalized, keepdims=False, derivative=1)[0] @@ -628,11 +625,18 @@ def warping_distance(warping1, warping2, *, eval_points=None, _check=True): warping1_data = warping1.derivative().data_matrix[0, ..., 0] warping2_data = warping2.derivative().data_matrix[0, ..., 0] + # Derivative approximations can have negatives, specially in the + # borders. + warping1_data[warping1_data < 0] = 0 + warping2_data[warping2_data < 0] = 0 + # In this case the srsf is the sqrt(gamma') srsf_warping1 = np.sqrt(warping1_data, out=warping1_data) srsf_warping2 = np.sqrt(warping2_data, out=warping2_data) product = np.multiply(srsf_warping1, srsf_warping2, out=srsf_warping1) + d = scipy.integrate.simps(product, x=warping1.sample_points[0]) + d = np.clip(d, -1, 1) return np.arccos(d) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index d7a4ab0eb..5aa1c32d3 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -647,6 +647,7 @@ def fdatagrid_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: FDataGrid): + # If using the default interpolation, finite differences are used if (not isinstance(basis.interpolator, SplineInterpolator) or basis.interpolator.interpolation_order != 1): return NotImplemented diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 420cac766..7034dc473 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -1,19 +1,18 @@ +import optimum_reparam + import scipy.integrate -from sklearn.utils.validation import check_is_fitted from sklearn.base import BaseEstimator, TransformerMixin - +from sklearn.utils.validation import check_is_fitted import numpy as np -import optimum_reparam - from . import invert_warping -from .base import RegistrationTransformer -from ._warping import _normalize_scale from ... import FDataGrid from ..._utils import check_is_univariate from ...representation.interpolation import SplineInterpolator +from ._warping import _normalize_scale +from .base import RegistrationTransformer __author__ = "Pablo Marcos Manchón" @@ -25,6 +24,7 @@ # and *ElasticFDA.jl* (https://github.com/jdtuck/ElasticFDA.jl). # ############################################################################### + class SRSF(BaseEstimator, TransformerMixin): r"""Square-Root Slope Function (SRSF) transform. @@ -92,9 +92,10 @@ class SRSF(BaseEstimator, TransformerMixin): >>> zero = fd - fd_pull_back >>> zero.data_matrix.flatten().round(3) - array([ 0. , 0. , 0. , ... ]) + array([ 0., 0., 0., ..., -0., -0., -0.]) """ + def __init__(self, output_points=None, initial_value=None): """Initializes the transformer. @@ -111,7 +112,6 @@ def __init__(self, output_points=None, initial_value=None): self.output_points = output_points self.initial_value = initial_value - def fit(self, X=None, y=None): """This transformer do not need to be fitted. @@ -125,8 +125,6 @@ def fit(self, X=None, y=None): """ return self - - def transform(self, X: FDataGrid, y=None): r"""Computes the square-root slope function (SRSF) transform. @@ -178,7 +176,6 @@ def transform(self, X: FDataGrid, y=None): return X.copy(data_matrix=data_matrix, sample_points=output_points) - def inverse_transform(self, X: FDataGrid, y=None): r"""Computes the inverse SRSF transform. @@ -277,7 +274,6 @@ def _elastic_alignment_array(template_data, q_data, penalty, grid_dim).T - class ElasticRegistration(RegistrationTransformer): r"""Align a FDatagrid using the SRSF framework. @@ -363,6 +359,7 @@ class ElasticRegistration(RegistrationTransformer): FDataGrid(...) """ + def __init__(self, template="elastic mean", penalty=0., output_points=None, grid_dim=7): """Initializes the registration transformer""" @@ -389,7 +386,7 @@ def fit(self, X: FDataGrid=None, y=None): """ if isinstance(self.template, FDataGrid): - self.template_ = self.template # Template already constructed + self.template_ = self.template # Template already constructed elif X is None: raise ValueError("Must be provided a dataset X to construct the " "template.") @@ -404,7 +401,6 @@ def fit(self, X: FDataGrid=None, y=None): return self - def transform(self, X: FDataGrid, y=None): """Apply elastic registration to the data. @@ -420,7 +416,7 @@ def transform(self, X: FDataGrid, y=None): check_is_univariate(X) if (len(self._template_srsf) != 1 and - len(X) != len(self._template_srsf)): + len(X) != len(self._template_srsf)): raise ValueError("The template should contain one sample to align " "all the curves to the same function or the " @@ -460,7 +456,6 @@ def transform(self, X: FDataGrid, y=None): self.warping_ = FDataGrid(gamma, output_points, interpolator=interpolator) - return X.compose(self.warping_, eval_points=output_points) def inverse_transform(self, X: FDataGrid, y=None): @@ -568,7 +563,6 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): arXiv:1103.3817v2. """ - eval_points = warping.sample_points[0] original_eval_points = eval_points @@ -590,7 +584,7 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): d = psi_data.sum(axis=1).argmin() # Get raw values to calculate - mu = psi[d].data_matrix[0,..., 0] + mu = psi[d].data_matrix[0, ..., 0] psi = psi.data_matrix[..., 0] vmean = np.empty((1, len(eval_points))) @@ -602,13 +596,13 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): for i in range(len(warping)): psi_i = psi[i] - inner = scipy.integrate.simps(mu*psi_i, x=eval_points) + inner = scipy.integrate.simps(mu * psi_i, x=eval_points) inner = max(min(inner, 1), -1) theta = np.arccos(inner) if theta > 1e-10: - vmean += theta / np.sin(theta) * (psi_i - np.cos(theta)*mu) + vmean += theta / np.sin(theta) * (psi_i - np.cos(theta) * mu) # Mean of shooting vectors vmean /= warping.n_samples @@ -619,9 +613,9 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): break # Calculate exponential map of mu - a = np.cos(step_size*v_norm) - b = np.sin(step_size*v_norm) / v_norm - mu = a * mu + b * vmean + a = np.cos(step_size * v_norm) + b = np.sin(step_size * v_norm) / v_norm + mu = a * mu + b * vmean # Recover mean in original gamma space warping_mean = scipy.integrate.cumtrapz(np.square(mu, out=mu)[0], @@ -752,13 +746,11 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, mu = mu_1 - if initial is None: initial = fdatagrid.data_matrix[:, 0].mean() srsf_transformer.set_params(initial_value=initial) - # Karcher mean orbit in space L2/Gamma karcher_mean = srsf_transformer.inverse_transform( fdatagrid.copy(data_matrix=[mu], sample_points=eval_points)) diff --git a/skfda/representation/_evaluation_trasformer.py b/skfda/representation/_evaluation_trasformer.py index f09f16c83..05845689e 100644 --- a/skfda/representation/_evaluation_trasformer.py +++ b/skfda/representation/_evaluation_trasformer.py @@ -83,14 +83,14 @@ class EvaluationTransformer(BaseEstimator, TransformerMixin): Evaluating derivative of a FDataGrid at all points. - >>> data_matrix = [[1, 2], [2, 3]] - >>> sample_points = [2, 4] + >>> data_matrix = [[1, 2, 3], [2, 3, 4]] + >>> sample_points = [2, 4, 6] >>> fd = FDataGrid(data_matrix, sample_points) >>> >>> transformer = EvaluationTransformer(derivative=1) >>> transformer.fit_transform(fd) - array([[ 0.5, 0.5], - [ 0.5, 0.5]]) + array([[ 0.5, 0.5, 0.5], + [ 0.5, 0.5, 0.5]]) Evaluation of the derivative of a functional data object at several points. diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 7fab00974..00f935a13 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -8,6 +8,7 @@ import copy import numbers +import findiff import pandas.api.extensions import scipy.stats.mstats @@ -406,24 +407,9 @@ def _evaluate_composed(self, eval_points, *, derivative=0): def derivative(self, order=1): r"""Differentiate a FDataGrid object. - It is calculated using lagged differences. If we call :math:`D` the - data_matrix, :math:`D^1` the derivative of order 1 and :math:`T` the - vector contaning the points of discretisation; :math:`D^1` is - calculated as it follows: - - .. math:: - - D^{1}_{ij} = \begin{cases} - \frac{D_{i1} - D_{i2}}{ T_{1} - T_{2}} & \mbox{if } j = 1 \\ - \frac{D_{i(m-1)} - D_{im}}{ T_{m-1} - T_m} & \mbox{if } - j = m \\ - \frac{D_{i(j-1)} - D_{i(j+1)}}{ T_{j-1} - T_{j+1}} & \mbox{if } - 1 < j < m - \end{cases} - - Where m is the number of columns of the matrix :math:`D`. - - Order > 1 derivatives are calculated by using derivative recursively. + It is calculated using central finite differences when possible. In + the extremes, forward and backward finite differences with accuracy + 2 are used. Args: order (int, optional): Order of the derivative. Defaults to one. @@ -434,11 +420,11 @@ def derivative(self, order=1): >>> fdata = FDataGrid([1,2,4,5,8], range(5)) >>> fdata.derivative() FDataGrid( - array([[[ 1. ], + array([[[ 0.5], [ 1.5], [ 1.5], [ 2. ], - [ 3. ]]]), + [ 4. ]]]), sample_points=[array([0, 1, 2, 3, 4])], domain_range=array([[0, 4]]), ...) @@ -448,11 +434,11 @@ def derivative(self, order=1): >>> fdata = FDataGrid([1,2,4,5,8], range(5)) >>> fdata.derivative(2) FDataGrid( - array([[[ 0.5 ], - [ 0.25], - [ 0.25], - [ 0.75], - [ 1. ]]]), + array([[[ 3.], + [ 1.], + [-1.], + [ 2.], + [ 5.]]]), sample_points=[array([0, 1, 2, 3, 4])], domain_range=array([[0, 4]]), ...) @@ -472,19 +458,9 @@ def derivative(self, order=1): if np.isnan(self.data_matrix).any(): raise ValueError("The FDataGrid object cannot contain nan " "elements.") - data_matrix = self.data_matrix[..., 0] - sample_points = self.sample_points[0] - for _ in range(order): - mdata = [] - for i in range(self.n_samples): - arr = (np.diff(data_matrix[i]) / - (sample_points[1:] - - sample_points[:-1])) - arr = np.append(arr, arr[-1]) - arr[1:-1] += arr[:-2] - arr[1:-1] /= 2 - mdata.append(arr) - data_matrix = np.array(mdata) + + operator = findiff.FinDiff(1, self.sample_points[0], order) + data_matrix = operator(self.data_matrix.astype(float)) if self.dataset_label: dataset_label = "{} - {} derivative".format(self.dataset_label, @@ -492,7 +468,7 @@ def derivative(self, order=1): else: dataset_label = None - return self.copy(data_matrix=data_matrix, sample_points=sample_points, + return self.copy(data_matrix=data_matrix, dataset_label=dataset_label) def __check_same_dimensions(self, other): diff --git a/tests/test_elastic.py b/tests/test_elastic.py index 4290b72b9..9876e1cc9 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -1,7 +1,3 @@ -import unittest - -import numpy as np - from skfda import FDataGrid from skfda.datasets import make_multimodal_samples, make_random_warping from skfda.misc.metrics import (fisher_rao_distance, amplitude_distance, @@ -12,6 +8,10 @@ normalize_warping) from skfda.preprocessing.registration.elastic import (SRSF, elastic_mean, warping_mean) +import unittest + +import numpy as np + metric = pairwise_distance(lp_distance) pairwise_fisher_rao = pairwise_distance(fisher_rao_distance) @@ -38,9 +38,9 @@ def test_to_srsf(self): srsf = SRSF().fit_transform(self.dummy_sample) - data_matrix = [[[-0.92155896], [-0.75559027], [0.25355399], + data_matrix = [[[-1.061897], [-0.75559027], [0.25355399], [0.81547327], [0.95333713], [0.81547327], - [0.25355399], [-0.75559027], [-0.92155896]]] + [0.25355399], [-0.75559027], [-1.06189697]]] np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) @@ -71,7 +71,6 @@ def test_from_srsf_with_output_points(self): np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) - def test_srsf_conversion(self): """Converts to srsf and pull backs""" @@ -117,9 +116,10 @@ def test_default_alignment(self): register = reg.fit_transform(self.unimodal_samples) values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.623701, 0.997427, 0.772248, 0.390317, 0.064725], - [0.639201, 0.997155, 0.791649, 0.382181, 0.050098], - [0.63332 , 0.997369, 0.785886, 0.376556, 0.048804]] + + expected = [[0.599058, 0.997427, 0.772248, 0.412342, 0.064725], + [0.626875, 0.997155, 0.791649, 0.382181, 0.050098], + [0.620992, 0.997369, 0.785886, 0.376556, 0.048804]] np.testing.assert_allclose(values, expected, atol=1e-4) @@ -130,9 +130,9 @@ def test_callable_alignment(self): register = reg.fit_transform(self.unimodal_samples) values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.623701, 0.997427, 0.772248, 0.390317, 0.064725], - [0.639201, 0.997155, 0.791649, 0.382181, 0.050098], - [0.63332 , 0.997369, 0.785886, 0.376556, 0.048804]] + expected = [[0.599058, 0.997427, 0.772248, 0.412342, 0.064725], + [0.626875, 0.997155, 0.791649, 0.382181, 0.050098], + [0.620992, 0.997369, 0.785886, 0.376556, 0.048804]] np.testing.assert_allclose(values, expected, atol=1e-4) @@ -172,16 +172,17 @@ def test_score(self): """Test score method of the transformer""" reg = ElasticRegistration() reg.fit(self.unimodal_samples) - score =reg.score(self.unimodal_samples) - np.testing.assert_almost_equal(score, 0.999666175) + score = reg.score(self.unimodal_samples) + np.testing.assert_almost_equal(score, 0.9994225) def test_warping_mean(self): warping = make_random_warping(start=-1, random_state=0) mean = warping_mean(warping) values = mean([-1, -.5, 0, .5, 1]) - expected = [[-1., -0.3762928 , 0.13613892, 0.59923733, 1. ]] + expected = [[-1., -0.376241, 0.136193, 0.599291, 1.]] np.testing.assert_array_almost_equal(values, expected) + class TestElasticDistances(unittest.TestCase): """Test elastic distances""" @@ -193,7 +194,7 @@ def test_fisher_rao(self): f = np.square(sample) g = np.power(sample, 0.5) - distance = [[0.62825868, 1.98009242], [1.98009242, 0.62825868]] + distance = [[0.64, 1.984], [1.984, 0.64]] res = pairwise_fisher_rao(f, g) np.testing.assert_almost_equal(res, distance, decimal=3) @@ -201,7 +202,7 @@ def test_fisher_rao(self): def test_fisher_rao_invariance(self): """Test invariance of fisher rao metric: d(f,g)= d(foh, goh)""" - t = np.linspace(0, np.pi) + t = np.linspace(0, np.pi, 1000) id = FDataGrid([t], t) cos = np.cos(id) sin = np.sin(id) @@ -217,11 +218,11 @@ def test_fisher_rao_invariance(self): sin.compose(gamma2)) # The error ~0.001 due to the derivation - np.testing.assert_almost_equal(distance_original, distance_warping, - decimal=2) + np.testing.assert_allclose(distance_original, distance_warping, + atol=0.01) - np.testing.assert_almost_equal(distance_original, distance_warping2, - decimal=2) + np.testing.assert_allclose(distance_original, distance_warping2, + atol=0.01) def test_amplitude_distance_limit(self): """Test limit of amplitude distance penalty""" @@ -244,7 +245,7 @@ def test_phase_distance_id(self): def test_warping_distance(self): """Test of warping distance""" - t = np.linspace(0, 1) + t = np.linspace(0, 1, 1000) w1 = FDataGrid([t**5], t) w2 = FDataGrid([t**3], t) diff --git a/tests/test_registration.py b/tests/test_registration.py index d81676d3c..e71dc56b8 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -1,21 +1,21 @@ -import unittest - -import numpy as np - from skfda import FDataGrid -from skfda.representation.interpolation import SplineInterpolator -from skfda.representation.basis import Fourier +from skfda._utils import _check_estimator from skfda.datasets import (make_multimodal_samples, make_multimodal_landmarks, make_sinusoidal_process) +from skfda.exploratory.stats import mean from skfda.preprocessing.registration import ( normalize_warping, invert_warping, landmark_shift_deltas, landmark_shift, landmark_registration_warping, landmark_registration, ShiftRegistration) -from skfda.exploratory.stats import mean +from skfda.preprocessing.registration.validation import ( + AmplitudePhaseDecomposition, LeastSquares, + SobolevLeastSquares, PairwiseCorrelation) +from skfda.representation.basis import Fourier +from skfda.representation.interpolation import SplineInterpolator +import unittest + from sklearn.exceptions import NotFittedError -from skfda._utils import _check_estimator -from skfda.preprocessing.registration.validation import \ - (AmplitudePhaseDecomposition, LeastSquares, - SobolevLeastSquares, PairwiseCorrelation) + +import numpy as np class TestWarping(unittest.TestCase): @@ -167,6 +167,7 @@ def test_landmark_registration(self): np.testing.assert_array_almost_equal(fd_reg(center), original_values, decimal=2) + class TestShiftRegistration(unittest.TestCase): """Test shift registration""" @@ -357,7 +358,7 @@ def test_least_squares_score(self): def test_sobolev_least_squares_score(self): scorer = SobolevLeastSquares() score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 0.762240135) + np.testing.assert_almost_equal(score, 0.7621990) def test_pairwise_correlation(self): scorer = PairwiseCorrelation() @@ -390,7 +391,6 @@ def test_raises_amplitude_phase(self): scorer.score_function(self.X, self.X, warping=self.X[:2]) - if __name__ == '__main__': print() unittest.main() From eaf87093fc48f76cb41922bef8361943fcdc659d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 May 2020 19:55:03 +0200 Subject: [PATCH 504/624] Remove approximation to finite differences. --- .../_linear_differential_operator.py | 89 ------------------- tests/test_fpca.py | 2 +- 2 files changed, 1 insertion(+), 90 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 5aa1c32d3..2519d2ca3 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -564,100 +564,11 @@ def bspline_penalty_matrix_optimized( return penalty_matrix -def _auxiliary_penalty_matrix(sample_points): - """ Computes the auxiliary matrix needed for the computation of the penalty - matrix. For more details please view the module fdata2pc of the library - fda.usc in R, and the referenced paper. - - Args: - sample_points: the points of discretization of the data matrix. - Returns: - (array_like): the auxiliary matrix used to compute the penalty matrix - - References. - [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized - partial least squares with applications to b-spline transformations - and functional data. Chemometrics and Intelligent Laboratory Systems, - 94:60–69, 11 2008. - - """ - diff_values = np.diff(sample_points) - hh = -(1 / np.mean(1 / diff_values)) / diff_values - aux_diff_matrix = np.diag(hh) - - n_points = len(sample_points) - - aux_matrix_1 = np.zeros((n_points - 1, n_points)) - aux_matrix_1[:, :-1] = aux_diff_matrix - aux_matrix_2 = np.zeros((n_points - 1, n_points)) - aux_matrix_2[:, 1:] = -aux_diff_matrix - - diff_matrix = aux_matrix_1 + aux_matrix_2 - - return diff_matrix - - -def regularization_penalty_matrix(sample_points, penalty): - """ Computes the penalty matrix for regularization of the principal - components in a grid representation. For more details please view the module - fdata2pc of the library fda.usc in R, and the referenced paper. - - Args: - sample_points: the points of discretization of the data matrix. - penalty (array_like): coefficients representing the differential - operator used in the computation of the penalty matrix. For example, - the array (1, 0, 1) means :math:`1 + D^{2}` - Returns: - (array_like): the penalty matrix used to regularize the components - - References. - [1] Nicole Krämer, Anne-Laure Boulesteix, and Gerhard Tutz. Penalized - partial least squares with applications to b-spline transformations - and functional data. Chemometrics and Intelligent Laboratory Systems, - 94:60–69, 11 2008. - - """ - penalty = np.array(penalty) - n_points = len(sample_points) - penalty_matrix = np.zeros((n_points, n_points)) - if np.sum(penalty) != 0: - # independent term - penalty_matrix = penalty_matrix + penalty[0] * np.diag( - np.ones(n_points)) - if len(penalty) > 1: - # for each term of the differential operator, we compute the penalty - # matrix of that order and then add it to the final penalty matrix - aux_penalty_1 = _auxiliary_penalty_matrix(sample_points) - aux_penalty_2 = _auxiliary_penalty_matrix(sample_points) - for i in range(1, len(penalty)): - if i > 1: - aux_penalty_1 = (aux_penalty_2[:(n_points - i), - :(n_points - i + 1)] - @ aux_penalty_1) - # applying the differential operator, as in each step the - # derivative degree increases by 1. - penalty_matrix = (penalty_matrix + - penalty[i] * (np.transpose( - aux_penalty_1) @ aux_penalty_1)) - return penalty_matrix - - @gramian_matrix_optimization.register def fdatagrid_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: FDataGrid): - # If using the default interpolation, finite differences are used - if (not isinstance(basis.interpolator, SplineInterpolator) - or basis.interpolator.interpolation_order != 1): - return NotImplemented - - coefs = linear_operator.constant_weights() - if coefs is None: - return NotImplemented - - return regularization_penalty_matrix(basis.sample_points[0], coefs) - evaluated_basis = sum( w(basis.sample_points[0]) * linear_operator.derivative_function( function=basis, points=basis.sample_points[0], derivative=i) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 9a390f3fc..6794231f4 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -326,7 +326,7 @@ def test_grid_fpca_regularization_fit_result(self): fpca.components_.data_matrix.reshape( fpca.components_.data_matrix.shape[:-1]), results, - rtol=1e-6) + rtol=1e-2) if __name__ == '__main__': From 9cad5517c352b1b658f1247d596ab5b0dea41aef Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 May 2020 20:44:48 +0200 Subject: [PATCH 505/624] Pass smoothing_parameter inside the regularization object. --- skfda/misc/regularization/_regularization.py | 17 ++++++++--------- skfda/ml/regression/linear.py | 9 +-------- .../dim_reduction/projection/_fpca.py | 19 +++++-------------- tests/test_fpca.py | 5 ++--- tests/test_regression.py | 5 +---- tests/test_regularization.py | 6 ++++-- 6 files changed, 21 insertions(+), 40 deletions(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 6386219cf..38e9e0e93 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -23,19 +23,23 @@ class TikhonovRegularization(BaseEstimator): (matrix for finite vectors). Parameters: - operator: linear operator used for regularization. + linear_operator: linear operator used for regularization. + regularization_parameter: scaling parameter of the penalization. """ - def __init__(self, linear_operator): + def __init__(self, linear_operator, + regularization_parameter=1): self.linear_operator = linear_operator + self.regularization_parameter = regularization_parameter def penalty_matrix(self, basis): r""" Return a penalty matrix for ordinary least squares. """ - return gramian_matrix(self.linear_operator, basis) + return self.regularization_parameter * gramian_matrix( + self.linear_operator, basis) def compute_penalty_matrix(basis_iterable, regularization_parameter, @@ -47,17 +51,12 @@ def compute_penalty_matrix(basis_iterable, regularization_parameter, """ # If there is no regularization, return 0 and rely on broadcasting - if regularization_parameter == 0: + if regularization_parameter == 0 or regularization is None: return 0 # Compute penalty matrix if not provided if penalty_matrix is None: - if regularization is None: - raise ValueError("The regularization parameter is " - f"{regularization_parameter} != 0 " - "and no regularization is specified") - if not isinstance(regularization, Iterable): regularization = (regularization,) diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 4271744b9..4fbace18e 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -123,12 +123,10 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): """ def __init__(self, *, coef_basis=None, fit_intercept=True, - regularization_parameter=0, regularization=None, penalty_matrix=None): self.coef_basis = coef_basis self.fit_intercept = fit_intercept - self.regularization_parameter = regularization_parameter self.regularization = regularization self.penalty_matrix = penalty_matrix @@ -138,7 +136,6 @@ def fit(self, X, y=None, sample_weight=None): X, y, sample_weight, self.coef_basis) regularization = self.regularization - regularization_parameter = self.regularization_parameter if self.fit_intercept: new_x = np.ones((len(y), 1)) @@ -150,10 +147,6 @@ def fit(self, X, y=None, sample_weight=None): elif regularization is not None: regularization = (None, regularization) - if isinstance(regularization_parameter, Iterable): - regularization_parameter = itertools.chain( - [0], regularization_parameter) - inner_products = [c.regression_matrix(x, y) for x, c in zip(X, coef_info)] @@ -169,7 +162,7 @@ def fit(self, X, y=None, sample_weight=None): penalty_matrix = compute_penalty_matrix( basis_iterable=(c.basis for c in coef_info), - regularization_parameter=regularization_parameter, + regularization_parameter=1, regularization=regularization, penalty_matrix=self.penalty_matrix) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 16a05254a..ddfb7c8c5 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -32,12 +32,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): def __init__(self, n_components=3, centering=True, - regularization_parameter=0, regularization=None): self.n_components = n_components self.centering = centering - # lambda in the regularization / penalization process - self.regularization_parameter = regularization_parameter self.regularization = regularization @abstractmethod @@ -103,8 +100,6 @@ class FPCABasis(FPCA): components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - regularization_parameter (float): this parameter determines the amount - of smoothing applied. Defaults to 0 regularization (Union[int, Iterable[float],'LinearDifferentialOperator']): Linear differential operator. If it is not a LinearDifferentialOperator object, it will be converted to one. @@ -138,10 +133,9 @@ def __init__(self, n_components=3, components_basis=None, centering=True, - regularization_parameter=0, regularization=None): super().__init__(n_components, centering, - regularization_parameter, regularization) + regularization) # basis that we want to use for the principal components self.components_basis = components_basis @@ -214,7 +208,7 @@ def fit(self, X: FDataBasis, y=None): # Apply regularization / penalty if applicable regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), - regularization_parameter=self.regularization_parameter, + regularization_parameter=1, regularization=self.regularization, penalty_matrix=None) @@ -289,8 +283,6 @@ class FPCAGrid(FPCA): computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the sample points of the passed FDataGrid object in the fit method. - regularization_parameter (float): this parameter determines the amount - of smoothing applied. Defaults to 0 penalty (Union[int, Iterable[float]]): the coefficients that will be used to calculate the penalty matrix for regularization. If you input an integer then the derivative of that degree will be @@ -326,10 +318,9 @@ def __init__(self, n_components=3, weights=None, centering=True, - regularization_parameter=0, - regularization=2): + regularization=None): super().__init__(n_components, centering, - regularization_parameter, regularization) + regularization) self.weights = weights def fit(self, X: FDataGrid, y=None): @@ -431,7 +422,7 @@ def fit(self, X: FDataGrid, y=None): regularization_matrix = compute_penalty_matrix( basis_iterable=(basis,), - regularization_parameter=self.regularization_parameter, + regularization_parameter=1, regularization=self.regularization, penalty_matrix=None) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 6794231f4..658129f32 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -60,9 +60,9 @@ def test_basis_fpca_fit_result(self): fd_basis = fd_data.to_basis(basis) fpca = FPCABasis(n_components=n_components, - regularization_parameter=1e5, regularization=TikhonovRegularization( - LinearDifferentialOperator(2))) + LinearDifferentialOperator(2), + regularization_parameter=1e5)) fpca.fit(fd_basis) # results obtained using Ramsay's R package @@ -229,7 +229,6 @@ def test_grid_fpca_regularization_fit_result(self): fpca = FPCAGrid( n_components=n_components, weights=[1] * 365, - regularization_parameter=1, regularization=TikhonovRegularization( LinearDifferentialOperator( 2, diff --git a/tests/test_regression.py b/tests/test_regression.py index 6e5fbe189..5e56fcd96 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -125,7 +125,6 @@ def test_regression_mixed_regularization(self): y = 2 + y_sum + y_integral scalar = MultivariateLinearRegression( - regularization_parameter=1, regularization=[TikhonovRegularization(lambda x: x), TikhonovRegularization( LinearDifferentialOperator(2))]) @@ -174,7 +173,6 @@ def test_regression_regularization(self): scalar = MultivariateLinearRegression( coef_basis=[beta_basis], - regularization_parameter=1, regularization=TikhonovRegularization( LinearDifferentialOperator(2))) scalar.fit(x_fd, y) @@ -196,7 +194,7 @@ def test_regression_regularization(self): y = [1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30] # Non regularized - scalar = MultivariateLinearRegression(regularization_parameter=0) + scalar = MultivariateLinearRegression() scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -211,7 +209,6 @@ def test_regression_regularization(self): y_reg = [5.333, 3.419, 2.697, 11.366] scalar_reg = MultivariateLinearRegression( - regularization_parameter=1, regularization=TikhonovRegularization( LinearDifferentialOperator(2))) scalar_reg.fit(x_fd, y) diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 2951d4646..532fb15bd 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -218,8 +218,10 @@ def ignore_scalar_warning(): sklearn_l2 = Ridge(alpha=regularization_parameter) skfda_l2 = MultivariateLinearRegression( - regularization=TikhonovRegularization(lambda x: x), - regularization_parameter=regularization_parameter) + regularization=TikhonovRegularization( + lambda x: x, + regularization_parameter=regularization_parameter), + ) sklearn_l2.fit(X_train, y_train) with warnings.catch_warnings(): From 144f5d1cd896f0b9d8a1c2e11bbce3e6a46f87cd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 May 2020 21:51:13 +0200 Subject: [PATCH 506/624] Remove explicitly passing penalty_matrix. --- skfda/misc/regularization/_regularization.py | 30 +++++++++---------- skfda/ml/regression/linear.py | 13 ++------ .../dim_reduction/projection/_fpca.py | 24 ++------------- skfda/preprocessing/smoothing/_basis.py | 11 ++----- 4 files changed, 20 insertions(+), 58 deletions(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 38e9e0e93..aedc1544d 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -43,7 +43,7 @@ def penalty_matrix(self, basis): def compute_penalty_matrix(basis_iterable, regularization_parameter, - regularization, penalty_matrix): + regularization): """ Computes the regularization matrix for a linear differential operator. @@ -55,20 +55,18 @@ def compute_penalty_matrix(basis_iterable, regularization_parameter, return 0 # Compute penalty matrix if not provided - if penalty_matrix is None: - - if not isinstance(regularization, Iterable): - regularization = (regularization,) - - if not isinstance(regularization_parameter, Iterable): - regularization_parameter = itertools.repeat( - regularization_parameter) - - penalty_blocks = [ - np.zeros((get_n_basis(b), get_n_basis(b))) if r is None else - a * r.penalty_matrix(b) - for b, r, a in zip(basis_iterable, regularization, - regularization_parameter)] - penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) + if not isinstance(regularization, Iterable): + regularization = (regularization,) + + if not isinstance(regularization_parameter, Iterable): + regularization_parameter = itertools.repeat( + regularization_parameter) + + penalty_blocks = [ + np.zeros((get_n_basis(b), get_n_basis(b))) if r is None else + a * r.penalty_matrix(b) + for b, r, a in zip(basis_iterable, regularization, + regularization_parameter)] + penalty_matrix = scipy.linalg.block_diag(*penalty_blocks) return penalty_matrix diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 4fbace18e..1b62ac2a2 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -41,9 +41,6 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): fit_intercept (bool): Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - regularization_parameter (int or float, optional): Regularization - parameter. Trying with several factors in a logarithm scale is - suggested. If 0 no regularization is performed. Defaults to 0. regularization (int, iterable or :class:`Regularization`): If it is not a :class:`Regularization` object, linear differential operator regularization is assumed. If it @@ -56,9 +53,6 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): numpy.sin) means :math:`1 + sin(x)D^{2}`. If not supplied this defaults to 2. Only used if penalty_matrix is ``None``. - penalty_matrix (array_like, optional): Penalty matrix. If - supplied the differential operator is not used and instead - the matrix supplied by this argument is used. Attributes: coef_ (iterable): A list containing the weight coefficient for each @@ -123,12 +117,10 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): """ def __init__(self, *, coef_basis=None, fit_intercept=True, - regularization=None, - penalty_matrix=None): + regularization=None): self.coef_basis = coef_basis self.fit_intercept = fit_intercept self.regularization = regularization - self.penalty_matrix = penalty_matrix def fit(self, X, y=None, sample_weight=None): @@ -163,8 +155,7 @@ def fit(self, X, y=None, sample_weight=None): penalty_matrix = compute_penalty_matrix( basis_iterable=(c.basis for c in coef_info), regularization_parameter=1, - regularization=regularization, - penalty_matrix=self.penalty_matrix) + regularization=regularization) if self.fit_intercept and hasattr(penalty_matrix, "shape"): # Intercept is not penalized diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index ddfb7c8c5..380d943a4 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -209,8 +209,7 @@ def fit(self, X: FDataBasis, y=None): regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), regularization_parameter=1, - regularization=self.regularization, - penalty_matrix=None) + regularization=self.regularization) # apply regularization g_matrix = (g_matrix + regularization_matrix) @@ -397,24 +396,6 @@ def fit(self, X: FDataGrid, y=None): weights_matrix = np.diag(self.weights) -# if self.regularization_parameter > 0: -# # if its an integer, we transform it to an array representing the -# # linear differential operator of that order -# if isinstance(self.regularization, int): -# self.regularization = np.append( -# np.zeros(self.regularization), 1) -# penalty_matrix = regularization_penalty_matrix(X.sample_points[0], -# self.regularization) -# -# # we need to invert aux matrix and multiply it to the data matrix -# aux_matrix = (np.diag(np.ones(n_points_discretization)) + -# self.regularization_parameter * penalty_matrix) -# # we use solve for better stability, P=aux matrix, X=data_matrix -# # we need X*P^-1 = ((P^T)^-1*X^T)^T, and np.solve gives -# # (P^T)^-1*X^T -# fd_data = np.transpose(np.linalg.solve(np.transpose(aux_matrix), -# np.transpose(fd_data))) - basis = FDataGrid( data_matrix=np.identity(n_points_discretization), sample_points=X.sample_points @@ -423,8 +404,7 @@ def fit(self, X: FDataGrid, y=None): regularization_matrix = compute_penalty_matrix( basis_iterable=(basis,), regularization_parameter=1, - regularization=self.regularization, - penalty_matrix=None) + regularization=self.regularization) fd_data = np.transpose(np.linalg.solve( np.transpose(basis.data_matrix[..., 0] + regularization_matrix), diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 3af4d48c2..bfc0cf934 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -179,9 +179,6 @@ class BasisSmoother(_LinearSmoother): numpy.sin) means :math:`1 + sin(x)D^{2}`. If not supplied this defaults to 2. Only used if penalty_matrix is ``None``. - penalty_matrix (array_like, optional): Penalty matrix. If - supplied the differential operator is not used and instead - the matrix supplied by this argument is used. method (str): Algorithm used for calculating the coefficients using the least squares method. The values admitted are 'cholesky', 'qr' and 'matrix' for Cholesky and QR factorisation methods, and matrix @@ -314,7 +311,6 @@ def __init__(self, weights=None, regularization: Union[int, Iterable[float], 'LinearDifferentialOperator'] = None, - penalty_matrix=None, output_points=None, method='cholesky', return_basis=False): @@ -322,7 +318,6 @@ def __init__(self, self.smoothing_parameter = smoothing_parameter self.weights = weights self.regularization = regularization - self.penalty_matrix = penalty_matrix self.output_points = output_points self.method = method self.return_basis = return_basis @@ -351,8 +346,7 @@ def _coef_matrix(self, input_points): penalty_matrix = compute_penalty_matrix( basis_iterable=(self.basis,), regularization_parameter=self.smoothing_parameter, - regularization=self.regularization, - penalty_matrix=self.penalty_matrix) + regularization=self.regularization) inv += penalty_matrix @@ -413,8 +407,7 @@ def fit_transform(self, X: FDataGrid, y=None): penalty_matrix = compute_penalty_matrix( basis_iterable=(self.basis,), regularization_parameter=self.smoothing_parameter, - regularization=self.regularization, - penalty_matrix=self.penalty_matrix) + regularization=self.regularization) # n is the samples # m is the observations From 810d67c8cc4e6625049c1c7983339d6405e41ee9 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 14 May 2020 14:02:16 +0200 Subject: [PATCH 507/624] Add L2 regularization. --- skfda/misc/operators/__init__.py | 1 + skfda/misc/operators/_identity.py | 34 ++++++++++++++++++++ skfda/misc/regularization/__init__.py | 1 + skfda/misc/regularization/_regularization.py | 18 +++++++++-- tests/test_regularization.py | 5 ++- 5 files changed, 54 insertions(+), 5 deletions(-) create mode 100644 skfda/misc/operators/_identity.py diff --git a/skfda/misc/operators/__init__.py b/skfda/misc/operators/__init__.py index 98ba5cb8b..b112a4c58 100644 --- a/skfda/misc/operators/__init__.py +++ b/skfda/misc/operators/__init__.py @@ -1,2 +1,3 @@ +from ._identity import Identity from ._linear_differential_operator import LinearDifferentialOperator from ._operators import Operator, gramian_matrix, gramian_matrix_optimization diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py new file mode 100644 index 000000000..3ccbaf22c --- /dev/null +++ b/skfda/misc/operators/_identity.py @@ -0,0 +1,34 @@ +import numpy as np + +from ...representation import FDataGrid +from ...representation.basis import Basis +from ._operators import Operator, gramian_matrix_optimization + + +class Identity(Operator): + """Identity operator. + + .. math:: + Ix = x + + """ + + def __call__(self, f): + return f + + +@gramian_matrix_optimization.register +def basis_penalty_matrix_optimized( + linear_operator: Identity, + basis: Basis): + + return basis.gram_matrix() + + +@gramian_matrix_optimization.register +def fdatagrid_penalty_matrix_optimized( + linear_operator: Identity, + basis: FDataGrid): + from ..metrics import norm_lp + + return np.diag(norm_lp(basis)**2) diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py index ec58432fd..01f89d797 100644 --- a/skfda/misc/regularization/__init__.py +++ b/skfda/misc/regularization/__init__.py @@ -1,2 +1,3 @@ from ._regularization import (TikhonovRegularization, + L2Regularization, compute_penalty_matrix) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index aedc1544d..8ee85e505 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -1,6 +1,6 @@ from collections.abc import Iterable import itertools -from skfda.misc.operators import gramian_matrix +from skfda.misc.operators import gramian_matrix, Identity import scipy.linalg from sklearn.base import BaseEstimator @@ -29,7 +29,7 @@ class TikhonovRegularization(BaseEstimator): """ def __init__(self, linear_operator, - regularization_parameter=1): + *, regularization_parameter=1): self.linear_operator = linear_operator self.regularization_parameter = regularization_parameter @@ -42,6 +42,20 @@ def penalty_matrix(self, basis): self.linear_operator, basis) +class L2Regularization(TikhonovRegularization): + r""" + Implements Tikhonov regularization. + + This is equivalent to Tikhonov regularization using the identity operator. + + """ + + def __init__(self, *, regularization_parameter=1): + return super().__init__( + linear_operator=Identity(), + regularization_parameter=regularization_parameter) + + def compute_penalty_matrix(basis_iterable, regularization_parameter, regularization): """ diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 532fb15bd..77d782e70 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -3,7 +3,7 @@ from skfda.misc.operators._linear_differential_operator import ( _monomial_evaluate_constant_linear_diff_op) from skfda.misc.operators._operators import gramian_matrix_numerical -from skfda.misc.regularization import TikhonovRegularization +from skfda.misc.regularization import TikhonovRegularization, L2Regularization from skfda.ml.regression.linear import MultivariateLinearRegression from skfda.representation.basis import Constant, Monomial, BSpline, Fourier import unittest @@ -218,8 +218,7 @@ def ignore_scalar_warning(): sklearn_l2 = Ridge(alpha=regularization_parameter) skfda_l2 = MultivariateLinearRegression( - regularization=TikhonovRegularization( - lambda x: x, + regularization=L2Regularization( regularization_parameter=regularization_parameter), ) From eef3b6677f5437a6c9b970a4e7568057bd59fd30 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 14 May 2020 15:44:14 +0200 Subject: [PATCH 508/624] Update docstring referring to regularization. --- .../dim_reduction/projection/_fpca.py | 20 +++++++------------ 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 380d943a4..e69e9aba2 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -26,7 +26,9 @@ class FPCA(ABC, BaseEstimator, TransformerMixin): n_components (int): number of principal components to obtain from functional principal component analysis. Defaults to 3. centering (bool): if True then calculate the mean of the functional data - object and center the data first + object and center the data first. + regularization (Regularization): + Regularization object to be applied. """ def __init__(self, @@ -100,11 +102,8 @@ class FPCABasis(FPCA): components_basis (Basis): the basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. - regularization (Union[int, Iterable[float],'LinearDifferentialOperator']): - Linear differential operator. If it is not a - LinearDifferentialOperator object, it will be converted to one. - If you input an integerthen the derivative of that degree will be - used to regularize the principal components. + regularization (Regularization): + Regularization object to be applied. Attributes: components_ (FDataBasis): this contains the principal components in a @@ -282,13 +281,8 @@ class FPCAGrid(FPCA): computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the sample points of the passed FDataGrid object in the fit method. - penalty (Union[int, Iterable[float]]): the coefficients that will be - used to calculate the penalty matrix for regularization. - If you input an integer then the derivative of that degree will be - used to regularize the principal components. If you input a vector - then it is considered as a differential operator. For example, - [0,1,2] penalizes first derivative and two times the second - derivative. + regularization (Regularization): + Regularization object to be applied. Attributes: components_ (FDataBasis): this contains the eigenvectors in a basis From 6e06537822278c98bdf7b275ed2cfbf4d55073db Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 14 May 2020 18:32:46 +0200 Subject: [PATCH 509/624] Fix doctests in new scikit-learn versions. Now the doctests work in versions where https://github.com/scikit-learn/scikit-learn/pull/17205 is merged. --- skfda/_neighbors/base.py | 8 ++-- skfda/_neighbors/classification.py | 12 +++--- skfda/_neighbors/outlier.py | 6 +-- skfda/_neighbors/regression.py | 8 ++-- skfda/_neighbors/unsupervised.py | 2 +- skfda/preprocessing/registration/elastic.py | 42 +++++++++------------ 6 files changed, 35 insertions(+), 43 deletions(-) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index 0ca33638b..a2ac25daf 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -3,8 +3,8 @@ from abc import ABC, abstractmethod from sklearn.base import BaseEstimator -from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from sklearn.base import RegressorMixin +from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted import numpy as np @@ -217,7 +217,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): >>> from skfda.ml.clustering import NearestNeighbors >>> neigh = NearestNeighbors() >>> neigh.fit(fd) - NearestNeighbors(algorithm='auto', leaf_size=30,...) + NearestNeighbors(...) Now we can query the k-nearest neighbors. @@ -274,7 +274,7 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'): >>> from skfda.ml.clustering import NearestNeighbors >>> neigh = NearestNeighbors() >>> neigh.fit(fd) - NearestNeighbors(algorithm='auto', leaf_size=30,...) + NearestNeighbors(...) Now we can obtain the graph of k-neighbors of a sample. @@ -343,7 +343,7 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True): >>> from skfda.ml.clustering import NearestNeighbors >>> neigh = NearestNeighbors(radius=.3) >>> neigh.fit(fd) - NearestNeighbors(algorithm='auto', leaf_size=30,...) + NearestNeighbors(...radius=0.3...) Now we can query the neighbors in the radius. diff --git a/skfda/_neighbors/classification.py b/skfda/_neighbors/classification.py index e914660f4..169fbf911 100644 --- a/skfda/_neighbors/classification.py +++ b/skfda/_neighbors/classification.py @@ -1,13 +1,13 @@ """Neighbor models for supervised classification.""" -from sklearn.utils.multiclass import check_classification_targets -from sklearn.preprocessing import LabelEncoder from sklearn.base import ClassifierMixin, BaseEstimator +from sklearn.preprocessing import LabelEncoder +from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted -from ..misc.metrics import lp_distance, pairwise_distance from ..exploratory.stats import mean as l2_mean +from ..misc.metrics import lp_distance, pairwise_distance from .base import (NeighborsBase, NeighborsMixin, KNeighborsMixin, NeighborsClassifierMixin, RadiusNeighborsMixin) @@ -78,7 +78,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, >>> from skfda.ml.classification import KNeighborsClassifier >>> neigh = KNeighborsClassifier() >>> neigh.fit(fd, y) - KNeighborsClassifier(algorithm='auto', leaf_size=30,...) + KNeighborsClassifier(...) We can predict the class of new samples @@ -97,7 +97,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin, :class:`~skfda.ml.regression.KNeighborsRegressor` :class:`~skfda.ml.regression.RadiusNeighborsRegressor` :class:`~skfda.ml.clustering.NearestNeighbors` - + Notes ----- @@ -241,7 +241,7 @@ class RadiusNeighborsClassifier(NeighborsBase, NeighborsMixin, >>> from skfda.ml.classification import RadiusNeighborsClassifier >>> neigh = RadiusNeighborsClassifier(radius=.3) >>> neigh.fit(fd, y) - RadiusNeighborsClassifier(algorithm='auto', leaf_size=30,...) + RadiusNeighborsClassifier(...radius=0.3...) We can predict the class of new samples. diff --git a/skfda/_neighbors/outlier.py b/skfda/_neighbors/outlier.py index 8ce41cb49..9b844575d 100644 --- a/skfda/_neighbors/outlier.py +++ b/skfda/_neighbors/outlier.py @@ -1,10 +1,10 @@ from sklearn.base import OutlierMixin -from .base import (NeighborsBase, NeighborsMixin, KNeighborsMixin, - _to_multivariate_metric) from ..misc.metrics import lp_distance +from .base import (NeighborsBase, NeighborsMixin, KNeighborsMixin, + _to_multivariate_metric) class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, @@ -136,7 +136,7 @@ class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, >>> lof = LocalOutlierFactor(novelty=True) >>> lof.fit(fd_train) - LocalOutlierFactor(algorithm='auto', ..., novelty=True) + LocalOutlierFactor(...novelty=True) Detection of annomalies for new samples. diff --git a/skfda/_neighbors/regression.py b/skfda/_neighbors/regression.py index 715d87935..69878cbf3 100644 --- a/skfda/_neighbors/regression.py +++ b/skfda/_neighbors/regression.py @@ -79,7 +79,7 @@ class KNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, >>> neigh = KNeighborsRegressor() >>> neigh.fit(X_train, y_train) - KNeighborsRegressor(algorithm='auto', leaf_size=30,...) + KNeighborsRegressor(...) We can predict the modes of new samples @@ -96,7 +96,7 @@ class KNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, We train the estimator with the functional response >>> neigh.fit(X_train, y_train) - KNeighborsRegressor(algorithm='auto', leaf_size=30,...) + KNeighborsRegressor(...) And predict the responses as in the first case. @@ -249,7 +249,7 @@ class RadiusNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, >>> neigh = RadiusNeighborsRegressor(radius=0.2) >>> neigh.fit(X_train, y_train) - RadiusNeighborsRegressor(algorithm='auto', leaf_size=30,...) + RadiusNeighborsRegressor(...radius=0.2...) We can predict the modes of new samples @@ -266,7 +266,7 @@ class RadiusNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, We train the estimator with the functional response >>> neigh.fit(X_train, y_train) - RadiusNeighborsRegressor(algorithm='auto', leaf_size=30,...) + RadiusNeighborsRegressor(...radius=0.2...) And predict the responses as in the first case. diff --git a/skfda/_neighbors/unsupervised.py b/skfda/_neighbors/unsupervised.py index b786cd425..dcc067ead 100644 --- a/skfda/_neighbors/unsupervised.py +++ b/skfda/_neighbors/unsupervised.py @@ -59,7 +59,7 @@ class NearestNeighbors(NeighborsBase, NeighborsMixin, KNeighborsMixin, >>> from skfda.ml.clustering import NearestNeighbors >>> neigh = NearestNeighbors(radius=.3) >>> neigh.fit(fd) - NearestNeighbors(algorithm='auto', leaf_size=30,...) + NearestNeighbors(...radius=0.3...) Now we can query the k-nearest neighbors. diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 420cac766..1403da807 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -1,19 +1,18 @@ +import optimum_reparam + import scipy.integrate -from sklearn.utils.validation import check_is_fitted from sklearn.base import BaseEstimator, TransformerMixin - +from sklearn.utils.validation import check_is_fitted import numpy as np -import optimum_reparam - from . import invert_warping -from .base import RegistrationTransformer -from ._warping import _normalize_scale from ... import FDataGrid from ..._utils import check_is_univariate from ...representation.interpolation import SplineInterpolator +from ._warping import _normalize_scale +from .base import RegistrationTransformer __author__ = "Pablo Marcos Manchón" @@ -25,6 +24,7 @@ # and *ElasticFDA.jl* (https://github.com/jdtuck/ElasticFDA.jl). # ############################################################################### + class SRSF(BaseEstimator, TransformerMixin): r"""Square-Root Slope Function (SRSF) transform. @@ -78,7 +78,7 @@ class SRSF(BaseEstimator, TransformerMixin): >>> fd = make_sinusoidal_process(error_std=0, random_state=0) >>> srsf = SRSF() >>> srsf - SRSF(initial_value=None, output_points=None) + SRSF(...) Fits the estimator (to apply the inverse transform) and apply the SRSF @@ -95,6 +95,7 @@ class SRSF(BaseEstimator, TransformerMixin): array([ 0. , 0. , 0. , ... ]) """ + def __init__(self, output_points=None, initial_value=None): """Initializes the transformer. @@ -111,7 +112,6 @@ def __init__(self, output_points=None, initial_value=None): self.output_points = output_points self.initial_value = initial_value - def fit(self, X=None, y=None): """This transformer do not need to be fitted. @@ -125,8 +125,6 @@ def fit(self, X=None, y=None): """ return self - - def transform(self, X: FDataGrid, y=None): r"""Computes the square-root slope function (SRSF) transform. @@ -178,7 +176,6 @@ def transform(self, X: FDataGrid, y=None): return X.copy(data_matrix=data_matrix, sample_points=output_points) - def inverse_transform(self, X: FDataGrid, y=None): r"""Computes the inverse SRSF transform. @@ -277,7 +274,6 @@ def _elastic_alignment_array(template_data, q_data, penalty, grid_dim).T - class ElasticRegistration(RegistrationTransformer): r"""Align a FDatagrid using the SRSF framework. @@ -363,6 +359,7 @@ class ElasticRegistration(RegistrationTransformer): FDataGrid(...) """ + def __init__(self, template="elastic mean", penalty=0., output_points=None, grid_dim=7): """Initializes the registration transformer""" @@ -389,7 +386,7 @@ def fit(self, X: FDataGrid=None, y=None): """ if isinstance(self.template, FDataGrid): - self.template_ = self.template # Template already constructed + self.template_ = self.template # Template already constructed elif X is None: raise ValueError("Must be provided a dataset X to construct the " "template.") @@ -404,7 +401,6 @@ def fit(self, X: FDataGrid=None, y=None): return self - def transform(self, X: FDataGrid, y=None): """Apply elastic registration to the data. @@ -420,7 +416,7 @@ def transform(self, X: FDataGrid, y=None): check_is_univariate(X) if (len(self._template_srsf) != 1 and - len(X) != len(self._template_srsf)): + len(X) != len(self._template_srsf)): raise ValueError("The template should contain one sample to align " "all the curves to the same function or the " @@ -460,7 +456,6 @@ def transform(self, X: FDataGrid, y=None): self.warping_ = FDataGrid(gamma, output_points, interpolator=interpolator) - return X.compose(self.warping_, eval_points=output_points) def inverse_transform(self, X: FDataGrid, y=None): @@ -568,7 +563,6 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): arXiv:1103.3817v2. """ - eval_points = warping.sample_points[0] original_eval_points = eval_points @@ -590,7 +584,7 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): d = psi_data.sum(axis=1).argmin() # Get raw values to calculate - mu = psi[d].data_matrix[0,..., 0] + mu = psi[d].data_matrix[0, ..., 0] psi = psi.data_matrix[..., 0] vmean = np.empty((1, len(eval_points))) @@ -602,13 +596,13 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): for i in range(len(warping)): psi_i = psi[i] - inner = scipy.integrate.simps(mu*psi_i, x=eval_points) + inner = scipy.integrate.simps(mu * psi_i, x=eval_points) inner = max(min(inner, 1), -1) theta = np.arccos(inner) if theta > 1e-10: - vmean += theta / np.sin(theta) * (psi_i - np.cos(theta)*mu) + vmean += theta / np.sin(theta) * (psi_i - np.cos(theta) * mu) # Mean of shooting vectors vmean /= warping.n_samples @@ -619,9 +613,9 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): break # Calculate exponential map of mu - a = np.cos(step_size*v_norm) - b = np.sin(step_size*v_norm) / v_norm - mu = a * mu + b * vmean + a = np.cos(step_size * v_norm) + b = np.sin(step_size * v_norm) / v_norm + mu = a * mu + b * vmean # Recover mean in original gamma space warping_mean = scipy.integrate.cumtrapz(np.square(mu, out=mu)[0], @@ -752,13 +746,11 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, mu = mu_1 - if initial is None: initial = fdatagrid.data_matrix[:, 0].mean() srsf_transformer.set_params(initial_value=initial) - # Karcher mean orbit in space L2/Gamma karcher_mean = srsf_transformer.inverse_transform( fdatagrid.copy(data_matrix=[mu], sample_points=eval_points)) From 8e60feee37db9a52547f3da842c765bf4f7eb9ef Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 16 May 2020 16:13:35 +0200 Subject: [PATCH 510/624] doc --- skfda/exploratory/stats/_stats.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 5f0e3897e..d84fece80 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -102,10 +102,9 @@ def trim_mean(fdatagrid, percentage of least deep curves. That is, we first remove the least deep curves and then we compute the mean as usual. - Note that the difference with the trim_mean method of scipy is that there is - no axis argument. This is because of the nature of the data that we are - dealing with. The data are functions, therefore there is only one possible - axis. + Note that in scipy the leftmost and rightmost proportiontocut data are + removed. In this case, as we order the data by the depth, we only remove + those that have the least depth values. Args: fdatagrid (FDataGrid): Object containing different samples of a From d4abbd2cd330341be4bd7f1e7120739f6a6ae3d3 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 16 May 2020 16:41:56 +0200 Subject: [PATCH 511/624] unify FPCAGrid and FPCABasis --- .../preprocessing/dim_reduction/fpca.rst | 12 +- examples/plot_fpca.py | 10 +- .../dim_reduction/projection/__init__.py | 2 +- .../dim_reduction/projection/_fpca.py | 239 ++++++++---------- tests/test_fpca.py | 20 +- 5 files changed, 117 insertions(+), 166 deletions(-) diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst index 5b1b8eb3e..c6cc9bfd8 100644 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ b/docs/modules/preprocessing/dim_reduction/fpca.rst @@ -15,18 +15,10 @@ For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, where the process is applied to several datasets in both discretized and basis forms. -FPCA for functional data in a basis representation +FPCA for functional data in both representations ---------------------------------------------------------------- .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.projection.FPCABasis - -FPCA for functional data in a discretized representation ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.preprocessing.dim_reduction.projection.FPCAGrid \ No newline at end of file + skfda.preprocessing.dim_reduction.projection.FPCA diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 513c94bf4..1498c125a 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -10,7 +10,7 @@ import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.preprocessing.dim_reduction.projection import FPCA from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.datasets import fetch_growth @@ -36,7 +36,7 @@ # obtain the first two components. By default, if we do not specify the number # of components, it's 3. Other parameters are weights and centering. For more # information please visit the documentation. -fpca_discretized = FPCAGrid(n_components=2) +fpca_discretized = FPCA(n_components=2) fpca_discretized.fit(fd) fpca_discretized.components_.plot() @@ -57,7 +57,7 @@ # first 2 principal components. By default the principal components are # expressed in the same basis as the data. We can see that the obtained result # is similar to the discretized case. -fpca = FPCABasis(n_components=2) +fpca = FPCA(n_components=2) fpca.fit(basis_fd) fpca.components_.plot() @@ -107,7 +107,7 @@ dataset = fetch_growth() fd = dataset['data'] basis_fd = fd.to_basis(BSpline(n_basis=7)) -fpca = FPCABasis(n_components=2, components_basis=Fourier(n_basis=7)) +fpca = FPCA(n_components=2, components_basis=Fourier(n_basis=7)) fpca.fit(basis_fd) fpca.components_.plot() @@ -120,6 +120,6 @@ dataset = fetch_growth() fd = dataset['data'] basis_fd = fd.to_basis(BSpline(n_basis=7)) -fpca = FPCABasis(n_components=2, components_basis=Monomial(n_basis=4)) +fpca = FPCA(n_components=2, components_basis=Monomial(n_basis=4)) fpca.fit(basis_fd) fpca.components_.plot() diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index fd2b66bf4..4b6cf980c 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1 +1 @@ -from ._fpca import FPCABasis, FPCAGrid +from ._fpca import FPCA diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index e69e9aba2..94a48c1ad 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,6 +1,5 @@ """Functional Principal Component Analysis Module.""" -from abc import ABC, abstractmethod import skfda from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -11,87 +10,17 @@ import numpy as np -from ....misc.regularization import compute_penalty_matrix +from skfda.misc.regularization import compute_penalty_matrix __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" -class FPCA(ABC, BaseEstimator, TransformerMixin): - """Defines the common structure shared between classes that do functional - principal component analysis - - Parameters: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. - regularization (Regularization): - Regularization object to be applied. - """ - - def __init__(self, - n_components=3, - centering=True, - regularization=None): - self.n_components = n_components - self.centering = centering - self.regularization = regularization - - @abstractmethod - def fit(self, X, y=None): - """Computes the n_components first principal components and saves them - inside the FPCA object. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - self (object) - """ - pass - - @abstractmethod - def transform(self, X, y=None): - """Computes the n_components first principal components score and - returns them. - - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present because of fit function convention - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - pass - - def fit_transform(self, X, y=None, **fit_params): - """Computes the n_components first principal components and their scores - and returns them. - Args: - X (FDataGrid or FDataBasis): - the functional data object to be analysed - y (None, not used): - only present for convention of a fit function - - Returns: - (array_like): the scores of the data with reference to the - principal components - """ - self.fit(X, y) - return self.transform(X, y) - - -class FPCABasis(FPCA): - """Functional principal component analysis for functional data represented - in basis form. +class FPCA(BaseEstimator, TransformerMixin): + """Class that implements functional principal component analysis for both + basis and grid representations of the data. Most parameters are shared + when fitting a FDataBasis or FDataGrid, except weights and components_basis. Parameters: n_components (int): number of principal components to obtain from @@ -99,11 +28,18 @@ class FPCABasis(FPCA): centering (bool): if True then calculate the mean of the functional data object and center the data first. Defaults to True. If True the passed FDataBasis object is modified. - components_basis (Basis): the basis in which we want the principal - components. We can use a different basis than the basis contained in - the passed FDataBasis object. regularization (Regularization): Regularization object to be applied. + components_basis (Basis): the basis in which we want the principal + components. We can use a different basis than the basis contained in + the passed FDataBasis object. This parameter is only used when + fitting a FDataBasis. + weights (numpy.array or callable): the weights vector used for + discrete integration. If none then the trapezoidal rule is used for + computing the weights. If a callable object is passed, then the + weight vector will be obtained by evaluating the object at the + sample points of the passed FDataGrid object in the fit method. + This parameter is only used when fitting a FDataGrid. Attributes: components_ (FDataBasis): this contains the principal components in a @@ -113,6 +49,7 @@ class FPCABasis(FPCA): explained_variance_ratio_ (array_like): this contains the percentage of variance explained by each principal component. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. The resulting principal components are not compared because there are @@ -123,22 +60,38 @@ class FPCABasis(FPCA): >>> fd = FDataGrid(data_matrix, sample_points) >>> basis = skfda.representation.basis.Monomial((0,1), n_basis=2) >>> basis_fd = fd.to_basis(basis) - >>> fpca_basis = FPCABasis(2) + >>> fpca_basis = FPCA(2) >>> fpca_basis = fpca_basis.fit(basis_fd) + In this example we apply discretized functional PCA with some simple + data to illustrate the usage of this class. We initialize the + FPCA object, fit the artificial data and obtain the scores. + The results are not tested because there are several equivalent + possibilities. + + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> sample_points = [0, 1] + >>> fd = FDataGrid(data_matrix, sample_points) + >>> fpca_grid = FPCA(2) + >>> fpca_grid = fpca_grid.fit(fd) + + """ def __init__(self, n_components=3, - components_basis=None, centering=True, - regularization=None): - super().__init__(n_components, centering, - regularization) - # basis that we want to use for the principal components + regularization=None, + weights=None, + components_basis=None + ): + self.n_components = n_components + self.centering = centering + self.regularization = regularization + self.weights = weights self.components_basis = components_basis - def fit(self, X: FDataBasis, y=None): + def fit_basis(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the @@ -247,7 +200,7 @@ def fit(self, X: FDataBasis, y=None): return self - def transform(self, X, y=None): + def transform_basis(self, X, y=None): """Computes the n_components first principal components score and returns them. @@ -265,58 +218,7 @@ def transform(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components_) - -class FPCAGrid(FPCA): - """Funcional principal component analysis for functional data represented - in discretized form. - - Parameters: - n_components (int): number of principal components to obtain from - functional principal component analysis. Defaults to 3. - centering (bool): if True then calculate the mean of the functional data - object and center the data first. Defaults to True. If True the - passed FDataBasis object is modified. - weights (numpy.array or callable): the weights vector used for - discrete integration. If none then the trapezoidal rule is used for - computing the weights. If a callable object is passed, then the - weight vector will be obtained by evaluating the object at the - sample points of the passed FDataGrid object in the fit method. - regularization (Regularization): - Regularization object to be applied. - - Attributes: - components_ (FDataBasis): this contains the eigenvectors in a basis - form. - explained_variance_ (array_like): The amount of variance explained by - each of the selected components. - explained_variance_ratio_ (array_like): this contains the percentage of - variance explained by each principal component. - - - Examples: - In this example we apply discretized functional PCA with some simple - data to illustrate the usage of this class. We initialize the - FPCADiscretized object, fit the artificial data and obtain the scores. - The results are not tested because there are several equivalent - possibilities. - - >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) - >>> sample_points = [0, 1] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> fpca_grid = FPCAGrid(2) - >>> fpca_grid = fpca_grid.fit(fd) - """ - - def __init__(self, - n_components=3, - weights=None, - centering=True, - regularization=None): - super().__init__(n_components, centering, - regularization) - self.weights = weights - - def fit(self, X: FDataGrid, y=None): + def fit_grid(self, X: FDataGrid, y=None): r"""Computes the n_components first principal components and saves them. The eigenvalues associated with these principal @@ -417,7 +319,7 @@ def fit(self, X: FDataGrid, y=None): return self - def transform(self, X, y=None): + def transform_grid(self, X : FDataGrid, y=None): """Computes the n_components first principal components score and returns them. @@ -438,3 +340,60 @@ def transform(self, X, y=None): X.data_matrix.shape[:-1]) @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1]))) + + def fit(self, X, y=None): + """Computes the n_components first principal components and saves them + inside the FPCA object, both FDataGrid and FDataBasis are accepted + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + self (object) + """ + if isinstance(X, FDataGrid): + return self.fit_grid(X, y) + elif isinstance(X, FDataBasis): + return self.fit_basis(X, y) + else: + raise AttributeError("X must be either FDataGrid or FDataBasis") + + def transform(self, X, y=None): + """Computes the n_components first principal components score and + returns them. + + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present because of fit function convention + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + if isinstance(X, FDataGrid): + return self.fit_grid(X, y) + elif isinstance(X, FDataBasis): + return self.fit_basis(X, y) + else: + raise AttributeError("X must be either FDataGrid or FDataBasis") + + def fit_transform(self, X, y=None, **fit_params): + """Computes the n_components first principal components and their scores + and returns them. + Args: + X (FDataGrid or FDataBasis): + the functional data object to be analysed + y (None, not used): + only present for convention of a fit function + + Returns: + (array_like): the scores of the data with reference to the + principal components + """ + self.fit(X, y) + return self.transform(X, y) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 658129f32..a9b0aca52 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -2,7 +2,7 @@ from skfda.datasets import fetch_weather from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import TikhonovRegularization -from skfda.preprocessing.dim_reduction.projection import FPCABasis, FPCAGrid +from skfda.preprocessing.dim_reduction.projection import FPCA from skfda.representation.basis import Fourier import unittest @@ -12,7 +12,7 @@ class FPCATestCase(unittest.TestCase): def test_basis_fpca_fit_attributes(self): - fpca = FPCABasis() + fpca = FPCA() with self.assertRaises(AttributeError): fpca.fit(None) @@ -30,7 +30,7 @@ def test_basis_fpca_fit_attributes(self): fpca.fit(fd) def test_discretized_fpca_fit_attributes(self): - fpca = FPCAGrid() + fpca = FPCA() with self.assertRaises(AttributeError): fpca.fit(None) @@ -59,10 +59,10 @@ def test_basis_fpca_fit_result(self): basis = Fourier(n_basis=9, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) - fpca = FPCABasis(n_components=n_components, - regularization=TikhonovRegularization( - LinearDifferentialOperator(2), - regularization_parameter=1e5)) + fpca = FPCA(n_components=n_components, + regularization=TikhonovRegularization( + LinearDifferentialOperator(2), + regularization_parameter=1e5)) fpca.fit(fd_basis) # results obtained using Ramsay's R package @@ -98,7 +98,7 @@ def test_basis_fpca_regularization_fit_result(self): basis = Fourier(n_basis=9, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) - fpca = FPCABasis(n_components=n_components) + fpca = FPCA(n_components=n_components) fpca.fit(fd_basis) # results obtained using Ramsay's R package @@ -125,7 +125,7 @@ def test_grid_fpca_fit_result(self): fd_data = fetch_weather()['data'].coordinates[0] - fpca = FPCAGrid(n_components=n_components, weights=[1] * 365) + fpca = FPCA(n_components=n_components, weights=[1] * 365) fpca.fit(fd_data) # results obtained using fda.usc for the first component @@ -227,7 +227,7 @@ def test_grid_fpca_regularization_fit_result(self): fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), np.arange(0.5, 365, 1)) - fpca = FPCAGrid( + fpca = FPCA( n_components=n_components, weights=[1] * 365, regularization=TikhonovRegularization( LinearDifferentialOperator( From d442990d1816e46aff67a95920283f9b7b29a87d Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 16 May 2020 18:13:40 +0200 Subject: [PATCH 512/624] perturbations over mean --- examples/plot_fpca.py | 17 ++------- .../dim_reduction/projection/_fpca.py | 36 +++++++++++++++++++ 2 files changed, 38 insertions(+), 15 deletions(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 1498c125a..f987d5a27 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -76,27 +76,14 @@ # Now we add and subtract a multiple of the first principal component. We can # then observe now that this principal component represents the variation in # growth between the children. -mean_fd.coefficients = np.vstack([mean_fd.coefficients, - mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[0, :]]) -mean_fd.coefficients = np.vstack([mean_fd.coefficients, - mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[0, :]]) -mean_fd.plot() +fpca.get_component_perturbations(basis_fd, index=0).plot() ############################################################################## # The second component is more interesting. The most appropriate explanation is # that it represents the differences between girls and boys. Girls tend to grow # faster at an early age and boys tend to start puberty later, therefore, their # growth is more significant later. Girls also stop growing early -mean_fd = basis_fd.mean() -mean_fd.coefficients = np.vstack([mean_fd.coefficients, - mean_fd.coefficients[0, :] + - 20 * fpca.components_.coefficients[1, :]]) -mean_fd.coefficients = np.vstack([mean_fd.coefficients, - mean_fd.coefficients[0, :] - - 20 * fpca.components_.coefficients[1, :]]) -mean_fd.plot() +fpca.get_component_perturbations(basis_fd, index=1).plot() ############################################################################## # We can also specify another basis for the principal components as argument diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 94a48c1ad..6175b4eef 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -397,3 +397,39 @@ def fit_transform(self, X, y=None, **fit_params): """ self.fit(X, y) return self.transform(X, y) + + def get_component_perturbations(self, X, index=0, multiple=30): + """ Computes the perturbations over the mean function of a principal + component at a certain index. The perturbations are defined as + variations over the mean. Adding a multiple of the principal component + curve to the mean function results in the positive perturbation and + subtracting a multiple of the principal component curve results in the + negative perturbation. + + Args: + X (FDataGrid or FDataBasis): + the functional data object from which we obtain the mean + index (int): + index of the component for which we want to compute the + perturbations + multiple (float): + multiple of the principal component curve to be added or + subtracted. + + Returns: + (FDataGrid or FDataBasis): this contains the mean function followed + by the positive perturbation and the negative perturbation. + """ + if not isinstance(X, FDataBasis) and not isinstance(X, FDataGrid): + raise AttributeError("X must be either FDataGrid or FDataBasis") + if self.components_ is None: + raise ValueError("The estimator must be fitted before calling " + "this method") + if index >= self.n_components: + raise AttributeError("Index out of range") + mean_fd = X.mean() + mean_fd = mean_fd.concatenate( + mean_fd[0] + multiple * self.components_[index]) + mean_fd = mean_fd.concatenate( + mean_fd[0] - multiple * self.components_[index]) + return mean_fd From 5c682910de580353a2823167c5c8d4a2d548dba4 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 17 May 2020 17:29:09 +0200 Subject: [PATCH 513/624] create fpca module in exploratory.visualization --- examples/plot_fpca.py | 13 ++-- skfda/exploratory/visualization/__init__.py | 1 + skfda/exploratory/visualization/fpca.py | 67 +++++++++++++++++++++ 3 files changed, 74 insertions(+), 7 deletions(-) create mode 100644 skfda/exploratory/visualization/fpca.py diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index f987d5a27..492e4081c 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,6 +13,7 @@ from skfda.preprocessing.dim_reduction.projection import FPCA from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.datasets import fetch_growth +from skfda.exploratory.visualization import plot_fpca_perturbation_graphs ############################################################################## # In this example we are going to use functional principal component analysis to @@ -73,17 +74,15 @@ mean_fd.plot() ############################################################################## -# Now we add and subtract a multiple of the first principal component. We can -# then observe now that this principal component represents the variation in -# growth between the children. -fpca.get_component_perturbations(basis_fd, index=0).plot() - -############################################################################## +# Now we add and subtract a multiple of the principal components. We can +# then observe now that this principal component represents the variation in the +# mean growth between the children. # The second component is more interesting. The most appropriate explanation is # that it represents the differences between girls and boys. Girls tend to grow # faster at an early age and boys tend to start puberty later, therefore, their # growth is more significant later. Girls also stop growing early -fpca.get_component_perturbations(basis_fd, index=1).plot() + +plot_fpca_perturbation_graphs(basis_fd.mean(), fpca.components_, 30) ############################################################################## # We can also specify another basis for the principal components as argument diff --git a/skfda/exploratory/visualization/__init__.py b/skfda/exploratory/visualization/__init__.py index 8f135ae5f..838c653f2 100644 --- a/skfda/exploratory/visualization/__init__.py +++ b/skfda/exploratory/visualization/__init__.py @@ -1,3 +1,4 @@ from . import clustering, representation from ._boxplot import Boxplot, SurfaceBoxplot from ._magnitude_shape_plot import MagnitudeShapePlot +from .fpca import plot_fpca_perturbation_graphs diff --git a/skfda/exploratory/visualization/fpca.py b/skfda/exploratory/visualization/fpca.py new file mode 100644 index 000000000..cd4a49245 --- /dev/null +++ b/skfda/exploratory/visualization/fpca.py @@ -0,0 +1,67 @@ +from matplotlib import pyplot as plt +from skfda.representation import FDataGrid, FDataBasis, FData + + +def plot_fpca_perturbation_graphs(mean, components, multiple, + fig: plt.figure = None, **kwargs): + """ Plots the perturbation graphs for the principal components. + The perturbations are defined as variations over the mean. Adding a multiple + of the principal component curve to the mean function results in the + positive perturbation and subtracting a multiple of the principal component + curve results in the negative perturbation. For each principal component + curve passed, a subplot with the mean and the perturbations is shown. + + Args: + mean (FDataGrid or FDataBasis): + the functional data object containing the mean function + components (FDataGrid or FDataBasis): + the principal components + multiple (float): + multiple of the principal component curve to be added or + subtracted. + fig (figure object, optional): + figure over which the graph is plotted. If not specified it will + be initialized + + Returns: + (FDataGrid or FDataBasis): this contains the mean function followed + by the positive perturbation and the negative perturbation. + """ + if fig is None: + fig = plt.figure(figsize=(6, 4 * len(components))) + axes = fig.subplots(nrows=len(components)) + + for i in range(len(axes)): + aux = _get_component_perturbations(mean, components, i, multiple) + aux.plot(axes[i], **kwargs) + axes[i].set_title('Principal component ' + str(i + 1)) + + return fig + + +def _get_component_perturbations(mean, components, index=0, multiple=30): + """ Computes the perturbations over the mean function of a principal + component at a certain index. + + Args: + X (FDataGrid or FDataBasis): + the functional data object from which we obtain the mean + index (int): + index of the component for which we want to compute the + perturbations + multiple (float): + multiple of the principal component curve to be added or + subtracted. + + Returns: + (FDataGrid or FDataBasis): this contains the mean function followed + by the positive perturbation and the negative perturbation. + """ + if not isinstance(mean, FData): + raise AttributeError("X must be a FData object") + perturbations = mean.copy() + perturbations = perturbations.concatenate( + perturbations[0] + multiple * components[index]) + perturbations = perturbations.concatenate( + perturbations[0] - multiple * components[index]) + return perturbations From 4167fae36e8b03205df3da09e73a5d15ea242681 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 17 May 2020 17:37:30 +0200 Subject: [PATCH 514/624] doc --- docs/modules/exploratory/visualization.rst | 3 ++- docs/modules/exploratory/visualization/fpca.rst | 14 ++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) create mode 100644 docs/modules/exploratory/visualization/fpca.rst diff --git a/docs/modules/exploratory/visualization.rst b/docs/modules/exploratory/visualization.rst index cb701b337..a2de8fb3a 100644 --- a/docs/modules/exploratory/visualization.rst +++ b/docs/modules/exploratory/visualization.rst @@ -10,4 +10,5 @@ the functional data, that highlight several important aspects of it. visualization/boxplot visualization/magnitude_shape_plot - visualization/clustering \ No newline at end of file + visualization/clustering + visualization/fpca \ No newline at end of file diff --git a/docs/modules/exploratory/visualization/fpca.rst b/docs/modules/exploratory/visualization/fpca.rst new file mode 100644 index 000000000..8f22e884e --- /dev/null +++ b/docs/modules/exploratory/visualization/fpca.rst @@ -0,0 +1,14 @@ +Functional Principal Component Analysis plots +============================================= +In order to show the modes of variation that the principal components represent, +the following function is implemented + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.visualization.fpca.plot_fpca_perturbation_graphs + +See the example :ref:`sphx_glr_auto_examples_plot_fpca.py` for detailed +explanation. + + From 96e76595a41ae0c32e0ccef9e93d88d0694cfe29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 18 May 2020 12:50:25 +0200 Subject: [PATCH 515/624] Changes in covariances tests. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- tests/test_covariances.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/tests/test_covariances.py b/tests/test_covariances.py index 878ed9d20..a4e29024d 100644 --- a/tests/test_covariances.py +++ b/tests/test_covariances.py @@ -11,14 +11,10 @@ def setUp(self): self.x = np.linspace(-1, 1, 1000)[:, np.newaxis] - def _test_compare_sklearn(self, cov: skfda.misc.covariances.Covariance, - eval_y=True): + def _test_compare_sklearn(self, cov: skfda.misc.covariances.Covariance): cov_sklearn = cov.to_sklearn() cov_matrix = cov(self.x, self.x) - if eval_y: - cov_sklearn_matrix = cov_sklearn(self.x, self.x) - else: - cov_sklearn_matrix = cov_sklearn(self.x) + cov_sklearn_matrix = cov_sklearn(self.x) np.testing.assert_array_almost_equal(cov_matrix, cov_sklearn_matrix) @@ -71,4 +67,4 @@ def test_white_noise(self): for variance in [1, 2]: with self.subTest(variance=variance): cov = skfda.misc.covariances.WhiteNoise(variance=variance) - self._test_compare_sklearn(cov, eval_y=False) + self._test_compare_sklearn(cov) From 50aeac409c0eaed2b96ab9c62ab0bda467852e6e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 18 May 2020 12:54:13 +0200 Subject: [PATCH 516/624] Removing installation of requirements in Travis MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 01a0438b2..e48c60eda 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,7 +26,6 @@ matrix: - PEP8COVERAGE=true # coverage test are only install: - pip3 install --upgrade pip cython numpy || pip3 install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' - - pip3 install -r requirements.txt - | if [[ $PEP8COVERAGE == true ]]; then pip3 install flake8 || pip3 install --user flake8 From abdd75220efd05244275a9dba3e50a25bfe33c6b Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 23 May 2020 13:24:47 +0200 Subject: [PATCH 517/624] quick fix --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 94a48c1ad..9d6b1f568 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -376,9 +376,9 @@ def transform(self, X, y=None): principal components """ if isinstance(X, FDataGrid): - return self.fit_grid(X, y) + return self.transform_grid(X, y) elif isinstance(X, FDataBasis): - return self.fit_basis(X, y) + return self.transform_basis(X, y) else: raise AttributeError("X must be either FDataGrid or FDataBasis") From 92609ff732ca9a5d75c0574c74e26d30a4929f3e Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sat, 23 May 2020 14:03:40 +0200 Subject: [PATCH 518/624] added tests --- tests/test_fpca.py | 90 +++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 89 insertions(+), 1 deletion(-) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index a9b0aca52..1c61a81bb 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -56,7 +56,7 @@ def test_basis_fpca_fit_result(self): np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) + basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) fpca = FPCA(n_components=n_components, @@ -85,6 +85,67 @@ def test_basis_fpca_fit_result(self): np.testing.assert_allclose(fpca.components_.coefficients, results, atol=1e-7) + def test_basis_fpca_transform_result(self): + + n_basis = 9 + n_components = 3 + + fd_data = fetch_weather()['data'].coordinates[0] + fd_data = FDataGrid(np.squeeze(fd_data.data_matrix), + np.arange(0.5, 365, 1)) + + # initialize basis data + basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) + fd_basis = fd_data.to_basis(basis) + + fpca = FPCA(n_components=n_components, + regularization=TikhonovRegularization( + LinearDifferentialOperator(2), + regularization_parameter=1e5)) + fpca.fit(fd_basis) + scores = fpca.transform(fd_basis) + + # results obtained using Ramsay's R package + results = [[-7.68307641e+01, 5.69034443e+01, -1.22440149e+01], + [-9.02873996e+01, 1.46262257e+01, -1.78574536e+01], + [-8.21155683e+01, 3.19159491e+01, -2.56212328e+01], + [-1.14163637e+02, 3.66425562e+01, -1.00810836e+01], + [-6.97263223e+01, 1.22817168e+01, -2.39417618e+01], + [-6.41886364e+01, -1.07261045e+01, -1.10587407e+01], + [1.35824412e+02, 2.03484658e+01, -9.04815324e+00], + [-1.46816399e+01, -2.66867491e+01, -1.20233465e+01], + [1.02507511e+00, -2.29840736e+01, -9.06081296e+00], + [-3.62936903e+01, -2.09520442e+01, -1.14799951e+01], + [-4.20649313e+01, -1.13618094e+01, -6.24909009e+00], + [-7.38115985e+01, -3.18423866e+01, -1.50298626e+01], + [-6.69822456e+01, -3.35518632e+01, -1.25167352e+01], + [-1.03534763e+02, -1.29513941e+01, -1.49103879e+01], + [-1.04542036e+02, -1.36794907e+01, -1.41555965e+01], + [-7.35863347e+00, -1.41171956e+01, -2.97562788e+00], + [7.28804530e+00, -5.34421830e+01, -3.39823418e+00], + [5.59974094e+01, -4.02154080e+01, 3.78800103e-01], + [1.80778702e+02, 1.87798201e+01, -1.99043247e+01], + [-3.69700617e+00, -4.19441020e+01, 6.45820740e+00], + [3.76527216e+01, -4.23056953e+01, 1.04221757e+01], + [1.23850646e+02, -4.24648130e+01, -2.22336786e-01], + [-7.23588457e+00, -1.20579536e+01, 2.07502089e+01], + [-4.96871011e+01, 8.88483448e+00, 2.02882768e+01], + [-1.36726355e+02, -1.86472599e+01, 1.89076217e+01], + [-1.83878661e+02, 4.12118550e+01, 1.78960356e+01], + [-1.81568820e+02, 5.20817910e+01, 2.01078870e+01], + [-5.08775852e+01, 1.34600555e+01, 3.18602712e+01], + [-1.37633866e+02, 7.50809631e+01, 2.42320782e+01], + [4.98276375e+01, 1.33401270e+00, 3.50611066e+01], + [1.51149934e+02, -5.47417776e+01, 3.97592325e+01], + [1.58366096e+02, -3.80762686e+01, -5.62415023e+00], + [2.17139548e+02, 6.34055987e+01, -1.98853635e+01], + [2.33615480e+02, -7.90787574e-02, 2.69069525e+00], + [3.45371437e+02, 9.58703622e+01, 8.47570770e+00]] + results = np.array(results) + + # compare results + np.testing.assert_allclose(scores, results, atol=1e-7) + def test_basis_fpca_regularization_fit_result(self): n_basis = 9 @@ -218,6 +279,33 @@ def test_grid_fpca_fit_result(self): results, rtol=1e-6) + def test_grid_fpca_transform_result(self): + + n_components = 1 + + fd_data = fetch_weather()['data'].coordinates[0] + + fpca = FPCA(n_components=n_components, weights=[1] * 365) + fpca.fit(fd_data) + scores = fpca.transform(fd_data) + + # results obtained + results = [[[-77.05020176]], [[-90.56072204]], [[-82.39565947]], + [[-114.45375934]], [[-69.99735931]], [[-64.44894047]], + [[135.58336775]], [[-14.93460852]], [[0.75024737]], + [[-36.4781038]], [[-42.35637749]], [[-73.98910492]], + [[-67.11253749]], [[-103.68269798]], [[-104.65948079]], + [[-7.42817782]], [[7.48125036]], [[56.29792942]], + [[181.00258791]], [[-3.53294736]], [[37.94673912]], + [[124.43819913]], [[-7.04274676]], [[-49.61134859]], + [[-136.86256785]], [[-184.03502398]], [[-181.72835749]], + [[-51.06323208]], [[-137.85606731]], [[50.10941466]], + [[151.68118097]], [[159.01360046]], [[217.17981302]], + [[234.40195237]], [[345.39374006]]] + results = np.array(results) + + np.testing.assert_allclose(scores.data_matrix, results, rtol=1e-6) + def test_grid_fpca_regularization_fit_result(self): n_components = 1 From b9702999cb3eae48f6d5f158593f984ceb7eba37 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 25 May 2020 03:16:30 +0200 Subject: [PATCH 519/624] Add MatrixOperator and IntegralTransform. --- skfda/misc/operators/__init__.py | 5 ++- skfda/misc/operators/_integral_transform.py | 36 +++++++++++++++++++++ skfda/misc/operators/_operators.py | 19 +++++++++++ 3 files changed, 59 insertions(+), 1 deletion(-) create mode 100644 skfda/misc/operators/_integral_transform.py diff --git a/skfda/misc/operators/__init__.py b/skfda/misc/operators/__init__.py index b112a4c58..62d78e994 100644 --- a/skfda/misc/operators/__init__.py +++ b/skfda/misc/operators/__init__.py @@ -1,3 +1,6 @@ from ._identity import Identity +from ._integral_transform import IntegralTransform from ._linear_differential_operator import LinearDifferentialOperator -from ._operators import Operator, gramian_matrix, gramian_matrix_optimization +from ._operators import (Operator, gramian_matrix, + gramian_matrix_optimization, + MatrixOperator) diff --git a/skfda/misc/operators/_integral_transform.py b/skfda/misc/operators/_integral_transform.py new file mode 100644 index 000000000..3af06ead6 --- /dev/null +++ b/skfda/misc/operators/_integral_transform.py @@ -0,0 +1,36 @@ +import scipy.integrate + +import numpy as np + +from ...representation import FData +from ._operators import Operator, get_n_basis, gramian_matrix_optimization + + +class IntegralTransform(Operator): + """Integral operator. + + + + Attributes: + kernel_function (callable): Kernel function corresponding to + the operator. + + """ + + def __init__(self, kernel_function): + self.kernel_function = kernel_function + + def __call__(self, f): + + def evaluate_covariance(points): + + def integral_body(integration_var): + return (f(integration_var) * + self.kernel_function(integration_var, points)) + + domain_range = f.domain_range[0] + + return scipy.integrate.quad_vec( + integral_body, domain_range[0], domain_range[1])[0] + + return evaluate_covariance diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 0f5669867..3ca027f26 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -113,3 +113,22 @@ def gramian_matrix(linear_operator, basis): return matrix return gramian_matrix_numerical(linear_operator, basis) + + +class MatrixOperator(Operator): + """Linear operator for finite spaces. + + Between finite dimensional spaces, every linear operator can be expressed + as a product by a matrix. + + Attributes: + matrix (array-like object): The matrix containing the linear + transformation. + + """ + + def __init__(self, matrix): + self.matrix = matrix + + def __call__(self, f): + return self.matrix @ f From a383fabd09f1a4720d59b824142f34e0aa343edd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 25 May 2020 17:33:50 +0200 Subject: [PATCH 520/624] Improved constant and monomial gram matrices. --- skfda/representation/basis/_basis.py | 28 +++++++++++++++++++------ skfda/representation/basis/_constant.py | 4 ++++ skfda/representation/basis/_monomial.py | 26 +++++++++++++++++++++++ 3 files changed, 52 insertions(+), 6 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index a0969101d..adf22f9da 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -226,6 +226,24 @@ def _inner_matrix(self, other=None): return inner + def _gram_matrix(self): + """ + Compute the Gram matrix. + + Subclasses may override this method for improving computation + of the Gram matrix. + """ + fbasis = self.to_basis() + + gram = np.zeros((self.n_basis, self.n_basis)) + + for i in range(fbasis.n_basis): + for j in range(i, fbasis.n_basis): + gram[i, j] = fbasis[i].inner_product(fbasis[j], None, None) + gram[j, i] = gram[i, j] + + return gram + def gram_matrix(self): r"""Return the Gram Matrix of a basis @@ -241,14 +259,12 @@ def gram_matrix(self): numpy.array: Gram Matrix of the basis. """ - fbasis = self.to_basis() - gram = np.zeros((self.n_basis, self.n_basis)) + cached = getattr(self, "_gram_matrix_cached", None) - for i in range(fbasis.n_basis): - for j in range(i, fbasis.n_basis): - gram[i, j] = fbasis[i].inner_product(fbasis[j], None, None) - gram[j, i] = gram[i, j] + if cached is None: + gram = self._gram_matrix() + self._gram_matrix_cached = gram return gram diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 329f1f804..62ddfa9f2 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -38,6 +38,10 @@ def _derivative(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) + def _gram_matrix(self): + return np.array([[self.domain_range[0][1] - + self.domain_range[0][0]]]) + def basis_of_product(self, other): """Multiplication of a Constant Basis with other Basis""" if not _same_domain(self, other): diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index cde90d506..649f669b9 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -1,4 +1,7 @@ +import scipy.linalg + import numpy as np + from ..._utils import _same_domain from ._basis import Basis @@ -74,6 +77,29 @@ def _derivative(self, coefs, order=1): np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])) + def _gram_matrix(self): + integral_coefs = np.polyint(np.ones(2 * self.n_basis - 1)) + + # We obtain the powers of both extremes in the domain range + power_domain_limits = np.vander( + self.domain_range[0], 2 * self.n_basis) + + # Subtract the powers (Barrow's rule) + power_domain_limits_diff = ( + power_domain_limits[1] - power_domain_limits[0]) + + # Multiply the constants that appear in the integration + evaluated_points = integral_coefs * power_domain_limits_diff + + # Order the powers, lower to higher, discarding the constant + # (it does not appear in the integral) + ordered_evaluated_points = evaluated_points[-2::-1] + + # Build the matrix + return scipy.linalg.hankel( + ordered_evaluated_points[:self.n_basis], + ordered_evaluated_points[self.n_basis - 1:]) + def basis_of_product(self, other): """Multiplication of a Monomial Basis with other Basis""" if not _same_domain(self, other): From 317b220154eb395e41fbd6cabab02bd2488359fd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 25 May 2020 20:07:38 +0200 Subject: [PATCH 521/624] Gram matrix optimized for Fourier and BSpline basis. --- skfda/representation/basis/_bspline.py | 70 ++++++++++++++++++++++++++ skfda/representation/basis/_fourier.py | 8 +++ tests/test_basis.py | 32 ++++++------ 3 files changed, 95 insertions(+), 15 deletions(-) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 7aea889c7..fa305fba1 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -259,6 +259,76 @@ def __eq__(self, other): and self.order == other.order and self.knots == other.knots) + def _gram_matrix(self): + # Places m knots at the boundaries + knots = self._evaluation_knots() + + # c is used the select which spline the function + # PPoly.from_spline below computes + c = np.zeros(len(knots)) + + # Initialise empty list to store the piecewise polynomials + ppoly_lst = [] + + no_0_intervals = np.where(np.diff(knots) > 0)[0] + + # For each basis gets its piecewise polynomial representation + for i in range(self.n_basis): + + # Write a 1 in c in the position of the spline + # transformed in each iteration + c[i] = 1 + + # Gets the piecewise polynomial representation and gets + # only the positions for no zero length intervals + # This polynomial are defined relatively to the knots + # meaning that the column i corresponds to the ith knot. + # Let the ith knot be a + # Then f(x) = pp(x - a) + pp = PPoly.from_spline((knots, c, self.order - 1)) + pp_coefs = pp.c[:, no_0_intervals] + + # We have the coefficients for each interval in coordinates + # (x - a), so we will need to subtract a when computing the + # definite integral + ppoly_lst.append(pp_coefs) + c[i] = 0 + + # Now for each pair of basis computes the inner product after + # applying the linear differential operator + matrix = np.zeros((self.n_basis, self.n_basis)) + + for interval in range(len(no_0_intervals)): + for i in range(self.n_basis): + poly_i = np.trim_zeros(ppoly_lst[i][:, + interval], 'f') + # Indefinite integral + square = polymul(poly_i, poly_i) + integral = polyint(square) + + # Definite integral + matrix[i, i] += np.diff(polyval( + integral, self.knots[interval: interval + 2] + - self.knots[interval]))[0] + + # The Gram matrix is banded, so not all intervals are used + for j in range(i + 1, min(i + self.order, self.n_basis)): + poly_j = np.trim_zeros(ppoly_lst[j][:, interval], 'f') + + # Indefinite integral + integral = polyint(polymul(poly_i, poly_j)) + + # Definite integral + matrix[i, j] += np.diff(polyval( + integral, self.knots[interval: interval + 2] + - self.knots[interval]) + )[0] + + # The matrix is symmetric + matrix[j, i] = matrix[i, j] + + return matrix + def basis_of_product(self, other): from ._constant import Constant diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 0369c0281..38edb3092 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -177,6 +177,14 @@ def _derivative(self, coefs, order=1): # normalise return self.copy(), deriv_coefs + def _gram_matrix(self): + + # Orthogonal in this case + if self.period == (self.domain_range[0][1] - self.domain_range[0][0]): + return np.identity(self.n_basis) + else: + return super()._gram_matrix() + def basis_of_product(self, other): """Multiplication of two Fourier Basis""" if not _same_domain(self, other): diff --git a/tests/test_basis.py b/tests/test_basis.py index ca6b29b37..2fee2431f 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -87,21 +87,23 @@ def test_basis_inner_matrix(self): # TODO testing with other basis def test_basis_gram_matrix(self): - np.testing.assert_array_almost_equal(Monomial(n_basis=3).gram_matrix(), - [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) - np.testing.assert_almost_equal(Fourier(n_basis=3).gram_matrix(), - np.identity(3)) - np.testing.assert_almost_equal(BSpline(n_basis=6).gram_matrix().round(4), - np.array([[4.760e-02, 2.920e-02, 6.200e-03, 4.000e-04, 0.000e+00, 0.000e+00], - [2.920e-02, 7.380e-02, 5.210e-02, - 1.150e-02, 1.000e-04, 0.000e+00], - [6.200e-03, 5.210e-02, 1.090e-01, - 7.100e-02, 1.150e-02, 4.000e-04], - [4.000e-04, 1.150e-02, 7.100e-02, - 1.090e-01, 5.210e-02, 6.200e-03], - [0.000e+00, 1.000e-04, 1.150e-02, - 5.210e-02, 7.380e-02, 2.920e-02], - [0.000e+00, 0.000e+00, 4.000e-04, 6.200e-03, 2.920e-02, 4.760e-02]])) + np.testing.assert_allclose(Monomial(n_basis=3).gram_matrix(), + [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) + np.testing.assert_allclose(Fourier(n_basis=3).gram_matrix(), + np.identity(3)) + np.testing.assert_allclose(BSpline(n_basis=6).gram_matrix().round(4), + np.array([[4.760e-02, 2.920e-02, 6.200e-03, + 4.000e-04, 0.000e+00, 0.000e+00], + [2.920e-02, 7.380e-02, 5.210e-02, + 1.150e-02, 1.000e-04, 0.000e+00], + [6.200e-03, 5.210e-02, 1.089e-01, + 7.100e-02, 1.150e-02, 4.000e-04], + [4.000e-04, 1.150e-02, 7.100e-02, + 1.089e-01, 5.210e-02, 6.200e-03], + [0.000e+00, 1.000e-04, 1.150e-02, + 5.210e-02, 7.380e-02, 2.920e-02], + [0.000e+00, 0.000e+00, 4.000e-04, + 6.200e-03, 2.920e-02, 4.760e-02]])) def test_basis_basis_inprod(self): monomial = Monomial(n_basis=4) From a6ee2c3309bfc170b7f4e8e6116148ce9d1ecabf Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 25 May 2020 20:19:13 +0200 Subject: [PATCH 522/624] Fix bug in Gram matrix caching. --- skfda/representation/basis/_basis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index adf22f9da..4b0fe720e 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -260,9 +260,9 @@ def gram_matrix(self): """ - cached = getattr(self, "_gram_matrix_cached", None) + gram = getattr(self, "_gram_matrix_cached", None) - if cached is None: + if gram is None: gram = self._gram_matrix() self._gram_matrix_cached = gram From 5054e6a8468599af3bdbb05c7c6b41b40508f1eb Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 27 May 2020 19:51:03 +0200 Subject: [PATCH 523/624] Renamed SplineInterpolator to SplineInterpolation --- examples/plot_composition.py | 2 +- examples/plot_elastic_registration.py | 2 +- examples/plot_interpolation.py | 25 ++++++++++--------- examples/plot_representation.py | 5 ++-- skfda/datasets/_samples_generators.py | 4 +-- skfda/exploratory/visualization/_boxplot.py | 9 ++----- .../visualization/_magnitude_shape_plot.py | 4 +-- .../_linear_differential_operator.py | 2 +- .../registration/_landmark_registration.py | 4 +-- skfda/preprocessing/registration/elastic.py | 8 +++--- skfda/representation/grid.py | 4 +-- skfda/representation/interpolation.py | 8 +++--- tests/test_interpolation.py | 24 +++++++++--------- tests/test_registration.py | 4 +-- 14 files changed, 49 insertions(+), 56 deletions(-) diff --git a/examples/plot_composition.py b/examples/plot_composition.py index b390bfc70..0ddf506ce 100644 --- a/examples/plot_composition.py +++ b/examples/plot_composition.py @@ -42,7 +42,7 @@ g = skfda.FDataGrid(data_matrix, sample_points) # Sets cubic interpolation -g.interpolator = skfda.representation.interpolation.SplineInterpolator( +g.interpolator = skfda.representation.interpolation.SplineInterpolation( interpolation_order=3) # Plots the surface diff --git a/examples/plot_elastic_registration.py b/examples/plot_elastic_registration.py index 4678ee6da..40b9df06e 100644 --- a/examples/plot_elastic_registration.py +++ b/examples/plot_elastic_registration.py @@ -78,7 +78,7 @@ fd = growth['data'][growth['target'] == 0] # Obtain velocity curves -fd.interpolator = skfda.representation.interpolation.SplineInterpolator(3) +fd.interpolator = skfda.representation.interpolation.SplineInterpolation(3) fd = fd.to_grid(np.linspace(*fd.domain_range[0], 200)).derivative() fd = fd.to_grid(np.linspace(*fd.domain_range[0], 50)) fd.plot() diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index 686c3f627..dc562fa2a 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -11,12 +11,13 @@ # sphinx_gallery_thumbnail_number = 3 +import skfda +from skfda.representation.interpolation import SplineInterpolation + from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np -import skfda -from skfda.representation.interpolation import SplineInterpolator ############################################################################## @@ -48,10 +49,10 @@ # the evaluation of the object. # # Polynomial spline interpolation could be performed using the interpolator -# :class:`~skfda.representation.interpolation.SplineInterpolator`. In the +# :class:`~skfda.representation.interpolation.SplineInterpolation. In the # following example a cubic interpolator is set. -fd.interpolator = SplineInterpolator(interpolation_order=3) +fd.interpolator = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) @@ -67,13 +68,13 @@ random_state=1, error_std=.3) # Cubic interpolator -fd_smooth.interpolator = SplineInterpolator(interpolation_order=3) +fd_smooth.interpolator = SplineInterpolation(interpolation_order=3) fig = fd_smooth.plot(label="Cubic") # Smooth interpolation -fd_smooth.interpolator = SplineInterpolator(interpolation_order=3, - smoothness_parameter=1.5) +fd_smooth.interpolator = SplineInterpolation(interpolation_order=3, + smoothness_parameter=1.5) fd_smooth.plot(fig=fig, label="Cubic smoothed") @@ -95,7 +96,7 @@ fig.add_subplot(1, 1, 1) for i in range(1, 4): - fd.interpolator = SplineInterpolator(interpolation_order=i) + fd.interpolator = SplineInterpolation(interpolation_order=i) fd.plot(fig=fig, derivative=1, label=f"Degree {i}") fig.legend() @@ -130,8 +131,8 @@ fig = fd_monotone.plot(linestyle='--', label="cubic") -fd_monotone.interpolator = SplineInterpolator(interpolation_order=3, - monotone=True) +fd_monotone.interpolator = SplineInterpolation(interpolation_order=3, + monotone=True) fd_monotone.plot(fig=fig, label="PCHIP") fd_monotone.scatter(fig=fig, c='C1') @@ -167,7 +168,7 @@ # -fd.interpolator = SplineInterpolator(interpolation_order=3) +fd.interpolator = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) @@ -183,7 +184,7 @@ ############################################################################## # The following table shows the interpolation methods available by the class -# :class:`SplineInterpolator` depending on the domain dimension. +# :class:`SplineInterpolation` depending on the domain dimension. # # +------------------+--------+----------------+----------+-------------+-------------+ # | Domain dimension | Linear | Up to degree 5 | Monotone | Derivatives | Smoothing | diff --git a/examples/plot_representation.py b/examples/plot_representation.py index bdeccc7f7..3968d51fb 100644 --- a/examples/plot_representation.py +++ b/examples/plot_representation.py @@ -10,8 +10,7 @@ import skfda import skfda.representation.basis as basis -from skfda.representation.interpolation import SplineInterpolator - +from skfda.representation.interpolation import SplineInterpolation ############################################################################## # In this example we are going to show the different representations of @@ -51,7 +50,7 @@ ############################################################################## # The interpolation used can however be changed. Here, we will use an # interpolation with degree 3 splines. -first_curve.interpolator = SplineInterpolator(3) +first_curve.interpolator = SplineInterpolation(3) first_curve.plot() ############################################################################## diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index 69c7260d6..4cff12950 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -7,7 +7,7 @@ from .. import FDataGrid from ..misc import covariances from ..preprocessing.registration import normalize_warping -from ..representation.interpolation import SplineInterpolator +from ..representation.interpolation import SplineInterpolation def make_gaussian_process(n_samples: int = 100, n_features: int = 100, *, @@ -348,7 +348,7 @@ def make_random_warping(n_samples: int = 15, n_features: int = 100, *, axis=0) warping = FDataGrid(data_matrix.T, sample_points=time[:, 0]) warping = normalize_warping(warping, domain_range=(start, stop)) - warping.interpolator = SplineInterpolator(interpolation_order=3, + warping.interpolator = SplineInterpolation(interpolation_order=3, monotone=True) return warping diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index 67bf52609..b81b3c8a8 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -160,10 +160,7 @@ class Boxplot(FDataBoxplot): domain_range=array([[ 0, 10]]), dataset_label='dataset', axes_labels=['x_label', 'y_label'], - extrapolation=None, - interpolator=SplineInterpolator(interpolation_order=1, - smoothness_parameter=0.0, monotone=False), - keepdims=False), + ...), median=array([[ 0.5], [ 0.5], [ 1. ], @@ -468,9 +465,7 @@ class SurfaceBoxplot(FDataBoxplot): dataset_label='dataset', axes_labels=['x1_label', 'x2_label', 'y_label'], extrapolation=None, - interpolator=SplineInterpolator(interpolation_order=1, - smoothness_parameter=0.0, monotone=False), - keepdims=False), + ...), median=array([[[ 1. ], [ 0.7], [ 1. ]], diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 345e6457f..752f041f7 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -105,9 +105,7 @@ class MagnitudeShapePlot: dataset_label=None, axes_labels=None, extrapolation=None, - interpolator=SplineInterpolator(interpolation_order=1, - smoothness_parameter=0.0, monotone=False), - keepdims=False), + ...), depth_method=projection_depth, pointwise_weights=None, alpha=0.993, diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 2519d2ca3..2aa10aaf7 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,7 @@ from ..._utils import _same_domain from ...representation import FDataGrid from ...representation.basis import Constant, Monomial, Fourier, BSpline -from ...representation.interpolation import SplineInterpolator +from ...representation.interpolation import SplineInterpolation from ._operators import Operator, gramian_matrix_optimization diff --git a/skfda/preprocessing/registration/_landmark_registration.py b/skfda/preprocessing/registration/_landmark_registration.py index 2036569fa..95fb78446 100644 --- a/skfda/preprocessing/registration/_landmark_registration.py +++ b/skfda/preprocessing/registration/_landmark_registration.py @@ -6,7 +6,7 @@ import numpy as np from ... import FDataGrid -from ...representation.interpolation import SplineInterpolator +from ...representation.interpolation import SplineInterpolation __author__ = "Pablo Marcos Manchón" __email__ = "pablo.marcosm@estudiante.uam.es" @@ -251,7 +251,7 @@ def landmark_registration_warping(fd, landmarks, *, location=None, sample_points[-1] = fd.domain_range[0][1] sample_points[1:-1] = location - interpolator = SplineInterpolator(interpolation_order=3, monotone=True) + interpolator = SplineInterpolation(interpolation_order=3, monotone=True) warping = FDataGrid(data_matrix=data_matrix, sample_points=sample_points, diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 4ef2533e9..27004188b 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -10,7 +10,7 @@ from . import invert_warping from ... import FDataGrid from ..._utils import check_is_univariate -from ...representation.interpolation import SplineInterpolator +from ...representation.interpolation import SplineInterpolation from ._warping import _normalize_scale from .base import RegistrationTransformer @@ -451,7 +451,7 @@ def transform(self, X: FDataGrid, y=None): gamma, a=output_points[0], b=output_points[-1]) # Interpolator - interpolator = SplineInterpolator(interpolation_order=3, monotone=True) + interpolator = SplineInterpolation(interpolation_order=3, monotone=True) self.warping_ = FDataGrid(gamma, output_points, interpolator=interpolator) @@ -626,7 +626,7 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): a=original_eval_points[0], b=original_eval_points[-1]) - monotone_interpolator = SplineInterpolator(interpolation_order=3, + monotone_interpolator = SplineInterpolation(interpolation_order=3, monotone=True) mean = FDataGrid([warping_mean], sample_points=original_eval_points, @@ -699,7 +699,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, eval_points_normalized = _normalize_scale(eval_points) y_scale = eval_points[-1] - eval_points[0] - interpolator = SplineInterpolator(interpolation_order=3, monotone=True) + interpolator = SplineInterpolation(interpolation_order=3, monotone=True) # Discretisation points fdatagrid_normalized = FDataGrid(fdatagrid(eval_points) / y_scale, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 0bf4b2b16..049399198 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -18,7 +18,7 @@ from . import basis as fdbasis from .._utils import _list_of_arrays, constants from ._functional_data import FData -from .interpolation import SplineInterpolator +from .interpolation import SplineInterpolation __author__ = "Miguel Carbajo Berrocal" @@ -356,7 +356,7 @@ def interpolator(self): def interpolator(self, new_interpolator): """Sets the interpolator of the FDataGrid.""" if new_interpolator is None: - new_interpolator = SplineInterpolator() + new_interpolator = SplineInterpolation() self._interpolator = new_interpolator self._interpolator_evaluator = None diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 316664289..1a57263a7 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -12,7 +12,7 @@ # Scipy interpolator methods used internally -class SplineInterpolator(EvaluatorConstructor): +class SplineInterpolation(EvaluatorConstructor): r"""Spline interpolator of :class:`FDataGrid`. Spline interpolator of discretized functional objects. Implements different @@ -41,7 +41,7 @@ class SplineInterpolator(EvaluatorConstructor): def __init__(self, interpolation_order=1, smoothness_parameter=0., monotone=False): - r"""Constructor of the SplineInterpolator. + r"""Constructor of the SplineInterpolation. Args: interpolator_order (int, optional): Order of the interpolation, 1 @@ -81,7 +81,7 @@ def monotone(self): return self._monotone def __eq__(self, other): - """Equality operator between SplineInterpolator""" + """Equality operator between SplineInterpolation""" return (super().__eq__(other) and self.interpolation_order == other.interpolation_order and self.smoothness_parameter == other.smoothness_parameter and @@ -113,7 +113,7 @@ def __repr__(self): class SplineInterpolatorEvaluator(Evaluator): r"""Spline interpolator evaluator of :class:`FDataGrid`. - It is generated by the SplineInterpolator, and it is used internally + It is generated by the SplineInterpolation, and it is used internally during the evaluation. Spline interpolator of discretized functional objects. Implements different diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index 369f21242..4d74f9333 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -1,7 +1,7 @@ import unittest from skfda import FDataGrid -from skfda.representation.interpolation import SplineInterpolator +from skfda.representation.interpolation import SplineInterpolation import numpy as np # TODO: Unitest for grids with domain dimension > 1 @@ -197,7 +197,7 @@ def test_evaluation_cubic_simple(self): """Test basic usage of evaluation""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolator(3)) + interpolator=SplineInterpolation(3)) # Test interpolation in nodes np.testing.assert_array_almost_equal(f(np.arange(10)).round(1), @@ -212,7 +212,7 @@ def test_evaluation_cubic_point(self): """Test the evaluation of a single point""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolator(3)) + interpolator=SplineInterpolation(3)) # Test a single point np.testing.assert_array_almost_equal(f(5.3).round(3), np.array([[28.09], @@ -225,7 +225,7 @@ def test_evaluation_cubic_point(self): def test_evaluation_cubic_derivative(self): """Test derivative""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolator(3)) + interpolator=SplineInterpolation(3)) # Derivate = [2*x, 2*(9-x)] np.testing.assert_array_almost_equal(f([0.5,1.5,2.5], derivative=1).round(3), @@ -236,7 +236,7 @@ def test_evaluation_cubic_grid(self): """Test grid evaluation. With domain dimension = 1""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolator(3)) + interpolator=SplineInterpolation(3)) t = [0.5,1.5,2.5] res = np.array([[ 0.25, 2.25, 6.25], [72.25, 56.25, 42.25]]) @@ -255,7 +255,7 @@ def test_evaluation_cubic_grid(self): def test_evaluation_cubic_composed(self): f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolator(3)) + interpolator=SplineInterpolation(3)) # Evaluate (x**2, (9-x)**2) in (1,8) @@ -276,7 +276,7 @@ def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" for degree in range(1,6): - interpolator = SplineInterpolator(degree) + interpolator = SplineInterpolation(degree) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), interpolator=interpolator) @@ -289,13 +289,13 @@ def test_error_degree(self): with np.testing.assert_raises(ValueError): - interpolator = SplineInterpolator(7) + interpolator = SplineInterpolation(7) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), interpolator=interpolator) f(1) with np.testing.assert_raises(ValueError): - interpolator = SplineInterpolator(0) + interpolator = SplineInterpolation(0) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), interpolator=interpolator) f(1) @@ -320,7 +320,7 @@ def setUp(self): self.data_matrix_1_n = np.dstack((data_1,data_2)) - self.interpolator = SplineInterpolator(interpolation_order=2) + self.interpolator = SplineInterpolation(interpolation_order=2) def test_evaluation_simple(self): @@ -373,7 +373,7 @@ def test_evaluation_grid(self): """Test grid evaluation. With domain dimension = 1""" f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=SplineInterpolator(2)) + interpolator=SplineInterpolation(2)) t = [1.5,2.5,3.5] res = np.array([[[ 2.25 , 0.08721158], @@ -428,7 +428,7 @@ def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" for degree in range(1,6): - interpolator = SplineInterpolator(degree) + interpolator = SplineInterpolation(degree) f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), interpolator=interpolator) diff --git a/tests/test_registration.py b/tests/test_registration.py index e71dc56b8..b47bb0d05 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -10,7 +10,7 @@ AmplitudePhaseDecomposition, LeastSquares, SobolevLeastSquares, PairwiseCorrelation) from skfda.representation.basis import Fourier -from skfda.representation.interpolation import SplineInterpolator +from skfda.representation.interpolation import SplineInterpolation import unittest from sklearn.exceptions import NotFittedError @@ -25,7 +25,7 @@ def setUp(self): """Initialization of samples""" self.time = np.linspace(-1, 1, 50) - interpolator = SplineInterpolator(3, monotone=True) + interpolator = SplineInterpolation(3, monotone=True) self.polynomial = FDataGrid([self.time**3, self.time**5], self.time, interpolator=interpolator) From ea1e773b44f59626a5fe3b0f6df4b47d397a31d9 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 27 May 2020 20:05:47 +0200 Subject: [PATCH 524/624] Rename `SplineInterpolatorEvaluator` to `SplineInterpolationEvaluator` --- skfda/representation/interpolation.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 1a57263a7..586519b8c 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -88,17 +88,17 @@ def __eq__(self, other): self.monotone == other.monotone) def evaluator(self, fdatagrid): - """Construct a SplineInterpolatorEvaluator used in the evaluation. + """Construct a SplineInterpolationEvaluator used in the evaluation. Args: fdatagrid (:class:`FDataGrid`): Functional object where the evaluator will be used. Returns: - (:class:`SplineInterpolatorEvaluator`): Evaluator of the fdatagrid. + (:class:`SplineInterpolationEvaluator`): Evaluator of the fdatagrid. """ - return SplineInterpolatorEvaluator(fdatagrid, self.interpolation_order, + return SplineInterpolationEvaluator(fdatagrid, self.interpolation_order, self.smoothness_parameter, self.monotone) @@ -110,7 +110,7 @@ def __repr__(self): f"monotone={self.monotone})") -class SplineInterpolatorEvaluator(Evaluator): +class SplineInterpolationEvaluator(Evaluator): r"""Spline interpolator evaluator of :class:`FDataGrid`. It is generated by the SplineInterpolation, and it is used internally @@ -141,7 +141,7 @@ class SplineInterpolatorEvaluator(Evaluator): """ def __init__(self, fdatagrid, k=1, s=0., monotone=False): - r"""Constructor of the SplineInterpolatorEvaluator. + r"""Constructor of the SplineInterpolationEvaluator. Args: fdatagir (fdatagrid): Grid to be interpolated. From 93f074aea3f2d5408e45e4898305e47e2b88d3b5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 28 May 2020 00:57:23 +0200 Subject: [PATCH 525/624] Rename grid attribute `interpolator` to `interpolation`. --- docs/modules/representation.rst | 2 +- examples/plot_composition.py | 5 +- examples/plot_elastic_registration.py | 7 +- examples/plot_interpolation.py | 30 +- examples/plot_representation.py | 7 +- skfda/datasets/_samples_generators.py | 4 +- .../registration/_landmark_registration.py | 4 +- skfda/preprocessing/registration/elastic.py | 17 +- skfda/representation/evaluator.py | 2 +- skfda/representation/grid.py | 44 +-- skfda/representation/interpolation.py | 64 ++--- tests/test_interpolation.py | 270 +++++++++--------- tests/test_registration.py | 4 +- 13 files changed, 230 insertions(+), 230 deletions(-) diff --git a/docs/modules/representation.rst b/docs/modules/representation.rst index f5c8719ad..50d25b35d 100644 --- a/docs/modules/representation.rst +++ b/docs/modules/representation.rst @@ -30,7 +30,7 @@ following class allows interpolation with different splines. .. autosummary:: :toctree: autosummary - skfda.representation.interpolation.SplineInterpolator + skfda.representation.interpolation.SplineInterpolation Basis representation diff --git a/examples/plot_composition.py b/examples/plot_composition.py index 0ddf506ce..9fdac453f 100644 --- a/examples/plot_composition.py +++ b/examples/plot_composition.py @@ -10,10 +10,11 @@ # sphinx_gallery_thumbnail_number = 3 +import skfda + from mpl_toolkits.mplot3d import axes3d import numpy as np -import skfda ############################################################################## @@ -42,7 +43,7 @@ g = skfda.FDataGrid(data_matrix, sample_points) # Sets cubic interpolation -g.interpolator = skfda.representation.interpolation.SplineInterpolation( +g.interpolation = skfda.representation.interpolation.SplineInterpolation( interpolation_order=3) # Plots the surface diff --git a/examples/plot_elastic_registration.py b/examples/plot_elastic_registration.py index 40b9df06e..7c9bc898e 100644 --- a/examples/plot_elastic_registration.py +++ b/examples/plot_elastic_registration.py @@ -10,13 +10,13 @@ # sphinx_gallery_thumbnail_number = 5 -import numpy as np import skfda - from skfda.datasets import make_multimodal_samples, fetch_growth from skfda.preprocessing.registration import ElasticRegistration from skfda.preprocessing.registration.elastic import elastic_mean +import numpy as np + ############################################################################## # In the example of pairwise alignment was shown the usage of @@ -46,7 +46,6 @@ # deformations of the curves. - fig = fd.mean().plot(label="L2 mean") elastic_mean(fd).plot(fig=fig, label="Elastic mean") fig.legend() @@ -78,7 +77,7 @@ fd = growth['data'][growth['target'] == 0] # Obtain velocity curves -fd.interpolator = skfda.representation.interpolation.SplineInterpolation(3) +fd.interpolation = skfda.representation.interpolation.SplineInterpolation(3) fd = fd.to_grid(np.linspace(*fd.domain_range[0], 200)).derivative() fd = fd.to_grid(np.linspace(*fd.domain_range[0], 50)) fd.plot() diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index dc562fa2a..ef3155e23 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -45,14 +45,14 @@ ############################################################################## # The interpolation method of the FDataGrid could be changed setting the -# attribute ``interpolator``. Once we have set an interpolator it is used for +# attribute ``interpolation``. Once we have set an interpolation it is used for # the evaluation of the object. # -# Polynomial spline interpolation could be performed using the interpolator +# Polynomial spline interpolation could be performed using the interpolation # :class:`~skfda.representation.interpolation.SplineInterpolation. In the -# following example a cubic interpolator is set. +# following example a cubic interpolation is set. -fd.interpolator = SplineInterpolation(interpolation_order=3) +fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) @@ -60,21 +60,21 @@ ############################################################################## # Smooth interpolation could be performed with the attribute -# ``smoothness_parameter`` of the spline interpolator. +# ``smoothness_parameter`` of the spline interpolation. # # Sample with noise fd_smooth = skfda.datasets.make_sinusoidal_process(n_samples=1, n_features=30, random_state=1, error_std=.3) -# Cubic interpolator -fd_smooth.interpolator = SplineInterpolation(interpolation_order=3) +# Cubic interpolation +fd_smooth.interpolation = SplineInterpolation(interpolation_order=3) fig = fd_smooth.plot(label="Cubic") # Smooth interpolation -fd_smooth.interpolator = SplineInterpolation(interpolation_order=3, - smoothness_parameter=1.5) +fd_smooth.interpolation = SplineInterpolation(interpolation_order=3, + smoothness_parameter=1.5) fd_smooth.plot(fig=fig, label="Cubic smoothed") @@ -96,7 +96,7 @@ fig.add_subplot(1, 1, 1) for i in range(1, 4): - fd.interpolator = SplineInterpolation(interpolation_order=i) + fd.interpolation = SplineInterpolation(interpolation_order=i) fd.plot(fig=fig, derivative=1, label=f"Degree {i}") fig.legend() @@ -131,15 +131,15 @@ fig = fd_monotone.plot(linestyle='--', label="cubic") -fd_monotone.interpolator = SplineInterpolation(interpolation_order=3, - monotone=True) +fd_monotone.interpolation = SplineInterpolation(interpolation_order=3, + monotone=True) fd_monotone.plot(fig=fig, label="PCHIP") fd_monotone.scatter(fig=fig, c='C1') fig.legend() ############################################################################## -# All the interpolators will work regardless of the dimension of the image, but +# All the interpolations will work regardless of the dimension of the image, but # depending on the domain dimension some methods will not be available. # # For the next examples it is constructed a surface, :math:`x_i: \mathbb{R}^2 @@ -161,14 +161,14 @@ # In the following figure it is shown the result of the cubic interpolation # applied to the surface. # -# The degree of the interpolator polynomial does not have to coincide in both +# The degree of the interpolation polynomial does not have to coincide in both # directions, for example, cubic interpolation in the first # component and quadratic in the second one could be defined using a tuple with # the values (3,2). # -fd.interpolator = SplineInterpolation(interpolation_order=3) +fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) diff --git a/examples/plot_representation.py b/examples/plot_representation.py index 3968d51fb..1763c4887 100644 --- a/examples/plot_representation.py +++ b/examples/plot_representation.py @@ -9,9 +9,11 @@ # License: MIT import skfda -import skfda.representation.basis as basis from skfda.representation.interpolation import SplineInterpolation +import skfda.representation.basis as basis + + ############################################################################## # In this example we are going to show the different representations of # functional data available in scikit-fda. @@ -20,7 +22,6 @@ # Growth Study. This dataset correspond to the height of several boys and # girls measured until the 18 years of age. The number and times of the # measurements are the same for each individual. - dataset = skfda.datasets.fetch_growth() fd = dataset['data'] y = dataset['target'] @@ -50,7 +51,7 @@ ############################################################################## # The interpolation used can however be changed. Here, we will use an # interpolation with degree 3 splines. -first_curve.interpolator = SplineInterpolation(3) +first_curve.interpolation = SplineInterpolation(3) first_curve.plot() ############################################################################## diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index 4cff12950..059cc3489 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -348,7 +348,7 @@ def make_random_warping(n_samples: int = 15, n_features: int = 100, *, axis=0) warping = FDataGrid(data_matrix.T, sample_points=time[:, 0]) warping = normalize_warping(warping, domain_range=(start, stop)) - warping.interpolator = SplineInterpolation(interpolation_order=3, - monotone=True) + warping.interpolation = SplineInterpolation(interpolation_order=3, + monotone=True) return warping diff --git a/skfda/preprocessing/registration/_landmark_registration.py b/skfda/preprocessing/registration/_landmark_registration.py index 95fb78446..5e8a96208 100644 --- a/skfda/preprocessing/registration/_landmark_registration.py +++ b/skfda/preprocessing/registration/_landmark_registration.py @@ -251,11 +251,11 @@ def landmark_registration_warping(fd, landmarks, *, location=None, sample_points[-1] = fd.domain_range[0][1] sample_points[1:-1] = location - interpolator = SplineInterpolation(interpolation_order=3, monotone=True) + interpolation = SplineInterpolation(interpolation_order=3, monotone=True) warping = FDataGrid(data_matrix=data_matrix, sample_points=sample_points, - interpolator=interpolator, + interpolation=interpolation, extrapolation='bounds') try: diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 27004188b..1bb6fec69 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -450,11 +450,12 @@ def transform(self, X: FDataGrid, y=None): gamma = _normalize_scale( gamma, a=output_points[0], b=output_points[-1]) - # Interpolator - interpolator = SplineInterpolation(interpolation_order=3, monotone=True) + # Interpolation + interpolation = SplineInterpolation( + interpolation_order=3, monotone=True) self.warping_ = FDataGrid(gamma, output_points, - interpolator=interpolator) + interpolation=interpolation) return X.compose(self.warping_, eval_points=output_points) @@ -626,11 +627,11 @@ def warping_mean(warping, *, max_iter=100, tol=1e-6, step_size=.3): a=original_eval_points[0], b=original_eval_points[-1]) - monotone_interpolator = SplineInterpolation(interpolation_order=3, - monotone=True) + monotone_interpolation = SplineInterpolation(interpolation_order=3, + monotone=True) mean = FDataGrid([warping_mean], sample_points=original_eval_points, - interpolator=monotone_interpolator) + interpolation=monotone_interpolation) return mean @@ -699,7 +700,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, eval_points_normalized = _normalize_scale(eval_points) y_scale = eval_points[-1] - eval_points[0] - interpolator = SplineInterpolation(interpolation_order=3, monotone=True) + interpolation = SplineInterpolation(interpolation_order=3, monotone=True) # Discretisation points fdatagrid_normalized = FDataGrid(fdatagrid(eval_points) / y_scale, @@ -724,7 +725,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, gammas = _elastic_alignment_array( mu, srsf, eval_points_normalized, penalty, grid_dim) gammas = FDataGrid(gammas, sample_points=eval_points_normalized, - interpolator=interpolator) + interpolation=interpolation) fdatagrid_normalized = fdatagrid_normalized.compose(gammas) srsf = srsf_transformer.transform( diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index 896a17147..4cf7c8423 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -41,7 +41,7 @@ class Evaluator(ABC): An evaluator defines how to evaluate points of a functional object, it can be used as extrapolator to evaluate points outside the domain range or - as interpolator in a :class:`FDataGrid`. The corresponding examples of + as interpolation in a :class:`FDataGrid`. The corresponding examples of Interpolation and Extrapolation shows the basic usage of this class. The evaluator is called internally by :func:`evaluate`. diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 049399198..6fb7440ae 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -47,7 +47,7 @@ class FDataGrid(FData): extrapolation. By default None, which does not apply any type of extrapolation. See `Extrapolation` for detailled information of the types of extrapolation. - interpolator (GridInterpolator): Defines the type of interpolation + interpolation (GridInterpolation): Defines the type of interpolation applied in `evaluate`. keepdims (bool): @@ -129,7 +129,7 @@ def __len__(self): def __init__(self, data_matrix, sample_points=None, domain_range=None, dataset_label=None, axes_labels=None, extrapolation=None, - interpolator=None, keepdims=False): + interpolation=None, keepdims=False): """Construct a FDataGrid object. Args: @@ -200,7 +200,7 @@ def __init__(self, data_matrix, sample_points=None, if self.data_matrix.ndim == 1 + self.dim_domain: self.data_matrix = self.data_matrix[..., np.newaxis] - self.interpolator = interpolator + self.interpolation = interpolation super().__init__(extrapolation, dataset_label, axes_labels, keepdims) @@ -348,27 +348,27 @@ def domain_range(self): return self._domain_range @property - def interpolator(self): + def interpolation(self): """Defines the type of interpolation applied in `evaluate`.""" - return self._interpolator + return self._interpolation - @interpolator.setter - def interpolator(self, new_interpolator): - """Sets the interpolator of the FDataGrid.""" - if new_interpolator is None: - new_interpolator = SplineInterpolation() + @interpolation.setter + def interpolation(self, new_interpolation): + """Sets the interpolation of the FDataGrid.""" + if new_interpolation is None: + new_interpolation = SplineInterpolation() - self._interpolator = new_interpolator - self._interpolator_evaluator = None + self._interpolation = new_interpolation + self._interpolation_evaluator = None @property def _evaluator(self): - """Return the evaluator constructed by the interpolator.""" + """Return the evaluator constructed by the interpolation.""" - if self._interpolator_evaluator is None: - self._interpolator_evaluator = self._interpolator.evaluator(self) + if self._interpolation_evaluator is None: + self._interpolation_evaluator = self._interpolation.evaluator(self) - return self._interpolator_evaluator + return self._interpolation_evaluator def _evaluate(self, eval_points, *, derivative=0): """"Evaluate the object or its derivatives at a list of values. @@ -586,7 +586,7 @@ def __eq__(self, other): if self.extrapolation != other.extrapolation: return False - if self.interpolator != other.interpolator: + if self.interpolation != other.interpolation: return False return True @@ -863,7 +863,7 @@ def copy(self, *, data_matrix=None, sample_points=None, domain_range=None, dataset_label=None, axes_labels=None, extrapolation=None, - interpolator=None, keepdims=None): + interpolation=None, keepdims=None): """Returns a copy of the FDataGrid. If an argument is provided the corresponding attribute in the new copy @@ -891,8 +891,8 @@ def copy(self, *, if extrapolation is None: extrapolation = self.extrapolation - if interpolator is None: - interpolator = self.interpolator + if interpolation is None: + interpolation = self.interpolation if keepdims is None: keepdims = self.keepdims @@ -901,7 +901,7 @@ def copy(self, *, domain_range=domain_range, dataset_label=dataset_label, axes_labels=axes_labels, extrapolation=extrapolation, - interpolator=interpolator, keepdims=keepdims) + interpolation=interpolation, keepdims=keepdims) def shift(self, shifts, *, restrict_domain=False, extrapolation=None, eval_points=None): @@ -1075,7 +1075,7 @@ def __repr__(self): f"\ndataset_label={repr(self.dataset_label)}," f"\naxes_labels={repr(axes_labels)}," f"\nextrapolation={repr(self.extrapolation)}," - f"\ninterpolator={repr(self.interpolator)}," + f"\ninterpolation={repr(self.interpolation)}," f"\nkeepdims={repr(self.keepdims)})").replace('\n', '\n ') def __getitem__(self, key): diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 586519b8c..2daf0cb28 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -11,18 +11,18 @@ from .evaluator import Evaluator, EvaluatorConstructor -# Scipy interpolator methods used internally +# Scipy interpolation methods used internally class SplineInterpolation(EvaluatorConstructor): - r"""Spline interpolator of :class:`FDataGrid`. + r"""Spline interpolation of :class:`FDataGrid`. - Spline interpolator of discretized functional objects. Implements different + Spline interpolation of discretized functional objects. Implements different interpolation methods based in splines, using the sample points of the grid as nodes to interpolate. See the interpolation example to a detailled explanation. Attributes: - interpolator_order (int, optional): Order of the interpolation, 1 + interpolation_order (int, optional): Order of the interpolation, 1 for linear interpolation, 2 for cuadratic, 3 for cubic and so on. In case of curves and surfaces there is available interpolation up to degree 5. For higher dimensional objects @@ -44,7 +44,7 @@ def __init__(self, interpolation_order=1, smoothness_parameter=0., r"""Constructor of the SplineInterpolation. Args: - interpolator_order (int, optional): Order of the interpolation, 1 + interpolation_order (int, optional): Order of the interpolation, 1 for linear interpolation, 2 for cuadratic, 3 for cubic and so on. In case of curves and surfaces there is available interpolation up to degree 5. For higher dimensional objects @@ -55,7 +55,7 @@ def __init__(self, interpolation_order=1, smoothness_parameter=0., surfaces. If 0 the residuals of the interpolation will be 0. Defaults 0. monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolator. Only valid for curves + curves using a PCHIP interpolation. Only valid for curves (domain dimension equal to 1) and interpolation order equal to 1 or 3. Defaults false. @@ -99,11 +99,11 @@ def evaluator(self, fdatagrid): """ return SplineInterpolationEvaluator(fdatagrid, self.interpolation_order, - self.smoothness_parameter, - self.monotone) + self.smoothness_parameter, + self.monotone) def __repr__(self): - """repr method of the interpolator""" + """repr method of the interpolation""" return (f"{type(self).__name__}(" f"interpolation_order={self.interpolation_order}, " f"smoothness_parameter={self.smoothness_parameter}, " @@ -111,19 +111,19 @@ def __repr__(self): class SplineInterpolationEvaluator(Evaluator): - r"""Spline interpolator evaluator of :class:`FDataGrid`. + r"""Spline interpolation evaluator of :class:`FDataGrid`. It is generated by the SplineInterpolation, and it is used internally during the evaluation. - Spline interpolator of discretized functional objects. Implements different + Spline interpolation of discretized functional objects. Implements different interpolation methods based in splines, using the sample points of the grid as nodes to interpolate. See the interpolation example to a detailled explanation. Attributes: - interpolator_order (int, optional): Order of the interpolation, 1 + interpolation_order (int, optional): Order of the interpolation, 1 for linear interpolation, 2 for cuadratic, 3 for cubic and so on. In case of curves and surfaces there is available interpolation up to degree 5. For higher dimensional objects @@ -145,7 +145,7 @@ def __init__(self, fdatagrid, k=1, s=0., monotone=False): Args: fdatagir (fdatagrid): Grid to be interpolated. - interpolator_order (int, optional): Order of the interpolation, 1 + interpolation_order (int, optional): Order of the interpolation, 1 for linear interpolation, 2 for cuadratic, 3 for cubic and so on. In case of curves and surfaces there is available interpolation up to degree 5. For higher dimensional objects @@ -156,7 +156,7 @@ def __init__(self, fdatagrid, k=1, s=0., monotone=False): surfaces. If 0 the residuals of the interpolation will be 0. Defaults 0. monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolator. Only valid for curves + curves using a PCHIP interpolation. Only valid for curves (domain dimension equal to 1) and interpolation order equal to 1 or 3. Defaults false. @@ -198,23 +198,23 @@ def __init__(self, fdatagrid, k=1, s=0., monotone=False): def _construct_spline_1_m(self, sample_points, data_matrix, k, s, monotone): - r"""Construct the matrix of interpolators for curves. + r"""Construct the matrix of interpolations for curves. - Constructs the matrix of interpolators for objects with domain + Constructs the matrix of interpolations for objects with domain dimension = 1. Calling internally during the creationg of the evaluator. - Uses internally the scipy interpolator UnivariateSpline or + Uses internally the scipy interpolation UnivariateSpline or PchipInterpolator. Args: sample_points (np.ndarray): Sample points of the fdatagrid. data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolators. + k (integer): Order of the spline interpolations. Returns: (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolator of the sample i, and image dimension j + corresponding interpolation of the sample i, and image dimension j in the entry (i,j) of the array. Raises: @@ -259,34 +259,34 @@ def _process_derivative_1_m(derivative): if monotone: def constructor(data): - """Constructs an unidimensional cubic monotone interpolator""" + """Constructs an unidimensional cubic monotone interpolation""" return PchipInterpolator(sample_points, data) else: def constructor(data): - """Constructs an unidimensional interpolator""" + """Constructs an unidimensional interpolation""" return UnivariateSpline(sample_points, data, s=s, k=k) return np.apply_along_axis(constructor, 1, data_matrix) def _construct_spline_2_m(self, sample_points, data_matrix, k, s): - r"""Construct the matrix of interpolators for surfaces. + r"""Construct the matrix of interpolations for surfaces. - Constructs the matrix of interpolators for objects with domain + Constructs the matrix of interpolations for objects with domain dimension = 2. Calling internally during the creationg of the evaluator. - Uses internally the scipy interpolator RectBivariateSpline. + Uses internally the scipy interpolation RectBivariateSpline. Args: sample_points (np.ndarray): Sample points of the fdatagrid. data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolators. + k (integer): Order of the spline interpolations. Returns: (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolator of the sample i, and image dimension j + corresponding interpolation of the sample i, and image dimension j in the entry (i,j) of the array. Raises: @@ -336,24 +336,24 @@ def _process_derivative_2_m(derivative): return spline def _construct_spline_n_m(self, sample_points, data_matrix, k): - r"""Construct the matrix of interpolators. + r"""Construct the matrix of interpolations. - Constructs the matrix of interpolators for objects with domain + Constructs the matrix of interpolations for objects with domain dimension > 2. Calling internally during the creationg of the evaluator. - Only linear and nearest interpolators are available for objects with - domain dimension >= 3. Uses internally the scipy interpolator + Only linear and nearest interpolations are available for objects with + domain dimension >= 3. Uses internally the scipy interpolation RegularGridInterpolator. Args: sample_points (np.ndarray): Sample points of the fdatagrid. data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolators. + k (integer): Order of the spline interpolations. Returns: (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolator of the sample i, and image dimension j + corresponding interpolation of the sample i, and image dimension j in the entry (i,j) of the array. Raises: diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index 4d74f9333..adebea9a4 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -1,13 +1,14 @@ -import unittest from skfda import FDataGrid from skfda.representation.interpolation import SplineInterpolation +import unittest + import numpy as np -# TODO: Unitest for grids with domain dimension > 1 +# TODO: Unitest for grids with domain dimension > 1 class TestEvaluationSpline1_1(unittest.TestCase): - """Test the evaluation of a grid spline interpolator with + """Test the evaluation of a grid spline interpolation with domain and image dimension equal to 1. """ @@ -19,7 +20,6 @@ def setUp(self): self.data_matrix_1_1 = [np.arange(10)**2, np.arange(start=9, stop=-1, step=-1)**2] - def test_evaluation_linear_simple(self): """Test basic usage of evaluation""" @@ -30,9 +30,9 @@ def test_evaluation_linear_simple(self): f(np.arange(10)), self.data_matrix_1_1) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([0.5,1.5,2.5]), - np.array([[ 0.5, 2.5, 6.5], - [72.5, 56.5, 42.5]])) + np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5]), + np.array([[0.5, 2.5, 6.5], + [72.5, 56.5, 42.5]])) def test_evaluation_linear_point(self): """Test the evaluation of a single point""" @@ -41,20 +41,19 @@ def test_evaluation_linear_point(self): # Test a single point np.testing.assert_array_almost_equal(f(5.3).round(1), - np.array([[28.3], [13.9]])) + np.array([[28.3], [13.9]])) np.testing.assert_array_almost_equal(f([3]), np.array([[9.], [36.]])) np.testing.assert_array_almost_equal(f((2,)), np.array([[4.], [49.]])) - def test_evaluation_linear_derivative(self): """Test derivative""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10)) # Derivate = [2*x, 2*(9-x)] np.testing.assert_array_almost_equal( - f([0.5,1.5,2.5], derivative=1).round(3), - np.array([[ 1., 3., 5.], - [-17., -15., -13.]])) + f([0.5, 1.5, 2.5], derivative=1).round(3), + np.array([[1., 3., 5.], + [-17., -15., -13.]])) def test_evaluation_linear_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -65,8 +64,8 @@ def test_evaluation_linear_grid(self): np.testing.assert_array_almost_equal(f(np.arange(10)), self.data_matrix_1_1) - res = np.array([[ 0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) - t = [0.5,1.5,2.5] + res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) + t = [0.5, 1.5, 2.5] # Test evaluation in a list of times np.testing.assert_array_almost_equal(f(t, grid=True), res) @@ -77,28 +76,29 @@ def test_evaluation_linear_grid(self): np.array([[9.], [36.]])) # Check erroneous axis - with np.testing.assert_raises(ValueError): f((t,t), grid=True) + with np.testing.assert_raises(ValueError): + f((t, t), grid=True) def test_evaluation_linear_composed(self): f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10)) # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal(f([[1],[8]], + np.testing.assert_array_almost_equal(f([[1], [8]], aligned_evaluation=False), - np.array([[1.], [1.]])) + np.array([[1.], [1.]])) - t = np.linspace(4,6,4) + t = np.linspace(4, 6, 4) np.testing.assert_array_almost_equal( - f([t,9-t], aligned_evaluation=False).round(2), - np.array([[16. , 22. , 28.67, 36. ], - [16. , 22. , 28.67, 36. ]])) + f([t, 9 - t], aligned_evaluation=False).round(2), + np.array([[16., 22., 28.67, 36.], + [16., 22., 28.67, 36.]])) # Same length than nsample - t = np.linspace(4,6,2) + t = np.linspace(4, 6, 2) np.testing.assert_array_almost_equal( - f([t,9-t], aligned_evaluation=False).round(2), - np.array([[16. , 36.], [16. , 36.]])) + f([t, 9 - t], aligned_evaluation=False).round(2), + np.array([[16., 36.], [16., 36.]])) def test_evaluation_linear_keepdims(self): """Test parameter keepdims""" @@ -111,10 +111,9 @@ def test_evaluation_linear_keepdims(self): fk = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), keepdims=True) - t = [0.5,1.5,2.5] - res = np.array([[ 0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) - res_keepdims = res.reshape((2,3,1)) - + t = [0.5, 1.5, 2.5] + res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) + res_keepdims = res.reshape((2, 3, 1)) # Test combinations of keepdims with list np.testing.assert_array_almost_equal(f(t), res) @@ -123,20 +122,23 @@ def test_evaluation_linear_keepdims(self): np.testing.assert_array_almost_equal(fk(t), res_keepdims) np.testing.assert_array_almost_equal(fk(t, keepdims=False), res) - np.testing.assert_array_almost_equal(fk(t, keepdims=True), res_keepdims) + np.testing.assert_array_almost_equal( + fk(t, keepdims=True), res_keepdims) t2 = 4 res2 = np.array([[16.], [25.]]) - res2_keepdims = res2.reshape(2,1,1) + res2_keepdims = res2.reshape(2, 1, 1) # Test combinations of keepdims with a single point np.testing.assert_array_almost_equal(f(t2), res2) np.testing.assert_array_almost_equal(f(t2, keepdims=False), res2) - np.testing.assert_array_almost_equal(f(t2, keepdims=True), res2_keepdims) + np.testing.assert_array_almost_equal( + f(t2, keepdims=True), res2_keepdims) np.testing.assert_array_almost_equal(fk(t2), res2_keepdims) np.testing.assert_array_almost_equal(fk(t2, keepdims=False), res2) - np.testing.assert_array_almost_equal(fk(t2, keepdims=True), res2_keepdims) + np.testing.assert_array_almost_equal( + fk(t2, keepdims=True), res2_keepdims) def test_evaluation_composed_linear_keepdims(self): """Test parameter keepdims with composed evaluation""" @@ -150,23 +152,24 @@ def test_evaluation_composed_linear_keepdims(self): keepdims=True) t = np.array([1, 2, 3]) - t = [t, 9 - t] - res = np.array([[ 1., 4., 9.], [ 1., 4., 9.]]) - res_keepdims = res.reshape((2,3,1)) + t = [t, 9 - t] + res = np.array([[1., 4., 9.], [1., 4., 9.]]) + res_keepdims = res.reshape((2, 3, 1)) # Test combinations of keepdims with list - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False), res) + np.testing.assert_array_almost_equal( + f(t, aligned_evaluation=False), res) np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=False), res) + keepdims=False), res) np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=True), res_keepdims) + keepdims=True), res_keepdims) np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False), - res_keepdims) + res_keepdims) np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False, - keepdims=False), res) + keepdims=False), res) np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False, - keepdims=True), res_keepdims) + keepdims=True), res_keepdims) def test_evaluation_grid_linear_keepdims(self): """Test grid evaluation with keepdims""" @@ -179,130 +182,132 @@ def test_evaluation_grid_linear_keepdims(self): fk = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), keepdims=True) - t = [0.5,1.5,2.5] - res = np.array([[ 0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) - res_keepdims = res.reshape(2,3,1) + t = [0.5, 1.5, 2.5] + res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) + res_keepdims = res.reshape(2, 3, 1) np.testing.assert_array_almost_equal(f(t, grid=True), res) np.testing.assert_array_almost_equal(f((t,), grid=True, keepdims=True), - res_keepdims) - np.testing.assert_array_almost_equal(f([t], grid=True, keepdims=False), res) + res_keepdims) + np.testing.assert_array_almost_equal( + f([t], grid=True, keepdims=False), res) np.testing.assert_array_almost_equal(fk(t, grid=True), res_keepdims) np.testing.assert_array_almost_equal(fk((t,), grid=True, keepdims=True), - res_keepdims) - np.testing.assert_array_almost_equal(fk([t], grid=True, keepdims=False), res) + res_keepdims) + np.testing.assert_array_almost_equal( + fk([t], grid=True, keepdims=False), res) def test_evaluation_cubic_simple(self): """Test basic usage of evaluation""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolation(3)) + interpolation=SplineInterpolation(3)) # Test interpolation in nodes np.testing.assert_array_almost_equal(f(np.arange(10)).round(1), - self.data_matrix_1_1) + self.data_matrix_1_1) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([0.5,1.5,2.5]).round(2), - np.array([[ 0.25, 2.25, 6.25], - [72.25, 56.25, 42.25]])) + np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5]).round(2), + np.array([[0.25, 2.25, 6.25], + [72.25, 56.25, 42.25]])) def test_evaluation_cubic_point(self): """Test the evaluation of a single point""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolation(3)) + interpolation=SplineInterpolation(3)) # Test a single point np.testing.assert_array_almost_equal(f(5.3).round(3), np.array([[28.09], - [13.69]])) - - np.testing.assert_array_almost_equal(f([3]).round(3), np.array([[9.], [36.]])) - np.testing.assert_array_almost_equal(f((2,)).round(3), np.array([[4.], [49.]])) + [13.69]])) + np.testing.assert_array_almost_equal( + f([3]).round(3), np.array([[9.], [36.]])) + np.testing.assert_array_almost_equal( + f((2,)).round(3), np.array([[4.], [49.]])) def test_evaluation_cubic_derivative(self): """Test derivative""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolation(3)) + interpolation=SplineInterpolation(3)) # Derivate = [2*x, 2*(9-x)] - np.testing.assert_array_almost_equal(f([0.5,1.5,2.5], derivative=1).round(3), - np.array([[ 1., 3., 5.], - [-17., -15., -13.]])) + np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5], derivative=1).round(3), + np.array([[1., 3., 5.], + [-17., -15., -13.]])) def test_evaluation_cubic_grid(self): """Test grid evaluation. With domain dimension = 1""" f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolation(3)) - - t = [0.5,1.5,2.5] - res = np.array([[ 0.25, 2.25, 6.25], [72.25, 56.25, 42.25]]) + interpolation=SplineInterpolation(3)) + t = [0.5, 1.5, 2.5] + res = np.array([[0.25, 2.25, 6.25], [72.25, 56.25, 42.25]]) # Test evaluation in a list of times np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) np.testing.assert_array_almost_equal(f((t,), grid=True).round(3), res) np.testing.assert_array_almost_equal(f([t], grid=True).round(3), res) # Single point with grid - np.testing.assert_array_almost_equal(f(3, grid=True), np.array([[9.], [36.]])) + np.testing.assert_array_almost_equal( + f(3, grid=True), np.array([[9.], [36.]])) # Check erroneous axis - with np.testing.assert_raises(ValueError): f((t,t), grid=True) + with np.testing.assert_raises(ValueError): + f((t, t), grid=True) def test_evaluation_cubic_composed(self): f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=SplineInterpolation(3)) - + interpolation=SplineInterpolation(3)) # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal(f([[1],[8]], aligned_evaluation=False).round(3) - ,np.array([[1.], [1.]])) + np.testing.assert_array_almost_equal( + f([[1], [8]], aligned_evaluation=False).round(3), np.array([[1.], [1.]])) - t = np.linspace(4,6,4) - np.testing.assert_array_almost_equal(f([t,9-t], aligned_evaluation=False).round(2), - np.array([[16. , 21.78, 28.44, 36. ], - [16. , 21.78, 28.44, 36. ]])) + t = np.linspace(4, 6, 4) + np.testing.assert_array_almost_equal(f([t, 9 - t], aligned_evaluation=False).round(2), + np.array([[16., 21.78, 28.44, 36.], + [16., 21.78, 28.44, 36.]])) # Same length than nsample - t = np.linspace(4,6,2) - np.testing.assert_array_almost_equal(f([t,9-t], aligned_evaluation=False).round(3), - np.array([[16. , 36.], [16. , 36.]])) + t = np.linspace(4, 6, 2) + np.testing.assert_array_almost_equal(f([t, 9 - t], aligned_evaluation=False).round(3), + np.array([[16., 36.], [16., 36.]])) def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" - for degree in range(1,6): - interpolator = SplineInterpolation(degree) + for degree in range(1, 6): + interpolation = SplineInterpolation(degree) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=interpolator) + interpolation=interpolation) # Test interpolation in nodes np.testing.assert_array_almost_equal(f(np.arange(10)).round(5), - self.data_matrix_1_1) + self.data_matrix_1_1) def test_error_degree(self): - with np.testing.assert_raises(ValueError): - interpolator = SplineInterpolation(7) + interpolation = SplineInterpolation(7) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=interpolator) + interpolation=interpolation) f(1) with np.testing.assert_raises(ValueError): - interpolator = SplineInterpolation(0) + interpolation = SplineInterpolation(0) f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolator=interpolator) + interpolation=interpolation) f(1) class TestEvaluationSpline1_n(unittest.TestCase): - """Test the evaluation of a grid spline interpolator with + """Test the evaluation of a grid spline interpolation with domain dimension equal to 1 and arbitary image dimension. """ @@ -316,29 +321,28 @@ def setUp(self): data_1 = np.array([np.arange(10)**2, np.arange(start=9, stop=-1, step=-1)**2]) - data_2 = np.sin(np.pi/81 * data_1) - - self.data_matrix_1_n = np.dstack((data_1,data_2)) + data_2 = np.sin(np.pi / 81 * data_1) - self.interpolator = SplineInterpolation(interpolation_order=2) + self.data_matrix_1_n = np.dstack((data_1, data_2)) + self.interpolation = SplineInterpolation(interpolation_order=2) def test_evaluation_simple(self): """Test basic usage of evaluation""" f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=self.interpolator) + interpolation=self.interpolation) # Test interpolation in nodes np.testing.assert_array_almost_equal(f(self.t), self.data_matrix_1_n) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([1.5,2.5,3.5]), - np.array([[[ 2.25 , 0.087212], - [ 6.25 , 0.240202], - [12.25 , 0.45773 ]], - [[56.25 , 0.816142], - [42.25 , 0.997589], - [30.25 , 0.922146]]] + np.testing.assert_array_almost_equal(f([1.5, 2.5, 3.5]), + np.array([[[2.25, 0.087212], + [6.25, 0.240202], + [12.25, 0.45773]], + [[56.25, 0.816142], + [42.25, 0.997589], + [30.25, 0.922146]]] ) ) @@ -346,42 +350,42 @@ def test_evaluation_point(self): """Test the evaluation of a single point""" f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=self.interpolator) + interpolation=self.interpolation) # Test a single point np.testing.assert_array_almost_equal(f(5.3), - np.array([[[28.09 , 0.885526]], - [[13.69 , 0.50697 ]]] + np.array([[[28.09, 0.885526]], + [[13.69, 0.50697]]] ) ) def test_evaluation_derivative(self): """Test derivative""" f = FDataGrid(self.data_matrix_1_n, sample_points=self.t, - interpolator=self.interpolator) + interpolation=self.interpolation) # [(2*x, d/dx sin(pi/81*x**2)), (2*(9-x), d/dx sin(pi/81*(9-x)**2))] - np.testing.assert_array_almost_equal(f([1.5,2.5,3.5], derivative=1), - np.array([[[ 3. , 0.1162381], - [ 5. , 0.1897434], - [ 7. , 0.2453124]], - [[-15. , 0.3385772], - [-13. , 0.0243172], - [-11. ,-0.1752035]]])) + np.testing.assert_array_almost_equal(f([1.5, 2.5, 3.5], derivative=1), + np.array([[[3., 0.1162381], + [5., 0.1897434], + [7., 0.2453124]], + [[-15., 0.3385772], + [-13., 0.0243172], + [-11., -0.1752035]]])) def test_evaluation_grid(self): """Test grid evaluation. With domain dimension = 1""" f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=SplineInterpolation(2)) + interpolation=SplineInterpolation(2)) - t = [1.5,2.5,3.5] - res = np.array([[[ 2.25 , 0.08721158], - [ 6.25 , 0.24020233], - [12.25 , 0.4577302 ]], - [[56.25 , 0.81614206], - [42.25 , 0.99758925], - [30.25 , 0.92214607]]]) + t = [1.5, 2.5, 3.5] + res = np.array([[[2.25, 0.08721158], + [6.25, 0.24020233], + [12.25, 0.4577302]], + [[56.25, 0.81614206], + [42.25, 0.99758925], + [30.25, 0.92214607]]]) # Test evaluation in a list of times np.testing.assert_array_almost_equal(f(t, grid=True), res) @@ -389,31 +393,30 @@ def test_evaluation_grid(self): np.testing.assert_array_almost_equal(f([t], grid=True), res) # Check erroneous axis - with np.testing.assert_raises(ValueError): f((t,t), grid=True) + with np.testing.assert_raises(ValueError): + f((t, t), grid=True) def test_evaluation_composed(self): f = FDataGrid(self.data_matrix_1_n, sample_points=self.t, - interpolator=self.interpolator) - + interpolation=self.interpolation) # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal(f([[1],[4]], + np.testing.assert_array_almost_equal(f([[1], [4]], aligned_evaluation=False)[0], f(1)[0]) - np.testing.assert_array_almost_equal(f([[1],[4]], + np.testing.assert_array_almost_equal(f([[1], [4]], aligned_evaluation=False)[1], f(4)[1]) - def test_evaluation_keepdims(self): """Test keepdims""" f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=self.interpolator, keepdims=True) + interpolation=self.interpolation, keepdims=True) fk = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=self.interpolator, keepdims=False) + interpolation=self.interpolation, keepdims=False) res = f(self.t) # Test interpolation in nodes @@ -423,25 +426,20 @@ def test_evaluation_keepdims(self): np.testing.assert_array_almost_equal(fk(self.t, keepdims=False), res) np.testing.assert_array_almost_equal(fk(self.t, keepdims=True), res) - def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" - for degree in range(1,6): - interpolator = SplineInterpolation(degree) + for degree in range(1, 6): + interpolation = SplineInterpolation(degree) f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolator=interpolator) + interpolation=interpolation) # Test interpolation in nodes np.testing.assert_array_almost_equal(f(np.arange(10)), self.data_matrix_1_n) - - - - if __name__ == '__main__': print() unittest.main() diff --git a/tests/test_registration.py b/tests/test_registration.py index b47bb0d05..07f997c73 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -25,9 +25,9 @@ def setUp(self): """Initialization of samples""" self.time = np.linspace(-1, 1, 50) - interpolator = SplineInterpolation(3, monotone=True) + interpolation = SplineInterpolation(3, monotone=True) self.polynomial = FDataGrid([self.time**3, self.time**5], - self.time, interpolator=interpolator) + self.time, interpolation=interpolation) def test_invert_warping(self): From 04d5469f95997b0eb87b528182b9e2e9a958eb48 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 28 May 2020 20:48:42 +0200 Subject: [PATCH 526/624] Fix documentation generation in Windows. --- docs/make.bat | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/make.bat b/docs/make.bat index aaea5cb30..820e54269 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -5,7 +5,7 @@ REM Command file for Sphinx documentation pushd %~dp0 if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=python -msphinx + set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . @@ -54,7 +54,7 @@ if "%1" == "clean" ( REM Check if sphinx-build is available %SPHINXBUILD% 1>NUL 2>NUL -if errorlevel 1 ( +if errorlevel 9009 ( echo. echo.The Sphinx module was not found. Make sure you have Sphinx installed, echo.then set the SPHINXBUILD environment variable to point to the full From 67209c4dfeeb1133cb9d3cb5de06728561960944 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 29 May 2020 16:45:25 +0200 Subject: [PATCH 527/624] Regularization documentation. --- docs/modules/misc.rst | 1 + docs/modules/misc/regularization.rst | 29 ++++++++- skfda/misc/regularization/_regularization.py | 65 ++++++++++++++++++-- 3 files changed, 88 insertions(+), 7 deletions(-) diff --git a/docs/modules/misc.rst b/docs/modules/misc.rst index 66654d9c6..db2bf14ad 100644 --- a/docs/modules/misc.rst +++ b/docs/modules/misc.rst @@ -8,3 +8,4 @@ Miscellaneous functions and objects. :caption: Modules: misc/metrics + misc/regularization diff --git a/docs/modules/misc/regularization.rst b/docs/modules/misc/regularization.rst index 731e22ea6..e68aeb7c2 100644 --- a/docs/modules/misc/regularization.rst +++ b/docs/modules/misc/regularization.rst @@ -4,10 +4,33 @@ Regularization This module contains several regularization techniques that can be applied in several situations, such as regression, PCA or basis smoothing. +These regularization methods are useful to obtain simple solutions and to +introduce known hypothesis to the model, such as periodicity or smoothness, +reducing the effects caused by noise in the observations. + +In functional data analysis is also common to have ill posed problems, because +of the infinite nature of the data and the finite sample size. The application +of regularization techniques in these kind of problems is then necessary to +obtain reasonable solutions. + +When dealing with multivariate data, a common choice for the regularization +is to penalize the squared Euclidean norm, or :math:`L_2` norm, of the vectors +in order to obtain simpler solutions. This can be done in scikit-fda for +both multivariate and functional data using the :class:`L2Regularization` +class. + +A more flexible generalization of this approach is the so called Tikhonov +regularization, available as :class:`TikhonovRegularization`, in which the +squared :math:`L_2` norm is penalized after a particular linear operator is +applied. This for example allows to penalize the second derivative of a curve, +which is a measure of its curvature, because the differential operator +is linear. As arbitrary Python callables can be used as operators (provided +that they correspond to a linear transformation), it is possible to penalize +the evaluation at a point, the difference between points or other arbitrary +linear operations. + .. autosummary:: :toctree: autosummary - skfda.misc.regularization.Regularization - skfda.misc.regularization.LinearDifferentialOperatorRegularization skfda.misc.regularization.L2Regularization - skfda.misc.regularization.EndpointsDifferenceRegularization + skfda.misc.regularization.TikhonovRegularization \ No newline at end of file diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 8ee85e505..5d22f8632 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -14,18 +14,67 @@ class TikhonovRegularization(BaseEstimator): r""" Implements Tikhonov regularization. - The penalization term in this type of regularization is + The penalization term in this type of regularization is the square of the + :math:`L_2` (Euclidean) norm of a linear operator applied to the function + or vector .. math:: \| \Gamma x \|_2^2 - where :math:`\Gamma``is the so called Tikhonov operator + where :math:`\Gamma` is the so called Tikhonov operator (matrix for finite vectors). + This linear operator can be an arbitrary Python callable that correspond + to a linear transformation. However, the :doc:`operators` module + provides several common linear operators. + Parameters: linear_operator: linear operator used for regularization. regularization_parameter: scaling parameter of the penalization. + Examples: + + Construct a regularization that penalizes the second derivative, + which is a measure of the curvature of the function. + + >>> from skfda.misc.regularization import TikhonovRegularization + >>> from skfda.misc.operators import LinearDifferentialOperator + >>> + >>> regularization = TikhonovRegularization( + ... LinearDifferentialOperator(2)) + + Construct a regularization that penalizes the identity operator, + that is, completely equivalent to the :math:`L_2` regularization ( + :class:`L2Regularization`). + + >>> from skfda.misc.regularization import TikhonovRegularization + >>> from skfda.misc.operators import Identity + >>> + >>> regularization = TikhonovRegularization(Identity()) + + Construct a regularization that penalizes the difference between + the points :math:`f(1)` and :math:`f(0)` of a function :math:`f`. + + >>> from skfda.misc.regularization import TikhonovRegularization + >>> + >>> regularization = TikhonovRegularization(lambda x: x(1) - x(0)) + + Construct a regularization that penalizes the harmonic acceleration + operator :math:`Lf = \omega^2 D f + D^3 f`, that, when the + regularization parameter is large, forces the function to be + :math:`f(t) = c_1 + c_2 \sin \omega t + c_3 \cos \omega t`, where + :math:`\omega` is the angular frequency. This is useful for some + periodic functions. + + >>> from skfda.misc.regularization import TikhonovRegularization + >>> from skfda.misc.operators import LinearDifferentialOperator + >>> import numpy as np + >>> + >>> period = 1 + >>> w = 2 * np.pi / period + >>> regularization = TikhonovRegularization( + ... LinearDifferentialOperator([0, w**2, 0, 1])) + """ def __init__(self, linear_operator, @@ -44,9 +93,17 @@ def penalty_matrix(self, basis): class L2Regularization(TikhonovRegularization): r""" - Implements Tikhonov regularization. + Implements :math:`L_2` regularization. + + The penalization term in this type of regularization is the square of the + :math:`L_2` (Euclidean) norm of the function or vector + + .. math:: + \| x \|_2^2 - This is equivalent to Tikhonov regularization using the identity operator. + This is equivalent to Tikhonov regularization ( + :class:`TikhonovRegularization`) using the identity operator ( + :class:`Identity`). """ From 48c3e90596873a5db5d39e1ab6044ae745823e1d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 29 May 2020 17:50:32 +0200 Subject: [PATCH 528/624] Add operators module. --- docs/modules/misc.rst | 1 + docs/modules/misc/operators.rst | 14 ++++++++++++++ skfda/misc/operators/_identity.py | 6 +++++- .../operators/_linear_differential_operator.py | 4 ++++ skfda/misc/regularization/_regularization.py | 18 +++++++++++++----- 5 files changed, 37 insertions(+), 6 deletions(-) create mode 100644 docs/modules/misc/operators.rst diff --git a/docs/modules/misc.rst b/docs/modules/misc.rst index db2bf14ad..19a22b91c 100644 --- a/docs/modules/misc.rst +++ b/docs/modules/misc.rst @@ -8,4 +8,5 @@ Miscellaneous functions and objects. :caption: Modules: misc/metrics + misc/operators misc/regularization diff --git a/docs/modules/misc/operators.rst b/docs/modules/misc/operators.rst new file mode 100644 index 000000000..d2a877a2e --- /dev/null +++ b/docs/modules/misc/operators.rst @@ -0,0 +1,14 @@ +Operators +========= + +This module contains several useful operators that can be applied to +functional data, and sometimes to multivariate data. + +The operators that are linear can also be used in the context of +:doc:`regularization`. + +.. autosummary:: + :toctree: autosummary + + skfda.misc.operators.Identity + skfda.misc.operators.LinearDifferentialOperator \ No newline at end of file diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py index 3ccbaf22c..b48dbef55 100644 --- a/skfda/misc/operators/_identity.py +++ b/skfda/misc/operators/_identity.py @@ -8,10 +8,14 @@ class Identity(Operator): """Identity operator. + Linear operator that returns its input. + .. math:: Ix = x - """ + Can be applied to both functional and multivariate data. + + """ def __call__(self, f): return f diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 2519d2ca3..a26ef04b3 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -24,7 +24,11 @@ class LinearDifferentialOperator(Operator): Lx(t) = b_0(t) x(t) + b_1(t) x'(x) + \\dots + b_{n-1}(t) d^{n-1}(x(t)) + b_n(t) d^n(x(t)) + Can only be applied to functional data, as multivariate data has no + derivatives. + Attributes: + weights (list): A list of callables. Examples: diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 5d22f8632..b33d82669 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -19,18 +19,20 @@ class TikhonovRegularization(BaseEstimator): or vector .. math:: - \| \Gamma x \|_2^2 + \lambda \| \Gamma x \|_2^2 where :math:`\Gamma` is the so called Tikhonov operator - (matrix for finite vectors). + (matrix for finite vectors) and :math:`\lambda` is a positive real number. This linear operator can be an arbitrary Python callable that correspond - to a linear transformation. However, the :doc:`operators` module + to a linear transformation. However, the + :doc:`operators ` module provides several common linear operators. Parameters: linear_operator: linear operator used for regularization. - regularization_parameter: scaling parameter of the penalization. + regularization_parameter: scaling parameter (:math:`\lambda`) of the + penalization. Examples: @@ -99,12 +101,18 @@ class L2Regularization(TikhonovRegularization): :math:`L_2` (Euclidean) norm of the function or vector .. math:: - \| x \|_2^2 + \lambda \| x \|_2^2 + + where :math:`\lambda` is a positive real number. This is equivalent to Tikhonov regularization ( :class:`TikhonovRegularization`) using the identity operator ( :class:`Identity`). + Parameters: + regularization_parameter: scaling parameter (:math:`\lambda`) of the + penalization. + """ def __init__(self, *, regularization_parameter=1): From cc1fba3042bc7e15dc931a0cef455c519c79ae1b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 29 May 2020 18:00:57 +0200 Subject: [PATCH 529/624] Remove unused import --- skfda/misc/operators/_linear_differential_operator.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index a26ef04b3..afa36163c 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,6 @@ from ..._utils import _same_domain from ...representation import FDataGrid from ...representation.basis import Constant, Monomial, Fourier, BSpline -from ...representation.interpolation import SplineInterpolator from ._operators import Operator, gramian_matrix_optimization From 02ccc68a472d23165c1d7ae21f9f2a5f711452cf Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 30 May 2020 03:21:24 +0200 Subject: [PATCH 530/624] Add covariances documentation. --- docs/conf.py | 3 +- docs/modules/misc.rst | 1 + docs/modules/misc/covariances.rst | 16 +++ skfda/misc/covariances.py | 166 ++++++++++++++++++++++++++++-- 4 files changed, 175 insertions(+), 11 deletions(-) create mode 100644 docs/modules/misc/covariances.rst diff --git a/docs/conf.py b/docs/conf.py index 177af1566..cb5e78d5e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -52,7 +52,8 @@ 'sphinx_rtd_theme', 'sphinx_gallery.gen_gallery', 'sphinx.ext.intersphinx', - 'sphinx.ext.doctest'] + 'sphinx.ext.doctest', + 'matplotlib.sphinxext.plot_directive'] autodoc_default_flags = ['members', 'inherited-members'] diff --git a/docs/modules/misc.rst b/docs/modules/misc.rst index 19a22b91c..e72660025 100644 --- a/docs/modules/misc.rst +++ b/docs/modules/misc.rst @@ -7,6 +7,7 @@ Miscellaneous functions and objects. :maxdepth: 4 :caption: Modules: + misc/covariances misc/metrics misc/operators misc/regularization diff --git a/docs/modules/misc/covariances.rst b/docs/modules/misc/covariances.rst new file mode 100644 index 000000000..52bef3925 --- /dev/null +++ b/docs/modules/misc/covariances.rst @@ -0,0 +1,16 @@ +Covariance functions +==================== + +This module contains several common covariance functions of Gaussian +processes. These functions can be used as covariances in +:func:`make_gaussian_process`. + +.. autosummary:: + :toctree: autosummary + + skfda.misc.covariances.Covariance + skfda.misc.covariances.Brownian + skfda.misc.covariances.Linear + skfda.misc.covariances.Polynomial + skfda.misc.covariances.Gaussian + skfda.misc.covariances.Exponential \ No newline at end of file diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index be8ff083c..4c4d154f3 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -55,15 +55,21 @@ class Covariance(abc.ABC): def __call__(self, x, y): pass - def heatmap(self): - x = np.linspace(-1, 1, 1000) + def heatmap(self, limits=(-1, 1)): + """ + Return a heatmap plot of the covariance function. + + """ + + x = np.linspace(*limits, 1000) cov_matrix = self(x, x) fig = _create_figure() ax = fig.add_subplot(1, 1, 1) - ax.imshow(cov_matrix, extent=[-1, 1, 1, -1]) - ax.set_title("Covariance function in [-1, 1]") + ax.imshow(cov_matrix, extent=[limits[0], limits[1], + limits[1], limits[0]]) + ax.set_title(f"Covariance function in [{limits[0]}, {limits[1]}]") return fig @@ -135,8 +141,41 @@ def to_sklearn(self): class Brownian(Covariance): - """Brownian covariance function.""" + r""" + Brownian covariance function. + + The covariance function is + + .. math:: + K(x, y) = \sigma^2 \frac{|x - \mathcal{O}| + |y - \mathcal{O}| + - |x-y|}{2} + + where :math:`\sigma^2` is the variance at distance 1 from + :math:`\mathcal{O}` and :math:`\mathcal{O}` is the origin point. + If :math:`\mathcal{O} = 0` (the default) and we only + consider positive values, the formula can be simplified as + + .. math:: + K(x, y) = \sigma^2 \min(x, y). + + Heatmap plot of the covariance function: + .. plot:: + + from skfda.misc.covariances import Brownian + + Brownian().heatmap(limits=(0, 1)) + + Example of Gaussian process trajectories using this covariance: + + .. plot:: + + from skfda.misc.covariances import Brownian + from skfda.datasets import make_gaussian_process + + make_gaussian_process(n_samples=10, cov=Brownian()).plot() + + """ _latex_formula = (r"K(x, y) = \sigma^2 \frac{|x - \mathcal{O}| + " r"|y - \mathcal{O}| - |x-y|}{2}") @@ -155,8 +194,35 @@ def __call__(self, x, y): class Linear(Covariance): - """Linear covariance function.""" + r""" + Linear covariance function. + The covariance function is + + .. math:: + K(x, y) = \sigma^2 (x^T y + c) + + where :math:`\sigma^2` is the scale of the variance and + :math:`c` is the intercept. + + Heatmap plot of the covariance function: + + .. plot:: + + from skfda.misc.covariances import Linear + + Linear().heatmap(limits=(0, 1)) + + Example of Gaussian process trajectories using this covariance: + + .. plot:: + + from skfda.misc.covariances import Linear + from skfda.datasets import make_gaussian_process + + make_gaussian_process(n_samples=10, cov=Linear()).plot() + + """ _latex_formula = r"K(x, y) = \sigma^2 (x^T y + c)" _parameters = [("variance", r"\sigma^2"), @@ -179,8 +245,36 @@ def to_sklearn(self): class Polynomial(Covariance): - """Polynomial covariance function.""" + r""" + Polynomial covariance function. + + The covariance function is + + .. math:: + K(x, y) = \sigma^2 (\alpha x^T y + c)^d + + where :math:`\sigma^2` is the scale of the variance, + :math:`\alpha` is the slope, :math:`d` the degree of the + polynomial and :math:`c` is the intercept. + + Heatmap plot of the covariance function: + .. plot:: + + from skfda.misc.covariances import Polynomial + + Polynomial().heatmap(limits=(0, 1)) + + Example of Gaussian process trajectories using this covariance: + + .. plot:: + + from skfda.misc.covariances import Polynomial + from skfda.datasets import make_gaussian_process + + make_gaussian_process(n_samples=10, cov=Polynomial()).plot() + + """ _latex_formula = r"K(x, y) = \sigma^2 (\alpha x^T y + c)^d" _parameters = [("variance", r"\sigma^2"), @@ -211,9 +305,35 @@ def to_sklearn(self): class Gaussian(Covariance): - """Gaussian covariance function.""" + r""" + Gaussian covariance function. + + The covariance function is + + .. math:: + K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||^2}{2l^2}\right) + + where :math:`\sigma^2` is the variance and :math:`l` is the length scale. + + Heatmap plot of the covariance function: + + .. plot:: + + from skfda.misc.covariances import Gaussian + + Gaussian().heatmap(limits=(0, 1)) + + Example of Gaussian process trajectories using this covariance: - _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||^2}{2l^2}" + .. plot:: + + from skfda.misc.covariances import Gaussian + from skfda.datasets import make_gaussian_process + + make_gaussian_process(n_samples=10, cov=Gaussian()).plot() + + """ + _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{\|x - y\|^2}{2l^2}" r"\right)") _parameters = [("variance", r"\sigma^2"), @@ -238,8 +358,34 @@ def to_sklearn(self): class Exponential(Covariance): - """Exponential covariance function.""" + r""" + Exponential covariance function. + + The covariance function is + + .. math:: + K(x, y) = \sigma^2 \exp\left(-\frac{\|x - y\|}{l}\right) + + where :math:`\sigma^2` is the variance and :math:`l` is the length scale. + + Heatmap plot of the covariance function: + .. plot:: + + from skfda.misc.covariances import Exponential + + Exponential().heatmap(limits=(0, 1)) + + Example of Gaussian process trajectories using this covariance: + + .. plot:: + + from skfda.misc.covariances import Exponential + from skfda.datasets import make_gaussian_process + + make_gaussian_process(n_samples=10, cov=Exponential()).plot() + + """ _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||}{l}" r"\right)") From 22369bd407b08144dfecb1b24678bd0d3ec8b3d6 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 31 May 2020 18:39:39 +0200 Subject: [PATCH 531/624] conform to api --- examples/plot_fpca.py | 6 +++- skfda/exploratory/visualization/fpca.py | 15 +++++--- .../dim_reduction/projection/_fpca.py | 36 ------------------- 3 files changed, 16 insertions(+), 41 deletions(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 492e4081c..5fc4d9fc4 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -14,6 +14,7 @@ from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.datasets import fetch_growth from skfda.exploratory.visualization import plot_fpca_perturbation_graphs +import matplotlib.pyplot as plt ############################################################################## # In this example we are going to use functional principal component analysis to @@ -82,7 +83,10 @@ # faster at an early age and boys tend to start puberty later, therefore, their # growth is more significant later. Girls also stop growing early -plot_fpca_perturbation_graphs(basis_fd.mean(), fpca.components_, 30) +plot_fpca_perturbation_graphs(basis_fd.mean(), + fpca.components_, + 30, + fig=plt.figure(figsize=(6, 2*4))) ############################################################################## # We can also specify another basis for the principal components as argument diff --git a/skfda/exploratory/visualization/fpca.py b/skfda/exploratory/visualization/fpca.py index cd4a49245..317b0c482 100644 --- a/skfda/exploratory/visualization/fpca.py +++ b/skfda/exploratory/visualization/fpca.py @@ -1,5 +1,6 @@ from matplotlib import pyplot as plt from skfda.representation import FDataGrid, FDataBasis, FData +from skfda.exploratory.visualization._utils import _get_figure_and_axes def plot_fpca_perturbation_graphs(mean, components, multiple, @@ -13,7 +14,8 @@ def plot_fpca_perturbation_graphs(mean, components, multiple, Args: mean (FDataGrid or FDataBasis): - the functional data object containing the mean function + the functional data object containing the mean function. + If len(mean) > 1, the mean is computed. components (FDataGrid or FDataBasis): the principal components multiple (float): @@ -27,9 +29,14 @@ def plot_fpca_perturbation_graphs(mean, components, multiple, (FDataGrid or FDataBasis): this contains the mean function followed by the positive perturbation and the negative perturbation. """ - if fig is None: - fig = plt.figure(figsize=(6, 4 * len(components))) - axes = fig.subplots(nrows=len(components)) + + if len(mean) > 1: + mean = mean.mean() + + fig, axes = _get_figure_and_axes(fig=fig) + + if not axes: + axes = fig.subplots(nrows=len(components)) for i in range(len(axes)): aux = _get_component_perturbations(mean, components, i, multiple) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index a9e7a1e84..9d6b1f568 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -397,39 +397,3 @@ def fit_transform(self, X, y=None, **fit_params): """ self.fit(X, y) return self.transform(X, y) - - def get_component_perturbations(self, X, index=0, multiple=30): - """ Computes the perturbations over the mean function of a principal - component at a certain index. The perturbations are defined as - variations over the mean. Adding a multiple of the principal component - curve to the mean function results in the positive perturbation and - subtracting a multiple of the principal component curve results in the - negative perturbation. - - Args: - X (FDataGrid or FDataBasis): - the functional data object from which we obtain the mean - index (int): - index of the component for which we want to compute the - perturbations - multiple (float): - multiple of the principal component curve to be added or - subtracted. - - Returns: - (FDataGrid or FDataBasis): this contains the mean function followed - by the positive perturbation and the negative perturbation. - """ - if not isinstance(X, FDataBasis) and not isinstance(X, FDataGrid): - raise AttributeError("X must be either FDataGrid or FDataBasis") - if self.components_ is None: - raise ValueError("The estimator must be fitted before calling " - "this method") - if index >= self.n_components: - raise AttributeError("Index out of range") - mean_fd = X.mean() - mean_fd = mean_fd.concatenate( - mean_fd[0] + multiple * self.components_[index]) - mean_fd = mean_fd.concatenate( - mean_fd[0] - multiple * self.components_[index]) - return mean_fd From 503514239dcb46af8cd1db9b58c7e19eb90c8019 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 1 Jun 2020 19:52:25 +0200 Subject: [PATCH 532/624] Matrix version of the statistics MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 34 ++++++++++++--------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index e098b6a67..44f365066 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -69,24 +69,19 @@ def v_sample_stat(fd, weights, p=2): Analysis*, 47:111-112, 02 2004 """ + weights = np.asarray(weights) if not isinstance(fd, FData): raise ValueError("Argument type must inherit FData.") if len(weights) != fd.n_samples: raise ValueError("Number of weights must match number of samples.") - n = fd.n_samples - size = (n * n - n) // 2 - ops = np.empty(size, dtype='object') - coef = np.empty(size) + fds = np.array([f for f in fd], dtype=FData).ravel() + t_ind = np.tril_indices(fd.n_samples, -1) - index = 0 - for j in range(n): - for i in range(j): - coef[index] = weights[i] - ops[index] = fd[i] - fd[j] - index += 1 + coef = (weights - 0 * weights[:, None])[t_ind] + ops = (fds - fds[:, None])[t_ind] - return np.dot(coef, norm_lp(concatenate(ops), p=p) ** p) + return np.sum(coef * norm_lp(concatenate(ops), p=p) ** p) def v_asymptotic_stat(fd, weights, p=2): @@ -150,19 +145,20 @@ def v_asymptotic_stat(fd, weights, p=2): anova test for functional data". *Computational Statistics Data Analysis*, 47:111-112, 02 2004 """ + weights = np.asarray(weights) if not isinstance(fd, FData): raise ValueError("Argument type must inherit FData.") if len(weights) != fd.n_samples: raise ValueError("Number of weights must match number of samples.") + if np.count_nonzero(weights) != len(weights): + raise ValueError("All weights must be non-zero.") + + fds = np.array([f for f in fd], dtype=FData).ravel() + t_ind = np.tril_indices(fd.n_samples, -1) + + w = np.sqrt(weights / weights[:, None]) + ops = (fds - w * fds[:, None])[t_ind] - n = fd.n_samples - size = (n * n - n) // 2 - ops = np.empty(size, dtype='object') - index = 0 - for j in range(n): - for i in range(j): - ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) - index += 1 return np.sum(norm_lp(concatenate(ops), p=p) ** p) From bfbd4481a86dc7f50561204d6e55d0a70d951cf5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 1 Jun 2020 20:09:57 +0200 Subject: [PATCH 533/624] Matrix version of the statistics - Faster version MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 44f365066..a7e9791f1 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -145,20 +145,19 @@ def v_asymptotic_stat(fd, weights, p=2): anova test for functional data". *Computational Statistics Data Analysis*, 47:111-112, 02 2004 """ - weights = np.asarray(weights) if not isinstance(fd, FData): raise ValueError("Argument type must inherit FData.") if len(weights) != fd.n_samples: raise ValueError("Number of weights must match number of samples.") - if np.count_nonzero(weights) != len(weights): - raise ValueError("All weights must be non-zero.") - - fds = np.array([f for f in fd], dtype=FData).ravel() - t_ind = np.tril_indices(fd.n_samples, -1) - - w = np.sqrt(weights / weights[:, None]) - ops = (fds - w * fds[:, None])[t_ind] + n = fd.n_samples + size = (n * n - n) // 2 + ops = np.empty(size, dtype='object') + index = 0 + for j in range(n): + for i in range(j): + ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) + index += 1 return np.sum(norm_lp(concatenate(ops), p=p) ** p) From 97d9c2e1398a3b6bd6524c7432a4293e838fb4f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 2 Jun 2020 17:15:37 +0200 Subject: [PATCH 534/624] Matrix version of the statistics - Faster version 2 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/anova/anova_oneway.py | 30 +++++++++++++-------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index a7e9791f1..8ebc11379 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -2,7 +2,7 @@ from sklearn.utils import check_random_state from skfda import concatenate -from skfda.misc.metrics import norm_lp +from skfda.misc.metrics import lp_distance from skfda.representation import FData, FDataGrid from skfda.datasets import make_gaussian_process @@ -75,13 +75,9 @@ def v_sample_stat(fd, weights, p=2): if len(weights) != fd.n_samples: raise ValueError("Number of weights must match number of samples.") - fds = np.array([f for f in fd], dtype=FData).ravel() t_ind = np.tril_indices(fd.n_samples, -1) - - coef = (weights - 0 * weights[:, None])[t_ind] - ops = (fds - fds[:, None])[t_ind] - - return np.sum(coef * norm_lp(concatenate(ops), p=p) ** p) + coef = weights[t_ind[1]] + return np.sum(coef * lp_distance(fd[t_ind[0]], fd[t_ind[1]], p=p) ** p) def v_asymptotic_stat(fd, weights, p=2): @@ -145,20 +141,22 @@ def v_asymptotic_stat(fd, weights, p=2): anova test for functional data". *Computational Statistics Data Analysis*, 47:111-112, 02 2004 """ + weights = np.asarray(weights) if not isinstance(fd, FData): raise ValueError("Argument type must inherit FData.") if len(weights) != fd.n_samples: raise ValueError("Number of weights must match number of samples.") + if np.count_nonzero(weights) != len(weights): + raise ValueError("All weights must be non-zero.") - n = fd.n_samples - size = (n * n - n) // 2 - ops = np.empty(size, dtype='object') - index = 0 - for j in range(n): - for i in range(j): - ops[index] = fd[i] - fd[j] * np.sqrt(weights[i] / weights[j]) - index += 1 - return np.sum(norm_lp(concatenate(ops), p=p) ** p) + t_ind = np.tril_indices(fd.n_samples, -1) + coef = np.sqrt(weights[t_ind[1]] / weights[t_ind[0]]) + left_fd = fd[t_ind[1]] + if isinstance(fd, FDataGrid): + right_fd = coef[:, None, np.newaxis] * fd[t_ind[0]] + else: + right_fd = fd[t_ind[0]].times(coef) + return np.sum(lp_distance(left_fd, right_fd, p=p) ** p) def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): From aaa074e87a87537c6f682f83c88c99aeb9c7ced3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 2 Jun 2020 17:34:35 +0200 Subject: [PATCH 535/624] Including new tests for statistics MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- tests/test_oneway_anova.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index c7534a1c6..f7c2ed87b 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -28,6 +28,9 @@ def test_v_stats_args(self): v_asymptotic_stat(1, [1]) with self.assertRaises(ValueError): v_asymptotic_stat(FDataGrid([0]), [0, 1]) + with self.assertRaises(ValueError): + v_asymptotic_stat(FDataGrid([[1, 1, 1], [1, 1, 1]]), [0, 0]) + def test_v_stats(self): n_features = 50 From 22b720dc54d6876327138c0cccc62c9a821e18e1 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 3 Jun 2020 10:19:39 +0200 Subject: [PATCH 536/624] change return type of transform_grid in fpca --- skfda/preprocessing/dim_reduction/projection/_fpca.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index 9d6b1f568..a145b7dbd 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -336,10 +336,10 @@ def transform_grid(self, X : FDataGrid, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return FDataGrid(data_matrix=X.data_matrix.reshape( + return X.data_matrix.reshape( X.data_matrix.shape[:-1]) @ np.transpose( self.components_.data_matrix.reshape( - self.components_.data_matrix.shape[:-1]))) + self.components_.data_matrix.shape[:-1])) def fit(self, X, y=None): """Computes the n_components first principal components and saves them From 286d92958290edd7a510b88104dfc8ad72c450af Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 8 Jun 2020 19:03:05 +0200 Subject: [PATCH 537/624] make auxiliary fit and transform methods internal --- .../dim_reduction/projection/_fpca.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index a145b7dbd..c581d97c1 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -91,7 +91,7 @@ def __init__(self, self.weights = weights self.components_basis = components_basis - def fit_basis(self, X: FDataBasis, y=None): + def _fit_basis(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also saved. For more details about how it is implemented please view the @@ -200,7 +200,7 @@ def fit_basis(self, X: FDataBasis, y=None): return self - def transform_basis(self, X, y=None): + def _transform_basis(self, X, y=None): """Computes the n_components first principal components score and returns them. @@ -218,7 +218,7 @@ def transform_basis(self, X, y=None): # in this case it is the inner product of our data with the components return X.inner_product(self.components_) - def fit_grid(self, X: FDataGrid, y=None): + def _fit_grid(self, X: FDataGrid, y=None): r"""Computes the n_components first principal components and saves them. The eigenvalues associated with these principal @@ -319,7 +319,7 @@ def fit_grid(self, X: FDataGrid, y=None): return self - def transform_grid(self, X : FDataGrid, y=None): + def _transform_grid(self, X : FDataGrid, y=None): """Computes the n_components first principal components score and returns them. @@ -355,9 +355,9 @@ def fit(self, X, y=None): self (object) """ if isinstance(X, FDataGrid): - return self.fit_grid(X, y) + return self._fit_grid(X, y) elif isinstance(X, FDataBasis): - return self.fit_basis(X, y) + return self._fit_basis(X, y) else: raise AttributeError("X must be either FDataGrid or FDataBasis") @@ -376,9 +376,9 @@ def transform(self, X, y=None): principal components """ if isinstance(X, FDataGrid): - return self.transform_grid(X, y) + return self._transform_grid(X, y) elif isinstance(X, FDataBasis): - return self.transform_basis(X, y) + return self._transform_basis(X, y) else: raise AttributeError("X must be either FDataGrid or FDataBasis") From dea1d3cf0a524929ac8d963353f369e83f65890b Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Mon, 8 Jun 2020 21:04:49 +0200 Subject: [PATCH 538/624] change test to conform new format --- tests/test_fpca.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 1c61a81bb..0b52a5394 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -290,21 +290,21 @@ def test_grid_fpca_transform_result(self): scores = fpca.transform(fd_data) # results obtained - results = [[[-77.05020176]], [[-90.56072204]], [[-82.39565947]], - [[-114.45375934]], [[-69.99735931]], [[-64.44894047]], - [[135.58336775]], [[-14.93460852]], [[0.75024737]], - [[-36.4781038]], [[-42.35637749]], [[-73.98910492]], - [[-67.11253749]], [[-103.68269798]], [[-104.65948079]], - [[-7.42817782]], [[7.48125036]], [[56.29792942]], - [[181.00258791]], [[-3.53294736]], [[37.94673912]], - [[124.43819913]], [[-7.04274676]], [[-49.61134859]], - [[-136.86256785]], [[-184.03502398]], [[-181.72835749]], - [[-51.06323208]], [[-137.85606731]], [[50.10941466]], - [[151.68118097]], [[159.01360046]], [[217.17981302]], - [[234.40195237]], [[345.39374006]]] + results = [[-77.05020176], [-90.56072204], [-82.39565947], + [-114.45375934], [-69.99735931], [-64.44894047], + [135.58336775], [-14.93460852], [0.75024737], + [-36.4781038], [-42.35637749], [-73.98910492], + [-67.11253749], [-103.68269798], [-104.65948079], + [-7.42817782], [7.48125036], [56.29792942], + [181.00258791], [-3.53294736], [37.94673912], + [124.43819913], [-7.04274676], [-49.61134859], + [-136.86256785], [-184.03502398], [-181.72835749], + [-51.06323208], [-137.85606731], [50.10941466], + [151.68118097], [159.01360046], [217.17981302], + [234.40195237], [345.39374006]] results = np.array(results) - np.testing.assert_allclose(scores.data_matrix, results, rtol=1e-6) + np.testing.assert_allclose(scores, results, rtol=1e-6) def test_grid_fpca_regularization_fit_result(self): From 930ed6d0173e1d79fa220a4fed26cd030a082cd6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 8 Jun 2020 23:00:06 +0200 Subject: [PATCH 539/624] Hotelling's T2 test for two samples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 215 +++++++++++++++++++++++++ 1 file changed, 215 insertions(+) create mode 100644 skfda/inference/hotelling/hotelling.py diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py new file mode 100644 index 000000000..3cfe78d25 --- /dev/null +++ b/skfda/inference/hotelling/hotelling.py @@ -0,0 +1,215 @@ +from skfda.representation import FDataBasis, FData +import numpy as np +import itertools +import scipy +from sklearn.utils import check_random_state + + +def hotelling_t2(fd1, fd2, weights=None): + r""" + Calculates Hotelling's :math:`T^2` over two samples in + :class:`skfda.representation.FData` objects with sizes :math:`n_1` + and :math:`n_2` as defined below: + + .. math:: + T^2 = n(\mathbf{m}_1 - \mathbf{m}_2)^\top \mathbf{W}^{1/2}( + \mathbf{W}^{1/2}\mathbf{K_{\operatorname{pooled}}} \mathbf{W}^{ + 1/2})^+ + \mathbf{W}^{1/2} (\mathbf{m}_1 - \mathbf{m}_2), + + where :math:`(\cdot)^{+}` indicates the Moore-Penrose pseudo-inverse + operator, :math:`n=n_1+n_2`, `W` is a matrix of weights + (usually Gram matrix), and :math:`\mathbf{m}_1, \mathbf{m}_2` are the + means of each ample, :math:`\mathbf{K}_{\operatorname{pooled}}` + matrix is defined as + + .. math:: + \mathbf{K}_{\operatorname{pooled}} := + \cfrac{n_1 - 1}{n_1 + n_2 - 2} \mathbf{K}_{n_1} + + \cfrac{n_2 - 1}{n_1 + n_2 - 2} \mathbf{K}_{n_2}, + + where :math:`\mathbf{K}_{n_1}`, :math:`\mathbf{K}_{n_2}` are the sample + covariance matrices, computed with the basis coefficients or using + the discrete representation, depending on the input. + + This statistic is defined in Pini, Stamm and Vantini[1]. + + Args: + fd1 (FData): Object with the first sample. + fd2 (FData): Object containing second sample. + weights (numpy.array, optional): Weights matrix. If no value + is passed then uses Gram matrix if data is in basis + representation. Identity matrix is used for discretized data. + + Returns: + The value of the statistic. + + Raises: + TypeError. If fd1 and fd2 types do not match or do not inherit + :class:`skfda.representation.FData`. + + Examples: + + >>> from skfda.inference.hotelling import hotelling_t2 + >>> from skfda.representation import FDataGrid, basis + + >>> fd1 = FDataGrid([[1, 1, 1], [3, 3, 3]]) + >>> fd2 = FDataGrid([[3, 3, 3], [5, 5, 5]]) + >>> '%.2f' % hotelling_t2(fd1, fd2) + '2.00' + >>> fd1 = fd1.to_basis(basis.Fourier(n_basis=2)) + >>> fd2 = fd2.to_basis(basis.Fourier(n_basis=2)) + >>> '%.2f' % hotelling_t2(fd1, fd2) + '2.00' + + References: + [1] A. Pini, A. Stamm and S. Vantini, "Hotelling's t2 in + separable hilbert spaces", *Jounal of Multivariate Analysis*, + 167 (2018), pp.284-305. + + """ + if not isinstance(fd1, FData): + raise TypeError("Argument type must inherit FData.") + + if not isinstance(fd2, type(fd1)): + raise TypeError("Both samples must be instances of the same type.") + + n1, n2 = fd1.n_samples, fd2.n_samples # Size of each sample + n = n1 + n2 # Size of full sample + m = fd1.mean() - fd2.mean() # Delta mean + + if isinstance(fd1, FDataBasis): + if fd1.basis != fd2.basis: + raise ValueError("Both FDataBasis objects must share the same " + "basis.") + # When working on basis representation we use the coefficients + m = m.coefficients[0] + k1 = np.cov(fd1.coefficients, rowvar=False) + k2 = np.cov(fd2.coefficients, rowvar=False) + # If no weight matrix is passed, then we compute the Gram Matrix + if weights is None: + weights = fd1.basis.gram_matrix() + weights = np.sqrt(np.abs(weights)) # TODO + else: + # Working with standard discretized data + m = m.data_matrix[0, ..., 0] + k1 = fd1.cov().data_matrix[0, ..., 0] + k2 = fd2.cov().data_matrix[0, ..., 0] + + m = m.reshape((-1, 1)) # Reshaping the mean for a proper matrix product + k_pool = ((n1 - 1) * k1 + (n2 - 1) * k2) / (n - 2) # Combination of covs + + if isinstance(fd1, FDataBasis): + # Product of pooled covariance with the weights and Moore-Penrose inv. + k_inv = np.linalg.pinv(np.linalg.multi_dot([weights, k_pool, weights])) + k_inv = weights.dot(k_inv).dot(weights) + else: + # If data is discrete no weights are needed + k_inv = np.linalg.pinv(k_pool) + + return n1 * n2 / n * m.T.dot(k_inv).dot(m)[0][0] + + +def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, + return_dist=False): + r""" + Calculate the :math:`T^2`-test for the means of two independent samples of + functional data. + + This is a two-sided test for the null hypothesis that 2 independent samples + have identical average (expected) values. This test assumes that the + populations have identical variances by default. + + The p-value of the test is calculated using a permutation test over the + statistic :func:`~skfda.inference.hotelling.hotelling_t2`. If a maximum + number of repetitions of the algorithm is provided then the permutations + tested are generated randomly. + + This procedure is from Pini, Stamm and Vantinni[1]. + + Args: + fd1,fd2 (FData): Samples of data. The FData objects must have the same + type. + + n_reps (int, optional): Maximum number of repetitions to compute + p-value. Default value is None. + + + random_state (optional): Random state. + + return_dist (bool, optional): Flag to indicate if the function should + return a numpy.array with the values of the statistic computed over + each permutation. + + + Returns: + Value of the sample statistic, one tailed p-value and a collection of + statistic values from permutations of the sample. + + Return type: + (float, float, numpy.array) + + Raises: + TypeError: In case of bad arguments. + + Examples: + >>> from skfda.inference.hotelling import hotelling_t2 + >>> from skfda.representation import FDataGrid, basis + >>> from numpy import printoptions + + >>> fd1 = FDataGrid([[1, 1, 1], [3, 3, 3]]) + >>> fd2 = FDataGrid([[3, 3, 3], [5, 5, 5]]) + >>> t2n, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True) + >>> '%.2f' % t2n + '2.00' + >>> '%.2f' % pval + '0.00' + >>> with printoptions(precision=4): + ... print(dist) + [2. 2. 0. 0. 2. 2.] + + References: + [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An + anova test for functional data". *Computational Statistics Data + Analysis*, 47:111-112, 02 2004 + """ + if not isinstance(fd1, FData): + raise TypeError("Argument type must inherit FData.") + + if not isinstance(fd2, type(fd1)): + raise TypeError("Both samples must be instances of the same type.") + + if n_reps is not None and n_reps < 1: + raise ValueError("Number of repetitions must be positive.") + + gram = fd1.basis.gram_matrix() if isinstance(fd1, FDataBasis) else None + + n1, n2 = fd1.n_samples, fd2.n_samples + t2_0 = hotelling_t2(fd1, fd2, gram) + n = n1 + n2 + sample = fd1.concatenate(fd2) + indices = np.arange(n) + + if n_reps: # Computing n_reps random permutations + random_state = check_random_state(random_state) + dist = np.empty(n_reps) + for i in range(n_reps): + random_state.shuffle(indices) + dist[i] = hotelling_t2(sample[indices[:n1]], sample[indices[n1:]], + gram) + + else: # Full permutation test + combinations = itertools.combinations(indices, n1) + dist = np.empty(int(scipy.special.comb(n, n1))) + for i, comb in enumerate(combinations): + sample1_i = np.asarray(comb) + sample2_i = np.setdiff1d(indices, sample1_i) + sample1, sample2 = sample[sample1_i], sample[sample2_i] + dist[i] = hotelling_t2(sample1, sample2, gram) + + p_value = np.sum(dist > t2_0) / len(dist) + + if return_dist: + return t2_0, p_value, dist + + return t2_0, p_value From 8b1e76b4867324b3f7dd1012ad2bda362df58570 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 8 Jun 2020 23:06:54 +0200 Subject: [PATCH 540/624] Hotelling's T2 test for two samples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/modules/inference.rst | 1 + docs/modules/inference/hotelling.rst | 27 ++++++++++++++++++++++++++ skfda/inference/__init__.py | 2 +- skfda/inference/hotelling/__init__.py | 2 ++ skfda/inference/hotelling/hotelling.py | 9 ++++----- 5 files changed, 35 insertions(+), 6 deletions(-) create mode 100644 docs/modules/inference/hotelling.rst create mode 100644 skfda/inference/hotelling/__init__.py diff --git a/docs/modules/inference.rst b/docs/modules/inference.rst index a06ebfba8..ad751703c 100644 --- a/docs/modules/inference.rst +++ b/docs/modules/inference.rst @@ -10,3 +10,4 @@ reliability of this results. :caption: Modules: inference/anova + inference/hotelling diff --git a/docs/modules/inference/hotelling.rst b/docs/modules/inference/hotelling.rst new file mode 100644 index 000000000..db8068019 --- /dev/null +++ b/docs/modules/inference/hotelling.rst @@ -0,0 +1,27 @@ +ANOVA +============== +This package groups a collection of statistical models, useful for analyzing +equality of means for different subsets of a sample. + +One-way functional ANOVA +------------------------ +Functionality to perform One-way ANOVA analysis, to compare means among +different samples. One-way stands for one functional response variable and +one unique variable of input. + +.. autosummary:: + :toctree: autosummary + + skfda.inference.hotelling.test_hotelling_ind + +Statistics +---------- +Statistics that measure the internal and external variability between +groups, used in the models above. + +.. autosummary:: + :toctree: autosummary + + skfda.inference.hotelling.hotelling_t2 + skfda.inference.hotelling.hotelling_t2 + diff --git a/skfda/inference/__init__.py b/skfda/inference/__init__.py index 23b76f4d2..73a2e789d 100644 --- a/skfda/inference/__init__.py +++ b/skfda/inference/__init__.py @@ -1 +1 @@ -from . import anova +from . import anova, hotelling diff --git a/skfda/inference/hotelling/__init__.py b/skfda/inference/hotelling/__init__.py new file mode 100644 index 000000000..6498f54bc --- /dev/null +++ b/skfda/inference/hotelling/__init__.py @@ -0,0 +1,2 @@ +from . import hotelling +from .hotelling import hotelling_t2, hotelling_test_ind diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 3cfe78d25..103264b69 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -45,8 +45,7 @@ def hotelling_t2(fd1, fd2, weights=None): The value of the statistic. Raises: - TypeError. If fd1 and fd2 types do not match or do not inherit - :class:`skfda.representation.FData`. + TypeError. Examples: @@ -169,9 +168,9 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, [2. 2. 0. 0. 2. 2.] References: - [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An - anova test for functional data". *Computational Statistics Data - Analysis*, 47:111-112, 02 2004 + [1] A. Pini, A. Stamm and S. Vantini, "Hotelling's t2 in + separable hilbert spaces", *Jounal of Multivariate Analysis*, + 167 (2018), pp.284-305. """ if not isinstance(fd1, FData): raise TypeError("Argument type must inherit FData.") From 20d0e890138b6a85850af9969092b5590d2a4372 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Mon, 8 Jun 2020 23:11:41 +0200 Subject: [PATCH 541/624] Trying to fix print error MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 103264b69..29d687293 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -165,7 +165,7 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, '0.00' >>> with printoptions(precision=4): ... print(dist) - [2. 2. 0. 0. 2. 2.] + [ 2. 2. 0. 0. 2. 2.] References: [1] A. Pini, A. Stamm and S. Vantini, "Hotelling's t2 in From def439933cd1bba3eb0cdb400c4257f3f2d9f62f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 9 Jun 2020 00:05:03 +0200 Subject: [PATCH 542/624] Including unit tests MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 4 +- tests/test_hotelling.py | 61 ++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 2 deletions(-) create mode 100644 tests/test_hotelling.py diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 29d687293..83cd87719 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -56,8 +56,8 @@ def hotelling_t2(fd1, fd2, weights=None): >>> fd2 = FDataGrid([[3, 3, 3], [5, 5, 5]]) >>> '%.2f' % hotelling_t2(fd1, fd2) '2.00' - >>> fd1 = fd1.to_basis(basis.Fourier(n_basis=2)) - >>> fd2 = fd2.to_basis(basis.Fourier(n_basis=2)) + >>> fd1 = fd1.to_basis(basis.Fourier(n_basis=3)) + >>> fd2 = fd2.to_basis(basis.Fourier(n_basis=3)) >>> '%.2f' % hotelling_t2(fd1, fd2) '2.00' diff --git a/tests/test_hotelling.py b/tests/test_hotelling.py new file mode 100644 index 000000000..8af06a370 --- /dev/null +++ b/tests/test_hotelling.py @@ -0,0 +1,61 @@ +import unittest +import pytest + +from skfda.representation import FDataGrid +from skfda.representation.basis import Fourier +from skfda.inference.hotelling import hotelling_t2, hotelling_test_ind + + +class HotellingTests(unittest.TestCase): + + def test_hotelling_test_ind_args(self): + fd1 = FDataGrid([[1, 1, 1]]) + with self.assertRaises(TypeError): + hotelling_test_ind(fd1, []) + with self.assertRaises(TypeError): + hotelling_test_ind([], fd1) + with self.assertRaises(TypeError): + hotelling_test_ind(fd1.to_basis(Fourier(n_basis=3)), fd1) + with self.assertRaises(TypeError): + hotelling_test_ind(fd1, fd1.to_basis(Fourier(n_basis=3))) + with self.assertRaises(ValueError): + hotelling_test_ind(fd1, fd1, n_reps=0) + + def test_hotelling_t2_args(self): + fd1 = FDataGrid([[1, 1, 1]]) + with self.assertRaises(TypeError): + hotelling_t2(fd1, []) + with self.assertRaises(TypeError): + hotelling_t2([], fd1) + with self.assertRaises(TypeError): + hotelling_t2(fd1.to_basis(Fourier(n_basis=3)), fd1) + with self.assertRaises(TypeError): + hotelling_t2(fd1, fd1.to_basis(Fourier(n_basis=3))) + + def test_hotelling_t2(self): + fd1 = FDataGrid([[1, 1, 1], [1, 1, 1]]) + fd2 = FDataGrid([[1, 1, 1], [2, 2, 2]]) + self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) + self.assertAlmostEqual(hotelling_t2(fd1, fd2), 1) + + fd1 = fd1.to_basis(Fourier(n_basis=3)) + fd2 = fd2.to_basis(Fourier(n_basis=3)) + self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) + self.assertAlmostEqual(hotelling_t2(fd1, fd2), 1) + + def test_hotelling_test(self): + fd1 = FDataGrid([[1, 1, 1], [1, 1, 1]]) + fd2 = FDataGrid([[3, 3, 3], [2, 2, 2]]) + t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True) + self.assertAlmostEqual(t2, 9) + self.assertAlmostEqual(pval, 0) + self.assertEqual(len(dist), 6) + reps = 5 + t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True, + n_reps=reps) + self.assertEqual(len(dist), reps) + + +if __name__ == '__main__': + print() + unittest.main() From b85ecd7c2e60a2705c9765ef5a6632d286b292bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 9 Jun 2020 11:32:22 +0200 Subject: [PATCH 543/624] Changes in documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/modules/inference/hotelling.rst | 23 ++++------------------- skfda/inference/hotelling/hotelling.py | 2 +- 2 files changed, 5 insertions(+), 20 deletions(-) diff --git a/docs/modules/inference/hotelling.rst b/docs/modules/inference/hotelling.rst index db8068019..959ad9714 100644 --- a/docs/modules/inference/hotelling.rst +++ b/docs/modules/inference/hotelling.rst @@ -1,27 +1,12 @@ -ANOVA +Hotelling ============== -This package groups a collection of statistical models, useful for analyzing -equality of means for different subsets of a sample. - -One-way functional ANOVA ------------------------- -Functionality to perform One-way ANOVA analysis, to compare means among -different samples. One-way stands for one functional response variable and -one unique variable of input. +This package groups a collection of statistical tests based on Hotelling's +statistic. .. autosummary:: :toctree: autosummary + skfda.inference.hotelling.hotelling_t2 skfda.inference.hotelling.test_hotelling_ind -Statistics ----------- -Statistics that measure the internal and external variability between -groups, used in the models above. - -.. autosummary:: - :toctree: autosummary - - skfda.inference.hotelling.hotelling_t2 - skfda.inference.hotelling.hotelling_t2 diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 83cd87719..5063c7e27 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -9,7 +9,7 @@ def hotelling_t2(fd1, fd2, weights=None): r""" Calculates Hotelling's :math:`T^2` over two samples in :class:`skfda.representation.FData` objects with sizes :math:`n_1` - and :math:`n_2` as defined below: + and :math:`n_2`. .. math:: T^2 = n(\mathbf{m}_1 - \mathbf{m}_2)^\top \mathbf{W}^{1/2}( From 1112bb28c805a51d2fef59abb2840f9e7f2c9fad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 9 Jun 2020 11:36:41 +0200 Subject: [PATCH 544/624] Including inference module in skfda namespace MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/__init__.py b/skfda/__init__.py index c66f69d38..eecd71b6c 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -34,7 +34,8 @@ from .representation import FDataGrid from .representation._functional_data import concatenate -from . import representation, datasets, preprocessing, exploratory, misc, ml +from . import representation, datasets, preprocessing, exploratory, misc, ml, \ + inference import os as _os From 5caa183d25f3a5c13df9ecb5f808f13f9d9eb1b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Tue, 9 Jun 2020 11:37:10 +0200 Subject: [PATCH 545/624] Including hotelling_test_ind in documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- docs/modules/inference/hotelling.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/modules/inference/hotelling.rst b/docs/modules/inference/hotelling.rst index 959ad9714..85ec04f1c 100644 --- a/docs/modules/inference/hotelling.rst +++ b/docs/modules/inference/hotelling.rst @@ -7,6 +7,6 @@ statistic. :toctree: autosummary skfda.inference.hotelling.hotelling_t2 - skfda.inference.hotelling.test_hotelling_ind + skfda.inference.hotelling.hotelling_test_ind From 68c91d1a3b3d9f648983e9d6c1422dedbb0f0268 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 10 Jun 2020 13:00:04 +0200 Subject: [PATCH 546/624] Added `eval_points` to derivative. --- .../representation/_evaluation_trasformer.py | 31 +--------- skfda/representation/basis/_basis.py | 61 +++++++++++++++++-- skfda/representation/basis/_bspline.py | 50 ++++++--------- skfda/representation/basis/_constant.py | 10 +-- skfda/representation/basis/_fdatabasis.py | 16 +++-- skfda/representation/basis/_fourier.py | 23 +++---- skfda/representation/basis/_monomial.py | 19 +++--- skfda/representation/grid.py | 13 ++-- 8 files changed, 119 insertions(+), 104 deletions(-) diff --git a/skfda/representation/_evaluation_trasformer.py b/skfda/representation/_evaluation_trasformer.py index 05845689e..927304a30 100644 --- a/skfda/representation/_evaluation_trasformer.py +++ b/skfda/representation/_evaluation_trasformer.py @@ -12,7 +12,6 @@ class EvaluationTransformer(BaseEstimator, TransformerMixin): eval_points (array_like): List of points where the functions are evaluated. If `None`, the functions must be `FDatagrid` objects and all points will be returned. - derivative (int, optional): Order of the derivative. Defaults to 0. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the @@ -81,35 +80,11 @@ class EvaluationTransformer(BaseEstimator, TransformerMixin): array([[ 0.5 , 0.784 , 1.5625, 2.3515, 4. ], [ 1.5 , 1.864 , 3.0625, 4.3315, 7. ]]) - Evaluating derivative of a FDataGrid at all points. - - >>> data_matrix = [[1, 2, 3], [2, 3, 4]] - >>> sample_points = [2, 4, 6] - >>> fd = FDataGrid(data_matrix, sample_points) - >>> - >>> transformer = EvaluationTransformer(derivative=1) - >>> transformer.fit_transform(fd) - array([[ 0.5, 0.5, 0.5], - [ 0.5, 0.5, 0.5]]) - - Evaluation of the derivative of a functional data object at several - points. - - >>> basis = Monomial(n_basis=4) - >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] - >>> fd = FDataBasis(basis, coefficients) - >>> - >>> transformer = EvaluationTransformer([0, 0.2, 0.5, 0.7, 1], - ... derivative=1) - >>> transformer.fit_transform(fd) - array([[ 1. , 1.86 , 3.375, 4.535, 6.5 ], - [ 1. , 2.66 , 5.375, 7.335, 10.5 ]]) """ - def __init__(self, eval_points=None, *, derivative=0, + def __init__(self, eval_points=None, *, extrapolation=None, grid=False): self.eval_points = eval_points - self.derivative = derivative self.extrapolation = extrapolation self.grid = grid @@ -128,11 +103,9 @@ def transform(self, X, y=None): check_is_fitted(self, '_is_fitted') if self.eval_points is None: - if self.derivative != 0: - X = X.derivative(self.derivative) evaluation = X.data_matrix.copy() else: - evaluation = X(self.eval_points, derivative=self.derivative, + evaluation = X(self.eval_points, extrapolation=self.extrapolation, grid=self.grid) evaluation = evaluation.reshape((X.n_samples, -1)) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 4b0fe720e..b7d71a35b 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -6,6 +6,7 @@ """ from abc import ABC, abstractmethod import copy +import warnings import scipy.integrate @@ -74,14 +75,10 @@ def domain_range(self, value): self._domain_range = value @abstractmethod - def _evaluate(self, eval_points, derivative=0): + def _evaluate(self, eval_points): """Subclasses must override this to provide basis evaluation.""" pass - @abstractmethod - def _derivative(self, coefs, order=1): - pass - def evaluate(self, eval_points, derivative=0): """Evaluate Basis objects and its derivatives. @@ -101,17 +98,69 @@ def evaluate(self, eval_points, derivative=0): """ if derivative < 0: raise ValueError("derivative only takes non-negative values.") + elif derivative != 0: + warnings.warn("Parameter derivative is deprecated. Use the " + "derivative function instead.", DeprecationWarning) + return self.derivative(eval_points, order=derivative) eval_points = np.atleast_1d(eval_points) if np.any(np.isnan(eval_points)): raise ValueError("The list of points where the function is " "evaluated can not contain nan values.") - return self._evaluate(eval_points, derivative) + return self._evaluate(eval_points) def __call__(self, *args, **kwargs): return self.evaluate(*args, **kwargs) + def _derivative(self, eval_points, order=1): + """ + Subclasses must override this to provide evaluation of derivatives. + + Order 0 derivatives (original function) are automatically + implemented. Subclasses can assume that the order passed is + nonzero. + + A basis can provide derivative evaluation at given points + without providing a basis representation for its derivatives, + although is recommended to provide both if possible. + + """ + return NotImplementedError(f"{type(self)} basis is not " + "differentiable.") + + def derivative(self, eval_points, order=1): + """Evaluate the basis derivative at given points. + + Args: + eval_points (array_like): List of points where the derivative of + the basis is evaluated. + order (int, optional): Order of the derivative. Defaults to 1. + + Returns: + (numpy.darray): Matrix whose rows are the values of the derivatives + of each basis function or its derivatives at the values specified + in eval_points. + + """ + if order == 0: + return self(eval_points) + else: + return self._derivative(eval_points, order=order) + + def _derivative_basis_and_coefs(self, coefs, order=1): + """ + Subclasses can override this to provide derivative construction. + + A basis can provide derivative evaluation at given points + without providing a basis representation for its derivatives, + although is recommended to provide both if possible. + + """ + return NotImplementedError(f"{type(self)} basis does not support " + "the construction of a basis of the " + "derivatives.") + def plot(self, chart=None, *, derivative=0, **kwargs): """Plot the basis object or its derivatives. diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index fa305fba1..b95905f47 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -156,33 +156,21 @@ def _evaluation_knots(self): return np.array([self.knots[0]] * (self.order - 1) + self.knots + [self.knots[-1]] * (self.order - 1)) - def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. - - It uses the scipy implementation of BSplines to compute the values - for each element of the basis. - - Args: - eval_points (array_like): List of points where the basis system is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - Implementation details: In order to allow a discontinuous behaviour at - the boundaries of the domain it is necessary to placing m knots at the - boundaries [RS05]_. This is automatically done so that the user only - has to specify a single knot at the boundaries. - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - - """ - if derivative > (self.order - 1): + def _evaluate(self, eval_points): + # The derivative method already works for 0 order. + return self._derivative(eval_points, 0) + + def _derivative(self, eval_points, order=1): + # Implementation details: In order to allow a discontinuous behaviour + # at the boundaries of the domain it is necessary to placing m knots + # at the boundaries [RS05]_. This is automatically done so that the + # user only has to specify a single knot at the boundaries. + # + # References: + # .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + # Analysis*. Springer. 50-51. + + if order > (self.order - 1): return np.zeros((self.n_basis, len(eval_points))) # Places m knots at the boundaries @@ -200,14 +188,14 @@ def _evaluate(self, eval_points, derivative=0): # iteration c[i] = 1 # compute the spline - mat[i] = scipy.interpolate.splev(eval_points, (knots, c, - self.order - 1), - der=derivative) + mat[i] = scipy.interpolate.splev(eval_points, + (knots, c, self.order - 1), + der=order) c[i] = 0 return mat - def _derivative(self, coefs, order=1): + def _derivative_basis_and_coefs(self, coefs, order=1): deriv_splines = [self._to_scipy_BSpline(coefs[i]).derivative(order) for i in range(coefs.shape[0])] diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 62ddfa9f2..4867d25f7 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -30,11 +30,13 @@ def __init__(self, domain_range=None): """ super().__init__(domain_range, 1) - def _evaluate(self, eval_points, derivative=0): - return (np.ones((1, len(eval_points))) if derivative == 0 - else np.zeros((1, len(eval_points)))) + def _evaluate(self, eval_points): + return np.ones((1, len(eval_points))) - def _derivative(self, coefs, order=1): + def _derivative(self, eval_points, order=1): + return np.zeros((1, len(eval_points))) + + def _derivative_basis_and_coefs(self, coefs, order=1): return (self.copy(), coefs.copy() if order == 0 else self.copy(), np.zeros(coefs.shape)) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 80c6ff7ee..f2c4560e0 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -376,7 +376,7 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, return FDataBasis.from_data(_data_matrix, eval_points, _basis, **kwargs) - def derivative(self, order=1): + def derivative(self, eval_points=None, *, order=1): r"""Differentiate a FDataBasis object. @@ -387,12 +387,18 @@ def derivative(self, order=1): if order < 0: raise ValueError("order only takes non-negative integer values.") - if order == 0: - return self.copy() + if eval_points is not None: + return np.sum( + self.basis.derivative(eval_points, order=order), axis=0) - basis, coefficients = self.basis._derivative(self.coefficients, order) + else: + if order == 0: + return self.copy() + + basis, coefficients = self.basis._derivative_basis_and_coefs( + self.coefficients, order) - return FDataBasis(basis, coefficients) + return FDataBasis(basis, coefficients) def mean(self, weights=None): """Compute the mean of all the samples in a FDataBasis object. diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 38edb3092..390b73204 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -113,23 +113,14 @@ def _functions_pairs_coefs_derivatives(self, derivative=0): return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs - def _evaluate(self, eval_points, derivative=0): - """Compute the basis or its derivatives given a list of values. + def _evaluate(self, eval_points): + # The derivative method already works for 0 order. + return self._derivative(eval_points, 0) - Args: - eval_points (array_like): List of points where the basis is - evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. - - Returns: - (:obj:`numpy.darray`): Matrix whose rows are the values of the each - basis function or its derivatives at the values specified in - eval_points. - - """ + def _derivative(self, eval_points, order=1): (functions, amplitude_coefs, - phase_coefs) = self._functions_pairs_coefs_derivatives(derivative) + phase_coefs) = self._functions_pairs_coefs_derivatives(order) normalization_denominator = np.sqrt(self.period / 2) @@ -146,7 +137,7 @@ def _evaluate(self, eval_points, derivative=0): res /= normalization_denominator # Add constant basis - if derivative == 0: + if order == 0: constant_basis = np.full( shape=(1, len(eval_points)), fill_value=1 / (np.sqrt(2) * normalization_denominator)) @@ -157,7 +148,7 @@ def _evaluate(self, eval_points, derivative=0): return res - def _derivative(self, coefs, order=1): + def _derivative_basis_and_coefs(self, coefs, order=1): omega = 2 * np.pi / self.period deriv_factor = (np.arange(1, (self.n_basis + 1) / 2) * omega) ** order diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index 649f669b9..9508752d7 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -46,7 +46,7 @@ class Monomial(Basis): """ - def _coefs_exps_derivatives(self, derivative): + def _coefs_exps_derivatives(self, order): """ Return coefficients and exponents of the derivatives. @@ -56,23 +56,24 @@ def _coefs_exps_derivatives(self, derivative): is zero) returns 0 as the exponent (to prevent division by zero). """ seq = np.arange(self.n_basis) - coef_mat = np.linspace(seq, seq - derivative + 1, - derivative, dtype=int) + coef_mat = np.linspace(seq, seq - order + 1, + order, dtype=int) coefs = np.prod(coef_mat, axis=0) - exps = np.maximum(seq - derivative, 0) + exps = np.maximum(seq - order, 0) return coefs, exps - def _evaluate(self, eval_points, derivative=0): - - coefs, exps = self._coefs_exps_derivatives(derivative) + def _evaluate(self, eval_points): + # The derivative method already works for 0 order. + return self._derivative(eval_points, 0) + def _derivative(self, eval_points, order=1): + coefs, exps = self._coefs_exps_derivatives(order) raised = np.power.outer(eval_points, exps) - return (coefs * raised).T - def _derivative(self, coefs, order=1): + def _derivative_basis_and_coefs(self, coefs, order=1): return (Monomial(self.domain_range, self.n_basis - order), np.array([np.polyder(x[::-1], order)[::-1] for x in coefs])) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 6fb7440ae..7e631ebb1 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -407,7 +407,7 @@ def _evaluate_composed(self, eval_points, *, derivative=0): return self._evaluator.evaluate_composed(eval_points, derivative=derivative) - def derivative(self, order=1): + def derivative(self, eval_points=None, *, order=1): r"""Differentiate a FDataGrid object. It is calculated using central finite differences when possible. In @@ -435,7 +435,7 @@ def derivative(self, order=1): Second order derivative >>> fdata = FDataGrid([1,2,4,5,8], range(5)) - >>> fdata.derivative(2) + >>> fdata.derivative(order=2) FDataGrid( array([[[ 3.], [ 1.], @@ -471,8 +471,13 @@ def derivative(self, order=1): else: dataset_label = None - return self.copy(data_matrix=data_matrix, - dataset_label=dataset_label) + fdatagrid = self.copy(data_matrix=data_matrix, + dataset_label=dataset_label) + + if eval_points is None: + return fdatagrid + else: + return fdatagrid(eval_points) def __check_same_dimensions(self, other): if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: From fe2716306ee3cabeaf6d15b7e84fe18aeabda3c8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 10 Jun 2020 19:59:32 +0200 Subject: [PATCH 547/624] Simplify evaluation. --- skfda/representation/_functional_data.py | 25 +-- skfda/representation/evaluator.py | 54 ++--- skfda/representation/extrapolation.py | 216 ++++++++---------- skfda/representation/grid.py | 16 +- skfda/representation/interpolation.py | 274 +++++++++-------------- 5 files changed, 215 insertions(+), 370 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 9ba2c101f..93e6e215b 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -118,19 +118,6 @@ def extrapolation(self, value): else: self._extrapolation = _parse_extrapolation(value) - self._extrapolator_evaluator = None - - @property - def extrapolator_evaluator(self): - """Return the evaluator constructed by the extrapolator.""" - if self.extrapolation is None: - return None - - elif self._extrapolator_evaluator is None: - self._extrapolator_evaluator = self._extrapolation.evaluator(self) - - return self._extrapolator_evaluator - @property @abstractmethod def domain_range(self): @@ -434,11 +421,9 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, """ if extrapolation is None: extrapolation = self.extrapolation - extrapolator_evaluator = self.extrapolator_evaluator else: # Gets the function to perform extrapolation or None extrapolation = _parse_extrapolation(extrapolation) - extrapolator_evaluator = None if grid: # Evaluation of a grid performed in auxiliar function return self._evaluate_grid(eval_points, @@ -468,10 +453,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, derivative=derivative) else: - # Evaluation using extrapolation - if extrapolator_evaluator is None: - extrapolator_evaluator = extrapolation.evaluator(self) - # Partition of eval points if aligned_evaluation: @@ -484,7 +465,8 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, # Direct evaluation res_evaluation = self._evaluate(eval_points_evaluation, derivative=derivative) - res_extrapolation = extrapolator_evaluator.evaluate( + res_extrapolation = extrapolation.evaluate( + self, eval_points_extrapolation, derivative=derivative) @@ -501,7 +483,8 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, derivative=derivative ) - res_extrapolation = extrapolator_evaluator.evaluate_composed( + res_extrapolation = extrapolation.evaluate_composed( + self, eval_points_extrapolation, derivative=derivative) diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index 4cf7c8423..da9eaae48 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -4,38 +4,6 @@ from abc import ABC, abstractmethod -class EvaluatorConstructor(ABC): - """Constructor of an evaluator. - - A constructor builds an Evaluator from a :class:`FData`, which is - used to the evaluation in the functional data object. - - The evaluator constructor should have a method :func:`evaluator` which - receives an fdata object and returns an :class:`Evaluator`. - - """ - - @abstractmethod - def evaluator(self, fdata): - """Construct an evaluator. - - Builds the evaluator from an functional data object. - - Args: - fdata (:class:`FData`): Functional object where the evaluator will - be used. - - Returns: - (:class:`Evaluator`): Evaluator of the fdata. - - """ - pass - - def __eq__(self, other): - """Equality operator between evaluators constructors""" - return type(self) == type(other) - - class Evaluator(ABC): """Structure of an evaluator. @@ -52,7 +20,7 @@ class Evaluator(ABC): """ @abstractmethod - def evaluate(self, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at the same evaluation points. The evaluation @@ -77,7 +45,7 @@ def evaluate(self, eval_points, *, derivative=0): pass @abstractmethod - def evaluate_composed(self, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -101,6 +69,13 @@ def evaluate_composed(self, eval_points, *, derivative=0): """ pass + def __repr__(self): + return f"{type(self)}()" + + def __eq__(self, other): + """Equality operator between evaluators.""" + return type(self) == type(other) + class GenericEvaluator(Evaluator): """Generic Evaluator. @@ -111,8 +86,7 @@ class GenericEvaluator(Evaluator): """ - def __init__(self, fdata, evaluate_func, evaluate_composed_func=None): - self.fdata = fdata + def __init__(self, evaluate_func, evaluate_composed_func=None): self.evaluate_func = evaluate_func if evaluate_composed_func is None: @@ -120,7 +94,7 @@ def __init__(self, fdata, evaluate_func, evaluate_composed_func=None): else: self.evaluate_composed_func = evaluate_composed_func - def evaluate(self, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at the same evaluation points. The evaluation @@ -143,10 +117,10 @@ def evaluate(self, eval_points, *, derivative=0): point. """ - return self.evaluate_func(self.fdata, eval_points, + return self.evaluate_func(fdata, eval_points, derivative=derivative) - def evaluate_composed(self, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -169,5 +143,5 @@ def evaluate_composed(self, eval_points, *, derivative=0): dimension of the i-th sample, at the j-th evaluation point. """ - return self.evaluate_composed_func(self.fdata, eval_points, + return self.evaluate_composed_func(fdata, eval_points, derivative=derivative) diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 60baddaca..c423b741a 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -6,10 +6,10 @@ import numpy as np -from .evaluator import EvaluatorConstructor, Evaluator, GenericEvaluator +from .evaluator import Evaluator -class PeriodicExtrapolation(EvaluatorConstructor): +class PeriodicExtrapolation(Evaluator): """Extends the domain range periodically. Examples: @@ -34,48 +34,41 @@ class PeriodicExtrapolation(EvaluatorConstructor): [-1.086, 0.759, -1.086]]) """ - def evaluator(self, fdata): - """Returns the evaluator used by :class:`FData`. + def evaluate(self, fdata, eval_points, *, derivative=0): + """Evaluate points outside the domain range. - Returns: - (:class:`Evaluator`): Evaluator of the periodic extrapolation. + Args: + fdata (:class:´FData´): Object where the evaluation is taken place. + eval_points (:class: numpy.ndarray): Numpy array with the evalation + points outside the domain range. The shape of the array may be + `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` + x `dim_codomain`. + derivate (numeric, optional): Order of derivative to be evaluated. + Returns: + (numpy.ndarray): numpy array with the evaluation of the points in + a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. """ - return GenericEvaluator(fdata, _periodic_evaluation) - - -def _periodic_evaluation(fdata, eval_points, *, derivative=0): - """Evaluate points outside the domain range. - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. + domain_range = np.asarray(fdata.domain_range) - Returns: - (numpy.ndarray): numpy array with the evaluation of the points in - a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. - """ + # Extends the domain periodically in each dimension + eval_points -= domain_range[:, 0] + eval_points %= domain_range[:, 1] - domain_range[:, 0] + eval_points += domain_range[:, 0] - domain_range = np.asarray(fdata.domain_range) + if eval_points.ndim == 3: + res = fdata._evaluate_composed(eval_points, derivative=derivative) + else: + res = fdata._evaluate(eval_points, derivative=derivative) - # Extends the domain periodically in each dimension - eval_points -= domain_range[:, 0] - eval_points %= domain_range[:, 1] - domain_range[:, 0] - eval_points += domain_range[:, 0] + return res - if eval_points.ndim == 3: - res = fdata._evaluate_composed(eval_points, derivative=derivative) - else: - res = fdata._evaluate(eval_points, derivative=derivative) + def evaluate_composed(self, *args, **kwargs): + return self.evaluate(*args, **kwargs) - return res - -class BoundaryExtrapolation(EvaluatorConstructor): +class BoundaryExtrapolation(Evaluator): """Extends the domain range using the boundary values. Examples: @@ -100,50 +93,43 @@ class BoundaryExtrapolation(EvaluatorConstructor): [ 0.759, 0.759, 1.125]]) """ - def evaluator(self, fdata): - """Returns the evaluator used by :class:`FData`. + def evaluate(self, fdata, eval_points, *, derivative=0): + """Evaluate points outside the domain range. - Returns: - (:class:`Evaluator`): Evaluator of the periodic boundary. + Args: + fdata (:class:´FData´): Object where the evaluation is taken place. + eval_points (:class: numpy.ndarray): Numpy array with the evalation + points outside the domain range. The shape of the array may be + `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` + x `dim_codomain`. + derivate (numeric, optional): Order of derivative to be evaluated. + Returns: + (numpy.ndarray): numpy array with the evaluation of the points in + a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. """ - return GenericEvaluator(fdata, _boundary_evaluation) - - -def _boundary_evaluation(fdata, eval_points, *, derivative=0): - """Evaluate points outside the domain range. - - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. - Returns: - (numpy.ndarray): numpy array with the evaluation of the points in - a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. - """ + domain_range = fdata.domain_range - domain_range = fdata.domain_range + for i in range(fdata.dim_domain): + a, b = domain_range[i] + eval_points[eval_points[..., i] < a, i] = a + eval_points[eval_points[..., i] > b, i] = b - for i in range(fdata.dim_domain): - a, b = domain_range[i] - eval_points[eval_points[..., i] < a, i] = a - eval_points[eval_points[..., i] > b, i] = b + if eval_points.ndim == 3: - if eval_points.ndim == 3: + res = fdata._evaluate_composed(eval_points, derivative=derivative) + else: - res = fdata._evaluate_composed(eval_points, derivative=derivative) - else: + res = fdata._evaluate(eval_points, derivative=derivative) - res = fdata._evaluate(eval_points, derivative=derivative) + return res - return res + def evaluate_composed(self, *args, **kwargs): + return self.evaluate(*args, **kwargs) -class ExceptionExtrapolation(EvaluatorConstructor): +class ExceptionExtrapolation(Evaluator): """Raise and exception. Examples: @@ -173,38 +159,31 @@ class ExceptionExtrapolation(EvaluatorConstructor): """ - def evaluator(self, fdata): - """Returns the evaluator used by :class:`FData`. + def evaluate(self, fdata, eval_points, *, derivative=0): + """Evaluate points outside the domain range. - Returns: - (:class:`Evaluator`): Evaluator of the periodic extrapolation. + Args: + fdata (:class:´FData´): Object where the evaluation is taken place. + eval_points (:class: numpy.ndarray): Numpy array with the evalation + points outside the domain range. The shape of the array may be + `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` + x `dim_codomain`. + derivate (numeric, optional): Order of derivative to be evaluated. + Raises: + ValueError: when the extrapolation method is called. """ - return GenericEvaluator(fdata, _exception_evaluation) - - -def _exception_evaluation(fdata, eval_points, *, derivative=0): - """Evaluate points outside the domain range. - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. - - Raises: - ValueError: when the extrapolation method is called. - """ + n_points = eval_points.shape[-2] - n_points = eval_points.shape[-2] + raise ValueError(f"Attempt to evaluate {n_points} points outside the " + f"domain range.") - raise ValueError(f"Attempt to evaluate {n_points} points outside the " - f"domain range.") + def evaluate_composed(self, *args, **kwargs): + return self.evaluate(*args, **kwargs) -class FillExtrapolation(EvaluatorConstructor): +class FillExtrapolation(Evaluator): """Values outside the domain range will be filled with a fixed value. Examples: @@ -230,44 +209,14 @@ class FillExtrapolation(EvaluatorConstructor): """ def __init__(self, fill_value): - """Returns the evaluator used by :class:`FData`. - - Returns: - (:class:`Evaluator`): Evaluator of the periodic extrapolation. - - """ - self._fill_value = fill_value - - super().__init__() - - @property - def fill_value(self): - """Returns the fill value of the extrapolation""" - return self._fill_value - - def __eq__(self, other): - """Equality operator bethween evaluator constructors""" - return (super().__eq__(other) and - (self.fill_value == other.fill_value - or self.fill_value is other.fill_value)) - - def evaluator(self, fdata): - - return FillExtrapolationEvaluator(fdata, self.fill_value) - - -class FillExtrapolationEvaluator(Evaluator): - - def __init__(self, fdata, fill_value): self.fill_value = fill_value - self.fdata = fdata - def _fill(self, eval_points): - shape = (self.fdata.n_samples, eval_points.shape[-2], - self.fdata.dim_codomain) + def _fill(self, fdata, eval_points): + shape = (fdata.n_samples, eval_points.shape[-2], + fdata.dim_codomain) return np.full(shape, self.fill_value) - def evaluate(self, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points, *, derivative=0): """ Evaluate points outside the domain range. @@ -284,9 +233,9 @@ def evaluate(self, eval_points, *, derivative=0): a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. """ - return self._fill(eval_points) + return self._fill(fdata, eval_points) - def evaluate_composed(self, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -309,7 +258,20 @@ def evaluate_composed(self, eval_points, *, derivative=0): dimension of the i-th sample, at the j-th evaluation point. """ - return self._fill(eval_points) + return self._fill(fdata, eval_points) + + def __repr__(self): + """repr method of FillExtrapolation""" + return (f"{type(self).__name__}(" + f"fill_value={self.fill_value})") + + def __eq__(self, other): + """Equality operator bethween FillExtrapolation instances.""" + return (super().__eq__(other) and + self.fill_value == other.fill_value + # NaNs compare unequal. Should we distinguish between + # different NaN types and payloads? + or np.isnan(self.fill_value) and np.isnan(other.fill_value)) def _parse_extrapolation(extrapolation): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 6fb7440ae..6d96b9df1 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -359,16 +359,6 @@ def interpolation(self, new_interpolation): new_interpolation = SplineInterpolation() self._interpolation = new_interpolation - self._interpolation_evaluator = None - - @property - def _evaluator(self): - """Return the evaluator constructed by the interpolation.""" - - if self._interpolation_evaluator is None: - self._interpolation_evaluator = self._interpolation.evaluator(self) - - return self._interpolation_evaluator def _evaluate(self, eval_points, *, derivative=0): """"Evaluate the object or its derivatives at a list of values. @@ -386,7 +376,7 @@ def _evaluate(self, eval_points, *, derivative=0): """ - return self._evaluator.evaluate(eval_points, derivative=derivative) + return self.interpolation.evaluate(self, eval_points, derivative=derivative) def _evaluate_composed(self, eval_points, *, derivative=0): """"Evaluate the object or its derivatives at a list of values. @@ -404,8 +394,8 @@ def _evaluate_composed(self, eval_points, *, derivative=0): """ - return self._evaluator.evaluate_composed(eval_points, - derivative=derivative) + return self.interpolation.evaluate_composed(self, eval_points, + derivative=derivative) def derivative(self, order=1): r"""Differentiate a FDataGrid object. diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 2daf0cb28..f9cbc847e 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -8,11 +8,10 @@ import numpy as np -from .evaluator import Evaluator, EvaluatorConstructor +from .evaluator import Evaluator -# Scipy interpolation methods used internally -class SplineInterpolation(EvaluatorConstructor): +class SplineInterpolation(Evaluator): r"""Spline interpolation of :class:`FDataGrid`. Spline interpolation of discretized functional objects. Implements different @@ -80,124 +79,36 @@ def monotone(self): "Returns flag to perform monotone interpolation" return self._monotone - def __eq__(self, other): - """Equality operator between SplineInterpolation""" - return (super().__eq__(other) and - self.interpolation_order == other.interpolation_order and - self.smoothness_parameter == other.smoothness_parameter and - self.monotone == other.monotone) - - def evaluator(self, fdatagrid): - """Construct a SplineInterpolationEvaluator used in the evaluation. - - Args: - fdatagrid (:class:`FDataGrid`): Functional object where the - evaluator will be used. - - Returns: - (:class:`SplineInterpolationEvaluator`): Evaluator of the fdatagrid. - - """ - return SplineInterpolationEvaluator(fdatagrid, self.interpolation_order, - self.smoothness_parameter, - self.monotone) - - def __repr__(self): - """repr method of the interpolation""" - return (f"{type(self).__name__}(" - f"interpolation_order={self.interpolation_order}, " - f"smoothness_parameter={self.smoothness_parameter}, " - f"monotone={self.monotone})") - - -class SplineInterpolationEvaluator(Evaluator): - r"""Spline interpolation evaluator of :class:`FDataGrid`. - - It is generated by the SplineInterpolation, and it is used internally - during the evaluation. - - Spline interpolation of discretized functional objects. Implements different - interpolation methods based in splines, using the sample points of the - grid as nodes to interpolate. - - See the interpolation example to a detailled explanation. - - Attributes: - interpolation_order (int, optional): Order of the interpolation, 1 - for linear interpolation, 2 for cuadratic, 3 for cubic and so - on. In case of curves and surfaces there is available - interpolation up to degree 5. For higher dimensional objects - only linear or nearest interpolation is available. Default - lineal interpolation. - smoothness_parameter (float, optional): Penalisation to perform - smoothness interpolation. Option only available for curves and - surfaces. If 0 the residuals of the interpolation will be 0. - Defaults 0. - monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolator. Only valid for curves (domain - dimension equal to 1) and interpolation order equal to 1 or 3. - Defaults false. - - """ - - def __init__(self, fdatagrid, k=1, s=0., monotone=False): - r"""Constructor of the SplineInterpolationEvaluator. - - Args: - fdatagir (fdatagrid): Grid to be interpolated. - interpolation_order (int, optional): Order of the interpolation, 1 - for linear interpolation, 2 for cuadratic, 3 for cubic and so - on. In case of curves and surfaces there is available - interpolation up to degree 5. For higher dimensional objects - only linear or nearest interpolation is available. Default - lineal interpolation. - smoothness_parameter (float, optional): Penalisation to perform - smoothness interpolation. Option only available for curves and - surfaces. If 0 the residuals of the interpolation will be 0. - Defaults 0. - monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolation. Only valid for curves - (domain dimension equal to 1) and interpolation order equal to - 1 or 3. - Defaults false. - - """ + def _build_interpolator(self, fdatagrid): sample_points = fdatagrid.sample_points data_matrix = fdatagrid.data_matrix - self._fdatagrid = fdatagrid - self._dim_codomain = fdatagrid.dim_codomain - self._dim_domain = fdatagrid.dim_domain - self._n_samples = fdatagrid.n_samples - self._keepdims = fdatagrid.keepdims - self._domain_range = fdatagrid.domain_range - - if self._dim_domain == 1: - self._splines = self._construct_spline_1_m(sample_points, - data_matrix, - k, s, monotone) - elif monotone: + if fdatagrid.dim_domain == 1: + return self._construct_spline_1_m( + sample_points, + data_matrix) + elif self.monotone: raise ValueError("Monotone interpolation is only supported with " "domain dimension equal to 1.") - elif self._dim_domain == 2: - self._splines = self._construct_spline_2_m(sample_points, - data_matrix, k, s) + elif fdatagrid.dim_domain == 2: + return self._construct_spline_2_m( + sample_points, + data_matrix, + self.interpolation_order, + self.smoothness_parameter) - elif s != 0: + elif self.smoothness_parameter != 0: raise ValueError("Smoothing interpolation is only supported with " "domain dimension up to 2, s should be 0.") else: - self._splines = self._construct_spline_n_m(sample_points, - data_matrix, k) + return self._construct_spline_n_m( + sample_points, + data_matrix, + self.interpolation_order) - # After the creation of the splines the fdatagrid reference can - # be deleted - self._fdatagrid = None - - def _construct_spline_1_m(self, sample_points, data_matrix, - k, s, monotone): + def _construct_spline_1_m(self, sample_points, data_matrix): r"""Construct the matrix of interpolations for curves. Constructs the matrix of interpolations for objects with domain @@ -221,25 +132,28 @@ def _construct_spline_1_m(self, sample_points, data_matrix, ValueError: If the value of the interpolation k is not valid. """ - if k > 5 or k < 1: - raise ValueError(f"Invalid degree of interpolation ({k}). Must be " + if self.interpolation_order > 5 or self.interpolation_order < 1: + raise ValueError(f"Invalid degree of interpolation " + f"({self.interpolation_order}). Must be " f"an integer greater than 0 and lower or " f"equal than 5.") - if monotone and s != 0: + if self.monotone and self.smoothness_parameter != 0: raise ValueError("Smoothing interpolation is not supported with " "monotone interpolation") - if monotone and (k == 2 or k == 4): - raise ValueError(f"monotone interpolation of degree {k}" + if self.monotone and (self.interpolation_order == 2 + or self.interpolation_order == 4): + raise ValueError(f"monotone interpolation of degree " + f"{self.interpolation_order}" f"not supported.") # Monotone interpolation of degree 1 is performed with linear spline - if monotone and k == 1: + monotone = self.monotone + if self.monotone and self.interpolation_order == 1: monotone = False # Evaluator of splines called in evaluate - def _spline_evaluator_1_m(spl, t, der): try: @@ -251,10 +165,6 @@ def _process_derivative_1_m(derivative): return derivative - self._spline_evaluator = _spline_evaluator_1_m - - self._process_derivative = _process_derivative_1_m - sample_points = sample_points[0] if monotone: @@ -266,11 +176,18 @@ def constructor(data): def constructor(data): """Constructs an unidimensional interpolation""" - return UnivariateSpline(sample_points, data, s=s, k=k) + return UnivariateSpline( + sample_points, data, + s=self.smoothness_parameter, + k=self.interpolation_order) + + splines = np.apply_along_axis(constructor, 1, data_matrix) + evaluator = _spline_evaluator_1_m + derivative = _process_derivative_1_m - return np.apply_along_axis(constructor, 1, data_matrix) + return (splines, evaluator, derivative) - def _construct_spline_2_m(self, sample_points, data_matrix, k, s): + def _construct_spline_2_m(self, sample_points, data_matrix): r"""Construct the matrix of interpolations for surfaces. Constructs the matrix of interpolations for objects with domain @@ -293,13 +210,13 @@ def _construct_spline_2_m(self, sample_points, data_matrix, k, s): ValueError: If the value of the interpolation k is not valid. """ - if np.isscalar(k): - kx = ky = k - elif len(k) != 2: + if np.isscalar(self.interpolation_order): + kx = ky = self.interpolation_order + elif len(self.interpolation_order) != 2: raise ValueError("k should be numeric or a tuple of length 2.") else: - kx = k[0] - ky = k[1] + kx = self.interpolation_order[0] + ky = self.interpolation_order[1] if kx > 5 or kx <= 0 or ky > 5 or ky <= 0: raise ValueError(f"Invalid degree of interpolation ({kx},{ky}). " @@ -319,23 +236,24 @@ def _process_derivative_2_m(derivative): return derivative - # Evaluator of splines called in evaluate - self._spline_evaluator = _spline_evaluator_2_m - self._process_derivative = _process_derivative_2_m - # Matrix of splines - spline = np.empty((self._n_samples, self._dim_codomain), dtype=object) + splines = np.empty((self._n_samples, self._dim_codomain), dtype=object) for i in range(self._n_samples): for j in range(self._dim_codomain): - spline[i, j] = RectBivariateSpline(sample_points[0], - sample_points[1], - data_matrix[i, :, :, j], - kx=kx, ky=ky, s=s) + splines[i, j] = RectBivariateSpline( + sample_points[0], + sample_points[1], + data_matrix[i, :, :, j], + kx=kx, ky=ky, + s=self.smoothness_parameter) + + evaluator = _spline_evaluator_2_m + derivative = _process_derivative_2_m - return spline + return (splines, evaluator, derivative) - def _construct_spline_n_m(self, sample_points, data_matrix, k): + def _construct_spline_n_m(self, sample_points, data_matrix): r"""Construct the matrix of interpolations. Constructs the matrix of interpolations for objects with domain @@ -361,9 +279,9 @@ def _construct_spline_n_m(self, sample_points, data_matrix, k): """ # Parses method of interpolation - if k == 0: + if self.interpolation_order == 0: method = 'nearest' - elif k == 1: + elif self.interpolation_order == 1: method = 'linear' else: raise ValueError("interpolation order should be 0 (nearest) or 1 " @@ -380,22 +298,19 @@ def _spline_evaluator_n_m(spl, t, derivative): return spl(t) - # Method to process derivative argument - self._process_derivative = _process_derivative_n_m - - # Evaluator of splines called in evaluate - self._spline_evaluator = _spline_evaluator_n_m - - spline = np.empty((self._n_samples, self._dim_codomain), dtype=object) + splines = np.empty((self._n_samples, self._dim_codomain), dtype=object) for i in range(self._n_samples): for j in range(self._dim_codomain): - spline[i, j] = RegularGridInterpolator( + splines[i, j] = RegularGridInterpolator( sample_points, data_matrix[i, ..., j], method, False) - return spline + evaluator = _spline_evaluator_n_m + derivative = _process_derivative_n_m - def evaluate(self, eval_points, *, derivative=0): + return (splines, evaluator, derivative) + + def evaluate(self, fdata, eval_points, *, derivative=0): r"""Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -422,28 +337,31 @@ def evaluate(self, eval_points, *, derivative=0): argument. """ - derivative = self._process_derivative(derivative) + + (splines, spline_evaluator, + process_derivative) = self._build_interpolator(fdata) + derivative = process_derivative(derivative) # Constructs the evaluator for t_eval - if self._dim_codomain == 1: + if fdata.dim_codomain == 1: def evaluator(spl): """Evaluator of object with image dimension equal to 1.""" - return self._spline_evaluator(spl[0], eval_points, derivative) + return spline_evaluator(spl[0], eval_points, derivative) else: def evaluator(spl_m): """Evaluator of multimensional object""" return np.dstack( - [self._spline_evaluator(spl, eval_points, derivative) + [spline_evaluator(spl, eval_points, derivative) for spl in spl_m]).flatten() # Points evaluated inside the domain - res = np.apply_along_axis(evaluator, 1, self._splines) - res = res.reshape(self._n_samples, eval_points.shape[0], - self._dim_codomain) + res = np.apply_along_axis(evaluator, 1, splines) + res = res.reshape(fdata.n_samples, eval_points.shape[0], + fdata.dim_codomain) return res - def evaluate_composed(self, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points, *, derivative=0): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -470,27 +388,45 @@ def evaluate_composed(self, eval_points, *, derivative=0): argument. """ - shape = (self._n_samples, eval_points.shape[1], self._dim_codomain) + shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) res = np.empty(shape) - derivative = self._process_derivative(derivative) + (splines, + spline_evaluator, + process_derivative) = self._build_interpolator(fdata) + + derivative = process_derivative(derivative) - if self._dim_codomain == 1: + if fdata.dim_codomain == 1: def evaluator(t, spl): """Evaluator of sample with image dimension equal to 1""" - return self._spline_evaluator(spl[0], t, derivative) + return spline_evaluator(spl[0], t, derivative) - for i in range(self._n_samples): - res[i] = evaluator(eval_points[i], self._splines[i]).reshape( - (eval_points.shape[1], self._dim_codomain)) + for i in range(fdata.n_samples): + res[i] = evaluator(eval_points[i], splines[i]).reshape( + (eval_points.shape[1], fdata.dim_codomain)) else: def evaluator(t, spl_m): """Evaluator of multidimensional sample""" - return np.array([self._spline_evaluator(spl, t, derivative) + return np.array([spline_evaluator(spl, t, derivative) for spl in spl_m]).T - for i in range(self._n_samples): - res[i] = evaluator(eval_points[i], self._splines[i]) + for i in range(fdata.n_samples): + res[i] = evaluator(eval_points[i], splines[i]) return res + + def __repr__(self): + """repr method of the interpolation""" + return (f"{type(self).__name__}(" + f"interpolation_order={self.interpolation_order}, " + f"smoothness_parameter={self.smoothness_parameter}, " + f"monotone={self.monotone})") + + def __eq__(self, other): + """Equality operator between SplineInterpolation""" + return (super().__eq__(other) and + self.interpolation_order == other.interpolation_order and + self.smoothness_parameter == other.smoothness_parameter and + self.monotone == other.monotone) From 3c1276289d0c155401e319acf5959d932bf525b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 10 Jun 2020 20:24:53 +0200 Subject: [PATCH 548/624] n_reps not None MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 5063c7e27..e8f2e2a29 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -189,7 +189,7 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, sample = fd1.concatenate(fd2) indices = np.arange(n) - if n_reps: # Computing n_reps random permutations + if n_reps is not None: # Computing n_reps random permutations random_state = check_random_state(random_state) dist = np.empty(n_reps) for i in range(n_reps): From ab9a3c882918548bceb893019f0992176fccef78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 10 Jun 2020 20:30:41 +0200 Subject: [PATCH 549/624] Comments on full permutation test MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index e8f2e2a29..599102ad5 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -201,8 +201,8 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, combinations = itertools.combinations(indices, n1) dist = np.empty(int(scipy.special.comb(n, n1))) for i, comb in enumerate(combinations): - sample1_i = np.asarray(comb) - sample2_i = np.setdiff1d(indices, sample1_i) + sample1_i = np.asarray(comb) # Comb is a selection of n1 indices + sample2_i = np.setdiff1d(indices, sample1_i) # Remaining n2 ind. sample1, sample2 = sample[sample1_i], sample[sample2_i] dist[i] = hotelling_t2(sample1, sample2, gram) From 4047df45c4711f472730a29af843f9608126c66c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 10 Jun 2020 20:43:55 +0200 Subject: [PATCH 550/624] Removing unnecessary parameter "weights" MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 23 ++++++++--------------- 1 file changed, 8 insertions(+), 15 deletions(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 599102ad5..0e3e31cd0 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -5,7 +5,7 @@ from sklearn.utils import check_random_state -def hotelling_t2(fd1, fd2, weights=None): +def hotelling_t2(fd1, fd2): r""" Calculates Hotelling's :math:`T^2` over two samples in :class:`skfda.representation.FData` objects with sizes :math:`n_1` @@ -18,9 +18,9 @@ def hotelling_t2(fd1, fd2, weights=None): \mathbf{W}^{1/2} (\mathbf{m}_1 - \mathbf{m}_2), where :math:`(\cdot)^{+}` indicates the Moore-Penrose pseudo-inverse - operator, :math:`n=n_1+n_2`, `W` is a matrix of weights - (usually Gram matrix), and :math:`\mathbf{m}_1, \mathbf{m}_2` are the - means of each ample, :math:`\mathbf{K}_{\operatorname{pooled}}` + operator, :math:`n=n_1+n_2`, `W` is Gram matrix (identity in case of + discretized data), :math:`\mathbf{m}_1, \mathbf{m}_2` are the + means of each ample and :math:`\mathbf{K}_{\operatorname{pooled}}` matrix is defined as .. math:: @@ -37,9 +37,6 @@ def hotelling_t2(fd1, fd2, weights=None): Args: fd1 (FData): Object with the first sample. fd2 (FData): Object containing second sample. - weights (numpy.array, optional): Weights matrix. If no value - is passed then uses Gram matrix if data is in basis - representation. Identity matrix is used for discretized data. Returns: The value of the statistic. @@ -86,8 +83,7 @@ def hotelling_t2(fd1, fd2, weights=None): k1 = np.cov(fd1.coefficients, rowvar=False) k2 = np.cov(fd2.coefficients, rowvar=False) # If no weight matrix is passed, then we compute the Gram Matrix - if weights is None: - weights = fd1.basis.gram_matrix() + weights = fd1.basis.gram_matrix() weights = np.sqrt(np.abs(weights)) # TODO else: # Working with standard discretized data @@ -181,10 +177,8 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, if n_reps is not None and n_reps < 1: raise ValueError("Number of repetitions must be positive.") - gram = fd1.basis.gram_matrix() if isinstance(fd1, FDataBasis) else None - n1, n2 = fd1.n_samples, fd2.n_samples - t2_0 = hotelling_t2(fd1, fd2, gram) + t2_0 = hotelling_t2(fd1, fd2) n = n1 + n2 sample = fd1.concatenate(fd2) indices = np.arange(n) @@ -194,8 +188,7 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, dist = np.empty(n_reps) for i in range(n_reps): random_state.shuffle(indices) - dist[i] = hotelling_t2(sample[indices[:n1]], sample[indices[n1:]], - gram) + dist[i] = hotelling_t2(sample[indices[:n1]], sample[indices[n1:]]) else: # Full permutation test combinations = itertools.combinations(indices, n1) @@ -204,7 +197,7 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, sample1_i = np.asarray(comb) # Comb is a selection of n1 indices sample2_i = np.setdiff1d(indices, sample1_i) # Remaining n2 ind. sample1, sample2 = sample[sample1_i], sample[sample2_i] - dist[i] = hotelling_t2(sample1, sample2, gram) + dist[i] = hotelling_t2(sample1, sample2) p_value = np.sum(dist > t2_0) / len(dist) From 825642987203682009a048dfa2224430036aa697 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Wed, 10 Jun 2020 20:54:35 +0200 Subject: [PATCH 551/624] Removing absolute value from matrix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 0e3e31cd0..75c6b7dd1 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -84,7 +84,7 @@ def hotelling_t2(fd1, fd2): k2 = np.cov(fd2.coefficients, rowvar=False) # If no weight matrix is passed, then we compute the Gram Matrix weights = fd1.basis.gram_matrix() - weights = np.sqrt(np.abs(weights)) # TODO + weights = np.sqrt(weights) else: # Working with standard discretized data m = m.data_matrix[0, ..., 0] From 878b43b39d1a3ee23294b528c1fbdaafc663df15 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 11 Jun 2020 17:49:56 +0200 Subject: [PATCH 552/624] Add test for multidimensional --- docs/modules/representation/extrapolation.rst | 5 +- skfda/representation/interpolation.py | 130 ++++++------------ tests/test_interpolation.py | 73 +++++++--- 3 files changed, 103 insertions(+), 105 deletions(-) diff --git a/docs/modules/representation/extrapolation.rst b/docs/modules/representation/extrapolation.rst index d1ab136e1..0736e883a 100644 --- a/docs/modules/representation/extrapolation.rst +++ b/docs/modules/representation/extrapolation.rst @@ -22,11 +22,10 @@ The following classes are used to define common methods of extrapolation. Custom Extrapolation -------------------- -Custom extrapolators could be done subclassing :class:`EvaluatorConstructor -`. +Custom extrapolators could be done subclassing :class:`Evaluator +`. .. autosummary:: :toctree: autosummary - skfda.representation.evaluator.EvaluatorConstructor skfda.representation.evaluator.Evaluator diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index f9cbc847e..8bd0f2052 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -80,35 +80,24 @@ def monotone(self): return self._monotone def _build_interpolator(self, fdatagrid): - sample_points = fdatagrid.sample_points - data_matrix = fdatagrid.data_matrix if fdatagrid.dim_domain == 1: - return self._construct_spline_1_m( - sample_points, - data_matrix) + return self._construct_spline_1_m(fdatagrid) elif self.monotone: raise ValueError("Monotone interpolation is only supported with " "domain dimension equal to 1.") elif fdatagrid.dim_domain == 2: - return self._construct_spline_2_m( - sample_points, - data_matrix, - self.interpolation_order, - self.smoothness_parameter) + return self._construct_spline_2_m(fdatagrid) elif self.smoothness_parameter != 0: raise ValueError("Smoothing interpolation is only supported with " "domain dimension up to 2, s should be 0.") else: - return self._construct_spline_n_m( - sample_points, - data_matrix, - self.interpolation_order) + return self._construct_spline_n_m(fdatagrid) - def _construct_spline_1_m(self, sample_points, data_matrix): + def _construct_spline_1_m(self, fdatagrid): r"""Construct the matrix of interpolations for curves. Constructs the matrix of interpolations for objects with domain @@ -154,18 +143,14 @@ def _construct_spline_1_m(self, sample_points, data_matrix): monotone = False # Evaluator of splines called in evaluate - def _spline_evaluator_1_m(spl, t, der): + def _spline_evaluator_1_m(spl, t, derivative): try: - return spl(t, der) + return spl(t, derivative) except ValueError: return np.zeros_like(t) - def _process_derivative_1_m(derivative): - - return derivative - - sample_points = sample_points[0] + sample_points = fdatagrid.sample_points[0] if monotone: def constructor(data): @@ -181,13 +166,12 @@ def constructor(data): s=self.smoothness_parameter, k=self.interpolation_order) - splines = np.apply_along_axis(constructor, 1, data_matrix) + splines = np.apply_along_axis(constructor, 1, fdatagrid.data_matrix) evaluator = _spline_evaluator_1_m - derivative = _process_derivative_1_m - return (splines, evaluator, derivative) + return (splines, evaluator) - def _construct_spline_2_m(self, sample_points, data_matrix): + def _construct_spline_2_m(self, fdatagrid): r"""Construct the matrix of interpolations for surfaces. Constructs the matrix of interpolations for objects with domain @@ -223,37 +207,34 @@ def _construct_spline_2_m(self, sample_points, data_matrix): f"Must be an integer greater than 0 and lower or " f"equal than 5.") - def _spline_evaluator_2_m(spl, t, der): - - return spl(t[:, 0], t[:, 1], dx=der[0], dy=der[1], grid=False) - - def _process_derivative_2_m(derivative): + def _spline_evaluator_2_m(spl, t, derivative): if np.isscalar(derivative): derivative = 2 * [derivative] elif len(derivative) != 2: raise ValueError("derivative should be a numeric value " "or a tuple of length 2 with (dx,dy).") - return derivative + return spl(t[:, 0], t[:, 1], dx=derivative[0], dy=derivative[1], + grid=False) # Matrix of splines - splines = np.empty((self._n_samples, self._dim_codomain), dtype=object) + splines = np.empty( + (fdatagrid.n_samples, fdatagrid.dim_codomain), dtype=object) - for i in range(self._n_samples): - for j in range(self._dim_codomain): + for i in range(fdatagrid.n_samples): + for j in range(fdatagrid.dim_codomain): splines[i, j] = RectBivariateSpline( - sample_points[0], - sample_points[1], - data_matrix[i, :, :, j], + fdatagrid.sample_points[0], + fdatagrid.sample_points[1], + fdatagrid.data_matrix[i, :, :, j], kx=kx, ky=ky, s=self.smoothness_parameter) evaluator = _spline_evaluator_2_m - derivative = _process_derivative_2_m - return (splines, evaluator, derivative) + return (splines, evaluator) - def _construct_spline_n_m(self, sample_points, data_matrix): + def _construct_spline_n_m(self, fdatagrid): r"""Construct the matrix of interpolations. Constructs the matrix of interpolations for objects with domain @@ -287,28 +268,26 @@ def _construct_spline_n_m(self, sample_points, data_matrix): raise ValueError("interpolation order should be 0 (nearest) or 1 " "(linear).") - def _process_derivative_n_m(derivative): + def _spline_evaluator_n_m(spl, t, derivative): + if derivative != 0: raise ValueError("derivates not suported for functional data " " with domain dimension greater than 2.") - return derivative - - def _spline_evaluator_n_m(spl, t, derivative): - return spl(t) - splines = np.empty((self._n_samples, self._dim_codomain), dtype=object) + splines = np.empty( + (fdatagrid.n_samples, fdatagrid.dim_codomain), dtype=object) - for i in range(self._n_samples): - for j in range(self._dim_codomain): + for i in range(fdatagrid.n_samples): + for j in range(fdatagrid.dim_codomain): splines[i, j] = RegularGridInterpolator( - sample_points, data_matrix[i, ..., j], method, False) + fdatagrid.sample_points, fdatagrid.data_matrix[i, ..., j], + method, False) evaluator = _spline_evaluator_n_m - derivative = _process_derivative_n_m - return (splines, evaluator, derivative) + return (splines, evaluator) def evaluate(self, fdata, eval_points, *, derivative=0): r"""Evaluation method. @@ -338,21 +317,13 @@ def evaluate(self, fdata, eval_points, *, derivative=0): """ - (splines, spline_evaluator, - process_derivative) = self._build_interpolator(fdata) - derivative = process_derivative(derivative) + (splines, spline_evaluator) = self._build_interpolator(fdata) - # Constructs the evaluator for t_eval - if fdata.dim_codomain == 1: - def evaluator(spl): - """Evaluator of object with image dimension equal to 1.""" - return spline_evaluator(spl[0], eval_points, derivative) - else: - def evaluator(spl_m): - """Evaluator of multimensional object""" - return np.dstack( - [spline_evaluator(spl, eval_points, derivative) - for spl in spl_m]).flatten() + def evaluator(spl_m): + """Evaluator of multimensional object""" + return np.dstack( + [spline_evaluator(spl, eval_points, derivative) + for spl in spl_m]).flatten() # Points evaluated inside the domain res = np.apply_along_axis(evaluator, 1, splines) @@ -392,28 +363,15 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): res = np.empty(shape) (splines, - spline_evaluator, - process_derivative) = self._build_interpolator(fdata) - - derivative = process_derivative(derivative) + spline_evaluator) = self._build_interpolator(fdata) - if fdata.dim_codomain == 1: - def evaluator(t, spl): - """Evaluator of sample with image dimension equal to 1""" - return spline_evaluator(spl[0], t, derivative) - - for i in range(fdata.n_samples): - res[i] = evaluator(eval_points[i], splines[i]).reshape( - (eval_points.shape[1], fdata.dim_codomain)) - - else: - def evaluator(t, spl_m): - """Evaluator of multidimensional sample""" - return np.array([spline_evaluator(spl, t, derivative) - for spl in spl_m]).T + def evaluator(t, spl_m): + """Evaluator of multidimensional sample""" + return np.array([spline_evaluator(spl, t, derivative) + for spl in spl_m]).T - for i in range(fdata.n_samples): - res[i] = evaluator(eval_points[i], splines[i]) + for i in range(fdata.n_samples): + res[i] = evaluator(eval_points[i], splines[i]) return res diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index adebea9a4..26289d9da 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -16,7 +16,7 @@ def setUp(self): # Data matrix of a datagrid with a dimension of domain and image equal # to 1. - # Matrix of functions (x**2, (9-x)**2) + # Matrix of functions (x**2, (9-x)**2) self.data_matrix_1_1 = [np.arange(10)**2, np.arange(start=9, stop=-1, step=-1)**2] @@ -71,7 +71,7 @@ def test_evaluation_linear_grid(self): np.testing.assert_array_almost_equal(f(t, grid=True), res) np.testing.assert_array_almost_equal(f((t,), grid=True), res) np.testing.assert_array_almost_equal(f([t], grid=True), res) - # Single point with grid + # Single point with grid np.testing.assert_array_almost_equal(f(3, grid=True), np.array([[9.], [36.]])) @@ -193,7 +193,8 @@ def test_evaluation_grid_linear_keepdims(self): f([t], grid=True, keepdims=False), res) np.testing.assert_array_almost_equal(fk(t, grid=True), res_keepdims) - np.testing.assert_array_almost_equal(fk((t,), grid=True, keepdims=True), + np.testing.assert_array_almost_equal(fk((t,), grid=True, + keepdims=True), res_keepdims) np.testing.assert_array_almost_equal( fk([t], grid=True, keepdims=False), res) @@ -220,8 +221,8 @@ def test_evaluation_cubic_point(self): interpolation=SplineInterpolation(3)) # Test a single point - np.testing.assert_array_almost_equal(f(5.3).round(3), np.array([[28.09], - [13.69]])) + np.testing.assert_array_almost_equal(f(5.3).round(3), + np.array([[28.09], [13.69]])) np.testing.assert_array_almost_equal( f([3]).round(3), np.array([[9.], [36.]])) @@ -234,9 +235,10 @@ def test_evaluation_cubic_derivative(self): interpolation=SplineInterpolation(3)) # Derivate = [2*x, 2*(9-x)] - np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5], derivative=1).round(3), - np.array([[1., 3., 5.], - [-17., -15., -13.]])) + np.testing.assert_array_almost_equal( + f([0.5, 1.5, 2.5], derivative=1).round(3), + np.array([[1., 3., 5.], + [-17., -15., -13.]])) def test_evaluation_cubic_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -251,7 +253,7 @@ def test_evaluation_cubic_grid(self): np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) np.testing.assert_array_almost_equal(f((t,), grid=True).round(3), res) np.testing.assert_array_almost_equal(f([t], grid=True).round(3), res) - # Single point with grid + # Single point with grid np.testing.assert_array_almost_equal( f(3, grid=True), np.array([[9.], [36.]])) @@ -266,17 +268,20 @@ def test_evaluation_cubic_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal( - f([[1], [8]], aligned_evaluation=False).round(3), np.array([[1.], [1.]])) + f([[1], [8]], aligned_evaluation=False).round(3), + np.array([[1.], [1.]])) t = np.linspace(4, 6, 4) - np.testing.assert_array_almost_equal(f([t, 9 - t], aligned_evaluation=False).round(2), - np.array([[16., 21.78, 28.44, 36.], - [16., 21.78, 28.44, 36.]])) + np.testing.assert_array_almost_equal( + f([t, 9 - t], aligned_evaluation=False).round(2), + np.array([[16., 21.78, 28.44, 36.], + [16., 21.78, 28.44, 36.]])) # Same length than nsample t = np.linspace(4, 6, 2) - np.testing.assert_array_almost_equal(f([t, 9 - t], aligned_evaluation=False).round(3), - np.array([[16., 36.], [16., 36.]])) + np.testing.assert_array_almost_equal( + f([t, 9 - t], aligned_evaluation=False).round(3), + np.array([[16., 36.], [16., 36.]])) def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" @@ -315,7 +320,7 @@ def setUp(self): # Data matrix of a datagrid with a dimension of domain and image equal # to 1. - # Matrix of functions (x**2, (9-x)**2) + # Matrix of functions (x**2, (9-x)**2) self.t = np.arange(10) @@ -440,6 +445,42 @@ def test_evaluation_nodes(self): self.data_matrix_1_n) +class TestEvaluationSplinem_n(unittest.TestCase): + """Test the evaluation of a grid spline interpolation with + arbitrary domain dimension and arbitary image dimension. + """ + + def test_evaluation_center_and_extreme_points_linear(self): + """Test linear interpolation in the middle point of a grid square.""" + + dim_codomain = 4 + n_samples = 2 + + @np.vectorize + def coordinate_function(*args): + _, *domain_indexes, _ = args + return np.sum(domain_indexes) + + for dim_domain in range(1, 6): + sample_points = [np.array([0, 1]) for _ in range(dim_domain)] + data_matrix = np.fromfunction( + function=coordinate_function, + shape=(n_samples,) + (2,) * dim_domain + (dim_codomain,)) + + f = FDataGrid(data_matrix, sample_points=sample_points) + + evaluation = f([[0.] * dim_domain, [0.5] * + dim_domain, [1.] * dim_domain]) + + self.assertEqual(evaluation.shape, (n_samples, 3, dim_codomain)) + + for i in range(n_samples): + for j in range(dim_codomain): + np.testing.assert_array_almost_equal( + evaluation[i, ..., j], + [0, dim_domain * 0.5, dim_domain]) + + if __name__ == '__main__': print() unittest.main() From 54b0f05b700e07f26aef2ac781a73450c742f359 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 12 Jun 2020 02:39:09 +0200 Subject: [PATCH 553/624] Simplify interpolation module. --- skfda/representation/interpolation.py | 457 +++++++++++++++----------- 1 file changed, 271 insertions(+), 186 deletions(-) diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 8bd0f2052..f1491a701 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -3,6 +3,8 @@ """ +import abc + from scipy.interpolate import (PchipInterpolator, UnivariateSpline, RectBivariateSpline, RegularGridInterpolator) @@ -11,16 +13,64 @@ from .evaluator import Evaluator -class SplineInterpolation(Evaluator): - r"""Spline interpolation of :class:`FDataGrid`. +class _SplineList(abc.ABC): + r"""ABC for list of interpolations.""" - Spline interpolation of discretized functional objects. Implements different - interpolation methods based in splines, using the sample points of the - grid as nodes to interpolate. + def __init__(self, fdatagrid, + interpolation_order=1, + smoothness_parameter=0.): - See the interpolation example to a detailled explanation. + super().__init__() - Attributes: + self.fdatagrid = fdatagrid + self.interpolation_order = interpolation_order + self.smoothness_parameter = smoothness_parameter + + @abc.abstractmethod + def _evaluate_one(self, spl, t, derivative=0): + """Evaluates one spline of the list.""" + pass + + def _evaluate_codomain(self, spl_m, t, derivative=0): + """Evaluator of multidimensional sample""" + return np.array([self._evaluate_one(spl, t, derivative) + for spl in spl_m]).T + + def evaluate(self, fdata, eval_points, *, derivative=0): + + # Points evaluated inside the domain + res = np.apply_along_axis( + self._evaluate_codomain, 1, + self.splines, eval_points, derivative) + res = res.reshape(fdata.n_samples, eval_points.shape[0], + fdata.dim_codomain) + + return res + + def evaluate_composed(self, fdata, eval_points, *, derivative=0): + + shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) + res = np.empty(shape) + + for i in range(fdata.n_samples): + res[i] = self._evaluate_codomain( + self.splines[i], eval_points[i], derivative=derivative) + + return res + + +class _SplineList1D(_SplineList): + r"""List of interpolations for curves. + + List of interpolations for objects with domain + dimension = 1. Calling internally during the creation of the + evaluator. + + Uses internally the scipy interpolation UnivariateSpline or + PchipInterpolator. + + Args: + fdatagrid (FDatagrid): Fdatagrid to interpolate. interpolation_order (int, optional): Order of the interpolation, 1 for linear interpolation, 2 for cuadratic, 3 for cubic and so on. In case of curves and surfaces there is available @@ -36,91 +86,28 @@ class SplineInterpolation(Evaluator): dimension equal to 1) and interpolation order equal to 1 or 3. Defaults false. - """ - - def __init__(self, interpolation_order=1, smoothness_parameter=0., - monotone=False): - r"""Constructor of the SplineInterpolation. - - Args: - interpolation_order (int, optional): Order of the interpolation, 1 - for linear interpolation, 2 for cuadratic, 3 for cubic and so - on. In case of curves and surfaces there is available - interpolation up to degree 5. For higher dimensional objects - only linear or nearest interpolation is available. Default - lineal interpolation. - smoothness_parameter (float, optional): Penalisation to perform - smoothness interpolation. Option only available for curves and - surfaces. If 0 the residuals of the interpolation will be 0. - Defaults 0. - monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolation. Only valid for curves - (domain dimension equal to 1) and interpolation order equal - to 1 or 3. - Defaults false. - - """ - self._interpolation_order = interpolation_order - self._smoothness_parameter = smoothness_parameter - self._monotone = monotone - - @property - def interpolation_order(self): - "Returns the interpolation order" - return self._interpolation_order - - @property - def smoothness_parameter(self): - "Returns the smoothness parameter" - return self._smoothness_parameter - - @property - def monotone(self): - "Returns flag to perform monotone interpolation" - return self._monotone - - def _build_interpolator(self, fdatagrid): - - if fdatagrid.dim_domain == 1: - return self._construct_spline_1_m(fdatagrid) - elif self.monotone: - raise ValueError("Monotone interpolation is only supported with " - "domain dimension equal to 1.") - - elif fdatagrid.dim_domain == 2: - return self._construct_spline_2_m(fdatagrid) - - elif self.smoothness_parameter != 0: - raise ValueError("Smoothing interpolation is only supported with " - "domain dimension up to 2, s should be 0.") - - else: - return self._construct_spline_n_m(fdatagrid) + Returns: + (np.ndarray): Array of size n_samples x dim_codomain with the + corresponding interpolation of the sample i, and image dimension j + in the entry (i,j) of the array. - def _construct_spline_1_m(self, fdatagrid): - r"""Construct the matrix of interpolations for curves. + Raises: + ValueError: If the value of the interpolation k is not valid. - Constructs the matrix of interpolations for objects with domain - dimension = 1. Calling internally during the creationg of the - evaluator. + """ - Uses internally the scipy interpolation UnivariateSpline or - PchipInterpolator. + def __init__(self, fdatagrid, + interpolation_order=1, + smoothness_parameter=0., + monotone=False): - Args: - sample_points (np.ndarray): Sample points of the fdatagrid. - data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolations. + super().__init__( + fdatagrid=fdatagrid, + interpolation_order=interpolation_order, + smoothness_parameter=smoothness_parameter) - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. + self.monotone = monotone - Raises: - ValueError: If the value of the interpolation k is not valid. - - """ if self.interpolation_order > 5 or self.interpolation_order < 1: raise ValueError(f"Invalid degree of interpolation " f"({self.interpolation_order}). Must be " @@ -142,14 +129,6 @@ def _construct_spline_1_m(self, fdatagrid): if self.monotone and self.interpolation_order == 1: monotone = False - # Evaluator of splines called in evaluate - def _spline_evaluator_1_m(spl, t, derivative): - - try: - return spl(t, derivative) - except ValueError: - return np.zeros_like(t) - sample_points = fdatagrid.sample_points[0] if monotone: @@ -166,34 +145,61 @@ def constructor(data): s=self.smoothness_parameter, k=self.interpolation_order) - splines = np.apply_along_axis(constructor, 1, fdatagrid.data_matrix) - evaluator = _spline_evaluator_1_m + self.splines = np.apply_along_axis( + constructor, 1, fdatagrid.data_matrix) - return (splines, evaluator) + def _evaluate_one(self, spl, t, derivative=0): + try: + return spl(t, derivative) + except ValueError: + return np.zeros_like(t) - def _construct_spline_2_m(self, fdatagrid): - r"""Construct the matrix of interpolations for surfaces. - Constructs the matrix of interpolations for objects with domain - dimension = 2. Calling internally during the creationg of the - evaluator. +class _SplineList2D(_SplineList): + r"""List of interpolations for surfaces. - Uses internally the scipy interpolation RectBivariateSpline. + List of interpolations for objects with domain + dimension = 2. Calling internally during the creationg of the + evaluator. - Args: - sample_points (np.ndarray): Sample points of the fdatagrid. - data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolations. + Uses internally the scipy interpolation RectBivariateSpline. - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. + Args: + fdatagrid (FDatagrid): Fdatagrid to interpolate. + interpolation_order (int, optional): Order of the interpolation, 1 + for linear interpolation, 2 for cuadratic, 3 for cubic and so + on. In case of curves and surfaces there is available + interpolation up to degree 5. For higher dimensional objects + only linear or nearest interpolation is available. Default + lineal interpolation. + smoothness_parameter (float, optional): Penalisation to perform + smoothness interpolation. Option only available for curves and + surfaces. If 0 the residuals of the interpolation will be 0. + Defaults 0. + monotone (boolean, optional): Performs monotone interpolation in + curves using a PCHIP interpolator. Only valid for curves (domain + dimension equal to 1) and interpolation order equal to 1 or 3. + Defaults false. - Raises: - ValueError: If the value of the interpolation k is not valid. + Returns: + (np.ndarray): Array of size n_samples x dim_codomain with the + corresponding interpolation of the sample i, and image dimension j + in the entry (i,j) of the array. + + Raises: + ValueError: If the value of the interpolation k is not valid. + + """ + + def __init__(self, fdatagrid, + interpolation_order=1, + smoothness_parameter=0.): + + super().__init__( + fdatagrid=fdatagrid, + interpolation_order=interpolation_order, + smoothness_parameter=smoothness_parameter) - """ if np.isscalar(self.interpolation_order): kx = ky = self.interpolation_order elif len(self.interpolation_order) != 2: @@ -207,58 +213,69 @@ def _construct_spline_2_m(self, fdatagrid): f"Must be an integer greater than 0 and lower or " f"equal than 5.") - def _spline_evaluator_2_m(spl, t, derivative): - if np.isscalar(derivative): - derivative = 2 * [derivative] - elif len(derivative) != 2: - raise ValueError("derivative should be a numeric value " - "or a tuple of length 2 with (dx,dy).") - - return spl(t[:, 0], t[:, 1], dx=derivative[0], dy=derivative[1], - grid=False) - # Matrix of splines - splines = np.empty( + self.splines = np.empty( (fdatagrid.n_samples, fdatagrid.dim_codomain), dtype=object) for i in range(fdatagrid.n_samples): for j in range(fdatagrid.dim_codomain): - splines[i, j] = RectBivariateSpline( + self.splines[i, j] = RectBivariateSpline( fdatagrid.sample_points[0], fdatagrid.sample_points[1], fdatagrid.data_matrix[i, :, :, j], kx=kx, ky=ky, s=self.smoothness_parameter) - evaluator = _spline_evaluator_2_m + def _evaluate_one(self, spl, t, derivative=0): + if np.isscalar(derivative): + derivative = 2 * [derivative] + elif len(derivative) != 2: + raise ValueError("derivative should be a numeric value " + "or a tuple of length 2 with (dx,dy).") - return (splines, evaluator) + return spl(t[:, 0], t[:, 1], dx=derivative[0], dy=derivative[1], + grid=False) - def _construct_spline_n_m(self, fdatagrid): - r"""Construct the matrix of interpolations. - Constructs the matrix of interpolations for objects with domain - dimension > 2. Calling internally during the creationg of the - evaluator. +class _SplineListND(_SplineList): + r"""List of interpolations. - Only linear and nearest interpolations are available for objects with - domain dimension >= 3. Uses internally the scipy interpolation - RegularGridInterpolator. + List of interpolations for objects with domain + dimension > 2. Calling internally during the creationg of the + evaluator. - Args: - sample_points (np.ndarray): Sample points of the fdatagrid. - data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolations. + Only linear and nearest interpolations are available for objects with + domain dimension >= 3. Uses internally the scipy interpolation + RegularGridInterpolator. - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. + Args: + sample_points (np.ndarray): Sample points of the fdatagrid. + data_matrix (np.ndarray): Data matrix of the fdatagrid. + k (integer): Order of the spline interpolations. - Raises: - ValueError: If the value of the interpolation k is not valid. + Returns: + (np.ndarray): Array of size n_samples x dim_codomain with the + corresponding interpolation of the sample i, and image dimension j + in the entry (i,j) of the array. + + Raises: + ValueError: If the value of the interpolation k is not valid. + + """ + + def __init__(self, fdatagrid, + interpolation_order=1, + smoothness_parameter=0.): + + super().__init__( + fdatagrid=fdatagrid, + interpolation_order=interpolation_order, + smoothness_parameter=smoothness_parameter) + + if self.smoothness_parameter != 0: + raise ValueError("Smoothing interpolation is only supported with " + "domain dimension up to 2, s should be 0.") - """ # Parses method of interpolation if self.interpolation_order == 0: method = 'nearest' @@ -268,26 +285,116 @@ def _construct_spline_n_m(self, fdatagrid): raise ValueError("interpolation order should be 0 (nearest) or 1 " "(linear).") - def _spline_evaluator_n_m(spl, t, derivative): - - if derivative != 0: - raise ValueError("derivates not suported for functional data " - " with domain dimension greater than 2.") - - return spl(t) - - splines = np.empty( + self.splines = np.empty( (fdatagrid.n_samples, fdatagrid.dim_codomain), dtype=object) for i in range(fdatagrid.n_samples): for j in range(fdatagrid.dim_codomain): - splines[i, j] = RegularGridInterpolator( + self.splines[i, j] = RegularGridInterpolator( fdatagrid.sample_points, fdatagrid.data_matrix[i, ..., j], method, False) - evaluator = _spline_evaluator_n_m + def _evaluate_one(self, spl, t, derivative=0): + + if derivative != 0: + raise ValueError("derivates not suported for functional data " + " with domain dimension greater than 2.") + + return spl(t) + + +class SplineInterpolation(Evaluator): + r"""Spline interpolation of :class:`FDataGrid`. + + Spline interpolation of discretized functional objects. Implements + different interpolation methods based in splines, using the sample + points of the grid as nodes to interpolate. + + See the interpolation example to a detailled explanation. + + Attributes: + interpolation_order (int, optional): Order of the interpolation, 1 + for linear interpolation, 2 for cuadratic, 3 for cubic and so + on. In case of curves and surfaces there is available + interpolation up to degree 5. For higher dimensional objects + only linear or nearest interpolation is available. Default + lineal interpolation. + smoothness_parameter (float, optional): Penalisation to perform + smoothness interpolation. Option only available for curves and + surfaces. If 0 the residuals of the interpolation will be 0. + Defaults 0. + monotone (boolean, optional): Performs monotone interpolation in + curves using a PCHIP interpolator. Only valid for curves (domain + dimension equal to 1) and interpolation order equal to 1 or 3. + Defaults false. + + """ + + def __init__(self, interpolation_order=1, *, smoothness_parameter=0., + monotone=False): + r"""Constructor of the SplineInterpolation. + + Args: + interpolation_order (int, optional): Order of the interpolation, 1 + for linear interpolation, 2 for cuadratic, 3 for cubic and so + on. In case of curves and surfaces there is available + interpolation up to degree 5. For higher dimensional objects + only linear or nearest interpolation is available. Default + lineal interpolation. + smoothness_parameter (float, optional): Penalisation to perform + smoothness interpolation. Option only available for curves and + surfaces. If 0 the residuals of the interpolation will be 0. + Defaults 0. + monotone (boolean, optional): Performs monotone interpolation in + curves using a PCHIP interpolation. Only valid for curves + (domain dimension equal to 1) and interpolation order equal + to 1 or 3. + Defaults false. + + """ + self._interpolation_order = interpolation_order + self._smoothness_parameter = smoothness_parameter + self._monotone = monotone + + @property + def interpolation_order(self): + "Returns the interpolation order" + return self._interpolation_order + + @property + def smoothness_parameter(self): + "Returns the smoothness parameter" + return self._smoothness_parameter + + @property + def monotone(self): + "Returns flag to perform monotone interpolation" + return self._monotone + + def _build_interpolator(self, fdatagrid): + + if fdatagrid.dim_domain == 1: + return _SplineList1D( + fdatagrid=fdatagrid, + interpolation_order=self.interpolation_order, + smoothness_parameter=self.smoothness_parameter, + monotone=self.monotone) + + elif self.monotone: + raise ValueError("Monotone interpolation is only supported with " + "domain dimension equal to 1.") + + elif fdatagrid.dim_domain == 2: + return _SplineList2D( + fdatagrid=fdatagrid, + interpolation_order=self.interpolation_order, + smoothness_parameter=self.smoothness_parameter) - return (splines, evaluator) + else: + return _SplineListND( + fdatagrid=fdatagrid, + interpolation_order=self.interpolation_order, + smoothness_parameter=self.smoothness_parameter) def evaluate(self, fdata, eval_points, *, derivative=0): r"""Evaluation method. @@ -317,20 +424,9 @@ def evaluate(self, fdata, eval_points, *, derivative=0): """ - (splines, spline_evaluator) = self._build_interpolator(fdata) - - def evaluator(spl_m): - """Evaluator of multimensional object""" - return np.dstack( - [spline_evaluator(spl, eval_points, derivative) - for spl in spl_m]).flatten() + spline_list = self._build_interpolator(fdata) - # Points evaluated inside the domain - res = np.apply_along_axis(evaluator, 1, splines) - res = res.reshape(fdata.n_samples, eval_points.shape[0], - fdata.dim_codomain) - - return res + return spline_list.evaluate(fdata, eval_points, derivative=derivative) def evaluate_composed(self, fdata, eval_points, *, derivative=0): """Evaluation method. @@ -359,21 +455,10 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): argument. """ - shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) - res = np.empty(shape) - - (splines, - spline_evaluator) = self._build_interpolator(fdata) - - def evaluator(t, spl_m): - """Evaluator of multidimensional sample""" - return np.array([spline_evaluator(spl, t, derivative) - for spl in spl_m]).T + spline_list = self._build_interpolator(fdata) - for i in range(fdata.n_samples): - res[i] = evaluator(eval_points[i], splines[i]) - - return res + return spline_list.evaluate_composed(fdata, eval_points, + derivative=derivative) def __repr__(self): """repr method of the interpolation""" From 392cb6f39579684ba7bdf79c201adb2f2d7766b4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 13 Jun 2020 20:55:05 +0200 Subject: [PATCH 554/624] Rename `MultivariateLinearRegression` to `LinearRegression`. In the future is expected that this class can perform both scalar and functional response prediction, depending on the target of the training data. --- docs/modules/ml/regression.rst | 2 +- skfda/ml/regression/__init__.py | 2 +- skfda/ml/regression/linear.py | 8 +++---- tests/test_regression.py | 40 ++++++++++++++++----------------- tests/test_regularization.py | 4 ++-- 5 files changed, 28 insertions(+), 28 deletions(-) diff --git a/docs/modules/ml/regression.rst b/docs/modules/ml/regression.rst index 700dbb7aa..ce416a58a 100644 --- a/docs/modules/ml/regression.rst +++ b/docs/modules/ml/regression.rst @@ -15,7 +15,7 @@ multivariate or functional). .. autosummary:: :toctree: autosummary - skfda.ml.regression.MultivariateLinearRegression + skfda.ml.regression.LinearRegression Nearest Neighbors ----------------- diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index ac29834dc..ed1ee3890 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,4 +1,4 @@ from ..._neighbors import KNeighborsRegressor, RadiusNeighborsRegressor -from .linear import MultivariateLinearRegression +from .linear import LinearRegression diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 1b62ac2a2..d695d5796 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -12,7 +12,7 @@ from ._coefficients import coefficient_info_from_covariate -class MultivariateLinearRegression(BaseEstimator, RegressorMixin): +class LinearRegression(BaseEstimator, RegressorMixin): r"""Linear regression with multivariate response. This is a regression algorithm equivalent to multivariate linear @@ -63,7 +63,7 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): Examples: - >>> from skfda.ml.regression import MultivariateLinearRegression + >>> from skfda.ml.regression import LinearRegression >>> from skfda.representation.basis import (FDataBasis, Monomial, ... Constant) @@ -76,7 +76,7 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): ... [0, 1, 1], ... [1, 0, 1]]) >>> y = [2, 3, 4, 5] - >>> linear = MultivariateLinearRegression() + >>> linear = LinearRegression() >>> _ = linear.fit(x_fd, y) >>> linear.coef_[0] FDataBasis( @@ -99,7 +99,7 @@ class MultivariateLinearRegression(BaseEstimator, RegressorMixin): ... [2, 2]]) >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] >>> y = [11, 10, 12, 6, 10, 13] - >>> linear = MultivariateLinearRegression( + >>> linear = LinearRegression( ... coef_basis=[None, Constant()]) >>> _ = linear.fit([x, x_fd], y) >>> linear.coef_[0] diff --git a/tests/test_regression.py b/tests/test_regression.py index 5e56fcd96..3f76270c1 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,6 +1,6 @@ from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import TikhonovRegularization -from skfda.ml.regression import MultivariateLinearRegression +from skfda.ml.regression import LinearRegression from skfda.representation.basis import (FDataBasis, Monomial, Fourier, BSpline) import unittest @@ -8,7 +8,7 @@ import numpy as np -class TestMultivariateLinearRegression(unittest.TestCase): +class TestScalarLinearRegression(unittest.TestCase): def test_regression_single_explanatory(self): @@ -25,7 +25,7 @@ def test_regression_single_explanatory(self): 0.10549625973303875, 0.11384314859153018] - scalar = MultivariateLinearRegression(coef_basis=[beta_basis]) + scalar = LinearRegression(coef_basis=[beta_basis]) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -35,8 +35,8 @@ def test_regression_single_explanatory(self): y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) - scalar = MultivariateLinearRegression(coef_basis=[beta_basis], - fit_intercept=False) + scalar = LinearRegression(coef_basis=[beta_basis], + fit_intercept=False) scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -53,7 +53,7 @@ def test_regression_multiple_explanatory(self): beta1 = BSpline(domain_range=(0, 1), n_basis=5) - scalar = MultivariateLinearRegression(coef_basis=[beta1]) + scalar = LinearRegression(coef_basis=[beta1]) scalar.fit(X, y) @@ -90,7 +90,7 @@ def test_regression_mixed(self): y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral - scalar = MultivariateLinearRegression() + scalar = LinearRegression() scalar.fit(X, y) np.testing.assert_allclose(scalar.intercept_, @@ -124,7 +124,7 @@ def test_regression_mixed_regularization(self): y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral - scalar = MultivariateLinearRegression( + scalar = LinearRegression( regularization=[TikhonovRegularization(lambda x: x), TikhonovRegularization( LinearDifferentialOperator(2))]) @@ -171,7 +171,7 @@ def test_regression_regularization(self): 0.023385, -0.001384] - scalar = MultivariateLinearRegression( + scalar = LinearRegression( coef_basis=[beta_basis], regularization=TikhonovRegularization( LinearDifferentialOperator(2))) @@ -194,7 +194,7 @@ def test_regression_regularization(self): y = [1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30] # Non regularized - scalar = MultivariateLinearRegression() + scalar = LinearRegression() scalar.fit(x_fd, y) np.testing.assert_allclose(scalar.coef_[0].coefficients, beta_fd.coefficients) @@ -208,7 +208,7 @@ def test_regression_regularization(self): beta_fd_reg = FDataBasis(x_basis, [2.812, 3.043, 0]) y_reg = [5.333, 3.419, 2.697, 11.366] - scalar_reg = MultivariateLinearRegression( + scalar_reg = LinearRegression( regularization=TikhonovRegularization( LinearDifferentialOperator(2))) scalar_reg.fit(x_fd, y) @@ -227,7 +227,7 @@ def test_error_X_not_FData(self): x_fd = np.identity(7) y = np.zeros(7) - scalar = MultivariateLinearRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = LinearRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_warns(UserWarning): scalar.fit([x_fd], y) @@ -238,7 +238,7 @@ def test_error_y_is_FData(self): x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) y = list(FDataBasis(Monomial(n_basis=7), np.identity(7))) - scalar = MultivariateLinearRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = LinearRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -251,11 +251,11 @@ def test_error_X_beta_len_distinct(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd, x_fd], y) - scalar = MultivariateLinearRegression(coef_basis=[beta, beta]) + scalar = LinearRegression(coef_basis=[beta, beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -267,7 +267,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(8)] beta = Fourier(n_basis=5) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -275,7 +275,7 @@ def test_error_y_X_samples_different(self): y = [1 for _ in range(7)] beta = Fourier(n_basis=5) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) @@ -286,7 +286,7 @@ def test_error_beta_not_basis(self): y = [1 for _ in range(7)] beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(TypeError): scalar.fit([x_fd], y) @@ -299,7 +299,7 @@ def test_error_weights_lenght(self): weights = [1 for _ in range(8)] beta = Monomial(n_basis=7) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) @@ -311,7 +311,7 @@ def test_error_weights_negative(self): weights = [-1 for _ in range(7)] beta = Monomial(n_basis=7) - scalar = MultivariateLinearRegression(coef_basis=[beta]) + scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 77d782e70..ea744ddf5 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -4,7 +4,7 @@ _monomial_evaluate_constant_linear_diff_op) from skfda.misc.operators._operators import gramian_matrix_numerical from skfda.misc.regularization import TikhonovRegularization, L2Regularization -from skfda.ml.regression.linear import MultivariateLinearRegression +from skfda.ml.regression.linear import LinearRegression from skfda.representation.basis import Constant, Monomial, BSpline, Fourier import unittest import warnings @@ -217,7 +217,7 @@ def ignore_scalar_warning(): regularization_parameter=regularization_parameter): sklearn_l2 = Ridge(alpha=regularization_parameter) - skfda_l2 = MultivariateLinearRegression( + skfda_l2 = LinearRegression( regularization=L2Regularization( regularization_parameter=regularization_parameter), ) From 9e997240164e4f7afd1b4e8a9bfeea605332ed09 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 14 Jun 2020 18:42:16 +0200 Subject: [PATCH 555/624] Remove debug print in aemet. --- skfda/datasets/_real_datasets.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 7f74bdeea..056c247d4 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -535,13 +535,12 @@ def fetch_aemet(return_X_y: bool = False): "logprecipitation", "wind speed (m/s)"]) - print(data['df']) if return_X_y: return curves, None else: return {"data": curves, "meta": np.asarray(data["df"])[:, - np.array([0, 1, 2, 3, 6, 7])], + np.array([0, 1, 2, 3, 6, 7])], "meta_names": ["ind", "place", "province", "altitude", "longitude", "latitude"], "meta_feature_names": ["location"], @@ -576,6 +575,7 @@ def fetch_aemet(return_X_y: bool = False): """ + def fetch_octane(return_X_y: bool = False): """Load near infrared spectra of gasoline samples. @@ -599,7 +599,7 @@ def fetch_octane(return_X_y: bool = False): # "The octane data set contains six outliers (25, 26, 36–39) to which # alcohol was added". target = np.zeros(len(data), dtype=int) - target[24] = target[25] = target [35:39] = 1 # Outliers 1 + target[24] = target[25] = target[35:39] = 1 # Outliers 1 axes_labels = ["wavelength (nm)", "absorbances"] From 579d8cd569168ce02bb194f40e4c2f6450942bd3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 14 Jun 2020 18:49:53 +0200 Subject: [PATCH 556/624] Including * for keyword-only args. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- skfda/inference/hotelling/hotelling.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/inference/hotelling/hotelling.py b/skfda/inference/hotelling/hotelling.py index 75c6b7dd1..f5fde264a 100644 --- a/skfda/inference/hotelling/hotelling.py +++ b/skfda/inference/hotelling/hotelling.py @@ -105,7 +105,7 @@ def hotelling_t2(fd1, fd2): return n1 * n2 / n * m.T.dot(k_inv).dot(m)[0][0] -def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, +def hotelling_test_ind(fd1, fd2, *, n_reps=None, random_state=None, return_dist=False): r""" Calculate the :math:`T^2`-test for the means of two independent samples of @@ -129,7 +129,6 @@ def hotelling_test_ind(fd1, fd2, n_reps=None, random_state=None, n_reps (int, optional): Maximum number of repetitions to compute p-value. Default value is None. - random_state (optional): Random state. return_dist (bool, optional): Flag to indicate if the function should From fb8daf9c3d88920b981d11491bd7f340e77599ff Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Sun, 14 Jun 2020 22:10:43 +0200 Subject: [PATCH 557/624] add axes and chart --- skfda/exploratory/visualization/fpca.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/visualization/fpca.py b/skfda/exploratory/visualization/fpca.py index 317b0c482..5edbc7fa8 100644 --- a/skfda/exploratory/visualization/fpca.py +++ b/skfda/exploratory/visualization/fpca.py @@ -4,7 +4,10 @@ def plot_fpca_perturbation_graphs(mean, components, multiple, - fig: plt.figure = None, **kwargs): + chart = None, + fig=None, + axes=None, + **kwargs): """ Plots the perturbation graphs for the principal components. The perturbations are defined as variations over the mean. Adding a multiple of the principal component curve to the mean function results in the @@ -24,6 +27,8 @@ def plot_fpca_perturbation_graphs(mean, components, multiple, fig (figure object, optional): figure over which the graph is plotted. If not specified it will be initialized + axes (axes object, optional): axis over where the graph is plotted. + If None, see param fig. Returns: (FDataGrid or FDataBasis): this contains the mean function followed @@ -33,7 +38,7 @@ def plot_fpca_perturbation_graphs(mean, components, multiple, if len(mean) > 1: mean = mean.mean() - fig, axes = _get_figure_and_axes(fig=fig) + fig, axes = _get_figure_and_axes(chart, fig, axes) if not axes: axes = fig.subplots(nrows=len(components)) From fffcd76ba7ba3b2815314b7325e34e677aa403f5 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 17 Jun 2020 10:59:35 +0200 Subject: [PATCH 558/624] change phonemes dataset domain range to 0-8kHz --- skfda/datasets/_real_datasets.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 056c247d4..de5f4bb26 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -218,9 +218,9 @@ def fetch_phoneme(return_X_y: bool = False): speaker = data["speaker"].values curves = FDataGrid(data_matrix=curve_data.values, - sample_points=range(0, 256), + sample_points=np.array(range(0, 256)) * 8 / 255, dataset_label="Phoneme", - axes_labels=["frequency", "log-periodogram"]) + axes_labels=["frequency (kHz)", "log-periodogram"]) if return_X_y: return curves, sound From 1911e27d3dd3c7b9fdb1bf9efbb2e14d15d9d289 Mon Sep 17 00:00:00 2001 From: Yujian Hong Date: Wed, 17 Jun 2020 11:26:34 +0200 Subject: [PATCH 559/624] small change --- skfda/datasets/_real_datasets.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index de5f4bb26..0c67619dc 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -218,7 +218,8 @@ def fetch_phoneme(return_X_y: bool = False): speaker = data["speaker"].values curves = FDataGrid(data_matrix=curve_data.values, - sample_points=np.array(range(0, 256)) * 8 / 255, + sample_points=np.linspace(0, 8, 256), + domain_range=[0, 8], dataset_label="Phoneme", axes_labels=["frequency (kHz)", "log-periodogram"]) From e2aa79e88e3ace201791d3321fcc16f38ebdc092 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 18 Jun 2020 14:02:21 +0200 Subject: [PATCH 560/624] Rework basis derivatives. --- .../_linear_differential_operator.py | 9 ++- skfda/representation/basis/_basis.py | 40 +++--------- skfda/representation/basis/_bspline.py | 35 +++++------ skfda/representation/basis/_constant.py | 7 +-- skfda/representation/basis/_fdatabasis.py | 17 ++--- skfda/representation/basis/_fourier.py | 49 +++------------ skfda/representation/basis/_monomial.py | 63 +++++++------------ skfda/representation/grid.py | 7 +-- 8 files changed, 74 insertions(+), 153 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index afa36163c..1c6d76e26 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -227,10 +227,13 @@ def constant_weights(self): def __call__(self, f): """Return the function that results of applying the operator.""" + + function_derivatives = [ + f.derivative(order=i) for i, _ in enumerate(self.weights)] + def applied_linear_diff_op(t): - return sum(w(t) * self.derivative_function( - function=f, points=t, derivative=i) - for i, w in enumerate(self.weights)) + return sum(w(t) * function_derivatives[i](t) + for i, w in enumerate(self.weights)) return applied_linear_diff_op diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index b7d71a35b..8c7a680d9 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -101,7 +101,7 @@ def evaluate(self, eval_points, derivative=0): elif derivative != 0: warnings.warn("Parameter derivative is deprecated. Use the " "derivative function instead.", DeprecationWarning) - return self.derivative(eval_points, order=derivative) + return self.derivative(order=derivative)(eval_points) eval_points = np.atleast_1d(eval_points) if np.any(np.isnan(eval_points)): @@ -113,40 +113,18 @@ def evaluate(self, eval_points, derivative=0): def __call__(self, *args, **kwargs): return self.evaluate(*args, **kwargs) - def _derivative(self, eval_points, order=1): - """ - Subclasses must override this to provide evaluation of derivatives. - - Order 0 derivatives (original function) are automatically - implemented. Subclasses can assume that the order passed is - nonzero. - - A basis can provide derivative evaluation at given points - without providing a basis representation for its derivatives, - although is recommended to provide both if possible. - - """ - return NotImplementedError(f"{type(self)} basis is not " - "differentiable.") - - def derivative(self, eval_points, order=1): - """Evaluate the basis derivative at given points. + def derivative(self, *, order=1): + """Construct a FDataBasis object containing the derivative. Args: - eval_points (array_like): List of points where the derivative of - the basis is evaluated. order (int, optional): Order of the derivative. Defaults to 1. Returns: - (numpy.darray): Matrix whose rows are the values of the derivatives - of each basis function or its derivatives at the values specified - in eval_points. + (FDataBasis): Derivative object. """ - if order == 0: - return self(eval_points) - else: - return self._derivative(eval_points, order=order) + + return self.to_basis().derivative(order=order) def _derivative_basis_and_coefs(self, coefs, order=1): """ @@ -157,9 +135,9 @@ def _derivative_basis_and_coefs(self, coefs, order=1): although is recommended to provide both if possible. """ - return NotImplementedError(f"{type(self)} basis does not support " - "the construction of a basis of the " - "derivatives.") + raise NotImplementedError(f"{type(self)} basis does not support " + "the construction of a basis of the " + "derivatives.") def plot(self, chart=None, *, derivative=0, **kwargs): """Plot the basis object or its derivatives. diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index b95905f47..c52f8d0de 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -55,14 +55,15 @@ class BSpline(Basis): set of points. >>> bss = BSpline(n_basis=3, order=3) - >>> bss.evaluate([0, 0.5, 1]) + >>> bss([0, 0.5, 1]) array([[ 1. , 0.25, 0. ], [ 0. , 0.5 , 0. ], [ 0. , 0.25, 1. ]]) And evaluates first derivative - >>> bss.evaluate([0, 0.5, 1], derivative=1) + >>> deriv = bss.derivative() + >>> deriv([0, 0.5, 1]) array([[-2., -1., 0.], [ 2., 0., -2.], [ 0., 1., 2.]]) @@ -157,21 +158,6 @@ def _evaluation_knots(self): [self.knots[-1]] * (self.order - 1)) def _evaluate(self, eval_points): - # The derivative method already works for 0 order. - return self._derivative(eval_points, 0) - - def _derivative(self, eval_points, order=1): - # Implementation details: In order to allow a discontinuous behaviour - # at the boundaries of the domain it is necessary to placing m knots - # at the boundaries [RS05]_. This is automatically done so that the - # user only has to specify a single knot at the boundaries. - # - # References: - # .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - # Analysis*. Springer. 50-51. - - if order > (self.order - 1): - return np.zeros((self.n_basis, len(eval_points))) # Places m knots at the boundaries knots = self._evaluation_knots() @@ -189,13 +175,17 @@ def _derivative(self, eval_points, order=1): c[i] = 1 # compute the spline mat[i] = scipy.interpolate.splev(eval_points, - (knots, c, self.order - 1), - der=order) + (knots, c, self.order - 1)) c[i] = 0 return mat def _derivative_basis_and_coefs(self, coefs, order=1): + if order >= self.order: + return ( + BSpline(n_basis=1, domain_range=self.domain_range, order=1), + np.zeros((len(coefs), 1))) + deriv_splines = [self._to_scipy_BSpline(coefs[i]).derivative(order) for i in range(coefs.shape[0])] @@ -383,7 +373,12 @@ def _to_scipy_BSpline(self, coefs): @staticmethod def _from_scipy_BSpline(bspline): order = bspline.k - knots = bspline.t[order: -order] + knots = bspline.t + + # Remove additional knots at the borders + if order != 0: + knots = knots[order: -order] + coefs = bspline.c domain_range = [knots[0], knots[-1]] diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 4867d25f7..fe139b826 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -33,12 +33,9 @@ def __init__(self, domain_range=None): def _evaluate(self, eval_points): return np.ones((1, len(eval_points))) - def _derivative(self, eval_points, order=1): - return np.zeros((1, len(eval_points))) - def _derivative_basis_and_coefs(self, coefs, order=1): - return (self.copy(), coefs.copy() if order == 0 - else self.copy(), np.zeros(coefs.shape)) + return ((self.copy(), coefs.copy()) if order == 0 + else (self.copy(), np.zeros(coefs.shape))) def _gram_matrix(self): return np.array([[self.domain_range[0][1] - diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index f2c4560e0..9f25d625f 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -376,7 +376,7 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, return FDataBasis.from_data(_data_matrix, eval_points, _basis, **kwargs) - def derivative(self, eval_points=None, *, order=1): + def derivative(self, *, order=1): r"""Differentiate a FDataBasis object. @@ -387,18 +387,13 @@ def derivative(self, eval_points=None, *, order=1): if order < 0: raise ValueError("order only takes non-negative integer values.") - if eval_points is not None: - return np.sum( - self.basis.derivative(eval_points, order=order), axis=0) + if order == 0: + return self.copy() - else: - if order == 0: - return self.copy() - - basis, coefficients = self.basis._derivative_basis_and_coefs( - self.coefficients, order) + basis, coefficients = self.basis._derivative_basis_and_coefs( + self.coefficients, order) - return FDataBasis(basis, coefficients) + return FDataBasis(basis, coefficients) def mean(self, weights=None): """Compute the mean of all the samples in a FDataBasis object. diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 390b73204..7ed31d04b 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -35,17 +35,17 @@ class Fourier(Basis): Constructs specifying number of basis, definition interval and period. >>> fb = Fourier((0, np.pi), n_basis=3, period=1) - >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) array([[ 1. , 1. , 1. , 1. ], [ 0. , -1.38, -0.61, 1.1 ], [ 1.41, 0.31, -1.28, 0.89]]) And evaluate second derivative - >>> fb.evaluate([0, np.pi / 4, np.pi / 2, np.pi], - ... derivative = 2).round(2) + >>> deriv2 = fb.derivative(order=2) + >>> deriv2([0, np.pi / 4, np.pi / 2, np.pi]).round(2) array([[ 0. , 0. , 0. , 0. ], - [ -0. , 54.46, 24.02, -43.37], + [ 0. , 54.46, 24.02, -43.37], [-55.83, -12.32, 50.4 , -35.16]]) @@ -91,38 +91,15 @@ def period(self): def period(self, value): self._period = value - def _functions_pairs_coefs_derivatives(self, derivative=0): - """ - Compute functions to use, amplitudes and phase of a derivative. - """ + def _evaluate(self, eval_points): functions = [np.sin, np.cos] - signs = [1, 1, -1, -1] omega = 2 * np.pi / self.period - deriv_functions = (functions[derivative % len(functions)], - functions[(derivative + 1) % len(functions)]) - - deriv_signs = (signs[derivative % len(signs)], - signs[(derivative + 1) % len(signs)]) + normalization_denominator = np.sqrt(self.period / 2) seq = 1 + np.arange((self.n_basis - 1) // 2) seq_pairs = np.array([seq, seq]).T - power_pairs = (omega * seq_pairs)**derivative - amplitude_coefs_pairs = deriv_signs * power_pairs - phase_coef_pairs = omega * seq_pairs - - return deriv_functions, amplitude_coefs_pairs, phase_coef_pairs - - def _evaluate(self, eval_points): - # The derivative method already works for 0 order. - return self._derivative(eval_points, 0) - - def _derivative(self, eval_points, order=1): - (functions, - amplitude_coefs, - phase_coefs) = self._functions_pairs_coefs_derivatives(order) - - normalization_denominator = np.sqrt(self.period / 2) + phase_coefs = omega * seq_pairs # Multiply the phase coefficients elementwise res = np.einsum('ij,k->ijk', phase_coefs, eval_points) @@ -131,18 +108,12 @@ def _derivative(self, eval_points, order=1): for i in [0, 1]: functions[i](res[:, i, :], out=res[:, i, :]) - # Multiply the amplitude and ravel the result - res *= amplitude_coefs[..., np.newaxis] res = res.reshape(-1, len(eval_points)) res /= normalization_denominator - # Add constant basis - if order == 0: - constant_basis = np.full( - shape=(1, len(eval_points)), - fill_value=1 / (np.sqrt(2) * normalization_denominator)) - else: - constant_basis = np.zeros(shape=(1, len(eval_points))) + constant_basis = np.full( + shape=(1, len(eval_points)), + fill_value=1 / (np.sqrt(2) * normalization_denominator)) res = np.concatenate((constant_basis, res)) diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index 9508752d7..bd4f13284 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -28,55 +28,40 @@ class Monomial(Basis): And evaluates all the functions in the basis in a list of descrete values. - >>> bs_mon.evaluate([0, 1, 2]) - array([[1, 1, 1], - [0, 1, 2], - [0, 1, 4]]) + >>> bs_mon([0., 1., 2.]) + array([[ 1., 1., 1.], + [ 0., 1., 2.], + [ 0., 1., 4.]]) And also evaluates its derivatives - >>> bs_mon.evaluate([0, 1, 2], derivative=1) - array([[0, 0, 0], - [1, 1, 1], - [0, 2, 4]]) - >>> bs_mon.evaluate([0, 1, 2], derivative=2) - array([[0, 0, 0], - [0, 0, 0], - [2, 2, 2]]) + >>> deriv = bs_mon.derivative() + >>> deriv([0, 1, 2]) + array([[ 0., 0., 0.], + [ 1., 1., 1.], + [ 0., 2., 4.]]) + >>> deriv2 = bs_mon.derivative(order=2) + >>> deriv2([0, 1, 2]) + array([[ 0., 0., 0.], + [ 0., 0., 0.], + [ 2., 2., 2.]]) """ - def _coefs_exps_derivatives(self, order): - """ - Return coefficients and exponents of the derivatives. - - This function is used for computing the basis functions and evaluate. - - When the exponent would be negative (the coefficient in that case - is zero) returns 0 as the exponent (to prevent division by zero). - """ - seq = np.arange(self.n_basis) - coef_mat = np.linspace(seq, seq - order + 1, - order, dtype=int) - coefs = np.prod(coef_mat, axis=0) - - exps = np.maximum(seq - order, 0) - - return coefs, exps - def _evaluate(self, eval_points): - # The derivative method already works for 0 order. - return self._derivative(eval_points, 0) - - def _derivative(self, eval_points, order=1): - coefs, exps = self._coefs_exps_derivatives(order) + exps = np.arange(self.n_basis) raised = np.power.outer(eval_points, exps) - return (coefs * raised).T + + return raised.T def _derivative_basis_and_coefs(self, coefs, order=1): - return (Monomial(self.domain_range, self.n_basis - order), - np.array([np.polyder(x[::-1], order)[::-1] - for x in coefs])) + if order >= self.n_basis: + return (Monomial(self.domain_range, 1), + np.zeros((len(coefs), 1))) + else: + return (Monomial(self.domain_range, self.n_basis - order), + np.array([np.polyder(x[::-1], order)[::-1] + for x in coefs])) def _gram_matrix(self): integral_coefs = np.polyint(np.ones(2 * self.n_basis - 1)) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 7e631ebb1..29ddf78fd 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -407,7 +407,7 @@ def _evaluate_composed(self, eval_points, *, derivative=0): return self._evaluator.evaluate_composed(eval_points, derivative=derivative) - def derivative(self, eval_points=None, *, order=1): + def derivative(self, *, order=1): r"""Differentiate a FDataGrid object. It is calculated using central finite differences when possible. In @@ -474,10 +474,7 @@ def derivative(self, eval_points=None, *, order=1): fdatagrid = self.copy(data_matrix=data_matrix, dataset_label=dataset_label) - if eval_points is None: - return fdatagrid - else: - return fdatagrid(eval_points) + return fdatagrid def __check_same_dimensions(self, other): if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: From 54ab65f443ac0e2465cfb0d244bf09a5f49a19e2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 18 Jun 2020 17:04:32 +0200 Subject: [PATCH 561/624] Fix basis warnings. --- .../_linear_differential_operator.py | 24 ++++--------------- .../registration/_shift_registration.py | 7 +++--- .../preprocessing/registration/validation.py | 11 ++++++--- tests/test_basis_evaluation.py | 9 ++++--- tests/test_fpca.py | 19 +++++++-------- tests/test_registration.py | 16 ++++++------- 6 files changed, 39 insertions(+), 47 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 1c6d76e26..f28c4ff52 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -104,8 +104,7 @@ class LinearDifferentialOperator(Operator): def __init__( self, order_or_weights=None, *, order=None, weights=None, - domain_range=None, - derivative_function=None): + domain_range=None): """Constructor. You have to provide either order or weights. If both are provided, it will raise an error. If a positional argument is supplied it will be considered the @@ -124,9 +123,6 @@ def __init__( and this is not, takes the domain range from them. Otherwise, defaults to (0,1). - derivative_function (callable): function used to evaluate the - derivatives. - """ from ...representation.basis import FDataBasis @@ -189,9 +185,6 @@ def __init__( "integers or FDataBasis objects") self.domain_range = real_domain_range - self.derivative_function = ( - LinearDifferentialOperator.evaluate_derivative - if derivative_function is None else derivative_function) def __repr__(self): """Representation of linear differential operator object.""" @@ -237,13 +230,6 @@ def applied_linear_diff_op(t): return applied_linear_diff_op - @staticmethod - def evaluate_derivative(function, points, derivative): - """ - Default function for evaluating derivatives. - """ - return function(points, derivative=derivative) - ############################################################# # @@ -479,8 +465,8 @@ def bspline_penalty_matrix_optimized( # defined between knots knots = np.array(basis.knots) mid_inter = (knots[1:] + knots[:-1]) / 2 - constants = basis.evaluate(mid_inter, - derivative=derivative_degree).T + basis_deriv = basis.derivative(order=derivative_degree) + constants = basis_deriv(mid_inter).T knots_intervals = np.diff(basis.knots) # Integration of product of constants return constants.T @ np.diag(knots_intervals) @ constants @@ -576,8 +562,8 @@ def fdatagrid_penalty_matrix_optimized( basis: FDataGrid): evaluated_basis = sum( - w(basis.sample_points[0]) * linear_operator.derivative_function( - function=basis, points=basis.sample_points[0], derivative=i) + w(basis.sample_points[0]) * + basis.derivative(order=i)(basis.sample_points[0]) for i, w in enumerate(linear_operator.weights)) indices = np.triu_indices(basis.n_samples) diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 81f20be79..7da35b7ae 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -8,9 +8,9 @@ import numpy as np +from ... import FData, FDataGrid from ..._utils import constants, check_is_univariate from .base import RegistrationTransformer -from ... import FData, FDataGrid class ShiftRegistration(RegistrationTransformer): @@ -101,7 +101,7 @@ class ShiftRegistration(RegistrationTransformer): Shifts applied during the transformation >>> reg.deltas_.round(3) - array([-0.126, 0.19 , 0.029, 0.036, -0.104, 0.116, ..., -0.058]) + array([-0.128, 0.187, 0.027, 0.034, -0.106, 0.114, ..., -0.06 ]) Registration and creation of a dataset in basis form using the @@ -184,7 +184,8 @@ def _compute_deltas(self, fd, template): delta_aux = np.empty(fd.n_samples) # Computes the derivate of originals curves in the mesh points - D1x = fd.evaluate(output_points, derivative=1, keepdims=False) + fd_deriv = fd.derivative(order=1) + D1x = fd_deriv(output_points, keepdims=False) # Second term of the second derivate estimation of REGSSE. The # first term has been dropped to improve convergence (see references) diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 2739494d8..4cebb44f7 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -1,8 +1,9 @@ """Methods and classes for validation of the registration procedures""" -import numpy as np from typing import NamedTuple +import numpy as np + from ..._utils import check_is_univariate, _to_grid @@ -38,6 +39,7 @@ class RegistrationScorer(): :class:`~PairwiseCorrelation` """ + def __init__(self, eval_points=None): """Initialize the transformer""" self.eval_points = eval_points @@ -224,10 +226,11 @@ class AmplitudePhaseDecomposition(RegistrationScorer): :class:`~PairwiseCorrelation` """ + def __init__(self, return_stats=False, eval_points=None): """Initialize the transformer""" super().__init__(eval_points) - self.return_stats=return_stats + self.return_stats = return_stats def __call__(self, estimator, X, y=None): """Compute the score of the transformation. @@ -420,6 +423,7 @@ class LeastSquares(AmplitudePhaseDecomposition): :class:`~PairwiseCorrelation` """ + def score_function(self, X, y): """Compute the score of the transformation performed. @@ -526,7 +530,7 @@ class SobolevLeastSquares(RegistrationScorer): >>> scorer = SobolevLeastSquares() >>> score = scorer(shift_registration, X) >>> round(score, 3) - 0.762 + 0.761 See also: :class:`~AmplitudePhaseDecomposition` @@ -534,6 +538,7 @@ class SobolevLeastSquares(RegistrationScorer): :class:`~PairwiseCorrelation` """ + def score_function(self, X, y): """Compute the score of the transformation performed. diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 9c77dad92..954b6ebe4 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -64,8 +64,9 @@ def test_evaluation_derivative_fourier(self): -4.81336320468984, -1.7123673353027, 6.52573053999253] ).reshape((2, 4)).round(3) + f_deriv = f.derivative() np.testing.assert_array_almost_equal( - f(t, derivative=1).round(3), res + f_deriv(t).round(3), res ) def test_evaluation_grid_fourier(self): @@ -317,8 +318,9 @@ def test_evaluation_derivative_bspline(self): t = np.linspace(0, 1, 4) + f_deriv = f.derivative() np.testing.assert_array_almost_equal( - f(t, derivative=1).round(3), + f_deriv(t).round(3), np.array([[2.927, 0.453, -1.229, 0.6], [4.3, -1.599, 1.016, -2.52]]) ) @@ -570,8 +572,9 @@ def test_evaluation_derivative_monomial(self): t = np.linspace(0, 1, 4) + f_deriv = f.derivative() np.testing.assert_array_almost_equal( - f(t, derivative=1).round(3), + f_deriv(t).round(3), np.array([[2., 4., 6., 8.], [1.4, 2.267, 3.133, 4.]]) ) diff --git a/tests/test_fpca.py b/tests/test_fpca.py index 0b52a5394..98f3c499f 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -61,12 +61,13 @@ def test_basis_fpca_fit_result(self): fpca = FPCA(n_components=n_components, regularization=TikhonovRegularization( - LinearDifferentialOperator(2), - regularization_parameter=1e5)) + LinearDifferentialOperator(2), + regularization_parameter=1e5)) fpca.fit(fd_basis) # results obtained using Ramsay's R package - results = [[0.92407552, 0.13544888, 0.35399023, 0.00805966, -0.02148108, + results = [[0.92407552, 0.13544888, 0.35399023, 0.00805966, + -0.02148108, -0.01709549, -0.00208469, -0.00297439, -0.00308224], [-0.33314436, -0.05116842, 0.89443418, 0.14673902, 0.21559073, @@ -100,8 +101,8 @@ def test_basis_fpca_transform_result(self): fpca = FPCA(n_components=n_components, regularization=TikhonovRegularization( - LinearDifferentialOperator(2), - regularization_parameter=1e5)) + LinearDifferentialOperator(2), + regularization_parameter=1e5)) fpca.fit(fd_basis) scores = fpca.transform(fd_basis) @@ -156,7 +157,7 @@ def test_basis_fpca_regularization_fit_result(self): np.arange(0.5, 365, 1)) # initialize basis data - basis = Fourier(n_basis=9, domain_range=(0, 365)) + basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) fpca = FPCA(n_components=n_components) @@ -318,11 +319,7 @@ def test_grid_fpca_regularization_fit_result(self): fpca = FPCA( n_components=n_components, weights=[1] * 365, regularization=TikhonovRegularization( - LinearDifferentialOperator( - 2, - derivative_function=( - lambda function, points, derivative: - function.derivative(order=derivative)(points))))) + LinearDifferentialOperator(2))) fpca.fit(fd_data) # results obtained using fda.usc for the first component diff --git a/tests/test_registration.py b/tests/test_registration.py index 07f997c73..d0b754945 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -213,7 +213,7 @@ def test_fit_and_transform(self): fd_registered = reg.transform(fd) deltas = reg.deltas_.round(3) - np.testing.assert_array_almost_equal(deltas, [0.071, -0.071]) + np.testing.assert_allclose(deltas, [0.071, -0.072]) def test_inverse_transform(self): @@ -331,39 +331,39 @@ def setUp(self): def test_amplitude_phase_score(self): scorer = AmplitudePhaseDecomposition() score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 0.972000160) + np.testing.assert_allclose(score, 0.972095, rtol=1e-6) def test_amplitude_phase_score_with_output_points(self): eval_points = self.X.sample_points[0] scorer = AmplitudePhaseDecomposition(eval_points=eval_points) score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 0.972000160) + np.testing.assert_allclose(score, 0.972095, rtol=1e-6) def test_amplitude_phase_score_with_basis(self): scorer = AmplitudePhaseDecomposition() X = self.X.to_basis(Fourier()) score = scorer(self.shift_registration, X) - np.testing.assert_almost_equal(score, 0.9950259588) + np.testing.assert_allclose(score, 0.995087, rtol=1e-6) def test_default_score(self): score = self.shift_registration.score(self.X) - np.testing.assert_almost_equal(score, 0.972000160) + np.testing.assert_allclose(score, 0.972095, rtol=1e-6) def test_least_squares_score(self): scorer = LeastSquares() score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 0.795742349) + np.testing.assert_allclose(score, 0.795933, rtol=1e-6) def test_sobolev_least_squares_score(self): scorer = SobolevLeastSquares() score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 0.7621990) + np.testing.assert_allclose(score, 0.76124, rtol=1e-6) def test_pairwise_correlation(self): scorer = PairwiseCorrelation() score = scorer(self.shift_registration, self.X) - np.testing.assert_almost_equal(score, 1.816298653) + np.testing.assert_allclose(score, 1.816228, rtol=1e-6) def test_mse_decomposition(self): From 5f0f4202702f1352b03263f2dc6352a9f9b6a878 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 18 Jun 2020 21:35:03 +0200 Subject: [PATCH 562/624] Rework derivatives for both basis and grid. --- skfda/misc/metrics.py | 30 ++++++++-------- .../preprocessing/registration/validation.py | 5 +-- skfda/representation/_functional_data.py | 34 +++++++++--------- skfda/representation/basis/_basis.py | 1 - skfda/representation/basis/_fdatabasis.py | 10 +++--- skfda/representation/evaluator.py | 18 ++++------ skfda/representation/extrapolation.py | 23 +++++------- skfda/representation/grid.py | 15 +++----- skfda/representation/interpolation.py | 11 +++--- tests/test_interpolation.py | 35 ------------------- tests/test_registration.py | 8 ++--- 11 files changed, 68 insertions(+), 122 deletions(-) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index ba70aa5ed..94a5e4f1c 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -5,7 +5,6 @@ from ..preprocessing.registration import normalize_warping, ElasticRegistration from ..preprocessing.registration._warping import _normalize_scale from ..preprocessing.registration.elastic import SRSF -from ..representation import FData from ..representation import FDataGrid, FDataBasis @@ -306,7 +305,8 @@ def norm_lp(fdata, p=2, p2=2): elif fdata.dim_domain == 1: - # Computes the norm, approximating the integral with Simpson's rule. + # Computes the norm, approximating the integral with Simpson's + # rule. res = scipy.integrate.simps(data_matrix[..., 0] ** p, x=fdata.sample_points) ** (1 / p) @@ -493,10 +493,11 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - elastic_registration = ElasticRegistration(template=fdata2, - penalty=lam, - output_points=eval_points_normalized, - **kwargs) + elastic_registration = ElasticRegistration( + template=fdata2, + penalty=lam, + output_points=eval_points_normalized, + **kwargs) fdata1_reg = elastic_registration.fit_transform(fdata1) @@ -507,9 +508,9 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, if lam != 0.0: # L2 norm || sqrt(Dh) - 1 ||^2 - warping = elastic_registration.warping_ - penalty = warping(eval_points_normalized, derivative=1, - keepdims=False)[0] + warping_deriv = elastic_registration.warping_.derivative() + penalty = warping_deriv(eval_points_normalized, + keepdims=False)[0] penalty = np.sqrt(penalty, out=penalty) penalty -= 1 penalty = np.square(penalty, out=penalty) @@ -573,14 +574,15 @@ def phase_distance(fdata1, fdata2, *, lam=0., eval_points=None, _check=True, fdata2 = fdata2.copy(sample_points=eval_points_normalized, domain_range=(0, 1)) - elastic_registration = ElasticRegistration(penalty=lam, template=fdata2, - output_points=eval_points_normalized) + elastic_registration = ElasticRegistration( + penalty=lam, template=fdata2, + output_points=eval_points_normalized) elastic_registration.fit_transform(fdata1) - derivative_warping = elastic_registration.warping_(eval_points_normalized, - keepdims=False, - derivative=1)[0] + warping_deriv = elastic_registration.warping_.derivative() + derivative_warping = warping_deriv(eval_points_normalized, + keepdims=False)[0] derivative_warping = np.sqrt(derivative_warping, out=derivative_warping) diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 4cebb44f7..6d1d368bf 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -311,8 +311,9 @@ def score_function(self, X, y, *, warping=None): # If the warping functions are not provided, are suppose independent if warping is not None: # Derivates warping functions - dh_fine = warping.evaluate(eval_points, derivative=1, - keepdims=False) + warping_deriv = warping.derivative() + dh_fine = warping_deriv(eval_points, + keepdims=False) dh_fine_mean = dh_fine.mean(axis=0) dh_fine_center = dh_fine - dh_fine_mean diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 93e6e215b..734389e1b 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -5,8 +5,10 @@ """ from abc import ABC, abstractmethod +import warnings import pandas.api.extensions + import numpy as np from .._utils import _coordinate_list, _list_of_arrays @@ -339,7 +341,7 @@ def _join_evaluation(self, index_matrix, index_ext, index_ev, return res @abstractmethod - def _evaluate(self, eval_points, *, derivative=0): + def _evaluate(self, eval_points): """Internal evaluation method, defines the evaluation of the FData. Evaluates the samples of an FData object at the same eval_points. @@ -351,7 +353,6 @@ def _evaluate(self, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape `(len(eval_points), dim_domain)` with the evaluation points. Each entry represents the coordinate of a point. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape `(n_samples, @@ -363,7 +364,7 @@ def _evaluate(self, eval_points, *, derivative=0): pass @abstractmethod - def _evaluate_composed(self, eval_points, *, derivative=0): + def _evaluate_composed(self, eval_points): """Internal evaluation method, defines the evaluation of a FData. Evaluates the samples of an FData object at different eval_points. @@ -375,7 +376,6 @@ def _evaluate_composed(self, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape `(n_samples, len(eval_points), dim_domain)` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape `(n_samples, @@ -419,6 +419,13 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, function at the values specified in eval_points. """ + if derivative < 0: + raise ValueError("derivative only takes non-negative values.") + elif derivative != 0: + warnings.warn("Parameter derivative is deprecated. Use the " + "derivative function instead.", DeprecationWarning) + return self.derivative(order=derivative)(eval_points) + if extrapolation is None: extrapolation = self.extrapolation else: @@ -427,7 +434,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, if grid: # Evaluation of a grid performed in auxiliar function return self._evaluate_grid(eval_points, - derivative=derivative, extrapolation=extrapolation, aligned_evaluation=aligned_evaluation, keepdims=keepdims) @@ -447,10 +453,9 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, if not extrapolate: # Direct evaluation if aligned_evaluation: - res = self._evaluate(eval_points, derivative=derivative) + res = self._evaluate(eval_points) else: - res = self._evaluate_composed(eval_points, - derivative=derivative) + res = self._evaluate_composed(eval_points) else: # Partition of eval points @@ -463,12 +468,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points_evaluation = eval_points[index_ev] # Direct evaluation - res_evaluation = self._evaluate(eval_points_evaluation, - derivative=derivative) + res_evaluation = self._evaluate(eval_points_evaluation) res_extrapolation = extrapolation.evaluate( self, - eval_points_extrapolation, - derivative=derivative) + eval_points_extrapolation) else: index_ext = np.logical_or.reduce(index_matrix, axis=0) @@ -479,14 +482,11 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, # Direct evaluation res_evaluation = self._evaluate_composed( - eval_points_evaluation, - derivative=derivative - ) + eval_points_evaluation) res_extrapolation = extrapolation.evaluate_composed( self, - eval_points_extrapolation, - derivative=derivative) + eval_points_extrapolation) res = self._join_evaluation(index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 8c7a680d9..3f1d1f614 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -88,7 +88,6 @@ def evaluate(self, eval_points, derivative=0): Args: eval_points (array_like): List of points where the basis is evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Matrix whose rows are the values of the each diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 9f25d625f..6205a7239 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -233,7 +233,7 @@ def domain_range(self): """Definition range.""" return self.basis.domain_range - def _evaluate(self, eval_points, *, derivative=0): + def _evaluate(self, eval_points): """"Evaluate the object or its derivatives at a list of values. Args: @@ -241,7 +241,6 @@ def _evaluate(self, eval_points, *, derivative=0): evaluated. If a matrix of shape `n_samples` x eval_points is given each sample is evaluated at the values in the corresponding row. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: @@ -253,13 +252,13 @@ def _evaluate(self, eval_points, *, derivative=0): eval_points = eval_points[:, 0] # each row contains the values of one element of the basis - basis_values = self.basis.evaluate(eval_points, derivative) + basis_values = self.basis.evaluate(eval_points) res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) return res.reshape((self.n_samples, len(eval_points), 1)) - def _evaluate_composed(self, eval_points, *, derivative=0): + def _evaluate_composed(self, eval_points): r"""Evaluate the object or its derivatives at a list of values with a different time for each sample. @@ -272,7 +271,6 @@ def _evaluate_composed(self, eval_points, *, derivative=0): Args: eval_points (numpy.ndarray): Matrix of size `n_samples`x n_points - derivative (int, optional): Order of the derivative. Defaults to 0. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default uses the method defined in fd. See extrapolation to @@ -290,7 +288,7 @@ def _evaluate_composed(self, eval_points, *, derivative=0): _matrix = np.empty((eval_points.shape[1], self.n_basis)) for i in range(self.n_samples): - basis_values = self.basis.evaluate(eval_points[i], derivative).T + basis_values = self.basis.evaluate(eval_points[i]).T np.multiply(basis_values, self.coefficients[i], out=_matrix) np.sum(_matrix, axis=1, out=res_matrix[i]) diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index da9eaae48..040a0c932 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -20,7 +20,7 @@ class Evaluator(ABC): """ @abstractmethod - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """Evaluation method. Evaluates the samples at the same evaluation points. The evaluation @@ -32,7 +32,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape ``(number_eval_points, dim_domain)`` with the evaluation points. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape @@ -45,7 +44,7 @@ def evaluate(self, fdata, eval_points, *, derivative=0): pass @abstractmethod - def evaluate_composed(self, fdata, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -57,7 +56,6 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape ``(n_samples, number_eval_points, dim_domain)`` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape @@ -94,7 +92,7 @@ def __init__(self, evaluate_func, evaluate_composed_func=None): else: self.evaluate_composed_func = evaluate_composed_func - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """Evaluation method. Evaluates the samples at the same evaluation points. The evaluation @@ -107,7 +105,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape `(len(eval_points), dim_domain)` with the evaluation points. Each entry represents the coordinate of a point. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3-d array with shape `(n_samples, @@ -117,10 +114,9 @@ def evaluate(self, fdata, eval_points, *, derivative=0): point. """ - return self.evaluate_func(fdata, eval_points, - derivative=derivative) + return self.evaluate_func(fdata, eval_points) - def evaluate_composed(self, fdata, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -134,7 +130,6 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape `(n_samples, number_eval_points, dim_domain)` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape `(n_samples, @@ -143,5 +138,4 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): dimension of the i-th sample, at the j-th evaluation point. """ - return self.evaluate_composed_func(fdata, eval_points, - derivative=derivative) + return self.evaluate_composed_func(fdata, eval_points) diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index c423b741a..59bad2df6 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -34,7 +34,7 @@ class PeriodicExtrapolation(Evaluator): [-1.086, 0.759, -1.086]]) """ - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """Evaluate points outside the domain range. Args: @@ -43,7 +43,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): points outside the domain range. The shape of the array may be `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. Returns: (numpy.ndarray): numpy array with the evaluation of the points in @@ -58,9 +57,9 @@ def evaluate(self, fdata, eval_points, *, derivative=0): eval_points += domain_range[:, 0] if eval_points.ndim == 3: - res = fdata._evaluate_composed(eval_points, derivative=derivative) + res = fdata._evaluate_composed(eval_points) else: - res = fdata._evaluate(eval_points, derivative=derivative) + res = fdata._evaluate(eval_points) return res @@ -93,7 +92,7 @@ class BoundaryExtrapolation(Evaluator): [ 0.759, 0.759, 1.125]]) """ - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """Evaluate points outside the domain range. Args: @@ -102,7 +101,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): points outside the domain range. The shape of the array may be `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. Returns: (numpy.ndarray): numpy array with the evaluation of the points in @@ -118,10 +116,10 @@ def evaluate(self, fdata, eval_points, *, derivative=0): if eval_points.ndim == 3: - res = fdata._evaluate_composed(eval_points, derivative=derivative) + res = fdata._evaluate_composed(eval_points) else: - res = fdata._evaluate(eval_points, derivative=derivative) + res = fdata._evaluate(eval_points) return res @@ -159,7 +157,7 @@ class ExceptionExtrapolation(Evaluator): """ - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """Evaluate points outside the domain range. Args: @@ -168,7 +166,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): points outside the domain range. The shape of the array may be `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. Raises: ValueError: when the extrapolation method is called. @@ -216,7 +213,7 @@ def _fill(self, fdata, eval_points): fdata.dim_codomain) return np.full(shape, self.fill_value) - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): """ Evaluate points outside the domain range. @@ -226,7 +223,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): points outside the domain range. The shape of the array may be `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` x `dim_codomain`. - derivate (numeric, optional): Order of derivative to be evaluated. Returns: (numpy.ndarray): numpy array with the evaluation of the points in @@ -235,7 +231,7 @@ def evaluate(self, fdata, eval_points, *, derivative=0): """ return self._fill(fdata, eval_points) - def evaluate_composed(self, fdata, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -249,7 +245,6 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): eval_points (numpy.ndarray): Numpy array with shape `(n_samples, number_eval_points, dim_domain)` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Numpy 3d array with shape `(n_samples, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 2a47a9bee..a86b21354 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -360,7 +360,7 @@ def interpolation(self, new_interpolation): self._interpolation = new_interpolation - def _evaluate(self, eval_points, *, derivative=0): + def _evaluate(self, eval_points): """"Evaluate the object or its derivatives at a list of values. Args: @@ -368,7 +368,6 @@ def _evaluate(self, eval_points, *, derivative=0): evaluated. If a matrix of shape nsample x eval_points is given each sample is evaluated at the values in the corresponding row in eval_points. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Matrix whose rows are the values of the each @@ -376,9 +375,9 @@ def _evaluate(self, eval_points, *, derivative=0): """ - return self.interpolation.evaluate(self, eval_points, derivative=derivative) + return self.interpolation.evaluate(self, eval_points) - def _evaluate_composed(self, eval_points, *, derivative=0): + def _evaluate_composed(self, eval_points): """"Evaluate the object or its derivatives at a list of values. Args: @@ -386,7 +385,6 @@ def _evaluate_composed(self, eval_points, *, derivative=0): evaluated. If a matrix of shape nsample x eval_points is given each sample is evaluated at the values in the corresponding row in eval_points. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (numpy.darray): Matrix whose rows are the values of the each @@ -394,8 +392,7 @@ def _evaluate_composed(self, eval_points, *, derivative=0): """ - return self.interpolation.evaluate_composed(self, eval_points, - derivative=derivative) + return self.interpolation.evaluate_composed(self, eval_points) def derivative(self, *, order=1): r"""Differentiate a FDataGrid object. @@ -445,9 +442,7 @@ def derivative(self, *, order=1): if order < 0: raise ValueError("The order of a derivative has to be greater " "or equal than 0.") - if self.dim_domain > 1 or self.dim_codomain > 1: - raise NotImplementedError("Not implemented for 2 or more" - " dimensional data.") + if np.isnan(self.data_matrix).any(): raise ValueError("The FDataGrid object cannot contain nan " "elements.") diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index f1491a701..7a1a52447 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -396,7 +396,7 @@ def _build_interpolator(self, fdatagrid): interpolation_order=self.interpolation_order, smoothness_parameter=self.smoothness_parameter) - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points): r"""Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -410,7 +410,6 @@ def evaluate(self, fdata, eval_points, *, derivative=0): eval_points (np.ndarray): Numpy array with shape `(n_samples, number_eval_points, dim_domain)` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (np.darray): Numpy 3d array with shape `(n_samples, @@ -426,9 +425,9 @@ def evaluate(self, fdata, eval_points, *, derivative=0): spline_list = self._build_interpolator(fdata) - return spline_list.evaluate(fdata, eval_points, derivative=derivative) + return spline_list.evaluate(fdata, eval_points) - def evaluate_composed(self, fdata, eval_points, *, derivative=0): + def evaluate_composed(self, fdata, eval_points): """Evaluation method. Evaluates the samples at different evaluation points. The evaluation @@ -442,7 +441,6 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): eval_points (np.ndarray): Numpy array with shape `(n_samples, number_eval_points, dim_domain)` with the evaluation points for each sample. - derivative (int, optional): Order of the derivative. Defaults to 0. Returns: (np.darray): Numpy 3d array with shape `(n_samples, @@ -457,8 +455,7 @@ def evaluate_composed(self, fdata, eval_points, *, derivative=0): """ spline_list = self._build_interpolator(fdata) - return spline_list.evaluate_composed(fdata, eval_points, - derivative=derivative) + return spline_list.evaluate_composed(fdata, eval_points) def __repr__(self): """repr method of the interpolation""" diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index 26289d9da..15047ea1b 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -45,16 +45,6 @@ def test_evaluation_linear_point(self): np.testing.assert_array_almost_equal(f([3]), np.array([[9.], [36.]])) np.testing.assert_array_almost_equal(f((2,)), np.array([[4.], [49.]])) - def test_evaluation_linear_derivative(self): - """Test derivative""" - f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10)) - - # Derivate = [2*x, 2*(9-x)] - np.testing.assert_array_almost_equal( - f([0.5, 1.5, 2.5], derivative=1).round(3), - np.array([[1., 3., 5.], - [-17., -15., -13.]])) - def test_evaluation_linear_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -229,17 +219,6 @@ def test_evaluation_cubic_point(self): np.testing.assert_array_almost_equal( f((2,)).round(3), np.array([[4.], [49.]])) - def test_evaluation_cubic_derivative(self): - """Test derivative""" - f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - interpolation=SplineInterpolation(3)) - - # Derivate = [2*x, 2*(9-x)] - np.testing.assert_array_almost_equal( - f([0.5, 1.5, 2.5], derivative=1).round(3), - np.array([[1., 3., 5.], - [-17., -15., -13.]])) - def test_evaluation_cubic_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -364,20 +343,6 @@ def test_evaluation_point(self): ) ) - def test_evaluation_derivative(self): - """Test derivative""" - f = FDataGrid(self.data_matrix_1_n, sample_points=self.t, - interpolation=self.interpolation) - - # [(2*x, d/dx sin(pi/81*x**2)), (2*(9-x), d/dx sin(pi/81*(9-x)**2))] - np.testing.assert_array_almost_equal(f([1.5, 2.5, 3.5], derivative=1), - np.array([[[3., 0.1162381], - [5., 0.1897434], - [7., 0.2453124]], - [[-15., 0.3385772], - [-13., 0.0243172], - [-11., -0.1752035]]])) - def test_evaluation_grid(self): """Test grid evaluation. With domain dimension = 1""" diff --git a/tests/test_registration.py b/tests/test_registration.py index d0b754945..1b1afd975 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -374,10 +374,10 @@ def test_mse_decomposition(self): fd_registered = fd.compose(warping) scorer = AmplitudePhaseDecomposition(return_stats=True) ret = scorer.score_function(fd, fd_registered, warping=warping) - np.testing.assert_almost_equal(ret.mse_amp, 0.0009866997121476962) - np.testing.assert_almost_equal(ret.mse_pha, 0.11576861468435257) - np.testing.assert_almost_equal(ret.r_squared, 0.9915489952877273) - np.testing.assert_almost_equal(ret.c_r, 0.9999963424653829) + np.testing.assert_allclose(ret.mse_amp, 0.0009866997121476962) + np.testing.assert_allclose(ret.mse_pha, 0.11576935495450151) + np.testing.assert_allclose(ret.r_squared, 0.9915489952877273) + np.testing.assert_allclose(ret.c_r, 0.999999, rtol=1e-6) def test_raises_amplitude_phase(self): scorer = AmplitudePhaseDecomposition() From dbdd0bbb5f1cbdc38c6de0d05f6abcb2b4b75658 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 19 Jun 2020 21:03:21 +0200 Subject: [PATCH 563/624] Remove derivative from plotting. --- examples/plot_explore.py | 7 +- examples/plot_interpolation.py | 65 +++---------------- .../visualization/representation.py | 21 ++---- skfda/representation/_functional_data.py | 20 +++--- skfda/representation/grid.py | 21 ++---- 5 files changed, 37 insertions(+), 97 deletions(-) diff --git a/examples/plot_explore.py b/examples/plot_explore.py index ddd73ba4d..632919eb1 100644 --- a/examples/plot_explore.py +++ b/examples/plot_explore.py @@ -9,9 +9,10 @@ # Author: Miguel Carbajo Berrocal # License: MIT -import numpy as np import skfda +import numpy as np + ############################################################################## # In this example we are going to explore the functional properties of the @@ -60,12 +61,12 @@ # # The first derivative is shown below: -fdd = fd.derivative(1) +fdd = fd.derivative() fig = fdd.plot(group=labels, group_colors=colors, linewidth=0.5, alpha=0.7, legend=True) ############################################################################## # We now show the second derivative: -fdd = fd.derivative(2) +fdd = fd.derivative(order=2) fig = fdd.plot(group=labels, group_colors=colors, linewidth=0.5, alpha=0.7, legend=True) diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index ef3155e23..1c3abf7fc 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -81,42 +81,6 @@ fd_smooth.scatter(fig=fig) fig.legend() - -############################################################################## -# It is possible to evaluate derivatives of the FDatagrid, -# but due to the fact that interpolation is performed first, the interpolation -# loses one degree for each order of derivation. In the next example, it is -# shown the first derivative of a sample using interpolation with different -# degrees. -# - -fd = fd[1] - -fig = plt.figure() -fig.add_subplot(1, 1, 1) - -for i in range(1, 4): - fd.interpolation = SplineInterpolation(interpolation_order=i) - fd.plot(fig=fig, derivative=1, label=f"Degree {i}") - -fig.legend() - -############################################################################## -# FDataGrids can be differentiate using lagged differences with the -# method :func:`~skfda.representation.grid.FDataGrid.derivative`, creating -# another FDataGrid which could be interpolated in order to avoid -# interpolating before differentiating. -# - -fd_derivative = fd.derivative() - -fig = fd_derivative.plot(label="Differentiation first") -fd_derivative.scatter(fig=fig) - -fd.plot(fig=fig, derivative=1, label="Interpolation first") - -fig.legend() - ############################################################################## # Sometimes our samples are required to be monotone, in these cases it is # possible to use monotone cubic interpolation with the attribute @@ -124,6 +88,7 @@ # will be used. # +fd = fd[1] fd_monotone = fd.copy(data_matrix=np.sort(fd.data_matrix, axis=1)) @@ -167,32 +132,22 @@ # the values (3,2). # - fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) -############################################################################## -# In case of surface derivatives could be taked in two directions, for this -# reason a tuple with the order of derivates in each direction could be passed. -# Let :math:`x(t,s)` be the surface, in the following example it is shown the -# derivative with respect to the second coordinate, :math:`\frac{\partial} -# {\partial s}x(t,s)`. - -fd.plot(derivative=(0, 1)) - ############################################################################## # The following table shows the interpolation methods available by the class # :class:`SplineInterpolation` depending on the domain dimension. # -# +------------------+--------+----------------+----------+-------------+-------------+ -# | Domain dimension | Linear | Up to degree 5 | Monotone | Derivatives | Smoothing | -# +==================+========+================+==========+=============+=============+ -# | 1 | ✔ | ✔ | ✔ | ✔ | ✔ | -# +------------------+--------+----------------+----------+-------------+-------------+ -# | 2 | ✔ | ✔ | ✖ | ✔ | ✔ | -# +------------------+--------+----------------+----------+-------------+-------------+ -# | 3 or more | ✔ | ✖ | ✖ | ✖ | ✖ | -# +------------------+--------+----------------+----------+-------------+-------------+ +# +------------------+--------+----------------+----------+-------------+ +# | Domain dimension | Linear | Up to degree 5 | Monotone | Smoothing | +# +==================+========+================+==========+=============+ +# | 1 | ✔ | ✔ | ✔ | ✔ | +# +------------------+--------+----------------+----------+-------------+ +# | 2 | ✔ | ✔ | ✖ | ✔ | +# +------------------+--------+----------------+----------+-------------+ +# | 3 or more | ✔ | ✖ | ✖ | ✖ | +# +------------------+--------+----------------+----------+-------------+ # diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 54d65e045..e70b4db14 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -77,7 +77,7 @@ def _get_color_info(fdata, group, group_names, group_colors, legend, kwargs): return sample_colors, patches -def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, +def plot_graph(fdata, chart=None, *, fig=None, axes=None, n_rows=None, n_cols=None, n_points=None, domain_range=None, group=None, group_colors=None, group_names=None, @@ -93,10 +93,6 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, with the graphs are plotted or axis over where the graphs are plotted. If None and ax is also None, the figure is initialized. - derivative (int or tuple, optional): Order of derivative to be - plotted. In case of surfaces a tuple with the order of - derivation in each direction can be passed. See - :func:`evaluate` to obtain more information. Defaults 0. fig (figure object, optional): figure over with the graphs are plotted in case ax is not specified. If None and ax is also None, the figure is initialized. @@ -164,7 +160,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, # Evaluates the object in a linspace eval_points = np.linspace(*domain_range[0], n_points) - mat = fdata(eval_points, derivative=derivative, keepdims=True) + mat = fdata(eval_points, keepdims=True) color_dict = {} @@ -193,7 +189,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, y = np.linspace(*domain_range[1], npoints[1]) # Evaluation of the functional object - Z = fdata((x, y), derivative=derivative, grid=True, keepdims=True) + Z = fdata((x, y), grid=True, keepdims=True) X, Y = np.meshgrid(x, y, indexing='ij') @@ -213,7 +209,7 @@ def plot_graph(fdata, chart=None, *, derivative=0, fig=None, axes=None, return fig -def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, +def plot_scatter(fdata, chart=None, *, sample_points=None, fig=None, axes=None, n_rows=None, n_cols=None, domain_range=None, group=None, group_colors=None, group_names=None, @@ -227,10 +223,6 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, plotted. If None and ax is also None, the figure is initialized. sample_points (ndarray): points to plot. - derivative (int or tuple, optional): Order of derivative to be - plotted. In case of surfaces a tuple with the order of - derivation in each direction can be passed. See - :func:`evaluate` to obtain more information. Defaults 0. fig (figure object, optional): figure over with the graphs are plotted in case ax is not specified. If None and ax is also None, the figure is initialized. @@ -278,12 +270,11 @@ def plot_scatter(fdata, chart=None, *, sample_points=None, derivative=0, if sample_points is None: # This can only be done for FDataGrid sample_points = fdata.sample_points - if derivative == 0: - evaluated_points = fdata.data_matrix + evaluated_points = fdata.data_matrix if evaluated_points is None: evaluated_points = fdata( - sample_points, grid=True, derivative=derivative) + sample_points, grid=True) fig, axes = _get_figure_and_axes(chart, fig, axes) fig, axes = _set_figure_layout_for_fdata(fdata, fig, axes, n_rows, n_cols) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 734389e1b..e3839b236 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -199,7 +199,7 @@ def _extrapolation_index(self, eval_points): return index - def _evaluate_grid(self, axes, *, derivative=0, extrapolation=None, + def _evaluate_grid(self, axes, *, extrapolation=None, aligned_evaluation=True, keepdims=None): """Evaluate the functional object in the cartesian grid. @@ -226,7 +226,6 @@ def _evaluate_grid(self, axes, *, derivative=0, extrapolation=None, Args: axes (array_like): List of axes to generated the grid where the object will be evaluated. - derivative (int, optional): Order of the derivative. Defaults to 0. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the @@ -260,7 +259,7 @@ def _evaluate_grid(self, axes, *, derivative=0, extrapolation=None, eval_points = _coordinate_list(axes) - res = self.evaluate(eval_points, derivative=derivative, + res = self.evaluate(eval_points, extrapolation=extrapolation, keepdims=True) elif self.dim_domain == 1: @@ -268,7 +267,6 @@ def _evaluate_grid(self, axes, *, derivative=0, extrapolation=None, eval_points = [ax.squeeze(0) for ax in axes] return self.evaluate(eval_points, - derivative=derivative, extrapolation=extrapolation, keepdims=keepdims, aligned_evaluation=False) @@ -289,7 +287,7 @@ def _evaluate_grid(self, axes, *, derivative=0, extrapolation=None, for i in range(self.n_samples): eval_points[i] = _coordinate_list(axes[i]) - res = self.evaluate(eval_points, derivative=derivative, + res = self.evaluate(eval_points, extrapolation=extrapolation, keepdims=True, aligned_evaluation=False) @@ -396,7 +394,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, evaluated. If a matrix of shape nsample x eval_points is given each sample is evaluated at the values in the corresponding row in eval_points. - derivative (int, optional): Order of the derivative. Defaults to 0. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the @@ -419,12 +416,15 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, function at the values specified in eval_points. """ - if derivative < 0: - raise ValueError("derivative only takes non-negative values.") - elif derivative != 0: + if derivative != 0: warnings.warn("Parameter derivative is deprecated. Use the " "derivative function instead.", DeprecationWarning) - return self.derivative(order=derivative)(eval_points) + return self.derivative(order=derivative)( + eval_points, + extrapolation=extrapolation, + grid=grid, + aligned_evaluation=aligned_evaluation, + keepdims=keepdims) if extrapolation is None: extrapolation = self.extrapolation diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index a86b21354..b94eeb49f 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -434,20 +434,13 @@ def derivative(self, *, order=1): ...) """ - if self.dim_domain != 1: - raise NotImplementedError( - "This method only works when the dimension " - "of the domain of the FDatagrid object is " - "one.") - if order < 0: - raise ValueError("The order of a derivative has to be greater " - "or equal than 0.") - - if np.isnan(self.data_matrix).any(): - raise ValueError("The FDataGrid object cannot contain nan " - "elements.") - - operator = findiff.FinDiff(1, self.sample_points[0], order) + order_list = np.atleast_1d(order) + if order_list.ndim != 1 or len(order_list) != self.dim_domain: + raise ValueError("The order for each partial should be specified.") + + operator = findiff.FinDiff(*[(1 + i, p, o) + for i, (p, o) in enumerate( + zip(self.sample_points, order_list))]) data_matrix = operator(self.data_matrix.astype(float)) if self.dataset_label: From 03e03de55cb6da6b2bd83b6d67cb58dbd67b3d21 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?David=20Garc=C3=ADa=20Fern=C3=A1ndez?= Date: Sun, 21 Jun 2020 17:44:04 +0200 Subject: [PATCH 564/624] Including equal_var parameter in ANOVA MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: David García Fernández --- examples/plot_oneway_synthetic.py | 39 +++++++-------------------- skfda/inference/anova/anova_oneway.py | 39 ++++++++++++++++++--------- 2 files changed, 35 insertions(+), 43 deletions(-) diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index a8844488f..ef68e9de0 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -9,11 +9,9 @@ # Author: David García Fernández # License: MIT -# sphinx_gallery_thumbnail_number = 2 import numpy as np -import skfda from skfda.representation import FDataGrid from skfda.inference.anova import oneway_anova from skfda.datasets import make_gaussian_process @@ -63,7 +61,7 @@ groups[20:] = 'Sample 3' ############################################################################### -# First simulation uses a low :math:`\sigma = 0.01` value. In this case the +# First simulation uses a low :math:`\sigma^2 = 0.01` value. In this case the # differences between the means of each group should be clear, and the # p-value for the test should be near to zero. @@ -83,16 +81,6 @@ print("Statistic: {:.3f}".format(stat)) print("p-value: {:.3f}".format(p_val)) -################################################################################ -# In the plot below we can see the simulated trajectories for each mean, -# and the averages for each group. - -fd = skfda.concatenate([fd1, fd2, fd3]) -fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, - fd3]]) -fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( - sigma2, p_val) -fd_total.plot() ################################################################################ # In the following, the same process will be followed incrementing sigma @@ -101,7 +89,7 @@ # refuse). ################################################################################ -# Plot for :math:`\sigma = 1`: +# Plot for :math:`\sigma^2 = 0.1`: sigma2 = 0.1 cov = WhiteNoise(variance=sigma2) @@ -115,17 +103,13 @@ n_features=n_features, random_state=3, start=t[0], stop=t[-1]) -_, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) +stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) +print("Statistic: {:.3f}".format(stat)) +print("p-value: {:.3f}".format(p_val)) -fd = skfda.concatenate([fd1, fd2, fd3]) -fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, - fd3]]) -fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( - sigma2, p_val) -fd_total.plot() ################################################################################ -# Plot for :math:`\sigma = 10`: +# Plot for :math:`\sigma^2 = 1`: sigma2 = 1 cov = WhiteNoise(variance=sigma2) @@ -140,14 +124,9 @@ n_features=n_features, random_state=3, start=t[0], stop=t[-1]) -_, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) - -fd = skfda.concatenate([fd1, fd2, fd3]) -fd_total = skfda.concatenate([fd.mean() for fd in [fd1, fd2, - fd3]]) -fd_total.dataset_label = "Sample with $\sigma^2$ = {}, p-value = {:.3f}".format( - sigma2, p_val) -fd_total.plot() +stat, p_val = oneway_anova(fd1, fd2, fd3, random_state=4) +print("Statistic: {:.3f}".format(stat)) +print("p-value: {:.3f}".format(p_val)) ################################################################################ # **References:** diff --git a/skfda/inference/anova/anova_oneway.py b/skfda/inference/anova/anova_oneway.py index 8ebc11379..9432f4daa 100644 --- a/skfda/inference/anova/anova_oneway.py +++ b/skfda/inference/anova/anova_oneway.py @@ -159,7 +159,8 @@ def v_asymptotic_stat(fd, weights, p=2): return np.sum(lp_distance(left_fd, right_fd, p=p) ** p) -def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): +def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2, + equal_var=True): n_groups = len(fd_grouped) if n_groups < 2: @@ -174,21 +175,27 @@ def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): sizes = [fd.n_samples for fd in fd_grouped] # List with sizes of each group - # Estimating covariances for each group - k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - - # Number of sample points for gaussian processes have to match the features - # of the covariances. - n_features = k_est[0].shape[0] - # Instance a random state object in case random_state is an int random_state = check_random_state(random_state) - # Simulating n_reps observations for each of the n_groups gaussian processes + if equal_var: + k_est = concatenate(fd_grouped).cov().data_matrix[0, ..., 0] + k_est = [k_est] * len(fd_grouped) + else: + # Estimating covariances for each group + k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] + + # Number of sample points for gaussian processes have to match + # the features of the covariances. + n_features = k_est[0].shape[0] + + # Simulating n_reps observations for each of the n_groups gaussian + # processes sim = [make_gaussian_process(n_reps, n_features=n_features, start=start, stop=stop, cov=k_est[i], random_state=random_state) for i in range(n_groups)] + v_samples = np.empty(n_reps) for i in range(n_reps): fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) @@ -196,7 +203,8 @@ def _anova_bootstrap(fd_grouped, n_reps, random_state=None, p=2): return v_samples -def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): +def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, + p=2, equal_var=True): r""" Performs one-way functional ANOVA. @@ -243,6 +251,10 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): than 1. If p='inf' or p=np.inf it is used the L infinity metric. Defaults to 2. + equal_var (bool, optional): If True (default), perform a One-way + ANOVA assuming the same covariance operator for all the groups, + else considers an independent covariance operator for each group. + Returns: Value of the sample statistic, p-value and sampling distribution of the simulated asymptotic statistic. @@ -262,13 +274,13 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): >>> fd = fetch_gait()["data"].coordinates[1] >>> fd1, fd2, fd3 = fd[:13], fd[13:26], fd[26:] >>> oneway_anova(fd1, fd2, fd3, random_state=RandomState(42)) - (179.52499999999998, 0.602) + (179.52499999999998, 0.5945) >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) >>> with printoptions(precision=4): ... print(dist) - [ 163.3577 208.595 229.7678] + [ 184.0698 212.7395 195.3663] References: [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An @@ -312,7 +324,8 @@ def oneway_anova(*args, n_reps=2000, return_dist=False, random_state=None, p=2): # Computing sampling distribution simulation = _anova_bootstrap(fd_groups, n_reps, - random_state=random_state, p=p) + random_state=random_state, p=p, + equal_var=equal_var) p_value = np.sum(simulation > vn) / len(simulation) From edb75fc10fb17a144783c7e45fee3dd60cd514d6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 21 Jun 2020 18:02:00 +0200 Subject: [PATCH 565/624] Remove keepdims parameter from FData objects. --- .../visualization/representation.py | 4 +- .../_linear_differential_operator.py | 4 +- skfda/misc/operators/_operators.py | 4 +- skfda/preprocessing/registration/_warping.py | 6 +- skfda/representation/_functional_data.py | 18 +- skfda/representation/basis/_bspline.py | 12 +- skfda/representation/basis/_fdatabasis.py | 24 +- skfda/representation/basis/_fourier.py | 17 +- skfda/representation/basis/_monomial.py | 25 +- skfda/representation/extrapolation.py | 48 ++- skfda/representation/grid.py | 16 +- tests/test_basis_evaluation.py | 375 +----------------- tests/test_elastic.py | 22 +- tests/test_extrapolation.py | 46 ++- tests/test_interpolation.py | 180 ++------- tests/test_registration.py | 30 +- 16 files changed, 216 insertions(+), 615 deletions(-) diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index e70b4db14..739fbbb57 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -160,7 +160,7 @@ def plot_graph(fdata, chart=None, *, fig=None, axes=None, # Evaluates the object in a linspace eval_points = np.linspace(*domain_range[0], n_points) - mat = fdata(eval_points, keepdims=True) + mat = fdata(eval_points) color_dict = {} @@ -189,7 +189,7 @@ def plot_graph(fdata, chart=None, *, fig=None, axes=None, y = np.linspace(*domain_range[1], npoints[1]) # Evaluation of the functional object - Z = fdata((x, y), grid=True, keepdims=True) + Z = fdata((x, y), grid=True) X, Y = np.meshgrid(x, y, indexing='ij') diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index f28c4ff52..5d4897c51 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -466,7 +466,7 @@ def bspline_penalty_matrix_optimized( knots = np.array(basis.knots) mid_inter = (knots[1:] + knots[:-1]) / 2 basis_deriv = basis.derivative(order=derivative_degree) - constants = basis_deriv(mid_inter).T + constants = basis_deriv(mid_inter)[..., 0].T knots_intervals = np.diff(basis.knots) # Integration of product of constants return constants.T @ np.diag(knots_intervals) @ constants @@ -569,7 +569,7 @@ def fdatagrid_penalty_matrix_optimized( indices = np.triu_indices(basis.n_samples) product = evaluated_basis[indices[0]] * evaluated_basis[indices[1]] - triang_vec = scipy.integrate.simps(product, x=basis.sample_points) + triang_vec = scipy.integrate.simps(product[..., 0], x=basis.sample_points) matrix = np.empty((basis.n_samples, basis.n_samples)) diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 3ca027f26..1f1e369ba 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -48,9 +48,11 @@ def cross_product(x): domain_range = basis.domain_range[0] # Obtain the integrals for the upper matrix - return scipy.integrate.quad_vec( + integral = scipy.integrate.quad_vec( cross_product, domain_range[0], domain_range[1])[0] + return integral[..., 0] + def compute_triang_multivariate(evaluated_basis, indices, diff --git a/skfda/preprocessing/registration/_warping.py b/skfda/preprocessing/registration/_warping.py index ff03622ea..a6d78c64f 100644 --- a/skfda/preprocessing/registration/_warping.py +++ b/skfda/preprocessing/registration/_warping.py @@ -64,7 +64,11 @@ def invert_warping(fdatagrid, *, output_points=None): >>> identity = gamma.compose(inverse) >>> identity([0, 0.25, 0.5, 0.75, 1]).round(3) - array([[ 0. , 0.25, 0.5 , 0.75, 1. ]]) + array([[[ 0. ], + [ 0.25], + [ 0.5 ], + [ 0.75], + [ 1. ]]]) """ diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index e3839b236..be24ca548 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -27,17 +27,14 @@ class FData(ABC, pandas.api.extensions.ExtensionArray): axes_labels (list): list containing the labels of the different axis. The first element is the x label, the second the y label and so on. - keepdims (bool): Default value of argument keepdims in - :func:`evaluate`. """ - def __init__(self, extrapolation, dataset_label, axes_labels, keepdims): + def __init__(self, extrapolation, dataset_label, axes_labels): self.extrapolation = extrapolation self.dataset_label = dataset_label self.axes_labels = axes_labels - self.keepdims = keepdims @property def axes_labels(self): @@ -200,7 +197,7 @@ def _extrapolation_index(self, eval_points): return index def _evaluate_grid(self, axes, *, extrapolation=None, - aligned_evaluation=True, keepdims=None): + aligned_evaluation=True, keepdims=True): """Evaluate the functional object in the cartesian grid. This method is called internally by :meth:`evaluate` when the argument @@ -293,9 +290,6 @@ def _evaluate_grid(self, axes, *, extrapolation=None, shape = [self.n_samples] + lengths - if keepdims is None: - keepdims = self.keepdims - if self.dim_codomain != 1 or keepdims: shape += [self.dim_codomain] @@ -385,7 +379,7 @@ def _evaluate_composed(self, eval_points): pass def evaluate(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True, keepdims=None): + grid=False, aligned_evaluation=True, keepdims=True): """Evaluate the object or its derivatives at a list of values or a grid. @@ -491,10 +485,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, res = self._join_evaluation(index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation) - # If not provided gets default value of keepdims - if keepdims is None: - keepdims = self.keepdims - # Delete last axis if not keepdims and if self.dim_codomain == 1 and not keepdims: res = res.reshape(res.shape[:-1]) @@ -502,7 +492,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, return res def __call__(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True, keepdims=None): + grid=False, aligned_evaluation=True, keepdims=True): """Evaluate the object or its derivatives at a list of values or a grid. This method is a wrapper of :meth:`evaluate`. diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index c52f8d0de..8a12185fd 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -64,9 +64,15 @@ class BSpline(Basis): >>> deriv = bss.derivative() >>> deriv([0, 0.5, 1]) - array([[-2., -1., 0.], - [ 2., 0., -2.], - [ 0., 1., 2.]]) + array([[[-2.], + [-1.], + [ 0.]], + [[ 2.], + [ 0.], + [-2.]], + [[ 0.], + [ 1.], + [ 2.]]]) References: .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 6205a7239..4f3245b0c 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -75,7 +75,7 @@ def __len__(self): return self._fdatabasis.dim_codomain def __init__(self, basis, coefficients, *, dataset_label=None, - axes_labels=None, extrapolation=None, keepdims=False): + axes_labels=None, extrapolation=None): """Construct a FDataBasis object. Args: @@ -92,11 +92,11 @@ def __init__(self, basis, coefficients, *, dataset_label=None, self.basis = basis self.coefficients = coefficients - super().__init__(extrapolation, dataset_label, axes_labels, keepdims) + super().__init__(extrapolation, dataset_label, axes_labels) @classmethod def from_data(cls, data_matrix, sample_points, basis, - method='cholesky', keepdims=False): + method='cholesky'): r"""Transform raw data to a smooth functional form. Takes functional data in a discrete form and makes an approximates it @@ -338,8 +338,7 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, _basis = self.basis.rescale((domain_range[0] + shifts, domain_range[1] + shifts)) - return FDataBasis.from_data(self.evaluate(eval_points, - keepdims=False), + return FDataBasis.from_data(self.evaluate(eval_points), eval_points + shifts, _basis, **kwargs) @@ -529,8 +528,7 @@ def to_grid(self, eval_points=None): return grid.FDataGrid(self.evaluate(eval_points, keepdims=False), sample_points=eval_points, - domain_range=self.domain_range, - keepdims=self.keepdims) + domain_range=self.domain_range) def to_basis(self, basis, eval_points=None, **kwargs): """Return the basis representation of the object. @@ -553,7 +551,7 @@ def to_list(self): return [self[i] for i in range(self.n_samples)] def copy(self, *, basis=None, coefficients=None, dataset_label=None, - axes_labels=None, extrapolation=None, keepdims=None): + axes_labels=None, extrapolation=None): """FDataBasis copy""" if basis is None: @@ -571,12 +569,8 @@ def copy(self, *, basis=None, coefficients=None, dataset_label=None, if extrapolation is None: extrapolation = self.extrapolation - if keepdims is None: - keepdims = self.keepdims - return FDataBasis(basis, coefficients, dataset_label=dataset_label, - axes_labels=axes_labels, extrapolation=extrapolation, - keepdims=keepdims) + axes_labels=axes_labels, extrapolation=extrapolation) def times(self, other): """"Provides a numerical approximation of the multiplication between @@ -740,8 +734,8 @@ def __repr__(self): f"\ncoefficients={self.coefficients}," f"\ndataset_label={self.dataset_label}," f"\naxes_labels={axes_labels}," - f"\nextrapolation={self.extrapolation}," - f"\nkeepdims={self.keepdims})").replace('\n', '\n ') + f"\nextrapolation={self.extrapolation})").replace( + '\n', '\n ') def __str__(self): """Return str(self).""" diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 7ed31d04b..37c8200b3 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -44,11 +44,18 @@ class Fourier(Basis): >>> deriv2 = fb.derivative(order=2) >>> deriv2([0, np.pi / 4, np.pi / 2, np.pi]).round(2) - array([[ 0. , 0. , 0. , 0. ], - [ 0. , 54.46, 24.02, -43.37], - [-55.83, -12.32, 50.4 , -35.16]]) - - + array([[[ 0. ], + [ 0. ], + [ 0. ], + [ 0. ]], + [[ 0. ], + [ 54.46], + [ 24.02], + [-43.37]], + [[-55.83], + [-12.32], + [ 50.4 ], + [-35.16]]]) """ diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index bd4f13284..d67899dc5 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -37,15 +37,26 @@ class Monomial(Basis): >>> deriv = bs_mon.derivative() >>> deriv([0, 1, 2]) - array([[ 0., 0., 0.], - [ 1., 1., 1.], - [ 0., 2., 4.]]) + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 2.], + [ 4.]]]) >>> deriv2 = bs_mon.derivative(order=2) >>> deriv2([0, 1, 2]) - array([[ 0., 0., 0.], - [ 0., 0., 0.], - [ 2., 2., 2.]]) - + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 0.], + [ 0.], + [ 0.]], + [[ 2.], + [ 2.], + [ 2.]]]) """ def _evaluate(self, eval_points): diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 59bad2df6..204ba9ad9 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -23,15 +23,23 @@ class PeriodicExtrapolation(Evaluator): >>> fd.extrapolation = PeriodicExtrapolation() >>> fd([-.5, 0, 1.5]).round(3) - array([[-0.724, 0.976, -0.724], - [-1.086, 0.759, -1.086]]) + array([[[-0.724], + [ 0.976], + [-0.724]], + [[-1.086], + [ 0.759], + [-1.086]]]) This extrapolator is equivalent to the string `"periodic"` >>> fd.extrapolation = 'periodic' >>> fd([-.5, 0, 1.5]).round(3) - array([[-0.724, 0.976, -0.724], - [-1.086, 0.759, -1.086]]) + array([[[-0.724], + [ 0.976], + [-0.724]], + [[-1.086], + [ 0.759], + [-1.086]]]) """ def evaluate(self, fdata, eval_points): @@ -81,15 +89,23 @@ class BoundaryExtrapolation(Evaluator): >>> fd.extrapolation = BoundaryExtrapolation() >>> fd([-.5, 0, 1.5]).round(3) - array([[ 0.976, 0.976, 0.797], - [ 0.759, 0.759, 1.125]]) + array([[[ 0.976], + [ 0.976], + [ 0.797]], + [[ 0.759], + [ 0.759], + [ 1.125]]]) This extrapolator is equivalent to the string `"bounds"`. >>> fd.extrapolation = 'bounds' >>> fd([-.5, 0, 1.5]).round(3) - array([[ 0.976, 0.976, 0.797], - [ 0.759, 0.759, 1.125]]) + array([[[ 0.976], + [ 0.976], + [ 0.797]], + [[ 0.759], + [ 0.759], + [ 1.125]]]) """ def evaluate(self, fdata, eval_points): @@ -193,16 +209,24 @@ class FillExtrapolation(Evaluator): >>> fd.extrapolation = FillExtrapolation(0) >>> fd([-.5, 0, 1.5]).round(3) - array([[ 0. , 0.976, 0. ], - [ 0. , 0.759, 0. ]]) + array([[[ 0. ], + [ 0.976], + [ 0. ]], + [[ 0. ], + [ 0.759], + [ 0. ]]]) The previous extrapolator is equivalent to the string `"zeros"`. In the same way FillExtrapolation(np.nan) is equivalent to `"nan"`. >>> fd.extrapolation = "nan" >>> fd([-.5, 0, 1.5]).round(3) - array([[ nan, 0.976, nan], - [ nan, 0.759, nan]]) + array([[[ nan], + [ 0.976], + [ nan]], + [[ nan], + [ 0.759], + [ nan]]]) """ def __init__(self, fill_value): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index b94eeb49f..922f86e83 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -129,7 +129,7 @@ def __len__(self): def __init__(self, data_matrix, sample_points=None, domain_range=None, dataset_label=None, axes_labels=None, extrapolation=None, - interpolation=None, keepdims=False): + interpolation=None): """Construct a FDataGrid object. Args: @@ -202,7 +202,7 @@ def __init__(self, data_matrix, sample_points=None, self.interpolation = interpolation - super().__init__(extrapolation, dataset_label, axes_labels, keepdims) + super().__init__(extrapolation, dataset_label, axes_labels) return @@ -816,7 +816,6 @@ def to_basis(self, basis, **kwargs): return fdbasis.FDataBasis.from_data(self.data_matrix[..., 0], self.sample_points[0], basis, - keepdims=self.keepdims, **kwargs) def to_grid(self, sample_points=None): @@ -843,7 +842,7 @@ def copy(self, *, data_matrix=None, sample_points=None, domain_range=None, dataset_label=None, axes_labels=None, extrapolation=None, - interpolation=None, keepdims=None): + interpolation=None): """Returns a copy of the FDataGrid. If an argument is provided the corresponding attribute in the new copy @@ -874,14 +873,11 @@ def copy(self, *, if interpolation is None: interpolation = self.interpolation - if keepdims is None: - keepdims = self.keepdims - return FDataGrid(data_matrix, sample_points=sample_points, domain_range=domain_range, dataset_label=dataset_label, axes_labels=axes_labels, extrapolation=extrapolation, - interpolation=interpolation, keepdims=keepdims) + interpolation=interpolation) def shift(self, shifts, *, restrict_domain=False, extrapolation=None, eval_points=None): @@ -1055,8 +1051,8 @@ def __repr__(self): f"\ndataset_label={repr(self.dataset_label)}," f"\naxes_labels={repr(axes_labels)}," f"\nextrapolation={repr(self.extrapolation)}," - f"\ninterpolation={repr(self.interpolation)}," - f"\nkeepdims={repr(self.keepdims)})").replace('\n', '\n ') + f"\ninterpolation={repr(self.interpolation)})").replace( + '\n', '\n ') def __getitem__(self, key): """Return self[key].""" diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 954b6ebe4..16ff51438 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -22,7 +22,7 @@ def test_evaluation_simple_fourier(self): res = np.array([[8.71, 9.66, 1.84, -4.71, -2.80, 2.71, 2.45, -3.82, -6.66, -0.30, 8.71], [22.24, 26.48, 10.57, -4.95, -3.58, 6.24, - 5.31, -7.69, -13.32, 1.13, 22.24]]) + 5.31, -7.69, -13.32, 1.13, 22.24]])[..., np.newaxis] np.testing.assert_array_almost_equal(f(t).round(2), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) @@ -38,7 +38,7 @@ def test_evaluation_point_fourier(self): # Test different ways of call f with a point res = np.array([-0.903918107989282, -0.267163981229459] - ).reshape((2, 1)).round(4) + ).reshape((2, 1, 1)).round(4) np.testing.assert_array_almost_equal(f([0.5]).round(4), res) np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) @@ -62,7 +62,7 @@ def test_evaluation_derivative_fourier(self): res = np.array([4.34138447771721, -7.09352774867064, 2.75214327095343, 4.34138447771721, 6.52573053999253, -4.81336320468984, -1.7123673353027, 6.52573053999253] - ).reshape((2, 4)).round(3) + ).reshape((2, 4, 1)).round(3) f_deriv = f.derivative() np.testing.assert_array_almost_equal( @@ -121,129 +121,6 @@ def test_evaluation_composed_fourier(self): f(t_multiple, aligned_evaluation=False)[1]) - def test_evaluation_keepdims_fourier(self): - """Test behaviour of keepdims """ - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - f_keepdims = FDataBasis(fourier, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([0.905482867989282, 0.146814813180645, - -1.04995054116993, 0.905482867989282, - 0.302725561229459, 0.774764356993855, - -1.02414754822331, 0.302725561229459] - ).reshape((2, 4)).round(3) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal( - f(t, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal( - f_keepdims(t).round(3), res_keepdims) - np.testing.assert_array_almost_equal(f_keepdims(t, keepdims=False - ).round(3), - res) - np.testing.assert_array_almost_equal(f_keepdims(t, keepdims=True - ).round(3), - res_keepdims) - - def test_evaluation_composed_keepdims_fourier(self): - """Test behaviour of keepdims with composed evaluation""" - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - f_keepdims = FDataBasis(fourier, coefficients, keepdims=True) - - t = [[0, 0.5, 0.6], [0.2, 0.7, 0.1]] - - res = np.array([0.905482867989282, -0.903918107989282, - -1.13726755517372, 1.09360302608278, - -1.05804144608278, 0.85878105128844] - ).reshape((2, 3)).round(3) - - res_keepdims = res.reshape((2, 3, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False - ).round(3), - res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims( - t, aligned_evaluation=False).round(3), res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=False).round(3), - res) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=True).round(3), - res_keepdims) - - def test_evaluation_grid_keepdims_fourier(self): - """Test behaviour of keepdims with grid evaluation""" - - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - f_keepdims = FDataBasis(fourier, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([0.905482867989282, 0.146814813180645, - -1.04995054116993, 0.905482867989282, - 0.302725561229459, 0.774764356993855, - -1.02414754822331, 0.302725561229459] - ).reshape((2, 4)).round(3) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) - np.testing.assert_array_almost_equal(f(t, grid=True, keepdims=False - ).round(3), - res) - - np.testing.assert_array_almost_equal(f(t, grid=True, keepdims=True - ).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims(t, grid=True - ).round(3), - res_keepdims) - np.testing.assert_array_almost_equal(f_keepdims(t, grid=True, - keepdims=False - ).round(3), res) - np.testing.assert_array_almost_equal( - f_keepdims(t, grid=True, keepdims=True).round(3), res_keepdims) - def test_domain_in_list_fourier(self): """Test the evaluation of FDataBasis""" for fourier in (Fourier(domain_range=[(0, 1)], n_basis=3), @@ -259,7 +136,7 @@ def test_domain_in_list_fourier(self): t = np.linspace(0, 1, 4) res = np.array([0.905, 0.147, -1.05, 0.905, 0.303, - 0.775, -1.024, 0.303]).reshape((2, 4)) + 0.775, -1.024, 0.303]).reshape((2, 4, 1)) np.testing.assert_array_almost_equal(f(t).round(3), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) @@ -282,7 +159,7 @@ def test_evaluation_simple_bspline(self): res = np.array([[1, 1.54, 1.99, 2.37, 2.7, 3, 3.3, 3.63, 4.01, 4.46, 5], [6, 6.54, 6.99, 7.37, 7.7, 8, - 8.3, 8.63, 9.01, 9.46, 10]]) + 8.3, 8.63, 9.01, 9.46, 10]])[..., np.newaxis] np.testing.assert_array_almost_equal(f(t).round(2), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) @@ -297,7 +174,7 @@ def test_evaluation_point_bspline(self): f = FDataBasis(bspline, coefficients) # Test different ways of call f with a point - res = np.array([[0.5696], [0.3104]]) + res = np.array([[0.5696], [0.3104]])[..., np.newaxis] np.testing.assert_array_almost_equal(f([0.5]).round(4), res) np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) @@ -322,7 +199,7 @@ def test_evaluation_derivative_bspline(self): np.testing.assert_array_almost_equal( f_deriv(t).round(3), np.array([[2.927, 0.453, -1.229, 0.6], - [4.3, -1.599, 1.016, -2.52]]) + [4.3, -1.599, 1.016, -2.52]])[..., np.newaxis] ) def test_evaluation_grid_bspline(self): @@ -377,120 +254,6 @@ def test_evaluation_composed_bspline(self): f(t_multiple, aligned_evaluation=False)[1]) - def test_evaluation_keepdims_bspline(self): - """Test behaviour of keepdims """ - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - f_keepdims = FDataBasis(bspline, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([[0.001, 0.564, 0.435, 0.33], - [0.018, 0.468, 0.371, 0.12]]) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal( - f(t, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal( - f_keepdims(t).round(3), res_keepdims) - np.testing.assert_array_almost_equal(f_keepdims(t, keepdims=False - ).round(3), - res) - np.testing.assert_array_almost_equal(f_keepdims(t, keepdims=True - ).round(3), - res_keepdims) - - def test_evaluation_composed_keepdims_bspline(self): - """Test behaviour of keepdims with composed evaluation""" - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - f_keepdims = FDataBasis(bspline, coefficients, keepdims=True) - - t = [[0, 0.5, 0.6], [0.2, 0.7, 0.1]] - - res = np.array([[0.001, 0.57, 0.506], - [0.524, 0.399, 0.359]]) - - res_keepdims = res.reshape((2, 3, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False - ).round(3), - res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=False).round(3), - res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False).round(3), res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=False).round(3), - res) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=True).round(3), - res_keepdims) - - def test_evaluation_grid_keepdims_bspline(self): - """Test behaviour of keepdims with grid evaluation""" - - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - f_keepdims = FDataBasis(bspline, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([[0.001, 0.564, 0.435, 0.33], - [0.018, 0.468, 0.371, 0.12]]) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) - np.testing.assert_array_almost_equal( - f(t, grid=True, keepdims=False).round(3), res) - - np.testing.assert_array_almost_equal( - f(t, grid=True, keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims(t, grid=True).round(3), - res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, grid=True, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal( - f_keepdims(t, grid=True, keepdims=True).round(3), - res_keepdims) - def test_domain_in_list_bspline(self): """Test the evaluation of FDataBasis""" @@ -510,7 +273,7 @@ def test_domain_in_list_bspline(self): t = np.linspace(0, 1, 4) res = np.array([[0.001, 0.564, 0.435, 0.33], - [0.018, 0.468, 0.371, 0.12]]) + [0.018, 0.468, 0.371, 0.12]])[..., np.newaxis] np.testing.assert_array_almost_equal(f(t).round(3), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) @@ -535,10 +298,11 @@ def test_evaluation_simple_monomial(self): t = np.linspace(0, 2, 11) # Results in R package fda - res = np.array([[1.00, 1.56, 2.66, 4.79, 8.62, 15.00, - 25.00, 39.86, 61.03, 90.14, 129.00], - [6.00, 7.81, 10.91, 16.32, 25.42, 40.00, - 62.21, 94.59, 140.08, 201.98, 284.00]]) + res = np.array( + [[1.00, 1.56, 2.66, 4.79, 8.62, 15.00, + 25.00, 39.86, 61.03, 90.14, 129.00], + [6.00, 7.81, 10.91, 16.32, 25.42, 40.00, + 62.21, 94.59, 140.08, 201.98, 284.00]])[..., np.newaxis] np.testing.assert_array_almost_equal(f(t).round(2), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) @@ -552,7 +316,7 @@ def test_evaluation_point_monomial(self): f = FDataBasis(monomial, coefficients) # Test different ways of call f with a point - res = np.array([[2.75], [1.525]]) + res = np.array([[2.75], [1.525]])[..., np.newaxis] np.testing.assert_array_almost_equal(f([0.5]).round(4), res) np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) @@ -576,7 +340,7 @@ def test_evaluation_derivative_monomial(self): np.testing.assert_array_almost_equal( f_deriv(t).round(3), np.array([[2., 4., 6., 8.], - [1.4, 2.267, 3.133, 4.]]) + [1.4, 2.267, 3.133, 4.]])[..., np.newaxis] ) def test_evaluation_grid_monomial(self): @@ -629,113 +393,6 @@ def test_evaluation_composed_monomial(self): f(t_multiple, aligned_evaluation=False)[1]) - def test_evaluation_keepdims_monomial(self): - """Test behaviour of keepdims """ - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - f_keepdims = FDataBasis(monomial, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([[1., 2., 3.667, 6.], - [0.5, 1.111, 2.011, 3.2]]) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal( - f(t, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal( - f_keepdims(t).round(3), res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal( - f_keepdims(t, keepdims=True).round(3), res_keepdims) - - def test_evaluation_composed_keepdims_monomial(self): - """Test behaviour of keepdims with composed evaluation""" - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - f_keepdims = FDataBasis(monomial, coefficients, keepdims=True) - - t = [[0, 0.5, 0.6], [0.2, 0.7, 0.1]] - - res = np.array([[1., 2.75, 3.28], - [0.832, 2.117, 0.653]]) - - res_keepdims = res.reshape((2, 3, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal( - f(t, aligned_evaluation=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=False).round(3), res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=True).round(3), - res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False).round(3), - res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=False).round(3), - res) - np.testing.assert_array_almost_equal( - f_keepdims(t, aligned_evaluation=False, keepdims=True).round(3), - res_keepdims) - - def test_evaluation_grid_keepdims_monomial(self): - """Test behaviour of keepdims with grid evaluation""" - - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - f_keepdims = FDataBasis(monomial, coefficients, keepdims=True) - - np.testing.assert_equal(f.keepdims, False) - np.testing.assert_equal(f_keepdims.keepdims, True) - - t = np.linspace(0, 1, 4) - - res = np.array([[1., 2., 3.667, 6.], - [0.5, 1.111, 2.011, 3.2]]) - - res_keepdims = res.reshape((2, 4, 1)) - - # Case default behaviour keepdims=False - np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) - np.testing.assert_array_almost_equal( - f(t, grid=True, keepdims=False).round(3), - res) - - np.testing.assert_array_almost_equal( - f(t, grid=True, keepdims=True).round(3), res_keepdims) - - # Case default behaviour keepdims=True - np.testing.assert_array_almost_equal(f_keepdims(t, grid=True).round(3), - res_keepdims) - np.testing.assert_array_almost_equal( - f_keepdims(t, grid=True, keepdims=False).round(3), res) - np.testing.assert_array_almost_equal( - f_keepdims(t, grid=True, keepdims=True).round(3), res_keepdims) - def test_domain_in_list_monomial(self): """Test the evaluation of FDataBasis""" @@ -751,7 +408,7 @@ def test_domain_in_list_monomial(self): t = np.linspace(0, 1, 4) res = np.array([[1., 2., 3.667, 6.], - [0.5, 1.111, 2.011, 3.2]]) + [0.5, 1.111, 2.011, 3.2]])[..., np.newaxis] np.testing.assert_array_almost_equal(f(t).round(3), res) np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) diff --git a/tests/test_elastic.py b/tests/test_elastic.py index 9876e1cc9..ea980d882 100644 --- a/tests/test_elastic.py +++ b/tests/test_elastic.py @@ -117,9 +117,12 @@ def test_default_alignment(self): values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.599058, 0.997427, 0.772248, 0.412342, 0.064725], - [0.626875, 0.997155, 0.791649, 0.382181, 0.050098], - [0.620992, 0.997369, 0.785886, 0.376556, 0.048804]] + expected = [[[0.599058], [0.997427], [0.772248], + [0.412342], [0.064725]], + [[0.626875], [0.997155], [0.791649], + [0.382181], [0.050098]], + [[0.620992], [0.997369], [0.785886], + [0.376556], [0.048804]]] np.testing.assert_allclose(values, expected, atol=1e-4) @@ -130,13 +133,16 @@ def test_callable_alignment(self): register = reg.fit_transform(self.unimodal_samples) values = register([-.25, -.1, 0, .1, .25]) - expected = [[0.599058, 0.997427, 0.772248, 0.412342, 0.064725], - [0.626875, 0.997155, 0.791649, 0.382181, 0.050098], - [0.620992, 0.997369, 0.785886, 0.376556, 0.048804]] + expected = [[[0.599058], [0.997427], [0.772248], + [0.412342], [0.064725]], + [[0.626875], [0.997155], [0.791649], + [0.382181], [0.050098]], + [[0.620992], [0.997369], [0.785886], + [0.376556], [0.048804]]] np.testing.assert_allclose(values, expected, atol=1e-4) - def test_simetry_of_aligment(self): + def test_simmetry_of_aligment(self): """Check registration using inverse composition""" reg = ElasticRegistration(template=self.template) reg.fit_transform(self.unimodal_samples) @@ -179,7 +185,7 @@ def test_warping_mean(self): warping = make_random_warping(start=-1, random_state=0) mean = warping_mean(warping) values = mean([-1, -.5, 0, .5, 1]) - expected = [[-1., -0.376241, 0.136193, 0.599291, 1.]] + expected = [[[-1.], [-0.376241], [0.136193], [0.599291], [1.]]] np.testing.assert_array_almost_equal(values, expected) diff --git a/tests/test_extrapolation.py b/tests/test_extrapolation.py index 993da57db..56281e702 100644 --- a/tests/test_extrapolation.py +++ b/tests/test_extrapolation.py @@ -1,14 +1,14 @@ """Test to check the extrapolation module""" -import unittest - -import numpy as np from skfda import FDataGrid, FDataBasis from skfda.datasets import make_sinusoidal_process from skfda.representation.basis import Fourier from skfda.representation.extrapolation import ( PeriodicExtrapolation, BoundaryExtrapolation, ExceptionExtrapolation, FillExtrapolation) +import unittest + +import numpy as np class TestBasis(unittest.TestCase): @@ -106,27 +106,31 @@ def test_periodic(self): self.grid.extrapolation = PeriodicExtrapolation() data = self.grid([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[-0.724, 0.976, -0.724], - [-1.086, 0.759, -1.086]]) + np.testing.assert_almost_equal(data[..., 0], + [[-0.724, 0.976, -0.724], + [-1.086, 0.759, -1.086]]) self.basis.extrapolation = "periodic" data = self.basis([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[-0.69, 0.692, -0.69], - [-1.021, 1.056, -1.021]]) + np.testing.assert_almost_equal(data[..., 0], + [[-0.69, 0.692, -0.69], + [-1.021, 1.056, -1.021]]) def test_boundary(self): self.grid.extrapolation = "bounds" data = self.grid([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[0.976, 0.976, 0.797], - [0.759, 0.759, 1.125]]) + np.testing.assert_almost_equal(data[..., 0], + [[0.976, 0.976, 0.797], + [0.759, 0.759, 1.125]]) self.basis.extrapolation = "bounds" data = self.basis([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[0.692, 0.692, 0.692], - [1.056, 1.056, 1.056]]) + np.testing.assert_almost_equal(data[..., 0], + [[0.692, 0.692, 0.692], + [1.056, 1.056, 1.056]]) def test_exception(self): self.grid.extrapolation = "exception" @@ -143,27 +147,31 @@ def test_zeros(self): self.grid.extrapolation = "zeros" data = self.grid([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[0., 0.976, 0.], - [0., 0.759, 0.]]) + np.testing.assert_almost_equal(data[..., 0], + [[0., 0.976, 0.], + [0., 0.759, 0.]]) self.basis.extrapolation = "zeros" data = self.basis([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[0, 0.692, 0], - [0, 1.056, 0]]) + np.testing.assert_almost_equal(data[..., 0], + [[0, 0.692, 0], + [0, 1.056, 0]]) def test_nan(self): self.grid.extrapolation = "nan" data = self.grid([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[np.nan, 0.976, np.nan], - [np.nan, 0.759, np.nan]]) + np.testing.assert_almost_equal(data[..., 0], + [[np.nan, 0.976, np.nan], + [np.nan, 0.759, np.nan]]) self.basis.extrapolation = "nan" data = self.basis([-.5, 0, 1.5]).round(3) - np.testing.assert_almost_equal(data, [[np.nan, 0.692, np.nan], - [np.nan, 1.056, np.nan]]) + np.testing.assert_almost_equal(data[..., 0], + [[np.nan, 0.692, np.nan], + [np.nan, 1.056, np.nan]]) if __name__ == '__main__': diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index 15047ea1b..386c872c1 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -27,12 +27,13 @@ def test_evaluation_linear_simple(self): # Test interpolation in nodes np.testing.assert_array_almost_equal( - f(np.arange(10)), self.data_matrix_1_1) + f(np.arange(10))[..., 0], self.data_matrix_1_1) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5]), - np.array([[0.5, 2.5, 6.5], - [72.5, 56.5, 42.5]])) + np.testing.assert_array_almost_equal( + f([0.5, 1.5, 2.5]), + np.array([[[0.5], [2.5], [6.5]], + [[72.5], [56.5], [42.5]]])) def test_evaluation_linear_point(self): """Test the evaluation of a single point""" @@ -41,9 +42,11 @@ def test_evaluation_linear_point(self): # Test a single point np.testing.assert_array_almost_equal(f(5.3).round(1), - np.array([[28.3], [13.9]])) - np.testing.assert_array_almost_equal(f([3]), np.array([[9.], [36.]])) - np.testing.assert_array_almost_equal(f((2,)), np.array([[4.], [49.]])) + np.array([[[28.3]], [[13.9]]])) + np.testing.assert_array_almost_equal( + f([3]), np.array([[[9.]], [[36.]]])) + np.testing.assert_array_almost_equal( + f((2,)), np.array([[[4.]], [[49.]]])) def test_evaluation_linear_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -51,10 +54,10 @@ def test_evaluation_linear_grid(self): f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10)) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10)), + np.testing.assert_array_almost_equal(f(np.arange(10))[..., 0], self.data_matrix_1_1) - res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) + res = np.array([[[0.5], [2.5], [6.5]], [[72.5], [56.5], [42.5]]]) t = [0.5, 1.5, 2.5] # Test evaluation in a list of times @@ -63,7 +66,7 @@ def test_evaluation_linear_grid(self): np.testing.assert_array_almost_equal(f([t], grid=True), res) # Single point with grid np.testing.assert_array_almost_equal(f(3, grid=True), - np.array([[9.], [36.]])) + np.array([[[9.]], [[36.]]])) # Check erroneous axis with np.testing.assert_raises(ValueError): @@ -76,118 +79,19 @@ def test_evaluation_linear_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal(f([[1], [8]], aligned_evaluation=False), - np.array([[1.], [1.]])) + np.array([[[1.]], [[1.]]])) t = np.linspace(4, 6, 4) np.testing.assert_array_almost_equal( f([t, 9 - t], aligned_evaluation=False).round(2), - np.array([[16., 22., 28.67, 36.], - [16., 22., 28.67, 36.]])) + np.array([[[16.], [22.], [28.67], [36.]], + [[16.], [22.], [28.67], [36.]]])) # Same length than nsample t = np.linspace(4, 6, 2) np.testing.assert_array_almost_equal( f([t, 9 - t], aligned_evaluation=False).round(2), - np.array([[16., 36.], [16., 36.]])) - - def test_evaluation_linear_keepdims(self): - """Test parameter keepdims""" - - # Default keepdims = False - f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=False) - - # Default keepdims = True - fk = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=True) - - t = [0.5, 1.5, 2.5] - res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) - res_keepdims = res.reshape((2, 3, 1)) - - # Test combinations of keepdims with list - np.testing.assert_array_almost_equal(f(t), res) - np.testing.assert_array_almost_equal(f(t, keepdims=False), res) - np.testing.assert_array_almost_equal(f(t, keepdims=True), res_keepdims) - - np.testing.assert_array_almost_equal(fk(t), res_keepdims) - np.testing.assert_array_almost_equal(fk(t, keepdims=False), res) - np.testing.assert_array_almost_equal( - fk(t, keepdims=True), res_keepdims) - - t2 = 4 - res2 = np.array([[16.], [25.]]) - res2_keepdims = res2.reshape(2, 1, 1) - - # Test combinations of keepdims with a single point - np.testing.assert_array_almost_equal(f(t2), res2) - np.testing.assert_array_almost_equal(f(t2, keepdims=False), res2) - np.testing.assert_array_almost_equal( - f(t2, keepdims=True), res2_keepdims) - - np.testing.assert_array_almost_equal(fk(t2), res2_keepdims) - np.testing.assert_array_almost_equal(fk(t2, keepdims=False), res2) - np.testing.assert_array_almost_equal( - fk(t2, keepdims=True), res2_keepdims) - - def test_evaluation_composed_linear_keepdims(self): - """Test parameter keepdims with composed evaluation""" - - # Default keepdims = False - f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=False) - - # Default keepdims = True - fk = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=True) - - t = np.array([1, 2, 3]) - t = [t, 9 - t] - res = np.array([[1., 4., 9.], [1., 4., 9.]]) - res_keepdims = res.reshape((2, 3, 1)) - - # Test combinations of keepdims with list - np.testing.assert_array_almost_equal( - f(t, aligned_evaluation=False), res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=False), res) - np.testing.assert_array_almost_equal(f(t, aligned_evaluation=False, - keepdims=True), res_keepdims) - - np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False), - res_keepdims) - np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False, - keepdims=False), res) - np.testing.assert_array_almost_equal(fk(t, aligned_evaluation=False, - keepdims=True), res_keepdims) - - def test_evaluation_grid_linear_keepdims(self): - """Test grid evaluation with keepdims""" - - # Default keepdims = False - f = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=False) - - # Default keepdims = True - fk = FDataGrid(self.data_matrix_1_1, sample_points=np.arange(10), - keepdims=True) - - t = [0.5, 1.5, 2.5] - res = np.array([[0.5, 2.5, 6.5], [72.5, 56.5, 42.5]]) - res_keepdims = res.reshape(2, 3, 1) - - np.testing.assert_array_almost_equal(f(t, grid=True), res) - np.testing.assert_array_almost_equal(f((t,), grid=True, keepdims=True), - res_keepdims) - np.testing.assert_array_almost_equal( - f([t], grid=True, keepdims=False), res) - - np.testing.assert_array_almost_equal(fk(t, grid=True), res_keepdims) - np.testing.assert_array_almost_equal(fk((t,), grid=True, - keepdims=True), - res_keepdims) - np.testing.assert_array_almost_equal( - fk([t], grid=True, keepdims=False), res) + np.array([[[16.], [36.]], [[16.], [36.]]])) def test_evaluation_cubic_simple(self): """Test basic usage of evaluation""" @@ -196,13 +100,14 @@ def test_evaluation_cubic_simple(self): interpolation=SplineInterpolation(3)) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10)).round(1), + np.testing.assert_array_almost_equal(f(np.arange(10)).round(1)[..., 0], self.data_matrix_1_1) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([0.5, 1.5, 2.5]).round(2), - np.array([[0.25, 2.25, 6.25], - [72.25, 56.25, 42.25]])) + np.testing.assert_array_almost_equal( + f([0.5, 1.5, 2.5]).round(2), + np.array([[[0.25], [2.25], [6.25]], + [[72.25], [56.25], [42.25]]])) def test_evaluation_cubic_point(self): """Test the evaluation of a single point""" @@ -212,12 +117,12 @@ def test_evaluation_cubic_point(self): # Test a single point np.testing.assert_array_almost_equal(f(5.3).round(3), - np.array([[28.09], [13.69]])) + np.array([[[28.09]], [[13.69]]])) np.testing.assert_array_almost_equal( - f([3]).round(3), np.array([[9.], [36.]])) + f([3]).round(3), np.array([[[9.]], [[36.]]])) np.testing.assert_array_almost_equal( - f((2,)).round(3), np.array([[4.], [49.]])) + f((2,)).round(3), np.array([[[4.]], [[49.]]])) def test_evaluation_cubic_grid(self): """Test grid evaluation. With domain dimension = 1""" @@ -226,7 +131,8 @@ def test_evaluation_cubic_grid(self): interpolation=SplineInterpolation(3)) t = [0.5, 1.5, 2.5] - res = np.array([[0.25, 2.25, 6.25], [72.25, 56.25, 42.25]]) + res = np.array([[[0.25], [2.25], [6.25]], + [[72.25], [56.25], [42.25]]]) # Test evaluation in a list of times np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) @@ -234,7 +140,7 @@ def test_evaluation_cubic_grid(self): np.testing.assert_array_almost_equal(f([t], grid=True).round(3), res) # Single point with grid np.testing.assert_array_almost_equal( - f(3, grid=True), np.array([[9.], [36.]])) + f(3, grid=True), np.array([[[9.]], [[36.]]])) # Check erroneous axis with np.testing.assert_raises(ValueError): @@ -248,19 +154,19 @@ def test_evaluation_cubic_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal( f([[1], [8]], aligned_evaluation=False).round(3), - np.array([[1.], [1.]])) + np.array([[[1.]], [[1.]]])) t = np.linspace(4, 6, 4) np.testing.assert_array_almost_equal( f([t, 9 - t], aligned_evaluation=False).round(2), - np.array([[16., 21.78, 28.44, 36.], - [16., 21.78, 28.44, 36.]])) + np.array([[[16.], [21.78], [28.44], [36.]], + [[16.], [21.78], [28.44], [36.]]])) # Same length than nsample t = np.linspace(4, 6, 2) np.testing.assert_array_almost_equal( f([t, 9 - t], aligned_evaluation=False).round(3), - np.array([[16., 36.], [16., 36.]])) + np.array([[[16.], [36.]], [[16.], [36.]]])) def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" @@ -272,8 +178,9 @@ def test_evaluation_nodes(self): interpolation=interpolation) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10)).round(5), - self.data_matrix_1_1) + np.testing.assert_array_almost_equal( + f(np.arange(10)).round(5)[..., 0], + self.data_matrix_1_1) def test_error_degree(self): @@ -379,23 +286,6 @@ def test_evaluation_composed(self): aligned_evaluation=False)[1], f(4)[1]) - def test_evaluation_keepdims(self): - """Test keepdims""" - - f = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolation=self.interpolation, keepdims=True) - - fk = FDataGrid(self.data_matrix_1_n, sample_points=np.arange(10), - interpolation=self.interpolation, keepdims=False) - - res = f(self.t) - # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(self.t, keepdims=False), res) - np.testing.assert_array_almost_equal(f(self.t, keepdims=True), res) - np.testing.assert_array_almost_equal(fk(self.t), res) - np.testing.assert_array_almost_equal(fk(self.t, keepdims=False), res) - np.testing.assert_array_almost_equal(fk(self.t, keepdims=True), res) - def test_evaluation_nodes(self): """Test interpolation in nodes for all dimensions""" diff --git a/tests/test_registration.py b/tests/test_registration.py index 1b1afd975..9f875df69 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -51,9 +51,11 @@ def test_standard_normalize_warping(self): np.testing.assert_array_almost_equal(normalized.sample_points[0], np.linspace(0, 1, 50)) - np.testing.assert_array_almost_equal(normalized(0), [[0.], [0.]]) + np.testing.assert_array_almost_equal( + normalized(0)[..., 0], [[0.], [0.]]) - np.testing.assert_array_almost_equal(normalized(1), [[1.], [1.]]) + np.testing.assert_array_almost_equal( + normalized(1)[..., 0], [[1.], [1.]]) def test_standard_normalize_warping_default_value(self): """Test normalization """ @@ -66,11 +68,13 @@ def test_standard_normalize_warping_default_value(self): np.testing.assert_array_almost_equal(normalized.sample_points[0], np.linspace(-1, 1, 50)) - np.testing.assert_array_almost_equal(normalized(-1), [[-1], [-1]]) + np.testing.assert_array_almost_equal( + normalized(-1)[..., 0], [[-1], [-1]]) - np.testing.assert_array_almost_equal(normalized(1), [[1.], [1.]]) + np.testing.assert_array_almost_equal( + normalized(1)[..., 0], [[1.], [1.]]) - def test_normalize_warpig(self): + def test_normalize_warping(self): """Test normalization to (a, b)""" a = -4 b = 3 @@ -83,9 +87,9 @@ def test_normalize_warpig(self): np.testing.assert_array_almost_equal(normalized.sample_points[0], np.linspace(*domain, 50)) - np.testing.assert_array_equal(normalized(a), [[a], [a]]) + np.testing.assert_array_equal(normalized(a)[..., 0], [[a], [a]]) - np.testing.assert_array_equal(normalized(b), [[b], [b]]) + np.testing.assert_array_equal(normalized(b)[..., 0], [[b], [b]]) def test_landmark_shift_deltas(self): @@ -104,12 +108,12 @@ def test_landmark_shift(self): original_modes = fd(landmarks.reshape((3, 1, 1)), aligned_evaluation=False) - # Test default location + # Test default location fd_registered = landmark_shift(fd, landmarks) center = (landmarks.max() + landmarks.min()) / 2 reg_modes = fd_registered(center) - # Test callable location + # Test callable location np.testing.assert_almost_equal(reg_modes, original_modes, decimal=2) fd_registered = landmark_shift(fd, landmarks, location=np.mean) @@ -125,7 +129,7 @@ def test_landmark_shift(self): np.testing.assert_almost_equal(reg_modes, original_modes, decimal=2) - # Test array location + # Test array location fd_registered = landmark_shift(fd, landmarks, location=[0, 0.1, 0.2]) reg_modes = fd_registered([[0], [.1], [.2]], aligned_evaluation=False) @@ -140,12 +144,14 @@ def test_landmark_registration_warping(self): # Default location warping = landmark_registration_warping(fd, landmarks) center = (landmarks.max(axis=0) + landmarks.min(axis=0)) / 2 - np.testing.assert_almost_equal(warping(center), landmarks, decimal=1) + np.testing.assert_almost_equal( + warping(center)[..., 0], landmarks, decimal=1) # Fixed location center = [.3, .6] warping = landmark_registration_warping(fd, landmarks, location=center) - np.testing.assert_almost_equal(warping(center), landmarks, decimal=3) + np.testing.assert_almost_equal( + warping(center)[..., 0], landmarks, decimal=3) def test_landmark_registration(self): fd = make_multimodal_samples(n_samples=3, n_modes=2, random_state=9) From 4cdaf6f64b728f096606f0bf1a92d7bbac939d10 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Jun 2020 01:09:38 +0200 Subject: [PATCH 566/624] Fix derivative error in plot. --- skfda/representation/basis/_basis.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 3f1d1f614..545cda40a 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -138,15 +138,13 @@ def _derivative_basis_and_coefs(self, coefs, order=1): "the construction of a basis of the " "derivatives.") - def plot(self, chart=None, *, derivative=0, **kwargs): + def plot(self, chart=None, **kwargs): """Plot the basis object or its derivatives. Args: chart (figure object, axe or list of axes, optional): figure over with the graphs are plotted or axis over where the graphs are plotted. - derivative (int or tuple, optional): Order of derivative to be - plotted. Defaults 0. **kwargs: keyword arguments to be passed to the fdata.plot function. @@ -154,7 +152,7 @@ def plot(self, chart=None, *, derivative=0, **kwargs): fig (figure): figure object in which the graphs are plotted. """ - self.to_basis().plot(chart=chart, derivative=derivative, **kwargs) + self.to_basis().plot(chart=chart, **kwargs) @abstractmethod def basis_of_product(self, other): From 8bb32e1d405b3a8b02d4b64adf0cda73d884d6a8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Jun 2020 18:27:34 +0200 Subject: [PATCH 567/624] Add pandas as a dependency. --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index b84b3ccff..6d2ca1f3f 100644 --- a/setup.py +++ b/setup.py @@ -82,6 +82,7 @@ install_requires=['numpy>=1.16', 'scipy>=1.3.0', 'scikit-learn>=0.20', + 'pandas', 'matplotlib', 'scikit-datasets[cran]>=0.1.24', 'rdata', From 284a8368bc863e46327fd8adc0836a54a792dfcc Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Jun 2020 19:43:38 +0200 Subject: [PATCH 568/624] Fix error in regression. --- skfda/representation/_functional_data.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index e3839b236..12d543c24 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -868,10 +868,6 @@ def to_numpy(self): return array - def __array__(self, dtype=None): - """Automatic conversion to numpy array""" - return self.to_numpy() - ##################################################################### # Pandas ExtensionArray methods ##################################################################### @@ -964,7 +960,7 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=0): # If the ExtensionArray is backed by an ndarray, then # just pass that here instead of coercing to object. - data = self.astype(object) + data = self.to_numpy() if allow_fill and fill_value is None: fill_value = self.dtype.na_value # fill value should always be translated from the scalar From 2c67ddd6ee5340f3f856a19f5355900691f8b434 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Jun 2020 20:14:32 +0200 Subject: [PATCH 569/624] Clip angle before passing it to `arccos`. --- skfda/misc/metrics.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 94a5e4f1c..390cb7965 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -587,6 +587,7 @@ def phase_distance(fdata1, fdata2, *, lam=0., eval_points=None, _check=True, derivative_warping = np.sqrt(derivative_warping, out=derivative_warping) d = scipy.integrate.simps(derivative_warping, x=eval_points_normalized) + d = np.clip(d, -1, 1) return np.arccos(d) From 0d73eee903390b2cf9327224d61a72416f4e3934 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Jun 2020 23:33:52 +0200 Subject: [PATCH 570/624] Fix `median_abs_deviation` warning. --- skfda/exploratory/depth/multivariate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/depth/multivariate.py b/skfda/exploratory/depth/multivariate.py index 2d12cc6d4..2fb9f6a2e 100644 --- a/skfda/exploratory/depth/multivariate.py +++ b/skfda/exploratory/depth/multivariate.py @@ -15,7 +15,7 @@ def _stagel_donoho_outlyingness(X, *, pointwise=False): m = X.data_matrix[..., 0] return (np.abs(m - np.median(m, axis=0)) / - scipy.stats.median_absolute_deviation(m, axis=0)) + scipy.stats.median_abs_deviation(m, axis=0, scale=1 / 1.4826)) else: raise NotImplementedError("Only implemented for one dimension") From 854df666b9badf803231ad6c9983cb6fbf412f40 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 23 Jun 2020 00:43:20 +0200 Subject: [PATCH 571/624] Fix numpy ragged array warning. --- skfda/_utils/_utils.py | 24 ++++++++++++++++-------- skfda/ml/regression/linear.py | 2 +- 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 271b7d0bc..571455f36 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -57,20 +57,28 @@ def _list_of_arrays(original_array): If the original list is two-dimensional (e.g. [[1, 2, 3], [4, 5]]), return a list containing other one-dimensional arrays (in this case - [array([1, 2, 3]), array([4, 5, 6])]). + [array([1, 2, 3]), array([4, 5])]). In any other case the behaviour is unespecified. """ - new_array = np.array([np.asarray(i) for i in - np.atleast_1d(original_array)]) - # Special case: Only one array, expand dimension - if len(new_array.shape) == 1 and not any(isinstance(s, np.ndarray) - for s in new_array): - new_array = np.atleast_2d(new_array) + unidimensional = False - return list(new_array) + try: + iter(original_array) + except TypeError: + original_array = [original_array] + + try: + iter(original_array[0]) + except TypeError: + unidimensional = True + + if unidimensional: + return [np.asarray(original_array)] + else: + return [np.asarray(i) for i in original_array] def _coordinate_list(axes): diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index d695d5796..2153d8685 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -202,7 +202,7 @@ def _inner_product_mixed(self, x, y): if inner_product is None: return y @ x else: - return inner_product(y) + return inner_product(y)[0] def _argcheck_X(self, X): if isinstance(X, FData) or isinstance(X, np.ndarray): From 1205672be4be40638c54ac13309f611eef1f7061 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Jun 2020 14:30:58 +0200 Subject: [PATCH 572/624] Keepdims removed. --- skfda/misc/metrics.py | 6 ++-- .../registration/_shift_registration.py | 11 ++++--- skfda/preprocessing/registration/_warping.py | 2 +- skfda/preprocessing/registration/elastic.py | 10 +++---- .../preprocessing/registration/validation.py | 7 ++--- skfda/representation/_functional_data.py | 29 ++++++------------- skfda/representation/basis/_fdatabasis.py | 9 +++--- skfda/representation/grid.py | 4 +-- 8 files changed, 31 insertions(+), 47 deletions(-) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 390cb7965..a177baa20 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -509,8 +509,7 @@ def amplitude_distance(fdata1, fdata2, *, lam=0., eval_points=None, if lam != 0.0: # L2 norm || sqrt(Dh) - 1 ||^2 warping_deriv = elastic_registration.warping_.derivative() - penalty = warping_deriv(eval_points_normalized, - keepdims=False)[0] + penalty = warping_deriv(eval_points_normalized)[0, ..., 0] penalty = np.sqrt(penalty, out=penalty) penalty -= 1 penalty = np.square(penalty, out=penalty) @@ -581,8 +580,7 @@ def phase_distance(fdata1, fdata2, *, lam=0., eval_points=None, _check=True, elastic_registration.fit_transform(fdata1) warping_deriv = elastic_registration.warping_.derivative() - derivative_warping = warping_deriv(eval_points_normalized, - keepdims=False)[0] + derivative_warping = warping_deriv(eval_points_normalized)[0, ..., 0] derivative_warping = np.sqrt(derivative_warping, out=derivative_warping) diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 7da35b7ae..9efb4cb4d 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -185,7 +185,7 @@ def _compute_deltas(self, fd, template): # Computes the derivate of originals curves in the mesh points fd_deriv = fd.derivative(order=1) - D1x = fd_deriv(output_points, keepdims=False) + D1x = fd_deriv(output_points)[..., 0] # Second term of the second derivate estimation of REGSSE. The # first term has been dropped to improve convergence (see references) @@ -197,7 +197,7 @@ def _compute_deltas(self, fd, template): # Case template fixed if isinstance(template, FData): original_template = template - tfine_aux = template.evaluate(output_points, keepdims=False)[0] + tfine_aux = template.evaluate(output_points)[0, ..., 0] if self.restrict_domain: template_points_aux = tfine_aux @@ -239,10 +239,9 @@ def _compute_deltas(self, fd, template): output_points_rep = np.outer(ones, output_points) # Computes the new values shifted - x = fd.evaluate(output_points_rep + np.atleast_2d(delta).T, - aligned_evaluation=False, - extrapolation=self.extrapolation, - keepdims=False) + x = fd(output_points_rep + np.atleast_2d(delta).T, + aligned_evaluation=False, + extrapolation=self.extrapolation)[..., 0] if template == "mean": x.mean(axis=0, out=tfine_aux) diff --git a/skfda/preprocessing/registration/_warping.py b/skfda/preprocessing/registration/_warping.py index a6d78c64f..90c5391ca 100644 --- a/skfda/preprocessing/registration/_warping.py +++ b/skfda/preprocessing/registration/_warping.py @@ -77,7 +77,7 @@ def invert_warping(fdatagrid, *, output_points=None): if output_points is None: output_points = fdatagrid.sample_points[0] - y = fdatagrid(output_points, keepdims=False) + y = fdatagrid(output_points)[..., 0] data_matrix = np.empty((fdatagrid.n_samples, len(output_points))) diff --git a/skfda/preprocessing/registration/elastic.py b/skfda/preprocessing/registration/elastic.py index 1bb6fec69..a073c2438 100644 --- a/skfda/preprocessing/registration/elastic.py +++ b/skfda/preprocessing/registration/elastic.py @@ -161,7 +161,7 @@ def transform(self, X: FDataGrid, y=None): g = X.derivative() # Evaluation with the corresponding interpolation - data_matrix = g(output_points, keepdims=False) + data_matrix = g(output_points)[..., 0] # SRSF(f) = sign(f) * sqrt|Df| (avoiding multiple allocation) sign_g = np.sign(data_matrix) @@ -222,7 +222,7 @@ def inverse_transform(self, X: FDataGrid, y=None): else: output_points = self.output_points - data_matrix = X(output_points, keepdims=True) + data_matrix = X(output_points) data_matrix *= np.abs(data_matrix) @@ -432,8 +432,8 @@ def transform(self, X: FDataGrid, y=None): output_points = self.output_points # Discretizacion in evaluation points - q_data = fdatagrid_srsf(output_points, keepdims=False) - template_data = self._template_srsf(output_points, keepdims=False) + q_data = fdatagrid_srsf(output_points)[..., 0] + template_data = self._template_srsf(output_points)[..., 0] if q_data.shape[0] == 1: q_data = q_data[0] @@ -706,7 +706,7 @@ def elastic_mean(fdatagrid, *, penalty=0., center=True, max_iter=20, tol=1e-3, fdatagrid_normalized = FDataGrid(fdatagrid(eval_points) / y_scale, sample_points=eval_points_normalized) - srsf = fdatagrid_srsf(eval_points, keepdims=False) + srsf = fdatagrid_srsf(eval_points)[..., 0] # Initialize with function closest to the L2 mean with the L2 distance centered = (srsf.T - srsf.mean(axis=0, keepdims=True).T).T diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 6d1d368bf..38870cdaa 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -293,8 +293,8 @@ def score_function(self, X, y, *, warping=None): else: eval_points = np.asarray(self.eval_points) - x_fine = X.evaluate(eval_points, keepdims=False) - y_fine = y.evaluate(eval_points, keepdims=False) + x_fine = X.evaluate(eval_points)[..., 0] + y_fine = y.evaluate(eval_points)[..., 0] mu_fine = x_fine.mean(axis=0) # Mean unregistered function eta_fine = y_fine.mean(axis=0) # Mean registered function mu_fine_sq = np.square(mu_fine) @@ -312,8 +312,7 @@ def score_function(self, X, y, *, warping=None): if warping is not None: # Derivates warping functions warping_deriv = warping.derivative() - dh_fine = warping_deriv(eval_points, - keepdims=False) + dh_fine = warping_deriv(eval_points)[..., 0] dh_fine_mean = dh_fine.mean(axis=0) dh_fine_center = dh_fine - dh_fine_mean diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index e0b3c3a9e..149504fa8 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -197,7 +197,7 @@ def _extrapolation_index(self, eval_points): return index def _evaluate_grid(self, axes, *, extrapolation=None, - aligned_evaluation=True, keepdims=True): + aligned_evaluation=True): """Evaluate the functional object in the cartesian grid. This method is called internally by :meth:`evaluate` when the argument @@ -257,7 +257,7 @@ def _evaluate_grid(self, axes, *, extrapolation=None, eval_points = _coordinate_list(axes) res = self.evaluate(eval_points, - extrapolation=extrapolation, keepdims=True) + extrapolation=extrapolation) elif self.dim_domain == 1: @@ -265,7 +265,6 @@ def _evaluate_grid(self, axes, *, extrapolation=None, return self.evaluate(eval_points, extrapolation=extrapolation, - keepdims=keepdims, aligned_evaluation=False) else: @@ -286,12 +285,9 @@ def _evaluate_grid(self, axes, *, extrapolation=None, res = self.evaluate(eval_points, extrapolation=extrapolation, - keepdims=True, aligned_evaluation=False) + aligned_evaluation=False) - shape = [self.n_samples] + lengths - - if self.dim_codomain != 1 or keepdims: - shape += [self.dim_codomain] + shape = [self.n_samples] + lengths + [self.dim_codomain] # Roll the list of result in a list return res.reshape(shape) @@ -379,7 +375,7 @@ def _evaluate_composed(self, eval_points): pass def evaluate(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True, keepdims=True): + grid=False, aligned_evaluation=True): """Evaluate the object or its derivatives at a list of values or a grid. @@ -417,8 +413,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points, extrapolation=extrapolation, grid=grid, - aligned_evaluation=aligned_evaluation, - keepdims=keepdims) + aligned_evaluation=aligned_evaluation) if extrapolation is None: extrapolation = self.extrapolation @@ -429,8 +424,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, if grid: # Evaluation of a grid performed in auxiliar function return self._evaluate_grid(eval_points, extrapolation=extrapolation, - aligned_evaluation=aligned_evaluation, - keepdims=keepdims) + aligned_evaluation=aligned_evaluation) # Convert to array and check dimensions of eval points eval_points = self._reshape_eval_points(eval_points, @@ -485,14 +479,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, res = self._join_evaluation(index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation) - # Delete last axis if not keepdims and - if self.dim_codomain == 1 and not keepdims: - res = res.reshape(res.shape[:-1]) - return res def __call__(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True, keepdims=True): + grid=False, aligned_evaluation=True): """Evaluate the object or its derivatives at a list of values or a grid. This method is a wrapper of :meth:`evaluate`. @@ -526,8 +516,7 @@ def __call__(self, eval_points, *, derivative=0, extrapolation=None, """ return self.evaluate(eval_points, derivative=derivative, extrapolation=extrapolation, grid=grid, - aligned_evaluation=aligned_evaluation, - keepdims=keepdims) + aligned_evaluation=aligned_evaluation) @abstractmethod def derivative(self, order=1): diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 4f3245b0c..a290c8e61 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -363,10 +363,9 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, points_shifted += np.atleast_2d(shifts).T # Matrix of shifted values - _data_matrix = self.evaluate(points_shifted, - aligned_evaluation=False, - extrapolation=extrapolation, - keepdims=False) + _data_matrix = self(points_shifted, + aligned_evaluation=False, + extrapolation=extrapolation)[..., 0] _basis = self.basis.rescale(domain) @@ -526,7 +525,7 @@ def to_grid(self, eval_points=None): constants.BASIS_MIN_FACTOR * self.n_basis) eval_points = np.linspace(*self.domain_range[0], npoints) - return grid.FDataGrid(self.evaluate(eval_points, keepdims=False), + return grid.FDataGrid(self.evaluate(eval_points), sample_points=eval_points, domain_range=self.domain_range) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 922f86e83..6d5d96301 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -1000,14 +1000,14 @@ def compose(self, fd, *, eval_points=None): eval_points = np.linspace(*fd.domain_range[0], constants.N_POINTS_COARSE_MESH) - eval_points_transformation = fd(eval_points, keepdims=False) + eval_points_transformation = fd(eval_points)[..., 0] data_matrix = self(eval_points_transformation, aligned_evaluation=False) else: if eval_points is None: eval_points = fd.sample_points - grid_transformation = fd(eval_points, grid=True, keepdims=True) + grid_transformation = fd(eval_points, grid=True) lengths = [len(ax) for ax in eval_points] From e68127cf644f5ee7472b218f0fabedc75a4dc38e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Jun 2020 18:06:49 +0200 Subject: [PATCH 573/624] Fix examples. --- examples/plot_composition.py | 2 +- examples/plot_extrapolation.py | 13 +++++++------ skfda/representation/grid.py | 9 +++------ tests/test_grid.py | 28 +++++++++++++++++++++++++--- 4 files changed, 36 insertions(+), 16 deletions(-) diff --git a/examples/plot_composition.py b/examples/plot_composition.py index 9fdac453f..ff9b33566 100644 --- a/examples/plot_composition.py +++ b/examples/plot_composition.py @@ -78,7 +78,7 @@ # Plots path along the surface path = f(t)[0] -fig.axes[0].plot(path[:, 0], path[:, 1], gof(t)[0], color="orange") +fig.axes[0].plot(path[:, 0], path[:, 1], gof(t)[0, ..., 0], color="orange") fig diff --git a/examples/plot_extrapolation.py b/examples/plot_extrapolation.py index 1bde81622..75a4ba18c 100644 --- a/examples/plot_extrapolation.py +++ b/examples/plot_extrapolation.py @@ -10,11 +10,12 @@ # sphinx_gallery_thumbnail_number = 2 +import skfda + import mpl_toolkits.mplot3d import matplotlib.pyplot as plt import numpy as np -import skfda ############################################################################## @@ -124,7 +125,7 @@ # Evaluation of the grid # Extrapolation supplied in the evaluation -values = fdgrid(t, extrapolation="periodic") +values = fdgrid(t, extrapolation="periodic")[..., 0] plt.plot(t, values.T, linestyle='--') @@ -146,7 +147,7 @@ fdgrid.extrapolation = "bounds" # Evaluation of the grid -values = fdgrid(t) +values = fdgrid(t)[..., 0] plt.plot(t, values.T, linestyle='--') plt.gca().set_prop_cycle(None) # Reset color cycle @@ -218,7 +219,7 @@ T, S = np.meshgrid(t, t) -ax.plot_wireframe(T, S, values[0], alpha=.3, color="C0") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") ############################################################################### @@ -231,7 +232,7 @@ fig = plt.figure() ax = fig.add_subplot(111, projection='3d') -ax.plot_wireframe(T, S, values[0], alpha=.3, color="C0") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") ############################################################################### @@ -243,5 +244,5 @@ fig = plt.figure() ax = fig.add_subplot(111, projection='3d') -ax.plot_wireframe(T, S, values[0], alpha=.3, color="C0") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 6d5d96301..ee8555671 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -1000,7 +1000,7 @@ def compose(self, fd, *, eval_points=None): eval_points = np.linspace(*fd.domain_range[0], constants.N_POINTS_COARSE_MESH) - eval_points_transformation = fd(eval_points)[..., 0] + eval_points_transformation = fd(eval_points) data_matrix = self(eval_points_transformation, aligned_evaluation=False) else: @@ -1020,11 +1020,8 @@ def compose(self, fd, *, eval_points=None): list(map(np.ravel, grid_transformation[i].T)) ).T - data_flatten = self(eval_points_transformation, - aligned_evaluation=False) - - data_matrix = data_flatten.reshape((self.n_samples, *lengths, - self.dim_codomain)) + data_matrix = self(eval_points_transformation, + aligned_evaluation=False) return self.copy(data_matrix=data_matrix, sample_points=eval_points, diff --git a/tests/test_grid.py b/tests/test_grid.py index 4213b1451..cfe76fb60 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -1,10 +1,11 @@ +from skfda import FDataGrid, concatenate +from skfda.exploratory import stats import unittest +from mpl_toolkits.mplot3d import axes3d import scipy.stats.mstats import numpy as np -from skfda import FDataGrid, concatenate -from skfda.exploratory import stats class TestFDataGrid(unittest.TestCase): @@ -99,7 +100,7 @@ def test_concatenate_coordinates(self): fd = fd1.concatenate(fd2, as_coordinates=True) np.testing.assert_equal(None, fd.axes_labels) - def test_concatenate(self): + def test_concatenate2(self): sample1 = np.arange(0, 10) sample2 = np.arange(10, 20) fd1 = FDataGrid([sample1]) @@ -174,6 +175,27 @@ def test_add(self): np.testing.assert_array_equal(fd2.data_matrix[..., 0], [[2, 4, 6, 8], [4, 6, 8, 10]]) + def test_composition(self): + X, Y, Z = axes3d.get_test_data(1.2) + + data_matrix = [Z.T] + sample_points = [X[0, :], Y[:, 0]] + + g = FDataGrid(data_matrix, sample_points) + self.assertEqual(g.dim_domain, 2) + self.assertEqual(g.dim_codomain, 1) + + t = np.linspace(0, 2 * np.pi, 100) + + data_matrix = [10 * np.array([np.cos(t), np.sin(t)]).T] + f = FDataGrid(data_matrix, t) + self.assertEqual(f.dim_domain, 1) + self.assertEqual(f.dim_codomain, 2) + + gof = g.compose(f) + self.assertEqual(gof.dim_domain, 1) + self.assertEqual(gof.dim_codomain, 1) + if __name__ == '__main__': print() From 8bb9f94ac6f7a8050534f91e1715a6a2fdb49ef5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Jun 2020 18:18:03 +0200 Subject: [PATCH 574/624] Remove keepdims from docs. --- skfda/representation/_functional_data.py | 15 --------------- skfda/representation/grid.py | 1 - 2 files changed, 16 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 149504fa8..f8dd24e84 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -229,11 +229,6 @@ def _evaluate_grid(self, axes, *, extrapolation=None, object. aligned_evaluation (bool, optional): If False evaluates each sample in a different grid. - keepdims (bool, optional): If the image dimension is equal to 1 and - keepdims is True the return matrix has shape - n_samples x eval_points x 1 else n_samples x eval_points. - By default is used the value given during the instance of the - object. Returns: (numpy.darray): Numpy array with dim_domain + 1 dimensions with @@ -395,11 +390,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, return matrix has shape n_samples x len(t1) x len(t2) x ... x len(t_dim_domain) x dim_codomain. If the domain dimension is 1 the parameter has no efect. Defaults to False. - keepdims (bool, optional): If the image dimension is equal to 1 and - keepdims is True the return matrix has shape - n_samples x eval_points x 1 else n_samples x eval_points. - By default is used the value given during the instance of the - object. Returns: (np.darray): Matrix whose rows are the values of the each @@ -503,11 +493,6 @@ def __call__(self, eval_points, *, derivative=0, extrapolation=None, return matrix has shape n_samples x len(t1) x len(t2) x ... x len(t_dim_domain) x dim_codomain. If the domain dimension is 1 the parameter has no efect. Defaults to False. - keepdims (bool, optional): If the image dimension is equal to 1 and - keepdims is True the return matrix has shape - n_samples x eval_points x 1 else n_samples x eval_points. - By default is used the value given during the instance of the - object. Returns: (np.ndarray): Matrix whose rows are the values of the each diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index ee8555671..a797f9baa 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -49,7 +49,6 @@ class FDataGrid(FData): types of extrapolation. interpolation (GridInterpolation): Defines the type of interpolation applied in `evaluate`. - keepdims (bool): Examples: Representation of a functional data object with 2 samples From 69dc75fcc469563c310facea448ea7c9910a4ed8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 18:44:04 +0200 Subject: [PATCH 575/624] Doc covariance functions. --- docs/conf.py | 2 +- docs/modules/misc/covariances.rst | 7 +- readthedocs-requirements.txt | 3 +- skfda/exploratory/visualization/_utils.py | 18 +- skfda/misc/covariances.py | 237 ++++++++++++++++------ 5 files changed, 203 insertions(+), 64 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index cb5e78d5e..3bc579b5f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -53,7 +53,7 @@ 'sphinx_gallery.gen_gallery', 'sphinx.ext.intersphinx', 'sphinx.ext.doctest', - 'matplotlib.sphinxext.plot_directive'] + 'jupyter_sphinx'] autodoc_default_flags = ['members', 'inherited-members'] diff --git a/docs/modules/misc/covariances.rst b/docs/modules/misc/covariances.rst index 52bef3925..f27137ef8 100644 --- a/docs/modules/misc/covariances.rst +++ b/docs/modules/misc/covariances.rst @@ -8,9 +8,10 @@ processes. These functions can be used as covariances in .. autosummary:: :toctree: autosummary - skfda.misc.covariances.Covariance skfda.misc.covariances.Brownian + skfda.misc.covariances.Covariance + skfda.misc.covariances.Exponential + skfda.misc.covariances.Gaussian skfda.misc.covariances.Linear skfda.misc.covariances.Polynomial - skfda.misc.covariances.Gaussian - skfda.misc.covariances.Exponential \ No newline at end of file + skfda.misc.covariances.WhiteNoise \ No newline at end of file diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 635d1b868..3562a73ad 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -11,4 +11,5 @@ matplotlib mpldatacursor setuptools>=41.2 multimethod>=1.2 -findiff \ No newline at end of file +findiff +jupyter-sphinx \ No newline at end of file diff --git a/skfda/exploratory/visualization/_utils.py b/skfda/exploratory/visualization/_utils.py index 9fd0d6198..e11189e5a 100644 --- a/skfda/exploratory/visualization/_utils.py +++ b/skfda/exploratory/visualization/_utils.py @@ -1,5 +1,6 @@ import io import math +import re import matplotlib.axes import matplotlib.backends.backend_svg @@ -7,6 +8,14 @@ import matplotlib.pyplot as plt +non_close_text = '[^>]*?' +svg_width_regex = re.compile( + f'()') +svg_width_replacement = r'\g<1>100%\g<2>' +svg_height_regex = re.compile( + f'()') +svg_height_replacement = r'\g<1>\g<2>' + def _create_figure(): """Create figure using the default backend.""" @@ -24,7 +33,14 @@ def _figure_to_svg(figure): figure.savefig(output, format='svg') figure.set_canvas(old_canvas) data = output.getvalue() - return data.decode('utf-8') + decoded_data = data.decode('utf-8') + + new_data = svg_width_regex.sub( + svg_width_replacement, decoded_data, count=1) + new_data = svg_height_regex.sub( + svg_height_replacement, new_data, count=1) + + return new_data def _get_figure_and_axes(chart=None, fig=None, axes=None): diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index 806f6897f..1ba97f2c2 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -1,7 +1,7 @@ import abc import numbers -import matplotlib +import matplotlib.pyplot as plt import numpy as np import sklearn.gaussian_process.kernels as sklearn_kern @@ -76,7 +76,8 @@ def heatmap(self, limits=(-1, 1)): def _sample_trajectories_plot(self): from ..datasets import make_gaussian_process - fd = make_gaussian_process(start=-1, cov=self) + fd = make_gaussian_process( + start=-1, n_samples=10, cov=self, random_state=0) fig = fd.plot() fig.axes[0].set_title("Sample trajectories") return fig @@ -106,27 +107,33 @@ def _repr_latex_(self): def _repr_html_(self): fig = self.heatmap() heatmap = _figure_to_svg(fig) + plt.close(fig) fig = self._sample_trajectories_plot() sample_trajectories = _figure_to_svg(fig) + plt.close(fig) - row_style = 'style="position:relative; display:table-row"' + row_style = '' - def column_style(percent): - return (f'style="width: {percent}%; display: table-cell; ' + def column_style(percent, margin_top=0): + return (f'style="display: inline-block; ' + f'margin:0; ' + f'margin-top: {margin_top}; ' + f'width:{percent}%; ' + f'height:auto;' f'vertical-align: middle"') html = f"""
-
+
\\[{self._latex_content()}\\]
-
+
{sample_trajectories}
-
+
{heatmap}
@@ -147,8 +154,8 @@ class Brownian(Covariance): The covariance function is .. math:: - K(x, y) = \sigma^2 \frac{|x - \mathcal{O}| + |y - \mathcal{O}| - - |x-y|}{2} + K(x, x') = \sigma^2 \frac{|x - \mathcal{O}| + |x' - \mathcal{O}| + - |x - x'|}{2} where :math:`\sigma^2` is the variance at distance 1 from :math:`\mathcal{O}` and :math:`\mathcal{O}` is the origin point. @@ -160,24 +167,38 @@ class Brownian(Covariance): Heatmap plot of the covariance function: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Brownian + from skfda.misc.covariances import Brownian + import matplotlib.pyplot as plt - Brownian().heatmap(limits=(0, 1)) + Brownian().heatmap(limits=(0, 1)) + plt.show() Example of Gaussian process trajectories using this covariance: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Brownian - from skfda.datasets import make_gaussian_process + from skfda.misc.covariances import Brownian + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt - make_gaussian_process(n_samples=10, cov=Brownian()).plot() + gp = make_gaussian_process( + n_samples=10, cov=Brownian(), random_state=0) + gp.plot() + plt.show() + + Default representation in a Jupyter notebook: + + .. jupyter-execute:: + + from skfda.misc.covariances import Brownian + + Brownian() """ - _latex_formula = (r"K(x, y) = \sigma^2 \frac{|x - \mathcal{O}| + " - r"|y - \mathcal{O}| - |x-y|}{2}") + _latex_formula = (r"K(x, x') = \sigma^2 \frac{|x - \mathcal{O}| + " + r"|x' - \mathcal{O}| - |x - x'|}{2}") _parameters = [("variance", r"\sigma^2"), ("origin", r"\mathcal{O}")] @@ -200,30 +221,44 @@ class Linear(Covariance): The covariance function is .. math:: - K(x, y) = \sigma^2 (x^T y + c) + K(x, x') = \sigma^2 (x^T x' + c) where :math:`\sigma^2` is the scale of the variance and :math:`c` is the intercept. Heatmap plot of the covariance function: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Linear + from skfda.misc.covariances import Linear + import matplotlib.pyplot as plt - Linear().heatmap(limits=(0, 1)) + Linear().heatmap(limits=(0, 1)) + plt.show() Example of Gaussian process trajectories using this covariance: - .. plot:: + .. jupyter-execute:: + + from skfda.misc.covariances import Linear + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt + + gp = make_gaussian_process( + n_samples=10, cov=Linear(), random_state=0) + gp.plot() + plt.show() - from skfda.misc.covariances import Linear - from skfda.datasets import make_gaussian_process + Default representation in a Jupyter notebook: - make_gaussian_process(n_samples=10, cov=Linear()).plot() + .. jupyter-execute:: + + from skfda.misc.covariances import Linear + + Linear() """ - _latex_formula = r"K(x, y) = \sigma^2 (x^T y + c)" + _latex_formula = r"K(x, x') = \sigma^2 (x^T x' + c)" _parameters = [("variance", r"\sigma^2"), ("intercept", r"c")] @@ -251,7 +286,7 @@ class Polynomial(Covariance): The covariance function is .. math:: - K(x, y) = \sigma^2 (\alpha x^T y + c)^d + K(x, x') = \sigma^2 (\alpha x^T x' + c)^d where :math:`\sigma^2` is the scale of the variance, :math:`\alpha` is the slope, :math:`d` the degree of the @@ -259,23 +294,37 @@ class Polynomial(Covariance): Heatmap plot of the covariance function: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Polynomial + from skfda.misc.covariances import Polynomial + import matplotlib.pyplot as plt - Polynomial().heatmap(limits=(0, 1)) + Polynomial().heatmap(limits=(0, 1)) + plt.show() Example of Gaussian process trajectories using this covariance: - .. plot:: + .. jupyter-execute:: + + from skfda.misc.covariances import Polynomial + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt + + gp = make_gaussian_process( + n_samples=10, cov=Polynomial(), random_state=0) + gp.plot() + plt.show() + + Default representation in a Jupyter notebook: - from skfda.misc.covariances import Polynomial - from skfda.datasets import make_gaussian_process + .. jupyter-execute:: - make_gaussian_process(n_samples=10, cov=Polynomial()).plot() + from skfda.misc.covariances import Polynomial + + Polynomial() """ - _latex_formula = r"K(x, y) = \sigma^2 (\alpha x^T y + c)^d" + _latex_formula = r"K(x, x') = \sigma^2 (\alpha x^T x' + c)^d" _parameters = [("variance", r"\sigma^2"), ("intercept", r"c"), @@ -311,29 +360,43 @@ class Gaussian(Covariance): The covariance function is .. math:: - K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||^2}{2l^2}\right) + K(x, x') = \sigma^2 \exp\left(-\frac{||x - x'||^2}{2l^2}\right) where :math:`\sigma^2` is the variance and :math:`l` is the length scale. Heatmap plot of the covariance function: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Gaussian + from skfda.misc.covariances import Gaussian + import matplotlib.pyplot as plt - Gaussian().heatmap(limits=(0, 1)) + Gaussian().heatmap(limits=(0, 1)) + plt.show() Example of Gaussian process trajectories using this covariance: - .. plot:: + .. jupyter-execute:: + + from skfda.misc.covariances import Gaussian + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt + + gp = make_gaussian_process( + n_samples=10, cov=Gaussian(), random_state=0) + gp.plot() + plt.show() - from skfda.misc.covariances import Gaussian - from skfda.datasets import make_gaussian_process + Default representation in a Jupyter notebook: - make_gaussian_process(n_samples=10, cov=Gaussian()).plot() + .. jupyter-execute:: + + from skfda.misc.covariances import Gaussian + + Gaussian() """ - _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{\|x - y\|^2}{2l^2}" + _latex_formula = (r"K(x, x') = \sigma^2 \exp\left(-\frac{\|x - x'\|^2}{2l^2}" r"\right)") _parameters = [("variance", r"\sigma^2"), @@ -364,29 +427,43 @@ class Exponential(Covariance): The covariance function is .. math:: - K(x, y) = \sigma^2 \exp\left(-\frac{\|x - y\|}{l}\right) + K(x, x') = \sigma^2 \exp\left(-\frac{\|x - x'\|}{l}\right) where :math:`\sigma^2` is the variance and :math:`l` is the length scale. Heatmap plot of the covariance function: - .. plot:: + .. jupyter-execute:: - from skfda.misc.covariances import Exponential + from skfda.misc.covariances import Exponential + import matplotlib.pyplot as plt - Exponential().heatmap(limits=(0, 1)) + Exponential().heatmap(limits=(0, 1)) + plt.show() Example of Gaussian process trajectories using this covariance: - .. plot:: + .. jupyter-execute:: + + from skfda.misc.covariances import Exponential + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt - from skfda.misc.covariances import Exponential - from skfda.datasets import make_gaussian_process + gp = make_gaussian_process( + n_samples=10, cov=Exponential(), random_state=0) + gp.plot() + plt.show() - make_gaussian_process(n_samples=10, cov=Exponential()).plot() + Default representation in a Jupyter notebook: + + .. jupyter-execute:: + + from skfda.misc.covariances import Exponential + + Exponential() """ - _latex_formula = (r"K(x, y) = \sigma^2 \exp\left(-\frac{||x - y||}{l}" + _latex_formula = (r"K(x, x') = \sigma^2 \exp\left(-\frac{||x - x'||}{l}" r"\right)") _parameters = [("variance", r"\sigma^2"), @@ -410,10 +487,54 @@ def to_sklearn(self): class WhiteNoise(Covariance): - """Gaussian covariance function.""" + r""" + Gaussian covariance function. + + The covariance function is + + .. math:: + K(x, x')= \begin{cases} + \sigma^2, \quad x = x' \\ + 0, \quad x \neq x'\\ + \end{cases} + + where :math:`\sigma^2` is the variance. + + Heatmap plot of the covariance function: + + .. jupyter-execute:: + + from skfda.misc.covariances import WhiteNoise + import matplotlib.pyplot as plt + + WhiteNoise().heatmap(limits=(0, 1)) + plt.show() + + Example of Gaussian process trajectories using this covariance: + + .. jupyter-execute:: + + from skfda.misc.covariances import WhiteNoise + from skfda.datasets import make_gaussian_process + import matplotlib.pyplot as plt + + gp = make_gaussian_process( + n_samples=10, cov=WhiteNoise(), random_state=0) + gp.plot() + plt.show() + + Default representation in a Jupyter notebook: + + .. jupyter-execute:: + + from skfda.misc.covariances import WhiteNoise + + WhiteNoise() + + """ - _latex_formula = (r"K(x,y)= \left\{ \begin{array}{lc} \sigma^2, & x = y " - r"\\ 0, & x \neq y\\ \end{array} \right.") + _latex_formula = (r"K(x, x')= \begin{cases} \sigma^2, \quad x = x' \\" + r"0, \quad x \neq x'\\ \end{cases}") _parameters = [("variance", r"\sigma^2")] From 9bb35824c5385928d9784b75a853fa2bedda9a57 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 19:48:26 +0200 Subject: [PATCH 576/624] Update RTD config. --- readthedocs.yml | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/readthedocs.yml b/readthedocs.yml index 232af1b0b..78fd45c8c 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -1,6 +1,26 @@ -build: - image: latest +# .readthedocs.yml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details -python: - version: 3.6 +# Required +version: 2 + +# Build documentation in the docs/ directory with Sphinx +sphinx: + builder: html + configuration: docs/conf.py + +# Build documentation with MkDocs +#mkdocs: +# configuration: mkdocs.yml +# Optionally build your docs in additional formats such as PDF +formats: + - pdf + +# Optionally set the version of Python and requirements required to build your docs +python: + version: 3.7 + install: + - requirements: readthedocs-requirements.txt + - method: pip \ No newline at end of file From 4d8ce09a8fe3cc42bc35d2ef59ea3714665357a8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 19:51:14 +0200 Subject: [PATCH 577/624] Add path key. --- readthedocs.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/readthedocs.yml b/readthedocs.yml index 78fd45c8c..30ac3b0de 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -23,4 +23,5 @@ python: version: 3.7 install: - requirements: readthedocs-requirements.txt - - method: pip \ No newline at end of file + - method: pip + path: . \ No newline at end of file From 5b9054ef51d2937a89f1673021e65fcfc92bedbd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 19:57:35 +0200 Subject: [PATCH 578/624] Disable pdf output. --- readthedocs.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/readthedocs.yml b/readthedocs.yml index 30ac3b0de..26b19d25f 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -16,7 +16,6 @@ sphinx: # Optionally build your docs in additional formats such as PDF formats: - - pdf # Optionally set the version of Python and requirements required to build your docs python: From 47b64bbed5ade45dc8aa5d4ce35b6eef0bf163ab Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 19:58:24 +0200 Subject: [PATCH 579/624] Fix empty list. --- readthedocs.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/readthedocs.yml b/readthedocs.yml index 26b19d25f..08775369f 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -15,7 +15,6 @@ sphinx: # configuration: mkdocs.yml # Optionally build your docs in additional formats such as PDF -formats: # Optionally set the version of Python and requirements required to build your docs python: From 3f2ea0e8c19f3137e824f4c780b9e9547865721d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 20:56:32 +0200 Subject: [PATCH 580/624] Improve functional boxplots. --- docs/conf.py | 44 ++++++++++++++ skfda/exploratory/visualization/_boxplot.py | 64 ++++++++++++++++++++- 2 files changed, 106 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 3bc579b5f..a5b3f2d07 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -237,3 +237,47 @@ } autosummary_generate = True + + +# Napoleon fix for attributes +# Taken from +# https://michaelgoerz.net/notes/extending-sphinx-napoleon-docstring-sections.html + +# -- Extensions to the Napoleon GoogleDocstring class --------------------- +from sphinx.ext.napoleon.docstring import GoogleDocstring + +# first, we define new methods for any new sections and add them to the class + + +def parse_keys_section(self, section): + return self._format_fields('Keys', self._consume_fields()) + + +GoogleDocstring._parse_keys_section = parse_keys_section + + +def parse_attributes_section(self, section): + return self._format_fields('Attributes', self._consume_fields()) + + +GoogleDocstring._parse_attributes_section = parse_attributes_section + + +def parse_class_attributes_section(self, section): + return self._format_fields('Class Attributes', self._consume_fields()) + + +GoogleDocstring._parse_class_attributes_section = parse_class_attributes_section + +# we now patch the parse method to guarantee that the the above methods are +# assigned to the _section dict + + +def patched_parse(self): + self._sections['keys'] = self._parse_keys_section + self._sections['class attributes'] = self._parse_class_attributes_section + self._unpatched_parse() + + +GoogleDocstring._unpatched_parse = GoogleDocstring._parse +GoogleDocstring._parse = patched_parse diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index b81b3c8a8..2662ed8a8 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -78,6 +78,8 @@ def plot(self, chart=None, *, fig=None, axes=None, def _repr_svg_(self): fig = self.plot() + plt.close(fig) + return _figure_to_svg(fig) @@ -96,7 +98,21 @@ class Boxplot(FDataBoxplot): detected in a functional boxplot by the 1.5 times the 50% central region empirical rule, analogous to the rule for classical boxplots. + Args: + + fdatagrid (FDataGrid): Object containing the data. + depth_method (:ref:`depth measure `, optional): + Method used to order the data. Defaults to :func:`modified + band depth + `. + prob (list of float, optional): List with float numbers (in the + range from 1 to 0) that indicate which central regions to + represent. + Defaults to [0.5] which represents the 50% central region. + factor (double): Number used to calculate the outlying envelope. + Attributes: + fdatagrid (FDataGrid): Object containing the data. median (array, (fdatagrid.dim_codomain, nsample_points)): contains the median/s. @@ -118,10 +134,27 @@ class Boxplot(FDataBoxplot): outside the box is plotted. If True, complete outling curves are plotted. - Example: + Representation in a Jupyter notebook: + + .. jupyter-execute:: + + from skfda.datasets import make_gaussian_process + from skfda.misc.covariances import Exponential + from skfda.exploratory.visualization import Boxplot + + fd = make_gaussian_process( + n_samples=20, cov=Exponential(), random_state=3) + + Boxplot(fd) + + + Examples: + Function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. >>> from skfda import FDataGrid + >>> from skfda.exploratory.visualization import Boxplot + >>> >>> data_matrix = [[1, 1, 2, 3, 2.5, 2], ... [0.5, 0.5, 1, 2, 1.5, 1], ... [-1, -1, -0.5, 1, 1, 0.5], @@ -202,6 +235,13 @@ class Boxplot(FDataBoxplot): [ 1. ]]))], outliers=array([ True, False, False, True])) + References: + + Sun, Y., & Genton, M. G. (2011). Functional Boxplots. Journal of + Computational and Graphical Statistics, 20(2), 316-334. + https://doi.org/10.1198/jcgs.2011.09224 + + """ def __init__(self, fdatagrid, depth_method=modified_band_depth, prob=[0.5], @@ -419,7 +459,20 @@ class SurfaceBoxplot(FDataBoxplot): 50% central region, the median curve, and the maximum non-outlying envelope. + Args: + + fdatagrid (FDataGrid): Object containing the data. + method (:ref:`depth measure `, optional): Method + used to order the data. Defaults to :func:`modified band depth + `. + prob (list of float, optional): List with float numbers (in the + range from 1 to 0) that indicate which central regions to + represent. + Defaults to [0.5] which represents the 50% central region. + factor (double): Number used to calculate the outlying envelope. + Attributes: + fdatagrid (FDataGrid): Object containing the data. median (array, (fdatagrid.dim_codomain, lx, ly)): contains the median/s. @@ -433,7 +486,8 @@ class SurfaceBoxplot(FDataBoxplot): envelope. outcol (string): Color of the outlying envelope. - Example: + Examples: + Function :math:`f : \mathbb{R^2}\longmapsto\mathbb{R}`. >>> from skfda import FDataGrid @@ -497,6 +551,12 @@ class SurfaceBoxplot(FDataBoxplot): [ 0.4], [ 5. ]]]))) + References: + + Sun, Y., & Genton, M. G. (2011). Functional Boxplots. Journal of + Computational and Graphical Statistics, 20(2), 316-334. + https://doi.org/10.1198/jcgs.2011.09224 + """ def __init__(self, fdatagrid, method=modified_band_depth, factor=1.5): From 3efc48bd5d9716065907603d0951b7676185371b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 27 Jun 2020 21:11:45 +0200 Subject: [PATCH 581/624] Magnitude shape plot. --- conftest.py | 2 +- .../visualization/_magnitude_shape_plot.py | 54 +++++++++++++++++++ 2 files changed, 55 insertions(+), 1 deletion(-) diff --git a/conftest.py b/conftest.py index 889066c3a..0bf55fc27 100644 --- a/conftest.py +++ b/conftest.py @@ -7,6 +7,6 @@ except TypeError: pass -collect_ignore = ['setup.py'] +collect_ignore = ['setup.py', 'docs/conf.py'] pytest.register_assert_rewrite("skfda") diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 752f041f7..80fade282 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -35,7 +35,39 @@ class MagnitudeShapePlot: The outliers are detected using an instance of :class:`DirectionalOutlierDetector`. + Args: + + fdatagrid (FDataGrid): Object containing the data. + depth_method (:ref:`depth measure `, optional): + Method used to order the data. Defaults to :func:`projection + depth `. + pointwise_weights (array_like, optional): an array containing the + weights of each points of discretisati on where values have + been recorded. + alpha (float, optional): Denotes the quantile to choose the cutoff + value for detecting outliers Defaults to 0.993, which is used + in the classical boxplot. + assume_centered (boolean, optional): If True, the support of the + robust location and the covariance estimates is computed, and a + covariance estimate is recomputed from it, without centering + the data. Useful to work with data whose mean is significantly + equal to zero but is not exactly zero. If False, default value, + the robust location and covariance are directly computed with + the FastMCD algorithm without additional treatment. + support_fraction (float, 0 < support_fraction < 1, optional): The + proportion of points to be included in the support of the + raw MCD estimate. + Default is None, which implies that the minimum value of + support_fraction will be used within the algorithm: + [n_sample + n_features + 1] / 2 + random_state (int, RandomState instance or None, optional): If int, + random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number + generator; If None, the random number generator is the + RandomState instance used by np.random. By default, it is 0. + Attributes: + fdatagrid (FDataGrid): Object to be visualized. depth_method (:ref:`depth measure `, optional): Method used to order the data. Defaults to :func:`modified band depth @@ -63,6 +95,19 @@ class MagnitudeShapePlot: variation of the directional outlyingness. title (string, optional): Title of the plot. defaults to 'MS-Plot'. + Representation in a Jupyter notebook: + + .. jupyter-execute:: + + from skfda.datasets import make_gaussian_process + from skfda.misc.covariances import Exponential + from skfda.exploratory.visualization import MagnitudeShapePlot + + fd = make_gaussian_process( + n_samples=20, cov=Exponential(), random_state=1) + + MagnitudeShapePlot(fd) + Example: >>> import skfda @@ -120,6 +165,14 @@ class MagnitudeShapePlot: xlabel='MO', ylabel='VO', title='MS-Plot') + + References: + + Dai, W., & Genton, M. G. (2018). Multivariate Functional Data + Visualization and Outlier Detection. Journal of Computational + and Graphical Statistics, 27(4), 923-934. + https://doi.org/10.1080/10618600.2018.1473781 + """ def __init__(self, fdatagrid, **kwargs): @@ -281,4 +334,5 @@ def __repr__(self): def _repr_svg_(self): fig = self.plot() + plt.close(fig) return _figure_to_svg(fig) From 6ce2c8afe62e2408d44f2fb5ee0e2000a60bab2f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 28 Jun 2020 19:29:21 +0200 Subject: [PATCH 582/624] Simplify grid. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 24 +++++-- skfda/representation/_functional_data.py | 83 ++++++++++++++---------- tests/test_grid.py | 60 +++++++++++++++++ 4 files changed, 128 insertions(+), 41 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 329b72770..63c0e00c3 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -1,6 +1,6 @@ from . import constants -from ._utils import (_list_of_arrays, _coordinate_list, +from ._utils import (_list_of_arrays, _cartesian_product, _check_estimator, parameter_aliases, _to_grid, check_is_univariate, _same_domain) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 571455f36..24770d4b0 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -81,8 +81,8 @@ def _list_of_arrays(original_array): return [np.asarray(i) for i in original_array] -def _coordinate_list(axes): - """Convert a list with axes in a list with coordinates. +def _cartesian_product(axes, flatten=True, return_shape=False): + """Computes the cartesian product of the axes. Computes the cartesian product of the axes and returns a numpy array of 1 dimension with all the possible combinations, for an arbitrary number of @@ -97,28 +97,38 @@ def _coordinate_list(axes): Examples: - >>> from skfda.representation._functional_data import _coordinate_list + >>> from skfda.representation._functional_data import _cartesian_product >>> axes = [[0,1],[2,3]] - >>> _coordinate_list(axes) + >>> _cartesian_product(axes) array([[0, 2], [0, 3], [1, 2], [1, 3]]) >>> axes = [[0,1],[2,3],[4]] - >>> _coordinate_list(axes) + >>> _cartesian_product(axes) array([[0, 2, 4], [0, 3, 4], [1, 2, 4], [1, 3, 4]]) >>> axes = [[0,1]] - >>> _coordinate_list(axes) + >>> _cartesian_product(axes) array([[0], [1]]) """ - return np.vstack(list(map(np.ravel, np.meshgrid(*axes, indexing='ij')))).T + cartesian = np.stack(np.meshgrid(*axes, indexing='ij'), -1) + + shape = cartesian.shape + + if flatten: + cartesian = cartesian.reshape(-1, len(axes)) + + if return_shape: + return cartesian, shape + else: + return cartesian def _same_domain(fd, fd2): diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index f8dd24e84..3b7f55e14 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -11,7 +11,7 @@ import numpy as np -from .._utils import _coordinate_list, _list_of_arrays +from .._utils import _cartesian_product, _list_of_arrays from .extrapolation import _parse_extrapolation @@ -196,6 +196,26 @@ def _extrapolation_index(self, eval_points): return index + def _one_grid_to_points(self, axes): + """ + Convert a list of ndarrays, one per domain dimension, in the points. + + Returns also the shape containing the information of how each point + is formed. + """ + axes = _list_of_arrays(axes) + + if len(axes) != self.dim_domain: + raise ValueError(f"Length of axes should be " + f"{self.dim_domain}") + + cartesian, shape = _cartesian_product(axes, return_shape=True) + + # Drop domain size dimension, as it is not needed to reshape the output + shape = shape[:-1] + + return cartesian, shape + def _evaluate_grid(self, axes, *, extrapolation=None, aligned_evaluation=True): """Evaluate the functional object in the cartesian grid. @@ -239,53 +259,50 @@ def _evaluate_grid(self, axes, *, extrapolation=None, dimension. """ - axes = _list_of_arrays(axes) + # If a numpy array is a ragged array (in unaligned_evaluation, if there + # are different points per sample) we have to add the object dtype + additional_numpy_params = {} + + # Compute intersection points and resulting shapes if aligned_evaluation: - lengths = [len(ax) for ax in axes] + eval_points, shape = self._one_grid_to_points(axes) - if len(axes) != self.dim_domain: - raise ValueError(f"Length of axes should be " - f"{self.dim_domain}") + else: - eval_points = _coordinate_list(axes) + axes = list(axes) - res = self.evaluate(eval_points, - extrapolation=extrapolation) + if len(axes) != self.n_samples: + raise ValueError("Should be provided a list of axis per " + "sample") - elif self.dim_domain == 1: + eval_points, shape = zip( + *[self._one_grid_to_points(a) for a in axes]) - eval_points = [ax.squeeze(0) for ax in axes] + if not all(s == shape[0] for s in shape): + additional_numpy_params['dtype'] = np.object_ - return self.evaluate(eval_points, - extrapolation=extrapolation, - aligned_evaluation=False) - else: + eval_points = np.array(eval_points, **additional_numpy_params) - if len(axes) != self.n_samples: - raise ValueError("Should be provided a list of axis per " - "sample") - elif len(axes[0]) != self.dim_domain: - raise ValueError(f"Incorrect length of axes. " - f"({self.dim_domain}) != {len(axes[0])}") + # Evaluate the points + res = self.evaluate(eval_points, + extrapolation=extrapolation, + aligned_evaluation=aligned_evaluation) - lengths = [len(ax) for ax in axes[0]] - eval_points = np.empty((self.n_samples, - np.prod(lengths), - self.dim_domain)) + # Reshape the result + if aligned_evaluation: - for i in range(self.n_samples): - eval_points[i] = _coordinate_list(axes[i]) + res = res.reshape([self.n_samples] + + list(shape) + [self.dim_codomain]) - res = self.evaluate(eval_points, - extrapolation=extrapolation, - aligned_evaluation=False) + else: - shape = [self.n_samples] + lengths + [self.dim_codomain] + res = np.array([ + r.reshape(list(s) + [self.dim_codomain]) + for r, s in zip(res, shape)], **additional_numpy_params) - # Roll the list of result in a list - return res.reshape(shape) + return res def _join_evaluation(self, index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation): diff --git a/tests/test_grid.py b/tests/test_grid.py index cfe76fb60..a8b2d3959 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -197,6 +197,66 @@ def test_composition(self): self.assertEqual(gof.dim_codomain, 1) +class TestEvaluateFDataGrid(unittest.TestCase): + + def setUp(self): + data_matrix = np.array( + [ + [ + [[0, 1, 2], [0, 1, 2]], + [[0, 1, 2], [0, 1, 2]] + ], + [ + [[3, 4, 5], [3, 4, 5]], + [[3, 4, 5], [3, 4, 5]] + ] + ]) + + sample_points = [[0, 1], [0, 1]] + + fd = FDataGrid(data_matrix, sample_points=sample_points) + self.assertEqual(fd.n_samples, 2) + self.assertEqual(fd.dim_domain, 2) + self.assertEqual(fd.dim_codomain, 3) + + self.fd = fd + + def test_evaluate_aligned(self): + + res = self.fd([(0, 0), (1, 1), (2, 2), (3, 3)]) + expected = np.array([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], + [[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + + np.testing.assert_allclose(res, expected) + + def test_evaluate_unaligned(self): + + res = self.fd([[(0, 0), (1, 1), (2, 2), (3, 3)], + [(1, 7), (5, 2), (3, 4), (6, 1)]], + aligned_evaluation=False) + expected = np.array([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], + [[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + + np.testing.assert_allclose(res, expected) + + def test_evaluate_grid_aligned(self): + + res = self.fd([[0, 1], [1, 2]], grid=True) + expected = np.array([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], + [[[3, 4, 5], [3, 4, 5]], [[3, 4, 5], [3, 4, 5]]]]) + + np.testing.assert_allclose(res, expected) + + def test_evaluate_grid_unaligned(self): + + res = self.fd([[[0, 1], [1, 2]], [[3, 4], [5, 6]]], + grid=True, aligned_evaluation=False) + expected = np.array([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], + [[[3, 4, 5], [3, 4, 5]], [[3, 4, 5], [3, 4, 5]]]]) + + np.testing.assert_allclose(res, expected) + + if __name__ == '__main__': print() unittest.main() From bc549836319076c0db794ad2842e9733207ad5e7 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 28 Jun 2020 19:57:46 +0200 Subject: [PATCH 583/624] Remove `_evaluate_composed` in `FData` objects. --- skfda/representation/_functional_data.py | 75 +++++++++-------------- skfda/representation/basis/_fdatabasis.py | 64 +++++-------------- skfda/representation/extrapolation.py | 8 +-- skfda/representation/grid.py | 37 ++--------- 4 files changed, 54 insertions(+), 130 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 3b7f55e14..bb0401866 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -341,41 +341,20 @@ def _join_evaluation(self, index_matrix, index_ext, index_ev, return res @abstractmethod - def _evaluate(self, eval_points): + def _evaluate(self, eval_points, *, aligned_evaluation=True): """Internal evaluation method, defines the evaluation of the FData. - Evaluates the samples of an FData object at the same eval_points. + Evaluates the samples of an FData object at several points. - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is True. - - Args: - eval_points (numpy.ndarray): Numpy array with shape - `(len(eval_points), dim_domain)` with the evaluation points. - Each entry represents the coordinate of a point. - - Returns: - (numpy.darray): Numpy 3d array with shape `(n_samples, - len(eval_points), dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th image - dimension of the i-th sample, at the j-th evaluation point. - - """ - pass - - @abstractmethod - def _evaluate_composed(self, eval_points): - """Internal evaluation method, defines the evaluation of a FData. - - Evaluates the samples of an FData object at different eval_points. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is False. + Subclasses must override this method to implement evaluation. Args: - eval_points (numpy.ndarray): Numpy array with shape - `(n_samples, len(eval_points), dim_domain)` with the - evaluation points for each sample. + eval_points (array_like): List of points where the functions are + evaluated. If `aligned_evaluation` is `True`, then a list of + lists of points must be passed, with one list per sample. + aligned_evaluation (bool, optional): Whether the input points are + the same for each sample, or an array of points per sample is + passed. Returns: (numpy.darray): Numpy 3d array with shape `(n_samples, @@ -393,9 +372,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, Args: eval_points (array_like): List of points where the functions are - evaluated. If a matrix of shape nsample x eval_points is given - each sample is evaluated at the values in the corresponding row - in eval_points. + evaluated. If `grid` is `True`, a list of axes, one per domain + dimension, must be passed instead. If `aligned_evaluation` is + `True`, then a list of lists (of points or axes, as explained) + must be passed, with one list per sample. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the @@ -407,6 +387,9 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, return matrix has shape n_samples x len(t1) x len(t2) x ... x len(t_dim_domain) x dim_codomain. If the domain dimension is 1 the parameter has no efect. Defaults to False. + aligned_evaluation (bool, optional): Whether the input points are + the same for each sample, or an array of points per sample is + passed. Returns: (np.darray): Matrix whose rows are the values of the each @@ -422,17 +405,17 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, grid=grid, aligned_evaluation=aligned_evaluation) + if grid: # Evaluation of a grid performed in auxiliar function + return self._evaluate_grid(eval_points, + extrapolation=extrapolation, + aligned_evaluation=aligned_evaluation) + if extrapolation is None: extrapolation = self.extrapolation else: # Gets the function to perform extrapolation or None extrapolation = _parse_extrapolation(extrapolation) - if grid: # Evaluation of a grid performed in auxiliar function - return self._evaluate_grid(eval_points, - extrapolation=extrapolation, - aligned_evaluation=aligned_evaluation) - # Convert to array and check dimensions of eval points eval_points = self._reshape_eval_points(eval_points, aligned_evaluation) @@ -447,10 +430,8 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, if not extrapolate: # Direct evaluation - if aligned_evaluation: - res = self._evaluate(eval_points) - else: - res = self._evaluate_composed(eval_points) + res = self._evaluate( + eval_points, aligned_evaluation=aligned_evaluation) else: # Partition of eval points @@ -463,7 +444,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points_evaluation = eval_points[index_ev] # Direct evaluation - res_evaluation = self._evaluate(eval_points_evaluation) + res_evaluation = self._evaluate( + eval_points_evaluation, + aligned_evaluation=aligned_evaluation) + res_extrapolation = extrapolation.evaluate( self, eval_points_extrapolation) @@ -476,8 +460,9 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points_evaluation = eval_points[:, index_ev] # Direct evaluation - res_evaluation = self._evaluate_composed( - eval_points_evaluation) + res_evaluation = self._evaluate( + eval_points_evaluation, + aligned_evaluation=aligned_evaluation) res_extrapolation = extrapolation.evaluate_composed( self, diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index a290c8e61..a97109376 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -233,67 +233,33 @@ def domain_range(self): """Definition range.""" return self.basis.domain_range - def _evaluate(self, eval_points): - """"Evaluate the object or its derivatives at a list of values. + def _evaluate(self, eval_points, *, aligned_evaluation=True): - Args: - eval_points (array_like): List of points where the functions are - evaluated. If a matrix of shape `n_samples` x eval_points is - given each sample is evaluated at the values in the - corresponding row. - - - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points. - - """ #  Only suported 1D objects - eval_points = eval_points[:, 0] - - # each row contains the values of one element of the basis - basis_values = self.basis.evaluate(eval_points) - - res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) - - return res.reshape((self.n_samples, len(eval_points), 1)) + eval_points = eval_points[..., 0] - def _evaluate_composed(self, eval_points): - r"""Evaluate the object or its derivatives at a list of values with a - different time for each sample. + if aligned_evaluation: - Returns a numpy array with the component (i,j) equal to :math:`f_i(t_j - + \delta_i)`. + # Each row contains the values of one element of the basis + basis_values = self.basis.evaluate(eval_points) - This method has to evaluate the basis values once per sample - instead of reuse the same evaluation for all the samples - as :func:`evaluate`. + res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) - Args: - eval_points (numpy.ndarray): Matrix of size `n_samples`x n_points - extrapolation (str or Extrapolation, optional): Controls the - extrapolation mode for elements outside the domain range. - By default uses the method defined in fd. See extrapolation to - more information. - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points with the - corresponding shift. - """ + return res.reshape((self.n_samples, len(eval_points), 1)) - eval_points = eval_points[..., 0] + else: - res_matrix = np.empty((self.n_samples, eval_points.shape[1])) + res_matrix = np.empty((self.n_samples, eval_points.shape[1])) - _matrix = np.empty((eval_points.shape[1], self.n_basis)) + _matrix = np.empty((eval_points.shape[1], self.n_basis)) - for i in range(self.n_samples): - basis_values = self.basis.evaluate(eval_points[i]).T + for i in range(self.n_samples): + basis_values = self.basis.evaluate(eval_points[i]).T - np.multiply(basis_values, self.coefficients[i], out=_matrix) - np.sum(_matrix, axis=1, out=res_matrix[i]) + np.multiply(basis_values, self.coefficients[i], out=_matrix) + np.sum(_matrix, axis=1, out=res_matrix[i]) - return res_matrix.reshape((self.n_samples, eval_points.shape[1], 1)) + return res_matrix.reshape((self.n_samples, eval_points.shape[1], 1)) def shift(self, shifts, *, restrict_domain=False, extrapolation=None, eval_points=None, **kwargs): diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 204ba9ad9..5d1814292 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -65,9 +65,9 @@ def evaluate(self, fdata, eval_points): eval_points += domain_range[:, 0] if eval_points.ndim == 3: - res = fdata._evaluate_composed(eval_points) + res = fdata(eval_points, aligned_evaluation=False) else: - res = fdata._evaluate(eval_points) + res = fdata(eval_points) return res @@ -132,10 +132,10 @@ def evaluate(self, fdata, eval_points): if eval_points.ndim == 3: - res = fdata._evaluate_composed(eval_points) + res = fdata(eval_points, aligned_evaluation=False) else: - res = fdata._evaluate(eval_points) + res = fdata(eval_points) return res diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index a797f9baa..cf9c0c7ab 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -359,39 +359,12 @@ def interpolation(self, new_interpolation): self._interpolation = new_interpolation - def _evaluate(self, eval_points): - """"Evaluate the object or its derivatives at a list of values. + def _evaluate(self, eval_points, *, aligned_evaluation=True): - Args: - eval_points (array_like): List of points where the functions are - evaluated. If a matrix of shape nsample x eval_points is given - each sample is evaluated at the values in the corresponding row - in eval_points. - - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points. - - """ - - return self.interpolation.evaluate(self, eval_points) - - def _evaluate_composed(self, eval_points): - """"Evaluate the object or its derivatives at a list of values. - - Args: - eval_points (array_like): List of points where the functions are - evaluated. If a matrix of shape nsample x eval_points is given - each sample is evaluated at the values in the corresponding row - in eval_points. - - Returns: - (numpy.darray): Matrix whose rows are the values of the each - function at the values specified in eval_points. - - """ - - return self.interpolation.evaluate_composed(self, eval_points) + if aligned_evaluation: + return self.interpolation.evaluate(self, eval_points) + else: + return self.interpolation.evaluate_composed(self, eval_points) def derivative(self, *, order=1): r"""Differentiate a FDataGrid object. From 4cef20fb5b780523fcf7b894700e53cc3615c014 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 28 Jun 2020 20:38:52 +0200 Subject: [PATCH 584/624] Simplify evaluator API. --- skfda/representation/_functional_data.py | 30 +++---- skfda/representation/evaluator.py | 98 +++------------------ skfda/representation/extrapolation.py | 105 ++--------------------- skfda/representation/grid.py | 6 +- skfda/representation/interpolation.py | 89 ++++--------------- 5 files changed, 47 insertions(+), 281 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index bb0401866..a3b16c48a 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -372,9 +372,9 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, Args: eval_points (array_like): List of points where the functions are - evaluated. If `grid` is `True`, a list of axes, one per domain - dimension, must be passed instead. If `aligned_evaluation` is - `True`, then a list of lists (of points or axes, as explained) + evaluated. If ``grid`` is ``True``, a list of axes, one per domain + dimension, must be passed instead. If ``aligned_evaluation`` is + ``True``, then a list of lists (of points or axes, as explained) must be passed, with one list per sample. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By @@ -443,15 +443,6 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points_extrapolation = eval_points[index_ext] eval_points_evaluation = eval_points[index_ev] - # Direct evaluation - res_evaluation = self._evaluate( - eval_points_evaluation, - aligned_evaluation=aligned_evaluation) - - res_extrapolation = extrapolation.evaluate( - self, - eval_points_extrapolation) - else: index_ext = np.logical_or.reduce(index_matrix, axis=0) eval_points_extrapolation = eval_points[:, index_ext] @@ -459,14 +450,15 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, index_ev = np.logical_or.reduce(~index_matrix, axis=0) eval_points_evaluation = eval_points[:, index_ev] - # Direct evaluation - res_evaluation = self._evaluate( - eval_points_evaluation, - aligned_evaluation=aligned_evaluation) + # Direct evaluation + res_evaluation = self._evaluate( + eval_points_evaluation, + aligned_evaluation=aligned_evaluation) - res_extrapolation = extrapolation.evaluate_composed( - self, - eval_points_extrapolation) + res_extrapolation = extrapolation.evaluate( + self, + eval_points_extrapolation, + aligned=aligned_evaluation) res = self._join_evaluation(index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation) diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index 040a0c932..7cdd3a41e 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -20,13 +20,13 @@ class Evaluator(ABC): """ @abstractmethod - def evaluate(self, fdata, eval_points): + def evaluate(self, fdata, eval_points, *, aligned=True): """Evaluation method. - Evaluates the samples at the same evaluation points. The evaluation - call will receive a 2-d array with the evaluation points. - This method is called internally by :meth:`evaluate` when the - argument ``aligned_evaluation`` is True. + Evaluates the samples at evaluation points. The evaluation + call will receive a 2-d array with the evaluation points, or + a 3-d array with the evaluation points per sample if ``aligned`` + is ``False``. Args: eval_points (numpy.ndarray): Numpy array with shape @@ -43,30 +43,6 @@ def evaluate(self, fdata, eval_points): """ pass - @abstractmethod - def evaluate_composed(self, fdata, eval_points): - """Evaluation method. - - Evaluates the samples at different evaluation points. The evaluation - call will receive a 3-d array with the evaluation points for each - sample. This method is called internally by :func:`evaluate` when - the argument ``aligned_evaluation`` is False. - - Args: - eval_points (numpy.ndarray): Numpy array with shape - ``(n_samples, number_eval_points, dim_domain)`` with the - evaluation points for each sample. - - Returns: - (numpy.darray): Numpy 3d array with shape - ``(n_samples, number_eval_points, dim_codomain)`` with the - result of the evaluation. The entry ``(i,j,k)`` will contain - the value k-th image dimension of the i-th sample, at the - j-th evaluation point. - - """ - pass - def __repr__(self): return f"{type(self)}()" @@ -78,64 +54,14 @@ def __eq__(self, other): class GenericEvaluator(Evaluator): """Generic Evaluator. - Generic evaluator that recibes two functions to construct the evaluator. - The functions will recieve an :class:`FData` as first argument, a numpy - array with the eval_points and a named argument derivative. + Generic evaluator that recibes a functions to construct the evaluator. + The function will recieve an :class:`FData` as first argument, a numpy + array with the eval_points and the ``aligned`` parameter. """ - def __init__(self, evaluate_func, evaluate_composed_func=None): - self.evaluate_func = evaluate_func - - if evaluate_composed_func is None: - self.evaluate_composed_func = evaluate_func - else: - self.evaluate_composed_func = evaluate_composed_func - - def evaluate(self, fdata, eval_points): - """Evaluation method. + def __init__(self, evaluate_function): + self.evaluate_function = evaluate_function - Evaluates the samples at the same evaluation points. The evaluation - call will receive a 2-d array with the evaluation points. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is True. - - Args: - eval_points (numpy.ndarray): Numpy array with shape - `(len(eval_points), dim_domain)` with the evaluation points. - Each entry represents the coordinate of a point. - - Returns: - (numpy.darray): Numpy 3-d array with shape `(n_samples, - len(eval_points), dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th - image dimension of the i-th sample, at the j-th evaluation - point. - - """ - return self.evaluate_func(fdata, eval_points) - - def evaluate_composed(self, fdata, eval_points): - """Evaluation method. - - Evaluates the samples at different evaluation points. The evaluation - call will receive a 3-d array with the evaluation points for each - sample. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is False. - - Args: - eval_points (numpy.ndarray): Numpy array with shape - `(n_samples, number_eval_points, dim_domain)` with the - evaluation points for each sample. - - Returns: - (numpy.darray): Numpy 3d array with shape `(n_samples, - number_eval_points, dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th image - dimension of the i-th sample, at the j-th evaluation point. - - """ - return self.evaluate_composed_func(fdata, eval_points) + def evaluate(self, fdata, eval_points, *, aligned=True): + return self.evaluate_function(fdata, eval_points, aligned=aligned) diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 5d1814292..2e89356a7 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -42,20 +42,7 @@ class PeriodicExtrapolation(Evaluator): [-1.086]]]) """ - def evaluate(self, fdata, eval_points): - """Evaluate points outside the domain range. - - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - - Returns: - (numpy.ndarray): numpy array with the evaluation of the points in - a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. - """ + def evaluate(self, fdata, eval_points, *, aligned=True): domain_range = np.asarray(fdata.domain_range) @@ -64,16 +51,10 @@ def evaluate(self, fdata, eval_points): eval_points %= domain_range[:, 1] - domain_range[:, 0] eval_points += domain_range[:, 0] - if eval_points.ndim == 3: - res = fdata(eval_points, aligned_evaluation=False) - else: - res = fdata(eval_points) + res = fdata(eval_points, aligned_evaluation=aligned) return res - def evaluate_composed(self, *args, **kwargs): - return self.evaluate(*args, **kwargs) - class BoundaryExtrapolation(Evaluator): """Extends the domain range using the boundary values. @@ -108,20 +89,7 @@ class BoundaryExtrapolation(Evaluator): [ 1.125]]]) """ - def evaluate(self, fdata, eval_points): - """Evaluate points outside the domain range. - - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - - Returns: - (numpy.ndarray): numpy array with the evaluation of the points in - a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. - """ + def evaluate(self, fdata, eval_points, *, aligned=True): domain_range = fdata.domain_range @@ -130,18 +98,10 @@ def evaluate(self, fdata, eval_points): eval_points[eval_points[..., i] < a, i] = a eval_points[eval_points[..., i] > b, i] = b - if eval_points.ndim == 3: - - res = fdata(eval_points, aligned_evaluation=False) - else: - - res = fdata(eval_points) + res = fdata(eval_points, aligned_evaluation=aligned) return res - def evaluate_composed(self, *args, **kwargs): - return self.evaluate(*args, **kwargs) - class ExceptionExtrapolation(Evaluator): """Raise and exception. @@ -173,28 +133,13 @@ class ExceptionExtrapolation(Evaluator): """ - def evaluate(self, fdata, eval_points): - """Evaluate points outside the domain range. - - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - - Raises: - ValueError: when the extrapolation method is called. - """ + def evaluate(self, fdata, eval_points, *, aligned=True): n_points = eval_points.shape[-2] raise ValueError(f"Attempt to evaluate {n_points} points outside the " f"domain range.") - def evaluate_composed(self, *args, **kwargs): - return self.evaluate(*args, **kwargs) - class FillExtrapolation(Evaluator): """Values outside the domain range will be filled with a fixed value. @@ -237,46 +182,8 @@ def _fill(self, fdata, eval_points): fdata.dim_codomain) return np.full(shape, self.fill_value) - def evaluate(self, fdata, eval_points): - """ - Evaluate points outside the domain range. - - Args: - fdata (:class:´FData´): Object where the evaluation is taken place. - eval_points (:class: numpy.ndarray): Numpy array with the evalation - points outside the domain range. The shape of the array may be - `n_eval_points` x `dim_codomain` or `n_samples` x `n_eval_points` - x `dim_codomain`. - - Returns: - (numpy.ndarray): numpy array with the evaluation of the points in - a matrix with shape `n_samples` x `n_eval_points`x `dim_codomain`. - - """ - return self._fill(fdata, eval_points) - - def evaluate_composed(self, fdata, eval_points): - """Evaluation method. - - Evaluates the samples at different evaluation points. The evaluation - call will receive a 3-d array with the evaluation points for - each sample. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is False. - - Args: - eval_points (numpy.ndarray): Numpy array with shape - `(n_samples, number_eval_points, dim_domain)` with the - evaluation points for each sample. - - Returns: - (numpy.darray): Numpy 3d array with shape `(n_samples, - number_eval_points, dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th image - dimension of the i-th sample, at the j-th evaluation point. + def evaluate(self, fdata, eval_points, *, aligned=True): - """ return self._fill(fdata, eval_points) def __repr__(self): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index cf9c0c7ab..52776d03f 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -361,10 +361,8 @@ def interpolation(self, new_interpolation): def _evaluate(self, eval_points, *, aligned_evaluation=True): - if aligned_evaluation: - return self.interpolation.evaluate(self, eval_points) - else: - return self.interpolation.evaluate_composed(self, eval_points) + return self.interpolation.evaluate(self, eval_points, + aligned=aligned_evaluation) def derivative(self, *, order=1): r"""Differentiate a FDataGrid object. diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 7a1a52447..f9942c473 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -36,25 +36,23 @@ def _evaluate_codomain(self, spl_m, t, derivative=0): return np.array([self._evaluate_one(spl, t, derivative) for spl in spl_m]).T - def evaluate(self, fdata, eval_points, *, derivative=0): + def evaluate(self, fdata, eval_points, *, derivative=0, aligned=True): - # Points evaluated inside the domain - res = np.apply_along_axis( - self._evaluate_codomain, 1, - self.splines, eval_points, derivative) - res = res.reshape(fdata.n_samples, eval_points.shape[0], - fdata.dim_codomain) + if aligned: + # Points evaluated inside the domain + res = np.apply_along_axis( + self._evaluate_codomain, 1, + self.splines, eval_points, derivative) + res = res.reshape(fdata.n_samples, eval_points.shape[0], + fdata.dim_codomain) - return res - - def evaluate_composed(self, fdata, eval_points, *, derivative=0): - - shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) - res = np.empty(shape) + else: + shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) + res = np.empty(shape) - for i in range(fdata.n_samples): - res[i] = self._evaluate_codomain( - self.splines[i], eval_points[i], derivative=derivative) + for i in range(fdata.n_samples): + res[i] = self._evaluate_codomain( + self.splines[i], eval_points[i], derivative=derivative) return res @@ -396,66 +394,11 @@ def _build_interpolator(self, fdatagrid): interpolation_order=self.interpolation_order, smoothness_parameter=self.smoothness_parameter) - def evaluate(self, fdata, eval_points): - r"""Evaluation method. - - Evaluates the samples at different evaluation points. The evaluation - call will receive a 3-d array with the evaluation points for - each sample. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is False. - - Args: - eval_points (np.ndarray): Numpy array with shape - `(n_samples, number_eval_points, dim_domain)` with the - evaluation points for each sample. - - Returns: - (np.darray): Numpy 3d array with shape `(n_samples, - number_eval_points, dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th image - dimension of the i-th sample, at the j-th evaluation point. - - Raises: - ValueError: In case of an incorrect value of the derivative - argument. - - """ + def evaluate(self, fdata, eval_points, *, aligned=True): spline_list = self._build_interpolator(fdata) - return spline_list.evaluate(fdata, eval_points) - - def evaluate_composed(self, fdata, eval_points): - """Evaluation method. - - Evaluates the samples at different evaluation points. The evaluation - call will receive a 3-d array with the evaluation points for - each sample. - - This method is called internally by :meth:`evaluate` when the argument - `aligned_evaluation` is False. - - Args: - eval_points (np.ndarray): Numpy array with shape - `(n_samples, number_eval_points, dim_domain)` with the - evaluation points for each sample. - - Returns: - (np.darray): Numpy 3d array with shape `(n_samples, - number_eval_points, dim_codomain)` with the result of the - evaluation. The entry (i,j,k) will contain the value k-th image - dimension of the i-th sample, at the j-th evaluation point. - - Raises: - ValueError: In case of an incorrect value of the derivative - argument. - - """ - spline_list = self._build_interpolator(fdata) - - return spline_list.evaluate_composed(fdata, eval_points) + return spline_list.evaluate(fdata, eval_points, aligned=aligned) def __repr__(self): """repr method of the interpolation""" From b68ea459d9db3fb5fac0ea152501ce86db41808c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 28 Jun 2020 20:48:05 +0200 Subject: [PATCH 585/624] Rename `aligned_evaluation` to `aligned`. --- .../registration/_shift_registration.py | 2 +- skfda/representation/_functional_data.py | 44 +++++++++---------- skfda/representation/basis/_fdatabasis.py | 6 +-- skfda/representation/extrapolation.py | 4 +- skfda/representation/grid.py | 10 ++--- tests/test_basis_evaluation.py | 24 +++++----- tests/test_grid.py | 4 +- tests/test_interpolation.py | 16 +++---- tests/test_registration.py | 6 +-- 9 files changed, 58 insertions(+), 58 deletions(-) diff --git a/skfda/preprocessing/registration/_shift_registration.py b/skfda/preprocessing/registration/_shift_registration.py index 9efb4cb4d..237165b8d 100644 --- a/skfda/preprocessing/registration/_shift_registration.py +++ b/skfda/preprocessing/registration/_shift_registration.py @@ -240,7 +240,7 @@ def _compute_deltas(self, fd, template): # Computes the new values shifted x = fd(output_points_rep + np.atleast_2d(delta).T, - aligned_evaluation=False, + aligned=False, extrapolation=self.extrapolation)[..., 0] if template == "mean": diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index a3b16c48a..11aeaceb4 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -217,7 +217,7 @@ def _one_grid_to_points(self, axes): return cartesian, shape def _evaluate_grid(self, axes, *, extrapolation=None, - aligned_evaluation=True): + aligned=True): """Evaluate the functional object in the cartesian grid. This method is called internally by :meth:`evaluate` when the argument @@ -232,7 +232,7 @@ def _evaluate_grid(self, axes, *, extrapolation=None, evaluation in the grid will be a matrix with :math:`m+1` dimensions and shape :math:`n_{samples} x n_1 x n_2 x ... x n_m`. - If `aligned_evaluation` is false each sample is evaluated in a + If `aligned` is false each sample is evaluated in a different grid, and the list of axes should contain a list of axes for each sample. @@ -247,7 +247,7 @@ def _evaluate_grid(self, axes, *, extrapolation=None, extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the object. - aligned_evaluation (bool, optional): If False evaluates each sample + aligned (bool, optional): If False evaluates each sample in a different grid. Returns: @@ -260,12 +260,12 @@ def _evaluate_grid(self, axes, *, extrapolation=None, """ - # If a numpy array is a ragged array (in unaligned_evaluation, if there + # If a numpy array is a ragged array (in unaligned evaluation, if there # are different points per sample) we have to add the object dtype additional_numpy_params = {} # Compute intersection points and resulting shapes - if aligned_evaluation: + if aligned: eval_points, shape = self._one_grid_to_points(axes) @@ -288,10 +288,10 @@ def _evaluate_grid(self, axes, *, extrapolation=None, # Evaluate the points res = self.evaluate(eval_points, extrapolation=extrapolation, - aligned_evaluation=aligned_evaluation) + aligned=aligned) # Reshape the result - if aligned_evaluation: + if aligned: res = res.reshape([self.n_samples] + list(shape) + [self.dim_codomain]) @@ -341,7 +341,7 @@ def _join_evaluation(self, index_matrix, index_ext, index_ev, return res @abstractmethod - def _evaluate(self, eval_points, *, aligned_evaluation=True): + def _evaluate(self, eval_points, *, aligned=True): """Internal evaluation method, defines the evaluation of the FData. Evaluates the samples of an FData object at several points. @@ -350,9 +350,9 @@ def _evaluate(self, eval_points, *, aligned_evaluation=True): Args: eval_points (array_like): List of points where the functions are - evaluated. If `aligned_evaluation` is `True`, then a list of + evaluated. If `aligned` is `True`, then a list of lists of points must be passed, with one list per sample. - aligned_evaluation (bool, optional): Whether the input points are + aligned (bool, optional): Whether the input points are the same for each sample, or an array of points per sample is passed. @@ -366,14 +366,14 @@ def _evaluate(self, eval_points, *, aligned_evaluation=True): pass def evaluate(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True): + grid=False, aligned=True): """Evaluate the object or its derivatives at a list of values or a grid. Args: eval_points (array_like): List of points where the functions are evaluated. If ``grid`` is ``True``, a list of axes, one per domain - dimension, must be passed instead. If ``aligned_evaluation`` is + dimension, must be passed instead. If ``aligned`` is ``True``, then a list of lists (of points or axes, as explained) must be passed, with one list per sample. extrapolation (str or Extrapolation, optional): Controls the @@ -387,7 +387,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, return matrix has shape n_samples x len(t1) x len(t2) x ... x len(t_dim_domain) x dim_codomain. If the domain dimension is 1 the parameter has no efect. Defaults to False. - aligned_evaluation (bool, optional): Whether the input points are + aligned (bool, optional): Whether the input points are the same for each sample, or an array of points per sample is passed. @@ -403,12 +403,12 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, eval_points, extrapolation=extrapolation, grid=grid, - aligned_evaluation=aligned_evaluation) + aligned=aligned) if grid: # Evaluation of a grid performed in auxiliar function return self._evaluate_grid(eval_points, extrapolation=extrapolation, - aligned_evaluation=aligned_evaluation) + aligned=aligned) if extrapolation is None: extrapolation = self.extrapolation @@ -418,7 +418,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, # Convert to array and check dimensions of eval points eval_points = self._reshape_eval_points(eval_points, - aligned_evaluation) + aligned) # Check if extrapolation should be applied if extrapolation is not None: @@ -431,11 +431,11 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, if not extrapolate: # Direct evaluation res = self._evaluate( - eval_points, aligned_evaluation=aligned_evaluation) + eval_points, aligned=aligned) else: # Partition of eval points - if aligned_evaluation: + if aligned: index_ext = index_matrix index_ev = ~index_matrix @@ -453,12 +453,12 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, # Direct evaluation res_evaluation = self._evaluate( eval_points_evaluation, - aligned_evaluation=aligned_evaluation) + aligned=aligned) res_extrapolation = extrapolation.evaluate( self, eval_points_extrapolation, - aligned=aligned_evaluation) + aligned=aligned) res = self._join_evaluation(index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation) @@ -466,7 +466,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, return res def __call__(self, eval_points, *, derivative=0, extrapolation=None, - grid=False, aligned_evaluation=True): + grid=False, aligned=True): """Evaluate the object or its derivatives at a list of values or a grid. This method is a wrapper of :meth:`evaluate`. @@ -495,7 +495,7 @@ def __call__(self, eval_points, *, derivative=0, extrapolation=None, """ return self.evaluate(eval_points, derivative=derivative, extrapolation=extrapolation, grid=grid, - aligned_evaluation=aligned_evaluation) + aligned=aligned) @abstractmethod def derivative(self, order=1): diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index a97109376..07705dca3 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -233,12 +233,12 @@ def domain_range(self): """Definition range.""" return self.basis.domain_range - def _evaluate(self, eval_points, *, aligned_evaluation=True): + def _evaluate(self, eval_points, *, aligned=True): #  Only suported 1D objects eval_points = eval_points[..., 0] - if aligned_evaluation: + if aligned: # Each row contains the values of one element of the basis basis_values = self.basis.evaluate(eval_points) @@ -330,7 +330,7 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, # Matrix of shifted values _data_matrix = self(points_shifted, - aligned_evaluation=False, + aligned=False, extrapolation=extrapolation)[..., 0] _basis = self.basis.rescale(domain) diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 2e89356a7..80aaec35a 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -51,7 +51,7 @@ def evaluate(self, fdata, eval_points, *, aligned=True): eval_points %= domain_range[:, 1] - domain_range[:, 0] eval_points += domain_range[:, 0] - res = fdata(eval_points, aligned_evaluation=aligned) + res = fdata(eval_points, aligned=aligned) return res @@ -98,7 +98,7 @@ def evaluate(self, fdata, eval_points, *, aligned=True): eval_points[eval_points[..., i] < a, i] = a eval_points[eval_points[..., i] > b, i] = b - res = fdata(eval_points, aligned_evaluation=aligned) + res = fdata(eval_points, aligned=aligned) return res diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 52776d03f..1bac3d41d 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -359,10 +359,10 @@ def interpolation(self, new_interpolation): self._interpolation = new_interpolation - def _evaluate(self, eval_points, *, aligned_evaluation=True): + def _evaluate(self, eval_points, *, aligned=True): return self.interpolation.evaluate(self, eval_points, - aligned=aligned_evaluation) + aligned=aligned) def derivative(self, *, order=1): r"""Differentiate a FDataGrid object. @@ -935,7 +935,7 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, data_matrix = self.evaluate(eval_points_shifted, extrapolation=extrapolation, - aligned_evaluation=False, + aligned=False, grid=True) return self.copy(data_matrix=data_matrix, sample_points=eval_points, @@ -972,7 +972,7 @@ def compose(self, fd, *, eval_points=None): eval_points_transformation = fd(eval_points) data_matrix = self(eval_points_transformation, - aligned_evaluation=False) + aligned=False) else: if eval_points is None: eval_points = fd.sample_points @@ -991,7 +991,7 @@ def compose(self, fd, *, eval_points=None): ).T data_matrix = self(eval_points_transformation, - aligned_evaluation=False) + aligned=False) return self.copy(data_matrix=data_matrix, sample_points=eval_points, diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 16ff51438..99727bc5e 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -108,18 +108,18 @@ def test_evaluation_composed_fourier(self): # Test same result than evaluation standart np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], - aligned_evaluation=False)) + aligned=False)) np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned_evaluation=False)) + aligned=False)) # Different evaluation times t_multiple = [[0, 0.5], [0.2, 0.7]] np.testing.assert_array_almost_equal(f(t_multiple[0])[0], f(t_multiple, - aligned_evaluation=False)[0]) + aligned=False)[0]) np.testing.assert_array_almost_equal(f(t_multiple[1])[1], f(t_multiple, - aligned_evaluation=False)[1]) + aligned=False)[1]) def test_domain_in_list_fourier(self): """Test the evaluation of FDataBasis""" @@ -241,18 +241,18 @@ def test_evaluation_composed_bspline(self): # Test same result than evaluation standart np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], - aligned_evaluation=False)) + aligned=False)) np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned_evaluation=False)) + aligned=False)) # Different evaluation times t_multiple = [[0, 0.5], [0.2, 0.7]] np.testing.assert_array_almost_equal(f(t_multiple[0])[0], f(t_multiple, - aligned_evaluation=False)[0]) + aligned=False)[0]) np.testing.assert_array_almost_equal(f(t_multiple[1])[1], f(t_multiple, - aligned_evaluation=False)[1]) + aligned=False)[1]) def test_domain_in_list_bspline(self): """Test the evaluation of FDataBasis""" @@ -380,18 +380,18 @@ def test_evaluation_composed_monomial(self): # Test same result than evaluation standart np.testing.assert_array_almost_equal(f([1]), f([[1], [1]], - aligned_evaluation=False)) + aligned=False)) np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned_evaluation=False)) + aligned=False)) # Different evaluation times t_multiple = [[0, 0.5], [0.2, 0.7]] np.testing.assert_array_almost_equal(f(t_multiple[0])[0], f(t_multiple, - aligned_evaluation=False)[0]) + aligned=False)[0]) np.testing.assert_array_almost_equal(f(t_multiple[1])[1], f(t_multiple, - aligned_evaluation=False)[1]) + aligned=False)[1]) def test_domain_in_list_monomial(self): """Test the evaluation of FDataBasis""" diff --git a/tests/test_grid.py b/tests/test_grid.py index a8b2d3959..a9a86dce7 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -233,7 +233,7 @@ def test_evaluate_unaligned(self): res = self.fd([[(0, 0), (1, 1), (2, 2), (3, 3)], [(1, 7), (5, 2), (3, 4), (6, 1)]], - aligned_evaluation=False) + aligned=False) expected = np.array([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], [[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]]]) @@ -250,7 +250,7 @@ def test_evaluate_grid_aligned(self): def test_evaluate_grid_unaligned(self): res = self.fd([[[0, 1], [1, 2]], [[3, 4], [5, 6]]], - grid=True, aligned_evaluation=False) + grid=True, aligned=False) expected = np.array([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], [[[3, 4, 5], [3, 4, 5]], [[3, 4, 5], [3, 4, 5]]]]) diff --git a/tests/test_interpolation.py b/tests/test_interpolation.py index 386c872c1..170a6204f 100644 --- a/tests/test_interpolation.py +++ b/tests/test_interpolation.py @@ -78,19 +78,19 @@ def test_evaluation_linear_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal(f([[1], [8]], - aligned_evaluation=False), + aligned=False), np.array([[[1.]], [[1.]]])) t = np.linspace(4, 6, 4) np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned_evaluation=False).round(2), + f([t, 9 - t], aligned=False).round(2), np.array([[[16.], [22.], [28.67], [36.]], [[16.], [22.], [28.67], [36.]]])) # Same length than nsample t = np.linspace(4, 6, 2) np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned_evaluation=False).round(2), + f([t, 9 - t], aligned=False).round(2), np.array([[[16.], [36.]], [[16.], [36.]]])) def test_evaluation_cubic_simple(self): @@ -153,19 +153,19 @@ def test_evaluation_cubic_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal( - f([[1], [8]], aligned_evaluation=False).round(3), + f([[1], [8]], aligned=False).round(3), np.array([[[1.]], [[1.]]])) t = np.linspace(4, 6, 4) np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned_evaluation=False).round(2), + f([t, 9 - t], aligned=False).round(2), np.array([[[16.], [21.78], [28.44], [36.]], [[16.], [21.78], [28.44], [36.]]])) # Same length than nsample t = np.linspace(4, 6, 2) np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned_evaluation=False).round(3), + f([t, 9 - t], aligned=False).round(3), np.array([[[16.], [36.]], [[16.], [36.]]])) def test_evaluation_nodes(self): @@ -280,10 +280,10 @@ def test_evaluation_composed(self): # Evaluate (x**2, (9-x)**2) in (1,8) np.testing.assert_array_almost_equal(f([[1], [4]], - aligned_evaluation=False)[0], + aligned=False)[0], f(1)[0]) np.testing.assert_array_almost_equal(f([[1], [4]], - aligned_evaluation=False)[1], + aligned=False)[1], f(4)[1]) def test_evaluation_nodes(self): diff --git a/tests/test_registration.py b/tests/test_registration.py index 9f875df69..411b0cacc 100644 --- a/tests/test_registration.py +++ b/tests/test_registration.py @@ -107,7 +107,7 @@ def test_landmark_shift(self): landmarks = landmarks.squeeze() original_modes = fd(landmarks.reshape((3, 1, 1)), - aligned_evaluation=False) + aligned=False) # Test default location fd_registered = landmark_shift(fd, landmarks) center = (landmarks.max() + landmarks.min()) / 2 @@ -131,7 +131,7 @@ def test_landmark_shift(self): # Test array location fd_registered = landmark_shift(fd, landmarks, location=[0, 0.1, 0.2]) - reg_modes = fd_registered([[0], [.1], [.2]], aligned_evaluation=False) + reg_modes = fd_registered([[0], [.1], [.2]], aligned=False) np.testing.assert_almost_equal(reg_modes, original_modes, decimal=2) @@ -159,7 +159,7 @@ def test_landmark_registration(self): random_state=9) landmarks = landmarks.squeeze() - original_values = fd(landmarks.reshape(3, 2), aligned_evaluation=False) + original_values = fd(landmarks.reshape(3, 2), aligned=False) # Default location fd_reg = landmark_registration(fd, landmarks) From 8c8d3ccd9af35e0ce6119b752ca0dd86cbff0228 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 29 Jun 2020 01:02:30 +0200 Subject: [PATCH 586/624] Allow ragged arrays in unaligned evaluation. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 30 +++++++++++++ skfda/representation/_functional_data.py | 53 ++++++++++------------- skfda/representation/basis/_fdatabasis.py | 13 +++--- skfda/representation/interpolation.py | 11 ++--- tests/test_grid.py | 23 ++++++++++ 6 files changed, 88 insertions(+), 44 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 63c0e00c3..5f3657fda 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -3,4 +3,4 @@ from ._utils import (_list_of_arrays, _cartesian_product, _check_estimator, parameter_aliases, _to_grid, check_is_univariate, - _same_domain) + _same_domain, _to_array_maybe_ragged) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 24770d4b0..357a95716 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -81,6 +81,36 @@ def _list_of_arrays(original_array): return [np.asarray(i) for i in original_array] +def _to_array_maybe_ragged(array, *, row_shape=None): + """ + Convert to an array where each element may or may not be of equal length. + + If each element is of equal length the array is multidimensional. + Otherwise it is a ragged array. + + """ + def convert_row(row): + r = np.array(row) + + if row_shape is not None: + r = r.reshape(row_shape) + + return r + + array_list = [convert_row(a) for a in array] + shapes = [a.shape for a in array_list] + + if all(s == shapes[0] for s in shapes): + return np.array(array_list) + else: + res = np.empty(len(array_list), dtype=np.object_) + + for i, a in enumerate(array_list): + res[i] = a + + return res + + def _cartesian_product(axes, flatten=True, return_shape=False): """Computes the cartesian product of the axes. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 11aeaceb4..23523fcb8 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -11,7 +11,8 @@ import numpy as np -from .._utils import _cartesian_product, _list_of_arrays +from .._utils import (_cartesian_product, _list_of_arrays, + _to_array_maybe_ragged) from .extrapolation import _parse_extrapolation @@ -127,13 +128,13 @@ def domain_range(self): """ pass - def _reshape_eval_points(self, eval_points, evaluation_aligned): + def _reshape_eval_points(self, eval_points, aligned): """Convert and reshape the eval_points to ndarray with the corresponding shape. Args: eval_points (array_like): Evaluation points to be reshaped. - evaluation_aligned (bool): Boolean flag. True if all the samples + aligned (bool): Boolean flag. True if all the samples will be evaluated at the same evaluation_points. Returns: @@ -145,30 +146,31 @@ def _reshape_eval_points(self, eval_points, evaluation_aligned): """ - # Case evaluation of a scalar value, i.e., f(0) - if np.isscalar(eval_points): - eval_points = [eval_points] + if aligned: + eval_points = np.asarray(eval_points) + else: + eval_points = _to_array_maybe_ragged( + eval_points, row_shape=(-1, self.dim_domain)) - # Creates a copy of the eval points, and convert to np.array - eval_points = np.array(eval_points, dtype=float) + # Case evaluation of a single value, i.e., f(0) + # Only allowed for aligned evaluation + if aligned and (eval_points.shape == (self.dim_domain,) + or (eval_points.ndim == 0 and self.dim_domain == 1)): + eval_points = np.array([eval_points]) - if evaluation_aligned: # Samples evaluated at same eval points + if aligned: # Samples evaluated at same eval points eval_points = eval_points.reshape((eval_points.shape[0], self.dim_domain)) else: # Different eval_points for each sample - if eval_points.ndim < 2 or eval_points.shape[0] != self.n_samples: + if eval_points.shape[0] != self.n_samples: raise ValueError(f"eval_points should be a list " f"of length {self.n_samples} with the " f"evaluation points for each sample.") - eval_points = eval_points.reshape((eval_points.shape[0], - eval_points.shape[1], - self.dim_domain)) - return eval_points def _extrapolation_index(self, eval_points): @@ -260,10 +262,6 @@ def _evaluate_grid(self, axes, *, extrapolation=None, """ - # If a numpy array is a ragged array (in unaligned evaluation, if there - # are different points per sample) we have to add the object dtype - additional_numpy_params = {} - # Compute intersection points and resulting shapes if aligned: @@ -280,10 +278,7 @@ def _evaluate_grid(self, axes, *, extrapolation=None, eval_points, shape = zip( *[self._one_grid_to_points(a) for a in axes]) - if not all(s == shape[0] for s in shape): - additional_numpy_params['dtype'] = np.object_ - - eval_points = np.array(eval_points, **additional_numpy_params) + eval_points = np.array(eval_points) # Evaluate the points res = self.evaluate(eval_points, @@ -298,9 +293,9 @@ def _evaluate_grid(self, axes, *, extrapolation=None, else: - res = np.array([ + res = _to_array_maybe_ragged([ r.reshape(list(s) + [self.dim_codomain]) - for r, s in zip(res, shape)], **additional_numpy_params) + for r, s in zip(res, shape)]) return res @@ -372,10 +367,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, Args: eval_points (array_like): List of points where the functions are - evaluated. If ``grid`` is ``True``, a list of axes, one per domain - dimension, must be passed instead. If ``aligned`` is - ``True``, then a list of lists (of points or axes, as explained) - must be passed, with one list per sample. + evaluated. If ``grid`` is ``True``, a list of axes, one per + domain dimension, must be passed instead. If ``aligned`` is + ``True``, then a list of lists (of points or axes, as + explained) must be passed, with one list per sample. extrapolation (str or Extrapolation, optional): Controls the extrapolation mode for elements outside the domain range. By default it is used the mode defined during the instance of the @@ -418,7 +413,7 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, # Convert to array and check dimensions of eval points eval_points = self._reshape_eval_points(eval_points, - aligned) + aligned=aligned) # Check if extrapolation should be applied if extrapolation is not None: diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 07705dca3..2f708aad2 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -470,13 +470,12 @@ def to_grid(self, eval_points=None): ... basis=Monomial((0,5), n_basis=3)) >>> fd.to_grid([0, 1, 2]) FDataGrid( - array([[[ 1.], - [ 3.], - [ 7.]], - - [[ 1.], - [ 2.], - [ 5.]]]), + array([[[1], + [3], + [7]], + [[1], + [2], + [5]]]), sample_points=[array([0, 1, 2])], domain_range=array([[0, 5]]), ...) diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index f9942c473..2967c29a8 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -47,12 +47,9 @@ def evaluate(self, fdata, eval_points, *, derivative=0, aligned=True): fdata.dim_codomain) else: - shape = (fdata.n_samples, eval_points.shape[1], fdata.dim_codomain) - res = np.empty(shape) - - for i in range(fdata.n_samples): - res[i] = self._evaluate_codomain( - self.splines[i], eval_points[i], derivative=derivative) + res = np.array([self._evaluate_codomain( + s, e, derivative=derivative) + for s, e in zip(self.splines, eval_points)]) return res @@ -148,7 +145,7 @@ def constructor(data): def _evaluate_one(self, spl, t, derivative=0): try: - return spl(t, derivative) + return spl(t, derivative)[:, 0] except ValueError: return np.zeros_like(t) diff --git a/tests/test_grid.py b/tests/test_grid.py index a9a86dce7..091fc54ae 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -239,6 +239,19 @@ def test_evaluate_unaligned(self): np.testing.assert_allclose(res, expected) + def test_evaluate_unaligned_ragged(self): + + res = self.fd([[(0, 0), (1, 1), (2, 2), (3, 3)], + [(1, 7), (5, 2), (3, 4)]], + aligned=False) + expected = ([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], + [[3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + + self.assertEqual(len(res), self.fd.n_samples) + + for r, e in zip(res, expected): + np.testing.assert_allclose(r, e) + def test_evaluate_grid_aligned(self): res = self.fd([[0, 1], [1, 2]], grid=True) @@ -256,6 +269,16 @@ def test_evaluate_grid_unaligned(self): np.testing.assert_allclose(res, expected) + def test_evaluate_grid_unaligned_ragged(self): + + res = self.fd([[[0, 1], [1, 2]], [[3, 4], [5]]], + grid=True, aligned=False) + expected = ([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], + [[[3, 4, 5]], [[3, 4, 5]]]]) + + for r, e in zip(res, expected): + np.testing.assert_allclose(r, e) + if __name__ == '__main__': print() From 7479fff6ec6d1c476a819912c09228a31cd30aba Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 29 Jun 2020 13:21:35 +0200 Subject: [PATCH 587/624] Fix FPCA modifying input dataset. --- .../dim_reduction/projection/_fpca.py | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index c581d97c1..efc224e2d 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -1,6 +1,7 @@ """Functional Principal Component Analysis Module.""" import skfda +from skfda.misc.regularization import compute_penalty_matrix from skfda.representation.basis import FDataBasis from skfda.representation.grid import FDataGrid @@ -10,8 +11,6 @@ import numpy as np -from skfda.misc.regularization import compute_penalty_matrix - __author__ = "Yujian Hong" __email__ = "yujian.hong@estudiante.uam.es" @@ -42,12 +41,13 @@ class FPCA(BaseEstimator, TransformerMixin): This parameter is only used when fitting a FDataGrid. Attributes: - components_ (FDataBasis): this contains the principal components in a + components_ (FData): this contains the principal components in a basis representation. explained_variance_ (array_like): The amount of variance explained by each of the selected components. explained_variance_ratio_ (array_like): this contains the percentage of variance explained by each principal component. + mean_ (FData): mean of the train data. Examples: @@ -91,6 +91,13 @@ def __init__(self, self.weights = weights self.components_basis = components_basis + def _center_if_necessary(self, X, *, learn_mean=True): + + if learn_mean: + self.mean_ = X.mean() + + return X - self.mean_ if self.centering else X + def _fit_basis(self, X: FDataBasis, y=None): """Computes the first n_components principal components and saves them. The eigenvalues associated with these principal components are also @@ -134,11 +141,7 @@ def _fit_basis(self, X: FDataBasis, y=None): # if centering is True then subtract the mean function to each function # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataBasis as a centering function - # subtract from each row the mean coefficient matrix - X.coefficients -= meanfd.coefficients + X = self._center_if_necessary(X) # setup principal component basis if not given components_basis = self.components_basis @@ -267,12 +270,7 @@ def _fit_grid(self, X: FDataGrid, y=None): # if centering is True then subtract the mean function to each function # in FDataBasis - if self.centering: - meanfd = X.mean() - # consider moving these lines to FDataGrid as a centering function - # subtract from each row the mean coefficient matrix - fd_data -= meanfd.data_matrix.reshape( - meanfd.data_matrix.shape[:-1]) + X = self._center_if_necessary(X) # establish weights for each point of discretization if not self.weights: @@ -319,7 +317,7 @@ def _fit_grid(self, X: FDataGrid, y=None): return self - def _transform_grid(self, X : FDataGrid, y=None): + def _transform_grid(self, X: FDataGrid, y=None): """Computes the n_components first principal components score and returns them. @@ -336,6 +334,7 @@ def _transform_grid(self, X : FDataGrid, y=None): # in this case its the coefficient matrix multiplied by the principal # components as column vectors + return X.data_matrix.reshape( X.data_matrix.shape[:-1]) @ np.transpose( self.components_.data_matrix.reshape( @@ -375,6 +374,8 @@ def transform(self, X, y=None): (array_like): the scores of the data with reference to the principal components """ + X = self._center_if_necessary(X, learn_mean=False) + if isinstance(X, FDataGrid): return self._transform_grid(X, y) elif isinstance(X, FDataBasis): From 6f00fac9b0db32a0bcadb93c9bc267de3eae014a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 29 Jun 2020 16:52:57 +0200 Subject: [PATCH 588/624] Remove useless refecth. --- examples/plot_fpca.py | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 5fc4d9fc4..460a1db7c 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -8,13 +8,15 @@ # Author: Yujian Hong # License: MIT -import numpy as np import skfda -from skfda.preprocessing.dim_reduction.projection import FPCA -from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.datasets import fetch_growth from skfda.exploratory.visualization import plot_fpca_perturbation_graphs +from skfda.preprocessing.dim_reduction.projection import FPCA +from skfda.representation.basis import BSpline, Fourier, Monomial + import matplotlib.pyplot as plt +import numpy as np + ############################################################################## # In this example we are going to use functional principal component analysis to @@ -66,18 +68,8 @@ ############################################################################## # To better illustrate the effects of the obtained two principal components, # we add and subtract a multiple of the components to the mean function. -# As the module modifies the original data, we have to fetch the data again. -# And then we get the mean function and plot it. -dataset = fetch_growth() -fd = dataset['data'] -basis_fd = fd.to_basis(BSpline(n_basis=7)) -mean_fd = basis_fd.mean() -mean_fd.plot() - -############################################################################## -# Now we add and subtract a multiple of the principal components. We can -# then observe now that this principal component represents the variation in the -# mean growth between the children. +# We can then observe now that this principal component represents the +# variation in the mean growth between the children. # The second component is more interesting. The most appropriate explanation is # that it represents the differences between girls and boys. Girls tend to grow # faster at an early age and boys tend to start puberty later, therefore, their @@ -86,14 +78,15 @@ plot_fpca_perturbation_graphs(basis_fd.mean(), fpca.components_, 30, - fig=plt.figure(figsize=(6, 2*4))) + fig=plt.figure(figsize=(6, 2 * 4))) ############################################################################## # We can also specify another basis for the principal components as argument # when creating the FPCABasis object. For example, if we use the Fourier basis # for the obtained principal components we can see that the components are # periodic. This example is only to illustrate the effect. In this dataset, as -# the functions are not periodic it does not make sense to use the Fourier basis +# the functions are not periodic it does not make sense to use the Fourier +# basis dataset = fetch_growth() fd = dataset['data'] basis_fd = fd.to_basis(BSpline(n_basis=7)) From 7be6fc142a0573ef08619a7eaf843dbddaa4bab3 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 2 Jul 2020 19:50:34 +0200 Subject: [PATCH 589/624] Add vector valued basis. --- docs/modules/representation.rst | 12 ++- skfda/representation/basis/__init__.py | 1 + skfda/representation/basis/_basis.py | 17 ++- skfda/representation/basis/_fdatabasis.py | 14 +-- skfda/representation/basis/_vector_basis.py | 112 ++++++++++++++++++++ tests/test_basis.py | 37 ++++++- 6 files changed, 174 insertions(+), 19 deletions(-) create mode 100644 skfda/representation/basis/_vector_basis.py diff --git a/docs/modules/representation.rst b/docs/modules/representation.rst index 50d25b35d..606372c3c 100644 --- a/docs/modules/representation.rst +++ b/docs/modules/representation.rst @@ -45,7 +45,8 @@ of elements of a basis function system. skfda.representation.basis.FDataBasis -The following classes are used to define different basis systems. +The following classes are used to define different basis for +:math:`\mathbb{R} \to \mathbb{R}` functions. .. autosummary:: :toctree: autosummary @@ -55,6 +56,15 @@ The following classes are used to define different basis systems. skfda.representation.basis.Monomial skfda.representation.basis.Constant +The following class, allows the construction of a basis for +:math:`\mathbb{R}^n \to \mathbb{R}^m` functions from +several :math:`\mathbb{R}^n \to \mathbb{R}` bases. + +.. autosummary:: + :toctree: autosummary + + skfda.representation.basis.VectorValued + Generic representation ---------------------- diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 3d65c4839..93953425d 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -5,3 +5,4 @@ from ._fdatabasis import FDataBasis, FDataBasisDType from ._fourier import Fourier from ._monomial import Monomial +from ._vector_basis import VectorValued diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 545cda40a..3c6e6a143 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -59,10 +59,17 @@ def __init__(self, domain_range=None, n_basis=1): self._domain_range = domain_range self.n_basis = n_basis - self._drop_index_lst = [] super().__init__() + @property + def dim_domain(self): + return 1 + + @property + def dim_codomain(self): + return 1 + @property def domain_range(self): if self._domain_range is None: @@ -189,14 +196,6 @@ def rescale(self, domain_range=None): return type(self)(domain_range, self.n_basis) - def same_domain(self, other): - r"""Returns if two basis are defined on the same domain range. - - Args: - other (Basis): Basis to check the domain range definition - """ - return _same_domain(self, other) - def copy(self): """Basis copy""" return copy.deepcopy(self) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 2f708aad2..bd847bb82 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -196,15 +196,13 @@ def n_samples(self): def dim_domain(self): """Return number of dimensions of the domain.""" - # Only domain dimension equal to 1 is supported - return 1 + return self.basis.dim_domain @property def dim_codomain(self): """Return number of dimensions of the image.""" - # Only image dimension equal to 1 is supported - return 1 + return self.basis.dim_codomain @property def coordinates(self): @@ -230,7 +228,7 @@ def n_basis(self): @property def domain_range(self): - """Definition range.""" + return self.basis.domain_range def _evaluate(self, eval_points, *, aligned=True): @@ -245,7 +243,8 @@ def _evaluate(self, eval_points, *, aligned=True): res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) - return res.reshape((self.n_samples, len(eval_points), 1)) + return res.reshape( + (self.n_samples, len(eval_points), self.dim_codomain)) else: @@ -259,7 +258,8 @@ def _evaluate(self, eval_points, *, aligned=True): np.multiply(basis_values, self.coefficients[i], out=_matrix) np.sum(_matrix, axis=1, out=res_matrix[i]) - return res_matrix.reshape((self.n_samples, eval_points.shape[1], 1)) + return res_matrix.reshape( + (self.n_samples, eval_points.shape[1], self.dim_codomain)) def shift(self, shifts, *, restrict_domain=False, extrapolation=None, eval_points=None, **kwargs): diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py new file mode 100644 index 000000000..c48e59efe --- /dev/null +++ b/skfda/representation/basis/_vector_basis.py @@ -0,0 +1,112 @@ +''' +Created on 2 jul. 2020 + +@author: Carlos +''' +import scipy.linalg + +import numpy as np + +from ..._utils import _same_domain +from ._basis import Basis + + +class VectorValued(Basis): + r"""Vector-valued basis. + + Basis for vector-valued functions constructed from scalar-valued bases. + + For each dimension in the codomain, it uses a scalar-valued basis + multiplying each basis by the corresponding unitary vector. + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a vector-valued base over the interval :math:`[0, 5]` + consisting on the functions + + .. math:: + + 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} + + >>> from skfda.representation.basis import VectorValued, Monomial + >>> + >>> basis_x = Monomial((0,5), n_basis=3) + >>> basis_y = Monomial((0,5), n_basis=2) + >>> + >>> basis = VectorValued([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([0., 1., 2.]) + array([[[ 1., 0.], + [ 1., 0.], + [ 1., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 2., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 4., 0.]], + [[ 0., 1.], + [ 0., 1.], + [ 0., 1.]], + [[ 0., 0.], + [ 0., 1.], + [ 0., 2.]]]) + + """ + + def __init__(self, basis_list): + + if not all(b.dim_codomain == 1 for b in basis_list): + raise ValueError("The basis functions must be " + "scalar valued") + + if any(b.dim_domain != basis_list[0].dim_domain or + not _same_domain(b, basis_list[0]) + for b in basis_list): + raise ValueError("The basis must all have the same domain " + "dimension an range") + + self.basis_list = basis_list + + super().__init__( + domain_range=basis_list[0].domain_range, + n_basis=sum(b.n_basis for b in basis_list)) + + @property + def dim_domain(self): + return self.basis_list[0].dim_domain + + @property + def dim_codomain(self): + return len(self.basis_list) + + def _evaluate(self, eval_points): + matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) + + n_basis_evaluated = 0 + + basis_evaluations = [b._evaluate(eval_points) for b in self.basis_list] + + for i, ev in enumerate(basis_evaluations): + + matrix[n_basis_evaluated:n_basis_evaluated + len(ev), :, i] = ev + n_basis_evaluated += len(ev) + + return matrix + + def _derivative_basis_and_coefs(self, coefs, order=1): + pass + + def basis_of_product(self, other): + pass + + def rbasis_of_product(self, other): + pass diff --git a/tests/test_basis.py b/tests/test_basis.py index f8d7859c4..e49af8017 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,7 +1,7 @@ +from skfda import concatenate from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, - BSpline, Fourier) + BSpline, Fourier, VectorValued) from skfda.representation.grid import FDataGrid -from skfda import concatenate import unittest import numpy as np @@ -450,6 +450,39 @@ def test_concatenate(self): np.testing.assert_array_equal(fd.coefficients, np.concatenate( [fd1.coefficients, fd2.coefficients])) + def test_vector_valued_constant(self): + + basis_first = Constant() + basis_second = Constant() + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) + + self.assertEqual(fd.dim_codomain, 2) + + res = np.array([[[1, 2]], [[3, 4]]]) + + np.testing.assert_allclose(fd(0), res) + + def test_vector_valued_constant_monomial(self): + + basis_first = Constant(domain_range=(0, 5)) + basis_second = Monomial(n_basis=3, domain_range=(0, 5)) + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis(basis=basis, coefficients=[[1, 2, 3, 4], [3, 4, 5, 6]]) + + self.assertEqual(fd.dim_codomain, 2) + + np.testing.assert_allclose(fd.domain_range[0], (0, 5)) + + res = np.array([[[1, 2], [1, 9], [1, 24]], [[3, 4], [3, 15], [3, 38]]]) + + np.testing.assert_allclose(fd([0, 1, 2]), res) + + if __name__ == '__main__': print() unittest.main() From 2d94ab906c78b4ed4cf7077f08b0a6fca115bf7c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 3 Jul 2020 01:08:43 +0200 Subject: [PATCH 590/624] Basis evaluation now has the same shape as FData evaluation. --- skfda/misc/operators/_operators.py | 4 +++- skfda/preprocessing/smoothing/_basis.py | 20 +++++++++++--------- skfda/representation/basis/_basis.py | 3 ++- skfda/representation/basis/_bspline.py | 12 +++++++++--- skfda/representation/basis/_fdatabasis.py | 14 ++++++-------- skfda/representation/basis/_fourier.py | 15 ++++++++++++--- skfda/representation/basis/_monomial.py | 12 +++++++++--- skfda/representation/grid.py | 16 ++++++++++------ tests/test_regularization.py | 3 ++- 9 files changed, 64 insertions(+), 35 deletions(-) diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 1f1e369ba..038dd4ff2 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -40,7 +40,9 @@ def compute_triang_functional(evaluated_basis, basis): def cross_product(x): """Multiply the two evaluations.""" - res = evaluated_basis([x])[:, 0] + res = evaluated_basis([x]) + + res = res.reshape((res.shape[0], -1)) return res[indices[0]] * res[indices[1]] diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index bfc0cf934..9f5ac92be 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -335,27 +335,28 @@ def _coef_matrix(self, input_points): """Get the matrix that gives the coefficients""" from ...misc.regularization import compute_penalty_matrix - basis_values_input = self.basis.evaluate(input_points).T + basis_values_input = self.basis.evaluate(input_points).reshape( + (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one weight_matrix = (self.weights if self.weights is not None else np.identity(basis_values_input.shape[0])) - inv = basis_values_input.T @ weight_matrix @ basis_values_input + ols_matrix = basis_values_input.T @ weight_matrix @ basis_values_input penalty_matrix = compute_penalty_matrix( basis_iterable=(self.basis,), regularization_parameter=self.smoothing_parameter, regularization=self.regularization) - inv += penalty_matrix + ols_matrix += penalty_matrix - inv = np.linalg.inv(inv) - - return inv @ basis_values_input.T @ weight_matrix + return np.linalg.solve( + ols_matrix, basis_values_input.T @ weight_matrix) def _hat_matrix(self, input_points, output_points): - basis_values_output = self.basis.evaluate(output_points).T + basis_values_output = self.basis.evaluate(output_points).reshape( + (self.basis.n_basis, -1)).T return basis_values_output @ self._coef_matrix(input_points) @@ -414,10 +415,11 @@ def fit_transform(self, X: FDataGrid, y=None): # k is the number of elements of the basis # Each sample in a column (m x n) - data_matrix = X.data_matrix[..., 0].T + data_matrix = X.data_matrix.reshape((X.n_samples, -1)).T # Each basis in a column - basis_values = self.basis.evaluate(self.input_points_).T + basis_values = self.basis.evaluate(self.input_points_).reshape( + (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one weight_matrix = (self.weights if self.weights is not None diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 3c6e6a143..313d98e25 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -114,7 +114,8 @@ def evaluate(self, eval_points, derivative=0): raise ValueError("The list of points where the function is " "evaluated can not contain nan values.") - return self._evaluate(eval_points) + return self._evaluate(eval_points).reshape( + (self.n_basis, len(eval_points), self.dim_codomain)) def __call__(self, *args, **kwargs): return self.evaluate(*args, **kwargs) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 8a12185fd..1a7f17294 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -56,9 +56,15 @@ class BSpline(Basis): >>> bss = BSpline(n_basis=3, order=3) >>> bss([0, 0.5, 1]) - array([[ 1. , 0.25, 0. ], - [ 0. , 0.5 , 0. ], - [ 0. , 0.25, 1. ]]) + array([[[ 1. ], + [ 0.25], + [ 0. ]], + [[ 0. ], + [ 0.5 ], + [ 0. ]], + [[ 0. ], + [ 0.25], + [ 1. ]]]) And evaluates first derivative diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index bd847bb82..165e73117 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -248,18 +248,16 @@ def _evaluate(self, eval_points, *, aligned=True): else: - res_matrix = np.empty((self.n_samples, eval_points.shape[1])) - - _matrix = np.empty((eval_points.shape[1], self.n_basis)) + res_matrix = np.empty( + (self.n_samples, eval_points.shape[1], self.dim_codomain)) for i in range(self.n_samples): - basis_values = self.basis.evaluate(eval_points[i]).T + basis_values = self.basis.evaluate(eval_points[i]) - np.multiply(basis_values, self.coefficients[i], out=_matrix) - np.sum(_matrix, axis=1, out=res_matrix[i]) + values = self.coefficients[i] * basis_values.T + np.sum(values.T, axis=0, out=res_matrix[i]) - return res_matrix.reshape( - (self.n_samples, eval_points.shape[1], self.dim_codomain)) + return res_matrix def shift(self, shifts, *, restrict_domain=False, extrapolation=None, eval_points=None, **kwargs): diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 37c8200b3..55698cbd1 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -36,9 +36,18 @@ class Fourier(Basis): >>> fb = Fourier((0, np.pi), n_basis=3, period=1) >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) - array([[ 1. , 1. , 1. , 1. ], - [ 0. , -1.38, -0.61, 1.1 ], - [ 1.41, 0.31, -1.28, 0.89]]) + array([[[ 1. ], + [ 1. ], + [ 1. ], + [ 1. ]], + [[ 0. ], + [-1.38], + [-0.61], + [ 1.1 ]], + [[ 1.41], + [ 0.31], + [-1.28], + [ 0.89]]]) And evaluate second derivative diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index d67899dc5..d6723b673 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -29,9 +29,15 @@ class Monomial(Basis): values. >>> bs_mon([0., 1., 2.]) - array([[ 1., 1., 1.], - [ 0., 1., 2.], - [ 0., 1., 4.]]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 1.], + [ 2.]], + [[ 0.], + [ 1.], + [ 4.]]]) And also evaluates its derivatives diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 1bac3d41d..de4c346a5 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -771,12 +771,16 @@ def to_basis(self, basis, **kwargs): array([[ 2. , 0.71, 0.71]]) """ - if self.dim_domain > 1: - raise NotImplementedError("Only support 1 dimension on the " - "domain.") - elif self.dim_codomain > 1: - raise NotImplementedError("Only support 1 dimension on the " - "image.") + if self.dim_domain != basis.dim_domain: + raise ValueError(f"The domain of the function has " + f"dimension {self.dim_domain} " + f"but the domain of the basis has " + f"dimension {basis.dim_domain}") + elif self.dim_codomain != basis.dim_codomain: + raise ValueError(f"The codomain of the function has " + f"dimension {self.dim_codomain} " + f"but the codomain of the basis has " + f"dimension {basis.dim_codomain}") # Readjust the domain range if there was not an explicit one if basis._domain_range is None: diff --git a/tests/test_regularization.py b/tests/test_regularization.py index ea744ddf5..3daadcb33 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -184,7 +184,8 @@ def test_basis_conversion(self): smoother = skfda.preprocessing.smoothing.BasisSmoother( basis=skfda.representation.basis.BSpline( n_basis=10, domain_range=fd.domain_range), - regularization=TikhonovRegularization(lambda x: x(1) - x(0)), + regularization=TikhonovRegularization( + lambda x: x(1)[:, 0] - x(0)[:, 0]), smoothing_parameter=10000) fd_basis = smoother.fit_transform(fd) From b370d676663f8543447f79b140029820bee5571b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 3 Jul 2020 02:28:42 +0200 Subject: [PATCH 591/624] Pass input with all dimensions to basis. --- skfda/_utils/__init__.py | 3 +- skfda/_utils/_utils.py | 47 ++++++++++++++++++++ skfda/representation/_functional_data.py | 53 +++-------------------- skfda/representation/basis/_basis.py | 10 ++--- skfda/representation/basis/_bspline.py | 3 ++ skfda/representation/basis/_fdatabasis.py | 3 -- skfda/representation/basis/_fourier.py | 4 ++ skfda/representation/basis/_monomial.py | 4 ++ tests/test_basis.py | 34 +-------------- tests/test_basis_evaluation.py | 40 ++++++++++++++++- 10 files changed, 110 insertions(+), 91 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 5f3657fda..3a718f43f 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -3,4 +3,5 @@ from ._utils import (_list_of_arrays, _cartesian_product, _check_estimator, parameter_aliases, _to_grid, check_is_univariate, - _same_domain, _to_array_maybe_ragged) + _same_domain, _to_array_maybe_ragged, + _reshape_eval_points) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 357a95716..24666c239 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -166,6 +166,53 @@ def _same_domain(fd, fd2): return np.array_equal(fd.domain_range, fd2.domain_range) +def _reshape_eval_points(eval_points, *, aligned, n_samples, dim_domain): + """Convert and reshape the eval_points to ndarray with the + corresponding shape. + + Args: + eval_points (array_like): Evaluation points to be reshaped. + aligned (bool): Boolean flag. True if all the samples + will be evaluated at the same evaluation_points. + dim_domain (int): Dimension of the domain. + + Returns: + (np.ndarray): Numpy array with the eval_points, if + evaluation_aligned is True with shape `number of evaluation points` + x `dim_domain`. If the points are not aligned the shape of the + points will be `n_samples` x `number of evaluation points` + x `dim_domain`. + + """ + + if aligned: + eval_points = np.asarray(eval_points) + else: + eval_points = _to_array_maybe_ragged( + eval_points, row_shape=(-1, dim_domain)) + + # Case evaluation of a single value, i.e., f(0) + # Only allowed for aligned evaluation + if aligned and (eval_points.shape == (dim_domain,) + or (eval_points.ndim == 0 and dim_domain == 1)): + eval_points = np.array([eval_points]) + + if aligned: # Samples evaluated at same eval points + + eval_points = eval_points.reshape((eval_points.shape[0], + dim_domain)) + + else: # Different eval_points for each sample + + if eval_points.shape[0] != n_samples: + + raise ValueError(f"eval_points should be a list " + f"of length {n_samples} with the " + f"evaluation points for each sample.") + + return eval_points + + def parameter_aliases(**alias_assignments): """Allows using aliases for parameters""" def decorator(f): diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 23523fcb8..a2cdce5da 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -12,7 +12,7 @@ import numpy as np from .._utils import (_cartesian_product, _list_of_arrays, - _to_array_maybe_ragged) + _to_array_maybe_ragged, _reshape_eval_points) from .extrapolation import _parse_extrapolation @@ -128,51 +128,6 @@ def domain_range(self): """ pass - def _reshape_eval_points(self, eval_points, aligned): - """Convert and reshape the eval_points to ndarray with the - corresponding shape. - - Args: - eval_points (array_like): Evaluation points to be reshaped. - aligned (bool): Boolean flag. True if all the samples - will be evaluated at the same evaluation_points. - - Returns: - (np.ndarray): Numpy array with the eval_points, if - evaluation_aligned is True with shape `number of evaluation points` - x `dim_domain`. If the points are not aligned the shape of the - points will be `n_samples` x `number of evaluation points` - x `dim_domain`. - - """ - - if aligned: - eval_points = np.asarray(eval_points) - else: - eval_points = _to_array_maybe_ragged( - eval_points, row_shape=(-1, self.dim_domain)) - - # Case evaluation of a single value, i.e., f(0) - # Only allowed for aligned evaluation - if aligned and (eval_points.shape == (self.dim_domain,) - or (eval_points.ndim == 0 and self.dim_domain == 1)): - eval_points = np.array([eval_points]) - - if aligned: # Samples evaluated at same eval points - - eval_points = eval_points.reshape((eval_points.shape[0], - self.dim_domain)) - - else: # Different eval_points for each sample - - if eval_points.shape[0] != self.n_samples: - - raise ValueError(f"eval_points should be a list " - f"of length {self.n_samples} with the " - f"evaluation points for each sample.") - - return eval_points - def _extrapolation_index(self, eval_points): """Checks the points that need to be extrapolated. @@ -412,8 +367,10 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, extrapolation = _parse_extrapolation(extrapolation) # Convert to array and check dimensions of eval points - eval_points = self._reshape_eval_points(eval_points, - aligned=aligned) + eval_points = _reshape_eval_points(eval_points, + aligned=aligned, + n_samples=self.n_samples, + dim_domain=self.dim_domain) # Check if extrapolation should be applied if extrapolation is not None: diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 313d98e25..5cc35d38f 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -12,7 +12,7 @@ import numpy as np -from ..._utils import _list_of_arrays, _same_domain +from ..._utils import _list_of_arrays, _same_domain, _reshape_eval_points __author__ = "Miguel Carbajo Berrocal" @@ -109,10 +109,10 @@ def evaluate(self, eval_points, derivative=0): "derivative function instead.", DeprecationWarning) return self.derivative(order=derivative)(eval_points) - eval_points = np.atleast_1d(eval_points) - if np.any(np.isnan(eval_points)): - raise ValueError("The list of points where the function is " - "evaluated can not contain nan values.") + eval_points = _reshape_eval_points(eval_points, + aligned=True, + n_samples=self.n_basis, + dim_domain=self.dim_domain) return self._evaluate(eval_points).reshape( (self.n_basis, len(eval_points), self.dim_codomain)) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 1a7f17294..a85ce8da9 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -171,6 +171,9 @@ def _evaluation_knots(self): def _evaluate(self, eval_points): + # Input is scalar + eval_points = eval_points[..., 0] + # Places m knots at the boundaries knots = self._evaluation_knots() diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 165e73117..0a7d3f66c 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -233,9 +233,6 @@ def domain_range(self): def _evaluate(self, eval_points, *, aligned=True): - #  Only suported 1D objects - eval_points = eval_points[..., 0] - if aligned: # Each row contains the values of one element of the basis diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 55698cbd1..4da88672a 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -108,6 +108,10 @@ def period(self, value): self._period = value def _evaluate(self, eval_points): + + # Input is scalar + eval_points = eval_points[..., 0] + functions = [np.sin, np.cos] omega = 2 * np.pi / self.period diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index d6723b673..ce1442cdd 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -66,6 +66,10 @@ class Monomial(Basis): """ def _evaluate(self, eval_points): + + # Input is scalar + eval_points = eval_points[..., 0] + exps = np.arange(self.n_basis) raised = np.power.outer(eval_points, exps) diff --git a/tests/test_basis.py b/tests/test_basis.py index e49af8017..ac2a30e1d 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,6 +1,6 @@ from skfda import concatenate from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, - BSpline, Fourier, VectorValued) + BSpline, Fourier) from skfda.representation.grid import FDataGrid import unittest @@ -450,38 +450,6 @@ def test_concatenate(self): np.testing.assert_array_equal(fd.coefficients, np.concatenate( [fd1.coefficients, fd2.coefficients])) - def test_vector_valued_constant(self): - - basis_first = Constant() - basis_second = Constant() - - basis = VectorValued([basis_first, basis_second]) - - fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) - - self.assertEqual(fd.dim_codomain, 2) - - res = np.array([[[1, 2]], [[3, 4]]]) - - np.testing.assert_allclose(fd(0), res) - - def test_vector_valued_constant_monomial(self): - - basis_first = Constant(domain_range=(0, 5)) - basis_second = Monomial(n_basis=3, domain_range=(0, 5)) - - basis = VectorValued([basis_first, basis_second]) - - fd = FDataBasis(basis=basis, coefficients=[[1, 2, 3, 4], [3, 4, 5, 6]]) - - self.assertEqual(fd.dim_codomain, 2) - - np.testing.assert_allclose(fd.domain_range[0], (0, 5)) - - res = np.array([[[1, 2], [1, 9], [1, 24]], [[3, 4], [3, 15], [3, 38]]]) - - np.testing.assert_allclose(fd([0, 1, 2]), res) - if __name__ == '__main__': print() diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 99727bc5e..654ad4404 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -1,5 +1,6 @@ -from skfda.representation.basis import FDataBasis, Monomial, BSpline, Fourier +from skfda.representation.basis import ( + FDataBasis, Monomial, BSpline, Fourier, Constant, VectorValued) import unittest import numpy as np @@ -414,6 +415,43 @@ def test_domain_in_list_monomial(self): np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) +class TestBasisEvaluationVectorValued(unittest.TestCase): + + def test_vector_valued_constant(self): + + basis_first = Constant() + basis_second = Constant() + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) + + self.assertEqual(fd.dim_codomain, 2) + + res = np.array([[[1, 2]], [[3, 4]]]) + + np.testing.assert_allclose(fd(0), res) + + def test_vector_valued_constant_monomial(self): + + basis_first = Constant(domain_range=(0, 5)) + basis_second = Monomial(n_basis=3, domain_range=(0, 5)) + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis(basis=basis, coefficients=[ + [1, 2, 3, 4], [3, 4, 5, 6]]) + + self.assertEqual(fd.dim_codomain, 2) + + np.testing.assert_allclose(fd.domain_range[0], (0, 5)) + + res = np.array([[[1, 2], [1, 9], [1, 24]], + [[3, 4], [3, 15], [3, 38]]]) + + np.testing.assert_allclose(fd([0, 1, 2]), res) + + if __name__ == '__main__': print() unittest.main() From 8993c6175a57ad67beb1304b2af7c187042e7363 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 3 Jul 2020 04:25:16 +0200 Subject: [PATCH 592/624] Basic conversion from grid to vector-valued working. --- skfda/preprocessing/smoothing/_basis.py | 2 -- skfda/representation/grid.py | 4 ++-- tests/test_basis.py | 13 +++++++++++++ 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 9f5ac92be..27b08c6ab 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -398,8 +398,6 @@ def fit_transform(self, X: FDataGrid, y=None): """ from ...misc.regularization import compute_penalty_matrix - _check_r_to_r(X) - self.input_points_ = X.sample_points[0] self.output_points_ = (self.output_points if self.output_points is not None diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index de4c346a5..ebe0090ad 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -787,8 +787,8 @@ def to_basis(self, basis, **kwargs): basis = basis.copy() basis.domain_range = self.domain_range - return fdbasis.FDataBasis.from_data(self.data_matrix[..., 0], - self.sample_points[0], + return fdbasis.FDataBasis.from_data(self.data_matrix, + self.sample_points, basis, **kwargs) diff --git a/tests/test_basis.py b/tests/test_basis.py index ac2a30e1d..7011a0731 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,4 +1,5 @@ from skfda import concatenate +import skfda from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) from skfda.representation.grid import FDataGrid @@ -450,6 +451,18 @@ def test_concatenate(self): np.testing.assert_array_equal(fd.coefficients, np.concatenate( [fd1.coefficients, fd2.coefficients])) + def test_vector_valued(self): + X, y = skfda.datasets.fetch_weather(return_X_y=True) + + basis = skfda.representation.basis.VectorValued( + [skfda.representation.basis.Fourier( + n_basis=7, domain_range=X.domain_range)] * 2 + ) + + X_basis = X.to_basis(basis) + + self.assertEqual(X_basis.dim_codomain, 2) + if __name__ == '__main__': print() From 4686193ab38a50fa70fd6fc347871f9b44ee7792 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 4 Jul 2020 03:16:13 +0200 Subject: [PATCH 593/624] Implement coordinates for FDataBasis. --- skfda/representation/basis/_basis.py | 31 +++++++++++++++++++++ skfda/representation/basis/_fdatabasis.py | 9 +++--- skfda/representation/basis/_vector_basis.py | 23 +++++++++++++++ tests/test_basis.py | 15 ++++++++-- 4 files changed, 71 insertions(+), 7 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 5cc35d38f..d129ae545 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -162,6 +162,37 @@ def plot(self, chart=None, **kwargs): """ self.to_basis().plot(chart=chart, **kwargs) + def _coordinate_nonfull(self, fdatabasis, key): + """ + Returns a fdatagrid for the coordinate functions indexed by key. + + Subclasses can override this to provide coordinate indexing. + + The key parameter has been already validated and is an integer or + slice in the range [0, self.dim_codomain. + + """ + raise NotImplementedError("Coordinate indexing not implemented") + + def _coordinate(self, fdatabasis, key): + """Returns a fdatagrid for the coordinate functions indexed by key.""" + + # Raises error if not in range and normalize key + r_key = range(self.dim_codomain)[key] + + if isinstance(r_key, range) and len(r_key) == 0: + raise IndexError("Empty number of coordinates selected") + + # Full fdatabasis case + if (self.dim_codomain == 1 and r_key == 0) or ( + isinstance(r_key, range) and len(r_key) == self.dim_codomain): + + return fdatabasis.copy() + + else: + + return self._coordinate_nonfull(fdatabasis=fdatabasis, key=r_key) + @abstractmethod def basis_of_product(self, other): pass diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 0a7d3f66c..5d9d44638 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -60,15 +60,14 @@ def __init__(self, fdatabasis): def __iter__(self): """Return an iterator through the image coordinates.""" - yield self._fdatabasis.copy() + + for i in range(len(self)): + yield self[i] def __getitem__(self, key): """Get a specific coordinate.""" - if key != 0: - return NotImplemented - - return self._fdatabasis.copy() + return self._fdatabasis.basis._coordinate(self._fdatabasis, key) def __len__(self): """Return the number of coordinates.""" diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index c48e59efe..b43a4e1b3 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -105,6 +105,29 @@ def _evaluate(self, eval_points): def _derivative_basis_and_coefs(self, coefs, order=1): pass + def _coordinate_nonfull(self, fdatabasis, key): + + r_key = key + if isinstance(r_key, int): + r_key = range(r_key, r_key + 1) + + coef_indexes = np.concatenate([ + np.ones(b.n_basis, dtype=np.bool_) if i in r_key + else np.zeros(b.n_basis, dtype=np.bool_) + for i, b in enumerate(self.basis_list)]) + + new_basis_list = self.basis_list[key] + + basis = (new_basis_list if isinstance(new_basis_list, Basis) + else VectorValued(new_basis_list)) + + coefs = fdatabasis.coefficients[:, coef_indexes] + + axes_labels = fdatabasis._get_labels_coordinates(key) + + return fdatabasis.copy(basis=basis, coefficients=coefs, + axes_labels=axes_labels) + def basis_of_product(self, other): pass diff --git a/tests/test_basis.py b/tests/test_basis.py index 7011a0731..787817e1d 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -454,15 +454,26 @@ def test_concatenate(self): def test_vector_valued(self): X, y = skfda.datasets.fetch_weather(return_X_y=True) + basis_dim = skfda.representation.basis.Fourier( + n_basis=7, domain_range=X.domain_range) basis = skfda.representation.basis.VectorValued( - [skfda.representation.basis.Fourier( - n_basis=7, domain_range=X.domain_range)] * 2 + [basis_dim] * 2 ) X_basis = X.to_basis(basis) self.assertEqual(X_basis.dim_codomain, 2) + self.assertEqual(X_basis.coordinates[0].basis, basis_dim) + np.testing.assert_allclose( + X_basis.coordinates[0].coefficients, + X.coordinates[0].to_basis(basis_dim).coefficients) + + self.assertEqual(X_basis.coordinates[1].basis, basis_dim) + np.testing.assert_allclose( + X_basis.coordinates[1].coefficients, + X.coordinates[1].to_basis(basis_dim).coefficients) + if __name__ == '__main__': print() From c7c8a13e556380a3ec6920edc6ce680a8f092e42 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 10 Jul 2020 02:00:20 +0200 Subject: [PATCH 594/624] Derivative and basis smoothing for vector valued basis. --- skfda/misc/operators/_operators.py | 8 +++- skfda/preprocessing/smoothing/_basis.py | 9 ++--- skfda/representation/basis/_vector_basis.py | 16 +++++++- tests/test_smoothing.py | 44 +++++++++++++++++++++ 4 files changed, 69 insertions(+), 8 deletions(-) diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 038dd4ff2..80ac9615c 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -42,7 +42,8 @@ def cross_product(x): """Multiply the two evaluations.""" res = evaluated_basis([x]) - res = res.reshape((res.shape[0], -1)) + # Remove n_points dimension + res = res[:, 0, :] return res[indices[0]] * res[indices[1]] @@ -53,7 +54,10 @@ def cross_product(x): integral = scipy.integrate.quad_vec( cross_product, domain_range[0], domain_range[1])[0] - return integral[..., 0] + # Sum the integrals of each codomain dimension + integral_sum = np.sum(integral, axis=-1) + + return integral_sum def compute_triang_multivariate(evaluated_basis, diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 27b08c6ab..7a22e68c4 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -108,7 +108,7 @@ def __call__(self, *, estimator, **_): def transform(self, estimator, X, y=None): if estimator.return_basis: - coefficients = (X.data_matrix[..., 0] + coefficients = (X.data_matrix.reshape((X.n_samples, -1)) @ estimator._cached_coef_matrix.T) fdatabasis = FDataBasis( @@ -325,11 +325,11 @@ def __init__(self, def _method_function(self): """ Return the method function""" method_function = self.method - if not callable(method_function): + if not isinstance(method_function, self.SolverMethod): method_function = self.SolverMethod[ - method_function.lower()].value + method_function.lower()] - return method_function + return method_function.value def _coef_matrix(self, input_points): """Get the matrix that gives the coefficients""" @@ -371,7 +371,6 @@ def fit(self, X: FDataGrid, y=None): self (object) """ - _check_r_to_r(X) self.input_points_ = X.sample_points[0] self.output_points_ = (self.output_points diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index b43a4e1b3..a419ca735 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -103,7 +103,21 @@ def _evaluate(self, eval_points): return matrix def _derivative_basis_and_coefs(self, coefs, order=1): - pass + + n_basis_list = [b.n_basis for b in self.basis_list] + indexes = np.cumsum(n_basis_list) + + coefs_per_basis = np.hsplit(coefs, indexes[:-1]) + + basis_and_coefs = [b._derivative_basis_and_coefs( + c, order=order) for b, c in zip(self.basis_list, coefs_per_basis)] + + new_basis_list, new_coefs_list = zip(*basis_and_coefs) + + new_basis = VectorValued(new_basis_list) + new_coefs = np.hstack(new_coefs_list) + + return new_basis, new_coefs def _coordinate_nonfull(self, fdatabasis, key): diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index 50d861b82..076ca5ed9 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -128,3 +128,47 @@ def test_monomial_smoothing(self): np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), np.array([[0.61, -0.88, 0.06, 0.02]])) + + def test_vector_valued_smoothing(self): + X, _ = skfda.datasets.fetch_weather(return_X_y=True) + + basis_dim = skfda.representation.basis.Fourier( + n_basis=7, domain_range=X.domain_range) + basis = skfda.representation.basis.VectorValued( + [basis_dim] * 2 + ) + + for method in smoothing.BasisSmoother.SolverMethod: + with self.subTest(method=method): + + basis_smoother = smoothing.BasisSmoother( + basis, + regularization=TikhonovRegularization( + LinearDifferentialOperator(2)), + return_basis=True, + smoothing_parameter=1, + method=method) + + basis_smoother_dim = smoothing.BasisSmoother( + basis_dim, + regularization=TikhonovRegularization( + LinearDifferentialOperator(2)), + return_basis=True, + smoothing_parameter=1, + method=method) + + X_basis = basis_smoother.fit_transform(X) + + self.assertEqual(X_basis.dim_codomain, 2) + + self.assertEqual(X_basis.coordinates[0].basis, basis_dim) + np.testing.assert_allclose( + X_basis.coordinates[0].coefficients, + basis_smoother_dim.fit_transform( + X.coordinates[0]).coefficients) + + self.assertEqual(X_basis.coordinates[1].basis, basis_dim) + np.testing.assert_allclose( + X_basis.coordinates[1].coefficients, + basis_smoother_dim.fit_transform( + X.coordinates[1]).coefficients) From 3f129b434bde4badaf66a58ac26f4057e7157545 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 12 Jul 2020 01:20:06 +0200 Subject: [PATCH 595/624] Update BasisSmoother documentation. --- skfda/preprocessing/smoothing/_basis.py | 29 ++++++++----------------- 1 file changed, 9 insertions(+), 20 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 7a22e68c4..4ad7039d2 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -166,19 +166,12 @@ class BasisSmoother(_LinearSmoother): observations. Defaults to the identity matrix. smoothing_parameter (int or float, optional): Smoothing parameter. Trying with several factors in a logarithm scale is - suggested. If 0 no smoothing is performed. Defaults to 0. - regularization (int, iterable or :class:`Regularization`): If it is - not a :class:`Regularization` object, linear differential - operator regularization is assumed. If it - is an integer, it indicates the order of the - derivative used in the computing of the penalty matrix. For - instance 2 means that the differential operator is - :math:`f''(x)`. If it is an iterable, it consists on coefficients - representing the differential operator used in the computing of - the penalty matrix. For instance the tuple (1, 0, - numpy.sin) means :math:`1 + sin(x)D^{2}`. If not supplied this - defaults to 2. Only used if penalty_matrix is - ``None``. + suggested. If 0 no smoothing is performed. Defaults to 1. + regularization (int, iterable or :class:`Regularization`): + Regularization object. This allows the penalization of + complicated models, which applies additional smoothing. By default + is ``None`` meaning that no additional smoothing has to take + place. method (str): Algorithm used for calculating the coefficients using the least squares method. The values admitted are 'cholesky', 'qr' and 'matrix' for Cholesky and QR factorisation methods, and matrix @@ -243,9 +236,8 @@ class BasisSmoother(_LinearSmoother): [ 0.14, -0.29, 0.29, 0.71, 0.14], [ 0.43, 0.14, -0.14, 0.14, 0.43]]) - If the smoothing parameter is set to something else than zero, we can - penalize approximations that are not smooth enough using some kind - of regularization: + We can penalize approximations that are not smooth enough using some + kind of regularization: >>> from skfda.misc.regularization import TikhonovRegularization >>> from skfda.misc.operators import LinearDifferentialOperator @@ -254,7 +246,6 @@ class BasisSmoother(_LinearSmoother): >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='cholesky', - ... smoothing_parameter=1, ... regularization=TikhonovRegularization( ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) @@ -266,7 +257,6 @@ class BasisSmoother(_LinearSmoother): >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='qr', - ... smoothing_parameter=1, ... regularization=TikhonovRegularization( ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) @@ -278,7 +268,6 @@ class BasisSmoother(_LinearSmoother): >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, method='matrix', - ... smoothing_parameter=1, ... regularization=TikhonovRegularization( ... LinearDifferentialOperator([0.1, 0.2])), ... return_basis=True) @@ -307,7 +296,7 @@ class SolverMethod(Enum): def __init__(self, basis, *, - smoothing_parameter: float = 0, + smoothing_parameter: float = 1., weights=None, regularization: Union[int, Iterable[float], 'LinearDifferentialOperator'] = None, From e2504d9b86efeedd617a0ef74125b3e11cbc681b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 14 Jul 2020 00:44:26 +0200 Subject: [PATCH 596/624] First version of tensor basis. --- skfda/representation/basis/__init__.py | 1 + skfda/representation/basis/_tensor_basis.py | 97 +++++++++++++++++++++ skfda/representation/basis/_vector_basis.py | 7 -- tests/test_basis_evaluation.py | 21 ++++- 4 files changed, 118 insertions(+), 8 deletions(-) create mode 100644 skfda/representation/basis/_tensor_basis.py diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 93953425d..7b2fa39e7 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -5,4 +5,5 @@ from ._fdatabasis import FDataBasis, FDataBasisDType from ._fourier import Fourier from ._monomial import Monomial +from ._tensor_basis import Tensor from ._vector_basis import VectorValued diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py new file mode 100644 index 000000000..c71d5fc20 --- /dev/null +++ b/skfda/representation/basis/_tensor_basis.py @@ -0,0 +1,97 @@ +import itertools + +import numpy as np + +from ..._utils import _same_domain +from ._basis import Basis + + +class Tensor(Basis): + r"""Tensor basis. + + Basis for multivariate functions constructed as a tensor product of + :math:`\mathbb{R} \to \mathbb{R}` bases. + + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a vector-valued base over the interval :math:`[0, 5]` + consisting on the functions + + .. math:: + + 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} + + >>> from skfda.representation.basis import VectorValued, Monomial + >>> + >>> basis_x = Monomial((0,5), n_basis=3) + >>> basis_y = Monomial((0,5), n_basis=2) + >>> + >>> basis = VectorValued([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([0., 1., 2.]) + array([[[ 1., 0.], + [ 1., 0.], + [ 1., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 2., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 4., 0.]], + [[ 0., 1.], + [ 0., 1.], + [ 0., 1.]], + [[ 0., 0.], + [ 0., 1.], + [ 0., 2.]]]) + + """ + + def __init__(self, basis_list): + + if not all(b.dim_domain == 1 and b.dim_codomain == 1 + for b in basis_list): + raise ValueError("The basis functions must be " + "univariate and scalar valued") + + self.basis_list = basis_list + + super().__init__( + domain_range=[b.domain_range[0] for b in basis_list], + n_basis=np.prod([b.n_basis for b in basis_list])) + + @property + def dim_domain(self): + return len(self.basis_list) + + def _evaluate(self, eval_points): + + matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) + + basis_evaluations = [b._evaluate(eval_points[:, i:i + 1]) + for i, b in enumerate(self.basis_list)] + + for i, ev in enumerate(itertools.product(*basis_evaluations)): + + matrix[i, :, 0] = np.prod(ev, axis=0) + + return matrix + + def _derivative_basis_and_coefs(self, coefs, order=1): + + pass + + def basis_of_product(self, other): + pass + + def rbasis_of_product(self, other): + pass diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index a419ca735..895d58c47 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -1,10 +1,3 @@ -''' -Created on 2 jul. 2020 - -@author: Carlos -''' -import scipy.linalg - import numpy as np from ..._utils import _same_domain diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index 654ad4404..c4249929c 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -1,6 +1,6 @@ from skfda.representation.basis import ( - FDataBasis, Monomial, BSpline, Fourier, Constant, VectorValued) + FDataBasis, Monomial, BSpline, Fourier, Constant, VectorValued, Tensor) import unittest import numpy as np @@ -452,6 +452,25 @@ def test_vector_valued_constant_monomial(self): np.testing.assert_allclose(fd([0, 1, 2]), res) +class TestBasisEvaluationTensor(unittest.TestCase): + + def test_tensor_monomial_constant(self): + + basis = Tensor([Monomial(n_basis=2), Constant()]) + + fd = FDataBasis(basis=basis, coefficients=[1, 1]) + + self.assertEqual(fd.dim_domain, 2) + self.assertEqual(fd.dim_codomain, 1) + + np.testing.assert_allclose(fd([0., 0.]), [[[1.]]]) + + np.testing.assert_allclose(fd([0.5, 0.5]), [[[1.5]]]) + + np.testing.assert_allclose( + fd([(0., 0.), (0.5, 0.5)]), [[[1.0], [1.5]]]) + + if __name__ == '__main__': print() unittest.main() From c211a6b82f15ee08c92da06f52f3752ad25326c4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 14 Jul 2020 01:09:04 +0200 Subject: [PATCH 597/624] Extract grid evaluation to util method. --- skfda/_utils/__init__.py | 3 +- skfda/_utils/_utils.py | 107 ++++++++++++++++++++- skfda/representation/_functional_data.py | 114 ++--------------------- skfda/representation/basis/_basis.py | 7 +- 4 files changed, 119 insertions(+), 112 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 3a718f43f..50a105de3 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -4,4 +4,5 @@ _check_estimator, parameter_aliases, _to_grid, check_is_univariate, _same_domain, _to_array_maybe_ragged, - _reshape_eval_points) + _reshape_eval_points, + _evaluate_grid) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 24666c239..6ad400d9e 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -127,7 +127,7 @@ def _cartesian_product(axes, flatten=True, return_shape=False): Examples: - >>> from skfda.representation._functional_data import _cartesian_product + >>> from skfda._utils import _cartesian_product >>> axes = [[0,1],[2,3]] >>> _cartesian_product(axes) array([[0, 2], @@ -213,6 +213,111 @@ def _reshape_eval_points(eval_points, *, aligned, n_samples, dim_domain): return eval_points +def _one_grid_to_points(axes, *, dim_domain): + """ + Convert a list of ndarrays, one per domain dimension, in the points. + + Returns also the shape containing the information of how each point + is formed. + """ + axes = _list_of_arrays(axes) + + if len(axes) != dim_domain: + raise ValueError(f"Length of axes should be " + f"{dim_domain}") + + cartesian, shape = _cartesian_product(axes, return_shape=True) + + # Drop domain size dimension, as it is not needed to reshape the output + shape = shape[:-1] + + return cartesian, shape + + +def _evaluate_grid(axes, *, evaluate_method, + n_samples, dim_domain, dim_codomain, + extrapolation=None, + aligned=True): + """Evaluate the functional object in the cartesian grid. + + This method is called internally by :meth:`evaluate` when the argument + `grid` is True. + + Evaluates the functional object in the grid generated by the cartesian + product of the axes. The length of the list of axes should be equal + than the domain dimension of the object. + + If the list of axes has lengths :math:`n_1, n_2, ..., n_m`, where + :math:`m` is equal than the dimension of the domain, the result of the + evaluation in the grid will be a matrix with :math:`m+1` dimensions and + shape :math:`n_{samples} x n_1 x n_2 x ... x n_m`. + + If `aligned` is false each sample is evaluated in a + different grid, and the list of axes should contain a list of axes for + each sample. + + If the domain dimension is 1, the result of the behaviour of the + evaluation will be the same than :meth:`evaluate` without the grid + option, but with worst performance. + + Args: + axes (array_like): List of axes to generated the grid where the + object will be evaluated. + extrapolation (str or Extrapolation, optional): Controls the + extrapolation mode for elements outside the domain range. By + default it is used the mode defined during the instance of the + object. + aligned (bool, optional): If False evaluates each sample + in a different grid. + + Returns: + (numpy.darray): Numpy array with dim_domain + 1 dimensions with + the result of the evaluation. + + Raises: + ValueError: If there are a different number of axes than the domain + dimension. + + """ + + # Compute intersection points and resulting shapes + if aligned: + + eval_points, shape = _one_grid_to_points(axes, dim_domain=dim_domain) + + else: + + axes = list(axes) + + if len(axes) != n_samples: + raise ValueError("Should be provided a list of axis per " + "sample") + + eval_points, shape = zip( + *[_one_grid_to_points(a, dim_domain=dim_domain) for a in axes]) + + eval_points = np.array(eval_points) + + # Evaluate the points + res = evaluate_method(eval_points, + extrapolation=extrapolation, + aligned=aligned) + + # Reshape the result + if aligned: + + res = res.reshape([n_samples] + + list(shape) + [dim_codomain]) + + else: + + res = _to_array_maybe_ragged([ + r.reshape(list(s) + [dim_codomain]) + for r, s in zip(res, shape)]) + + return res + + def parameter_aliases(**alias_assignments): """Allows using aliases for parameters""" def decorator(f): diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index a2cdce5da..9e4536b20 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -11,8 +11,7 @@ import numpy as np -from .._utils import (_cartesian_product, _list_of_arrays, - _to_array_maybe_ragged, _reshape_eval_points) +from .._utils import (_evaluate_grid, _reshape_eval_points) from .extrapolation import _parse_extrapolation @@ -153,107 +152,6 @@ def _extrapolation_index(self, eval_points): return index - def _one_grid_to_points(self, axes): - """ - Convert a list of ndarrays, one per domain dimension, in the points. - - Returns also the shape containing the information of how each point - is formed. - """ - axes = _list_of_arrays(axes) - - if len(axes) != self.dim_domain: - raise ValueError(f"Length of axes should be " - f"{self.dim_domain}") - - cartesian, shape = _cartesian_product(axes, return_shape=True) - - # Drop domain size dimension, as it is not needed to reshape the output - shape = shape[:-1] - - return cartesian, shape - - def _evaluate_grid(self, axes, *, extrapolation=None, - aligned=True): - """Evaluate the functional object in the cartesian grid. - - This method is called internally by :meth:`evaluate` when the argument - `grid` is True. - - Evaluates the functional object in the grid generated by the cartesian - product of the axes. The length of the list of axes should be equal - than the domain dimension of the object. - - If the list of axes has lengths :math:`n_1, n_2, ..., n_m`, where - :math:`m` is equal than the dimension of the domain, the result of the - evaluation in the grid will be a matrix with :math:`m+1` dimensions and - shape :math:`n_{samples} x n_1 x n_2 x ... x n_m`. - - If `aligned` is false each sample is evaluated in a - different grid, and the list of axes should contain a list of axes for - each sample. - - If the domain dimension is 1, the result of the behaviour of the - evaluation will be the same than :meth:`evaluate` without the grid - option, but with worst performance. - - Args: - axes (array_like): List of axes to generated the grid where the - object will be evaluated. - extrapolation (str or Extrapolation, optional): Controls the - extrapolation mode for elements outside the domain range. By - default it is used the mode defined during the instance of the - object. - aligned (bool, optional): If False evaluates each sample - in a different grid. - - Returns: - (numpy.darray): Numpy array with dim_domain + 1 dimensions with - the result of the evaluation. - - Raises: - ValueError: If there are a different number of axes than the domain - dimension. - - """ - - # Compute intersection points and resulting shapes - if aligned: - - eval_points, shape = self._one_grid_to_points(axes) - - else: - - axes = list(axes) - - if len(axes) != self.n_samples: - raise ValueError("Should be provided a list of axis per " - "sample") - - eval_points, shape = zip( - *[self._one_grid_to_points(a) for a in axes]) - - eval_points = np.array(eval_points) - - # Evaluate the points - res = self.evaluate(eval_points, - extrapolation=extrapolation, - aligned=aligned) - - # Reshape the result - if aligned: - - res = res.reshape([self.n_samples] + - list(shape) + [self.dim_codomain]) - - else: - - res = _to_array_maybe_ragged([ - r.reshape(list(s) + [self.dim_codomain]) - for r, s in zip(res, shape)]) - - return res - def _join_evaluation(self, index_matrix, index_ext, index_ev, res_extrapolation, res_evaluation): """Join the points evaluated. @@ -356,9 +254,13 @@ def evaluate(self, eval_points, *, derivative=0, extrapolation=None, aligned=aligned) if grid: # Evaluation of a grid performed in auxiliar function - return self._evaluate_grid(eval_points, - extrapolation=extrapolation, - aligned=aligned) + return _evaluate_grid(eval_points, + evaluate_method=self.evaluate, + n_samples=self.n_samples, + dim_domain=self.dim_domain, + dim_codomain=self.dim_codomain, + extrapolation=extrapolation, + aligned=aligned) if extrapolation is None: extrapolation = self.extrapolation diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index d129ae545..327e3fa3c 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -8,11 +8,10 @@ import copy import warnings -import scipy.integrate - import numpy as np -from ..._utils import _list_of_arrays, _same_domain, _reshape_eval_points +from ..._utils import (_list_of_arrays, _same_domain, + _reshape_eval_points, _evaluate_grid) __author__ = "Miguel Carbajo Berrocal" @@ -86,7 +85,7 @@ def _evaluate(self, eval_points): """Subclasses must override this to provide basis evaluation.""" pass - def evaluate(self, eval_points, derivative=0): + def evaluate(self, eval_points, *, derivative=0): """Evaluate Basis objects and its derivatives. Evaluates the basis function system or its derivatives at a list of From 1bb6b576ee7a75d5d0137a89051e3c05088b5f8b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 14 Jul 2020 01:55:16 +0200 Subject: [PATCH 598/624] Conversion from grid to multivariate functions in basis. --- skfda/preprocessing/smoothing/_basis.py | 21 +++++++++++++-------- skfda/representation/_functional_data.py | 6 +++--- skfda/representation/basis/_fdatabasis.py | 13 +++++++------ skfda/representation/grid.py | 11 ----------- 4 files changed, 23 insertions(+), 28 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 4ad7039d2..9e2871e78 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -13,7 +13,8 @@ from ... import FDataBasis from ... import FDataGrid -from ._linear import _LinearSmoother, _check_r_to_r +from ..._utils import _cartesian_product +from ._linear import _LinearSmoother class _Cholesky(): @@ -324,7 +325,8 @@ def _coef_matrix(self, input_points): """Get the matrix that gives the coefficients""" from ...misc.regularization import compute_penalty_matrix - basis_values_input = self.basis.evaluate(input_points).reshape( + basis_values_input = self.basis.evaluate( + _cartesian_product(input_points)).reshape( (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one @@ -344,7 +346,8 @@ def _coef_matrix(self, input_points): ols_matrix, basis_values_input.T @ weight_matrix) def _hat_matrix(self, input_points, output_points): - basis_values_output = self.basis.evaluate(output_points).reshape( + basis_values_output = self.basis.evaluate(_cartesian_product( + output_points)).reshape( (self.basis.n_basis, -1)).T return basis_values_output @ self._coef_matrix(input_points) @@ -361,7 +364,7 @@ def fit(self, X: FDataGrid, y=None): """ - self.input_points_ = X.sample_points[0] + self.input_points_ = X.sample_points self.output_points_ = (self.output_points if self.output_points is not None else self.input_points_) @@ -386,7 +389,7 @@ def fit_transform(self, X: FDataGrid, y=None): """ from ...misc.regularization import compute_penalty_matrix - self.input_points_ = X.sample_points[0] + self.input_points_ = X.sample_points self.output_points_ = (self.output_points if self.output_points is not None else self.input_points_) @@ -404,7 +407,8 @@ def fit_transform(self, X: FDataGrid, y=None): data_matrix = X.data_matrix.reshape((X.n_samples, -1)).T # Each basis in a column - basis_values = self.basis.evaluate(self.input_points_).reshape( + basis_values = self.basis.evaluate( + _cartesian_product(self.input_points_)).reshape( (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one @@ -454,7 +458,7 @@ def fit_transform(self, X: FDataGrid, y=None): if self.return_basis: return fdatabasis else: - return fdatabasis.to_grid(eval_points=self.output_points_) + return fdatabasis.to_grid(sample_points=self.output_points_) return self @@ -470,7 +474,8 @@ def transform(self, X: FDataGrid, y=None): """ - assert all(self.input_points_ == X.sample_points[0]) + assert all([all(i == s) + for i, s in zip(self.input_points_, X.sample_points)]) method = self._method_function() diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 9e4536b20..29aba4375 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -532,12 +532,12 @@ def mean(self, weights=None): pass @abstractmethod - def to_grid(self, eval_points=None): + def to_grid(self, sample_points=None): """Return the discrete representation of the object. Args: - eval_points (array_like, optional): Set of points where the - functions are evaluated. + sample_points (array_like, optional): Points per axis + where the function is going to be evaluated. Returns: FDataGrid: Discrete representation of the functional data diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 5d9d44638..5159c4bc0 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -443,11 +443,11 @@ def cov(self, eval_points=None): """ return self.to_grid(eval_points).cov() - def to_grid(self, eval_points=None): + def to_grid(self, sample_points=None): """Return the discrete representation of the object. Args: - eval_points (array_like, optional): Set of points where the + sample_points (array_like, optional): Points per axis where the functions are evaluated. If none are passed it calls numpy.linspace with bounds equal to the ones defined in self.domain_range and the number of points the maximum @@ -479,13 +479,14 @@ def to_grid(self, eval_points=None): if self.dim_codomain > 1 or self.dim_domain > 1: raise NotImplementedError - if eval_points is None: + if sample_points is None: npoints = max(constants.N_POINTS_FINE_MESH, constants.BASIS_MIN_FACTOR * self.n_basis) - eval_points = np.linspace(*self.domain_range[0], npoints) + sample_points = [np.linspace(*r, npoints) + for r in self.domain_range] - return grid.FDataGrid(self.evaluate(eval_points), - sample_points=eval_points, + return grid.FDataGrid(self.evaluate(sample_points, grid=True), + sample_points=sample_points, domain_range=self.domain_range) def to_basis(self, basis, eval_points=None, **kwargs): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index ebe0090ad..5b49004c2 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -793,18 +793,7 @@ def to_basis(self, basis, **kwargs): **kwargs) def to_grid(self, sample_points=None): - """Return the discrete representation of the object. - Args: - sample_points (array_like, optional): 2 dimension matrix where - each row contains the points of dicretisation for each axis of - data_matrix. - - Returns: - FDataGrid: Discrete representation of the functional data - object. - - """ if sample_points is None: sample_points = self.sample_points From 03c08c5fe287897e379a56f3c1fc86a1866dd4f1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 15 Jul 2020 00:44:58 +0200 Subject: [PATCH 599/624] Test of tensor basis conversion. --- skfda/preprocessing/smoothing/_basis.py | 27 +++++++++++++++-------- skfda/representation/basis/_fdatabasis.py | 3 --- tests/test_basis_evaluation.py | 6 +++++ 3 files changed, 24 insertions(+), 12 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 9e2871e78..8258dbbb9 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -23,8 +23,13 @@ class _Cholesky(): def __call__(self, *, basis_values, weight_matrix, data_matrix, penalty_matrix, **_): - right_matrix = basis_values.T @ weight_matrix @ data_matrix - left_matrix = basis_values.T @ weight_matrix @ basis_values + common_matrix = basis_values.T + + if weight_matrix is not None: + common_matrix @= weight_matrix + + right_matrix = common_matrix @ data_matrix + left_matrix = common_matrix @ basis_values # Adds the roughness penalty to the equation if penalty_matrix is not None: @@ -330,10 +335,11 @@ def _coef_matrix(self, input_points): (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one - weight_matrix = (self.weights if self.weights is not None - else np.identity(basis_values_input.shape[0])) - - ols_matrix = basis_values_input.T @ weight_matrix @ basis_values_input + if self.weights is not None: + ols_matrix = (basis_values_input.T @ self.weights + @ basis_values_input) + else: + ols_matrix = basis_values_input.T @ basis_values_input penalty_matrix = compute_penalty_matrix( basis_iterable=(self.basis,), @@ -342,8 +348,12 @@ def _coef_matrix(self, input_points): ols_matrix += penalty_matrix + right_side = basis_values_input.T + if self.weights is not None: + right_side @= self.weights + return np.linalg.solve( - ols_matrix, basis_values_input.T @ weight_matrix) + ols_matrix, right_side) def _hat_matrix(self, input_points, output_points): basis_values_output = self.basis.evaluate(_cartesian_product( @@ -412,8 +422,7 @@ def fit_transform(self, X: FDataGrid, y=None): (self.basis.n_basis, -1)).T # If no weight matrix is given all the weights are one - weight_matrix = (self.weights if self.weights is not None - else np.identity(basis_values.shape[0])) + weight_matrix = self.weights # We need to solve the equation # (phi' W phi + lambda * R) C = phi' W Y diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 5159c4bc0..59706e8d6 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -476,9 +476,6 @@ def to_grid(self, sample_points=None): """ - if self.dim_codomain > 1 or self.dim_domain > 1: - raise NotImplementedError - if sample_points is None: npoints = max(constants.N_POINTS_FINE_MESH, constants.BASIS_MIN_FACTOR * self.n_basis) diff --git a/tests/test_basis_evaluation.py b/tests/test_basis_evaluation.py index c4249929c..0ff5727a0 100644 --- a/tests/test_basis_evaluation.py +++ b/tests/test_basis_evaluation.py @@ -470,6 +470,12 @@ def test_tensor_monomial_constant(self): np.testing.assert_allclose( fd([(0., 0.), (0.5, 0.5)]), [[[1.0], [1.5]]]) + fd_grid = fd.to_grid() + + fd2 = fd_grid.to_basis(basis) + + np.testing.assert_allclose(fd.coefficients, fd2.coefficients) + if __name__ == '__main__': print() From 767058d7cc26e3af0a882a0c0f3e9fc43a8224dd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 15 Jul 2020 01:40:36 +0200 Subject: [PATCH 600/624] Tensor basis documentation. --- docs/modules/representation.rst | 9 ++++ skfda/representation/basis/_tensor_basis.py | 46 +++++++++++---------- 2 files changed, 34 insertions(+), 21 deletions(-) diff --git a/docs/modules/representation.rst b/docs/modules/representation.rst index 606372c3c..83efe532a 100644 --- a/docs/modules/representation.rst +++ b/docs/modules/representation.rst @@ -55,6 +55,15 @@ The following classes are used to define different basis for skfda.representation.basis.Fourier skfda.representation.basis.Monomial skfda.representation.basis.Constant + +The following class, allows the construction of a basis for +:math:`\mathbb{R}^n \to \mathbb{R}` functions from +several :math:`\mathbb{R} \to \mathbb{R}` bases. + +.. autosummary:: + :toctree: autosummary + + skfda.representation.basis.Tensor The following class, allows the construction of a basis for :math:`\mathbb{R}^n \to \mathbb{R}^m` functions from diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index c71d5fc20..4f4c76337 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -19,40 +19,44 @@ class Tensor(Basis): n_basis (int): number of functions in the basis. Examples: - Defines a vector-valued base over the interval :math:`[0, 5]` + + Defines a tensor basis over the interval :math:`[0, 5] \times [0, 3]` consisting on the functions .. math:: - 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} + 1, v, u, uv, u^2, u^2v - >>> from skfda.representation.basis import VectorValued, Monomial + >>> from skfda.representation.basis import Tensor, Monomial >>> >>> basis_x = Monomial((0,5), n_basis=3) - >>> basis_y = Monomial((0,5), n_basis=2) + >>> basis_y = Monomial((0,3), n_basis=2) >>> - >>> basis = VectorValued([basis_x, basis_y]) + >>> basis = Tensor([basis_x, basis_y]) And evaluates all the functions in the basis in a list of descrete values. - >>> basis([0., 1., 2.]) - array([[[ 1., 0.], - [ 1., 0.], - [ 1., 0.]], - [[ 0., 0.], - [ 1., 0.], - [ 2., 0.]], - [[ 0., 0.], - [ 1., 0.], - [ 4., 0.]], - [[ 0., 1.], - [ 0., 1.], - [ 0., 1.]], - [[ 0., 0.], - [ 0., 1.], - [ 0., 2.]]]) + >>> basis([(0., 2.), (3., 0), (2., 3.)]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 2.], + [ 0.], + [ 3.]], + [[ 0.], + [ 3.], + [ 2.]], + [[ 0.], + [ 0.], + [ 6.]], + [[ 0.], + [ 9.], + [ 4.]], + [[ 0.], + [ 0.], + [ 12.]]]) """ From 04805cf6ec18f20debc4ee6ad9ed21a7db422317 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 15 Jul 2020 22:15:28 +0200 Subject: [PATCH 601/624] Remove useless operators in inner product. --- skfda/representation/basis/_basis.py | 2 +- skfda/representation/basis/_fdatabasis.py | 29 +++++++---------------- tests/test_basis.py | 2 +- 3 files changed, 11 insertions(+), 22 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 327e3fa3c..efdaa221a 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -276,7 +276,7 @@ def _inner_matrix(self, other=None): for i in range(self.n_basis): for j in range(other.n_basis): - inner[i, j] = first[i].inner_product(second[j], None, None) + inner[i, j] = first[i].inner_product(second[j]) return inner diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 59706e8d6..0af2f3eb5 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -1,3 +1,4 @@ +from builtins import isinstance import copy import pandas.api.extensions @@ -500,6 +501,9 @@ def to_basis(self, basis, eval_points=None, **kwargs): object. """ + if basis == self.basis: + return self.copy() + return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) def to_list(self): @@ -580,8 +584,7 @@ def times(self, other): coefs = np.transpose(np.atleast_2d(other)) return self.copy(coefficients=self.coefficients * coefs) - def inner_product(self, other, lfd_self=None, lfd_other=None, - weights=None): + def inner_product(self, other, weights=None): r"""Return an inner product matrix given a FDataBasis object. The inner product of two functions is defined as @@ -604,12 +607,6 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, other (FDataBasis, Basis): FDataBasis object containing the second object to make the inner product - lfd_self (Lfd): LinearDifferentialOperator object for the first - function evaluation - - lfd_other (Lfd): LinearDifferentialOperator object for the second - function evaluation - weights(FDataBasis): a FDataBasis object with only one sample that defines the weight to calculate the inner product @@ -617,19 +614,11 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, numpy.array: Inner Product matrix. """ - from ...misc.operators import LinearDifferentialOperator - from ..basis import Basis - if not _same_domain(self.domain_range, other.domain_range): raise ValueError("Both Objects should have the same domain_range") - if isinstance(other, Basis): - other = other.to_basis() - # TODO this will be used when lfd evaluation is ready - lfd_self = (LinearDifferentialOperator(0) if lfd_self is None - else lfd_self) - lfd_other = (LinearDifferentialOperator(0) if (lfd_other is None) - else lfd_other) + if not isinstance(other, FDataBasis): + other = other.to_basis() if weights is not None: other = other.times(weights) @@ -639,9 +628,9 @@ def inner_product(self, other, lfd_self=None, lfd_other=None, self.basis._inner_matrix(other.basis) @ other.coefficients.T) else: - return self._inner_product_integrate(other, lfd_self, lfd_other) + return self._inner_product_integrate(other) - def _inner_product_integrate(self, other, lfd_self, lfd_other): + def _inner_product_integrate(self, other): matrix = np.empty((self.n_samples, other.n_samples)) (left, right) = self.domain_range[0] diff --git a/tests/test_basis.py b/tests/test_basis.py index 787817e1d..02f478918 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -159,7 +159,7 @@ def test_fdatabasis_fdatabasis_inprod(self): np.testing.assert_array_almost_equal( monomialfd._inner_product_integrate( - bsplinefd, None, None).round(3), + bsplinefd).round(3), np.array([[16.14797697, 52.81464364, 89.4813103], [11.55565285, 38.22211951, 64.88878618], [18.14698361, 55.64698361, 93.14698361], From 84ad569bdc1af08e7207453fd7dcf7d3765f0d04 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 16 Jul 2020 02:18:29 +0200 Subject: [PATCH 602/624] Remove times usage. --- skfda/representation/basis/_fdatabasis.py | 8 ++--- tests/test_basis.py | 39 +++++++++++------------ tests/test_regression.py | 18 +++++------ 3 files changed, 29 insertions(+), 36 deletions(-) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 0af2f3eb5..0e0539259 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -584,7 +584,7 @@ def times(self, other): coefs = np.transpose(np.atleast_2d(other)) return self.copy(coefficients=self.coefficients * coefs) - def inner_product(self, other, weights=None): + def inner_product(self, other): r"""Return an inner product matrix given a FDataBasis object. The inner product of two functions is defined as @@ -620,9 +620,6 @@ def inner_product(self, other, weights=None): if not isinstance(other, FDataBasis): other = other.to_basis() - if weights is not None: - other = other.times(weights) - if self.n_samples * other.n_samples > self.n_basis * other.n_basis: return (self.coefficients @ self.basis._inner_matrix(other.basis) @ @@ -637,9 +634,8 @@ def _inner_product_integrate(self, other): for i in range(self.n_samples): for j in range(other.n_samples): - fd = self[i].times(other[j]) matrix[i, j] = scipy.integrate.quad( - lambda x: fd.evaluate([x])[0], left, right)[0] + lambda x: self[i]([x]) * other[j]([x])[0], left, right)[0] return matrix diff --git a/tests/test_basis.py b/tests/test_basis.py index 02f478918..9b360c25e 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -111,15 +111,14 @@ def test_basis_gram_matrix(self): def test_basis_basis_inprod(self): monomial = Monomial(n_basis=4) bspline = BSpline(n_basis=5, order=4) - np.testing.assert_array_almost_equal( - monomial.inner_product(bspline).round(3), + np.testing.assert_allclose( + monomial.inner_product(bspline), np.array( [[0.12499983, 0.25000035, 0.24999965, 0.25000035, 0.12499983], [0.01249991, 0.07500017, 0.12499983, 0.17500017, 0.11249991], [0.00208338, 0.02916658, 0.07083342, 0.12916658, 0.10208338], - [0.00044654, 0.01339264, 0.04375022, 0.09910693, 0.09330368]]) - .round(3) - ) + [0.00044654, 0.01339264, 0.04375022, 0.09910693, 0.09330368] + ]), rtol=1e-3) np.testing.assert_array_almost_equal( monomial.inner_product(bspline), bspline.inner_product(monomial).T @@ -130,13 +129,12 @@ def test_basis_fdatabasis_inprod(self): bspline = BSpline(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) - np.testing.assert_array_almost_equal( - monomial.inner_product(bsplinefd).round(3), + np.testing.assert_allclose( + monomial.inner_product(bsplinefd), np.array([[2., 7., 12.], [1.29626206, 3.79626206, 6.29626206], [0.96292873, 2.62959539, 4.29626206], - [0.7682873, 2.0182873, 3.2682873]]).round(3) - ) + [0.7682873, 2.0182873, 3.2682873]]), rtol=1e-4) def test_fdatabasis_fdatabasis_inprod(self): monomial = Monomial(n_basis=4) @@ -148,33 +146,32 @@ def test_fdatabasis_fdatabasis_inprod(self): bspline = BSpline(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) - np.testing.assert_array_almost_equal( - monomialfd.inner_product(bsplinefd).round(3), + np.testing.assert_allclose( + monomialfd.inner_product(bsplinefd), np.array([[16.14797697, 52.81464364, 89.4813103], [11.55565285, 38.22211951, 64.88878618], [18.14698361, 55.64698361, 93.14698361], [15.2495976, 48.9995976, 82.7495976], - [19.70392982, 63.03676315, 106.37009648]]).round(3) - ) + [19.70392982, 63.03676315, 106.37009648]]), + rtol=1e-4) - np.testing.assert_array_almost_equal( - monomialfd._inner_product_integrate( - bsplinefd).round(3), + np.testing.assert_allclose( + monomialfd._inner_product_integrate(bsplinefd), np.array([[16.14797697, 52.81464364, 89.4813103], [11.55565285, 38.22211951, 64.88878618], [18.14698361, 55.64698361, 93.14698361], [15.2495976, 48.9995976, 82.7495976], - [19.70392982, 63.03676315, 106.37009648]]).round(3) - ) + [19.70392982, 63.03676315, 106.37009648]]), + rtol=1e-4) def test_comutativity_inprod(self): monomial = Monomial(n_basis=4) bspline = BSpline(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) - np.testing.assert_array_almost_equal( - bsplinefd.inner_product(monomial).round(3), - np.transpose(monomial.inner_product(bsplinefd).round(3)) + np.testing.assert_allclose( + bsplinefd.inner_product(monomial), + np.transpose(monomial.inner_product(bsplinefd)) ) def test_fdatabasis_times_fdatabasis_fdatabasis(self): diff --git a/tests/test_regression.py b/tests/test_regression.py index 3f76270c1..edd661582 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -17,13 +17,13 @@ def test_regression_single_explanatory(self): beta_basis = Fourier(n_basis=5) beta_fd = FDataBasis(beta_basis, [1, 1, 1, 1, 1]) - y = [1.0000684777229512, - 0.1623672257830915, - 0.08521053851548224, - 0.08514200869281137, - 0.09529138749665378, - 0.10549625973303875, - 0.11384314859153018] + y = [0.9999999999999993, + 0.162381381441085, + 0.08527083481359901, + 0.08519946930844623, + 0.09532291032042489, + 0.10550022969639987, + 0.11382675064746171] scalar = LinearRegression(coef_basis=[beta_basis]) scalar.fit(x_fd, y) @@ -58,7 +58,7 @@ def test_regression_multiple_explanatory(self): scalar.fit(X, y) np.testing.assert_allclose(scalar.intercept_.round(4), - np.array([32.6518])) + np.array([32.65]), rtol=1e-3) np.testing.assert_allclose( scalar.coef_[0].coefficients.round(4), @@ -66,7 +66,7 @@ def test_regression_multiple_explanatory(self): 80.3996, -188.587, 236.5832, - -481.3449]])) + -481.3449]]), rtol=1e-3) y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) From 1198db5431bef9bb9f267c811aa2acc99f81006c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 16 Jul 2020 03:20:10 +0200 Subject: [PATCH 603/624] Rename and make public the inner product matrix between basis. --- skfda/representation/basis/_basis.py | 2 +- skfda/representation/basis/_fdatabasis.py | 2 +- tests/test_basis.py | 17 +++++++++++------ 3 files changed, 13 insertions(+), 8 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index efdaa221a..5d90c240f 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -244,7 +244,7 @@ def _list_to_R(self, knots): def _to_R(self): raise NotImplementedError - def _inner_matrix(self, other=None): + def inner_product_matrix(self, other=None): r"""Return the Inner Product Matrix of a pair of basis. The Inner Product Matrix is defined as diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 0e0539259..db804509e 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -622,7 +622,7 @@ def inner_product(self, other): if self.n_samples * other.n_samples > self.n_basis * other.n_basis: return (self.coefficients @ - self.basis._inner_matrix(other.basis) @ + self.basis.inner_product_matrix(other.basis) @ other.coefficients.T) else: return self._inner_product_integrate(other) diff --git a/tests/test_basis.py b/tests/test_basis.py index 9b360c25e..21bfcd826 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -78,14 +78,19 @@ def test_basis_bspline_product(self): self.assertEqual(bspline.basis_of_product(bspline2), prod) def test_basis_inner_matrix(self): - np.testing.assert_array_almost_equal(Monomial(n_basis=3)._inner_matrix(), - [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) + np.testing.assert_array_almost_equal( + Monomial(n_basis=3).inner_product_matrix(), + [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) - np.testing.assert_array_almost_equal(Monomial(n_basis=3)._inner_matrix(Monomial(n_basis=3)), - [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) + np.testing.assert_array_almost_equal( + Monomial(n_basis=3).inner_product_matrix(Monomial(n_basis=3)), + [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) - np.testing.assert_array_almost_equal(Monomial(n_basis=3)._inner_matrix(Monomial(n_basis=4)), - [[1, 1 / 2, 1 / 3, 1 / 4], [1 / 2, 1 / 3, 1 / 4, 1 / 5], [1 / 3, 1 / 4, 1 / 5, 1 / 6]]) + np.testing.assert_array_almost_equal( + Monomial(n_basis=3).inner_product_matrix(Monomial(n_basis=4)), + [[1, 1 / 2, 1 / 3, 1 / 4], + [1 / 2, 1 / 3, 1 / 4, 1 / 5], + [1 / 3, 1 / 4, 1 / 5, 1 / 6]]) # TODO testing with other basis From 986fee60c65f67b8238edc69b393b47f1a3e46a5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 17 Jul 2020 03:45:24 +0200 Subject: [PATCH 604/624] Refactor inner product. --- skfda/misc/_math.py | 194 +++++++++++++++--- skfda/ml/regression/_coefficients.py | 2 +- skfda/ml/regression/linear.py | 14 +- .../dim_reduction/projection/_fpca.py | 12 +- skfda/representation/basis/_basis.py | 19 +- skfda/representation/basis/_fdatabasis.py | 55 ----- tests/test_basis.py | 24 +-- 7 files changed, 195 insertions(+), 125 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 22cd635fc..36792a4fb 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -4,10 +4,18 @@ package. FDataBasis and FDataGrid. """ +from builtins import isinstance +from typing import Union + +import multimethod import scipy.integrate import numpy as np +from .._utils import _same_domain +from ..representation import FDataGrid, FDataBasis +from ..representation.basis import Basis + __author__ = "Miguel Carbajo Berrocal" __license__ = "GPL3" @@ -135,68 +143,192 @@ def cumsum(fdatagrid): axis=0)) -def inner_product(fdatagrid, fdatagrid2): - r"""Return inner product for FDataGrid. +@multimethod.multidispatch +def inner_product(arg1, arg2): + r"""Return the usual (:math:`L_2`) inner product. - Calculates the inner product amongst all the samples in two + Calculates the inner product between matching samples in two FDataGrid objects. - For each pair of samples f and g the inner product is defined as: + For two samples x and y the inner product is defined as: .. math:: - = \int_a^bf(x)g(x)dx + = \sum_i x_i y_i - The integral is approximated using Simpson's rule. + for multivariate data and + + .. math:: + = \int_a^b x(t)y(t)dt + + for functional data. + + The two arguments must have the same number of samples, or one should + contain only one sample (and will be broadcasted). Args: - fdatagrid (FDataGrid): First FDataGrid object. - fdatagrid2 (FDataGrid): Second FDataGrid object. + + arg1: First sample. + arg2: Second sample. Returns: - numpy.darray: Matrix with as many rows as samples in the first - object and as many columns as samples in the second one. Each - element (i, j) of the matrix is the inner product of the ith sample - of the first object and the jth sample of the second one. + + numpy.darray: Vector with the inner products of each pair of + samples. Examples: + + This function can compute the multivariate inner product. + + >>> import numpy as np + >>> from skfda.misc import inner_product + >>> + >>> array1 = np.array([1, 2, 3]) + >>> array2 = np.array([4, 5, 6]) + >>> inner_product(array1, array2) + 32 + + If the arrays contain more than one sample + + >>> array1 = np.array([[1, 2, 3], [2, 3, 4]]) + >>> array2 = np.array([[4, 5, 6], [1, 1, 1]]) + >>> inner_product(array1, array2) + array([32, 9]) + The inner product of the :math:'f(x) = x` and the constant :math:`y=1` defined over the interval [0,1] is the area of the triangle delimited by the the lines y = 0, x = 1 and y = x; 0.5. >>> import skfda - >>> x = np.linspace(0,1,1001) + >>> + >>> x = np.linspace(0,1,1000) + >>> >>> fd1 = skfda.FDataGrid(x,x) >>> fd2 = skfda.FDataGrid(np.ones(len(x)),x) >>> inner_product(fd1, fd2) - array([[ 0.5]]) + array([ 0.5]) If the FDataGrid object contains more than one sample >>> fd1 = skfda.FDataGrid([x, np.ones(len(x))], x) >>> fd2 = skfda.FDataGrid([np.ones(len(x)), x] ,x) >>> inner_product(fd1, fd2).round(2) - array([[ 0.5 , 0.33], - [ 1. , 0.5 ]]) + array([ 0.5, 0.5]) + + If one argument contains only one sample it is + broadcasted. + + >>> fd1 = skfda.FDataGrid([x, np.ones(len(x))], x) + >>> fd2 = skfda.FDataGrid([np.ones(len(x))] ,x) + >>> inner_product(fd1, fd2).round(2) + array([ 0.5, 1. ]) + + It also work with basis objects + + >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> + >>> fd1 = skfda.FDataBasis(basis, [0, 1, 0]) + >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) + >>> inner_product(fd1, fd2) + array([ 0.5]) + + >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> + >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) + >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) + >>> inner_product(fd1, fd2) + array([ 0.5 , 0.33333333]) + + >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> + >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) + >>> fd2 = skfda.FDataBasis(basis, [[1, 0, 0], [0, 1, 0]]) + >>> inner_product(fd1, fd2) + array([ 0.5 , 0.25]) """ - if fdatagrid.dim_domain != 1: + + return (arg1 * arg2).sum(axis=-1) + + +@inner_product.register +def inner_product_fdatagrid(arg1: FDataGrid, arg2: FDataGrid): + + if arg1.dim_domain != 1: raise NotImplementedError("This method only works when the dimension " "of the domain of the FDatagrid object is " "one.") - # Checks - if not np.array_equal(fdatagrid.sample_points, - fdatagrid2.sample_points): + + if not np.array_equal(arg1.sample_points, + arg2.sample_points): raise ValueError("Sample points for both objects must be equal") - # Creates an empty matrix with the desired size to store the results. - matrix = np.empty([fdatagrid.n_samples, fdatagrid2.n_samples]) - # Iterates over the different samples of both objects. - for i in range(fdatagrid.n_samples): - for j in range(fdatagrid2.n_samples): - # Calculates the inner product using Simpson's rule. - matrix[i, j] = (scipy.integrate.simps( - fdatagrid.data_matrix[i, ..., 0] * - fdatagrid2.data_matrix[j, ..., 0], - x=fdatagrid.sample_points[0] - )) + integrand = arg1.data_matrix * arg2.data_matrix + + integral = scipy.integrate.simps(integrand, + x=arg1.sample_points[0], + axis=1) + + return np.sum(integral, axis=-1) + + +@inner_product.register(FDataBasis, FDataBasis) +@inner_product.register(FDataBasis, Basis) +@inner_product.register(Basis, FDataBasis) +@inner_product.register(Basis, Basis) +def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], + arg2: Union[FDataBasis, Basis]): + + if not _same_domain(arg1, arg2): + raise ValueError("Both Objects should have the same domain_range") + + if isinstance(arg1, Basis): + arg1 = arg1.to_basis() + + if isinstance(arg2, Basis): + arg2 = arg2.to_basis() + + if max(arg1.n_samples, arg2.n_samples) > arg1.n_basis * arg2.n_basis: + return (arg1.coefficients @ + arg1.basis.inner_product_matrix(arg2.basis) * + arg2.coefficients).sum(axis=-1) + else: + return _inner_product_integrate(arg1, arg2) + + +def _inner_product_integrate(arg1, arg2): + + if not np.array_equal(arg1.domain_range, + arg2.domain_range): + raise ValueError("Domain range for both objects must be equal") + + if arg1.dim_domain != 1: + raise NotImplementedError("This method only works when the dimension " + "of the domain of the FDatagrid object is " + "one.") + + (left, right) = arg1.domain_range[0] + + integral = scipy.integrate.quad_vec( + lambda x: arg1([x])[:, 0, :] * arg2([x])[:, 0, :], + left, right)[0] + + return np.sum(integral, axis=-1) + + +def _inner_product_matrix(arg1, arg2): + """ + Currently only used for testing purposes. + """ + + if isinstance(arg1, Basis): + arg1 = arg1.to_basis() + if isinstance(arg2, Basis): + arg2 = arg2.to_basis() + + matrix = np.empty((arg1.n_samples, arg2.n_samples)) + + for i in range(arg1.n_samples): + for j in range(arg2.n_samples): + matrix[i, j] = inner_product(arg1[i], arg2[j]) + return matrix diff --git a/skfda/ml/regression/_coefficients.py b/skfda/ml/regression/_coefficients.py index f5156252e..185953059 100644 --- a/skfda/ml/regression/_coefficients.py +++ b/skfda/ml/regression/_coefficients.py @@ -56,7 +56,7 @@ class CoefficientInfoFDataBasis(CoefficientInfo): def regression_matrix(self, X, y): xcoef = X.coefficients - inner_basis = X.basis.inner_product(self.basis) + inner_basis = X.basis.inner_product_matrix(self.basis) return xcoef @ inner_basis def convert_from_constant_coefs(self, coefs): diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index 2153d8685..bba34ec5a 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -1,6 +1,5 @@ from collections.abc import Iterable import itertools -from skfda.representation import FData import warnings from sklearn.base import BaseEstimator, RegressorMixin @@ -9,6 +8,7 @@ import numpy as np from ...misc.regularization import compute_penalty_matrix +from ...representation import FData from ._coefficients import coefficient_info_from_covariate @@ -183,10 +183,12 @@ def fit(self, X, y=None, sample_weight=None): return self def predict(self, X): + from ...misc import inner_product + check_is_fitted(self) X = self._argcheck_X(X) - result = np.sum([self._inner_product_mixed( + result = np.sum([inner_product( coef, x) for coef, x in zip(self.coef_, X)], axis=0) result += self.intercept_ @@ -196,14 +198,6 @@ def predict(self, X): return result - def _inner_product_mixed(self, x, y): - inner_product = getattr(x, "inner_product", None) - - if inner_product is None: - return y @ x - else: - return inner_product(y)[0] - def _argcheck_X(self, X): if isinstance(X, FData) or isinstance(X, np.ndarray): X = [X] diff --git a/skfda/preprocessing/dim_reduction/projection/_fpca.py b/skfda/preprocessing/dim_reduction/projection/_fpca.py index efc224e2d..95ca70b1f 100644 --- a/skfda/preprocessing/dim_reduction/projection/_fpca.py +++ b/skfda/preprocessing/dim_reduction/projection/_fpca.py @@ -152,7 +152,7 @@ def _fit_basis(self, X: FDataBasis, y=None): # the matrix that are in charge of changing the computed principal # components to target matrix is essentially the inner product # of both basis. - j_matrix = X.basis.inner_product(components_basis) + j_matrix = X.basis.inner_product_matrix(components_basis) else: # if no other basis is specified we use the same basis as the passed # FDataBasis Object @@ -160,6 +160,9 @@ def _fit_basis(self, X: FDataBasis, y=None): g_matrix = components_basis.gram_matrix() j_matrix = g_matrix + self._X_basis = X.basis + self._j_matrix = j_matrix + # Apply regularization / penalty if applicable regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), @@ -218,8 +221,13 @@ def _transform_basis(self, X, y=None): principal components """ + if X.basis != self._X_basis: + raise ValueError("The basis used in fit is different from " + "the basis used in transform.") + # in this case it is the inner product of our data with the components - return X.inner_product(self.components_) + return (X.coefficients @ self._j_matrix + @ self.components_.coefficients.T) def _fit_grid(self, X: FDataGrid, y=None): r"""Computes the n_components first principal components and saves them. diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 5d90c240f..b3887f592 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -266,19 +266,19 @@ def inner_product_matrix(self, other=None): numpy.array: Inner Product Matrix of two basis """ + from ...misc import inner_product + if other is None or self == other: return self.gram_matrix() first = self.to_basis() second = other.to_basis() - inner = np.zeros((self.n_basis, other.n_basis)) - - for i in range(self.n_basis): - for j in range(other.n_basis): - inner[i, j] = first[i].inner_product(second[j]) + indices = np.indices((self.n_basis, other.n_basis)) - return inner + return inner_product( + first[indices[0].ravel()], second[indices[1].ravel()]).reshape( + (self.n_basis, other.n_basis)) def _gram_matrix(self): """ @@ -287,13 +287,15 @@ def _gram_matrix(self): Subclasses may override this method for improving computation of the Gram matrix. """ + from ...misc import inner_product + fbasis = self.to_basis() gram = np.zeros((self.n_basis, self.n_basis)) for i in range(fbasis.n_basis): for j in range(i, fbasis.n_basis): - gram[i, j] = fbasis[i].inner_product(fbasis[j], None, None) + gram[i, j] = inner_product(fbasis[i], fbasis[j]) gram[j, i] = gram[i, j] return gram @@ -322,9 +324,6 @@ def gram_matrix(self): return gram - def inner_product(self, other): - return self.to_basis().inner_product(other) - def _add_same_basis(self, coefs1, coefs2): return self.copy(), coefs1 + coefs2 diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index db804509e..c79d096eb 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -584,61 +584,6 @@ def times(self, other): coefs = np.transpose(np.atleast_2d(other)) return self.copy(coefficients=self.coefficients * coefs) - def inner_product(self, other): - r"""Return an inner product matrix given a FDataBasis object. - - The inner product of two functions is defined as - - .. math:: - = \int_a^b x(t)y(t) dt - - When we talk abaout FDataBasis objects, they have many samples, so we - talk about inner product matrix instead. So, for two FDataBasis objects - we define the inner product matrix as - - .. math:: - a_{ij} = = \int_a^b x_i(s) y_j(s) ds - - where :math:`f_i(s), g_j(s)` are the :math:`i^{th} j^{th}` sample of - each object. The return matrix has a shape of :math:`IxJ` where I and - J are the number of samples of each object respectively. - - Args: - other (FDataBasis, Basis): FDataBasis object containing the second - object to make the inner product - - weights(FDataBasis): a FDataBasis object with only one sample that - defines the weight to calculate the inner product - - Returns: - numpy.array: Inner Product matrix. - - """ - if not _same_domain(self.domain_range, other.domain_range): - raise ValueError("Both Objects should have the same domain_range") - - if not isinstance(other, FDataBasis): - other = other.to_basis() - - if self.n_samples * other.n_samples > self.n_basis * other.n_basis: - return (self.coefficients @ - self.basis.inner_product_matrix(other.basis) @ - other.coefficients.T) - else: - return self._inner_product_integrate(other) - - def _inner_product_integrate(self, other): - - matrix = np.empty((self.n_samples, other.n_samples)) - (left, right) = self.domain_range[0] - - for i in range(self.n_samples): - for j in range(other.n_samples): - matrix[i, j] = scipy.integrate.quad( - lambda x: self[i]([x]) * other[j]([x])[0], left, right)[0] - - return matrix - def _to_R(self): """Gives the code to build the object on fda package on R""" return ("fd(coef = " + self._array_to_R(self.coefficients, True) + diff --git a/tests/test_basis.py b/tests/test_basis.py index 21bfcd826..c49a28dfc 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,5 +1,6 @@ from skfda import concatenate import skfda +from skfda.misc._math import inner_product, _inner_product_matrix from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) from skfda.representation.grid import FDataGrid @@ -117,7 +118,7 @@ def test_basis_basis_inprod(self): monomial = Monomial(n_basis=4) bspline = BSpline(n_basis=5, order=4) np.testing.assert_allclose( - monomial.inner_product(bspline), + monomial.inner_product_matrix(bspline), np.array( [[0.12499983, 0.25000035, 0.24999965, 0.25000035, 0.12499983], [0.01249991, 0.07500017, 0.12499983, 0.17500017, 0.11249991], @@ -125,8 +126,8 @@ def test_basis_basis_inprod(self): [0.00044654, 0.01339264, 0.04375022, 0.09910693, 0.09330368] ]), rtol=1e-3) np.testing.assert_array_almost_equal( - monomial.inner_product(bspline), - bspline.inner_product(monomial).T + monomial.inner_product_matrix(bspline), + bspline.inner_product_matrix(monomial).T ) def test_basis_fdatabasis_inprod(self): @@ -135,7 +136,7 @@ def test_basis_fdatabasis_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - monomial.inner_product(bsplinefd), + _inner_product_matrix(monomial, bsplinefd), np.array([[2., 7., 12.], [1.29626206, 3.79626206, 6.29626206], [0.96292873, 2.62959539, 4.29626206], @@ -152,16 +153,7 @@ def test_fdatabasis_fdatabasis_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - monomialfd.inner_product(bsplinefd), - np.array([[16.14797697, 52.81464364, 89.4813103], - [11.55565285, 38.22211951, 64.88878618], - [18.14698361, 55.64698361, 93.14698361], - [15.2495976, 48.9995976, 82.7495976], - [19.70392982, 63.03676315, 106.37009648]]), - rtol=1e-4) - - np.testing.assert_allclose( - monomialfd._inner_product_integrate(bsplinefd), + _inner_product_matrix(monomialfd, bsplinefd), np.array([[16.14797697, 52.81464364, 89.4813103], [11.55565285, 38.22211951, 64.88878618], [18.14698361, 55.64698361, 93.14698361], @@ -175,8 +167,8 @@ def test_comutativity_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - bsplinefd.inner_product(monomial), - np.transpose(monomial.inner_product(bsplinefd)) + _inner_product_matrix(bsplinefd, monomial), + np.transpose(_inner_product_matrix(monomial, bsplinefd)) ) def test_fdatabasis_times_fdatabasis_fdatabasis(self): From 76787071b93556a79b99e0c4d0315e7d1b7f0a59 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 17 Jul 2020 13:56:33 +0200 Subject: [PATCH 605/624] Optimize Gram matrix. --- skfda/representation/basis/_basis.py | 32 +++++--- skfda/representation/basis/_fdatabasis.py | 8 +- tests/test_basis.py | 90 +++++++++++++++-------- 3 files changed, 88 insertions(+), 42 deletions(-) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index b3887f592..a0bfde137 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -280,26 +280,40 @@ def inner_product_matrix(self, other=None): first[indices[0].ravel()], second[indices[1].ravel()]).reshape( (self.n_basis, other.n_basis)) - def _gram_matrix(self): + def _gram_matrix_numerical(self): """ - Compute the Gram matrix. + Compute the Gram matrix numerically. - Subclasses may override this method for improving computation - of the Gram matrix. """ from ...misc import inner_product fbasis = self.to_basis() - gram = np.zeros((self.n_basis, self.n_basis)) + indices = np.triu_indices(self.n_basis) - for i in range(fbasis.n_basis): - for j in range(i, fbasis.n_basis): - gram[i, j] = inner_product(fbasis[i], fbasis[j]) - gram[j, i] = gram[i, j] + gram = np.empty((self.n_basis, self.n_basis)) + + triang_vec = inner_product( + fbasis[indices[0]], fbasis[indices[1]]) + + # Set upper matrix + gram[indices] = triang_vec + + # Set lower matrix + gram[(indices[1], indices[0])] = triang_vec return gram + def _gram_matrix(self): + """ + Compute the Gram matrix. + + Subclasses may override this method for improving computation + of the Gram matrix. + + """ + return self._gram_matrix_numerical() + def gram_matrix(self): r"""Return the Gram Matrix of a basis diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index c79d096eb..2404e71a5 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -706,7 +706,7 @@ def __add__(self, other): """Addition for FDataBasis object.""" if isinstance(other, FDataBasis): if self.basis != other.basis: - raise NotImplementedError + return NotImplemented else: basis, coefs = self.basis._add_same_basis(self.coefficients, other.coefficients) @@ -728,7 +728,7 @@ def __sub__(self, other): """Subtraction for FDataBasis object.""" if isinstance(other, FDataBasis): if self.basis != other.basis: - raise NotImplementedError + return NotImplemented else: basis, coefs = self.basis._sub_same_basis(self.coefficients, other.coefficients) @@ -748,7 +748,7 @@ def __rsub__(self, other): def __mul__(self, other): """Multiplication for FDataBasis object.""" if isinstance(other, FDataBasis): - raise NotImplementedError + return NotImplemented try: basis, coefs = self.basis._mul_constant(self.coefficients, other) @@ -776,7 +776,7 @@ def __truediv__(self, other): def __rtruediv__(self, other): """Right division for FDataBasis object.""" - raise NotImplementedError + return NotImplemented ##################################################################### # Pandas ExtensionArray methods diff --git a/tests/test_basis.py b/tests/test_basis.py index c49a28dfc..bd8c44a19 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -95,24 +95,55 @@ def test_basis_inner_matrix(self): # TODO testing with other basis - def test_basis_gram_matrix(self): - np.testing.assert_allclose(Monomial(n_basis=3).gram_matrix(), - [[1, 1 / 2, 1 / 3], [1 / 2, 1 / 3, 1 / 4], [1 / 3, 1 / 4, 1 / 5]]) - np.testing.assert_allclose(Fourier(n_basis=3).gram_matrix(), - np.identity(3)) - np.testing.assert_allclose(BSpline(n_basis=6).gram_matrix().round(4), - np.array([[4.760e-02, 2.920e-02, 6.200e-03, - 4.000e-04, 0.000e+00, 0.000e+00], - [2.920e-02, 7.380e-02, 5.210e-02, - 1.150e-02, 1.000e-04, 0.000e+00], - [6.200e-03, 5.210e-02, 1.089e-01, - 7.100e-02, 1.150e-02, 4.000e-04], - [4.000e-04, 1.150e-02, 7.100e-02, - 1.089e-01, 5.210e-02, 6.200e-03], - [0.000e+00, 1.000e-04, 1.150e-02, - 5.210e-02, 7.380e-02, 2.920e-02], - [0.000e+00, 0.000e+00, 4.000e-04, - 6.200e-03, 2.920e-02, 4.760e-02]])) + def test_basis_gram_matrix_monomial(self): + + basis = Monomial(n_basis=3) + gram_matrix = basis.gram_matrix() + gram_matrix_numerical = basis._gram_matrix_numerical() + gram_matrix_res = np.array([[1, 1 / 2, 1 / 3], + [1 / 2, 1 / 3, 1 / 4], + [1 / 3, 1 / 4, 1 / 5]]) + + np.testing.assert_allclose( + gram_matrix, gram_matrix_res) + np.testing.assert_allclose( + gram_matrix_numerical, gram_matrix_res) + + def test_basis_gram_matrix_fourier(self): + + basis = Fourier(n_basis=3) + gram_matrix = basis.gram_matrix() + gram_matrix_numerical = basis._gram_matrix_numerical() + gram_matrix_res = np.identity(3) + + np.testing.assert_allclose( + gram_matrix, gram_matrix_res) + np.testing.assert_allclose( + gram_matrix_numerical, gram_matrix_res, atol=1e-15, rtol=1e-15) + + def test_basis_gram_matrix_bspline(self): + + basis = BSpline(n_basis=6) + gram_matrix = basis.gram_matrix() + gram_matrix_numerical = basis._gram_matrix_numerical() + gram_matrix_res = np.array( + [[0.04761905, 0.02916667, 0.00615079, + 0.00039683, 0., 0.], + [0.02916667, 0.07380952, 0.05208333, + 0.01145833, 0.00014881, 0.], + [0.00615079, 0.05208333, 0.10892857, 0.07098214, + 0.01145833, 0.00039683], + [0.00039683, 0.01145833, 0.07098214, 0.10892857, + 0.05208333, 0.00615079], + [0., 0.00014881, 0.01145833, 0.05208333, + 0.07380952, 0.02916667], + [0., 0., 0.00039683, 0.00615079, + 0.02916667, 0.04761905]]) + + np.testing.assert_allclose( + gram_matrix, gram_matrix_res, rtol=1e-4) + np.testing.assert_allclose( + gram_matrix_numerical, gram_matrix_res, rtol=1e-4) def test_basis_basis_inprod(self): monomial = Monomial(n_basis=4) @@ -227,9 +258,9 @@ def test_fdatabasis__add__(self): FDataBasis(Monomial(n_basis=3), [[2, 2, 3], [5, 4, 5]])) - np.testing.assert_raises(NotImplementedError, monomial2.__add__, - FDataBasis(Fourier(n_basis=3), - [[2, 2, 3], [5, 4, 5]])) + with np.testing.assert_raises(TypeError): + monomial2 + FDataBasis(Fourier(n_basis=3), + [[2, 2, 3], [5, 4, 5]]) def test_fdatabasis__sub__(self): monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) @@ -251,9 +282,9 @@ def test_fdatabasis__sub__(self): FDataBasis(Monomial(n_basis=3), [[0, -2, -3], [-1, -4, -5]])) - np.testing.assert_raises(NotImplementedError, monomial2.__sub__, - FDataBasis(Fourier(n_basis=3), - [[2, 2, 3], [5, 4, 5]])) + with np.testing.assert_raises(TypeError): + monomial2 - FDataBasis(Fourier(n_basis=3), + [[2, 2, 3], [5, 4, 5]]) def test_fdatabasis__mul__(self): monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) @@ -275,11 +306,12 @@ def test_fdatabasis__mul__(self): FDataBasis(Monomial(n_basis=3), [[1, 2, 3], [6, 8, 10]])) - np.testing.assert_raises(NotImplementedError, monomial2.__mul__, - FDataBasis(Fourier(n_basis=3), - [[2, 2, 3], [5, 4, 5]])) - np.testing.assert_raises(NotImplementedError, monomial2.__mul__, - monomial2) + with np.testing.assert_raises(TypeError): + monomial2 * FDataBasis(Fourier(n_basis=3), + [[2, 2, 3], [5, 4, 5]]) + + with np.testing.assert_raises(TypeError): + monomial2 * monomial2 def test_fdatabasis__mul__2(self): monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) From 7be6e1268d5bac01b770ec8c63d9b19acba5ea7e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 17 Jul 2020 19:33:43 +0200 Subject: [PATCH 606/624] Improve inner product in linear regression. --- skfda/misc/_math.py | 15 ++++++--- skfda/ml/regression/_coefficients.py | 46 ++++++++++++++++++++-------- skfda/ml/regression/linear.py | 35 +++++++++++---------- 3 files changed, 62 insertions(+), 34 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 36792a4fb..4a6d030aa 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -144,7 +144,7 @@ def cumsum(fdatagrid): @multimethod.multidispatch -def inner_product(arg1, arg2): +def inner_product(arg1, arg2, **kwargs): r"""Return the usual (:math:`L_2`) inner product. Calculates the inner product between matching samples in two @@ -276,7 +276,9 @@ def inner_product_fdatagrid(arg1: FDataGrid, arg2: FDataGrid): @inner_product.register(Basis, FDataBasis) @inner_product.register(Basis, Basis) def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], - arg2: Union[FDataBasis, Basis]): + arg2: Union[FDataBasis, Basis], + *, + inner_product_matrix=None): if not _same_domain(arg1, arg2): raise ValueError("Both Objects should have the same domain_range") @@ -287,9 +289,14 @@ def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], if isinstance(arg2, Basis): arg2 = arg2.to_basis() - if max(arg1.n_samples, arg2.n_samples) > arg1.n_basis * arg2.n_basis: + if inner_product_matrix is not None or ( + max(arg1.n_samples, arg2.n_samples) > arg1.n_basis * arg2.n_basis): + + if inner_product_matrix is None: + inner_product_matrix = arg1.basis.inner_product_matrix(arg2.basis) + return (arg1.coefficients @ - arg1.basis.inner_product_matrix(arg2.basis) * + inner_product_matrix * arg2.coefficients).sum(axis=-1) else: return _inner_product_integrate(arg1, arg2) diff --git a/skfda/ml/regression/_coefficients.py b/skfda/ml/regression/_coefficients.py index 185953059..67f30ab16 100644 --- a/skfda/ml/regression/_coefficients.py +++ b/skfda/ml/regression/_coefficients.py @@ -2,6 +2,7 @@ import numpy as np +from ...misc._math import inner_product from ...representation.basis import Basis, FDataBasis @@ -9,12 +10,8 @@ class CoefficientInfo(): """ Information about an estimated coefficient. - At the very least it should have a type and a shape, but it may have - additional information depending on its type. - Parameters: - coef_type: Class of the coefficient. - shape: Shape of the constant coefficients form. + basis: Basis of the coefficient. """ @@ -42,26 +39,49 @@ def convert_from_constant_coefs(self, coefs): """ return coefs + def inner_product(self, coefs, X): + """ + Compute the inner product between the coefficient and + the covariate. + + """ + return inner_product(coefs, X) -@singledispatch -def coefficient_info_from_covariate(X, y, **kwargs) -> CoefficientInfo: - """ - Make a coefficient info object from a covariate. +class CoefficientInfoFDataBasis(CoefficientInfo): """ - return CoefficientInfo(basis=np.identity(X.shape[1], dtype=X.dtype)) + Information about a FDataBasis coefficient. + Parameters: + basis: Basis of the coefficient. -class CoefficientInfoFDataBasis(CoefficientInfo): + """ def regression_matrix(self, X, y): + # The matrix is the matrix of coefficients multiplied by + # the matrix of inner products. + xcoef = X.coefficients - inner_basis = X.basis.inner_product_matrix(self.basis) - return xcoef @ inner_basis + self.inner_basis = X.basis.inner_product_matrix(self.basis) + return xcoef @ self.inner_basis def convert_from_constant_coefs(self, coefs): return FDataBasis(self.basis, coefs.T) + def inner_product(self, coefs, X): + # Efficient implementation of the inner product using the + # inner product matrix previously computed + return inner_product(coefs, X, inner_product_matrix=self.inner_basis.T) + + +@singledispatch +def coefficient_info_from_covariate(X, y, **kwargs) -> CoefficientInfo: + """ + Make a coefficient info object from a covariate. + + """ + return CoefficientInfo(basis=np.identity(X.shape[1], dtype=X.dtype)) + @coefficient_info_from_covariate.register(FDataBasis) def coefficient_info_from_covariate_fdatabasis( diff --git a/skfda/ml/regression/linear.py b/skfda/ml/regression/linear.py index bba34ec5a..30cbe5faf 100644 --- a/skfda/ml/regression/linear.py +++ b/skfda/ml/regression/linear.py @@ -139,16 +139,13 @@ def fit(self, X, y=None, sample_weight=None): elif regularization is not None: regularization = (None, regularization) - inner_products = [c.regression_matrix(x, y) - for x, c in zip(X, coef_info)] - - coef_lengths = np.array([i.shape[1] for i in inner_products]) - coef_start = np.cumsum(coef_lengths) + inner_products_list = [c.regression_matrix(x, y) + for x, c in zip(X, coef_info)] # This is C @ J - inner_products = np.concatenate(inner_products, axis=1) + inner_products = np.concatenate(inner_products_list, axis=1) - if any(w != 1 for w in sample_weight): + if sample_weight is not None: inner_products = inner_products * np.sqrt(sample_weight) y = y * np.sqrt(sample_weight) @@ -164,6 +161,9 @@ def fit(self, X, y=None, sample_weight=None): gram_inner_x_coef = inner_products.T @ inner_products + penalty_matrix inner_x_coef_y = inner_products.T @ y + coef_lengths = np.array([i.shape[1] for i in inner_products_list]) + coef_start = np.cumsum(coef_lengths) + basiscoefs = np.linalg.solve(gram_inner_x_coef, inner_x_coef_y) basiscoef_list = np.split(basiscoefs, coef_start) @@ -178,6 +178,7 @@ def fit(self, X, y=None, sample_weight=None): self.intercept_ = 0.0 self.coef_ = coefs + self._coef_info = coef_info self._target_ndim = y.ndim return self @@ -188,8 +189,9 @@ def predict(self, X): check_is_fitted(self) X = self._argcheck_X(X) - result = np.sum([inner_product( - coef, x) for coef, x in zip(self.coef_, X)], axis=0) + result = np.sum([coef_info.inner_product(coef, x) + for coef, x, coef_info + in zip(self.coef_, X, self._coef_info)], axis=0) result += self.intercept_ @@ -237,15 +239,14 @@ def _argcheck_X_y(self, X, y, sample_weight=None, coef_basis=None): coef_info = [coefficient_info_from_covariate(x, y, basis=b) for x, b in zip(X, coef_basis)] - if sample_weight is None: - sample_weight = np.ones(len(y)) + if sample_weight is not None: - if len(sample_weight) != len(y): - raise ValueError("The number of sample weights should be equal to" - "the number of samples.") + if len(sample_weight) != len(y): + raise ValueError("The number of sample weights should be " + "equal to the number of samples.") - if np.any(np.array(sample_weight) < 0): - raise ValueError( - "The sample weights should be non negative values") + if np.any(np.array(sample_weight) < 0): + raise ValueError( + "The sample weights should be non negative values") return X, y, sample_weight, coef_info From d60211e8a75249adc1dc81863a72f51876c95e7c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 18 Jul 2020 00:33:23 +0200 Subject: [PATCH 607/624] Inner product for FDataGrid with multiple variables. --- skfda/misc/_math.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 4a6d030aa..3663c6699 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -253,22 +253,18 @@ def inner_product(arg1, arg2, **kwargs): @inner_product.register def inner_product_fdatagrid(arg1: FDataGrid, arg2: FDataGrid): - if arg1.dim_domain != 1: - raise NotImplementedError("This method only works when the dimension " - "of the domain of the FDatagrid object is " - "one.") - if not np.array_equal(arg1.sample_points, arg2.sample_points): raise ValueError("Sample points for both objects must be equal") integrand = arg1.data_matrix * arg2.data_matrix - integral = scipy.integrate.simps(integrand, - x=arg1.sample_points[0], - axis=1) + for s in arg1.sample_points: + integrand = scipy.integrate.simps(integrand, + x=s, + axis=1) - return np.sum(integral, axis=-1) + return np.sum(integrand, axis=-1) @inner_product.register(FDataBasis, FDataBasis) From b2ca0ffe3776935241d623cb481fc306c5b6e38f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 18 Jul 2020 20:04:27 +0200 Subject: [PATCH 608/624] Inner product of several variables, for both grid and basis. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 18 ++++++++ skfda/misc/_math.py | 39 ++++++++++------- skfda/representation/basis/_basis.py | 6 ++- skfda/representation/basis/_tensor_basis.py | 11 +++++ tests/test_math.py | 46 +++++++++++++++++++++ 6 files changed, 105 insertions(+), 17 deletions(-) create mode 100644 tests/test_math.py diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 50a105de3..b6fb2d5bf 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -5,4 +5,4 @@ _to_grid, check_is_univariate, _same_domain, _to_array_maybe_ragged, _reshape_eval_points, - _evaluate_grid) + _evaluate_grid, nquad_vec) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 6ad400d9e..9e6c6fbcb 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -3,6 +3,8 @@ import functools import types +import scipy.integrate + import numpy as np @@ -318,6 +320,22 @@ def _evaluate_grid(axes, *, evaluate_method, return res +def nquad_vec(func, ranges): + + initial_depth = len(ranges) - 1 + + def integrate(*args, depth): + + if depth == 0: + f = functools.partial(func, *args) + else: + f = functools.partial(integrate, *args, depth=depth - 1) + + return scipy.integrate.quad_vec(f, *ranges[initial_depth - depth])[0] + + return integrate(depth=initial_depth) + + def parameter_aliases(**alias_assignments): """Allows using aliases for parameters""" def decorator(f): diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 3663c6699..51b9cb70c 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -12,7 +12,7 @@ import numpy as np -from .._utils import _same_domain +from .._utils import _same_domain, nquad_vec from ..representation import FDataGrid, FDataBasis from ..representation.basis import Basis @@ -274,7 +274,8 @@ def inner_product_fdatagrid(arg1: FDataGrid, arg2: FDataGrid): def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], arg2: Union[FDataBasis, Basis], *, - inner_product_matrix=None): + inner_product_matrix=None, + force_numerical=False): if not _same_domain(arg1, arg2): raise ValueError("Both Objects should have the same domain_range") @@ -285,8 +286,25 @@ def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], if isinstance(arg2, Basis): arg2 = arg2.to_basis() - if inner_product_matrix is not None or ( - max(arg1.n_samples, arg2.n_samples) > arg1.n_basis * arg2.n_basis): + # Now several cases where computing the matrix is preferrable + # + # First, if force_numerical is True, the matrix is NOT used + # Otherwise, if the matrix is given, it is used + # Two other cases follow + + # The basis is the same: most basis can optimize this case, + # and also the Gram matrix is cached the first time, so computing + # it is usually worthwhile + same_basis = arg1.basis == arg2.basis + + # The number of operations is less usinf the matrix + n_ops_best_with_matrix = max( + arg1.n_samples, arg2.n_samples) > arg1.n_basis * arg2.n_basis + + if not force_numerical and ( + inner_product_matrix is not None + or same_basis + or n_ops_best_with_matrix): if inner_product_matrix is None: inner_product_matrix = arg1.basis.inner_product_matrix(arg2.basis) @@ -304,16 +322,9 @@ def _inner_product_integrate(arg1, arg2): arg2.domain_range): raise ValueError("Domain range for both objects must be equal") - if arg1.dim_domain != 1: - raise NotImplementedError("This method only works when the dimension " - "of the domain of the FDatagrid object is " - "one.") - - (left, right) = arg1.domain_range[0] - - integral = scipy.integrate.quad_vec( - lambda x: arg1([x])[:, 0, :] * arg2([x])[:, 0, :], - left, right)[0] + integral = nquad_vec( + lambda *args: arg1([*args])[:, 0, :] * arg2([*args])[:, 0, :], + arg1.domain_range) return np.sum(integral, axis=-1) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index a0bfde137..3ec23a973 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -277,7 +277,8 @@ def inner_product_matrix(self, other=None): indices = np.indices((self.n_basis, other.n_basis)) return inner_product( - first[indices[0].ravel()], second[indices[1].ravel()]).reshape( + first[indices[0].ravel()], second[indices[1].ravel()], + force_numerical=True).reshape( (self.n_basis, other.n_basis)) def _gram_matrix_numerical(self): @@ -294,7 +295,8 @@ def _gram_matrix_numerical(self): gram = np.empty((self.n_basis, self.n_basis)) triang_vec = inner_product( - fbasis[indices[0]], fbasis[indices[1]]) + fbasis[indices[0]], fbasis[indices[1]], + force_numerical=True) # Set upper matrix gram[indices] = triang_vec diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index 4f4c76337..b1d96aa35 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -94,6 +94,17 @@ def _derivative_basis_and_coefs(self, coefs, order=1): pass + def _gram_matrix(self): + + gram_matrices = [b.gram_matrix().ravel() for b in self.basis_list] + + gram = gram_matrices[0] + + for g in gram_matrices[1:]: + gram = np.outer(gram, g).ravel() + + return gram.reshape((self.n_basis, self.n_basis)) + def basis_of_product(self, other): pass diff --git a/tests/test_math.py b/tests/test_math.py new file mode 100644 index 000000000..787a29d6e --- /dev/null +++ b/tests/test_math.py @@ -0,0 +1,46 @@ +import skfda +from skfda.representation.basis import Monomial, Tensor +import unittest +import numpy as np + + +def ndm(*args): + return [x[(None,) * i + (slice(None),) + (None,) * (len(args) - i - 1)] + for i, x in enumerate(args)] + + +class InnerProductTest(unittest.TestCase): + + def test_several_variables(self): + + def f(x, y, z): + return x * y * z + + t = np.linspace(0, 1, 100) + + x2, y2, z2 = ndm(t, 2 * t, 3 * t) + + data_matrix = f(x2, y2, z2) + + sample_points = [t, 2 * t, 3 * t] + + fd = skfda.FDataGrid( + data_matrix[None, ...], sample_points=sample_points) + + basis = Tensor([Monomial(n_basis=5, domain_range=(0, 1)), + Monomial(n_basis=5, domain_range=(0, 2)), + Monomial(n_basis=5, domain_range=(0, 3))]) + + fd_basis = fd.to_basis(basis) + + res = 8 + + np.testing.assert_allclose( + skfda.misc.inner_product(fd, fd), res, rtol=1e-5) + np.testing.assert_allclose( + skfda.misc.inner_product(fd_basis, fd_basis), res, rtol=1e-5) + + +if __name__ == "__main__": + #import sys;sys.argv = ['', 'Test.testName'] + unittest.main() From fa7704eb9d2fd01434db859486fa1bc5ab9b6fd4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 18 Jul 2020 23:23:34 +0200 Subject: [PATCH 609/624] Inner product for vector valued functions. --- skfda/representation/basis/_vector_basis.py | 8 +++++ tests/test_math.py | 33 +++++++++++++++++++-- 2 files changed, 39 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 895d58c47..a15a6dfaf 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -1,3 +1,5 @@ +import scipy.linalg + import numpy as np from ..._utils import _same_domain @@ -112,6 +114,12 @@ def _derivative_basis_and_coefs(self, coefs, order=1): return new_basis, new_coefs + def _gram_matrix(self): + + gram_matrices = [b.gram_matrix() for b in self.basis_list] + + return scipy.linalg.block_diag(*gram_matrices) + def _coordinate_nonfull(self, fdatabasis, key): r_key = key diff --git a/tests/test_math.py b/tests/test_math.py index 787a29d6e..a53729c8c 100644 --- a/tests/test_math.py +++ b/tests/test_math.py @@ -1,5 +1,5 @@ import skfda -from skfda.representation.basis import Monomial, Tensor +from skfda.representation.basis import Monomial, Tensor, VectorValued import unittest import numpy as np @@ -25,7 +25,7 @@ def f(x, y, z): sample_points = [t, 2 * t, 3 * t] fd = skfda.FDataGrid( - data_matrix[None, ...], sample_points=sample_points) + data_matrix[np.newaxis, ...], sample_points=sample_points) basis = Tensor([Monomial(n_basis=5, domain_range=(0, 1)), Monomial(n_basis=5, domain_range=(0, 2)), @@ -40,6 +40,35 @@ def f(x, y, z): np.testing.assert_allclose( skfda.misc.inner_product(fd_basis, fd_basis), res, rtol=1e-5) + def test_vector_valued(self): + + def f(x): + return x**2 + + def g(y): + return 3 * y + + t = np.linspace(0, 1, 100) + + data_matrix = np.array([np.array([f(t), g(t)]).T]) + + sample_points = [t] + + fd = skfda.FDataGrid( + data_matrix, sample_points=sample_points) + + basis = VectorValued([Monomial(n_basis=5), + Monomial(n_basis=5)]) + + fd_basis = fd.to_basis(basis) + + res = 1 / 5 + 3 + + np.testing.assert_allclose( + skfda.misc.inner_product(fd, fd), res, rtol=1e-5) + np.testing.assert_allclose( + skfda.misc.inner_product(fd_basis, fd_basis), res, rtol=1e-5) + if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] From d9c99439effcc0c79841e1c526e8848c471a0de7 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 02:19:50 +0200 Subject: [PATCH 610/624] Rename `norm_lp` to `lp_norm`. --- docs/modules/misc/metrics.rst | 2 +- skfda/misc/metrics.py | 12 ++++++------ skfda/misc/operators/_identity.py | 4 ++-- tests/test_metrics.py | 30 +++++++++++++++--------------- 4 files changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/modules/misc/metrics.rst b/docs/modules/misc/metrics.rst index e6ca52f93..d8f72872d 100644 --- a/docs/modules/misc/metrics.rst +++ b/docs/modules/misc/metrics.rst @@ -12,7 +12,7 @@ The following functions computes the norms and distances used in Lp spaces. .. autosummary:: :toctree: autosummary - skfda.misc.metrics.norm_lp + skfda.misc.metrics.lp_norm skfda.misc.metrics.lp_distance diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index a177baa20..b441a2405 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -150,7 +150,7 @@ def distance_from_norm(norm, **kwargs): To construct the :math:`\mathbb{L}^2` distance it is used the :math:`\mathbb{L}^2` norm wich it is used to compute the distance. - >>> l2_distance = distance_from_norm(norm_lp, p=2) + >>> l2_distance = distance_from_norm(lp_norm, p=2) >>> d = l2_distance(fd, fd2) >>> float('%.3f'% d) 0.289 @@ -208,7 +208,7 @@ def pairwise(fdata1, fdata2): return pairwise -def norm_lp(fdata, p=2, p2=2): +def lp_norm(fdata, p=2, p2=2): r"""Calculate the norm of all the samples in a FDataGrid object. For each sample sample f the Lp norm is defined as: @@ -263,12 +263,12 @@ def norm_lp(fdata, p=2, p2=2): >>> x = np.linspace(0,1,1001) >>> fd = FDataGrid([np.ones(len(x)), x] ,x) - >>> norm_lp(fd).round(2) + >>> lp_norm(fd).round(2) array([ 1. , 0.58]) The lp norm is only defined if p >= 1. - >>> norm_lp(fd, p = 0.5) + >>> lp_norm(fd, p = 0.5) Traceback (most recent call last): .... ValueError: p must be equal or greater than 1. @@ -339,7 +339,7 @@ def lp_distance(fdata1, fdata2, p=2, p2=2, *, eval_points=None, _check=True): than 1. If p='inf' or p=np.inf it is used the L infinity metric. Defaults to 2. p2 (int, optional): p index of the vectorial norm applied in case of - multivariate objects. Defaults to 2. See :func:`norm_lp`. + multivariate objects. Defaults to 2. See :func:`lp_norm`. Examples: Computes the distances between an object containing functional data @@ -371,7 +371,7 @@ def lp_distance(fdata1, fdata2, p=2, p2=2, *, eval_points=None, _check=True): fdata1, fdata2 = _cast_to_grid(fdata1, fdata2, eval_points=eval_points, _check=_check) - return norm_lp(fdata1 - fdata2, p=p, p2=p2) + return lp_norm(fdata1 - fdata2, p=p, p2=p2) def fisher_rao_distance(fdata1, fdata2, *, eval_points=None, _check=True): diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py index b48dbef55..16067002e 100644 --- a/skfda/misc/operators/_identity.py +++ b/skfda/misc/operators/_identity.py @@ -33,6 +33,6 @@ def basis_penalty_matrix_optimized( def fdatagrid_penalty_matrix_optimized( linear_operator: Identity, basis: FDataGrid): - from ..metrics import norm_lp + from ..metrics import lp_norm - return np.diag(norm_lp(basis)**2) + return np.diag(lp_norm(basis)**2) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index aa6dc39f8..f1eb76720 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,13 +1,13 @@ +from skfda import FDataGrid, FDataBasis +from skfda.datasets import make_multimodal_samples +from skfda.exploratory import stats +from skfda.misc.metrics import lp_distance, lp_norm, vectorial_norm +from skfda.representation.basis import Monomial import unittest import scipy.stats.mstats import numpy as np -from skfda import FDataGrid, FDataBasis -from skfda.datasets import make_multimodal_samples -from skfda.exploratory import stats -from skfda.misc.metrics import lp_distance, norm_lp, vectorial_norm -from skfda.representation.basis import Monomial class TestLpMetrics(unittest.TestCase): @@ -44,26 +44,26 @@ def test_vectorial_norm_surface(self): np.testing.assert_array_almost_equal(vec.data_matrix, self.fd_surface.data_matrix) - def test_norm_lp(self): + def test_lp_norm(self): - np.testing.assert_allclose(norm_lp(self.fd, p=1), [16., 41.33333333]) - np.testing.assert_allclose(norm_lp(self.fd, p='inf'), [6, 25]) + np.testing.assert_allclose(lp_norm(self.fd, p=1), [16., 41.33333333]) + np.testing.assert_allclose(lp_norm(self.fd, p='inf'), [6, 25]) - def test_norm_lp_curve(self): + def test_lp_norm_curve(self): - np.testing.assert_allclose(norm_lp(self.fd_curve, p=1, p2=1), + np.testing.assert_allclose(lp_norm(self.fd_curve, p=1, p2=1), [32., 82.666667]) - np.testing.assert_allclose(norm_lp(self.fd_curve, p='inf', p2='inf'), + np.testing.assert_allclose(lp_norm(self.fd_curve, p='inf', p2='inf'), [6, 25]) - def test_norm_lp_surface_inf(self): - np.testing.assert_allclose(norm_lp(self.fd_surface, p='inf').round(5), + def test_lp_norm_surface_inf(self): + np.testing.assert_allclose(lp_norm(self.fd_surface, p='inf').round(5), [0.99994, 0.99793, 0.99868]) - def test_norm_lp_surface(self): + def test_lp_norm_surface(self): # Integration of surfaces not implemented, add test case after # implementation - self.assertEqual(norm_lp(self.fd_surface), NotImplemented) + self.assertEqual(lp_norm(self.fd_surface), NotImplemented) def test_lp_error_dimensions(self): # Case internal arrays From 917b352385cc549a0b198fa899a3d5561d778675 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 12:26:35 +0200 Subject: [PATCH 611/624] Implement `inner_product_matrix`. The inner product matrix and Gram matrix of basis is implemented calling this function. --- skfda/misc/__init__.py | 3 +- skfda/misc/_math.py | 41 +++++++++++++++++++++++----- skfda/representation/basis/_basis.py | 32 +++------------------- tests/test_basis.py | 10 +++---- 4 files changed, 45 insertions(+), 41 deletions(-) diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 099e3b17f..f3ef87ad1 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,4 +1,5 @@ from . import covariances, kernels, metrics from . import operators from . import regularization -from ._math import log, log2, log10, exp, sqrt, cumsum, inner_product +from ._math import (log, log2, log10, exp, sqrt, cumsum, + inner_product, inner_product_matrix) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 51b9cb70c..cade7d067 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -329,9 +329,17 @@ def _inner_product_integrate(arg1, arg2): return np.sum(integral, axis=-1) -def _inner_product_matrix(arg1, arg2): +def inner_product_matrix(arg1, arg2=None, **kwargs): """ - Currently only used for testing purposes. + Returns the inner product matrix between is arguments. + + If arg2 is ``None`` returns the Gram matrix. + + Args: + + arg1: First sample. + arg2: Second sample. + """ if isinstance(arg1, Basis): @@ -339,10 +347,29 @@ def _inner_product_matrix(arg1, arg2): if isinstance(arg2, Basis): arg2 = arg2.to_basis() - matrix = np.empty((arg1.n_samples, arg2.n_samples)) + if arg2 is None: + + indices = np.triu_indices(len(arg1)) + + matrix = np.empty((len(arg1), len(arg1))) + + triang_vec = inner_product( + arg1[indices[0]], arg1[indices[1]], + **kwargs) + + # Set upper matrix + matrix[indices] = triang_vec + + # Set lower matrix + matrix[(indices[1], indices[0])] = triang_vec + + return matrix + + else: - for i in range(arg1.n_samples): - for j in range(arg2.n_samples): - matrix[i, j] = inner_product(arg1[i], arg2[j]) + indices = np.indices((len(arg1), len(arg2))) - return matrix + return inner_product( + arg1[indices[0].ravel()], arg2[indices[1].ravel()], + **kwargs).reshape( + (len(arg1), len(arg2))) diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 3ec23a973..4b51602fc 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -266,45 +266,21 @@ def inner_product_matrix(self, other=None): numpy.array: Inner Product Matrix of two basis """ - from ...misc import inner_product + from ...misc import inner_product_matrix if other is None or self == other: return self.gram_matrix() - first = self.to_basis() - second = other.to_basis() - - indices = np.indices((self.n_basis, other.n_basis)) - - return inner_product( - first[indices[0].ravel()], second[indices[1].ravel()], - force_numerical=True).reshape( - (self.n_basis, other.n_basis)) + return inner_product_matrix(self, other) def _gram_matrix_numerical(self): """ Compute the Gram matrix numerically. """ - from ...misc import inner_product - - fbasis = self.to_basis() - - indices = np.triu_indices(self.n_basis) - - gram = np.empty((self.n_basis, self.n_basis)) + from ...misc import inner_product_matrix - triang_vec = inner_product( - fbasis[indices[0]], fbasis[indices[1]], - force_numerical=True) - - # Set upper matrix - gram[indices] = triang_vec - - # Set lower matrix - gram[(indices[1], indices[0])] = triang_vec - - return gram + return inner_product_matrix(self, force_numerical=True) def _gram_matrix(self): """ diff --git a/tests/test_basis.py b/tests/test_basis.py index bd8c44a19..da2531eaf 100644 --- a/tests/test_basis.py +++ b/tests/test_basis.py @@ -1,6 +1,6 @@ from skfda import concatenate import skfda -from skfda.misc._math import inner_product, _inner_product_matrix +from skfda.misc import inner_product, inner_product_matrix from skfda.representation.basis import (Basis, FDataBasis, Constant, Monomial, BSpline, Fourier) from skfda.representation.grid import FDataGrid @@ -167,7 +167,7 @@ def test_basis_fdatabasis_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - _inner_product_matrix(monomial, bsplinefd), + inner_product_matrix(monomial, bsplinefd), np.array([[2., 7., 12.], [1.29626206, 3.79626206, 6.29626206], [0.96292873, 2.62959539, 4.29626206], @@ -184,7 +184,7 @@ def test_fdatabasis_fdatabasis_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - _inner_product_matrix(monomialfd, bsplinefd), + inner_product_matrix(monomialfd, bsplinefd), np.array([[16.14797697, 52.81464364, 89.4813103], [11.55565285, 38.22211951, 64.88878618], [18.14698361, 55.64698361, 93.14698361], @@ -198,8 +198,8 @@ def test_comutativity_inprod(self): bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( - _inner_product_matrix(bsplinefd, monomial), - np.transpose(_inner_product_matrix(monomial, bsplinefd)) + inner_product_matrix(bsplinefd, monomial), + np.transpose(inner_product_matrix(monomial, bsplinefd)) ) def test_fdatabasis_times_fdatabasis_fdatabasis(self): From 6cd3855ddaddab0fff4fae366e004f155f5e883d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 14:22:14 +0200 Subject: [PATCH 612/624] Implemented gramian matrix for operators in terms of the inner product. --- skfda/_utils/__init__.py | 3 +- skfda/_utils/_utils.py | 29 +++++++++ skfda/misc/_math.py | 7 ++- skfda/misc/operators/_integral_transform.py | 5 +- .../_linear_differential_operator.py | 6 +- skfda/misc/operators/_operators.py | 63 +------------------ skfda/misc/regularization/_regularization.py | 4 +- skfda/representation/basis/_basis.py | 3 + 8 files changed, 48 insertions(+), 72 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index b6fb2d5bf..bfd587b1b 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -5,4 +5,5 @@ _to_grid, check_is_univariate, _same_domain, _to_array_maybe_ragged, _reshape_eval_points, - _evaluate_grid, nquad_vec) + _evaluate_grid, nquad_vec, + _FDataCallable) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 9e6c6fbcb..eb77e7e3b 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -8,6 +8,35 @@ import numpy as np +class _FDataCallable(): + + def __init__(self, function, *, domain_range, n_samples=1): + + self.function = function + self.domain_range = domain_range + self.n_samples = n_samples + + def __call__(self, *args, **kwargs): + + return self.function(*args, **kwargs) + + def __len__(self): + + return self.n_samples + + def __getitem__(self, key): + + def new_function(*args, **kwargs): + return self.function(*args, **kwargs)[key] + + tmp = np.empty(self.n_samples) + new_nsamples = len(tmp[key]) + + return _FDataCallable(new_function, + domain_range=self.domain_range, + n_samples=new_nsamples) + + def check_is_univariate(fd): """Checks if an FData is univariate and raises an error diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index cade7d067..af5f9df21 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -12,7 +12,7 @@ import numpy as np -from .._utils import _same_domain, nquad_vec +from .._utils import _same_domain, nquad_vec, _FDataCallable from ..representation import FDataGrid, FDataBasis from ..representation.basis import Basis @@ -316,6 +316,11 @@ def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], return _inner_product_integrate(arg1, arg2) +@inner_product.register +def inner_product_fdatacallable(arg1: _FDataCallable, arg2: _FDataCallable): + return _inner_product_integrate(arg1, arg2) + + def _inner_product_integrate(arg1, arg2): if not np.array_equal(arg1.domain_range, diff --git a/skfda/misc/operators/_integral_transform.py b/skfda/misc/operators/_integral_transform.py index 3af06ead6..aab01d5ad 100644 --- a/skfda/misc/operators/_integral_transform.py +++ b/skfda/misc/operators/_integral_transform.py @@ -1,9 +1,6 @@ import scipy.integrate -import numpy as np - -from ...representation import FData -from ._operators import Operator, get_n_basis, gramian_matrix_optimization +from ._operators import Operator class IntegralTransform(Operator): diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 5d4897c51..8589bf66a 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -6,7 +6,7 @@ import numpy as np -from ..._utils import _same_domain +from ..._utils import _same_domain, _FDataCallable from ...representation import FDataGrid from ...representation.basis import Constant, Monomial, Fourier, BSpline from ._operators import Operator, gramian_matrix_optimization @@ -228,7 +228,9 @@ def applied_linear_diff_op(t): return sum(w(t) * function_derivatives[i](t) for i, w in enumerate(self.weights)) - return applied_linear_diff_op + return _FDataCallable(applied_linear_diff_op, + domain_range=f.domain_range, + n_samples=len(f)) ############################################################# diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 80ac9615c..8d1b955d5 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -1,9 +1,6 @@ import abc import multimethod -import scipy.integrate - -import numpy as np class Operator(abc.ABC): @@ -27,49 +24,6 @@ def gramian_matrix_optimization(linear_operator, basis): return NotImplemented -def get_n_basis(basis): - n_basis = getattr(basis, "n_basis", None) - if n_basis is None: - n_basis = len(basis) - - return n_basis - - -def compute_triang_functional(evaluated_basis, - indices, - basis): - def cross_product(x): - """Multiply the two evaluations.""" - res = evaluated_basis([x]) - - # Remove n_points dimension - res = res[:, 0, :] - - return res[indices[0]] * res[indices[1]] - - # Range of first dimension - domain_range = basis.domain_range[0] - - # Obtain the integrals for the upper matrix - integral = scipy.integrate.quad_vec( - cross_product, domain_range[0], domain_range[1])[0] - - # Sum the integrals of each codomain dimension - integral_sum = np.sum(integral, axis=-1) - - return integral_sum - - -def compute_triang_multivariate(evaluated_basis, - indices, - basis): - - cross_product = evaluated_basis[indices[0]] * evaluated_basis[indices[1]] - - # Obtain the integrals for the upper matrix - return np.sum(cross_product, axis=-1) - - def gramian_matrix_numerical(linear_operator, basis): r""" Return the gramian matrix given a basis, computed numerically. @@ -77,24 +31,11 @@ def gramian_matrix_numerical(linear_operator, basis): This method should work for every linear operator. """ - n_basis = get_n_basis(basis) - - indices = np.triu_indices(n_basis) + from .. import inner_product_matrix evaluated_basis = linear_operator(basis) - compute_triang = (compute_triang_functional if callable( - evaluated_basis) else compute_triang_multivariate) - triang_vec = compute_triang(evaluated_basis, indices, basis) - - matrix = np.empty((n_basis, n_basis)) - - # Set upper matrix - matrix[indices] = triang_vec - - # Set lower matrix - matrix[(indices[1], indices[0])] = triang_vec - return matrix + return inner_product_matrix(evaluated_basis) def gramian_matrix(linear_operator, basis): diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index b33d82669..42a496ac6 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -7,8 +7,6 @@ import numpy as np -from ..operators._operators import get_n_basis - class TikhonovRegularization(BaseEstimator): r""" @@ -142,7 +140,7 @@ def compute_penalty_matrix(basis_iterable, regularization_parameter, regularization_parameter) penalty_blocks = [ - np.zeros((get_n_basis(b), get_n_basis(b))) if r is None else + np.zeros((len(b), len(b))) if r is None else a * r.penalty_matrix(b) for b, r, a in zip(basis_iterable, regularization, regularization_parameter)] diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 4b51602fc..4bc3e3ed1 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -119,6 +119,9 @@ def evaluate(self, eval_points, *, derivative=0): def __call__(self, *args, **kwargs): return self.evaluate(*args, **kwargs) + def __len__(self): + return self.n_basis + def derivative(self, *, order=1): """Construct a FDataBasis object containing the derivative. From 66366cc5517e5b312e4955e111c343d70d2f0c59 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 22:04:54 +0200 Subject: [PATCH 613/624] Optimize `lp_norm` in the `p=2` case using the inner product. --- skfda/_neighbors/base.py | 4 ++-- skfda/misc/metrics.py | 13 +++++++++++-- tests/test_metrics.py | 2 +- 3 files changed, 14 insertions(+), 5 deletions(-) diff --git a/skfda/_neighbors/base.py b/skfda/_neighbors/base.py index a2ac25daf..8b2ffc76d 100644 --- a/skfda/_neighbors/base.py +++ b/skfda/_neighbors/base.py @@ -73,13 +73,13 @@ def _to_multivariate_metric(metric, sample_points): >>> fd = FDataGrid([np.ones(len(x))], x) >>> fd2 = FDataGrid([np.zeros(len(x))], x) >>> lp_distance(fd, fd2).round(2) - 1.0 + array([ 1.]) Creation of the sklearn-style metric. >>> sklearn_lp_distance = _to_multivariate_metric(lp_distance, [x]) >>> sklearn_lp_distance(np.ones(len(x)), np.zeros(len(x))).round(2) - 1.0 + array([ 1.]) """ # Shape -> (n_samples = 1, domain_dims...., image_dimension (-1)) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index b441a2405..3c4aa3046 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -208,7 +208,7 @@ def pairwise(fdata1, fdata2): return pairwise -def lp_norm(fdata, p=2, p2=2): +def lp_norm(fdata, p=2, p2=None): r"""Calculate the norm of all the samples in a FDataGrid object. For each sample sample f the Lp norm is defined as: @@ -274,6 +274,15 @@ def lp_norm(fdata, p=2, p2=2): ValueError: p must be equal or greater than 1. """ + from ..misc import inner_product + + if p2 is None: + p2 = p + + # Special case, the inner product is heavily optimized + if p == p2 == 2: + return np.sqrt(inner_product(fdata, fdata)) + # Checks that the lp normed is well defined if not (p == 'inf' or np.isinf(p)) and p < 1: raise ValueError(f"p must be equal or greater than 1.") @@ -352,7 +361,7 @@ def lp_distance(fdata1, fdata2, p=2, p2=2, *, eval_points=None, _check=True): >>> fd = FDataGrid([np.ones(len(x))], x) >>> fd2 = FDataGrid([np.zeros(len(x))], x) >>> lp_distance(fd, fd2).round(2) - 1.0 + array([ 1.]) If the functional data are defined over a different set of points of diff --git a/tests/test_metrics.py b/tests/test_metrics.py index f1eb76720..b939bac1b 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -63,7 +63,7 @@ def test_lp_norm_surface_inf(self): def test_lp_norm_surface(self): # Integration of surfaces not implemented, add test case after # implementation - self.assertEqual(lp_norm(self.fd_surface), NotImplemented) + self.assertEqual(lp_norm(self.fd_surface, p=1), NotImplemented) def test_lp_error_dimensions(self): # Case internal arrays From b419d6b9882b210f1638266bcaec464a2706d4fa Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 23:32:47 +0200 Subject: [PATCH 614/624] Compute `lp_distance` for `FDataGrid` without casting. --- skfda/misc/_math.py | 12 +++++------- skfda/misc/metrics.py | 27 ++++++++++++++------------- tests/test_metrics.py | 10 ---------- 3 files changed, 19 insertions(+), 30 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index af5f9df21..f5d4b2f4e 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -12,7 +12,7 @@ import numpy as np -from .._utils import _same_domain, nquad_vec, _FDataCallable +from .._utils import _same_domain, nquad_vec from ..representation import FDataGrid, FDataBasis from ..representation.basis import Basis @@ -247,7 +247,10 @@ def inner_product(arg1, arg2, **kwargs): """ - return (arg1 * arg2).sum(axis=-1) + if callable(arg1): + return _inner_product_integrate(arg1, arg2) + else: + return (arg1 * arg2).sum(axis=-1) @inner_product.register @@ -316,11 +319,6 @@ def inner_product_fdatabasis(arg1: Union[FDataBasis, Basis], return _inner_product_integrate(arg1, arg2) -@inner_product.register -def inner_product_fdatacallable(arg1: _FDataCallable, arg2: _FDataCallable): - return _inner_product_integrate(arg1, arg2) - - def _inner_product_integrate(arg1, arg2): if not np.array_equal(arg1.domain_range, diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 3c4aa3046..cbb078a34 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -8,6 +8,16 @@ from ..representation import FDataGrid, FDataBasis +def _check_compatible(fdata1, fdata2): + + if (fdata2.dim_codomain != fdata1.dim_codomain or + fdata2.dim_domain != fdata1.dim_domain): + raise ValueError("Objects should have the same dimensions") + + if not np.array_equal(fdata1.domain_range, fdata2.domain_range): + raise ValueError("Domain ranges for both objects must be equal") + + def _cast_to_grid(fdata1, fdata2, eval_points=None, _check=True, **kwargs): """Checks if the fdatas passed as argument are unidimensional and compatible and converts them to FDatagrid to compute their distances. @@ -24,16 +34,10 @@ def _cast_to_grid(fdata1, fdata2, eval_points=None, _check=True, **kwargs): if not _check: return fdata1, fdata2 - elif (fdata2.dim_codomain != fdata1.dim_codomain or - fdata2.dim_domain != fdata1.dim_domain): - raise ValueError("Objects should have the same dimensions") - - # Case different domain ranges - elif not np.array_equal(fdata1.domain_range, fdata2.domain_range): - raise ValueError("Domain ranges for both objects must be equal") + _check_compatible(fdata1, fdata2) # Case new evaluation points specified - elif eval_points is not None: + if eval_points is not None: fdata1 = fdata1.to_grid(eval_points) fdata2 = fdata2.to_grid(eval_points) @@ -372,13 +376,10 @@ def lp_distance(fdata1, fdata2, p=2, p2=2, *, eval_points=None, _check=True): >>> lp_distance(fd, fd2) Traceback (most recent call last): .... - ValueError: Domain ranges for both objects must be equal + ValueError: ... """ - # Checks - - fdata1, fdata2 = _cast_to_grid(fdata1, fdata2, eval_points=eval_points, - _check=_check) + _check_compatible(fdata1, fdata2) return lp_norm(fdata1 - fdata2, p=p, p2=p2) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index b939bac1b..394ebea1a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -92,16 +92,6 @@ def test_lp_error_sample_points(self): with np.testing.assert_raises(ValueError): lp_distance(self.fd, fd2) - def test_lp_grid_basis(self): - - np.testing.assert_allclose(lp_distance(self.fd, self.fd_basis), 0) - np.testing.assert_allclose(lp_distance(self.fd_basis, self.fd), 0) - np.testing.assert_allclose( - lp_distance(self.fd_basis, - self.fd_basis, eval_points=[1, 2, 3, 4, 5]), 0) - np.testing.assert_allclose(lp_distance(self.fd_basis, self.fd_basis), - 0) - if __name__ == '__main__': print() From 3b8165b0c437d991764711591f038a8c91dac713 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 19 Jul 2020 23:50:50 +0200 Subject: [PATCH 615/624] Vectorize pairwise distances. --- skfda/_utils/__init__.py | 2 +- skfda/_utils/_utils.py | 33 +++++++++++++++++++++++++++++++++ skfda/misc/_math.py | 29 ++--------------------------- skfda/misc/metrics.py | 16 +++------------- 4 files changed, 39 insertions(+), 41 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index bfd587b1b..c58ce4023 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -6,4 +6,4 @@ _same_domain, _to_array_maybe_ragged, _reshape_eval_points, _evaluate_grid, nquad_vec, - _FDataCallable) + _FDataCallable, _pairwise_commutative) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index eb77e7e3b..b2332041a 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -365,6 +365,39 @@ def integrate(*args, depth): return integrate(depth=initial_depth) +def _pairwise_commutative(function, arg1, arg2=None, **kwargs): + """ + Compute pairwise a commutative function. + + """ + if arg2 is None: + + indices = np.triu_indices(len(arg1)) + + matrix = np.empty((len(arg1), len(arg1))) + + triang_vec = function( + arg1[indices[0]], arg1[indices[1]], + **kwargs) + + # Set upper matrix + matrix[indices] = triang_vec + + # Set lower matrix + matrix[(indices[1], indices[0])] = triang_vec + + return matrix + + else: + + indices = np.indices((len(arg1), len(arg2))) + + return function( + arg1[indices[0].ravel()], arg2[indices[1].ravel()], + **kwargs).reshape( + (len(arg1), len(arg2))) + + def parameter_aliases(**alias_assignments): """Allows using aliases for parameters""" def decorator(f): diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index f5d4b2f4e..fbf9b5af9 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -12,7 +12,7 @@ import numpy as np -from .._utils import _same_domain, nquad_vec +from .._utils import _same_domain, nquad_vec, _pairwise_commutative from ..representation import FDataGrid, FDataBasis from ..representation.basis import Basis @@ -350,29 +350,4 @@ def inner_product_matrix(arg1, arg2=None, **kwargs): if isinstance(arg2, Basis): arg2 = arg2.to_basis() - if arg2 is None: - - indices = np.triu_indices(len(arg1)) - - matrix = np.empty((len(arg1), len(arg1))) - - triang_vec = inner_product( - arg1[indices[0]], arg1[indices[1]], - **kwargs) - - # Set upper matrix - matrix[indices] = triang_vec - - # Set lower matrix - matrix[(indices[1], indices[0])] = triang_vec - - return matrix - - else: - - indices = np.indices((len(arg1), len(arg2))) - - return inner_product( - arg1[indices[0].ravel()], arg2[indices[1].ravel()], - **kwargs).reshape( - (len(arg1), len(arg2))) + return _pairwise_commutative(inner_product, arg1, arg2, **kwargs) diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index cbb078a34..1474e60f3 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -2,6 +2,7 @@ import numpy as np +from .._utils import _pairwise_commutative from ..preprocessing.registration import normalize_warping, ElasticRegistration from ..preprocessing.registration._warping import _normalize_scale from ..preprocessing.registration.elastic import SRSF @@ -192,20 +193,9 @@ def pairwise_distance(distance, **kwargs): :obj:`Function`: Pairwise distance function, wich accepts two functional data objects and returns the pairwise distance matrix. """ - def pairwise(fdata1, fdata2): + def pairwise(fdata1, fdata2=None): - fdata1, fdata2 = _cast_to_grid(fdata1, fdata2, **kwargs) - - # Creates an empty matrix with the desired size to store the results. - matrix = np.empty((fdata1.n_samples, fdata2.n_samples)) - - # Iterates over the different samples of both objects. - for i in range(fdata1.n_samples): - for j in range(fdata2.n_samples): - matrix[i, j] = distance(fdata1[i], fdata2[j], _check=False, - **kwargs) - # Computes the metric between all piars of x and y. - return matrix + return _pairwise_commutative(distance, fdata1, fdata2) pairwise.__name__ = f"pairwise_{distance.__name__}" From bede46311965268eed27d98f7820dd826ced68a4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 21 Jul 2020 18:30:04 +0200 Subject: [PATCH 616/624] Fixes the seed of ANOVA and Hotelling tests. --- tests/test_hotelling.py | 13 +++++++------ tests/test_oneway_anova.py | 28 +++++++++++++++------------- 2 files changed, 22 insertions(+), 19 deletions(-) diff --git a/tests/test_hotelling.py b/tests/test_hotelling.py index 8af06a370..fdea10d27 100644 --- a/tests/test_hotelling.py +++ b/tests/test_hotelling.py @@ -1,9 +1,9 @@ -import unittest -import pytest - +from skfda.inference.hotelling import hotelling_t2, hotelling_test_ind from skfda.representation import FDataGrid from skfda.representation.basis import Fourier -from skfda.inference.hotelling import hotelling_t2, hotelling_test_ind +import unittest + +import pytest class HotellingTests(unittest.TestCase): @@ -46,13 +46,14 @@ def test_hotelling_t2(self): def test_hotelling_test(self): fd1 = FDataGrid([[1, 1, 1], [1, 1, 1]]) fd2 = FDataGrid([[3, 3, 3], [2, 2, 2]]) - t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True) + t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True, + random_state=0) self.assertAlmostEqual(t2, 9) self.assertAlmostEqual(pval, 0) self.assertEqual(len(dist), 6) reps = 5 t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True, - n_reps=reps) + n_reps=reps, random_state=1) self.assertEqual(len(dist), reps) diff --git a/tests/test_oneway_anova.py b/tests/test_oneway_anova.py index f7c2ed87b..31eed81b7 100644 --- a/tests/test_oneway_anova.py +++ b/tests/test_oneway_anova.py @@ -1,12 +1,13 @@ -import unittest -import numpy as np -import pytest - -from skfda.representation import FDataGrid -from skfda.representation.basis import Fourier from skfda.datasets import fetch_gait from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ v_sample_stat +from skfda.representation import FDataGrid +from skfda.representation.basis import Fourier +import unittest + +import pytest + +import numpy as np class OnewayAnovaTests(unittest.TestCase): @@ -31,7 +32,6 @@ def test_v_stats_args(self): with self.assertRaises(ValueError): v_asymptotic_stat(FDataGrid([[1, 1, 1], [1, 1, 1]]), [0, 0]) - def test_v_stats(self): n_features = 50 weights = [1, 2, 3] @@ -58,20 +58,22 @@ def test_asymptotic_behaviour(self): n_little_sim = 10 - sims = np.array([oneway_anova(fd1, fd2, fd3, n_reps=500)[1] for _ in - range(n_little_sim)]) + sims = np.array([oneway_anova( + fd1, fd2, fd3, n_reps=500, random_state=i)[1] + for i in range(n_little_sim)]) little_sim = np.mean(sims) - big_sim = oneway_anova(fd1, fd2, fd3, n_reps=2000)[1] + big_sim = oneway_anova(fd1, fd2, fd3, n_reps=2000, random_state=100)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.05) fd = fd.to_basis(Fourier(n_basis=5)) fd1 = fd[0:5] fd2 = fd[5:10] - sims = np.array([oneway_anova(fd1, fd2, n_reps=500)[1] for _ in - range(n_little_sim)]) + sims = np.array([oneway_anova( + fd1, fd2, n_reps=500, random_state=i)[1] + for i in range(n_little_sim)]) little_sim = np.mean(sims) - big_sim = oneway_anova(fd1, fd2, n_reps=2000)[1] + big_sim = oneway_anova(fd1, fd2, n_reps=2000, random_state=100)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.05) From b8ba05736e949800390cc97cd4b1e47111eb3cb2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 21 Jul 2020 18:57:43 +0200 Subject: [PATCH 617/624] Remove indexing in FDataGrid additional dimensions. --- skfda/representation/grid.py | 11 ----------- tests/test_grid.py | 9 +++------ 2 files changed, 3 insertions(+), 17 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 5b49004c2..00c26b969 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -1016,17 +1016,6 @@ def __repr__(self): def __getitem__(self, key): """Return self[key].""" - if isinstance(key, tuple): - # If there are not values for every dimension, the remaining ones - # are kept - key += (slice(None),) * (self.dim_domain + 1 - len(key)) - - sample_points = [self.sample_points[i][subkey] - for i, subkey in enumerate( - key[1:1 + self.dim_domain])] - - return self.copy(data_matrix=self.data_matrix[key], - sample_points=sample_points) if isinstance(key, numbers.Integral): # To accept also numpy ints key = int(key) diff --git a/tests/test_grid.py b/tests/test_grid.py index 091fc54ae..809201419 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -49,15 +49,12 @@ def test_gmean(self): np.array([[0., 0.25, 0.5, 0.75, 1.]])) def test_slice(self): - t = 10 + t = (5, 3) fd = FDataGrid(data_matrix=np.ones(t)) - fd = fd[:, 0] + fd = fd[1:3] np.testing.assert_array_equal( fd.data_matrix[..., 0], - np.array([[1]])) - np.testing.assert_array_equal( - fd.sample_points, - np.array([[0]])) + np.array([[1, 1, 1], [1, 1, 1]])) def test_concatenate(self): fd1 = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) From 3193b31d3e5f9b6a1cae180db169c75553a2e2e5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 21 Jul 2020 19:15:30 +0200 Subject: [PATCH 618/624] Remove `FDataBasis` method `to_list` as it is redundant. --- skfda/misc/operators/_linear_differential_operator.py | 6 +++--- skfda/representation/basis/_fdatabasis.py | 4 ---- tests/test_linear_differential_operator.py | 8 ++++---- 3 files changed, 7 insertions(+), 11 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 8589bf66a..fa55f4ee5 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -160,9 +160,9 @@ def __init__( raise ValueError("You have to provide one weight at least") if all(isinstance(n, numbers.Real) for n in weights): - self.weights = (FDataBasis(Constant(real_domain_range), - np.array(weights) - .reshape(-1, 1)).to_list()) + self.weights = list(FDataBasis(Constant(real_domain_range), + np.array(weights) + .reshape(-1, 1))) elif all(isinstance(n, FDataBasis) for n in weights): if all([_same_domain(weights[0], x) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 2404e71a5..de8fac66a 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -506,10 +506,6 @@ def to_basis(self, basis, eval_points=None, **kwargs): return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) - def to_list(self): - """Splits FDataBasis samples into a list""" - return [self[i] for i in range(self.n_samples)] - def copy(self, *, basis=None, coefficients=None, dataset_label=None, axes_labels=None, extrapolation=None): """FDataBasis copy""" diff --git a/tests/test_linear_differential_operator.py b/tests/test_linear_differential_operator.py index 46fea7ba8..9bdd506a5 100644 --- a/tests/test_linear_differential_operator.py +++ b/tests/test_linear_differential_operator.py @@ -5,7 +5,7 @@ import numpy as np -class TestLfd(unittest.TestCase): +class TestLinearDifferentialOperator(unittest.TestCase): def test_init_default(self): """Tests default initialization (do not penalize).""" @@ -30,7 +30,7 @@ def test_init_integer(self): # Checks for a non zero order Lfd object lfd_3 = LinearDifferentialOperator(3) consfd = FDataBasis(Constant((0, 1)), [[0], [0], [0], [1]]) - bwtlist3 = consfd.to_list() + bwtlist3 = list(consfd) np.testing.assert_equal( lfd_3.weights, bwtlist3, @@ -51,7 +51,7 @@ def test_init_list_int(self): lfd = LinearDifferentialOperator(weights=coefficients) np.testing.assert_equal( - lfd.weights, fd.to_list(), + lfd.weights, list(fd), "Wrong list of weight functions of the linear operator") def test_init_list_fdatabasis(self): @@ -70,7 +70,7 @@ def test_init_list_fdatabasis(self): lfd = LinearDifferentialOperator(weights=fdlist) np.testing.assert_equal( - lfd.weights, fd.to_list(), + lfd.weights, list(fd), "Wrong list of weight functions of the linear operator") # Check failure if intervals do not match From cd96e4ecd02a3a0b7837e1ccfbb66d2b6f681070 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 21 Jul 2020 19:50:55 +0200 Subject: [PATCH 619/624] Update Travis link. --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 6b58b841e..02e7bc444 100644 --- a/README.rst +++ b/README.rst @@ -105,7 +105,7 @@ license_ can be found along with the code. .. |build-status| image:: https://travis-ci.org/GAA-UAM/scikit-fda.svg?branch=develop :alt: build status :scale: 100% - :target: https://travis-ci.org/GAA-UAM/scikit-fda + :target: https://travis-ci.com/GAA-UAM/scikit-fda .. |docs| image:: https://readthedocs.org/projects/fda/badge/?version=latest :alt: Documentation Status From 1cb6dcf8ab3a9d4e0675bdfbf4e8feacba38f731 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 21 Jul 2020 19:58:36 +0200 Subject: [PATCH 620/624] Update dependencies. --- README.rst | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 02e7bc444..aef40ddda 100644 --- a/README.rst +++ b/README.rst @@ -61,16 +61,18 @@ Requirements ------------ *scikit-fda* depends on the following packages: -* `setuptools `_ - Python Packaging * `cython `_ - Python to C compiler -* `numpy `_ - The fundamental package for scientific computing with Python -* `pandas `_ - Powerful Python data analysis toolkit -* `scipy `_ - Scientific computation in Python -* `scikit-learn `_ - Machine learning in Python +* `findiff `_ - Finite differences * `matplotlib `_ - Plotting with Python * `mpldatacursor `_ - Interactive data cursors for matplotlib +* `multimethod `_ - Multiple dispatch +* `numpy `_ - The fundamental package for scientific computing with Python +* `pandas `_ - Powerful Python data analysis toolkit * `rdata `_ - Reader of R datasets in .rda format in Python * `scikit-datasets `_ - Scikit-learn compatible datasets +* `scikit-learn `_ - Machine learning in Python +* `scipy `_ - Scientific computation in Python +* `setuptools `_ - Python Packaging The dependencies are automatically installed. From b6a6f7fc36c53f86a91378caacdeeaa3f48c8973 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 22 Jul 2020 23:36:39 +0200 Subject: [PATCH 621/624] Replace `axes_labels` with `argument_names` and `coordinate_names`. --- docs/modules/misc/metrics.rst | 1 - examples/plot_discrete_representation.py | 6 +- skfda/datasets/_real_datasets.py | 36 +++--- skfda/exploratory/visualization/_boxplot.py | 12 +- .../visualization/_magnitude_shape_plot.py | 3 - skfda/exploratory/visualization/_utils.py | 29 +++-- skfda/misc/metrics.py | 59 ---------- skfda/representation/_functional_data.py | 111 +++++++++--------- skfda/representation/basis/_fdatabasis.py | 36 ++++-- skfda/representation/basis/_vector_basis.py | 5 +- skfda/representation/grid.py | 79 +++++++------ tests/test_grid.py | 37 +++--- tests/test_magnitude_shape.py | 11 +- tests/test_metrics.py | 24 +--- 14 files changed, 197 insertions(+), 252 deletions(-) diff --git a/docs/modules/misc/metrics.rst b/docs/modules/misc/metrics.rst index d8f72872d..a7ac7c7e7 100644 --- a/docs/modules/misc/metrics.rst +++ b/docs/modules/misc/metrics.rst @@ -38,6 +38,5 @@ Utils .. autosummary:: :toctree: autosummary - skfda.misc.metrics.vectorial_norm skfda.misc.metrics.distance_from_norm skfda.misc.metrics.pairwise_distance diff --git a/examples/plot_discrete_representation.py b/examples/plot_discrete_representation.py index 143eb4644..0d25378fb 100644 --- a/examples/plot_discrete_representation.py +++ b/examples/plot_discrete_representation.py @@ -10,9 +10,10 @@ # sphinx_gallery_thumbnail_number = 2 -import numpy as np from skfda import FDataGrid +import numpy as np + ############################################################################## # We will construct a dataset containing several sinusoidal functions with @@ -29,7 +30,8 @@ fd = FDataGrid(data, sample_points, dataset_label='Sinusoidal curves', - axes_labels=['t', 'x(t)']) + argument_names=['t'], + coordinate_names=['x(t)']) fd = fd[:5] diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 0c67619dc..fc4481b33 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -24,7 +24,8 @@ def fdata_constructor(obj, attrs): sample_points=obj["argvals"], domain_range=obj["rangeval"], dataset_label=names['main'][0], - axes_labels=[names['xlab'][0], names['ylab'][0]]) + argument_names=(names['xlab'][0],), + coordinate_names=(names['ylab'][0],)) def functional_constructor(obj, attrs): @@ -52,7 +53,8 @@ def functional_constructor(obj, attrs): sample_points=sample_points, domain_range=(args_init, args_end), dataset_label=name[0], - axes_labels=[args_label[0], values_label[0]]), target) + argument_names=(args_label[0],), + coordinate_names=(values_label[0],)), target) def fetch_cran(name, package_name, *, converter=None, @@ -221,7 +223,8 @@ def fetch_phoneme(return_X_y: bool = False): sample_points=np.linspace(0, 8, 256), domain_range=[0, 8], dataset_label="Phoneme", - axes_labels=["frequency (kHz)", "log-periodogram"]) + argument_names=("frequency (kHz)",), + coordinate_names=("log-periodogram",)) if return_X_y: return curves, sound @@ -274,7 +277,8 @@ def fetch_growth(return_X_y: bool = False): curves = FDataGrid(data_matrix=np.concatenate((males, females), axis=0), sample_points=ages, dataset_label="Berkeley Growth Study", - axes_labels=["age", "height"]) + argument_names=("age",), + coordinate_names=("height",)) sex = np.array([0] * males.shape[0] + [1] * females.shape[0]) @@ -468,8 +472,9 @@ def fetch_weather(return_X_y: bool = False): curves = FDataGrid(data_matrix=temp_prec_daily, sample_points=range(1, 366), dataset_label="Canadian Weather", - axes_labels=["day", "temperature (ºC)", - "precipitation (mm.)"]) + argument_names=("day",), + coordinate_names=("temperature (ºC)", + "precipitation (mm.)")) target_names, target = np.unique(data["region"], return_inverse=True) @@ -531,10 +536,10 @@ def fetch_aemet(return_X_y: bool = False): curves = data["temp"].copy(data_matrix=data_matrix, dataset_label="AEMET", - axes_labels=["day", - "temperature (ºC)", - "logprecipitation", - "wind speed (m/s)"]) + argument_names=("day",), + coordinate_names=("temperature (ºC)", + "logprecipitation", + "wind speed (m/s)")) if return_X_y: return curves, None @@ -602,12 +607,11 @@ def fetch_octane(return_X_y: bool = False): target = np.zeros(len(data), dtype=int) target[24] = target[25] = target[35:39] = 1 # Outliers 1 - axes_labels = ["wavelength (nm)", "absorbances"] - curves = FDataGrid(data, sample_points=sample_points, dataset_label="Octane", - axes_labels=axes_labels) + argument_names=("wavelength (nm)",), + coordinate_names=("absorbances",)) if return_X_y: return curves, target @@ -654,9 +658,9 @@ def fetch_gait(return_X_y: bool = False): curves = FDataGrid(data_matrix=data_matrix, sample_points=sample_points, dataset_label="GAIT", - axes_labels=["Time (proportion of gait cycle)", - "Hip angle (degrees)", - "Knee angle (degrees)"]) + argument_names=("Time (proportion of gait cycle)",), + coordinate_names=("Hip angle (degrees)", + "Knee angle (degrees)")) meta_names, meta = np.unique(np.asarray(data.coords.get('dim_1')), return_inverse=True) diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index 2662ed8a8..d4ff8c2c3 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -161,7 +161,8 @@ class Boxplot(FDataBoxplot): ... [-0.5, -0.5, -0.5, -1, -1, -1]] >>> sample_points = [0, 2, 4, 6, 8, 10] >>> fd = FDataGrid(data_matrix, sample_points, dataset_label="dataset", - ... axes_labels=["x_label", "y_label"]) + ... argument_names=["x_label"], + ... coordinate_names=["y_label"]) >>> Boxplot(fd) Boxplot( FDataGrid=FDataGrid( @@ -192,7 +193,8 @@ class Boxplot(FDataBoxplot): sample_points=[array([ 0, 2, 4, 6, 8, 10])], domain_range=array([[ 0, 10]]), dataset_label='dataset', - axes_labels=['x_label', 'y_label'], + argument_names=('x_label',), + coordinate_names=('y_label',), ...), median=array([[ 0.5], [ 0.5], @@ -497,7 +499,8 @@ class SurfaceBoxplot(FDataBoxplot): ... [[3], [0.6], [3]]]] >>> sample_points = [[2, 4], [3, 6, 8]] >>> fd = FDataGrid(data_matrix, sample_points, dataset_label="dataset", - ... axes_labels=["x1_label", "x2_label", "y_label"]) + ... argument_names=["x1_label", "x2_label"], + ... coordinate_names=["y_label"]) >>> SurfaceBoxplot(fd) SurfaceBoxplot( FDataGrid=FDataGrid( @@ -517,7 +520,8 @@ class SurfaceBoxplot(FDataBoxplot): domain_range=array([[2, 4], [3, 8]]), dataset_label='dataset', - axes_labels=['x1_label', 'x2_label', 'y_label'], + argument_names=('x1_label', 'x2_label'), + coordinate_names=('y_label',), extrapolation=None, ...), median=array([[[ 1. ], diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 80fade282..5b21f60f4 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -147,9 +147,6 @@ class MagnitudeShapePlot: [-1. ]]]), sample_points=[array([ 0, 2, 4, 6, 8, 10])], domain_range=array([[ 0, 10]]), - dataset_label=None, - axes_labels=None, - extrapolation=None, ...), depth_method=projection_depth, pointwise_weights=None, diff --git a/skfda/exploratory/visualization/_utils.py b/skfda/exploratory/visualization/_utils.py index e11189e5a..ff6407610 100644 --- a/skfda/exploratory/visualization/_utils.py +++ b/skfda/exploratory/visualization/_utils.py @@ -218,21 +218,20 @@ def _set_labels(fdata, fig=None, axes=None, patches=None): axes[0].legend(handles=patches) # Axis labels - if fdata.axes_labels is not None: - if axes[0].name == '3d': - for i in range(fdata.dim_codomain): - if fdata.axes_labels[0] is not None: - axes[i].set_xlabel(fdata.axes_labels[0]) - if fdata.axes_labels[1] is not None: - axes[i].set_ylabel(fdata.axes_labels[1]) - if fdata.axes_labels[i + 2] is not None: - axes[i].set_zlabel(fdata.axes_labels[i + 2]) - else: - for i in range(fdata.dim_codomain): - if fdata.axes_labels[0] is not None: - axes[i].set_xlabel(fdata.axes_labels[0]) - if fdata.axes_labels[i + 1] is not None: - axes[i].set_ylabel(fdata.axes_labels[i + 1]) + if axes[0].name == '3d': + for i in range(fdata.dim_codomain): + if fdata.argument_names[0] is not None: + axes[i].set_xlabel(fdata.argument_names[0]) + if fdata.argument_names[1] is not None: + axes[i].set_ylabel(fdata.argument_names[1]) + if fdata.coordinate_names[i] is not None: + axes[i].set_zlabel(fdata.coordinate_names[i]) + else: + for i in range(fdata.dim_codomain): + if fdata.argument_names[0] is not None: + axes[i].set_xlabel(fdata.argument_names[0]) + if fdata.coordinate_names[i] is not None: + axes[i].set_ylabel(fdata.coordinate_names[i]) def _change_luminosity(color, amount=0.5): diff --git a/skfda/misc/metrics.py b/skfda/misc/metrics.py index 1474e60f3..18188c40a 100644 --- a/skfda/misc/metrics.py +++ b/skfda/misc/metrics.py @@ -63,65 +63,6 @@ def _cast_to_grid(fdata1, fdata2, eval_points=None, _check=True, **kwargs): return fdata1, fdata2 -def vectorial_norm(fdatagrid, p=2): - r"""Apply a vectorial norm to a multivariate function. - - Given a multivariate function :math:`f:\mathbb{R}^n\rightarrow - \mathbb{R}^d` applies a vectorial norm :math:`\| \cdot \|` to produce a - function :math:`\|f\|:\mathbb{R}^n\rightarrow \mathbb{R}`. - - For example, let :math:`f:\mathbb{R} \rightarrow \mathbb{R}^2` be - :math:`f(t)=(f_1(t), f_2(t))` and :math:`\| \cdot \|_2` the euclidian norm. - - .. math:: - \|f\|_2(t) = \sqrt { |f_1(t)|^2 + |f_2(t)|^2 } - - In general if :math:`p \neq \pm \infty` and :math:`f:\mathbb{R}^n - \rightarrow \mathbb{R}^d` - - .. math:: - \|f\|_p(x_1, ... x_n) = \left ( \sum_{k=1}^{d} |f_k(x_1, ..., x_n)|^p - \right )^{(1/p)} - - Args: - fdatagrid (:class:`FDatagrid`): Functional object to be transformed. - p (int, optional): Exponent in the lp norm. If p is a number then - it is applied sum(abs(x)**p)**(1./p), if p is inf then max(abs(x)), - and if p is -inf it is applied min(abs(x)). See numpy.linalg.norm - to more information. Defaults to 2. - - Returns: - (:class:`FDatagrid`): FDatagrid with image dimension equal to 1. - - Examples: - - >>> from skfda.datasets import make_multimodal_samples - >>> from skfda.misc.metrics import vectorial_norm - - First we will construct an example dataset with curves in - :math:`\mathbb{R}^2`. - - >>> fd = make_multimodal_samples(dim_codomain=2, random_state=0) - >>> fd.dim_codomain - 2 - - We will apply the euclidean norm - - >>> fd = vectorial_norm(fd, p=2) - >>> fd.dim_codomain - 1 - - """ - - if p == 'inf': - p = np.inf - - data_matrix = np.linalg.norm(fdatagrid.data_matrix, ord=p, axis=-1, - keepdims=True) - - return fdatagrid.copy(data_matrix=data_matrix) - - def distance_from_norm(norm, **kwargs): r"""Returns the distance induced by a norm. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 29aba4375..b648e89d4 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -30,16 +30,54 @@ class FData(ABC, pandas.api.extensions.ExtensionArray): """ - def __init__(self, extrapolation, dataset_label, axes_labels): + def __init__(self, *, extrapolation, dataset_label, axes_labels=None, + argument_names=None, coordinate_names=None): self.extrapolation = extrapolation self.dataset_label = dataset_label + self.argument_names = argument_names + self.coordinate_names = coordinate_names self.axes_labels = axes_labels + @property + def argument_names(self): + return self._argument_names + + @argument_names.setter + def argument_names(self, labels): + if labels is None: + labels = (None,) * self.dim_domain + else: + labels = tuple(labels) + if len(labels) != self.dim_domain: + raise ValueError("There must be a label for each of the " + "dimensions of the domain.") + + self._argument_names = labels + + @property + def coordinate_names(self): + return self._coordinate_names + + @coordinate_names.setter + def coordinate_names(self, labels): + if labels is None: + labels = (None,) * self.dim_codomain + else: + labels = tuple(labels) + if len(labels) != self.dim_codomain: + raise ValueError("There must be a label for each of the " + "dimensions of the codomain.") + + self._coordinate_names = labels + @property def axes_labels(self): - """Return the list of axes labels""" - return self._axes_labels + warnings.warn("Parameter axes_labels is deprecated. Use the " + "parameters argument_names and " + "coordinate_names instead.", DeprecationWarning) + + return self.argument_names + self.coordinate_names @axes_labels.setter def axes_labels(self, labels): @@ -47,6 +85,10 @@ def axes_labels(self, labels): if labels is not None: + warnings.warn("Parameter axes_labels is deprecated. Use the " + "parameters argument_names and " + "coordinate_names instead.", DeprecationWarning) + labels = np.asarray(labels) if len(labels) > (self.dim_domain + self.dim_codomain): raise ValueError("There must be a label for each of the " @@ -55,7 +97,8 @@ def axes_labels(self, labels): diff = (self.dim_domain + self.dim_codomain) - len(labels) labels = np.concatenate((labels, diff * [None])) - self._axes_labels = labels + self.argument_names = labels[:self.dim_domain] + self.coordinate_names = labels[self.dim_domain:] @property @abstractmethod @@ -392,58 +435,6 @@ def shift(self, shifts, *, restrict_domain=False, extrapolation=None, """ pass - def _get_labels_coordinates(self, key): - """Return the labels of a function when it is indexed by its components. - - Args: - key (int, tuple, slice): Key used to index the coordinates. - - Returns: - (list): labels of the object fd.coordinates[key. - - """ - if self.axes_labels is None: - labels = None - else: - - labels = self.axes_labels[:self.dim_domain].tolist() - image_label = np.atleast_1d( - self.axes_labels[self.dim_domain:][key]) - labels.extend(image_label.tolist()) - - return labels - - def _join_labels_coordinates(self, *others): - """Return the labels of the concatenation as new coordinates of multiple - functional objects. - - Args: - others (:obj:`FData`) Objects to be concatenated. - - Returns: - (list): labels of the object - self.concatenate(*others, as_coordinates=True). - - """ - # Labels should be None or a list of length self.dim_domain + - # self.dim_codomain. - - if self.axes_labels is None: - labels = (self.dim_domain + self.dim_codomain) * [None] - else: - labels = self.axes_labels.tolist() - - for other in others: - if other.axes_labels is None: - labels.extend(other.dim_codomain * [None]) - else: - labels.extend(list(other.axes_labels[self.dim_domain:])) - - if all(label is None for label in labels): - labels = None - - return labels - def plot(self, *args, **kwargs): """Plot the FDatGrid object. @@ -606,6 +597,14 @@ def __getitem__(self, key): pass + def __eq__(self, other): + return ( + self.extrapolation == other.extrapolation + and self.dataset_label == other.dataset_label + and self.argument_names == other.argument_names + and self.coordinate_names == other.coordinate_names + ) + @abstractmethod def __add__(self, other): """Addition for FData object.""" diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index de8fac66a..238190e0a 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -75,7 +75,8 @@ def __len__(self): return self._fdatabasis.dim_codomain def __init__(self, basis, coefficients, *, dataset_label=None, - axes_labels=None, extrapolation=None): + axes_labels=None, argument_names=None, + coordinate_names=None, extrapolation=None): """Construct a FDataBasis object. Args: @@ -92,7 +93,11 @@ def __init__(self, basis, coefficients, *, dataset_label=None, self.basis = basis self.coefficients = coefficients - super().__init__(extrapolation, dataset_label, axes_labels) + super().__init__(extrapolation=extrapolation, + dataset_label=dataset_label, + axes_labels=axes_labels, + argument_names=argument_names, + coordinate_names=coordinate_names) @classmethod def from_data(cls, data_matrix, sample_points, basis, @@ -507,7 +512,9 @@ def to_basis(self, basis, eval_points=None, **kwargs): return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) def copy(self, *, basis=None, coefficients=None, dataset_label=None, - axes_labels=None, extrapolation=None): + argument_names=None, + coordinate_names=None, + extrapolation=None): """FDataBasis copy""" if basis is None: @@ -519,14 +526,19 @@ def copy(self, *, basis=None, coefficients=None, dataset_label=None, if dataset_label is None: dataset_label = copy.deepcopy(dataset_label) - if axes_labels is None: - axes_labels = copy.deepcopy(axes_labels) + if argument_names is None: + argument_names = self.argument_names + + if coordinate_names is None: + coordinate_names = self.coordinate_names if extrapolation is None: extrapolation = self.extrapolation return FDataBasis(basis, coefficients, dataset_label=dataset_label, - axes_labels=axes_labels, extrapolation=extrapolation) + argument_names=argument_names, + coordinate_names=coordinate_names, + extrapolation=extrapolation) def times(self, other): """"Provides a numerical approximation of the multiplication between @@ -606,16 +618,13 @@ def _array_to_R(self, coefficients, transpose=False): def __repr__(self): """Representation of FDataBasis object.""" - if self.axes_labels is None: - axes_labels = None - else: - axes_labels = self.axes_labels.tolist() return (f"{self.__class__.__name__}(" f"\nbasis={self.basis}," f"\ncoefficients={self.coefficients}," f"\ndataset_label={self.dataset_label}," - f"\naxes_labels={axes_labels}," + f"\nargument_names={repr(self.argument_names)}," + f"\ncoordinate_names={repr(self.coordinate_names)}," f"\nextrapolation={self.extrapolation})").replace( '\n', '\n ') @@ -629,8 +638,9 @@ def __str__(self): def __eq__(self, other): """Equality of FDataBasis""" # TODO check all other params - return (self.basis == other.basis and - np.all(self.coefficients == other.coefficients)) + return (super().__eq__(other) + and self.basis == other.basis + and np.all(self.coefficients == other.coefficients)) def concatenate(self, *others, as_coordinates=False): """Join samples from a similar FDataBasis object. diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index a15a6dfaf..c59c046c2 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -125,6 +125,7 @@ def _coordinate_nonfull(self, fdatabasis, key): r_key = key if isinstance(r_key, int): r_key = range(r_key, r_key + 1) + s_key = slice(r_key.start, r_key.stop, r_key.step) coef_indexes = np.concatenate([ np.ones(b.n_basis, dtype=np.bool_) if i in r_key @@ -138,10 +139,10 @@ def _coordinate_nonfull(self, fdatabasis, key): coefs = fdatabasis.coefficients[:, coef_indexes] - axes_labels = fdatabasis._get_labels_coordinates(key) + coordinate_names = np.array(fdatabasis.coordinate_names)[s_key] return fdatabasis.copy(basis=basis, coefficients=coefs, - axes_labels=axes_labels) + coordinate_names=coordinate_names) def basis_of_product(self, other): pass diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 00c26b969..fa1e31410 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -115,11 +115,17 @@ def __iter__(self): def __getitem__(self, key): """Get a specific coordinate.""" - axes_labels = self._fdatagrid._get_labels_coordinates(key) + + s_key = key + if isinstance(s_key, int): + s_key = slice(s_key, s_key + 1) + + coordinate_names = np.array( + self._fdatagrid.coordinate_names)[s_key] return self._fdatagrid.copy( data_matrix=self._fdatagrid.data_matrix[..., key], - axes_labels=axes_labels) + coordinate_names=coordinate_names) def __len__(self): """Return the number of coordinates.""" @@ -127,6 +133,8 @@ def __len__(self): def __init__(self, data_matrix, sample_points=None, domain_range=None, dataset_label=None, + argument_names=None, + coordinate_names=None, axes_labels=None, extrapolation=None, interpolation=None): """Construct a FDataGrid object. @@ -201,9 +209,11 @@ def __init__(self, data_matrix, sample_points=None, self.interpolation = interpolation - super().__init__(extrapolation, dataset_label, axes_labels) - - return + super().__init__(extrapolation=extrapolation, + dataset_label=dataset_label, + axes_labels=axes_labels, + argument_names=argument_names, + coordinate_names=coordinate_names) def round(self, decimals=0): """Evenly round to the given number of decimals. @@ -486,7 +496,8 @@ def cov(self): self.sample_points[0]], domain_range=[self.domain_range[0], self.domain_range[0]], - dataset_label=dataset_label) + dataset_label=dataset_label, + argument_names=self.argument_names * 2) def gmean(self): """Compute the geometric mean of all samples in the FDataGrid object. @@ -505,6 +516,9 @@ def __eq__(self, other): if not isinstance(other, FDataGrid): return NotImplemented + if not super().__eq__(other): + return False + if not np.array_equal(self.data_matrix, other.data_matrix): return False @@ -518,24 +532,6 @@ def __eq__(self, other): if not np.array_equal(self.domain_range, other.domain_range): return False - if self.dataset_label != other.dataset_label: - return False - - if self.axes_labels is None or other.axes_labels is None: - # Both must be None - if self.axes_labels is not other.axes_labels: - return False - else: - if len(self.axes_labels) != len(other.axes_labels): - return False - - for a, b in zip(self.axes_labels, other.axes_labels): - if a != b: - return False - - if self.extrapolation != other.extrapolation: - return False - if self.interpolation != other.interpolation: return False @@ -709,9 +705,12 @@ def concatenate(self, *others, as_coordinates=False): data = [self.data_matrix] + [other.data_matrix for other in others] if as_coordinates: + + coordinate_names = [ + fd.coordinate_names for fd in [self, *others]] + return self.copy(data_matrix=np.concatenate(data, axis=-1), - axes_labels=( - self._join_labels_coordinates(*others))) + coordinate_names=sum(coordinate_names, ())) else: return self.copy(data_matrix=np.concatenate(data, axis=0)) @@ -804,7 +803,9 @@ def copy(self, *, deep=False, # For Pandas compatibility data_matrix=None, sample_points=None, domain_range=None, dataset_label=None, - axes_labels=None, extrapolation=None, + argument_names=None, + coordinate_names=None, + extrapolation=None, interpolation=None): """Returns a copy of the FDataGrid. @@ -827,8 +828,13 @@ def copy(self, *, if dataset_label is None: dataset_label = copy.copy(self.dataset_label) - if axes_labels is None: - axes_labels = copy.copy(self.axes_labels) + if argument_names is None: + # Tuple, immutable + argument_names = self.argument_names + + if coordinate_names is None: + # Tuple, immutable + coordinate_names = self.coordinate_names if extrapolation is None: extrapolation = self.extrapolation @@ -839,7 +845,9 @@ def copy(self, *, return FDataGrid(data_matrix, sample_points=sample_points, domain_range=domain_range, dataset_label=dataset_label, - axes_labels=axes_labels, extrapolation=extrapolation, + argument_names=argument_names, + coordinate_names=coordinate_names, + extrapolation=extrapolation, interpolation=interpolation) def shift(self, shifts, *, restrict_domain=False, extrapolation=None, @@ -988,7 +996,8 @@ def compose(self, fd, *, eval_points=None): return self.copy(data_matrix=data_matrix, sample_points=eval_points, - domain_range=fd.domain_range) + domain_range=fd.domain_range, + argument_names=fd.argument_names) def __str__(self): """Return str(self).""" @@ -999,17 +1008,13 @@ def __str__(self): def __repr__(self): """Return repr(self).""" - if self.axes_labels is None: - axes_labels = None - else: - axes_labels = self.axes_labels.tolist() - return (f"FDataGrid(" f"\n{repr(self.data_matrix)}," f"\nsample_points={repr(self.sample_points)}," f"\ndomain_range={repr(self.domain_range)}," f"\ndataset_label={repr(self.dataset_label)}," - f"\naxes_labels={repr(axes_labels)}," + f"\nargument_names={repr(self.argument_names)}," + f"\ncoordinate_names={repr(self.coordinate_names)}," f"\nextrapolation={repr(self.extrapolation)}," f"\ninterpolation={repr(self.interpolation)})").replace( '\n', '\n ') diff --git a/tests/test_grid.py b/tests/test_grid.py index 809201419..e39db303a 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -60,7 +60,8 @@ def test_concatenate(self): fd1 = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) fd2 = FDataGrid([[3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) - fd1.axes_labels = ["x", "y"] + fd1.argument_names = ["x"] + fd1.coordinate_names = ["y"] fd = fd1.concatenate(fd2) np.testing.assert_equal(fd.n_samples, 4) @@ -69,14 +70,18 @@ def test_concatenate(self): np.testing.assert_array_equal(fd.data_matrix[..., 0], [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) - np.testing.assert_array_equal(fd1.axes_labels, fd.axes_labels) + np.testing.assert_array_equal(fd1.argument_names, fd.argument_names) + np.testing.assert_array_equal( + fd1.coordinate_names, fd.coordinate_names) def test_concatenate_coordinates(self): fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) - fd1.axes_labels = ["x", "y"] - fd2.axes_labels = ["w", "t"] + fd1.argument_names = ["x"] + fd1.coordinate_names = ["y"] + fd2.argument_names = ["w"] + fd2.coordinate_names = ["t"] fd = fd1.concatenate(fd2, as_coordinates=True) np.testing.assert_equal(fd.n_samples, 2) @@ -88,14 +93,13 @@ def test_concatenate_coordinates(self): [[2, 4], [3, 5], [4, 6], [5, 7]]]) # Testing labels - np.testing.assert_array_equal(["x", "y", "t"], fd.axes_labels) - fd1.axes_labels = ["x", "y"] - fd2.axes_labels = None + np.testing.assert_array_equal(("y", "t"), fd.coordinate_names) + fd2.coordinate_names = None fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_array_equal(["x", "y", None], fd.axes_labels) - fd1.axes_labels = None + np.testing.assert_array_equal(("y", None), fd.coordinate_names) + fd1.coordinate_names = None fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_equal(None, fd.axes_labels) + np.testing.assert_equal((None, None), fd.coordinate_names) def test_concatenate2(self): sample1 = np.arange(0, 10) @@ -103,7 +107,8 @@ def test_concatenate2(self): fd1 = FDataGrid([sample1]) fd2 = FDataGrid([sample2]) - fd1.axes_labels = ["x", "y"] + fd1.argument_names = ["x"] + fd1.coordinate_names = ["y"] fd = concatenate([fd1, fd2]) np.testing.assert_equal(fd.n_samples, 2) @@ -111,11 +116,14 @@ def test_concatenate2(self): np.testing.assert_equal(fd.dim_domain, 1) np.testing.assert_array_equal(fd.data_matrix[..., 0], [sample1, sample2]) - np.testing.assert_array_equal(fd1.axes_labels, fd.axes_labels) + np.testing.assert_array_equal(fd1.argument_names, fd.argument_names) + np.testing.assert_array_equal( + fd1.coordinate_names, fd.coordinate_names) def test_coordinates(self): fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) - fd1.axes_labels = ["x", "y"] + fd1.argument_names = ["x"] + fd1.coordinate_names = ["y"] fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) fd = fd1.concatenate(fd2, as_coordinates=True) @@ -138,7 +146,8 @@ def test_coordinates(self): np.testing.assert_array_equal(fd3.coordinates[-2:].data_matrix, fd.data_matrix) np.testing.assert_array_equal( - fd3.coordinates[(False, False, True, False, True)].data_matrix, + fd3.coordinates[np.array( + (False, False, True, False, True))].data_matrix, fd.data_matrix) def test_add(self): diff --git a/tests/test_magnitude_shape.py b/tests/test_magnitude_shape.py index 77fd0a4d5..50509e483 100644 --- a/tests/test_magnitude_shape.py +++ b/tests/test_magnitude_shape.py @@ -1,20 +1,17 @@ -import unittest - -import numpy as np from skfda import FDataGrid from skfda.datasets import fetch_weather from skfda.exploratory.depth import modified_band_depth from skfda.exploratory.visualization import MagnitudeShapePlot +import unittest + +import numpy as np class TestMagnitudeShapePlot(unittest.TestCase): def test_magnitude_shape_plot(self): fd = fetch_weather()["data"] - fd_temperatures = FDataGrid(data_matrix=fd.data_matrix[:, :, 0], - sample_points=fd.sample_points, - dataset_label=fd.dataset_label, - axes_labels=fd.axes_labels[0:2]) + fd_temperatures = fd.coordinates[0] msplot = MagnitudeShapePlot( fd_temperatures, depth_method=modified_band_depth) np.testing.assert_allclose(msplot.points, diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 394ebea1a..e95371f1b 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,7 +1,7 @@ from skfda import FDataGrid, FDataBasis from skfda.datasets import make_multimodal_samples from skfda.exploratory import stats -from skfda.misc.metrics import lp_distance, lp_norm, vectorial_norm +from skfda.misc.metrics import lp_distance, lp_norm from skfda.representation.basis import Monomial import unittest @@ -22,28 +22,6 @@ def setUp(self): self.fd_surface = make_multimodal_samples(n_samples=3, dim_domain=2, random_state=0) - def test_vectorial_norm(self): - - vec = vectorial_norm(self.fd_curve, p=2) - np.testing.assert_array_almost_equal(vec.data_matrix, - np.sqrt(2) * self.fd.data_matrix) - - vec = vectorial_norm(self.fd_curve, p='inf') - np.testing.assert_array_almost_equal(vec.data_matrix, - self.fd.data_matrix) - - def test_vectorial_norm_surface(self): - - fd_surface_curve = self.fd_surface.concatenate(self.fd_surface, - as_coordinates=True) - vec = vectorial_norm(fd_surface_curve, p=2) - np.testing.assert_array_almost_equal( - vec.data_matrix, np.sqrt(2) * self.fd_surface.data_matrix) - - vec = vectorial_norm(fd_surface_curve, p='inf') - np.testing.assert_array_almost_equal(vec.data_matrix, - self.fd_surface.data_matrix) - def test_lp_norm(self): np.testing.assert_allclose(lp_norm(self.fd, p=1), [16., 41.33333333]) From 3e5dcb577064f6aaba50a1836eba4b8f4cef9303 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 23 Jul 2020 00:00:57 +0200 Subject: [PATCH 622/624] Rename `dataset_label` as `dataset_name`. --- examples/plot_discrete_representation.py | 2 +- examples/plot_elastic_registration.py | 2 +- examples/plot_explore.py | 2 +- examples/plot_extrapolation.py | 16 +++--- examples/plot_oneway_synthetic.py | 27 +++++----- examples/plot_surface_boxplot.py | 9 ++-- skfda/datasets/_real_datasets.py | 16 +++--- skfda/exploratory/visualization/_boxplot.py | 8 +-- skfda/exploratory/visualization/_utils.py | 4 +- skfda/representation/_functional_data.py | 58 ++++++++++++++------- skfda/representation/basis/_fdatabasis.py | 14 +++-- skfda/representation/grid.py | 34 ++++++------ 12 files changed, 111 insertions(+), 81 deletions(-) diff --git a/examples/plot_discrete_representation.py b/examples/plot_discrete_representation.py index 0d25378fb..47e6afb80 100644 --- a/examples/plot_discrete_representation.py +++ b/examples/plot_discrete_representation.py @@ -29,7 +29,7 @@ # that are measured at the same points. fd = FDataGrid(data, sample_points, - dataset_label='Sinusoidal curves', + dataset_name='Sinusoidal curves', argument_names=['t'], coordinate_names=['x(t)']) diff --git a/examples/plot_elastic_registration.py b/examples/plot_elastic_registration.py index 7c9bc898e..1688126ad 100644 --- a/examples/plot_elastic_registration.py +++ b/examples/plot_elastic_registration.py @@ -86,7 +86,7 @@ # We now show the aligned curves: fd_align = elastic_registration.fit_transform(fd) -fd_align.dataset_label += " - aligned" +fd_align.dataset_name += " - aligned" fd_align.plot() diff --git a/examples/plot_explore.py b/examples/plot_explore.py index 632919eb1..035d502b5 100644 --- a/examples/plot_explore.py +++ b/examples/plot_explore.py @@ -49,7 +49,7 @@ means = mean_high.concatenate(mean_low) -means.dataset_label = fd.dataset_label + ' - means' +means.dataset_name = fd.dataset_name + ' - means' means.plot(group=['high fat', 'low fat'], group_colors=colors, linewidth=0.5, legend=True) diff --git a/examples/plot_extrapolation.py b/examples/plot_extrapolation.py index 75a4ba18c..afab1caf4 100644 --- a/examples/plot_extrapolation.py +++ b/examples/plot_extrapolation.py @@ -42,16 +42,16 @@ # fdgrid = skfda.datasets.make_sinusoidal_process( n_samples=2, error_std=0, random_state=0) -fdgrid.dataset_label = "Grid" +fdgrid.dataset_name = "Grid" fd_fourier = fdgrid.to_basis(skfda.representation.basis.Fourier()) -fd_fourier.dataset_label = "Fourier Basis" +fd_fourier.dataset_name = "Fourier Basis" fd_monomial = fdgrid.to_basis(skfda.representation.basis.Monomial(n_basis=5)) -fd_monomial.dataset_label = "Monomial Basis" +fd_monomial.dataset_name = "Monomial Basis" fd_bspline = fdgrid.to_basis(skfda.representation.basis.BSpline(n_basis=5)) -fd_bspline.dataset_label = "BSpline Basis" +fd_bspline.dataset_name = "BSpline Basis" # Plot of diferent representations @@ -66,7 +66,7 @@ ax[0][1].set_xticks([]) # Clear title for next plots -fdgrid.dataset_label = "" +fdgrid.dataset_name = "" ############################################################################## @@ -121,7 +121,7 @@ t = np.linspace(*domain_extended) fig = plt.figure() -fdgrid.dataset_label = "Periodic extrapolation" +fdgrid.dataset_name = "Periodic extrapolation" # Evaluation of the grid # Extrapolation supplied in the evaluation @@ -141,7 +141,7 @@ # fig = plt.figure() -fdgrid.dataset_label = "Boundary extrapolation" +fdgrid.dataset_name = "Boundary extrapolation" # Other way to call the extrapolation, changing the default value fdgrid.extrapolation = "bounds" @@ -163,7 +163,7 @@ # ``extrapolation=FillExtrapolation(0)``. # -fdgrid.dataset_label = "Fill with zeros" +fdgrid.dataset_name = "Fill with zeros" # Evaluation of the grid filling with zeros fdgrid.extrapolation = "zeros" diff --git a/examples/plot_oneway_synthetic.py b/examples/plot_oneway_synthetic.py index ef68e9de0..2d210d08a 100644 --- a/examples/plot_oneway_synthetic.py +++ b/examples/plot_oneway_synthetic.py @@ -10,14 +10,15 @@ # License: MIT -import numpy as np - -from skfda.representation import FDataGrid -from skfda.inference.anova import oneway_anova from skfda.datasets import make_gaussian_process +from skfda.inference.anova import oneway_anova from skfda.misc.covariances import WhiteNoise +from skfda.representation import FDataGrid -################################################################################ +import numpy as np + + +########################################################################## # *One-way ANOVA* (analysis of variance) is a test that can be used to # compare the means of different samples of data. # Let :math:`X_{ij}(t), j=1, \dots, n_i` be trajectories corresponding to @@ -33,10 +34,8 @@ # process by adding to them white noise. The main objective of the # test is to illustrate the differences in the results of the ANOVA method # when the covariance function of the brownian processes changes. - -################################################################################ +########################################################################## # First, the means for the future processes are drawn. - n_samples = 10 n_features = 100 n_groups = 3 @@ -50,9 +49,9 @@ m3 = t ** 3 * (1 - t) ** 3 _ = FDataGrid([m1, m2, m3], - dataset_label="Means to be used in the simulation").plot() + dataset_name="Means to be used in the simulation").plot() -################################################################################ +########################################################################## # A total of `n_samples` trajectories will be created for each mean, so a array # of labels is created to identify them when plotting. @@ -82,13 +81,13 @@ print("p-value: {:.3f}".format(p_val)) -################################################################################ +########################################################################## # In the following, the same process will be followed incrementing sigma # value, this way the differences between the averages of each group will be # lower and the p-values will increase (the null hypothesis will be harder to # refuse). -################################################################################ +########################################################################## # Plot for :math:`\sigma^2 = 0.1`: sigma2 = 0.1 cov = WhiteNoise(variance=sigma2) @@ -108,7 +107,7 @@ print("p-value: {:.3f}".format(p_val)) -################################################################################ +########################################################################## # Plot for :math:`\sigma^2 = 1`: sigma2 = 1 @@ -128,7 +127,7 @@ print("Statistic: {:.3f}".format(stat)) print("p-value: {:.3f}".format(p_val)) -################################################################################ +########################################################################## # **References:** # # [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An anova test diff --git a/examples/plot_surface_boxplot.py b/examples/plot_surface_boxplot.py index c15bc7223..d64dbb6a3 100644 --- a/examples/plot_surface_boxplot.py +++ b/examples/plot_surface_boxplot.py @@ -11,12 +11,13 @@ # sphinx_gallery_thumbnail_number = 3 -import matplotlib.pyplot as plt -import numpy as np from skfda import FDataGrid from skfda.datasets import make_gaussian_process from skfda.exploratory.visualization import SurfaceBoxplot, Boxplot +import matplotlib.pyplot as plt +import numpy as np + ############################################################################## # In order to instantiate a @@ -35,7 +36,7 @@ fd = make_gaussian_process(n_samples=n_samples, n_features=n_features, random_state=1) -fd.dataset_label = "Brownian process" +fd.dataset_name = "Brownian process" ############################################################################## # After, those values generated for one dimension on the domain are extruded @@ -50,7 +51,7 @@ fd_2 = FDataGrid(data_matrix=cube, sample_points=np.tile(fd.sample_points, (2, 1)), - dataset_label="Extruded Brownian process") + dataset_name="Extruded Brownian process") fd_2.plot() diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index fc4481b33..2b3f362a2 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -23,7 +23,7 @@ def fdata_constructor(obj, attrs): return FDataGrid(data_matrix=obj["data"], sample_points=obj["argvals"], domain_range=obj["rangeval"], - dataset_label=names['main'][0], + dataset_name=names['main'][0], argument_names=(names['xlab'][0],), coordinate_names=(names['ylab'][0],)) @@ -52,7 +52,7 @@ def functional_constructor(obj, attrs): return (FDataGrid(data_matrix=data_matrix, sample_points=sample_points, domain_range=(args_init, args_end), - dataset_label=name[0], + dataset_name=name[0], argument_names=(args_label[0],), coordinate_names=(values_label[0],)), target) @@ -222,7 +222,7 @@ def fetch_phoneme(return_X_y: bool = False): curves = FDataGrid(data_matrix=curve_data.values, sample_points=np.linspace(0, 8, 256), domain_range=[0, 8], - dataset_label="Phoneme", + dataset_name="Phoneme", argument_names=("frequency (kHz)",), coordinate_names=("log-periodogram",)) @@ -276,7 +276,7 @@ def fetch_growth(return_X_y: bool = False): curves = FDataGrid(data_matrix=np.concatenate((males, females), axis=0), sample_points=ages, - dataset_label="Berkeley Growth Study", + dataset_name="Berkeley Growth Study", argument_names=("age",), coordinate_names=("height",)) @@ -471,7 +471,7 @@ def fetch_weather(return_X_y: bool = False): curves = FDataGrid(data_matrix=temp_prec_daily, sample_points=range(1, 366), - dataset_label="Canadian Weather", + dataset_name="Canadian Weather", argument_names=("day",), coordinate_names=("temperature (ºC)", "precipitation (mm.)")) @@ -535,7 +535,7 @@ def fetch_aemet(return_X_y: bool = False): data_matrix[:, :, 2] = data["wind.speed"].data_matrix[:, :, 0] curves = data["temp"].copy(data_matrix=data_matrix, - dataset_label="AEMET", + dataset_name="AEMET", argument_names=("day",), coordinate_names=("temperature (ºC)", "logprecipitation", @@ -609,7 +609,7 @@ def fetch_octane(return_X_y: bool = False): curves = FDataGrid(data, sample_points=sample_points, - dataset_label="Octane", + dataset_name="Octane", argument_names=("wavelength (nm)",), coordinate_names=("absorbances",)) @@ -657,7 +657,7 @@ def fetch_gait(return_X_y: bool = False): curves = FDataGrid(data_matrix=data_matrix, sample_points=sample_points, - dataset_label="GAIT", + dataset_name="GAIT", argument_names=("Time (proportion of gait cycle)",), coordinate_names=("Hip angle (degrees)", "Knee angle (degrees)")) diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index d4ff8c2c3..90e1f0ab9 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -160,7 +160,7 @@ class Boxplot(FDataBoxplot): ... [-1, -1, -0.5, 1, 1, 0.5], ... [-0.5, -0.5, -0.5, -1, -1, -1]] >>> sample_points = [0, 2, 4, 6, 8, 10] - >>> fd = FDataGrid(data_matrix, sample_points, dataset_label="dataset", + >>> fd = FDataGrid(data_matrix, sample_points, dataset_name="dataset", ... argument_names=["x_label"], ... coordinate_names=["y_label"]) >>> Boxplot(fd) @@ -192,7 +192,7 @@ class Boxplot(FDataBoxplot): [-1. ]]]), sample_points=[array([ 0, 2, 4, 6, 8, 10])], domain_range=array([[ 0, 10]]), - dataset_label='dataset', + dataset_name='dataset', argument_names=('x_label',), coordinate_names=('y_label',), ...), @@ -498,7 +498,7 @@ class SurfaceBoxplot(FDataBoxplot): ... [[[2], [0.5], [2]], ... [[3], [0.6], [3]]]] >>> sample_points = [[2, 4], [3, 6, 8]] - >>> fd = FDataGrid(data_matrix, sample_points, dataset_label="dataset", + >>> fd = FDataGrid(data_matrix, sample_points, dataset_name="dataset", ... argument_names=["x1_label", "x2_label"], ... coordinate_names=["y_label"]) >>> SurfaceBoxplot(fd) @@ -519,7 +519,7 @@ class SurfaceBoxplot(FDataBoxplot): sample_points=[array([2, 4]), array([3, 6, 8])], domain_range=array([[2, 4], [3, 8]]), - dataset_label='dataset', + dataset_name='dataset', argument_names=('x1_label', 'x2_label'), coordinate_names=('y_label',), extrapolation=None, diff --git a/skfda/exploratory/visualization/_utils.py b/skfda/exploratory/visualization/_utils.py index ff6407610..021f11832 100644 --- a/skfda/exploratory/visualization/_utils.py +++ b/skfda/exploratory/visualization/_utils.py @@ -208,8 +208,8 @@ def _set_labels(fdata, fig=None, axes=None, patches=None): """ # Dataset name - if fdata.dataset_label is not None: - fig.suptitle(fdata.dataset_label) + if fdata.dataset_name is not None: + fig.suptitle(fdata.dataset_name) # Legend if patches is not None: diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index b648e89d4..496e9b463 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -30,46 +30,68 @@ class FData(ABC, pandas.api.extensions.ExtensionArray): """ - def __init__(self, *, extrapolation, dataset_label, axes_labels=None, - argument_names=None, coordinate_names=None): + def __init__(self, *, extrapolation, + dataset_name=None, + dataset_label=None, + axes_labels=None, + argument_names=None, + coordinate_names=None): self.extrapolation = extrapolation - self.dataset_label = dataset_label + self.dataset_name = dataset_name + + if dataset_label is not None: + self.dataset_label = dataset_label + self.argument_names = argument_names self.coordinate_names = coordinate_names self.axes_labels = axes_labels + @property + def dataset_label(self): + warnings.warn("Parameter dataset_label is deprecated. Use the " + "parameter dataset_name instead.", + DeprecationWarning) + return self.dataset_name + + @dataset_label.setter + def dataset_label(self, name): + warnings.warn("Parameter dataset_label is deprecated. Use the " + "parameter dataset_name instead.", + DeprecationWarning) + self.dataset_name = name + @property def argument_names(self): return self._argument_names @argument_names.setter - def argument_names(self, labels): - if labels is None: - labels = (None,) * self.dim_domain + def argument_names(self, names): + if names is None: + names = (None,) * self.dim_domain else: - labels = tuple(labels) - if len(labels) != self.dim_domain: - raise ValueError("There must be a label for each of the " + names = tuple(names) + if len(names) != self.dim_domain: + raise ValueError("There must be a name for each of the " "dimensions of the domain.") - self._argument_names = labels + self._argument_names = names @property def coordinate_names(self): return self._coordinate_names @coordinate_names.setter - def coordinate_names(self, labels): - if labels is None: - labels = (None,) * self.dim_codomain + def coordinate_names(self, names): + if names is None: + names = (None,) * self.dim_codomain else: - labels = tuple(labels) - if len(labels) != self.dim_codomain: - raise ValueError("There must be a label for each of the " + names = tuple(names) + if len(names) != self.dim_codomain: + raise ValueError("There must be a name for each of the " "dimensions of the codomain.") - self._coordinate_names = labels + self._coordinate_names = names @property def axes_labels(self): @@ -600,7 +622,7 @@ def __getitem__(self, key): def __eq__(self, other): return ( self.extrapolation == other.extrapolation - and self.dataset_label == other.dataset_label + and self.dataset_name == other.dataset_name and self.argument_names == other.argument_names and self.coordinate_names == other.coordinate_names ) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 238190e0a..5998bf998 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -75,6 +75,7 @@ def __len__(self): return self._fdatabasis.dim_codomain def __init__(self, basis, coefficients, *, dataset_label=None, + dataset_name=None, axes_labels=None, argument_names=None, coordinate_names=None, extrapolation=None): """Construct a FDataBasis object. @@ -95,6 +96,7 @@ def __init__(self, basis, coefficients, *, dataset_label=None, super().__init__(extrapolation=extrapolation, dataset_label=dataset_label, + dataset_name=dataset_name, axes_labels=axes_labels, argument_names=argument_names, coordinate_names=coordinate_names) @@ -511,7 +513,8 @@ def to_basis(self, basis, eval_points=None, **kwargs): return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs) - def copy(self, *, basis=None, coefficients=None, dataset_label=None, + def copy(self, *, basis=None, coefficients=None, + dataset_name=None, argument_names=None, coordinate_names=None, extrapolation=None): @@ -523,8 +526,8 @@ def copy(self, *, basis=None, coefficients=None, dataset_label=None, if coefficients is None: coefficients = self.coefficients - if dataset_label is None: - dataset_label = copy.deepcopy(dataset_label) + if dataset_name is None: + dataset_name = self.dataset_name if argument_names is None: argument_names = self.argument_names @@ -535,7 +538,8 @@ def copy(self, *, basis=None, coefficients=None, dataset_label=None, if extrapolation is None: extrapolation = self.extrapolation - return FDataBasis(basis, coefficients, dataset_label=dataset_label, + return FDataBasis(basis, coefficients, + dataset_name=dataset_name, argument_names=argument_names, coordinate_names=coordinate_names, extrapolation=extrapolation) @@ -622,7 +626,7 @@ def __repr__(self): return (f"{self.__class__.__name__}(" f"\nbasis={self.basis}," f"\ncoefficients={self.coefficients}," - f"\ndataset_label={self.dataset_label}," + f"\ndataset_name={self.dataset_name}," f"\nargument_names={repr(self.argument_names)}," f"\ncoordinate_names={repr(self.coordinate_names)}," f"\nextrapolation={self.extrapolation})").replace( diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index fa1e31410..fd5c2ba71 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -132,7 +132,9 @@ def __len__(self): return self._fdatagrid.dim_codomain def __init__(self, data_matrix, sample_points=None, - domain_range=None, dataset_label=None, + domain_range=None, + dataset_label=None, + dataset_name=None, argument_names=None, coordinate_names=None, axes_labels=None, extrapolation=None, @@ -211,6 +213,7 @@ def __init__(self, data_matrix, sample_points=None, super().__init__(extrapolation=extrapolation, dataset_label=dataset_label, + dataset_name=dataset_name, axes_labels=axes_labels, argument_names=argument_names, coordinate_names=coordinate_names) @@ -423,14 +426,14 @@ def derivative(self, *, order=1): zip(self.sample_points, order_list))]) data_matrix = operator(self.data_matrix.astype(float)) - if self.dataset_label: - dataset_label = "{} - {} derivative".format(self.dataset_label, - order) + if self.dataset_name: + dataset_name = "{} - {} derivative".format(self.dataset_name, + order) else: - dataset_label = None + dataset_name = None fdatagrid = self.copy(data_matrix=data_matrix, - dataset_label=dataset_label) + dataset_name=dataset_name) return fdatagrid @@ -481,10 +484,10 @@ def cov(self): """ - if self.dataset_label is not None: - dataset_label = self.dataset_label + ' - covariance' + if self.dataset_name is not None: + dataset_name = self.dataset_name + ' - covariance' else: - dataset_label = None + dataset_name = None if self.dim_domain != 1 or self.dim_codomain != 1: raise NotImplementedError("Covariance only implemented " @@ -496,7 +499,7 @@ def cov(self): self.sample_points[0]], domain_range=[self.domain_range[0], self.domain_range[0]], - dataset_label=dataset_label, + dataset_name=dataset_name, argument_names=self.argument_names * 2) def gmean(self): @@ -802,7 +805,8 @@ def to_grid(self, sample_points=None): def copy(self, *, deep=False, # For Pandas compatibility data_matrix=None, sample_points=None, - domain_range=None, dataset_label=None, + domain_range=None, + dataset_name=None, argument_names=None, coordinate_names=None, extrapolation=None, @@ -825,8 +829,8 @@ def copy(self, *, if domain_range is None: domain_range = copy.deepcopy(self.domain_range) - if dataset_label is None: - dataset_label = copy.copy(self.dataset_label) + if dataset_name is None: + dataset_name = self.dataset_name if argument_names is None: # Tuple, immutable @@ -844,7 +848,7 @@ def copy(self, *, return FDataGrid(data_matrix, sample_points=sample_points, domain_range=domain_range, - dataset_label=dataset_label, + dataset_name=dataset_name, argument_names=argument_names, coordinate_names=coordinate_names, extrapolation=extrapolation, @@ -1012,7 +1016,7 @@ def __repr__(self): f"\n{repr(self.data_matrix)}," f"\nsample_points={repr(self.sample_points)}," f"\ndomain_range={repr(self.domain_range)}," - f"\ndataset_label={repr(self.dataset_label)}," + f"\ndataset_name={repr(self.dataset_name)}," f"\nargument_names={repr(self.argument_names)}," f"\ncoordinate_names={repr(self.coordinate_names)}," f"\nextrapolation={repr(self.extrapolation)}," From 42a03d0ebb0efd835ed6a546c676ad32fe9d21f6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 23 Jul 2020 00:12:20 +0200 Subject: [PATCH 623/624] Update docs. --- skfda/representation/_functional_data.py | 9 +++++---- skfda/representation/basis/_fdatabasis.py | 15 +++++++++++++-- skfda/representation/grid.py | 8 +++++--- 3 files changed, 23 insertions(+), 9 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 496e9b463..b62acdb92 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -23,10 +23,11 @@ class FData(ABC, pandas.api.extensions.ExtensionArray): dim_domain (int): Dimension of the domain. dim_codomain (int): Dimension of the image. extrapolation (Extrapolation): Default extrapolation mode. - dataset_label (str): name of the dataset. - axes_labels (list): list containing the labels of the different - axis. The first element is the x label, the second the y label - and so on. + dataset_name (str): name of the dataset. + argument_names (tuple): tuple containing the names of the different + arguments. + coordinate_names (tuple): tuple containing the names of the different + coordinate functions. """ diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 5998bf998..8a719a7db 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -2,7 +2,6 @@ import copy import pandas.api.extensions -import scipy.integrate import numpy as np @@ -35,9 +34,21 @@ class FDataBasis(FData): function in the basis. If a matrix, each row contains the coefficients that multiplied by the basis functions produce each functional datum. + domain_range (numpy.ndarray): 2 dimension matrix where each row + contains the bounds of the interval in which the functional data + is considered to exist for each one of the axies. + dataset_name (str): name of the dataset. + argument_names (tuple): tuple containing the names of the different + arguments. + coordinate_names (tuple): tuple containing the names of the different + coordinate functions. + extrapolation (str or Extrapolation): defines the default type of + extrapolation. By default None, which does not apply any type of + extrapolation. See `Extrapolation` for detailled information of the + types of extrapolation. Examples: - >>> from skfda.representation.basis import FDataBasis, Monomial + >>> from skfda.representation.basis import FDataBasis, Monomial >>> >>> basis = Monomial(n_basis=4) >>> coefficients = [1, 1, 3, .5] diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index fd5c2ba71..7d67d965e 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -40,9 +40,11 @@ class FDataGrid(FData): domain_range (numpy.ndarray): 2 dimension matrix where each row contains the bounds of the interval in which the functional data is considered to exist for each one of the axies. - dataset_label (str): name of the dataset. - axes_labels (list): list containing the labels of the different - axis. + dataset_name (str): name of the dataset. + argument_names (tuple): tuple containing the names of the different + arguments. + coordinate_names (tuple): tuple containing the names of the different + coordinate functions. extrapolation (str or Extrapolation): defines the default type of extrapolation. By default None, which does not apply any type of extrapolation. See `Extrapolation` for detailled information of the From 46825525c23cbe3033aada15437ba5145cc2dda8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 23 Jul 2020 17:40:24 +0200 Subject: [PATCH 624/624] Update version for new release. --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index be5863417..bd73f4707 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -0.3 +0.4